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//
// 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("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("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));
}
#elif defined(ARMNN_TFLITE_DELEGATE)
#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 '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()
{
// Set to true if it is preferred to throw an exception rather than use ARMNN_LOG
bool throwExc = false;
try
{
if (m_DynamicBackendsPath == "")
{
// Check compute devices are valid unless they are dynamically loaded at runtime
std::string invalidBackends;
if (!CheckRequestedBackendsAreValid(m_ComputeDevices, armnn::Optional<std::string&>(invalidBackends)))
{
ARMNN_LOG(fatal) << "The list of preferred devices contains invalid backend IDs: "
<< invalidBackends;
}
}
CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices);
if (m_EnableBf16TurboMode && m_EnableFp16TurboMode)
{
ARMNN_LOG(fatal) << "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()))
{
ARMNN_LOG(fatal) << "input-name and input-tensor-shape must have the same amount of elements. ";
}
if (m_InputTensorDataFilePaths.size() != 0)
{
if (!ValidatePaths(m_InputTensorDataFilePaths, true))
{
ARMNN_LOG(fatal) << "One or more input data file paths are not valid. ";
}
if (!m_Concurrent && m_InputTensorDataFilePaths.size() != m_InputNames.size())
{
ARMNN_LOG(fatal) << "input-name and input-tensor-data must have the same amount of elements. ";
}
if (m_InputTensorDataFilePaths.size() < m_SimultaneousIterations * m_InputNames.size())
{
ARMNN_LOG(fatal) << "There is not enough input data for " << m_SimultaneousIterations << " execution.";
}
if (m_InputTensorDataFilePaths.size() > m_SimultaneousIterations * m_InputNames.size())
{
ARMNN_LOG(fatal) << "There is more input data for " << m_SimultaneousIterations << " execution.";
}
}
if ((m_OutputTensorFiles.size() != 0) &&
(m_OutputTensorFiles.size() != m_OutputNames.size()))
{
ARMNN_LOG(fatal) << "output-name and write-outputs-to-file must have the same amount of elements. ";
}
if ((m_OutputTensorFiles.size() != 0)
&& m_OutputTensorFiles.size() != m_SimultaneousIterations * m_OutputNames.size())
{
ARMNN_LOG(fatal) << "There is not enough output data for " << m_SimultaneousIterations << " execution.";
}
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()))
{
ARMNN_LOG(fatal) << "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()))
{
ARMNN_LOG(fatal) << "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)
{
ARMNN_LOG(fatal) << "Threshold time supplied as a command line argument is less than zero.";
}
}
catch (std::string& exc)
{
if (throwExc)
{
throw armnn::InvalidArgumentException(exc);
}
else
{
std::cout << exc;
exit(EXIT_FAILURE);
}
}
// Check turbo modes
// 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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