Revert "Revert "IVGCVSW-6650 Refactor ExecuteNetwork""
This reverts commit 1a7f033768acb27da11503bd29abb468d2e77f9e.
List of fixes to be able to add this code again:
* "emplacing_back" the vector inputTensors into the vector m_InputTensorsVec outside the for loop
* GetIOInfo() uses IOptimizedNetwork instead of INetwork, where the infered shapes are not saved
* Add missing data type Signed32 to SetupInputsAndOutputs()
* PrintOutputTensors() prints the actual output without dequantizing
* Add profilingDetailsMethod as input in networkProperties in ArmNNExecutor constructor
* Fix typos
Change-Id: I91de166f87228282db3efa27431fe91458834442
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ic6634d48892d11e5f146cdf285e1e333e93e9937
Signed-off-by: Francis Murtagh <francis.murtagh@arm.com>
diff --git a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
index cc75bb4..f341c30 100644
--- a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
@@ -1,76 +1,15 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 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));
- }
-}
+#include <armnnUtils/Filesystem.hpp>
void CheckClTuningParameter(const int& tuningLevel,
const std::string& tuningPath,
@@ -105,7 +44,6 @@
ARMNN_LOG(warning) << "To use Cl Tuning the compute device GpuAcc needs to be active.";
}
}
-
}
void ExecuteNetworkParams::ValidateParams()
@@ -120,7 +58,6 @@
<< invalidBackends;
}
}
-
CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices);
if (m_EnableBf16TurboMode && m_EnableFp16TurboMode)
@@ -129,10 +66,6 @@
"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()))
@@ -157,68 +90,6 @@
m_InputNames.size(),
m_InputTensorDataFilePaths.size()));
}
- else if (m_InputTensorDataFilePaths.size() % m_InputNames.size() != 0)
- {
- throw armnn::InvalidArgumentException(
- fmt::format("According to the number of input names the user provided the network has {} "
- "inputs. The user specified {} input-tensor-data file paths which is not "
- "divisible by the number of inputs.",
- m_InputNames.size(),
- m_InputTensorDataFilePaths.size()));
- }
- }
-
- 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.");
- }
-
- // Make sure that the number of input files given is divisible by the number of inputs of the model
- if (!(m_InputTensorDataFilePaths.size() % m_InputNames.size() == 0))
- {
- throw armnn::InvalidArgumentException(
- fmt::format("The number of input-tensor-data files ({0}) is not divisible by the "
- "number of inputs ({1} according to the number of input names).",
- m_InputTensorDataFilePaths.size(),
- m_InputNames.size()));
- }
-
- 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.");
- }
-
- // Make sure that the number of output files given is equal to the number of outputs of the model
- // or equal to the number of outputs of the model multiplied with the number of iterations
- if (!m_OutputTensorFiles.empty())
- {
- if ((m_OutputTensorFiles.size() != m_OutputNames.size()) &&
- (m_OutputTensorFiles.size() != m_OutputNames.size() * m_Iterations))
- {
- std::stringstream errmsg;
- auto numOutputs = m_OutputNames.size();
- throw armnn::InvalidArgumentException(
- fmt::format("The user provided {0} output-tensor files. The only allowed number of output-tensor "
- "files is the number of outputs of the network ({1} according to the number of "
- "output names) or the number of outputs multiplied with the number of times the "
- "network should be executed (NumOutputs * NumIterations = {1} * {2} = {3}).",
- m_OutputTensorFiles.size(),
- numOutputs,
- m_Iterations,
- numOutputs*m_Iterations));
- }
}
// Check that threshold time is not less than zero
@@ -310,4 +181,5 @@
return delegateOptions;
}
+
#endif