Different input data for every iteration of ExecuteNetwork
* Allows to supply different input data for every execution of a
model when using the 'iterations' option in ExecuteNetwork
* Removes the option 'simultaneous-iterations' because it's functionallity
is now covered by 'iterations'
* Adds a deprecation warning message to notify users
* Little refactor of warning messages
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ib3ab0d6533f6952bfee20d098a890b653c34cc12
diff --git a/tests/ExecuteNetwork/ExecuteNetwork.cpp b/tests/ExecuteNetwork/ExecuteNetwork.cpp
index a9b5a3c..e757d2c 100644
--- a/tests/ExecuteNetwork/ExecuteNetwork.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetwork.cpp
@@ -356,15 +356,31 @@
model.GetInputQuantizationParams()) :
armnn::EmptyOptional();
- for(unsigned int j = 0; j < params.m_SimultaneousIterations ; ++j)
+ if (params.m_InputTensorDataFilePaths.size() > numInputs)
+ {
+ ARMNN_LOG(info) << "Given network has " << numInputs << " input/s. One input-tensor-data file is required "
+ << "for each input. The user provided "
+ << params.m_InputTensorDataFilePaths.size()
+ << " input-tensor-data file/s which will be used to fill the input/s.\n";
+ }
+
+ for(unsigned int j = 0; j < params.m_Iterations ; ++j)
{
std::vector<TContainer> inputDataContainers;
for(unsigned int i = 0; i < numInputs; ++i)
{
+ // If there are less input files given than required for the execution of
+ // params.m_Iterations we simply start with the first input file again
+ size_t inputFileIndex = j * numInputs + i;
+ if (!params.m_InputTensorDataFilePaths.empty())
+ {
+ inputFileIndex = inputFileIndex % params.m_InputTensorDataFilePaths.size();
+ }
+
armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ?
armnn::EmptyOptional() :
armnn::MakeOptional<std::string>(
- params.m_InputTensorDataFilePaths[(j * numInputs) + i]);
+ params.m_InputTensorDataFilePaths.at(inputFileIndex));
unsigned int numElements = model.GetInputSize(i);
if (params.m_InputTensorShapes.size() > i && params.m_InputTensorShapes[i])
@@ -388,7 +404,7 @@
const size_t numOutputs = inferenceModelParams.m_OutputBindings.size();
- for (unsigned int j = 0; j < params.m_SimultaneousIterations; ++j)
+ for (unsigned int j = 0; j < params.m_Iterations; ++j)
{
std::vector <TContainer> outputDataContainers;
for (unsigned int i = 0; i < numOutputs; ++i)
@@ -418,13 +434,30 @@
outputs.push_back(outputDataContainers);
}
+ if (params.m_Iterations > 1)
+ {
+ std::stringstream msg;
+ msg << "Network will be executed " << params.m_Iterations;
+ if (params.m_Concurrent)
+ {
+ msg << " times in an asynchronous manner. ";
+ }
+ else
+ {
+ msg << " times successively. ";
+ }
+ msg << "The input-tensor-data files will be reused recursively if the user didn't provide enough to "
+ "cover each execution.";
+ ARMNN_LOG(info) << msg.str();
+ }
+
// Synchronous execution
if (!params.m_Concurrent)
{
for (size_t x = 0; x < params.m_Iterations; x++)
{
// model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
- auto inference_duration = model.Run(inputs[0], outputs[0]);
+ auto inference_duration = model.Run(inputs[x], outputs[x]);
if (params.m_GenerateTensorData)
{
@@ -436,13 +469,29 @@
for (size_t i = 0; i < numOutputs; i++)
{
const armnn::TensorInfo& infoOut = infosOut[i].second;
- auto outputTensorFile = params.m_OutputTensorFiles.empty() ? "" : params.m_OutputTensorFiles[i];
+
+ // We've made sure before that the number of output files either equals numOutputs, in which case
+ // we override those files when processing the results of each iteration (only the result of the
+ // last iteration will be stored), or there are enough
+ // output files for each output of each iteration.
+ size_t outputFileIndex = x * numOutputs + i;
+ if (!params.m_OutputTensorFiles.empty())
+ {
+ outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
+ ARMNN_LOG(info) << "Writing output " << i << " named: '"
+ << inferenceModelParams.m_OutputBindings[i]
+ << "' of iteration: " << x+1 << " to file: '"
+ << params.m_OutputTensorFiles[outputFileIndex] << "'";
+ }
+ auto outputTensorFile = params.m_OutputTensorFiles.empty()
+ ? ""
+ : params.m_OutputTensorFiles[outputFileIndex];
TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
infoOut,
outputTensorFile,
params.m_DequantizeOutput);
- mapbox::util::apply_visitor(printer, outputs[0][i]);
+ mapbox::util::apply_visitor(printer, outputs[x][i]);
}
ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
@@ -481,7 +530,7 @@
// For the asynchronous execution, we are adding a pool of working memory handles (1 per thread) in the
// LoadedNetwork with each scheduled inference having a specific priority
- for (size_t i = 0; i < params.m_SimultaneousIterations; ++i)
+ for (size_t i = 0; i < params.m_Iterations; ++i)
{
std::shared_ptr<armnn::AsyncExecutionCallback> cb = callbackManager.GetNewCallback();
inferenceOutputMap.insert({cb->GetInferenceId(), outputs[i]});
@@ -490,7 +539,7 @@
// Check the results
unsigned int j = 0;
- for (size_t iteration = 0; iteration < params.m_SimultaneousIterations; ++iteration)
+ for (size_t iteration = 0; iteration < params.m_Iterations; ++iteration)
{
auto cb = callbackManager.GetNotifiedCallback();
@@ -522,10 +571,24 @@
const auto& infosOut = model.GetOutputBindingInfos();
for (size_t i = 0; i < numOutputs; i++)
{
+ // We've made sure before that the number of output files either equals numOutputs, in which
+ // case we override those files when processing the results of each iteration (only the result
+ // of the last iteration will be stored), or there are enough
+ // output files for each output of each iteration.
+ size_t outputFileIndex = iteration * numOutputs + i;
+ if (!params.m_OutputTensorFiles.empty())
+ {
+ outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
+ ARMNN_LOG(info) << "Writing output " << i << " named: '"
+ << inferenceModelParams.m_OutputBindings[i]
+ << "' of iteration: " << iteration+1 << " to file: '"
+ << params.m_OutputTensorFiles[outputFileIndex] << "'";
+ }
+
const armnn::TensorInfo& infoOut = infosOut[i].second;
auto outputTensorFile = params.m_OutputTensorFiles.empty()
? ""
- : params.m_OutputTensorFiles[(j * numOutputs) + i];
+ : params.m_OutputTensorFiles[outputFileIndex];
TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
infoOut,
@@ -575,12 +638,12 @@
ARMNN_LOG(info) << "Asynchronous Execution with std::launch:async... \n";
std::vector<std::future<std::tuple<unsigned int,
std::chrono::duration<double, std::milli>>>> inferenceResults;
- inferenceResults.reserve(params.m_SimultaneousIterations);
+ inferenceResults.reserve(params.m_Iterations);
// Create WorkingMemHandles for each inference
std::vector<std::unique_ptr<armnn::experimental::IWorkingMemHandle>> workingMemHandles;
- workingMemHandles.reserve(params.m_SimultaneousIterations);
- for (unsigned int i = 0; i < params.m_SimultaneousIterations; ++i)
+ workingMemHandles.reserve(params.m_Iterations);
+ for (unsigned int i = 0; i < params.m_Iterations; ++i)
{
workingMemHandles.push_back(model.CreateWorkingMemHandle());
}
@@ -588,7 +651,7 @@
// Run each inference in its own thread
// start a timer
const auto start_time = armnn::GetTimeNow();
- for (unsigned int i = 0; i < params.m_SimultaneousIterations; ++i)
+ for (unsigned int i = 0; i < params.m_Iterations; ++i)
{
armnn::experimental::IWorkingMemHandle& workingMemHandleRef = *workingMemHandles[i].get();
@@ -616,10 +679,23 @@
const auto& infosOut = model.GetOutputBindingInfos();
for (size_t i = 0; i < numOutputs; i++)
{
+ // We've made sure before that the number of output files either equals numOutputs, in which
+ // case we override those files when processing the results of each iteration (only the result
+ // of the last iteration will be stored), or there are enough
+ // output files for each output of each iteration.
+ size_t outputFileIndex = j * numOutputs + i;
+ if (!params.m_OutputTensorFiles.empty())
+ {
+ outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size();
+ ARMNN_LOG(info) << "Writing output " << i << " named: '"
+ << inferenceModelParams.m_OutputBindings[i]
+ << "' of iteration: " << j+1 << " to file: '"
+ << params.m_OutputTensorFiles[outputFileIndex] << "'";
+ }
const armnn::TensorInfo& infoOut = infosOut[i].second;
auto outputTensorFile = params.m_OutputTensorFiles.empty()
? ""
- : params.m_OutputTensorFiles[(j * numOutputs) + i];
+ : params.m_OutputTensorFiles[outputFileIndex];
TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
infoOut,
@@ -683,7 +759,14 @@
// Get ExecuteNetwork parameters and runtime options from command line
- ProgramOptions ProgramOptions(argc, argv);
+ // This might throw an InvalidArgumentException if the user provided invalid inputs
+ ProgramOptions ProgramOptions;
+ try {
+ ProgramOptions.ParseOptions(argc, argv);
+ } catch (const std::exception &e){
+ ARMNN_LOG(fatal) << e.what();
+ return EXIT_FAILURE;
+ }
// Create runtime
std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions));