public int ExecuteGraph(IEnumerable <TensorProxy> inputs_it, IEnumerable <TensorProxy> outputs_it)
{
Profiler.BeginSample("TFSharpInferenceComponent.ExecuteGraph");
TensorProxy[] inputs = inputs_it.ToArray();
TensorProxy[] outputs = outputs_it.ToArray();
// TODO: Can/should we pre-allocate that?
TFSession.Runner runner = m_session.GetRunner();
inputs.ToList().ForEach((TensorProxy input) =>
{
if (input.Shape.Length == 0)
{
var data = input.Data[0];
if (input.DataType == typeof(int))
{
runner.AddInput(m_graph[input.Name][0], (int)data);
}
else
{
runner.AddInput(m_graph[input.Name][0], (float)data);
}
}
else
{
runner.AddInput(m_graph[input.Name][0], input.DataType == typeof(int) ?
TensorUtils.BarracudaToIntArray(input.Data) :
TensorUtils.BarracudaToFloatArray(input.Data));
}
});
// TODO: better way to pre-allocate this?
outputs.ToList().ForEach(s => runner.Fetch(s.Name));
TFStatus status = new TFStatus();
Profiler.BeginSample("TFSharpInferenceComponent.ExecuteGraph.RunnerRun");
var out_tensors = runner.Run(status);
Profiler.EndSample();
if (!status.Ok)
{
Debug.LogError(status.StatusMessage);
return(-1);
}
Debug.Assert(outputs.Length == out_tensors.Length);
for (var i = 0; i < outputs.Length; ++i)
{
if (outputs[i].Shape.Length == 0)
{
// Handle scalars
outputs[i].Data = new Tensor(1, 1);
outputs[i].Data[0] = (float)(int)out_tensors[i].GetValue();
}
else
{
outputs[i].Data = TensorUtils.ArrayToBarracuda(out_tensors[i].GetValue() as Array);
}
}
Profiler.EndSample();
// TODO: create error codes
return(0);
}