public void TestGetOutputTensors2()
{
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var outputNames = BarracudaModelParamLoader.GetOutputNames(model);
Assert.Contains(TensorNames.ActionOutput, outputNames);
// TODO : There are some memory tensors as well
}
public void TestGetOutputTensors1()
{
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var outputNames = BarracudaModelParamLoader.GetOutputNames(model);
Assert.Contains(TensorNames.ActionOutput, outputNames);
Assert.AreEqual(1, outputNames.Count());
Assert.AreEqual(0, BarracudaModelParamLoader.GetOutputNames(null).Count());
}
/// <summary>
/// Initializes the Brain with the Model that it will use when selecting actions for
/// the agents
/// </summary>
/// <param name="seed"> The seed that will be used to initialize the RandomNormal
/// and Multinomial obsjects used when running inference.</param>
/// <exception cref="UnityAgentsException">Throws an error when the model is null
/// </exception>
public void ReloadModel(int seed = 0)
{
if (m_TensorAllocator == null)
{
m_TensorAllocator = new TensorCachingAllocator();
}
if (model != null)
{
#if BARRACUDA_VERBOSE
_verbose = true;
#endif
D.logEnabled = m_Verbose;
// Cleanup previous instance
if (m_Engine != null)
{
m_Engine.Dispose();
}
m_BarracudaModel = ModelLoader.Load(model.Value);
var executionDevice = inferenceDevice == InferenceDevice.GPU
? BarracudaWorkerFactory.Type.ComputePrecompiled
: BarracudaWorkerFactory.Type.CSharp;
m_Engine = BarracudaWorkerFactory.CreateWorker(executionDevice, m_BarracudaModel, m_Verbose);
}
else
{
m_BarracudaModel = null;
m_Engine = null;
}
m_ModelParamLoader = BarracudaModelParamLoader.GetLoaderAndCheck(m_Engine, m_BarracudaModel, brainParameters);
m_InferenceInputs = m_ModelParamLoader.GetInputTensors();
m_OutputNames = m_ModelParamLoader.GetOutputNames();
m_TensorGenerator = new TensorGenerator(brainParameters, seed, m_TensorAllocator, m_BarracudaModel);
m_TensorApplier = new TensorApplier(brainParameters, seed, m_TensorAllocator, m_BarracudaModel);
}