/// <summary>
/// Initializes the Brain with the Model that it will use when selecting actions for
/// the agents
/// </summary>
/// <param name="model"> The Barracuda model to load </param>
/// <param name="brainParameters"> The parameters of the Brain used to generate the
/// placeholder tensors </param>
/// <param name="inferenceDevice"> Inference execution device. CPU is the fastest
/// option for most of ML Agents models. </param>
/// <param name="seed"> The seed that will be used to initialize the RandomNormal
/// and Multinomial objects used when running inference.</param>
/// <exception cref="UnityAgentsException">Throws an error when the model is null
/// </exception>
public ModelRunner(
NNModel model,
BrainParameters brainParameters,
InferenceDevice inferenceDevice = InferenceDevice.CPU,
int seed = 0)
{
Model barracudaModel;
m_Model = model;
m_InferenceDevice = inferenceDevice;
m_TensorAllocator = new TensorCachingAllocator();
if (model != null)
{
#if BARRACUDA_VERBOSE
m_Verbose = true;
#endif
D.logEnabled = m_Verbose;
barracudaModel = ModelLoader.Load(model);
var executionDevice = inferenceDevice == InferenceDevice.GPU
? WorkerFactory.Type.ComputePrecompiled
: WorkerFactory.Type.CSharp;
m_Engine = WorkerFactory.CreateWorker(executionDevice, barracudaModel, m_Verbose);
}
else
{
barracudaModel = null;
m_Engine = null;
}
m_InferenceInputs = BarracudaModelParamLoader.GetInputTensors(barracudaModel);
m_OutputNames = BarracudaModelParamLoader.GetOutputNames(barracudaModel);
m_TensorGenerator = new TensorGenerator(
seed, m_TensorAllocator, m_Memories, barracudaModel);
m_TensorApplier = new TensorApplier(
brainParameters, seed, m_TensorAllocator, m_Memories, barracudaModel);
}
// Start is called before the first frame update
void Start()
{
ops = new PrecompiledComputeOps(ComputeShaderSingleton.Instance.kernels, ComputeShaderSingleton.Instance.referenceKernels);
//ops = new BurstCPUOps();
TextureLoader loader = new TextureLoader();
print(TextureLoader.instance.semInput.shape);
inputs[name1] = TextureLoader.instance.semInput;
inputs[name2] = TextureLoader.instance.embedInput;
var model = ModelLoader.Load(modelSource);
worker = WorkerFactory.CreateWorker(WorkerFactory.Type.ComputePrecompiled, model);
//worker.Execute(inputs);
//output = worker.PeekOutput();
//output.PrintDataPart(100);
//output = Normalize(output, ops);
//texture = output.ToRenderTexture(0, 0, 1, 0, null);
count = Time.frameCount;
}
public static IOps CreateOps(WorkerFactory.Type type, bool verbose = false)
{
WorkerFactory.ValidateType(type);
switch (type)
{
case WorkerFactory.Type.ComputePrecompiled:
return new PrecompiledComputeOps(ComputeShaderSingleton.Instance.kernels,
ComputeShaderSingleton.Instance.referenceKernels, verbose: verbose);
case WorkerFactory.Type.Compute:
return new ComputeOps(ComputeShaderSingleton.Instance.kernels,
ComputeShaderSingleton.Instance.referenceKernels, verbose: verbose);
case WorkerFactory.Type.ComputeRef:
return new ReferenceComputeOps(ComputeShaderSingleton.Instance.referenceKernels);
case WorkerFactory.Type.CSharp:
return new UnsafeArrayCPUOps();
default:
return new ReferenceCPUOps();
}
}
BuiltinHandlerActivator StartBus(int workers, int parallelism, WorkerFactory workerFactory)
{
var activator = new BuiltinHandlerActivator();
Using(activator);
Configure.With(activator)
.Logging(l => l.ColoredConsole(minLevel: LogLevel.Info))
.Transport(t => t.UseInMemoryTransport(new InMemNetwork(), "threads"))
.Options(o =>
{
o.SetNumberOfWorkers(workers);
o.SetMaxParallelism(parallelism);
if (workerFactory == WorkerFactory.TaskParallelLibrary)
{
o.UseTplToReceiveMessages();
}
})
.Start();
return(activator);
}
public SolverDataManager(ProjectDataSet model, ConstraintSolverResult data)
{
_model = model;
_solverResult = data;
var jobFactory = new JobFactory();
var skillFactory = new SkillFactory();
var workerFactory = new WorkerFactory();
var toolFactory = new ToolFactory();
var zoneFactory = new ZoneFactory();
var shiftFactory = new ShiftFactory();
var shiftConverter = new ShiftConverter(shiftFactory);
var skillConverter = new SkillConverter(skillFactory);
var toolConverter = new ToolConverter(toolFactory, shiftConverter);
var zoneConverter = new ZoneConverter(zoneFactory);
var laborConverter = new LaborConverter(workerFactory, shiftConverter, skillConverter);
var jobConverter = new JobConverter(jobFactory, skillConverter, toolConverter, zoneConverter);
_shiftManager = new ShiftManager(shiftConverter, skillConverter, laborConverter, toolConverter);
_jobManager = new JobManager(jobConverter);
_zoneManager = new ZoneManager(zoneConverter);
}
// Use this for initialization
IEnumerator Start()
{
Application.targetFrameRate = 60;
labels = labelsAsset.text.Split('\n');
model = ModelLoader.Load(srcModel, false);
// Append Softmax() layer to the end of the network
var modelBuilder = new ModelBuilder(model);
modelBuilder.Softmax(output_name, model.outputs[0]);
modelBuilder.Output(output_name);
engine = WorkerFactory.CreateWorker(model, useGPU ? WorkerFactory.Device.GPU : WorkerFactory.Device.CSharp);
var input = new Tensor(PrepareTextureForInput(inputImage, !useGPU), 3);
inputs["data"] = input;
yield return(null);
StartCoroutine(RunInference());
}
public static GameObject GetWorker(this RoomInfo room)
{
if (room == null || room.CustomProperties[WorkerPropKey] == null || !room.CustomProperties.ContainsKey(WorkerPropKey))
{
return(null);
}
var WorkerFactory = new WorkerFactory();
string data = (string)room.CustomProperties[WorkerPropKey];
List <string> workerD = data.Split(',').ToList();
GameObject go = WorkerFactory.Create();
Worker worker = go.GetComponent <Worker>();
foreach (Player player in PhotonNetwork.PlayerList)
{
if (player.ActorNumber == int.Parse(workerD[0]))
{
worker.Owner = player;
}
}
worker.Pos = new Vector2(int.Parse(workerD[1]), int.Parse(workerD[2]));
return(go);
}
public SolverDataManager(ProjectDataSet model, ConstraintSolverResult data)
{
_model = model;
_solverResult = data;
var jobFactory = new JobFactory();
var skillFactory = new SkillFactory();
var workerFactory = new WorkerFactory();
var toolFactory = new ToolFactory();
var zoneFactory = new ZoneFactory();
var shiftFactory = new ShiftFactory();
var shiftConverter = new ShiftConverter(shiftFactory);
var skillConverter = new SkillConverter(skillFactory);
var toolConverter = new ToolConverter(toolFactory, shiftConverter);
var zoneConverter = new ZoneConverter(zoneFactory);
var laborConverter = new LaborConverter(workerFactory, shiftConverter, skillConverter);
var jobConverter = new JobConverter(jobFactory, skillConverter, toolConverter, zoneConverter);
_shiftManager = new ShiftManager(shiftConverter, skillConverter, laborConverter, toolConverter);
_jobManager = new JobManager(jobConverter);
_zoneManager = new ZoneManager(zoneConverter);
}
/// <summary>
/// Updates the output layer names based on the selected model architecture
/// and initializes the Barracuda inference engine witht the selected model.
/// </summary>
private void InitializeBarracuda()
{
// The compiled model used for performing inference
Model m_RunTimeModel;
if (modelType == ModelType.MobileNet)
{
preProcessFunction = Utils.PreprocessMobileNet;
// Compile the model asset into an object oriented representation
m_RunTimeModel = ModelLoader.Load(mobileNetModelAsset);
displacementFWDLayer = m_RunTimeModel.outputs[2];
displacementBWDLayer = m_RunTimeModel.outputs[3];
}
else
{
preProcessFunction = Utils.PreprocessResNet;
// Compile the model asset into an object oriented representation
m_RunTimeModel = ModelLoader.Load(resnetModelAsset);
displacementFWDLayer = m_RunTimeModel.outputs[3];
displacementBWDLayer = m_RunTimeModel.outputs[2];
}
heatmapLayer = m_RunTimeModel.outputs[0];
offsetsLayer = m_RunTimeModel.outputs[1];
// Create a model builder to modify the m_RunTimeModel
ModelBuilder modelBuilder = new ModelBuilder(m_RunTimeModel);
// Add a new Sigmoid layer that takes the output of the heatmap layer
modelBuilder.Sigmoid(predictionLayer, heatmapLayer);
// Validate if backend is supported, otherwise use fallback type.
workerType = WorkerFactory.ValidateType(workerType);
// Create a worker that will execute the model with the selected backend
engine = new Engine(workerType, modelBuilder.model, modelType);
}
/// <summary>
/// 根据关键字查询雇员集合
/// </summary>
/// <param name="sql"></param>
/// <returns></returns>
public static List <IEmployee> GetEmployeeByKeyword(string sql)
{
using (SqlConnection conn = sqlHelper.GetConnection())
{
conn.Open();
SqlCommand cmd = new SqlCommand(sql, conn);
using (SqlDataReader reader = cmd.ExecuteReader())
{
List <IEmployee> list = new List <IEmployee>();
while (reader.Read())
{
System.Data.IDataRecord record = reader;
IEmployee emp = null;
if (record.GetString(4).Equals("Worker"))
{
emp = new WorkerFactory().Get();
}
else if (record.GetString(4).Equals("Manager"))
{
emp = new ManagerFactory().Get();
}
else
{
emp = new DriverFactory().Get();
}
emp.Id = record.GetInt32(0);
emp.Name = record.GetString(1);
emp.Sex = record.GetString(2);
emp.Age = record.GetInt32(3);
list.Add(emp);
}
return(list);
}
}
}
/// <summary>
/// Gère la reception des demandes contenues dans l'objet de transfert depuis un noeud
/// </summary>
/// <param name="input">objet de transfert</param>
/// <param name="node">noeud emetteur</param>
public override void ProcessInput(DataInput input, Node node)
{
TaskExecutor executor = WorkerFactory.GetWorker(input.Method);
if (MethodIsNotInfra(input.Method) && input.MsgType == MessageType.Response)
{
ViewModelLocator.VmlMonitorUcStatic.RefreshStateFromTaskResult(input);
}
object res = executor.DoWork(input);
if (res != null)
{
DataInput resp = new DataInput()
{
ClientGuid = input.ClientGuid,
NodeGuid = NodeGuid,
TaskId = input.TaskId,
Method = input.Method,
Data = res,
MsgType = MessageType.Response
};
SendData(node, resp);
}
}
public void ReadBarracudaModel()
{
resultText.text = "Loading";
var worker = WorkerFactory.CreateWorker(WorkerFactory.Type.ComputePrecompiled, model);
resultText.text = "Loaded";
var tensor = new Tensor(drawTexture, 1);
worker.Execute(tensor);
var result = worker.PeekOutput();
var scores = result.AsFloats().ToList();
float bestScore = 0;
for (var i = 0; i < 10; i++)
{
if (scores[i] > bestScore)
{
bestScore = scores[i];
}
}
worker.Dispose();
resultText.text = scores.IndexOf(bestScore).ToString();
}
static void Main(string[] args)
{
//есть сервис, в нем обработчики, которые что то делают с определенной периодичностью,
//1 из обработчиков будет создавать задачу на выполнение
var worker = new WorkerFactory(new WorkerRepository()).GetService();
worker.AddTaskAsync(new Models.BO.NewTaskToWork("test", 50, false));
List <ITaskWorkHandler> handlers = new List <ITaskWorkHandler>();
while (true)
{
worker.StartActualTaskHandleAsync(handlers);
Thread.Sleep(500);
Console.WriteLine("write 'y' if stop else write anything");
if (Console.ReadKey().KeyChar == 'y')
{
break;
}
}
Console.WriteLine("Hello World!");
}
public override IWorkFacade[] InitWorkFacades(IInputDataContainer inputDataContainer, int dataSize)
{
return(new[]
{
#region IJob
#region Persistent
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
#region Temp
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
#endregion
#region TempJob
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
#endregion
#endregion
#region IJobParallelFor
#region Persistent
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
#region Temp
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Temp),
new DataConfigUnityCollection(Allocator.Temp),
}
),
#endregion
#region TempJob
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BaseBurstIJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.TempJob),
new DataConfigUnityCollection(Allocator.TempJob),
}
),
#endregion
#endregion
});
}
public override IWorkFacade[] InitWorkFacades(IInputDataContainer inputDataContainer, int dataSize)
{
return(new[]
{
#region Plain
WorkerFactory <Vector3[], int[]> .Create <SeparateAllPlain>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigDefault(),
new IDataConfig[]
{
new DataConfigDefault(),
new DataConfigDefault(),
}
),
WorkerFactory <Vector3[], int[]> .Create <SeparateVerticesTrianglesPlain>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigDefault(),
new IDataConfig[]
{
new DataConfigDefault(),
new DataConfigDefault(),
}
),
WorkerFactory <Vector3[], int[]> .Create <AllJoinPlain>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigDefault(),
new IDataConfig[]
{
new DataConfigDefault(),
new DataConfigDefault(),
}
),
WorkerFactory <Vector3[], int[]> .Create <AllJoinSingleForPlain>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigDefault(),
new IDataConfig[]
{
new DataConfigDefault(),
new DataConfigDefault(),
}
),
#endregion
#region Job
WorkerFactory <NativeArray <Vector3>, NativeArray <int> > .Create <SeparateAllJob>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <Vector3>, NativeArray <int> > .Create <SeparateVerticesTrianglesJob>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <Vector3>, NativeArray <int> > .Create <AllJoinJob>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <Vector3>, NativeArray <int> > .Create <AllJoinSingleForJob>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
#region JobParallelFor
WorkerFactory <NativeArray <Vector3>, NativeArray <int> > .Create <AllJoinSingleForJobParallelFor>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
#region SystemParallelFor
WorkerFactory <Vector3[], int[]> .Create <AllJoinSingleForSystemParallelFor>(
TestName(),
inputDataContainer.GetData <Vector3>(TypeConfig.DataVector3),
inputDataContainer.GetData <int>(TypeConfig.DataInt1),
new WorkConfigDefault(),
new IDataConfig[]
{
new DataConfigDefault(),
new DataConfigDefault(),
}
),
#endregion
});
}
public WorkerScheduler(ServiceProvider sp, IDictionary <string, int> dic)
{
_wf = new WorkerFactory(sp);
SetupTimers(dic);
}
// Start is called before the first frame update
void Start()
{
m_RuntimeModel = ModelLoader.Load(modelAsset);
worker = WorkerFactory.CreateWorker(WorkerFactory.Type.ComputePrecompiled, m_RuntimeModel);
}
// TODO: refactor with FuseShapesIntoConstants
public void InferAllShapes(Model model, ref IDictionary <string, TensorShape?> shapesByName, ref IDictionary <string, int?> ranksByName)
{
var toRunnableNCHW = new IntermediateToRunnableNCHWPass();
var knownLayersValue = new Dictionary <string, Tensor>();
var newKnownLayers = new HashSet <string>();
var keepLayers = new HashSet <string>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (layer.flags == Layer.Flags.Preserve)
{
keepLayers.Add(layer.name);
}
// NN is a directed graph, if we just fused constants + shapes, update following nodes
// re-evaluate shapes
FuseInputsIntoLayer(ref layer, knownLayersValue, ranksByName, null);//TODO handle potential folding errors/warnings
// TODO optimization, pass in index, or add shape
IRShapeInferenceHelper.RankInference.UpdateKnownTensorRanks(model, ranksByName);
IRShapeInferenceHelper.ShapeInference.UpdateKnownTensorShapesNCHW(model, ranksByName, ref shapesByName);
if (ModelOptimizer.IsLayerConstant(layer))
{
knownLayersValue[layer.name] = new Tensor(layer.datasets[0].shape, layer.weights);
}
else if (layer.type == Layer.Type.Shape)
{
// assert inputs.Lenght == 1
var input = layer.inputs[0];
if (shapesByName.ContainsKey(input) && shapesByName[input] != null &&
ranksByName.ContainsKey(input) && ranksByName[input] != null
)
{
var shape = shapesByName[input].Value;
var rank = ranksByName[input].Value;
knownLayersValue[layer.name] = ShapeToNCHWTensor(shape, rank);
newKnownLayers.Add(layer.name);
continue;
}
}
bool allInputsAreKnown = layer.inputs.Length > 0 ? knownLayersValue.ContainsKey(layer.inputs[0]) : false;
for (int i = 1; i < layer.inputs.Length; i++)
{
allInputsAreKnown &= knownLayersValue.ContainsKey(layer.inputs[i]);
}
// if all inputs are known, execute layer
if (!allInputsAreKnown)
{
continue;
}
var layerInputs = new Dictionary <string, Tensor>();
var opsModel = new Model();
opsModel.layout = "iNCHW";
for (int i = 0; i < layer.inputs.Length; i++)
{
Model.Input input;
input.name = layer.inputs[i];
input.shape = shapesByName[input.name].Value.ToArray();
input.rank = ranksByName[input.name].Value;
opsModel.inputs.Add(input);
layerInputs[input.name] = knownLayersValue[input.name];
}
Layer newLayer = new Layer(layer.name.ToString(), layer.activation);
newLayer.type = layer.type;
newLayer.activation = layer.activation;
newLayer.pad = layer.pad.ToArray();
newLayer.stride = layer.stride.ToArray();
newLayer.pool = layer.pool.ToArray();
newLayer.axis = layer.axis;
newLayer.alpha = layer.alpha;
newLayer.beta = layer.beta;
newLayer.inputs = layer.inputs.ToArray();
newLayer.datasets = layer.datasets;
newLayer.weights = layer.weights;
if (layer.outputs != null)
{
newLayer.outputs = layer.outputs.ToArray();
}
if (layer.axes != null)
{
newLayer.axes = layer.axes.ToArray();
}
opsModel.layers.Add(newLayer);
opsModel.outputs.Add(newLayer.name);
toRunnableNCHW.Run(ref opsModel);
toRunnableNCHW.Run(ref opsModel);
// bake
var useCPUforBaking = WorkerFactory.Device.CPU;
using (var worker = WorkerFactory.CreateWorker(opsModel, useCPUforBaking))
{
var bakedConstant = worker.Execute(layerInputs).PeekOutput();
bakedConstant.TakeOwnership();
knownLayersValue[layer.name] = bakedConstant;
newKnownLayers.Add(layer.name);
}
}
// clear allocated tensors
foreach (var l in knownLayersValue)
{
l.Value.Dispose();
}
// remove unused constants
var removeUnusedLayersPass = new Cleanup.RemoveUnusedLayersPass();
removeUnusedLayersPass.Run(ref model);
}
public void LoadModel(NNModel model)
{
mRuntimeModel = ModelLoader.Load(model);
mWorker = WorkerFactory.CreateWorker(WorkerFactory.Type.ComputePrecompiled, mRuntimeModel);
Debug.Log("model loaded");
}
public override IWorkFacade[] InitWorkFacades(IInputDataContainer inputDataContainer, int dataSize)
{
return(new[]
{
#region IJob
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDefaultJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDefaultJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeFastJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeFastJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeStrictJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeStrictJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDeterministicJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDeterministicJob>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJob(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
#region IJobParallelFor
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDefaultJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDefaultJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeFastJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeFastJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeStrictJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeStrictJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDeterministicJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
WorkerFactory <NativeArray <float>, NativeArray <float> > .Create <BurstFloatModeDeterministicJobParallelFor>(
TestName(),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
inputDataContainer.GetData <float>(TypeConfig.DataFloat1),
new WorkConfigIJobParallelFor(false),
new IDataConfig[]
{
new DataConfigUnityCollection(Allocator.Persistent),
new DataConfigUnityCollection(Allocator.Persistent),
}
),
#endregion
});
}
public void OnGUI()
{
m_position = EditorGUILayout.Vector3Field("Position the agent should point to:", m_position);
kinectModel = EditorGUILayout.ObjectField("Kinect NN model:", kinectModel, typeof(NNModel), false) as NNModel;
leapModel = EditorGUILayout.ObjectField("Leap NN model:", leapModel, typeof(NNModel), false) as NNModel;
exampleKinectClip = EditorGUILayout.ObjectField("Example Kinect Animation Clip", exampleKinectClip, typeof(AnimationClip), false) as AnimationClip;
exampleLeapClip = EditorGUILayout.ObjectField("Example Leap Animation Clip", exampleLeapClip, typeof(AnimationClip), false) as AnimationClip;
if (GUILayout.Button("Load Animation Clips"))
{
string filePath = Application.dataPath + "/Resources/Recordings/";
animList.Clear();
LoadAnimationClips(animList, filePath);
Debug.Log(animList.Count);
}
if (GUILayout.Button("Save Clips Data to CSV Files"))
{
// Retrieve current time in MM-dd-yyyy_HH:mm:ss
DateTime now = DateTime.Now;
string path = Application.dataPath + "/Resources/clips2csv_" + now.ToString("MM-dd-yyyy_HH-mm-ss");
TextWriter sw_Kinect = new StreamWriter(path + "_Kinect.csv");
TextWriter sw_Leap = new StreamWriter(path + "_Leap.csv");
StringBuilder line_Kinect = new StringBuilder(); // each line in the csv file
StringBuilder line_Leap = new StringBuilder();
foreach (AnimationClip clip in animList)
{
line_Kinect.Clear();
line_Leap.Clear();
if (clip != null)
{
string device = clip.name.Split('_')[0];
if (device == "Kinect")
{
RecordAnimationClip2CSV(clip, line_Kinect, sw_Kinect, true);
}
else if (device == "LeapRight")
{
RecordAnimationClip2CSV(clip, line_Leap, sw_Leap, false);
}
}
}
sw_Kinect.Close();
sw_Leap.Close();
// Debug.Log(maxPos);
// Debug.Log(maxRot);
// Debug.Log(minScale);
}
if (GUILayout.Button("Recorded transform path and property name"))
{
// Retrieve current time in MM-dd-yyyy_HH:mm:ss
DateTime now = DateTime.Now;
string path = Application.dataPath + "/Resources/propertynames_" + now.ToString("MM-dd-yyyy_HH-mm-ss");
TextWriter sw_Kinect = new StreamWriter(path + "_Kinect.csv");
TextWriter sw_Leap = new StreamWriter(path + "_Leap.csv");
RecordPathPropertyNames(exampleKinectClip, sw_Kinect, true);
RecordPathPropertyNames(exampleLeapClip, sw_Leap, false);
sw_Kinect.Close();
sw_Leap.Close();
}
if (GUILayout.Button("Inference Kinect Body from Position"))
{
var model = ModelLoader.Load(kinectModel, false);
var worker = WorkerFactory.CreateWorker(WorkerFactory.Type.Compute, model);
// Pass the input to the model
Tensor input = new Tensor(1, 10, new float[10] {
m_position.x, m_position.y, m_position.z, 0f, 0f, 1.25f, 0f, 0.5f, 0f, 1.8f
}); // create a tensor
// Debug.Log(input[0]);
var output = worker.Execute(input).PeekOutput();
// From the example Kinect animation clip, find all transforms that the output should match with
// and assign each output to either position, rotation, or scale of the transform
InferenceFromModel(true, output);
}
if (GUILayout.Button("Inference Leap Hand from Position"))
{
var model = ModelLoader.Load(leapModel, false);
var worker = WorkerFactory.CreateWorker(WorkerFactory.Type.Compute, model);
// Pass the input to the model
Tensor input = new Tensor(1, 10, new float[10] {
m_position.x, m_position.y, m_position.z, 0f, 0f, 1.25f, 0f, 0.5f, 0f, 1.8f
}); // create a tensor
// Debug.Log(input[0]);
var output = worker.Execute(input).PeekOutput();
// From the example Leap animation clip, find all transforms that the output should match with
// and assign each output to either position, rotation, or scale of the transform
InferenceFromModel(false, output);
// Debug.Log(i);
// Debug.Log(GameObject.Find("R_Palm").transform.localRotation);
}
}
private void Start()
{
// デバッグ用ファイルを開く
//Encoding enc = Encoding.GetEncoding("Shift_JIS");
//var csvPath = System.IO.Path.Combine(Application.streamingAssetsPath, "data.csv");
//writer = new StreamWriter(csvPath, false, enc);
if (DebugMode)
{
if (User3Input)
{
UpdateVNectModel = new UpdateVNectModelDelegate(UpdateVNectAsync);
}
else
{
UpdateVNectModel = new UpdateVNectModelDelegate(UpdateVNect);
}
/*
* var streamingPath = System.IO.Path.Combine(Application.streamingAssetsPath, HighQualityModelName);
* var writer = new BinaryWriter(new FileStream(streamingPath, FileMode.Create));
* writer.Write(NNModel.modelData.Value);
* writer.Close();
*/
_model = ModelLoader.Load(NNModel, Verbose);
}
else
{
var streamingPath = System.IO.Path.Combine(Application.streamingAssetsPath, HighQualityModelName);
if (!File.Exists(streamingPath))
{
ModelQuality = 0;
}
if (ModelQuality == 0)
{
InputImageSize = 224;
HeatMapCol = 14;
User3Input = false;
UpdateVNectModel = new UpdateVNectModelDelegate(UpdateVNect);
_model = ModelLoader.Load(NNModel, Verbose);
}
else
{
InputImageSize = 448;
HeatMapCol = 28;
User3Input = true;
UpdateVNectModel = new UpdateVNectModelDelegate(UpdateVNectAsync);
_model = ModelLoader.LoadFromStreamingAssets(streamingPath);
}
}
// Init VideoCapture
videoCapture.Init(InputImageSize, InputImageSize);
videoCapture.VideoReady += videoCapture_VideoReady;
HeatMapCol_Half = HeatMapCol / 2;
HeatMapCol_Squared = HeatMapCol * HeatMapCol;
HeatMapCol_Cube = HeatMapCol * HeatMapCol * HeatMapCol;
HeatMapCol_JointNum = HeatMapCol * JointNum;
heatMap2D = new float[JointNum * HeatMapCol_Squared];
offset2D = new float[JointNum * HeatMapCol_Squared * 2];
heatMap3D = new float[JointNum * HeatMapCol_Cube];
offset3D = new float[JointNum * HeatMapCol_Cube * 3];
InputImageSizeF = InputImageSize;
InputImageSizeHalf = InputImageSizeF / 2f;
unit = 1f / (float)HeatMapCol;
cubeOffsetLinear = HeatMapCol * JointNum_Cube;
cubeOffsetSquared = HeatMapCol_Squared * JointNum_Cube;
// Disabel sleep
Screen.sleepTimeout = SleepTimeout.NeverSleep;
_worker = WorkerFactory.CreateWorker(WorkerType, _model, Verbose);
StartCoroutine("WaitLoad");
var texture = new RenderTexture(InputImageSize, InputImageSize, 0, RenderTextureFormat.RGB565, RenderTextureReadWrite.sRGB)
{
useMipMap = false,
autoGenerateMips = false,
wrapMode = TextureWrapMode.Clamp,
filterMode = FilterMode.Point,
};
if (User3Input)
{
inputs[inputName_1] = new Tensor(texture, 3);
inputs[inputName_2] = new Tensor(texture, 3);
inputs[inputName_3] = new Tensor(texture, 3);
_worker.Execute(inputs);
inputs[inputName_1].Dispose();
inputs[inputName_2].Dispose();
inputs[inputName_3].Dispose();
}
else
{
input = new Tensor(texture, 3);
_worker.Execute(input);
input.Dispose();
}
}
/// <summary>
/// Constructeur initialisant un TaskExecutor avec la méthode de traitement métier pour le Module 1
/// </summary>
/// <param name="name"></param>
/// <param name="adress"></param>
/// <param name="port"></param>
public DnaClient(string name, string adress, int port) : base(name, adress, port)
{
WorkerFactory.AddWorker(DnaQuantMethod, new TaskExecutor(this, DnaQuantStatDisplay, null, null));
}
public void MLP_Calc()
{
TensorCachingAllocator tca = new TensorCachingAllocator();
var shape = new MultiLayerPerception.Shape {
inputSize = 2,
outputSize = 3,
hiddenSize = 2
};
MultiLayerPerception mlp = new MultiLayerPerception(shape);
int layerCnt = 0;
foreach (Layer layer in mlp.model.layers)
{
layerCnt++;
for (int iWB = 0; iWB < layer.weights.Length; iWB++)
{
layer.weights[iWB] = iWB * layerCnt;
}
if (layer.datasets.Length == 2)
{
Debug.Log($"" +
$"{layer.name} " +
$"({layer.weights.Length}: W{layer.datasets[0].length} + B{layer.datasets[1].length}): " +
$"<{string.Join(", ", layer.weights)}>");
}
}
string HiddenLayer = MultiLayerPerception.LayerNames.Hidden;
IWorker worker = WorkerFactory.CreateWorker(mlp.model, new string[] { HiddenLayer }, WorkerFactory.Device.GPU);
Tensor inTensor = tca.Alloc(new TensorShape(1, 1, 1, shape.inputSize));
for (int i = 0; i < shape.inputSize; i++)
{
inTensor[i] = i;
Debug.Log($"input[{i}] = {inTensor[i]}");
}
IWorker ex = worker.Execute(inTensor);
ex.FlushSchedule(true);
Tensor hTensor = ex.PeekOutput(HiddenLayer);
Debug.Assert(hTensor.length == shape.hiddenSize);
for (int i = 0; i < hTensor.length; i++)
{
Debug.Log($"hidden1[{i}] = {hTensor[i]}");
}
Tensor output = ex.PeekOutput();
Debug.Assert(output.length == shape.outputSize);
for (int i = 0; i < output.length; i++)
{
Debug.Log($"output[{i}] = {output[i]}");
}
for (int iHNode = 0; iHNode < shape.hiddenSize; iHNode++)
{
string str = "";
float sum = 0;
for (int iINode = 0; iINode < shape.inputSize; iINode++)
{
float w = mlp.GetWeight(HiddenLayer, iINode, iHNode);
str += $"{w} * {inTensor[iINode]} + ";
sum += w * inTensor[iINode];
}
float b = mlp.GetBias(HiddenLayer, iHNode);
str += $"{b}";
sum += b;
str += $"= {hTensor[iHNode]} ({sum})";
Debug.Assert(Mathf.Approximately(sum, hTensor[iHNode]));
Debug.Log(str);
}
tca.Dispose();
ex.Dispose();
worker.Dispose();
Debug.Assert(true);
}
private void Start()
{
var model = ModelLoader.Load(ModelFinalOut);
worker = WorkerFactory.CreateWorker(WorkerFactory.Type.ComputePrecompiled, model);
}
private static async Task ProcessJobs(CancellationToken cancellationToken)
{
IWorker worker = null;
ClientJob job = null;
var whenLastJobCompleted = DateTime.MinValue;
var waitForMoreJobs = false;
while (!cancellationToken.IsCancellationRequested)
{
var allJobs = _jobs.GetAll();
// Dequeue the first job. We will only pass jobs that have
// the same SpanId to the current worker.
job = allJobs.FirstOrDefault(newJob =>
{
// If the job is null then we don't have a span id to match against.
// Otherwise we want to pick jobs with the same span id.
return(job == null || string.Equals(newJob.SpanId, job.SpanId, StringComparison.OrdinalIgnoreCase));
});
if (job != null)
{
// A spanId means that a span is defined and we might run
// multiple jobs.
if (!string.IsNullOrEmpty(job.SpanId))
{
waitForMoreJobs = true;
}
if (job.State == ClientState.Waiting)
{
Log($"Starting '{job.Client}' worker");
Log($"Current Job SpanId '{job.SpanId}'");
job.State = ClientState.Starting;
try
{
if (worker == null)
{
worker = WorkerFactory.CreateWorker(job);
}
if (worker == null)
{
Log($"Error while creating the worker");
job.State = ClientState.Deleting;
whenLastJobCompleted = DateTime.UtcNow;
}
else
{
await worker.StartJobAsync(job);
}
}
catch (Exception e)
{
Log($"An unexpected error occurred while starting the job {job.Id}");
Log(e.ToString());
job.State = ClientState.Deleting;
}
}
else if (job.State == ClientState.Running || job.State == ClientState.Completed)
{
var now = DateTime.UtcNow;
// Clean the job in case the driver is not running
if (now - job.LastDriverCommunicationUtc > TimeSpan.FromSeconds(30))
{
Log($"Driver didn't communicate for {now - job.LastDriverCommunicationUtc}. Halting job.");
job.State = ClientState.Deleting;
}
}
else if (job.State == ClientState.Deleting)
{
Log($"Deleting job {worker?.JobLogText ?? "no worker found"}");
try
{
if (worker != null)
{
await worker.StopJobAsync();
// Reset the last job completed indicator.
whenLastJobCompleted = DateTime.UtcNow;
}
}
finally
{
_jobs.Remove(job.Id);
job = null;
}
}
}
await Task.Delay(100);
// job will be null if there aren't any more jobs with the same spanId.
if (job == null)
{
// Currently no jobs with the same span id exist so we check if we can
// clear out the worker to signal to the worker factory to create
// a new one.
if (worker != null)
{
var now = DateTime.UtcNow;
// Disposing the worker conditions
// 1. A span isn't defined so there won't be any more jobs for this worker
// 2. We check that whenLastJob completed is something other that it's default value
// and 10 seconds have passed since the last job was completed.
if (!waitForMoreJobs || (whenLastJobCompleted != DateTime.MinValue && now - whenLastJobCompleted > TimeSpan.FromSeconds(10)))
{
Log("Job queuing timeout reached. Disposing worker.");
waitForMoreJobs = false;
await worker.DisposeAsync();
worker = null;
}
}
}
}
}
public async Task CheckThreads(int messageCount, int workers, int parallelism, WorkerFactory workerFactory)
{
var(activator, starter) = CreateBus(workers, parallelism, workerFactory);
var counter = new SharedCounter(messageCount);
activator.Handle <string>(async str => counter.Decrement());
var bus = starter.Start();
await Task.WhenAll(Enumerable.Range(0, messageCount)
.Select(n => bus.SendLocal($"THIS IS MESSAGE {n}")));
counter.WaitForResetEvent(timeoutSeconds: 15);
}
public NNHandler(NNModel nnmodel)
{
model = ModelLoader.Load(nnmodel);
worker = WorkerFactory.CreateWorker(model);
}
/// <summary>
/// Initialized a new World.
/// </summary>
public void initialize(string saveName, NewWorldSettings settings)
{
this.saveName = saveName;
this.seed = settings.getSeed();
this.preInitialization();
// Create land plots
this.plotManager.initializeFirstTime(this.seed);
// Generate the map.
for (int depth = 0; depth < this.storage.layerCount; depth++)
{
this.mapGenerator.generateLayer(this, depth);
}
// Unlock the plot that contains the Dumptruck and set the
// Worker spawn point.
List <CellBehaviorMasterDepositPoint> masters = this.getAllBehaviors <CellBehaviorMasterDepositPoint>();
if (masters.Count > 0)
{
CellBehaviorMasterDepositPoint m = masters[0];
this.plotManager.getPlot(m.pos.x, m.pos.y).isOwned = true;
this.storage.workerSpawnPoint = m.pos.add(-1, -1);
}
else
{
Debug.LogWarning("No MasterDepositPoint could be found when generating a new map, there must always be at least one!");
}
// Set starting money.
this.money.value = this.mapGenerator.startingMoney;
// Setup the new Player.
CameraController.instance.initNewPlayer(settings);
// Spawn the starting Workers.
WorkerFactory factory = Main.instance.workerFactory;
foreach (WorkerType workerType in this.mapGenerator.startingWorkers)
{
if (workerType != null)
{
int xShift = UnityEngine.Random.Range(-1, 2);
int yShift = UnityEngine.Random.Range(0, 2);
EntityWorker worker = factory.spawnWorker(
this,
this.storage.workerSpawnPoint.add(xShift, -yShift),
factory.generateWorkerInfo(workerType, Main.instance.personalityRegistry.getDefaultPersonality()),
workerType);
}
}
this.postInitialization();
CameraController.instance.changeLayer(this.mapGenerator.playerStartLayer);
}
public static void FuseConstants(ref Model model)
{
var knownLayersValue = new Dictionary <string, Tensor>();
var newKnownLayers = new HashSet <string>();
var keepLayers = new HashSet <string>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (layer.flags == Layer.Flags.Preserve)
{
keepLayers.Add(layer.name);
}
// NN is a directed graph, if we just fused constants + shapes, update following nodes
// TODO optimization, pass in index, or add shape
if (ModelOptimizer.IsLayerConstant(layer))
{
knownLayersValue[layer.name] = new Tensor(layer.datasets[0].shape, layer.weights);
}
bool allInputsAreKnown = layer.inputs.Length > 0 ? knownLayersValue.ContainsKey(layer.inputs[0]) : false;
for (int i = 1; i < layer.inputs.Length; i++)
{
allInputsAreKnown &= knownLayersValue.ContainsKey(layer.inputs[i]);
}
// if all inputs are known, execute layer
if (!allInputsAreKnown)
{
continue;
}
var layerInputs = new Dictionary <string, Tensor>();
var opsModel = new Model();
for (int i = 0; i < layer.inputs.Length; i++)
{
Model.Input input;
input.name = layer.inputs[i];
input.shape = knownLayersValue[input.name].shape.ToArray();
input.rank = knownLayersValue[input.name].shape.dimensions;
opsModel.inputs.Add(input);
layerInputs[input.name] = knownLayersValue[input.name];
}
opsModel.layers.Add(layer);
opsModel.outputs.Add(layer.name);
// bake
var useCPUforBaking = WorkerFactory.Device.CPU;
using (var worker = WorkerFactory.CreateWorker(opsModel, useCPUforBaking))
{
// TODO use ModelIR2RunnableNCHWPass
var bakedConstant = worker.Execute(layerInputs).PeekOutput();
bakedConstant.TakeOwnership();
knownLayersValue[layer.name] = bakedConstant;
newKnownLayers.Add(layer.name);
}
}
// remove new baked layers since we will insert constants for those
model.layers.RemoveAll(x => newKnownLayers.Contains(x.name) && !keepLayers.Contains(x.name));
// TODO use ModelBuilder?
foreach (var l in newKnownLayers)
{
if (keepLayers.Contains(l))
{
continue;
}
var name = l;
var tensor = knownLayersValue[name];
Layer c = new Layer(name, Layer.Type.Load);
c.datasets = new Layer.DataSet[1];
c.datasets[0].name = name;
c.datasets[0].shape = tensor.shape;
c.datasets[0].itemSizeInBytes = 4;
c.datasets[0].length = tensor.shape.length;
c.datasets[0].offset = 0;
c.axis = tensor.shape.dimensions;
c.weights = new BarracudaArray(tensor.length);
BarracudaArray.Copy(tensor.ToReadOnlyArray(), c.weights, tensor.length);
model.layers.Insert(0, c);
}
// clear allocated tensors
foreach (var l in knownLayersValue)
{
l.Value.Dispose();
}
// remove unused constants
var removeUnusedLayersPass = new Cleanup.RemoveUnusedLayersPass();
removeUnusedLayersPass.Run(ref model);
}
private void FuseShapesIntoConstants(ref Model model, IDictionary <string, TensorShape?> shapesByName, IDictionary <string, int?> ranksByName, ref List <Model.ImporterWarning> warnings)
{
var toRunnableNCHW = new IntermediateToRunnableNCHWPass();
var knownLayersValue = new Dictionary <string, Tensor>();
var newKnownLayers = new HashSet <string>();
var keepLayers = new HashSet <string>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (layer.flags == Layer.Flags.Preserve)
{
keepLayers.Add(layer.name);
}
// NN is a directed graph, if we just fused constants + shapes, update following nodes
// re-evaluate shapes
FuseInputsIntoLayer(ref layer, knownLayersValue, ranksByName, warnings);
// TODO optimization, pass in index, or add shape
IRShapeInferenceHelper.RankInference.UpdateKnownTensorRanks(model, ranksByName);
IRShapeInferenceHelper.ShapeInference.UpdateKnownTensorShapesNCHW(model, ranksByName, ref shapesByName);
if (ModelOptimizer.IsLayerConstant(layer))
{
knownLayersValue[layer.name] = new Tensor(layer.datasets[0].shape, layer.weights);
}
else if (layer.type == Layer.Type.Shape)
{
// assert inputs.Lenght == 1
var input = layer.inputs[0];
if (shapesByName.ContainsKey(input) && shapesByName[input] != null &&
ranksByName.ContainsKey(input) && ranksByName[input] != null
)
{
var shape = shapesByName[input].Value;
var rank = ranksByName[input].Value;
knownLayersValue[layer.name] = ShapeToNCHWTensor(shape, rank);
newKnownLayers.Add(layer.name);
continue;
}
}
bool allInputsAreKnown = layer.inputs.Length > 0 ? knownLayersValue.ContainsKey(layer.inputs[0]) : false;
for (int i = 1; i < layer.inputs.Length; i++)
{
allInputsAreKnown &= knownLayersValue.ContainsKey(layer.inputs[i]);
}
// if all inputs are known, execute layer
if (!allInputsAreKnown)
{
continue;
}
var layerInputs = new Dictionary <string, Tensor>();
var opsModel = new Model();
opsModel.layout = "iNCHW";
for (int i = 0; i < layer.inputs.Length; i++)
{
Model.Input input;
input.name = layer.inputs[i];
input.shape = shapesByName[input.name].Value.ToArray();
input.rank = ranksByName[input.name].Value;
opsModel.inputs.Add(input);
layerInputs[input.name] = knownLayersValue[input.name];
}
Layer newLayer = new Layer(layer.name.ToString(), layer.activation);
newLayer.type = layer.type;
newLayer.activation = layer.activation;
newLayer.pad = layer.pad.ToArray();
newLayer.stride = layer.stride.ToArray();
newLayer.pool = layer.pool.ToArray();
newLayer.axis = layer.axis;
newLayer.alpha = layer.alpha;
newLayer.beta = layer.beta;
newLayer.inputs = layer.inputs.ToArray();
newLayer.datasets = layer.datasets;
newLayer.weights = layer.weights;
if (layer.outputs != null)
{
newLayer.outputs = layer.outputs.ToArray();
}
if (layer.axes != null)
{
newLayer.axes = layer.axes.ToArray();
}
opsModel.layers.Add(newLayer);
opsModel.outputs.Add(newLayer.name);
toRunnableNCHW.Run(ref opsModel);
// bake
var useCPUforBaking = WorkerFactory.Device.CPU;
using (var worker = WorkerFactory.CreateWorker(opsModel, useCPUforBaking))
{
var bakedConstant = worker.Execute(layerInputs).CopyOutput();
knownLayersValue[layer.name] = bakedConstant;
newKnownLayers.Add(layer.name);
}
}
// remove new baked layers since we will insert constants for those
model.layers.RemoveAll(x => newKnownLayers.Contains(x.name) && !keepLayers.Contains(x.name));
// TODO use ModelBuilder?
foreach (var l in newKnownLayers)
{
if (keepLayers.Contains(l))
{
continue;
}
var name = l;
var tensor = knownLayersValue[name];
Layer c = new Layer(name, Layer.Type.Load);
c.datasets = new Layer.DataSet[1];
c.datasets[0].name = name;
c.datasets[0].shape = tensor.shape;
c.datasets[0].itemSizeInBytes = 4;
c.datasets[0].length = tensor.shape.length;
c.datasets[0].offset = 0;
c.axis = ranksByName[c.name].Value;
c.weights = new BarracudaArray(tensor.length);
BarracudaArray.Copy(tensor.ToReadOnlyArray(), c.weights, tensor.length);
model.layers.Insert(0, c);
}
foreach (var l in knownLayersValue)
{
l.Value.Dispose();
}
// TODO remove?
// remove unused constants
var removeUnusedLayersPass = new Cleanup.RemoveUnusedLayersPass();
removeUnusedLayersPass.Run(ref model);
}
private void InitializeWorkers()
{
_workerFactory = new WorkerFactory();
_laborConverter = new LaborConverter(_workerFactory, _shiftConverter, _skillConverter);
_workers = new ConcurrentDictionary<int, IWorker>();
_laborsMap = new ConcurrentDictionary<int, ILabor>();
}
