using System.Collections.Generic; using Unity.Barracuda; using UnityEngine.Profiling; using Unity.MLAgents.Actuators; using Unity.MLAgents.Policies; using Unity.MLAgents.Sensors; namespace Unity.MLAgents.Inference { internal struct AgentInfoSensorsPair { public AgentInfo agentInfo; public List sensors; } internal class ModelRunner { List m_Infos = new List(); Dictionary m_LastActionsReceived = new Dictionary(); List m_OrderedAgentsRequestingDecisions = new List(); ITensorAllocator m_TensorAllocator; TensorGenerator m_TensorGenerator; TensorApplier m_TensorApplier; NNModel m_Model; string m_ModelName; InferenceDevice m_InferenceDevice; IWorker m_Engine; bool m_Verbose = false; string[] m_OutputNames; IReadOnlyList m_InferenceInputs; List m_InferenceOutputs; Dictionary m_InputsByName; Dictionary> m_Memories = new Dictionary>(); SensorShapeValidator m_SensorShapeValidator = new SensorShapeValidator(); bool m_VisualObservationsInitialized; /// /// Initializes the Brain with the Model that it will use when selecting actions for /// the agents /// /// The Barracuda model to load /// Description of the actions for the Agent. /// Inference execution device. CPU is the fastest /// option for most of ML Agents models. /// The seed that will be used to initialize the RandomNormal /// and Multinomial objects used when running inference. /// Throws an error when the model is null /// public ModelRunner( NNModel model, ActionSpec actionSpec, InferenceDevice inferenceDevice = InferenceDevice.CPU, int seed = 0) { Model barracudaModel; m_Model = model; m_ModelName = model.name; 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 = barracudaModel.GetInputTensors(); m_OutputNames = barracudaModel.GetOutputNames(); m_TensorGenerator = new TensorGenerator( seed, m_TensorAllocator, m_Memories, barracudaModel); m_TensorApplier = new TensorApplier( actionSpec, seed, m_TensorAllocator, m_Memories, barracudaModel); m_InputsByName = new Dictionary(); m_InferenceOutputs = new List(); } public InferenceDevice InferenceDevice { get { return m_InferenceDevice; } } public NNModel Model { get { return m_Model; } } void PrepareBarracudaInputs(IReadOnlyList infInputs) { m_InputsByName.Clear(); for (var i = 0; i < infInputs.Count; i++) { var inp = infInputs[i]; m_InputsByName[inp.name] = inp.data; } } public void Dispose() { if (m_Engine != null) m_Engine.Dispose(); m_TensorAllocator?.Reset(false); } void FetchBarracudaOutputs(string[] names) { m_InferenceOutputs.Clear(); foreach (var n in names) { var output = m_Engine.PeekOutput(n); m_InferenceOutputs.Add(TensorUtils.TensorProxyFromBarracuda(output, n)); } } public void PutObservations(AgentInfo info, List sensors) { #if DEBUG m_SensorShapeValidator.ValidateSensors(sensors); #endif m_Infos.Add(new AgentInfoSensorsPair { agentInfo = info, sensors = sensors }); // We add the episodeId to this list to maintain the order in which the decisions were requested m_OrderedAgentsRequestingDecisions.Add(info.episodeId); if (!m_LastActionsReceived.ContainsKey(info.episodeId)) { m_LastActionsReceived[info.episodeId] = ActionBuffers.Empty; } if (info.done) { // If the agent is done, we remove the key from the last action dictionary since no action // should be taken. m_LastActionsReceived.Remove(info.episodeId); } } public void DecideBatch() { var currentBatchSize = m_Infos.Count; if (currentBatchSize == 0) { return; } if (!m_VisualObservationsInitialized) { // Just grab the first agent in the collection (any will suffice, really). // We check for an empty Collection above, so this will always return successfully. var firstInfo = m_Infos[0]; m_TensorGenerator.InitializeObservations(firstInfo.sensors, m_TensorAllocator); m_VisualObservationsInitialized = true; } Profiler.BeginSample("ModelRunner.DecideAction"); Profiler.BeginSample(m_ModelName); Profiler.BeginSample($"GenerateTensors"); // Prepare the input tensors to be feed into the engine m_TensorGenerator.GenerateTensors(m_InferenceInputs, currentBatchSize, m_Infos); Profiler.EndSample(); Profiler.BeginSample($"PrepareBarracudaInputs"); PrepareBarracudaInputs(m_InferenceInputs); Profiler.EndSample(); // Execute the Model Profiler.BeginSample($"ExecuteGraph"); m_Engine.Execute(m_InputsByName); Profiler.EndSample(); Profiler.BeginSample($"FetchBarracudaOutputs"); FetchBarracudaOutputs(m_OutputNames); Profiler.EndSample(); Profiler.BeginSample($"ApplyTensors"); // Update the outputs m_TensorApplier.ApplyTensors(m_InferenceOutputs, m_OrderedAgentsRequestingDecisions, m_LastActionsReceived); Profiler.EndSample(); Profiler.EndSample(); // end name Profiler.EndSample(); // end ModelRunner.DecideAction m_Infos.Clear(); m_OrderedAgentsRequestingDecisions.Clear(); } public bool HasModel(NNModel other, InferenceDevice otherInferenceDevice) { return m_Model == other && m_InferenceDevice == otherInferenceDevice; } public ActionBuffers GetAction(int agentId) { if (m_LastActionsReceived.ContainsKey(agentId)) { return m_LastActionsReceived[agentId]; } return ActionBuffers.Empty; } } }