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4 年前
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e04d8731
共有 16 个文件被更改，包括 486 次插入 和 4 次删除
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6README.md
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6com.unity.ml-agents/CHANGELOG.md
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4com.unity.ml-agents/Documentation~/com.unity.ml-agents.md
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10com.unity.ml-agents/Runtime/Inference/ModelRunner.cs
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24com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs
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5com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs
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3com.unity.ml-agents/Runtime/Analytics.meta
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3com.unity.ml-agents/Tests/Editor/Analytics.meta
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89com.unity.ml-agents/Runtime/Analytics/Events.cs
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3com.unity.ml-agents/Runtime/Analytics/Events.cs.meta
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263com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs
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3com.unity.ml-agents/Runtime/Analytics/InferenceAnalytics.cs.meta
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68com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs
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3com.unity.ml-agents/Tests/Editor/Analytics/InferenceAnalyticsTests.cs.meta
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fileFormatVersion: 2 |
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guid: 8b12ac54c5224758af88c67e2af4a01e |
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timeCreated: 1604359666 |
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fileFormatVersion: 2 |
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guid: adbf291ff40848a296523d69a5be65a5 |
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timeCreated: 1607379470 |
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using System; |
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using System.Collections.Generic; |
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using Unity.MLAgents.Actuators; |
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using Unity.MLAgents.Sensors; |
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namespace Unity.MLAgents.Analytics |
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{ |
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internal struct InferenceEvent |
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{ |
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/// <summary>
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/// Hash of the BehaviorName.
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/// </summary>
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public string BehaviorName; |
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public string BarracudaModelSource; |
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public string BarracudaModelVersion; |
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public string BarracudaModelProducer; |
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public string BarracudaPackageVersion; |
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/// <summary>
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/// Whether inference is performed on CPU (0) or GPU (1).
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/// </summary>
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public int InferenceDevice; |
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public List<EventObservationSpec> ObservationSpecs; |
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public EventActionSpec ActionSpec; |
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public int MemorySize; |
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public long TotalWeightSizeBytes; |
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public string ModelHash; |
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} |
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/// <summary>
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/// Simplified version of ActionSpec struct for use in analytics
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/// </summary>
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[Serializable] |
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internal struct EventActionSpec |
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{ |
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public int NumContinuousActions; |
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public int NumDiscreteActions; |
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public int[] BranchSizes; |
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public static EventActionSpec FromActionSpec(ActionSpec actionSpec) |
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{ |
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var branchSizes = actionSpec.BranchSizes ?? Array.Empty<int>(); |
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return new EventActionSpec |
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{ |
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NumContinuousActions = actionSpec.NumContinuousActions, |
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NumDiscreteActions = actionSpec.NumDiscreteActions, |
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BranchSizes = branchSizes, |
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}; |
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} |
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} |
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/// <summary>
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/// Information about one dimension of an observation.
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/// </summary>
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[Serializable] |
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internal struct EventObservationDimensionInfo |
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{ |
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public int Size; |
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public int Flags; |
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} |
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/// <summary>
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/// Simplified summary of Agent observations for use in analytics
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/// </summary>
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[Serializable] |
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internal struct EventObservationSpec |
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{ |
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public string SensorName; |
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public string CompressionType; |
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public EventObservationDimensionInfo[] DimensionInfos; |
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public static EventObservationSpec FromSensor(ISensor sensor) |
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{ |
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var shape = sensor.GetObservationShape(); |
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var dimInfos = new EventObservationDimensionInfo[shape.Length]; |
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for (var i = 0; i < shape.Length; i++) |
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{ |
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dimInfos[i].Size = shape[i]; |
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// TODO copy flags when we have them
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} |
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return new EventObservationSpec |
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{ |
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SensorName = sensor.GetName(), |
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CompressionType = sensor.GetCompressionType().ToString(), |
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DimensionInfos = dimInfos, |
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}; |
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} |
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} |
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} |
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fileFormatVersion: 2 |
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guid: 0a0d7cda6d74425a80775769a9283ba6 |
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timeCreated: 1604359798 |
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using System; |
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using System.Collections.Generic; |
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using Unity.Barracuda; |
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using Unity.MLAgents.Actuators; |
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using Unity.MLAgents.Inference; |
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using Unity.MLAgents.Policies; |
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using Unity.MLAgents.Sensors; |
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using UnityEngine; |
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using UnityEngine.Analytics; |
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#if UNITY_EDITOR
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using UnityEditor; |
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using UnityEditor.Analytics; |
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#endif
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namespace Unity.MLAgents.Analytics |
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{ |
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internal class InferenceAnalytics |
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{ |
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const string k_VendorKey = "unity.ml-agents"; |
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const string k_EventName = "ml_agents_inferencemodelset"; |
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/// <summary>
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/// Whether or not we've registered this particular event yet
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/// </summary>
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static bool s_EventRegistered = false; |
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/// <summary>
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/// Hourly limit for this event name
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/// </summary>
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const int k_MaxEventsPerHour = 1000; |
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/// <summary>
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/// Maximum number of items in this event.
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/// </summary>
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const int k_MaxNumberOfElements = 1000; |
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/// <summary>
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/// Models that we've already sent events for.
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/// </summary>
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private static HashSet<NNModel> s_SentModels; |
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static bool EnableAnalytics() |
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{ |
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if (s_EventRegistered) |
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{ |
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return true; |
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} |
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#if UNITY_EDITOR
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AnalyticsResult result = EditorAnalytics.RegisterEventWithLimit(k_EventName, k_MaxEventsPerHour, k_MaxNumberOfElements, k_VendorKey); |
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#else
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AnalyticsResult result = AnalyticsResult.UnsupportedPlatform; |
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#endif
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if (result == AnalyticsResult.Ok) |
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{ |
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s_EventRegistered = true; |
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} |
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if (s_EventRegistered && s_SentModels == null) |
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{ |
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s_SentModels = new HashSet<NNModel>(); |
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} |
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return s_EventRegistered; |
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} |
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public static bool IsAnalyticsEnabled() |
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{ |
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#if UNITY_EDITOR
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return EditorAnalytics.enabled; |
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#else
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return false; |
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#endif
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} |
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/// <summary>
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/// Send an analytics event for the NNModel when it is set up for inference.
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/// No events will be sent if analytics are disabled, and at most one event
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/// will be sent per model instance.
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/// </summary>
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/// <param name="nnModel">The NNModel being used for inference.</param>
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/// <param name="behaviorName">The BehaviorName of the Agent using the model</param>
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/// <param name="inferenceDevice">Whether inference is being performed on the CPU or GPU</param>
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/// <param name="sensors">List of ISensors for the Agent. Used to generate information about the observation space.</param>
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/// <param name="actionSpec">ActionSpec for the Agent. Used to generate information about the action space.</param>
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/// <returns></returns>
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public static void InferenceModelSet( |
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NNModel nnModel, |
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string behaviorName, |
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InferenceDevice inferenceDevice, |
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IList<ISensor> sensors, |
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ActionSpec actionSpec |
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) |
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{ |
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// The event shouldn't be able to report if this is disabled but if we know we're not going to report
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// Lets early out and not waste time gathering all the data
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if (!IsAnalyticsEnabled()) |
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return; |
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if (!EnableAnalytics()) |
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return; |
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var added = s_SentModels.Add(nnModel); |
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if (!added) |
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{ |
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// We previously added this model. Exit so we don't resend.
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return; |
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} |
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var data = GetEventForModel(nnModel, behaviorName, inferenceDevice, sensors, actionSpec); |
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// Note - to debug, use JsonUtility.ToJson on the event.
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// Debug.Log(JsonUtility.ToJson(data, true));
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#if UNITY_EDITOR
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EditorAnalytics.SendEventWithLimit(k_EventName, data); |
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#else
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return; |
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#endif
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} |
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/// <summary>
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/// Generate an InferenceEvent for the model.
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/// </summary>
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/// <param name="nnModel"></param>
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/// <param name="behaviorName"></param>
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/// <param name="inferenceDevice"></param>
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/// <param name="sensors"></param>
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/// <param name="actionSpec"></param>
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/// <returns></returns>
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internal static InferenceEvent GetEventForModel( |
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NNModel nnModel, |
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string behaviorName, |
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InferenceDevice inferenceDevice, |
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IList<ISensor> sensors, |
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ActionSpec actionSpec |
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) |
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{ |
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var barracudaModel = ModelLoader.Load(nnModel); |
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var inferenceEvent = new InferenceEvent(); |
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// Hash the behavior name so that there's no concern about PII or "secret" data being leaked.
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var behaviorNameHash = Hash128.Compute(behaviorName); |
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inferenceEvent.BehaviorName = behaviorNameHash.ToString(); |
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inferenceEvent.BarracudaModelSource = barracudaModel.IrSource; |
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inferenceEvent.BarracudaModelVersion = barracudaModel.IrVersion; |
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inferenceEvent.BarracudaModelProducer = barracudaModel.ProducerName; |
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inferenceEvent.MemorySize = (int)barracudaModel.GetTensorByName(TensorNames.MemorySize)[0]; |
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inferenceEvent.InferenceDevice = (int)inferenceDevice; |
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if (barracudaModel.ProducerName == "Script") |
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{ |
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// .nn files don't have these fields set correctly. Assign some placeholder values.
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inferenceEvent.BarracudaModelSource = "NN"; |
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inferenceEvent.BarracudaModelProducer = "tensorflow_to_barracuda.py"; |
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} |
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#if UNITY_2019_3_OR_NEWER && UNITY_EDITOR
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var barracudaPackageInfo = UnityEditor.PackageManager.PackageInfo.FindForAssembly(typeof(Tensor).Assembly); |
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inferenceEvent.BarracudaPackageVersion = barracudaPackageInfo.version; |
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#else
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inferenceEvent.BarracudaPackageVersion = null; |
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#endif
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inferenceEvent.ActionSpec = EventActionSpec.FromActionSpec(actionSpec); |
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inferenceEvent.ObservationSpecs = new List<EventObservationSpec>(sensors.Count); |
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foreach (var sensor in sensors) |
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{ |
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inferenceEvent.ObservationSpecs.Add(EventObservationSpec.FromSensor(sensor)); |
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} |
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inferenceEvent.TotalWeightSizeBytes = GetModelWeightSize(barracudaModel); |
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inferenceEvent.ModelHash = GetModelHash(barracudaModel); |
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return inferenceEvent; |
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} |
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/// <summary>
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/// Compute the total model weight size in bytes.
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/// This corresponds to the "Total weight size" display in the Barracuda inspector,
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/// and the calculations are the same.
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/// </summary>
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/// <param name="barracudaModel"></param>
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/// <returns></returns>
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static long GetModelWeightSize(Model barracudaModel) |
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{ |
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long totalWeightsSizeInBytes = 0; |
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for (var l = 0; l < barracudaModel.layers.Count; ++l) |
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{ |
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for (var d = 0; d < barracudaModel.layers[l].datasets.Length; ++d) |
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{ |
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totalWeightsSizeInBytes += barracudaModel.layers[l].datasets[d].length; |
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} |
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} |
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return totalWeightsSizeInBytes; |
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} |
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/// <summary>
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/// Wrapper around Hash128 that supports Append(float[], int, int)
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/// </summary>
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struct MLAgentsHash128 |
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{ |
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private Hash128 m_Hash; |
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public void Append(float[] values, int count) |
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{ |
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if (values == null) |
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{ |
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return; |
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} |
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// Pre-2020 versions of Unity don't have Hash128.Append() (can only hash strings and scalars)
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// For these versions, we'll hash element by element.
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#if UNITY_2020_1_OR_NEWER
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m_Hash.Append(values, 0, count); |
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#else
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for (var i = 0; i < count; i++) |
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{ |
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var tempHash = new Hash128(); |
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HashUtilities.ComputeHash128(ref values[i], ref tempHash); |
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HashUtilities.AppendHash(ref tempHash, ref m_Hash); |
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} |
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#endif
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} |
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public void Append(string value) |
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{ |
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var tempHash = Hash128.Compute(value); |
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HashUtilities.AppendHash(ref tempHash, ref m_Hash); |
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} |
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public override string ToString() |
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{ |
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return m_Hash.ToString(); |
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} |
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} |
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/// <summary>
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/// Compute a hash of the model's layer data and return it as a string.
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/// A subset of the layer weights are used for performance.
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/// This increases the chance of a collision, but this should still be extremely rare.
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/// </summary>
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/// <param name="barracudaModel"></param>
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/// <returns></returns>
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static string GetModelHash(Model barracudaModel) |
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{ |
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var hash = new MLAgentsHash128(); |
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// Limit the max number of float bytes that we hash for performance.
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const int kMaxFloats = 256; |
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foreach (var layer in barracudaModel.layers) |
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{ |
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hash.Append(layer.name); |
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var numFloatsToHash = Mathf.Min(layer.weights.Length, kMaxFloats); |
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hash.Append(layer.weights, numFloatsToHash); |
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} |
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return hash.ToString(); |
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} |
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} |
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} |
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fileFormatVersion: 2 |
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guid: ac4c40c2394d481ebf602caa600a32f3 |
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timeCreated: 1604359787 |
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using System.Collections.Generic; |
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using NUnit.Framework; |
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using Unity.MLAgents.Sensors; |
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using UnityEngine; |
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using Unity.Barracuda; |
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using Unity.MLAgents.Actuators; |
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using Unity.MLAgents.Analytics; |
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using Unity.MLAgents.Policies; |
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using UnityEditor; |
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namespace Unity.MLAgents.Tests.Analytics |
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{ |
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[TestFixture] |
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public class InferenceAnalyticsTests |
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{ |
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const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx"; |
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NNModel continuousONNXModel; |
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Test3DSensorComponent sensor_21_20_3; |
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Test3DSensorComponent sensor_20_22_3; |
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ActionSpec GetContinuous2vis8vec2actionActionSpec() |
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{ |
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return ActionSpec.MakeContinuous(2); |
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} |
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[SetUp] |
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public void SetUp() |
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{ |
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continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel)); |
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var go = new GameObject("SensorA"); |
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sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>(); |
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sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3); |
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sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>(); |
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sensor_20_22_3.Sensor = new Test3DSensor("SensorB", 20, 22, 3); |
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} |
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[Test] |
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public void TestModelEvent() |
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{ |
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var sensors = new List<ISensor> { sensor_21_20_3.Sensor, sensor_20_22_3.Sensor }; |
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var behaviorName = "continuousModel"; |
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var continuousEvent = InferenceAnalytics.GetEventForModel( |
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continuousONNXModel, behaviorName, |
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InferenceDevice.CPU, sensors, GetContinuous2vis8vec2actionActionSpec() |
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); |
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// The behavior name should be hashed, not pass-through.
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Assert.AreNotEqual(behaviorName, continuousEvent.BehaviorName); |
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Assert.AreEqual(2, continuousEvent.ActionSpec.NumContinuousActions); |
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Assert.AreEqual(0, continuousEvent.ActionSpec.NumDiscreteActions); |
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Assert.AreEqual(2, continuousEvent.ObservationSpecs.Count); |
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Assert.AreEqual(3, continuousEvent.ObservationSpecs[0].DimensionInfos.Length); |
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Assert.AreEqual(20, continuousEvent.ObservationSpecs[0].DimensionInfos[0].Size); |
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Assert.AreEqual("None", continuousEvent.ObservationSpecs[0].CompressionType); |
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Assert.AreNotEqual(null, continuousEvent.ModelHash); |
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// Make sure nested fields get serialized
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var jsonString = JsonUtility.ToJson(continuousEvent, true); |
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Assert.IsTrue(jsonString.Contains("ObservationSpecs")); |
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Assert.IsTrue(jsonString.Contains("ActionSpec")); |
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Assert.IsTrue(jsonString.Contains("NumDiscreteActions")); |
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Assert.IsTrue(jsonString.Contains("SensorName")); |
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Assert.IsTrue(jsonString.Contains("Flags")); |
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} |
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} |
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} |
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fileFormatVersion: 2 |
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guid: 9f054f620b8b468bbd8ccf7d2cc14ccd |
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timeCreated: 1607379491 |
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