using System.Collections.Generic; using Unity.Barracuda; using NUnit.Framework; using UnityEngine; using Unity.MLAgents.Actuators; using Unity.MLAgents.Inference; using Unity.MLAgents.Policies; using Unity.MLAgents.Sensors.Reflection; namespace Unity.MLAgents.Tests { [TestFixture] public class EditModeTestInternalBrainTensorGenerator { [SetUp] public void SetUp() { if (Academy.IsInitialized) { Academy.Instance.Dispose(); } } static List GetFakeAgents(ObservableAttributeOptions observableAttributeOptions = ObservableAttributeOptions.Ignore) { var goA = new GameObject("goA"); var bpA = goA.AddComponent(); bpA.BrainParameters.VectorObservationSize = 3; bpA.BrainParameters.NumStackedVectorObservations = 1; bpA.ObservableAttributeHandling = observableAttributeOptions; var agentA = goA.AddComponent(); var goB = new GameObject("goB"); var bpB = goB.AddComponent(); bpB.BrainParameters.VectorObservationSize = 3; bpB.BrainParameters.NumStackedVectorObservations = 1; bpB.ObservableAttributeHandling = observableAttributeOptions; var agentB = goB.AddComponent(); var agents = new List { agentA, agentB }; foreach (var agent in agents) { agent.LazyInitialize(); } agentA.collectObservationsSensor.AddObservation(new Vector3(1, 2, 3)); agentB.collectObservationsSensor.AddObservation(new Vector3(4, 5, 6)); var infoA = new AgentInfo { storedActions = new ActionBuffers(null, new[] { 1, 2 }), discreteActionMasks = null, }; var infoB = new AgentInfo { storedActions = new ActionBuffers(null, new[] { 3, 4 }), discreteActionMasks = new[] { true, false, false, false, false }, }; agentA._Info = infoA; agentB._Info = infoB; return agents; } [Test] public void Construction() { var alloc = new TensorCachingAllocator(); var mem = new Dictionary>(); var tensorGenerator = new TensorGenerator(0, alloc, mem); Assert.IsNotNull(tensorGenerator); alloc.Dispose(); } [Test] public void GenerateBatchSize() { var inputTensor = new TensorProxy(); var alloc = new TensorCachingAllocator(); const int batchSize = 4; var generator = new BatchSizeGenerator(alloc); generator.Generate(inputTensor, batchSize, null); Assert.IsNotNull(inputTensor.data); Assert.AreEqual(inputTensor.data[0], batchSize); alloc.Dispose(); } [Test] public void GenerateSequenceLength() { var inputTensor = new TensorProxy(); var alloc = new TensorCachingAllocator(); const int batchSize = 4; var generator = new SequenceLengthGenerator(alloc); generator.Generate(inputTensor, batchSize, null); Assert.IsNotNull(inputTensor.data); Assert.AreEqual(inputTensor.data[0], 1); alloc.Dispose(); } [Test] public void GenerateVectorObservation() { var inputTensor = new TensorProxy { shape = new long[] { 2, 4 } }; const int batchSize = 4; var agentInfos = GetFakeAgents(ObservableAttributeOptions.ExamineAll); var alloc = new TensorCachingAllocator(); var generator = new VectorObservationGenerator(alloc); generator.AddSensorIndex(0); // ObservableAttribute (size 1) generator.AddSensorIndex(1); // TestSensor (size 0) generator.AddSensorIndex(2); // TestSensor (size 0) generator.AddSensorIndex(3); // VectorSensor (size 3) var agent0 = agentInfos[0]; var agent1 = agentInfos[1]; var inputs = new List { new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors}, new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors}, }; generator.Generate(inputTensor, batchSize, inputs); Assert.IsNotNull(inputTensor.data); Assert.AreEqual(inputTensor.data[0, 1], 1); Assert.AreEqual(inputTensor.data[0, 3], 3); Assert.AreEqual(inputTensor.data[1, 1], 4); Assert.AreEqual(inputTensor.data[1, 3], 6); alloc.Dispose(); } [Test] public void GeneratePreviousActionInput() { var inputTensor = new TensorProxy { shape = new long[] { 2, 2 }, valueType = TensorProxy.TensorType.Integer }; const int batchSize = 4; var agentInfos = GetFakeAgents(); var alloc = new TensorCachingAllocator(); var generator = new PreviousActionInputGenerator(alloc); var agent0 = agentInfos[0]; var agent1 = agentInfos[1]; var inputs = new List { new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors}, new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors}, }; generator.Generate(inputTensor, batchSize, inputs); Assert.IsNotNull(inputTensor.data); Assert.AreEqual(inputTensor.data[0, 0], 1); Assert.AreEqual(inputTensor.data[0, 1], 2); Assert.AreEqual(inputTensor.data[1, 0], 3); Assert.AreEqual(inputTensor.data[1, 1], 4); alloc.Dispose(); } [Test] public void GenerateActionMaskInput() { var inputTensor = new TensorProxy { shape = new long[] { 2, 5 }, valueType = TensorProxy.TensorType.FloatingPoint }; const int batchSize = 4; var agentInfos = GetFakeAgents(); var alloc = new TensorCachingAllocator(); var generator = new ActionMaskInputGenerator(alloc); var agent0 = agentInfos[0]; var agent1 = agentInfos[1]; var inputs = new List { new AgentInfoSensorsPair {agentInfo = agent0._Info, sensors = agent0.sensors}, new AgentInfoSensorsPair {agentInfo = agent1._Info, sensors = agent1.sensors}, }; generator.Generate(inputTensor, batchSize, inputs); Assert.IsNotNull(inputTensor.data); Assert.AreEqual(inputTensor.data[0, 0], 1); Assert.AreEqual(inputTensor.data[0, 4], 1); Assert.AreEqual(inputTensor.data[1, 0], 0); Assert.AreEqual(inputTensor.data[1, 4], 1); alloc.Dispose(); } } }