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using System;
using System.Linq;
using NUnit.Framework;
using UnityEngine;
using UnityEditor;
using Unity.Barracuda;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Policies;
using System.Collections.Generic;
namespace Unity.MLAgents.Tests
{
public class FloatThresholdComparer : IEqualityComparer<float>
{
private readonly float _threshold;
public FloatThresholdComparer(float threshold)
{
_threshold = threshold;
}
public bool Equals(float x, float y)
{
return Math.Abs(x - y) < _threshold;
}
public int GetHashCode(float f)
{
throw new NotImplementedException("Unable to generate a hash code for threshold floats, do not use this method");
}
}
[TestFixture]
public class ModelRunnerTest
{
const string k_hybrid_ONNX_recurr_v2 = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis8vec_2c_2_3d_v2_0.onnx";
const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_v1_0.onnx";
const string k_discreteONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_obsolete_recurr_v1_0.onnx";
const string k_hybridONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction_v1_0.onnx";
const string k_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated_v1_0.nn";
const string k_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated_v1_0.nn";
// models with deterministic action tensors
private const string k_deterministic_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/deterDiscrete1obs3action_v2_0.onnx";
private const string k_deterministic_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/deterContinuous2vis8vec2action_v2_0.onnx";
NNModel hybridONNXModelV2;
NNModel continuousONNXModel;
NNModel discreteONNXModel;
NNModel hybridONNXModel;
NNModel continuousNNModel;
NNModel discreteNNModel;
NNModel deterministicDiscreteNNModel;
NNModel deterministicContinuousNNModel;
Test3DSensorComponent sensor_21_20_3;
Test3DSensorComponent sensor_20_22_3;
ActionSpec GetContinuous2vis8vec2actionActionSpec()
{
return ActionSpec.MakeContinuous(2);
}
ActionSpec GetDiscrete1vis0vec_2_3action_recurrModelActionSpec()
{
return ActionSpec.MakeDiscrete(2, 3);
}
ActionSpec GetHybrid0vis53vec_3c_2dActionSpec()
{
return new ActionSpec(3, new[] { 2 });
}
[SetUp]
public void SetUp()
{
hybridONNXModelV2 = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybrid_ONNX_recurr_v2, typeof(NNModel));
continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
discreteONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteONNXPath, typeof(NNModel));
hybridONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybridONNXPath, typeof(NNModel));
continuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousNNPath, typeof(NNModel));
discreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteNNPath, typeof(NNModel));
deterministicDiscreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_deterministic_discreteNNPath, typeof(NNModel));
deterministicContinuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_deterministic_continuousNNPath, typeof(NNModel));
var go = new GameObject("SensorA");
sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);
sensor_20_22_3 = go.AddComponent<Test3DSensorComponent>();
sensor_20_22_3.Sensor = new Test3DSensor("SensorB", 20, 22, 3);
}
[Test]
public void TestModelExist()
{
Assert.IsNotNull(continuousONNXModel);
Assert.IsNotNull(discreteONNXModel);
Assert.IsNotNull(hybridONNXModel);
Assert.IsNotNull(continuousNNModel);
Assert.IsNotNull(discreteNNModel);
Assert.IsNotNull(hybridONNXModelV2);
Assert.IsNotNull(deterministicDiscreteNNModel);
Assert.IsNotNull(deterministicContinuousNNModel);
}
[Test]
public void TestCreation()
{
var inferenceDevice = InferenceDevice.Burst;
var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec(), inferenceDevice);
modelRunner.Dispose();
Assert.Throws<UnityAgentsException>(() =>
{
// Cannot load a model trained with 1.x that has an LSTM
modelRunner = new ModelRunner(discreteONNXModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec(), inferenceDevice);
modelRunner.Dispose();
});
modelRunner = new ModelRunner(hybridONNXModel, GetHybrid0vis53vec_3c_2dActionSpec(), inferenceDevice);
modelRunner.Dispose();
modelRunner = new ModelRunner(continuousNNModel, GetContinuous2vis8vec2actionActionSpec(), inferenceDevice);
modelRunner.Dispose();
Assert.Throws<UnityAgentsException>(() =>
{
// Cannot load a model trained with 1.x that has an LSTM
modelRunner = new ModelRunner(discreteNNModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec(), inferenceDevice);
modelRunner.Dispose();
});
// This one was trained with 2.0 so it should not raise an error:
modelRunner = new ModelRunner(hybridONNXModelV2, new ActionSpec(2, new[] { 2, 3 }), inferenceDevice);
modelRunner.Dispose();
// V2.0 Model that has serialized deterministic action tensors, discrete
modelRunner = new ModelRunner(deterministicDiscreteNNModel, new ActionSpec(0, new[] { 7 }), inferenceDevice);
modelRunner.Dispose();
// V2.0 Model that has serialized deterministic action tensors, continuous
modelRunner = new ModelRunner(deterministicContinuousNNModel,
GetContinuous2vis8vec2actionActionSpec(), inferenceDevice,
deterministicInference: true);
modelRunner.Dispose();
}
[Test]
public void TestHasModel()
{
var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec(), InferenceDevice.CPU);
Assert.True(modelRunner.HasModel(continuousONNXModel, InferenceDevice.CPU));
Assert.False(modelRunner.HasModel(continuousONNXModel, InferenceDevice.GPU));
Assert.False(modelRunner.HasModel(discreteONNXModel, InferenceDevice.CPU));
modelRunner.Dispose();
}
[Test]
public void TestRunModel()
{
var actionSpec = GetContinuous2vis8vec2actionActionSpec();
var modelRunner = new ModelRunner(continuousONNXModel, actionSpec, InferenceDevice.Burst);
var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
var info1 = new AgentInfo();
info1.episodeId = 1;
modelRunner.PutObservations(info1, new[]
{
sensor_8,
sensor_21_20_3.CreateSensors()[0],
sensor_20_22_3.CreateSensors()[0]
}.ToList());
var info2 = new AgentInfo();
info2.episodeId = 2;
modelRunner.PutObservations(info2, new[]
{
sensor_8,
sensor_21_20_3.CreateSensors()[0],
sensor_20_22_3.CreateSensors()[0]
}.ToList());
modelRunner.DecideBatch();
Assert.IsFalse(modelRunner.GetAction(1).Equals(ActionBuffers.Empty));
Assert.IsFalse(modelRunner.GetAction(2).Equals(ActionBuffers.Empty));
Assert.IsTrue(modelRunner.GetAction(3).Equals(ActionBuffers.Empty));
Assert.AreEqual(actionSpec.NumDiscreteActions, modelRunner.GetAction(1).DiscreteActions.Length);
modelRunner.Dispose();
}
[Test]
public void TestRunModel_stochastic()
{
var actionSpec = GetContinuous2vis8vec2actionActionSpec();
// deterministicInference = false by default
var modelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst);
var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
var info1 = new AgentInfo();
var obs = new[]
{
sensor_8,
sensor_21_20_3.CreateSensors()[0],
sensor_20_22_3.CreateSensors()[0]
}.ToList();
info1.episodeId = 1;
modelRunner.PutObservations(info1, obs);
modelRunner.DecideBatch();
var stochAction1 = (float[])modelRunner.GetAction(1).ContinuousActions.Array.Clone();
modelRunner.PutObservations(info1, obs);
modelRunner.DecideBatch();
var stochAction2 = (float[])modelRunner.GetAction(1).ContinuousActions.Array.Clone();
// Stochastic action selection should output randomly different action values with same obs
Assert.IsFalse(Enumerable.SequenceEqual(stochAction1, stochAction2, new FloatThresholdComparer(0.001f)));
modelRunner.Dispose();
}
[Test]
public void TestRunModel_deterministic()
{
var actionSpec = GetContinuous2vis8vec2actionActionSpec();
var modelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst);
var sensor_8 = new Sensors.VectorSensor(8, "VectorSensor8");
var info1 = new AgentInfo();
var obs = new[]
{
sensor_8,
sensor_21_20_3.CreateSensors()[0],
sensor_20_22_3.CreateSensors()[0]
}.ToList();
var deterministicModelRunner = new ModelRunner(deterministicContinuousNNModel, actionSpec, InferenceDevice.Burst,
deterministicInference: true);
info1.episodeId = 1;
deterministicModelRunner.PutObservations(info1, obs);
deterministicModelRunner.DecideBatch();
var deterministicAction1 = (float[])deterministicModelRunner.GetAction(1).ContinuousActions.Array.Clone();
deterministicModelRunner.PutObservations(info1, obs);
deterministicModelRunner.DecideBatch();
var deterministicAction2 = (float[])deterministicModelRunner.GetAction(1).ContinuousActions.Array.Clone();
// Deterministic action selection should output same action everytime
Assert.IsTrue(Enumerable.SequenceEqual(deterministicAction1, deterministicAction2, new FloatThresholdComparer(0.001f)));
modelRunner.Dispose();
}
}
}
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