using System; using Unity.Barracuda; using NUnit.Framework; using UnityEngine; using MLAgents.Inference; using MLAgents.Inference.Utils; namespace MLAgents.Tests { public class DiscreteActionOutputApplierTest { [Test] public void TestEvalP() { var m = new Multinomial(2018); var src = new TensorProxy { data = new Tensor(1, 3, new[] {0.1f, 0.2f, 0.7f}), valueType = TensorProxy.TensorType.FloatingPoint }; var dst = new TensorProxy { data = new Tensor(1, 3), valueType = TensorProxy.TensorType.FloatingPoint }; DiscreteActionOutputApplier.Eval(src, dst, m); float[] reference = {2, 2, 1}; for (var i = 0; i < dst.data.length; i++) { Assert.AreEqual(reference[i], dst.data[i]); ++i; } } [Test] public void TestEvalLogits() { var m = new Multinomial(2018); var src = new TensorProxy { data = new Tensor( 1, 3, new[] { Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50 }), valueType = TensorProxy.TensorType.FloatingPoint }; var dst = new TensorProxy { data = new Tensor(1, 3), valueType = TensorProxy.TensorType.FloatingPoint }; DiscreteActionOutputApplier.Eval(src, dst, m); float[] reference = {2, 2, 2}; for (var i = 0; i < dst.data.length; i++) { Assert.AreEqual(reference[i], dst.data[i]); ++i; } } [Test] public void TestEvalBatching() { var m = new Multinomial(2018); var src = new TensorProxy { data = new Tensor(2, 3, new[] { Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50, Mathf.Log(0.3f) - 25, Mathf.Log(0.4f) - 25, Mathf.Log(0.3f) - 25 }), valueType = TensorProxy.TensorType.FloatingPoint }; var dst = new TensorProxy { data = new Tensor(2, 3), valueType = TensorProxy.TensorType.FloatingPoint }; DiscreteActionOutputApplier.Eval(src, dst, m); float[] reference = {2, 2, 2, 0, 1, 0}; for (var i = 0; i < dst.data.length; i++) { Assert.AreEqual(reference[i], dst.data[i]); ++i; } } [Test] public void TestSrcInt() { var m = new Multinomial(2018); var src = new TensorProxy { valueType = TensorProxy.TensorType.Integer }; Assert.Throws( () => DiscreteActionOutputApplier.Eval(src, null, m)); } [Test] public void TestDstInt() { var m = new Multinomial(2018); var src = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint }; var dst = new TensorProxy { valueType = TensorProxy.TensorType.Integer }; Assert.Throws( () => DiscreteActionOutputApplier.Eval(src, dst, m)); } [Test] public void TestSrcDataNull() { var m = new Multinomial(2018); var src = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint }; var dst = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint }; Assert.Throws( () => DiscreteActionOutputApplier.Eval(src, dst, m)); } [Test] public void TestDstDataNull() { var m = new Multinomial(2018); var src = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint, data = new Tensor(0, 1) }; var dst = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint }; Assert.Throws( () => DiscreteActionOutputApplier.Eval(src, dst, m)); } [Test] public void TestUnequalBatchSize() { var m = new Multinomial(2018); var src = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint, data = new Tensor(1, 1) }; var dst = new TensorProxy { valueType = TensorProxy.TensorType.FloatingPoint, data = new Tensor(2, 1) }; Assert.Throws( () => DiscreteActionOutputApplier.Eval(src, dst, m)); } } }