Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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123 行
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using System;
using System.Collections.Generic;
using Barracuda;
using MLAgents.InferenceBrain.Utils;
namespace MLAgents.InferenceBrain
{
/// <summary>
/// Tensor - A class to encapsulate a Tensor used for inference.
///
/// This class contains the Array that holds the data array, the shapes, type and the
/// placeholder in the execution graph. All the fields are editable in the inspector,
/// allowing the user to specify everything but the data in a graphical way.
/// </summary>
[Serializable]
public class TensorProxy
{
public enum TensorType
{
Integer,
FloatingPoint
};
private static readonly Dictionary<TensorType, Type> k_TypeMap =
new Dictionary<TensorType, Type>()
{
{TensorType.FloatingPoint, typeof(float)},
{TensorType.Integer, typeof(int)}
};
public string name;
public TensorType valueType;
// Since Type is not serializable, we use the DisplayType for the Inspector
public Type DataType => k_TypeMap[valueType];
public long[] shape;
public Tensor data;
}
public static class TensorUtils
{
public static void ResizeTensor(TensorProxy tensor, int batch, ITensorAllocator allocator)
{
if (tensor.shape[0] == batch &&
tensor.data != null && tensor.data.batch == batch)
{
return;
}
tensor.data?.Dispose();
tensor.shape[0] = batch;
if (tensor.shape.Length == 4)
{
tensor.data = allocator.Alloc(
new TensorShape(
batch,
(int)tensor.shape[1],
(int)tensor.shape[2],
(int)tensor.shape[3]));
}
else
{
tensor.data = allocator.Alloc(
new TensorShape(
batch,
(int)tensor.shape[tensor.shape.Length - 1]));
}
}
internal static long[] TensorShapeFromBarracuda(TensorShape src)
{
if (src.height == 1 && src.width == 1)
{
return new long[] {src.batch, src.channels};
}
return new long[] {src.batch, src.height, src.width, src.channels};
}
public static TensorProxy TensorProxyFromBarracuda(Tensor src, string nameOverride = null)
{
var shape = TensorShapeFromBarracuda(src.shape);
return new TensorProxy
{
name = nameOverride ?? src.name,
valueType = TensorProxy.TensorType.FloatingPoint,
shape = shape,
data = src
};
}
/// <summary>
/// Fill a pre-allocated Tensor with random numbers
/// </summary>
/// <param name="tensorProxy">The pre-allocated Tensor to fill</param>
/// <param name="randomNormal">RandomNormal object used to populate tensor</param>
/// <exception cref="NotImplementedException">
/// Throws when trying to fill a Tensor of type other than float
/// </exception>
/// <exception cref="ArgumentNullException">
/// Throws when the Tensor is not allocated
/// </exception>
public static void FillTensorWithRandomNormal(
TensorProxy tensorProxy, RandomNormal randomNormal)
{
if (tensorProxy.DataType != typeof(float))
{
throw new NotImplementedException("Only float data types are currently supported");
}
if (tensorProxy.data == null)
{
throw new ArgumentNullException();
}
for (var i = 0; i < tensorProxy.data.length; i++)
{
tensorProxy.data[i] = (float)randomNormal.NextDouble();
}
}
}
}