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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139 行
4.9 KiB

using UnityEngine;
using System.Collections;
using System.Collections.Generic;
using Barracuda;
using MLAgents.InferenceBrain;
namespace MLAgents
{
public class Utilities
{
/// <summary>
/// Converts a list of Texture2D into a Tensor.
/// </summary>
/// <param name="tensorProxy">
/// Tensor proxy to fill with Texture data.
/// </param>
/// <param name="textures">
/// The list of textures to be put into the tensor.
/// Note that the textures must have same width and height.
/// </param>
/// <param name="blackAndWhite">
/// If set to <c>true</c> the textures
/// will be converted to grayscale before being stored in the tensor.
/// </param>
/// <param name="allocator">Tensor allocator</param>
public static void TextureToTensorProxy(TensorProxy tensorProxy, List<Texture2D> textures, bool blackAndWhite,
ITensorAllocator allocator)
{
var batchSize = textures.Count;
var width = textures[0].width;
var height = textures[0].height;
var data = tensorProxy.Data;
for (var b = 0; b < batchSize; b++)
{
var cc = textures[b].GetPixels32();
for (var h = height - 1; h >= 0; h--)
{
for (var w = 0; w < width; w++)
{
var currentPixel = cc[(height - h - 1) * width + w];
if (!blackAndWhite)
{
// For Color32, the r, g and b values are between
// 0 and 255.
data[b, h, w, 0] = currentPixel.r / 255.0f;
data[b, h, w, 1] = currentPixel.g / 255.0f;
data[b, h, w,2] = currentPixel.b / 255.0f;
}
else
{
data[b, h, w, 0] = (currentPixel.r + currentPixel.g + currentPixel.b)
/ 3f / 255.0f;
}
}
}
}
}
/// <summary>
/// Calculates the cumulative sum of an integer array. The result array will be one element
/// larger than the input array since it has a padded 0 at the begining.
/// If the input is [a, b, c], the result will be [0, a, a+b, a+b+c]
/// </summary>
/// <returns> The cumulative sum of the input array.</returns>
public static int[] CumSum(int [] array)
{
var runningSum = 0;
var result = new int[array.Length + 1];
for (var actionIndex = 0; actionIndex < array.Length; actionIndex++)
{
runningSum += array[actionIndex];
result[actionIndex + 1] = runningSum;
}
return result;
}
/// <summary>
/// Shifts list elements to the left by the specified amount.
/// <param name="list">
/// Target list
/// </param>
/// <param name="amount">
/// Shift amount
/// </param>
/// </summary>
public static void ShiftLeft<T>(List<T> list, int amount)
{
for (var i = amount; i < list.Count; i++)
{
list[i - amount] = list[i];
}
}
/// <summary>
/// Replaces target list elements with source list elements starting at specified position in target list.
/// <param name="dst">
/// Target list
/// </param>
/// <param name="src">
/// Source list
/// </param>
/// <param name="start">
/// Offset in target list
/// </param>
/// </summary>
public static void ReplaceRange<T>(List<T> dst, List<T> src, int start)
{
for (var i = 0; i < src.Count; i++)
{
dst[i + start] = src[i];
}
}
/// <summary>
/// Adds elements to list without extra temp allocations (assuming it fits pre-allocated capacity of the list).
/// Regular List<T>.AddRange() unfortunately allocates temp list to add items.
/// https://stackoverflow.com/questions/2123161/listt-addrange-implementation-suboptimal
/// Note: this implementation might be slow with large numbers of elements in the source array.
/// <param name="dst">
/// Target list
/// </param>
/// <param name="src">
/// Source array
/// </param>
/// </summary>
public static void AddRangeNoAlloc<T>(List<T> dst, T[] src)
{
var offset = dst.Count;
for (var i = 0; i < src.Length; i++)
{
dst.Add(src[i]);
}
}
}
}