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154 行
5.7 KiB
154 行
5.7 KiB
using UnityEngine;
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using System.Collections.Generic;
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namespace MLAgents
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{
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internal static class Utilities
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{
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/// <summary>
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/// Puts a Texture2D into a WriteAdapter.
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/// </summary>
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/// <param name="texture">
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/// The texture to be put into the tensor.
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/// </param>
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/// <param name="adapter">
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/// Adapter to fill with Texture data.
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/// </param>
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/// <param name="grayScale">
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/// If set to <c>true</c> the textures will be converted to grayscale before
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/// being stored in the tensor.
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/// </param>
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/// <returns>The number of floats written</returns>
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public static int TextureToTensorProxy(
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Texture2D texture,
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WriteAdapter adapter,
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bool grayScale)
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{
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var width = texture.width;
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var height = texture.height;
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var texturePixels = texture.GetPixels32();
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// During training, we convert from Texture to PNG before sending to the trainer, which has the
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// effect of flipping the image. We need another flip here at inference time to match this.
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for (var h = height - 1; h >= 0; h--)
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{
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for (var w = 0; w < width; w++)
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{
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var currentPixel = texturePixels[(height - h - 1) * width + w];
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if (grayScale)
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{
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adapter[h, w, 0] =
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(currentPixel.r + currentPixel.g + currentPixel.b) / 3f / 255.0f;
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}
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else
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{
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// For Color32, the r, g and b values are between 0 and 255.
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adapter[h, w, 0] = currentPixel.r / 255.0f;
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adapter[h, w, 1] = currentPixel.g / 255.0f;
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adapter[h, w, 2] = currentPixel.b / 255.0f;
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}
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}
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}
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return height * width * (grayScale ? 1 : 3);
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}
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/// <summary>
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/// Calculates the cumulative sum of an integer array. The result array will be one element
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/// larger than the input array since it has a padded 0 at the beginning.
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/// If the input is [a, b, c], the result will be [0, a, a+b, a+b+c]
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/// </summary>
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/// <param name="input">
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/// Input array whose elements will be cumulatively added
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/// </param>
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/// <returns> The cumulative sum of the input array.</returns>
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public static int[] CumSum(int[] input)
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{
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var runningSum = 0;
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var result = new int[input.Length + 1];
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for (var actionIndex = 0; actionIndex < input.Length; actionIndex++)
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{
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runningSum += input[actionIndex];
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result[actionIndex + 1] = runningSum;
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}
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return result;
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}
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/// <summary>
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/// Shifts list elements to the left by the specified amount (in-place).
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/// <param name="list">
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/// List whose elements will be shifted
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/// </param>
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/// <param name="shiftAmount">
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/// Amount to shift the elements to the left by
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/// </param>
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/// </summary>
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public static void ShiftLeft<T>(List<T> list, int shiftAmount)
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{
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for (var i = shiftAmount; i < list.Count; i++)
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{
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list[i - shiftAmount] = list[i];
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}
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}
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/// <summary>
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/// Replaces target list elements with source list elements starting at specified position
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/// in target list.
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/// <param name="dst">
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/// Target list, where the elements are added to
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/// </param>
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/// <param name="src">
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/// Source array, where the elements are copied from
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/// </param>
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/// <param name="start">
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/// Starting position in target list to copy elements to
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/// </param>
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/// </summary>
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public static void ReplaceRange<T>(List<T> dst, List<T> src, int start)
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{
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for (var i = 0; i < src.Count; i++)
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{
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dst[i + start] = src[i];
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}
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}
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/// <summary>
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/// Adds elements to list without extra temp allocations (assuming it fits pre-allocated
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/// capacity of the list). The built-in List/<T/>.AddRange() unfortunately allocates
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/// a temporary list to add items (even if the original array has sufficient capacity):
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/// https://stackoverflow.com/questions/2123161/listt-addrange-implementation-suboptimal
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/// Note: this implementation might be slow with a large source array.
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/// <param name="dst">
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/// Target list, where the elements are added to
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/// </param>
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/// <param name="src">
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/// Source array, where the elements are copied from
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/// </param>
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/// </summary>
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// ReSharper disable once ParameterTypeCanBeEnumerable.Global
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public static void AddRangeNoAlloc<T>(List<T> dst, T[] src)
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{
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// ReSharper disable once LoopCanBeConvertedToQuery
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foreach (var item in src)
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{
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dst.Add(item);
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}
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}
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/// <summary>
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/// Calculates the number of uncompressed floats in a list of ISensor
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/// </summary>
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public static int GetSensorFloatObservationSize(this List<ISensor> sensors)
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{
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int numFloatObservations = 0;
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for (var i = 0; i < sensors.Count; i++)
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{
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if (sensors[i].GetCompressionType() == SensorCompressionType.None)
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{
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numFloatObservations += sensors[i].ObservationSize();
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}
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}
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return numFloatObservations;
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}
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}
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}
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