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using Microsoft.ML.OnnxRuntime.Tensors;
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using ZeroLevel.ML.DNN.Models;
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using System.Runtime.CompilerServices;
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using System;
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using System.Collections.Generic;
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namespace ZeroLevel.ML.DNN.Detectors
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{
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public class NanodetDetector
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: SSDNN, IObjectDetector
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{
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private static float[] mean_vals = new float[3] { 103.53f, 116.28f, 123.675f };
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private static float[] norm_vals = new float[3] { 0.017429f, 0.017507f, 0.017125f };
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private static int[] strides = new int[4] { 8, 16, 32, 64 };
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private static int reg_max = 7;
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private static int input_s = 640;
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public float RNorm(float x) => (x + mean_vals[0]) * norm_vals[0];
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public float GNorm(float x) => (x + mean_vals[1]) * norm_vals[1];
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public float BNorm(float x) => (x + mean_vals[2]) * norm_vals[2];
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public NanodetDetector(string modelPath, int deviceId = 0)
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: base(modelPath, deviceId)
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{
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}
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#region Fastexp dict
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static double[] ExpAdjustment = new double[256] {
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1.040389835,
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1.039159306,
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1.037945888,
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1.036749401,
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1.035569671,
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1.034406528,
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1.033259801,
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1.032129324,
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1.031014933,
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1.029916467,
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1.028833767,
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1.027766676,
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1.02671504,
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1.025678708,
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1.02465753,
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1.023651359,
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1.022660049,
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1.021683458,
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1.020721446,
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1.019773873,
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1.018840604,
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1.017921503,
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1.017016438,
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1.016125279,
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1.015247897,
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1.014384165,
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1.013533958,
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1.012697153,
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1.011873629,
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1.011063266,
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1.010265947,
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1.009481555,
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1.008709975,
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1.007951096,
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1.007204805,
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1.006470993,
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1.005749552,
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1.005040376,
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1.004343358,
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1.003658397,
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1.002985389,
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1.002324233,
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1.001674831,
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1.001037085,
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1.000410897,
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0.999796173,
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0.999192819,
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0.998600742,
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0.998019851,
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0.997450055,
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0.996891266,
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0.996343396,
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0.995806358,
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0.995280068,
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0.99476444,
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0.994259393,
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0.993764844,
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0.993280711,
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0.992806917,
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0.992343381,
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0.991890026,
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0.991446776,
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0.991013555,
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0.990590289,
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0.990176903,
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0.989773325,
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0.989379484,
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0.988995309,
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0.988620729,
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0.988255677,
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0.987900083,
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0.987553882,
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0.987217006,
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0.98688939,
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0.98657097,
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0.986261682,
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0.985961463,
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0.985670251,
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0.985387985,
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0.985114604,
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0.984850048,
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0.984594259,
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0.984347178,
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0.984108748,
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0.983878911,
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0.983657613,
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0.983444797,
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0.983240409,
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0.983044394,
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0.982856701,
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0.982677276,
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0.982506066,
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0.982343022,
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0.982188091,
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0.982041225,
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0.981902373,
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0.981771487,
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0.981648519,
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0.981533421,
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0.981426146,
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0.981326648,
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0.98123488,
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0.981150798,
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0.981074356,
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0.981005511,
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0.980944219,
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0.980890437,
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0.980844122,
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0.980805232,
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0.980773726,
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0.980749562,
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0.9807327,
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0.9807231,
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0.980720722,
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0.980725528,
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0.980737478,
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0.980756534,
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0.98078266,
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0.980815817,
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0.980855968,
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0.980903079,
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0.980955475,
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0.981017942,
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0.981085714,
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0.981160303,
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0.981241675,
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0.981329796,
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0.981424634,
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0.981526154,
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0.981634325,
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0.981749114,
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0.981870489,
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0.981998419,
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0.982132873,
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0.98227382,
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0.982421229,
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0.982575072,
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0.982735318,
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0.982901937,
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0.983074902,
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0.983254183,
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0.983439752,
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0.983631582,
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0.983829644,
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0.984033912,
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0.984244358,
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0.984460956,
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0.984683681,
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0.984912505,
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0.985147403,
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0.985388349,
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0.98563532,
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0.98588829,
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0.986147234,
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0.986412128,
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0.986682949,
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0.986959673,
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0.987242277,
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0.987530737,
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0.987825031,
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0.988125136,
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0.98843103,
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0.988742691,
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0.989060098,
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0.989383229,
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0.989712063,
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0.990046579,
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0.990386756,
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0.990732574,
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0.991084012,
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0.991441052,
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0.991803672,
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0.992171854,
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0.992545578,
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0.992924825,
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0.993309578,
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0.993699816,
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0.994095522,
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0.994496677,
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0.994903265,
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0.995315266,
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0.995732665,
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0.996155442,
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0.996583582,
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0.997017068,
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0.997455883,
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0.99790001,
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0.998349434,
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0.998804138,
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0.999264107,
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0.999729325,
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1.000199776,
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1.000675446,
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1.001156319,
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1.001642381,
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1.002133617,
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1.002630011,
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1.003131551,
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1.003638222,
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1.00415001,
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1.004666901,
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1.005188881,
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1.005715938,
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1.006248058,
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1.006785227,
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1.007327434,
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1.007874665,
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1.008426907,
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1.008984149,
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1.009546377,
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1.010113581,
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1.010685747,
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1.011262865,
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1.011844922,
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1.012431907,
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1.013023808,
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1.013620615,
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1.014222317,
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1.014828902,
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1.01544036,
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1.016056681,
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1.016677853,
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1.017303866,
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1.017934711,
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1.018570378,
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1.019210855,
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1.019856135,
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1.020506206,
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1.02116106,
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1.021820687,
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1.022485078,
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1.023154224,
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1.023828116,
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1.024506745,
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1.025190103,
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1.02587818,
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1.026570969,
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1.027268461,
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1.027970647,
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1.02867752,
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1.029389072,
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1.030114973,
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1.030826088,
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1.03155163,
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1.032281819,
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1.03301665,
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1.033756114,
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1.034500204,
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1.035248913,
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1.036002235,
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1.036760162,
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1.037522688,
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1.038289806,
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1.039061509,
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1.039837792,
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1.040618648
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};
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#endregion
|
|
|
|
|
|
static double FastExp(double x)
|
|
|
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|
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{
|
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|
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var tmp = (long)(1512775 * x + 1072632447);
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int index = (int)(tmp >> 12) & 0xFF;
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return BitConverter.Int64BitsToDouble(tmp << 32) * ExpAdjustment[index];
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}
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|
[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
|
|
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|
|
public static float Sigmoid(double value)
|
|
|
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|
|
{
|
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|
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float k = (float)Math.Exp(value);
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return k / (1.0f + k);
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}
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|
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
|
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|
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private static unsafe void Softmax(float[] input, int start, float[] dst)
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{
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var sum = 0f;
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for (var i = 0; i < 8; ++i)
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{
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var e = (float)Math.Exp(input[start + i]);
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dst[i] = e;
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sum += e;
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}
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var sumInv = 1f / sum;
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for (var i = 0; i < 8; ++i)
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dst[i] *= sumInv;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private float[] GetCurrentRow(Tensor<float> output, int b, int s)
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{
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float[] row = new float[33];
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for (int i = 0; i < row.Length; i++)
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{
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row[i] = output[b, s, i];
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}
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return row;
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}
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private List<YoloPrediction> Decoder(int batchIndex, Tensor<float> output, float threshold)
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{
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var result = new List<YoloPrediction>();
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var proceedLength = 0;
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for (int i = 0; i < strides.Length; i++)
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{
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var stride = strides[i];
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int feature_size = (int)Math.Ceiling((float)input_s / stride);
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for (int y = 0; y < feature_size; y++)
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{
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for (int x = 0; x < feature_size; x++)
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{
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var startIndex = proceedLength + y * feature_size + x;
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var row = GetCurrentRow(output, batchIndex, startIndex);
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var score = row[0];
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if (score > threshold)
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{
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float ct_y = (x + 0.5f) * stride;
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float ct_x = (y + 0.5f) * stride;
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float[] dis_pred = new float[4];
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for (int di = 0; di < 4; di++)
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{
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float dis = 0;
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float[] dis_after_sm = new float[reg_max + 1];
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Softmax(row, 1 + (reg_max + 1) * di, dis_after_sm);
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for (int j = 0; j < reg_max + 1; j++)
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{
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dis += j * dis_after_sm[j];
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}
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dis *= stride;
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dis_pred[di] = dis;
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}
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float xmin = Math.Max(ct_x - dis_pred[0], .0f);
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float ymin = Math.Max(ct_y - dis_pred[1], .0f);
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float xmax = Math.Min(ct_x + dis_pred[2], (float)(input_s));
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float ymax = Math.Min(ct_y + dis_pred[3], (float)(input_s));
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var cx = (xmin + xmax) * 0.5f;
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var cy = (ymin + ymax) * 0.5f;
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var h = (xmax - xmin);
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var w = (ymax - ymin);
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result.Add(new YoloPrediction
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{
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Cx = cx,
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Cy = cy,
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W = w,
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H = h,
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Class = 0,
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Label = "0",
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Score = score
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});
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}
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}
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}
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proceedLength += feature_size * feature_size;
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}
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return result;
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}
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public List<YoloPrediction> Predict(FastTensorPool inputs, float threshold)
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{
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var result = new List<YoloPrediction>();
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float normalization_koef_x = 1.0f / inputs.Width;
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float normalization_koef_y = 1.0f / inputs.Height;
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Extract(new Dictionary<string, Tensor<float>> { { "data", inputs.Tensor } }, d =>
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{
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Tensor<float> output = d["output"];
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if (output != null)
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{
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for (int tensorIndex = 0; tensorIndex < inputs.TensorSize; tensorIndex++)
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{
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var tensor = inputs.GetTensor(tensorIndex);
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foreach (var box in Decoder(tensorIndex, output, threshold))
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{
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box.Cx += tensor.StartX;
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box.Cy += tensor.StartY;
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box.Cx *= normalization_koef_x;
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box.Cy *= normalization_koef_y;
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box.W *= normalization_koef_x;
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box.H *= normalization_koef_y;
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result.Add(box);
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}
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}
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}
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});
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NMS.Apply(result);
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return result;
|
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}
|
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}
|
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}
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