mxnet复现SSD系列文章目录

一、数据集的导入.
二、SSD模型架构.
三、训练脚本的实现.
四、损失、评价函数.
五、预测结果.

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前言

本项目是按照pascal voc的格式读取数据集，数据集为kaggle官网提供的口罩检测数据集，地址：Face Mask Detection，模型架构参考自gluoncv ssd_300_vgg16_atrous_voc源码

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一、损失函数

类别损失函数采用SoftmaxCrossEntropyLoss
boundingbox损失函数采用smooth_l1或focal loss

代码实现

from mxnet.gluon.loss import Lossclass smooth_l1(Loss):def __init__(self, weight=None, batch_axis=0, **kwargs):super(smooth_l1, self).__init__(weight, batch_axis, **kwargs)def hybrid_forward(self, F, pred, label):loss = F.smooth_l1(pred-label, scalar=1.)return F.mean(loss, axis=self._batch_axis, exclude=True)class FocalLoss(Loss):def __init__(self,axis=-1,alpha=0.25,gamma=2,batch_axis=0,**kwargs):super(FocalLoss,self).__init__(None,batch_axis,**kwargs)self.alpha = alphaself.gamma = gammaself.axis = axisself.batch_axis = batch_axisdef hybrid_forward(self, F, y, label):y = F.softmax(y)pt = F.pick(y, label, axis=self.axis, keepdims=True)loss = -self.alpha * ((1 - pt) ** self.gamma) * F.log(pt)return F.mean(loss, axis=self._batch_axis, exclude=True)

二、评价函数

评价函数采用计算每个类别的recall, precision和AP值

代码实现

class_recs = {}npos = 0for imagename in imagenames:R = [obj for obj in recs[imagename] if obj['name'] == classname] bbox = np.array([x['bbox'] for x in R])difficult = np.array([x['difficult'] for x in R]).astype(np.bool)det = [False] * len(R) #这个值是用来判断是否重复检测的npos = npos + sum(~difficult)class_recs[imagename] = {'bbox': bbox,'difficult': difficult,'det': det}# read detsdetfile = detpath.format(classname)with open(detfile, 'r') as f:lines = f.readlines()splitlines = [x.strip().split(' ') for x in lines]image_ids = [x[0] for x in splitlines]confidence = np.array([float(x[1]) for x in splitlines])BB = np.array([[float(z) for z in x[2:]] for x in splitlines])# sort by confidence sorted_ind = np.argsort(-confidence)BB = BB[sorted_ind, :]image_ids = [image_ids[x] for x in sorted_ind]# go down dets and mark TPs and FPsnd = len(image_ids) tp = np.zeros(nd)fp = np.zeros(nd)for d in range(nd):R = class_recs[image_ids[d]]bb = BB[d, :].astype(float)ovmax = -np.inf BBGT = R['bbox'].astype(float)if BBGT.size > 0:# compute overlaps# intersectionixmin = np.maximum(BBGT[:, 0], bb[0])iymin = np.maximum(BBGT[:, 1], bb[1])ixmax = np.minimum(BBGT[:, 2], bb[2])iymax = np.minimum(BBGT[:, 3], bb[3])iw = np.maximum(ixmax - ixmin + 1., 0.)ih = np.maximum(iymax - iymin + 1., 0.)inters = iw * ih# unionuni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +(BBGT[:, 2] - BBGT[:, 0] + 1.) *(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)overlaps = inters / uniovmax = np.max(overlaps)jmax = np.argmax(overlaps)if ovmax > ovthresh:if not R['difficult'][jmax]: if not R['det'][jmax]:tp[d] = 1.R['det'][jmax] = 1 #判断是否重复检测，检测过一次以后，值就从False变为1了else:fp[d] = 1.else:fp[d] = 1.# compute precision recallfp = np.cumsum(fp) tp = np.cumsum(tp)rec = tp / float(npos)# avoid divide by zero in case the first detection matches a difficult# ground truthprec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)ap = voc_ap(rec, prec, use_07_metric)return rec, prec, ap

计算AP值

def voc_ap(rec, prec, use_07_metric=False):"""Compute VOC AP given precision and recall. If use_07_metric is true, usesthe VOC 07 11-point method (default:False)."""if use_07_metric:# 11 point metricap = 0.for t in np.arange(0., 1.1, 0.1):if np.sum(rec >= t) == 0:p = 0 else:p = np.max(prec[rec >= t])ap = ap + p / 11.else:# correct AP calculation# first append sentinel values at the endmrec = np.concatenate(([0.], rec, [1.]))mpre = np.concatenate(([0.], prec, [0.]))# compute the precision envelopefor i in range(mpre.size - 1, 0, -1):mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])i = np.where(mrec[1:] != mrec[:-1])[0]# and sum (\Delta recall) * precap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) #计算面积return ap

计算mAP值

def mAP():detpath,annopath,imagesetfile,cachedir,class_path = get_dir('kitti')ovthresh=0.3,use_07_metric=Falserec = 0; prec = 0; mAP = 0class_list = get_classlist(class_path)for classname in class_list:rec, prec, ap = voc_eval(detpath,annopath,imagesetfile,classname,cachedir,ovthresh=0.5,use_07_metric=False,kitti=True)print('on {}, the ap is {}, recall is {}, precision is {}'.format(classname, ap, rec[-1], prec[-1]))mAP += apmAP = float(mAP) / len(class_list)return mAP

参考链接

https://zhuanlan.zhihu.com/p/43068926