深度学习（PYTORCH）-3.sphereface-pytorch.lfw_eval.py详解

pytorch版本sphereface的原作者地址：https://github.com/clcarwin/sphereface_pytorch

由于接触深度学习不久，所以花了较长时间来阅读源码，以下对项目中的lfw_eval.py文件做了详细解释

（不知是版本问题还是作者code有误，原代码存在很多的bug，需要自行一一纠正，另：由于在windows下运行，故而去掉了gpu加速以及多线程）

 #-*- coding:utf-8 -*-
 from __future__ import print_function

 import torch
 import torch.nn as nn
 import torch.optim as optim
 import torch.nn.functional as F
 from torch.autograd import Variable
 torch.backends.cudnn.bencmark = True

 import os,sys,cv2,random,datetime
 import argparse
 import numpy as np
 import zipfile

 from dataset import ImageDataset
 from matlab_cp2tform import get_similarity_transform_for_cv2
 import net_sphere
 from matplotlib import pyplot as plt

 #图像对齐和裁剪
 def alignment(src_img,src_pts):
     #使用标准人脸坐标对图像进行仿射
     ref_pts = [ [30.2946, 51.6963],[65.5318, 51.5014],
         [48.0252, 71.7366],[33.5493, 92.3655],[62.7299, 92.2041] ]
     crop_size = (96, 112)
     src_pts = np.array(src_pts).reshape(5,2)

     s = np.array(src_pts).astype(np.float32)
     r = np.array(ref_pts).astype(np.float32)

     tfm = get_similarity_transform_for_cv2(s, r)
     face_img = cv2.warpAffine(src_img, tfm, crop_size)
     return face_img

 #k-fold cross validation（k-折叠交叉验证）
 #将n份数据分为n_folds份，以次将第i份作为测试集，其余部分作为训练集
 def KFold(n=200, n_folds=10, shuffle=False):
     folds = []
     base = list(range(n))
     for i in range(n_folds):
         test = base[(i*n//n_folds):((i+1)*n//n_folds)]
         train = list(set(base)-set(test))
         folds.append([train,test])
     return folds

 #求解当前阈值时的准确率
 def eval_acc(threshold, diff):
     y_true = []
     y_predict = []
     for d in diff:
         same = 1 if float(d[2]) > threshold else 0
         y_predict.append(same)
         y_true.append(int(d[3]))
     y_true = np.array(y_true)
     y_predict = np.array(y_predict)
     accuracy = 1.0*np.count_nonzero(y_true==y_predict)/len(y_true)
     return accuracy

 #eval_acc和find_best_threshold共同工作，来求试图找到最佳阈值，
 #
 def find_best_threshold(thresholds, predicts):
     #threshould 阈值
     best_threshold = best_acc = 0
     for threshold in thresholds:
         accuracy = eval_acc(threshold, predicts)
         if accuracy >= best_acc:
             best_acc = accuracy
             best_threshold = threshold
     return best_threshold

 #命令行参数
 parser = argparse.ArgumentParser(description='PyTorch sphereface lfw')
 parser.add_argument('--net','-n', default='sphere20a', type=str)
 parser.add_argument('--lfw', default='../DataSet/lfw.zip', type=str)
 parser.add_argument('--model','-m', default='./sphere20a_20171020.pth', type=str)
 args = parser.parse_args()

 predicts=[]

 #加载网络
 net = getattr(net_sphere,args.net)()
 #加载模型
 net.load_state_dict(torch.load(args.model))
 #
 net.eval()
 #
 net.feature = True

 #加载图片数据
 zfile = zipfile.ZipFile(args.lfw)

 #加载landmark，每张照片包括五个特征点，共五组坐标
 landmark = {}
 with open('data/lfw_landmark.txt') as f:
     landmark_lines = f.readlines()
 #对每一行进行处理
 for line in landmark_lines:
     l = line.replace('\n','').split('\t')
     #将每一组数据转化为字典形式
     landmark[l[0]] = [int(k) for k in l[1:]]

 #加载pairs
 with open('data/pairs.txt') as f:
     pairs_lines = f.readlines()[1:]

 #range表示测试的图片对数
 for i in range(600):
     print(str(i)+" start")
     p = pairs_lines[i].replace('\n','').split('\t')
     # pairs.txt一共有6000行，存在两种形式，
     # 分别表示进行对比的两张照片，形式1是同一个人，形式2是不同人：
     # name 数字1 数字2
     # name 数字1 name数字2
     if 3==len(p):
         sameflag = 1
         #形式例如：Woody_Allen/Woody_Allen_0002.jpg
         name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
         name2 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[2]))
     if 4==len(p):
         sameflag = 0
         name1 = p[0]+'/'+p[0]+'_'+'{:04}.jpg'.format(int(p[1]))
         name2 = p[2]+'/'+p[2]+'_'+'{:04}.jpg'.format(int(p[3]))

     #分别加载两张照片，并对其进行图像对齐
     org_img1=cv2.imdecode(np.frombuffer(zfile.read("lfw/lfw/"+name1),np.uint8),1)
     org_img2=cv2.imdecode(np.frombuffer(zfile.read("lfw/lfw/"+name2),np.uint8),1)
     img1 = alignment(org_img1,landmark[name1])
     img2 = alignment(org_img2,landmark[name2])
     #1.对输出图像使用cv2进行展示
     # cv2.imshow("org_img1", org_img1)
     # cv2.imshow("org_img2", org_img2)
     # cv2.imshow("img1",img1)
     # cv2.imshow("img2", img2)
     # cv2.waitKey(0)
     # cv2.destroyAllWindows()
     #2.对输出图像使用matplotlib进行展示
     fig_new=plt.figure()
     img_list=[[org_img1,221],[org_img2,222],[img1,223],[img2,224]]
     for p,q in img_list:
         ax=fig_new.add_subplot(q)
         p = p[:, :, (2, 1, 0)]
         ax.imshow(p)
     plt.show()

     #cv.flip图像翻转,第二个参数：1：水平翻转，0：垂直翻转，-1：水平垂直翻转
     imglist = [img1,cv2.flip(img1,1),img2,cv2.flip(img2,1)]
     #分别对图片进行
     for m in range(len(imglist)):
         imglist[m] = imglist[m].transpose(2, 0, 1).reshape((1,3,112,96))
         imglist[m] = (imglist[m]-127.5)/128.0

     # p.vstack: 垂直（按照行顺序）的把数组给堆叠起来
     #******举例******
     # import numpy as np
     # a = [1, 2, 3]
     # b = [4, 5, 6]
     # print(np.vstack((a, b)))
     #
     # 输出：
     # [[1 2 3]
     #  [4 5 6]]
     img = np.vstack(imglist)
     #将numpy形式转化为variable形式
     img = Variable(torch.from_numpy(img).float(),volatile=True)
     output = net(img)
     #得到计算结果,f1和f2均为512维向量形式
     f = output.data
     f1,f2 = f[0],f[2]
     #计算二者的余弦相似度，后面加上常量是为了防止分母为0
     #关于余弦相似度请自行百度或google
     #这里给出一个简单说明的链接：http://blog.csdn.net/huangfei711/article/details/78469614
     #a*b/|a||b|
     cosdistance = f1.dot(f2)/(f1.norm()*f2.norm()+1e-5)
     predicts.append('{}\t{}\t{}\t{}\n'.format(name1,name2,cosdistance,sameflag))
     print(str(i) + " end")

 #准确率
 accuracy = []
 #（最佳）阈值
 thd = []
 #k-fold cross validation（k-折叠交叉验证）
 #folds的形式为[[train,test],[train,test].....]
 folds = KFold(n=600, n_folds=10, shuffle=False)
 #取数组为-1到1，步长为0.005
 thresholds = np.arange(-1.0, 1.0, 0.005)
 # 此处为原作者code，疑似有误，已做修改
 # predicts = np.array(map(lambda line:frd.append(line.strip('\n').split()), predicts))
 predicts = np.array([k.strip('\n').split() for k in predicts])
 for idx, (train, test) in enumerate(folds):
     # predicts[train/test]形式为：
     # [['Doris_Roberts/Doris_Roberts_0001.jpg'
     # 'Doris_Roberts/Doris_Roberts_0003.jpg' '0.6532696413605743' '1'],.....]
     #寻找最佳阈值
     best_thresh = find_best_threshold(thresholds, predicts[train])
     #通过上面的得到的最佳阈值来对test数据集进行测试得到准确率
     accuracy.append(eval_acc(best_thresh, predicts[test]))
     #thd阈值
     thd.append(best_thresh)
 #np.mean：计算均值，np.std：计算标准差
 #输出结果分别为：准确率均值，准确率标准差，阈值均值
 print('LFWACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))
 #例如结果为 LFWACC=0.9800 std=0.0600 thd=0.3490
 #则说明准确率为98%，准确率标准差为0.06，阈值的均值为0.3490
 #因此我们可以认为余弦相似度大于0.3490的两张图片里是同一个人