eval_cfp.py

#!/usr/bin/env python
# encoding: utf-8
'''
@author: wujiyang
@contact: wujiyang@hust.edu.cn
@file: eval_cfp.py
@time: 2018/12/26 16:23
@desc: this code is very similar with eval_lfw.py and eval_agedb30.py
'''


import numpy as np
import scipy.io
import os
import torch.utils.data
from backbone import mobilefacenet, resnet, arcfacenet, cbam
from dataset.cfp import CFP_FP
import torchvision.transforms as transforms
from torch.nn import DataParallel
import argparse

def getAccuracy(scores, flags, threshold):
    p = np.sum(scores[flags == 1] > threshold)
    n = np.sum(scores[flags == -1] < threshold)
    return 1.0 * (p + n) / len(scores)

def getThreshold(scores, flags, thrNum):
    accuracys = np.zeros((2 * thrNum + 1, 1))
    thresholds = np.arange(-thrNum, thrNum + 1) * 1.0 / thrNum
    for i in range(2 * thrNum + 1):
        accuracys[i] = getAccuracy(scores, flags, thresholds[i])
    max_index = np.squeeze(accuracys == np.max(accuracys))
    bestThreshold = np.mean(thresholds[max_index])
    return bestThreshold

def evaluation_10_fold(feature_path='./result/cur_epoch_cfp_result.mat'):
    ACCs = np.zeros(10)
    result = scipy.io.loadmat(feature_path)
    for i in range(10):
        fold = result['fold']
        flags = result['flag']
        featureLs = result['fl']
        featureRs = result['fr']

        valFold = fold != i
        testFold = fold == i
        flags = np.squeeze(flags)

        mu = np.mean(np.concatenate((featureLs[valFold[0], :], featureRs[valFold[0], :]), 0), 0)
        mu = np.expand_dims(mu, 0)
        featureLs = featureLs - mu
        featureRs = featureRs - mu
        featureLs = featureLs / np.expand_dims(np.sqrt(np.sum(np.power(featureLs, 2), 1)), 1)
        featureRs = featureRs / np.expand_dims(np.sqrt(np.sum(np.power(featureRs, 2), 1)), 1)

        scores = np.sum(np.multiply(featureLs, featureRs), 1)
        threshold = getThreshold(scores[valFold[0]], flags[valFold[0]], 10000)
        ACCs[i] = getAccuracy(scores[testFold[0]], flags[testFold[0]], threshold)

    return ACCs

def loadModel(data_root, file_list, backbone_net, gpus='0', resume=None):

    if backbone_net == 'MobileFace':
        net = mobilefacenet.MobileFaceNet()
    elif backbone_net == 'CBAM_50':
        net = cbam.CBAMResNet(50, feature_dim=args.feature_dim, mode='ir')
    elif backbone_net == 'CBAM_50_SE':
        net = cbam.CBAMResNet(50, feature_dim=args.feature_dim, mode='ir_se')
    elif backbone_net == 'CBAM_100':
        net = cbam.CBAMResNet(100, feature_dim=args.feature_dim, mode='ir')
    elif backbone_net == 'CBAM_100_SE':
        net = cbam.CBAMResNet(100, feature_dim=args.feature_dim, mode='ir_se')
    else:
        print(backbone_net, ' is not available!')

    # gpu init
    multi_gpus = False
    if len(gpus.split(',')) > 1:
        multi_gpus = True
    os.environ['CUDA_VISIBLE_DEVICES'] = gpus
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    net.load_state_dict(torch.load(resume)['net_state_dict'])

    if multi_gpus:
        net = DataParallel(net).to(device)
    else:
        net = net.to(device)

    transform = transforms.Compose([
        transforms.ToTensor(),  # range [0, 255] -> [0.0,1.0]
        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))  # range [0.0, 1.0] -> [-1.0,1.0]
    ])
    cfp_dataset = CFP_FP(data_root, file_list, transform=transform)
    cfp_loader = torch.utils.data.DataLoader(cfp_dataset, batch_size=128,
                                             shuffle=False, num_workers=4, drop_last=False)

    return net.eval(), device, cfp_dataset, cfp_loader

def getFeatureFromTorch(feature_save_dir, net, device, data_set, data_loader):
    featureLs = None
    featureRs = None
    count = 0
    for data in data_loader:
        for i in range(len(data)):
            data[i] = data[i].to(device)
        count += data[0].size(0)
        #print('extracing deep features from the face pair {}...'.format(count))
        with torch.no_grad():
            res = [net(d).data.cpu().numpy() for d in data]
        featureL = np.concatenate((res[0], res[1]), 1)
        featureR = np.concatenate((res[2], res[3]), 1)
        # print(featureL.shape, featureR.shape)
        if featureLs is None:
            featureLs = featureL
        else:
            featureLs = np.concatenate((featureLs, featureL), 0)
        if featureRs is None:
            featureRs = featureR
        else:
            featureRs = np.concatenate((featureRs, featureR), 0)
        # print(featureLs.shape, featureRs.shape)

    result = {'fl': featureLs, 'fr': featureRs, 'fold': data_set.folds, 'flag': data_set.flags}
    scipy.io.savemat(feature_save_dir, result)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Testing')
    parser.add_argument('--root', type=str, default='/media/sda/CFP-FP/cfp_fp_aligned_112', help='The path of lfw data')
    parser.add_argument('--file_list', type=str, default='/media/sda/CFP-FP/cfp_fp_pair.txt', help='The path of lfw data')
    parser.add_argument('--resume', type=str, default='./model/SERES100_SERES100_IR_20190528_132635/Iter_342000_net.ckpt', help='The path pf save model')
    parser.add_argument('--backbone_net', type=str, default='CBAM_100_SE', help='MobileFace, CBAM_50, CBAM_50_SE, CBAM_100, CBAM_100_SE')
    parser.add_argument('--feature_dim', type=int, default=512, help='feature dimension')
    parser.add_argument('--feature_save_path', type=str, default='./result/cur_epoch_cfp_result.mat',
                        help='The path of the extract features save, must be .mat file')
    parser.add_argument('--gpus', type=str, default='2,3', help='gpu list')
    args = parser.parse_args()

    net, device, agedb_dataset, agedb_loader = loadModel(args.root, args.file_list, args.backbone_net, args.gpus, args.resume)
    getFeatureFromTorch(args.feature_save_path, net, device, agedb_dataset, agedb_loader)
    ACCs = evaluation_10_fold(args.feature_save_path)
    for i in range(len(ACCs)):
        print('{}    {:.2f}'.format(i + 1, ACCs[i] * 100))
    print('--------')
    print('AVE    {:.4f}'.format(np.mean(ACCs) * 100))