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#!/usr/bin/env python
# encoding: utf-8
'''
@author: MingDong
@file: eval_agedb30.py
@desc: The AgeDB-30 test protocol is same with LFW, so I just copy the code from eval_lfw.py
'''
import os
import argparse
import numpy as np
import scipy.io
import torch.utils.data
import torchvision.transforms as transforms
from torch.nn import DataParallel
from model import mobilefacenet, cbam
from dataloader.agedb import AgeDB30
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_agedb_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]
])
agedb_dataset = AgeDB30(data_root, file_list, transform=transform)
agedb_loader = torch.utils.data.DataLoader(agedb_dataset, batch_size=128,
shuffle=False, num_workers=2, drop_last=False)
return net.eval(), device, agedb_dataset, agedb_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 enumerate(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/AgeDB-30/agedb30_align_112', help='The path of lfw data')
parser.add_argument('--file_list', type=str, default='/media/sda/AgeDB-30/agedb_30_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_agedb_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 enumerate(ACCs):
print(f'{i + 1} {ACCs[i] * 100:.2f}')
print('--------')
print(f'AVE {np.mean(ACCs) * 100:.4f}')
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