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"""
@author: MingDong
@file: eval_cfp.py
@desc: this code is very similar with eval_lfw.py and eval_agedb30.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.cfp import CFP_FP
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 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/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='./checkpoints/SERES100_SERES100_IR_20190528_132635/Iter_342000_net.ckpt', help='The path pf save checkpoints')
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 enumerate(ACCs):
print(f'{i + 1} {ACCs[i] * 100:.2f}')
print('--------')
print(f'AVE {np.mean(ACCs) * 100:.4f}')
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