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#!/usr/bin/env python
# encoding: utf-8
"""
@author: MingDong
@file: eval_lfw.py
@desc:
"""
import os
import argparse
import numpy as np
import scipy.io
import onnxruntime as ort
import torch.utils.data
import torchvision.transforms as transforms
from torch.nn import DataParallel
from model import mobilefacenet, resnet, cbam
from dataloader.lfw import LFW, LFWDataset
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_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 load_model(data_root, file_list, backbone_net, gpus='0', resume=None):
if backbone_net == 'MobileFace':
net = mobilefacenet.MobileFaceNet()
elif backbone_net == 'Res50':
net = resnet.ResNet50()
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]
])
lfw_dataset = LFW(data_root, file_list, transform=transform)
lfw_loader = torch.utils.data.DataLoader(lfw_dataset, batch_size=128,
shuffle=False, num_workers=2, drop_last=False)
return net.eval(), device, lfw_dataset, lfw_loader
def load_onnx_model(data_root, file_list):
ort_session = ort.InferenceSession('checkpoints/resnet50_Quant.onnx')
lfw_dataset = LFWDataset(data_root, file_list)
return ort_session, lfw_dataset
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)
def getFeatureFromOnnx(feature_save_dir, net, data_set):
featureLs = None
featureRs = None
count = 0
for data in data_set:
res = []
for _, i in enumerate(data):
feat = net.run(None, {"input": data[i]})
res.append(feat)
count += data[0].size(0)
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='/datasets/public1/upload/datasets/lfw', help='The path of lfw data')
parser.add_argument('--file_list', type=str, default='/datasets/public1/upload/datasets/lfw_pair.txt', help='The path of lfw data')
parser.add_argument('--backbone_net', type=str, default='Res50', help='MobileFace, Res50, 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('--resume', type=str, default='./checkpoints/Res50_RES50_20210711_091848/Iter_066000_net.ckpt',
help='The path pf save checkpoints')
parser.add_argument('--feature_save_path', type=str, default='./result/cur_epoch_lfw_result.mat',
help='The path of the extract features save, must be .mat file')
parser.add_argument('--gpus', type=str, default='0', help='gpu list')
args = parser.parse_args()
# inference by torch
# net, device, lfw_dataset, lfw_loader = load_model(args.root, args.file_list, args.backbone_net, args.gpus, args.resume)
# getFeatureFromTorch(args.feature_save_path, net, device, lfw_dataset, lfw_loader)
# ACCs = evaluation_10_fold(args.feature_save_path)
# inference by onnx
net, lfw_dataset = load_onnx_model(args.root, args.file_list)
getFeatureFromOnnx(args.feature_save_path, net, lfw_dataset)
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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