def lfw_verification(net, feature_dim, device, resolution = 'hr', N =1, step=None):
net.eval()
if resolution == 'hr':
dataloader = get_loader('LFWDataset_gz', batch_size=256).dataloader
elif resolution == 'lr':
dataloader = get_loader('LFWDataset_gz_lr', batch_size=256, N=N).dataloader
# 6000 Image Pairs ( 3000 genuine and 3000 impostor matches)
features11_total = torch.Tensor(np.zeros((6000, feature_dim), dtype=np.float32)).to(device)
features12_total, features21_total, features22_total = torch.zeros_like(features11_total), \
torch.zeros_like(features11_total), \
torch.zeros_like(features11_total)
labels = torch.Tensor(np.zeros((6000, 1), dtype=np.float32)).to(device)
with torch.no_grad():
bs_total = 0
for index, (img1, img2, img1_flip, img2_flip, targets) in enumerate(dataloader):
bs = len(targets)
img1, img1_flip = img1.to(device), img1_flip.to(device)
img2, img2_flip = img2.to(device), img2_flip.to(device)
features11 = net(img1)
features12 = net(img1_flip)
features21 = net(img2)
features22 = net(img2_flip)
features11_total[bs_total:bs_total + bs] = features11
features12_total[bs_total:bs_total + bs] = features12
features21_total[bs_total:bs_total + bs] = features21
features22_total[bs_total:bs_total + bs] = features22
labels[bs_total:bs_total + bs] = targets
bs_total += bs
assert bs_total == 6000, print('LFW pairs should be 6,000!')
labels = labels.cpu().numpy()
for cal_type in ['concat']: # cal_type: concat/sum/normal
scores = tensor_pair_cosine_distance(features11_total, features12_total, features21_total, features22_total, type=cal_type)
fpr, tpr, _ = roc_curve(labels, scores) # false positive rate / true positive rate
# if step == args.iterations:
# np.savez('cnn_roc_{}.npz'.format(args.loss_type), name1=fpr, name2=tpr)
# message = 'The fpr and tpr is saved to cnn_roc_{}.npz'.format(args.loss_type)
# save_log(message, args.log_path)
roc_auc = auc(fpr, tpr)
accuracy = []
thd = []
folds = KFold(n=6000, n_folds=10, shuffle=False) # 1 for test, 9 for train
thresholds = np.linspace(-10000, 10000, 10000 + 1)
thresholds = thresholds / 10000
predicts = np.hstack((scores, labels))
for idx, (train, test) in enumerate(folds):
best_thresh = find_best_threshold(thresholds, predicts[train]) # find the best threshold to say if these two persons are same or not
accuracy.append(eval_acc(best_thresh, predicts[test]))
thd.append(best_thresh)
mean_acc, std = np.mean(accuracy), np.std(accuracy) # have 10 accuracies and then find the mean and std
if step is not None:
message = 'LFWACC={:.4f} std={:.4f} auc={:.4f} type:{} at {}iter.'.format(mean_acc, std, roc_auc, cal_type, step)
else:
message = 'LFWACC={:.4f} std={:.4f} auc={:.4f} type:{} at testing.'.format(mean_acc, std, roc_auc, cal_type)
if step is None:
np.savez('/home/tangjiawei/PycharmProjects/FYP_Face_Verification/saved/alexnet_hr/resnet_roc_{}.npz'.format(N), name1=fpr, name2=tpr)
print(message)
def run(net, feature_dim, device, N =1, step=None):
net.eval()
dataloader = get_loader('LFWDataset_gz_lr', batch_size=256, N=N).dataloader
# 6000 Image Pairs ( 3000 genuine and 3000 impostor matches)
features11_total = torch.Tensor(np.zeros((6000, feature_dim), dtype=np.float32)).to(device)
features12_total, features21_total, features22_total = torch.zeros_like(features11_total), \
torch.zeros_like(features11_total), \
torch.zeros_like(features11_total)
labels = torch.Tensor(np.zeros((6000, 1), dtype=np.float32)).to(device)
with torch.no_grad():
bs_total = 0
for index, (img1, img2, img1_flip, img2_flip, targets) in enumerate(dataloader):
bs = len(targets)
img1, img1_flip = img1.to(device), img1_flip.to(device)
img2, img2_flip = img2.to(device), img2_flip.to(device)
features11 = net(img1)
features12 = net(img1_flip)
features21 = net(img2)
features22 = net(img2_flip)
features11_total[bs_total:bs_total + bs] = features11
features12_total[bs_total:bs_total + bs] = features12
features21_total[bs_total:bs_total + bs] = features21
features22_total[bs_total:bs_total + bs] = features22
labels[bs_total:bs_total + bs] = targets
bs_total += bs
assert bs_total == 6000, print('LFW pairs should be 6,000!')
labels = labels.cpu().numpy()
for cal_type in ['concat']: # cal_type: concat/sum/normal
scores = tensor_pair_cosine_distance(features11_total, features12_total, features21_total, features22_total, type=cal_type)
fpr, tpr, _ = roc_curve(labels, scores) # false positive rate / true positive rate
roc_auc = auc(fpr, tpr)
accuracy = []
thd = []
folds = KFold(n=6000, n_folds=10, shuffle=False) # 1 for test, 9 for train
thresholds = np.linspace(-10000, 10000, 10000 + 1)
thresholds = thresholds / 10000
predicts = np.hstack((scores, labels))
for idx, (train, test) in enumerate(folds):
best_thresh = find_best_threshold(thresholds, predicts[train]) # find the best threshold to say if these two persons are same or not
accuracy.append(eval_acc(best_thresh, predicts[test]))
thd.append(best_thresh)
mean_acc, std = np.mean(accuracy), np.std(accuracy) # have 10 accuracies and then find the mean and std
if step is not None:
message = 'DF={} LFWACC={:.4f} std={:.4f} auc={:.4f} type:{} at {}iter.'.format(N, mean_acc, std, roc_auc, cal_type, step)
logging.info(message)
else:
message = 'DF={} LFWACC={:.4f} std={:.4f} auc={:.4f} type:{} at testing.'.format(N, mean_acc, std, roc_auc, cal_type)
logging.info(message)
print(message)