def load_test_data(batch_size, dataset = 'Faces_emore'):
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]
root = 'E:\\dataset/LFW/lfw_align_112'
file_list = 'E:\\dataset/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = 'E:\\dataset/shunde'
file_list = 'E:\\dataset/shunde/pairs.txt'
dataset_privacy= CFP_FP(root, file_list, transform=transform)
root = 'E:\\dataset/CFP-FP/CFP_FP_aligned_112'
file_list = 'E:\\dataset/CFP-FP/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = 'E:\\dataset/AgeDB-30/agedb30_align_112'
file_list = 'E:\\dataset/AgeDB-30/agedb_30_pair.txt'
dataset_AgeDB30 = AgeDB30(root, file_list, transform=transform)
dataloaders = {'LFW': DataLoaderX(dataset_LFW, batch_size=batch_size, shuffle=False, num_workers=2),
'privacy': DataLoaderX(dataset_privacy, batch_size=batch_size, shuffle=False, num_workers=2),
'CFP_FP': DataLoaderX(dataset_CFP_FP, batch_size=batch_size, shuffle=False, num_workers=2),
'AgeDB30': DataLoaderX(dataset_AgeDB30, batch_size=batch_size, shuffle=False, num_workers=2)}
dataset = {'LFW': dataset_LFW,'CFP_FP': dataset_CFP_FP, 'AgeDB30': dataset_AgeDB30,'privacy':dataset_privacy}
dataset_sizes = {'LFW': len(dataset_LFW),'privacy':len(dataset_privacy),
'CFP_FP': len(dataset_CFP_FP), 'AgeDB30': len(dataset_AgeDB30)}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset
def load_data(batch_size, dataset = 'Faces_emore'):
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]
root = 'data_set/LFW/lfw_align_112'
file_list = 'data_set/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = 'data_set/CFP-FP/CFP_FP_aligned_112'
file_list = 'data_set/CFP-FP/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = 'data_set/AgeDB-30/agedb30_align_112'
file_list = 'data_set/AgeDB-30/agedb_30_pair.txt'
dataset_AgeDB30 = AgeDB30(root, file_list, transform=transform)
if dataset == 'CASIA':
root = 'data_set/CASIA_Webface_Image'
file_list = 'data_set/CASIA_Webface_Image/webface_align_112.txt'
dataset_train = CASIAWebFace(root, file_list, transform=transform)
elif dataset == 'Faces_emore':
root = 'data_set/faces_emore_images'
file_list = 'data_set/faces_emore_images/faces_emore_align_112.txt'
dataset_train = MS1M(root, file_list, transform=transform)
else:
raise NameError('no training data exist!')
dataloaders = {'train': data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=0),
'LFW': data.DataLoader(dataset_LFW, batch_size=batch_size, shuffle=False, num_workers=0),
'CFP_FP': data.DataLoader(dataset_CFP_FP, batch_size=batch_size, shuffle=False, num_workers=0),
'AgeDB30': data.DataLoader(dataset_AgeDB30, batch_size=batch_size, shuffle=False, num_workers=0)}
dataset = {'train': dataset_train,'LFW': dataset_LFW,
'CFP_FP': dataset_CFP_FP, 'AgeDB30': dataset_AgeDB30}
dataset_sizes = {'train': len(dataset_train), 'LFW': len(dataset_LFW),
'CFP_FP': len(dataset_CFP_FP), 'AgeDB30': len(dataset_AgeDB30)}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset
def load_data_dataparallel(batch_size, dataset='Faces_emore'):
transform = transforms.Compose([
#transforms.RandomHorizontalFlip(),
# transforms.Resize((120, 120), interpolation=3),
# transforms.RandomCrop(112),
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]
train_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.Resize((120, 120), interpolation=3),
transforms.RandomCrop(112),
transforms.ToTensor(),
Cutout(n_holes=1, length=16),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
root = '/data/face_dataset/LFW/lfw_align_112'
file_list = '/data/face_dataset/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = '/data/face_dataset/CFP-FP/CFP_FP_aligned_112'
file_list = '/data/face_dataset/CFP-FP/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = '/data/face_dataset/AgeDB-30/agedb30_align_112'
file_list = '/data/face_dataset/AgeDB-30/agedb_30_pair.txt'
dataset_AgeDB30 = AgeDB30(root, file_list, transform=transform)
if dataset == 'CASIA':
root = 'data_set/CASIA_Webface_Image'
file_list = 'data_set/CASIA_Webface_Image/webface_align_112.txt'
dataset_train = CASIAWebFace(root, file_list, transform=transform)
elif dataset == 'Faces_emore':
root = '/data/face_dataset/imgs'
file_list = '/data/face_dataset/imgs/faces_emore_align_112.txt'
dataset_train = MS1M(root, file_list, transform=train_transform)
# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
else:
raise NameError('no training data exist!')
dataset_size = len(dataset_train)
train_size = int(0.8 * dataset_size)
valid_size = dataset_size - train_size
train_subset, valid_subset = torch.utils.data.random_split(
dataset_train, [train_size, valid_size])
# 'train_dataset': data.DataLoader(dataset_train, batch_size=batch_size, sampler=train_sampler),
dataloaders = {
'train_dataset':
data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True),
'train_subset':
data.DataLoader(train_subset, batch_size=batch_size, shuffle=True),
'valid_subset':
data.DataLoader(valid_subset, batch_size=batch_size, shuffle=True),
'LFW':
data.DataLoader(dataset_LFW, batch_size=batch_size, shuffle=False),
'CFP_FP':
data.DataLoader(dataset_CFP_FP, batch_size=batch_size, shuffle=False),
'AgeDB30':
data.DataLoader(dataset_AgeDB30, batch_size=batch_size, shuffle=False)
}
dataset = {
'train_dataset': dataset_train,
'train_subset': train_subset,
'valid_subset': valid_subset,
'LFW': dataset_LFW,
'CFP_FP': dataset_CFP_FP,
'AgeDB30': dataset_AgeDB30
}
dataset_sizes = {
'train': len(dataset_train),
'train_subset': len(train_subset),
'valid_subset': len(valid_subset),
'LFW': len(dataset_LFW),
'CFP_FP': len(dataset_CFP_FP),
'AgeDB30': len(dataset_AgeDB30)
}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset
def load_data_with_MXNet_dali(batch_size, args, dataset='Faces_emore'):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
root = '/data/face_dataset/LFW/lfw_align_112'
file_list = '/data/face_dataset/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = '/data/face_dataset/CFP-FP/CFP_FP_aligned_112'
file_list = '/data/face_dataset/CFP-FP/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = '/data/face_dataset/AgeDB-30/agedb30_align_112'
file_list = '/data/face_dataset/AgeDB-30/agedb_30_pair.txt'
dataset_AgeDB30 = AgeDB30(root, file_list, transform=transform)
if dataset == 'CASIA':
root = 'data_set/CASIA_Webface_Image'
file_list = 'data_set/CASIA_Webface_Image/webface_align_112.txt'
dataset_train = CASIAWebFace(root, file_list, transform=transform)
elif dataset == 'Faces_emore':
path = "/data/face_dataset/"
pipes = MXNetReaderPipeline(path,
batch_size=batch_size,
num_threads=4,
device_id=args.local_rank,
num_gpus=torch.cuda.device_count())
pipes.build()
train_loader = DALIGenericIterator(pipes, ['data', 'label'],
pipes.epoch_size("Reader"))
root = '/data/face_dataset/imgs/'
file_list = '/data/face_dataset/imgs/faces_emore_align_112.txt'
dataset_train = MS1M(root, file_list, transform=transform)
else:
raise NameError('no training data exist!')
dataloaders = {
'train_dataset':
train_loader,
'LFW':
data.DataLoader(dataset_LFW,
batch_size=batch_size,
pin_memory=True,
shuffle=False),
'CFP_FP':
data.DataLoader(dataset_CFP_FP,
batch_size=batch_size,
pin_memory=True,
shuffle=False),
'AgeDB30':
data.DataLoader(dataset_AgeDB30,
batch_size=batch_size,
pin_memory=True,
shuffle=False)
}
dataset = {
'train_dataset': dataset_train,
'LFW': dataset_LFW,
'CFP_FP': dataset_CFP_FP,
'AgeDB30': dataset_AgeDB30
}
dataset_sizes = {
'LFW': len(dataset_LFW),
'CFP_FP': len(dataset_CFP_FP),
'AgeDB30': len(dataset_AgeDB30)
}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset
if select_dataset == 'LFW':
root = 'data_set/LFW/lfw_align_112'
file_list = 'data_set/LFW/pairs.txt'
dataset = LFW(root, file_list, transform=transform)
elif select_dataset == 'CFP-FP':
root = 'data_set/CFP-FP/CFP_FP_aligned_112'
file_list = 'data_set/CFP-FP/cfp_fp_pair.txt'
dataset = CFP_FP(root, file_list, transform=transform)
elif select_dataset == 'AgeDB-30':
root = 'data_set/AgeDB-30/agedb30_align_112'
file_list = 'data_set/AgeDB-30/agedb_30_pair.txt'
dataset = AgeDB30(root, file_list, transform=transform)
dataloader = data.DataLoader(dataset, batch_size=128, shuffle=False, num_workers=2, drop_last=False)
print('{} data is loaded with length'.format(select_dataset), len(dataset))
featureLs, featureRs = getFeature(detect_model, dataloader, device, flip = args.flip)
scores_l2 = np.sum(np.power((featureLs.numpy() - featureRs.numpy()), 2), 1) # L2 distance
ACCs, threshold = evaluation_10_fold(featureLs, featureRs, dataset, method = args.method)
for i in range(len(ACCs)):
print('{} accuracy: {:.2f} threshold: {:.4f}'.format(i+1, ACCs[i] * 100, threshold[i]))
print('--------')
print('Average Acc:{:.4f} Average Threshold:{:.4f}'.format(np.mean(ACCs) * 100, np.mean(threshold)))
def load_data(batch_size, dataset = 'Faces_emore'):
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))])
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize((120, 120), interpolation=3),
transforms.RandomCrop(112),
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))])
train_transform.transforms.append(Cutout(length=20))
root = 'E:\\dataset/LFW/lfw_align_112'
file_list = 'E:\\dataset/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = 'E:\\dataset/CFP-FP/CFP_FP_aligned_112'
file_list = 'E:\\dataset/CFP-FP/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = 'E:\\dataset/AgeDB-30/agedb30_align_112'
file_list = 'E:\\dataset/AgeDB-30/agedb_30_pair.txt'
dataset_AgeDB30 = AgeDB30(root, file_list, transform=transform)
if dataset == 'CASIA':
root = 'data_set/CASIA_Webface_Image'
file_list = 'data_set/CASIA_Webface_Image/webface_align_112.txt'
dataset_train = CASIAWebFace(root, file_list, transform=train_transform)
elif dataset == 'Faces_emore':
root = 'E:\\faces_emore_images'
file_list = 'E:\\faces_emore_images\\faces_emore_align_112.txt'
dataset_train = MS1M(root, file_list, transform=train_transform)
else:
raise NameError('no training data exist!')
dataset_size = len(dataset_train)
train_size = int(0.8 * dataset_size)
valid_size = dataset_size - train_size
train_subset, valid_subset = torch.utils.data.random_split(dataset_train, [train_size, valid_size])
dataloaders = {'train_dataset': data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=1),
'train_subset': data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=1),
'valid_subset': data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=1),
'LFW': data.DataLoader(dataset_LFW, batch_size=batch_size, shuffle=False, num_workers=1),
'CFP_FP': data.DataLoader(dataset_CFP_FP, batch_size=batch_size, shuffle=False, num_workers=1),
'AgeDB30': data.DataLoader(dataset_AgeDB30, batch_size=batch_size, shuffle=False, num_workers=1)}
dataset = {'train_dataset': dataset_train,'train_subset': train_subset,'valid_subset': valid_subset,'LFW': dataset_LFW,
'CFP_FP': dataset_CFP_FP, 'AgeDB30': dataset_AgeDB30}
dataset_sizes = {'train': len(dataset_train),'train_subset':len(train_subset),'valid_subset':len(valid_subset),'LFW': len(dataset_LFW),
'CFP_FP': len(dataset_CFP_FP), 'AgeDB30': len(dataset_AgeDB30)}
print('training and validation data loaded')
return dataloaders, dataset_sizes, dataset