def load_data_train_fix_from_lmdb(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.ToTensor(),
Cutout(n_holes=1, length=16),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
root = '/root/faces_emore/LFW/lfw_align_112/'
file_list = '/root/faces_emore/LFW/pairs.txt'
dataset_LFW = LFW(root, file_list, transform=transform)
root = '/root/faces_emore/cfp_fp/'
file_list = '/root/faces_emore/cfp_fp/cfp_fp_pair.txt'
dataset_CFP_FP = CFP_FP(root, file_list, transform=transform)
root = '/root/faces_emore/agedb_30/'
file_list = '/root/faces_emore/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/ms1m.lmdb"
dataset_train = ImageFolderLMDB(path, transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
else:
raise NameError('no training data exist!')
dataset_size = len(dataset_train)
dataloaders = {'train_dataset': data.DataLoader(dataset_train, batch_size=batch_size,pin_memory=True, sampler=train_sampler),
'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 = {'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_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 = 2)
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 = {'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