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dataloader.py
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dataloader.py
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import os
import pickle
import numpy as np
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from trainer import AverageMeter, accuracy
__all__ = ['setup_dataset', 'generate_softlogit_unlabel']
class k150_dataset(Dataset):
def __init__(self, _dir, transform):
super(k150_dataset, self).__init__()
self.imgdir=_dir
self.transforms=transform
self.all_data = pickle.load(open(self.imgdir,'rb'))
self.image = self.all_data['data']
self.label = self.all_data['label']
self.number = self.image.shape[0]
def __len__(self):
return self.number
def __getitem__(self, index):
img = self.image[index]
target = self.label[index]
img = self.transforms(img)
return img, target
class Labeled_dataset(Dataset):
def __init__(self, _dir, transform, target_list, offset=0, num=None):
super(Labeled_dataset, self).__init__()
self.imgdir=_dir
self.transforms=transform
self.all_image = pickle.load(open(self.imgdir,'rb'))
self.img = []
self.target = []
print('target list = ', target_list)
for i,idx in enumerate(target_list):
self.img.append(self.all_image[idx])
self.target.append((i+offset)*np.ones(self.all_image[idx].shape[0]))
self.image = np.concatenate(self.img, 0)
self.label = np.concatenate(self.target, 0)
self.number = self.image.shape[0]
if num:
index = np.random.permutation(self.number)
select_index = index[:int(num)]
self.image = self.image[select_index]
self.label = self.label[select_index]
self.number = num
def __len__(self):
return self.number
def __getitem__(self, index):
img = self.image[index]
target = self.label[index]
img = self.transforms(img)
return img, target
class unlabel_logit_dataset(Dataset):
# output: img, soft-logits for random branch, soft-logits for balance branch
def __init__(self, _dir):
super(unlabel_logit_dataset, self).__init__()
self.imgdir = _dir + '_img.npy'
self.softlogit_dir = _dir + '_dis_label.npy'
self.softlogit_main_dir = _dir + '_dis_label_main.npy'
self.image = np.load(self.imgdir)
self.softlogit = np.load(self.softlogit_dir)
self.softlogit_main = np.load(self.softlogit_main_dir)
self.number = self.image.shape[0]
def __len__(self):
return self.number
def __getitem__(self, index):
img = self.image[index]
target = self.softlogit[index]
main_target = self.softlogit_main[index]
img = torch.from_numpy(img)
target = torch.from_numpy(target)
main_target = torch.from_numpy(main_target)
return img, target, main_target
def setup_dataset(args, task_id, train=True, all_test=False):
if args.dataset == 'cifar10':
normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
train_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize
])
val_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
normalize
])
path_head = args.data
train_path = os.path.join(path_head, 'cifar10_train.pkl')
saved_img_path = os.path.join(path_head, 'cifar10_save_100.pkl')
val_path = os.path.join(path_head, 'cifar10_val.pkl')
test_path = os.path.join(path_head,'cifar10_test.pkl')
unlabel_path = os.path.join(path_head,'cifar10_80m_150k.pkl')
if os.path.isfile('npy_files/cifar10_class_order.txt'):
sequence = np.loadtxt('npy_files/cifar10_class_order.txt')
else:
sequence = np.random.permutation(10)
np.savetxt('npy_files/cifar10_class_order.txt', sequence)
print('cifar10 incremental task sequence:', sequence)
elif args.dataset == 'cifar100':
normalize = transforms.Normalize(mean=[0.5071, 0.4866, 0.4409], std=[0.2009, 0.1984, 0.2023])
train_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize
])
val_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
normalize
])
path_head = args.data
train_path = os.path.join(path_head, 'cifar100_train.pkl')
saved_img_path = os.path.join(path_head, 'cifar100_save_100.pkl')
val_path = os.path.join(path_head, 'cifar100_val.pkl')
test_path = os.path.join(path_head,'cifar100_test.pkl')
unlabel_path = os.path.join(path_head,'cifar100_80m_150k.pkl')
if os.path.isfile('npy_files/cifar100_class_order.txt'):
sequence = np.loadtxt('npy_files/cifar100_class_order.txt')
else:
sequence = np.random.permutation(100)
np.savetxt('npy_files/cifar100_class_order.txt', sequence)
print('cifar100 incremental task sequence:', sequence)
else:
raise ValueError('Unknow Dataset')
class_per_state = args.classes_per_classifier
if train:
if task_id == 0:
train_dataset = Labeled_dataset(train_path, train_trans,
sequence[:class_per_state], offset=0)
val_dataset = Labeled_dataset(val_path , val_trans,
sequence[:class_per_state], offset=0)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size, shuffle=True,
num_workers=2, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
return train_loader, val_loader
else:
train_dataset = Labeled_dataset(train_path, train_trans,
sequence[task_id*class_per_state:(task_id+1)*class_per_state],
offset=task_id*class_per_state)
val_dataset = Labeled_dataset(val_path, val_trans,
sequence[:(task_id+1)*class_per_state], offset=0)
train_saved_dataset = Labeled_dataset(saved_img_path, train_trans,
sequence[:task_id*class_per_state], offset=0)
train_random_dataset = torch.utils.data.dataset.ConcatDataset((train_dataset, train_saved_dataset))
unlabel_dataset = unlabel_logit_dataset(os.path.join(args.save_data_path, 'task'+str(task_id)))
sub_batch_size = args.batch_size // 4
train_loader_random = torch.utils.data.DataLoader(
train_random_dataset,
batch_size=sub_batch_size, shuffle=True,
num_workers=2, pin_memory=True)
train_loader_balance_new = torch.utils.data.DataLoader(
train_dataset,
batch_size=int(sub_batch_size/(1+task_id)), shuffle=True,
num_workers=2, pin_memory=True)
train_loader_balance_saved = torch.utils.data.DataLoader(
train_saved_dataset,
batch_size=int(sub_batch_size*task_id/(1+task_id)), shuffle=True,
num_workers=2, pin_memory=True)
unlabel_loader = torch.utils.data.DataLoader(
unlabel_dataset,
batch_size=sub_batch_size, shuffle=True,
num_workers=2, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
return train_loader_random, train_loader_balance_new, train_loader_balance_saved, unlabel_loader, val_loader
else:
if all_test:
test_dataset = Labeled_dataset(test_path, val_trans,
sequence[0:(task_id+1)*class_per_state], offset=0)
else:
test_dataset = Labeled_dataset(test_path, val_trans,
sequence[task_id*class_per_state:(task_id+1)*class_per_state], offset=task_id*class_per_state)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
return test_loader
def select_knn(target_feature, dataset, number):
offset = 1e+20
target_number = target_feature.size(0)
all_number = dataset.size(0)
dataset_trans = torch.transpose(dataset, 0, 1)
target_norm = torch.norm(target_feature, p=2, dim=1).pow(2)
dataset_norm = torch.norm(dataset, p=2, dim=1).pow(2)
target = target_norm.repeat(all_number,1)
all_dis = dataset_norm.repeat(target_number,1)
distance_matrix = torch.transpose(target, 0, 1) +all_dis - 2*torch.mm(target_feature, dataset_trans)
select_img = []
for index in range(target_number):
distance_one = distance_matrix[index,:]
nearest = torch.argsort(distance_one)[0:number]
nearest = nearest.tolist()
select_img.extend(nearest)
distance_matrix[:,select_img] = offset
select_img = list(set(select_img))
print('selected numbers of unlabel images', len(select_img))
return select_img
def label_extract(train_loader, model, criterion, _dir, fc_num):
img = []
label = []
dis_label = []
dis_label_main = []
# switch to evaluate mode
model.eval()
for i, (input, target) in enumerate(train_loader):
input = input.cuda()
target = target.long().cuda()
input_data = {'x': input, 'out_idx': fc_num, 'main_fc': False}
input_data_main = {'x': input, 'out_idx': fc_num, 'main_fc': True}
# compute output
with torch.no_grad():
output = model(**input_data)
output_main = model(**input_data_main)
img.append(input.cpu().numpy())
label.append(target.cpu().numpy())
dis_label.append(output.cpu().numpy())
dis_label_main.append(output_main.cpu().numpy())
img = np.concatenate(img,0)
label = np.concatenate(label,0)
dis_label = np.concatenate(dis_label,0)
dis_label_main = np.concatenate(dis_label_main,0)
print(img.shape)
print(label.shape)
print(dis_label.shape)
print(dis_label_main.shape)
np.save(os.path.join(_dir,'task'+str(fc_num)+'_img.npy'),img)
np.save(os.path.join(_dir,'task'+str(fc_num)+'_label.npy'),label)
np.save(os.path.join(_dir,'task'+str(fc_num)+'_dis_label.npy'),dis_label)
np.save(os.path.join(_dir,'task'+str(fc_num)+'_dis_label_main.npy'),dis_label_main)
def feature_extract(train_loader, model, criterion):
losses = AverageMeter()
top1 = AverageMeter()
all_feature = []
# switch to evaluate mode
model.eval()
for i, (input, target) in enumerate(train_loader):
input = input.cuda()
inputs_data = {'x': input, 'is_feature': True}
# compute output
with torch.no_grad():
feature = model(**inputs_data)
all_feature.append(feature.cpu())
all_feature = torch.cat(all_feature, dim=0)
print('all_feature_size', all_feature.size())
return all_feature
def generate_softlogit_unlabel(args, task_id, model, criterion):
if args.dataset == 'cifar10':
normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
train_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize
])
val_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
normalize
])
path_head = args.data
train_path = os.path.join(path_head, 'cifar10_train.pkl')
saved_img_path = os.path.join(path_head, 'cifar10_save_100.pkl')
val_path = os.path.join(path_head, 'cifar10_val.pkl')
test_path = os.path.join(path_head,'cifar10_test.pkl')
unlabel_path = os.path.join(path_head,'cifar10_80m_150k.pkl')
if os.path.isfile('npy_files/cifar10_class_order.txt'):
sequence = np.loadtxt('npy_files/cifar10_class_order.txt')
else:
sequence = np.random.permutation(10)
np.savetxt('npy_files/cifar10_class_order.txt', sequence)
print('cifar10 incremental task sequence:', sequence)
elif args.dataset == 'cifar100':
normalize = transforms.Normalize(mean=[0.5071, 0.4866, 0.4409], std=[0.2009, 0.1984, 0.2023])
train_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize
])
val_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
normalize
])
path_head = args.data
train_path = os.path.join(path_head, 'cifar100_train.pkl')
saved_img_path = os.path.join(path_head, 'cifar100_save_100.pkl')
val_path = os.path.join(path_head, 'cifar100_val.pkl')
test_path = os.path.join(path_head,'cifar100_test.pkl')
unlabel_path = os.path.join(path_head,'cifar100_80m_150k.pkl')
if os.path.isfile('npy_files/cifar100_class_order.txt'):
sequence = np.loadtxt('npy_files/cifar100_class_order.txt')
else:
sequence = np.random.permutation(100)
np.savetxt('npy_files/cifar100_class_order.txt', sequence)
print('cifar100 incremental task sequence:', sequence)
else:
raise ValueError('Unknow Dataset')
class_per_state = args.classes_per_classifier
saved_dataset = Labeled_dataset(saved_img_path, val_trans, sequence[:task_id*class_per_state], offset=0)
k15_dataset = k150_dataset(unlabel_path, val_trans)
saved_loader = torch.utils.data.DataLoader(
saved_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
k15_loader = torch.utils.data.DataLoader(
k15_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
all_feature = feature_extract(k15_loader, model, criterion)
target_feature = feature_extract(saved_loader, model, criterion)
index_select = select_knn(target_feature, all_feature, args.unlabel_num)
np.save(os.path.join(args.save_data_path,'task'+str(task_id)+'_select_index.npy'), np.array(index_select))
all_data = pickle.load(open(unlabel_path,'rb'))
all_image = all_data['data']
all_label = all_data['label']
new_data = {}
new_data['data'] = all_image[index_select,:,:,:]
new_data['label'] = all_label[index_select]
print(new_data['data'].shape)
print(new_data['label'].shape)
pickle.dump(new_data, open(os.path.join(args.save_data_path,'selected_unlabel_task'+str(task_id)+'.pkl'), 'wb'))
extract_dataset = k150_dataset(os.path.join(args.save_data_path,'selected_unlabel_task'+str(task_id)+'.pkl'), train_trans)
extract_loader = torch.utils.data.DataLoader(
extract_dataset,
batch_size=args.batch_size, shuffle=True,
num_workers=2, pin_memory=True)
label_extract(extract_loader, model, criterion, args.save_data_path, fc_num=task_id)