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cifar.py
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import os,sys
import numpy as np
import torch
#import utils
from torchvision import datasets,transforms
from sklearn.utils import shuffle
import torch.utils.data as Data
def get(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
t_num=2
# CIFAR10
if not os.path.isdir('./data/binary_cifar_/'):
os.makedirs('./data/binary_cifar_')
t_num = 2
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(10//t_num):
data[t] = {}
data[t]['name'] = 'cifar10-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 10 // t_num
data[t] = {}
data[t]['name'] = 'cifar10-all'
data[t]['ncla'] = 10
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar_'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar_'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(6))
print('Task order =', ids)
for i in range(6):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar10->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n=0
for t in data.keys():
print("T",t)
taskcla.append((t, data[t]['ncla']))
n+=data[t]['ncla']
data['ncla'] = n
Loder={}
Loder_test={}
for t in range(5):
print("t",t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder_test[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
u2 = torch.tensor(data[t]['test']['x'].reshape(-1, 3, 32, 32))
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
#u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=10,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
#Loder[t]['valid'] = valid_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
test_dataset= datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))#Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset=datasets.CIFAR10('./data/', train=False, download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10//data[0]['ncla']], size,Loder,test_loader
def get_pretrain_AOP(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
t_num=2
# CIFAR10
import clip
device = "cuda" if torch.cuda.is_available() else "cpu"
import pdb
# pdb.set_trace()
model, preprocess = clip.load('ViT-B/32', device)
if not os.path.isdir('./data/binary_cifar_pretr/'):
os.makedirs('./data/binary_cifar_pretr')
t_num = 2
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(10//t_num):
print(t,"t")
num=0
data[t] = {}
data[t]['name'] = 'cifar10-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
num+=1
if num%100==0:
print(num)
# import pdb
# pdb.set_trace()
with torch.no_grad():
image=transforms.ToPILImage()(image.squeeze(0))
image_input = preprocess(image).unsqueeze(0).to(device)
image_features = model.encode_image(image_input)
image=image_features.squeeze(0)
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 10 // t_num
data[t] = {}
data[t]['name'] = 'cifar10-all'
data[t]['ncla'] = 10
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
with torch.no_grad():
image = transforms.ToPILImage()(image.squeeze(0))
image_input = preprocess(image).unsqueeze(0).to(device)
image_features = model.encode_image(image_input)
image = image_features.squeeze(0)
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, 512)
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar_p'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar_p'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(6))
print('Task order =', ids)
for i in range(6):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_p'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_p'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar10->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n = 0
for t in data.keys():
taskcla.append((t, data[t]['ncla']))
n += data[t]['ncla']
data['ncla'] = n
# pdb.set_trace()
return data, taskcla[:10 // data[0]['ncla']], size
def get_pretrain(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
t_num=2
# CIFAR10
import clip
device = "cuda" if torch.cuda.is_available() else "cpu"
import pdb
# pdb.set_trace()
model, preprocess = clip.load('ViT-B/32', device)
if not os.path.isdir('./data/binary_cifar_pretr/'):
os.makedirs('./data/binary_cifar_pretr')
t_num = 2
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(10//t_num):
print(t,"t")
num=0
data[t] = {}
data[t]['name'] = 'cifar10-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
num+=1
if num%100==0:
print(num)
# import pdb
# pdb.set_trace()
with torch.no_grad():
image=transforms.ToPILImage()(image.squeeze(0))
image_input = preprocess(image).unsqueeze(0).to(device)
image_features = model.encode_image(image_input)
image=image_features.squeeze(0)
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 10 // t_num
data[t] = {}
data[t]['name'] = 'cifar10-all'
data[t]['ncla'] = 10
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
with torch.no_grad():
image = transforms.ToPILImage()(image.squeeze(0))
image_input = preprocess(image).unsqueeze(0).to(device)
image_features = model.encode_image(image_input)
image = image_features.squeeze(0)
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, 512)
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar_p'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar_p'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(6))
print('Task order =', ids)
for i in range(6):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_p'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_p'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar10->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n=0
for t in data.keys():
print("T",t)
taskcla.append((t, data[t]['ncla']))
n+=data[t]['ncla']
data['ncla'] = n
Loder={}
Loder_test={}
for t in range(5):
print("t",t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder_test[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 512)) # .item()
u2 = torch.tensor(data[t]['test']['x'].reshape(-1, 512))
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
#u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=32,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=32,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
#Loder[t]['valid'] = valid_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
test_dataset= datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))#Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset=datasets.CIFAR10('./data/', train=False, download=True,transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]))
#import pdb
#pdb.set_trace()
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=32,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10//data[0]['ncla']], size,Loder,test_loader
def get_a_order(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
t_num=2
# CIFAR10
if not os.path.isdir('./data/binary_cifar_a1/'):
os.makedirs('./data/binary_cifar_a1')
t_num = 2
np.random.seed(101)
cls_list = [i for i in range(10)]
np.random.shuffle(cls_list)
class_mapping = np.array(cls_list, copy=True)
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(10//t_num):
data[t] = {}
data[t]['name'] = 'cifar10-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if cls_list.index(label) in range(t_num*t, t_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(cls_list.index(label))
t = 10 // t_num
data[t] = {}
data[t]['name'] = 'cifar10-all'
data[t]['ncla'] = 10
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(cls_list.index(label))
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar_a1'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar_a1'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(6))
print('Task order =', ids)
for i in range(6):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_a1'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_a1'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar10->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n=0
for t in data.keys():
print("T",t)
taskcla.append((t, data[t]['ncla']))
n+=data[t]['ncla']
data['ncla'] = n
Loder={}
Loder_test={}
for t in range(5):
print("t",t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder_test[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
u2 = torch.tensor(data[t]['test']['x'].reshape(-1, 3, 32, 32))
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
#u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=32,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
#Loder[t]['valid'] = valid_loader
# mean = [x / 255 for x in [125.3, 123.0, 113.9]]
# std = [x / 255 for x in [63.0, 62.1, 66.7]]
# test_dataset= datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))#Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
# test_dataset=datasets.CIFAR10('./data/', train=False, download=True,
# transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]))
dataset_new_test = Data.TensorDataset(data[5]['test']['x'], data[5]['test']['y'])
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
#test_loader = torch.utils.data.DataLoader(
# test_dataset,
# batch_size=64,
# shuffle=True,
#)
print("Loder is prepared")
return data, taskcla[:10//data[0]['ncla']], size,Loder,test_loader
def get_revisit(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
t_num=2
# CIFAR10
if not os.path.isdir('./data/binary_cifar_/'):
os.makedirs('./data/binary_cifar_')
t_num = 2
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(10//t_num):
data[t] = {}
data[t]['name'] = 'cifar10-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 10 // t_num
data[t] = {}
data[t]['name'] = 'cifar10-all'
data[t]['ncla'] = 10
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar_'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar_'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(6))
print('Task order =', ids)
for i in range(6):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar_'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar10->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n=0
for t in data.keys():
print("T",t)
taskcla.append((t, data[t]['ncla']))
n+=data[t]['ncla']
data['ncla'] = n
Loder={}
for t in range(5):
print("t",t)
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
#u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
for i in range(2):
dataset_new_train = Data.TensorDataset(data[t]['train']['x'][i*int(TOTAL_NUM/2):(i+1)*int(TOTAL_NUM/2)], data[t]['train']['y'][i*int(TOTAL_NUM/2):(i+1)*int(TOTAL_NUM/2)])
#dataset_new_valid = Data.TensorDataset(data[t]['valid']['x'], data[t]['valid']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=10,
shuffle=True,
)
Loder[2 * t+ i] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[2*t+i]['train'] = train_loader
#Loder[t]['valid'] = valid_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
test_dataset= datasets.CIFAR10('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))#Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset=datasets.CIFAR10('./data/', train=False, download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10//data[0]['ncla']], size,Loder,test_loader
def get_cifar100(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
# CIFAR10
if not os.path.isdir('./data/binary_cifar100_100/'):
os.makedirs('./data/binary_cifar100_100')
t_num = 10
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR100('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(100//t_num):
print(t)
data[t] = {}
data[t]['name'] = 'cifar100-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 100 // t_num
data[t] = {}
data[t]['name'] = 'cifar100-all'
data[t]['ncla'] = 100
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar100_100'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar100_100'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(11))
print('Task order =', ids)
for i in range(11):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_100'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_100'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar100->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n = 0
for t in data.keys():
print("T", t)
taskcla.append((t, data[t]['ncla']))
n += data[t]['ncla']
data['ncla'] = n
Loder = {}
for t in range(10):
print("t", t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
# u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=10,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
# test_dataset = datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose(
# [transforms.ToTensor(), transforms.Normalize(mean,
# std)])) # Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset = datasets.CIFAR100('./data/', train=False, download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10 // data[0]['ncla']], size, Loder, test_loader
def get_cifar100_joint(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
# CIFAR10
if not os.path.isdir('./data/binary_cifar100_j/'):
os.makedirs('./data/binary_cifar100_j')
t_num = 100
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR100('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(100//t_num):
print(t)
data[t] = {}
data[t]['name'] = 'cifar100-' + str(t_num*t) + '-' + str(t_num*(t+1)-1)
data[t]['ncla'] = t_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_num*t, t_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 100 // t_num
data[t] = {}
data[t]['name'] = 'cifar100-all'
data[t]['ncla'] = 100
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar100_j'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar100_j'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(2))
print('Task order =', ids)
for i in range(2):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_j'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_j'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar100->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n = 0
for t in data.keys():
print("T", t)
taskcla.append((t, data[t]['ncla']))
n += data[t]['ncla']
data['ncla'] = n
Loder = {}
for t in range(1):
print("t", t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
# u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=10,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
# test_dataset = datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose(
# [transforms.ToTensor(), transforms.Normalize(mean,
# std)])) # Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset = datasets.CIFAR100('./data/', train=False, download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10 // data[0]['ncla']], size, Loder, test_loader
def get_cifar100_50(seed=0,pc_valid=0.10):
data = {}
taskcla = []
size = [3, 32, 32]
# CIFAR10
if not os.path.isdir('./data/binary_cifar100_22/'):
os.makedirs('./data/binary_cifar100_22')
t_class_num = 2
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
dat={}
dat['train']=datasets.CIFAR100('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
dat['test']=datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
for t in range(100//t_class_num):
print(t)
data[t] = {}
data[t]['name'] = 'cifar100-' + str(t_class_num*t) + '-' + str(t_class_num*(t+1)-1)
data[t]['ncla'] = t_class_num
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
if label in range(t_class_num*t, t_class_num*(t+1)):
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
t = 100 // t_class_num
data[t] = {}
data[t]['name'] = 'cifar100-all'
data[t]['ncla'] = 100
for s in ['train', 'test']:
loader = torch.utils.data.DataLoader(dat[s], batch_size=1, shuffle=False)
data[t][s] = {'x': [], 'y': []}
for image, target in loader:
label = target.numpy()[0]
data[t][s]['x'].append(image)
data[t][s]['y'].append(label)
# "Unify" and save
for t in data.keys():
for s in ['train', 'test']:
data[t][s]['x'] = torch.stack(data[t][s]['x']).view(-1, size[0], size[1], size[2])
data[t][s]['y'] = torch.LongTensor(np.array(data[t][s]['y'], dtype=int)).view(-1)
torch.save(data[t][s]['x'],
os.path.join(os.path.expanduser('./data/binary_cifar100_2'), 'data' + str(t) + s + 'x.bin'))
torch.save(data[t][s]['y'],
os.path.join(os.path.expanduser('./data/binary_cifar100_2'), 'data' + str(t) + s + 'y.bin'))
# Load binary files
data = {}
ids = list(np.arange(51))
print('Task order =', ids)
for i in range(51):
data[i] = dict.fromkeys(['name','ncla','train','test'])
for s in ['train','test']:
data[i][s]={'x':[],'y':[]}
data[i][s]['x']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_2'),'data'+str(ids[i])+s+'x.bin'))
data[i][s]['y']=torch.load(os.path.join(os.path.expanduser('./data/binary_cifar100_2'),'data'+str(ids[i])+s+'y.bin'))
data[i]['ncla'] = len(np.unique(data[i]['train']['y'].numpy()))
data[i]['name'] = 'cifar100->>>' + str(i * data[i]['ncla']) + '-' + str(data[i]['ncla'] * (i + 1) - 1)
# Others
n = 0
for t in data.keys():
print("T", t)
taskcla.append((t, data[t]['ncla']))
n += data[t]['ncla']
data['ncla'] = n
Loder = {}
for t in range(50):
print("t", t)
Loder[t] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
u1 = torch.tensor(data[t]['train']['x'].reshape(-1, 3, 32, 32)) # .item()
# print("u1",u1.size())
TOTAL_NUM = u1.size()[0]
NUM_VALID = int(round(TOTAL_NUM * 0.1))
NUM_TRAIN = int(round(TOTAL_NUM - NUM_VALID))
# u1.size()[0]
# u2=torch.tensor(data[t]['train']['y'].reshape(-1))
# u3 = data[t]['valid']['x']
# print("u3",u3.size(),s)
# u4=data[t]['valid']['y']
dataset_new_train = Data.TensorDataset(data[t]['train']['x'], data[t]['train']['y'])
dataset_new_test = Data.TensorDataset(data[t]['test']['x'], data[t]['test']['y'])
train_loader = torch.utils.data.DataLoader(
dataset_new_train,
batch_size=10,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
dataset_new_test,
batch_size=64,
shuffle=True,
)
Loder[t]['train'] = train_loader
Loder[t]['test'] = test_loader
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
# test_dataset = datasets.CIFAR100('./data/', train=False, download=True, transform=transforms.Compose(
# [transforms.ToTensor(), transforms.Normalize(mean,
# std)])) # Data.TensorDataset(data[10//t_num]['test']['x'], data[10//t_num]['test']['y'])
'''
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_dataset=datasets.CIFAR10('./data/', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean,std)]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=64,
shuffle=True,
)
Loder={}
Loder[0] = dict.fromkeys(['name', 'ncla', 'train', 'test', 'valid'])
Loder[0]['train']=train_loader
'''
test_dataset = datasets.CIFAR100('./data/', train=False, download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)]))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=True,
)
print("Loder is prepared")
return data, taskcla[:10 // data[0]['ncla']], size, Loder, test_loader
def get_mnist(seed=0,pc_valid=0.10):