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from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import random
import numpy as np
from PIL import Image
import json
import os
import torch
from torchnet.meter import AUCMeter
def unpickle(file):
import _pickle as cPickle
with open(file, 'rb') as fo:
dict = cPickle.load(fo, encoding='latin1')
return dict
class cifar_dataset(Dataset):
def __init__(self, dataset, r, noise_mode, root_dir, transform, mode, noise_file='', pred=[], probability=[], log='', clean_idx=[], test_form = None):
self.r = r # noise ratio
self.transform = transform
self.test_form = test_form
self.mode = mode
self.transition = {0:0,2:0,4:7,7:7,1:1,9:1,3:5,5:3,6:6,8:8} # class transition for asymmetric noise
self.noise_file = noise_file
if self.mode=='test':
if dataset=='cifar10':
test_dic = unpickle('%s/test_batch'%root_dir)
self.test_data = test_dic['data']
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
self.test_label = test_dic['labels']
elif dataset=='cifar100':
test_dic = unpickle('%s/test'%root_dir)
self.test_data = test_dic['data']
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
self.test_label = test_dic['fine_labels']
else:
train_data=[]
train_label=[]
if dataset=='cifar10':
for n in range(1,6):
dpath = '%s/data_batch_%d'%(root_dir,n)
data_dic = unpickle(dpath)
train_data.append(data_dic['data'])
train_label = train_label+data_dic['labels']
train_data = np.concatenate(train_data)
elif dataset=='cifar100':
train_dic = unpickle('%s/train'%root_dir)
train_data = train_dic['data']
train_label = train_dic['fine_labels']
train_data = train_data.reshape((50000, 3, 32, 32))
train_data = train_data.transpose((0, 2, 3, 1))
self.clean_label = np.array(train_label)
if os.path.exists(noise_file):
noise_label = json.load(open(noise_file,"r"))
else: #inject noise
noise_label = []
idx = list(range(50000))
random.shuffle(idx)
num_noise = int(self.r*50000)
noise_idx = idx[:num_noise]
for i in range(50000):
if i in noise_idx:
if noise_mode=='sym':
if dataset=='cifar10':
noiselabel = random.randint(0,9)
elif dataset=='cifar100':
noiselabel = random.randint(0,99)
noise_label.append(noiselabel)
elif noise_mode=='asym':
noiselabel = self.transition[train_label[i]]
noise_label.append(noiselabel)
else:
noise_label.append(train_label[i])
print("save noisy labels to %s ..."%noise_file)
json.dump(noise_label,open(noise_file,"w"))
if self.mode == 'all':
self.train_data = train_data
self.noise_label = np.array(noise_label).astype(np.int64)
else:
if self.mode == "labeled":
pred_idx = pred.nonzero()[0]
self.probability = [probability[i] for i in pred_idx]
clean = (np.array(noise_label)==np.array(train_label))
auc_meter = AUCMeter()
auc_meter.reset()
auc_meter.add(probability,clean)
auc,_,_ = auc_meter.value()
clean_index = np.where(np.array(noise_label)[pred_idx.tolist()] == np.array(self.clean_label)[pred_idx.tolist()])[0]
num_per_class = []
for i in range(max(noise_label)):
temp = np.where(np.array(noise_label)[clean_index.tolist()] == i)[0]
num_per_class.append(len(temp))
num_per_class2 = []
for i in range(max(noise_label)):
temp = np.where(np.array(noise_label)[pred_idx.tolist()] == i)[0]
num_per_class2.append(len(temp))
print('clean num per class:', num_per_class, num_per_class2)
log.write('Numer of labeled samples:%d AUC:%.3f corrected clean num:%d, uncorrected noisy num:%d\n'
% (pred.sum(), auc, len(clean_index), len(pred_idx) - len(clean_index)))
log.flush()
elif self.mode == "unlabeled":
pred_idx = (1-pred).nonzero()[0]
noise_index = np.where(np.array(noise_label)[pred_idx.tolist()] != np.array(self.clean_label)[pred_idx.tolist()])[0]
log.write('Numer of unlabeled samples:%d corrected noisy num:%d, uncorrected clean num:%d\n'
% (pred.sum(), len(noise_index), len(pred_idx) - len(noise_index)))
log.flush()
elif self.mode == 'boost':
pred_idx = clean_idx
self.train_data = train_data[pred_idx]
self.noise_label = [noise_label[i] for i in pred_idx]
print("%s data has a size of %d"%(self.mode,len(self.noise_label)))
def if_noise(self, pred=None):
if pred is None:
noise_index = np.where(self.noise_label[:] != self.clean_label[:])[0]
clean_index = np.where(self.noise_label[:] == self.clean_label[:])[0]
return noise_index, clean_index
else:
pred_idx1 = pred.nonzero()[0].tolist()
clean_index = np.where(np.array(self.noise_label)[pred_idx1] == np.array(self.clean_label)[pred_idx1])[0]
pred_idx = (1 - pred).nonzero()[0].tolist()
noise_index = np.where(np.array(self.noise_label)[pred_idx] != np.array(self.clean_label)[pred_idx])[0]
print(
f'选择的非mask样本中正确选取的干净标签数量{len(clean_index)}, 不正确选取的非干净数量{len(pred_idx1) - len(clean_index)}.\t '
f'选择的mask样本中正确选取的不干净标签数量{len(noise_index)}, 不正确选取的干净数量{len(pred_idx) - len(noise_index)}')
return len(clean_index), (len(pred_idx1) - len(clean_index)), len(noise_index), len(pred_idx) - len(
noise_index)
def print_noise_rate(self, new_y):
temp_y = np.array(new_y.reshape(1, -1).squeeze())
clean_index = np.where(temp_y[:] == np.array(self.clean_label)[:])
print(f'clean rate is: {len(clean_index[0]) / len(self.clean_label)}')
def load_train_label(self, new_y):
temp_y = np.array(new_y.reshape(1, -1).squeeze()).astype(np.int64)
self.noise_label[:] = np.array(temp_y)[:]
if os.path.exists(self.noise_file):
result = os.path.splitext(self.noise_file)
noise_file_temp = result[0]+'_old'+result[1]
if not os.path.exists(noise_file_temp):
os.rename(self.noise_file, noise_file_temp)
# 覆盖原来的noise_file
json.dump(self.noise_label.tolist(), open(self.noise_file, "w"))
def __getitem__(self, index):
if self.mode=='labeled':
img, target, prob = self.train_data[index], self.noise_label[index], self.probability[index]
img = Image.fromarray(img)
img1 = self.transform(img)
img2 = self.transform(img)
return img1, img2, target, prob
elif self.mode=='unlabeled':
img = self.train_data[index]
img = Image.fromarray(img)
img1 = self.transform(img)
img2 = self.transform(img)
return img1, img2
elif self.mode=='all':
img, target = self.train_data[index], self.noise_label[index]
img = Image.fromarray(img)
img = self.transform(img)
return img, target, index
elif self.mode=='test':
img, target = self.test_data[index], self.test_label[index]
img = Image.fromarray(img)
img = self.transform(img)
return img, target
elif self.mode=='boost':
img, target = self.train_data[index], self.noise_label[index]
img = Image.fromarray(img)
img_no_da = self.test_form(img)
img = self.transform(img)
return img, img_no_da, target, index
def __len__(self):
if self.mode!='test':
return len(self.train_data)
else:
return len(self.test_data)
class cifar_dataloader():
def __init__(self, dataset, r, noise_mode, batch_size, num_workers, root_dir, log, noise_file=''):
self.dataset = dataset
self.r = r
self.noise_mode = noise_mode
self.batch_size = batch_size
self.num_workers = num_workers
self.root_dir = root_dir
self.log = log
self.noise_file = noise_file
if self.dataset=='cifar10':
self.transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)),
])
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)),
])
elif self.dataset=='cifar100':
self.transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
])
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
])
def run(self,mode,pred=[],prob=[], clean_idx=[]):
if mode=='warmup':
all_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_train, mode="all",noise_file=self.noise_file)
trainloader = DataLoader(
dataset=all_dataset,
batch_size=self.batch_size*2,
shuffle=True,
num_workers=self.num_workers)
return trainloader
elif mode=='train':
labeled_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_train, mode="labeled", noise_file=self.noise_file, pred=pred, probability=prob,log=self.log)
labeled_trainloader = DataLoader(
dataset=labeled_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers)
unlabeled_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_train, mode="unlabeled", noise_file=self.noise_file, pred=pred, log=self.log)
unlabeled_trainloader = DataLoader(
dataset=unlabeled_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers)
return labeled_trainloader, unlabeled_trainloader
elif mode=='test':
test_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_test, mode='test')
test_loader = DataLoader(
dataset=test_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers)
return test_loader
elif mode=='eval_train':
eval_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_test, mode='all', noise_file=self.noise_file)
eval_loader = DataLoader(
dataset=eval_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers)
return eval_loader
elif mode=='boost':
eval_dataset = cifar_dataset(dataset=self.dataset, noise_mode=self.noise_mode, r=self.r, root_dir=self.root_dir, transform=self.transform_train, mode=mode, noise_file=self.noise_file, clean_idx=clean_idx, test_form=self.transform_test)
eval_loader = DataLoader(
dataset=eval_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers)
return eval_loader |