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dataloader_cifar.py
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dataloader_cifar.py
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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=''):
self.r = r # noise ratio
self.transform = transform
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
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))
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 = noise_label
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()
log.write('Numer of labeled samples:%d AUC:%.3f\n'%(pred.sum(),auc))
log.flush()
elif self.mode == "unlabeled":
pred_idx = (1-pred).nonzero()[0]
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 __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
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=[]):
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)
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