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cifar_noisy.py
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from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
import copy
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import torch
import torch.utils.data as data
from utils import download_url, check_integrity, noisify, noisify_instance
class CIFAR10_noisy(data.Dataset):
"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``cifar-10-batches-py`` exists or will be saved to if download is set to True.
train (bool, optional): If True, creates dataset from training set, otherwise
creates from test set.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
base_folder = 'cifar-10-batches-py'
url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
filename = "cifar-10-python.tar.gz"
tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
]
def __init__(self, root, train=True,indexes=None,
transform=None, target_transform=None,
download=False,down_sample=False,
noise_type=None, noise_rate=0.2, random_state=0):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
self.dataset='cifar10'
self.noise_type=noise_type
self.nb_classes=10
idx_each_class_noisy = [[] for i in range(10)]
#if download:
# self.download()
#if not self._check_integrity():
# raise RuntimeError('Dataset not found or corrupted.' +
# ' You can use download=True to download it')
# now load the picked numpy arrays
if self.train:
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.train_data.append(entry['data'])
if 'labels' in entry:
self.train_labels += entry['labels']
else:
self.train_labels += entry['fine_labels']
fo.close()
self.true_labels = copy.deepcopy(self.train_labels)
self.train_data = np.concatenate(self.train_data)
self.train_data = self.train_data.reshape((50000, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1)) # convert to HWC
#if noise_type is not None:
if noise_type !='clean':
# noisify train data
if noise_type in ['symmetric','pairflip']:
self.train_labels=np.asarray([[self.train_labels[i]] for i in range(len(self.train_labels))])
self.train_noisy_labels, self.actual_noise_rate = noisify(dataset=self.dataset, train_labels=self.train_labels, noise_type=noise_type, noise_rate=noise_rate, random_state=random_state, nb_classes=self.nb_classes)
self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
_train_labels=[i[0] for i in self.train_labels]
for i in range(len(_train_labels)):
idx_each_class_noisy[self.train_noisy_labels[i]].append(i)
class_size_noisy = [len(idx_each_class_noisy[i]) for i in range(10)]
self.noise_prior = np.array(class_size_noisy)/sum(class_size_noisy)
print(f'The noisy data ratio in each class is {self.noise_prior}')
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(_train_labels)
elif noise_type == 'human':
self.train_noisy_labels = torch.load('CIFAR-10_human.pt')['worse_label']
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(self.train_labels)
elif noise_type == 'instance':
self.train_noisy_labels, self.actual_noise_rate = noisify_instance(self.train_data, self.train_labels,noise_rate=noise_rate)
#self.train_noisy_labels, self.actual_noise_rate = noisify_instance_new(self.train_data, self.train_labels,noise_rate=noise_rate)
print('over all noise rate is ', self.actual_noise_rate)
#self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
#self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
#_train_labels=[i[0] for i in self.train_labels]
for i in range(len(self.train_labels)):
idx_each_class_noisy[self.train_noisy_labels[i]].append(i)
class_size_noisy = [len(idx_each_class_noisy[i]) for i in range(10)]
self.noise_prior = np.array(class_size_noisy)/sum(class_size_noisy)
print(f'The noisy data ratio in each class is {self.noise_prior}')
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(self.train_labels)
#self.train_noisy_labels = list(torch.load('CIFAR-10_human.pt')['worse_label'])
if indexes is not None:
self.train_data = self.train_data[indexes]
self.train_noisy_labels = list(np.array(self.train_noisy_labels)[indexes])
self.true_labels = list(np.array(self.true_labels)[indexes])
if down_sample:
count_number =[]
for i in range(self.nb_classes):
idxs = np.where(np.array(self.train_noisy_labels) == i)[0]
count_number.append(len(idxs))
min_num = min(count_number)
train_labeled_idxs = []
for i in range(self.nb_classes):
idxs = np.where(np.array(self.train_noisy_labels) == i)[0]
np.random.shuffle(idxs)
train_labeled_idxs.extend(idxs[:min_num])
np.random.shuffle(train_labeled_idxs)
self.train_data = self.train_data[train_labeled_idxs]
self.train_labels = list(np.array(self.train_labels)[train_labeled_idxs])
self.train_noisy_labels = list(np.array(self.train_noisy_labels)[train_labeled_idxs])
self.true_labels = list(np.array(self.true_labels)[train_labeled_idxs])
else:
f = self.test_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.test_data = entry['data']
if 'labels' in entry:
self.test_labels = entry['labels']
else:
self.test_labels = entry['fine_labels']
fo.close()
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1)) # convert to HWC
self.true_labels = copy.deepcopy(self.test_labels)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
if self.noise_type !='clean':
img, target = self.train_data[index], self.train_noisy_labels[index]
else:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
true_target = self.true_labels[index]
return img, target, true_target, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.test_data)
def _check_integrity(self):
root = self.root
for fentry in (self.train_list + self.test_list):
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(root, self.base_folder, filename)
if not check_integrity(fpath, md5):
return False
return True
def download(self):
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
root = self.root
download_url(self.url, root, self.filename, self.tgz_md5)
# extract file
cwd = os.getcwd()
tar = tarfile.open(os.path.join(root, self.filename), "r:gz")
os.chdir(root)
tar.extractall()
tar.close()
os.chdir(cwd)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class CIFAR100_noisy(data.Dataset):
"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): Root directory of dataset where directory
``cifar-10-batches-py`` exists or will be saved to if download is set to True.
train (bool, optional): If True, creates dataset from training set, otherwise
creates from test set.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
base_folder = 'cifar-100-python'
url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
filename = "cifar-100-python.tar.gz"
tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
train_list = [
['train', '16019d7e3df5f24257cddd939b257f8d'],
]
test_list = [
['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
]
def __init__(self, root, train=True,indexes=None,
transform=None, target_transform=None,
download=False,down_sample=False,
noise_type=None, noise_rate=0.2, random_state=0):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
self.dataset='cifar100'
self.noise_type=noise_type
self.nb_classes=100
idx_each_class_noisy = [[] for i in range(100)]
#if download:
# self.download()
#if not self._check_integrity():
# raise RuntimeError('Dataset not found or corrupted.' +
# ' You can use download=True to download it')
# now load the picked numpy arrays
if self.train:
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.train_data.append(entry['data'])
if 'labels' in entry:
self.train_labels += entry['labels']
else:
self.train_labels += entry['fine_labels']
fo.close()
self.true_labels = copy.deepcopy(self.train_labels)
self.train_data = np.concatenate(self.train_data)
self.train_data = self.train_data.reshape((50000, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1)) # convert to HWC
#if noise_type is not None:
if noise_type !='clean':
# noisify train data
if noise_type in ['symmetric','pairflip']:
self.train_labels=np.asarray([[self.train_labels[i]] for i in range(len(self.train_labels))])
self.train_noisy_labels, self.actual_noise_rate = noisify(dataset=self.dataset, train_labels=self.train_labels, noise_type=noise_type, noise_rate=noise_rate, random_state=random_state, nb_classes=self.nb_classes)
self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
_train_labels=[i[0] for i in self.train_labels]
for i in range(len(_train_labels)):
idx_each_class_noisy[self.train_noisy_labels[i]].append(i)
class_size_noisy = [len(idx_each_class_noisy[i]) for i in range(100)]
self.noise_prior = np.array(class_size_noisy)/sum(class_size_noisy)
print(f'The noisy data ratio in each class is {self.noise_prior}')
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(_train_labels)
elif noise_type == 'human':
self.train_noisy_labels = torch.load('CIFAR-100_human.pt')['noisy_label']
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(self.train_labels)
elif noise_type == 'instance':
self.train_noisy_labels, self.actual_noise_rate = noisify_instance(self.train_data, self.train_labels,noise_rate=noise_rate)
#self.train_noisy_labels, self.actual_noise_rate = noisify_instance_new(self.train_data, self.train_labels,noise_rate=noise_rate)
print('over all noise rate is ', self.actual_noise_rate)
#self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
#self.train_noisy_labels=[i[0] for i in self.train_noisy_labels]
#_train_labels=[i[0] for i in self.train_labels]
for i in range(len(self.train_labels)):
idx_each_class_noisy[self.train_noisy_labels[i]].append(i)
class_size_noisy = [len(idx_each_class_noisy[i]) for i in range(100)]
self.noise_prior = np.array(class_size_noisy)/sum(class_size_noisy)
print(f'The noisy data ratio in each class is {self.noise_prior}')
self.noise_or_not = np.transpose(self.train_noisy_labels)!=np.transpose(self.train_labels)
#self.train_noisy_labels = list(torch.load('CIFAR-10_human.pt')['worse_label'])
if indexes is not None:
self.train_data = self.train_data[indexes]
self.train_noisy_labels = list(np.array(self.train_noisy_labels)[indexes])
self.true_labels = list(np.array(self.true_labels)[indexes])
if down_sample:
count_number =[]
for i in range(self.nb_classes):
idxs = np.where(np.array(self.train_noisy_labels) == i)[0]
count_number.append(len(idxs))
min_num = min(count_number)
train_labeled_idxs = []
for i in range(self.nb_classes):
idxs = np.where(np.array(self.train_noisy_labels) == i)[0]
np.random.shuffle(idxs)
train_labeled_idxs.extend(idxs[:min_num])
np.random.shuffle(train_labeled_idxs)
self.train_data = self.train_data[train_labeled_idxs]
self.train_labels = list(np.array(self.train_labels)[train_labeled_idxs])
self.train_noisy_labels = list(np.array(self.train_noisy_labels)[train_labeled_idxs])
self.true_labels = list(np.array(self.true_labels)[train_labeled_idxs])
else:
f = self.test_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.test_data = entry['data']
if 'labels' in entry:
self.test_labels = entry['labels']
else:
self.test_labels = entry['fine_labels']
fo.close()
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1)) # convert to HWC
self.true_labels = copy.deepcopy(self.test_labels)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
if self.noise_type !='clean':
img, target = self.train_data[index], self.train_noisy_labels[index]
else:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
true_target = self.true_labels[index]
return img, target, true_target, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.test_data)
def _check_integrity(self):
root = self.root
for fentry in (self.train_list + self.test_list):
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(root, self.base_folder, filename)
if not check_integrity(fpath, md5):
return False
return True
def download(self):
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
root = self.root
download_url(self.url, root, self.filename, self.tgz_md5)
# extract file
cwd = os.getcwd()
tar = tarfile.open(os.path.join(root, self.filename), "r:gz")
os.chdir(root)
tar.extractall()
tar.close()
os.chdir(cwd)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str