/
noisy_long_tail_CIFAR.py
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/
noisy_long_tail_CIFAR.py
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import copy
import numpy as np
import torchvision.datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
def uniform_corruption(corruption_ratio, num_classes):
eye = np.eye(num_classes)
noise = np.full((num_classes, num_classes), 1/num_classes)
corruption_matrix = eye * (1 - corruption_ratio) + noise * corruption_ratio
return corruption_matrix
def flip1_corruption(corruption_ratio, num_classes):
corruption_matrix = np.eye(num_classes) * (1 - corruption_ratio)
row_indices = np.arange(num_classes)
for i in range(num_classes):
corruption_matrix[i][np.random.choice(row_indices[row_indices != i])] = corruption_ratio
return corruption_matrix
def flip2_corruption(corruption_ratio, num_classes):
corruption_matrix = np.eye(num_classes) * (1 - corruption_ratio)
row_indices = np.arange(num_classes)
for i in range(num_classes):
corruption_matrix[i][np.random.choice(row_indices[row_indices != i], 2, replace=False)] = corruption_ratio / 2
return corruption_matrix
def build_dataloader(
seed=1,
dataset='cifar10',
num_meta_total=1000,
imbalanced_factor=None,
corruption_type=None,
corruption_ratio=0.,
batch_size=100,
):
np.random.seed(seed)
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
)
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
normalize,
])
dataset_list = {
'cifar10': torchvision.datasets.CIFAR10,
'cifar100': torchvision.datasets.CIFAR100,
}
corruption_list = {
'uniform': uniform_corruption,
'flip1': flip1_corruption,
'flip2': flip2_corruption,
}
train_dataset = dataset_list[dataset](root='../data', train=True, download=True, transform=train_transforms)
test_dataset = dataset_list[dataset](root='../data', train=False, transform=test_transforms)
num_classes = len(train_dataset.classes)
num_meta = int(num_meta_total / num_classes)
index_to_meta = []
index_to_train = []
if imbalanced_factor is not None:
imbalanced_num_list = []
sample_num = int((len(train_dataset.targets) - num_meta_total) / num_classes)
for class_index in range(num_classes):
imbalanced_num = sample_num / (imbalanced_factor ** (class_index / (num_classes - 1)))
imbalanced_num_list.append(int(imbalanced_num))
np.random.shuffle(imbalanced_num_list)
print(imbalanced_num_list)
else:
imbalanced_num_list = None
for class_index in range(num_classes):
index_to_class = [index for index, label in enumerate(train_dataset.targets) if label == class_index]
np.random.shuffle(index_to_class)
index_to_meta.extend(index_to_class[:num_meta])
index_to_class_for_train = index_to_class[num_meta:]
if imbalanced_num_list is not None:
index_to_class_for_train = index_to_class_for_train[:imbalanced_num_list[class_index]]
index_to_train.extend(index_to_class_for_train)
meta_dataset = copy.deepcopy(train_dataset)
train_dataset.data = train_dataset.data[index_to_train]
train_dataset.targets = list(np.array(train_dataset.targets)[index_to_train])
meta_dataset.data = meta_dataset.data[index_to_meta]
meta_dataset.targets = list(np.array(meta_dataset.targets)[index_to_meta])
if corruption_type is not None:
corruption_matrix = corruption_list[corruption_type](corruption_ratio, num_classes)
print(corruption_matrix)
for index in range(len(train_dataset.targets)):
p = corruption_matrix[train_dataset.targets[index]]
train_dataset.targets[index] = np.random.choice(num_classes, p=p)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True)
meta_dataloader = DataLoader(meta_dataset, batch_size=batch_size, shuffle=True, pin_memory=True)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, pin_memory=True)
return train_dataloader, meta_dataloader, test_dataloader, imbalanced_num_list