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utils.py
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from datasets import KMNIST, TIMG
import torch
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import Dataset
def set_all_seeds(SEED):
# REPRODUCIBILITY
torch.manual_seed(SEED)
np.random.seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def uniform_noise(labels, num_class, noise_level):
noise_level = noise_level# * num_class / (num_class-1)
rand_class = np.random.randint(num_class,size=labels.shape[0])
checks = np.random.rand(labels.shape[0])
noise_tag = np.zeros_like(labels)
for i in range(labels.shape[0]):
if checks[i] < noise_level:
labels[i] = rand_class[i]
noise_tag[i] = 1
return labels, noise_tag
def classDep_noise(labels, num_class, noise_level):
checks = np.random.rand(labels.shape[0])
noise_tag = np.zeros_like(labels)
for i in range(labels.shape[0]):
if checks[i] < noise_level:
labels[i] = (labels[i] + 1) % num_class
noise_tag[i] = 1
return labels, noise_tag
def classDep_news20(labels, noise_level):
transY = np.arange(20)
checks = np.random.rand(labels.shape[0])
transY[2] = 5
transY[5] = 2
transY[3] = 4
transY[4] = 3
transY[7] = 8
transY[8] = 7
transY[9] = 10
transY[10] = 9
transY[11] = 12
transY[12] = 11
transY[15] = 18
transY[18] = 15
noise_tag = np.zeros_like(labels)
for i in range(labels.shape[0]):
if checks[i] < noise_level:
labels[i] = transY[labels[i]]
noise_tag[i] = 1
return labels, noise_tag
def classDep_letter(labels, noise_level):
transY = np.arange(26)
checks = np.random.rand(labels.shape[0])
transY[1] = 3
transY[3] = 1
transY[2] = 6
transY[6] = 2
transY[4] = 5
transY[5] = 4
transY[7] = 13
transY[13] = 7
transY[8] = 11
transY[11] = 8
transY[10] = 23
transY[23] = 10
transY[12] = 22
transY[22] = 12
transY[14] = 16
transY[16] = 14
transY[15] = 17
transY[17] = 15
transY[20] = 21
transY[21] = 20
noise_tag = np.zeros_like(labels)
for i in range(labels.shape[0]):
if checks[i] < noise_level:
labels[i] = transY[labels[i]]
noise_tag[i] = 1
return labels, noise_tag
def classDep_vowel(labels, noise_level):
transY = np.arange(11)
checks = np.random.rand(labels.shape[0])
transY[0] = 1
transY[1] = 0
transY[2] = 3
transY[3] = 2
transY[4] = 5
transY[5] = 4
transY[6] = 7
transY[7] = 6
transY[8] = 9
transY[9] = 8
noise_tag = np.zeros_like(labels)
for i in range(labels.shape[0]):
if checks[i] < noise_level:
labels[i] = transY[labels[i]]
noise_tag[i] = 1
return labels, noise_tag
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.shape[0]
#print(output)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].float().sum()
res.append(correct_k.mul_(100.0 / batch_size).detach().numpy())
return res
def balanced_accuracy(output, target, num_class, topk=(1,)):
accs = []
for cls in range(num_class):
mask = (target == cls)
tmp_acc = accuracy(output[mask],target[mask],topk)
accs.append(tmp_acc)
return np.mean(accs, axis=0)
class ImageDataset(Dataset):
def __init__(self, images, targets, image_size=32, crop_size=30, mode='train'):
self.images = images.astype(np.uint8)
self.targets = targets[0]
self.noise_tag = targets[1]
self.mode = mode
self.transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.RandomCrop((crop_size, crop_size), padding=None),
transforms.RandomHorizontalFlip(),
transforms.Resize((image_size, image_size)),
])
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((image_size, image_size)),
])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image = self.images[idx]
target = self.targets[idx]
noise_tag = self.noise_tag[idx]
if self.mode == 'train':
image = self.transform_train(image)
else:
image = self.transform_test(image)
return image, target, noise_tag, int(idx)
def get_labels(self):
return np.array(self.targets).reshape(-1)
class TabularDataset(Dataset):
def __init__(self, data, targets):
self.data = data
self.targets = targets[0]
self.noise_tag = targets[1]
def __len__(self):
return self.data.shape[0]
def __getitem__(self, idx):
data = self.data[idx]
target = self.targets[idx]
noise_tag = self.noise_tag[idx]
return data, target, noise_tag, int(idx)
def get_labels(self):
return np.array(self.targets).reshape(-1)
class ImageDataset_cifar(Dataset):
def __init__(self, images, targets, image_size=32, crop_size=30, mode='train'):
self.images = images.astype(np.uint8)
self.targets = targets[0]
self.noise_tag = targets[1]
self.mode = mode
self.transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.RandomCrop((crop_size, crop_size), padding=None),
transforms.RandomHorizontalFlip(),
transforms.Resize((image_size, image_size)),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((image_size, image_size)),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image = self.images[idx]
target = self.targets[idx]
noise_tag = self.noise_tag[idx]
if self.mode == 'train':
image = self.transform_train(image)
else:
image = self.transform_test(image)
return image, target, noise_tag, int(idx)
def get_labels(self):
return np.array(self.targets).reshape(-1)