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data_load.py
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import numpy as np
import torch.utils.data as Data
from PIL import Image
import tools
import torch
from random import choice
import random
class mnist_dataset(Data.Dataset):
def __init__(self, train=True, transform=None, target_transform=None, dataset='mnist', noise_type='symmetric', noise_rate=0.5, split_per=0.9, random_seed=1, num_class=10):
self.transform = transform
self.target_transform = target_transform
self.train = train
original_images = np.load('data/mnist/train_images.npy')
original_labels = np.load('data/mnist/train_labels.npy')
# clean images and noisy labels (training and validation)
self.train_data, self.val_data, self.train_labels, self.val_labels = tools.dataset_split(original_images,
original_labels, dataset, noise_type, noise_rate, split_per, random_seed, num_class)
def __getitem__(self, index):
if self.train:
img, label = self.train_data[index], self.train_labels[index]
else:
img, label = self.val_data[index], self.val_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.val_data)
class mnist_test_dataset(Data.Dataset):
def __init__(self, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.test_data = np.load('data/mnist/test_images.npy')
self.test_labels = np.load('data/mnist/test_labels.npy') - 1 # 0-9
def __getitem__(self, index):
img, label = self.test_data[index], self.test_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
return len(self.test_data)
class cifar10_dataset(Data.Dataset):
def __init__(self, train=True, transform=None, target_transform=None, dataset='cifar10', noise_type='symmetric', noise_rate=0.5, split_per=0.9, random_seed=1, num_class=10):
self.transform = transform
self.target_transform = target_transform
self.train = train
original_images = np.load('data/cifar10/train_images.npy')
original_labels = np.load('data/cifar10/train_labels.npy')
# clean images and noisy labels (training and validation)
self.train_data, self.val_data, self.train_labels, self.val_labels = tools.dataset_split(original_images,
original_labels, dataset, noise_type, noise_rate, split_per, random_seed, num_class)
if self.train:
self.train_data = self.train_data.reshape((-1, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1))
else:
self.val_data = self.val_data.reshape((-1, 3, 32, 32))
self.val_data = self.val_data.transpose((0, 2, 3, 1))
def __getitem__(self, index):
if self.train:
img, label = self.train_data[index], self.train_labels[index]
else:
img, label = self.val_data[index], self.val_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.val_data)
class cifar10_test_dataset(Data.Dataset):
def __init__(self, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.test_data = np.load('data/cifar10/test_images.npy')
self.test_labels = np.load('data/cifar10/test_labels.npy')
self.test_data = self.test_data.reshape((-1, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
def __getitem__(self, index):
img, label = self.test_data[index], self.test_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
return len(self.test_data)
class cifar100_dataset(Data.Dataset):
def __init__(self, train=True, transform=None, target_transform=None, dataset='cifar100', noise_type='symmetric', noise_rate=0.5, split_per=0.9, random_seed=1, num_class=100):
self.transform = transform
self.target_transform = target_transform
self.train = train
original_images = np.load('data/cifar100/train_images.npy')
original_labels = np.load('data/cifar100/train_labels.npy')
# clean images and noisy labels (training and validation)
self.train_data, self.val_data, self.train_labels, self.val_labels = tools.dataset_split(original_images,
original_labels, dataset, noise_type, noise_rate, split_per, random_seed, num_class)
if self.train:
self.train_data = self.train_data.reshape((-1, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1))
else:
self.val_data = self.val_data.reshape((-1, 3, 32, 32))
self.val_data = self.val_data.transpose((0, 2, 3, 1))
def __getitem__(self, index):
if self.train:
img, label = self.train_data[index], self.train_labels[index]
else:
img, label = self.val_data[index], self.val_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.val_data)
class cifar100_test_dataset(Data.Dataset):
def __init__(self, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.test_data = np.load('data/cifar100/test_images.npy')
self.test_labels = np.load('data/cifar100/test_labels.npy')
self.test_data = self.test_data.reshape((-1, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
def __getitem__(self, index):
img, label = self.test_data[index], self.test_labels[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
return len(self.test_data)
class fmnist_dataset(Data.Dataset):
def __init__(self, train=True, transform=None, target_transform=None, dataset='fmnist', noise_type='symmetric', noise_rate=0.5, split_per=0.9, random_seed=1, num_class=10):
self.transform = transform
self.target_transform = target_transform
self.train = train
original_images = np.load('data/fashionmnist/train_images.npy').reshape((-1, 1, 28, 28))
original_labels = np.load('data/fashionmnist/train_labels.npy')
self.train_data, self.val_data, self.train_labels, self.val_labels = tools.dataset_split(original_images,
original_labels, dataset, noise_type, noise_rate, split_per, random_seed, num_class)
def __getitem__(self, index):
if self.train:
img, label = self.train_data[index], self.train_labels[index]
else:
img, label = self.val_data[index], self.val_labels[index]
img = torch.from_numpy(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.val_data)
class fmnist_test_dataset(Data.Dataset):
def __init__(self, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.test_data = np.load('data/fashionmnist/test_images.npy').reshape((-1, 1, 28, 28))
self.test_labels = np.load('data/fashionmnist/test_labels.npy')
def __getitem__(self, index):
img, label = self.test_data[index], self.test_labels[index]
img = torch.from_numpy(img)
if self.target_transform is not None:
label = self.target_transform(label)
return img, label, index
def __len__(self):
return len(self.test_data)