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fullg_data.py
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import time
import torch
import random
import torchvision.datasets as dset
import torchvision.transforms as transforms
import numpy as np
from utils import CustomSubset
########################################################################################################################
# Load Data
########################################################################################################################
def load_data(args):
"""
Load data for training and testing.
Returns:
train_loader: DataLoader for training data.
test_loader: DataLoader for testing data.
"""
train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55 = load_dataset(args)
return train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55
def load_dataset(args):
"""
Load dataset based on the specified dataset in args.
Returns:
train_loader: DataLoader for training data.
test_loader: DataLoader for testing data.
"""
if args.dataset == 'cifar10':
train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55 = load_cifar10(args)
elif args.dataset == 'cifar100':
train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55 = load_cifar100(args)
else:
raise NotImplementedError("Dataset not supported: {}".format(args.dataset))
return train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55
def random_prune_dataset(dataset, rate):
total_size = len(dataset)
num_to_keep = int(total_size * rate)
result = torch.randperm(total_size)
indices, _ = torch.sort(result[:num_to_keep])
other_indices, _ = torch.sort(result[num_to_keep:])
coreset = CustomSubset(dataset, indices)
otherset = CustomSubset(dataset, other_indices)
return coreset
def random_split(dataset, num_splits=5):
total_size = len(dataset)
part_size = total_size // num_splits
indices = torch.randperm(total_size)
subsets = []
complements = []
start = 0
for i in range(num_splits):
end = start + part_size if i < num_splits - 1 else total_size
subset_indices = indices[start:end]
subset_indices, _ = torch.sort(subset_indices)
# Create the subset
subsets.append(CustomSubset(dataset, subset_indices))
# Create the complement of the subset
complement_indices = torch.cat((indices[:start], indices[end:]))
complement_indices, _ = torch.sort(complement_indices)
complements.append(CustomSubset(dataset, complement_indices))
start = end
return subsets, complements
def load_cifar10(args):
"""
Load CIFAR-10 dataset.
Returns:
train_loader: DataLoader for training data.
test_loader: DataLoader for testing data.
"""
print('Loading CIFAR-10... ', end='')
time_start = time.time()
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
train_data = dset.CIFAR10(args.data_path, train=True, transform=train_transform, download=True)
train_data.target = train_data.targets
target_index = [[train_data.targets[i], i] for i in range(len(train_data.targets))]
train_data.targets = target_index
subsets, complements = random_split(train_data, 5)
dataset1,dataset2,dataset3,dataset4,dataset5=subsets
dataset11,dataset22,dataset33,dataset44,dataset55=complements
train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_1 = torch.utils.data.DataLoader(dataset1, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_2 = torch.utils.data.DataLoader(dataset2, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_3 = torch.utils.data.DataLoader(dataset3, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_4 = torch.utils.data.DataLoader(dataset4, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_5 = torch.utils.data.DataLoader(dataset5, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_11 = torch.utils.data.DataLoader(dataset11, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_22 = torch.utils.data.DataLoader(dataset22, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_33 = torch.utils.data.DataLoader(dataset33, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_44 = torch.utils.data.DataLoader(dataset44, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_55 = torch.utils.data.DataLoader(dataset55, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_data = dset.CIFAR10(args.data_path, train=False, transform=test_transform, download=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
print(f"done in {time.time() - time_start:.2f} seconds.")
return train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55
def load_cifar100(args):
"""
Load CIFAR-100 dataset.
Returns:
train_loader: DataLoader for training data.
test_loader: DataLoader for testing data.
"""
print('Loading CIFAR-100... ', end='')
time_start = time.time()
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
std = [x / 255 for x in [68.2, 65.4, 70.4]]
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
train_data = dset.CIFAR100(args.data_path, train=True, transform=train_transform, download=True)
train_data.target = train_data.targets
target_index = [[train_data.targets[i], i] for i in range(len(train_data.targets))]
train_data.targets = target_index
subsets, complements = random_split(train_data, 5)
dataset1,dataset2,dataset3,dataset4,dataset5=subsets
dataset11,dataset22,dataset33,dataset44,dataset55=complements
train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_1 = torch.utils.data.DataLoader(dataset1, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_2 = torch.utils.data.DataLoader(dataset2, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_3 = torch.utils.data.DataLoader(dataset3, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_4 = torch.utils.data.DataLoader(dataset4, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_5 = torch.utils.data.DataLoader(dataset5, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_11 = torch.utils.data.DataLoader(dataset11, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_22 = torch.utils.data.DataLoader(dataset22, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_33 = torch.utils.data.DataLoader(dataset33, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_44 = torch.utils.data.DataLoader(dataset44, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_55 = torch.utils.data.DataLoader(dataset55, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_data = dset.CIFAR100(args.data_path, train=False, transform=test_transform, download=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
print(f"done in {time.time() - time_start:.2f} seconds.")
return train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55
def load_ImageNet(args):
"""
Load CIFAR-100 dataset.
Returns:
train_loader: DataLoader for training data.
test_loader: DataLoader for testing data.
"""
print('Loading CIFAR-100... ', end='')
time_start = time.time()
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
std = [x / 255 for x in [68.2, 65.4, 70.4]]
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
train_data = dset.CIFAR100(args.data_path, train=True, transform=train_transform, download=True)
train_data.target = train_data.targets
target_index = [[train_data.targets[i], i] for i in range(len(train_data.targets))]
train_data.targets = target_index
subsets, complements = random_split(train_data, 5)
dataset1,dataset2,dataset3,dataset4,dataset5=subsets
dataset11,dataset22,dataset33,dataset44,dataset55=complements
train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_1 = torch.utils.data.DataLoader(dataset1, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_2 = torch.utils.data.DataLoader(dataset2, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_3 = torch.utils.data.DataLoader(dataset3, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_4 = torch.utils.data.DataLoader(dataset4, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_5 = torch.utils.data.DataLoader(dataset5, args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
train_loader_11 = torch.utils.data.DataLoader(dataset11, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_22 = torch.utils.data.DataLoader(dataset22, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_33 = torch.utils.data.DataLoader(dataset33, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_44 = torch.utils.data.DataLoader(dataset44, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader_55 = torch.utils.data.DataLoader(dataset55, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
train_loader = torch.utils.data.DataLoader(train_data, args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_data = dset.CIFAR100(args.data_path, train=False, transform=test_transform, download=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
print(f"done in {time.time() - time_start:.2f} seconds.")
return train_data, train_loader, test_loader, train_loader_1, train_loader_2, train_loader_3, train_loader_4, train_loader_5, train_loader_11, train_loader_22, train_loader_33, train_loader_44, train_loader_55