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"""
Data loading and preprocessing for CIFAR-10 dataset
"""
import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import config
def get_transforms(train=True):
"""
Get data transformations for training or testing
Args:
train (bool): If True, returns training transforms with augmentation
Returns:
torchvision.transforms.Compose: Composed transforms
"""
if train and config.USE_AUGMENTATION:
transform = transforms.Compose([
transforms.RandomCrop(32, padding=config.RANDOM_CROP_PADDING),
transforms.RandomHorizontalFlip(p=config.RANDOM_HORIZONTAL_FLIP),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4914, 0.4822, 0.4465],
std=[0.2470, 0.2435, 0.2616]
)
])
else:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4914, 0.4822, 0.4465],
std=[0.2470, 0.2435, 0.2616]
)
])
return transform
def get_data_loaders():
"""
Create train and test data loaders for CIFAR-10
Returns:
tuple: (train_loader, test_loader)
"""
# Get transforms
train_transform = get_transforms(train=True)
test_transform = get_transforms(train=False)
# Load datasets
train_dataset = datasets.CIFAR10(
root=config.DATA_DIR,
train=True,
download=True,
transform=train_transform
)
test_dataset = datasets.CIFAR10(
root=config.DATA_DIR,
train=False,
download=True,
transform=test_transform
)
# Create data loaders
train_loader = DataLoader(
train_dataset,
batch_size=config.BATCH_SIZE,
shuffle=True,
num_workers=config.NUM_WORKERS,
pin_memory=True if config.DEVICE.type == 'cuda' else False
)
test_loader = DataLoader(
test_dataset,
batch_size=config.BATCH_SIZE,
shuffle=False,
num_workers=config.NUM_WORKERS,
pin_memory=True if config.DEVICE.type == 'cuda' else False
)
return train_loader, test_loader
def denormalize(tensor):
"""
Denormalize a tensor image for visualization
Args:
tensor: Normalized tensor image
Returns:
tensor: Denormalized tensor image
"""
mean = torch.tensor([0.4914, 0.4822, 0.4465]).view(3, 1, 1)
std = torch.tensor([0.2470, 0.2435, 0.2616]).view(3, 1, 1)
return tensor * std + mean
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