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import torchvision.transforms as transforms
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
def MNISTDataLoader(data_dir, batch_size, img_size=32):
train_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST(root=data_dir, train=True,
download=True, transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
test_dataset = datasets.MNIST(root=data_dir, train=False,
transform=test_transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, test_loader
def CIFAR10DataLoader(data_dir, batch_size, img_size=32):
train_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root=data_dir, train=True,
download=True, transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
test_dataset = datasets.CIFAR10(root=data_dir, train=False,
download=True, transform=test_transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, test_loader
def CIFAR100DataLoader(data_dir, batch_size, img_size=32):
train_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR100(root=data_dir, train=True,
download=True, transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
test_dataset = datasets.CIFAR100(root=data_dir, train=False,
download=True, transform=test_transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, test_loader
def ImageNetDataLoader(train_data_root, val_data_root, batch_size=128, num_workers=8):
# https://github.com/floydhub/imagenet/blob/master/main.py
img_size = 224
train_transform = transforms.Compose([
transforms.RandomResizedCrop(img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = ImageFolder(train_data_root, transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True)
test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_dataset = ImageFolder(val_data_root, transform=test_transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)
return train_loader, test_loader
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