代码拉取完成,页面将自动刷新
import torch
from torchvision import datasets, transforms
# CIFAR-10,
# mean, [0.4914, 0.4822, 0.4465]
# std, [0.2470, 0.2435, 0.2616]
# CIFAR-100,
# mean, [0.5071, 0.4865, 0.4409]
# std, [0.2673, 0.2564, 0.2762]
def load_data(args):
if args.dataset_mode is "CIFAR10":
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('data', train=True, download=True, transform=transform_train),
batch_size=args.batch_size,
shuffle=True,
num_workers=2
)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('data', train=False, transform=transform_test),
batch_size=args.batch_size,
shuffle=False,
num_workers=2
)
elif args.dataset_mode is "CIFAR100":
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4865, 0.4409), (0.2673, 0.2564, 0.2762)),
])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('data', train=True, download=True, transform=transform_train),
batch_size=args.batch_size,
shuffle=True,
num_workers=2
)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('data', train=False, transform=transform_test),
batch_size=args.batch_size,
shuffle=False,
num_workers=2
)
elif args.dataset_mode is "MNIST":
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('data', train=True, download=True, transform=transform_train),
batch_size=args.batch_size,
shuffle=True,
num_workers=2
)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('data', train=False, transform=transform_test),
batch_size=args.batch_size,
shuffle=True,
num_workers=2
)
return train_loader, test_loader
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。