本文整理汇总了Python中torchvision.datasets.FakeData方法的典型用法代码示例。如果您正苦于以下问题:Python datasets.FakeData方法的具体用法?Python datasets.FakeData怎么用?Python datasets.FakeData使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torchvision.datasets
的用法示例。
在下文中一共展示了datasets.FakeData方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: check_dataset
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def check_dataset(args):
transform = transforms.Compose(
[
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255)),
]
)
if args.dataset in {"folder", "mscoco"}:
train_dataset = datasets.ImageFolder(args.dataroot, transform)
elif args.dataset == "test":
train_dataset = datasets.FakeData(
size=args.batch_size, image_size=(3, 32, 32), num_classes=1, transform=transform
)
else:
raise RuntimeError("Invalid dataset name: {}".format(args.dataset))
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0)
return train_loader
示例2: __init__
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def __init__(self, batch_size=256, subset_size=256, test_batch_size=256):
trans = transforms.Compose([transforms.ToTensor()])
root = './data_fake_fmnist'
train_set = dset.FakeData(image_size=(1, 28, 28),transform=transforms.ToTensor())
test_set = dset.FakeData(image_size=(1, 28, 28),transform=transforms.ToTensor())
indices = torch.randperm(len(train_set))[:subset_size]
train_set = torch.utils.data.Subset(train_set, indices)
self.train_loader = torch.utils.data.DataLoader(
dataset=train_set,
batch_size=batch_size,
shuffle=True)
self.test_loader = torch.utils.data.DataLoader(
dataset=test_set,
batch_size=test_batch_size,
shuffle=False)
self.name = "fakemnist"
self.data_dims = [28, 28, 1]
self.train_size = len(self.train_loader)
self.test_size = len(self.test_loader)
self.range = [0.0, 1.0]
self.batch_size = batch_size
self.num_training_instances = len(train_set)
self.num_test_instances = len(test_set)
self.likelihood_type = 'gaussian'
self.output_activation_type = 'sigmoid'
示例3: make_dataloader
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def make_dataloader(batch_size, dataset_type, data_path, shuffle=True, drop_last=True, dataloader_args={},
resize=True, imsize=128, centercrop=False, centercrop_size=128, totensor=True,
normalize=False, norm_mean=(0.5, 0.5, 0.5), norm_std=(0.5, 0.5, 0.5)):
# Make transform
transform = make_transform(resize=resize, imsize=imsize,
centercrop=centercrop, centercrop_size=centercrop_size,
totensor=totensor,
normalize=normalize, norm_mean=norm_mean, norm_std=norm_std)
# Make dataset
if dataset_type in ['folder', 'imagenet', 'lfw']:
# folder dataset
assert os.path.exists(data_path), "data_path does not exist! Given: " + data_path
dataset = dset.ImageFolder(root=data_path, transform=transform)
elif dataset_type == 'lsun':
assert os.path.exists(data_path), "data_path does not exist! Given: " + data_path
dataset = dset.LSUN(root=data_path, classes=['bedroom_train'], transform=transform)
elif dataset_type == 'cifar10':
if not os.path.exists(data_path):
print("data_path does not exist! Given: {}\nDownloading CIFAR10 dataset...".format(data_path))
dataset = dset.CIFAR10(root=data_path, download=True, transform=transform)
elif dataset_type == 'fake':
dataset = dset.FakeData(image_size=(3, centercrop_size, centercrop_size), transform=transforms.ToTensor())
assert dataset
num_of_classes = len(dataset.classes)
print("Data found! # of images =", len(dataset), ", # of classes =", num_of_classes, ", classes:", dataset.classes)
# Make dataloader from dataset
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, **dataloader_args)
return dataloader, num_of_classes
示例4: test_init_fit_predict
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def test_init_fit_predict(self):
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from lale.lib.pytorch import ResNet50
transform = transforms.Compose([transforms.ToTensor()])
data_train = datasets.FakeData(size = 50, num_classes=2 , transform = transform)#, target_transform = transform)
clf = ResNet50(num_classes=2,num_epochs = 1)
clf.fit(data_train)
predicted = clf.predict(data_train)
示例5: genFakeData
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def genFakeData(
self, imgSize: Tuple[int, int, int], batch_size: int = 1, num_batches: int = 1
) -> DataLoader:
self.ds = FakeData(
size=num_batches,
image_size=imgSize,
num_classes=2,
transform=transforms.Compose([transforms.ToTensor()]),
)
return DataLoader(self.ds, batch_size=batch_size)
示例6: setUp_data
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def setUp_data(self):
self.ds = FakeData(
size=self.DATA_SIZE,
image_size=(1, 35, 35),
num_classes=10,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
)
self.dl = DataLoader(self.ds, batch_size=self.DATA_SIZE)
示例7: setUp_data
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def setUp_data(self):
self.ds = FakeData(
size=self.DATA_SIZE,
image_size=(1, 35, 35),
num_classes=10,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
)
self.dl = DataLoader(self.ds, batch_size=self.BATCH_SIZE)
示例8: check_dataset
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def check_dataset(dataset, dataroot):
"""
Args:
dataset (str): Name of the dataset to use. See CLI help for details
dataroot (str): root directory where the dataset will be stored.
Returns:
dataset (data.Dataset): torchvision Dataset object
"""
resize = transforms.Resize(64)
crop = transforms.CenterCrop(64)
to_tensor = transforms.ToTensor()
normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
if dataset in {"imagenet", "folder", "lfw"}:
dataset = dset.ImageFolder(root=dataroot, transform=transforms.Compose([resize, crop, to_tensor, normalize]))
nc = 3
elif dataset == "lsun":
dataset = dset.LSUN(
root=dataroot, classes=["bedroom_train"], transform=transforms.Compose([resize, crop, to_tensor, normalize])
)
nc = 3
elif dataset == "cifar10":
dataset = dset.CIFAR10(
root=dataroot, download=True, transform=transforms.Compose([resize, to_tensor, normalize])
)
nc = 3
elif dataset == "mnist":
dataset = dset.MNIST(root=dataroot, download=True, transform=transforms.Compose([resize, to_tensor, normalize]))
nc = 1
elif dataset == "fake":
dataset = dset.FakeData(size=256, image_size=(3, 64, 64), transform=to_tensor)
nc = 3
else:
raise RuntimeError("Invalid dataset name: {}".format(dataset))
return dataset, nc
示例9: get_data_loader
# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import FakeData [as 别名]
def get_data_loader(dataset, dataroot, workers, image_size, batch_size):
if dataset in ['imagenet', 'folder', 'lfw']:
# folder dataset
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif dataset == 'lsun':
dataset = dset.LSUN(root=dataroot, classes=['bedroom_train'],
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif dataset == 'cifar10':
dataset = dset.CIFAR10(root=dataroot, download=True,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif dataset == 'mnist':
dataset = dset.MNIST(root=dataroot, train=True, download=True,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif dataset == 'fake':
dataset = dset.FakeData(image_size=(3, image_size, image_size),
transform=transforms.ToTensor())
else:
assert False
assert dataset
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=True,
num_workers=int(workers))
return data_loader