本文整理汇总了Python中torchvision.transforms.ToTensor方法的典型用法代码示例。如果您正苦于以下问题:Python transforms.ToTensor方法的具体用法?Python transforms.ToTensor怎么用?Python transforms.ToTensor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.transforms
的用法示例。
在下文中一共展示了transforms.ToTensor方法的15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: load_data
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_data(root_path, dir, batch_size, phase):
transform_dict = {
'src': transforms.Compose(
[transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
'tar': transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])}
data = datasets.ImageFolder(root=root_path + dir, transform=transform_dict[phase])
data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
return data_loader
示例2: __init__
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def __init__(self, args, train=True):
self.root_dir = args.data
if train:
self.data_set_list = train_set_list
elif args.use_test_for_val:
self.data_set_list = test_set_list
else:
self.data_set_list = val_set_list
self.data_set_list = ['%06d.png' % (x) for x in self.data_set_list]
self.args = args
self.read_features = args.read_features
self.features_dir = args.features_dir
self.transform = transforms.Compose([
transforms.Scale((args.image_size, args.image_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
self.transform_segmentation = transforms.Compose([
transforms.Scale((args.segmentation_size, args.segmentation_size)),
transforms.ToTensor(),
])
示例3: load_training
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_training(root_path, dir, batch_size, kwargs):
transform = transforms.Compose(
[transforms.Resize([256, 256]),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
data = datasets.ImageFolder(root=root_path + dir, transform=transform)
train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True, **kwargs)
return train_loader
示例4: main
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def main():
best_acc = 0
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('==> Preparing data..')
transforms_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))])
dataset_train = CIFAR10(root='../data', train=True, download=True,
transform=transforms_train)
train_loader = DataLoader(dataset_train, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_worker)
# there are 10 classes so the dataset name is cifar-10
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
print('==> Making model..')
net = pyramidnet()
net = nn.DataParallel(net)
net = net.to(device)
num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print('The number of parameters of model is', num_params)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=args.lr)
# optimizer = optim.SGD(net.parameters(), lr=args.lr,
# momentum=0.9, weight_decay=1e-4)
train(net, criterion, optimizer, train_loader, device)
示例5: __init__
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def __init__(self, config):
self.config = config
if config.data_mode == "imgs":
transform = v_transforms.Compose(
[v_transforms.ToTensor(),
v_transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
dataset = v_datasets.ImageFolder(self.config.data_folder, transform=transform)
self.dataset_len = len(dataset)
self.num_iterations = (self.dataset_len + config.batch_size - 1) // config.batch_size
self.loader = DataLoader(dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.data_loader_workers,
pin_memory=config.pin_memory)
elif config.data_mode == "numpy":
raise NotImplementedError("This mode is not implemented YET")
else:
raise Exception("Please specify in the json a specified mode in data_mode")
示例6: load_data
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_data(data_folder, batch_size, phase='train', train_val_split=True, train_ratio=.8):
transform_dict = {
'train': transforms.Compose(
[transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
'test': transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])}
data = datasets.ImageFolder(root=data_folder, transform=transform_dict[phase])
if phase == 'train':
if train_val_split:
train_size = int(train_ratio * len(data))
test_size = len(data) - train_size
data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=True,
num_workers=4)
val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=False, drop_last=False,
num_workers=4)
return [train_loader, val_loader]
else:
train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True,
num_workers=4)
return train_loader
else:
test_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=False, drop_last=False,
num_workers=4)
return test_loader
## Below are for ImageCLEF datasets
示例7: load_imageclef_train
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_imageclef_train(root_path, domain, batch_size, phase):
transform_dict = {
'src': transforms.Compose(
[transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
'tar': transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])}
data = ImageCLEF(root_dir=root_path, domain=domain, transform=transform_dict[phase])
train_size = int(0.8 * len(data))
test_size = len(data) - train_size
data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=False,
num_workers=4)
val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=True, drop_last=False,
num_workers=4)
return train_loader, val_loader
示例8: load_imageclef_test
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_imageclef_test(root_path, domain, batch_size, phase):
transform_dict = {
'src': transforms.Compose(
[transforms.Resize((256,256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
'tar': transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])}
data = ImageCLEF(root_dir=root_path, domain=domain, transform=transform_dict[phase])
data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
return data_loader
示例9: load_data
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_data(data_folder, batch_size, train, kwargs):
transform = {
'train': transforms.Compose(
[transforms.Resize([256, 256]),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]),
'test': transforms.Compose(
[transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
}
data = datasets.ImageFolder(root = data_folder, transform=transform['train' if train else 'test'])
data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, **kwargs, drop_last = True if train else False)
return data_loader
示例10: load_train
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def load_train(root_path, dir, batch_size, phase):
transform_dict = {
'src': transforms.Compose(
[transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
'tar': transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])}
data = datasets.ImageFolder(root=root_path + dir, transform=transform_dict[phase])
train_size = int(0.8 * len(data))
test_size = len(data) - train_size
data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
return train_loader, val_loader
示例11: __init__
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def __init__(self, train_mode, loader_params, dataset_params, augmentation_params):
super().__init__(train_mode, loader_params, dataset_params, augmentation_params)
self.image_transform = transforms.Compose([transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
transforms.Normalize(mean=self.dataset_params.MEAN,
std=self.dataset_params.STD),
])
self.mask_transform = transforms.Compose([transforms.Lambda(to_array),
transforms.Lambda(to_tensor),
])
self.image_augment_train = ImgAug(self.augmentation_params['image_augment_train'])
self.image_augment_with_target_train = ImgAug(self.augmentation_params['image_augment_with_target_train'])
self.image_augment_inference = ImgAug(self.augmentation_params['image_augment_inference'])
self.image_augment_with_target_inference = ImgAug(
self.augmentation_params['image_augment_with_target_inference'])
if self.dataset_params.target_format == 'png':
self.dataset = ImageSegmentationPngDataset
elif self.dataset_params.target_format == 'json':
self.dataset = ImageSegmentationJsonDataset
else:
raise Exception('files must be png or json')
示例12: __init__
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def __init__(self):
self.batch_size = 64
self.test_batch_size = 100
self.learning_rate = 0.01
self.sgd_momentum = 0.9
self.log_interval = 100
# Fetch MNIST data set.
self.train_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.batch_size,
shuffle=True)
self.test_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.test_batch_size,
shuffle=True)
self.network = Net()
# Train the network for several epochs, validating after each epoch.
示例13: __init__
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def __init__(self):
self.batch_size = 64
self.test_batch_size = 100
self.learning_rate = 0.0025
self.sgd_momentum = 0.9
self.log_interval = 100
# Fetch MNIST data set.
self.train_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.batch_size,
shuffle=True)
self.test_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.test_batch_size,
shuffle=True)
self.network = Net()
# Train the network for one or more epochs, validating after each epoch.
示例14: loaders_mnist
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def loaders_mnist(dataset, batch_size=64, cuda=0,
train_size=50000, val_size=10000, test_size=10000,
test_batch_size=1000, **kwargs):
assert dataset == 'mnist'
root = '{}/{}'.format(os.environ['VISION_DATA'], dataset)
# Data loading code
normalize = transforms.Normalize(mean=(0.1307,),
std=(0.3081,))
transform = transforms.Compose([transforms.ToTensor(), normalize])
# define two datasets in order to have different transforms
# on training and validation
dataset_train = datasets.MNIST(root=root, train=True, transform=transform)
dataset_val = datasets.MNIST(root=root, train=True, transform=transform)
dataset_test = datasets.MNIST(root=root, train=False, transform=transform)
return create_loaders(dataset_train, dataset_val,
dataset_test, train_size, val_size, test_size,
batch_size=batch_size,
test_batch_size=test_batch_size,
cuda=cuda, num_workers=0)
示例15: get_usps
# 需要导入模块: from torchvision import transforms [as 别名]
# 或者: from torchvision.transforms import ToTensor [as 别名]
def get_usps(train, get_dataset=False, batch_size=cfg.batch_size):
"""Get USPS dataset loader."""
# image pre-processing
pre_process = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(
mean=cfg.dataset_mean,
std=cfg.dataset_std)])
# dataset and data loader
usps_dataset = USPS(root=cfg.data_root,
train=train,
transform=pre_process,
download=True)
if get_dataset:
return usps_dataset
else:
usps_data_loader = torch.utils.data.DataLoader(
dataset=usps_dataset,
batch_size=batch_size,
shuffle=True)
return usps_data_loader