def get_data_loader(name, train=True):
"""Get data loader by name."""
if name == "MNIST":
return get_mnist(train)
elif name == "USPS":
return get_usps(train)
elif name == "SVHN":
return get_svhn(train)
def get_data_loader(name, train=True, get_dataset=False):
"""Get data loader by name."""
if name == "MNIST":
return get_mnist(train, get_dataset)
elif name == "MNIST-M":
return get_mnist_m(train, get_dataset)
elif name == "SVHN":
return get_svhn(train, get_dataset)
elif name == "USPS":
return get_usps(train, get_dataset)
def get_data_loader(name, train=True):
"""Get data loader by name."""
if name == "MNIST":
return get_mnist(train)
elif name == "USPS":
return get_usps(train)
elif name == "CIFAR10":
return get_cifar10(train)
elif "CIFAR10-" in name:
corruption = name.split('-')[1]
return get_cifar10(train, corruption=corruption)
def get_data_loader(name, train=True,domain=None,visualize=False):
"""Get data loader by name."""
if name == "MNIST":
raise NotImplementedError
return get_mnist(train)
elif name == "USPS":
raise NotImplementedError
return get_usps(train)
elif name == "Dollarstreet":
return get_dollarstreet(visualize,domain)
else:
raise NotImplementedError
def get_data_loader(name, train=True):
"""Get data loader by name."""
if name == "MNIST":
dataset = get_mnist(train)
elif name == "MNISTM":
dataset = get_mnistm(train)
elif name == "USPS":
dataset = get_usps(train)
elif name == "SVHN":
dataset = get_svhn(train)
if train:
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=params.batch_size,
shuffle=True)
else:
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=1,
shuffle=False)
return data_loader
def get_data_loader(name, train=True, dataset=None, sample=False):
"""Get data loader by name."""
if name == "MNIST":
return get_mnist(train, sample)
elif name == "MNIST-M":
return get_mnist_m(train)
elif name == "SVHN":
return get_svhn(train)
elif name == "USPS":
return get_usps(train, sample)
elif name == "SYNTH":
return get_synth(train)
elif name == "SYNTHSIGN":
return get_synthsign(train)
elif name == "GTSRB":
return get_gtsrb(train)
elif name == "STL":
return get_stl(train)
elif name == "CIFA":
return get_cifa(train)
elif name == "PIE27":
return get_pie27(train)
elif name == "PIE05":
return get_pie05(train)
elif name == "PIE09":
return get_pie09(train)
elif name == "PIE37":
return get_pie37(train)
elif name == "PIE25":
return get_pie25(train)
elif name == "PIE02":
return get_pie02(train)
elif name == "taskcv_S":
return get_taskcv_s(train)
elif name == "taskcv_T":
return get_taskcv_t(train)
elif name == "OFFICE":
return get_office(dataset)
import clusters
import datasets as ds
import estimate_k as ek
from sklearn.cluster import KMeans
from sklearn.cluster import SpectralClustering
from pymongo import MongoClient
# prepare datasets
# uci
uci_data, uci_target = ds.get_uci()
uci_data = uci_data.astype('float32') / 16.
# usps
usps_data, _, usps_test, usps_test_lable = ds.get_usps()
# mnist
mnist_train,_,mnist_test,mnist_test_lable = ds.get_mnist()
mnist_train = mnist_train.astype('float32') / 255.
mnist_train = mnist_train.reshape((len(mnist_train),np.prod(mnist_train.shape[1:]))) # make 28*28 img to 784 demission matrix
mnist_test = mnist_test.astype('float32') / 255.
mnist_test = mnist_test.reshape((len(mnist_test), np.prod(mnist_test.shape[1:])))
# fashion
fashion_train,_,fashion_test,fashion_test_lable = ds.get_fashion_mnist()
fashion_train = fashion_train.astype('float32') / 255.
fashion_train = fashion_train.reshape((len(fashion_train),np.prod(fashion_train.shape[1:]))) # make 28*28 img to 784 demission matrix
fashion_test = fashion_test.astype('float32') / 255.
fashion_test = fashion_test.reshape((len(fashion_test), np.prod(fashion_test.shape[1:])))