def cifar_to_stl(resize_mode='i',normalize=True):
import numpy as np
from keras.datasets import cifar10
(source_traindata, source_trainlabel), (source_testdata, source_testlabel) = cifar10.load_data()
# remove the class 'frog' label = '6'
def remove(data, label, lind):
ind1 = (label < lind) + (label > lind)
ind1 = ind1.ravel()
data = data[ind1]
label = label[ind1]
ind2 = label > lind
label[ind2] = label[ind2] - 1
return data, label
source_traindata, source_trainlabel = remove(source_traindata, source_trainlabel, 6)
source_testdata, source_testlabel = remove(source_testdata, source_testlabel, 6)
source_size = source_traindata.shape
if resize_mode=='imagenet':
resize =True
resize_size = 224
else:
resize =False
resize_size =32
from preprocess import zero_mean_unitvarince, resize_data
if resize == True:
source_traindata = resize_data(source_traindata, resize_size=resize_size)
source_testdata = resize_data(source_testdata, resize_size=resize_size)
source_size = source_traindata.shape
if normalize == True:
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
from DatasetLoad import stl10_dataload
target_traindata, target_trainlabel, target_testdata, target_testlabel = stl10_dataload()
# remove the class name 'monkey', label = '7'
target_traindata, target_trainlabel = remove(target_traindata, target_trainlabel, 7)
target_testdata, target_testlabel = remove(target_testdata, target_testlabel, 7)
if resize_mode=='imagenet':
resize =True
resize_size = 224
else:
resize =True
resize_size =32
from preprocess import zero_mean_unitvarince, resize_data
if resize == True:
target_traindata = resize_data(target_traindata, resize_size=resize_size)
target_testdata = resize_data(target_testdata, resize_size=resize_size)
from preprocess import zero_mean_unitvarince
target_traindata = zero_mean_unitvarince(target_traindata, scaling=True)
target_testdata = zero_mean_unitvarince(target_testdata, scaling=True)
return (source_traindata, source_trainlabel,source_testdata, source_testlabel), (target_traindata, target_trainlabel, target_testdata, target_testlabel)
def mnist_to_usps():
from keras.datasets import mnist
(source_traindata,
source_trainlabel), (source_testdata,
source_testlabel) = mnist.load_data()
source_size = source_traindata.shape
resize = False
resize_size = 16
from preprocess import zero_mean_unitvarince, resize_data
if resize == True:
source_traindata = resize_data(source_traindata,
resize_size=resize_size)
source_testdata = resize_data(source_testdata, resize_size=resize_size)
source_size = source_traindata.shape
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
source_traindata = source_traindata.reshape(-1, source_size[1],
source_size[2], 1)
source_testdata = source_testdata.reshape(-1, source_size[1],
source_size[2], 1)
#%%
from DatasetLoad import usps_digit_dataload
target_traindata, target_trainlabel, target_testdata, target_testlabel = usps_digit_dataload(
)
target_trainlabel = target_trainlabel - 1
target_testlabel = target_testlabel - 1
target_traindata = target_traindata.reshape(-1, 16, 16, 1)
target_testdata = target_testdata.reshape(-1, 16, 16, 1)
print(target_traindata.shape)
resize = True
resize_size = 28
if resize:
npad = ((0, 0), (6, 6), (6, 6), (0, 0))
target_traindata = np.pad(target_traindata,
pad_width=npad,
mode='constant')
target_testdata = np.pad(target_testdata,
pad_width=npad,
mode='constant')
# target_traindata = resize_data(target_traindata, resize_size=resize_size)
# target_testdata = resize_data(target_testdata, resize_size=resize_size)
target_traindata = target_traindata.reshape(-1, 28, 28, 1)
target_testdata = target_testdata.reshape(-1, 28, 28, 1)
target_traindata = zero_mean_unitvarince(target_traindata, scaling=True)
target_testdata = zero_mean_unitvarince(target_testdata, scaling=True)
return (source_traindata, source_trainlabel, source_testdata,
source_testlabel), (target_traindata, target_trainlabel,
target_testdata, target_testlabel)
def svhnn_to_mnist(method = 'zero_mean_unitvarince', **params):
from skimage.color import rgb2gray
from scipy.misc import imresize
from DatasetLoad import SVHN_dataload
source_traindata, source_trainlabel, source_testdata, source_testlabel = SVHN_dataload()
source_size = source_traindata.shape
from preprocess import zero_mean_unitvarince, instance_zero_mean_unitvar, min_max_scaling
if method =='instance_zero_mean_unitvar':
source_traindata = instance_zero_mean_unitvar(source_traindata, scaling=True)
source_testdata = instance_zero_mean_unitvar(source_testdata, scaling=True)
elif method =='min_max':
source_traindata = min_max_scaling(source_traindata, **params)
source_testdata = min_max_scaling(source_testdata, **params)
else:
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
source_trainlabel = source_trainlabel*(source_trainlabel!=10)
source_testlabel = source_testlabel*(source_testlabel!=10)
from keras.datasets import mnist
(target_traindata, target_trainlabel), (target_testdata, target_testlabel) = mnist.load_data()
target_size = target_traindata.shape
resize = True
resize_size =32
from preprocess import zero_mean_unitvarince,resize_data
if resize == True:
target_traindata = resize_data(target_traindata, resize_size=resize_size)
target_testdata = resize_data(target_testdata, resize_size=resize_size)
if method =='instance_zero_mean_unitvar':
target_traindata = instance_zero_mean_unitvar(target_traindata, scaling=True)
target_testdata = instance_zero_mean_unitvar(target_testdata, scaling=True)
elif method =='min_max':
target_traindata = min_max_scaling(target_traindata, **params)
target_testdata = min_max_scaling(target_testdata, **params)
else:
target_traindata = zero_mean_unitvarince(target_traindata,scaling=True)
target_testdata = zero_mean_unitvarince(target_testdata,scaling=True)
convert_rgb=1
if convert_rgb:
target_traindata = np.stack((target_traindata,target_traindata,target_traindata), axis=3)
target_testdata = np.stack((target_testdata,target_testdata,target_testdata), axis=3)
return (source_traindata, source_trainlabel,source_testdata, source_testlabel), (target_traindata, target_trainlabel, target_testdata, target_testlabel)
def mnist_to_mnistm():
from keras.datasets import mnist
(source_traindata,
source_trainlabel), (source_testdata,
source_testlabel) = mnist.load_data()
source_size = source_traindata.shape
resize = False
resize_size = 32
from preprocess import zero_mean_unitvarince, resize_data
if resize == True:
source_traindata = resize_data(source_traindata,
resize_size=resize_size)
source_testdata = resize_data(source_testdata, resize_size=resize_size)
source_size = source_traindata.shape
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
convert_rgb = 1
if convert_rgb:
source_traindata = np.stack(
(source_traindata, source_traindata, source_traindata), axis=3)
source_testdata = np.stack(
(source_testdata, source_testdata, source_testdata), axis=3)
from DatasetLoad import mnist_m_dataload
from skimage.color import rgb2gray
target_traindata, target_trainlabel, target_testdata, target_testlabel = mnist_m_dataload(
)
target_size = target_traindata.shape
resize = False
resize_size = 28
if resize == True:
target_traindata = resize_data(target_traindata,
resize_size=resize_size)
target_testdata = resize_data(target_testdata, resize_size=resize_size)
target_size = target_traindata.shape
target_traindata = zero_mean_unitvarince(target_traindata, scaling=True)
target_testdata = zero_mean_unitvarince(target_testdata, scaling=True)
return (source_traindata, source_trainlabel, source_testdata,
source_testlabel), (target_traindata, target_trainlabel,
target_testdata, target_testlabel)
def usps_to_mnist():
from DatasetLoad import usps_digit_dataload
source_traindata, source_trainlabel, source_testdata, source_testlabel = usps_digit_dataload(
)
source_trainlabel = source_trainlabel - 1
source_testlabel = source_testlabel - 1
# 2d to 3d for CNN
source_traindata = source_traindata.reshape(-1, 16, 16, 1)
source_testdata = source_testdata.reshape(-1, 16, 16, 1)
from preprocess import zero_mean_unitvarince, resize_data
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
#
from keras.datasets import mnist
(target_traindata,
target_trainlabel), (target_testdata,
target_testlabel) = mnist.load_data()
target_size = target_traindata.shape
resize = True
resize_size = 16
if resize == True:
target_traindata = resize_data(target_traindata,
resize_size=resize_size)
target_testdata = resize_data(target_testdata, resize_size=resize_size)
target_size = target_traindata.shape
target_traindata = zero_mean_unitvarince(target_traindata, scaling=True)
target_testdata = zero_mean_unitvarince(target_testdata, scaling=True)
target_traindata = target_traindata.reshape(-1, target_size[1],
target_size[2], 1)
target_testdata = target_testdata.reshape(-1, target_size[1],
target_size[2], 1)
return (source_traindata, source_trainlabel, source_testdata,
source_testlabel), (target_traindata, target_trainlabel,
target_testdata, target_testlabel)
def mnist_to_svhnn():
from keras.datasets import mnist
(source_traindata,
source_trainlabel), (source_testdata,
source_testlabel) = mnist.load_data()
source_size = source_traindata.shape
resize = False
resize_size = 32
from preprocess import zero_mean_unitvarince, resize_data
if resize == True:
source_traindata = resize_data(source_traindata,
resize_size=resize_size)
source_testdata = resize_data(source_testdata, resize_size=resize_size)
source_size = source_traindata.shape
source_traindata = zero_mean_unitvarince(source_traindata, scaling=True)
source_testdata = zero_mean_unitvarince(source_testdata, scaling=True)
convert_rgb = 1
if convert_rgb:
source_traindata = np.stack(
(source_traindata, source_traindata, source_traindata), axis=3)
source_testdata = np.stack(
(source_testdata, source_testdata, source_testdata), axis=3)
#########################################
from skimage.color import rgb2gray
from scipy.misc import imresize
from DatasetLoad import SVHN_dataload
target_traindata, label = SVHN_dataload()
target_size = target_traindata.shape
from preprocess import zero_mean_unitvarince
target_traindata = zero_mean_unitvarince(target_traindata, scaling=True)
target_trainlabel = label * (label != 10)
target_size = target_traindata.shape
return source_traindata, source_trainlabel, source_testdata, source_testlabel, target_traindata, target_trainlabel
