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handlers.py
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import numpy as np
from torchvision import transforms
from torch.utils.data import Dataset
from PIL import Image
class MNIST_Handler(Dataset):
def __init__(self, X, Y, transform):
self.X = X
self.Y = Y
self.transform = transform
def __getitem__(self, index):
x, y = self.X[index], self.Y[index]
x = Image.fromarray(x.numpy(), mode='L')
x = self.transform(x)
return x, y, index
def __len__(self):
return len(self.X)
class SVHN_Handler(Dataset):
def __init__(self, X, Y, transform):
self.X = X
self.Y = Y
self.transform = transform
def __getitem__(self, index):
x, y = self.X[index], self.Y[index]
x = Image.fromarray(np.transpose(x, (1, 2, 0)))
x = self.transform(x)
return x, y, index
def __len__(self):
return len(self.X)
class CIFAR10_Handler(Dataset):
def __init__(self, X, Y, transform):
self.X = X
self.Y = Y
self.transform = transform
def __getitem__(self, index):
x, y = self.X[index], self.Y[index]
x = Image.fromarray(x)
x = self.transform(x)
return x, y, index
def __len__(self):
return len(self.X)
class openml_Handler(Dataset):
def __init__(self, X, Y, transform):
self.X = X
self.Y = Y
self.transform = transform
def __getitem__(self, index):
x, y = self.X[index], self.Y[index]
return x, y, index
def __len__(self):
return len(self.X)
class MNIST_Handler_joint(Dataset):
def __init__(self,X_1, Y_1, X_2, Y_2, transform):
"""
:param X_1: covariate from the first distribution
:param Y_1: label from the first distribution
:param X_2:
:param Y_2:
:param transform:
"""
self.X1 = X_1
self.Y1 = Y_1
self.X2 = X_2
self.Y2 = Y_2
self.transform = transform
def __len__(self):
# returning the minimum length of two data-sets
return max(len(self.X1),len(self.X2))
def __getitem__(self, index):
Len1 = len(self.Y1)
Len2 = len(self.Y2)
# checking the index in the range or not
if index < Len1:
x_1 = self.X1[index]
y_1 = self.Y1[index]
else:
# rescaling the index to the range of Len1
re_index = index % Len1
x_1 = self.X1[re_index]
y_1 = self.Y1[re_index]
# checking second datasets
if index < Len2:
x_2 = self.X2[index]
y_2 = self.Y2[index]
else:
# rescaling the index to the range of Len2
re_index = index % Len2
x_2 = self.X2[re_index]
y_2 = self.Y2[re_index]
if self.transform is not None:
x_1 = Image.fromarray(x_1.numpy(), mode='L')
x_1 = self.transform(x_1)
x_2 = Image.fromarray(x_2.numpy(), mode='L')
x_2 = self.transform(x_2)
return index,x_1,y_1,x_2,y_2
class SVHN_Handler_joint(Dataset):
def __init__(self,X_1, Y_1, X_2, Y_2, transform = None):
"""
:param X_1: covariate from the first distribution
:param Y_1: label from the first distribution
:param X_2:
:param Y_2:
:param transform:
"""
self.X1 = X_1
self.Y1 = Y_1
self.X2 = X_2
self.Y2 = Y_2
self.transform = transform
def __len__(self):
# returning the minimum length of two data-sets
return max(len(self.X1),len(self.X2))
def __getitem__(self, index):
Len1 = len(self.Y1)
Len2 = len(self.Y2)
# checking the index in the range or not
if index < Len1:
x_1 = self.X1[index]
y_1 = self.Y1[index]
else:
# rescaling the index to the range of Len1
re_index = index % Len1
x_1 = self.X1[re_index]
y_1 = self.Y1[re_index]
# checking second datasets
if index < Len2:
x_2 = self.X2[index]
y_2 = self.Y2[index]
else:
# rescaling the index to the range of Len2
re_index = index % Len2
x_2 = self.X2[re_index]
y_2 = self.Y2[re_index]
if self.transform is not None:
x_1 = Image.fromarray(np.transpose(x_1, (1, 2, 0)))
x_1 = self.transform(x_1)
x_2 = Image.fromarray(np.transpose(x_2, (1, 2, 0)))
x_2 = self.transform(x_2)
return index,x_1,y_1,x_2,y_2
class CIFAR10_Handler_joint(Dataset):
def __init__(self,X_1, Y_1, X_2, Y_2, transform = None):
"""
:param X_1: covariate from the first distribution
:param Y_1: label from the first distribution
:param X_2:
:param Y_2:
:param transform:
"""
self.X1 = X_1
self.Y1 = Y_1
self.X2 = X_2
self.Y2 = Y_2
self.transform = transform
def __len__(self):
# returning the minimum length of two data-sets
return max(len(self.X1),len(self.X2))
def __getitem__(self, index):
Len1 = len(self.Y1)
Len2 = len(self.Y2)
# checking the index in the range or not
if index < Len1:
x_1 = self.X1[index]
y_1 = self.Y1[index]
else:
# rescaling the index to the range of Len1
re_index = index % Len1
x_1 = self.X1[re_index]
y_1 = self.Y1[re_index]
# checking second datasets
if index < Len2:
x_2 = self.X2[index]
y_2 = self.Y2[index]
else:
# rescaling the index to the range of Len2
re_index = index % Len2
x_2 = self.X2[re_index]
y_2 = self.Y2[re_index]
if self.transform is not None:
x_1 = Image.fromarray(x_1)
x_1 = self.transform(x_1)
x_2 = Image.fromarray(x_2)
x_2 = self.transform(x_2)
return index,x_1,y_1,x_2,y_2