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data_loading.py
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"""TODO: docstring
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from torchvision import transforms
from torch.utils.data import DataLoader
import util
def data_loading (img_size, num_tr_smpl,num_test_smpl, tsk_list ):
our_transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor()])
source_dataset, source_dataset_test, source_dataset_validation = [], [] , []
for tsk in tsk_list:
print ('LLLLLLLLLLLLLLL loading the current task '+tsk)
if tsk =='mnist':
source_dataset.append(util.Local_Dataset_digit(data_name='mnist', set='train', data_path='data/mnist', transform=our_transform,
num_samples=num_tr_smpl))
source_dataset_test.append(
util.Local_Dataset_digit(data_name='mnist', set='test', data_path='data/mnist', transform=our_transform,
num_samples=num_test_smpl))
source_dataset_validation.append(
util.Local_Dataset_digit(data_name='mnist', set='validation', data_path='data/mnist',
transform=our_transform,
num_samples=1000))
if tsk == 'm_mnist':
source_dataset.append(util.Local_Dataset_digit(data_name='m_mnist', set='train', data_path='data/mnist_m',
transform=our_transform,
num_samples=num_tr_smpl))
source_dataset_test.append(
util.Local_Dataset_digit(data_name='m_mnist', set='test', data_path='data/mnist_m', transform=our_transform,
num_samples=num_test_smpl))
source_dataset_validation.append(
util.Local_Dataset_digit(data_name='m_mnist', set='validation', data_path='data/mnist_m',
transform=our_transform,
num_samples=1000))
if tsk =='usps':
source_dataset.append(util.Local_Dataset_digit(data_name='usps', set='train', data_path='data/USPSdata',
transform=our_transform,
num_samples=num_tr_smpl))
source_dataset_test.append(
util.Local_Dataset_digit(data_name='usps', set='test', data_path='data/USPSdata', transform=our_transform,
num_samples=num_test_smpl))
source_dataset_validation.append(
util.Local_Dataset_digit(data_name='usps', set='validation', data_path='data/USPSdata',
transform=our_transform,
num_samples=1000))
if tsk =='svhn':
source_dataset.append(util.Local_SVHN(root='data/SVHN', split='train', transform=our_transform, download=True,
num_smpl=num_tr_smpl))
source_dataset_test.append(
util.Local_SVHN(root='data/SVHN', split='test', transform=our_transform, download=True,
num_smpl=num_test_smpl))
source_dataset_validation.append(
util.Local_SVHN(root='data/SVHN', split='extra', transform=our_transform, download=True,
num_smpl=1000))
train_loader = [DataLoader(source_dataset[t], batch_size=16, shuffle=True, num_workers=0)
for t in range(len(source_dataset_test))]
test_loader = [
DataLoader(source_dataset_test[t], batch_size=128, shuffle=False, num_workers=0) for t in
range(len(source_dataset_test))]
validation_loader = [
DataLoader(source_dataset_validation[t], batch_size=128, shuffle=False, num_workers=0) for t in
range(len(source_dataset_test))]
return train_loader,test_loader,validation_loader