from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import OneCycleLR
import matplotlib.pyplot as plt
#%matplotlib inline
import numpy as np
import torchvision
import torchsummary
from torchsummary import summary
from utils.data_albument import TrainAlbumentation,TestAlbumentation


	
def download_load(train_bs,test_bs):	
	use_cuda = torch.cuda.is_available()

	cuda = torch.cuda.is_available()
	print("CUDA Available?", cuda)
	SEED=1
	# For reproducibility
	torch.manual_seed(SEED)

	if cuda:
		torch.cuda.manual_seed(SEED)
	transform_train = TrainAlbumentation()
	transform = TestAlbumentation()
	#transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
	#transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

	trainoader_args = dict(shuffle=True, batch_size=train_bs, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
	testloader_args = dict(shuffle=False, batch_size=test_bs, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)

	trainset = datasets.CIFAR10(root='./data', train=True,download=True, transform=transform_train)
	testset = datasets.CIFAR10(root='./data', train=False,download=True, transform=transform)
                                       

	train_loader = torch.utils.data.DataLoader(trainset, **trainoader_args)
	test_loader= torch.utils.data.DataLoader(testset, **testloader_args)

	classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
	return trainset, testset, train_loader, test_loader, classes
		 

61c605410ee8cf3ac67ef1acbdf143511e9884f6
<mask token>

<mask token>


def download_load(train_bs, test_bs):
    use_cuda = torch.cuda.is_available()
    cuda = torch.cuda.is_available()
    print('CUDA Available?', cuda)
    SEED = 1
    torch.manual_seed(SEED)
    if cuda:
        torch.cuda.manual_seed(SEED)
    transform_train = TrainAlbumentation()
    transform = TestAlbumentation()
    trainoader_args = dict(shuffle=True, batch_size=train_bs, num_workers=4,
        pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
    testloader_args = dict(shuffle=False, batch_size=test_bs, num_workers=4,
        pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
    trainset = datasets.CIFAR10(root='./data', train=True, download=True,
        transform=transform_train)
    testset = datasets.CIFAR10(root='./data', train=False, download=True,
        transform=transform)
    train_loader = torch.utils.data.DataLoader(trainset, **trainoader_args)
    test_loader = torch.utils.data.DataLoader(testset, **testloader_args)
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog',
        'horse', 'ship', 'truck')
    return trainset, testset, train_loader, test_loader, classes

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import OneCycleLR
import matplotlib.pyplot as plt
import numpy as np
import torchvision
import torchsummary
from torchsummary import summary
from utils.data_albument import TrainAlbumentation, TestAlbumentation


def download_load(train_bs, test_bs):
    use_cuda = torch.cuda.is_available()
    cuda = torch.cuda.is_available()
    print('CUDA Available?', cuda)
    SEED = 1
    torch.manual_seed(SEED)
    if cuda:
        torch.cuda.manual_seed(SEED)
    transform_train = TrainAlbumentation()
    transform = TestAlbumentation()
    trainoader_args = dict(shuffle=True, batch_size=train_bs, num_workers=4,
        pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
    testloader_args = dict(shuffle=False, batch_size=test_bs, num_workers=4,
        pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
    trainset = datasets.CIFAR10(root='./data', train=True, download=True,
        transform=transform_train)
    testset = datasets.CIFAR10(root='./data', train=False, download=True,
        transform=transform)
    train_loader = torch.utils.data.DataLoader(trainset, **trainoader_args)
    test_loader = torch.utils.data.DataLoader(testset, **testloader_args)
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog',
        'horse', 'ship', 'truck')
    return trainset, testset, train_loader, test_loader, classes

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import OneCycleLR
import matplotlib.pyplot as plt
#%matplotlib inline
import numpy as np
import torchvision
import torchsummary
from torchsummary import summary
from utils.data_albument import TrainAlbumentation,TestAlbumentation


	
def download_load(train_bs,test_bs):	
	use_cuda = torch.cuda.is_available()

	cuda = torch.cuda.is_available()
	print("CUDA Available?", cuda)
	SEED=1
	# For reproducibility
	torch.manual_seed(SEED)

	if cuda:
		torch.cuda.manual_seed(SEED)
	transform_train = TrainAlbumentation()
	transform = TestAlbumentation()
	#transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
	#transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

	trainoader_args = dict(shuffle=True, batch_size=train_bs, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
	testloader_args = dict(shuffle=False, batch_size=test_bs, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)

	trainset = datasets.CIFAR10(root='./data', train=True,download=True, transform=transform_train)
	testset = datasets.CIFAR10(root='./data', train=False,download=True, transform=transform)
                                       

	train_loader = torch.utils.data.DataLoader(trainset, **trainoader_args)
	test_loader= torch.utils.data.DataLoader(testset, **testloader_args)

	classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
	return trainset, testset, train_loader, test_loader, classes
		 
