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data.py
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import os
import torch
import torchvision
import torchvision.transforms as transforms
class MNIST:
transform = transforms.Compose([
transforms.ToTensor(),
])
imageSize = 28
batchSize = 128
numClass = 10
imageChannel = 1
dataName = 'MNIST'
def __init__(self, fileDir):
self.fileDir = fileDir
def getTrainLoader(self):
trainSet = torchvision.datasets.MNIST(root=self.fileDir, train=True, download=True, transform=MNIST.transform)
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=MNIST.batchSize, shuffle=True, num_workers=2)
return trainLoader
def getTestLoader(self):
testSet = torchvision.datasets.MNIST(root=self.fileDir, train=False, download=True, transform=MNIST.transform)
testLoader = torch.utils.data.DataLoader(testSet, batch_size=MNIST.batchSize, shuffle=False, num_workers=2)
return testLoader
class SVHN:
transformTrain = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transformTest = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
imageSize = 32
batchSize = 128
numClass = 10
imageChannel = 3
dataName = 'SVHN'
def __init__(self, fileDir):
self.fileDir = fileDir
def getTrainLoader(self):
trainSet = torchvision.datasets.SVHN(root=self.fileDir, split='train', download=True, transform=SVHN.transformTrain)
extraSet = torchvision.datasets.SVHN(root=self.fileDir, split='extra', download=True, transform=SVHN.transformTrain)
trainSet += extraSet
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=SVHN.batchSize, shuffle=True, num_workers=2)
return trainLoader
def getTestLoader(self):
testSet = torchvision.datasets.SVHN(root=self.fileDir, split='test', download=True, transform=SVHN.transformTest)
testLoader = torch.utils.data.DataLoader(testSet, batch_size=SVHN.batchSize, shuffle=False, num_workers=2)
return testLoader
class CIFAR10:
transformTrainWithoutDataAug = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transformTrainWithDataAug = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transformTest = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
imageSize = 32
batchSize = 128
numClass = 10
imageChannel = 3
dataName = 'CIFAR10'
def __init__(self, fileDir, dataAug=True):
self.fileDir = fileDir
self.dataAug = dataAug
def getTrainLoader(self):
if self.dataAug:
transformTrain = CIFAR10.transformTrainWithDataAug
else:
transformTrain = CIFAR10.transformTrainWithoutDataAug
trainSet = torchvision.datasets.CIFAR10(root=self.fileDir, train=True, download=True, transform=transformTrain)
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=CIFAR10.batchSize, shuffle=True, num_workers=2)
return trainLoader
def getTestLoader(self):
testSet = torchvision.datasets.CIFAR10(root=self.fileDir, train=False, download=True, transform=CIFAR10.transformTest)
testLoader = torch.utils.data.DataLoader(testSet, batch_size=CIFAR10.batchSize, shuffle=False, num_workers=2)
return testLoader
class CIFAR100:
transformTrainWithoutDataAug = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transformTrainWithDataAug = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transformTest = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
imageSize = 32
batchSize = 128
numClass = 100
imageChannel = 3
dataName = 'CIFAR100'
def __init__(self, fileDir, dataAug=True):
self.fileDir = fileDir
self.dataAug = dataAug
def getTrainLoader(self):
if self.dataAug:
transformTrain = CIFAR100.transformTrainWithDataAug
else:
transformTrain = CIFAR100.transformTrainWithoutDataAug
trainSet = torchvision.datasets.CIFAR100(root=self.fileDir, train=True, download=True, transform=transformTrain)
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=CIFAR100.batchSize, shuffle=True, num_workers=2)
return trainLoader
def getTestLoader(self):
testSet = torchvision.datasets.CIFAR100(root=self.fileDir, train=False, download=True,
transform=CIFAR100.transformTest)
testLoader = torch.utils.data.DataLoader(testSet, batch_size=CIFAR100.batchSize, shuffle=False, num_workers=2)
return testLoader
class ImageNet:
imageSize = 224
batchSize = 128
numClass = 1000
imageChannel = 3
dataName = 'ImageNet'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
def __init__(self, fileDir):
self.fileDir = fileDir
def getTrainLoader(self):
trainDir = os.path.join(self.fileDir, 'train')
trainLoader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(
trainDir, transforms.Compose([
transforms.RandomResizedCrop(self.imageSize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
self.normalize,
])),
batch_size=self.batchSize, shuffle=True,
num_workers=8, pin_memory=True)
return trainLoader
def getTestLoader(self):
testDir = os.path.join(self.fileDir, 'val')
testLoader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(
testDir, transforms.Compose([
transforms.Resize(int(self.imageSize / 0.875)),
transforms.CenterCrop(self.imageSize),
transforms.ToTensor(),
self.normalize,
])),
batch_size=self.batchSize, shuffle=False,
num_workers=8, pin_memory=True)
return testLoader