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data.py
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data.py
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import os
import torch
import torchvision
from torchvision import transforms
def dataloader(datasetname : str):
""" returns dataloaders for a given dataset """
datasets = { 'mnist': _mnist,
'cifar10': _cifar10,
'cifar100': _cifar100,
'tinyimagenet': _tinyimagenet }
return datasets[datasetname.lower()]
def _mnist(
batchsize : int, testbatchsize : int, datasetfolder : str,
augment : bool = False, nworkers : int = 8):
""" MNIST, 60000 28x28x1 images, 10 classes, 10000 test images """
transform_totensor = transforms.Compose([
transforms.ToTensor(),
])
trainset = torchvision.datasets.MNIST(
root=datasetfolder, train=True,
download=True, transform=transform_totensor)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=batchsize, shuffle=True,
num_workers=nworkers)
testset = torchvision.datasets.MNIST(
root=datasetfolder, train=False,
download=True, transform=transform_totensor)
testloader = torch.utils.data.DataLoader(
testset, batch_size=testbatchsize, shuffle=False,
num_workers=nworkers)
return trainset, testset, trainloader, testloader
def _cifar10(
batchsize : int, testbatchsize : int, datasetfolder : str,
augment : bool = True, nworkers : int = 8):
""" CIFAR-10, 50000 32x32x3 images, 10 classes, 10000 test images """
train_mean = (0.4914, 0.4822, 0.4465)
train_std = (0.2023, 0.1994, 0.2010)
if augment:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
else:
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
trainset = torchvision.datasets.CIFAR10(
root=datasetfolder, train=True,
download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=batchsize, shuffle=True,
num_workers=nworkers, drop_last=True)
testset = torchvision.datasets.CIFAR10(
root=datasetfolder, train=False,
download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
testset, batch_size=testbatchsize, shuffle=False,
num_workers=nworkers, drop_last=False)
return trainset, testset, trainloader, testloader
def _cifar100(
batchsize : int, testbatchsize : int, datasetfolder : str,
augment : bool = True, nworkers : int = 8):
""" CIFAR100, 50000 32x32x3 images, 100 classes, 10000 test images """
train_mean = (0.5071, 0.4865, 0.4409)
train_std = (0.2673, 0.2564, 0.2761)
if augment:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
else:
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
trainset = torchvision.datasets.CIFAR100(
root=datasetfolder, train=True,
download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=batchsize, shuffle=True,
num_workers=nworkers, drop_last=True)
testset = torchvision.datasets.CIFAR100(
root=datasetfolder, train=False,
download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
testset, batch_size=testbatchsize, shuffle=False,
num_workers=nworkers, drop_last=False)
return trainset, testset, trainloader, testloader
def _tinyimagenet(batchsize : int, testbatchsize : int, datasetfolder : str,
augment : bool = True, nworkers : int = 8):
""" TinyImageNet, 100000 64x64x3 images, 200 classes, 10000 test images """
train_mean = (0.485, 0.456, 0.406)
train_std = (0.229, 0.224, 0.225)
data_dir = os.path.join(datasetfolder, 'tiny-imagenet-200')
train_dir = os.path.join(data_dir, 'train')
valid_dir = os.path.join(data_dir, 'val/images')
if augment:
transform_train = transforms.Compose([
transforms.RandomCrop(64, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
else:
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std),
])
trainset = torchvision.datasets.ImageFolder(
train_dir, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=batchsize,
shuffle=True, num_workers=nworkers, drop_last=True)
testset = torchvision.datasets.ImageFolder(
valid_dir, transform=transform_test)
testloader = torch.utils.data.DataLoader(
testset, batch_size=testbatchsize,
shuffle=False, num_workers=nworkers, drop_last=False)
return trainset, testset, trainloader, testloader