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dataset.py
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import os
import torch
from torchvision.datasets import MNIST, CIFAR10, CIFAR100
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torchvision.datasets import VisionDataset
from torchvision.datasets.folder import default_loader
from torchvision.datasets.utils import extract_archive, check_integrity, download_url, verify_str_arg
from utils import load_obj
def getDataLoader(config):
loaders = {
'cifar10': cifar10_loaders,
'cifar100': cifar100_loaders,
'tiny_imagenet': tiny_imagenet_loaders,
'square_wave': square_wave_loaders
}[config.dataset]
return loaders(config)
## square-wave ----------------------------------------------------------
def square_wave_loaders(config):
fpath = "./data/square-wave.pt"
train_size, test_size = 300, 200
if os.path.isfile(fpath):
data = torch.load(fpath)
else:
x, y = square_wave(train_size)
xt, yt = square_wave(test_size, xrand=False)
data = {"xt":xt, "yt":yt, "x":x, "y":y}
torch.save(data, fpath)
train_dataset = Curve(data['x'], data['y'])
test_dataset = Curve(data['xt'], data['yt'])
trainLoader = DataLoader(train_dataset,batch_size=config.train_batch_size, shuffle=True, pin_memory=True, num_workers=config.num_workers)
testLoader = DataLoader(test_dataset, batch_size=config.test_batch_size, shuffle=False, pin_memory=True, num_workers=config.num_workers)
return trainLoader, testLoader
def square_wave(size, xrand=True):
if xrand:
x = 2*(2*torch.rand(size,1) - 1)
else:
x = torch.linspace(-2.0, 2.0, size).reshape((size,1))
y = torch.zeros((size,1))
for i in range(size):
if x[i, 0] <= -1.0:
y[i, 0] = 1.0
if x[i, 0] > 0.0 and x[i, 0] <= 1.0:
y[i, 0] = 1.0
return x, y
class Curve(torch.utils.data.Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
self.size = x.shape[0]
def __len__(self):
return self.size
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
def tiny_imagenet_loaders(config):
mean = [0.485, 0.456, 0.406]
if config.normalized:
std = [0.229, 0.224, 0.225]
else:
std = [1.0, 1.0, 1.0]
normalize = transforms.Normalize(mean=mean, std=std)
hue = 0.02
saturation = (.3, 2.)
brightness = 0.1
contrast = (.5, 2.)
transforms_list = [transforms.RandomCrop(64, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=brightness, contrast=contrast,
saturation=saturation, hue=hue),
transforms.ToTensor(),
normalize]
train_dset = TinyImageNet('data',
split='train',
download=True,
transform=transforms.Compose(transforms_list))
test_dset = TinyImageNet('data',
split='val',
transform=transforms.Compose([
transforms.ToTensor(),
normalize
]))
trainLoader = DataLoader(train_dset, batch_size=config.train_batch_size, shuffle=True, drop_last=True, pin_memory=True, num_workers=config.num_workers)
testLoader = DataLoader(test_dset, batch_size=config.test_batch_size, shuffle=False, pin_memory=True,num_workers=config.num_workers)
return trainLoader, testLoader
##------------------------------------------------------------------------------------------
## from https://github.com/araujoalexandre/lipschitz-sll-networks
class TinyImageNet(VisionDataset):
"""`tiny-imageNet <http://cs231n.stanford.edu/tiny-imagenet-200.zip>`_ Dataset.
Args:
root (string): Root directory of the dataset.
split (string, optional): The dataset split, supports ``train``, or ``val``.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
base_folder = 'tiny-imagenet-200/'
url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip'
filename = 'tiny-imagenet-200.zip'
md5 = '90528d7ca1a48142e341f4ef8d21d0de'
def __init__(self, root, split='train', transform=None, target_transform=None, download=False):
super(TinyImageNet, self).__init__(root, transform=transform, target_transform=target_transform)
self.dataset_path = os.path.join(root, self.base_folder)
self.loader = default_loader
self.split = verify_str_arg(split, "split", ("train", "val",))
if self._check_integrity():
# print('Files already downloaded and verified.')
pass
elif download:
self._download()
else:
raise RuntimeError(
'Dataset not found. You can use download=True to download it.')
if not os.path.isdir(self.dataset_path):
print('Extracting...')
extract_archive(os.path.join(root, self.filename))
_, class_to_idx = find_classes(os.path.join(self.dataset_path, 'wnids.txt'))
self.data = make_dataset(self.root, self.base_folder, self.split, class_to_idx)
def _download(self):
print('Downloading...')
download_url(self.url, root=self.root, filename=self.filename)
print('Extracting...')
extract_archive(os.path.join(self.root, self.filename))
def _check_integrity(self):
return check_integrity(os.path.join(self.root, self.filename), self.md5)
def __getitem__(self, index):
img_path, target = self.data[index]
image = self.loader(img_path)
if self.transform is not None:
image = self.transform(image)
if self.target_transform is not None:
target = self.target_transform(target)
return image, target
def __len__(self):
return len(self.data)
def find_classes(class_file):
with open(class_file) as r:
classes = list(map(lambda s: s.strip(), r.readlines()))
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
def make_dataset(root, base_folder, dirname, class_to_idx):
images = []
dir_path = os.path.join(root, base_folder, dirname)
if dirname == 'train':
for fname in sorted(os.listdir(dir_path)):
cls_fpath = os.path.join(dir_path, fname)
if os.path.isdir(cls_fpath):
cls_imgs_path = os.path.join(cls_fpath, 'images')
for imgname in sorted(os.listdir(cls_imgs_path)):
path = os.path.join(cls_imgs_path, imgname)
item = (path, class_to_idx[fname])
images.append(item)
else:
imgs_path = os.path.join(dir_path, 'images')
imgs_annotations = os.path.join(dir_path, 'val_annotations.txt')
with open(imgs_annotations) as r:
data_info = map(lambda s: s.split('\t'), r.readlines())
cls_map = {line_data[0]: line_data[1] for line_data in data_info}
for imgname in sorted(os.listdir(imgs_path)):
path = os.path.join(imgs_path, imgname)
item = (path, class_to_idx[cls_map[imgname]])
images.append(item)
return images
##------------------------------------------------------------------------------------------
def cifar100_loaders(config):
mean = [0.5071, 0.4865, 0.4409]
if config.normalized:
std = [0.2675, 0.2565, 0.2761]
else:
std = [1.0, 1.0, 1.0]
normalize = transforms.Normalize(mean=mean, std=std)
hue = 0.02
saturation = (.3, 2.)
brightness = 0.1
contrast = (.5, 2.)
transforms_list = [transforms.RandomCrop(32, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=brightness, contrast=contrast,
saturation=saturation, hue=hue),
transforms.ToTensor(),
normalize]
train_dset = CIFAR100('data',train=True,download=True, transform=transforms.Compose(transforms_list))
test_dset = CIFAR100('data', train=False,transform=transforms.Compose([transforms.ToTensor(),normalize]))
trainLoader = DataLoader(train_dset, batch_size=config.train_batch_size, shuffle=True, drop_last=True, pin_memory=False, num_workers=config.num_workers)
testLoader = DataLoader(test_dset, batch_size=config.test_batch_size, shuffle=False, pin_memory=False,num_workers=config.num_workers)
return trainLoader, testLoader
def cifar10_loaders(config):
mean = [0.4914, 0.4822, 0.4465]
if config.normalized:
std = [0.2470, 0.2435, 0.2616]
else:
std = [1.0, 1.0, 1.0]
normalize = transforms.Normalize(mean=mean, std=std)
hue = 0.02
saturation = (.3, 2.)
brightness = 0.1
contrast = (.5, 2.)
transforms_list = [transforms.RandomCrop(32, padding=4, padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=brightness, contrast=contrast,
saturation=saturation, hue=hue),
transforms.ToTensor(),
normalize]
train_dset = CIFAR10('data',train=True,download=True,transform=transforms.Compose(transforms_list))
test_dset = CIFAR10('data',train=False,transform=transforms.Compose([transforms.ToTensor(),normalize]))
trainLoader = DataLoader(train_dset, batch_size=config.train_batch_size, shuffle=True, drop_last=True, pin_memory=True, num_workers=config.num_workers)
testLoader = DataLoader(test_dset, batch_size=config.test_batch_size, shuffle=False, pin_memory=True, num_workers=config.num_workers)
return trainLoader, testLoader
def mnist_loaders(config):
mean = (0.1307,)
if config.normalized:
std = (0.3081,)
else:
std = (1.0, )
trainLoader = DataLoader(MNIST('data',train=True,download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])),
batch_size=config.train_batch_size,
shuffle=True, pin_memory=True, num_workers=config.num_workers)
testLoader = DataLoader(MNIST('data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])),
batch_size=config.test_batch_size,
shuffle=False, pin_memory=True, num_workers=config.num_workers)
return trainLoader, testLoader