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dataloader.py
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dataloader.py
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import glob
import os
import random
from typing import Callable, Optional
import numpy as np
from PIL import Image
from PIL import ImageFilter
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torchvision import datasets, transforms
__all__ = ['prepare_dataset']
EXTENSION = 'JPEG'
NUM_IMAGES_PER_CLASS = 500
CLASS_LIST_FILE = 'wnids.txt'
VAL_ANNOTATION_FILE = 'val_annotations.txt'
class GaussianBlur(object):
"""Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
sigma = random.uniform(self.sigma[0], self.sigma[1])
x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
return x
class TinyImagenet(Dataset):
"""Tiny ImageNet data set available from `http://cs231n.stanford.edu/tiny-imagenet-200.zip`.
Parameters
----------
root: string
Root directory including `train`, `test` and `val` subdirectories.
split: string
Indicating which split to return as a data set.
Valid option: [`train`, `test`, `val`]
transform: torchvision.transforms
A (series) of valid transformation(s).
in_memory: bool
Set to True if there is enough memory (about 5G) and want to minimize disk IO overhead.
"""
def __init__(self, root, split='train', transform: Optional[Callable] = None, target_transform=None,
in_memory=False):
self.root = os.path.expanduser(root)
self.split = split
self.transform = transform
self.target_transform = target_transform
self.in_memory = in_memory
self.split_dir = os.path.join(root, self.split)
self.image_paths = sorted(glob.iglob(os.path.join(self.split_dir, '**', '*.%s' % EXTENSION), recursive=True))
self.labels = {} # fname - label number mapping
self.images = [] # used for in-memory processing
# build class label - number mapping
with open(os.path.join(self.root, CLASS_LIST_FILE), 'r') as fp:
self.label_texts = sorted([text.strip() for text in fp.readlines()])
self.label_text_to_number = {text: i for i, text in enumerate(self.label_texts)}
if self.split == 'train':
for label_text, i in self.label_text_to_number.items():
for cnt in range(NUM_IMAGES_PER_CLASS):
self.labels['%s_%d.%s' % (label_text, cnt, EXTENSION)] = i
elif self.split == 'val':
with open(os.path.join(self.split_dir, VAL_ANNOTATION_FILE), 'r') as fp:
for line in fp.readlines():
terms = line.split('\t')
file_name, label_text = terms[0], terms[1]
self.labels[file_name] = self.label_text_to_number[label_text]
# read all images into torch tensor in memory to minimize disk IO overhead
if self.in_memory:
self.images = [self.read_image(path) for path in self.image_paths]
def __len__(self):
return len(self.image_paths)
def __getitem__(self, index):
file_path = self.image_paths[index]
if self.in_memory:
img = self.images[index]
else:
img = self.read_image(file_path)
if self.split == 'test':
# return index, img
return img
else:
# file_name = file_path.split('/')[-1]
# return index, img, self.labels[os.path.basename(file_path)]
return img, self.labels[os.path.basename(file_path)]
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = self.split
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
def read_image(self, path):
img = Image.open(path).convert('RGB')
return self.transform(img) # if self.transform is not None else img
class TinyImagenetPair(TinyImagenet):
def __init__(self, root, transform, weak_aug=None):
super().__init__(root, transform)
self.weak_aug = weak_aug
def __getitem__(self, index):
path, _ = self.samples[index]
img = self.load_image(path)
pos_1 = self.transform(img)
if self.weak_aug is not None:
pos_2 = self.weak_aug(img)
else:
pos_2 = self.transform(img)
return pos_1, pos_2
class STL10Pair(datasets.STL10):
def __init__(self, root, split='train', transform=None, target_transform=None, download=False, weak_aug=None):
super().__init__(root, split=split, transform=transform, target_transform=target_transform, download=download)
self.weak_aug = weak_aug
def __getitem__(self, index):
if self.labels is not None:
img, target = self.data[index], int(self.labels[index])
else:
img, target = self.data[index], None
img = Image.fromarray(np.transpose(img, (1, 2, 0)))
if self.transform is not None:
pos_1 = self.transform(img)
if self.weak_aug is not None:
pos_2 = self.weak_aug(img)
else:
pos_2 = self.transform(img)
return pos_1, pos_2
class CIFAR10Pair(datasets.CIFAR10):
def __init__(self, root, train=True, transform=None, target_transform=None, download=False, weak_aug=None):
super().__init__(root, train=train, transform=transform, target_transform=target_transform, download=download)
self.weak_aug = weak_aug
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
img = Image.fromarray(img)
if self.transform is not None:
pos_1 = self.transform(img)
if self.weak_aug is not None:
pos_2 = self.weak_aug(img)
else:
pos_2 = self.transform(img)
return pos_1, pos_2
class CIFAR100Pair(datasets.CIFAR100):
def __init__(self, root, train=True, transform=None, target_transform=None, download=False, weak_aug=None):
super().__init__(root, train=train, transform=transform, target_transform=target_transform, download=download)
self.weak_aug = weak_aug
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
img = Image.fromarray(img)
if self.transform is not None:
pos_1 = self.transform(img)
if self.weak_aug is not None:
pos_2 = self.weak_aug(img)
else:
pos_2 = self.transform(img)
return pos_1, pos_2
class TwoCrop:
def __init__(self, weak, strong):
self.weak = weak
self.strong = strong
def __call__(self, img):
im_1 = self.strong(img)
im_2 = self.weak(img)
return im_1, im_2
class GaussianBlur(object):
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
sigma = random.uniform(self.sigma[0], self.sigma[1])
x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
return x
def get_strong_augment(dataset):
size = 32
if dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
elif dataset == 'stl10':
mean = (0.4408, 0.4279, 0.3867)
std = (0.2682, 0.2610, 0.2686)
size = 64
elif dataset == 'tinyimagenet':
mean = (0.4802, 0.4481, 0.3975)
std = (0.2302, 0.2265, 0.2262)
size = 64
else:
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=size, scale=(0.2, 1)),
transforms.RandomHorizontalFlip(),
transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.RandomApply([GaussianBlur([.1, 2.])], p=0.5),
transforms.ToTensor(),
normalize,
])
return train_transform
def get_weak_augment(dataset):
size = 32
if dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
elif dataset == 'stl10':
mean = (0.4408, 0.4279, 0.3867)
std = (0.2682, 0.2610, 0.2686)
size = 64
elif dataset == 'tinyimagenet':
mean = (0.4802, 0.4481, 0.3975)
std = (0.2302, 0.2265, 0.2262)
size = 64
else:
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=size, scale=(0.2, 1)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
return train_transform
def get_linear_augment(dataset):
size = 32
if dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
elif dataset == 'stl10':
mean = (0.4408, 0.4279, 0.3867)
std = (0.2682, 0.2610, 0.2686)
size = 64
elif dataset == 'tinyimagenet':
mean = (0.4802, 0.4481, 0.3975)
std = (0.2302, 0.2265, 0.2262)
size = 64
else:
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
return train_transform
def get_test_augment(dataset):
if dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
elif dataset == 'stl10':
mean = (0.4408, 0.4279, 0.3867)
std = (0.2682, 0.2610, 0.2686)
elif dataset == 'tinyimagenet':
mean = (0.4802, 0.4481, 0.3975)
std = (0.2302, 0.2265, 0.2262)
else:
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
normalize = transforms.Normalize(mean=mean, std=std)
none_transform = transforms.Compose([
transforms.ToTensor(),
normalize,
])
return none_transform
def prepare_dataset(args):
"""Define train/test"""
strong_transform = get_strong_augment(args.dataset)
weak_transform = eval(f'get_{args.aug}')(args.dataset)
test_transform = get_test_augment(args.dataset)
linear_transform = get_linear_augment(args.dataset)
if args.dataset == 'cifar10':
train_dataset = datasets.CIFAR10(root=args.data_path, download=True,
transform=TwoCrop(weak_transform, strong_transform))
memory_dataset = datasets.CIFAR10(root=args.data_path, train=True, download=True, transform=test_transform)
linear_dataset = datasets.CIFAR10(root=args.data_path, train=True, download=True, transform=linear_transform)
test_dataset = datasets.CIFAR10(root=args.data_path, train=False, download=True, transform=test_transform)
args.num_classes = 10
elif args.dataset == 'stl10':
train_dataset = datasets.STL10(root=args.data_path, download=True, split='train+unlabeled',
transform=TwoCrop(weak_transform, strong_transform))
memory_dataset = datasets.STL10(root=args.data_path, download=True, split='train', transform=test_transform)
linear_dataset = datasets.STL10(root=args.data_path, download=True, split='train', transform=linear_transform)
test_dataset = datasets.STL10(root=args.data_path, download=True, split='test', transform=test_transform)
args.num_classes = 10
elif args.dataset == 'tinyimagenet':
train_dataset = TinyImagenet(root=args.data_path, split='train',
transform=TwoCrop(weak_transform, strong_transform))
memory_dataset = TinyImagenet(root=args.data_path, split='train', transform=test_transform)
linear_dataset = TinyImagenet(root=args.data_path, split='train', transform=linear_transform)
test_dataset = TinyImagenet(root=args.data_path, split='val', transform=test_transform)
args.num_classes = 200
else:
train_dataset = datasets.CIFAR100(root=args.data_path, download=True,
transform=TwoCrop(weak_transform, strong_transform))
memory_dataset = datasets.CIFAR100(root=args.data_path, train=True, download=True, transform=test_transform)
linear_dataset = datasets.CIFAR100(root=args.data_path, train=True, download=True, transform=linear_transform)
test_dataset = datasets.CIFAR100(root=args.data_path, train=False, download=True, transform=test_transform)
args.num_classes = 100
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=6, pin_memory=True,
drop_last=True)
memory_loader = DataLoader(memory_dataset, batch_size=args.batch_size, shuffle=False, num_workers=6,
pin_memory=True)
linear_loader = DataLoader(linear_dataset, batch_size=args.batch_size, shuffle=True, num_workers=6, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=6, pin_memory=True)
return train_loader, memory_loader, linear_loader, test_loader