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data.py
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import logging
import os
import numpy as np
import torch
import torchvision
from PIL import Image
from torch.utils.data import SubsetRandomSampler, Subset, Dataset
from torchvision.transforms import transforms
from sklearn.model_selection import StratifiedShuffleSplit
from theconf import Config as C
from archive import autoaug_policy, autoaug_paper_cifar10, fa_reduced_cifar10
from augmentations import *
from common import get_logger
from samplers.stratified_sampler import StratifiedSampler
logger = get_logger('Unsupervised Data Augmentation')
logger.setLevel(logging.INFO)
def get_dataloaders(dataset, batch, batch_unsup, dataroot):
if 'cifar' in dataset:
transform_train = 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)),
])
transform_valid = 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)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
else:
raise ValueError('dataset=%s' % dataset)
autoaug = transforms.Compose([])
if isinstance(C.get()['aug'], list):
logger.debug('augmentation provided.')
autoaug.transforms.insert(0, Augmentation(C.get()['aug']))
else:
logger.debug('augmentation: %s' % C.get()['aug'])
if C.get()['aug'] == 'fa_reduced_cifar10':
autoaug.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
elif C.get()['aug'] == 'autoaug_cifar10':
autoaug.transforms.insert(0, Augmentation(autoaug_paper_cifar10()))
elif C.get()['aug'] == 'autoaug_extend':
autoaug.transforms.insert(0, Augmentation(autoaug_policy()))
elif C.get()['aug'] == 'default':
pass
else:
raise ValueError('not found augmentations. %s' % C.get()['aug'])
transform_train.transforms.insert(0, autoaug)
if C.get()['cutout'] > 0:
transform_train.transforms.append(CutoutDefault(C.get()['cutout']))
if dataset in ['cifar10', 'cifar100']:
if dataset == 'cifar10':
total_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=transform_train)
unsup_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=None)
testset = torchvision.datasets.CIFAR10(root=dataroot, train=False, download=True, transform=transform_test)
elif dataset == 'cifar100':
total_trainset = torchvision.datasets.CIFAR100(root=dataroot, train=True, download=True, transform=transform_train)
unsup_trainset = torchvision.datasets.CIFAR100(root=dataroot, train=True, download=True, transform=None)
testset = torchvision.datasets.CIFAR100(root=dataroot, train=False, download=True, transform=transform_test)
else:
raise ValueError
sss = StratifiedShuffleSplit(n_splits=1, test_size=46000, random_state=0) # 4000 trainset
sss = sss.split(list(range(len(total_trainset))), total_trainset.targets)
train_idx, valid_idx = next(sss)
train_labels = [total_trainset.targets[idx] for idx in train_idx]
trainset = Subset(total_trainset, train_idx) # for supervised
trainset.train_labels = train_labels
otherset = Subset(unsup_trainset, valid_idx) # for unsupervised
# otherset = unsup_trainset
otherset = UnsupervisedDataset(otherset, transform_valid, autoaug, cutout=C.get()['cutout'])
else:
raise ValueError('invalid dataset name=%s' % dataset)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=batch, shuffle=False, num_workers=8, pin_memory=True,
sampler=StratifiedSampler(trainset.train_labels), drop_last=True)
unsuploader = torch.utils.data.DataLoader(
otherset, batch_size=batch_unsup, shuffle=True, num_workers=8, pin_memory=True,
sampler=None, drop_last=True)
testloader = torch.utils.data.DataLoader(
testset, batch_size=batch, shuffle=False, num_workers=32, pin_memory=True,
drop_last=False
)
return trainloader, unsuploader, testloader
class CutoutDefault(object):
"""
Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
"""
def __init__(self, length):
self.length = length
def __call__(self, img):
if self.length <= 0:
return img
h, w = img.size(1), img.size(2)
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return img
class Augmentation(object):
def __init__(self, policies):
self.policies = policies
def __call__(self, img):
for _ in range(1):
policy = random.choice(self.policies)
for name, pr, level in policy:
if random.random() > pr:
continue
img = apply_augment(img, name, level)
return img
class UnsupervisedDataset(Dataset):
def __init__(self, dataset, transform_default, transform_aug, cutout=0):
self.dataset = dataset
self.transform_default = transform_default
self.transform_aug = transform_aug
self.transform_cutout = CutoutDefault(cutout) # issue 4 : https://github.com/ildoonet/unsupervised-data-augmentation/issues/4
def __getitem__(self, index):
img, _ = self.dataset[index]
img1 = self.transform_default(img)
img2 = self.transform_default(self.transform_aug(img))
img2 = self.transform_cutout(img2)
return img1, img2
def __len__(self):
return len(self.dataset)