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_02_data.py
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import logging
import math
import numpy as np
from PIL import Image
from torchvision import datasets
from torchvision import transforms
from augmentation import RandAugment
from _02_custom_dataset import CustomDataset
logger = logging.getLogger(__name__)
cifar10_mean = (0.4914, 0.4822, 0.4465)
cifar10_std = (0.2471, 0.2435, 0.2616)
cifar100_mean = (0.5071, 0.4867, 0.4408)
cifar100_std = (0.2675, 0.2565, 0.2761)
normal_mean = (0.5, 0.5, 0.5)
#normal_std = (0.5, 0.5, 0.5)
normal_std = (0.25, 0.25, 0.25)
def get_cifar10(args):
transform_labeled = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=args.resize,
padding=int(args.resize*0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=cifar10_mean, std=cifar10_std)
])
transform_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cifar10_mean, std=cifar10_std)
])
base_dataset = datasets.CIFAR10(args.data_path, train=True, download=True)
# base_dataset.targets <- list임
train_labeled_idxs, train_unlabeled_idxs = x_u_split(args, base_dataset.targets)
# train_labeled_idxs, train_unlabeled_idxs = x_u_split_test(args, base_dataset.targets)
train_labeled_dataset = CIFAR10SSL(
args.data_path, train_labeled_idxs, train=True,
transform=transform_labeled
)
train_unlabeled_dataset = CIFAR10SSL(
args.data_path, train_unlabeled_idxs,
train=True,
transform=TransformMPL(args, mean=cifar10_mean, std=cifar10_std)
)
test_dataset = datasets.CIFAR10(args.data_path, train=False,
transform=transform_val, download=False)
return train_labeled_dataset, train_unlabeled_dataset, test_dataset
def get_cifar100(args):
transform_labeled = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=args.resize,
padding=int(args.resize*0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=cifar100_mean, std=cifar100_std)])
transform_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cifar100_mean, std=cifar100_std)])
base_dataset = datasets.CIFAR100(args.data_path, train=True, download=True)
train_labeled_idxs, train_unlabeled_idxs = x_u_split(args, base_dataset.targets)
train_labeled_dataset = CIFAR100SSL(
args.data_path, train_labeled_idxs, train=True,
transform=transform_labeled
)
train_unlabeled_dataset = CIFAR100SSL(
args.data_path, train_unlabeled_idxs, train=True,
transform=TransformMPL(args, mean=cifar100_mean, std=cifar100_std)
)
test_dataset = datasets.CIFAR100(args.data_path, train=False,
transform=transform_val, download=False)
return train_labeled_dataset, train_unlabeled_dataset, test_dataset
def get_custom(args):
transform_labeled = transforms.Compose([
transforms.Resize(size=(args.resize,args.resize)), # resize 부분 추가
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=args.resize,
padding=int(args.resize*0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=normal_mean, std=normal_std)
])
transform_val = transforms.Compose([
transforms.Resize(size=(args.resize,args.resize)),
transforms.ToTensor(),
transforms.Normalize(mean=normal_mean, std=normal_std)
])
base_dataset = CustomDataset(args.csv_train_filename,args.data_path, train=True)
# base_dataset.targets <- list임
# num_labeled 값 설정(train data의 length 대비하여 x_u_split 함수 호출 전에 설정). num_classes의 배수
#args.num_labeled = args.num_classes * (len(base_dataset.targets) // args.num_classes)
train_labeled_idxs, train_unlabeled_idxs = x_u_split(args, base_dataset.targets)
# train_labeled_idxs, train_unlabeled_idxs = x_u_split_test(args, base_dataset.targets)
train_labeled_dataset = CustomSSL(
args.csv_train_filename,
args.data_path, train_labeled_idxs, train=True,
transform=transform_labeled
)
# print("data here!!!!!!!!!!!!!!!!!!!!!!!!!!")
# print(transform_labeled)
train_unlabeled_dataset = CustomSSL(
args.csv_train_filename,
args.data_path, train_unlabeled_idxs,
train=True,
# 20220114 수정
transform=TransformMPL(args, mean=cifar10_mean, std=cifar10_std)
# transform=TransformMPL(args, mean=normal_mean, std=normal_std)
)
test_dataset = CustomDataset(args.csv_test_filename,args.data_path, train=False,
transform=transform_val)
return train_labeled_dataset, train_unlabeled_dataset, test_dataset
def x_u_split(args, labels):
#label_per_class = args.num_labeled // args.num_classes # 10개 class arg에서 최초 label된 것은 4000개임
label_per_class = args.num_labeled // args.num_classes
labels = np.array(labels) # class당 400개 label
labeled_idx = []
# unlabeled data: all training data
unlabeled_idx = np.array(range(len(labels))) # 0~50000 숫자 array
for i in range(args.num_classes):
idx = np.where(labels == i)[0] # i번째 class와 일치하는 인덱스의 array
idx = np.random.choice(idx, label_per_class, True) # idx array에서 랜덤으로 400개 추출
# idx = np.random.choice(idx, label_per_class, False) # idx array에서 랜덤으로 400개 추출
labeled_idx.extend(idx)
labeled_idx = np.array(labeled_idx)
assert len(labeled_idx) == args.num_labeled # 4000개 labeled
if args.expand_labels or args.num_labeled < args.batch_size:
num_expand_x = math.ceil(
args.batch_size * args.eval_step / args.num_labeled)
labeled_idx = np.hstack([labeled_idx for _ in range(num_expand_x)])
np.random.shuffle(labeled_idx)
return labeled_idx, unlabeled_idx # labeled idx는 추출작업을 했지만 unlabeled_idx는 추출작업 없음
def x_u_split_test(args, labels):
label_per_class = args.num_labeled // args.num_classes
labels = np.array(labels)
labeled_idx = []
unlabeled_idx = []
for i in range(args.num_classes):
idx = np.where(labels == i)[0]
np.random.shuffle(idx)
labeled_idx.extend(idx[:label_per_class])
unlabeled_idx.extend(idx[label_per_class:])
labeled_idx = np.array(labeled_idx)
unlabeled_idx = np.array(unlabeled_idx)
assert len(labeled_idx) == args.num_labeled
if args.expand_labels or args.num_labeled < args.batch_size:
num_expand_x = math.ceil(
args.batch_size * args.eval_step / args.num_labeled)
labeled_idx = np.hstack([labeled_idx for _ in range(num_expand_x)])
np.random.shuffle(labeled_idx)
np.random.shuffle(unlabeled_idx)
return labeled_idx, unlabeled_idx
class TransformMPL(object):
def __init__(self, args, mean, std):
if args.randaug:
n, m = args.randaug
else:
n, m = 2, 10 # default
self.ori = transforms.Compose([
transforms.Resize(size=(args.resize,args.resize)), # resize 때문에 추가
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=args.resize,
padding=int(args.resize*0.125),
padding_mode='reflect')])
self.aug = transforms.Compose([
transforms.Resize(size=(args.resize,args.resize)), # resize 때문에 추가
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size=args.resize,
padding=int(args.resize*0.125),
padding_mode='reflect'),
RandAugment(n=n, m=m)])
self.normalize = transforms.Compose([
transforms.ToTensor(), # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor
# 2022.01.14 주석처리
# transforms.Normalize(mean=mean, std=std)
]) #Normalize a tensor image with mean and standard deviation.
def __call__(self, x):
ori = self.ori(x) # weak augmentation 에 해당??
aug = self.aug(x) # strong augmentation 에 해당
return self.normalize(ori), self.normalize(aug)
class CIFAR10SSL(datasets.CIFAR10):
def __init__(self, root, indexs, train=True,
transform=None, target_transform=None,
download=False):
super().__init__(root, train=train,
transform=transform,
target_transform=target_transform,
download=download)
if indexs is not None:
self.data = self.data[indexs]
self.targets = np.array(self.targets)[indexs]
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
class CIFAR100SSL(datasets.CIFAR100):
def __init__(self, root, indexs, train=True,
transform=None, target_transform=None,
download=False):
super().__init__(root, train=train,
transform=transform,
target_transform=target_transform,
download=download)
if indexs is not None:
self.data = self.data[indexs]
self.targets = np.array(self.targets)[indexs]
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
import os
from skimage import io
class CustomSSL(CustomDataset):
def __init__(self, csv_file,root, indexs, train=True,
transform=None, target_transform=None,
download=False):
super().__init__(csv_file,root, train=train,
transform=transform,
target_transform=target_transform)
if indexs is not None:
self.data = self.data.iloc[indexs] #csv를 읽은 df에서 0번째 컬럼
self.targets = np.array(self.targets)[indexs]
def __getitem__(self, index):
img_path=os.path.join(self.root_dir, str(self.data.iloc[index,0]))
target=self.targets[index]
# ---- 변경 코드 시작-----------
img = io.imread(img_path, pilmode='RGB')
# ---- 변경 코드 끝-----------
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
DATASET_GETTERS = {'cifar10': get_cifar10,
'cifar100': get_cifar100,
'custom': get_custom}