75 lines
3.2 KiB
Python
75 lines
3.2 KiB
Python
|
import os, sys, torch
|
||
|
|
import os.path as osp
|
||
|
|
import torchvision.datasets as dset
|
||
|
|
import torch.backends.cudnn as cudnn
|
||
|
|
import torchvision.transforms as transforms
|
||
|
|
|
||
|
|
from utils import Cutout
|
||
|
|
from .TieredImageNet import TieredImageNet
|
||
|
|
|
||
|
|
Dataset2Class = {'cifar10' : 10,
|
||
|
|
'cifar100': 100,
|
||
|
|
'tiered' : -1,
|
||
|
|
'imagnet-1k' : 1000,
|
||
|
|
'imagenet-100': 100}
|
||
|
|
|
||
|
|
|
||
|
|
def get_datasets(name, root, cutout):
|
||
|
|
|
||
|
|
# Mean + Std
|
||
|
|
if name == 'cifar10':
|
||
|
|
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
|
||
|
|
std = [x / 255 for x in [63.0, 62.1, 66.7]]
|
||
|
|
elif name == 'cifar100':
|
||
|
|
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
|
||
|
|
std = [x / 255 for x in [68.2, 65.4, 70.4]]
|
||
|
|
elif name == 'tiered':
|
||
|
|
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
|
||
|
|
elif name == 'imagnet-1k' or name == 'imagenet-100':
|
||
|
|
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||
|
|
else: raise TypeError("Unknow dataset : {:}".format(name))
|
||
|
|
|
||
|
|
|
||
|
|
# Data Argumentation
|
||
|
|
if name == 'cifar10' or name == 'cifar100':
|
||
|
|
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
|
||
|
|
transforms.Normalize(mean, std)]
|
||
|
|
if cutout > 0 : lists += [Cutout(cutout)]
|
||
|
|
train_transform = transforms.Compose(lists)
|
||
|
|
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
|
||
|
|
elif name == 'tiered':
|
||
|
|
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
|
||
|
|
if cutout > 0 : lists += [Cutout(cutout)]
|
||
|
|
train_transform = transforms.Compose(lists)
|
||
|
|
test_transform = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
|
||
|
|
elif name == 'imagnet-1k' or name == 'imagenet-100':
|
||
|
|
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||
|
|
train_transform = transforms.Compose([
|
||
|
|
transforms.RandomResizedCrop(224),
|
||
|
|
transforms.RandomHorizontalFlip(),
|
||
|
|
transforms.ColorJitter(
|
||
|
|
brightness=0.4,
|
||
|
|
contrast=0.4,
|
||
|
|
saturation=0.4,
|
||
|
|
hue=0.2),
|
||
|
|
transforms.ToTensor(),
|
||
|
|
normalize,
|
||
|
|
])
|
||
|
|
test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
|
||
|
|
else: raise TypeError("Unknow dataset : {:}".format(name))
|
||
|
|
train_data = TieredImageNet(root, 'train-val', train_transform)
|
||
|
|
test_data = None
|
||
|
|
if name == 'cifar10':
|
||
|
|
train_data = dset.CIFAR10(root, train=True, transform=train_transform, download=True)
|
||
|
|
test_data = dset.CIFAR10(root, train=True, transform=test_transform , download=True)
|
||
|
|
elif name == 'cifar100':
|
||
|
|
train_data = dset.CIFAR100(root, train=True, transform=train_transform, download=True)
|
||
|
|
test_data = dset.CIFAR100(root, train=True, transform=test_transform , download=True)
|
||
|
|
elif name == 'imagnet-1k' or name == 'imagenet-100':
|
||
|
|
train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
|
||
|
|
test_data = dset.ImageFolder(osp.join(root, 'val'), train_transform)
|
||
|
|
else: raise TypeError("Unknow dataset : {:}".format(name))
|
||
|
|
|
||
|
|
class_num = Dataset2Class[name]
|
||
|
|
return train_data, test_data, class_num
|