78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, torch
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import os.path as osp
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import torchvision.datasets as dset
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import torch.backends.cudnn as cudnn
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import torchvision.transforms as transforms
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from utils import Cutout
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from .TieredImageNet import TieredImageNet
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Dataset2Class = {'cifar10' : 10,
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'cifar100': 100,
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'tiered' : -1,
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'imagenet-1k' : 1000,
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'imagenet-100': 100}
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def get_datasets(name, root, cutout):
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# Mean + Std
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if name == 'cifar10':
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mean = [x / 255 for x in [125.3, 123.0, 113.9]]
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std = [x / 255 for x in [63.0, 62.1, 66.7]]
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elif name == 'cifar100':
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mean = [x / 255 for x in [129.3, 124.1, 112.4]]
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std = [x / 255 for x in [68.2, 65.4, 70.4]]
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elif name == 'tiered':
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mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
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elif name == 'imagenet-1k' or name == 'imagenet-100':
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mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
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else: raise TypeError("Unknow dataset : {:}".format(name))
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# Data Argumentation
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if name == 'cifar10' or name == 'cifar100':
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lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
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transforms.Normalize(mean, std)]
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if cutout > 0 : lists += [Cutout(cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
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elif name == 'tiered':
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lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
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if cutout > 0 : lists += [Cutout(cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
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elif name == 'imagenet-1k' or name == 'imagenet-100':
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_transform = transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ColorJitter(
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.2),
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transforms.ToTensor(),
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normalize,
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])
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test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
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else: raise TypeError("Unknow dataset : {:}".format(name))
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if name == 'cifar10':
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train_data = dset.CIFAR10 (root, train=True , transform=train_transform, download=True)
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test_data = dset.CIFAR10 (root, train=False, transform=test_transform , download=True)
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elif name == 'cifar100':
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train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
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test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
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elif name == 'imagenet-1k' or name == 'imagenet-100':
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train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
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test_data = dset.ImageFolder(osp.join(root, 'val'), test_transform)
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else: raise TypeError("Unknow dataset : {:}".format(name))
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class_num = Dataset2Class[name]
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return train_data, test_data, class_num
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