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import numpy as np
from torchvision import datasets, transforms
from utils.toolkit import split_images_labels
import os
class iData(object):
train_trsf = []
test_trsf = []
common_trsf = []
class_order = None
class iCIFAR10(iData):
use_path = False
train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
]
test_trsf = [transforms.ToTensor()]
common_trsf = [
transforms.Normalize(
mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010)
),
]
class_order = np.arange(10).tolist()
def download_data(self):
train_dataset = datasets.cifar.CIFAR10("./data", train=True, download=True)
test_dataset = datasets.cifar.CIFAR10("./data", train=False, download=True)
self.train_data, self.train_targets = train_dataset.data, np.array(
train_dataset.targets
)
self.test_data, self.test_targets = test_dataset.data, np.array(
test_dataset.targets
)
class iCIFAR100(iData):
use_path = False
train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor()
]
test_trsf = [transforms.ToTensor()]
common_trsf = [
transforms.Normalize(
mean=(0.5071, 0.4867, 0.4408), std=(0.2675, 0.2565, 0.2761)
),
]
class_order = np.arange(100).tolist()
def download_data(self):
train_dataset = datasets.cifar.CIFAR100("./data", train=True, download=True)
test_dataset = datasets.cifar.CIFAR100("./data", train=False, download=True)
self.train_data, self.train_targets = train_dataset.data, np.array(
train_dataset.targets
)
self.test_data, self.test_targets = test_dataset.data, np.array(
test_dataset.targets
)
class iImageNet1000(iData):
use_path = True
train_trsf = [
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(25, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=8),
transforms.ColorJitter(),
]
test_trsf = [
transforms.Resize(256),
transforms.CenterCrop(224),
]
common_trsf = [
transforms.ToTensor(),
transforms.Normalize(
mean=[0.470, 0.460, 0.455],
std=[0.267, 0.266, 0.270]
),
]
class_order = np.arange(1000).tolist()
def download_data(self):
assert 0, "You should specify the folder of your dataset"
train_dir = "[DATA-PATH]/train/"
test_dir = "[DATA-PATH]/val/"
train_dset = datasets.ImageFolder(train_dir)
test_dset = datasets.ImageFolder(test_dir)
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
class StanfordCar(iData):
use_path = True
train_trsf = [
transforms.Resize(320),
transforms.CenterCrop(320),
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(25, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=8),
transforms.ColorJitter(),
]
test_trsf = [
transforms.Resize(320),
transforms.CenterCrop(320),
]
common_trsf = [
transforms.ToTensor(),
transforms.Normalize(
mean=[0.470, 0.460, 0.455],
std=[0.267, 0.266, 0.270]
),
]
class_order = np.arange(196).tolist()
def download_data(self):
path = './car_data/car_data'
train_dset = datasets.ImageFolder(os.path.join(path, "train"))
test_dset = datasets.ImageFolder(os.path.join(path, "test"))
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
class GeneralDataset(iData):
def __init__(
self,
path,
init_class_list = [-1],
train_transform = None,
test_transform = None,
common_transform = None):
self.use_path = True
self.path = path
self.train_trsf = train_transform
if self.train_trsf == None:
self.train_trsf = [
transforms.RandomAffine(25, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=8),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness = 0.3, saturation = 0.2),
]
self.test_trsf = test_transform
if self.test_trsf == None:
self.test_trsf = [
transforms.Resize(224),
transforms.CenterCrop(224),
]
self.common_trsf = common_transform
if self.common_trsf == None:
self.common_trsf = [
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]
),
]
self.init_index = max(init_class_list) + 1
self.class_order = np.arange(self.init_index, self.init_index + len(os.listdir(os.path.join(self.path, "train"))))
def download_data(self):
train_dset = datasets.ImageFolder(os.path.join(self.path, "train"))
test_dset = datasets.ImageFolder(os.path.join(self.path, "val"))
self.train_data, self.train_targets = split_images_labels(train_dset.imgs, start_index = self.init_index)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs, start_index = self.init_index)
return train_dset.classes
class iImageNet100(iData):
use_path = True
train_trsf = [
transforms.Resize(320),
transforms.CenterCrop(320),
]
test_trsf = [
transforms.Resize(320),
transforms.CenterCrop(320),
]
common_trsf = [
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
class_order = np.arange(1000).tolist()
def download_data(self):
assert 0, "You should specify the folder of your dataset"
train_dir = "[DATA-PATH]/train/"
test_dir = "[DATA-PATH]/val/"
train_dset = datasets.ImageFolder(train_dir)
test_dset = datasets.ImageFolder(test_dir)
self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
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