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class DataLoader(object):
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"""
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data loader for CV data sets
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"""
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def __init__(self, dataset, batch_size, n_threads=4,
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ten_crop=False, data_path='/home/dataset/', logger=None):
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"""
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create data loader for specific data set
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:params n_treads: number of threads to load data, default: 4
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:params ten_crop: use ten crop for testing, default: False
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:params data_path: path to data set, default: /home/dataset/
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"""
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self.dataset = dataset
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self.batch_size = batch_size
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self.n_threads = n_threads
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self.ten_crop = ten_crop
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self.data_path = data_path
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self.logger = logger
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self.dataset_root = data_path
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self.logger.info("|===>Creating data loader for " + self.dataset)
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if self.dataset in ["cifar100","cifar10"]:
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self.train_loader, self.test_loader = self.cifar(
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dataset=self.dataset)
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elif self.dataset in ["imagenet"]:
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self.train_loader, self.test_loader = self.imagenet(
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dataset=self.dataset)
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else:
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assert False, "invalid data set"
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def getloader(self):
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"""
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get train_loader and test_loader
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"""
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return self.train_loader, self.test_loader
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def imagenet(self, dataset="imagenet"):
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traindir = os.path.join(self.data_path, "train")
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testdir = os.path.join(self.data_path, "val")
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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train_loader = None
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test_transform = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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normalize
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])
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test_loader = torch.utils.data.DataLoader(
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dsets.ImageFolder(testdir, test_transform),
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batch_size=self.batch_size,
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shuffle=False,
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num_workers=self.n_threads,
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pin_memory=False)
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return train_loader, test_loader
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def cifar(self, dataset="cifar100"):
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"""
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dataset: cifar
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"""
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if dataset == "cifar10":
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norm_mean = [0.49139968, 0.48215827, 0.44653124]
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norm_std = [0.24703233, 0.24348505, 0.26158768]
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elif dataset == "cifar100":
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norm_mean = [0.50705882, 0.48666667, 0.44078431]
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norm_std = [0.26745098, 0.25568627, 0.27607843]
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else:
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assert False, "Invalid cifar dataset"
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test_data_root = self.dataset_root
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test_transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(norm_mean, norm_std)])
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if self.dataset == "cifar10":
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test_dataset = dsets.CIFAR10(root=test_data_root,
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train=False,
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transform=test_transform,download=True)
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elif self.dataset == "cifar100":
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test_dataset = dsets.CIFAR100(root=test_data_root,
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train=False,
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transform=test_transform,
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download=True)
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else:
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assert False, "invalid data set"
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test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
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batch_size=200,
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shuffle=False,
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pin_memory=True,
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