69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
import os.path as osp
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from dassl.utils import listdir_nohidden
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from ..build import DATASET_REGISTRY
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from ..base_dataset import Datum, DatasetBase
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@DATASET_REGISTRY.register()
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class CIFARSTL(DatasetBase):
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"""CIFAR-10 and STL-10.
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CIFAR-10:
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- 60,000 32x32 colour images.
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- 10 classes, with 6,000 images per class.
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- 50,000 training images and 10,000 test images.
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- URL: https://www.cs.toronto.edu/~kriz/cifar.html.
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STL-10:
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- 10 classes: airplane, bird, car, cat, deer, dog, horse,
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monkey, ship, truck.
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- Images are 96x96 pixels, color.
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- 500 training images (10 pre-defined folds), 800 test images
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per class.
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- URL: https://cs.stanford.edu/~acoates/stl10/.
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Reference:
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- Krizhevsky. Learning Multiple Layers of Features
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from Tiny Images. Tech report.
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- Coates et al. An Analysis of Single Layer Networks in
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Unsupervised Feature Learning. AISTATS 2011.
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"""
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dataset_dir = "cifar_stl"
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domains = ["cifar", "stl"]
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def __init__(self, cfg):
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root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.check_input_domains(
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cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
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)
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train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
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train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
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test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
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super().__init__(train_x=train_x, train_u=train_u, test=test)
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def _read_data(self, input_domains, split="train"):
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items = []
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for domain, dname in enumerate(input_domains):
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data_dir = osp.join(self.dataset_dir, dname, split)
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class_names = listdir_nohidden(data_dir)
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for class_name in class_names:
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class_dir = osp.join(data_dir, class_name)
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imnames = listdir_nohidden(class_dir)
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label = int(class_name.split("_")[0])
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for imname in imnames:
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impath = osp.join(class_dir, imname)
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item = Datum(impath=impath, label=label, domain=domain)
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items.append(item)
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return items
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