98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
import glob
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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 DigitsDG(DatasetBase):
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"""Digits-DG.
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It contains 4 digit datasets:
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- MNIST: hand-written digits.
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- MNIST-M: variant of MNIST with blended background.
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- SVHN: street view house number.
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- SYN: synthetic digits.
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Reference:
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- Lecun et al. Gradient-based learning applied to document
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recognition. IEEE 1998.
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- Ganin et al. Domain-adversarial training of neural networks.
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JMLR 2016.
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- Netzer et al. Reading digits in natural images with unsupervised
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feature learning. NIPS-W 2011.
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- Zhou et al. Deep Domain-Adversarial Image Generation for Domain
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Generalisation. AAAI 2020.
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"""
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dataset_dir = "digits_dg"
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domains = ["mnist", "mnist_m", "svhn", "syn"]
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data_url = "https://drive.google.com/uc?id=15V7EsHfCcfbKgsDmzQKj_DfXt_XYp_P7"
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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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if not osp.exists(self.dataset_dir):
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dst = osp.join(root, "digits_dg.zip")
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self.download_data(self.data_url, dst, from_gdrive=True)
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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 = self.read_data(
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self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "train"
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)
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val = self.read_data(
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self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, "val"
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)
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test = self.read_data(
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self.dataset_dir, cfg.DATASET.TARGET_DOMAINS, "all"
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)
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super().__init__(train_x=train, val=val, test=test)
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@staticmethod
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def read_data(dataset_dir, input_domains, split):
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def _load_data_from_directory(directory):
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folders = listdir_nohidden(directory)
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folders.sort()
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items_ = []
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for label, folder in enumerate(folders):
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impaths = glob.glob(osp.join(directory, folder, "*.jpg"))
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for impath in impaths:
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items_.append((impath, label))
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return items_
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items = []
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for domain, dname in enumerate(input_domains):
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if split == "all":
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train_dir = osp.join(dataset_dir, dname, "train")
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impath_label_list = _load_data_from_directory(train_dir)
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val_dir = osp.join(dataset_dir, dname, "val")
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impath_label_list += _load_data_from_directory(val_dir)
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else:
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split_dir = osp.join(dataset_dir, dname, split)
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impath_label_list = _load_data_from_directory(split_dir)
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for impath, label in impath_label_list:
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class_name = impath.split("/")[-2].lower()
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item = Datum(
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impath=impath,
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label=label,
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domain=domain,
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classname=class_name
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)
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items.append(item)
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return items
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