Dassl.pytorch/dassl/data/datasets/da/office_home.py (37 lines of code) (raw):

import os.path as osp from dassl.utils import listdir_nohidden from ..build import DATASET_REGISTRY from ..base_dataset import Datum, DatasetBase @DATASET_REGISTRY.register() class OfficeHome(DatasetBase): """Office-Home. Statistics: - Around 15,500 images. - 65 classes related to office and home objects. - 4 domains: Art, Clipart, Product, Real World. - URL: http://hemanthdv.org/OfficeHome-Dataset/. Reference: - Venkateswara et al. Deep Hashing Network for Unsupervised Domain Adaptation. CVPR 2017. """ dataset_dir = "office_home" domains = ["art", "clipart", "product", "real_world"] def __init__(self, cfg): root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT)) self.dataset_dir = osp.join(root, self.dataset_dir) self.check_input_domains( cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS ) train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS) train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS) test = self._read_data(cfg.DATASET.TARGET_DOMAINS) super().__init__(train_x=train_x, train_u=train_u, test=test) def _read_data(self, input_domains): items = [] for domain, dname in enumerate(input_domains): domain_dir = osp.join(self.dataset_dir, dname) class_names = listdir_nohidden(domain_dir) class_names.sort() for label, class_name in enumerate(class_names): class_path = osp.join(domain_dir, class_name) imnames = listdir_nohidden(class_path) for imname in imnames: impath = osp.join(class_path, imname) item = Datum( impath=impath, label=label, domain=domain, classname=class_name.lower(), ) items.append(item) return items