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