64 lines
2.0 KiB
Python
64 lines
2.0 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 OfficeHome(DatasetBase):
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"""Office-Home.
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Statistics:
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- Around 15,500 images.
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- 65 classes related to office and home objects.
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- 4 domains: Art, Clipart, Product, Real World.
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- URL: http://hemanthdv.org/OfficeHome-Dataset/.
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Reference:
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- Venkateswara et al. Deep Hashing Network for Unsupervised
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Domain Adaptation. CVPR 2017.
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"""
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dataset_dir = "office_home"
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domains = ["art", "clipart", "product", "real_world"]
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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)
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train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS)
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test = self._read_data(cfg.DATASET.TARGET_DOMAINS)
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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):
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items = []
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for domain, dname in enumerate(input_domains):
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domain_dir = osp.join(self.dataset_dir, dname)
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class_names = listdir_nohidden(domain_dir)
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class_names.sort()
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for label, class_name in enumerate(class_names):
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class_path = osp.join(domain_dir, class_name)
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imnames = listdir_nohidden(class_path)
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for imname in imnames:
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impath = osp.join(class_path, imname)
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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.lower(),
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)
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items.append(item)
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return items
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