release code

Dassl.ProGrad.pytorch/dassl/data/datasets/da/office_home.py

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+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