release code

Dassl.ProGrad.pytorch/dassl/data/datasets/dg/vlcs.py

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+import glob
+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 VLCS(DatasetBase):
+    """VLCS.
+
+    Statistics:
+        - 4 domains: CALTECH, LABELME, PASCAL, SUN
+        - 5 categories: bird, car, chair, dog, and person.
+
+    Reference:
+        - Torralba and Efros. Unbiased look at dataset bias. CVPR 2011.
+    """
+
+    dataset_dir = "VLCS"
+    domains = ["caltech", "labelme", "pascal", "sun"]
+    data_url = "https://drive.google.com/uc?id=1r0WL5DDqKfSPp9E3tRENwHaXNs1olLZd"
+
+    def __init__(self, cfg):
+        root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+        self.dataset_dir = osp.join(root, self.dataset_dir)
+
+        if not osp.exists(self.dataset_dir):
+            dst = osp.join(root, "vlcs.zip")
+            self.download_data(self.data_url, dst, from_gdrive=True)
+
+        self.check_input_domains(
+            cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+        )
+
+        train = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "train")
+        val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, "crossval")
+        test = self._read_data(cfg.DATASET.TARGET_DOMAINS, "test")
+
+        super().__init__(train_x=train, val=val, test=test)
+
+    def _read_data(self, input_domains, split):
+        items = []
+
+        for domain, dname in enumerate(input_domains):
+            dname = dname.upper()
+            path = osp.join(self.dataset_dir, dname, split)
+            folders = listdir_nohidden(path)
+            folders.sort()
+
+            for label, folder in enumerate(folders):
+                impaths = glob.glob(osp.join(path, folder, "*.jpg"))
+
+                for impath in impaths:
+                    item = Datum(impath=impath, label=label, domain=domain)
+                    items.append(item)
+
+        return items