release code

Dassl.ProGrad.pytorch/dassl/data/datasets/da/cifarstl.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 CIFARSTL(DatasetBase):
+    """CIFAR-10 and STL-10.
+
+    CIFAR-10:
+        - 60,000 32x32 colour images.
+        - 10 classes, with 6,000 images per class.
+        - 50,000 training images and 10,000 test images.
+        - URL: https://www.cs.toronto.edu/~kriz/cifar.html.
+
+    STL-10:
+        - 10 classes: airplane, bird, car, cat, deer, dog, horse,
+        monkey, ship, truck.
+        - Images are 96x96 pixels, color.
+        - 500 training images (10 pre-defined folds), 800 test images
+        per class.
+        - URL: https://cs.stanford.edu/~acoates/stl10/.
+
+    Reference:
+        - Krizhevsky. Learning Multiple Layers of Features
+        from Tiny Images. Tech report.
+        - Coates et al. An Analysis of Single Layer Networks in
+        Unsupervised Feature Learning. AISTATS 2011.
+    """
+
+    dataset_dir = "cifar_stl"
+    domains = ["cifar", "stl"]
+
+    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, split="train")
+        train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
+        test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
+
+        super().__init__(train_x=train_x, train_u=train_u, test=test)
+
+    def _read_data(self, input_domains, split="train"):
+        items = []
+
+        for domain, dname in enumerate(input_domains):
+            data_dir = osp.join(self.dataset_dir, dname, split)
+            class_names = listdir_nohidden(data_dir)
+
+            for class_name in class_names:
+                class_dir = osp.join(data_dir, class_name)
+                imnames = listdir_nohidden(class_dir)
+                label = int(class_name.split("_")[0])
+
+                for imname in imnames:
+                    impath = osp.join(class_dir, imname)
+                    item = Datum(impath=impath, label=label, domain=domain)
+                    items.append(item)
+
+        return items