release code

Dassl.ProGrad.pytorch/dassl/data/datasets/dg/cifar_c.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
+
+AVAI_C_TYPES = [
+    "brightness",
+    "contrast",
+    "defocus_blur",
+    "elastic_transform",
+    "fog",
+    "frost",
+    "gaussian_blur",
+    "gaussian_noise",
+    "glass_blur",
+    "impulse_noise",
+    "jpeg_compression",
+    "motion_blur",
+    "pixelate",
+    "saturate",
+    "shot_noise",
+    "snow",
+    "spatter",
+    "speckle_noise",
+    "zoom_blur",
+]
+
+
+@DATASET_REGISTRY.register()
+class CIFAR10C(DatasetBase):
+    """CIFAR-10 -> CIFAR-10-C.
+
+    Dataset link: https://zenodo.org/record/2535967#.YFwtV2Qzb0o
+
+    Statistics:
+        - 2 domains: the normal CIFAR-10 vs. a corrupted CIFAR-10
+        - 10 categories
+
+    Reference:
+        - Hendrycks et al. Benchmarking neural network robustness
+        to common corruptions and perturbations. ICLR 2019.
+    """
+
+    dataset_dir = ""
+    domains = ["cifar10", "cifar10_c"]
+
+    def __init__(self, cfg):
+        root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
+        self.dataset_dir = root
+
+        self.check_input_domains(
+            cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
+        )
+        source_domain = cfg.DATASET.SOURCE_DOMAINS[0]
+        target_domain = cfg.DATASET.TARGET_DOMAINS[0]
+        assert source_domain == self.domains[0]
+        assert target_domain == self.domains[1]
+
+        c_type = cfg.DATASET.CIFAR_C_TYPE
+        c_level = cfg.DATASET.CIFAR_C_LEVEL
+
+        if not c_type:
+            raise ValueError(
+                "Please specify DATASET.CIFAR_C_TYPE in the config file"
+            )
+
+        assert (
+            c_type in AVAI_C_TYPES
+        ), f'C_TYPE is expected to belong to {AVAI_C_TYPES}, but got "{c_type}"'
+        assert 1 <= c_level <= 5
+
+        train_dir = osp.join(self.dataset_dir, source_domain, "train")
+        test_dir = osp.join(
+            self.dataset_dir, target_domain, c_type, str(c_level)
+        )
+
+        if not osp.exists(test_dir):
+            raise ValueError
+
+        train = self._read_data(train_dir)
+        test = self._read_data(test_dir)
+
+        super().__init__(train_x=train, test=test)
+
+    def _read_data(self, data_dir):
+        class_names = listdir_nohidden(data_dir)
+        class_names.sort()
+        items = []
+
+        for label, class_name in enumerate(class_names):
+            class_dir = osp.join(data_dir, class_name)
+            imnames = listdir_nohidden(class_dir)
+
+            for imname in imnames:
+                impath = osp.join(class_dir, imname)
+                item = Datum(impath=impath, label=label, domain=0)
+                items.append(item)
+
+        return items
+
+
+@DATASET_REGISTRY.register()
+class CIFAR100C(CIFAR10C):
+    """CIFAR-100 -> CIFAR-100-C.
+
+    Dataset link: https://zenodo.org/record/3555552#.YFxpQmQzb0o
+
+    Statistics:
+        - 2 domains: the normal CIFAR-100 vs. a corrupted CIFAR-100
+        - 10 categories
+
+    Reference:
+        - Hendrycks et al. Benchmarking neural network robustness
+        to common corruptions and perturbations. ICLR 2019.
+    """
+
+    dataset_dir = ""
+    domains = ["cifar100", "cifar100_c"]
+
+    def __init__(self, cfg):
+        super().__init__(cfg)