release code

Dassl.ProGrad.pytorch/configs/datasets/dg/cifar100_c.yaml

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+INPUT:
+  SIZE: (32, 32)
+  TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+  PIXEL_MEAN: [0.5, 0.5, 0.5]
+  PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+  NAME: "CIFAR100C"
+  CIFAR_C_TYPE: "fog"
+  CIFAR_C_LEVEL: 5
+
+MODEL:
+  BACKBONE:
+    NAME: "wide_resnet_16_4"

Dassl.ProGrad.pytorch/configs/datasets/dg/cifar10_c.yaml

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+INPUT:
+  SIZE: (32, 32)
+  TRANSFORMS: ["random_flip", "random_crop", "normalize"]
+  PIXEL_MEAN: [0.5, 0.5, 0.5]
+  PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+  NAME: "CIFAR10C"
+  CIFAR_C_TYPE: "fog"
+  CIFAR_C_LEVEL: 5
+
+MODEL:
+  BACKBONE:
+    NAME: "wide_resnet_16_4"

Dassl.ProGrad.pytorch/configs/datasets/dg/digit_single.yaml

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+INPUT:
+  SIZE: (32, 32)
+  TRANSFORMS: ["normalize"]
+  PIXEL_MEAN: [0.5, 0.5, 0.5]
+  PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+  NAME: "DigitSingle"
+
+MODEL:
+  BACKBONE:
+    NAME: "cnn_digitsingle"

Dassl.ProGrad.pytorch/configs/datasets/dg/digits_dg.yaml

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+INPUT:
+  SIZE: (32, 32)
+  TRANSFORMS: ["normalize"]
+  PIXEL_MEAN: [0.5, 0.5, 0.5]
+  PIXEL_STD: [0.5, 0.5, 0.5]
+
+DATASET:
+  NAME: "DigitsDG"
+
+MODEL:
+  BACKBONE:
+    NAME: "cnn_digitsdg"

Dassl.ProGrad.pytorch/configs/datasets/dg/office_home_dg.yaml

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+INPUT:
+  SIZE: (224, 224)
+  TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+  NAME: "OfficeHomeDG"
+
+MODEL:
+  BACKBONE:
+    NAME: "resnet18"
+    PRETRAINED: True

Dassl.ProGrad.pytorch/configs/datasets/dg/pacs.yaml

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+INPUT:
+  SIZE: (224, 224)
+  TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+  NAME: "PACS"
+
+MODEL:
+  BACKBONE:
+    NAME: "resnet18"
+    PRETRAINED: True

Dassl.ProGrad.pytorch/configs/datasets/dg/vlcs.yaml

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+INPUT:
+  SIZE: (224, 224)
+  TRANSFORMS: ["random_flip", "random_translation", "normalize"]
+
+DATASET:
+  NAME: "VLCS"
+
+MODEL:
+  BACKBONE:
+    NAME: "resnet18"
+    PRETRAINED: True