release code

Dassl.ProGrad.pytorch/configs/trainers/da/dael/digit5.yaml

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+DATALOADER:
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 256
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 64
+  TEST:
+    BATCH_SIZE: 256
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.05
+  STEPSIZE: [30]
+  MAX_EPOCH: 30
+  LR_SCHEDULER: "cosine"
+
+TRAINER:
+  DAEL:
+    STRONG_TRANSFORMS: ["randaugment2", "normalize"]

Dassl.ProGrad.pytorch/configs/trainers/da/dael/domainnet.yaml

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+DATALOADER:
+  NUM_WORKERS: 4
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 30
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 6
+  TEST:
+    BATCH_SIZE: 30
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.002
+  MAX_EPOCH: 40
+  LR_SCHEDULER: "cosine"
+
+TRAINER:
+  DAEL:
+    STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]

Dassl.ProGrad.pytorch/configs/trainers/da/dael/mini_domainnet.yaml

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+DATALOADER:
+  NUM_WORKERS: 8
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 192
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 64
+  TEST:
+    BATCH_SIZE: 200
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.005
+  MAX_EPOCH: 60
+  LR_SCHEDULER: "cosine"
+
+TRAINER:
+  DAEL:
+    STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]

Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/digit5.yaml

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+DATALOADER:
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 256
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 64
+  TEST:
+    BATCH_SIZE: 256
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.05
+  STEPSIZE: [30]
+  MAX_EPOCH: 30
+  LR_SCHEDULER: "cosine"

Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/domainnet.yaml

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+DATALOADER:
+  NUM_WORKERS: 4
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 30
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 6
+  TEST:
+    BATCH_SIZE: 30
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.002
+  MAX_EPOCH: 40
+  LR_SCHEDULER: "cosine"

Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/mini_domainnet.yaml

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+DATALOADER:
+  NUM_WORKERS: 8
+  TRAIN_X:
+    SAMPLER: "RandomDomainSampler"
+    BATCH_SIZE: 192
+  TRAIN_U:
+    SAME_AS_X: False
+    BATCH_SIZE: 64
+  TEST:
+    BATCH_SIZE: 200
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.005
+  MAX_EPOCH: 60
+  LR_SCHEDULER: "cosine"

Dassl.ProGrad.pytorch/configs/trainers/da/source_only/digit5.yaml

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+DATALOADER:
+  TRAIN_X:
+    BATCH_SIZE: 256
+  TEST:
+    BATCH_SIZE: 256
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.05
+  STEPSIZE: [30]
+  MAX_EPOCH: 30
+  LR_SCHEDULER: "cosine"

Dassl.ProGrad.pytorch/configs/trainers/da/source_only/mini_domainnet.yaml

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+DATALOADER:
+  NUM_WORKERS: 8
+  TRAIN_X:
+    BATCH_SIZE: 128
+  TEST:
+    BATCH_SIZE: 128
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.005
+  MAX_EPOCH: 60
+  LR_SCHEDULER: "cosine"

Dassl.ProGrad.pytorch/configs/trainers/da/source_only/office31.yaml

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+DATALOADER:
+  TRAIN_X:
+    BATCH_SIZE: 32
+  TEST:
+    BATCH_SIZE: 32
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.002
+  STEPSIZE: [20]
+  MAX_EPOCH: 20

Dassl.ProGrad.pytorch/configs/trainers/da/source_only/visda17.yaml

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+DATALOADER:
+  TRAIN_X:
+    BATCH_SIZE: 32
+  TEST:
+    BATCH_SIZE: 32
+
+OPTIM:
+  NAME: "sgd"
+  LR: 0.0001
+  STEPSIZE: [2]
+  MAX_EPOCH: 2
+
+TRAIN:
+  PRINT_FREQ: 50
+  COUNT_ITER: "train_u"