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miunangel
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Dassl.ProGrad.pytorch/configs/README.md Normal file
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The `datasets/` folder contains dataset-specific config files which define the standard protocols (e.g., image size, data augmentation, network architecture) used by most papers. The `trainers/` folder contains method-specific config files which define optimization algorithms (e.g., optimizer, epoch) and hyperparameter settings.

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Dassl.ProGrad.pytorch/configs/datasets/da/cifar_stl.yaml Normal file
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INPUT:
SIZE: (32, 32)
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
DATASET:
NAME: "CIFARSTL"

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Dassl.ProGrad.pytorch/configs/datasets/da/digit5.yaml Normal file
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INPUT:
SIZE: (32, 32)
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
TRANSFORMS: ["normalize"]
DATASET:
NAME: "Digit5"
MODEL:
BACKBONE:
NAME: "cnn_digit5_m3sda"

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Dassl.ProGrad.pytorch/configs/datasets/da/domainnet.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "DomainNet"
MODEL:
BACKBONE:
NAME: "resnet101"

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Dassl.ProGrad.pytorch/configs/datasets/da/mini_domainnet.yaml Normal file
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INPUT:
SIZE: (96, 96)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "miniDomainNet"
MODEL:
BACKBONE:
NAME: "resnet18"

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Dassl.ProGrad.pytorch/configs/datasets/da/office31.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "Office31"
MODEL:
BACKBONE:
NAME: "resnet50"
HEAD:
NAME: "mlp"
HIDDEN_LAYERS: [256]
DROPOUT: 0.

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Dassl.ProGrad.pytorch/configs/datasets/da/office_home.yaml Normal file
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INPUT:
SIZE: (224, 224)
DATASET:
NAME: "OfficeHome"

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Dassl.ProGrad.pytorch/configs/datasets/da/visda17.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "center_crop", "normalize"]
DATASET:
NAME: "VisDA17"
MODEL:
BACKBONE:
NAME: "resnet101"
TEST:
PER_CLASS_RESULT: True

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Dassl.ProGrad.pytorch/configs/datasets/dg/cifar100_c.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/datasets/dg/cifar10_c.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/datasets/dg/digit_single.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/datasets/dg/digits_dg.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/datasets/dg/office_home_dg.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "OfficeHomeDG"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/dg/pacs.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "PACS"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/dg/vlcs.yaml Normal file
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INPUT:
SIZE: (224, 224)
TRANSFORMS: ["random_flip", "random_translation", "normalize"]
DATASET:
NAME: "VLCS"
MODEL:
BACKBONE:
NAME: "resnet18"
PRETRAINED: True

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Dassl.ProGrad.pytorch/configs/datasets/ssl/cifar10.yaml Normal file
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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: "CIFAR10"
NUM_LABELED: 4000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/cifar100.yaml Normal file
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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]
CROP_PADDING: 4
DATASET:
NAME: "CIFAR100"
NUM_LABELED: 10000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/stl10.yaml Normal file
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INPUT:
SIZE: (96, 96)
TRANSFORMS: ["random_flip", "random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
CROP_PADDING: 4
DATASET:
NAME: "STL10"
STL10_FOLD: 0
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/datasets/ssl/svhn.yaml Normal file
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INPUT:
SIZE: (32, 32)
TRANSFORMS: ["random_crop", "normalize"]
PIXEL_MEAN: [0.5, 0.5, 0.5]
PIXEL_STD: [0.5, 0.5, 0.5]
CROP_PADDING: 4
DATASET:
NAME: "SVHN"
NUM_LABELED: 1000
VAL_PERCENT: 0.
MODEL:
BACKBONE:
NAME: "wide_resnet_28_2"

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Dassl.ProGrad.pytorch/configs/trainers/da/dael/digit5.yaml Normal file
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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"]

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Dassl.ProGrad.pytorch/configs/trainers/da/dael/domainnet.yaml Normal file
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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"]

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Dassl.ProGrad.pytorch/configs/trainers/da/dael/mini_domainnet.yaml Normal file
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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"]

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Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/digit5.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/domainnet.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/da/m3sda/mini_domainnet.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/da/source_only/digit5.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/da/source_only/mini_domainnet.yaml Normal file
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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"

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

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Dassl.ProGrad.pytorch/configs/trainers/da/source_only/visda17.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/dg/dael/digits_dg.yaml Normal file
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DATALOADER:
TRAIN_X:
SAMPLER: "RandomDomainSampler"
BATCH_SIZE: 120
TEST:
BATCH_SIZE: 100
OPTIM:
NAME: "sgd"
LR: 0.05
STEPSIZE: [20]
MAX_EPOCH: 50
TRAINER:
DAEL:
STRONG_TRANSFORMS: ["randaugment2", "normalize"]

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Dassl.ProGrad.pytorch/configs/trainers/dg/dael/office_home_dg.yaml Normal file
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DATALOADER:
TRAIN_X:
SAMPLER: "RandomDomainSampler"
BATCH_SIZE: 30
TEST:
BATCH_SIZE: 100
OPTIM:
NAME: "sgd"
LR: 0.002
MAX_EPOCH: 40
LR_SCHEDULER: "cosine"
TRAINER:
DAEL:
STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]

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Dassl.ProGrad.pytorch/configs/trainers/dg/dael/pacs.yaml Normal file
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DATALOADER:
TRAIN_X:
SAMPLER: "RandomDomainSampler"
BATCH_SIZE: 30
TEST:
BATCH_SIZE: 100
OPTIM:
NAME: "sgd"
LR: 0.002
MAX_EPOCH: 40
LR_SCHEDULER: "cosine"
TRAINER:
DAEL:
STRONG_TRANSFORMS: ["random_flip", "cutout", "randaugment2", "normalize"]

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Dassl.ProGrad.pytorch/configs/trainers/dg/ddaig/digits_dg.yaml Normal file
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INPUT:
PIXEL_MEAN: [0., 0., 0.]
PIXEL_STD: [1., 1., 1.]
DATALOADER:
TRAIN_X:
BATCH_SIZE: 128
TEST:
BATCH_SIZE: 128
OPTIM:
NAME: "sgd"
LR: 0.05
STEPSIZE: [20]
MAX_EPOCH: 50
TRAINER:
DDAIG:
G_ARCH: "fcn_3x32_gctx"
LMDA: 0.3

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Dassl.ProGrad.pytorch/configs/trainers/dg/ddaig/office_home_dg.yaml Normal file
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INPUT:
PIXEL_MEAN: [0., 0., 0.]
PIXEL_STD: [1., 1., 1.]
DATALOADER:
TRAIN_X:
BATCH_SIZE: 16
TEST:
BATCH_SIZE: 16
OPTIM:
NAME: "sgd"
LR: 0.0005
STEPSIZE: [20]
MAX_EPOCH: 25
TRAINER:
DDAIG:
G_ARCH: "fcn_3x64_gctx"
WARMUP: 3
LMDA: 0.3

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Dassl.ProGrad.pytorch/configs/trainers/dg/ddaig/pacs.yaml Normal file
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INPUT:
PIXEL_MEAN: [0., 0., 0.]
PIXEL_STD: [1., 1., 1.]
DATALOADER:
TRAIN_X:
BATCH_SIZE: 16
TEST:
BATCH_SIZE: 16
OPTIM:
NAME: "sgd"
LR: 0.0005
STEPSIZE: [20]
MAX_EPOCH: 25
TRAINER:
DDAIG:
G_ARCH: "fcn_3x64_gctx"
WARMUP: 3
LMDA: 0.3

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Dassl.ProGrad.pytorch/configs/trainers/dg/vanilla/digits_dg.yaml Normal file
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DATALOADER:
TRAIN_X:
BATCH_SIZE: 128
TEST:
BATCH_SIZE: 100
NUM_WORKERS: 8
OPTIM:
NAME: "sgd"
LR: 0.05
STEPSIZE: [20]
MAX_EPOCH: 50
TRAIN:
PRINT_FREQ: 20

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Dassl.ProGrad.pytorch/configs/trainers/dg/vanilla/mini_domainnet.yaml Normal file
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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"

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Dassl.ProGrad.pytorch/configs/trainers/dg/vanilla/office_home_dg.yaml Normal file
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DATALOADER:
TRAIN_X:
BATCH_SIZE: 64
TEST:
BATCH_SIZE: 100
NUM_WORKERS: 8
OPTIM:
NAME: "sgd"
LR: 0.001
MAX_EPOCH: 50
LR_SCHEDULER: "cosine"

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Dassl.ProGrad.pytorch/configs/trainers/dg/vanilla/pacs.yaml Normal file
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DATALOADER:
TRAIN_X:
BATCH_SIZE: 64
TEST:
BATCH_SIZE: 100
NUM_WORKERS: 8
OPTIM:
NAME: "sgd"
LR: 0.001
MAX_EPOCH: 50
LR_SCHEDULER: "cosine"

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Dassl.ProGrad.pytorch/configs/trainers/ssl/fixmatch/cifar10.yaml Normal file
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DATALOADER:
TRAIN_X:
BATCH_SIZE: 64
TRAIN_U:
SAME_AS_X: False
BATCH_SIZE: 448
TEST:
BATCH_SIZE: 500
OPTIM:
NAME: "sgd"
LR: 0.05
STEPSIZE: [4000]
MAX_EPOCH: 4000
LR_SCHEDULER: "cosine"
TRAIN:
COUNT_ITER: "train_u"
PRINT_FREQ: 10
TRAINER:
FIXMATCH:
STRONG_TRANSFORMS: ["random_flip", "randaugment_fixmatch", "normalize", "cutout"]