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model_paths.py
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model_paths.py
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'''
Specify model paths of all models that you wish to benchmark using either
gen_eval.py or gen_attack_stats.py
'''
import collections
class MNIST_models():
def __init__(self):
file_base = ('SavedModels/base/'
'base_MNIST_lr0.001_augm_flagTrue_train_typeplain.pth'
)
file_MCDO = ('SavedModels/other/mcdo/MNIST.pth')
file_DE = ('SavedModels/other/deep_ensemble/MNIST.pth')
file_GAN = ('SavedModels/other/gan/MNIST.pth')
file_ODIN = ('SavedModels/other/odin/'
'MNIST_ODIN.pth'
)
file_single_maha = ('SavedModels/other/single_mahalanobis/MNIST.pth')
file_edl = ('SavedModels/other/edl/MNIST.pth')
file_ACET_OUT = ('SavedModels/base/'
'base_MNIST_lr0.001_augm_flagTrue_train_typeACET_OUT_steps40.pth'
)
file_ACET = ('SavedModels/base/'
'base_MNIST_lr0.001_augm_flagTrue_train_typeACET_steps40.pth'
)
file_CEDA_ACET = ('SavedModels/base/'
'base_MNIST_lr0.001_augm_flagTrue_train_typeCEDA_ACET_OUT_steps40.pth'
)
file_hendrycks = ('SavedModels/other/outlier-exposure/hendrycks_MNIST.pth')
file_CCU_double = ('SavedModels/'
'gmm__PCAMNIST_lam0.0_n100_lr0.001_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMM_OUTgrad_vars mu var_OUT.pth')
file_CCU_single = ('SavedModels/'
'gmm__PCAMNIST_lam0.0_n100_lr0.001_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMMgrad_vars mu var.pth'
)
files = [file_base, file_MCDO, file_edl, file_DE, file_GAN,
file_ODIN, file_single_maha, file_ACET_OUT, file_ACET,
file_hendrycks, file_CCU_double, file_CCU_single]
keys = ['Base', 'MCD', 'EDL', 'DE', 'GAN', 'ODIN', 'Maha',
'ACET', 'ACET2', 'OE', 'CCU', 'CCUs']
file_GAN = ('SavedModels/other/gan/MNIST_batchstack.pth')
self.file_dict = collections.OrderedDict(zip(keys, files))
class FMNIST_models():
def __init__(self):
file_base = ('SavedModels/base/'
'base_FMNIST_lr0.1_augm_flagTrue_train_typeplain.pth'
)
file_MCDO = ('SavedModels/other/mcdo/FMNIST.pth')
file_DE = ('SavedModels/other/deep_ensemble/FMNIST.pth')
file_GAN = ('SavedModels/other/gan/FMNIST.pth')
file_ODIN = ('SavedModels/other/odin/'
'FMNIST_ODIN.pth'
)
file_single_maha = ('SavedModels/other/single_mahalanobis/FMNIST.pth')
file_edl = ('SavedModels/other/edl/FMNIST.pth')
file_ACET_OUT = ('SavedModels/base/'
'base_FMNIST_lr0.1_augm_flagTrue_train_typeACET_OUT_steps40.pth'
)
file_ACET = ('SavedModels/base/'
'base_FMNIST_lr0.1_augm_flagTrue_train_typeACET_steps40.pth'
)
file_CEDA_ACET = ('SavedModels/base/'
'base_FMNIST_lr0.1_augm_flagTrue_train_typeCEDA_ACET_OUT_steps40.pth'
)
file_hendrycks = ('SavedModels/other/outlier-exposure/hendrycks_FMNIST.pth')
file_CCU_double = ('SavedModels/'
'gmm__PCAFMNIST_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMM_OUTgrad_vars mu var_OUT.pth')
file_CCU_single = ('SavedModels/'
'gmm__PCAFMNIST_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMMgrad_vars mu var.pth'
)
files = [file_base, file_MCDO, file_edl, file_DE, file_GAN,
file_ODIN, file_single_maha, file_ACET_OUT, file_ACET,
file_hendrycks, file_CCU_double, file_CCU_single]
keys = ['Base', 'MCD', 'EDL', 'DE', 'GAN', 'ODIN', 'Maha',
'ACET', 'ACET2', 'OE', 'CCU', 'CCUs']
self.file_dict = collections.OrderedDict(zip(keys, files))
class SVHN_models():
def __init__(self):
file_base = ('SavedModels/base/'
'base_SVHN_lr0.1_augm_flagTrue_train_typeplain.pth'
)
file_MCDO = ('SavedModels/other/mcdo/SVHN.pth')
file_DE = ('SavedModels/other/deep_ensemble/SVHN.pth')
file_GAN = ('SavedModels/other/gan/SVHN.pth')
file_ODIN = ('SavedModels/other/odin/'
'SVHN_ODIN.pth'
)
file_single_maha = ('SavedModels/other/single_mahalanobis/SVHN.pth')
file_edl = ('SavedModels/other/edl/SVHN.pth')
file_ACET_OUT = ('SavedModels/base/'
'base_SVHN_lr0.1_augm_flagTrue_train_typeACET_OUT_steps40.pth'
)
file_ACET = ('SavedModels/base/'
'base_SVHN_lr0.1_augm_flagTrue_train_typeACET_steps40.pth'
)
file_CEDA_ACET = ('SavedModels/base/'
'base_SVHN_lr0.1_augm_flagTrue_train_typeCEDA_ACET_OUT_steps40.pth'
)
file_hendrycks = ('SavedModels/other/outlier-exposure/hendrycks_SVHN.pth')
file_CCU_double = ('SavedModels/'
'gmm__PCASVHN_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMM_OUTgrad_vars mu var_OUT.pth')
file_CCU_single = ('SavedModels/'
'gmm__PCASVHN_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMMgrad_vars mu var.pth'
)
files = [file_base, file_MCDO, file_edl, file_DE, file_GAN,
file_ODIN, file_single_maha, file_ACET_OUT, file_ACET,
file_hendrycks, file_CCU_double, file_CCU_single]
keys = ['Base', 'MCD', 'EDL', 'DE', 'GAN', 'ODIN', 'Maha',
'ACET', 'ACET2', 'OE', 'CCU', 'CCUs']
self.file_dict = collections.OrderedDict(zip(keys, files))
class CIFAR10_models():
def __init__(self):
file_base = ('SavedModels/base/'
'base_CIFAR10_lr0.1_augm_flagTrue_train_typeplain.pth'
)
file_MCDO = ('SavedModels/other/mcdo/CIFAR10.pth')
file_DE = ('SavedModels/other/deep_ensemble/CIFAR10.pth')
file_GAN = ('SavedModels/other/gan/CIFAR10.pth')
file_ODIN = ('SavedModels/other/odin/'
'CIFAR10_ODIN.pth'
)
file_single_maha = ('SavedModels/other/single_mahalanobis/CIFAR10.pth')
file_edl = ('SavedModels/other/edl/CIFAR10.pth')
file_ACET_OUT = ('SavedModels/base/'
'base_CIFAR10_lr0.1_augm_flagTrue_train_typeACET_OUT_steps40.pth'
)
file_ACET = ('SavedModels/base/'
'base_CIFAR10_lr0.1_augm_flagTrue_train_typeACET_steps40.pth'
)
file_CEDA_ACET = ('SavedModels/base/'
'base_CIFAR10_lr0.1_augm_flagTrue_train_typeCEDA_ACET_OUT_steps40.pth'
)
file_hendrycks = ('SavedModels/other/outlier-exposure/hendrycks_CIFAR10.pth')
file_CCU_double = ('SavedModels/'
'gmm__PCACIFAR10_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMM_OUTgrad_vars mu var_OUT.pth')
file_CCU_single = ('SavedModels/'
'gmm__PCACIFAR10_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMMgrad_vars mu var.pth'
)
files = [file_base, file_MCDO, file_edl, file_DE, file_GAN,
file_ODIN, file_single_maha, file_ACET_OUT, file_ACET,
file_hendrycks, file_CCU_double, file_CCU_single]
keys = ['Base', 'MCD', 'EDL', 'DE', 'GAN', 'ODIN', 'Maha',
'ACET', 'ACET2', 'OE', 'CCU', 'CCUs']
self.file_dict = collections.OrderedDict(zip(keys, files))
class CIFAR100_models():
def __init__(self):
file_base = ('SavedModels/base/'
'base_CIFAR100_lr0.1_augm_flagTrue_train_typeplain.pth'
)
file_MCDO = ('SavedModels/other/mcdo/CIFAR100.pth')
file_DE = ('SavedModels/other/deep_ensemble/CIFAR100.pth')
file_GAN = ('SavedModels/other/gan/CIFAR100.pth')
file_ODIN = ('SavedModels/other/odin/'
'CIFAR100_ODIN.pth'
)
file_single_maha = ('SavedModels/other/single_mahalanobis/CIFAR100.pth')
file_edl = ('SavedModels/other/edl/CIFAR100.pth')
file_ACET_OUT = ('SavedModels/base/'
'base_CIFAR100_lr0.1_augm_flagTrue_train_typeACET_OUT_steps40.pth'
)
file_ACET = ('SavedModels/base/'
'base_CIFAR100_lr0.1_augm_flagTrue_train_typeACET_steps40.pth'
)
file_CEDA_ACET = ('SavedModels/base/'
'base_CIFAR100_lr0.1_augm_flagTrue_train_typeCEDA_ACET_OUT_steps40.pth'
)
file_hendrycks = ('SavedModels/other/outlier-exposure/hendrycks_CIFAR100.pth')
file_CCU_double = ('SavedModels/'
'gmm__PCACIFAR100_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMM_OUTgrad_vars mu var_OUT.pth')
file_CCU_single = ('SavedModels/'
'gmm__PCACIFAR100_lam0.0_n100_lr0.1_lrgmm1e-05_'
'augm_flagTrue_train_typeCEDA_GMMgrad_vars mu var.pth'
)
files = [file_base, file_MCDO, file_edl, file_DE, file_GAN,
file_ODIN, file_single_maha, file_ACET_OUT, file_ACET,
file_hendrycks, file_CCU_double, file_CCU_single]
keys = ['Base', 'MCD', 'EDL', 'DE', 'GAN', 'ODIN', 'Maha',
'ACET', 'ACET2', 'OE', 'CCU', 'CCUs']
self.file_dict = collections.OrderedDict(zip(keys, files))
model_dict = {'MNIST': MNIST_models,
'FMNIST': FMNIST_models,
'SVHN': SVHN_models,
'CIFAR10': CIFAR10_models,
'CIFAR100': CIFAR100_models,
}