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x = x_orig[counter * bs:min((counter + 1) * bs, x_orig.shape[0])].clone().to(device)
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y = y_orig[counter * bs:min((counter + 1) * bs, x_orig.shape[0])].clone().to(device)
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output = model(x)
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acc += (output.max(1)[1] == y).float().sum()
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return (acc / x_orig.shape[0]).item()
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def get_image_classifier(classifier_name):
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class _Wrapper_ResNet(nn.Module):
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def __init__(self, resnet):
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super().__init__()
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self.resnet = resnet
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self.mu = torch.Tensor([0.485, 0.456, 0.406]).float().view(3, 1, 1)
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self.sigma = torch.Tensor([0.229, 0.224, 0.225]).float().view(3, 1, 1)
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def forward(self, x):
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x = (x - self.mu.to(x.device)) / self.sigma.to(x.device)
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return self.resnet(x)
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if 'imagenet' in classifier_name:
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if 'resnet18' in classifier_name:
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print('using imagenet resnet18...')
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model = models.resnet18(pretrained=True).eval()
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elif 'resnet50' in classifier_name:
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print('using imagenet resnet50...')
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model = models.resnet50(pretrained=True).eval()
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elif 'resnet101' in classifier_name:
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print('using imagenet resnet101...')
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model = models.resnet101(pretrained=True).eval()
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elif 'wideresnet-50-2' in classifier_name:
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print('using imagenet wideresnet-50-2...')
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model = models.wide_resnet50_2(pretrained=True).eval()
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elif 'deit-s' in classifier_name:
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print('using imagenet deit-s...')
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model = torch.hub.load('facebookresearch/deit:main', 'deit_small_patch16_224', pretrained=True).eval()
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else:
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raise NotImplementedError(f'unknown {classifier_name}')
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wrapper_resnet = _Wrapper_ResNet(model)
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elif 'cifar10' in classifier_name:
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if 'wideresnet-28-10' in classifier_name:
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print('using cifar10 wideresnet-28-10...')
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model = load_model(model_name='Standard', dataset='cifar10', threat_model='Linf') # pixel in [0, 1]
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elif 'wrn-28-10-at0' in classifier_name:
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print('using cifar10 wrn-28-10-at0...')
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model = load_model(model_name='Gowal2021Improving_28_10_ddpm_100m', dataset='cifar10',
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threat_model='Linf') # pixel in [0, 1]
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elif 'wrn-28-10-at1' in classifier_name:
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print('using cifar10 wrn-28-10-at1...')
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model = load_model(model_name='Gowal2020Uncovering_28_10_extra', dataset='cifar10',
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threat_model='Linf') # pixel in [0, 1]
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elif 'wrn-70-16-at0' in classifier_name:
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print('using cifar10 wrn-70-16-at0...')
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model = load_model(model_name='Gowal2021Improving_70_16_ddpm_100m', dataset='cifar10',
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threat_model='Linf') # pixel in [0, 1]
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elif 'wrn-70-16-at1' in classifier_name:
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print('using cifar10 wrn-70-16-at1...')
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model = load_model(model_name='Rebuffi2021Fixing_70_16_cutmix_extra', dataset='cifar10',
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threat_model='Linf') # pixel in [0, 1]
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elif 'wrn-70-16-L2-at1' in classifier_name:
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print('using cifar10 wrn-70-16-L2-at1...')
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model = load_model(model_name='Rebuffi2021Fixing_70_16_cutmix_extra', dataset='cifar10',
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threat_model='L2') # pixel in [0, 1]
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elif 'wideresnet-70-16' in classifier_name:
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print('using cifar10 wideresnet-70-16 (dm_wrn-70-16)...')
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from robustbench.model_zoo.architectures.dm_wide_resnet import DMWideResNet, Swish
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model = DMWideResNet(num_classes=10, depth=70, width=16, activation_fn=Swish) # pixel in [0, 1]
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model_path = 'pretrained/cifar10/wresnet-76-10/weights-best.pt'
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print(f"=> loading wideresnet-70-16 checkpoint '{model_path}'")
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model.load_state_dict(update_state_dict(torch.load(model_path)['model_state_dict']))
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model.eval()
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print(f"=> loaded wideresnet-70-16 checkpoint")
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elif 'resnet-50' in classifier_name:
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print('using cifar10 resnet-50...')
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from classifiers.cifar10_resnet import ResNet50
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model = ResNet50() # pixel in [0, 1]
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model_path = 'pretrained/cifar10/resnet-50/weights.pt'
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print(f"=> loading resnet-50 checkpoint '{model_path}'")
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model.load_state_dict(update_state_dict(torch.load(model_path), idx_start=7))
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model.eval()
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print(f"=> loaded resnet-50 checkpoint")
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elif 'wrn-70-16-dropout' in classifier_name:
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print('using cifar10 wrn-70-16-dropout (standard wrn-70-16-dropout)...')
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from classifiers.cifar10_resnet import WideResNet_70_16_dropout
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model = WideResNet_70_16_dropout() # pixel in [0, 1]
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model_path = 'pretrained/cifar10/wrn-70-16-dropout/weights.pt'
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print(f"=> loading wrn-70-16-dropout checkpoint '{model_path}'")
|
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