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models.py
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""" Loader of the models used to estimate the correlations in terms of robustness between benchmarks """
import torchvision
import torch
import os
import antialiased_cnns
def load(model_name, model_path="Results/trained_models", nb_gpu=1):
model_path = os.path.join(model_path,model_name)
if os.path.exists(model_path):
checkpoint_name = os.listdir(model_path)[0]
model_path = os.path.join(model_path,checkpoint_name)
# Plain models
if model_name == "resnet50":
classifier = torchvision.models.resnet50(pretrained=True)
elif model_name == "resnet18":
classifier = torchvision.models.resnet18(pretrained=True)
elif model_name == "densenet121":
classifier = torchvision.models.densenet121(pretrained=True)
elif model_name == "alexnet":
classifier = torchvision.models.alexnet(pretrained=True)
elif model_name == "resnet152":
classifier = torchvision.models.resnet152(pretrained=True)
elif model_name == "efficientnet_b0":
classifier = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'tf_efficientnet_b0', pretrained=True)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "resnext101_32x8d":
classifier = torchvision.models.resnext101_32x8d(pretrained=True)
# Robust models
elif model_name == "ANT":
classifier = torchvision.models.resnet50(pretrained=False)
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint['model_state_dict'])
elif model_name == "SIN":
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint['state_dict'])
elif model_name == "Augmix":
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint['state_dict'])
elif model_name == "DeepAugment":
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint['state_dict'])
elif model_name == "Cutmix":
classifier = torchvision.models.resnet152(pretrained=False)
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "FastAutoAugment":
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint)
elif model_name == "RSC":
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(model_path)
classifier.load_state_dict(checkpoint['state_dict'])
elif model_name == "MoPro":
classifier = torchvision.models.resnet50(pretrained=False)
checkpoint = torch.load(model_path)['state_dict']
for k in list(checkpoint.keys()):
if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
checkpoint[k[len("module.encoder_q."):]] = checkpoint[k]
del checkpoint[k]
checkpoint['fc.weight'] = checkpoint['classifier.weight']
checkpoint['fc.bias'] = checkpoint['classifier.bias']
del checkpoint['classifier.weight']
del checkpoint['classifier.bias']
classifier.load_state_dict(checkpoint)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "AdvProp":
classifier = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'tf_efficientnet_b0_ap', pretrained=True)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "AT_Linf_4":
classifier = torchvision.models.resnet50(pretrained=False)
checkpoint = torch.load(model_path)['model']
for k in list(checkpoint.keys()):
if 'module.model.' not in k:
checkpoint.pop(k)
checkpoint = {k.split('model.')[1]:v for k,v in checkpoint.items()}
classifier.load_state_dict(checkpoint)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "NoisyStudent":
classifier = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'tf_efficientnet_b0_ns', pretrained=True)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "SpatialAdv":
classifier = torchvision.models.resnet18(pretrained=False)
checkpoint = torch.load(model_path)['model']
for k in list(checkpoint.keys()):
if 'attacker' in k:
checkpoint.pop(k)
for k in list(checkpoint.keys()):
if 'module.model.' not in k:
checkpoint.pop(k)
checkpoint = {k.split('model.')[1]:v for k,v in checkpoint.items()}
classifier.load_state_dict(checkpoint)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "Anti_Alias":
classifier = antialiased_cnns.densenet121(pretrained=True, filter_size=4)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "WSL":
classifier = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
elif model_name == "SSL":
classifier = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x16d_ssl')
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
else:
# Load models trained with data augmentation
classifier = torchvision.models.resnet50(pretrained=False)
classifier = torch.nn.DataParallel(classifier, device_ids=list(range(nb_gpu)))
checkpoint = torch.load(os.path.join(model_path,'{}/checkpoint'.format(model_name)))
classifier.load_state_dict(checkpoint)
return classifier