def classifier_loader():
if name == 'googlenet/inceptionv1':
model = torch_models.__dict__[d['arch']](pretrained=False,
aux_logits=False,
transform_input=True)
else:
model = torch_models.__dict__[d['arch']](pretrained=False)
load_model_state_dict(model, name)
return model
def classifier_loader():
model = torch_models.__dict__[d['arch']](pretrained=False)
load_model_state_dict(model, name)
return model
def classifier_loader():
model = torch_models.resnet50()
load_model_state_dict(model, 'resnet50_adv-train-free')
return model
def classifier_loader():
model = torch_models.resnet50()
load_model_state_dict(model, 'resnet50_augmix')
return model
def noisystudent_loader():
model = timm.create_model('tf_efficientnet_l2_ns', pretrained=False)
load_model_state_dict(model, 'efficientnet-l2-noisystudent')
return model
def classifier_loader():
model = torch.hub.load('facebookresearch/WSL-Images',
d['arch'] + '_wsl')
load_model_state_dict(model, name)
return model
def classifier_loader():
model = EfficientNet.from_name(d['arch'])
load_model_state_dict(model, name)
return model
def classifier_loader():
model = KNOWN_MODELS[d['arch']](head_size=1000)
load_model_state_dict(model, name)
return model
def classifier_loader():
model = torch_models.__dict__[d['arch']]()
load_model_state_dict(model, name)
model = Smooth(model, d['noise_sigma'], d['n'], d['alpha'], d['mean'],
d['std'])
return model
def classifier_loader():
model = getattr(models_lpf, d['arch'])(filter_size=d['filter_size'])
load_model_state_dict(model, name)
return model
def classifier_loader():
model = pretrainedmodels.__dict__[d['arch']](num_classes=1000,
pretrained=None)
load_model_state_dict(model, name)
return model
