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