dataset=m["dataset"],
architecture=m["architecture"],
debug=args.debug,
model_idx=args.model_idx)
basenet = baseNN(inp_shape, out_size, *list(base_m.values()))
basenet_savedir = get_savedir(model="baseNN",
dataset=m["dataset"],
architecture=m["architecture"],
debug=args.debug,
model_idx=m["baseNN_idx"])
basenet.load(savedir=basenet_savedir, device=args.device)
hyp = get_hyperparams(m)
net = redBNN(dataset_name=m["dataset"],
inference=m["inference"],
base_net=basenet,
hyperparams=hyp,
layer_idx=args.layer_idx)
else:
raise NotImplementedError
if args.debug:
bayesian_defence_samples = [1]
else:
if m["inference"] == "svi":
bayesian_defence_samples = [1, 10, 50]
elif m["inference"] == "hmc":
bayesian_defence_samples = [1, 5, 10]
def load_test_net(model_idx,
model_type,
n_inputs,
device,
load_dir,
return_data_loader=True):
if model_type == "baseNN":
model = baseNN_settings["model_" + str(model_idx)]
x_test, y_test, inp_shape, out_size = load_dataset(
dataset_name=model["dataset"], n_inputs=n_inputs)[2:]
net = baseNN(inp_shape, out_size, *list(model.values()))
net.load(device=device, rel_path=load_dir)
elif model_type == "fullBNN":
dataset_name, model = fullBNN_settings["model_" + str(model_idx)]
x_test, y_test, inp_shape, out_size = load_dataset(
dataset_name=dataset_name, n_inputs=n_inputs)[2:]
net = BNN(dataset_name, *list(model.values()), inp_shape, out_size)
net.load(device=device, rel_path=load_dir)
elif model_type == "redBNN":
m = redBNN_settingss["model_" + str(model_idx)]
dataset_name = m["dataset"]
x_test, y_test, inp_shape, out_size = load_dataset(
dataset_name=dataset_name, n_inputs=n_inputs)[2:]
nn = baseNN(dataset_name=m["dataset"],
input_shape=inp_shape,
output_size=out_size,
epochs=m["baseNN_epochs"],
lr=m["baseNN_lr"],
hidden_size=m["hidden_size"],
activation=m["activation"],
architecture=m["architecture"])
nn.load(rel_path=load_dir, device=device)
hyp = get_hyperparams(m)
net = redBNN(dataset_name=m["dataset"],
inference=m["inference"],
base_net=nn,
hyperparams=hyp)
net.load(n_inputs=m["BNN_inputs"], device=device, rel_path=load_dir)
else:
raise AssertionError("Wrong model name.")
if return_data_loader:
test_loader = DataLoader(dataset=list(zip(x_test, y_test)),
batch_size=128,
shuffle=True)
return test_loader, net
else:
return (x_test, y_test), net