def test_cnn_mlp_match_sliding_window(self):
num_steps = 1000
for seed in range(1):
verif_instance = test_utils.make_toy_verif_instance(seed,
nn='cnn_slide')
key = jax.random.PRNGKey(0)
lb, ub, params_mlp = test_utils.make_mlp_verif_instance_from_cnn(
verif_instance)
bounds_mlp = sdp_verify.boundprop(
params_mlp,
sdp_verify.IntBound(lb=lb, ub=ub, lb_pre=None, ub_pre=None))
verif_instance_mlp = utils.make_nn_verif_instance(
params_mlp, bounds_mlp)
dual_ub_cnn, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance),
key,
num_steps=num_steps,
verbose=False,
use_exact_eig_train=True)
dual_ub_mlp, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance_mlp),
key,
num_steps=num_steps,
verbose=False,
use_exact_eig_train=True)
assert abs(dual_ub_cnn - dual_ub_mlp) < 5e-3, (
'Dual upper bound for MLP and CNN (sliding filter) should match.'
f'Seed is {seed}. Vals are CNN: {dual_ub_cnn} MLP: {dual_ub_mlp}'
)
def test_cnn_mlp_match_fixed_window(self):
num_steps = 1000
for seed in range(1):
verif_instance = test_utils.make_toy_verif_instance(seed, nn='cnn_simple')
key = jax.random.PRNGKey(0)
params_cnn = verif_instance.params_full
in_shape = int(np.prod(np.array(params_cnn[0]['W'].shape[:-1])))
# Input and filter size match -> filter is applied at just one location.
# Number of layer 1 neurons = no. channels out of conv filter (last dim).
out_shape = params_cnn[0]['W'].shape[-1]
params_mlp = [(jnp.reshape(params_cnn[0]['W'],
(in_shape, out_shape)), params_cnn[0]['b']),
(params_cnn[1][0], params_cnn[1][1])]
bounds_mlp = sdp_verify.boundprop(
params_mlp,
sdp_verify.IntBound(
lb=np.zeros((1, in_shape)),
ub=1 * np.ones((1, in_shape)),
lb_pre=None,
ub_pre=None))
verif_instance_mlp = utils.make_nn_verif_instance(params_mlp, bounds_mlp)
dual_ub_cnn, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key,
num_steps=num_steps, verbose=False, use_exact_eig_train=True)
dual_ub_mlp, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance_mlp), key,
num_steps=num_steps, verbose=False, use_exact_eig_train=True)
assert abs(dual_ub_cnn - dual_ub_mlp) < 1e-2, (
'Dual upper bound for MLP and CNN (simple CNN) should match.'
f'Seed is {seed}. Vals are CNN: {dual_ub_cnn} MLP: {dual_ub_mlp}')
def test_dual_sdp_no_crash(self):
for nn in ['cnn', 'mlp']:
verif_instance = test_utils.make_toy_verif_instance(
seed=0, target_label=1, label=2, nn=nn)
key = jax.random.PRNGKey(0)
dual_val, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=10,
n_iter_lanczos=5)
assert isinstance(dual_val, float)
def test_crossing_bounds(self):
loss_margin = 1e-3
seed = random.randint(1, 10000)
verif_instance = test_utils.make_toy_verif_instance(seed)
key = jax.random.PRNGKey(0)
primal_opt, _ = cvxpy_verify.solve_mip_mlp_elided(verif_instance)
dual_ub, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=1000)
assert dual_ub > primal_opt - loss_margin, (
'Dual upper bound should be greater than optimal primal objective.'
f'Seed is {seed}. Vals are Dual: {dual_ub} Primal: {primal_opt}')
def _test_tight_duality_gap(self, seed, loss_margin=0.003, num_steps=3000):
verif_instance = test_utils.make_toy_verif_instance(
seed, label=1, target_label=2)
key = jax.random.PRNGKey(0)
primal_opt, _ = cvxpy_verify.solve_sdp_mlp_elided(verif_instance)
dual_ub, _ = sdp_verify.solve_sdp_dual(
utils.make_sdp_verif_instance(verif_instance), key, num_steps=num_steps,
verbose=False)
assert dual_ub - primal_opt < loss_margin, (
'Primal and dual vals should be close. '
f'Seed: {seed}. Primal: {primal_opt}, Dual: {dual_ub}')
assert dual_ub > primal_opt - 1e-3, 'crossing bounds'