def test_loss_augmented_inference_exhaustive_grid():
crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2)
for i in range(10):
w = np.random.normal(size=18)
y = np.random.randint(2, size=(2, 2))
x = np.random.normal(size=(2, 2, 2))
h_hat = crf.loss_augmented_inference(x, y * 2, w)
h = exhaustive_loss_augmented_inference(crf, x, y * 2, w)
assert_array_equal(h, h_hat)
def test_loss_augmented_inference_exhaustive_grid():
crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2)
for i in xrange(10):
w = np.random.normal(size=18)
y = np.random.randint(2, size=(2, 2))
x = np.random.normal(size=(2, 2, 2))
h_hat = crf.loss_augmented_inference(x, y * 2, w)
h = exhaustive_loss_augmented_inference(crf, x, y * 2, w)
assert_array_equal(h, h_hat)
def test_binary_crf_exhaustive_loss_augmented():
# tests qpbo inference against brute force
# on random data / weights
np.random.seed(0)
for inference_method in get_installed(["qpbo", "lp"]):
crf = GridCRF(n_states=2, n_features=2, inference_method=inference_method)
for i in xrange(10):
# generate data and weights
y = np.random.randint(2, size=(3, 2))
x = np.random.uniform(-1, 1, size=(3, 2))
x = np.dstack([-x, np.zeros_like(x)])
w = np.random.uniform(-1, 1, size=7)
# check loss augmented map inference
y_hat = crf.loss_augmented_inference(x, y, w)
y_ex = exhaustive_loss_augmented_inference(crf, x, y, w)
assert_array_equal(y_hat, y_ex)
def test_binary_crf_exhaustive_loss_augmented():
# tests qpbo inference against brute force
# on random data / weights
np.random.seed(0)
for inference_method in get_installed(['qpbo', 'lp']):
crf = GridCRF(n_states=2, n_features=2,
inference_method=inference_method)
for i in xrange(10):
# generate data and weights
y = np.random.randint(2, size=(3, 2))
x = np.random.uniform(-1, 1, size=(3, 2))
x = np.dstack([-x, np.zeros_like(x)])
w = np.random.uniform(-1, 1, size=7)
# check loss augmented map inference
y_hat = crf.loss_augmented_inference(x, y, w)
y_ex = exhaustive_loss_augmented_inference(crf, x, y, w)
assert_array_equal(y_hat, y_ex)
def test_binary_crf_exhaustive_loss_augmented():
# tests graph cut inference against brute force
# on random data / weights
np.random.seed(0)
for inference_method in ['qpbo', 'lp']:
crf = GridCRF(inference_method=inference_method)
for i in xrange(50):
# generate data and weights
y = np.random.randint(2, size=(3, 3))
x = np.random.uniform(-1, 1, size=(3, 3))
x = np.dstack([-x, np.zeros_like(x)])
w = np.random.uniform(-1, 1, size=7)
# check loss augmented map inference
y_hat = crf.loss_augmented_inference(x, y, w)
y_ex = exhaustive_loss_augmented_inference(crf, x, y, w)
#print(y_hat)
#print(y_ex)
#print("++++++++++++++++++++++")
assert_array_equal(y_hat, y_ex)
