def test_energy_continuous():
# make sure that energy as computed by ssvm is the same as by lp
np.random.seed(0)
for inference_method in get_installed(["lp", "ad3"]):
found_fractional = False
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
while not found_fractional:
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
res, energy = crf.inference(x, w, relaxed=True, return_energy=True)
found_fractional = np.any(np.max(res[0], axis=-1) != 1)
joint_feature = crf.joint_feature(x, res)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, -energy_svm)
def test_energy_discrete():
for inference_method in get_installed(["qpbo", "ad3"]):
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
for i in range(10):
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
y_hat = crf.inference(x, w, relaxed=False)
energy = compute_energy(crf._get_unary_potentials(x, w),
crf._get_pairwise_potentials(x, w), edges,
y_hat)
joint_feature = crf.joint_feature(x, y_hat)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, energy_svm)
def test_joint_feature_continuous():
# FIXME
# first make perfect prediction, including pairwise part
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
y = y.ravel()
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# create crf, assemble weight, make prediction
for inference_method in get_installed(["lp", "ad3"]):
crf = EdgeFeatureGraphCRF(inference_method=inference_method)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
crf.initialize([x], [y])
y_pred = crf.inference(x, w, relaxed=True)
# compute joint_feature for prediction
joint_feature_y = crf.joint_feature(x, y_pred)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
def test_energy_discrete():
for inference_method in get_installed(["qpbo", "ad3"]):
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
for i in xrange(10):
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
y_hat = crf.inference(x, w, relaxed=False)
energy = compute_energy(crf._get_unary_potentials(x, w),
crf._get_pairwise_potentials(x, w), edges,
y_hat)
joint_feature = crf.joint_feature(x, y_hat)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, energy_svm)
def test_energy_continuous():
# make sure that energy as computed by ssvm is the same as by lp
np.random.seed(0)
for inference_method in get_installed(["lp", "ad3"]):
found_fractional = False
crf = EdgeFeatureGraphCRF(n_states=3,
inference_method=inference_method,
n_edge_features=2, n_features=3)
while not found_fractional:
x = np.random.normal(size=(7, 8, 3))
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
res, energy = crf.inference(x, w, relaxed=True, return_energy=True)
found_fractional = np.any(np.max(res[0], axis=-1) != 1)
joint_feature = crf.joint_feature(x, res)
energy_svm = np.dot(joint_feature, w)
assert_almost_equal(energy, -energy_svm)
def test_joint_feature_continuous():
# FIXME
# first make perfect prediction, including pairwise part
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
y = y.ravel()
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# create crf, assemble weight, make prediction
for inference_method in get_installed(["lp", "ad3"]):
crf = EdgeFeatureGraphCRF(inference_method=inference_method)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
crf.initialize([x], [y])
y_pred = crf.inference(x, w, relaxed=True)
# compute joint_feature for prediction
joint_feature_y = crf.joint_feature(x, y_pred)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
def test_joint_feature_discrete():
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
y_flat = y.ravel()
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
crf = EdgeFeatureGraphCRF(inference_method=inference_method)
crf.initialize([x], [y_flat])
joint_feature_y = crf.joint_feature(x, y_flat)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature, ))
# first horizontal, then vertical
# we trust the unaries ;)
pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[
crf.n_states * crf.n_features:].reshape(2, crf.n_states,
crf.n_states)
xx, yy = np.indices(y.shape)
assert_array_equal(pw_joint_feature_vert,
np.diag([9 * 4, 9 * 4, 9 * 4]))
vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3])
vert_joint_feature[0, 1] = 10
vert_joint_feature[1, 2] = 10
assert_array_equal(pw_joint_feature_horz, vert_joint_feature)
def test_joint_feature_discrete():
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
edge_features = edge_list_to_features(edge_list)
x = (x.reshape(-1, 3), edges, edge_features)
y_flat = y.ravel()
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
crf = EdgeFeatureGraphCRF(inference_method=inference_method)
crf.initialize([x], [y_flat])
joint_feature_y = crf.joint_feature(x, y_flat)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
# first horizontal, then vertical
# we trust the unaries ;)
pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states *
crf.n_features:].reshape(
2, crf.n_states, crf.n_states)
xx, yy = np.indices(y.shape)
assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4]))
vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3])
vert_joint_feature[0, 1] = 10
vert_joint_feature[1, 2] = 10
assert_array_equal(pw_joint_feature_horz, vert_joint_feature)
