def test_HealpyMonomial():
# create the layer
tf.random.set_seed(11)
L = tf.random.normal(shape=(3, 3), seed=11)
# make sym
L = tf.matmul(L, tf.transpose(L))
x = tf.random.normal(shape=(5, 3, 7), seed=12)
Fout = 3
K = 4
# create the layer
stddev = 0.1
initializer = tf.initializers.RandomNormal(stddev=stddev, seed=13)
mon = healpy_layers.HealpyMonomial(Fout=Fout,
K=K,
initializer=initializer,
activation=tf.keras.activations.linear)
mon = mon._get_layer(L)
new = mon(x)
mon = healpy_layers.HealpyMonomial(Fout=Fout,
K=K,
initializer=initializer,
activation=tf.keras.activations.linear,
use_bias=True,
use_bn=True)
mon = mon._get_layer(L)
new = mon(x)
def test_HealpyMonomial():
# this is the result from Deepsphere with tf 1.x
result = np.array([[[0.04206353, 0.46168754, 0.10546149],
[-0.5492798, -0.32608002, 0.5628096],
[-0.11329696, -0.7900159, 0.92530084]],
[[0.06915615, 0.03369189, 0.0245935],
[-0.89208144, -0.11626951, -0.10967396],
[0.01909873, -0.16593638, -0.1462554]],
[[-0.29119226, -0.12377091, -0.0128078],
[0.36727118, 0.30154356, -0.02591037],
[-0.23363924, -0.14655769, 0.3258103]],
[[0.00471622, -0.03371258, 0.00214787],
[0.31400114, -0.57628125, 1.5108933],
[0.09324764, -0.75300777, 0.40933472]],
[[0.12954447, 0.06049673, 0.15058015],
[0.38768154, -0.24916826, 0.43720144],
[-0.1512235, 0.01706326, 0.14433491]]],
dtype=np.float32)
# create the layer
tf.random.set_seed(11)
L = tf.random.normal(shape=(3, 3), seed=11)
# make sym
L = tf.matmul(L, tf.transpose(L))
x = tf.random.normal(shape=(5, 3, 7), seed=12)
Fout = 3
K = 4
# create the layer
stddev = 0.1
initializer = tf.initializers.RandomNormal(stddev=stddev, seed=13)
mon = healpy_layers.HealpyMonomial(Fout=Fout,
K=K,
initializer=initializer,
activation=tf.keras.activations.linear)
mon = mon._get_layer(L)
new = mon(x)
assert np.all(np.abs(new.numpy() - result) < 1e-5)
mon = healpy_layers.HealpyMonomial(Fout=Fout,
K=K,
initializer=initializer,
activation=tf.keras.activations.linear,
use_bias=True,
use_bn=True)
mon = mon._get_layer(L)
new = mon(x)
def test_HealpyGCNN():
# clear session
tf.keras.backend.clear_session()
# we get a random map
nside_in = 256
n_pix = hp.nside2npix(nside_in)
np.random.seed(11)
m_in = np.random.normal(size=[3, n_pix, 1]).astype(np.float32)
indices = np.arange(n_pix)
# define some layers
layers = [hp_nn.HealpyPseudoConv(p=1, Fout=4),
hp_nn.HealpyPool(p=1),
hp_nn.HealpyChebyshev(K=5, Fout=8),
hp_nn.HealpyPseudoConv(p=2, Fout=16),
hp_nn.HealpyPseudoConv_Transpose(p=2, Fout=16),
hp_nn.HealpyPseudoConv(p=2, Fout=16),
hp_nn.HealpyMonomial(K=5, Fout=32),
hp_nn.Healpy_ResidualLayer("CHEBY", layer_kwargs={"K": 5}),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4)]
tf.random.set_seed(11)
model = HealpyGCNN(nside=nside_in, indices=indices, layers=layers)
model.build(input_shape=(3, n_pix, 1))
model.summary(line_length=128)
out = model(m_in)
assert out.numpy().shape == (3,4)
# now we check if we can save this
with tempfile.TemporaryDirectory() as tempdir:
# save the current weight
model.save_weights(tempdir)
# create new model
tf.random.set_seed(12)
model = HealpyGCNN(nside=nside_in, indices=indices, layers=layers)
model.build(input_shape=(3, n_pix, 1))
out_new = model(m_in)
# output should be different
assert not np.all(np.isclose(out.numpy(), out_new.numpy()))
# restore weights
model.load_weights(tempdir)
# now it should be the same
out_new = model(m_in)
assert np.all(np.isclose(out.numpy(), out_new.numpy(), atol=1e-6))
# test the use 4 graphing
with pytest.raises(NotImplementedError):
model = HealpyGCNN(nside=nside_in, indices=indices, layers=layers, n_neighbors=12)
# more channels
tf.keras.backend.clear_session()
# we get a random map
nside_in = 256
n_pix = hp.nside2npix(nside_in)
np.random.seed(11)
m_in = np.random.normal(size=[3, n_pix, 2]).astype(np.float32)
indices = np.arange(n_pix)
# define some layers
layers = [hp_nn.HealpyPseudoConv(p=1, Fout=4),
hp_nn.HealpyPool(p=1),
hp_nn.HealpyChebyshev(K=5, Fout=8),
hp_nn.HealpyPseudoConv(p=2, Fout=16),
hp_nn.HealpyPseudoConv_Transpose(p=2, Fout=16),
hp_nn.HealpyPseudoConv(p=2, Fout=16),
hp_nn.HealpyMonomial(K=5, Fout=32),
hp_nn.Healpy_ResidualLayer("CHEBY", layer_kwargs={"K": 5}),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4)]
tf.random.set_seed(11)
model = HealpyGCNN(nside=nside_in, indices=indices, layers=layers)
model.build(input_shape=(3, n_pix, 2))
model.summary(line_length=128)
out = model(m_in)
assert out.numpy().shape == (3, 4)
def test_HealpyGCNN_plotting():
# create dir for plots
os.makedirs("./tests/test_plots", exist_ok=True)
# clear session
tf.keras.backend.clear_session()
# we get a random map
nside_in = 256
n_pix = hp.nside2npix(nside_in)
np.random.seed(11)
m_in = np.random.normal(size=[3, n_pix, 1]).astype(np.float32)
indices = np.arange(n_pix)
# define some layers
layers = [hp_nn.HealpyPseudoConv(p=1, Fout=4),
hp_nn.HealpyPool(p=1),
hp_nn.HealpyChebyshev(K=5, Fout=8),
hp_nn.HealpyPseudoConv(p=2, Fout=16),
hp_nn.HealpyMonomial(K=5, Fout=32),
hp_nn.Healpy_ResidualLayer("CHEBY", layer_kwargs={"K": 5}),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4)]
tf.random.set_seed(11)
model = HealpyGCNN(nside=nside_in, indices=indices, layers=layers)
model.build(input_shape=(3, n_pix, 1))
model.summary()
with pytest.raises(ValueError):
filters1 = model.get_gsp_filters(3)
# get some filters
filters1 = model.get_gsp_filters("chebyshev")
filters2 = model.get_gsp_filters("gcnn__residual_layer")
# plot some filters (coeff)
ax = model.plot_chebyshev_coeffs("chebyshev")
base_path, _ = os.path.split(__file__)
plt.savefig(os.path.join(base_path, "test_plots/plot_chebyshev_coeffs_cheby5.png"))
plt.clf()
ax = model.plot_chebyshev_coeffs("gcnn__residual_layer")
plt.savefig(os.path.join(base_path, "test_plots/plot_chebyshev_coeffs_res.png"))
plt.clf()
# plot some filters (spectral)
ax = model.plot_filters_spectral("chebyshev")
plt.savefig(os.path.join(base_path, "test_plots/plot_filters_spectral_cheby5.png"))
plt.clf()
ax = model.plot_filters_spectral("gcnn__residual_layer")
plt.savefig(os.path.join(base_path, "test_plots/plot_filters_spectral_res.png"))
plt.clf()
# plot some filters (section)
figs = model.plot_filters_section("chebyshev", ind_in=[0], ind_out=[0])
figs[0].savefig(os.path.join(base_path, "test_plots/plot_filters_section_cheby5.png"))
plt.clf()
figs = model.plot_filters_section("gcnn__residual_layer", ind_in=[0], ind_out=[0])
figs[0].savefig(os.path.join(base_path, "test_plots/plot_filters_section_res_1.png"))
plt.clf()
# plot some filters (gnomonic)
figs = model.plot_filters_gnomonic("chebyshev", ind_in=[0], ind_out=[0])
figs[0].savefig(os.path.join(base_path, "test_plots/plot_filters_gnomonic_cheby5.png"))
plt.clf()
figs = model.plot_filters_gnomonic("gcnn__residual_layer", ind_in=[0,1,2], ind_out=[0])
figs[0].savefig(os.path.join(base_path, "test_plots/plot_filters_gnomonic_res_1.png"))
plt.clf()
# get the output
out = model(m_in)
assert out.numpy().shape == (3, 4)