def test_HealpyPool():
# we get a random map to pool
n_pix = hp.nside2npix(4)
np.random.seed(11)
m_in = np.random.normal(size=n_pix)
# check exception
with pytest.raises(IOError):
avg_layer = healpy_layers.HealpyPool(0, pool_type="MAX")
with pytest.raises(IOError):
avg_layer = healpy_layers.HealpyPool(2, pool_type="HUHU")
# we pool with healpy
m_avg = hp.ud_grade(map_in=m_in,
nside_out=2,
order_in="NEST",
order_out="NEST",
power=None)
# avg layer
avg_layer = healpy_layers.HealpyPool(1, pool_type="AVG")
m_avg_tf = avg_layer(m_in[None, :, None])
assert np.all(np.abs(m_avg - m_avg_tf.numpy().ravel()) < 1e-5)
# maxpool normal
m_max = np.max(m_in.reshape((n_pix // 4, 4)), axis=1)
# max layer
max_layer = healpy_layers.HealpyPool(1, pool_type="MAX")
m_max_tf = max_layer(m_in[None, :, None])
assert np.all(np.abs(m_max - m_max_tf.numpy().ravel()) < 1e-5)
def HealpyPool(self, current_nside, current_indices, p, pool):
"""
Input:
current_nside
current_indices
+
p reduction factor >=1 of the nside -> number of nodes reduces by 4^p, note that the layer only checks if the dimensionality of the input is evenly divisible by 4^p and not if the ordering is correct (should be nested ordering).
pool type of pooling, can be "MAX" or "AVG"
Layer Parameters: (That have been set)
None
"""
layer = hp_layer.HealpyPool(p, pool)
new_nside = int(current_nside // 2**layer.p)
self.current_indices = self._transform_indices(nside_in=current_nside,
nside_out=new_nside,
indices=current_indices)
self.current_nside = new_nside
return layer
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)