def test_HealpyPseudoConv_Transpose():
# we get a random map to conv
n_pix = hp.nside2npix(8)
np.random.seed(11)
m_in = np.random.normal(size=n_pix)
# layer
hp_conv = healpy_layers.HealpyPseudoConv_Transpose(3, 5)
m_conv_tf = hp_conv(m_in[None, :, None])
assert m_conv_tf.numpy().shape == (1, n_pix * int(4**3), 5)
def HealpyPseudoConv_Transpose(self, current_nside, current_indices, p,
Fout, kernel_initializer):
"""
:param p: Boost factor >=1 of the nside -> number of nodes increases 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)
:param Fout: number of output channels
:param kernel_initializer: initializer for kernel init
"""
layer = hp_layer.HealpyPseudoConv_Transpose(p, Fout,
kernel_initializer)
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)