def get_icosahedron_weights(nodes, depth):
"""Get the icosahedron laplacian list for a certain depth.
Args:
nodes (int): initial number of nodes.
depth (int): the depth of the UNet.
laplacian_type ["combinatorial", "normalized"]: the type of the laplacian.
Returns:
laps (list): increasing list of laplacians.
"""
edge_list = []
weight_list = []
order = icosahedron_order_calculator(nodes)
for _ in range(depth):
nodes = icosahedron_nodes_calculator(order)
order_initial = icosahedron_order_calculator(nodes)
coords = get_ico_coords(int(order_initial))
coords = torch.from_numpy(coords)
edge_index = knn_graph(coords, 6 if order else 5)
if order:
dist = torch.norm(coords[edge_index[0]] - coords[edge_index[1]],
p=2,
dim=1)
_, extra_idx = torch.topk(dist, 12)
edge_index[0, extra_idx] = edge_index[1, extra_idx]
edge_index, _ = remove_self_loops(edge_index)
edge_list.append(edge_index)
weight_list.append(None)
order -= 1
return edge_list[::-1], weight_list
def get_icosahedron_laplacians(nodes, depth, laplacian_type):
"""Get the icosahedron laplacian list for a certain depth.
Args:
nodes (int): initial number of nodes.
depth (int): the depth of the UNet.
laplacian_type ["combinatorial", "normalized"]: the type of the laplacian.
Returns:
laps (list): increasing list of laplacians.
"""
laps = []
order = icosahedron_order_calculator(nodes)
for _ in range(depth):
nodes = icosahedron_nodes_calculator(order)
order_initial = icosahedron_order_calculator(nodes)
G = SphereIcosahedron(level=int(order_initial))
G.compute_laplacian(laplacian_type)
laplacian = prepare_laplacian(G.L)
laps.append(laplacian)
order -= 1
return laps[::-1]