def test_basic():
num_layers = 2
g = generate_rand_graph(100, connect_more=True)
nf = create_full_nodeflow(g, num_layers)
assert nf.number_of_nodes() == g.number_of_nodes() * (num_layers + 1)
assert nf.number_of_edges() == g.number_of_edges() * num_layers
assert nf.num_layers == num_layers + 1
assert nf.layer_size(0) == g.number_of_nodes()
assert nf.layer_size(1) == g.number_of_nodes()
check_basic(g, nf)
parent_nids = F.copy_to(F.arange(0, g.number_of_nodes()), F.cpu())
nids = nf.map_from_parent_nid(0, parent_nids, remap_local=True)
assert_array_equal(F.asnumpy(nids), F.asnumpy(parent_nids))
# should also work for negative layer ids
for l in range(-1, -num_layers, -1):
nids1 = nf.map_from_parent_nid(l, parent_nids, remap_local=True)
nids2 = nf.map_from_parent_nid(l + num_layers,
parent_nids,
remap_local=True)
assert_array_equal(F.asnumpy(nids1), F.asnumpy(nids2))
g = generate_rand_graph(100)
nf = create_mini_batch(g, num_layers)
assert nf.num_layers == num_layers + 1
check_basic(g, nf)
g = generate_rand_graph(100, add_self_loop=True)
nf = create_mini_batch(g, num_layers, add_self_loop=True)
assert nf.num_layers == num_layers + 1
check_basic(g, nf)
def test_basic():
num_layers = 2
g = generate_rand_graph(100, connect_more=True)
nf = create_full_nodeflow(g, num_layers)
assert nf.number_of_nodes() == g.number_of_nodes() * (num_layers + 1)
assert nf.number_of_edges() == g.number_of_edges() * num_layers
assert nf.num_layers == num_layers + 1
assert nf.layer_size(0) == g.number_of_nodes()
assert nf.layer_size(1) == g.number_of_nodes()
check_basic(g, nf)
parent_nids = F.arange(0, g.number_of_nodes())
nids = nf.map_from_parent_nid(0, parent_nids)
assert F.array_equal(nids, parent_nids)
g = generate_rand_graph(100)
nf = create_mini_batch(g, num_layers)
assert nf.num_layers == num_layers + 1
check_basic(g, nf)