def test_feed_data(self):
networkx = [_generate_graph(batch_index) for batch_index in range(16)]
placeholders = utils_tf.placeholders_from_networkxs(
networkx, force_dynamic_num_graphs=True)
# Does not need to be the same size
networkxs = [_generate_graph(batch_index) for batch_index in range(2)]
with self.test_session() as sess:
output = sess.run(
placeholders,
utils_tf.get_feed_dict(
placeholders,
utils_np.networkxs_to_graphs_tuple(networkxs)))
self.assertAllEqual(
np.array([[0, 0], [1, 0], [2, 0], [3, 0], [0, 1], [1, 1], [2, 1],
[3, 1]]), output.nodes)
self.assertEqual(np.float32, output.nodes.dtype)
self.assertAllEqual(np.array([[0], [1]]), output.globals)
self.assertEqual(np.float32, output.globals.dtype)
sorted_edges_content = sorted([
(x, y, z)
for x, y, z in zip(output.receivers, output.senders, output.edges)
])
self.assertAllEqual([0, 0, 1, 4, 4, 5],
[x[0] for x in sorted_edges_content])
self.assertAllEqual([1, 2, 3, 5, 6, 7],
[x[1] for x in sorted_edges_content])
self.assertEqual(np.float64, output.edges.dtype)
self.assertAllEqual(
np.array([[0, 1, 0], [1, 2, 0], [2, 3, 0], [0, 1, 1], [1, 2, 1],
[2, 3, 1]]), [x[2] for x in sorted_edges_content])
def test_get_feed_dict_raises(self, none_fields):
networkxs = [_generate_graph(batch_index) for batch_index in range(16)]
placeholders = utils_tf.placeholders_from_networkxs(networkxs)
feed_values = utils_np.networkxs_to_graphs_tuple(networkxs)
with self.assertRaisesRegexp(ValueError, ""):
utils_tf.get_feed_dict(
placeholders.map(lambda _: None, none_fields), feed_values)
with self.assertRaisesRegexp(ValueError, ""):
utils_tf.get_feed_dict(
placeholders, feed_values.map(lambda _: None, none_fields))
def test_placeholders_from_networkxs(self):
num_graphs = 16
networkxs = [
_generate_graph(batch_index) for batch_index in range(num_graphs)
]
placeholders = utils_tf.placeholders_from_networkxs(
networkxs, force_dynamic_num_graphs=False)
self._assert_expected_shapes(placeholders, num_graphs=num_graphs)
self.assertEqual(tf.float32, placeholders.nodes.dtype)
self.assertEqual(tf.float64, placeholders.edges.dtype)
def test_placeholders_from_networkxs_missing_edges(self):
num_graphs = 16
networkxs = [
_generate_graph(batch_index, add_edges=False)
for batch_index in range(num_graphs)
]
placeholders = utils_tf.placeholders_from_networkxs(
networkxs, force_dynamic_num_graphs=False)
self.assertEqual(None, placeholders.edges)
self._assert_expected_shapes(placeholders,
but_for=["edges"],
num_graphs=num_graphs)
def test_placeholders_from_networkxs_hints(self):
num_graphs = 16
networkxs = [
_generate_graph(batch_index, n_nodes=0, add_edges=False)
for batch_index in range(num_graphs)
]
placeholders = utils_tf.placeholders_from_networkxs(
networkxs,
node_shape_hint=[14],
edge_shape_hint=[17],
data_type_hint=tf.float64,
force_dynamic_num_graphs=False)
self.assertAllEqual([None, 14], placeholders.nodes.shape.as_list())
self.assertAllEqual([None, 17], placeholders.edges.shape.as_list())
self._assert_expected_shapes(placeholders,
but_for=["nodes", "edges"],
num_graphs=num_graphs)
self.assertEqual(tf.float64, placeholders.nodes.dtype)
self.assertEqual(tf.float64, placeholders.edges.dtype)
def test_feed_data_no_nodes(self):
networkx = [
_generate_graph(batch_index, n_nodes=0, add_edges=False)
for batch_index in range(16)
]
placeholders = utils_tf.placeholders_from_networkxs(
networkx, force_dynamic_num_graphs=True)
# Does not need to be the same size
networkxs = [
_generate_graph(batch_index, n_nodes=0, add_edges=False)
for batch_index in range(2)
]
self.assertEqual(None, placeholders.nodes)
self.assertEqual(None, placeholders.edges)
with self.test_session() as sess:
output = sess.run(
placeholders.replace(nodes=tf.no_op(), edges=tf.no_op()),
utils_tf.get_feed_dict(
placeholders,
utils_np.networkxs_to_graphs_tuple(networkxs)))
self.assertAllEqual(np.array([[0], [1]]), output.globals)
self.assertEqual(np.float32, output.globals.dtype)
