def _build(self,
sequence_graph, #graphs tuple with a single graph for the sequence graph
pattern_graph, #graphs tuple with a single graph for each pattern
num_processing_steps):
latent_seq_graph = self.sequence_encoder(sequence_graph)
latent_seq_graph0 = latent_seq_graph
latent_pattern_graph = self.pattern_encoder(pattern_graph)
latent_pattern_graph0 = latent_pattern_graph
for _ in range(num_processing_steps):
#core input is the concatenation of original input graph and the graph from the last iteration
seq_core_input = utils_tf.concat([latent_seq_graph0, latent_seq_graph], axis=1)
#the latent representation of the input graph in the current iteration
latent_seq_graph = self.sequence_graph_core(seq_core_input)
#copy node tensors from the latent sequence graph to the pattern graph
latent_pattern_graph = latent_pattern_graph.replace(nodes = latent_seq_graph.nodes)
pattern_core_input = utils_tf.concat([latent_pattern_graph0, latent_pattern_graph], axis=1)
latent_pattern_graph = self.pattern_graph_core(pattern_core_input)
latent_seq_graph = latent_seq_graph.replace(nodes = latent_pattern_graph.nodes)
decoded_op = self.decoder(latent_pattern_graph)
return self.output_transform(decoded_op)
def create_graph(self, num_graphs, is_training=True):
if not self.idx_mgr:
raise ValueError("No Doublet Graph is created")
inputs = []
targets = []
for _ in range(num_graphs):
idx = self.idx_mgr.next(is_training)
input_dd, target_dd = self.graphs[idx]
inputs.append(input_dd)
targets.append(target_dd)
return utils_tf.concat(inputs, axis=0), utils_tf.concat(targets, axis=0)
def get_input_signature(dataset, batch_size):
with_batch_dim = False
input_list = []
target_list = []
for dd in dataset.take(batch_size).as_numpy_iterator():
input_list.append(dd[0])
target_list.append(dd[1])
inputs = utils_tf.concat(input_list, axis=0)
targets = utils_tf.concat(target_list, axis=0)
input_signature = (graph.specs_from_graphs_tuple(inputs, with_batch_dim),
graph.specs_from_graphs_tuple(targets, with_batch_dim),
tf.TensorSpec(shape=[], dtype=tf.bool))
return input_signature
def loop_dataset(datasets, batch_size):
if batch_size > 0:
in_list = []
target_list = []
for dataset in datasets:
inputs_tr, targets_tr = dataset
in_list.append(inputs_tr)
target_list.append(targets_tr)
if len(in_list) == batch_size:
inputs_tr = utils_tf.concat(in_list, axis=0)
targets_tr = utils_tf.concat(target_list, axis=0)
yield (inputs_tr, targets_tr)
else:
for dataset in datasets:
yield dataset
def test_raise_all_or_no_nones(self, none_field):
graph_0 = utils_np.networkxs_to_graphs_tuple(
[_generate_graph(0, 3),
_generate_graph(1, 2)])
graph_1 = utils_np.networkxs_to_graphs_tuple([_generate_graph(2, 2)])
graph_2 = utils_np.networkxs_to_graphs_tuple([_generate_graph(3, 3)])
graphs_ = [
gr.map(tf.convert_to_tensor, graphs.ALL_FIELDS)
for gr in [graph_0, graph_1, graph_2]
]
graphs_[1] = graphs_[1].replace(**{none_field: None})
with self.assertRaisesRegex(
ValueError,
"Different set of keys found when iterating over data dictionaries."
):
utils_tf.concat(graphs_, axis=0)
def input_signature(self):
with_batch_dim = False
input_list = []
target_list = []
for dd in self.data_train.take(
self.train_batch_size).as_numpy_iterator():
input_list.append(dd[0])
target_list.append(dd[1])
inputs = utils_tf.concat(input_list, axis=0)
targets = utils_tf.concat(target_list, axis=0)
input_signature = (
graph.specs_from_graphs_tuple(inputs, with_batch_dim),
graph.specs_from_graphs_tuple(targets, with_batch_dim),
)
return input_signature
def _build(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
latent0 = latent
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
return latent
def _build(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
latent0 = latent
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
return latent
def __call__(self, input_op, num_processing_steps):
####
fea1 = input_op.nodes[:, 0]
fea2 = input_op.globals[:, 0]
fea3 = input_op.edges[:, 0]
embed1 = self._embed_mod_symptomNode(fea1) # node
embed2 = self._embed_mod_global(fea2) # global
embed3 = self._embed_mod_edge(fea3) # edge
embed1 = tf.cast(embed1, tf.float64)
embed2 = tf.cast(embed2, tf.float64)
embed3 = tf.cast(embed3, tf.float64)
input_op = input_op.replace(nodes=embed1, globals=embed2,
edges=embed3) # node [? 256] edge [?,]
###3
latent = self._encoder(input_op)
#ws = self.trainable_variables
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
#ws = self.trainable_variables
decoded_op = self._decoder(latent)
#ws = self.trainable_variables
output_ops.append(self._output_transform(decoded_op))
###
#ws=self.trainable_variables
return output_ops
def create_linked_list_target(batch_size, input_graphs):
"""Creates linked list targets.
Returns a graph with the same number of nodes as `input_graph`. Each node
contains a 2d vector with targets for a 1-class classification where only one
node is `True`, the smallest value in the array. The vector contains two
values: [prob_true, prob_false].
It also contains edges connecting all nodes. These are again 2d vectors with
softmax targets [prob_true, prob_false]. An edge is True
if n+1 is the element immediately after n in the sorted list.
Args:
batch_size: batch size for the `input_graphs`.
input_graphs: a `graphs.GraphsTuple` which contains a batch of inputs.
Returns:
A `graphs.GraphsTuple` with the targets, which encode the linked list.
"""
target_graphs = []
for i in range(batch_size):
input_graph = utils_tf.get_graph(input_graphs, i)
num_elements = tf.shape(input_graph.nodes)[0]
si = tf.cast(tf.squeeze(input_graph.nodes), tf.int32)
nodes = tf.reshape(tf.one_hot(si[:1], num_elements), (-1, 1))
x = tf.stack((si[:-1], si[1:]))[None]
y = tf.stack((input_graph.senders, input_graph.receivers),
axis=1)[:, :, None]
edges = tf.reshape(
tf.cast(
tf.reduce_any(tf.reduce_all(tf.equal(x, y), axis=1), axis=1),
tf.float32), (-1, 1))
target_graphs.append(input_graph._replace(nodes=nodes, edges=edges))
return utils_tf.concat(target_graphs, axis=0)
def test_batch_reshape():
data = dict(nodes=tf.reshape(tf.range(3 * 2), (3, 2)),
edges=tf.reshape(tf.range(40 * 5), (40, 5)),
senders=tf.random.uniform((40, ),
minval=0,
maxval=3,
dtype=tf.int32),
receivers=tf.random.uniform((40, ),
minval=0,
maxval=3,
dtype=tf.int32),
n_node=tf.constant([3]),
n_edge=tf.constant([40]),
globals=None)
graph = GraphsTuple(**data)
graphs = utils_tf.concat([graph] * 4, axis=0)
batched_graphs = graph_batch_reshape(graphs)
assert tf.reduce_all(
batched_graphs.nodes[0] == batched_graphs.nodes[1]).numpy()
assert tf.reduce_all(
batched_graphs.edges[0] == batched_graphs.edges[1]).numpy()
assert tf.reduce_all(
batched_graphs.senders[0] +
graphs.n_node[0] == batched_graphs.senders[1]).numpy()
assert tf.reduce_all(
batched_graphs.receivers[0] +
graphs.n_node[0] == batched_graphs.receivers[1]).numpy()
# print(batched_graphs)
unbatched_graphs = graph_unbatch_reshape(batched_graphs)
for (t1, t2) in zip(graphs, unbatched_graphs):
if t1 is not None:
assert tf.reduce_all(t1 == t2).numpy()
def _build(self, input_op, num_processing_steps):
fea1 = input_op.nodes[:, 0]
fea2 = input_op.globals[:, 0]
fea3 = input_op.edges[:, 0]
embed1 = self._embed_mod_symptomNode(fea1) # node
embed2 = self._embed_mod_global(fea2) # global
embed3 = self._embed_mod_edge(fea3) # edge
embed1 = tf.cast(embed1, tf.float64)
embed2 = tf.cast(embed2, tf.float64)
embed3 = tf.cast(embed3, tf.float64)
input_op = input_op.replace(nodes=embed1, globals=embed2,
edges=embed3) # node [? 256] edge [?,]
#
latent = self._encoder(
input_op) # graph_out=GN(graph_in) latent [? 256]
latent0 = latent #[?node_n,hid16] node[? 16] edge [? 16]
output_ops = []
for _ in range(num_processing_steps): # how many cores GN block
core_input = utils_tf.concat(
[latent0, latent], axis=1) # node 256 -> 512 ,edge 25->50
latent = self._core(core_input)
decoded_op = self._decoder(latent)
output_ops.append(self._output_transform(decoded_op))
return output_ops
def _build(self, input_op):
# Initial step gets encoder output as input.
latents = [self._encoder(input_op)]
# Each sucsessive step gets all previous steps' outputs as input.
for step, core in enumerate(self._cores):
latents.append(core(utils_tf.concat(latents, axis=1)))
# Return a single list of one output: decode of final core's output.
return [self._output_transform(self._decoder(latents[-1]))]
def _build(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
if self._skip_encoder_decoder:
decoder_input = utils_tf.concat([latent0, latent], axis=1)
else:
decoder_input = latent
if self._edge_type_concat:
decoder_input = decoder_input._replace(edges=tf.concat(
[input_op.edges[:, :8], decoder_input.edges], axis=1))
decoded_op = self._decoder(decoder_input)
output_ops.append(self._output_transform(decoded_op))
return output_ops
def _build(self, input_op):
latent = self._encoder(input_op)
output_ops = [self._decoder(latent)] # K = 0
for i in range(self._num_processing_steps):
latent = self._core(latent)
decoded_op = self._decoder(latent)
output_ops.append(decoded_op) # K = 1, 2, 3, ...
return self._output_transform(utils_tf.concat(output_ops, axis=1))
def test_nested_features(self):
graph_0 = utils_np.networkxs_to_graphs_tuple(
[_generate_graph(0, 3),
_generate_graph(1, 2)])
graph_1 = utils_np.networkxs_to_graphs_tuple([_generate_graph(2, 2)])
graph_2 = utils_np.networkxs_to_graphs_tuple([_generate_graph(3, 3)])
graphs_ = [
gr.map(tf.convert_to_tensor, graphs.ALL_FIELDS)
for gr in [graph_0, graph_1, graph_2]
]
def _create_nested_fields(graphs_tuple):
new_nodes = ({
"a": graphs_tuple.nodes,
"b": [graphs_tuple.nodes + 1, graphs_tuple.nodes + 2]
}, )
new_edges = [{
"c": graphs_tuple.edges + 5,
"d": (graphs_tuple.edges + 1, graphs_tuple.edges + 3),
}]
new_globals = []
return graphs_tuple.replace(nodes=new_nodes,
edges=new_edges,
globals=new_globals)
graphs_ = [_create_nested_fields(gr) for gr in graphs_]
concat_graph = utils_tf.concat(graphs_, axis=0)
actual_nodes = concat_graph.nodes
actual_edges = concat_graph.edges
actual_globals = concat_graph.globals
expected_nodes = tree.map_structure(lambda *x: tf.concat(x, axis=0),
*[gr.nodes for gr in graphs_])
expected_edges = tree.map_structure(lambda *x: tf.concat(x, axis=0),
*[gr.edges for gr in graphs_])
expected_globals = tree.map_structure(lambda *x: tf.concat(x, axis=0),
*[gr.globals for gr in graphs_])
tree.assert_same_structure(expected_nodes, actual_nodes)
tree.assert_same_structure(expected_edges, actual_edges)
tree.assert_same_structure(expected_globals, actual_globals)
tree.map_structure(self.assertAllEqual, expected_nodes, actual_nodes)
tree.map_structure(self.assertAllEqual, expected_edges, actual_edges)
tree.map_structure(self.assertAllEqual, expected_globals,
actual_globals)
# Borrowed from `test_concat_first_axis`:
self.assertAllEqual(np.array([3, 2, 2, 3]), concat_graph.n_node)
self.assertAllEqual(np.array([2, 1, 1, 2]), concat_graph.n_edge)
self.assertAllEqual(np.array([1, 2, 4, 6, 8, 9]), concat_graph.senders)
self.assertAllEqual(np.array([0, 0, 3, 5, 7, 7]),
concat_graph.receivers)
def _build(self, input_op):
latent = self._encoder(input_op) # latent size = 16
output_ops = [self._decoder(latent)] # K = 0 data
for i in range(self._num_processing_steps):
for c in self._cores:
latent = c(latent)
decoded_op = self._decoder(latent)
output_ops.append(decoded_op) # K = 1, 2, 3... data
return self._output_transform(utils_tf.concat(
output_ops, axis=1)) # K * 16 for every node
def __call__(self, input_op, num_processing_steps):
latent = self._edge_block(self._node_encoder_block(input_op))
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
output_ops.append(self._output_transform(latent))
return output_ops
def _build(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
decoded_op = self._decoder(latent)
output_ops.append(self._output_transform(decoded_op))
return output_ops
def data_dicts_to_graphs_tuple(input_dd, target_dd, with_batch_dim=True):
# if type(input_dd) is not list:
# input_dd = [input_dd]
# if type(target_dd) is not list:
# target_dd = [target_dd]
# input_graphs = utils_tf.data_dicts_to_graphs_tuple(input_dd)
# target_graphs = utils_tf.data_dicts_to_graphs_tuple(target_dd)
input_graphs = utils_tf.concat(input_dd, axis=0)
target_graphs = utils_tf.concat(target_dd, axis=0)
# # fill zeros
# input_graphs = utils_tf.set_zero_global_features(input_graphs, 1, dtype=tf.float64)
# target_graphs = utils_tf.set_zero_global_features(target_graphs, 1, dtype=tf.float64)
# target_graphs = utils_tf.set_zero_node_features(target_graphs, 1, dtype=tf.float64)
# expand dims
if with_batch_dim:
input_graphs = add_batch_dim(input_graphs)
target_graphs = add_batch_dim(target_graphs)
return input_graphs, target_graphs
def _build(self, input_op, num_processing_steps, is_training, sess):
print("EncodeProcessDecode is running in mode: full global data")
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
decoded_op = self._decoder(latent)#, is_training)
#output_ops.append(self._output_transform(decoded_op))
output_ops.append(decoded_op)
return output_ops
def __call__(self, input_op, num_processing_steps, is_training=True):
latent = self._global_block(
self._edge_block(self._node_encoder_block(input_op)))
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
output = latent.replace(
globals=self._global_nn(latent.globals, is_training))
output_ops.append(output)
return output_ops
def __call__(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
ws = self.trainable_variables
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
ws = self.trainable_variables
decoded_op = self._decoder(latent)
ws = self.trainable_variables
output_ops.append(self._output_transform(decoded_op))
###
ws = self.trainable_variables
return output_ops
def _build(self, input_op, num_processing_steps):
"""
Feed the input through the GraphAttention network
:param input_op: The graph_nets graph input of the network
:param num_processing_steps: The number of feeding forward on the data
:return: The output graphs calculated
"""
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._network(core_input)
decoded_op = self._output_transform(latent)
output_ops.append(decoded_op)
return output_ops
def __call__(self, input_op, num_processing_steps, is_training=True):
node_kwargs = edge_kwargs = dict(is_training=is_training)
latent = self._edge_block(
self._node_encoder_block(input_op, node_kwargs), edge_kwargs)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input,
edge_model_kwargs=edge_kwargs,
node_model_kwargs=node_kwargs)
output_ops.append(self._output_transform(latent))
return output_ops
def test_concat_last_axis(self):
graph0 = utils_np.networkxs_to_graphs_tuple(
[_generate_graph(0, 3), _generate_graph(1, 2)])
graph1 = utils_np.networkxs_to_graphs_tuple(
[_generate_graph(2, 3), _generate_graph(3, 2)])
graph0 = graph0.map(tf.convert_to_tensor, graphs.ALL_FIELDS)
graph1 = graph1.map(tf.convert_to_tensor, graphs.ALL_FIELDS)
concat_graph = utils_tf.concat([graph0, graph1], axis=-1)
self.assertAllEqual(
np.array([[0, 0, 0, 2], [1, 0, 1, 2], [2, 0, 2, 2], [0, 1, 0, 3],
[1, 1, 1, 3]]), concat_graph.nodes)
self.assertAllEqual(
np.array([[0, 1, 0, 0, 1, 2], [1, 2, 0, 1, 2, 2], [0, 1, 1, 0, 1, 3]]),
concat_graph.edges)
self.assertAllEqual(np.array([3, 2]), concat_graph.n_node)
self.assertAllEqual(np.array([2, 1]), concat_graph.n_edge)
self.assertAllEqual(np.array([1, 2, 4]), concat_graph.senders)
self.assertAllEqual(np.array([0, 0, 3]), concat_graph.receivers)
self.assertAllEqual(np.array([[0, 2], [1, 3]]), concat_graph.globals)
def test_concat_first_axis(self, none_fields):
graph_0 = utils_np.networkxs_to_graphs_tuple(
[_generate_graph(0, 3),
_generate_graph(1, 2)])
graph_1 = utils_np.networkxs_to_graphs_tuple([_generate_graph(2, 2)])
graph_2 = utils_np.networkxs_to_graphs_tuple([_generate_graph(3, 3)])
graphs_ = [
gr.map(tf.convert_to_tensor, graphs.ALL_FIELDS)
for gr in [graph_0, graph_1, graph_2]
]
graphs_ = [gr.map(lambda _: None, none_fields) for gr in graphs_]
concat_graph = utils_tf.concat(graphs_, axis=0)
for none_field in none_fields:
self.assertEqual(None, getattr(concat_graph, none_field))
concat_graph = concat_graph.map(tf.no_op, none_fields)
with self.test_session() as sess:
concat_graph = sess.run(concat_graph)
if "nodes" not in none_fields:
self.assertAllEqual(np.array([0, 1, 2, 0, 1, 0, 1, 0, 1, 2]),
[x[0] for x in concat_graph.nodes])
self.assertAllEqual(np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3]),
[x[1] for x in concat_graph.nodes])
if "edges" not in none_fields:
self.assertAllEqual(np.array([0, 1, 0, 0, 0, 1]),
[x[0] for x in concat_graph.edges])
self.assertAllEqual(np.array([0, 0, 1, 2, 3, 3]),
[x[2] for x in concat_graph.edges])
self.assertAllEqual(np.array([3, 2, 2, 3]), concat_graph.n_node)
self.assertAllEqual(np.array([2, 1, 1, 2]), concat_graph.n_edge)
if "senders" not in none_fields:
# [1, 2], [1], [1], [1, 2] and 3, 2, 2, 3 nodes
# So we are summing [1, 2, 1, 1, 2] with [0, 0, 3, 5, 7, 7]
self.assertAllEqual(np.array([1, 2, 4, 6, 8, 9]),
concat_graph.senders)
if "receivers" not in none_fields:
# [0, 0], [0], [0], [0, 0] and 3, 2, 2, 3 nodes
# So we are summing [0, 0, 0, 0, 0, 0] with [0, 0, 3, 5, 7, 7]
self.assertAllEqual(np.array([0, 0, 3, 5, 7, 7]),
concat_graph.receivers)
if "globals" not in none_fields:
self.assertAllEqual(np.array([[0], [1], [2], [3]]),
concat_graph.globals)
def _concat_batch_dim(G):
"""
G is a GraphNtuple Tensor, with additional dimension for batch-size.
Concatenate them along the axis for batch
"""
input_graphs = []
for ibatch in [0, 1]:
data_dict = {
"nodes": G.nodes[ibatch],
"edges": G.edges[ibatch],
"receivers": G.receivers[ibatch],
'senders': G.senders[ibatch],
'globals': G.globals[ibatch],
'n_node': G.n_node[ibatch],
'n_edge': G.n_edge[ibatch],
}
input_graphs.append(graphs.GraphsTuple(**data_dict))
return (tf.add(ibatch, 1), input_graphs)
print("{} graphs".format(len(input_graphs)))
return utils_tf.concat(input_graphs, axis=0)
def _build(self, graphs_tuple):
"""Connects the model into the tensorflow graph.
Args:
graphs_tuple: input graph tensor as defined in `graphs_tuple.graphs`.
Returns:
tensor with shape [n_particles] containing the predicted particle
mobilities.
"""
encoded = self._encoder(graphs_tuple)
outputs = encoded
for _ in range(self._n_recurrences):
# Adds skip connections.
inputs = utils_tf.concat([outputs, encoded], axis=-1)
outputs = self._propagation_network(inputs)
decoded = self._decoder(outputs)
return tf.squeeze(decoded.globals, axis=-1)
def __call__(self, input_op, num_processing_steps):
###node
node = input_op.nodes[:, 0] #[n,1]
edge = input_op.edges[:, 0] #[n,1]
globals = input_op.globals
node_f = self._embed(node)
edge_f = self._embed_edge(edge)
g_f = self._embed_global(globals)
input_op = input_op.replace(nodes=node_f, edges=edge_f, globals=g_f)
### edge
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
decoded_op = self._decoder(latent)
output_ops.append(self._output_transform(decoded_op))
return output_ops
def padding(g, max_nodes, max_edges, do_concat=True):
f_dtype = np.float32
n_nodes = np.sum(g.n_node)
n_edges = np.sum(g.n_edge)
n_nodes_pad = max_nodes - n_nodes
n_edges_pad = max_edges - n_edges
if n_nodes_pad < 0:
raise ValueError("Max Nodes: {}, but {} nodes in graph".format(max_nodes, n_nodes))
if n_edges_pad < 0:
raise ValueError("Max Edges: {}, but {} edges in graph".format(max_edges, n_edges))
# padding edges all pointing to the last node
# TODO: make the graphs more general <xju>
edges_idx = tf.constant([0] * n_edges_pad, dtype=np.int32)
# print(edges_idx)
zeros = np.array([0.0], dtype=f_dtype)
n_node_features = g.nodes.shape[-1]
n_edge_features = g.edges.shape[-1]
# print("input graph global: ", g.globals.shape)
# print("zeros: ", np.zeros_like(g.globals.numpy()))
# print("input edges", n_edges, "padding edges:", n_edges_pad)
padding_datadict = {
"n_node": n_nodes_pad,
"n_edge": n_edges_pad,
"nodes": np.zeros((n_nodes_pad, n_node_features), dtype=f_dtype),
'edges': np.zeros((n_edges_pad, n_edge_features), dtype=f_dtype),
'receivers': edges_idx,
'senders': edges_idx,
'globals':zeros
}
padding_graph = utils_tf.data_dicts_to_graphs_tuple([padding_datadict])
if do_concat:
return utils_tf.concat([g, padding_graph], axis=0)
else:
return padding_graph
