def __init__(self,
edge_model_fn,
node_encoder_model_fn,
node_model_fn,
reducer=tf.unsorted_segment_sum,
name="oricomm_net"):
super(OriCommNet, self).__init__(name=name)
with self._enter_variable_scope():
# Computes $\Psi_{com}(x_j)$ in Eq. (2) of 1706.06122
self._edge_block = blocks.EdgeBlock(edge_model_fn=edge_model_fn,
use_edges=False,
use_receiver_nodes=False,
use_sender_nodes=True,
use_globals=False)
# Computes $\Phi(x_i)$ in Eq. (2) of 1706.06122
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=node_encoder_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer,
name="node_encoder_block")
# Computes $\Theta(..)$ in Eq.(2) of 1706.06122
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
use_received_edges=True,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer)
def test_same_as_subblocks(self, reducer, none_field=None):
"""Compares the output to explicit subblocks output.
Args:
reducer: The reducer used in the `NodeBlock`s.
none_field: (string, default=None) If not None, the corresponding field
is removed from the input graph.
"""
input_graph = self._get_input_graph(none_field)
comm_net = self._get_model(reducer)
output_graph = comm_net(input_graph)
output_nodes = output_graph.nodes
edge_subblock = blocks.EdgeBlock(
edge_model_fn=lambda: comm_net._edge_block._edge_model,
use_edges=False,
use_receiver_nodes=False,
use_sender_nodes=True,
use_globals=False)
node_encoder_subblock = blocks.NodeBlock(
node_model_fn=lambda: comm_net._node_encoder_block._node_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer)
node_subblock = blocks.NodeBlock(
node_model_fn=lambda: comm_net._node_block._node_model,
use_received_edges=True,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer)
edge_block_out = edge_subblock(input_graph)
encoded_nodes = node_encoder_subblock(input_graph).nodes
node_input_graph = input_graph.replace(
edges=edge_block_out.edges, nodes=encoded_nodes)
node_block_out = node_subblock(node_input_graph)
expected_nodes = node_block_out.nodes
self.assertAllEqual(input_graph.globals, output_graph.globals)
self.assertAllEqual(input_graph.edges, output_graph.edges)
self.assertAllEqual(input_graph.receivers, output_graph.receivers,)
self.assertAllEqual(input_graph.senders, output_graph.senders)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
actual_nodes_output, expected_nodes_output = sess.run(
[output_nodes, expected_nodes])
self._assert_all_none_or_all_close(expected_nodes_output,
actual_nodes_output)
def __init__(self,
edge_model_fn,
node_encoder_model_fn,
node_model_fn,
reducer=tf.math.unsorted_segment_sum,
name="comm_net"):
"""Initializes the CommNet module.
Args:
edge_model_fn: A callable to be passed to EdgeBlock. The callable must
return a Sonnet module (or equivalent; see EdgeBlock for details).
node_encoder_model_fn: A callable to be passed to the NodeBlock
responsible for the first encoding of the nodes. The callable must
return a Sonnet module (or equivalent; see NodeBlock for details). The
shape of this module's output should match the shape of the module built
by `edge_model_fn`, but for the first and last dimension.
node_model_fn: A callable to be passed to NodeBlock. The callable must
return a Sonnet module (or equivalent; see NodeBlock for details).
reducer: Reduction to be used when aggregating the edges in the nodes.
This should be a callable whose signature matches
tf.unsorted_segment_sum.
name: The module name.
"""
super(CommNet, self).__init__(name=name)
#with self._enter_variable_scope():
if 2 > 1:
# Computes $\Psi_{com}(x_j)$ in Eq. (2) of 1706.06122
self._edge_block = blocks.EdgeBlock(edge_model_fn=edge_model_fn,
use_edges=False,
use_receiver_nodes=False,
use_sender_nodes=True,
use_globals=False)
# Computes $\Phi(x_i)$ in Eq. (2) of 1706.06122
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=node_encoder_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer,
name="node_encoder_block")
# Computes $\Theta(..)$ in Eq.(2) of 1706.06122
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
use_received_edges=True,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
received_edges_reducer=reducer)
def test_compatible_higher_rank_no_raise(self):
"""No exception should occur with higher ranks tensors."""
input_graph = self._get_shaped_input_graph()
input_graph = input_graph.map(lambda v: tf.transpose(v, [0, 2, 1, 3]))
network = blocks.NodeBlock(
functools.partial(snt.Conv2D, output_channels=10, kernel_shape=[3, 3]))
self._assert_build_and_run(network, input_graph)
def __init__(self, node_model_fn, global_model_fn, name="rwrd_gnn"):
# aggregator_fn = tf.math.unsorted_segment_sum,
"""Initializes the reward model"""
super(GraphNeuralNetwork_reward, self).__init__(name=name)
with self._enter_variable_scope():
# self._edge_block = blocks.EdgeBlock(
# edge_model_fn=edge_model_fn,
# use_edges=False,
# use_receiver_nodes=True,
# use_sender_nodes=True,
# use_globals=True,
# name='edge_block')
self._node_block = blocks.NodeBlock(
node_model_fn=node_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=True,
received_edges_reducer=tf.math.unsorted_segment_sum,
sent_edges_reducer=tf.math.unsorted_segment_sum,
name="node_block")
self._global_block = blocks.GlobalBlock(
global_model_fn=global_model_fn,
use_edges=False,
use_nodes=True,
use_globals=True,
nodes_reducer=tf.math.unsorted_segment_sum,
edges_reducer=tf.math.unsorted_segment_sum,
name="global_block")
def __init__(self, conf, name="encoder-attention-tsp"):
"""Inits the module.
Args:
name: The module name.
"""
super(Encoder, self).__init__(name=name)
self.conf = conf
self.training = True
with self._enter_variable_scope():
self._initial_projection = snt.Linear(
output_size=self.conf.embedding_dim,
initializers={
'w': utils.initializer(conf.init_dim),
'b': utils.initializer(conf.init_dim)
},
name="initial_projection")
self._initial_projection_block = blocks.NodeBlock(
lambda: self._initial_projection,
use_received_edges=False,
use_nodes=True,
use_globals=False,
name="initial_block_projection")
self._encoder_layers = [
EncoderLayer(conf, "encoder_layer_%i" % i)
for i in range(self.conf.encoder_nbr_layers)
]
def test_unused_field_can_be_none(
self, use_edges, use_nodes, use_globals, none_field):
"""Checks that computation can handle non-necessary fields left None."""
input_graph = self._get_input_graph([none_field])
node_block = blocks.NodeBlock(
node_model_fn=self._node_model_fn,
use_received_edges=use_edges,
use_sent_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals)
output_graph = node_block(input_graph)
model_inputs = []
if use_edges:
model_inputs.append(
blocks.ReceivedEdgesToNodesAggregator(
tf.unsorted_segment_sum)(input_graph))
model_inputs.append(
blocks.SentEdgesToNodesAggregator(
tf.unsorted_segment_sum)(input_graph))
if use_nodes:
model_inputs.append(input_graph.nodes)
if use_globals:
model_inputs.append(blocks.broadcast_globals_to_nodes(input_graph))
model_inputs = tf.concat(model_inputs, axis=-1)
self.assertEqual(input_graph.edges, output_graph.edges)
self.assertEqual(input_graph.globals, output_graph.globals)
with self.test_session() as sess:
actual_nodes, model_inputs_out = sess.run(
(output_graph.nodes, model_inputs))
expected_output_nodes = model_inputs_out * self._scale
self.assertNDArrayNear(expected_output_nodes, actual_nodes, err=1e-4)
def __init__(self, name="DecaySimulator"):
super(DecaySimulator, self).__init__(name=name)
self._node_linear = make_mlp_model()
self._node_rnn = snt.GRU(hidden_size=LATENT_SIZE, name='node_rnn')
self._node_proper = snt.nets.MLP([4], activate_final=False)
self._edge_block = blocks.EdgeBlock(edge_model_fn=make_mlp_model,
use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=make_mlp_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
name='node_encoder_block')
self._global_encoder_block = blocks.GlobalBlock(
global_model_fn=make_mlp_model,
use_edges=True,
use_nodes=True,
use_globals=False,
nodes_reducer=tf.math.unsorted_segment_sum,
edges_reducer=tf.math.unsorted_segment_sum,
name='global_encoder_block')
self._core = MLPGraphNetwork()
# self._core = InteractionNetwork(
# edge_model_fn=make_mlp_model,
# node_model_fn=make_mlp_model,
# reducer=tf.math.unsorted_segment_sum
# )
# # Transforms the outputs into appropriate shapes.
node_output_size = 64
node_fn = lambda: snt.Sequential([
snt.nets.MLP(
[node_output_size],
activation=tf.nn.relu, # default is relu
name='node_output')
])
global_output_size = 1
global_fn = lambda: snt.Sequential([
snt.nets.MLP(
[global_output_size],
activation=tf.nn.relu, # default is relu
name='global_output'),
tf.sigmoid
])
self._output_transform = modules.GraphIndependent(
edge_model_fn=None,
node_model_fn=node_fn,
global_model_fn=global_fn)
def __init__(self, name="DeepGraphInfoMax"):
super(DeepGraphInfoMax, self).__init__(name=name)
self._edge_block = blocks.EdgeBlock(
edge_model_fn=lambda: snt.nets.MLP([LATENT_SIZE] * 2,
activation=tf.nn.relu,
activate_final=True,
use_dropout=True),
use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=make_mlp_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
name='node_encoder_block')
self._core = modules.InteractionNetwork(
edge_model_fn=make_mlp_model,
node_model_fn=make_mlp_model,
reducer=tf.unsorted_segment_sum)
def __init__(self, node_model_fn, name=None):
super(DecoderNetwork, self).__init__(name=name)
self.node_block = blocks.NodeBlock(node_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=False,
use_globals=True)
def __init__(self, name="SegmentClassifier"):
super(SegmentClassifier, self).__init__(name=name)
self._edge_block = blocks.EdgeBlock(edge_model_fn=make_mlp_model,
use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=make_mlp_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
name='node_encoder_block')
self._core = InteractionNetwork(edge_model_fn=make_mlp_model,
node_model_fn=make_mlp_model,
reducer=tf.math.unsorted_segment_sum)
# Transforms the outputs into appropriate shapes.
edge_output_size = 1
edge_fn = lambda: snt.Sequential([
snt.nets.MLP(
[edge_output_size],
activation=tf.nn.relu, # default is relu
name='edge_output'),
tf.sigmoid
])
self._output_transform = modules.GraphIndependent(edge_fn, None, None)
def test_same_as_subblocks(self, reducer):
"""Compares the output to explicit subblocks output.
Args:
reducer: The reducer used in the `NodeBlock` and `GlobalBlock`.
"""
input_graph = self._get_input_graph()
edge_model_fn = functools.partial(snt.Linear, output_size=5)
node_model_fn = functools.partial(snt.Linear, output_size=10)
global_model_fn = functools.partial(snt.Linear, output_size=15)
graph_network = modules.GraphNetwork(
edge_model_fn=edge_model_fn,
node_model_fn=node_model_fn,
global_model_fn=global_model_fn,
reducer=reducer)
output_graph = graph_network(input_graph)
edge_block = blocks.EdgeBlock(
edge_model_fn=lambda: graph_network._edge_block._edge_model,
use_sender_nodes=True,
use_edges=True,
use_receiver_nodes=True,
use_globals=True)
node_block = blocks.NodeBlock(
node_model_fn=lambda: graph_network._node_block._node_model,
use_nodes=True,
use_sent_edges=False,
use_received_edges=True,
use_globals=True,
received_edges_reducer=reducer)
global_block = blocks.GlobalBlock(
global_model_fn=lambda: graph_network._global_block._global_model,
use_nodes=True,
use_edges=True,
use_globals=True,
edges_reducer=reducer,
nodes_reducer=reducer)
expected_output_edge_block = edge_block(input_graph)
expected_output_node_block = node_block(expected_output_edge_block)
expected_output_global_block = global_block(expected_output_node_block)
expected_edges = expected_output_edge_block.edges
expected_nodes = expected_output_node_block.nodes
expected_globals = expected_output_global_block.globals
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
(output_graph_out,
expected_edges_out, expected_nodes_out, expected_globals_out) = sess.run(
(output_graph, expected_edges, expected_nodes, expected_globals))
self._assert_all_none_or_all_close(expected_edges_out,
output_graph_out.edges)
self._assert_all_none_or_all_close(expected_nodes_out,
output_graph_out.nodes)
self._assert_all_none_or_all_close(expected_globals_out,
output_graph_out.globals)
def __init__(self,
edge_model_fn,
node_model_fn,
reducer=tf.unsorted_segment_sum,
name="interaction_network"):
"""Initializes the InteractionNetwork module.
Args:
edge_model_fn: A callable that will be passed to `EdgeBlock` to perform
per-edge computations. The callable must return a Sonnet module (or
equivalent; see `blocks.EdgeBlock` for details), and the shape of the
output of this module must match the one of the input nodes, but for the
first and last axis.
node_model_fn: A callable that will be passed to `NodeBlock` to perform
per-node computations. The callable must return a Sonnet module (or
equivalent; see `blocks.NodeBlock` for details).
reducer: Reducer to be used by NodeBlock to aggregate edges. Defaults to
tf.unsorted_segment_sum.
name: The module name.
"""
super(InteractionNetwork, self).__init__(name=name)
with self._enter_variable_scope():
self._edge_block = blocks.EdgeBlock(edge_model_fn=edge_model_fn,
use_globals=False)
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
use_received_edges=True,
use_sent_edges=True,
use_globals=False,
received_edges_reducer=reducer)
def __init__(self, name="FourTopPredictor"):
super(FourTopPredictor, self).__init__(name=name)
self._edge_block = blocks.EdgeBlock(edge_model_fn=make_mlp_model,
use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=make_mlp_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
name='node_encoder_block')
self._global_block = blocks.GlobalBlock(
global_model_fn=make_mlp_model,
use_edges=True,
use_nodes=True,
use_globals=False,
)
self._core = MLPGraphNetwork()
# Transforms the outputs into appropriate shapes.
global_output_size = n_target_node_features * n_max_tops
self._global_nn = snt.nets.MLP(
[128, 128, global_output_size],
activation=tf.nn.leaky_relu, # default is relu, tanh
dropout_rate=0.30,
name='global_output')
def __init__(self,
node_model_fn,
global_model_fn,
reducer=tf.unsorted_segment_sum,
name="deep_sets"):
"""Initializes the DeepSets module.
Args:
node_model_fn: A callable to be passed to NodeBlock. The callable must
return a Sonnet module (or equivalent; see NodeBlock for details). The
shape of this module's output must equal the shape of the input graph's
global features, but for the first and last axis.
global_model_fn: A callable to be passed to GlobalBlock. The callable must
return a Sonnet module (or equivalent; see GlobalBlock for details).
reducer: Reduction to be used when aggregating the nodes in the globals.
This should be a callable whose signature matches
tf.unsorted_segment_sum.
name: The module name.
"""
super(DeepSets, self).__init__(name=name)
with self._enter_variable_scope():
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=True)
self._global_block = blocks.GlobalBlock(
global_model_fn=global_model_fn,
use_edges=False,
use_nodes=True,
use_globals=False,
nodes_reducer=reducer)
def __init__(self, name="GlobalClassifierNoEdgeInfo"):
super(GlobalClassifierNoEdgeInfo, self).__init__(name=name)
self._edge_block = blocks.EdgeBlock(edge_model_fn=make_mlp_model,
use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=make_mlp_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False,
name='node_encoder_block')
self._global_block = blocks.GlobalBlock(
global_model_fn=make_mlp_model,
use_edges=True,
use_nodes=True,
use_globals=False,
)
self._core = MLPGraphNetwork()
# Transforms the outputs into appropriate shapes.
global_output_size = 1
global_fn = lambda: snt.Sequential([
snt.nets.MLP([LATENT_SIZE, global_output_size],
name='global_output'), tf.sigmoid
])
self._output_transform = modules.GraphIndependent(
None, None, global_fn)
def test_created_variables(self, use_received_edges, use_sent_edges,
use_nodes, use_globals, expected_first_dim_w):
"""Verifies the variable names and shapes created by a NodeBlock."""
output_size = 10
expected_var_shapes_dict = {
"node_block/mlp/linear_0/b:0": [output_size],
"node_block/mlp/linear_0/w:0": [expected_first_dim_w, output_size]
}
input_graph = self._get_input_graph()
node_block = blocks.NodeBlock(node_model_fn=functools.partial(
snt.nets.MLP, output_sizes=[output_size]),
use_received_edges=use_received_edges,
use_sent_edges=use_sent_edges,
use_nodes=use_nodes,
use_globals=use_globals)
node_block(input_graph)
variables = node_block.get_variables()
var_shapes_dict = {
var.name: var.get_shape().as_list()
for var in variables
}
self.assertDictEqual(expected_var_shapes_dict, var_shapes_dict)
def test_no_input_raises_exception(self):
"""Checks that receiving no input raises an exception."""
with self.assertRaisesRegexp(ValueError, "At least one of "):
blocks.NodeBlock(node_model_fn=self._node_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=False,
use_globals=False)
def __init__(self,
mlp_size=16,
cluster_encoded_size=10,
num_heads=10,
core_steps=10,
name=None):
super(Model, self).__init__(name=name)
self.epd_encoder = EncodeProcessDecode_E(
encoder=EncoderNetwork(edge_model_fn=lambda: snt.nets.MLP(
[mlp_size], activate_final=True, activation=tf.nn.leaky_relu),
node_model_fn=lambda: snt.Linear(
cluster_encoded_size),
global_model_fn=lambda: snt.
nets.MLP([mlp_size],
activate_final=True,
activation=tf.nn.leaky_relu)),
core=CoreNetwork(num_heads=num_heads,
multi_head_output_size=cluster_encoded_size,
input_node_size=cluster_encoded_size),
decoder=EncoderNetwork(edge_model_fn=lambda: snt.nets.MLP(
[mlp_size], activate_final=True, activation=tf.nn.leaky_relu),
node_model_fn=lambda: snt.Linear(
cluster_encoded_size),
global_model_fn=lambda: snt.nets.MLP(
[32, 32, 64],
activate_final=True,
activation=tf.nn.leaky_relu)))
self.epd_decoder = EncodeProcessDecode_D(
encoder=DecoderNetwork(node_model_fn=lambda: snt.nets.MLP(
[32, 32, cluster_encoded_size],
activate_final=True,
activation=tf.nn.leaky_relu)),
core=CoreNetwork(num_heads=num_heads,
multi_head_output_size=cluster_encoded_size,
input_node_size=cluster_encoded_size),
decoder=snt.Sequential([
RelationNetwork(edge_model_fn=lambda: snt.nets.MLP(
[mlp_size],
activate_final=True,
activation=tf.nn.leaky_relu),
global_model_fn=lambda: snt.nets.MLP(
[mlp_size],
activate_final=True,
activation=tf.nn.leaky_relu)),
blocks.NodeBlock(node_model_fn=lambda: snt.nets.MLP(
[cluster_encoded_size - 3],
activate_final=True,
activation=tf.nn.leaky_relu),
use_received_edges=True,
use_sent_edges=True,
use_nodes=True,
use_globals=True)
]))
self._core_steps = core_steps
def test_optional_arguments(self, scale, offset):
"""Assesses the correctness of the NodeBlock using arguments."""
input_graph = self._get_input_graph()
node_block = blocks.NodeBlock(node_model_fn=self._node_model_args_fn)
output_graph_out = node_block(
input_graph, node_model_kwargs=dict(scale=scale, offset=offset))
fixed_scale = scale
fixed_offset = offset
model_fn = lambda: lambda features: features * fixed_scale + fixed_offset
hardcoded_node_block = blocks.NodeBlock(node_model_fn=model_fn)
expected_graph_out = hardcoded_node_block(input_graph)
self.assertIs(expected_graph_out.edges, output_graph_out.edges)
self.assertIs(expected_graph_out.globals, output_graph_out.globals)
self.assertNDArrayNear(
expected_graph_out.nodes.numpy(),
output_graph_out.nodes.numpy(),
err=1e-4)
def tinet(input_graph):
embedding = blocks.NodeBlock(
node_model_fn=lambda: tf.keras.layers.Embedding(800, 32),
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False)
graph_network_layer1 = blocks.NodeBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
node_model_fn=lambda: tf.layers.Dense(32, activation=tf.nn.relu))
# global_model_fn = lambda: tf.layers.Dense(8, activation=tf.nn.relu))
graph_network_layer2 = blocks.NodeBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
node_model_fn=lambda: tf.layers.Dense(32, activation=tf.nn.relu))
# global_model_fn = lambda: tf.layers.Dense(8, activation=tf.nn.relu))
graph_network_layer3 = blocks.NodeBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
node_model_fn=lambda: tf.layers.Dense(32, activation=tf.nn.relu))
# global_model_fn = lambda: tf.layers.Dense(8, activation=tf.nn.relu))
graph_network_layer4 = blocks.NodeBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
node_model_fn=lambda: tf.layers.Dense(32, activation=tf.nn.relu))
# global_model_fn = lambda: tf.layers.Dense(8, activation=tf.nn.relu))
graph_network_layer5 = blocks.GlobalBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
# node_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
global_model_fn=lambda: tf.layers.Dense(40, activation=tf.nn.relu))
graph_network_layer6 = blocks.GlobalBlock(
# edge_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
# node_model_fn = lambda: tf.layers.Dense(16, activation=tf.nn.relu),
global_model_fn=lambda: tf.layers.Dense(40, activation=tf.nn.relu))
h0 = embedding(input_graph)
h1 = graph_network_layer1(h0)
h2 = graph_network_layer2(h1)
h3 = graph_network_layer3(h2)
h4 = graph_network_layer4(h3)
h5 = graph_network_layer5(h4)
h6 = graph_network_layer6(h5)
out = h6.globals
return tf.layers.dense(out, 4, activation=None)
def test_missing_field_raises_exception(self, use_received_edges,
use_sent_edges, use_nodes,
use_globals, none_fields):
"""Checks that missing a required field raises an exception."""
input_graph = self._get_input_graph(none_fields)
node_block = blocks.NodeBlock(node_model_fn=self._node_model_fn,
use_received_edges=use_received_edges,
use_sent_edges=use_sent_edges,
use_nodes=use_nodes,
use_globals=use_globals)
with self.assertRaisesRegexp(ValueError, "field cannot be None"):
node_block(input_graph)
def __init__(self,
edge_fn,
with_edge_inputs=False,
with_node_inputs=True,
encoder_size: list = None,
core_size: list = None,
name="EdgeLearnerBase",
**kwargs):
super(EdgeLearnerBase, self).__init__(name=name)
if encoder_size is not None:
encoder_mlp_fn = partial(make_mlp_model,
mlp_size=encoder_size,
**kwargs)
else:
encoder_mlp_fn = partial(make_mlp_model, **kwargs)
edge_block_args = dict(use_edges=False,
use_receiver_nodes=True,
use_sender_nodes=True,
use_globals=False)
node_block_args = dict(use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False)
if with_edge_inputs:
edge_block_args['use_edges'] = True
node_block_args['use_received_edges'] = True
node_block_args['use_sent_edges'] = True
if not with_node_inputs:
edge_block_args['use_receiver_nodes'] = False
edge_block_args['use_sender_nodes'] = False
node_block_args['use_nodes'] = False
self._edge_block = blocks.EdgeBlock(edge_model_fn=encoder_mlp_fn,
**edge_block_args,
name='edge_encoder_block')
self._node_encoder_block = blocks.NodeBlock(
node_model_fn=encoder_mlp_fn,
**node_block_args,
name='node_encoder_block')
if core_size is not None:
core_mlp_fn = partial(make_mlp_model, mlp_size=core_size, **kwargs)
else:
core_mlp_fn = partial(make_mlp_model, **kwargs)
self._core = InteractionNetwork(edge_model_fn=core_mlp_fn,
node_model_fn=core_mlp_fn,
reducer=tf.math.unsorted_segment_sum)
self._output_transform = modules.GraphIndependent(edge_fn, None, None)
def __init__(self,
edge_model_fn,
node_model_fn,
global_model_fn,
reducer=tf.math.unsorted_segment_sum,
edge_block_opt=None,
node_block_opt=None,
global_block_opt=None,
name="graph_network"):
"""Initializes the GraphNetwork module.
Args:
edge_model_fn: A callable that will be passed to EdgeBlock to perform
per-edge computations. The callable must return a Sonnet module (or
equivalent; see EdgeBlock for details).
node_model_fn: A callable that will be passed to NodeBlock to perform
per-node computations. The callable must return a Sonnet module (or
equivalent; see NodeBlock for details).
global_model_fn: A callable that will be passed to GlobalBlock to perform
per-global computations. The callable must return a Sonnet module (or
equivalent; see GlobalBlock for details).
reducer: Reducer to be used by NodeBlock and GlobalBlock to aggregate
nodes and edges. Defaults to tf.unsorted_segment_sum. This will be
overridden by the reducers specified in `node_block_opt` and
`global_block_opt`, if any.
edge_block_opt: Additional options to be passed to the EdgeBlock. Can
contain keys `use_edges`, `use_receiver_nodes`, `use_sender_nodes`,
`use_globals`. By default, these are all True.
node_block_opt: Additional options to be passed to the NodeBlock. Can
contain the keys `use_received_edges`, `use_sent_edges`, `use_nodes`,
`use_globals` (all set to True by default), and
`received_edges_reducer`, `sent_edges_reducer` (default to `reducer`).
global_block_opt: Additional options to be passed to the GlobalBlock. Can
contain the keys `use_edges`, `use_nodes`, `use_globals` (all set to
True by default), and `edges_reducer`, `nodes_reducer` (defaults to
`reducer`).
name: The module name.
"""
super(GraphNetwork, self).__init__(name=name)
edge_block_opt = _make_default_edge_block_opt(edge_block_opt)
node_block_opt = _make_default_node_block_opt(node_block_opt, reducer)
global_block_opt = _make_default_global_block_opt(
global_block_opt, reducer)
#with self._enter_variable_scope():
if 2 > 1:
self._edge_block = blocks.EdgeBlock(edge_model_fn=edge_model_fn,
**edge_block_opt)
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
**node_block_opt)
self._global_block = blocks.GlobalBlock(
global_model_fn=global_model_fn, **global_block_opt)
def __init__(self,
edge_model_fn,
node_model_fn,
reducer=tf.math.unsorted_segment_sum,
name="interaction_network"):
super(InteractionNetwork, self).__init__(name=name)
self._edge_block = blocks.EdgeBlock(edge_model_fn=edge_model_fn,
use_globals=False)
self._node_block = blocks.NodeBlock(node_model_fn=node_model_fn,
use_received_edges=True,
use_sent_edges=True,
use_globals=False,
received_edges_reducer=reducer)
def test_missing_aggregation_raises_exception(
self, use_received_edges, use_sent_edges,
received_edges_reducer, sent_edges_reducer):
"""Checks that missing a required aggregation argument raises an error."""
with self.assertRaisesRegexp(ValueError, "should not be None"):
blocks.NodeBlock(
node_model_fn=self._node_model_fn,
use_received_edges=use_received_edges,
use_sent_edges=use_sent_edges,
use_nodes=False,
use_globals=False,
received_edges_reducer=received_edges_reducer,
sent_edges_reducer=sent_edges_reducer)
def __init__(self,
edge_model_fn,
node_model_fn,
global_model_fn,
name=None):
super(EncoderNetwork, self).__init__(name=name)
self.node_block = blocks.NodeBlock(node_model_fn,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=False)
self.relation_network = RelationNetwork(edge_model_fn=edge_model_fn,
global_model_fn=global_model_fn)
def test_incompatible_higher_rank_inputs_no_raise(self, use_received_edges,
use_sent_edges,
use_nodes, use_globals,
field):
"""No exception should occur if a differently shapped field is not used."""
input_graph = self._get_shaped_input_graph()
input_graph = input_graph.replace(
**{field: tf.transpose(getattr(input_graph, field), [0, 2, 1, 3])})
network = blocks.NodeBlock(functools.partial(snt.Conv2D,
output_channels=10,
kernel_shape=[3, 3]),
use_received_edges=use_received_edges,
use_sent_edges=use_sent_edges,
use_nodes=use_nodes,
use_globals=use_globals)
self._assert_build_and_run(network, input_graph)
def test_incompatible_higher_rank_inputs_raises(self, use_received_edges,
use_sent_edges, use_nodes,
use_globals, field):
"""A exception should be raised if the inputs have incompatible shapes."""
input_graph = self._get_shaped_input_graph()
input_graph = input_graph.replace(
**{field: tf.transpose(getattr(input_graph, field), [0, 2, 1, 3])})
network = blocks.NodeBlock(functools.partial(snt.Conv2D,
output_channels=10,
kernel_shape=[3, 3]),
use_received_edges=use_received_edges,
use_sent_edges=use_sent_edges,
use_nodes=use_nodes,
use_globals=use_globals)
with self.assertRaisesRegexp(ValueError,
"in both shapes must be equal"):
network(input_graph)
def test_same_as_subblocks(self, reducer, none_fields):
"""Compares the output to explicit subblocks output.
Args:
reducer: The reducer used in the NodeBlock.
none_fields: (list of strings) The corresponding fields are removed from
the input graph.
"""
input_graph = self._get_input_graph()
input_graph = input_graph.map(lambda _: None, none_fields)
deep_sets = self._get_model(reducer)
output_graph = deep_sets(input_graph)
output_nodes = output_graph.nodes
output_globals = output_graph.globals
node_block = blocks.NodeBlock(
node_model_fn=lambda: deep_sets._node_block._node_model,
use_received_edges=False,
use_sent_edges=False,
use_nodes=True,
use_globals=True)
global_block = blocks.GlobalBlock(
global_model_fn=lambda: deep_sets._global_block._global_model,
use_edges=False,
use_nodes=True,
use_globals=False,
nodes_reducer=reducer)
node_block_out = node_block(input_graph)
expected_nodes = node_block_out.nodes
expected_globals = global_block(node_block_out).globals
self.assertAllEqual(input_graph.edges, output_graph.edges)
self.assertAllEqual(input_graph.receivers, output_graph.receivers)
self.assertAllEqual(input_graph.senders, output_graph.senders)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
(output_nodes_, output_globals_, expected_nodes_,
expected_globals_) = sess.run(
[output_nodes, output_globals, expected_nodes, expected_globals])
self._assert_all_none_or_all_close(expected_nodes_, output_nodes_)
self._assert_all_none_or_all_close(expected_globals_, output_globals_)