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,
edge_model_fn,
global_model_fn,
reducer=tf.unsorted_segment_sum,
name="relation_network"):
"""Initializes the RelationNetwork 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).
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 GlobalBlock to aggregate edges. Defaults to
tf.unsorted_segment_sum.
name: The module name.
"""
super(RelationNetwork, 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=False)
self._global_block = blocks.GlobalBlock(
global_model_fn=global_model_fn,
use_edges=True,
use_nodes=False,
use_globals=False,
edges_reducer=reducer)
def test_created_variables(self, use_edges, use_nodes, use_globals,
expected_first_dim_w):
"""Verifies the variable names and shapes created by a GlobalBlock."""
output_size = 10
expected_var_shapes_dict = {
"global_block/mlp/linear_0/b:0": [output_size],
"global_block/mlp/linear_0/w:0":
[expected_first_dim_w, output_size]
}
input_graph = self._get_input_graph()
global_block = blocks.GlobalBlock(global_model_fn=functools.partial(
snt.nets.MLP, output_sizes=[output_size]),
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals)
global_block(input_graph)
variables = global_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_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])
global_block = blocks.GlobalBlock(
global_model_fn=self._global_model_fn,
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals)
output_graph = global_block(input_graph)
model_inputs = []
if use_edges:
model_inputs.append(
blocks.EdgesToGlobalsAggregator(
tf.unsorted_segment_sum)(input_graph))
if use_nodes:
model_inputs.append(
blocks.NodesToGlobalsAggregator(
tf.unsorted_segment_sum)(input_graph))
if use_globals:
model_inputs.append(input_graph.globals)
model_inputs = tf.concat(model_inputs, axis=-1)
self.assertEqual(input_graph.edges, output_graph.edges)
self.assertEqual(input_graph.nodes, output_graph.nodes)
with self.test_session() as sess:
actual_globals, model_inputs_out = sess.run(
(output_graph.globals, model_inputs))
expected_output_globals = model_inputs_out * self._scale
self.assertNDArrayNear(expected_output_globals,
actual_globals,
err=1e-4)
def test_output_values(self, use_edges, use_nodes, use_globals,
edges_reducer, nodes_reducer):
"""Compares the output of a GlobalBlock to an explicit computation."""
input_graph = self._get_input_graph()
global_block = blocks.GlobalBlock(
global_model_fn=self._global_model_fn,
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals,
edges_reducer=edges_reducer,
nodes_reducer=nodes_reducer)
output_graph = global_block(input_graph)
model_inputs = []
if use_edges:
model_inputs.append(
blocks.EdgesToGlobalsAggregator(edges_reducer)(input_graph))
if use_nodes:
model_inputs.append(
blocks.NodesToGlobalsAggregator(nodes_reducer)(input_graph))
if use_globals:
model_inputs.append(input_graph.globals)
model_inputs = tf.concat(model_inputs, axis=-1)
self.assertEqual(input_graph.edges, output_graph.edges)
self.assertEqual(input_graph.nodes, output_graph.nodes)
with self.test_session() as sess:
output_graph_out, model_inputs_out = sess.run(
(output_graph, model_inputs))
expected_output_globals = model_inputs_out * self._scale
self.assertNDArrayNear(expected_output_globals,
output_graph_out.globals,
err=1e-4)
def test_no_input_raises_exception(self):
"""Checks that receiving no input raises an exception."""
with self.assertRaisesRegexp(ValueError, "At least one of "):
blocks.GlobalBlock(global_model_fn=self._global_model_fn,
use_edges=False,
use_nodes=False,
use_globals=False)
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.GlobalBlock(
functools.partial(snt.Conv2D,
output_channels=10,
kernel_shape=[3, 3]))
self._assert_build_and_run(network, input_graph)
def test_missing_field_raises_exception(self, use_edges, use_nodes,
use_globals, none_field):
"""Checks that missing a required field raises an exception."""
input_graph = self._get_input_graph([none_field])
global_block = blocks.GlobalBlock(
global_model_fn=self._global_model_fn,
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals)
with self.assertRaisesRegexp(ValueError, "field cannot be None"):
global_block(input_graph)
def test_missing_aggregation_raises_exception(self, use_edges, use_nodes,
edges_reducer,
nodes_reducer):
"""Checks that missing a required aggregation argument raises an error."""
with self.assertRaisesRegexp(ValueError, "should not be None"):
blocks.GlobalBlock(global_model_fn=self._global_model_fn,
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=False,
edges_reducer=edges_reducer,
nodes_reducer=nodes_reducer)
def __init__(self,
edge_model_fn,
node_model_fn,
global_model_fn,
reducer=tf.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 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():
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 test_incompatible_higher_rank_inputs_no_raise(self, use_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.GlobalBlock(functools.partial(snt.Conv2D,
output_channels=10,
kernel_shape=[3, 3]),
use_edges=use_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_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.GlobalBlock(functools.partial(snt.Conv2D,
output_channels=10,
kernel_shape=[3, 3]),
use_edges=use_edges,
use_nodes=use_nodes,
use_globals=use_globals)
with self.assertRaisesRegexp(ValueError,
"in both shapes must be equal"):
network(input_graph)
