def find_and_replace_pattern(self, graph: Graph):
for cbv in graph.get_op_nodes(op='ClipByValueTF'):
cbv_name = cbv.soft_get('name', cbv.id)
minimum = Minimum(graph, {
'name': cbv_name + '/CLipMinimum'
}).create_node()
maximum = Maximum(graph, {
'name': cbv_name + '/CLipMaximum'
}).create_node()
minimum.in_port(0).connect(cbv.in_port(0).get_source())
minimum.in_port(1).connect(cbv.in_port(2).get_source())
maximum.in_port(0).connect(minimum.out_port(0))
maximum.in_port(1).connect(cbv.in_port(1).get_source())
cbv.out_port(0).get_connection().set_source(maximum.out_port(0))
rename_nodes([(cbv, cbv_name + '/TBR'), (maximum, cbv_name)])
graph.remove_node(cbv.id)
def replace_pattern(self, graph: Graph, match: dict):
clamp = match['clamp']
name = clamp.soft_get('name', clamp.id)
min_value = max_value = None
port_1_exist = clamp.has_port(
'in', 1) and not clamp.in_port(1).disconnected()
port_2_exist = clamp.has_port(
'in', 2) and not clamp.in_port(2).disconnected()
if port_1_exist and clamp.in_port(1).get_source().node.soft_get(
'type') == 'Const':
min_value = clamp.in_port(1).data.get_value()
if port_2_exist and clamp.in_port(2).get_source().node.soft_get(
'type') == 'Const':
max_value = clamp.in_port(2).data.get_value()
rename_node(clamp, name + '/TBR')
if min_value is None or max_value is None:
max_node = min_node = None
if port_1_exist:
max_node = Maximum(graph, {}).create_node()
clamp.in_port(0).get_connection().set_destination(
max_node.in_port(0))
clamp.in_port(1).get_connection().set_destination(
max_node.in_port(1))
clamp.out_port(0).get_connection().set_source(
max_node.out_port(0))
if port_2_exist:
min_node = Minimum(graph, {}).create_node()
if max_node is not None:
max_node.out_port(0).get_connection().set_source(
min_node.out_port(0))
max_node.out_port(0).connect(min_node.in_port(0))
else:
clamp.in_port(0).get_connection().set_destination(
min_node.in_port(0))
clamp.out_port(0).get_connection().set_source(
min_node.out_port(0))
clamp.in_port(2).get_connection().set_destination(
min_node.in_port(1))
assert min_node is not None or max_node is not None, 'Clamp node should have either min or max input used'
rename_node(min_node if min_node is not None else max_node, name)
else:
a_clamp = AttributedClamp(graph, {
'name': name,
'min': min_value,
'max': max_value
}).create_node()
rename_node(a_clamp, name)
clamp.in_port(0).get_connection().set_destination(
a_clamp.in_port(0))
clamp.out_port(0).get_connection().set_source(a_clamp.out_port(0))
def extract(cls, node):
Minimum.update_node_stat(
node, {'data_type': tf_dtype_extractor(node.pb.attr["T"].type)})
return cls.enabled
def extract(cls, node):
Minimum.update_node_stat(node)
return cls.enabled
def extract(node):
Minimum.update_node_stat(node)
return __class__.enabled