def apply_final_node_renames(self):
"""Applies node renames in self.final_node_renames to self.output_graph."""
old_graph = self.output_graph
self.output_graph = graph_pb2.GraphDef()
for node in old_graph.node:
node.name = self.final_node_renames.get(node.name, node.name)
for index, input_name in enumerate(node.input):
node_name = helper.node_name_from_input(input_name)
input_full_name = helper.ensure_tensor_name_has_port(
input_name)
if node_name in self.final_node_renames:
node.input[index] = "%s%s" % (
self.final_node_renames[node_name],
input_full_name[len(node_name):])
self.add_output_graph_node(node)
return self.output_graph
def remove_redundant_quantization(self, old_graph):
old_nodes_map = self.create_nodes_map(old_graph)
self.output_graph = graph_pb2.GraphDef()
inputs_to_rename = {}
# We go through all the nodes, looking for any that match the patterns we
# know how to optimize away.
for node in old_graph.node:
# We always start with a Quantize node, and examine its inputs to see if
# they are in a form that can be removed.
if node.op not in ["Quantize", "QuantizeV2"]:
continue
dequantize_node_name = helper.node_name_from_input(node.input[0])
if dequantize_node_name not in old_nodes_map:
raise ValueError("Input node name '" + dequantize_node_name +
"' not found in node '" + node.name + "'")
dequantize_node = old_nodes_map[dequantize_node_name]
# Do we have a Dequantize feeding in, with the same type as the Quantize?
if dequantize_node.op != "Dequantize":
continue
if node.attr["T"] != dequantize_node.attr["T"]:
continue
# Now look at the other inputs, and ensure they're Min/Max nodes.
min_node_name = helper.node_name_from_input(node.input[1])
max_node_name = helper.node_name_from_input(node.input[2])
min_node = old_nodes_map[min_node_name]
max_node = old_nodes_map[max_node_name]
is_min_right_type = (min_node.op in ["Min", "Dequantize"])
is_max_right_type = (max_node.op in ["Max", "Dequantize"])
if not is_min_right_type or not is_max_right_type:
print("Didn't find expected types on inputs : %s, %s." %
(min_node.op, max_node.op))
continue
min_node_input_name = helper.node_name_from_input(
min_node.input[0])
max_node_input_name = helper.node_name_from_input(
max_node.input[0])
# There are two different patterns for Min nodes we can recognize, one
# where the input comes directly from the same one as the Max, and
# another where we run it through another Min first, so check for both.
is_same_input = False
if min_node_input_name == max_node_input_name:
is_same_input = True
else:
first_min_node_input = old_nodes_map[min_node_input_name]
if first_min_node_input.op == "Concat":
second_min_node_name = helper.node_name_from_input(
first_min_node_input.input[1])
second_min_node = old_nodes_map[second_min_node_name]
if second_min_node.op == "Min":
second_min_node_input_name = helper.node_name_from_input(
second_min_node.input[0])
is_same_input = (
second_min_node_input_name == max_node_input_name)
if not is_same_input:
print("Different min/max inputs: " + min_node_input_name)
continue
# We recognize this pattern, so mark the graph edges to be rewired to
# route around it entirely, since we know it's a no-op.
dequantize_source_name = helper.node_name_from_input(
dequantize_node.input[0])
node_tensor_name = helper.ensure_tensor_name_has_port(node.name)
min_tensor_name = node.name + ":1"
max_tensor_name = node.name + ":2"
inputs_to_rename[node_tensor_name] = dequantize_source_name
inputs_to_rename[min_tensor_name] = dequantize_node.input[1]
inputs_to_rename[max_tensor_name] = dequantize_node.input[2]
# Finally we apply all the rewiring we've marked to the graph.
for node in old_graph.node:
for index, input_full_name in enumerate(node.input):
input_name = helper.ensure_tensor_name_has_port(
input_full_name)
if input_name in inputs_to_rename:
node.input[index] = inputs_to_rename[input_name]
self.add_output_graph_node(node)
return self.output_graph
