openvino/model-optimizer/extensions/back/TopKNormalizer.py

75 lines
2.9 KiB
Python

"""
Copyright (C) 2018-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from extensions.back.Reshape0DToSqueeze import Reshape0DToSqueeze
from extensions.back.ScalarConstNormalize import ScalarNormalize
from mo.back.replacement import BackReplacementPattern
from mo.front.common.partial_infer.utils import int64_array
from mo.front.tf.graph_utils import create_op_node_with_second_input
from mo.graph.graph import Graph, Node
from mo.ops.reshape import Reshape
from mo.ops.result import Result
class TopKNormalizer(BackReplacementPattern):
"""
The transformation converts the second input to the TopK layer from 0D to 1D.
Also the transformation adds the Result Op if there are no consumers of TopK outputs. However the Result for output
with values is not added if the node has attribute 'remove_values_output' which is set to True for Caffe models
where ArgMax does not have separate output with values.
TODO this pass should be removed when IE supports 0D tensors.
"""
enabled = True
def run_after(self):
return [ScalarNormalize]
def run_before(self):
return [Reshape0DToSqueeze]
@staticmethod
def pattern():
return dict(
nodes=[('result', {'type': 'TopK'})],
edges=[],
)
@staticmethod
def replace_pattern(graph: Graph, match: dict):
node = match['result']
is_scalar = graph.graph['cmd_params'].generate_experimental_IR_V10
reshape = create_op_node_with_second_input(graph, Reshape, int64_array([]) if is_scalar else int64_array([1]),
{'override_output_shape': True})
node.in_port(1).get_connection().insert_node(reshape)
TopKNormalizer.normalize_outputs(node)
@staticmethod
def normalize_outputs(node: Node):
"""
This function adds missed outputs for TopK node.
"""
if node.out_port(0).disconnected():
output = Result(node.graph, {'name': node.name + '/Result_port_0/',
'remove_from_xml': node.has_and_set('remove_values_output')}).create_node()
node.out_port(0).get_connection().set_destination(output.in_port(0))
if node.out_port(1).disconnected():
output = Result(node.graph, {'name': node.name + '/Result_port_1/'}).create_node()
node.out_port(1).get_connection().set_destination(output.in_port(0))