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Result rename operation (#4242)

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* Added result rename operation

* Optimize imports

* Added ResultRename to package_BOM

* ResultRename moved to the end of back phase, code refactoring

* Revert incorrect changes

* Optimize imports

* Added comments and optimized imports.
This commit is contained in:
Anastasia Popova authored and GitHub committed 2021-02-12 22:44:21 +03:00
1 parent 2bf0e88b30
commit eeb7835128
8 files changed
+113 -25
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inference-engine/src/readers/ir_reader/ie_ir_parser.cpp
+1 -1
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@@ -392,7 +392,7 @@ std::shared_ptr<ngraph::Function> V10Parser::XmlDeserializer::parse_function(con
// Read all layers and store their parameters in params map
FOREACH_CHILD(node, root.child("layers"), "layer") {
auto node_param = parseGenericParams(node);
if (opName.find(node_param.name) != opName.end())
if (opName.find(node_param.name) != opName.end() && node_param.type != "Result")
THROW_IE_EXCEPTION << "Invalid IR! " << node_param.name << " name is not unique!";
opName.insert(node_param.name);
params[node_param.layerId] = {node, node_param};
model-optimizer/automation/package_BOM.txt
+1
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@@ -47,6 +47,7 @@ extensions/back/RemoveUselessConvert.py
extensions/back/Reshape0DToSqueeze.py
extensions/back/ReshapeMutation.py
extensions/back/ResultNormalizer.py
extensions/back/ResultRename.py
extensions/back/ReverseInputChannels.py
extensions/back/RNNSequenceTypeRename.py
extensions/back/ScalarConstNormalize.py
model-optimizer/extensions/back/ResultRename.py
+38
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@@ -0,0 +1,38 @@
"""
Copyright (C) 2018-2021 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 mo.back.replacement import BackReplacementPattern
from mo.graph.graph import Graph
class ResultRename(BackReplacementPattern):
# This transformation sets the Result operation name equal to the incoming tensor name.
# For some frameworks like kaldi and onnx this may result in appearance of nodes with identical names,
# which can lead to errors in other transformations.
# So ResultRename should be launched at the end of back phase.
enabled = False
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(type='Result'):
if node.in_ports():
prev_node_out_port = node.in_port(0).get_connection().get_source()
tensor_names = prev_node_out_port.get_tensor_names()
if tensor_names:
result_name = tensor_names[0]
else:
result_name = prev_node_out_port.node.soft_get('name', prev_node_out_port.node.id) + \
'/sink_port_' + str(prev_node_out_port.idx)
node['name'] = result_name
model-optimizer/extensions/back/ResultRename_test.py
+48
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@@ -0,0 +1,48 @@
"""
Copyright (C) 2018-2021 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.
"""
import unittest
from extensions.back.ResultRename import ResultRename
from mo.graph.graph import Node
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op, result
nodes = {
**regular_op('Op1', {'type': 'Op1', 'kind': 'op', 'op': 'Op1'}),
**result('result'),
'Op1_data': {'kind': 'data', 'fw_tensor_debug_info': [('Op1', 0, 'Op1_tensor')]}
}
class ResultRenameTest(unittest.TestCase):
def test_case1(self):
graph = build_graph(nodes, [('Op1', 'Op1_data'), ('Op1_data', 'result')])
graph_ref = build_graph(nodes, [('Op1', 'Op1_data'), ('Op1_data', 'result')])
res_node = Node(graph_ref, 'result')
res_node['name'] = 'Op1_tensor'
ResultRename().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_case2(self):
graph = build_graph(nodes, [])
graph_ref = build_graph(nodes, [])
ResultRename().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
model-optimizer/mo/back/ie_ir_ver_2/emitter.py
+2 -2
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@@ -174,8 +174,8 @@ def xml_ports(node: Node, element: Element, edges: Element):
assert node.graph.node[v]['shape'] is not None, 'Output shape is not calculated properly for node {}' \
''.format(node.id)
tensor_names = node.out_port(port_id).get_tensor_names(port_renumber=True)
if tensor_names is not None:
port.set('names', tensor_names)
if tensor_names:
port.set('names', ','.join(tensor_names))
xml_shape(node.graph.node[v]['shape'], port)
model-optimizer/mo/graph/port.py
+2 -4
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@@ -1,5 +1,5 @@
"""
Copyright (C) 2018-2020 Intel Corporation
Copyright (C) 2018-2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -310,9 +310,7 @@ class Port:
if node_idx in self.node.out_nodes():
out_node = self.node.out_node(node_idx)
fw_names += get_tensor_names_list(out_node.attrs())
if len(fw_names) > 0:
return ','.join(fw_names)
return None
return fw_names
def disconnect(self):
if self.type == 'out':
model-optimizer/mo/graph/port_test.py
+18 -18
View File
@@ -39,50 +39,50 @@ class TestsGetTensorNames(unittest.TestCase):
('Op1', 0, 'Op1,Op2')]})])
graph.stage = 'front'
input_node = Node(graph, 'input')
self.assertTrue(input_node.out_port(0).get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node.out_port(0).get_tensor_names() == ['input', 'Op1\\,Op2'])
op1_node = Node(graph, 'Op1')
op1_node.add_output_port(0)
self.assertTrue(op1_node.out_port(0).get_tensor_names() is None)
self.assertTrue(op1_node.out_port(0).get_tensor_names() == [])
def test_middle(self):
graph = build_graph(nodes, [('input', 'input_data'), ('input_data', 'Op1'),
('input_data', 'Op2')])
input_node = Node(graph, 'input')
self.assertTrue(input_node.out_port(0).get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node.out_port(0).get_tensor_names() == ['input', 'Op1\\,Op2'])
op1_node = Node(graph, 'Op1')
op1_node.add_output_port(0)
self.assertTrue(op1_node.out_port(0).get_tensor_names() is None)
self.assertTrue(op1_node.out_port(0).get_tensor_names() == [])
op2_node = Node(graph, 'Op2')
op2_node.add_output_port(0)
self.assertTrue(op2_node.out_port(0).get_tensor_names() is None)
self.assertTrue(op2_node.out_port(0).get_tensor_names() == [])
def test_port_renumber(self):
graph = build_graph(nodes, [('input', 'input_data'), ('input_data', 'Op1'),
('Op1', 'Op1_data', {'out': 1}), ('Op1_data', 'Op2')])
input_node = Node(graph, 'input')
self.assertTrue(input_node.out_port(0).get_tensor_names(port_renumber=True) == 'input,Op1\\,Op2')
self.assertTrue(input_node.out_port(0).get_tensor_names(port_renumber=True) == ['input', 'Op1\\,Op2'])
op1_node = Node(graph, 'Op1')
op1_node.add_output_port(0)
self.assertTrue(op1_node.out_port(0).get_tensor_names(port_renumber=True) == 'Op1\\,Op2')
self.assertTrue(op1_node.out_port(0).get_tensor_names(port_renumber=True) == ['Op1\\,Op2'])
def test_reconnect_middle_case1(self):
graph = build_graph(nodes, [('input', 'input_data'), ('input_data', 'Op1'), ('Op3', 'Op3_data')])
input_node = Node(graph, 'input')
input_node_out_port = input_node.out_port(0)
self.assertTrue(input_node_out_port.get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == ['input', 'Op1\\,Op2'])
op3_node = Node(graph, 'Op3')
input_node_out_port.get_connection().set_source(op3_node.out_port(0))
self.assertTrue(input_node_out_port.get_tensor_names() is None)
self.assertTrue(op3_node.out_port(0).get_tensor_names() == 'Op3,input,Op1\\,Op2')
self.assertTrue(op3_node.out_port(0).get_tensor_names() == ['Op3', 'input', 'Op1\\,Op2'])
def test_reconnect_front_case1(self):
graph = build_graph(nodes, [('input', 'Op1', {'in': 0, 'out': 0, 'fw_tensor_debug_info': [('input', 0, 'input'),
@@ -93,26 +93,26 @@ class TestsGetTensorNames(unittest.TestCase):
input_node = Node(graph, 'input')
input_node_out_port = input_node.out_port(0)
self.assertTrue(input_node_out_port.get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == ['input', 'Op1\\,Op2'])
op3_node = Node(graph, 'Op3')
input_node_out_port.get_connection().set_source(op3_node.out_port(0))
self.assertTrue(input_node_out_port.get_tensor_names() is None)
self.assertTrue(op3_node.out_port(0).get_tensor_names() == 'Op3,input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == [])
self.assertTrue(op3_node.out_port(0).get_tensor_names() == ['Op3', 'input', 'Op1\\,Op2'])
def test_reconnect_middle_case1(self):
graph = build_graph(nodes, [('input', 'input_data'), ('input_data', 'Op1'), ('Op3', 'Op3_data')])
input_node = Node(graph, 'input')
input_node_out_port = input_node.out_port(0)
self.assertTrue(input_node_out_port.get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == ['input', 'Op1\\,Op2'])
op3_node = Node(graph, 'Op3')
input_node_out_port.get_connection().set_source(op3_node.out_port(0))
self.assertTrue(input_node_out_port.get_tensor_names() is None)
self.assertTrue(op3_node.out_port(0).get_tensor_names() == 'Op3,input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == [])
self.assertTrue(op3_node.out_port(0).get_tensor_names() == ['Op3', 'input', 'Op1\\,Op2'])
def test_reconnect_middle_case2(self):
graph = build_graph(nodes, [('input', 'input_data'), ('input_data', 'Op1', {'out': 0}),
@@ -120,10 +120,10 @@ class TestsGetTensorNames(unittest.TestCase):
input_node = Node(graph, 'input')
input_node_out_port = input_node.out_port(0)
self.assertTrue(input_node_out_port.get_tensor_names() == 'input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == ['input', 'Op1\\,Op2'])
op3_node = Node(graph, 'Op3')
input_node_out_port.get_connection().set_source(op3_node.out_port(0))
self.assertTrue(input_node_out_port.get_tensor_names() is None)
self.assertTrue(op3_node.out_port(0).get_tensor_names() == 'Op3,input,Op1\\,Op2')
self.assertTrue(input_node_out_port.get_tensor_names() == [])
self.assertTrue(op3_node.out_port(0).get_tensor_names() == ['Op3', 'input', 'Op1\\,Op2'])
model-optimizer/mo/pipeline/common.py
+3
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@@ -22,6 +22,7 @@ from operator import itemgetter
import networkx as nx
from extensions.back.RemoveUselessConvert import RemoveUselessConvert
from extensions.back.ResultRename import ResultRename
from extensions.back.op_versioning import OpVersioning
from extensions.ops.Cast import Cast
from mo.back.ie_ir_ver_2.emitter import port_renumber, serialize_constants, generate_ie_ir, serialize_mean_image
@@ -208,6 +209,8 @@ def prepare_emit_ir(graph: Graph, data_type: str, output_dir: str, output_model_
type_infer(graph)
RemoveUselessConvert().find_and_replace_pattern(graph)
ResultRename().find_and_replace_pattern(graph)
for sub_graph in [graph] + collect_sub_graphs(graph):
op_order, data_order = determined_sort(get_sorted_outputs(sub_graph))
mapping = {v: u for u, v in enumerate(op_order)}