Add hsigmoid fusing for MO (#2750)

* Add hsigmoid fusing for MO

* Update Bom file

* Remove comments

* Refactoring hsigmoid fusion according to review

* Add div and mul patterns for hsigmoid fusion

* Refactoring code according to review

* Fix HSigmoid fusion transformation

model-optimizer/automation/package_BOM.txt

@@ -127,6 +127,7 @@ extensions/front/freeze_placeholder_value.py
 extensions/front/GeLUMerger_Erf.py
 extensions/front/GeLUMerger_Tanh.py
 extensions/front/global_pooling_to_reduce.py
+extensions/front/HSigmoid_fusion.py
 extensions/front/HSwish_fusion.py
 extensions/front/image_scaler.py
 extensions/front/input_cut.py

model-optimizer/extensions/front/HSigmoid_fusion.py

@@ -0,0 +1,181 @@
+"""
+ 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.
+"""
+import numpy as np
+
+from extensions.front.AttributedClampNormalizer import AttributedClampNormalizer
+from extensions.ops.activation_ops import HSigmoid
+from mo.front.common.replacement import FrontReplacementSubgraph
+from mo.front.subgraph_matcher import SubgraphMatch
+from mo.graph.graph import Graph, rename_nodes
+from mo.utils.graph import Node
+
+
+def replace_with_hsigmoid(graph: Graph, first_node: Node, last_node: Node):
+    # determine the input port of first and last nodes which gets the 'input' node output
+    add_input_port_idx = int(first_node.in_port(0).get_connection().get_source().node.soft_get('op') == 'Const')
+    last_node_name = last_node.soft_get('name', last_node.id)
+
+    hsigmoid = HSigmoid(graph, {}).create_node()
+    hsigmoid.in_port(0).connect(first_node.in_port(add_input_port_idx).get_source())
+    last_node.out_port(0).get_connection().set_source(hsigmoid.out_port(0))
+
+    rename_nodes([(last_node, last_node_name + '/TBR'), (hsigmoid, last_node_name)])
+
+
+class HSigmoidWithClamp(FrontReplacementSubgraph):
+    """
+    The transformation looks for the pattern with ReLU6 (Clamp) defining the HSigmoid function:
+    HSigmoid(x) = Relu6(x + 3.0) / 6.0.
+    """
+    enabled = True
+
+    def run_after(self):
+        return [AttributedClampNormalizer]
+
+    def pattern(self):
+        return dict(
+            nodes=[
+                ('input', dict()),
+                ('add', dict(op='Add')),
+                ('const_0', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 0.0, atol=1e-6))),
+                ('const_3', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
+                ('const_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
+                ('const_1_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 1.0 / 6.0, atol=1e-6))),
+                ('clamp', dict(op='Clamp')),
+                ('mul_2', dict(op='Mul')),
+            ],
+            edges=[
+                ('input', 'add', {}),
+                ('const_3', 'add', {}),
+                ('add', 'clamp', {'in': 0}),
+                ('const_0', 'clamp', {'in': 1}),
+                ('const_6', 'clamp', {'in': 2}),
+                ('clamp', 'mul_2', {}),
+                ('const_1_6', 'mul_2', {}),
+            ])
+
+    def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
+        replace_with_hsigmoid(graph, match['add'], match['mul_2'])
+
+
+class HSigmoidWithMinMax(FrontReplacementSubgraph):
+    """
+    The transformation looks for the pattern with Min/Max defining the HSigmoid function:
+    HSigmoid(x) = Min(Max(x + 3.0, 0), 6.0) / 6.0.
+    """
+    enabled = True
+
+    def run_after(self):
+        return [AttributedClampNormalizer]
+
+    def pattern(self):
+        return dict(
+            nodes=[
+                ('input', dict()),
+                ('add', dict(op='Add')),
+                ('const_0', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 0.0, atol=1e-6))),
+                ('const_3', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
+                ('const_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
+                ('const_1_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 1.0 / 6.0, atol=1e-6))),
+                ('max', dict(op='Maximum')),
+                ('min', dict(op='Minimum')),
+                ('mul_2', dict(op='Mul')),
+            ],
+            edges=[
+                ('input', 'add', {'out': 0}),
+                ('const_3', 'add', {}),
+                ('add', 'max', {}),
+                ('const_0', 'max', {}),
+                ('max', 'min', {}),
+                ('const_6', 'min', {}),
+                ('min', 'mul_2', {}),
+                ('const_1_6', 'mul_2', {}),
+            ])
+
+    def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
+        replace_with_hsigmoid(graph, match['add'], match['mul_2'])
+
+
+class HSigmoidWithReluDiv(FrontReplacementSubgraph):
+    """
+    The transformation looks for the pattern with Relu/Div defining the HSigmoid function:
+    HSigmoid(x) = Min(Relu(x + 3.0), 6.0) / 6.0
+    """
+    enabled = True
+
+    def run_after(self):
+        return [AttributedClampNormalizer]
+
+    def pattern(self):
+        return dict(
+            nodes=[
+                ('input', dict()),
+                ('add_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
+                ('add', dict(op='Add')),
+                ('relu', dict(op='ReLU')),
+                ('min_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
+                ('min', dict(op='Minimum')),
+                ('div_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
+                ('div', dict(op='Div')),
+            ],
+            edges=[
+                ('input', 'add', {'out': 0}),
+                ('add_const', 'add', {}),
+                ('add', 'relu', {}),
+                ('relu', 'min', {}),
+                ('min_const', 'min', {}),
+                ('min', 'div', {}),
+                ('div_const', 'div', {}),
+            ])
+
+    def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
+        replace_with_hsigmoid(graph, match['add'], match['div'])
+
+
+class HSigmoidWithReluMul(FrontReplacementSubgraph):
+    """
+    The transformation looks for the pattern with Relu/Mul defining the HSigmoid function:
+    HSigmoid(x) = Min(Relu(x + 3.0), 6.0) * 1.0/6.0
+    """
+    enabled = True
+
+    def run_after(self):
+        return [AttributedClampNormalizer]
+
+    def pattern(self):
+        return dict(
+            nodes=[
+                ('input', dict()),
+                ('add_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
+                ('add', dict(op='Add')),
+                ('relu', dict(op='ReLU')),
+                ('min_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
+                ('min', dict(op='Minimum')),
+                ('mul_const', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 1.0/6.0, atol=1e-6))),
+                ('mul', dict(op='Mul')),
+            ],
+            edges=[
+                ('input', 'add', {'out': 0}),
+                ('add_const', 'add', {}),
+                ('add', 'relu', {}),
+                ('relu', 'min', {}),
+                ('min_const', 'min', {}),
+                ('min', 'mul', {}),
+                ('mul_const', 'mul', {}),
+            ])
+
+    def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
+        replace_with_hsigmoid(graph, match['add'], match['mul'])

model-optimizer/extensions/front/HSigmoid_fusion_test.py

@@ -0,0 +1,355 @@
+"""
+ 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.
+"""
+
+import unittest
+
+from extensions.front.HSigmoid_fusion import HSigmoidWithClamp, HSigmoidWithMinMax, HSigmoidWithReluDiv, \
+    HSigmoidWithReluMul
+from mo.front.common.partial_infer.utils import float_array
+from mo.utils.ir_engine.compare_graphs import compare_graphs
+from mo.utils.unittest.graph import build_graph, const, regular_op, result, build_graph_with_edge_attrs
+
+ref_nodes = {**regular_op('input', {'type': 'Parameter'}),
+             **regular_op('hsigmoid', {'type': 'HSigmoid', 'name': 'final_mul'}),
+             **result('result')
+             }
+ref_edges = [('input', 'hsigmoid'), ('hsigmoid', 'result')]
+
+
+class HSigmoidWithClampTest(unittest.TestCase):
+    nodes = {
+        **regular_op('input', {'type': 'Parameter'}),
+        **regular_op('add', {'op': 'Add'}),
+        **regular_op('relu6', {'op': 'Clamp'}),
+        **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+        **const('const_0', float_array([0.0])),
+        **const('const_3', float_array([3.0])),
+        **const('const_6', float_array([6.0])),
+        **const('const_1_6', float_array([1.0 / 6.0])),
+        **result('result'),
+    }
+
+    edges = [('input', 'add', {'in': 0, 'out': 0}),
+             ('const_3', 'add', {'in': 1, 'out': 0}),
+             ('add', 'relu6', {'in': 0, 'out': 0}),
+             ('const_0', 'relu6', {'in': 1, 'out': 0}),
+             ('const_6', 'relu6', {'in': 2, 'out': 0}),
+             ('relu6', 'mul_2', {'in': 1, 'out': 0}),
+             ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+             ('mul_2', 'result', {'in': 0, 'out': 0})]
+
+    def test_hsigmoid_with_clamp(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
+
+        graph_ref = build_graph(ref_nodes, ref_edges)
+        graph.stage = 'front'
+
+        HSigmoidWithClamp().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+        self.assertTrue(len(graph.get_op_nodes(name='final_mul')) == 1 and
+                        graph.get_op_nodes(name='final_mul')[0].op == 'HSigmoid')
+
+    def test_hsigmoid_with_clamp_wrong_constant(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {'const_0': {'value': float_array([0.00001])}})
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithClamp().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+    def test_hsigmoid_with_clamp_different_tensors(self):
+        graph = build_graph_with_edge_attrs({
+            **regular_op('input', {'type': 'Parameter'}),
+            **regular_op('input_2', {'type': 'Parameter'}),
+            **regular_op('add', {'op': 'Add'}),
+            **regular_op('relu6', {'op': 'Clamp'}),
+            **regular_op('mul', {'op': 'Mul'}),
+            **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+            **const('const_0', float_array([0.0])),
+            **const('const_3', float_array([3.0])),
+            **const('const_6', float_array([6.0])),
+            **const('const_1_6', float_array([1.0 / 6.0])),
+            **result('result'),
+        }, [('input', 'mul', {'in': 0, 'out': 0}),
+            ('input_2', 'add', {'in': 0, 'out': 0}),
+            ('const_3', 'add', {'in': 1, 'out': 0}),
+            ('add', 'relu6', {'in': 0, 'out': 0}),
+            ('const_0', 'relu6', {'in': 1, 'out': 0}),
+            ('const_6', 'relu6', {'in': 2, 'out': 0}),
+            ('relu6', 'mul', {'in': 1, 'out': 0}),
+            ('mul', 'mul_2', {'in': 0, 'out': 0}),
+            ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+            ('mul_2', 'result', {'in': 0, 'out': 0})])
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithClamp().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+
+class HSigmoidWithMinMaxTest(unittest.TestCase):
+    nodes = {
+        **regular_op('input', {'type': 'Parameter'}),
+        **regular_op('add', {'op': 'Add'}),
+        **regular_op('max', {'op': 'Maximum'}),
+        **regular_op('min', {'op': 'Minimum'}),
+        **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+        **const('const_0', float_array([0.0])),
+        **const('const_3', float_array([3.0])),
+        **const('const_6', float_array([6.0])),
+        **const('const_1_6', float_array([1.0 / 6.0])),
+        **result('result'),
+    }
+
+    edges = [('input', 'add', {'in': 0, 'out': 0}),
+             ('const_3', 'add', {'in': 1, 'out': 0}),
+             ('add', 'max', {'in': 0, 'out': 0}),
+             ('const_0', 'max', {'in': 1, 'out': 0}),
+             ('max', 'min', {'in': 0, 'out': 0}),
+             ('const_6', 'min', {'in': 1, 'out': 0}),
+             ('min', 'mul_2', {'in': 0, 'out': 0}),
+             ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+             ('mul_2', 'result', {'in': 0, 'out': 0})]
+
+    def test_hsigmoid_with_min_max(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
+
+        graph_ref = build_graph(ref_nodes, ref_edges)
+        graph.stage = 'front'
+
+        HSigmoidWithMinMax().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+        self.assertTrue(len(graph.get_op_nodes(name='final_mul')) == 1 and
+                        graph.get_op_nodes(name='final_mul')[0].op == 'HSigmoid')
+
+    def test_hsigmoid_with_min_max_wrong_constant(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {'const_0': {'value': float_array([0.00001])}})
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithMinMax().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+    def test_hsigmoid_with_min_max_different_tensors(self):
+        graph = build_graph_with_edge_attrs({
+            **regular_op('input', {'type': 'Parameter'}),
+            **regular_op('input_2', {'type': 'Parameter'}),
+            **regular_op('add', {'op': 'Add'}),
+            **regular_op('max', {'op': 'Maximum'}),
+            **regular_op('min', {'op': 'Minimum'}),
+            **regular_op('mul', {'op': 'Mul'}),
+            **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+            **const('const_0', float_array([0.0])),
+            **const('const_3', float_array([3.0])),
+            **const('const_6', float_array([6.0])),
+            **const('const_1_6', float_array([1.0 / 6.0])),
+            **result('result'),
+        }, [('input_2', 'mul', {'in': 1, 'out': 0}),
+            ('input', 'add', {'in': 0, 'out': 0}),
+            ('const_3', 'add', {'in': 1, 'out': 0}),
+            ('add', 'max', {'in': 0, 'out': 0}),
+            ('const_0', 'max', {'in': 1, 'out': 0}),
+            ('max', 'min', {'in': 0, 'out': 0}),
+            ('const_6', 'min', {'in': 1, 'out': 0}),
+            ('min', 'mul', {'in': 0, 'out': 0}),
+            ('mul', 'mul_2', {'in': 0, 'out': 0}),
+            ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+            ('mul_2', 'result', {'in': 0, 'out': 0})])
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithMinMax().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+
+class HSigmoidWithReluDivTest(unittest.TestCase):
+    nodes = {
+        **regular_op('input', {'type': 'Parameter'}),
+        **regular_op('add', {'op': 'Add'}),
+        **regular_op('relu', {'op': 'ReLU'}),
+        **regular_op('min', {'op': 'Minimum'}),
+        **regular_op('div', {'op': 'Div', 'name': 'final_div'}),
+        **const('add_const', float_array([3.0])),
+        **const('min_const', float_array([6.0])),
+        **const('div_const', float_array([6.0])),
+        **result('result'),
+    }
+
+    edges = [('input', 'add', {'in': 0, 'out': 0}),
+             ('add_const', 'add', {'in': 1, 'out': 0}),
+             ('add', 'relu', {'in': 0, 'out': 0}),
+             ('relu', 'min', {'in': 0, 'out': 0}),
+             ('min_const', 'min', {'in': 1, 'out': 0}),
+             ('min', 'div', {'in': 0, 'out': 0}),
+             ('div_const', 'div', {'in': 1, 'out': 0}),
+             ('div', 'result', {'in': 0, 'out': 0})]
+
+    def test_hsigmoid_with_relu_div(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
+
+        graph_ref = build_graph(ref_nodes, ref_edges)
+        graph.stage = 'front'
+
+        HSigmoidWithReluDiv().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+        self.assertTrue(len(graph.get_op_nodes(name='final_div')) == 1 and
+                        graph.get_op_nodes(name='final_div')[0].op == 'HSigmoid')
+        self.assertTrue(graph.get_op_nodes(name='final_div')[0].out_nodes()[0].node == 'result')
+
+    def test_hsigmoid_with_relu_div_wrong_constant(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {'add_const': {'value': float_array([0.00001])}})
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithReluDiv().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+    def test_hsigmoid_with_relu_div_different_tensors(self):
+        graph = build_graph_with_edge_attrs({
+            **regular_op('input', {'type': 'Parameter'}),
+            **regular_op('input_2', {'type': 'Parameter'}),
+            **regular_op('add', {'op': 'Add'}),
+            **regular_op('max', {'op': 'Maximum'}),
+            **regular_op('min', {'op': 'Minimum'}),
+            **regular_op('mul', {'op': 'Mul'}),
+            **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+            **const('const_0', float_array([0.0])),
+            **const('const_3', float_array([3.0])),
+            **const('const_6', float_array([6.0])),
+            **const('const_1_6', float_array([1.0 / 6.0])),
+            **result('result'),
+        }, [('input_2', 'mul', {'in': 1, 'out': 0}),
+            ('input', 'add', {'in': 0, 'out': 0}),
+            ('const_3', 'add', {'in': 1, 'out': 0}),
+            ('add', 'max', {'in': 0, 'out': 0}),
+            ('const_0', 'max', {'in': 1, 'out': 0}),
+            ('max', 'min', {'in': 0, 'out': 0}),
+            ('const_6', 'min', {'in': 1, 'out': 0}),
+            ('min', 'mul', {'in': 0, 'out': 0}),
+            ('mul', 'mul_2', {'in': 0, 'out': 0}),
+            ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+            ('mul_2', 'result', {'in': 0, 'out': 0})])
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithReluDiv().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+
+class HSigmoidWithReluMulTest(unittest.TestCase):
+    nodes = {
+        **regular_op('input', {'type': 'Parameter'}),
+        **regular_op('add', {'op': 'Add'}),
+        **regular_op('relu', {'op': 'ReLU'}),
+        **regular_op('min', {'op': 'Minimum'}),
+        **regular_op('mul', {'op': 'Mul', 'name': 'final_mul'}),
+        **const('add_const', float_array([3.0])),
+        **const('min_const', float_array([6.0])),
+        **const('mul_const', float_array([1.0/6.0])),
+        **result('result'),
+    }
+
+    edges = [('input', 'add', {'in': 0, 'out': 0}),
+             ('add_const', 'add', {'in': 1, 'out': 0}),
+             ('add', 'relu', {'in': 0, 'out': 0}),
+             ('relu', 'min', {'in': 0, 'out': 0}),
+             ('min_const', 'min', {'in': 1, 'out': 0}),
+             ('min', 'mul', {'in': 0, 'out': 0}),
+             ('mul_const', 'mul', {'in': 1, 'out': 0}),
+             ('mul', 'result', {'in': 0, 'out': 0})]
+
+    def test_hsigmoid_with_relu_mul(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
+
+        graph_ref = build_graph(ref_nodes, ref_edges)
+        graph.stage = 'front'
+
+        HSigmoidWithReluMul().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+        self.assertTrue(len(graph.get_op_nodes(name='final_mul')) == 1 and
+                        graph.get_op_nodes(name='final_mul')[0].op == 'HSigmoid')
+        self.assertTrue(graph.get_op_nodes(name='final_mul')[0].out_nodes()[0].node == 'result')
+
+    def test_hsigmoid_with_relu_mul_wrong_constant(self):
+        graph = build_graph_with_edge_attrs(self.nodes, self.edges, {'add_const': {'value': float_array([0.00001])}})
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithReluMul().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+    def test_hsigmoid_with_relu_mul_different_tensors(self):
+        graph = build_graph_with_edge_attrs({
+            **regular_op('input', {'type': 'Parameter'}),
+            **regular_op('input_2', {'type': 'Parameter'}),
+            **regular_op('add', {'op': 'Add'}),
+            **regular_op('max', {'op': 'Maximum'}),
+            **regular_op('min', {'op': 'Minimum'}),
+            **regular_op('mul', {'op': 'Mul'}),
+            **regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
+            **const('const_0', float_array([0.0])),
+            **const('const_3', float_array([3.0])),
+            **const('const_6', float_array([6.0])),
+            **const('const_1_6', float_array([1.0 / 6.0])),
+            **result('result'),
+        }, [('input_2', 'mul', {'in': 1, 'out': 0}),
+            ('input', 'add', {'in': 0, 'out': 0}),
+            ('const_3', 'add', {'in': 1, 'out': 0}),
+            ('add', 'max', {'in': 0, 'out': 0}),
+            ('const_0', 'max', {'in': 1, 'out': 0}),
+            ('max', 'min', {'in': 0, 'out': 0}),
+            ('const_6', 'min', {'in': 1, 'out': 0}),
+            ('min', 'mul', {'in': 0, 'out': 0}),
+            ('mul', 'mul_2', {'in': 0, 'out': 0}),
+            ('const_1_6', 'mul_2', {'in': 1, 'out': 0}),
+            ('mul_2', 'result', {'in': 0, 'out': 0})])
+
+        graph_ref = graph.copy()
+        graph.stage = 'front'
+
+        HSigmoidWithReluMul().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)

model-optimizer/extensions/ops/activation_ops.py

@@ -248,6 +248,12 @@ class HSwish(Activation):
     operation = staticmethod(lambda x: x * np.minimum(np.maximum(x + 3.0, 0.0), 6.0) / 6.0)
 
 
+class HSigmoid(Activation):
+    op = 'HSigmoid'
+    version = 'opset5'
+    operation = staticmethod(lambda x: np.minimum(np.maximum(x + 3.0, 0.0), 6.0) / 6.0)
+
+
 class Swish(Op):
     op = 'Swish'