124 lines
4.9 KiB
Python
124 lines
4.9 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from extensions.front.AttributedClampNormalizer import AttributedClampNormalizer
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from extensions.ops.activation_ops import HSwish
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.front.subgraph_matcher import SubgraphMatch
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from mo.graph.graph import Graph, rename_nodes
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def replace_with_hswish(graph: Graph, match: [dict, SubgraphMatch]):
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add = match['add']
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mul = match['mul']
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mul_2 = match['mul_2']
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# determine the input port of Add and Mul which gets the 'input' node output
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add_input_port_idx = int(add.in_port(0).get_connection().get_source().node.soft_get('op') == 'Const')
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mul_input_port_idx = int(mul.in_port(0).get_connection().get_source().node.soft_get('op') in ['Clamp', 'Minimum'])
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# check that the same tensor provided as input to Add and Mul
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if add.in_port(add_input_port_idx).get_source() != mul.in_port(mul_input_port_idx).get_source():
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return
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mul_2_name = mul_2.soft_get('name', mul_2.id)
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hswish = HSwish(graph, {}).create_node()
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hswish.in_port(0).connect(add.in_port(add_input_port_idx).get_source())
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mul_2.out_port(0).get_connection().set_source(hswish.out_port(0))
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rename_nodes([(mul_2, mul_2_name + '/TBR'), (hswish, mul_2_name)])
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class HSwishWithClamp(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with ReLU6 (Clamp) defining the HSwish function:
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HSwish(x) = x * Relu6(x + 3) / 6.0.
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"""
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enabled = True
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def run_after(self):
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return [AttributedClampNormalizer]
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def pattern(self):
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return dict(
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nodes=[
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('input', dict()),
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('add', dict(op='Add')),
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('const_0', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 0.0, atol=1e-6))),
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('const_3', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
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('const_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
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('const_1_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 1 / 6.0, atol=1e-6))),
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('clamp', dict(op='Clamp')),
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('mul', dict(op='Mul')),
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('mul_2', dict(op='Mul')),
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],
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edges=[
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('input', 'add', {}),
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('input', 'mul', {}),
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('const_3', 'add', {}),
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('add', 'clamp', {'in': 0}),
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('const_0', 'clamp', {'in': 1}),
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('const_6', 'clamp', {'in': 2}),
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('clamp', 'mul', {}),
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('mul', 'mul_2', {}),
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('const_1_6', 'mul_2', {}),
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])
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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replace_with_hswish(graph, match)
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class HSwishWithMinMax(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with Min/Max defining the HSwish function:
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HSwish(x) = x * Min(Max(x + 3, 0), 6) / 6.0.
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"""
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enabled = True
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def run_after(self):
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return [AttributedClampNormalizer]
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def pattern(self):
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return dict(
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nodes=[
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('input', dict()),
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('add', dict(op='Add')),
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('const_0', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 0.0, atol=1e-6))),
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('const_3', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 3.0, atol=1e-6))),
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('const_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 6.0, atol=1e-6))),
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('const_1_6', dict(op='Const', value=lambda v: v is not None and np.allclose(v, 1 / 6.0, atol=1e-6))),
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('max', dict(op='Maximum')),
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('min', dict(op='Minimum')),
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('mul', dict(op='Mul')),
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('mul_2', dict(op='Mul')),
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],
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edges=[
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('input', 'add', {'out': 0}),
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('input', 'mul', {'out': 0}),
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('const_3', 'add', {}),
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('add', 'max', {}),
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('const_0', 'max', {}),
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('max', 'min', {}),
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('const_6', 'min', {}),
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('min', 'mul', {}),
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('mul', 'mul_2', {}),
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('const_1_6', 'mul_2', {}),
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])
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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replace_with_hswish(graph, match)
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