179 lines
7.0 KiB
Python
179 lines
7.0 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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 HSigmoid
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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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from mo.middle.pattern_match import check_value
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from mo.utils.graph import Node
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def replace_with_hsigmoid(graph: Graph, first_node: Node, last_node: Node):
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# determine the input port of first and last nodes which gets the 'input' node output
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add_input_port_idx = int(first_node.in_port(0).get_connection().get_source().node.soft_get('op') == 'Const')
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last_node_name = last_node.soft_get('name', last_node.id)
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hsigmoid = HSigmoid(graph, {}).create_node()
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hsigmoid.in_port(0).connect(first_node.in_port(add_input_port_idx).get_source())
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last_node.out_port(0).get_connection().set_source(hsigmoid.out_port(0))
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rename_nodes([(last_node, last_node_name + '/TBR'), (hsigmoid, last_node_name)])
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class HSigmoidWithClamp(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with ReLU6 (Clamp) defining the HSigmoid function:
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HSigmoid(x) = Relu6(x + 3.0) / 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: check_value(v, lambda x: np.allclose(x, 0.0, atol=1e-6)))),
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('const_3', dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 3.0, atol=1e-6)))),
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('const_6', dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 6.0, atol=1e-6)))),
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('const_1_6',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 1.0 / 6.0, atol=1e-6)))),
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('clamp', dict(op='Clamp')),
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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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('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_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_hsigmoid(graph, match['add'], match['mul_2'])
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class HSigmoidWithMinMax(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with Min/Max defining the HSigmoid function:
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HSigmoid(x) = Min(Max(x + 3.0, 0), 6.0) / 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: check_value(v, lambda x: np.allclose(x, 0.0, atol=1e-6)))),
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('const_3', dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 3.0, atol=1e-6)))),
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('const_6', dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 6.0, atol=1e-6)))),
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('const_1_6',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 1.0 / 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_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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('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_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_hsigmoid(graph, match['add'], match['mul_2'])
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class HSigmoidWithReluDiv(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with Relu/Div defining the HSigmoid function:
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HSigmoid(x) = Min(Relu(x + 3.0), 6.0) / 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_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 3.0, atol=1e-6)))),
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('add', dict(op='Add')),
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('relu', dict(op='ReLU')),
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('min_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 6.0, atol=1e-6)))),
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('min', dict(op='Minimum')),
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('div_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 6.0, atol=1e-6)))),
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('div', dict(op='Div')),
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],
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edges=[
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('input', 'add', {'out': 0}),
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('add_const', 'add', {}),
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('add', 'relu', {}),
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('relu', 'min', {}),
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('min_const', 'min', {}),
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('min', 'div', {}),
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('div_const', 'div', {}),
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])
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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replace_with_hsigmoid(graph, match['add'], match['div'])
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class HSigmoidWithReluMul(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern with Relu/Mul defining the HSigmoid function:
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HSigmoid(x) = Min(Relu(x + 3.0), 6.0) * 1.0/6.0
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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_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 3.0, atol=1e-6)))),
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('add', dict(op='Add')),
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('relu', dict(op='ReLU')),
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('min_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 6.0, atol=1e-6)))),
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('min', dict(op='Minimum')),
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('mul_const',
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dict(op='Const', value=lambda v: check_value(v, lambda x: np.allclose(x, 1.0 / 6.0, atol=1e-6)))),
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('mul', dict(op='Mul')),
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],
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edges=[
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('input', 'add', {'out': 0}),
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('add_const', 'add', {}),
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('add', 'relu', {}),
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('relu', 'min', {}),
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('min_const', 'min', {}),
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('min', 'mul', {}),
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('mul_const', 'mul', {}),
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])
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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replace_with_hsigmoid(graph, match['add'], match['mul'])
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