Evgeny Lazarev
e065cf554a
* Fixed L2NormFusing transformastion * Refactor L2NormFusing transformation
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""
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Copyright (C) 2018-2021 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 mo.graph.graph import Graph, Node
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from mo.graph.perm_inputs import PermuteInputs
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from mo.ops.op import Op
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class NormalizeL2Op(Op):
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op = 'NormalizeL2'
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enabled = True
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def __init__(self, graph: Graph, attrs: dict):
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super().__init__(graph, {
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'type': self.op,
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'op': self.op,
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'version': 'opset1',
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'eps': None,
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'p': None,
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'eps_mode': None,
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'in_ports_count': 2,
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'out_ports_count': 1,
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'infer': self.infer
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}, attrs)
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def supported_attrs(self):
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return ['eps', 'eps_mode']
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@staticmethod
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def infer(node: Node):
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input_shape = node.in_port(0).data.get_shape()
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if input_shape is None:
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return
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input_value = node.in_port(0).data.get_value()
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axes = node.in_port(1).data.get_value()
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if input_value is not None and axes is not None:
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norm_value = np.linalg.norm(input_value, node.p, axes, keepdims=True)
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if node.eps_mode == 'add':
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norm_value = norm_value + node.eps
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elif node.eps_mode == 'max':
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norm_value = np.max(norm_value, node.eps)
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else:
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assert False, 'Unsupported "eps_mode" = {}'.format(node.eps_mode)
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node.out_port(0).data.set_value(input_value / norm_value)
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else:
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node.out_port(0).data.set_shape(input_shape)
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PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:0', 'axis')
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