74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import numpy as np
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from extensions.front.reduce_axis_normalizer import ReduceAxisNormalizer
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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
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from mo.ops.softmax import Softmax
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class SoftmaxFromKeras(FrontReplacementSubgraph):
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"""
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The transformation looks for the pattern that Keras produces for SoftMax layer. The transformation works if the
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softmax is performed over one pre-defined axis.
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"""
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enabled = True
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def run_after(self):
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return [ReduceAxisNormalizer]
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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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('reduce_max', dict(op='ReduceMax')),
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('reduce_indices_max', dict(op='Const', value=lambda x: x is not None and x.size != 0)),
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('sub', dict(op='Sub')),
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('exp', dict(op='Exp')),
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('reduce_sum', dict(op='ReduceSum')),
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('reduce_indices_sum', dict(op='Const', value=lambda x: x is not None and x.size != 0)),
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('div', dict(op='Div')),
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],
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edges=[
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('input', 'sub', {'in': 0}),
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('input', 'reduce_max', {'in': 0}),
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('reduce_indices_max', 'reduce_max', {'in': 1}),
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('reduce_max', 'sub', {'in': 1}),
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('sub', 'exp', {'in': 0}),
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('exp', 'div', {'in': 0}),
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('exp', 'reduce_sum', {'in': 0}),
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('reduce_indices_sum', 'reduce_sum', {'in': 1}),
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('reduce_sum', 'div', {'in': 1}),
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])
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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reduce_max_axis = match['reduce_indices_max'].value
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reduce_sum_axis = match['reduce_indices_sum'].value
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if reduce_max_axis.ndim == 0:
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reduce_max_axis = reduce_max_axis.reshape([1])
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if reduce_sum_axis.ndim == 0:
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reduce_sum_axis = reduce_sum_axis.reshape([1])
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if len(reduce_max_axis) != 1:
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log.info('The reductions indices contain more than 1 element. Cannot convert to Softmax.')
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return
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if not np.array_equal(reduce_max_axis, reduce_sum_axis):
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log.info('The reduce indices are not equal: {} vs {}. Cannot convert to Softmax'
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''.format(reduce_max_axis, reduce_sum_axis))
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return
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softmax = Softmax(graph, {'name': match['input'].name + '/Softmax', 'axis': reduce_sum_axis[0]}).create_node()
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match['input'].out_port(0).connect(softmax.in_port(0))
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match['div'].out_port(0).get_connection().set_source(softmax.out_port(0))
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log.debug('Successfully created SoftMax node')
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