55f1f9606f
* [MO] Implement transformation to avoid RandomUniform in a particular drop-out block Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove old RandomUniform transformation and correct const values Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix atol value Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Move DropoutWithRandomUniform transformation to the front Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Change just RandomUniform to Broadcast in the transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Correct comment for the transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove redundant line Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
"""
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Copyright (C) 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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import unittest
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from extensions.front.DropoutWithRandomUniformReplacer import DropoutWithRandomUniformReplacer
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from mo.utils.unittest.graph import build_graph, result, regular_op
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class DropoutWithRandomUniformReplacerTest(unittest.TestCase):
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def test(self):
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nodes = {
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**regular_op('input', {'type': 'Parameter'}),
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**regular_op('shape', {'type': 'ShapeOf', 'kind': 'op', 'op': 'ShapeOf'}),
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**regular_op('random_uniform', {'type': 'RandomUniform', 'kind': 'op', 'op': 'RandomUniform',
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'name': 'dropout/RU'}),
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**regular_op('mul', {'type': 'Mul', 'kind': 'op', 'op': 'Mul'}),
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**regular_op('add', {'type': 'Add', 'kind': 'op', 'op': 'Add'}),
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**regular_op('add2', {'type': 'Add', 'kind': 'op', 'op': 'Add'}),
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**regular_op('floor', {'type': 'Floor', 'kind': 'op', 'op': 'Floor'}),
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'add_const': {'kind': 'op', 'op': 'Const', 'value': np.array(0.0), 'data_type': np.float32},
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**result('result'),
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# new nodes to be added
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'broadcast_const': {'kind': 'op', 'op': 'Const', 'value': np.array(0.5), 'data_type': np.float32},
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**regular_op('broadcast', {'type': 'Broadcast', 'kind': 'op', 'op': 'Broadcast'}),
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}
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edges = [('input', 'shape'),
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('shape', 'random_uniform'),
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('random_uniform', 'mul'),
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('mul', 'add'),
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('add_const', 'add'),
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('add', 'add2'),
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('add2', 'floor'),
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('floor', 'result')]
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graph = build_graph(nodes, edges, nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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graph.stage = 'front'
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DropoutWithRandomUniformReplacer().find_and_replace_pattern(graph)
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edges_ref = [('input', 'shape'),
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('broadcast_const', 'broadcast'),
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('shape', 'broadcast'),
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('broadcast', 'mul'),
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('mul', 'add'),
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('add_const', 'add'),
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('add', 'add2'),
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('add2', 'floor'),
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('floor', 'result')]
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graph_ref = build_graph(nodes, edges_ref, nodes_with_edges_only=True)
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# check graph structure after the transformation and output name
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(flag, resp) = compare_graphs(graph, graph_ref, 'result')
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self.assertTrue(flag, resp)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(op='Broadcast')[0]]['name'] == 'dropout/RU')
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