Eugeny Volosenkov
6f512142b6
* add class ConcatFrontExtractor for onnx * add class ConcatFrontExtractor for onnx * Delete concat from mo/front/onnx/extractor.py * Add identity, reshape extractors classes for onnx * import FrontExtractorOp * Added BatchNormalizationExtractor * Added extra line * Fix import modules * fix caffe bn.py and bn_test.py * Fix BatchNormInference * Modify convert_batch_norm * Modify convert_batch_norm * Modify bn_test.py * Fix old comments BN->batchNormInference
81 lines
3.1 KiB
Python
81 lines
3.1 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 unittest
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import numpy as np
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from extensions.front.caffe.bn import BNToScaleShift
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from mo.graph.graph import Node
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from mo.utils.unittest.extractors import FakeParam
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from mo.utils.unittest.graph import build_graph_with_edge_attrs, build_graph_with_attrs
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class FakeBNProtoLayer:
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def __init__(self, val):
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self.bn_param = val
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class FakeBNBinLayer:
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def __init__(self, val):
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self.blobs = val
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class TestBNReplacer(unittest.TestCase):
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def test_bn(self):
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bn_pb = FakeBNProtoLayer(FakeParam('eps', 0.0001))
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mean = [1, 2.5, 3]
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var = [0.5, 0.1, 1.2]
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scale = [2.3, 3.4, 4.5]
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shift = [0.8, 0.6, 0.4]
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bn_bin = FakeBNBinLayer([FakeParam('data', mean),
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FakeParam('data', var),
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FakeParam('data', scale),
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FakeParam('data', shift)])
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nodes = [
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('input', {'kind': 'op', 'type': 'Identity', 'op': 'Identity'}),
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('bn', {'type': None, 'kind': 'op', 'op': 'batchNormInference', 'pb': bn_pb, 'model_pb': bn_bin}),
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('output', {'kind': 'op', 'type': 'Identity', 'op': 'Identity'}),
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]
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edges = [
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('input', 'bn', {'in': 0, 'out': 0}),
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('bn', 'output', {'in': 0, 'out': 0}),
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]
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graph = build_graph_with_attrs(nodes, edges)
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node = Node(graph, 'bn')
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graph.stage = 'front'
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BNToScaleShift().find_and_replace_pattern(graph)
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ref_nodes = {
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'input': {'kind': 'op', 'type': 'Identity', 'op': 'Identity'},
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'scale': {'kind': 'op', 'type': 'Const', 'op': 'Const',
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'value': np.array([1.11796412, 3.2272172, 4.74282367])},
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'shift': {'kind': 'op', 'type': 'Const', 'op': 'Const',
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'value': np.array([-2.07131747, -10.87253847, -20.14270653])},
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'ss': {'type': 'ScaleShift', 'kind': 'op', 'op': 'ScaleShift'},
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'output': {'kind': 'op', 'type': 'Identity', 'op': 'Identity'},
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}
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ref_edges = [
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('input', 'ss', {'in': 0, 'out': 0}),
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('scale', 'ss', {'in': 1, 'out': 0}),
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('shift', 'ss', {'in': 2, 'out': 0}),
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('ss', 'output', {'in': 0, 'out': 0}),
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]
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ref_graph = build_graph_with_edge_attrs(ref_nodes, ref_edges)
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(flag, resp) = compare_graphs(graph, ref_graph, 'input', check_op_attrs=True)
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self.assertTrue(flag, resp)
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