1a787cb3ba
* move crop extractor * Add concat_ext.py * Add roipooling_ext.py * Add roipooling_ext * Add scale extractor * Add scale extractor * Add bn_ext.py and dropout_ext.py * Add bn_ext.py and dropout_ext.py * Add bn_ext.py and dropout_ext.py * Fix bn.ext.py * Sort fix * Fix bn_test.py * rename to batchnorm_ext * Add bn_ext * Fix batchnorm_ext.py * small fix * Small fix
54 lines
1.8 KiB
Python
54 lines
1.8 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 extensions.ops.BatchNormInference import BatchNormInference
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from mo.front.caffe.extractors.utils import embed_input
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from mo.front.extractor import FrontExtractorOp
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class BatchNormalizationExtractor(FrontExtractorOp):
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op = 'batchnorm'
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enabled = True
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@classmethod
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def extract(cls, node):
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eps = node.pb.batch_norm_param.eps
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attrs = {
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'eps': eps
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}
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pb_model = None if not node.soft_get('model_pb', None) else node.model_pb
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if pb_model:
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blobs = pb_model.blobs
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assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs'
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mean = np.array(blobs[0].data)
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variance = np.array(blobs[1].data)
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if len(blobs) == 3:
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scale = blobs[2].data[0]
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if scale != 0:
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scale = 1.0 / scale
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mean *= scale
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variance *= scale
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embed_input(attrs, 1, 'gamma', np.ones(mean.shape), 'gamma')
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embed_input(attrs, 2, 'beta', np.zeros(variance.shape), 'beta')
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embed_input(attrs, 3, 'mean', mean, 'biases')
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embed_input(attrs, 4, 'variance', variance, 'weights')
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BatchNormInference.update_node_stat(node, attrs)
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return cls.enabled
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