64 lines
2.0 KiB
Python
64 lines
2.0 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 numpy as np
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from mo.front.caffe.extractors.utils import embed_input
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from mo.front.common.partial_infer.elemental import copy_shape_infer
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def batch_norm_ext(pb_layer, pb_model):
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"""
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Extracts properties of the BatchNorm layer.
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In case of scale, scale is merged into mean and variance
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Args:
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pb_layer: proto layer, contains own properties of the layer, i.e epsilon
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pb_model: caffemodel layer, contains blobs with 0: mean, 1: variance, (opt)2: scale
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Returns:
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attrs object with type, partial inference function and mean/variance properties.
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"""
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assert pb_layer, 'Protobuf layer can not be empty'
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param = pb_layer.batch_norm_param
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attrs = {
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'op': 'BatchNormalization',
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'type': 'BatchNormalization',
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'eps': param.eps,
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'infer': copy_shape_infer
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}
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if not pb_model:
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return attrs
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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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return attrs
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