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
56 lines
2.1 KiB
Python
56 lines
2.1 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 mo.front.caffe.extractors.utils import embed_input, weights_biases
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from mo.front.common.partial_infer.elemental import copy_shape_infer
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from mo.front.extractor import FrontExtractorOp
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from mo.ops.scale_shift import ScaleShiftOp
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from mo.utils.utils import NamedAttrsClass
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class ScaleFrontExtractor(FrontExtractorOp):
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op = 'scale'
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enabled = True
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@classmethod
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def extract(cls, node):
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pb = node.pb
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model = node.model_pb
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param = pb.scale_param
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attrs = {
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'axis': param.axis,
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}
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if model is None and len(pb.bottom) == 1:
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# default weights and biases for scale layer if the caffemodel file doesn't contain them
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model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([1])}),
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NamedAttrsClass({'data': np.array([0])})])})
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# scale with 1 input and 1 or 2 blobs
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if model and len(model.blobs) != 0 and len(pb.bottom) == 1:
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attrs.update(weights_biases(param.bias_term, model))
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# 2 inputs + bias
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elif len(pb.bottom) == 2 and param.bias_term:
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if model is None or len(model.blobs) == 0:
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# default bias for scale layer with 2 inputs if the caffemodel file doesn't contain them
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model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([0])})])})
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embed_input(attrs, 1, 'biases', model.blobs[0].data)
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ScaleShiftOp.update_node_stat(node, attrs)
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return cls.enabled
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