94 lines
2.8 KiB
Python
94 lines
2.8 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 extensions.ops.elementwise import Add, Mul, Maximum
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from mo.front.caffe.collect_attributes import merge_attrs
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from mo.front.caffe.extractors.utils import embed_input
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from mo.front.common.extractors.utils import layout_attrs
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from mo.front.extractor import FrontExtractorOp
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from mo.graph.graph import Node
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from mo.ops.eltwise_n import EltwiseNMul, EltwiseNAdd, EltwiseNMax
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from mo.ops.power import AttributedPower
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class BiasToAdd(FrontExtractorOp):
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"""
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Replaces Bias layer with Add.
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"""
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op = "Bias"
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enabled = True
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@classmethod
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def extract(cls, node: Node):
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attrs = {'axis': node.pb.bias_param.axis}
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embed_input(attrs, 1, 'bias', node.model_pb.blobs[0].data, 'biases')
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Add.update_node_stat(node, attrs)
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return cls.enabled
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class EltwiseExtractor(FrontExtractorOp):
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op = 'Eltwise'
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enabled = True
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@classmethod
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def extract(cls, node):
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proto_layer = node.pb
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param = proto_layer.eltwise_param
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input_len = len(node.in_edges())
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eltwise_caffe_map = {
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0: EltwiseNMul if input_len > 2 else Mul,
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1: EltwiseNAdd if input_len > 2 else Add,
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2: EltwiseNMax if input_len > 2 else Maximum,
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}
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operation = int(param.operation)
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if operation not in eltwise_caffe_map:
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raise Exception('Unsupported type of operation in Eltwise layer: ' + node.name)
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lin_op_class = eltwise_caffe_map[operation]
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mapping_rule = merge_attrs(param, {'coeff': np.array(param.coeff)})
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mapping_rule.update(layout_attrs())
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assert len(param.coeff) <= input_len
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lin_op_class.update_node_stat(node, mapping_rule)
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return cls.enabled
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class PowerExtractor(FrontExtractorOp):
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op = 'power'
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enabled = True
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@classmethod
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def extract(cls, node: Node):
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pb = node.pb
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assert pb, 'Protobuf layer can not be empty'
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param = pb.power_param
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attrs = {
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'output_spatial_shape': None,
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'power': param.power,
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'scale': param.scale,
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'shift': param.shift,
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}
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AttributedPower.update_node_stat(node, attrs)
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return cls.enabled
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