44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from extensions.front.caffe.conv_ext import conv_create_attrs, conv_set_params
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from mo.front.caffe.extractors.utils import weights_biases
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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.ops.convolution import Convolution
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from mo.utils.error import Error
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class ConvFrontExtractor(FrontExtractorOp):
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op = 'ConvolutionBinary'
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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, model_layer = node.pb, node.model_pb
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if not proto_layer:
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raise Error('Protobuf layer can not be empty')
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conv_param = proto_layer.convolution_param
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conv_type = 'ConvND' if len(proto_layer.bottom) > 1 else 'Conv2D'
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params = conv_set_params(conv_param, conv_type)
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attrs = conv_create_attrs(params)
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attrs.update({'op': __class__.op,
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'get_group': lambda node: node.group,
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'get_output_feature_dim': lambda node: node.output,
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'weights_index': 1 if conv_type == 'Conv2D' else 2
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})
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# Embed weights and biases as attributes
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# It will be moved to a separate nodes in special pass
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attrs.update(
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weights_biases(conv_param.bias_term, model_layer, start_index=len(proto_layer.bottom), proto=conv_param))
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attrs.update(layout_attrs())
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# update the attributes of the node
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Convolution.update_node_stat(node, attrs)
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return cls.enabled
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