80 lines
2.8 KiB
Python
80 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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from extensions.back.op_versioning import OpVersioning
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from mo.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph
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class BlobNormalizer(BackReplacementPattern):
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"""
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This pass affects Convolution and FullyConnected weights and biases form in IR.
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Old version of those layers included weights and biases as blobs:
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<layer ... type="Convolution">
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...
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<blobs>
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<weights offset="***" size="***"/>
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<biases offset="***" size="***"/>
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</blobs>
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</layer>
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New version (after BlobNormalizer execution) weighs and biases are represented
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as inputs to Convolution/FullyConnected layer
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"""
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enabled = True
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graph_condition = [
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lambda graph: graph.graph['cmd_params'].blobs_as_inputs or
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graph.graph['cmd_params'].generate_experimental_IR_V10
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]
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def run_before(self):
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return []
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def run_after(self):
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from extensions.back.pass_separator import BackFinish
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return [BackFinish]
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@staticmethod
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def pattern():
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return dict(
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nodes=[('conv', dict(type=lambda type: type in ['Convolution', 'Deconvolution', 'FullyConnected']))],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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conv = match['conv']
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for i in [1, 2]:
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if i in conv.in_edges() and conv.in_edges()[i] and 'bin' in conv.in_edges()[i]:
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del conv.in_edges()[i]['bin']
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def find_and_replace_pattern(self, graph: Graph):
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if graph.graph['cmd_params'].generate_experimental_IR_V10:
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for node in graph.get_op_nodes():
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if node.soft_get('type').lower() not in OpVersioning.opset_1_types and \
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not node.soft_get('version') in ["opset2", "opset3"]:
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continue
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for _, d in node.in_edges().items():
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if 'bin' in d:
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del d['bin']
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for node in graph.get_data_nodes():
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for d in node.in_edges():
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if 'bin' in d:
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del d['bin']
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else:
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if len(graph.get_op_nodes(type='FakeQuantize')):
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BackReplacementPattern.find_and_replace_pattern(self, graph)
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