60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
"""
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Copyright (c) 2019 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.back.ReshapeMutation import ReshapeMutation
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from mo.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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from mo.ops.reshape import Reshape
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class MatMulNormalizer(BackReplacementPattern):
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""" Work-around for incorrect models (likely from ONNX) where GEMM takes tensors with rank > 2
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ONNX/Gemm supports 2D inputs only. We observe some models that are constructed in a way where
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GEMM has an input with 4D input. It should be flattenned before.
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"""
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enabled = True
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graph_condition = [lambda graph: graph.graph['cmd_params'].generate_experimental_IR_V10]
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force_clean_up = True
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def run_before(self):
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return [ReshapeMutation]
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@staticmethod
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def pattern():
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return dict(
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nodes=[
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('op', dict(kind='op', type='MatMul'))],
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edges=[]
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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node = match['op']
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input_shape = node.in_port(0).data.get_shape()
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if len(input_shape) > 2:
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new_shape = Const(graph, {'value': np.array([0, -1], dtype=np.int64)}).create_node()
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reshape = Reshape(graph, {}).create_node()
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source = node.in_port(0).get_source()
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node.in_port(0).get_connection().set_source(reshape.out_port(0))
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source.connect(reshape.in_port(0))
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new_shape.out_port(0).connect(reshape.in_port(1))
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new_shape.infer(new_shape)
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reshape.infer(reshape)
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