c2394508c1
* Implement LookupTableInsertV2 shape inference It is needed if other nodes not beeing pruned in the graph have a conditional dependence on LookupTableInsertV2 node. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix after core-review #1 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix the code after review #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix after code review #3
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
"""
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Copyright (C) 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 mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op
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class LookupTableInsert(Op):
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'''
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This operation has only output control flow edges and no output data edges in some models.
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And for these cases implementation of the shape inference is needed since the shape inference is executed
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before control flow edges resolving. This operation has non-tensor output so the output shape is empty.
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'''
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enabled = False
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op = 'LookupTableInsert'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': None,
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'op': self.op,
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'infer': self.infer,
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'in_ports_count': 3,
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'out_ports_count': 1,
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}
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super().__init__(graph, mandatory_props, attrs)
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@staticmethod
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def infer(node: Node):
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node_name = node.soft_get('name', node.id)
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connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
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assert len(connected_in_ports) == 3, \
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"Incorrect number of inputs for {} node".format(node_name)
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# check shapes of input tensors
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keys_shape = node.in_port(1).data.get_shape()
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values_shape = node.in_port(2).data.get_shape()
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assert np.array_equal(keys_shape, values_shape), \
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'Shapes of tensors with keys and values must be equal for {} node'.format(node_name)
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# set output shape that must be empty
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# since output is not a tensor
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node.out_port(0).data.set_shape(int64_array([]))
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