Anton Chetverikov
a788c02c3d
* Fix missed/redundant attrs for some operations * Align auto_pad attr values in spec * Update MO IR Reader extenders for appropriate operations * Allign auto_pad attr values for appropriate operations * Remove changes in extenders * Update backend_attrs for some operations * Changes in shape_infer functions to correct work with explicit mode * Apply offline comments
73 lines
3.0 KiB
Python
73 lines
3.0 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 mo.front.common.partial_infer.utils import int64_array, broadcast_shape
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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 Select(Op):
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op = 'Select'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'op': __class__.op,
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'type': __class__.op,
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'version': 'opset1',
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'in_ports_count': 3,
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'out_ports_count': 1,
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'infer': __class__.infer,
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'type_infer': __class__.type_infer,
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'auto_broadcast': 'numpy'
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}
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super().__init__(graph, mandatory_props, attrs)
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def backend_attrs(self):
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return ['auto_broadcast']
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@staticmethod
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def infer(node: Node):
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assert len([port for port in node.in_ports().values() if not port.disconnected()]) == 3, "Select operation must have 3 inputs:" \
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" \'condition\', \'then\' and \'else\' tensors"
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condition_value = node.in_port(0).data.get_value()
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resulting_tensors = [node.in_port(1).data.get_value(), node.in_port(2).data.get_value()]
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a_shape = node.in_port(1).data.get_shape()
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b_shape = node.in_port(2).data.get_shape()
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node.out_port(0).data.set_shape(broadcast_shape(a_shape, b_shape))
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# Case with unknown condition
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if condition_value is not None:
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output_value = np.where(condition_value, resulting_tensors[0], resulting_tensors[1])
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if condition_value.size != 1:
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if np.any(output_value == None):
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# If any element of output value is None that means that we use the value from 'then' or 'else' tensor
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# which is not defined, this means that we cannot perform value propagation.
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output_value = None
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else:
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output_value = np.array(output_value, dtype=resulting_tensors[not np.bool(condition_value.item(0))].dtype)
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if output_value is not None:
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node.out_port(0).data.set_value(np.array(output_value))
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@staticmethod
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def type_infer(node: Node):
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assert node.in_port(1).get_source().get_data_type() == node.in_port(2).get_source().get_data_type(), \
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'The data type of the second and the third inputs must be equal for the node {}'.format(node.name)
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node.out_port(0).set_data_type(node.in_port(1).get_source().get_data_type())
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