66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
"""
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Copyright (c) 2018-2019 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 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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'in_ports_count': 3,
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'out_ports_count': 1,
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'infer': __class__.infer,
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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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assert len(node.in_nodes()) == 3, "Select operation must have 3 inputs by TensorFlow reference:" \
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" \'condition\', \'then\' and \'else\' tensors"
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condition_node = node.in_node(0)
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resulting_tensors = [node.in_node(1), node.in_node(2)]
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assert np.array_equal(resulting_tensors[0].shape, resulting_tensors[1].shape), \
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"TensorFlow \'Select\' operation has 3 inputs: \'condition\', \'then\' and \'else\' tensors." \
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"\'then\' and \'else\' tensors must have the same shape by TensorFlow reference"
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output_shape = resulting_tensors[0].shape
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# Case with unknown condition
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if not condition_node.has_valid('value'):
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# infer only shapes
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for out in node.out_nodes():
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node.out_node(out).shape = np.array(output_shape)
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return
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assert condition_node.value.size == 1
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condition_value = condition_node.value.item(0)
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assert isinstance(condition_value, np.bool), \
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"TensorFlow \'Select\' operation has 3 inputs: \'condition\', \'then\' and \'else\' tensors. " \
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"Value of \'condition\' tensor must be boolen by TensorFlow reference"
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output_value = resulting_tensors[not condition_value].value
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for _, out_node in node.graph.out_edges(node.id):
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node.graph.node[out_node]['shape'] = np.array(output_shape)
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node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
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