72 lines
2.8 KiB
Python
72 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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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.middle.passes.convert_data_type import np_data_type_to_destination_type
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from mo.ops.op import Op
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class NonZero(Op):
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op = 'NonZero'
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enabled = False
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def __init__(self, graph: Graph, attrs: dict):
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assert 'output_type' in attrs, 'NonZero has mandatory `output_type` attribute'
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mandatory_props = {
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'op': self.op,
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'type': self.op,
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'version': 'opset3',
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'infer': self.infer,
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'type_infer': self.type_infer,
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'in_ports_count': 1,
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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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def backend_attrs(self):
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return [
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('output_type', lambda node: np_data_type_to_destination_type(node.output_type)),
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]
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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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input_shape = node.in_port(0).data.get_shape()
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assert input_shape is not None, 'The input shape for node "{}" is None'.format(node_name)
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assert node.has_valid('output_type'), \
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'`output_type` attribute is not set for NonZero node `{}`'.format(node_name)
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assert node.output_type in [np.int64, np.int32], \
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'NonZero `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name)
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input_value = node.in_port(0).data.get_value()
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if input_value is not None:
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node.out_port(0).data.set_value(np.array(np.nonzero(input_value), dtype=node.output_type))
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else:
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# output shape of NonZero should be [input_rank, dynamic]
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# having restriction to save IR with static shape only we count upper-bound shape value here
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node.out_port(0).data.set_shape(int64_array([len(input_shape), np.prod(input_shape)]))
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@staticmethod
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def type_infer(node):
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assert node.output_type in [np.int64, np.int32], \
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'NonZero `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name)
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node.out_port(0).set_data_type(node.output_type)
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