6b04eca3c2
* Add Range-4 support * Add type infer and IR Reader extender * Implement ngraph Range-4 * Implement evaluate for Range-4 * Remove not needed types * Resolve review feedback * Add RTTI defines
95 lines
3.3 KiB
Python
95 lines
3.3 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 logging as log
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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.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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from mo.utils.error import Error
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class Range(Op):
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"""
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Some notes on the automatic result data type infer. The tf.range does is differently than np.arange. Numpy
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by default creates array with elements of type int64 and float64, but TF does not widen data types and
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keep them int32 and float32.
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Compare:
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>>> tf.range(1, 5, 0.5)
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<tf.Tensor 'range_1:0' shape = (8,) dtype = float32>
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>>> tf.range(1, 5, 2)
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<tf.Tensor 'range_2:0' shape = (2,) dtype = int32>
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>>> np.array([0.5], dtype=np.float32)
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array([0.5], dtype=float32)
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>>> np.arange(np.array([1], dtype=np.int32), np.array([5], dtype=np.int32), np.array([2], dtype=np.int32)).dtype
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dtype('int64')
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>>> np.arange(np.array([1], dtype=np.int32), np.array([5], dtype=np.int32), np.array([0.5], dtype=np.float32)).dtype
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dtype('float64')
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"""
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op = 'Range'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': self.op,
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'op': self.op,
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'version': 'opset4',
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'infer': self.infer,
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'type_infer': self.type_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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def backend_attrs(self):
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version = self.get_opset()
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if version == 'opset4':
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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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elif version == 'opset1':
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return []
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else:
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raise Error('Unknown opset version "{}"'.format(version))
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@staticmethod
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def type_infer(node: Node):
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node.out_port(0).set_data_type(node['output_type'])
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@staticmethod
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def infer(node: Node):
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name = node.soft_get('name', node.id)
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connected_input_ports = [in_port.idx for in_port in node.in_ports().values() if not in_port.disconnected()]
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assert len(connected_input_ports) == 3 and [0, 1, 2] == sorted(connected_input_ports), \
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'Range operation should have 3 inputs, {} found for {}'.format(len(connected_input_ports), name)
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start = node.in_port(0).data.get_value()
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limit = node.in_port(1).data.get_value()
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delta = node.in_port(2).data.get_value()
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assert start is not None and limit is not None and delta is not None, \
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'Range operation {} with dynamic inputs is not supported'.format(name)
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if not node.has_valid('output_type'):
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node['output_type'] = start.dtype
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node.out_port(0).data.set_value(np.arange(start, limit, delta, dtype=node['output_type']))
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