72 lines
2.7 KiB
Python
72 lines
2.7 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 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.ops.op import Op
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class Range(Op):
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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': __class__.op,
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'op': __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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start = node.in_node(0)
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limit = node.in_node(1)
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delta = node.in_node(2)
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output = node.out_node()
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if not start.has_valid('value') or not limit.has_valid('value') or not delta.has_valid('value'):
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log.error("Range operation is supported with constant inputs only")
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return
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if node.has_valid('pb') and 'type' in node.pb.attr:
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from mo.front.tf.extractors.utils import tf_dtype_extractor
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result_data_type = tf_dtype_extractor(node.pb.attr["type"].type)
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else:
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result_data_type = start.value.dtype
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output.value = np.arange(start.value, limit.value, delta.value, dtype=result_data_type)
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output.shape = np.array(output.value.shape, dtype=np.int64)
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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 keep them
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# 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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