87 lines
3.4 KiB
Python
87 lines
3.4 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, PermuteAttrs
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class Tile(Op):
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op = 'Tile'
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enabled = True
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def __init__(self, graph: Graph, attrs: dict):
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super().__init__(graph, {
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'kind': 'op',
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'type': __class__.op,
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'op': __class__.op,
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'in_ports_count': 1,
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'out_ports_count': 1,
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'infer': Tile.infer
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}, attrs)
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def supported_attrs(self):
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return ['axis', 'tiles']
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@staticmethod
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def infer(node: Node):
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shape = node.in_node().shape
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if shape is None:
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log.error("Undefined shape for the input tiles for the Tile operation '{}'.".format(node.node))
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return
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shape = np.copy(shape)
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if len(node.in_nodes()) == 2:
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tile_array = node.in_node(1).value
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if tile_array is None:
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log.error('A tile values are None for a node "{}".'.format(node.name))
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return
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if len(shape) != len(tile_array):
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log.error('Shape mismatch for a node "{}": {} vs {}.'.format(node.name, shape.shape, tile_array.shape))
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return
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non_one_tile = np.argwhere(tile_array != 1)
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if len(non_one_tile) == 0:
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log.info(
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'Redundant "Tile" operation "{}" with tile values for all dimensions equal to 1.'.format(node.name))
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node['axis'] = 0
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node['tiles'] = 1
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elif len(non_one_tile) == 1:
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node['axis'] = non_one_tile[0][0]
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node['tiles'] = tile_array[node['axis']]
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else:
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node['type'] = None
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node['tile_array'] = tile_array
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log.warning("Tile operation with more than one dimension not equal to 1 is not supported.")
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# do not return here to allow infer shape and values for the constant propagation case
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node.graph.remove_edge(node.in_node(1).id, node.id)
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elif len(node.in_nodes()) == 1: # case when tiled dimension and count are specified in node attributes
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if not node.has_valid('axis') or not node.has_valid('tiles'):
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log.error('Mandatory attributes "axis" or "tiles" are not specified for a Tile node "{}"'.
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format(node.name))
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return
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tile_array = np.ones([len(shape)], dtype=np.int64)
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tile_array[node.axis] = node.tiles
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else:
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log.error('Unsupported number of input parameters to Tile node "{}"'.format(node.name))
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return
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PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:0')])
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node.out_node().shape = shape * tile_array
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if node.in_node(0).value is not None:
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node.out_node().value = np.tile(node.in_node(0).value, tile_array)
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