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"""
Copyright (c) 2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import logging as log
import networkx as nx
import numpy as np
from mo.graph.graph import Node
from mo.ops.op import Op, PermuteAttrs
class Tile(Op):
op = 'Tile'
enabled = True
def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
super().__init__(graph, {
'kind': 'op',
'type': __class__.op,
'op': __class__.op,
'infer': Tile.infer
}, attrs)
def supported_attrs(self):
return ['axis', 'tiles']
@staticmethod
def infer(node: Node):
shape = node.in_node().shape
if shape is None:
log.error("Undefined shape for the input tiles for the Tile operation '{}'.".format(node.node))
return
shape = np.copy(shape)
if len(node.in_nodes()) == 2:
tile_array = node.in_node(1).value
if tile_array is None:
log.error('A tile values are None for a node "{}".'.format(node.name))
return
if len(shape) != len(tile_array):
log.error('Shape mismatch for a node "{}": {} vs {}.'.format(node.name, shape.shape, tile_array.shape))
return
non_one_tile = np.argwhere(tile_array != 1)
if len(non_one_tile) == 0:
log.info(
'Redundant "Tile" operation "{}" with tile values for all dimensions equal to 1.'.format(node.name))
node['axis'] = 0
node['tiles'] = 1
elif len(non_one_tile) == 1:
node['axis'] = non_one_tile[0][0]
node['tiles'] = tile_array[node['axis']]
else:
node['type'] = None
log.warning("Tile operation with more than one dimension not equal to 1 is not supported.")
# do not return here to allow infer shape and values for the constant propagation case
node.graph.remove_edge(node.in_node(1).id, node.id)
elif len(node.in_nodes()) == 1: # case when tiled dimension and count are specified in node attributes
if not node.has_valid('axis') or not node.has_valid('tiles'):
log.error('Mandatory attributes "axis" or "tiles" are not specified for a Tile node "{}"'.
format(node.name))
return
tile_array = np.ones([len(shape)], dtype=np.int64)
tile_array[node.axis] = node.tiles
else:
log.error('Unsupported number of input parameters to Tile node "{}"'.format(node.name))
return
PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:0')])
node.out_node().shape = shape * tile_array
if node.in_node(0).value is not None:
node.out_node().value = np.tile(node.in_node(0).value, tile_array)
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