62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
"""
|
|
Copyright (C) 2018-2020 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 numpy as np
|
|
|
|
from mo.graph.graph import Node, Graph
|
|
from mo.ops.op import Op
|
|
|
|
|
|
class TensorArray(Op):
|
|
op = "TensorArrayV3"
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
mandatory_props = {
|
|
'type': None,
|
|
'op': __class__.op,
|
|
'infer': TensorArray.array_infer,
|
|
}
|
|
super().__init__(graph, mandatory_props, attrs)
|
|
|
|
@staticmethod
|
|
def array_infer(node: Node):
|
|
size = node.in_node(0)
|
|
assert size.value is not None
|
|
|
|
# 0 port: handle
|
|
if 0 in node.out_nodes().keys():
|
|
if node.has_valid('element_shape'):
|
|
element_shape = node['element_shape']
|
|
else:
|
|
element_shape = None
|
|
|
|
out_node = node.out_node(0).id
|
|
output_value = node.out_node(0).id
|
|
node.graph.node[out_node]['value'] = np.array(output_value)
|
|
|
|
output_shape = node.graph.node[out_node]['value'].shape
|
|
node.graph.node[out_node]['shape'] = np.array(output_shape)
|
|
|
|
node.graph.node[out_node]['element_shape'] = np.array(element_shape)
|
|
node.graph.node[out_node]['size'] = size.value
|
|
# 1 port flow
|
|
if 1 in node.out_nodes().keys():
|
|
output_value = None
|
|
|
|
out_node = node.out_node(1).id
|
|
node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
|
|
node.graph.node[out_node]['shape'] = np.array(output_shape)
|