63 lines
2.1 KiB
Python
63 lines
2.1 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
|
|
from mo.utils.utils import symm_match_shapes
|
|
|
|
|
|
class TensorArrayGather(Op):
|
|
op = "TensorArrayGatherV3"
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
mandatory_props = {
|
|
'type': None,
|
|
'op': __class__.op,
|
|
'infer': TensorArrayGather.array_infer,
|
|
}
|
|
super().__init__(graph, mandatory_props, attrs)
|
|
|
|
@staticmethod
|
|
def array_infer(node: Node):
|
|
assert len(node.in_nodes()) == 3
|
|
|
|
handle = node.in_node(0)
|
|
indices = node.in_node(1)
|
|
flow_in = node.in_node(2)
|
|
|
|
ta_node = Node(node.graph, str(handle.value))
|
|
|
|
if ta_node.has_valid('element_shape') and ta_node.element_shape is not None and len(ta_node.element_shape) > 0:
|
|
assert symm_match_shapes(ta_node['element_shape'], node.element_shape)
|
|
else:
|
|
ta_node['element_shape'] = node.element_shape
|
|
data_shape = ta_node['element_shape']
|
|
assert -1 not in data_shape or data_shape.size == 2 and data_shape[0] == -1 and data_shape[1] != -1
|
|
|
|
assert ta_node.has_valid('size')
|
|
size = ta_node['size']
|
|
|
|
assert size > 0
|
|
|
|
output_shape = [size] + [data_shape[i] for i in range(len(data_shape))]
|
|
output_value = None
|
|
|
|
for _, out_node in node.graph.out_edges(node.id):
|
|
node.graph.node[out_node]['shape'] = np.array(output_shape)
|
|
node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
|