70 lines
2.5 KiB
Python
70 lines
2.5 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.front.common.partial_infer.utils import int64_array
|
|
from mo.graph.graph import Node, Graph
|
|
from mo.ops.op import Op
|
|
|
|
|
|
class Merge(Op):
|
|
op = 'Merge'
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
mandatory_props = {
|
|
'op': __class__.op,
|
|
'infer': __class__.merge_infer,
|
|
'cf_infer': __class__.control_flow_infer,
|
|
}
|
|
super().__init__(graph, mandatory_props, attrs)
|
|
|
|
@staticmethod
|
|
def merge_infer(node: Node):
|
|
# we infer only through executable input nodes
|
|
inferred_nodes = [n for n in node.in_nodes().values() if n['is_partial_inferred']]
|
|
assert len(inferred_nodes) != 0
|
|
|
|
if len(inferred_nodes) < len(node.in_nodes()):
|
|
node['is_not_fully_inferred'] = True
|
|
else:
|
|
node['is_not_fully_inferred'] = False
|
|
assert np.all(node.shape == inferred_nodes[0].shape for node in inferred_nodes)
|
|
|
|
inferred_and_executable = [n for n in node.in_nodes().values() if n['is_partial_inferred'] and
|
|
'executable' in n and n['executable']]
|
|
tensor = inferred_and_executable[0]
|
|
|
|
if all([np.all(tensor.value == n.value) for n in inferred_and_executable]):
|
|
node.out_node().value = tensor.value.copy() if tensor.has_valid('value') else None
|
|
|
|
tensor = inferred_nodes[0]
|
|
node.out_node().shape = int64_array(tensor.shape)
|
|
|
|
@staticmethod
|
|
def control_flow_infer(node: Node, is_executable: bool, mark_executability: callable):
|
|
graph = node.graph
|
|
|
|
in_data_nodes = node.in_nodes(control_flow=True)
|
|
out_data_nodes = node.out_nodes(control_flow=True)
|
|
|
|
is_executable = any([d.has_and_set('executable') for i, d in in_data_nodes.items()]
|
|
if len(in_data_nodes) else [False])
|
|
|
|
for i, d in out_data_nodes.items():
|
|
mark_executability(d.id, is_executable)
|
|
|