67 lines
2.4 KiB
Python
67 lines
2.4 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 typing import Dict
|
|
|
|
from mo.front.tf.graph_utils import create_op_with_const_inputs
|
|
from mo.graph.graph import Graph, Node
|
|
from mo.middle.replacement import MiddleReplacementPattern
|
|
from mo.ops.broadcast import Broadcast
|
|
|
|
|
|
class RandomUniformReplacer(MiddleReplacementPattern):
|
|
"""
|
|
Replaces RandomUniform operation with Broadcast of ones in sub-graph:
|
|
|
|
ShapeOf ---> RandomUniform ---> Mul
|
|
|
|
"""
|
|
|
|
enabled = True
|
|
|
|
@staticmethod
|
|
def pattern():
|
|
return dict(
|
|
nodes=[
|
|
('shape', dict(op='ShapeOf')),
|
|
('shape_data', dict()),
|
|
('random_uniform', dict(op='RandomUniform')),
|
|
('random_uniform_data', dict()),
|
|
('mul', dict(op='Mul')),
|
|
('mul_const', dict(op='Const')),
|
|
('mul_const_data', dict())
|
|
],
|
|
edges=[
|
|
('shape', 'shape_data'),
|
|
('shape_data', 'random_uniform'),
|
|
('random_uniform', 'random_uniform_data'),
|
|
('random_uniform_data', 'mul'),
|
|
('mul_const', 'mul_const_data'),
|
|
('mul_const_data', 'mul')
|
|
]
|
|
)
|
|
|
|
@staticmethod
|
|
def replace_pattern(graph: Graph, match: Dict[str, Node]):
|
|
node = match['random_uniform']
|
|
node_name = node.soft_get('name', node.id)
|
|
data_type = match['mul_const'].out_port(0).get_data_type()
|
|
broadcast_node = create_op_with_const_inputs(graph, Broadcast, port_value_dict={0: np.array([1], dtype=data_type)},
|
|
op_attrs={'name': node_name + '/Broadcast', 'mode': 'numpy'})
|
|
node.in_port(0).get_connection().set_destination(broadcast_node.in_port(1))
|
|
node.out_port(0).get_connection().set_source(broadcast_node.out_port(0))
|