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openvino/model-optimizer/extensions/back/CutMemory.py

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3.1 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 extensions.back.ParameterToPlaceholder import ParameterToInput
from extensions.ops.parameter import Parameter
from mo.back.replacement import BackReplacementPattern
from mo.graph.graph import Graph
from mo.ops.crop import Crop
from mo.utils.logger import log
class CutMemoryInput(BackReplacementPattern):
"""
Cut Memory layers and have inputs/outputs in graph instead of them
"""
enabled = True
graph_condition = [lambda graph: graph.graph['fw'] == "kaldi" and graph.graph['cmd_params'].remove_memory]
force_clean_up = True
def run_before(self):
return [ParameterToInput]
@staticmethod
def pattern():
return dict(
nodes=[
('op', dict(kind='op', op='ReadValue'))],
edges=[]
)
@staticmethod
def replace_pattern(graph: Graph, match: dict):
node = match['op']
node_id = node['variable_id']
i = 0
node.in_port(0).disconnect()
for dest in node.out_port(0).get_destinations():
new_in = Parameter(graph, {'name': "Parameter_"+str(i)+"_for_"+node_id,
'shape': dest.data.get_shape()}).create_node()
i += 1
dest.disconnect()
new_in.out_port(0).connect(dest)
log.error("Add input/output mapped {} -> {} ".format(new_in.name, "Result_for_"+node_id),
extra={'is_warning': True})
class CutMemoryOutput(BackReplacementPattern):
"""
Cut Memory layers and have inputs/outputs in graph instead of them
"""
enabled = True
graph_condition = [lambda graph: graph.graph['fw'] == "kaldi" and graph.graph['cmd_params'].remove_memory]
force_clean_up = True
def run_before(self):
return [ParameterToInput]
@staticmethod
def pattern():
return dict(
nodes=[
('op', dict(kind='op', op='Assign'))],
edges=[]
)
@staticmethod
def replace_pattern(graph: Graph, match: dict):
node = match['op']
node_id = node['variable_id']
out_node_port = node.out_port(0).get_destination()
in_node_port = node.in_port(0).get_source()
node.in_port(0).disconnect()
node.out_port(0).disconnect()
crop = Crop(graph, {'name': 'Result_for_'+node_id, 'dim': np.array([1]), 'offset': np.array([0]),
'axis': np.array([0])}).create_node()
in_node_port.connect(crop.in_port(0))
crop.out_port(0).connect(out_node_port)