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

122 lines
4.3 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.ops.split import AttributedSplit
from mo.back.replacement import BackReplacementPattern
from mo.front.common.partial_infer.utils import int64_array
from mo.graph.graph import Graph
class SplitNormalizer(BackReplacementPattern):
enabled = True
graph_condition = [lambda graph: not graph.graph['cmd_params'].generate_experimental_IR_V10]
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='Split'):
name = node.soft_get('name', node.id)
input_shape = node.in_port(0).data.get_shape()
assert input_shape is not None
axis = node.in_port(1).data.get_value()
assert axis is not None
num_splits = node.soft_get('num_splits', None)
assert num_splits is not None
if axis < 0:
axis += input_shape.size
split = AttributedSplit(graph, {'name': name, 'axis': axis, 'num_splits': num_splits}).create_node()
for idx, port in node.out_ports().items():
node.out_port(idx).get_connection().set_source(split.out_port(idx))
node.in_port(0).get_connection().set_destination(split.in_port(0))
graph.remove_node(node.id)
class VariadicSplitNormalizer(BackReplacementPattern):
enabled = True
graph_condition = [lambda graph: not graph.graph['cmd_params'].generate_experimental_IR_V10]
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='VariadicSplit'):
name = node.soft_get('name', node.id)
input_shape = node.in_port(0).data.get_shape()
assert input_shape is not None
axis = node.in_port(1).data.get_value()
assert axis is not None
size_splits = node.in_port(2).data.get_value()
assert size_splits is not None
connected_outputs = {idx: port for idx, port in node.out_ports().items() if not port.disconnected()}
assert len(size_splits) >= len(connected_outputs)
split_size = connected_outputs[list(connected_outputs.keys())[0]].data.get_shape()[axis]
if np.unique(size_splits).size != 1:
return
# all split sizes are equal
assert input_shape[axis] % split_size == 0
num_splits = int64_array(input_shape[axis] / split_size)
assert num_splits is not None
if axis < 0:
axis += input_shape.size
split = AttributedSplit(graph, {'name': name, 'axis': axis, 'num_splits': num_splits}).create_node()
for idx, port in node.out_ports().items():
node.out_port(idx).get_connection().set_source(split.out_port(idx))
node.in_port(0).get_connection().set_destination(split.in_port(0))
graph.remove_node(node.id)
class PassVariadicSplitAsIs(BackReplacementPattern):
enabled = True
force_clean_up = True
graph_condition = [lambda graph: not graph.graph['cmd_params'].generate_experimental_IR_V10]
def run_after(self):
return [VariadicSplitNormalizer]
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='VariadicSplit'):
input_shape = node.in_port(0).data.get_shape()
assert input_shape is not None
axis = node.in_port(1).data.get_value()
assert axis is not None
node.in_port(1).disconnect()
size_splits = node.in_port(2).data.get_value()
assert size_splits is not None
node.in_port(2).disconnect()
if axis < 0:
axis += input_shape.size
node['type'] = 'Split'
node['axis'] = axis