openvino/model-optimizer/extensions/middle/DilatedConvolution.py

90 lines
3.4 KiB
Python

"""
Copyright (c) 2019 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.
"""
from mo.graph.graph import Graph
from mo.middle.replacement import MiddleReplacementPattern
class DilatedConvolutionConverter(MiddleReplacementPattern):
enabled = True
force_clean_up = True
def run_after(self):
from extensions.middle.pass_separator import PreMiddleStart
return [PreMiddleStart]
def run_before(self):
from extensions.middle.pass_separator import MiddleStart
return [MiddleStart]
def pattern(self):
return dict(
nodes=[
('conv', dict(kind='op', op=lambda value: value in ['Conv2D', 'DepthwiseConv2dNative', 'Conv3D'])),
('space_to_batch', dict(kind='op', op='SpaceToBatchND')),
('batch_to_space', dict(kind='op', op='BatchToSpaceND')),
('input', dict(kind='data')),
('output', dict(kind='data')),
('conv_output', dict(kind='data')),
('stb_output', dict(kind='data')),
('stb_bs', dict(kind='data')),
('stb_pad', dict(kind='data')),
('bts_bs', dict(kind='data')),
('bts_crop', dict(kind='data'))
],
edges=[
('input', 'space_to_batch', {'in': 0}),
('stb_bs', 'space_to_batch', {'in': 1}),
('stb_pad', 'space_to_batch', {'in': 2}),
('space_to_batch', 'stb_output', {'out': 0}),
('stb_output', 'conv', {'in': 0}),
('conv', 'conv_output', {'out': 0}),
('conv_output', 'batch_to_space', {'in': 0}),
('bts_bs', 'batch_to_space', {'in': 1}),
('bts_crop', 'batch_to_space', {'in': 2}),
('batch_to_space', 'output', {'out': 0}),
])
def replace_pattern(self, graph: Graph, match: dict):
conv = match['conv']
stb = match['space_to_batch']
bts = match['batch_to_space']
block_size = match['stb_bs']
input = match['input']
output = match['output']
stb_out = match['stb_output']
conv_out = match['conv_output']
in_edge_attrs = graph.get_edge_data(input.id, stb.id)[0]
out_edge_attrs = graph.get_edge_data(bts.id, output.id)[0]
graph.remove_edge(input.id, stb.id)
graph.remove_edge(stb_out.id, conv.id)
graph.remove_edge(conv.id, conv_out.id)
graph.remove_edge(bts.id, output.id)
conv.dilation[conv.spatial_dims] = block_size.value
pad = match['stb_pad'].value - match['bts_crop'].value
conv.pad[conv.spatial_dims] = [[pad[x][0], pad[x][1]] for x in range(len(pad))]
conv['auto_pad'] = None
graph.add_edges_from([
(input.id, conv.id, {'in': 0, **in_edge_attrs}),
(conv.id, output.id, {'out': 0, **out_edge_attrs}),
])