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openvino/model-optimizer/extensions/back/LayoutChangeForGatherND.py
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Roman Kazantsev e3b879ad3b Provide GatherND with original layout for inputs and output (#3002)
* Provide GatherND with original layout for inputs and output

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Fix code review #1

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
2020-11-10 17:24:04 +03:00

62 lines
2.8 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.transpose import Transpose
from mo.front.common.partial_infer.utils import int64_array
from mo.front.tf.graph_utils import create_op_with_const_inputs
from mo.graph.graph import Graph, Port
from mo.back.replacement import BackReplacementPattern
class LayoutChangeForGatherND(BackReplacementPattern):
"""
Return original layout for inputs and output of GatherND operation
since the operation is designed for NHWC layout.
"""
enabled = True
force_shape_inference = True
graph_condition = [lambda graph: graph.graph['fw'] == 'tf']
@staticmethod
def insert_transpose(graph: Graph, input_port: Port, before_input=True):
input_rank = len(input_port.data.get_shape())
if input_rank > 3:
if before_input:
axis_order = np.concatenate((int64_array([0]),
int64_array(list(range(2, input_rank))),
int64_array([1])))
source_node = input_port.get_source().node
transpose_name = source_node.soft_get('name', source_node.id) + '/TransposeToNHWC'
else:
axis_order = np.concatenate(
(int64_array([0]),
int64_array([input_rank - 1]),
int64_array(list(range(1, input_rank - 1)))))
transpose_name = input_port.node.soft_get('name', input_port.node.id) + '/TransposeToNCHW'
input_port.node['need_shape_inference'] = True
input_port.node['override_output_shape'] = True
transpose = create_op_with_const_inputs(graph, Transpose, {1: axis_order}, {'name': transpose_name})
input_port.get_connection().insert_node(transpose)
transpose['need_shape_inference'] = True
transpose['override_output_shape'] = True
def find_and_replace_pattern(self, graph: Graph):
for gathernd in graph.get_op_nodes(type='GatherND'):
self.insert_transpose(graph, gathernd.in_port(0), before_input=True)
self.insert_transpose(graph, gathernd.in_port(1), before_input=True)
self.insert_transpose(graph, gathernd.out_port(0), before_input=False)