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openvino/model-optimizer/extensions/front/interpolate_reshape.py
T
Vladimir Gavrilov dca30b4522 Extend MO for support of Interpolate-4 (#2026)
* Commit.

* Added opset4 version in the class Interpolate.

* Added class ONNXResize11Op to read ONNX Resize with opset version >= 11.

* Added support for Interpolate-4 into transformations TestInterpolateReshapeWA and InterpolateConcat.

* Added support for Interpolate-4 into transformation InterpolateWithConcat.

* Deleted redundant checks from the transformation UpsampleToResample.

* Reverted last changes.

* Changed ONNX Resize extractor to support for Interpolate-4.

* Added conversion of ONNXResize11Op into Interpolate-4.

* Added support for Interpolate-4 into the transformation InterpolateSequenceToInterpolate.

* Small fix for formatting.

* Written tests for MO version of Interpolate-4 with shape_calculation_mode = sizes.

* Written tests for infer function of Interpolate-4.

* Now transformations InterpolateWithConcat, InterpolateConcat, InterpolateReshapeWA skip Interpolate-4.

* Used create_op_with_const_inputs in the transformation InterpolateSequenceToInterpolate.

* The transformation ONNXResize11ToInterpolate4 was rewritten using find_and_replace_pattern.

* Now the dictionary infers (dictionary of infer functions of Interpolate) is a class static attribute.

* Deleted unused variable.

* Restored original logic of find_and_replace_pattern method of the class InterpolateReshapeWA.

* Used create_op_with_const_inputs() in the transformation InterpolateSequenceToInterpolate for opset1 case.

* Replaced resize_name by resize.soft_get('name', resize.id).

* Small fixes.

* Added two tests for Interpolate-4 infer function.

* Fixed the transformation ONNXResize11ToInterpolateV4 for the case when ONNXResize11 operation has 3 inputs.

* Added conversion of ONNXResize11 with tf_crop_and_resize_mode to ROIPooling + ONNXResize11.

* Fixed bugs in the transformation ONNXResize11ToInterpolateV4 and in the infer function of the operation ONNXResize11.

* Small changes.

* Renamed transformation that converts ONNXResize11 into ROIPooling + ONNXResize11 and fixed BOM-file.

* Fixed tests for the transformation InterpolateSequenceToInterpolate.

* Small change.

* Now the transformation InterpolateSequenceToInterpolate preserves output layer name.

* Deleted the transformation ONNXResize11ToTFCropAndResize.
2020-09-09 16:28:52 +03:00

185 lines
8.5 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.gather import Gather
from extensions.ops.interpolate import Interpolate
from mo.front.common.partial_infer.utils import int64_array
from mo.front.common.replacement import FrontReplacementPattern
from mo.front.tf.graph_utils import create_op_with_const_inputs
from mo.graph.graph import Graph, Node
from mo.ops.shape import Shape
class InterpolateWithConcat(FrontReplacementPattern):
"""
Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph using the following Concat inputs
BEFORE:
input Const
shape=[1, 3, 30, 40] value=[60, 160]
\ /
Interpolate(axes=(2, 3)) input_1
shape=[1, 3, 60, 160] shape=[1, 4, 60, 160]
\ /
Concat(axis=1)
shape=[1, 7, 60, 160]
AFTER:
input
shape=[1, 3, 30, 40] input_1
| shape=[1, 4, 60, 160]
| / |
| ShapeOf |
| | |
| Gather |
| indices=(2, 3); axis=0 |
\ | |
Interpolate(axes=(2, 3)) |
shape=[1, 3, 60, 160] |
\ /
Concat(axis=1)
shape=[1, 7, 60, 160]
1. Searches for Interpolate operation which output is connected to Concat (through identity operation or directly).
Interpolate -- [identity] --> Concat
2. Checks that Interpolate has positive axes parameter
3. Checks that Concat has positive axis (from attribute and N-input)
4. Checks that interpolation takes place over different dimensions than concatenation
5. Searches for Concat sources that are not connected to Interpolate operations
and checks that we have at least one such source (we could create a loop if we won't check)
6. If any of this checks are failed -- transformation doesn't do anything
7. Otherwise, we take the first Concat source from the (5) item.
Taking ShapeOf of this source and Gather'ing dimensions by the Interpolate::axes indices
we connect them to the second Interpolate input
This is how we get updated Interpolate second input that will fit the following Concat operation restrictions.
We perform this transformation of the FRONT phase for MO to be able to reshape this Interpolate layer too.
There is a similar transformation with less restrictions on the BACK phase.
"""
enabled = True
def run_after(self):
from extensions.front.InterpolateNormalizer import InterpolateNormalizer
return [InterpolateNormalizer]
@staticmethod
def get_concat_axis(concat: Node):
# Concat axis may be stored as an attribute and as an input (TF) and this is not resolved yet
# TODO: should be removed after Concat operation normalization
assert concat.soft_get('type') == 'Concat'
if concat.has_valid('axis'):
return concat.axis
if concat.has_valid('N'):
axis_node = concat.in_port(concat.N).get_source().node
if axis_node.has_valid('value'):
return axis_node.value.item(0)
return None
@staticmethod
def get_single_output_destination_safely(node: Node, idx: int = 0):
"""
Checks if node has exactly one used output port and this output port is only used by one consumer
If the checks passed, function returns consumer_node, otherwise None
"""
connected_out_ports = [port for port in node.out_ports().values() if not port.disconnected()]
if len(connected_out_ports) == 1 and connected_out_ports[0].idx == idx:
dsts = node.out_port(idx).get_destinations()
if len(dsts) == 1:
return dsts[0].node
return None
@staticmethod
def get_single_input_source_safely(node: Node, idx: int = 0):
"""
Checks if node has exactly one used input port
If the check passed, function returns input_node otherwise None
"""
connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
if len(connected_in_ports) == 1 and connected_in_ports[0].idx == idx:
return node.in_port(idx).get_source().node
return None
def get_non_interpolate_concat_sources(self, concat: Node):
"""
Traverses Concat input ports up to find which of them are not connected to Interpolate operations directly
or through identity operation sequence. Returns the list of Concat sources that satisfy the condition.
"""
assert concat.soft_get('type') == 'Concat'
sources, ports_to_omit = [], []
if concat.has_valid('N'):
# TODO: should be removed after Concat operation normalization
ports_to_omit.append(concat.N)
for in_port in concat.in_ports().values():
if in_port.disconnected() or in_port.idx in ports_to_omit:
continue
next_node = in_port.get_source().node
while next_node.soft_get('type') != 'Interpolate' and next_node.has_and_set('identity'):
node = self.get_single_input_source_safely(next_node)
if node is not None:
next_node = node
else:
break
if next_node.soft_get('type') != 'Interpolate':
sources.append(in_port.get_connection().get_source())
return sources
def make_interpolate_reshape_able(self, interpolate: Node, concat: Node):
assert interpolate.soft_get('type') == 'Interpolate'
assert concat.soft_get('type') == 'Concat'
interp_axes = Interpolate.get_axes(interpolate)
concat_axis = self.get_concat_axis(concat)
if concat_axis is None or interp_axes is None \
or np.any(interp_axes < 0) or concat_axis < 0 \
or concat_axis in interp_axes:
# checks that interpolate axes and concat axis are valid and do not intersect
return
non_interp_concat_srcs = self.get_non_interpolate_concat_sources(concat)
if not len(non_interp_concat_srcs):
# there is no Concat input to take input from
return
graph = interpolate.graph
src = non_interp_concat_srcs[0]
shape = Shape(graph, {'name': src.node.soft_get('name', src.node.id) + '/Shape'}).create_node()
shape.in_port(0).connect(src)
gather = create_op_with_const_inputs(graph, Gather,
{1: np.array(interp_axes, dtype=np.int32), 2: int64_array(0)},
{'name': shape.name + '/Gathered'}, input_node=shape)
interpolate.in_port(1).get_connection().set_source(gather.out_port(0))
def find_and_replace_pattern(self, graph: Graph):
for interpolate in graph.get_op_nodes(type='Interpolate', version='opset1'):
if interpolate.in_port(1).get_source().node.soft_get('type') != 'Const':
continue
# Interpolate could be connected to Concat through identity operations, skipping them
next_node = self.get_single_output_destination_safely(interpolate)
if next_node is not None:
while next_node.soft_get('type') != 'Concat' and next_node.has_and_set('identity'):
node = self.get_single_output_destination_safely(next_node)
if node is not None:
next_node = node
else:
break
if next_node.soft_get('type') == 'Concat':
self.make_interpolate_reshape_able(interpolate, next_node)