+++ /dev/null
-"""
- 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 unittest
-
-import numpy as np
-
-from extensions.middle.EltwiseInputNormalization import EltwiseInputNormalize
-from mo.front.common.partial_infer.utils import int64_array
-from mo.middle.passes.eliminate_test import build_graph
-from mo.utils.ir_engine.compare_graphs import compare_graphs
-
-# The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
-# dictionary with node attributes.
-nodes_attributes = {
- # Placeholder layers
- 'placeholder_1': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
- 'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
- 'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
- 'placeholder_3_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
- 'placeholder_4_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
-
- # Reshape layers
- 'reshape_1': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
- 'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
- 'reshape_1_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
- 'reshape_1_const_data': {'kind': 'data', 'value': None, 'shape': None},
-
- 'reshape_2': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
- 'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
- 'reshape_2_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
- 'reshape_2_const_data': {'kind': 'data', 'value': None, 'shape': None},
-
- # Eltwise consumes layers
- 'eltwise_1': {'kind': 'op', 'is_eltwise': True},
- 'eltwise_1_data': {'value': None, 'shape': None, 'kind': 'data'},
-
- 'eltwise_2': {'kind': 'op', 'is_eltwise': True},
- 'eltwise_2_data': {'value': None, 'shape': None, 'kind': 'data'},
-
- 'eltwise_3': {'kind': 'op', 'is_eltwise': True},
- 'eltwise_3_data': {'value': None, 'shape': None, 'kind': 'data'},
-
- 'eltwise_4': {'kind': 'op', 'is_eltwise': True},
- 'eltwise_4_data': {'value': None, 'shape': None, 'kind': 'data'},
-
- # Concat
- 'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
-}
-
-
-class EltwiseInputNormalizationTest(unittest.TestCase):
- def test1_not_constant(self):
- #
- # data1(1,3,64,64)----. data(1,3,64,64)-------.
- # data2(1,64,1)-------->Eltwise-->data(1,3,64,64) => data(1,64,1)->Reshape->data(1,1,64,1)-->Eltwise->...
- # data3(64,1)------' data(64,1)->Reshape->data(1,1,64,1)-'
- #
- graph = build_graph(nodes_attributes, [
- ('placeholder_1', 'placeholder_1_data'),
- ('placeholder_1', 'placeholder_2_data'),
- ('placeholder_1', 'placeholder_3_data'),
- ('placeholder_1_data', 'eltwise_1'),
- ('placeholder_2_data', 'eltwise_1'),
- ('placeholder_3_data', 'eltwise_1'),
- ('eltwise_1', 'eltwise_1_data')
- ],
- {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'placeholder_2_data': {'shape': np.array([1, 64, 1])},
- 'placeholder_3_data': {'shape': np.array([64, 1])},
- 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
- }, nodes_with_edges_only=True)
-
- graph_ref = build_graph(nodes_attributes,
- [
- ('placeholder_1', 'placeholder_1_data'),
- ('placeholder_1', 'placeholder_2_data'),
- ('placeholder_1', 'placeholder_3_data'),
- ('placeholder_1_data', 'eltwise_1'),
- ('placeholder_2_data', 'reshape_1'),
- ('reshape_1_const', 'reshape_1_const_data'),
- ('reshape_1_const_data', 'reshape_1'),
- ('placeholder_3_data', 'reshape_2'),
- ('reshape_2_const', 'reshape_2_const_data'),
- ('reshape_2_const_data', 'reshape_2'),
- ('reshape_1', 'reshape_1_data'),
- ('reshape_2', 'reshape_2_data'),
- ('reshape_1_data', 'eltwise_1'),
- ('reshape_2_data', 'eltwise_1'),
- ('eltwise_1', 'eltwise_1_data')
- ],
- {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'reshape_1_const': {'value': int64_array([1, 1, 64, 1]), 'shape': int64_array([4])},
- 'reshape_1_const_data': {'value': int64_array([1, 1, 64, 1]),
- 'shape': int64_array([4])},
- 'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
- 'reshape_2_const': {'value': int64_array([1, 1, 64, 1]), 'shape': int64_array([4])},
- 'reshape_2_const_data': {'value': int64_array([1, 1, 64, 1]),
- 'shape': int64_array([4])},
- 'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
- 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
- }, nodes_with_edges_only=True)
-
- pattern = EltwiseInputNormalize()
- pattern.find_and_replace_pattern(graph)
-
- (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
- self.assertTrue(flag, resp)
-
- def test_mega_hardcore(self):
- # ORIGINAL GRAPH
- #
- # data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
- # /\ /\ /\
- # data2(64,1)-----,-'--------------------------------'------------------------------'
- # \/ /
- # data3(64,1)----`-->Eltwise3->data(64,1)----------'
- #
- # REFERENCE GRAPH AFTER TRANSFORMATION
- #
- # data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
- # /\ /\ /\
- # data2(1,1,64,1)---'--------------------------------'-------------------------------'
- # /
- # data4(64,1)-------, Reshape(1,1,64,1)
- # \/ |
- # data3(64,1)------`---->Eltwise3->data(64,1)---'
- #
- graph = build_graph(nodes_attributes,
- [('placeholder_1_data', 'eltwise_1'),
- ('placeholder_2_data', 'eltwise_1'),
- ('eltwise_1', 'eltwise_1_data'),
- ('eltwise_1_data', 'eltwise_2'),
- ('placeholder_2_data', 'eltwise_3'),
- ('placeholder_3_data', 'eltwise_3'),
- ('eltwise_3', 'eltwise_3_data'),
- ('eltwise_3_data', 'eltwise_2'),
- ('eltwise_2', 'eltwise_2_data'),
- ('eltwise_2_data', 'eltwise_4'),
- ('placeholder_2_data', 'eltwise_4'),
- ('eltwise_4', 'eltwise_4_data'),
- ],
- {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'placeholder_2_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
- 'placeholder_3_data': {'shape': np.array([64, 1])},
- 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
- 'eltwise_3_data': {'shape': np.array([64, 1])},
- 'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
- }, nodes_with_edges_only=True)
-
- graph_ref = build_graph(nodes_attributes,
- [('placeholder_1_data', 'eltwise_1'),
- ('placeholder_2_data', 'eltwise_1'),
- ('eltwise_1', 'eltwise_1_data'),
- ('eltwise_1_data', 'eltwise_2'),
- ('placeholder_4_data', 'eltwise_3'),
- ('placeholder_3_data', 'eltwise_3'),
- ('eltwise_3', 'eltwise_3_data'),
- ('eltwise_3_data', 'reshape_1'),
- ('reshape_1_const', 'reshape_1_const_data'),
- ('reshape_1_const_data', 'reshape_1'),
- ('reshape_1', 'reshape_1_data'),
- ('reshape_1_data', 'eltwise_2'),
- ('eltwise_2', 'eltwise_2_data'),
- ('eltwise_2_data', 'eltwise_4'),
- ('placeholder_2_data', 'eltwise_4'),
- ('eltwise_4', 'eltwise_4_data'),
- ],
- {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'placeholder_2_data': {'shape': np.array([1, 1, 64, 1]),
- 'value': np.ones([1, 1, 64, 1])},
- 'placeholder_3_data': {'shape': np.array([64, 1])},
- 'placeholder_4_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
- 'reshape_1_const': {'value': int64_array([1, 1, 64, 1]), 'shape': int64_array([4])},
- 'reshape_1_const_data': {'value': int64_array([1, 1, 64, 1]),
- 'shape': int64_array([4])},
- 'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
- 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
- 'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
- 'eltwise_3_data': {'shape': np.array([64, 1])},
- 'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
- }, nodes_with_edges_only=True)
-
- pattern = EltwiseInputNormalize()
- pattern.find_and_replace_pattern(graph)
-
- (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_4', check_op_attrs=True)
- self.assertTrue(flag, resp)
import numpy as np
-from extensions.middle.EltwiseInputReshape import EltwiseInputReshape
+from extensions.middle.EltwiseInputReshape import normalize_eltwise_inputs
from mo.front.common.partial_infer.utils import int64_array
from mo.middle.passes.eliminate_test import build_graph
from mo.utils.ir_engine.compare_graphs import compare_graphs
nodes_attributes = {
# Placeholder layers
'placeholder_1': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
+ 'placeholder_2': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
+ 'placeholder_3': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
'placeholder_3_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
+ 'placeholder_4_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
# Reshape layers
- 'reshape_1': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
+ 'reshape_1': {'type': 'Unsqueeze', 'value': None, 'kind': 'op', 'op': 'Unsqueeze'},
'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
'reshape_1_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
'reshape_1_const_data': {'kind': 'data', 'value': None, 'shape': None},
- 'reshape_2': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
+ 'reshape_2': {'type': 'Unsqueeze', 'value': None, 'kind': 'op', 'op': 'Unsqueeze'},
'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
'reshape_2_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
'reshape_2_const_data': {'kind': 'data', 'value': None, 'shape': None},
- # Fake consumes layers
- 'consumer_1': {'type': 'Consumer', 'value': None, 'kind': 'op', 'op': 'Consumer'},
- 'consumer_2': {'type': 'Consumer', 'value': None, 'kind': 'op', 'op': 'Consumer'},
- 'consumer_3': {'type': 'Consumer', 'value': None, 'kind': 'op', 'op': 'Consumer'},
+ # Eltwise consumes layers
+ 'eltwise_1': {'kind': 'op', 'is_eltwise': True},
+ 'eltwise_1_data': {'value': None, 'shape': None, 'kind': 'data'},
+
+ 'eltwise_2': {'kind': 'op', 'is_eltwise': True},
+ 'eltwise_2_data': {'value': None, 'shape': None, 'kind': 'data'},
+
+ 'eltwise_3': {'kind': 'op', 'is_eltwise': True},
+ 'eltwise_3_data': {'value': None, 'shape': None, 'kind': 'data'},
+
+ 'eltwise_4': {'kind': 'op', 'is_eltwise': True},
+ 'eltwise_4_data': {'value': None, 'shape': None, 'kind': 'data'},
# Concat
'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
}
-class EltwiseInputReshapeTest(unittest.TestCase):
+class EltwiseInputNormalizationTest(unittest.TestCase):
def test1_not_constant(self):
+ #
+ # data1(1,3,64,64)----. data(1,3,64,64)-------.
+ # data2(1,64,1)-------->Eltwise-->data(1,3,64,64) => data(1,64,1)->Reshape->data(1,1,64,1)-->Eltwise->...
+ # data3(64,1)------' data(64,1)->Reshape->data(1,1,64,1)-'
+ #
+ graph = build_graph(nodes_attributes, [
+ ('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1', 'placeholder_2_data'),
+ ('placeholder_1', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'eltwise_1'),
+ ('placeholder_3_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data')
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'placeholder_2_data': {'shape': np.array([1, 64, 1])},
+ 'placeholder_3_data': {'shape': np.array([64, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
+ }, nodes_with_edges_only=True)
+
+ graph_ref = build_graph(nodes_attributes,
+ [
+ ('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1', 'placeholder_2_data'),
+ ('placeholder_1', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'reshape_1'),
+ ('reshape_1_const', 'reshape_1_const_data'),
+ ('reshape_1_const_data', 'reshape_1'),
+ ('placeholder_3_data', 'reshape_2'),
+ ('reshape_2_const', 'reshape_2_const_data'),
+ ('reshape_2_const_data', 'reshape_2'),
+ ('reshape_1', 'reshape_1_data'),
+ ('reshape_2', 'reshape_2_data'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('reshape_2_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data')
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'reshape_1_const': {'value': int64_array([0]), 'shape': int64_array([1])},
+ 'reshape_1_const_data': {'value': int64_array([0]),
+ 'shape': int64_array([1])},
+ 'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
+ 'reshape_2_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_2_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
+ }, nodes_with_edges_only=True)
+
+ normalize_eltwise_inputs(graph)
+
+ (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
+ self.assertTrue(flag, resp)
+
+ def test_mega_hardcore(self):
+ # ORIGINAL GRAPH
+ #
+ # data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
+ # /\ /\ /\
+ # data2(64,1)-----,-'--------------------------------'------------------------------'
+ # \/ /
+ # data3(64,1)----`-->Eltwise3->data(64,1)----------'
+ #
+ # REFERENCE GRAPH AFTER TRANSFORMATION
+ #
+ # data1(1,3,64,64)---------------------,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
+ # /\ /\ /\
+ # data2(64,1)-,- Reshape1(1,1,64,64)--'--------------------------------o-------------------------------'
+ # | |
+ # | Reshape(1,1,64,1)
+ # \/ |
+ # data3(64,1)----------->Eltwise3->data(64,1)--------------------------'
+ #
+ graph = build_graph(nodes_attributes,
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_2', 'placeholder_2_data'),
+ ('placeholder_3', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_1_data', 'eltwise_2'),
+ ('placeholder_2_data', 'eltwise_3'),
+ ('placeholder_3_data', 'eltwise_3'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_3_data', 'eltwise_2'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_2_data', 'eltwise_4'),
+ ('placeholder_2_data', 'eltwise_4'),
+ ('eltwise_4', 'eltwise_4_data'),
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'placeholder_2_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
+ 'placeholder_3_data': {'shape': np.array([64, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
+ 'eltwise_3_data': {'shape': np.array([64, 1])},
+ 'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
+ }, nodes_with_edges_only=True)
+
+ graph_ref = build_graph(nodes_attributes,
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_2', 'placeholder_2_data'),
+ ('placeholder_3', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'reshape_1'),
+ ('reshape_1_const', 'reshape_1_const_data'),
+ ('reshape_1_const_data', 'reshape_1'),
+ ('reshape_1', 'reshape_1_data'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_1_data', 'eltwise_2'),
+ ('placeholder_2_data', 'eltwise_3'),
+ ('placeholder_3_data', 'eltwise_3'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_3_data', 'reshape_2'),
+ ('reshape_2_const', 'reshape_2_const_data'),
+ ('reshape_2_const_data', 'reshape_2'),
+ ('reshape_2', 'reshape_2_data'),
+ ('reshape_2_data', 'eltwise_2'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_2_data', 'eltwise_4'),
+ ('reshape_1_data', 'eltwise_4'),
+ ('eltwise_4', 'eltwise_4_data'),
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'placeholder_2_data': {'shape': np.array([64, 1]),
+ 'value': np.ones([64, 1])},
+ 'placeholder_3_data': {'shape': np.array([64, 1])},
+ 'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_1_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
+
+ 'reshape_2_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_2_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
+ 'eltwise_3_data': {'shape': np.array([64, 1])},
+ 'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
+ }, nodes_with_edges_only=True)
+
+ normalize_eltwise_inputs(graph)
+
+ (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_4', check_op_attrs=True)
+ self.assertTrue(flag, resp)
+
+ def test2_not_constant(self):
# ,-------------->consumer3 ,------------>consumer3
# data---(new_shape1)-->consumer1 => data---->Reshape-->consumer1
# `-(new_shape2)-->consumer2 `-->Reshape-->consumer2
#
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
- ('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3, 1, 1])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 1, 3])}),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
+ {'placeholder_1_data': {'shape': int64_array([1, 3])},
+ 'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
+ 'eltwise_2_data': {'shape': int64_array([1, 1, 3])},
+ 'eltwise_3_data': {'shape': int64_array([1, 3])},
+ },
+ nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[
('placeholder_1_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
- ('placeholder_1_data', 'consumer_3'),
+ ('placeholder_1_data', 'eltwise_3'),
('reshape_1', 'reshape_1_data'),
('reshape_2', 'reshape_2_data'),
- ('reshape_1_data', 'consumer_1'),
- ('reshape_2_data', 'consumer_2'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('reshape_2_data', 'eltwise_2'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])},
- 'reshape_1_const': {'value': int64_array([1, 3, 1, 1]), 'shape': int64_array([4])},
- 'reshape_1_const_data': {'value': int64_array([1, 3, 1, 1]),
- 'shape': int64_array([4])},
- 'reshape_1_data': {'shape': int64_array([1, 3, 1, 1])},
- 'reshape_2_const': {'value': int64_array([1, 1, 3]), 'shape': int64_array([3])},
- 'reshape_2_const_data': {'value': int64_array([1, 1, 3]), 'shape': int64_array([3])},
+ 'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_1_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_1_data': {'shape': int64_array([1, 1, 1, 3])},
+ 'reshape_2_const': {'value': int64_array([0]), 'shape': int64_array([1])},
+ 'reshape_2_const_data': {'value': int64_array([0]), 'shape': int64_array([1])},
'reshape_2_data': {'shape': int64_array([1, 1, 3])},
}, nodes_with_edges_only=True)
- pattern = EltwiseInputReshape()
- pattern.find_and_replace_pattern(graph)
+ normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
- def test2_not_constant(self):
+ def test3_not_constant(self):
# ,--------------->consumer3 ,----------->consumer3
# data---(new_shape1)-->consumer1 => data-->Reshape-->consumer1
# `-(new_shape1)-->consumer2 `-->consumer2
graph = build_graph(nodes_attributes,
[
('placeholder_1', 'placeholder_1_data'),
- ('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3, 1, 1])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 3, 1, 1])}),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
+ {'placeholder_1_data': {'shape': int64_array([1, 3])},
+ 'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
+ 'eltwise_2_data': {'shape': int64_array([1, 1, 1, 3])},
+ 'eltwise_3_data': {'shape': int64_array([1, 3])},
+ },
+ nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[
('placeholder_1_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
- ('placeholder_1_data', 'consumer_3'),
+ ('placeholder_1_data', 'eltwise_3'),
('reshape_1', 'reshape_1_data'),
- ('reshape_1_data', 'consumer_1'),
- ('reshape_1_data', 'consumer_2'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('reshape_1_data', 'eltwise_2'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])},
- 'reshape_1_const': {'value': int64_array([1, 3, 1, 1]), 'shape': int64_array([4])},
- 'reshape_1_const_data': {'value': int64_array([1, 3, 1, 1]),
- 'shape': int64_array([4])},
- 'reshape_1_data': {'shape': int64_array([1, 3, 1, 1])},
+ 'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_1_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_1_data': {'shape': int64_array([1, 1, 1, 3])},
}, nodes_with_edges_only=True)
- pattern = EltwiseInputReshape()
- pattern.find_and_replace_pattern(graph)
+ normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
- def test3_constant(self):
- # ,--------------->consumer3 data-->consumer3
- # data---(new_shape1)-->consumer1 => data-->consumer1
- # `-(new_shape2)-->consumer2 data-->consumer2
+ def test4_constant(self):
+ # ,--------------->consumer3 ,------------>consumer3
+ # data---(new_shape1)-->consumer1 => data--->reshape1-->consumer1
+ # `-(new_shape2)-->consumer2 `->reshape2-->consumer2
#
graph = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3, 1, 1])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 1, 3])}),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])}},
+ {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
+ 'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
+ 'eltwise_2_data': {'shape': int64_array([1, 1, 3])},
+ 'eltwise_3_data': {'shape': int64_array([1, 3])},
+ },
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1'),
- ('placeholder_2_data', 'consumer_2'),
- ('placeholder_3_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'reshape_1'),
+ ('reshape_1_const', 'reshape_1_const_data'),
+ ('reshape_1_const_data', 'reshape_1'),
+ ('reshape_1', 'reshape_1_data'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'reshape_2'),
+ ('reshape_2_const', 'reshape_2_const_data'),
+ ('reshape_2_const_data', 'reshape_2'),
+ ('reshape_2', 'reshape_2_data'),
+ ('reshape_2_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3, 1, 1]),
- 'value': np.ones([1, 3, 1, 1])},
- 'placeholder_2_data': {'shape': int64_array([1, 1, 3]), 'value': np.ones([1, 1, 3])},
- 'placeholder_3_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
+ {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
+ 'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
+ 'reshape_1_const_data': {'value': int64_array([0, 1]),
+ 'shape': int64_array([2])},
+ 'reshape_1_data': {'shape': int64_array([1, 1, 1, 3])},
+
+ 'reshape_2_const': {'value': int64_array([0]), 'shape': int64_array([1])},
+ 'reshape_2_const_data': {'value': int64_array([0]),
+ 'shape': int64_array([1])},
+ 'reshape_2_data': {'shape': int64_array([1, 1, 3])},
}, nodes_with_edges_only=True)
- pattern = EltwiseInputReshape()
- pattern.find_and_replace_pattern(graph)
+ normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
- def test4_constant(self):
- # ,--------------->consumer3 ,-->consumer3
- # data---(new_shape1)-->consumer1 => data-->consumer1
- # `-(new_shape2)-->consumer2 `->consumer2
+ def test5_constant(self):
+ # ,-(new_shape)-->consumer3 ,-->consumer3
+ # data---(new_shape)-->consumer1 => data-->reshape---->consumer1
+ # `-(new_shape)-->consumer2 `-->consumer2
#
graph = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([3, 1, 1])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([3, 1, 1])}),
- ('placeholder_1_data', 'consumer_3', {'new_shape': int64_array([3, 1, 1])}),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])}},
+ {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
+ 'eltwise_1_data': {'shape': int64_array([1, 1, 3])},
+ 'eltwise_2_data': {'shape': int64_array([1, 1, 3])},
+ 'eltwise_3_data': {'shape': int64_array([1, 1, 3])},
+ },
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1'),
- ('placeholder_1_data', 'consumer_2'),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'reshape_1'),
+ ('reshape_1_const', 'reshape_1_const_data'),
+ ('reshape_1_const_data', 'reshape_1'),
+ ('reshape_1', 'reshape_1_data'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('reshape_1_data', 'eltwise_2'),
+ ('reshape_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([3, 1, 1]), 'value': np.ones([3, 1, 1])}
+ {'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
+ 'reshape_1_const': {'value': int64_array([0]), 'shape': int64_array([1])},
+ 'reshape_1_const_data': {'value': int64_array([0]),
+ 'shape': int64_array([1])},
+ 'reshape_1_data': {'shape': int64_array([1, 1, 3])},
}, nodes_with_edges_only=True)
- pattern = EltwiseInputReshape()
- pattern.find_and_replace_pattern(graph)
+ normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
- def test5_not_constant(self):
+ def test6_not_constant(self):
# ,--------------->consumer3 ,->consumer3
# data---(new_shape1)-->consumer1 => data----->consumer1
# `-(new_shape1)-->consumer2 `-->consumer2
#
graph = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 3])}),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
- {'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
+ {'placeholder_1_data': {'shape': int64_array([1, 3])},
+ 'eltwise_1_data': {'shape': int64_array([1, 3])},
+ 'eltwise_2_data': {'shape': int64_array([1, 3])},
+ 'eltwise_3_data': {'shape': int64_array([1, 3])},
+ }, nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
- [('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3])}),
- ('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 3])}),
- ('placeholder_1_data', 'consumer_3'),
- ('consumer_1', 'concat'),
- ('consumer_2', 'concat'),
- ('consumer_3', 'concat'),
+ [('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_1_data', 'eltwise_2'),
+ ('placeholder_1_data', 'eltwise_3'),
+ ('eltwise_1', 'eltwise_1_data'),
+ ('eltwise_2', 'eltwise_2_data'),
+ ('eltwise_3', 'eltwise_3_data'),
+ ('eltwise_1_data', 'concat'),
+ ('eltwise_2_data', 'concat'),
+ ('eltwise_3_data', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
- pattern = EltwiseInputReshape()
- pattern.find_and_replace_pattern(graph)
+ normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
+
+ def test7_axis1_not_constant(self):
+ #
+ # data1(1,3,64,64)----. data(1,3,64,64)-------.
+ # data2(3,64,1)-------->Eltwise-->data(1,3,64,64)=> data(3,64,1)->Unsqueeze(0)->data(1,3,64,1)-->Eltwise->...
+ # data3(3,1)------' data(3,1)->Unsqueeze(2, 0)->data(1,3,1,1)-'
+ #
+ graph = build_graph(nodes_attributes, [
+ ('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_2', 'placeholder_2_data'),
+ ('placeholder_3', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'eltwise_1'),
+ ('placeholder_3_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data')
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'placeholder_2_data': {'shape': np.array([3, 64, 1])},
+ 'placeholder_3_data': {'shape': np.array([3, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'eltwise_1' : {'axis': 1}
+ }, nodes_with_edges_only=True)
+
+ graph_ref = build_graph(nodes_attributes,
+ [
+ ('placeholder_1', 'placeholder_1_data'),
+ ('placeholder_2', 'placeholder_2_data'),
+ ('placeholder_3', 'placeholder_3_data'),
+ ('placeholder_1_data', 'eltwise_1'),
+ ('placeholder_2_data', 'reshape_1'),
+ ('reshape_1_const', 'reshape_1_const_data'),
+ ('reshape_1_const_data', 'reshape_1'),
+ ('placeholder_3_data', 'reshape_2'),
+ ('reshape_2_const', 'reshape_2_const_data'),
+ ('reshape_2_const_data', 'reshape_2'),
+ ('reshape_1', 'reshape_1_data'),
+ ('reshape_2', 'reshape_2_data'),
+ ('reshape_1_data', 'eltwise_1'),
+ ('reshape_2_data', 'eltwise_1'),
+ ('eltwise_1', 'eltwise_1_data')
+ ],
+ {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
+ 'placeholder_2_data': {'shape': np.array([3, 64, 1])},
+ 'placeholder_3_data': {'shape': np.array([3, 1])},
+ 'reshape_1_const': {'value': int64_array([0]), 'shape': int64_array([1])},
+ 'reshape_1_const_data': {'value': int64_array([0]),
+ 'shape': int64_array([1])},
+ 'reshape_1_data': {'shape': np.array([1, 3, 64, 1])},
+ 'reshape_2_const': {'value': int64_array([2, 0]), 'shape': int64_array([2])},
+ 'reshape_2_const_data': {'value': int64_array([2, 0]),
+ 'shape': int64_array([2])},
+ 'reshape_2_data': {'shape': np.array([1, 3, 1, 1])},
+ 'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
+ }, nodes_with_edges_only=True)
+
+ normalize_eltwise_inputs(graph)
+
+ (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
+ self.assertTrue(flag, resp)