Roman Kazantsev
10b18a00c6
* Fix ElementwiseInputReshape transformation Reshape node always needs to be inserted in order to preserve ShapeOf nodes (reshapability of a model) that can potentially be above elementwise node. Refactor EltwiseInputReshape_test and EltwiseInputNormalization_test since the logic of maintaining reshape for eltwise has been changed. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Merge EltwiseInputNormalization and EltwiseInputReshape transformations Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove Unsqueeze from Fused_op Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after code review #1 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after review #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code review #4 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Perform full normalization based on shapes of all inputs to eltwise Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Refactor much to avoid old API and edges with unsqueeze_dims attribute Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after review Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
545 lines
32 KiB
Python
545 lines
32 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from extensions.middle.EltwiseInputReshape import normalize_eltwise_inputs
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from mo.front.common.partial_infer.utils import int64_array
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from mo.middle.passes.eliminate_test import build_graph
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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# The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
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# dictionary with node attributes.
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nodes_attributes = {
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# Placeholder layers
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'placeholder_1': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_2': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_3': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
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'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
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'placeholder_3_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
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'placeholder_4_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
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# Reshape layers
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'reshape_1': {'type': 'Unsqueeze', 'value': None, 'kind': 'op', 'op': 'Unsqueeze'},
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'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_1_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
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'reshape_1_const_data': {'kind': 'data', 'value': None, 'shape': None},
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'reshape_2': {'type': 'Unsqueeze', 'value': None, 'kind': 'op', 'op': 'Unsqueeze'},
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'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_2_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
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'reshape_2_const_data': {'kind': 'data', 'value': None, 'shape': None},
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# Eltwise consumes layers
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'eltwise_1': {'kind': 'op', 'is_eltwise': True},
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'eltwise_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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'eltwise_2': {'kind': 'op', 'is_eltwise': True},
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'eltwise_2_data': {'value': None, 'shape': None, 'kind': 'data'},
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'eltwise_3': {'kind': 'op', 'is_eltwise': True},
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'eltwise_3_data': {'value': None, 'shape': None, 'kind': 'data'},
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'eltwise_4': {'kind': 'op', 'is_eltwise': True},
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'eltwise_4_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Concat
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'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
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}
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class EltwiseInputNormalizationTest(unittest.TestCase):
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def test1_not_constant(self):
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#
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# data1(1,3,64,64)----. data(1,3,64,64)-------.
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# data2(1,64,1)-------->Eltwise-->data(1,3,64,64) => data(1,64,1)->Reshape->data(1,1,64,1)-->Eltwise->...
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# data3(64,1)------' data(64,1)->Reshape->data(1,1,64,1)-'
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#
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graph = build_graph(nodes_attributes, [
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1', 'placeholder_2_data'),
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('placeholder_1', 'placeholder_3_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_2_data', 'eltwise_1'),
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('placeholder_3_data', 'eltwise_1'),
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('eltwise_1', 'eltwise_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
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'placeholder_2_data': {'shape': np.array([1, 64, 1])},
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'placeholder_3_data': {'shape': np.array([64, 1])},
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'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1', 'placeholder_2_data'),
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('placeholder_1', 'placeholder_3_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_2_data', 'reshape_1'),
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('reshape_1_const', 'reshape_1_const_data'),
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('reshape_1_const_data', 'reshape_1'),
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('placeholder_3_data', 'reshape_2'),
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('reshape_2_const', 'reshape_2_const_data'),
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('reshape_2_const_data', 'reshape_2'),
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('reshape_1', 'reshape_1_data'),
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('reshape_2', 'reshape_2_data'),
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('reshape_1_data', 'eltwise_1'),
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('reshape_2_data', 'eltwise_1'),
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('eltwise_1', 'eltwise_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
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'reshape_1_const': {'value': int64_array([0]), 'shape': int64_array([1])},
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'reshape_1_const_data': {'value': int64_array([0]),
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'shape': int64_array([1])},
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'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
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'reshape_2_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
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'reshape_2_const_data': {'value': int64_array([0, 1]),
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'shape': int64_array([2])},
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'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
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'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
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}, nodes_with_edges_only=True)
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normalize_eltwise_inputs(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mega_hardcore(self):
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# ORIGINAL GRAPH
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#
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# data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
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# /\ /\ /\
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# data2(64,1)-----,-'--------------------------------'------------------------------'
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# \/ /
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# data3(64,1)----`-->Eltwise3->data(64,1)----------'
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#
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# REFERENCE GRAPH AFTER TRANSFORMATION
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#
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# data1(1,3,64,64)---------------------,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
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# /\ /\ /\
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# data2(64,1)-,- Reshape1(1,1,64,64)--'--------------------------------o-------------------------------'
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# | |
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# | Reshape(1,1,64,1)
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# \/ |
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# data3(64,1)----------->Eltwise3->data(64,1)--------------------------'
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#
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graph = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_1_data'),
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('placeholder_2', 'placeholder_2_data'),
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('placeholder_3', 'placeholder_3_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_2_data', 'eltwise_1'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_1_data', 'eltwise_2'),
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('placeholder_2_data', 'eltwise_3'),
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('placeholder_3_data', 'eltwise_3'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_3_data', 'eltwise_2'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_2_data', 'eltwise_4'),
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('placeholder_2_data', 'eltwise_4'),
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('eltwise_4', 'eltwise_4_data'),
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],
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{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
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'placeholder_2_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
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'placeholder_3_data': {'shape': np.array([64, 1])},
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'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
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'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
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'eltwise_3_data': {'shape': np.array([64, 1])},
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'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_1_data'),
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('placeholder_2', 'placeholder_2_data'),
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('placeholder_3', 'placeholder_3_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_2_data', 'reshape_1'),
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('reshape_1_const', 'reshape_1_const_data'),
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('reshape_1_const_data', 'reshape_1'),
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('reshape_1', 'reshape_1_data'),
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('reshape_1_data', 'eltwise_1'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_1_data', 'eltwise_2'),
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('placeholder_2_data', 'eltwise_3'),
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('placeholder_3_data', 'eltwise_3'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_3_data', 'reshape_2'),
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('reshape_2_const', 'reshape_2_const_data'),
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('reshape_2_const_data', 'reshape_2'),
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('reshape_2', 'reshape_2_data'),
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('reshape_2_data', 'eltwise_2'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_2_data', 'eltwise_4'),
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('reshape_1_data', 'eltwise_4'),
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('eltwise_4', 'eltwise_4_data'),
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],
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{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
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'placeholder_2_data': {'shape': np.array([64, 1]),
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'value': np.ones([64, 1])},
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'placeholder_3_data': {'shape': np.array([64, 1])},
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'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
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'reshape_1_const_data': {'value': int64_array([0, 1]),
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'shape': int64_array([2])},
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'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
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'reshape_2_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
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'reshape_2_const_data': {'value': int64_array([0, 1]),
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'shape': int64_array([2])},
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'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
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'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
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'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
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'eltwise_3_data': {'shape': np.array([64, 1])},
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'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
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}, nodes_with_edges_only=True)
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normalize_eltwise_inputs(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_4', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test2_not_constant(self):
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# ,-------------->consumer3 ,------------>consumer3
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# data---(new_shape1)-->consumer1 => data---->Reshape-->consumer1
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# `-(new_shape2)-->consumer2 `-->Reshape-->consumer2
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#
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graph = build_graph(nodes_attributes, [
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_1_data', 'eltwise_2'),
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('placeholder_1_data', 'eltwise_3'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_1_data', 'concat'),
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('eltwise_2_data', 'concat'),
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('eltwise_3_data', 'concat'),
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],
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{'placeholder_1_data': {'shape': int64_array([1, 3])},
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'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
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'eltwise_2_data': {'shape': int64_array([1, 1, 3])},
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'eltwise_3_data': {'shape': int64_array([1, 3])},
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},
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nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'reshape_1'),
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('reshape_1_const', 'reshape_1_const_data'),
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('reshape_1_const_data', 'reshape_1'),
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('placeholder_1_data', 'reshape_2'),
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('reshape_2_const', 'reshape_2_const_data'),
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('reshape_2_const_data', 'reshape_2'),
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('placeholder_1_data', 'eltwise_3'),
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('reshape_1', 'reshape_1_data'),
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('reshape_2', 'reshape_2_data'),
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('reshape_1_data', 'eltwise_1'),
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('reshape_2_data', 'eltwise_2'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_1_data', 'concat'),
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('eltwise_2_data', 'concat'),
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('eltwise_3_data', 'concat'),
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],
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{'placeholder_1_data': {'shape': int64_array([1, 3])},
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'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
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'reshape_1_const_data': {'value': int64_array([0, 1]),
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'shape': int64_array([2])},
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'reshape_1_data': {'shape': int64_array([1, 1, 1, 3])},
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'reshape_2_const': {'value': int64_array([0]), 'shape': int64_array([1])},
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'reshape_2_const_data': {'value': int64_array([0]), 'shape': int64_array([1])},
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'reshape_2_data': {'shape': int64_array([1, 1, 3])},
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}, nodes_with_edges_only=True)
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normalize_eltwise_inputs(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test3_not_constant(self):
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# ,--------------->consumer3 ,----------->consumer3
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# data---(new_shape1)-->consumer1 => data-->Reshape-->consumer1
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# `-(new_shape1)-->consumer2 `-->consumer2
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#
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graph = build_graph(nodes_attributes,
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[
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_1_data', 'eltwise_2'),
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('placeholder_1_data', 'eltwise_3'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_1_data', 'concat'),
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('eltwise_2_data', 'concat'),
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('eltwise_3_data', 'concat'),
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],
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{'placeholder_1_data': {'shape': int64_array([1, 3])},
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'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
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'eltwise_2_data': {'shape': int64_array([1, 1, 1, 3])},
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'eltwise_3_data': {'shape': int64_array([1, 3])},
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},
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nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[
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('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'reshape_1'),
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('reshape_1_const', 'reshape_1_const_data'),
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('reshape_1_const_data', 'reshape_1'),
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('placeholder_1_data', 'eltwise_3'),
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('reshape_1', 'reshape_1_data'),
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('reshape_1_data', 'eltwise_1'),
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('reshape_1_data', 'eltwise_2'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_1_data', 'concat'),
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('eltwise_2_data', 'concat'),
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('eltwise_3_data', 'concat'),
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],
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{'placeholder_1_data': {'shape': int64_array([1, 3])},
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'reshape_1_const': {'value': int64_array([0, 1]), 'shape': int64_array([2])},
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'reshape_1_const_data': {'value': int64_array([0, 1]),
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'shape': int64_array([2])},
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'reshape_1_data': {'shape': int64_array([1, 1, 1, 3])},
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}, nodes_with_edges_only=True)
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normalize_eltwise_inputs(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test4_constant(self):
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# ,--------------->consumer3 ,------------>consumer3
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# data---(new_shape1)-->consumer1 => data--->reshape1-->consumer1
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# `-(new_shape2)-->consumer2 `->reshape2-->consumer2
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#
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graph = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'eltwise_1'),
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('placeholder_1_data', 'eltwise_2'),
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('placeholder_1_data', 'eltwise_3'),
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('eltwise_1', 'eltwise_1_data'),
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('eltwise_2', 'eltwise_2_data'),
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('eltwise_3', 'eltwise_3_data'),
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('eltwise_1_data', 'concat'),
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('eltwise_2_data', 'concat'),
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('eltwise_3_data', 'concat'),
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],
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{'placeholder_1_data': {'shape': int64_array([1, 3]), 'value': np.ones([1, 3])},
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'eltwise_1_data': {'shape': int64_array([1, 1, 1, 3])},
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'eltwise_2_data': {'shape': int64_array([1, 1, 3])},
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'eltwise_3_data': {'shape': int64_array([1, 3])},
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},
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nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_1_data'),
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('placeholder_1_data', 'reshape_1'),
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('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]), '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)
|
|
|
|
normalize_eltwise_inputs(graph)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|
|
|
|
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', '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])},
|
|
'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', '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([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)
|
|
|
|
normalize_eltwise_inputs(graph)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|
|
|
|
def test6_not_constant(self):
|
|
# ,--------------->consumer3 ,->consumer3
|
|
# data---(new_shape1)-->consumer1 => data----->consumer1
|
|
# `-(new_shape1)-->consumer2 `-->consumer2
|
|
#
|
|
graph = build_graph(nodes_attributes,
|
|
[('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])},
|
|
'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', '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)
|
|
|
|
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)
|