150 lines
8.1 KiB
Python
150 lines
8.1 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.back.ShufflenetReLUReorder import ShufflenetReLUReorder
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from mo.utils.unittest.graph import build_graph
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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_1': {'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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# ReLU
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'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'ReLU'},
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'relu_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Reshape layers
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'reshape_1': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
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'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_2': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
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'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_3': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
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'reshape_3_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Transpose layer
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'transpose_1': {'type': 'Transpose', 'kind': 'op', 'op': 'Transpose'},
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'transpose_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Conv layer
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'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'Conv2d'},
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'conv_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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}
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class ShufflenetReLUReorderTests(unittest.TestCase):
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def test_1(self):
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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', 'relu_1'),
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('relu_1', 'relu_1_data'),
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('relu_1_data', 'reshape_1'),
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('reshape_1', 'reshape_1_data'),
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('reshape_1_data', 'transpose_1'),
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('transpose_1', 'transpose_1_data'),
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('transpose_1_data', 'reshape_2'),
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('reshape_2', 'reshape_2_data'),
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('reshape_2_data', 'conv_1'),
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('conv_1', 'conv_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
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'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
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'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
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'transpose_1': {'order': np.array([0, 1, 3, 2])},
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'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
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'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
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'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
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'conv_1': {'pad': np.array([1, 1])}
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})
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graph.graph['layout'] = 'NHWC'
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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', 'reshape_1_data'),
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('reshape_1_data', 'transpose_1'),
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('transpose_1', 'transpose_1_data'),
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('transpose_1_data', 'reshape_2'),
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('reshape_2', 'reshape_2_data'),
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('reshape_2_data', 'relu_1'),
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('relu_1', 'relu_1_data'),
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('relu_1_data', 'conv_1'),
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('conv_1', 'conv_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
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'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
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'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
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'transpose_1': {'order': np.array([0, 1, 3, 2])},
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'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
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'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
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'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
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})
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pattern = ShufflenetReLUReorder()
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pattern.find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'conv_1_data', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_2_neg(self):
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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', 'reshape_1'),
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('reshape_1', 'reshape_1_data'),
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('reshape_1_data', 'transpose_1'),
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('transpose_1', 'transpose_1_data'),
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('transpose_1_data', 'reshape_2'),
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('reshape_2', 'reshape_2_data'),
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('reshape_2_data', 'conv_1'),
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('conv_1', 'conv_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
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'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
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'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
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'transpose_1': {'order': np.array([0, 1, 3, 2])},
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'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
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'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
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'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
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})
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graph.graph['layout'] = 'NHWC'
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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', 'reshape_1_data'),
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('reshape_1_data', 'transpose_1'),
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('transpose_1', 'transpose_1_data'),
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('transpose_1_data', 'reshape_2'),
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('reshape_2', 'reshape_2_data'),
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('reshape_2_data', 'conv_1'),
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('conv_1', 'conv_1_data')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
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'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
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'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
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'transpose_1': {'order': np.array([0, 1, 3, 2])},
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'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
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'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
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'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
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})
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pattern = ShufflenetReLUReorder()
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pattern.find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'conv_1_data', check_op_attrs=True)
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self.assertTrue(flag, resp)
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