Yegor Kruglov
465707eba7
* added value propagation for slice_like op * Mark slice_lice as undead node * fixes in mark_undead_nodes and unittests update
516 lines
30 KiB
Python
516 lines
30 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 mo.middle.passes.fusing.decomposition import convert_scale_shift_to_mul_add, convert_batch_norm
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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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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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'placeholder_2': {'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
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# ScaleShift layer
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'scaleshift_1': {'type': 'ScaleShift', 'kind': 'op', 'op': 'ScaleShift', 'axis': 0},
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'const_scaleshift_1_w': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'scaleshift_1_w': {'value': None, 'shape': None, 'kind': 'data'},
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'const_scaleshift_1_b': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'scaleshift_1_b': {'value': None, 'shape': None, 'kind': 'data'},
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'scaleshift_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Mul and Add operations
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'mul_1': {'type': None, 'value': None, 'kind': 'op', 'op': 'Mul'},
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'const_mul_1_w': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'mul_1_w': {'value': None, 'shape': None, 'kind': 'data'},
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'mul_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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'add_1': {'type': None, 'kind': 'op', 'op': 'Add'},
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'const_add_1_w': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'add_1_w': {'value': None, 'shape': None, 'kind': 'data'},
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'add_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Mul and Add operations
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'mul_2': {'type': None, 'kind': 'op', 'op': 'Mul'},
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'const_mul_2_w': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'mul_2_w': {'value': None, 'shape': None, 'kind': 'data'},
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'mul_2_data': {'value': None, 'shape': None, 'kind': 'data'},
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'add_2': {'type': None, 'kind': 'op', 'op': 'Add'},
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'const_add_2_w': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'add_2_w': {'value': None, 'shape': None, 'kind': 'data'},
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'add_2_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Reshape
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'placeholder_2/Reshape_': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
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'placeholder_2/Reshape_data': {'value': None, 'shape': None, 'kind': 'data'},
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'placeholder_2/Reshape_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
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'placeholder_2/Reshape_const_data': {'kind': 'data', 'value': None, 'shape': None},
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# BatchNorm operation
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'bn_op': {'type': None, 'kind': 'op', 'op': 'BatchNorm', 'can_be_fused': True},
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'const_bn_const': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'bn_const': {'value': None, 'shape': None, 'kind': 'data'},
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'const_bn_beta': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'bn_beta': {'value': None, 'shape': None, 'kind': 'data'},
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'const_bn_mean': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'bn_mean': {'value': None, 'shape': None, 'kind': 'data'},
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'const_bn_var': {'value': None, 'shape': None, 'kind': 'op', 'op': 'Const'},
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'bn_var': {'value': None, 'shape': None, 'kind': 'data'},
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'bn_data': {'value': None, 'shape': None, 'kind': 'data'},
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# Concat1 operation
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'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
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'concat_data': {'value': None, 'shape': None, 'kind': 'data'},
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'op_output': {'kind': 'op', 'op': 'Result'}
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}
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class ScaleShiftToMulAdd(unittest.TestCase):
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# ScaleShift -> Mul
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def test_scaleshift_to_mul_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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'scaleshift_1_data': {}
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})
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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', 'mul_1'),
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('const_mul_1_w', 'mul_1_w'),
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('mul_1_w', 'mul_1'),
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('mul_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'const_mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'mul_1': {'can_be_fused': True},
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'scaleshift_1_data': {}
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})
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift 2 inputs-> Mul
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def test_scaleshift2_to_mul(self):
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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_1_data', 'scaleshift_1'),
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('placeholder_2_data', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'placeholder_2_data': {'shape': np.array([1, 227])},
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'scaleshift_1_data': {}
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})
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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_2_data', 'placeholder_2/Reshape_'),
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('placeholder_2/Reshape_const', 'placeholder_2/Reshape_const_data'),
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('placeholder_2/Reshape_const_data', 'placeholder_2/Reshape_'),
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('placeholder_2/Reshape_', 'placeholder_2/Reshape_data'),
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('placeholder_1_data', 'mul_1'),
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('placeholder_2/Reshape_data', 'mul_1'),
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('mul_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'placeholder_2_data': {'shape': np.array([1, 227])},
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'placeholder_2/Reshape_const': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
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'placeholder_2/Reshape_const_data': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
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'placeholder_2/Reshape_data': {'shape': np.array([1, 227, 1, 1])},
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'mul_1': {'can_be_fused': True},
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'scaleshift_1_data': {}
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})
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift 2 inputs-> Mul (axis = 1)
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def test_scaleshift2_axis1_to_mul(self):
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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_1_data', 'scaleshift_1'),
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('placeholder_2_data', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'placeholder_2_data': {'shape': np.array([227])},
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'scaleshift_1': {'axis': 1},
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'scaleshift_1_data': {}
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})
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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_2_data', 'placeholder_2/Reshape_'),
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('placeholder_2/Reshape_const', 'placeholder_2/Reshape_const_data'),
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('placeholder_2/Reshape_const_data', 'placeholder_2/Reshape_'),
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('placeholder_2/Reshape_', 'placeholder_2/Reshape_data'),
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('placeholder_1_data', 'mul_1'),
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('placeholder_2/Reshape_data', 'mul_1'),
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('mul_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'placeholder_2_data': {'shape': np.array([227])},
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'placeholder_2/Reshape_const': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
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'placeholder_2/Reshape_const_data': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
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'placeholder_2/Reshape_data': {'shape': np.array([1, 227, 1, 1])},
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'mul_1': {'can_be_fused': True},
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'scaleshift_1_data': {}
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})
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift -> Mul (Zero biases)
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def test_scaleshift_to_mul_2(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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('const_scaleshift_1_b', 'scaleshift_1_b'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1_b', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([0, 0, 0])},
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'scaleshift_1_data': {}
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})
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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', 'mul_1'),
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('const_mul_1_w', 'mul_1_w'),
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('mul_1_w', 'mul_1'),
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('mul_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'const_mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'mul_1': {'can_be_fused': True},
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'scaleshift_1_data': {}
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})
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift -> Mul->Add
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def test_scaleshift_to_mul_add(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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('const_scaleshift_1_b', 'scaleshift_1_b'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1_b', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([3, 2, 1])},
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'scaleshift_1_data': {}
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})
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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', 'mul_1'),
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('const_mul_1_w', 'mul_1_w'),
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('mul_1_w', 'mul_1'),
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('mul_1', 'mul_1_data'),
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('mul_1_data', 'add_1'),
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('const_add_1_w', 'add_1_w'),
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('add_1_w', 'add_1'),
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('add_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'const_mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
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'const_add_1_w': {'shape': np.array([3]), 'value': np.array([3, 2, 1])},
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'add_1_w': {'shape': np.array([3]), 'value': np.array([3, 2, 1])},
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'mul_1_data': {'shape': np.array([1, 227, 227, 3])},
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'add_1': {'can_be_fused': True},
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'mul_1': {'can_be_fused': True},
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'scaleshift_1_data': {}
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})
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift -> None (Zero weights and biases)
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def test_scaleshift_to_nothing(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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('const_scaleshift_1_b', 'scaleshift_1_b'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1_b', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 1, 1])},
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'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([0, 0, 0])},
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'scaleshift_1_data': {'shape': np.array([1, 227, 227, 3])}
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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', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])}}
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,nodes_with_edges_only=True)
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graph.graph['layout'] = 'NHWC'
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
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self.assertTrue(flag, resp)
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# ScaleShift -> ScaleShift (can_be_fused=False)
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def test_scaleshift_can_be_fused(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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('const_scaleshift_1_b', 'scaleshift_1_b'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1_b', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 1, 1])},
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'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([0, 0, 0])},
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'scaleshift_1': {'can_be_fused': False},
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'scaleshift_1_data': {'shape': np.array([1, 227, 227, 3])}
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})
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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', 'scaleshift_1'),
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('const_scaleshift_1_w', 'scaleshift_1_w'),
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('const_scaleshift_1_b', 'scaleshift_1_b'),
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('scaleshift_1_w', 'scaleshift_1'),
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('scaleshift_1_b', 'scaleshift_1'),
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('scaleshift_1', 'scaleshift_1_data'),
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('scaleshift_1_data', 'op_output')
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],
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{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
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'const_scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 1, 1])},
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'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array([1, 1, 1])},
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'const_scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([0, 0, 0])},
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'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([0, 0, 0])},
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'scaleshift_1': {'can_be_fused': False},
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'scaleshift_1_data': {'shape': np.array([1, 227, 227, 3])}
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})
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convert_scale_shift_to_mul_add(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
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self.assertTrue(flag, resp)
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class BatchNormDecomposition(unittest.TestCase):
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def test_bn_decomposition_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', 'bn_op'),
|
|
('const_bn_const', 'bn_const'),
|
|
('const_bn_beta', 'bn_beta'),
|
|
('const_bn_mean', 'bn_mean'),
|
|
('const_bn_var', 'bn_var'),
|
|
('bn_const', 'bn_op'),
|
|
('bn_beta', 'bn_op'),
|
|
('bn_mean', 'bn_op'),
|
|
('bn_var', 'bn_op'),
|
|
('bn_op', 'bn_data'),
|
|
('concat', 'concat_data'),
|
|
('bn_data', 'concat'),
|
|
('concat_data', 'op_output')
|
|
],
|
|
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'bn_op': {'eps': 1.2},
|
|
'bn_const': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_beta': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_mean': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_var': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'concat_data': {}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
graph_ref = build_graph(nodes_attributes,
|
|
[('placeholder_1', 'placeholder_1_data'),
|
|
('placeholder_1_data', 'mul_1'),
|
|
('const_mul_1_w', 'mul_1_w'),
|
|
('mul_1_w', 'mul_1'),
|
|
('mul_1', 'mul_1_data'),
|
|
('mul_1_data', 'add_1'),
|
|
('const_add_1_w', 'add_1_w'),
|
|
('add_1_w', 'add_1'),
|
|
('add_1', 'add_1_data'),
|
|
('add_1_data', 'mul_2'),
|
|
('const_mul_2_w', 'mul_2_w'),
|
|
('mul_2_w', 'mul_2'),
|
|
('mul_2', 'mul_2_data'),
|
|
('mul_2_data', 'add_2'),
|
|
('const_add_2_w', 'add_2_w'),
|
|
('add_2_w', 'add_2'),
|
|
('add_2', 'add_2_data'),
|
|
('concat', 'concat_data'),
|
|
('add_2_data', 'concat'),
|
|
('concat_data', 'op_output')
|
|
],
|
|
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'const_mul_1_w': {'shape': np.array([3]),
|
|
'value': np.array([0.67419986, 0.55901699, 0.48795004])},
|
|
'mul_1_w': {'shape': np.array([3]),
|
|
'value': np.array([0.67419986, 0.55901699, 0.48795004])},
|
|
'const_mul_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'mul_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'const_add_1_w': {'shape': np.array([3]),
|
|
'value': np.array([-0.67419986, -1.11803399, -1.46385011])},
|
|
'add_1_w': {'shape': np.array([3]),
|
|
'value': np.array([-0.67419986, -1.11803399, -1.46385011])},
|
|
'const_add_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'add_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'add_2_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'mul_1': {'can_be_fused': True},
|
|
'mul_2': {'can_be_fused': True},
|
|
'add_1': {'can_be_fused': True},
|
|
'add_2': {'can_be_fused': True},
|
|
'concat_data': {}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
graph.graph['layout'] = 'NHWC'
|
|
convert_batch_norm(graph)
|
|
graph.clean_up()
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'concat_data')
|
|
self.assertTrue(flag, resp)
|
|
|
|
# 'can_be_fused': False for BatchNorm
|
|
def test_bn_decomposition_2(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[('placeholder_1', 'placeholder_1_data'),
|
|
('placeholder_1_data', 'bn_op'),
|
|
('const_bn_const', 'bn_const'),
|
|
('const_bn_beta', 'bn_beta'),
|
|
('const_bn_mean', 'bn_mean'),
|
|
('const_bn_var', 'bn_var'),
|
|
('bn_const', 'bn_op'),
|
|
('bn_beta', 'bn_op'),
|
|
('bn_mean', 'bn_op'),
|
|
('bn_var', 'bn_op'),
|
|
('bn_op', 'bn_data'),
|
|
('concat', 'concat_data'),
|
|
('bn_data', 'concat'),
|
|
('concat_data', 'op_output')
|
|
],
|
|
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'bn_op': {'eps': 1.2, 'can_be_fused': False},
|
|
'bn_const': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_beta': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_mean': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_var': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'bn_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'concat_data': {}
|
|
})
|
|
|
|
graph_ref = build_graph(nodes_attributes,
|
|
[('placeholder_1', 'placeholder_1_data'),
|
|
('placeholder_1_data', 'mul_1'),
|
|
('const_mul_1_w', 'mul_1_w'),
|
|
('mul_1_w', 'mul_1'),
|
|
('mul_1', 'mul_1_data'),
|
|
('mul_1_data', 'add_1'),
|
|
('const_add_1_w', 'add_1_w'),
|
|
('add_1_w', 'add_1'),
|
|
('add_1', 'add_1_data'),
|
|
('add_1_data', 'mul_2'),
|
|
('const_mul_2_w', 'mul_2_w'),
|
|
('mul_2_w', 'mul_2'),
|
|
('mul_2', 'mul_2_data'),
|
|
('mul_2_data', 'add_2'),
|
|
('const_add_2_w', 'add_2_w'),
|
|
('add_2_w', 'add_2'),
|
|
('add_2', 'add_2_data'),
|
|
('concat', 'concat_data'),
|
|
('add_2_data', 'concat'),
|
|
('concat_data', 'op_output')
|
|
],
|
|
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'const_mul_1_w': {'shape': np.array([3]),
|
|
'value': np.array([0.67419986, 0.55901699, 0.48795004])},
|
|
'mul_1_w': {'shape': np.array([3]),
|
|
'value': np.array([0.67419986, 0.55901699, 0.48795004])},
|
|
'const_mul_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'mul_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'const_add_1_w': {'shape': np.array([3]),
|
|
'value': np.array([-0.67419986, -1.11803399, -1.46385011])},
|
|
'add_1_w': {'shape': np.array([3]),
|
|
'value': np.array([-0.67419986, -1.11803399, -1.46385011])},
|
|
'const_add_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'add_2_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
|
|
'add_2_data': {'shape': np.array([1, 227, 227, 3])},
|
|
'mul_1': {'can_be_fused': False},
|
|
'mul_2': {'can_be_fused': False},
|
|
'add_1': {'can_be_fused': False},
|
|
'add_2': {'can_be_fused': False},
|
|
'concat_data': {}
|
|
})
|
|
|
|
graph.graph['layout'] = 'NHWC'
|
|
convert_batch_norm(graph)
|
|
graph.clean_up()
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'concat_data')
|
|
self.assertTrue(flag, resp) |