253 lines
12 KiB
Python
253 lines
12 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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from argparse import Namespace
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import numpy as np
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from extensions.middle.AddMeanScaleValues import AddMeanScaleValues
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from mo.graph.graph import Node
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from mo.utils.cli_parser import get_mean_scale_dictionary, parse_tuple_pairs
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from mo.utils.unittest.graph import build_graph
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nodes_attributes = {'node_1': {'type': 'Identity', 'value': None, 'kind': 'op'},
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'node_1_data': {'value': None, 'kind': 'data', 'data_type': None},
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'node_2': {'type': 'Identity', 'value': None, 'kind': 'op'},
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'concat': {'type': 'Concat', 'value': None, 'kind': 'op'},
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'node_3': {'type': 'Identity', 'value': None, 'kind': 'op'},
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'node_3_data': {'value': None, 'kind': 'data', 'data_type': None},
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# Placeholders
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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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'pl_1': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'pl_1_data': {'value': None, 'kind': 'data', 'data_type': None},
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'pl_2': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'pl_2_data': {'value': 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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# ScaleShift layer
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'scaleshift_1': {'type': 'ScaleShift', 'kind': 'op', 'op': 'ScaleShift'},
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'scaleshift_1_w': {'value': None, 'shape': None, 'kind': 'data'},
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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 op
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'mul_1': {'type': None, 'kind': 'op', 'op': 'Mul'},
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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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'op_output': {'kind': 'op', 'op': 'Result', 'infer': lambda x: None}
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}
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class AddMeanScaleValuesTest(unittest.TestCase):
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def test_add_mean_scale_values_with_data_name(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None, 'data_type': None},
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'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Parameter', 'name': 'data',
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'data_type': None}
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},
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nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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mean_values = parse_tuple_pairs('(124,117,104)')
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scale_values = parse_tuple_pairs('')
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# input = 'data'
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mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
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argv = Namespace(mean_scale_values=mean_scale)
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graph.graph['cmd_params'] = argv
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self.assertEqual(len(graph), 3)
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AddMeanScaleValues().find_and_replace_pattern(graph)
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self.assertEqual(len(graph), 6)
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def test_add_mean_scale_values_without_data_name(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None, 'data_type': None},
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'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Parameter', 'name': 'data',
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'data_type': None}
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},
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nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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mean_values = parse_tuple_pairs('(124,117,104)')
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scale_values = parse_tuple_pairs('')
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# input = None
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mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
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argv = Namespace(mean_scale_values=mean_scale)
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graph.graph['cmd_params'] = argv
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self.assertEqual(len(graph), 3)
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AddMeanScaleValues().find_and_replace_pattern(graph)
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self.assertEqual(len(graph), 6)
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def test_add_mean_scale_values1(self):
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graph = build_graph(nodes_attributes,
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[('pl_1', 'pl_1_data'), ('pl_2', 'pl_2_data')],
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{'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
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'pl_2_data': {'shape': np.array([1, 6]), 'infer': None},
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'pl_1': {'shape': np.array([1, 3, 38, 38])},
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'pl_2': {'shape': np.array([1, 6])},
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},
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nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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argv = Namespace(
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mean_scale_values={'pl_1': {'mean': np.array([1., 2., 3.])}, 'pl_2': {'mean': np.array([0., 0., 0.])}})
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graph.graph['cmd_params'] = argv
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graph.graph['cmd_params'] = argv
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AddMeanScaleValues().find_and_replace_pattern(graph)
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mul_op_cnt = 0
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add_op_cnt = 0
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('op') and node.op == 'Mul':
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mul_op_cnt += 1
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if node.has_valid('op') and node.op == 'Add':
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add_op_cnt += 1
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self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
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self.assertEqual(mul_op_cnt, 0, "Found Mul op in graph")
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def test_optimize_scale_and_add_mean_values(self):
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graph = build_graph(
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nodes_attributes,
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[
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('pl_1', 'pl_1_data')
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],
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{
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'pl_1_data': {
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'shape': np.array([1, 3, 38, 38]),
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'infer': None
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},
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'pl_1': {
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'shape': np.array([1, 3, 38, 38])
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}
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},
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nodes_with_edges_only=True
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)
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graph.graph['layout'] = 'NCHW'
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argv = Namespace(mean_scale_values={'pl_1': {'scale': np.array([1.]), 'mean': np.array([1., 2., 3.])}})
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graph.graph['cmd_params'] = argv
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AddMeanScaleValues().find_and_replace_pattern(graph)
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mul_op_cnt = 0
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add_op_cnt = 0
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('op') and node.op == 'Mul':
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mul_op_cnt += 1
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if node.has_valid('op') and node.op == 'Add':
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add_op_cnt += 1
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self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
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self.assertEqual(mul_op_cnt, 0, "Found Mul op in graph")
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def test_optimize_mean_and_add_scale_values(self):
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graph = build_graph(
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nodes_attributes,
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[
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('pl_1', 'pl_1_data')
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],
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{
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'pl_1_data': {
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'shape': np.array([1, 3, 38, 38]),
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'infer': None
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},
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'pl_1': {
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'shape': np.array([1, 3, 38, 38])
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}
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},
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nodes_with_edges_only=True
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)
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graph.graph['layout'] = 'NCHW'
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argv = Namespace(mean_scale_values={'pl_1': {'scale': np.array([1.43]), 'mean': np.array([0., 0., 0.])}})
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graph.graph['cmd_params'] = argv
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AddMeanScaleValues().find_and_replace_pattern(graph)
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mul_op_cnt = 0
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add_op_cnt = 0
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('op') and node.op == 'Mul':
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mul_op_cnt += 1
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if node.has_valid('op') and node.op == 'Add':
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add_op_cnt += 1
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self.assertEqual(add_op_cnt, 0, "Found more than one Add op in graph")
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self.assertEqual(mul_op_cnt, 1, "Found Mul op in graph")
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def test_add_mean_scale_values3(self):
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graph = build_graph(nodes_attributes,
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[('pl_1', 'pl_1_data')],
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{'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
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'pl_1': {'shape': np.array([1, 3, 38, 38])},
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},
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nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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argv = Namespace(mean_scale_values=[[np.array([1., 2., 3.]), np.array([1., 2., 3.])]])
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graph.graph['cmd_params'] = argv
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AddMeanScaleValues().find_and_replace_pattern(graph)
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mul_op_cnt = 0
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add_op_cnt = 0
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('op') and node.op == 'Mul':
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mul_op_cnt += 1
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if node.has_valid('op') and node.op == 'Add':
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add_op_cnt += 1
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self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
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self.assertEqual(mul_op_cnt, 1, "Found more than one Nul op in graph")
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def test_add_mean_scale_values_cut_graph(self):
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"""
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Test case when user cutted start of the network and specified mean/scale value to the new input node 'node_3'.
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"""
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graph = build_graph(nodes_attributes,
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[('pl_1', 'pl_1_data'),
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('pl_2', 'pl_2_data'),
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('pl_2_data', 'node_3'),
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('node_3', 'node_3_data'),
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('pl_1_data', 'node_1'),
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('node_3_data', 'node_1'),
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],
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{'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
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'pl_2_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
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'pl_2': {'initial_node_name': 'node_3', 'shape': np.array([1, 3, 38, 38])},
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'pl_1': {'shape': np.array([1, 3, 38, 38])},
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},
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nodes_with_edges_only=True)
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graph.graph['layout'] = 'NCHW'
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argv = Namespace(
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mean_scale_values={'pl_1': {'mean': np.array([1, 2, 3])}, 'node_3': {'scale': np.array([1, 2, 3])}})
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graph.graph['cmd_params'] = argv
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AddMeanScaleValues().find_and_replace_pattern(graph)
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mul_op_cnt = 0
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add_op_cnt = 0
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for node in graph.nodes():
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node = Node(graph, node)
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if node.has_valid('op') and node.op == 'Mul':
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mul_op_cnt += 1
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if node.has_valid('op') and node.op == 'Add':
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add_op_cnt += 1
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self.assertEqual(add_op_cnt, 1, "There should be exactly one Add op")
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self.assertEqual(mul_op_cnt, 1, "There should be exactly one Mul op")
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self.assertEqual(Node(graph, 'pl_2').out_node().out_node().op, 'Mul', "The Mul op should be added after pl_2")
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self.assertEqual(Node(graph, 'pl_1').out_node().out_node().op, 'Add', "The Add op should be added after pl_1")
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