238 lines
11 KiB
Python
238 lines
11 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 extensions.middle.ScaleInput import ScaleInput
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from mo.utils.cli_parser import get_mean_scale_dictionary, parse_tuple_pairs
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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, regular_op_with_shaped_data, result, connect, connect_data, \
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valued_const_with_data
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nodes = {
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**regular_op_with_shaped_data('parameter', [1, 3, 227, 227],
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{'type': 'Parameter', 'op': 'Parameter', 'shape': [1, 3, 227, 227]}),
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**regular_op_with_shaped_data('parameter_2', [1, 3, 227, 227],
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{'type': 'Parameter', 'op': 'Parameter', 'shape': [1, 3, 227, 227]}),
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**regular_op_with_shaped_data('mul_scale', [1, 3, 227, 227], {'type': 'Multiply', 'op': 'Mul'}),
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**regular_op_with_shaped_data('add_mean', [1, 3, 227, 227], {'type': 'Add', 'op': 'Add'}),
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**valued_const_with_data('scale', np.array([1. / 1., 1. / 2., 1. / 3.]).reshape((1, 3, 1, 1))),
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**valued_const_with_data('mean', np.array([-1., -2., -3.]).reshape((1, 3, 1, 1))),
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**regular_op_with_shaped_data('shape_of', [4], {'type': 'ShapeOf', 'op': 'ShapeOf'}),
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**regular_op_with_shaped_data('op', [1, 3, 227, 227], {}),
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**result('result'),
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**result('result_2'),
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}
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class AddMeanScaleValuesTest(unittest.TestCase):
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def test_mean_values_with_data_name(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', 'result'),
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])
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mean_values = parse_tuple_pairs('(1,2,3)')
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scale_values = parse_tuple_pairs('')
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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 = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_without_data_name(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', 'result'),
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])
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mean_values = parse_tuple_pairs('(1,2,3)')
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scale_values = parse_tuple_pairs('')
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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 = build_graph(nodes, [*connect('parameter', 'result')], {'parameter': {'name': 'None'}},
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nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_explicit_and_optimized(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', 'result'),
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*connect('parameter_2', 'result_2'),
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])
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argv = Namespace(mean_scale_values={'parameter': {'mean': np.array([1., 2., 3.])},
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'parameter_2': {'mean': np.array([0., 0., 0.])}})
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graph = build_graph(nodes, [*connect('parameter', 'result'), *connect('parameter_2', 'result_2')],
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nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result_2', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_explicit_and_scale_values_optimized(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', 'result'),
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])
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argv = Namespace(mean_scale_values={'parameter': {'scale': np.array([1.]), 'mean': np.array([1., 2., 3.])}})
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graph = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_optimized_and_scale_values_explicit(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:mul_scale'),
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*connect('scale', '1:mul_scale'),
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*connect('mul_scale', 'result'),
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])
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argv = Namespace(
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mean_scale_values={'parameter': {'scale': np.array([1., 2., 3.]), 'mean': np.array([0., 0., 0.])}})
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graph = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_explicit_and_scale_values_explicit(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', '0:mul_scale'),
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*connect('scale', '1:mul_scale'),
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*connect('mul_scale', 'result'),
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])
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argv = Namespace(mean_scale_values=[[np.array([1., 2., 3.]), np.array([1., 2., 3.])]])
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graph = build_graph(nodes, [*connect('parameter', 'result')],
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nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_explicit_and_scale_values_explicit_on_cutted_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_ref = build_graph(nodes, [
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', 'result'),
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*connect('parameter_2', '0:mul_scale'),
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*connect('scale', '1:mul_scale'),
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*connect('mul_scale', 'op'),
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*connect('op', 'result_2'),
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])
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argv = Namespace(
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mean_scale_values={'parameter': {'mean': np.array([1, 2, 3])}, 'op': {'scale': np.array([1, 2, 3])}})
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graph = build_graph(
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nodes, [*connect('parameter', 'result'), *connect('parameter_2', 'op'), *connect('op', 'result_2')],
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{'parameter_2': {'initial_node_name': 'op'}}, nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result_2', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_mean_values_explicit_and_scale_values_explicit_with_shape_of(self):
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graph_ref = build_graph(nodes,
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[
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*connect('parameter', '0:add_mean'),
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*connect('mean', '1:add_mean'),
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*connect('add_mean', '0:mul_scale'),
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*connect('scale', '1:mul_scale'),
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*connect('mul_scale', 'result'),
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*connect_data('parameter', 'shape_of'),
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*connect('shape_of', 'result_2'),
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],
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nodes_with_edges_only=True)
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argv = Namespace(
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mean_scale_values={'parameter': {'mean': np.array([1, 2, 3]), 'scale': np.array([1, 2, 3])}})
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graph = build_graph(nodes,
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[
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*connect('parameter', 'result'),
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*connect_data('parameter', 'shape_of'),
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*connect('shape_of', 'result_2'),
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],
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nodes_with_edges_only=True, cli=argv)
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graph.graph['layout'] = 'NCHW'
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AddMeanScaleValues().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result_2', check_op_attrs=True)
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self.assertTrue(flag, resp)
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class ScaleInputTests(unittest.TestCase):
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def test_scale_input(self):
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graph_ref = build_graph(nodes, [
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*connect('parameter', '0:mul_scale'),
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*connect('scale', '1:mul_scale'),
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*connect('mul_scale', 'result'),
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], {'scale': {'shape': [1, 1, 1, 1], 'value': np.array(1/255)},
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'scale_d': {'shape': [1, 1, 1, 1], 'value': np.array(1/255)}})
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graph = build_graph(nodes, connect('parameter', 'result'), nodes_with_edges_only=True, cli=Namespace(scale=255))
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graph.graph['layout'] = 'NCHW'
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ScaleInput().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result')
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self.assertTrue(flag, resp)
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def test_scale_input_2(self):
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graph_ref = build_graph(nodes, connect('parameter', 'result'), nodes_with_edges_only=True)
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graph = build_graph(nodes, connect('parameter', 'result'), nodes_with_edges_only=True, cli=Namespace(scale=1))
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graph.graph['layout'] = 'NCHW'
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ScaleInput().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result')
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self.assertTrue(flag, resp) |