215 lines
13 KiB
Python
215 lines
13 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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import numpy as np
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from generator import generator, generate
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from extensions.ops.select import Select
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from mo.graph.graph import Node
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph_with_attrs
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@generator
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class TestSelect(unittest.TestCase):
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nodes = [
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('than', {'kind': 'op'}),
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('than_data', {'value': np.ones((2, 2)), 'kind': 'data', 'executable': True, 'shape': np.array([2, 2])}),
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('else', {'kind': 'op'}),
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('else_data', {'value': np.zeros((2, 2)), 'kind': 'data', 'executable': True, 'shape': np.array([2, 2])}),
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('condition', {'value': None, 'kind': 'op'}),
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('condition_data', {'value': None, 'kind': 'data', 'executable': True, 'shape': np.array([2, 2])}),
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('select', {'type': 'Select', 'kind': 'op', 'op': 'Select'}),
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('select_output', {'value': None, 'kind': 'data', 'executable': True, 'shape': None}),
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]
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edges = [
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('condition', 'condition_data'),
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('condition_data', 'select', {'in': 0}),
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('than', 'than_data'),
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('than_data', 'select', {'in': 1}),
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('else', 'else_data'),
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('else_data', 'select', {'in': 2}),
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('select', 'select_output', {'out': 0}),
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]
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def test_select_infer_no_condition(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges)
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# We should propagate only shapes
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
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edges_with_attrs=self.edges,
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update_nodes_attributes=[('select_output', {'shape': np.array([2, 2])})])
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tested_class = Select(graph=graph, attrs={})
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node = Node(graph, 'select')
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tested_class.infer(node)
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(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_select_infer_condition_true(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[('condition', {'value': np.array([True])}),
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('select_output', {'shape': np.array([2, 2]),
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'value': np.ones((2, 2))})
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])
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# We should propagate shapes and values
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
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edges_with_attrs=self.edges,
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update_nodes_attributes=[('select_output', {'shape': np.array([2, 2]),
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'value': np.ones((2, 2))})])
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tested_class = Select(graph=graph, attrs={})
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node = Node(graph, 'select')
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tested_class.infer(node)
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(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_select_infer_condition_false(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[('condition', {'value': np.array([False])}),
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('select_output', {'shape': np.array([2, 2]),
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'value': np.zeros((2, 2))})
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])
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# We should propagate shapes and values
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
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edges_with_attrs=self.edges,
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update_nodes_attributes=[('select_output', {'shape': np.array([2, 2]),
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'value': np.zeros((2, 2))})])
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tested_class = Select(graph=graph, attrs={})
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node = Node(graph, 'select')
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tested_class.infer(node)
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(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_select_infer_assert_shapes(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[('else_data', {'shape': np.array([3, 3]), 'value':np.zeros((3, 3))})])
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tested_class = Select(graph=graph, attrs={})
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node = Node(graph, 'select')
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with self.assertRaisesRegex(AssertionError, "Input shape do not broadcast"):
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tested_class.infer(node)
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@generate(*[
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([5, 6], [1], [5, 6]),
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([15, 3, 5], [15, 1, 5], [15, 3, 5]),
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([2, 3, 4, 5], [], [2, 3, 4, 5]),
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([2, 3, 4, 5], [5], [2, 3, 4, 5]),
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([2, 3, 4, 5], [2, 1, 1, 5], [2, 3, 4, 5]),
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([2, 3, 4, 5], [1, 3, 1, 5], [2, 3, 4, 5]),
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])
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def test_select_infer_condition_shapes_broadcast(self, else_data_shape, than_data_shape, select_output_shape):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[('else_data', {'shape': np.array(else_data_shape),
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'value': np.zeros(else_data_shape, dtype=np.float)}),
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('than_data', {'shape': np.array(than_data_shape),
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'value': np.zeros(than_data_shape, dtype=np.float)}),
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('select_output', {'shape': np.array(select_output_shape),
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'value': np.zeros(select_output_shape, dtype=np.float)})
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])
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# We should propagate shapes and values
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[
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('else_data', {'shape': np.array(else_data_shape),
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'value': np.zeros(else_data_shape, dtype=np.float)}),
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('than_data', {'shape': np.array(than_data_shape),
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'value': np.zeros(than_data_shape, dtype=np.float)}),
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('select_output', {'shape': np.array(select_output_shape), 'value': np.zeros(select_output_shape)})])
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tested_class = Select(graph=graph, attrs={})
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node = Node(graph, 'select')
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tested_class.infer(node)
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(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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@generate(*[
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([5, 6], [5, 6], [5, 6], [5, 6], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: None, lambda x: np.ones(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float)),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float), lambda x: None, lambda x: None),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.zeros(x, dtype=np.float),
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lambda x: None, lambda x: np.ones(x, dtype=np.float), lambda x: None),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.zeros(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float), lambda x: None, lambda x: np.ones(x, dtype=np.float)),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.array([True], np.bool),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([15, 3, 5], [15, 3, 5], [15, 1, 5], [15, 3, 5], lambda x: np.array([False], np.bool),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float)),
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([2, 3, 4, 5], [2, 3, 4, 5], [], [2, 3, 4, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([2, 3, 4, 5], [2, 3, 4, 5], [5], [2, 3, 4, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([2, 3, 1, 1], [2, 1, 1, 5], [2, 3, 4, 5], [2, 3, 4, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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([2, 3, 4, 1], [2, 1, 1, 5], [1, 3, 1, 5], [2, 3, 4, 5], lambda x: np.ones(x, dtype=np.float),
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lambda x: np.zeros(x, dtype=np.float), lambda x: np.ones(x, dtype=np.float),
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lambda x: np.ones(x, dtype=np.float)),
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])
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def test_select_infer_condition_with_value(self, condition_shape, else_data_shape, than_data_shape, select_output_shape,
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condition_value, else_value, than_value, output_value):
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"""
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Unit tests generator can sporadic throw exception if we try
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to run generator with call numpy array generation functions.
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So we need to use lambda function for escape the problem.
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"""
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condition_value = condition_value(condition_shape)
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else_value = else_value(else_data_shape)
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than_value = than_value(than_data_shape)
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output_value = output_value(select_output_shape)
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[('condition_data', {'shape': np.array(condition_shape),
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'value': condition_value}),
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('else_data', {'shape': np.array(else_data_shape),
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'value': else_value}),
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('than_data', {'shape': np.array(than_data_shape),
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'value': than_value}),
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('select_output',
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{'shape': np.array(select_output_shape),
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'value': None})
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])
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[
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('condition_data', {'shape': np.array(condition_shape),
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'value': condition_value}),
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('else_data', {'shape': np.array(else_data_shape),
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'value': else_value}),
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('than_data', {'shape': np.array(than_data_shape),
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'value': than_value}),
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('select_output', {'shape': np.array(select_output_shape),
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'value': output_value})])
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node = Node(graph, 'select')
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Select.infer(node)
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if else_value is not None and than_value is not None:
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(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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self.assertTrue(np.array_equal(graph.nodes['select_output']['value'], graph_ref.nodes['select_output']['value']))
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