def test_merge_infer_only_second_executable(self):
graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('first', {'executable': False, 'value': np.ones([2, 2]), 'shape': int64_array([2, 2])}),
('second', {'executable': True, 'value': np.zeros([4, 4]), 'shape': int64_array([4, 4])})
]
)
ref_graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('first', {'executable': False, 'value': np.ones([2, 2]), 'shape': int64_array([2, 2])}),
('second', {'executable': True, 'value': np.zeros([4, 4]), 'shape': int64_array([4, 4])}),
('merge', {'is_not_fully_inferred': False}),
('merge_output', {'shape': int64_array([4, 4]), 'value': np.zeros([4, 4])})
]
)
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
(flag, resp) = compare_graphs(graph, ref_graph, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_merge_infer_simple_case_one_executable(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges)
# We should propagate value of the first input since only this input is executable
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('merge_output', {'shape': np.array([2, 2]),
'value': np.ones((2, 2))}),
('merge', {'is_not_fully_inferred': False})])
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
(flag, resp) = compare_graphs(graph, graph_ref, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_merge_infer_complex_case(self):
"""
Case as in cycles when in first visit only one input are inferred and in the second -- both.
"""
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('first', {'is_partial_inferred': False,
'value': None}),
('second', {'executable': True})])
# In first visit we should propagate only shapes
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('second', {'executable': True}),
('first', {'is_partial_inferred': False,
'value': None}),
('merge_output', {'shape': np.array([2, 2]),
'value': None}),
('merge', {'is_not_fully_inferred': True})])
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
(flag, resp) = compare_graphs(graph, graph_ref, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)
# Imitate that inputs nodes now is inferred
graph.node['first']['is_partial_inferred'] = True
# Run infer second time
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('second', {'executable': True}),
('first', {'is_partial_inferred': True,
'value': None}),
('merge_output', {'shape': np.array([2, 2]),
'value': None}),
('merge', {'is_not_fully_inferred': False})])
(flag, resp) = compare_graphs(graph, graph_ref, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)