def test_select_infer_condition_true(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('condition', {
'value': np.array([True])
}),
('select_output', {
'shape': np.array([2, 2]),
'value': np.ones((2, 2))
})
])
# We should propagate shapes and values
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('select_output', {
'shape': np.array([2, 2]),
'value': np.ones((2, 2))
})
])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
tested_class.infer(node)
(flag, resp) = compare_graphs(graph,
graph_ref,
'select_output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_select_infer_condition_with_value(self, else_data_shape, than_data_shape, select_output_shape,
condition_value, else_value, than_value, output_value):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('condition_data', {'shape': np.array(select_output_shape),
'value': condition_value}),
('else_data', {'shape': np.array(else_data_shape),
'value': else_value}),
('than_data', {'shape': np.array(than_data_shape),
'value': than_value}),
('select_output',
{'shape': np.array(select_output_shape),
'value': None})
])
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[
('condition_data', {'shape': np.array(else_data_shape),
'value': condition_value}),
('else_data', {'shape': np.array(else_data_shape),
'value': else_value}),
('than_data', {'shape': np.array(than_data_shape),
'value': than_value}),
('select_output', {'shape': np.array(select_output_shape),
'value': output_value})])
node = Node(graph, 'select')
Select.infer(node)
if else_value is not None and than_value is not None:
(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(np.array_equal(graph.nodes['select_output']['value'], graph_ref.nodes['select_output']['value']))
def test_select_infer_condition_shapes_broadcast(self, else_data_shape, than_data_shape, select_output_shape):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else_data', {'shape': np.array(else_data_shape),
'value': np.zeros(else_data_shape, dtype=np.float)}),
('than_data', {'shape': np.array(than_data_shape),
'value': np.zeros(than_data_shape, dtype=np.float)}),
('select_output', {'shape': np.array(select_output_shape),
'value': np.zeros(select_output_shape, dtype=np.float)})
])
# We should propagate shapes and values
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[
('else_data', {'shape': np.array(else_data_shape),
'value': np.zeros(else_data_shape, dtype=np.float)}),
('than_data', {'shape': np.array(than_data_shape),
'value': np.zeros(than_data_shape, dtype=np.float)}),
('select_output', {'shape': np.array(select_output_shape), 'value': np.zeros(select_output_shape)})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
tested_class.infer(node)
(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_select_infer_assert_shapes(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else_data', {'shape': np.array([3, 3]), 'value':np.zeros((3, 3))})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "Input shape do not broadcast"):
tested_class.infer(node)
def build_select_graph_and_infer(condition_value,
then_value,
else_value,
out_value,
condition_shape=None,
then_shape=None,
else_shape=None,
out_shape=None,
auto_broadcast='numpy',
fw_format=None):
if then_value is not None:
then_shape = int64_array(then_value.shape)
if else_value is not None:
else_shape = int64_array(else_value.shape)
nodes = {
**valued_const_with_data('then', then_value, then_shape),
**valued_const_with_data('else', else_value, else_shape),
**valued_const_with_data('condition', condition_value, condition_shape),
**regular_op_with_empty_data(
'select', {
'op': 'Select',
'auto_broadcast': auto_broadcast,
'format': fw_format
}),
**result('out'),
}
edges = [
*connect('condition', '0:select'),
*connect('then', '1:select'),
*connect('else', '2:select'),
*connect('select', 'out'),
]
graph = build_graph(nodes, edges)
select_node = Node(graph, 'select')
Select.infer(select_node)
select_out_node = Node(graph, 'select_d')
value_desc = 'values'
ref_val = out_value
actual_val = select_out_node['value']
if out_shape is not None:
value_desc = 'shapes'
ref_val = out_shape
actual_val = select_out_node['shape']
assert select_out_node[
'value'] is None, "if 'out_shape' is defined manually 'value' must be None"
flag = strict_compare_tensors(actual_val, ref_val)
msg = '' if flag else 'reference {} and actual {} {} do not match\n'.format(
ref_val, actual_val, value_desc)
return flag, msg
def test_select_infer_assert_shapes(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else', {'shape': np.array([3,3]), 'value':np.zeros((3,3))})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "TensorFlow \'Select\' operation has 3 inputs: \'condition\',"
" \'then\' and \'else\' tensors.\'then\' and \'else\' tensors"
" must have the same shape by TensorFlow reference"):
tested_class.infer(node)
def test_select_infer_assert_condition_bool(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('condition', {'value': np.array([3])})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "TensorFlow \'Select\' operation has 3 inputs: \'condition\',"
" \'then\' and \'else\' tensors. Value of \'condition\' tensor"
" must be boolen by TensorFlow reference"):
tested_class.infer(node)
def test_select_infer_condition_with_value(self, condition_shape,
else_data_shape,
than_data_shape,
select_output_shape,
condition_value, else_value,
than_value, output_value):
"""
Unit tests generator can sporadic throw exception if we try
to run generator with call numpy array generation functions.
So we need to use lambda function for escape the problem.
"""
condition_value = condition_value(condition_shape)
else_value = else_value(else_data_shape)
than_value = than_value(than_data_shape)
output_value = output_value(select_output_shape)
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('condition_data', {
'shape':
np.array(condition_shape),
'value': condition_value
}),
('else_data', {
'shape':
np.array(else_data_shape),
'value': else_value
}),
('than_data', {
'shape':
np.array(than_data_shape),
'value': than_value
}),
('select_output', {
'shape':
np.array(select_output_shape),
'value':
None
})
])
graph_ref = build_graph_with_attrs(
nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('condition_data', {
'shape': np.array(condition_shape),
'value': condition_value
}),
('else_data', {
'shape': np.array(else_data_shape),
'value': else_value
}),
('than_data', {
'shape': np.array(than_data_shape),
'value': than_value
}),
('select_output', {
'shape':
np.array(select_output_shape),
'value': output_value
})])
node = Node(graph, 'select')
Select.infer(node)
if else_value is not None and than_value is not None:
(flag, resp) = compare_graphs(graph,
graph_ref,
'select_output',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(
np.array_equal(graph.nodes['select_output']['value'],
graph_ref.nodes['select_output']['value']))