"""
- Copyright (c) 2018 Intel Corporation
+ Copyright (c) 2018-2019 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
from mo.front.common.partial_infer.concat import concat_infer
from mo.graph.graph import Node
-from mo.middle.passes.infer import override_placeholder_shapes, partial_infer, add_mean_scale_values, scale_input, \
- check_for_cycle
-from mo.utils.cli_parser import get_mean_scale_dictionary, parse_tuple_pairs
+from mo.middle.passes.infer import override_placeholder_shapes, partial_infer
from mo.utils.error import Error
-from mo.utils.unittest.graph import build_graph, compare_graphs
+from mo.utils.unittest.graph import build_graph
nodes_attributes = {'node_1': {'type': 'Identity', 'value': None, 'kind': 'op'},
'node_1_data': {'value': None, 'kind': 'data', 'data_type': None},
'mul_1': {'type': None, 'kind': 'op', 'op': 'Mul'},
'mul_1_w': {'value': None, 'shape': None, 'kind': 'data'},
'mul_1_data': {'value': None, 'shape': None, 'kind': 'data'},
+ 'op_output': { 'kind': 'op', 'op': 'OpOutput', 'infer': lambda x: None}
}
Test for overriding shape in placeholder by shape from user_shapes.
"""
graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None},
+ [('node_1', 'node_2'),
+ ('node_2', 'op_output')
+ ],
+ {'node_2': {'shape': None},
'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder'}
},
nodes_with_edges_only=True)
Test for case when user_shapes is not defined.
"""
graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None, 'op': 'Placeholder'},
+ [('node_1', 'node_2'),
+ ('node_2', 'op_output')
+ ],
+ {'node_2': {'shape': None, 'op': 'Placeholder'},
'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder'}
},
nodes_with_edges_only=True)
Test for case when user_shapes is not None, but it shouldn't rewrite shapes.
"""
graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None},
+ [('node_1', 'node_2'),
+ ('node_2', 'op_output')
+ ],
+ {'node_2': {'shape': None},
'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder'}
},
nodes_with_edges_only=True)
def test_override_placeholder_shapes_dict(self):
graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None, 'op': 'Placeholder'},
+ [('node_1', 'node_2'),
+ ('node_2', 'op_output')
+ ],
+ {'node_2': {'shape': None, 'op': 'Placeholder'},
'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder'}
},
nodes_with_edges_only=True)
graph = build_graph(nodes_attributes,
[('node_1', 'concat'),
('node_2', 'concat'),
- ('concat', 'node_3')],
- {'node_3': {'kind': 'data', 'is_output': True, 'shape': None, 'infer': None},
+ ('concat', 'node_3'),
+ ('node_3', 'op_output')
+ ],
+ {'node_3': {'kind': 'data', 'shape': None, 'infer': None},
'node_1': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'node_2': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'concat': {'kind': 'op', 'axis': 2, 'infer': concat_infer}
def test_partial_infer_no_shape(self):
graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None, 'infer': None},
+ [('node_1', 'node_2'),
+ ('node_2', 'op_output')
+ ],
+ {'node_2': {'shape': None, 'infer': None},
'node_1': {'shape': None, 'infer': None}
},
nodes_with_edges_only=True)
[('node_1', 'concat'),
('node_2', 'concat'),
('concat', 'node_3'),
- ('node_3', 'concat')],
- {'node_3': {'kind': 'data', 'is_output': True, 'shape': None, 'infer': None},
+ ('node_3', 'concat'),
+ ('node_3', 'op_output')
+ ],
+ {'node_3': {'kind': 'data', 'shape': None, 'infer': None},
'node_1': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'node_2': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'concat': {'kind': 'op', 'axis': 2, 'infer': concat_infer}
start_node = 'concat'
self.assertRaises(Error, partial_infer, graph, start_node)
- def test_add_mean_scale_values_with_data_name(self):
- graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None, 'data_type': None},
- 'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder', 'name': 'data',
- 'data_type': None}
- },
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- mean_values = parse_tuple_pairs('(124,117,104)')
- scale_values = parse_tuple_pairs('')
-
- # input = 'data'
- mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
- self.assertEqual(len(graph), 2)
- add_mean_scale_values(graph, mean_scale)
- self.assertEqual(len(graph), 5)
-
- def test_add_mean_scale_values_without_data_name(self):
- graph = build_graph(nodes_attributes,
- [('node_1', 'node_2')],
- {'node_2': {'is_output': True, 'shape': None, 'data_type': None},
- 'node_1': {'shape': np.array([1, 3, 227, 227]), 'op': 'Placeholder', 'name': 'data',
- 'data_type': None}
- },
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- mean_values = parse_tuple_pairs('(124,117,104)')
- scale_values = parse_tuple_pairs('')
- # input = None
- mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
- self.assertEqual(len(graph), 2)
- add_mean_scale_values(graph, mean_scale)
- self.assertEqual(len(graph), 5)
-
- def test_add_mean_scale_values1(self):
- graph = build_graph(nodes_attributes,
- [('pl_1', 'pl_1_data'), ('pl_2', 'pl_2_data')],
- {'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
- 'pl_2_data': {'shape': np.array([1, 6]), 'infer': None},
- 'pl_1': {'shape': np.array([1,3,38,38])},
- 'pl_2': {'shape': np.array([1,6])},
- },
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- add_mean_scale_values(graph,
- {'pl_1': {'mean': np.array([1., 2., 3.])}, 'pl_2': {'mean': np.array([0., 0., 0.])}})
- mul_op_cnt = 0
- add_op_cnt = 0
- for node in graph.nodes():
- node = Node(graph, node)
- if node.has_valid('op') and node.op == 'Mul':
- mul_op_cnt += 1
- if node.has_valid('op') and node.op == 'Add':
- add_op_cnt += 1
-
- self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
- self.assertEqual(mul_op_cnt, 0, "Found Mul op in graph")
-
- def test_optimize_scale_and_add_mean_values(self):
- graph = build_graph(
- nodes_attributes,
- [
- ('pl_1', 'pl_1_data')
- ],
- {
- 'pl_1_data': {
- 'shape': np.array([1, 3, 38, 38]),
- 'infer': None
- },
- 'pl_1': {
- 'shape': np.array([1,3,38,38])
- }
- },
- nodes_with_edges_only=True
- )
- graph.graph['layout'] = 'NCHW'
- add_mean_scale_values(graph,
- {
- 'pl_1': {
- 'scale': np.array([1.]),
- 'mean': np.array([1., 2., 3.])
- }
- })
- mul_op_cnt = 0
- add_op_cnt = 0
- for node in graph.nodes():
- node = Node(graph, node)
- if node.has_valid('op') and node.op == 'Mul':
- mul_op_cnt += 1
- if node.has_valid('op') and node.op == 'Add':
- add_op_cnt += 1
-
- self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
- self.assertEqual(mul_op_cnt, 0, "Found Mul op in graph")
-
- def test_optimize_mean_and_add_scale_values(self):
- graph = build_graph(
- nodes_attributes,
- [
- ('pl_1', 'pl_1_data')
- ],
- {
- 'pl_1_data': {
- 'shape': np.array([1, 3, 38, 38]),
- 'infer': None
- },
- 'pl_1': {
- 'shape': np.array([1,3,38,38])
- }
- },
- nodes_with_edges_only=True
- )
- graph.graph['layout'] = 'NCHW'
- add_mean_scale_values(graph,
- {
- 'pl_1': {
- 'scale': np.array([1.43]),
- 'mean': np.array([0., 0., 0.])
- }
- })
- mul_op_cnt = 0
- add_op_cnt = 0
- for node in graph.nodes():
- node = Node(graph, node)
- if node.has_valid('op') and node.op == 'Mul':
- mul_op_cnt += 1
- if node.has_valid('op') and node.op == 'Add':
- add_op_cnt += 1
-
- self.assertEqual(add_op_cnt, 0, "Found more than one Add op in graph")
- self.assertEqual(mul_op_cnt, 1, "Found Mul op in graph")
-
- def test_add_mean_scale_values3(self):
- graph = build_graph(nodes_attributes,
- [('pl_1', 'pl_1_data')],
- {'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
- 'pl_1': {'shape': np.array([1,3,38,38])},
- },
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- add_mean_scale_values(graph, [[np.array([1., 2., 3.]), np.array([1., 2., 3.])]])
-
- mul_op_cnt = 0
- add_op_cnt = 0
- for node in graph.nodes():
- node = Node(graph, node)
- if node.has_valid('op') and node.op == 'Mul':
- mul_op_cnt += 1
- if node.has_valid('op') and node.op == 'Add':
- add_op_cnt += 1
-
- self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
- self.assertEqual(mul_op_cnt, 1, "Found more than one Nul op in graph")
-
- def test_add_mean_scale_values_cut_graph(self):
- """
- Test case when user cutted start of the network and specified mean/scale value to the new input node 'node_3'.
- """
- graph = build_graph(nodes_attributes,
- [('pl_1', 'pl_1_data'),
- ('pl_2', 'pl_2_data'),
- ('pl_2_data', 'node_3'),
- ('node_3', 'node_3_data'),
- ('pl_1_data', 'node_1'),
- ('node_3_data', 'node_1'),
- ],
- {'pl_1_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
- 'pl_2_data': {'shape': np.array([1, 3, 38, 38]), 'infer': None},
- 'pl_2': {'initial_node_name': 'node_3', 'shape': np.array([1,3,38,38])},
- 'pl_1': {'shape': np.array([1,3,38,38])},
- },
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- add_mean_scale_values(graph, {'pl_1': {'mean': np.array([1, 2, 3])}, 'node_3': {'scale': np.array([1, 2, 3])}})
-
- mul_op_cnt = 0
- add_op_cnt = 0
- for node in graph.nodes():
- node = Node(graph, node)
- if node.has_valid('op') and node.op == 'Mul':
- mul_op_cnt += 1
- if node.has_valid('op') and node.op == 'Add':
- add_op_cnt += 1
-
- self.assertEqual(add_op_cnt, 1, "There should be exactly one Add op")
- self.assertEqual(mul_op_cnt, 1, "There should be exactly one Mul op")
- self.assertEqual(Node(graph, 'pl_2').out_node().out_node().op, 'Mul', "The Mul op should be added after pl_2")
- self.assertEqual(Node(graph, 'pl_1').out_node().out_node().op, 'Add', "The Add op should be added after pl_1")
-
-
-class ScaleInputTests(unittest.TestCase):
- def test_scale_input_1(self):
- graph = build_graph(nodes_attributes,
- [('placeholder_1', 'placeholder_1_data')],
- {'placeholder_1_data': {'is_output': True},
- 'placeholder_1': {'shape': np.array([1, 3, 224, 224])}
- },
- nodes_with_edges_only=True)
-
- graph_ref = build_graph(nodes_attributes,
- [('placeholder_1', 'mul_1_data'),
- ('mul_1_data', 'mul_1'),
- ('mul_1_w', 'mul_1'),
- ('mul_1', 'placeholder_1_data')],
- {'mul_1_w': {'shape': np.array([1, 1, 1]), 'value': np.array([1 / 255])},
- 'placeholder_1_data': {'is_output': True}},
- nodes_with_edges_only=True)
- graph.graph['layout'] = 'NCHW'
- scale_input(graph, 255)
- (flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1_data')
- self.assertTrue(flag, resp)
-
- def test_scale_input_2(self):
- graph = build_graph(nodes_attributes,
- [('placeholder_1', 'placeholder_1_data')],
- {'placeholder_1_data': {'is_output': True}},
- nodes_with_edges_only=True)
-
- graph_ref = build_graph(nodes_attributes,
- [('placeholder_1', 'placeholder_1_data')],
- {'placeholder_1_data': {'is_output': True}},
- nodes_with_edges_only=True)
-
- scale_input(graph, 1)
- (flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1_data')
- self.assertTrue(flag, resp)
-
- def test_check_for_cycle1(self):
- # cyclic case
- graph = build_graph(nodes_attributes,
- [('node_1', 'node_1_data'),
- ('node_1_data', 'node_3'),
- ('node_3', 'node_3_data'),
- ('node_3_data', 'node_1')],
- nodes_with_edges_only=True)
- with self.assertRaisesRegex(Error, 'Graph contains a cycle. Can not proceed.*'):
- check_for_cycle(graph)
-
- def test_check_for_cycle2(self):
- # acyclic case
- graph = build_graph(nodes_attributes,
- [('node_1', 'node_1_data'),
- ('node_1_data', 'node_3'),
- ('node_3', 'node_3_data'),
- ('node_3_data', 'mul_1'),
- ('mul_1_w', 'mul_1'),
- ('mul_1', 'mul_1_data')
- ],
- nodes_with_edges_only=True)
- try:
- check_for_cycle(graph)
- except Error:
- self.fail("Unexpected Error raised")
+class CycleTest(unittest.TestCase):
def test_is_not_fully_inferred_param(self):
# Node that have is_not_fully_inferred=True
graph = build_graph(nodes_attributes,
[('node_1', 'concat'),
('node_2', 'concat'),
- ('concat', 'node_3')],
- {'node_3': {'kind': 'data', 'is_output': True, 'shape': None, 'infer': None},
+ ('concat', 'node_3'),
+ ('node_3', 'op_output')
+ ],
+ {'node_3': {'kind': 'data', 'shape': None, 'infer': None},
'node_1': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'node_2': {'kind': 'data', 'shape': np.array([1, 3, 227, 227]), 'infer': None},
'concat': {'kind': 'op', 'axis': 2, 'infer': concat_infer, 'is_not_fully_inferred': True}
projects / platform / upstream / dldt.git / blobdiff