121 lines
6.0 KiB
Python
121 lines
6.0 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from extensions.ops.cumsum import CumSum
|
|
from mo.front.common.partial_infer.utils import int64_array
|
|
from mo.graph.graph import Node
|
|
from unit_tests.utils.graph import build_graph, valued_const_with_data, regular_op_with_shaped_data, result, connect
|
|
|
|
nodes_attributes = {
|
|
**regular_op_with_shaped_data('data', [1, 3, 224, 224], {'type': 'Parameter', 'value': None,
|
|
'_out_port_data_type': {0: np.float32}}),
|
|
**valued_const_with_data('axis', int64_array(0)),
|
|
**regular_op_with_shaped_data('cumsum', None, {'op': 'CumSum', 'type': 'CumSum', 'name': 'cumsum'}),
|
|
**regular_op_with_shaped_data('identity', None, {'op': 'Identity', 'name': 'identity'}),
|
|
**result('output'),
|
|
}
|
|
|
|
|
|
class TestCumSum(unittest.TestCase):
|
|
def test_cumsum_axis(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
*connect('cumsum', '0:identity'),
|
|
('identity', 'identity_d', {'out': 0}),
|
|
('identity_d', 'output'),
|
|
],
|
|
{'cumsum': {'reverse': False, 'exclusive': False}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_shape(), int64_array([1, 3, 224, 224])))
|
|
|
|
def test_cumsum_value_prop(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
('cumsum', 'cumsum_d', {'out': 0}),
|
|
('cumsum_d', 'output'),
|
|
],
|
|
{'data_d': {'value': np.array([1., 2., 3., 4., 5.]).astype(np.float32), 'shape': [5]},
|
|
'cumsum': {'reverse': False, 'exclusive': False}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_value(),
|
|
np.array([1., 3., 6., 10., 15.]).astype(np.float32)))
|
|
|
|
def test_cumsum_value_prop_exclusive(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
('cumsum', 'cumsum_d', {'out': 0}),
|
|
('cumsum_d', 'output'),
|
|
],
|
|
{'data_d': {'value': np.array([1., 2., 3., 4., 5.]).astype(np.float32), 'shape': [5]},
|
|
'cumsum': {'reverse': False, 'exclusive': True}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_value(),
|
|
np.array([0., 1., 3., 6., 10.]).astype(np.float32)))
|
|
|
|
def test_cumsum_value_prop_reverse(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
('cumsum', 'cumsum_d', {'out': 0}),
|
|
('cumsum_d', 'output'),
|
|
],
|
|
{'data_d': {'value': np.array([1., 2., 3., 4., 5.]).astype(np.float32), 'shape': [5]},
|
|
'cumsum': {'reverse': True, 'exclusive': False}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_value(),
|
|
np.array([15., 14., 12., 9., 5.]).astype(np.float32)))
|
|
|
|
def test_cumsum_value_prop_exclusive_reverse(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
('cumsum', 'cumsum_d', {'out': 0}),
|
|
('cumsum_d', 'output'),
|
|
],
|
|
{'data_d': {'value': np.array([1., 2., 3., 4., 5.]).astype(np.float32), 'shape': [5]},
|
|
'cumsum': {'reverse': True, 'exclusive': True}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_value(),
|
|
np.array([14., 12., 9., 5., 0.]).astype(np.float32)))
|
|
|
|
def test_cumsum_value_prop_axis_1(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[*connect('data', '0:cumsum'),
|
|
*connect('axis', '1:cumsum'),
|
|
('cumsum', 'cumsum_d', {'out': 0}),
|
|
('cumsum_d', 'output'),
|
|
],
|
|
{'data_d': {'value': np.array([[1., 2., 3.], [4., 5., 6.]]).astype(np.float32),
|
|
'shape': [2, 3]},
|
|
'axis_d': {'value': int64_array(1),
|
|
'shape': []},
|
|
'cumsum': {'reverse': False, 'exclusive': False}
|
|
}, nodes_with_edges_only=True)
|
|
|
|
cumsum_node = Node(graph, 'cumsum')
|
|
CumSum.infer(cumsum_node)
|
|
self.assertTrue(np.array_equal(cumsum_node.out_port(0).data.get_value(),
|
|
np.array([[1., 3., 6.], [4., 9., 15.]]).astype(np.float32)))
|