forked from PaddlePaddle/Paddle
/
test_count_nonzero_api.py
112 lines (95 loc) · 3.98 KB
/
test_count_nonzero_api.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
np.random.seed(10)
class TestCountNonzeroAPI(unittest.TestCase):
# test paddle.tensor.math.count_nonzero
def setUp(self):
self.x_shape = [2, 3, 4, 5]
self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_api_static(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.data('X', self.x_shape)
out1 = paddle.count_nonzero(x)
out2 = paddle.tensor.count_nonzero(x)
out3 = paddle.tensor.math.count_nonzero(x)
axis = np.arange(len(self.x_shape)).tolist()
out4 = paddle.count_nonzero(x, axis)
out5 = paddle.count_nonzero(x, tuple(axis))
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x},
fetch_list=[out1, out2, out3, out4, out5])
out_ref = np.count_nonzero(self.x)
for out in res:
self.assertEqual(np.allclose(out, out_ref), True)
def test_api_dygraph(self):
paddle.disable_static(self.place)
def test_case(x, axis=None, keepdim=False):
x_tensor = paddle.to_tensor(x)
out = paddle.count_nonzero(x_tensor, axis=axis, keepdim=keepdim)
if isinstance(axis, list):
axis = tuple(axis)
if len(axis) == 0:
axis = None
out_ref = np.count_nonzero(x, axis, keepdims=keepdim)
self.assertEqual(np.allclose(out.numpy(), out_ref), True)
test_case(self.x)
test_case(self.x, None)
test_case(self.x, -1)
test_case(self.x, keepdim=True)
test_case(self.x, 2, keepdim=True)
test_case(self.x, [0, 2])
test_case(self.x, (0, 2))
test_case(self.x, (0, 1, 3))
test_case(self.x, [0, 1, 2, 3])
paddle.enable_static()
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.data('X', [10, 12], 'int32')
self.assertRaises(ValueError, paddle.count_nonzero, x, axis=10)
def test_api_dygraph_grad(self):
paddle.disable_static(self.place)
def test_case(x, axis=None, keepdim=False):
if isinstance(axis, list):
axis = list(axis)
if len(axis) == 0:
axis = None
x_tensor = paddle.to_tensor(x, stop_gradient=False)
y = paddle.count_nonzero(x_tensor, axis, keepdim)
dx = paddle.grad(y, x_tensor)[0].numpy()
self.assertEqual(
np.allclose(np.sum(np.ones_like(x, dtype=np.int64)), dx.sum()),
True)
test_case(self.x)
test_case(self.x, None)
test_case(self.x, -1)
test_case(self.x, keepdim=True)
test_case(self.x, 2, keepdim=True)
test_case(self.x, [0, 2])
test_case(self.x, (0, 2))
test_case(self.x, (0, 1, 3))
test_case(self.x, [0, 1, 2, 3])
paddle.enable_static()
if __name__ == "__main__":
unittest.main()