forked from NifTK/NiftyNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
resampler_test.py
executable file
·231 lines (208 loc) · 9.67 KB
/
resampler_test.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
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
from __future__ import absolute_import, print_function, division
import tensorflow as tf
from niftynet.layer.resampler import ResamplerLayer
class ResamplerTest(tf.test.TestCase):
def get_2d_input(self):
test_case = tf.constant(
[[[[1, 2, -1], [3, 4, -2]], [[5, 6, -3], [7, 8, -4]]],
[[[9, 10, -5], [11, 12, -6]], [[13, 14, -7], [15, 16, -8]]]],
dtype=tf.float32)
return test_case
def get_3d_input1(self):
test_case = tf.constant(
[[[[1, 2, -1], [3, 4, -2]], [[5, 6, -3], [7, 8, -4]]],
[[[9, 10, -5], [11, 12, -6]], [[13, 14, -7], [15, 16, -8]]]],
dtype=tf.float32)
return tf.expand_dims(test_case, 4)
def get_3d_input2(self):
return tf.concat([self.get_3d_input1(), 100 + self.get_3d_input1()], 4)
def _test_correctness(
self, input, grid, interpolation, boundary, expected_value):
resampler = ResamplerLayer(interpolation=interpolation,
boundary=boundary)
out = resampler(input, grid)
with self.test_session() as sess:
out_value = sess.run(out)
# print(expected_value)
# print(out_value)
self.assertAllClose(expected_value, out_value)
def test_resampler_3d_multivariate_zero_weight_idw_correctness(self):
test_grid = tf.constant(
[[[0, 1, 2], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[-2, 98], [3.87344956, 103.873459]],
[[12.70884895, 112.70884705], [11.45574856, 111.45578003]]]
self._test_correctness(input=self.get_3d_input2(),
grid=test_grid,
interpolation='IDW',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_3d_multivariate_replicate_idw_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[3.03804874, 103.03804016], [3.87344956, 103.87354279]],
[[12.70884895, 112.70884705], [11.45574856, 111.45578003]]]
self._test_correctness(input=self.get_3d_input2(),
grid=test_grid,
interpolation='IDW',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_2d_replicate_linear_correctness(self):
test_grid = tf.constant(
[[[.25, .25], [.25, .78]],
[[.62, .25], [.25, .28]]],
dtype=tf.float32)
expected = [[[2.5, 3.5, -1.75],
[3.56, 4.56, -2.28]],
[[11.98, 12.98, -6.49],
[10.56, 11.56, -5.78]]]
self._test_correctness(input=self.get_2d_input(),
grid=test_grid,
interpolation='LINEAR',
boundary='ZERO',
expected_value=expected)
def test_resampler_3d_multivariate_replicate_linear_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[2.75, 102.75], [3.75, 103.75]],
[[12.75, 112.75], [11.25, 111.25]]]
self._test_correctness(input=self.get_3d_input2(),
grid=test_grid,
interpolation='LINEAR',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_3d_replicate_nearest_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[1, 101], [3, 103]],
[[13, 113], [10, 110]]]
self._test_correctness(input=self.get_3d_input2(),
grid=test_grid,
interpolation='NEAREST',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_3d_zero_nearest_correctness(self):
test_grid = tf.constant(
[[[-5.2, .25, .25], [.25, .95, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[0, 0], [3, 103]],
[[13, 113], [10, 110]]]
self._test_correctness(input=self.get_3d_input2(),
grid=test_grid,
interpolation='NEAREST',
boundary='ZERO',
expected_value=expected)
def test_resampler_3d_replicate_linear_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[2.75], [3.75]],
[[12.75], [11.25]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='LINEAR',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_3d_replicate_cubic_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[3.20869954], [3.93501790]],
[[12.63008626], [10.33280436]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='BSPLINE',
boundary='REPLICATE',
expected_value=expected)
def test_resampler_3d_circular_nearest_correctness(self):
test_grid = tf.constant(
[[[.25, .25, .25], [.25, .75, .25]],
[[.75, .25, .25], [.25, .25, .75]]],
dtype=tf.float32)
expected = [[[1], [3]], [[13], [10]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='NEAREST',
boundary='CIRCULAR',
expected_value=expected)
def test_resampler_3d_circular_linear_correctness(self):
test_grid = tf.constant(
[[[.25, .25 + 2, .25 + 3],
[.25 - 2, .75 - 2, .25 - 3]],
[[.75 + 2, .25 - 2, .25 - 3],
[.25 + 2, .25 - 2, .75 + 3]]],
dtype=tf.float32)
expected = [[[2.75], [3.75]],
[[12.75], [11.25]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='LINEAR',
boundary='CIRCULAR',
expected_value=expected)
def test_resampler_3d_symmetric_nearest_correctness(self):
test_grid = tf.constant(
[[[-.25, -.25, -.25],
[.25 + 2, .75 + 2, .25 + 4]],
[[.75, .25, -.25 + 4],
[.25, .25, .75]]],
dtype=tf.float32)
expected = [[[1], [3]], [[13], [10]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='NEAREST',
boundary='SYMMETRIC',
expected_value=expected)
def test_resampler_3d_symmetric_linear_correctness(self):
test_grid = tf.constant(
[[[-.25, -.25, -.25],
[.25 + 2, .75 + 2, .25 + 4]],
[[.75, .25, -.25 + 4],
[.25, .25, .75]]],
dtype=tf.float32)
expected = [[[2.75], [3.75]],
[[12.75], [11.25]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='LINEAR',
boundary='SYMMETRIC',
expected_value=expected)
def test_resampler_3d_symmetric_cubic_correctness(self):
test_grid = tf.constant(
[[[-.25, -.25, -.25],
[.25 + 2, .75 + 2, .25 + 4]],
[[.75, .25, -.25 + 4],
[.25, .25, .75]]],
dtype=tf.float32)
expected = [[[3.683675], [4.140218]],
[[12.56551075], [10.69881153]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='BSPLINE',
boundary='SYMMETRIC',
expected_value=expected)
def test_resampler_3d_circular_cubic_correctness(self):
test_grid = tf.constant(
[[[-.25, -.25, -.25],
[.25 + 2, .75 + 2, .25 + 4]],
[[.75, .25, -.25 + 4],
[.25, .25, .75]]],
dtype=tf.float32)
expected = [[[1.66219068], [2.44295263]],
[[11.46712303], [10.65071392]]]
self._test_correctness(input=self.get_3d_input1(),
grid=test_grid,
interpolation='BSPLINE',
boundary='CIRCULAR',
expected_value=expected)
if __name__ == "__main__":
tf.test.main()