forked from numenta/nupic.torch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
k_winners2d_local_test.py
268 lines (222 loc) · 9.34 KB
/
k_winners2d_local_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
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2019, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
#
import unittest
import torch
from torch.testing import assert_allclose
from nupic.torch.modules import KWinners2d
class KWinner2dLocalTest(unittest.TestCase):
def setUp(self):
x = torch.zeros(3, 4, 2, 2)
x[0, :, 0, 0] = torch.FloatTensor([1, 2, 3, 4])
x[0, :, 0, 1] = torch.FloatTensor([2, 3, 0, 6])
x[0, :, 1, 0] = torch.FloatTensor([-1, -2, -3, -4])
x[0, :, 1, 1] = torch.FloatTensor([10, 11, 12, 13])
x[1, :, 0, 0] = torch.FloatTensor([10, 12, 31, 42])
x[1, :, 0, 1] = torch.FloatTensor([0, 1, 0, 6])
x[1, :, 1, 0] = torch.FloatTensor([-2, -10, -11, -4])
x[1, :, 1, 1] = torch.FloatTensor([7, 1, 10, 3])
self.x = x
def test_k_winners2d_one(self):
"""
Equal duty cycle, boost_strength=0, percent_on=0.5, batch size=1
"""
x = self.x[0:1]
n, c, h, w = x.shape
expected = torch.zeros_like(x)
expected[0, [2, 3], 0, 0] = x[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = x[0, [1, 3], 0, 1]
expected[0, [0, 1], 1, 0] = x[0, [0, 1], 1, 0]
expected[0, [2, 3], 1, 1] = x[0, [2, 3], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5, # k=2
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=True,
break_ties=break_ties,
)
kw.train(mode=False)
result = kw(x)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_k_winners2d_one_relu(self):
"""
Equal duty cycle, boost_strength=0, percent_on=0.5, batch size=1, relu
"""
x = self.x[0:1]
n, c, h, w = x.shape
expected = torch.zeros_like(x)
expected[0, [2, 3], 0, 0] = x[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = x[0, [1, 3], 0, 1]
expected[0, [2, 3], 1, 1] = x[0, [2, 3], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5, # k=2
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=True,
break_ties=break_ties,
relu=True,
)
kw.train(mode=False)
result = kw(x)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_k_winners2d_two(self):
"""
Equal duty cycle, boost_strength=0, percent_on=0.5, batch size=2
"""
x = self.x[0:2]
n, c, h, w = x.shape
expected = torch.zeros_like(x)
expected[0, [2, 3], 0, 0] = x[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = x[0, [1, 3], 0, 1]
expected[0, [0, 1], 1, 0] = x[0, [0, 1], 1, 0]
expected[0, [2, 3], 1, 1] = x[0, [2, 3], 1, 1]
expected[1, [2, 3], 0, 0] = x[1, [2, 3], 0, 0]
expected[1, [1, 3], 0, 1] = x[1, [1, 3], 0, 1]
expected[1, [0, 3], 1, 0] = x[1, [0, 3], 1, 0]
expected[1, [0, 2], 1, 1] = x[1, [0, 2], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5, # k=2
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=True,
break_ties=break_ties,
)
kw.train(mode=False)
result = kw(x)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_k_winners2d_two_relu(self):
"""
Equal duty cycle, boost_strength=0, percent_on=0.5, batch size=2, relu
"""
x = self.x[0:2]
n, c, h, w = x.shape
expected = torch.zeros_like(x)
expected[0, [2, 3], 0, 0] = x[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = x[0, [1, 3], 0, 1]
expected[0, [2, 3], 1, 1] = x[0, [2, 3], 1, 1]
expected[1, [2, 3], 0, 0] = x[1, [2, 3], 0, 0]
expected[1, [1, 3], 0, 1] = x[1, [1, 3], 0, 1]
expected[1, [0, 2], 1, 1] = x[1, [0, 2], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5, # k=2
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=True,
break_ties=break_ties,
relu=True,
)
kw.train(mode=False)
result = kw(x)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_k_winners2d_train(self):
"""
Test training
Changing duty cycle, boost_strength=1, percent_on=0.5, batch size=2
"""
x = self.x[0:2]
n, c, h, w = x.shape
# Expectation due to boosting after the second training step
expected = torch.zeros_like(x)
expected[0, [2, 3], 0, 0] = x[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = x[0, [1, 3], 0, 1]
expected[0, [0, 1], 1, 0] = x[0, [0, 1], 1, 0]
expected[0, [0, 1], 1, 1] = x[0, [0, 1], 1, 1]
expected[1, [2, 3], 0, 0] = x[1, [2, 3], 0, 0]
expected[1, [1, 3], 0, 1] = x[1, [1, 3], 0, 1]
expected[1, [0, 3], 1, 0] = x[1, [0, 3], 1, 0]
expected[1, [0, 2], 1, 1] = x[1, [0, 2], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5,
channels=c,
boost_strength=1.0,
duty_cycle_period=10,
local=True,
break_ties=break_ties,
)
kw.train(mode=True)
result = kw(x)
result = kw(x)
self.assertTrue(result.eq(expected).all())
# Expectation due to boosting after the fourth training step
expected_boosted = expected.clone()
expected_boosted[0, [0, 1], 1, 1] = 0
expected_boosted[0, [0, 2], 1, 1] = x[0, [0, 2], 1, 1]
result = kw(x)
result = kw(x)
self.assertTrue(result.eq(expected_boosted).all())
def test_k_winners2d_local_grad(self):
"""
Test gradient
"""
x = self.x[0:2].clone().detach().requires_grad_(True)
n, c, h, w = x.shape
grad = torch.rand_like(x, requires_grad=True)
expected = torch.zeros_like(grad, requires_grad=False)
expected[0, [2, 3], 0, 0] = grad[0, [2, 3], 0, 0]
expected[0, [1, 3], 0, 1] = grad[0, [1, 3], 0, 1]
expected[0, [0, 1], 1, 0] = grad[0, [0, 1], 1, 0]
expected[0, [2, 3], 1, 1] = grad[0, [2, 3], 1, 1]
expected[1, [2, 3], 0, 0] = grad[1, [2, 3], 0, 0]
expected[1, [1, 3], 0, 1] = grad[1, [1, 3], 0, 1]
expected[1, [0, 3], 1, 0] = grad[1, [0, 3], 1, 0]
expected[1, [0, 2], 1, 1] = grad[1, [0, 2], 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.5, # k=2
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=True,
break_ties=break_ties,
)
kw.train(mode=True)
y = kw(x)
y.backward(grad)
assert_allclose(x.grad, expected)
x.grad.zero_()
if __name__ == "__main__":
unittest.main()