RuntimeError: The size of tensor a (2000) must match the size of tensor b (200) at non-singleton dimension 1
eyalol opened this issue · 2 comments
eyalol commented
Hello Hugues.
As you probably already know, I'm using KPConv for semantic segmentation on DALES dataset, i've managed to configure everything, and the network starts to train, although when it comes to iteration ~500 it crashes with:
RuntimeError: The size of tensor a (2000) must match the size of tensor b (200) at non-singleton dimension 1
I'll add the log:
Starting Calibration (use verbose=True for more details)
Previous calibration found:
Check batch limit dictionary
"random_10.000_0.250_4": ?
Check neighbors limit dictionary
"0.250_0.625": ?
"0.500_1.250": ?
"1.000_6.000": ?
"2.000_12.000": ?
"4.000_24.000": ?
Step 1 estim_b = 0.10 batch_limit = 301
Step 33 estim_b = 1.29 batch_limit = 9301
Step 51 estim_b = 2.01 batch_limit = 12901
Step 68 estim_b = 2.42 batch_limit = 15301
Step 86 estim_b = 3.18 batch_limit = 16401
Step 105 estim_b = 3.23 batch_limit = 17401
Step 119 estim_b = 3.25 batch_limit = 18301
Step 136 estim_b = 3.31 batch_limit = 18901
Step 151 estim_b = 3.38 batch_limit = 19201
Step 169 estim_b = 3.39 batch_limit = 20101
Step 187 estim_b = 3.54 batch_limit = 19601
Step 201 estim_b = 3.60 batch_limit = 19601
Step 218 estim_b = 3.71 batch_limit = 19101
Step 231 estim_b = 3.75 batch_limit = 19001
Step 248 estim_b = 3.76 batch_limit = 19301
Step 266 estim_b = 3.78 batch_limit = 19601
Step 279 estim_b = 3.81 batch_limit = 19501
Step 294 estim_b = 3.79 batch_limit = 20001
Step 310 estim_b = 3.81 batch_limit = 20201
Step 324 estim_b = 3.83 batch_limit = 20201
Step 336 estim_b = 3.84 batch_limit = 20401
Step 353 estim_b = 3.85 batch_limit = 20501
Step 367 estim_b = 3.90 batch_limit = 20201
**************************************************
neighbors_num | layer 0 | layer 1 | layer 2 | layer 3 | layer 4
0 | 0 | 0 | 0 | 0 | 0
1 | 29917 | 2172 | 11 | 0 | 0
2 | 59842 | 3536 | 22 | 0 | 0
3 | 81952 | 5895 | 31 | 0 | 0
4 | 97794 | 8707 | 17 | 0 | 0
5 | 115214 | 12084 | 33 | 0 | 0
6 | 131731 | 16144 | 41 | 2 | 12
7 | 150861 | 19491 | 103 | 0 | 0
8 | 173694 | 22398 | 91 | 4 | 56
9 | 200662 | 25750 | 125 | 3 | 36
10 | 228412 | 29971 | 116 | 16 | 40
11 | 257407 | 35774 | 130 | 11 | 33
12 | 282680 | 43301 | 134 | 19 | 84
13 | 308353 | 53801 | 144 | 29 | 65
14 | 343635 | 63568 | 161 | 41 | 98
15 | 399433 | 72270 | 182 | 43 | 75
16 | 474887 | 78824 | 176 | 23 | 112
17 | 558377 | 82443 | 143 | 45 | 170
18 | 614705 | 85977 | 171 | 20 | 90
19 | 623315 | 93346 | 209 | 36 | 152
20 | 574134 | 107658 | 234 | 79 | 80
21 | 489556 | 138659 | 229 | 39 | 126
22 | 393761 | 151712 | 248 | 56 | 132
23 | 310102 | 184121 | 290 | 60 | 391
24 | 237647 | 211094 | 296 | 53 | 240
25 | 178217 | 218331 | 316 | 59 | 150
26 | 131371 | 198576 | 359 | 106 | 130
27 | 93399 | 163348 | 346 | 85 | 405
28 | 65337 | 127002 | 342 | 91 | 392
29 | 44434 | 98029 | 331 | 107 | 377
30 | 29366 | 78552 | 340 | 144 | 390
31 | 18523 | 65405 | 365 | 99 | 589
32 | 11192 | 56750 | 359 | 126 | 832
33 | 6416 | 50338 | 370 | 148 | 858
34 | 3655 | 44878 | 431 | 160 | 884
35 | 1943 | 40251 | 419 | 150 | 1400
36 | 957 | 36280 | 422 | 113 | 900
37 | 502 | 32474 | 406 | 193 | 2072
38 | 228 | 29241 | 483 | 104 | 950
39 | 104 | 26295 | 508 | 136 | 1365
40 | 37 | 23592 | 469 | 146 | 1720
41 | 28 | 21054 | 451 | 117 | 1435
42 | 2 | 18578 | 479 | 163 | 2058
43 | 3 | 16494 | 484 | 169 | 1892
44 | 0 | 14687 | 533 | 176 | 2112
45 | 0 | 13058 | 609 | 155 | 1800
46 | 1 | 11088 | 654 | 165 | 1978
47 | 0 | 9717 | 619 | 210 | 1927
48 | 0 | 8211 | 608 | 199 | 1920
49 | 0 | 6994 | 545 | 248 | 1862
50 | 0 | 5813 | 559 | 185 | 1700
51 | 0 | 4739 | 596 | 253 | 1530
52 | 0 | 3912 | 612 | 262 | 1716
53 | 0 | 3085 | 608 | 296 | 1484
54 | 0 | 2480 | 635 | 266 | 1998
55 | 0 | 1819 | 670 | 345 | 1595
56 | 0 | 1452 | 712 | 309 | 1232
57 | 0 | 1041 | 724 | 308 | 1140
58 | 0 | 742 | 797 | 383 | 870
59 | 0 | 552 | 878 | 386 | 1239
60 | 0 | 375 | 850 | 379 | 780
61 | 0 | 218 | 966 | 452 | 854
62 | 0 | 136 | 1059 | 422 | 620
63 | 0 | 97 | 1146 | 522 | 441
64 | 0 | 57 | 1257 | 540 | 576
65 | 0 | 22 | 1320 | 584 | 325
66 | 0 | 12 | 1415 | 654 | 528
67 | 0 | 9 | 1570 | 727 | 603
68 | 0 | 0 | 1770 | 757 | 408
69 | 0 | 1 | 1803 | 760 | 414
70 | 0 | 2 | 2064 | 825 | 560
71 | 0 | 0 | 2107 | 856 | 142
72 | 0 | 0 | 2217 | 868 | 504
73 | 0 | 0 | 2334 | 984 | 365
74 | 0 | 0 | 2389 | 891 | 0
75 | 0 | 0 | 2438 | 992 | 525
76 | 0 | 0 | 2612 | 1022 | 304
77 | 0 | 0 | 2639 | 1073 | 231
78 | 0 | 0 | 2701 | 1176 | 624
79 | 0 | 0 | 2760 | 1117 | 79
80 | 0 | 0 | 2871 | 1140 | 160
81 | 0 | 0 | 2890 | 1206 | 243
82 | 0 | 0 | 2954 | 1219 | 0
83 | 0 | 0 | 3134 | 1341 | 83
84 | 0 | 0 | 3073 | 1419 | 168
85 | 0 | 0 | 3053 | 1418 | 85
86 | 0 | 0 | 3222 | 1361 | 0
87 | 0 | 0 | 3270 | 1508 | 174
88 | 0 | 0 | 3250 | 1431 | 176
89 | 0 | 0 | 3281 | 1523 | 0
90 | 0 | 0 | 3322 | 1574 | 180
91 | 0 | 0 | 3425 | 1630 | 0
92 | 0 | 0 | 3333 | 1600 | 0
93 | 0 | 0 | 3444 | 1660 | 93
94 | 0 | 0 | 3485 | 1656 | 188
95 | 0 | 0 | 3507 | 1733 | 0
96 | 0 | 0 | 3585 | 1686 | 96
97 | 0 | 0 | 3608 | 1677 | 97
98 | 0 | 0 | 3647 | 1832 | 98
99 | 0 | 0 | 3632 | 1728 | 99
100 | 0 | 0 | 3630 | 1768 | 0
101 | 0 | 0 | 3700 | 1799 | 0
102 | 0 | 0 | 3728 | 1801 | 102
103 | 0 | 0 | 3616 | 1753 | 103
104 | 0 | 0 | 3592 | 1820 | 0
105 | 0 | 0 | 3666 | 1843 | 0
106 | 0 | 0 | 3657 | 1858 | 0
107 | 0 | 0 | 3856 | 1874 | 0
108 | 0 | 0 | 3750 | 1966 | 0
109 | 0 | 0 | 3805 | 1837 | 0
110 | 0 | 0 | 3833 | 1943 | 0
111 | 0 | 0 | 3854 | 1815 | 0
112 | 0 | 0 | 3792 | 2006 | 112
113 | 0 | 0 | 3715 | 1862 | 0
114 | 0 | 0 | 3858 | 1932 | 0
115 | 0 | 0 | 3972 | 1762 | 0
116 | 0 | 0 | 3971 | 1820 | 0
117 | 0 | 0 | 3848 | 1779 | 0
118 | 0 | 0 | 3818 | 1793 | 0
119 | 0 | 0 | 3907 | 1828 | 0
120 | 0 | 0 | 3948 | 1789 | 0
121 | 0 | 0 | 3945 | 1687 | 0
122 | 0 | 0 | 3970 | 1650 | 0
123 | 0 | 0 | 3833 | 1695 | 0
124 | 0 | 0 | 4010 | 1730 | 0
125 | 0 | 0 | 3855 | 1753 | 0
126 | 0 | 0 | 4015 | 1597 | 0
127 | 0 | 0 | 3996 | 1793 | 0
128 | 0 | 0 | 4071 | 1647 | 0
129 | 0 | 0 | 4054 | 1700 | 0
130 | 0 | 0 | 3953 | 1607 | 0
131 | 0 | 0 | 4038 | 1667 | 0
132 | 0 | 0 | 3981 | 1516 | 0
133 | 0 | 0 | 3893 | 1568 | 0
134 | 0 | 0 | 4074 | 1586 | 0
135 | 0 | 0 | 4057 | 1561 | 0
136 | 0 | 0 | 4065 | 1574 | 0
137 | 0 | 0 | 4044 | 1654 | 0
138 | 0 | 0 | 4015 | 1523 | 0
139 | 0 | 0 | 4037 | 1489 | 0
140 | 0 | 0 | 4105 | 1542 | 0
141 | 0 | 0 | 4047 | 1458 | 0
142 | 0 | 0 | 4164 | 1487 | 0
143 | 0 | 0 | 4016 | 1387 | 0
144 | 0 | 0 | 3998 | 1398 | 0
145 | 0 | 0 | 4100 | 1339 | 0
146 | 0 | 0 | 4123 | 1391 | 0
147 | 0 | 0 | 3961 | 1391 | 0
148 | 0 | 0 | 4039 | 1516 | 0
149 | 0 | 0 | 4089 | 1315 | 0
150 | 0 | 0 | 4029 | 1276 | 0
151 | 0 | 0 | 4057 | 1243 | 0
152 | 0 | 0 | 4081 | 1303 | 0
153 | 0 | 0 | 3999 | 1223 | 0
154 | 0 | 0 | 4029 | 1273 | 0
155 | 0 | 0 | 4109 | 1290 | 0
156 | 0 | 0 | 4132 | 1148 | 0
157 | 0 | 0 | 3996 | 1193 | 0
158 | 0 | 0 | 3995 | 1172 | 0
159 | 0 | 0 | 4001 | 1217 | 0
160 | 0 | 0 | 4051 | 1170 | 0
161 | 0 | 0 | 3874 | 1091 | 0
162 | 0 | 0 | 3892 | 1085 | 0
163 | 0 | 0 | 4010 | 1047 | 0
164 | 0 | 0 | 3872 | 1060 | 0
165 | 0 | 0 | 3898 | 1091 | 0
166 | 0 | 0 | 3775 | 1012 | 0
167 | 0 | 0 | 3913 | 1088 | 0
168 | 0 | 0 | 3719 | 973 | 0
169 | 0 | 0 | 3929 | 1030 | 0
170 | 0 | 0 | 3818 | 887 | 0
171 | 0 | 0 | 3964 | 1040 | 0
172 | 0 | 0 | 3978 | 1034 | 0
173 | 0 | 0 | 3793 | 896 | 0
174 | 0 | 0 | 3819 | 944 | 0
175 | 0 | 0 | 3771 | 912 | 0
176 | 0 | 0 | 3814 | 871 | 0
177 | 0 | 0 | 3731 | 865 | 0
178 | 0 | 0 | 3746 | 818 | 0
179 | 0 | 0 | 3716 | 772 | 0
180 | 0 | 0 | 3969 | 764 | 0
181 | 0 | 0 | 3813 | 885 | 0
182 | 0 | 0 | 3779 | 790 | 0
183 | 0 | 0 | 3650 | 912 | 0
184 | 0 | 0 | 3643 | 828 | 0
185 | 0 | 0 | 3651 | 805 | 0
186 | 0 | 0 | 3751 | 765 | 0
187 | 0 | 0 | 3632 | 821 | 0
188 | 0 | 0 | 3604 | 783 | 0
189 | 0 | 0 | 3550 | 751 | 0
190 | 0 | 0 | 3535 | 781 | 0
191 | 0 | 0 | 3565 | 681 | 0
192 | 0 | 0 | 3607 | 740 | 0
193 | 0 | 0 | 3479 | 762 | 0
194 | 0 | 0 | 3485 | 700 | 0
195 | 0 | 0 | 3463 | 714 | 0
196 | 0 | 0 | 3349 | 681 | 0
197 | 0 | 0 | 3407 | 646 | 0
198 | 0 | 0 | 3401 | 585 | 0
199 | 0 | 0 | 3385 | 682 | 0
200 | 0 | 0 | 3425 | 605 | 0
201 | 0 | 0 | 3323 | 601 | 0
202 | 0 | 0 | 3203 | 613 | 0
203 | 0 | 0 | 3383 | 628 | 0
204 | 0 | 0 | 3187 | 534 | 0
205 | 0 | 0 | 3217 | 553 | 0
206 | 0 | 0 | 3199 | 592 | 0
207 | 0 | 0 | 3137 | 532 | 0
208 | 0 | 0 | 3161 | 516 | 0
209 | 0 | 0 | 3200 | 578 | 0
210 | 0 | 0 | 3088 | 510 | 0
211 | 0 | 0 | 3060 | 532 | 0
212 | 0 | 0 | 3035 | 545 | 0
213 | 0 | 0 | 3022 | 545 | 0
214 | 0 | 0 | 2998 | 538 | 0
215 | 0 | 0 | 3023 | 469 | 0
216 | 0 | 0 | 2906 | 477 | 0
217 | 0 | 0 | 2961 | 498 | 0
218 | 0 | 0 | 2947 | 512 | 0
219 | 0 | 0 | 2887 | 526 | 0
220 | 0 | 0 | 2833 | 542 | 0
221 | 0 | 0 | 2810 | 478 | 0
222 | 0 | 0 | 2840 | 490 | 0
223 | 0 | 0 | 2703 | 497 | 0
224 | 0 | 0 | 2747 | 453 | 0
225 | 0 | 0 | 2806 | 473 | 0
226 | 0 | 0 | 2705 | 440 | 0
227 | 0 | 0 | 2691 | 441 | 0
228 | 0 | 0 | 2735 | 423 | 0
229 | 0 | 0 | 2707 | 427 | 0
230 | 0 | 0 | 2611 | 404 | 0
231 | 0 | 0 | 2691 | 468 | 0
232 | 0 | 0 | 2497 | 434 | 0
233 | 0 | 0 | 2522 | 383 | 0
234 | 0 | 0 | 2561 | 372 | 0
235 | 0 | 0 | 2548 | 379 | 0
236 | 0 | 0 | 2427 | 415 | 0
237 | 0 | 0 | 2551 | 344 | 0
238 | 0 | 0 | 2438 | 383 | 0
239 | 0 | 0 | 2403 | 381 | 0
240 | 0 | 0 | 2371 | 361 | 0
241 | 0 | 0 | 2361 | 367 | 0
242 | 0 | 0 | 2366 | 383 | 0
243 | 0 | 0 | 2316 | 393 | 0
244 | 0 | 0 | 2339 | 338 | 0
245 | 0 | 0 | 2279 | 317 | 0
246 | 0 | 0 | 2297 | 350 | 0
247 | 0 | 0 | 2242 | 356 | 0
248 | 0 | 0 | 2233 | 306 | 0
249 | 0 | 0 | 2241 | 299 | 0
250 | 0 | 0 | 2174 | 262 | 0
251 | 0 | 0 | 2139 | 350 | 0
252 | 0 | 0 | 2068 | 323 | 0
253 | 0 | 0 | 2160 | 295 | 0
254 | 0 | 0 | 2071 | 289 | 0
255 | 0 | 0 | 2087 | 274 | 0
256 | 0 | 0 | 2041 | 301 | 0
257 | 0 | 0 | 2008 | 298 | 0
258 | 0 | 0 | 1996 | 294 | 0
259 | 0 | 0 | 1917 | 259 | 0
260 | 0 | 0 | 2008 | 281 | 0
261 | 0 | 0 | 1960 | 244 | 0
262 | 0 | 0 | 1926 | 234 | 0
263 | 0 | 0 | 1917 | 273 | 0
264 | 0 | 0 | 1902 | 283 | 0
265 | 0 | 0 | 1872 | 208 | 0
266 | 0 | 0 | 1819 | 189 | 0
267 | 0 | 0 | 1835 | 186 | 0
268 | 0 | 0 | 1800 | 233 | 0
269 | 0 | 0 | 1812 | 233 | 0
270 | 0 | 0 | 1858 | 244 | 0
271 | 0 | 0 | 1750 | 223 | 0
272 | 0 | 0 | 1739 | 198 | 0
273 | 0 | 0 | 1717 | 194 | 0
274 | 0 | 0 | 1697 | 206 | 0
275 | 0 | 0 | 1684 | 189 | 0
276 | 0 | 0 | 1710 | 162 | 0
277 | 0 | 0 | 1631 | 217 | 0
278 | 0 | 0 | 1637 | 190 | 0
279 | 0 | 0 | 1605 | 166 | 0
280 | 0 | 0 | 1544 | 195 | 0
281 | 0 | 0 | 1646 | 188 | 0
282 | 0 | 0 | 1632 | 203 | 0
283 | 0 | 0 | 1579 | 178 | 0
284 | 0 | 0 | 1514 | 161 | 0
285 | 0 | 0 | 1494 | 167 | 0
286 | 0 | 0 | 1568 | 187 | 0
287 | 0 | 0 | 1519 | 166 | 0
288 | 0 | 0 | 1529 | 182 | 0
289 | 0 | 0 | 1488 | 176 | 0
290 | 0 | 0 | 1426 | 162 | 0
291 | 0 | 0 | 1460 | 144 | 0
292 | 0 | 0 | 1473 | 161 | 0
293 | 0 | 0 | 1426 | 122 | 0
294 | 0 | 0 | 1482 | 125 | 0
295 | 0 | 0 | 1370 | 144 | 0
296 | 0 | 0 | 1428 | 128 | 0
297 | 0 | 0 | 1421 | 98 | 0
298 | 0 | 0 | 1377 | 117 | 0
299 | 0 | 0 | 1414 | 131 | 0
300 | 0 | 0 | 1346 | 114 | 0
301 | 0 | 0 | 1318 | 143 | 0
302 | 0 | 0 | 1255 | 114 | 0
303 | 0 | 0 | 1299 | 112 | 0
304 | 0 | 0 | 1267 | 105 | 0
305 | 0 | 0 | 1250 | 98 | 0
306 | 0 | 0 | 1261 | 131 | 0
307 | 0 | 0 | 1265 | 114 | 0
308 | 0 | 0 | 1229 | 106 | 0
309 | 0 | 0 | 1258 | 85 | 0
310 | 0 | 0 | 1191 | 104 | 0
311 | 0 | 0 | 1225 | 107 | 0
312 | 0 | 0 | 1168 | 100 | 0
313 | 0 | 0 | 1173 | 118 | 0
314 | 0 | 0 | 1132 | 87 | 0
315 | 0 | 0 | 1211 | 92 | 0
316 | 0 | 0 | 1187 | 85 | 0
317 | 0 | 0 | 1162 | 77 | 0
318 | 0 | 0 | 1125 | 80 | 0
319 | 0 | 0 | 1164 | 98 | 0
320 | 0 | 0 | 1118 | 80 | 0
321 | 0 | 0 | 1164 | 86 | 0
322 | 0 | 0 | 1089 | 75 | 0
323 | 0 | 0 | 1038 | 113 | 0
324 | 0 | 0 | 1078 | 78 | 0
325 | 0 | 0 | 1124 | 83 | 0
326 | 0 | 0 | 1031 | 82 | 0
327 | 0 | 0 | 1018 | 89 | 0
328 | 0 | 0 | 1063 | 57 | 0
329 | 0 | 0 | 1036 | 53 | 0
330 | 0 | 0 | 1029 | 48 | 0
331 | 0 | 0 | 1023 | 54 | 0
332 | 0 | 0 | 993 | 65 | 0
333 | 0 | 0 | 1028 | 61 | 0
334 | 0 | 0 | 983 | 54 | 0
335 | 0 | 0 | 962 | 63 | 0
336 | 0 | 0 | 926 | 51 | 0
337 | 0 | 0 | 945 | 62 | 0
338 | 0 | 0 | 927 | 82 | 0
339 | 0 | 0 | 913 | 54 | 0
340 | 0 | 0 | 914 | 70 | 0
341 | 0 | 0 | 938 | 63 | 0
342 | 0 | 0 | 905 | 60 | 0
343 | 0 | 0 | 901 | 57 | 0
344 | 0 | 0 | 941 | 54 | 0
345 | 0 | 0 | 907 | 34 | 0
346 | 0 | 0 | 892 | 77 | 0
347 | 0 | 0 | 884 | 50 | 0
348 | 0 | 0 | 901 | 47 | 0
349 | 0 | 0 | 834 | 45 | 0
350 | 0 | 0 | 867 | 39 | 0
351 | 0 | 0 | 837 | 45 | 0
352 | 0 | 0 | 841 | 46 | 0
353 | 0 | 0 | 857 | 43 | 0
354 | 0 | 0 | 826 | 39 | 0
355 | 0 | 0 | 832 | 55 | 0
356 | 0 | 0 | 780 | 42 | 0
357 | 0 | 0 | 824 | 38 | 0
358 | 0 | 0 | 819 | 27 | 0
359 | 0 | 0 | 808 | 46 | 0
360 | 0 | 0 | 745 | 50 | 0
361 | 0 | 0 | 749 | 42 | 0
362 | 0 | 0 | 769 | 31 | 0
363 | 0 | 0 | 732 | 27 | 0
364 | 0 | 0 | 752 | 38 | 0
365 | 0 | 0 | 733 | 27 | 0
366 | 0 | 0 | 699 | 25 | 0
367 | 0 | 0 | 732 | 45 | 0
368 | 0 | 0 | 692 | 25 | 0
369 | 0 | 0 | 753 | 27 | 0
370 | 0 | 0 | 679 | 26 | 0
371 | 0 | 0 | 756 | 26 | 0
372 | 0 | 0 | 705 | 35 | 0
373 | 0 | 0 | 713 | 40 | 0
374 | 0 | 0 | 673 | 30 | 0
375 | 0 | 0 | 733 | 57 | 0
376 | 0 | 0 | 672 | 23 | 0
377 | 0 | 0 | 684 | 30 | 0
378 | 0 | 0 | 700 | 14 | 0
379 | 0 | 0 | 653 | 18 | 0
380 | 0 | 0 | 681 | 24 | 0
381 | 0 | 0 | 666 | 23 | 0
382 | 0 | 0 | 691 | 19 | 0
383 | 0 | 0 | 639 | 17 | 0
384 | 0 | 0 | 598 | 21 | 0
385 | 0 | 0 | 614 | 22 | 0
386 | 0 | 0 | 656 | 35 | 0
387 | 0 | 0 | 610 | 20 | 0
388 | 0 | 0 | 599 | 12 | 0
389 | 0 | 0 | 607 | 14 | 0
390 | 0 | 0 | 676 | 20 | 0
391 | 0 | 0 | 603 | 11 | 0
392 | 0 | 0 | 598 | 22 | 0
393 | 0 | 0 | 555 | 10 | 0
394 | 0 | 0 | 585 | 7 | 0
395 | 0 | 0 | 586 | 10 | 0
396 | 0 | 0 | 550 | 13 | 0
397 | 0 | 0 | 581 | 18 | 0
398 | 0 | 0 | 577 | 15 | 0
399 | 0 | 0 | 575 | 13 | 0
400 | 0 | 0 | 591 | 9 | 0
401 | 0 | 0 | 571 | 17 | 0
402 | 0 | 0 | 537 | 16 | 0
403 | 0 | 0 | 553 | 11 | 0
404 | 0 | 0 | 545 | 12 | 0
405 | 0 | 0 | 520 | 13 | 0
406 | 0 | 0 | 509 | 10 | 0
407 | 0 | 0 | 552 | 10 | 0
408 | 0 | 0 | 505 | 13 | 0
409 | 0 | 0 | 493 | 10 | 0
410 | 0 | 0 | 516 | 15 | 0
411 | 0 | 0 | 518 | 9 | 0
412 | 0 | 0 | 516 | 9 | 0
413 | 0 | 0 | 482 | 11 | 0
414 | 0 | 0 | 522 | 16 | 0
415 | 0 | 0 | 497 | 9 | 0
416 | 0 | 0 | 468 | 9 | 0
417 | 0 | 0 | 518 | 6 | 0
418 | 0 | 0 | 528 | 9 | 0
419 | 0 | 0 | 488 | 5 | 0
420 | 0 | 0 | 464 | 11 | 0
421 | 0 | 0 | 473 | 3 | 0
422 | 0 | 0 | 473 | 15 | 0
423 | 0 | 0 | 491 | 5 | 0
424 | 0 | 0 | 465 | 10 | 0
425 | 0 | 0 | 440 | 5 | 0
426 | 0 | 0 | 473 | 13 | 0
427 | 0 | 0 | 449 | 12 | 0
428 | 0 | 0 | 458 | 6 | 0
429 | 0 | 0 | 438 | 12 | 0
430 | 0 | 0 | 439 | 6 | 0
431 | 0 | 0 | 381 | 6 | 0
432 | 0 | 0 | 433 | 7 | 0
433 | 0 | 0 | 416 | 11 | 0
434 | 0 | 0 | 370 | 6 | 0
435 | 0 | 0 | 403 | 7 | 0
436 | 0 | 0 | 413 | 1 | 0
437 | 0 | 0 | 408 | 6 | 0
438 | 0 | 0 | 421 | 12 | 0
439 | 0 | 0 | 378 | 3 | 0
440 | 0 | 0 | 393 | 2 | 0
441 | 0 | 0 | 393 | 2 | 0
442 | 0 | 0 | 389 | 3 | 0
443 | 0 | 0 | 422 | 9 | 0
444 | 0 | 0 | 410 | 7 | 0
445 | 0 | 0 | 388 | 3 | 0
446 | 0 | 0 | 364 | 4 | 0
447 | 0 | 0 | 384 | 2 | 0
448 | 0 | 0 | 361 | 4 | 0
449 | 0 | 0 | 402 | 6 | 0
450 | 0 | 0 | 372 | 7 | 0
451 | 0 | 0 | 373 | 10 | 0
452 | 0 | 0 | 348 | 2 | 0
453 | 0 | 0 | 389 | 4 | 0
454 | 0 | 0 | 372 | 4 | 0
455 | 0 | 0 | 351 | 8 | 0
456 | 0 | 0 | 362 | 3 | 0
457 | 0 | 0 | 351 | 10 | 0
458 | 0 | 0 | 331 | 2 | 0
459 | 0 | 0 | 369 | 2 | 0
460 | 0 | 0 | 344 | 5 | 0
461 | 0 | 0 | 318 | 2 | 0
462 | 0 | 0 | 319 | 5 | 0
463 | 0 | 0 | 353 | 1 | 0
464 | 0 | 0 | 311 | 10 | 0
465 | 0 | 0 | 331 | 1 | 0
466 | 0 | 0 | 325 | 3 | 0
467 | 0 | 0 | 296 | 8 | 0
468 | 0 | 0 | 325 | 2 | 0
469 | 0 | 0 | 340 | 3 | 0
470 | 0 | 0 | 325 | 4 | 0
471 | 0 | 0 | 329 | 3 | 0
472 | 0 | 0 | 301 | 4 | 0
473 | 0 | 0 | 285 | 2 | 0
474 | 0 | 0 | 327 | 0 | 0
475 | 0 | 0 | 270 | 2 | 0
476 | 0 | 0 | 279 | 4 | 0
477 | 0 | 0 | 275 | 0 | 0
478 | 0 | 0 | 304 | 2 | 0
479 | 0 | 0 | 250 | 3 | 0
480 | 0 | 0 | 271 | 3 | 0
481 | 0 | 0 | 287 | 5 | 0
482 | 0 | 0 | 292 | 3 | 0
483 | 0 | 0 | 262 | 1 | 0
484 | 0 | 0 | 287 | 1 | 0
485 | 0 | 0 | 269 | 5 | 0
486 | 0 | 0 | 224 | 1 | 0
487 | 0 | 0 | 251 | 2 | 0
488 | 0 | 0 | 269 | 0 | 0
489 | 0 | 0 | 234 | 2 | 0
490 | 0 | 0 | 247 | 8 | 0
491 | 0 | 0 | 267 | 1 | 0
492 | 0 | 0 | 239 | 6 | 0
493 | 0 | 0 | 245 | 1 | 0
494 | 0 | 0 | 234 | 0 | 0
495 | 0 | 0 | 224 | 1 | 0
496 | 0 | 0 | 239 | 0 | 0
497 | 0 | 0 | 218 | 0 | 0
498 | 0 | 0 | 225 | 0 | 0
499 | 0 | 0 | 225 | 0 | 0
500 | 0 | 0 | 230 | 0 | 0
501 | 0 | 0 | 243 | 0 | 0
502 | 0 | 0 | 234 | 0 | 0
503 | 0 | 0 | 227 | 0 | 0
504 | 0 | 0 | 207 | 1 | 0
505 | 0 | 0 | 197 | 0 | 0
506 | 0 | 0 | 213 | 1 | 0
507 | 0 | 0 | 224 | 1 | 0
508 | 0 | 0 | 230 | 2 | 0
509 | 0 | 0 | 199 | 0 | 0
510 | 0 | 0 | 192 | 0 | 0
511 | 0 | 0 | 214 | 0 | 0
512 | 0 | 0 | 191 | 1 | 0
513 | 0 | 0 | 223 | 1 | 0
514 | 0 | 0 | 187 | 0 | 0
515 | 0 | 0 | 194 | 0 | 0
516 | 0 | 0 | 223 | 0 | 0
517 | 0 | 0 | 217 | 0 | 0
518 | 0 | 0 | 214 | 0 | 0
519 | 0 | 0 | 203 | 0 | 0
520 | 0 | 0 | 190 | 0 | 0
521 | 0 | 0 | 212 | 0 | 0
522 | 0 | 0 | 186 | 0 | 0
523 | 0 | 0 | 165 | 1 | 0
524 | 0 | 0 | 164 | 0 | 0
525 | 0 | 0 | 166 | 0 | 0
526 | 0 | 0 | 193 | 0 | 0
527 | 0 | 0 | 159 | 0 | 0
528 | 0 | 0 | 190 | 0 | 0
529 | 0 | 0 | 145 | 1 | 0
530 | 0 | 0 | 170 | 0 | 0
531 | 0 | 0 | 181 | 0 | 0
532 | 0 | 0 | 171 | 1 | 0
533 | 0 | 0 | 183 | 0 | 0
534 | 0 | 0 | 204 | 0 | 0
535 | 0 | 0 | 176 | 0 | 0
536 | 0 | 0 | 175 | 0 | 0
537 | 0 | 0 | 159 | 0 | 0
538 | 0 | 0 | 174 | 2 | 0
539 | 0 | 0 | 156 | 0 | 0
540 | 0 | 0 | 166 | 0 | 0
541 | 0 | 0 | 165 | 0 | 0
542 | 0 | 0 | 116 | 0 | 0
543 | 0 | 0 | 147 | 0 | 0
544 | 0 | 0 | 159 | 0 | 0
545 | 0 | 0 | 152 | 0 | 0
546 | 0 | 0 | 149 | 0 | 0
547 | 0 | 0 | 146 | 0 | 0
548 | 0 | 0 | 146 | 0 | 0
549 | 0 | 0 | 126 | 0 | 0
550 | 0 | 0 | 155 | 0 | 0
551 | 0 | 0 | 134 | 0 | 0
552 | 0 | 0 | 130 | 0 | 0
553 | 0 | 0 | 147 | 0 | 0
554 | 0 | 0 | 134 | 0 | 0
555 | 0 | 0 | 132 | 0 | 0
556 | 0 | 0 | 119 | 0 | 0
557 | 0 | 0 | 153 | 0 | 0
558 | 0 | 0 | 135 | 0 | 0
559 | 0 | 0 | 130 | 0 | 0
560 | 0 | 0 | 122 | 0 | 0
561 | 0 | 0 | 125 | 0 | 0
562 | 0 | 0 | 124 | 0 | 0
563 | 0 | 0 | 129 | 0 | 0
564 | 0 | 0 | 131 | 0 | 0
565 | 0 | 0 | 119 | 0 | 0
566 | 0 | 0 | 108 | 0 | 0
567 | 0 | 0 | 116 | 0 | 0
568 | 0 | 0 | 126 | 0 | 0
569 | 0 | 0 | 105 | 0 | 0
570 | 0 | 0 | 101 | 0 | 0
571 | 0 | 0 | 101 | 0 | 0
572 | 0 | 0 | 104 | 0 | 0
573 | 0 | 0 | 106 | 0 | 0
574 | 0 | 0 | 112 | 0 | 0
575 | 0 | 0 | 100 | 0 | 0
576 | 0 | 0 | 117 | 0 | 0
577 | 0 | 0 | 122 | 0 | 0
578 | 0 | 0 | 90 | 0 | 0
579 | 0 | 0 | 100 | 0 | 0
580 | 0 | 0 | 100 | 0 | 0
581 | 0 | 0 | 115 | 0 | 0
582 | 0 | 0 | 99 | 0 | 0
583 | 0 | 0 | 91 | 0 | 0
584 | 0 | 0 | 96 | 0 | 0
585 | 0 | 0 | 97 | 0 | 0
586 | 0 | 0 | 76 | 0 | 0
587 | 0 | 0 | 99 | 0 | 0
588 | 0 | 0 | 100 | 0 | 0
589 | 0 | 0 | 94 | 0 | 0
590 | 0 | 0 | 102 | 0 | 0
591 | 0 | 0 | 73 | 0 | 0
592 | 0 | 0 | 104 | 0 | 0
593 | 0 | 0 | 71 | 0 | 0
594 | 0 | 0 | 86 | 0 | 0
595 | 0 | 0 | 78 | 0 | 0
596 | 0 | 0 | 87 | 0 | 0
597 | 0 | 0 | 77 | 0 | 0
598 | 0 | 0 | 95 | 0 | 0
599 | 0 | 0 | 84 | 0 | 0
600 | 0 | 0 | 70 | 0 | 0
601 | 0 | 0 | 67 | 0 | 0
602 | 0 | 0 | 71 | 0 | 0
603 | 0 | 0 | 70 | 0 | 0
604 | 0 | 0 | 69 | 0 | 0
605 | 0 | 0 | 71 | 0 | 0
606 | 0 | 0 | 67 | 0 | 0
607 | 0 | 0 | 73 | 0 | 0
608 | 0 | 0 | 62 | 0 | 0
609 | 0 | 0 | 73 | 0 | 0
610 | 0 | 0 | 53 | 0 | 0
611 | 0 | 0 | 59 | 0 | 0
612 | 0 | 0 | 64 | 0 | 0
613 | 0 | 0 | 50 | 0 | 0
614 | 0 | 0 | 51 | 0 | 0
615 | 0 | 0 | 72 | 0 | 0
616 | 0 | 0 | 70 | 0 | 0
617 | 0 | 0 | 50 | 0 | 0
618 | 0 | 0 | 56 | 0 | 0
619 | 0 | 0 | 50 | 0 | 0
620 | 0 | 0 | 65 | 0 | 0
621 | 0 | 0 | 62 | 0 | 0
622 | 0 | 0 | 62 | 0 | 0
623 | 0 | 0 | 55 | 0 | 0
624 | 0 | 0 | 44 | 0 | 0
625 | 0 | 0 | 42 | 0 | 0
626 | 0 | 0 | 48 | 0 | 0
627 | 0 | 0 | 54 | 0 | 0
628 | 0 | 0 | 57 | 0 | 0
629 | 0 | 0 | 52 | 0 | 0
630 | 0 | 0 | 59 | 0 | 0
631 | 0 | 0 | 43 | 0 | 0
632 | 0 | 0 | 44 | 0 | 0
633 | 0 | 0 | 48 | 0 | 0
634 | 0 | 0 | 38 | 0 | 0
635 | 0 | 0 | 39 | 0 | 0
636 | 0 | 0 | 57 | 0 | 0
637 | 0 | 0 | 41 | 0 | 0
638 | 0 | 0 | 57 | 0 | 0
639 | 0 | 0 | 39 | 0 | 0
640 | 0 | 0 | 32 | 0 | 0
641 | 0 | 0 | 41 | 0 | 0
642 | 0 | 0 | 41 | 0 | 0
643 | 0 | 0 | 41 | 0 | 0
644 | 0 | 0 | 39 | 0 | 0
645 | 0 | 0 | 36 | 0 | 0
646 | 0 | 0 | 49 | 0 | 0
647 | 0 | 0 | 48 | 0 | 0
648 | 0 | 0 | 42 | 0 | 0
649 | 0 | 0 | 38 | 0 | 0
650 | 0 | 0 | 43 | 0 | 0
651 | 0 | 0 | 42 | 0 | 0
652 | 0 | 0 | 46 | 0 | 0
653 | 0 | 0 | 36 | 0 | 0
654 | 0 | 0 | 43 | 0 | 0
655 | 0 | 0 | 32 | 0 | 0
656 | 0 | 0 | 39 | 0 | 0
657 | 0 | 0 | 37 | 0 | 0
658 | 0 | 0 | 31 | 0 | 0
659 | 0 | 0 | 39 | 0 | 0
660 | 0 | 0 | 32 | 0 | 0
661 | 0 | 0 | 32 | 0 | 0
662 | 0 | 0 | 23 | 0 | 0
663 | 0 | 0 | 28 | 0 | 0
664 | 0 | 0 | 34 | 0 | 0
665 | 0 | 0 | 40 | 0 | 0
666 | 0 | 0 | 34 | 0 | 0
667 | 0 | 0 | 41 | 0 | 0
668 | 0 | 0 | 35 | 0 | 0
669 | 0 | 0 | 32 | 0 | 0
670 | 0 | 0 | 30 | 0 | 0
671 | 0 | 0 | 30 | 0 | 0
672 | 0 | 0 | 18 | 0 | 0
673 | 0 | 0 | 25 | 0 | 0
674 | 0 | 0 | 28 | 0 | 0
675 | 0 | 0 | 26 | 0 | 0
676 | 0 | 0 | 28 | 0 | 0
677 | 0 | 0 | 30 | 0 | 0
678 | 0 | 0 | 14 | 0 | 0
679 | 0 | 0 | 31 | 0 | 0
680 | 0 | 0 | 24 | 0 | 0
681 | 0 | 0 | 24 | 0 | 0
682 | 0 | 0 | 22 | 0 | 0
683 | 0 | 0 | 21 | 0 | 0
684 | 0 | 0 | 22 | 0 | 0
685 | 0 | 0 | 32 | 0 | 0
686 | 0 | 0 | 26 | 0 | 0
687 | 0 | 0 | 21 | 0 | 0
688 | 0 | 0 | 19 | 0 | 0
689 | 0 | 0 | 25 | 0 | 0
690 | 0 | 0 | 17 | 0 | 0
691 | 0 | 0 | 28 | 0 | 0
692 | 0 | 0 | 19 | 0 | 0
693 | 0 | 0 | 13 | 0 | 0
694 | 0 | 0 | 18 | 0 | 0
695 | 0 | 0 | 18 | 0 | 0
696 | 0 | 0 | 15 | 0 | 0
697 | 0 | 0 | 13 | 0 | 0
698 | 0 | 0 | 18 | 0 | 0
699 | 0 | 0 | 20 | 0 | 0
700 | 0 | 0 | 31 | 0 | 0
701 | 0 | 0 | 10 | 0 | 0
702 | 0 | 0 | 9 | 0 | 0
703 | 0 | 0 | 11 | 0 | 0
704 | 0 | 0 | 15 | 0 | 0
705 | 0 | 0 | 17 | 0 | 0
706 | 0 | 0 | 10 | 0 | 0
707 | 0 | 0 | 9 | 0 | 0
708 | 0 | 0 | 10 | 0 | 0
709 | 0 | 0 | 23 | 0 | 0
710 | 0 | 0 | 10 | 0 | 0
711 | 0 | 0 | 18 | 0 | 0
712 | 0 | 0 | 11 | 0 | 0
713 | 0 | 0 | 11 | 0 | 0
714 | 0 | 0 | 9 | 0 | 0
715 | 0 | 0 | 11 | 0 | 0
716 | 0 | 0 | 10 | 0 | 0
717 | 0 | 0 | 7 | 0 | 0
718 | 0 | 0 | 16 | 0 | 0
719 | 0 | 0 | 10 | 0 | 0
720 | 0 | 0 | 6 | 0 | 0
721 | 0 | 0 | 9 | 0 | 0
722 | 0 | 0 | 9 | 0 | 0
723 | 0 | 0 | 12 | 0 | 0
724 | 0 | 0 | 7 | 0 | 0
725 | 0 | 0 | 9 | 0 | 0
726 | 0 | 0 | 14 | 0 | 0
727 | 0 | 0 | 8 | 0 | 0
728 | 0 | 0 | 8 | 0 | 0
729 | 0 | 0 | 4 | 0 | 0
730 | 0 | 0 | 12 | 0 | 0
731 | 0 | 0 | 3 | 0 | 0
732 | 0 | 0 | 8 | 0 | 0
733 | 0 | 0 | 5 | 0 | 0
734 | 0 | 0 | 3 | 0 | 0
735 | 0 | 0 | 5 | 0 | 0
736 | 0 | 0 | 10 | 0 | 0
737 | 0 | 0 | 4 | 0 | 0
738 | 0 | 0 | 10 | 0 | 0
739 | 0 | 0 | 6 | 0 | 0
740 | 0 | 0 | 4 | 0 | 0
741 | 0 | 0 | 8 | 0 | 0
742 | 0 | 0 | 7 | 0 | 0
743 | 0 | 0 | 8 | 0 | 0
744 | 0 | 0 | 3 | 0 | 0
745 | 0 | 0 | 4 | 0 | 0
746 | 0 | 0 | 6 | 0 | 0
747 | 0 | 0 | 2 | 0 | 0
748 | 0 | 0 | 5 | 0 | 0
749 | 0 | 0 | 5 | 0 | 0
750 | 0 | 0 | 6 | 0 | 0
751 | 0 | 0 | 3 | 0 | 0
752 | 0 | 0 | 5 | 0 | 0
753 | 0 | 0 | 4 | 0 | 0
754 | 0 | 0 | 3 | 0 | 0
755 | 0 | 0 | 3 | 0 | 0
756 | 0 | 0 | 2 | 0 | 0
757 | 0 | 0 | 2 | 0 | 0
758 | 0 | 0 | 6 | 0 | 0
759 | 0 | 0 | 1 | 0 | 0
760 | 0 | 0 | 1 | 0 | 0
761 | 0 | 0 | 6 | 0 | 0
762 | 0 | 0 | 2 | 0 | 0
763 | 0 | 0 | 4 | 0 | 0
764 | 0 | 0 | 4 | 0 | 0
765 | 0 | 0 | 2 | 0 | 0
766 | 0 | 0 | 2 | 0 | 0
767 | 0 | 0 | 2 | 0 | 0
768 | 0 | 0 | 2 | 0 | 0
769 | 0 | 0 | 1 | 0 | 0
770 | 0 | 0 | 1 | 0 | 0
771 | 0 | 0 | 5 | 0 | 0
772 | 0 | 0 | 1 | 0 | 0
773 | 0 | 0 | 1 | 0 | 0
774 | 0 | 0 | 3 | 0 | 0
775 | 0 | 0 | 2 | 0 | 0
776 | 0 | 0 | 0 | 0 | 0
777 | 0 | 0 | 3 | 0 | 0
778 | 0 | 0 | 3 | 0 | 0
779 | 0 | 0 | 1 | 0 | 0
780 | 0 | 0 | 0 | 0 | 0
781 | 0 | 0 | 1 | 0 | 0
782 | 0 | 0 | 0 | 0 | 0
783 | 0 | 0 | 1 | 0 | 0
784 | 0 | 0 | 2 | 0 | 0
785 | 0 | 0 | 1 | 0 | 0
786 | 0 | 0 | 1 | 0 | 0
787 | 0 | 0 | 0 | 0 | 0
788 | 0 | 0 | 2 | 0 | 0
789 | 0 | 0 | 2 | 0 | 0
790 | 0 | 0 | 0 | 0 | 0
791 | 0 | 0 | 0 | 0 | 0
792 | 0 | 0 | 1 | 0 | 0
793 | 0 | 0 | 0 | 0 | 0
794 | 0 | 0 | 0 | 0 | 0
795 | 0 | 0 | 2 | 0 | 0
**************************************************
chosen neighbors limits: [ 24 35 354 233 69]
Calibration done in 33.9s
Starting Calibration (use verbose=True for more details)
Previous calibration found:
Check batch limit dictionary
"random_10.000_0.250_4": 20001
Check neighbors limit dictionary
"0.250_0.625": 24
"0.500_1.250": 35
"1.000_6.000": 354
"2.000_12.000": 233
"4.000_24.000": 69
Calibration done in 0.0s
Model Preparation
*****************
Done in 0.2s
Start training
**************
e000-i0000 => L=11.884 acc= 12% / t(ms): 5879.4 540.4 132.9)
e000-i0006 => L=9.903 acc= 71% / t(ms): 0.6 95.1 90.7)
e000-i0012 => L=9.432 acc= 56% / t(ms): 0.6 91.1 89.1)
e000-i0018 => L=8.345 acc= 56% / t(ms): 0.5 91.1 88.6)
e000-i0024 => L=6.867 acc= 78% / t(ms): 0.5 90.1 92.4)
e000-i0030 => L=6.602 acc= 76% / t(ms): 0.5 89.5 89.7)
e000-i0036 => L=6.338 acc= 82% / t(ms): 0.5 91.3 90.8)
e000-i0042 => L=5.990 acc= 76% / t(ms): 0.5 92.6 89.6)
e000-i0048 => L=6.122 acc= 60% / t(ms): 0.5 90.5 87.5)
e000-i0054 => L=6.363 acc= 57% / t(ms): 0.5 92.1 87.1)
e000-i0060 => L=5.932 acc= 56% / t(ms): 0.5 92.5 85.8)
e000-i0066 => L=4.704 acc= 82% / t(ms): 0.5 92.1 85.3)
e000-i0072 => L=6.151 acc= 73% / t(ms): 0.5 93.3 86.6)
e000-i0078 => L=5.078 acc= 78% / t(ms): 0.5 91.7 85.8)
e000-i0084 => L=5.348 acc= 76% / t(ms): 0.5 90.8 85.7)
e000-i0089 => L=4.916 acc= 73% / t(ms): 0.6 91.9 119.2)
e000-i0095 => L=3.722 acc= 92% / t(ms): 0.6 92.5 105.3)
e000-i0101 => L=4.323 acc= 86% / t(ms): 0.6 92.9 94.5)
e000-i0107 => L=4.097 acc= 82% / t(ms): 0.5 90.3 90.6)
e000-i0113 => L=4.236 acc= 75% / t(ms): 0.7 91.0 87.6)
e000-i0119 => L=5.085 acc= 85% / t(ms): 0.6 94.5 87.4)
e000-i0125 => L=4.579 acc= 79% / t(ms): 0.5 94.0 87.1)
e000-i0131 => L=5.377 acc= 58% / t(ms): 0.6 97.7 86.2)
e000-i0137 => L=5.015 acc= 79% / t(ms): 0.5 95.2 88.6)
e000-i0143 => L=4.035 acc= 85% / t(ms): 0.5 92.8 87.8)
e000-i0149 => L=4.213 acc= 88% / t(ms): 0.5 89.4 85.9)
e000-i0155 => L=4.573 acc= 67% / t(ms): 0.6 90.6 84.7)
e000-i0161 => L=4.079 acc= 81% / t(ms): 0.5 95.3 86.1)
e000-i0167 => L=4.180 acc= 85% / t(ms): 0.6 100.6 87.0)
e000-i0173 => L=5.872 acc= 38% / t(ms): 0.6 97.4 88.0)
e000-i0179 => L=3.926 acc= 88% / t(ms): 0.5 100.6 87.5)
e000-i0185 => L=4.443 acc= 66% / t(ms): 0.5 100.2 86.7)
e000-i0191 => L=4.128 acc= 85% / t(ms): 0.5 102.3 87.3)
e000-i0197 => L=4.184 acc= 85% / t(ms): 0.5 97.8 87.1)
e000-i0203 => L=4.159 acc= 83% / t(ms): 0.5 94.5 88.2)
e000-i0209 => L=4.431 acc= 77% / t(ms): 0.5 90.3 86.3)
e000-i0215 => L=3.964 acc= 83% / t(ms): 0.5 91.3 87.3)
e000-i0221 => L=4.175 acc= 84% / t(ms): 0.5 89.9 88.5)
e000-i0227 => L=5.604 acc= 51% / t(ms): 0.5 89.4 88.5)
e000-i0233 => L=3.665 acc= 92% / t(ms): 0.5 89.8 91.0)
e000-i0239 => L=4.036 acc= 88% / t(ms): 0.5 89.8 89.2)
e000-i0245 => L=4.366 acc= 75% / t(ms): 0.5 90.8 85.2)
e000-i0251 => L=4.653 acc= 69% / t(ms): 0.5 89.2 88.8)
e000-i0257 => L=3.956 acc= 84% / t(ms): 0.5 92.4 86.9)
e000-i0263 => L=4.219 acc= 81% / t(ms): 0.5 99.4 84.7)
e000-i0269 => L=3.944 acc= 89% / t(ms): 0.6 102.2 85.1)
e000-i0275 => L=3.688 acc= 89% / t(ms): 0.5 95.1 84.7)
e000-i0281 => L=4.962 acc= 57% / t(ms): 0.5 101.5 85.1)
e000-i0287 => L=4.364 acc= 80% / t(ms): 0.5 105.1 86.7)
e000-i0293 => L=3.929 acc= 73% / t(ms): 0.5 101.0 87.1)
e000-i0299 => L=3.881 acc= 77% / t(ms): 0.5 100.0 86.1)
e000-i0305 => L=3.819 acc= 80% / t(ms): 0.6 101.8 86.8)
e000-i0310 => L=3.902 acc= 94% / t(ms): 0.6 107.0 89.2)
e000-i0316 => L=4.751 acc= 68% / t(ms): 0.6 108.1 91.4)
e000-i0322 => L=3.716 acc= 85% / t(ms): 0.5 101.7 87.0)
e000-i0328 => L=3.748 acc= 85% / t(ms): 0.5 97.1 90.4)
e000-i0334 => L=3.445 acc= 93% / t(ms): 0.5 93.3 90.2)
e000-i0340 => L=3.953 acc= 83% / t(ms): 0.5 94.2 91.0)
e000-i0346 => L=3.555 acc= 86% / t(ms): 0.5 95.0 91.7)
e000-i0352 => L=4.309 acc= 79% / t(ms): 0.7 91.4 90.4)
e000-i0358 => L=4.337 acc= 82% / t(ms): 0.6 92.4 88.0)
e000-i0364 => L=3.702 acc= 83% / t(ms): 0.6 97.2 87.7)
e000-i0370 => L=4.338 acc= 70% / t(ms): 0.5 100.5 86.4)
e000-i0376 => L=3.974 acc= 84% / t(ms): 0.6 102.6 87.5)
e000-i0382 => L=3.644 acc= 88% / t(ms): 0.5 100.4 87.0)
e000-i0388 => L=4.484 acc= 65% / t(ms): 0.6 96.6 87.5)
e000-i0394 => L=3.507 acc= 82% / t(ms): 0.5 93.8 86.0)
e000-i0400 => L=3.639 acc= 84% / t(ms): 0.5 92.0 86.4)
e000-i0406 => L=3.330 acc= 88% / t(ms): 0.5 91.5 85.7)
e000-i0412 => L=4.530 acc= 65% / t(ms): 0.5 90.6 87.6)
e000-i0418 => L=5.494 acc= 29% / t(ms): 0.5 89.6 86.5)
e000-i0424 => L=3.699 acc= 80% / t(ms): 0.5 92.1 87.5)
e000-i0430 => L=3.585 acc= 93% / t(ms): 0.5 91.2 87.9)
e000-i0436 => L=3.841 acc= 70% / t(ms): 0.5 90.8 87.8)
e000-i0442 => L=3.335 acc= 94% / t(ms): 0.6 96.7 86.5)
e000-i0448 => L=3.729 acc= 82% / t(ms): 0.5 99.1 86.1)
e000-i0453 => L=3.874 acc= 89% / t(ms): 0.5 105.7 88.9)
e000-i0459 => L=3.645 acc= 90% / t(ms): 0.5 96.7 86.6)
e000-i0465 => L=3.646 acc= 88% / t(ms): 0.5 95.6 87.6)
e000-i0471 => L=3.521 acc= 77% / t(ms): 0.5 92.0 87.3)
e000-i0477 => L=3.965 acc= 87% / t(ms): 0.5 92.7 89.0)
e000-i0483 => L=3.235 acc= 89% / t(ms): 0.5 91.5 85.7)
e000-i0489 => L=3.755 acc= 82% / t(ms): 0.5 89.4 88.1)
e000-i0495 => L=3.583 acc= 84% / t(ms): 0.4 86.4 89.0)
Traceback (most recent call last):
File "/home/op/users/shibolet/segmentation/KPConv-PyTorch/train_dales.py", line 277, in <module>
trainer.train(net, training_loader, test_loader, config)
File "/home/op/users/shibolet/segmentation/KPConv-PyTorch/utils/trainer.py", line 271, in train
self.validation(net, val_loader, config)
File "/home/op/users/shibolet/segmentation/KPConv-PyTorch/utils/trainer.py", line 287, in validation
self.cloud_segmentation_validation(net, val_loader, config)
File "/home/op/users/shibolet/segmentation/KPConv-PyTorch/utils/trainer.py", line 468, in cloud_segmentation_validation
for i, batch in enumerate(val_loader):
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 352, in __iter__
return self._get_iterator()
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 827, in __init__
self._reset(loader, first_iter=True)
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 857, in _reset
self._try_put_index()
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1091, in _try_put_index
index = self._next_index()
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/anaconda/envs/seg/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
for idx in self.sampler:
File "/home/op/users/shibolet/segmentation/KPConv-PyTorch/datasets/dales.py", line 911, in __iter__
self.dataset.epoch_inds += torch.from_numpy(all_epoch_inds[:, :num_centers])
RuntimeError: The size of tensor a (2000) must match the size of tensor b (200) at non-singleton dimension 1
Thanks very much.
eyalol commented
self.dataset.epoch_inds is of shape (2, 2000) while all_epoch_inds[:, num_centers]) is of shape (2, 200).
HuguesTHOMAS commented
self.dataset.epoch_inds is initialized in the beginning (in the dataset __init__ function). And num_centers is defined while the code is running. They both should be defined in the same way but it seems they are not in your dataset implementation. Especially, it seems the error occurs when you start the validation process. The number of epoch_inds should be small during validation so for me, num_centers is right and the mistake comes from self.dataset.epoch_inds. Check where this variable is defined if you do it right especially in the case of the validation dataset.
N.B. I close this issue here because this is a KPConv-PyTorch issue. Reopen an issue on the KPConv-PyTorch repo if you have further problems.