Upload folder using huggingface_hub (part 10)
Browse files- .gitattributes +2 -0
- results_resnet_augment/images/000040_02_plot.pdf +0 -0
- results_resnet_augment/images/000040_03_plot.pdf +0 -0
- results_resnet_augment/images/000040_04_plot.pdf +0 -0
- results_resnet_augment/images/000040_05_plot.pdf +0 -0
- results_resnet_augment/images/000050_00_plot.pdf +0 -0
- results_resnet_augment/images/000050_01_plot.pdf +0 -0
- results_resnet_augment/images/000050_02_plot.pdf +0 -0
- results_resnet_augment/images/000050_03_plot.pdf +0 -0
- results_resnet_augment/images/000050_04_plot.pdf +0 -0
- results_resnet_augment/images/000050_05_plot.pdf +0 -0
- results_resnet_augment/images/overview_accuracy.pdf +0 -0
- results_resnet_augment/images/overview_f2.pdf +0 -0
- results_resnet_augment/results/summary.txt +360 -0
- results_resnet_augment/results/summary_rev.txt +360 -0
- results_resnet_augment/uncertainties/000000_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000000_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000000_epistemic_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000010_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000010_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000010_epistemic_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000020_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000020_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000020_epistemic_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000030_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000030_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000030_epistemic_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000040_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000040_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000040_epistemic_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000050_aleatoric_diag.pkl +3 -0
- results_resnet_augment/uncertainties/000050_confusion.pkl +3 -0
- results_resnet_augment/uncertainties/000050_epistemic_diag.pkl +3 -0
- test_0_1F.txt +0 -0
- test_1_2F.txt +0 -0
- test_2_3C.txt +0 -0
- test_3_3C2.txt +0 -0
- test_4_3F.txt +3 -0
- test_5_5F6F.txt +1458 -0
- test_resnet.py +172 -0
- train_0_1F.txt +0 -0
- train_1_2F.txt +0 -0
- train_2_3C.txt +0 -0
- train_3_3C2.txt +0 -0
- train_4_3F.txt +3 -0
- train_5_5F6F.txt +0 -0
- train_resnet.py +193 -0
- uncertainty.py +117 -0
- utilities.py +151 -0
.gitattributes
CHANGED
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@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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test_4_3F.txt filter=lfs diff=lfs merge=lfs -text
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train_4_3F.txt filter=lfs diff=lfs merge=lfs -text
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results_resnet_augment/images/000040_02_plot.pdf
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results_resnet_augment/images/000040_03_plot.pdf
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results_resnet_augment/images/000040_04_plot.pdf
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results_resnet_augment/images/000040_05_plot.pdf
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results_resnet_augment/images/000050_01_plot.pdf
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results_resnet_augment/images/000050_02_plot.pdf
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results_resnet_augment/images/000050_03_plot.pdf
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results_resnet_augment/images/000050_04_plot.pdf
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results_resnet_augment/images/000050_05_plot.pdf
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results_resnet_augment/images/overview_accuracy.pdf
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results_resnet_augment/images/overview_f2.pdf
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results_resnet_augment/results/summary.txt
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0.00062 0.06203 0.11682 0.24851 0.63230 0.06203 1.00000
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| 273 |
+
0.32538 0.98623 0.99307 0.99722 0.99946 0.98623 1.00000
|
| 274 |
+
0.16117 0.77587 0.87379 0.94538 0.98901 0.77587 1.00000
|
| 275 |
+
0.12924 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 276 |
+
0.96433 0.99451 0.99587 0.99340 0.99208 1.00000 0.99177
|
| 277 |
+
0.00005 0.00140 0.00279 0.00695 0.03510 0.00140 1.00000
|
| 278 |
+
0.00000 0.06203 0.11682 0.24851 0.63230 0.06203 1.00000
|
| 279 |
+
0.44955 0.98623 0.99307 0.99722 0.99946 0.98623 1.00000
|
| 280 |
+
0.22066 0.77587 0.87379 0.94538 0.98901 0.77587 1.00000
|
| 281 |
+
0.19952 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 282 |
+
0.97051 0.99588 0.99690 0.99506 0.99406 1.00000 0.99383
|
| 283 |
+
0.00008 0.00140 0.00279 0.00695 0.03510 0.00140 1.00000
|
| 284 |
+
0.00093 0.06203 0.11682 0.24851 0.63230 0.06203 1.00000
|
| 285 |
+
0.63129 0.98623 0.99307 0.99722 0.99946 0.98623 1.00000
|
| 286 |
+
0.26826 0.77587 0.87379 0.94538 0.98901 0.77587 1.00000
|
| 287 |
+
0.12924 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 288 |
+
0.97051 0.99520 0.99639 0.99423 0.99307 1.00000 0.99280
|
| 289 |
+
0.00008 0.00140 0.00279 0.00695 0.03510 0.00140 1.00000
|
| 290 |
+
0.00093 0.06203 0.11682 0.24851 0.63230 0.06203 1.00000
|
| 291 |
+
0.71961 0.98623 0.99307 0.99722 0.99946 0.98623 1.00000
|
| 292 |
+
0.30019 0.77587 0.87379 0.94538 0.98901 0.77587 1.00000
|
| 293 |
+
0.17931 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 294 |
+
0.96296 0.99588 0.99690 0.99506 0.99406 1.00000 0.99383
|
| 295 |
+
0.00008 0.00140 0.00279 0.00695 0.03510 0.00140 1.00000
|
| 296 |
+
0.00558 0.06203 0.11682 0.24851 0.63230 0.06203 1.00000
|
| 297 |
+
0.64542 0.98623 0.99307 0.99722 0.99946 0.98623 1.00000
|
| 298 |
+
0.24550 0.77587 0.87379 0.94538 0.98901 0.77587 1.00000
|
| 299 |
+
0.13517 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 300 |
+
0.96091 0.99588 0.99690 0.99506 0.99406 1.00000 0.99383
|
| 301 |
+
0.08085 0.08164 0.00281 0.00700 0.03529 0.00141 0.92727
|
| 302 |
+
0.32320 0.32320 0.15492 0.31427 0.70442 0.08396 1.00000
|
| 303 |
+
0.98469 0.99739 0.99867 0.99788 0.99745 1.00000 0.99735
|
| 304 |
+
0.97848 0.99183 0.99471 0.99180 0.99023 0.99960 0.98986
|
| 305 |
+
0.52493 0.75157 0.85817 0.93799 0.98745 0.75157 1.00000
|
| 306 |
+
0.88889 0.95816 0.96760 0.94916 0.93951 1.00000 0.93724
|
| 307 |
+
0.99337 0.99385 0.21429 0.34884 0.52703 0.13043 0.60000
|
| 308 |
+
0.94355 0.94386 0.60394 0.65279 0.68252 0.53696 0.69000
|
| 309 |
+
0.95726 0.99632 0.99813 0.99716 0.99663 0.99976 0.99651
|
| 310 |
+
0.96166 0.99022 0.99367 0.99118 0.98985 0.99785 0.98953
|
| 311 |
+
0.79092 0.84884 0.88903 0.83707 0.81153 0.99163 0.80567
|
| 312 |
+
0.88134 0.98697 0.99013 0.98430 0.98119 1.00000 0.98045
|
| 313 |
+
0.99909 0.99924 0.62500 0.51020 0.46429 1.00000 0.45455
|
| 314 |
+
0.98976 0.98976 0.91105 0.87024 0.84974 0.98830 0.84500
|
| 315 |
+
0.98979 0.99620 0.99807 0.99721 0.99674 0.99952 0.99663
|
| 316 |
+
0.97160 0.98965 0.99332 0.99204 0.99136 0.99545 0.99120
|
| 317 |
+
0.99918 0.99966 0.99977 0.99969 0.99964 0.99992 0.99963
|
| 318 |
+
0.89781 0.95953 0.96870 0.95084 0.94150 1.00000 0.93930
|
| 319 |
+
0.99848 0.99883 0.64062 0.69966 0.73619 0.56164 0.74545
|
| 320 |
+
0.98914 0.98914 0.91003 0.89484 0.88688 0.93651 0.88500
|
| 321 |
+
0.99395 0.99739 0.99868 0.99831 0.99812 0.99928 0.99807
|
| 322 |
+
0.98241 0.99436 0.99636 0.99546 0.99498 0.99786 0.99487
|
| 323 |
+
0.98851 0.99824 0.99883 0.99825 0.99793 0.99980 0.99786
|
| 324 |
+
0.86557 0.94307 0.95540 0.93050 0.91762 1.00000 0.91461
|
| 325 |
+
0.99792 0.99825 0.44800 0.48276 0.50381 0.40000 0.50909
|
| 326 |
+
0.98418 0.98480 0.88525 0.92016 0.94012 0.83260 0.94500
|
| 327 |
+
0.98219 0.99774 0.99886 0.99817 0.99780 1.00000 0.99771
|
| 328 |
+
0.96673 0.99333 0.99568 0.99355 0.99240 0.99926 0.99213
|
| 329 |
+
0.99824 0.99954 0.99969 0.99954 0.99945 0.99995 0.99943
|
| 330 |
+
0.82785 0.94170 0.95428 0.92880 0.91563 1.00000 0.91255
|
| 331 |
+
0.99886 0.99901 0.53012 0.44355 0.40770 0.78571 0.40000
|
| 332 |
+
0.99038 0.99038 0.92072 0.90817 0.90156 0.94241 0.90000
|
| 333 |
+
0.97234 0.99454 0.99722 0.99557 0.99467 1.00000 0.99446
|
| 334 |
+
0.94226 0.99069 0.99396 0.99054 0.98870 0.99973 0.98826
|
| 335 |
+
0.95776 0.99278 0.99518 0.99234 0.99082 0.99994 0.99046
|
| 336 |
+
0.86694 0.99177 0.99379 0.99010 0.98812 1.00000 0.98765
|
| 337 |
+
0.84602 0.84646 0.01499 0.03650 0.16039 0.00756 0.83636
|
| 338 |
+
0.09801 0.11166 0.11877 0.25065 0.62335 0.06328 0.96500
|
| 339 |
+
0.98813 0.99739 0.99868 0.99839 0.99823 0.99916 0.99819
|
| 340 |
+
0.97377 0.99286 0.99539 0.99407 0.99337 0.99759 0.99320
|
| 341 |
+
0.90133 0.94208 0.96217 0.97278 0.97859 0.94499 0.97998
|
| 342 |
+
0.86763 0.96433 0.97252 0.95674 0.94845 1.00000 0.94650
|
| 343 |
+
0.99883 0.99893 0.46154 0.37037 0.33476 0.78261 0.32727
|
| 344 |
+
0.97922 0.98015 0.81176 0.73404 0.69805 0.98571 0.69000
|
| 345 |
+
0.98172 0.98754 0.99364 0.98986 0.98784 1.00000 0.98736
|
| 346 |
+
0.95349 0.98888 0.99279 0.98902 0.98701 0.99912 0.98653
|
| 347 |
+
0.90184 0.93267 0.95326 0.92897 0.91639 0.99669 0.91345
|
| 348 |
+
0.87243 0.95405 0.96430 0.94409 0.93354 1.00000 0.93107
|
| 349 |
+
0.95347 0.95385 0.04419 0.10165 0.33903 0.02275 0.76364
|
| 350 |
+
0.97984 0.98015 0.83420 0.81643 0.80717 0.86559 0.80500
|
| 351 |
+
0.87547 0.99561 0.99777 0.99643 0.99572 1.00000 0.99555
|
| 352 |
+
0.89740 0.99270 0.99529 0.99476 0.99447 0.99619 0.99440
|
| 353 |
+
0.97324 0.99656 0.99771 0.99646 0.99578 0.99980 0.99562
|
| 354 |
+
0.81687 0.93621 0.95362 0.97135 0.98117 0.92546 0.98354
|
| 355 |
+
0.75819 0.75857 0.00752 0.01848 0.08590 0.00378 0.65455
|
| 356 |
+
0.98449 0.98542 0.86981 0.81686 0.79093 0.97516 0.78500
|
| 357 |
+
0.91346 0.99537 0.99765 0.99631 0.99560 0.99988 0.99543
|
| 358 |
+
0.94862 0.99224 0.99498 0.99287 0.99173 0.99852 0.99146
|
| 359 |
+
0.93768 0.97541 0.98337 0.97369 0.96855 0.99994 0.96734
|
| 360 |
+
0.79081 0.92936 0.94853 0.96502 0.97414 0.92225 0.97634
|
results_resnet_augment/results/summary_rev.txt
ADDED
|
@@ -0,0 +1,360 @@
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|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
0.99919 0.99890 0.99874 0.99967 0.99870
|
| 2 |
+
0.99375 0.99710 0.99891 0.98823 0.99934
|
| 3 |
+
0.06346 0.14486 0.46832 0.03277 1.00000
|
| 4 |
+
0.62897 0.80908 0.95659 0.45875 1.00000
|
| 5 |
+
0.49580 0.71079 0.92731 0.32963 0.99983
|
| 6 |
+
0.50000 0.71429 0.92857 0.33333 1.00000
|
| 7 |
+
0.99972 0.99980 0.99984 0.99959 0.99985
|
| 8 |
+
0.98775 0.99506 0.99905 0.97580 1.00000
|
| 9 |
+
0.05415 0.12522 0.42671 0.02783 1.00000
|
| 10 |
+
0.53515 0.74214 0.93737 0.36532 1.00000
|
| 11 |
+
0.48921 0.70534 0.92552 0.32383 0.99983
|
| 12 |
+
0.50000 0.71429 0.92857 0.33333 1.00000
|
| 13 |
+
0.99971 0.99965 0.99963 0.99980 0.99962
|
| 14 |
+
0.98822 0.99467 0.99817 0.97767 0.99901
|
| 15 |
+
0.11658 0.24808 0.63176 0.06190 1.00000
|
| 16 |
+
0.78075 0.89902 0.97886 0.64035 1.00000
|
| 17 |
+
0.55156 0.75454 0.94100 0.38082 0.99983
|
| 18 |
+
0.50784 0.72064 0.93062 0.34034 1.00000
|
| 19 |
+
0.99892 0.99842 0.99816 0.99975 0.99809
|
| 20 |
+
0.98678 0.99467 0.99897 0.97391 1.00000
|
| 21 |
+
0.07822 0.17502 0.52452 0.04070 1.00000
|
| 22 |
+
0.57618 0.77266 0.94645 0.40467 1.00000
|
| 23 |
+
0.51824 0.72889 0.93313 0.34977 0.99983
|
| 24 |
+
0.50546 0.71872 0.93001 0.33820 1.00000
|
| 25 |
+
0.99947 0.99928 0.99918 0.99977 0.99916
|
| 26 |
+
0.98984 0.99492 0.99769 0.98147 0.99835
|
| 27 |
+
0.07426 0.16705 0.51049 0.03856 1.00000
|
| 28 |
+
0.66142 0.83004 0.96212 0.49413 1.00000
|
| 29 |
+
0.50891 0.72145 0.93076 0.34132 0.99983
|
| 30 |
+
0.50000 0.71429 0.92857 0.33333 1.00000
|
| 31 |
+
0.99882 0.99821 0.99789 0.99982 0.99781
|
| 32 |
+
0.98822 0.99427 0.99756 0.97829 0.99835
|
| 33 |
+
0.09404 0.20604 0.57437 0.04934 1.00000
|
| 34 |
+
0.73761 0.87543 0.97337 0.58430 1.00000
|
| 35 |
+
0.55032 0.75361 0.94072 0.37964 0.99983
|
| 36 |
+
0.51374 0.72537 0.93213 0.34566 1.00000
|
| 37 |
+
0.99958 0.99944 0.99936 0.99982 0.99934
|
| 38 |
+
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results_resnet_augment/uncertainties/000000_aleatoric_diag.pkl
ADDED
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test_1_2F.txt
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test_4_3F.txt
ADDED
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test_5_5F6F.txt
ADDED
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Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00069_1.npy 6
|
| 1305 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00069_2.npy 6
|
| 1306 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00069_3.npy 6
|
| 1307 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00069_4.npy 6
|
| 1308 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00069_5.npy 6
|
| 1309 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_0.npy 6
|
| 1310 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_1.npy 6
|
| 1311 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_2.npy 6
|
| 1312 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_3.npy 6
|
| 1313 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_4.npy 6
|
| 1314 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00070_5.npy 6
|
| 1315 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_0.npy 6
|
| 1316 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_1.npy 6
|
| 1317 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_2.npy 6
|
| 1318 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_3.npy 6
|
| 1319 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_4.npy 6
|
| 1320 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00071_5.npy 6
|
| 1321 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_0.npy 6
|
| 1322 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_1.npy 6
|
| 1323 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_2.npy 6
|
| 1324 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_3.npy 6
|
| 1325 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_4.npy 6
|
| 1326 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00072_5.npy 6
|
| 1327 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_0.npy 4
|
| 1328 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_1.npy 4
|
| 1329 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_2.npy 4
|
| 1330 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_3.npy 4
|
| 1331 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_4.npy 4
|
| 1332 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01112_5.npy 4
|
| 1333 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_0.npy 4
|
| 1334 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_1.npy 4
|
| 1335 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_2.npy 4
|
| 1336 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_3.npy 4
|
| 1337 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_4.npy 4
|
| 1338 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01024_5.npy 4
|
| 1339 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_0.npy 4
|
| 1340 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_1.npy 4
|
| 1341 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_2.npy 4
|
| 1342 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_3.npy 4
|
| 1343 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_4.npy 4
|
| 1344 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00827_5.npy 4
|
| 1345 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_0.npy 4
|
| 1346 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_1.npy 4
|
| 1347 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_2.npy 4
|
| 1348 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_3.npy 4
|
| 1349 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_4.npy 4
|
| 1350 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01012_5.npy 4
|
| 1351 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_0.npy 4
|
| 1352 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_1.npy 4
|
| 1353 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_2.npy 4
|
| 1354 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_3.npy 4
|
| 1355 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_4.npy 4
|
| 1356 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01124_5.npy 4
|
| 1357 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_0.npy 4
|
| 1358 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_1.npy 4
|
| 1359 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_2.npy 4
|
| 1360 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_3.npy 4
|
| 1361 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_4.npy 4
|
| 1362 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01115_5.npy 4
|
| 1363 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_0.npy 4
|
| 1364 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_1.npy 4
|
| 1365 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_2.npy 4
|
| 1366 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_3.npy 4
|
| 1367 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_4.npy 4
|
| 1368 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01008_5.npy 4
|
| 1369 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_0.npy 4
|
| 1370 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_1.npy 4
|
| 1371 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_2.npy 4
|
| 1372 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_3.npy 4
|
| 1373 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_4.npy 4
|
| 1374 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00823_5.npy 4
|
| 1375 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_0.npy 4
|
| 1376 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_1.npy 4
|
| 1377 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_2.npy 4
|
| 1378 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_3.npy 4
|
| 1379 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_4.npy 4
|
| 1380 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01009_5.npy 4
|
| 1381 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_0.npy 4
|
| 1382 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_1.npy 4
|
| 1383 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_2.npy 4
|
| 1384 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_3.npy 4
|
| 1385 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_4.npy 4
|
| 1386 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00923_5.npy 4
|
| 1387 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_0.npy 4
|
| 1388 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_1.npy 4
|
| 1389 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_2.npy 4
|
| 1390 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_3.npy 4
|
| 1391 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_4.npy 4
|
| 1392 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01111_5.npy 4
|
| 1393 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_0.npy 4
|
| 1394 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_1.npy 4
|
| 1395 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_2.npy 4
|
| 1396 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_3.npy 4
|
| 1397 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_4.npy 4
|
| 1398 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00831_5.npy 4
|
| 1399 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_0.npy 4
|
| 1400 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_1.npy 4
|
| 1401 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_2.npy 4
|
| 1402 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_3.npy 4
|
| 1403 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_4.npy 4
|
| 1404 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01028_5.npy 4
|
| 1405 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_0.npy 4
|
| 1406 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_1.npy 4
|
| 1407 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_2.npy 4
|
| 1408 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_3.npy 4
|
| 1409 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_4.npy 4
|
| 1410 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00821_5.npy 4
|
| 1411 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_0.npy 4
|
| 1412 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_1.npy 4
|
| 1413 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_2.npy 4
|
| 1414 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_3.npy 4
|
| 1415 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_4.npy 4
|
| 1416 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00818_5.npy 4
|
| 1417 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_0.npy 4
|
| 1418 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_1.npy 4
|
| 1419 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_2.npy 4
|
| 1420 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_3.npy 4
|
| 1421 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_4.npy 4
|
| 1422 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00917_5.npy 4
|
| 1423 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_0.npy 4
|
| 1424 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_1.npy 4
|
| 1425 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_2.npy 4
|
| 1426 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_3.npy 4
|
| 1427 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_4.npy 4
|
| 1428 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00934_5.npy 4
|
| 1429 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_0.npy 4
|
| 1430 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_1.npy 4
|
| 1431 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_2.npy 4
|
| 1432 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_3.npy 4
|
| 1433 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_4.npy 4
|
| 1434 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01128_5.npy 4
|
| 1435 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_0.npy 4
|
| 1436 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_1.npy 4
|
| 1437 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_2.npy 4
|
| 1438 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_3.npy 4
|
| 1439 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_4.npy 4
|
| 1440 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00819_5.npy 4
|
| 1441 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_0.npy 4
|
| 1442 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_1.npy 4
|
| 1443 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_2.npy 4
|
| 1444 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_3.npy 4
|
| 1445 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_4.npy 4
|
| 1446 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/01123_5.npy 4
|
| 1447 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_0.npy 7
|
| 1448 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_1.npy 7
|
| 1449 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_2.npy 7
|
| 1450 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_3.npy 7
|
| 1451 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_4.npy 7
|
| 1452 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00188_5.npy 7
|
| 1453 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_0.npy 7
|
| 1454 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_1.npy 7
|
| 1455 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_2.npy 7
|
| 1456 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_3.npy 7
|
| 1457 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_4.npy 7
|
| 1458 |
+
Spectrum_LC_laboratory/GNSS_dataset/data_FIOT/data/00074_5.npy 7
|
test_resnet.py
ADDED
|
@@ -0,0 +1,172 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import random
|
| 2 |
+
import numpy as np
|
| 3 |
+
import torch
|
| 4 |
+
from tqdm import tqdm
|
| 5 |
+
from torch.utils.data import DataLoader
|
| 6 |
+
import argparse
|
| 7 |
+
import os
|
| 8 |
+
import time
|
| 9 |
+
from torch import nn
|
| 10 |
+
from torch.optim import SGD, Optimizer
|
| 11 |
+
from torch.optim.lr_scheduler import MultiStepLR
|
| 12 |
+
from torch.utils.data import Dataset
|
| 13 |
+
import torch.nn.functional as F
|
| 14 |
+
from glob import glob
|
| 15 |
+
import utilities
|
| 16 |
+
|
| 17 |
+
torch.autograd.set_detect_anomaly(True)
|
| 18 |
+
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
| 19 |
+
print(device)
|
| 20 |
+
parser = argparse.ArgumentParser()
|
| 21 |
+
parser.add_argument('--folder', help='Folder of the runs. Either resnet or resnet_augment', required=False, default="resnet")
|
| 22 |
+
parser.add_argument('--run_num', help='Number of run', required=False, default="0")
|
| 23 |
+
parser.add_argument('--use_min_loss', help='Whether to use the epoch with min loss for testing or just the last epoch', required=False, default="0")
|
| 24 |
+
|
| 25 |
+
args = parser.parse_args()
|
| 26 |
+
cfolder = str(args.folder)
|
| 27 |
+
run_num = int(args.run_num)
|
| 28 |
+
use_min_loss = int(args.use_min_loss)
|
| 29 |
+
|
| 30 |
+
min_max = {0: [-133.38071, 0.0], 1: [-135.69371, 0.0], 2: [-182.77412, -20.55280], 3: [-184.71960, -18.33420], 4: [-136.01241, 27.37697], 5: [0.0, 255.0]}
|
| 31 |
+
norm_dict = {key: [val[0], val[1] - val[0]] for key, val in min_max.items()}
|
| 32 |
+
bp = "./"
|
| 33 |
+
all_files = {0: bp + "test_0_1F.txt", 1: bp + "test_1_2F.txt", 2: bp + "test_2_3C.txt", 3: bp + "test_3_3C2.txt", 4: bp + "test_4_3F.txt", 5: bp + "test_5_5F6F.txt"}
|
| 34 |
+
time_size = 243
|
| 35 |
+
num_classes = 9
|
| 36 |
+
|
| 37 |
+
# Create Folders
|
| 38 |
+
time.sleep(np.random.randint(5))
|
| 39 |
+
train_output_folder = "results_" + cfolder
|
| 40 |
+
folder = glob(bp + train_output_folder + "/%06d_202*" % run_num)[0]
|
| 41 |
+
resnet_to_use = int(folder.split("_")[-1])
|
| 42 |
+
use_norm = int(folder.split("_")[-2])
|
| 43 |
+
weights = glob(folder + "/weights/*.h5")
|
| 44 |
+
weights.sort()
|
| 45 |
+
if len(weights) == 0:
|
| 46 |
+
print("Skipped %d: Training not finished." % training_num)
|
| 47 |
+
exit()
|
| 48 |
+
if not os.path.exists(folder + "/test/"):
|
| 49 |
+
os.makedirs(folder + "/test/")
|
| 50 |
+
else:
|
| 51 |
+
print("Skipped %d: Already Tested." % run_num)
|
| 52 |
+
exit()
|
| 53 |
+
# For getting best weights --- #
|
| 54 |
+
start_we = -1
|
| 55 |
+
for wi, we in enumerate(weights):
|
| 56 |
+
if use_min_loss:
|
| 57 |
+
if "_min_loss.h5" in we:
|
| 58 |
+
start_we = wi
|
| 59 |
+
else:
|
| 60 |
+
if "_min_loss.h5" not in we:
|
| 61 |
+
start_we = wi
|
| 62 |
+
startweight = weights[start_we]
|
| 63 |
+
print(startweight)
|
| 64 |
+
|
| 65 |
+
# Load Model #
|
| 66 |
+
if resnet_to_use == 18:
|
| 67 |
+
from torchvision.models import resnet18
|
| 68 |
+
model = resnet18(weights='IMAGENET1K_V1')
|
| 69 |
+
model.fc = nn.Linear(512, num_classes)
|
| 70 |
+
model = model.to(device)
|
| 71 |
+
model.load_state_dict(torch.load(startweight, map_location=device))
|
| 72 |
+
model.eval()
|
| 73 |
+
elif resnet_to_use == 34:
|
| 74 |
+
from torchvision.models import resnet34
|
| 75 |
+
model = resnet34(weights='IMAGENET1K_V1')
|
| 76 |
+
model.fc = nn.Linear(512, num_classes)
|
| 77 |
+
model = model.to(device)
|
| 78 |
+
model.load_state_dict(torch.load(startweight, map_location=device))
|
| 79 |
+
model.eval()
|
| 80 |
+
elif resnet_to_use == 50:
|
| 81 |
+
from torchvision.models import resnet50
|
| 82 |
+
model = resnet50(weights='IMAGENET1K_V1')
|
| 83 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 84 |
+
model = model.to(device)
|
| 85 |
+
model.load_state_dict(torch.load(startweight, map_location=device))
|
| 86 |
+
model.eval()
|
| 87 |
+
elif resnet_to_use == 101:
|
| 88 |
+
from torchvision.models import resnet101
|
| 89 |
+
model = resnet101(weights='IMAGENET1K_V1')
|
| 90 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 91 |
+
model = model.to(device)
|
| 92 |
+
model.load_state_dict(torch.load(startweight, map_location=device))
|
| 93 |
+
model.eval()
|
| 94 |
+
elif resnet_to_use == 152:
|
| 95 |
+
from torchvision.models import resnet152
|
| 96 |
+
model = resnet152(weights='IMAGENET1K_V1')
|
| 97 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 98 |
+
model = model.to(device)
|
| 99 |
+
model.load_state_dict(torch.load(startweight, map_location=device))
|
| 100 |
+
model.eval()
|
| 101 |
+
else:
|
| 102 |
+
print("Invalid Resnet Number: ", resnet_to_use)
|
| 103 |
+
exit()
|
| 104 |
+
# ---------- #
|
| 105 |
+
|
| 106 |
+
for ts_key, ts_value in all_files.items():
|
| 107 |
+
print("Dealing with %d: " % ts_key, ts_value)
|
| 108 |
+
# Data Loader #
|
| 109 |
+
data_set_test = utilities.get_dataset([ts_value], norm_dict, norm=True)
|
| 110 |
+
test_loader = DataLoader(data_set_test, batch_size=1, shuffle=False, num_workers=0, pin_memory=True, drop_last=True)
|
| 111 |
+
# ----------- #
|
| 112 |
+
|
| 113 |
+
save_file = folder + "/test/summary_%d.txt" % ts_key
|
| 114 |
+
save_file_rev = folder + "/test/summary_rev_%d.txt" % ts_key
|
| 115 |
+
save_file_all_vals = folder + "/test/preds_%d.npy" % ts_key
|
| 116 |
+
save_file_all_vals_classes = folder + "/test/labs_%d.npy" % ts_key
|
| 117 |
+
preds = []
|
| 118 |
+
labs = []
|
| 119 |
+
total_predictions = 0
|
| 120 |
+
correct_predictions = 0
|
| 121 |
+
with torch.no_grad():
|
| 122 |
+
with tqdm(enumerate(test_loader), total=len(test_loader)) as tqdm_eval:
|
| 123 |
+
for vi, (x_dat, y_dat) in tqdm_eval:
|
| 124 |
+
y_batch = y_dat.numpy()
|
| 125 |
+
predictions = model(x_dat.to(device)).float().detach().cpu().numpy()
|
| 126 |
+
preds.extend(predictions)
|
| 127 |
+
labs.extend(y_batch)
|
| 128 |
+
number_of_correct_predictions = np.sum(np.argmax(predictions, axis=-1) == y_batch)
|
| 129 |
+
total_predictions += len(y_batch)
|
| 130 |
+
correct_predictions += number_of_correct_predictions
|
| 131 |
+
tqdm_eval.set_postfix(accuracy=correct_predictions / total_predictions)
|
| 132 |
+
multi_acc = correct_predictions / total_predictions
|
| 133 |
+
preds = np.array(preds)
|
| 134 |
+
labs = np.array(labs)
|
| 135 |
+
np.save(save_file_all_vals, preds)
|
| 136 |
+
np.save(save_file_all_vals_classes, labs)
|
| 137 |
+
|
| 138 |
+
pred_bool = np.argmax(preds, axis=-1) > 0
|
| 139 |
+
lab_bool = labs > 0
|
| 140 |
+
acc = np.mean(pred_bool == lab_bool)
|
| 141 |
+
true_positives = np.sum(pred_bool * lab_bool)
|
| 142 |
+
true_negatives = np.sum((~pred_bool) * (~lab_bool))
|
| 143 |
+
false_positives = np.sum(pred_bool * (~lab_bool))
|
| 144 |
+
false_negatives = np.sum((~pred_bool) * lab_bool)
|
| 145 |
+
precision = true_positives/(true_positives + false_positives)
|
| 146 |
+
recall = true_positives/(true_positives + false_negatives)
|
| 147 |
+
f1_score = (2 * precision * recall) / (precision + recall)
|
| 148 |
+
f2_score = ((1 + 2**2) * precision * recall) / (2**2 * precision + recall)
|
| 149 |
+
f5_score = ((1 + 5**2) * precision * recall) / (5**2 * precision + recall)
|
| 150 |
+
print('Test Multi Acc: %.5f, Acc: %.5f, F1: %.5f, F2: %.5f, F5: %.5f, Prec: %.5f, Rec: %.5f' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall))
|
| 151 |
+
with open(save_file, "w") as f:
|
| 152 |
+
f.write('MAcc\tAcc\tF1\tF2\tF5\tPrec\tRec\n')
|
| 153 |
+
f.write('%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\n' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall))
|
| 154 |
+
# Same thing for reverse F1 Score (Swap positive and negative classes)
|
| 155 |
+
pred_bool = np.argmax(preds, axis=-1) == 0
|
| 156 |
+
lab_bool = labs == 0
|
| 157 |
+
acc = np.mean(pred_bool == lab_bool)
|
| 158 |
+
true_positives = np.sum(pred_bool * lab_bool)
|
| 159 |
+
true_negatives = np.sum((~pred_bool) * (~lab_bool))
|
| 160 |
+
false_positives = np.sum(pred_bool * (~lab_bool))
|
| 161 |
+
false_negatives = np.sum((~pred_bool) * lab_bool)
|
| 162 |
+
precision = true_positives/(true_positives + false_positives)
|
| 163 |
+
recall = true_positives/(true_positives + false_negatives)
|
| 164 |
+
f1_score = (2 * precision * recall) / (precision + recall)
|
| 165 |
+
f2_score = ((1 + 2**2) * precision * recall) / (2**2 * precision + recall)
|
| 166 |
+
f5_score = ((1 + 5**2) * precision * recall) / (5**2 * precision + recall)
|
| 167 |
+
print('Test Multi Acc: %.5f, Acc: %.5f, F1: %.5f, F2: %.5f, F5: %.5f, Prec: %.5f, Rec: %.5f' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall))
|
| 168 |
+
with open(save_file_rev, "w") as f:
|
| 169 |
+
f.write('MAcc\tAcc\tF1\tF2\tF5\tPrec\tRec\n')
|
| 170 |
+
f.write('%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\n' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall))
|
| 171 |
+
print("Done")
|
| 172 |
+
|
train_0_1F.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
train_1_2F.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
train_2_3C.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
train_3_3C2.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
train_4_3F.txt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c12d0eca370f75ceb2c8a29caf14670863e73ee5c303b344f45e86dbca2df73b
|
| 3 |
+
size 109927492
|
train_5_5F6F.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
train_resnet.py
ADDED
|
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import random
|
| 2 |
+
import numpy as np
|
| 3 |
+
import torch
|
| 4 |
+
from tqdm import tqdm
|
| 5 |
+
from torch.utils.data import DataLoader
|
| 6 |
+
import argparse
|
| 7 |
+
import os
|
| 8 |
+
import time
|
| 9 |
+
from torch import nn
|
| 10 |
+
from torch.optim import SGD, Optimizer
|
| 11 |
+
from torch.optim.lr_scheduler import MultiStepLR
|
| 12 |
+
from torch.utils.data import Dataset
|
| 13 |
+
import torch.nn.functional as F
|
| 14 |
+
from glob import glob
|
| 15 |
+
import utilities
|
| 16 |
+
|
| 17 |
+
torch.autograd.set_detect_anomaly(True)
|
| 18 |
+
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
| 19 |
+
print(device)
|
| 20 |
+
parser = argparse.ArgumentParser()
|
| 21 |
+
parser.add_argument('--timestamps', help='Epochs to evaluate and save weights at', required=False, default=1000)
|
| 22 |
+
parser.add_argument('--epochs', help='Number of epochs', required=False, default="100")
|
| 23 |
+
parser.add_argument('--run_num', help='Number of run', required=False, default="0")
|
| 24 |
+
parser.add_argument('--norm', help='Use Norm', required=False, default="1")
|
| 25 |
+
parser.add_argument('--seed', help='Seed for the randomness', required=False, default="0")
|
| 26 |
+
parser.add_argument('--class_lim', help='Number of images per class. If >= 0 the samples per class are limited to this number', required=False, default="-1")
|
| 27 |
+
parser.add_argument('--data', help='Inds of data files to use: 0: 1F, 1: 2F, 2: 3C, 3: 3C2, 4: 3F, 5: 5F6F; Split multiple inds with ,', required=False, default="0")
|
| 28 |
+
parser.add_argument('--res', help='Resnet to use. 18, 34, 50, 101, 152', required=False, default="18")
|
| 29 |
+
parser.add_argument('--augment', help='Whether to use augmentation or not', required=False, default="0")
|
| 30 |
+
parser.add_argument('--flip', help='Flipping Prob', required=False, default="0.5")
|
| 31 |
+
parser.add_argument('--noise', help='Adding Noise Prob', required=False, default="0.5")
|
| 32 |
+
parser.add_argument('--noise_max_amp', help='Max noise added', required=False, default="0.1")
|
| 33 |
+
parser.add_argument('--zoom', help='Zooming Prob', required=False, default="0.5")
|
| 34 |
+
parser.add_argument('--zoom_min', help='Min zoom area', required=False, default="0.7")
|
| 35 |
+
parser.add_argument('--zoom_max', help='Max zoom area', required=False, default="1.0")
|
| 36 |
+
parser.add_argument('--crop', help='Cropping Prob', required=False, default="0.5")
|
| 37 |
+
parser.add_argument('--intensity_mul', help='Intensity multiplying Prob', required=False, default="0.5")
|
| 38 |
+
parser.add_argument('--intensity_min', help='Min intensity multiplier', required=False, default="0.8")
|
| 39 |
+
parser.add_argument('--intensity_max', help='Max intensity multiplier', required=False, default="1.2")
|
| 40 |
+
|
| 41 |
+
args = parser.parse_args()
|
| 42 |
+
timestamps = int(args.timestamps)
|
| 43 |
+
epochs = int(args.epochs)
|
| 44 |
+
run_num = int(args.run_num)
|
| 45 |
+
use_norm = int(args.norm)
|
| 46 |
+
random_seed = int(args.seed)
|
| 47 |
+
class_limit = int(args.class_lim)
|
| 48 |
+
data_inds = [int(x) for x in args.data.split(",")]
|
| 49 |
+
data_inds_str = "-".join([str(x) for x in data_inds])
|
| 50 |
+
resnet_to_use = int(args.res)
|
| 51 |
+
use_augment = int(args.augment)
|
| 52 |
+
flipping = float(args.flip)
|
| 53 |
+
noise = float(args.noise)
|
| 54 |
+
noise_max_amp = float(args.noise_max_amp)
|
| 55 |
+
zooming = float(args.zoom)
|
| 56 |
+
zoom_min_max = [float(args.zoom_min), float(args.zoom_max)]
|
| 57 |
+
cropping = float(args.crop)
|
| 58 |
+
intensity_mul = float(args.intensity_mul)
|
| 59 |
+
intensity_min_max = [float(args.intensity_min), float(args.intensity_max)]
|
| 60 |
+
|
| 61 |
+
min_max = {0: [-133.38071, 0.0], 1: [-135.69371, 0.0], 2: [-182.77412, -20.55280], 3: [-184.71960, -18.33420], 4: [-136.01241, 27.37697], 5: [0.0, 255.0]}
|
| 62 |
+
norm_dict = {key: [val[0], val[1] - val[0]] for key, val in min_max.items()}
|
| 63 |
+
bp = "./"
|
| 64 |
+
all_files = {0: bp + "train_0_1F.txt", 1: bp + "train_1_2F.txt", 2: bp + "train_2_3C.txt", 3: bp + "train_3_3C2.txt", 4: bp + "train_4_3F.txt", 5: bp + "train_5_5F6F.txt"}
|
| 65 |
+
data_files = [x for key, x in all_files.items() if key in data_inds]
|
| 66 |
+
batch_size = 64
|
| 67 |
+
time_size = 243
|
| 68 |
+
num_classes = 9
|
| 69 |
+
|
| 70 |
+
# Create Folders
|
| 71 |
+
time.sleep(np.random.randint(5))
|
| 72 |
+
if use_augment:
|
| 73 |
+
train_output_folder = "results_resnet_augment"
|
| 74 |
+
else:
|
| 75 |
+
train_output_folder = "results_resnet"
|
| 76 |
+
all_folders = glob("./" + train_output_folder + "/*_202*")
|
| 77 |
+
all_folders = np.array([int(x.split("/")[-1].split("_")[0]) for x in all_folders])
|
| 78 |
+
if run_num in all_folders:
|
| 79 |
+
print("Skipped: ", run_num, epochs, random_seed, class_limit, data_inds)
|
| 80 |
+
exit()
|
| 81 |
+
print("Running: ", run_num, epochs, random_seed, class_limit, data_inds)
|
| 82 |
+
currentDate = time.strftime('%Y-%m-%d_%H-%M-%S', time.localtime())
|
| 83 |
+
if not os.path.exists("./" + train_output_folder):
|
| 84 |
+
os.mkdir("./" + train_output_folder)
|
| 85 |
+
if use_augment:
|
| 86 |
+
base_path = "./" + train_output_folder + "/%06d_" % (run_num) + str(currentDate) + "_" + str(random_seed) + "_data_inds-" + data_inds_str + "_" + str(class_limit) + "_" + str(int(flipping * 100)) + "_" + str(int(noise * 100)) + "_" + str(int(noise_max_amp * 100)) + "_" + str(int(zooming * 100)) + "_" + str(int(zoom_min_max[0] * 100)) + "_" + str(int(zoom_min_max[1] * 100)) + "_" + str(int(cropping * 100)) + "_" + str(int(intensity_mul * 100)) + "_" + str(int(intensity_min_max[0] * 100)) + "_" + str(int(intensity_min_max[1] * 100)) + "_" + str(use_norm) + "_" + str(resnet_to_use)
|
| 87 |
+
else:
|
| 88 |
+
base_path = "./" + train_output_folder + "/%06d_" % (run_num) + str(currentDate) + "_" + str(random_seed) + "_data_inds-" + data_inds_str + "_" + str(class_limit) + "_" + str(use_norm) + "_" + str(resnet_to_use)
|
| 89 |
+
os.mkdir(base_path)
|
| 90 |
+
os.mkdir(base_path + "/weights/")
|
| 91 |
+
outputWeightspath = base_path + "/weights/"
|
| 92 |
+
log_loss = open(base_path + "/loss.txt", "w")
|
| 93 |
+
log = open(base_path + "/log.txt", "w")
|
| 94 |
+
# ------------ #
|
| 95 |
+
|
| 96 |
+
# Make deterministic #
|
| 97 |
+
np.random.seed(random_seed)
|
| 98 |
+
torch.manual_seed(random_seed)
|
| 99 |
+
random.seed(random_seed)
|
| 100 |
+
torch.backends.cudnn.deterministic = True
|
| 101 |
+
torch.backends.cudnn.benchmark = False
|
| 102 |
+
# ------------------ #
|
| 103 |
+
|
| 104 |
+
# Data Loader #
|
| 105 |
+
data_set_train = utilities.get_dataset(data_files, norm_dict, norm=True, class_limit=class_limit, augment=use_augment, flipping=flipping, noise=noise, noise_max_amp=noise_max_amp, zooming=zooming, zoom_min_max=zoom_min_max, cropping=cropping, intensity_mul=intensity_mul, intensity_min_max=intensity_min_max)
|
| 106 |
+
train_loader = DataLoader(data_set_train, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True, drop_last=True)
|
| 107 |
+
# ----------- #
|
| 108 |
+
|
| 109 |
+
# Load Model #
|
| 110 |
+
if resnet_to_use == 18:
|
| 111 |
+
from torchvision.models import resnet18
|
| 112 |
+
model = resnet18(weights='IMAGENET1K_V1')
|
| 113 |
+
model.fc = nn.Linear(512, num_classes)
|
| 114 |
+
model = model.to(device)
|
| 115 |
+
elif resnet_to_use == 34:
|
| 116 |
+
from torchvision.models import resnet34
|
| 117 |
+
model = resnet34(weights='IMAGENET1K_V1')
|
| 118 |
+
model.fc = nn.Linear(512, num_classes)
|
| 119 |
+
model = model.to(device)
|
| 120 |
+
elif resnet_to_use == 50:
|
| 121 |
+
from torchvision.models import resnet50
|
| 122 |
+
model = resnet50(weights='IMAGENET1K_V1')
|
| 123 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 124 |
+
model = model.to(device)
|
| 125 |
+
elif resnet_to_use == 101:
|
| 126 |
+
from torchvision.models import resnet101
|
| 127 |
+
model = resnet101(weights='IMAGENET1K_V1')
|
| 128 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 129 |
+
model = model.to(device)
|
| 130 |
+
elif resnet_to_use == 152:
|
| 131 |
+
from torchvision.models import resnet152
|
| 132 |
+
model = resnet152(weights='IMAGENET1K_V1')
|
| 133 |
+
model.fc = nn.Linear(2048, num_classes)
|
| 134 |
+
model = model.to(device)
|
| 135 |
+
else:
|
| 136 |
+
exit()
|
| 137 |
+
# ---------- #
|
| 138 |
+
|
| 139 |
+
train_optimizer = SGD(model.parameters(), lr=1e-2, momentum=0.9, weight_decay=5e-4)
|
| 140 |
+
train_scheduler = MultiStepLR(train_optimizer, milestones=[int(epochs * 0.6), int(epochs * 0.8)], gamma=0.1)
|
| 141 |
+
criterion = torch.nn.CrossEntropyLoss().to(device)
|
| 142 |
+
|
| 143 |
+
def training_epoch(model, data_loader, optimizer):
|
| 144 |
+
all_loss = []
|
| 145 |
+
model.train()
|
| 146 |
+
with tqdm(enumerate(data_loader), total=len(data_loader), desc="Training") as tqdm_train:
|
| 147 |
+
for episode_index, (x_batch, y_batch) in tqdm_train:
|
| 148 |
+
optimizer.zero_grad()
|
| 149 |
+
preds = model(x_batch.to(device)).float()
|
| 150 |
+
loss = criterion(preds, y_batch.to(device))
|
| 151 |
+
loss.backward()
|
| 152 |
+
optimizer.step()
|
| 153 |
+
model.zero_grad()
|
| 154 |
+
all_loss.append(loss.item())
|
| 155 |
+
tqdm_train.set_postfix(loss=np.mean(all_loss))
|
| 156 |
+
return np.mean(all_loss)
|
| 157 |
+
|
| 158 |
+
def evaluate(model, data_loader, device='cpu', tqdm_prefix="Validation"):
|
| 159 |
+
total_predictions = 0
|
| 160 |
+
correct_predictions = 0
|
| 161 |
+
model.eval()
|
| 162 |
+
with torch.no_grad():
|
| 163 |
+
with tqdm(enumerate(data_loader), total=len(data_loader), desc=tqdm_prefix) as tqdm_eval:
|
| 164 |
+
for vi, (x_dat, y_dat) in tqdm_eval:
|
| 165 |
+
predictions = model(x_dat.to(device)).detach().data
|
| 166 |
+
number_of_correct_predictions = int((torch.max(predictions, 1)[1] == y_dat.to(device)).sum().item())
|
| 167 |
+
total_predictions += len(y_dat)
|
| 168 |
+
correct_predictions += number_of_correct_predictions
|
| 169 |
+
tqdm_eval.set_postfix(accuracy=correct_predictions / total_predictions)
|
| 170 |
+
return correct_predictions / total_predictions
|
| 171 |
+
|
| 172 |
+
losses = []
|
| 173 |
+
min_loss_dist = 0
|
| 174 |
+
for e in range(epochs):
|
| 175 |
+
print("Epoch: %d/%d" % (e, epochs))
|
| 176 |
+
average_loss = training_epoch(model, train_loader, train_optimizer)
|
| 177 |
+
losses.append(average_loss)
|
| 178 |
+
log_loss.write("%f\n" % average_loss)
|
| 179 |
+
log_loss.flush()
|
| 180 |
+
if average_loss == np.min(losses):
|
| 181 |
+
torch.save(model.state_dict(), outputWeightspath + 'weights_min_loss.h5')
|
| 182 |
+
min_loss_dist = 0
|
| 183 |
+
else:
|
| 184 |
+
min_loss_dist += 1
|
| 185 |
+
if min_loss_dist >= 25:
|
| 186 |
+
break
|
| 187 |
+
if (e + 1) % timestamps == 0:
|
| 188 |
+
torch.save(model.state_dict(), outputWeightspath + ('weights_%d.h5' % e))
|
| 189 |
+
torch.save(model.state_dict(), outputWeightspath + 'weights.h5')
|
| 190 |
+
log.close()
|
| 191 |
+
log_loss.close()
|
| 192 |
+
print("Done")
|
| 193 |
+
|
uncertainty.py
ADDED
|
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import re
|
| 3 |
+
import os
|
| 4 |
+
from glob import glob
|
| 5 |
+
import pickle as pkl
|
| 6 |
+
from itertools import chain, combinations, permutations
|
| 7 |
+
import argparse
|
| 8 |
+
|
| 9 |
+
parser = argparse.ArgumentParser()
|
| 10 |
+
parser.add_argument('--folder', help='Folder of the runs. Either resnet or resnet_augment', required=False, default="resnet")
|
| 11 |
+
parser.add_argument('--repeats', help='The number of times each model was trained for the uncertainty calculation', required=False, default="10")
|
| 12 |
+
|
| 13 |
+
args = parser.parse_args()
|
| 14 |
+
cfolder = str(args.folder)
|
| 15 |
+
repeats = int(args.repeats)
|
| 16 |
+
save_big_files = False
|
| 17 |
+
|
| 18 |
+
def to_softmax(inarr):
|
| 19 |
+
ret_arr = inarr - np.max(inarr, axis=1)[:, None]
|
| 20 |
+
return np.exp(ret_arr)/np.sum(np.exp(ret_arr), axis=1)[:, None]
|
| 21 |
+
|
| 22 |
+
def calc_uncertainty(preds, lab, num_c=9, ret_big=False):
|
| 23 |
+
preds_arg = np.argmax(np.sum(preds, axis=0), axis=-1)
|
| 24 |
+
conf_mat = np.zeros((num_c, num_c))
|
| 25 |
+
for iim, im in enumerate(range(num_c)):
|
| 26 |
+
for jjm, jm in enumerate(range(num_c)):
|
| 27 |
+
conf_mat[iim, jjm] = np.sum(preds_arg[lab == im] == jm)
|
| 28 |
+
# Equation 4 from: https://openreview.net/pdf?id=Sk_P2Q9sG
|
| 29 |
+
# For Multiclass: Shape (T, N, C)
|
| 30 |
+
p_mean = np.mean(preds, axis=0)
|
| 31 |
+
p_hat_minus_p_mean = preds - p_mean[None, :] # shape (T, N, C)
|
| 32 |
+
epistemic = np.mean(np.matmul(p_hat_minus_p_mean[:, :, :, None], p_hat_minus_p_mean[:, :, None, :]), axis=0) # shape (N, C, C)
|
| 33 |
+
diag_p = np.zeros((preds.shape[0], preds.shape[1], preds.shape[2], preds.shape[2]))
|
| 34 |
+
diag_p[:, :, np.arange(preds.shape[2]).astype(int), np.arange(preds.shape[2]).astype(int)] = preds
|
| 35 |
+
aleatoric = np.mean(diag_p - np.matmul(preds[:, :, :, None], preds[:, :, None, :]), axis=0) # shape (N, C, C)
|
| 36 |
+
#
|
| 37 |
+
ep_diag = []
|
| 38 |
+
al_diag = []
|
| 39 |
+
for i in range(num_c):
|
| 40 |
+
if np.sum(lab == i) == 0:
|
| 41 |
+
ep_diag_c = np.zeros(num_c)
|
| 42 |
+
al_diag_c = np.zeros(num_c)
|
| 43 |
+
else:
|
| 44 |
+
ep_diag_c = np.diag(np.mean(epistemic[lab == i], axis=0))
|
| 45 |
+
al_diag_c = np.diag(np.mean(aleatoric[lab == i], axis=0))
|
| 46 |
+
ep_diag.append(ep_diag_c)
|
| 47 |
+
al_diag.append(al_diag_c)
|
| 48 |
+
if ret_big:
|
| 49 |
+
return conf_mat, np.array(al_diag), np.array(ep_diag), aleatoric, epistemic
|
| 50 |
+
else:
|
| 51 |
+
return conf_mat, np.array(al_diag), np.array(ep_diag)
|
| 52 |
+
|
| 53 |
+
# Data folders ------------------------------------------------------------------- #
|
| 54 |
+
data_base = "./"
|
| 55 |
+
data_base_folder = data_base + "results_" + cfolder
|
| 56 |
+
split_files = [data_base + "test_0_1F.txt", data_base + "test_1_2F.txt", data_base + "test_2_3C.txt", data_base + "test_3_3C2.txt", data_base + "test_4_3F.txt", data_base + "test_5_5F6F.txt"]
|
| 57 |
+
all_lab = []
|
| 58 |
+
for split_file in split_files:
|
| 59 |
+
with open(split_file, "r") as f:
|
| 60 |
+
cur_lab = f.readlines()
|
| 61 |
+
cur_lab = np.array([int(x.split("\n")[0].split("\t")[1]) for x in cur_lab])
|
| 62 |
+
all_lab.append(cur_lab)
|
| 63 |
+
sp_base = data_base_folder + "/uncertainties/"
|
| 64 |
+
if not os.path.exists(sp_base):
|
| 65 |
+
os.makedirs(sp_base)
|
| 66 |
+
# -------------------------------------------------------------------------------- #
|
| 67 |
+
|
| 68 |
+
re_c = re.compile(".*/results_" + cfolder + r'/0.*_202.*/test/preds_(?P<m1>.*).npy')
|
| 69 |
+
re_c_folder = re.compile(".*/results_" + cfolder + r'/(?P<num>\d{1,6})_202.*')
|
| 70 |
+
folders = glob(data_base_folder + "/0*_202*")
|
| 71 |
+
folders.sort(key=lambda x: int(re_c_folder.match(x).group(1)))
|
| 72 |
+
folders = np.array(folders).reshape((-1, repeats))
|
| 73 |
+
print(folders)
|
| 74 |
+
for cfi, cfolders in enumerate(folders):
|
| 75 |
+
cfolder = cfolders[0] # Always save output in the first folder with the current number
|
| 76 |
+
cfolder_num = int(re_c_folder.match(cfolder).group(1))
|
| 77 |
+
print("Dealing with count %d " % cfi, cfolder)
|
| 78 |
+
sp_aleatoric_diag = sp_base + "%06d_aleatoric_diag.pkl" % cfolder_num
|
| 79 |
+
sp_epistemic_diag = sp_base + "%06d_epistemic_diag.pkl" % cfolder_num
|
| 80 |
+
sp_aleatoric = sp_base + "%06d_aleatoric.pkl" % cfolder_num
|
| 81 |
+
sp_epistemic = sp_base + "%06d_epistemic.pkl" % cfolder_num
|
| 82 |
+
sp_confusion = sp_base + "%06d_confusion.pkl" % cfolder_num
|
| 83 |
+
aleatoric_diag_tests = {}
|
| 84 |
+
epistemic_diag_tests = {}
|
| 85 |
+
aleatoric_tests = {}
|
| 86 |
+
epistemic_tests = {}
|
| 87 |
+
confusion_tests = {}
|
| 88 |
+
for pi in range(6):
|
| 89 |
+
c1_preds = []
|
| 90 |
+
for cfii, cfolder in enumerate(cfolders):
|
| 91 |
+
cur_pred_file = cfolder + "/test/preds_%d.npy" % pi
|
| 92 |
+
cur_pred = np.load(cur_pred_file)
|
| 93 |
+
c1_pred = to_softmax(cur_pred)
|
| 94 |
+
c1_preds.append(c1_pred)
|
| 95 |
+
c1_preds = np.array(c1_preds)
|
| 96 |
+
if save_big_files:
|
| 97 |
+
cm, ald, epd, al, ep = calc_uncertainty(c1_preds, all_lab[pi], num_c=9, ret_big=True)
|
| 98 |
+
aleatoric_tests["T%d" % pi] = al
|
| 99 |
+
epistemic_tests["T%d" % pi] = ep
|
| 100 |
+
else:
|
| 101 |
+
cm, ald, epd = calc_uncertainty(c1_preds, all_lab[pi], num_c=9)
|
| 102 |
+
aleatoric_diag_tests["T%d" % pi] = ald
|
| 103 |
+
epistemic_diag_tests["T%d" % pi] = epd
|
| 104 |
+
confusion_tests["T%d" % pi] = cm
|
| 105 |
+
with open(sp_aleatoric_diag, "wb") as f:
|
| 106 |
+
pkl.dump(aleatoric_diag_tests, f)
|
| 107 |
+
with open(sp_epistemic_diag, "wb") as f:
|
| 108 |
+
pkl.dump(epistemic_diag_tests, f)
|
| 109 |
+
with open(sp_confusion, "wb") as f:
|
| 110 |
+
pkl.dump(confusion_tests, f)
|
| 111 |
+
if save_big_files:
|
| 112 |
+
with open(sp_aleatoric, "wb") as f:
|
| 113 |
+
pkl.dump(aleatoric_tests, f)
|
| 114 |
+
with open(sp_epistemic, "wb") as f:
|
| 115 |
+
pkl.dump(epistemic_tests, f)
|
| 116 |
+
print("Done")
|
| 117 |
+
|
utilities.py
ADDED
|
@@ -0,0 +1,151 @@
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import random
|
| 3 |
+
import torch
|
| 4 |
+
from torch.utils.data import Dataset
|
| 5 |
+
from torchvision.transforms import v2
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
class custom_dataset(Dataset):
|
| 9 |
+
def __init__(self, file_list, label_list, data_types_list, norm_dict, norm=True, time_size=243, augment=False, flipping=0.5, noise=0.5, noise_max_amp=0.1, zooming=0.5, zoom_min_max=[0.7, 1.0], cropping=0.5, intensity_mul=0.5, intensity_min_max=[0.8, 1.2]):
|
| 10 |
+
self.use_norm = norm
|
| 11 |
+
self.time_size = time_size
|
| 12 |
+
self.norm_dict = norm_dict
|
| 13 |
+
self.files = file_list
|
| 14 |
+
self.labels = label_list
|
| 15 |
+
self.data_types = data_types_list
|
| 16 |
+
self.indices = np.arange(len(self.files)).astype(int)
|
| 17 |
+
self.augment = augment
|
| 18 |
+
self.flipping = flipping
|
| 19 |
+
self.noise = noise
|
| 20 |
+
self.noise_max_amp = noise_max_amp
|
| 21 |
+
self.zooming = zooming
|
| 22 |
+
self.zoom_min_max = zoom_min_max
|
| 23 |
+
self.cropping = cropping
|
| 24 |
+
self.intensity_mul = intensity_mul
|
| 25 |
+
self.intensity_min_max = intensity_min_max
|
| 26 |
+
|
| 27 |
+
def __getitem__(self, item: int):
|
| 28 |
+
cur_dtype = self.data_types[item]
|
| 29 |
+
cur_y_data = self.labels[item]
|
| 30 |
+
if cur_dtype <= 1:
|
| 31 |
+
cur_x_data = np.load(self.files[item])
|
| 32 |
+
if self.use_norm:
|
| 33 |
+
cur_x_data -= self.norm_dict[cur_dtype][0]
|
| 34 |
+
cur_x_data /= self.norm_dict[cur_dtype][1]
|
| 35 |
+
if self.augment:
|
| 36 |
+
cur_x_data, zz, zs, org_size = augment_data(cur_x_data, flipping=self.flipping, noise=self.noise, noise_max_amp=self.noise_max_amp, zooming=self.zooming, zoom_min_max=self.zoom_min_max, cropping=self.cropping, intensity_mul=self.intensity_mul, intensity_min_max=self.intensity_min_max)
|
| 37 |
+
cur_x_data = torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32"))
|
| 38 |
+
if zz is not None:
|
| 39 |
+
cur_x_data = v2.Resize(size=org_size)(cur_x_data[:, zs[0]: zs[0] + zz[0], zs[1]: zs[1] + zz[1]])
|
| 40 |
+
return cur_x_data, torch.tensor(cur_y_data, dtype=torch.long)
|
| 41 |
+
return torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32")), torch.tensor(cur_y_data, dtype=torch.long)
|
| 42 |
+
elif cur_dtype == 2 or cur_dtype == 3:
|
| 43 |
+
cur_x_data = (np.load(self.files[item])[:, 256:-256]).transpose((1, 0))
|
| 44 |
+
if self.use_norm:
|
| 45 |
+
cur_x_data -= self.norm_dict[cur_dtype][0]
|
| 46 |
+
cur_x_data /= self.norm_dict[cur_dtype][1]
|
| 47 |
+
if len(cur_x_data[0]) != 34:
|
| 48 |
+
cur_x_data = np.concatenate([np.tile(cur_x_data, (1, 7)), cur_x_data[:, :5], np.zeros((512, 21))], axis=1)
|
| 49 |
+
else:
|
| 50 |
+
cur_x_data = np.concatenate([np.tile(cur_x_data, (1, 7)), cur_x_data[:, :5]], axis=1)
|
| 51 |
+
if self.augment:
|
| 52 |
+
cur_x_data, zz, zs, org_size = augment_data(cur_x_data, flipping=self.flipping, noise=self.noise, noise_max_amp=self.noise_max_amp, zooming=self.zooming, zoom_min_max=self.zoom_min_max, cropping=self.cropping, intensity_mul=self.intensity_mul, intensity_min_max=self.intensity_min_max)
|
| 53 |
+
cur_x_data = torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32"))
|
| 54 |
+
if zz is not None:
|
| 55 |
+
cur_x_data = v2.Resize(size=org_size)(cur_x_data[:, zs[0]: zs[0] + zz[0], zs[1]: zs[1] + zz[1]])
|
| 56 |
+
return cur_x_data, torch.tensor(cur_y_data, dtype=torch.long)
|
| 57 |
+
return torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32")), torch.tensor(cur_y_data, dtype=torch.long)
|
| 58 |
+
elif cur_dtype == 4:
|
| 59 |
+
cur_x_data = np.load(self.files[item])[:, :243]
|
| 60 |
+
if self.use_norm:
|
| 61 |
+
cur_x_data -= self.norm_dict[cur_dtype][0]
|
| 62 |
+
cur_x_data /= self.norm_dict[cur_dtype][1]
|
| 63 |
+
if self.augment:
|
| 64 |
+
cur_x_data, zz, zs, org_size = augment_data(cur_x_data, flipping=self.flipping, noise=self.noise, noise_max_amp=self.noise_max_amp, zooming=self.zooming, zoom_min_max=self.zoom_min_max, cropping=self.cropping, intensity_mul=self.intensity_mul, intensity_min_max=self.intensity_min_max)
|
| 65 |
+
cur_x_data = torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32"))
|
| 66 |
+
if zz is not None:
|
| 67 |
+
cur_x_data = v2.Resize(size=org_size)(cur_x_data[:, zs[0]: zs[0] + zz[0], zs[1]: zs[1] + zz[1]])
|
| 68 |
+
return cur_x_data, torch.tensor(cur_y_data, dtype=torch.long)
|
| 69 |
+
return torch.from_numpy(np.repeat(cur_x_data[None, :, :], 3, axis=0).astype("float32")), torch.tensor(cur_y_data, dtype=torch.long)
|
| 70 |
+
else:
|
| 71 |
+
cur_x_data = np.load(self.files[item]).astype("float")
|
| 72 |
+
if self.use_norm:
|
| 73 |
+
cur_x_data -= self.norm_dict[cur_dtype][0]
|
| 74 |
+
cur_x_data /= self.norm_dict[cur_dtype][1]
|
| 75 |
+
if self.augment:
|
| 76 |
+
cur_x_data, zz, zs, org_size = augment_data(cur_x_data, flipping=self.flipping, noise=self.noise, noise_max_amp=self.noise_max_amp, zooming=self.zooming, zoom_min_max=self.zoom_min_max, cropping=self.cropping, intensity_mul=self.intensity_mul, intensity_min_max=self.intensity_min_max)
|
| 77 |
+
cur_x_data = torch.from_numpy(cur_x_data.transpose((2, 0, 1)).astype("float32"))
|
| 78 |
+
if zz is not None:
|
| 79 |
+
cur_x_data = v2.Resize(size=org_size)(cur_x_data[:, zs[0]: zs[0] + zz[0], zs[1]: zs[1] + zz[1]])
|
| 80 |
+
return cur_x_data, torch.tensor(cur_y_data, dtype=torch.long)
|
| 81 |
+
return torch.from_numpy(cur_x_data.transpose((2, 0, 1)).astype("float32")), torch.tensor(cur_y_data, dtype=torch.long)
|
| 82 |
+
|
| 83 |
+
def __len__(self) -> int:
|
| 84 |
+
return len(self.labels)
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
def get_dataset(data_files, norm_dict, norm=True, time_size=243, class_limit=-1, augment=False, flipping=0.5, noise=0.5, noise_max_amp=0.1, zooming=0.5, zoom_min_max=[0.7, 1.0], cropping=0.5, intensity_mul=0.5, intensity_min_max=[0.8, 1.2]):
|
| 88 |
+
print("Files: ", data_files)
|
| 89 |
+
dp_base = "../"
|
| 90 |
+
file_list = []
|
| 91 |
+
label_list = []
|
| 92 |
+
data_types_list = []
|
| 93 |
+
for data_file in data_files:
|
| 94 |
+
class_counts = np.zeros(9)
|
| 95 |
+
cd_type = int(data_file.split("_")[-2])
|
| 96 |
+
with open(data_file, "r") as f:
|
| 97 |
+
data_info = f.readlines()
|
| 98 |
+
data_info = np.array([x.split("\n")[0].split("\t") for x in data_info])
|
| 99 |
+
x_files = []
|
| 100 |
+
y_labels = []
|
| 101 |
+
for x in data_info:
|
| 102 |
+
cur_y_lab = int(x[1])
|
| 103 |
+
if class_limit != -1 and class_counts[cur_y_lab] >= class_limit:
|
| 104 |
+
continue
|
| 105 |
+
class_counts[cur_y_lab] += 1
|
| 106 |
+
x_files.append(dp_base + x[0])
|
| 107 |
+
y_labels.append(cur_y_lab)
|
| 108 |
+
file_list.extend(x_files)
|
| 109 |
+
label_list.extend(y_labels)
|
| 110 |
+
data_types_list.extend([cd_type] * len(x_files))
|
| 111 |
+
return custom_dataset(file_list, label_list, data_types_list, norm_dict, norm=norm, time_size=time_size, augment=augment, flipping=flipping, noise=noise, noise_max_amp=noise_max_amp, zooming=zooming, zoom_min_max=zoom_min_max, cropping=cropping, intensity_mul=intensity_mul, intensity_min_max=intensity_min_max)
|
| 112 |
+
|
| 113 |
+
def get_dataset_files(data_files, norm_dict, norm=False, time_size=243, augment=False, flipping=0.5, noise=0.5, noise_max_amp=0.1, zooming=0.5, zoom_min_max=[0.7, 1.0], cropping=0.5, intensity_mul=0.5, intensity_min_max=[0.8, 1.2]):
|
| 114 |
+
return custom_dataset([x[0] for x in data_files], [x[1] for x in data_files], [x[2] for x in data_files], norm_dict, norm=norm, time_size=time_size, augment=augment, flipping=flipping, noise=noise, noise_max_amp=noise_max_amp, zooming=zooming, zoom_min_max=zoom_min_max, cropping=cropping, intensity_mul=intensity_mul, intensity_min_max=intensity_min_max)
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
def augment_data(data_in, flipping=0.5, noise=0.5, noise_max_amp=0.1, zooming=0.5, zoom_min_max=[0.7, 1.0], cropping=0.5, intensity_mul=0.5, intensity_min_max=[0.8, 1.2]):
|
| 118 |
+
yx_shape = data_in.shape
|
| 119 |
+
flip = np.random.random() < flipping
|
| 120 |
+
if flip:
|
| 121 |
+
flip_type = np.random.randint(1, 4)
|
| 122 |
+
if flip_type == 1:
|
| 123 |
+
data_in = data_in[:, ::-1]
|
| 124 |
+
elif flip_type == 2:
|
| 125 |
+
data_in = data_in[:, ::-1]
|
| 126 |
+
else:
|
| 127 |
+
data_in = data_in[::-1, ::-1]
|
| 128 |
+
add_noise = np.random.random() < noise
|
| 129 |
+
if add_noise:
|
| 130 |
+
data_in += (np.random.random(size=yx_shape) * 2 - 1) * noise_max_amp
|
| 131 |
+
mult_intensity = np.random.random() < intensity_mul
|
| 132 |
+
if mult_intensity:
|
| 133 |
+
data_in *= (np.random.random() * (intensity_min_max[1] - intensity_min_max[0]) + intensity_min_max[0])
|
| 134 |
+
data_in[data_in < 0] = 0.0
|
| 135 |
+
data_in[data_in > 1] = 1.0
|
| 136 |
+
zoom = np.random.random() < zooming
|
| 137 |
+
crop = np.random.random() < cropping
|
| 138 |
+
if zoom or crop:
|
| 139 |
+
zoom_xy = ((np.random.random(2) * (zoom_min_max[1] - zoom_min_max[0]) + zoom_min_max[0]) * yx_shape[:2]).astype(int)
|
| 140 |
+
zoom_starts = (np.random.random(2) * (yx_shape[:2] - zoom_xy)).astype(int)
|
| 141 |
+
else:
|
| 142 |
+
zoom_xy = None
|
| 143 |
+
zoom_starts = None
|
| 144 |
+
if not zoom and crop:
|
| 145 |
+
crop_mask = np.zeros(yx_shape)
|
| 146 |
+
crop_mask[zoom_starts[0]: zoom_starts[0] + zoom_xy[0], zoom_starts[1]: zoom_starts[1] + zoom_xy[1]] = 1.0
|
| 147 |
+
data_in *= crop_mask
|
| 148 |
+
zoom_xy = None
|
| 149 |
+
zoom_starts = None
|
| 150 |
+
return data_in, zoom_xy, zoom_starts, yx_shape[:2]
|
| 151 |
+
|