Meta-causal/code/56717.log

/home/yuqian_fu
{'gpu': '0', 'data': 'art_painting', 'ntr': None, 'translate': None, 'autoaug': 'CA_multiple', 'n': 3, 'stride': 5, 'factor_num': 16, 'epochs': 70, 'nbatch': 100, 'batchsize': 6, 'lr': 0.01, 'lr_scheduler': 'cosine', 'svroot': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//art_painting/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal', 'clsadapt': True, 'lambda_causal': 1.0, 'lambda_re': 1.0, 'randm': True, 'randn': True, 'network': 'resnet18'}
stride: 5
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/art_painting_train.hdf5 torch.Size([1840, 3, 227, 227]) torch.Size([1840])
--------------------------CA_multiple--------------------------
---------------------------16 factors-----------------
randm: True
randn: True
n: 3
randm: False
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/art_painting_val.hdf5 torch.Size([208, 3, 227, 227]) torch.Size([208])
-------------------------------------loading pretrain weights----------------------------------
306
0.01
changing lr
---------------------saving model at epoch 0----------------------------------------------------
epoch 0, time 396.56, cls_loss 6.7564 cls_loss_mapping 1.5193 cls_loss_causal 1.7521 re_mapping 1.0575 re_causal 1.0584 /// teacc 81.25 lr 0.00999497
306
0.009994965332706574
changing lr
---------------------saving model at epoch 1----------------------------------------------------
epoch 1, time 415.32, cls_loss 2.1970 cls_loss_mapping 0.9096 cls_loss_causal 1.4403 re_mapping 0.7024 re_causal 0.7051 /// teacc 83.65 lr 0.00997987
306
0.009979871469976196
changing lr
---------------------saving model at epoch 2----------------------------------------------------
epoch 2, time 457.96, cls_loss 1.3065 cls_loss_mapping 0.6322 cls_loss_causal 1.2780 re_mapping 0.6032 re_causal 0.6057 /// teacc 88.46 lr 0.00995475
306
0.009954748808839675
changing lr
epoch 3, time 451.75, cls_loss 0.5818 cls_loss_mapping 0.5055 cls_loss_causal 1.1465 re_mapping 0.5267 re_causal 0.5293 /// teacc 87.02 lr 0.00991965
306
0.009919647942993149
changing lr
epoch 4, time 451.48, cls_loss 0.3909 cls_loss_mapping 0.4012 cls_loss_causal 1.0889 re_mapping 0.4649 re_causal 0.4683 /// teacc 84.62 lr 0.00987464
306
0.009874639560909117
changing lr
epoch 5, time 441.59, cls_loss 0.3191 cls_loss_mapping 0.3555 cls_loss_causal 1.0670 re_mapping 0.3968 re_causal 0.4013 /// teacc 86.06 lr 0.00981981
306
0.009819814303479266
changing lr
epoch 6, time 432.93, cls_loss 0.1327 cls_loss_mapping 0.2760 cls_loss_causal 1.0002 re_mapping 0.3232 re_causal 0.3278 /// teacc 83.17 lr 0.00975528
306
0.009755282581475767
changing lr
epoch 7, time 444.85, cls_loss 0.0411 cls_loss_mapping 0.2236 cls_loss_causal 0.9368 re_mapping 0.2592 re_causal 0.2641 /// teacc 88.46 lr 0.00968117
306
0.009681174353198686
changing lr
epoch 8, time 448.36, cls_loss 0.0723 cls_loss_mapping 0.2492 cls_loss_causal 0.9911 re_mapping 0.2174 re_causal 0.2224 /// teacc 86.54 lr 0.00959764
306
0.009597638862757255
changing lr
epoch 9, time 446.26, cls_loss 0.0174 cls_loss_mapping 0.1853 cls_loss_causal 0.8733 re_mapping 0.1873 re_causal 0.1925 /// teacc 86.54 lr 0.00950484
306
0.009504844339512096
changing lr
---------------------saving model at epoch 10----------------------------------------------------
epoch 10, time 457.12, cls_loss 0.0358 cls_loss_mapping 0.1781 cls_loss_causal 0.8735 re_mapping 0.1610 re_causal 0.1661 /// teacc 89.90 lr 0.00940298
306
0.009402977659283692
changing lr
epoch 11, time 443.50, cls_loss 0.0162 cls_loss_mapping 0.1514 cls_loss_causal 0.8453 re_mapping 0.1432 re_causal 0.1486 /// teacc 89.90 lr 0.00929224
306
0.009292243968009333
changing lr
---------------------saving model at epoch 12----------------------------------------------------
epoch 12, time 453.53, cls_loss 0.0101 cls_loss_mapping 0.1383 cls_loss_causal 0.8002 re_mapping 0.1270 re_causal 0.1328 /// teacc 90.87 lr 0.00917287
306
0.009172866268606516
changing lr
epoch 13, time 466.47, cls_loss 0.0092 cls_loss_mapping 0.1432 cls_loss_causal 0.8412 re_mapping 0.1167 re_causal 0.1224 /// teacc 90.38 lr 0.00904508
306
0.00904508497187474
changing lr
epoch 14, time 448.78, cls_loss 0.0063 cls_loss_mapping 0.1207 cls_loss_causal 0.7912 re_mapping 0.1077 re_causal 0.1140 /// teacc 90.38 lr 0.00890916
306
0.008909157412340152
changing lr
epoch 15, time 442.01, cls_loss 0.0075 cls_loss_mapping 0.1148 cls_loss_causal 0.7640 re_mapping 0.0982 re_causal 0.1047 /// teacc 89.90 lr 0.00876536
306
0.00876535733001806
changing lr
epoch 16, time 451.70, cls_loss 0.0050 cls_loss_mapping 0.1000 cls_loss_causal 0.7562 re_mapping 0.0898 re_causal 0.0964 /// teacc 90.87 lr 0.00861397
306
0.008613974319136962
changing lr
epoch 17, time 454.36, cls_loss 0.0084 cls_loss_mapping 0.0986 cls_loss_causal 0.7422 re_mapping 0.0817 re_causal 0.0883 /// teacc 89.90 lr 0.00845531
306
0.008455313244934327
changing lr
epoch 18, time 450.70, cls_loss 0.0033 cls_loss_mapping 0.0951 cls_loss_causal 0.7426 re_mapping 0.0760 re_causal 0.0827 /// teacc 89.42 lr 0.00828969
306
0.008289693629698565
changing lr
epoch 19, time 456.56, cls_loss 0.0051 cls_loss_mapping 0.0938 cls_loss_causal 0.7288 re_mapping 0.0711 re_causal 0.0787 /// teacc 88.94 lr 0.00811745
306
0.00811744900929367
changing lr
epoch 20, time 444.31, cls_loss 0.0025 cls_loss_mapping 0.0920 cls_loss_causal 0.7432 re_mapping 0.0652 re_causal 0.0723 /// teacc 89.90 lr 0.00793893
306
0.007938926261462368
changing lr
epoch 21, time 436.20, cls_loss 0.0028 cls_loss_mapping 0.0782 cls_loss_causal 0.7226 re_mapping 0.0605 re_causal 0.0677 /// teacc 90.87 lr 0.00775448
306
0.007754484907260515
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epoch 22, time 447.42, cls_loss 0.0020 cls_loss_mapping 0.0778 cls_loss_causal 0.6694 re_mapping 0.0571 re_causal 0.0641 /// teacc 90.38 lr 0.00756450
306
0.007564496387029534
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epoch 23, time 443.40, cls_loss 0.0019 cls_loss_mapping 0.0766 cls_loss_causal 0.7606 re_mapping 0.0533 re_causal 0.0621 /// teacc 89.42 lr 0.00736934
306
0.007369343312364995
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epoch 24, time 439.80, cls_loss 0.0045 cls_loss_mapping 0.0782 cls_loss_causal 0.7261 re_mapping 0.0521 re_causal 0.0608 /// teacc 90.38 lr 0.00716942
306
0.0071694186955877925
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epoch 25, time 430.50, cls_loss 0.0020 cls_loss_mapping 0.0645 cls_loss_causal 0.7059 re_mapping 0.0500 re_causal 0.0593 /// teacc 90.87 lr 0.00696513
306
0.0069651251582696205
changing lr
epoch 26, time 444.21, cls_loss 0.0008 cls_loss_mapping 0.0529 cls_loss_causal 0.6660 re_mapping 0.0448 re_causal 0.0527 /// teacc 90.87 lr 0.00675687
306
0.006756874120406716
changing lr
epoch 27, time 451.19, cls_loss 0.0027 cls_loss_mapping 0.0633 cls_loss_causal 0.7457 re_mapping 0.0430 re_causal 0.0520 /// teacc 90.87 lr 0.00654508
306
0.00654508497187474
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---------------------saving model at epoch 28----------------------------------------------------
epoch 28, time 444.91, cls_loss 0.0045 cls_loss_mapping 0.0630 cls_loss_causal 0.6839 re_mapping 0.0409 re_causal 0.0485 /// teacc 91.35 lr 0.00633018
306
0.006330184227833378
changing lr
epoch 29, time 454.38, cls_loss 0.0030 cls_loss_mapping 0.0528 cls_loss_causal 0.6373 re_mapping 0.0388 re_causal 0.0468 /// teacc 88.94 lr 0.00611260
306
0.006112604669781575
changing lr
epoch 30, time 455.36, cls_loss 0.0023 cls_loss_mapping 0.0479 cls_loss_causal 0.6459 re_mapping 0.0382 re_causal 0.0462 /// teacc 91.35 lr 0.00589278
306
0.005892784473993186
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epoch 31, time 447.61, cls_loss 0.0014 cls_loss_mapping 0.0532 cls_loss_causal 0.6553 re_mapping 0.0365 re_causal 0.0447 /// teacc 91.35 lr 0.00567117
306
0.00567116632908828
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epoch 32, time 455.74, cls_loss 0.0019 cls_loss_mapping 0.0470 cls_loss_causal 0.6156 re_mapping 0.0346 re_causal 0.0422 /// teacc 90.38 lr 0.00544820
306
0.00544819654451717
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epoch 33, time 458.62, cls_loss 0.0026 cls_loss_mapping 0.0475 cls_loss_causal 0.6128 re_mapping 0.0336 re_causal 0.0415 /// teacc 91.35 lr 0.00522432
306
0.005224324151752577
changing lr
epoch 34, time 443.89, cls_loss 0.0034 cls_loss_mapping 0.0503 cls_loss_causal 0.6216 re_mapping 0.0331 re_causal 0.0412 /// teacc 90.87 lr 0.00500000
306
0.005000000000000003
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---------------------saving model at epoch 35----------------------------------------------------
epoch 35, time 474.23, cls_loss 0.0025 cls_loss_mapping 0.0398 cls_loss_causal 0.5884 re_mapping 0.0317 re_causal 0.0397 /// teacc 91.83 lr 0.00477568
306
0.004775675848247429
changing lr
epoch 36, time 456.46, cls_loss 0.0023 cls_loss_mapping 0.0434 cls_loss_causal 0.6319 re_mapping 0.0308 re_causal 0.0386 /// teacc 91.35 lr 0.00455180
306
0.004551803455482836
changing lr
epoch 37, time 460.36, cls_loss 0.0024 cls_loss_mapping 0.0376 cls_loss_causal 0.6052 re_mapping 0.0290 re_causal 0.0364 /// teacc 90.87 lr 0.00432883
306
0.004328833670911726
changing lr
epoch 38, time 456.58, cls_loss 0.0013 cls_loss_mapping 0.0368 cls_loss_causal 0.6265 re_mapping 0.0276 re_causal 0.0354 /// teacc 90.38 lr 0.00410722
306
0.0041072155260068206
changing lr
epoch 39, time 468.90, cls_loss 0.0019 cls_loss_mapping 0.0310 cls_loss_causal 0.6240 re_mapping 0.0264 re_causal 0.0344 /// teacc 90.87 lr 0.00388740
306
0.0038873953302184317
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epoch 40, time 457.96, cls_loss 0.0020 cls_loss_mapping 0.0328 cls_loss_causal 0.6230 re_mapping 0.0257 re_causal 0.0335 /// teacc 90.87 lr 0.00366982
306
0.003669815772166629
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---------------------saving model at epoch 41----------------------------------------------------
epoch 41, time 469.29, cls_loss 0.0023 cls_loss_mapping 0.0376 cls_loss_causal 0.6061 re_mapping 0.0249 re_causal 0.0320 /// teacc 92.31 lr 0.00345492
306
0.0034549150281252667
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epoch 42, time 475.72, cls_loss 0.0025 cls_loss_mapping 0.0311 cls_loss_causal 0.6195 re_mapping 0.0243 re_causal 0.0322 /// teacc 90.87 lr 0.00324313
306
0.0032431258795932905
changing lr
epoch 43, time 450.85, cls_loss 0.0018 cls_loss_mapping 0.0341 cls_loss_causal 0.6223 re_mapping 0.0235 re_causal 0.0310 /// teacc 90.87 lr 0.00303487
306
0.0030348748417303863
changing lr
epoch 44, time 441.78, cls_loss 0.0019 cls_loss_mapping 0.0317 cls_loss_causal 0.6072 re_mapping 0.0228 re_causal 0.0304 /// teacc 90.38 lr 0.00283058
306
0.0028305813044122124
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---------------------saving model at epoch 45----------------------------------------------------
epoch 45, time 462.98, cls_loss 0.0013 cls_loss_mapping 0.0307 cls_loss_causal 0.5641 re_mapping 0.0222 re_causal 0.0291 /// teacc 93.75 lr 0.00263066
306
0.0026306566876350096
changing lr
epoch 46, time 474.81, cls_loss 0.0028 cls_loss_mapping 0.0323 cls_loss_causal 0.6004 re_mapping 0.0218 re_causal 0.0287 /// teacc 91.83 lr 0.00243550
306
0.0024355036129704724
changing lr
epoch 47, time 465.56, cls_loss 0.0013 cls_loss_mapping 0.0291 cls_loss_causal 0.6082 re_mapping 0.0213 re_causal 0.0289 /// teacc 92.31 lr 0.00224552
306
0.00224551509273949
changing lr
epoch 48, time 458.33, cls_loss 0.0011 cls_loss_mapping 0.0269 cls_loss_causal 0.6051 re_mapping 0.0208 re_causal 0.0289 /// teacc 91.35 lr 0.00206107
306
0.002061073738537637
changing lr
epoch 49, time 450.51, cls_loss 0.0012 cls_loss_mapping 0.0242 cls_loss_causal 0.5558 re_mapping 0.0200 re_causal 0.0273 /// teacc 91.35 lr 0.00188255
306
0.0018825509907063344
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epoch 50, time 462.46, cls_loss 0.0009 cls_loss_mapping 0.0237 cls_loss_causal 0.5775 re_mapping 0.0194 re_causal 0.0261 /// teacc 90.38 lr 0.00171031
306
0.0017103063703014388
changing lr
epoch 51, time 458.67, cls_loss 0.0017 cls_loss_mapping 0.0239 cls_loss_causal 0.5359 re_mapping 0.0184 re_causal 0.0244 /// teacc 91.35 lr 0.00154469
306
0.0015446867550656784
changing lr
epoch 52, time 439.55, cls_loss 0.0016 cls_loss_mapping 0.0239 cls_loss_causal 0.5782 re_mapping 0.0180 re_causal 0.0248 /// teacc 92.31 lr 0.00138603
306
0.001386025680863044
changing lr
epoch 53, time 468.39, cls_loss 0.0011 cls_loss_mapping 0.0221 cls_loss_causal 0.5797 re_mapping 0.0174 re_causal 0.0241 /// teacc 90.38 lr 0.00123464
306
0.0012346426699819469
changing lr
epoch 54, time 478.52, cls_loss 0.0011 cls_loss_mapping 0.0208 cls_loss_causal 0.5323 re_mapping 0.0171 re_causal 0.0233 /// teacc 91.35 lr 0.00109084
306
0.0010908425876598518
changing lr
epoch 55, time 451.23, cls_loss 0.0018 cls_loss_mapping 0.0228 cls_loss_causal 0.5217 re_mapping 0.0167 re_causal 0.0227 /// teacc 91.35 lr 0.00095492
306
0.000954915028125264
changing lr
epoch 56, time 455.62, cls_loss 0.0008 cls_loss_mapping 0.0185 cls_loss_causal 0.5520 re_mapping 0.0165 re_causal 0.0225 /// teacc 90.87 lr 0.00082713
306
0.0008271337313934874
changing lr
epoch 57, time 455.64, cls_loss 0.0015 cls_loss_mapping 0.0242 cls_loss_causal 0.5776 re_mapping 0.0162 re_causal 0.0225 /// teacc 90.87 lr 0.00070776
306
0.00070775603199067
changing lr
epoch 58, time 446.78, cls_loss 0.0009 cls_loss_mapping 0.0185 cls_loss_causal 0.5541 re_mapping 0.0158 re_causal 0.0221 /// teacc 91.35 lr 0.00059702
306
0.0005970223407163104
changing lr
epoch 59, time 451.88, cls_loss 0.0025 cls_loss_mapping 0.0193 cls_loss_causal 0.5280 re_mapping 0.0156 re_causal 0.0217 /// teacc 92.31 lr 0.00049516
306
0.0004951556604879052
changing lr
epoch 60, time 459.80, cls_loss 0.0019 cls_loss_mapping 0.0191 cls_loss_causal 0.5650 re_mapping 0.0154 re_causal 0.0212 /// teacc 91.83 lr 0.00040236
306
0.00040236113724274745
changing lr
epoch 61, time 456.30, cls_loss 0.0013 cls_loss_mapping 0.0195 cls_loss_causal 0.5573 re_mapping 0.0151 re_causal 0.0209 /// teacc 90.87 lr 0.00031883
306
0.00031882564680131423
changing lr
epoch 62, time 461.25, cls_loss 0.0016 cls_loss_mapping 0.0184 cls_loss_causal 0.5320 re_mapping 0.0149 re_causal 0.0203 /// teacc 91.83 lr 0.00024472
306
0.0002447174185242325
changing lr
epoch 63, time 461.95, cls_loss 0.0025 cls_loss_mapping 0.0234 cls_loss_causal 0.5478 re_mapping 0.0148 re_causal 0.0203 /// teacc 91.35 lr 0.00018019
306
0.0001801856965207339
changing lr
epoch 64, time 443.04, cls_loss 0.0012 cls_loss_mapping 0.0208 cls_loss_causal 0.5022 re_mapping 0.0147 re_causal 0.0200 /// teacc 91.35 lr 0.00012536
306
0.000125360439090882
changing lr
epoch 65, time 454.35, cls_loss 0.0012 cls_loss_mapping 0.0176 cls_loss_causal 0.5745 re_mapping 0.0147 re_causal 0.0203 /// teacc 91.83 lr 0.00008035
306
8.03520570068517e-05
changing lr
epoch 66, time 462.74, cls_loss 0.0018 cls_loss_mapping 0.0228 cls_loss_causal 0.5579 re_mapping 0.0147 re_causal 0.0201 /// teacc 91.35 lr 0.00004525
306
4.5251191160326525e-05
changing lr
epoch 67, time 470.10, cls_loss 0.0012 cls_loss_mapping 0.0186 cls_loss_causal 0.5288 re_mapping 0.0147 re_causal 0.0205 /// teacc 92.31 lr 0.00002013
306
2.0128530023804673e-05
changing lr
epoch 68, time 446.31, cls_loss 0.0011 cls_loss_mapping 0.0165 cls_loss_causal 0.5339 re_mapping 0.0146 re_causal 0.0202 /// teacc 89.42 lr 0.00000503
306
5.034667293427056e-06
changing lr
epoch 69, time 458.08, cls_loss 0.0013 cls_loss_mapping 0.0148 cls_loss_causal 0.5422 re_mapping 0.0146 re_causal 0.0204 /// teacc 92.31 lr 0.00000000
---------------------saving last model at epoch 69----------------------------------------------------
/home/yuqian_fu
{'gpu': '0', 'svroot': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//art_painting/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal', 'source_domain': 'art_painting', 'svpath': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//art_painting/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal/art_painting_16factor_last_test_check.csv', 'factor_num': 16, 'epoch': 'last', 'stride': 5, 'eval_mapping': False, 'network': 'resnet18'}
-------------------------------------loading pretrain weights----------------------------------
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randm: False
stride: 5
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columns: ['art_painting', 'cartoon', 'photo', 'sketch']
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/art_painting_test.hdf5 torch.Size([2048, 3, 227, 227]) torch.Size([2048])
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/cartoon_test.hdf5 torch.Size([2344, 3, 227, 227]) torch.Size([2344])
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/photo_test.hdf5 torch.Size([1670, 3, 227, 227]) torch.Size([1670])
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/sketch_test.hdf5 torch.Size([3929, 3, 227, 227]) torch.Size([3929])
                     art_painting    cartoon      photo     sketch        Avg
w/o do (original x)     99.169922  65.784983  95.209581  64.596589  75.197051
    art_painting    cartoon      photo     sketch        Avg
do     99.072266  64.803754  95.269461  64.342072  74.805096