/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 changing lr 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 changing lr 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 changing lr 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 changing lr 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 changing lr ---------------------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 changing lr 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 changing lr 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 changing lr 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 changing lr ---------------------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 changing lr 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 changing lr ---------------------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 changing lr 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 changing lr ---------------------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 changing lr 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---------------------------------- loading weight of last randm: False stride: 5 loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last loading weight of last 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