/home/yuqian_fu {'gpu': '0', 'data': 'cartoon', '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//cartoon/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/cartoon_train.hdf5 torch.Size([2107, 3, 227, 227]) torch.Size([2107]) --------------------------CA_multiple-------------------------- ---------------------------16 factors----------------- randm: True randn: True n: 3 randm: False /data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/cartoon_val.hdf5 torch.Size([237, 3, 227, 227]) torch.Size([237]) -------------------------------------loading pretrain weights---------------------------------- 351 0.01 changing lr ---------------------saving model at epoch 0---------------------------------------------------- epoch 0, time 500.68, cls_loss 5.0126 cls_loss_mapping 1.4019 cls_loss_causal 1.7210 re_mapping 1.0578 re_causal 1.0584 /// teacc 83.12 lr 0.00999497 351 0.009994965332706574 changing lr ---------------------saving model at epoch 1---------------------------------------------------- epoch 1, time 530.56, cls_loss 2.0306 cls_loss_mapping 0.7946 cls_loss_causal 1.3288 re_mapping 0.6527 re_causal 0.6538 /// teacc 87.76 lr 0.00997987 351 0.009979871469976196 changing lr ---------------------saving model at epoch 2---------------------------------------------------- epoch 2, time 536.46, cls_loss 0.6382 cls_loss_mapping 0.4834 cls_loss_causal 1.1278 re_mapping 0.3952 re_causal 0.3957 /// teacc 91.98 lr 0.00995475 351 0.009954748808839675 changing lr ---------------------saving model at epoch 3---------------------------------------------------- epoch 3, time 514.73, cls_loss 0.2115 cls_loss_mapping 0.3043 cls_loss_causal 0.9479 re_mapping 0.2605 re_causal 0.2608 /// teacc 92.41 lr 0.00991965 351 0.009919647942993149 changing lr epoch 4, time 518.58, cls_loss 0.1048 cls_loss_mapping 0.2504 cls_loss_causal 0.8913 re_mapping 0.2075 re_causal 0.2080 /// teacc 92.41 lr 0.00987464 351 0.009874639560909117 changing lr ---------------------saving model at epoch 5---------------------------------------------------- epoch 5, time 522.88, cls_loss 0.0517 cls_loss_mapping 0.2038 cls_loss_causal 0.8571 re_mapping 0.1746 re_causal 0.1753 /// teacc 95.36 lr 0.00981981 351 0.009819814303479266 changing lr epoch 6, time 515.51, cls_loss 0.0244 cls_loss_mapping 0.1830 cls_loss_causal 0.7905 re_mapping 0.1502 re_causal 0.1512 /// teacc 94.51 lr 0.00975528 351 0.009755282581475767 changing lr epoch 7, time 516.68, cls_loss 0.0226 cls_loss_mapping 0.1536 cls_loss_causal 0.7386 re_mapping 0.1335 re_causal 0.1347 /// teacc 94.94 lr 0.00968117 351 0.009681174353198686 changing lr epoch 8, time 512.83, cls_loss 0.0311 cls_loss_mapping 0.1488 cls_loss_causal 0.7284 re_mapping 0.1200 re_causal 0.1218 /// teacc 91.56 lr 0.00959764 351 0.009597638862757255 changing lr ---------------------saving model at epoch 9---------------------------------------------------- epoch 9, time 515.75, cls_loss 0.0257 cls_loss_mapping 0.1258 cls_loss_causal 0.7038 re_mapping 0.1090 re_causal 0.1110 /// teacc 95.78 lr 0.00950484 351 0.009504844339512096 changing lr epoch 10, time 508.97, cls_loss 0.0086 cls_loss_mapping 0.1049 cls_loss_causal 0.7078 re_mapping 0.0973 re_causal 0.0997 /// teacc 94.09 lr 0.00940298 351 0.009402977659283692 changing lr epoch 11, time 522.72, cls_loss 0.0121 cls_loss_mapping 0.1017 cls_loss_causal 0.6880 re_mapping 0.0899 re_causal 0.0929 /// teacc 95.36 lr 0.00929224 351 0.009292243968009333 changing lr epoch 12, time 511.13, cls_loss 0.0138 cls_loss_mapping 0.0946 cls_loss_causal 0.7011 re_mapping 0.0820 re_causal 0.0855 /// teacc 94.94 lr 0.00917287 351 0.009172866268606516 changing lr epoch 13, time 522.62, cls_loss 0.0104 cls_loss_mapping 0.0844 cls_loss_causal 0.6675 re_mapping 0.0747 re_causal 0.0784 /// teacc 94.51 lr 0.00904508 351 0.00904508497187474 changing lr epoch 14, time 530.18, cls_loss 0.0122 cls_loss_mapping 0.0736 cls_loss_causal 0.6363 re_mapping 0.0698 re_causal 0.0745 /// teacc 95.78 lr 0.00890916 351 0.008909157412340152 changing lr epoch 15, time 513.03, cls_loss 0.0108 cls_loss_mapping 0.0735 cls_loss_causal 0.6055 re_mapping 0.0623 re_causal 0.0673 /// teacc 94.94 lr 0.00876536 351 0.00876535733001806 changing lr ---------------------saving model at epoch 16---------------------------------------------------- epoch 16, time 515.62, cls_loss 0.0097 cls_loss_mapping 0.0626 cls_loss_causal 0.6328 re_mapping 0.0572 re_causal 0.0629 /// teacc 97.05 lr 0.00861397 351 0.008613974319136962 changing lr epoch 17, time 534.26, cls_loss 0.0145 cls_loss_mapping 0.0706 cls_loss_causal 0.6484 re_mapping 0.0533 re_causal 0.0603 /// teacc 96.20 lr 0.00845531 351 0.008455313244934327 changing lr epoch 18, time 532.03, cls_loss 0.0106 cls_loss_mapping 0.0571 cls_loss_causal 0.5705 re_mapping 0.0492 re_causal 0.0567 /// teacc 96.62 lr 0.00828969 351 0.008289693629698565 changing lr epoch 19, time 518.07, cls_loss 0.0076 cls_loss_mapping 0.0474 cls_loss_causal 0.5525 re_mapping 0.0441 re_causal 0.0513 /// teacc 95.78 lr 0.00811745 351 0.00811744900929367 changing lr epoch 20, time 530.26, cls_loss 0.0081 cls_loss_mapping 0.0546 cls_loss_causal 0.5926 re_mapping 0.0409 re_causal 0.0491 /// teacc 97.05 lr 0.00793893 351 0.007938926261462368 changing lr epoch 21, time 534.00, cls_loss 0.0104 cls_loss_mapping 0.0511 cls_loss_causal 0.5469 re_mapping 0.0373 re_causal 0.0451 /// teacc 95.78 lr 0.00775448 351 0.007754484907260515 changing lr epoch 22, time 534.87, cls_loss 0.0148 cls_loss_mapping 0.0474 cls_loss_causal 0.5694 re_mapping 0.0353 re_causal 0.0430 /// teacc 95.36 lr 0.00756450 351 0.007564496387029534 changing lr epoch 23, time 515.04, cls_loss 0.0053 cls_loss_mapping 0.0395 cls_loss_causal 0.5557 re_mapping 0.0324 re_causal 0.0409 /// teacc 96.62 lr 0.00736934 351 0.007369343312364995 changing lr epoch 24, time 527.73, cls_loss 0.0083 cls_loss_mapping 0.0487 cls_loss_causal 0.5594 re_mapping 0.0306 re_causal 0.0402 /// teacc 94.94 lr 0.00716942 351 0.0071694186955877925 changing lr epoch 25, time 521.10, cls_loss 0.0080 cls_loss_mapping 0.0392 cls_loss_causal 0.5600 re_mapping 0.0291 re_causal 0.0390 /// teacc 97.05 lr 0.00696513 351 0.0069651251582696205 changing lr epoch 26, time 528.30, cls_loss 0.0054 cls_loss_mapping 0.0316 cls_loss_causal 0.5380 re_mapping 0.0270 re_causal 0.0366 /// teacc 96.20 lr 0.00675687 351 0.006756874120406716 changing lr epoch 27, time 526.16, cls_loss 0.0075 cls_loss_mapping 0.0354 cls_loss_causal 0.5384 re_mapping 0.0251 re_causal 0.0347 /// teacc 97.05 lr 0.00654508 351 0.00654508497187474 changing lr epoch 28, time 520.46, cls_loss 0.0066 cls_loss_mapping 0.0281 cls_loss_causal 0.5043 re_mapping 0.0240 re_causal 0.0354 /// teacc 96.62 lr 0.00633018 351 0.006330184227833378 changing lr epoch 29, time 536.80, cls_loss 0.0074 cls_loss_mapping 0.0305 cls_loss_causal 0.5296 re_mapping 0.0227 re_causal 0.0341 /// teacc 95.78 lr 0.00611260 351 0.006112604669781575 changing lr epoch 30, time 530.96, cls_loss 0.0062 cls_loss_mapping 0.0301 cls_loss_causal 0.5251 re_mapping 0.0214 re_causal 0.0317 /// teacc 96.20 lr 0.00589278 351 0.005892784473993186 changing lr epoch 31, time 528.74, cls_loss 0.0051 cls_loss_mapping 0.0263 cls_loss_causal 0.5350 re_mapping 0.0205 re_causal 0.0317 /// teacc 96.20 lr 0.00567117 351 0.00567116632908828 changing lr ---------------------saving model at epoch 32---------------------------------------------------- epoch 32, time 517.42, cls_loss 0.0051 cls_loss_mapping 0.0225 cls_loss_causal 0.5060 re_mapping 0.0197 re_causal 0.0305 /// teacc 97.47 lr 0.00544820 351 0.00544819654451717 changing lr ---------------------saving model at epoch 33---------------------------------------------------- epoch 33, time 532.29, cls_loss 0.0050 cls_loss_mapping 0.0196 cls_loss_causal 0.5099 re_mapping 0.0185 re_causal 0.0291 /// teacc 97.89 lr 0.00522432 351 0.005224324151752577 changing lr epoch 34, time 521.23, cls_loss 0.0079 cls_loss_mapping 0.0235 cls_loss_causal 0.5058 re_mapping 0.0177 re_causal 0.0285 /// teacc 97.89 lr 0.00500000 351 0.005000000000000003 changing lr epoch 35, time 521.42, cls_loss 0.0054 cls_loss_mapping 0.0236 cls_loss_causal 0.4683 re_mapping 0.0178 re_causal 0.0281 /// teacc 97.05 lr 0.00477568 351 0.004775675848247429 changing lr epoch 36, time 526.29, cls_loss 0.0057 cls_loss_mapping 0.0231 cls_loss_causal 0.5159 re_mapping 0.0172 re_causal 0.0278 /// teacc 97.05 lr 0.00455180 351 0.004551803455482836 changing lr epoch 37, time 535.59, cls_loss 0.0063 cls_loss_mapping 0.0199 cls_loss_causal 0.4658 re_mapping 0.0163 re_causal 0.0267 /// teacc 97.47 lr 0.00432883 351 0.004328833670911726 changing lr epoch 38, time 512.58, cls_loss 0.0045 cls_loss_mapping 0.0199 cls_loss_causal 0.4925 re_mapping 0.0155 re_causal 0.0258 /// teacc 97.05 lr 0.00410722 351 0.0041072155260068206 changing lr epoch 39, time 532.69, cls_loss 0.0056 cls_loss_mapping 0.0220 cls_loss_causal 0.4772 re_mapping 0.0150 re_causal 0.0253 /// teacc 97.47 lr 0.00388740 351 0.0038873953302184317 changing lr epoch 40, time 536.18, cls_loss 0.0044 cls_loss_mapping 0.0185 cls_loss_causal 0.4992 re_mapping 0.0146 re_causal 0.0241 /// teacc 97.47 lr 0.00366982 351 0.003669815772166629 changing lr epoch 41, time 531.87, cls_loss 0.0044 cls_loss_mapping 0.0147 cls_loss_causal 0.4840 re_mapping 0.0144 re_causal 0.0246 /// teacc 97.89 lr 0.00345492 351 0.0034549150281252667 changing lr ---------------------saving model at epoch 42---------------------------------------------------- epoch 42, time 509.65, cls_loss 0.0045 cls_loss_mapping 0.0164 cls_loss_causal 0.4600 re_mapping 0.0136 re_causal 0.0224 /// teacc 98.31 lr 0.00324313 351 0.0032431258795932905 changing lr epoch 43, time 520.56, cls_loss 0.0051 cls_loss_mapping 0.0169 cls_loss_causal 0.5021 re_mapping 0.0137 re_causal 0.0235 /// teacc 97.47 lr 0.00303487 351 0.0030348748417303863 changing lr ---------------------saving model at epoch 44---------------------------------------------------- epoch 44, time 532.35, cls_loss 0.0042 cls_loss_mapping 0.0153 cls_loss_causal 0.4512 re_mapping 0.0131 re_causal 0.0230 /// teacc 98.73 lr 0.00283058 351 0.0028305813044122124 changing lr epoch 45, time 523.83, cls_loss 0.0053 cls_loss_mapping 0.0159 cls_loss_causal 0.4523 re_mapping 0.0130 re_causal 0.0219 /// teacc 97.89 lr 0.00263066 351 0.0026306566876350096 changing lr epoch 46, time 536.05, cls_loss 0.0050 cls_loss_mapping 0.0148 cls_loss_causal 0.4521 re_mapping 0.0125 re_causal 0.0215 /// teacc 96.62 lr 0.00243550 351 0.0024355036129704724 changing lr epoch 47, time 509.13, cls_loss 0.0043 cls_loss_mapping 0.0159 cls_loss_causal 0.4864 re_mapping 0.0121 re_causal 0.0214 /// teacc 97.89 lr 0.00224552 351 0.00224551509273949 changing lr epoch 48, time 524.58, cls_loss 0.0037 cls_loss_mapping 0.0109 cls_loss_causal 0.4474 re_mapping 0.0120 re_causal 0.0208 /// teacc 98.31 lr 0.00206107 351 0.002061073738537637 changing lr epoch 49, time 517.27, cls_loss 0.0033 cls_loss_mapping 0.0125 cls_loss_causal 0.4527 re_mapping 0.0117 re_causal 0.0205 /// teacc 97.89 lr 0.00188255 351 0.0018825509907063344 changing lr epoch 50, time 516.76, cls_loss 0.0039 cls_loss_mapping 0.0142 cls_loss_causal 0.4602 re_mapping 0.0116 re_causal 0.0204 /// teacc 97.47 lr 0.00171031 351 0.0017103063703014388 changing lr epoch 51, time 513.81, cls_loss 0.0025 cls_loss_mapping 0.0098 cls_loss_causal 0.4081 re_mapping 0.0116 re_causal 0.0197 /// teacc 98.31 lr 0.00154469 351 0.0015446867550656784 changing lr epoch 52, time 514.01, cls_loss 0.0042 cls_loss_mapping 0.0125 cls_loss_causal 0.4603 re_mapping 0.0114 re_causal 0.0195 /// teacc 97.89 lr 0.00138603 351 0.001386025680863044 changing lr epoch 53, time 524.35, cls_loss 0.0051 cls_loss_mapping 0.0127 cls_loss_causal 0.4572 re_mapping 0.0111 re_causal 0.0193 /// teacc 97.89 lr 0.00123464 351 0.0012346426699819469 changing lr epoch 54, time 514.44, cls_loss 0.0044 cls_loss_mapping 0.0127 cls_loss_causal 0.4353 re_mapping 0.0111 re_causal 0.0187 /// teacc 97.47 lr 0.00109084 351 0.0010908425876598518 changing lr epoch 55, time 522.77, cls_loss 0.0037 cls_loss_mapping 0.0112 cls_loss_causal 0.4375 re_mapping 0.0109 re_causal 0.0188 /// teacc 98.31 lr 0.00095492 351 0.000954915028125264 changing lr epoch 56, time 523.02, cls_loss 0.0041 cls_loss_mapping 0.0109 cls_loss_causal 0.4403 re_mapping 0.0108 re_causal 0.0186 /// teacc 97.05 lr 0.00082713 351 0.0008271337313934874 changing lr epoch 57, time 527.11, cls_loss 0.0028 cls_loss_mapping 0.0091 cls_loss_causal 0.4157 re_mapping 0.0108 re_causal 0.0176 /// teacc 97.47 lr 0.00070776 351 0.00070775603199067 changing lr epoch 58, time 504.49, cls_loss 0.0031 cls_loss_mapping 0.0086 cls_loss_causal 0.4095 re_mapping 0.0108 re_causal 0.0171 /// teacc 97.89 lr 0.00059702 351 0.0005970223407163104 changing lr epoch 59, time 497.53, cls_loss 0.0053 cls_loss_mapping 0.0115 cls_loss_causal 0.4429 re_mapping 0.0105 re_causal 0.0172 /// teacc 97.05 lr 0.00049516 351 0.0004951556604879052 changing lr epoch 60, time 507.85, cls_loss 0.0043 cls_loss_mapping 0.0108 cls_loss_causal 0.4240 re_mapping 0.0103 re_causal 0.0166 /// teacc 98.31 lr 0.00040236 351 0.00040236113724274745 changing lr epoch 61, time 489.10, cls_loss 0.0040 cls_loss_mapping 0.0104 cls_loss_causal 0.4613 re_mapping 0.0103 re_causal 0.0175 /// teacc 97.05 lr 0.00031883 351 0.00031882564680131423 changing lr epoch 62, time 487.44, cls_loss 0.0040 cls_loss_mapping 0.0101 cls_loss_causal 0.4445 re_mapping 0.0102 re_causal 0.0167 /// teacc 98.31 lr 0.00024472 351 0.0002447174185242325 changing lr epoch 63, time 492.60, cls_loss 0.0030 cls_loss_mapping 0.0067 cls_loss_causal 0.3786 re_mapping 0.0102 re_causal 0.0165 /// teacc 97.89 lr 0.00018019 351 0.0001801856965207339 changing lr epoch 64, time 493.59, cls_loss 0.0040 cls_loss_mapping 0.0106 cls_loss_causal 0.4459 re_mapping 0.0101 re_causal 0.0165 /// teacc 96.62 lr 0.00012536 351 0.000125360439090882 changing lr epoch 65, time 485.22, cls_loss 0.0051 cls_loss_mapping 0.0094 cls_loss_causal 0.4355 re_mapping 0.0101 re_causal 0.0162 /// teacc 97.47 lr 0.00008035 351 8.03520570068517e-05 changing lr epoch 66, time 475.77, cls_loss 0.0036 cls_loss_mapping 0.0086 cls_loss_causal 0.4274 re_mapping 0.0101 re_causal 0.0165 /// teacc 97.89 lr 0.00004525 351 4.5251191160326525e-05 changing lr epoch 67, time 483.43, cls_loss 0.0043 cls_loss_mapping 0.0107 cls_loss_causal 0.4531 re_mapping 0.0102 re_causal 0.0168 /// teacc 96.62 lr 0.00002013 351 2.0128530023804673e-05 changing lr epoch 68, time 484.93, cls_loss 0.0030 cls_loss_mapping 0.0073 cls_loss_causal 0.4376 re_mapping 0.0102 re_causal 0.0166 /// teacc 97.89 lr 0.00000503 351 5.034667293427056e-06 changing lr epoch 69, time 479.93, cls_loss 0.0041 cls_loss_mapping 0.0089 cls_loss_causal 0.4412 re_mapping 0.0100 re_causal 0.0165 /// teacc 96.20 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//cartoon/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal', 'source_domain': 'cartoon', 'svpath': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//cartoon/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal/cartoon_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: ['cartoon', 'art_painting', 'photo', 'sketch'] /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/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/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]) cartoon art_painting photo sketch Avg w/o do (original x) 99.616041 76.806641 89.700599 72.613897 79.707045 cartoon art_painting photo sketch Avg do 99.573379 75.537109 89.760479 73.631967 79.643185