/home/yuqian_fu {'gpu': '0', 'data': 'photo', '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//photo/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/photo_train.hdf5 torch.Size([1499, 3, 227, 227]) torch.Size([1499]) --------------------------CA_multiple-------------------------- ---------------------------16 factors----------------- randm: True randn: True n: 3 randm: False /data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/photo_val.hdf5 torch.Size([171, 3, 227, 227]) torch.Size([171]) -------------------------------------loading pretrain weights---------------------------------- 249 0.01 changing lr ---------------------saving model at epoch 0---------------------------------------------------- epoch 0, time 330.26, cls_loss 2.1901 cls_loss_mapping 1.0966 cls_loss_causal 1.5411 re_mapping 1.2660 re_causal 1.2689 /// teacc 95.32 lr 0.00999497 249 0.009994965332706574 changing lr epoch 1, time 329.57, cls_loss 1.1155 cls_loss_mapping 0.7787 cls_loss_causal 1.4901 re_mapping 0.9558 re_causal 0.9646 /// teacc 93.57 lr 0.00997987 249 0.009979871469976196 changing lr epoch 2, time 330.39, cls_loss 0.9288 cls_loss_mapping 0.7032 cls_loss_causal 1.4747 re_mapping 0.7723 re_causal 0.7847 /// teacc 75.44 lr 0.00995475 249 0.009954748808839675 changing lr epoch 3, time 328.90, cls_loss 1.2627 cls_loss_mapping 0.6321 cls_loss_causal 1.5390 re_mapping 0.6502 re_causal 0.6690 /// teacc 85.96 lr 0.00991965 249 0.009919647942993149 changing lr epoch 4, time 327.35, cls_loss 0.9500 cls_loss_mapping 0.7241 cls_loss_causal 1.5912 re_mapping 0.6164 re_causal 0.6345 /// teacc 93.57 lr 0.00987464 249 0.009874639560909117 changing lr epoch 5, time 326.62, cls_loss 1.4824 cls_loss_mapping 0.9000 cls_loss_causal 1.6461 re_mapping 0.5127 re_causal 0.5278 /// teacc 79.53 lr 0.00981981 249 0.009819814303479266 changing lr epoch 6, time 329.86, cls_loss 0.5391 cls_loss_mapping 0.6994 cls_loss_causal 1.5297 re_mapping 0.4445 re_causal 0.4580 /// teacc 91.23 lr 0.00975528 249 0.009755282581475767 changing lr epoch 7, time 326.96, cls_loss 0.4282 cls_loss_mapping 0.7985 cls_loss_causal 1.5762 re_mapping 0.4031 re_causal 0.4239 /// teacc 90.06 lr 0.00968117 249 0.009681174353198686 changing lr epoch 8, time 324.18, cls_loss 0.4582 cls_loss_mapping 0.6437 cls_loss_causal 1.4929 re_mapping 0.3704 re_causal 0.4016 /// teacc 92.40 lr 0.00959764 249 0.009597638862757255 changing lr epoch 9, time 329.22, cls_loss 0.4196 cls_loss_mapping 0.6434 cls_loss_causal 1.5206 re_mapping 0.3366 re_causal 0.3732 /// teacc 91.23 lr 0.00950484 249 0.009504844339512096 changing lr epoch 10, time 326.22, cls_loss 0.6899 cls_loss_mapping 0.6931 cls_loss_causal 1.4531 re_mapping 0.3138 re_causal 0.3465 /// teacc 91.81 lr 0.00940298 249 0.009402977659283692 changing lr epoch 11, time 332.53, cls_loss 0.2100 cls_loss_mapping 0.5305 cls_loss_causal 1.2812 re_mapping 0.2652 re_causal 0.3015 /// teacc 94.15 lr 0.00929224 249 0.009292243968009333 changing lr epoch 12, time 328.24, cls_loss 1.9157 cls_loss_mapping 1.1807 cls_loss_causal 1.8542 re_mapping 0.2875 re_causal 0.3153 /// teacc 81.29 lr 0.00917287 249 0.009172866268606516 changing lr epoch 13, time 330.89, cls_loss 0.5559 cls_loss_mapping 0.9412 cls_loss_causal 1.5863 re_mapping 0.2804 re_causal 0.3010 /// teacc 88.30 lr 0.00904508 249 0.00904508497187474 changing lr epoch 14, time 327.08, cls_loss 0.2945 cls_loss_mapping 0.7027 cls_loss_causal 1.4399 re_mapping 0.2493 re_causal 0.2637 /// teacc 89.47 lr 0.00890916 249 0.008909157412340152 changing lr epoch 15, time 327.50, cls_loss 0.1556 cls_loss_mapping 0.5735 cls_loss_causal 1.3348 re_mapping 0.2367 re_causal 0.2499 /// teacc 90.64 lr 0.00876536 249 0.00876535733001806 changing lr epoch 16, time 325.93, cls_loss 0.5865 cls_loss_mapping 0.6469 cls_loss_causal 1.4535 re_mapping 0.2249 re_causal 0.2442 /// teacc 83.63 lr 0.00861397 249 0.008613974319136962 changing lr epoch 17, time 325.67, cls_loss 0.2541 cls_loss_mapping 0.5530 cls_loss_causal 1.3152 re_mapping 0.1981 re_causal 0.2108 /// teacc 90.64 lr 0.00845531 249 0.008455313244934327 changing lr epoch 18, time 328.07, cls_loss 0.1021 cls_loss_mapping 0.4746 cls_loss_causal 1.2840 re_mapping 0.1724 re_causal 0.1940 /// teacc 91.81 lr 0.00828969 249 0.008289693629698565 changing lr epoch 19, time 327.66, cls_loss 0.2583 cls_loss_mapping 0.4658 cls_loss_causal 1.3477 re_mapping 0.1511 re_causal 0.1725 /// teacc 85.38 lr 0.00811745 249 0.00811744900929367 changing lr epoch 20, time 330.32, cls_loss 0.2436 cls_loss_mapping 0.4640 cls_loss_causal 1.2885 re_mapping 0.1358 re_causal 0.1612 /// teacc 89.47 lr 0.00793893 249 0.007938926261462368 changing lr epoch 21, time 327.13, cls_loss 0.0809 cls_loss_mapping 0.3624 cls_loss_causal 1.1645 re_mapping 0.1276 re_causal 0.1497 /// teacc 92.40 lr 0.00775448 249 0.007754484907260515 changing lr ---------------------saving model at epoch 22---------------------------------------------------- epoch 22, time 327.51, cls_loss 0.0782 cls_loss_mapping 0.2983 cls_loss_causal 1.0244 re_mapping 0.1161 re_causal 0.1302 /// teacc 95.91 lr 0.00756450 249 0.007564496387029534 changing lr epoch 23, time 328.44, cls_loss 0.0508 cls_loss_mapping 0.2665 cls_loss_causal 1.0062 re_mapping 0.1035 re_causal 0.1238 /// teacc 92.40 lr 0.00736934 249 0.007369343312364995 changing lr epoch 24, time 326.85, cls_loss 0.0439 cls_loss_mapping 0.2489 cls_loss_causal 0.9377 re_mapping 0.0935 re_causal 0.1083 /// teacc 93.57 lr 0.00716942 249 0.0071694186955877925 changing lr epoch 25, time 328.40, cls_loss 0.0447 cls_loss_mapping 0.2510 cls_loss_causal 0.9697 re_mapping 0.0891 re_causal 0.1042 /// teacc 95.32 lr 0.00696513 249 0.0069651251582696205 changing lr epoch 26, time 326.49, cls_loss 0.0183 cls_loss_mapping 0.2090 cls_loss_causal 0.9070 re_mapping 0.0889 re_causal 0.1054 /// teacc 94.15 lr 0.00675687 249 0.006756874120406716 changing lr epoch 27, time 329.18, cls_loss 0.0199 cls_loss_mapping 0.2252 cls_loss_causal 0.9563 re_mapping 0.0849 re_causal 0.1040 /// teacc 92.40 lr 0.00654508 249 0.00654508497187474 changing lr epoch 28, time 331.00, cls_loss 0.0349 cls_loss_mapping 0.1811 cls_loss_causal 0.8829 re_mapping 0.0737 re_causal 0.0947 /// teacc 94.15 lr 0.00633018 249 0.006330184227833378 changing lr epoch 29, time 330.93, cls_loss 0.0173 cls_loss_mapping 0.1582 cls_loss_causal 0.8307 re_mapping 0.0685 re_causal 0.0870 /// teacc 95.91 lr 0.00611260 249 0.006112604669781575 changing lr ---------------------saving model at epoch 30---------------------------------------------------- epoch 30, time 333.04, cls_loss 0.0136 cls_loss_mapping 0.1520 cls_loss_causal 0.8025 re_mapping 0.0632 re_causal 0.0809 /// teacc 97.08 lr 0.00589278 249 0.005892784473993186 changing lr epoch 31, time 328.71, cls_loss 0.0093 cls_loss_mapping 0.1464 cls_loss_causal 0.7705 re_mapping 0.0664 re_causal 0.0860 /// teacc 95.32 lr 0.00567117 249 0.00567116632908828 changing lr epoch 32, time 331.65, cls_loss 0.0048 cls_loss_mapping 0.1322 cls_loss_causal 0.7072 re_mapping 0.0552 re_causal 0.0736 /// teacc 95.32 lr 0.00544820 249 0.00544819654451717 changing lr ---------------------saving model at epoch 33---------------------------------------------------- epoch 33, time 331.93, cls_loss 0.0196 cls_loss_mapping 0.1406 cls_loss_causal 0.7016 re_mapping 0.0551 re_causal 0.0790 /// teacc 97.66 lr 0.00522432 249 0.005224324151752577 changing lr epoch 34, time 326.51, cls_loss 0.0110 cls_loss_mapping 0.1272 cls_loss_causal 0.7379 re_mapping 0.0532 re_causal 0.0753 /// teacc 95.91 lr 0.00500000 249 0.005000000000000003 changing lr epoch 35, time 326.79, cls_loss 0.0039 cls_loss_mapping 0.1204 cls_loss_causal 0.7016 re_mapping 0.0500 re_causal 0.0750 /// teacc 96.49 lr 0.00477568 249 0.004775675848247429 changing lr epoch 36, time 328.75, cls_loss 0.0098 cls_loss_mapping 0.1122 cls_loss_causal 0.6372 re_mapping 0.0458 re_causal 0.0661 /// teacc 95.32 lr 0.00455180 249 0.004551803455482836 changing lr epoch 37, time 333.82, cls_loss 0.0088 cls_loss_mapping 0.1083 cls_loss_causal 0.6648 re_mapping 0.0459 re_causal 0.0701 /// teacc 95.91 lr 0.00432883 249 0.004328833670911726 changing lr epoch 38, time 328.36, cls_loss 0.0111 cls_loss_mapping 0.1082 cls_loss_causal 0.6774 re_mapping 0.0479 re_causal 0.0716 /// teacc 94.74 lr 0.00410722 249 0.0041072155260068206 changing lr epoch 39, time 329.81, cls_loss 0.0019 cls_loss_mapping 0.0890 cls_loss_causal 0.6447 re_mapping 0.0461 re_causal 0.0699 /// teacc 95.32 lr 0.00388740 249 0.0038873953302184317 changing lr epoch 40, time 329.55, cls_loss 0.0031 cls_loss_mapping 0.0853 cls_loss_causal 0.5882 re_mapping 0.0445 re_causal 0.0632 /// teacc 94.74 lr 0.00366982 249 0.003669815772166629 changing lr epoch 41, time 330.31, cls_loss 0.0050 cls_loss_mapping 0.0811 cls_loss_causal 0.5662 re_mapping 0.0384 re_causal 0.0568 /// teacc 95.32 lr 0.00345492 249 0.0034549150281252667 changing lr epoch 42, time 333.18, cls_loss 0.0062 cls_loss_mapping 0.0839 cls_loss_causal 0.6104 re_mapping 0.0375 re_causal 0.0582 /// teacc 95.91 lr 0.00324313 249 0.0032431258795932905 changing lr epoch 43, time 329.10, cls_loss 0.0014 cls_loss_mapping 0.0792 cls_loss_causal 0.5998 re_mapping 0.0385 re_causal 0.0578 /// teacc 96.49 lr 0.00303487 249 0.0030348748417303863 changing lr epoch 44, time 327.44, cls_loss 0.0038 cls_loss_mapping 0.0816 cls_loss_causal 0.5993 re_mapping 0.0363 re_causal 0.0564 /// teacc 96.49 lr 0.00283058 249 0.0028305813044122124 changing lr epoch 45, time 328.69, cls_loss 0.0064 cls_loss_mapping 0.0724 cls_loss_causal 0.5434 re_mapping 0.0350 re_causal 0.0566 /// teacc 97.08 lr 0.00263066 249 0.0026306566876350096 changing lr epoch 46, time 329.11, cls_loss 0.0036 cls_loss_mapping 0.0732 cls_loss_causal 0.6550 re_mapping 0.0336 re_causal 0.0560 /// teacc 97.66 lr 0.00243550 249 0.0024355036129704724 changing lr epoch 47, time 330.95, cls_loss 0.0028 cls_loss_mapping 0.0696 cls_loss_causal 0.5213 re_mapping 0.0347 re_causal 0.0540 /// teacc 95.32 lr 0.00224552 249 0.00224551509273949 changing lr epoch 48, time 329.49, cls_loss 0.0022 cls_loss_mapping 0.0614 cls_loss_causal 0.5186 re_mapping 0.0319 re_causal 0.0531 /// teacc 97.08 lr 0.00206107 249 0.002061073738537637 changing lr epoch 49, time 327.39, cls_loss 0.0030 cls_loss_mapping 0.0631 cls_loss_causal 0.5368 re_mapping 0.0315 re_causal 0.0477 /// teacc 97.08 lr 0.00188255 249 0.0018825509907063344 changing lr epoch 50, time 330.68, cls_loss 0.0025 cls_loss_mapping 0.0624 cls_loss_causal 0.5418 re_mapping 0.0308 re_causal 0.0501 /// teacc 95.91 lr 0.00171031 249 0.0017103063703014388 changing lr epoch 51, time 331.11, cls_loss 0.0024 cls_loss_mapping 0.0666 cls_loss_causal 0.6219 re_mapping 0.0303 re_causal 0.0463 /// teacc 94.15 lr 0.00154469 249 0.0015446867550656784 changing lr epoch 52, time 329.80, cls_loss 0.0037 cls_loss_mapping 0.0624 cls_loss_causal 0.5204 re_mapping 0.0305 re_causal 0.0459 /// teacc 95.91 lr 0.00138603 249 0.001386025680863044 changing lr epoch 53, time 330.30, cls_loss 0.0021 cls_loss_mapping 0.0573 cls_loss_causal 0.4976 re_mapping 0.0330 re_causal 0.0522 /// teacc 96.49 lr 0.00123464 249 0.0012346426699819469 changing lr epoch 54, time 328.15, cls_loss 0.0037 cls_loss_mapping 0.0636 cls_loss_causal 0.5476 re_mapping 0.0300 re_causal 0.0478 /// teacc 94.74 lr 0.00109084 249 0.0010908425876598518 changing lr epoch 55, time 330.82, cls_loss 0.0019 cls_loss_mapping 0.0573 cls_loss_causal 0.4965 re_mapping 0.0298 re_causal 0.0464 /// teacc 94.74 lr 0.00095492 249 0.000954915028125264 changing lr epoch 56, time 327.34, cls_loss 0.0026 cls_loss_mapping 0.0569 cls_loss_causal 0.5251 re_mapping 0.0303 re_causal 0.0466 /// teacc 95.91 lr 0.00082713 249 0.0008271337313934874 changing lr epoch 57, time 333.58, cls_loss 0.0042 cls_loss_mapping 0.0546 cls_loss_causal 0.5309 re_mapping 0.0287 re_causal 0.0428 /// teacc 95.32 lr 0.00070776 249 0.00070775603199067 changing lr epoch 58, time 328.86, cls_loss 0.0031 cls_loss_mapping 0.0587 cls_loss_causal 0.5149 re_mapping 0.0288 re_causal 0.0456 /// teacc 96.49 lr 0.00059702 249 0.0005970223407163104 changing lr epoch 59, time 328.86, cls_loss 0.0046 cls_loss_mapping 0.0559 cls_loss_causal 0.5242 re_mapping 0.0292 re_causal 0.0461 /// teacc 95.32 lr 0.00049516 249 0.0004951556604879052 changing lr epoch 60, time 329.33, cls_loss 0.0035 cls_loss_mapping 0.0531 cls_loss_causal 0.5105 re_mapping 0.0286 re_causal 0.0415 /// teacc 94.74 lr 0.00040236 249 0.00040236113724274745 changing lr epoch 61, time 329.57, cls_loss 0.0024 cls_loss_mapping 0.0552 cls_loss_causal 0.5395 re_mapping 0.0269 re_causal 0.0440 /// teacc 95.91 lr 0.00031883 249 0.00031882564680131423 changing lr epoch 62, time 333.79, cls_loss 0.0025 cls_loss_mapping 0.0505 cls_loss_causal 0.5307 re_mapping 0.0257 re_causal 0.0430 /// teacc 95.32 lr 0.00024472 249 0.0002447174185242325 changing lr epoch 63, time 325.49, cls_loss 0.0033 cls_loss_mapping 0.0561 cls_loss_causal 0.5009 re_mapping 0.0285 re_causal 0.0429 /// teacc 96.49 lr 0.00018019 249 0.0001801856965207339 changing lr epoch 64, time 325.60, cls_loss 0.0020 cls_loss_mapping 0.0478 cls_loss_causal 0.5195 re_mapping 0.0274 re_causal 0.0416 /// teacc 95.32 lr 0.00012536 249 0.000125360439090882 changing lr epoch 65, time 329.45, cls_loss 0.0022 cls_loss_mapping 0.0502 cls_loss_causal 0.4924 re_mapping 0.0274 re_causal 0.0425 /// teacc 94.15 lr 0.00008035 249 8.03520570068517e-05 changing lr epoch 66, time 331.82, cls_loss 0.0036 cls_loss_mapping 0.0536 cls_loss_causal 0.5226 re_mapping 0.0276 re_causal 0.0429 /// teacc 95.91 lr 0.00004525 249 4.5251191160326525e-05 changing lr epoch 67, time 328.42, cls_loss 0.0030 cls_loss_mapping 0.0563 cls_loss_causal 0.5390 re_mapping 0.0282 re_causal 0.0435 /// teacc 95.32 lr 0.00002013 249 2.0128530023804673e-05 changing lr epoch 68, time 331.35, cls_loss 0.0034 cls_loss_mapping 0.0501 cls_loss_causal 0.5100 re_mapping 0.0269 re_causal 0.0424 /// teacc 95.32 lr 0.00000503 249 5.034667293427056e-06 changing lr epoch 69, time 332.59, cls_loss 0.0023 cls_loss_mapping 0.0540 cls_loss_causal 0.5166 re_mapping 0.0279 re_causal 0.0451 /// teacc 97.08 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//photo/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal', 'source_domain': 'photo', 'svpath': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//photo/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal/photo_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: ['photo', 'art_painting', 'cartoon', 'sketch'] /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/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/sketch_test.hdf5 torch.Size([3929, 3, 227, 227]) torch.Size([3929]) photo art_painting cartoon sketch Avg w/o do (original x) 99.700599 60.253906 43.515358 57.724612 53.831292 photo art_painting cartoon sketch Avg do 99.760479 60.15625 49.274744 60.57521 56.668735