Meta-causal/code/56722.log

/home/yuqian_fu
{'gpu': '0', 'data': 'sketch', '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//sketch/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/sketch_train.hdf5 torch.Size([3531, 3, 227, 227]) torch.Size([3531])
--------------------------CA_multiple--------------------------
---------------------------16 factors-----------------
randm: True
randn: True
n: 3
randm: False
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/sketch_val.hdf5 torch.Size([398, 3, 227, 227]) torch.Size([398])
-------------------------------------loading pretrain weights----------------------------------
588
0.01
changing lr
---------------------saving model at epoch 0----------------------------------------------------
epoch 0, time 845.95, cls_loss 3.6738 cls_loss_mapping 1.1243 cls_loss_causal 1.4459 re_mapping 0.6948 re_causal 0.6950 /// teacc 87.69 lr 0.00999497
588
0.009994965332706574
changing lr
---------------------saving model at epoch 1----------------------------------------------------
epoch 1, time 854.64, cls_loss 0.5577 cls_loss_mapping 0.4581 cls_loss_causal 1.0021 re_mapping 0.2545 re_causal 0.2541 /// teacc 90.20 lr 0.00997987
588
0.009979871469976196
changing lr
epoch 2, time 878.75, cls_loss 0.1988 cls_loss_mapping 0.2884 cls_loss_causal 0.8433 re_mapping 0.1588 re_causal 0.1584 /// teacc 89.70 lr 0.00995475
588
0.009954748808839675
changing lr
---------------------saving model at epoch 3----------------------------------------------------
epoch 3, time 892.14, cls_loss 0.1337 cls_loss_mapping 0.2165 cls_loss_causal 0.7708 re_mapping 0.1261 re_causal 0.1263 /// teacc 92.96 lr 0.00991965
588
0.009919647942993149
changing lr
---------------------saving model at epoch 4----------------------------------------------------
epoch 4, time 852.27, cls_loss 0.0720 cls_loss_mapping 0.1604 cls_loss_causal 0.6995 re_mapping 0.1031 re_causal 0.1040 /// teacc 93.22 lr 0.00987464
588
0.009874639560909117
changing lr
---------------------saving model at epoch 5----------------------------------------------------
epoch 5, time 847.00, cls_loss 0.0390 cls_loss_mapping 0.1253 cls_loss_causal 0.6512 re_mapping 0.0839 re_causal 0.0858 /// teacc 93.72 lr 0.00981981
588
0.009819814303479266
changing lr
epoch 6, time 868.82, cls_loss 0.0280 cls_loss_mapping 0.1074 cls_loss_causal 0.6153 re_mapping 0.0698 re_causal 0.0724 /// teacc 92.46 lr 0.00975528
588
0.009755282581475767
changing lr
---------------------saving model at epoch 7----------------------------------------------------
epoch 7, time 845.22, cls_loss 0.0251 cls_loss_mapping 0.0936 cls_loss_causal 0.5946 re_mapping 0.0582 re_causal 0.0616 /// teacc 93.97 lr 0.00968117
588
0.009681174353198686
changing lr
epoch 8, time 850.90, cls_loss 0.0209 cls_loss_mapping 0.0815 cls_loss_causal 0.5643 re_mapping 0.0500 re_causal 0.0549 /// teacc 92.71 lr 0.00959764
588
0.009597638862757255
changing lr
---------------------saving model at epoch 9----------------------------------------------------
epoch 9, time 861.45, cls_loss 0.0196 cls_loss_mapping 0.0758 cls_loss_causal 0.5799 re_mapping 0.0425 re_causal 0.0492 /// teacc 94.97 lr 0.00950484
588
0.009504844339512096
changing lr
epoch 10, time 887.24, cls_loss 0.0124 cls_loss_mapping 0.0557 cls_loss_causal 0.5474 re_mapping 0.0349 re_causal 0.0424 /// teacc 94.22 lr 0.00940298
588
0.009402977659283692
changing lr
epoch 11, time 860.82, cls_loss 0.0155 cls_loss_mapping 0.0521 cls_loss_causal 0.5327 re_mapping 0.0303 re_causal 0.0397 /// teacc 92.96 lr 0.00929224
588
0.009292243968009333
changing lr
epoch 12, time 882.17, cls_loss 0.0122 cls_loss_mapping 0.0491 cls_loss_causal 0.5306 re_mapping 0.0254 re_causal 0.0353 /// teacc 92.46 lr 0.00917287
588
0.009172866268606516
changing lr
epoch 13, time 886.31, cls_loss 0.0123 cls_loss_mapping 0.0488 cls_loss_causal 0.5114 re_mapping 0.0231 re_causal 0.0341 /// teacc 93.97 lr 0.00904508
588
0.00904508497187474
changing lr
epoch 14, time 860.16, cls_loss 0.0097 cls_loss_mapping 0.0391 cls_loss_causal 0.5384 re_mapping 0.0201 re_causal 0.0320 /// teacc 94.47 lr 0.00890916
588
0.008909157412340152
changing lr
epoch 15, time 851.33, cls_loss 0.0083 cls_loss_mapping 0.0374 cls_loss_causal 0.4965 re_mapping 0.0180 re_causal 0.0299 /// teacc 92.71 lr 0.00876536
588
0.00876535733001806
changing lr
epoch 16, time 856.91, cls_loss 0.0087 cls_loss_mapping 0.0368 cls_loss_causal 0.4716 re_mapping 0.0163 re_causal 0.0278 /// teacc 91.96 lr 0.00861397
588
0.008613974319136962
changing lr
epoch 17, time 881.17, cls_loss 0.0070 cls_loss_mapping 0.0307 cls_loss_causal 0.4890 re_mapping 0.0146 re_causal 0.0269 /// teacc 93.47 lr 0.00845531
588
0.008455313244934327
changing lr
epoch 18, time 868.14, cls_loss 0.0060 cls_loss_mapping 0.0262 cls_loss_causal 0.4835 re_mapping 0.0132 re_causal 0.0256 /// teacc 93.47 lr 0.00828969
588
0.008289693629698565
changing lr
epoch 19, time 883.13, cls_loss 0.0067 cls_loss_mapping 0.0258 cls_loss_causal 0.4667 re_mapping 0.0123 re_causal 0.0250 /// teacc 93.72 lr 0.00811745
588
0.00811744900929367
changing lr
epoch 20, time 884.58, cls_loss 0.0064 cls_loss_mapping 0.0251 cls_loss_causal 0.4629 re_mapping 0.0117 re_causal 0.0239 /// teacc 94.72 lr 0.00793893
588
0.007938926261462368
changing lr
epoch 21, time 875.05, cls_loss 0.0051 cls_loss_mapping 0.0202 cls_loss_causal 0.4715 re_mapping 0.0107 re_causal 0.0234 /// teacc 92.46 lr 0.00775448
588
0.007754484907260515
changing lr
epoch 22, time 896.60, cls_loss 0.0054 cls_loss_mapping 0.0194 cls_loss_causal 0.4351 re_mapping 0.0099 re_causal 0.0214 /// teacc 94.97 lr 0.00756450
588
0.007564496387029534
changing lr
epoch 23, time 860.93, cls_loss 0.0049 cls_loss_mapping 0.0175 cls_loss_causal 0.4279 re_mapping 0.0094 re_causal 0.0210 /// teacc 92.71 lr 0.00736934
588
0.007369343312364995
changing lr
epoch 24, time 870.01, cls_loss 0.0046 cls_loss_mapping 0.0183 cls_loss_causal 0.4499 re_mapping 0.0094 re_causal 0.0216 /// teacc 94.22 lr 0.00716942
588
0.0071694186955877925
changing lr
epoch 25, time 881.12, cls_loss 0.0059 cls_loss_mapping 0.0212 cls_loss_causal 0.4502 re_mapping 0.0092 re_causal 0.0210 /// teacc 94.22 lr 0.00696513
588
0.0069651251582696205
changing lr
epoch 26, time 883.73, cls_loss 0.0052 cls_loss_mapping 0.0151 cls_loss_causal 0.4330 re_mapping 0.0088 re_causal 0.0207 /// teacc 94.47 lr 0.00675687
588
0.006756874120406716
changing lr
epoch 27, time 876.67, cls_loss 0.0050 cls_loss_mapping 0.0183 cls_loss_causal 0.4334 re_mapping 0.0082 re_causal 0.0200 /// teacc 93.22 lr 0.00654508
588
0.00654508497187474
changing lr
epoch 28, time 849.09, cls_loss 0.0067 cls_loss_mapping 0.0154 cls_loss_causal 0.4283 re_mapping 0.0084 re_causal 0.0204 /// teacc 93.47 lr 0.00633018
588
0.006330184227833378
changing lr
epoch 29, time 851.32, cls_loss 0.0044 cls_loss_mapping 0.0147 cls_loss_causal 0.3901 re_mapping 0.0077 re_causal 0.0185 /// teacc 92.96 lr 0.00611260
588
0.006112604669781575
changing lr
epoch 30, time 854.67, cls_loss 0.0034 cls_loss_mapping 0.0126 cls_loss_causal 0.4241 re_mapping 0.0076 re_causal 0.0193 /// teacc 93.72 lr 0.00589278
588
0.005892784473993186
changing lr
epoch 31, time 861.52, cls_loss 0.0048 cls_loss_mapping 0.0151 cls_loss_causal 0.4106 re_mapping 0.0072 re_causal 0.0186 /// teacc 93.22 lr 0.00567117
588
0.00567116632908828
changing lr
epoch 32, time 886.70, cls_loss 0.0034 cls_loss_mapping 0.0119 cls_loss_causal 0.4174 re_mapping 0.0070 re_causal 0.0183 /// teacc 93.72 lr 0.00544820
588
0.00544819654451717
changing lr
epoch 33, time 865.62, cls_loss 0.0038 cls_loss_mapping 0.0111 cls_loss_causal 0.4096 re_mapping 0.0068 re_causal 0.0178 /// teacc 92.96 lr 0.00522432
588
0.005224324151752577
changing lr
epoch 34, time 853.80, cls_loss 0.0039 cls_loss_mapping 0.0117 cls_loss_causal 0.4176 re_mapping 0.0066 re_causal 0.0176 /// teacc 93.22 lr 0.00500000
588
0.005000000000000003
changing lr
epoch 35, time 873.63, cls_loss 0.0043 cls_loss_mapping 0.0126 cls_loss_causal 0.4324 re_mapping 0.0065 re_causal 0.0176 /// teacc 93.22 lr 0.00477568
588
0.004775675848247429
changing lr
epoch 36, time 847.64, cls_loss 0.0035 cls_loss_mapping 0.0099 cls_loss_causal 0.4156 re_mapping 0.0062 re_causal 0.0166 /// teacc 93.72 lr 0.00455180
588
0.004551803455482836
changing lr
epoch 37, time 821.88, cls_loss 0.0038 cls_loss_mapping 0.0099 cls_loss_causal 0.4130 re_mapping 0.0059 re_causal 0.0165 /// teacc 94.22 lr 0.00432883
588
0.004328833670911726
changing lr
epoch 38, time 833.52, cls_loss 0.0039 cls_loss_mapping 0.0113 cls_loss_causal 0.3887 re_mapping 0.0059 re_causal 0.0166 /// teacc 94.97 lr 0.00410722
588
0.0041072155260068206
changing lr
epoch 39, time 803.24, cls_loss 0.0032 cls_loss_mapping 0.0079 cls_loss_causal 0.4193 re_mapping 0.0058 re_causal 0.0165 /// teacc 94.72 lr 0.00388740
588
0.0038873953302184317
changing lr
epoch 40, time 810.38, cls_loss 0.0034 cls_loss_mapping 0.0082 cls_loss_causal 0.3832 re_mapping 0.0056 re_causal 0.0154 /// teacc 93.47 lr 0.00366982
588
0.003669815772166629
changing lr
epoch 41, time 798.30, cls_loss 0.0038 cls_loss_mapping 0.0093 cls_loss_causal 0.3853 re_mapping 0.0054 re_causal 0.0152 /// teacc 93.72 lr 0.00345492
588
0.0034549150281252667
changing lr
epoch 42, time 770.71, cls_loss 0.0038 cls_loss_mapping 0.0078 cls_loss_causal 0.4206 re_mapping 0.0052 re_causal 0.0155 /// teacc 93.22 lr 0.00324313
588
0.0032431258795932905
changing lr
epoch 43, time 769.73, cls_loss 0.0032 cls_loss_mapping 0.0085 cls_loss_causal 0.3786 re_mapping 0.0052 re_causal 0.0147 /// teacc 94.22 lr 0.00303487
588
0.0030348748417303863
changing lr
epoch 44, time 781.30, cls_loss 0.0030 cls_loss_mapping 0.0066 cls_loss_causal 0.3762 re_mapping 0.0052 re_causal 0.0141 /// teacc 92.96 lr 0.00283058
588
0.0028305813044122124
changing lr
epoch 45, time 763.23, cls_loss 0.0028 cls_loss_mapping 0.0060 cls_loss_causal 0.3935 re_mapping 0.0050 re_causal 0.0143 /// teacc 93.97 lr 0.00263066
588
0.0026306566876350096
changing lr
epoch 46, time 756.78, cls_loss 0.0030 cls_loss_mapping 0.0072 cls_loss_causal 0.3847 re_mapping 0.0049 re_causal 0.0141 /// teacc 94.47 lr 0.00243550
588
0.0024355036129704724
changing lr
epoch 47, time 753.45, cls_loss 0.0027 cls_loss_mapping 0.0062 cls_loss_causal 0.3732 re_mapping 0.0048 re_causal 0.0134 /// teacc 93.72 lr 0.00224552
588
0.00224551509273949
changing lr
epoch 48, time 753.93, cls_loss 0.0029 cls_loss_mapping 0.0050 cls_loss_causal 0.3621 re_mapping 0.0047 re_causal 0.0131 /// teacc 94.22 lr 0.00206107
588
0.002061073738537637
changing lr
epoch 49, time 760.37, cls_loss 0.0028 cls_loss_mapping 0.0057 cls_loss_causal 0.3736 re_mapping 0.0048 re_causal 0.0132 /// teacc 93.47 lr 0.00188255
588
0.0018825509907063344
changing lr
---------------------saving model at epoch 50----------------------------------------------------
epoch 50, time 761.62, cls_loss 0.0025 cls_loss_mapping 0.0047 cls_loss_causal 0.3886 re_mapping 0.0047 re_causal 0.0133 /// teacc 95.48 lr 0.00171031
588
0.0017103063703014388
changing lr
epoch 51, time 757.83, cls_loss 0.0026 cls_loss_mapping 0.0051 cls_loss_causal 0.3723 re_mapping 0.0047 re_causal 0.0131 /// teacc 92.21 lr 0.00154469
588
0.0015446867550656784
changing lr
epoch 52, time 756.68, cls_loss 0.0027 cls_loss_mapping 0.0047 cls_loss_causal 0.3874 re_mapping 0.0046 re_causal 0.0131 /// teacc 92.71 lr 0.00138603
588
0.001386025680863044
changing lr
epoch 53, time 758.80, cls_loss 0.0027 cls_loss_mapping 0.0049 cls_loss_causal 0.3915 re_mapping 0.0046 re_causal 0.0130 /// teacc 93.72 lr 0.00123464
588
0.0012346426699819469
changing lr
epoch 54, time 754.88, cls_loss 0.0026 cls_loss_mapping 0.0049 cls_loss_causal 0.3825 re_mapping 0.0045 re_causal 0.0130 /// teacc 94.22 lr 0.00109084
588
0.0010908425876598518
changing lr
epoch 55, time 758.50, cls_loss 0.0030 cls_loss_mapping 0.0050 cls_loss_causal 0.3839 re_mapping 0.0045 re_causal 0.0126 /// teacc 93.22 lr 0.00095492
588
0.000954915028125264
changing lr
epoch 56, time 757.06, cls_loss 0.0025 cls_loss_mapping 0.0044 cls_loss_causal 0.3577 re_mapping 0.0045 re_causal 0.0122 /// teacc 94.22 lr 0.00082713
588
0.0008271337313934874
changing lr
epoch 57, time 758.09, cls_loss 0.0023 cls_loss_mapping 0.0046 cls_loss_causal 0.3461 re_mapping 0.0046 re_causal 0.0118 /// teacc 93.47 lr 0.00070776
588
0.00070775603199067
changing lr
epoch 58, time 760.13, cls_loss 0.0023 cls_loss_mapping 0.0039 cls_loss_causal 0.3523 re_mapping 0.0046 re_causal 0.0118 /// teacc 94.47 lr 0.00059702
588
0.0005970223407163104
changing lr
epoch 59, time 756.85, cls_loss 0.0021 cls_loss_mapping 0.0035 cls_loss_causal 0.3762 re_mapping 0.0045 re_causal 0.0121 /// teacc 93.22 lr 0.00049516
588
0.0004951556604879052
changing lr
epoch 60, time 754.41, cls_loss 0.0023 cls_loss_mapping 0.0041 cls_loss_causal 0.3579 re_mapping 0.0044 re_causal 0.0115 /// teacc 92.71 lr 0.00040236
588
0.00040236113724274745
changing lr
epoch 61, time 758.79, cls_loss 0.0026 cls_loss_mapping 0.0042 cls_loss_causal 0.3682 re_mapping 0.0044 re_causal 0.0114 /// teacc 93.72 lr 0.00031883
588
0.00031882564680131423
changing lr
epoch 62, time 752.52, cls_loss 0.0025 cls_loss_mapping 0.0039 cls_loss_causal 0.3746 re_mapping 0.0044 re_causal 0.0117 /// teacc 92.46 lr 0.00024472
588
0.0002447174185242325
changing lr
epoch 63, time 758.14, cls_loss 0.0025 cls_loss_mapping 0.0034 cls_loss_causal 0.3751 re_mapping 0.0044 re_causal 0.0115 /// teacc 93.72 lr 0.00018019
588
0.0001801856965207339
changing lr
epoch 64, time 751.51, cls_loss 0.0024 cls_loss_mapping 0.0034 cls_loss_causal 0.3590 re_mapping 0.0044 re_causal 0.0114 /// teacc 93.47 lr 0.00012536
588
0.000125360439090882
changing lr
epoch 65, time 760.25, cls_loss 0.0030 cls_loss_mapping 0.0049 cls_loss_causal 0.3519 re_mapping 0.0044 re_causal 0.0112 /// teacc 93.97 lr 0.00008035
588
8.03520570068517e-05
changing lr
epoch 66, time 759.68, cls_loss 0.0023 cls_loss_mapping 0.0036 cls_loss_causal 0.3571 re_mapping 0.0044 re_causal 0.0114 /// teacc 92.96 lr 0.00004525
588
4.5251191160326525e-05
changing lr
epoch 67, time 758.83, cls_loss 0.0025 cls_loss_mapping 0.0043 cls_loss_causal 0.3541 re_mapping 0.0044 re_causal 0.0113 /// teacc 92.21 lr 0.00002013
588
2.0128530023804673e-05
changing lr
epoch 68, time 755.82, cls_loss 0.0021 cls_loss_mapping 0.0032 cls_loss_causal 0.3385 re_mapping 0.0044 re_causal 0.0113 /// teacc 93.22 lr 0.00000503
588
5.034667293427056e-06
changing lr
epoch 69, time 757.80, cls_loss 0.0027 cls_loss_mapping 0.0040 cls_loss_causal 0.3563 re_mapping 0.0044 re_causal 0.0113 /// teacc 93.22 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//sketch/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal', 'source_domain': 'sketch', 'svpath': '/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/saved-PACS//sketch/CA_multiple_16fa_v2_ep70_lr0.01_cosine_base0.01_bs6_lamCa_1_lamRe1_adt4_cls1_EW2_70_rmTrue_rnTrue_str5_ReProduceMetaCausal/sketch_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: ['sketch', 'art_painting', 'cartoon', 'photo']
/data/work-gcp-europe-west4-a/yuqian_fu/datasets/SingleSourceDG/data/PACS/sketch_test.hdf5 torch.Size([3929, 3, 227, 227]) torch.Size([3929])
/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])
                        sketch  art_painting    cartoon      photo        Avg
w/o do (original x)  99.312802     55.029297  67.491468  57.964072  60.161612
       sketch  art_painting   cartoon      photo        Avg
do  99.312802     49.804688  63.90785  55.449102  56.387213