diff --git "a/POLYPGEN/logs/EAT_PolypGEN_log.txt" "b/POLYPGEN/logs/EAT_PolypGEN_log.txt" new file mode 100644--- /dev/null +++ "b/POLYPGEN/logs/EAT_PolypGEN_log.txt" @@ -0,0 +1,32234 @@ +, + 'EDD_seg': , + 'Kvasir_SEG': , + 'PolypGen': , + 'Sun_seg': + >, + 'finetune': , + 'models': , + 'branch5': + >, + 'cfp_net': , + 'branch5': + >, + 'cvc_unetr': , + 'branch5': + >, + 'cvc_unetr_v4': , + 'branch5': + >, + 'duat': , + 'branch5': + >, + 'swin_unetr': , + 'branch5': + >, + 'trans_unet': , + 'branch5': + >, + 'u_netr': , + 'branch5': + >, + 'unet': , + 'branch5': + > + >, + 'shared path': '/root/.cache/huggingface/forget/model_stores/', + 'trainer': , + 'visualization': +> +Load Model... +Load Dataloader... +Successfully loaded the training model！ +Loading training state successfully! Start training from 3396, Best Acc: tensor([0.9514]) +Start Training！ +Epoch [3397/4000] Training [1/39] Loss: 0.00835 +Epoch [3397/4000] Training [2/39] Loss: 0.00688 +Epoch [3397/4000] Training [3/39] Loss: 0.00327 +Epoch [3397/4000] Training [4/39] Loss: 0.12930 +Epoch [3397/4000] Training [5/39] Loss: 0.12786 +Epoch [3397/4000] Training [6/39] Loss: 0.13034 +Epoch [3397/4000] Training [7/39] Loss: 0.00653 +Epoch [3397/4000] Training [8/39] Loss: 0.00529 +Epoch [3397/4000] Training [9/39] Loss: 0.00856 +Epoch [3397/4000] Training [10/39] Loss: 0.12949 +Epoch [3397/4000] Training [11/39] Loss: 0.00482 +Epoch [3397/4000] Training [12/39] Loss: 0.00617 +Epoch [3397/4000] Training [13/39] Loss: 0.00435 +Epoch [3397/4000] Training [14/39] Loss: 0.12967 +Epoch [3397/4000] Training [15/39] Loss: 0.00560 +Epoch [3397/4000] Training [16/39] Loss: 0.13104 +Epoch [3397/4000] Training [17/39] Loss: 0.13032 +Epoch [3397/4000] Training [18/39] Loss: 0.12890 +Epoch [3397/4000] Training [19/39] Loss: 0.13158 +Epoch [3397/4000] Training [20/39] Loss: 0.00738 +Epoch [3397/4000] Training [21/39] Loss: 0.00989 +Epoch [3397/4000] Training [22/39] Loss: 0.00344 +Epoch [3397/4000] Training [23/39] Loss: 0.12895 +Epoch [3397/4000] Training [24/39] Loss: 0.00734 +Epoch [3397/4000] Training [25/39] Loss: 0.00520 +Epoch [3397/4000] Training [26/39] Loss: 0.00677 +Epoch [3397/4000] Training [27/39] Loss: 0.00623 +Epoch [3397/4000] Training [28/39] Loss: 0.00545 +Epoch [3397/4000] Training [29/39] Loss: 0.00566 +Epoch [3397/4000] Training [30/39] Loss: 0.00454 +Epoch [3397/4000] Training [31/39] Loss: 0.00366 +Epoch [3397/4000] Training [32/39] Loss: 0.00356 +Epoch [3397/4000] Training [33/39] Loss: 0.00643 +Epoch [3397/4000] Training [34/39] Loss: 0.12843 +Epoch [3397/4000] Training [35/39] Loss: 0.00606 +Epoch [3397/4000] Training [36/39] Loss: 0.13053 +Epoch [3397/4000] Training [37/39] Loss: 0.00593 +Epoch [3397/4000] Training [38/39] Loss: 0.00459 +Epoch [3397/4000] Training [39/39] Loss: 0.00607 +Epoch [3397/4000] Training metric {'Train/mean dice_metric': 0.9956825375556946, 'Train/mean miou_metric': 0.9918822050094604, 'Train/mean f1': 0.9965717196464539, 'Train/mean precision': 0.9961875081062317, 'Train/mean recall': 0.9969562292098999, 'Train/mean hd95_metric': 1.0427749156951904} +Epoch [3397/4000] Validation [1/10] Loss: 0.64150 focal_loss 0.55442 dice_loss 0.08708 +Epoch [3397/4000] Validation [2/10] Loss: 0.44349 focal_loss 0.34784 dice_loss 0.09566 +Epoch [3397/4000] Validation [3/10] Loss: 0.36280 focal_loss 0.24974 dice_loss 0.11306 +Epoch [3397/4000] Validation [4/10] Loss: 0.87399 focal_loss 0.29399 dice_loss 0.58000 +Epoch [3397/4000] Validation [5/10] Loss: 2.87424 focal_loss 2.20160 dice_loss 0.67264 +Epoch [3397/4000] Validation [6/10] Loss: 1.20350 focal_loss 0.49765 dice_loss 0.70585 +Epoch [3397/4000] Validation [7/10] Loss: 1.10925 focal_loss 0.45927 dice_loss 0.64998 +Epoch [3397/4000] Validation [8/10] Loss: 2.24414 focal_loss 1.62046 dice_loss 0.62368 +Epoch [3397/4000] Validation [9/10] Loss: 1.34723 focal_loss 0.80349 dice_loss 0.54374 +Epoch [3397/4000] Validation [10/10] Loss: 1.69105 focal_loss 0.96786 dice_loss 0.72319 +Epoch [3397/4000] Validation metric {'Val/mean dice_metric': 0.9507534503936768, 'Val/mean miou_metric': 0.934364914894104, 'Val/mean f1': 0.9502304196357727, 'Val/mean precision': 0.947417676448822, 'Val/mean recall': 0.9530599117279053, 'Val/mean hd95_metric': 10.582368850708008} +Cheakpoint... +Epoch [3397/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507534503936768, 'Val/mean miou_metric': 0.934364914894104, 'Val/mean f1': 0.9502304196357727, 'Val/mean precision': 0.947417676448822, 'Val/mean recall': 0.9530599117279053, 'Val/mean hd95_metric': 10.582368850708008} +Epoch [3398/4000] Training [1/39] Loss: 0.14375 +Epoch [3398/4000] Training [2/39] Loss: 0.13006 +Epoch [3398/4000] Training [3/39] Loss: 0.25803 +Epoch [3398/4000] Training [4/39] Loss: 0.00747 +Epoch [3398/4000] Training [5/39] Loss: 0.00638 +Epoch [3398/4000] Training [6/39] Loss: 0.00673 +Epoch [3398/4000] Training [7/39] Loss: 0.00445 +Epoch [3398/4000] Training [8/39] Loss: 0.13188 +Epoch [3398/4000] Training [9/39] Loss: 0.00524 +Epoch [3398/4000] Training [10/39] Loss: 0.00661 +Epoch [3398/4000] Training [11/39] Loss: 0.00655 +Epoch [3398/4000] Training [12/39] Loss: 0.00883 +Epoch [3398/4000] Training [13/39] Loss: 0.12783 +Epoch [3398/4000] Training [14/39] Loss: 0.13131 +Epoch [3398/4000] Training [15/39] Loss: 0.12894 +Epoch [3398/4000] Training [16/39] Loss: 0.00951 +Epoch [3398/4000] Training [17/39] Loss: 0.01090 +Epoch [3398/4000] Training [18/39] Loss: 0.00681 +Epoch [3398/4000] Training [19/39] Loss: 0.00506 +Epoch [3398/4000] Training [20/39] Loss: 0.13079 +Epoch [3398/4000] Training [21/39] Loss: 0.00608 +Epoch [3398/4000] Training [22/39] Loss: 0.00556 +Epoch [3398/4000] Training [23/39] Loss: 0.00462 +Epoch [3398/4000] Training [24/39] Loss: 0.12978 +Epoch [3398/4000] Training [25/39] Loss: 0.13145 +Epoch [3398/4000] Training [26/39] Loss: 0.00472 +Epoch [3398/4000] Training [27/39] Loss: 0.12932 +Epoch [3398/4000] Training [28/39] Loss: 0.00521 +Epoch [3398/4000] Training [29/39] Loss: 0.00431 +Epoch [3398/4000] Training [30/39] Loss: 0.13144 +Epoch [3398/4000] Training [31/39] Loss: 0.13117 +Epoch [3398/4000] Training [32/39] Loss: 0.00633 +Epoch [3398/4000] Training [33/39] Loss: 0.00908 +Epoch [3398/4000] Training [34/39] Loss: 0.00689 +Epoch [3398/4000] Training [35/39] Loss: 0.00701 +Epoch [3398/4000] Training [36/39] Loss: 0.00602 +Epoch [3398/4000] Training [37/39] Loss: 0.00668 +Epoch [3398/4000] Training [38/39] Loss: 0.12974 +Epoch [3398/4000] Training [39/39] Loss: 0.04294 +Epoch [3398/4000] Training metric {'Train/mean dice_metric': 0.9943206310272217, 'Train/mean miou_metric': 0.9899555444717407, 'Train/mean f1': 0.9960043430328369, 'Train/mean precision': 0.9954976439476013, 'Train/mean recall': 0.9965115785598755, 'Train/mean hd95_metric': 1.1248791217803955} +Epoch [3398/4000] Validation [1/10] Loss: 0.76316 focal_loss 0.66388 dice_loss 0.09927 +Epoch [3398/4000] Validation [2/10] Loss: 0.45248 focal_loss 0.36550 dice_loss 0.08699 +Epoch [3398/4000] Validation [3/10] Loss: 0.34531 focal_loss 0.23392 dice_loss 0.11139 +Epoch [3398/4000] Validation [4/10] Loss: 0.92201 focal_loss 0.34642 dice_loss 0.57559 +Epoch [3398/4000] Validation [5/10] Loss: 2.93734 focal_loss 2.26657 dice_loss 0.67077 +Epoch [3398/4000] Validation [6/10] Loss: 1.32499 focal_loss 0.61124 dice_loss 0.71375 +Epoch [3398/4000] Validation [7/10] Loss: 1.18538 focal_loss 0.52886 dice_loss 0.65652 +Epoch [3398/4000] Validation [8/10] Loss: 1.97484 focal_loss 1.40328 dice_loss 0.57157 +Epoch [3398/4000] Validation [9/10] Loss: 1.42877 focal_loss 0.91622 dice_loss 0.51255 +Epoch [3398/4000] Validation [10/10] Loss: 1.88499 focal_loss 1.14833 dice_loss 0.73666 +Epoch [3398/4000] Validation metric {'Val/mean dice_metric': 0.9493808746337891, 'Val/mean miou_metric': 0.9322203397750854, 'Val/mean f1': 0.9456930756568909, 'Val/mean precision': 0.9354326725006104, 'Val/mean recall': 0.9561808705329895, 'Val/mean hd95_metric': 11.559012413024902} +Cheakpoint... +Epoch [3398/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9494], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9493808746337891, 'Val/mean miou_metric': 0.9322203397750854, 'Val/mean f1': 0.9456930756568909, 'Val/mean precision': 0.9354326725006104, 'Val/mean recall': 0.9561808705329895, 'Val/mean hd95_metric': 11.559012413024902} +Epoch [3399/4000] Training [1/39] Loss: 0.00614 +Epoch [3399/4000] Training [2/39] Loss: 0.12972 +Epoch [3399/4000] Training [3/39] Loss: 0.00509 +Epoch [3399/4000] Training [4/39] Loss: 0.12872 +Epoch [3399/4000] Training [5/39] Loss: 0.00509 +Epoch [3399/4000] Training [6/39] Loss: 0.00382 +Epoch [3399/4000] Training [7/39] Loss: 0.00558 +Epoch [3399/4000] Training [8/39] Loss: 0.00656 +Epoch [3399/4000] Training [9/39] Loss: 0.00741 +Epoch [3399/4000] Training [10/39] Loss: 0.00436 +Epoch [3399/4000] Training [11/39] Loss: 0.01105 +Epoch [3399/4000] Training [12/39] Loss: 0.13013 +Epoch [3399/4000] Training [13/39] Loss: 0.00548 +Epoch [3399/4000] Training [14/39] Loss: 0.00957 +Epoch [3399/4000] Training [15/39] Loss: 0.00396 +Epoch [3399/4000] Training [16/39] Loss: 0.00536 +Epoch [3399/4000] Training [17/39] Loss: 0.01089 +Epoch [3399/4000] Training [18/39] Loss: 0.00616 +Epoch [3399/4000] Training [19/39] Loss: 0.00527 +Epoch [3399/4000] Training [20/39] Loss: 0.00516 +Epoch [3399/4000] Training [21/39] Loss: 0.13123 +Epoch [3399/4000] Training [22/39] Loss: 0.00457 +Epoch [3399/4000] Training [23/39] Loss: 0.13139 +Epoch [3399/4000] Training [24/39] Loss: 0.00507 +Epoch [3399/4000] Training [25/39] Loss: 0.00590 +Epoch [3399/4000] Training [26/39] Loss: 0.12830 +Epoch [3399/4000] Training [27/39] Loss: 0.00753 +Epoch [3399/4000] Training [28/39] Loss: 0.00738 +Epoch [3399/4000] Training [29/39] Loss: 0.12898 +Epoch [3399/4000] Training [30/39] Loss: 0.12987 +Epoch [3399/4000] Training [31/39] Loss: 0.13072 +Epoch [3399/4000] Training [32/39] Loss: 0.00356 +Epoch [3399/4000] Training [33/39] Loss: 0.13118 +Epoch [3399/4000] Training [34/39] Loss: 0.12866 +Epoch [3399/4000] Training [35/39] Loss: 0.03942 +Epoch [3399/4000] Training [36/39] Loss: 0.00555 +Epoch [3399/4000] Training [37/39] Loss: 0.00511 +Epoch [3399/4000] Training [38/39] Loss: 0.00411 +Epoch [3399/4000] Training [39/39] Loss: 0.13051 +Epoch [3399/4000] Training metric {'Train/mean dice_metric': 0.995800256729126, 'Train/mean miou_metric': 0.9920532703399658, 'Train/mean f1': 0.9965879917144775, 'Train/mean precision': 0.9961168169975281, 'Train/mean recall': 0.9970596432685852, 'Train/mean hd95_metric': 1.0351821184158325} +Epoch [3399/4000] Validation [1/10] Loss: 0.76945 focal_loss 0.67000 dice_loss 0.09945 +Epoch [3399/4000] Validation [2/10] Loss: 0.44703 focal_loss 0.36179 dice_loss 0.08524 +Epoch [3399/4000] Validation [3/10] Loss: 0.33699 focal_loss 0.22639 dice_loss 0.11060 +Epoch [3399/4000] Validation [4/10] Loss: 0.89831 focal_loss 0.32920 dice_loss 0.56911 +Epoch [3399/4000] Validation [5/10] Loss: 2.95417 focal_loss 2.28406 dice_loss 0.67011 +Epoch [3399/4000] Validation [6/10] Loss: 1.32606 focal_loss 0.60933 dice_loss 0.71673 +Epoch [3399/4000] Validation [7/10] Loss: 1.19014 focal_loss 0.53416 dice_loss 0.65598 +Epoch [3399/4000] Validation [8/10] Loss: 1.95309 focal_loss 1.38501 dice_loss 0.56808 +Epoch [3399/4000] Validation [9/10] Loss: 1.43591 focal_loss 0.92274 dice_loss 0.51317 +Epoch [3399/4000] Validation [10/10] Loss: 1.90091 focal_loss 1.16598 dice_loss 0.73492 +Epoch [3399/4000] Validation metric {'Val/mean dice_metric': 0.9504171013832092, 'Val/mean miou_metric': 0.9339461922645569, 'Val/mean f1': 0.9460100531578064, 'Val/mean precision': 0.9347219467163086, 'Val/mean recall': 0.9575742483139038, 'Val/mean hd95_metric': 11.437238693237305} +Cheakpoint... +Epoch [3399/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504171013832092, 'Val/mean miou_metric': 0.9339461922645569, 'Val/mean f1': 0.9460100531578064, 'Val/mean precision': 0.9347219467163086, 'Val/mean recall': 0.9575742483139038, 'Val/mean hd95_metric': 11.437238693237305} +Epoch [3400/4000] Training [1/39] Loss: 0.00582 +Epoch [3400/4000] Training [2/39] Loss: 0.13085 +Epoch [3400/4000] Training [3/39] Loss: 0.00504 +Epoch [3400/4000] Training [4/39] Loss: 0.00544 +Epoch [3400/4000] Training [5/39] Loss: 0.00421 +Epoch [3400/4000] Training [6/39] Loss: 0.00925 +Epoch [3400/4000] Training [7/39] Loss: 0.00582 +Epoch [3400/4000] Training [8/39] Loss: 0.00467 +Epoch [3400/4000] Training [9/39] Loss: 0.00722 +Epoch [3400/4000] Training [10/39] Loss: 0.00597 +Epoch [3400/4000] Training [11/39] Loss: 0.00727 +Epoch [3400/4000] Training [12/39] Loss: 0.12822 +Epoch [3400/4000] Training [13/39] Loss: 0.00409 +Epoch [3400/4000] Training [14/39] Loss: 0.00491 +Epoch [3400/4000] Training [15/39] Loss: 0.00482 +Epoch [3400/4000] Training [16/39] Loss: 0.00616 +Epoch [3400/4000] Training [17/39] Loss: 0.00372 +Epoch [3400/4000] Training [18/39] Loss: 0.00721 +Epoch [3400/4000] Training [19/39] Loss: 0.00462 +Epoch [3400/4000] Training [20/39] Loss: 0.12796 +Epoch [3400/4000] Training [21/39] Loss: 0.13141 +Epoch [3400/4000] Training [22/39] Loss: 0.00384 +Epoch [3400/4000] Training [23/39] Loss: 0.00664 +Epoch [3400/4000] Training [24/39] Loss: 0.00566 +Epoch [3400/4000] Training [25/39] Loss: 0.00379 +Epoch [3400/4000] Training [26/39] Loss: 0.25441 +Epoch [3400/4000] Training [27/39] Loss: 0.12851 +Epoch [3400/4000] Training [28/39] Loss: 0.00660 +Epoch [3400/4000] Training [29/39] Loss: 0.13061 +Epoch [3400/4000] Training [30/39] Loss: 0.00718 +Epoch [3400/4000] Training [31/39] Loss: 0.00755 +Epoch [3400/4000] Training [32/39] Loss: 0.00584 +Epoch [3400/4000] Training [33/39] Loss: 0.00503 +Epoch [3400/4000] Training [34/39] Loss: 0.00528 +Epoch [3400/4000] Training [35/39] Loss: 0.00783 +Epoch [3400/4000] Training [36/39] Loss: 0.00524 +Epoch [3400/4000] Training [37/39] Loss: 0.13248 +Epoch [3400/4000] Training [38/39] Loss: 0.00576 +Epoch [3400/4000] Training [39/39] Loss: 0.00470 +Epoch [3400/4000] Training metric {'Train/mean dice_metric': 0.9956687688827515, 'Train/mean miou_metric': 0.9917925596237183, 'Train/mean f1': 0.9964442253112793, 'Train/mean precision': 0.9959814548492432, 'Train/mean recall': 0.9969074726104736, 'Train/mean hd95_metric': 1.0372072458267212} +Epoch [3400/4000] Validation [1/10] Loss: 0.74854 focal_loss 0.65149 dice_loss 0.09705 +Epoch [3400/4000] Validation [2/10] Loss: 0.44305 focal_loss 0.35756 dice_loss 0.08549 +Epoch [3400/4000] Validation [3/10] Loss: 0.35165 focal_loss 0.23977 dice_loss 0.11188 +Epoch [3400/4000] Validation [4/10] Loss: 0.89281 focal_loss 0.32563 dice_loss 0.56718 +Epoch [3400/4000] Validation [5/10] Loss: 2.95437 focal_loss 2.28329 dice_loss 0.67108 +Epoch [3400/4000] Validation [6/10] Loss: 1.31068 focal_loss 0.59811 dice_loss 0.71257 +Epoch [3400/4000] Validation [7/10] Loss: 1.18394 focal_loss 0.52493 dice_loss 0.65902 +Epoch [3400/4000] Validation [8/10] Loss: 2.00880 focal_loss 1.43273 dice_loss 0.57607 +Epoch [3400/4000] Validation [9/10] Loss: 1.41251 focal_loss 0.88165 dice_loss 0.53086 +Epoch [3400/4000] Validation [10/10] Loss: 1.86232 focal_loss 1.12831 dice_loss 0.73401 +Epoch [3400/4000] Validation metric {'Val/mean dice_metric': 0.9503322839736938, 'Val/mean miou_metric': 0.9337994456291199, 'Val/mean f1': 0.9467515349388123, 'Val/mean precision': 0.9370712637901306, 'Val/mean recall': 0.9566339254379272, 'Val/mean hd95_metric': 11.399377822875977} +Cheakpoint... +Epoch [3400/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9503], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503322839736938, 'Val/mean miou_metric': 0.9337994456291199, 'Val/mean f1': 0.9467515349388123, 'Val/mean precision': 0.9370712637901306, 'Val/mean recall': 0.9566339254379272, 'Val/mean hd95_metric': 11.399377822875977} +Epoch [3401/4000] Training [1/39] Loss: 0.00456 +Epoch [3401/4000] Training [2/39] Loss: 0.00576 +Epoch [3401/4000] Training [3/39] Loss: 0.00430 +Epoch [3401/4000] Training [4/39] Loss: 0.00842 +Epoch [3401/4000] Training [5/39] Loss: 0.00510 +Epoch [3401/4000] Training [6/39] Loss: 0.00416 +Epoch [3401/4000] Training [7/39] Loss: 0.12968 +Epoch [3401/4000] Training [8/39] Loss: 0.00536 +Epoch [3401/4000] Training [9/39] Loss: 0.00437 +Epoch [3401/4000] Training [10/39] Loss: 0.13005 +Epoch [3401/4000] Training [11/39] Loss: 0.00691 +Epoch [3401/4000] Training [12/39] Loss: 0.00333 +Epoch [3401/4000] Training [13/39] Loss: 0.00507 +Epoch [3401/4000] Training [14/39] Loss: 0.00697 +Epoch [3401/4000] Training [15/39] Loss: 0.08373 +Epoch [3401/4000] Training [16/39] Loss: 0.12832 +Epoch [3401/4000] Training [17/39] Loss: 0.00810 +Epoch [3401/4000] Training [18/39] Loss: 0.00524 +Epoch [3401/4000] Training [19/39] Loss: 0.00654 +Epoch [3401/4000] Training [20/39] Loss: 0.00652 +Epoch [3401/4000] Training [21/39] Loss: 0.00509 +Epoch [3401/4000] Training [22/39] Loss: 0.00298 +Epoch [3401/4000] Training [23/39] Loss: 0.00336 +Epoch [3401/4000] Training [24/39] Loss: 0.00855 +Epoch [3401/4000] Training [25/39] Loss: 0.00499 +Epoch [3401/4000] Training [26/39] Loss: 0.13065 +Epoch [3401/4000] Training [27/39] Loss: 0.12921 +Epoch [3401/4000] Training [28/39] Loss: 0.00791 +Epoch [3401/4000] Training [29/39] Loss: 0.00377 +Epoch [3401/4000] Training [30/39] Loss: 0.00298 +Epoch [3401/4000] Training [31/39] Loss: 0.00391 +Epoch [3401/4000] Training [32/39] Loss: 0.00663 +Epoch [3401/4000] Training [33/39] Loss: 0.00777 +Epoch [3401/4000] Training [34/39] Loss: 0.00489 +Epoch [3401/4000] Training [35/39] Loss: 0.00815 +Epoch [3401/4000] Training [36/39] Loss: 0.00408 +Epoch [3401/4000] Training [37/39] Loss: 0.00471 +Epoch [3401/4000] Training [38/39] Loss: 0.00410 +Epoch [3401/4000] Training [39/39] Loss: 0.00469 +Epoch [3401/4000] Training metric {'Train/mean dice_metric': 0.9957834482192993, 'Train/mean miou_metric': 0.9920111298561096, 'Train/mean f1': 0.9964816570281982, 'Train/mean precision': 0.9960487484931946, 'Train/mean recall': 0.9969149231910706, 'Train/mean hd95_metric': 1.0452951192855835} +Epoch [3401/4000] Validation [1/10] Loss: 0.71166 focal_loss 0.61754 dice_loss 0.09412 +Epoch [3401/4000] Validation [2/10] Loss: 0.45324 focal_loss 0.35743 dice_loss 0.09581 +Epoch [3401/4000] Validation [3/10] Loss: 0.36001 focal_loss 0.24712 dice_loss 0.11289 +Epoch [3401/4000] Validation [4/10] Loss: 0.87879 focal_loss 0.31192 dice_loss 0.56687 +Epoch [3401/4000] Validation [5/10] Loss: 2.95672 focal_loss 2.28500 dice_loss 0.67171 +Epoch [3401/4000] Validation [6/10] Loss: 1.26043 focal_loss 0.55351 dice_loss 0.70692 +Epoch [3401/4000] Validation [7/10] Loss: 1.14571 focal_loss 0.49377 dice_loss 0.65194 +Epoch [3401/4000] Validation [8/10] Loss: 2.08139 focal_loss 1.48256 dice_loss 0.59883 +Epoch [3401/4000] Validation [9/10] Loss: 1.40451 focal_loss 0.86166 dice_loss 0.54285 +Epoch [3401/4000] Validation [10/10] Loss: 1.77841 focal_loss 1.04678 dice_loss 0.73163 +Epoch [3401/4000] Validation metric {'Val/mean dice_metric': 0.9504718780517578, 'Val/mean miou_metric': 0.934082567691803, 'Val/mean f1': 0.9475099444389343, 'Val/mean precision': 0.9397058486938477, 'Val/mean recall': 0.9554446935653687, 'Val/mean hd95_metric': 10.806648254394531} +Cheakpoint... +Epoch [3401/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504718780517578, 'Val/mean miou_metric': 0.934082567691803, 'Val/mean f1': 0.9475099444389343, 'Val/mean precision': 0.9397058486938477, 'Val/mean recall': 0.9554446935653687, 'Val/mean hd95_metric': 10.806648254394531} +Epoch [3402/4000] Training [1/39] Loss: 0.01106 +Epoch [3402/4000] Training [2/39] Loss: 0.00758 +Epoch [3402/4000] Training [3/39] Loss: 0.25480 +Epoch [3402/4000] Training [4/39] Loss: 0.00512 +Epoch [3402/4000] Training [5/39] Loss: 0.12809 +Epoch [3402/4000] Training [6/39] Loss: 0.25358 +Epoch [3402/4000] Training [7/39] Loss: 0.00472 +Epoch [3402/4000] Training [8/39] Loss: 0.00485 +Epoch [3402/4000] Training [9/39] Loss: 0.00635 +Epoch [3402/4000] Training [10/39] Loss: 0.00367 +Epoch [3402/4000] Training [11/39] Loss: 0.12955 +Epoch [3402/4000] Training [12/39] Loss: 0.13378 +Epoch [3402/4000] Training [13/39] Loss: 0.04714 +Epoch [3402/4000] Training [14/39] Loss: 0.12883 +Epoch [3402/4000] Training [15/39] Loss: 0.00464 +Epoch [3402/4000] Training [16/39] Loss: 0.00380 +Epoch [3402/4000] Training [17/39] Loss: 0.21689 +Epoch [3402/4000] Training [18/39] Loss: 0.12863 +Epoch [3402/4000] Training [19/39] Loss: 0.00511 +Epoch [3402/4000] Training [20/39] Loss: 0.00527 +Epoch [3402/4000] Training [21/39] Loss: 0.12866 +Epoch [3402/4000] Training [22/39] Loss: 0.00300 +Epoch [3402/4000] Training [23/39] Loss: 0.12945 +Epoch [3402/4000] Training [24/39] Loss: 0.00316 +Epoch [3402/4000] Training [25/39] Loss: 0.13020 +Epoch [3402/4000] Training [26/39] Loss: 0.00762 +Epoch [3402/4000] Training [27/39] Loss: 0.00617 +Epoch [3402/4000] Training [28/39] Loss: 0.00479 +Epoch [3402/4000] Training [29/39] Loss: 0.00373 +Epoch [3402/4000] Training [30/39] Loss: 0.00849 +Epoch [3402/4000] Training [31/39] Loss: 0.00369 +Epoch [3402/4000] Training [32/39] Loss: 0.00490 +Epoch [3402/4000] Training [33/39] Loss: 0.00545 +Epoch [3402/4000] Training [34/39] Loss: 0.00639 +Epoch [3402/4000] Training [35/39] Loss: 0.00729 +Epoch [3402/4000] Training [36/39] Loss: 0.00493 +Epoch [3402/4000] Training [37/39] Loss: 0.00367 +Epoch [3402/4000] Training [38/39] Loss: 0.00705 +Epoch [3402/4000] Training [39/39] Loss: 0.25384 +Epoch [3402/4000] Training metric {'Train/mean dice_metric': 0.9956958889961243, 'Train/mean miou_metric': 0.9918653964996338, 'Train/mean f1': 0.996564507484436, 'Train/mean precision': 0.9960877895355225, 'Train/mean recall': 0.997041642665863, 'Train/mean hd95_metric': 1.0493988990783691} +Epoch [3402/4000] Validation [1/10] Loss: 0.71184 focal_loss 0.61767 dice_loss 0.09418 +Epoch [3402/4000] Validation [2/10] Loss: 0.45821 focal_loss 0.36678 dice_loss 0.09144 +Epoch [3402/4000] Validation [3/10] Loss: 0.36440 focal_loss 0.25146 dice_loss 0.11294 +Epoch [3402/4000] Validation [4/10] Loss: 0.89807 focal_loss 0.33049 dice_loss 0.56758 +Epoch [3402/4000] Validation [5/10] Loss: 2.95186 focal_loss 2.28030 dice_loss 0.67157 +Epoch [3402/4000] Validation [6/10] Loss: 1.30148 focal_loss 0.59444 dice_loss 0.70704 +Epoch [3402/4000] Validation [7/10] Loss: 1.16624 focal_loss 0.51428 dice_loss 0.65195 +Epoch [3402/4000] Validation [8/10] Loss: 2.16713 focal_loss 1.56363 dice_loss 0.60350 +Epoch [3402/4000] Validation [9/10] Loss: 1.43908 focal_loss 0.89460 dice_loss 0.54448 +Epoch [3402/4000] Validation [10/10] Loss: 1.89615 focal_loss 1.16003 dice_loss 0.73612 +Epoch [3402/4000] Validation metric {'Val/mean dice_metric': 0.9504844546318054, 'Val/mean miou_metric': 0.9339554905891418, 'Val/mean f1': 0.9468755722045898, 'Val/mean precision': 0.9389150738716125, 'Val/mean recall': 0.9549720883369446, 'Val/mean hd95_metric': 11.332086563110352} +Cheakpoint... +Epoch [3402/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504844546318054, 'Val/mean miou_metric': 0.9339554905891418, 'Val/mean f1': 0.9468755722045898, 'Val/mean precision': 0.9389150738716125, 'Val/mean recall': 0.9549720883369446, 'Val/mean hd95_metric': 11.332086563110352} +Epoch [3403/4000] Training [1/39] Loss: 0.00485 +Epoch [3403/4000] Training [2/39] Loss: 0.00388 +Epoch [3403/4000] Training [3/39] Loss: 0.00848 +Epoch [3403/4000] Training [4/39] Loss: 0.00638 +Epoch [3403/4000] Training [5/39] Loss: 0.00613 +Epoch [3403/4000] Training [6/39] Loss: 0.00409 +Epoch [3403/4000] Training [7/39] Loss: 0.00643 +Epoch [3403/4000] Training [8/39] Loss: 0.00386 +Epoch [3403/4000] Training [9/39] Loss: 0.00382 +Epoch [3403/4000] Training [10/39] Loss: 0.00416 +Epoch [3403/4000] Training [11/39] Loss: 0.13526 +Epoch [3403/4000] Training [12/39] Loss: 0.00559 +Epoch [3403/4000] Training [13/39] Loss: 0.00394 +Epoch [3403/4000] Training [14/39] Loss: 0.00490 +Epoch [3403/4000] Training [15/39] Loss: 0.00461 +Epoch [3403/4000] Training [16/39] Loss: 0.00362 +Epoch [3403/4000] Training [17/39] Loss: 0.12877 +Epoch [3403/4000] Training [18/39] Loss: 0.00524 +Epoch [3403/4000] Training [19/39] Loss: 0.13351 +Epoch [3403/4000] Training [20/39] Loss: 0.00547 +Epoch [3403/4000] Training [21/39] Loss: 0.01296 +Epoch [3403/4000] Training [22/39] Loss: 0.13049 +Epoch [3403/4000] Training [23/39] Loss: 0.00500 +Epoch [3403/4000] Training [24/39] Loss: 0.00656 +Epoch [3403/4000] Training [25/39] Loss: 0.13054 +Epoch [3403/4000] Training [26/39] Loss: 0.00526 +Epoch [3403/4000] Training [27/39] Loss: 0.13138 +Epoch [3403/4000] Training [28/39] Loss: 0.00458 +Epoch [3403/4000] Training [29/39] Loss: 0.00664 +Epoch [3403/4000] Training [30/39] Loss: 0.00510 +Epoch [3403/4000] Training [31/39] Loss: 0.00815 +Epoch [3403/4000] Training [32/39] Loss: 0.00439 +Epoch [3403/4000] Training [33/39] Loss: 0.00639 +Epoch [3403/4000] Training [34/39] Loss: 0.00539 +Epoch [3403/4000] Training [35/39] Loss: 0.12825 +Epoch [3403/4000] Training [36/39] Loss: 0.00581 +Epoch [3403/4000] Training [37/39] Loss: 0.13415 +Epoch [3403/4000] Training [38/39] Loss: 0.00483 +Epoch [3403/4000] Training [39/39] Loss: 0.13036 +Epoch [3403/4000] Training metric {'Train/mean dice_metric': 0.9956321716308594, 'Train/mean miou_metric': 0.9917252063751221, 'Train/mean f1': 0.9964076280593872, 'Train/mean precision': 0.9959861040115356, 'Train/mean recall': 0.9968293905258179, 'Train/mean hd95_metric': 1.057863473892212} +Epoch [3403/4000] Validation [1/10] Loss: 0.69343 focal_loss 0.60275 dice_loss 0.09067 +Epoch [3403/4000] Validation [2/10] Loss: 0.44552 focal_loss 0.35661 dice_loss 0.08891 +Epoch [3403/4000] Validation [3/10] Loss: 0.34215 focal_loss 0.23313 dice_loss 0.10902 +Epoch [3403/4000] Validation [4/10] Loss: 0.88708 focal_loss 0.31871 dice_loss 0.56837 +Epoch [3403/4000] Validation [5/10] Loss: 2.94490 focal_loss 2.27309 dice_loss 0.67181 +Epoch [3403/4000] Validation [6/10] Loss: 1.27962 focal_loss 0.56800 dice_loss 0.71162 +Epoch [3403/4000] Validation [7/10] Loss: 1.17208 focal_loss 0.52110 dice_loss 0.65097 +Epoch [3403/4000] Validation [8/10] Loss: 2.17574 focal_loss 1.57041 dice_loss 0.60533 +Epoch [3403/4000] Validation [9/10] Loss: 1.39166 focal_loss 0.84699 dice_loss 0.54466 +Epoch [3403/4000] Validation [10/10] Loss: 1.81051 focal_loss 1.08358 dice_loss 0.72693 +Epoch [3403/4000] Validation metric {'Val/mean dice_metric': 0.9507774114608765, 'Val/mean miou_metric': 0.9343039989471436, 'Val/mean f1': 0.9477533102035522, 'Val/mean precision': 0.9409648180007935, 'Val/mean recall': 0.9546406269073486, 'Val/mean hd95_metric': 11.017829895019531} +Cheakpoint... +Epoch [3403/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507774114608765, 'Val/mean miou_metric': 0.9343039989471436, 'Val/mean f1': 0.9477533102035522, 'Val/mean precision': 0.9409648180007935, 'Val/mean recall': 0.9546406269073486, 'Val/mean hd95_metric': 11.017829895019531} +Epoch [3404/4000] Training [1/39] Loss: 0.00466 +Epoch [3404/4000] Training [2/39] Loss: 0.00494 +Epoch [3404/4000] Training [3/39] Loss: 0.00536 +Epoch [3404/4000] Training [4/39] Loss: 0.00514 +Epoch [3404/4000] Training [5/39] Loss: 0.12934 +Epoch [3404/4000] Training [6/39] Loss: 0.12920 +Epoch [3404/4000] Training [7/39] Loss: 0.00534 +Epoch [3404/4000] Training [8/39] Loss: 0.00361 +Epoch [3404/4000] Training [9/39] Loss: 0.00615 +Epoch [3404/4000] Training [10/39] Loss: 0.00400 +Epoch [3404/4000] Training [11/39] Loss: 0.12807 +Epoch [3404/4000] Training [12/39] Loss: 0.00539 +Epoch [3404/4000] Training [13/39] Loss: 0.00714 +Epoch [3404/4000] Training [14/39] Loss: 0.25274 +Epoch [3404/4000] Training [15/39] Loss: 0.00389 +Epoch [3404/4000] Training [16/39] Loss: 0.12885 +Epoch [3404/4000] Training [17/39] Loss: 0.00435 +Epoch [3404/4000] Training [18/39] Loss: 0.00803 +Epoch [3404/4000] Training [19/39] Loss: 0.13151 +Epoch [3404/4000] Training [20/39] Loss: 0.00399 +Epoch [3404/4000] Training [21/39] Loss: 0.00592 +Epoch [3404/4000] Training [22/39] Loss: 0.00562 +Epoch [3404/4000] Training [23/39] Loss: 0.00392 +Epoch [3404/4000] Training [24/39] Loss: 0.00656 +Epoch [3404/4000] Training [25/39] Loss: 0.00752 +Epoch [3404/4000] Training [26/39] Loss: 0.00533 +Epoch [3404/4000] Training [27/39] Loss: 0.04344 +Epoch [3404/4000] Training [28/39] Loss: 0.00606 +Epoch [3404/4000] Training [29/39] Loss: 0.00625 +Epoch [3404/4000] Training [30/39] Loss: 0.00485 +Epoch [3404/4000] Training [31/39] Loss: 0.12903 +Epoch [3404/4000] Training [32/39] Loss: 0.00596 +Epoch [3404/4000] Training [33/39] Loss: 0.00850 +Epoch [3404/4000] Training [34/39] Loss: 0.00652 +Epoch [3404/4000] Training [35/39] Loss: 0.13143 +Epoch [3404/4000] Training [36/39] Loss: 0.00373 +Epoch [3404/4000] Training [37/39] Loss: 0.00573 +Epoch [3404/4000] Training [38/39] Loss: 0.00427 +Epoch [3404/4000] Training [39/39] Loss: 0.00437 +Epoch [3404/4000] Training metric {'Train/mean dice_metric': 0.9958857297897339, 'Train/mean miou_metric': 0.992234468460083, 'Train/mean f1': 0.9967366456985474, 'Train/mean precision': 0.9962283968925476, 'Train/mean recall': 0.9972453117370605, 'Train/mean hd95_metric': 1.0300428867340088} +Epoch [3404/4000] Validation [1/10] Loss: 0.70063 focal_loss 0.61085 dice_loss 0.08979 +Epoch [3404/4000] Validation [2/10] Loss: 0.45859 focal_loss 0.36642 dice_loss 0.09216 +Epoch [3404/4000] Validation [3/10] Loss: 0.36654 focal_loss 0.25467 dice_loss 0.11187 +Epoch [3404/4000] Validation [4/10] Loss: 0.88178 focal_loss 0.31716 dice_loss 0.56462 +Epoch [3404/4000] Validation [5/10] Loss: 3.02723 focal_loss 2.35465 dice_loss 0.67259 +Epoch [3404/4000] Validation [6/10] Loss: 1.26296 focal_loss 0.55458 dice_loss 0.70837 +Epoch [3404/4000] Validation [7/10] Loss: 1.15841 focal_loss 0.50509 dice_loss 0.65332 +Epoch [3404/4000] Validation [8/10] Loss: 2.15643 focal_loss 1.55345 dice_loss 0.60298 +Epoch [3404/4000] Validation [9/10] Loss: 1.41493 focal_loss 0.90672 dice_loss 0.50820 +Epoch [3404/4000] Validation [10/10] Loss: 1.81154 focal_loss 1.08452 dice_loss 0.72703 +Epoch [3404/4000] Validation metric {'Val/mean dice_metric': 0.95107102394104, 'Val/mean miou_metric': 0.9347680807113647, 'Val/mean f1': 0.9475615620613098, 'Val/mean precision': 0.9402327537536621, 'Val/mean recall': 0.9550053477287292, 'Val/mean hd95_metric': 10.840325355529785} +Cheakpoint... +Epoch [3404/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.95107102394104, 'Val/mean miou_metric': 0.9347680807113647, 'Val/mean f1': 0.9475615620613098, 'Val/mean precision': 0.9402327537536621, 'Val/mean recall': 0.9550053477287292, 'Val/mean hd95_metric': 10.840325355529785} +Epoch [3405/4000] Training [1/39] Loss: 0.00735 +Epoch [3405/4000] Training [2/39] Loss: 0.13073 +Epoch [3405/4000] Training [3/39] Loss: 0.01195 +Epoch [3405/4000] Training [4/39] Loss: 0.13220 +Epoch [3405/4000] Training [5/39] Loss: 0.00559 +Epoch [3405/4000] Training [6/39] Loss: 0.00488 +Epoch [3405/4000] Training [7/39] Loss: 0.00876 +Epoch [3405/4000] Training [8/39] Loss: 0.00693 +Epoch [3405/4000] Training [9/39] Loss: 0.00471 +Epoch [3405/4000] Training [10/39] Loss: 0.08737 +Epoch [3405/4000] Training [11/39] Loss: 0.00563 +Epoch [3405/4000] Training [12/39] Loss: 0.00416 +Epoch [3405/4000] Training [13/39] Loss: 0.00736 +Epoch [3405/4000] Training [14/39] Loss: 0.00877 +Epoch [3405/4000] Training [15/39] Loss: 0.00390 +Epoch [3405/4000] Training [16/39] Loss: 0.13059 +Epoch [3405/4000] Training [17/39] Loss: 0.00773 +Epoch [3405/4000] Training [18/39] Loss: 0.12780 +Epoch [3405/4000] Training [19/39] Loss: 0.00654 +Epoch [3405/4000] Training [20/39] Loss: 0.13155 +Epoch [3405/4000] Training [21/39] Loss: 0.00689 +Epoch [3405/4000] Training [22/39] Loss: 0.00563 +Epoch [3405/4000] Training [23/39] Loss: 0.12989 +Epoch [3405/4000] Training [24/39] Loss: 0.00439 +Epoch [3405/4000] Training [25/39] Loss: 0.00532 +Epoch [3405/4000] Training [26/39] Loss: 0.00666 +Epoch [3405/4000] Training [27/39] Loss: 0.00699 +Epoch [3405/4000] Training [28/39] Loss: 0.12839 +Epoch [3405/4000] Training [29/39] Loss: 0.00537 +Epoch [3405/4000] Training [30/39] Loss: 0.12853 +Epoch [3405/4000] Training [31/39] Loss: 0.12829 +Epoch [3405/4000] Training [32/39] Loss: 0.00440 +Epoch [3405/4000] Training [33/39] Loss: 0.25678 +Epoch [3405/4000] Training [34/39] Loss: 0.00510 +Epoch [3405/4000] Training [35/39] Loss: 0.00839 +Epoch [3405/4000] Training [36/39] Loss: 0.00617 +Epoch [3405/4000] Training [37/39] Loss: 0.00411 +Epoch [3405/4000] Training [38/39] Loss: 0.13142 +Epoch [3405/4000] Training [39/39] Loss: 0.00422 +Epoch [3405/4000] Training metric {'Train/mean dice_metric': 0.9956254363059998, 'Train/mean miou_metric': 0.99171382188797, 'Train/mean f1': 0.9964694976806641, 'Train/mean precision': 0.9960480332374573, 'Train/mean recall': 0.9968913197517395, 'Train/mean hd95_metric': 1.0514013767242432} +Epoch [3405/4000] Validation [1/10] Loss: 0.72319 focal_loss 0.63127 dice_loss 0.09193 +Epoch [3405/4000] Validation [2/10] Loss: 0.48217 focal_loss 0.37761 dice_loss 0.10456 +Epoch [3405/4000] Validation [3/10] Loss: 0.36169 focal_loss 0.25180 dice_loss 0.10989 +Epoch [3405/4000] Validation [4/10] Loss: 0.85938 focal_loss 0.29481 dice_loss 0.56457 +Epoch [3405/4000] Validation [5/10] Loss: 2.98626 focal_loss 2.31318 dice_loss 0.67308 +Epoch [3405/4000] Validation [6/10] Loss: 1.23915 focal_loss 0.52211 dice_loss 0.71704 +Epoch [3405/4000] Validation [7/10] Loss: 1.14897 focal_loss 0.49672 dice_loss 0.65225 +Epoch [3405/4000] Validation [8/10] Loss: 2.15388 focal_loss 1.54506 dice_loss 0.60881 +Epoch [3405/4000] Validation [9/10] Loss: 1.39050 focal_loss 0.84651 dice_loss 0.54399 +Epoch [3405/4000] Validation [10/10] Loss: 1.68089 focal_loss 0.96285 dice_loss 0.71804 +Epoch [3405/4000] Validation metric {'Val/mean dice_metric': 0.9503646492958069, 'Val/mean miou_metric': 0.9338456988334656, 'Val/mean f1': 0.9484552145004272, 'Val/mean precision': 0.9445498585700989, 'Val/mean recall': 0.9523929357528687, 'Val/mean hd95_metric': 10.699708938598633} +Cheakpoint... +Epoch [3405/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503646492958069, 'Val/mean miou_metric': 0.9338456988334656, 'Val/mean f1': 0.9484552145004272, 'Val/mean precision': 0.9445498585700989, 'Val/mean recall': 0.9523929357528687, 'Val/mean hd95_metric': 10.699708938598633} +Epoch [3406/4000] Training [1/39] Loss: 0.00409 +Epoch [3406/4000] Training [2/39] Loss: 0.00980 +Epoch [3406/4000] Training [3/39] Loss: 0.01083 +Epoch [3406/4000] Training [4/39] Loss: 0.00349 +Epoch [3406/4000] Training [5/39] Loss: 0.00587 +Epoch [3406/4000] Training [6/39] Loss: 0.00486 +Epoch [3406/4000] Training [7/39] Loss: 0.13291 +Epoch [3406/4000] Training [8/39] Loss: 0.00373 +Epoch [3406/4000] Training [9/39] Loss: 0.00618 +Epoch [3406/4000] Training [10/39] Loss: 0.00666 +Epoch [3406/4000] Training [11/39] Loss: 0.12854 +Epoch [3406/4000] Training [12/39] Loss: 0.00447 +Epoch [3406/4000] Training [13/39] Loss: 0.00791 +Epoch [3406/4000] Training [14/39] Loss: 0.00408 +Epoch [3406/4000] Training [15/39] Loss: 0.00623 +Epoch [3406/4000] Training [16/39] Loss: 0.12818 +Epoch [3406/4000] Training [17/39] Loss: 0.00579 +Epoch [3406/4000] Training [18/39] Loss: 0.13085 +Epoch [3406/4000] Training [19/39] Loss: 0.00458 +Epoch [3406/4000] Training [20/39] Loss: 0.00378 +Epoch [3406/4000] Training [21/39] Loss: 0.12797 +Epoch [3406/4000] Training [22/39] Loss: 0.00478 +Epoch [3406/4000] Training [23/39] Loss: 0.00633 +Epoch [3406/4000] Training [24/39] Loss: 0.00723 +Epoch [3406/4000] Training [25/39] Loss: 0.00483 +Epoch [3406/4000] Training [26/39] Loss: 0.00589 +Epoch [3406/4000] Training [27/39] Loss: 0.00470 +Epoch [3406/4000] Training [28/39] Loss: 0.12845 +Epoch [3406/4000] Training [29/39] Loss: 0.12906 +Epoch [3406/4000] Training [30/39] Loss: 0.00422 +Epoch [3406/4000] Training [31/39] Loss: 0.12822 +Epoch [3406/4000] Training [32/39] Loss: 0.00506 +Epoch [3406/4000] Training [33/39] Loss: 0.00656 +Epoch [3406/4000] Training [34/39] Loss: 0.00506 +Epoch [3406/4000] Training [35/39] Loss: 0.00816 +Epoch [3406/4000] Training [36/39] Loss: 0.00982 +Epoch [3406/4000] Training [37/39] Loss: 0.00378 +Epoch [3406/4000] Training [38/39] Loss: 0.00495 +Epoch [3406/4000] Training [39/39] Loss: 0.12924 +Epoch [3406/4000] Training metric {'Train/mean dice_metric': 0.9957133531570435, 'Train/mean miou_metric': 0.9918881058692932, 'Train/mean f1': 0.996604323387146, 'Train/mean precision': 0.9961269497871399, 'Train/mean recall': 0.9970820546150208, 'Train/mean hd95_metric': 1.0563093423843384} +Epoch [3406/4000] Validation [1/10] Loss: 0.71578 focal_loss 0.62478 dice_loss 0.09100 +Epoch [3406/4000] Validation [2/10] Loss: 0.47695 focal_loss 0.37533 dice_loss 0.10162 +Epoch [3406/4000] Validation [3/10] Loss: 0.37330 focal_loss 0.26227 dice_loss 0.11103 +Epoch [3406/4000] Validation [4/10] Loss: 0.86861 focal_loss 0.29519 dice_loss 0.57341 +Epoch [3406/4000] Validation [5/10] Loss: 3.03237 focal_loss 2.35867 dice_loss 0.67370 +Epoch [3406/4000] Validation [6/10] Loss: 1.25290 focal_loss 0.53962 dice_loss 0.71328 +Epoch [3406/4000] Validation [7/10] Loss: 1.13802 focal_loss 0.48883 dice_loss 0.64919 +Epoch [3406/4000] Validation [8/10] Loss: 2.46616 focal_loss 1.82842 dice_loss 0.63774 +Epoch [3406/4000] Validation [9/10] Loss: 1.32111 focal_loss 0.79491 dice_loss 0.52620 +Epoch [3406/4000] Validation [10/10] Loss: 1.68569 focal_loss 0.96927 dice_loss 0.71642 +Epoch [3406/4000] Validation metric {'Val/mean dice_metric': 0.9505374431610107, 'Val/mean miou_metric': 0.9340397119522095, 'Val/mean f1': 0.9486750364303589, 'Val/mean precision': 0.9464694261550903, 'Val/mean recall': 0.9508910179138184, 'Val/mean hd95_metric': 10.619917869567871} +Cheakpoint... +Epoch [3406/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505374431610107, 'Val/mean miou_metric': 0.9340397119522095, 'Val/mean f1': 0.9486750364303589, 'Val/mean precision': 0.9464694261550903, 'Val/mean recall': 0.9508910179138184, 'Val/mean hd95_metric': 10.619917869567871} +Epoch [3407/4000] Training [1/39] Loss: 0.13046 +Epoch [3407/4000] Training [2/39] Loss: 0.12859 +Epoch [3407/4000] Training [3/39] Loss: 0.00608 +Epoch [3407/4000] Training [4/39] Loss: 0.00716 +Epoch [3407/4000] Training [5/39] Loss: 0.12810 +Epoch [3407/4000] Training [6/39] Loss: 0.00606 +Epoch [3407/4000] Training [7/39] Loss: 0.00534 +Epoch [3407/4000] Training [8/39] Loss: 0.00568 +Epoch [3407/4000] Training [9/39] Loss: 0.00386 +Epoch [3407/4000] Training [10/39] Loss: 0.00381 +Epoch [3407/4000] Training [11/39] Loss: 0.12880 +Epoch [3407/4000] Training [12/39] Loss: 0.00878 +Epoch [3407/4000] Training [13/39] Loss: 0.00580 +Epoch [3407/4000] Training [14/39] Loss: 0.00319 +Epoch [3407/4000] Training [15/39] Loss: 0.00669 +Epoch [3407/4000] Training [16/39] Loss: 0.00612 +Epoch [3407/4000] Training [17/39] Loss: 0.00678 +Epoch [3407/4000] Training [18/39] Loss: 0.00382 +Epoch [3407/4000] Training [19/39] Loss: 0.00616 +Epoch [3407/4000] Training [20/39] Loss: 0.00698 +Epoch [3407/4000] Training [21/39] Loss: 0.00972 +Epoch [3407/4000] Training [22/39] Loss: 0.00526 +Epoch [3407/4000] Training [23/39] Loss: 0.13014 +Epoch [3407/4000] Training [24/39] Loss: 0.13000 +Epoch [3407/4000] Training [25/39] Loss: 0.00626 +Epoch [3407/4000] Training [26/39] Loss: 0.00645 +Epoch [3407/4000] Training [27/39] Loss: 0.00475 +Epoch [3407/4000] Training [28/39] Loss: 0.00602 +Epoch [3407/4000] Training [29/39] Loss: 0.00276 +Epoch [3407/4000] Training [30/39] Loss: 0.25410 +Epoch [3407/4000] Training [31/39] Loss: 0.00516 +Epoch [3407/4000] Training [32/39] Loss: 0.13183 +Epoch [3407/4000] Training [33/39] Loss: 0.00492 +Epoch [3407/4000] Training [34/39] Loss: 0.00343 +Epoch [3407/4000] Training [35/39] Loss: 0.00373 +Epoch [3407/4000] Training [36/39] Loss: 0.00536 +Epoch [3407/4000] Training [37/39] Loss: 0.00705 +Epoch [3407/4000] Training [38/39] Loss: 0.00483 +Epoch [3407/4000] Training [39/39] Loss: 0.00547 +Epoch [3407/4000] Training metric {'Train/mean dice_metric': 0.9956284165382385, 'Train/mean miou_metric': 0.9917152523994446, 'Train/mean f1': 0.9964720010757446, 'Train/mean precision': 0.996050238609314, 'Train/mean recall': 0.9968939423561096, 'Train/mean hd95_metric': 1.0715935230255127} +Epoch [3407/4000] Validation [1/10] Loss: 0.69130 focal_loss 0.59999 dice_loss 0.09132 +Epoch [3407/4000] Validation [2/10] Loss: 0.44409 focal_loss 0.35358 dice_loss 0.09051 +Epoch [3407/4000] Validation [3/10] Loss: 0.34843 focal_loss 0.23914 dice_loss 0.10929 +Epoch [3407/4000] Validation [4/10] Loss: 0.87699 focal_loss 0.30819 dice_loss 0.56880 +Epoch [3407/4000] Validation [5/10] Loss: 2.99299 focal_loss 2.32024 dice_loss 0.67275 +Epoch [3407/4000] Validation [6/10] Loss: 1.27260 focal_loss 0.56104 dice_loss 0.71156 +Epoch [3407/4000] Validation [7/10] Loss: 1.14784 focal_loss 0.49895 dice_loss 0.64889 +Epoch [3407/4000] Validation [8/10] Loss: 2.07444 focal_loss 1.47755 dice_loss 0.59689 +Epoch [3407/4000] Validation [9/10] Loss: 1.31889 focal_loss 0.82014 dice_loss 0.49875 +Epoch [3407/4000] Validation [10/10] Loss: 1.73613 focal_loss 1.01096 dice_loss 0.72517 +Epoch [3407/4000] Validation metric {'Val/mean dice_metric': 0.9508475065231323, 'Val/mean miou_metric': 0.9343180656433105, 'Val/mean f1': 0.9482110142707825, 'Val/mean precision': 0.9423496127128601, 'Val/mean recall': 0.9541457891464233, 'Val/mean hd95_metric': 10.682729721069336} +Cheakpoint... +Epoch [3407/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508475065231323, 'Val/mean miou_metric': 0.9343180656433105, 'Val/mean f1': 0.9482110142707825, 'Val/mean precision': 0.9423496127128601, 'Val/mean recall': 0.9541457891464233, 'Val/mean hd95_metric': 10.682729721069336} +Epoch [3408/4000] Training [1/39] Loss: 0.00595 +Epoch [3408/4000] Training [2/39] Loss: 0.13307 +Epoch [3408/4000] Training [3/39] Loss: 0.25327 +Epoch [3408/4000] Training [4/39] Loss: 0.00789 +Epoch [3408/4000] Training [5/39] Loss: 0.00742 +Epoch [3408/4000] Training [6/39] Loss: 0.13007 +Epoch [3408/4000] Training [7/39] Loss: 0.00692 +Epoch [3408/4000] Training [8/39] Loss: 0.00697 +Epoch [3408/4000] Training [9/39] Loss: 0.01015 +Epoch [3408/4000] Training [10/39] Loss: 0.00397 +Epoch [3408/4000] Training [11/39] Loss: 0.00820 +Epoch [3408/4000] Training [12/39] Loss: 0.00467 +Epoch [3408/4000] Training [13/39] Loss: 0.00525 +Epoch [3408/4000] Training [14/39] Loss: 0.00442 +Epoch [3408/4000] Training [15/39] Loss: 0.12927 +Epoch [3408/4000] Training [16/39] Loss: 0.12823 +Epoch [3408/4000] Training [17/39] Loss: 0.00907 +Epoch [3408/4000] Training [18/39] Loss: 0.00745 +Epoch [3408/4000] Training [19/39] Loss: 0.01002 +Epoch [3408/4000] Training [20/39] Loss: 0.13012 +Epoch [3408/4000] Training [21/39] Loss: 0.12952 +Epoch [3408/4000] Training [22/39] Loss: 0.03852 +Epoch [3408/4000] Training [23/39] Loss: 0.13101 +Epoch [3408/4000] Training [24/39] Loss: 0.00747 +Epoch [3408/4000] Training [25/39] Loss: 0.00522 +Epoch [3408/4000] Training [26/39] Loss: 0.25435 +Epoch [3408/4000] Training [27/39] Loss: 0.13136 +Epoch [3408/4000] Training [28/39] Loss: 0.00511 +Epoch [3408/4000] Training [29/39] Loss: 0.00554 +Epoch [3408/4000] Training [30/39] Loss: 0.00527 +Epoch [3408/4000] Training [31/39] Loss: 0.13304 +Epoch [3408/4000] Training [32/39] Loss: 0.00457 +Epoch [3408/4000] Training [33/39] Loss: 0.00309 +Epoch [3408/4000] Training [34/39] Loss: 0.00468 +Epoch [3408/4000] Training [35/39] Loss: 0.00455 +Epoch [3408/4000] Training [36/39] Loss: 0.00341 +Epoch [3408/4000] Training [37/39] Loss: 0.00533 +Epoch [3408/4000] Training [38/39] Loss: 0.01391 +Epoch [3408/4000] Training [39/39] Loss: 0.00482 +Epoch [3408/4000] Training metric {'Train/mean dice_metric': 0.9945009350776672, 'Train/mean miou_metric': 0.990302324295044, 'Train/mean f1': 0.9962111711502075, 'Train/mean precision': 0.9957288503646851, 'Train/mean recall': 0.9966939687728882, 'Train/mean hd95_metric': 1.1737302541732788} +Epoch [3408/4000] Validation [1/10] Loss: 0.74949 focal_loss 0.65516 dice_loss 0.09433 +Epoch [3408/4000] Validation [2/10] Loss: 0.44854 focal_loss 0.35878 dice_loss 0.08976 +Epoch [3408/4000] Validation [3/10] Loss: 0.34204 focal_loss 0.23355 dice_loss 0.10849 +Epoch [3408/4000] Validation [4/10] Loss: 0.89208 focal_loss 0.32592 dice_loss 0.56616 +Epoch [3408/4000] Validation [5/10] Loss: 2.98438 focal_loss 2.31226 dice_loss 0.67211 +Epoch [3408/4000] Validation [6/10] Loss: 1.28652 focal_loss 0.57515 dice_loss 0.71136 +Epoch [3408/4000] Validation [7/10] Loss: 1.18217 focal_loss 0.52568 dice_loss 0.65649 +Epoch [3408/4000] Validation [8/10] Loss: 2.05766 focal_loss 1.46400 dice_loss 0.59366 +Epoch [3408/4000] Validation [9/10] Loss: 1.35674 focal_loss 0.85164 dice_loss 0.50510 +Epoch [3408/4000] Validation [10/10] Loss: 1.83052 focal_loss 1.09590 dice_loss 0.73462 +Epoch [3408/4000] Validation metric {'Val/mean dice_metric': 0.9496222734451294, 'Val/mean miou_metric': 0.9328582882881165, 'Val/mean f1': 0.9475864768028259, 'Val/mean precision': 0.9397510886192322, 'Val/mean recall': 0.9555534720420837, 'Val/mean hd95_metric': 10.9991455078125} +Cheakpoint... +Epoch [3408/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9496], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9496222734451294, 'Val/mean miou_metric': 0.9328582882881165, 'Val/mean f1': 0.9475864768028259, 'Val/mean precision': 0.9397510886192322, 'Val/mean recall': 0.9555534720420837, 'Val/mean hd95_metric': 10.9991455078125} +Epoch [3409/4000] Training [1/39] Loss: 0.13005 +Epoch [3409/4000] Training [2/39] Loss: 0.00683 +Epoch [3409/4000] Training [3/39] Loss: 0.00389 +Epoch [3409/4000] Training [4/39] Loss: 0.00674 +Epoch [3409/4000] Training [5/39] Loss: 0.00456 +Epoch [3409/4000] Training [6/39] Loss: 0.00600 +Epoch [3409/4000] Training [7/39] Loss: 0.00728 +Epoch [3409/4000] Training [8/39] Loss: 0.13026 +Epoch [3409/4000] Training [9/39] Loss: 0.12900 +Epoch [3409/4000] Training [10/39] Loss: 0.00488 +Epoch [3409/4000] Training [11/39] Loss: 0.00756 +Epoch [3409/4000] Training [12/39] Loss: 0.13209 +Epoch [3409/4000] Training [13/39] Loss: 0.04883 +Epoch [3409/4000] Training [14/39] Loss: 0.00507 +Epoch [3409/4000] Training [15/39] Loss: 0.13144 +Epoch [3409/4000] Training [16/39] Loss: 0.00476 +Epoch [3409/4000] Training [17/39] Loss: 0.12858 +Epoch [3409/4000] Training [18/39] Loss: 0.00593 +Epoch [3409/4000] Training [19/39] Loss: 0.00424 +Epoch [3409/4000] Training [20/39] Loss: 0.00553 +Epoch [3409/4000] Training [21/39] Loss: 0.00464 +Epoch [3409/4000] Training [22/39] Loss: 0.00374 +Epoch [3409/4000] Training [23/39] Loss: 0.00352 +Epoch [3409/4000] Training [24/39] Loss: 0.00365 +Epoch [3409/4000] Training [25/39] Loss: 0.00548 +Epoch [3409/4000] Training [26/39] Loss: 0.12991 +Epoch [3409/4000] Training [27/39] Loss: 0.00638 +Epoch [3409/4000] Training [28/39] Loss: 0.00856 +Epoch [3409/4000] Training [29/39] Loss: 0.00659 +Epoch [3409/4000] Training [30/39] Loss: 0.13167 +Epoch [3409/4000] Training [31/39] Loss: 0.00467 +Epoch [3409/4000] Training [32/39] Loss: 0.13141 +Epoch [3409/4000] Training [33/39] Loss: 0.25354 +Epoch [3409/4000] Training [34/39] Loss: 0.00745 +Epoch [3409/4000] Training [35/39] Loss: 0.00574 +Epoch [3409/4000] Training [36/39] Loss: 0.00778 +Epoch [3409/4000] Training [37/39] Loss: 0.01072 +Epoch [3409/4000] Training [38/39] Loss: 0.00810 +Epoch [3409/4000] Training [39/39] Loss: 0.12896 +Epoch [3409/4000] Training metric {'Train/mean dice_metric': 0.9948912262916565, 'Train/mean miou_metric': 0.9910019636154175, 'Train/mean f1': 0.9964888095855713, 'Train/mean precision': 0.996107816696167, 'Train/mean recall': 0.9968701004981995, 'Train/mean hd95_metric': 1.0385149717330933} +Epoch [3409/4000] Validation [1/10] Loss: 0.68373 focal_loss 0.59548 dice_loss 0.08825 +Epoch [3409/4000] Validation [2/10] Loss: 0.46016 focal_loss 0.36990 dice_loss 0.09027 +Epoch [3409/4000] Validation [3/10] Loss: 0.35248 focal_loss 0.24342 dice_loss 0.10906 +Epoch [3409/4000] Validation [4/10] Loss: 0.89497 focal_loss 0.32519 dice_loss 0.56979 +Epoch [3409/4000] Validation [5/10] Loss: 2.98898 focal_loss 2.31645 dice_loss 0.67253 +Epoch [3409/4000] Validation [6/10] Loss: 1.28551 focal_loss 0.57441 dice_loss 0.71110 +Epoch [3409/4000] Validation [7/10] Loss: 1.16615 focal_loss 0.51779 dice_loss 0.64835 +Epoch [3409/4000] Validation [8/10] Loss: 2.31327 focal_loss 1.68946 dice_loss 0.62381 +Epoch [3409/4000] Validation [9/10] Loss: 1.37877 focal_loss 0.84138 dice_loss 0.53739 +Epoch [3409/4000] Validation [10/10] Loss: 1.79609 focal_loss 1.06479 dice_loss 0.73130 +Epoch [3409/4000] Validation metric {'Val/mean dice_metric': 0.9499630331993103, 'Val/mean miou_metric': 0.9334654211997986, 'Val/mean f1': 0.948376476764679, 'Val/mean precision': 0.943649411201477, 'Val/mean recall': 0.9531509876251221, 'Val/mean hd95_metric': 10.91499137878418} +Cheakpoint... +Epoch [3409/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9500], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9499630331993103, 'Val/mean miou_metric': 0.9334654211997986, 'Val/mean f1': 0.948376476764679, 'Val/mean precision': 0.943649411201477, 'Val/mean recall': 0.9531509876251221, 'Val/mean hd95_metric': 10.91499137878418} +Epoch [3410/4000] Training [1/39] Loss: 0.00574 +Epoch [3410/4000] Training [2/39] Loss: 0.12918 +Epoch [3410/4000] Training [3/39] Loss: 0.00717 +Epoch [3410/4000] Training [4/39] Loss: 0.00792 +Epoch [3410/4000] Training [5/39] Loss: 0.00555 +Epoch [3410/4000] Training [6/39] Loss: 0.00451 +Epoch [3410/4000] Training [7/39] Loss: 0.00451 +Epoch [3410/4000] Training [8/39] Loss: 0.00545 +Epoch [3410/4000] Training [9/39] Loss: 0.12907 +Epoch [3410/4000] Training [10/39] Loss: 0.00579 +Epoch [3410/4000] Training [11/39] Loss: 0.00470 +Epoch [3410/4000] Training [12/39] Loss: 0.00482 +Epoch [3410/4000] Training [13/39] Loss: 0.13012 +Epoch [3410/4000] Training [14/39] Loss: 0.00428 +Epoch [3410/4000] Training [15/39] Loss: 0.00847 +Epoch [3410/4000] Training [16/39] Loss: 0.00549 +Epoch [3410/4000] Training [17/39] Loss: 0.00389 +Epoch [3410/4000] Training [18/39] Loss: 0.00499 +Epoch [3410/4000] Training [19/39] Loss: 0.13477 +Epoch [3410/4000] Training [20/39] Loss: 0.00426 +Epoch [3410/4000] Training [21/39] Loss: 0.00496 +Epoch [3410/4000] Training [22/39] Loss: 0.00543 +Epoch [3410/4000] Training [23/39] Loss: 0.00466 +Epoch [3410/4000] Training [24/39] Loss: 0.00468 +Epoch [3410/4000] Training [25/39] Loss: 0.12779 +Epoch [3410/4000] Training [26/39] Loss: 0.00417 +Epoch [3410/4000] Training [27/39] Loss: 0.00642 +Epoch [3410/4000] Training [28/39] Loss: 0.12908 +Epoch [3410/4000] Training [29/39] Loss: 0.00473 +Epoch [3410/4000] Training [30/39] Loss: 0.00826 +Epoch [3410/4000] Training [31/39] Loss: 0.00643 +Epoch [3410/4000] Training [32/39] Loss: 0.00702 +Epoch [3410/4000] Training [33/39] Loss: 0.00720 +Epoch [3410/4000] Training [34/39] Loss: 0.00487 +Epoch [3410/4000] Training [35/39] Loss: 0.00404 +Epoch [3410/4000] Training [36/39] Loss: 0.00877 +Epoch [3410/4000] Training [37/39] Loss: 0.01078 +Epoch [3410/4000] Training [38/39] Loss: 0.12845 +Epoch [3410/4000] Training [39/39] Loss: 0.00295 +Epoch [3410/4000] Training metric {'Train/mean dice_metric': 0.9957355260848999, 'Train/mean miou_metric': 0.9919210076332092, 'Train/mean f1': 0.9964386820793152, 'Train/mean precision': 0.9960347414016724, 'Train/mean recall': 0.9968429207801819, 'Train/mean hd95_metric': 1.0498586893081665} +Epoch [3410/4000] Validation [1/10] Loss: 0.66064 focal_loss 0.57434 dice_loss 0.08630 +Epoch [3410/4000] Validation [2/10] Loss: 0.45748 focal_loss 0.36303 dice_loss 0.09446 +Epoch [3410/4000] Validation [3/10] Loss: 0.36581 focal_loss 0.25481 dice_loss 0.11100 +Epoch [3410/4000] Validation [4/10] Loss: 0.87052 focal_loss 0.30010 dice_loss 0.57043 +Epoch [3410/4000] Validation [5/10] Loss: 2.94918 focal_loss 2.27632 dice_loss 0.67286 +Epoch [3410/4000] Validation [6/10] Loss: 1.24080 focal_loss 0.53160 dice_loss 0.70919 +Epoch [3410/4000] Validation [7/10] Loss: 1.14456 focal_loss 0.49621 dice_loss 0.64835 +Epoch [3410/4000] Validation [8/10] Loss: 2.46745 focal_loss 1.82848 dice_loss 0.63897 +Epoch [3410/4000] Validation [9/10] Loss: 1.30410 focal_loss 0.79187 dice_loss 0.51223 +Epoch [3410/4000] Validation [10/10] Loss: 1.69613 focal_loss 0.97366 dice_loss 0.72247 +Epoch [3410/4000] Validation metric {'Val/mean dice_metric': 0.9510809779167175, 'Val/mean miou_metric': 0.9346771240234375, 'Val/mean f1': 0.9490221738815308, 'Val/mean precision': 0.9459640383720398, 'Val/mean recall': 0.9521002173423767, 'Val/mean hd95_metric': 10.460257530212402} +Cheakpoint... +Epoch [3410/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510809779167175, 'Val/mean miou_metric': 0.9346771240234375, 'Val/mean f1': 0.9490221738815308, 'Val/mean precision': 0.9459640383720398, 'Val/mean recall': 0.9521002173423767, 'Val/mean hd95_metric': 10.460257530212402} +Epoch [3411/4000] Training [1/39] Loss: 0.00478 +Epoch [3411/4000] Training [2/39] Loss: 0.00667 +Epoch [3411/4000] Training [3/39] Loss: 0.00501 +Epoch [3411/4000] Training [4/39] Loss: 0.00454 +Epoch [3411/4000] Training [5/39] Loss: 0.00563 +Epoch [3411/4000] Training [6/39] Loss: 0.00458 +Epoch [3411/4000] Training [7/39] Loss: 0.00639 +Epoch [3411/4000] Training [8/39] Loss: 0.04622 +Epoch [3411/4000] Training [9/39] Loss: 0.00534 +Epoch [3411/4000] Training [10/39] Loss: 0.00425 +Epoch [3411/4000] Training [11/39] Loss: 0.25307 +Epoch [3411/4000] Training [12/39] Loss: 0.00339 +Epoch [3411/4000] Training [13/39] Loss: 0.00615 +Epoch [3411/4000] Training [14/39] Loss: 0.00479 +Epoch [3411/4000] Training [15/39] Loss: 0.12954 +Epoch [3411/4000] Training [16/39] Loss: 0.00588 +Epoch [3411/4000] Training [17/39] Loss: 0.00513 +Epoch [3411/4000] Training [18/39] Loss: 0.00566 +Epoch [3411/4000] Training [19/39] Loss: 0.00370 +Epoch [3411/4000] Training [20/39] Loss: 0.00620 +Epoch [3411/4000] Training [21/39] Loss: 0.00686 +Epoch [3411/4000] Training [22/39] Loss: 0.00442 +Epoch [3411/4000] Training [23/39] Loss: 0.00710 +Epoch [3411/4000] Training [24/39] Loss: 0.00711 +Epoch [3411/4000] Training [25/39] Loss: 0.00451 +Epoch [3411/4000] Training [26/39] Loss: 0.12967 +Epoch [3411/4000] Training [27/39] Loss: 0.00470 +Epoch [3411/4000] Training [28/39] Loss: 0.00869 +Epoch [3411/4000] Training [29/39] Loss: 0.25521 +Epoch [3411/4000] Training [30/39] Loss: 0.00893 +Epoch [3411/4000] Training [31/39] Loss: 0.00480 +Epoch [3411/4000] Training [32/39] Loss: 0.00795 +Epoch [3411/4000] Training [33/39] Loss: 0.00847 +Epoch [3411/4000] Training [34/39] Loss: 0.00471 +Epoch [3411/4000] Training [35/39] Loss: 0.00502 +Epoch [3411/4000] Training [36/39] Loss: 0.00619 +Epoch [3411/4000] Training [37/39] Loss: 0.13045 +Epoch [3411/4000] Training [38/39] Loss: 0.00730 +Epoch [3411/4000] Training [39/39] Loss: 0.00493 +Epoch [3411/4000] Training metric {'Train/mean dice_metric': 0.9949634075164795, 'Train/mean miou_metric': 0.9912592768669128, 'Train/mean f1': 0.9964724183082581, 'Train/mean precision': 0.9959962368011475, 'Train/mean recall': 0.9969488978385925, 'Train/mean hd95_metric': 1.0335851907730103} +Epoch [3411/4000] Validation [1/10] Loss: 0.71608 focal_loss 0.62497 dice_loss 0.09111 +Epoch [3411/4000] Validation [2/10] Loss: 0.45625 focal_loss 0.36317 dice_loss 0.09308 +Epoch [3411/4000] Validation [3/10] Loss: 0.36791 focal_loss 0.25636 dice_loss 0.11155 +Epoch [3411/4000] Validation [4/10] Loss: 0.88024 focal_loss 0.31561 dice_loss 0.56463 +Epoch [3411/4000] Validation [5/10] Loss: 2.99560 focal_loss 2.32317 dice_loss 0.67244 +Epoch [3411/4000] Validation [6/10] Loss: 1.26830 focal_loss 0.55357 dice_loss 0.71473 +Epoch [3411/4000] Validation [7/10] Loss: 1.16133 focal_loss 0.50704 dice_loss 0.65429 +Epoch [3411/4000] Validation [8/10] Loss: 2.24770 focal_loss 1.63408 dice_loss 0.61362 +Epoch [3411/4000] Validation [9/10] Loss: 1.35901 focal_loss 0.83930 dice_loss 0.51971 +Epoch [3411/4000] Validation [10/10] Loss: 1.76631 focal_loss 1.03919 dice_loss 0.72712 +Epoch [3411/4000] Validation metric {'Val/mean dice_metric': 0.9502354264259338, 'Val/mean miou_metric': 0.9339576959609985, 'Val/mean f1': 0.9482883214950562, 'Val/mean precision': 0.9425593018531799, 'Val/mean recall': 0.9540873765945435, 'Val/mean hd95_metric': 10.560403823852539} +Cheakpoint... +Epoch [3411/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502354264259338, 'Val/mean miou_metric': 0.9339576959609985, 'Val/mean f1': 0.9482883214950562, 'Val/mean precision': 0.9425593018531799, 'Val/mean recall': 0.9540873765945435, 'Val/mean hd95_metric': 10.560403823852539} +Epoch [3412/4000] Training [1/39] Loss: 0.13081 +Epoch [3412/4000] Training [2/39] Loss: 0.00728 +Epoch [3412/4000] Training [3/39] Loss: 0.00479 +Epoch [3412/4000] Training [4/39] Loss: 0.00337 +Epoch [3412/4000] Training [5/39] Loss: 0.00567 +Epoch [3412/4000] Training [6/39] Loss: 0.00584 +Epoch [3412/4000] Training [7/39] Loss: 0.00594 +Epoch [3412/4000] Training [8/39] Loss: 0.00415 +Epoch [3412/4000] Training [9/39] Loss: 0.00422 +Epoch [3412/4000] Training [10/39] Loss: 0.00526 +Epoch [3412/4000] Training [11/39] Loss: 0.00617 +Epoch [3412/4000] Training [12/39] Loss: 0.04357 +Epoch [3412/4000] Training [13/39] Loss: 0.12891 +Epoch [3412/4000] Training [14/39] Loss: 0.13071 +Epoch [3412/4000] Training [15/39] Loss: 0.00477 +Epoch [3412/4000] Training [16/39] Loss: 0.00733 +Epoch [3412/4000] Training [17/39] Loss: 0.13128 +Epoch [3412/4000] Training [18/39] Loss: 0.00410 +Epoch [3412/4000] Training [19/39] Loss: 0.00609 +Epoch [3412/4000] Training [20/39] Loss: 0.00633 +Epoch [3412/4000] Training [21/39] Loss: 0.00463 +Epoch [3412/4000] Training [22/39] Loss: 0.00637 +Epoch [3412/4000] Training [23/39] Loss: 0.00338 +Epoch [3412/4000] Training [24/39] Loss: 0.13130 +Epoch [3412/4000] Training [25/39] Loss: 0.12940 +Epoch [3412/4000] Training [26/39] Loss: 0.00701 +Epoch [3412/4000] Training [27/39] Loss: 0.00464 +Epoch [3412/4000] Training [28/39] Loss: 0.13238 +Epoch [3412/4000] Training [29/39] Loss: 0.00382 +Epoch [3412/4000] Training [30/39] Loss: 0.12851 +Epoch [3412/4000] Training [31/39] Loss: 0.00529 +Epoch [3412/4000] Training [32/39] Loss: 0.00655 +Epoch [3412/4000] Training [33/39] Loss: 0.00464 +Epoch [3412/4000] Training [34/39] Loss: 0.00394 +Epoch [3412/4000] Training [35/39] Loss: 0.00735 +Epoch [3412/4000] Training [36/39] Loss: 0.00554 +Epoch [3412/4000] Training [37/39] Loss: 0.00481 +Epoch [3412/4000] Training [38/39] Loss: 0.00674 +Epoch [3412/4000] Training [39/39] Loss: 0.12819 +Epoch [3412/4000] Training metric {'Train/mean dice_metric': 0.9958187341690063, 'Train/mean miou_metric': 0.9921256303787231, 'Train/mean f1': 0.9965593218803406, 'Train/mean precision': 0.9960845708847046, 'Train/mean recall': 0.9970346689224243, 'Train/mean hd95_metric': 1.0365350246429443} +Epoch [3412/4000] Validation [1/10] Loss: 0.69687 focal_loss 0.60668 dice_loss 0.09019 +Epoch [3412/4000] Validation [2/10] Loss: 0.44578 focal_loss 0.35300 dice_loss 0.09278 +Epoch [3412/4000] Validation [3/10] Loss: 0.36644 focal_loss 0.25508 dice_loss 0.11136 +Epoch [3412/4000] Validation [4/10] Loss: 0.87124 focal_loss 0.30618 dice_loss 0.56506 +Epoch [3412/4000] Validation [5/10] Loss: 2.99736 focal_loss 2.32491 dice_loss 0.67245 +Epoch [3412/4000] Validation [6/10] Loss: 1.27086 focal_loss 0.55434 dice_loss 0.71652 +Epoch [3412/4000] Validation [7/10] Loss: 1.14917 focal_loss 0.50012 dice_loss 0.64905 +Epoch [3412/4000] Validation [8/10] Loss: 2.07283 focal_loss 1.47452 dice_loss 0.59831 +Epoch [3412/4000] Validation [9/10] Loss: 1.33008 focal_loss 0.82197 dice_loss 0.50811 +Epoch [3412/4000] Validation [10/10] Loss: 1.77101 focal_loss 1.04338 dice_loss 0.72764 +Epoch [3412/4000] Validation metric {'Val/mean dice_metric': 0.9510524272918701, 'Val/mean miou_metric': 0.9348024129867554, 'Val/mean f1': 0.948394238948822, 'Val/mean precision': 0.9418831467628479, 'Val/mean recall': 0.9549958109855652, 'Val/mean hd95_metric': 10.476268768310547} +Cheakpoint... +Epoch [3412/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510524272918701, 'Val/mean miou_metric': 0.9348024129867554, 'Val/mean f1': 0.948394238948822, 'Val/mean precision': 0.9418831467628479, 'Val/mean recall': 0.9549958109855652, 'Val/mean hd95_metric': 10.476268768310547} +Epoch [3413/4000] Training [1/39] Loss: 0.00357 +Epoch [3413/4000] Training [2/39] Loss: 0.00580 +Epoch [3413/4000] Training [3/39] Loss: 0.00546 +Epoch [3413/4000] Training [4/39] Loss: 0.00593 +Epoch [3413/4000] Training [5/39] Loss: 0.12834 +Epoch [3413/4000] Training [6/39] Loss: 0.00733 +Epoch [3413/4000] Training [7/39] Loss: 0.00927 +Epoch [3413/4000] Training [8/39] Loss: 0.00542 +Epoch [3413/4000] Training [9/39] Loss: 0.13012 +Epoch [3413/4000] Training [10/39] Loss: 0.00736 +Epoch [3413/4000] Training [11/39] Loss: 0.00431 +Epoch [3413/4000] Training [12/39] Loss: 0.00391 +Epoch [3413/4000] Training [13/39] Loss: 0.00866 +Epoch [3413/4000] Training [14/39] Loss: 0.00596 +Epoch [3413/4000] Training [15/39] Loss: 0.13167 +Epoch [3413/4000] Training [16/39] Loss: 0.00651 +Epoch [3413/4000] Training [17/39] Loss: 0.00644 +Epoch [3413/4000] Training [18/39] Loss: 0.00785 +Epoch [3413/4000] Training [19/39] Loss: 0.00832 +Epoch [3413/4000] Training [20/39] Loss: 0.00870 +Epoch [3413/4000] Training [21/39] Loss: 0.25447 +Epoch [3413/4000] Training [22/39] Loss: 0.00484 +Epoch [3413/4000] Training [23/39] Loss: 0.00695 +Epoch [3413/4000] Training [24/39] Loss: 0.13289 +Epoch [3413/4000] Training [25/39] Loss: 0.00385 +Epoch [3413/4000] Training [26/39] Loss: 0.12937 +Epoch [3413/4000] Training [27/39] Loss: 0.00662 +Epoch [3413/4000] Training [28/39] Loss: 0.00434 +Epoch [3413/4000] Training [29/39] Loss: 0.00457 +Epoch [3413/4000] Training [30/39] Loss: 0.13331 +Epoch [3413/4000] Training [31/39] Loss: 0.12914 +Epoch [3413/4000] Training [32/39] Loss: 0.00964 +Epoch [3413/4000] Training [33/39] Loss: 0.00396 +Epoch [3413/4000] Training [34/39] Loss: 0.00676 +Epoch [3413/4000] Training [35/39] Loss: 0.00339 +Epoch [3413/4000] Training [36/39] Loss: 0.00478 +Epoch [3413/4000] Training [37/39] Loss: 0.00576 +Epoch [3413/4000] Training [38/39] Loss: 0.00574 +Epoch [3413/4000] Training [39/39] Loss: 0.00466 +Epoch [3413/4000] Training metric {'Train/mean dice_metric': 0.9954466223716736, 'Train/mean miou_metric': 0.9913648366928101, 'Train/mean f1': 0.9963109493255615, 'Train/mean precision': 0.9958336353302002, 'Train/mean recall': 0.9967886209487915, 'Train/mean hd95_metric': 1.136887788772583} +Epoch [3413/4000] Validation [1/10] Loss: 0.69448 focal_loss 0.60449 dice_loss 0.08999 +Epoch [3413/4000] Validation [2/10] Loss: 0.45385 focal_loss 0.35931 dice_loss 0.09454 +Epoch [3413/4000] Validation [3/10] Loss: 0.37097 focal_loss 0.25988 dice_loss 0.11109 +Epoch [3413/4000] Validation [4/10] Loss: 0.86932 focal_loss 0.30591 dice_loss 0.56341 +Epoch [3413/4000] Validation [5/10] Loss: 2.96473 focal_loss 2.29242 dice_loss 0.67231 +Epoch [3413/4000] Validation [6/10] Loss: 1.23594 focal_loss 0.52866 dice_loss 0.70728 +Epoch [3413/4000] Validation [7/10] Loss: 1.14378 focal_loss 0.49406 dice_loss 0.64971 +Epoch [3413/4000] Validation [8/10] Loss: 2.21912 focal_loss 1.60242 dice_loss 0.61671 +Epoch [3413/4000] Validation [9/10] Loss: 1.36138 focal_loss 0.81929 dice_loss 0.54209 +Epoch [3413/4000] Validation [10/10] Loss: 1.73223 focal_loss 1.00236 dice_loss 0.72986 +Epoch [3413/4000] Validation metric {'Val/mean dice_metric': 0.9504231214523315, 'Val/mean miou_metric': 0.9338465929031372, 'Val/mean f1': 0.9482508897781372, 'Val/mean precision': 0.9432392120361328, 'Val/mean recall': 0.9533162713050842, 'Val/mean hd95_metric': 10.803454399108887} +Cheakpoint... +Epoch [3413/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504231214523315, 'Val/mean miou_metric': 0.9338465929031372, 'Val/mean f1': 0.9482508897781372, 'Val/mean precision': 0.9432392120361328, 'Val/mean recall': 0.9533162713050842, 'Val/mean hd95_metric': 10.803454399108887} +Epoch [3414/4000] Training [1/39] Loss: 0.01111 +Epoch [3414/4000] Training [2/39] Loss: 0.13079 +Epoch [3414/4000] Training [3/39] Loss: 0.12880 +Epoch [3414/4000] Training [4/39] Loss: 0.00800 +Epoch [3414/4000] Training [5/39] Loss: 0.00374 +Epoch [3414/4000] Training [6/39] Loss: 0.12831 +Epoch [3414/4000] Training [7/39] Loss: 0.16469 +Epoch [3414/4000] Training [8/39] Loss: 0.12902 +Epoch [3414/4000] Training [9/39] Loss: 0.00445 +Epoch [3414/4000] Training [10/39] Loss: 0.00541 +Epoch [3414/4000] Training [11/39] Loss: 0.00836 +Epoch [3414/4000] Training [12/39] Loss: 0.00651 +Epoch [3414/4000] Training [13/39] Loss: 0.25527 +Epoch [3414/4000] Training [14/39] Loss: 0.00517 +Epoch [3414/4000] Training [15/39] Loss: 0.00463 +Epoch [3414/4000] Training [16/39] Loss: 0.00707 +Epoch [3414/4000] Training [17/39] Loss: 0.00490 +Epoch [3414/4000] Training [18/39] Loss: 0.12878 +Epoch [3414/4000] Training [19/39] Loss: 0.00498 +Epoch [3414/4000] Training [20/39] Loss: 0.00468 +Epoch [3414/4000] Training [21/39] Loss: 0.13170 +Epoch [3414/4000] Training [22/39] Loss: 0.00383 +Epoch [3414/4000] Training [23/39] Loss: 0.00419 +Epoch [3414/4000] Training [24/39] Loss: 0.00624 +Epoch [3414/4000] Training [25/39] Loss: 0.00576 +Epoch [3414/4000] Training [26/39] Loss: 0.00852 +Epoch [3414/4000] Training [27/39] Loss: 0.00663 +Epoch [3414/4000] Training [28/39] Loss: 0.12878 +Epoch [3414/4000] Training [29/39] Loss: 0.12796 +Epoch [3414/4000] Training [30/39] Loss: 0.00870 +Epoch [3414/4000] Training [31/39] Loss: 0.00593 +Epoch [3414/4000] Training [32/39] Loss: 0.00415 +Epoch [3414/4000] Training [33/39] Loss: 0.00637 +Epoch [3414/4000] Training [34/39] Loss: 0.00373 +Epoch [3414/4000] Training [35/39] Loss: 0.13065 +Epoch [3414/4000] Training [36/39] Loss: 0.13146 +Epoch [3414/4000] Training [37/39] Loss: 0.00652 +Epoch [3414/4000] Training [38/39] Loss: 0.00486 +Epoch [3414/4000] Training [39/39] Loss: 0.00505 +Epoch [3414/4000] Training metric {'Train/mean dice_metric': 0.9957071542739868, 'Train/mean miou_metric': 0.9918760061264038, 'Train/mean f1': 0.9964569211006165, 'Train/mean precision': 0.9960129261016846, 'Train/mean recall': 0.9969013929367065, 'Train/mean hd95_metric': 1.1564056873321533} +Epoch [3414/4000] Validation [1/10] Loss: 0.67311 focal_loss 0.58426 dice_loss 0.08886 +Epoch [3414/4000] Validation [2/10] Loss: 0.45450 focal_loss 0.36288 dice_loss 0.09162 +Epoch [3414/4000] Validation [3/10] Loss: 0.36433 focal_loss 0.25433 dice_loss 0.11000 +Epoch [3414/4000] Validation [4/10] Loss: 0.87151 focal_loss 0.30717 dice_loss 0.56434 +Epoch [3414/4000] Validation [5/10] Loss: 2.94863 focal_loss 2.27582 dice_loss 0.67282 +Epoch [3414/4000] Validation [6/10] Loss: 1.27123 focal_loss 0.56152 dice_loss 0.70971 +Epoch [3414/4000] Validation [7/10] Loss: 1.15308 focal_loss 0.50343 dice_loss 0.64965 +Epoch [3414/4000] Validation [8/10] Loss: 2.19296 focal_loss 1.58011 dice_loss 0.61285 +Epoch [3414/4000] Validation [9/10] Loss: 1.36070 focal_loss 0.81880 dice_loss 0.54190 +Epoch [3414/4000] Validation [10/10] Loss: 1.75881 focal_loss 1.03022 dice_loss 0.72859 +Epoch [3414/4000] Validation metric {'Val/mean dice_metric': 0.9506406188011169, 'Val/mean miou_metric': 0.9342620968818665, 'Val/mean f1': 0.9479731917381287, 'Val/mean precision': 0.9422316551208496, 'Val/mean recall': 0.9537850618362427, 'Val/mean hd95_metric': 10.83499813079834} +Cheakpoint... +Epoch [3414/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506406188011169, 'Val/mean miou_metric': 0.9342620968818665, 'Val/mean f1': 0.9479731917381287, 'Val/mean precision': 0.9422316551208496, 'Val/mean recall': 0.9537850618362427, 'Val/mean hd95_metric': 10.83499813079834} +Epoch [3415/4000] Training [1/39] Loss: 0.12939 +Epoch [3415/4000] Training [2/39] Loss: 0.00621 +Epoch [3415/4000] Training [3/39] Loss: 0.25523 +Epoch [3415/4000] Training [4/39] Loss: 0.00547 +Epoch [3415/4000] Training [5/39] Loss: 0.13037 +Epoch [3415/4000] Training [6/39] Loss: 0.00433 +Epoch [3415/4000] Training [7/39] Loss: 0.00505 +Epoch [3415/4000] Training [8/39] Loss: 0.00659 +Epoch [3415/4000] Training [9/39] Loss: 0.12846 +Epoch [3415/4000] Training [10/39] Loss: 0.00487 +Epoch [3415/4000] Training [11/39] Loss: 0.02073 +Epoch [3415/4000] Training [12/39] Loss: 0.00402 +Epoch [3415/4000] Training [13/39] Loss: 0.13358 +Epoch [3415/4000] Training [14/39] Loss: 0.00408 +Epoch [3415/4000] Training [15/39] Loss: 0.13109 +Epoch [3415/4000] Training [16/39] Loss: 0.12984 +Epoch [3415/4000] Training [17/39] Loss: 0.00731 +Epoch [3415/4000] Training [18/39] Loss: 0.00354 +Epoch [3415/4000] Training [19/39] Loss: 0.00384 +Epoch [3415/4000] Training [20/39] Loss: 0.00444 +Epoch [3415/4000] Training [21/39] Loss: 0.12900 +Epoch [3415/4000] Training [22/39] Loss: 0.00451 +Epoch [3415/4000] Training [23/39] Loss: 0.00470 +Epoch [3415/4000] Training [24/39] Loss: 0.00614 +Epoch [3415/4000] Training [25/39] Loss: 0.13037 +Epoch [3415/4000] Training [26/39] Loss: 0.00495 +Epoch [3415/4000] Training [27/39] Loss: 0.00318 +Epoch [3415/4000] Training [28/39] Loss: 0.00374 +Epoch [3415/4000] Training [29/39] Loss: 0.00500 +Epoch [3415/4000] Training [30/39] Loss: 0.00406 +Epoch [3415/4000] Training [31/39] Loss: 0.00634 +Epoch [3415/4000] Training [32/39] Loss: 0.00440 +Epoch [3415/4000] Training [33/39] Loss: 0.00428 +Epoch [3415/4000] Training [34/39] Loss: 0.12826 +Epoch [3415/4000] Training [35/39] Loss: 0.13343 +Epoch [3415/4000] Training [36/39] Loss: 0.00879 +Epoch [3415/4000] Training [37/39] Loss: 0.00554 +Epoch [3415/4000] Training [38/39] Loss: 0.00764 +Epoch [3415/4000] Training [39/39] Loss: 0.00813 +Epoch [3415/4000] Training metric {'Train/mean dice_metric': 0.994915246963501, 'Train/mean miou_metric': 0.9911279082298279, 'Train/mean f1': 0.9964830875396729, 'Train/mean precision': 0.9960456490516663, 'Train/mean recall': 0.9969208240509033, 'Train/mean hd95_metric': 1.1999520063400269} +Epoch [3415/4000] Validation [1/10] Loss: 0.69408 focal_loss 0.60584 dice_loss 0.08824 +Epoch [3415/4000] Validation [2/10] Loss: 0.46687 focal_loss 0.37356 dice_loss 0.09331 +Epoch [3415/4000] Validation [3/10] Loss: 0.37627 focal_loss 0.26580 dice_loss 0.11047 +Epoch [3415/4000] Validation [4/10] Loss: 0.89420 focal_loss 0.31676 dice_loss 0.57744 +Epoch [3415/4000] Validation [5/10] Loss: 3.00055 focal_loss 2.32743 dice_loss 0.67311 +Epoch [3415/4000] Validation [6/10] Loss: 1.30036 focal_loss 0.58066 dice_loss 0.71970 +Epoch [3415/4000] Validation [7/10] Loss: 1.14379 focal_loss 0.49297 dice_loss 0.65082 +Epoch [3415/4000] Validation [8/10] Loss: 2.25360 focal_loss 1.64001 dice_loss 0.61359 +Epoch [3415/4000] Validation [9/10] Loss: 1.39044 focal_loss 0.88639 dice_loss 0.50405 +Epoch [3415/4000] Validation [10/10] Loss: 1.77800 focal_loss 1.04729 dice_loss 0.73072 +Epoch [3415/4000] Validation metric {'Val/mean dice_metric': 0.9497146606445312, 'Val/mean miou_metric': 0.9332769513130188, 'Val/mean f1': 0.9474220275878906, 'Val/mean precision': 0.942374050617218, 'Val/mean recall': 0.9525241851806641, 'Val/mean hd95_metric': 10.93528938293457} +Cheakpoint... +Epoch [3415/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9497], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9497146606445312, 'Val/mean miou_metric': 0.9332769513130188, 'Val/mean f1': 0.9474220275878906, 'Val/mean precision': 0.942374050617218, 'Val/mean recall': 0.9525241851806641, 'Val/mean hd95_metric': 10.93528938293457} +Epoch [3416/4000] Training [1/39] Loss: 0.00565 +Epoch [3416/4000] Training [2/39] Loss: 0.12846 +Epoch [3416/4000] Training [3/39] Loss: 0.13001 +Epoch [3416/4000] Training [4/39] Loss: 0.00530 +Epoch [3416/4000] Training [5/39] Loss: 0.00597 +Epoch [3416/4000] Training [6/39] Loss: 0.13170 +Epoch [3416/4000] Training [7/39] Loss: 0.00424 +Epoch [3416/4000] Training [8/39] Loss: 0.00450 +Epoch [3416/4000] Training [9/39] Loss: 0.13142 +Epoch [3416/4000] Training [10/39] Loss: 0.00596 +Epoch [3416/4000] Training [11/39] Loss: 0.00579 +Epoch [3416/4000] Training [12/39] Loss: 0.12855 +Epoch [3416/4000] Training [13/39] Loss: 0.25548 +Epoch [3416/4000] Training [14/39] Loss: 0.00676 +Epoch [3416/4000] Training [15/39] Loss: 0.00284 +Epoch [3416/4000] Training [16/39] Loss: 0.00570 +Epoch [3416/4000] Training [17/39] Loss: 0.00525 +Epoch [3416/4000] Training [18/39] Loss: 0.00485 +Epoch [3416/4000] Training [19/39] Loss: 0.00673 +Epoch [3416/4000] Training [20/39] Loss: 0.13042 +Epoch [3416/4000] Training [21/39] Loss: 0.00861 +Epoch [3416/4000] Training [22/39] Loss: 0.00566 +Epoch [3416/4000] Training [23/39] Loss: 0.00560 +Epoch [3416/4000] Training [24/39] Loss: 0.00435 +Epoch [3416/4000] Training [25/39] Loss: 0.12950 +Epoch [3416/4000] Training [26/39] Loss: 0.12936 +Epoch [3416/4000] Training [27/39] Loss: 0.00548 +Epoch [3416/4000] Training [28/39] Loss: 0.00608 +Epoch [3416/4000] Training [29/39] Loss: 0.13198 +Epoch [3416/4000] Training [30/39] Loss: 0.00507 +Epoch [3416/4000] Training [31/39] Loss: 0.00615 +Epoch [3416/4000] Training [32/39] Loss: 0.00560 +Epoch [3416/4000] Training [33/39] Loss: 0.00420 +Epoch [3416/4000] Training [34/39] Loss: 0.00617 +Epoch [3416/4000] Training [35/39] Loss: 0.00523 +Epoch [3416/4000] Training [36/39] Loss: 0.00453 +Epoch [3416/4000] Training [37/39] Loss: 0.00709 +Epoch [3416/4000] Training [38/39] Loss: 0.00802 +Epoch [3416/4000] Training [39/39] Loss: 0.00298 +Epoch [3416/4000] Training metric {'Train/mean dice_metric': 0.9957224726676941, 'Train/mean miou_metric': 0.9918967485427856, 'Train/mean f1': 0.9964857697486877, 'Train/mean precision': 0.9960947632789612, 'Train/mean recall': 0.996877133846283, 'Train/mean hd95_metric': 1.0338994264602661} +Epoch [3416/4000] Validation [1/10] Loss: 0.69590 focal_loss 0.60671 dice_loss 0.08919 +Epoch [3416/4000] Validation [2/10] Loss: 0.46791 focal_loss 0.37082 dice_loss 0.09709 +Epoch [3416/4000] Validation [3/10] Loss: 0.38108 focal_loss 0.26986 dice_loss 0.11122 +Epoch [3416/4000] Validation [4/10] Loss: 0.87842 focal_loss 0.30711 dice_loss 0.57132 +Epoch [3416/4000] Validation [5/10] Loss: 2.99851 focal_loss 2.32524 dice_loss 0.67327 +Epoch [3416/4000] Validation [6/10] Loss: 1.26439 focal_loss 0.55090 dice_loss 0.71350 +Epoch [3416/4000] Validation [7/10] Loss: 1.13522 focal_loss 0.48538 dice_loss 0.64984 +Epoch [3416/4000] Validation [8/10] Loss: 2.34746 focal_loss 1.71789 dice_loss 0.62957 +Epoch [3416/4000] Validation [9/10] Loss: 1.33909 focal_loss 0.82100 dice_loss 0.51809 +Epoch [3416/4000] Validation [10/10] Loss: 1.74596 focal_loss 1.01339 dice_loss 0.73257 +Epoch [3416/4000] Validation metric {'Val/mean dice_metric': 0.9504448771476746, 'Val/mean miou_metric': 0.9339757561683655, 'Val/mean f1': 0.9477816820144653, 'Val/mean precision': 0.9434991478919983, 'Val/mean recall': 0.952103316783905, 'Val/mean hd95_metric': 10.812718391418457} +Cheakpoint... +Epoch [3416/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504448771476746, 'Val/mean miou_metric': 0.9339757561683655, 'Val/mean f1': 0.9477816820144653, 'Val/mean precision': 0.9434991478919983, 'Val/mean recall': 0.952103316783905, 'Val/mean hd95_metric': 10.812718391418457} +Epoch [3417/4000] Training [1/39] Loss: 0.00867 +Epoch [3417/4000] Training [2/39] Loss: 0.13085 +Epoch [3417/4000] Training [3/39] Loss: 0.00743 +Epoch [3417/4000] Training [4/39] Loss: 0.00433 +Epoch [3417/4000] Training [5/39] Loss: 0.00388 +Epoch [3417/4000] Training [6/39] Loss: 0.00614 +Epoch [3417/4000] Training [7/39] Loss: 0.00570 +Epoch [3417/4000] Training [8/39] Loss: 0.00565 +Epoch [3417/4000] Training [9/39] Loss: 0.00671 +Epoch [3417/4000] Training [10/39] Loss: 0.13123 +Epoch [3417/4000] Training [11/39] Loss: 0.00595 +Epoch [3417/4000] Training [12/39] Loss: 0.00648 +Epoch [3417/4000] Training [13/39] Loss: 0.00638 +Epoch [3417/4000] Training [14/39] Loss: 0.00624 +Epoch [3417/4000] Training [15/39] Loss: 0.00578 +Epoch [3417/4000] Training [16/39] Loss: 0.00624 +Epoch [3417/4000] Training [17/39] Loss: 0.00674 +Epoch [3417/4000] Training [18/39] Loss: 0.00594 +Epoch [3417/4000] Training [19/39] Loss: 0.12961 +Epoch [3417/4000] Training [20/39] Loss: 0.00438 +Epoch [3417/4000] Training [21/39] Loss: 0.00634 +Epoch [3417/4000] Training [22/39] Loss: 0.00404 +Epoch [3417/4000] Training [23/39] Loss: 0.00365 +Epoch [3417/4000] Training [24/39] Loss: 0.00900 +Epoch [3417/4000] Training [25/39] Loss: 0.00864 +Epoch [3417/4000] Training [26/39] Loss: 0.00373 +Epoch [3417/4000] Training [27/39] Loss: 0.00666 +Epoch [3417/4000] Training [28/39] Loss: 0.00571 +Epoch [3417/4000] Training [29/39] Loss: 0.00489 +Epoch [3417/4000] Training [30/39] Loss: 0.01083 +Epoch [3417/4000] Training [31/39] Loss: 0.12794 +Epoch [3417/4000] Training [32/39] Loss: 0.12796 +Epoch [3417/4000] Training [33/39] Loss: 0.01034 +Epoch [3417/4000] Training [34/39] Loss: 0.00559 +Epoch [3417/4000] Training [35/39] Loss: 0.00601 +Epoch [3417/4000] Training [36/39] Loss: 0.00721 +Epoch [3417/4000] Training [37/39] Loss: 0.00470 +Epoch [3417/4000] Training [38/39] Loss: 0.00698 +Epoch [3417/4000] Training [39/39] Loss: 0.12989 +Epoch [3417/4000] Training metric {'Train/mean dice_metric': 0.9955306649208069, 'Train/mean miou_metric': 0.9915133714675903, 'Train/mean f1': 0.9964240789413452, 'Train/mean precision': 0.9959156513214111, 'Train/mean recall': 0.9969329833984375, 'Train/mean hd95_metric': 1.0857852697372437} +Epoch [3417/4000] Validation [1/10] Loss: 0.70563 focal_loss 0.61457 dice_loss 0.09105 +Epoch [3417/4000] Validation [2/10] Loss: 0.46464 focal_loss 0.37015 dice_loss 0.09449 +Epoch [3417/4000] Validation [3/10] Loss: 0.36696 focal_loss 0.25684 dice_loss 0.11012 +Epoch [3417/4000] Validation [4/10] Loss: 0.87720 focal_loss 0.30894 dice_loss 0.56826 +Epoch [3417/4000] Validation [5/10] Loss: 2.96481 focal_loss 2.29207 dice_loss 0.67274 +Epoch [3417/4000] Validation [6/10] Loss: 1.28447 focal_loss 0.56594 dice_loss 0.71853 +Epoch [3417/4000] Validation [7/10] Loss: 1.14082 focal_loss 0.49172 dice_loss 0.64910 +Epoch [3417/4000] Validation [8/10] Loss: 2.32615 focal_loss 1.70317 dice_loss 0.62298 +Epoch [3417/4000] Validation [9/10] Loss: 1.33435 focal_loss 0.82156 dice_loss 0.51279 +Epoch [3417/4000] Validation [10/10] Loss: 1.75465 focal_loss 1.02607 dice_loss 0.72858 +Epoch [3417/4000] Validation metric {'Val/mean dice_metric': 0.9504227638244629, 'Val/mean miou_metric': 0.9338456988334656, 'Val/mean f1': 0.9480588436126709, 'Val/mean precision': 0.9429210424423218, 'Val/mean recall': 0.9532528519630432, 'Val/mean hd95_metric': 10.798047065734863} +Cheakpoint... +Epoch [3417/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504227638244629, 'Val/mean miou_metric': 0.9338456988334656, 'Val/mean f1': 0.9480588436126709, 'Val/mean precision': 0.9429210424423218, 'Val/mean recall': 0.9532528519630432, 'Val/mean hd95_metric': 10.798047065734863} +Epoch [3418/4000] Training [1/39] Loss: 0.00483 +Epoch [3418/4000] Training [2/39] Loss: 0.13018 +Epoch [3418/4000] Training [3/39] Loss: 0.00434 +Epoch [3418/4000] Training [4/39] Loss: 0.00638 +Epoch [3418/4000] Training [5/39] Loss: 0.00449 +Epoch [3418/4000] Training [6/39] Loss: 0.00673 +Epoch [3418/4000] Training [7/39] Loss: 0.00420 +Epoch [3418/4000] Training [8/39] Loss: 0.00349 +Epoch [3418/4000] Training [9/39] Loss: 0.12962 +Epoch [3418/4000] Training [10/39] Loss: 0.00350 +Epoch [3418/4000] Training [11/39] Loss: 0.01140 +Epoch [3418/4000] Training [12/39] Loss: 0.00546 +Epoch [3418/4000] Training [13/39] Loss: 0.00619 +Epoch [3418/4000] Training [14/39] Loss: 0.00840 +Epoch [3418/4000] Training [15/39] Loss: 0.00471 +Epoch [3418/4000] Training [16/39] Loss: 0.00637 +Epoch [3418/4000] Training [17/39] Loss: 0.00470 +Epoch [3418/4000] Training [18/39] Loss: 0.00456 +Epoch [3418/4000] Training [19/39] Loss: 0.00497 +Epoch [3418/4000] Training [20/39] Loss: 0.25569 +Epoch [3418/4000] Training [21/39] Loss: 0.00539 +Epoch [3418/4000] Training [22/39] Loss: 0.13112 +Epoch [3418/4000] Training [23/39] Loss: 0.00466 +Epoch [3418/4000] Training [24/39] Loss: 0.00568 +Epoch [3418/4000] Training [25/39] Loss: 0.00527 +Epoch [3418/4000] Training [26/39] Loss: 0.00512 +Epoch [3418/4000] Training [27/39] Loss: 0.13102 +Epoch [3418/4000] Training [28/39] Loss: 0.08152 +Epoch [3418/4000] Training [29/39] Loss: 0.00630 +Epoch [3418/4000] Training [30/39] Loss: 0.00524 +Epoch [3418/4000] Training [31/39] Loss: 0.00431 +Epoch [3418/4000] Training [32/39] Loss: 0.12936 +Epoch [3418/4000] Training [33/39] Loss: 0.13077 +Epoch [3418/4000] Training [34/39] Loss: 0.00567 +Epoch [3418/4000] Training [35/39] Loss: 0.00302 +Epoch [3418/4000] Training [36/39] Loss: 0.12910 +Epoch [3418/4000] Training [37/39] Loss: 0.12868 +Epoch [3418/4000] Training [38/39] Loss: 0.13161 +Epoch [3418/4000] Training [39/39] Loss: 0.00571 +Epoch [3418/4000] Training metric {'Train/mean dice_metric': 0.9957877397537231, 'Train/mean miou_metric': 0.9920231103897095, 'Train/mean f1': 0.9965561628341675, 'Train/mean precision': 0.9961473345756531, 'Train/mean recall': 0.9969653487205505, 'Train/mean hd95_metric': 1.0199787616729736} +Epoch [3418/4000] Validation [1/10] Loss: 0.67478 focal_loss 0.58684 dice_loss 0.08794 +Epoch [3418/4000] Validation [2/10] Loss: 0.46933 focal_loss 0.37363 dice_loss 0.09570 +Epoch [3418/4000] Validation [3/10] Loss: 0.37480 focal_loss 0.26444 dice_loss 0.11036 +Epoch [3418/4000] Validation [4/10] Loss: 0.88763 focal_loss 0.30845 dice_loss 0.57918 +Epoch [3418/4000] Validation [5/10] Loss: 2.92907 focal_loss 2.25675 dice_loss 0.67232 +Epoch [3418/4000] Validation [6/10] Loss: 1.29413 focal_loss 0.57823 dice_loss 0.71590 +Epoch [3418/4000] Validation [7/10] Loss: 1.14715 focal_loss 0.49641 dice_loss 0.65074 +Epoch [3418/4000] Validation [8/10] Loss: 2.60849 focal_loss 1.96437 dice_loss 0.64413 +Epoch [3418/4000] Validation [9/10] Loss: 1.37976 focal_loss 0.83753 dice_loss 0.54223 +Epoch [3418/4000] Validation [10/10] Loss: 1.75261 focal_loss 1.02486 dice_loss 0.72775 +Epoch [3418/4000] Validation metric {'Val/mean dice_metric': 0.9505742192268372, 'Val/mean miou_metric': 0.9342222809791565, 'Val/mean f1': 0.9488439559936523, 'Val/mean precision': 0.945702314376831, 'Val/mean recall': 0.9520065784454346, 'Val/mean hd95_metric': 10.76237678527832} +Cheakpoint... +Epoch [3418/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505742192268372, 'Val/mean miou_metric': 0.9342222809791565, 'Val/mean f1': 0.9488439559936523, 'Val/mean precision': 0.945702314376831, 'Val/mean recall': 0.9520065784454346, 'Val/mean hd95_metric': 10.76237678527832} +Epoch [3419/4000] Training [1/39] Loss: 0.00406 +Epoch [3419/4000] Training [2/39] Loss: 0.00284 +Epoch [3419/4000] Training [3/39] Loss: 0.12890 +Epoch [3419/4000] Training [4/39] Loss: 0.00726 +Epoch [3419/4000] Training [5/39] Loss: 0.00400 +Epoch [3419/4000] Training [6/39] Loss: 0.12959 +Epoch [3419/4000] Training [7/39] Loss: 0.00534 +Epoch [3419/4000] Training [8/39] Loss: 0.00730 +Epoch [3419/4000] Training [9/39] Loss: 0.00651 +Epoch [3419/4000] Training [10/39] Loss: 0.00386 +Epoch [3419/4000] Training [11/39] Loss: 0.00651 +Epoch [3419/4000] Training [12/39] Loss: 0.00470 +Epoch [3419/4000] Training [13/39] Loss: 0.25293 +Epoch [3419/4000] Training [14/39] Loss: 0.00453 +Epoch [3419/4000] Training [15/39] Loss: 0.00449 +Epoch [3419/4000] Training [16/39] Loss: 0.00444 +Epoch [3419/4000] Training [17/39] Loss: 0.00695 +Epoch [3419/4000] Training [18/39] Loss: 0.00566 +Epoch [3419/4000] Training [19/39] Loss: 0.00737 +Epoch [3419/4000] Training [20/39] Loss: 0.00603 +Epoch [3419/4000] Training [21/39] Loss: 0.12990 +Epoch [3419/4000] Training [22/39] Loss: 0.00580 +Epoch [3419/4000] Training [23/39] Loss: 0.00673 +Epoch [3419/4000] Training [24/39] Loss: 0.15625 +Epoch [3419/4000] Training [25/39] Loss: 0.00634 +Epoch [3419/4000] Training [26/39] Loss: 0.00389 +Epoch [3419/4000] Training [27/39] Loss: 0.00568 +Epoch [3419/4000] Training [28/39] Loss: 0.00554 +Epoch [3419/4000] Training [29/39] Loss: 0.00546 +Epoch [3419/4000] Training [30/39] Loss: 0.13389 +Epoch [3419/4000] Training [31/39] Loss: 0.00613 +Epoch [3419/4000] Training [32/39] Loss: 0.00475 +Epoch [3419/4000] Training [33/39] Loss: 0.13599 +Epoch [3419/4000] Training [34/39] Loss: 0.01243 +Epoch [3419/4000] Training [35/39] Loss: 0.00339 +Epoch [3419/4000] Training [36/39] Loss: 0.12811 +Epoch [3419/4000] Training [37/39] Loss: 0.00537 +Epoch [3419/4000] Training [38/39] Loss: 0.12822 +Epoch [3419/4000] Training [39/39] Loss: 0.25425 +Epoch [3419/4000] Training metric {'Train/mean dice_metric': 0.9957379102706909, 'Train/mean miou_metric': 0.9919889569282532, 'Train/mean f1': 0.9965673089027405, 'Train/mean precision': 0.9961197376251221, 'Train/mean recall': 0.9970153570175171, 'Train/mean hd95_metric': 1.0658663511276245} +Epoch [3419/4000] Validation [1/10] Loss: 0.66382 focal_loss 0.57881 dice_loss 0.08501 +Epoch [3419/4000] Validation [2/10] Loss: 0.46831 focal_loss 0.37384 dice_loss 0.09447 +Epoch [3419/4000] Validation [3/10] Loss: 0.36917 focal_loss 0.26024 dice_loss 0.10893 +Epoch [3419/4000] Validation [4/10] Loss: 0.89784 focal_loss 0.31574 dice_loss 0.58210 +Epoch [3419/4000] Validation [5/10] Loss: 2.93801 focal_loss 2.26568 dice_loss 0.67233 +Epoch [3419/4000] Validation [6/10] Loss: 1.28427 focal_loss 0.57139 dice_loss 0.71289 +Epoch [3419/4000] Validation [7/10] Loss: 1.13576 focal_loss 0.48534 dice_loss 0.65042 +Epoch [3419/4000] Validation [8/10] Loss: 2.59399 focal_loss 1.95116 dice_loss 0.64283 +Epoch [3419/4000] Validation [9/10] Loss: 1.33446 focal_loss 0.82016 dice_loss 0.51429 +Epoch [3419/4000] Validation [10/10] Loss: 1.78604 focal_loss 1.05602 dice_loss 0.73003 +Epoch [3419/4000] Validation metric {'Val/mean dice_metric': 0.9506857991218567, 'Val/mean miou_metric': 0.9343215823173523, 'Val/mean f1': 0.9487611651420593, 'Val/mean precision': 0.9452888369560242, 'Val/mean recall': 0.9522590041160583, 'Val/mean hd95_metric': 10.76246452331543} +Cheakpoint... +Epoch [3419/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506857991218567, 'Val/mean miou_metric': 0.9343215823173523, 'Val/mean f1': 0.9487611651420593, 'Val/mean precision': 0.9452888369560242, 'Val/mean recall': 0.9522590041160583, 'Val/mean hd95_metric': 10.76246452331543} +Epoch [3420/4000] Training [1/39] Loss: 0.00326 +Epoch [3420/4000] Training [2/39] Loss: 0.00412 +Epoch [3420/4000] Training [3/39] Loss: 0.00529 +Epoch [3420/4000] Training [4/39] Loss: 0.00342 +Epoch [3420/4000] Training [5/39] Loss: 0.00628 +Epoch [3420/4000] Training [6/39] Loss: 0.12866 +Epoch [3420/4000] Training [7/39] Loss: 0.00416 +Epoch [3420/4000] Training [8/39] Loss: 0.00393 +Epoch [3420/4000] Training [9/39] Loss: 0.00540 +Epoch [3420/4000] Training [10/39] Loss: 0.13218 +Epoch [3420/4000] Training [11/39] Loss: 0.00962 +Epoch [3420/4000] Training [12/39] Loss: 0.00460 +Epoch [3420/4000] Training [13/39] Loss: 0.00529 +Epoch [3420/4000] Training [14/39] Loss: 0.00370 +Epoch [3420/4000] Training [15/39] Loss: 0.00614 +Epoch [3420/4000] Training [16/39] Loss: 0.12934 +Epoch [3420/4000] Training [17/39] Loss: 0.00473 +Epoch [3420/4000] Training [18/39] Loss: 0.00739 +Epoch [3420/4000] Training [19/39] Loss: 0.13021 +Epoch [3420/4000] Training [20/39] Loss: 0.00529 +Epoch [3420/4000] Training [21/39] Loss: 0.00416 +Epoch [3420/4000] Training [22/39] Loss: 0.00563 +Epoch [3420/4000] Training [23/39] Loss: 0.00503 +Epoch [3420/4000] Training [24/39] Loss: 0.00535 +Epoch [3420/4000] Training [25/39] Loss: 0.00348 +Epoch [3420/4000] Training [26/39] Loss: 0.00627 +Epoch [3420/4000] Training [27/39] Loss: 0.00683 +Epoch [3420/4000] Training [28/39] Loss: 0.00512 +Epoch [3420/4000] Training [29/39] Loss: 0.00613 +Epoch [3420/4000] Training [30/39] Loss: 0.00691 +Epoch [3420/4000] Training [31/39] Loss: 0.13155 +Epoch [3420/4000] Training [32/39] Loss: 0.12864 +Epoch [3420/4000] Training [33/39] Loss: 0.12933 +Epoch [3420/4000] Training [34/39] Loss: 0.00453 +Epoch [3420/4000] Training [35/39] Loss: 0.00561 +Epoch [3420/4000] Training [36/39] Loss: 0.00436 +Epoch [3420/4000] Training [37/39] Loss: 0.00449 +Epoch [3420/4000] Training [38/39] Loss: 0.12774 +Epoch [3420/4000] Training [39/39] Loss: 0.00424 +Epoch [3420/4000] Training metric {'Train/mean dice_metric': 0.9957901835441589, 'Train/mean miou_metric': 0.9920970797538757, 'Train/mean f1': 0.9965758919715881, 'Train/mean precision': 0.9961119890213013, 'Train/mean recall': 0.997040331363678, 'Train/mean hd95_metric': 1.1345425844192505} +Epoch [3420/4000] Validation [1/10] Loss: 0.69250 focal_loss 0.60453 dice_loss 0.08797 +Epoch [3420/4000] Validation [2/10] Loss: 0.46913 focal_loss 0.37250 dice_loss 0.09664 +Epoch [3420/4000] Validation [3/10] Loss: 0.36789 focal_loss 0.25873 dice_loss 0.10916 +Epoch [3420/4000] Validation [4/10] Loss: 0.88823 focal_loss 0.31254 dice_loss 0.57569 +Epoch [3420/4000] Validation [5/10] Loss: 2.99760 focal_loss 2.32542 dice_loss 0.67219 +Epoch [3420/4000] Validation [6/10] Loss: 1.25916 focal_loss 0.54258 dice_loss 0.71659 +Epoch [3420/4000] Validation [7/10] Loss: 1.13781 focal_loss 0.48611 dice_loss 0.65170 +Epoch [3420/4000] Validation [8/10] Loss: 2.29536 focal_loss 1.67445 dice_loss 0.62091 +Epoch [3420/4000] Validation [9/10] Loss: 1.31976 focal_loss 0.79940 dice_loss 0.52036 +Epoch [3420/4000] Validation [10/10] Loss: 1.75787 focal_loss 1.02921 dice_loss 0.72867 +Epoch [3420/4000] Validation metric {'Val/mean dice_metric': 0.9506360292434692, 'Val/mean miou_metric': 0.9344141483306885, 'Val/mean f1': 0.9483349919319153, 'Val/mean precision': 0.9435040950775146, 'Val/mean recall': 0.9532156586647034, 'Val/mean hd95_metric': 10.719367980957031} +Cheakpoint... +Epoch [3420/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506360292434692, 'Val/mean miou_metric': 0.9344141483306885, 'Val/mean f1': 0.9483349919319153, 'Val/mean precision': 0.9435040950775146, 'Val/mean recall': 0.9532156586647034, 'Val/mean hd95_metric': 10.719367980957031} +Epoch [3421/4000] Training [1/39] Loss: 0.00403 +Epoch [3421/4000] Training [2/39] Loss: 0.12834 +Epoch [3421/4000] Training [3/39] Loss: 0.00665 +Epoch [3421/4000] Training [4/39] Loss: 0.00449 +Epoch [3421/4000] Training [5/39] Loss: 0.13137 +Epoch [3421/4000] Training [6/39] Loss: 0.00658 +Epoch [3421/4000] Training [7/39] Loss: 0.12934 +Epoch [3421/4000] Training [8/39] Loss: 0.12904 +Epoch [3421/4000] Training [9/39] Loss: 0.38395 +Epoch [3421/4000] Training [10/39] Loss: 0.00585 +Epoch [3421/4000] Training [11/39] Loss: 0.00943 +Epoch [3421/4000] Training [12/39] Loss: 0.00942 +Epoch [3421/4000] Training [13/39] Loss: 0.00422 +Epoch [3421/4000] Training [14/39] Loss: 0.00672 +Epoch [3421/4000] Training [15/39] Loss: 0.00594 +Epoch [3421/4000] Training [16/39] Loss: 0.00701 +Epoch [3421/4000] Training [17/39] Loss: 0.00466 +Epoch [3421/4000] Training [18/39] Loss: 0.00454 +Epoch [3421/4000] Training [19/39] Loss: 0.00404 +Epoch [3421/4000] Training [20/39] Loss: 0.00657 +Epoch [3421/4000] Training [21/39] Loss: 0.00534 +Epoch [3421/4000] Training [22/39] Loss: 0.00410 +Epoch [3421/4000] Training [23/39] Loss: 0.00609 +Epoch [3421/4000] Training [24/39] Loss: 0.00493 +Epoch [3421/4000] Training [25/39] Loss: 0.00711 +Epoch [3421/4000] Training [26/39] Loss: 0.00902 +Epoch [3421/4000] Training [27/39] Loss: 0.13021 +Epoch [3421/4000] Training [28/39] Loss: 0.00519 +Epoch [3421/4000] Training [29/39] Loss: 0.00966 +Epoch [3421/4000] Training [30/39] Loss: 0.00612 +Epoch [3421/4000] Training [31/39] Loss: 0.00499 +Epoch [3421/4000] Training [32/39] Loss: 0.00603 +Epoch [3421/4000] Training [33/39] Loss: 0.00454 +Epoch [3421/4000] Training [34/39] Loss: 0.01009 +Epoch [3421/4000] Training [35/39] Loss: 0.00443 +Epoch [3421/4000] Training [36/39] Loss: 0.00656 +Epoch [3421/4000] Training [37/39] Loss: 0.00886 +Epoch [3421/4000] Training [38/39] Loss: 0.13110 +Epoch [3421/4000] Training [39/39] Loss: 0.12830 +Epoch [3421/4000] Training metric {'Train/mean dice_metric': 0.9955224394798279, 'Train/mean miou_metric': 0.9915169477462769, 'Train/mean f1': 0.9963252544403076, 'Train/mean precision': 0.9959889054298401, 'Train/mean recall': 0.996661901473999, 'Train/mean hd95_metric': 1.030821442604065} +Epoch [3421/4000] Validation [1/10] Loss: 0.70000 focal_loss 0.61152 dice_loss 0.08848 +Epoch [3421/4000] Validation [2/10] Loss: 0.47681 focal_loss 0.38041 dice_loss 0.09640 +Epoch [3421/4000] Validation [3/10] Loss: 0.38023 focal_loss 0.26994 dice_loss 0.11028 +Epoch [3421/4000] Validation [4/10] Loss: 0.87984 focal_loss 0.30192 dice_loss 0.57792 +Epoch [3421/4000] Validation [5/10] Loss: 3.04176 focal_loss 2.36862 dice_loss 0.67313 +Epoch [3421/4000] Validation [6/10] Loss: 1.26102 focal_loss 0.54544 dice_loss 0.71559 +Epoch [3421/4000] Validation [7/10] Loss: 1.13714 focal_loss 0.48401 dice_loss 0.65312 +Epoch [3421/4000] Validation [8/10] Loss: 2.54155 focal_loss 1.90042 dice_loss 0.64114 +Epoch [3421/4000] Validation [9/10] Loss: 1.35377 focal_loss 0.81391 dice_loss 0.53986 +Epoch [3421/4000] Validation [10/10] Loss: 1.71823 focal_loss 0.99198 dice_loss 0.72625 +Epoch [3421/4000] Validation metric {'Val/mean dice_metric': 0.950408399105072, 'Val/mean miou_metric': 0.9338740110397339, 'Val/mean f1': 0.9484264850616455, 'Val/mean precision': 0.9461882710456848, 'Val/mean recall': 0.9506754279136658, 'Val/mean hd95_metric': 10.576608657836914} +Cheakpoint... +Epoch [3421/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950408399105072, 'Val/mean miou_metric': 0.9338740110397339, 'Val/mean f1': 0.9484264850616455, 'Val/mean precision': 0.9461882710456848, 'Val/mean recall': 0.9506754279136658, 'Val/mean hd95_metric': 10.576608657836914} +Epoch [3422/4000] Training [1/39] Loss: 0.00494 +Epoch [3422/4000] Training [2/39] Loss: 0.00384 +Epoch [3422/4000] Training [3/39] Loss: 0.00577 +Epoch [3422/4000] Training [4/39] Loss: 0.00395 +Epoch [3422/4000] Training [5/39] Loss: 0.00540 +Epoch [3422/4000] Training [6/39] Loss: 0.00344 +Epoch [3422/4000] Training [7/39] Loss: 0.12791 +Epoch [3422/4000] Training [8/39] Loss: 0.00732 +Epoch [3422/4000] Training [9/39] Loss: 0.04385 +Epoch [3422/4000] Training [10/39] Loss: 0.00415 +Epoch [3422/4000] Training [11/39] Loss: 0.00343 +Epoch [3422/4000] Training [12/39] Loss: 0.12912 +Epoch [3422/4000] Training [13/39] Loss: 0.00521 +Epoch [3422/4000] Training [14/39] Loss: 0.01453 +Epoch [3422/4000] Training [15/39] Loss: 0.00963 +Epoch [3422/4000] Training [16/39] Loss: 0.00660 +Epoch [3422/4000] Training [17/39] Loss: 0.00586 +Epoch [3422/4000] Training [18/39] Loss: 0.25364 +Epoch [3422/4000] Training [19/39] Loss: 0.00747 +Epoch [3422/4000] Training [20/39] Loss: 0.12928 +Epoch [3422/4000] Training [21/39] Loss: 0.00476 +Epoch [3422/4000] Training [22/39] Loss: 0.00752 +Epoch [3422/4000] Training [23/39] Loss: 0.00587 +Epoch [3422/4000] Training [24/39] Loss: 0.00385 +Epoch [3422/4000] Training [25/39] Loss: 0.00726 +Epoch [3422/4000] Training [26/39] Loss: 0.00768 +Epoch [3422/4000] Training [27/39] Loss: 0.00633 +Epoch [3422/4000] Training [28/39] Loss: 0.13186 +Epoch [3422/4000] Training [29/39] Loss: 0.00637 +Epoch [3422/4000] Training [30/39] Loss: 0.12888 +Epoch [3422/4000] Training [31/39] Loss: 0.00338 +Epoch [3422/4000] Training [32/39] Loss: 0.00330 +Epoch [3422/4000] Training [33/39] Loss: 0.00671 +Epoch [3422/4000] Training [34/39] Loss: 0.00367 +Epoch [3422/4000] Training [35/39] Loss: 0.00382 +Epoch [3422/4000] Training [36/39] Loss: 0.00541 +Epoch [3422/4000] Training [37/39] Loss: 0.00620 +Epoch [3422/4000] Training [38/39] Loss: 0.12881 +Epoch [3422/4000] Training [39/39] Loss: 0.00758 +Epoch [3422/4000] Training metric {'Train/mean dice_metric': 0.9958735704421997, 'Train/mean miou_metric': 0.9922061562538147, 'Train/mean f1': 0.9965908527374268, 'Train/mean precision': 0.9961064457893372, 'Train/mean recall': 0.9970757365226746, 'Train/mean hd95_metric': 1.0226531028747559} +Epoch [3422/4000] Validation [1/10] Loss: 0.66409 focal_loss 0.57637 dice_loss 0.08772 +Epoch [3422/4000] Validation [2/10] Loss: 0.45437 focal_loss 0.36419 dice_loss 0.09018 +Epoch [3422/4000] Validation [3/10] Loss: 0.35361 focal_loss 0.24459 dice_loss 0.10902 +Epoch [3422/4000] Validation [4/10] Loss: 0.87525 focal_loss 0.30779 dice_loss 0.56745 +Epoch [3422/4000] Validation [5/10] Loss: 2.94917 focal_loss 2.27648 dice_loss 0.67268 +Epoch [3422/4000] Validation [6/10] Loss: 1.27955 focal_loss 0.56972 dice_loss 0.70983 +Epoch [3422/4000] Validation [7/10] Loss: 1.16136 focal_loss 0.50425 dice_loss 0.65711 +Epoch [3422/4000] Validation [8/10] Loss: 2.23675 focal_loss 1.62247 dice_loss 0.61428 +Epoch [3422/4000] Validation [9/10] Loss: 1.33272 focal_loss 0.82219 dice_loss 0.51053 +Epoch [3422/4000] Validation [10/10] Loss: 1.79322 focal_loss 1.05989 dice_loss 0.73332 +Epoch [3422/4000] Validation metric {'Val/mean dice_metric': 0.9510785937309265, 'Val/mean miou_metric': 0.9347409605979919, 'Val/mean f1': 0.9485964179039001, 'Val/mean precision': 0.9432791471481323, 'Val/mean recall': 0.9539739489555359, 'Val/mean hd95_metric': 10.757658004760742} +Cheakpoint... +Epoch [3422/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510785937309265, 'Val/mean miou_metric': 0.9347409605979919, 'Val/mean f1': 0.9485964179039001, 'Val/mean precision': 0.9432791471481323, 'Val/mean recall': 0.9539739489555359, 'Val/mean hd95_metric': 10.757658004760742} +Epoch [3423/4000] Training [1/39] Loss: 0.00451 +Epoch [3423/4000] Training [2/39] Loss: 0.00468 +Epoch [3423/4000] Training [3/39] Loss: 0.00515 +Epoch [3423/4000] Training [4/39] Loss: 0.12937 +Epoch [3423/4000] Training [5/39] Loss: 0.00478 +Epoch [3423/4000] Training [6/39] Loss: 0.12833 +Epoch [3423/4000] Training [7/39] Loss: 0.00847 +Epoch [3423/4000] Training [8/39] Loss: 0.12854 +Epoch [3423/4000] Training [9/39] Loss: 0.01148 +Epoch [3423/4000] Training [10/39] Loss: 0.12748 +Epoch [3423/4000] Training [11/39] Loss: 0.00583 +Epoch [3423/4000] Training [12/39] Loss: 0.00683 +Epoch [3423/4000] Training [13/39] Loss: 0.00503 +Epoch [3423/4000] Training [14/39] Loss: 0.00456 +Epoch [3423/4000] Training [15/39] Loss: 0.00525 +Epoch [3423/4000] Training [16/39] Loss: 0.00714 +Epoch [3423/4000] Training [17/39] Loss: 0.00376 +Epoch [3423/4000] Training [18/39] Loss: 0.00468 +Epoch [3423/4000] Training [19/39] Loss: 0.00625 +Epoch [3423/4000] Training [20/39] Loss: 0.00653 +Epoch [3423/4000] Training [21/39] Loss: 0.12931 +Epoch [3423/4000] Training [22/39] Loss: 0.00712 +Epoch [3423/4000] Training [23/39] Loss: 0.25539 +Epoch [3423/4000] Training [24/39] Loss: 0.13003 +Epoch [3423/4000] Training [25/39] Loss: 0.00527 +Epoch [3423/4000] Training [26/39] Loss: 0.12925 +Epoch [3423/4000] Training [27/39] Loss: 0.00950 +Epoch [3423/4000] Training [28/39] Loss: 0.00531 +Epoch [3423/4000] Training [29/39] Loss: 0.00475 +Epoch [3423/4000] Training [30/39] Loss: 0.00780 +Epoch [3423/4000] Training [31/39] Loss: 0.00598 +Epoch [3423/4000] Training [32/39] Loss: 0.13126 +Epoch [3423/4000] Training [33/39] Loss: 0.00605 +Epoch [3423/4000] Training [34/39] Loss: 0.13184 +Epoch [3423/4000] Training [35/39] Loss: 0.00834 +Epoch [3423/4000] Training [36/39] Loss: 0.00481 +Epoch [3423/4000] Training [37/39] Loss: 0.12924 +Epoch [3423/4000] Training [38/39] Loss: 0.00410 +Epoch [3423/4000] Training [39/39] Loss: 0.00615 +Epoch [3423/4000] Training metric {'Train/mean dice_metric': 0.9951640367507935, 'Train/mean miou_metric': 0.9910973906517029, 'Train/mean f1': 0.9962120652198792, 'Train/mean precision': 0.9957849383354187, 'Train/mean recall': 0.9966395497322083, 'Train/mean hd95_metric': 1.1452418565750122} +Epoch [3423/4000] Validation [1/10] Loss: 0.62025 focal_loss 0.53756 dice_loss 0.08269 +Epoch [3423/4000] Validation [2/10] Loss: 0.44981 focal_loss 0.35461 dice_loss 0.09520 +Epoch [3423/4000] Validation [3/10] Loss: 0.36043 focal_loss 0.24925 dice_loss 0.11118 +Epoch [3423/4000] Validation [4/10] Loss: 0.85538 focal_loss 0.25208 dice_loss 0.60330 +Epoch [3423/4000] Validation [5/10] Loss: 2.90207 focal_loss 2.22860 dice_loss 0.67347 +Epoch [3423/4000] Validation [6/10] Loss: 1.18975 focal_loss 0.47262 dice_loss 0.71712 +Epoch [3423/4000] Validation [7/10] Loss: 1.06744 focal_loss 0.41700 dice_loss 0.65044 +Epoch [3423/4000] Validation [8/10] Loss: 2.82504 focal_loss 2.15731 dice_loss 0.66773 +Epoch [3423/4000] Validation [9/10] Loss: 1.26122 focal_loss 0.72500 dice_loss 0.53622 +Epoch [3423/4000] Validation [10/10] Loss: 1.64320 focal_loss 0.92332 dice_loss 0.71988 +Epoch [3423/4000] Validation metric {'Val/mean dice_metric': 0.9496168494224548, 'Val/mean miou_metric': 0.9331345558166504, 'Val/mean f1': 0.950526773929596, 'Val/mean precision': 0.9509191513061523, 'Val/mean recall': 0.9501346945762634, 'Val/mean hd95_metric': 10.435368537902832} +Cheakpoint... +Epoch [3423/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9496], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9496168494224548, 'Val/mean miou_metric': 0.9331345558166504, 'Val/mean f1': 0.950526773929596, 'Val/mean precision': 0.9509191513061523, 'Val/mean recall': 0.9501346945762634, 'Val/mean hd95_metric': 10.435368537902832} +Epoch [3424/4000] Training [1/39] Loss: 0.12986 +Epoch [3424/4000] Training [2/39] Loss: 0.12873 +Epoch [3424/4000] Training [3/39] Loss: 0.00405 +Epoch [3424/4000] Training [4/39] Loss: 0.00439 +Epoch [3424/4000] Training [5/39] Loss: 0.00548 +Epoch [3424/4000] Training [6/39] Loss: 0.00460 +Epoch [3424/4000] Training [7/39] Loss: 0.00514 +Epoch [3424/4000] Training [8/39] Loss: 0.00445 +Epoch [3424/4000] Training [9/39] Loss: 0.00426 +Epoch [3424/4000] Training [10/39] Loss: 0.00510 +Epoch [3424/4000] Training [11/39] Loss: 0.00923 +Epoch [3424/4000] Training [12/39] Loss: 0.00434 +Epoch [3424/4000] Training [13/39] Loss: 0.12930 +Epoch [3424/4000] Training [14/39] Loss: 0.12966 +Epoch [3424/4000] Training [15/39] Loss: 0.12853 +Epoch [3424/4000] Training [16/39] Loss: 0.00577 +Epoch [3424/4000] Training [17/39] Loss: 0.01038 +Epoch [3424/4000] Training [18/39] Loss: 0.12963 +Epoch [3424/4000] Training [19/39] Loss: 0.00507 +Epoch [3424/4000] Training [20/39] Loss: 0.00501 +Epoch [3424/4000] Training [21/39] Loss: 0.12925 +Epoch [3424/4000] Training [22/39] Loss: 0.00380 +Epoch [3424/4000] Training [23/39] Loss: 0.00460 +Epoch [3424/4000] Training [24/39] Loss: 0.25332 +Epoch [3424/4000] Training [25/39] Loss: 0.00504 +Epoch [3424/4000] Training [26/39] Loss: 0.00561 +Epoch [3424/4000] Training [27/39] Loss: 0.00468 +Epoch [3424/4000] Training [28/39] Loss: 0.00423 +Epoch [3424/4000] Training [29/39] Loss: 0.00437 +Epoch [3424/4000] Training [30/39] Loss: 0.00585 +Epoch [3424/4000] Training [31/39] Loss: 0.00545 +Epoch [3424/4000] Training [32/39] Loss: 0.00560 +Epoch [3424/4000] Training [33/39] Loss: 0.00774 +Epoch [3424/4000] Training [34/39] Loss: 0.12831 +Epoch [3424/4000] Training [35/39] Loss: 0.00817 +Epoch [3424/4000] Training [36/39] Loss: 0.00520 +Epoch [3424/4000] Training [37/39] Loss: 0.00779 +Epoch [3424/4000] Training [38/39] Loss: 0.00734 +Epoch [3424/4000] Training [39/39] Loss: 0.00439 +Epoch [3424/4000] Training metric {'Train/mean dice_metric': 0.9958646297454834, 'Train/mean miou_metric': 0.9921613335609436, 'Train/mean f1': 0.996679961681366, 'Train/mean precision': 0.9962373375892639, 'Train/mean recall': 0.9971229434013367, 'Train/mean hd95_metric': 1.1362147331237793} +Epoch [3424/4000] Validation [1/10] Loss: 0.63302 focal_loss 0.55036 dice_loss 0.08266 +Epoch [3424/4000] Validation [2/10] Loss: 0.46048 focal_loss 0.36342 dice_loss 0.09706 +Epoch [3424/4000] Validation [3/10] Loss: 0.37111 focal_loss 0.26079 dice_loss 0.11032 +Epoch [3424/4000] Validation [4/10] Loss: 0.85704 focal_loss 0.25854 dice_loss 0.59850 +Epoch [3424/4000] Validation [5/10] Loss: 3.02280 focal_loss 2.34920 dice_loss 0.67360 +Epoch [3424/4000] Validation [6/10] Loss: 1.21171 focal_loss 0.49268 dice_loss 0.71903 +Epoch [3424/4000] Validation [7/10] Loss: 1.07266 focal_loss 0.42280 dice_loss 0.64986 +Epoch [3424/4000] Validation [8/10] Loss: 2.74006 focal_loss 2.08130 dice_loss 0.65877 +Epoch [3424/4000] Validation [9/10] Loss: 1.29869 focal_loss 0.76328 dice_loss 0.53541 +Epoch [3424/4000] Validation [10/10] Loss: 1.67746 focal_loss 0.95357 dice_loss 0.72390 +Epoch [3424/4000] Validation metric {'Val/mean dice_metric': 0.9502136707305908, 'Val/mean miou_metric': 0.9339953660964966, 'Val/mean f1': 0.9502352476119995, 'Val/mean precision': 0.9497646689414978, 'Val/mean recall': 0.950706422328949, 'Val/mean hd95_metric': 10.42602825164795} +Cheakpoint... +Epoch [3424/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502136707305908, 'Val/mean miou_metric': 0.9339953660964966, 'Val/mean f1': 0.9502352476119995, 'Val/mean precision': 0.9497646689414978, 'Val/mean recall': 0.950706422328949, 'Val/mean hd95_metric': 10.42602825164795} +Epoch [3425/4000] Training [1/39] Loss: 0.13039 +Epoch [3425/4000] Training [2/39] Loss: 0.00615 +Epoch [3425/4000] Training [3/39] Loss: 0.00611 +Epoch [3425/4000] Training [4/39] Loss: 0.00650 +Epoch [3425/4000] Training [5/39] Loss: 0.00430 +Epoch [3425/4000] Training [6/39] Loss: 0.00422 +Epoch [3425/4000] Training [7/39] Loss: 0.00460 +Epoch [3425/4000] Training [8/39] Loss: 0.13029 +Epoch [3425/4000] Training [9/39] Loss: 0.00687 +Epoch [3425/4000] Training [10/39] Loss: 0.00480 +Epoch [3425/4000] Training [11/39] Loss: 0.00591 +Epoch [3425/4000] Training [12/39] Loss: 0.00528 +Epoch [3425/4000] Training [13/39] Loss: 0.00716 +Epoch [3425/4000] Training [14/39] Loss: 0.12891 +Epoch [3425/4000] Training [15/39] Loss: 0.00555 +Epoch [3425/4000] Training [16/39] Loss: 0.12899 +Epoch [3425/4000] Training [17/39] Loss: 0.16652 +Epoch [3425/4000] Training [18/39] Loss: 0.12963 +Epoch [3425/4000] Training [19/39] Loss: 0.00553 +Epoch [3425/4000] Training [20/39] Loss: 0.00518 +Epoch [3425/4000] Training [21/39] Loss: 0.12866 +Epoch [3425/4000] Training [22/39] Loss: 0.00402 +Epoch [3425/4000] Training [23/39] Loss: 0.12888 +Epoch [3425/4000] Training [24/39] Loss: 0.00533 +Epoch [3425/4000] Training [25/39] Loss: 0.00629 +Epoch [3425/4000] Training [26/39] Loss: 0.00784 +Epoch [3425/4000] Training [27/39] Loss: 0.00466 +Epoch [3425/4000] Training [28/39] Loss: 0.13306 +Epoch [3425/4000] Training [29/39] Loss: 0.00548 +Epoch [3425/4000] Training [30/39] Loss: 0.00457 +Epoch [3425/4000] Training [31/39] Loss: 0.00327 +Epoch [3425/4000] Training [32/39] Loss: 0.13109 +Epoch [3425/4000] Training [33/39] Loss: 0.00462 +Epoch [3425/4000] Training [34/39] Loss: 0.00470 +Epoch [3425/4000] Training [35/39] Loss: 0.00492 +Epoch [3425/4000] Training [36/39] Loss: 0.13003 +Epoch [3425/4000] Training [37/39] Loss: 0.00499 +Epoch [3425/4000] Training [38/39] Loss: 0.00312 +Epoch [3425/4000] Training [39/39] Loss: 0.12967 +Epoch [3425/4000] Training metric {'Train/mean dice_metric': 0.9958991408348083, 'Train/mean miou_metric': 0.9922454953193665, 'Train/mean f1': 0.9966500401496887, 'Train/mean precision': 0.9961972832679749, 'Train/mean recall': 0.997103214263916, 'Train/mean hd95_metric': 1.0371733903884888} +Epoch [3425/4000] Validation [1/10] Loss: 0.64840 focal_loss 0.56432 dice_loss 0.08408 +Epoch [3425/4000] Validation [2/10] Loss: 0.45676 focal_loss 0.36258 dice_loss 0.09417 +Epoch [3425/4000] Validation [3/10] Loss: 0.36859 focal_loss 0.25808 dice_loss 0.11051 +Epoch [3425/4000] Validation [4/10] Loss: 0.85755 focal_loss 0.27075 dice_loss 0.58680 +Epoch [3425/4000] Validation [5/10] Loss: 2.99526 focal_loss 2.32208 dice_loss 0.67318 +Epoch [3425/4000] Validation [6/10] Loss: 1.23717 focal_loss 0.51658 dice_loss 0.72059 +Epoch [3425/4000] Validation [7/10] Loss: 1.09960 focal_loss 0.44925 dice_loss 0.65035 +Epoch [3425/4000] Validation [8/10] Loss: 2.46293 focal_loss 1.82475 dice_loss 0.63818 +Epoch [3425/4000] Validation [9/10] Loss: 1.31131 focal_loss 0.77383 dice_loss 0.53748 +Epoch [3425/4000] Validation [10/10] Loss: 1.70927 focal_loss 0.98199 dice_loss 0.72728 +Epoch [3425/4000] Validation metric {'Val/mean dice_metric': 0.9504815340042114, 'Val/mean miou_metric': 0.9343492984771729, 'Val/mean f1': 0.9499056339263916, 'Val/mean precision': 0.9476211071014404, 'Val/mean recall': 0.9522010684013367, 'Val/mean hd95_metric': 10.411582946777344} +Cheakpoint... +Epoch [3425/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504815340042114, 'Val/mean miou_metric': 0.9343492984771729, 'Val/mean f1': 0.9499056339263916, 'Val/mean precision': 0.9476211071014404, 'Val/mean recall': 0.9522010684013367, 'Val/mean hd95_metric': 10.411582946777344} +Epoch [3426/4000] Training [1/39] Loss: 0.00393 +Epoch [3426/4000] Training [2/39] Loss: 0.12921 +Epoch [3426/4000] Training [3/39] Loss: 0.00337 +Epoch [3426/4000] Training [4/39] Loss: 0.00546 +Epoch [3426/4000] Training [5/39] Loss: 0.25420 +Epoch [3426/4000] Training [6/39] Loss: 0.13039 +Epoch [3426/4000] Training [7/39] Loss: 0.12873 +Epoch [3426/4000] Training [8/39] Loss: 0.00787 +Epoch [3426/4000] Training [9/39] Loss: 0.00534 +Epoch [3426/4000] Training [10/39] Loss: 0.25390 +Epoch [3426/4000] Training [11/39] Loss: 0.00329 +Epoch [3426/4000] Training [12/39] Loss: 0.00315 +Epoch [3426/4000] Training [13/39] Loss: 0.00530 +Epoch [3426/4000] Training [14/39] Loss: 0.00419 +Epoch [3426/4000] Training [15/39] Loss: 0.00740 +Epoch [3426/4000] Training [16/39] Loss: 0.13205 +Epoch [3426/4000] Training [17/39] Loss: 0.00513 +Epoch [3426/4000] Training [18/39] Loss: 0.00400 +Epoch [3426/4000] Training [19/39] Loss: 0.12937 +Epoch [3426/4000] Training [20/39] Loss: 0.00601 +Epoch [3426/4000] Training [21/39] Loss: 0.00615 +Epoch [3426/4000] Training [22/39] Loss: 0.00592 +Epoch [3426/4000] Training [23/39] Loss: 0.01104 +Epoch [3426/4000] Training [24/39] Loss: 0.00709 +Epoch [3426/4000] Training [25/39] Loss: 0.01064 +Epoch [3426/4000] Training [26/39] Loss: 0.00407 +Epoch [3426/4000] Training [27/39] Loss: 0.00695 +Epoch [3426/4000] Training [28/39] Loss: 0.13087 +Epoch [3426/4000] Training [29/39] Loss: 0.00606 +Epoch [3426/4000] Training [30/39] Loss: 0.16693 +Epoch [3426/4000] Training [31/39] Loss: 0.00722 +Epoch [3426/4000] Training [32/39] Loss: 0.00405 +Epoch [3426/4000] Training [33/39] Loss: 0.00366 +Epoch [3426/4000] Training [34/39] Loss: 0.12883 +Epoch [3426/4000] Training [35/39] Loss: 0.00605 +Epoch [3426/4000] Training [36/39] Loss: 0.00437 +Epoch [3426/4000] Training [37/39] Loss: 0.00524 +Epoch [3426/4000] Training [38/39] Loss: 0.00581 +Epoch [3426/4000] Training [39/39] Loss: 0.00528 +Epoch [3426/4000] Training metric {'Train/mean dice_metric': 0.9949501156806946, 'Train/mean miou_metric': 0.9912124872207642, 'Train/mean f1': 0.9965941905975342, 'Train/mean precision': 0.9961211681365967, 'Train/mean recall': 0.9970676302909851, 'Train/mean hd95_metric': 1.0455135107040405} +Epoch [3426/4000] Validation [1/10] Loss: 0.64661 focal_loss 0.56220 dice_loss 0.08441 +Epoch [3426/4000] Validation [2/10] Loss: 0.46516 focal_loss 0.37035 dice_loss 0.09481 +Epoch [3426/4000] Validation [3/10] Loss: 0.36498 focal_loss 0.25475 dice_loss 0.11023 +Epoch [3426/4000] Validation [4/10] Loss: 0.87332 focal_loss 0.28240 dice_loss 0.59092 +Epoch [3426/4000] Validation [5/10] Loss: 2.99695 focal_loss 2.32396 dice_loss 0.67299 +Epoch [3426/4000] Validation [6/10] Loss: 1.25227 focal_loss 0.53368 dice_loss 0.71859 +Epoch [3426/4000] Validation [7/10] Loss: 1.10601 focal_loss 0.45526 dice_loss 0.65075 +Epoch [3426/4000] Validation [8/10] Loss: 2.71420 focal_loss 2.05967 dice_loss 0.65453 +Epoch [3426/4000] Validation [9/10] Loss: 1.32026 focal_loss 0.78236 dice_loss 0.53790 +Epoch [3426/4000] Validation [10/10] Loss: 1.71225 focal_loss 0.98816 dice_loss 0.72409 +Epoch [3426/4000] Validation metric {'Val/mean dice_metric': 0.9498741030693054, 'Val/mean miou_metric': 0.933628261089325, 'Val/mean f1': 0.9499167203903198, 'Val/mean precision': 0.9481691122055054, 'Val/mean recall': 0.9516708254814148, 'Val/mean hd95_metric': 10.564958572387695} +Cheakpoint... +Epoch [3426/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9499], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9498741030693054, 'Val/mean miou_metric': 0.933628261089325, 'Val/mean f1': 0.9499167203903198, 'Val/mean precision': 0.9481691122055054, 'Val/mean recall': 0.9516708254814148, 'Val/mean hd95_metric': 10.564958572387695} +Epoch [3427/4000] Training [1/39] Loss: 0.00273 +Epoch [3427/4000] Training [2/39] Loss: 0.00911 +Epoch [3427/4000] Training [3/39] Loss: 0.00341 +Epoch [3427/4000] Training [4/39] Loss: 0.00540 +Epoch [3427/4000] Training [5/39] Loss: 0.00439 +Epoch [3427/4000] Training [6/39] Loss: 0.12916 +Epoch [3427/4000] Training [7/39] Loss: 0.00459 +Epoch [3427/4000] Training [8/39] Loss: 0.13127 +Epoch [3427/4000] Training [9/39] Loss: 0.00428 +Epoch [3427/4000] Training [10/39] Loss: 0.12903 +Epoch [3427/4000] Training [11/39] Loss: 0.00541 +Epoch [3427/4000] Training [12/39] Loss: 0.00351 +Epoch [3427/4000] Training [13/39] Loss: 0.00642 +Epoch [3427/4000] Training [14/39] Loss: 0.13026 +Epoch [3427/4000] Training [15/39] Loss: 0.00418 +Epoch [3427/4000] Training [16/39] Loss: 0.00914 +Epoch [3427/4000] Training [17/39] Loss: 0.13525 +Epoch [3427/4000] Training [18/39] Loss: 0.09272 +Epoch [3427/4000] Training [19/39] Loss: 0.13007 +Epoch [3427/4000] Training [20/39] Loss: 0.12930 +Epoch [3427/4000] Training [21/39] Loss: 0.00331 +Epoch [3427/4000] Training [22/39] Loss: 0.00616 +Epoch [3427/4000] Training [23/39] Loss: 0.00389 +Epoch [3427/4000] Training [24/39] Loss: 0.00547 +Epoch [3427/4000] Training [25/39] Loss: 0.12866 +Epoch [3427/4000] Training [26/39] Loss: 0.00592 +Epoch [3427/4000] Training [27/39] Loss: 0.00442 +Epoch [3427/4000] Training [28/39] Loss: 0.25533 +Epoch [3427/4000] Training [29/39] Loss: 0.13276 +Epoch [3427/4000] Training [30/39] Loss: 0.00554 +Epoch [3427/4000] Training [31/39] Loss: 0.00593 +Epoch [3427/4000] Training [32/39] Loss: 0.00564 +Epoch [3427/4000] Training [33/39] Loss: 0.12953 +Epoch [3427/4000] Training [34/39] Loss: 0.00392 +Epoch [3427/4000] Training [35/39] Loss: 0.00596 +Epoch [3427/4000] Training [36/39] Loss: 0.00368 +Epoch [3427/4000] Training [37/39] Loss: 0.00689 +Epoch [3427/4000] Training [38/39] Loss: 0.00619 +Epoch [3427/4000] Training [39/39] Loss: 0.00506 +Epoch [3427/4000] Training metric {'Train/mean dice_metric': 0.9948785305023193, 'Train/mean miou_metric': 0.9910590648651123, 'Train/mean f1': 0.9964703917503357, 'Train/mean precision': 0.9959850907325745, 'Train/mean recall': 0.9969559907913208, 'Train/mean hd95_metric': 1.2634092569351196} +Epoch [3427/4000] Validation [1/10] Loss: 0.65251 focal_loss 0.56919 dice_loss 0.08332 +Epoch [3427/4000] Validation [2/10] Loss: 0.46829 focal_loss 0.36738 dice_loss 0.10091 +Epoch [3427/4000] Validation [3/10] Loss: 0.37113 focal_loss 0.26003 dice_loss 0.11110 +Epoch [3427/4000] Validation [4/10] Loss: 0.86336 focal_loss 0.27602 dice_loss 0.58734 +Epoch [3427/4000] Validation [5/10] Loss: 2.94625 focal_loss 2.27310 dice_loss 0.67315 +Epoch [3427/4000] Validation [6/10] Loss: 1.20185 focal_loss 0.48202 dice_loss 0.71984 +Epoch [3427/4000] Validation [7/10] Loss: 1.06677 focal_loss 0.41591 dice_loss 0.65085 +Epoch [3427/4000] Validation [8/10] Loss: 2.36595 focal_loss 1.72978 dice_loss 0.63616 +Epoch [3427/4000] Validation [9/10] Loss: 1.22265 focal_loss 0.72449 dice_loss 0.49816 +Epoch [3427/4000] Validation [10/10] Loss: 1.65349 focal_loss 0.93289 dice_loss 0.72060 +Epoch [3427/4000] Validation metric {'Val/mean dice_metric': 0.949798583984375, 'Val/mean miou_metric': 0.9336138963699341, 'Val/mean f1': 0.949982762336731, 'Val/mean precision': 0.9484561681747437, 'Val/mean recall': 0.9515142440795898, 'Val/mean hd95_metric': 10.175714492797852} +Cheakpoint... +Epoch [3427/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9498], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.949798583984375, 'Val/mean miou_metric': 0.9336138963699341, 'Val/mean f1': 0.949982762336731, 'Val/mean precision': 0.9484561681747437, 'Val/mean recall': 0.9515142440795898, 'Val/mean hd95_metric': 10.175714492797852} +Epoch [3428/4000] Training [1/39] Loss: 0.00348 +Epoch [3428/4000] Training [2/39] Loss: 0.00430 +Epoch [3428/4000] Training [3/39] Loss: 0.00528 +Epoch [3428/4000] Training [4/39] Loss: 0.12932 +Epoch [3428/4000] Training [5/39] Loss: 0.00612 +Epoch [3428/4000] Training [6/39] Loss: 0.00413 +Epoch [3428/4000] Training [7/39] Loss: 0.00462 +Epoch [3428/4000] Training [8/39] Loss: 0.00430 +Epoch [3428/4000] Training [9/39] Loss: 0.00393 +Epoch [3428/4000] Training [10/39] Loss: 0.12992 +Epoch [3428/4000] Training [11/39] Loss: 0.00626 +Epoch [3428/4000] Training [12/39] Loss: 0.00832 +Epoch [3428/4000] Training [13/39] Loss: 0.00444 +Epoch [3428/4000] Training [14/39] Loss: 0.00768 +Epoch [3428/4000] Training [15/39] Loss: 0.00985 +Epoch [3428/4000] Training [16/39] Loss: 0.08509 +Epoch [3428/4000] Training [17/39] Loss: 0.13036 +Epoch [3428/4000] Training [18/39] Loss: 0.00750 +Epoch [3428/4000] Training [19/39] Loss: 0.00616 +Epoch [3428/4000] Training [20/39] Loss: 0.00613 +Epoch [3428/4000] Training [21/39] Loss: 0.00540 +Epoch [3428/4000] Training [22/39] Loss: 0.13241 +Epoch [3428/4000] Training [23/39] Loss: 0.16802 +Epoch [3428/4000] Training [24/39] Loss: 0.00387 +Epoch [3428/4000] Training [25/39] Loss: 0.25420 +Epoch [3428/4000] Training [26/39] Loss: 0.13109 +Epoch [3428/4000] Training [27/39] Loss: 0.00674 +Epoch [3428/4000] Training [28/39] Loss: 0.13237 +Epoch [3428/4000] Training [29/39] Loss: 0.00638 +Epoch [3428/4000] Training [30/39] Loss: 0.00674 +Epoch [3428/4000] Training [31/39] Loss: 0.12858 +Epoch [3428/4000] Training [32/39] Loss: 0.00460 +Epoch [3428/4000] Training [33/39] Loss: 0.00856 +Epoch [3428/4000] Training [34/39] Loss: 0.00576 +Epoch [3428/4000] Training [35/39] Loss: 0.12962 +Epoch [3428/4000] Training [36/39] Loss: 0.12804 +Epoch [3428/4000] Training [37/39] Loss: 0.00610 +Epoch [3428/4000] Training [38/39] Loss: 0.13011 +Epoch [3428/4000] Training [39/39] Loss: 0.13140 +Epoch [3428/4000] Training metric {'Train/mean dice_metric': 0.9956958889961243, 'Train/mean miou_metric': 0.9918467402458191, 'Train/mean f1': 0.9964621663093567, 'Train/mean precision': 0.9959867596626282, 'Train/mean recall': 0.9969379901885986, 'Train/mean hd95_metric': 1.0201740264892578} +Epoch [3428/4000] Validation [1/10] Loss: 0.67486 focal_loss 0.58789 dice_loss 0.08697 +Epoch [3428/4000] Validation [2/10] Loss: 0.45894 focal_loss 0.36888 dice_loss 0.09006 +Epoch [3428/4000] Validation [3/10] Loss: 0.35554 focal_loss 0.24704 dice_loss 0.10850 +Epoch [3428/4000] Validation [4/10] Loss: 0.88800 focal_loss 0.29035 dice_loss 0.59765 +Epoch [3428/4000] Validation [5/10] Loss: 2.96215 focal_loss 2.28917 dice_loss 0.67298 +Epoch [3428/4000] Validation [6/10] Loss: 1.26817 focal_loss 0.54826 dice_loss 0.71991 +Epoch [3428/4000] Validation [7/10] Loss: 1.10746 focal_loss 0.45797 dice_loss 0.64949 +Epoch [3428/4000] Validation [8/10] Loss: 2.54795 focal_loss 1.90597 dice_loss 0.64198 +Epoch [3428/4000] Validation [9/10] Loss: 1.31869 focal_loss 0.78203 dice_loss 0.53666 +Epoch [3428/4000] Validation [10/10] Loss: 1.76030 focal_loss 1.03505 dice_loss 0.72525 +Epoch [3428/4000] Validation metric {'Val/mean dice_metric': 0.9504343867301941, 'Val/mean miou_metric': 0.9341248869895935, 'Val/mean f1': 0.9492971897125244, 'Val/mean precision': 0.9466025233268738, 'Val/mean recall': 0.9520072937011719, 'Val/mean hd95_metric': 10.5875883102417} +Cheakpoint... +Epoch [3428/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504343867301941, 'Val/mean miou_metric': 0.9341248869895935, 'Val/mean f1': 0.9492971897125244, 'Val/mean precision': 0.9466025233268738, 'Val/mean recall': 0.9520072937011719, 'Val/mean hd95_metric': 10.5875883102417} +Epoch [3429/4000] Training [1/39] Loss: 0.00576 +Epoch [3429/4000] Training [2/39] Loss: 0.12915 +Epoch [3429/4000] Training [3/39] Loss: 0.00440 +Epoch [3429/4000] Training [4/39] Loss: 0.00482 +Epoch [3429/4000] Training [5/39] Loss: 0.00466 +Epoch [3429/4000] Training [6/39] Loss: 0.25445 +Epoch [3429/4000] Training [7/39] Loss: 0.12952 +Epoch [3429/4000] Training [8/39] Loss: 0.00485 +Epoch [3429/4000] Training [9/39] Loss: 0.00356 +Epoch [3429/4000] Training [10/39] Loss: 0.00627 +Epoch [3429/4000] Training [11/39] Loss: 0.00594 +Epoch [3429/4000] Training [12/39] Loss: 0.00458 +Epoch [3429/4000] Training [13/39] Loss: 0.01038 +Epoch [3429/4000] Training [14/39] Loss: 0.00605 +Epoch [3429/4000] Training [15/39] Loss: 0.00337 +Epoch [3429/4000] Training [16/39] Loss: 0.00642 +Epoch [3429/4000] Training [17/39] Loss: 0.00560 +Epoch [3429/4000] Training [18/39] Loss: 0.12759 +Epoch [3429/4000] Training [19/39] Loss: 0.00498 +Epoch [3429/4000] Training [20/39] Loss: 0.00522 +Epoch [3429/4000] Training [21/39] Loss: 0.00656 +Epoch [3429/4000] Training [22/39] Loss: 0.12880 +Epoch [3429/4000] Training [23/39] Loss: 0.00482 +Epoch [3429/4000] Training [24/39] Loss: 0.00439 +Epoch [3429/4000] Training [25/39] Loss: 0.00571 +Epoch [3429/4000] Training [26/39] Loss: 0.13067 +Epoch [3429/4000] Training [27/39] Loss: 0.00330 +Epoch [3429/4000] Training [28/39] Loss: 0.00700 +Epoch [3429/4000] Training [29/39] Loss: 0.00481 +Epoch [3429/4000] Training [30/39] Loss: 0.25406 +Epoch [3429/4000] Training [31/39] Loss: 0.12954 +Epoch [3429/4000] Training [32/39] Loss: 0.00422 +Epoch [3429/4000] Training [33/39] Loss: 0.12880 +Epoch [3429/4000] Training [34/39] Loss: 0.13166 +Epoch [3429/4000] Training [35/39] Loss: 0.00376 +Epoch [3429/4000] Training [36/39] Loss: 0.12883 +Epoch [3429/4000] Training [37/39] Loss: 0.00396 +Epoch [3429/4000] Training [38/39] Loss: 0.00532 +Epoch [3429/4000] Training [39/39] Loss: 0.00416 +Epoch [3429/4000] Training metric {'Train/mean dice_metric': 0.9960359334945679, 'Train/mean miou_metric': 0.9925337433815002, 'Train/mean f1': 0.9967694282531738, 'Train/mean precision': 0.9962676167488098, 'Train/mean recall': 0.9972716569900513, 'Train/mean hd95_metric': 1.016380786895752} +Epoch [3429/4000] Validation [1/10] Loss: 0.64389 focal_loss 0.55857 dice_loss 0.08532 +Epoch [3429/4000] Validation [2/10] Loss: 0.45433 focal_loss 0.35990 dice_loss 0.09443 +Epoch [3429/4000] Validation [3/10] Loss: 0.37879 focal_loss 0.26661 dice_loss 0.11218 +Epoch [3429/4000] Validation [4/10] Loss: 0.88253 focal_loss 0.28298 dice_loss 0.59955 +Epoch [3429/4000] Validation [5/10] Loss: 2.92265 focal_loss 2.24894 dice_loss 0.67371 +Epoch [3429/4000] Validation [6/10] Loss: 1.21795 focal_loss 0.50654 dice_loss 0.71141 +Epoch [3429/4000] Validation [7/10] Loss: 1.06757 focal_loss 0.41662 dice_loss 0.65094 +Epoch [3429/4000] Validation [8/10] Loss: 2.82568 focal_loss 2.16102 dice_loss 0.66466 +Epoch [3429/4000] Validation [9/10] Loss: 1.27082 focal_loss 0.73766 dice_loss 0.53316 +Epoch [3429/4000] Validation [10/10] Loss: 1.67038 focal_loss 0.94883 dice_loss 0.72154 +Epoch [3429/4000] Validation metric {'Val/mean dice_metric': 0.9507054090499878, 'Val/mean miou_metric': 0.9346754550933838, 'Val/mean f1': 0.9499592185020447, 'Val/mean precision': 0.949195921421051, 'Val/mean recall': 0.9507238268852234, 'Val/mean hd95_metric': 10.360591888427734} +Cheakpoint... +Epoch [3429/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507054090499878, 'Val/mean miou_metric': 0.9346754550933838, 'Val/mean f1': 0.9499592185020447, 'Val/mean precision': 0.949195921421051, 'Val/mean recall': 0.9507238268852234, 'Val/mean hd95_metric': 10.360591888427734} +Epoch [3430/4000] Training [1/39] Loss: 0.12998 +Epoch [3430/4000] Training [2/39] Loss: 0.00532 +Epoch [3430/4000] Training [3/39] Loss: 0.00474 +Epoch [3430/4000] Training [4/39] Loss: 0.00618 +Epoch [3430/4000] Training [5/39] Loss: 0.00620 +Epoch [3430/4000] Training [6/39] Loss: 0.00488 +Epoch [3430/4000] Training [7/39] Loss: 0.00406 +Epoch [3430/4000] Training [8/39] Loss: 0.00720 +Epoch [3430/4000] Training [9/39] Loss: 0.00659 +Epoch [3430/4000] Training [10/39] Loss: 0.00674 +Epoch [3430/4000] Training [11/39] Loss: 0.00658 +Epoch [3430/4000] Training [12/39] Loss: 0.00741 +Epoch [3430/4000] Training [13/39] Loss: 0.00334 +Epoch [3430/4000] Training [14/39] Loss: 0.12918 +Epoch [3430/4000] Training [15/39] Loss: 0.00806 +Epoch [3430/4000] Training [16/39] Loss: 0.00417 +Epoch [3430/4000] Training [17/39] Loss: 0.00424 +Epoch [3430/4000] Training [18/39] Loss: 0.00538 +Epoch [3430/4000] Training [19/39] Loss: 0.00408 +Epoch [3430/4000] Training [20/39] Loss: 0.25305 +Epoch [3430/4000] Training [21/39] Loss: 0.09937 +Epoch [3430/4000] Training [22/39] Loss: 0.00348 +Epoch [3430/4000] Training [23/39] Loss: 0.00373 +Epoch [3430/4000] Training [24/39] Loss: 0.00787 +Epoch [3430/4000] Training [25/39] Loss: 0.00424 +Epoch [3430/4000] Training [26/39] Loss: 0.00389 +Epoch [3430/4000] Training [27/39] Loss: 0.00536 +Epoch [3430/4000] Training [28/39] Loss: 0.00499 +Epoch [3430/4000] Training [29/39] Loss: 0.00456 +Epoch [3430/4000] Training [30/39] Loss: 0.00333 +Epoch [3430/4000] Training [31/39] Loss: 0.12850 +Epoch [3430/4000] Training [32/39] Loss: 0.25272 +Epoch [3430/4000] Training [33/39] Loss: 0.00676 +Epoch [3430/4000] Training [34/39] Loss: 0.01277 +Epoch [3430/4000] Training [35/39] Loss: 0.12896 +Epoch [3430/4000] Training [36/39] Loss: 0.01008 +Epoch [3430/4000] Training [37/39] Loss: 0.13083 +Epoch [3430/4000] Training [38/39] Loss: 0.00398 +Epoch [3430/4000] Training [39/39] Loss: 0.00499 +Epoch [3430/4000] Training metric {'Train/mean dice_metric': 0.9957005381584167, 'Train/mean miou_metric': 0.9918874502182007, 'Train/mean f1': 0.9965240955352783, 'Train/mean precision': 0.9960947036743164, 'Train/mean recall': 0.9969538450241089, 'Train/mean hd95_metric': 1.0379031896591187} +Epoch [3430/4000] Validation [1/10] Loss: 0.66350 focal_loss 0.57756 dice_loss 0.08594 +Epoch [3430/4000] Validation [2/10] Loss: 0.46445 focal_loss 0.37193 dice_loss 0.09252 +Epoch [3430/4000] Validation [3/10] Loss: 0.37123 focal_loss 0.26090 dice_loss 0.11033 +Epoch [3430/4000] Validation [4/10] Loss: 0.88505 focal_loss 0.28792 dice_loss 0.59713 +Epoch [3430/4000] Validation [5/10] Loss: 2.99794 focal_loss 2.32501 dice_loss 0.67293 +Epoch [3430/4000] Validation [6/10] Loss: 1.23928 focal_loss 0.52531 dice_loss 0.71397 +Epoch [3430/4000] Validation [7/10] Loss: 1.11446 focal_loss 0.46393 dice_loss 0.65053 +Epoch [3430/4000] Validation [8/10] Loss: 2.49857 focal_loss 1.86013 dice_loss 0.63844 +Epoch [3430/4000] Validation [9/10] Loss: 1.27887 focal_loss 0.74836 dice_loss 0.53051 +Epoch [3430/4000] Validation [10/10] Loss: 1.72869 focal_loss 1.00055 dice_loss 0.72814 +Epoch [3430/4000] Validation metric {'Val/mean dice_metric': 0.9504532814025879, 'Val/mean miou_metric': 0.9341846108436584, 'Val/mean f1': 0.9494333863258362, 'Val/mean precision': 0.9468036890029907, 'Val/mean recall': 0.9520778059959412, 'Val/mean hd95_metric': 10.567353248596191} +Cheakpoint... +Epoch [3430/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504532814025879, 'Val/mean miou_metric': 0.9341846108436584, 'Val/mean f1': 0.9494333863258362, 'Val/mean precision': 0.9468036890029907, 'Val/mean recall': 0.9520778059959412, 'Val/mean hd95_metric': 10.567353248596191} +Epoch [3431/4000] Training [1/39] Loss: 0.00954 +Epoch [3431/4000] Training [2/39] Loss: 0.00675 +Epoch [3431/4000] Training [3/39] Loss: 0.12934 +Epoch [3431/4000] Training [4/39] Loss: 0.00665 +Epoch [3431/4000] Training [5/39] Loss: 0.25378 +Epoch [3431/4000] Training [6/39] Loss: 0.00612 +Epoch [3431/4000] Training [7/39] Loss: 0.12883 +Epoch [3431/4000] Training [8/39] Loss: 0.12912 +Epoch [3431/4000] Training [9/39] Loss: 0.12871 +Epoch [3431/4000] Training [10/39] Loss: 0.00934 +Epoch [3431/4000] Training [11/39] Loss: 0.13160 +Epoch [3431/4000] Training [12/39] Loss: 0.25347 +Epoch [3431/4000] Training [13/39] Loss: 0.00376 +Epoch [3431/4000] Training [14/39] Loss: 0.00474 +Epoch [3431/4000] Training [15/39] Loss: 0.00440 +Epoch [3431/4000] Training [16/39] Loss: 0.00495 +Epoch [3431/4000] Training [17/39] Loss: 0.00437 +Epoch [3431/4000] Training [18/39] Loss: 0.00657 +Epoch [3431/4000] Training [19/39] Loss: 0.00790 +Epoch [3431/4000] Training [20/39] Loss: 0.00415 +Epoch [3431/4000] Training [21/39] Loss: 0.12948 +Epoch [3431/4000] Training [22/39] Loss: 0.13017 +Epoch [3431/4000] Training [23/39] Loss: 0.00506 +Epoch [3431/4000] Training [24/39] Loss: 0.00684 +Epoch [3431/4000] Training [25/39] Loss: 0.00668 +Epoch [3431/4000] Training [26/39] Loss: 0.00761 +Epoch [3431/4000] Training [27/39] Loss: 0.00741 +Epoch [3431/4000] Training [28/39] Loss: 0.00656 +Epoch [3431/4000] Training [29/39] Loss: 0.00392 +Epoch [3431/4000] Training [30/39] Loss: 0.00565 +Epoch [3431/4000] Training [31/39] Loss: 0.00434 +Epoch [3431/4000] Training [32/39] Loss: 0.00646 +Epoch [3431/4000] Training [33/39] Loss: 0.00963 +Epoch [3431/4000] Training [34/39] Loss: 0.12770 +Epoch [3431/4000] Training [35/39] Loss: 0.00585 +Epoch [3431/4000] Training [36/39] Loss: 0.00500 +Epoch [3431/4000] Training [37/39] Loss: 0.00849 +Epoch [3431/4000] Training [38/39] Loss: 0.00465 +Epoch [3431/4000] Training [39/39] Loss: 0.00633 +Epoch [3431/4000] Training metric {'Train/mean dice_metric': 0.9949770569801331, 'Train/mean miou_metric': 0.9912410378456116, 'Train/mean f1': 0.9966124892234802, 'Train/mean precision': 0.9961828589439392, 'Train/mean recall': 0.9970423579216003, 'Train/mean hd95_metric': 1.0300060510635376} +Epoch [3431/4000] Validation [1/10] Loss: 0.64920 focal_loss 0.56508 dice_loss 0.08412 +Epoch [3431/4000] Validation [2/10] Loss: 0.45896 focal_loss 0.36830 dice_loss 0.09066 +Epoch [3431/4000] Validation [3/10] Loss: 0.35748 focal_loss 0.24792 dice_loss 0.10956 +Epoch [3431/4000] Validation [4/10] Loss: 0.87575 focal_loss 0.28232 dice_loss 0.59343 +Epoch [3431/4000] Validation [5/10] Loss: 2.96364 focal_loss 2.29089 dice_loss 0.67274 +Epoch [3431/4000] Validation [6/10] Loss: 1.24341 focal_loss 0.53119 dice_loss 0.71222 +Epoch [3431/4000] Validation [7/10] Loss: 1.12467 focal_loss 0.47445 dice_loss 0.65022 +Epoch [3431/4000] Validation [8/10] Loss: 2.37031 focal_loss 1.74771 dice_loss 0.62260 +Epoch [3431/4000] Validation [9/10] Loss: 1.27537 focal_loss 0.74749 dice_loss 0.52788 +Epoch [3431/4000] Validation [10/10] Loss: 1.74226 focal_loss 1.01563 dice_loss 0.72663 +Epoch [3431/4000] Validation metric {'Val/mean dice_metric': 0.9502110481262207, 'Val/mean miou_metric': 0.9340721964836121, 'Val/mean f1': 0.949971079826355, 'Val/mean precision': 0.9466950297355652, 'Val/mean recall': 0.9532697796821594, 'Val/mean hd95_metric': 10.648035049438477} +Cheakpoint... +Epoch [3431/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502110481262207, 'Val/mean miou_metric': 0.9340721964836121, 'Val/mean f1': 0.949971079826355, 'Val/mean precision': 0.9466950297355652, 'Val/mean recall': 0.9532697796821594, 'Val/mean hd95_metric': 10.648035049438477} +Epoch [3432/4000] Training [1/39] Loss: 0.00559 +Epoch [3432/4000] Training [2/39] Loss: 0.00525 +Epoch [3432/4000] Training [3/39] Loss: 0.00501 +Epoch [3432/4000] Training [4/39] Loss: 0.00473 +Epoch [3432/4000] Training [5/39] Loss: 0.00619 +Epoch [3432/4000] Training [6/39] Loss: 0.00365 +Epoch [3432/4000] Training [7/39] Loss: 0.25299 +Epoch [3432/4000] Training [8/39] Loss: 0.00596 +Epoch [3432/4000] Training [9/39] Loss: 0.00616 +Epoch [3432/4000] Training [10/39] Loss: 0.12843 +Epoch [3432/4000] Training [11/39] Loss: 0.25324 +Epoch [3432/4000] Training [12/39] Loss: 0.00592 +Epoch [3432/4000] Training [13/39] Loss: 0.12977 +Epoch [3432/4000] Training [14/39] Loss: 0.00523 +Epoch [3432/4000] Training [15/39] Loss: 0.00461 +Epoch [3432/4000] Training [16/39] Loss: 0.00690 +Epoch [3432/4000] Training [17/39] Loss: 0.00922 +Epoch [3432/4000] Training [18/39] Loss: 0.00572 +Epoch [3432/4000] Training [19/39] Loss: 0.00427 +Epoch [3432/4000] Training [20/39] Loss: 0.00590 +Epoch [3432/4000] Training [21/39] Loss: 0.00661 +Epoch [3432/4000] Training [22/39] Loss: 0.00307 +Epoch [3432/4000] Training [23/39] Loss: 0.00568 +Epoch [3432/4000] Training [24/39] Loss: 0.00611 +Epoch [3432/4000] Training [25/39] Loss: 0.12835 +Epoch [3432/4000] Training [26/39] Loss: 0.25249 +Epoch [3432/4000] Training [27/39] Loss: 0.12751 +Epoch [3432/4000] Training [28/39] Loss: 0.00428 +Epoch [3432/4000] Training [29/39] Loss: 0.00771 +Epoch [3432/4000] Training [30/39] Loss: 0.00473 +Epoch [3432/4000] Training [31/39] Loss: 0.12913 +Epoch [3432/4000] Training [32/39] Loss: 0.25245 +Epoch [3432/4000] Training [33/39] Loss: 0.00742 +Epoch [3432/4000] Training [34/39] Loss: 0.00800 +Epoch [3432/4000] Training [35/39] Loss: 0.00545 +Epoch [3432/4000] Training [36/39] Loss: 0.01002 +Epoch [3432/4000] Training [37/39] Loss: 0.00628 +Epoch [3432/4000] Training [38/39] Loss: 0.00343 +Epoch [3432/4000] Training [39/39] Loss: 0.00447 +Epoch [3432/4000] Training metric {'Train/mean dice_metric': 0.9959046244621277, 'Train/mean miou_metric': 0.9922773241996765, 'Train/mean f1': 0.9966232776641846, 'Train/mean precision': 0.9961214065551758, 'Train/mean recall': 0.9971256852149963, 'Train/mean hd95_metric': 1.0168685913085938} +Epoch [3432/4000] Validation [1/10] Loss: 0.63615 focal_loss 0.55479 dice_loss 0.08136 +Epoch [3432/4000] Validation [2/10] Loss: 0.47450 focal_loss 0.37346 dice_loss 0.10104 +Epoch [3432/4000] Validation [3/10] Loss: 0.38959 focal_loss 0.27768 dice_loss 0.11191 +Epoch [3432/4000] Validation [4/10] Loss: 0.86311 focal_loss 0.26661 dice_loss 0.59650 +Epoch [3432/4000] Validation [5/10] Loss: 2.96703 focal_loss 2.29382 dice_loss 0.67321 +Epoch [3432/4000] Validation [6/10] Loss: 1.22480 focal_loss 0.50792 dice_loss 0.71689 +Epoch [3432/4000] Validation [7/10] Loss: 1.08132 focal_loss 0.43086 dice_loss 0.65046 +Epoch [3432/4000] Validation [8/10] Loss: 2.60947 focal_loss 1.95782 dice_loss 0.65164 +Epoch [3432/4000] Validation [9/10] Loss: 1.34622 focal_loss 0.81037 dice_loss 0.53585 +Epoch [3432/4000] Validation [10/10] Loss: 1.65588 focal_loss 0.93664 dice_loss 0.71924 +Epoch [3432/4000] Validation metric {'Val/mean dice_metric': 0.950855016708374, 'Val/mean miou_metric': 0.9348002076148987, 'Val/mean f1': 0.9502537250518799, 'Val/mean precision': 0.9507265090942383, 'Val/mean recall': 0.9497813582420349, 'Val/mean hd95_metric': 10.295984268188477} +Cheakpoint... +Epoch [3432/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950855016708374, 'Val/mean miou_metric': 0.9348002076148987, 'Val/mean f1': 0.9502537250518799, 'Val/mean precision': 0.9507265090942383, 'Val/mean recall': 0.9497813582420349, 'Val/mean hd95_metric': 10.295984268188477} +Epoch [3433/4000] Training [1/39] Loss: 0.00668 +Epoch [3433/4000] Training [2/39] Loss: 0.00601 +Epoch [3433/4000] Training [3/39] Loss: 0.12941 +Epoch [3433/4000] Training [4/39] Loss: 0.00508 +Epoch [3433/4000] Training [5/39] Loss: 0.00548 +Epoch [3433/4000] Training [6/39] Loss: 0.13041 +Epoch [3433/4000] Training [7/39] Loss: 0.00405 +Epoch [3433/4000] Training [8/39] Loss: 0.12937 +Epoch [3433/4000] Training [9/39] Loss: 0.12751 +Epoch [3433/4000] Training [10/39] Loss: 0.12948 +Epoch [3433/4000] Training [11/39] Loss: 0.00551 +Epoch [3433/4000] Training [12/39] Loss: 0.00612 +Epoch [3433/4000] Training [13/39] Loss: 0.00447 +Epoch [3433/4000] Training [14/39] Loss: 0.00396 +Epoch [3433/4000] Training [15/39] Loss: 0.12841 +Epoch [3433/4000] Training [16/39] Loss: 0.00612 +Epoch [3433/4000] Training [17/39] Loss: 0.00386 +Epoch [3433/4000] Training [18/39] Loss: 0.00416 +Epoch [3433/4000] Training [19/39] Loss: 0.00558 +Epoch [3433/4000] Training [20/39] Loss: 0.00490 +Epoch [3433/4000] Training [21/39] Loss: 0.00468 +Epoch [3433/4000] Training [22/39] Loss: 0.00723 +Epoch [3433/4000] Training [23/39] Loss: 0.00626 +Epoch [3433/4000] Training [24/39] Loss: 0.13092 +Epoch [3433/4000] Training [25/39] Loss: 0.04844 +Epoch [3433/4000] Training [26/39] Loss: 0.13179 +Epoch [3433/4000] Training [27/39] Loss: 0.00573 +Epoch [3433/4000] Training [28/39] Loss: 0.00304 +Epoch [3433/4000] Training [29/39] Loss: 0.00514 +Epoch [3433/4000] Training [30/39] Loss: 0.00462 +Epoch [3433/4000] Training [31/39] Loss: 0.00308 +Epoch [3433/4000] Training [32/39] Loss: 0.00492 +Epoch [3433/4000] Training [33/39] Loss: 0.12989 +Epoch [3433/4000] Training [34/39] Loss: 0.00497 +Epoch [3433/4000] Training [35/39] Loss: 0.01211 +Epoch [3433/4000] Training [36/39] Loss: 0.00497 +Epoch [3433/4000] Training [37/39] Loss: 0.00307 +Epoch [3433/4000] Training [38/39] Loss: 0.00455 +Epoch [3433/4000] Training [39/39] Loss: 0.01008 +Epoch [3433/4000] Training metric {'Train/mean dice_metric': 0.995825469493866, 'Train/mean miou_metric': 0.9921086430549622, 'Train/mean f1': 0.9965084195137024, 'Train/mean precision': 0.9960793852806091, 'Train/mean recall': 0.9969378113746643, 'Train/mean hd95_metric': 1.0324872732162476} +Epoch [3433/4000] Validation [1/10] Loss: 0.68703 focal_loss 0.59995 dice_loss 0.08709 +Epoch [3433/4000] Validation [2/10] Loss: 0.45491 focal_loss 0.36070 dice_loss 0.09421 +Epoch [3433/4000] Validation [3/10] Loss: 0.38401 focal_loss 0.27166 dice_loss 0.11234 +Epoch [3433/4000] Validation [4/10] Loss: 0.88972 focal_loss 0.29236 dice_loss 0.59736 +Epoch [3433/4000] Validation [5/10] Loss: 3.02676 focal_loss 2.35366 dice_loss 0.67311 +Epoch [3433/4000] Validation [6/10] Loss: 1.25355 focal_loss 0.53727 dice_loss 0.71628 +Epoch [3433/4000] Validation [7/10] Loss: 1.10791 focal_loss 0.45769 dice_loss 0.65021 +Epoch [3433/4000] Validation [8/10] Loss: 2.51646 focal_loss 1.87509 dice_loss 0.64137 +Epoch [3433/4000] Validation [9/10] Loss: 1.28877 focal_loss 0.75820 dice_loss 0.53057 +Epoch [3433/4000] Validation [10/10] Loss: 1.73680 focal_loss 1.00815 dice_loss 0.72866 +Epoch [3433/4000] Validation metric {'Val/mean dice_metric': 0.9505864977836609, 'Val/mean miou_metric': 0.9344733953475952, 'Val/mean f1': 0.9496868848800659, 'Val/mean precision': 0.9469354152679443, 'Val/mean recall': 0.9524543881416321, 'Val/mean hd95_metric': 10.39803695678711} +Cheakpoint... +Epoch [3433/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505864977836609, 'Val/mean miou_metric': 0.9344733953475952, 'Val/mean f1': 0.9496868848800659, 'Val/mean precision': 0.9469354152679443, 'Val/mean recall': 0.9524543881416321, 'Val/mean hd95_metric': 10.39803695678711} +Epoch [3434/4000] Training [1/39] Loss: 0.00598 +Epoch [3434/4000] Training [2/39] Loss: 0.00393 +Epoch [3434/4000] Training [3/39] Loss: 0.00476 +Epoch [3434/4000] Training [4/39] Loss: 0.12902 +Epoch [3434/4000] Training [5/39] Loss: 0.00517 +Epoch [3434/4000] Training [6/39] Loss: 0.00374 +Epoch [3434/4000] Training [7/39] Loss: 0.00507 +Epoch [3434/4000] Training [8/39] Loss: 0.00483 +Epoch [3434/4000] Training [9/39] Loss: 0.00577 +Epoch [3434/4000] Training [10/39] Loss: 0.00659 +Epoch [3434/4000] Training [11/39] Loss: 0.00315 +Epoch [3434/4000] Training [12/39] Loss: 0.12808 +Epoch [3434/4000] Training [13/39] Loss: 0.00403 +Epoch [3434/4000] Training [14/39] Loss: 0.13044 +Epoch [3434/4000] Training [15/39] Loss: 0.12768 +Epoch [3434/4000] Training [16/39] Loss: 0.00482 +Epoch [3434/4000] Training [17/39] Loss: 0.00575 +Epoch [3434/4000] Training [18/39] Loss: 0.00773 +Epoch [3434/4000] Training [19/39] Loss: 0.00455 +Epoch [3434/4000] Training [20/39] Loss: 0.00437 +Epoch [3434/4000] Training [21/39] Loss: 0.13066 +Epoch [3434/4000] Training [22/39] Loss: 0.00510 +Epoch [3434/4000] Training [23/39] Loss: 0.00467 +Epoch [3434/4000] Training [24/39] Loss: 0.00300 +Epoch [3434/4000] Training [25/39] Loss: 0.13018 +Epoch [3434/4000] Training [26/39] Loss: 0.00414 +Epoch [3434/4000] Training [27/39] Loss: 0.00527 +Epoch [3434/4000] Training [28/39] Loss: 0.00935 +Epoch [3434/4000] Training [29/39] Loss: 0.12986 +Epoch [3434/4000] Training [30/39] Loss: 0.00639 +Epoch [3434/4000] Training [31/39] Loss: 0.00399 +Epoch [3434/4000] Training [32/39] Loss: 0.13139 +Epoch [3434/4000] Training [33/39] Loss: 0.00721 +Epoch [3434/4000] Training [34/39] Loss: 0.00567 +Epoch [3434/4000] Training [35/39] Loss: 0.13080 +Epoch [3434/4000] Training [36/39] Loss: 0.00566 +Epoch [3434/4000] Training [37/39] Loss: 0.00763 +Epoch [3434/4000] Training [38/39] Loss: 0.25354 +Epoch [3434/4000] Training [39/39] Loss: 0.12875 +Epoch [3434/4000] Training metric {'Train/mean dice_metric': 0.9959253072738647, 'Train/mean miou_metric': 0.9923040866851807, 'Train/mean f1': 0.9966265559196472, 'Train/mean precision': 0.996117115020752, 'Train/mean recall': 0.9971364140510559, 'Train/mean hd95_metric': 1.0196683406829834} +Epoch [3434/4000] Validation [1/10] Loss: 0.69742 focal_loss 0.60911 dice_loss 0.08831 +Epoch [3434/4000] Validation [2/10] Loss: 0.46597 focal_loss 0.37475 dice_loss 0.09122 +Epoch [3434/4000] Validation [3/10] Loss: 0.37786 focal_loss 0.26794 dice_loss 0.10992 +Epoch [3434/4000] Validation [4/10] Loss: 0.89709 focal_loss 0.30371 dice_loss 0.59338 +Epoch [3434/4000] Validation [5/10] Loss: 3.07309 focal_loss 2.40084 dice_loss 0.67224 +Epoch [3434/4000] Validation [6/10] Loss: 1.28395 focal_loss 0.57144 dice_loss 0.71251 +Epoch [3434/4000] Validation [7/10] Loss: 1.13511 focal_loss 0.48486 dice_loss 0.65026 +Epoch [3434/4000] Validation [8/10] Loss: 2.50113 focal_loss 1.86322 dice_loss 0.63792 +Epoch [3434/4000] Validation [9/10] Loss: 1.35352 focal_loss 0.81383 dice_loss 0.53969 +Epoch [3434/4000] Validation [10/10] Loss: 1.79797 focal_loss 1.06559 dice_loss 0.73238 +Epoch [3434/4000] Validation metric {'Val/mean dice_metric': 0.9507437944412231, 'Val/mean miou_metric': 0.9346357583999634, 'Val/mean f1': 0.9493027329444885, 'Val/mean precision': 0.945720374584198, 'Val/mean recall': 0.9529123902320862, 'Val/mean hd95_metric': 10.62679672241211} +Cheakpoint... +Epoch [3434/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507437944412231, 'Val/mean miou_metric': 0.9346357583999634, 'Val/mean f1': 0.9493027329444885, 'Val/mean precision': 0.945720374584198, 'Val/mean recall': 0.9529123902320862, 'Val/mean hd95_metric': 10.62679672241211} +Epoch [3435/4000] Training [1/39] Loss: 0.00447 +Epoch [3435/4000] Training [2/39] Loss: 0.00540 +Epoch [3435/4000] Training [3/39] Loss: 0.00567 +Epoch [3435/4000] Training [4/39] Loss: 0.01262 +Epoch [3435/4000] Training [5/39] Loss: 0.00551 +Epoch [3435/4000] Training [6/39] Loss: 0.00709 +Epoch [3435/4000] Training [7/39] Loss: 0.00557 +Epoch [3435/4000] Training [8/39] Loss: 0.00567 +Epoch [3435/4000] Training [9/39] Loss: 0.00352 +Epoch [3435/4000] Training [10/39] Loss: 0.00559 +Epoch [3435/4000] Training [11/39] Loss: 0.00537 +Epoch [3435/4000] Training [12/39] Loss: 0.00625 +Epoch [3435/4000] Training [13/39] Loss: 0.00413 +Epoch [3435/4000] Training [14/39] Loss: 0.13118 +Epoch [3435/4000] Training [15/39] Loss: 0.00392 +Epoch [3435/4000] Training [16/39] Loss: 0.00445 +Epoch [3435/4000] Training [17/39] Loss: 0.00452 +Epoch [3435/4000] Training [18/39] Loss: 0.00379 +Epoch [3435/4000] Training [19/39] Loss: 0.13213 +Epoch [3435/4000] Training [20/39] Loss: 0.00561 +Epoch [3435/4000] Training [21/39] Loss: 0.00378 +Epoch [3435/4000] Training [22/39] Loss: 0.12883 +Epoch [3435/4000] Training [23/39] Loss: 0.00385 +Epoch [3435/4000] Training [24/39] Loss: 0.00799 +Epoch [3435/4000] Training [25/39] Loss: 0.09320 +Epoch [3435/4000] Training [26/39] Loss: 0.00490 +Epoch [3435/4000] Training [27/39] Loss: 0.12962 +Epoch [3435/4000] Training [28/39] Loss: 0.00331 +Epoch [3435/4000] Training [29/39] Loss: 0.12982 +Epoch [3435/4000] Training [30/39] Loss: 0.00515 +Epoch [3435/4000] Training [31/39] Loss: 0.25449 +Epoch [3435/4000] Training [32/39] Loss: 0.00545 +Epoch [3435/4000] Training [33/39] Loss: 0.00504 +Epoch [3435/4000] Training [34/39] Loss: 0.00631 +Epoch [3435/4000] Training [35/39] Loss: 0.00611 +Epoch [3435/4000] Training [36/39] Loss: 0.12965 +Epoch [3435/4000] Training [37/39] Loss: 0.00475 +Epoch [3435/4000] Training [38/39] Loss: 0.00580 +Epoch [3435/4000] Training [39/39] Loss: 0.00336 +Epoch [3435/4000] Training metric {'Train/mean dice_metric': 0.9958118796348572, 'Train/mean miou_metric': 0.9921067357063293, 'Train/mean f1': 0.9965753555297852, 'Train/mean precision': 0.9961344599723816, 'Train/mean recall': 0.9970166087150574, 'Train/mean hd95_metric': 1.05865478515625} +Epoch [3435/4000] Validation [1/10] Loss: 0.67002 focal_loss 0.58460 dice_loss 0.08541 +Epoch [3435/4000] Validation [2/10] Loss: 0.45323 focal_loss 0.36020 dice_loss 0.09304 +Epoch [3435/4000] Validation [3/10] Loss: 0.37695 focal_loss 0.26615 dice_loss 0.11081 +Epoch [3435/4000] Validation [4/10] Loss: 0.88502 focal_loss 0.29575 dice_loss 0.58927 +Epoch [3435/4000] Validation [5/10] Loss: 2.95643 focal_loss 2.28361 dice_loss 0.67282 +Epoch [3435/4000] Validation [6/10] Loss: 1.24199 focal_loss 0.53393 dice_loss 0.70806 +Epoch [3435/4000] Validation [7/10] Loss: 1.08719 focal_loss 0.43644 dice_loss 0.65075 +Epoch [3435/4000] Validation [8/10] Loss: 2.38176 focal_loss 1.74646 dice_loss 0.63530 +Epoch [3435/4000] Validation [9/10] Loss: 1.33526 focal_loss 0.79501 dice_loss 0.54026 +Epoch [3435/4000] Validation [10/10] Loss: 1.70163 focal_loss 0.97593 dice_loss 0.72570 +Epoch [3435/4000] Validation metric {'Val/mean dice_metric': 0.9506382346153259, 'Val/mean miou_metric': 0.9344387650489807, 'Val/mean f1': 0.9499233961105347, 'Val/mean precision': 0.9478073716163635, 'Val/mean recall': 0.9520488381385803, 'Val/mean hd95_metric': 10.551688194274902} +Cheakpoint... +Epoch [3435/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506382346153259, 'Val/mean miou_metric': 0.9344387650489807, 'Val/mean f1': 0.9499233961105347, 'Val/mean precision': 0.9478073716163635, 'Val/mean recall': 0.9520488381385803, 'Val/mean hd95_metric': 10.551688194274902} +Epoch [3436/4000] Training [1/39] Loss: 0.00621 +Epoch [3436/4000] Training [2/39] Loss: 0.00290 +Epoch [3436/4000] Training [3/39] Loss: 0.00724 +Epoch [3436/4000] Training [4/39] Loss: 0.00435 +Epoch [3436/4000] Training [5/39] Loss: 0.00400 +Epoch [3436/4000] Training [6/39] Loss: 0.00687 +Epoch [3436/4000] Training [7/39] Loss: 0.00578 +Epoch [3436/4000] Training [8/39] Loss: 0.00558 +Epoch [3436/4000] Training [9/39] Loss: 0.00672 +Epoch [3436/4000] Training [10/39] Loss: 0.00575 +Epoch [3436/4000] Training [11/39] Loss: 0.00641 +Epoch [3436/4000] Training [12/39] Loss: 0.00633 +Epoch [3436/4000] Training [13/39] Loss: 0.00484 +Epoch [3436/4000] Training [14/39] Loss: 0.00354 +Epoch [3436/4000] Training [15/39] Loss: 0.12798 +Epoch [3436/4000] Training [16/39] Loss: 0.00324 +Epoch [3436/4000] Training [17/39] Loss: 0.00402 +Epoch [3436/4000] Training [18/39] Loss: 0.12908 +Epoch [3436/4000] Training [19/39] Loss: 0.00603 +Epoch [3436/4000] Training [20/39] Loss: 0.00506 +Epoch [3436/4000] Training [21/39] Loss: 0.00429 +Epoch [3436/4000] Training [22/39] Loss: 0.12800 +Epoch [3436/4000] Training [23/39] Loss: 0.00618 +Epoch [3436/4000] Training [24/39] Loss: 0.00432 +Epoch [3436/4000] Training [25/39] Loss: 0.00445 +Epoch [3436/4000] Training [26/39] Loss: 0.12997 +Epoch [3436/4000] Training [27/39] Loss: 0.12892 +Epoch [3436/4000] Training [28/39] Loss: 0.00480 +Epoch [3436/4000] Training [29/39] Loss: 0.00475 +Epoch [3436/4000] Training [30/39] Loss: 0.21232 +Epoch [3436/4000] Training [31/39] Loss: 0.00506 +Epoch [3436/4000] Training [32/39] Loss: 0.00522 +Epoch [3436/4000] Training [33/39] Loss: 0.00531 +Epoch [3436/4000] Training [34/39] Loss: 0.00718 +Epoch [3436/4000] Training [35/39] Loss: 0.00887 +Epoch [3436/4000] Training [36/39] Loss: 0.00835 +Epoch [3436/4000] Training [37/39] Loss: 0.00942 +Epoch [3436/4000] Training [38/39] Loss: 0.00733 +Epoch [3436/4000] Training [39/39] Loss: 0.12920 +Epoch [3436/4000] Training metric {'Train/mean dice_metric': 0.9960732460021973, 'Train/mean miou_metric': 0.9926008582115173, 'Train/mean f1': 0.9967748522758484, 'Train/mean precision': 0.9963467121124268, 'Train/mean recall': 0.9972032904624939, 'Train/mean hd95_metric': 1.1541852951049805} +Epoch [3436/4000] Validation [1/10] Loss: 0.69600 focal_loss 0.60500 dice_loss 0.09099 +Epoch [3436/4000] Validation [2/10] Loss: 0.45421 focal_loss 0.36611 dice_loss 0.08810 +Epoch [3436/4000] Validation [3/10] Loss: 0.35851 focal_loss 0.24881 dice_loss 0.10970 +Epoch [3436/4000] Validation [4/10] Loss: 0.92293 focal_loss 0.32222 dice_loss 0.60071 +Epoch [3436/4000] Validation [5/10] Loss: 2.94586 focal_loss 2.27327 dice_loss 0.67258 +Epoch [3436/4000] Validation [6/10] Loss: 1.27589 focal_loss 0.56515 dice_loss 0.71074 +Epoch [3436/4000] Validation [7/10] Loss: 1.13126 focal_loss 0.47659 dice_loss 0.65467 +Epoch [3436/4000] Validation [8/10] Loss: 2.34789 focal_loss 1.72156 dice_loss 0.62633 +Epoch [3436/4000] Validation [9/10] Loss: 1.33481 focal_loss 0.79419 dice_loss 0.54062 +Epoch [3436/4000] Validation [10/10] Loss: 1.80453 focal_loss 1.07243 dice_loss 0.73210 +Epoch [3436/4000] Validation metric {'Val/mean dice_metric': 0.9510314464569092, 'Val/mean miou_metric': 0.9349657893180847, 'Val/mean f1': 0.9493653178215027, 'Val/mean precision': 0.9445275664329529, 'Val/mean recall': 0.9542527198791504, 'Val/mean hd95_metric': 10.777229309082031} +Cheakpoint... +Epoch [3436/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510314464569092, 'Val/mean miou_metric': 0.9349657893180847, 'Val/mean f1': 0.9493653178215027, 'Val/mean precision': 0.9445275664329529, 'Val/mean recall': 0.9542527198791504, 'Val/mean hd95_metric': 10.777229309082031} +Epoch [3437/4000] Training [1/39] Loss: 0.00704 +Epoch [3437/4000] Training [2/39] Loss: 0.00514 +Epoch [3437/4000] Training [3/39] Loss: 0.00587 +Epoch [3437/4000] Training [4/39] Loss: 0.00622 +Epoch [3437/4000] Training [5/39] Loss: 0.12832 +Epoch [3437/4000] Training [6/39] Loss: 0.00841 +Epoch [3437/4000] Training [7/39] Loss: 0.00332 +Epoch [3437/4000] Training [8/39] Loss: 0.13576 +Epoch [3437/4000] Training [9/39] Loss: 0.13013 +Epoch [3437/4000] Training [10/39] Loss: 0.13209 +Epoch [3437/4000] Training [11/39] Loss: 0.00645 +Epoch [3437/4000] Training [12/39] Loss: 0.00705 +Epoch [3437/4000] Training [13/39] Loss: 0.00485 +Epoch [3437/4000] Training [14/39] Loss: 0.00570 +Epoch [3437/4000] Training [15/39] Loss: 0.00493 +Epoch [3437/4000] Training [16/39] Loss: 0.00428 +Epoch [3437/4000] Training [17/39] Loss: 0.12868 +Epoch [3437/4000] Training [18/39] Loss: 0.00541 +Epoch [3437/4000] Training [19/39] Loss: 0.00458 +Epoch [3437/4000] Training [20/39] Loss: 0.00464 +Epoch [3437/4000] Training [21/39] Loss: 0.00572 +Epoch [3437/4000] Training [22/39] Loss: 0.00302 +Epoch [3437/4000] Training [23/39] Loss: 0.00617 +Epoch [3437/4000] Training [24/39] Loss: 0.00239 +Epoch [3437/4000] Training [25/39] Loss: 0.13218 +Epoch [3437/4000] Training [26/39] Loss: 0.00591 +Epoch [3437/4000] Training [27/39] Loss: 0.00345 +Epoch [3437/4000] Training [28/39] Loss: 0.00540 +Epoch [3437/4000] Training [29/39] Loss: 0.12946 +Epoch [3437/4000] Training [30/39] Loss: 0.12920 +Epoch [3437/4000] Training [31/39] Loss: 0.12819 +Epoch [3437/4000] Training [32/39] Loss: 0.00393 +Epoch [3437/4000] Training [33/39] Loss: 0.00467 +Epoch [3437/4000] Training [34/39] Loss: 0.01268 +Epoch [3437/4000] Training [35/39] Loss: 0.00682 +Epoch [3437/4000] Training [36/39] Loss: 0.00431 +Epoch [3437/4000] Training [37/39] Loss: 0.00562 +Epoch [3437/4000] Training [38/39] Loss: 0.00420 +Epoch [3437/4000] Training [39/39] Loss: 0.13485 +Epoch [3437/4000] Training metric {'Train/mean dice_metric': 0.9956225156784058, 'Train/mean miou_metric': 0.9918407797813416, 'Train/mean f1': 0.9963322281837463, 'Train/mean precision': 0.9959527850151062, 'Train/mean recall': 0.9967119097709656, 'Train/mean hd95_metric': 1.0671530961990356} +Epoch [3437/4000] Validation [1/10] Loss: 0.77882 focal_loss 0.68175 dice_loss 0.09707 +Epoch [3437/4000] Validation [2/10] Loss: 0.45744 focal_loss 0.36944 dice_loss 0.08800 +Epoch [3437/4000] Validation [3/10] Loss: 0.32831 focal_loss 0.21984 dice_loss 0.10847 +Epoch [3437/4000] Validation [4/10] Loss: 0.87897 focal_loss 0.31125 dice_loss 0.56772 +Epoch [3437/4000] Validation [5/10] Loss: 2.97878 focal_loss 2.30806 dice_loss 0.67072 +Epoch [3437/4000] Validation [6/10] Loss: 1.29914 focal_loss 0.59004 dice_loss 0.70910 +Epoch [3437/4000] Validation [7/10] Loss: 1.20560 focal_loss 0.54288 dice_loss 0.66272 +Epoch [3437/4000] Validation [8/10] Loss: 1.84386 focal_loss 1.28622 dice_loss 0.55764 +Epoch [3437/4000] Validation [9/10] Loss: 1.29483 focal_loss 0.76041 dice_loss 0.53443 +Epoch [3437/4000] Validation [10/10] Loss: 1.95065 focal_loss 1.20858 dice_loss 0.74206 +Epoch [3437/4000] Validation metric {'Val/mean dice_metric': 0.9508111476898193, 'Val/mean miou_metric': 0.9346325993537903, 'Val/mean f1': 0.947316586971283, 'Val/mean precision': 0.9358930587768555, 'Val/mean recall': 0.959022581577301, 'Val/mean hd95_metric': 10.875908851623535} +Cheakpoint... +Epoch [3437/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508111476898193, 'Val/mean miou_metric': 0.9346325993537903, 'Val/mean f1': 0.947316586971283, 'Val/mean precision': 0.9358930587768555, 'Val/mean recall': 0.959022581577301, 'Val/mean hd95_metric': 10.875908851623535} +Epoch [3438/4000] Training [1/39] Loss: 0.00954 +Epoch [3438/4000] Training [2/39] Loss: 0.00419 +Epoch [3438/4000] Training [3/39] Loss: 0.00328 +Epoch [3438/4000] Training [4/39] Loss: 0.00692 +Epoch [3438/4000] Training [5/39] Loss: 0.00401 +Epoch [3438/4000] Training [6/39] Loss: 0.01164 +Epoch [3438/4000] Training [7/39] Loss: 0.13132 +Epoch [3438/4000] Training [8/39] Loss: 0.00380 +Epoch [3438/4000] Training [9/39] Loss: 0.00807 +Epoch [3438/4000] Training [10/39] Loss: 0.00362 +Epoch [3438/4000] Training [11/39] Loss: 0.12905 +Epoch [3438/4000] Training [12/39] Loss: 0.00539 +Epoch [3438/4000] Training [13/39] Loss: 0.00425 +Epoch [3438/4000] Training [14/39] Loss: 0.13339 +Epoch [3438/4000] Training [15/39] Loss: 0.00310 +Epoch [3438/4000] Training [16/39] Loss: 0.00601 +Epoch [3438/4000] Training [17/39] Loss: 0.00585 +Epoch [3438/4000] Training [18/39] Loss: 0.00533 +Epoch [3438/4000] Training [19/39] Loss: 0.00659 +Epoch [3438/4000] Training [20/39] Loss: 0.00625 +Epoch [3438/4000] Training [21/39] Loss: 0.00376 +Epoch [3438/4000] Training [22/39] Loss: 0.00661 +Epoch [3438/4000] Training [23/39] Loss: 0.00531 +Epoch [3438/4000] Training [24/39] Loss: 0.12817 +Epoch [3438/4000] Training [25/39] Loss: 0.00581 +Epoch [3438/4000] Training [26/39] Loss: 0.00812 +Epoch [3438/4000] Training [27/39] Loss: 0.00741 +Epoch [3438/4000] Training [28/39] Loss: 0.00453 +Epoch [3438/4000] Training [29/39] Loss: 0.13612 +Epoch [3438/4000] Training [30/39] Loss: 0.13109 +Epoch [3438/4000] Training [31/39] Loss: 0.00347 +Epoch [3438/4000] Training [32/39] Loss: 0.00525 +Epoch [3438/4000] Training [33/39] Loss: 0.00540 +Epoch [3438/4000] Training [34/39] Loss: 0.13177 +Epoch [3438/4000] Training [35/39] Loss: 0.00331 +Epoch [3438/4000] Training [36/39] Loss: 0.00673 +Epoch [3438/4000] Training [37/39] Loss: 0.12881 +Epoch [3438/4000] Training [38/39] Loss: 0.00534 +Epoch [3438/4000] Training [39/39] Loss: 0.17165 +Epoch [3438/4000] Training metric {'Train/mean dice_metric': 0.9955586194992065, 'Train/mean miou_metric': 0.9916157722473145, 'Train/mean f1': 0.9963164329528809, 'Train/mean precision': 0.9958311319351196, 'Train/mean recall': 0.9968021512031555, 'Train/mean hd95_metric': 1.015048861503601} +Epoch [3438/4000] Validation [1/10] Loss: 0.72437 focal_loss 0.63088 dice_loss 0.09349 +Epoch [3438/4000] Validation [2/10] Loss: 0.45088 focal_loss 0.36079 dice_loss 0.09009 +Epoch [3438/4000] Validation [3/10] Loss: 0.36510 focal_loss 0.25403 dice_loss 0.11107 +Epoch [3438/4000] Validation [4/10] Loss: 0.85210 focal_loss 0.28942 dice_loss 0.56268 +Epoch [3438/4000] Validation [5/10] Loss: 3.04183 focal_loss 2.37011 dice_loss 0.67172 +Epoch [3438/4000] Validation [6/10] Loss: 1.25983 focal_loss 0.55065 dice_loss 0.70918 +Epoch [3438/4000] Validation [7/10] Loss: 1.16509 focal_loss 0.50713 dice_loss 0.65796 +Epoch [3438/4000] Validation [8/10] Loss: 2.14134 focal_loss 1.53451 dice_loss 0.60684 +Epoch [3438/4000] Validation [9/10] Loss: 1.33601 focal_loss 0.79570 dice_loss 0.54031 +Epoch [3438/4000] Validation [10/10] Loss: 1.84573 focal_loss 1.10773 dice_loss 0.73800 +Epoch [3438/4000] Validation metric {'Val/mean dice_metric': 0.9506771564483643, 'Val/mean miou_metric': 0.9343482255935669, 'Val/mean f1': 0.9485218524932861, 'Val/mean precision': 0.9420164823532104, 'Val/mean recall': 0.9551177024841309, 'Val/mean hd95_metric': 10.540647506713867} +Cheakpoint... +Epoch [3438/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506771564483643, 'Val/mean miou_metric': 0.9343482255935669, 'Val/mean f1': 0.9485218524932861, 'Val/mean precision': 0.9420164823532104, 'Val/mean recall': 0.9551177024841309, 'Val/mean hd95_metric': 10.540647506713867} +Epoch [3439/4000] Training [1/39] Loss: 0.00653 +Epoch [3439/4000] Training [2/39] Loss: 0.00436 +Epoch [3439/4000] Training [3/39] Loss: 0.00483 +Epoch [3439/4000] Training [4/39] Loss: 0.00757 +Epoch [3439/4000] Training [5/39] Loss: 0.00518 +Epoch [3439/4000] Training [6/39] Loss: 0.00479 +Epoch [3439/4000] Training [7/39] Loss: 0.00545 +Epoch [3439/4000] Training [8/39] Loss: 0.12887 +Epoch [3439/4000] Training [9/39] Loss: 0.00648 +Epoch [3439/4000] Training [10/39] Loss: 0.13037 +Epoch [3439/4000] Training [11/39] Loss: 0.00842 +Epoch [3439/4000] Training [12/39] Loss: 0.00767 +Epoch [3439/4000] Training [13/39] Loss: 0.00610 +Epoch [3439/4000] Training [14/39] Loss: 0.12817 +Epoch [3439/4000] Training [15/39] Loss: 0.13171 +Epoch [3439/4000] Training [16/39] Loss: 0.04452 +Epoch [3439/4000] Training [17/39] Loss: 0.00455 +Epoch [3439/4000] Training [18/39] Loss: 0.00702 +Epoch [3439/4000] Training [19/39] Loss: 0.01079 +Epoch [3439/4000] Training [20/39] Loss: 0.13000 +Epoch [3439/4000] Training [21/39] Loss: 0.13037 +Epoch [3439/4000] Training [22/39] Loss: 0.00617 +Epoch [3439/4000] Training [23/39] Loss: 0.00496 +Epoch [3439/4000] Training [24/39] Loss: 0.00736 +Epoch [3439/4000] Training [25/39] Loss: 0.13107 +Epoch [3439/4000] Training [26/39] Loss: 0.00564 +Epoch [3439/4000] Training [27/39] Loss: 0.00472 +Epoch [3439/4000] Training [28/39] Loss: 0.08771 +Epoch [3439/4000] Training [29/39] Loss: 0.13105 +Epoch [3439/4000] Training [30/39] Loss: 0.00538 +Epoch [3439/4000] Training [31/39] Loss: 0.12911 +Epoch [3439/4000] Training [32/39] Loss: 0.13237 +Epoch [3439/4000] Training [33/39] Loss: 0.00816 +Epoch [3439/4000] Training [34/39] Loss: 0.00486 +Epoch [3439/4000] Training [35/39] Loss: 0.00882 +Epoch [3439/4000] Training [36/39] Loss: 0.00570 +Epoch [3439/4000] Training [37/39] Loss: 0.12999 +Epoch [3439/4000] Training [38/39] Loss: 0.00771 +Epoch [3439/4000] Training [39/39] Loss: 0.00315 +Epoch [3439/4000] Training metric {'Train/mean dice_metric': 0.9945953488349915, 'Train/mean miou_metric': 0.9904907941818237, 'Train/mean f1': 0.9961254000663757, 'Train/mean precision': 0.9954514503479004, 'Train/mean recall': 0.9968003630638123, 'Train/mean hd95_metric': 1.077490210533142} +Epoch [3439/4000] Validation [1/10] Loss: 0.69996 focal_loss 0.60691 dice_loss 0.09305 +Epoch [3439/4000] Validation [2/10] Loss: 0.47310 focal_loss 0.37271 dice_loss 0.10039 +Epoch [3439/4000] Validation [3/10] Loss: 0.42760 focal_loss 0.31213 dice_loss 0.11547 +Epoch [3439/4000] Validation [4/10] Loss: 0.89446 focal_loss 0.29540 dice_loss 0.59906 +Epoch [3439/4000] Validation [5/10] Loss: 2.91317 focal_loss 2.23945 dice_loss 0.67372 +Epoch [3439/4000] Validation [6/10] Loss: 1.21589 focal_loss 0.51252 dice_loss 0.70337 +Epoch [3439/4000] Validation [7/10] Loss: 1.02215 focal_loss 0.37184 dice_loss 0.65031 +Epoch [3439/4000] Validation [8/10] Loss: 3.37038 focal_loss 2.68538 dice_loss 0.68500 +Epoch [3439/4000] Validation [9/10] Loss: 1.49305 focal_loss 0.94220 dice_loss 0.55085 +Epoch [3439/4000] Validation [10/10] Loss: 1.70920 focal_loss 0.98631 dice_loss 0.72289 +Epoch [3439/4000] Validation metric {'Val/mean dice_metric': 0.9495384097099304, 'Val/mean miou_metric': 0.9328656196594238, 'Val/mean f1': 0.949211835861206, 'Val/mean precision': 0.9526802897453308, 'Val/mean recall': 0.9457685947418213, 'Val/mean hd95_metric': 10.435318946838379} +Cheakpoint... +Epoch [3439/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9495], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9495384097099304, 'Val/mean miou_metric': 0.9328656196594238, 'Val/mean f1': 0.949211835861206, 'Val/mean precision': 0.9526802897453308, 'Val/mean recall': 0.9457685947418213, 'Val/mean hd95_metric': 10.435318946838379} +Epoch [3440/4000] Training [1/39] Loss: 0.00513 +Epoch [3440/4000] Training [2/39] Loss: 0.00529 +Epoch [3440/4000] Training [3/39] Loss: 0.00408 +Epoch [3440/4000] Training [4/39] Loss: 0.12977 +Epoch [3440/4000] Training [5/39] Loss: 0.00440 +Epoch [3440/4000] Training [6/39] Loss: 0.00761 +Epoch [3440/4000] Training [7/39] Loss: 0.00591 +Epoch [3440/4000] Training [8/39] Loss: 0.13403 +Epoch [3440/4000] Training [9/39] Loss: 0.00451 +Epoch [3440/4000] Training [10/39] Loss: 0.00562 +Epoch [3440/4000] Training [11/39] Loss: 0.00536 +Epoch [3440/4000] Training [12/39] Loss: 0.00776 +Epoch [3440/4000] Training [13/39] Loss: 0.00430 +Epoch [3440/4000] Training [14/39] Loss: 0.25482 +Epoch [3440/4000] Training [15/39] Loss: 0.00511 +Epoch [3440/4000] Training [16/39] Loss: 0.12915 +Epoch [3440/4000] Training [17/39] Loss: 0.00523 +Epoch [3440/4000] Training [18/39] Loss: 0.00436 +Epoch [3440/4000] Training [19/39] Loss: 0.00593 +Epoch [3440/4000] Training [20/39] Loss: 0.00698 +Epoch [3440/4000] Training [21/39] Loss: 0.00593 +Epoch [3440/4000] Training [22/39] Loss: 0.12810 +Epoch [3440/4000] Training [23/39] Loss: 0.00764 +Epoch [3440/4000] Training [24/39] Loss: 0.13009 +Epoch [3440/4000] Training [25/39] Loss: 0.25449 +Epoch [3440/4000] Training [26/39] Loss: 0.00645 +Epoch [3440/4000] Training [27/39] Loss: 0.00672 +Epoch [3440/4000] Training [28/39] Loss: 0.00444 +Epoch [3440/4000] Training [29/39] Loss: 0.13047 +Epoch [3440/4000] Training [30/39] Loss: 0.12912 +Epoch [3440/4000] Training [31/39] Loss: 0.00448 +Epoch [3440/4000] Training [32/39] Loss: 0.00323 +Epoch [3440/4000] Training [33/39] Loss: 0.00784 +Epoch [3440/4000] Training [34/39] Loss: 0.00603 +Epoch [3440/4000] Training [35/39] Loss: 0.00462 +Epoch [3440/4000] Training [36/39] Loss: 0.12995 +Epoch [3440/4000] Training [37/39] Loss: 0.00431 +Epoch [3440/4000] Training [38/39] Loss: 0.00455 +Epoch [3440/4000] Training [39/39] Loss: 0.12914 +Epoch [3440/4000] Training metric {'Train/mean dice_metric': 0.9957603216171265, 'Train/mean miou_metric': 0.9919758439064026, 'Train/mean f1': 0.996427059173584, 'Train/mean precision': 0.9960324764251709, 'Train/mean recall': 0.9968219995498657, 'Train/mean hd95_metric': 1.0563637018203735} +Epoch [3440/4000] Validation [1/10] Loss: 0.72989 focal_loss 0.63445 dice_loss 0.09544 +Epoch [3440/4000] Validation [2/10] Loss: 0.45742 focal_loss 0.36055 dice_loss 0.09686 +Epoch [3440/4000] Validation [3/10] Loss: 0.40511 focal_loss 0.29055 dice_loss 0.11456 +Epoch [3440/4000] Validation [4/10] Loss: 0.85532 focal_loss 0.27942 dice_loss 0.57591 +Epoch [3440/4000] Validation [5/10] Loss: 2.95897 focal_loss 2.28634 dice_loss 0.67263 +Epoch [3440/4000] Validation [6/10] Loss: 1.22934 focal_loss 0.52308 dice_loss 0.70626 +Epoch [3440/4000] Validation [7/10] Loss: 1.08282 focal_loss 0.43049 dice_loss 0.65234 +Epoch [3440/4000] Validation [8/10] Loss: 3.03174 focal_loss 2.36249 dice_loss 0.66925 +Epoch [3440/4000] Validation [9/10] Loss: 1.43424 focal_loss 0.88563 dice_loss 0.54861 +Epoch [3440/4000] Validation [10/10] Loss: 1.73938 focal_loss 1.01068 dice_loss 0.72869 +Epoch [3440/4000] Validation metric {'Val/mean dice_metric': 0.9510103464126587, 'Val/mean miou_metric': 0.9345560073852539, 'Val/mean f1': 0.949176013469696, 'Val/mean precision': 0.949505090713501, 'Val/mean recall': 0.9488471746444702, 'Val/mean hd95_metric': 10.623495101928711} +Cheakpoint... +Epoch [3440/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510103464126587, 'Val/mean miou_metric': 0.9345560073852539, 'Val/mean f1': 0.949176013469696, 'Val/mean precision': 0.949505090713501, 'Val/mean recall': 0.9488471746444702, 'Val/mean hd95_metric': 10.623495101928711} +Epoch [3441/4000] Training [1/39] Loss: 0.13290 +Epoch [3441/4000] Training [2/39] Loss: 0.12794 +Epoch [3441/4000] Training [3/39] Loss: 0.00570 +Epoch [3441/4000] Training [4/39] Loss: 0.00482 +Epoch [3441/4000] Training [5/39] Loss: 0.00401 +Epoch [3441/4000] Training [6/39] Loss: 0.00549 +Epoch [3441/4000] Training [7/39] Loss: 0.12889 +Epoch [3441/4000] Training [8/39] Loss: 0.12851 +Epoch [3441/4000] Training [9/39] Loss: 0.00657 +Epoch [3441/4000] Training [10/39] Loss: 0.12941 +Epoch [3441/4000] Training [11/39] Loss: 0.25513 +Epoch [3441/4000] Training [12/39] Loss: 0.00409 +Epoch [3441/4000] Training [13/39] Loss: 0.00485 +Epoch [3441/4000] Training [14/39] Loss: 0.00893 +Epoch [3441/4000] Training [15/39] Loss: 0.00683 +Epoch [3441/4000] Training [16/39] Loss: 0.00543 +Epoch [3441/4000] Training [17/39] Loss: 0.00443 +Epoch [3441/4000] Training [18/39] Loss: 0.00641 +Epoch [3441/4000] Training [19/39] Loss: 0.00421 +Epoch [3441/4000] Training [20/39] Loss: 0.13135 +Epoch [3441/4000] Training [21/39] Loss: 0.00664 +Epoch [3441/4000] Training [22/39] Loss: 0.00521 +Epoch [3441/4000] Training [23/39] Loss: 0.00764 +Epoch [3441/4000] Training [24/39] Loss: 0.00313 +Epoch [3441/4000] Training [25/39] Loss: 0.00417 +Epoch [3441/4000] Training [26/39] Loss: 0.00688 +Epoch [3441/4000] Training [27/39] Loss: 0.12873 +Epoch [3441/4000] Training [28/39] Loss: 0.13002 +Epoch [3441/4000] Training [29/39] Loss: 0.00463 +Epoch [3441/4000] Training [30/39] Loss: 0.00415 +Epoch [3441/4000] Training [31/39] Loss: 0.12956 +Epoch [3441/4000] Training [32/39] Loss: 0.00574 +Epoch [3441/4000] Training [33/39] Loss: 0.12882 +Epoch [3441/4000] Training [34/39] Loss: 0.12852 +Epoch [3441/4000] Training [35/39] Loss: 0.00617 +Epoch [3441/4000] Training [36/39] Loss: 0.00704 +Epoch [3441/4000] Training [37/39] Loss: 0.13045 +Epoch [3441/4000] Training [38/39] Loss: 0.00470 +Epoch [3441/4000] Training [39/39] Loss: 0.25371 +Epoch [3441/4000] Training metric {'Train/mean dice_metric': 0.995081901550293, 'Train/mean miou_metric': 0.9914536476135254, 'Train/mean f1': 0.996699869632721, 'Train/mean precision': 0.9962379336357117, 'Train/mean recall': 0.9971622824668884, 'Train/mean hd95_metric': 1.039849877357483} +Epoch [3441/4000] Validation [1/10] Loss: 0.70090 focal_loss 0.61103 dice_loss 0.08987 +Epoch [3441/4000] Validation [2/10] Loss: 0.45263 focal_loss 0.35413 dice_loss 0.09850 +Epoch [3441/4000] Validation [3/10] Loss: 0.41346 focal_loss 0.29846 dice_loss 0.11500 +Epoch [3441/4000] Validation [4/10] Loss: 0.86395 focal_loss 0.27832 dice_loss 0.58563 +Epoch [3441/4000] Validation [5/10] Loss: 3.01810 focal_loss 2.34479 dice_loss 0.67331 +Epoch [3441/4000] Validation [6/10] Loss: 1.21072 focal_loss 0.50495 dice_loss 0.70577 +Epoch [3441/4000] Validation [7/10] Loss: 1.07999 focal_loss 0.42960 dice_loss 0.65039 +Epoch [3441/4000] Validation [8/10] Loss: 2.94595 focal_loss 2.27664 dice_loss 0.66931 +Epoch [3441/4000] Validation [9/10] Loss: 1.39526 focal_loss 0.85260 dice_loss 0.54266 +Epoch [3441/4000] Validation [10/10] Loss: 1.70915 focal_loss 0.98253 dice_loss 0.72662 +Epoch [3441/4000] Validation metric {'Val/mean dice_metric': 0.9502192735671997, 'Val/mean miou_metric': 0.9340043067932129, 'Val/mean f1': 0.9499092102050781, 'Val/mean precision': 0.951441764831543, 'Val/mean recall': 0.9483814835548401, 'Val/mean hd95_metric': 10.627462387084961} +Cheakpoint... +Epoch [3441/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502192735671997, 'Val/mean miou_metric': 0.9340043067932129, 'Val/mean f1': 0.9499092102050781, 'Val/mean precision': 0.951441764831543, 'Val/mean recall': 0.9483814835548401, 'Val/mean hd95_metric': 10.627462387084961} +Epoch [3442/4000] Training [1/39] Loss: 0.00652 +Epoch [3442/4000] Training [2/39] Loss: 0.12949 +Epoch [3442/4000] Training [3/39] Loss: 0.00557 +Epoch [3442/4000] Training [4/39] Loss: 0.00513 +Epoch [3442/4000] Training [5/39] Loss: 0.13116 +Epoch [3442/4000] Training [6/39] Loss: 0.00594 +Epoch [3442/4000] Training [7/39] Loss: 0.12856 +Epoch [3442/4000] Training [8/39] Loss: 0.12790 +Epoch [3442/4000] Training [9/39] Loss: 0.09253 +Epoch [3442/4000] Training [10/39] Loss: 0.00631 +Epoch [3442/4000] Training [11/39] Loss: 0.00794 +Epoch [3442/4000] Training [12/39] Loss: 0.13228 +Epoch [3442/4000] Training [13/39] Loss: 0.00534 +Epoch [3442/4000] Training [14/39] Loss: 0.12766 +Epoch [3442/4000] Training [15/39] Loss: 0.12868 +Epoch [3442/4000] Training [16/39] Loss: 0.00466 +Epoch [3442/4000] Training [17/39] Loss: 0.00589 +Epoch [3442/4000] Training [18/39] Loss: 0.00543 +Epoch [3442/4000] Training [19/39] Loss: 0.12988 +Epoch [3442/4000] Training [20/39] Loss: 0.13065 +Epoch [3442/4000] Training [21/39] Loss: 0.13160 +Epoch [3442/4000] Training [22/39] Loss: 0.00447 +Epoch [3442/4000] Training [23/39] Loss: 0.00391 +Epoch [3442/4000] Training [24/39] Loss: 0.00561 +Epoch [3442/4000] Training [25/39] Loss: 0.00432 +Epoch [3442/4000] Training [26/39] Loss: 0.00510 +Epoch [3442/4000] Training [27/39] Loss: 0.01034 +Epoch [3442/4000] Training [28/39] Loss: 0.00628 +Epoch [3442/4000] Training [29/39] Loss: 0.12840 +Epoch [3442/4000] Training [30/39] Loss: 0.00622 +Epoch [3442/4000] Training [31/39] Loss: 0.13025 +Epoch [3442/4000] Training [32/39] Loss: 0.13114 +Epoch [3442/4000] Training [33/39] Loss: 0.25472 +Epoch [3442/4000] Training [34/39] Loss: 0.00604 +Epoch [3442/4000] Training [35/39] Loss: 0.00460 +Epoch [3442/4000] Training [36/39] Loss: 0.00586 +Epoch [3442/4000] Training [37/39] Loss: 0.00890 +Epoch [3442/4000] Training [38/39] Loss: 0.13001 +Epoch [3442/4000] Training [39/39] Loss: 0.25437 +Epoch [3442/4000] Training metric {'Train/mean dice_metric': 0.9957113265991211, 'Train/mean miou_metric': 0.9919008016586304, 'Train/mean f1': 0.9964569807052612, 'Train/mean precision': 0.9960440397262573, 'Train/mean recall': 0.9968704581260681, 'Train/mean hd95_metric': 1.0264170169830322} +Epoch [3442/4000] Validation [1/10] Loss: 0.75201 focal_loss 0.65832 dice_loss 0.09369 +Epoch [3442/4000] Validation [2/10] Loss: 0.46547 focal_loss 0.37294 dice_loss 0.09254 +Epoch [3442/4000] Validation [3/10] Loss: 0.38414 focal_loss 0.27340 dice_loss 0.11074 +Epoch [3442/4000] Validation [4/10] Loss: 0.85871 focal_loss 0.29130 dice_loss 0.56740 +Epoch [3442/4000] Validation [5/10] Loss: 3.09276 focal_loss 2.42080 dice_loss 0.67196 +Epoch [3442/4000] Validation [6/10] Loss: 1.27856 focal_loss 0.56397 dice_loss 0.71459 +Epoch [3442/4000] Validation [7/10] Loss: 1.14962 focal_loss 0.49059 dice_loss 0.65903 +Epoch [3442/4000] Validation [8/10] Loss: 2.61434 focal_loss 1.96740 dice_loss 0.64695 +Epoch [3442/4000] Validation [9/10] Loss: 1.41021 focal_loss 0.86636 dice_loss 0.54386 +Epoch [3442/4000] Validation [10/10] Loss: 1.79664 focal_loss 1.06689 dice_loss 0.72975 +Epoch [3442/4000] Validation metric {'Val/mean dice_metric': 0.9504491090774536, 'Val/mean miou_metric': 0.9340118169784546, 'Val/mean f1': 0.9484513401985168, 'Val/mean precision': 0.9456632733345032, 'Val/mean recall': 0.9512558579444885, 'Val/mean hd95_metric': 10.670195579528809} +Cheakpoint... +Epoch [3442/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504491090774536, 'Val/mean miou_metric': 0.9340118169784546, 'Val/mean f1': 0.9484513401985168, 'Val/mean precision': 0.9456632733345032, 'Val/mean recall': 0.9512558579444885, 'Val/mean hd95_metric': 10.670195579528809} +Epoch [3443/4000] Training [1/39] Loss: 0.00566 +Epoch [3443/4000] Training [2/39] Loss: 0.13113 +Epoch [3443/4000] Training [3/39] Loss: 0.12835 +Epoch [3443/4000] Training [4/39] Loss: 0.01346 +Epoch [3443/4000] Training [5/39] Loss: 0.13075 +Epoch [3443/4000] Training [6/39] Loss: 0.12891 +Epoch [3443/4000] Training [7/39] Loss: 0.04127 +Epoch [3443/4000] Training [8/39] Loss: 0.00380 +Epoch [3443/4000] Training [9/39] Loss: 0.00336 +Epoch [3443/4000] Training [10/39] Loss: 0.00350 +Epoch [3443/4000] Training [11/39] Loss: 0.00830 +Epoch [3443/4000] Training [12/39] Loss: 0.00862 +Epoch [3443/4000] Training [13/39] Loss: 0.00645 +Epoch [3443/4000] Training [14/39] Loss: 0.12951 +Epoch [3443/4000] Training [15/39] Loss: 0.00402 +Epoch [3443/4000] Training [16/39] Loss: 0.00373 +Epoch [3443/4000] Training [17/39] Loss: 0.00673 +Epoch [3443/4000] Training [18/39] Loss: 0.00512 +Epoch [3443/4000] Training [19/39] Loss: 0.00419 +Epoch [3443/4000] Training [20/39] Loss: 0.00720 +Epoch [3443/4000] Training [21/39] Loss: 0.13132 +Epoch [3443/4000] Training [22/39] Loss: 0.00386 +Epoch [3443/4000] Training [23/39] Loss: 0.00634 +Epoch [3443/4000] Training [24/39] Loss: 0.00533 +Epoch [3443/4000] Training [25/39] Loss: 0.00795 +Epoch [3443/4000] Training [26/39] Loss: 0.00519 +Epoch [3443/4000] Training [27/39] Loss: 0.00542 +Epoch [3443/4000] Training [28/39] Loss: 0.12938 +Epoch [3443/4000] Training [29/39] Loss: 0.00369 +Epoch [3443/4000] Training [30/39] Loss: 0.00563 +Epoch [3443/4000] Training [31/39] Loss: 0.00627 +Epoch [3443/4000] Training [32/39] Loss: 0.00458 +Epoch [3443/4000] Training [33/39] Loss: 0.00552 +Epoch [3443/4000] Training [34/39] Loss: 0.00494 +Epoch [3443/4000] Training [35/39] Loss: 0.00551 +Epoch [3443/4000] Training [36/39] Loss: 0.12963 +Epoch [3443/4000] Training [37/39] Loss: 0.13158 +Epoch [3443/4000] Training [38/39] Loss: 0.00528 +Epoch [3443/4000] Training [39/39] Loss: 0.13170 +Epoch [3443/4000] Training metric {'Train/mean dice_metric': 0.9957355260848999, 'Train/mean miou_metric': 0.9919307827949524, 'Train/mean f1': 0.9965900182723999, 'Train/mean precision': 0.9960963129997253, 'Train/mean recall': 0.9970842003822327, 'Train/mean hd95_metric': 1.0410370826721191} +Epoch [3443/4000] Validation [1/10] Loss: 0.73763 focal_loss 0.64439 dice_loss 0.09324 +Epoch [3443/4000] Validation [2/10] Loss: 0.44739 focal_loss 0.35675 dice_loss 0.09064 +Epoch [3443/4000] Validation [3/10] Loss: 0.38118 focal_loss 0.26822 dice_loss 0.11296 +Epoch [3443/4000] Validation [4/10] Loss: 0.85718 focal_loss 0.29016 dice_loss 0.56701 +Epoch [3443/4000] Validation [5/10] Loss: 3.03712 focal_loss 2.36520 dice_loss 0.67192 +Epoch [3443/4000] Validation [6/10] Loss: 1.27587 focal_loss 0.56487 dice_loss 0.71100 +Epoch [3443/4000] Validation [7/10] Loss: 1.13973 focal_loss 0.48098 dice_loss 0.65875 +Epoch [3443/4000] Validation [8/10] Loss: 2.54666 focal_loss 1.90491 dice_loss 0.64175 +Epoch [3443/4000] Validation [9/10] Loss: 1.39459 focal_loss 0.85044 dice_loss 0.54414 +Epoch [3443/4000] Validation [10/10] Loss: 1.77709 focal_loss 1.04714 dice_loss 0.72994 +Epoch [3443/4000] Validation metric {'Val/mean dice_metric': 0.950691282749176, 'Val/mean miou_metric': 0.9342505931854248, 'Val/mean f1': 0.9484807848930359, 'Val/mean precision': 0.9450889825820923, 'Val/mean recall': 0.9518970251083374, 'Val/mean hd95_metric': 10.764244079589844} +Cheakpoint... +Epoch [3443/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950691282749176, 'Val/mean miou_metric': 0.9342505931854248, 'Val/mean f1': 0.9484807848930359, 'Val/mean precision': 0.9450889825820923, 'Val/mean recall': 0.9518970251083374, 'Val/mean hd95_metric': 10.764244079589844} +Epoch [3444/4000] Training [1/39] Loss: 0.00515 +Epoch [3444/4000] Training [2/39] Loss: 0.00661 +Epoch [3444/4000] Training [3/39] Loss: 0.00568 +Epoch [3444/4000] Training [4/39] Loss: 0.00498 +Epoch [3444/4000] Training [5/39] Loss: 0.01043 +Epoch [3444/4000] Training [6/39] Loss: 0.25462 +Epoch [3444/4000] Training [7/39] Loss: 0.00394 +Epoch [3444/4000] Training [8/39] Loss: 0.25596 +Epoch [3444/4000] Training [9/39] Loss: 0.00390 +Epoch [3444/4000] Training [10/39] Loss: 0.12784 +Epoch [3444/4000] Training [11/39] Loss: 0.12854 +Epoch [3444/4000] Training [12/39] Loss: 0.00485 +Epoch [3444/4000] Training [13/39] Loss: 0.00489 +Epoch [3444/4000] Training [14/39] Loss: 0.13092 +Epoch [3444/4000] Training [15/39] Loss: 0.12835 +Epoch [3444/4000] Training [16/39] Loss: 0.00518 +Epoch [3444/4000] Training [17/39] Loss: 0.00494 +Epoch [3444/4000] Training [18/39] Loss: 0.00505 +Epoch [3444/4000] Training [19/39] Loss: 0.20571 +Epoch [3444/4000] Training [20/39] Loss: 0.00618 +Epoch [3444/4000] Training [21/39] Loss: 0.12905 +Epoch [3444/4000] Training [22/39] Loss: 0.00284 +Epoch [3444/4000] Training [23/39] Loss: 0.00475 +Epoch [3444/4000] Training [24/39] Loss: 0.00518 +Epoch [3444/4000] Training [25/39] Loss: 0.00580 +Epoch [3444/4000] Training [26/39] Loss: 0.00419 +Epoch [3444/4000] Training [27/39] Loss: 0.00576 +Epoch [3444/4000] Training [28/39] Loss: 0.00473 +Epoch [3444/4000] Training [29/39] Loss: 0.00549 +Epoch [3444/4000] Training [30/39] Loss: 0.00504 +Epoch [3444/4000] Training [31/39] Loss: 0.12956 +Epoch [3444/4000] Training [32/39] Loss: 0.00397 +Epoch [3444/4000] Training [33/39] Loss: 0.00555 +Epoch [3444/4000] Training [34/39] Loss: 0.00552 +Epoch [3444/4000] Training [35/39] Loss: 0.00488 +Epoch [3444/4000] Training [36/39] Loss: 0.00482 +Epoch [3444/4000] Training [37/39] Loss: 0.01315 +Epoch [3444/4000] Training [38/39] Loss: 0.00427 +Epoch [3444/4000] Training [39/39] Loss: 0.00539 +Epoch [3444/4000] Training metric {'Train/mean dice_metric': 0.9958429336547852, 'Train/mean miou_metric': 0.9921481013298035, 'Train/mean f1': 0.9966080188751221, 'Train/mean precision': 0.9961730241775513, 'Train/mean recall': 0.9970434904098511, 'Train/mean hd95_metric': 1.0277425050735474} +Epoch [3444/4000] Validation [1/10] Loss: 0.71287 focal_loss 0.62169 dice_loss 0.09118 +Epoch [3444/4000] Validation [2/10] Loss: 0.44924 focal_loss 0.35705 dice_loss 0.09219 +Epoch [3444/4000] Validation [3/10] Loss: 0.37449 focal_loss 0.26324 dice_loss 0.11124 +Epoch [3444/4000] Validation [4/10] Loss: 0.87738 focal_loss 0.30338 dice_loss 0.57399 +Epoch [3444/4000] Validation [5/10] Loss: 3.01040 focal_loss 2.33970 dice_loss 0.67069 +Epoch [3444/4000] Validation [6/10] Loss: 1.27229 focal_loss 0.55786 dice_loss 0.71443 +Epoch [3444/4000] Validation [7/10] Loss: 1.14553 focal_loss 0.48532 dice_loss 0.66021 +Epoch [3444/4000] Validation [8/10] Loss: 2.54544 focal_loss 1.90361 dice_loss 0.64183 +Epoch [3444/4000] Validation [9/10] Loss: 1.38379 focal_loss 0.84031 dice_loss 0.54348 +Epoch [3444/4000] Validation [10/10] Loss: 1.79608 focal_loss 1.06171 dice_loss 0.73437 +Epoch [3444/4000] Validation metric {'Val/mean dice_metric': 0.9506098628044128, 'Val/mean miou_metric': 0.9342554211616516, 'Val/mean f1': 0.9481357336044312, 'Val/mean precision': 0.9441823363304138, 'Val/mean recall': 0.9521224498748779, 'Val/mean hd95_metric': 10.821619987487793} +Cheakpoint... +Epoch [3444/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506098628044128, 'Val/mean miou_metric': 0.9342554211616516, 'Val/mean f1': 0.9481357336044312, 'Val/mean precision': 0.9441823363304138, 'Val/mean recall': 0.9521224498748779, 'Val/mean hd95_metric': 10.821619987487793} +Epoch [3445/4000] Training [1/39] Loss: 0.00443 +Epoch [3445/4000] Training [2/39] Loss: 0.00339 +Epoch [3445/4000] Training [3/39] Loss: 0.00504 +Epoch [3445/4000] Training [4/39] Loss: 0.00838 +Epoch [3445/4000] Training [5/39] Loss: 0.00376 +Epoch [3445/4000] Training [6/39] Loss: 0.00385 +Epoch [3445/4000] Training [7/39] Loss: 0.13070 +Epoch [3445/4000] Training [8/39] Loss: 0.12794 +Epoch [3445/4000] Training [9/39] Loss: 0.12958 +Epoch [3445/4000] Training [10/39] Loss: 0.00692 +Epoch [3445/4000] Training [11/39] Loss: 0.13350 +Epoch [3445/4000] Training [12/39] Loss: 0.25377 +Epoch [3445/4000] Training [13/39] Loss: 0.00563 +Epoch [3445/4000] Training [14/39] Loss: 0.12943 +Epoch [3445/4000] Training [15/39] Loss: 0.00831 +Epoch [3445/4000] Training [16/39] Loss: 0.00536 +Epoch [3445/4000] Training [17/39] Loss: 0.00583 +Epoch [3445/4000] Training [18/39] Loss: 0.00672 +Epoch [3445/4000] Training [19/39] Loss: 0.21956 +Epoch [3445/4000] Training [20/39] Loss: 0.12750 +Epoch [3445/4000] Training [21/39] Loss: 0.00526 +Epoch [3445/4000] Training [22/39] Loss: 0.13157 +Epoch [3445/4000] Training [23/39] Loss: 0.25364 +Epoch [3445/4000] Training [24/39] Loss: 0.00804 +Epoch [3445/4000] Training [25/39] Loss: 0.00354 +Epoch [3445/4000] Training [26/39] Loss: 0.00698 +Epoch [3445/4000] Training [27/39] Loss: 0.00529 +Epoch [3445/4000] Training [28/39] Loss: 0.00517 +Epoch [3445/4000] Training [29/39] Loss: 0.00592 +Epoch [3445/4000] Training [30/39] Loss: 0.13167 +Epoch [3445/4000] Training [31/39] Loss: 0.00503 +Epoch [3445/4000] Training [32/39] Loss: 0.00736 +Epoch [3445/4000] Training [33/39] Loss: 0.00519 +Epoch [3445/4000] Training [34/39] Loss: 0.00562 +Epoch [3445/4000] Training [35/39] Loss: 0.00635 +Epoch [3445/4000] Training [36/39] Loss: 0.00459 +Epoch [3445/4000] Training [37/39] Loss: 0.12920 +Epoch [3445/4000] Training [38/39] Loss: 0.00606 +Epoch [3445/4000] Training [39/39] Loss: 0.13185 +Epoch [3445/4000] Training metric {'Train/mean dice_metric': 0.9956395626068115, 'Train/mean miou_metric': 0.9917466640472412, 'Train/mean f1': 0.996393620967865, 'Train/mean precision': 0.9958732724189758, 'Train/mean recall': 0.9969145059585571, 'Train/mean hd95_metric': 1.0195996761322021} +Epoch [3445/4000] Validation [1/10] Loss: 0.70822 focal_loss 0.61572 dice_loss 0.09250 +Epoch [3445/4000] Validation [2/10] Loss: 0.45741 focal_loss 0.36854 dice_loss 0.08886 +Epoch [3445/4000] Validation [3/10] Loss: 0.36016 focal_loss 0.24967 dice_loss 0.11049 +Epoch [3445/4000] Validation [4/10] Loss: 0.88529 focal_loss 0.31026 dice_loss 0.57504 +Epoch [3445/4000] Validation [5/10] Loss: 2.97071 focal_loss 2.29934 dice_loss 0.67137 +Epoch [3445/4000] Validation [6/10] Loss: 1.30287 focal_loss 0.58894 dice_loss 0.71393 +Epoch [3445/4000] Validation [7/10] Loss: 1.15855 focal_loss 0.50094 dice_loss 0.65761 +Epoch [3445/4000] Validation [8/10] Loss: 2.45854 focal_loss 1.82504 dice_loss 0.63350 +Epoch [3445/4000] Validation [9/10] Loss: 1.37935 focal_loss 0.83794 dice_loss 0.54141 +Epoch [3445/4000] Validation [10/10] Loss: 1.86315 focal_loss 1.12677 dice_loss 0.73638 +Epoch [3445/4000] Validation metric {'Val/mean dice_metric': 0.9503805041313171, 'Val/mean miou_metric': 0.9339004755020142, 'Val/mean f1': 0.9479340314865112, 'Val/mean precision': 0.9426248073577881, 'Val/mean recall': 0.9533035159111023, 'Val/mean hd95_metric': 10.784708023071289} +Cheakpoint... +Epoch [3445/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503805041313171, 'Val/mean miou_metric': 0.9339004755020142, 'Val/mean f1': 0.9479340314865112, 'Val/mean precision': 0.9426248073577881, 'Val/mean recall': 0.9533035159111023, 'Val/mean hd95_metric': 10.784708023071289} +Epoch [3446/4000] Training [1/39] Loss: 0.00301 +Epoch [3446/4000] Training [2/39] Loss: 0.00827 +Epoch [3446/4000] Training [3/39] Loss: 0.00560 +Epoch [3446/4000] Training [4/39] Loss: 0.00333 +Epoch [3446/4000] Training [5/39] Loss: 0.00875 +Epoch [3446/4000] Training [6/39] Loss: 0.12796 +Epoch [3446/4000] Training [7/39] Loss: 0.00634 +Epoch [3446/4000] Training [8/39] Loss: 0.12961 +Epoch [3446/4000] Training [9/39] Loss: 0.00466 +Epoch [3446/4000] Training [10/39] Loss: 0.00695 +Epoch [3446/4000] Training [11/39] Loss: 0.00504 +Epoch [3446/4000] Training [12/39] Loss: 0.13053 +Epoch [3446/4000] Training [13/39] Loss: 0.00348 +Epoch [3446/4000] Training [14/39] Loss: 0.12971 +Epoch [3446/4000] Training [15/39] Loss: 0.12891 +Epoch [3446/4000] Training [16/39] Loss: 0.00719 +Epoch [3446/4000] Training [17/39] Loss: 0.00675 +Epoch [3446/4000] Training [18/39] Loss: 0.12847 +Epoch [3446/4000] Training [19/39] Loss: 0.00493 +Epoch [3446/4000] Training [20/39] Loss: 0.00556 +Epoch [3446/4000] Training [21/39] Loss: 0.00674 +Epoch [3446/4000] Training [22/39] Loss: 0.00781 +Epoch [3446/4000] Training [23/39] Loss: 0.00616 +Epoch [3446/4000] Training [24/39] Loss: 0.00409 +Epoch [3446/4000] Training [25/39] Loss: 0.00879 +Epoch [3446/4000] Training [26/39] Loss: 0.00540 +Epoch [3446/4000] Training [27/39] Loss: 0.12826 +Epoch [3446/4000] Training [28/39] Loss: 0.00413 +Epoch [3446/4000] Training [29/39] Loss: 0.12926 +Epoch [3446/4000] Training [30/39] Loss: 0.00586 +Epoch [3446/4000] Training [31/39] Loss: 0.00436 +Epoch [3446/4000] Training [32/39] Loss: 0.00494 +Epoch [3446/4000] Training [33/39] Loss: 0.12975 +Epoch [3446/4000] Training [34/39] Loss: 0.13384 +Epoch [3446/4000] Training [35/39] Loss: 0.00551 +Epoch [3446/4000] Training [36/39] Loss: 0.00708 +Epoch [3446/4000] Training [37/39] Loss: 0.00426 +Epoch [3446/4000] Training [38/39] Loss: 0.12902 +Epoch [3446/4000] Training [39/39] Loss: 0.00398 +Epoch [3446/4000] Training metric {'Train/mean dice_metric': 0.9958851337432861, 'Train/mean miou_metric': 0.9922367930412292, 'Train/mean f1': 0.9965436458587646, 'Train/mean precision': 0.996132493019104, 'Train/mean recall': 0.9969553351402283, 'Train/mean hd95_metric': 1.0614200830459595} +Epoch [3446/4000] Validation [1/10] Loss: 0.65892 focal_loss 0.57192 dice_loss 0.08700 +Epoch [3446/4000] Validation [2/10] Loss: 0.45041 focal_loss 0.35310 dice_loss 0.09732 +Epoch [3446/4000] Validation [3/10] Loss: 0.37909 focal_loss 0.26555 dice_loss 0.11354 +Epoch [3446/4000] Validation [4/10] Loss: 0.88203 focal_loss 0.30401 dice_loss 0.57802 +Epoch [3446/4000] Validation [5/10] Loss: 2.96066 focal_loss 2.28954 dice_loss 0.67113 +Epoch [3446/4000] Validation [6/10] Loss: 1.24839 focal_loss 0.53689 dice_loss 0.71149 +Epoch [3446/4000] Validation [7/10] Loss: 1.12998 focal_loss 0.47951 dice_loss 0.65048 +Epoch [3446/4000] Validation [8/10] Loss: 2.58727 focal_loss 1.93856 dice_loss 0.64872 +Epoch [3446/4000] Validation [9/10] Loss: 1.34436 focal_loss 0.80272 dice_loss 0.54164 +Epoch [3446/4000] Validation [10/10] Loss: 1.77996 focal_loss 1.04514 dice_loss 0.73482 +Epoch [3446/4000] Validation metric {'Val/mean dice_metric': 0.9508070349693298, 'Val/mean miou_metric': 0.9345241189002991, 'Val/mean f1': 0.9489390850067139, 'Val/mean precision': 0.9460455179214478, 'Val/mean recall': 0.9518502354621887, 'Val/mean hd95_metric': 10.74620246887207} +Cheakpoint... +Epoch [3446/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508070349693298, 'Val/mean miou_metric': 0.9345241189002991, 'Val/mean f1': 0.9489390850067139, 'Val/mean precision': 0.9460455179214478, 'Val/mean recall': 0.9518502354621887, 'Val/mean hd95_metric': 10.74620246887207} +Epoch [3447/4000] Training [1/39] Loss: 0.00476 +Epoch [3447/4000] Training [2/39] Loss: 0.00426 +Epoch [3447/4000] Training [3/39] Loss: 0.12969 +Epoch [3447/4000] Training [4/39] Loss: 0.13087 +Epoch [3447/4000] Training [5/39] Loss: 0.12732 +Epoch [3447/4000] Training [6/39] Loss: 0.13261 +Epoch [3447/4000] Training [7/39] Loss: 0.00467 +Epoch [3447/4000] Training [8/39] Loss: 0.12831 +Epoch [3447/4000] Training [9/39] Loss: 0.00527 +Epoch [3447/4000] Training [10/39] Loss: 0.12934 +Epoch [3447/4000] Training [11/39] Loss: 0.00391 +Epoch [3447/4000] Training [12/39] Loss: 0.25386 +Epoch [3447/4000] Training [13/39] Loss: 0.00369 +Epoch [3447/4000] Training [14/39] Loss: 0.13016 +Epoch [3447/4000] Training [15/39] Loss: 0.00380 +Epoch [3447/4000] Training [16/39] Loss: 0.00610 +Epoch [3447/4000] Training [17/39] Loss: 0.00470 +Epoch [3447/4000] Training [18/39] Loss: 0.13092 +Epoch [3447/4000] Training [19/39] Loss: 0.12806 +Epoch [3447/4000] Training [20/39] Loss: 0.00701 +Epoch [3447/4000] Training [21/39] Loss: 0.13109 +Epoch [3447/4000] Training [22/39] Loss: 0.00743 +Epoch [3447/4000] Training [23/39] Loss: 0.00511 +Epoch [3447/4000] Training [24/39] Loss: 0.00800 +Epoch [3447/4000] Training [25/39] Loss: 0.00572 +Epoch [3447/4000] Training [26/39] Loss: 0.00600 +Epoch [3447/4000] Training [27/39] Loss: 0.00431 +Epoch [3447/4000] Training [28/39] Loss: 0.00676 +Epoch [3447/4000] Training [29/39] Loss: 0.00770 +Epoch [3447/4000] Training [30/39] Loss: 0.00483 +Epoch [3447/4000] Training [31/39] Loss: 0.00384 +Epoch [3447/4000] Training [32/39] Loss: 0.00600 +Epoch [3447/4000] Training [33/39] Loss: 0.00383 +Epoch [3447/4000] Training [34/39] Loss: 0.00944 +Epoch [3447/4000] Training [35/39] Loss: 0.13089 +Epoch [3447/4000] Training [36/39] Loss: 0.00781 +Epoch [3447/4000] Training [37/39] Loss: 0.00403 +Epoch [3447/4000] Training [38/39] Loss: 0.12953 +Epoch [3447/4000] Training [39/39] Loss: 0.00422 +Epoch [3447/4000] Training metric {'Train/mean dice_metric': 0.9957677125930786, 'Train/mean miou_metric': 0.9920098781585693, 'Train/mean f1': 0.9964168667793274, 'Train/mean precision': 0.995948076248169, 'Train/mean recall': 0.9968860745429993, 'Train/mean hd95_metric': 1.0314337015151978} +Epoch [3447/4000] Validation [1/10] Loss: 0.74662 focal_loss 0.65306 dice_loss 0.09356 +Epoch [3447/4000] Validation [2/10] Loss: 0.45638 focal_loss 0.36435 dice_loss 0.09203 +Epoch [3447/4000] Validation [3/10] Loss: 0.38099 focal_loss 0.26823 dice_loss 0.11277 +Epoch [3447/4000] Validation [4/10] Loss: 0.87161 focal_loss 0.30801 dice_loss 0.56360 +Epoch [3447/4000] Validation [5/10] Loss: 3.01663 focal_loss 2.34594 dice_loss 0.67069 +Epoch [3447/4000] Validation [6/10] Loss: 1.27428 focal_loss 0.56400 dice_loss 0.71028 +Epoch [3447/4000] Validation [7/10] Loss: 1.15402 focal_loss 0.49613 dice_loss 0.65789 +Epoch [3447/4000] Validation [8/10] Loss: 2.31212 focal_loss 1.69341 dice_loss 0.61871 +Epoch [3447/4000] Validation [9/10] Loss: 1.36152 focal_loss 0.82141 dice_loss 0.54010 +Epoch [3447/4000] Validation [10/10] Loss: 1.84053 focal_loss 1.10322 dice_loss 0.73731 +Epoch [3447/4000] Validation metric {'Val/mean dice_metric': 0.9506378173828125, 'Val/mean miou_metric': 0.9343106150627136, 'Val/mean f1': 0.9481297731399536, 'Val/mean precision': 0.9425464868545532, 'Val/mean recall': 0.9537794589996338, 'Val/mean hd95_metric': 10.835643768310547} +Cheakpoint... +Epoch [3447/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506378173828125, 'Val/mean miou_metric': 0.9343106150627136, 'Val/mean f1': 0.9481297731399536, 'Val/mean precision': 0.9425464868545532, 'Val/mean recall': 0.9537794589996338, 'Val/mean hd95_metric': 10.835643768310547} +Epoch [3448/4000] Training [1/39] Loss: 0.12949 +Epoch [3448/4000] Training [2/39] Loss: 0.00434 +Epoch [3448/4000] Training [3/39] Loss: 0.00711 +Epoch [3448/4000] Training [4/39] Loss: 0.00614 +Epoch [3448/4000] Training [5/39] Loss: 0.00580 +Epoch [3448/4000] Training [6/39] Loss: 0.00917 +Epoch [3448/4000] Training [7/39] Loss: 0.00691 +Epoch [3448/4000] Training [8/39] Loss: 0.00706 +Epoch [3448/4000] Training [9/39] Loss: 0.00380 +Epoch [3448/4000] Training [10/39] Loss: 0.00596 +Epoch [3448/4000] Training [11/39] Loss: 0.00525 +Epoch [3448/4000] Training [12/39] Loss: 0.00457 +Epoch [3448/4000] Training [13/39] Loss: 0.13330 +Epoch [3448/4000] Training [14/39] Loss: 0.00685 +Epoch [3448/4000] Training [15/39] Loss: 0.00829 +Epoch [3448/4000] Training [16/39] Loss: 0.00362 +Epoch [3448/4000] Training [17/39] Loss: 0.12927 +Epoch [3448/4000] Training [18/39] Loss: 0.00726 +Epoch [3448/4000] Training [19/39] Loss: 0.00824 +Epoch [3448/4000] Training [20/39] Loss: 0.00499 +Epoch [3448/4000] Training [21/39] Loss: 0.12843 +Epoch [3448/4000] Training [22/39] Loss: 0.00758 +Epoch [3448/4000] Training [23/39] Loss: 0.00701 +Epoch [3448/4000] Training [24/39] Loss: 0.00372 +Epoch [3448/4000] Training [25/39] Loss: 0.00692 +Epoch [3448/4000] Training [26/39] Loss: 0.00571 +Epoch [3448/4000] Training [27/39] Loss: 0.00616 +Epoch [3448/4000] Training [28/39] Loss: 0.13009 +Epoch [3448/4000] Training [29/39] Loss: 0.12924 +Epoch [3448/4000] Training [30/39] Loss: 0.00618 +Epoch [3448/4000] Training [31/39] Loss: 0.00285 +Epoch [3448/4000] Training [32/39] Loss: 0.00615 +Epoch [3448/4000] Training [33/39] Loss: 0.00545 +Epoch [3448/4000] Training [34/39] Loss: 0.13117 +Epoch [3448/4000] Training [35/39] Loss: 0.00447 +Epoch [3448/4000] Training [36/39] Loss: 0.00389 +Epoch [3448/4000] Training [37/39] Loss: 0.00721 +Epoch [3448/4000] Training [38/39] Loss: 0.00811 +Epoch [3448/4000] Training [39/39] Loss: 0.00573 +Epoch [3448/4000] Training metric {'Train/mean dice_metric': 0.9956264495849609, 'Train/mean miou_metric': 0.9917582869529724, 'Train/mean f1': 0.996436595916748, 'Train/mean precision': 0.9960266351699829, 'Train/mean recall': 0.9968467950820923, 'Train/mean hd95_metric': 1.01077139377594} +Epoch [3448/4000] Validation [1/10] Loss: 0.68816 focal_loss 0.59812 dice_loss 0.09004 +Epoch [3448/4000] Validation [2/10] Loss: 0.45066 focal_loss 0.35613 dice_loss 0.09453 +Epoch [3448/4000] Validation [3/10] Loss: 0.36828 focal_loss 0.25702 dice_loss 0.11126 +Epoch [3448/4000] Validation [4/10] Loss: 0.89265 focal_loss 0.30602 dice_loss 0.58663 +Epoch [3448/4000] Validation [5/10] Loss: 2.95074 focal_loss 2.27909 dice_loss 0.67165 +Epoch [3448/4000] Validation [6/10] Loss: 1.24696 focal_loss 0.53780 dice_loss 0.70915 +Epoch [3448/4000] Validation [7/10] Loss: 1.12045 focal_loss 0.46907 dice_loss 0.65138 +Epoch [3448/4000] Validation [8/10] Loss: 2.51963 focal_loss 1.87448 dice_loss 0.64515 +Epoch [3448/4000] Validation [9/10] Loss: 1.35090 focal_loss 0.80866 dice_loss 0.54224 +Epoch [3448/4000] Validation [10/10] Loss: 1.75175 focal_loss 1.02109 dice_loss 0.73066 +Epoch [3448/4000] Validation metric {'Val/mean dice_metric': 0.9507399797439575, 'Val/mean miou_metric': 0.9343283176422119, 'Val/mean f1': 0.9490126371383667, 'Val/mean precision': 0.946384847164154, 'Val/mean recall': 0.9516550898551941, 'Val/mean hd95_metric': 10.696298599243164} +Cheakpoint... +Epoch [3448/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507399797439575, 'Val/mean miou_metric': 0.9343283176422119, 'Val/mean f1': 0.9490126371383667, 'Val/mean precision': 0.946384847164154, 'Val/mean recall': 0.9516550898551941, 'Val/mean hd95_metric': 10.696298599243164} +Epoch [3449/4000] Training [1/39] Loss: 0.25623 +Epoch [3449/4000] Training [2/39] Loss: 0.00408 +Epoch [3449/4000] Training [3/39] Loss: 0.00632 +Epoch [3449/4000] Training [4/39] Loss: 0.00509 +Epoch [3449/4000] Training [5/39] Loss: 0.00626 +Epoch [3449/4000] Training [6/39] Loss: 0.12901 +Epoch [3449/4000] Training [7/39] Loss: 0.12947 +Epoch [3449/4000] Training [8/39] Loss: 0.01311 +Epoch [3449/4000] Training [9/39] Loss: 0.00912 +Epoch [3449/4000] Training [10/39] Loss: 0.00517 +Epoch [3449/4000] Training [11/39] Loss: 0.00687 +Epoch [3449/4000] Training [12/39] Loss: 0.12937 +Epoch [3449/4000] Training [13/39] Loss: 0.00717 +Epoch [3449/4000] Training [14/39] Loss: 0.00338 +Epoch [3449/4000] Training [15/39] Loss: 0.12926 +Epoch [3449/4000] Training [16/39] Loss: 0.00494 +Epoch [3449/4000] Training [17/39] Loss: 0.00365 +Epoch [3449/4000] Training [18/39] Loss: 0.00755 +Epoch [3449/4000] Training [19/39] Loss: 0.00759 +Epoch [3449/4000] Training [20/39] Loss: 0.12920 +Epoch [3449/4000] Training [21/39] Loss: 0.00666 +Epoch [3449/4000] Training [22/39] Loss: 0.00405 +Epoch [3449/4000] Training [23/39] Loss: 0.00471 +Epoch [3449/4000] Training [24/39] Loss: 0.13199 +Epoch [3449/4000] Training [25/39] Loss: 0.00341 +Epoch [3449/4000] Training [26/39] Loss: 0.25451 +Epoch [3449/4000] Training [27/39] Loss: 0.00716 +Epoch [3449/4000] Training [28/39] Loss: 0.00553 +Epoch [3449/4000] Training [29/39] Loss: 0.00612 +Epoch [3449/4000] Training [30/39] Loss: 0.00584 +Epoch [3449/4000] Training [31/39] Loss: 0.00576 +Epoch [3449/4000] Training [32/39] Loss: 0.00421 +Epoch [3449/4000] Training [33/39] Loss: 0.00406 +Epoch [3449/4000] Training [34/39] Loss: 0.25337 +Epoch [3449/4000] Training [35/39] Loss: 0.00575 +Epoch [3449/4000] Training [36/39] Loss: 0.12913 +Epoch [3449/4000] Training [37/39] Loss: 0.00645 +Epoch [3449/4000] Training [38/39] Loss: 0.12765 +Epoch [3449/4000] Training [39/39] Loss: 0.00477 +Epoch [3449/4000] Training metric {'Train/mean dice_metric': 0.9948630928993225, 'Train/mean miou_metric': 0.9910190105438232, 'Train/mean f1': 0.996545672416687, 'Train/mean precision': 0.9960403442382812, 'Train/mean recall': 0.9970515370368958, 'Train/mean hd95_metric': 1.1251211166381836} +Epoch [3449/4000] Validation [1/10] Loss: 0.70940 focal_loss 0.61968 dice_loss 0.08972 +Epoch [3449/4000] Validation [2/10] Loss: 0.47117 focal_loss 0.37057 dice_loss 0.10060 +Epoch [3449/4000] Validation [3/10] Loss: 0.38544 focal_loss 0.27326 dice_loss 0.11218 +Epoch [3449/4000] Validation [4/10] Loss: 0.90564 focal_loss 0.30785 dice_loss 0.59779 +Epoch [3449/4000] Validation [5/10] Loss: 2.99796 focal_loss 2.32548 dice_loss 0.67248 +Epoch [3449/4000] Validation [6/10] Loss: 1.24234 focal_loss 0.52936 dice_loss 0.71298 +Epoch [3449/4000] Validation [7/10] Loss: 1.12168 focal_loss 0.47025 dice_loss 0.65143 +Epoch [3449/4000] Validation [8/10] Loss: 2.82550 focal_loss 2.16474 dice_loss 0.66076 +Epoch [3449/4000] Validation [9/10] Loss: 1.39716 focal_loss 0.85427 dice_loss 0.54289 +Epoch [3449/4000] Validation [10/10] Loss: 1.72643 focal_loss 1.00085 dice_loss 0.72558 +Epoch [3449/4000] Validation metric {'Val/mean dice_metric': 0.9497762322425842, 'Val/mean miou_metric': 0.933394193649292, 'Val/mean f1': 0.9495137929916382, 'Val/mean precision': 0.9490699768066406, 'Val/mean recall': 0.9499579071998596, 'Val/mean hd95_metric': 10.67061996459961} +Cheakpoint... +Epoch [3449/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9498], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9497762322425842, 'Val/mean miou_metric': 0.933394193649292, 'Val/mean f1': 0.9495137929916382, 'Val/mean precision': 0.9490699768066406, 'Val/mean recall': 0.9499579071998596, 'Val/mean hd95_metric': 10.67061996459961} +Epoch [3450/4000] Training [1/39] Loss: 0.12886 +Epoch [3450/4000] Training [2/39] Loss: 0.00451 +Epoch [3450/4000] Training [3/39] Loss: 0.12945 +Epoch [3450/4000] Training [4/39] Loss: 0.00504 +Epoch [3450/4000] Training [5/39] Loss: 0.13062 +Epoch [3450/4000] Training [6/39] Loss: 0.12813 +Epoch [3450/4000] Training [7/39] Loss: 0.13153 +Epoch [3450/4000] Training [8/39] Loss: 0.00600 +Epoch [3450/4000] Training [9/39] Loss: 0.12928 +Epoch [3450/4000] Training [10/39] Loss: 0.12888 +Epoch [3450/4000] Training [11/39] Loss: 0.13043 +Epoch [3450/4000] Training [12/39] Loss: 0.12925 +Epoch [3450/4000] Training [13/39] Loss: 0.00600 +Epoch [3450/4000] Training [14/39] Loss: 0.00605 +Epoch [3450/4000] Training [15/39] Loss: 0.00496 +Epoch [3450/4000] Training [16/39] Loss: 0.00457 +Epoch [3450/4000] Training [17/39] Loss: 0.00541 +Epoch [3450/4000] Training [18/39] Loss: 0.00468 +Epoch [3450/4000] Training [19/39] Loss: 0.00325 +Epoch [3450/4000] Training [20/39] Loss: 0.12997 +Epoch [3450/4000] Training [21/39] Loss: 0.00587 +Epoch [3450/4000] Training [22/39] Loss: 0.00393 +Epoch [3450/4000] Training [23/39] Loss: 0.00521 +Epoch [3450/4000] Training [24/39] Loss: 0.00604 +Epoch [3450/4000] Training [25/39] Loss: 0.00559 +Epoch [3450/4000] Training [26/39] Loss: 0.00514 +Epoch [3450/4000] Training [27/39] Loss: 0.00461 +Epoch [3450/4000] Training [28/39] Loss: 0.00711 +Epoch [3450/4000] Training [29/39] Loss: 0.25246 +Epoch [3450/4000] Training [30/39] Loss: 0.00493 +Epoch [3450/4000] Training [31/39] Loss: 0.00528 +Epoch [3450/4000] Training [32/39] Loss: 0.00510 +Epoch [3450/4000] Training [33/39] Loss: 0.00809 +Epoch [3450/4000] Training [34/39] Loss: 0.08366 +Epoch [3450/4000] Training [35/39] Loss: 0.00414 +Epoch [3450/4000] Training [36/39] Loss: 0.00367 +Epoch [3450/4000] Training [37/39] Loss: 0.13261 +Epoch [3450/4000] Training [38/39] Loss: 0.00504 +Epoch [3450/4000] Training [39/39] Loss: 0.12937 +Epoch [3450/4000] Training metric {'Train/mean dice_metric': 0.9959278106689453, 'Train/mean miou_metric': 0.9923045039176941, 'Train/mean f1': 0.9966251850128174, 'Train/mean precision': 0.9962035417556763, 'Train/mean recall': 0.9970470666885376, 'Train/mean hd95_metric': 1.0211960077285767} +Epoch [3450/4000] Validation [1/10] Loss: 0.72750 focal_loss 0.63674 dice_loss 0.09075 +Epoch [3450/4000] Validation [2/10] Loss: 0.47175 focal_loss 0.37379 dice_loss 0.09796 +Epoch [3450/4000] Validation [3/10] Loss: 0.38985 focal_loss 0.27731 dice_loss 0.11255 +Epoch [3450/4000] Validation [4/10] Loss: 0.88153 focal_loss 0.30072 dice_loss 0.58081 +Epoch [3450/4000] Validation [5/10] Loss: 3.06239 focal_loss 2.38972 dice_loss 0.67266 +Epoch [3450/4000] Validation [6/10] Loss: 1.25673 focal_loss 0.54027 dice_loss 0.71647 +Epoch [3450/4000] Validation [7/10] Loss: 1.14425 focal_loss 0.49059 dice_loss 0.65366 +Epoch [3450/4000] Validation [8/10] Loss: 2.72043 focal_loss 2.06836 dice_loss 0.65207 +Epoch [3450/4000] Validation [9/10] Loss: 1.38299 focal_loss 0.84176 dice_loss 0.54123 +Epoch [3450/4000] Validation [10/10] Loss: 1.77340 focal_loss 1.04161 dice_loss 0.73179 +Epoch [3450/4000] Validation metric {'Val/mean dice_metric': 0.9508404731750488, 'Val/mean miou_metric': 0.9345901608467102, 'Val/mean f1': 0.9494118690490723, 'Val/mean precision': 0.9477570056915283, 'Val/mean recall': 0.951072633266449, 'Val/mean hd95_metric': 10.711013793945312} +Cheakpoint... +Epoch [3450/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508404731750488, 'Val/mean miou_metric': 0.9345901608467102, 'Val/mean f1': 0.9494118690490723, 'Val/mean precision': 0.9477570056915283, 'Val/mean recall': 0.951072633266449, 'Val/mean hd95_metric': 10.711013793945312} +Epoch [3451/4000] Training [1/39] Loss: 0.00448 +Epoch [3451/4000] Training [2/39] Loss: 0.00465 +Epoch [3451/4000] Training [3/39] Loss: 0.00382 +Epoch [3451/4000] Training [4/39] Loss: 0.13001 +Epoch [3451/4000] Training [5/39] Loss: 0.00410 +Epoch [3451/4000] Training [6/39] Loss: 0.00768 +Epoch [3451/4000] Training [7/39] Loss: 0.00649 +Epoch [3451/4000] Training [8/39] Loss: 0.13065 +Epoch [3451/4000] Training [9/39] Loss: 0.00415 +Epoch [3451/4000] Training [10/39] Loss: 0.00523 +Epoch [3451/4000] Training [11/39] Loss: 0.12830 +Epoch [3451/4000] Training [12/39] Loss: 0.00834 +Epoch [3451/4000] Training [13/39] Loss: 0.00617 +Epoch [3451/4000] Training [14/39] Loss: 0.00700 +Epoch [3451/4000] Training [15/39] Loss: 0.00778 +Epoch [3451/4000] Training [16/39] Loss: 0.00487 +Epoch [3451/4000] Training [17/39] Loss: 0.00540 +Epoch [3451/4000] Training [18/39] Loss: 0.00346 +Epoch [3451/4000] Training [19/39] Loss: 0.13103 +Epoch [3451/4000] Training [20/39] Loss: 0.04344 +Epoch [3451/4000] Training [21/39] Loss: 0.00412 +Epoch [3451/4000] Training [22/39] Loss: 0.09356 +Epoch [3451/4000] Training [23/39] Loss: 0.00474 +Epoch [3451/4000] Training [24/39] Loss: 0.00517 +Epoch [3451/4000] Training [25/39] Loss: 0.13067 +Epoch [3451/4000] Training [26/39] Loss: 0.12898 +Epoch [3451/4000] Training [27/39] Loss: 0.00426 +Epoch [3451/4000] Training [28/39] Loss: 0.00606 +Epoch [3451/4000] Training [29/39] Loss: 0.00389 +Epoch [3451/4000] Training [30/39] Loss: 0.12849 +Epoch [3451/4000] Training [31/39] Loss: 0.00581 +Epoch [3451/4000] Training [32/39] Loss: 0.12826 +Epoch [3451/4000] Training [33/39] Loss: 0.00461 +Epoch [3451/4000] Training [34/39] Loss: 0.00517 +Epoch [3451/4000] Training [35/39] Loss: 0.00668 +Epoch [3451/4000] Training [36/39] Loss: 0.13170 +Epoch [3451/4000] Training [37/39] Loss: 0.00545 +Epoch [3451/4000] Training [38/39] Loss: 0.25550 +Epoch [3451/4000] Training [39/39] Loss: 0.00620 +Epoch [3451/4000] Training metric {'Train/mean dice_metric': 0.9947909712791443, 'Train/mean miou_metric': 0.9908829927444458, 'Train/mean f1': 0.9964451193809509, 'Train/mean precision': 0.9959811568260193, 'Train/mean recall': 0.996909499168396, 'Train/mean hd95_metric': 1.1178427934646606} +Epoch [3451/4000] Validation [1/10] Loss: 0.73522 focal_loss 0.64394 dice_loss 0.09128 +Epoch [3451/4000] Validation [2/10] Loss: 0.49992 focal_loss 0.39470 dice_loss 0.10522 +Epoch [3451/4000] Validation [3/10] Loss: 0.39219 focal_loss 0.28055 dice_loss 0.11164 +Epoch [3451/4000] Validation [4/10] Loss: 0.87392 focal_loss 0.27989 dice_loss 0.59402 +Epoch [3451/4000] Validation [5/10] Loss: 3.02560 focal_loss 2.35288 dice_loss 0.67272 +Epoch [3451/4000] Validation [6/10] Loss: 1.23071 focal_loss 0.51578 dice_loss 0.71493 +Epoch [3451/4000] Validation [7/10] Loss: 1.11351 focal_loss 0.46352 dice_loss 0.64998 +Epoch [3451/4000] Validation [8/10] Loss: 2.91582 focal_loss 2.25265 dice_loss 0.66317 +Epoch [3451/4000] Validation [9/10] Loss: 1.39112 focal_loss 0.85026 dice_loss 0.54086 +Epoch [3451/4000] Validation [10/10] Loss: 1.71666 focal_loss 0.99384 dice_loss 0.72282 +Epoch [3451/4000] Validation metric {'Val/mean dice_metric': 0.949962317943573, 'Val/mean miou_metric': 0.9335718154907227, 'Val/mean f1': 0.9494957327842712, 'Val/mean precision': 0.9492713809013367, 'Val/mean recall': 0.9497202038764954, 'Val/mean hd95_metric': 10.682353973388672} +Cheakpoint... +Epoch [3451/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9500], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.949962317943573, 'Val/mean miou_metric': 0.9335718154907227, 'Val/mean f1': 0.9494957327842712, 'Val/mean precision': 0.9492713809013367, 'Val/mean recall': 0.9497202038764954, 'Val/mean hd95_metric': 10.682353973388672} +Epoch [3452/4000] Training [1/39] Loss: 0.12843 +Epoch [3452/4000] Training [2/39] Loss: 0.25312 +Epoch [3452/4000] Training [3/39] Loss: 0.00824 +Epoch [3452/4000] Training [4/39] Loss: 0.00546 +Epoch [3452/4000] Training [5/39] Loss: 0.13055 +Epoch [3452/4000] Training [6/39] Loss: 0.12896 +Epoch [3452/4000] Training [7/39] Loss: 0.00717 +Epoch [3452/4000] Training [8/39] Loss: 0.00449 +Epoch [3452/4000] Training [9/39] Loss: 0.00576 +Epoch [3452/4000] Training [10/39] Loss: 0.00611 +Epoch [3452/4000] Training [11/39] Loss: 0.00721 +Epoch [3452/4000] Training [12/39] Loss: 0.00401 +Epoch [3452/4000] Training [13/39] Loss: 0.00434 +Epoch [3452/4000] Training [14/39] Loss: 0.00377 +Epoch [3452/4000] Training [15/39] Loss: 0.00582 +Epoch [3452/4000] Training [16/39] Loss: 0.00438 +Epoch [3452/4000] Training [17/39] Loss: 0.00567 +Epoch [3452/4000] Training [18/39] Loss: 0.13302 +Epoch [3452/4000] Training [19/39] Loss: 0.13067 +Epoch [3452/4000] Training [20/39] Loss: 0.00534 +Epoch [3452/4000] Training [21/39] Loss: 0.09209 +Epoch [3452/4000] Training [22/39] Loss: 0.00383 +Epoch [3452/4000] Training [23/39] Loss: 0.00451 +Epoch [3452/4000] Training [24/39] Loss: 0.00524 +Epoch [3452/4000] Training [25/39] Loss: 0.00387 +Epoch [3452/4000] Training [26/39] Loss: 0.00488 +Epoch [3452/4000] Training [27/39] Loss: 0.00405 +Epoch [3452/4000] Training [28/39] Loss: 0.00381 +Epoch [3452/4000] Training [29/39] Loss: 0.25298 +Epoch [3452/4000] Training [30/39] Loss: 0.00550 +Epoch [3452/4000] Training [31/39] Loss: 0.00478 +Epoch [3452/4000] Training [32/39] Loss: 0.00480 +Epoch [3452/4000] Training [33/39] Loss: 0.00498 +Epoch [3452/4000] Training [34/39] Loss: 0.13243 +Epoch [3452/4000] Training [35/39] Loss: 0.00431 +Epoch [3452/4000] Training [36/39] Loss: 0.00310 +Epoch [3452/4000] Training [37/39] Loss: 0.13098 +Epoch [3452/4000] Training [38/39] Loss: 0.00448 +Epoch [3452/4000] Training [39/39] Loss: 0.00348 +Epoch [3452/4000] Training metric {'Train/mean dice_metric': 0.9959924221038818, 'Train/mean miou_metric': 0.9924613237380981, 'Train/mean f1': 0.9966853260993958, 'Train/mean precision': 0.9961917400360107, 'Train/mean recall': 0.9971792697906494, 'Train/mean hd95_metric': 1.108751654624939} +Epoch [3452/4000] Validation [1/10] Loss: 0.74497 focal_loss 0.65174 dice_loss 0.09323 +Epoch [3452/4000] Validation [2/10] Loss: 0.47809 focal_loss 0.37554 dice_loss 0.10255 +Epoch [3452/4000] Validation [3/10] Loss: 0.36970 focal_loss 0.25847 dice_loss 0.11123 +Epoch [3452/4000] Validation [4/10] Loss: 0.85000 focal_loss 0.28897 dice_loss 0.56102 +Epoch [3452/4000] Validation [5/10] Loss: 2.93542 focal_loss 2.26459 dice_loss 0.67083 +Epoch [3452/4000] Validation [6/10] Loss: 1.26645 focal_loss 0.54537 dice_loss 0.72108 +Epoch [3452/4000] Validation [7/10] Loss: 1.18029 focal_loss 0.52119 dice_loss 0.65909 +Epoch [3452/4000] Validation [8/10] Loss: 2.12926 focal_loss 1.52270 dice_loss 0.60656 +Epoch [3452/4000] Validation [9/10] Loss: 1.34635 focal_loss 0.80503 dice_loss 0.54132 +Epoch [3452/4000] Validation [10/10] Loss: 1.83831 focal_loss 1.09681 dice_loss 0.74150 +Epoch [3452/4000] Validation metric {'Val/mean dice_metric': 0.9508515000343323, 'Val/mean miou_metric': 0.9348174929618835, 'Val/mean f1': 0.9486089944839478, 'Val/mean precision': 0.9423811435699463, 'Val/mean recall': 0.9549195170402527, 'Val/mean hd95_metric': 10.759900093078613} +Cheakpoint... +Epoch [3452/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508515000343323, 'Val/mean miou_metric': 0.9348174929618835, 'Val/mean f1': 0.9486089944839478, 'Val/mean precision': 0.9423811435699463, 'Val/mean recall': 0.9549195170402527, 'Val/mean hd95_metric': 10.759900093078613} +Epoch [3453/4000] Training [1/39] Loss: 0.00399 +Epoch [3453/4000] Training [2/39] Loss: 0.00345 +Epoch [3453/4000] Training [3/39] Loss: 0.12821 +Epoch [3453/4000] Training [4/39] Loss: 0.00414 +Epoch [3453/4000] Training [5/39] Loss: 0.12925 +Epoch [3453/4000] Training [6/39] Loss: 0.00504 +Epoch [3453/4000] Training [7/39] Loss: 0.00580 +Epoch [3453/4000] Training [8/39] Loss: 0.00589 +Epoch [3453/4000] Training [9/39] Loss: 0.25757 +Epoch [3453/4000] Training [10/39] Loss: 0.00338 +Epoch [3453/4000] Training [11/39] Loss: 0.00461 +Epoch [3453/4000] Training [12/39] Loss: 0.00646 +Epoch [3453/4000] Training [13/39] Loss: 0.00535 +Epoch [3453/4000] Training [14/39] Loss: 0.12866 +Epoch [3453/4000] Training [15/39] Loss: 0.13164 +Epoch [3453/4000] Training [16/39] Loss: 0.00627 +Epoch [3453/4000] Training [17/39] Loss: 0.00401 +Epoch [3453/4000] Training [18/39] Loss: 0.00386 +Epoch [3453/4000] Training [19/39] Loss: 0.00916 +Epoch [3453/4000] Training [20/39] Loss: 0.00638 +Epoch [3453/4000] Training [21/39] Loss: 0.00395 +Epoch [3453/4000] Training [22/39] Loss: 0.00526 +Epoch [3453/4000] Training [23/39] Loss: 0.00602 +Epoch [3453/4000] Training [24/39] Loss: 0.00349 +Epoch [3453/4000] Training [25/39] Loss: 0.13082 +Epoch [3453/4000] Training [26/39] Loss: 0.00453 +Epoch [3453/4000] Training [27/39] Loss: 0.00602 +Epoch [3453/4000] Training [28/39] Loss: 0.00789 +Epoch [3453/4000] Training [29/39] Loss: 0.12960 +Epoch [3453/4000] Training [30/39] Loss: 0.00364 +Epoch [3453/4000] Training [31/39] Loss: 0.00504 +Epoch [3453/4000] Training [32/39] Loss: 0.00509 +Epoch [3453/4000] Training [33/39] Loss: 0.00368 +Epoch [3453/4000] Training [34/39] Loss: 0.00417 +Epoch [3453/4000] Training [35/39] Loss: 0.25343 +Epoch [3453/4000] Training [36/39] Loss: 0.00652 +Epoch [3453/4000] Training [37/39] Loss: 0.12789 +Epoch [3453/4000] Training [38/39] Loss: 0.00829 +Epoch [3453/4000] Training [39/39] Loss: 0.00845 +Epoch [3453/4000] Training metric {'Train/mean dice_metric': 0.9959511160850525, 'Train/mean miou_metric': 0.9923619031906128, 'Train/mean f1': 0.9966841340065002, 'Train/mean precision': 0.9962076544761658, 'Train/mean recall': 0.9971612095832825, 'Train/mean hd95_metric': 1.0481537580490112} +Epoch [3453/4000] Validation [1/10] Loss: 0.72034 focal_loss 0.62729 dice_loss 0.09305 +Epoch [3453/4000] Validation [2/10] Loss: 0.47864 focal_loss 0.37977 dice_loss 0.09887 +Epoch [3453/4000] Validation [3/10] Loss: 0.36219 focal_loss 0.25222 dice_loss 0.10997 +Epoch [3453/4000] Validation [4/10] Loss: 0.89509 focal_loss 0.30049 dice_loss 0.59461 +Epoch [3453/4000] Validation [5/10] Loss: 2.93446 focal_loss 2.26302 dice_loss 0.67144 +Epoch [3453/4000] Validation [6/10] Loss: 1.28156 focal_loss 0.56495 dice_loss 0.71662 +Epoch [3453/4000] Validation [7/10] Loss: 1.16388 focal_loss 0.50342 dice_loss 0.66046 +Epoch [3453/4000] Validation [8/10] Loss: 2.40870 focal_loss 1.77797 dice_loss 0.63073 +Epoch [3453/4000] Validation [9/10] Loss: 1.34705 focal_loss 0.80532 dice_loss 0.54173 +Epoch [3453/4000] Validation [10/10] Loss: 1.83099 focal_loss 1.09254 dice_loss 0.73846 +Epoch [3453/4000] Validation metric {'Val/mean dice_metric': 0.9509180784225464, 'Val/mean miou_metric': 0.9347428679466248, 'Val/mean f1': 0.9490206241607666, 'Val/mean precision': 0.944725751876831, 'Val/mean recall': 0.9533546566963196, 'Val/mean hd95_metric': 10.717913627624512} +Cheakpoint... +Epoch [3453/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509180784225464, 'Val/mean miou_metric': 0.9347428679466248, 'Val/mean f1': 0.9490206241607666, 'Val/mean precision': 0.944725751876831, 'Val/mean recall': 0.9533546566963196, 'Val/mean hd95_metric': 10.717913627624512} +Epoch [3454/4000] Training [1/39] Loss: 0.00931 +Epoch [3454/4000] Training [2/39] Loss: 0.00393 +Epoch [3454/4000] Training [3/39] Loss: 0.13088 +Epoch [3454/4000] Training [4/39] Loss: 0.00453 +Epoch [3454/4000] Training [5/39] Loss: 0.00739 +Epoch [3454/4000] Training [6/39] Loss: 0.12948 +Epoch [3454/4000] Training [7/39] Loss: 0.00503 +Epoch [3454/4000] Training [8/39] Loss: 0.01019 +Epoch [3454/4000] Training [9/39] Loss: 0.00445 +Epoch [3454/4000] Training [10/39] Loss: 0.00667 +Epoch [3454/4000] Training [11/39] Loss: 0.00551 +Epoch [3454/4000] Training [12/39] Loss: 0.00411 +Epoch [3454/4000] Training [13/39] Loss: 0.12887 +Epoch [3454/4000] Training [14/39] Loss: 0.00340 +Epoch [3454/4000] Training [15/39] Loss: 0.00895 +Epoch [3454/4000] Training [16/39] Loss: 0.00751 +Epoch [3454/4000] Training [17/39] Loss: 0.00626 +Epoch [3454/4000] Training [18/39] Loss: 0.00769 +Epoch [3454/4000] Training [19/39] Loss: 0.00440 +Epoch [3454/4000] Training [20/39] Loss: 0.00590 +Epoch [3454/4000] Training [21/39] Loss: 0.12746 +Epoch [3454/4000] Training [22/39] Loss: 0.00713 +Epoch [3454/4000] Training [23/39] Loss: 0.00409 +Epoch [3454/4000] Training [24/39] Loss: 0.00652 +Epoch [3454/4000] Training [25/39] Loss: 0.12929 +Epoch [3454/4000] Training [26/39] Loss: 0.00348 +Epoch [3454/4000] Training [27/39] Loss: 0.12820 +Epoch [3454/4000] Training [28/39] Loss: 0.00947 +Epoch [3454/4000] Training [29/39] Loss: 0.00575 +Epoch [3454/4000] Training [30/39] Loss: 0.00463 +Epoch [3454/4000] Training [31/39] Loss: 0.00630 +Epoch [3454/4000] Training [32/39] Loss: 0.00531 +Epoch [3454/4000] Training [33/39] Loss: 0.00572 +Epoch [3454/4000] Training [34/39] Loss: 0.12959 +Epoch [3454/4000] Training [35/39] Loss: 0.00598 +Epoch [3454/4000] Training [36/39] Loss: 0.00685 +Epoch [3454/4000] Training [37/39] Loss: 0.12914 +Epoch [3454/4000] Training [38/39] Loss: 0.12817 +Epoch [3454/4000] Training [39/39] Loss: 0.16623 +Epoch [3454/4000] Training metric {'Train/mean dice_metric': 0.9955849647521973, 'Train/mean miou_metric': 0.9916401505470276, 'Train/mean f1': 0.9964376091957092, 'Train/mean precision': 0.9960399270057678, 'Train/mean recall': 0.996835470199585, 'Train/mean hd95_metric': 1.0846607685089111} +Epoch [3454/4000] Validation [1/10] Loss: 0.69005 focal_loss 0.60474 dice_loss 0.08531 +Epoch [3454/4000] Validation [2/10] Loss: 0.47902 focal_loss 0.38113 dice_loss 0.09789 +Epoch [3454/4000] Validation [3/10] Loss: 0.38218 focal_loss 0.27228 dice_loss 0.10990 +Epoch [3454/4000] Validation [4/10] Loss: 0.89999 focal_loss 0.30586 dice_loss 0.59413 +Epoch [3454/4000] Validation [5/10] Loss: 3.04007 focal_loss 2.36741 dice_loss 0.67266 +Epoch [3454/4000] Validation [6/10] Loss: 1.26892 focal_loss 0.54817 dice_loss 0.72075 +Epoch [3454/4000] Validation [7/10] Loss: 1.13865 focal_loss 0.48520 dice_loss 0.65345 +Epoch [3454/4000] Validation [8/10] Loss: 2.57638 focal_loss 1.93733 dice_loss 0.63905 +Epoch [3454/4000] Validation [9/10] Loss: 1.37783 focal_loss 0.83820 dice_loss 0.53963 +Epoch [3454/4000] Validation [10/10] Loss: 1.76544 focal_loss 1.03556 dice_loss 0.72988 +Epoch [3454/4000] Validation metric {'Val/mean dice_metric': 0.9503297209739685, 'Val/mean miou_metric': 0.933992326259613, 'Val/mean f1': 0.9493615031242371, 'Val/mean precision': 0.9473476409912109, 'Val/mean recall': 0.9513840079307556, 'Val/mean hd95_metric': 10.721773147583008} +Cheakpoint... +Epoch [3454/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9503], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503297209739685, 'Val/mean miou_metric': 0.933992326259613, 'Val/mean f1': 0.9493615031242371, 'Val/mean precision': 0.9473476409912109, 'Val/mean recall': 0.9513840079307556, 'Val/mean hd95_metric': 10.721773147583008} +Epoch [3455/4000] Training [1/39] Loss: 0.00505 +Epoch [3455/4000] Training [2/39] Loss: 0.00649 +Epoch [3455/4000] Training [3/39] Loss: 0.00386 +Epoch [3455/4000] Training [4/39] Loss: 0.00500 +Epoch [3455/4000] Training [5/39] Loss: 0.12943 +Epoch [3455/4000] Training [6/39] Loss: 0.00536 +Epoch [3455/4000] Training [7/39] Loss: 0.00411 +Epoch [3455/4000] Training [8/39] Loss: 0.00378 +Epoch [3455/4000] Training [9/39] Loss: 0.00523 +Epoch [3455/4000] Training [10/39] Loss: 0.00530 +Epoch [3455/4000] Training [11/39] Loss: 0.00436 +Epoch [3455/4000] Training [12/39] Loss: 0.00670 +Epoch [3455/4000] Training [13/39] Loss: 0.37680 +Epoch [3455/4000] Training [14/39] Loss: 0.00977 +Epoch [3455/4000] Training [15/39] Loss: 0.00458 +Epoch [3455/4000] Training [16/39] Loss: 0.00528 +Epoch [3455/4000] Training [17/39] Loss: 0.12909 +Epoch [3455/4000] Training [18/39] Loss: 0.00465 +Epoch [3455/4000] Training [19/39] Loss: 0.13299 +Epoch [3455/4000] Training [20/39] Loss: 0.00592 +Epoch [3455/4000] Training [21/39] Loss: 0.00313 +Epoch [3455/4000] Training [22/39] Loss: 0.13027 +Epoch [3455/4000] Training [23/39] Loss: 0.13014 +Epoch [3455/4000] Training [24/39] Loss: 0.00772 +Epoch [3455/4000] Training [25/39] Loss: 0.00863 +Epoch [3455/4000] Training [26/39] Loss: 0.12998 +Epoch [3455/4000] Training [27/39] Loss: 0.00712 +Epoch [3455/4000] Training [28/39] Loss: 0.00559 +Epoch [3455/4000] Training [29/39] Loss: 0.00716 +Epoch [3455/4000] Training [30/39] Loss: 0.00741 +Epoch [3455/4000] Training [31/39] Loss: 0.00815 +Epoch [3455/4000] Training [32/39] Loss: 0.00562 +Epoch [3455/4000] Training [33/39] Loss: 0.00655 +Epoch [3455/4000] Training [34/39] Loss: 0.00632 +Epoch [3455/4000] Training [35/39] Loss: 0.00761 +Epoch [3455/4000] Training [36/39] Loss: 0.00685 +Epoch [3455/4000] Training [37/39] Loss: 0.12994 +Epoch [3455/4000] Training [38/39] Loss: 0.12809 +Epoch [3455/4000] Training [39/39] Loss: 0.00453 +Epoch [3455/4000] Training metric {'Train/mean dice_metric': 0.9948574304580688, 'Train/mean miou_metric': 0.9910297989845276, 'Train/mean f1': 0.9963953495025635, 'Train/mean precision': 0.9959605932235718, 'Train/mean recall': 0.9968305230140686, 'Train/mean hd95_metric': 1.0288755893707275} +Epoch [3455/4000] Validation [1/10] Loss: 0.70085 focal_loss 0.61042 dice_loss 0.09043 +Epoch [3455/4000] Validation [2/10] Loss: 0.46670 focal_loss 0.36570 dice_loss 0.10101 +Epoch [3455/4000] Validation [3/10] Loss: 0.37444 focal_loss 0.26350 dice_loss 0.11094 +Epoch [3455/4000] Validation [4/10] Loss: 0.84376 focal_loss 0.27886 dice_loss 0.56491 +Epoch [3455/4000] Validation [5/10] Loss: 2.93994 focal_loss 2.26758 dice_loss 0.67236 +Epoch [3455/4000] Validation [6/10] Loss: 1.23117 focal_loss 0.51884 dice_loss 0.71233 +Epoch [3455/4000] Validation [7/10] Loss: 1.13732 focal_loss 0.48193 dice_loss 0.65538 +Epoch [3455/4000] Validation [8/10] Loss: 2.39257 focal_loss 1.75712 dice_loss 0.63546 +Epoch [3455/4000] Validation [9/10] Loss: 1.34090 focal_loss 0.79909 dice_loss 0.54181 +Epoch [3455/4000] Validation [10/10] Loss: 1.73984 focal_loss 1.00611 dice_loss 0.73373 +Epoch [3455/4000] Validation metric {'Val/mean dice_metric': 0.9502754211425781, 'Val/mean miou_metric': 0.9339345693588257, 'Val/mean f1': 0.9491546154022217, 'Val/mean precision': 0.9461873769760132, 'Val/mean recall': 0.9521405100822449, 'Val/mean hd95_metric': 10.650680541992188} +Cheakpoint... +Epoch [3455/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9503], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502754211425781, 'Val/mean miou_metric': 0.9339345693588257, 'Val/mean f1': 0.9491546154022217, 'Val/mean precision': 0.9461873769760132, 'Val/mean recall': 0.9521405100822449, 'Val/mean hd95_metric': 10.650680541992188} +Epoch [3456/4000] Training [1/39] Loss: 0.08557 +Epoch [3456/4000] Training [2/39] Loss: 0.12802 +Epoch [3456/4000] Training [3/39] Loss: 0.00500 +Epoch [3456/4000] Training [4/39] Loss: 0.00429 +Epoch [3456/4000] Training [5/39] Loss: 0.00617 +Epoch [3456/4000] Training [6/39] Loss: 0.00565 +Epoch [3456/4000] Training [7/39] Loss: 0.00304 +Epoch [3456/4000] Training [8/39] Loss: 0.00539 +Epoch [3456/4000] Training [9/39] Loss: 0.25631 +Epoch [3456/4000] Training [10/39] Loss: 0.00466 +Epoch [3456/4000] Training [11/39] Loss: 0.00804 +Epoch [3456/4000] Training [12/39] Loss: 0.00319 +Epoch [3456/4000] Training [13/39] Loss: 0.00600 +Epoch [3456/4000] Training [14/39] Loss: 0.00767 +Epoch [3456/4000] Training [15/39] Loss: 0.00402 +Epoch [3456/4000] Training [16/39] Loss: 0.00571 +Epoch [3456/4000] Training [17/39] Loss: 0.12936 +Epoch [3456/4000] Training [18/39] Loss: 0.12965 +Epoch [3456/4000] Training [19/39] Loss: 0.00584 +Epoch [3456/4000] Training [20/39] Loss: 0.00757 +Epoch [3456/4000] Training [21/39] Loss: 0.00360 +Epoch [3456/4000] Training [22/39] Loss: 0.13070 +Epoch [3456/4000] Training [23/39] Loss: 0.00417 +Epoch [3456/4000] Training [24/39] Loss: 0.00363 +Epoch [3456/4000] Training [25/39] Loss: 0.00590 +Epoch [3456/4000] Training [26/39] Loss: 0.12955 +Epoch [3456/4000] Training [27/39] Loss: 0.00353 +Epoch [3456/4000] Training [28/39] Loss: 0.00675 +Epoch [3456/4000] Training [29/39] Loss: 0.00535 +Epoch [3456/4000] Training [30/39] Loss: 0.00642 +Epoch [3456/4000] Training [31/39] Loss: 0.12957 +Epoch [3456/4000] Training [32/39] Loss: 0.00763 +Epoch [3456/4000] Training [33/39] Loss: 0.00499 +Epoch [3456/4000] Training [34/39] Loss: 0.25483 +Epoch [3456/4000] Training [35/39] Loss: 0.00584 +Epoch [3456/4000] Training [36/39] Loss: 0.01091 +Epoch [3456/4000] Training [37/39] Loss: 0.00414 +Epoch [3456/4000] Training [38/39] Loss: 0.00514 +Epoch [3456/4000] Training [39/39] Loss: 0.00755 +Epoch [3456/4000] Training metric {'Train/mean dice_metric': 0.9954468011856079, 'Train/mean miou_metric': 0.9917158484458923, 'Train/mean f1': 0.9963041543960571, 'Train/mean precision': 0.9955258369445801, 'Train/mean recall': 0.9970836639404297, 'Train/mean hd95_metric': 1.0747848749160767} +Epoch [3456/4000] Validation [1/10] Loss: 0.69909 focal_loss 0.60973 dice_loss 0.08936 +Epoch [3456/4000] Validation [2/10] Loss: 0.45822 focal_loss 0.36332 dice_loss 0.09491 +Epoch [3456/4000] Validation [3/10] Loss: 0.37575 focal_loss 0.26515 dice_loss 0.11060 +Epoch [3456/4000] Validation [4/10] Loss: 0.86581 focal_loss 0.29183 dice_loss 0.57398 +Epoch [3456/4000] Validation [5/10] Loss: 2.99150 focal_loss 2.32024 dice_loss 0.67126 +Epoch [3456/4000] Validation [6/10] Loss: 1.25998 focal_loss 0.54713 dice_loss 0.71286 +Epoch [3456/4000] Validation [7/10] Loss: 1.15896 focal_loss 0.50019 dice_loss 0.65877 +Epoch [3456/4000] Validation [8/10] Loss: 2.34918 focal_loss 1.72553 dice_loss 0.62366 +Epoch [3456/4000] Validation [9/10] Loss: 1.35800 focal_loss 0.81598 dice_loss 0.54202 +Epoch [3456/4000] Validation [10/10] Loss: 1.81755 focal_loss 1.08016 dice_loss 0.73739 +Epoch [3456/4000] Validation metric {'Val/mean dice_metric': 0.9504052996635437, 'Val/mean miou_metric': 0.9341548085212708, 'Val/mean f1': 0.9486802220344543, 'Val/mean precision': 0.9440849423408508, 'Val/mean recall': 0.9533206820487976, 'Val/mean hd95_metric': 10.780254364013672} +Cheakpoint... +Epoch [3456/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504052996635437, 'Val/mean miou_metric': 0.9341548085212708, 'Val/mean f1': 0.9486802220344543, 'Val/mean precision': 0.9440849423408508, 'Val/mean recall': 0.9533206820487976, 'Val/mean hd95_metric': 10.780254364013672} +Epoch [3457/4000] Training [1/39] Loss: 0.00362 +Epoch [3457/4000] Training [2/39] Loss: 0.00525 +Epoch [3457/4000] Training [3/39] Loss: 0.13426 +Epoch [3457/4000] Training [4/39] Loss: 0.00566 +Epoch [3457/4000] Training [5/39] Loss: 0.00319 +Epoch [3457/4000] Training [6/39] Loss: 0.00881 +Epoch [3457/4000] Training [7/39] Loss: 0.00863 +Epoch [3457/4000] Training [8/39] Loss: 0.00520 +Epoch [3457/4000] Training [9/39] Loss: 0.13043 +Epoch [3457/4000] Training [10/39] Loss: 0.12919 +Epoch [3457/4000] Training [11/39] Loss: 0.00331 +Epoch [3457/4000] Training [12/39] Loss: 0.00290 +Epoch [3457/4000] Training [13/39] Loss: 0.00462 +Epoch [3457/4000] Training [14/39] Loss: 0.00421 +Epoch [3457/4000] Training [15/39] Loss: 0.00758 +Epoch [3457/4000] Training [16/39] Loss: 0.00352 +Epoch [3457/4000] Training [17/39] Loss: 0.00290 +Epoch [3457/4000] Training [18/39] Loss: 0.00376 +Epoch [3457/4000] Training [19/39] Loss: 0.00903 +Epoch [3457/4000] Training [20/39] Loss: 0.00624 +Epoch [3457/4000] Training [21/39] Loss: 0.12936 +Epoch [3457/4000] Training [22/39] Loss: 0.03012 +Epoch [3457/4000] Training [23/39] Loss: 0.12804 +Epoch [3457/4000] Training [24/39] Loss: 0.12958 +Epoch [3457/4000] Training [25/39] Loss: 0.00275 +Epoch [3457/4000] Training [26/39] Loss: 0.12976 +Epoch [3457/4000] Training [27/39] Loss: 0.12945 +Epoch [3457/4000] Training [28/39] Loss: 0.00731 +Epoch [3457/4000] Training [29/39] Loss: 0.00425 +Epoch [3457/4000] Training [30/39] Loss: 0.00585 +Epoch [3457/4000] Training [31/39] Loss: 0.00435 +Epoch [3457/4000] Training [32/39] Loss: 0.00508 +Epoch [3457/4000] Training [33/39] Loss: 0.13055 +Epoch [3457/4000] Training [34/39] Loss: 0.00470 +Epoch [3457/4000] Training [35/39] Loss: 0.00396 +Epoch [3457/4000] Training [36/39] Loss: 0.00452 +Epoch [3457/4000] Training [37/39] Loss: 0.00787 +Epoch [3457/4000] Training [38/39] Loss: 0.13052 +Epoch [3457/4000] Training [39/39] Loss: 0.00782 +Epoch [3457/4000] Training metric {'Train/mean dice_metric': 0.9956881999969482, 'Train/mean miou_metric': 0.991888701915741, 'Train/mean f1': 0.9965062737464905, 'Train/mean precision': 0.9960907697677612, 'Train/mean recall': 0.9969220757484436, 'Train/mean hd95_metric': 1.1002243757247925} +Epoch [3457/4000] Validation [1/10] Loss: 0.70897 focal_loss 0.61636 dice_loss 0.09261 +Epoch [3457/4000] Validation [2/10] Loss: 0.49484 focal_loss 0.39095 dice_loss 0.10390 +Epoch [3457/4000] Validation [3/10] Loss: 0.37919 focal_loss 0.26750 dice_loss 0.11169 +Epoch [3457/4000] Validation [4/10] Loss: 0.88356 focal_loss 0.29187 dice_loss 0.59169 +Epoch [3457/4000] Validation [5/10] Loss: 2.81994 focal_loss 2.14586 dice_loss 0.67409 +Epoch [3457/4000] Validation [6/10] Loss: 1.21613 focal_loss 0.50750 dice_loss 0.70863 +Epoch [3457/4000] Validation [7/10] Loss: 1.08204 focal_loss 0.43122 dice_loss 0.65081 +Epoch [3457/4000] Validation [8/10] Loss: 2.90367 focal_loss 2.24304 dice_loss 0.66063 +Epoch [3457/4000] Validation [9/10] Loss: 1.39316 focal_loss 0.84877 dice_loss 0.54439 +Epoch [3457/4000] Validation [10/10] Loss: 1.68747 focal_loss 0.96670 dice_loss 0.72077 +Epoch [3457/4000] Validation metric {'Val/mean dice_metric': 0.9508033990859985, 'Val/mean miou_metric': 0.9344625473022461, 'Val/mean f1': 0.9500888586044312, 'Val/mean precision': 0.9510073661804199, 'Val/mean recall': 0.9491721987724304, 'Val/mean hd95_metric': 10.807514190673828} +Cheakpoint... +Epoch [3457/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508033990859985, 'Val/mean miou_metric': 0.9344625473022461, 'Val/mean f1': 0.9500888586044312, 'Val/mean precision': 0.9510073661804199, 'Val/mean recall': 0.9491721987724304, 'Val/mean hd95_metric': 10.807514190673828} +Epoch [3458/4000] Training [1/39] Loss: 0.12868 +Epoch [3458/4000] Training [2/39] Loss: 0.00606 +Epoch [3458/4000] Training [3/39] Loss: 0.13479 +Epoch [3458/4000] Training [4/39] Loss: 0.00575 +Epoch [3458/4000] Training [5/39] Loss: 0.00391 +Epoch [3458/4000] Training [6/39] Loss: 0.12877 +Epoch [3458/4000] Training [7/39] Loss: 0.13020 +Epoch [3458/4000] Training [8/39] Loss: 0.00476 +Epoch [3458/4000] Training [9/39] Loss: 0.12975 +Epoch [3458/4000] Training [10/39] Loss: 0.00555 +Epoch [3458/4000] Training [11/39] Loss: 0.12819 +Epoch [3458/4000] Training [12/39] Loss: 0.00593 +Epoch [3458/4000] Training [13/39] Loss: 0.00582 +Epoch [3458/4000] Training [14/39] Loss: 0.00556 +Epoch [3458/4000] Training [15/39] Loss: 0.00378 +Epoch [3458/4000] Training [16/39] Loss: 0.12794 +Epoch [3458/4000] Training [17/39] Loss: 0.00457 +Epoch [3458/4000] Training [18/39] Loss: 0.00410 +Epoch [3458/4000] Training [19/39] Loss: 0.00735 +Epoch [3458/4000] Training [20/39] Loss: 0.00845 +Epoch [3458/4000] Training [21/39] Loss: 0.00453 +Epoch [3458/4000] Training [22/39] Loss: 0.00865 +Epoch [3458/4000] Training [23/39] Loss: 0.00535 +Epoch [3458/4000] Training [24/39] Loss: 0.13367 +Epoch [3458/4000] Training [25/39] Loss: 0.00523 +Epoch [3458/4000] Training [26/39] Loss: 0.00716 +Epoch [3458/4000] Training [27/39] Loss: 0.12937 +Epoch [3458/4000] Training [28/39] Loss: 0.00561 +Epoch [3458/4000] Training [29/39] Loss: 0.13113 +Epoch [3458/4000] Training [30/39] Loss: 0.00658 +Epoch [3458/4000] Training [31/39] Loss: 0.00413 +Epoch [3458/4000] Training [32/39] Loss: 0.13158 +Epoch [3458/4000] Training [33/39] Loss: 0.00508 +Epoch [3458/4000] Training [34/39] Loss: 0.13211 +Epoch [3458/4000] Training [35/39] Loss: 0.00707 +Epoch [3458/4000] Training [36/39] Loss: 0.12980 +Epoch [3458/4000] Training [37/39] Loss: 0.00421 +Epoch [3458/4000] Training [38/39] Loss: 0.00527 +Epoch [3458/4000] Training [39/39] Loss: 0.12930 +Epoch [3458/4000] Training metric {'Train/mean dice_metric': 0.9951390027999878, 'Train/mean miou_metric': 0.9911136031150818, 'Train/mean f1': 0.9960917830467224, 'Train/mean precision': 0.9953480362892151, 'Train/mean recall': 0.9968367218971252, 'Train/mean hd95_metric': 1.0771398544311523} +Epoch [3458/4000] Validation [1/10] Loss: 0.70047 focal_loss 0.60965 dice_loss 0.09082 +Epoch [3458/4000] Validation [2/10] Loss: 0.48507 focal_loss 0.38044 dice_loss 0.10463 +Epoch [3458/4000] Validation [3/10] Loss: 0.39064 focal_loss 0.27813 dice_loss 0.11252 +Epoch [3458/4000] Validation [4/10] Loss: 0.86559 focal_loss 0.28643 dice_loss 0.57916 +Epoch [3458/4000] Validation [5/10] Loss: 2.86657 focal_loss 2.19314 dice_loss 0.67342 +Epoch [3458/4000] Validation [6/10] Loss: 1.19933 focal_loss 0.48870 dice_loss 0.71063 +Epoch [3458/4000] Validation [7/10] Loss: 1.10608 focal_loss 0.45204 dice_loss 0.65404 +Epoch [3458/4000] Validation [8/10] Loss: 2.78703 focal_loss 2.12677 dice_loss 0.66026 +Epoch [3458/4000] Validation [9/10] Loss: 1.36806 focal_loss 0.82438 dice_loss 0.54367 +Epoch [3458/4000] Validation [10/10] Loss: 1.67931 focal_loss 0.95778 dice_loss 0.72153 +Epoch [3458/4000] Validation metric {'Val/mean dice_metric': 0.9504155516624451, 'Val/mean miou_metric': 0.9338993430137634, 'Val/mean f1': 0.9493602514266968, 'Val/mean precision': 0.9488866925239563, 'Val/mean recall': 0.9498342275619507, 'Val/mean hd95_metric': 10.827383995056152} +Cheakpoint... +Epoch [3458/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504155516624451, 'Val/mean miou_metric': 0.9338993430137634, 'Val/mean f1': 0.9493602514266968, 'Val/mean precision': 0.9488866925239563, 'Val/mean recall': 0.9498342275619507, 'Val/mean hd95_metric': 10.827383995056152} +Epoch [3459/4000] Training [1/39] Loss: 0.00600 +Epoch [3459/4000] Training [2/39] Loss: 0.00689 +Epoch [3459/4000] Training [3/39] Loss: 0.00346 +Epoch [3459/4000] Training [4/39] Loss: 0.12851 +Epoch [3459/4000] Training [5/39] Loss: 0.00540 +Epoch [3459/4000] Training [6/39] Loss: 0.13016 +Epoch [3459/4000] Training [7/39] Loss: 0.01127 +Epoch [3459/4000] Training [8/39] Loss: 0.04591 +Epoch [3459/4000] Training [9/39] Loss: 0.00326 +Epoch [3459/4000] Training [10/39] Loss: 0.12867 +Epoch [3459/4000] Training [11/39] Loss: 0.00599 +Epoch [3459/4000] Training [12/39] Loss: 0.00624 +Epoch [3459/4000] Training [13/39] Loss: 0.12868 +Epoch [3459/4000] Training [14/39] Loss: 0.00641 +Epoch [3459/4000] Training [15/39] Loss: 0.00467 +Epoch [3459/4000] Training [16/39] Loss: 0.00520 +Epoch [3459/4000] Training [17/39] Loss: 0.00458 +Epoch [3459/4000] Training [18/39] Loss: 0.13040 +Epoch [3459/4000] Training [19/39] Loss: 0.00462 +Epoch [3459/4000] Training [20/39] Loss: 0.13068 +Epoch [3459/4000] Training [21/39] Loss: 0.00603 +Epoch [3459/4000] Training [22/39] Loss: 0.00643 +Epoch [3459/4000] Training [23/39] Loss: 0.13121 +Epoch [3459/4000] Training [24/39] Loss: 0.00353 +Epoch [3459/4000] Training [25/39] Loss: 0.00453 +Epoch [3459/4000] Training [26/39] Loss: 0.00544 +Epoch [3459/4000] Training [27/39] Loss: 0.00549 +Epoch [3459/4000] Training [28/39] Loss: 0.00405 +Epoch [3459/4000] Training [29/39] Loss: 0.00401 +Epoch [3459/4000] Training [30/39] Loss: 0.00576 +Epoch [3459/4000] Training [31/39] Loss: 0.00657 +Epoch [3459/4000] Training [32/39] Loss: 0.00644 +Epoch [3459/4000] Training [33/39] Loss: 0.00523 +Epoch [3459/4000] Training [34/39] Loss: 0.00593 +Epoch [3459/4000] Training [35/39] Loss: 0.00479 +Epoch [3459/4000] Training [36/39] Loss: 0.00459 +Epoch [3459/4000] Training [37/39] Loss: 0.13047 +Epoch [3459/4000] Training [38/39] Loss: 0.13060 +Epoch [3459/4000] Training [39/39] Loss: 0.00393 +Epoch [3459/4000] Training metric {'Train/mean dice_metric': 0.9959830641746521, 'Train/mean miou_metric': 0.9924153089523315, 'Train/mean f1': 0.9966530799865723, 'Train/mean precision': 0.9961578249931335, 'Train/mean recall': 0.9971489310264587, 'Train/mean hd95_metric': 1.0001978874206543} +Epoch [3459/4000] Validation [1/10] Loss: 0.69409 focal_loss 0.60562 dice_loss 0.08847 +Epoch [3459/4000] Validation [2/10] Loss: 0.47014 focal_loss 0.37050 dice_loss 0.09965 +Epoch [3459/4000] Validation [3/10] Loss: 0.39272 focal_loss 0.28059 dice_loss 0.11213 +Epoch [3459/4000] Validation [4/10] Loss: 0.86772 focal_loss 0.28633 dice_loss 0.58139 +Epoch [3459/4000] Validation [5/10] Loss: 2.92301 focal_loss 2.24926 dice_loss 0.67376 +Epoch [3459/4000] Validation [6/10] Loss: 1.20774 focal_loss 0.50036 dice_loss 0.70738 +Epoch [3459/4000] Validation [7/10] Loss: 1.09822 focal_loss 0.44722 dice_loss 0.65100 +Epoch [3459/4000] Validation [8/10] Loss: 2.71498 focal_loss 2.05916 dice_loss 0.65582 +Epoch [3459/4000] Validation [9/10] Loss: 1.36175 focal_loss 0.82092 dice_loss 0.54083 +Epoch [3459/4000] Validation [10/10] Loss: 1.70087 focal_loss 0.97456 dice_loss 0.72632 +Epoch [3459/4000] Validation metric {'Val/mean dice_metric': 0.9509716033935547, 'Val/mean miou_metric': 0.9348161220550537, 'Val/mean f1': 0.9497030973434448, 'Val/mean precision': 0.9497233629226685, 'Val/mean recall': 0.9496829509735107, 'Val/mean hd95_metric': 10.762672424316406} +Cheakpoint... +Epoch [3459/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509716033935547, 'Val/mean miou_metric': 0.9348161220550537, 'Val/mean f1': 0.9497030973434448, 'Val/mean precision': 0.9497233629226685, 'Val/mean recall': 0.9496829509735107, 'Val/mean hd95_metric': 10.762672424316406} +Epoch [3460/4000] Training [1/39] Loss: 0.00512 +Epoch [3460/4000] Training [2/39] Loss: 0.00712 +Epoch [3460/4000] Training [3/39] Loss: 0.04265 +Epoch [3460/4000] Training [4/39] Loss: 0.12966 +Epoch [3460/4000] Training [5/39] Loss: 0.00650 +Epoch [3460/4000] Training [6/39] Loss: 0.00572 +Epoch [3460/4000] Training [7/39] Loss: 0.13070 +Epoch [3460/4000] Training [8/39] Loss: 0.12879 +Epoch [3460/4000] Training [9/39] Loss: 0.37751 +Epoch [3460/4000] Training [10/39] Loss: 0.25673 +Epoch [3460/4000] Training [11/39] Loss: 0.25525 +Epoch [3460/4000] Training [12/39] Loss: 0.00550 +Epoch [3460/4000] Training [13/39] Loss: 0.12932 +Epoch [3460/4000] Training [14/39] Loss: 0.00437 +Epoch [3460/4000] Training [15/39] Loss: 0.00415 +Epoch [3460/4000] Training [16/39] Loss: 0.00697 +Epoch [3460/4000] Training [17/39] Loss: 0.00734 +Epoch [3460/4000] Training [18/39] Loss: 0.13119 +Epoch [3460/4000] Training [19/39] Loss: 0.00279 +Epoch [3460/4000] Training [20/39] Loss: 0.00351 +Epoch [3460/4000] Training [21/39] Loss: 0.25308 +Epoch [3460/4000] Training [22/39] Loss: 0.00391 +Epoch [3460/4000] Training [23/39] Loss: 0.00677 +Epoch [3460/4000] Training [24/39] Loss: 0.00450 +Epoch [3460/4000] Training [25/39] Loss: 0.00339 +Epoch [3460/4000] Training [26/39] Loss: 0.00624 +Epoch [3460/4000] Training [27/39] Loss: 0.00634 +Epoch [3460/4000] Training [28/39] Loss: 0.12911 +Epoch [3460/4000] Training [29/39] Loss: 0.13012 +Epoch [3460/4000] Training [30/39] Loss: 0.00444 +Epoch [3460/4000] Training [31/39] Loss: 0.00519 +Epoch [3460/4000] Training [32/39] Loss: 0.00456 +Epoch [3460/4000] Training [33/39] Loss: 0.12809 +Epoch [3460/4000] Training [34/39] Loss: 0.00647 +Epoch [3460/4000] Training [35/39] Loss: 0.00472 +Epoch [3460/4000] Training [36/39] Loss: 0.00785 +Epoch [3460/4000] Training [37/39] Loss: 0.00591 +Epoch [3460/4000] Training [38/39] Loss: 0.00420 +Epoch [3460/4000] Training [39/39] Loss: 0.00727 +Epoch [3460/4000] Training metric {'Train/mean dice_metric': 0.9951281547546387, 'Train/mean miou_metric': 0.9915406703948975, 'Train/mean f1': 0.9966802597045898, 'Train/mean precision': 0.9961671233177185, 'Train/mean recall': 0.9971941113471985, 'Train/mean hd95_metric': 1.006919264793396} +Epoch [3460/4000] Validation [1/10] Loss: 0.71410 focal_loss 0.62253 dice_loss 0.09157 +Epoch [3460/4000] Validation [2/10] Loss: 0.47198 focal_loss 0.37362 dice_loss 0.09836 +Epoch [3460/4000] Validation [3/10] Loss: 0.36655 focal_loss 0.25643 dice_loss 0.11012 +Epoch [3460/4000] Validation [4/10] Loss: 0.85239 focal_loss 0.28762 dice_loss 0.56478 +Epoch [3460/4000] Validation [5/10] Loss: 2.97193 focal_loss 2.29907 dice_loss 0.67286 +Epoch [3460/4000] Validation [6/10] Loss: 1.26339 focal_loss 0.54404 dice_loss 0.71935 +Epoch [3460/4000] Validation [7/10] Loss: 1.13980 focal_loss 0.48619 dice_loss 0.65361 +Epoch [3460/4000] Validation [8/10] Loss: 2.41981 focal_loss 1.78539 dice_loss 0.63442 +Epoch [3460/4000] Validation [9/10] Loss: 1.35516 focal_loss 0.81471 dice_loss 0.54045 +Epoch [3460/4000] Validation [10/10] Loss: 1.78175 focal_loss 1.04775 dice_loss 0.73400 +Epoch [3460/4000] Validation metric {'Val/mean dice_metric': 0.9501972794532776, 'Val/mean miou_metric': 0.9339894652366638, 'Val/mean f1': 0.9491779208183289, 'Val/mean precision': 0.9458240866661072, 'Val/mean recall': 0.9525555372238159, 'Val/mean hd95_metric': 10.725438117980957} +Cheakpoint... +Epoch [3460/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9501972794532776, 'Val/mean miou_metric': 0.9339894652366638, 'Val/mean f1': 0.9491779208183289, 'Val/mean precision': 0.9458240866661072, 'Val/mean recall': 0.9525555372238159, 'Val/mean hd95_metric': 10.725438117980957} +Epoch [3461/4000] Training [1/39] Loss: 0.25440 +Epoch [3461/4000] Training [2/39] Loss: 0.25271 +Epoch [3461/4000] Training [3/39] Loss: 0.12953 +Epoch [3461/4000] Training [4/39] Loss: 0.00423 +Epoch [3461/4000] Training [5/39] Loss: 0.00400 +Epoch [3461/4000] Training [6/39] Loss: 0.00648 +Epoch [3461/4000] Training [7/39] Loss: 0.00452 +Epoch [3461/4000] Training [8/39] Loss: 0.00819 +Epoch [3461/4000] Training [9/39] Loss: 0.13168 +Epoch [3461/4000] Training [10/39] Loss: 0.00373 +Epoch [3461/4000] Training [11/39] Loss: 0.13179 +Epoch [3461/4000] Training [12/39] Loss: 0.12951 +Epoch [3461/4000] Training [13/39] Loss: 0.00603 +Epoch [3461/4000] Training [14/39] Loss: 0.00883 +Epoch [3461/4000] Training [15/39] Loss: 0.00593 +Epoch [3461/4000] Training [16/39] Loss: 0.00339 +Epoch [3461/4000] Training [17/39] Loss: 0.00413 +Epoch [3461/4000] Training [18/39] Loss: 0.25408 +Epoch [3461/4000] Training [19/39] Loss: 0.25242 +Epoch [3461/4000] Training [20/39] Loss: 0.04976 +Epoch [3461/4000] Training [21/39] Loss: 0.12770 +Epoch [3461/4000] Training [22/39] Loss: 0.00387 +Epoch [3461/4000] Training [23/39] Loss: 0.25535 +Epoch [3461/4000] Training [24/39] Loss: 0.00504 +Epoch [3461/4000] Training [25/39] Loss: 0.00547 +Epoch [3461/4000] Training [26/39] Loss: 0.00588 +Epoch [3461/4000] Training [27/39] Loss: 0.00467 +Epoch [3461/4000] Training [28/39] Loss: 0.37850 +Epoch [3461/4000] Training [29/39] Loss: 0.00840 +Epoch [3461/4000] Training [30/39] Loss: 0.00660 +Epoch [3461/4000] Training [31/39] Loss: 0.00436 +Epoch [3461/4000] Training [32/39] Loss: 0.00343 +Epoch [3461/4000] Training [33/39] Loss: 0.00368 +Epoch [3461/4000] Training [34/39] Loss: 0.00501 +Epoch [3461/4000] Training [35/39] Loss: 0.00390 +Epoch [3461/4000] Training [36/39] Loss: 0.00513 +Epoch [3461/4000] Training [37/39] Loss: 0.12929 +Epoch [3461/4000] Training [38/39] Loss: 0.00550 +Epoch [3461/4000] Training [39/39] Loss: 0.00423 +Epoch [3461/4000] Training metric {'Train/mean dice_metric': 0.9957932233810425, 'Train/mean miou_metric': 0.992065966129303, 'Train/mean f1': 0.9964605569839478, 'Train/mean precision': 0.9960615038871765, 'Train/mean recall': 0.9968599081039429, 'Train/mean hd95_metric': 1.0158004760742188} +Epoch [3461/4000] Validation [1/10] Loss: 0.68242 focal_loss 0.59509 dice_loss 0.08734 +Epoch [3461/4000] Validation [2/10] Loss: 0.49077 focal_loss 0.38843 dice_loss 0.10234 +Epoch [3461/4000] Validation [3/10] Loss: 0.38789 focal_loss 0.27561 dice_loss 0.11228 +Epoch [3461/4000] Validation [4/10] Loss: 0.85936 focal_loss 0.27879 dice_loss 0.58057 +Epoch [3461/4000] Validation [5/10] Loss: 2.88450 focal_loss 2.21099 dice_loss 0.67350 +Epoch [3461/4000] Validation [6/10] Loss: 1.23311 focal_loss 0.51564 dice_loss 0.71747 +Epoch [3461/4000] Validation [7/10] Loss: 1.11936 focal_loss 0.46258 dice_loss 0.65678 +Epoch [3461/4000] Validation [8/10] Loss: 2.79501 focal_loss 2.13564 dice_loss 0.65937 +Epoch [3461/4000] Validation [9/10] Loss: 1.35859 focal_loss 0.81881 dice_loss 0.53979 +Epoch [3461/4000] Validation [10/10] Loss: 1.72866 focal_loss 1.00098 dice_loss 0.72768 +Epoch [3461/4000] Validation metric {'Val/mean dice_metric': 0.950353741645813, 'Val/mean miou_metric': 0.9339517951011658, 'Val/mean f1': 0.9491906762123108, 'Val/mean precision': 0.9496372938156128, 'Val/mean recall': 0.9487442374229431, 'Val/mean hd95_metric': 10.940077781677246} +Cheakpoint... +Epoch [3461/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950353741645813, 'Val/mean miou_metric': 0.9339517951011658, 'Val/mean f1': 0.9491906762123108, 'Val/mean precision': 0.9496372938156128, 'Val/mean recall': 0.9487442374229431, 'Val/mean hd95_metric': 10.940077781677246} +Epoch [3462/4000] Training [1/39] Loss: 0.00717 +Epoch [3462/4000] Training [2/39] Loss: 0.25513 +Epoch [3462/4000] Training [3/39] Loss: 0.25301 +Epoch [3462/4000] Training [4/39] Loss: 0.13304 +Epoch [3462/4000] Training [5/39] Loss: 0.00875 +Epoch [3462/4000] Training [6/39] Loss: 0.00455 +Epoch [3462/4000] Training [7/39] Loss: 0.13059 +Epoch [3462/4000] Training [8/39] Loss: 0.00483 +Epoch [3462/4000] Training [9/39] Loss: 0.12939 +Epoch [3462/4000] Training [10/39] Loss: 0.00520 +Epoch [3462/4000] Training [11/39] Loss: 0.00424 +Epoch [3462/4000] Training [12/39] Loss: 0.13224 +Epoch [3462/4000] Training [13/39] Loss: 0.00485 +Epoch [3462/4000] Training [14/39] Loss: 0.12978 +Epoch [3462/4000] Training [15/39] Loss: 0.13036 +Epoch [3462/4000] Training [16/39] Loss: 0.00319 +Epoch [3462/4000] Training [17/39] Loss: 0.00511 +Epoch [3462/4000] Training [18/39] Loss: 0.13062 +Epoch [3462/4000] Training [19/39] Loss: 0.00292 +Epoch [3462/4000] Training [20/39] Loss: 0.00342 +Epoch [3462/4000] Training [21/39] Loss: 0.12866 +Epoch [3462/4000] Training [22/39] Loss: 0.00663 +Epoch [3462/4000] Training [23/39] Loss: 0.13086 +Epoch [3462/4000] Training [24/39] Loss: 0.25337 +Epoch [3462/4000] Training [25/39] Loss: 0.00691 +Epoch [3462/4000] Training [26/39] Loss: 0.12855 +Epoch [3462/4000] Training [27/39] Loss: 0.00370 +Epoch [3462/4000] Training [28/39] Loss: 0.00696 +Epoch [3462/4000] Training [29/39] Loss: 0.00735 +Epoch [3462/4000] Training [30/39] Loss: 0.13116 +Epoch [3462/4000] Training [31/39] Loss: 0.00756 +Epoch [3462/4000] Training [32/39] Loss: 0.00506 +Epoch [3462/4000] Training [33/39] Loss: 0.12830 +Epoch [3462/4000] Training [34/39] Loss: 0.13403 +Epoch [3462/4000] Training [35/39] Loss: 0.00282 +Epoch [3462/4000] Training [36/39] Loss: 0.00467 +Epoch [3462/4000] Training [37/39] Loss: 0.00595 +Epoch [3462/4000] Training [38/39] Loss: 0.00341 +Epoch [3462/4000] Training [39/39] Loss: 0.00418 +Epoch [3462/4000] Training metric {'Train/mean dice_metric': 0.9959067702293396, 'Train/mean miou_metric': 0.9922686815261841, 'Train/mean f1': 0.9965711236000061, 'Train/mean precision': 0.9960954785346985, 'Train/mean recall': 0.9970474243164062, 'Train/mean hd95_metric': 1.0409716367721558} +Epoch [3462/4000] Validation [1/10] Loss: 0.73606 focal_loss 0.64376 dice_loss 0.09231 +Epoch [3462/4000] Validation [2/10] Loss: 0.47901 focal_loss 0.38113 dice_loss 0.09788 +Epoch [3462/4000] Validation [3/10] Loss: 0.36805 focal_loss 0.25732 dice_loss 0.11074 +Epoch [3462/4000] Validation [4/10] Loss: 0.85483 focal_loss 0.29193 dice_loss 0.56290 +Epoch [3462/4000] Validation [5/10] Loss: 2.93495 focal_loss 2.26207 dice_loss 0.67288 +Epoch [3462/4000] Validation [6/10] Loss: 1.28270 focal_loss 0.56423 dice_loss 0.71846 +Epoch [3462/4000] Validation [7/10] Loss: 1.14813 focal_loss 0.49038 dice_loss 0.65775 +Epoch [3462/4000] Validation [8/10] Loss: 2.23516 focal_loss 1.62005 dice_loss 0.61511 +Epoch [3462/4000] Validation [9/10] Loss: 1.36700 focal_loss 0.82450 dice_loss 0.54250 +Epoch [3462/4000] Validation [10/10] Loss: 1.78121 focal_loss 1.04756 dice_loss 0.73365 +Epoch [3462/4000] Validation metric {'Val/mean dice_metric': 0.9511170387268066, 'Val/mean miou_metric': 0.9349066019058228, 'Val/mean f1': 0.9489057660102844, 'Val/mean precision': 0.9449281692504883, 'Val/mean recall': 0.9529169797897339, 'Val/mean hd95_metric': 10.760372161865234} +Cheakpoint... +Epoch [3462/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511170387268066, 'Val/mean miou_metric': 0.9349066019058228, 'Val/mean f1': 0.9489057660102844, 'Val/mean precision': 0.9449281692504883, 'Val/mean recall': 0.9529169797897339, 'Val/mean hd95_metric': 10.760372161865234} +Epoch [3463/4000] Training [1/39] Loss: 0.00628 +Epoch [3463/4000] Training [2/39] Loss: 0.00392 +Epoch [3463/4000] Training [3/39] Loss: 0.00597 +Epoch [3463/4000] Training [4/39] Loss: 0.25603 +Epoch [3463/4000] Training [5/39] Loss: 0.00730 +Epoch [3463/4000] Training [6/39] Loss: 0.00632 +Epoch [3463/4000] Training [7/39] Loss: 0.00776 +Epoch [3463/4000] Training [8/39] Loss: 0.00551 +Epoch [3463/4000] Training [9/39] Loss: 0.00576 +Epoch [3463/4000] Training [10/39] Loss: 0.00499 +Epoch [3463/4000] Training [11/39] Loss: 0.00503 +Epoch [3463/4000] Training [12/39] Loss: 0.00623 +Epoch [3463/4000] Training [13/39] Loss: 0.00844 +Epoch [3463/4000] Training [14/39] Loss: 0.00960 +Epoch [3463/4000] Training [15/39] Loss: 0.00781 +Epoch [3463/4000] Training [16/39] Loss: 0.00506 +Epoch [3463/4000] Training [17/39] Loss: 0.00632 +Epoch [3463/4000] Training [18/39] Loss: 0.00592 +Epoch [3463/4000] Training [19/39] Loss: 0.00517 +Epoch [3463/4000] Training [20/39] Loss: 0.00517 +Epoch [3463/4000] Training [21/39] Loss: 0.00638 +Epoch [3463/4000] Training [22/39] Loss: 0.00746 +Epoch [3463/4000] Training [23/39] Loss: 0.12802 +Epoch [3463/4000] Training [24/39] Loss: 0.12883 +Epoch [3463/4000] Training [25/39] Loss: 0.00467 +Epoch [3463/4000] Training [26/39] Loss: 0.00361 +Epoch [3463/4000] Training [27/39] Loss: 0.00650 +Epoch [3463/4000] Training [28/39] Loss: 0.13347 +Epoch [3463/4000] Training [29/39] Loss: 0.00594 +Epoch [3463/4000] Training [30/39] Loss: 0.13110 +Epoch [3463/4000] Training [31/39] Loss: 0.00390 +Epoch [3463/4000] Training [32/39] Loss: 0.00364 +Epoch [3463/4000] Training [33/39] Loss: 0.00541 +Epoch [3463/4000] Training [34/39] Loss: 0.13069 +Epoch [3463/4000] Training [35/39] Loss: 0.00515 +Epoch [3463/4000] Training [36/39] Loss: 0.00561 +Epoch [3463/4000] Training [37/39] Loss: 0.00460 +Epoch [3463/4000] Training [38/39] Loss: 0.00385 +Epoch [3463/4000] Training [39/39] Loss: 0.00394 +Epoch [3463/4000] Training metric {'Train/mean dice_metric': 0.995589017868042, 'Train/mean miou_metric': 0.9916432499885559, 'Train/mean f1': 0.9964182376861572, 'Train/mean precision': 0.9960161447525024, 'Train/mean recall': 0.9968207478523254, 'Train/mean hd95_metric': 1.0334333181381226} +Epoch [3463/4000] Validation [1/10] Loss: 0.69966 focal_loss 0.61323 dice_loss 0.08643 +Epoch [3463/4000] Validation [2/10] Loss: 0.48960 focal_loss 0.38508 dice_loss 0.10452 +Epoch [3463/4000] Validation [3/10] Loss: 0.39860 focal_loss 0.28545 dice_loss 0.11315 +Epoch [3463/4000] Validation [4/10] Loss: 0.84548 focal_loss 0.26970 dice_loss 0.57578 +Epoch [3463/4000] Validation [5/10] Loss: 2.94103 focal_loss 2.26747 dice_loss 0.67356 +Epoch [3463/4000] Validation [6/10] Loss: 1.21869 focal_loss 0.49908 dice_loss 0.71961 +Epoch [3463/4000] Validation [7/10] Loss: 1.08420 focal_loss 0.43480 dice_loss 0.64940 +Epoch [3463/4000] Validation [8/10] Loss: 2.60155 focal_loss 1.95080 dice_loss 0.65074 +Epoch [3463/4000] Validation [9/10] Loss: 1.37046 focal_loss 0.82973 dice_loss 0.54072 +Epoch [3463/4000] Validation [10/10] Loss: 1.69382 focal_loss 0.96991 dice_loss 0.72391 +Epoch [3463/4000] Validation metric {'Val/mean dice_metric': 0.9504912495613098, 'Val/mean miou_metric': 0.9340737462043762, 'Val/mean f1': 0.9500409364700317, 'Val/mean precision': 0.9506024122238159, 'Val/mean recall': 0.9494801759719849, 'Val/mean hd95_metric': 10.525960922241211} +Cheakpoint... +Epoch [3463/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504912495613098, 'Val/mean miou_metric': 0.9340737462043762, 'Val/mean f1': 0.9500409364700317, 'Val/mean precision': 0.9506024122238159, 'Val/mean recall': 0.9494801759719849, 'Val/mean hd95_metric': 10.525960922241211} +Epoch [3464/4000] Training [1/39] Loss: 0.00360 +Epoch [3464/4000] Training [2/39] Loss: 0.00462 +Epoch [3464/4000] Training [3/39] Loss: 0.00926 +Epoch [3464/4000] Training [4/39] Loss: 0.13091 +Epoch [3464/4000] Training [5/39] Loss: 0.12720 +Epoch [3464/4000] Training [6/39] Loss: 0.12824 +Epoch [3464/4000] Training [7/39] Loss: 0.13092 +Epoch [3464/4000] Training [8/39] Loss: 0.13032 +Epoch [3464/4000] Training [9/39] Loss: 0.12781 +Epoch [3464/4000] Training [10/39] Loss: 0.00624 +Epoch [3464/4000] Training [11/39] Loss: 0.00573 +Epoch [3464/4000] Training [12/39] Loss: 0.00530 +Epoch [3464/4000] Training [13/39] Loss: 0.00383 +Epoch [3464/4000] Training [14/39] Loss: 0.00477 +Epoch [3464/4000] Training [15/39] Loss: 0.13173 +Epoch [3464/4000] Training [16/39] Loss: 0.00308 +Epoch [3464/4000] Training [17/39] Loss: 0.00526 +Epoch [3464/4000] Training [18/39] Loss: 0.00576 +Epoch [3464/4000] Training [19/39] Loss: 0.12883 +Epoch [3464/4000] Training [20/39] Loss: 0.00571 +Epoch [3464/4000] Training [21/39] Loss: 0.00426 +Epoch [3464/4000] Training [22/39] Loss: 0.00618 +Epoch [3464/4000] Training [23/39] Loss: 0.00610 +Epoch [3464/4000] Training [24/39] Loss: 0.00505 +Epoch [3464/4000] Training [25/39] Loss: 0.00362 +Epoch [3464/4000] Training [26/39] Loss: 0.13118 +Epoch [3464/4000] Training [27/39] Loss: 0.00504 +Epoch [3464/4000] Training [28/39] Loss: 0.12921 +Epoch [3464/4000] Training [29/39] Loss: 0.12919 +Epoch [3464/4000] Training [30/39] Loss: 0.12797 +Epoch [3464/4000] Training [31/39] Loss: 0.12987 +Epoch [3464/4000] Training [32/39] Loss: 0.00519 +Epoch [3464/4000] Training [33/39] Loss: 0.00508 +Epoch [3464/4000] Training [34/39] Loss: 0.00469 +Epoch [3464/4000] Training [35/39] Loss: 0.12918 +Epoch [3464/4000] Training [36/39] Loss: 0.00500 +Epoch [3464/4000] Training [37/39] Loss: 0.00608 +Epoch [3464/4000] Training [38/39] Loss: 0.00706 +Epoch [3464/4000] Training [39/39] Loss: 0.12917 +Epoch [3464/4000] Training metric {'Train/mean dice_metric': 0.9951131343841553, 'Train/mean miou_metric': 0.991514265537262, 'Train/mean f1': 0.9966928362846375, 'Train/mean precision': 0.9962427616119385, 'Train/mean recall': 0.9971433281898499, 'Train/mean hd95_metric': 1.192676067352295} +Epoch [3464/4000] Validation [1/10] Loss: 0.69592 focal_loss 0.61026 dice_loss 0.08566 +Epoch [3464/4000] Validation [2/10] Loss: 0.47588 focal_loss 0.37583 dice_loss 0.10006 +Epoch [3464/4000] Validation [3/10] Loss: 0.38548 focal_loss 0.27414 dice_loss 0.11134 +Epoch [3464/4000] Validation [4/10] Loss: 0.84793 focal_loss 0.27109 dice_loss 0.57683 +Epoch [3464/4000] Validation [5/10] Loss: 2.97728 focal_loss 2.30413 dice_loss 0.67315 +Epoch [3464/4000] Validation [6/10] Loss: 1.25129 focal_loss 0.52507 dice_loss 0.72622 +Epoch [3464/4000] Validation [7/10] Loss: 1.09190 focal_loss 0.44242 dice_loss 0.64948 +Epoch [3464/4000] Validation [8/10] Loss: 2.44597 focal_loss 1.80941 dice_loss 0.63656 +Epoch [3464/4000] Validation [9/10] Loss: 1.35009 focal_loss 0.81030 dice_loss 0.53979 +Epoch [3464/4000] Validation [10/10] Loss: 1.75447 focal_loss 1.02563 dice_loss 0.72883 +Epoch [3464/4000] Validation metric {'Val/mean dice_metric': 0.9504621028900146, 'Val/mean miou_metric': 0.934429943561554, 'Val/mean f1': 0.9504005312919617, 'Val/mean precision': 0.9495062232017517, 'Val/mean recall': 0.9512965679168701, 'Val/mean hd95_metric': 10.266751289367676} +Cheakpoint... +Epoch [3464/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504621028900146, 'Val/mean miou_metric': 0.934429943561554, 'Val/mean f1': 0.9504005312919617, 'Val/mean precision': 0.9495062232017517, 'Val/mean recall': 0.9512965679168701, 'Val/mean hd95_metric': 10.266751289367676} +Epoch [3465/4000] Training [1/39] Loss: 0.00794 +Epoch [3465/4000] Training [2/39] Loss: 0.00663 +Epoch [3465/4000] Training [3/39] Loss: 0.12843 +Epoch [3465/4000] Training [4/39] Loss: 0.00479 +Epoch [3465/4000] Training [5/39] Loss: 0.00572 +Epoch [3465/4000] Training [6/39] Loss: 0.00323 +Epoch [3465/4000] Training [7/39] Loss: 0.00607 +Epoch [3465/4000] Training [8/39] Loss: 0.00591 +Epoch [3465/4000] Training [9/39] Loss: 0.00596 +Epoch [3465/4000] Training [10/39] Loss: 0.00413 +Epoch [3465/4000] Training [11/39] Loss: 0.12933 +Epoch [3465/4000] Training [12/39] Loss: 0.00698 +Epoch [3465/4000] Training [13/39] Loss: 0.00652 +Epoch [3465/4000] Training [14/39] Loss: 0.00530 +Epoch [3465/4000] Training [15/39] Loss: 0.00532 +Epoch [3465/4000] Training [16/39] Loss: 0.00591 +Epoch [3465/4000] Training [17/39] Loss: 0.00698 +Epoch [3465/4000] Training [18/39] Loss: 0.00671 +Epoch [3465/4000] Training [19/39] Loss: 0.00489 +Epoch [3465/4000] Training [20/39] Loss: 0.12914 +Epoch [3465/4000] Training [21/39] Loss: 0.00361 +Epoch [3465/4000] Training [22/39] Loss: 0.00780 +Epoch [3465/4000] Training [23/39] Loss: 0.25315 +Epoch [3465/4000] Training [24/39] Loss: 0.00665 +Epoch [3465/4000] Training [25/39] Loss: 0.12929 +Epoch [3465/4000] Training [26/39] Loss: 0.00627 +Epoch [3465/4000] Training [27/39] Loss: 0.00331 +Epoch [3465/4000] Training [28/39] Loss: 0.00662 +Epoch [3465/4000] Training [29/39] Loss: 0.00817 +Epoch [3465/4000] Training [30/39] Loss: 0.04773 +Epoch [3465/4000] Training [31/39] Loss: 0.00680 +Epoch [3465/4000] Training [32/39] Loss: 0.12914 +Epoch [3465/4000] Training [33/39] Loss: 0.00355 +Epoch [3465/4000] Training [34/39] Loss: 0.12889 +Epoch [3465/4000] Training [35/39] Loss: 0.00579 +Epoch [3465/4000] Training [36/39] Loss: 0.12848 +Epoch [3465/4000] Training [37/39] Loss: 0.12858 +Epoch [3465/4000] Training [38/39] Loss: 0.00500 +Epoch [3465/4000] Training [39/39] Loss: 0.00677 +Epoch [3465/4000] Training metric {'Train/mean dice_metric': 0.9958381652832031, 'Train/mean miou_metric': 0.9921327233314514, 'Train/mean f1': 0.9965260624885559, 'Train/mean precision': 0.9960764646530151, 'Train/mean recall': 0.9969760179519653, 'Train/mean hd95_metric': 1.2176724672317505} +Epoch [3465/4000] Validation [1/10] Loss: 0.67774 focal_loss 0.59375 dice_loss 0.08400 +Epoch [3465/4000] Validation [2/10] Loss: 0.51539 focal_loss 0.40901 dice_loss 0.10639 +Epoch [3465/4000] Validation [3/10] Loss: 0.38255 focal_loss 0.27212 dice_loss 0.11044 +Epoch [3465/4000] Validation [4/10] Loss: 0.85541 focal_loss 0.28292 dice_loss 0.57249 +Epoch [3465/4000] Validation [5/10] Loss: 2.96926 focal_loss 2.29616 dice_loss 0.67310 +Epoch [3465/4000] Validation [6/10] Loss: 1.27264 focal_loss 0.54520 dice_loss 0.72744 +Epoch [3465/4000] Validation [7/10] Loss: 1.12274 focal_loss 0.47225 dice_loss 0.65049 +Epoch [3465/4000] Validation [8/10] Loss: 2.55243 focal_loss 1.90835 dice_loss 0.64408 +Epoch [3465/4000] Validation [9/10] Loss: 1.38910 focal_loss 0.84754 dice_loss 0.54156 +Epoch [3465/4000] Validation [10/10] Loss: 1.75453 focal_loss 1.02669 dice_loss 0.72784 +Epoch [3465/4000] Validation metric {'Val/mean dice_metric': 0.9510250091552734, 'Val/mean miou_metric': 0.9348886609077454, 'Val/mean f1': 0.9497659802436829, 'Val/mean precision': 0.9489336013793945, 'Val/mean recall': 0.950599730014801, 'Val/mean hd95_metric': 10.607617378234863} +Cheakpoint... +Epoch [3465/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510250091552734, 'Val/mean miou_metric': 0.9348886609077454, 'Val/mean f1': 0.9497659802436829, 'Val/mean precision': 0.9489336013793945, 'Val/mean recall': 0.950599730014801, 'Val/mean hd95_metric': 10.607617378234863} +Epoch [3466/4000] Training [1/39] Loss: 0.00479 +Epoch [3466/4000] Training [2/39] Loss: 0.00886 +Epoch [3466/4000] Training [3/39] Loss: 0.00230 +Epoch [3466/4000] Training [4/39] Loss: 0.00299 +Epoch [3466/4000] Training [5/39] Loss: 0.00344 +Epoch [3466/4000] Training [6/39] Loss: 0.12882 +Epoch [3466/4000] Training [7/39] Loss: 0.05092 +Epoch [3466/4000] Training [8/39] Loss: 0.00395 +Epoch [3466/4000] Training [9/39] Loss: 0.00508 +Epoch [3466/4000] Training [10/39] Loss: 0.00513 +Epoch [3466/4000] Training [11/39] Loss: 0.12775 +Epoch [3466/4000] Training [12/39] Loss: 0.00612 +Epoch [3466/4000] Training [13/39] Loss: 0.25581 +Epoch [3466/4000] Training [14/39] Loss: 0.00482 +Epoch [3466/4000] Training [15/39] Loss: 0.00462 +Epoch [3466/4000] Training [16/39] Loss: 0.00468 +Epoch [3466/4000] Training [17/39] Loss: 0.12997 +Epoch [3466/4000] Training [18/39] Loss: 0.13125 +Epoch [3466/4000] Training [19/39] Loss: 0.00554 +Epoch [3466/4000] Training [20/39] Loss: 0.00488 +Epoch [3466/4000] Training [21/39] Loss: 0.00589 +Epoch [3466/4000] Training [22/39] Loss: 0.00522 +Epoch [3466/4000] Training [23/39] Loss: 0.00455 +Epoch [3466/4000] Training [24/39] Loss: 0.00358 +Epoch [3466/4000] Training [25/39] Loss: 0.00582 +Epoch [3466/4000] Training [26/39] Loss: 0.13162 +Epoch [3466/4000] Training [27/39] Loss: 0.00560 +Epoch [3466/4000] Training [28/39] Loss: 0.00773 +Epoch [3466/4000] Training [29/39] Loss: 0.00369 +Epoch [3466/4000] Training [30/39] Loss: 0.12997 +Epoch [3466/4000] Training [31/39] Loss: 0.00611 +Epoch [3466/4000] Training [32/39] Loss: 0.12877 +Epoch [3466/4000] Training [33/39] Loss: 0.00689 +Epoch [3466/4000] Training [34/39] Loss: 0.00508 +Epoch [3466/4000] Training [35/39] Loss: 0.00702 +Epoch [3466/4000] Training [36/39] Loss: 0.00833 +Epoch [3466/4000] Training [37/39] Loss: 0.00708 +Epoch [3466/4000] Training [38/39] Loss: 0.00431 +Epoch [3466/4000] Training [39/39] Loss: 0.12863 +Epoch [3466/4000] Training metric {'Train/mean dice_metric': 0.9957444071769714, 'Train/mean miou_metric': 0.9919573664665222, 'Train/mean f1': 0.9965471625328064, 'Train/mean precision': 0.9960498809814453, 'Train/mean recall': 0.99704509973526, 'Train/mean hd95_metric': 1.0271170139312744} +Epoch [3466/4000] Validation [1/10] Loss: 0.68641 focal_loss 0.60033 dice_loss 0.08609 +Epoch [3466/4000] Validation [2/10] Loss: 0.49299 focal_loss 0.39096 dice_loss 0.10204 +Epoch [3466/4000] Validation [3/10] Loss: 0.37043 focal_loss 0.26079 dice_loss 0.10964 +Epoch [3466/4000] Validation [4/10] Loss: 0.84222 focal_loss 0.27999 dice_loss 0.56223 +Epoch [3466/4000] Validation [5/10] Loss: 2.95481 focal_loss 2.28167 dice_loss 0.67313 +Epoch [3466/4000] Validation [6/10] Loss: 1.25182 focal_loss 0.53235 dice_loss 0.71948 +Epoch [3466/4000] Validation [7/10] Loss: 1.12062 focal_loss 0.46567 dice_loss 0.65495 +Epoch [3466/4000] Validation [8/10] Loss: 2.38124 focal_loss 1.74775 dice_loss 0.63349 +Epoch [3466/4000] Validation [9/10] Loss: 1.35513 focal_loss 0.81386 dice_loss 0.54127 +Epoch [3466/4000] Validation [10/10] Loss: 1.75057 focal_loss 1.02025 dice_loss 0.73033 +Epoch [3466/4000] Validation metric {'Val/mean dice_metric': 0.9510334730148315, 'Val/mean miou_metric': 0.9347901344299316, 'Val/mean f1': 0.9500471353530884, 'Val/mean precision': 0.9476627707481384, 'Val/mean recall': 0.9524434208869934, 'Val/mean hd95_metric': 10.66405200958252} +Cheakpoint... +Epoch [3466/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510334730148315, 'Val/mean miou_metric': 0.9347901344299316, 'Val/mean f1': 0.9500471353530884, 'Val/mean precision': 0.9476627707481384, 'Val/mean recall': 0.9524434208869934, 'Val/mean hd95_metric': 10.66405200958252} +Epoch [3467/4000] Training [1/39] Loss: 0.12855 +Epoch [3467/4000] Training [2/39] Loss: 0.08528 +Epoch [3467/4000] Training [3/39] Loss: 0.13068 +Epoch [3467/4000] Training [4/39] Loss: 0.12841 +Epoch [3467/4000] Training [5/39] Loss: 0.00664 +Epoch [3467/4000] Training [6/39] Loss: 0.00495 +Epoch [3467/4000] Training [7/39] Loss: 0.00738 +Epoch [3467/4000] Training [8/39] Loss: 0.12929 +Epoch [3467/4000] Training [9/39] Loss: 0.00476 +Epoch [3467/4000] Training [10/39] Loss: 0.00377 +Epoch [3467/4000] Training [11/39] Loss: 0.25555 +Epoch [3467/4000] Training [12/39] Loss: 0.00467 +Epoch [3467/4000] Training [13/39] Loss: 0.00511 +Epoch [3467/4000] Training [14/39] Loss: 0.13087 +Epoch [3467/4000] Training [15/39] Loss: 0.00635 +Epoch [3467/4000] Training [16/39] Loss: 0.13054 +Epoch [3467/4000] Training [17/39] Loss: 0.00811 +Epoch [3467/4000] Training [18/39] Loss: 0.13006 +Epoch [3467/4000] Training [19/39] Loss: 0.00654 +Epoch [3467/4000] Training [20/39] Loss: 0.00537 +Epoch [3467/4000] Training [21/39] Loss: 0.00433 +Epoch [3467/4000] Training [22/39] Loss: 0.00544 +Epoch [3467/4000] Training [23/39] Loss: 0.13028 +Epoch [3467/4000] Training [24/39] Loss: 0.00467 +Epoch [3467/4000] Training [25/39] Loss: 0.00711 +Epoch [3467/4000] Training [26/39] Loss: 0.00696 +Epoch [3467/4000] Training [27/39] Loss: 0.00739 +Epoch [3467/4000] Training [28/39] Loss: 0.00714 +Epoch [3467/4000] Training [29/39] Loss: 0.00544 +Epoch [3467/4000] Training [30/39] Loss: 0.00669 +Epoch [3467/4000] Training [31/39] Loss: 0.12847 +Epoch [3467/4000] Training [32/39] Loss: 0.00356 +Epoch [3467/4000] Training [33/39] Loss: 0.00663 +Epoch [3467/4000] Training [34/39] Loss: 0.13157 +Epoch [3467/4000] Training [35/39] Loss: 0.00670 +Epoch [3467/4000] Training [36/39] Loss: 0.12872 +Epoch [3467/4000] Training [37/39] Loss: 0.00668 +Epoch [3467/4000] Training [38/39] Loss: 0.00644 +Epoch [3467/4000] Training [39/39] Loss: 0.00497 +Epoch [3467/4000] Training metric {'Train/mean dice_metric': 0.9954698085784912, 'Train/mean miou_metric': 0.9914087653160095, 'Train/mean f1': 0.9961360692977905, 'Train/mean precision': 0.9956747889518738, 'Train/mean recall': 0.9965976476669312, 'Train/mean hd95_metric': 1.0762697458267212} +Epoch [3467/4000] Validation [1/10] Loss: 0.70873 focal_loss 0.62001 dice_loss 0.08872 +Epoch [3467/4000] Validation [2/10] Loss: 0.47229 focal_loss 0.37638 dice_loss 0.09591 +Epoch [3467/4000] Validation [3/10] Loss: 0.36275 focal_loss 0.25400 dice_loss 0.10876 +Epoch [3467/4000] Validation [4/10] Loss: 0.85177 focal_loss 0.28852 dice_loss 0.56325 +Epoch [3467/4000] Validation [5/10] Loss: 2.99506 focal_loss 2.32252 dice_loss 0.67255 +Epoch [3467/4000] Validation [6/10] Loss: 1.28713 focal_loss 0.56253 dice_loss 0.72459 +Epoch [3467/4000] Validation [7/10] Loss: 1.14650 focal_loss 0.49253 dice_loss 0.65397 +Epoch [3467/4000] Validation [8/10] Loss: 2.33752 focal_loss 1.71373 dice_loss 0.62379 +Epoch [3467/4000] Validation [9/10] Loss: 1.35864 focal_loss 0.81610 dice_loss 0.54254 +Epoch [3467/4000] Validation [10/10] Loss: 1.80356 focal_loss 1.07066 dice_loss 0.73290 +Epoch [3467/4000] Validation metric {'Val/mean dice_metric': 0.9509502649307251, 'Val/mean miou_metric': 0.9344938397407532, 'Val/mean f1': 0.9490959644317627, 'Val/mean precision': 0.9455352425575256, 'Val/mean recall': 0.9526835680007935, 'Val/mean hd95_metric': 10.753142356872559} +Cheakpoint... +Epoch [3467/4000] best acc:tensor([0.9514], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509502649307251, 'Val/mean miou_metric': 0.9344938397407532, 'Val/mean f1': 0.9490959644317627, 'Val/mean precision': 0.9455352425575256, 'Val/mean recall': 0.9526835680007935, 'Val/mean hd95_metric': 10.753142356872559} +Epoch [3468/4000] Training [1/39] Loss: 0.12939 +Epoch [3468/4000] Training [2/39] Loss: 0.00482 +Epoch [3468/4000] Training [3/39] Loss: 0.00469 +Epoch [3468/4000] Training [4/39] Loss: 0.00464 +Epoch [3468/4000] Training [5/39] Loss: 0.00500 +Epoch [3468/4000] Training [6/39] Loss: 0.00671 +Epoch [3468/4000] Training [7/39] Loss: 0.12806 +Epoch [3468/4000] Training [8/39] Loss: 0.00427 +Epoch [3468/4000] Training [9/39] Loss: 0.00395 +Epoch [3468/4000] Training [10/39] Loss: 0.25387 +Epoch [3468/4000] Training [11/39] Loss: 0.00547 +Epoch [3468/4000] Training [12/39] Loss: 0.00724 +Epoch [3468/4000] Training [13/39] Loss: 0.00346 +Epoch [3468/4000] Training [14/39] Loss: 0.00367 +Epoch [3468/4000] Training [15/39] Loss: 0.25353 +Epoch [3468/4000] Training [16/39] Loss: 0.00380 +Epoch [3468/4000] Training [17/39] Loss: 0.00504 +Epoch [3468/4000] Training [18/39] Loss: 0.00620 +Epoch [3468/4000] Training [19/39] Loss: 0.12911 +Epoch [3468/4000] Training [20/39] Loss: 0.00351 +Epoch [3468/4000] Training [21/39] Loss: 0.13025 +Epoch [3468/4000] Training [22/39] Loss: 0.00440 +Epoch [3468/4000] Training [23/39] Loss: 0.13148 +Epoch [3468/4000] Training [24/39] Loss: 0.12852 +Epoch [3468/4000] Training [25/39] Loss: 0.00467 +Epoch [3468/4000] Training [26/39] Loss: 0.00436 +Epoch [3468/4000] Training [27/39] Loss: 0.00538 +Epoch [3468/4000] Training [28/39] Loss: 0.37769 +Epoch [3468/4000] Training [29/39] Loss: 0.00432 +Epoch [3468/4000] Training [30/39] Loss: 0.12895 +Epoch [3468/4000] Training [31/39] Loss: 0.00638 +Epoch [3468/4000] Training [32/39] Loss: 0.00444 +Epoch [3468/4000] Training [33/39] Loss: 0.00365 +Epoch [3468/4000] Training [34/39] Loss: 0.00848 +Epoch [3468/4000] Training [35/39] Loss: 0.00664 +Epoch [3468/4000] Training [36/39] Loss: 0.00605 +Epoch [3468/4000] Training [37/39] Loss: 0.00650 +Epoch [3468/4000] Training [38/39] Loss: 0.13100 +Epoch [3468/4000] Training [39/39] Loss: 0.00497 +Epoch [3468/4000] Training metric {'Train/mean dice_metric': 0.9961189031600952, 'Train/mean miou_metric': 0.9927023649215698, 'Train/mean f1': 0.9967296123504639, 'Train/mean precision': 0.9962524175643921, 'Train/mean recall': 0.9972071647644043, 'Train/mean hd95_metric': 0.9982233047485352} +Epoch [3468/4000] Validation [1/10] Loss: 0.67526 focal_loss 0.58850 dice_loss 0.08675 +Epoch [3468/4000] Validation [2/10] Loss: 0.48057 focal_loss 0.38258 dice_loss 0.09799 +Epoch [3468/4000] Validation [3/10] Loss: 0.36510 focal_loss 0.25553 dice_loss 0.10957 +Epoch [3468/4000] Validation [4/10] Loss: 0.86008 focal_loss 0.29693 dice_loss 0.56316 +Epoch [3468/4000] Validation [5/10] Loss: 2.99105 focal_loss 2.31817 dice_loss 0.67288 +Epoch [3468/4000] Validation [6/10] Loss: 1.30448 focal_loss 0.58623 dice_loss 0.71826 +Epoch [3468/4000] Validation [7/10] Loss: 1.14270 focal_loss 0.48750 dice_loss 0.65521 +Epoch [3468/4000] Validation [8/10] Loss: 2.42272 focal_loss 1.79611 dice_loss 0.62661 +Epoch [3468/4000] Validation [9/10] Loss: 1.37160 focal_loss 0.82942 dice_loss 0.54218 +Epoch [3468/4000] Validation [10/10] Loss: 1.81041 focal_loss 1.07955 dice_loss 0.73086 +Epoch [3468/4000] Validation metric {'Val/mean dice_metric': 0.9516221284866333, 'Val/mean miou_metric': 0.9357436299324036, 'Val/mean f1': 0.9498816728591919, 'Val/mean precision': 0.9461127519607544, 'Val/mean recall': 0.953680694103241, 'Val/mean hd95_metric': 10.63013744354248} +Cheakpoint... +Epoch [3468/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516221284866333, 'Val/mean miou_metric': 0.9357436299324036, 'Val/mean f1': 0.9498816728591919, 'Val/mean precision': 0.9461127519607544, 'Val/mean recall': 0.953680694103241, 'Val/mean hd95_metric': 10.63013744354248} +Epoch [3469/4000] Training [1/39] Loss: 0.13152 +Epoch [3469/4000] Training [2/39] Loss: 0.13132 +Epoch [3469/4000] Training [3/39] Loss: 0.00629 +Epoch [3469/4000] Training [4/39] Loss: 0.00901 +Epoch [3469/4000] Training [5/39] Loss: 0.00808 +Epoch [3469/4000] Training [6/39] Loss: 0.12822 +Epoch [3469/4000] Training [7/39] Loss: 0.00444 +Epoch [3469/4000] Training [8/39] Loss: 0.00378 +Epoch [3469/4000] Training [9/39] Loss: 0.00613 +Epoch [3469/4000] Training [10/39] Loss: 0.00608 +Epoch [3469/4000] Training [11/39] Loss: 0.00567 +Epoch [3469/4000] Training [12/39] Loss: 0.00537 +Epoch [3469/4000] Training [13/39] Loss: 0.00522 +Epoch [3469/4000] Training [14/39] Loss: 0.00449 +Epoch [3469/4000] Training [15/39] Loss: 0.00511 +Epoch [3469/4000] Training [16/39] Loss: 0.00371 +Epoch [3469/4000] Training [17/39] Loss: 0.00642 +Epoch [3469/4000] Training [18/39] Loss: 0.00441 +Epoch [3469/4000] Training [19/39] Loss: 0.12969 +Epoch [3469/4000] Training [20/39] Loss: 0.00468 +Epoch [3469/4000] Training [21/39] Loss: 0.00557 +Epoch [3469/4000] Training [22/39] Loss: 0.13002 +Epoch [3469/4000] Training [23/39] Loss: 0.00499 +Epoch [3469/4000] Training [24/39] Loss: 0.08775 +Epoch [3469/4000] Training [25/39] Loss: 0.00292 +Epoch [3469/4000] Training [26/39] Loss: 0.00521 +Epoch [3469/4000] Training [27/39] Loss: 0.00451 +Epoch [3469/4000] Training [28/39] Loss: 0.12839 +Epoch [3469/4000] Training [29/39] Loss: 0.00576 +Epoch [3469/4000] Training [30/39] Loss: 0.00540 +Epoch [3469/4000] Training [31/39] Loss: 0.13289 +Epoch [3469/4000] Training [32/39] Loss: 0.00794 +Epoch [3469/4000] Training [33/39] Loss: 0.00494 +Epoch [3469/4000] Training [34/39] Loss: 0.25441 +Epoch [3469/4000] Training [35/39] Loss: 0.00360 +Epoch [3469/4000] Training [36/39] Loss: 0.00581 +Epoch [3469/4000] Training [37/39] Loss: 0.00664 +Epoch [3469/4000] Training [38/39] Loss: 0.00545 +Epoch [3469/4000] Training [39/39] Loss: 0.00562 +Epoch [3469/4000] Training metric {'Train/mean dice_metric': 0.9958095550537109, 'Train/mean miou_metric': 0.992076575756073, 'Train/mean f1': 0.9965169429779053, 'Train/mean precision': 0.9960851669311523, 'Train/mean recall': 0.9969489574432373, 'Train/mean hd95_metric': 1.0239408016204834} +Epoch [3469/4000] Validation [1/10] Loss: 0.67047 focal_loss 0.58320 dice_loss 0.08727 +Epoch [3469/4000] Validation [2/10] Loss: 0.46092 focal_loss 0.36590 dice_loss 0.09503 +Epoch [3469/4000] Validation [3/10] Loss: 0.35472 focal_loss 0.24639 dice_loss 0.10833 +Epoch [3469/4000] Validation [4/10] Loss: 0.86195 focal_loss 0.29930 dice_loss 0.56265 +Epoch [3469/4000] Validation [5/10] Loss: 2.94949 focal_loss 2.27736 dice_loss 0.67213 +Epoch [3469/4000] Validation [6/10] Loss: 1.29501 focal_loss 0.57439 dice_loss 0.72062 +Epoch [3469/4000] Validation [7/10] Loss: 1.14724 focal_loss 0.49332 dice_loss 0.65392 +Epoch [3469/4000] Validation [8/10] Loss: 2.16876 focal_loss 1.55996 dice_loss 0.60880 +Epoch [3469/4000] Validation [9/10] Loss: 1.35098 focal_loss 0.80741 dice_loss 0.54357 +Epoch [3469/4000] Validation [10/10] Loss: 1.82415 focal_loss 1.08716 dice_loss 0.73699 +Epoch [3469/4000] Validation metric {'Val/mean dice_metric': 0.9512196779251099, 'Val/mean miou_metric': 0.9349838495254517, 'Val/mean f1': 0.9488722681999207, 'Val/mean precision': 0.9435071349143982, 'Val/mean recall': 0.9542989134788513, 'Val/mean hd95_metric': 10.799725532531738} +Cheakpoint... +Epoch [3469/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512196779251099, 'Val/mean miou_metric': 0.9349838495254517, 'Val/mean f1': 0.9488722681999207, 'Val/mean precision': 0.9435071349143982, 'Val/mean recall': 0.9542989134788513, 'Val/mean hd95_metric': 10.799725532531738} +Epoch [3470/4000] Training [1/39] Loss: 0.25427 +Epoch [3470/4000] Training [2/39] Loss: 0.00462 +Epoch [3470/4000] Training [3/39] Loss: 0.00548 +Epoch [3470/4000] Training [4/39] Loss: 0.00625 +Epoch [3470/4000] Training [5/39] Loss: 0.25476 +Epoch [3470/4000] Training [6/39] Loss: 0.13341 +Epoch [3470/4000] Training [7/39] Loss: 0.00719 +Epoch [3470/4000] Training [8/39] Loss: 0.00487 +Epoch [3470/4000] Training [9/39] Loss: 0.00473 +Epoch [3470/4000] Training [10/39] Loss: 0.00609 +Epoch [3470/4000] Training [11/39] Loss: 0.00444 +Epoch [3470/4000] Training [12/39] Loss: 0.00645 +Epoch [3470/4000] Training [13/39] Loss: 0.13339 +Epoch [3470/4000] Training [14/39] Loss: 0.00378 +Epoch [3470/4000] Training [15/39] Loss: 0.00456 +Epoch [3470/4000] Training [16/39] Loss: 0.00460 +Epoch [3470/4000] Training [17/39] Loss: 0.12896 +Epoch [3470/4000] Training [18/39] Loss: 0.00530 +Epoch [3470/4000] Training [19/39] Loss: 0.00505 +Epoch [3470/4000] Training [20/39] Loss: 0.00464 +Epoch [3470/4000] Training [21/39] Loss: 0.00381 +Epoch [3470/4000] Training [22/39] Loss: 0.12858 +Epoch [3470/4000] Training [23/39] Loss: 0.00373 +Epoch [3470/4000] Training [24/39] Loss: 0.00519 +Epoch [3470/4000] Training [25/39] Loss: 0.00528 +Epoch [3470/4000] Training [26/39] Loss: 0.12820 +Epoch [3470/4000] Training [27/39] Loss: 0.00414 +Epoch [3470/4000] Training [28/39] Loss: 0.00479 +Epoch [3470/4000] Training [29/39] Loss: 0.00697 +Epoch [3470/4000] Training [30/39] Loss: 0.00505 +Epoch [3470/4000] Training [31/39] Loss: 0.00476 +Epoch [3470/4000] Training [32/39] Loss: 0.12996 +Epoch [3470/4000] Training [33/39] Loss: 0.12911 +Epoch [3470/4000] Training [34/39] Loss: 0.12883 +Epoch [3470/4000] Training [35/39] Loss: 0.00778 +Epoch [3470/4000] Training [36/39] Loss: 0.00416 +Epoch [3470/4000] Training [37/39] Loss: 0.00939 +Epoch [3470/4000] Training [38/39] Loss: 0.00577 +Epoch [3470/4000] Training [39/39] Loss: 0.00832 +Epoch [3470/4000] Training metric {'Train/mean dice_metric': 0.9958398342132568, 'Train/mean miou_metric': 0.992149293422699, 'Train/mean f1': 0.9964718818664551, 'Train/mean precision': 0.996001660823822, 'Train/mean recall': 0.9969425201416016, 'Train/mean hd95_metric': 1.0103079080581665} +Epoch [3470/4000] Validation [1/10] Loss: 0.70724 focal_loss 0.61759 dice_loss 0.08965 +Epoch [3470/4000] Validation [2/10] Loss: 0.49784 focal_loss 0.39568 dice_loss 0.10216 +Epoch [3470/4000] Validation [3/10] Loss: 0.36924 focal_loss 0.25986 dice_loss 0.10938 +Epoch [3470/4000] Validation [4/10] Loss: 0.85854 focal_loss 0.29470 dice_loss 0.56385 +Epoch [3470/4000] Validation [5/10] Loss: 2.98081 focal_loss 2.30868 dice_loss 0.67213 +Epoch [3470/4000] Validation [6/10] Loss: 1.27300 focal_loss 0.55433 dice_loss 0.71867 +Epoch [3470/4000] Validation [7/10] Loss: 1.14114 focal_loss 0.48758 dice_loss 0.65356 +Epoch [3470/4000] Validation [8/10] Loss: 2.50895 focal_loss 1.87427 dice_loss 0.63467 +Epoch [3470/4000] Validation [9/10] Loss: 1.40001 focal_loss 0.85565 dice_loss 0.54436 +Epoch [3470/4000] Validation [10/10] Loss: 1.78896 focal_loss 1.05959 dice_loss 0.72937 +Epoch [3470/4000] Validation metric {'Val/mean dice_metric': 0.9510266780853271, 'Val/mean miou_metric': 0.9348073601722717, 'Val/mean f1': 0.9489597082138062, 'Val/mean precision': 0.9460973739624023, 'Val/mean recall': 0.9518393278121948, 'Val/mean hd95_metric': 10.716001510620117} +Cheakpoint... +Epoch [3470/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510266780853271, 'Val/mean miou_metric': 0.9348073601722717, 'Val/mean f1': 0.9489597082138062, 'Val/mean precision': 0.9460973739624023, 'Val/mean recall': 0.9518393278121948, 'Val/mean hd95_metric': 10.716001510620117} +Epoch [3471/4000] Training [1/39] Loss: 0.12897 +Epoch [3471/4000] Training [2/39] Loss: 0.00750 +Epoch [3471/4000] Training [3/39] Loss: 0.13048 +Epoch [3471/4000] Training [4/39] Loss: 0.00349 +Epoch [3471/4000] Training [5/39] Loss: 0.00604 +Epoch [3471/4000] Training [6/39] Loss: 0.00599 +Epoch [3471/4000] Training [7/39] Loss: 0.00472 +Epoch [3471/4000] Training [8/39] Loss: 0.00599 +Epoch [3471/4000] Training [9/39] Loss: 0.13359 +Epoch [3471/4000] Training [10/39] Loss: 0.00369 +Epoch [3471/4000] Training [11/39] Loss: 0.08731 +Epoch [3471/4000] Training [12/39] Loss: 0.00485 +Epoch [3471/4000] Training [13/39] Loss: 0.00607 +Epoch [3471/4000] Training [14/39] Loss: 0.00405 +Epoch [3471/4000] Training [15/39] Loss: 0.00366 +Epoch [3471/4000] Training [16/39] Loss: 0.12936 +Epoch [3471/4000] Training [17/39] Loss: 0.00692 +Epoch [3471/4000] Training [18/39] Loss: 0.00932 +Epoch [3471/4000] Training [19/39] Loss: 0.00612 +Epoch [3471/4000] Training [20/39] Loss: 0.00565 +Epoch [3471/4000] Training [21/39] Loss: 0.00509 +Epoch [3471/4000] Training [22/39] Loss: 0.12793 +Epoch [3471/4000] Training [23/39] Loss: 0.00566 +Epoch [3471/4000] Training [24/39] Loss: 0.00593 +Epoch [3471/4000] Training [25/39] Loss: 0.13000 +Epoch [3471/4000] Training [26/39] Loss: 0.00499 +Epoch [3471/4000] Training [27/39] Loss: 0.00572 +Epoch [3471/4000] Training [28/39] Loss: 0.00533 +Epoch [3471/4000] Training [29/39] Loss: 0.00694 +Epoch [3471/4000] Training [30/39] Loss: 0.00528 +Epoch [3471/4000] Training [31/39] Loss: 0.00404 +Epoch [3471/4000] Training [32/39] Loss: 0.13026 +Epoch [3471/4000] Training [33/39] Loss: 0.00448 +Epoch [3471/4000] Training [34/39] Loss: 0.00388 +Epoch [3471/4000] Training [35/39] Loss: 0.00510 +Epoch [3471/4000] Training [36/39] Loss: 0.13020 +Epoch [3471/4000] Training [37/39] Loss: 0.00759 +Epoch [3471/4000] Training [38/39] Loss: 0.00482 +Epoch [3471/4000] Training [39/39] Loss: 0.13107 +Epoch [3471/4000] Training metric {'Train/mean dice_metric': 0.9956803321838379, 'Train/mean miou_metric': 0.9918221831321716, 'Train/mean f1': 0.9964535236358643, 'Train/mean precision': 0.9960314035415649, 'Train/mean recall': 0.9968761205673218, 'Train/mean hd95_metric': 1.0074126720428467} +Epoch [3471/4000] Validation [1/10] Loss: 0.70499 focal_loss 0.61869 dice_loss 0.08631 +Epoch [3471/4000] Validation [2/10] Loss: 0.50026 focal_loss 0.39886 dice_loss 0.10140 +Epoch [3471/4000] Validation [3/10] Loss: 0.38519 focal_loss 0.27482 dice_loss 0.11038 +Epoch [3471/4000] Validation [4/10] Loss: 0.86702 focal_loss 0.29966 dice_loss 0.56736 +Epoch [3471/4000] Validation [5/10] Loss: 2.99852 focal_loss 2.32491 dice_loss 0.67361 +Epoch [3471/4000] Validation [6/10] Loss: 1.26891 focal_loss 0.55709 dice_loss 0.71182 +Epoch [3471/4000] Validation [7/10] Loss: 1.11930 focal_loss 0.46880 dice_loss 0.65051 +Epoch [3471/4000] Validation [8/10] Loss: 2.54745 focal_loss 1.90961 dice_loss 0.63784 +Epoch [3471/4000] Validation [9/10] Loss: 1.41747 focal_loss 0.87495 dice_loss 0.54252 +Epoch [3471/4000] Validation [10/10] Loss: 1.75409 focal_loss 1.02678 dice_loss 0.72731 +Epoch [3471/4000] Validation metric {'Val/mean dice_metric': 0.9510898590087891, 'Val/mean miou_metric': 0.9347543716430664, 'Val/mean f1': 0.9496882557868958, 'Val/mean precision': 0.9488272666931152, 'Val/mean recall': 0.9505507349967957, 'Val/mean hd95_metric': 10.612019538879395} +Cheakpoint... +Epoch [3471/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510898590087891, 'Val/mean miou_metric': 0.9347543716430664, 'Val/mean f1': 0.9496882557868958, 'Val/mean precision': 0.9488272666931152, 'Val/mean recall': 0.9505507349967957, 'Val/mean hd95_metric': 10.612019538879395} +Epoch [3472/4000] Training [1/39] Loss: 0.00677 +Epoch [3472/4000] Training [2/39] Loss: 0.00482 +Epoch [3472/4000] Training [3/39] Loss: 0.00504 +Epoch [3472/4000] Training [4/39] Loss: 0.00744 +Epoch [3472/4000] Training [5/39] Loss: 0.00405 +Epoch [3472/4000] Training [6/39] Loss: 0.00524 +Epoch [3472/4000] Training [7/39] Loss: 0.00503 +Epoch [3472/4000] Training [8/39] Loss: 0.00740 +Epoch [3472/4000] Training [9/39] Loss: 0.00489 +Epoch [3472/4000] Training [10/39] Loss: 0.00378 +Epoch [3472/4000] Training [11/39] Loss: 0.00318 +Epoch [3472/4000] Training [12/39] Loss: 0.12803 +Epoch [3472/4000] Training [13/39] Loss: 0.00285 +Epoch [3472/4000] Training [14/39] Loss: 0.25218 +Epoch [3472/4000] Training [15/39] Loss: 0.12914 +Epoch [3472/4000] Training [16/39] Loss: 0.00480 +Epoch [3472/4000] Training [17/39] Loss: 0.00394 +Epoch [3472/4000] Training [18/39] Loss: 0.00368 +Epoch [3472/4000] Training [19/39] Loss: 0.00446 +Epoch [3472/4000] Training [20/39] Loss: 0.00363 +Epoch [3472/4000] Training [21/39] Loss: 0.12866 +Epoch [3472/4000] Training [22/39] Loss: 0.00845 +Epoch [3472/4000] Training [23/39] Loss: 0.00645 +Epoch [3472/4000] Training [24/39] Loss: 0.20970 +Epoch [3472/4000] Training [25/39] Loss: 0.12990 +Epoch [3472/4000] Training [26/39] Loss: 0.12847 +Epoch [3472/4000] Training [27/39] Loss: 0.00524 +Epoch [3472/4000] Training [28/39] Loss: 0.00741 +Epoch [3472/4000] Training [29/39] Loss: 0.00449 +Epoch [3472/4000] Training [30/39] Loss: 0.00653 +Epoch [3472/4000] Training [31/39] Loss: 0.00604 +Epoch [3472/4000] Training [32/39] Loss: 0.00431 +Epoch [3472/4000] Training [33/39] Loss: 0.01166 +Epoch [3472/4000] Training [34/39] Loss: 0.00504 +Epoch [3472/4000] Training [35/39] Loss: 0.13010 +Epoch [3472/4000] Training [36/39] Loss: 0.00371 +Epoch [3472/4000] Training [37/39] Loss: 0.00517 +Epoch [3472/4000] Training [38/39] Loss: 0.00478 +Epoch [3472/4000] Training [39/39] Loss: 0.00651 +Epoch [3472/4000] Training metric {'Train/mean dice_metric': 0.9961178302764893, 'Train/mean miou_metric': 0.992682695388794, 'Train/mean f1': 0.9967800378799438, 'Train/mean precision': 0.9963079690933228, 'Train/mean recall': 0.9972525835037231, 'Train/mean hd95_metric': 1.0630459785461426} +Epoch [3472/4000] Validation [1/10] Loss: 0.69404 focal_loss 0.60671 dice_loss 0.08732 +Epoch [3472/4000] Validation [2/10] Loss: 0.48555 focal_loss 0.38768 dice_loss 0.09787 +Epoch [3472/4000] Validation [3/10] Loss: 0.36231 focal_loss 0.25386 dice_loss 0.10845 +Epoch [3472/4000] Validation [4/10] Loss: 0.86615 focal_loss 0.30353 dice_loss 0.56262 +Epoch [3472/4000] Validation [5/10] Loss: 2.99113 focal_loss 2.31872 dice_loss 0.67241 +Epoch [3472/4000] Validation [6/10] Loss: 1.27179 focal_loss 0.55654 dice_loss 0.71525 +Epoch [3472/4000] Validation [7/10] Loss: 1.13689 focal_loss 0.48372 dice_loss 0.65317 +Epoch [3472/4000] Validation [8/10] Loss: 2.32392 focal_loss 1.70154 dice_loss 0.62238 +Epoch [3472/4000] Validation [9/10] Loss: 1.38594 focal_loss 0.84187 dice_loss 0.54407 +Epoch [3472/4000] Validation [10/10] Loss: 1.79163 focal_loss 1.05912 dice_loss 0.73251 +Epoch [3472/4000] Validation metric {'Val/mean dice_metric': 0.9515065550804138, 'Val/mean miou_metric': 0.9355258941650391, 'Val/mean f1': 0.9492934942245483, 'Val/mean precision': 0.945703387260437, 'Val/mean recall': 0.9529109001159668, 'Val/mean hd95_metric': 10.732453346252441} +Cheakpoint... +Epoch [3472/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515065550804138, 'Val/mean miou_metric': 0.9355258941650391, 'Val/mean f1': 0.9492934942245483, 'Val/mean precision': 0.945703387260437, 'Val/mean recall': 0.9529109001159668, 'Val/mean hd95_metric': 10.732453346252441} +Epoch [3473/4000] Training [1/39] Loss: 0.00430 +Epoch [3473/4000] Training [2/39] Loss: 0.13352 +Epoch [3473/4000] Training [3/39] Loss: 0.00709 +Epoch [3473/4000] Training [4/39] Loss: 0.00423 +Epoch [3473/4000] Training [5/39] Loss: 0.00480 +Epoch [3473/4000] Training [6/39] Loss: 0.13323 +Epoch [3473/4000] Training [7/39] Loss: 0.00748 +Epoch [3473/4000] Training [8/39] Loss: 0.00362 +Epoch [3473/4000] Training [9/39] Loss: 0.13063 +Epoch [3473/4000] Training [10/39] Loss: 0.00553 +Epoch [3473/4000] Training [11/39] Loss: 0.00689 +Epoch [3473/4000] Training [12/39] Loss: 0.12902 +Epoch [3473/4000] Training [13/39] Loss: 0.00401 +Epoch [3473/4000] Training [14/39] Loss: 0.00655 +Epoch [3473/4000] Training [15/39] Loss: 0.00389 +Epoch [3473/4000] Training [16/39] Loss: 0.12910 +Epoch [3473/4000] Training [17/39] Loss: 0.12984 +Epoch [3473/4000] Training [18/39] Loss: 0.00606 +Epoch [3473/4000] Training [19/39] Loss: 0.13199 +Epoch [3473/4000] Training [20/39] Loss: 0.00579 +Epoch [3473/4000] Training [21/39] Loss: 0.00593 +Epoch [3473/4000] Training [22/39] Loss: 0.00604 +Epoch [3473/4000] Training [23/39] Loss: 0.00508 +Epoch [3473/4000] Training [24/39] Loss: 0.01072 +Epoch [3473/4000] Training [25/39] Loss: 0.00540 +Epoch [3473/4000] Training [26/39] Loss: 0.00972 +Epoch [3473/4000] Training [27/39] Loss: 0.13258 +Epoch [3473/4000] Training [28/39] Loss: 0.00464 +Epoch [3473/4000] Training [29/39] Loss: 0.00587 +Epoch [3473/4000] Training [30/39] Loss: 0.00562 +Epoch [3473/4000] Training [31/39] Loss: 0.13350 +Epoch [3473/4000] Training [32/39] Loss: 0.00500 +Epoch [3473/4000] Training [33/39] Loss: 0.00623 +Epoch [3473/4000] Training [34/39] Loss: 0.00566 +Epoch [3473/4000] Training [35/39] Loss: 0.12896 +Epoch [3473/4000] Training [36/39] Loss: 0.00484 +Epoch [3473/4000] Training [37/39] Loss: 0.00516 +Epoch [3473/4000] Training [38/39] Loss: 0.00467 +Epoch [3473/4000] Training [39/39] Loss: 0.00332 +Epoch [3473/4000] Training metric {'Train/mean dice_metric': 0.9955657124519348, 'Train/mean miou_metric': 0.9915966987609863, 'Train/mean f1': 0.9963407516479492, 'Train/mean precision': 0.9958575367927551, 'Train/mean recall': 0.996824324131012, 'Train/mean hd95_metric': 1.041369915008545} +Epoch [3473/4000] Validation [1/10] Loss: 0.67447 focal_loss 0.58956 dice_loss 0.08491 +Epoch [3473/4000] Validation [2/10] Loss: 0.49529 focal_loss 0.39231 dice_loss 0.10298 +Epoch [3473/4000] Validation [3/10] Loss: 0.37651 focal_loss 0.26637 dice_loss 0.11014 +Epoch [3473/4000] Validation [4/10] Loss: 0.85485 focal_loss 0.29285 dice_loss 0.56200 +Epoch [3473/4000] Validation [5/10] Loss: 2.98342 focal_loss 2.31093 dice_loss 0.67249 +Epoch [3473/4000] Validation [6/10] Loss: 1.24575 focal_loss 0.52829 dice_loss 0.71746 +Epoch [3473/4000] Validation [7/10] Loss: 1.11158 focal_loss 0.46170 dice_loss 0.64989 +Epoch [3473/4000] Validation [8/10] Loss: 2.51076 focal_loss 1.86437 dice_loss 0.64640 +Epoch [3473/4000] Validation [9/10] Loss: 1.38163 focal_loss 0.83739 dice_loss 0.54424 +Epoch [3473/4000] Validation [10/10] Loss: 1.70393 focal_loss 0.97882 dice_loss 0.72511 +Epoch [3473/4000] Validation metric {'Val/mean dice_metric': 0.9508135914802551, 'Val/mean miou_metric': 0.9344104528427124, 'Val/mean f1': 0.9494421482086182, 'Val/mean precision': 0.9488823413848877, 'Val/mean recall': 0.9500026702880859, 'Val/mean hd95_metric': 10.480487823486328} +Cheakpoint... +Epoch [3473/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508135914802551, 'Val/mean miou_metric': 0.9344104528427124, 'Val/mean f1': 0.9494421482086182, 'Val/mean precision': 0.9488823413848877, 'Val/mean recall': 0.9500026702880859, 'Val/mean hd95_metric': 10.480487823486328} +Epoch [3474/4000] Training [1/39] Loss: 0.00409 +Epoch [3474/4000] Training [2/39] Loss: 0.00595 +Epoch [3474/4000] Training [3/39] Loss: 0.00314 +Epoch [3474/4000] Training [4/39] Loss: 0.13094 +Epoch [3474/4000] Training [5/39] Loss: 0.00503 +Epoch [3474/4000] Training [6/39] Loss: 0.00663 +Epoch [3474/4000] Training [7/39] Loss: 0.00370 +Epoch [3474/4000] Training [8/39] Loss: 0.12999 +Epoch [3474/4000] Training [9/39] Loss: 0.00691 +Epoch [3474/4000] Training [10/39] Loss: 0.00498 +Epoch [3474/4000] Training [11/39] Loss: 0.00720 +Epoch [3474/4000] Training [12/39] Loss: 0.00585 +Epoch [3474/4000] Training [13/39] Loss: 0.00318 +Epoch [3474/4000] Training [14/39] Loss: 0.00575 +Epoch [3474/4000] Training [15/39] Loss: 0.00701 +Epoch [3474/4000] Training [16/39] Loss: 0.00559 +Epoch [3474/4000] Training [17/39] Loss: 0.37939 +Epoch [3474/4000] Training [18/39] Loss: 0.01001 +Epoch [3474/4000] Training [19/39] Loss: 0.00506 +Epoch [3474/4000] Training [20/39] Loss: 0.01058 +Epoch [3474/4000] Training [21/39] Loss: 0.12866 +Epoch [3474/4000] Training [22/39] Loss: 0.00809 +Epoch [3474/4000] Training [23/39] Loss: 0.00448 +Epoch [3474/4000] Training [24/39] Loss: 0.00341 +Epoch [3474/4000] Training [25/39] Loss: 0.25300 +Epoch [3474/4000] Training [26/39] Loss: 0.12756 +Epoch [3474/4000] Training [27/39] Loss: 0.00391 +Epoch [3474/4000] Training [28/39] Loss: 0.00514 +Epoch [3474/4000] Training [29/39] Loss: 0.00446 +Epoch [3474/4000] Training [30/39] Loss: 0.00516 +Epoch [3474/4000] Training [31/39] Loss: 0.13388 +Epoch [3474/4000] Training [32/39] Loss: 0.13041 +Epoch [3474/4000] Training [33/39] Loss: 0.00467 +Epoch [3474/4000] Training [34/39] Loss: 0.00383 +Epoch [3474/4000] Training [35/39] Loss: 0.00663 +Epoch [3474/4000] Training [36/39] Loss: 0.00440 +Epoch [3474/4000] Training [37/39] Loss: 0.00451 +Epoch [3474/4000] Training [38/39] Loss: 0.00749 +Epoch [3474/4000] Training [39/39] Loss: 0.00472 +Epoch [3474/4000] Training metric {'Train/mean dice_metric': 0.9960371851921082, 'Train/mean miou_metric': 0.9925178289413452, 'Train/mean f1': 0.9966942071914673, 'Train/mean precision': 0.9962741732597351, 'Train/mean recall': 0.9971144795417786, 'Train/mean hd95_metric': 0.979961633682251} +Epoch [3474/4000] Validation [1/10] Loss: 0.70898 focal_loss 0.62018 dice_loss 0.08880 +Epoch [3474/4000] Validation [2/10] Loss: 0.47859 focal_loss 0.38073 dice_loss 0.09786 +Epoch [3474/4000] Validation [3/10] Loss: 0.38458 focal_loss 0.27356 dice_loss 0.11101 +Epoch [3474/4000] Validation [4/10] Loss: 0.86259 focal_loss 0.29988 dice_loss 0.56271 +Epoch [3474/4000] Validation [5/10] Loss: 3.04276 focal_loss 2.36982 dice_loss 0.67294 +Epoch [3474/4000] Validation [6/10] Loss: 1.25974 focal_loss 0.54591 dice_loss 0.71383 +Epoch [3474/4000] Validation [7/10] Loss: 1.13436 focal_loss 0.48256 dice_loss 0.65180 +Epoch [3474/4000] Validation [8/10] Loss: 2.38701 focal_loss 1.75902 dice_loss 0.62799 +Epoch [3474/4000] Validation [9/10] Loss: 1.38334 focal_loss 0.83950 dice_loss 0.54384 +Epoch [3474/4000] Validation [10/10] Loss: 1.76974 focal_loss 1.03880 dice_loss 0.73094 +Epoch [3474/4000] Validation metric {'Val/mean dice_metric': 0.9513463377952576, 'Val/mean miou_metric': 0.9352641701698303, 'Val/mean f1': 0.9492993950843811, 'Val/mean precision': 0.9461545348167419, 'Val/mean recall': 0.952465295791626, 'Val/mean hd95_metric': 10.629379272460938} +Cheakpoint... +Epoch [3474/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513463377952576, 'Val/mean miou_metric': 0.9352641701698303, 'Val/mean f1': 0.9492993950843811, 'Val/mean precision': 0.9461545348167419, 'Val/mean recall': 0.952465295791626, 'Val/mean hd95_metric': 10.629379272460938} +Epoch [3475/4000] Training [1/39] Loss: 0.00448 +Epoch [3475/4000] Training [2/39] Loss: 0.00462 +Epoch [3475/4000] Training [3/39] Loss: 0.00489 +Epoch [3475/4000] Training [4/39] Loss: 0.00373 +Epoch [3475/4000] Training [5/39] Loss: 0.13066 +Epoch [3475/4000] Training [6/39] Loss: 0.00607 +Epoch [3475/4000] Training [7/39] Loss: 0.13361 +Epoch [3475/4000] Training [8/39] Loss: 0.12998 +Epoch [3475/4000] Training [9/39] Loss: 0.00452 +Epoch [3475/4000] Training [10/39] Loss: 0.12876 +Epoch [3475/4000] Training [11/39] Loss: 0.00576 +Epoch [3475/4000] Training [12/39] Loss: 0.00287 +Epoch [3475/4000] Training [13/39] Loss: 0.00564 +Epoch [3475/4000] Training [14/39] Loss: 0.00363 +Epoch [3475/4000] Training [15/39] Loss: 0.00497 +Epoch [3475/4000] Training [16/39] Loss: 0.00433 +Epoch [3475/4000] Training [17/39] Loss: 0.00773 +Epoch [3475/4000] Training [18/39] Loss: 0.00760 +Epoch [3475/4000] Training [19/39] Loss: 0.00496 +Epoch [3475/4000] Training [20/39] Loss: 0.00363 +Epoch [3475/4000] Training [21/39] Loss: 0.12974 +Epoch [3475/4000] Training [22/39] Loss: 0.00602 +Epoch [3475/4000] Training [23/39] Loss: 0.13026 +Epoch [3475/4000] Training [24/39] Loss: 0.00553 +Epoch [3475/4000] Training [25/39] Loss: 0.12972 +Epoch [3475/4000] Training [26/39] Loss: 0.00401 +Epoch [3475/4000] Training [27/39] Loss: 0.00458 +Epoch [3475/4000] Training [28/39] Loss: 0.00743 +Epoch [3475/4000] Training [29/39] Loss: 0.12902 +Epoch [3475/4000] Training [30/39] Loss: 0.00581 +Epoch [3475/4000] Training [31/39] Loss: 0.13106 +Epoch [3475/4000] Training [32/39] Loss: 0.12942 +Epoch [3475/4000] Training [33/39] Loss: 0.00452 +Epoch [3475/4000] Training [34/39] Loss: 0.00467 +Epoch [3475/4000] Training [35/39] Loss: 0.00626 +Epoch [3475/4000] Training [36/39] Loss: 0.00664 +Epoch [3475/4000] Training [37/39] Loss: 0.00891 +Epoch [3475/4000] Training [38/39] Loss: 0.21169 +Epoch [3475/4000] Training [39/39] Loss: 0.00616 +Epoch [3475/4000] Training metric {'Train/mean dice_metric': 0.9957736134529114, 'Train/mean miou_metric': 0.9920064806938171, 'Train/mean f1': 0.9965242147445679, 'Train/mean precision': 0.9960016012191772, 'Train/mean recall': 0.9970473051071167, 'Train/mean hd95_metric': 1.0089393854141235} +Epoch [3475/4000] Validation [1/10] Loss: 0.74379 focal_loss 0.65202 dice_loss 0.09177 +Epoch [3475/4000] Validation [2/10] Loss: 0.48760 focal_loss 0.39178 dice_loss 0.09581 +Epoch [3475/4000] Validation [3/10] Loss: 0.36718 focal_loss 0.25814 dice_loss 0.10903 +Epoch [3475/4000] Validation [4/10] Loss: 0.87807 focal_loss 0.31441 dice_loss 0.56366 +Epoch [3475/4000] Validation [5/10] Loss: 3.02283 focal_loss 2.35021 dice_loss 0.67262 +Epoch [3475/4000] Validation [6/10] Loss: 1.30363 focal_loss 0.58584 dice_loss 0.71780 +Epoch [3475/4000] Validation [7/10] Loss: 1.15997 focal_loss 0.50109 dice_loss 0.65888 +Epoch [3475/4000] Validation [8/10] Loss: 2.29840 focal_loss 1.68433 dice_loss 0.61407 +Epoch [3475/4000] Validation [9/10] Loss: 1.39908 focal_loss 0.85525 dice_loss 0.54383 +Epoch [3475/4000] Validation [10/10] Loss: 1.83187 focal_loss 1.09962 dice_loss 0.73225 +Epoch [3475/4000] Validation metric {'Val/mean dice_metric': 0.9511251449584961, 'Val/mean miou_metric': 0.9347866773605347, 'Val/mean f1': 0.9487360715866089, 'Val/mean precision': 0.9442044496536255, 'Val/mean recall': 0.9533115029335022, 'Val/mean hd95_metric': 10.681591987609863} +Cheakpoint... +Epoch [3475/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511251449584961, 'Val/mean miou_metric': 0.9347866773605347, 'Val/mean f1': 0.9487360715866089, 'Val/mean precision': 0.9442044496536255, 'Val/mean recall': 0.9533115029335022, 'Val/mean hd95_metric': 10.681591987609863} +Epoch [3476/4000] Training [1/39] Loss: 0.00712 +Epoch [3476/4000] Training [2/39] Loss: 0.00532 +Epoch [3476/4000] Training [3/39] Loss: 0.00389 +Epoch [3476/4000] Training [4/39] Loss: 0.13086 +Epoch [3476/4000] Training [5/39] Loss: 0.00406 +Epoch [3476/4000] Training [6/39] Loss: 0.00440 +Epoch [3476/4000] Training [7/39] Loss: 0.00409 +Epoch [3476/4000] Training [8/39] Loss: 0.13335 +Epoch [3476/4000] Training [9/39] Loss: 0.12807 +Epoch [3476/4000] Training [10/39] Loss: 0.00469 +Epoch [3476/4000] Training [11/39] Loss: 0.00235 +Epoch [3476/4000] Training [12/39] Loss: 0.00502 +Epoch [3476/4000] Training [13/39] Loss: 0.00726 +Epoch [3476/4000] Training [14/39] Loss: 0.12345 +Epoch [3476/4000] Training [15/39] Loss: 0.00430 +Epoch [3476/4000] Training [16/39] Loss: 0.12898 +Epoch [3476/4000] Training [17/39] Loss: 0.00467 +Epoch [3476/4000] Training [18/39] Loss: 0.00621 +Epoch [3476/4000] Training [19/39] Loss: 0.01243 +Epoch [3476/4000] Training [20/39] Loss: 0.00636 +Epoch [3476/4000] Training [21/39] Loss: 0.00642 +Epoch [3476/4000] Training [22/39] Loss: 0.00432 +Epoch [3476/4000] Training [23/39] Loss: 0.00575 +Epoch [3476/4000] Training [24/39] Loss: 0.00386 +Epoch [3476/4000] Training [25/39] Loss: 0.00536 +Epoch [3476/4000] Training [26/39] Loss: 0.12818 +Epoch [3476/4000] Training [27/39] Loss: 0.00994 +Epoch [3476/4000] Training [28/39] Loss: 0.12867 +Epoch [3476/4000] Training [29/39] Loss: 0.13387 +Epoch [3476/4000] Training [30/39] Loss: 0.00420 +Epoch [3476/4000] Training [31/39] Loss: 0.00600 +Epoch [3476/4000] Training [32/39] Loss: 0.00489 +Epoch [3476/4000] Training [33/39] Loss: 0.00654 +Epoch [3476/4000] Training [34/39] Loss: 0.13007 +Epoch [3476/4000] Training [35/39] Loss: 0.00469 +Epoch [3476/4000] Training [36/39] Loss: 0.00360 +Epoch [3476/4000] Training [37/39] Loss: 0.12986 +Epoch [3476/4000] Training [38/39] Loss: 0.00456 +Epoch [3476/4000] Training [39/39] Loss: 0.00615 +Epoch [3476/4000] Training metric {'Train/mean dice_metric': 0.9956762194633484, 'Train/mean miou_metric': 0.9919646382331848, 'Train/mean f1': 0.9965288043022156, 'Train/mean precision': 0.9961683750152588, 'Train/mean recall': 0.9968894720077515, 'Train/mean hd95_metric': 1.002068042755127} +Epoch [3476/4000] Validation [1/10] Loss: 0.72569 focal_loss 0.63366 dice_loss 0.09203 +Epoch [3476/4000] Validation [2/10] Loss: 0.45305 focal_loss 0.36390 dice_loss 0.08915 +Epoch [3476/4000] Validation [3/10] Loss: 0.36878 focal_loss 0.25928 dice_loss 0.10950 +Epoch [3476/4000] Validation [4/10] Loss: 0.87467 focal_loss 0.31116 dice_loss 0.56351 +Epoch [3476/4000] Validation [5/10] Loss: 3.02604 focal_loss 2.35338 dice_loss 0.67266 +Epoch [3476/4000] Validation [6/10] Loss: 1.30451 focal_loss 0.58550 dice_loss 0.71901 +Epoch [3476/4000] Validation [7/10] Loss: 1.14115 focal_loss 0.49031 dice_loss 0.65085 +Epoch [3476/4000] Validation [8/10] Loss: 2.29768 focal_loss 1.67698 dice_loss 0.62069 +Epoch [3476/4000] Validation [9/10] Loss: 1.35281 focal_loss 0.82483 dice_loss 0.52799 +Epoch [3476/4000] Validation [10/10] Loss: 1.82209 focal_loss 1.08693 dice_loss 0.73515 +Epoch [3476/4000] Validation metric {'Val/mean dice_metric': 0.9508563280105591, 'Val/mean miou_metric': 0.9346018433570862, 'Val/mean f1': 0.9487252235412598, 'Val/mean precision': 0.9439406394958496, 'Val/mean recall': 0.9535585045814514, 'Val/mean hd95_metric': 10.893738746643066} +Cheakpoint... +Epoch [3476/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508563280105591, 'Val/mean miou_metric': 0.9346018433570862, 'Val/mean f1': 0.9487252235412598, 'Val/mean precision': 0.9439406394958496, 'Val/mean recall': 0.9535585045814514, 'Val/mean hd95_metric': 10.893738746643066} +Epoch [3477/4000] Training [1/39] Loss: 0.00550 +Epoch [3477/4000] Training [2/39] Loss: 0.00711 +Epoch [3477/4000] Training [3/39] Loss: 0.13039 +Epoch [3477/4000] Training [4/39] Loss: 0.00470 +Epoch [3477/4000] Training [5/39] Loss: 0.00511 +Epoch [3477/4000] Training [6/39] Loss: 0.13022 +Epoch [3477/4000] Training [7/39] Loss: 0.00547 +Epoch [3477/4000] Training [8/39] Loss: 0.00461 +Epoch [3477/4000] Training [9/39] Loss: 0.12841 +Epoch [3477/4000] Training [10/39] Loss: 0.00397 +Epoch [3477/4000] Training [11/39] Loss: 0.00463 +Epoch [3477/4000] Training [12/39] Loss: 0.00412 +Epoch [3477/4000] Training [13/39] Loss: 0.00528 +Epoch [3477/4000] Training [14/39] Loss: 0.13063 +Epoch [3477/4000] Training [15/39] Loss: 0.00651 +Epoch [3477/4000] Training [16/39] Loss: 0.00397 +Epoch [3477/4000] Training [17/39] Loss: 0.00835 +Epoch [3477/4000] Training [18/39] Loss: 0.13282 +Epoch [3477/4000] Training [19/39] Loss: 0.00666 +Epoch [3477/4000] Training [20/39] Loss: 0.12790 +Epoch [3477/4000] Training [21/39] Loss: 0.00415 +Epoch [3477/4000] Training [22/39] Loss: 0.00388 +Epoch [3477/4000] Training [23/39] Loss: 0.00550 +Epoch [3477/4000] Training [24/39] Loss: 0.00447 +Epoch [3477/4000] Training [25/39] Loss: 0.00435 +Epoch [3477/4000] Training [26/39] Loss: 0.13129 +Epoch [3477/4000] Training [27/39] Loss: 0.00452 +Epoch [3477/4000] Training [28/39] Loss: 0.00592 +Epoch [3477/4000] Training [29/39] Loss: 0.12866 +Epoch [3477/4000] Training [30/39] Loss: 0.00488 +Epoch [3477/4000] Training [31/39] Loss: 0.00872 +Epoch [3477/4000] Training [32/39] Loss: 0.12946 +Epoch [3477/4000] Training [33/39] Loss: 0.00434 +Epoch [3477/4000] Training [34/39] Loss: 0.00747 +Epoch [3477/4000] Training [35/39] Loss: 0.00395 +Epoch [3477/4000] Training [36/39] Loss: 0.00338 +Epoch [3477/4000] Training [37/39] Loss: 0.00678 +Epoch [3477/4000] Training [38/39] Loss: 0.00438 +Epoch [3477/4000] Training [39/39] Loss: 0.00545 +Epoch [3477/4000] Training metric {'Train/mean dice_metric': 0.9959466457366943, 'Train/mean miou_metric': 0.9923439621925354, 'Train/mean f1': 0.9967014193534851, 'Train/mean precision': 0.9962289929389954, 'Train/mean recall': 0.9971742033958435, 'Train/mean hd95_metric': 0.9956715106964111} +Epoch [3477/4000] Validation [1/10] Loss: 0.72640 focal_loss 0.63312 dice_loss 0.09328 +Epoch [3477/4000] Validation [2/10] Loss: 0.46765 focal_loss 0.37334 dice_loss 0.09431 +Epoch [3477/4000] Validation [3/10] Loss: 0.36014 focal_loss 0.25045 dice_loss 0.10969 +Epoch [3477/4000] Validation [4/10] Loss: 0.88519 focal_loss 0.31755 dice_loss 0.56764 +Epoch [3477/4000] Validation [5/10] Loss: 2.93678 focal_loss 2.26471 dice_loss 0.67207 +Epoch [3477/4000] Validation [6/10] Loss: 1.29529 focal_loss 0.58096 dice_loss 0.71433 +Epoch [3477/4000] Validation [7/10] Loss: 1.13847 focal_loss 0.48754 dice_loss 0.65093 +Epoch [3477/4000] Validation [8/10] Loss: 2.16980 focal_loss 1.55575 dice_loss 0.61404 +Epoch [3477/4000] Validation [9/10] Loss: 1.36851 focal_loss 0.83012 dice_loss 0.53840 +Epoch [3477/4000] Validation [10/10] Loss: 1.83893 focal_loss 1.09955 dice_loss 0.73939 +Epoch [3477/4000] Validation metric {'Val/mean dice_metric': 0.951176106929779, 'Val/mean miou_metric': 0.9348398447036743, 'Val/mean f1': 0.9480466246604919, 'Val/mean precision': 0.9419953227043152, 'Val/mean recall': 0.9541762471199036, 'Val/mean hd95_metric': 10.826281547546387} +Cheakpoint... +Epoch [3477/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951176106929779, 'Val/mean miou_metric': 0.9348398447036743, 'Val/mean f1': 0.9480466246604919, 'Val/mean precision': 0.9419953227043152, 'Val/mean recall': 0.9541762471199036, 'Val/mean hd95_metric': 10.826281547546387} +Epoch [3478/4000] Training [1/39] Loss: 0.00693 +Epoch [3478/4000] Training [2/39] Loss: 0.00322 +Epoch [3478/4000] Training [3/39] Loss: 0.00534 +Epoch [3478/4000] Training [4/39] Loss: 0.00324 +Epoch [3478/4000] Training [5/39] Loss: 0.00640 +Epoch [3478/4000] Training [6/39] Loss: 0.00488 +Epoch [3478/4000] Training [7/39] Loss: 0.01059 +Epoch [3478/4000] Training [8/39] Loss: 0.00564 +Epoch [3478/4000] Training [9/39] Loss: 0.13148 +Epoch [3478/4000] Training [10/39] Loss: 0.00602 +Epoch [3478/4000] Training [11/39] Loss: 0.00536 +Epoch [3478/4000] Training [12/39] Loss: 0.00792 +Epoch [3478/4000] Training [13/39] Loss: 0.00665 +Epoch [3478/4000] Training [14/39] Loss: 0.00511 +Epoch [3478/4000] Training [15/39] Loss: 0.13275 +Epoch [3478/4000] Training [16/39] Loss: 0.12807 +Epoch [3478/4000] Training [17/39] Loss: 0.00476 +Epoch [3478/4000] Training [18/39] Loss: 0.00378 +Epoch [3478/4000] Training [19/39] Loss: 0.00726 +Epoch [3478/4000] Training [20/39] Loss: 0.00480 +Epoch [3478/4000] Training [21/39] Loss: 0.13033 +Epoch [3478/4000] Training [22/39] Loss: 0.00653 +Epoch [3478/4000] Training [23/39] Loss: 0.00607 +Epoch [3478/4000] Training [24/39] Loss: 0.00625 +Epoch [3478/4000] Training [25/39] Loss: 0.12891 +Epoch [3478/4000] Training [26/39] Loss: 0.00394 +Epoch [3478/4000] Training [27/39] Loss: 0.00657 +Epoch [3478/4000] Training [28/39] Loss: 0.00454 +Epoch [3478/4000] Training [29/39] Loss: 0.00467 +Epoch [3478/4000] Training [30/39] Loss: 0.00482 +Epoch [3478/4000] Training [31/39] Loss: 0.00469 +Epoch [3478/4000] Training [32/39] Loss: 0.08712 +Epoch [3478/4000] Training [33/39] Loss: 0.00416 +Epoch [3478/4000] Training [34/39] Loss: 0.00478 +Epoch [3478/4000] Training [35/39] Loss: 0.00797 +Epoch [3478/4000] Training [36/39] Loss: 0.00519 +Epoch [3478/4000] Training [37/39] Loss: 0.00676 +Epoch [3478/4000] Training [38/39] Loss: 0.13046 +Epoch [3478/4000] Training [39/39] Loss: 0.00651 +Epoch [3478/4000] Training metric {'Train/mean dice_metric': 0.994783341884613, 'Train/mean miou_metric': 0.9908661246299744, 'Train/mean f1': 0.9963809251785278, 'Train/mean precision': 0.9958823919296265, 'Train/mean recall': 0.9968799352645874, 'Train/mean hd95_metric': 1.3686153888702393} +Epoch [3478/4000] Validation [1/10] Loss: 0.68199 focal_loss 0.59397 dice_loss 0.08802 +Epoch [3478/4000] Validation [2/10] Loss: 0.47877 focal_loss 0.38390 dice_loss 0.09487 +Epoch [3478/4000] Validation [3/10] Loss: 0.35284 focal_loss 0.24414 dice_loss 0.10869 +Epoch [3478/4000] Validation [4/10] Loss: 0.87485 focal_loss 0.31159 dice_loss 0.56327 +Epoch [3478/4000] Validation [5/10] Loss: 2.94477 focal_loss 2.27195 dice_loss 0.67282 +Epoch [3478/4000] Validation [6/10] Loss: 1.29735 focal_loss 0.58179 dice_loss 0.71556 +Epoch [3478/4000] Validation [7/10] Loss: 1.14552 focal_loss 0.49585 dice_loss 0.64967 +Epoch [3478/4000] Validation [8/10] Loss: 2.30643 focal_loss 1.68543 dice_loss 0.62100 +Epoch [3478/4000] Validation [9/10] Loss: 1.38293 focal_loss 0.83826 dice_loss 0.54467 +Epoch [3478/4000] Validation [10/10] Loss: 1.83288 focal_loss 1.09848 dice_loss 0.73439 +Epoch [3478/4000] Validation metric {'Val/mean dice_metric': 0.9503579139709473, 'Val/mean miou_metric': 0.9338558316230774, 'Val/mean f1': 0.9482558369636536, 'Val/mean precision': 0.9442682266235352, 'Val/mean recall': 0.9522772431373596, 'Val/mean hd95_metric': 10.893847465515137} +Cheakpoint... +Epoch [3478/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503579139709473, 'Val/mean miou_metric': 0.9338558316230774, 'Val/mean f1': 0.9482558369636536, 'Val/mean precision': 0.9442682266235352, 'Val/mean recall': 0.9522772431373596, 'Val/mean hd95_metric': 10.893847465515137} +Epoch [3479/4000] Training [1/39] Loss: 0.00431 +Epoch [3479/4000] Training [2/39] Loss: 0.00910 +Epoch [3479/4000] Training [3/39] Loss: 0.12914 +Epoch [3479/4000] Training [4/39] Loss: 0.00713 +Epoch [3479/4000] Training [5/39] Loss: 0.00573 +Epoch [3479/4000] Training [6/39] Loss: 0.00409 +Epoch [3479/4000] Training [7/39] Loss: 0.00495 +Epoch [3479/4000] Training [8/39] Loss: 0.00348 +Epoch [3479/4000] Training [9/39] Loss: 0.00600 +Epoch [3479/4000] Training [10/39] Loss: 0.00610 +Epoch [3479/4000] Training [11/39] Loss: 0.00421 +Epoch [3479/4000] Training [12/39] Loss: 0.00514 +Epoch [3479/4000] Training [13/39] Loss: 0.12791 +Epoch [3479/4000] Training [14/39] Loss: 0.00318 +Epoch [3479/4000] Training [15/39] Loss: 0.00442 +Epoch [3479/4000] Training [16/39] Loss: 0.13026 +Epoch [3479/4000] Training [17/39] Loss: 0.12945 +Epoch [3479/4000] Training [18/39] Loss: 0.00617 +Epoch [3479/4000] Training [19/39] Loss: 0.00700 +Epoch [3479/4000] Training [20/39] Loss: 0.00254 +Epoch [3479/4000] Training [21/39] Loss: 0.00530 +Epoch [3479/4000] Training [22/39] Loss: 0.00461 +Epoch [3479/4000] Training [23/39] Loss: 0.12809 +Epoch [3479/4000] Training [24/39] Loss: 0.00477 +Epoch [3479/4000] Training [25/39] Loss: 0.00420 +Epoch [3479/4000] Training [26/39] Loss: 0.00398 +Epoch [3479/4000] Training [27/39] Loss: 0.00817 +Epoch [3479/4000] Training [28/39] Loss: 0.00631 +Epoch [3479/4000] Training [29/39] Loss: 0.13159 +Epoch [3479/4000] Training [30/39] Loss: 0.00417 +Epoch [3479/4000] Training [31/39] Loss: 0.13044 +Epoch [3479/4000] Training [32/39] Loss: 0.13002 +Epoch [3479/4000] Training [33/39] Loss: 0.00732 +Epoch [3479/4000] Training [34/39] Loss: 0.00476 +Epoch [3479/4000] Training [35/39] Loss: 0.00516 +Epoch [3479/4000] Training [36/39] Loss: 0.00624 +Epoch [3479/4000] Training [37/39] Loss: 0.00681 +Epoch [3479/4000] Training [38/39] Loss: 0.00567 +Epoch [3479/4000] Training [39/39] Loss: 0.00408 +Epoch [3479/4000] Training metric {'Train/mean dice_metric': 0.9960028529167175, 'Train/mean miou_metric': 0.9924750924110413, 'Train/mean f1': 0.9967149496078491, 'Train/mean precision': 0.996245265007019, 'Train/mean recall': 0.9971851706504822, 'Train/mean hd95_metric': 1.0136995315551758} +Epoch [3479/4000] Validation [1/10] Loss: 0.69471 focal_loss 0.60670 dice_loss 0.08800 +Epoch [3479/4000] Validation [2/10] Loss: 0.46176 focal_loss 0.36685 dice_loss 0.09492 +Epoch [3479/4000] Validation [3/10] Loss: 0.36507 focal_loss 0.25588 dice_loss 0.10919 +Epoch [3479/4000] Validation [4/10] Loss: 0.86055 focal_loss 0.29800 dice_loss 0.56255 +Epoch [3479/4000] Validation [5/10] Loss: 2.99530 focal_loss 2.32246 dice_loss 0.67284 +Epoch [3479/4000] Validation [6/10] Loss: 1.27398 focal_loss 0.55601 dice_loss 0.71797 +Epoch [3479/4000] Validation [7/10] Loss: 1.12967 focal_loss 0.47840 dice_loss 0.65127 +Epoch [3479/4000] Validation [8/10] Loss: 2.28478 focal_loss 1.66336 dice_loss 0.62142 +Epoch [3479/4000] Validation [9/10] Loss: 1.35391 focal_loss 0.81232 dice_loss 0.54159 +Epoch [3479/4000] Validation [10/10] Loss: 1.77639 focal_loss 1.04485 dice_loss 0.73155 +Epoch [3479/4000] Validation metric {'Val/mean dice_metric': 0.9513744115829468, 'Val/mean miou_metric': 0.9352886080741882, 'Val/mean f1': 0.948946475982666, 'Val/mean precision': 0.945543646812439, 'Val/mean recall': 0.9523740410804749, 'Val/mean hd95_metric': 10.552450180053711} +Cheakpoint... +Epoch [3479/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513744115829468, 'Val/mean miou_metric': 0.9352886080741882, 'Val/mean f1': 0.948946475982666, 'Val/mean precision': 0.945543646812439, 'Val/mean recall': 0.9523740410804749, 'Val/mean hd95_metric': 10.552450180053711} +Epoch [3480/4000] Training [1/39] Loss: 0.13537 +Epoch [3480/4000] Training [2/39] Loss: 0.00316 +Epoch [3480/4000] Training [3/39] Loss: 0.00805 +Epoch [3480/4000] Training [4/39] Loss: 0.25641 +Epoch [3480/4000] Training [5/39] Loss: 0.00669 +Epoch [3480/4000] Training [6/39] Loss: 0.00512 +Epoch [3480/4000] Training [7/39] Loss: 0.00459 +Epoch [3480/4000] Training [8/39] Loss: 0.00397 +Epoch [3480/4000] Training [9/39] Loss: 0.00570 +Epoch [3480/4000] Training [10/39] Loss: 0.00655 +Epoch [3480/4000] Training [11/39] Loss: 0.00378 +Epoch [3480/4000] Training [12/39] Loss: 0.12880 +Epoch [3480/4000] Training [13/39] Loss: 0.00575 +Epoch [3480/4000] Training [14/39] Loss: 0.00350 +Epoch [3480/4000] Training [15/39] Loss: 0.00442 +Epoch [3480/4000] Training [16/39] Loss: 0.00463 +Epoch [3480/4000] Training [17/39] Loss: 0.00625 +Epoch [3480/4000] Training [18/39] Loss: 0.00506 +Epoch [3480/4000] Training [19/39] Loss: 0.12918 +Epoch [3480/4000] Training [20/39] Loss: 0.00632 +Epoch [3480/4000] Training [21/39] Loss: 0.00773 +Epoch [3480/4000] Training [22/39] Loss: 0.00493 +Epoch [3480/4000] Training [23/39] Loss: 0.13229 +Epoch [3480/4000] Training [24/39] Loss: 0.12990 +Epoch [3480/4000] Training [25/39] Loss: 0.12895 +Epoch [3480/4000] Training [26/39] Loss: 0.00628 +Epoch [3480/4000] Training [27/39] Loss: 0.00646 +Epoch [3480/4000] Training [28/39] Loss: 0.13042 +Epoch [3480/4000] Training [29/39] Loss: 0.00525 +Epoch [3480/4000] Training [30/39] Loss: 0.25621 +Epoch [3480/4000] Training [31/39] Loss: 0.00561 +Epoch [3480/4000] Training [32/39] Loss: 0.00596 +Epoch [3480/4000] Training [33/39] Loss: 0.00513 +Epoch [3480/4000] Training [34/39] Loss: 0.00420 +Epoch [3480/4000] Training [35/39] Loss: 0.00860 +Epoch [3480/4000] Training [36/39] Loss: 0.00543 +Epoch [3480/4000] Training [37/39] Loss: 0.13024 +Epoch [3480/4000] Training [38/39] Loss: 0.13648 +Epoch [3480/4000] Training [39/39] Loss: 0.00354 +Epoch [3480/4000] Training metric {'Train/mean dice_metric': 0.9957857728004456, 'Train/mean miou_metric': 0.9920321702957153, 'Train/mean f1': 0.9964962005615234, 'Train/mean precision': 0.9960730075836182, 'Train/mean recall': 0.9969197511672974, 'Train/mean hd95_metric': 1.025519609451294} +Epoch [3480/4000] Validation [1/10] Loss: 0.68601 focal_loss 0.59768 dice_loss 0.08834 +Epoch [3480/4000] Validation [2/10] Loss: 0.47075 focal_loss 0.37535 dice_loss 0.09541 +Epoch [3480/4000] Validation [3/10] Loss: 0.36454 focal_loss 0.25540 dice_loss 0.10915 +Epoch [3480/4000] Validation [4/10] Loss: 0.87021 focal_loss 0.30681 dice_loss 0.56340 +Epoch [3480/4000] Validation [5/10] Loss: 2.92552 focal_loss 2.25233 dice_loss 0.67320 +Epoch [3480/4000] Validation [6/10] Loss: 1.27792 focal_loss 0.56207 dice_loss 0.71586 +Epoch [3480/4000] Validation [7/10] Loss: 1.12207 focal_loss 0.47077 dice_loss 0.65130 +Epoch [3480/4000] Validation [8/10] Loss: 2.50181 focal_loss 1.86326 dice_loss 0.63855 +Epoch [3480/4000] Validation [9/10] Loss: 1.37802 focal_loss 0.83447 dice_loss 0.54354 +Epoch [3480/4000] Validation [10/10] Loss: 1.79518 focal_loss 1.06318 dice_loss 0.73200 +Epoch [3480/4000] Validation metric {'Val/mean dice_metric': 0.9511408805847168, 'Val/mean miou_metric': 0.9348379969596863, 'Val/mean f1': 0.9490395784378052, 'Val/mean precision': 0.9467234015464783, 'Val/mean recall': 0.9513671398162842, 'Val/mean hd95_metric': 10.636114120483398} +Cheakpoint... +Epoch [3480/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511408805847168, 'Val/mean miou_metric': 0.9348379969596863, 'Val/mean f1': 0.9490395784378052, 'Val/mean precision': 0.9467234015464783, 'Val/mean recall': 0.9513671398162842, 'Val/mean hd95_metric': 10.636114120483398} +Epoch [3481/4000] Training [1/39] Loss: 0.00478 +Epoch [3481/4000] Training [2/39] Loss: 0.12936 +Epoch [3481/4000] Training [3/39] Loss: 0.00406 +Epoch [3481/4000] Training [4/39] Loss: 0.00621 +Epoch [3481/4000] Training [5/39] Loss: 0.00370 +Epoch [3481/4000] Training [6/39] Loss: 0.00502 +Epoch [3481/4000] Training [7/39] Loss: 0.12918 +Epoch [3481/4000] Training [8/39] Loss: 0.00482 +Epoch [3481/4000] Training [9/39] Loss: 0.00728 +Epoch [3481/4000] Training [10/39] Loss: 0.12942 +Epoch [3481/4000] Training [11/39] Loss: 0.00505 +Epoch [3481/4000] Training [12/39] Loss: 0.00565 +Epoch [3481/4000] Training [13/39] Loss: 0.13097 +Epoch [3481/4000] Training [14/39] Loss: 0.00435 +Epoch [3481/4000] Training [15/39] Loss: 0.00558 +Epoch [3481/4000] Training [16/39] Loss: 0.00483 +Epoch [3481/4000] Training [17/39] Loss: 0.00630 +Epoch [3481/4000] Training [18/39] Loss: 0.12917 +Epoch [3481/4000] Training [19/39] Loss: 0.00740 +Epoch [3481/4000] Training [20/39] Loss: 0.12926 +Epoch [3481/4000] Training [21/39] Loss: 0.25344 +Epoch [3481/4000] Training [22/39] Loss: 0.00856 +Epoch [3481/4000] Training [23/39] Loss: 0.12922 +Epoch [3481/4000] Training [24/39] Loss: 0.00599 +Epoch [3481/4000] Training [25/39] Loss: 0.00420 +Epoch [3481/4000] Training [26/39] Loss: 0.00579 +Epoch [3481/4000] Training [27/39] Loss: 0.00905 +Epoch [3481/4000] Training [28/39] Loss: 0.12829 +Epoch [3481/4000] Training [29/39] Loss: 0.00484 +Epoch [3481/4000] Training [30/39] Loss: 0.12850 +Epoch [3481/4000] Training [31/39] Loss: 0.12966 +Epoch [3481/4000] Training [32/39] Loss: 0.25262 +Epoch [3481/4000] Training [33/39] Loss: 0.00587 +Epoch [3481/4000] Training [34/39] Loss: 0.13042 +Epoch [3481/4000] Training [35/39] Loss: 0.13029 +Epoch [3481/4000] Training [36/39] Loss: 0.00622 +Epoch [3481/4000] Training [37/39] Loss: 0.00356 +Epoch [3481/4000] Training [38/39] Loss: 0.00591 +Epoch [3481/4000] Training [39/39] Loss: 0.00506 +Epoch [3481/4000] Training metric {'Train/mean dice_metric': 0.9958478808403015, 'Train/mean miou_metric': 0.9921556711196899, 'Train/mean f1': 0.9966092109680176, 'Train/mean precision': 0.9960893392562866, 'Train/mean recall': 0.9971297979354858, 'Train/mean hd95_metric': 1.0178934335708618} +Epoch [3481/4000] Validation [1/10] Loss: 0.72256 focal_loss 0.63025 dice_loss 0.09231 +Epoch [3481/4000] Validation [2/10] Loss: 0.46454 focal_loss 0.37005 dice_loss 0.09449 +Epoch [3481/4000] Validation [3/10] Loss: 0.36785 focal_loss 0.25834 dice_loss 0.10950 +Epoch [3481/4000] Validation [4/10] Loss: 0.87576 focal_loss 0.31209 dice_loss 0.56367 +Epoch [3481/4000] Validation [5/10] Loss: 2.99315 focal_loss 2.32036 dice_loss 0.67279 +Epoch [3481/4000] Validation [6/10] Loss: 1.27449 focal_loss 0.55608 dice_loss 0.71841 +Epoch [3481/4000] Validation [7/10] Loss: 1.14277 focal_loss 0.48702 dice_loss 0.65575 +Epoch [3481/4000] Validation [8/10] Loss: 2.22805 focal_loss 1.61245 dice_loss 0.61560 +Epoch [3481/4000] Validation [9/10] Loss: 1.37298 focal_loss 0.83180 dice_loss 0.54118 +Epoch [3481/4000] Validation [10/10] Loss: 1.81288 focal_loss 1.07795 dice_loss 0.73493 +Epoch [3481/4000] Validation metric {'Val/mean dice_metric': 0.9510114789009094, 'Val/mean miou_metric': 0.9347019195556641, 'Val/mean f1': 0.9489356279373169, 'Val/mean precision': 0.9443228244781494, 'Val/mean recall': 0.9535936713218689, 'Val/mean hd95_metric': 10.709429740905762} +Cheakpoint... +Epoch [3481/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510114789009094, 'Val/mean miou_metric': 0.9347019195556641, 'Val/mean f1': 0.9489356279373169, 'Val/mean precision': 0.9443228244781494, 'Val/mean recall': 0.9535936713218689, 'Val/mean hd95_metric': 10.709429740905762} +Epoch [3482/4000] Training [1/39] Loss: 0.00560 +Epoch [3482/4000] Training [2/39] Loss: 0.00951 +Epoch [3482/4000] Training [3/39] Loss: 0.00657 +Epoch [3482/4000] Training [4/39] Loss: 0.00960 +Epoch [3482/4000] Training [5/39] Loss: 0.00407 +Epoch [3482/4000] Training [6/39] Loss: 0.12874 +Epoch [3482/4000] Training [7/39] Loss: 0.00491 +Epoch [3482/4000] Training [8/39] Loss: 0.00377 +Epoch [3482/4000] Training [9/39] Loss: 0.00234 +Epoch [3482/4000] Training [10/39] Loss: 0.25400 +Epoch [3482/4000] Training [11/39] Loss: 0.00607 +Epoch [3482/4000] Training [12/39] Loss: 0.13277 +Epoch [3482/4000] Training [13/39] Loss: 0.00596 +Epoch [3482/4000] Training [14/39] Loss: 0.00642 +Epoch [3482/4000] Training [15/39] Loss: 0.00453 +Epoch [3482/4000] Training [16/39] Loss: 0.12946 +Epoch [3482/4000] Training [17/39] Loss: 0.12846 +Epoch [3482/4000] Training [18/39] Loss: 0.12966 +Epoch [3482/4000] Training [19/39] Loss: 0.00455 +Epoch [3482/4000] Training [20/39] Loss: 0.12882 +Epoch [3482/4000] Training [21/39] Loss: 0.00441 +Epoch [3482/4000] Training [22/39] Loss: 0.12778 +Epoch [3482/4000] Training [23/39] Loss: 0.00714 +Epoch [3482/4000] Training [24/39] Loss: 0.00504 +Epoch [3482/4000] Training [25/39] Loss: 0.01585 +Epoch [3482/4000] Training [26/39] Loss: 0.00621 +Epoch [3482/4000] Training [27/39] Loss: 0.00945 +Epoch [3482/4000] Training [28/39] Loss: 0.00410 +Epoch [3482/4000] Training [29/39] Loss: 0.00504 +Epoch [3482/4000] Training [30/39] Loss: 0.12983 +Epoch [3482/4000] Training [31/39] Loss: 0.00513 +Epoch [3482/4000] Training [32/39] Loss: 0.00499 +Epoch [3482/4000] Training [33/39] Loss: 0.00776 +Epoch [3482/4000] Training [34/39] Loss: 0.00406 +Epoch [3482/4000] Training [35/39] Loss: 0.00325 +Epoch [3482/4000] Training [36/39] Loss: 0.01272 +Epoch [3482/4000] Training [37/39] Loss: 0.00455 +Epoch [3482/4000] Training [38/39] Loss: 0.00346 +Epoch [3482/4000] Training [39/39] Loss: 0.00800 +Epoch [3482/4000] Training metric {'Train/mean dice_metric': 0.9959006905555725, 'Train/mean miou_metric': 0.9922781586647034, 'Train/mean f1': 0.9966570138931274, 'Train/mean precision': 0.9962359070777893, 'Train/mean recall': 0.9970785975456238, 'Train/mean hd95_metric': 1.0483858585357666} +Epoch [3482/4000] Validation [1/10] Loss: 0.73161 focal_loss 0.63926 dice_loss 0.09235 +Epoch [3482/4000] Validation [2/10] Loss: 0.47753 focal_loss 0.38016 dice_loss 0.09737 +Epoch [3482/4000] Validation [3/10] Loss: 0.36209 focal_loss 0.25266 dice_loss 0.10943 +Epoch [3482/4000] Validation [4/10] Loss: 0.86977 focal_loss 0.30586 dice_loss 0.56391 +Epoch [3482/4000] Validation [5/10] Loss: 2.96136 focal_loss 2.28946 dice_loss 0.67190 +Epoch [3482/4000] Validation [6/10] Loss: 1.27717 focal_loss 0.56223 dice_loss 0.71494 +Epoch [3482/4000] Validation [7/10] Loss: 1.15055 focal_loss 0.49472 dice_loss 0.65582 +Epoch [3482/4000] Validation [8/10] Loss: 2.28866 focal_loss 1.66924 dice_loss 0.61942 +Epoch [3482/4000] Validation [9/10] Loss: 1.38560 focal_loss 0.84169 dice_loss 0.54390 +Epoch [3482/4000] Validation [10/10] Loss: 1.82134 focal_loss 1.08675 dice_loss 0.73458 +Epoch [3482/4000] Validation metric {'Val/mean dice_metric': 0.9514244794845581, 'Val/mean miou_metric': 0.9352360963821411, 'Val/mean f1': 0.9490424394607544, 'Val/mean precision': 0.944845974445343, 'Val/mean recall': 0.953276515007019, 'Val/mean hd95_metric': 10.687883377075195} +Cheakpoint... +Epoch [3482/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514244794845581, 'Val/mean miou_metric': 0.9352360963821411, 'Val/mean f1': 0.9490424394607544, 'Val/mean precision': 0.944845974445343, 'Val/mean recall': 0.953276515007019, 'Val/mean hd95_metric': 10.687883377075195} +Epoch [3483/4000] Training [1/39] Loss: 0.00314 +Epoch [3483/4000] Training [2/39] Loss: 0.12883 +Epoch [3483/4000] Training [3/39] Loss: 0.13048 +Epoch [3483/4000] Training [4/39] Loss: 0.00424 +Epoch [3483/4000] Training [5/39] Loss: 0.01074 +Epoch [3483/4000] Training [6/39] Loss: 0.00688 +Epoch [3483/4000] Training [7/39] Loss: 0.00555 +Epoch [3483/4000] Training [8/39] Loss: 0.00592 +Epoch [3483/4000] Training [9/39] Loss: 0.00410 +Epoch [3483/4000] Training [10/39] Loss: 0.00494 +Epoch [3483/4000] Training [11/39] Loss: 0.00322 +Epoch [3483/4000] Training [12/39] Loss: 0.00468 +Epoch [3483/4000] Training [13/39] Loss: 0.00857 +Epoch [3483/4000] Training [14/39] Loss: 0.00449 +Epoch [3483/4000] Training [15/39] Loss: 0.00516 +Epoch [3483/4000] Training [16/39] Loss: 0.00942 +Epoch [3483/4000] Training [17/39] Loss: 0.12803 +Epoch [3483/4000] Training [18/39] Loss: 0.01165 +Epoch [3483/4000] Training [19/39] Loss: 0.00687 +Epoch [3483/4000] Training [20/39] Loss: 0.12996 +Epoch [3483/4000] Training [21/39] Loss: 0.13064 +Epoch [3483/4000] Training [22/39] Loss: 0.00334 +Epoch [3483/4000] Training [23/39] Loss: 0.00628 +Epoch [3483/4000] Training [24/39] Loss: 0.00431 +Epoch [3483/4000] Training [25/39] Loss: 0.12881 +Epoch [3483/4000] Training [26/39] Loss: 0.00654 +Epoch [3483/4000] Training [27/39] Loss: 0.12851 +Epoch [3483/4000] Training [28/39] Loss: 0.00550 +Epoch [3483/4000] Training [29/39] Loss: 0.00639 +Epoch [3483/4000] Training [30/39] Loss: 0.00640 +Epoch [3483/4000] Training [31/39] Loss: 0.12975 +Epoch [3483/4000] Training [32/39] Loss: 0.00851 +Epoch [3483/4000] Training [33/39] Loss: 0.00418 +Epoch [3483/4000] Training [34/39] Loss: 0.00873 +Epoch [3483/4000] Training [35/39] Loss: 0.00446 +Epoch [3483/4000] Training [36/39] Loss: 0.00292 +Epoch [3483/4000] Training [37/39] Loss: 0.00723 +Epoch [3483/4000] Training [38/39] Loss: 0.12866 +Epoch [3483/4000] Training [39/39] Loss: 0.00505 +Epoch [3483/4000] Training metric {'Train/mean dice_metric': 0.9956796169281006, 'Train/mean miou_metric': 0.9918680787086487, 'Train/mean f1': 0.9964374899864197, 'Train/mean precision': 0.9959916472434998, 'Train/mean recall': 0.996883749961853, 'Train/mean hd95_metric': 1.008984923362732} +Epoch [3483/4000] Validation [1/10] Loss: 0.64225 focal_loss 0.56052 dice_loss 0.08173 +Epoch [3483/4000] Validation [2/10] Loss: 0.49163 focal_loss 0.38859 dice_loss 0.10304 +Epoch [3483/4000] Validation [3/10] Loss: 0.38962 focal_loss 0.27838 dice_loss 0.11125 +Epoch [3483/4000] Validation [4/10] Loss: 0.89354 focal_loss 0.29697 dice_loss 0.59656 +Epoch [3483/4000] Validation [5/10] Loss: 2.89914 focal_loss 2.22636 dice_loss 0.67278 +Epoch [3483/4000] Validation [6/10] Loss: 1.20598 focal_loss 0.49448 dice_loss 0.71149 +Epoch [3483/4000] Validation [7/10] Loss: 1.09778 focal_loss 0.44749 dice_loss 0.65029 +Epoch [3483/4000] Validation [8/10] Loss: 2.89607 focal_loss 2.22984 dice_loss 0.66623 +Epoch [3483/4000] Validation [9/10] Loss: 1.37376 focal_loss 0.83286 dice_loss 0.54090 +Epoch [3483/4000] Validation [10/10] Loss: 1.69308 focal_loss 0.96817 dice_loss 0.72491 +Epoch [3483/4000] Validation metric {'Val/mean dice_metric': 0.9507450461387634, 'Val/mean miou_metric': 0.9343441128730774, 'Val/mean f1': 0.9502826929092407, 'Val/mean precision': 0.9529111385345459, 'Val/mean recall': 0.9476686716079712, 'Val/mean hd95_metric': 10.641254425048828} +Cheakpoint... +Epoch [3483/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507450461387634, 'Val/mean miou_metric': 0.9343441128730774, 'Val/mean f1': 0.9502826929092407, 'Val/mean precision': 0.9529111385345459, 'Val/mean recall': 0.9476686716079712, 'Val/mean hd95_metric': 10.641254425048828} +Epoch [3484/4000] Training [1/39] Loss: 0.00835 +Epoch [3484/4000] Training [2/39] Loss: 0.01170 +Epoch [3484/4000] Training [3/39] Loss: 0.12936 +Epoch [3484/4000] Training [4/39] Loss: 0.00903 +Epoch [3484/4000] Training [5/39] Loss: 0.00895 +Epoch [3484/4000] Training [6/39] Loss: 0.00514 +Epoch [3484/4000] Training [7/39] Loss: 0.00587 +Epoch [3484/4000] Training [8/39] Loss: 0.00447 +Epoch [3484/4000] Training [9/39] Loss: 0.08780 +Epoch [3484/4000] Training [10/39] Loss: 0.00490 +Epoch [3484/4000] Training [11/39] Loss: 0.00481 +Epoch [3484/4000] Training [12/39] Loss: 0.00494 +Epoch [3484/4000] Training [13/39] Loss: 0.00677 +Epoch [3484/4000] Training [14/39] Loss: 0.12960 +Epoch [3484/4000] Training [15/39] Loss: 0.12880 +Epoch [3484/4000] Training [16/39] Loss: 0.00493 +Epoch [3484/4000] Training [17/39] Loss: 0.00407 +Epoch [3484/4000] Training [18/39] Loss: 0.00631 +Epoch [3484/4000] Training [19/39] Loss: 0.00621 +Epoch [3484/4000] Training [20/39] Loss: 0.00565 +Epoch [3484/4000] Training [21/39] Loss: 0.25401 +Epoch [3484/4000] Training [22/39] Loss: 0.00891 +Epoch [3484/4000] Training [23/39] Loss: 0.00472 +Epoch [3484/4000] Training [24/39] Loss: 0.12986 +Epoch [3484/4000] Training [25/39] Loss: 0.00916 +Epoch [3484/4000] Training [26/39] Loss: 0.00390 +Epoch [3484/4000] Training [27/39] Loss: 0.00388 +Epoch [3484/4000] Training [28/39] Loss: 0.01003 +Epoch [3484/4000] Training [29/39] Loss: 0.00496 +Epoch [3484/4000] Training [30/39] Loss: 0.13076 +Epoch [3484/4000] Training [31/39] Loss: 0.00337 +Epoch [3484/4000] Training [32/39] Loss: 0.00354 +Epoch [3484/4000] Training [33/39] Loss: 0.00432 +Epoch [3484/4000] Training [34/39] Loss: 0.12884 +Epoch [3484/4000] Training [35/39] Loss: 0.00515 +Epoch [3484/4000] Training [36/39] Loss: 0.00566 +Epoch [3484/4000] Training [37/39] Loss: 0.00656 +Epoch [3484/4000] Training [38/39] Loss: 0.00709 +Epoch [3484/4000] Training [39/39] Loss: 0.00486 +Epoch [3484/4000] Training metric {'Train/mean dice_metric': 0.9956945776939392, 'Train/mean miou_metric': 0.9918519854545593, 'Train/mean f1': 0.9964877963066101, 'Train/mean precision': 0.9960427284240723, 'Train/mean recall': 0.9969331622123718, 'Train/mean hd95_metric': 1.0197721719741821} +Epoch [3484/4000] Validation [1/10] Loss: 0.67363 focal_loss 0.58952 dice_loss 0.08411 +Epoch [3484/4000] Validation [2/10] Loss: 0.49736 focal_loss 0.39503 dice_loss 0.10233 +Epoch [3484/4000] Validation [3/10] Loss: 0.38972 focal_loss 0.27899 dice_loss 0.11073 +Epoch [3484/4000] Validation [4/10] Loss: 0.87974 focal_loss 0.30233 dice_loss 0.57741 +Epoch [3484/4000] Validation [5/10] Loss: 2.99168 focal_loss 2.31942 dice_loss 0.67226 +Epoch [3484/4000] Validation [6/10] Loss: 1.24256 focal_loss 0.52740 dice_loss 0.71516 +Epoch [3484/4000] Validation [7/10] Loss: 1.13029 focal_loss 0.48015 dice_loss 0.65014 +Epoch [3484/4000] Validation [8/10] Loss: 2.67927 focal_loss 2.02685 dice_loss 0.65243 +Epoch [3484/4000] Validation [9/10] Loss: 1.38817 focal_loss 0.84768 dice_loss 0.54050 +Epoch [3484/4000] Validation [10/10] Loss: 1.76410 focal_loss 1.03435 dice_loss 0.72974 +Epoch [3484/4000] Validation metric {'Val/mean dice_metric': 0.9510855674743652, 'Val/mean miou_metric': 0.934726893901825, 'Val/mean f1': 0.9498085379600525, 'Val/mean precision': 0.9492852091789246, 'Val/mean recall': 0.950332522392273, 'Val/mean hd95_metric': 10.658730506896973} +Cheakpoint... +Epoch [3484/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510855674743652, 'Val/mean miou_metric': 0.934726893901825, 'Val/mean f1': 0.9498085379600525, 'Val/mean precision': 0.9492852091789246, 'Val/mean recall': 0.950332522392273, 'Val/mean hd95_metric': 10.658730506896973} +Epoch [3485/4000] Training [1/39] Loss: 0.00556 +Epoch [3485/4000] Training [2/39] Loss: 0.00515 +Epoch [3485/4000] Training [3/39] Loss: 0.25319 +Epoch [3485/4000] Training [4/39] Loss: 0.00919 +Epoch [3485/4000] Training [5/39] Loss: 0.00271 +Epoch [3485/4000] Training [6/39] Loss: 0.00500 +Epoch [3485/4000] Training [7/39] Loss: 0.00717 +Epoch [3485/4000] Training [8/39] Loss: 0.13237 +Epoch [3485/4000] Training [9/39] Loss: 0.01091 +Epoch [3485/4000] Training [10/39] Loss: 0.00635 +Epoch [3485/4000] Training [11/39] Loss: 0.00427 +Epoch [3485/4000] Training [12/39] Loss: 0.00400 +Epoch [3485/4000] Training [13/39] Loss: 0.00621 +Epoch [3485/4000] Training [14/39] Loss: 0.00353 +Epoch [3485/4000] Training [15/39] Loss: 0.00424 +Epoch [3485/4000] Training [16/39] Loss: 0.25561 +Epoch [3485/4000] Training [17/39] Loss: 0.00754 +Epoch [3485/4000] Training [18/39] Loss: 0.00421 +Epoch [3485/4000] Training [19/39] Loss: 0.12972 +Epoch [3485/4000] Training [20/39] Loss: 0.00412 +Epoch [3485/4000] Training [21/39] Loss: 0.00363 +Epoch [3485/4000] Training [22/39] Loss: 0.00457 +Epoch [3485/4000] Training [23/39] Loss: 0.13199 +Epoch [3485/4000] Training [24/39] Loss: 0.00250 +Epoch [3485/4000] Training [25/39] Loss: 0.00481 +Epoch [3485/4000] Training [26/39] Loss: 0.00505 +Epoch [3485/4000] Training [27/39] Loss: 0.13181 +Epoch [3485/4000] Training [28/39] Loss: 0.12847 +Epoch [3485/4000] Training [29/39] Loss: 0.13245 +Epoch [3485/4000] Training [30/39] Loss: 0.00564 +Epoch [3485/4000] Training [31/39] Loss: 0.00629 +Epoch [3485/4000] Training [32/39] Loss: 0.25385 +Epoch [3485/4000] Training [33/39] Loss: 0.00560 +Epoch [3485/4000] Training [34/39] Loss: 0.00449 +Epoch [3485/4000] Training [35/39] Loss: 0.00484 +Epoch [3485/4000] Training [36/39] Loss: 0.00285 +Epoch [3485/4000] Training [37/39] Loss: 0.12946 +Epoch [3485/4000] Training [38/39] Loss: 0.00368 +Epoch [3485/4000] Training [39/39] Loss: 0.13083 +Epoch [3485/4000] Training metric {'Train/mean dice_metric': 0.9958816766738892, 'Train/mean miou_metric': 0.992218554019928, 'Train/mean f1': 0.9965546727180481, 'Train/mean precision': 0.9961172342300415, 'Train/mean recall': 0.9969925880432129, 'Train/mean hd95_metric': 1.0049999952316284} +Epoch [3485/4000] Validation [1/10] Loss: 0.66145 focal_loss 0.57599 dice_loss 0.08546 +Epoch [3485/4000] Validation [2/10] Loss: 0.49808 focal_loss 0.39697 dice_loss 0.10112 +Epoch [3485/4000] Validation [3/10] Loss: 0.36846 focal_loss 0.25921 dice_loss 0.10925 +Epoch [3485/4000] Validation [4/10] Loss: 0.86326 focal_loss 0.29748 dice_loss 0.56578 +Epoch [3485/4000] Validation [5/10] Loss: 2.93409 focal_loss 2.26199 dice_loss 0.67210 +Epoch [3485/4000] Validation [6/10] Loss: 1.25644 focal_loss 0.54146 dice_loss 0.71498 +Epoch [3485/4000] Validation [7/10] Loss: 1.12848 focal_loss 0.47887 dice_loss 0.64961 +Epoch [3485/4000] Validation [8/10] Loss: 2.63492 focal_loss 1.98872 dice_loss 0.64621 +Epoch [3485/4000] Validation [9/10] Loss: 1.37713 focal_loss 0.83644 dice_loss 0.54069 +Epoch [3485/4000] Validation [10/10] Loss: 1.78878 focal_loss 1.05748 dice_loss 0.73130 +Epoch [3485/4000] Validation metric {'Val/mean dice_metric': 0.9513633847236633, 'Val/mean miou_metric': 0.9351560473442078, 'Val/mean f1': 0.9497333765029907, 'Val/mean precision': 0.9484400153160095, 'Val/mean recall': 0.951030433177948, 'Val/mean hd95_metric': 10.605413436889648} +Cheakpoint... +Epoch [3485/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513633847236633, 'Val/mean miou_metric': 0.9351560473442078, 'Val/mean f1': 0.9497333765029907, 'Val/mean precision': 0.9484400153160095, 'Val/mean recall': 0.951030433177948, 'Val/mean hd95_metric': 10.605413436889648} +Epoch [3486/4000] Training [1/39] Loss: 0.00467 +Epoch [3486/4000] Training [2/39] Loss: 0.00830 +Epoch [3486/4000] Training [3/39] Loss: 0.00731 +Epoch [3486/4000] Training [4/39] Loss: 0.12979 +Epoch [3486/4000] Training [5/39] Loss: 0.12849 +Epoch [3486/4000] Training [6/39] Loss: 0.00527 +Epoch [3486/4000] Training [7/39] Loss: 0.00492 +Epoch [3486/4000] Training [8/39] Loss: 0.00296 +Epoch [3486/4000] Training [9/39] Loss: 0.00473 +Epoch [3486/4000] Training [10/39] Loss: 0.04542 +Epoch [3486/4000] Training [11/39] Loss: 0.12918 +Epoch [3486/4000] Training [12/39] Loss: 0.25293 +Epoch [3486/4000] Training [13/39] Loss: 0.13025 +Epoch [3486/4000] Training [14/39] Loss: 0.00756 +Epoch [3486/4000] Training [15/39] Loss: 0.00836 +Epoch [3486/4000] Training [16/39] Loss: 0.00304 +Epoch [3486/4000] Training [17/39] Loss: 0.00506 +Epoch [3486/4000] Training [18/39] Loss: 0.00665 +Epoch [3486/4000] Training [19/39] Loss: 0.00636 +Epoch [3486/4000] Training [20/39] Loss: 0.13151 +Epoch [3486/4000] Training [21/39] Loss: 0.00910 +Epoch [3486/4000] Training [22/39] Loss: 0.00432 +Epoch [3486/4000] Training [23/39] Loss: 0.00530 +Epoch [3486/4000] Training [24/39] Loss: 0.00855 +Epoch [3486/4000] Training [25/39] Loss: 0.25412 +Epoch [3486/4000] Training [26/39] Loss: 0.00615 +Epoch [3486/4000] Training [27/39] Loss: 0.00540 +Epoch [3486/4000] Training [28/39] Loss: 0.00589 +Epoch [3486/4000] Training [29/39] Loss: 0.01110 +Epoch [3486/4000] Training [30/39] Loss: 0.00598 +Epoch [3486/4000] Training [31/39] Loss: 0.00295 +Epoch [3486/4000] Training [32/39] Loss: 0.00622 +Epoch [3486/4000] Training [33/39] Loss: 0.00361 +Epoch [3486/4000] Training [34/39] Loss: 0.13663 +Epoch [3486/4000] Training [35/39] Loss: 0.13390 +Epoch [3486/4000] Training [36/39] Loss: 0.00634 +Epoch [3486/4000] Training [37/39] Loss: 0.00524 +Epoch [3486/4000] Training [38/39] Loss: 0.00515 +Epoch [3486/4000] Training [39/39] Loss: 0.00532 +Epoch [3486/4000] Training metric {'Train/mean dice_metric': 0.9956896305084229, 'Train/mean miou_metric': 0.9918445348739624, 'Train/mean f1': 0.996488630771637, 'Train/mean precision': 0.9960099458694458, 'Train/mean recall': 0.9969679117202759, 'Train/mean hd95_metric': 1.2490177154541016} +Epoch [3486/4000] Validation [1/10] Loss: 0.67520 focal_loss 0.58928 dice_loss 0.08592 +Epoch [3486/4000] Validation [2/10] Loss: 0.48770 focal_loss 0.38706 dice_loss 0.10063 +Epoch [3486/4000] Validation [3/10] Loss: 0.36905 focal_loss 0.25966 dice_loss 0.10939 +Epoch [3486/4000] Validation [4/10] Loss: 0.87203 focal_loss 0.29629 dice_loss 0.57574 +Epoch [3486/4000] Validation [5/10] Loss: 2.93033 focal_loss 2.25756 dice_loss 0.67276 +Epoch [3486/4000] Validation [6/10] Loss: 1.24555 focal_loss 0.53057 dice_loss 0.71498 +Epoch [3486/4000] Validation [7/10] Loss: 1.12486 focal_loss 0.47493 dice_loss 0.64993 +Epoch [3486/4000] Validation [8/10] Loss: 2.77008 focal_loss 2.11437 dice_loss 0.65571 +Epoch [3486/4000] Validation [9/10] Loss: 1.36077 focal_loss 0.82187 dice_loss 0.53890 +Epoch [3486/4000] Validation [10/10] Loss: 1.71291 focal_loss 0.99055 dice_loss 0.72236 +Epoch [3486/4000] Validation metric {'Val/mean dice_metric': 0.951171338558197, 'Val/mean miou_metric': 0.9347583055496216, 'Val/mean f1': 0.9501999616622925, 'Val/mean precision': 0.950156033039093, 'Val/mean recall': 0.9502436518669128, 'Val/mean hd95_metric': 10.866230010986328} +Cheakpoint... +Epoch [3486/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951171338558197, 'Val/mean miou_metric': 0.9347583055496216, 'Val/mean f1': 0.9501999616622925, 'Val/mean precision': 0.950156033039093, 'Val/mean recall': 0.9502436518669128, 'Val/mean hd95_metric': 10.866230010986328} +Epoch [3487/4000] Training [1/39] Loss: 0.25498 +Epoch [3487/4000] Training [2/39] Loss: 0.13215 +Epoch [3487/4000] Training [3/39] Loss: 0.00927 +Epoch [3487/4000] Training [4/39] Loss: 0.00658 +Epoch [3487/4000] Training [5/39] Loss: 0.00334 +Epoch [3487/4000] Training [6/39] Loss: 0.00634 +Epoch [3487/4000] Training [7/39] Loss: 0.13028 +Epoch [3487/4000] Training [8/39] Loss: 0.00543 +Epoch [3487/4000] Training [9/39] Loss: 0.12961 +Epoch [3487/4000] Training [10/39] Loss: 0.00584 +Epoch [3487/4000] Training [11/39] Loss: 0.00441 +Epoch [3487/4000] Training [12/39] Loss: 0.00586 +Epoch [3487/4000] Training [13/39] Loss: 0.00725 +Epoch [3487/4000] Training [14/39] Loss: 0.00459 +Epoch [3487/4000] Training [15/39] Loss: 0.00419 +Epoch [3487/4000] Training [16/39] Loss: 0.12939 +Epoch [3487/4000] Training [17/39] Loss: 0.00456 +Epoch [3487/4000] Training [18/39] Loss: 0.00696 +Epoch [3487/4000] Training [19/39] Loss: 0.00421 +Epoch [3487/4000] Training [20/39] Loss: 0.00586 +Epoch [3487/4000] Training [21/39] Loss: 0.00741 +Epoch [3487/4000] Training [22/39] Loss: 0.00686 +Epoch [3487/4000] Training [23/39] Loss: 0.12852 +Epoch [3487/4000] Training [24/39] Loss: 0.00395 +Epoch [3487/4000] Training [25/39] Loss: 0.00459 +Epoch [3487/4000] Training [26/39] Loss: 0.13054 +Epoch [3487/4000] Training [27/39] Loss: 0.00397 +Epoch [3487/4000] Training [28/39] Loss: 0.00643 +Epoch [3487/4000] Training [29/39] Loss: 0.00519 +Epoch [3487/4000] Training [30/39] Loss: 0.00562 +Epoch [3487/4000] Training [31/39] Loss: 0.00370 +Epoch [3487/4000] Training [32/39] Loss: 0.00579 +Epoch [3487/4000] Training [33/39] Loss: 0.18055 +Epoch [3487/4000] Training [34/39] Loss: 0.25469 +Epoch [3487/4000] Training [35/39] Loss: 0.00933 +Epoch [3487/4000] Training [36/39] Loss: 0.13155 +Epoch [3487/4000] Training [37/39] Loss: 0.25341 +Epoch [3487/4000] Training [38/39] Loss: 0.00564 +Epoch [3487/4000] Training [39/39] Loss: 0.13042 +Epoch [3487/4000] Training metric {'Train/mean dice_metric': 0.9945952892303467, 'Train/mean miou_metric': 0.9905072450637817, 'Train/mean f1': 0.996030867099762, 'Train/mean precision': 0.9956207871437073, 'Train/mean recall': 0.9964411854743958, 'Train/mean hd95_metric': 1.0809979438781738} +Epoch [3487/4000] Validation [1/10] Loss: 0.69998 focal_loss 0.61326 dice_loss 0.08672 +Epoch [3487/4000] Validation [2/10] Loss: 0.47238 focal_loss 0.37670 dice_loss 0.09568 +Epoch [3487/4000] Validation [3/10] Loss: 0.37934 focal_loss 0.26931 dice_loss 0.11002 +Epoch [3487/4000] Validation [4/10] Loss: 0.87493 focal_loss 0.30999 dice_loss 0.56495 +Epoch [3487/4000] Validation [5/10] Loss: 3.00336 focal_loss 2.33084 dice_loss 0.67251 +Epoch [3487/4000] Validation [6/10] Loss: 1.26749 focal_loss 0.55482 dice_loss 0.71267 +Epoch [3487/4000] Validation [7/10] Loss: 1.13081 focal_loss 0.48004 dice_loss 0.65077 +Epoch [3487/4000] Validation [8/10] Loss: 2.51131 focal_loss 1.87045 dice_loss 0.64086 +Epoch [3487/4000] Validation [9/10] Loss: 1.37530 focal_loss 0.83634 dice_loss 0.53897 +Epoch [3487/4000] Validation [10/10] Loss: 1.76840 focal_loss 1.03980 dice_loss 0.72860 +Epoch [3487/4000] Validation metric {'Val/mean dice_metric': 0.9502208828926086, 'Val/mean miou_metric': 0.933678388595581, 'Val/mean f1': 0.9490234851837158, 'Val/mean precision': 0.9468818306922913, 'Val/mean recall': 0.9511749744415283, 'Val/mean hd95_metric': 10.625676155090332} +Cheakpoint... +Epoch [3487/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9502], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9502208828926086, 'Val/mean miou_metric': 0.933678388595581, 'Val/mean f1': 0.9490234851837158, 'Val/mean precision': 0.9468818306922913, 'Val/mean recall': 0.9511749744415283, 'Val/mean hd95_metric': 10.625676155090332} +Epoch [3488/4000] Training [1/39] Loss: 0.00450 +Epoch [3488/4000] Training [2/39] Loss: 0.00677 +Epoch [3488/4000] Training [3/39] Loss: 0.00566 +Epoch [3488/4000] Training [4/39] Loss: 0.00327 +Epoch [3488/4000] Training [5/39] Loss: 0.00550 +Epoch [3488/4000] Training [6/39] Loss: 0.00568 +Epoch [3488/4000] Training [7/39] Loss: 0.00796 +Epoch [3488/4000] Training [8/39] Loss: 0.13096 +Epoch [3488/4000] Training [9/39] Loss: 0.12965 +Epoch [3488/4000] Training [10/39] Loss: 0.13083 +Epoch [3488/4000] Training [11/39] Loss: 0.00598 +Epoch [3488/4000] Training [12/39] Loss: 0.25683 +Epoch [3488/4000] Training [13/39] Loss: 0.00496 +Epoch [3488/4000] Training [14/39] Loss: 0.00723 +Epoch [3488/4000] Training [15/39] Loss: 0.00658 +Epoch [3488/4000] Training [16/39] Loss: 0.12840 +Epoch [3488/4000] Training [17/39] Loss: 0.25598 +Epoch [3488/4000] Training [18/39] Loss: 0.13070 +Epoch [3488/4000] Training [19/39] Loss: 0.12892 +Epoch [3488/4000] Training [20/39] Loss: 0.00444 +Epoch [3488/4000] Training [21/39] Loss: 0.00513 +Epoch [3488/4000] Training [22/39] Loss: 0.00714 +Epoch [3488/4000] Training [23/39] Loss: 0.00959 +Epoch [3488/4000] Training [24/39] Loss: 0.12903 +Epoch [3488/4000] Training [25/39] Loss: 0.00510 +Epoch [3488/4000] Training [26/39] Loss: 0.00559 +Epoch [3488/4000] Training [27/39] Loss: 0.12951 +Epoch [3488/4000] Training [28/39] Loss: 0.13052 +Epoch [3488/4000] Training [29/39] Loss: 0.00440 +Epoch [3488/4000] Training [30/39] Loss: 0.00377 +Epoch [3488/4000] Training [31/39] Loss: 0.00411 +Epoch [3488/4000] Training [32/39] Loss: 0.00420 +Epoch [3488/4000] Training [33/39] Loss: 0.00655 +Epoch [3488/4000] Training [34/39] Loss: 0.00414 +Epoch [3488/4000] Training [35/39] Loss: 0.13003 +Epoch [3488/4000] Training [36/39] Loss: 0.00487 +Epoch [3488/4000] Training [37/39] Loss: 0.00507 +Epoch [3488/4000] Training [38/39] Loss: 0.12846 +Epoch [3488/4000] Training [39/39] Loss: 0.00836 +Epoch [3488/4000] Training metric {'Train/mean dice_metric': 0.9957570433616638, 'Train/mean miou_metric': 0.9919900298118591, 'Train/mean f1': 0.9964741468429565, 'Train/mean precision': 0.9960141181945801, 'Train/mean recall': 0.9969345927238464, 'Train/mean hd95_metric': 1.022452473640442} +Epoch [3488/4000] Validation [1/10] Loss: 0.70591 focal_loss 0.61826 dice_loss 0.08765 +Epoch [3488/4000] Validation [2/10] Loss: 0.46956 focal_loss 0.37572 dice_loss 0.09384 +Epoch [3488/4000] Validation [3/10] Loss: 0.38255 focal_loss 0.27174 dice_loss 0.11081 +Epoch [3488/4000] Validation [4/10] Loss: 0.88161 focal_loss 0.31799 dice_loss 0.56362 +Epoch [3488/4000] Validation [5/10] Loss: 3.02223 focal_loss 2.35004 dice_loss 0.67220 +Epoch [3488/4000] Validation [6/10] Loss: 1.28916 focal_loss 0.57461 dice_loss 0.71455 +Epoch [3488/4000] Validation [7/10] Loss: 1.14841 focal_loss 0.49693 dice_loss 0.65148 +Epoch [3488/4000] Validation [8/10] Loss: 2.43809 focal_loss 1.80434 dice_loss 0.63375 +Epoch [3488/4000] Validation [9/10] Loss: 1.38831 focal_loss 0.84788 dice_loss 0.54044 +Epoch [3488/4000] Validation [10/10] Loss: 1.80389 focal_loss 1.07206 dice_loss 0.73183 +Epoch [3488/4000] Validation metric {'Val/mean dice_metric': 0.9512026309967041, 'Val/mean miou_metric': 0.9349625706672668, 'Val/mean f1': 0.9492948055267334, 'Val/mean precision': 0.9460180401802063, 'Val/mean recall': 0.9525944590568542, 'Val/mean hd95_metric': 10.488852500915527} +Cheakpoint... +Epoch [3488/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512026309967041, 'Val/mean miou_metric': 0.9349625706672668, 'Val/mean f1': 0.9492948055267334, 'Val/mean precision': 0.9460180401802063, 'Val/mean recall': 0.9525944590568542, 'Val/mean hd95_metric': 10.488852500915527} +Epoch [3489/4000] Training [1/39] Loss: 0.00817 +Epoch [3489/4000] Training [2/39] Loss: 0.12892 +Epoch [3489/4000] Training [3/39] Loss: 0.00356 +Epoch [3489/4000] Training [4/39] Loss: 0.00801 +Epoch [3489/4000] Training [5/39] Loss: 0.00558 +Epoch [3489/4000] Training [6/39] Loss: 0.13051 +Epoch [3489/4000] Training [7/39] Loss: 0.00433 +Epoch [3489/4000] Training [8/39] Loss: 0.00630 +Epoch [3489/4000] Training [9/39] Loss: 0.00381 +Epoch [3489/4000] Training [10/39] Loss: 0.00509 +Epoch [3489/4000] Training [11/39] Loss: 0.12983 +Epoch [3489/4000] Training [12/39] Loss: 0.12904 +Epoch [3489/4000] Training [13/39] Loss: 0.00386 +Epoch [3489/4000] Training [14/39] Loss: 0.00757 +Epoch [3489/4000] Training [15/39] Loss: 0.25381 +Epoch [3489/4000] Training [16/39] Loss: 0.00634 +Epoch [3489/4000] Training [17/39] Loss: 0.01053 +Epoch [3489/4000] Training [18/39] Loss: 0.00752 +Epoch [3489/4000] Training [19/39] Loss: 0.13011 +Epoch [3489/4000] Training [20/39] Loss: 0.00820 +Epoch [3489/4000] Training [21/39] Loss: 0.12948 +Epoch [3489/4000] Training [22/39] Loss: 0.12800 +Epoch [3489/4000] Training [23/39] Loss: 0.00755 +Epoch [3489/4000] Training [24/39] Loss: 0.12970 +Epoch [3489/4000] Training [25/39] Loss: 0.13141 +Epoch [3489/4000] Training [26/39] Loss: 0.00524 +Epoch [3489/4000] Training [27/39] Loss: 0.00427 +Epoch [3489/4000] Training [28/39] Loss: 0.00444 +Epoch [3489/4000] Training [29/39] Loss: 0.00644 +Epoch [3489/4000] Training [30/39] Loss: 0.13270 +Epoch [3489/4000] Training [31/39] Loss: 0.00454 +Epoch [3489/4000] Training [32/39] Loss: 0.00571 +Epoch [3489/4000] Training [33/39] Loss: 0.00403 +Epoch [3489/4000] Training [34/39] Loss: 0.00639 +Epoch [3489/4000] Training [35/39] Loss: 0.00382 +Epoch [3489/4000] Training [36/39] Loss: 0.12989 +Epoch [3489/4000] Training [37/39] Loss: 0.12874 +Epoch [3489/4000] Training [38/39] Loss: 0.00581 +Epoch [3489/4000] Training [39/39] Loss: 0.00553 +Epoch [3489/4000] Training metric {'Train/mean dice_metric': 0.9947152733802795, 'Train/mean miou_metric': 0.9907813668251038, 'Train/mean f1': 0.9960939884185791, 'Train/mean precision': 0.9959076642990112, 'Train/mean recall': 0.9962804913520813, 'Train/mean hd95_metric': 1.067500352859497} +Epoch [3489/4000] Validation [1/10] Loss: 0.73938 focal_loss 0.64609 dice_loss 0.09329 +Epoch [3489/4000] Validation [2/10] Loss: 0.46965 focal_loss 0.38047 dice_loss 0.08918 +Epoch [3489/4000] Validation [3/10] Loss: 0.34440 focal_loss 0.23631 dice_loss 0.10809 +Epoch [3489/4000] Validation [4/10] Loss: 0.90232 focal_loss 0.33272 dice_loss 0.56960 +Epoch [3489/4000] Validation [5/10] Loss: 2.96660 focal_loss 2.29675 dice_loss 0.66985 +Epoch [3489/4000] Validation [6/10] Loss: 1.32687 focal_loss 0.61280 dice_loss 0.71407 +Epoch [3489/4000] Validation [7/10] Loss: 1.22409 focal_loss 0.55711 dice_loss 0.66698 +Epoch [3489/4000] Validation [8/10] Loss: 1.92989 focal_loss 1.35442 dice_loss 0.57547 +Epoch [3489/4000] Validation [9/10] Loss: 1.41333 focal_loss 0.86863 dice_loss 0.54469 +Epoch [3489/4000] Validation [10/10] Loss: 1.91641 focal_loss 1.17446 dice_loss 0.74195 +Epoch [3489/4000] Validation metric {'Val/mean dice_metric': 0.9500247836112976, 'Val/mean miou_metric': 0.9334083199501038, 'Val/mean f1': 0.9467810392379761, 'Val/mean precision': 0.9370459318161011, 'Val/mean recall': 0.9567204713821411, 'Val/mean hd95_metric': 10.90262222290039} +Cheakpoint... +Epoch [3489/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9500], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9500247836112976, 'Val/mean miou_metric': 0.9334083199501038, 'Val/mean f1': 0.9467810392379761, 'Val/mean precision': 0.9370459318161011, 'Val/mean recall': 0.9567204713821411, 'Val/mean hd95_metric': 10.90262222290039} +Epoch [3490/4000] Training [1/39] Loss: 0.00474 +Epoch [3490/4000] Training [2/39] Loss: 0.00427 +Epoch [3490/4000] Training [3/39] Loss: 0.00301 +Epoch [3490/4000] Training [4/39] Loss: 0.00446 +Epoch [3490/4000] Training [5/39] Loss: 0.00602 +Epoch [3490/4000] Training [6/39] Loss: 0.13130 +Epoch [3490/4000] Training [7/39] Loss: 0.00463 +Epoch [3490/4000] Training [8/39] Loss: 0.00338 +Epoch [3490/4000] Training [9/39] Loss: 0.12815 +Epoch [3490/4000] Training [10/39] Loss: 0.00411 +Epoch [3490/4000] Training [11/39] Loss: 0.12845 +Epoch [3490/4000] Training [12/39] Loss: 0.00319 +Epoch [3490/4000] Training [13/39] Loss: 0.00793 +Epoch [3490/4000] Training [14/39] Loss: 0.00499 +Epoch [3490/4000] Training [15/39] Loss: 0.00495 +Epoch [3490/4000] Training [16/39] Loss: 0.00360 +Epoch [3490/4000] Training [17/39] Loss: 0.00774 +Epoch [3490/4000] Training [18/39] Loss: 0.00418 +Epoch [3490/4000] Training [19/39] Loss: 0.00511 +Epoch [3490/4000] Training [20/39] Loss: 0.00483 +Epoch [3490/4000] Training [21/39] Loss: 0.00526 +Epoch [3490/4000] Training [22/39] Loss: 0.00552 +Epoch [3490/4000] Training [23/39] Loss: 0.00435 +Epoch [3490/4000] Training [24/39] Loss: 0.13020 +Epoch [3490/4000] Training [25/39] Loss: 0.12869 +Epoch [3490/4000] Training [26/39] Loss: 0.00501 +Epoch [3490/4000] Training [27/39] Loss: 0.00346 +Epoch [3490/4000] Training [28/39] Loss: 0.13362 +Epoch [3490/4000] Training [29/39] Loss: 0.00639 +Epoch [3490/4000] Training [30/39] Loss: 0.12955 +Epoch [3490/4000] Training [31/39] Loss: 0.00600 +Epoch [3490/4000] Training [32/39] Loss: 0.00530 +Epoch [3490/4000] Training [33/39] Loss: 0.12860 +Epoch [3490/4000] Training [34/39] Loss: 0.00379 +Epoch [3490/4000] Training [35/39] Loss: 0.00415 +Epoch [3490/4000] Training [36/39] Loss: 0.00399 +Epoch [3490/4000] Training [37/39] Loss: 0.00663 +Epoch [3490/4000] Training [38/39] Loss: 0.12810 +Epoch [3490/4000] Training [39/39] Loss: 0.00730 +Epoch [3490/4000] Training metric {'Train/mean dice_metric': 0.9952570199966431, 'Train/mean miou_metric': 0.9918023943901062, 'Train/mean f1': 0.9967435002326965, 'Train/mean precision': 0.9962539672851562, 'Train/mean recall': 0.9972333312034607, 'Train/mean hd95_metric': 1.1353462934494019} +Epoch [3490/4000] Validation [1/10] Loss: 0.73045 focal_loss 0.63891 dice_loss 0.09155 +Epoch [3490/4000] Validation [2/10] Loss: 0.49082 focal_loss 0.39756 dice_loss 0.09326 +Epoch [3490/4000] Validation [3/10] Loss: 0.35523 focal_loss 0.24750 dice_loss 0.10773 +Epoch [3490/4000] Validation [4/10] Loss: 0.89295 focal_loss 0.32736 dice_loss 0.56558 +Epoch [3490/4000] Validation [5/10] Loss: 2.99012 focal_loss 2.31880 dice_loss 0.67132 +Epoch [3490/4000] Validation [6/10] Loss: 1.33550 focal_loss 0.62321 dice_loss 0.71229 +Epoch [3490/4000] Validation [7/10] Loss: 1.20873 focal_loss 0.54323 dice_loss 0.66549 +Epoch [3490/4000] Validation [8/10] Loss: 2.15090 focal_loss 1.55080 dice_loss 0.60010 +Epoch [3490/4000] Validation [9/10] Loss: 1.41479 focal_loss 0.86970 dice_loss 0.54509 +Epoch [3490/4000] Validation [10/10] Loss: 1.87500 focal_loss 1.13802 dice_loss 0.73698 +Epoch [3490/4000] Validation metric {'Val/mean dice_metric': 0.950579822063446, 'Val/mean miou_metric': 0.9344240427017212, 'Val/mean f1': 0.9480670690536499, 'Val/mean precision': 0.9409988522529602, 'Val/mean recall': 0.9552422761917114, 'Val/mean hd95_metric': 10.880537033081055} +Cheakpoint... +Epoch [3490/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950579822063446, 'Val/mean miou_metric': 0.9344240427017212, 'Val/mean f1': 0.9480670690536499, 'Val/mean precision': 0.9409988522529602, 'Val/mean recall': 0.9552422761917114, 'Val/mean hd95_metric': 10.880537033081055} +Epoch [3491/4000] Training [1/39] Loss: 0.00374 +Epoch [3491/4000] Training [2/39] Loss: 0.00721 +Epoch [3491/4000] Training [3/39] Loss: 0.00390 +Epoch [3491/4000] Training [4/39] Loss: 0.00734 +Epoch [3491/4000] Training [5/39] Loss: 0.00610 +Epoch [3491/4000] Training [6/39] Loss: 0.25497 +Epoch [3491/4000] Training [7/39] Loss: 0.00367 +Epoch [3491/4000] Training [8/39] Loss: 0.00461 +Epoch [3491/4000] Training [9/39] Loss: 0.00539 +Epoch [3491/4000] Training [10/39] Loss: 0.00405 +Epoch [3491/4000] Training [11/39] Loss: 0.00396 +Epoch [3491/4000] Training [12/39] Loss: 0.12907 +Epoch [3491/4000] Training [13/39] Loss: 0.00422 +Epoch [3491/4000] Training [14/39] Loss: 0.00820 +Epoch [3491/4000] Training [15/39] Loss: 0.12983 +Epoch [3491/4000] Training [16/39] Loss: 0.00388 +Epoch [3491/4000] Training [17/39] Loss: 0.00480 +Epoch [3491/4000] Training [18/39] Loss: 0.13037 +Epoch [3491/4000] Training [19/39] Loss: 0.13016 +Epoch [3491/4000] Training [20/39] Loss: 0.00454 +Epoch [3491/4000] Training [21/39] Loss: 0.00496 +Epoch [3491/4000] Training [22/39] Loss: 0.12759 +Epoch [3491/4000] Training [23/39] Loss: 0.00679 +Epoch [3491/4000] Training [24/39] Loss: 0.00406 +Epoch [3491/4000] Training [25/39] Loss: 0.00622 +Epoch [3491/4000] Training [26/39] Loss: 0.13135 +Epoch [3491/4000] Training [27/39] Loss: 0.00741 +Epoch [3491/4000] Training [28/39] Loss: 0.00739 +Epoch [3491/4000] Training [29/39] Loss: 0.00464 +Epoch [3491/4000] Training [30/39] Loss: 0.00578 +Epoch [3491/4000] Training [31/39] Loss: 0.00549 +Epoch [3491/4000] Training [32/39] Loss: 0.12978 +Epoch [3491/4000] Training [33/39] Loss: 0.00428 +Epoch [3491/4000] Training [34/39] Loss: 0.00294 +Epoch [3491/4000] Training [35/39] Loss: 0.00440 +Epoch [3491/4000] Training [36/39] Loss: 0.00703 +Epoch [3491/4000] Training [37/39] Loss: 0.00425 +Epoch [3491/4000] Training [38/39] Loss: 0.12939 +Epoch [3491/4000] Training [39/39] Loss: 0.00943 +Epoch [3491/4000] Training metric {'Train/mean dice_metric': 0.9957382082939148, 'Train/mean miou_metric': 0.9920040369033813, 'Train/mean f1': 0.9963213801383972, 'Train/mean precision': 0.9956994652748108, 'Train/mean recall': 0.996944010257721, 'Train/mean hd95_metric': 1.2001898288726807} +Epoch [3491/4000] Validation [1/10] Loss: 0.73896 focal_loss 0.64755 dice_loss 0.09141 +Epoch [3491/4000] Validation [2/10] Loss: 0.48974 focal_loss 0.39636 dice_loss 0.09339 +Epoch [3491/4000] Validation [3/10] Loss: 0.35454 focal_loss 0.24671 dice_loss 0.10783 +Epoch [3491/4000] Validation [4/10] Loss: 0.88963 focal_loss 0.32248 dice_loss 0.56715 +Epoch [3491/4000] Validation [5/10] Loss: 2.99584 focal_loss 2.32474 dice_loss 0.67110 +Epoch [3491/4000] Validation [6/10] Loss: 1.33234 focal_loss 0.61678 dice_loss 0.71556 +Epoch [3491/4000] Validation [7/10] Loss: 1.19561 focal_loss 0.53400 dice_loss 0.66161 +Epoch [3491/4000] Validation [8/10] Loss: 2.09682 focal_loss 1.50314 dice_loss 0.59368 +Epoch [3491/4000] Validation [9/10] Loss: 1.41947 focal_loss 0.87473 dice_loss 0.54474 +Epoch [3491/4000] Validation [10/10] Loss: 1.87601 focal_loss 1.13866 dice_loss 0.73735 +Epoch [3491/4000] Validation metric {'Val/mean dice_metric': 0.9510753750801086, 'Val/mean miou_metric': 0.9347187876701355, 'Val/mean f1': 0.9478229284286499, 'Val/mean precision': 0.9404580593109131, 'Val/mean recall': 0.9553040862083435, 'Val/mean hd95_metric': 10.946420669555664} +Cheakpoint... +Epoch [3491/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510753750801086, 'Val/mean miou_metric': 0.9347187876701355, 'Val/mean f1': 0.9478229284286499, 'Val/mean precision': 0.9404580593109131, 'Val/mean recall': 0.9553040862083435, 'Val/mean hd95_metric': 10.946420669555664} +Epoch [3492/4000] Training [1/39] Loss: 0.00320 +Epoch [3492/4000] Training [2/39] Loss: 0.13024 +Epoch [3492/4000] Training [3/39] Loss: 0.12851 +Epoch [3492/4000] Training [4/39] Loss: 0.00399 +Epoch [3492/4000] Training [5/39] Loss: 0.25362 +Epoch [3492/4000] Training [6/39] Loss: 0.12926 +Epoch [3492/4000] Training [7/39] Loss: 0.00475 +Epoch [3492/4000] Training [8/39] Loss: 0.00489 +Epoch [3492/4000] Training [9/39] Loss: 0.00531 +Epoch [3492/4000] Training [10/39] Loss: 0.13025 +Epoch [3492/4000] Training [11/39] Loss: 0.12983 +Epoch [3492/4000] Training [12/39] Loss: 0.00414 +Epoch [3492/4000] Training [13/39] Loss: 0.00550 +Epoch [3492/4000] Training [14/39] Loss: 0.00533 +Epoch [3492/4000] Training [15/39] Loss: 0.13006 +Epoch [3492/4000] Training [16/39] Loss: 0.00951 +Epoch [3492/4000] Training [17/39] Loss: 0.13103 +Epoch [3492/4000] Training [18/39] Loss: 0.00666 +Epoch [3492/4000] Training [19/39] Loss: 0.00512 +Epoch [3492/4000] Training [20/39] Loss: 0.00568 +Epoch [3492/4000] Training [21/39] Loss: 0.00519 +Epoch [3492/4000] Training [22/39] Loss: 0.00456 +Epoch [3492/4000] Training [23/39] Loss: 0.00511 +Epoch [3492/4000] Training [24/39] Loss: 0.00625 +Epoch [3492/4000] Training [25/39] Loss: 0.12779 +Epoch [3492/4000] Training [26/39] Loss: 0.12978 +Epoch [3492/4000] Training [27/39] Loss: 0.00617 +Epoch [3492/4000] Training [28/39] Loss: 0.00501 +Epoch [3492/4000] Training [29/39] Loss: 0.00547 +Epoch [3492/4000] Training [30/39] Loss: 0.12891 +Epoch [3492/4000] Training [31/39] Loss: 0.00957 +Epoch [3492/4000] Training [32/39] Loss: 0.00752 +Epoch [3492/4000] Training [33/39] Loss: 0.00466 +Epoch [3492/4000] Training [34/39] Loss: 0.00404 +Epoch [3492/4000] Training [35/39] Loss: 0.00435 +Epoch [3492/4000] Training [36/39] Loss: 0.00434 +Epoch [3492/4000] Training [37/39] Loss: 0.25472 +Epoch [3492/4000] Training [38/39] Loss: 0.00504 +Epoch [3492/4000] Training [39/39] Loss: 0.13201 +Epoch [3492/4000] Training metric {'Train/mean dice_metric': 0.9951273202896118, 'Train/mean miou_metric': 0.9915515780448914, 'Train/mean f1': 0.9966743588447571, 'Train/mean precision': 0.9962671995162964, 'Train/mean recall': 0.9970816969871521, 'Train/mean hd95_metric': 0.9955630302429199} +Epoch [3492/4000] Validation [1/10] Loss: 0.71770 focal_loss 0.62823 dice_loss 0.08948 +Epoch [3492/4000] Validation [2/10] Loss: 0.48093 focal_loss 0.38763 dice_loss 0.09330 +Epoch [3492/4000] Validation [3/10] Loss: 0.36108 focal_loss 0.25274 dice_loss 0.10834 +Epoch [3492/4000] Validation [4/10] Loss: 0.87841 focal_loss 0.31277 dice_loss 0.56564 +Epoch [3492/4000] Validation [5/10] Loss: 2.99240 focal_loss 2.32094 dice_loss 0.67146 +Epoch [3492/4000] Validation [6/10] Loss: 1.31053 focal_loss 0.59289 dice_loss 0.71764 +Epoch [3492/4000] Validation [7/10] Loss: 1.16895 focal_loss 0.50812 dice_loss 0.66083 +Epoch [3492/4000] Validation [8/10] Loss: 2.22583 focal_loss 1.60797 dice_loss 0.61786 +Epoch [3492/4000] Validation [9/10] Loss: 1.39508 focal_loss 0.85084 dice_loss 0.54424 +Epoch [3492/4000] Validation [10/10] Loss: 1.81370 focal_loss 1.08140 dice_loss 0.73231 +Epoch [3492/4000] Validation metric {'Val/mean dice_metric': 0.9505821466445923, 'Val/mean miou_metric': 0.9344766736030579, 'Val/mean f1': 0.9488279819488525, 'Val/mean precision': 0.9433317184448242, 'Val/mean recall': 0.9543887376785278, 'Val/mean hd95_metric': 10.732831001281738} +Cheakpoint... +Epoch [3492/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505821466445923, 'Val/mean miou_metric': 0.9344766736030579, 'Val/mean f1': 0.9488279819488525, 'Val/mean precision': 0.9433317184448242, 'Val/mean recall': 0.9543887376785278, 'Val/mean hd95_metric': 10.732831001281738} +Epoch [3493/4000] Training [1/39] Loss: 0.00325 +Epoch [3493/4000] Training [2/39] Loss: 0.00362 +Epoch [3493/4000] Training [3/39] Loss: 0.00603 +Epoch [3493/4000] Training [4/39] Loss: 0.00369 +Epoch [3493/4000] Training [5/39] Loss: 0.12846 +Epoch [3493/4000] Training [6/39] Loss: 0.00420 +Epoch [3493/4000] Training [7/39] Loss: 0.00638 +Epoch [3493/4000] Training [8/39] Loss: 0.00549 +Epoch [3493/4000] Training [9/39] Loss: 0.00587 +Epoch [3493/4000] Training [10/39] Loss: 0.12878 +Epoch [3493/4000] Training [11/39] Loss: 0.12784 +Epoch [3493/4000] Training [12/39] Loss: 0.00658 +Epoch [3493/4000] Training [13/39] Loss: 0.00578 +Epoch [3493/4000] Training [14/39] Loss: 0.00713 +Epoch [3493/4000] Training [15/39] Loss: 0.13058 +Epoch [3493/4000] Training [16/39] Loss: 0.00498 +Epoch [3493/4000] Training [17/39] Loss: 0.25307 +Epoch [3493/4000] Training [18/39] Loss: 0.12852 +Epoch [3493/4000] Training [19/39] Loss: 0.00358 +Epoch [3493/4000] Training [20/39] Loss: 0.12973 +Epoch [3493/4000] Training [21/39] Loss: 0.00367 +Epoch [3493/4000] Training [22/39] Loss: 0.00476 +Epoch [3493/4000] Training [23/39] Loss: 0.00531 +Epoch [3493/4000] Training [24/39] Loss: 0.13243 +Epoch [3493/4000] Training [25/39] Loss: 0.00463 +Epoch [3493/4000] Training [26/39] Loss: 0.00348 +Epoch [3493/4000] Training [27/39] Loss: 0.00669 +Epoch [3493/4000] Training [28/39] Loss: 0.00534 +Epoch [3493/4000] Training [29/39] Loss: 0.00543 +Epoch [3493/4000] Training [30/39] Loss: 0.00557 +Epoch [3493/4000] Training [31/39] Loss: 0.12952 +Epoch [3493/4000] Training [32/39] Loss: 0.04284 +Epoch [3493/4000] Training [33/39] Loss: 0.13065 +Epoch [3493/4000] Training [34/39] Loss: 0.00454 +Epoch [3493/4000] Training [35/39] Loss: 0.12859 +Epoch [3493/4000] Training [36/39] Loss: 0.12872 +Epoch [3493/4000] Training [37/39] Loss: 0.00685 +Epoch [3493/4000] Training [38/39] Loss: 0.00538 +Epoch [3493/4000] Training [39/39] Loss: 0.12826 +Epoch [3493/4000] Training metric {'Train/mean dice_metric': 0.9961243867874146, 'Train/mean miou_metric': 0.9926974773406982, 'Train/mean f1': 0.9967590570449829, 'Train/mean precision': 0.9963003993034363, 'Train/mean recall': 0.9972181916236877, 'Train/mean hd95_metric': 0.9954421520233154} +Epoch [3493/4000] Validation [1/10] Loss: 0.69883 focal_loss 0.61066 dice_loss 0.08817 +Epoch [3493/4000] Validation [2/10] Loss: 0.48817 focal_loss 0.39044 dice_loss 0.09773 +Epoch [3493/4000] Validation [3/10] Loss: 0.36761 focal_loss 0.25821 dice_loss 0.10940 +Epoch [3493/4000] Validation [4/10] Loss: 0.86478 focal_loss 0.30122 dice_loss 0.56356 +Epoch [3493/4000] Validation [5/10] Loss: 2.95659 focal_loss 2.28430 dice_loss 0.67228 +Epoch [3493/4000] Validation [6/10] Loss: 1.27868 focal_loss 0.56200 dice_loss 0.71668 +Epoch [3493/4000] Validation [7/10] Loss: 1.13494 focal_loss 0.47815 dice_loss 0.65679 +Epoch [3493/4000] Validation [8/10] Loss: 2.36173 focal_loss 1.72638 dice_loss 0.63534 +Epoch [3493/4000] Validation [9/10] Loss: 1.38995 focal_loss 0.84697 dice_loss 0.54299 +Epoch [3493/4000] Validation [10/10] Loss: 1.73996 focal_loss 1.01339 dice_loss 0.72656 +Epoch [3493/4000] Validation metric {'Val/mean dice_metric': 0.9513518810272217, 'Val/mean miou_metric': 0.9354025721549988, 'Val/mean f1': 0.9496681690216064, 'Val/mean precision': 0.9464268088340759, 'Val/mean recall': 0.9529317617416382, 'Val/mean hd95_metric': 10.59196949005127} +Cheakpoint... +Epoch [3493/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513518810272217, 'Val/mean miou_metric': 0.9354025721549988, 'Val/mean f1': 0.9496681690216064, 'Val/mean precision': 0.9464268088340759, 'Val/mean recall': 0.9529317617416382, 'Val/mean hd95_metric': 10.59196949005127} +Epoch [3494/4000] Training [1/39] Loss: 0.00724 +Epoch [3494/4000] Training [2/39] Loss: 0.00448 +Epoch [3494/4000] Training [3/39] Loss: 0.12919 +Epoch [3494/4000] Training [4/39] Loss: 0.00494 +Epoch [3494/4000] Training [5/39] Loss: 0.00473 +Epoch [3494/4000] Training [6/39] Loss: 0.13054 +Epoch [3494/4000] Training [7/39] Loss: 0.00619 +Epoch [3494/4000] Training [8/39] Loss: 0.12991 +Epoch [3494/4000] Training [9/39] Loss: 0.00665 +Epoch [3494/4000] Training [10/39] Loss: 0.00429 +Epoch [3494/4000] Training [11/39] Loss: 0.00663 +Epoch [3494/4000] Training [12/39] Loss: 0.00572 +Epoch [3494/4000] Training [13/39] Loss: 0.00537 +Epoch [3494/4000] Training [14/39] Loss: 0.00239 +Epoch [3494/4000] Training [15/39] Loss: 0.00429 +Epoch [3494/4000] Training [16/39] Loss: 0.12971 +Epoch [3494/4000] Training [17/39] Loss: 0.00467 +Epoch [3494/4000] Training [18/39] Loss: 0.13176 +Epoch [3494/4000] Training [19/39] Loss: 0.00443 +Epoch [3494/4000] Training [20/39] Loss: 0.00768 +Epoch [3494/4000] Training [21/39] Loss: 0.00421 +Epoch [3494/4000] Training [22/39] Loss: 0.12852 +Epoch [3494/4000] Training [23/39] Loss: 0.13174 +Epoch [3494/4000] Training [24/39] Loss: 0.00393 +Epoch [3494/4000] Training [25/39] Loss: 0.00340 +Epoch [3494/4000] Training [26/39] Loss: 0.00687 +Epoch [3494/4000] Training [27/39] Loss: 0.00691 +Epoch [3494/4000] Training [28/39] Loss: 0.00484 +Epoch [3494/4000] Training [29/39] Loss: 0.00617 +Epoch [3494/4000] Training [30/39] Loss: 0.00454 +Epoch [3494/4000] Training [31/39] Loss: 0.25612 +Epoch [3494/4000] Training [32/39] Loss: 0.00592 +Epoch [3494/4000] Training [33/39] Loss: 0.00407 +Epoch [3494/4000] Training [34/39] Loss: 0.00517 +Epoch [3494/4000] Training [35/39] Loss: 0.00534 +Epoch [3494/4000] Training [36/39] Loss: 0.00732 +Epoch [3494/4000] Training [37/39] Loss: 0.25274 +Epoch [3494/4000] Training [38/39] Loss: 0.00429 +Epoch [3494/4000] Training [39/39] Loss: 0.00603 +Epoch [3494/4000] Training metric {'Train/mean dice_metric': 0.9951133131980896, 'Train/mean miou_metric': 0.9915159344673157, 'Train/mean f1': 0.9967095255851746, 'Train/mean precision': 0.9962206482887268, 'Train/mean recall': 0.9971988201141357, 'Train/mean hd95_metric': 1.0321897268295288} +Epoch [3494/4000] Validation [1/10] Loss: 0.67427 focal_loss 0.58825 dice_loss 0.08601 +Epoch [3494/4000] Validation [2/10] Loss: 0.46649 focal_loss 0.37242 dice_loss 0.09406 +Epoch [3494/4000] Validation [3/10] Loss: 0.38533 focal_loss 0.27337 dice_loss 0.11196 +Epoch [3494/4000] Validation [4/10] Loss: 0.86176 focal_loss 0.29834 dice_loss 0.56342 +Epoch [3494/4000] Validation [5/10] Loss: 2.94734 focal_loss 2.27471 dice_loss 0.67263 +Epoch [3494/4000] Validation [6/10] Loss: 1.26582 focal_loss 0.55181 dice_loss 0.71401 +Epoch [3494/4000] Validation [7/10] Loss: 1.13178 focal_loss 0.47339 dice_loss 0.65839 +Epoch [3494/4000] Validation [8/10] Loss: 2.37632 focal_loss 1.73951 dice_loss 0.63682 +Epoch [3494/4000] Validation [9/10] Loss: 1.37867 focal_loss 0.83591 dice_loss 0.54277 +Epoch [3494/4000] Validation [10/10] Loss: 1.74379 focal_loss 1.01588 dice_loss 0.72791 +Epoch [3494/4000] Validation metric {'Val/mean dice_metric': 0.9506527185440063, 'Val/mean miou_metric': 0.9345239400863647, 'Val/mean f1': 0.9497868418693542, 'Val/mean precision': 0.9465917944908142, 'Val/mean recall': 0.9530034065246582, 'Val/mean hd95_metric': 10.709758758544922} +Cheakpoint... +Epoch [3494/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506527185440063, 'Val/mean miou_metric': 0.9345239400863647, 'Val/mean f1': 0.9497868418693542, 'Val/mean precision': 0.9465917944908142, 'Val/mean recall': 0.9530034065246582, 'Val/mean hd95_metric': 10.709758758544922} +Epoch [3495/4000] Training [1/39] Loss: 0.00482 +Epoch [3495/4000] Training [2/39] Loss: 0.00449 +Epoch [3495/4000] Training [3/39] Loss: 0.00691 +Epoch [3495/4000] Training [4/39] Loss: 0.00633 +Epoch [3495/4000] Training [5/39] Loss: 0.00393 +Epoch [3495/4000] Training [6/39] Loss: 0.12897 +Epoch [3495/4000] Training [7/39] Loss: 0.13086 +Epoch [3495/4000] Training [8/39] Loss: 0.12890 +Epoch [3495/4000] Training [9/39] Loss: 0.00716 +Epoch [3495/4000] Training [10/39] Loss: 0.00473 +Epoch [3495/4000] Training [11/39] Loss: 0.00678 +Epoch [3495/4000] Training [12/39] Loss: 0.12832 +Epoch [3495/4000] Training [13/39] Loss: 0.00621 +Epoch [3495/4000] Training [14/39] Loss: 0.00482 +Epoch [3495/4000] Training [15/39] Loss: 0.12830 +Epoch [3495/4000] Training [16/39] Loss: 0.00463 +Epoch [3495/4000] Training [17/39] Loss: 0.00556 +Epoch [3495/4000] Training [18/39] Loss: 0.00422 +Epoch [3495/4000] Training [19/39] Loss: 0.00548 +Epoch [3495/4000] Training [20/39] Loss: 0.13034 +Epoch [3495/4000] Training [21/39] Loss: 0.13044 +Epoch [3495/4000] Training [22/39] Loss: 0.00341 +Epoch [3495/4000] Training [23/39] Loss: 0.00369 +Epoch [3495/4000] Training [24/39] Loss: 0.12730 +Epoch [3495/4000] Training [25/39] Loss: 0.00455 +Epoch [3495/4000] Training [26/39] Loss: 0.00329 +Epoch [3495/4000] Training [27/39] Loss: 0.12692 +Epoch [3495/4000] Training [28/39] Loss: 0.00461 +Epoch [3495/4000] Training [29/39] Loss: 0.00612 +Epoch [3495/4000] Training [30/39] Loss: 0.00720 +Epoch [3495/4000] Training [31/39] Loss: 0.00624 +Epoch [3495/4000] Training [32/39] Loss: 0.00585 +Epoch [3495/4000] Training [33/39] Loss: 0.13126 +Epoch [3495/4000] Training [34/39] Loss: 0.13125 +Epoch [3495/4000] Training [35/39] Loss: 0.12765 +Epoch [3495/4000] Training [36/39] Loss: 0.25647 +Epoch [3495/4000] Training [37/39] Loss: 0.00766 +Epoch [3495/4000] Training [38/39] Loss: 0.00649 +Epoch [3495/4000] Training [39/39] Loss: 0.00581 +Epoch [3495/4000] Training metric {'Train/mean dice_metric': 0.9958932995796204, 'Train/mean miou_metric': 0.992253303527832, 'Train/mean f1': 0.9965206384658813, 'Train/mean precision': 0.9960862398147583, 'Train/mean recall': 0.9969554543495178, 'Train/mean hd95_metric': 1.056181788444519} +Epoch [3495/4000] Validation [1/10] Loss: 0.67704 focal_loss 0.59103 dice_loss 0.08601 +Epoch [3495/4000] Validation [2/10] Loss: 0.48418 focal_loss 0.38992 dice_loss 0.09426 +Epoch [3495/4000] Validation [3/10] Loss: 0.36883 focal_loss 0.25811 dice_loss 0.11073 +Epoch [3495/4000] Validation [4/10] Loss: 0.88149 focal_loss 0.31606 dice_loss 0.56543 +Epoch [3495/4000] Validation [5/10] Loss: 2.94297 focal_loss 2.27046 dice_loss 0.67251 +Epoch [3495/4000] Validation [6/10] Loss: 1.32342 focal_loss 0.60597 dice_loss 0.71745 +Epoch [3495/4000] Validation [7/10] Loss: 1.17031 focal_loss 0.51043 dice_loss 0.65988 +Epoch [3495/4000] Validation [8/10] Loss: 2.31389 focal_loss 1.69187 dice_loss 0.62202 +Epoch [3495/4000] Validation [9/10] Loss: 1.40418 focal_loss 0.86000 dice_loss 0.54418 +Epoch [3495/4000] Validation [10/10] Loss: 1.81979 focal_loss 1.08821 dice_loss 0.73158 +Epoch [3495/4000] Validation metric {'Val/mean dice_metric': 0.9513119459152222, 'Val/mean miou_metric': 0.9351015090942383, 'Val/mean f1': 0.9490716457366943, 'Val/mean precision': 0.9446166753768921, 'Val/mean recall': 0.9535687565803528, 'Val/mean hd95_metric': 10.754155158996582} +Cheakpoint... +Epoch [3495/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513119459152222, 'Val/mean miou_metric': 0.9351015090942383, 'Val/mean f1': 0.9490716457366943, 'Val/mean precision': 0.9446166753768921, 'Val/mean recall': 0.9535687565803528, 'Val/mean hd95_metric': 10.754155158996582} +Epoch [3496/4000] Training [1/39] Loss: 0.00700 +Epoch [3496/4000] Training [2/39] Loss: 0.00357 +Epoch [3496/4000] Training [3/39] Loss: 0.00465 +Epoch [3496/4000] Training [4/39] Loss: 0.12902 +Epoch [3496/4000] Training [5/39] Loss: 0.25446 +Epoch [3496/4000] Training [6/39] Loss: 0.00608 +Epoch [3496/4000] Training [7/39] Loss: 0.00469 +Epoch [3496/4000] Training [8/39] Loss: 0.00814 +Epoch [3496/4000] Training [9/39] Loss: 0.00756 +Epoch [3496/4000] Training [10/39] Loss: 0.00536 +Epoch [3496/4000] Training [11/39] Loss: 0.00410 +Epoch [3496/4000] Training [12/39] Loss: 0.00592 +Epoch [3496/4000] Training [13/39] Loss: 0.00497 +Epoch [3496/4000] Training [14/39] Loss: 0.00634 +Epoch [3496/4000] Training [15/39] Loss: 0.13040 +Epoch [3496/4000] Training [16/39] Loss: 0.00513 +Epoch [3496/4000] Training [17/39] Loss: 0.00546 +Epoch [3496/4000] Training [18/39] Loss: 0.00451 +Epoch [3496/4000] Training [19/39] Loss: 0.13024 +Epoch [3496/4000] Training [20/39] Loss: 0.13364 +Epoch [3496/4000] Training [21/39] Loss: 0.25321 +Epoch [3496/4000] Training [22/39] Loss: 0.00498 +Epoch [3496/4000] Training [23/39] Loss: 0.00695 +Epoch [3496/4000] Training [24/39] Loss: 0.00461 +Epoch [3496/4000] Training [25/39] Loss: 0.12956 +Epoch [3496/4000] Training [26/39] Loss: 0.00462 +Epoch [3496/4000] Training [27/39] Loss: 0.00634 +Epoch [3496/4000] Training [28/39] Loss: 0.13099 +Epoch [3496/4000] Training [29/39] Loss: 0.09025 +Epoch [3496/4000] Training [30/39] Loss: 0.12919 +Epoch [3496/4000] Training [31/39] Loss: 0.00459 +Epoch [3496/4000] Training [32/39] Loss: 0.00633 +Epoch [3496/4000] Training [33/39] Loss: 0.00546 +Epoch [3496/4000] Training [34/39] Loss: 0.12896 +Epoch [3496/4000] Training [35/39] Loss: 0.00551 +Epoch [3496/4000] Training [36/39] Loss: 0.00492 +Epoch [3496/4000] Training [37/39] Loss: 0.00551 +Epoch [3496/4000] Training [38/39] Loss: 0.25317 +Epoch [3496/4000] Training [39/39] Loss: 0.00584 +Epoch [3496/4000] Training metric {'Train/mean dice_metric': 0.9957686066627502, 'Train/mean miou_metric': 0.9920089244842529, 'Train/mean f1': 0.9965438842773438, 'Train/mean precision': 0.9961197972297668, 'Train/mean recall': 0.9969685673713684, 'Train/mean hd95_metric': 1.1687254905700684} +Epoch [3496/4000] Validation [1/10] Loss: 0.67515 focal_loss 0.58974 dice_loss 0.08541 +Epoch [3496/4000] Validation [2/10] Loss: 0.48330 focal_loss 0.38961 dice_loss 0.09368 +Epoch [3496/4000] Validation [3/10] Loss: 0.36910 focal_loss 0.26020 dice_loss 0.10890 +Epoch [3496/4000] Validation [4/10] Loss: 0.86802 focal_loss 0.30374 dice_loss 0.56428 +Epoch [3496/4000] Validation [5/10] Loss: 2.95510 focal_loss 2.28322 dice_loss 0.67188 +Epoch [3496/4000] Validation [6/10] Loss: 1.30568 focal_loss 0.58583 dice_loss 0.71985 +Epoch [3496/4000] Validation [7/10] Loss: 1.18548 focal_loss 0.52447 dice_loss 0.66101 +Epoch [3496/4000] Validation [8/10] Loss: 2.26155 focal_loss 1.64275 dice_loss 0.61880 +Epoch [3496/4000] Validation [9/10] Loss: 1.40209 focal_loss 0.85885 dice_loss 0.54324 +Epoch [3496/4000] Validation [10/10] Loss: 1.79341 focal_loss 1.06265 dice_loss 0.73076 +Epoch [3496/4000] Validation metric {'Val/mean dice_metric': 0.9510995745658875, 'Val/mean miou_metric': 0.9347882270812988, 'Val/mean f1': 0.9490430355072021, 'Val/mean precision': 0.9445701837539673, 'Val/mean recall': 0.9535585045814514, 'Val/mean hd95_metric': 10.956555366516113} +Cheakpoint... +Epoch [3496/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510995745658875, 'Val/mean miou_metric': 0.9347882270812988, 'Val/mean f1': 0.9490430355072021, 'Val/mean precision': 0.9445701837539673, 'Val/mean recall': 0.9535585045814514, 'Val/mean hd95_metric': 10.956555366516113} +Epoch [3497/4000] Training [1/39] Loss: 0.00650 +Epoch [3497/4000] Training [2/39] Loss: 0.13072 +Epoch [3497/4000] Training [3/39] Loss: 0.00525 +Epoch [3497/4000] Training [4/39] Loss: 0.00762 +Epoch [3497/4000] Training [5/39] Loss: 0.00750 +Epoch [3497/4000] Training [6/39] Loss: 0.00340 +Epoch [3497/4000] Training [7/39] Loss: 0.00392 +Epoch [3497/4000] Training [8/39] Loss: 0.00682 +Epoch [3497/4000] Training [9/39] Loss: 0.25228 +Epoch [3497/4000] Training [10/39] Loss: 0.00295 +Epoch [3497/4000] Training [11/39] Loss: 0.00393 +Epoch [3497/4000] Training [12/39] Loss: 0.00674 +Epoch [3497/4000] Training [13/39] Loss: 0.00420 +Epoch [3497/4000] Training [14/39] Loss: 0.00714 +Epoch [3497/4000] Training [15/39] Loss: 0.00429 +Epoch [3497/4000] Training [16/39] Loss: 0.00806 +Epoch [3497/4000] Training [17/39] Loss: 0.00538 +Epoch [3497/4000] Training [18/39] Loss: 0.00734 +Epoch [3497/4000] Training [19/39] Loss: 0.25343 +Epoch [3497/4000] Training [20/39] Loss: 0.00482 +Epoch [3497/4000] Training [21/39] Loss: 0.00520 +Epoch [3497/4000] Training [22/39] Loss: 0.12896 +Epoch [3497/4000] Training [23/39] Loss: 0.00601 +Epoch [3497/4000] Training [24/39] Loss: 0.00561 +Epoch [3497/4000] Training [25/39] Loss: 0.12897 +Epoch [3497/4000] Training [26/39] Loss: 0.00546 +Epoch [3497/4000] Training [27/39] Loss: 0.00631 +Epoch [3497/4000] Training [28/39] Loss: 0.00590 +Epoch [3497/4000] Training [29/39] Loss: 0.12925 +Epoch [3497/4000] Training [30/39] Loss: 0.00560 +Epoch [3497/4000] Training [31/39] Loss: 0.00399 +Epoch [3497/4000] Training [32/39] Loss: 0.12811 +Epoch [3497/4000] Training [33/39] Loss: 0.12886 +Epoch [3497/4000] Training [34/39] Loss: 0.00676 +Epoch [3497/4000] Training [35/39] Loss: 0.12954 +Epoch [3497/4000] Training [36/39] Loss: 0.12949 +Epoch [3497/4000] Training [37/39] Loss: 0.00559 +Epoch [3497/4000] Training [38/39] Loss: 0.12947 +Epoch [3497/4000] Training [39/39] Loss: 0.00463 +Epoch [3497/4000] Training metric {'Train/mean dice_metric': 0.9951821565628052, 'Train/mean miou_metric': 0.9916407465934753, 'Train/mean f1': 0.996616780757904, 'Train/mean precision': 0.9961301684379578, 'Train/mean recall': 0.9971038699150085, 'Train/mean hd95_metric': 1.0094289779663086} +Epoch [3497/4000] Validation [1/10] Loss: 0.66278 focal_loss 0.57743 dice_loss 0.08535 +Epoch [3497/4000] Validation [2/10] Loss: 0.46995 focal_loss 0.37626 dice_loss 0.09369 +Epoch [3497/4000] Validation [3/10] Loss: 0.36535 focal_loss 0.25633 dice_loss 0.10902 +Epoch [3497/4000] Validation [4/10] Loss: 0.86435 focal_loss 0.30005 dice_loss 0.56430 +Epoch [3497/4000] Validation [5/10] Loss: 2.93166 focal_loss 2.25918 dice_loss 0.67248 +Epoch [3497/4000] Validation [6/10] Loss: 1.28816 focal_loss 0.56959 dice_loss 0.71857 +Epoch [3497/4000] Validation [7/10] Loss: 1.16427 focal_loss 0.50391 dice_loss 0.66035 +Epoch [3497/4000] Validation [8/10] Loss: 2.31187 focal_loss 1.68576 dice_loss 0.62612 +Epoch [3497/4000] Validation [9/10] Loss: 1.35995 focal_loss 0.81835 dice_loss 0.54160 +Epoch [3497/4000] Validation [10/10] Loss: 1.75089 focal_loss 1.02329 dice_loss 0.72759 +Epoch [3497/4000] Validation metric {'Val/mean dice_metric': 0.9506056904792786, 'Val/mean miou_metric': 0.9344758987426758, 'Val/mean f1': 0.9489072561264038, 'Val/mean precision': 0.9450362324714661, 'Val/mean recall': 0.9528102278709412, 'Val/mean hd95_metric': 10.829707145690918} +Cheakpoint... +Epoch [3497/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506056904792786, 'Val/mean miou_metric': 0.9344758987426758, 'Val/mean f1': 0.9489072561264038, 'Val/mean precision': 0.9450362324714661, 'Val/mean recall': 0.9528102278709412, 'Val/mean hd95_metric': 10.829707145690918} +Epoch [3498/4000] Training [1/39] Loss: 0.00663 +Epoch [3498/4000] Training [2/39] Loss: 0.00494 +Epoch [3498/4000] Training [3/39] Loss: 0.00561 +Epoch [3498/4000] Training [4/39] Loss: 0.25505 +Epoch [3498/4000] Training [5/39] Loss: 0.00438 +Epoch [3498/4000] Training [6/39] Loss: 0.12995 +Epoch [3498/4000] Training [7/39] Loss: 0.00365 +Epoch [3498/4000] Training [8/39] Loss: 0.00533 +Epoch [3498/4000] Training [9/39] Loss: 0.00371 +Epoch [3498/4000] Training [10/39] Loss: 0.00549 +Epoch [3498/4000] Training [11/39] Loss: 0.00382 +Epoch [3498/4000] Training [12/39] Loss: 0.12783 +Epoch [3498/4000] Training [13/39] Loss: 0.12927 +Epoch [3498/4000] Training [14/39] Loss: 0.00493 +Epoch [3498/4000] Training [15/39] Loss: 0.12807 +Epoch [3498/4000] Training [16/39] Loss: 0.00918 +Epoch [3498/4000] Training [17/39] Loss: 0.00562 +Epoch [3498/4000] Training [18/39] Loss: 0.00965 +Epoch [3498/4000] Training [19/39] Loss: 0.00429 +Epoch [3498/4000] Training [20/39] Loss: 0.00454 +Epoch [3498/4000] Training [21/39] Loss: 0.00411 +Epoch [3498/4000] Training [22/39] Loss: 0.00511 +Epoch [3498/4000] Training [23/39] Loss: 0.13012 +Epoch [3498/4000] Training [24/39] Loss: 0.12912 +Epoch [3498/4000] Training [25/39] Loss: 0.00480 +Epoch [3498/4000] Training [26/39] Loss: 0.25185 +Epoch [3498/4000] Training [27/39] Loss: 0.00390 +Epoch [3498/4000] Training [28/39] Loss: 0.00529 +Epoch [3498/4000] Training [29/39] Loss: 0.00568 +Epoch [3498/4000] Training [30/39] Loss: 0.00652 +Epoch [3498/4000] Training [31/39] Loss: 0.00626 +Epoch [3498/4000] Training [32/39] Loss: 0.12817 +Epoch [3498/4000] Training [33/39] Loss: 0.00478 +Epoch [3498/4000] Training [34/39] Loss: 0.00442 +Epoch [3498/4000] Training [35/39] Loss: 0.00566 +Epoch [3498/4000] Training [36/39] Loss: 0.00454 +Epoch [3498/4000] Training [37/39] Loss: 0.00360 +Epoch [3498/4000] Training [38/39] Loss: 0.00523 +Epoch [3498/4000] Training [39/39] Loss: 0.00455 +Epoch [3498/4000] Training metric {'Train/mean dice_metric': 0.9961227774620056, 'Train/mean miou_metric': 0.9926942586898804, 'Train/mean f1': 0.9967973828315735, 'Train/mean precision': 0.9963472485542297, 'Train/mean recall': 0.9972478151321411, 'Train/mean hd95_metric': 0.9936172366142273} +Epoch [3498/4000] Validation [1/10] Loss: 0.71901 focal_loss 0.63077 dice_loss 0.08824 +Epoch [3498/4000] Validation [2/10] Loss: 0.47911 focal_loss 0.38459 dice_loss 0.09452 +Epoch [3498/4000] Validation [3/10] Loss: 0.38304 focal_loss 0.27348 dice_loss 0.10956 +Epoch [3498/4000] Validation [4/10] Loss: 0.86929 focal_loss 0.30567 dice_loss 0.56362 +Epoch [3498/4000] Validation [5/10] Loss: 3.02463 focal_loss 2.35162 dice_loss 0.67302 +Epoch [3498/4000] Validation [6/10] Loss: 1.29259 focal_loss 0.57316 dice_loss 0.71943 +Epoch [3498/4000] Validation [7/10] Loss: 1.16563 focal_loss 0.50731 dice_loss 0.65832 +Epoch [3498/4000] Validation [8/10] Loss: 2.26011 focal_loss 1.64437 dice_loss 0.61573 +Epoch [3498/4000] Validation [9/10] Loss: 1.39360 focal_loss 0.85264 dice_loss 0.54096 +Epoch [3498/4000] Validation [10/10] Loss: 1.80011 focal_loss 1.06833 dice_loss 0.73177 +Epoch [3498/4000] Validation metric {'Val/mean dice_metric': 0.9514609575271606, 'Val/mean miou_metric': 0.9354133009910583, 'Val/mean f1': 0.9489330649375916, 'Val/mean precision': 0.9443426728248596, 'Val/mean recall': 0.953568160533905, 'Val/mean hd95_metric': 10.69148063659668} +Cheakpoint... +Epoch [3498/4000] best acc:tensor([0.9516], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514609575271606, 'Val/mean miou_metric': 0.9354133009910583, 'Val/mean f1': 0.9489330649375916, 'Val/mean precision': 0.9443426728248596, 'Val/mean recall': 0.953568160533905, 'Val/mean hd95_metric': 10.69148063659668} +Epoch [3499/4000] Training [1/39] Loss: 0.13183 +Epoch [3499/4000] Training [2/39] Loss: 0.00418 +Epoch [3499/4000] Training [3/39] Loss: 0.00610 +Epoch [3499/4000] Training [4/39] Loss: 0.12718 +Epoch [3499/4000] Training [5/39] Loss: 0.12840 +Epoch [3499/4000] Training [6/39] Loss: 0.12926 +Epoch [3499/4000] Training [7/39] Loss: 0.00392 +Epoch [3499/4000] Training [8/39] Loss: 0.00576 +Epoch [3499/4000] Training [9/39] Loss: 0.12842 +Epoch [3499/4000] Training [10/39] Loss: 0.13098 +Epoch [3499/4000] Training [11/39] Loss: 0.00785 +Epoch [3499/4000] Training [12/39] Loss: 0.00407 +Epoch [3499/4000] Training [13/39] Loss: 0.00333 +Epoch [3499/4000] Training [14/39] Loss: 0.25272 +Epoch [3499/4000] Training [15/39] Loss: 0.00731 +Epoch [3499/4000] Training [16/39] Loss: 0.00430 +Epoch [3499/4000] Training [17/39] Loss: 0.25406 +Epoch [3499/4000] Training [18/39] Loss: 0.00538 +Epoch [3499/4000] Training [19/39] Loss: 0.00401 +Epoch [3499/4000] Training [20/39] Loss: 0.00502 +Epoch [3499/4000] Training [21/39] Loss: 0.01017 +Epoch [3499/4000] Training [22/39] Loss: 0.00313 +Epoch [3499/4000] Training [23/39] Loss: 0.00401 +Epoch [3499/4000] Training [24/39] Loss: 0.00534 +Epoch [3499/4000] Training [25/39] Loss: 0.12933 +Epoch [3499/4000] Training [26/39] Loss: 0.00677 +Epoch [3499/4000] Training [27/39] Loss: 0.00421 +Epoch [3499/4000] Training [28/39] Loss: 0.00533 +Epoch [3499/4000] Training [29/39] Loss: 0.00565 +Epoch [3499/4000] Training [30/39] Loss: 0.37697 +Epoch [3499/4000] Training [31/39] Loss: 0.00342 +Epoch [3499/4000] Training [32/39] Loss: 0.00386 +Epoch [3499/4000] Training [33/39] Loss: 0.00361 +Epoch [3499/4000] Training [34/39] Loss: 0.12949 +Epoch [3499/4000] Training [35/39] Loss: 0.13041 +Epoch [3499/4000] Training [36/39] Loss: 0.00409 +Epoch [3499/4000] Training [37/39] Loss: 0.13268 +Epoch [3499/4000] Training [38/39] Loss: 0.00499 +Epoch [3499/4000] Training [39/39] Loss: 0.00684 +Epoch [3499/4000] Training metric {'Train/mean dice_metric': 0.9960849285125732, 'Train/mean miou_metric': 0.9926234483718872, 'Train/mean f1': 0.9967715740203857, 'Train/mean precision': 0.9963433146476746, 'Train/mean recall': 0.9972003698348999, 'Train/mean hd95_metric': 0.9794030785560608} +Epoch [3499/4000] Validation [1/10] Loss: 0.69301 focal_loss 0.60499 dice_loss 0.08802 +Epoch [3499/4000] Validation [2/10] Loss: 0.46951 focal_loss 0.37702 dice_loss 0.09249 +Epoch [3499/4000] Validation [3/10] Loss: 0.36829 focal_loss 0.25916 dice_loss 0.10912 +Epoch [3499/4000] Validation [4/10] Loss: 0.86965 focal_loss 0.30549 dice_loss 0.56416 +Epoch [3499/4000] Validation [5/10] Loss: 2.96437 focal_loss 2.29150 dice_loss 0.67287 +Epoch [3499/4000] Validation [6/10] Loss: 1.29736 focal_loss 0.57805 dice_loss 0.71931 +Epoch [3499/4000] Validation [7/10] Loss: 1.16119 focal_loss 0.50003 dice_loss 0.66116 +Epoch [3499/4000] Validation [8/10] Loss: 2.21687 focal_loss 1.60729 dice_loss 0.60958 +Epoch [3499/4000] Validation [9/10] Loss: 1.37268 focal_loss 0.83054 dice_loss 0.54214 +Epoch [3499/4000] Validation [10/10] Loss: 1.80101 focal_loss 1.06922 dice_loss 0.73179 +Epoch [3499/4000] Validation metric {'Val/mean dice_metric': 0.9516609907150269, 'Val/mean miou_metric': 0.935590922832489, 'Val/mean f1': 0.9491075873374939, 'Val/mean precision': 0.9442276358604431, 'Val/mean recall': 0.9540382027626038, 'Val/mean hd95_metric': 10.689754486083984} +Cheakpoint... +Epoch [3499/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516609907150269, 'Val/mean miou_metric': 0.935590922832489, 'Val/mean f1': 0.9491075873374939, 'Val/mean precision': 0.9442276358604431, 'Val/mean recall': 0.9540382027626038, 'Val/mean hd95_metric': 10.689754486083984} +Epoch [3500/4000] Training [1/39] Loss: 0.00438 +Epoch [3500/4000] Training [2/39] Loss: 0.00489 +Epoch [3500/4000] Training [3/39] Loss: 0.12781 +Epoch [3500/4000] Training [4/39] Loss: 0.00493 +Epoch [3500/4000] Training [5/39] Loss: 0.12768 +Epoch [3500/4000] Training [6/39] Loss: 0.12777 +Epoch [3500/4000] Training [7/39] Loss: 0.00653 +Epoch [3500/4000] Training [8/39] Loss: 0.00291 +Epoch [3500/4000] Training [9/39] Loss: 0.00505 +Epoch [3500/4000] Training [10/39] Loss: 0.00736 +Epoch [3500/4000] Training [11/39] Loss: 0.12982 +Epoch [3500/4000] Training [12/39] Loss: 0.00361 +Epoch [3500/4000] Training [13/39] Loss: 0.00519 +Epoch [3500/4000] Training [14/39] Loss: 0.00373 +Epoch [3500/4000] Training [15/39] Loss: 0.00653 +Epoch [3500/4000] Training [16/39] Loss: 0.00445 +Epoch [3500/4000] Training [17/39] Loss: 0.00619 +Epoch [3500/4000] Training [18/39] Loss: 0.13225 +Epoch [3500/4000] Training [19/39] Loss: 0.13058 +Epoch [3500/4000] Training [20/39] Loss: 0.00748 +Epoch [3500/4000] Training [21/39] Loss: 0.00394 +Epoch [3500/4000] Training [22/39] Loss: 0.00553 +Epoch [3500/4000] Training [23/39] Loss: 0.00937 +Epoch [3500/4000] Training [24/39] Loss: 0.00655 +Epoch [3500/4000] Training [25/39] Loss: 0.12833 +Epoch [3500/4000] Training [26/39] Loss: 0.12845 +Epoch [3500/4000] Training [27/39] Loss: 0.00271 +Epoch [3500/4000] Training [28/39] Loss: 0.00557 +Epoch [3500/4000] Training [29/39] Loss: 0.12956 +Epoch [3500/4000] Training [30/39] Loss: 0.00577 +Epoch [3500/4000] Training [31/39] Loss: 0.00408 +Epoch [3500/4000] Training [32/39] Loss: 0.00520 +Epoch [3500/4000] Training [33/39] Loss: 0.00476 +Epoch [3500/4000] Training [34/39] Loss: 0.00497 +Epoch [3500/4000] Training [35/39] Loss: 0.00665 +Epoch [3500/4000] Training [36/39] Loss: 0.12965 +Epoch [3500/4000] Training [37/39] Loss: 0.13204 +Epoch [3500/4000] Training [38/39] Loss: 0.00660 +Epoch [3500/4000] Training [39/39] Loss: 0.00704 +Epoch [3500/4000] Training metric {'Train/mean dice_metric': 0.9951595664024353, 'Train/mean miou_metric': 0.9916006922721863, 'Train/mean f1': 0.996612012386322, 'Train/mean precision': 0.996146559715271, 'Train/mean recall': 0.9970778822898865, 'Train/mean hd95_metric': 1.007275104522705} +Epoch [3500/4000] Validation [1/10] Loss: 0.73598 focal_loss 0.64560 dice_loss 0.09038 +Epoch [3500/4000] Validation [2/10] Loss: 0.48488 focal_loss 0.38813 dice_loss 0.09675 +Epoch [3500/4000] Validation [3/10] Loss: 0.39499 focal_loss 0.28378 dice_loss 0.11121 +Epoch [3500/4000] Validation [4/10] Loss: 0.87480 focal_loss 0.31128 dice_loss 0.56352 +Epoch [3500/4000] Validation [5/10] Loss: 3.01762 focal_loss 2.34492 dice_loss 0.67271 +Epoch [3500/4000] Validation [6/10] Loss: 1.28298 focal_loss 0.56581 dice_loss 0.71717 +Epoch [3500/4000] Validation [7/10] Loss: 1.15630 focal_loss 0.49713 dice_loss 0.65917 +Epoch [3500/4000] Validation [8/10] Loss: 2.37298 focal_loss 1.74553 dice_loss 0.62745 +Epoch [3500/4000] Validation [9/10] Loss: 1.38982 focal_loss 0.84884 dice_loss 0.54098 +Epoch [3500/4000] Validation [10/10] Loss: 1.77052 focal_loss 1.04002 dice_loss 0.73050 +Epoch [3500/4000] Validation metric {'Val/mean dice_metric': 0.9506087899208069, 'Val/mean miou_metric': 0.9344691634178162, 'Val/mean f1': 0.9489796161651611, 'Val/mean precision': 0.9449793100357056, 'Val/mean recall': 0.9530141353607178, 'Val/mean hd95_metric': 10.549715995788574} +Cheakpoint... +Epoch [3500/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506087899208069, 'Val/mean miou_metric': 0.9344691634178162, 'Val/mean f1': 0.9489796161651611, 'Val/mean precision': 0.9449793100357056, 'Val/mean recall': 0.9530141353607178, 'Val/mean hd95_metric': 10.549715995788574} +Epoch [3501/4000] Training [1/39] Loss: 0.00310 +Epoch [3501/4000] Training [2/39] Loss: 0.00330 +Epoch [3501/4000] Training [3/39] Loss: 0.12889 +Epoch [3501/4000] Training [4/39] Loss: 0.00666 +Epoch [3501/4000] Training [5/39] Loss: 0.00745 +Epoch [3501/4000] Training [6/39] Loss: 0.00500 +Epoch [3501/4000] Training [7/39] Loss: 0.00526 +Epoch [3501/4000] Training [8/39] Loss: 0.25267 +Epoch [3501/4000] Training [9/39] Loss: 0.00549 +Epoch [3501/4000] Training [10/39] Loss: 0.00395 +Epoch [3501/4000] Training [11/39] Loss: 0.13002 +Epoch [3501/4000] Training [12/39] Loss: 0.00610 +Epoch [3501/4000] Training [13/39] Loss: 0.00356 +Epoch [3501/4000] Training [14/39] Loss: 0.00585 +Epoch [3501/4000] Training [15/39] Loss: 0.00783 +Epoch [3501/4000] Training [16/39] Loss: 0.12982 +Epoch [3501/4000] Training [17/39] Loss: 0.12885 +Epoch [3501/4000] Training [18/39] Loss: 0.00464 +Epoch [3501/4000] Training [19/39] Loss: 0.25586 +Epoch [3501/4000] Training [20/39] Loss: 0.00415 +Epoch [3501/4000] Training [21/39] Loss: 0.12930 +Epoch [3501/4000] Training [22/39] Loss: 0.00423 +Epoch [3501/4000] Training [23/39] Loss: 0.13020 +Epoch [3501/4000] Training [24/39] Loss: 0.00470 +Epoch [3501/4000] Training [25/39] Loss: 0.16915 +Epoch [3501/4000] Training [26/39] Loss: 0.00878 +Epoch [3501/4000] Training [27/39] Loss: 0.00592 +Epoch [3501/4000] Training [28/39] Loss: 0.00844 +Epoch [3501/4000] Training [29/39] Loss: 0.25385 +Epoch [3501/4000] Training [30/39] Loss: 0.00909 +Epoch [3501/4000] Training [31/39] Loss: 0.12943 +Epoch [3501/4000] Training [32/39] Loss: 0.00611 +Epoch [3501/4000] Training [33/39] Loss: 0.00331 +Epoch [3501/4000] Training [34/39] Loss: 0.00396 +Epoch [3501/4000] Training [35/39] Loss: 0.00344 +Epoch [3501/4000] Training [36/39] Loss: 0.13223 +Epoch [3501/4000] Training [37/39] Loss: 0.00611 +Epoch [3501/4000] Training [38/39] Loss: 0.00714 +Epoch [3501/4000] Training [39/39] Loss: 0.00436 +Epoch [3501/4000] Training metric {'Train/mean dice_metric': 0.9957204461097717, 'Train/mean miou_metric': 0.991935670375824, 'Train/mean f1': 0.9965794086456299, 'Train/mean precision': 0.996059775352478, 'Train/mean recall': 0.9970995783805847, 'Train/mean hd95_metric': 1.1228420734405518} +Epoch [3501/4000] Validation [1/10] Loss: 0.69589 focal_loss 0.61042 dice_loss 0.08548 +Epoch [3501/4000] Validation [2/10] Loss: 0.46036 focal_loss 0.37129 dice_loss 0.08907 +Epoch [3501/4000] Validation [3/10] Loss: 0.38469 focal_loss 0.27465 dice_loss 0.11004 +Epoch [3501/4000] Validation [4/10] Loss: 0.86895 focal_loss 0.30482 dice_loss 0.56413 +Epoch [3501/4000] Validation [5/10] Loss: 3.00248 focal_loss 2.32980 dice_loss 0.67268 +Epoch [3501/4000] Validation [6/10] Loss: 1.28394 focal_loss 0.56875 dice_loss 0.71519 +Epoch [3501/4000] Validation [7/10] Loss: 1.15008 focal_loss 0.49360 dice_loss 0.65647 +Epoch [3501/4000] Validation [8/10] Loss: 2.35982 focal_loss 1.73519 dice_loss 0.62463 +Epoch [3501/4000] Validation [9/10] Loss: 1.40150 focal_loss 0.85958 dice_loss 0.54192 +Epoch [3501/4000] Validation [10/10] Loss: 1.79198 focal_loss 1.05957 dice_loss 0.73240 +Epoch [3501/4000] Validation metric {'Val/mean dice_metric': 0.951323390007019, 'Val/mean miou_metric': 0.9349902272224426, 'Val/mean f1': 0.949157178401947, 'Val/mean precision': 0.9449670314788818, 'Val/mean recall': 0.9533846974372864, 'Val/mean hd95_metric': 10.808712005615234} +Cheakpoint... +Epoch [3501/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951323390007019, 'Val/mean miou_metric': 0.9349902272224426, 'Val/mean f1': 0.949157178401947, 'Val/mean precision': 0.9449670314788818, 'Val/mean recall': 0.9533846974372864, 'Val/mean hd95_metric': 10.808712005615234} +Epoch [3502/4000] Training [1/39] Loss: 0.13088 +Epoch [3502/4000] Training [2/39] Loss: 0.00411 +Epoch [3502/4000] Training [3/39] Loss: 0.00470 +Epoch [3502/4000] Training [4/39] Loss: 0.00417 +Epoch [3502/4000] Training [5/39] Loss: 0.00629 +Epoch [3502/4000] Training [6/39] Loss: 0.00586 +Epoch [3502/4000] Training [7/39] Loss: 0.00256 +Epoch [3502/4000] Training [8/39] Loss: 0.12794 +Epoch [3502/4000] Training [9/39] Loss: 0.00505 +Epoch [3502/4000] Training [10/39] Loss: 0.00683 +Epoch [3502/4000] Training [11/39] Loss: 0.00405 +Epoch [3502/4000] Training [12/39] Loss: 0.00780 +Epoch [3502/4000] Training [13/39] Loss: 0.00358 +Epoch [3502/4000] Training [14/39] Loss: 0.00927 +Epoch [3502/4000] Training [15/39] Loss: 0.00542 +Epoch [3502/4000] Training [16/39] Loss: 0.00623 +Epoch [3502/4000] Training [17/39] Loss: 0.12909 +Epoch [3502/4000] Training [18/39] Loss: 0.00517 +Epoch [3502/4000] Training [19/39] Loss: 0.00361 +Epoch [3502/4000] Training [20/39] Loss: 0.00699 +Epoch [3502/4000] Training [21/39] Loss: 0.00518 +Epoch [3502/4000] Training [22/39] Loss: 0.12868 +Epoch [3502/4000] Training [23/39] Loss: 0.00943 +Epoch [3502/4000] Training [24/39] Loss: 0.12991 +Epoch [3502/4000] Training [25/39] Loss: 0.13032 +Epoch [3502/4000] Training [26/39] Loss: 0.00534 +Epoch [3502/4000] Training [27/39] Loss: 0.12808 +Epoch [3502/4000] Training [28/39] Loss: 0.00538 +Epoch [3502/4000] Training [29/39] Loss: 0.00592 +Epoch [3502/4000] Training [30/39] Loss: 0.00684 +Epoch [3502/4000] Training [31/39] Loss: 0.12984 +Epoch [3502/4000] Training [32/39] Loss: 0.00498 +Epoch [3502/4000] Training [33/39] Loss: 0.00617 +Epoch [3502/4000] Training [34/39] Loss: 0.12957 +Epoch [3502/4000] Training [35/39] Loss: 0.00407 +Epoch [3502/4000] Training [36/39] Loss: 0.00632 +Epoch [3502/4000] Training [37/39] Loss: 0.00322 +Epoch [3502/4000] Training [38/39] Loss: 0.12845 +Epoch [3502/4000] Training [39/39] Loss: 0.00331 +Epoch [3502/4000] Training metric {'Train/mean dice_metric': 0.9960392713546753, 'Train/mean miou_metric': 0.9925665855407715, 'Train/mean f1': 0.9967015385627747, 'Train/mean precision': 0.9962701797485352, 'Train/mean recall': 0.9971332550048828, 'Train/mean hd95_metric': 1.0232837200164795} +Epoch [3502/4000] Validation [1/10] Loss: 0.72451 focal_loss 0.63424 dice_loss 0.09027 +Epoch [3502/4000] Validation [2/10] Loss: 0.47067 focal_loss 0.37280 dice_loss 0.09787 +Epoch [3502/4000] Validation [3/10] Loss: 0.42300 focal_loss 0.30763 dice_loss 0.11537 +Epoch [3502/4000] Validation [4/10] Loss: 0.85240 focal_loss 0.28928 dice_loss 0.56312 +Epoch [3502/4000] Validation [5/10] Loss: 2.96231 focal_loss 2.28940 dice_loss 0.67291 +Epoch [3502/4000] Validation [6/10] Loss: 1.25689 focal_loss 0.54561 dice_loss 0.71128 +Epoch [3502/4000] Validation [7/10] Loss: 1.11893 focal_loss 0.46355 dice_loss 0.65538 +Epoch [3502/4000] Validation [8/10] Loss: 2.45149 focal_loss 1.81377 dice_loss 0.63772 +Epoch [3502/4000] Validation [9/10] Loss: 1.35863 focal_loss 0.82036 dice_loss 0.53827 +Epoch [3502/4000] Validation [10/10] Loss: 1.68946 focal_loss 0.96611 dice_loss 0.72335 +Epoch [3502/4000] Validation metric {'Val/mean dice_metric': 0.9513581395149231, 'Val/mean miou_metric': 0.9353137612342834, 'Val/mean f1': 0.9496855139732361, 'Val/mean precision': 0.9475176930427551, 'Val/mean recall': 0.9518633484840393, 'Val/mean hd95_metric': 10.614341735839844} +Cheakpoint... +Epoch [3502/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513581395149231, 'Val/mean miou_metric': 0.9353137612342834, 'Val/mean f1': 0.9496855139732361, 'Val/mean precision': 0.9475176930427551, 'Val/mean recall': 0.9518633484840393, 'Val/mean hd95_metric': 10.614341735839844} +Epoch [3503/4000] Training [1/39] Loss: 0.00730 +Epoch [3503/4000] Training [2/39] Loss: 0.00624 +Epoch [3503/4000] Training [3/39] Loss: 0.12899 +Epoch [3503/4000] Training [4/39] Loss: 0.00482 +Epoch [3503/4000] Training [5/39] Loss: 0.12965 +Epoch [3503/4000] Training [6/39] Loss: 0.00853 +Epoch [3503/4000] Training [7/39] Loss: 0.13231 +Epoch [3503/4000] Training [8/39] Loss: 0.00431 +Epoch [3503/4000] Training [9/39] Loss: 0.00762 +Epoch [3503/4000] Training [10/39] Loss: 0.12992 +Epoch [3503/4000] Training [11/39] Loss: 0.12813 +Epoch [3503/4000] Training [12/39] Loss: 0.00686 +Epoch [3503/4000] Training [13/39] Loss: 0.00401 +Epoch [3503/4000] Training [14/39] Loss: 0.00867 +Epoch [3503/4000] Training [15/39] Loss: 0.25434 +Epoch [3503/4000] Training [16/39] Loss: 0.00483 +Epoch [3503/4000] Training [17/39] Loss: 0.12790 +Epoch [3503/4000] Training [18/39] Loss: 0.12944 +Epoch [3503/4000] Training [19/39] Loss: 0.00467 +Epoch [3503/4000] Training [20/39] Loss: 0.00648 +Epoch [3503/4000] Training [21/39] Loss: 0.00680 +Epoch [3503/4000] Training [22/39] Loss: 0.13057 +Epoch [3503/4000] Training [23/39] Loss: 0.00460 +Epoch [3503/4000] Training [24/39] Loss: 0.00606 +Epoch [3503/4000] Training [25/39] Loss: 0.00546 +Epoch [3503/4000] Training [26/39] Loss: 0.13020 +Epoch [3503/4000] Training [27/39] Loss: 0.13086 +Epoch [3503/4000] Training [28/39] Loss: 0.00465 +Epoch [3503/4000] Training [29/39] Loss: 0.00533 +Epoch [3503/4000] Training [30/39] Loss: 0.00468 +Epoch [3503/4000] Training [31/39] Loss: 0.00398 +Epoch [3503/4000] Training [32/39] Loss: 0.00511 +Epoch [3503/4000] Training [33/39] Loss: 0.00525 +Epoch [3503/4000] Training [34/39] Loss: 0.00397 +Epoch [3503/4000] Training [35/39] Loss: 0.13080 +Epoch [3503/4000] Training [36/39] Loss: 0.00745 +Epoch [3503/4000] Training [37/39] Loss: 0.00596 +Epoch [3503/4000] Training [38/39] Loss: 0.00813 +Epoch [3503/4000] Training [39/39] Loss: 0.25408 +Epoch [3503/4000] Training metric {'Train/mean dice_metric': 0.9958240389823914, 'Train/mean miou_metric': 0.9920978546142578, 'Train/mean f1': 0.9964858293533325, 'Train/mean precision': 0.9960445761680603, 'Train/mean recall': 0.9969272613525391, 'Train/mean hd95_metric': 1.0286771059036255} +Epoch [3503/4000] Validation [1/10] Loss: 0.71761 focal_loss 0.62909 dice_loss 0.08852 +Epoch [3503/4000] Validation [2/10] Loss: 0.47860 focal_loss 0.38539 dice_loss 0.09321 +Epoch [3503/4000] Validation [3/10] Loss: 0.39418 focal_loss 0.28258 dice_loss 0.11160 +Epoch [3503/4000] Validation [4/10] Loss: 0.86989 focal_loss 0.30230 dice_loss 0.56759 +Epoch [3503/4000] Validation [5/10] Loss: 2.98381 focal_loss 2.31067 dice_loss 0.67314 +Epoch [3503/4000] Validation [6/10] Loss: 1.29506 focal_loss 0.58131 dice_loss 0.71375 +Epoch [3503/4000] Validation [7/10] Loss: 1.14024 focal_loss 0.48134 dice_loss 0.65889 +Epoch [3503/4000] Validation [8/10] Loss: 2.39029 focal_loss 1.76457 dice_loss 0.62572 +Epoch [3503/4000] Validation [9/10] Loss: 1.38158 focal_loss 0.84233 dice_loss 0.53924 +Epoch [3503/4000] Validation [10/10] Loss: 1.75485 focal_loss 1.02934 dice_loss 0.72551 +Epoch [3503/4000] Validation metric {'Val/mean dice_metric': 0.9513847231864929, 'Val/mean miou_metric': 0.935081958770752, 'Val/mean f1': 0.9494282603263855, 'Val/mean precision': 0.9465524554252625, 'Val/mean recall': 0.9523215889930725, 'Val/mean hd95_metric': 10.663511276245117} +Cheakpoint... +Epoch [3503/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513847231864929, 'Val/mean miou_metric': 0.935081958770752, 'Val/mean f1': 0.9494282603263855, 'Val/mean precision': 0.9465524554252625, 'Val/mean recall': 0.9523215889930725, 'Val/mean hd95_metric': 10.663511276245117} +Epoch [3504/4000] Training [1/39] Loss: 0.00517 +Epoch [3504/4000] Training [2/39] Loss: 0.37942 +Epoch [3504/4000] Training [3/39] Loss: 0.00713 +Epoch [3504/4000] Training [4/39] Loss: 0.00580 +Epoch [3504/4000] Training [5/39] Loss: 0.12894 +Epoch [3504/4000] Training [6/39] Loss: 0.00372 +Epoch [3504/4000] Training [7/39] Loss: 0.00489 +Epoch [3504/4000] Training [8/39] Loss: 0.13050 +Epoch [3504/4000] Training [9/39] Loss: 0.13153 +Epoch [3504/4000] Training [10/39] Loss: 0.00463 +Epoch [3504/4000] Training [11/39] Loss: 0.00630 +Epoch [3504/4000] Training [12/39] Loss: 0.12899 +Epoch [3504/4000] Training [13/39] Loss: 0.00315 +Epoch [3504/4000] Training [14/39] Loss: 0.12904 +Epoch [3504/4000] Training [15/39] Loss: 0.00375 +Epoch [3504/4000] Training [16/39] Loss: 0.00644 +Epoch [3504/4000] Training [17/39] Loss: 0.00432 +Epoch [3504/4000] Training [18/39] Loss: 0.25285 +Epoch [3504/4000] Training [19/39] Loss: 0.12848 +Epoch [3504/4000] Training [20/39] Loss: 0.12782 +Epoch [3504/4000] Training [21/39] Loss: 0.00925 +Epoch [3504/4000] Training [22/39] Loss: 0.13165 +Epoch [3504/4000] Training [23/39] Loss: 0.00461 +Epoch [3504/4000] Training [24/39] Loss: 0.00659 +Epoch [3504/4000] Training [25/39] Loss: 0.04328 +Epoch [3504/4000] Training [26/39] Loss: 0.00313 +Epoch [3504/4000] Training [27/39] Loss: 0.13118 +Epoch [3504/4000] Training [28/39] Loss: 0.12906 +Epoch [3504/4000] Training [29/39] Loss: 0.00644 +Epoch [3504/4000] Training [30/39] Loss: 0.00612 +Epoch [3504/4000] Training [31/39] Loss: 0.00547 +Epoch [3504/4000] Training [32/39] Loss: 0.12790 +Epoch [3504/4000] Training [33/39] Loss: 0.00426 +Epoch [3504/4000] Training [34/39] Loss: 0.00400 +Epoch [3504/4000] Training [35/39] Loss: 0.00466 +Epoch [3504/4000] Training [36/39] Loss: 0.12934 +Epoch [3504/4000] Training [37/39] Loss: 0.00387 +Epoch [3504/4000] Training [38/39] Loss: 0.00333 +Epoch [3504/4000] Training [39/39] Loss: 0.00539 +Epoch [3504/4000] Training metric {'Train/mean dice_metric': 0.9956040382385254, 'Train/mean miou_metric': 0.9920487999916077, 'Train/mean f1': 0.9963933825492859, 'Train/mean precision': 0.9956595301628113, 'Train/mean recall': 0.9971283674240112, 'Train/mean hd95_metric': 1.1881251335144043} +Epoch [3504/4000] Validation [1/10] Loss: 0.69917 focal_loss 0.61202 dice_loss 0.08715 +Epoch [3504/4000] Validation [2/10] Loss: 0.49265 focal_loss 0.39255 dice_loss 0.10011 +Epoch [3504/4000] Validation [3/10] Loss: 0.41347 focal_loss 0.30048 dice_loss 0.11298 +Epoch [3504/4000] Validation [4/10] Loss: 0.85478 focal_loss 0.28308 dice_loss 0.57170 +Epoch [3504/4000] Validation [5/10] Loss: 2.97847 focal_loss 2.30502 dice_loss 0.67345 +Epoch [3504/4000] Validation [6/10] Loss: 1.25791 focal_loss 0.54708 dice_loss 0.71083 +Epoch [3504/4000] Validation [7/10] Loss: 1.11827 focal_loss 0.46302 dice_loss 0.65525 +Epoch [3504/4000] Validation [8/10] Loss: 2.39545 focal_loss 1.76434 dice_loss 0.63110 +Epoch [3504/4000] Validation [9/10] Loss: 1.38598 focal_loss 0.84710 dice_loss 0.53888 +Epoch [3504/4000] Validation [10/10] Loss: 1.70948 focal_loss 0.98382 dice_loss 0.72566 +Epoch [3504/4000] Validation metric {'Val/mean dice_metric': 0.9510108232498169, 'Val/mean miou_metric': 0.9348525404930115, 'Val/mean f1': 0.9497265815734863, 'Val/mean precision': 0.9481838941574097, 'Val/mean recall': 0.9512743353843689, 'Val/mean hd95_metric': 10.74089527130127} +Cheakpoint... +Epoch [3504/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510108232498169, 'Val/mean miou_metric': 0.9348525404930115, 'Val/mean f1': 0.9497265815734863, 'Val/mean precision': 0.9481838941574097, 'Val/mean recall': 0.9512743353843689, 'Val/mean hd95_metric': 10.74089527130127} +Epoch [3505/4000] Training [1/39] Loss: 0.13025 +Epoch [3505/4000] Training [2/39] Loss: 0.13279 +Epoch [3505/4000] Training [3/39] Loss: 0.12838 +Epoch [3505/4000] Training [4/39] Loss: 0.01169 +Epoch [3505/4000] Training [5/39] Loss: 0.00542 +Epoch [3505/4000] Training [6/39] Loss: 0.00348 +Epoch [3505/4000] Training [7/39] Loss: 0.00698 +Epoch [3505/4000] Training [8/39] Loss: 0.00470 +Epoch [3505/4000] Training [9/39] Loss: 0.12830 +Epoch [3505/4000] Training [10/39] Loss: 0.13023 +Epoch [3505/4000] Training [11/39] Loss: 0.00451 +Epoch [3505/4000] Training [12/39] Loss: 0.12955 +Epoch [3505/4000] Training [13/39] Loss: 0.25635 +Epoch [3505/4000] Training [14/39] Loss: 0.00447 +Epoch [3505/4000] Training [15/39] Loss: 0.08245 +Epoch [3505/4000] Training [16/39] Loss: 0.00524 +Epoch [3505/4000] Training [17/39] Loss: 0.00518 +Epoch [3505/4000] Training [18/39] Loss: 0.13266 +Epoch [3505/4000] Training [19/39] Loss: 0.00701 +Epoch [3505/4000] Training [20/39] Loss: 0.00537 +Epoch [3505/4000] Training [21/39] Loss: 0.00329 +Epoch [3505/4000] Training [22/39] Loss: 0.00718 +Epoch [3505/4000] Training [23/39] Loss: 0.00505 +Epoch [3505/4000] Training [24/39] Loss: 0.00579 +Epoch [3505/4000] Training [25/39] Loss: 0.00540 +Epoch [3505/4000] Training [26/39] Loss: 0.01231 +Epoch [3505/4000] Training [27/39] Loss: 0.00515 +Epoch [3505/4000] Training [28/39] Loss: 0.00520 +Epoch [3505/4000] Training [29/39] Loss: 0.00500 +Epoch [3505/4000] Training [30/39] Loss: 0.00462 +Epoch [3505/4000] Training [31/39] Loss: 0.00594 +Epoch [3505/4000] Training [32/39] Loss: 0.00552 +Epoch [3505/4000] Training [33/39] Loss: 0.00379 +Epoch [3505/4000] Training [34/39] Loss: 0.25343 +Epoch [3505/4000] Training [35/39] Loss: 0.00409 +Epoch [3505/4000] Training [36/39] Loss: 0.00357 +Epoch [3505/4000] Training [37/39] Loss: 0.12912 +Epoch [3505/4000] Training [38/39] Loss: 0.13190 +Epoch [3505/4000] Training [39/39] Loss: 0.12906 +Epoch [3505/4000] Training metric {'Train/mean dice_metric': 0.9951167702674866, 'Train/mean miou_metric': 0.991517961025238, 'Train/mean f1': 0.9965910911560059, 'Train/mean precision': 0.9961479902267456, 'Train/mean recall': 0.9970346689224243, 'Train/mean hd95_metric': 1.101948857307434} +Epoch [3505/4000] Validation [1/10] Loss: 0.73221 focal_loss 0.64255 dice_loss 0.08966 +Epoch [3505/4000] Validation [2/10] Loss: 0.48817 focal_loss 0.39147 dice_loss 0.09671 +Epoch [3505/4000] Validation [3/10] Loss: 0.40288 focal_loss 0.29120 dice_loss 0.11168 +Epoch [3505/4000] Validation [4/10] Loss: 0.86243 focal_loss 0.29713 dice_loss 0.56530 +Epoch [3505/4000] Validation [5/10] Loss: 3.01870 focal_loss 2.34558 dice_loss 0.67312 +Epoch [3505/4000] Validation [6/10] Loss: 1.29750 focal_loss 0.58503 dice_loss 0.71247 +Epoch [3505/4000] Validation [7/10] Loss: 1.12416 focal_loss 0.46466 dice_loss 0.65950 +Epoch [3505/4000] Validation [8/10] Loss: 2.42728 focal_loss 1.79995 dice_loss 0.62733 +Epoch [3505/4000] Validation [9/10] Loss: 1.40567 focal_loss 0.86529 dice_loss 0.54038 +Epoch [3505/4000] Validation [10/10] Loss: 1.74502 focal_loss 1.02088 dice_loss 0.72413 +Epoch [3505/4000] Validation metric {'Val/mean dice_metric': 0.9507269263267517, 'Val/mean miou_metric': 0.9345439672470093, 'Val/mean f1': 0.949086606502533, 'Val/mean precision': 0.9462000131607056, 'Val/mean recall': 0.9519907236099243, 'Val/mean hd95_metric': 10.783059120178223} +Cheakpoint... +Epoch [3505/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507269263267517, 'Val/mean miou_metric': 0.9345439672470093, 'Val/mean f1': 0.949086606502533, 'Val/mean precision': 0.9462000131607056, 'Val/mean recall': 0.9519907236099243, 'Val/mean hd95_metric': 10.783059120178223} +Epoch [3506/4000] Training [1/39] Loss: 0.00494 +Epoch [3506/4000] Training [2/39] Loss: 0.00501 +Epoch [3506/4000] Training [3/39] Loss: 0.00300 +Epoch [3506/4000] Training [4/39] Loss: 0.00636 +Epoch [3506/4000] Training [5/39] Loss: 0.00760 +Epoch [3506/4000] Training [6/39] Loss: 0.00455 +Epoch [3506/4000] Training [7/39] Loss: 0.00602 +Epoch [3506/4000] Training [8/39] Loss: 0.00481 +Epoch [3506/4000] Training [9/39] Loss: 0.00529 +Epoch [3506/4000] Training [10/39] Loss: 0.00678 +Epoch [3506/4000] Training [11/39] Loss: 0.00778 +Epoch [3506/4000] Training [12/39] Loss: 0.13095 +Epoch [3506/4000] Training [13/39] Loss: 0.00561 +Epoch [3506/4000] Training [14/39] Loss: 0.00592 +Epoch [3506/4000] Training [15/39] Loss: 0.00469 +Epoch [3506/4000] Training [16/39] Loss: 0.00419 +Epoch [3506/4000] Training [17/39] Loss: 0.00666 +Epoch [3506/4000] Training [18/39] Loss: 0.00366 +Epoch [3506/4000] Training [19/39] Loss: 0.00468 +Epoch [3506/4000] Training [20/39] Loss: 0.00820 +Epoch [3506/4000] Training [21/39] Loss: 0.13081 +Epoch [3506/4000] Training [22/39] Loss: 0.00550 +Epoch [3506/4000] Training [23/39] Loss: 0.00567 +Epoch [3506/4000] Training [24/39] Loss: 0.00727 +Epoch [3506/4000] Training [25/39] Loss: 0.12966 +Epoch [3506/4000] Training [26/39] Loss: 0.12918 +Epoch [3506/4000] Training [27/39] Loss: 0.08243 +Epoch [3506/4000] Training [28/39] Loss: 0.00372 +Epoch [3506/4000] Training [29/39] Loss: 0.13005 +Epoch [3506/4000] Training [30/39] Loss: 0.00362 +Epoch [3506/4000] Training [31/39] Loss: 0.00428 +Epoch [3506/4000] Training [32/39] Loss: 0.00445 +Epoch [3506/4000] Training [33/39] Loss: 0.00648 +Epoch [3506/4000] Training [34/39] Loss: 0.00551 +Epoch [3506/4000] Training [35/39] Loss: 0.00655 +Epoch [3506/4000] Training [36/39] Loss: 0.00532 +Epoch [3506/4000] Training [37/39] Loss: 0.00810 +Epoch [3506/4000] Training [38/39] Loss: 0.04871 +Epoch [3506/4000] Training [39/39] Loss: 0.00470 +Epoch [3506/4000] Training metric {'Train/mean dice_metric': 0.9957540035247803, 'Train/mean miou_metric': 0.9919679164886475, 'Train/mean f1': 0.9965270161628723, 'Train/mean precision': 0.9960681796073914, 'Train/mean recall': 0.996986448764801, 'Train/mean hd95_metric': 1.0119881629943848} +Epoch [3506/4000] Validation [1/10] Loss: 0.68256 focal_loss 0.59736 dice_loss 0.08520 +Epoch [3506/4000] Validation [2/10] Loss: 0.47979 focal_loss 0.38354 dice_loss 0.09626 +Epoch [3506/4000] Validation [3/10] Loss: 0.38523 focal_loss 0.27466 dice_loss 0.11057 +Epoch [3506/4000] Validation [4/10] Loss: 0.87360 focal_loss 0.30793 dice_loss 0.56567 +Epoch [3506/4000] Validation [5/10] Loss: 2.98543 focal_loss 2.31240 dice_loss 0.67304 +Epoch [3506/4000] Validation [6/10] Loss: 1.29362 focal_loss 0.58352 dice_loss 0.71010 +Epoch [3506/4000] Validation [7/10] Loss: 1.13960 focal_loss 0.48333 dice_loss 0.65627 +Epoch [3506/4000] Validation [8/10] Loss: 2.36046 focal_loss 1.73710 dice_loss 0.62335 +Epoch [3506/4000] Validation [9/10] Loss: 1.40207 focal_loss 0.85930 dice_loss 0.54278 +Epoch [3506/4000] Validation [10/10] Loss: 1.75592 focal_loss 1.02682 dice_loss 0.72910 +Epoch [3506/4000] Validation metric {'Val/mean dice_metric': 0.9513156414031982, 'Val/mean miou_metric': 0.9349930882453918, 'Val/mean f1': 0.9492709636688232, 'Val/mean precision': 0.9458873271942139, 'Val/mean recall': 0.9526787400245667, 'Val/mean hd95_metric': 10.717462539672852} +Cheakpoint... +Epoch [3506/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513156414031982, 'Val/mean miou_metric': 0.9349930882453918, 'Val/mean f1': 0.9492709636688232, 'Val/mean precision': 0.9458873271942139, 'Val/mean recall': 0.9526787400245667, 'Val/mean hd95_metric': 10.717462539672852} +Epoch [3507/4000] Training [1/39] Loss: 0.00556 +Epoch [3507/4000] Training [2/39] Loss: 0.00561 +Epoch [3507/4000] Training [3/39] Loss: 0.00459 +Epoch [3507/4000] Training [4/39] Loss: 0.25324 +Epoch [3507/4000] Training [5/39] Loss: 0.25327 +Epoch [3507/4000] Training [6/39] Loss: 0.13149 +Epoch [3507/4000] Training [7/39] Loss: 0.00473 +Epoch [3507/4000] Training [8/39] Loss: 0.00719 +Epoch [3507/4000] Training [9/39] Loss: 0.00523 +Epoch [3507/4000] Training [10/39] Loss: 0.00637 +Epoch [3507/4000] Training [11/39] Loss: 0.00463 +Epoch [3507/4000] Training [12/39] Loss: 0.00446 +Epoch [3507/4000] Training [13/39] Loss: 0.00498 +Epoch [3507/4000] Training [14/39] Loss: 0.13006 +Epoch [3507/4000] Training [15/39] Loss: 0.00425 +Epoch [3507/4000] Training [16/39] Loss: 0.12965 +Epoch [3507/4000] Training [17/39] Loss: 0.00931 +Epoch [3507/4000] Training [18/39] Loss: 0.12833 +Epoch [3507/4000] Training [19/39] Loss: 0.00485 +Epoch [3507/4000] Training [20/39] Loss: 0.00373 +Epoch [3507/4000] Training [21/39] Loss: 0.00591 +Epoch [3507/4000] Training [22/39] Loss: 0.00578 +Epoch [3507/4000] Training [23/39] Loss: 0.13088 +Epoch [3507/4000] Training [24/39] Loss: 0.13132 +Epoch [3507/4000] Training [25/39] Loss: 0.00638 +Epoch [3507/4000] Training [26/39] Loss: 0.00615 +Epoch [3507/4000] Training [27/39] Loss: 0.00465 +Epoch [3507/4000] Training [28/39] Loss: 0.00341 +Epoch [3507/4000] Training [29/39] Loss: 0.00763 +Epoch [3507/4000] Training [30/39] Loss: 0.12972 +Epoch [3507/4000] Training [31/39] Loss: 0.12921 +Epoch [3507/4000] Training [32/39] Loss: 0.00462 +Epoch [3507/4000] Training [33/39] Loss: 0.00678 +Epoch [3507/4000] Training [34/39] Loss: 0.00468 +Epoch [3507/4000] Training [35/39] Loss: 0.00314 +Epoch [3507/4000] Training [36/39] Loss: 0.00863 +Epoch [3507/4000] Training [37/39] Loss: 0.00664 +Epoch [3507/4000] Training [38/39] Loss: 0.13084 +Epoch [3507/4000] Training [39/39] Loss: 0.00585 +Epoch [3507/4000] Training metric {'Train/mean dice_metric': 0.9949101805686951, 'Train/mean miou_metric': 0.9911290407180786, 'Train/mean f1': 0.9963997006416321, 'Train/mean precision': 0.9959213733673096, 'Train/mean recall': 0.9968783259391785, 'Train/mean hd95_metric': 1.0161548852920532} +Epoch [3507/4000] Validation [1/10] Loss: 0.70173 focal_loss 0.61510 dice_loss 0.08663 +Epoch [3507/4000] Validation [2/10] Loss: 0.48551 focal_loss 0.38736 dice_loss 0.09815 +Epoch [3507/4000] Validation [3/10] Loss: 0.39384 focal_loss 0.28254 dice_loss 0.11130 +Epoch [3507/4000] Validation [4/10] Loss: 0.87820 focal_loss 0.31341 dice_loss 0.56479 +Epoch [3507/4000] Validation [5/10] Loss: 3.02552 focal_loss 2.35257 dice_loss 0.67296 +Epoch [3507/4000] Validation [6/10] Loss: 1.30042 focal_loss 0.58695 dice_loss 0.71347 +Epoch [3507/4000] Validation [7/10] Loss: 1.14459 focal_loss 0.48639 dice_loss 0.65820 +Epoch [3507/4000] Validation [8/10] Loss: 2.36283 focal_loss 1.73974 dice_loss 0.62309 +Epoch [3507/4000] Validation [9/10] Loss: 1.41285 focal_loss 0.86933 dice_loss 0.54352 +Epoch [3507/4000] Validation [10/10] Loss: 1.78416 focal_loss 1.05162 dice_loss 0.73255 +Epoch [3507/4000] Validation metric {'Val/mean dice_metric': 0.9503864645957947, 'Val/mean miou_metric': 0.9341067671775818, 'Val/mean f1': 0.948687732219696, 'Val/mean precision': 0.944541871547699, 'Val/mean recall': 0.9528701901435852, 'Val/mean hd95_metric': 10.70309066772461} +Cheakpoint... +Epoch [3507/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503864645957947, 'Val/mean miou_metric': 0.9341067671775818, 'Val/mean f1': 0.948687732219696, 'Val/mean precision': 0.944541871547699, 'Val/mean recall': 0.9528701901435852, 'Val/mean hd95_metric': 10.70309066772461} +Epoch [3508/4000] Training [1/39] Loss: 0.00522 +Epoch [3508/4000] Training [2/39] Loss: 0.00393 +Epoch [3508/4000] Training [3/39] Loss: 0.00377 +Epoch [3508/4000] Training [4/39] Loss: 0.00619 +Epoch [3508/4000] Training [5/39] Loss: 0.00497 +Epoch [3508/4000] Training [6/39] Loss: 0.00406 +Epoch [3508/4000] Training [7/39] Loss: 0.00413 +Epoch [3508/4000] Training [8/39] Loss: 0.12807 +Epoch [3508/4000] Training [9/39] Loss: 0.00602 +Epoch [3508/4000] Training [10/39] Loss: 0.12845 +Epoch [3508/4000] Training [11/39] Loss: 0.00546 +Epoch [3508/4000] Training [12/39] Loss: 0.12870 +Epoch [3508/4000] Training [13/39] Loss: 0.00389 +Epoch [3508/4000] Training [14/39] Loss: 0.12840 +Epoch [3508/4000] Training [15/39] Loss: 0.00456 +Epoch [3508/4000] Training [16/39] Loss: 0.13037 +Epoch [3508/4000] Training [17/39] Loss: 0.00413 +Epoch [3508/4000] Training [18/39] Loss: 0.00530 +Epoch [3508/4000] Training [19/39] Loss: 0.00568 +Epoch [3508/4000] Training [20/39] Loss: 0.04378 +Epoch [3508/4000] Training [21/39] Loss: 0.00366 +Epoch [3508/4000] Training [22/39] Loss: 0.13258 +Epoch [3508/4000] Training [23/39] Loss: 0.12817 +Epoch [3508/4000] Training [24/39] Loss: 0.00591 +Epoch [3508/4000] Training [25/39] Loss: 0.00740 +Epoch [3508/4000] Training [26/39] Loss: 0.09753 +Epoch [3508/4000] Training [27/39] Loss: 0.00501 +Epoch [3508/4000] Training [28/39] Loss: 0.00536 +Epoch [3508/4000] Training [29/39] Loss: 0.00608 +Epoch [3508/4000] Training [30/39] Loss: 0.00578 +Epoch [3508/4000] Training [31/39] Loss: 0.12963 +Epoch [3508/4000] Training [32/39] Loss: 0.01124 +Epoch [3508/4000] Training [33/39] Loss: 0.00482 +Epoch [3508/4000] Training [34/39] Loss: 0.13343 +Epoch [3508/4000] Training [35/39] Loss: 0.00462 +Epoch [3508/4000] Training [36/39] Loss: 0.00414 +Epoch [3508/4000] Training [37/39] Loss: 0.00420 +Epoch [3508/4000] Training [38/39] Loss: 0.00455 +Epoch [3508/4000] Training [39/39] Loss: 0.00515 +Epoch [3508/4000] Training metric {'Train/mean dice_metric': 0.9960065484046936, 'Train/mean miou_metric': 0.9924928545951843, 'Train/mean f1': 0.9966956377029419, 'Train/mean precision': 0.9962224960327148, 'Train/mean recall': 0.9971692562103271, 'Train/mean hd95_metric': 0.9891830682754517} +Epoch [3508/4000] Validation [1/10] Loss: 0.68068 focal_loss 0.59530 dice_loss 0.08537 +Epoch [3508/4000] Validation [2/10] Loss: 0.48117 focal_loss 0.38304 dice_loss 0.09813 +Epoch [3508/4000] Validation [3/10] Loss: 0.39764 focal_loss 0.28523 dice_loss 0.11241 +Epoch [3508/4000] Validation [4/10] Loss: 0.86170 focal_loss 0.29670 dice_loss 0.56500 +Epoch [3508/4000] Validation [5/10] Loss: 2.98321 focal_loss 2.31058 dice_loss 0.67263 +Epoch [3508/4000] Validation [6/10] Loss: 1.27995 focal_loss 0.56753 dice_loss 0.71242 +Epoch [3508/4000] Validation [7/10] Loss: 1.10003 focal_loss 0.44420 dice_loss 0.65584 +Epoch [3508/4000] Validation [8/10] Loss: 2.27593 focal_loss 1.65357 dice_loss 0.62236 +Epoch [3508/4000] Validation [9/10] Loss: 1.38164 focal_loss 0.83996 dice_loss 0.54168 +Epoch [3508/4000] Validation [10/10] Loss: 1.74170 focal_loss 1.01254 dice_loss 0.72916 +Epoch [3508/4000] Validation metric {'Val/mean dice_metric': 0.9514359831809998, 'Val/mean miou_metric': 0.9353983402252197, 'Val/mean f1': 0.9493062496185303, 'Val/mean precision': 0.9463946223258972, 'Val/mean recall': 0.9522359371185303, 'Val/mean hd95_metric': 10.581268310546875} +Cheakpoint... +Epoch [3508/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514359831809998, 'Val/mean miou_metric': 0.9353983402252197, 'Val/mean f1': 0.9493062496185303, 'Val/mean precision': 0.9463946223258972, 'Val/mean recall': 0.9522359371185303, 'Val/mean hd95_metric': 10.581268310546875} +Epoch [3509/4000] Training [1/39] Loss: 0.00494 +Epoch [3509/4000] Training [2/39] Loss: 0.12820 +Epoch [3509/4000] Training [3/39] Loss: 0.00640 +Epoch [3509/4000] Training [4/39] Loss: 0.00369 +Epoch [3509/4000] Training [5/39] Loss: 0.00718 +Epoch [3509/4000] Training [6/39] Loss: 0.00376 +Epoch [3509/4000] Training [7/39] Loss: 0.00466 +Epoch [3509/4000] Training [8/39] Loss: 0.13084 +Epoch [3509/4000] Training [9/39] Loss: 0.00393 +Epoch [3509/4000] Training [10/39] Loss: 0.00653 +Epoch [3509/4000] Training [11/39] Loss: 0.00523 +Epoch [3509/4000] Training [12/39] Loss: 0.00544 +Epoch [3509/4000] Training [13/39] Loss: 0.00733 +Epoch [3509/4000] Training [14/39] Loss: 0.00750 +Epoch [3509/4000] Training [15/39] Loss: 0.00420 +Epoch [3509/4000] Training [16/39] Loss: 0.00504 +Epoch [3509/4000] Training [17/39] Loss: 0.00613 +Epoch [3509/4000] Training [18/39] Loss: 0.00558 +Epoch [3509/4000] Training [19/39] Loss: 0.00538 +Epoch [3509/4000] Training [20/39] Loss: 0.00507 +Epoch [3509/4000] Training [21/39] Loss: 0.12986 +Epoch [3509/4000] Training [22/39] Loss: 0.25583 +Epoch [3509/4000] Training [23/39] Loss: 0.00459 +Epoch [3509/4000] Training [24/39] Loss: 0.00462 +Epoch [3509/4000] Training [25/39] Loss: 0.00386 +Epoch [3509/4000] Training [26/39] Loss: 0.00377 +Epoch [3509/4000] Training [27/39] Loss: 0.00304 +Epoch [3509/4000] Training [28/39] Loss: 0.00298 +Epoch [3509/4000] Training [29/39] Loss: 0.00621 +Epoch [3509/4000] Training [30/39] Loss: 0.00456 +Epoch [3509/4000] Training [31/39] Loss: 0.00427 +Epoch [3509/4000] Training [32/39] Loss: 0.00449 +Epoch [3509/4000] Training [33/39] Loss: 0.00581 +Epoch [3509/4000] Training [34/39] Loss: 0.12894 +Epoch [3509/4000] Training [35/39] Loss: 0.00439 +Epoch [3509/4000] Training [36/39] Loss: 0.00427 +Epoch [3509/4000] Training [37/39] Loss: 0.00683 +Epoch [3509/4000] Training [38/39] Loss: 0.25401 +Epoch [3509/4000] Training [39/39] Loss: 0.00844 +Epoch [3509/4000] Training metric {'Train/mean dice_metric': 0.9961261749267578, 'Train/mean miou_metric': 0.9927112460136414, 'Train/mean f1': 0.9966604709625244, 'Train/mean precision': 0.9962233304977417, 'Train/mean recall': 0.9970980286598206, 'Train/mean hd95_metric': 1.0127655267715454} +Epoch [3509/4000] Validation [1/10] Loss: 0.70477 focal_loss 0.61721 dice_loss 0.08756 +Epoch [3509/4000] Validation [2/10] Loss: 0.48905 focal_loss 0.39316 dice_loss 0.09589 +Epoch [3509/4000] Validation [3/10] Loss: 0.37019 focal_loss 0.26111 dice_loss 0.10908 +Epoch [3509/4000] Validation [4/10] Loss: 0.86820 focal_loss 0.30361 dice_loss 0.56459 +Epoch [3509/4000] Validation [5/10] Loss: 2.99522 focal_loss 2.32240 dice_loss 0.67282 +Epoch [3509/4000] Validation [6/10] Loss: 1.31189 focal_loss 0.59479 dice_loss 0.71711 +Epoch [3509/4000] Validation [7/10] Loss: 1.15502 focal_loss 0.49593 dice_loss 0.65909 +Epoch [3509/4000] Validation [8/10] Loss: 2.20588 focal_loss 1.59610 dice_loss 0.60978 +Epoch [3509/4000] Validation [9/10] Loss: 1.38213 focal_loss 0.83935 dice_loss 0.54278 +Epoch [3509/4000] Validation [10/10] Loss: 1.81519 focal_loss 1.08125 dice_loss 0.73393 +Epoch [3509/4000] Validation metric {'Val/mean dice_metric': 0.9514973759651184, 'Val/mean miou_metric': 0.9355404376983643, 'Val/mean f1': 0.9487928748130798, 'Val/mean precision': 0.943709135055542, 'Val/mean recall': 0.9539317488670349, 'Val/mean hd95_metric': 10.742755889892578} +Cheakpoint... +Epoch [3509/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514973759651184, 'Val/mean miou_metric': 0.9355404376983643, 'Val/mean f1': 0.9487928748130798, 'Val/mean precision': 0.943709135055542, 'Val/mean recall': 0.9539317488670349, 'Val/mean hd95_metric': 10.742755889892578} +Epoch [3510/4000] Training [1/39] Loss: 0.12834 +Epoch [3510/4000] Training [2/39] Loss: 0.00410 +Epoch [3510/4000] Training [3/39] Loss: 0.00637 +Epoch [3510/4000] Training [4/39] Loss: 0.00557 +Epoch [3510/4000] Training [5/39] Loss: 0.00791 +Epoch [3510/4000] Training [6/39] Loss: 0.00349 +Epoch [3510/4000] Training [7/39] Loss: 0.00423 +Epoch [3510/4000] Training [8/39] Loss: 0.00495 +Epoch [3510/4000] Training [9/39] Loss: 0.13095 +Epoch [3510/4000] Training [10/39] Loss: 0.00299 +Epoch [3510/4000] Training [11/39] Loss: 0.12832 +Epoch [3510/4000] Training [12/39] Loss: 0.00604 +Epoch [3510/4000] Training [13/39] Loss: 0.00759 +Epoch [3510/4000] Training [14/39] Loss: 0.00633 +Epoch [3510/4000] Training [15/39] Loss: 0.12945 +Epoch [3510/4000] Training [16/39] Loss: 0.12758 +Epoch [3510/4000] Training [17/39] Loss: 0.00319 +Epoch [3510/4000] Training [18/39] Loss: 0.00401 +Epoch [3510/4000] Training [19/39] Loss: 0.00666 +Epoch [3510/4000] Training [20/39] Loss: 0.00668 +Epoch [3510/4000] Training [21/39] Loss: 0.12976 +Epoch [3510/4000] Training [22/39] Loss: 0.12855 +Epoch [3510/4000] Training [23/39] Loss: 0.00956 +Epoch [3510/4000] Training [24/39] Loss: 0.00684 +Epoch [3510/4000] Training [25/39] Loss: 0.09624 +Epoch [3510/4000] Training [26/39] Loss: 0.00419 +Epoch [3510/4000] Training [27/39] Loss: 0.00466 +Epoch [3510/4000] Training [28/39] Loss: 0.00365 +Epoch [3510/4000] Training [29/39] Loss: 0.00634 +Epoch [3510/4000] Training [30/39] Loss: 0.00487 +Epoch [3510/4000] Training [31/39] Loss: 0.00493 +Epoch [3510/4000] Training [32/39] Loss: 0.12888 +Epoch [3510/4000] Training [33/39] Loss: 0.00758 +Epoch [3510/4000] Training [34/39] Loss: 0.13015 +Epoch [3510/4000] Training [35/39] Loss: 0.25309 +Epoch [3510/4000] Training [36/39] Loss: 0.00553 +Epoch [3510/4000] Training [37/39] Loss: 0.12921 +Epoch [3510/4000] Training [38/39] Loss: 0.00337 +Epoch [3510/4000] Training [39/39] Loss: 0.00650 +Epoch [3510/4000] Training metric {'Train/mean dice_metric': 0.9959540963172913, 'Train/mean miou_metric': 0.992378830909729, 'Train/mean f1': 0.9967041015625, 'Train/mean precision': 0.9962571263313293, 'Train/mean recall': 0.9971516728401184, 'Train/mean hd95_metric': 0.9895339012145996} +Epoch [3510/4000] Validation [1/10] Loss: 0.71614 focal_loss 0.62920 dice_loss 0.08694 +Epoch [3510/4000] Validation [2/10] Loss: 0.48650 focal_loss 0.38857 dice_loss 0.09793 +Epoch [3510/4000] Validation [3/10] Loss: 0.39539 focal_loss 0.28346 dice_loss 0.11193 +Epoch [3510/4000] Validation [4/10] Loss: 0.86111 focal_loss 0.29693 dice_loss 0.56418 +Epoch [3510/4000] Validation [5/10] Loss: 3.00090 focal_loss 2.32796 dice_loss 0.67294 +Epoch [3510/4000] Validation [6/10] Loss: 1.28424 focal_loss 0.57036 dice_loss 0.71388 +Epoch [3510/4000] Validation [7/10] Loss: 1.14090 focal_loss 0.48418 dice_loss 0.65672 +Epoch [3510/4000] Validation [8/10] Loss: 2.36016 focal_loss 1.73178 dice_loss 0.62838 +Epoch [3510/4000] Validation [9/10] Loss: 1.37757 focal_loss 0.83689 dice_loss 0.54068 +Epoch [3510/4000] Validation [10/10] Loss: 1.75340 focal_loss 1.02559 dice_loss 0.72781 +Epoch [3510/4000] Validation metric {'Val/mean dice_metric': 0.9511293768882751, 'Val/mean miou_metric': 0.9350132346153259, 'Val/mean f1': 0.9492660164833069, 'Val/mean precision': 0.9460384845733643, 'Val/mean recall': 0.9525156021118164, 'Val/mean hd95_metric': 10.607937812805176} +Cheakpoint... +Epoch [3510/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511293768882751, 'Val/mean miou_metric': 0.9350132346153259, 'Val/mean f1': 0.9492660164833069, 'Val/mean precision': 0.9460384845733643, 'Val/mean recall': 0.9525156021118164, 'Val/mean hd95_metric': 10.607937812805176} +Epoch [3511/4000] Training [1/39] Loss: 0.12881 +Epoch [3511/4000] Training [2/39] Loss: 0.00575 +Epoch [3511/4000] Training [3/39] Loss: 0.25345 +Epoch [3511/4000] Training [4/39] Loss: 0.00338 +Epoch [3511/4000] Training [5/39] Loss: 0.12942 +Epoch [3511/4000] Training [6/39] Loss: 0.00489 +Epoch [3511/4000] Training [7/39] Loss: 0.00576 +Epoch [3511/4000] Training [8/39] Loss: 0.00689 +Epoch [3511/4000] Training [9/39] Loss: 0.00751 +Epoch [3511/4000] Training [10/39] Loss: 0.00329 +Epoch [3511/4000] Training [11/39] Loss: 0.12766 +Epoch [3511/4000] Training [12/39] Loss: 0.00243 +Epoch [3511/4000] Training [13/39] Loss: 0.00617 +Epoch [3511/4000] Training [14/39] Loss: 0.13125 +Epoch [3511/4000] Training [15/39] Loss: 0.13082 +Epoch [3511/4000] Training [16/39] Loss: 0.00734 +Epoch [3511/4000] Training [17/39] Loss: 0.00609 +Epoch [3511/4000] Training [18/39] Loss: 0.00426 +Epoch [3511/4000] Training [19/39] Loss: 0.00505 +Epoch [3511/4000] Training [20/39] Loss: 0.00572 +Epoch [3511/4000] Training [21/39] Loss: 0.12890 +Epoch [3511/4000] Training [22/39] Loss: 0.00597 +Epoch [3511/4000] Training [23/39] Loss: 0.00689 +Epoch [3511/4000] Training [24/39] Loss: 0.12847 +Epoch [3511/4000] Training [25/39] Loss: 0.25416 +Epoch [3511/4000] Training [26/39] Loss: 0.00388 +Epoch [3511/4000] Training [27/39] Loss: 0.00599 +Epoch [3511/4000] Training [28/39] Loss: 0.00614 +Epoch [3511/4000] Training [29/39] Loss: 0.12871 +Epoch [3511/4000] Training [30/39] Loss: 0.00506 +Epoch [3511/4000] Training [31/39] Loss: 0.00706 +Epoch [3511/4000] Training [32/39] Loss: 0.00353 +Epoch [3511/4000] Training [33/39] Loss: 0.00565 +Epoch [3511/4000] Training [34/39] Loss: 0.00475 +Epoch [3511/4000] Training [35/39] Loss: 0.12853 +Epoch [3511/4000] Training [36/39] Loss: 0.00650 +Epoch [3511/4000] Training [37/39] Loss: 0.00560 +Epoch [3511/4000] Training [38/39] Loss: 0.13058 +Epoch [3511/4000] Training [39/39] Loss: 0.12800 +Epoch [3511/4000] Training metric {'Train/mean dice_metric': 0.9958637356758118, 'Train/mean miou_metric': 0.9921795725822449, 'Train/mean f1': 0.9966011643409729, 'Train/mean precision': 0.9961413145065308, 'Train/mean recall': 0.9970616102218628, 'Train/mean hd95_metric': 1.0726124048233032} +Epoch [3511/4000] Validation [1/10] Loss: 0.73764 focal_loss 0.64839 dice_loss 0.08925 +Epoch [3511/4000] Validation [2/10] Loss: 0.48634 focal_loss 0.38789 dice_loss 0.09846 +Epoch [3511/4000] Validation [3/10] Loss: 0.38826 focal_loss 0.27685 dice_loss 0.11141 +Epoch [3511/4000] Validation [4/10] Loss: 0.88060 focal_loss 0.31324 dice_loss 0.56736 +Epoch [3511/4000] Validation [5/10] Loss: 2.97480 focal_loss 2.30228 dice_loss 0.67252 +Epoch [3511/4000] Validation [6/10] Loss: 1.29903 focal_loss 0.58142 dice_loss 0.71761 +Epoch [3511/4000] Validation [7/10] Loss: 1.14263 focal_loss 0.48649 dice_loss 0.65614 +Epoch [3511/4000] Validation [8/10] Loss: 2.36496 focal_loss 1.73451 dice_loss 0.63045 +Epoch [3511/4000] Validation [9/10] Loss: 1.40883 focal_loss 0.86640 dice_loss 0.54243 +Epoch [3511/4000] Validation [10/10] Loss: 1.74409 focal_loss 1.01917 dice_loss 0.72491 +Epoch [3511/4000] Validation metric {'Val/mean dice_metric': 0.9510546922683716, 'Val/mean miou_metric': 0.934840202331543, 'Val/mean f1': 0.9491852521896362, 'Val/mean precision': 0.946196973323822, 'Val/mean recall': 0.952192485332489, 'Val/mean hd95_metric': 10.71168041229248} +Cheakpoint... +Epoch [3511/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510546922683716, 'Val/mean miou_metric': 0.934840202331543, 'Val/mean f1': 0.9491852521896362, 'Val/mean precision': 0.946196973323822, 'Val/mean recall': 0.952192485332489, 'Val/mean hd95_metric': 10.71168041229248} +Epoch [3512/4000] Training [1/39] Loss: 0.00522 +Epoch [3512/4000] Training [2/39] Loss: 0.00596 +Epoch [3512/4000] Training [3/39] Loss: 0.00281 +Epoch [3512/4000] Training [4/39] Loss: 0.00425 +Epoch [3512/4000] Training [5/39] Loss: 0.00572 +Epoch [3512/4000] Training [6/39] Loss: 0.00377 +Epoch [3512/4000] Training [7/39] Loss: 0.00509 +Epoch [3512/4000] Training [8/39] Loss: 0.00571 +Epoch [3512/4000] Training [9/39] Loss: 0.00678 +Epoch [3512/4000] Training [10/39] Loss: 0.00431 +Epoch [3512/4000] Training [11/39] Loss: 0.00861 +Epoch [3512/4000] Training [12/39] Loss: 0.00406 +Epoch [3512/4000] Training [13/39] Loss: 0.12914 +Epoch [3512/4000] Training [14/39] Loss: 0.00489 +Epoch [3512/4000] Training [15/39] Loss: 0.12901 +Epoch [3512/4000] Training [16/39] Loss: 0.00742 +Epoch [3512/4000] Training [17/39] Loss: 0.00416 +Epoch [3512/4000] Training [18/39] Loss: 0.00577 +Epoch [3512/4000] Training [19/39] Loss: 0.00389 +Epoch [3512/4000] Training [20/39] Loss: 0.00502 +Epoch [3512/4000] Training [21/39] Loss: 0.00433 +Epoch [3512/4000] Training [22/39] Loss: 0.13091 +Epoch [3512/4000] Training [23/39] Loss: 0.00498 +Epoch [3512/4000] Training [24/39] Loss: 0.00759 +Epoch [3512/4000] Training [25/39] Loss: 0.00315 +Epoch [3512/4000] Training [26/39] Loss: 0.00471 +Epoch [3512/4000] Training [27/39] Loss: 0.12777 +Epoch [3512/4000] Training [28/39] Loss: 0.08463 +Epoch [3512/4000] Training [29/39] Loss: 0.00571 +Epoch [3512/4000] Training [30/39] Loss: 0.12809 +Epoch [3512/4000] Training [31/39] Loss: 0.00505 +Epoch [3512/4000] Training [32/39] Loss: 0.12752 +Epoch [3512/4000] Training [33/39] Loss: 0.13095 +Epoch [3512/4000] Training [34/39] Loss: 0.00417 +Epoch [3512/4000] Training [35/39] Loss: 0.00388 +Epoch [3512/4000] Training [36/39] Loss: 0.00470 +Epoch [3512/4000] Training [37/39] Loss: 0.00704 +Epoch [3512/4000] Training [38/39] Loss: 0.12942 +Epoch [3512/4000] Training [39/39] Loss: 0.00448 +Epoch [3512/4000] Training metric {'Train/mean dice_metric': 0.9952846169471741, 'Train/mean miou_metric': 0.9918507933616638, 'Train/mean f1': 0.9966141581535339, 'Train/mean precision': 0.9961696267127991, 'Train/mean recall': 0.9970589876174927, 'Train/mean hd95_metric': 1.0052270889282227} +Epoch [3512/4000] Validation [1/10] Loss: 0.73897 focal_loss 0.64656 dice_loss 0.09241 +Epoch [3512/4000] Validation [2/10] Loss: 0.49969 focal_loss 0.40093 dice_loss 0.09876 +Epoch [3512/4000] Validation [3/10] Loss: 0.35143 focal_loss 0.24234 dice_loss 0.10909 +Epoch [3512/4000] Validation [4/10] Loss: 0.90298 focal_loss 0.32753 dice_loss 0.57545 +Epoch [3512/4000] Validation [5/10] Loss: 2.92981 focal_loss 2.25770 dice_loss 0.67211 +Epoch [3512/4000] Validation [6/10] Loss: 1.34849 focal_loss 0.62442 dice_loss 0.72407 +Epoch [3512/4000] Validation [7/10] Loss: 1.18711 focal_loss 0.52905 dice_loss 0.65805 +Epoch [3512/4000] Validation [8/10] Loss: 2.02440 focal_loss 1.43058 dice_loss 0.59382 +Epoch [3512/4000] Validation [9/10] Loss: 1.47435 focal_loss 0.93012 dice_loss 0.54423 +Epoch [3512/4000] Validation [10/10] Loss: 1.85659 focal_loss 1.11861 dice_loss 0.73797 +Epoch [3512/4000] Validation metric {'Val/mean dice_metric': 0.9503849148750305, 'Val/mean miou_metric': 0.9343219995498657, 'Val/mean f1': 0.9478477239608765, 'Val/mean precision': 0.9400432705879211, 'Val/mean recall': 0.955782949924469, 'Val/mean hd95_metric': 10.863560676574707} +Cheakpoint... +Epoch [3512/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503849148750305, 'Val/mean miou_metric': 0.9343219995498657, 'Val/mean f1': 0.9478477239608765, 'Val/mean precision': 0.9400432705879211, 'Val/mean recall': 0.955782949924469, 'Val/mean hd95_metric': 10.863560676574707} +Epoch [3513/4000] Training [1/39] Loss: 0.00804 +Epoch [3513/4000] Training [2/39] Loss: 0.00366 +Epoch [3513/4000] Training [3/39] Loss: 0.00453 +Epoch [3513/4000] Training [4/39] Loss: 0.00565 +Epoch [3513/4000] Training [5/39] Loss: 0.13034 +Epoch [3513/4000] Training [6/39] Loss: 0.00302 +Epoch [3513/4000] Training [7/39] Loss: 0.00611 +Epoch [3513/4000] Training [8/39] Loss: 0.13023 +Epoch [3513/4000] Training [9/39] Loss: 0.00443 +Epoch [3513/4000] Training [10/39] Loss: 0.00686 +Epoch [3513/4000] Training [11/39] Loss: 0.00562 +Epoch [3513/4000] Training [12/39] Loss: 0.00619 +Epoch [3513/4000] Training [13/39] Loss: 0.12843 +Epoch [3513/4000] Training [14/39] Loss: 0.00436 +Epoch [3513/4000] Training [15/39] Loss: 0.00462 +Epoch [3513/4000] Training [16/39] Loss: 0.00412 +Epoch [3513/4000] Training [17/39] Loss: 0.00546 +Epoch [3513/4000] Training [18/39] Loss: 0.00720 +Epoch [3513/4000] Training [19/39] Loss: 0.13259 +Epoch [3513/4000] Training [20/39] Loss: 0.00405 +Epoch [3513/4000] Training [21/39] Loss: 0.00434 +Epoch [3513/4000] Training [22/39] Loss: 0.12954 +Epoch [3513/4000] Training [23/39] Loss: 0.00746 +Epoch [3513/4000] Training [24/39] Loss: 0.13004 +Epoch [3513/4000] Training [25/39] Loss: 0.00952 +Epoch [3513/4000] Training [26/39] Loss: 0.00600 +Epoch [3513/4000] Training [27/39] Loss: 0.00859 +Epoch [3513/4000] Training [28/39] Loss: 0.00641 +Epoch [3513/4000] Training [29/39] Loss: 0.00405 +Epoch [3513/4000] Training [30/39] Loss: 0.00607 +Epoch [3513/4000] Training [31/39] Loss: 0.00641 +Epoch [3513/4000] Training [32/39] Loss: 0.00461 +Epoch [3513/4000] Training [33/39] Loss: 0.00535 +Epoch [3513/4000] Training [34/39] Loss: 0.13309 +Epoch [3513/4000] Training [35/39] Loss: 0.04933 +Epoch [3513/4000] Training [36/39] Loss: 0.00390 +Epoch [3513/4000] Training [37/39] Loss: 0.00433 +Epoch [3513/4000] Training [38/39] Loss: 0.00704 +Epoch [3513/4000] Training [39/39] Loss: 0.00820 +Epoch [3513/4000] Training metric {'Train/mean dice_metric': 0.9958171844482422, 'Train/mean miou_metric': 0.992087721824646, 'Train/mean f1': 0.9965354800224304, 'Train/mean precision': 0.9960526823997498, 'Train/mean recall': 0.9970188140869141, 'Train/mean hd95_metric': 1.0049223899841309} +Epoch [3513/4000] Validation [1/10] Loss: 0.77452 focal_loss 0.68096 dice_loss 0.09356 +Epoch [3513/4000] Validation [2/10] Loss: 0.50748 focal_loss 0.40961 dice_loss 0.09788 +Epoch [3513/4000] Validation [3/10] Loss: 0.36067 focal_loss 0.25196 dice_loss 0.10871 +Epoch [3513/4000] Validation [4/10] Loss: 0.90225 focal_loss 0.33007 dice_loss 0.57218 +Epoch [3513/4000] Validation [5/10] Loss: 3.02946 focal_loss 2.35701 dice_loss 0.67246 +Epoch [3513/4000] Validation [6/10] Loss: 1.36244 focal_loss 0.64412 dice_loss 0.71832 +Epoch [3513/4000] Validation [7/10] Loss: 1.19646 focal_loss 0.53911 dice_loss 0.65735 +Epoch [3513/4000] Validation [8/10] Loss: 2.09196 focal_loss 1.49636 dice_loss 0.59560 +Epoch [3513/4000] Validation [9/10] Loss: 1.48388 focal_loss 0.93924 dice_loss 0.54464 +Epoch [3513/4000] Validation [10/10] Loss: 1.89513 focal_loss 1.15728 dice_loss 0.73785 +Epoch [3513/4000] Validation metric {'Val/mean dice_metric': 0.9509279131889343, 'Val/mean miou_metric': 0.9346212148666382, 'Val/mean f1': 0.9473989605903625, 'Val/mean precision': 0.9396718740463257, 'Val/mean recall': 0.9552542567253113, 'Val/mean hd95_metric': 10.801007270812988} +Cheakpoint... +Epoch [3513/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509279131889343, 'Val/mean miou_metric': 0.9346212148666382, 'Val/mean f1': 0.9473989605903625, 'Val/mean precision': 0.9396718740463257, 'Val/mean recall': 0.9552542567253113, 'Val/mean hd95_metric': 10.801007270812988} +Epoch [3514/4000] Training [1/39] Loss: 0.12901 +Epoch [3514/4000] Training [2/39] Loss: 0.12993 +Epoch [3514/4000] Training [3/39] Loss: 0.00603 +Epoch [3514/4000] Training [4/39] Loss: 0.00470 +Epoch [3514/4000] Training [5/39] Loss: 0.12870 +Epoch [3514/4000] Training [6/39] Loss: 0.13103 +Epoch [3514/4000] Training [7/39] Loss: 0.00425 +Epoch [3514/4000] Training [8/39] Loss: 0.00476 +Epoch [3514/4000] Training [9/39] Loss: 0.13282 +Epoch [3514/4000] Training [10/39] Loss: 0.00335 +Epoch [3514/4000] Training [11/39] Loss: 0.00717 +Epoch [3514/4000] Training [12/39] Loss: 0.00403 +Epoch [3514/4000] Training [13/39] Loss: 0.00301 +Epoch [3514/4000] Training [14/39] Loss: 0.00404 +Epoch [3514/4000] Training [15/39] Loss: 0.01019 +Epoch [3514/4000] Training [16/39] Loss: 0.00598 +Epoch [3514/4000] Training [17/39] Loss: 0.13054 +Epoch [3514/4000] Training [18/39] Loss: 0.00417 +Epoch [3514/4000] Training [19/39] Loss: 0.00576 +Epoch [3514/4000] Training [20/39] Loss: 0.12854 +Epoch [3514/4000] Training [21/39] Loss: 0.12797 +Epoch [3514/4000] Training [22/39] Loss: 0.00608 +Epoch [3514/4000] Training [23/39] Loss: 0.00368 +Epoch [3514/4000] Training [24/39] Loss: 0.00505 +Epoch [3514/4000] Training [25/39] Loss: 0.00388 +Epoch [3514/4000] Training [26/39] Loss: 0.00474 +Epoch [3514/4000] Training [27/39] Loss: 0.12854 +Epoch [3514/4000] Training [28/39] Loss: 0.00402 +Epoch [3514/4000] Training [29/39] Loss: 0.00729 +Epoch [3514/4000] Training [30/39] Loss: 0.13122 +Epoch [3514/4000] Training [31/39] Loss: 0.12864 +Epoch [3514/4000] Training [32/39] Loss: 0.01047 +Epoch [3514/4000] Training [33/39] Loss: 0.00492 +Epoch [3514/4000] Training [34/39] Loss: 0.00569 +Epoch [3514/4000] Training [35/39] Loss: 0.12842 +Epoch [3514/4000] Training [36/39] Loss: 0.00601 +Epoch [3514/4000] Training [37/39] Loss: 0.00801 +Epoch [3514/4000] Training [38/39] Loss: 0.00977 +Epoch [3514/4000] Training [39/39] Loss: 0.00534 +Epoch [3514/4000] Training metric {'Train/mean dice_metric': 0.9958893656730652, 'Train/mean miou_metric': 0.9922477602958679, 'Train/mean f1': 0.9966104626655579, 'Train/mean precision': 0.9961376190185547, 'Train/mean recall': 0.9970836639404297, 'Train/mean hd95_metric': 1.1146209239959717} +Epoch [3514/4000] Validation [1/10] Loss: 0.74466 focal_loss 0.65301 dice_loss 0.09164 +Epoch [3514/4000] Validation [2/10] Loss: 0.49162 focal_loss 0.39595 dice_loss 0.09567 +Epoch [3514/4000] Validation [3/10] Loss: 0.35996 focal_loss 0.25128 dice_loss 0.10867 +Epoch [3514/4000] Validation [4/10] Loss: 0.88596 focal_loss 0.31976 dice_loss 0.56620 +Epoch [3514/4000] Validation [5/10] Loss: 2.99961 focal_loss 2.32700 dice_loss 0.67261 +Epoch [3514/4000] Validation [6/10] Loss: 1.36105 focal_loss 0.64073 dice_loss 0.72032 +Epoch [3514/4000] Validation [7/10] Loss: 1.18352 focal_loss 0.52916 dice_loss 0.65436 +Epoch [3514/4000] Validation [8/10] Loss: 2.13573 focal_loss 1.53445 dice_loss 0.60127 +Epoch [3514/4000] Validation [9/10] Loss: 1.48624 focal_loss 0.94440 dice_loss 0.54183 +Epoch [3514/4000] Validation [10/10] Loss: 1.89253 focal_loss 1.15570 dice_loss 0.73683 +Epoch [3514/4000] Validation metric {'Val/mean dice_metric': 0.9510919451713562, 'Val/mean miou_metric': 0.934889018535614, 'Val/mean f1': 0.9484850764274597, 'Val/mean precision': 0.941781222820282, 'Val/mean recall': 0.9552850127220154, 'Val/mean hd95_metric': 10.834443092346191} +Cheakpoint... +Epoch [3514/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510919451713562, 'Val/mean miou_metric': 0.934889018535614, 'Val/mean f1': 0.9484850764274597, 'Val/mean precision': 0.941781222820282, 'Val/mean recall': 0.9552850127220154, 'Val/mean hd95_metric': 10.834443092346191} +Epoch [3515/4000] Training [1/39] Loss: 0.00418 +Epoch [3515/4000] Training [2/39] Loss: 0.00774 +Epoch [3515/4000] Training [3/39] Loss: 0.25472 +Epoch [3515/4000] Training [4/39] Loss: 0.00565 +Epoch [3515/4000] Training [5/39] Loss: 0.00502 +Epoch [3515/4000] Training [6/39] Loss: 0.13104 +Epoch [3515/4000] Training [7/39] Loss: 0.00481 +Epoch [3515/4000] Training [8/39] Loss: 0.00411 +Epoch [3515/4000] Training [9/39] Loss: 0.00447 +Epoch [3515/4000] Training [10/39] Loss: 0.00774 +Epoch [3515/4000] Training [11/39] Loss: 0.13066 +Epoch [3515/4000] Training [12/39] Loss: 0.12874 +Epoch [3515/4000] Training [13/39] Loss: 0.00577 +Epoch [3515/4000] Training [14/39] Loss: 0.12919 +Epoch [3515/4000] Training [15/39] Loss: 0.12766 +Epoch [3515/4000] Training [16/39] Loss: 0.00578 +Epoch [3515/4000] Training [17/39] Loss: 0.00498 +Epoch [3515/4000] Training [18/39] Loss: 0.13074 +Epoch [3515/4000] Training [19/39] Loss: 0.00495 +Epoch [3515/4000] Training [20/39] Loss: 0.00499 +Epoch [3515/4000] Training [21/39] Loss: 0.00393 +Epoch [3515/4000] Training [22/39] Loss: 0.00658 +Epoch [3515/4000] Training [23/39] Loss: 0.00445 +Epoch [3515/4000] Training [24/39] Loss: 0.00445 +Epoch [3515/4000] Training [25/39] Loss: 0.12730 +Epoch [3515/4000] Training [26/39] Loss: 0.00580 +Epoch [3515/4000] Training [27/39] Loss: 0.37965 +Epoch [3515/4000] Training [28/39] Loss: 0.00574 +Epoch [3515/4000] Training [29/39] Loss: 0.00609 +Epoch [3515/4000] Training [30/39] Loss: 0.25415 +Epoch [3515/4000] Training [31/39] Loss: 0.00934 +Epoch [3515/4000] Training [32/39] Loss: 0.00482 +Epoch [3515/4000] Training [33/39] Loss: 0.00351 +Epoch [3515/4000] Training [34/39] Loss: 0.00512 +Epoch [3515/4000] Training [35/39] Loss: 0.13148 +Epoch [3515/4000] Training [36/39] Loss: 0.00599 +Epoch [3515/4000] Training [37/39] Loss: 0.00378 +Epoch [3515/4000] Training [38/39] Loss: 0.01020 +Epoch [3515/4000] Training [39/39] Loss: 0.00521 +Epoch [3515/4000] Training metric {'Train/mean dice_metric': 0.9950588941574097, 'Train/mean miou_metric': 0.9914007186889648, 'Train/mean f1': 0.9965626001358032, 'Train/mean precision': 0.9960922598838806, 'Train/mean recall': 0.997033417224884, 'Train/mean hd95_metric': 1.1381874084472656} +Epoch [3515/4000] Validation [1/10] Loss: 0.73180 focal_loss 0.64139 dice_loss 0.09041 +Epoch [3515/4000] Validation [2/10] Loss: 0.49196 focal_loss 0.39521 dice_loss 0.09675 +Epoch [3515/4000] Validation [3/10] Loss: 0.37115 focal_loss 0.26184 dice_loss 0.10931 +Epoch [3515/4000] Validation [4/10] Loss: 0.87458 focal_loss 0.30979 dice_loss 0.56479 +Epoch [3515/4000] Validation [5/10] Loss: 3.03047 focal_loss 2.35786 dice_loss 0.67260 +Epoch [3515/4000] Validation [6/10] Loss: 1.33296 focal_loss 0.61416 dice_loss 0.71881 +Epoch [3515/4000] Validation [7/10] Loss: 1.17554 focal_loss 0.51926 dice_loss 0.65628 +Epoch [3515/4000] Validation [8/10] Loss: 2.18802 focal_loss 1.58237 dice_loss 0.60564 +Epoch [3515/4000] Validation [9/10] Loss: 1.45413 focal_loss 0.91004 dice_loss 0.54409 +Epoch [3515/4000] Validation [10/10] Loss: 1.84647 focal_loss 1.11202 dice_loss 0.73445 +Epoch [3515/4000] Validation metric {'Val/mean dice_metric': 0.9505624771118164, 'Val/mean miou_metric': 0.9343658685684204, 'Val/mean f1': 0.9486655592918396, 'Val/mean precision': 0.9427651166915894, 'Val/mean recall': 0.9546403288841248, 'Val/mean hd95_metric': 10.868743896484375} +Cheakpoint... +Epoch [3515/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505624771118164, 'Val/mean miou_metric': 0.9343658685684204, 'Val/mean f1': 0.9486655592918396, 'Val/mean precision': 0.9427651166915894, 'Val/mean recall': 0.9546403288841248, 'Val/mean hd95_metric': 10.868743896484375} +Epoch [3516/4000] Training [1/39] Loss: 0.00371 +Epoch [3516/4000] Training [2/39] Loss: 0.00530 +Epoch [3516/4000] Training [3/39] Loss: 0.04311 +Epoch [3516/4000] Training [4/39] Loss: 0.25344 +Epoch [3516/4000] Training [5/39] Loss: 0.00567 +Epoch [3516/4000] Training [6/39] Loss: 0.00565 +Epoch [3516/4000] Training [7/39] Loss: 0.00702 +Epoch [3516/4000] Training [8/39] Loss: 0.12901 +Epoch [3516/4000] Training [9/39] Loss: 0.12903 +Epoch [3516/4000] Training [10/39] Loss: 0.00429 +Epoch [3516/4000] Training [11/39] Loss: 0.00608 +Epoch [3516/4000] Training [12/39] Loss: 0.00552 +Epoch [3516/4000] Training [13/39] Loss: 0.00685 +Epoch [3516/4000] Training [14/39] Loss: 0.00840 +Epoch [3516/4000] Training [15/39] Loss: 0.00502 +Epoch [3516/4000] Training [16/39] Loss: 0.12825 +Epoch [3516/4000] Training [17/39] Loss: 0.00384 +Epoch [3516/4000] Training [18/39] Loss: 0.00378 +Epoch [3516/4000] Training [19/39] Loss: 0.00411 +Epoch [3516/4000] Training [20/39] Loss: 0.13147 +Epoch [3516/4000] Training [21/39] Loss: 0.00780 +Epoch [3516/4000] Training [22/39] Loss: 0.00348 +Epoch [3516/4000] Training [23/39] Loss: 0.00503 +Epoch [3516/4000] Training [24/39] Loss: 0.00332 +Epoch [3516/4000] Training [25/39] Loss: 0.00945 +Epoch [3516/4000] Training [26/39] Loss: 0.00850 +Epoch [3516/4000] Training [27/39] Loss: 0.00441 +Epoch [3516/4000] Training [28/39] Loss: 0.00566 +Epoch [3516/4000] Training [29/39] Loss: 0.38178 +Epoch [3516/4000] Training [30/39] Loss: 0.00598 +Epoch [3516/4000] Training [31/39] Loss: 0.12985 +Epoch [3516/4000] Training [32/39] Loss: 0.00505 +Epoch [3516/4000] Training [33/39] Loss: 0.00507 +Epoch [3516/4000] Training [34/39] Loss: 0.00532 +Epoch [3516/4000] Training [35/39] Loss: 0.00383 +Epoch [3516/4000] Training [36/39] Loss: 0.00417 +Epoch [3516/4000] Training [37/39] Loss: 0.12868 +Epoch [3516/4000] Training [38/39] Loss: 0.12776 +Epoch [3516/4000] Training [39/39] Loss: 0.00547 +Epoch [3516/4000] Training metric {'Train/mean dice_metric': 0.9959217309951782, 'Train/mean miou_metric': 0.9923053979873657, 'Train/mean f1': 0.9965789318084717, 'Train/mean precision': 0.9960858225822449, 'Train/mean recall': 0.9970726370811462, 'Train/mean hd95_metric': 1.0016196966171265} +Epoch [3516/4000] Validation [1/10] Loss: 0.72328 focal_loss 0.63473 dice_loss 0.08854 +Epoch [3516/4000] Validation [2/10] Loss: 0.50754 focal_loss 0.40708 dice_loss 0.10047 +Epoch [3516/4000] Validation [3/10] Loss: 0.38247 focal_loss 0.27240 dice_loss 0.11006 +Epoch [3516/4000] Validation [4/10] Loss: 0.87411 focal_loss 0.31001 dice_loss 0.56410 +Epoch [3516/4000] Validation [5/10] Loss: 3.04040 focal_loss 2.36708 dice_loss 0.67332 +Epoch [3516/4000] Validation [6/10] Loss: 1.33510 focal_loss 0.61499 dice_loss 0.72011 +Epoch [3516/4000] Validation [7/10] Loss: 1.16939 focal_loss 0.51628 dice_loss 0.65312 +Epoch [3516/4000] Validation [8/10] Loss: 2.24636 focal_loss 1.62948 dice_loss 0.61687 +Epoch [3516/4000] Validation [9/10] Loss: 1.45264 focal_loss 0.91127 dice_loss 0.54137 +Epoch [3516/4000] Validation [10/10] Loss: 1.83838 focal_loss 1.10508 dice_loss 0.73329 +Epoch [3516/4000] Validation metric {'Val/mean dice_metric': 0.9511400461196899, 'Val/mean miou_metric': 0.9349786043167114, 'Val/mean f1': 0.9487295746803284, 'Val/mean precision': 0.943880021572113, 'Val/mean recall': 0.9536292552947998, 'Val/mean hd95_metric': 10.732942581176758} +Cheakpoint... +Epoch [3516/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511400461196899, 'Val/mean miou_metric': 0.9349786043167114, 'Val/mean f1': 0.9487295746803284, 'Val/mean precision': 0.943880021572113, 'Val/mean recall': 0.9536292552947998, 'Val/mean hd95_metric': 10.732942581176758} +Epoch [3517/4000] Training [1/39] Loss: 0.00659 +Epoch [3517/4000] Training [2/39] Loss: 0.00836 +Epoch [3517/4000] Training [3/39] Loss: 0.00620 +Epoch [3517/4000] Training [4/39] Loss: 0.00478 +Epoch [3517/4000] Training [5/39] Loss: 0.00417 +Epoch [3517/4000] Training [6/39] Loss: 0.00330 +Epoch [3517/4000] Training [7/39] Loss: 0.00729 +Epoch [3517/4000] Training [8/39] Loss: 0.00502 +Epoch [3517/4000] Training [9/39] Loss: 0.00560 +Epoch [3517/4000] Training [10/39] Loss: 0.00424 +Epoch [3517/4000] Training [11/39] Loss: 0.00447 +Epoch [3517/4000] Training [12/39] Loss: 0.00757 +Epoch [3517/4000] Training [13/39] Loss: 0.00594 +Epoch [3517/4000] Training [14/39] Loss: 0.13041 +Epoch [3517/4000] Training [15/39] Loss: 0.12921 +Epoch [3517/4000] Training [16/39] Loss: 0.00620 +Epoch [3517/4000] Training [17/39] Loss: 0.00451 +Epoch [3517/4000] Training [18/39] Loss: 0.00498 +Epoch [3517/4000] Training [19/39] Loss: 0.00495 +Epoch [3517/4000] Training [20/39] Loss: 0.00432 +Epoch [3517/4000] Training [21/39] Loss: 0.25444 +Epoch [3517/4000] Training [22/39] Loss: 0.13169 +Epoch [3517/4000] Training [23/39] Loss: 0.00537 +Epoch [3517/4000] Training [24/39] Loss: 0.00597 +Epoch [3517/4000] Training [25/39] Loss: 0.00754 +Epoch [3517/4000] Training [26/39] Loss: 0.00594 +Epoch [3517/4000] Training [27/39] Loss: 0.00681 +Epoch [3517/4000] Training [28/39] Loss: 0.00406 +Epoch [3517/4000] Training [29/39] Loss: 0.00550 +Epoch [3517/4000] Training [30/39] Loss: 0.00695 +Epoch [3517/4000] Training [31/39] Loss: 0.00562 +Epoch [3517/4000] Training [32/39] Loss: 0.00714 +Epoch [3517/4000] Training [33/39] Loss: 0.13063 +Epoch [3517/4000] Training [34/39] Loss: 0.00685 +Epoch [3517/4000] Training [35/39] Loss: 0.12872 +Epoch [3517/4000] Training [36/39] Loss: 0.04654 +Epoch [3517/4000] Training [37/39] Loss: 0.00528 +Epoch [3517/4000] Training [38/39] Loss: 0.00532 +Epoch [3517/4000] Training [39/39] Loss: 0.00316 +Epoch [3517/4000] Training metric {'Train/mean dice_metric': 0.9959005117416382, 'Train/mean miou_metric': 0.9922566413879395, 'Train/mean f1': 0.9966164231300354, 'Train/mean precision': 0.9962013959884644, 'Train/mean recall': 0.9970316290855408, 'Train/mean hd95_metric': 0.9862394332885742} +Epoch [3517/4000] Validation [1/10] Loss: 0.69177 focal_loss 0.60379 dice_loss 0.08798 +Epoch [3517/4000] Validation [2/10] Loss: 0.50276 focal_loss 0.40180 dice_loss 0.10097 +Epoch [3517/4000] Validation [3/10] Loss: 0.36343 focal_loss 0.25445 dice_loss 0.10899 +Epoch [3517/4000] Validation [4/10] Loss: 0.85984 focal_loss 0.29596 dice_loss 0.56388 +Epoch [3517/4000] Validation [5/10] Loss: 2.95490 focal_loss 2.28177 dice_loss 0.67313 +Epoch [3517/4000] Validation [6/10] Loss: 1.33146 focal_loss 0.61010 dice_loss 0.72136 +Epoch [3517/4000] Validation [7/10] Loss: 1.15492 focal_loss 0.50347 dice_loss 0.65145 +Epoch [3517/4000] Validation [8/10] Loss: 2.22675 focal_loss 1.60880 dice_loss 0.61794 +Epoch [3517/4000] Validation [9/10] Loss: 1.40703 focal_loss 0.86161 dice_loss 0.54542 +Epoch [3517/4000] Validation [10/10] Loss: 1.81073 focal_loss 1.07634 dice_loss 0.73439 +Epoch [3517/4000] Validation metric {'Val/mean dice_metric': 0.9511827826499939, 'Val/mean miou_metric': 0.9350496530532837, 'Val/mean f1': 0.9486320614814758, 'Val/mean precision': 0.9436920881271362, 'Val/mean recall': 0.9536240696907043, 'Val/mean hd95_metric': 10.635591506958008} +Cheakpoint... +Epoch [3517/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511827826499939, 'Val/mean miou_metric': 0.9350496530532837, 'Val/mean f1': 0.9486320614814758, 'Val/mean precision': 0.9436920881271362, 'Val/mean recall': 0.9536240696907043, 'Val/mean hd95_metric': 10.635591506958008} +Epoch [3518/4000] Training [1/39] Loss: 0.00599 +Epoch [3518/4000] Training [2/39] Loss: 0.12898 +Epoch [3518/4000] Training [3/39] Loss: 0.12849 +Epoch [3518/4000] Training [4/39] Loss: 0.00531 +Epoch [3518/4000] Training [5/39] Loss: 0.12806 +Epoch [3518/4000] Training [6/39] Loss: 0.00629 +Epoch [3518/4000] Training [7/39] Loss: 0.12782 +Epoch [3518/4000] Training [8/39] Loss: 0.00774 +Epoch [3518/4000] Training [9/39] Loss: 0.00792 +Epoch [3518/4000] Training [10/39] Loss: 0.00449 +Epoch [3518/4000] Training [11/39] Loss: 0.00457 +Epoch [3518/4000] Training [12/39] Loss: 0.00532 +Epoch [3518/4000] Training [13/39] Loss: 0.00317 +Epoch [3518/4000] Training [14/39] Loss: 0.00358 +Epoch [3518/4000] Training [15/39] Loss: 0.12995 +Epoch [3518/4000] Training [16/39] Loss: 0.00557 +Epoch [3518/4000] Training [17/39] Loss: 0.00489 +Epoch [3518/4000] Training [18/39] Loss: 0.12915 +Epoch [3518/4000] Training [19/39] Loss: 0.12873 +Epoch [3518/4000] Training [20/39] Loss: 0.00482 +Epoch [3518/4000] Training [21/39] Loss: 0.00351 +Epoch [3518/4000] Training [22/39] Loss: 0.00376 +Epoch [3518/4000] Training [23/39] Loss: 0.00831 +Epoch [3518/4000] Training [24/39] Loss: 0.12958 +Epoch [3518/4000] Training [25/39] Loss: 0.00491 +Epoch [3518/4000] Training [26/39] Loss: 0.12883 +Epoch [3518/4000] Training [27/39] Loss: 0.12890 +Epoch [3518/4000] Training [28/39] Loss: 0.00658 +Epoch [3518/4000] Training [29/39] Loss: 0.00359 +Epoch [3518/4000] Training [30/39] Loss: 0.00464 +Epoch [3518/4000] Training [31/39] Loss: 0.00509 +Epoch [3518/4000] Training [32/39] Loss: 0.13113 +Epoch [3518/4000] Training [33/39] Loss: 0.00588 +Epoch [3518/4000] Training [34/39] Loss: 0.00485 +Epoch [3518/4000] Training [35/39] Loss: 0.00598 +Epoch [3518/4000] Training [36/39] Loss: 0.00441 +Epoch [3518/4000] Training [37/39] Loss: 0.00408 +Epoch [3518/4000] Training [38/39] Loss: 0.00479 +Epoch [3518/4000] Training [39/39] Loss: 0.00449 +Epoch [3518/4000] Training metric {'Train/mean dice_metric': 0.9961176514625549, 'Train/mean miou_metric': 0.9926808476448059, 'Train/mean f1': 0.9967290163040161, 'Train/mean precision': 0.9962913990020752, 'Train/mean recall': 0.9971670508384705, 'Train/mean hd95_metric': 1.115507960319519} +Epoch [3518/4000] Validation [1/10] Loss: 0.68713 focal_loss 0.60068 dice_loss 0.08645 +Epoch [3518/4000] Validation [2/10] Loss: 0.49271 focal_loss 0.39587 dice_loss 0.09683 +Epoch [3518/4000] Validation [3/10] Loss: 0.37401 focal_loss 0.26403 dice_loss 0.10998 +Epoch [3518/4000] Validation [4/10] Loss: 0.85935 focal_loss 0.29591 dice_loss 0.56344 +Epoch [3518/4000] Validation [5/10] Loss: 3.01223 focal_loss 2.33905 dice_loss 0.67318 +Epoch [3518/4000] Validation [6/10] Loss: 1.33722 focal_loss 0.61597 dice_loss 0.72125 +Epoch [3518/4000] Validation [7/10] Loss: 1.15786 focal_loss 0.50289 dice_loss 0.65496 +Epoch [3518/4000] Validation [8/10] Loss: 2.26940 focal_loss 1.65125 dice_loss 0.61815 +Epoch [3518/4000] Validation [9/10] Loss: 1.40500 focal_loss 0.85915 dice_loss 0.54586 +Epoch [3518/4000] Validation [10/10] Loss: 1.85279 focal_loss 1.11568 dice_loss 0.73710 +Epoch [3518/4000] Validation metric {'Val/mean dice_metric': 0.9513832926750183, 'Val/mean miou_metric': 0.9354167580604553, 'Val/mean f1': 0.9488214254379272, 'Val/mean precision': 0.9437394142150879, 'Val/mean recall': 0.9539585113525391, 'Val/mean hd95_metric': 10.76272964477539} +Cheakpoint... +Epoch [3518/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513832926750183, 'Val/mean miou_metric': 0.9354167580604553, 'Val/mean f1': 0.9488214254379272, 'Val/mean precision': 0.9437394142150879, 'Val/mean recall': 0.9539585113525391, 'Val/mean hd95_metric': 10.76272964477539} +Epoch [3519/4000] Training [1/39] Loss: 0.00518 +Epoch [3519/4000] Training [2/39] Loss: 0.13158 +Epoch [3519/4000] Training [3/39] Loss: 0.00434 +Epoch [3519/4000] Training [4/39] Loss: 0.00534 +Epoch [3519/4000] Training [5/39] Loss: 0.12824 +Epoch [3519/4000] Training [6/39] Loss: 0.12893 +Epoch [3519/4000] Training [7/39] Loss: 0.12964 +Epoch [3519/4000] Training [8/39] Loss: 0.37733 +Epoch [3519/4000] Training [9/39] Loss: 0.00457 +Epoch [3519/4000] Training [10/39] Loss: 0.00482 +Epoch [3519/4000] Training [11/39] Loss: 0.00478 +Epoch [3519/4000] Training [12/39] Loss: 0.00366 +Epoch [3519/4000] Training [13/39] Loss: 0.13297 +Epoch [3519/4000] Training [14/39] Loss: 0.00606 +Epoch [3519/4000] Training [15/39] Loss: 0.00686 +Epoch [3519/4000] Training [16/39] Loss: 0.00641 +Epoch [3519/4000] Training [17/39] Loss: 0.12840 +Epoch [3519/4000] Training [18/39] Loss: 0.00677 +Epoch [3519/4000] Training [19/39] Loss: 0.12976 +Epoch [3519/4000] Training [20/39] Loss: 0.00503 +Epoch [3519/4000] Training [21/39] Loss: 0.25314 +Epoch [3519/4000] Training [22/39] Loss: 0.00427 +Epoch [3519/4000] Training [23/39] Loss: 0.00589 +Epoch [3519/4000] Training [24/39] Loss: 0.00461 +Epoch [3519/4000] Training [25/39] Loss: 0.00308 +Epoch [3519/4000] Training [26/39] Loss: 0.00457 +Epoch [3519/4000] Training [27/39] Loss: 0.12974 +Epoch [3519/4000] Training [28/39] Loss: 0.00653 +Epoch [3519/4000] Training [29/39] Loss: 0.00771 +Epoch [3519/4000] Training [30/39] Loss: 0.13147 +Epoch [3519/4000] Training [31/39] Loss: 0.00391 +Epoch [3519/4000] Training [32/39] Loss: 0.13102 +Epoch [3519/4000] Training [33/39] Loss: 0.12949 +Epoch [3519/4000] Training [34/39] Loss: 0.09027 +Epoch [3519/4000] Training [35/39] Loss: 0.12897 +Epoch [3519/4000] Training [36/39] Loss: 0.00472 +Epoch [3519/4000] Training [37/39] Loss: 0.00577 +Epoch [3519/4000] Training [38/39] Loss: 0.00396 +Epoch [3519/4000] Training [39/39] Loss: 0.12865 +Epoch [3519/4000] Training metric {'Train/mean dice_metric': 0.9961138367652893, 'Train/mean miou_metric': 0.9926813840866089, 'Train/mean f1': 0.9969002604484558, 'Train/mean precision': 0.9963760375976562, 'Train/mean recall': 0.9974250793457031, 'Train/mean hd95_metric': 0.9944841265678406} +Epoch [3519/4000] Validation [1/10] Loss: 0.70796 focal_loss 0.61908 dice_loss 0.08888 +Epoch [3519/4000] Validation [2/10] Loss: 0.52562 focal_loss 0.42263 dice_loss 0.10300 +Epoch [3519/4000] Validation [3/10] Loss: 0.37486 focal_loss 0.26478 dice_loss 0.11009 +Epoch [3519/4000] Validation [4/10] Loss: 0.87687 focal_loss 0.31168 dice_loss 0.56519 +Epoch [3519/4000] Validation [5/10] Loss: 3.01239 focal_loss 2.33961 dice_loss 0.67277 +Epoch [3519/4000] Validation [6/10] Loss: 1.36089 focal_loss 0.63740 dice_loss 0.72350 +Epoch [3519/4000] Validation [7/10] Loss: 1.15982 focal_loss 0.50563 dice_loss 0.65419 +Epoch [3519/4000] Validation [8/10] Loss: 2.31379 focal_loss 1.68867 dice_loss 0.62512 +Epoch [3519/4000] Validation [9/10] Loss: 1.42451 focal_loss 0.87853 dice_loss 0.54597 +Epoch [3519/4000] Validation [10/10] Loss: 1.86068 focal_loss 1.12540 dice_loss 0.73528 +Epoch [3519/4000] Validation metric {'Val/mean dice_metric': 0.9513505101203918, 'Val/mean miou_metric': 0.9353551864624023, 'Val/mean f1': 0.9485511183738708, 'Val/mean precision': 0.9434938430786133, 'Val/mean recall': 0.9536629319190979, 'Val/mean hd95_metric': 10.634178161621094} +Cheakpoint... +Epoch [3519/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513505101203918, 'Val/mean miou_metric': 0.9353551864624023, 'Val/mean f1': 0.9485511183738708, 'Val/mean precision': 0.9434938430786133, 'Val/mean recall': 0.9536629319190979, 'Val/mean hd95_metric': 10.634178161621094} +Epoch [3520/4000] Training [1/39] Loss: 0.00791 +Epoch [3520/4000] Training [2/39] Loss: 0.00509 +Epoch [3520/4000] Training [3/39] Loss: 0.00651 +Epoch [3520/4000] Training [4/39] Loss: 0.00556 +Epoch [3520/4000] Training [5/39] Loss: 0.00374 +Epoch [3520/4000] Training [6/39] Loss: 0.00324 +Epoch [3520/4000] Training [7/39] Loss: 0.01019 +Epoch [3520/4000] Training [8/39] Loss: 0.13123 +Epoch [3520/4000] Training [9/39] Loss: 0.00768 +Epoch [3520/4000] Training [10/39] Loss: 0.12934 +Epoch [3520/4000] Training [11/39] Loss: 0.12817 +Epoch [3520/4000] Training [12/39] Loss: 0.00648 +Epoch [3520/4000] Training [13/39] Loss: 0.00586 +Epoch [3520/4000] Training [14/39] Loss: 0.00451 +Epoch [3520/4000] Training [15/39] Loss: 0.00595 +Epoch [3520/4000] Training [16/39] Loss: 0.00497 +Epoch [3520/4000] Training [17/39] Loss: 0.12910 +Epoch [3520/4000] Training [18/39] Loss: 0.12953 +Epoch [3520/4000] Training [19/39] Loss: 0.00362 +Epoch [3520/4000] Training [20/39] Loss: 0.00646 +Epoch [3520/4000] Training [21/39] Loss: 0.12918 +Epoch [3520/4000] Training [22/39] Loss: 0.00639 +Epoch [3520/4000] Training [23/39] Loss: 0.00467 +Epoch [3520/4000] Training [24/39] Loss: 0.00727 +Epoch [3520/4000] Training [25/39] Loss: 0.00390 +Epoch [3520/4000] Training [26/39] Loss: 0.12763 +Epoch [3520/4000] Training [27/39] Loss: 0.00406 +Epoch [3520/4000] Training [28/39] Loss: 0.00306 +Epoch [3520/4000] Training [29/39] Loss: 0.00850 +Epoch [3520/4000] Training [30/39] Loss: 0.13282 +Epoch [3520/4000] Training [31/39] Loss: 0.00538 +Epoch [3520/4000] Training [32/39] Loss: 0.00365 +Epoch [3520/4000] Training [33/39] Loss: 0.12954 +Epoch [3520/4000] Training [34/39] Loss: 0.00680 +Epoch [3520/4000] Training [35/39] Loss: 0.00552 +Epoch [3520/4000] Training [36/39] Loss: 0.00392 +Epoch [3520/4000] Training [37/39] Loss: 0.00455 +Epoch [3520/4000] Training [38/39] Loss: 0.00578 +Epoch [3520/4000] Training [39/39] Loss: 0.00485 +Epoch [3520/4000] Training metric {'Train/mean dice_metric': 0.9952250719070435, 'Train/mean miou_metric': 0.9917288422584534, 'Train/mean f1': 0.9966070652008057, 'Train/mean precision': 0.9961997866630554, 'Train/mean recall': 0.9970147609710693, 'Train/mean hd95_metric': 0.9740386009216309} +Epoch [3520/4000] Validation [1/10] Loss: 0.71107 focal_loss 0.62183 dice_loss 0.08924 +Epoch [3520/4000] Validation [2/10] Loss: 0.49231 focal_loss 0.39375 dice_loss 0.09856 +Epoch [3520/4000] Validation [3/10] Loss: 0.36385 focal_loss 0.25444 dice_loss 0.10941 +Epoch [3520/4000] Validation [4/10] Loss: 0.87050 focal_loss 0.30697 dice_loss 0.56352 +Epoch [3520/4000] Validation [5/10] Loss: 2.97867 focal_loss 2.30563 dice_loss 0.67303 +Epoch [3520/4000] Validation [6/10] Loss: 1.33382 focal_loss 0.61317 dice_loss 0.72065 +Epoch [3520/4000] Validation [7/10] Loss: 1.16322 focal_loss 0.50638 dice_loss 0.65684 +Epoch [3520/4000] Validation [8/10] Loss: 2.16968 focal_loss 1.56315 dice_loss 0.60653 +Epoch [3520/4000] Validation [9/10] Loss: 1.40040 focal_loss 0.85570 dice_loss 0.54469 +Epoch [3520/4000] Validation [10/10] Loss: 1.84306 focal_loss 1.10866 dice_loss 0.73440 +Epoch [3520/4000] Validation metric {'Val/mean dice_metric': 0.9508010745048523, 'Val/mean miou_metric': 0.9348239898681641, 'Val/mean f1': 0.9485802054405212, 'Val/mean precision': 0.942537248134613, 'Val/mean recall': 0.9547010064125061, 'Val/mean hd95_metric': 10.607084274291992} +Cheakpoint... +Epoch [3520/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508010745048523, 'Val/mean miou_metric': 0.9348239898681641, 'Val/mean f1': 0.9485802054405212, 'Val/mean precision': 0.942537248134613, 'Val/mean recall': 0.9547010064125061, 'Val/mean hd95_metric': 10.607084274291992} +Epoch [3521/4000] Training [1/39] Loss: 0.00308 +Epoch [3521/4000] Training [2/39] Loss: 0.12895 +Epoch [3521/4000] Training [3/39] Loss: 0.12851 +Epoch [3521/4000] Training [4/39] Loss: 0.00572 +Epoch [3521/4000] Training [5/39] Loss: 0.00467 +Epoch [3521/4000] Training [6/39] Loss: 0.12987 +Epoch [3521/4000] Training [7/39] Loss: 0.00499 +Epoch [3521/4000] Training [8/39] Loss: 0.00399 +Epoch [3521/4000] Training [9/39] Loss: 0.00891 +Epoch [3521/4000] Training [10/39] Loss: 0.00511 +Epoch [3521/4000] Training [11/39] Loss: 0.00514 +Epoch [3521/4000] Training [12/39] Loss: 0.00491 +Epoch [3521/4000] Training [13/39] Loss: 0.00489 +Epoch [3521/4000] Training [14/39] Loss: 0.00510 +Epoch [3521/4000] Training [15/39] Loss: 0.00427 +Epoch [3521/4000] Training [16/39] Loss: 0.00496 +Epoch [3521/4000] Training [17/39] Loss: 0.21257 +Epoch [3521/4000] Training [18/39] Loss: 0.00780 +Epoch [3521/4000] Training [19/39] Loss: 0.00484 +Epoch [3521/4000] Training [20/39] Loss: 0.13199 +Epoch [3521/4000] Training [21/39] Loss: 0.00401 +Epoch [3521/4000] Training [22/39] Loss: 0.00629 +Epoch [3521/4000] Training [23/39] Loss: 0.00709 +Epoch [3521/4000] Training [24/39] Loss: 0.00346 +Epoch [3521/4000] Training [25/39] Loss: 0.12910 +Epoch [3521/4000] Training [26/39] Loss: 0.00415 +Epoch [3521/4000] Training [27/39] Loss: 0.00377 +Epoch [3521/4000] Training [28/39] Loss: 0.00541 +Epoch [3521/4000] Training [29/39] Loss: 0.12939 +Epoch [3521/4000] Training [30/39] Loss: 0.12830 +Epoch [3521/4000] Training [31/39] Loss: 0.25283 +Epoch [3521/4000] Training [32/39] Loss: 0.00474 +Epoch [3521/4000] Training [33/39] Loss: 0.00441 +Epoch [3521/4000] Training [34/39] Loss: 0.13177 +Epoch [3521/4000] Training [35/39] Loss: 0.00332 +Epoch [3521/4000] Training [36/39] Loss: 0.12943 +Epoch [3521/4000] Training [37/39] Loss: 0.00578 +Epoch [3521/4000] Training [38/39] Loss: 0.00419 +Epoch [3521/4000] Training [39/39] Loss: 0.00540 +Epoch [3521/4000] Training metric {'Train/mean dice_metric': 0.996040403842926, 'Train/mean miou_metric': 0.9925353527069092, 'Train/mean f1': 0.9967517256736755, 'Train/mean precision': 0.9962794780731201, 'Train/mean recall': 0.9972244501113892, 'Train/mean hd95_metric': 1.010925054550171} +Epoch [3521/4000] Validation [1/10] Loss: 0.71228 focal_loss 0.62278 dice_loss 0.08950 +Epoch [3521/4000] Validation [2/10] Loss: 0.48950 focal_loss 0.39168 dice_loss 0.09782 +Epoch [3521/4000] Validation [3/10] Loss: 0.36620 focal_loss 0.25699 dice_loss 0.10921 +Epoch [3521/4000] Validation [4/10] Loss: 0.87168 focal_loss 0.30783 dice_loss 0.56384 +Epoch [3521/4000] Validation [5/10] Loss: 2.98696 focal_loss 2.31381 dice_loss 0.67315 +Epoch [3521/4000] Validation [6/10] Loss: 1.32925 focal_loss 0.61129 dice_loss 0.71796 +Epoch [3521/4000] Validation [7/10] Loss: 1.17547 focal_loss 0.51968 dice_loss 0.65579 +Epoch [3521/4000] Validation [8/10] Loss: 2.22293 focal_loss 1.60890 dice_loss 0.61403 +Epoch [3521/4000] Validation [9/10] Loss: 1.39993 focal_loss 0.85666 dice_loss 0.54328 +Epoch [3521/4000] Validation [10/10] Loss: 1.84335 focal_loss 1.10750 dice_loss 0.73585 +Epoch [3521/4000] Validation metric {'Val/mean dice_metric': 0.9513656497001648, 'Val/mean miou_metric': 0.9353369474411011, 'Val/mean f1': 0.9488784670829773, 'Val/mean precision': 0.9430251717567444, 'Val/mean recall': 0.9548048973083496, 'Val/mean hd95_metric': 10.685494422912598} +Cheakpoint... +Epoch [3521/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513656497001648, 'Val/mean miou_metric': 0.9353369474411011, 'Val/mean f1': 0.9488784670829773, 'Val/mean precision': 0.9430251717567444, 'Val/mean recall': 0.9548048973083496, 'Val/mean hd95_metric': 10.685494422912598} +Epoch [3522/4000] Training [1/39] Loss: 0.00507 +Epoch [3522/4000] Training [2/39] Loss: 0.00605 +Epoch [3522/4000] Training [3/39] Loss: 0.00528 +Epoch [3522/4000] Training [4/39] Loss: 0.00355 +Epoch [3522/4000] Training [5/39] Loss: 0.13099 +Epoch [3522/4000] Training [6/39] Loss: 0.00632 +Epoch [3522/4000] Training [7/39] Loss: 0.00372 +Epoch [3522/4000] Training [8/39] Loss: 0.25227 +Epoch [3522/4000] Training [9/39] Loss: 0.00438 +Epoch [3522/4000] Training [10/39] Loss: 0.00592 +Epoch [3522/4000] Training [11/39] Loss: 0.12972 +Epoch [3522/4000] Training [12/39] Loss: 0.00451 +Epoch [3522/4000] Training [13/39] Loss: 0.00418 +Epoch [3522/4000] Training [14/39] Loss: 0.12961 +Epoch [3522/4000] Training [15/39] Loss: 0.00427 +Epoch [3522/4000] Training [16/39] Loss: 0.00598 +Epoch [3522/4000] Training [17/39] Loss: 0.25344 +Epoch [3522/4000] Training [18/39] Loss: 0.00413 +Epoch [3522/4000] Training [19/39] Loss: 0.00582 +Epoch [3522/4000] Training [20/39] Loss: 0.00780 +Epoch [3522/4000] Training [21/39] Loss: 0.00446 +Epoch [3522/4000] Training [22/39] Loss: 0.00536 +Epoch [3522/4000] Training [23/39] Loss: 0.00770 +Epoch [3522/4000] Training [24/39] Loss: 0.00331 +Epoch [3522/4000] Training [25/39] Loss: 0.00457 +Epoch [3522/4000] Training [26/39] Loss: 0.12816 +Epoch [3522/4000] Training [27/39] Loss: 0.00334 +Epoch [3522/4000] Training [28/39] Loss: 0.08609 +Epoch [3522/4000] Training [29/39] Loss: 0.00425 +Epoch [3522/4000] Training [30/39] Loss: 0.00454 +Epoch [3522/4000] Training [31/39] Loss: 0.00469 +Epoch [3522/4000] Training [32/39] Loss: 0.00396 +Epoch [3522/4000] Training [33/39] Loss: 0.00426 +Epoch [3522/4000] Training [34/39] Loss: 0.00442 +Epoch [3522/4000] Training [35/39] Loss: 0.00573 +Epoch [3522/4000] Training [36/39] Loss: 0.00512 +Epoch [3522/4000] Training [37/39] Loss: 0.00486 +Epoch [3522/4000] Training [38/39] Loss: 0.00736 +Epoch [3522/4000] Training [39/39] Loss: 0.00388 +Epoch [3522/4000] Training metric {'Train/mean dice_metric': 0.99616938829422, 'Train/mean miou_metric': 0.9927854537963867, 'Train/mean f1': 0.9967859983444214, 'Train/mean precision': 0.9963943958282471, 'Train/mean recall': 0.9971780776977539, 'Train/mean hd95_metric': 0.9917661547660828} +Epoch [3522/4000] Validation [1/10] Loss: 0.71002 focal_loss 0.62137 dice_loss 0.08865 +Epoch [3522/4000] Validation [2/10] Loss: 0.47485 focal_loss 0.37955 dice_loss 0.09530 +Epoch [3522/4000] Validation [3/10] Loss: 0.37779 focal_loss 0.26778 dice_loss 0.11002 +Epoch [3522/4000] Validation [4/10] Loss: 0.86758 focal_loss 0.30352 dice_loss 0.56406 +Epoch [3522/4000] Validation [5/10] Loss: 3.02185 focal_loss 2.34813 dice_loss 0.67372 +Epoch [3522/4000] Validation [6/10] Loss: 1.31426 focal_loss 0.59573 dice_loss 0.71853 +Epoch [3522/4000] Validation [7/10] Loss: 1.15883 focal_loss 0.50778 dice_loss 0.65105 +Epoch [3522/4000] Validation [8/10] Loss: 2.23914 focal_loss 1.62646 dice_loss 0.61267 +Epoch [3522/4000] Validation [9/10] Loss: 1.39662 focal_loss 0.85424 dice_loss 0.54238 +Epoch [3522/4000] Validation [10/10] Loss: 1.84108 focal_loss 1.10469 dice_loss 0.73640 +Epoch [3522/4000] Validation metric {'Val/mean dice_metric': 0.9516222476959229, 'Val/mean miou_metric': 0.935727059841156, 'Val/mean f1': 0.9487972855567932, 'Val/mean precision': 0.9432604908943176, 'Val/mean recall': 0.9543994069099426, 'Val/mean hd95_metric': 10.739727020263672} +Cheakpoint... +Epoch [3522/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516222476959229, 'Val/mean miou_metric': 0.935727059841156, 'Val/mean f1': 0.9487972855567932, 'Val/mean precision': 0.9432604908943176, 'Val/mean recall': 0.9543994069099426, 'Val/mean hd95_metric': 10.739727020263672} +Epoch [3523/4000] Training [1/39] Loss: 0.00383 +Epoch [3523/4000] Training [2/39] Loss: 0.00462 +Epoch [3523/4000] Training [3/39] Loss: 0.12786 +Epoch [3523/4000] Training [4/39] Loss: 0.12848 +Epoch [3523/4000] Training [5/39] Loss: 0.12864 +Epoch [3523/4000] Training [6/39] Loss: 0.25416 +Epoch [3523/4000] Training [7/39] Loss: 0.00722 +Epoch [3523/4000] Training [8/39] Loss: 0.00289 +Epoch [3523/4000] Training [9/39] Loss: 0.00614 +Epoch [3523/4000] Training [10/39] Loss: 0.00388 +Epoch [3523/4000] Training [11/39] Loss: 0.00475 +Epoch [3523/4000] Training [12/39] Loss: 0.00482 +Epoch [3523/4000] Training [13/39] Loss: 0.25224 +Epoch [3523/4000] Training [14/39] Loss: 0.00504 +Epoch [3523/4000] Training [15/39] Loss: 0.00571 +Epoch [3523/4000] Training [16/39] Loss: 0.00634 +Epoch [3523/4000] Training [17/39] Loss: 0.00640 +Epoch [3523/4000] Training [18/39] Loss: 0.00769 +Epoch [3523/4000] Training [19/39] Loss: 0.00888 +Epoch [3523/4000] Training [20/39] Loss: 0.00715 +Epoch [3523/4000] Training [21/39] Loss: 0.00597 +Epoch [3523/4000] Training [22/39] Loss: 0.00475 +Epoch [3523/4000] Training [23/39] Loss: 0.00588 +Epoch [3523/4000] Training [24/39] Loss: 0.00747 +Epoch [3523/4000] Training [25/39] Loss: 0.13045 +Epoch [3523/4000] Training [26/39] Loss: 0.00387 +Epoch [3523/4000] Training [27/39] Loss: 0.13195 +Epoch [3523/4000] Training [28/39] Loss: 0.00383 +Epoch [3523/4000] Training [29/39] Loss: 0.13293 +Epoch [3523/4000] Training [30/39] Loss: 0.00291 +Epoch [3523/4000] Training [31/39] Loss: 0.00451 +Epoch [3523/4000] Training [32/39] Loss: 0.00478 +Epoch [3523/4000] Training [33/39] Loss: 0.12825 +Epoch [3523/4000] Training [34/39] Loss: 0.00458 +Epoch [3523/4000] Training [35/39] Loss: 0.00355 +Epoch [3523/4000] Training [36/39] Loss: 0.00404 +Epoch [3523/4000] Training [37/39] Loss: 0.13066 +Epoch [3523/4000] Training [38/39] Loss: 0.00499 +Epoch [3523/4000] Training [39/39] Loss: 0.13112 +Epoch [3523/4000] Training metric {'Train/mean dice_metric': 0.9961600303649902, 'Train/mean miou_metric': 0.9927651286125183, 'Train/mean f1': 0.9968491792678833, 'Train/mean precision': 0.9964058995246887, 'Train/mean recall': 0.9972928166389465, 'Train/mean hd95_metric': 0.979986846446991} +Epoch [3523/4000] Validation [1/10] Loss: 0.73147 focal_loss 0.64342 dice_loss 0.08805 +Epoch [3523/4000] Validation [2/10] Loss: 0.49585 focal_loss 0.39817 dice_loss 0.09768 +Epoch [3523/4000] Validation [3/10] Loss: 0.39287 focal_loss 0.28213 dice_loss 0.11074 +Epoch [3523/4000] Validation [4/10] Loss: 0.87036 focal_loss 0.30760 dice_loss 0.56276 +Epoch [3523/4000] Validation [5/10] Loss: 3.07626 focal_loss 2.40254 dice_loss 0.67372 +Epoch [3523/4000] Validation [6/10] Loss: 1.31609 focal_loss 0.59674 dice_loss 0.71934 +Epoch [3523/4000] Validation [7/10] Loss: 1.16413 focal_loss 0.51015 dice_loss 0.65398 +Epoch [3523/4000] Validation [8/10] Loss: 2.43377 focal_loss 1.80540 dice_loss 0.62837 +Epoch [3523/4000] Validation [9/10] Loss: 1.42537 focal_loss 0.88275 dice_loss 0.54262 +Epoch [3523/4000] Validation [10/10] Loss: 1.83494 focal_loss 1.10167 dice_loss 0.73327 +Epoch [3523/4000] Validation metric {'Val/mean dice_metric': 0.9515116214752197, 'Val/mean miou_metric': 0.9355428218841553, 'Val/mean f1': 0.9497237801551819, 'Val/mean precision': 0.9458832144737244, 'Val/mean recall': 0.9535956382751465, 'Val/mean hd95_metric': 10.624481201171875} +Cheakpoint... +Epoch [3523/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515116214752197, 'Val/mean miou_metric': 0.9355428218841553, 'Val/mean f1': 0.9497237801551819, 'Val/mean precision': 0.9458832144737244, 'Val/mean recall': 0.9535956382751465, 'Val/mean hd95_metric': 10.624481201171875} +Epoch [3524/4000] Training [1/39] Loss: 0.00409 +Epoch [3524/4000] Training [2/39] Loss: 0.00608 +Epoch [3524/4000] Training [3/39] Loss: 0.00692 +Epoch [3524/4000] Training [4/39] Loss: 0.12809 +Epoch [3524/4000] Training [5/39] Loss: 0.00283 +Epoch [3524/4000] Training [6/39] Loss: 0.00671 +Epoch [3524/4000] Training [7/39] Loss: 0.12800 +Epoch [3524/4000] Training [8/39] Loss: 0.00499 +Epoch [3524/4000] Training [9/39] Loss: 0.25301 +Epoch [3524/4000] Training [10/39] Loss: 0.00493 +Epoch [3524/4000] Training [11/39] Loss: 0.12953 +Epoch [3524/4000] Training [12/39] Loss: 0.00482 +Epoch [3524/4000] Training [13/39] Loss: 0.00329 +Epoch [3524/4000] Training [14/39] Loss: 0.12955 +Epoch [3524/4000] Training [15/39] Loss: 0.00446 +Epoch [3524/4000] Training [16/39] Loss: 0.00497 +Epoch [3524/4000] Training [17/39] Loss: 0.00700 +Epoch [3524/4000] Training [18/39] Loss: 0.12720 +Epoch [3524/4000] Training [19/39] Loss: 0.00668 +Epoch [3524/4000] Training [20/39] Loss: 0.00483 +Epoch [3524/4000] Training [21/39] Loss: 0.00487 +Epoch [3524/4000] Training [22/39] Loss: 0.13188 +Epoch [3524/4000] Training [23/39] Loss: 0.00576 +Epoch [3524/4000] Training [24/39] Loss: 0.12731 +Epoch [3524/4000] Training [25/39] Loss: 0.00611 +Epoch [3524/4000] Training [26/39] Loss: 0.12989 +Epoch [3524/4000] Training [27/39] Loss: 0.13080 +Epoch [3524/4000] Training [28/39] Loss: 0.00368 +Epoch [3524/4000] Training [29/39] Loss: 0.12832 +Epoch [3524/4000] Training [30/39] Loss: 0.00488 +Epoch [3524/4000] Training [31/39] Loss: 0.00575 +Epoch [3524/4000] Training [32/39] Loss: 0.12808 +Epoch [3524/4000] Training [33/39] Loss: 0.00392 +Epoch [3524/4000] Training [34/39] Loss: 0.00533 +Epoch [3524/4000] Training [35/39] Loss: 0.00425 +Epoch [3524/4000] Training [36/39] Loss: 0.12986 +Epoch [3524/4000] Training [37/39] Loss: 0.25511 +Epoch [3524/4000] Training [38/39] Loss: 0.00692 +Epoch [3524/4000] Training [39/39] Loss: 0.00357 +Epoch [3524/4000] Training metric {'Train/mean dice_metric': 0.995292603969574, 'Train/mean miou_metric': 0.9918622374534607, 'Train/mean f1': 0.9967968463897705, 'Train/mean precision': 0.9963312149047852, 'Train/mean recall': 0.9972628951072693, 'Train/mean hd95_metric': 0.9797402024269104} +Epoch [3524/4000] Validation [1/10] Loss: 0.71930 focal_loss 0.62944 dice_loss 0.08986 +Epoch [3524/4000] Validation [2/10] Loss: 0.49209 focal_loss 0.39402 dice_loss 0.09807 +Epoch [3524/4000] Validation [3/10] Loss: 0.36656 focal_loss 0.25750 dice_loss 0.10907 +Epoch [3524/4000] Validation [4/10] Loss: 0.87466 focal_loss 0.31123 dice_loss 0.56343 +Epoch [3524/4000] Validation [5/10] Loss: 2.99526 focal_loss 2.32191 dice_loss 0.67335 +Epoch [3524/4000] Validation [6/10] Loss: 1.31787 focal_loss 0.60393 dice_loss 0.71394 +Epoch [3524/4000] Validation [7/10] Loss: 1.17558 focal_loss 0.52006 dice_loss 0.65553 +Epoch [3524/4000] Validation [8/10] Loss: 2.21945 focal_loss 1.60861 dice_loss 0.61085 +Epoch [3524/4000] Validation [9/10] Loss: 1.39804 focal_loss 0.85471 dice_loss 0.54333 +Epoch [3524/4000] Validation [10/10] Loss: 1.84502 focal_loss 1.11036 dice_loss 0.73466 +Epoch [3524/4000] Validation metric {'Val/mean dice_metric': 0.9509979486465454, 'Val/mean miou_metric': 0.9349514245986938, 'Val/mean f1': 0.948552131652832, 'Val/mean precision': 0.9427886605262756, 'Val/mean recall': 0.9543865919113159, 'Val/mean hd95_metric': 10.708722114562988} +Cheakpoint... +Epoch [3524/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509979486465454, 'Val/mean miou_metric': 0.9349514245986938, 'Val/mean f1': 0.948552131652832, 'Val/mean precision': 0.9427886605262756, 'Val/mean recall': 0.9543865919113159, 'Val/mean hd95_metric': 10.708722114562988} +Epoch [3525/4000] Training [1/39] Loss: 0.12872 +Epoch [3525/4000] Training [2/39] Loss: 0.00298 +Epoch [3525/4000] Training [3/39] Loss: 0.12931 +Epoch [3525/4000] Training [4/39] Loss: 0.00744 +Epoch [3525/4000] Training [5/39] Loss: 0.13195 +Epoch [3525/4000] Training [6/39] Loss: 0.01082 +Epoch [3525/4000] Training [7/39] Loss: 0.00655 +Epoch [3525/4000] Training [8/39] Loss: 0.00439 +Epoch [3525/4000] Training [9/39] Loss: 0.00774 +Epoch [3525/4000] Training [10/39] Loss: 0.00675 +Epoch [3525/4000] Training [11/39] Loss: 0.08883 +Epoch [3525/4000] Training [12/39] Loss: 0.12998 +Epoch [3525/4000] Training [13/39] Loss: 0.13030 +Epoch [3525/4000] Training [14/39] Loss: 0.12831 +Epoch [3525/4000] Training [15/39] Loss: 0.00432 +Epoch [3525/4000] Training [16/39] Loss: 0.00503 +Epoch [3525/4000] Training [17/39] Loss: 0.00561 +Epoch [3525/4000] Training [18/39] Loss: 0.00541 +Epoch [3525/4000] Training [19/39] Loss: 0.00560 +Epoch [3525/4000] Training [20/39] Loss: 0.00539 +Epoch [3525/4000] Training [21/39] Loss: 0.01120 +Epoch [3525/4000] Training [22/39] Loss: 0.00585 +Epoch [3525/4000] Training [23/39] Loss: 0.00661 +Epoch [3525/4000] Training [24/39] Loss: 0.12916 +Epoch [3525/4000] Training [25/39] Loss: 0.00398 +Epoch [3525/4000] Training [26/39] Loss: 0.00318 +Epoch [3525/4000] Training [27/39] Loss: 0.00396 +Epoch [3525/4000] Training [28/39] Loss: 0.00434 +Epoch [3525/4000] Training [29/39] Loss: 0.00489 +Epoch [3525/4000] Training [30/39] Loss: 0.25397 +Epoch [3525/4000] Training [31/39] Loss: 0.00692 +Epoch [3525/4000] Training [32/39] Loss: 0.00858 +Epoch [3525/4000] Training [33/39] Loss: 0.00533 +Epoch [3525/4000] Training [34/39] Loss: 0.12873 +Epoch [3525/4000] Training [35/39] Loss: 0.25404 +Epoch [3525/4000] Training [36/39] Loss: 0.12825 +Epoch [3525/4000] Training [37/39] Loss: 0.00438 +Epoch [3525/4000] Training [38/39] Loss: 0.12971 +Epoch [3525/4000] Training [39/39] Loss: 0.13034 +Epoch [3525/4000] Training metric {'Train/mean dice_metric': 0.9957420229911804, 'Train/mean miou_metric': 0.9919539093971252, 'Train/mean f1': 0.9964415431022644, 'Train/mean precision': 0.9959813356399536, 'Train/mean recall': 0.9969022274017334, 'Train/mean hd95_metric': 1.0219662189483643} +Epoch [3525/4000] Validation [1/10] Loss: 0.71269 focal_loss 0.62299 dice_loss 0.08970 +Epoch [3525/4000] Validation [2/10] Loss: 0.48899 focal_loss 0.39073 dice_loss 0.09825 +Epoch [3525/4000] Validation [3/10] Loss: 0.36370 focal_loss 0.25450 dice_loss 0.10920 +Epoch [3525/4000] Validation [4/10] Loss: 0.86148 focal_loss 0.29867 dice_loss 0.56281 +Epoch [3525/4000] Validation [5/10] Loss: 3.01457 focal_loss 2.34219 dice_loss 0.67238 +Epoch [3525/4000] Validation [6/10] Loss: 1.30375 focal_loss 0.58895 dice_loss 0.71480 +Epoch [3525/4000] Validation [7/10] Loss: 1.17625 focal_loss 0.51762 dice_loss 0.65863 +Epoch [3525/4000] Validation [8/10] Loss: 2.22378 focal_loss 1.60966 dice_loss 0.61411 +Epoch [3525/4000] Validation [9/10] Loss: 1.38786 focal_loss 0.84528 dice_loss 0.54258 +Epoch [3525/4000] Validation [10/10] Loss: 1.83134 focal_loss 1.09613 dice_loss 0.73521 +Epoch [3525/4000] Validation metric {'Val/mean dice_metric': 0.9512998461723328, 'Val/mean miou_metric': 0.9350142478942871, 'Val/mean f1': 0.9488987326622009, 'Val/mean precision': 0.9430641531944275, 'Val/mean recall': 0.9548060894012451, 'Val/mean hd95_metric': 10.720651626586914} +Cheakpoint... +Epoch [3525/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512998461723328, 'Val/mean miou_metric': 0.9350142478942871, 'Val/mean f1': 0.9488987326622009, 'Val/mean precision': 0.9430641531944275, 'Val/mean recall': 0.9548060894012451, 'Val/mean hd95_metric': 10.720651626586914} +Epoch [3526/4000] Training [1/39] Loss: 0.12913 +Epoch [3526/4000] Training [2/39] Loss: 0.00609 +Epoch [3526/4000] Training [3/39] Loss: 0.00736 +Epoch [3526/4000] Training [4/39] Loss: 0.00410 +Epoch [3526/4000] Training [5/39] Loss: 0.00400 +Epoch [3526/4000] Training [6/39] Loss: 0.00737 +Epoch [3526/4000] Training [7/39] Loss: 0.00308 +Epoch [3526/4000] Training [8/39] Loss: 0.00489 +Epoch [3526/4000] Training [9/39] Loss: 0.00488 +Epoch [3526/4000] Training [10/39] Loss: 0.00700 +Epoch [3526/4000] Training [11/39] Loss: 0.00497 +Epoch [3526/4000] Training [12/39] Loss: 0.00424 +Epoch [3526/4000] Training [13/39] Loss: 0.00385 +Epoch [3526/4000] Training [14/39] Loss: 0.12884 +Epoch [3526/4000] Training [15/39] Loss: 0.12898 +Epoch [3526/4000] Training [16/39] Loss: 0.13072 +Epoch [3526/4000] Training [17/39] Loss: 0.00619 +Epoch [3526/4000] Training [18/39] Loss: 0.00407 +Epoch [3526/4000] Training [19/39] Loss: 0.13008 +Epoch [3526/4000] Training [20/39] Loss: 0.12990 +Epoch [3526/4000] Training [21/39] Loss: 0.00507 +Epoch [3526/4000] Training [22/39] Loss: 0.00496 +Epoch [3526/4000] Training [23/39] Loss: 0.00527 +Epoch [3526/4000] Training [24/39] Loss: 0.00555 +Epoch [3526/4000] Training [25/39] Loss: 0.00463 +Epoch [3526/4000] Training [26/39] Loss: 0.00580 +Epoch [3526/4000] Training [27/39] Loss: 0.13033 +Epoch [3526/4000] Training [28/39] Loss: 0.00445 +Epoch [3526/4000] Training [29/39] Loss: 0.00454 +Epoch [3526/4000] Training [30/39] Loss: 0.00470 +Epoch [3526/4000] Training [31/39] Loss: 0.01519 +Epoch [3526/4000] Training [32/39] Loss: 0.00621 +Epoch [3526/4000] Training [33/39] Loss: 0.00577 +Epoch [3526/4000] Training [34/39] Loss: 0.12904 +Epoch [3526/4000] Training [35/39] Loss: 0.13067 +Epoch [3526/4000] Training [36/39] Loss: 0.00612 +Epoch [3526/4000] Training [37/39] Loss: 0.00557 +Epoch [3526/4000] Training [38/39] Loss: 0.00411 +Epoch [3526/4000] Training [39/39] Loss: 0.00427 +Epoch [3526/4000] Training metric {'Train/mean dice_metric': 0.9959301948547363, 'Train/mean miou_metric': 0.992328941822052, 'Train/mean f1': 0.9966443181037903, 'Train/mean precision': 0.9962087273597717, 'Train/mean recall': 0.9970800876617432, 'Train/mean hd95_metric': 0.9895755052566528} +Epoch [3526/4000] Validation [1/10] Loss: 0.69826 focal_loss 0.61096 dice_loss 0.08730 +Epoch [3526/4000] Validation [2/10] Loss: 0.48263 focal_loss 0.38637 dice_loss 0.09625 +Epoch [3526/4000] Validation [3/10] Loss: 0.36780 focal_loss 0.25874 dice_loss 0.10905 +Epoch [3526/4000] Validation [4/10] Loss: 0.86830 focal_loss 0.30455 dice_loss 0.56376 +Epoch [3526/4000] Validation [5/10] Loss: 3.01532 focal_loss 2.34193 dice_loss 0.67338 +Epoch [3526/4000] Validation [6/10] Loss: 1.31666 focal_loss 0.60399 dice_loss 0.71267 +Epoch [3526/4000] Validation [7/10] Loss: 1.16045 focal_loss 0.50890 dice_loss 0.65155 +Epoch [3526/4000] Validation [8/10] Loss: 2.21887 focal_loss 1.60677 dice_loss 0.61210 +Epoch [3526/4000] Validation [9/10] Loss: 1.41543 focal_loss 0.86938 dice_loss 0.54605 +Epoch [3526/4000] Validation [10/10] Loss: 1.81477 focal_loss 1.08531 dice_loss 0.72946 +Epoch [3526/4000] Validation metric {'Val/mean dice_metric': 0.9515753388404846, 'Val/mean miou_metric': 0.9354735612869263, 'Val/mean f1': 0.9489141702651978, 'Val/mean precision': 0.9440163373947144, 'Val/mean recall': 0.9538630247116089, 'Val/mean hd95_metric': 10.697099685668945} +Cheakpoint... +Epoch [3526/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515753388404846, 'Val/mean miou_metric': 0.9354735612869263, 'Val/mean f1': 0.9489141702651978, 'Val/mean precision': 0.9440163373947144, 'Val/mean recall': 0.9538630247116089, 'Val/mean hd95_metric': 10.697099685668945} +Epoch [3527/4000] Training [1/39] Loss: 0.00589 +Epoch [3527/4000] Training [2/39] Loss: 0.00359 +Epoch [3527/4000] Training [3/39] Loss: 0.12964 +Epoch [3527/4000] Training [4/39] Loss: 0.13320 +Epoch [3527/4000] Training [5/39] Loss: 0.03616 +Epoch [3527/4000] Training [6/39] Loss: 0.00559 +Epoch [3527/4000] Training [7/39] Loss: 0.00423 +Epoch [3527/4000] Training [8/39] Loss: 0.25330 +Epoch [3527/4000] Training [9/39] Loss: 0.00605 +Epoch [3527/4000] Training [10/39] Loss: 0.00701 +Epoch [3527/4000] Training [11/39] Loss: 0.00455 +Epoch [3527/4000] Training [12/39] Loss: 0.00568 +Epoch [3527/4000] Training [13/39] Loss: 0.00337 +Epoch [3527/4000] Training [14/39] Loss: 0.00625 +Epoch [3527/4000] Training [15/39] Loss: 0.00426 +Epoch [3527/4000] Training [16/39] Loss: 0.13005 +Epoch [3527/4000] Training [17/39] Loss: 0.00499 +Epoch [3527/4000] Training [18/39] Loss: 0.12952 +Epoch [3527/4000] Training [19/39] Loss: 0.00555 +Epoch [3527/4000] Training [20/39] Loss: 0.00395 +Epoch [3527/4000] Training [21/39] Loss: 0.12835 +Epoch [3527/4000] Training [22/39] Loss: 0.00655 +Epoch [3527/4000] Training [23/39] Loss: 0.00393 +Epoch [3527/4000] Training [24/39] Loss: 0.00564 +Epoch [3527/4000] Training [25/39] Loss: 0.13089 +Epoch [3527/4000] Training [26/39] Loss: 0.12817 +Epoch [3527/4000] Training [27/39] Loss: 0.00471 +Epoch [3527/4000] Training [28/39] Loss: 0.25425 +Epoch [3527/4000] Training [29/39] Loss: 0.00371 +Epoch [3527/4000] Training [30/39] Loss: 0.00425 +Epoch [3527/4000] Training [31/39] Loss: 0.00681 +Epoch [3527/4000] Training [32/39] Loss: 0.00604 +Epoch [3527/4000] Training [33/39] Loss: 0.00428 +Epoch [3527/4000] Training [34/39] Loss: 0.00371 +Epoch [3527/4000] Training [35/39] Loss: 0.00497 +Epoch [3527/4000] Training [36/39] Loss: 0.00621 +Epoch [3527/4000] Training [37/39] Loss: 0.12898 +Epoch [3527/4000] Training [38/39] Loss: 0.00630 +Epoch [3527/4000] Training [39/39] Loss: 0.00470 +Epoch [3527/4000] Training metric {'Train/mean dice_metric': 0.996090829372406, 'Train/mean miou_metric': 0.9926154017448425, 'Train/mean f1': 0.9967111349105835, 'Train/mean precision': 0.996256411075592, 'Train/mean recall': 0.9971662759780884, 'Train/mean hd95_metric': 0.9993107914924622} +Epoch [3527/4000] Validation [1/10] Loss: 0.70214 focal_loss 0.61521 dice_loss 0.08693 +Epoch [3527/4000] Validation [2/10] Loss: 0.48094 focal_loss 0.38488 dice_loss 0.09606 +Epoch [3527/4000] Validation [3/10] Loss: 0.37405 focal_loss 0.26432 dice_loss 0.10972 +Epoch [3527/4000] Validation [4/10] Loss: 0.88082 focal_loss 0.31710 dice_loss 0.56372 +Epoch [3527/4000] Validation [5/10] Loss: 3.04296 focal_loss 2.36942 dice_loss 0.67354 +Epoch [3527/4000] Validation [6/10] Loss: 1.31320 focal_loss 0.60358 dice_loss 0.70962 +Epoch [3527/4000] Validation [7/10] Loss: 1.16061 focal_loss 0.50960 dice_loss 0.65101 +Epoch [3527/4000] Validation [8/10] Loss: 2.34641 focal_loss 1.72143 dice_loss 0.62498 +Epoch [3527/4000] Validation [9/10] Loss: 1.42034 focal_loss 0.87551 dice_loss 0.54482 +Epoch [3527/4000] Validation [10/10] Loss: 1.80563 focal_loss 1.07410 dice_loss 0.73152 +Epoch [3527/4000] Validation metric {'Val/mean dice_metric': 0.9516925811767578, 'Val/mean miou_metric': 0.9356649518013, 'Val/mean f1': 0.9492892026901245, 'Val/mean precision': 0.944972038269043, 'Val/mean recall': 0.9536459445953369, 'Val/mean hd95_metric': 10.67794418334961} +Cheakpoint... +Epoch [3527/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516925811767578, 'Val/mean miou_metric': 0.9356649518013, 'Val/mean f1': 0.9492892026901245, 'Val/mean precision': 0.944972038269043, 'Val/mean recall': 0.9536459445953369, 'Val/mean hd95_metric': 10.67794418334961} +Epoch [3528/4000] Training [1/39] Loss: 0.00450 +Epoch [3528/4000] Training [2/39] Loss: 0.00493 +Epoch [3528/4000] Training [3/39] Loss: 0.00462 +Epoch [3528/4000] Training [4/39] Loss: 0.00481 +Epoch [3528/4000] Training [5/39] Loss: 0.12904 +Epoch [3528/4000] Training [6/39] Loss: 0.12818 +Epoch [3528/4000] Training [7/39] Loss: 0.00529 +Epoch [3528/4000] Training [8/39] Loss: 0.00494 +Epoch [3528/4000] Training [9/39] Loss: 0.00473 +Epoch [3528/4000] Training [10/39] Loss: 0.00570 +Epoch [3528/4000] Training [11/39] Loss: 0.12874 +Epoch [3528/4000] Training [12/39] Loss: 0.00300 +Epoch [3528/4000] Training [13/39] Loss: 0.00280 +Epoch [3528/4000] Training [14/39] Loss: 0.00725 +Epoch [3528/4000] Training [15/39] Loss: 0.00537 +Epoch [3528/4000] Training [16/39] Loss: 0.00468 +Epoch [3528/4000] Training [17/39] Loss: 0.00505 +Epoch [3528/4000] Training [18/39] Loss: 0.00606 +Epoch [3528/4000] Training [19/39] Loss: 0.12856 +Epoch [3528/4000] Training [20/39] Loss: 0.13171 +Epoch [3528/4000] Training [21/39] Loss: 0.00451 +Epoch [3528/4000] Training [22/39] Loss: 0.12897 +Epoch [3528/4000] Training [23/39] Loss: 0.00618 +Epoch [3528/4000] Training [24/39] Loss: 0.00690 +Epoch [3528/4000] Training [25/39] Loss: 0.00376 +Epoch [3528/4000] Training [26/39] Loss: 0.00508 +Epoch [3528/4000] Training [27/39] Loss: 0.13062 +Epoch [3528/4000] Training [28/39] Loss: 0.00395 +Epoch [3528/4000] Training [29/39] Loss: 0.00502 +Epoch [3528/4000] Training [30/39] Loss: 0.00499 +Epoch [3528/4000] Training [31/39] Loss: 0.00548 +Epoch [3528/4000] Training [32/39] Loss: 0.00506 +Epoch [3528/4000] Training [33/39] Loss: 0.00505 +Epoch [3528/4000] Training [34/39] Loss: 0.00542 +Epoch [3528/4000] Training [35/39] Loss: 0.12857 +Epoch [3528/4000] Training [36/39] Loss: 0.12975 +Epoch [3528/4000] Training [37/39] Loss: 0.00658 +Epoch [3528/4000] Training [38/39] Loss: 0.00627 +Epoch [3528/4000] Training [39/39] Loss: 0.00805 +Epoch [3528/4000] Training metric {'Train/mean dice_metric': 0.9960840344429016, 'Train/mean miou_metric': 0.9926131367683411, 'Train/mean f1': 0.9966956377029419, 'Train/mean precision': 0.9962178468704224, 'Train/mean recall': 0.9971737861633301, 'Train/mean hd95_metric': 1.091176152229309} +Epoch [3528/4000] Validation [1/10] Loss: 0.71035 focal_loss 0.62372 dice_loss 0.08662 +Epoch [3528/4000] Validation [2/10] Loss: 0.49626 focal_loss 0.39820 dice_loss 0.09807 +Epoch [3528/4000] Validation [3/10] Loss: 0.37562 focal_loss 0.26597 dice_loss 0.10965 +Epoch [3528/4000] Validation [4/10] Loss: 0.88784 focal_loss 0.32374 dice_loss 0.56410 +Epoch [3528/4000] Validation [5/10] Loss: 3.04829 focal_loss 2.37509 dice_loss 0.67320 +Epoch [3528/4000] Validation [6/10] Loss: 1.33762 focal_loss 0.62547 dice_loss 0.71215 +Epoch [3528/4000] Validation [7/10] Loss: 1.17682 focal_loss 0.52084 dice_loss 0.65598 +Epoch [3528/4000] Validation [8/10] Loss: 2.32194 focal_loss 1.70205 dice_loss 0.61989 +Epoch [3528/4000] Validation [9/10] Loss: 1.43297 focal_loss 0.88844 dice_loss 0.54453 +Epoch [3528/4000] Validation [10/10] Loss: 1.86497 focal_loss 1.12941 dice_loss 0.73556 +Epoch [3528/4000] Validation metric {'Val/mean dice_metric': 0.9516136050224304, 'Val/mean miou_metric': 0.9355430006980896, 'Val/mean f1': 0.9489617943763733, 'Val/mean precision': 0.9441871643066406, 'Val/mean recall': 0.9537850618362427, 'Val/mean hd95_metric': 10.802714347839355} +Cheakpoint... +Epoch [3528/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516136050224304, 'Val/mean miou_metric': 0.9355430006980896, 'Val/mean f1': 0.9489617943763733, 'Val/mean precision': 0.9441871643066406, 'Val/mean recall': 0.9537850618362427, 'Val/mean hd95_metric': 10.802714347839355} +Epoch [3529/4000] Training [1/39] Loss: 0.00504 +Epoch [3529/4000] Training [2/39] Loss: 0.00711 +Epoch [3529/4000] Training [3/39] Loss: 0.00338 +Epoch [3529/4000] Training [4/39] Loss: 0.12769 +Epoch [3529/4000] Training [5/39] Loss: 0.00461 +Epoch [3529/4000] Training [6/39] Loss: 0.00513 +Epoch [3529/4000] Training [7/39] Loss: 0.00426 +Epoch [3529/4000] Training [8/39] Loss: 0.12900 +Epoch [3529/4000] Training [9/39] Loss: 0.00443 +Epoch [3529/4000] Training [10/39] Loss: 0.12777 +Epoch [3529/4000] Training [11/39] Loss: 0.00661 +Epoch [3529/4000] Training [12/39] Loss: 0.12945 +Epoch [3529/4000] Training [13/39] Loss: 0.12889 +Epoch [3529/4000] Training [14/39] Loss: 0.13081 +Epoch [3529/4000] Training [15/39] Loss: 0.00549 +Epoch [3529/4000] Training [16/39] Loss: 0.00706 +Epoch [3529/4000] Training [17/39] Loss: 0.00480 +Epoch [3529/4000] Training [18/39] Loss: 0.12749 +Epoch [3529/4000] Training [19/39] Loss: 0.00824 +Epoch [3529/4000] Training [20/39] Loss: 0.00383 +Epoch [3529/4000] Training [21/39] Loss: 0.00397 +Epoch [3529/4000] Training [22/39] Loss: 0.00462 +Epoch [3529/4000] Training [23/39] Loss: 0.00783 +Epoch [3529/4000] Training [24/39] Loss: 0.12913 +Epoch [3529/4000] Training [25/39] Loss: 0.00742 +Epoch [3529/4000] Training [26/39] Loss: 0.00458 +Epoch [3529/4000] Training [27/39] Loss: 0.00341 +Epoch [3529/4000] Training [28/39] Loss: 0.12823 +Epoch [3529/4000] Training [29/39] Loss: 0.00706 +Epoch [3529/4000] Training [30/39] Loss: 0.00517 +Epoch [3529/4000] Training [31/39] Loss: 0.12743 +Epoch [3529/4000] Training [32/39] Loss: 0.00929 +Epoch [3529/4000] Training [33/39] Loss: 0.00675 +Epoch [3529/4000] Training [34/39] Loss: 0.00575 +Epoch [3529/4000] Training [35/39] Loss: 0.00742 +Epoch [3529/4000] Training [36/39] Loss: 0.00652 +Epoch [3529/4000] Training [37/39] Loss: 0.13095 +Epoch [3529/4000] Training [38/39] Loss: 0.00523 +Epoch [3529/4000] Training [39/39] Loss: 0.13078 +Epoch [3529/4000] Training metric {'Train/mean dice_metric': 0.995929479598999, 'Train/mean miou_metric': 0.9923111796379089, 'Train/mean f1': 0.9965640902519226, 'Train/mean precision': 0.9961501359939575, 'Train/mean recall': 0.9969783425331116, 'Train/mean hd95_metric': 1.0035260915756226} +Epoch [3529/4000] Validation [1/10] Loss: 0.71293 focal_loss 0.62538 dice_loss 0.08755 +Epoch [3529/4000] Validation [2/10] Loss: 0.49061 focal_loss 0.39554 dice_loss 0.09506 +Epoch [3529/4000] Validation [3/10] Loss: 0.36658 focal_loss 0.25749 dice_loss 0.10909 +Epoch [3529/4000] Validation [4/10] Loss: 0.88653 focal_loss 0.32181 dice_loss 0.56471 +Epoch [3529/4000] Validation [5/10] Loss: 3.01628 focal_loss 2.34259 dice_loss 0.67369 +Epoch [3529/4000] Validation [6/10] Loss: 1.33842 focal_loss 0.62399 dice_loss 0.71443 +Epoch [3529/4000] Validation [7/10] Loss: 1.17128 focal_loss 0.51547 dice_loss 0.65582 +Epoch [3529/4000] Validation [8/10] Loss: 2.28986 focal_loss 1.67453 dice_loss 0.61533 +Epoch [3529/4000] Validation [9/10] Loss: 1.41870 focal_loss 0.87563 dice_loss 0.54307 +Epoch [3529/4000] Validation [10/10] Loss: 1.87478 focal_loss 1.14017 dice_loss 0.73461 +Epoch [3529/4000] Validation metric {'Val/mean dice_metric': 0.9515022039413452, 'Val/mean miou_metric': 0.935356616973877, 'Val/mean f1': 0.9484125375747681, 'Val/mean precision': 0.9427518248558044, 'Val/mean recall': 0.9541416168212891, 'Val/mean hd95_metric': 10.722311019897461} +Cheakpoint... +Epoch [3529/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515022039413452, 'Val/mean miou_metric': 0.935356616973877, 'Val/mean f1': 0.9484125375747681, 'Val/mean precision': 0.9427518248558044, 'Val/mean recall': 0.9541416168212891, 'Val/mean hd95_metric': 10.722311019897461} +Epoch [3530/4000] Training [1/39] Loss: 0.00355 +Epoch [3530/4000] Training [2/39] Loss: 0.12910 +Epoch [3530/4000] Training [3/39] Loss: 0.00455 +Epoch [3530/4000] Training [4/39] Loss: 0.00580 +Epoch [3530/4000] Training [5/39] Loss: 0.00816 +Epoch [3530/4000] Training [6/39] Loss: 0.00504 +Epoch [3530/4000] Training [7/39] Loss: 0.00479 +Epoch [3530/4000] Training [8/39] Loss: 0.12760 +Epoch [3530/4000] Training [9/39] Loss: 0.00537 +Epoch [3530/4000] Training [10/39] Loss: 0.12948 +Epoch [3530/4000] Training [11/39] Loss: 0.13152 +Epoch [3530/4000] Training [12/39] Loss: 0.00447 +Epoch [3530/4000] Training [13/39] Loss: 0.13255 +Epoch [3530/4000] Training [14/39] Loss: 0.00394 +Epoch [3530/4000] Training [15/39] Loss: 0.25542 +Epoch [3530/4000] Training [16/39] Loss: 0.00491 +Epoch [3530/4000] Training [17/39] Loss: 0.00312 +Epoch [3530/4000] Training [18/39] Loss: 0.00279 +Epoch [3530/4000] Training [19/39] Loss: 0.00416 +Epoch [3530/4000] Training [20/39] Loss: 0.00450 +Epoch [3530/4000] Training [21/39] Loss: 0.01037 +Epoch [3530/4000] Training [22/39] Loss: 0.00536 +Epoch [3530/4000] Training [23/39] Loss: 0.00447 +Epoch [3530/4000] Training [24/39] Loss: 0.00382 +Epoch [3530/4000] Training [25/39] Loss: 0.00522 +Epoch [3530/4000] Training [26/39] Loss: 0.00484 +Epoch [3530/4000] Training [27/39] Loss: 0.00416 +Epoch [3530/4000] Training [28/39] Loss: 0.00472 +Epoch [3530/4000] Training [29/39] Loss: 0.00612 +Epoch [3530/4000] Training [30/39] Loss: 0.00758 +Epoch [3530/4000] Training [31/39] Loss: 0.12921 +Epoch [3530/4000] Training [32/39] Loss: 0.00451 +Epoch [3530/4000] Training [33/39] Loss: 0.12864 +Epoch [3530/4000] Training [34/39] Loss: 0.12917 +Epoch [3530/4000] Training [35/39] Loss: 0.00568 +Epoch [3530/4000] Training [36/39] Loss: 0.00523 +Epoch [3530/4000] Training [37/39] Loss: 0.00517 +Epoch [3530/4000] Training [38/39] Loss: 0.00409 +Epoch [3530/4000] Training [39/39] Loss: 0.00633 +Epoch [3530/4000] Training metric {'Train/mean dice_metric': 0.996106743812561, 'Train/mean miou_metric': 0.9927082061767578, 'Train/mean f1': 0.9967175722122192, 'Train/mean precision': 0.9962496757507324, 'Train/mean recall': 0.9971858859062195, 'Train/mean hd95_metric': 0.9862432479858398} +Epoch [3530/4000] Validation [1/10] Loss: 0.70076 focal_loss 0.61406 dice_loss 0.08671 +Epoch [3530/4000] Validation [2/10] Loss: 0.49582 focal_loss 0.39558 dice_loss 0.10024 +Epoch [3530/4000] Validation [3/10] Loss: 0.38625 focal_loss 0.27496 dice_loss 0.11129 +Epoch [3530/4000] Validation [4/10] Loss: 0.87182 focal_loss 0.30771 dice_loss 0.56411 +Epoch [3530/4000] Validation [5/10] Loss: 3.00892 focal_loss 2.33520 dice_loss 0.67372 +Epoch [3530/4000] Validation [6/10] Loss: 1.31777 focal_loss 0.60244 dice_loss 0.71532 +Epoch [3530/4000] Validation [7/10] Loss: 1.14922 focal_loss 0.49554 dice_loss 0.65368 +Epoch [3530/4000] Validation [8/10] Loss: 2.32144 focal_loss 1.69925 dice_loss 0.62219 +Epoch [3530/4000] Validation [9/10] Loss: 1.39372 focal_loss 0.85088 dice_loss 0.54283 +Epoch [3530/4000] Validation [10/10] Loss: 1.82361 focal_loss 1.09162 dice_loss 0.73199 +Epoch [3530/4000] Validation metric {'Val/mean dice_metric': 0.9516657590866089, 'Val/mean miou_metric': 0.9357348680496216, 'Val/mean f1': 0.9491446614265442, 'Val/mean precision': 0.9450196027755737, 'Val/mean recall': 0.9533059597015381, 'Val/mean hd95_metric': 10.646656036376953} +Cheakpoint... +Epoch [3530/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516657590866089, 'Val/mean miou_metric': 0.9357348680496216, 'Val/mean f1': 0.9491446614265442, 'Val/mean precision': 0.9450196027755737, 'Val/mean recall': 0.9533059597015381, 'Val/mean hd95_metric': 10.646656036376953} +Epoch [3531/4000] Training [1/39] Loss: 0.00536 +Epoch [3531/4000] Training [2/39] Loss: 0.12859 +Epoch [3531/4000] Training [3/39] Loss: 0.00322 +Epoch [3531/4000] Training [4/39] Loss: 0.00309 +Epoch [3531/4000] Training [5/39] Loss: 0.00360 +Epoch [3531/4000] Training [6/39] Loss: 0.00485 +Epoch [3531/4000] Training [7/39] Loss: 0.00388 +Epoch [3531/4000] Training [8/39] Loss: 0.00657 +Epoch [3531/4000] Training [9/39] Loss: 0.12883 +Epoch [3531/4000] Training [10/39] Loss: 0.00520 +Epoch [3531/4000] Training [11/39] Loss: 0.00465 +Epoch [3531/4000] Training [12/39] Loss: 0.00477 +Epoch [3531/4000] Training [13/39] Loss: 0.25610 +Epoch [3531/4000] Training [14/39] Loss: 0.00786 +Epoch [3531/4000] Training [15/39] Loss: 0.00716 +Epoch [3531/4000] Training [16/39] Loss: 0.12912 +Epoch [3531/4000] Training [17/39] Loss: 0.00446 +Epoch [3531/4000] Training [18/39] Loss: 0.00482 +Epoch [3531/4000] Training [19/39] Loss: 0.00582 +Epoch [3531/4000] Training [20/39] Loss: 0.00535 +Epoch [3531/4000] Training [21/39] Loss: 0.12787 +Epoch [3531/4000] Training [22/39] Loss: 0.00790 +Epoch [3531/4000] Training [23/39] Loss: 0.00388 +Epoch [3531/4000] Training [24/39] Loss: 0.00418 +Epoch [3531/4000] Training [25/39] Loss: 0.00489 +Epoch [3531/4000] Training [26/39] Loss: 0.25337 +Epoch [3531/4000] Training [27/39] Loss: 0.00455 +Epoch [3531/4000] Training [28/39] Loss: 0.00526 +Epoch [3531/4000] Training [29/39] Loss: 0.00479 +Epoch [3531/4000] Training [30/39] Loss: 0.00534 +Epoch [3531/4000] Training [31/39] Loss: 0.00312 +Epoch [3531/4000] Training [32/39] Loss: 0.00631 +Epoch [3531/4000] Training [33/39] Loss: 0.00602 +Epoch [3531/4000] Training [34/39] Loss: 0.00696 +Epoch [3531/4000] Training [35/39] Loss: 0.12813 +Epoch [3531/4000] Training [36/39] Loss: 0.00452 +Epoch [3531/4000] Training [37/39] Loss: 0.00636 +Epoch [3531/4000] Training [38/39] Loss: 0.00598 +Epoch [3531/4000] Training [39/39] Loss: 0.13162 +Epoch [3531/4000] Training metric {'Train/mean dice_metric': 0.9961663484573364, 'Train/mean miou_metric': 0.9927778840065002, 'Train/mean f1': 0.9967681765556335, 'Train/mean precision': 0.9963726997375488, 'Train/mean recall': 0.9971638321876526, 'Train/mean hd95_metric': 1.0005329847335815} +Epoch [3531/4000] Validation [1/10] Loss: 0.73670 focal_loss 0.64639 dice_loss 0.09030 +Epoch [3531/4000] Validation [2/10] Loss: 0.48794 focal_loss 0.39290 dice_loss 0.09504 +Epoch [3531/4000] Validation [3/10] Loss: 0.38321 focal_loss 0.27332 dice_loss 0.10989 +Epoch [3531/4000] Validation [4/10] Loss: 0.88620 focal_loss 0.32157 dice_loss 0.56463 +Epoch [3531/4000] Validation [5/10] Loss: 3.04596 focal_loss 2.37272 dice_loss 0.67323 +Epoch [3531/4000] Validation [6/10] Loss: 1.32020 focal_loss 0.60371 dice_loss 0.71649 +Epoch [3531/4000] Validation [7/10] Loss: 1.17465 focal_loss 0.51594 dice_loss 0.65871 +Epoch [3531/4000] Validation [8/10] Loss: 2.27047 focal_loss 1.65500 dice_loss 0.61547 +Epoch [3531/4000] Validation [9/10] Loss: 1.43522 focal_loss 0.89305 dice_loss 0.54217 +Epoch [3531/4000] Validation [10/10] Loss: 1.87111 focal_loss 1.13481 dice_loss 0.73631 +Epoch [3531/4000] Validation metric {'Val/mean dice_metric': 0.9516439437866211, 'Val/mean miou_metric': 0.9356539845466614, 'Val/mean f1': 0.9487902522087097, 'Val/mean precision': 0.9434289932250977, 'Val/mean recall': 0.9542126059532166, 'Val/mean hd95_metric': 10.78345012664795} +Cheakpoint... +Epoch [3531/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516439437866211, 'Val/mean miou_metric': 0.9356539845466614, 'Val/mean f1': 0.9487902522087097, 'Val/mean precision': 0.9434289932250977, 'Val/mean recall': 0.9542126059532166, 'Val/mean hd95_metric': 10.78345012664795} +Epoch [3532/4000] Training [1/39] Loss: 0.00626 +Epoch [3532/4000] Training [2/39] Loss: 0.00294 +Epoch [3532/4000] Training [3/39] Loss: 0.00505 +Epoch [3532/4000] Training [4/39] Loss: 0.13548 +Epoch [3532/4000] Training [5/39] Loss: 0.13379 +Epoch [3532/4000] Training [6/39] Loss: 0.12882 +Epoch [3532/4000] Training [7/39] Loss: 0.00544 +Epoch [3532/4000] Training [8/39] Loss: 0.00361 +Epoch [3532/4000] Training [9/39] Loss: 0.00433 +Epoch [3532/4000] Training [10/39] Loss: 0.00459 +Epoch [3532/4000] Training [11/39] Loss: 0.00597 +Epoch [3532/4000] Training [12/39] Loss: 0.00562 +Epoch [3532/4000] Training [13/39] Loss: 0.00636 +Epoch [3532/4000] Training [14/39] Loss: 0.00462 +Epoch [3532/4000] Training [15/39] Loss: 0.00646 +Epoch [3532/4000] Training [16/39] Loss: 0.00595 +Epoch [3532/4000] Training [17/39] Loss: 0.26007 +Epoch [3532/4000] Training [18/39] Loss: 0.13008 +Epoch [3532/4000] Training [19/39] Loss: 0.00444 +Epoch [3532/4000] Training [20/39] Loss: 0.00722 +Epoch [3532/4000] Training [21/39] Loss: 0.00658 +Epoch [3532/4000] Training [22/39] Loss: 0.13070 +Epoch [3532/4000] Training [23/39] Loss: 0.00711 +Epoch [3532/4000] Training [24/39] Loss: 0.00371 +Epoch [3532/4000] Training [25/39] Loss: 0.13114 +Epoch [3532/4000] Training [26/39] Loss: 0.00465 +Epoch [3532/4000] Training [27/39] Loss: 0.00322 +Epoch [3532/4000] Training [28/39] Loss: 0.00509 +Epoch [3532/4000] Training [29/39] Loss: 0.00463 +Epoch [3532/4000] Training [30/39] Loss: 0.00380 +Epoch [3532/4000] Training [31/39] Loss: 0.00551 +Epoch [3532/4000] Training [32/39] Loss: 0.25247 +Epoch [3532/4000] Training [33/39] Loss: 0.00611 +Epoch [3532/4000] Training [34/39] Loss: 0.01028 +Epoch [3532/4000] Training [35/39] Loss: 0.00464 +Epoch [3532/4000] Training [36/39] Loss: 0.00501 +Epoch [3532/4000] Training [37/39] Loss: 0.00486 +Epoch [3532/4000] Training [38/39] Loss: 0.00547 +Epoch [3532/4000] Training [39/39] Loss: 0.25353 +Epoch [3532/4000] Training metric {'Train/mean dice_metric': 0.994917094707489, 'Train/mean miou_metric': 0.9911190271377563, 'Train/mean f1': 0.9964747428894043, 'Train/mean precision': 0.9960013628005981, 'Train/mean recall': 0.9969485998153687, 'Train/mean hd95_metric': 1.0877586603164673} +Epoch [3532/4000] Validation [1/10] Loss: 0.72576 focal_loss 0.63907 dice_loss 0.08670 +Epoch [3532/4000] Validation [2/10] Loss: 0.48773 focal_loss 0.39205 dice_loss 0.09568 +Epoch [3532/4000] Validation [3/10] Loss: 0.39162 focal_loss 0.28117 dice_loss 0.11045 +Epoch [3532/4000] Validation [4/10] Loss: 0.88603 focal_loss 0.31824 dice_loss 0.56779 +Epoch [3532/4000] Validation [5/10] Loss: 3.04140 focal_loss 2.36744 dice_loss 0.67397 +Epoch [3532/4000] Validation [6/10] Loss: 1.29548 focal_loss 0.58363 dice_loss 0.71185 +Epoch [3532/4000] Validation [7/10] Loss: 1.13992 focal_loss 0.48429 dice_loss 0.65563 +Epoch [3532/4000] Validation [8/10] Loss: 2.52638 focal_loss 1.89053 dice_loss 0.63585 +Epoch [3532/4000] Validation [9/10] Loss: 1.39522 focal_loss 0.85512 dice_loss 0.54011 +Epoch [3532/4000] Validation [10/10] Loss: 1.79797 focal_loss 1.06955 dice_loss 0.72842 +Epoch [3532/4000] Validation metric {'Val/mean dice_metric': 0.9504491090774536, 'Val/mean miou_metric': 0.9341356754302979, 'Val/mean f1': 0.9492013454437256, 'Val/mean precision': 0.9466812014579773, 'Val/mean recall': 0.9517349004745483, 'Val/mean hd95_metric': 10.697303771972656} +Cheakpoint... +Epoch [3532/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504491090774536, 'Val/mean miou_metric': 0.9341356754302979, 'Val/mean f1': 0.9492013454437256, 'Val/mean precision': 0.9466812014579773, 'Val/mean recall': 0.9517349004745483, 'Val/mean hd95_metric': 10.697303771972656} +Epoch [3533/4000] Training [1/39] Loss: 0.00685 +Epoch [3533/4000] Training [2/39] Loss: 0.00478 +Epoch [3533/4000] Training [3/39] Loss: 0.00435 +Epoch [3533/4000] Training [4/39] Loss: 0.13196 +Epoch [3533/4000] Training [5/39] Loss: 0.00480 +Epoch [3533/4000] Training [6/39] Loss: 0.00778 +Epoch [3533/4000] Training [7/39] Loss: 0.00409 +Epoch [3533/4000] Training [8/39] Loss: 0.00485 +Epoch [3533/4000] Training [9/39] Loss: 0.00652 +Epoch [3533/4000] Training [10/39] Loss: 0.12952 +Epoch [3533/4000] Training [11/39] Loss: 0.12933 +Epoch [3533/4000] Training [12/39] Loss: 0.12827 +Epoch [3533/4000] Training [13/39] Loss: 0.00560 +Epoch [3533/4000] Training [14/39] Loss: 0.13181 +Epoch [3533/4000] Training [15/39] Loss: 0.01293 +Epoch [3533/4000] Training [16/39] Loss: 0.00431 +Epoch [3533/4000] Training [17/39] Loss: 0.00383 +Epoch [3533/4000] Training [18/39] Loss: 0.00508 +Epoch [3533/4000] Training [19/39] Loss: 0.00633 +Epoch [3533/4000] Training [20/39] Loss: 0.00549 +Epoch [3533/4000] Training [21/39] Loss: 0.00475 +Epoch [3533/4000] Training [22/39] Loss: 0.00399 +Epoch [3533/4000] Training [23/39] Loss: 0.12869 +Epoch [3533/4000] Training [24/39] Loss: 0.12765 +Epoch [3533/4000] Training [25/39] Loss: 0.00430 +Epoch [3533/4000] Training [26/39] Loss: 0.12937 +Epoch [3533/4000] Training [27/39] Loss: 0.00289 +Epoch [3533/4000] Training [28/39] Loss: 0.00519 +Epoch [3533/4000] Training [29/39] Loss: 0.25366 +Epoch [3533/4000] Training [30/39] Loss: 0.00461 +Epoch [3533/4000] Training [31/39] Loss: 0.00576 +Epoch [3533/4000] Training [32/39] Loss: 0.00486 +Epoch [3533/4000] Training [33/39] Loss: 0.13112 +Epoch [3533/4000] Training [34/39] Loss: 0.00356 +Epoch [3533/4000] Training [35/39] Loss: 0.13268 +Epoch [3533/4000] Training [36/39] Loss: 0.13144 +Epoch [3533/4000] Training [37/39] Loss: 0.00524 +Epoch [3533/4000] Training [38/39] Loss: 0.00524 +Epoch [3533/4000] Training [39/39] Loss: 0.00675 +Epoch [3533/4000] Training metric {'Train/mean dice_metric': 0.9959060549736023, 'Train/mean miou_metric': 0.9922783374786377, 'Train/mean f1': 0.9967687129974365, 'Train/mean precision': 0.9963366389274597, 'Train/mean recall': 0.9972010850906372, 'Train/mean hd95_metric': 0.9959121346473694} +Epoch [3533/4000] Validation [1/10] Loss: 0.70229 focal_loss 0.61820 dice_loss 0.08410 +Epoch [3533/4000] Validation [2/10] Loss: 0.49140 focal_loss 0.39305 dice_loss 0.09835 +Epoch [3533/4000] Validation [3/10] Loss: 0.40062 focal_loss 0.28929 dice_loss 0.11133 +Epoch [3533/4000] Validation [4/10] Loss: 0.88410 focal_loss 0.30679 dice_loss 0.57732 +Epoch [3533/4000] Validation [5/10] Loss: 3.03238 focal_loss 2.35809 dice_loss 0.67428 +Epoch [3533/4000] Validation [6/10] Loss: 1.27466 focal_loss 0.56010 dice_loss 0.71456 +Epoch [3533/4000] Validation [7/10] Loss: 1.12530 focal_loss 0.47544 dice_loss 0.64987 +Epoch [3533/4000] Validation [8/10] Loss: 2.64460 focal_loss 1.99974 dice_loss 0.64486 +Epoch [3533/4000] Validation [9/10] Loss: 1.41758 focal_loss 0.87717 dice_loss 0.54040 +Epoch [3533/4000] Validation [10/10] Loss: 1.76041 focal_loss 1.03535 dice_loss 0.72506 +Epoch [3533/4000] Validation metric {'Val/mean dice_metric': 0.9512010216712952, 'Val/mean miou_metric': 0.9350547194480896, 'Val/mean f1': 0.9499403238296509, 'Val/mean precision': 0.9488474130630493, 'Val/mean recall': 0.9510357975959778, 'Val/mean hd95_metric': 10.46609878540039} +Cheakpoint... +Epoch [3533/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512010216712952, 'Val/mean miou_metric': 0.9350547194480896, 'Val/mean f1': 0.9499403238296509, 'Val/mean precision': 0.9488474130630493, 'Val/mean recall': 0.9510357975959778, 'Val/mean hd95_metric': 10.46609878540039} +Epoch [3534/4000] Training [1/39] Loss: 0.00689 +Epoch [3534/4000] Training [2/39] Loss: 0.12767 +Epoch [3534/4000] Training [3/39] Loss: 0.13005 +Epoch [3534/4000] Training [4/39] Loss: 0.00544 +Epoch [3534/4000] Training [5/39] Loss: 0.00457 +Epoch [3534/4000] Training [6/39] Loss: 0.00381 +Epoch [3534/4000] Training [7/39] Loss: 0.00407 +Epoch [3534/4000] Training [8/39] Loss: 0.00378 +Epoch [3534/4000] Training [9/39] Loss: 0.00577 +Epoch [3534/4000] Training [10/39] Loss: 0.00595 +Epoch [3534/4000] Training [11/39] Loss: 0.00370 +Epoch [3534/4000] Training [12/39] Loss: 0.00283 +Epoch [3534/4000] Training [13/39] Loss: 0.05261 +Epoch [3534/4000] Training [14/39] Loss: 0.00487 +Epoch [3534/4000] Training [15/39] Loss: 0.00631 +Epoch [3534/4000] Training [16/39] Loss: 0.00547 +Epoch [3534/4000] Training [17/39] Loss: 0.00732 +Epoch [3534/4000] Training [18/39] Loss: 0.00504 +Epoch [3534/4000] Training [19/39] Loss: 0.13375 +Epoch [3534/4000] Training [20/39] Loss: 0.12964 +Epoch [3534/4000] Training [21/39] Loss: 0.12956 +Epoch [3534/4000] Training [22/39] Loss: 0.13042 +Epoch [3534/4000] Training [23/39] Loss: 0.00619 +Epoch [3534/4000] Training [24/39] Loss: 0.00278 +Epoch [3534/4000] Training [25/39] Loss: 0.00522 +Epoch [3534/4000] Training [26/39] Loss: 0.00320 +Epoch [3534/4000] Training [27/39] Loss: 0.00383 +Epoch [3534/4000] Training [28/39] Loss: 0.00558 +Epoch [3534/4000] Training [29/39] Loss: 0.00401 +Epoch [3534/4000] Training [30/39] Loss: 0.00894 +Epoch [3534/4000] Training [31/39] Loss: 0.00790 +Epoch [3534/4000] Training [32/39] Loss: 0.00565 +Epoch [3534/4000] Training [33/39] Loss: 0.00532 +Epoch [3534/4000] Training [34/39] Loss: 0.13030 +Epoch [3534/4000] Training [35/39] Loss: 0.12942 +Epoch [3534/4000] Training [36/39] Loss: 0.25567 +Epoch [3534/4000] Training [37/39] Loss: 0.13145 +Epoch [3534/4000] Training [38/39] Loss: 0.00831 +Epoch [3534/4000] Training [39/39] Loss: 0.00710 +Epoch [3534/4000] Training metric {'Train/mean dice_metric': 0.9960561990737915, 'Train/mean miou_metric': 0.9925643801689148, 'Train/mean f1': 0.9966851472854614, 'Train/mean precision': 0.9961903095245361, 'Train/mean recall': 0.9971804618835449, 'Train/mean hd95_metric': 0.9933014512062073} +Epoch [3534/4000] Validation [1/10] Loss: 0.71099 focal_loss 0.62532 dice_loss 0.08567 +Epoch [3534/4000] Validation [2/10] Loss: 0.48610 focal_loss 0.39296 dice_loss 0.09313 +Epoch [3534/4000] Validation [3/10] Loss: 0.39946 focal_loss 0.28892 dice_loss 0.11053 +Epoch [3534/4000] Validation [4/10] Loss: 0.88749 focal_loss 0.31306 dice_loss 0.57443 +Epoch [3534/4000] Validation [5/10] Loss: 3.07685 focal_loss 2.40289 dice_loss 0.67395 +Epoch [3534/4000] Validation [6/10] Loss: 1.32083 focal_loss 0.60238 dice_loss 0.71845 +Epoch [3534/4000] Validation [7/10] Loss: 1.15523 focal_loss 0.49755 dice_loss 0.65768 +Epoch [3534/4000] Validation [8/10] Loss: 2.50122 focal_loss 1.87551 dice_loss 0.62571 +Epoch [3534/4000] Validation [9/10] Loss: 1.41362 focal_loss 0.87312 dice_loss 0.54050 +Epoch [3534/4000] Validation [10/10] Loss: 1.81860 focal_loss 1.08926 dice_loss 0.72934 +Epoch [3534/4000] Validation metric {'Val/mean dice_metric': 0.9513594508171082, 'Val/mean miou_metric': 0.9352936148643494, 'Val/mean f1': 0.9492427110671997, 'Val/mean precision': 0.9459418654441833, 'Val/mean recall': 0.9525666236877441, 'Val/mean hd95_metric': 10.67574691772461} +Cheakpoint... +Epoch [3534/4000] best acc:tensor([0.9517], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513594508171082, 'Val/mean miou_metric': 0.9352936148643494, 'Val/mean f1': 0.9492427110671997, 'Val/mean precision': 0.9459418654441833, 'Val/mean recall': 0.9525666236877441, 'Val/mean hd95_metric': 10.67574691772461} +Epoch [3535/4000] Training [1/39] Loss: 0.00466 +Epoch [3535/4000] Training [2/39] Loss: 0.00486 +Epoch [3535/4000] Training [3/39] Loss: 0.00510 +Epoch [3535/4000] Training [4/39] Loss: 0.00408 +Epoch [3535/4000] Training [5/39] Loss: 0.00680 +Epoch [3535/4000] Training [6/39] Loss: 0.00392 +Epoch [3535/4000] Training [7/39] Loss: 0.00753 +Epoch [3535/4000] Training [8/39] Loss: 0.00587 +Epoch [3535/4000] Training [9/39] Loss: 0.21826 +Epoch [3535/4000] Training [10/39] Loss: 0.00634 +Epoch [3535/4000] Training [11/39] Loss: 0.25503 +Epoch [3535/4000] Training [12/39] Loss: 0.00418 +Epoch [3535/4000] Training [13/39] Loss: 0.00503 +Epoch [3535/4000] Training [14/39] Loss: 0.13016 +Epoch [3535/4000] Training [15/39] Loss: 0.00500 +Epoch [3535/4000] Training [16/39] Loss: 0.00463 +Epoch [3535/4000] Training [17/39] Loss: 0.00475 +Epoch [3535/4000] Training [18/39] Loss: 0.00351 +Epoch [3535/4000] Training [19/39] Loss: 0.00478 +Epoch [3535/4000] Training [20/39] Loss: 0.12980 +Epoch [3535/4000] Training [21/39] Loss: 0.12960 +Epoch [3535/4000] Training [22/39] Loss: 0.12886 +Epoch [3535/4000] Training [23/39] Loss: 0.00618 +Epoch [3535/4000] Training [24/39] Loss: 0.00366 +Epoch [3535/4000] Training [25/39] Loss: 0.00452 +Epoch [3535/4000] Training [26/39] Loss: 0.00407 +Epoch [3535/4000] Training [27/39] Loss: 0.12815 +Epoch [3535/4000] Training [28/39] Loss: 0.00538 +Epoch [3535/4000] Training [29/39] Loss: 0.12789 +Epoch [3535/4000] Training [30/39] Loss: 0.12905 +Epoch [3535/4000] Training [31/39] Loss: 0.00493 +Epoch [3535/4000] Training [32/39] Loss: 0.00575 +Epoch [3535/4000] Training [33/39] Loss: 0.00907 +Epoch [3535/4000] Training [34/39] Loss: 0.00496 +Epoch [3535/4000] Training [35/39] Loss: 0.00594 +Epoch [3535/4000] Training [36/39] Loss: 0.00378 +Epoch [3535/4000] Training [37/39] Loss: 0.12931 +Epoch [3535/4000] Training [38/39] Loss: 0.00439 +Epoch [3535/4000] Training [39/39] Loss: 0.00774 +Epoch [3535/4000] Training metric {'Train/mean dice_metric': 0.9963293671607971, 'Train/mean miou_metric': 0.9931381344795227, 'Train/mean f1': 0.9969112873077393, 'Train/mean precision': 0.996488094329834, 'Train/mean recall': 0.997334897518158, 'Train/mean hd95_metric': 0.9580140709877014} +Epoch [3535/4000] Validation [1/10] Loss: 0.69424 focal_loss 0.60950 dice_loss 0.08474 +Epoch [3535/4000] Validation [2/10] Loss: 0.49303 focal_loss 0.39515 dice_loss 0.09788 +Epoch [3535/4000] Validation [3/10] Loss: 0.38903 focal_loss 0.27863 dice_loss 0.11040 +Epoch [3535/4000] Validation [4/10] Loss: 0.87457 focal_loss 0.30701 dice_loss 0.56755 +Epoch [3535/4000] Validation [5/10] Loss: 3.03854 focal_loss 2.36563 dice_loss 0.67291 +Epoch [3535/4000] Validation [6/10] Loss: 1.31384 focal_loss 0.59268 dice_loss 0.72116 +Epoch [3535/4000] Validation [7/10] Loss: 1.14177 focal_loss 0.49165 dice_loss 0.65012 +Epoch [3535/4000] Validation [8/10] Loss: 2.46256 focal_loss 1.83314 dice_loss 0.62941 +Epoch [3535/4000] Validation [9/10] Loss: 1.39928 focal_loss 0.85705 dice_loss 0.54222 +Epoch [3535/4000] Validation [10/10] Loss: 1.82440 focal_loss 1.09211 dice_loss 0.73229 +Epoch [3535/4000] Validation metric {'Val/mean dice_metric': 0.9517925381660461, 'Val/mean miou_metric': 0.9360669851303101, 'Val/mean f1': 0.9503430724143982, 'Val/mean precision': 0.9470710754394531, 'Val/mean recall': 0.9536377191543579, 'Val/mean hd95_metric': 10.567523002624512} +Cheakpoint... +Epoch [3535/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9518], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9517925381660461, 'Val/mean miou_metric': 0.9360669851303101, 'Val/mean f1': 0.9503430724143982, 'Val/mean precision': 0.9470710754394531, 'Val/mean recall': 0.9536377191543579, 'Val/mean hd95_metric': 10.567523002624512} +Epoch [3536/4000] Training [1/39] Loss: 0.00537 +Epoch [3536/4000] Training [2/39] Loss: 0.00463 +Epoch [3536/4000] Training [3/39] Loss: 0.00351 +Epoch [3536/4000] Training [4/39] Loss: 0.13204 +Epoch [3536/4000] Training [5/39] Loss: 0.00582 +Epoch [3536/4000] Training [6/39] Loss: 0.00348 +Epoch [3536/4000] Training [7/39] Loss: 0.00474 +Epoch [3536/4000] Training [8/39] Loss: 0.12835 +Epoch [3536/4000] Training [9/39] Loss: 0.00620 +Epoch [3536/4000] Training [10/39] Loss: 0.00464 +Epoch [3536/4000] Training [11/39] Loss: 0.00970 +Epoch [3536/4000] Training [12/39] Loss: 0.00279 +Epoch [3536/4000] Training [13/39] Loss: 0.00494 +Epoch [3536/4000] Training [14/39] Loss: 0.00484 +Epoch [3536/4000] Training [15/39] Loss: 0.00453 +Epoch [3536/4000] Training [16/39] Loss: 0.12835 +Epoch [3536/4000] Training [17/39] Loss: 0.13209 +Epoch [3536/4000] Training [18/39] Loss: 0.00344 +Epoch [3536/4000] Training [19/39] Loss: 0.00307 +Epoch [3536/4000] Training [20/39] Loss: 0.00625 +Epoch [3536/4000] Training [21/39] Loss: 0.00381 +Epoch [3536/4000] Training [22/39] Loss: 0.12856 +Epoch [3536/4000] Training [23/39] Loss: 0.00362 +Epoch [3536/4000] Training [24/39] Loss: 0.12906 +Epoch [3536/4000] Training [25/39] Loss: 0.00505 +Epoch [3536/4000] Training [26/39] Loss: 0.00651 +Epoch [3536/4000] Training [27/39] Loss: 0.00362 +Epoch [3536/4000] Training [28/39] Loss: 0.00496 +Epoch [3536/4000] Training [29/39] Loss: 0.00444 +Epoch [3536/4000] Training [30/39] Loss: 0.00569 +Epoch [3536/4000] Training [31/39] Loss: 0.00339 +Epoch [3536/4000] Training [32/39] Loss: 0.00564 +Epoch [3536/4000] Training [33/39] Loss: 0.00544 +Epoch [3536/4000] Training [34/39] Loss: 0.00491 +Epoch [3536/4000] Training [35/39] Loss: 0.12966 +Epoch [3536/4000] Training [36/39] Loss: 0.12879 +Epoch [3536/4000] Training [37/39] Loss: 0.00488 +Epoch [3536/4000] Training [38/39] Loss: 0.13023 +Epoch [3536/4000] Training [39/39] Loss: 0.12984 +Epoch [3536/4000] Training metric {'Train/mean dice_metric': 0.9962334632873535, 'Train/mean miou_metric': 0.9928985238075256, 'Train/mean f1': 0.9968580007553101, 'Train/mean precision': 0.9964446425437927, 'Train/mean recall': 0.997271716594696, 'Train/mean hd95_metric': 0.9714331030845642} +Epoch [3536/4000] Validation [1/10] Loss: 0.70174 focal_loss 0.61516 dice_loss 0.08658 +Epoch [3536/4000] Validation [2/10] Loss: 0.49592 focal_loss 0.39897 dice_loss 0.09695 +Epoch [3536/4000] Validation [3/10] Loss: 0.37204 focal_loss 0.26257 dice_loss 0.10948 +Epoch [3536/4000] Validation [4/10] Loss: 0.88050 focal_loss 0.31155 dice_loss 0.56895 +Epoch [3536/4000] Validation [5/10] Loss: 2.97220 focal_loss 2.29924 dice_loss 0.67296 +Epoch [3536/4000] Validation [6/10] Loss: 1.32992 focal_loss 0.60941 dice_loss 0.72051 +Epoch [3536/4000] Validation [7/10] Loss: 1.14213 focal_loss 0.48995 dice_loss 0.65218 +Epoch [3536/4000] Validation [8/10] Loss: 2.38772 focal_loss 1.76388 dice_loss 0.62385 +Epoch [3536/4000] Validation [9/10] Loss: 1.41051 focal_loss 0.86770 dice_loss 0.54281 +Epoch [3536/4000] Validation [10/10] Loss: 1.83486 focal_loss 1.10402 dice_loss 0.73084 +Epoch [3536/4000] Validation metric {'Val/mean dice_metric': 0.95174241065979, 'Val/mean miou_metric': 0.935853123664856, 'Val/mean f1': 0.9497633576393127, 'Val/mean precision': 0.9460129737854004, 'Val/mean recall': 0.9535437226295471, 'Val/mean hd95_metric': 10.588595390319824} +Cheakpoint... +Epoch [3536/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.95174241065979, 'Val/mean miou_metric': 0.935853123664856, 'Val/mean f1': 0.9497633576393127, 'Val/mean precision': 0.9460129737854004, 'Val/mean recall': 0.9535437226295471, 'Val/mean hd95_metric': 10.588595390319824} +Epoch [3537/4000] Training [1/39] Loss: 0.00424 +Epoch [3537/4000] Training [2/39] Loss: 0.00720 +Epoch [3537/4000] Training [3/39] Loss: 0.12901 +Epoch [3537/4000] Training [4/39] Loss: 0.00659 +Epoch [3537/4000] Training [5/39] Loss: 0.00614 +Epoch [3537/4000] Training [6/39] Loss: 0.00706 +Epoch [3537/4000] Training [7/39] Loss: 0.00528 +Epoch [3537/4000] Training [8/39] Loss: 0.12858 +Epoch [3537/4000] Training [9/39] Loss: 0.00772 +Epoch [3537/4000] Training [10/39] Loss: 0.00752 +Epoch [3537/4000] Training [11/39] Loss: 0.12980 +Epoch [3537/4000] Training [12/39] Loss: 0.12847 +Epoch [3537/4000] Training [13/39] Loss: 0.00385 +Epoch [3537/4000] Training [14/39] Loss: 0.00525 +Epoch [3537/4000] Training [15/39] Loss: 0.00672 +Epoch [3537/4000] Training [16/39] Loss: 0.13235 +Epoch [3537/4000] Training [17/39] Loss: 0.00641 +Epoch [3537/4000] Training [18/39] Loss: 0.00578 +Epoch [3537/4000] Training [19/39] Loss: 0.00628 +Epoch [3537/4000] Training [20/39] Loss: 0.00536 +Epoch [3537/4000] Training [21/39] Loss: 0.12859 +Epoch [3537/4000] Training [22/39] Loss: 0.00462 +Epoch [3537/4000] Training [23/39] Loss: 0.00528 +Epoch [3537/4000] Training [24/39] Loss: 0.00541 +Epoch [3537/4000] Training [25/39] Loss: 0.00629 +Epoch [3537/4000] Training [26/39] Loss: 0.00326 +Epoch [3537/4000] Training [27/39] Loss: 0.00635 +Epoch [3537/4000] Training [28/39] Loss: 0.00470 +Epoch [3537/4000] Training [29/39] Loss: 0.12903 +Epoch [3537/4000] Training [30/39] Loss: 0.12905 +Epoch [3537/4000] Training [31/39] Loss: 0.00379 +Epoch [3537/4000] Training [32/39] Loss: 0.00782 +Epoch [3537/4000] Training [33/39] Loss: 0.00421 +Epoch [3537/4000] Training [34/39] Loss: 0.12872 +Epoch [3537/4000] Training [35/39] Loss: 0.00500 +Epoch [3537/4000] Training [36/39] Loss: 0.00468 +Epoch [3537/4000] Training [37/39] Loss: 0.00681 +Epoch [3537/4000] Training [38/39] Loss: 0.00515 +Epoch [3537/4000] Training [39/39] Loss: 0.00463 +Epoch [3537/4000] Training metric {'Train/mean dice_metric': 0.9958301186561584, 'Train/mean miou_metric': 0.9921382069587708, 'Train/mean f1': 0.9966428875923157, 'Train/mean precision': 0.9962130784988403, 'Train/mean recall': 0.9970731735229492, 'Train/mean hd95_metric': 1.0078632831573486} +Epoch [3537/4000] Validation [1/10] Loss: 0.71488 focal_loss 0.62860 dice_loss 0.08628 +Epoch [3537/4000] Validation [2/10] Loss: 0.49455 focal_loss 0.39626 dice_loss 0.09828 +Epoch [3537/4000] Validation [3/10] Loss: 0.40354 focal_loss 0.29176 dice_loss 0.11179 +Epoch [3537/4000] Validation [4/10] Loss: 0.87496 focal_loss 0.30199 dice_loss 0.57297 +Epoch [3537/4000] Validation [5/10] Loss: 3.00412 focal_loss 2.33043 dice_loss 0.67369 +Epoch [3537/4000] Validation [6/10] Loss: 1.29049 focal_loss 0.57153 dice_loss 0.71896 +Epoch [3537/4000] Validation [7/10] Loss: 1.12292 focal_loss 0.47196 dice_loss 0.65096 +Epoch [3537/4000] Validation [8/10] Loss: 2.61177 focal_loss 1.96944 dice_loss 0.64233 +Epoch [3537/4000] Validation [9/10] Loss: 1.39949 focal_loss 0.85756 dice_loss 0.54194 +Epoch [3537/4000] Validation [10/10] Loss: 1.78916 focal_loss 1.06054 dice_loss 0.72862 +Epoch [3537/4000] Validation metric {'Val/mean dice_metric': 0.951094388961792, 'Val/mean miou_metric': 0.93488609790802, 'Val/mean f1': 0.9501336812973022, 'Val/mean precision': 0.9481520056724548, 'Val/mean recall': 0.952123761177063, 'Val/mean hd95_metric': 10.723522186279297} +Cheakpoint... +Epoch [3537/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951094388961792, 'Val/mean miou_metric': 0.93488609790802, 'Val/mean f1': 0.9501336812973022, 'Val/mean precision': 0.9481520056724548, 'Val/mean recall': 0.952123761177063, 'Val/mean hd95_metric': 10.723522186279297} +Epoch [3538/4000] Training [1/39] Loss: 0.13037 +Epoch [3538/4000] Training [2/39] Loss: 0.00302 +Epoch [3538/4000] Training [3/39] Loss: 0.00622 +Epoch [3538/4000] Training [4/39] Loss: 0.00434 +Epoch [3538/4000] Training [5/39] Loss: 0.00441 +Epoch [3538/4000] Training [6/39] Loss: 0.00546 +Epoch [3538/4000] Training [7/39] Loss: 0.37821 +Epoch [3538/4000] Training [8/39] Loss: 0.00414 +Epoch [3538/4000] Training [9/39] Loss: 0.00430 +Epoch [3538/4000] Training [10/39] Loss: 0.12813 +Epoch [3538/4000] Training [11/39] Loss: 0.12971 +Epoch [3538/4000] Training [12/39] Loss: 0.00662 +Epoch [3538/4000] Training [13/39] Loss: 0.00469 +Epoch [3538/4000] Training [14/39] Loss: 0.13029 +Epoch [3538/4000] Training [15/39] Loss: 0.00523 +Epoch [3538/4000] Training [16/39] Loss: 0.00715 +Epoch [3538/4000] Training [17/39] Loss: 0.13020 +Epoch [3538/4000] Training [18/39] Loss: 0.00986 +Epoch [3538/4000] Training [19/39] Loss: 0.13184 +Epoch [3538/4000] Training [20/39] Loss: 0.00515 +Epoch [3538/4000] Training [21/39] Loss: 0.00329 +Epoch [3538/4000] Training [22/39] Loss: 0.00514 +Epoch [3538/4000] Training [23/39] Loss: 0.12822 +Epoch [3538/4000] Training [24/39] Loss: 0.00632 +Epoch [3538/4000] Training [25/39] Loss: 0.13008 +Epoch [3538/4000] Training [26/39] Loss: 0.00897 +Epoch [3538/4000] Training [27/39] Loss: 0.00776 +Epoch [3538/4000] Training [28/39] Loss: 0.12982 +Epoch [3538/4000] Training [29/39] Loss: 0.37811 +Epoch [3538/4000] Training [30/39] Loss: 0.00819 +Epoch [3538/4000] Training [31/39] Loss: 0.00418 +Epoch [3538/4000] Training [32/39] Loss: 0.00776 +Epoch [3538/4000] Training [33/39] Loss: 0.00507 +Epoch [3538/4000] Training [34/39] Loss: 0.00774 +Epoch [3538/4000] Training [35/39] Loss: 0.00466 +Epoch [3538/4000] Training [36/39] Loss: 0.00594 +Epoch [3538/4000] Training [37/39] Loss: 0.00483 +Epoch [3538/4000] Training [38/39] Loss: 0.00418 +Epoch [3538/4000] Training [39/39] Loss: 0.12725 +Epoch [3538/4000] Training metric {'Train/mean dice_metric': 0.995947539806366, 'Train/mean miou_metric': 0.9923468232154846, 'Train/mean f1': 0.9965327382087708, 'Train/mean precision': 0.9960987567901611, 'Train/mean recall': 0.9969672560691833, 'Train/mean hd95_metric': 1.0085698366165161} +Epoch [3538/4000] Validation [1/10] Loss: 0.70380 focal_loss 0.61723 dice_loss 0.08658 +Epoch [3538/4000] Validation [2/10] Loss: 0.49300 focal_loss 0.39296 dice_loss 0.10004 +Epoch [3538/4000] Validation [3/10] Loss: 0.38785 focal_loss 0.27702 dice_loss 0.11084 +Epoch [3538/4000] Validation [4/10] Loss: 0.86769 focal_loss 0.30197 dice_loss 0.56572 +Epoch [3538/4000] Validation [5/10] Loss: 2.98331 focal_loss 2.31002 dice_loss 0.67329 +Epoch [3538/4000] Validation [6/10] Loss: 1.28278 focal_loss 0.56309 dice_loss 0.71969 +Epoch [3538/4000] Validation [7/10] Loss: 1.14192 focal_loss 0.48835 dice_loss 0.65357 +Epoch [3538/4000] Validation [8/10] Loss: 2.35032 focal_loss 1.72757 dice_loss 0.62275 +Epoch [3538/4000] Validation [9/10] Loss: 1.38263 focal_loss 0.84104 dice_loss 0.54159 +Epoch [3538/4000] Validation [10/10] Loss: 1.80203 focal_loss 1.06954 dice_loss 0.73249 +Epoch [3538/4000] Validation metric {'Val/mean dice_metric': 0.9513251781463623, 'Val/mean miou_metric': 0.9352441430091858, 'Val/mean f1': 0.9497065544128418, 'Val/mean precision': 0.9462245106697083, 'Val/mean recall': 0.9532143473625183, 'Val/mean hd95_metric': 10.765580177307129} +Cheakpoint... +Epoch [3538/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513251781463623, 'Val/mean miou_metric': 0.9352441430091858, 'Val/mean f1': 0.9497065544128418, 'Val/mean precision': 0.9462245106697083, 'Val/mean recall': 0.9532143473625183, 'Val/mean hd95_metric': 10.765580177307129} +Epoch [3539/4000] Training [1/39] Loss: 0.00490 +Epoch [3539/4000] Training [2/39] Loss: 0.00477 +Epoch [3539/4000] Training [3/39] Loss: 0.00632 +Epoch [3539/4000] Training [4/39] Loss: 0.00505 +Epoch [3539/4000] Training [5/39] Loss: 0.12852 +Epoch [3539/4000] Training [6/39] Loss: 0.12913 +Epoch [3539/4000] Training [7/39] Loss: 0.00535 +Epoch [3539/4000] Training [8/39] Loss: 0.00558 +Epoch [3539/4000] Training [9/39] Loss: 0.00462 +Epoch [3539/4000] Training [10/39] Loss: 0.00406 +Epoch [3539/4000] Training [11/39] Loss: 0.13032 +Epoch [3539/4000] Training [12/39] Loss: 0.00476 +Epoch [3539/4000] Training [13/39] Loss: 0.00752 +Epoch [3539/4000] Training [14/39] Loss: 0.00514 +Epoch [3539/4000] Training [15/39] Loss: 0.13097 +Epoch [3539/4000] Training [16/39] Loss: 0.12786 +Epoch [3539/4000] Training [17/39] Loss: 0.00519 +Epoch [3539/4000] Training [18/39] Loss: 0.00511 +Epoch [3539/4000] Training [19/39] Loss: 0.00392 +Epoch [3539/4000] Training [20/39] Loss: 0.00470 +Epoch [3539/4000] Training [21/39] Loss: 0.13146 +Epoch [3539/4000] Training [22/39] Loss: 0.00452 +Epoch [3539/4000] Training [23/39] Loss: 0.00412 +Epoch [3539/4000] Training [24/39] Loss: 0.00470 +Epoch [3539/4000] Training [25/39] Loss: 0.12969 +Epoch [3539/4000] Training [26/39] Loss: 0.00548 +Epoch [3539/4000] Training [27/39] Loss: 0.00337 +Epoch [3539/4000] Training [28/39] Loss: 0.00367 +Epoch [3539/4000] Training [29/39] Loss: 0.00474 +Epoch [3539/4000] Training [30/39] Loss: 0.12930 +Epoch [3539/4000] Training [31/39] Loss: 0.00418 +Epoch [3539/4000] Training [32/39] Loss: 0.00523 +Epoch [3539/4000] Training [33/39] Loss: 0.00538 +Epoch [3539/4000] Training [34/39] Loss: 0.00773 +Epoch [3539/4000] Training [35/39] Loss: 0.00350 +Epoch [3539/4000] Training [36/39] Loss: 0.00651 +Epoch [3539/4000] Training [37/39] Loss: 0.12819 +Epoch [3539/4000] Training [38/39] Loss: 0.12777 +Epoch [3539/4000] Training [39/39] Loss: 0.00365 +Epoch [3539/4000] Training metric {'Train/mean dice_metric': 0.9961777925491333, 'Train/mean miou_metric': 0.9928143620491028, 'Train/mean f1': 0.9967753887176514, 'Train/mean precision': 0.996317982673645, 'Train/mean recall': 0.9972332715988159, 'Train/mean hd95_metric': 0.9673492908477783} +Epoch [3539/4000] Validation [1/10] Loss: 0.68936 focal_loss 0.60424 dice_loss 0.08512 +Epoch [3539/4000] Validation [2/10] Loss: 0.48697 focal_loss 0.38710 dice_loss 0.09987 +Epoch [3539/4000] Validation [3/10] Loss: 0.38575 focal_loss 0.27487 dice_loss 0.11088 +Epoch [3539/4000] Validation [4/10] Loss: 0.86480 focal_loss 0.29711 dice_loss 0.56769 +Epoch [3539/4000] Validation [5/10] Loss: 2.93993 focal_loss 2.26637 dice_loss 0.67356 +Epoch [3539/4000] Validation [6/10] Loss: 1.27373 focal_loss 0.55809 dice_loss 0.71563 +Epoch [3539/4000] Validation [7/10] Loss: 1.13801 focal_loss 0.48666 dice_loss 0.65135 +Epoch [3539/4000] Validation [8/10] Loss: 2.58190 focal_loss 1.93985 dice_loss 0.64205 +Epoch [3539/4000] Validation [9/10] Loss: 1.38623 focal_loss 0.84376 dice_loss 0.54247 +Epoch [3539/4000] Validation [10/10] Loss: 1.77226 focal_loss 1.04312 dice_loss 0.72914 +Epoch [3539/4000] Validation metric {'Val/mean dice_metric': 0.9514961242675781, 'Val/mean miou_metric': 0.935581386089325, 'Val/mean f1': 0.9500052332878113, 'Val/mean precision': 0.9481205344200134, 'Val/mean recall': 0.9518973231315613, 'Val/mean hd95_metric': 10.544140815734863} +Cheakpoint... +Epoch [3539/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514961242675781, 'Val/mean miou_metric': 0.935581386089325, 'Val/mean f1': 0.9500052332878113, 'Val/mean precision': 0.9481205344200134, 'Val/mean recall': 0.9518973231315613, 'Val/mean hd95_metric': 10.544140815734863} +Epoch [3540/4000] Training [1/39] Loss: 0.00495 +Epoch [3540/4000] Training [2/39] Loss: 0.00310 +Epoch [3540/4000] Training [3/39] Loss: 0.00458 +Epoch [3540/4000] Training [4/39] Loss: 0.00495 +Epoch [3540/4000] Training [5/39] Loss: 0.00427 +Epoch [3540/4000] Training [6/39] Loss: 0.00367 +Epoch [3540/4000] Training [7/39] Loss: 0.12920 +Epoch [3540/4000] Training [8/39] Loss: 0.00329 +Epoch [3540/4000] Training [9/39] Loss: 0.00596 +Epoch [3540/4000] Training [10/39] Loss: 0.00434 +Epoch [3540/4000] Training [11/39] Loss: 0.00423 +Epoch [3540/4000] Training [12/39] Loss: 0.00633 +Epoch [3540/4000] Training [13/39] Loss: 0.13046 +Epoch [3540/4000] Training [14/39] Loss: 0.12925 +Epoch [3540/4000] Training [15/39] Loss: 0.13307 +Epoch [3540/4000] Training [16/39] Loss: 0.00353 +Epoch [3540/4000] Training [17/39] Loss: 0.00511 +Epoch [3540/4000] Training [18/39] Loss: 0.25290 +Epoch [3540/4000] Training [19/39] Loss: 0.00443 +Epoch [3540/4000] Training [20/39] Loss: 0.13006 +Epoch [3540/4000] Training [21/39] Loss: 0.00403 +Epoch [3540/4000] Training [22/39] Loss: 0.00531 +Epoch [3540/4000] Training [23/39] Loss: 0.12846 +Epoch [3540/4000] Training [24/39] Loss: 0.25396 +Epoch [3540/4000] Training [25/39] Loss: 0.00472 +Epoch [3540/4000] Training [26/39] Loss: 0.12940 +Epoch [3540/4000] Training [27/39] Loss: 0.08300 +Epoch [3540/4000] Training [28/39] Loss: 0.00613 +Epoch [3540/4000] Training [29/39] Loss: 0.12932 +Epoch [3540/4000] Training [30/39] Loss: 0.00683 +Epoch [3540/4000] Training [31/39] Loss: 0.00662 +Epoch [3540/4000] Training [32/39] Loss: 0.00719 +Epoch [3540/4000] Training [33/39] Loss: 0.00430 +Epoch [3540/4000] Training [34/39] Loss: 0.00869 +Epoch [3540/4000] Training [35/39] Loss: 0.00648 +Epoch [3540/4000] Training [36/39] Loss: 0.12846 +Epoch [3540/4000] Training [37/39] Loss: 0.00466 +Epoch [3540/4000] Training [38/39] Loss: 0.00491 +Epoch [3540/4000] Training [39/39] Loss: 0.00478 +Epoch [3540/4000] Training metric {'Train/mean dice_metric': 0.995243489742279, 'Train/mean miou_metric': 0.9917691946029663, 'Train/mean f1': 0.9967648386955261, 'Train/mean precision': 0.9962844848632812, 'Train/mean recall': 0.9972456693649292, 'Train/mean hd95_metric': 0.9697268605232239} +Epoch [3540/4000] Validation [1/10] Loss: 0.71034 focal_loss 0.62455 dice_loss 0.08579 +Epoch [3540/4000] Validation [2/10] Loss: 0.49500 focal_loss 0.39475 dice_loss 0.10024 +Epoch [3540/4000] Validation [3/10] Loss: 0.38425 focal_loss 0.27382 dice_loss 0.11044 +Epoch [3540/4000] Validation [4/10] Loss: 0.87571 focal_loss 0.30656 dice_loss 0.56915 +Epoch [3540/4000] Validation [5/10] Loss: 2.95432 focal_loss 2.28090 dice_loss 0.67343 +Epoch [3540/4000] Validation [6/10] Loss: 1.29341 focal_loss 0.57930 dice_loss 0.71411 +Epoch [3540/4000] Validation [7/10] Loss: 1.14372 focal_loss 0.49355 dice_loss 0.65017 +Epoch [3540/4000] Validation [8/10] Loss: 2.66476 focal_loss 2.01960 dice_loss 0.64516 +Epoch [3540/4000] Validation [9/10] Loss: 1.41366 focal_loss 0.87091 dice_loss 0.54275 +Epoch [3540/4000] Validation [10/10] Loss: 1.78686 focal_loss 1.05919 dice_loss 0.72766 +Epoch [3540/4000] Validation metric {'Val/mean dice_metric': 0.9507061243057251, 'Val/mean miou_metric': 0.9346669912338257, 'Val/mean f1': 0.9497552514076233, 'Val/mean precision': 0.9480670094490051, 'Val/mean recall': 0.9514496326446533, 'Val/mean hd95_metric': 10.431855201721191} +Cheakpoint... +Epoch [3540/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507061243057251, 'Val/mean miou_metric': 0.9346669912338257, 'Val/mean f1': 0.9497552514076233, 'Val/mean precision': 0.9480670094490051, 'Val/mean recall': 0.9514496326446533, 'Val/mean hd95_metric': 10.431855201721191} +Epoch [3541/4000] Training [1/39] Loss: 0.13182 +Epoch [3541/4000] Training [2/39] Loss: 0.00524 +Epoch [3541/4000] Training [3/39] Loss: 0.00560 +Epoch [3541/4000] Training [4/39] Loss: 0.12985 +Epoch [3541/4000] Training [5/39] Loss: 0.00566 +Epoch [3541/4000] Training [6/39] Loss: 0.00391 +Epoch [3541/4000] Training [7/39] Loss: 0.00421 +Epoch [3541/4000] Training [8/39] Loss: 0.00756 +Epoch [3541/4000] Training [9/39] Loss: 0.00422 +Epoch [3541/4000] Training [10/39] Loss: 0.00573 +Epoch [3541/4000] Training [11/39] Loss: 0.00470 +Epoch [3541/4000] Training [12/39] Loss: 0.00771 +Epoch [3541/4000] Training [13/39] Loss: 0.00332 +Epoch [3541/4000] Training [14/39] Loss: 0.12962 +Epoch [3541/4000] Training [15/39] Loss: 0.00742 +Epoch [3541/4000] Training [16/39] Loss: 0.00442 +Epoch [3541/4000] Training [17/39] Loss: 0.00544 +Epoch [3541/4000] Training [18/39] Loss: 0.00430 +Epoch [3541/4000] Training [19/39] Loss: 0.00816 +Epoch [3541/4000] Training [20/39] Loss: 0.00476 +Epoch [3541/4000] Training [21/39] Loss: 0.13272 +Epoch [3541/4000] Training [22/39] Loss: 0.00492 +Epoch [3541/4000] Training [23/39] Loss: 0.00378 +Epoch [3541/4000] Training [24/39] Loss: 0.13252 +Epoch [3541/4000] Training [25/39] Loss: 0.00484 +Epoch [3541/4000] Training [26/39] Loss: 0.00317 +Epoch [3541/4000] Training [27/39] Loss: 0.00517 +Epoch [3541/4000] Training [28/39] Loss: 0.00544 +Epoch [3541/4000] Training [29/39] Loss: 0.12876 +Epoch [3541/4000] Training [30/39] Loss: 0.00415 +Epoch [3541/4000] Training [31/39] Loss: 0.00275 +Epoch [3541/4000] Training [32/39] Loss: 0.12894 +Epoch [3541/4000] Training [33/39] Loss: 0.00467 +Epoch [3541/4000] Training [34/39] Loss: 0.00545 +Epoch [3541/4000] Training [35/39] Loss: 0.00681 +Epoch [3541/4000] Training [36/39] Loss: 0.00567 +Epoch [3541/4000] Training [37/39] Loss: 0.00460 +Epoch [3541/4000] Training [38/39] Loss: 0.12835 +Epoch [3541/4000] Training [39/39] Loss: 0.00600 +Epoch [3541/4000] Training metric {'Train/mean dice_metric': 0.9959176182746887, 'Train/mean miou_metric': 0.9922707080841064, 'Train/mean f1': 0.9966627359390259, 'Train/mean precision': 0.996279776096344, 'Train/mean recall': 0.9970459342002869, 'Train/mean hd95_metric': 1.091923475265503} +Epoch [3541/4000] Validation [1/10] Loss: 0.71945 focal_loss 0.63380 dice_loss 0.08565 +Epoch [3541/4000] Validation [2/10] Loss: 0.49747 focal_loss 0.39607 dice_loss 0.10139 +Epoch [3541/4000] Validation [3/10] Loss: 0.40082 focal_loss 0.28959 dice_loss 0.11122 +Epoch [3541/4000] Validation [4/10] Loss: 0.87811 focal_loss 0.29906 dice_loss 0.57906 +Epoch [3541/4000] Validation [5/10] Loss: 3.03855 focal_loss 2.36526 dice_loss 0.67328 +Epoch [3541/4000] Validation [6/10] Loss: 1.26642 focal_loss 0.55096 dice_loss 0.71546 +Epoch [3541/4000] Validation [7/10] Loss: 1.10819 focal_loss 0.45810 dice_loss 0.65008 +Epoch [3541/4000] Validation [8/10] Loss: 2.81920 focal_loss 2.16572 dice_loss 0.65348 +Epoch [3541/4000] Validation [9/10] Loss: 1.42336 focal_loss 0.88090 dice_loss 0.54246 +Epoch [3541/4000] Validation [10/10] Loss: 1.72404 focal_loss 1.00113 dice_loss 0.72291 +Epoch [3541/4000] Validation metric {'Val/mean dice_metric': 0.9510193467140198, 'Val/mean miou_metric': 0.9347805976867676, 'Val/mean f1': 0.9499694108963013, 'Val/mean precision': 0.9497553110122681, 'Val/mean recall': 0.950183629989624, 'Val/mean hd95_metric': 10.604628562927246} +Cheakpoint... +Epoch [3541/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510193467140198, 'Val/mean miou_metric': 0.9347805976867676, 'Val/mean f1': 0.9499694108963013, 'Val/mean precision': 0.9497553110122681, 'Val/mean recall': 0.950183629989624, 'Val/mean hd95_metric': 10.604628562927246} +Epoch [3542/4000] Training [1/39] Loss: 0.13150 +Epoch [3542/4000] Training [2/39] Loss: 0.12887 +Epoch [3542/4000] Training [3/39] Loss: 0.00380 +Epoch [3542/4000] Training [4/39] Loss: 0.00418 +Epoch [3542/4000] Training [5/39] Loss: 0.00520 +Epoch [3542/4000] Training [6/39] Loss: 0.13106 +Epoch [3542/4000] Training [7/39] Loss: 0.00381 +Epoch [3542/4000] Training [8/39] Loss: 0.00480 +Epoch [3542/4000] Training [9/39] Loss: 0.00476 +Epoch [3542/4000] Training [10/39] Loss: 0.12885 +Epoch [3542/4000] Training [11/39] Loss: 0.13083 +Epoch [3542/4000] Training [12/39] Loss: 0.00394 +Epoch [3542/4000] Training [13/39] Loss: 0.00576 +Epoch [3542/4000] Training [14/39] Loss: 0.00339 +Epoch [3542/4000] Training [15/39] Loss: 0.13409 +Epoch [3542/4000] Training [16/39] Loss: 0.00822 +Epoch [3542/4000] Training [17/39] Loss: 0.00404 +Epoch [3542/4000] Training [18/39] Loss: 0.00650 +Epoch [3542/4000] Training [19/39] Loss: 0.00601 +Epoch [3542/4000] Training [20/39] Loss: 0.25277 +Epoch [3542/4000] Training [21/39] Loss: 0.00314 +Epoch [3542/4000] Training [22/39] Loss: 0.00394 +Epoch [3542/4000] Training [23/39] Loss: 0.00441 +Epoch [3542/4000] Training [24/39] Loss: 0.00531 +Epoch [3542/4000] Training [25/39] Loss: 0.13025 +Epoch [3542/4000] Training [26/39] Loss: 0.00416 +Epoch [3542/4000] Training [27/39] Loss: 0.12747 +Epoch [3542/4000] Training [28/39] Loss: 0.12898 +Epoch [3542/4000] Training [29/39] Loss: 0.00461 +Epoch [3542/4000] Training [30/39] Loss: 0.13025 +Epoch [3542/4000] Training [31/39] Loss: 0.00537 +Epoch [3542/4000] Training [32/39] Loss: 0.00590 +Epoch [3542/4000] Training [33/39] Loss: 0.00349 +Epoch [3542/4000] Training [34/39] Loss: 0.00371 +Epoch [3542/4000] Training [35/39] Loss: 0.00490 +Epoch [3542/4000] Training [36/39] Loss: 0.00460 +Epoch [3542/4000] Training [37/39] Loss: 0.00678 +Epoch [3542/4000] Training [38/39] Loss: 0.00380 +Epoch [3542/4000] Training [39/39] Loss: 0.12971 +Epoch [3542/4000] Training metric {'Train/mean dice_metric': 0.9962777495384216, 'Train/mean miou_metric': 0.9929980039596558, 'Train/mean f1': 0.9968841671943665, 'Train/mean precision': 0.9964345097541809, 'Train/mean recall': 0.9973342418670654, 'Train/mean hd95_metric': 1.1408061981201172} +Epoch [3542/4000] Validation [1/10] Loss: 0.71047 focal_loss 0.62505 dice_loss 0.08542 +Epoch [3542/4000] Validation [2/10] Loss: 0.48239 focal_loss 0.38350 dice_loss 0.09890 +Epoch [3542/4000] Validation [3/10] Loss: 0.40516 focal_loss 0.29313 dice_loss 0.11202 +Epoch [3542/4000] Validation [4/10] Loss: 0.86583 focal_loss 0.30093 dice_loss 0.56490 +Epoch [3542/4000] Validation [5/10] Loss: 3.05566 focal_loss 2.38211 dice_loss 0.67356 +Epoch [3542/4000] Validation [6/10] Loss: 1.25230 focal_loss 0.53707 dice_loss 0.71523 +Epoch [3542/4000] Validation [7/10] Loss: 1.10619 focal_loss 0.45575 dice_loss 0.65045 +Epoch [3542/4000] Validation [8/10] Loss: 2.63958 focal_loss 1.99368 dice_loss 0.64590 +Epoch [3542/4000] Validation [9/10] Loss: 1.39572 focal_loss 0.85384 dice_loss 0.54188 +Epoch [3542/4000] Validation [10/10] Loss: 1.72494 focal_loss 1.00038 dice_loss 0.72456 +Epoch [3542/4000] Validation metric {'Val/mean dice_metric': 0.9515577554702759, 'Val/mean miou_metric': 0.9357256293296814, 'Val/mean f1': 0.9503664970397949, 'Val/mean precision': 0.9489298462867737, 'Val/mean recall': 0.9518076181411743, 'Val/mean hd95_metric': 10.718911170959473} +Cheakpoint... +Epoch [3542/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515577554702759, 'Val/mean miou_metric': 0.9357256293296814, 'Val/mean f1': 0.9503664970397949, 'Val/mean precision': 0.9489298462867737, 'Val/mean recall': 0.9518076181411743, 'Val/mean hd95_metric': 10.718911170959473} +Epoch [3543/4000] Training [1/39] Loss: 0.00512 +Epoch [3543/4000] Training [2/39] Loss: 0.00699 +Epoch [3543/4000] Training [3/39] Loss: 0.00424 +Epoch [3543/4000] Training [4/39] Loss: 0.00514 +Epoch [3543/4000] Training [5/39] Loss: 0.00618 +Epoch [3543/4000] Training [6/39] Loss: 0.12746 +Epoch [3543/4000] Training [7/39] Loss: 0.00336 +Epoch [3543/4000] Training [8/39] Loss: 0.13002 +Epoch [3543/4000] Training [9/39] Loss: 0.00500 +Epoch [3543/4000] Training [10/39] Loss: 0.00270 +Epoch [3543/4000] Training [11/39] Loss: 0.00303 +Epoch [3543/4000] Training [12/39] Loss: 0.00482 +Epoch [3543/4000] Training [13/39] Loss: 0.00398 +Epoch [3543/4000] Training [14/39] Loss: 0.12927 +Epoch [3543/4000] Training [15/39] Loss: 0.00466 +Epoch [3543/4000] Training [16/39] Loss: 0.00541 +Epoch [3543/4000] Training [17/39] Loss: 0.13347 +Epoch [3543/4000] Training [18/39] Loss: 0.00453 +Epoch [3543/4000] Training [19/39] Loss: 0.00533 +Epoch [3543/4000] Training [20/39] Loss: 0.12855 +Epoch [3543/4000] Training [21/39] Loss: 0.00315 +Epoch [3543/4000] Training [22/39] Loss: 0.00433 +Epoch [3543/4000] Training [23/39] Loss: 0.00728 +Epoch [3543/4000] Training [24/39] Loss: 0.13096 +Epoch [3543/4000] Training [25/39] Loss: 0.00551 +Epoch [3543/4000] Training [26/39] Loss: 0.00684 +Epoch [3543/4000] Training [27/39] Loss: 0.00468 +Epoch [3543/4000] Training [28/39] Loss: 0.00517 +Epoch [3543/4000] Training [29/39] Loss: 0.00774 +Epoch [3543/4000] Training [30/39] Loss: 0.00530 +Epoch [3543/4000] Training [31/39] Loss: 0.00391 +Epoch [3543/4000] Training [32/39] Loss: 0.09891 +Epoch [3543/4000] Training [33/39] Loss: 0.00627 +Epoch [3543/4000] Training [34/39] Loss: 0.00633 +Epoch [3543/4000] Training [35/39] Loss: 0.00554 +Epoch [3543/4000] Training [36/39] Loss: 0.13260 +Epoch [3543/4000] Training [37/39] Loss: 0.00428 +Epoch [3543/4000] Training [38/39] Loss: 0.00559 +Epoch [3543/4000] Training [39/39] Loss: 0.00413 +Epoch [3543/4000] Training metric {'Train/mean dice_metric': 0.9960381388664246, 'Train/mean miou_metric': 0.9925751090049744, 'Train/mean f1': 0.9967159628868103, 'Train/mean precision': 0.9961851239204407, 'Train/mean recall': 0.9972472786903381, 'Train/mean hd95_metric': 0.9784343242645264} +Epoch [3543/4000] Validation [1/10] Loss: 0.68333 focal_loss 0.59851 dice_loss 0.08482 +Epoch [3543/4000] Validation [2/10] Loss: 0.48608 focal_loss 0.39046 dice_loss 0.09562 +Epoch [3543/4000] Validation [3/10] Loss: 0.38657 focal_loss 0.27616 dice_loss 0.11042 +Epoch [3543/4000] Validation [4/10] Loss: 0.86936 focal_loss 0.30325 dice_loss 0.56611 +Epoch [3543/4000] Validation [5/10] Loss: 3.00473 focal_loss 2.33171 dice_loss 0.67302 +Epoch [3543/4000] Validation [6/10] Loss: 1.28510 focal_loss 0.56629 dice_loss 0.71882 +Epoch [3543/4000] Validation [7/10] Loss: 1.12801 focal_loss 0.47693 dice_loss 0.65108 +Epoch [3543/4000] Validation [8/10] Loss: 2.60099 focal_loss 1.96029 dice_loss 0.64070 +Epoch [3543/4000] Validation [9/10] Loss: 1.41085 focal_loss 0.86705 dice_loss 0.54380 +Epoch [3543/4000] Validation [10/10] Loss: 1.80645 focal_loss 1.07642 dice_loss 0.73004 +Epoch [3543/4000] Validation metric {'Val/mean dice_metric': 0.9512917995452881, 'Val/mean miou_metric': 0.9352594017982483, 'Val/mean f1': 0.9495357275009155, 'Val/mean precision': 0.9473190307617188, 'Val/mean recall': 0.951762855052948, 'Val/mean hd95_metric': 10.599775314331055} +Cheakpoint... +Epoch [3543/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512917995452881, 'Val/mean miou_metric': 0.9352594017982483, 'Val/mean f1': 0.9495357275009155, 'Val/mean precision': 0.9473190307617188, 'Val/mean recall': 0.951762855052948, 'Val/mean hd95_metric': 10.599775314331055} +Epoch [3544/4000] Training [1/39] Loss: 0.00552 +Epoch [3544/4000] Training [2/39] Loss: 0.12822 +Epoch [3544/4000] Training [3/39] Loss: 0.00455 +Epoch [3544/4000] Training [4/39] Loss: 0.08234 +Epoch [3544/4000] Training [5/39] Loss: 0.00471 +Epoch [3544/4000] Training [6/39] Loss: 0.00348 +Epoch [3544/4000] Training [7/39] Loss: 0.00307 +Epoch [3544/4000] Training [8/39] Loss: 0.00442 +Epoch [3544/4000] Training [9/39] Loss: 0.00497 +Epoch [3544/4000] Training [10/39] Loss: 0.00520 +Epoch [3544/4000] Training [11/39] Loss: 0.00355 +Epoch [3544/4000] Training [12/39] Loss: 0.13062 +Epoch [3544/4000] Training [13/39] Loss: 0.00330 +Epoch [3544/4000] Training [14/39] Loss: 0.00397 +Epoch [3544/4000] Training [15/39] Loss: 0.38168 +Epoch [3544/4000] Training [16/39] Loss: 0.00370 +Epoch [3544/4000] Training [17/39] Loss: 0.00616 +Epoch [3544/4000] Training [18/39] Loss: 0.01034 +Epoch [3544/4000] Training [19/39] Loss: 0.00614 +Epoch [3544/4000] Training [20/39] Loss: 0.00362 +Epoch [3544/4000] Training [21/39] Loss: 0.00588 +Epoch [3544/4000] Training [22/39] Loss: 0.00454 +Epoch [3544/4000] Training [23/39] Loss: 0.00516 +Epoch [3544/4000] Training [24/39] Loss: 0.00383 +Epoch [3544/4000] Training [25/39] Loss: 0.00628 +Epoch [3544/4000] Training [26/39] Loss: 0.00604 +Epoch [3544/4000] Training [27/39] Loss: 0.00456 +Epoch [3544/4000] Training [28/39] Loss: 0.13168 +Epoch [3544/4000] Training [29/39] Loss: 0.00450 +Epoch [3544/4000] Training [30/39] Loss: 0.00589 +Epoch [3544/4000] Training [31/39] Loss: 0.00699 +Epoch [3544/4000] Training [32/39] Loss: 0.12892 +Epoch [3544/4000] Training [33/39] Loss: 0.03618 +Epoch [3544/4000] Training [34/39] Loss: 0.00342 +Epoch [3544/4000] Training [35/39] Loss: 0.00506 +Epoch [3544/4000] Training [36/39] Loss: 0.00478 +Epoch [3544/4000] Training [37/39] Loss: 0.12711 +Epoch [3544/4000] Training [38/39] Loss: 0.00326 +Epoch [3544/4000] Training [39/39] Loss: 0.25667 +Epoch [3544/4000] Training metric {'Train/mean dice_metric': 0.9960848093032837, 'Train/mean miou_metric': 0.992616593837738, 'Train/mean f1': 0.9967957139015198, 'Train/mean precision': 0.9963902831077576, 'Train/mean recall': 0.9972013831138611, 'Train/mean hd95_metric': 1.0338131189346313} +Epoch [3544/4000] Validation [1/10] Loss: 0.71172 focal_loss 0.62483 dice_loss 0.08689 +Epoch [3544/4000] Validation [2/10] Loss: 0.49414 focal_loss 0.39858 dice_loss 0.09555 +Epoch [3544/4000] Validation [3/10] Loss: 0.38145 focal_loss 0.27193 dice_loss 0.10952 +Epoch [3544/4000] Validation [4/10] Loss: 0.89376 focal_loss 0.32106 dice_loss 0.57270 +Epoch [3544/4000] Validation [5/10] Loss: 3.02937 focal_loss 2.35650 dice_loss 0.67287 +Epoch [3544/4000] Validation [6/10] Loss: 1.28131 focal_loss 0.56682 dice_loss 0.71450 +Epoch [3544/4000] Validation [7/10] Loss: 1.14069 focal_loss 0.48765 dice_loss 0.65304 +Epoch [3544/4000] Validation [8/10] Loss: 2.54187 focal_loss 1.90478 dice_loss 0.63709 +Epoch [3544/4000] Validation [9/10] Loss: 1.42487 focal_loss 0.88047 dice_loss 0.54441 +Epoch [3544/4000] Validation [10/10] Loss: 1.82595 focal_loss 1.09426 dice_loss 0.73169 +Epoch [3544/4000] Validation metric {'Val/mean dice_metric': 0.9511362314224243, 'Val/mean miou_metric': 0.9350526928901672, 'Val/mean f1': 0.9495259523391724, 'Val/mean precision': 0.9466602802276611, 'Val/mean recall': 0.952409029006958, 'Val/mean hd95_metric': 10.757336616516113} +Cheakpoint... +Epoch [3544/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511362314224243, 'Val/mean miou_metric': 0.9350526928901672, 'Val/mean f1': 0.9495259523391724, 'Val/mean precision': 0.9466602802276611, 'Val/mean recall': 0.952409029006958, 'Val/mean hd95_metric': 10.757336616516113} +Epoch [3545/4000] Training [1/39] Loss: 0.12929 +Epoch [3545/4000] Training [2/39] Loss: 0.12902 +Epoch [3545/4000] Training [3/39] Loss: 0.00611 +Epoch [3545/4000] Training [4/39] Loss: 0.00534 +Epoch [3545/4000] Training [5/39] Loss: 0.00764 +Epoch [3545/4000] Training [6/39] Loss: 0.00537 +Epoch [3545/4000] Training [7/39] Loss: 0.00382 +Epoch [3545/4000] Training [8/39] Loss: 0.12886 +Epoch [3545/4000] Training [9/39] Loss: 0.13020 +Epoch [3545/4000] Training [10/39] Loss: 0.12869 +Epoch [3545/4000] Training [11/39] Loss: 0.00453 +Epoch [3545/4000] Training [12/39] Loss: 0.13235 +Epoch [3545/4000] Training [13/39] Loss: 0.13009 +Epoch [3545/4000] Training [14/39] Loss: 0.00536 +Epoch [3545/4000] Training [15/39] Loss: 0.13004 +Epoch [3545/4000] Training [16/39] Loss: 0.00338 +Epoch [3545/4000] Training [17/39] Loss: 0.00497 +Epoch [3545/4000] Training [18/39] Loss: 0.00434 +Epoch [3545/4000] Training [19/39] Loss: 0.00399 +Epoch [3545/4000] Training [20/39] Loss: 0.00538 +Epoch [3545/4000] Training [21/39] Loss: 0.00555 +Epoch [3545/4000] Training [22/39] Loss: 0.13011 +Epoch [3545/4000] Training [23/39] Loss: 0.00625 +Epoch [3545/4000] Training [24/39] Loss: 0.25432 +Epoch [3545/4000] Training [25/39] Loss: 0.00420 +Epoch [3545/4000] Training [26/39] Loss: 0.00395 +Epoch [3545/4000] Training [27/39] Loss: 0.00663 +Epoch [3545/4000] Training [28/39] Loss: 0.00325 +Epoch [3545/4000] Training [29/39] Loss: 0.00382 +Epoch [3545/4000] Training [30/39] Loss: 0.00440 +Epoch [3545/4000] Training [31/39] Loss: 0.00530 +Epoch [3545/4000] Training [32/39] Loss: 0.00631 +Epoch [3545/4000] Training [33/39] Loss: 0.12839 +Epoch [3545/4000] Training [34/39] Loss: 0.00338 +Epoch [3545/4000] Training [35/39] Loss: 0.00463 +Epoch [3545/4000] Training [36/39] Loss: 0.00380 +Epoch [3545/4000] Training [37/39] Loss: 0.00420 +Epoch [3545/4000] Training [38/39] Loss: 0.00414 +Epoch [3545/4000] Training [39/39] Loss: 0.00412 +Epoch [3545/4000] Training metric {'Train/mean dice_metric': 0.9961767792701721, 'Train/mean miou_metric': 0.9928020238876343, 'Train/mean f1': 0.9968234300613403, 'Train/mean precision': 0.9963407516479492, 'Train/mean recall': 0.9973066449165344, 'Train/mean hd95_metric': 0.9615209698677063} +Epoch [3545/4000] Validation [1/10] Loss: 0.71838 focal_loss 0.63171 dice_loss 0.08667 +Epoch [3545/4000] Validation [2/10] Loss: 0.50228 focal_loss 0.40108 dice_loss 0.10120 +Epoch [3545/4000] Validation [3/10] Loss: 0.40334 focal_loss 0.29172 dice_loss 0.11162 +Epoch [3545/4000] Validation [4/10] Loss: 0.89081 focal_loss 0.32137 dice_loss 0.56944 +Epoch [3545/4000] Validation [5/10] Loss: 3.05642 focal_loss 2.38325 dice_loss 0.67317 +Epoch [3545/4000] Validation [6/10] Loss: 1.27085 focal_loss 0.55387 dice_loss 0.71698 +Epoch [3545/4000] Validation [7/10] Loss: 1.11955 focal_loss 0.46826 dice_loss 0.65128 +Epoch [3545/4000] Validation [8/10] Loss: 2.60941 focal_loss 1.96678 dice_loss 0.64263 +Epoch [3545/4000] Validation [9/10] Loss: 1.43040 focal_loss 0.88881 dice_loss 0.54159 +Epoch [3545/4000] Validation [10/10] Loss: 1.78142 focal_loss 1.05349 dice_loss 0.72793 +Epoch [3545/4000] Validation metric {'Val/mean dice_metric': 0.9512008428573608, 'Val/mean miou_metric': 0.935245156288147, 'Val/mean f1': 0.9496040940284729, 'Val/mean precision': 0.9478150606155396, 'Val/mean recall': 0.9514000415802002, 'Val/mean hd95_metric': 10.587913513183594} +Cheakpoint... +Epoch [3545/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512008428573608, 'Val/mean miou_metric': 0.935245156288147, 'Val/mean f1': 0.9496040940284729, 'Val/mean precision': 0.9478150606155396, 'Val/mean recall': 0.9514000415802002, 'Val/mean hd95_metric': 10.587913513183594} +Epoch [3546/4000] Training [1/39] Loss: 0.00427 +Epoch [3546/4000] Training [2/39] Loss: 0.00477 +Epoch [3546/4000] Training [3/39] Loss: 0.12817 +Epoch [3546/4000] Training [4/39] Loss: 0.00541 +Epoch [3546/4000] Training [5/39] Loss: 0.13020 +Epoch [3546/4000] Training [6/39] Loss: 0.13168 +Epoch [3546/4000] Training [7/39] Loss: 0.00464 +Epoch [3546/4000] Training [8/39] Loss: 0.00428 +Epoch [3546/4000] Training [9/39] Loss: 0.00498 +Epoch [3546/4000] Training [10/39] Loss: 0.00466 +Epoch [3546/4000] Training [11/39] Loss: 0.13036 +Epoch [3546/4000] Training [12/39] Loss: 0.12962 +Epoch [3546/4000] Training [13/39] Loss: 0.00608 +Epoch [3546/4000] Training [14/39] Loss: 0.00458 +Epoch [3546/4000] Training [15/39] Loss: 0.00519 +Epoch [3546/4000] Training [16/39] Loss: 0.12921 +Epoch [3546/4000] Training [17/39] Loss: 0.00434 +Epoch [3546/4000] Training [18/39] Loss: 0.00514 +Epoch [3546/4000] Training [19/39] Loss: 0.00414 +Epoch [3546/4000] Training [20/39] Loss: 0.00616 +Epoch [3546/4000] Training [21/39] Loss: 0.00836 +Epoch [3546/4000] Training [22/39] Loss: 0.00510 +Epoch [3546/4000] Training [23/39] Loss: 0.00618 +Epoch [3546/4000] Training [24/39] Loss: 0.12759 +Epoch [3546/4000] Training [25/39] Loss: 0.00690 +Epoch [3546/4000] Training [26/39] Loss: 0.25528 +Epoch [3546/4000] Training [27/39] Loss: 0.25481 +Epoch [3546/4000] Training [28/39] Loss: 0.00449 +Epoch [3546/4000] Training [29/39] Loss: 0.12945 +Epoch [3546/4000] Training [30/39] Loss: 0.12958 +Epoch [3546/4000] Training [31/39] Loss: 0.00390 +Epoch [3546/4000] Training [32/39] Loss: 0.13051 +Epoch [3546/4000] Training [33/39] Loss: 0.00700 +Epoch [3546/4000] Training [34/39] Loss: 0.00369 +Epoch [3546/4000] Training [35/39] Loss: 0.12929 +Epoch [3546/4000] Training [36/39] Loss: 0.12778 +Epoch [3546/4000] Training [37/39] Loss: 0.00337 +Epoch [3546/4000] Training [38/39] Loss: 0.00458 +Epoch [3546/4000] Training [39/39] Loss: 0.00641 +Epoch [3546/4000] Training metric {'Train/mean dice_metric': 0.9962681531906128, 'Train/mean miou_metric': 0.9929714202880859, 'Train/mean f1': 0.9968413710594177, 'Train/mean precision': 0.9963983297348022, 'Train/mean recall': 0.9972848296165466, 'Train/mean hd95_metric': 1.0908159017562866} +Epoch [3546/4000] Validation [1/10] Loss: 0.70028 focal_loss 0.61638 dice_loss 0.08390 +Epoch [3546/4000] Validation [2/10] Loss: 0.51861 focal_loss 0.41405 dice_loss 0.10456 +Epoch [3546/4000] Validation [3/10] Loss: 0.43060 focal_loss 0.31710 dice_loss 0.11350 +Epoch [3546/4000] Validation [4/10] Loss: 0.88148 focal_loss 0.30797 dice_loss 0.57352 +Epoch [3546/4000] Validation [5/10] Loss: 3.10178 focal_loss 2.42813 dice_loss 0.67365 +Epoch [3546/4000] Validation [6/10] Loss: 1.25651 focal_loss 0.53975 dice_loss 0.71675 +Epoch [3546/4000] Validation [7/10] Loss: 1.11475 focal_loss 0.46480 dice_loss 0.64996 +Epoch [3546/4000] Validation [8/10] Loss: 2.80515 focal_loss 2.14981 dice_loss 0.65534 +Epoch [3546/4000] Validation [9/10] Loss: 1.44318 focal_loss 0.90390 dice_loss 0.53929 +Epoch [3546/4000] Validation [10/10] Loss: 1.74851 focal_loss 1.02196 dice_loss 0.72655 +Epoch [3546/4000] Validation metric {'Val/mean dice_metric': 0.9512550234794617, 'Val/mean miou_metric': 0.9353536367416382, 'Val/mean f1': 0.9500643014907837, 'Val/mean precision': 0.9506849050521851, 'Val/mean recall': 0.9494444727897644, 'Val/mean hd95_metric': 10.597389221191406} +Cheakpoint... +Epoch [3546/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512550234794617, 'Val/mean miou_metric': 0.9353536367416382, 'Val/mean f1': 0.9500643014907837, 'Val/mean precision': 0.9506849050521851, 'Val/mean recall': 0.9494444727897644, 'Val/mean hd95_metric': 10.597389221191406} +Epoch [3547/4000] Training [1/39] Loss: 0.00587 +Epoch [3547/4000] Training [2/39] Loss: 0.00563 +Epoch [3547/4000] Training [3/39] Loss: 0.12916 +Epoch [3547/4000] Training [4/39] Loss: 0.12937 +Epoch [3547/4000] Training [5/39] Loss: 0.00369 +Epoch [3547/4000] Training [6/39] Loss: 0.12913 +Epoch [3547/4000] Training [7/39] Loss: 0.13074 +Epoch [3547/4000] Training [8/39] Loss: 0.00580 +Epoch [3547/4000] Training [9/39] Loss: 0.25303 +Epoch [3547/4000] Training [10/39] Loss: 0.13097 +Epoch [3547/4000] Training [11/39] Loss: 0.00556 +Epoch [3547/4000] Training [12/39] Loss: 0.00277 +Epoch [3547/4000] Training [13/39] Loss: 0.00436 +Epoch [3547/4000] Training [14/39] Loss: 0.00599 +Epoch [3547/4000] Training [15/39] Loss: 0.00363 +Epoch [3547/4000] Training [16/39] Loss: 0.00489 +Epoch [3547/4000] Training [17/39] Loss: 0.29441 +Epoch [3547/4000] Training [18/39] Loss: 0.12923 +Epoch [3547/4000] Training [19/39] Loss: 0.00408 +Epoch [3547/4000] Training [20/39] Loss: 0.00399 +Epoch [3547/4000] Training [21/39] Loss: 0.00330 +Epoch [3547/4000] Training [22/39] Loss: 0.13036 +Epoch [3547/4000] Training [23/39] Loss: 0.12764 +Epoch [3547/4000] Training [24/39] Loss: 0.00376 +Epoch [3547/4000] Training [25/39] Loss: 0.00692 +Epoch [3547/4000] Training [26/39] Loss: 0.13021 +Epoch [3547/4000] Training [27/39] Loss: 0.00444 +Epoch [3547/4000] Training [28/39] Loss: 0.13324 +Epoch [3547/4000] Training [29/39] Loss: 0.12970 +Epoch [3547/4000] Training [30/39] Loss: 0.00367 +Epoch [3547/4000] Training [31/39] Loss: 0.00596 +Epoch [3547/4000] Training [32/39] Loss: 0.12910 +Epoch [3547/4000] Training [33/39] Loss: 0.00370 +Epoch [3547/4000] Training [34/39] Loss: 0.00630 +Epoch [3547/4000] Training [35/39] Loss: 0.00680 +Epoch [3547/4000] Training [36/39] Loss: 0.00510 +Epoch [3547/4000] Training [37/39] Loss: 0.00368 +Epoch [3547/4000] Training [38/39] Loss: 0.00646 +Epoch [3547/4000] Training [39/39] Loss: 0.00548 +Epoch [3547/4000] Training metric {'Train/mean dice_metric': 0.9959352016448975, 'Train/mean miou_metric': 0.992317259311676, 'Train/mean f1': 0.996612548828125, 'Train/mean precision': 0.9961658120155334, 'Train/mean recall': 0.9970598220825195, 'Train/mean hd95_metric': 1.0039393901824951} +Epoch [3547/4000] Validation [1/10] Loss: 0.69943 focal_loss 0.61264 dice_loss 0.08680 +Epoch [3547/4000] Validation [2/10] Loss: 0.49318 focal_loss 0.39579 dice_loss 0.09739 +Epoch [3547/4000] Validation [3/10] Loss: 0.38151 focal_loss 0.27115 dice_loss 0.11036 +Epoch [3547/4000] Validation [4/10] Loss: 0.87678 focal_loss 0.31257 dice_loss 0.56421 +Epoch [3547/4000] Validation [5/10] Loss: 2.98749 focal_loss 2.31409 dice_loss 0.67340 +Epoch [3547/4000] Validation [6/10] Loss: 1.28851 focal_loss 0.56891 dice_loss 0.71960 +Epoch [3547/4000] Validation [7/10] Loss: 1.12083 focal_loss 0.46780 dice_loss 0.65304 +Epoch [3547/4000] Validation [8/10] Loss: 2.50981 focal_loss 1.87541 dice_loss 0.63439 +Epoch [3547/4000] Validation [9/10] Loss: 1.40445 focal_loss 0.86271 dice_loss 0.54174 +Epoch [3547/4000] Validation [10/10] Loss: 1.81377 focal_loss 1.08294 dice_loss 0.73084 +Epoch [3547/4000] Validation metric {'Val/mean dice_metric': 0.9511515498161316, 'Val/mean miou_metric': 0.9350391626358032, 'Val/mean f1': 0.9496841430664062, 'Val/mean precision': 0.946904718875885, 'Val/mean recall': 0.9524799585342407, 'Val/mean hd95_metric': 10.776725769042969} +Cheakpoint... +Epoch [3547/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511515498161316, 'Val/mean miou_metric': 0.9350391626358032, 'Val/mean f1': 0.9496841430664062, 'Val/mean precision': 0.946904718875885, 'Val/mean recall': 0.9524799585342407, 'Val/mean hd95_metric': 10.776725769042969} +Epoch [3548/4000] Training [1/39] Loss: 0.13157 +Epoch [3548/4000] Training [2/39] Loss: 0.00562 +Epoch [3548/4000] Training [3/39] Loss: 0.00477 +Epoch [3548/4000] Training [4/39] Loss: 0.00416 +Epoch [3548/4000] Training [5/39] Loss: 0.12959 +Epoch [3548/4000] Training [6/39] Loss: 0.00500 +Epoch [3548/4000] Training [7/39] Loss: 0.00532 +Epoch [3548/4000] Training [8/39] Loss: 0.00555 +Epoch [3548/4000] Training [9/39] Loss: 0.12847 +Epoch [3548/4000] Training [10/39] Loss: 0.00409 +Epoch [3548/4000] Training [11/39] Loss: 0.12957 +Epoch [3548/4000] Training [12/39] Loss: 0.00413 +Epoch [3548/4000] Training [13/39] Loss: 0.00376 +Epoch [3548/4000] Training [14/39] Loss: 0.00382 +Epoch [3548/4000] Training [15/39] Loss: 0.00420 +Epoch [3548/4000] Training [16/39] Loss: 0.00461 +Epoch [3548/4000] Training [17/39] Loss: 0.25435 +Epoch [3548/4000] Training [18/39] Loss: 0.00675 +Epoch [3548/4000] Training [19/39] Loss: 0.00450 +Epoch [3548/4000] Training [20/39] Loss: 0.12932 +Epoch [3548/4000] Training [21/39] Loss: 0.00384 +Epoch [3548/4000] Training [22/39] Loss: 0.12775 +Epoch [3548/4000] Training [23/39] Loss: 0.00688 +Epoch [3548/4000] Training [24/39] Loss: 0.00607 +Epoch [3548/4000] Training [25/39] Loss: 0.12916 +Epoch [3548/4000] Training [26/39] Loss: 0.12983 +Epoch [3548/4000] Training [27/39] Loss: 0.08689 +Epoch [3548/4000] Training [28/39] Loss: 0.00514 +Epoch [3548/4000] Training [29/39] Loss: 0.00535 +Epoch [3548/4000] Training [30/39] Loss: 0.00495 +Epoch [3548/4000] Training [31/39] Loss: 0.00383 +Epoch [3548/4000] Training [32/39] Loss: 0.00812 +Epoch [3548/4000] Training [33/39] Loss: 0.00509 +Epoch [3548/4000] Training [34/39] Loss: 0.00771 +Epoch [3548/4000] Training [35/39] Loss: 0.00326 +Epoch [3548/4000] Training [36/39] Loss: 0.00427 +Epoch [3548/4000] Training [37/39] Loss: 0.00558 +Epoch [3548/4000] Training [38/39] Loss: 0.00497 +Epoch [3548/4000] Training [39/39] Loss: 0.25516 +Epoch [3548/4000] Training metric {'Train/mean dice_metric': 0.9953231811523438, 'Train/mean miou_metric': 0.991932213306427, 'Train/mean f1': 0.9966535568237305, 'Train/mean precision': 0.9961779117584229, 'Train/mean recall': 0.9971296787261963, 'Train/mean hd95_metric': 0.9740313291549683} +Epoch [3548/4000] Validation [1/10] Loss: 0.67900 focal_loss 0.59524 dice_loss 0.08376 +Epoch [3548/4000] Validation [2/10] Loss: 0.50123 focal_loss 0.39972 dice_loss 0.10151 +Epoch [3548/4000] Validation [3/10] Loss: 0.39979 focal_loss 0.28802 dice_loss 0.11177 +Epoch [3548/4000] Validation [4/10] Loss: 0.87356 focal_loss 0.30906 dice_loss 0.56450 +Epoch [3548/4000] Validation [5/10] Loss: 3.04361 focal_loss 2.36972 dice_loss 0.67389 +Epoch [3548/4000] Validation [6/10] Loss: 1.28997 focal_loss 0.57095 dice_loss 0.71902 +Epoch [3548/4000] Validation [7/10] Loss: 1.12375 focal_loss 0.47356 dice_loss 0.65020 +Epoch [3548/4000] Validation [8/10] Loss: 2.59757 focal_loss 1.95508 dice_loss 0.64249 +Epoch [3548/4000] Validation [9/10] Loss: 1.41497 focal_loss 0.87276 dice_loss 0.54221 +Epoch [3548/4000] Validation [10/10] Loss: 1.76168 focal_loss 1.03410 dice_loss 0.72757 +Epoch [3548/4000] Validation metric {'Val/mean dice_metric': 0.9506358504295349, 'Val/mean miou_metric': 0.9347124695777893, 'Val/mean f1': 0.9499466419219971, 'Val/mean precision': 0.9487403631210327, 'Val/mean recall': 0.9511560797691345, 'Val/mean hd95_metric': 10.446304321289062} +Cheakpoint... +Epoch [3548/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506358504295349, 'Val/mean miou_metric': 0.9347124695777893, 'Val/mean f1': 0.9499466419219971, 'Val/mean precision': 0.9487403631210327, 'Val/mean recall': 0.9511560797691345, 'Val/mean hd95_metric': 10.446304321289062} +Epoch [3549/4000] Training [1/39] Loss: 0.12813 +Epoch [3549/4000] Training [2/39] Loss: 0.00446 +Epoch [3549/4000] Training [3/39] Loss: 0.20778 +Epoch [3549/4000] Training [4/39] Loss: 0.00367 +Epoch [3549/4000] Training [5/39] Loss: 0.00529 +Epoch [3549/4000] Training [6/39] Loss: 0.00378 +Epoch [3549/4000] Training [7/39] Loss: 0.00362 +Epoch [3549/4000] Training [8/39] Loss: 0.00532 +Epoch [3549/4000] Training [9/39] Loss: 0.12791 +Epoch [3549/4000] Training [10/39] Loss: 0.00517 +Epoch [3549/4000] Training [11/39] Loss: 0.00671 +Epoch [3549/4000] Training [12/39] Loss: 0.00453 +Epoch [3549/4000] Training [13/39] Loss: 0.13009 +Epoch [3549/4000] Training [14/39] Loss: 0.12988 +Epoch [3549/4000] Training [15/39] Loss: 0.12748 +Epoch [3549/4000] Training [16/39] Loss: 0.00311 +Epoch [3549/4000] Training [17/39] Loss: 0.00597 +Epoch [3549/4000] Training [18/39] Loss: 0.29945 +Epoch [3549/4000] Training [19/39] Loss: 0.00493 +Epoch [3549/4000] Training [20/39] Loss: 0.00478 +Epoch [3549/4000] Training [21/39] Loss: 0.00584 +Epoch [3549/4000] Training [22/39] Loss: 0.13143 +Epoch [3549/4000] Training [23/39] Loss: 0.00437 +Epoch [3549/4000] Training [24/39] Loss: 0.12845 +Epoch [3549/4000] Training [25/39] Loss: 0.00519 +Epoch [3549/4000] Training [26/39] Loss: 0.00640 +Epoch [3549/4000] Training [27/39] Loss: 0.00664 +Epoch [3549/4000] Training [28/39] Loss: 0.00546 +Epoch [3549/4000] Training [29/39] Loss: 0.00419 +Epoch [3549/4000] Training [30/39] Loss: 0.00463 +Epoch [3549/4000] Training [31/39] Loss: 0.00556 +Epoch [3549/4000] Training [32/39] Loss: 0.00572 +Epoch [3549/4000] Training [33/39] Loss: 0.12840 +Epoch [3549/4000] Training [34/39] Loss: 0.00351 +Epoch [3549/4000] Training [35/39] Loss: 0.13142 +Epoch [3549/4000] Training [36/39] Loss: 0.00465 +Epoch [3549/4000] Training [37/39] Loss: 0.00439 +Epoch [3549/4000] Training [38/39] Loss: 0.13259 +Epoch [3549/4000] Training [39/39] Loss: 0.25394 +Epoch [3549/4000] Training metric {'Train/mean dice_metric': 0.9960691928863525, 'Train/mean miou_metric': 0.9925886988639832, 'Train/mean f1': 0.9967446327209473, 'Train/mean precision': 0.9963041543960571, 'Train/mean recall': 0.9971854090690613, 'Train/mean hd95_metric': 1.215193271636963} +Epoch [3549/4000] Validation [1/10] Loss: 0.70350 focal_loss 0.61725 dice_loss 0.08626 +Epoch [3549/4000] Validation [2/10] Loss: 0.49669 focal_loss 0.39926 dice_loss 0.09743 +Epoch [3549/4000] Validation [3/10] Loss: 0.38947 focal_loss 0.27909 dice_loss 0.11038 +Epoch [3549/4000] Validation [4/10] Loss: 0.89350 focal_loss 0.32747 dice_loss 0.56602 +Epoch [3549/4000] Validation [5/10] Loss: 3.05231 focal_loss 2.37881 dice_loss 0.67350 +Epoch [3549/4000] Validation [6/10] Loss: 1.31181 focal_loss 0.59280 dice_loss 0.71901 +Epoch [3549/4000] Validation [7/10] Loss: 1.14401 focal_loss 0.49298 dice_loss 0.65103 +Epoch [3549/4000] Validation [8/10] Loss: 2.60079 focal_loss 1.96391 dice_loss 0.63689 +Epoch [3549/4000] Validation [9/10] Loss: 1.42340 focal_loss 0.88108 dice_loss 0.54232 +Epoch [3549/4000] Validation [10/10] Loss: 1.82154 focal_loss 1.09067 dice_loss 0.73087 +Epoch [3549/4000] Validation metric {'Val/mean dice_metric': 0.9512401223182678, 'Val/mean miou_metric': 0.9352098107337952, 'Val/mean f1': 0.9495474100112915, 'Val/mean precision': 0.9470384120941162, 'Val/mean recall': 0.9520697593688965, 'Val/mean hd95_metric': 10.8468017578125} +Cheakpoint... +Epoch [3549/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512401223182678, 'Val/mean miou_metric': 0.9352098107337952, 'Val/mean f1': 0.9495474100112915, 'Val/mean precision': 0.9470384120941162, 'Val/mean recall': 0.9520697593688965, 'Val/mean hd95_metric': 10.8468017578125} +Epoch [3550/4000] Training [1/39] Loss: 0.00306 +Epoch [3550/4000] Training [2/39] Loss: 0.00477 +Epoch [3550/4000] Training [3/39] Loss: 0.00424 +Epoch [3550/4000] Training [4/39] Loss: 0.00547 +Epoch [3550/4000] Training [5/39] Loss: 0.00946 +Epoch [3550/4000] Training [6/39] Loss: 0.00327 +Epoch [3550/4000] Training [7/39] Loss: 0.00514 +Epoch [3550/4000] Training [8/39] Loss: 0.00443 +Epoch [3550/4000] Training [9/39] Loss: 0.00650 +Epoch [3550/4000] Training [10/39] Loss: 0.00383 +Epoch [3550/4000] Training [11/39] Loss: 0.00853 +Epoch [3550/4000] Training [12/39] Loss: 0.12877 +Epoch [3550/4000] Training [13/39] Loss: 0.00577 +Epoch [3550/4000] Training [14/39] Loss: 0.12931 +Epoch [3550/4000] Training [15/39] Loss: 0.00604 +Epoch [3550/4000] Training [16/39] Loss: 0.00731 +Epoch [3550/4000] Training [17/39] Loss: 0.00413 +Epoch [3550/4000] Training [18/39] Loss: 0.00498 +Epoch [3550/4000] Training [19/39] Loss: 0.00380 +Epoch [3550/4000] Training [20/39] Loss: 0.00619 +Epoch [3550/4000] Training [21/39] Loss: 0.00544 +Epoch [3550/4000] Training [22/39] Loss: 0.00781 +Epoch [3550/4000] Training [23/39] Loss: 0.00590 +Epoch [3550/4000] Training [24/39] Loss: 0.13043 +Epoch [3550/4000] Training [25/39] Loss: 0.00477 +Epoch [3550/4000] Training [26/39] Loss: 0.00513 +Epoch [3550/4000] Training [27/39] Loss: 0.00614 +Epoch [3550/4000] Training [28/39] Loss: 0.00643 +Epoch [3550/4000] Training [29/39] Loss: 0.00512 +Epoch [3550/4000] Training [30/39] Loss: 0.00628 +Epoch [3550/4000] Training [31/39] Loss: 0.12803 +Epoch [3550/4000] Training [32/39] Loss: 0.00721 +Epoch [3550/4000] Training [33/39] Loss: 0.00354 +Epoch [3550/4000] Training [34/39] Loss: 0.00455 +Epoch [3550/4000] Training [35/39] Loss: 0.00523 +Epoch [3550/4000] Training [36/39] Loss: 0.12764 +Epoch [3550/4000] Training [37/39] Loss: 0.00389 +Epoch [3550/4000] Training [38/39] Loss: 0.00601 +Epoch [3550/4000] Training [39/39] Loss: 0.13201 +Epoch [3550/4000] Training metric {'Train/mean dice_metric': 0.996025025844574, 'Train/mean miou_metric': 0.9925719499588013, 'Train/mean f1': 0.9966839551925659, 'Train/mean precision': 0.9961027503013611, 'Train/mean recall': 0.9972658157348633, 'Train/mean hd95_metric': 1.0395503044128418} +Epoch [3550/4000] Validation [1/10] Loss: 0.71654 focal_loss 0.62893 dice_loss 0.08761 +Epoch [3550/4000] Validation [2/10] Loss: 0.49747 focal_loss 0.39627 dice_loss 0.10120 +Epoch [3550/4000] Validation [3/10] Loss: 0.40563 focal_loss 0.29349 dice_loss 0.11214 +Epoch [3550/4000] Validation [4/10] Loss: 0.87587 focal_loss 0.31267 dice_loss 0.56320 +Epoch [3550/4000] Validation [5/10] Loss: 3.03613 focal_loss 2.36307 dice_loss 0.67306 +Epoch [3550/4000] Validation [6/10] Loss: 1.29898 focal_loss 0.57909 dice_loss 0.71989 +Epoch [3550/4000] Validation [7/10] Loss: 1.13833 focal_loss 0.48532 dice_loss 0.65301 +Epoch [3550/4000] Validation [8/10] Loss: 2.44544 focal_loss 1.81670 dice_loss 0.62875 +Epoch [3550/4000] Validation [9/10] Loss: 1.41205 focal_loss 0.87010 dice_loss 0.54194 +Epoch [3550/4000] Validation [10/10] Loss: 1.78597 focal_loss 1.05510 dice_loss 0.73088 +Epoch [3550/4000] Validation metric {'Val/mean dice_metric': 0.9511957168579102, 'Val/mean miou_metric': 0.9352470636367798, 'Val/mean f1': 0.9495797157287598, 'Val/mean precision': 0.9466074705123901, 'Val/mean recall': 0.9525706768035889, 'Val/mean hd95_metric': 10.759345054626465} +Cheakpoint... +Epoch [3550/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511957168579102, 'Val/mean miou_metric': 0.9352470636367798, 'Val/mean f1': 0.9495797157287598, 'Val/mean precision': 0.9466074705123901, 'Val/mean recall': 0.9525706768035889, 'Val/mean hd95_metric': 10.759345054626465} +Epoch [3551/4000] Training [1/39] Loss: 0.00470 +Epoch [3551/4000] Training [2/39] Loss: 0.00599 +Epoch [3551/4000] Training [3/39] Loss: 0.00446 +Epoch [3551/4000] Training [4/39] Loss: 0.00550 +Epoch [3551/4000] Training [5/39] Loss: 0.12981 +Epoch [3551/4000] Training [6/39] Loss: 0.20916 +Epoch [3551/4000] Training [7/39] Loss: 0.00458 +Epoch [3551/4000] Training [8/39] Loss: 0.00452 +Epoch [3551/4000] Training [9/39] Loss: 0.12983 +Epoch [3551/4000] Training [10/39] Loss: 0.00996 +Epoch [3551/4000] Training [11/39] Loss: 0.00348 +Epoch [3551/4000] Training [12/39] Loss: 0.00518 +Epoch [3551/4000] Training [13/39] Loss: 0.00767 +Epoch [3551/4000] Training [14/39] Loss: 0.00747 +Epoch [3551/4000] Training [15/39] Loss: 0.00434 +Epoch [3551/4000] Training [16/39] Loss: 0.00371 +Epoch [3551/4000] Training [17/39] Loss: 0.00382 +Epoch [3551/4000] Training [18/39] Loss: 0.00740 +Epoch [3551/4000] Training [19/39] Loss: 0.00452 +Epoch [3551/4000] Training [20/39] Loss: 0.12763 +Epoch [3551/4000] Training [21/39] Loss: 0.00292 +Epoch [3551/4000] Training [22/39] Loss: 0.00507 +Epoch [3551/4000] Training [23/39] Loss: 0.00726 +Epoch [3551/4000] Training [24/39] Loss: 0.00506 +Epoch [3551/4000] Training [25/39] Loss: 0.00784 +Epoch [3551/4000] Training [26/39] Loss: 0.00411 +Epoch [3551/4000] Training [27/39] Loss: 0.00475 +Epoch [3551/4000] Training [28/39] Loss: 0.00493 +Epoch [3551/4000] Training [29/39] Loss: 0.13006 +Epoch [3551/4000] Training [30/39] Loss: 0.00404 +Epoch [3551/4000] Training [31/39] Loss: 0.12777 +Epoch [3551/4000] Training [32/39] Loss: 0.00611 +Epoch [3551/4000] Training [33/39] Loss: 0.00526 +Epoch [3551/4000] Training [34/39] Loss: 0.00421 +Epoch [3551/4000] Training [35/39] Loss: 0.00319 +Epoch [3551/4000] Training [36/39] Loss: 0.00483 +Epoch [3551/4000] Training [37/39] Loss: 0.12925 +Epoch [3551/4000] Training [38/39] Loss: 0.00532 +Epoch [3551/4000] Training [39/39] Loss: 0.12895 +Epoch [3551/4000] Training metric {'Train/mean dice_metric': 0.9960934519767761, 'Train/mean miou_metric': 0.992633581161499, 'Train/mean f1': 0.996583104133606, 'Train/mean precision': 0.9961238503456116, 'Train/mean recall': 0.9970427751541138, 'Train/mean hd95_metric': 0.9969887137413025} +Epoch [3551/4000] Validation [1/10] Loss: 0.70449 focal_loss 0.61807 dice_loss 0.08642 +Epoch [3551/4000] Validation [2/10] Loss: 0.49054 focal_loss 0.39326 dice_loss 0.09728 +Epoch [3551/4000] Validation [3/10] Loss: 0.38605 focal_loss 0.27536 dice_loss 0.11069 +Epoch [3551/4000] Validation [4/10] Loss: 0.89101 focal_loss 0.32706 dice_loss 0.56395 +Epoch [3551/4000] Validation [5/10] Loss: 3.02234 focal_loss 2.34930 dice_loss 0.67305 +Epoch [3551/4000] Validation [6/10] Loss: 1.32296 focal_loss 0.60822 dice_loss 0.71474 +Epoch [3551/4000] Validation [7/10] Loss: 1.15469 focal_loss 0.50243 dice_loss 0.65226 +Epoch [3551/4000] Validation [8/10] Loss: 2.41686 focal_loss 1.79214 dice_loss 0.62472 +Epoch [3551/4000] Validation [9/10] Loss: 1.41284 focal_loss 0.87000 dice_loss 0.54284 +Epoch [3551/4000] Validation [10/10] Loss: 1.84068 focal_loss 1.10639 dice_loss 0.73430 +Epoch [3551/4000] Validation metric {'Val/mean dice_metric': 0.9514417052268982, 'Val/mean miou_metric': 0.9354376792907715, 'Val/mean f1': 0.9494657516479492, 'Val/mean precision': 0.9458022713661194, 'Val/mean recall': 0.9531577229499817, 'Val/mean hd95_metric': 10.679802894592285} +Cheakpoint... +Epoch [3551/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514417052268982, 'Val/mean miou_metric': 0.9354376792907715, 'Val/mean f1': 0.9494657516479492, 'Val/mean precision': 0.9458022713661194, 'Val/mean recall': 0.9531577229499817, 'Val/mean hd95_metric': 10.679802894592285} +Epoch [3552/4000] Training [1/39] Loss: 0.00561 +Epoch [3552/4000] Training [2/39] Loss: 0.00456 +Epoch [3552/4000] Training [3/39] Loss: 0.00421 +Epoch [3552/4000] Training [4/39] Loss: 0.00571 +Epoch [3552/4000] Training [5/39] Loss: 0.13211 +Epoch [3552/4000] Training [6/39] Loss: 0.00440 +Epoch [3552/4000] Training [7/39] Loss: 0.25503 +Epoch [3552/4000] Training [8/39] Loss: 0.00418 +Epoch [3552/4000] Training [9/39] Loss: 0.00451 +Epoch [3552/4000] Training [10/39] Loss: 0.00346 +Epoch [3552/4000] Training [11/39] Loss: 0.00350 +Epoch [3552/4000] Training [12/39] Loss: 0.00572 +Epoch [3552/4000] Training [13/39] Loss: 0.00466 +Epoch [3552/4000] Training [14/39] Loss: 0.00550 +Epoch [3552/4000] Training [15/39] Loss: 0.00681 +Epoch [3552/4000] Training [16/39] Loss: 0.00601 +Epoch [3552/4000] Training [17/39] Loss: 0.00473 +Epoch [3552/4000] Training [18/39] Loss: 0.00315 +Epoch [3552/4000] Training [19/39] Loss: 0.00539 +Epoch [3552/4000] Training [20/39] Loss: 0.05084 +Epoch [3552/4000] Training [21/39] Loss: 0.00374 +Epoch [3552/4000] Training [22/39] Loss: 0.12979 +Epoch [3552/4000] Training [23/39] Loss: 0.00651 +Epoch [3552/4000] Training [24/39] Loss: 0.00474 +Epoch [3552/4000] Training [25/39] Loss: 0.00397 +Epoch [3552/4000] Training [26/39] Loss: 0.00569 +Epoch [3552/4000] Training [27/39] Loss: 0.00460 +Epoch [3552/4000] Training [28/39] Loss: 0.13200 +Epoch [3552/4000] Training [29/39] Loss: 0.00750 +Epoch [3552/4000] Training [30/39] Loss: 0.12875 +Epoch [3552/4000] Training [31/39] Loss: 0.00500 +Epoch [3552/4000] Training [32/39] Loss: 0.00315 +Epoch [3552/4000] Training [33/39] Loss: 0.00304 +Epoch [3552/4000] Training [34/39] Loss: 0.12881 +Epoch [3552/4000] Training [35/39] Loss: 0.00754 +Epoch [3552/4000] Training [36/39] Loss: 0.13291 +Epoch [3552/4000] Training [37/39] Loss: 0.00344 +Epoch [3552/4000] Training [38/39] Loss: 0.00617 +Epoch [3552/4000] Training [39/39] Loss: 0.00389 +Epoch [3552/4000] Training metric {'Train/mean dice_metric': 0.995955765247345, 'Train/mean miou_metric': 0.9923691153526306, 'Train/mean f1': 0.9966705441474915, 'Train/mean precision': 0.9961901307106018, 'Train/mean recall': 0.9971514344215393, 'Train/mean hd95_metric': 0.9888121485710144} +Epoch [3552/4000] Validation [1/10] Loss: 0.69360 focal_loss 0.60805 dice_loss 0.08555 +Epoch [3552/4000] Validation [2/10] Loss: 0.48841 focal_loss 0.39114 dice_loss 0.09727 +Epoch [3552/4000] Validation [3/10] Loss: 0.38595 focal_loss 0.27520 dice_loss 0.11076 +Epoch [3552/4000] Validation [4/10] Loss: 0.88012 focal_loss 0.31558 dice_loss 0.56454 +Epoch [3552/4000] Validation [5/10] Loss: 3.03998 focal_loss 2.36635 dice_loss 0.67363 +Epoch [3552/4000] Validation [6/10] Loss: 1.30565 focal_loss 0.58765 dice_loss 0.71800 +Epoch [3552/4000] Validation [7/10] Loss: 1.13612 focal_loss 0.48330 dice_loss 0.65283 +Epoch [3552/4000] Validation [8/10] Loss: 2.60387 focal_loss 1.96239 dice_loss 0.64149 +Epoch [3552/4000] Validation [9/10] Loss: 1.39288 focal_loss 0.85202 dice_loss 0.54086 +Epoch [3552/4000] Validation [10/10] Loss: 1.79320 focal_loss 1.06247 dice_loss 0.73073 +Epoch [3552/4000] Validation metric {'Val/mean dice_metric': 0.9512190818786621, 'Val/mean miou_metric': 0.9351430535316467, 'Val/mean f1': 0.9499533772468567, 'Val/mean precision': 0.9476990699768066, 'Val/mean recall': 0.9522184133529663, 'Val/mean hd95_metric': 10.608887672424316} +Cheakpoint... +Epoch [3552/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512190818786621, 'Val/mean miou_metric': 0.9351430535316467, 'Val/mean f1': 0.9499533772468567, 'Val/mean precision': 0.9476990699768066, 'Val/mean recall': 0.9522184133529663, 'Val/mean hd95_metric': 10.608887672424316} +Epoch [3553/4000] Training [1/39] Loss: 0.12979 +Epoch [3553/4000] Training [2/39] Loss: 0.00485 +Epoch [3553/4000] Training [3/39] Loss: 0.01002 +Epoch [3553/4000] Training [4/39] Loss: 0.00458 +Epoch [3553/4000] Training [5/39] Loss: 0.00666 +Epoch [3553/4000] Training [6/39] Loss: 0.13125 +Epoch [3553/4000] Training [7/39] Loss: 0.12789 +Epoch [3553/4000] Training [8/39] Loss: 0.00661 +Epoch [3553/4000] Training [9/39] Loss: 0.00563 +Epoch [3553/4000] Training [10/39] Loss: 0.12885 +Epoch [3553/4000] Training [11/39] Loss: 0.00532 +Epoch [3553/4000] Training [12/39] Loss: 0.12742 +Epoch [3553/4000] Training [13/39] Loss: 0.00429 +Epoch [3553/4000] Training [14/39] Loss: 0.00583 +Epoch [3553/4000] Training [15/39] Loss: 0.00457 +Epoch [3553/4000] Training [16/39] Loss: 0.00736 +Epoch [3553/4000] Training [17/39] Loss: 0.00367 +Epoch [3553/4000] Training [18/39] Loss: 0.00272 +Epoch [3553/4000] Training [19/39] Loss: 0.00745 +Epoch [3553/4000] Training [20/39] Loss: 0.00545 +Epoch [3553/4000] Training [21/39] Loss: 0.00506 +Epoch [3553/4000] Training [22/39] Loss: 0.00725 +Epoch [3553/4000] Training [23/39] Loss: 0.00496 +Epoch [3553/4000] Training [24/39] Loss: 0.00939 +Epoch [3553/4000] Training [25/39] Loss: 0.00560 +Epoch [3553/4000] Training [26/39] Loss: 0.12755 +Epoch [3553/4000] Training [27/39] Loss: 0.00755 +Epoch [3553/4000] Training [28/39] Loss: 0.12985 +Epoch [3553/4000] Training [29/39] Loss: 0.00527 +Epoch [3553/4000] Training [30/39] Loss: 0.00634 +Epoch [3553/4000] Training [31/39] Loss: 0.00323 +Epoch [3553/4000] Training [32/39] Loss: 0.13132 +Epoch [3553/4000] Training [33/39] Loss: 0.00386 +Epoch [3553/4000] Training [34/39] Loss: 0.00473 +Epoch [3553/4000] Training [35/39] Loss: 0.00448 +Epoch [3553/4000] Training [36/39] Loss: 0.00581 +Epoch [3553/4000] Training [37/39] Loss: 0.00964 +Epoch [3553/4000] Training [38/39] Loss: 0.00793 +Epoch [3553/4000] Training [39/39] Loss: 0.00583 +Epoch [3553/4000] Training metric {'Train/mean dice_metric': 0.9957515597343445, 'Train/mean miou_metric': 0.9919570684432983, 'Train/mean f1': 0.9963877201080322, 'Train/mean precision': 0.9959449768066406, 'Train/mean recall': 0.9968309998512268, 'Train/mean hd95_metric': 1.015949010848999} +Epoch [3553/4000] Validation [1/10] Loss: 0.69851 focal_loss 0.61278 dice_loss 0.08573 +Epoch [3553/4000] Validation [2/10] Loss: 0.48857 focal_loss 0.39374 dice_loss 0.09483 +Epoch [3553/4000] Validation [3/10] Loss: 0.39387 focal_loss 0.28257 dice_loss 0.11130 +Epoch [3553/4000] Validation [4/10] Loss: 0.89072 focal_loss 0.32466 dice_loss 0.56606 +Epoch [3553/4000] Validation [5/10] Loss: 3.04790 focal_loss 2.37439 dice_loss 0.67350 +Epoch [3553/4000] Validation [6/10] Loss: 1.32802 focal_loss 0.60739 dice_loss 0.72063 +Epoch [3553/4000] Validation [7/10] Loss: 1.14031 focal_loss 0.48371 dice_loss 0.65661 +Epoch [3553/4000] Validation [8/10] Loss: 2.62744 focal_loss 1.98879 dice_loss 0.63865 +Epoch [3553/4000] Validation [9/10] Loss: 1.41812 focal_loss 0.87520 dice_loss 0.54292 +Epoch [3553/4000] Validation [10/10] Loss: 1.81832 focal_loss 1.08942 dice_loss 0.72890 +Epoch [3553/4000] Validation metric {'Val/mean dice_metric': 0.9510083794593811, 'Val/mean miou_metric': 0.9347580075263977, 'Val/mean f1': 0.949361264705658, 'Val/mean precision': 0.9469417929649353, 'Val/mean recall': 0.9517929553985596, 'Val/mean hd95_metric': 10.684574127197266} +Cheakpoint... +Epoch [3553/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510083794593811, 'Val/mean miou_metric': 0.9347580075263977, 'Val/mean f1': 0.949361264705658, 'Val/mean precision': 0.9469417929649353, 'Val/mean recall': 0.9517929553985596, 'Val/mean hd95_metric': 10.684574127197266} +Epoch [3554/4000] Training [1/39] Loss: 0.13018 +Epoch [3554/4000] Training [2/39] Loss: 0.12912 +Epoch [3554/4000] Training [3/39] Loss: 0.00387 +Epoch [3554/4000] Training [4/39] Loss: 0.00386 +Epoch [3554/4000] Training [5/39] Loss: 0.00568 +Epoch [3554/4000] Training [6/39] Loss: 0.08774 +Epoch [3554/4000] Training [7/39] Loss: 0.12902 +Epoch [3554/4000] Training [8/39] Loss: 0.00586 +Epoch [3554/4000] Training [9/39] Loss: 0.12895 +Epoch [3554/4000] Training [10/39] Loss: 0.12993 +Epoch [3554/4000] Training [11/39] Loss: 0.00398 +Epoch [3554/4000] Training [12/39] Loss: 0.00548 +Epoch [3554/4000] Training [13/39] Loss: 0.12965 +Epoch [3554/4000] Training [14/39] Loss: 0.00478 +Epoch [3554/4000] Training [15/39] Loss: 0.00622 +Epoch [3554/4000] Training [16/39] Loss: 0.00512 +Epoch [3554/4000] Training [17/39] Loss: 0.00275 +Epoch [3554/4000] Training [18/39] Loss: 0.00412 +Epoch [3554/4000] Training [19/39] Loss: 0.00535 +Epoch [3554/4000] Training [20/39] Loss: 0.00368 +Epoch [3554/4000] Training [21/39] Loss: 0.00382 +Epoch [3554/4000] Training [22/39] Loss: 0.00322 +Epoch [3554/4000] Training [23/39] Loss: 0.00509 +Epoch [3554/4000] Training [24/39] Loss: 0.00613 +Epoch [3554/4000] Training [25/39] Loss: 0.00298 +Epoch [3554/4000] Training [26/39] Loss: 0.00494 +Epoch [3554/4000] Training [27/39] Loss: 0.00726 +Epoch [3554/4000] Training [28/39] Loss: 0.00542 +Epoch [3554/4000] Training [29/39] Loss: 0.00587 +Epoch [3554/4000] Training [30/39] Loss: 0.12816 +Epoch [3554/4000] Training [31/39] Loss: 0.00429 +Epoch [3554/4000] Training [32/39] Loss: 0.12884 +Epoch [3554/4000] Training [33/39] Loss: 0.00683 +Epoch [3554/4000] Training [34/39] Loss: 0.00531 +Epoch [3554/4000] Training [35/39] Loss: 0.00479 +Epoch [3554/4000] Training [36/39] Loss: 0.00504 +Epoch [3554/4000] Training [37/39] Loss: 0.00322 +Epoch [3554/4000] Training [38/39] Loss: 0.00589 +Epoch [3554/4000] Training [39/39] Loss: 0.00430 +Epoch [3554/4000] Training metric {'Train/mean dice_metric': 0.9963043928146362, 'Train/mean miou_metric': 0.9930546879768372, 'Train/mean f1': 0.9969089031219482, 'Train/mean precision': 0.9964543581008911, 'Train/mean recall': 0.9973639249801636, 'Train/mean hd95_metric': 0.9787304401397705} +Epoch [3554/4000] Validation [1/10] Loss: 0.69256 focal_loss 0.60619 dice_loss 0.08637 +Epoch [3554/4000] Validation [2/10] Loss: 0.49088 focal_loss 0.39421 dice_loss 0.09667 +Epoch [3554/4000] Validation [3/10] Loss: 0.37549 focal_loss 0.26540 dice_loss 0.11009 +Epoch [3554/4000] Validation [4/10] Loss: 0.88696 focal_loss 0.32313 dice_loss 0.56383 +Epoch [3554/4000] Validation [5/10] Loss: 2.97453 focal_loss 2.30122 dice_loss 0.67331 +Epoch [3554/4000] Validation [6/10] Loss: 1.32723 focal_loss 0.60685 dice_loss 0.72038 +Epoch [3554/4000] Validation [7/10] Loss: 1.14702 focal_loss 0.49003 dice_loss 0.65699 +Epoch [3554/4000] Validation [8/10] Loss: 2.32403 focal_loss 1.70616 dice_loss 0.61788 +Epoch [3554/4000] Validation [9/10] Loss: 1.40434 focal_loss 0.86038 dice_loss 0.54396 +Epoch [3554/4000] Validation [10/10] Loss: 1.84484 focal_loss 1.11074 dice_loss 0.73410 +Epoch [3554/4000] Validation metric {'Val/mean dice_metric': 0.9515724778175354, 'Val/mean miou_metric': 0.9357815384864807, 'Val/mean f1': 0.9493054151535034, 'Val/mean precision': 0.9453374743461609, 'Val/mean recall': 0.9533069133758545, 'Val/mean hd95_metric': 10.744735717773438} +Cheakpoint... +Epoch [3554/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515724778175354, 'Val/mean miou_metric': 0.9357815384864807, 'Val/mean f1': 0.9493054151535034, 'Val/mean precision': 0.9453374743461609, 'Val/mean recall': 0.9533069133758545, 'Val/mean hd95_metric': 10.744735717773438} +Epoch [3555/4000] Training [1/39] Loss: 0.00698 +Epoch [3555/4000] Training [2/39] Loss: 0.00648 +Epoch [3555/4000] Training [3/39] Loss: 0.00758 +Epoch [3555/4000] Training [4/39] Loss: 0.00731 +Epoch [3555/4000] Training [5/39] Loss: 0.00344 +Epoch [3555/4000] Training [6/39] Loss: 0.00581 +Epoch [3555/4000] Training [7/39] Loss: 0.00545 +Epoch [3555/4000] Training [8/39] Loss: 0.00372 +Epoch [3555/4000] Training [9/39] Loss: 0.00510 +Epoch [3555/4000] Training [10/39] Loss: 0.00742 +Epoch [3555/4000] Training [11/39] Loss: 0.00385 +Epoch [3555/4000] Training [12/39] Loss: 0.00536 +Epoch [3555/4000] Training [13/39] Loss: 0.13096 +Epoch [3555/4000] Training [14/39] Loss: 0.00540 +Epoch [3555/4000] Training [15/39] Loss: 0.01007 +Epoch [3555/4000] Training [16/39] Loss: 0.00441 +Epoch [3555/4000] Training [17/39] Loss: 0.01092 +Epoch [3555/4000] Training [18/39] Loss: 0.00460 +Epoch [3555/4000] Training [19/39] Loss: 0.00543 +Epoch [3555/4000] Training [20/39] Loss: 0.00348 +Epoch [3555/4000] Training [21/39] Loss: 0.12916 +Epoch [3555/4000] Training [22/39] Loss: 0.12873 +Epoch [3555/4000] Training [23/39] Loss: 0.25415 +Epoch [3555/4000] Training [24/39] Loss: 0.00488 +Epoch [3555/4000] Training [25/39] Loss: 0.00390 +Epoch [3555/4000] Training [26/39] Loss: 0.00451 +Epoch [3555/4000] Training [27/39] Loss: 0.00549 +Epoch [3555/4000] Training [28/39] Loss: 0.00443 +Epoch [3555/4000] Training [29/39] Loss: 0.00359 +Epoch [3555/4000] Training [30/39] Loss: 0.13040 +Epoch [3555/4000] Training [31/39] Loss: 0.00639 +Epoch [3555/4000] Training [32/39] Loss: 0.00629 +Epoch [3555/4000] Training [33/39] Loss: 0.00591 +Epoch [3555/4000] Training [34/39] Loss: 0.25324 +Epoch [3555/4000] Training [35/39] Loss: 0.00519 +Epoch [3555/4000] Training [36/39] Loss: 0.00510 +Epoch [3555/4000] Training [37/39] Loss: 0.00517 +Epoch [3555/4000] Training [38/39] Loss: 0.25265 +Epoch [3555/4000] Training [39/39] Loss: 0.13011 +Epoch [3555/4000] Training metric {'Train/mean dice_metric': 0.9950746297836304, 'Train/mean miou_metric': 0.9914340972900391, 'Train/mean f1': 0.996646523475647, 'Train/mean precision': 0.9961940050125122, 'Train/mean recall': 0.9970993399620056, 'Train/mean hd95_metric': 0.9764644503593445} +Epoch [3555/4000] Validation [1/10] Loss: 0.68616 focal_loss 0.60075 dice_loss 0.08541 +Epoch [3555/4000] Validation [2/10] Loss: 0.50095 focal_loss 0.40243 dice_loss 0.09852 +Epoch [3555/4000] Validation [3/10] Loss: 0.37973 focal_loss 0.26958 dice_loss 0.11016 +Epoch [3555/4000] Validation [4/10] Loss: 0.88904 focal_loss 0.32489 dice_loss 0.56415 +Epoch [3555/4000] Validation [5/10] Loss: 2.99219 focal_loss 2.31839 dice_loss 0.67379 +Epoch [3555/4000] Validation [6/10] Loss: 1.32184 focal_loss 0.60255 dice_loss 0.71929 +Epoch [3555/4000] Validation [7/10] Loss: 1.14832 focal_loss 0.49425 dice_loss 0.65407 +Epoch [3555/4000] Validation [8/10] Loss: 2.41696 focal_loss 1.79032 dice_loss 0.62664 +Epoch [3555/4000] Validation [9/10] Loss: 1.41186 focal_loss 0.86848 dice_loss 0.54339 +Epoch [3555/4000] Validation [10/10] Loss: 1.85139 focal_loss 1.11793 dice_loss 0.73346 +Epoch [3555/4000] Validation metric {'Val/mean dice_metric': 0.9505464434623718, 'Val/mean miou_metric': 0.9344239830970764, 'Val/mean f1': 0.9497718214988708, 'Val/mean precision': 0.9463935494422913, 'Val/mean recall': 0.953174352645874, 'Val/mean hd95_metric': 10.662909507751465} +Cheakpoint... +Epoch [3555/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505464434623718, 'Val/mean miou_metric': 0.9344239830970764, 'Val/mean f1': 0.9497718214988708, 'Val/mean precision': 0.9463935494422913, 'Val/mean recall': 0.953174352645874, 'Val/mean hd95_metric': 10.662909507751465} +Epoch [3556/4000] Training [1/39] Loss: 0.00728 +Epoch [3556/4000] Training [2/39] Loss: 0.00817 +Epoch [3556/4000] Training [3/39] Loss: 0.00635 +Epoch [3556/4000] Training [4/39] Loss: 0.00469 +Epoch [3556/4000] Training [5/39] Loss: 0.00555 +Epoch [3556/4000] Training [6/39] Loss: 0.00676 +Epoch [3556/4000] Training [7/39] Loss: 0.00517 +Epoch [3556/4000] Training [8/39] Loss: 0.25307 +Epoch [3556/4000] Training [9/39] Loss: 0.00707 +Epoch [3556/4000] Training [10/39] Loss: 0.00482 +Epoch [3556/4000] Training [11/39] Loss: 0.00447 +Epoch [3556/4000] Training [12/39] Loss: 0.00459 +Epoch [3556/4000] Training [13/39] Loss: 0.00810 +Epoch [3556/4000] Training [14/39] Loss: 0.00430 +Epoch [3556/4000] Training [15/39] Loss: 0.13151 +Epoch [3556/4000] Training [16/39] Loss: 0.00428 +Epoch [3556/4000] Training [17/39] Loss: 0.00481 +Epoch [3556/4000] Training [18/39] Loss: 0.12963 +Epoch [3556/4000] Training [19/39] Loss: 0.00344 +Epoch [3556/4000] Training [20/39] Loss: 0.00484 +Epoch [3556/4000] Training [21/39] Loss: 0.13047 +Epoch [3556/4000] Training [22/39] Loss: 0.00368 +Epoch [3556/4000] Training [23/39] Loss: 0.00397 +Epoch [3556/4000] Training [24/39] Loss: 0.12730 +Epoch [3556/4000] Training [25/39] Loss: 0.00378 +Epoch [3556/4000] Training [26/39] Loss: 0.00420 +Epoch [3556/4000] Training [27/39] Loss: 0.00579 +Epoch [3556/4000] Training [28/39] Loss: 0.00506 +Epoch [3556/4000] Training [29/39] Loss: 0.12906 +Epoch [3556/4000] Training [30/39] Loss: 0.13034 +Epoch [3556/4000] Training [31/39] Loss: 0.12863 +Epoch [3556/4000] Training [32/39] Loss: 0.00956 +Epoch [3556/4000] Training [33/39] Loss: 0.00655 +Epoch [3556/4000] Training [34/39] Loss: 0.13027 +Epoch [3556/4000] Training [35/39] Loss: 0.12862 +Epoch [3556/4000] Training [36/39] Loss: 0.00741 +Epoch [3556/4000] Training [37/39] Loss: 0.00374 +Epoch [3556/4000] Training [38/39] Loss: 0.00377 +Epoch [3556/4000] Training [39/39] Loss: 0.00328 +Epoch [3556/4000] Training metric {'Train/mean dice_metric': 0.9960169792175293, 'Train/mean miou_metric': 0.992488443851471, 'Train/mean f1': 0.9966363906860352, 'Train/mean precision': 0.9961668848991394, 'Train/mean recall': 0.9971065521240234, 'Train/mean hd95_metric': 0.999251127243042} +Epoch [3556/4000] Validation [1/10] Loss: 0.68555 focal_loss 0.59951 dice_loss 0.08604 +Epoch [3556/4000] Validation [2/10] Loss: 0.48052 focal_loss 0.38370 dice_loss 0.09682 +Epoch [3556/4000] Validation [3/10] Loss: 0.38537 focal_loss 0.27395 dice_loss 0.11142 +Epoch [3556/4000] Validation [4/10] Loss: 0.88049 focal_loss 0.31660 dice_loss 0.56389 +Epoch [3556/4000] Validation [5/10] Loss: 2.97711 focal_loss 2.30355 dice_loss 0.67356 +Epoch [3556/4000] Validation [6/10] Loss: 1.30851 focal_loss 0.59233 dice_loss 0.71618 +Epoch [3556/4000] Validation [7/10] Loss: 1.15031 focal_loss 0.49743 dice_loss 0.65288 +Epoch [3556/4000] Validation [8/10] Loss: 2.37709 focal_loss 1.75056 dice_loss 0.62653 +Epoch [3556/4000] Validation [9/10] Loss: 1.39055 focal_loss 0.84694 dice_loss 0.54361 +Epoch [3556/4000] Validation [10/10] Loss: 1.85254 focal_loss 1.11662 dice_loss 0.73591 +Epoch [3556/4000] Validation metric {'Val/mean dice_metric': 0.9513054490089417, 'Val/mean miou_metric': 0.9352511167526245, 'Val/mean f1': 0.949176013469696, 'Val/mean precision': 0.9452396035194397, 'Val/mean recall': 0.9531452059745789, 'Val/mean hd95_metric': 10.807310104370117} +Cheakpoint... +Epoch [3556/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513054490089417, 'Val/mean miou_metric': 0.9352511167526245, 'Val/mean f1': 0.949176013469696, 'Val/mean precision': 0.9452396035194397, 'Val/mean recall': 0.9531452059745789, 'Val/mean hd95_metric': 10.807310104370117} +Epoch [3557/4000] Training [1/39] Loss: 0.00620 +Epoch [3557/4000] Training [2/39] Loss: 0.00525 +Epoch [3557/4000] Training [3/39] Loss: 0.13021 +Epoch [3557/4000] Training [4/39] Loss: 0.00510 +Epoch [3557/4000] Training [5/39] Loss: 0.00399 +Epoch [3557/4000] Training [6/39] Loss: 0.00523 +Epoch [3557/4000] Training [7/39] Loss: 0.00583 +Epoch [3557/4000] Training [8/39] Loss: 0.00425 +Epoch [3557/4000] Training [9/39] Loss: 0.13023 +Epoch [3557/4000] Training [10/39] Loss: 0.25346 +Epoch [3557/4000] Training [11/39] Loss: 0.13025 +Epoch [3557/4000] Training [12/39] Loss: 0.00464 +Epoch [3557/4000] Training [13/39] Loss: 0.09209 +Epoch [3557/4000] Training [14/39] Loss: 0.00488 +Epoch [3557/4000] Training [15/39] Loss: 0.00685 +Epoch [3557/4000] Training [16/39] Loss: 0.12980 +Epoch [3557/4000] Training [17/39] Loss: 0.12829 +Epoch [3557/4000] Training [18/39] Loss: 0.00457 +Epoch [3557/4000] Training [19/39] Loss: 0.00568 +Epoch [3557/4000] Training [20/39] Loss: 0.00343 +Epoch [3557/4000] Training [21/39] Loss: 0.12929 +Epoch [3557/4000] Training [22/39] Loss: 0.00449 +Epoch [3557/4000] Training [23/39] Loss: 0.00492 +Epoch [3557/4000] Training [24/39] Loss: 0.00443 +Epoch [3557/4000] Training [25/39] Loss: 0.00616 +Epoch [3557/4000] Training [26/39] Loss: 0.00493 +Epoch [3557/4000] Training [27/39] Loss: 0.00496 +Epoch [3557/4000] Training [28/39] Loss: 0.00610 +Epoch [3557/4000] Training [29/39] Loss: 0.12972 +Epoch [3557/4000] Training [30/39] Loss: 0.13093 +Epoch [3557/4000] Training [31/39] Loss: 0.25399 +Epoch [3557/4000] Training [32/39] Loss: 0.00514 +Epoch [3557/4000] Training [33/39] Loss: 0.00631 +Epoch [3557/4000] Training [34/39] Loss: 0.00916 +Epoch [3557/4000] Training [35/39] Loss: 0.25368 +Epoch [3557/4000] Training [36/39] Loss: 0.00529 +Epoch [3557/4000] Training [37/39] Loss: 0.00424 +Epoch [3557/4000] Training [38/39] Loss: 0.00594 +Epoch [3557/4000] Training [39/39] Loss: 0.00587 +Epoch [3557/4000] Training metric {'Train/mean dice_metric': 0.9959098696708679, 'Train/mean miou_metric': 0.9922851324081421, 'Train/mean f1': 0.9966223835945129, 'Train/mean precision': 0.9961543679237366, 'Train/mean recall': 0.997090756893158, 'Train/mean hd95_metric': 1.0149106979370117} +Epoch [3557/4000] Validation [1/10] Loss: 0.70754 focal_loss 0.61781 dice_loss 0.08972 +Epoch [3557/4000] Validation [2/10] Loss: 0.46881 focal_loss 0.37706 dice_loss 0.09175 +Epoch [3557/4000] Validation [3/10] Loss: 0.35397 focal_loss 0.24593 dice_loss 0.10804 +Epoch [3557/4000] Validation [4/10] Loss: 0.89613 focal_loss 0.33057 dice_loss 0.56556 +Epoch [3557/4000] Validation [5/10] Loss: 2.99817 focal_loss 2.32543 dice_loss 0.67274 +Epoch [3557/4000] Validation [6/10] Loss: 1.34806 focal_loss 0.63238 dice_loss 0.71568 +Epoch [3557/4000] Validation [7/10] Loss: 1.18009 focal_loss 0.52262 dice_loss 0.65747 +Epoch [3557/4000] Validation [8/10] Loss: 2.14762 focal_loss 1.54771 dice_loss 0.59991 +Epoch [3557/4000] Validation [9/10] Loss: 1.42586 focal_loss 0.88043 dice_loss 0.54543 +Epoch [3557/4000] Validation [10/10] Loss: 1.93372 focal_loss 1.19227 dice_loss 0.74145 +Epoch [3557/4000] Validation metric {'Val/mean dice_metric': 0.9511698484420776, 'Val/mean miou_metric': 0.9349817037582397, 'Val/mean f1': 0.9484965205192566, 'Val/mean precision': 0.9411288499832153, 'Val/mean recall': 0.9559804797172546, 'Val/mean hd95_metric': 10.877890586853027} +Cheakpoint... +Epoch [3557/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511698484420776, 'Val/mean miou_metric': 0.9349817037582397, 'Val/mean f1': 0.9484965205192566, 'Val/mean precision': 0.9411288499832153, 'Val/mean recall': 0.9559804797172546, 'Val/mean hd95_metric': 10.877890586853027} +Epoch [3558/4000] Training [1/39] Loss: 0.00437 +Epoch [3558/4000] Training [2/39] Loss: 0.12798 +Epoch [3558/4000] Training [3/39] Loss: 0.00595 +Epoch [3558/4000] Training [4/39] Loss: 0.12902 +Epoch [3558/4000] Training [5/39] Loss: 0.00650 +Epoch [3558/4000] Training [6/39] Loss: 0.00461 +Epoch [3558/4000] Training [7/39] Loss: 0.00415 +Epoch [3558/4000] Training [8/39] Loss: 0.00374 +Epoch [3558/4000] Training [9/39] Loss: 0.00369 +Epoch [3558/4000] Training [10/39] Loss: 0.12971 +Epoch [3558/4000] Training [11/39] Loss: 0.12950 +Epoch [3558/4000] Training [12/39] Loss: 0.12831 +Epoch [3558/4000] Training [13/39] Loss: 0.00377 +Epoch [3558/4000] Training [14/39] Loss: 0.00551 +Epoch [3558/4000] Training [15/39] Loss: 0.00620 +Epoch [3558/4000] Training [16/39] Loss: 0.13008 +Epoch [3558/4000] Training [17/39] Loss: 0.00447 +Epoch [3558/4000] Training [18/39] Loss: 0.00392 +Epoch [3558/4000] Training [19/39] Loss: 0.00439 +Epoch [3558/4000] Training [20/39] Loss: 0.00892 +Epoch [3558/4000] Training [21/39] Loss: 0.25393 +Epoch [3558/4000] Training [22/39] Loss: 0.00632 +Epoch [3558/4000] Training [23/39] Loss: 0.00590 +Epoch [3558/4000] Training [24/39] Loss: 0.00481 +Epoch [3558/4000] Training [25/39] Loss: 0.00446 +Epoch [3558/4000] Training [26/39] Loss: 0.00419 +Epoch [3558/4000] Training [27/39] Loss: 0.00416 +Epoch [3558/4000] Training [28/39] Loss: 0.00645 +Epoch [3558/4000] Training [29/39] Loss: 0.25292 +Epoch [3558/4000] Training [30/39] Loss: 0.00477 +Epoch [3558/4000] Training [31/39] Loss: 0.12842 +Epoch [3558/4000] Training [32/39] Loss: 0.00311 +Epoch [3558/4000] Training [33/39] Loss: 0.00534 +Epoch [3558/4000] Training [34/39] Loss: 0.00403 +Epoch [3558/4000] Training [35/39] Loss: 0.00631 +Epoch [3558/4000] Training [36/39] Loss: 0.00652 +Epoch [3558/4000] Training [37/39] Loss: 0.00485 +Epoch [3558/4000] Training [38/39] Loss: 0.12925 +Epoch [3558/4000] Training [39/39] Loss: 0.00384 +Epoch [3558/4000] Training metric {'Train/mean dice_metric': 0.996211051940918, 'Train/mean miou_metric': 0.9928821325302124, 'Train/mean f1': 0.9968629479408264, 'Train/mean precision': 0.9963894486427307, 'Train/mean recall': 0.9973368644714355, 'Train/mean hd95_metric': 0.9724380373954773} +Epoch [3558/4000] Validation [1/10] Loss: 0.68185 focal_loss 0.59606 dice_loss 0.08579 +Epoch [3558/4000] Validation [2/10] Loss: 0.48481 focal_loss 0.38783 dice_loss 0.09699 +Epoch [3558/4000] Validation [3/10] Loss: 0.36675 focal_loss 0.25713 dice_loss 0.10963 +Epoch [3558/4000] Validation [4/10] Loss: 0.88576 focal_loss 0.32198 dice_loss 0.56378 +Epoch [3558/4000] Validation [5/10] Loss: 2.99043 focal_loss 2.31711 dice_loss 0.67332 +Epoch [3558/4000] Validation [6/10] Loss: 1.35222 focal_loss 0.63199 dice_loss 0.72023 +Epoch [3558/4000] Validation [7/10] Loss: 1.15988 focal_loss 0.50155 dice_loss 0.65833 +Epoch [3558/4000] Validation [8/10] Loss: 2.21550 focal_loss 1.60837 dice_loss 0.60714 +Epoch [3558/4000] Validation [9/10] Loss: 1.42149 focal_loss 0.87615 dice_loss 0.54534 +Epoch [3558/4000] Validation [10/10] Loss: 1.88117 focal_loss 1.14554 dice_loss 0.73563 +Epoch [3558/4000] Validation metric {'Val/mean dice_metric': 0.9515170454978943, 'Val/mean miou_metric': 0.9356479644775391, 'Val/mean f1': 0.949131429195404, 'Val/mean precision': 0.9436905980110168, 'Val/mean recall': 0.9546352624893188, 'Val/mean hd95_metric': 10.68532943725586} +Cheakpoint... +Epoch [3558/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515170454978943, 'Val/mean miou_metric': 0.9356479644775391, 'Val/mean f1': 0.949131429195404, 'Val/mean precision': 0.9436905980110168, 'Val/mean recall': 0.9546352624893188, 'Val/mean hd95_metric': 10.68532943725586} +Epoch [3559/4000] Training [1/39] Loss: 0.12977 +Epoch [3559/4000] Training [2/39] Loss: 0.13253 +Epoch [3559/4000] Training [3/39] Loss: 0.00723 +Epoch [3559/4000] Training [4/39] Loss: 0.00543 +Epoch [3559/4000] Training [5/39] Loss: 0.12846 +Epoch [3559/4000] Training [6/39] Loss: 0.00558 +Epoch [3559/4000] Training [7/39] Loss: 0.00549 +Epoch [3559/4000] Training [8/39] Loss: 0.00386 +Epoch [3559/4000] Training [9/39] Loss: 0.00707 +Epoch [3559/4000] Training [10/39] Loss: 0.00478 +Epoch [3559/4000] Training [11/39] Loss: 0.00356 +Epoch [3559/4000] Training [12/39] Loss: 0.00789 +Epoch [3559/4000] Training [13/39] Loss: 0.00629 +Epoch [3559/4000] Training [14/39] Loss: 0.00558 +Epoch [3559/4000] Training [15/39] Loss: 0.00384 +Epoch [3559/4000] Training [16/39] Loss: 0.12883 +Epoch [3559/4000] Training [17/39] Loss: 0.00385 +Epoch [3559/4000] Training [18/39] Loss: 0.12934 +Epoch [3559/4000] Training [19/39] Loss: 0.00551 +Epoch [3559/4000] Training [20/39] Loss: 0.00531 +Epoch [3559/4000] Training [21/39] Loss: 0.00546 +Epoch [3559/4000] Training [22/39] Loss: 0.00737 +Epoch [3559/4000] Training [23/39] Loss: 0.00592 +Epoch [3559/4000] Training [24/39] Loss: 0.00568 +Epoch [3559/4000] Training [25/39] Loss: 0.25568 +Epoch [3559/4000] Training [26/39] Loss: 0.00492 +Epoch [3559/4000] Training [27/39] Loss: 0.00528 +Epoch [3559/4000] Training [28/39] Loss: 0.00572 +Epoch [3559/4000] Training [29/39] Loss: 0.00593 +Epoch [3559/4000] Training [30/39] Loss: 0.00509 +Epoch [3559/4000] Training [31/39] Loss: 0.00514 +Epoch [3559/4000] Training [32/39] Loss: 0.00810 +Epoch [3559/4000] Training [33/39] Loss: 0.00424 +Epoch [3559/4000] Training [34/39] Loss: 0.00553 +Epoch [3559/4000] Training [35/39] Loss: 0.00466 +Epoch [3559/4000] Training [36/39] Loss: 0.00543 +Epoch [3559/4000] Training [37/39] Loss: 0.00458 +Epoch [3559/4000] Training [38/39] Loss: 0.00585 +Epoch [3559/4000] Training [39/39] Loss: 0.00290 +Epoch [3559/4000] Training metric {'Train/mean dice_metric': 0.9960516095161438, 'Train/mean miou_metric': 0.9925477504730225, 'Train/mean f1': 0.9966627359390259, 'Train/mean precision': 0.996198296546936, 'Train/mean recall': 0.9971277713775635, 'Train/mean hd95_metric': 0.9948216080665588} +Epoch [3559/4000] Validation [1/10] Loss: 0.70328 focal_loss 0.61570 dice_loss 0.08758 +Epoch [3559/4000] Validation [2/10] Loss: 0.48086 focal_loss 0.38558 dice_loss 0.09528 +Epoch [3559/4000] Validation [3/10] Loss: 0.36483 focal_loss 0.25579 dice_loss 0.10904 +Epoch [3559/4000] Validation [4/10] Loss: 0.88584 focal_loss 0.32166 dice_loss 0.56418 +Epoch [3559/4000] Validation [5/10] Loss: 3.03199 focal_loss 2.35932 dice_loss 0.67267 +Epoch [3559/4000] Validation [6/10] Loss: 1.34398 focal_loss 0.62706 dice_loss 0.71692 +Epoch [3559/4000] Validation [7/10] Loss: 1.16363 focal_loss 0.50566 dice_loss 0.65797 +Epoch [3559/4000] Validation [8/10] Loss: 2.22242 focal_loss 1.61804 dice_loss 0.60438 +Epoch [3559/4000] Validation [9/10] Loss: 1.43506 focal_loss 0.89003 dice_loss 0.54503 +Epoch [3559/4000] Validation [10/10] Loss: 1.90063 focal_loss 1.16186 dice_loss 0.73877 +Epoch [3559/4000] Validation metric {'Val/mean dice_metric': 0.9512000679969788, 'Val/mean miou_metric': 0.9351586699485779, 'Val/mean f1': 0.9487212896347046, 'Val/mean precision': 0.942277729511261, 'Val/mean recall': 0.9552536606788635, 'Val/mean hd95_metric': 10.789688110351562} +Cheakpoint... +Epoch [3559/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512000679969788, 'Val/mean miou_metric': 0.9351586699485779, 'Val/mean f1': 0.9487212896347046, 'Val/mean precision': 0.942277729511261, 'Val/mean recall': 0.9552536606788635, 'Val/mean hd95_metric': 10.789688110351562} +Epoch [3560/4000] Training [1/39] Loss: 0.00741 +Epoch [3560/4000] Training [2/39] Loss: 0.00403 +Epoch [3560/4000] Training [3/39] Loss: 0.00502 +Epoch [3560/4000] Training [4/39] Loss: 0.00339 +Epoch [3560/4000] Training [5/39] Loss: 0.13252 +Epoch [3560/4000] Training [6/39] Loss: 0.00347 +Epoch [3560/4000] Training [7/39] Loss: 0.12924 +Epoch [3560/4000] Training [8/39] Loss: 0.00380 +Epoch [3560/4000] Training [9/39] Loss: 0.00337 +Epoch [3560/4000] Training [10/39] Loss: 0.00702 +Epoch [3560/4000] Training [11/39] Loss: 0.13048 +Epoch [3560/4000] Training [12/39] Loss: 0.12945 +Epoch [3560/4000] Training [13/39] Loss: 0.00488 +Epoch [3560/4000] Training [14/39] Loss: 0.00577 +Epoch [3560/4000] Training [15/39] Loss: 0.13150 +Epoch [3560/4000] Training [16/39] Loss: 0.00395 +Epoch [3560/4000] Training [17/39] Loss: 0.12912 +Epoch [3560/4000] Training [18/39] Loss: 0.00592 +Epoch [3560/4000] Training [19/39] Loss: 0.00680 +Epoch [3560/4000] Training [20/39] Loss: 0.00346 +Epoch [3560/4000] Training [21/39] Loss: 0.12964 +Epoch [3560/4000] Training [22/39] Loss: 0.00613 +Epoch [3560/4000] Training [23/39] Loss: 0.13055 +Epoch [3560/4000] Training [24/39] Loss: 0.00412 +Epoch [3560/4000] Training [25/39] Loss: 0.00382 +Epoch [3560/4000] Training [26/39] Loss: 0.00740 +Epoch [3560/4000] Training [27/39] Loss: 0.00775 +Epoch [3560/4000] Training [28/39] Loss: 0.00588 +Epoch [3560/4000] Training [29/39] Loss: 0.13053 +Epoch [3560/4000] Training [30/39] Loss: 0.00312 +Epoch [3560/4000] Training [31/39] Loss: 0.00345 +Epoch [3560/4000] Training [32/39] Loss: 0.00552 +Epoch [3560/4000] Training [33/39] Loss: 0.00394 +Epoch [3560/4000] Training [34/39] Loss: 0.00329 +Epoch [3560/4000] Training [35/39] Loss: 0.00427 +Epoch [3560/4000] Training [36/39] Loss: 0.00538 +Epoch [3560/4000] Training [37/39] Loss: 0.00618 +Epoch [3560/4000] Training [38/39] Loss: 0.00382 +Epoch [3560/4000] Training [39/39] Loss: 0.00824 +Epoch [3560/4000] Training metric {'Train/mean dice_metric': 0.9951916337013245, 'Train/mean miou_metric': 0.9916611909866333, 'Train/mean f1': 0.996755063533783, 'Train/mean precision': 0.9962748885154724, 'Train/mean recall': 0.9972356557846069, 'Train/mean hd95_metric': 1.0854413509368896} +Epoch [3560/4000] Validation [1/10] Loss: 0.70365 focal_loss 0.61786 dice_loss 0.08579 +Epoch [3560/4000] Validation [2/10] Loss: 0.48079 focal_loss 0.38271 dice_loss 0.09809 +Epoch [3560/4000] Validation [3/10] Loss: 0.38222 focal_loss 0.27196 dice_loss 0.11027 +Epoch [3560/4000] Validation [4/10] Loss: 0.87489 focal_loss 0.31208 dice_loss 0.56280 +Epoch [3560/4000] Validation [5/10] Loss: 3.07347 focal_loss 2.40023 dice_loss 0.67324 +Epoch [3560/4000] Validation [6/10] Loss: 1.30847 focal_loss 0.59339 dice_loss 0.71508 +Epoch [3560/4000] Validation [7/10] Loss: 1.13731 focal_loss 0.48757 dice_loss 0.64974 +Epoch [3560/4000] Validation [8/10] Loss: 2.39517 focal_loss 1.77130 dice_loss 0.62388 +Epoch [3560/4000] Validation [9/10] Loss: 1.40703 focal_loss 0.86859 dice_loss 0.53844 +Epoch [3560/4000] Validation [10/10] Loss: 1.84609 focal_loss 1.10965 dice_loss 0.73644 +Epoch [3560/4000] Validation metric {'Val/mean dice_metric': 0.9506012201309204, 'Val/mean miou_metric': 0.9345539212226868, 'Val/mean f1': 0.9491564631462097, 'Val/mean precision': 0.9452289342880249, 'Val/mean recall': 0.953116774559021, 'Val/mean hd95_metric': 10.505728721618652} +Cheakpoint... +Epoch [3560/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506012201309204, 'Val/mean miou_metric': 0.9345539212226868, 'Val/mean f1': 0.9491564631462097, 'Val/mean precision': 0.9452289342880249, 'Val/mean recall': 0.953116774559021, 'Val/mean hd95_metric': 10.505728721618652} +Epoch [3561/4000] Training [1/39] Loss: 0.12881 +Epoch [3561/4000] Training [2/39] Loss: 0.13397 +Epoch [3561/4000] Training [3/39] Loss: 0.13064 +Epoch [3561/4000] Training [4/39] Loss: 0.13036 +Epoch [3561/4000] Training [5/39] Loss: 0.00958 +Epoch [3561/4000] Training [6/39] Loss: 0.00861 +Epoch [3561/4000] Training [7/39] Loss: 0.00486 +Epoch [3561/4000] Training [8/39] Loss: 0.25231 +Epoch [3561/4000] Training [9/39] Loss: 0.12823 +Epoch [3561/4000] Training [10/39] Loss: 0.00516 +Epoch [3561/4000] Training [11/39] Loss: 0.00350 +Epoch [3561/4000] Training [12/39] Loss: 0.00691 +Epoch [3561/4000] Training [13/39] Loss: 0.12848 +Epoch [3561/4000] Training [14/39] Loss: 0.00891 +Epoch [3561/4000] Training [15/39] Loss: 0.13271 +Epoch [3561/4000] Training [16/39] Loss: 0.00376 +Epoch [3561/4000] Training [17/39] Loss: 0.00358 +Epoch [3561/4000] Training [18/39] Loss: 0.00418 +Epoch [3561/4000] Training [19/39] Loss: 0.00690 +Epoch [3561/4000] Training [20/39] Loss: 0.00484 +Epoch [3561/4000] Training [21/39] Loss: 0.00687 +Epoch [3561/4000] Training [22/39] Loss: 0.00508 +Epoch [3561/4000] Training [23/39] Loss: 0.13005 +Epoch [3561/4000] Training [24/39] Loss: 0.12892 +Epoch [3561/4000] Training [25/39] Loss: 0.00523 +Epoch [3561/4000] Training [26/39] Loss: 0.00261 +Epoch [3561/4000] Training [27/39] Loss: 0.00360 +Epoch [3561/4000] Training [28/39] Loss: 0.00569 +Epoch [3561/4000] Training [29/39] Loss: 0.00749 +Epoch [3561/4000] Training [30/39] Loss: 0.00434 +Epoch [3561/4000] Training [31/39] Loss: 0.00279 +Epoch [3561/4000] Training [32/39] Loss: 0.13016 +Epoch [3561/4000] Training [33/39] Loss: 0.00360 +Epoch [3561/4000] Training [34/39] Loss: 0.13381 +Epoch [3561/4000] Training [35/39] Loss: 0.25775 +Epoch [3561/4000] Training [36/39] Loss: 0.00330 +Epoch [3561/4000] Training [37/39] Loss: 0.13038 +Epoch [3561/4000] Training [38/39] Loss: 0.00325 +Epoch [3561/4000] Training [39/39] Loss: 0.12841 +Epoch [3561/4000] Training metric {'Train/mean dice_metric': 0.9962257742881775, 'Train/mean miou_metric': 0.9928916096687317, 'Train/mean f1': 0.996708333492279, 'Train/mean precision': 0.9962687492370605, 'Train/mean recall': 0.9971482157707214, 'Train/mean hd95_metric': 0.9583942294120789} +Epoch [3561/4000] Validation [1/10] Loss: 0.69098 focal_loss 0.60600 dice_loss 0.08499 +Epoch [3561/4000] Validation [2/10] Loss: 0.48516 focal_loss 0.38828 dice_loss 0.09687 +Epoch [3561/4000] Validation [3/10] Loss: 0.37023 focal_loss 0.26121 dice_loss 0.10902 +Epoch [3561/4000] Validation [4/10] Loss: 0.88457 focal_loss 0.32213 dice_loss 0.56244 +Epoch [3561/4000] Validation [5/10] Loss: 3.04814 focal_loss 2.37487 dice_loss 0.67327 +Epoch [3561/4000] Validation [6/10] Loss: 1.33238 focal_loss 0.61632 dice_loss 0.71605 +Epoch [3561/4000] Validation [7/10] Loss: 1.15937 focal_loss 0.50886 dice_loss 0.65051 +Epoch [3561/4000] Validation [8/10] Loss: 2.32321 focal_loss 1.70802 dice_loss 0.61519 +Epoch [3561/4000] Validation [9/10] Loss: 1.46605 focal_loss 0.92006 dice_loss 0.54599 +Epoch [3561/4000] Validation [10/10] Loss: 1.88725 focal_loss 1.14958 dice_loss 0.73767 +Epoch [3561/4000] Validation metric {'Val/mean dice_metric': 0.9514695405960083, 'Val/mean miou_metric': 0.9355834722518921, 'Val/mean f1': 0.9488047957420349, 'Val/mean precision': 0.9440892338752747, 'Val/mean recall': 0.9535676836967468, 'Val/mean hd95_metric': 10.629542350769043} +Cheakpoint... +Epoch [3561/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514695405960083, 'Val/mean miou_metric': 0.9355834722518921, 'Val/mean f1': 0.9488047957420349, 'Val/mean precision': 0.9440892338752747, 'Val/mean recall': 0.9535676836967468, 'Val/mean hd95_metric': 10.629542350769043} +Epoch [3562/4000] Training [1/39] Loss: 0.00605 +Epoch [3562/4000] Training [2/39] Loss: 0.00726 +Epoch [3562/4000] Training [3/39] Loss: 0.00431 +Epoch [3562/4000] Training [4/39] Loss: 0.00223 +Epoch [3562/4000] Training [5/39] Loss: 0.00295 +Epoch [3562/4000] Training [6/39] Loss: 0.00560 +Epoch [3562/4000] Training [7/39] Loss: 0.12795 +Epoch [3562/4000] Training [8/39] Loss: 0.12988 +Epoch [3562/4000] Training [9/39] Loss: 0.00580 +Epoch [3562/4000] Training [10/39] Loss: 0.25289 +Epoch [3562/4000] Training [11/39] Loss: 0.00637 +Epoch [3562/4000] Training [12/39] Loss: 0.00973 +Epoch [3562/4000] Training [13/39] Loss: 0.00555 +Epoch [3562/4000] Training [14/39] Loss: 0.00363 +Epoch [3562/4000] Training [15/39] Loss: 0.00865 +Epoch [3562/4000] Training [16/39] Loss: 0.12896 +Epoch [3562/4000] Training [17/39] Loss: 0.12890 +Epoch [3562/4000] Training [18/39] Loss: 0.12874 +Epoch [3562/4000] Training [19/39] Loss: 0.00558 +Epoch [3562/4000] Training [20/39] Loss: 0.00595 +Epoch [3562/4000] Training [21/39] Loss: 0.00898 +Epoch [3562/4000] Training [22/39] Loss: 0.12948 +Epoch [3562/4000] Training [23/39] Loss: 0.00832 +Epoch [3562/4000] Training [24/39] Loss: 0.00480 +Epoch [3562/4000] Training [25/39] Loss: 0.12979 +Epoch [3562/4000] Training [26/39] Loss: 0.00577 +Epoch [3562/4000] Training [27/39] Loss: 0.00462 +Epoch [3562/4000] Training [28/39] Loss: 0.00407 +Epoch [3562/4000] Training [29/39] Loss: 0.00548 +Epoch [3562/4000] Training [30/39] Loss: 0.13091 +Epoch [3562/4000] Training [31/39] Loss: 0.00656 +Epoch [3562/4000] Training [32/39] Loss: 0.12981 +Epoch [3562/4000] Training [33/39] Loss: 0.12941 +Epoch [3562/4000] Training [34/39] Loss: 0.00495 +Epoch [3562/4000] Training [35/39] Loss: 0.00841 +Epoch [3562/4000] Training [36/39] Loss: 0.00313 +Epoch [3562/4000] Training [37/39] Loss: 0.00639 +Epoch [3562/4000] Training [38/39] Loss: 0.00448 +Epoch [3562/4000] Training [39/39] Loss: 0.00504 +Epoch [3562/4000] Training metric {'Train/mean dice_metric': 0.9959320425987244, 'Train/mean miou_metric': 0.9923441410064697, 'Train/mean f1': 0.9964969754219055, 'Train/mean precision': 0.9959699511528015, 'Train/mean recall': 0.997024655342102, 'Train/mean hd95_metric': 1.0357990264892578} +Epoch [3562/4000] Validation [1/10] Loss: 0.67112 focal_loss 0.58646 dice_loss 0.08466 +Epoch [3562/4000] Validation [2/10] Loss: 0.48483 focal_loss 0.38713 dice_loss 0.09770 +Epoch [3562/4000] Validation [3/10] Loss: 0.37255 focal_loss 0.26263 dice_loss 0.10993 +Epoch [3562/4000] Validation [4/10] Loss: 0.88254 focal_loss 0.31956 dice_loss 0.56298 +Epoch [3562/4000] Validation [5/10] Loss: 2.97122 focal_loss 2.29761 dice_loss 0.67361 +Epoch [3562/4000] Validation [6/10] Loss: 1.32288 focal_loss 0.60787 dice_loss 0.71501 +Epoch [3562/4000] Validation [7/10] Loss: 1.13114 focal_loss 0.48143 dice_loss 0.64972 +Epoch [3562/4000] Validation [8/10] Loss: 2.38117 focal_loss 1.75630 dice_loss 0.62487 +Epoch [3562/4000] Validation [9/10] Loss: 1.43425 focal_loss 0.88904 dice_loss 0.54521 +Epoch [3562/4000] Validation [10/10] Loss: 1.84431 focal_loss 1.10970 dice_loss 0.73461 +Epoch [3562/4000] Validation metric {'Val/mean dice_metric': 0.9511388540267944, 'Val/mean miou_metric': 0.935035765171051, 'Val/mean f1': 0.9491441249847412, 'Val/mean precision': 0.9456419348716736, 'Val/mean recall': 0.9526724219322205, 'Val/mean hd95_metric': 10.723071098327637} +Cheakpoint... +Epoch [3562/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511388540267944, 'Val/mean miou_metric': 0.935035765171051, 'Val/mean f1': 0.9491441249847412, 'Val/mean precision': 0.9456419348716736, 'Val/mean recall': 0.9526724219322205, 'Val/mean hd95_metric': 10.723071098327637} +Epoch [3563/4000] Training [1/39] Loss: 0.12891 +Epoch [3563/4000] Training [2/39] Loss: 0.00325 +Epoch [3563/4000] Training [3/39] Loss: 0.00735 +Epoch [3563/4000] Training [4/39] Loss: 0.00383 +Epoch [3563/4000] Training [5/39] Loss: 0.00700 +Epoch [3563/4000] Training [6/39] Loss: 0.00621 +Epoch [3563/4000] Training [7/39] Loss: 0.00442 +Epoch [3563/4000] Training [8/39] Loss: 0.00415 +Epoch [3563/4000] Training [9/39] Loss: 0.00537 +Epoch [3563/4000] Training [10/39] Loss: 0.00421 +Epoch [3563/4000] Training [11/39] Loss: 0.29215 +Epoch [3563/4000] Training [12/39] Loss: 0.01359 +Epoch [3563/4000] Training [13/39] Loss: 0.00385 +Epoch [3563/4000] Training [14/39] Loss: 0.13041 +Epoch [3563/4000] Training [15/39] Loss: 0.00630 +Epoch [3563/4000] Training [16/39] Loss: 0.00273 +Epoch [3563/4000] Training [17/39] Loss: 0.13368 +Epoch [3563/4000] Training [18/39] Loss: 0.00644 +Epoch [3563/4000] Training [19/39] Loss: 0.00409 +Epoch [3563/4000] Training [20/39] Loss: 0.12902 +Epoch [3563/4000] Training [21/39] Loss: 0.00433 +Epoch [3563/4000] Training [22/39] Loss: 0.00456 +Epoch [3563/4000] Training [23/39] Loss: 0.12845 +Epoch [3563/4000] Training [24/39] Loss: 0.13094 +Epoch [3563/4000] Training [25/39] Loss: 0.00444 +Epoch [3563/4000] Training [26/39] Loss: 0.01046 +Epoch [3563/4000] Training [27/39] Loss: 0.00543 +Epoch [3563/4000] Training [28/39] Loss: 0.13019 +Epoch [3563/4000] Training [29/39] Loss: 0.00449 +Epoch [3563/4000] Training [30/39] Loss: 0.00472 +Epoch [3563/4000] Training [31/39] Loss: 0.00273 +Epoch [3563/4000] Training [32/39] Loss: 0.00512 +Epoch [3563/4000] Training [33/39] Loss: 0.00512 +Epoch [3563/4000] Training [34/39] Loss: 0.00532 +Epoch [3563/4000] Training [35/39] Loss: 0.00499 +Epoch [3563/4000] Training [36/39] Loss: 0.00499 +Epoch [3563/4000] Training [37/39] Loss: 0.00388 +Epoch [3563/4000] Training [38/39] Loss: 0.00516 +Epoch [3563/4000] Training [39/39] Loss: 0.00479 +Epoch [3563/4000] Training metric {'Train/mean dice_metric': 0.9958234429359436, 'Train/mean miou_metric': 0.9921761155128479, 'Train/mean f1': 0.9964866042137146, 'Train/mean precision': 0.9961309432983398, 'Train/mean recall': 0.9968425035476685, 'Train/mean hd95_metric': 1.0428509712219238} +Epoch [3563/4000] Validation [1/10] Loss: 0.77676 focal_loss 0.68389 dice_loss 0.09287 +Epoch [3563/4000] Validation [2/10] Loss: 0.45356 focal_loss 0.36550 dice_loss 0.08806 +Epoch [3563/4000] Validation [3/10] Loss: 0.34416 focal_loss 0.23668 dice_loss 0.10748 +Epoch [3563/4000] Validation [4/10] Loss: 0.92945 focal_loss 0.35483 dice_loss 0.57461 +Epoch [3563/4000] Validation [5/10] Loss: 3.02467 focal_loss 2.35253 dice_loss 0.67214 +Epoch [3563/4000] Validation [6/10] Loss: 1.36124 focal_loss 0.64344 dice_loss 0.71780 +Epoch [3563/4000] Validation [7/10] Loss: 1.21325 focal_loss 0.55163 dice_loss 0.66162 +Epoch [3563/4000] Validation [8/10] Loss: 1.82973 focal_loss 1.27639 dice_loss 0.55334 +Epoch [3563/4000] Validation [9/10] Loss: 1.74193 focal_loss 1.23923 dice_loss 0.50270 +Epoch [3563/4000] Validation [10/10] Loss: 2.00147 focal_loss 1.25735 dice_loss 0.74411 +Epoch [3563/4000] Validation metric {'Val/mean dice_metric': 0.9507854580879211, 'Val/mean miou_metric': 0.9345158934593201, 'Val/mean f1': 0.9459779262542725, 'Val/mean precision': 0.9332714676856995, 'Val/mean recall': 0.9590350389480591, 'Val/mean hd95_metric': 11.020326614379883} +Cheakpoint... +Epoch [3563/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507854580879211, 'Val/mean miou_metric': 0.9345158934593201, 'Val/mean f1': 0.9459779262542725, 'Val/mean precision': 0.9332714676856995, 'Val/mean recall': 0.9590350389480591, 'Val/mean hd95_metric': 11.020326614379883} +Epoch [3564/4000] Training [1/39] Loss: 0.00317 +Epoch [3564/4000] Training [2/39] Loss: 0.00574 +Epoch [3564/4000] Training [3/39] Loss: 0.00451 +Epoch [3564/4000] Training [4/39] Loss: 0.12858 +Epoch [3564/4000] Training [5/39] Loss: 0.00390 +Epoch [3564/4000] Training [6/39] Loss: 0.12912 +Epoch [3564/4000] Training [7/39] Loss: 0.00441 +Epoch [3564/4000] Training [8/39] Loss: 0.00792 +Epoch [3564/4000] Training [9/39] Loss: 0.12924 +Epoch [3564/4000] Training [10/39] Loss: 0.00696 +Epoch [3564/4000] Training [11/39] Loss: 0.00802 +Epoch [3564/4000] Training [12/39] Loss: 0.13288 +Epoch [3564/4000] Training [13/39] Loss: 0.12982 +Epoch [3564/4000] Training [14/39] Loss: 0.00612 +Epoch [3564/4000] Training [15/39] Loss: 0.13320 +Epoch [3564/4000] Training [16/39] Loss: 0.00486 +Epoch [3564/4000] Training [17/39] Loss: 0.12890 +Epoch [3564/4000] Training [18/39] Loss: 0.00375 +Epoch [3564/4000] Training [19/39] Loss: 0.00415 +Epoch [3564/4000] Training [20/39] Loss: 0.00491 +Epoch [3564/4000] Training [21/39] Loss: 0.00581 +Epoch [3564/4000] Training [22/39] Loss: 0.00510 +Epoch [3564/4000] Training [23/39] Loss: 0.00856 +Epoch [3564/4000] Training [24/39] Loss: 0.00779 +Epoch [3564/4000] Training [25/39] Loss: 0.00342 +Epoch [3564/4000] Training [26/39] Loss: 0.25416 +Epoch [3564/4000] Training [27/39] Loss: 0.33465 +Epoch [3564/4000] Training [28/39] Loss: 0.00405 +Epoch [3564/4000] Training [29/39] Loss: 0.00371 +Epoch [3564/4000] Training [30/39] Loss: 0.00534 +Epoch [3564/4000] Training [31/39] Loss: 0.00540 +Epoch [3564/4000] Training [32/39] Loss: 0.00485 +Epoch [3564/4000] Training [33/39] Loss: 0.00757 +Epoch [3564/4000] Training [34/39] Loss: 0.00690 +Epoch [3564/4000] Training [35/39] Loss: 0.00534 +Epoch [3564/4000] Training [36/39] Loss: 0.00624 +Epoch [3564/4000] Training [37/39] Loss: 0.00434 +Epoch [3564/4000] Training [38/39] Loss: 0.00608 +Epoch [3564/4000] Training [39/39] Loss: 0.13109 +Epoch [3564/4000] Training metric {'Train/mean dice_metric': 0.9951762557029724, 'Train/mean miou_metric': 0.9916372299194336, 'Train/mean f1': 0.9967156052589417, 'Train/mean precision': 0.9962184429168701, 'Train/mean recall': 0.9972134232521057, 'Train/mean hd95_metric': 0.9838011860847473} +Epoch [3564/4000] Validation [1/10] Loss: 0.76252 focal_loss 0.67047 dice_loss 0.09205 +Epoch [3564/4000] Validation [2/10] Loss: 0.46576 focal_loss 0.37706 dice_loss 0.08869 +Epoch [3564/4000] Validation [3/10] Loss: 0.35173 focal_loss 0.24395 dice_loss 0.10779 +Epoch [3564/4000] Validation [4/10] Loss: 0.91229 focal_loss 0.34677 dice_loss 0.56552 +Epoch [3564/4000] Validation [5/10] Loss: 3.02615 focal_loss 2.35342 dice_loss 0.67273 +Epoch [3564/4000] Validation [6/10] Loss: 1.37252 focal_loss 0.65554 dice_loss 0.71698 +Epoch [3564/4000] Validation [7/10] Loss: 1.20935 focal_loss 0.54988 dice_loss 0.65947 +Epoch [3564/4000] Validation [8/10] Loss: 2.00894 focal_loss 1.43495 dice_loss 0.57399 +Epoch [3564/4000] Validation [9/10] Loss: 1.67879 focal_loss 1.17299 dice_loss 0.50581 +Epoch [3564/4000] Validation [10/10] Loss: 1.97401 focal_loss 1.23471 dice_loss 0.73930 +Epoch [3564/4000] Validation metric {'Val/mean dice_metric': 0.9505733847618103, 'Val/mean miou_metric': 0.9343710541725159, 'Val/mean f1': 0.9469393491744995, 'Val/mean precision': 0.9368065595626831, 'Val/mean recall': 0.957293689250946, 'Val/mean hd95_metric': 10.776650428771973} +Cheakpoint... +Epoch [3564/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505733847618103, 'Val/mean miou_metric': 0.9343710541725159, 'Val/mean f1': 0.9469393491744995, 'Val/mean precision': 0.9368065595626831, 'Val/mean recall': 0.957293689250946, 'Val/mean hd95_metric': 10.776650428771973} +Epoch [3565/4000] Training [1/39] Loss: 0.00340 +Epoch [3565/4000] Training [2/39] Loss: 0.00362 +Epoch [3565/4000] Training [3/39] Loss: 0.00572 +Epoch [3565/4000] Training [4/39] Loss: 0.00436 +Epoch [3565/4000] Training [5/39] Loss: 0.00654 +Epoch [3565/4000] Training [6/39] Loss: 0.00377 +Epoch [3565/4000] Training [7/39] Loss: 0.00567 +Epoch [3565/4000] Training [8/39] Loss: 0.00436 +Epoch [3565/4000] Training [9/39] Loss: 0.00564 +Epoch [3565/4000] Training [10/39] Loss: 0.00608 +Epoch [3565/4000] Training [11/39] Loss: 0.00479 +Epoch [3565/4000] Training [12/39] Loss: 0.00600 +Epoch [3565/4000] Training [13/39] Loss: 0.00516 +Epoch [3565/4000] Training [14/39] Loss: 0.00405 +Epoch [3565/4000] Training [15/39] Loss: 0.00747 +Epoch [3565/4000] Training [16/39] Loss: 0.12850 +Epoch [3565/4000] Training [17/39] Loss: 0.00499 +Epoch [3565/4000] Training [18/39] Loss: 0.12939 +Epoch [3565/4000] Training [19/39] Loss: 0.00480 +Epoch [3565/4000] Training [20/39] Loss: 0.12884 +Epoch [3565/4000] Training [21/39] Loss: 0.00693 +Epoch [3565/4000] Training [22/39] Loss: 0.00511 +Epoch [3565/4000] Training [23/39] Loss: 0.00626 +Epoch [3565/4000] Training [24/39] Loss: 0.00597 +Epoch [3565/4000] Training [25/39] Loss: 0.25315 +Epoch [3565/4000] Training [26/39] Loss: 0.00523 +Epoch [3565/4000] Training [27/39] Loss: 0.13004 +Epoch [3565/4000] Training [28/39] Loss: 0.08405 +Epoch [3565/4000] Training [29/39] Loss: 0.00562 +Epoch [3565/4000] Training [30/39] Loss: 0.25570 +Epoch [3565/4000] Training [31/39] Loss: 0.00580 +Epoch [3565/4000] Training [32/39] Loss: 0.00367 +Epoch [3565/4000] Training [33/39] Loss: 0.12754 +Epoch [3565/4000] Training [34/39] Loss: 0.00551 +Epoch [3565/4000] Training [35/39] Loss: 0.00594 +Epoch [3565/4000] Training [36/39] Loss: 0.00991 +Epoch [3565/4000] Training [37/39] Loss: 0.12823 +Epoch [3565/4000] Training [38/39] Loss: 0.00356 +Epoch [3565/4000] Training [39/39] Loss: 0.00407 +Epoch [3565/4000] Training metric {'Train/mean dice_metric': 0.995967447757721, 'Train/mean miou_metric': 0.9923901557922363, 'Train/mean f1': 0.9966720342636108, 'Train/mean precision': 0.9961589574813843, 'Train/mean recall': 0.9971856474876404, 'Train/mean hd95_metric': 0.9900820851325989} +Epoch [3565/4000] Validation [1/10] Loss: 0.73809 focal_loss 0.64672 dice_loss 0.09137 +Epoch [3565/4000] Validation [2/10] Loss: 0.46423 focal_loss 0.37068 dice_loss 0.09355 +Epoch [3565/4000] Validation [3/10] Loss: 0.36376 focal_loss 0.25434 dice_loss 0.10941 +Epoch [3565/4000] Validation [4/10] Loss: 0.89370 focal_loss 0.32979 dice_loss 0.56391 +Epoch [3565/4000] Validation [5/10] Loss: 2.99334 focal_loss 2.32067 dice_loss 0.67267 +Epoch [3565/4000] Validation [6/10] Loss: 1.33753 focal_loss 0.62092 dice_loss 0.71661 +Epoch [3565/4000] Validation [7/10] Loss: 1.17124 focal_loss 0.51307 dice_loss 0.65817 +Epoch [3565/4000] Validation [8/10] Loss: 2.07590 focal_loss 1.48726 dice_loss 0.58863 +Epoch [3565/4000] Validation [9/10] Loss: 1.56444 focal_loss 1.05153 dice_loss 0.51291 +Epoch [3565/4000] Validation [10/10] Loss: 1.92949 focal_loss 1.18889 dice_loss 0.74060 +Epoch [3565/4000] Validation metric {'Val/mean dice_metric': 0.9512426257133484, 'Val/mean miou_metric': 0.9350128173828125, 'Val/mean f1': 0.9472513794898987, 'Val/mean precision': 0.9391383528709412, 'Val/mean recall': 0.9555057287216187, 'Val/mean hd95_metric': 10.729884147644043} +Cheakpoint... +Epoch [3565/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512426257133484, 'Val/mean miou_metric': 0.9350128173828125, 'Val/mean f1': 0.9472513794898987, 'Val/mean precision': 0.9391383528709412, 'Val/mean recall': 0.9555057287216187, 'Val/mean hd95_metric': 10.729884147644043} +Epoch [3566/4000] Training [1/39] Loss: 0.00549 +Epoch [3566/4000] Training [2/39] Loss: 0.13345 +Epoch [3566/4000] Training [3/39] Loss: 0.00453 +Epoch [3566/4000] Training [4/39] Loss: 0.00665 +Epoch [3566/4000] Training [5/39] Loss: 0.12851 +Epoch [3566/4000] Training [6/39] Loss: 0.13027 +Epoch [3566/4000] Training [7/39] Loss: 0.00374 +Epoch [3566/4000] Training [8/39] Loss: 0.13006 +Epoch [3566/4000] Training [9/39] Loss: 0.25370 +Epoch [3566/4000] Training [10/39] Loss: 0.00391 +Epoch [3566/4000] Training [11/39] Loss: 0.00686 +Epoch [3566/4000] Training [12/39] Loss: 0.00632 +Epoch [3566/4000] Training [13/39] Loss: 0.00476 +Epoch [3566/4000] Training [14/39] Loss: 0.00687 +Epoch [3566/4000] Training [15/39] Loss: 0.12968 +Epoch [3566/4000] Training [16/39] Loss: 0.13281 +Epoch [3566/4000] Training [17/39] Loss: 0.00748 +Epoch [3566/4000] Training [18/39] Loss: 0.00407 +Epoch [3566/4000] Training [19/39] Loss: 0.12881 +Epoch [3566/4000] Training [20/39] Loss: 0.00499 +Epoch [3566/4000] Training [21/39] Loss: 0.12888 +Epoch [3566/4000] Training [22/39] Loss: 0.00800 +Epoch [3566/4000] Training [23/39] Loss: 0.12970 +Epoch [3566/4000] Training [24/39] Loss: 0.00938 +Epoch [3566/4000] Training [25/39] Loss: 0.13305 +Epoch [3566/4000] Training [26/39] Loss: 0.00367 +Epoch [3566/4000] Training [27/39] Loss: 0.00394 +Epoch [3566/4000] Training [28/39] Loss: 0.00562 +Epoch [3566/4000] Training [29/39] Loss: 0.12879 +Epoch [3566/4000] Training [30/39] Loss: 0.00623 +Epoch [3566/4000] Training [31/39] Loss: 0.00596 +Epoch [3566/4000] Training [32/39] Loss: 0.00511 +Epoch [3566/4000] Training [33/39] Loss: 0.00624 +Epoch [3566/4000] Training [34/39] Loss: 0.00549 +Epoch [3566/4000] Training [35/39] Loss: 0.00577 +Epoch [3566/4000] Training [36/39] Loss: 0.00520 +Epoch [3566/4000] Training [37/39] Loss: 0.00917 +Epoch [3566/4000] Training [38/39] Loss: 0.00614 +Epoch [3566/4000] Training [39/39] Loss: 0.05023 +Epoch [3566/4000] Training metric {'Train/mean dice_metric': 0.9958174228668213, 'Train/mean miou_metric': 0.9920911192893982, 'Train/mean f1': 0.9964890480041504, 'Train/mean precision': 0.9961245656013489, 'Train/mean recall': 0.9968538880348206, 'Train/mean hd95_metric': 1.0109528303146362} +Epoch [3566/4000] Validation [1/10] Loss: 0.75927 focal_loss 0.66761 dice_loss 0.09167 +Epoch [3566/4000] Validation [2/10] Loss: 0.47366 focal_loss 0.38188 dice_loss 0.09178 +Epoch [3566/4000] Validation [3/10] Loss: 0.36903 focal_loss 0.26031 dice_loss 0.10873 +Epoch [3566/4000] Validation [4/10] Loss: 0.89874 focal_loss 0.33446 dice_loss 0.56428 +Epoch [3566/4000] Validation [5/10] Loss: 3.07908 focal_loss 2.40630 dice_loss 0.67278 +Epoch [3566/4000] Validation [6/10] Loss: 1.34296 focal_loss 0.62503 dice_loss 0.71793 +Epoch [3566/4000] Validation [7/10] Loss: 1.17954 focal_loss 0.52051 dice_loss 0.65904 +Epoch [3566/4000] Validation [8/10] Loss: 2.15769 focal_loss 1.56274 dice_loss 0.59495 +Epoch [3566/4000] Validation [9/10] Loss: 1.51840 focal_loss 0.97583 dice_loss 0.54257 +Epoch [3566/4000] Validation [10/10] Loss: 1.91292 focal_loss 1.17559 dice_loss 0.73733 +Epoch [3566/4000] Validation metric {'Val/mean dice_metric': 0.9510036110877991, 'Val/mean miou_metric': 0.9347438812255859, 'Val/mean f1': 0.9476537108421326, 'Val/mean precision': 0.9404160380363464, 'Val/mean recall': 0.9550036191940308, 'Val/mean hd95_metric': 10.714325904846191} +Cheakpoint... +Epoch [3566/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510036110877991, 'Val/mean miou_metric': 0.9347438812255859, 'Val/mean f1': 0.9476537108421326, 'Val/mean precision': 0.9404160380363464, 'Val/mean recall': 0.9550036191940308, 'Val/mean hd95_metric': 10.714325904846191} +Epoch [3567/4000] Training [1/39] Loss: 0.00663 +Epoch [3567/4000] Training [2/39] Loss: 0.00722 +Epoch [3567/4000] Training [3/39] Loss: 0.00377 +Epoch [3567/4000] Training [4/39] Loss: 0.00490 +Epoch [3567/4000] Training [5/39] Loss: 0.00380 +Epoch [3567/4000] Training [6/39] Loss: 0.00563 +Epoch [3567/4000] Training [7/39] Loss: 0.00482 +Epoch [3567/4000] Training [8/39] Loss: 0.00671 +Epoch [3567/4000] Training [9/39] Loss: 0.00519 +Epoch [3567/4000] Training [10/39] Loss: 0.00499 +Epoch [3567/4000] Training [11/39] Loss: 0.13064 +Epoch [3567/4000] Training [12/39] Loss: 0.12774 +Epoch [3567/4000] Training [13/39] Loss: 0.00610 +Epoch [3567/4000] Training [14/39] Loss: 0.00373 +Epoch [3567/4000] Training [15/39] Loss: 0.00469 +Epoch [3567/4000] Training [16/39] Loss: 0.13100 +Epoch [3567/4000] Training [17/39] Loss: 0.00508 +Epoch [3567/4000] Training [18/39] Loss: 0.00375 +Epoch [3567/4000] Training [19/39] Loss: 0.00390 +Epoch [3567/4000] Training [20/39] Loss: 0.25157 +Epoch [3567/4000] Training [21/39] Loss: 0.00741 +Epoch [3567/4000] Training [22/39] Loss: 0.08054 +Epoch [3567/4000] Training [23/39] Loss: 0.12962 +Epoch [3567/4000] Training [24/39] Loss: 0.00400 +Epoch [3567/4000] Training [25/39] Loss: 0.00578 +Epoch [3567/4000] Training [26/39] Loss: 0.00462 +Epoch [3567/4000] Training [27/39] Loss: 0.00459 +Epoch [3567/4000] Training [28/39] Loss: 0.00555 +Epoch [3567/4000] Training [29/39] Loss: 0.00278 +Epoch [3567/4000] Training [30/39] Loss: 0.12876 +Epoch [3567/4000] Training [31/39] Loss: 0.00575 +Epoch [3567/4000] Training [32/39] Loss: 0.00414 +Epoch [3567/4000] Training [33/39] Loss: 0.00405 +Epoch [3567/4000] Training [34/39] Loss: 0.00403 +Epoch [3567/4000] Training [35/39] Loss: 0.00865 +Epoch [3567/4000] Training [36/39] Loss: 0.00427 +Epoch [3567/4000] Training [37/39] Loss: 0.00479 +Epoch [3567/4000] Training [38/39] Loss: 0.00357 +Epoch [3567/4000] Training [39/39] Loss: 0.00583 +Epoch [3567/4000] Training metric {'Train/mean dice_metric': 0.99623042345047, 'Train/mean miou_metric': 0.9929034113883972, 'Train/mean f1': 0.9967831373214722, 'Train/mean precision': 0.9962745904922485, 'Train/mean recall': 0.9972923398017883, 'Train/mean hd95_metric': 1.060171127319336} +Epoch [3567/4000] Validation [1/10] Loss: 0.73166 focal_loss 0.64034 dice_loss 0.09132 +Epoch [3567/4000] Validation [2/10] Loss: 0.47179 focal_loss 0.38129 dice_loss 0.09050 +Epoch [3567/4000] Validation [3/10] Loss: 0.35022 focal_loss 0.24223 dice_loss 0.10799 +Epoch [3567/4000] Validation [4/10] Loss: 0.90631 focal_loss 0.33954 dice_loss 0.56677 +Epoch [3567/4000] Validation [5/10] Loss: 2.96963 focal_loss 2.29723 dice_loss 0.67240 +Epoch [3567/4000] Validation [6/10] Loss: 1.36155 focal_loss 0.64267 dice_loss 0.71889 +Epoch [3567/4000] Validation [7/10] Loss: 1.19780 focal_loss 0.53782 dice_loss 0.65998 +Epoch [3567/4000] Validation [8/10] Loss: 2.10616 focal_loss 1.51665 dice_loss 0.58951 +Epoch [3567/4000] Validation [9/10] Loss: 1.54434 focal_loss 1.00401 dice_loss 0.54034 +Epoch [3567/4000] Validation [10/10] Loss: 1.94630 focal_loss 1.20631 dice_loss 0.73999 +Epoch [3567/4000] Validation metric {'Val/mean dice_metric': 0.9513591527938843, 'Val/mean miou_metric': 0.935366153717041, 'Val/mean f1': 0.9471917152404785, 'Val/mean precision': 0.9390318393707275, 'Val/mean recall': 0.9554944634437561, 'Val/mean hd95_metric': 10.891703605651855} +Cheakpoint... +Epoch [3567/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513591527938843, 'Val/mean miou_metric': 0.935366153717041, 'Val/mean f1': 0.9471917152404785, 'Val/mean precision': 0.9390318393707275, 'Val/mean recall': 0.9554944634437561, 'Val/mean hd95_metric': 10.891703605651855} +Epoch [3568/4000] Training [1/39] Loss: 0.13021 +Epoch [3568/4000] Training [2/39] Loss: 0.00505 +Epoch [3568/4000] Training [3/39] Loss: 0.00481 +Epoch [3568/4000] Training [4/39] Loss: 0.12949 +Epoch [3568/4000] Training [5/39] Loss: 0.00667 +Epoch [3568/4000] Training [6/39] Loss: 0.00736 +Epoch [3568/4000] Training [7/39] Loss: 0.00575 +Epoch [3568/4000] Training [8/39] Loss: 0.12850 +Epoch [3568/4000] Training [9/39] Loss: 0.00512 +Epoch [3568/4000] Training [10/39] Loss: 0.12799 +Epoch [3568/4000] Training [11/39] Loss: 0.00505 +Epoch [3568/4000] Training [12/39] Loss: 0.37752 +Epoch [3568/4000] Training [13/39] Loss: 0.00407 +Epoch [3568/4000] Training [14/39] Loss: 0.00628 +Epoch [3568/4000] Training [15/39] Loss: 0.00691 +Epoch [3568/4000] Training [16/39] Loss: 0.00473 +Epoch [3568/4000] Training [17/39] Loss: 0.00361 +Epoch [3568/4000] Training [18/39] Loss: 0.00548 +Epoch [3568/4000] Training [19/39] Loss: 0.00524 +Epoch [3568/4000] Training [20/39] Loss: 0.00396 +Epoch [3568/4000] Training [21/39] Loss: 0.14115 +Epoch [3568/4000] Training [22/39] Loss: 0.00521 +Epoch [3568/4000] Training [23/39] Loss: 0.00312 +Epoch [3568/4000] Training [24/39] Loss: 0.13164 +Epoch [3568/4000] Training [25/39] Loss: 0.00711 +Epoch [3568/4000] Training [26/39] Loss: 0.00578 +Epoch [3568/4000] Training [27/39] Loss: 0.00536 +Epoch [3568/4000] Training [28/39] Loss: 0.00391 +Epoch [3568/4000] Training [29/39] Loss: 0.00590 +Epoch [3568/4000] Training [30/39] Loss: 0.12866 +Epoch [3568/4000] Training [31/39] Loss: 0.00511 +Epoch [3568/4000] Training [32/39] Loss: 0.00335 +Epoch [3568/4000] Training [33/39] Loss: 0.00364 +Epoch [3568/4000] Training [34/39] Loss: 0.00361 +Epoch [3568/4000] Training [35/39] Loss: 0.00390 +Epoch [3568/4000] Training [36/39] Loss: 0.00748 +Epoch [3568/4000] Training [37/39] Loss: 0.00615 +Epoch [3568/4000] Training [38/39] Loss: 0.00533 +Epoch [3568/4000] Training [39/39] Loss: 0.13066 +Epoch [3568/4000] Training metric {'Train/mean dice_metric': 0.9961286783218384, 'Train/mean miou_metric': 0.9926944375038147, 'Train/mean f1': 0.9967256784439087, 'Train/mean precision': 0.9962641000747681, 'Train/mean recall': 0.9971876740455627, 'Train/mean hd95_metric': 1.026728868484497} +Epoch [3568/4000] Validation [1/10] Loss: 0.76679 focal_loss 0.67422 dice_loss 0.09257 +Epoch [3568/4000] Validation [2/10] Loss: 0.46015 focal_loss 0.37307 dice_loss 0.08709 +Epoch [3568/4000] Validation [3/10] Loss: 0.35247 focal_loss 0.24497 dice_loss 0.10751 +Epoch [3568/4000] Validation [4/10] Loss: 0.90648 focal_loss 0.33936 dice_loss 0.56712 +Epoch [3568/4000] Validation [5/10] Loss: 3.01791 focal_loss 2.34554 dice_loss 0.67237 +Epoch [3568/4000] Validation [6/10] Loss: 1.35435 focal_loss 0.63381 dice_loss 0.72054 +Epoch [3568/4000] Validation [7/10] Loss: 1.19183 focal_loss 0.53427 dice_loss 0.65756 +Epoch [3568/4000] Validation [8/10] Loss: 2.02203 focal_loss 1.44033 dice_loss 0.58170 +Epoch [3568/4000] Validation [9/10] Loss: 1.57198 focal_loss 1.05738 dice_loss 0.51460 +Epoch [3568/4000] Validation [10/10] Loss: 1.94767 focal_loss 1.20648 dice_loss 0.74119 +Epoch [3568/4000] Validation metric {'Val/mean dice_metric': 0.9515714645385742, 'Val/mean miou_metric': 0.9354618787765503, 'Val/mean f1': 0.947711169719696, 'Val/mean precision': 0.9391183853149414, 'Val/mean recall': 0.956462562084198, 'Val/mean hd95_metric': 10.787653923034668} +Cheakpoint... +Epoch [3568/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515714645385742, 'Val/mean miou_metric': 0.9354618787765503, 'Val/mean f1': 0.947711169719696, 'Val/mean precision': 0.9391183853149414, 'Val/mean recall': 0.956462562084198, 'Val/mean hd95_metric': 10.787653923034668} +Epoch [3569/4000] Training [1/39] Loss: 0.13069 +Epoch [3569/4000] Training [2/39] Loss: 0.00744 +Epoch [3569/4000] Training [3/39] Loss: 0.00539 +Epoch [3569/4000] Training [4/39] Loss: 0.01139 +Epoch [3569/4000] Training [5/39] Loss: 0.00455 +Epoch [3569/4000] Training [6/39] Loss: 0.00320 +Epoch [3569/4000] Training [7/39] Loss: 0.00732 +Epoch [3569/4000] Training [8/39] Loss: 0.00403 +Epoch [3569/4000] Training [9/39] Loss: 0.00429 +Epoch [3569/4000] Training [10/39] Loss: 0.00383 +Epoch [3569/4000] Training [11/39] Loss: 0.00462 +Epoch [3569/4000] Training [12/39] Loss: 0.25546 +Epoch [3569/4000] Training [13/39] Loss: 0.00632 +Epoch [3569/4000] Training [14/39] Loss: 0.00564 +Epoch [3569/4000] Training [15/39] Loss: 0.00538 +Epoch [3569/4000] Training [16/39] Loss: 0.00501 +Epoch [3569/4000] Training [17/39] Loss: 0.00585 +Epoch [3569/4000] Training [18/39] Loss: 0.00449 +Epoch [3569/4000] Training [19/39] Loss: 0.00984 +Epoch [3569/4000] Training [20/39] Loss: 0.00321 +Epoch [3569/4000] Training [21/39] Loss: 0.00502 +Epoch [3569/4000] Training [22/39] Loss: 0.12936 +Epoch [3569/4000] Training [23/39] Loss: 0.00579 +Epoch [3569/4000] Training [24/39] Loss: 0.00354 +Epoch [3569/4000] Training [25/39] Loss: 0.00383 +Epoch [3569/4000] Training [26/39] Loss: 0.00359 +Epoch [3569/4000] Training [27/39] Loss: 0.00619 +Epoch [3569/4000] Training [28/39] Loss: 0.00621 +Epoch [3569/4000] Training [29/39] Loss: 0.00900 +Epoch [3569/4000] Training [30/39] Loss: 0.00502 +Epoch [3569/4000] Training [31/39] Loss: 0.00431 +Epoch [3569/4000] Training [32/39] Loss: 0.00642 +Epoch [3569/4000] Training [33/39] Loss: 0.12935 +Epoch [3569/4000] Training [34/39] Loss: 0.00471 +Epoch [3569/4000] Training [35/39] Loss: 0.00656 +Epoch [3569/4000] Training [36/39] Loss: 0.00384 +Epoch [3569/4000] Training [37/39] Loss: 0.00795 +Epoch [3569/4000] Training [38/39] Loss: 0.37808 +Epoch [3569/4000] Training [39/39] Loss: 0.00483 +Epoch [3569/4000] Training metric {'Train/mean dice_metric': 0.9959129691123962, 'Train/mean miou_metric': 0.9923087358474731, 'Train/mean f1': 0.9965761303901672, 'Train/mean precision': 0.9961947798728943, 'Train/mean recall': 0.9969577789306641, 'Train/mean hd95_metric': 1.0469728708267212} +Epoch [3569/4000] Validation [1/10] Loss: 0.75893 focal_loss 0.66638 dice_loss 0.09255 +Epoch [3569/4000] Validation [2/10] Loss: 0.45977 focal_loss 0.37142 dice_loss 0.08836 +Epoch [3569/4000] Validation [3/10] Loss: 0.34852 focal_loss 0.24102 dice_loss 0.10750 +Epoch [3569/4000] Validation [4/10] Loss: 0.91133 focal_loss 0.34252 dice_loss 0.56881 +Epoch [3569/4000] Validation [5/10] Loss: 3.04320 focal_loss 2.37082 dice_loss 0.67238 +Epoch [3569/4000] Validation [6/10] Loss: 1.35894 focal_loss 0.64046 dice_loss 0.71847 +Epoch [3569/4000] Validation [7/10] Loss: 1.19799 focal_loss 0.53841 dice_loss 0.65959 +Epoch [3569/4000] Validation [8/10] Loss: 1.99013 focal_loss 1.41467 dice_loss 0.57546 +Epoch [3569/4000] Validation [9/10] Loss: 1.59314 focal_loss 1.07038 dice_loss 0.52276 +Epoch [3569/4000] Validation [10/10] Loss: 1.96210 focal_loss 1.21979 dice_loss 0.74231 +Epoch [3569/4000] Validation metric {'Val/mean dice_metric': 0.9512568116188049, 'Val/mean miou_metric': 0.9350097179412842, 'Val/mean f1': 0.9468789100646973, 'Val/mean precision': 0.9374829530715942, 'Val/mean recall': 0.9564652442932129, 'Val/mean hd95_metric': 10.775007247924805} +Cheakpoint... +Epoch [3569/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512568116188049, 'Val/mean miou_metric': 0.9350097179412842, 'Val/mean f1': 0.9468789100646973, 'Val/mean precision': 0.9374829530715942, 'Val/mean recall': 0.9564652442932129, 'Val/mean hd95_metric': 10.775007247924805} +Epoch [3570/4000] Training [1/39] Loss: 0.00658 +Epoch [3570/4000] Training [2/39] Loss: 0.12836 +Epoch [3570/4000] Training [3/39] Loss: 0.00444 +Epoch [3570/4000] Training [4/39] Loss: 0.13001 +Epoch [3570/4000] Training [5/39] Loss: 0.00500 +Epoch [3570/4000] Training [6/39] Loss: 0.00466 +Epoch [3570/4000] Training [7/39] Loss: 0.00642 +Epoch [3570/4000] Training [8/39] Loss: 0.00526 +Epoch [3570/4000] Training [9/39] Loss: 0.13091 +Epoch [3570/4000] Training [10/39] Loss: 0.12821 +Epoch [3570/4000] Training [11/39] Loss: 0.00552 +Epoch [3570/4000] Training [12/39] Loss: 0.00445 +Epoch [3570/4000] Training [13/39] Loss: 0.12909 +Epoch [3570/4000] Training [14/39] Loss: 0.00549 +Epoch [3570/4000] Training [15/39] Loss: 0.00430 +Epoch [3570/4000] Training [16/39] Loss: 0.00721 +Epoch [3570/4000] Training [17/39] Loss: 0.00548 +Epoch [3570/4000] Training [18/39] Loss: 0.12931 +Epoch [3570/4000] Training [19/39] Loss: 0.00601 +Epoch [3570/4000] Training [20/39] Loss: 0.13187 +Epoch [3570/4000] Training [21/39] Loss: 0.00548 +Epoch [3570/4000] Training [22/39] Loss: 0.00315 +Epoch [3570/4000] Training [23/39] Loss: 0.08048 +Epoch [3570/4000] Training [24/39] Loss: 0.00611 +Epoch [3570/4000] Training [25/39] Loss: 0.00570 +Epoch [3570/4000] Training [26/39] Loss: 0.00428 +Epoch [3570/4000] Training [27/39] Loss: 0.00342 +Epoch [3570/4000] Training [28/39] Loss: 0.00390 +Epoch [3570/4000] Training [29/39] Loss: 0.00684 +Epoch [3570/4000] Training [30/39] Loss: 0.00628 +Epoch [3570/4000] Training [31/39] Loss: 0.12907 +Epoch [3570/4000] Training [32/39] Loss: 0.00392 +Epoch [3570/4000] Training [33/39] Loss: 0.00569 +Epoch [3570/4000] Training [34/39] Loss: 0.00338 +Epoch [3570/4000] Training [35/39] Loss: 0.00407 +Epoch [3570/4000] Training [36/39] Loss: 0.00519 +Epoch [3570/4000] Training [37/39] Loss: 0.13164 +Epoch [3570/4000] Training [38/39] Loss: 0.00620 +Epoch [3570/4000] Training [39/39] Loss: 0.00410 +Epoch [3570/4000] Training metric {'Train/mean dice_metric': 0.9961872100830078, 'Train/mean miou_metric': 0.9928191304206848, 'Train/mean f1': 0.9967231154441833, 'Train/mean precision': 0.996229350566864, 'Train/mean recall': 0.9972174167633057, 'Train/mean hd95_metric': 1.0309034585952759} +Epoch [3570/4000] Validation [1/10] Loss: 0.73670 focal_loss 0.64470 dice_loss 0.09200 +Epoch [3570/4000] Validation [2/10] Loss: 0.46725 focal_loss 0.37584 dice_loss 0.09142 +Epoch [3570/4000] Validation [3/10] Loss: 0.35312 focal_loss 0.24473 dice_loss 0.10840 +Epoch [3570/4000] Validation [4/10] Loss: 0.90683 focal_loss 0.33954 dice_loss 0.56728 +Epoch [3570/4000] Validation [5/10] Loss: 2.96134 focal_loss 2.28847 dice_loss 0.67288 +Epoch [3570/4000] Validation [6/10] Loss: 1.35595 focal_loss 0.63738 dice_loss 0.71856 +Epoch [3570/4000] Validation [7/10] Loss: 1.18433 focal_loss 0.52645 dice_loss 0.65789 +Epoch [3570/4000] Validation [8/10] Loss: 2.06997 focal_loss 1.48161 dice_loss 0.58837 +Epoch [3570/4000] Validation [9/10] Loss: 1.53995 focal_loss 1.00804 dice_loss 0.53191 +Epoch [3570/4000] Validation [10/10] Loss: 1.92452 focal_loss 1.18591 dice_loss 0.73860 +Epoch [3570/4000] Validation metric {'Val/mean dice_metric': 0.9514987468719482, 'Val/mean miou_metric': 0.9355063438415527, 'Val/mean f1': 0.947543203830719, 'Val/mean precision': 0.9396882057189941, 'Val/mean recall': 0.9555305242538452, 'Val/mean hd95_metric': 10.826150894165039} +Cheakpoint... +Epoch [3570/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514987468719482, 'Val/mean miou_metric': 0.9355063438415527, 'Val/mean f1': 0.947543203830719, 'Val/mean precision': 0.9396882057189941, 'Val/mean recall': 0.9555305242538452, 'Val/mean hd95_metric': 10.826150894165039} +Epoch [3571/4000] Training [1/39] Loss: 0.12865 +Epoch [3571/4000] Training [2/39] Loss: 0.00756 +Epoch [3571/4000] Training [3/39] Loss: 0.00514 +Epoch [3571/4000] Training [4/39] Loss: 0.00417 +Epoch [3571/4000] Training [5/39] Loss: 0.00525 +Epoch [3571/4000] Training [6/39] Loss: 0.00308 +Epoch [3571/4000] Training [7/39] Loss: 0.12898 +Epoch [3571/4000] Training [8/39] Loss: 0.00533 +Epoch [3571/4000] Training [9/39] Loss: 0.00333 +Epoch [3571/4000] Training [10/39] Loss: 0.00362 +Epoch [3571/4000] Training [11/39] Loss: 0.00602 +Epoch [3571/4000] Training [12/39] Loss: 0.00549 +Epoch [3571/4000] Training [13/39] Loss: 0.12898 +Epoch [3571/4000] Training [14/39] Loss: 0.00506 +Epoch [3571/4000] Training [15/39] Loss: 0.00587 +Epoch [3571/4000] Training [16/39] Loss: 0.12886 +Epoch [3571/4000] Training [17/39] Loss: 0.00454 +Epoch [3571/4000] Training [18/39] Loss: 0.00800 +Epoch [3571/4000] Training [19/39] Loss: 0.00427 +Epoch [3571/4000] Training [20/39] Loss: 0.00739 +Epoch [3571/4000] Training [21/39] Loss: 0.01075 +Epoch [3571/4000] Training [22/39] Loss: 0.13055 +Epoch [3571/4000] Training [23/39] Loss: 0.00726 +Epoch [3571/4000] Training [24/39] Loss: 0.00533 +Epoch [3571/4000] Training [25/39] Loss: 0.00608 +Epoch [3571/4000] Training [26/39] Loss: 0.00573 +Epoch [3571/4000] Training [27/39] Loss: 0.00429 +Epoch [3571/4000] Training [28/39] Loss: 0.13211 +Epoch [3571/4000] Training [29/39] Loss: 0.10174 +Epoch [3571/4000] Training [30/39] Loss: 0.00772 +Epoch [3571/4000] Training [31/39] Loss: 0.00616 +Epoch [3571/4000] Training [32/39] Loss: 0.00620 +Epoch [3571/4000] Training [33/39] Loss: 0.00647 +Epoch [3571/4000] Training [34/39] Loss: 0.13194 +Epoch [3571/4000] Training [35/39] Loss: 0.12956 +Epoch [3571/4000] Training [36/39] Loss: 0.00472 +Epoch [3571/4000] Training [37/39] Loss: 0.00364 +Epoch [3571/4000] Training [38/39] Loss: 0.12920 +Epoch [3571/4000] Training [39/39] Loss: 0.00382 +Epoch [3571/4000] Training metric {'Train/mean dice_metric': 0.9959647059440613, 'Train/mean miou_metric': 0.992428719997406, 'Train/mean f1': 0.99667888879776, 'Train/mean precision': 0.9962466955184937, 'Train/mean recall': 0.9971115589141846, 'Train/mean hd95_metric': 1.1501832008361816} +Epoch [3571/4000] Validation [1/10] Loss: 0.71765 focal_loss 0.62813 dice_loss 0.08952 +Epoch [3571/4000] Validation [2/10] Loss: 0.47550 focal_loss 0.38177 dice_loss 0.09373 +Epoch [3571/4000] Validation [3/10] Loss: 0.36302 focal_loss 0.25376 dice_loss 0.10926 +Epoch [3571/4000] Validation [4/10] Loss: 0.89862 focal_loss 0.33290 dice_loss 0.56572 +Epoch [3571/4000] Validation [5/10] Loss: 2.96512 focal_loss 2.29261 dice_loss 0.67251 +Epoch [3571/4000] Validation [6/10] Loss: 1.37293 focal_loss 0.65045 dice_loss 0.72248 +Epoch [3571/4000] Validation [7/10] Loss: 1.20422 focal_loss 0.54474 dice_loss 0.65949 +Epoch [3571/4000] Validation [8/10] Loss: 2.13393 focal_loss 1.53950 dice_loss 0.59443 +Epoch [3571/4000] Validation [9/10] Loss: 1.57367 focal_loss 1.03196 dice_loss 0.54171 +Epoch [3571/4000] Validation [10/10] Loss: 1.95542 focal_loss 1.21634 dice_loss 0.73908 +Epoch [3571/4000] Validation metric {'Val/mean dice_metric': 0.9512702226638794, 'Val/mean miou_metric': 0.9351996183395386, 'Val/mean f1': 0.9479953646659851, 'Val/mean precision': 0.9408882260322571, 'Val/mean recall': 0.9552107453346252, 'Val/mean hd95_metric': 10.804163932800293} +Cheakpoint... +Epoch [3571/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512702226638794, 'Val/mean miou_metric': 0.9351996183395386, 'Val/mean f1': 0.9479953646659851, 'Val/mean precision': 0.9408882260322571, 'Val/mean recall': 0.9552107453346252, 'Val/mean hd95_metric': 10.804163932800293} +Epoch [3572/4000] Training [1/39] Loss: 0.00493 +Epoch [3572/4000] Training [2/39] Loss: 0.00538 +Epoch [3572/4000] Training [3/39] Loss: 0.00454 +Epoch [3572/4000] Training [4/39] Loss: 0.00392 +Epoch [3572/4000] Training [5/39] Loss: 0.00379 +Epoch [3572/4000] Training [6/39] Loss: 0.00392 +Epoch [3572/4000] Training [7/39] Loss: 0.25373 +Epoch [3572/4000] Training [8/39] Loss: 0.00533 +Epoch [3572/4000] Training [9/39] Loss: 0.00475 +Epoch [3572/4000] Training [10/39] Loss: 0.13037 +Epoch [3572/4000] Training [11/39] Loss: 0.00448 +Epoch [3572/4000] Training [12/39] Loss: 0.00837 +Epoch [3572/4000] Training [13/39] Loss: 0.00400 +Epoch [3572/4000] Training [14/39] Loss: 0.00680 +Epoch [3572/4000] Training [15/39] Loss: 0.00543 +Epoch [3572/4000] Training [16/39] Loss: 0.13132 +Epoch [3572/4000] Training [17/39] Loss: 0.12896 +Epoch [3572/4000] Training [18/39] Loss: 0.00403 +Epoch [3572/4000] Training [19/39] Loss: 0.00472 +Epoch [3572/4000] Training [20/39] Loss: 0.00511 +Epoch [3572/4000] Training [21/39] Loss: 0.00550 +Epoch [3572/4000] Training [22/39] Loss: 0.00416 +Epoch [3572/4000] Training [23/39] Loss: 0.00416 +Epoch [3572/4000] Training [24/39] Loss: 0.00484 +Epoch [3572/4000] Training [25/39] Loss: 0.13009 +Epoch [3572/4000] Training [26/39] Loss: 0.13085 +Epoch [3572/4000] Training [27/39] Loss: 0.00641 +Epoch [3572/4000] Training [28/39] Loss: 0.00694 +Epoch [3572/4000] Training [29/39] Loss: 0.00415 +Epoch [3572/4000] Training [30/39] Loss: 0.00448 +Epoch [3572/4000] Training [31/39] Loss: 0.00401 +Epoch [3572/4000] Training [32/39] Loss: 0.12906 +Epoch [3572/4000] Training [33/39] Loss: 0.00480 +Epoch [3572/4000] Training [34/39] Loss: 0.00472 +Epoch [3572/4000] Training [35/39] Loss: 0.00637 +Epoch [3572/4000] Training [36/39] Loss: 0.00378 +Epoch [3572/4000] Training [37/39] Loss: 0.25209 +Epoch [3572/4000] Training [38/39] Loss: 0.00354 +Epoch [3572/4000] Training [39/39] Loss: 0.12956 +Epoch [3572/4000] Training metric {'Train/mean dice_metric': 0.9961962103843689, 'Train/mean miou_metric': 0.9928671717643738, 'Train/mean f1': 0.9968448877334595, 'Train/mean precision': 0.9963976740837097, 'Train/mean recall': 0.9972926378250122, 'Train/mean hd95_metric': 0.9695419669151306} +Epoch [3572/4000] Validation [1/10] Loss: 0.72574 focal_loss 0.63663 dice_loss 0.08911 +Epoch [3572/4000] Validation [2/10] Loss: 0.47465 focal_loss 0.38108 dice_loss 0.09357 +Epoch [3572/4000] Validation [3/10] Loss: 0.36845 focal_loss 0.25905 dice_loss 0.10940 +Epoch [3572/4000] Validation [4/10] Loss: 0.90547 focal_loss 0.33737 dice_loss 0.56810 +Epoch [3572/4000] Validation [5/10] Loss: 3.02974 focal_loss 2.35744 dice_loss 0.67230 +Epoch [3572/4000] Validation [6/10] Loss: 1.36851 focal_loss 0.65132 dice_loss 0.71719 +Epoch [3572/4000] Validation [7/10] Loss: 1.19925 focal_loss 0.54048 dice_loss 0.65877 +Epoch [3572/4000] Validation [8/10] Loss: 2.12673 focal_loss 1.53416 dice_loss 0.59257 +Epoch [3572/4000] Validation [9/10] Loss: 1.60191 focal_loss 1.07163 dice_loss 0.53028 +Epoch [3572/4000] Validation [10/10] Loss: 1.95829 focal_loss 1.21800 dice_loss 0.74029 +Epoch [3572/4000] Validation metric {'Val/mean dice_metric': 0.9515917301177979, 'Val/mean miou_metric': 0.9356298446655273, 'Val/mean f1': 0.9477234482765198, 'Val/mean precision': 0.9403172135353088, 'Val/mean recall': 0.9552471041679382, 'Val/mean hd95_metric': 10.659890174865723} +Cheakpoint... +Epoch [3572/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515917301177979, 'Val/mean miou_metric': 0.9356298446655273, 'Val/mean f1': 0.9477234482765198, 'Val/mean precision': 0.9403172135353088, 'Val/mean recall': 0.9552471041679382, 'Val/mean hd95_metric': 10.659890174865723} +Epoch [3573/4000] Training [1/39] Loss: 0.00889 +Epoch [3573/4000] Training [2/39] Loss: 0.00844 +Epoch [3573/4000] Training [3/39] Loss: 0.00379 +Epoch [3573/4000] Training [4/39] Loss: 0.12896 +Epoch [3573/4000] Training [5/39] Loss: 0.00416 +Epoch [3573/4000] Training [6/39] Loss: 0.00560 +Epoch [3573/4000] Training [7/39] Loss: 0.12879 +Epoch [3573/4000] Training [8/39] Loss: 0.00482 +Epoch [3573/4000] Training [9/39] Loss: 0.25326 +Epoch [3573/4000] Training [10/39] Loss: 0.00361 +Epoch [3573/4000] Training [11/39] Loss: 0.00449 +Epoch [3573/4000] Training [12/39] Loss: 0.00377 +Epoch [3573/4000] Training [13/39] Loss: 0.00687 +Epoch [3573/4000] Training [14/39] Loss: 0.00671 +Epoch [3573/4000] Training [15/39] Loss: 0.00479 +Epoch [3573/4000] Training [16/39] Loss: 0.00419 +Epoch [3573/4000] Training [17/39] Loss: 0.00458 +Epoch [3573/4000] Training [18/39] Loss: 0.00433 +Epoch [3573/4000] Training [19/39] Loss: 0.00415 +Epoch [3573/4000] Training [20/39] Loss: 0.00374 +Epoch [3573/4000] Training [21/39] Loss: 0.00443 +Epoch [3573/4000] Training [22/39] Loss: 0.12911 +Epoch [3573/4000] Training [23/39] Loss: 0.00498 +Epoch [3573/4000] Training [24/39] Loss: 0.00642 +Epoch [3573/4000] Training [25/39] Loss: 0.13190 +Epoch [3573/4000] Training [26/39] Loss: 0.00420 +Epoch [3573/4000] Training [27/39] Loss: 0.12756 +Epoch [3573/4000] Training [28/39] Loss: 0.00738 +Epoch [3573/4000] Training [29/39] Loss: 0.00453 +Epoch [3573/4000] Training [30/39] Loss: 0.00634 +Epoch [3573/4000] Training [31/39] Loss: 0.00228 +Epoch [3573/4000] Training [32/39] Loss: 0.00825 +Epoch [3573/4000] Training [33/39] Loss: 0.25347 +Epoch [3573/4000] Training [34/39] Loss: 0.00273 +Epoch [3573/4000] Training [35/39] Loss: 0.00631 +Epoch [3573/4000] Training [36/39] Loss: 0.00776 +Epoch [3573/4000] Training [37/39] Loss: 0.00584 +Epoch [3573/4000] Training [38/39] Loss: 0.00328 +Epoch [3573/4000] Training [39/39] Loss: 0.00384 +Epoch [3573/4000] Training metric {'Train/mean dice_metric': 0.9961950182914734, 'Train/mean miou_metric': 0.9928438067436218, 'Train/mean f1': 0.9968162775039673, 'Train/mean precision': 0.99632328748703, 'Train/mean recall': 0.9973098635673523, 'Train/mean hd95_metric': 1.0068525075912476} +Epoch [3573/4000] Validation [1/10] Loss: 0.72638 focal_loss 0.63642 dice_loss 0.08995 +Epoch [3573/4000] Validation [2/10] Loss: 0.48075 focal_loss 0.38426 dice_loss 0.09648 +Epoch [3573/4000] Validation [3/10] Loss: 0.37275 focal_loss 0.26219 dice_loss 0.11056 +Epoch [3573/4000] Validation [4/10] Loss: 0.89804 focal_loss 0.33071 dice_loss 0.56733 +Epoch [3573/4000] Validation [5/10] Loss: 3.00109 focal_loss 2.32884 dice_loss 0.67224 +Epoch [3573/4000] Validation [6/10] Loss: 1.35939 focal_loss 0.64012 dice_loss 0.71926 +Epoch [3573/4000] Validation [7/10] Loss: 1.19293 focal_loss 0.53042 dice_loss 0.66250 +Epoch [3573/4000] Validation [8/10] Loss: 2.13564 focal_loss 1.54004 dice_loss 0.59559 +Epoch [3573/4000] Validation [9/10] Loss: 1.56709 focal_loss 1.04228 dice_loss 0.52481 +Epoch [3573/4000] Validation [10/10] Loss: 1.89308 focal_loss 1.15706 dice_loss 0.73602 +Epoch [3573/4000] Validation metric {'Val/mean dice_metric': 0.9516400694847107, 'Val/mean miou_metric': 0.9356383085250854, 'Val/mean f1': 0.947917103767395, 'Val/mean precision': 0.9407888650894165, 'Val/mean recall': 0.9551542401313782, 'Val/mean hd95_metric': 10.777770042419434} +Cheakpoint... +Epoch [3573/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516400694847107, 'Val/mean miou_metric': 0.9356383085250854, 'Val/mean f1': 0.947917103767395, 'Val/mean precision': 0.9407888650894165, 'Val/mean recall': 0.9551542401313782, 'Val/mean hd95_metric': 10.777770042419434} +Epoch [3574/4000] Training [1/39] Loss: 0.00364 +Epoch [3574/4000] Training [2/39] Loss: 0.00330 +Epoch [3574/4000] Training [3/39] Loss: 0.00616 +Epoch [3574/4000] Training [4/39] Loss: 0.00643 +Epoch [3574/4000] Training [5/39] Loss: 0.13077 +Epoch [3574/4000] Training [6/39] Loss: 0.13188 +Epoch [3574/4000] Training [7/39] Loss: 0.13094 +Epoch [3574/4000] Training [8/39] Loss: 0.12939 +Epoch [3574/4000] Training [9/39] Loss: 0.00522 +Epoch [3574/4000] Training [10/39] Loss: 0.00391 +Epoch [3574/4000] Training [11/39] Loss: 0.00726 +Epoch [3574/4000] Training [12/39] Loss: 0.00421 +Epoch [3574/4000] Training [13/39] Loss: 0.00660 +Epoch [3574/4000] Training [14/39] Loss: 0.12930 +Epoch [3574/4000] Training [15/39] Loss: 0.00377 +Epoch [3574/4000] Training [16/39] Loss: 0.12965 +Epoch [3574/4000] Training [17/39] Loss: 0.01007 +Epoch [3574/4000] Training [18/39] Loss: 0.00984 +Epoch [3574/4000] Training [19/39] Loss: 0.00415 +Epoch [3574/4000] Training [20/39] Loss: 0.00437 +Epoch [3574/4000] Training [21/39] Loss: 0.25226 +Epoch [3574/4000] Training [22/39] Loss: 0.00451 +Epoch [3574/4000] Training [23/39] Loss: 0.00616 +Epoch [3574/4000] Training [24/39] Loss: 0.13034 +Epoch [3574/4000] Training [25/39] Loss: 0.12932 +Epoch [3574/4000] Training [26/39] Loss: 0.13016 +Epoch [3574/4000] Training [27/39] Loss: 0.12982 +Epoch [3574/4000] Training [28/39] Loss: 0.25261 +Epoch [3574/4000] Training [29/39] Loss: 0.12751 +Epoch [3574/4000] Training [30/39] Loss: 0.12766 +Epoch [3574/4000] Training [31/39] Loss: 0.00605 +Epoch [3574/4000] Training [32/39] Loss: 0.00604 +Epoch [3574/4000] Training [33/39] Loss: 0.00411 +Epoch [3574/4000] Training [34/39] Loss: 0.13096 +Epoch [3574/4000] Training [35/39] Loss: 0.00457 +Epoch [3574/4000] Training [36/39] Loss: 0.00496 +Epoch [3574/4000] Training [37/39] Loss: 0.25224 +Epoch [3574/4000] Training [38/39] Loss: 0.00518 +Epoch [3574/4000] Training [39/39] Loss: 0.00504 +Epoch [3574/4000] Training metric {'Train/mean dice_metric': 0.9956134557723999, 'Train/mean miou_metric': 0.9920939207077026, 'Train/mean f1': 0.9963653683662415, 'Train/mean precision': 0.9955940842628479, 'Train/mean recall': 0.9971379041671753, 'Train/mean hd95_metric': 1.1560007333755493} +Epoch [3574/4000] Validation [1/10] Loss: 0.71453 focal_loss 0.62625 dice_loss 0.08828 +Epoch [3574/4000] Validation [2/10] Loss: 0.48355 focal_loss 0.39048 dice_loss 0.09307 +Epoch [3574/4000] Validation [3/10] Loss: 0.36823 focal_loss 0.25921 dice_loss 0.10901 +Epoch [3574/4000] Validation [4/10] Loss: 0.89489 focal_loss 0.32962 dice_loss 0.56527 +Epoch [3574/4000] Validation [5/10] Loss: 3.02079 focal_loss 2.34848 dice_loss 0.67231 +Epoch [3574/4000] Validation [6/10] Loss: 1.37850 focal_loss 0.65857 dice_loss 0.71993 +Epoch [3574/4000] Validation [7/10] Loss: 1.19204 focal_loss 0.53398 dice_loss 0.65806 +Epoch [3574/4000] Validation [8/10] Loss: 2.13493 focal_loss 1.54077 dice_loss 0.59416 +Epoch [3574/4000] Validation [9/10] Loss: 1.54383 focal_loss 0.99902 dice_loss 0.54481 +Epoch [3574/4000] Validation [10/10] Loss: 1.92865 focal_loss 1.19230 dice_loss 0.73635 +Epoch [3574/4000] Validation metric {'Val/mean dice_metric': 0.9511767625808716, 'Val/mean miou_metric': 0.9351310133934021, 'Val/mean f1': 0.9478720426559448, 'Val/mean precision': 0.9410140514373779, 'Val/mean recall': 0.9548308849334717, 'Val/mean hd95_metric': 10.771475791931152} +Cheakpoint... +Epoch [3574/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511767625808716, 'Val/mean miou_metric': 0.9351310133934021, 'Val/mean f1': 0.9478720426559448, 'Val/mean precision': 0.9410140514373779, 'Val/mean recall': 0.9548308849334717, 'Val/mean hd95_metric': 10.771475791931152} +Epoch [3575/4000] Training [1/39] Loss: 0.00497 +Epoch [3575/4000] Training [2/39] Loss: 0.12862 +Epoch [3575/4000] Training [3/39] Loss: 0.00329 +Epoch [3575/4000] Training [4/39] Loss: 0.00343 +Epoch [3575/4000] Training [5/39] Loss: 0.00524 +Epoch [3575/4000] Training [6/39] Loss: 0.00803 +Epoch [3575/4000] Training [7/39] Loss: 0.00548 +Epoch [3575/4000] Training [8/39] Loss: 0.00334 +Epoch [3575/4000] Training [9/39] Loss: 0.00242 +Epoch [3575/4000] Training [10/39] Loss: 0.01128 +Epoch [3575/4000] Training [11/39] Loss: 0.00496 +Epoch [3575/4000] Training [12/39] Loss: 0.00406 +Epoch [3575/4000] Training [13/39] Loss: 0.00491 +Epoch [3575/4000] Training [14/39] Loss: 0.12869 +Epoch [3575/4000] Training [15/39] Loss: 0.00396 +Epoch [3575/4000] Training [16/39] Loss: 0.00331 +Epoch [3575/4000] Training [17/39] Loss: 0.13232 +Epoch [3575/4000] Training [18/39] Loss: 0.00673 +Epoch [3575/4000] Training [19/39] Loss: 0.13058 +Epoch [3575/4000] Training [20/39] Loss: 0.00608 +Epoch [3575/4000] Training [21/39] Loss: 0.00411 +Epoch [3575/4000] Training [22/39] Loss: 0.00723 +Epoch [3575/4000] Training [23/39] Loss: 0.00819 +Epoch [3575/4000] Training [24/39] Loss: 0.12807 +Epoch [3575/4000] Training [25/39] Loss: 0.00354 +Epoch [3575/4000] Training [26/39] Loss: 0.00556 +Epoch [3575/4000] Training [27/39] Loss: 0.00552 +Epoch [3575/4000] Training [28/39] Loss: 0.13230 +Epoch [3575/4000] Training [29/39] Loss: 0.12893 +Epoch [3575/4000] Training [30/39] Loss: 0.00360 +Epoch [3575/4000] Training [31/39] Loss: 0.12754 +Epoch [3575/4000] Training [32/39] Loss: 0.00336 +Epoch [3575/4000] Training [33/39] Loss: 0.00370 +Epoch [3575/4000] Training [34/39] Loss: 0.00434 +Epoch [3575/4000] Training [35/39] Loss: 0.00513 +Epoch [3575/4000] Training [36/39] Loss: 0.00613 +Epoch [3575/4000] Training [37/39] Loss: 0.12882 +Epoch [3575/4000] Training [38/39] Loss: 0.00620 +Epoch [3575/4000] Training [39/39] Loss: 0.00349 +Epoch [3575/4000] Training metric {'Train/mean dice_metric': 0.9961510300636292, 'Train/mean miou_metric': 0.9927988648414612, 'Train/mean f1': 0.9968057870864868, 'Train/mean precision': 0.9963666200637817, 'Train/mean recall': 0.9972453117370605, 'Train/mean hd95_metric': 0.9598391652107239} +Epoch [3575/4000] Validation [1/10] Loss: 0.72783 focal_loss 0.63832 dice_loss 0.08950 +Epoch [3575/4000] Validation [2/10] Loss: 0.47612 focal_loss 0.38166 dice_loss 0.09446 +Epoch [3575/4000] Validation [3/10] Loss: 0.36680 focal_loss 0.25741 dice_loss 0.10939 +Epoch [3575/4000] Validation [4/10] Loss: 0.88249 focal_loss 0.31773 dice_loss 0.56476 +Epoch [3575/4000] Validation [5/10] Loss: 3.02120 focal_loss 2.34911 dice_loss 0.67209 +Epoch [3575/4000] Validation [6/10] Loss: 1.35380 focal_loss 0.63287 dice_loss 0.72093 +Epoch [3575/4000] Validation [7/10] Loss: 1.18179 focal_loss 0.52396 dice_loss 0.65783 +Epoch [3575/4000] Validation [8/10] Loss: 2.02305 focal_loss 1.43709 dice_loss 0.58596 +Epoch [3575/4000] Validation [9/10] Loss: 1.51743 focal_loss 0.97612 dice_loss 0.54131 +Epoch [3575/4000] Validation [10/10] Loss: 1.90113 focal_loss 1.16232 dice_loss 0.73881 +Epoch [3575/4000] Validation metric {'Val/mean dice_metric': 0.9514902234077454, 'Val/mean miou_metric': 0.9355581402778625, 'Val/mean f1': 0.9483209252357483, 'Val/mean precision': 0.9411168694496155, 'Val/mean recall': 0.9556360840797424, 'Val/mean hd95_metric': 10.656673431396484} +Cheakpoint... +Epoch [3575/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514902234077454, 'Val/mean miou_metric': 0.9355581402778625, 'Val/mean f1': 0.9483209252357483, 'Val/mean precision': 0.9411168694496155, 'Val/mean recall': 0.9556360840797424, 'Val/mean hd95_metric': 10.656673431396484} +Epoch [3576/4000] Training [1/39] Loss: 0.00446 +Epoch [3576/4000] Training [2/39] Loss: 0.00668 +Epoch [3576/4000] Training [3/39] Loss: 0.00518 +Epoch [3576/4000] Training [4/39] Loss: 0.00462 +Epoch [3576/4000] Training [5/39] Loss: 0.00443 +Epoch [3576/4000] Training [6/39] Loss: 0.00458 +Epoch [3576/4000] Training [7/39] Loss: 0.00332 +Epoch [3576/4000] Training [8/39] Loss: 0.00316 +Epoch [3576/4000] Training [9/39] Loss: 0.12839 +Epoch [3576/4000] Training [10/39] Loss: 0.00593 +Epoch [3576/4000] Training [11/39] Loss: 0.00350 +Epoch [3576/4000] Training [12/39] Loss: 0.13180 +Epoch [3576/4000] Training [13/39] Loss: 0.12918 +Epoch [3576/4000] Training [14/39] Loss: 0.00511 +Epoch [3576/4000] Training [15/39] Loss: 0.00617 +Epoch [3576/4000] Training [16/39] Loss: 0.00504 +Epoch [3576/4000] Training [17/39] Loss: 0.00368 +Epoch [3576/4000] Training [18/39] Loss: 0.00330 +Epoch [3576/4000] Training [19/39] Loss: 0.00628 +Epoch [3576/4000] Training [20/39] Loss: 0.00597 +Epoch [3576/4000] Training [21/39] Loss: 0.00390 +Epoch [3576/4000] Training [22/39] Loss: 0.12976 +Epoch [3576/4000] Training [23/39] Loss: 0.00616 +Epoch [3576/4000] Training [24/39] Loss: 0.12925 +Epoch [3576/4000] Training [25/39] Loss: 0.00476 +Epoch [3576/4000] Training [26/39] Loss: 0.00500 +Epoch [3576/4000] Training [27/39] Loss: 0.12826 +Epoch [3576/4000] Training [28/39] Loss: 0.00551 +Epoch [3576/4000] Training [29/39] Loss: 0.00381 +Epoch [3576/4000] Training [30/39] Loss: 0.00614 +Epoch [3576/4000] Training [31/39] Loss: 0.00459 +Epoch [3576/4000] Training [32/39] Loss: 0.00442 +Epoch [3576/4000] Training [33/39] Loss: 0.12844 +Epoch [3576/4000] Training [34/39] Loss: 0.00459 +Epoch [3576/4000] Training [35/39] Loss: 0.00316 +Epoch [3576/4000] Training [36/39] Loss: 0.00452 +Epoch [3576/4000] Training [37/39] Loss: 0.00368 +Epoch [3576/4000] Training [38/39] Loss: 0.00343 +Epoch [3576/4000] Training [39/39] Loss: 0.12901 +Epoch [3576/4000] Training metric {'Train/mean dice_metric': 0.9956426024436951, 'Train/mean miou_metric': 0.9925495386123657, 'Train/mean f1': 0.9970278143882751, 'Train/mean precision': 0.9966192841529846, 'Train/mean recall': 0.9974367618560791, 'Train/mean hd95_metric': 0.9564187526702881} +Epoch [3576/4000] Validation [1/10] Loss: 0.74104 focal_loss 0.64969 dice_loss 0.09135 +Epoch [3576/4000] Validation [2/10] Loss: 0.47575 focal_loss 0.38321 dice_loss 0.09254 +Epoch [3576/4000] Validation [3/10] Loss: 0.35370 focal_loss 0.24542 dice_loss 0.10827 +Epoch [3576/4000] Validation [4/10] Loss: 0.90231 focal_loss 0.33553 dice_loss 0.56678 +Epoch [3576/4000] Validation [5/10] Loss: 2.98057 focal_loss 2.30810 dice_loss 0.67247 +Epoch [3576/4000] Validation [6/10] Loss: 1.36980 focal_loss 0.65105 dice_loss 0.71875 +Epoch [3576/4000] Validation [7/10] Loss: 1.19065 focal_loss 0.53048 dice_loss 0.66017 +Epoch [3576/4000] Validation [8/10] Loss: 1.99769 focal_loss 1.41720 dice_loss 0.58049 +Epoch [3576/4000] Validation [9/10] Loss: 1.56040 focal_loss 1.02059 dice_loss 0.53980 +Epoch [3576/4000] Validation [10/10] Loss: 1.92123 focal_loss 1.18115 dice_loss 0.74009 +Epoch [3576/4000] Validation metric {'Val/mean dice_metric': 0.9510651230812073, 'Val/mean miou_metric': 0.9353165626525879, 'Val/mean f1': 0.9478954076766968, 'Val/mean precision': 0.9397954344749451, 'Val/mean recall': 0.9561362266540527, 'Val/mean hd95_metric': 10.80714225769043} +Cheakpoint... +Epoch [3576/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510651230812073, 'Val/mean miou_metric': 0.9353165626525879, 'Val/mean f1': 0.9478954076766968, 'Val/mean precision': 0.9397954344749451, 'Val/mean recall': 0.9561362266540527, 'Val/mean hd95_metric': 10.80714225769043} +Epoch [3577/4000] Training [1/39] Loss: 0.12835 +Epoch [3577/4000] Training [2/39] Loss: 0.00443 +Epoch [3577/4000] Training [3/39] Loss: 0.13077 +Epoch [3577/4000] Training [4/39] Loss: 0.00949 +Epoch [3577/4000] Training [5/39] Loss: 0.12805 +Epoch [3577/4000] Training [6/39] Loss: 0.00456 +Epoch [3577/4000] Training [7/39] Loss: 0.00458 +Epoch [3577/4000] Training [8/39] Loss: 0.00360 +Epoch [3577/4000] Training [9/39] Loss: 0.00587 +Epoch [3577/4000] Training [10/39] Loss: 0.00333 +Epoch [3577/4000] Training [11/39] Loss: 0.00396 +Epoch [3577/4000] Training [12/39] Loss: 0.00406 +Epoch [3577/4000] Training [13/39] Loss: 0.00535 +Epoch [3577/4000] Training [14/39] Loss: 0.12901 +Epoch [3577/4000] Training [15/39] Loss: 0.00447 +Epoch [3577/4000] Training [16/39] Loss: 0.12883 +Epoch [3577/4000] Training [17/39] Loss: 0.12886 +Epoch [3577/4000] Training [18/39] Loss: 0.13062 +Epoch [3577/4000] Training [19/39] Loss: 0.00347 +Epoch [3577/4000] Training [20/39] Loss: 0.00672 +Epoch [3577/4000] Training [21/39] Loss: 0.00452 +Epoch [3577/4000] Training [22/39] Loss: 0.13208 +Epoch [3577/4000] Training [23/39] Loss: 0.00676 +Epoch [3577/4000] Training [24/39] Loss: 0.13035 +Epoch [3577/4000] Training [25/39] Loss: 0.00362 +Epoch [3577/4000] Training [26/39] Loss: 0.00604 +Epoch [3577/4000] Training [27/39] Loss: 0.00646 +Epoch [3577/4000] Training [28/39] Loss: 0.00309 +Epoch [3577/4000] Training [29/39] Loss: 0.00713 +Epoch [3577/4000] Training [30/39] Loss: 0.00411 +Epoch [3577/4000] Training [31/39] Loss: 0.12954 +Epoch [3577/4000] Training [32/39] Loss: 0.00599 +Epoch [3577/4000] Training [33/39] Loss: 0.00520 +Epoch [3577/4000] Training [34/39] Loss: 0.00673 +Epoch [3577/4000] Training [35/39] Loss: 0.13088 +Epoch [3577/4000] Training [36/39] Loss: 0.00327 +Epoch [3577/4000] Training [37/39] Loss: 0.00580 +Epoch [3577/4000] Training [38/39] Loss: 0.12774 +Epoch [3577/4000] Training [39/39] Loss: 0.00438 +Epoch [3577/4000] Training metric {'Train/mean dice_metric': 0.9962760806083679, 'Train/mean miou_metric': 0.9930184483528137, 'Train/mean f1': 0.9967517256736755, 'Train/mean precision': 0.9962933659553528, 'Train/mean recall': 0.9972104430198669, 'Train/mean hd95_metric': 0.9737001657485962} +Epoch [3577/4000] Validation [1/10] Loss: 0.77104 focal_loss 0.67966 dice_loss 0.09139 +Epoch [3577/4000] Validation [2/10] Loss: 0.47867 focal_loss 0.38339 dice_loss 0.09528 +Epoch [3577/4000] Validation [3/10] Loss: 0.38036 focal_loss 0.27045 dice_loss 0.10991 +Epoch [3577/4000] Validation [4/10] Loss: 0.89715 focal_loss 0.33179 dice_loss 0.56536 +Epoch [3577/4000] Validation [5/10] Loss: 3.07722 focal_loss 2.40410 dice_loss 0.67312 +Epoch [3577/4000] Validation [6/10] Loss: 1.34407 focal_loss 0.62740 dice_loss 0.71667 +Epoch [3577/4000] Validation [7/10] Loss: 1.17586 focal_loss 0.51608 dice_loss 0.65978 +Epoch [3577/4000] Validation [8/10] Loss: 2.17885 focal_loss 1.57684 dice_loss 0.60202 +Epoch [3577/4000] Validation [9/10] Loss: 1.50978 focal_loss 0.97557 dice_loss 0.53420 +Epoch [3577/4000] Validation [10/10] Loss: 1.87580 focal_loss 1.13974 dice_loss 0.73607 +Epoch [3577/4000] Validation metric {'Val/mean dice_metric': 0.9515082836151123, 'Val/mean miou_metric': 0.9356107115745544, 'Val/mean f1': 0.9480708837509155, 'Val/mean precision': 0.9418561458587646, 'Val/mean recall': 0.9543682336807251, 'Val/mean hd95_metric': 10.642897605895996} +Cheakpoint... +Epoch [3577/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515082836151123, 'Val/mean miou_metric': 0.9356107115745544, 'Val/mean f1': 0.9480708837509155, 'Val/mean precision': 0.9418561458587646, 'Val/mean recall': 0.9543682336807251, 'Val/mean hd95_metric': 10.642897605895996} +Epoch [3578/4000] Training [1/39] Loss: 0.00397 +Epoch [3578/4000] Training [2/39] Loss: 0.00445 +Epoch [3578/4000] Training [3/39] Loss: 0.13058 +Epoch [3578/4000] Training [4/39] Loss: 0.00686 +Epoch [3578/4000] Training [5/39] Loss: 0.00516 +Epoch [3578/4000] Training [6/39] Loss: 0.00486 +Epoch [3578/4000] Training [7/39] Loss: 0.00383 +Epoch [3578/4000] Training [8/39] Loss: 0.00400 +Epoch [3578/4000] Training [9/39] Loss: 0.00713 +Epoch [3578/4000] Training [10/39] Loss: 0.00498 +Epoch [3578/4000] Training [11/39] Loss: 0.00455 +Epoch [3578/4000] Training [12/39] Loss: 0.00307 +Epoch [3578/4000] Training [13/39] Loss: 0.00323 +Epoch [3578/4000] Training [14/39] Loss: 0.13378 +Epoch [3578/4000] Training [15/39] Loss: 0.00325 +Epoch [3578/4000] Training [16/39] Loss: 0.12745 +Epoch [3578/4000] Training [17/39] Loss: 0.00409 +Epoch [3578/4000] Training [18/39] Loss: 0.00333 +Epoch [3578/4000] Training [19/39] Loss: 0.00399 +Epoch [3578/4000] Training [20/39] Loss: 0.00296 +Epoch [3578/4000] Training [21/39] Loss: 0.00458 +Epoch [3578/4000] Training [22/39] Loss: 0.00668 +Epoch [3578/4000] Training [23/39] Loss: 0.00497 +Epoch [3578/4000] Training [24/39] Loss: 0.00644 +Epoch [3578/4000] Training [25/39] Loss: 0.12743 +Epoch [3578/4000] Training [26/39] Loss: 0.00514 +Epoch [3578/4000] Training [27/39] Loss: 0.00445 +Epoch [3578/4000] Training [28/39] Loss: 0.00700 +Epoch [3578/4000] Training [29/39] Loss: 0.00360 +Epoch [3578/4000] Training [30/39] Loss: 0.00481 +Epoch [3578/4000] Training [31/39] Loss: 0.00658 +Epoch [3578/4000] Training [32/39] Loss: 0.13103 +Epoch [3578/4000] Training [33/39] Loss: 0.00372 +Epoch [3578/4000] Training [34/39] Loss: 0.00417 +Epoch [3578/4000] Training [35/39] Loss: 0.12942 +Epoch [3578/4000] Training [36/39] Loss: 0.00901 +Epoch [3578/4000] Training [37/39] Loss: 0.00573 +Epoch [3578/4000] Training [38/39] Loss: 0.00454 +Epoch [3578/4000] Training [39/39] Loss: 0.00360 +Epoch [3578/4000] Training metric {'Train/mean dice_metric': 0.9962384104728699, 'Train/mean miou_metric': 0.9929282069206238, 'Train/mean f1': 0.9968069195747375, 'Train/mean precision': 0.9963298439979553, 'Train/mean recall': 0.9972844123840332, 'Train/mean hd95_metric': 0.9572064280509949} +Epoch [3578/4000] Validation [1/10] Loss: 0.75935 focal_loss 0.66936 dice_loss 0.08999 +Epoch [3578/4000] Validation [2/10] Loss: 0.47897 focal_loss 0.38432 dice_loss 0.09465 +Epoch [3578/4000] Validation [3/10] Loss: 0.38089 focal_loss 0.27122 dice_loss 0.10967 +Epoch [3578/4000] Validation [4/10] Loss: 0.89891 focal_loss 0.33353 dice_loss 0.56538 +Epoch [3578/4000] Validation [5/10] Loss: 3.08837 focal_loss 2.41571 dice_loss 0.67266 +Epoch [3578/4000] Validation [6/10] Loss: 1.34176 focal_loss 0.62365 dice_loss 0.71811 +Epoch [3578/4000] Validation [7/10] Loss: 1.17964 focal_loss 0.52263 dice_loss 0.65701 +Epoch [3578/4000] Validation [8/10] Loss: 2.13660 focal_loss 1.53933 dice_loss 0.59726 +Epoch [3578/4000] Validation [9/10] Loss: 1.54147 focal_loss 1.00179 dice_loss 0.53969 +Epoch [3578/4000] Validation [10/10] Loss: 1.90143 focal_loss 1.16318 dice_loss 0.73825 +Epoch [3578/4000] Validation metric {'Val/mean dice_metric': 0.9515619277954102, 'Val/mean miou_metric': 0.9356852769851685, 'Val/mean f1': 0.9482065439224243, 'Val/mean precision': 0.9418755769729614, 'Val/mean recall': 0.9546232223510742, 'Val/mean hd95_metric': 10.579227447509766} +Cheakpoint... +Epoch [3578/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515619277954102, 'Val/mean miou_metric': 0.9356852769851685, 'Val/mean f1': 0.9482065439224243, 'Val/mean precision': 0.9418755769729614, 'Val/mean recall': 0.9546232223510742, 'Val/mean hd95_metric': 10.579227447509766} +Epoch [3579/4000] Training [1/39] Loss: 0.00460 +Epoch [3579/4000] Training [2/39] Loss: 0.00599 +Epoch [3579/4000] Training [3/39] Loss: 0.12803 +Epoch [3579/4000] Training [4/39] Loss: 0.00857 +Epoch [3579/4000] Training [5/39] Loss: 0.00351 +Epoch [3579/4000] Training [6/39] Loss: 0.00434 +Epoch [3579/4000] Training [7/39] Loss: 0.12837 +Epoch [3579/4000] Training [8/39] Loss: 0.12898 +Epoch [3579/4000] Training [9/39] Loss: 0.12763 +Epoch [3579/4000] Training [10/39] Loss: 0.00572 +Epoch [3579/4000] Training [11/39] Loss: 0.00646 +Epoch [3579/4000] Training [12/39] Loss: 0.00571 +Epoch [3579/4000] Training [13/39] Loss: 0.12876 +Epoch [3579/4000] Training [14/39] Loss: 0.00437 +Epoch [3579/4000] Training [15/39] Loss: 0.12952 +Epoch [3579/4000] Training [16/39] Loss: 0.00526 +Epoch [3579/4000] Training [17/39] Loss: 0.00357 +Epoch [3579/4000] Training [18/39] Loss: 0.00433 +Epoch [3579/4000] Training [19/39] Loss: 0.13141 +Epoch [3579/4000] Training [20/39] Loss: 0.00520 +Epoch [3579/4000] Training [21/39] Loss: 0.00451 +Epoch [3579/4000] Training [22/39] Loss: 0.00384 +Epoch [3579/4000] Training [23/39] Loss: 0.13076 +Epoch [3579/4000] Training [24/39] Loss: 0.25367 +Epoch [3579/4000] Training [25/39] Loss: 0.00441 +Epoch [3579/4000] Training [26/39] Loss: 0.00569 +Epoch [3579/4000] Training [27/39] Loss: 0.00450 +Epoch [3579/4000] Training [28/39] Loss: 0.00630 +Epoch [3579/4000] Training [29/39] Loss: 0.00544 +Epoch [3579/4000] Training [30/39] Loss: 0.00431 +Epoch [3579/4000] Training [31/39] Loss: 0.12865 +Epoch [3579/4000] Training [32/39] Loss: 0.00460 +Epoch [3579/4000] Training [33/39] Loss: 0.00378 +Epoch [3579/4000] Training [34/39] Loss: 0.00366 +Epoch [3579/4000] Training [35/39] Loss: 0.13055 +Epoch [3579/4000] Training [36/39] Loss: 0.00511 +Epoch [3579/4000] Training [37/39] Loss: 0.12835 +Epoch [3579/4000] Training [38/39] Loss: 0.00593 +Epoch [3579/4000] Training [39/39] Loss: 0.00285 +Epoch [3579/4000] Training metric {'Train/mean dice_metric': 0.9963133335113525, 'Train/mean miou_metric': 0.9930886030197144, 'Train/mean f1': 0.9968979358673096, 'Train/mean precision': 0.9964815974235535, 'Train/mean recall': 0.9973146915435791, 'Train/mean hd95_metric': 0.9962754249572754} +Epoch [3579/4000] Validation [1/10] Loss: 0.75158 focal_loss 0.66179 dice_loss 0.08980 +Epoch [3579/4000] Validation [2/10] Loss: 0.46601 focal_loss 0.37358 dice_loss 0.09243 +Epoch [3579/4000] Validation [3/10] Loss: 0.37649 focal_loss 0.26678 dice_loss 0.10971 +Epoch [3579/4000] Validation [4/10] Loss: 0.89176 focal_loss 0.32751 dice_loss 0.56425 +Epoch [3579/4000] Validation [5/10] Loss: 3.04571 focal_loss 2.37319 dice_loss 0.67251 +Epoch [3579/4000] Validation [6/10] Loss: 1.34313 focal_loss 0.62292 dice_loss 0.72021 +Epoch [3579/4000] Validation [7/10] Loss: 1.17884 focal_loss 0.52106 dice_loss 0.65778 +Epoch [3579/4000] Validation [8/10] Loss: 2.12410 focal_loss 1.52459 dice_loss 0.59950 +Epoch [3579/4000] Validation [9/10] Loss: 1.52124 focal_loss 0.98503 dice_loss 0.53621 +Epoch [3579/4000] Validation [10/10] Loss: 1.88994 focal_loss 1.15123 dice_loss 0.73871 +Epoch [3579/4000] Validation metric {'Val/mean dice_metric': 0.9515820145606995, 'Val/mean miou_metric': 0.9357686638832092, 'Val/mean f1': 0.9482935070991516, 'Val/mean precision': 0.9415850639343262, 'Val/mean recall': 0.9550982117652893, 'Val/mean hd95_metric': 10.620199203491211} +Cheakpoint... +Epoch [3579/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515820145606995, 'Val/mean miou_metric': 0.9357686638832092, 'Val/mean f1': 0.9482935070991516, 'Val/mean precision': 0.9415850639343262, 'Val/mean recall': 0.9550982117652893, 'Val/mean hd95_metric': 10.620199203491211} +Epoch [3580/4000] Training [1/39] Loss: 0.00453 +Epoch [3580/4000] Training [2/39] Loss: 0.13094 +Epoch [3580/4000] Training [3/39] Loss: 0.00450 +Epoch [3580/4000] Training [4/39] Loss: 0.12880 +Epoch [3580/4000] Training [5/39] Loss: 0.12828 +Epoch [3580/4000] Training [6/39] Loss: 0.00525 +Epoch [3580/4000] Training [7/39] Loss: 0.00579 +Epoch [3580/4000] Training [8/39] Loss: 0.00314 +Epoch [3580/4000] Training [9/39] Loss: 0.00535 +Epoch [3580/4000] Training [10/39] Loss: 0.00336 +Epoch [3580/4000] Training [11/39] Loss: 0.00408 +Epoch [3580/4000] Training [12/39] Loss: 0.00482 +Epoch [3580/4000] Training [13/39] Loss: 0.00518 +Epoch [3580/4000] Training [14/39] Loss: 0.00453 +Epoch [3580/4000] Training [15/39] Loss: 0.00280 +Epoch [3580/4000] Training [16/39] Loss: 0.25508 +Epoch [3580/4000] Training [17/39] Loss: 0.00383 +Epoch [3580/4000] Training [18/39] Loss: 0.00622 +Epoch [3580/4000] Training [19/39] Loss: 0.12961 +Epoch [3580/4000] Training [20/39] Loss: 0.13258 +Epoch [3580/4000] Training [21/39] Loss: 0.00626 +Epoch [3580/4000] Training [22/39] Loss: 0.00737 +Epoch [3580/4000] Training [23/39] Loss: 0.00494 +Epoch [3580/4000] Training [24/39] Loss: 0.00509 +Epoch [3580/4000] Training [25/39] Loss: 0.13308 +Epoch [3580/4000] Training [26/39] Loss: 0.00600 +Epoch [3580/4000] Training [27/39] Loss: 0.25367 +Epoch [3580/4000] Training [28/39] Loss: 0.00404 +Epoch [3580/4000] Training [29/39] Loss: 0.13039 +Epoch [3580/4000] Training [30/39] Loss: 0.00384 +Epoch [3580/4000] Training [31/39] Loss: 0.12853 +Epoch [3580/4000] Training [32/39] Loss: 0.12933 +Epoch [3580/4000] Training [33/39] Loss: 0.00836 +Epoch [3580/4000] Training [34/39] Loss: 0.12838 +Epoch [3580/4000] Training [35/39] Loss: 0.00330 +Epoch [3580/4000] Training [36/39] Loss: 0.12854 +Epoch [3580/4000] Training [37/39] Loss: 0.00927 +Epoch [3580/4000] Training [38/39] Loss: 0.12811 +Epoch [3580/4000] Training [39/39] Loss: 0.00393 +Epoch [3580/4000] Training metric {'Train/mean dice_metric': 0.9962189793586731, 'Train/mean miou_metric': 0.9928768277168274, 'Train/mean f1': 0.9968058466911316, 'Train/mean precision': 0.9963730573654175, 'Train/mean recall': 0.9972389340400696, 'Train/mean hd95_metric': 0.9913687109947205} +Epoch [3580/4000] Validation [1/10] Loss: 0.75201 focal_loss 0.66105 dice_loss 0.09096 +Epoch [3580/4000] Validation [2/10] Loss: 0.46499 focal_loss 0.37371 dice_loss 0.09128 +Epoch [3580/4000] Validation [3/10] Loss: 0.35994 focal_loss 0.25153 dice_loss 0.10841 +Epoch [3580/4000] Validation [4/10] Loss: 0.89362 focal_loss 0.32772 dice_loss 0.56590 +Epoch [3580/4000] Validation [5/10] Loss: 2.99579 focal_loss 2.32359 dice_loss 0.67221 +Epoch [3580/4000] Validation [6/10] Loss: 1.35960 focal_loss 0.64033 dice_loss 0.71927 +Epoch [3580/4000] Validation [7/10] Loss: 1.19237 focal_loss 0.53471 dice_loss 0.65766 +Epoch [3580/4000] Validation [8/10] Loss: 2.08845 focal_loss 1.50094 dice_loss 0.58752 +Epoch [3580/4000] Validation [9/10] Loss: 1.58591 focal_loss 1.04540 dice_loss 0.54051 +Epoch [3580/4000] Validation [10/10] Loss: 1.95545 focal_loss 1.21431 dice_loss 0.74114 +Epoch [3580/4000] Validation metric {'Val/mean dice_metric': 0.9515343904495239, 'Val/mean miou_metric': 0.9356176257133484, 'Val/mean f1': 0.9478388428688049, 'Val/mean precision': 0.9398863911628723, 'Val/mean recall': 0.9559271335601807, 'Val/mean hd95_metric': 10.780838966369629} +Cheakpoint... +Epoch [3580/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515343904495239, 'Val/mean miou_metric': 0.9356176257133484, 'Val/mean f1': 0.9478388428688049, 'Val/mean precision': 0.9398863911628723, 'Val/mean recall': 0.9559271335601807, 'Val/mean hd95_metric': 10.780838966369629} +Epoch [3581/4000] Training [1/39] Loss: 0.00492 +Epoch [3581/4000] Training [2/39] Loss: 0.25816 +Epoch [3581/4000] Training [3/39] Loss: 0.00527 +Epoch [3581/4000] Training [4/39] Loss: 0.00515 +Epoch [3581/4000] Training [5/39] Loss: 0.00634 +Epoch [3581/4000] Training [6/39] Loss: 0.13084 +Epoch [3581/4000] Training [7/39] Loss: 0.00427 +Epoch [3581/4000] Training [8/39] Loss: 0.00322 +Epoch [3581/4000] Training [9/39] Loss: 0.00308 +Epoch [3581/4000] Training [10/39] Loss: 0.00461 +Epoch [3581/4000] Training [11/39] Loss: 0.25342 +Epoch [3581/4000] Training [12/39] Loss: 0.00518 +Epoch [3581/4000] Training [13/39] Loss: 0.00542 +Epoch [3581/4000] Training [14/39] Loss: 0.00429 +Epoch [3581/4000] Training [15/39] Loss: 0.00517 +Epoch [3581/4000] Training [16/39] Loss: 0.00516 +Epoch [3581/4000] Training [17/39] Loss: 0.00516 +Epoch [3581/4000] Training [18/39] Loss: 0.00296 +Epoch [3581/4000] Training [19/39] Loss: 0.13008 +Epoch [3581/4000] Training [20/39] Loss: 0.00607 +Epoch [3581/4000] Training [21/39] Loss: 0.00669 +Epoch [3581/4000] Training [22/39] Loss: 0.00358 +Epoch [3581/4000] Training [23/39] Loss: 0.00564 +Epoch [3581/4000] Training [24/39] Loss: 0.00427 +Epoch [3581/4000] Training [25/39] Loss: 0.00355 +Epoch [3581/4000] Training [26/39] Loss: 0.12798 +Epoch [3581/4000] Training [27/39] Loss: 0.00390 +Epoch [3581/4000] Training [28/39] Loss: 0.12906 +Epoch [3581/4000] Training [29/39] Loss: 0.00523 +Epoch [3581/4000] Training [30/39] Loss: 0.00664 +Epoch [3581/4000] Training [31/39] Loss: 0.00486 +Epoch [3581/4000] Training [32/39] Loss: 0.00601 +Epoch [3581/4000] Training [33/39] Loss: 0.00482 +Epoch [3581/4000] Training [34/39] Loss: 0.00443 +Epoch [3581/4000] Training [35/39] Loss: 0.00437 +Epoch [3581/4000] Training [36/39] Loss: 0.00640 +Epoch [3581/4000] Training [37/39] Loss: 0.00292 +Epoch [3581/4000] Training [38/39] Loss: 0.12913 +Epoch [3581/4000] Training [39/39] Loss: 0.12818 +Epoch [3581/4000] Training metric {'Train/mean dice_metric': 0.9961047172546387, 'Train/mean miou_metric': 0.9926579594612122, 'Train/mean f1': 0.9967564940452576, 'Train/mean precision': 0.9962816834449768, 'Train/mean recall': 0.9972316026687622, 'Train/mean hd95_metric': 0.9653822183609009} +Epoch [3581/4000] Validation [1/10] Loss: 0.76140 focal_loss 0.67084 dice_loss 0.09056 +Epoch [3581/4000] Validation [2/10] Loss: 0.47016 focal_loss 0.37630 dice_loss 0.09387 +Epoch [3581/4000] Validation [3/10] Loss: 0.37350 focal_loss 0.26426 dice_loss 0.10924 +Epoch [3581/4000] Validation [4/10] Loss: 0.89169 focal_loss 0.32658 dice_loss 0.56511 +Epoch [3581/4000] Validation [5/10] Loss: 3.10489 focal_loss 2.43233 dice_loss 0.67256 +Epoch [3581/4000] Validation [6/10] Loss: 1.34272 focal_loss 0.62853 dice_loss 0.71419 +Epoch [3581/4000] Validation [7/10] Loss: 1.17895 focal_loss 0.52352 dice_loss 0.65543 +Epoch [3581/4000] Validation [8/10] Loss: 2.12442 focal_loss 1.52815 dice_loss 0.59627 +Epoch [3581/4000] Validation [9/10] Loss: 1.56532 focal_loss 1.02428 dice_loss 0.54104 +Epoch [3581/4000] Validation [10/10] Loss: 1.92564 focal_loss 1.18413 dice_loss 0.74151 +Epoch [3581/4000] Validation metric {'Val/mean dice_metric': 0.9513877034187317, 'Val/mean miou_metric': 0.9353376626968384, 'Val/mean f1': 0.9481526613235474, 'Val/mean precision': 0.9413291215896606, 'Val/mean recall': 0.9550759196281433, 'Val/mean hd95_metric': 10.64159107208252} +Cheakpoint... +Epoch [3581/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513877034187317, 'Val/mean miou_metric': 0.9353376626968384, 'Val/mean f1': 0.9481526613235474, 'Val/mean precision': 0.9413291215896606, 'Val/mean recall': 0.9550759196281433, 'Val/mean hd95_metric': 10.64159107208252} +Epoch [3582/4000] Training [1/39] Loss: 0.00426 +Epoch [3582/4000] Training [2/39] Loss: 0.00494 +Epoch [3582/4000] Training [3/39] Loss: 0.12989 +Epoch [3582/4000] Training [4/39] Loss: 0.00777 +Epoch [3582/4000] Training [5/39] Loss: 0.12875 +Epoch [3582/4000] Training [6/39] Loss: 0.00325 +Epoch [3582/4000] Training [7/39] Loss: 0.00518 +Epoch [3582/4000] Training [8/39] Loss: 0.12929 +Epoch [3582/4000] Training [9/39] Loss: 0.13096 +Epoch [3582/4000] Training [10/39] Loss: 0.00397 +Epoch [3582/4000] Training [11/39] Loss: 0.00397 +Epoch [3582/4000] Training [12/39] Loss: 0.00368 +Epoch [3582/4000] Training [13/39] Loss: 0.00487 +Epoch [3582/4000] Training [14/39] Loss: 0.00517 +Epoch [3582/4000] Training [15/39] Loss: 0.12895 +Epoch [3582/4000] Training [16/39] Loss: 0.13136 +Epoch [3582/4000] Training [17/39] Loss: 0.00439 +Epoch [3582/4000] Training [18/39] Loss: 0.00347 +Epoch [3582/4000] Training [19/39] Loss: 0.00456 +Epoch [3582/4000] Training [20/39] Loss: 0.00578 +Epoch [3582/4000] Training [21/39] Loss: 0.25297 +Epoch [3582/4000] Training [22/39] Loss: 0.12899 +Epoch [3582/4000] Training [23/39] Loss: 0.00522 +Epoch [3582/4000] Training [24/39] Loss: 0.00443 +Epoch [3582/4000] Training [25/39] Loss: 0.00369 +Epoch [3582/4000] Training [26/39] Loss: 0.00468 +Epoch [3582/4000] Training [27/39] Loss: 0.00600 +Epoch [3582/4000] Training [28/39] Loss: 0.00586 +Epoch [3582/4000] Training [29/39] Loss: 0.00448 +Epoch [3582/4000] Training [30/39] Loss: 0.12936 +Epoch [3582/4000] Training [31/39] Loss: 0.00401 +Epoch [3582/4000] Training [32/39] Loss: 0.00504 +Epoch [3582/4000] Training [33/39] Loss: 0.00461 +Epoch [3582/4000] Training [34/39] Loss: 0.00453 +Epoch [3582/4000] Training [35/39] Loss: 0.00431 +Epoch [3582/4000] Training [36/39] Loss: 0.00668 +Epoch [3582/4000] Training [37/39] Loss: 0.12822 +Epoch [3582/4000] Training [38/39] Loss: 0.00508 +Epoch [3582/4000] Training [39/39] Loss: 0.13131 +Epoch [3582/4000] Training metric {'Train/mean dice_metric': 0.9952452182769775, 'Train/mean miou_metric': 0.9918039441108704, 'Train/mean f1': 0.9967654943466187, 'Train/mean precision': 0.9963480234146118, 'Train/mean recall': 0.9971833825111389, 'Train/mean hd95_metric': 1.0928964614868164} +Epoch [3582/4000] Validation [1/10] Loss: 0.76672 focal_loss 0.67539 dice_loss 0.09133 +Epoch [3582/4000] Validation [2/10] Loss: 0.47520 focal_loss 0.38334 dice_loss 0.09186 +Epoch [3582/4000] Validation [3/10] Loss: 0.36927 focal_loss 0.26020 dice_loss 0.10906 +Epoch [3582/4000] Validation [4/10] Loss: 0.90462 focal_loss 0.33859 dice_loss 0.56603 +Epoch [3582/4000] Validation [5/10] Loss: 3.07103 focal_loss 2.39827 dice_loss 0.67277 +Epoch [3582/4000] Validation [6/10] Loss: 1.36854 focal_loss 0.65397 dice_loss 0.71458 +Epoch [3582/4000] Validation [7/10] Loss: 1.20305 focal_loss 0.54331 dice_loss 0.65974 +Epoch [3582/4000] Validation [8/10] Loss: 2.12161 focal_loss 1.53181 dice_loss 0.58980 +Epoch [3582/4000] Validation [9/10] Loss: 1.58215 focal_loss 1.03545 dice_loss 0.54670 +Epoch [3582/4000] Validation [10/10] Loss: 1.96185 focal_loss 1.22067 dice_loss 0.74118 +Epoch [3582/4000] Validation metric {'Val/mean dice_metric': 0.9506876468658447, 'Val/mean miou_metric': 0.9346196055412292, 'Val/mean f1': 0.9479090571403503, 'Val/mean precision': 0.9406439661979675, 'Val/mean recall': 0.9552871584892273, 'Val/mean hd95_metric': 10.695478439331055} +Cheakpoint... +Epoch [3582/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506876468658447, 'Val/mean miou_metric': 0.9346196055412292, 'Val/mean f1': 0.9479090571403503, 'Val/mean precision': 0.9406439661979675, 'Val/mean recall': 0.9552871584892273, 'Val/mean hd95_metric': 10.695478439331055} +Epoch [3583/4000] Training [1/39] Loss: 0.12870 +Epoch [3583/4000] Training [2/39] Loss: 0.00365 +Epoch [3583/4000] Training [3/39] Loss: 0.12743 +Epoch [3583/4000] Training [4/39] Loss: 0.00477 +Epoch [3583/4000] Training [5/39] Loss: 0.00704 +Epoch [3583/4000] Training [6/39] Loss: 0.12824 +Epoch [3583/4000] Training [7/39] Loss: 0.00320 +Epoch [3583/4000] Training [8/39] Loss: 0.00515 +Epoch [3583/4000] Training [9/39] Loss: 0.00558 +Epoch [3583/4000] Training [10/39] Loss: 0.00400 +Epoch [3583/4000] Training [11/39] Loss: 0.00849 +Epoch [3583/4000] Training [12/39] Loss: 0.00693 +Epoch [3583/4000] Training [13/39] Loss: 0.00410 +Epoch [3583/4000] Training [14/39] Loss: 0.00595 +Epoch [3583/4000] Training [15/39] Loss: 0.00485 +Epoch [3583/4000] Training [16/39] Loss: 0.00547 +Epoch [3583/4000] Training [17/39] Loss: 0.13033 +Epoch [3583/4000] Training [18/39] Loss: 0.00588 +Epoch [3583/4000] Training [19/39] Loss: 0.00791 +Epoch [3583/4000] Training [20/39] Loss: 0.12873 +Epoch [3583/4000] Training [21/39] Loss: 0.25593 +Epoch [3583/4000] Training [22/39] Loss: 0.00539 +Epoch [3583/4000] Training [23/39] Loss: 0.00451 +Epoch [3583/4000] Training [24/39] Loss: 0.00466 +Epoch [3583/4000] Training [25/39] Loss: 0.12866 +Epoch [3583/4000] Training [26/39] Loss: 0.00442 +Epoch [3583/4000] Training [27/39] Loss: 0.00333 +Epoch [3583/4000] Training [28/39] Loss: 0.25782 +Epoch [3583/4000] Training [29/39] Loss: 0.00388 +Epoch [3583/4000] Training [30/39] Loss: 0.00455 +Epoch [3583/4000] Training [31/39] Loss: 0.00709 +Epoch [3583/4000] Training [32/39] Loss: 0.00550 +Epoch [3583/4000] Training [33/39] Loss: 0.00721 +Epoch [3583/4000] Training [34/39] Loss: 0.12901 +Epoch [3583/4000] Training [35/39] Loss: 0.00443 +Epoch [3583/4000] Training [36/39] Loss: 0.00484 +Epoch [3583/4000] Training [37/39] Loss: 0.00599 +Epoch [3583/4000] Training [38/39] Loss: 0.12715 +Epoch [3583/4000] Training [39/39] Loss: 0.00623 +Epoch [3583/4000] Training metric {'Train/mean dice_metric': 0.9960702061653137, 'Train/mean miou_metric': 0.9925850629806519, 'Train/mean f1': 0.9967597126960754, 'Train/mean precision': 0.996296226978302, 'Train/mean recall': 0.9972238540649414, 'Train/mean hd95_metric': 0.9874627590179443} +Epoch [3583/4000] Validation [1/10] Loss: 0.73677 focal_loss 0.64684 dice_loss 0.08993 +Epoch [3583/4000] Validation [2/10] Loss: 0.46999 focal_loss 0.37767 dice_loss 0.09232 +Epoch [3583/4000] Validation [3/10] Loss: 0.36254 focal_loss 0.25411 dice_loss 0.10843 +Epoch [3583/4000] Validation [4/10] Loss: 0.89493 focal_loss 0.32973 dice_loss 0.56520 +Epoch [3583/4000] Validation [5/10] Loss: 3.02453 focal_loss 2.35159 dice_loss 0.67294 +Epoch [3583/4000] Validation [6/10] Loss: 1.35699 focal_loss 0.64303 dice_loss 0.71396 +Epoch [3583/4000] Validation [7/10] Loss: 1.19293 focal_loss 0.53536 dice_loss 0.65757 +Epoch [3583/4000] Validation [8/10] Loss: 2.09282 focal_loss 1.50182 dice_loss 0.59100 +Epoch [3583/4000] Validation [9/10] Loss: 1.52677 focal_loss 0.97979 dice_loss 0.54699 +Epoch [3583/4000] Validation [10/10] Loss: 1.92882 focal_loss 1.18886 dice_loss 0.73996 +Epoch [3583/4000] Validation metric {'Val/mean dice_metric': 0.9515150785446167, 'Val/mean miou_metric': 0.9354872703552246, 'Val/mean f1': 0.9484344720840454, 'Val/mean precision': 0.941599428653717, 'Val/mean recall': 0.9553695917129517, 'Val/mean hd95_metric': 10.786907196044922} +Cheakpoint... +Epoch [3583/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515150785446167, 'Val/mean miou_metric': 0.9354872703552246, 'Val/mean f1': 0.9484344720840454, 'Val/mean precision': 0.941599428653717, 'Val/mean recall': 0.9553695917129517, 'Val/mean hd95_metric': 10.786907196044922} +Epoch [3584/4000] Training [1/39] Loss: 0.12797 +Epoch [3584/4000] Training [2/39] Loss: 0.00575 +Epoch [3584/4000] Training [3/39] Loss: 0.00615 +Epoch [3584/4000] Training [4/39] Loss: 0.00363 +Epoch [3584/4000] Training [5/39] Loss: 0.00527 +Epoch [3584/4000] Training [6/39] Loss: 0.00752 +Epoch [3584/4000] Training [7/39] Loss: 0.00549 +Epoch [3584/4000] Training [8/39] Loss: 0.00323 +Epoch [3584/4000] Training [9/39] Loss: 0.00521 +Epoch [3584/4000] Training [10/39] Loss: 0.12754 +Epoch [3584/4000] Training [11/39] Loss: 0.00454 +Epoch [3584/4000] Training [12/39] Loss: 0.00401 +Epoch [3584/4000] Training [13/39] Loss: 0.00279 +Epoch [3584/4000] Training [14/39] Loss: 0.13284 +Epoch [3584/4000] Training [15/39] Loss: 0.00620 +Epoch [3584/4000] Training [16/39] Loss: 0.00456 +Epoch [3584/4000] Training [17/39] Loss: 0.12886 +Epoch [3584/4000] Training [18/39] Loss: 0.00649 +Epoch [3584/4000] Training [19/39] Loss: 0.00542 +Epoch [3584/4000] Training [20/39] Loss: 0.00415 +Epoch [3584/4000] Training [21/39] Loss: 0.00506 +Epoch [3584/4000] Training [22/39] Loss: 0.09832 +Epoch [3584/4000] Training [23/39] Loss: 0.00378 +Epoch [3584/4000] Training [24/39] Loss: 0.00390 +Epoch [3584/4000] Training [25/39] Loss: 0.00376 +Epoch [3584/4000] Training [26/39] Loss: 0.00360 +Epoch [3584/4000] Training [27/39] Loss: 0.00369 +Epoch [3584/4000] Training [28/39] Loss: 0.00510 +Epoch [3584/4000] Training [29/39] Loss: 0.12932 +Epoch [3584/4000] Training [30/39] Loss: 0.12869 +Epoch [3584/4000] Training [31/39] Loss: 0.00444 +Epoch [3584/4000] Training [32/39] Loss: 0.13082 +Epoch [3584/4000] Training [33/39] Loss: 0.25287 +Epoch [3584/4000] Training [34/39] Loss: 0.00284 +Epoch [3584/4000] Training [35/39] Loss: 0.00557 +Epoch [3584/4000] Training [36/39] Loss: 0.00313 +Epoch [3584/4000] Training [37/39] Loss: 0.00505 +Epoch [3584/4000] Training [38/39] Loss: 0.00421 +Epoch [3584/4000] Training [39/39] Loss: 0.25441 +Epoch [3584/4000] Training metric {'Train/mean dice_metric': 0.9962970018386841, 'Train/mean miou_metric': 0.9930830597877502, 'Train/mean f1': 0.9969047904014587, 'Train/mean precision': 0.9964585900306702, 'Train/mean recall': 0.9973512291908264, 'Train/mean hd95_metric': 0.9419959783554077} +Epoch [3584/4000] Validation [1/10] Loss: 0.75324 focal_loss 0.66406 dice_loss 0.08918 +Epoch [3584/4000] Validation [2/10] Loss: 0.48835 focal_loss 0.39473 dice_loss 0.09362 +Epoch [3584/4000] Validation [3/10] Loss: 0.37240 focal_loss 0.26402 dice_loss 0.10838 +Epoch [3584/4000] Validation [4/10] Loss: 0.91125 focal_loss 0.34543 dice_loss 0.56582 +Epoch [3584/4000] Validation [5/10] Loss: 3.11554 focal_loss 2.44264 dice_loss 0.67290 +Epoch [3584/4000] Validation [6/10] Loss: 1.38547 focal_loss 0.66951 dice_loss 0.71596 +Epoch [3584/4000] Validation [7/10] Loss: 1.21141 focal_loss 0.55275 dice_loss 0.65867 +Epoch [3584/4000] Validation [8/10] Loss: 2.17076 focal_loss 1.57611 dice_loss 0.59465 +Epoch [3584/4000] Validation [9/10] Loss: 1.61317 focal_loss 1.06543 dice_loss 0.54774 +Epoch [3584/4000] Validation [10/10] Loss: 1.97852 focal_loss 1.23763 dice_loss 0.74089 +Epoch [3584/4000] Validation metric {'Val/mean dice_metric': 0.9515572190284729, 'Val/mean miou_metric': 0.9356892704963684, 'Val/mean f1': 0.9480384588241577, 'Val/mean precision': 0.9410735964775085, 'Val/mean recall': 0.9551073312759399, 'Val/mean hd95_metric': 10.731053352355957} +Cheakpoint... +Epoch [3584/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515572190284729, 'Val/mean miou_metric': 0.9356892704963684, 'Val/mean f1': 0.9480384588241577, 'Val/mean precision': 0.9410735964775085, 'Val/mean recall': 0.9551073312759399, 'Val/mean hd95_metric': 10.731053352355957} +Epoch [3585/4000] Training [1/39] Loss: 0.00333 +Epoch [3585/4000] Training [2/39] Loss: 0.00522 +Epoch [3585/4000] Training [3/39] Loss: 0.13226 +Epoch [3585/4000] Training [4/39] Loss: 0.00374 +Epoch [3585/4000] Training [5/39] Loss: 0.25230 +Epoch [3585/4000] Training [6/39] Loss: 0.00375 +Epoch [3585/4000] Training [7/39] Loss: 0.00628 +Epoch [3585/4000] Training [8/39] Loss: 0.00615 +Epoch [3585/4000] Training [9/39] Loss: 0.00408 +Epoch [3585/4000] Training [10/39] Loss: 0.13050 +Epoch [3585/4000] Training [11/39] Loss: 0.12992 +Epoch [3585/4000] Training [12/39] Loss: 0.12976 +Epoch [3585/4000] Training [13/39] Loss: 0.25304 +Epoch [3585/4000] Training [14/39] Loss: 0.00783 +Epoch [3585/4000] Training [15/39] Loss: 0.00472 +Epoch [3585/4000] Training [16/39] Loss: 0.13085 +Epoch [3585/4000] Training [17/39] Loss: 0.00555 +Epoch [3585/4000] Training [18/39] Loss: 0.00750 +Epoch [3585/4000] Training [19/39] Loss: 0.00666 +Epoch [3585/4000] Training [20/39] Loss: 0.00497 +Epoch [3585/4000] Training [21/39] Loss: 0.00405 +Epoch [3585/4000] Training [22/39] Loss: 0.00715 +Epoch [3585/4000] Training [23/39] Loss: 0.12969 +Epoch [3585/4000] Training [24/39] Loss: 0.00492 +Epoch [3585/4000] Training [25/39] Loss: 0.00558 +Epoch [3585/4000] Training [26/39] Loss: 0.13055 +Epoch [3585/4000] Training [27/39] Loss: 0.00685 +Epoch [3585/4000] Training [28/39] Loss: 0.00409 +Epoch [3585/4000] Training [29/39] Loss: 0.00341 +Epoch [3585/4000] Training [30/39] Loss: 0.00510 +Epoch [3585/4000] Training [31/39] Loss: 0.00566 +Epoch [3585/4000] Training [32/39] Loss: 0.00336 +Epoch [3585/4000] Training [33/39] Loss: 0.00666 +Epoch [3585/4000] Training [34/39] Loss: 0.13027 +Epoch [3585/4000] Training [35/39] Loss: 0.00299 +Epoch [3585/4000] Training [36/39] Loss: 0.00397 +Epoch [3585/4000] Training [37/39] Loss: 0.12782 +Epoch [3585/4000] Training [38/39] Loss: 0.00642 +Epoch [3585/4000] Training [39/39] Loss: 0.00436 +Epoch [3585/4000] Training metric {'Train/mean dice_metric': 0.9955534338951111, 'Train/mean miou_metric': 0.9919623136520386, 'Train/mean f1': 0.9963892102241516, 'Train/mean precision': 0.9956561326980591, 'Train/mean recall': 0.9971233606338501, 'Train/mean hd95_metric': 1.0939112901687622} +Epoch [3585/4000] Validation [1/10] Loss: 0.74208 focal_loss 0.65252 dice_loss 0.08956 +Epoch [3585/4000] Validation [2/10] Loss: 0.47543 focal_loss 0.38039 dice_loss 0.09504 +Epoch [3585/4000] Validation [3/10] Loss: 0.37070 focal_loss 0.26209 dice_loss 0.10861 +Epoch [3585/4000] Validation [4/10] Loss: 0.89138 focal_loss 0.32641 dice_loss 0.56497 +Epoch [3585/4000] Validation [5/10] Loss: 3.10481 focal_loss 2.43220 dice_loss 0.67262 +Epoch [3585/4000] Validation [6/10] Loss: 1.33693 focal_loss 0.61952 dice_loss 0.71741 +Epoch [3585/4000] Validation [7/10] Loss: 1.19095 focal_loss 0.53525 dice_loss 0.65569 +Epoch [3585/4000] Validation [8/10] Loss: 2.08229 focal_loss 1.48857 dice_loss 0.59372 +Epoch [3585/4000] Validation [9/10] Loss: 1.54200 focal_loss 0.99666 dice_loss 0.54535 +Epoch [3585/4000] Validation [10/10] Loss: 1.93393 focal_loss 1.19145 dice_loss 0.74248 +Epoch [3585/4000] Validation metric {'Val/mean dice_metric': 0.9509073495864868, 'Val/mean miou_metric': 0.9347843527793884, 'Val/mean f1': 0.9478083252906799, 'Val/mean precision': 0.9402756690979004, 'Val/mean recall': 0.9554626941680908, 'Val/mean hd95_metric': 10.882112503051758} +Cheakpoint... +Epoch [3585/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509073495864868, 'Val/mean miou_metric': 0.9347843527793884, 'Val/mean f1': 0.9478083252906799, 'Val/mean precision': 0.9402756690979004, 'Val/mean recall': 0.9554626941680908, 'Val/mean hd95_metric': 10.882112503051758} +Epoch [3586/4000] Training [1/39] Loss: 0.00460 +Epoch [3586/4000] Training [2/39] Loss: 0.00467 +Epoch [3586/4000] Training [3/39] Loss: 0.00427 +Epoch [3586/4000] Training [4/39] Loss: 0.00740 +Epoch [3586/4000] Training [5/39] Loss: 0.00904 +Epoch [3586/4000] Training [6/39] Loss: 0.00386 +Epoch [3586/4000] Training [7/39] Loss: 0.00714 +Epoch [3586/4000] Training [8/39] Loss: 0.00418 +Epoch [3586/4000] Training [9/39] Loss: 0.00798 +Epoch [3586/4000] Training [10/39] Loss: 0.16840 +Epoch [3586/4000] Training [11/39] Loss: 0.12969 +Epoch [3586/4000] Training [12/39] Loss: 0.00662 +Epoch [3586/4000] Training [13/39] Loss: 0.00745 +Epoch [3586/4000] Training [14/39] Loss: 0.00471 +Epoch [3586/4000] Training [15/39] Loss: 0.00319 +Epoch [3586/4000] Training [16/39] Loss: 0.00615 +Epoch [3586/4000] Training [17/39] Loss: 0.08250 +Epoch [3586/4000] Training [18/39] Loss: 0.00594 +Epoch [3586/4000] Training [19/39] Loss: 0.00493 +Epoch [3586/4000] Training [20/39] Loss: 0.12711 +Epoch [3586/4000] Training [21/39] Loss: 0.25363 +Epoch [3586/4000] Training [22/39] Loss: 0.00560 +Epoch [3586/4000] Training [23/39] Loss: 0.00565 +Epoch [3586/4000] Training [24/39] Loss: 0.00386 +Epoch [3586/4000] Training [25/39] Loss: 0.00490 +Epoch [3586/4000] Training [26/39] Loss: 0.00546 +Epoch [3586/4000] Training [27/39] Loss: 0.00483 +Epoch [3586/4000] Training [28/39] Loss: 0.00371 +Epoch [3586/4000] Training [29/39] Loss: 0.13021 +Epoch [3586/4000] Training [30/39] Loss: 0.00498 +Epoch [3586/4000] Training [31/39] Loss: 0.00429 +Epoch [3586/4000] Training [32/39] Loss: 0.00582 +Epoch [3586/4000] Training [33/39] Loss: 0.12777 +Epoch [3586/4000] Training [34/39] Loss: 0.12904 +Epoch [3586/4000] Training [35/39] Loss: 0.00346 +Epoch [3586/4000] Training [36/39] Loss: 0.00391 +Epoch [3586/4000] Training [37/39] Loss: 0.12816 +Epoch [3586/4000] Training [38/39] Loss: 0.00503 +Epoch [3586/4000] Training [39/39] Loss: 0.00442 +Epoch [3586/4000] Training metric {'Train/mean dice_metric': 0.9960847496986389, 'Train/mean miou_metric': 0.9926242828369141, 'Train/mean f1': 0.9967606067657471, 'Train/mean precision': 0.9962253570556641, 'Train/mean recall': 0.9972963929176331, 'Train/mean hd95_metric': 0.9789190292358398} +Epoch [3586/4000] Validation [1/10] Loss: 0.76321 focal_loss 0.67235 dice_loss 0.09086 +Epoch [3586/4000] Validation [2/10] Loss: 0.48766 focal_loss 0.39154 dice_loss 0.09612 +Epoch [3586/4000] Validation [3/10] Loss: 0.37352 focal_loss 0.26472 dice_loss 0.10880 +Epoch [3586/4000] Validation [4/10] Loss: 0.89803 focal_loss 0.33250 dice_loss 0.56553 +Epoch [3586/4000] Validation [5/10] Loss: 3.12730 focal_loss 2.45412 dice_loss 0.67319 +Epoch [3586/4000] Validation [6/10] Loss: 1.34985 focal_loss 0.63536 dice_loss 0.71449 +Epoch [3586/4000] Validation [7/10] Loss: 1.18617 focal_loss 0.53097 dice_loss 0.65520 +Epoch [3586/4000] Validation [8/10] Loss: 2.16127 focal_loss 1.56166 dice_loss 0.59961 +Epoch [3586/4000] Validation [9/10] Loss: 1.56047 focal_loss 1.01404 dice_loss 0.54643 +Epoch [3586/4000] Validation [10/10] Loss: 1.93399 focal_loss 1.19351 dice_loss 0.74048 +Epoch [3586/4000] Validation metric {'Val/mean dice_metric': 0.9513881206512451, 'Val/mean miou_metric': 0.935364842414856, 'Val/mean f1': 0.9481035470962524, 'Val/mean precision': 0.9412719011306763, 'Val/mean recall': 0.9550349712371826, 'Val/mean hd95_metric': 10.751923561096191} +Cheakpoint... +Epoch [3586/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513881206512451, 'Val/mean miou_metric': 0.935364842414856, 'Val/mean f1': 0.9481035470962524, 'Val/mean precision': 0.9412719011306763, 'Val/mean recall': 0.9550349712371826, 'Val/mean hd95_metric': 10.751923561096191} +Epoch [3587/4000] Training [1/39] Loss: 0.00368 +Epoch [3587/4000] Training [2/39] Loss: 0.00363 +Epoch [3587/4000] Training [3/39] Loss: 0.00323 +Epoch [3587/4000] Training [4/39] Loss: 0.00468 +Epoch [3587/4000] Training [5/39] Loss: 0.00363 +Epoch [3587/4000] Training [6/39] Loss: 0.00576 +Epoch [3587/4000] Training [7/39] Loss: 0.00426 +Epoch [3587/4000] Training [8/39] Loss: 0.00511 +Epoch [3587/4000] Training [9/39] Loss: 0.12841 +Epoch [3587/4000] Training [10/39] Loss: 0.00993 +Epoch [3587/4000] Training [11/39] Loss: 0.00421 +Epoch [3587/4000] Training [12/39] Loss: 0.00856 +Epoch [3587/4000] Training [13/39] Loss: 0.00468 +Epoch [3587/4000] Training [14/39] Loss: 0.01140 +Epoch [3587/4000] Training [15/39] Loss: 0.13143 +Epoch [3587/4000] Training [16/39] Loss: 0.00537 +Epoch [3587/4000] Training [17/39] Loss: 0.00365 +Epoch [3587/4000] Training [18/39] Loss: 0.00388 +Epoch [3587/4000] Training [19/39] Loss: 0.00464 +Epoch [3587/4000] Training [20/39] Loss: 0.00543 +Epoch [3587/4000] Training [21/39] Loss: 0.00284 +Epoch [3587/4000] Training [22/39] Loss: 0.00427 +Epoch [3587/4000] Training [23/39] Loss: 0.00693 +Epoch [3587/4000] Training [24/39] Loss: 0.00523 +Epoch [3587/4000] Training [25/39] Loss: 0.00658 +Epoch [3587/4000] Training [26/39] Loss: 0.00466 +Epoch [3587/4000] Training [27/39] Loss: 0.00545 +Epoch [3587/4000] Training [28/39] Loss: 0.00543 +Epoch [3587/4000] Training [29/39] Loss: 0.00318 +Epoch [3587/4000] Training [30/39] Loss: 0.00388 +Epoch [3587/4000] Training [31/39] Loss: 0.12834 +Epoch [3587/4000] Training [32/39] Loss: 0.00790 +Epoch [3587/4000] Training [33/39] Loss: 0.12946 +Epoch [3587/4000] Training [34/39] Loss: 0.00520 +Epoch [3587/4000] Training [35/39] Loss: 0.00503 +Epoch [3587/4000] Training [36/39] Loss: 0.12844 +Epoch [3587/4000] Training [37/39] Loss: 0.00518 +Epoch [3587/4000] Training [38/39] Loss: 0.00232 +Epoch [3587/4000] Training [39/39] Loss: 0.00451 +Epoch [3587/4000] Training metric {'Train/mean dice_metric': 0.9961608052253723, 'Train/mean miou_metric': 0.9927664995193481, 'Train/mean f1': 0.996699869632721, 'Train/mean precision': 0.9962561130523682, 'Train/mean recall': 0.9971440434455872, 'Train/mean hd95_metric': 0.9685741066932678} +Epoch [3587/4000] Validation [1/10] Loss: 0.74262 focal_loss 0.65281 dice_loss 0.08981 +Epoch [3587/4000] Validation [2/10] Loss: 0.48428 focal_loss 0.39027 dice_loss 0.09401 +Epoch [3587/4000] Validation [3/10] Loss: 0.36449 focal_loss 0.25628 dice_loss 0.10821 +Epoch [3587/4000] Validation [4/10] Loss: 0.89958 focal_loss 0.33409 dice_loss 0.56548 +Epoch [3587/4000] Validation [5/10] Loss: 3.05303 focal_loss 2.37985 dice_loss 0.67318 +Epoch [3587/4000] Validation [6/10] Loss: 1.35508 focal_loss 0.63992 dice_loss 0.71516 +Epoch [3587/4000] Validation [7/10] Loss: 1.18821 focal_loss 0.53184 dice_loss 0.65638 +Epoch [3587/4000] Validation [8/10] Loss: 2.11334 focal_loss 1.52288 dice_loss 0.59046 +Epoch [3587/4000] Validation [9/10] Loss: 1.56457 focal_loss 1.01804 dice_loss 0.54653 +Epoch [3587/4000] Validation [10/10] Loss: 1.93966 focal_loss 1.20071 dice_loss 0.73895 +Epoch [3587/4000] Validation metric {'Val/mean dice_metric': 0.9514909982681274, 'Val/mean miou_metric': 0.9355371594429016, 'Val/mean f1': 0.9481391906738281, 'Val/mean precision': 0.9407920241355896, 'Val/mean recall': 0.9556020498275757, 'Val/mean hd95_metric': 10.678526878356934} +Cheakpoint... +Epoch [3587/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514909982681274, 'Val/mean miou_metric': 0.9355371594429016, 'Val/mean f1': 0.9481391906738281, 'Val/mean precision': 0.9407920241355896, 'Val/mean recall': 0.9556020498275757, 'Val/mean hd95_metric': 10.678526878356934} +Epoch [3588/4000] Training [1/39] Loss: 0.00390 +Epoch [3588/4000] Training [2/39] Loss: 0.00506 +Epoch [3588/4000] Training [3/39] Loss: 0.00363 +Epoch [3588/4000] Training [4/39] Loss: 0.00443 +Epoch [3588/4000] Training [5/39] Loss: 0.00297 +Epoch [3588/4000] Training [6/39] Loss: 0.00345 +Epoch [3588/4000] Training [7/39] Loss: 0.13340 +Epoch [3588/4000] Training [8/39] Loss: 0.12987 +Epoch [3588/4000] Training [9/39] Loss: 0.00578 +Epoch [3588/4000] Training [10/39] Loss: 0.12919 +Epoch [3588/4000] Training [11/39] Loss: 0.12988 +Epoch [3588/4000] Training [12/39] Loss: 0.00479 +Epoch [3588/4000] Training [13/39] Loss: 0.00459 +Epoch [3588/4000] Training [14/39] Loss: 0.13130 +Epoch [3588/4000] Training [15/39] Loss: 0.12870 +Epoch [3588/4000] Training [16/39] Loss: 0.00502 +Epoch [3588/4000] Training [17/39] Loss: 0.00603 +Epoch [3588/4000] Training [18/39] Loss: 0.00425 +Epoch [3588/4000] Training [19/39] Loss: 0.00404 +Epoch [3588/4000] Training [20/39] Loss: 0.00500 +Epoch [3588/4000] Training [21/39] Loss: 0.00454 +Epoch [3588/4000] Training [22/39] Loss: 0.12692 +Epoch [3588/4000] Training [23/39] Loss: 0.12738 +Epoch [3588/4000] Training [24/39] Loss: 0.25752 +Epoch [3588/4000] Training [25/39] Loss: 0.00495 +Epoch [3588/4000] Training [26/39] Loss: 0.12969 +Epoch [3588/4000] Training [27/39] Loss: 0.12959 +Epoch [3588/4000] Training [28/39] Loss: 0.00327 +Epoch [3588/4000] Training [29/39] Loss: 0.00548 +Epoch [3588/4000] Training [30/39] Loss: 0.00450 +Epoch [3588/4000] Training [31/39] Loss: 0.04692 +Epoch [3588/4000] Training [32/39] Loss: 0.00441 +Epoch [3588/4000] Training [33/39] Loss: 0.00836 +Epoch [3588/4000] Training [34/39] Loss: 0.00422 +Epoch [3588/4000] Training [35/39] Loss: 0.00590 +Epoch [3588/4000] Training [36/39] Loss: 0.00694 +Epoch [3588/4000] Training [37/39] Loss: 0.00622 +Epoch [3588/4000] Training [38/39] Loss: 0.00555 +Epoch [3588/4000] Training [39/39] Loss: 0.13316 +Epoch [3588/4000] Training metric {'Train/mean dice_metric': 0.9963095784187317, 'Train/mean miou_metric': 0.9930609464645386, 'Train/mean f1': 0.9968722462654114, 'Train/mean precision': 0.9964540600776672, 'Train/mean recall': 0.997290849685669, 'Train/mean hd95_metric': 0.9753965139389038} +Epoch [3588/4000] Validation [1/10] Loss: 0.75637 focal_loss 0.66578 dice_loss 0.09059 +Epoch [3588/4000] Validation [2/10] Loss: 0.49075 focal_loss 0.39617 dice_loss 0.09458 +Epoch [3588/4000] Validation [3/10] Loss: 0.36799 focal_loss 0.25991 dice_loss 0.10809 +Epoch [3588/4000] Validation [4/10] Loss: 0.89580 focal_loss 0.33087 dice_loss 0.56493 +Epoch [3588/4000] Validation [5/10] Loss: 3.06210 focal_loss 2.38842 dice_loss 0.67367 +Epoch [3588/4000] Validation [6/10] Loss: 1.35156 focal_loss 0.63742 dice_loss 0.71414 +Epoch [3588/4000] Validation [7/10] Loss: 1.19069 focal_loss 0.53542 dice_loss 0.65527 +Epoch [3588/4000] Validation [8/10] Loss: 2.23512 focal_loss 1.63400 dice_loss 0.60113 +Epoch [3588/4000] Validation [9/10] Loss: 1.52568 focal_loss 0.97885 dice_loss 0.54683 +Epoch [3588/4000] Validation [10/10] Loss: 1.91842 focal_loss 1.18105 dice_loss 0.73736 +Epoch [3588/4000] Validation metric {'Val/mean dice_metric': 0.9515300393104553, 'Val/mean miou_metric': 0.9356346726417542, 'Val/mean f1': 0.9484661817550659, 'Val/mean precision': 0.9427822232246399, 'Val/mean recall': 0.9542191028594971, 'Val/mean hd95_metric': 10.633304595947266} +Cheakpoint... +Epoch [3588/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515300393104553, 'Val/mean miou_metric': 0.9356346726417542, 'Val/mean f1': 0.9484661817550659, 'Val/mean precision': 0.9427822232246399, 'Val/mean recall': 0.9542191028594971, 'Val/mean hd95_metric': 10.633304595947266} +Epoch [3589/4000] Training [1/39] Loss: 0.00413 +Epoch [3589/4000] Training [2/39] Loss: 0.00436 +Epoch [3589/4000] Training [3/39] Loss: 0.00234 +Epoch [3589/4000] Training [4/39] Loss: 0.00510 +Epoch [3589/4000] Training [5/39] Loss: 0.00399 +Epoch [3589/4000] Training [6/39] Loss: 0.13015 +Epoch [3589/4000] Training [7/39] Loss: 0.25252 +Epoch [3589/4000] Training [8/39] Loss: 0.12913 +Epoch [3589/4000] Training [9/39] Loss: 0.00284 +Epoch [3589/4000] Training [10/39] Loss: 0.12905 +Epoch [3589/4000] Training [11/39] Loss: 0.00533 +Epoch [3589/4000] Training [12/39] Loss: 0.00462 +Epoch [3589/4000] Training [13/39] Loss: 0.00599 +Epoch [3589/4000] Training [14/39] Loss: 0.00387 +Epoch [3589/4000] Training [15/39] Loss: 0.12851 +Epoch [3589/4000] Training [16/39] Loss: 0.00555 +Epoch [3589/4000] Training [17/39] Loss: 0.00589 +Epoch [3589/4000] Training [18/39] Loss: 0.00625 +Epoch [3589/4000] Training [19/39] Loss: 0.00424 +Epoch [3589/4000] Training [20/39] Loss: 0.00491 +Epoch [3589/4000] Training [21/39] Loss: 0.00658 +Epoch [3589/4000] Training [22/39] Loss: 0.00523 +Epoch [3589/4000] Training [23/39] Loss: 0.00356 +Epoch [3589/4000] Training [24/39] Loss: 0.12898 +Epoch [3589/4000] Training [25/39] Loss: 0.00346 +Epoch [3589/4000] Training [26/39] Loss: 0.00586 +Epoch [3589/4000] Training [27/39] Loss: 0.00726 +Epoch [3589/4000] Training [28/39] Loss: 0.00607 +Epoch [3589/4000] Training [29/39] Loss: 0.00462 +Epoch [3589/4000] Training [30/39] Loss: 0.00631 +Epoch [3589/4000] Training [31/39] Loss: 0.00918 +Epoch [3589/4000] Training [32/39] Loss: 0.00481 +Epoch [3589/4000] Training [33/39] Loss: 0.00521 +Epoch [3589/4000] Training [34/39] Loss: 0.12833 +Epoch [3589/4000] Training [35/39] Loss: 0.00537 +Epoch [3589/4000] Training [36/39] Loss: 0.00710 +Epoch [3589/4000] Training [37/39] Loss: 0.00555 +Epoch [3589/4000] Training [38/39] Loss: 0.00459 +Epoch [3589/4000] Training [39/39] Loss: 0.00525 +Epoch [3589/4000] Training metric {'Train/mean dice_metric': 0.9961379766464233, 'Train/mean miou_metric': 0.9927536249160767, 'Train/mean f1': 0.9968241453170776, 'Train/mean precision': 0.9963228702545166, 'Train/mean recall': 0.9973259568214417, 'Train/mean hd95_metric': 0.9918646812438965} +Epoch [3589/4000] Validation [1/10] Loss: 0.72427 focal_loss 0.63648 dice_loss 0.08779 +Epoch [3589/4000] Validation [2/10] Loss: 0.49103 focal_loss 0.39526 dice_loss 0.09577 +Epoch [3589/4000] Validation [3/10] Loss: 0.37136 focal_loss 0.26280 dice_loss 0.10856 +Epoch [3589/4000] Validation [4/10] Loss: 0.88650 focal_loss 0.32208 dice_loss 0.56442 +Epoch [3589/4000] Validation [5/10] Loss: 3.05856 focal_loss 2.38452 dice_loss 0.67405 +Epoch [3589/4000] Validation [6/10] Loss: 1.34228 focal_loss 0.63042 dice_loss 0.71185 +Epoch [3589/4000] Validation [7/10] Loss: 1.17769 focal_loss 0.52534 dice_loss 0.65235 +Epoch [3589/4000] Validation [8/10] Loss: 2.29876 focal_loss 1.68963 dice_loss 0.60913 +Epoch [3589/4000] Validation [9/10] Loss: 1.47138 focal_loss 0.92445 dice_loss 0.54693 +Epoch [3589/4000] Validation [10/10] Loss: 1.88005 focal_loss 1.14523 dice_loss 0.73482 +Epoch [3589/4000] Validation metric {'Val/mean dice_metric': 0.9515622854232788, 'Val/mean miou_metric': 0.9356249570846558, 'Val/mean f1': 0.9493664503097534, 'Val/mean precision': 0.9447979927062988, 'Val/mean recall': 0.9539791941642761, 'Val/mean hd95_metric': 10.56954574584961} +Cheakpoint... +Epoch [3589/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515622854232788, 'Val/mean miou_metric': 0.9356249570846558, 'Val/mean f1': 0.9493664503097534, 'Val/mean precision': 0.9447979927062988, 'Val/mean recall': 0.9539791941642761, 'Val/mean hd95_metric': 10.56954574584961} +Epoch [3590/4000] Training [1/39] Loss: 0.00386 +Epoch [3590/4000] Training [2/39] Loss: 0.12972 +Epoch [3590/4000] Training [3/39] Loss: 0.12872 +Epoch [3590/4000] Training [4/39] Loss: 0.25453 +Epoch [3590/4000] Training [5/39] Loss: 0.12852 +Epoch [3590/4000] Training [6/39] Loss: 0.00466 +Epoch [3590/4000] Training [7/39] Loss: 0.00485 +Epoch [3590/4000] Training [8/39] Loss: 0.16531 +Epoch [3590/4000] Training [9/39] Loss: 0.12938 +Epoch [3590/4000] Training [10/39] Loss: 0.00319 +Epoch [3590/4000] Training [11/39] Loss: 0.00734 +Epoch [3590/4000] Training [12/39] Loss: 0.00493 +Epoch [3590/4000] Training [13/39] Loss: 0.00375 +Epoch [3590/4000] Training [14/39] Loss: 0.00344 +Epoch [3590/4000] Training [15/39] Loss: 0.00601 +Epoch [3590/4000] Training [16/39] Loss: 0.00342 +Epoch [3590/4000] Training [17/39] Loss: 0.00361 +Epoch [3590/4000] Training [18/39] Loss: 0.00585 +Epoch [3590/4000] Training [19/39] Loss: 0.00768 +Epoch [3590/4000] Training [20/39] Loss: 0.12881 +Epoch [3590/4000] Training [21/39] Loss: 0.00501 +Epoch [3590/4000] Training [22/39] Loss: 0.00613 +Epoch [3590/4000] Training [23/39] Loss: 0.00470 +Epoch [3590/4000] Training [24/39] Loss: 0.00458 +Epoch [3590/4000] Training [25/39] Loss: 0.12872 +Epoch [3590/4000] Training [26/39] Loss: 0.00559 +Epoch [3590/4000] Training [27/39] Loss: 0.00415 +Epoch [3590/4000] Training [28/39] Loss: 0.00495 +Epoch [3590/4000] Training [29/39] Loss: 0.00514 +Epoch [3590/4000] Training [30/39] Loss: 0.00825 +Epoch [3590/4000] Training [31/39] Loss: 0.00379 +Epoch [3590/4000] Training [32/39] Loss: 0.00424 +Epoch [3590/4000] Training [33/39] Loss: 0.00612 +Epoch [3590/4000] Training [34/39] Loss: 0.00494 +Epoch [3590/4000] Training [35/39] Loss: 0.00482 +Epoch [3590/4000] Training [36/39] Loss: 0.00563 +Epoch [3590/4000] Training [37/39] Loss: 0.00350 +Epoch [3590/4000] Training [38/39] Loss: 0.00433 +Epoch [3590/4000] Training [39/39] Loss: 0.00606 +Epoch [3590/4000] Training metric {'Train/mean dice_metric': 0.9962505102157593, 'Train/mean miou_metric': 0.9929406046867371, 'Train/mean f1': 0.9968317151069641, 'Train/mean precision': 0.9963890314102173, 'Train/mean recall': 0.9972746968269348, 'Train/mean hd95_metric': 0.9572587609291077} +Epoch [3590/4000] Validation [1/10] Loss: 0.75517 focal_loss 0.66427 dice_loss 0.09090 +Epoch [3590/4000] Validation [2/10] Loss: 0.49169 focal_loss 0.39510 dice_loss 0.09659 +Epoch [3590/4000] Validation [3/10] Loss: 0.37712 focal_loss 0.26801 dice_loss 0.10911 +Epoch [3590/4000] Validation [4/10] Loss: 0.88575 focal_loss 0.32120 dice_loss 0.56455 +Epoch [3590/4000] Validation [5/10] Loss: 3.05756 focal_loss 2.38345 dice_loss 0.67411 +Epoch [3590/4000] Validation [6/10] Loss: 1.34635 focal_loss 0.63474 dice_loss 0.71161 +Epoch [3590/4000] Validation [7/10] Loss: 1.17346 focal_loss 0.51771 dice_loss 0.65575 +Epoch [3590/4000] Validation [8/10] Loss: 2.30796 focal_loss 1.69762 dice_loss 0.61035 +Epoch [3590/4000] Validation [9/10] Loss: 1.49878 focal_loss 0.95175 dice_loss 0.54703 +Epoch [3590/4000] Validation [10/10] Loss: 1.87414 focal_loss 1.14004 dice_loss 0.73410 +Epoch [3590/4000] Validation metric {'Val/mean dice_metric': 0.9514698386192322, 'Val/mean miou_metric': 0.9355262517929077, 'Val/mean f1': 0.9486036896705627, 'Val/mean precision': 0.94343501329422, 'Val/mean recall': 0.9538293480873108, 'Val/mean hd95_metric': 10.600279808044434} +Cheakpoint... +Epoch [3590/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514698386192322, 'Val/mean miou_metric': 0.9355262517929077, 'Val/mean f1': 0.9486036896705627, 'Val/mean precision': 0.94343501329422, 'Val/mean recall': 0.9538293480873108, 'Val/mean hd95_metric': 10.600279808044434} +Epoch [3591/4000] Training [1/39] Loss: 0.00541 +Epoch [3591/4000] Training [2/39] Loss: 0.00740 +Epoch [3591/4000] Training [3/39] Loss: 0.00653 +Epoch [3591/4000] Training [4/39] Loss: 0.00360 +Epoch [3591/4000] Training [5/39] Loss: 0.13088 +Epoch [3591/4000] Training [6/39] Loss: 0.00575 +Epoch [3591/4000] Training [7/39] Loss: 0.12915 +Epoch [3591/4000] Training [8/39] Loss: 0.00547 +Epoch [3591/4000] Training [9/39] Loss: 0.00429 +Epoch [3591/4000] Training [10/39] Loss: 0.00471 +Epoch [3591/4000] Training [11/39] Loss: 0.25397 +Epoch [3591/4000] Training [12/39] Loss: 0.25918 +Epoch [3591/4000] Training [13/39] Loss: 0.00716 +Epoch [3591/4000] Training [14/39] Loss: 0.00596 +Epoch [3591/4000] Training [15/39] Loss: 0.00392 +Epoch [3591/4000] Training [16/39] Loss: 0.25288 +Epoch [3591/4000] Training [17/39] Loss: 0.00457 +Epoch [3591/4000] Training [18/39] Loss: 0.00411 +Epoch [3591/4000] Training [19/39] Loss: 0.12876 +Epoch [3591/4000] Training [20/39] Loss: 0.03651 +Epoch [3591/4000] Training [21/39] Loss: 0.00624 +Epoch [3591/4000] Training [22/39] Loss: 0.00600 +Epoch [3591/4000] Training [23/39] Loss: 0.00465 +Epoch [3591/4000] Training [24/39] Loss: 0.00366 +Epoch [3591/4000] Training [25/39] Loss: 0.13027 +Epoch [3591/4000] Training [26/39] Loss: 0.12930 +Epoch [3591/4000] Training [27/39] Loss: 0.00601 +Epoch [3591/4000] Training [28/39] Loss: 0.12851 +Epoch [3591/4000] Training [29/39] Loss: 0.00636 +Epoch [3591/4000] Training [30/39] Loss: 0.00790 +Epoch [3591/4000] Training [31/39] Loss: 0.00505 +Epoch [3591/4000] Training [32/39] Loss: 0.00468 +Epoch [3591/4000] Training [33/39] Loss: 0.00686 +Epoch [3591/4000] Training [34/39] Loss: 0.00533 +Epoch [3591/4000] Training [35/39] Loss: 0.00313 +Epoch [3591/4000] Training [36/39] Loss: 0.13009 +Epoch [3591/4000] Training [37/39] Loss: 0.12960 +Epoch [3591/4000] Training [38/39] Loss: 0.00351 +Epoch [3591/4000] Training [39/39] Loss: 0.12891 +Epoch [3591/4000] Training metric {'Train/mean dice_metric': 0.9953522086143494, 'Train/mean miou_metric': 0.9919818639755249, 'Train/mean f1': 0.996752142906189, 'Train/mean precision': 0.9962866306304932, 'Train/mean recall': 0.9972180724143982, 'Train/mean hd95_metric': 0.9942396879196167} +Epoch [3591/4000] Validation [1/10] Loss: 0.74763 focal_loss 0.65800 dice_loss 0.08963 +Epoch [3591/4000] Validation [2/10] Loss: 0.48214 focal_loss 0.38849 dice_loss 0.09365 +Epoch [3591/4000] Validation [3/10] Loss: 0.38211 focal_loss 0.27272 dice_loss 0.10939 +Epoch [3591/4000] Validation [4/10] Loss: 0.89395 focal_loss 0.32998 dice_loss 0.56397 +Epoch [3591/4000] Validation [5/10] Loss: 3.05683 focal_loss 2.38304 dice_loss 0.67380 +Epoch [3591/4000] Validation [6/10] Loss: 1.34846 focal_loss 0.63672 dice_loss 0.71174 +Epoch [3591/4000] Validation [7/10] Loss: 1.18746 focal_loss 0.53310 dice_loss 0.65436 +Epoch [3591/4000] Validation [8/10] Loss: 2.31350 focal_loss 1.70379 dice_loss 0.60971 +Epoch [3591/4000] Validation [9/10] Loss: 1.58261 focal_loss 1.04078 dice_loss 0.54182 +Epoch [3591/4000] Validation [10/10] Loss: 1.91618 focal_loss 1.17908 dice_loss 0.73710 +Epoch [3591/4000] Validation metric {'Val/mean dice_metric': 0.9507066011428833, 'Val/mean miou_metric': 0.9346899390220642, 'Val/mean f1': 0.9482943415641785, 'Val/mean precision': 0.9429271817207336, 'Val/mean recall': 0.9537228345870972, 'Val/mean hd95_metric': 10.637992858886719} +Cheakpoint... +Epoch [3591/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507066011428833, 'Val/mean miou_metric': 0.9346899390220642, 'Val/mean f1': 0.9482943415641785, 'Val/mean precision': 0.9429271817207336, 'Val/mean recall': 0.9537228345870972, 'Val/mean hd95_metric': 10.637992858886719} +Epoch [3592/4000] Training [1/39] Loss: 0.00627 +Epoch [3592/4000] Training [2/39] Loss: 0.00705 +Epoch [3592/4000] Training [3/39] Loss: 0.13261 +Epoch [3592/4000] Training [4/39] Loss: 0.00613 +Epoch [3592/4000] Training [5/39] Loss: 0.25324 +Epoch [3592/4000] Training [6/39] Loss: 0.00433 +Epoch [3592/4000] Training [7/39] Loss: 0.12856 +Epoch [3592/4000] Training [8/39] Loss: 0.12914 +Epoch [3592/4000] Training [9/39] Loss: 0.00367 +Epoch [3592/4000] Training [10/39] Loss: 0.00412 +Epoch [3592/4000] Training [11/39] Loss: 0.00518 +Epoch [3592/4000] Training [12/39] Loss: 0.00506 +Epoch [3592/4000] Training [13/39] Loss: 0.00914 +Epoch [3592/4000] Training [14/39] Loss: 0.20533 +Epoch [3592/4000] Training [15/39] Loss: 0.13064 +Epoch [3592/4000] Training [16/39] Loss: 0.00798 +Epoch [3592/4000] Training [17/39] Loss: 0.37844 +Epoch [3592/4000] Training [18/39] Loss: 0.00407 +Epoch [3592/4000] Training [19/39] Loss: 0.00337 +Epoch [3592/4000] Training [20/39] Loss: 0.00469 +Epoch [3592/4000] Training [21/39] Loss: 0.12824 +Epoch [3592/4000] Training [22/39] Loss: 0.01322 +Epoch [3592/4000] Training [23/39] Loss: 0.00460 +Epoch [3592/4000] Training [24/39] Loss: 0.13042 +Epoch [3592/4000] Training [25/39] Loss: 0.00697 +Epoch [3592/4000] Training [26/39] Loss: 0.00399 +Epoch [3592/4000] Training [27/39] Loss: 0.00480 +Epoch [3592/4000] Training [28/39] Loss: 0.12947 +Epoch [3592/4000] Training [29/39] Loss: 0.00500 +Epoch [3592/4000] Training [30/39] Loss: 0.00383 +Epoch [3592/4000] Training [31/39] Loss: 0.00407 +Epoch [3592/4000] Training [32/39] Loss: 0.00475 +Epoch [3592/4000] Training [33/39] Loss: 0.00571 +Epoch [3592/4000] Training [34/39] Loss: 0.00353 +Epoch [3592/4000] Training [35/39] Loss: 0.12960 +Epoch [3592/4000] Training [36/39] Loss: 0.00452 +Epoch [3592/4000] Training [37/39] Loss: 0.00507 +Epoch [3592/4000] Training [38/39] Loss: 0.00343 +Epoch [3592/4000] Training [39/39] Loss: 0.00617 +Epoch [3592/4000] Training metric {'Train/mean dice_metric': 0.9960141777992249, 'Train/mean miou_metric': 0.9924737811088562, 'Train/mean f1': 0.9967437386512756, 'Train/mean precision': 0.9963001012802124, 'Train/mean recall': 0.9971879124641418, 'Train/mean hd95_metric': 0.9851841330528259} +Epoch [3592/4000] Validation [1/10] Loss: 0.74968 focal_loss 0.65766 dice_loss 0.09203 +Epoch [3592/4000] Validation [2/10] Loss: 0.46890 focal_loss 0.37867 dice_loss 0.09023 +Epoch [3592/4000] Validation [3/10] Loss: 0.36485 focal_loss 0.25648 dice_loss 0.10837 +Epoch [3592/4000] Validation [4/10] Loss: 0.91023 focal_loss 0.34288 dice_loss 0.56735 +Epoch [3592/4000] Validation [5/10] Loss: 3.01143 focal_loss 2.33814 dice_loss 0.67329 +Epoch [3592/4000] Validation [6/10] Loss: 1.35625 focal_loss 0.64630 dice_loss 0.70995 +Epoch [3592/4000] Validation [7/10] Loss: 1.19563 focal_loss 0.53773 dice_loss 0.65791 +Epoch [3592/4000] Validation [8/10] Loss: 2.12006 focal_loss 1.52891 dice_loss 0.59115 +Epoch [3592/4000] Validation [9/10] Loss: 1.59150 focal_loss 1.04688 dice_loss 0.54461 +Epoch [3592/4000] Validation [10/10] Loss: 1.94519 focal_loss 1.20455 dice_loss 0.74064 +Epoch [3592/4000] Validation metric {'Val/mean dice_metric': 0.951232373714447, 'Val/mean miou_metric': 0.935045063495636, 'Val/mean f1': 0.9472228288650513, 'Val/mean precision': 0.9393066167831421, 'Val/mean recall': 0.9552736878395081, 'Val/mean hd95_metric': 10.75057601928711} +Cheakpoint... +Epoch [3592/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951232373714447, 'Val/mean miou_metric': 0.935045063495636, 'Val/mean f1': 0.9472228288650513, 'Val/mean precision': 0.9393066167831421, 'Val/mean recall': 0.9552736878395081, 'Val/mean hd95_metric': 10.75057601928711} +Epoch [3593/4000] Training [1/39] Loss: 0.12997 +Epoch [3593/4000] Training [2/39] Loss: 0.00439 +Epoch [3593/4000] Training [3/39] Loss: 0.00413 +Epoch [3593/4000] Training [4/39] Loss: 0.00873 +Epoch [3593/4000] Training [5/39] Loss: 0.00515 +Epoch [3593/4000] Training [6/39] Loss: 0.00522 +Epoch [3593/4000] Training [7/39] Loss: 0.00815 +Epoch [3593/4000] Training [8/39] Loss: 0.00714 +Epoch [3593/4000] Training [9/39] Loss: 0.01029 +Epoch [3593/4000] Training [10/39] Loss: 0.00439 +Epoch [3593/4000] Training [11/39] Loss: 0.12959 +Epoch [3593/4000] Training [12/39] Loss: 0.13123 +Epoch [3593/4000] Training [13/39] Loss: 0.00566 +Epoch [3593/4000] Training [14/39] Loss: 0.00554 +Epoch [3593/4000] Training [15/39] Loss: 0.00450 +Epoch [3593/4000] Training [16/39] Loss: 0.25633 +Epoch [3593/4000] Training [17/39] Loss: 0.00318 +Epoch [3593/4000] Training [18/39] Loss: 0.00627 +Epoch [3593/4000] Training [19/39] Loss: 0.12908 +Epoch [3593/4000] Training [20/39] Loss: 0.00525 +Epoch [3593/4000] Training [21/39] Loss: 0.12822 +Epoch [3593/4000] Training [22/39] Loss: 0.12719 +Epoch [3593/4000] Training [23/39] Loss: 0.00520 +Epoch [3593/4000] Training [24/39] Loss: 0.00448 +Epoch [3593/4000] Training [25/39] Loss: 0.00713 +Epoch [3593/4000] Training [26/39] Loss: 0.00430 +Epoch [3593/4000] Training [27/39] Loss: 0.00359 +Epoch [3593/4000] Training [28/39] Loss: 0.00369 +Epoch [3593/4000] Training [29/39] Loss: 0.00371 +Epoch [3593/4000] Training [30/39] Loss: 0.00568 +Epoch [3593/4000] Training [31/39] Loss: 0.13201 +Epoch [3593/4000] Training [32/39] Loss: 0.00543 +Epoch [3593/4000] Training [33/39] Loss: 0.00616 +Epoch [3593/4000] Training [34/39] Loss: 0.00952 +Epoch [3593/4000] Training [35/39] Loss: 0.00308 +Epoch [3593/4000] Training [36/39] Loss: 0.00610 +Epoch [3593/4000] Training [37/39] Loss: 0.00927 +Epoch [3593/4000] Training [38/39] Loss: 0.00437 +Epoch [3593/4000] Training [39/39] Loss: 0.00385 +Epoch [3593/4000] Training metric {'Train/mean dice_metric': 0.9957644939422607, 'Train/mean miou_metric': 0.9920487999916077, 'Train/mean f1': 0.9964436888694763, 'Train/mean precision': 0.9959142804145813, 'Train/mean recall': 0.9969736933708191, 'Train/mean hd95_metric': 1.3116101026535034} +Epoch [3593/4000] Validation [1/10] Loss: 0.73223 focal_loss 0.64336 dice_loss 0.08886 +Epoch [3593/4000] Validation [2/10] Loss: 0.47641 focal_loss 0.38259 dice_loss 0.09382 +Epoch [3593/4000] Validation [3/10] Loss: 0.38504 focal_loss 0.27481 dice_loss 0.11023 +Epoch [3593/4000] Validation [4/10] Loss: 0.88034 focal_loss 0.31583 dice_loss 0.56451 +Epoch [3593/4000] Validation [5/10] Loss: 3.01910 focal_loss 2.34482 dice_loss 0.67428 +Epoch [3593/4000] Validation [6/10] Loss: 1.32179 focal_loss 0.61086 dice_loss 0.71093 +Epoch [3593/4000] Validation [7/10] Loss: 1.16170 focal_loss 0.51127 dice_loss 0.65042 +Epoch [3593/4000] Validation [8/10] Loss: 2.39485 focal_loss 1.77365 dice_loss 0.62120 +Epoch [3593/4000] Validation [9/10] Loss: 1.51595 focal_loss 0.96877 dice_loss 0.54718 +Epoch [3593/4000] Validation [10/10] Loss: 1.85586 focal_loss 1.12209 dice_loss 0.73377 +Epoch [3593/4000] Validation metric {'Val/mean dice_metric': 0.9512726068496704, 'Val/mean miou_metric': 0.9350076913833618, 'Val/mean f1': 0.9484423995018005, 'Val/mean precision': 0.9446843266487122, 'Val/mean recall': 0.9522305130958557, 'Val/mean hd95_metric': 10.895052909851074} +Cheakpoint... +Epoch [3593/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512726068496704, 'Val/mean miou_metric': 0.9350076913833618, 'Val/mean f1': 0.9484423995018005, 'Val/mean precision': 0.9446843266487122, 'Val/mean recall': 0.9522305130958557, 'Val/mean hd95_metric': 10.895052909851074} +Epoch [3594/4000] Training [1/39] Loss: 0.13053 +Epoch [3594/4000] Training [2/39] Loss: 0.00353 +Epoch [3594/4000] Training [3/39] Loss: 0.25486 +Epoch [3594/4000] Training [4/39] Loss: 0.00496 +Epoch [3594/4000] Training [5/39] Loss: 0.13173 +Epoch [3594/4000] Training [6/39] Loss: 0.00447 +Epoch [3594/4000] Training [7/39] Loss: 0.00492 +Epoch [3594/4000] Training [8/39] Loss: 0.00430 +Epoch [3594/4000] Training [9/39] Loss: 0.00523 +Epoch [3594/4000] Training [10/39] Loss: 0.00341 +Epoch [3594/4000] Training [11/39] Loss: 0.00346 +Epoch [3594/4000] Training [12/39] Loss: 0.13194 +Epoch [3594/4000] Training [13/39] Loss: 0.00343 +Epoch [3594/4000] Training [14/39] Loss: 0.12874 +Epoch [3594/4000] Training [15/39] Loss: 0.00444 +Epoch [3594/4000] Training [16/39] Loss: 0.00434 +Epoch [3594/4000] Training [17/39] Loss: 0.00374 +Epoch [3594/4000] Training [18/39] Loss: 0.12861 +Epoch [3594/4000] Training [19/39] Loss: 0.00632 +Epoch [3594/4000] Training [20/39] Loss: 0.12798 +Epoch [3594/4000] Training [21/39] Loss: 0.00385 +Epoch [3594/4000] Training [22/39] Loss: 0.25248 +Epoch [3594/4000] Training [23/39] Loss: 0.00652 +Epoch [3594/4000] Training [24/39] Loss: 0.00605 +Epoch [3594/4000] Training [25/39] Loss: 0.00412 +Epoch [3594/4000] Training [26/39] Loss: 0.00615 +Epoch [3594/4000] Training [27/39] Loss: 0.12995 +Epoch [3594/4000] Training [28/39] Loss: 0.00584 +Epoch [3594/4000] Training [29/39] Loss: 0.13070 +Epoch [3594/4000] Training [30/39] Loss: 0.00582 +Epoch [3594/4000] Training [31/39] Loss: 0.00736 +Epoch [3594/4000] Training [32/39] Loss: 0.00609 +Epoch [3594/4000] Training [33/39] Loss: 0.00671 +Epoch [3594/4000] Training [34/39] Loss: 0.00508 +Epoch [3594/4000] Training [35/39] Loss: 0.00384 +Epoch [3594/4000] Training [36/39] Loss: 0.13089 +Epoch [3594/4000] Training [37/39] Loss: 0.12939 +Epoch [3594/4000] Training [38/39] Loss: 0.00501 +Epoch [3594/4000] Training [39/39] Loss: 0.00420 +Epoch [3594/4000] Training metric {'Train/mean dice_metric': 0.9962173700332642, 'Train/mean miou_metric': 0.9929059147834778, 'Train/mean f1': 0.9968025088310242, 'Train/mean precision': 0.9963569045066833, 'Train/mean recall': 0.9972484111785889, 'Train/mean hd95_metric': 1.0393421649932861} +Epoch [3594/4000] Validation [1/10] Loss: 0.74489 focal_loss 0.65420 dice_loss 0.09068 +Epoch [3594/4000] Validation [2/10] Loss: 0.47374 focal_loss 0.38162 dice_loss 0.09212 +Epoch [3594/4000] Validation [3/10] Loss: 0.38586 focal_loss 0.27540 dice_loss 0.11046 +Epoch [3594/4000] Validation [4/10] Loss: 0.88384 focal_loss 0.31934 dice_loss 0.56450 +Epoch [3594/4000] Validation [5/10] Loss: 3.04209 focal_loss 2.36839 dice_loss 0.67370 +Epoch [3594/4000] Validation [6/10] Loss: 1.33631 focal_loss 0.62092 dice_loss 0.71538 +Epoch [3594/4000] Validation [7/10] Loss: 1.17677 focal_loss 0.52667 dice_loss 0.65010 +Epoch [3594/4000] Validation [8/10] Loss: 2.34171 focal_loss 1.72523 dice_loss 0.61648 +Epoch [3594/4000] Validation [9/10] Loss: 1.51512 focal_loss 0.96840 dice_loss 0.54672 +Epoch [3594/4000] Validation [10/10] Loss: 1.88194 focal_loss 1.14810 dice_loss 0.73383 +Epoch [3594/4000] Validation metric {'Val/mean dice_metric': 0.9515582323074341, 'Val/mean miou_metric': 0.9356751441955566, 'Val/mean f1': 0.9480384588241577, 'Val/mean precision': 0.9428813457489014, 'Val/mean recall': 0.9532521963119507, 'Val/mean hd95_metric': 10.717463493347168} +Cheakpoint... +Epoch [3594/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515582323074341, 'Val/mean miou_metric': 0.9356751441955566, 'Val/mean f1': 0.9480384588241577, 'Val/mean precision': 0.9428813457489014, 'Val/mean recall': 0.9532521963119507, 'Val/mean hd95_metric': 10.717463493347168} +Epoch [3595/4000] Training [1/39] Loss: 0.01133 +Epoch [3595/4000] Training [2/39] Loss: 0.13139 +Epoch [3595/4000] Training [3/39] Loss: 0.00645 +Epoch [3595/4000] Training [4/39] Loss: 0.00394 +Epoch [3595/4000] Training [5/39] Loss: 0.00344 +Epoch [3595/4000] Training [6/39] Loss: 0.12858 +Epoch [3595/4000] Training [7/39] Loss: 0.12858 +Epoch [3595/4000] Training [8/39] Loss: 0.00478 +Epoch [3595/4000] Training [9/39] Loss: 0.12952 +Epoch [3595/4000] Training [10/39] Loss: 0.00672 +Epoch [3595/4000] Training [11/39] Loss: 0.00589 +Epoch [3595/4000] Training [12/39] Loss: 0.00645 +Epoch [3595/4000] Training [13/39] Loss: 0.00584 +Epoch [3595/4000] Training [14/39] Loss: 0.00470 +Epoch [3595/4000] Training [15/39] Loss: 0.00624 +Epoch [3595/4000] Training [16/39] Loss: 0.00418 +Epoch [3595/4000] Training [17/39] Loss: 0.00423 +Epoch [3595/4000] Training [18/39] Loss: 0.00576 +Epoch [3595/4000] Training [19/39] Loss: 0.00556 +Epoch [3595/4000] Training [20/39] Loss: 0.13120 +Epoch [3595/4000] Training [21/39] Loss: 0.00486 +Epoch [3595/4000] Training [22/39] Loss: 0.00329 +Epoch [3595/4000] Training [23/39] Loss: 0.13100 +Epoch [3595/4000] Training [24/39] Loss: 0.00563 +Epoch [3595/4000] Training [25/39] Loss: 0.00490 +Epoch [3595/4000] Training [26/39] Loss: 0.25236 +Epoch [3595/4000] Training [27/39] Loss: 0.00535 +Epoch [3595/4000] Training [28/39] Loss: 0.25385 +Epoch [3595/4000] Training [29/39] Loss: 0.00385 +Epoch [3595/4000] Training [30/39] Loss: 0.00678 +Epoch [3595/4000] Training [31/39] Loss: 0.00421 +Epoch [3595/4000] Training [32/39] Loss: 0.12860 +Epoch [3595/4000] Training [33/39] Loss: 0.13093 +Epoch [3595/4000] Training [34/39] Loss: 0.00724 +Epoch [3595/4000] Training [35/39] Loss: 0.00806 +Epoch [3595/4000] Training [36/39] Loss: 0.00708 +Epoch [3595/4000] Training [37/39] Loss: 0.00459 +Epoch [3595/4000] Training [38/39] Loss: 0.00647 +Epoch [3595/4000] Training [39/39] Loss: 0.12934 +Epoch [3595/4000] Training metric {'Train/mean dice_metric': 0.9950350522994995, 'Train/mean miou_metric': 0.9913515448570251, 'Train/mean f1': 0.9964621067047119, 'Train/mean precision': 0.9960238933563232, 'Train/mean recall': 0.9969008564949036, 'Train/mean hd95_metric': 0.982270359992981} +Epoch [3595/4000] Validation [1/10] Loss: 0.72048 focal_loss 0.63186 dice_loss 0.08862 +Epoch [3595/4000] Validation [2/10] Loss: 0.47443 focal_loss 0.38395 dice_loss 0.09048 +Epoch [3595/4000] Validation [3/10] Loss: 0.36039 focal_loss 0.25200 dice_loss 0.10839 +Epoch [3595/4000] Validation [4/10] Loss: 0.88711 focal_loss 0.32270 dice_loss 0.56440 +Epoch [3595/4000] Validation [5/10] Loss: 2.99876 focal_loss 2.32525 dice_loss 0.67351 +Epoch [3595/4000] Validation [6/10] Loss: 1.34630 focal_loss 0.62871 dice_loss 0.71759 +Epoch [3595/4000] Validation [7/10] Loss: 1.17892 focal_loss 0.52856 dice_loss 0.65037 +Epoch [3595/4000] Validation [8/10] Loss: 2.31183 focal_loss 1.69853 dice_loss 0.61330 +Epoch [3595/4000] Validation [9/10] Loss: 1.49482 focal_loss 0.94669 dice_loss 0.54814 +Epoch [3595/4000] Validation [10/10] Loss: 1.91169 focal_loss 1.17581 dice_loss 0.73587 +Epoch [3595/4000] Validation metric {'Val/mean dice_metric': 0.9506093263626099, 'Val/mean miou_metric': 0.9344645738601685, 'Val/mean f1': 0.9485435485839844, 'Val/mean precision': 0.9430875778198242, 'Val/mean recall': 0.9540628790855408, 'Val/mean hd95_metric': 10.636492729187012} +Cheakpoint... +Epoch [3595/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506093263626099, 'Val/mean miou_metric': 0.9344645738601685, 'Val/mean f1': 0.9485435485839844, 'Val/mean precision': 0.9430875778198242, 'Val/mean recall': 0.9540628790855408, 'Val/mean hd95_metric': 10.636492729187012} +Epoch [3596/4000] Training [1/39] Loss: 0.00584 +Epoch [3596/4000] Training [2/39] Loss: 0.13256 +Epoch [3596/4000] Training [3/39] Loss: 0.13085 +Epoch [3596/4000] Training [4/39] Loss: 0.00782 +Epoch [3596/4000] Training [5/39] Loss: 0.00484 +Epoch [3596/4000] Training [6/39] Loss: 0.00419 +Epoch [3596/4000] Training [7/39] Loss: 0.00502 +Epoch [3596/4000] Training [8/39] Loss: 0.00371 +Epoch [3596/4000] Training [9/39] Loss: 0.00407 +Epoch [3596/4000] Training [10/39] Loss: 0.00412 +Epoch [3596/4000] Training [11/39] Loss: 0.12873 +Epoch [3596/4000] Training [12/39] Loss: 0.00623 +Epoch [3596/4000] Training [13/39] Loss: 0.00309 +Epoch [3596/4000] Training [14/39] Loss: 0.00377 +Epoch [3596/4000] Training [15/39] Loss: 0.00570 +Epoch [3596/4000] Training [16/39] Loss: 0.00475 +Epoch [3596/4000] Training [17/39] Loss: 0.00452 +Epoch [3596/4000] Training [18/39] Loss: 0.00338 +Epoch [3596/4000] Training [19/39] Loss: 0.00431 +Epoch [3596/4000] Training [20/39] Loss: 0.12783 +Epoch [3596/4000] Training [21/39] Loss: 0.00334 +Epoch [3596/4000] Training [22/39] Loss: 0.00409 +Epoch [3596/4000] Training [23/39] Loss: 0.00528 +Epoch [3596/4000] Training [24/39] Loss: 0.13071 +Epoch [3596/4000] Training [25/39] Loss: 0.00910 +Epoch [3596/4000] Training [26/39] Loss: 0.00644 +Epoch [3596/4000] Training [27/39] Loss: 0.00260 +Epoch [3596/4000] Training [28/39] Loss: 0.29516 +Epoch [3596/4000] Training [29/39] Loss: 0.00758 +Epoch [3596/4000] Training [30/39] Loss: 0.00387 +Epoch [3596/4000] Training [31/39] Loss: 0.00656 +Epoch [3596/4000] Training [32/39] Loss: 0.00484 +Epoch [3596/4000] Training [33/39] Loss: 0.12787 +Epoch [3596/4000] Training [34/39] Loss: 0.08044 +Epoch [3596/4000] Training [35/39] Loss: 0.00405 +Epoch [3596/4000] Training [36/39] Loss: 0.00461 +Epoch [3596/4000] Training [37/39] Loss: 0.00387 +Epoch [3596/4000] Training [38/39] Loss: 0.00578 +Epoch [3596/4000] Training [39/39] Loss: 0.00378 +Epoch [3596/4000] Training metric {'Train/mean dice_metric': 0.9953873753547668, 'Train/mean miou_metric': 0.9920398592948914, 'Train/mean f1': 0.9967642426490784, 'Train/mean precision': 0.9962978959083557, 'Train/mean recall': 0.997231125831604, 'Train/mean hd95_metric': 0.9630298614501953} +Epoch [3596/4000] Validation [1/10] Loss: 0.72573 focal_loss 0.63725 dice_loss 0.08848 +Epoch [3596/4000] Validation [2/10] Loss: 0.48156 focal_loss 0.38613 dice_loss 0.09543 +Epoch [3596/4000] Validation [3/10] Loss: 0.37011 focal_loss 0.26079 dice_loss 0.10932 +Epoch [3596/4000] Validation [4/10] Loss: 0.89031 focal_loss 0.32542 dice_loss 0.56489 +Epoch [3596/4000] Validation [5/10] Loss: 3.00894 focal_loss 2.33492 dice_loss 0.67401 +Epoch [3596/4000] Validation [6/10] Loss: 1.32459 focal_loss 0.60989 dice_loss 0.71470 +Epoch [3596/4000] Validation [7/10] Loss: 1.16770 focal_loss 0.51646 dice_loss 0.65124 +Epoch [3596/4000] Validation [8/10] Loss: 2.29778 focal_loss 1.68293 dice_loss 0.61485 +Epoch [3596/4000] Validation [9/10] Loss: 1.48065 focal_loss 0.93624 dice_loss 0.54440 +Epoch [3596/4000] Validation [10/10] Loss: 1.87603 focal_loss 1.14035 dice_loss 0.73569 +Epoch [3596/4000] Validation metric {'Val/mean dice_metric': 0.950909435749054, 'Val/mean miou_metric': 0.9350041151046753, 'Val/mean f1': 0.9483252167701721, 'Val/mean precision': 0.9432365894317627, 'Val/mean recall': 0.9534690976142883, 'Val/mean hd95_metric': 10.782203674316406} +Cheakpoint... +Epoch [3596/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950909435749054, 'Val/mean miou_metric': 0.9350041151046753, 'Val/mean f1': 0.9483252167701721, 'Val/mean precision': 0.9432365894317627, 'Val/mean recall': 0.9534690976142883, 'Val/mean hd95_metric': 10.782203674316406} +Epoch [3597/4000] Training [1/39] Loss: 0.00473 +Epoch [3597/4000] Training [2/39] Loss: 0.12885 +Epoch [3597/4000] Training [3/39] Loss: 0.00522 +Epoch [3597/4000] Training [4/39] Loss: 0.00483 +Epoch [3597/4000] Training [5/39] Loss: 0.12960 +Epoch [3597/4000] Training [6/39] Loss: 0.12935 +Epoch [3597/4000] Training [7/39] Loss: 0.00412 +Epoch [3597/4000] Training [8/39] Loss: 0.00459 +Epoch [3597/4000] Training [9/39] Loss: 0.00361 +Epoch [3597/4000] Training [10/39] Loss: 0.00554 +Epoch [3597/4000] Training [11/39] Loss: 0.00823 +Epoch [3597/4000] Training [12/39] Loss: 0.00500 +Epoch [3597/4000] Training [13/39] Loss: 0.00472 +Epoch [3597/4000] Training [14/39] Loss: 0.12936 +Epoch [3597/4000] Training [15/39] Loss: 0.00677 +Epoch [3597/4000] Training [16/39] Loss: 0.00717 +Epoch [3597/4000] Training [17/39] Loss: 0.00567 +Epoch [3597/4000] Training [18/39] Loss: 0.12982 +Epoch [3597/4000] Training [19/39] Loss: 0.12989 +Epoch [3597/4000] Training [20/39] Loss: 0.00503 +Epoch [3597/4000] Training [21/39] Loss: 0.00524 +Epoch [3597/4000] Training [22/39] Loss: 0.00373 +Epoch [3597/4000] Training [23/39] Loss: 0.00375 +Epoch [3597/4000] Training [24/39] Loss: 0.12799 +Epoch [3597/4000] Training [25/39] Loss: 0.00397 +Epoch [3597/4000] Training [26/39] Loss: 0.13076 +Epoch [3597/4000] Training [27/39] Loss: 0.00822 +Epoch [3597/4000] Training [28/39] Loss: 0.00586 +Epoch [3597/4000] Training [29/39] Loss: 0.25356 +Epoch [3597/4000] Training [30/39] Loss: 0.12977 +Epoch [3597/4000] Training [31/39] Loss: 0.00394 +Epoch [3597/4000] Training [32/39] Loss: 0.01070 +Epoch [3597/4000] Training [33/39] Loss: 0.01167 +Epoch [3597/4000] Training [34/39] Loss: 0.00637 +Epoch [3597/4000] Training [35/39] Loss: 0.00499 +Epoch [3597/4000] Training [36/39] Loss: 0.00439 +Epoch [3597/4000] Training [37/39] Loss: 0.00416 +Epoch [3597/4000] Training [38/39] Loss: 0.12835 +Epoch [3597/4000] Training [39/39] Loss: 0.00380 +Epoch [3597/4000] Training metric {'Train/mean dice_metric': 0.9959056377410889, 'Train/mean miou_metric': 0.9922998547554016, 'Train/mean f1': 0.996630847454071, 'Train/mean precision': 0.9961693286895752, 'Train/mean recall': 0.9970928430557251, 'Train/mean hd95_metric': 1.1738466024398804} +Epoch [3597/4000] Validation [1/10] Loss: 0.71626 focal_loss 0.62895 dice_loss 0.08732 +Epoch [3597/4000] Validation [2/10] Loss: 0.48059 focal_loss 0.38599 dice_loss 0.09460 +Epoch [3597/4000] Validation [3/10] Loss: 0.37173 focal_loss 0.26225 dice_loss 0.10948 +Epoch [3597/4000] Validation [4/10] Loss: 0.88431 focal_loss 0.31916 dice_loss 0.56515 +Epoch [3597/4000] Validation [5/10] Loss: 3.01299 focal_loss 2.33856 dice_loss 0.67443 +Epoch [3597/4000] Validation [6/10] Loss: 1.33262 focal_loss 0.61922 dice_loss 0.71340 +Epoch [3597/4000] Validation [7/10] Loss: 1.17315 focal_loss 0.52012 dice_loss 0.65303 +Epoch [3597/4000] Validation [8/10] Loss: 2.32711 focal_loss 1.71225 dice_loss 0.61486 +Epoch [3597/4000] Validation [9/10] Loss: 1.47968 focal_loss 0.93234 dice_loss 0.54735 +Epoch [3597/4000] Validation [10/10] Loss: 1.85617 focal_loss 1.12243 dice_loss 0.73374 +Epoch [3597/4000] Validation metric {'Val/mean dice_metric': 0.9513471722602844, 'Val/mean miou_metric': 0.9351907968521118, 'Val/mean f1': 0.948563277721405, 'Val/mean precision': 0.9440800547599792, 'Val/mean recall': 0.9530892968177795, 'Val/mean hd95_metric': 10.769793510437012} +Cheakpoint... +Epoch [3597/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513471722602844, 'Val/mean miou_metric': 0.9351907968521118, 'Val/mean f1': 0.948563277721405, 'Val/mean precision': 0.9440800547599792, 'Val/mean recall': 0.9530892968177795, 'Val/mean hd95_metric': 10.769793510437012} +Epoch [3598/4000] Training [1/39] Loss: 0.00437 +Epoch [3598/4000] Training [2/39] Loss: 0.00682 +Epoch [3598/4000] Training [3/39] Loss: 0.00288 +Epoch [3598/4000] Training [4/39] Loss: 0.00655 +Epoch [3598/4000] Training [5/39] Loss: 0.00806 +Epoch [3598/4000] Training [6/39] Loss: 0.00443 +Epoch [3598/4000] Training [7/39] Loss: 0.00626 +Epoch [3598/4000] Training [8/39] Loss: 0.00373 +Epoch [3598/4000] Training [9/39] Loss: 0.12948 +Epoch [3598/4000] Training [10/39] Loss: 0.00480 +Epoch [3598/4000] Training [11/39] Loss: 0.00532 +Epoch [3598/4000] Training [12/39] Loss: 0.25573 +Epoch [3598/4000] Training [13/39] Loss: 0.20781 +Epoch [3598/4000] Training [14/39] Loss: 0.00360 +Epoch [3598/4000] Training [15/39] Loss: 0.00532 +Epoch [3598/4000] Training [16/39] Loss: 0.00621 +Epoch [3598/4000] Training [17/39] Loss: 0.00423 +Epoch [3598/4000] Training [18/39] Loss: 0.12759 +Epoch [3598/4000] Training [19/39] Loss: 0.12879 +Epoch [3598/4000] Training [20/39] Loss: 0.00798 +Epoch [3598/4000] Training [21/39] Loss: 0.00394 +Epoch [3598/4000] Training [22/39] Loss: 0.00394 +Epoch [3598/4000] Training [23/39] Loss: 0.00526 +Epoch [3598/4000] Training [24/39] Loss: 0.01291 +Epoch [3598/4000] Training [25/39] Loss: 0.25160 +Epoch [3598/4000] Training [26/39] Loss: 0.00487 +Epoch [3598/4000] Training [27/39] Loss: 0.25412 +Epoch [3598/4000] Training [28/39] Loss: 0.00388 +Epoch [3598/4000] Training [29/39] Loss: 0.00390 +Epoch [3598/4000] Training [30/39] Loss: 0.00405 +Epoch [3598/4000] Training [31/39] Loss: 0.13065 +Epoch [3598/4000] Training [32/39] Loss: 0.00482 +Epoch [3598/4000] Training [33/39] Loss: 0.00607 +Epoch [3598/4000] Training [34/39] Loss: 0.00626 +Epoch [3598/4000] Training [35/39] Loss: 0.00776 +Epoch [3598/4000] Training [36/39] Loss: 0.00473 +Epoch [3598/4000] Training [37/39] Loss: 0.00416 +Epoch [3598/4000] Training [38/39] Loss: 0.00411 +Epoch [3598/4000] Training [39/39] Loss: 0.12858 +Epoch [3598/4000] Training metric {'Train/mean dice_metric': 0.9961961507797241, 'Train/mean miou_metric': 0.992837131023407, 'Train/mean f1': 0.9968538880348206, 'Train/mean precision': 0.9964306950569153, 'Train/mean recall': 0.997277557849884, 'Train/mean hd95_metric': 1.0458073616027832} +Epoch [3598/4000] Validation [1/10] Loss: 0.73390 focal_loss 0.64654 dice_loss 0.08736 +Epoch [3598/4000] Validation [2/10] Loss: 0.48659 focal_loss 0.39110 dice_loss 0.09549 +Epoch [3598/4000] Validation [3/10] Loss: 0.38517 focal_loss 0.27519 dice_loss 0.10998 +Epoch [3598/4000] Validation [4/10] Loss: 0.89487 focal_loss 0.32955 dice_loss 0.56532 +Epoch [3598/4000] Validation [5/10] Loss: 3.07982 focal_loss 2.40577 dice_loss 0.67404 +Epoch [3598/4000] Validation [6/10] Loss: 1.33872 focal_loss 0.62627 dice_loss 0.71244 +Epoch [3598/4000] Validation [7/10] Loss: 1.17800 focal_loss 0.52727 dice_loss 0.65073 +Epoch [3598/4000] Validation [8/10] Loss: 2.28031 focal_loss 1.67352 dice_loss 0.60678 +Epoch [3598/4000] Validation [9/10] Loss: 1.57082 focal_loss 1.02493 dice_loss 0.54589 +Epoch [3598/4000] Validation [10/10] Loss: 1.90245 focal_loss 1.16571 dice_loss 0.73674 +Epoch [3598/4000] Validation metric {'Val/mean dice_metric': 0.9515889883041382, 'Val/mean miou_metric': 0.9356631636619568, 'Val/mean f1': 0.948139488697052, 'Val/mean precision': 0.9425593018531799, 'Val/mean recall': 0.9537861347198486, 'Val/mean hd95_metric': 10.708353996276855} +Cheakpoint... +Epoch [3598/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515889883041382, 'Val/mean miou_metric': 0.9356631636619568, 'Val/mean f1': 0.948139488697052, 'Val/mean precision': 0.9425593018531799, 'Val/mean recall': 0.9537861347198486, 'Val/mean hd95_metric': 10.708353996276855} +Epoch [3599/4000] Training [1/39] Loss: 0.00507 +Epoch [3599/4000] Training [2/39] Loss: 0.12933 +Epoch [3599/4000] Training [3/39] Loss: 0.12979 +Epoch [3599/4000] Training [4/39] Loss: 0.00597 +Epoch [3599/4000] Training [5/39] Loss: 0.00488 +Epoch [3599/4000] Training [6/39] Loss: 0.00517 +Epoch [3599/4000] Training [7/39] Loss: 0.00351 +Epoch [3599/4000] Training [8/39] Loss: 0.00433 +Epoch [3599/4000] Training [9/39] Loss: 0.00458 +Epoch [3599/4000] Training [10/39] Loss: 0.00500 +Epoch [3599/4000] Training [11/39] Loss: 0.00564 +Epoch [3599/4000] Training [12/39] Loss: 0.00624 +Epoch [3599/4000] Training [13/39] Loss: 0.00384 +Epoch [3599/4000] Training [14/39] Loss: 0.00512 +Epoch [3599/4000] Training [15/39] Loss: 0.00333 +Epoch [3599/4000] Training [16/39] Loss: 0.00663 +Epoch [3599/4000] Training [17/39] Loss: 0.00424 +Epoch [3599/4000] Training [18/39] Loss: 0.00266 +Epoch [3599/4000] Training [19/39] Loss: 0.13122 +Epoch [3599/4000] Training [20/39] Loss: 0.00477 +Epoch [3599/4000] Training [21/39] Loss: 0.00436 +Epoch [3599/4000] Training [22/39] Loss: 0.12950 +Epoch [3599/4000] Training [23/39] Loss: 0.00412 +Epoch [3599/4000] Training [24/39] Loss: 0.12804 +Epoch [3599/4000] Training [25/39] Loss: 0.00439 +Epoch [3599/4000] Training [26/39] Loss: 0.00562 +Epoch [3599/4000] Training [27/39] Loss: 0.00477 +Epoch [3599/4000] Training [28/39] Loss: 0.13216 +Epoch [3599/4000] Training [29/39] Loss: 0.00726 +Epoch [3599/4000] Training [30/39] Loss: 0.00530 +Epoch [3599/4000] Training [31/39] Loss: 0.00437 +Epoch [3599/4000] Training [32/39] Loss: 0.12971 +Epoch [3599/4000] Training [33/39] Loss: 0.13019 +Epoch [3599/4000] Training [34/39] Loss: 0.00351 +Epoch [3599/4000] Training [35/39] Loss: 0.12993 +Epoch [3599/4000] Training [36/39] Loss: 0.00507 +Epoch [3599/4000] Training [37/39] Loss: 0.00339 +Epoch [3599/4000] Training [38/39] Loss: 0.00562 +Epoch [3599/4000] Training [39/39] Loss: 0.13106 +Epoch [3599/4000] Training metric {'Train/mean dice_metric': 0.9961888790130615, 'Train/mean miou_metric': 0.9928401112556458, 'Train/mean f1': 0.9969040155410767, 'Train/mean precision': 0.9964662790298462, 'Train/mean recall': 0.997342050075531, 'Train/mean hd95_metric': 0.9855494499206543} +Epoch [3599/4000] Validation [1/10] Loss: 0.72984 focal_loss 0.64090 dice_loss 0.08894 +Epoch [3599/4000] Validation [2/10] Loss: 0.48229 focal_loss 0.38921 dice_loss 0.09308 +Epoch [3599/4000] Validation [3/10] Loss: 0.36647 focal_loss 0.25759 dice_loss 0.10888 +Epoch [3599/4000] Validation [4/10] Loss: 0.90323 focal_loss 0.33671 dice_loss 0.56652 +Epoch [3599/4000] Validation [5/10] Loss: 3.01495 focal_loss 2.34122 dice_loss 0.67373 +Epoch [3599/4000] Validation [6/10] Loss: 1.36937 focal_loss 0.65440 dice_loss 0.71497 +Epoch [3599/4000] Validation [7/10] Loss: 1.19718 focal_loss 0.54316 dice_loss 0.65402 +Epoch [3599/4000] Validation [8/10] Loss: 2.12277 focal_loss 1.53446 dice_loss 0.58831 +Epoch [3599/4000] Validation [9/10] Loss: 1.64004 focal_loss 1.10252 dice_loss 0.53752 +Epoch [3599/4000] Validation [10/10] Loss: 1.97355 focal_loss 1.23263 dice_loss 0.74091 +Epoch [3599/4000] Validation metric {'Val/mean dice_metric': 0.9515621662139893, 'Val/mean miou_metric': 0.9355597496032715, 'Val/mean f1': 0.9479910731315613, 'Val/mean precision': 0.9404036998748779, 'Val/mean recall': 0.9557018280029297, 'Val/mean hd95_metric': 10.866644859313965} +Cheakpoint... +Epoch [3599/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515621662139893, 'Val/mean miou_metric': 0.9355597496032715, 'Val/mean f1': 0.9479910731315613, 'Val/mean precision': 0.9404036998748779, 'Val/mean recall': 0.9557018280029297, 'Val/mean hd95_metric': 10.866644859313965} +Epoch [3600/4000] Training [1/39] Loss: 0.13018 +Epoch [3600/4000] Training [2/39] Loss: 0.00606 +Epoch [3600/4000] Training [3/39] Loss: 0.13119 +Epoch [3600/4000] Training [4/39] Loss: 0.00681 +Epoch [3600/4000] Training [5/39] Loss: 0.13008 +Epoch [3600/4000] Training [6/39] Loss: 0.00593 +Epoch [3600/4000] Training [7/39] Loss: 0.00950 +Epoch [3600/4000] Training [8/39] Loss: 0.00540 +Epoch [3600/4000] Training [9/39] Loss: 0.00349 +Epoch [3600/4000] Training [10/39] Loss: 0.00544 +Epoch [3600/4000] Training [11/39] Loss: 0.00378 +Epoch [3600/4000] Training [12/39] Loss: 0.00531 +Epoch [3600/4000] Training [13/39] Loss: 0.00334 +Epoch [3600/4000] Training [14/39] Loss: 0.12982 +Epoch [3600/4000] Training [15/39] Loss: 0.00375 +Epoch [3600/4000] Training [16/39] Loss: 0.00300 +Epoch [3600/4000] Training [17/39] Loss: 0.00421 +Epoch [3600/4000] Training [18/39] Loss: 0.12941 +Epoch [3600/4000] Training [19/39] Loss: 0.28370 +Epoch [3600/4000] Training [20/39] Loss: 0.00426 +Epoch [3600/4000] Training [21/39] Loss: 0.00754 +Epoch [3600/4000] Training [22/39] Loss: 0.13067 +Epoch [3600/4000] Training [23/39] Loss: 0.12778 +Epoch [3600/4000] Training [24/39] Loss: 0.01007 +Epoch [3600/4000] Training [25/39] Loss: 0.00779 +Epoch [3600/4000] Training [26/39] Loss: 0.00580 +Epoch [3600/4000] Training [27/39] Loss: 0.13379 +Epoch [3600/4000] Training [28/39] Loss: 0.00498 +Epoch [3600/4000] Training [29/39] Loss: 0.00706 +Epoch [3600/4000] Training [30/39] Loss: 0.00486 +Epoch [3600/4000] Training [31/39] Loss: 0.00824 +Epoch [3600/4000] Training [32/39] Loss: 0.00604 +Epoch [3600/4000] Training [33/39] Loss: 0.00417 +Epoch [3600/4000] Training [34/39] Loss: 0.00548 +Epoch [3600/4000] Training [35/39] Loss: 0.00682 +Epoch [3600/4000] Training [36/39] Loss: 0.00512 +Epoch [3600/4000] Training [37/39] Loss: 0.00817 +Epoch [3600/4000] Training [38/39] Loss: 0.00579 +Epoch [3600/4000] Training [39/39] Loss: 0.12795 +Epoch [3600/4000] Training metric {'Train/mean dice_metric': 0.9958459734916687, 'Train/mean miou_metric': 0.9921574592590332, 'Train/mean f1': 0.9964697360992432, 'Train/mean precision': 0.9959121346473694, 'Train/mean recall': 0.9970279932022095, 'Train/mean hd95_metric': 1.0842186212539673} +Epoch [3600/4000] Validation [1/10] Loss: 0.72232 focal_loss 0.63378 dice_loss 0.08853 +Epoch [3600/4000] Validation [2/10] Loss: 0.49147 focal_loss 0.39147 dice_loss 0.10000 +Epoch [3600/4000] Validation [3/10] Loss: 0.38277 focal_loss 0.27191 dice_loss 0.11086 +Epoch [3600/4000] Validation [4/10] Loss: 0.87943 focal_loss 0.31483 dice_loss 0.56460 +Epoch [3600/4000] Validation [5/10] Loss: 3.00915 focal_loss 2.33543 dice_loss 0.67372 +Epoch [3600/4000] Validation [6/10] Loss: 1.32833 focal_loss 0.61062 dice_loss 0.71771 +Epoch [3600/4000] Validation [7/10] Loss: 1.17044 focal_loss 0.51980 dice_loss 0.65064 +Epoch [3600/4000] Validation [8/10] Loss: 2.50879 focal_loss 1.87291 dice_loss 0.63588 +Epoch [3600/4000] Validation [9/10] Loss: 1.49549 focal_loss 0.95004 dice_loss 0.54546 +Epoch [3600/4000] Validation [10/10] Loss: 1.88337 focal_loss 1.14727 dice_loss 0.73610 +Epoch [3600/4000] Validation metric {'Val/mean dice_metric': 0.9509068131446838, 'Val/mean miou_metric': 0.934614896774292, 'Val/mean f1': 0.9483314752578735, 'Val/mean precision': 0.9446169137954712, 'Val/mean recall': 0.9520753026008606, 'Val/mean hd95_metric': 10.71415901184082} +Cheakpoint... +Epoch [3600/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509068131446838, 'Val/mean miou_metric': 0.934614896774292, 'Val/mean f1': 0.9483314752578735, 'Val/mean precision': 0.9446169137954712, 'Val/mean recall': 0.9520753026008606, 'Val/mean hd95_metric': 10.71415901184082} +Epoch [3601/4000] Training [1/39] Loss: 0.00663 +Epoch [3601/4000] Training [2/39] Loss: 0.00662 +Epoch [3601/4000] Training [3/39] Loss: 0.00535 +Epoch [3601/4000] Training [4/39] Loss: 0.00492 +Epoch [3601/4000] Training [5/39] Loss: 0.00368 +Epoch [3601/4000] Training [6/39] Loss: 0.00484 +Epoch [3601/4000] Training [7/39] Loss: 0.12887 +Epoch [3601/4000] Training [8/39] Loss: 0.00353 +Epoch [3601/4000] Training [9/39] Loss: 0.13027 +Epoch [3601/4000] Training [10/39] Loss: 0.12898 +Epoch [3601/4000] Training [11/39] Loss: 0.00671 +Epoch [3601/4000] Training [12/39] Loss: 0.00601 +Epoch [3601/4000] Training [13/39] Loss: 0.00542 +Epoch [3601/4000] Training [14/39] Loss: 0.00406 +Epoch [3601/4000] Training [15/39] Loss: 0.00351 +Epoch [3601/4000] Training [16/39] Loss: 0.00391 +Epoch [3601/4000] Training [17/39] Loss: 0.25298 +Epoch [3601/4000] Training [18/39] Loss: 0.00597 +Epoch [3601/4000] Training [19/39] Loss: 0.00325 +Epoch [3601/4000] Training [20/39] Loss: 0.00633 +Epoch [3601/4000] Training [21/39] Loss: 0.12856 +Epoch [3601/4000] Training [22/39] Loss: 0.12856 +Epoch [3601/4000] Training [23/39] Loss: 0.00476 +Epoch [3601/4000] Training [24/39] Loss: 0.00399 +Epoch [3601/4000] Training [25/39] Loss: 0.00330 +Epoch [3601/4000] Training [26/39] Loss: 0.12935 +Epoch [3601/4000] Training [27/39] Loss: 0.00567 +Epoch [3601/4000] Training [28/39] Loss: 0.00454 +Epoch [3601/4000] Training [29/39] Loss: 0.00661 +Epoch [3601/4000] Training [30/39] Loss: 0.00442 +Epoch [3601/4000] Training [31/39] Loss: 0.13207 +Epoch [3601/4000] Training [32/39] Loss: 0.00445 +Epoch [3601/4000] Training [33/39] Loss: 0.00490 +Epoch [3601/4000] Training [34/39] Loss: 0.00893 +Epoch [3601/4000] Training [35/39] Loss: 0.00565 +Epoch [3601/4000] Training [36/39] Loss: 0.00495 +Epoch [3601/4000] Training [37/39] Loss: 0.12927 +Epoch [3601/4000] Training [38/39] Loss: 0.00692 +Epoch [3601/4000] Training [39/39] Loss: 0.25463 +Epoch [3601/4000] Training metric {'Train/mean dice_metric': 0.996099054813385, 'Train/mean miou_metric': 0.9926353693008423, 'Train/mean f1': 0.9967873096466064, 'Train/mean precision': 0.9963801503181458, 'Train/mean recall': 0.9971948862075806, 'Train/mean hd95_metric': 1.1090089082717896} +Epoch [3601/4000] Validation [1/10] Loss: 0.73016 focal_loss 0.64128 dice_loss 0.08887 +Epoch [3601/4000] Validation [2/10] Loss: 0.48474 focal_loss 0.38977 dice_loss 0.09497 +Epoch [3601/4000] Validation [3/10] Loss: 0.37092 focal_loss 0.26156 dice_loss 0.10936 +Epoch [3601/4000] Validation [4/10] Loss: 0.88560 focal_loss 0.32090 dice_loss 0.56470 +Epoch [3601/4000] Validation [5/10] Loss: 3.02803 focal_loss 2.35441 dice_loss 0.67362 +Epoch [3601/4000] Validation [6/10] Loss: 1.34251 focal_loss 0.62267 dice_loss 0.71984 +Epoch [3601/4000] Validation [7/10] Loss: 1.17853 focal_loss 0.52777 dice_loss 0.65076 +Epoch [3601/4000] Validation [8/10] Loss: 2.39020 focal_loss 1.76690 dice_loss 0.62330 +Epoch [3601/4000] Validation [9/10] Loss: 1.51032 focal_loss 0.96446 dice_loss 0.54586 +Epoch [3601/4000] Validation [10/10] Loss: 1.90551 focal_loss 1.16907 dice_loss 0.73644 +Epoch [3601/4000] Validation metric {'Val/mean dice_metric': 0.9513232111930847, 'Val/mean miou_metric': 0.9352208971977234, 'Val/mean f1': 0.9483204483985901, 'Val/mean precision': 0.9435008764266968, 'Val/mean recall': 0.9531892538070679, 'Val/mean hd95_metric': 10.711566925048828} +Cheakpoint... +Epoch [3601/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513232111930847, 'Val/mean miou_metric': 0.9352208971977234, 'Val/mean f1': 0.9483204483985901, 'Val/mean precision': 0.9435008764266968, 'Val/mean recall': 0.9531892538070679, 'Val/mean hd95_metric': 10.711566925048828} +Epoch [3602/4000] Training [1/39] Loss: 0.00451 +Epoch [3602/4000] Training [2/39] Loss: 0.00498 +Epoch [3602/4000] Training [3/39] Loss: 0.00496 +Epoch [3602/4000] Training [4/39] Loss: 0.13147 +Epoch [3602/4000] Training [5/39] Loss: 0.00415 +Epoch [3602/4000] Training [6/39] Loss: 0.00501 +Epoch [3602/4000] Training [7/39] Loss: 0.00324 +Epoch [3602/4000] Training [8/39] Loss: 0.13054 +Epoch [3602/4000] Training [9/39] Loss: 0.20765 +Epoch [3602/4000] Training [10/39] Loss: 0.25274 +Epoch [3602/4000] Training [11/39] Loss: 0.00673 +Epoch [3602/4000] Training [12/39] Loss: 0.00429 +Epoch [3602/4000] Training [13/39] Loss: 0.12965 +Epoch [3602/4000] Training [14/39] Loss: 0.00364 +Epoch [3602/4000] Training [15/39] Loss: 0.00640 +Epoch [3602/4000] Training [16/39] Loss: 0.00287 +Epoch [3602/4000] Training [17/39] Loss: 0.00496 +Epoch [3602/4000] Training [18/39] Loss: 0.00439 +Epoch [3602/4000] Training [19/39] Loss: 0.00497 +Epoch [3602/4000] Training [20/39] Loss: 0.00585 +Epoch [3602/4000] Training [21/39] Loss: 0.00372 +Epoch [3602/4000] Training [22/39] Loss: 0.12860 +Epoch [3602/4000] Training [23/39] Loss: 0.00893 +Epoch [3602/4000] Training [24/39] Loss: 0.13034 +Epoch [3602/4000] Training [25/39] Loss: 0.25233 +Epoch [3602/4000] Training [26/39] Loss: 0.00485 +Epoch [3602/4000] Training [27/39] Loss: 0.12717 +Epoch [3602/4000] Training [28/39] Loss: 0.00423 +Epoch [3602/4000] Training [29/39] Loss: 0.25259 +Epoch [3602/4000] Training [30/39] Loss: 0.00577 +Epoch [3602/4000] Training [31/39] Loss: 0.00502 +Epoch [3602/4000] Training [32/39] Loss: 0.00479 +Epoch [3602/4000] Training [33/39] Loss: 0.13008 +Epoch [3602/4000] Training [34/39] Loss: 0.17319 +Epoch [3602/4000] Training [35/39] Loss: 0.12997 +Epoch [3602/4000] Training [36/39] Loss: 0.00490 +Epoch [3602/4000] Training [37/39] Loss: 0.00701 +Epoch [3602/4000] Training [38/39] Loss: 0.00549 +Epoch [3602/4000] Training [39/39] Loss: 0.00441 +Epoch [3602/4000] Training metric {'Train/mean dice_metric': 0.9962881803512573, 'Train/mean miou_metric': 0.9930240511894226, 'Train/mean f1': 0.99686199426651, 'Train/mean precision': 0.9964314699172974, 'Train/mean recall': 0.9972929358482361, 'Train/mean hd95_metric': 0.9499830603599548} +Epoch [3602/4000] Validation [1/10] Loss: 0.74540 focal_loss 0.65574 dice_loss 0.08966 +Epoch [3602/4000] Validation [2/10] Loss: 0.49382 focal_loss 0.39804 dice_loss 0.09578 +Epoch [3602/4000] Validation [3/10] Loss: 0.37392 focal_loss 0.26459 dice_loss 0.10933 +Epoch [3602/4000] Validation [4/10] Loss: 0.88898 focal_loss 0.32433 dice_loss 0.56465 +Epoch [3602/4000] Validation [5/10] Loss: 3.05396 focal_loss 2.38025 dice_loss 0.67372 +Epoch [3602/4000] Validation [6/10] Loss: 1.34491 focal_loss 0.62762 dice_loss 0.71729 +Epoch [3602/4000] Validation [7/10] Loss: 1.18531 focal_loss 0.53485 dice_loss 0.65046 +Epoch [3602/4000] Validation [8/10] Loss: 2.46246 focal_loss 1.83623 dice_loss 0.62623 +Epoch [3602/4000] Validation [9/10] Loss: 1.50126 focal_loss 0.95548 dice_loss 0.54577 +Epoch [3602/4000] Validation [10/10] Loss: 1.91715 focal_loss 1.18123 dice_loss 0.73591 +Epoch [3602/4000] Validation metric {'Val/mean dice_metric': 0.9515622854232788, 'Val/mean miou_metric': 0.9356171488761902, 'Val/mean f1': 0.9486669301986694, 'Val/mean precision': 0.944288432598114, 'Val/mean recall': 0.9530863165855408, 'Val/mean hd95_metric': 10.569600105285645} +Cheakpoint... +Epoch [3602/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515622854232788, 'Val/mean miou_metric': 0.9356171488761902, 'Val/mean f1': 0.9486669301986694, 'Val/mean precision': 0.944288432598114, 'Val/mean recall': 0.9530863165855408, 'Val/mean hd95_metric': 10.569600105285645} +Epoch [3603/4000] Training [1/39] Loss: 0.00348 +Epoch [3603/4000] Training [2/39] Loss: 0.00358 +Epoch [3603/4000] Training [3/39] Loss: 0.00539 +Epoch [3603/4000] Training [4/39] Loss: 0.25277 +Epoch [3603/4000] Training [5/39] Loss: 0.12760 +Epoch [3603/4000] Training [6/39] Loss: 0.00328 +Epoch [3603/4000] Training [7/39] Loss: 0.00539 +Epoch [3603/4000] Training [8/39] Loss: 0.00419 +Epoch [3603/4000] Training [9/39] Loss: 0.00502 +Epoch [3603/4000] Training [10/39] Loss: 0.00489 +Epoch [3603/4000] Training [11/39] Loss: 0.00261 +Epoch [3603/4000] Training [12/39] Loss: 0.12915 +Epoch [3603/4000] Training [13/39] Loss: 0.13497 +Epoch [3603/4000] Training [14/39] Loss: 0.13030 +Epoch [3603/4000] Training [15/39] Loss: 0.00727 +Epoch [3603/4000] Training [16/39] Loss: 0.08339 +Epoch [3603/4000] Training [17/39] Loss: 0.00432 +Epoch [3603/4000] Training [18/39] Loss: 0.00677 +Epoch [3603/4000] Training [19/39] Loss: 0.00668 +Epoch [3603/4000] Training [20/39] Loss: 0.00371 +Epoch [3603/4000] Training [21/39] Loss: 0.13175 +Epoch [3603/4000] Training [22/39] Loss: 0.25471 +Epoch [3603/4000] Training [23/39] Loss: 0.00288 +Epoch [3603/4000] Training [24/39] Loss: 0.00471 +Epoch [3603/4000] Training [25/39] Loss: 0.00423 +Epoch [3603/4000] Training [26/39] Loss: 0.00502 +Epoch [3603/4000] Training [27/39] Loss: 0.00475 +Epoch [3603/4000] Training [28/39] Loss: 0.00335 +Epoch [3603/4000] Training [29/39] Loss: 0.00444 +Epoch [3603/4000] Training [30/39] Loss: 0.12821 +Epoch [3603/4000] Training [31/39] Loss: 0.00384 +Epoch [3603/4000] Training [32/39] Loss: 0.12747 +Epoch [3603/4000] Training [33/39] Loss: 0.00402 +Epoch [3603/4000] Training [34/39] Loss: 0.00641 +Epoch [3603/4000] Training [35/39] Loss: 0.00390 +Epoch [3603/4000] Training [36/39] Loss: 0.00223 +Epoch [3603/4000] Training [37/39] Loss: 0.13124 +Epoch [3603/4000] Training [38/39] Loss: 0.12912 +Epoch [3603/4000] Training [39/39] Loss: 0.00496 +Epoch [3603/4000] Training metric {'Train/mean dice_metric': 0.9964901804924011, 'Train/mean miou_metric': 0.9934206008911133, 'Train/mean f1': 0.996957004070282, 'Train/mean precision': 0.996475875377655, 'Train/mean recall': 0.9974385499954224, 'Train/mean hd95_metric': 0.926838219165802} +Epoch [3603/4000] Validation [1/10] Loss: 0.72971 focal_loss 0.64106 dice_loss 0.08865 +Epoch [3603/4000] Validation [2/10] Loss: 0.48194 focal_loss 0.38630 dice_loss 0.09563 +Epoch [3603/4000] Validation [3/10] Loss: 0.37861 focal_loss 0.26849 dice_loss 0.11012 +Epoch [3603/4000] Validation [4/10] Loss: 0.87979 focal_loss 0.31551 dice_loss 0.56429 +Epoch [3603/4000] Validation [5/10] Loss: 3.02279 focal_loss 2.34887 dice_loss 0.67392 +Epoch [3603/4000] Validation [6/10] Loss: 1.32644 focal_loss 0.60647 dice_loss 0.71997 +Epoch [3603/4000] Validation [7/10] Loss: 1.17072 focal_loss 0.52084 dice_loss 0.64988 +Epoch [3603/4000] Validation [8/10] Loss: 2.38946 focal_loss 1.76524 dice_loss 0.62421 +Epoch [3603/4000] Validation [9/10] Loss: 1.51426 focal_loss 0.96883 dice_loss 0.54543 +Epoch [3603/4000] Validation [10/10] Loss: 1.88049 focal_loss 1.14397 dice_loss 0.73652 +Epoch [3603/4000] Validation metric {'Val/mean dice_metric': 0.9516857862472534, 'Val/mean miou_metric': 0.9359963536262512, 'Val/mean f1': 0.9485240578651428, 'Val/mean precision': 0.9442588686943054, 'Val/mean recall': 0.952828049659729, 'Val/mean hd95_metric': 10.504404067993164} +Cheakpoint... +Epoch [3603/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516857862472534, 'Val/mean miou_metric': 0.9359963536262512, 'Val/mean f1': 0.9485240578651428, 'Val/mean precision': 0.9442588686943054, 'Val/mean recall': 0.952828049659729, 'Val/mean hd95_metric': 10.504404067993164} +Epoch [3604/4000] Training [1/39] Loss: 0.00794 +Epoch [3604/4000] Training [2/39] Loss: 0.12970 +Epoch [3604/4000] Training [3/39] Loss: 0.00472 +Epoch [3604/4000] Training [4/39] Loss: 0.00513 +Epoch [3604/4000] Training [5/39] Loss: 0.01009 +Epoch [3604/4000] Training [6/39] Loss: 0.00831 +Epoch [3604/4000] Training [7/39] Loss: 0.00510 +Epoch [3604/4000] Training [8/39] Loss: 0.12800 +Epoch [3604/4000] Training [9/39] Loss: 0.00411 +Epoch [3604/4000] Training [10/39] Loss: 0.00486 +Epoch [3604/4000] Training [11/39] Loss: 0.12770 +Epoch [3604/4000] Training [12/39] Loss: 0.00653 +Epoch [3604/4000] Training [13/39] Loss: 0.00453 +Epoch [3604/4000] Training [14/39] Loss: 0.00589 +Epoch [3604/4000] Training [15/39] Loss: 0.00361 +Epoch [3604/4000] Training [16/39] Loss: 0.16917 +Epoch [3604/4000] Training [17/39] Loss: 0.13014 +Epoch [3604/4000] Training [18/39] Loss: 0.12973 +Epoch [3604/4000] Training [19/39] Loss: 0.00629 +Epoch [3604/4000] Training [20/39] Loss: 0.00458 +Epoch [3604/4000] Training [21/39] Loss: 0.00741 +Epoch [3604/4000] Training [22/39] Loss: 0.08294 +Epoch [3604/4000] Training [23/39] Loss: 0.00342 +Epoch [3604/4000] Training [24/39] Loss: 0.12850 +Epoch [3604/4000] Training [25/39] Loss: 0.13012 +Epoch [3604/4000] Training [26/39] Loss: 0.00409 +Epoch [3604/4000] Training [27/39] Loss: 0.12921 +Epoch [3604/4000] Training [28/39] Loss: 0.00432 +Epoch [3604/4000] Training [29/39] Loss: 0.00430 +Epoch [3604/4000] Training [30/39] Loss: 0.50243 +Epoch [3604/4000] Training [31/39] Loss: 0.12982 +Epoch [3604/4000] Training [32/39] Loss: 0.00370 +Epoch [3604/4000] Training [33/39] Loss: 0.00402 +Epoch [3604/4000] Training [34/39] Loss: 0.00491 +Epoch [3604/4000] Training [35/39] Loss: 0.00687 +Epoch [3604/4000] Training [36/39] Loss: 0.00434 +Epoch [3604/4000] Training [37/39] Loss: 0.13299 +Epoch [3604/4000] Training [38/39] Loss: 0.00457 +Epoch [3604/4000] Training [39/39] Loss: 0.13415 +Epoch [3604/4000] Training metric {'Train/mean dice_metric': 0.9959782958030701, 'Train/mean miou_metric': 0.9924107789993286, 'Train/mean f1': 0.9967631101608276, 'Train/mean precision': 0.9963125586509705, 'Train/mean recall': 0.9972140192985535, 'Train/mean hd95_metric': 1.0161489248275757} +Epoch [3604/4000] Validation [1/10] Loss: 0.75191 focal_loss 0.66220 dice_loss 0.08970 +Epoch [3604/4000] Validation [2/10] Loss: 0.48779 focal_loss 0.39256 dice_loss 0.09523 +Epoch [3604/4000] Validation [3/10] Loss: 0.37496 focal_loss 0.26539 dice_loss 0.10957 +Epoch [3604/4000] Validation [4/10] Loss: 0.89182 focal_loss 0.32711 dice_loss 0.56471 +Epoch [3604/4000] Validation [5/10] Loss: 3.06680 focal_loss 2.39319 dice_loss 0.67360 +Epoch [3604/4000] Validation [6/10] Loss: 1.33985 focal_loss 0.62049 dice_loss 0.71936 +Epoch [3604/4000] Validation [7/10] Loss: 1.18863 focal_loss 0.53641 dice_loss 0.65222 +Epoch [3604/4000] Validation [8/10] Loss: 2.39134 focal_loss 1.77257 dice_loss 0.61877 +Epoch [3604/4000] Validation [9/10] Loss: 1.51910 focal_loss 0.97334 dice_loss 0.54576 +Epoch [3604/4000] Validation [10/10] Loss: 1.90604 focal_loss 1.16921 dice_loss 0.73683 +Epoch [3604/4000] Validation metric {'Val/mean dice_metric': 0.9513360857963562, 'Val/mean miou_metric': 0.9352074861526489, 'Val/mean f1': 0.9482563734054565, 'Val/mean precision': 0.9432352185249329, 'Val/mean recall': 0.9533313512802124, 'Val/mean hd95_metric': 10.66505241394043} +Cheakpoint... +Epoch [3604/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513360857963562, 'Val/mean miou_metric': 0.9352074861526489, 'Val/mean f1': 0.9482563734054565, 'Val/mean precision': 0.9432352185249329, 'Val/mean recall': 0.9533313512802124, 'Val/mean hd95_metric': 10.66505241394043} +Epoch [3605/4000] Training [1/39] Loss: 0.00548 +Epoch [3605/4000] Training [2/39] Loss: 0.00383 +Epoch [3605/4000] Training [3/39] Loss: 0.00358 +Epoch [3605/4000] Training [4/39] Loss: 0.00545 +Epoch [3605/4000] Training [5/39] Loss: 0.00376 +Epoch [3605/4000] Training [6/39] Loss: 0.13018 +Epoch [3605/4000] Training [7/39] Loss: 0.00650 +Epoch [3605/4000] Training [8/39] Loss: 0.00462 +Epoch [3605/4000] Training [9/39] Loss: 0.00416 +Epoch [3605/4000] Training [10/39] Loss: 0.00345 +Epoch [3605/4000] Training [11/39] Loss: 0.00885 +Epoch [3605/4000] Training [12/39] Loss: 0.00334 +Epoch [3605/4000] Training [13/39] Loss: 0.00455 +Epoch [3605/4000] Training [14/39] Loss: 0.12671 +Epoch [3605/4000] Training [15/39] Loss: 0.00393 +Epoch [3605/4000] Training [16/39] Loss: 0.13023 +Epoch [3605/4000] Training [17/39] Loss: 0.00449 +Epoch [3605/4000] Training [18/39] Loss: 0.00741 +Epoch [3605/4000] Training [19/39] Loss: 0.00448 +Epoch [3605/4000] Training [20/39] Loss: 0.00709 +Epoch [3605/4000] Training [21/39] Loss: 0.00644 +Epoch [3605/4000] Training [22/39] Loss: 0.00572 +Epoch [3605/4000] Training [23/39] Loss: 0.00728 +Epoch [3605/4000] Training [24/39] Loss: 0.12870 +Epoch [3605/4000] Training [25/39] Loss: 0.00524 +Epoch [3605/4000] Training [26/39] Loss: 0.00522 +Epoch [3605/4000] Training [27/39] Loss: 0.00475 +Epoch [3605/4000] Training [28/39] Loss: 0.07870 +Epoch [3605/4000] Training [29/39] Loss: 0.00403 +Epoch [3605/4000] Training [30/39] Loss: 0.00322 +Epoch [3605/4000] Training [31/39] Loss: 0.00730 +Epoch [3605/4000] Training [32/39] Loss: 0.12984 +Epoch [3605/4000] Training [33/39] Loss: 0.00441 +Epoch [3605/4000] Training [34/39] Loss: 0.00684 +Epoch [3605/4000] Training [35/39] Loss: 0.00396 +Epoch [3605/4000] Training [36/39] Loss: 0.00560 +Epoch [3605/4000] Training [37/39] Loss: 0.12959 +Epoch [3605/4000] Training [38/39] Loss: 0.00663 +Epoch [3605/4000] Training [39/39] Loss: 0.13026 +Epoch [3605/4000] Training metric {'Train/mean dice_metric': 0.996242880821228, 'Train/mean miou_metric': 0.9929229617118835, 'Train/mean f1': 0.9968107342720032, 'Train/mean precision': 0.9963761568069458, 'Train/mean recall': 0.9972456097602844, 'Train/mean hd95_metric': 0.9457367062568665} +Epoch [3605/4000] Validation [1/10] Loss: 0.73835 focal_loss 0.64904 dice_loss 0.08932 +Epoch [3605/4000] Validation [2/10] Loss: 0.48010 focal_loss 0.38782 dice_loss 0.09228 +Epoch [3605/4000] Validation [3/10] Loss: 0.37016 focal_loss 0.26121 dice_loss 0.10895 +Epoch [3605/4000] Validation [4/10] Loss: 0.89915 focal_loss 0.33391 dice_loss 0.56524 +Epoch [3605/4000] Validation [5/10] Loss: 3.03400 focal_loss 2.36024 dice_loss 0.67376 +Epoch [3605/4000] Validation [6/10] Loss: 1.34710 focal_loss 0.63286 dice_loss 0.71424 +Epoch [3605/4000] Validation [7/10] Loss: 1.19366 focal_loss 0.53830 dice_loss 0.65536 +Epoch [3605/4000] Validation [8/10] Loss: 2.36197 focal_loss 1.74785 dice_loss 0.61412 +Epoch [3605/4000] Validation [9/10] Loss: 1.50564 focal_loss 0.95962 dice_loss 0.54602 +Epoch [3605/4000] Validation [10/10] Loss: 1.93838 focal_loss 1.20025 dice_loss 0.73813 +Epoch [3605/4000] Validation metric {'Val/mean dice_metric': 0.9516861438751221, 'Val/mean miou_metric': 0.9356832504272461, 'Val/mean f1': 0.9484017491340637, 'Val/mean precision': 0.943056583404541, 'Val/mean recall': 0.9538079500198364, 'Val/mean hd95_metric': 10.905162811279297} +Cheakpoint... +Epoch [3605/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516861438751221, 'Val/mean miou_metric': 0.9356832504272461, 'Val/mean f1': 0.9484017491340637, 'Val/mean precision': 0.943056583404541, 'Val/mean recall': 0.9538079500198364, 'Val/mean hd95_metric': 10.905162811279297} +Epoch [3606/4000] Training [1/39] Loss: 0.00354 +Epoch [3606/4000] Training [2/39] Loss: 0.00554 +Epoch [3606/4000] Training [3/39] Loss: 0.00690 +Epoch [3606/4000] Training [4/39] Loss: 0.00591 +Epoch [3606/4000] Training [5/39] Loss: 0.12903 +Epoch [3606/4000] Training [6/39] Loss: 0.00318 +Epoch [3606/4000] Training [7/39] Loss: 0.00447 +Epoch [3606/4000] Training [8/39] Loss: 0.12880 +Epoch [3606/4000] Training [9/39] Loss: 0.00623 +Epoch [3606/4000] Training [10/39] Loss: 0.00483 +Epoch [3606/4000] Training [11/39] Loss: 0.00575 +Epoch [3606/4000] Training [12/39] Loss: 0.00762 +Epoch [3606/4000] Training [13/39] Loss: 0.00393 +Epoch [3606/4000] Training [14/39] Loss: 0.00649 +Epoch [3606/4000] Training [15/39] Loss: 0.00502 +Epoch [3606/4000] Training [16/39] Loss: 0.00318 +Epoch [3606/4000] Training [17/39] Loss: 0.12798 +Epoch [3606/4000] Training [18/39] Loss: 0.12995 +Epoch [3606/4000] Training [19/39] Loss: 0.00568 +Epoch [3606/4000] Training [20/39] Loss: 0.00546 +Epoch [3606/4000] Training [21/39] Loss: 0.12881 +Epoch [3606/4000] Training [22/39] Loss: 0.00326 +Epoch [3606/4000] Training [23/39] Loss: 0.13123 +Epoch [3606/4000] Training [24/39] Loss: 0.00337 +Epoch [3606/4000] Training [25/39] Loss: 0.00668 +Epoch [3606/4000] Training [26/39] Loss: 0.00419 +Epoch [3606/4000] Training [27/39] Loss: 0.00798 +Epoch [3606/4000] Training [28/39] Loss: 0.00629 +Epoch [3606/4000] Training [29/39] Loss: 0.12830 +Epoch [3606/4000] Training [30/39] Loss: 0.00692 +Epoch [3606/4000] Training [31/39] Loss: 0.00636 +Epoch [3606/4000] Training [32/39] Loss: 0.00358 +Epoch [3606/4000] Training [33/39] Loss: 0.25273 +Epoch [3606/4000] Training [34/39] Loss: 0.00502 +Epoch [3606/4000] Training [35/39] Loss: 0.12996 +Epoch [3606/4000] Training [36/39] Loss: 0.12793 +Epoch [3606/4000] Training [37/39] Loss: 0.00449 +Epoch [3606/4000] Training [38/39] Loss: 0.00464 +Epoch [3606/4000] Training [39/39] Loss: 0.00477 +Epoch [3606/4000] Training metric {'Train/mean dice_metric': 0.9960958957672119, 'Train/mean miou_metric': 0.9926681518554688, 'Train/mean f1': 0.9966943264007568, 'Train/mean precision': 0.9961575269699097, 'Train/mean recall': 0.997231662273407, 'Train/mean hd95_metric': 1.0641838312149048} +Epoch [3606/4000] Validation [1/10] Loss: 0.70726 focal_loss 0.62007 dice_loss 0.08719 +Epoch [3606/4000] Validation [2/10] Loss: 0.47081 focal_loss 0.38117 dice_loss 0.08964 +Epoch [3606/4000] Validation [3/10] Loss: 0.36563 focal_loss 0.25673 dice_loss 0.10890 +Epoch [3606/4000] Validation [4/10] Loss: 0.91129 focal_loss 0.34572 dice_loss 0.56558 +Epoch [3606/4000] Validation [5/10] Loss: 3.01277 focal_loss 2.33925 dice_loss 0.67352 +Epoch [3606/4000] Validation [6/10] Loss: 1.37088 focal_loss 0.65323 dice_loss 0.71765 +Epoch [3606/4000] Validation [7/10] Loss: 1.19605 focal_loss 0.53973 dice_loss 0.65632 +Epoch [3606/4000] Validation [8/10] Loss: 2.20287 focal_loss 1.60642 dice_loss 0.59645 +Epoch [3606/4000] Validation [9/10] Loss: 1.63266 focal_loss 1.08921 dice_loss 0.54345 +Epoch [3606/4000] Validation [10/10] Loss: 1.97774 focal_loss 1.23560 dice_loss 0.74214 +Epoch [3606/4000] Validation metric {'Val/mean dice_metric': 0.9513192772865295, 'Val/mean miou_metric': 0.9351863265037537, 'Val/mean f1': 0.9474048018455505, 'Val/mean precision': 0.9400181174278259, 'Val/mean recall': 0.9549084901809692, 'Val/mean hd95_metric': 11.076311111450195} +Cheakpoint... +Epoch [3606/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513192772865295, 'Val/mean miou_metric': 0.9351863265037537, 'Val/mean f1': 0.9474048018455505, 'Val/mean precision': 0.9400181174278259, 'Val/mean recall': 0.9549084901809692, 'Val/mean hd95_metric': 11.076311111450195} +Epoch [3607/4000] Training [1/39] Loss: 0.00646 +Epoch [3607/4000] Training [2/39] Loss: 0.00494 +Epoch [3607/4000] Training [3/39] Loss: 0.00696 +Epoch [3607/4000] Training [4/39] Loss: 0.00673 +Epoch [3607/4000] Training [5/39] Loss: 0.00342 +Epoch [3607/4000] Training [6/39] Loss: 0.00336 +Epoch [3607/4000] Training [7/39] Loss: 0.00357 +Epoch [3607/4000] Training [8/39] Loss: 0.13009 +Epoch [3607/4000] Training [9/39] Loss: 0.01011 +Epoch [3607/4000] Training [10/39] Loss: 0.00375 +Epoch [3607/4000] Training [11/39] Loss: 0.00451 +Epoch [3607/4000] Training [12/39] Loss: 0.13061 +Epoch [3607/4000] Training [13/39] Loss: 0.00605 +Epoch [3607/4000] Training [14/39] Loss: 0.00563 +Epoch [3607/4000] Training [15/39] Loss: 0.00388 +Epoch [3607/4000] Training [16/39] Loss: 0.00551 +Epoch [3607/4000] Training [17/39] Loss: 0.01013 +Epoch [3607/4000] Training [18/39] Loss: 0.13020 +Epoch [3607/4000] Training [19/39] Loss: 0.00903 +Epoch [3607/4000] Training [20/39] Loss: 0.12789 +Epoch [3607/4000] Training [21/39] Loss: 0.00409 +Epoch [3607/4000] Training [22/39] Loss: 0.12865 +Epoch [3607/4000] Training [23/39] Loss: 0.00606 +Epoch [3607/4000] Training [24/39] Loss: 0.00440 +Epoch [3607/4000] Training [25/39] Loss: 0.00529 +Epoch [3607/4000] Training [26/39] Loss: 0.13326 +Epoch [3607/4000] Training [27/39] Loss: 0.12945 +Epoch [3607/4000] Training [28/39] Loss: 0.12935 +Epoch [3607/4000] Training [29/39] Loss: 0.00482 +Epoch [3607/4000] Training [30/39] Loss: 0.13259 +Epoch [3607/4000] Training [31/39] Loss: 0.00538 +Epoch [3607/4000] Training [32/39] Loss: 0.00852 +Epoch [3607/4000] Training [33/39] Loss: 0.00541 +Epoch [3607/4000] Training [34/39] Loss: 0.00394 +Epoch [3607/4000] Training [35/39] Loss: 0.00545 +Epoch [3607/4000] Training [36/39] Loss: 0.00555 +Epoch [3607/4000] Training [37/39] Loss: 0.00551 +Epoch [3607/4000] Training [38/39] Loss: 0.12846 +Epoch [3607/4000] Training [39/39] Loss: 0.00361 +Epoch [3607/4000] Training metric {'Train/mean dice_metric': 0.9954975843429565, 'Train/mean miou_metric': 0.9918551445007324, 'Train/mean f1': 0.9962995052337646, 'Train/mean precision': 0.9956101179122925, 'Train/mean recall': 0.9969899654388428, 'Train/mean hd95_metric': 1.0347137451171875} +Epoch [3607/4000] Validation [1/10] Loss: 0.70082 focal_loss 0.61427 dice_loss 0.08655 +Epoch [3607/4000] Validation [2/10] Loss: 0.47234 focal_loss 0.37972 dice_loss 0.09262 +Epoch [3607/4000] Validation [3/10] Loss: 0.36967 focal_loss 0.26070 dice_loss 0.10897 +Epoch [3607/4000] Validation [4/10] Loss: 0.90264 focal_loss 0.33669 dice_loss 0.56595 +Epoch [3607/4000] Validation [5/10] Loss: 3.02716 focal_loss 2.35375 dice_loss 0.67341 +Epoch [3607/4000] Validation [6/10] Loss: 1.35826 focal_loss 0.63990 dice_loss 0.71836 +Epoch [3607/4000] Validation [7/10] Loss: 1.18393 focal_loss 0.53183 dice_loss 0.65211 +Epoch [3607/4000] Validation [8/10] Loss: 2.23268 focal_loss 1.63011 dice_loss 0.60257 +Epoch [3607/4000] Validation [9/10] Loss: 1.57597 focal_loss 1.02920 dice_loss 0.54677 +Epoch [3607/4000] Validation [10/10] Loss: 1.94313 focal_loss 1.20222 dice_loss 0.74091 +Epoch [3607/4000] Validation metric {'Val/mean dice_metric': 0.9510433077812195, 'Val/mean miou_metric': 0.9348195195198059, 'Val/mean f1': 0.9477535486221313, 'Val/mean precision': 0.9412354230880737, 'Val/mean recall': 0.954362690448761, 'Val/mean hd95_metric': 11.038594245910645} +Cheakpoint... +Epoch [3607/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510433077812195, 'Val/mean miou_metric': 0.9348195195198059, 'Val/mean f1': 0.9477535486221313, 'Val/mean precision': 0.9412354230880737, 'Val/mean recall': 0.954362690448761, 'Val/mean hd95_metric': 11.038594245910645} +Epoch [3608/4000] Training [1/39] Loss: 0.00676 +Epoch [3608/4000] Training [2/39] Loss: 0.00864 +Epoch [3608/4000] Training [3/39] Loss: 0.00331 +Epoch [3608/4000] Training [4/39] Loss: 0.00444 +Epoch [3608/4000] Training [5/39] Loss: 0.12824 +Epoch [3608/4000] Training [6/39] Loss: 0.00392 +Epoch [3608/4000] Training [7/39] Loss: 0.00454 +Epoch [3608/4000] Training [8/39] Loss: 0.12889 +Epoch [3608/4000] Training [9/39] Loss: 0.00484 +Epoch [3608/4000] Training [10/39] Loss: 0.00492 +Epoch [3608/4000] Training [11/39] Loss: 0.00846 +Epoch [3608/4000] Training [12/39] Loss: 0.13067 +Epoch [3608/4000] Training [13/39] Loss: 0.00456 +Epoch [3608/4000] Training [14/39] Loss: 0.12834 +Epoch [3608/4000] Training [15/39] Loss: 0.00327 +Epoch [3608/4000] Training [16/39] Loss: 0.00503 +Epoch [3608/4000] Training [17/39] Loss: 0.12801 +Epoch [3608/4000] Training [18/39] Loss: 0.00240 +Epoch [3608/4000] Training [19/39] Loss: 0.00403 +Epoch [3608/4000] Training [20/39] Loss: 0.12779 +Epoch [3608/4000] Training [21/39] Loss: 0.00544 +Epoch [3608/4000] Training [22/39] Loss: 0.00426 +Epoch [3608/4000] Training [23/39] Loss: 0.00378 +Epoch [3608/4000] Training [24/39] Loss: 0.00373 +Epoch [3608/4000] Training [25/39] Loss: 0.12838 +Epoch [3608/4000] Training [26/39] Loss: 0.00326 +Epoch [3608/4000] Training [27/39] Loss: 0.00733 +Epoch [3608/4000] Training [28/39] Loss: 0.00596 +Epoch [3608/4000] Training [29/39] Loss: 0.00427 +Epoch [3608/4000] Training [30/39] Loss: 0.00640 +Epoch [3608/4000] Training [31/39] Loss: 0.00501 +Epoch [3608/4000] Training [32/39] Loss: 0.00400 +Epoch [3608/4000] Training [33/39] Loss: 0.12950 +Epoch [3608/4000] Training [34/39] Loss: 0.09587 +Epoch [3608/4000] Training [35/39] Loss: 0.00612 +Epoch [3608/4000] Training [36/39] Loss: 0.00648 +Epoch [3608/4000] Training [37/39] Loss: 0.13029 +Epoch [3608/4000] Training [38/39] Loss: 0.00488 +Epoch [3608/4000] Training [39/39] Loss: 0.25222 +Epoch [3608/4000] Training metric {'Train/mean dice_metric': 0.9962243437767029, 'Train/mean miou_metric': 0.9929351210594177, 'Train/mean f1': 0.9968681931495667, 'Train/mean precision': 0.9964108467102051, 'Train/mean recall': 0.9973258972167969, 'Train/mean hd95_metric': 0.9642447829246521} +Epoch [3608/4000] Validation [1/10] Loss: 0.71476 focal_loss 0.62692 dice_loss 0.08784 +Epoch [3608/4000] Validation [2/10] Loss: 0.47881 focal_loss 0.38637 dice_loss 0.09244 +Epoch [3608/4000] Validation [3/10] Loss: 0.36543 focal_loss 0.25695 dice_loss 0.10849 +Epoch [3608/4000] Validation [4/10] Loss: 0.90481 focal_loss 0.33882 dice_loss 0.56598 +Epoch [3608/4000] Validation [5/10] Loss: 3.04167 focal_loss 2.36799 dice_loss 0.67368 +Epoch [3608/4000] Validation [6/10] Loss: 1.36384 focal_loss 0.64782 dice_loss 0.71602 +Epoch [3608/4000] Validation [7/10] Loss: 1.18867 focal_loss 0.53568 dice_loss 0.65299 +Epoch [3608/4000] Validation [8/10] Loss: 2.31926 focal_loss 1.70905 dice_loss 0.61021 +Epoch [3608/4000] Validation [9/10] Loss: 1.50273 focal_loss 0.95627 dice_loss 0.54646 +Epoch [3608/4000] Validation [10/10] Loss: 1.93441 focal_loss 1.19469 dice_loss 0.73972 +Epoch [3608/4000] Validation metric {'Val/mean dice_metric': 0.9516761302947998, 'Val/mean miou_metric': 0.9357652068138123, 'Val/mean f1': 0.9483423233032227, 'Val/mean precision': 0.9424784183502197, 'Val/mean recall': 0.9542795419692993, 'Val/mean hd95_metric': 10.905487060546875} +Cheakpoint... +Epoch [3608/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516761302947998, 'Val/mean miou_metric': 0.9357652068138123, 'Val/mean f1': 0.9483423233032227, 'Val/mean precision': 0.9424784183502197, 'Val/mean recall': 0.9542795419692993, 'Val/mean hd95_metric': 10.905487060546875} +Epoch [3609/4000] Training [1/39] Loss: 0.00725 +Epoch [3609/4000] Training [2/39] Loss: 0.13036 +Epoch [3609/4000] Training [3/39] Loss: 0.25446 +Epoch [3609/4000] Training [4/39] Loss: 0.01180 +Epoch [3609/4000] Training [5/39] Loss: 0.00483 +Epoch [3609/4000] Training [6/39] Loss: 0.00452 +Epoch [3609/4000] Training [7/39] Loss: 0.00623 +Epoch [3609/4000] Training [8/39] Loss: 0.25164 +Epoch [3609/4000] Training [9/39] Loss: 0.00467 +Epoch [3609/4000] Training [10/39] Loss: 0.00583 +Epoch [3609/4000] Training [11/39] Loss: 0.00370 +Epoch [3609/4000] Training [12/39] Loss: 0.13034 +Epoch [3609/4000] Training [13/39] Loss: 0.00638 +Epoch [3609/4000] Training [14/39] Loss: 0.00334 +Epoch [3609/4000] Training [15/39] Loss: 0.00530 +Epoch [3609/4000] Training [16/39] Loss: 0.00660 +Epoch [3609/4000] Training [17/39] Loss: 0.00539 +Epoch [3609/4000] Training [18/39] Loss: 0.00492 +Epoch [3609/4000] Training [19/39] Loss: 0.00506 +Epoch [3609/4000] Training [20/39] Loss: 0.00376 +Epoch [3609/4000] Training [21/39] Loss: 0.00469 +Epoch [3609/4000] Training [22/39] Loss: 0.00420 +Epoch [3609/4000] Training [23/39] Loss: 0.00482 +Epoch [3609/4000] Training [24/39] Loss: 0.00340 +Epoch [3609/4000] Training [25/39] Loss: 0.00461 +Epoch [3609/4000] Training [26/39] Loss: 0.00839 +Epoch [3609/4000] Training [27/39] Loss: 0.00430 +Epoch [3609/4000] Training [28/39] Loss: 0.00482 +Epoch [3609/4000] Training [29/39] Loss: 0.00483 +Epoch [3609/4000] Training [30/39] Loss: 0.00500 +Epoch [3609/4000] Training [31/39] Loss: 0.00587 +Epoch [3609/4000] Training [32/39] Loss: 0.00405 +Epoch [3609/4000] Training [33/39] Loss: 0.12851 +Epoch [3609/4000] Training [34/39] Loss: 0.00359 +Epoch [3609/4000] Training [35/39] Loss: 0.00566 +Epoch [3609/4000] Training [36/39] Loss: 0.12883 +Epoch [3609/4000] Training [37/39] Loss: 0.13003 +Epoch [3609/4000] Training [38/39] Loss: 0.00507 +Epoch [3609/4000] Training [39/39] Loss: 0.00609 +Epoch [3609/4000] Training metric {'Train/mean dice_metric': 0.9960566163063049, 'Train/mean miou_metric': 0.9925532937049866, 'Train/mean f1': 0.9966636896133423, 'Train/mean precision': 0.9961910843849182, 'Train/mean recall': 0.997136652469635, 'Train/mean hd95_metric': 1.0443068742752075} +Epoch [3609/4000] Validation [1/10] Loss: 0.75058 focal_loss 0.65964 dice_loss 0.09094 +Epoch [3609/4000] Validation [2/10] Loss: 0.48268 focal_loss 0.38939 dice_loss 0.09329 +Epoch [3609/4000] Validation [3/10] Loss: 0.37282 focal_loss 0.26395 dice_loss 0.10887 +Epoch [3609/4000] Validation [4/10] Loss: 0.90897 focal_loss 0.34118 dice_loss 0.56779 +Epoch [3609/4000] Validation [5/10] Loss: 3.09439 focal_loss 2.42080 dice_loss 0.67359 +Epoch [3609/4000] Validation [6/10] Loss: 1.35991 focal_loss 0.64411 dice_loss 0.71579 +Epoch [3609/4000] Validation [7/10] Loss: 1.20221 focal_loss 0.54288 dice_loss 0.65934 +Epoch [3609/4000] Validation [8/10] Loss: 2.24024 focal_loss 1.64026 dice_loss 0.59998 +Epoch [3609/4000] Validation [9/10] Loss: 1.50540 focal_loss 0.96000 dice_loss 0.54540 +Epoch [3609/4000] Validation [10/10] Loss: 1.95385 focal_loss 1.21290 dice_loss 0.74095 +Epoch [3609/4000] Validation metric {'Val/mean dice_metric': 0.9514539241790771, 'Val/mean miou_metric': 0.9353147745132446, 'Val/mean f1': 0.9477263689041138, 'Val/mean precision': 0.9406951665878296, 'Val/mean recall': 0.954863429069519, 'Val/mean hd95_metric': 11.014494895935059} +Cheakpoint... +Epoch [3609/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514539241790771, 'Val/mean miou_metric': 0.9353147745132446, 'Val/mean f1': 0.9477263689041138, 'Val/mean precision': 0.9406951665878296, 'Val/mean recall': 0.954863429069519, 'Val/mean hd95_metric': 11.014494895935059} +Epoch [3610/4000] Training [1/39] Loss: 0.00401 +Epoch [3610/4000] Training [2/39] Loss: 0.12913 +Epoch [3610/4000] Training [3/39] Loss: 0.00603 +Epoch [3610/4000] Training [4/39] Loss: 0.00290 +Epoch [3610/4000] Training [5/39] Loss: 0.00440 +Epoch [3610/4000] Training [6/39] Loss: 0.00421 +Epoch [3610/4000] Training [7/39] Loss: 0.00691 +Epoch [3610/4000] Training [8/39] Loss: 0.12778 +Epoch [3610/4000] Training [9/39] Loss: 0.12872 +Epoch [3610/4000] Training [10/39] Loss: 0.00395 +Epoch [3610/4000] Training [11/39] Loss: 0.00488 +Epoch [3610/4000] Training [12/39] Loss: 0.12850 +Epoch [3610/4000] Training [13/39] Loss: 0.00519 +Epoch [3610/4000] Training [14/39] Loss: 0.00416 +Epoch [3610/4000] Training [15/39] Loss: 0.00558 +Epoch [3610/4000] Training [16/39] Loss: 0.12795 +Epoch [3610/4000] Training [17/39] Loss: 0.00296 +Epoch [3610/4000] Training [18/39] Loss: 0.12998 +Epoch [3610/4000] Training [19/39] Loss: 0.00720 +Epoch [3610/4000] Training [20/39] Loss: 0.00453 +Epoch [3610/4000] Training [21/39] Loss: 0.00512 +Epoch [3610/4000] Training [22/39] Loss: 0.00545 +Epoch [3610/4000] Training [23/39] Loss: 0.00639 +Epoch [3610/4000] Training [24/39] Loss: 0.00308 +Epoch [3610/4000] Training [25/39] Loss: 0.00436 +Epoch [3610/4000] Training [26/39] Loss: 0.12859 +Epoch [3610/4000] Training [27/39] Loss: 0.12996 +Epoch [3610/4000] Training [28/39] Loss: 0.00564 +Epoch [3610/4000] Training [29/39] Loss: 0.00669 +Epoch [3610/4000] Training [30/39] Loss: 0.00592 +Epoch [3610/4000] Training [31/39] Loss: 0.00533 +Epoch [3610/4000] Training [32/39] Loss: 0.12885 +Epoch [3610/4000] Training [33/39] Loss: 0.00423 +Epoch [3610/4000] Training [34/39] Loss: 0.12844 +Epoch [3610/4000] Training [35/39] Loss: 0.13021 +Epoch [3610/4000] Training [36/39] Loss: 0.00389 +Epoch [3610/4000] Training [37/39] Loss: 0.00345 +Epoch [3610/4000] Training [38/39] Loss: 0.00516 +Epoch [3610/4000] Training [39/39] Loss: 0.00566 +Epoch [3610/4000] Training metric {'Train/mean dice_metric': 0.9964155554771423, 'Train/mean miou_metric': 0.9932571649551392, 'Train/mean f1': 0.996882975101471, 'Train/mean precision': 0.9964257478713989, 'Train/mean recall': 0.997340738773346, 'Train/mean hd95_metric': 0.978263795375824} +Epoch [3610/4000] Validation [1/10] Loss: 0.72750 focal_loss 0.63845 dice_loss 0.08905 +Epoch [3610/4000] Validation [2/10] Loss: 0.47744 focal_loss 0.38492 dice_loss 0.09252 +Epoch [3610/4000] Validation [3/10] Loss: 0.36730 focal_loss 0.25855 dice_loss 0.10875 +Epoch [3610/4000] Validation [4/10] Loss: 0.90354 focal_loss 0.33758 dice_loss 0.56596 +Epoch [3610/4000] Validation [5/10] Loss: 3.02237 focal_loss 2.34886 dice_loss 0.67351 +Epoch [3610/4000] Validation [6/10] Loss: 1.36305 focal_loss 0.64450 dice_loss 0.71855 +Epoch [3610/4000] Validation [7/10] Loss: 1.20370 focal_loss 0.54775 dice_loss 0.65595 +Epoch [3610/4000] Validation [8/10] Loss: 2.26892 focal_loss 1.66716 dice_loss 0.60176 +Epoch [3610/4000] Validation [9/10] Loss: 1.48743 focal_loss 0.93980 dice_loss 0.54764 +Epoch [3610/4000] Validation [10/10] Loss: 1.94512 focal_loss 1.20702 dice_loss 0.73810 +Epoch [3610/4000] Validation metric {'Val/mean dice_metric': 0.9518125057220459, 'Val/mean miou_metric': 0.9360127449035645, 'Val/mean f1': 0.9486430287361145, 'Val/mean precision': 0.9424237608909607, 'Val/mean recall': 0.9549449682235718, 'Val/mean hd95_metric': 10.64108657836914} +Cheakpoint... +Epoch [3610/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9518], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9518125057220459, 'Val/mean miou_metric': 0.9360127449035645, 'Val/mean f1': 0.9486430287361145, 'Val/mean precision': 0.9424237608909607, 'Val/mean recall': 0.9549449682235718, 'Val/mean hd95_metric': 10.64108657836914} +Epoch [3611/4000] Training [1/39] Loss: 0.00507 +Epoch [3611/4000] Training [2/39] Loss: 0.00472 +Epoch [3611/4000] Training [3/39] Loss: 0.12886 +Epoch [3611/4000] Training [4/39] Loss: 0.00536 +Epoch [3611/4000] Training [5/39] Loss: 0.00353 +Epoch [3611/4000] Training [6/39] Loss: 0.12942 +Epoch [3611/4000] Training [7/39] Loss: 0.00538 +Epoch [3611/4000] Training [8/39] Loss: 0.00319 +Epoch [3611/4000] Training [9/39] Loss: 0.00431 +Epoch [3611/4000] Training [10/39] Loss: 0.00766 +Epoch [3611/4000] Training [11/39] Loss: 0.12975 +Epoch [3611/4000] Training [12/39] Loss: 0.12766 +Epoch [3611/4000] Training [13/39] Loss: 0.00555 +Epoch [3611/4000] Training [14/39] Loss: 0.13666 +Epoch [3611/4000] Training [15/39] Loss: 0.00713 +Epoch [3611/4000] Training [16/39] Loss: 0.00552 +Epoch [3611/4000] Training [17/39] Loss: 0.00555 +Epoch [3611/4000] Training [18/39] Loss: 0.12976 +Epoch [3611/4000] Training [19/39] Loss: 0.00684 +Epoch [3611/4000] Training [20/39] Loss: 0.00355 +Epoch [3611/4000] Training [21/39] Loss: 0.00693 +Epoch [3611/4000] Training [22/39] Loss: 0.00432 +Epoch [3611/4000] Training [23/39] Loss: 0.00596 +Epoch [3611/4000] Training [24/39] Loss: 0.00272 +Epoch [3611/4000] Training [25/39] Loss: 0.00268 +Epoch [3611/4000] Training [26/39] Loss: 0.00385 +Epoch [3611/4000] Training [27/39] Loss: 0.00604 +Epoch [3611/4000] Training [28/39] Loss: 0.25242 +Epoch [3611/4000] Training [29/39] Loss: 0.00361 +Epoch [3611/4000] Training [30/39] Loss: 0.13182 +Epoch [3611/4000] Training [31/39] Loss: 0.00446 +Epoch [3611/4000] Training [32/39] Loss: 0.00660 +Epoch [3611/4000] Training [33/39] Loss: 0.00531 +Epoch [3611/4000] Training [34/39] Loss: 0.00680 +Epoch [3611/4000] Training [35/39] Loss: 0.00350 +Epoch [3611/4000] Training [36/39] Loss: 0.12820 +Epoch [3611/4000] Training [37/39] Loss: 0.00531 +Epoch [3611/4000] Training [38/39] Loss: 0.12835 +Epoch [3611/4000] Training [39/39] Loss: 0.00689 +Epoch [3611/4000] Training metric {'Train/mean dice_metric': 0.9961825609207153, 'Train/mean miou_metric': 0.9928210973739624, 'Train/mean f1': 0.9968211054801941, 'Train/mean precision': 0.996406078338623, 'Train/mean recall': 0.9972366094589233, 'Train/mean hd95_metric': 0.9553359746932983} +Epoch [3611/4000] Validation [1/10] Loss: 0.74170 focal_loss 0.65256 dice_loss 0.08915 +Epoch [3611/4000] Validation [2/10] Loss: 0.47833 focal_loss 0.38542 dice_loss 0.09291 +Epoch [3611/4000] Validation [3/10] Loss: 0.38191 focal_loss 0.27263 dice_loss 0.10928 +Epoch [3611/4000] Validation [4/10] Loss: 0.89888 focal_loss 0.33304 dice_loss 0.56584 +Epoch [3611/4000] Validation [5/10] Loss: 3.10181 focal_loss 2.42823 dice_loss 0.67358 +Epoch [3611/4000] Validation [6/10] Loss: 1.34929 focal_loss 0.62886 dice_loss 0.72043 +Epoch [3611/4000] Validation [7/10] Loss: 1.19635 focal_loss 0.54144 dice_loss 0.65491 +Epoch [3611/4000] Validation [8/10] Loss: 2.25819 focal_loss 1.65739 dice_loss 0.60080 +Epoch [3611/4000] Validation [9/10] Loss: 1.52373 focal_loss 0.97714 dice_loss 0.54659 +Epoch [3611/4000] Validation [10/10] Loss: 1.92369 focal_loss 1.18535 dice_loss 0.73834 +Epoch [3611/4000] Validation metric {'Val/mean dice_metric': 0.9515403509140015, 'Val/mean miou_metric': 0.9355840086936951, 'Val/mean f1': 0.9482854008674622, 'Val/mean precision': 0.94235759973526, 'Val/mean recall': 0.954288125038147, 'Val/mean hd95_metric': 10.582483291625977} +Cheakpoint... +Epoch [3611/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515403509140015, 'Val/mean miou_metric': 0.9355840086936951, 'Val/mean f1': 0.9482854008674622, 'Val/mean precision': 0.94235759973526, 'Val/mean recall': 0.954288125038147, 'Val/mean hd95_metric': 10.582483291625977} +Epoch [3612/4000] Training [1/39] Loss: 0.00621 +Epoch [3612/4000] Training [2/39] Loss: 0.00566 +Epoch [3612/4000] Training [3/39] Loss: 0.00367 +Epoch [3612/4000] Training [4/39] Loss: 0.12980 +Epoch [3612/4000] Training [5/39] Loss: 0.00403 +Epoch [3612/4000] Training [6/39] Loss: 0.00435 +Epoch [3612/4000] Training [7/39] Loss: 0.00747 +Epoch [3612/4000] Training [8/39] Loss: 0.00413 +Epoch [3612/4000] Training [9/39] Loss: 0.00282 +Epoch [3612/4000] Training [10/39] Loss: 0.12879 +Epoch [3612/4000] Training [11/39] Loss: 0.12922 +Epoch [3612/4000] Training [12/39] Loss: 0.00684 +Epoch [3612/4000] Training [13/39] Loss: 0.00418 +Epoch [3612/4000] Training [14/39] Loss: 0.00664 +Epoch [3612/4000] Training [15/39] Loss: 0.12897 +Epoch [3612/4000] Training [16/39] Loss: 0.12893 +Epoch [3612/4000] Training [17/39] Loss: 0.12879 +Epoch [3612/4000] Training [18/39] Loss: 0.12979 +Epoch [3612/4000] Training [19/39] Loss: 0.12845 +Epoch [3612/4000] Training [20/39] Loss: 0.00456 +Epoch [3612/4000] Training [21/39] Loss: 0.00757 +Epoch [3612/4000] Training [22/39] Loss: 0.00456 +Epoch [3612/4000] Training [23/39] Loss: 0.00312 +Epoch [3612/4000] Training [24/39] Loss: 0.00635 +Epoch [3612/4000] Training [25/39] Loss: 0.12906 +Epoch [3612/4000] Training [26/39] Loss: 0.00438 +Epoch [3612/4000] Training [27/39] Loss: 0.00635 +Epoch [3612/4000] Training [28/39] Loss: 0.00513 +Epoch [3612/4000] Training [29/39] Loss: 0.00554 +Epoch [3612/4000] Training [30/39] Loss: 0.12781 +Epoch [3612/4000] Training [31/39] Loss: 0.13248 +Epoch [3612/4000] Training [32/39] Loss: 0.00618 +Epoch [3612/4000] Training [33/39] Loss: 0.00428 +Epoch [3612/4000] Training [34/39] Loss: 0.00514 +Epoch [3612/4000] Training [35/39] Loss: 0.12841 +Epoch [3612/4000] Training [36/39] Loss: 0.00286 +Epoch [3612/4000] Training [37/39] Loss: 0.00663 +Epoch [3612/4000] Training [38/39] Loss: 0.00414 +Epoch [3612/4000] Training [39/39] Loss: 0.13079 +Epoch [3612/4000] Training metric {'Train/mean dice_metric': 0.9954512119293213, 'Train/mean miou_metric': 0.9922143220901489, 'Train/mean f1': 0.9967769384384155, 'Train/mean precision': 0.9963165521621704, 'Train/mean recall': 0.9972378015518188, 'Train/mean hd95_metric': 0.9535576701164246} +Epoch [3612/4000] Validation [1/10] Loss: 0.71356 focal_loss 0.62454 dice_loss 0.08901 +Epoch [3612/4000] Validation [2/10] Loss: 0.47030 focal_loss 0.37858 dice_loss 0.09173 +Epoch [3612/4000] Validation [3/10] Loss: 0.36302 focal_loss 0.25413 dice_loss 0.10890 +Epoch [3612/4000] Validation [4/10] Loss: 0.89700 focal_loss 0.33095 dice_loss 0.56605 +Epoch [3612/4000] Validation [5/10] Loss: 2.98855 focal_loss 2.31537 dice_loss 0.67318 +Epoch [3612/4000] Validation [6/10] Loss: 1.36345 focal_loss 0.64338 dice_loss 0.72008 +Epoch [3612/4000] Validation [7/10] Loss: 1.19578 focal_loss 0.54285 dice_loss 0.65292 +Epoch [3612/4000] Validation [8/10] Loss: 2.17814 focal_loss 1.58027 dice_loss 0.59787 +Epoch [3612/4000] Validation [9/10] Loss: 1.54287 focal_loss 0.99540 dice_loss 0.54747 +Epoch [3612/4000] Validation [10/10] Loss: 1.94344 focal_loss 1.20241 dice_loss 0.74103 +Epoch [3612/4000] Validation metric {'Val/mean dice_metric': 0.950851321220398, 'Val/mean miou_metric': 0.9349610209465027, 'Val/mean f1': 0.9476203322410583, 'Val/mean precision': 0.9408913254737854, 'Val/mean recall': 0.9544461965560913, 'Val/mean hd95_metric': 10.628351211547852} +Cheakpoint... +Epoch [3612/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950851321220398, 'Val/mean miou_metric': 0.9349610209465027, 'Val/mean f1': 0.9476203322410583, 'Val/mean precision': 0.9408913254737854, 'Val/mean recall': 0.9544461965560913, 'Val/mean hd95_metric': 10.628351211547852} +Epoch [3613/4000] Training [1/39] Loss: 0.00353 +Epoch [3613/4000] Training [2/39] Loss: 0.00637 +Epoch [3613/4000] Training [3/39] Loss: 0.00488 +Epoch [3613/4000] Training [4/39] Loss: 0.00506 +Epoch [3613/4000] Training [5/39] Loss: 0.00424 +Epoch [3613/4000] Training [6/39] Loss: 0.12862 +Epoch [3613/4000] Training [7/39] Loss: 0.00553 +Epoch [3613/4000] Training [8/39] Loss: 0.12892 +Epoch [3613/4000] Training [9/39] Loss: 0.00473 +Epoch [3613/4000] Training [10/39] Loss: 0.01016 +Epoch [3613/4000] Training [11/39] Loss: 0.01210 +Epoch [3613/4000] Training [12/39] Loss: 0.09324 +Epoch [3613/4000] Training [13/39] Loss: 0.12943 +Epoch [3613/4000] Training [14/39] Loss: 0.00492 +Epoch [3613/4000] Training [15/39] Loss: 0.12852 +Epoch [3613/4000] Training [16/39] Loss: 0.12817 +Epoch [3613/4000] Training [17/39] Loss: 0.12794 +Epoch [3613/4000] Training [18/39] Loss: 0.25247 +Epoch [3613/4000] Training [19/39] Loss: 0.00775 +Epoch [3613/4000] Training [20/39] Loss: 0.00396 +Epoch [3613/4000] Training [21/39] Loss: 0.12849 +Epoch [3613/4000] Training [22/39] Loss: 0.00383 +Epoch [3613/4000] Training [23/39] Loss: 0.00371 +Epoch [3613/4000] Training [24/39] Loss: 0.00331 +Epoch [3613/4000] Training [25/39] Loss: 0.00654 +Epoch [3613/4000] Training [26/39] Loss: 0.00565 +Epoch [3613/4000] Training [27/39] Loss: 0.00462 +Epoch [3613/4000] Training [28/39] Loss: 0.12866 +Epoch [3613/4000] Training [29/39] Loss: 0.00376 +Epoch [3613/4000] Training [30/39] Loss: 0.00653 +Epoch [3613/4000] Training [31/39] Loss: 0.00456 +Epoch [3613/4000] Training [32/39] Loss: 0.12901 +Epoch [3613/4000] Training [33/39] Loss: 0.00526 +Epoch [3613/4000] Training [34/39] Loss: 0.12853 +Epoch [3613/4000] Training [35/39] Loss: 0.00452 +Epoch [3613/4000] Training [36/39] Loss: 0.13176 +Epoch [3613/4000] Training [37/39] Loss: 0.13395 +Epoch [3613/4000] Training [38/39] Loss: 0.00404 +Epoch [3613/4000] Training [39/39] Loss: 0.00455 +Epoch [3613/4000] Training metric {'Train/mean dice_metric': 0.9953600168228149, 'Train/mean miou_metric': 0.9920285940170288, 'Train/mean f1': 0.9967755079269409, 'Train/mean precision': 0.9963499903678894, 'Train/mean recall': 0.9972015619277954, 'Train/mean hd95_metric': 0.9778807759284973} +Epoch [3613/4000] Validation [1/10] Loss: 0.71734 focal_loss 0.62886 dice_loss 0.08848 +Epoch [3613/4000] Validation [2/10] Loss: 0.47590 focal_loss 0.38478 dice_loss 0.09112 +Epoch [3613/4000] Validation [3/10] Loss: 0.36554 focal_loss 0.25667 dice_loss 0.10887 +Epoch [3613/4000] Validation [4/10] Loss: 0.89786 focal_loss 0.33149 dice_loss 0.56637 +Epoch [3613/4000] Validation [5/10] Loss: 3.04859 focal_loss 2.37480 dice_loss 0.67379 +Epoch [3613/4000] Validation [6/10] Loss: 1.38467 focal_loss 0.66530 dice_loss 0.71937 +Epoch [3613/4000] Validation [7/10] Loss: 1.20915 focal_loss 0.55447 dice_loss 0.65469 +Epoch [3613/4000] Validation [8/10] Loss: 2.21157 focal_loss 1.61509 dice_loss 0.59648 +Epoch [3613/4000] Validation [9/10] Loss: 1.56691 focal_loss 1.01892 dice_loss 0.54799 +Epoch [3613/4000] Validation [10/10] Loss: 1.97258 focal_loss 1.23224 dice_loss 0.74034 +Epoch [3613/4000] Validation metric {'Val/mean dice_metric': 0.9508745670318604, 'Val/mean miou_metric': 0.9349645376205444, 'Val/mean f1': 0.9475302696228027, 'Val/mean precision': 0.9402502775192261, 'Val/mean recall': 0.954923689365387, 'Val/mean hd95_metric': 10.601801872253418} +Cheakpoint... +Epoch [3613/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508745670318604, 'Val/mean miou_metric': 0.9349645376205444, 'Val/mean f1': 0.9475302696228027, 'Val/mean precision': 0.9402502775192261, 'Val/mean recall': 0.954923689365387, 'Val/mean hd95_metric': 10.601801872253418} +Epoch [3614/4000] Training [1/39] Loss: 0.12981 +Epoch [3614/4000] Training [2/39] Loss: 0.00386 +Epoch [3614/4000] Training [3/39] Loss: 0.00573 +Epoch [3614/4000] Training [4/39] Loss: 0.00353 +Epoch [3614/4000] Training [5/39] Loss: 0.00421 +Epoch [3614/4000] Training [6/39] Loss: 0.01055 +Epoch [3614/4000] Training [7/39] Loss: 0.00414 +Epoch [3614/4000] Training [8/39] Loss: 0.13064 +Epoch [3614/4000] Training [9/39] Loss: 0.00688 +Epoch [3614/4000] Training [10/39] Loss: 0.12719 +Epoch [3614/4000] Training [11/39] Loss: 0.00414 +Epoch [3614/4000] Training [12/39] Loss: 0.00313 +Epoch [3614/4000] Training [13/39] Loss: 0.00420 +Epoch [3614/4000] Training [14/39] Loss: 0.00960 +Epoch [3614/4000] Training [15/39] Loss: 0.45894 +Epoch [3614/4000] Training [16/39] Loss: 0.00766 +Epoch [3614/4000] Training [17/39] Loss: 0.00384 +Epoch [3614/4000] Training [18/39] Loss: 0.12805 +Epoch [3614/4000] Training [19/39] Loss: 0.12874 +Epoch [3614/4000] Training [20/39] Loss: 0.12815 +Epoch [3614/4000] Training [21/39] Loss: 0.00445 +Epoch [3614/4000] Training [22/39] Loss: 0.00369 +Epoch [3614/4000] Training [23/39] Loss: 0.12703 +Epoch [3614/4000] Training [24/39] Loss: 0.12905 +Epoch [3614/4000] Training [25/39] Loss: 0.00588 +Epoch [3614/4000] Training [26/39] Loss: 0.12898 +Epoch [3614/4000] Training [27/39] Loss: 0.12774 +Epoch [3614/4000] Training [28/39] Loss: 0.12845 +Epoch [3614/4000] Training [29/39] Loss: 0.00568 +Epoch [3614/4000] Training [30/39] Loss: 0.00463 +Epoch [3614/4000] Training [31/39] Loss: 0.00484 +Epoch [3614/4000] Training [32/39] Loss: 0.00380 +Epoch [3614/4000] Training [33/39] Loss: 0.00849 +Epoch [3614/4000] Training [34/39] Loss: 0.00527 +Epoch [3614/4000] Training [35/39] Loss: 0.00292 +Epoch [3614/4000] Training [36/39] Loss: 0.00362 +Epoch [3614/4000] Training [37/39] Loss: 0.12808 +Epoch [3614/4000] Training [38/39] Loss: 0.00432 +Epoch [3614/4000] Training [39/39] Loss: 0.00748 +Epoch [3614/4000] Training metric {'Train/mean dice_metric': 0.9963394999504089, 'Train/mean miou_metric': 0.9931243658065796, 'Train/mean f1': 0.996936559677124, 'Train/mean precision': 0.9964191317558289, 'Train/mean recall': 0.9974545240402222, 'Train/mean hd95_metric': 0.9528906345367432} +Epoch [3614/4000] Validation [1/10] Loss: 0.70953 focal_loss 0.62138 dice_loss 0.08815 +Epoch [3614/4000] Validation [2/10] Loss: 0.46948 focal_loss 0.38084 dice_loss 0.08864 +Epoch [3614/4000] Validation [3/10] Loss: 0.36083 focal_loss 0.25214 dice_loss 0.10869 +Epoch [3614/4000] Validation [4/10] Loss: 0.90167 focal_loss 0.33289 dice_loss 0.56878 +Epoch [3614/4000] Validation [5/10] Loss: 2.99845 focal_loss 2.32462 dice_loss 0.67383 +Epoch [3614/4000] Validation [6/10] Loss: 1.39100 focal_loss 0.67222 dice_loss 0.71879 +Epoch [3614/4000] Validation [7/10] Loss: 1.20621 focal_loss 0.55004 dice_loss 0.65617 +Epoch [3614/4000] Validation [8/10] Loss: 2.19660 focal_loss 1.60199 dice_loss 0.59461 +Epoch [3614/4000] Validation [9/10] Loss: 1.53061 focal_loss 0.98193 dice_loss 0.54868 +Epoch [3614/4000] Validation [10/10] Loss: 1.96532 focal_loss 1.22495 dice_loss 0.74036 +Epoch [3614/4000] Validation metric {'Val/mean dice_metric': 0.9516796469688416, 'Val/mean miou_metric': 0.9358018636703491, 'Val/mean f1': 0.9478849172592163, 'Val/mean precision': 0.9407613277435303, 'Val/mean recall': 0.9551172852516174, 'Val/mean hd95_metric': 10.703526496887207} +Cheakpoint... +Epoch [3614/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516796469688416, 'Val/mean miou_metric': 0.9358018636703491, 'Val/mean f1': 0.9478849172592163, 'Val/mean precision': 0.9407613277435303, 'Val/mean recall': 0.9551172852516174, 'Val/mean hd95_metric': 10.703526496887207} +Epoch [3615/4000] Training [1/39] Loss: 0.00508 +Epoch [3615/4000] Training [2/39] Loss: 0.00463 +Epoch [3615/4000] Training [3/39] Loss: 0.00572 +Epoch [3615/4000] Training [4/39] Loss: 0.00506 +Epoch [3615/4000] Training [5/39] Loss: 0.12939 +Epoch [3615/4000] Training [6/39] Loss: 0.00309 +Epoch [3615/4000] Training [7/39] Loss: 0.00609 +Epoch [3615/4000] Training [8/39] Loss: 0.12930 +Epoch [3615/4000] Training [9/39] Loss: 0.00597 +Epoch [3615/4000] Training [10/39] Loss: 0.13177 +Epoch [3615/4000] Training [11/39] Loss: 0.00417 +Epoch [3615/4000] Training [12/39] Loss: 0.04454 +Epoch [3615/4000] Training [13/39] Loss: 0.00360 +Epoch [3615/4000] Training [14/39] Loss: 0.13189 +Epoch [3615/4000] Training [15/39] Loss: 0.00336 +Epoch [3615/4000] Training [16/39] Loss: 0.12997 +Epoch [3615/4000] Training [17/39] Loss: 0.12912 +Epoch [3615/4000] Training [18/39] Loss: 0.25434 +Epoch [3615/4000] Training [19/39] Loss: 0.00471 +Epoch [3615/4000] Training [20/39] Loss: 0.12844 +Epoch [3615/4000] Training [21/39] Loss: 0.13189 +Epoch [3615/4000] Training [22/39] Loss: 0.12829 +Epoch [3615/4000] Training [23/39] Loss: 0.00510 +Epoch [3615/4000] Training [24/39] Loss: 0.00380 +Epoch [3615/4000] Training [25/39] Loss: 0.13036 +Epoch [3615/4000] Training [26/39] Loss: 0.00537 +Epoch [3615/4000] Training [27/39] Loss: 0.12779 +Epoch [3615/4000] Training [28/39] Loss: 0.00435 +Epoch [3615/4000] Training [29/39] Loss: 0.00305 +Epoch [3615/4000] Training [30/39] Loss: 0.00388 +Epoch [3615/4000] Training [31/39] Loss: 0.00786 +Epoch [3615/4000] Training [32/39] Loss: 0.00356 +Epoch [3615/4000] Training [33/39] Loss: 0.00792 +Epoch [3615/4000] Training [34/39] Loss: 0.00372 +Epoch [3615/4000] Training [35/39] Loss: 0.00533 +Epoch [3615/4000] Training [36/39] Loss: 0.00507 +Epoch [3615/4000] Training [37/39] Loss: 0.00671 +Epoch [3615/4000] Training [38/39] Loss: 0.00690 +Epoch [3615/4000] Training [39/39] Loss: 0.00591 +Epoch [3615/4000] Training metric {'Train/mean dice_metric': 0.9962521195411682, 'Train/mean miou_metric': 0.9929458498954773, 'Train/mean f1': 0.9968365430831909, 'Train/mean precision': 0.9964059591293335, 'Train/mean recall': 0.9972675442695618, 'Train/mean hd95_metric': 0.9504280686378479} +Epoch [3615/4000] Validation [1/10] Loss: 0.76672 focal_loss 0.67511 dice_loss 0.09160 +Epoch [3615/4000] Validation [2/10] Loss: 0.47987 focal_loss 0.38999 dice_loss 0.08989 +Epoch [3615/4000] Validation [3/10] Loss: 0.36500 focal_loss 0.25654 dice_loss 0.10845 +Epoch [3615/4000] Validation [4/10] Loss: 0.91223 focal_loss 0.34397 dice_loss 0.56825 +Epoch [3615/4000] Validation [5/10] Loss: 3.08166 focal_loss 2.40812 dice_loss 0.67354 +Epoch [3615/4000] Validation [6/10] Loss: 1.40507 focal_loss 0.68536 dice_loss 0.71971 +Epoch [3615/4000] Validation [7/10] Loss: 1.22265 focal_loss 0.56506 dice_loss 0.65760 +Epoch [3615/4000] Validation [8/10] Loss: 2.17245 focal_loss 1.58602 dice_loss 0.58642 +Epoch [3615/4000] Validation [9/10] Loss: 1.59503 focal_loss 1.04620 dice_loss 0.54883 +Epoch [3615/4000] Validation [10/10] Loss: 2.01108 focal_loss 1.26767 dice_loss 0.74341 +Epoch [3615/4000] Validation metric {'Val/mean dice_metric': 0.9515318274497986, 'Val/mean miou_metric': 0.9355432391166687, 'Val/mean f1': 0.9473610520362854, 'Val/mean precision': 0.9394909143447876, 'Val/mean recall': 0.9553642868995667, 'Val/mean hd95_metric': 10.69332504272461} +Cheakpoint... +Epoch [3615/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515318274497986, 'Val/mean miou_metric': 0.9355432391166687, 'Val/mean f1': 0.9473610520362854, 'Val/mean precision': 0.9394909143447876, 'Val/mean recall': 0.9553642868995667, 'Val/mean hd95_metric': 10.69332504272461} +Epoch [3616/4000] Training [1/39] Loss: 0.00544 +Epoch [3616/4000] Training [2/39] Loss: 0.08373 +Epoch [3616/4000] Training [3/39] Loss: 0.00557 +Epoch [3616/4000] Training [4/39] Loss: 0.00531 +Epoch [3616/4000] Training [5/39] Loss: 0.00484 +Epoch [3616/4000] Training [6/39] Loss: 0.12803 +Epoch [3616/4000] Training [7/39] Loss: 0.00605 +Epoch [3616/4000] Training [8/39] Loss: 0.00351 +Epoch [3616/4000] Training [9/39] Loss: 0.13080 +Epoch [3616/4000] Training [10/39] Loss: 0.25219 +Epoch [3616/4000] Training [11/39] Loss: 0.00576 +Epoch [3616/4000] Training [12/39] Loss: 0.00954 +Epoch [3616/4000] Training [13/39] Loss: 0.00600 +Epoch [3616/4000] Training [14/39] Loss: 0.00556 +Epoch [3616/4000] Training [15/39] Loss: 0.00540 +Epoch [3616/4000] Training [16/39] Loss: 0.00354 +Epoch [3616/4000] Training [17/39] Loss: 0.00608 +Epoch [3616/4000] Training [18/39] Loss: 0.00484 +Epoch [3616/4000] Training [19/39] Loss: 0.00446 +Epoch [3616/4000] Training [20/39] Loss: 0.00551 +Epoch [3616/4000] Training [21/39] Loss: 0.00588 +Epoch [3616/4000] Training [22/39] Loss: 0.13256 +Epoch [3616/4000] Training [23/39] Loss: 0.00640 +Epoch [3616/4000] Training [24/39] Loss: 0.00532 +Epoch [3616/4000] Training [25/39] Loss: 0.00841 +Epoch [3616/4000] Training [26/39] Loss: 0.13242 +Epoch [3616/4000] Training [27/39] Loss: 0.00378 +Epoch [3616/4000] Training [28/39] Loss: 0.00724 +Epoch [3616/4000] Training [29/39] Loss: 0.00769 +Epoch [3616/4000] Training [30/39] Loss: 0.25285 +Epoch [3616/4000] Training [31/39] Loss: 0.12866 +Epoch [3616/4000] Training [32/39] Loss: 0.00668 +Epoch [3616/4000] Training [33/39] Loss: 0.00519 +Epoch [3616/4000] Training [34/39] Loss: 0.00420 +Epoch [3616/4000] Training [35/39] Loss: 0.00498 +Epoch [3616/4000] Training [36/39] Loss: 0.00422 +Epoch [3616/4000] Training [37/39] Loss: 0.00384 +Epoch [3616/4000] Training [38/39] Loss: 0.00787 +Epoch [3616/4000] Training [39/39] Loss: 0.00388 +Epoch [3616/4000] Training metric {'Train/mean dice_metric': 0.9959303736686707, 'Train/mean miou_metric': 0.9923180937767029, 'Train/mean f1': 0.9965690970420837, 'Train/mean precision': 0.9961016178131104, 'Train/mean recall': 0.9970369935035706, 'Train/mean hd95_metric': 0.9817215204238892} +Epoch [3616/4000] Validation [1/10] Loss: 0.74958 focal_loss 0.66081 dice_loss 0.08877 +Epoch [3616/4000] Validation [2/10] Loss: 0.47551 focal_loss 0.38366 dice_loss 0.09185 +Epoch [3616/4000] Validation [3/10] Loss: 0.38604 focal_loss 0.27633 dice_loss 0.10971 +Epoch [3616/4000] Validation [4/10] Loss: 0.89735 focal_loss 0.33100 dice_loss 0.56635 +Epoch [3616/4000] Validation [5/10] Loss: 3.12574 focal_loss 2.45209 dice_loss 0.67365 +Epoch [3616/4000] Validation [6/10] Loss: 1.36363 focal_loss 0.64802 dice_loss 0.71561 +Epoch [3616/4000] Validation [7/10] Loss: 1.20072 focal_loss 0.54954 dice_loss 0.65118 +Epoch [3616/4000] Validation [8/10] Loss: 2.31610 focal_loss 1.70660 dice_loss 0.60950 +Epoch [3616/4000] Validation [9/10] Loss: 1.56621 focal_loss 1.01783 dice_loss 0.54838 +Epoch [3616/4000] Validation [10/10] Loss: 1.94464 focal_loss 1.20362 dice_loss 0.74102 +Epoch [3616/4000] Validation metric {'Val/mean dice_metric': 0.9512968063354492, 'Val/mean miou_metric': 0.9351112246513367, 'Val/mean f1': 0.9478788375854492, 'Val/mean precision': 0.9422361254692078, 'Val/mean recall': 0.9535894989967346, 'Val/mean hd95_metric': 10.59490966796875} +Cheakpoint... +Epoch [3616/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512968063354492, 'Val/mean miou_metric': 0.9351112246513367, 'Val/mean f1': 0.9478788375854492, 'Val/mean precision': 0.9422361254692078, 'Val/mean recall': 0.9535894989967346, 'Val/mean hd95_metric': 10.59490966796875} +Epoch [3617/4000] Training [1/39] Loss: 0.00502 +Epoch [3617/4000] Training [2/39] Loss: 0.00601 +Epoch [3617/4000] Training [3/39] Loss: 0.00650 +Epoch [3617/4000] Training [4/39] Loss: 0.00641 +Epoch [3617/4000] Training [5/39] Loss: 0.00437 +Epoch [3617/4000] Training [6/39] Loss: 0.00503 +Epoch [3617/4000] Training [7/39] Loss: 0.00520 +Epoch [3617/4000] Training [8/39] Loss: 0.00517 +Epoch [3617/4000] Training [9/39] Loss: 0.00406 +Epoch [3617/4000] Training [10/39] Loss: 0.00360 +Epoch [3617/4000] Training [11/39] Loss: 0.00428 +Epoch [3617/4000] Training [12/39] Loss: 0.00524 +Epoch [3617/4000] Training [13/39] Loss: 0.00493 +Epoch [3617/4000] Training [14/39] Loss: 0.25256 +Epoch [3617/4000] Training [15/39] Loss: 0.00388 +Epoch [3617/4000] Training [16/39] Loss: 0.00454 +Epoch [3617/4000] Training [17/39] Loss: 0.00362 +Epoch [3617/4000] Training [18/39] Loss: 0.00478 +Epoch [3617/4000] Training [19/39] Loss: 0.00669 +Epoch [3617/4000] Training [20/39] Loss: 0.12858 +Epoch [3617/4000] Training [21/39] Loss: 0.00535 +Epoch [3617/4000] Training [22/39] Loss: 0.00518 +Epoch [3617/4000] Training [23/39] Loss: 0.00382 +Epoch [3617/4000] Training [24/39] Loss: 0.00366 +Epoch [3617/4000] Training [25/39] Loss: 0.12902 +Epoch [3617/4000] Training [26/39] Loss: 0.00657 +Epoch [3617/4000] Training [27/39] Loss: 0.12792 +Epoch [3617/4000] Training [28/39] Loss: 0.00632 +Epoch [3617/4000] Training [29/39] Loss: 0.00396 +Epoch [3617/4000] Training [30/39] Loss: 0.00702 +Epoch [3617/4000] Training [31/39] Loss: 0.00253 +Epoch [3617/4000] Training [32/39] Loss: 0.00952 +Epoch [3617/4000] Training [33/39] Loss: 0.00690 +Epoch [3617/4000] Training [34/39] Loss: 0.13102 +Epoch [3617/4000] Training [35/39] Loss: 0.00266 +Epoch [3617/4000] Training [36/39] Loss: 0.25460 +Epoch [3617/4000] Training [37/39] Loss: 0.00435 +Epoch [3617/4000] Training [38/39] Loss: 0.00563 +Epoch [3617/4000] Training [39/39] Loss: 0.12928 +Epoch [3617/4000] Training metric {'Train/mean dice_metric': 0.9962032437324524, 'Train/mean miou_metric': 0.992851197719574, 'Train/mean f1': 0.9967812895774841, 'Train/mean precision': 0.9963548183441162, 'Train/mean recall': 0.9972081780433655, 'Train/mean hd95_metric': 0.9555268883705139} +Epoch [3617/4000] Validation [1/10] Loss: 0.78244 focal_loss 0.69017 dice_loss 0.09228 +Epoch [3617/4000] Validation [2/10] Loss: 0.47447 focal_loss 0.38557 dice_loss 0.08890 +Epoch [3617/4000] Validation [3/10] Loss: 0.36326 focal_loss 0.25508 dice_loss 0.10818 +Epoch [3617/4000] Validation [4/10] Loss: 0.92599 focal_loss 0.35333 dice_loss 0.57266 +Epoch [3617/4000] Validation [5/10] Loss: 3.05551 focal_loss 2.38260 dice_loss 0.67291 +Epoch [3617/4000] Validation [6/10] Loss: 1.39810 focal_loss 0.67953 dice_loss 0.71857 +Epoch [3617/4000] Validation [7/10] Loss: 1.22795 focal_loss 0.57113 dice_loss 0.65683 +Epoch [3617/4000] Validation [8/10] Loss: 2.09557 focal_loss 1.51649 dice_loss 0.57908 +Epoch [3617/4000] Validation [9/10] Loss: 1.71034 focal_loss 1.16140 dice_loss 0.54894 +Epoch [3617/4000] Validation [10/10] Loss: 2.03818 focal_loss 1.29263 dice_loss 0.74555 +Epoch [3617/4000] Validation metric {'Val/mean dice_metric': 0.9512226581573486, 'Val/mean miou_metric': 0.935145378112793, 'Val/mean f1': 0.9466297030448914, 'Val/mean precision': 0.9376659989356995, 'Val/mean recall': 0.9557665586471558, 'Val/mean hd95_metric': 10.975940704345703} +Cheakpoint... +Epoch [3617/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512226581573486, 'Val/mean miou_metric': 0.935145378112793, 'Val/mean f1': 0.9466297030448914, 'Val/mean precision': 0.9376659989356995, 'Val/mean recall': 0.9557665586471558, 'Val/mean hd95_metric': 10.975940704345703} +Epoch [3618/4000] Training [1/39] Loss: 0.12904 +Epoch [3618/4000] Training [2/39] Loss: 0.00394 +Epoch [3618/4000] Training [3/39] Loss: 0.00483 +Epoch [3618/4000] Training [4/39] Loss: 0.00554 +Epoch [3618/4000] Training [5/39] Loss: 0.00668 +Epoch [3618/4000] Training [6/39] Loss: 0.00451 +Epoch [3618/4000] Training [7/39] Loss: 0.13181 +Epoch [3618/4000] Training [8/39] Loss: 0.00347 +Epoch [3618/4000] Training [9/39] Loss: 0.12975 +Epoch [3618/4000] Training [10/39] Loss: 0.00403 +Epoch [3618/4000] Training [11/39] Loss: 0.00489 +Epoch [3618/4000] Training [12/39] Loss: 0.12860 +Epoch [3618/4000] Training [13/39] Loss: 0.00407 +Epoch [3618/4000] Training [14/39] Loss: 0.00825 +Epoch [3618/4000] Training [15/39] Loss: 0.13032 +Epoch [3618/4000] Training [16/39] Loss: 0.00309 +Epoch [3618/4000] Training [17/39] Loss: 0.00386 +Epoch [3618/4000] Training [18/39] Loss: 0.00499 +Epoch [3618/4000] Training [19/39] Loss: 0.00516 +Epoch [3618/4000] Training [20/39] Loss: 0.00522 +Epoch [3618/4000] Training [21/39] Loss: 0.00471 +Epoch [3618/4000] Training [22/39] Loss: 0.12935 +Epoch [3618/4000] Training [23/39] Loss: 0.00832 +Epoch [3618/4000] Training [24/39] Loss: 0.00690 +Epoch [3618/4000] Training [25/39] Loss: 0.00312 +Epoch [3618/4000] Training [26/39] Loss: 0.00513 +Epoch [3618/4000] Training [27/39] Loss: 0.00490 +Epoch [3618/4000] Training [28/39] Loss: 0.00463 +Epoch [3618/4000] Training [29/39] Loss: 0.00594 +Epoch [3618/4000] Training [30/39] Loss: 0.00433 +Epoch [3618/4000] Training [31/39] Loss: 0.00348 +Epoch [3618/4000] Training [32/39] Loss: 0.00271 +Epoch [3618/4000] Training [33/39] Loss: 0.00410 +Epoch [3618/4000] Training [34/39] Loss: 0.00506 +Epoch [3618/4000] Training [35/39] Loss: 0.00662 +Epoch [3618/4000] Training [36/39] Loss: 0.25371 +Epoch [3618/4000] Training [37/39] Loss: 0.00526 +Epoch [3618/4000] Training [38/39] Loss: 0.00708 +Epoch [3618/4000] Training [39/39] Loss: 0.12882 +Epoch [3618/4000] Training metric {'Train/mean dice_metric': 0.995391845703125, 'Train/mean miou_metric': 0.9920709133148193, 'Train/mean f1': 0.9968045353889465, 'Train/mean precision': 0.996361255645752, 'Train/mean recall': 0.9972482323646545, 'Train/mean hd95_metric': 0.9614966511726379} +Epoch [3618/4000] Validation [1/10] Loss: 0.74708 focal_loss 0.65606 dice_loss 0.09102 +Epoch [3618/4000] Validation [2/10] Loss: 0.47379 focal_loss 0.38318 dice_loss 0.09061 +Epoch [3618/4000] Validation [3/10] Loss: 0.35800 focal_loss 0.24973 dice_loss 0.10827 +Epoch [3618/4000] Validation [4/10] Loss: 0.90875 focal_loss 0.33847 dice_loss 0.57028 +Epoch [3618/4000] Validation [5/10] Loss: 3.00341 focal_loss 2.33025 dice_loss 0.67317 +Epoch [3618/4000] Validation [6/10] Loss: 1.37914 focal_loss 0.65951 dice_loss 0.71963 +Epoch [3618/4000] Validation [7/10] Loss: 1.21647 focal_loss 0.56076 dice_loss 0.65571 +Epoch [3618/4000] Validation [8/10] Loss: 2.13691 focal_loss 1.54786 dice_loss 0.58906 +Epoch [3618/4000] Validation [9/10] Loss: 1.64811 focal_loss 1.09947 dice_loss 0.54863 +Epoch [3618/4000] Validation [10/10] Loss: 1.98848 focal_loss 1.24474 dice_loss 0.74374 +Epoch [3618/4000] Validation metric {'Val/mean dice_metric': 0.9507420063018799, 'Val/mean miou_metric': 0.9347444176673889, 'Val/mean f1': 0.947106122970581, 'Val/mean precision': 0.9390693306922913, 'Val/mean recall': 0.9552815556526184, 'Val/mean hd95_metric': 10.84522819519043} +Cheakpoint... +Epoch [3618/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507420063018799, 'Val/mean miou_metric': 0.9347444176673889, 'Val/mean f1': 0.947106122970581, 'Val/mean precision': 0.9390693306922913, 'Val/mean recall': 0.9552815556526184, 'Val/mean hd95_metric': 10.84522819519043} +Epoch [3619/4000] Training [1/39] Loss: 0.00399 +Epoch [3619/4000] Training [2/39] Loss: 0.08979 +Epoch [3619/4000] Training [3/39] Loss: 0.00499 +Epoch [3619/4000] Training [4/39] Loss: 0.00473 +Epoch [3619/4000] Training [5/39] Loss: 0.12790 +Epoch [3619/4000] Training [6/39] Loss: 0.00649 +Epoch [3619/4000] Training [7/39] Loss: 0.00363 +Epoch [3619/4000] Training [8/39] Loss: 0.12818 +Epoch [3619/4000] Training [9/39] Loss: 0.00469 +Epoch [3619/4000] Training [10/39] Loss: 0.00604 +Epoch [3619/4000] Training [11/39] Loss: 0.00666 +Epoch [3619/4000] Training [12/39] Loss: 0.00349 +Epoch [3619/4000] Training [13/39] Loss: 0.00311 +Epoch [3619/4000] Training [14/39] Loss: 0.12981 +Epoch [3619/4000] Training [15/39] Loss: 0.00730 +Epoch [3619/4000] Training [16/39] Loss: 0.13011 +Epoch [3619/4000] Training [17/39] Loss: 0.00586 +Epoch [3619/4000] Training [18/39] Loss: 0.12902 +Epoch [3619/4000] Training [19/39] Loss: 0.12781 +Epoch [3619/4000] Training [20/39] Loss: 0.00448 +Epoch [3619/4000] Training [21/39] Loss: 0.00489 +Epoch [3619/4000] Training [22/39] Loss: 0.00665 +Epoch [3619/4000] Training [23/39] Loss: 0.00551 +Epoch [3619/4000] Training [24/39] Loss: 0.00458 +Epoch [3619/4000] Training [25/39] Loss: 0.12771 +Epoch [3619/4000] Training [26/39] Loss: 0.12816 +Epoch [3619/4000] Training [27/39] Loss: 0.00710 +Epoch [3619/4000] Training [28/39] Loss: 0.00759 +Epoch [3619/4000] Training [29/39] Loss: 0.00344 +Epoch [3619/4000] Training [30/39] Loss: 0.00921 +Epoch [3619/4000] Training [31/39] Loss: 0.00544 +Epoch [3619/4000] Training [32/39] Loss: 0.00272 +Epoch [3619/4000] Training [33/39] Loss: 0.12834 +Epoch [3619/4000] Training [34/39] Loss: 0.00786 +Epoch [3619/4000] Training [35/39] Loss: 0.00365 +Epoch [3619/4000] Training [36/39] Loss: 0.25323 +Epoch [3619/4000] Training [37/39] Loss: 0.12938 +Epoch [3619/4000] Training [38/39] Loss: 0.00309 +Epoch [3619/4000] Training [39/39] Loss: 0.00502 +Epoch [3619/4000] Training metric {'Train/mean dice_metric': 0.9954634308815002, 'Train/mean miou_metric': 0.9922075867652893, 'Train/mean f1': 0.9968031048774719, 'Train/mean precision': 0.9962900280952454, 'Train/mean recall': 0.9973165988922119, 'Train/mean hd95_metric': 0.96580570936203} +Epoch [3619/4000] Validation [1/10] Loss: 0.75615 focal_loss 0.66473 dice_loss 0.09141 +Epoch [3619/4000] Validation [2/10] Loss: 0.46921 focal_loss 0.38010 dice_loss 0.08911 +Epoch [3619/4000] Validation [3/10] Loss: 0.35875 focal_loss 0.25028 dice_loss 0.10848 +Epoch [3619/4000] Validation [4/10] Loss: 0.90974 focal_loss 0.34097 dice_loss 0.56878 +Epoch [3619/4000] Validation [5/10] Loss: 3.02625 focal_loss 2.35305 dice_loss 0.67319 +Epoch [3619/4000] Validation [6/10] Loss: 1.39418 focal_loss 0.67395 dice_loss 0.72023 +Epoch [3619/4000] Validation [7/10] Loss: 1.21468 focal_loss 0.55781 dice_loss 0.65686 +Epoch [3619/4000] Validation [8/10] Loss: 2.11532 focal_loss 1.53032 dice_loss 0.58500 +Epoch [3619/4000] Validation [9/10] Loss: 1.68130 focal_loss 1.13236 dice_loss 0.54894 +Epoch [3619/4000] Validation [10/10] Loss: 2.01299 focal_loss 1.26727 dice_loss 0.74572 +Epoch [3619/4000] Validation metric {'Val/mean dice_metric': 0.9507716298103333, 'Val/mean miou_metric': 0.9347806572914124, 'Val/mean f1': 0.946906328201294, 'Val/mean precision': 0.9385247230529785, 'Val/mean recall': 0.955439031124115, 'Val/mean hd95_metric': 10.731425285339355} +Cheakpoint... +Epoch [3619/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507716298103333, 'Val/mean miou_metric': 0.9347806572914124, 'Val/mean f1': 0.946906328201294, 'Val/mean precision': 0.9385247230529785, 'Val/mean recall': 0.955439031124115, 'Val/mean hd95_metric': 10.731425285339355} +Epoch [3620/4000] Training [1/39] Loss: 0.00382 +Epoch [3620/4000] Training [2/39] Loss: 0.00657 +Epoch [3620/4000] Training [3/39] Loss: 0.00540 +Epoch [3620/4000] Training [4/39] Loss: 0.00562 +Epoch [3620/4000] Training [5/39] Loss: 0.00541 +Epoch [3620/4000] Training [6/39] Loss: 0.00393 +Epoch [3620/4000] Training [7/39] Loss: 0.00445 +Epoch [3620/4000] Training [8/39] Loss: 0.00316 +Epoch [3620/4000] Training [9/39] Loss: 0.12832 +Epoch [3620/4000] Training [10/39] Loss: 0.00496 +Epoch [3620/4000] Training [11/39] Loss: 0.00692 +Epoch [3620/4000] Training [12/39] Loss: 0.00285 +Epoch [3620/4000] Training [13/39] Loss: 0.00441 +Epoch [3620/4000] Training [14/39] Loss: 0.12786 +Epoch [3620/4000] Training [15/39] Loss: 0.00477 +Epoch [3620/4000] Training [16/39] Loss: 0.12898 +Epoch [3620/4000] Training [17/39] Loss: 0.00558 +Epoch [3620/4000] Training [18/39] Loss: 0.13043 +Epoch [3620/4000] Training [19/39] Loss: 0.00448 +Epoch [3620/4000] Training [20/39] Loss: 0.00555 +Epoch [3620/4000] Training [21/39] Loss: 0.00592 +Epoch [3620/4000] Training [22/39] Loss: 0.12988 +Epoch [3620/4000] Training [23/39] Loss: 0.25728 +Epoch [3620/4000] Training [24/39] Loss: 0.12969 +Epoch [3620/4000] Training [25/39] Loss: 0.13107 +Epoch [3620/4000] Training [26/39] Loss: 0.00476 +Epoch [3620/4000] Training [27/39] Loss: 0.00693 +Epoch [3620/4000] Training [28/39] Loss: 0.00330 +Epoch [3620/4000] Training [29/39] Loss: 0.00294 +Epoch [3620/4000] Training [30/39] Loss: 0.00572 +Epoch [3620/4000] Training [31/39] Loss: 0.00489 +Epoch [3620/4000] Training [32/39] Loss: 0.12857 +Epoch [3620/4000] Training [33/39] Loss: 0.13013 +Epoch [3620/4000] Training [34/39] Loss: 0.08411 +Epoch [3620/4000] Training [35/39] Loss: 0.00425 +Epoch [3620/4000] Training [36/39] Loss: 0.13006 +Epoch [3620/4000] Training [37/39] Loss: 0.00502 +Epoch [3620/4000] Training [38/39] Loss: 0.00353 +Epoch [3620/4000] Training [39/39] Loss: 0.00528 +Epoch [3620/4000] Training metric {'Train/mean dice_metric': 0.995414137840271, 'Train/mean miou_metric': 0.9921059608459473, 'Train/mean f1': 0.9969726800918579, 'Train/mean precision': 0.9965660572052002, 'Train/mean recall': 0.9973796010017395, 'Train/mean hd95_metric': 0.9699392318725586} +Epoch [3620/4000] Validation [1/10] Loss: 0.76524 focal_loss 0.67294 dice_loss 0.09230 +Epoch [3620/4000] Validation [2/10] Loss: 0.47016 focal_loss 0.37907 dice_loss 0.09109 +Epoch [3620/4000] Validation [3/10] Loss: 0.36325 focal_loss 0.25434 dice_loss 0.10890 +Epoch [3620/4000] Validation [4/10] Loss: 0.90526 focal_loss 0.33693 dice_loss 0.56834 +Epoch [3620/4000] Validation [5/10] Loss: 3.01699 focal_loss 2.34349 dice_loss 0.67351 +Epoch [3620/4000] Validation [6/10] Loss: 1.37418 focal_loss 0.65597 dice_loss 0.71820 +Epoch [3620/4000] Validation [7/10] Loss: 1.20935 focal_loss 0.55190 dice_loss 0.65745 +Epoch [3620/4000] Validation [8/10] Loss: 2.13027 focal_loss 1.54097 dice_loss 0.58930 +Epoch [3620/4000] Validation [9/10] Loss: 1.61187 focal_loss 1.06411 dice_loss 0.54776 +Epoch [3620/4000] Validation [10/10] Loss: 1.96113 focal_loss 1.21842 dice_loss 0.74271 +Epoch [3620/4000] Validation metric {'Val/mean dice_metric': 0.9507227540016174, 'Val/mean miou_metric': 0.9346995949745178, 'Val/mean f1': 0.9473538994789124, 'Val/mean precision': 0.9396384954452515, 'Val/mean recall': 0.9551971554756165, 'Val/mean hd95_metric': 10.702471733093262} +Cheakpoint... +Epoch [3620/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507227540016174, 'Val/mean miou_metric': 0.9346995949745178, 'Val/mean f1': 0.9473538994789124, 'Val/mean precision': 0.9396384954452515, 'Val/mean recall': 0.9551971554756165, 'Val/mean hd95_metric': 10.702471733093262} +Epoch [3621/4000] Training [1/39] Loss: 0.00624 +Epoch [3621/4000] Training [2/39] Loss: 0.12966 +Epoch [3621/4000] Training [3/39] Loss: 0.00518 +Epoch [3621/4000] Training [4/39] Loss: 0.12821 +Epoch [3621/4000] Training [5/39] Loss: 0.00406 +Epoch [3621/4000] Training [6/39] Loss: 0.12798 +Epoch [3621/4000] Training [7/39] Loss: 0.13000 +Epoch [3621/4000] Training [8/39] Loss: 0.00631 +Epoch [3621/4000] Training [9/39] Loss: 0.00499 +Epoch [3621/4000] Training [10/39] Loss: 0.25380 +Epoch [3621/4000] Training [11/39] Loss: 0.00709 +Epoch [3621/4000] Training [12/39] Loss: 0.12882 +Epoch [3621/4000] Training [13/39] Loss: 0.00545 +Epoch [3621/4000] Training [14/39] Loss: 0.00544 +Epoch [3621/4000] Training [15/39] Loss: 0.00845 +Epoch [3621/4000] Training [16/39] Loss: 0.08295 +Epoch [3621/4000] Training [17/39] Loss: 0.00392 +Epoch [3621/4000] Training [18/39] Loss: 0.00456 +Epoch [3621/4000] Training [19/39] Loss: 0.00484 +Epoch [3621/4000] Training [20/39] Loss: 0.00402 +Epoch [3621/4000] Training [21/39] Loss: 0.13383 +Epoch [3621/4000] Training [22/39] Loss: 0.00432 +Epoch [3621/4000] Training [23/39] Loss: 0.00316 +Epoch [3621/4000] Training [24/39] Loss: 0.25392 +Epoch [3621/4000] Training [25/39] Loss: 0.00525 +Epoch [3621/4000] Training [26/39] Loss: 0.00686 +Epoch [3621/4000] Training [27/39] Loss: 0.00357 +Epoch [3621/4000] Training [28/39] Loss: 0.00765 +Epoch [3621/4000] Training [29/39] Loss: 0.00667 +Epoch [3621/4000] Training [30/39] Loss: 0.00372 +Epoch [3621/4000] Training [31/39] Loss: 0.00512 +Epoch [3621/4000] Training [32/39] Loss: 0.00592 +Epoch [3621/4000] Training [33/39] Loss: 0.13025 +Epoch [3621/4000] Training [34/39] Loss: 0.00431 +Epoch [3621/4000] Training [35/39] Loss: 0.00525 +Epoch [3621/4000] Training [36/39] Loss: 0.00488 +Epoch [3621/4000] Training [37/39] Loss: 0.00520 +Epoch [3621/4000] Training [38/39] Loss: 0.00373 +Epoch [3621/4000] Training [39/39] Loss: 0.13137 +Epoch [3621/4000] Training metric {'Train/mean dice_metric': 0.9961777925491333, 'Train/mean miou_metric': 0.992799699306488, 'Train/mean f1': 0.9966580867767334, 'Train/mean precision': 0.9961671829223633, 'Train/mean recall': 0.9971492290496826, 'Train/mean hd95_metric': 0.9531700015068054} +Epoch [3621/4000] Validation [1/10] Loss: 0.76507 focal_loss 0.67337 dice_loss 0.09170 +Epoch [3621/4000] Validation [2/10] Loss: 0.47151 focal_loss 0.38436 dice_loss 0.08715 +Epoch [3621/4000] Validation [3/10] Loss: 0.35942 focal_loss 0.25141 dice_loss 0.10801 +Epoch [3621/4000] Validation [4/10] Loss: 0.91816 focal_loss 0.34928 dice_loss 0.56889 +Epoch [3621/4000] Validation [5/10] Loss: 3.04569 focal_loss 2.37227 dice_loss 0.67342 +Epoch [3621/4000] Validation [6/10] Loss: 1.40371 focal_loss 0.68685 dice_loss 0.71686 +Epoch [3621/4000] Validation [7/10] Loss: 1.22916 focal_loss 0.57114 dice_loss 0.65802 +Epoch [3621/4000] Validation [8/10] Loss: 2.14992 focal_loss 1.56495 dice_loss 0.58496 +Epoch [3621/4000] Validation [9/10] Loss: 1.68212 focal_loss 1.13399 dice_loss 0.54813 +Epoch [3621/4000] Validation [10/10] Loss: 2.01859 focal_loss 1.27427 dice_loss 0.74432 +Epoch [3621/4000] Validation metric {'Val/mean dice_metric': 0.9514127373695374, 'Val/mean miou_metric': 0.9352883696556091, 'Val/mean f1': 0.9473380446434021, 'Val/mean precision': 0.9392130374908447, 'Val/mean recall': 0.9556049108505249, 'Val/mean hd95_metric': 11.000560760498047} +Cheakpoint... +Epoch [3621/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514127373695374, 'Val/mean miou_metric': 0.9352883696556091, 'Val/mean f1': 0.9473380446434021, 'Val/mean precision': 0.9392130374908447, 'Val/mean recall': 0.9556049108505249, 'Val/mean hd95_metric': 11.000560760498047} +Epoch [3622/4000] Training [1/39] Loss: 0.00563 +Epoch [3622/4000] Training [2/39] Loss: 0.13081 +Epoch [3622/4000] Training [3/39] Loss: 0.00759 +Epoch [3622/4000] Training [4/39] Loss: 0.00720 +Epoch [3622/4000] Training [5/39] Loss: 0.00543 +Epoch [3622/4000] Training [6/39] Loss: 0.00483 +Epoch [3622/4000] Training [7/39] Loss: 0.00455 +Epoch [3622/4000] Training [8/39] Loss: 0.00336 +Epoch [3622/4000] Training [9/39] Loss: 0.00555 +Epoch [3622/4000] Training [10/39] Loss: 0.00387 +Epoch [3622/4000] Training [11/39] Loss: 0.13255 +Epoch [3622/4000] Training [12/39] Loss: 0.12853 +Epoch [3622/4000] Training [13/39] Loss: 0.13261 +Epoch [3622/4000] Training [14/39] Loss: 0.00247 +Epoch [3622/4000] Training [15/39] Loss: 0.00310 +Epoch [3622/4000] Training [16/39] Loss: 0.00583 +Epoch [3622/4000] Training [17/39] Loss: 0.13022 +Epoch [3622/4000] Training [18/39] Loss: 0.12782 +Epoch [3622/4000] Training [19/39] Loss: 0.00474 +Epoch [3622/4000] Training [20/39] Loss: 0.00464 +Epoch [3622/4000] Training [21/39] Loss: 0.00644 +Epoch [3622/4000] Training [22/39] Loss: 0.00471 +Epoch [3622/4000] Training [23/39] Loss: 0.00371 +Epoch [3622/4000] Training [24/39] Loss: 0.00322 +Epoch [3622/4000] Training [25/39] Loss: 0.00494 +Epoch [3622/4000] Training [26/39] Loss: 0.12728 +Epoch [3622/4000] Training [27/39] Loss: 0.00408 +Epoch [3622/4000] Training [28/39] Loss: 0.00555 +Epoch [3622/4000] Training [29/39] Loss: 0.00501 +Epoch [3622/4000] Training [30/39] Loss: 0.12941 +Epoch [3622/4000] Training [31/39] Loss: 0.00673 +Epoch [3622/4000] Training [32/39] Loss: 0.00450 +Epoch [3622/4000] Training [33/39] Loss: 0.37797 +Epoch [3622/4000] Training [34/39] Loss: 0.00577 +Epoch [3622/4000] Training [35/39] Loss: 0.00580 +Epoch [3622/4000] Training [36/39] Loss: 0.00622 +Epoch [3622/4000] Training [37/39] Loss: 0.00628 +Epoch [3622/4000] Training [38/39] Loss: 0.12784 +Epoch [3622/4000] Training [39/39] Loss: 0.12978 +Epoch [3622/4000] Training metric {'Train/mean dice_metric': 0.9963157773017883, 'Train/mean miou_metric': 0.9930735230445862, 'Train/mean f1': 0.9969877600669861, 'Train/mean precision': 0.996515154838562, 'Train/mean recall': 0.9974607229232788, 'Train/mean hd95_metric': 0.9445016384124756} +Epoch [3622/4000] Validation [1/10] Loss: 0.76889 focal_loss 0.67794 dice_loss 0.09095 +Epoch [3622/4000] Validation [2/10] Loss: 0.48119 focal_loss 0.38965 dice_loss 0.09154 +Epoch [3622/4000] Validation [3/10] Loss: 0.36910 focal_loss 0.26037 dice_loss 0.10874 +Epoch [3622/4000] Validation [4/10] Loss: 0.91651 focal_loss 0.34894 dice_loss 0.56757 +Epoch [3622/4000] Validation [5/10] Loss: 3.06804 focal_loss 2.39452 dice_loss 0.67352 +Epoch [3622/4000] Validation [6/10] Loss: 1.39902 focal_loss 0.68088 dice_loss 0.71814 +Epoch [3622/4000] Validation [7/10] Loss: 1.22722 focal_loss 0.56958 dice_loss 0.65764 +Epoch [3622/4000] Validation [8/10] Loss: 2.21347 focal_loss 1.61840 dice_loss 0.59507 +Epoch [3622/4000] Validation [9/10] Loss: 1.64928 focal_loss 1.10144 dice_loss 0.54784 +Epoch [3622/4000] Validation [10/10] Loss: 1.98245 focal_loss 1.24188 dice_loss 0.74058 +Epoch [3622/4000] Validation metric {'Val/mean dice_metric': 0.9513968825340271, 'Val/mean miou_metric': 0.9354190230369568, 'Val/mean f1': 0.9473732113838196, 'Val/mean precision': 0.9401937127113342, 'Val/mean recall': 0.9546632170677185, 'Val/mean hd95_metric': 10.657180786132812} +Cheakpoint... +Epoch [3622/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513968825340271, 'Val/mean miou_metric': 0.9354190230369568, 'Val/mean f1': 0.9473732113838196, 'Val/mean precision': 0.9401937127113342, 'Val/mean recall': 0.9546632170677185, 'Val/mean hd95_metric': 10.657180786132812} +Epoch [3623/4000] Training [1/39] Loss: 0.00376 +Epoch [3623/4000] Training [2/39] Loss: 0.00355 +Epoch [3623/4000] Training [3/39] Loss: 0.00527 +Epoch [3623/4000] Training [4/39] Loss: 0.00457 +Epoch [3623/4000] Training [5/39] Loss: 0.37683 +Epoch [3623/4000] Training [6/39] Loss: 0.00375 +Epoch [3623/4000] Training [7/39] Loss: 0.00557 +Epoch [3623/4000] Training [8/39] Loss: 0.00756 +Epoch [3623/4000] Training [9/39] Loss: 0.25335 +Epoch [3623/4000] Training [10/39] Loss: 0.00471 +Epoch [3623/4000] Training [11/39] Loss: 0.00480 +Epoch [3623/4000] Training [12/39] Loss: 0.00583 +Epoch [3623/4000] Training [13/39] Loss: 0.00347 +Epoch [3623/4000] Training [14/39] Loss: 0.00550 +Epoch [3623/4000] Training [15/39] Loss: 0.00412 +Epoch [3623/4000] Training [16/39] Loss: 0.12756 +Epoch [3623/4000] Training [17/39] Loss: 0.00570 +Epoch [3623/4000] Training [18/39] Loss: 0.00440 +Epoch [3623/4000] Training [19/39] Loss: 0.00532 +Epoch [3623/4000] Training [20/39] Loss: 0.00370 +Epoch [3623/4000] Training [21/39] Loss: 0.00328 +Epoch [3623/4000] Training [22/39] Loss: 0.00680 +Epoch [3623/4000] Training [23/39] Loss: 0.00347 +Epoch [3623/4000] Training [24/39] Loss: 0.12878 +Epoch [3623/4000] Training [25/39] Loss: 0.00496 +Epoch [3623/4000] Training [26/39] Loss: 0.25245 +Epoch [3623/4000] Training [27/39] Loss: 0.00671 +Epoch [3623/4000] Training [28/39] Loss: 0.00452 +Epoch [3623/4000] Training [29/39] Loss: 0.00422 +Epoch [3623/4000] Training [30/39] Loss: 0.00329 +Epoch [3623/4000] Training [31/39] Loss: 0.00623 +Epoch [3623/4000] Training [32/39] Loss: 0.00461 +Epoch [3623/4000] Training [33/39] Loss: 0.12827 +Epoch [3623/4000] Training [34/39] Loss: 0.00546 +Epoch [3623/4000] Training [35/39] Loss: 0.00551 +Epoch [3623/4000] Training [36/39] Loss: 0.13088 +Epoch [3623/4000] Training [37/39] Loss: 0.08267 +Epoch [3623/4000] Training [38/39] Loss: 0.00527 +Epoch [3623/4000] Training [39/39] Loss: 0.12844 +Epoch [3623/4000] Training metric {'Train/mean dice_metric': 0.9964310526847839, 'Train/mean miou_metric': 0.9933010339736938, 'Train/mean f1': 0.9968960285186768, 'Train/mean precision': 0.9963970184326172, 'Train/mean recall': 0.9973956346511841, 'Train/mean hd95_metric': 0.9413110613822937} +Epoch [3623/4000] Validation [1/10] Loss: 0.74461 focal_loss 0.65399 dice_loss 0.09062 +Epoch [3623/4000] Validation [2/10] Loss: 0.48005 focal_loss 0.38962 dice_loss 0.09043 +Epoch [3623/4000] Validation [3/10] Loss: 0.35834 focal_loss 0.24995 dice_loss 0.10839 +Epoch [3623/4000] Validation [4/10] Loss: 0.91301 focal_loss 0.34559 dice_loss 0.56742 +Epoch [3623/4000] Validation [5/10] Loss: 2.98829 focal_loss 2.31457 dice_loss 0.67372 +Epoch [3623/4000] Validation [6/10] Loss: 1.39727 focal_loss 0.67551 dice_loss 0.72176 +Epoch [3623/4000] Validation [7/10] Loss: 1.21681 focal_loss 0.56219 dice_loss 0.65462 +Epoch [3623/4000] Validation [8/10] Loss: 2.16413 focal_loss 1.57403 dice_loss 0.59010 +Epoch [3623/4000] Validation [9/10] Loss: 1.63286 focal_loss 1.08486 dice_loss 0.54800 +Epoch [3623/4000] Validation [10/10] Loss: 1.97504 focal_loss 1.23451 dice_loss 0.74053 +Epoch [3623/4000] Validation metric {'Val/mean dice_metric': 0.9515842795372009, 'Val/mean miou_metric': 0.9356870651245117, 'Val/mean f1': 0.9471907615661621, 'Val/mean precision': 0.9396923780441284, 'Val/mean recall': 0.9548097848892212, 'Val/mean hd95_metric': 10.829912185668945} +Cheakpoint... +Epoch [3623/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515842795372009, 'Val/mean miou_metric': 0.9356870651245117, 'Val/mean f1': 0.9471907615661621, 'Val/mean precision': 0.9396923780441284, 'Val/mean recall': 0.9548097848892212, 'Val/mean hd95_metric': 10.829912185668945} +Epoch [3624/4000] Training [1/39] Loss: 0.12958 +Epoch [3624/4000] Training [2/39] Loss: 0.00385 +Epoch [3624/4000] Training [3/39] Loss: 0.00421 +Epoch [3624/4000] Training [4/39] Loss: 0.00400 +Epoch [3624/4000] Training [5/39] Loss: 0.13045 +Epoch [3624/4000] Training [6/39] Loss: 0.12961 +Epoch [3624/4000] Training [7/39] Loss: 0.00395 +Epoch [3624/4000] Training [8/39] Loss: 0.00400 +Epoch [3624/4000] Training [9/39] Loss: 0.00714 +Epoch [3624/4000] Training [10/39] Loss: 0.00469 +Epoch [3624/4000] Training [11/39] Loss: 0.13086 +Epoch [3624/4000] Training [12/39] Loss: 0.00626 +Epoch [3624/4000] Training [13/39] Loss: 0.12735 +Epoch [3624/4000] Training [14/39] Loss: 0.00487 +Epoch [3624/4000] Training [15/39] Loss: 0.00497 +Epoch [3624/4000] Training [16/39] Loss: 0.00495 +Epoch [3624/4000] Training [17/39] Loss: 0.00407 +Epoch [3624/4000] Training [18/39] Loss: 0.00514 +Epoch [3624/4000] Training [19/39] Loss: 0.00568 +Epoch [3624/4000] Training [20/39] Loss: 0.00493 +Epoch [3624/4000] Training [21/39] Loss: 0.00424 +Epoch [3624/4000] Training [22/39] Loss: 0.00433 +Epoch [3624/4000] Training [23/39] Loss: 0.12837 +Epoch [3624/4000] Training [24/39] Loss: 0.13060 +Epoch [3624/4000] Training [25/39] Loss: 0.12803 +Epoch [3624/4000] Training [26/39] Loss: 0.00562 +Epoch [3624/4000] Training [27/39] Loss: 0.00575 +Epoch [3624/4000] Training [28/39] Loss: 0.12942 +Epoch [3624/4000] Training [29/39] Loss: 0.00535 +Epoch [3624/4000] Training [30/39] Loss: 0.00441 +Epoch [3624/4000] Training [31/39] Loss: 0.00398 +Epoch [3624/4000] Training [32/39] Loss: 0.00373 +Epoch [3624/4000] Training [33/39] Loss: 0.00479 +Epoch [3624/4000] Training [34/39] Loss: 0.00533 +Epoch [3624/4000] Training [35/39] Loss: 0.00518 +Epoch [3624/4000] Training [36/39] Loss: 0.00780 +Epoch [3624/4000] Training [37/39] Loss: 0.12997 +Epoch [3624/4000] Training [38/39] Loss: 0.12921 +Epoch [3624/4000] Training [39/39] Loss: 0.00509 +Epoch [3624/4000] Training metric {'Train/mean dice_metric': 0.9961144924163818, 'Train/mean miou_metric': 0.9927577376365662, 'Train/mean f1': 0.996711015701294, 'Train/mean precision': 0.9961647391319275, 'Train/mean recall': 0.9972578287124634, 'Train/mean hd95_metric': 1.0879460573196411} +Epoch [3624/4000] Validation [1/10] Loss: 0.76260 focal_loss 0.67012 dice_loss 0.09248 +Epoch [3624/4000] Validation [2/10] Loss: 0.48161 focal_loss 0.38884 dice_loss 0.09277 +Epoch [3624/4000] Validation [3/10] Loss: 0.36778 focal_loss 0.25826 dice_loss 0.10952 +Epoch [3624/4000] Validation [4/10] Loss: 0.89039 focal_loss 0.32466 dice_loss 0.56573 +Epoch [3624/4000] Validation [5/10] Loss: 2.98335 focal_loss 2.31015 dice_loss 0.67320 +Epoch [3624/4000] Validation [6/10] Loss: 1.35821 focal_loss 0.63748 dice_loss 0.72073 +Epoch [3624/4000] Validation [7/10] Loss: 1.19603 focal_loss 0.53881 dice_loss 0.65722 +Epoch [3624/4000] Validation [8/10] Loss: 2.24837 focal_loss 1.64185 dice_loss 0.60652 +Epoch [3624/4000] Validation [9/10] Loss: 1.46469 focal_loss 0.91822 dice_loss 0.54647 +Epoch [3624/4000] Validation [10/10] Loss: 1.89356 focal_loss 1.15726 dice_loss 0.73629 +Epoch [3624/4000] Validation metric {'Val/mean dice_metric': 0.9512106776237488, 'Val/mean miou_metric': 0.9352077841758728, 'Val/mean f1': 0.947783350944519, 'Val/mean precision': 0.9422097206115723, 'Val/mean recall': 0.9534234404563904, 'Val/mean hd95_metric': 10.881911277770996} +Cheakpoint... +Epoch [3624/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512106776237488, 'Val/mean miou_metric': 0.9352077841758728, 'Val/mean f1': 0.947783350944519, 'Val/mean precision': 0.9422097206115723, 'Val/mean recall': 0.9534234404563904, 'Val/mean hd95_metric': 10.881911277770996} +Epoch [3625/4000] Training [1/39] Loss: 0.00566 +Epoch [3625/4000] Training [2/39] Loss: 0.13254 +Epoch [3625/4000] Training [3/39] Loss: 0.13148 +Epoch [3625/4000] Training [4/39] Loss: 0.00414 +Epoch [3625/4000] Training [5/39] Loss: 0.00457 +Epoch [3625/4000] Training [6/39] Loss: 0.12879 +Epoch [3625/4000] Training [7/39] Loss: 0.12773 +Epoch [3625/4000] Training [8/39] Loss: 0.00752 +Epoch [3625/4000] Training [9/39] Loss: 0.00257 +Epoch [3625/4000] Training [10/39] Loss: 0.00505 +Epoch [3625/4000] Training [11/39] Loss: 0.12856 +Epoch [3625/4000] Training [12/39] Loss: 0.25459 +Epoch [3625/4000] Training [13/39] Loss: 0.00318 +Epoch [3625/4000] Training [14/39] Loss: 0.00603 +Epoch [3625/4000] Training [15/39] Loss: 0.12913 +Epoch [3625/4000] Training [16/39] Loss: 0.00510 +Epoch [3625/4000] Training [17/39] Loss: 0.00589 +Epoch [3625/4000] Training [18/39] Loss: 0.00642 +Epoch [3625/4000] Training [19/39] Loss: 0.12805 +Epoch [3625/4000] Training [20/39] Loss: 0.25404 +Epoch [3625/4000] Training [21/39] Loss: 0.00337 +Epoch [3625/4000] Training [22/39] Loss: 0.00619 +Epoch [3625/4000] Training [23/39] Loss: 0.00590 +Epoch [3625/4000] Training [24/39] Loss: 0.00413 +Epoch [3625/4000] Training [25/39] Loss: 0.00488 +Epoch [3625/4000] Training [26/39] Loss: 0.00462 +Epoch [3625/4000] Training [27/39] Loss: 0.00475 +Epoch [3625/4000] Training [28/39] Loss: 0.00466 +Epoch [3625/4000] Training [29/39] Loss: 0.00566 +Epoch [3625/4000] Training [30/39] Loss: 0.00690 +Epoch [3625/4000] Training [31/39] Loss: 0.00537 +Epoch [3625/4000] Training [32/39] Loss: 0.00779 +Epoch [3625/4000] Training [33/39] Loss: 0.00514 +Epoch [3625/4000] Training [34/39] Loss: 0.00782 +Epoch [3625/4000] Training [35/39] Loss: 0.00690 +Epoch [3625/4000] Training [36/39] Loss: 0.00605 +Epoch [3625/4000] Training [37/39] Loss: 0.13048 +Epoch [3625/4000] Training [38/39] Loss: 0.04755 +Epoch [3625/4000] Training [39/39] Loss: 0.12913 +Epoch [3625/4000] Training metric {'Train/mean dice_metric': 0.9961261749267578, 'Train/mean miou_metric': 0.9926996231079102, 'Train/mean f1': 0.9966932535171509, 'Train/mean precision': 0.9962192177772522, 'Train/mean recall': 0.997167706489563, 'Train/mean hd95_metric': 0.9535303711891174} +Epoch [3625/4000] Validation [1/10] Loss: 0.74784 focal_loss 0.65712 dice_loss 0.09073 +Epoch [3625/4000] Validation [2/10] Loss: 0.47548 focal_loss 0.38340 dice_loss 0.09208 +Epoch [3625/4000] Validation [3/10] Loss: 0.36715 focal_loss 0.25793 dice_loss 0.10922 +Epoch [3625/4000] Validation [4/10] Loss: 0.89197 focal_loss 0.32550 dice_loss 0.56646 +Epoch [3625/4000] Validation [5/10] Loss: 3.01010 focal_loss 2.33668 dice_loss 0.67342 +Epoch [3625/4000] Validation [6/10] Loss: 1.35579 focal_loss 0.63627 dice_loss 0.71953 +Epoch [3625/4000] Validation [7/10] Loss: 1.20168 focal_loss 0.54550 dice_loss 0.65619 +Epoch [3625/4000] Validation [8/10] Loss: 2.17527 focal_loss 1.57760 dice_loss 0.59768 +Epoch [3625/4000] Validation [9/10] Loss: 1.49474 focal_loss 0.94801 dice_loss 0.54673 +Epoch [3625/4000] Validation [10/10] Loss: 1.90378 focal_loss 1.16562 dice_loss 0.73816 +Epoch [3625/4000] Validation metric {'Val/mean dice_metric': 0.9515166282653809, 'Val/mean miou_metric': 0.9354738593101501, 'Val/mean f1': 0.9481826424598694, 'Val/mean precision': 0.9422343373298645, 'Val/mean recall': 0.9542064666748047, 'Val/mean hd95_metric': 10.708192825317383} +Cheakpoint... +Epoch [3625/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515166282653809, 'Val/mean miou_metric': 0.9354738593101501, 'Val/mean f1': 0.9481826424598694, 'Val/mean precision': 0.9422343373298645, 'Val/mean recall': 0.9542064666748047, 'Val/mean hd95_metric': 10.708192825317383} +Epoch [3626/4000] Training [1/39] Loss: 0.00603 +Epoch [3626/4000] Training [2/39] Loss: 0.00515 +Epoch [3626/4000] Training [3/39] Loss: 0.00665 +Epoch [3626/4000] Training [4/39] Loss: 0.12809 +Epoch [3626/4000] Training [5/39] Loss: 0.00591 +Epoch [3626/4000] Training [6/39] Loss: 0.12950 +Epoch [3626/4000] Training [7/39] Loss: 0.08051 +Epoch [3626/4000] Training [8/39] Loss: 0.00531 +Epoch [3626/4000] Training [9/39] Loss: 0.00379 +Epoch [3626/4000] Training [10/39] Loss: 0.00337 +Epoch [3626/4000] Training [11/39] Loss: 0.00487 +Epoch [3626/4000] Training [12/39] Loss: 0.00697 +Epoch [3626/4000] Training [13/39] Loss: 0.00518 +Epoch [3626/4000] Training [14/39] Loss: 0.13196 +Epoch [3626/4000] Training [15/39] Loss: 0.00488 +Epoch [3626/4000] Training [16/39] Loss: 0.12833 +Epoch [3626/4000] Training [17/39] Loss: 0.00542 +Epoch [3626/4000] Training [18/39] Loss: 0.00499 +Epoch [3626/4000] Training [19/39] Loss: 0.00354 +Epoch [3626/4000] Training [20/39] Loss: 0.00387 +Epoch [3626/4000] Training [21/39] Loss: 0.00375 +Epoch [3626/4000] Training [22/39] Loss: 0.00534 +Epoch [3626/4000] Training [23/39] Loss: 0.00446 +Epoch [3626/4000] Training [24/39] Loss: 0.00506 +Epoch [3626/4000] Training [25/39] Loss: 0.00303 +Epoch [3626/4000] Training [26/39] Loss: 0.13345 +Epoch [3626/4000] Training [27/39] Loss: 0.00415 +Epoch [3626/4000] Training [28/39] Loss: 0.00610 +Epoch [3626/4000] Training [29/39] Loss: 0.00451 +Epoch [3626/4000] Training [30/39] Loss: 0.00390 +Epoch [3626/4000] Training [31/39] Loss: 0.12942 +Epoch [3626/4000] Training [32/39] Loss: 0.12963 +Epoch [3626/4000] Training [33/39] Loss: 0.12934 +Epoch [3626/4000] Training [34/39] Loss: 0.00460 +Epoch [3626/4000] Training [35/39] Loss: 0.12928 +Epoch [3626/4000] Training [36/39] Loss: 0.00417 +Epoch [3626/4000] Training [37/39] Loss: 0.00513 +Epoch [3626/4000] Training [38/39] Loss: 0.13138 +Epoch [3626/4000] Training [39/39] Loss: 0.00546 +Epoch [3626/4000] Training metric {'Train/mean dice_metric': 0.9961534142494202, 'Train/mean miou_metric': 0.9927409291267395, 'Train/mean f1': 0.9967834949493408, 'Train/mean precision': 0.9963489770889282, 'Train/mean recall': 0.9972184300422668, 'Train/mean hd95_metric': 1.0296013355255127} +Epoch [3626/4000] Validation [1/10] Loss: 0.76314 focal_loss 0.67207 dice_loss 0.09107 +Epoch [3626/4000] Validation [2/10] Loss: 0.48024 focal_loss 0.38907 dice_loss 0.09117 +Epoch [3626/4000] Validation [3/10] Loss: 0.37461 focal_loss 0.26512 dice_loss 0.10949 +Epoch [3626/4000] Validation [4/10] Loss: 0.90310 focal_loss 0.33553 dice_loss 0.56757 +Epoch [3626/4000] Validation [5/10] Loss: 3.08168 focal_loss 2.40832 dice_loss 0.67336 +Epoch [3626/4000] Validation [6/10] Loss: 1.38596 focal_loss 0.66649 dice_loss 0.71947 +Epoch [3626/4000] Validation [7/10] Loss: 1.22139 focal_loss 0.56342 dice_loss 0.65797 +Epoch [3626/4000] Validation [8/10] Loss: 2.24789 focal_loss 1.64993 dice_loss 0.59795 +Epoch [3626/4000] Validation [9/10] Loss: 1.51761 focal_loss 0.96941 dice_loss 0.54820 +Epoch [3626/4000] Validation [10/10] Loss: 1.96026 focal_loss 1.22088 dice_loss 0.73938 +Epoch [3626/4000] Validation metric {'Val/mean dice_metric': 0.9514995813369751, 'Val/mean miou_metric': 0.9354376792907715, 'Val/mean f1': 0.9480887055397034, 'Val/mean precision': 0.9418455958366394, 'Val/mean recall': 0.954414963722229, 'Val/mean hd95_metric': 10.73653507232666} +Cheakpoint... +Epoch [3626/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514995813369751, 'Val/mean miou_metric': 0.9354376792907715, 'Val/mean f1': 0.9480887055397034, 'Val/mean precision': 0.9418455958366394, 'Val/mean recall': 0.954414963722229, 'Val/mean hd95_metric': 10.73653507232666} +Epoch [3627/4000] Training [1/39] Loss: 0.00389 +Epoch [3627/4000] Training [2/39] Loss: 0.00452 +Epoch [3627/4000] Training [3/39] Loss: 0.00425 +Epoch [3627/4000] Training [4/39] Loss: 0.00558 +Epoch [3627/4000] Training [5/39] Loss: 0.00455 +Epoch [3627/4000] Training [6/39] Loss: 0.00481 +Epoch [3627/4000] Training [7/39] Loss: 0.00504 +Epoch [3627/4000] Training [8/39] Loss: 0.00533 +Epoch [3627/4000] Training [9/39] Loss: 0.00542 +Epoch [3627/4000] Training [10/39] Loss: 0.12894 +Epoch [3627/4000] Training [11/39] Loss: 0.00650 +Epoch [3627/4000] Training [12/39] Loss: 0.00381 +Epoch [3627/4000] Training [13/39] Loss: 0.13077 +Epoch [3627/4000] Training [14/39] Loss: 0.00300 +Epoch [3627/4000] Training [15/39] Loss: 0.00572 +Epoch [3627/4000] Training [16/39] Loss: 0.00651 +Epoch [3627/4000] Training [17/39] Loss: 0.00486 +Epoch [3627/4000] Training [18/39] Loss: 0.00547 +Epoch [3627/4000] Training [19/39] Loss: 0.00339 +Epoch [3627/4000] Training [20/39] Loss: 0.00400 +Epoch [3627/4000] Training [21/39] Loss: 0.00421 +Epoch [3627/4000] Training [22/39] Loss: 0.00392 +Epoch [3627/4000] Training [23/39] Loss: 0.00692 +Epoch [3627/4000] Training [24/39] Loss: 0.12935 +Epoch [3627/4000] Training [25/39] Loss: 0.00555 +Epoch [3627/4000] Training [26/39] Loss: 0.00348 +Epoch [3627/4000] Training [27/39] Loss: 0.00450 +Epoch [3627/4000] Training [28/39] Loss: 0.12724 +Epoch [3627/4000] Training [29/39] Loss: 0.00687 +Epoch [3627/4000] Training [30/39] Loss: 0.12964 +Epoch [3627/4000] Training [31/39] Loss: 0.00359 +Epoch [3627/4000] Training [32/39] Loss: 0.00630 +Epoch [3627/4000] Training [33/39] Loss: 0.00623 +Epoch [3627/4000] Training [34/39] Loss: 0.12940 +Epoch [3627/4000] Training [35/39] Loss: 0.00477 +Epoch [3627/4000] Training [36/39] Loss: 0.00877 +Epoch [3627/4000] Training [37/39] Loss: 0.12900 +Epoch [3627/4000] Training [38/39] Loss: 0.00413 +Epoch [3627/4000] Training [39/39] Loss: 0.00558 +Epoch [3627/4000] Training metric {'Train/mean dice_metric': 0.9954074621200562, 'Train/mean miou_metric': 0.9921177625656128, 'Train/mean f1': 0.9967969059944153, 'Train/mean precision': 0.9963091015815735, 'Train/mean recall': 0.9972853064537048, 'Train/mean hd95_metric': 1.0171031951904297} +Epoch [3627/4000] Validation [1/10] Loss: 0.73614 focal_loss 0.64688 dice_loss 0.08926 +Epoch [3627/4000] Validation [2/10] Loss: 0.47826 focal_loss 0.38403 dice_loss 0.09423 +Epoch [3627/4000] Validation [3/10] Loss: 0.37472 focal_loss 0.26472 dice_loss 0.11000 +Epoch [3627/4000] Validation [4/10] Loss: 0.89453 focal_loss 0.32856 dice_loss 0.56597 +Epoch [3627/4000] Validation [5/10] Loss: 3.04582 focal_loss 2.37204 dice_loss 0.67377 +Epoch [3627/4000] Validation [6/10] Loss: 1.36822 focal_loss 0.65145 dice_loss 0.71677 +Epoch [3627/4000] Validation [7/10] Loss: 1.20514 focal_loss 0.55154 dice_loss 0.65360 +Epoch [3627/4000] Validation [8/10] Loss: 2.28412 focal_loss 1.67707 dice_loss 0.60705 +Epoch [3627/4000] Validation [9/10] Loss: 1.48996 focal_loss 0.94301 dice_loss 0.54695 +Epoch [3627/4000] Validation [10/10] Loss: 1.93003 focal_loss 1.19124 dice_loss 0.73879 +Epoch [3627/4000] Validation metric {'Val/mean dice_metric': 0.9508705735206604, 'Val/mean miou_metric': 0.9349520206451416, 'Val/mean f1': 0.9480909705162048, 'Val/mean precision': 0.9426077604293823, 'Val/mean recall': 0.9536383152008057, 'Val/mean hd95_metric': 10.859994888305664} +Cheakpoint... +Epoch [3627/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508705735206604, 'Val/mean miou_metric': 0.9349520206451416, 'Val/mean f1': 0.9480909705162048, 'Val/mean precision': 0.9426077604293823, 'Val/mean recall': 0.9536383152008057, 'Val/mean hd95_metric': 10.859994888305664} +Epoch [3628/4000] Training [1/39] Loss: 0.00427 +Epoch [3628/4000] Training [2/39] Loss: 0.25305 +Epoch [3628/4000] Training [3/39] Loss: 0.00388 +Epoch [3628/4000] Training [4/39] Loss: 0.12870 +Epoch [3628/4000] Training [5/39] Loss: 0.25323 +Epoch [3628/4000] Training [6/39] Loss: 0.00449 +Epoch [3628/4000] Training [7/39] Loss: 0.13036 +Epoch [3628/4000] Training [8/39] Loss: 0.00453 +Epoch [3628/4000] Training [9/39] Loss: 0.00386 +Epoch [3628/4000] Training [10/39] Loss: 0.00559 +Epoch [3628/4000] Training [11/39] Loss: 0.00372 +Epoch [3628/4000] Training [12/39] Loss: 0.00545 +Epoch [3628/4000] Training [13/39] Loss: 0.12833 +Epoch [3628/4000] Training [14/39] Loss: 0.00463 +Epoch [3628/4000] Training [15/39] Loss: 0.00482 +Epoch [3628/4000] Training [16/39] Loss: 0.25355 +Epoch [3628/4000] Training [17/39] Loss: 0.12826 +Epoch [3628/4000] Training [18/39] Loss: 0.00333 +Epoch [3628/4000] Training [19/39] Loss: 0.00460 +Epoch [3628/4000] Training [20/39] Loss: 0.00406 +Epoch [3628/4000] Training [21/39] Loss: 0.00383 +Epoch [3628/4000] Training [22/39] Loss: 0.00284 +Epoch [3628/4000] Training [23/39] Loss: 0.25312 +Epoch [3628/4000] Training [24/39] Loss: 0.00658 +Epoch [3628/4000] Training [25/39] Loss: 0.00470 +Epoch [3628/4000] Training [26/39] Loss: 0.13238 +Epoch [3628/4000] Training [27/39] Loss: 0.00484 +Epoch [3628/4000] Training [28/39] Loss: 0.00674 +Epoch [3628/4000] Training [29/39] Loss: 0.00836 +Epoch [3628/4000] Training [30/39] Loss: 0.00375 +Epoch [3628/4000] Training [31/39] Loss: 0.13015 +Epoch [3628/4000] Training [32/39] Loss: 0.00442 +Epoch [3628/4000] Training [33/39] Loss: 0.12951 +Epoch [3628/4000] Training [34/39] Loss: 0.00538 +Epoch [3628/4000] Training [35/39] Loss: 0.00565 +Epoch [3628/4000] Training [36/39] Loss: 0.00456 +Epoch [3628/4000] Training [37/39] Loss: 0.00544 +Epoch [3628/4000] Training [38/39] Loss: 0.13223 +Epoch [3628/4000] Training [39/39] Loss: 0.00565 +Epoch [3628/4000] Training metric {'Train/mean dice_metric': 0.9962962865829468, 'Train/mean miou_metric': 0.9930346012115479, 'Train/mean f1': 0.9968503713607788, 'Train/mean precision': 0.9963930249214172, 'Train/mean recall': 0.9973081946372986, 'Train/mean hd95_metric': 0.9428445100784302} +Epoch [3628/4000] Validation [1/10] Loss: 0.75886 focal_loss 0.66816 dice_loss 0.09071 +Epoch [3628/4000] Validation [2/10] Loss: 0.48241 focal_loss 0.39144 dice_loss 0.09097 +Epoch [3628/4000] Validation [3/10] Loss: 0.38077 focal_loss 0.27100 dice_loss 0.10977 +Epoch [3628/4000] Validation [4/10] Loss: 0.90106 focal_loss 0.33382 dice_loss 0.56724 +Epoch [3628/4000] Validation [5/10] Loss: 3.09370 focal_loss 2.41975 dice_loss 0.67395 +Epoch [3628/4000] Validation [6/10] Loss: 1.36570 focal_loss 0.65041 dice_loss 0.71530 +Epoch [3628/4000] Validation [7/10] Loss: 1.21395 focal_loss 0.55838 dice_loss 0.65557 +Epoch [3628/4000] Validation [8/10] Loss: 2.28798 focal_loss 1.68417 dice_loss 0.60381 +Epoch [3628/4000] Validation [9/10] Loss: 1.50953 focal_loss 0.96297 dice_loss 0.54656 +Epoch [3628/4000] Validation [10/10] Loss: 1.96006 focal_loss 1.22028 dice_loss 0.73978 +Epoch [3628/4000] Validation metric {'Val/mean dice_metric': 0.951542317867279, 'Val/mean miou_metric': 0.935613214969635, 'Val/mean f1': 0.9486501216888428, 'Val/mean precision': 0.942791759967804, 'Val/mean recall': 0.9545816779136658, 'Val/mean hd95_metric': 10.5910062789917} +Cheakpoint... +Epoch [3628/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951542317867279, 'Val/mean miou_metric': 0.935613214969635, 'Val/mean f1': 0.9486501216888428, 'Val/mean precision': 0.942791759967804, 'Val/mean recall': 0.9545816779136658, 'Val/mean hd95_metric': 10.5910062789917} +Epoch [3629/4000] Training [1/39] Loss: 0.00493 +Epoch [3629/4000] Training [2/39] Loss: 0.00485 +Epoch [3629/4000] Training [3/39] Loss: 0.25381 +Epoch [3629/4000] Training [4/39] Loss: 0.00528 +Epoch [3629/4000] Training [5/39] Loss: 0.00479 +Epoch [3629/4000] Training [6/39] Loss: 0.00621 +Epoch [3629/4000] Training [7/39] Loss: 0.00437 +Epoch [3629/4000] Training [8/39] Loss: 0.00620 +Epoch [3629/4000] Training [9/39] Loss: 0.00410 +Epoch [3629/4000] Training [10/39] Loss: 0.00457 +Epoch [3629/4000] Training [11/39] Loss: 0.00842 +Epoch [3629/4000] Training [12/39] Loss: 0.12835 +Epoch [3629/4000] Training [13/39] Loss: 0.12874 +Epoch [3629/4000] Training [14/39] Loss: 0.00455 +Epoch [3629/4000] Training [15/39] Loss: 0.00430 +Epoch [3629/4000] Training [16/39] Loss: 0.00449 +Epoch [3629/4000] Training [17/39] Loss: 0.12848 +Epoch [3629/4000] Training [18/39] Loss: 0.00498 +Epoch [3629/4000] Training [19/39] Loss: 0.12878 +Epoch [3629/4000] Training [20/39] Loss: 0.00405 +Epoch [3629/4000] Training [21/39] Loss: 0.00407 +Epoch [3629/4000] Training [22/39] Loss: 0.00364 +Epoch [3629/4000] Training [23/39] Loss: 0.12867 +Epoch [3629/4000] Training [24/39] Loss: 0.00586 +Epoch [3629/4000] Training [25/39] Loss: 0.00275 +Epoch [3629/4000] Training [26/39] Loss: 0.00468 +Epoch [3629/4000] Training [27/39] Loss: 0.12964 +Epoch [3629/4000] Training [28/39] Loss: 0.00439 +Epoch [3629/4000] Training [29/39] Loss: 0.00480 +Epoch [3629/4000] Training [30/39] Loss: 0.00404 +Epoch [3629/4000] Training [31/39] Loss: 0.00519 +Epoch [3629/4000] Training [32/39] Loss: 0.00641 +Epoch [3629/4000] Training [33/39] Loss: 0.00458 +Epoch [3629/4000] Training [34/39] Loss: 0.12942 +Epoch [3629/4000] Training [35/39] Loss: 0.00562 +Epoch [3629/4000] Training [36/39] Loss: 0.00734 +Epoch [3629/4000] Training [37/39] Loss: 0.00311 +Epoch [3629/4000] Training [38/39] Loss: 0.00532 +Epoch [3629/4000] Training [39/39] Loss: 0.00622 +Epoch [3629/4000] Training metric {'Train/mean dice_metric': 0.9962558746337891, 'Train/mean miou_metric': 0.99295574426651, 'Train/mean f1': 0.9968908429145813, 'Train/mean precision': 0.9964550733566284, 'Train/mean recall': 0.9973270893096924, 'Train/mean hd95_metric': 0.9610795378684998} +Epoch [3629/4000] Validation [1/10] Loss: 0.74541 focal_loss 0.65660 dice_loss 0.08881 +Epoch [3629/4000] Validation [2/10] Loss: 0.48636 focal_loss 0.39302 dice_loss 0.09333 +Epoch [3629/4000] Validation [3/10] Loss: 0.38734 focal_loss 0.27665 dice_loss 0.11069 +Epoch [3629/4000] Validation [4/10] Loss: 0.89538 focal_loss 0.32964 dice_loss 0.56574 +Epoch [3629/4000] Validation [5/10] Loss: 3.11265 focal_loss 2.43876 dice_loss 0.67389 +Epoch [3629/4000] Validation [6/10] Loss: 1.36222 focal_loss 0.64425 dice_loss 0.71797 +Epoch [3629/4000] Validation [7/10] Loss: 1.19306 focal_loss 0.54161 dice_loss 0.65145 +Epoch [3629/4000] Validation [8/10] Loss: 2.36716 focal_loss 1.75545 dice_loss 0.61171 +Epoch [3629/4000] Validation [9/10] Loss: 1.52112 focal_loss 0.97404 dice_loss 0.54708 +Epoch [3629/4000] Validation [10/10] Loss: 1.91041 focal_loss 1.17511 dice_loss 0.73530 +Epoch [3629/4000] Validation metric {'Val/mean dice_metric': 0.9515640139579773, 'Val/mean miou_metric': 0.9356961250305176, 'Val/mean f1': 0.9485783576965332, 'Val/mean precision': 0.9438772201538086, 'Val/mean recall': 0.9533265233039856, 'Val/mean hd95_metric': 10.513832092285156} +Cheakpoint... +Epoch [3629/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515640139579773, 'Val/mean miou_metric': 0.9356961250305176, 'Val/mean f1': 0.9485783576965332, 'Val/mean precision': 0.9438772201538086, 'Val/mean recall': 0.9533265233039856, 'Val/mean hd95_metric': 10.513832092285156} +Epoch [3630/4000] Training [1/39] Loss: 0.00501 +Epoch [3630/4000] Training [2/39] Loss: 0.00623 +Epoch [3630/4000] Training [3/39] Loss: 0.00467 +Epoch [3630/4000] Training [4/39] Loss: 0.00473 +Epoch [3630/4000] Training [5/39] Loss: 0.00342 +Epoch [3630/4000] Training [6/39] Loss: 0.01516 +Epoch [3630/4000] Training [7/39] Loss: 0.12897 +Epoch [3630/4000] Training [8/39] Loss: 0.12802 +Epoch [3630/4000] Training [9/39] Loss: 0.12765 +Epoch [3630/4000] Training [10/39] Loss: 0.00366 +Epoch [3630/4000] Training [11/39] Loss: 0.37797 +Epoch [3630/4000] Training [12/39] Loss: 0.01404 +Epoch [3630/4000] Training [13/39] Loss: 0.13220 +Epoch [3630/4000] Training [14/39] Loss: 0.00325 +Epoch [3630/4000] Training [15/39] Loss: 0.00276 +Epoch [3630/4000] Training [16/39] Loss: 0.00509 +Epoch [3630/4000] Training [17/39] Loss: 0.00667 +Epoch [3630/4000] Training [18/39] Loss: 0.00534 +Epoch [3630/4000] Training [19/39] Loss: 0.04476 +Epoch [3630/4000] Training [20/39] Loss: 0.00686 +Epoch [3630/4000] Training [21/39] Loss: 0.00527 +Epoch [3630/4000] Training [22/39] Loss: 0.00397 +Epoch [3630/4000] Training [23/39] Loss: 0.13030 +Epoch [3630/4000] Training [24/39] Loss: 0.00495 +Epoch [3630/4000] Training [25/39] Loss: 0.00468 +Epoch [3630/4000] Training [26/39] Loss: 0.00349 +Epoch [3630/4000] Training [27/39] Loss: 0.12864 +Epoch [3630/4000] Training [28/39] Loss: 0.00789 +Epoch [3630/4000] Training [29/39] Loss: 0.25265 +Epoch [3630/4000] Training [30/39] Loss: 0.00504 +Epoch [3630/4000] Training [31/39] Loss: 0.00549 +Epoch [3630/4000] Training [32/39] Loss: 0.00367 +Epoch [3630/4000] Training [33/39] Loss: 0.00349 +Epoch [3630/4000] Training [34/39] Loss: 0.00345 +Epoch [3630/4000] Training [35/39] Loss: 0.00385 +Epoch [3630/4000] Training [36/39] Loss: 0.00949 +Epoch [3630/4000] Training [37/39] Loss: 0.00426 +Epoch [3630/4000] Training [38/39] Loss: 0.12862 +Epoch [3630/4000] Training [39/39] Loss: 0.13038 +Epoch [3630/4000] Training metric {'Train/mean dice_metric': 0.9961820244789124, 'Train/mean miou_metric': 0.9928284883499146, 'Train/mean f1': 0.9967043399810791, 'Train/mean precision': 0.9963158965110779, 'Train/mean recall': 0.9970930218696594, 'Train/mean hd95_metric': 1.0331815481185913} +Epoch [3630/4000] Validation [1/10] Loss: 0.75802 focal_loss 0.66718 dice_loss 0.09084 +Epoch [3630/4000] Validation [2/10] Loss: 0.47581 focal_loss 0.38729 dice_loss 0.08852 +Epoch [3630/4000] Validation [3/10] Loss: 0.36695 focal_loss 0.25799 dice_loss 0.10897 +Epoch [3630/4000] Validation [4/10] Loss: 0.91457 focal_loss 0.34643 dice_loss 0.56814 +Epoch [3630/4000] Validation [5/10] Loss: 3.08636 focal_loss 2.41357 dice_loss 0.67279 +Epoch [3630/4000] Validation [6/10] Loss: 1.38430 focal_loss 0.66177 dice_loss 0.72253 +Epoch [3630/4000] Validation [7/10] Loss: 1.22256 focal_loss 0.56305 dice_loss 0.65951 +Epoch [3630/4000] Validation [8/10] Loss: 2.12658 focal_loss 1.54339 dice_loss 0.58319 +Epoch [3630/4000] Validation [9/10] Loss: 1.66552 focal_loss 1.11901 dice_loss 0.54651 +Epoch [3630/4000] Validation [10/10] Loss: 1.98611 focal_loss 1.24605 dice_loss 0.74006 +Epoch [3630/4000] Validation metric {'Val/mean dice_metric': 0.9511855840682983, 'Val/mean miou_metric': 0.9351730346679688, 'Val/mean f1': 0.9473849534988403, 'Val/mean precision': 0.9393320083618164, 'Val/mean recall': 0.9555771946907043, 'Val/mean hd95_metric': 10.801189422607422} +Cheakpoint... +Epoch [3630/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511855840682983, 'Val/mean miou_metric': 0.9351730346679688, 'Val/mean f1': 0.9473849534988403, 'Val/mean precision': 0.9393320083618164, 'Val/mean recall': 0.9555771946907043, 'Val/mean hd95_metric': 10.801189422607422} +Epoch [3631/4000] Training [1/39] Loss: 0.12989 +Epoch [3631/4000] Training [2/39] Loss: 0.00779 +Epoch [3631/4000] Training [3/39] Loss: 0.00457 +Epoch [3631/4000] Training [4/39] Loss: 0.00517 +Epoch [3631/4000] Training [5/39] Loss: 0.00391 +Epoch [3631/4000] Training [6/39] Loss: 0.00507 +Epoch [3631/4000] Training [7/39] Loss: 0.00459 +Epoch [3631/4000] Training [8/39] Loss: 0.00551 +Epoch [3631/4000] Training [9/39] Loss: 0.00658 +Epoch [3631/4000] Training [10/39] Loss: 0.00505 +Epoch [3631/4000] Training [11/39] Loss: 0.00490 +Epoch [3631/4000] Training [12/39] Loss: 0.00700 +Epoch [3631/4000] Training [13/39] Loss: 0.00539 +Epoch [3631/4000] Training [14/39] Loss: 0.00436 +Epoch [3631/4000] Training [15/39] Loss: 0.12910 +Epoch [3631/4000] Training [16/39] Loss: 0.00461 +Epoch [3631/4000] Training [17/39] Loss: 0.25296 +Epoch [3631/4000] Training [18/39] Loss: 0.00424 +Epoch [3631/4000] Training [19/39] Loss: 0.00486 +Epoch [3631/4000] Training [20/39] Loss: 0.01017 +Epoch [3631/4000] Training [21/39] Loss: 0.12759 +Epoch [3631/4000] Training [22/39] Loss: 0.12795 +Epoch [3631/4000] Training [23/39] Loss: 0.00364 +Epoch [3631/4000] Training [24/39] Loss: 0.00404 +Epoch [3631/4000] Training [25/39] Loss: 0.00356 +Epoch [3631/4000] Training [26/39] Loss: 0.00494 +Epoch [3631/4000] Training [27/39] Loss: 0.00390 +Epoch [3631/4000] Training [28/39] Loss: 0.00469 +Epoch [3631/4000] Training [29/39] Loss: 0.00513 +Epoch [3631/4000] Training [30/39] Loss: 0.00407 +Epoch [3631/4000] Training [31/39] Loss: 0.00571 +Epoch [3631/4000] Training [32/39] Loss: 0.12971 +Epoch [3631/4000] Training [33/39] Loss: 0.00565 +Epoch [3631/4000] Training [34/39] Loss: 0.00433 +Epoch [3631/4000] Training [35/39] Loss: 0.00390 +Epoch [3631/4000] Training [36/39] Loss: 0.12851 +Epoch [3631/4000] Training [37/39] Loss: 0.13043 +Epoch [3631/4000] Training [38/39] Loss: 0.00457 +Epoch [3631/4000] Training [39/39] Loss: 0.12856 +Epoch [3631/4000] Training metric {'Train/mean dice_metric': 0.9963449835777283, 'Train/mean miou_metric': 0.9931482076644897, 'Train/mean f1': 0.9968360066413879, 'Train/mean precision': 0.9963417649269104, 'Train/mean recall': 0.9973305463790894, 'Train/mean hd95_metric': 0.9697762727737427} +Epoch [3631/4000] Validation [1/10] Loss: 0.74451 focal_loss 0.65472 dice_loss 0.08979 +Epoch [3631/4000] Validation [2/10] Loss: 0.46947 focal_loss 0.38035 dice_loss 0.08912 +Epoch [3631/4000] Validation [3/10] Loss: 0.36986 focal_loss 0.26016 dice_loss 0.10970 +Epoch [3631/4000] Validation [4/10] Loss: 0.89998 focal_loss 0.33334 dice_loss 0.56665 +Epoch [3631/4000] Validation [5/10] Loss: 3.05944 focal_loss 2.38617 dice_loss 0.67327 +Epoch [3631/4000] Validation [6/10] Loss: 1.36856 focal_loss 0.64757 dice_loss 0.72100 +Epoch [3631/4000] Validation [7/10] Loss: 1.20295 focal_loss 0.54693 dice_loss 0.65603 +Epoch [3631/4000] Validation [8/10] Loss: 2.21217 focal_loss 1.61490 dice_loss 0.59727 +Epoch [3631/4000] Validation [9/10] Loss: 1.58666 focal_loss 1.03917 dice_loss 0.54750 +Epoch [3631/4000] Validation [10/10] Loss: 1.92189 focal_loss 1.18520 dice_loss 0.73669 +Epoch [3631/4000] Validation metric {'Val/mean dice_metric': 0.951528012752533, 'Val/mean miou_metric': 0.935714066028595, 'Val/mean f1': 0.9475347399711609, 'Val/mean precision': 0.9407353401184082, 'Val/mean recall': 0.9544331431388855, 'Val/mean hd95_metric': 10.765849113464355} +Cheakpoint... +Epoch [3631/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951528012752533, 'Val/mean miou_metric': 0.935714066028595, 'Val/mean f1': 0.9475347399711609, 'Val/mean precision': 0.9407353401184082, 'Val/mean recall': 0.9544331431388855, 'Val/mean hd95_metric': 10.765849113464355} +Epoch [3632/4000] Training [1/39] Loss: 0.00656 +Epoch [3632/4000] Training [2/39] Loss: 0.12867 +Epoch [3632/4000] Training [3/39] Loss: 0.00598 +Epoch [3632/4000] Training [4/39] Loss: 0.00705 +Epoch [3632/4000] Training [5/39] Loss: 0.00375 +Epoch [3632/4000] Training [6/39] Loss: 0.00565 +Epoch [3632/4000] Training [7/39] Loss: 0.00389 +Epoch [3632/4000] Training [8/39] Loss: 0.00454 +Epoch [3632/4000] Training [9/39] Loss: 0.00520 +Epoch [3632/4000] Training [10/39] Loss: 0.00518 +Epoch [3632/4000] Training [11/39] Loss: 0.13560 +Epoch [3632/4000] Training [12/39] Loss: 0.00620 +Epoch [3632/4000] Training [13/39] Loss: 0.00574 +Epoch [3632/4000] Training [14/39] Loss: 0.00601 +Epoch [3632/4000] Training [15/39] Loss: 0.00446 +Epoch [3632/4000] Training [16/39] Loss: 0.00447 +Epoch [3632/4000] Training [17/39] Loss: 0.12871 +Epoch [3632/4000] Training [18/39] Loss: 0.12875 +Epoch [3632/4000] Training [19/39] Loss: 0.00278 +Epoch [3632/4000] Training [20/39] Loss: 0.00369 +Epoch [3632/4000] Training [21/39] Loss: 0.00495 +Epoch [3632/4000] Training [22/39] Loss: 0.00699 +Epoch [3632/4000] Training [23/39] Loss: 0.00463 +Epoch [3632/4000] Training [24/39] Loss: 0.12877 +Epoch [3632/4000] Training [25/39] Loss: 0.00966 +Epoch [3632/4000] Training [26/39] Loss: 0.00523 +Epoch [3632/4000] Training [27/39] Loss: 0.00510 +Epoch [3632/4000] Training [28/39] Loss: 0.00879 +Epoch [3632/4000] Training [29/39] Loss: 0.00468 +Epoch [3632/4000] Training [30/39] Loss: 0.25601 +Epoch [3632/4000] Training [31/39] Loss: 0.13113 +Epoch [3632/4000] Training [32/39] Loss: 0.00449 +Epoch [3632/4000] Training [33/39] Loss: 0.00359 +Epoch [3632/4000] Training [34/39] Loss: 0.00533 +Epoch [3632/4000] Training [35/39] Loss: 0.00449 +Epoch [3632/4000] Training [36/39] Loss: 0.00503 +Epoch [3632/4000] Training [37/39] Loss: 0.00387 +Epoch [3632/4000] Training [38/39] Loss: 0.12867 +Epoch [3632/4000] Training [39/39] Loss: 0.12782 +Epoch [3632/4000] Training metric {'Train/mean dice_metric': 0.9961482882499695, 'Train/mean miou_metric': 0.9927178621292114, 'Train/mean f1': 0.9967089891433716, 'Train/mean precision': 0.9962487816810608, 'Train/mean recall': 0.9971694946289062, 'Train/mean hd95_metric': 1.0269829034805298} +Epoch [3632/4000] Validation [1/10] Loss: 0.74877 focal_loss 0.65875 dice_loss 0.09002 +Epoch [3632/4000] Validation [2/10] Loss: 0.47111 focal_loss 0.38178 dice_loss 0.08934 +Epoch [3632/4000] Validation [3/10] Loss: 0.37526 focal_loss 0.26547 dice_loss 0.10979 +Epoch [3632/4000] Validation [4/10] Loss: 0.90631 focal_loss 0.33957 dice_loss 0.56675 +Epoch [3632/4000] Validation [5/10] Loss: 3.05431 focal_loss 2.38056 dice_loss 0.67375 +Epoch [3632/4000] Validation [6/10] Loss: 1.36443 focal_loss 0.64862 dice_loss 0.71581 +Epoch [3632/4000] Validation [7/10] Loss: 1.20731 focal_loss 0.55472 dice_loss 0.65259 +Epoch [3632/4000] Validation [8/10] Loss: 2.23319 focal_loss 1.63298 dice_loss 0.60021 +Epoch [3632/4000] Validation [9/10] Loss: 1.57943 focal_loss 1.03192 dice_loss 0.54751 +Epoch [3632/4000] Validation [10/10] Loss: 1.94289 focal_loss 1.20582 dice_loss 0.73707 +Epoch [3632/4000] Validation metric {'Val/mean dice_metric': 0.9514797329902649, 'Val/mean miou_metric': 0.9354850649833679, 'Val/mean f1': 0.9480614066123962, 'Val/mean precision': 0.9416955709457397, 'Val/mean recall': 0.9545139074325562, 'Val/mean hd95_metric': 10.70795726776123} +Cheakpoint... +Epoch [3632/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514797329902649, 'Val/mean miou_metric': 0.9354850649833679, 'Val/mean f1': 0.9480614066123962, 'Val/mean precision': 0.9416955709457397, 'Val/mean recall': 0.9545139074325562, 'Val/mean hd95_metric': 10.70795726776123} +Epoch [3633/4000] Training [1/39] Loss: 0.00259 +Epoch [3633/4000] Training [2/39] Loss: 0.00450 +Epoch [3633/4000] Training [3/39] Loss: 0.00543 +Epoch [3633/4000] Training [4/39] Loss: 0.12724 +Epoch [3633/4000] Training [5/39] Loss: 0.13258 +Epoch [3633/4000] Training [6/39] Loss: 0.00299 +Epoch [3633/4000] Training [7/39] Loss: 0.12918 +Epoch [3633/4000] Training [8/39] Loss: 0.00377 +Epoch [3633/4000] Training [9/39] Loss: 0.00327 +Epoch [3633/4000] Training [10/39] Loss: 0.00573 +Epoch [3633/4000] Training [11/39] Loss: 0.00551 +Epoch [3633/4000] Training [12/39] Loss: 0.00534 +Epoch [3633/4000] Training [13/39] Loss: 0.00589 +Epoch [3633/4000] Training [14/39] Loss: 0.00542 +Epoch [3633/4000] Training [15/39] Loss: 0.00391 +Epoch [3633/4000] Training [16/39] Loss: 0.00636 +Epoch [3633/4000] Training [17/39] Loss: 0.00455 +Epoch [3633/4000] Training [18/39] Loss: 0.00346 +Epoch [3633/4000] Training [19/39] Loss: 0.12875 +Epoch [3633/4000] Training [20/39] Loss: 0.00573 +Epoch [3633/4000] Training [21/39] Loss: 0.12897 +Epoch [3633/4000] Training [22/39] Loss: 0.00620 +Epoch [3633/4000] Training [23/39] Loss: 0.00337 +Epoch [3633/4000] Training [24/39] Loss: 0.08322 +Epoch [3633/4000] Training [25/39] Loss: 0.00541 +Epoch [3633/4000] Training [26/39] Loss: 0.00885 +Epoch [3633/4000] Training [27/39] Loss: 0.00808 +Epoch [3633/4000] Training [28/39] Loss: 0.00588 +Epoch [3633/4000] Training [29/39] Loss: 0.12814 +Epoch [3633/4000] Training [30/39] Loss: 0.00460 +Epoch [3633/4000] Training [31/39] Loss: 0.00642 +Epoch [3633/4000] Training [32/39] Loss: 0.12951 +Epoch [3633/4000] Training [33/39] Loss: 0.00460 +Epoch [3633/4000] Training [34/39] Loss: 0.00507 +Epoch [3633/4000] Training [35/39] Loss: 0.00647 +Epoch [3633/4000] Training [36/39] Loss: 0.00296 +Epoch [3633/4000] Training [37/39] Loss: 0.00405 +Epoch [3633/4000] Training [38/39] Loss: 0.00595 +Epoch [3633/4000] Training [39/39] Loss: 0.00328 +Epoch [3633/4000] Training metric {'Train/mean dice_metric': 0.9955220222473145, 'Train/mean miou_metric': 0.9923205375671387, 'Train/mean f1': 0.9969525337219238, 'Train/mean precision': 0.9965339303016663, 'Train/mean recall': 0.9973714351654053, 'Train/mean hd95_metric': 1.2002906799316406} +Epoch [3633/4000] Validation [1/10] Loss: 0.76716 focal_loss 0.67591 dice_loss 0.09125 +Epoch [3633/4000] Validation [2/10] Loss: 0.48153 focal_loss 0.38885 dice_loss 0.09268 +Epoch [3633/4000] Validation [3/10] Loss: 0.37891 focal_loss 0.26897 dice_loss 0.10994 +Epoch [3633/4000] Validation [4/10] Loss: 0.89982 focal_loss 0.33320 dice_loss 0.56662 +Epoch [3633/4000] Validation [5/10] Loss: 3.07932 focal_loss 2.40597 dice_loss 0.67335 +Epoch [3633/4000] Validation [6/10] Loss: 1.35521 focal_loss 0.63902 dice_loss 0.71618 +Epoch [3633/4000] Validation [7/10] Loss: 1.20034 focal_loss 0.54776 dice_loss 0.65258 +Epoch [3633/4000] Validation [8/10] Loss: 2.24650 focal_loss 1.64420 dice_loss 0.60231 +Epoch [3633/4000] Validation [9/10] Loss: 1.52723 focal_loss 0.97973 dice_loss 0.54750 +Epoch [3633/4000] Validation [10/10] Loss: 1.91764 focal_loss 1.18108 dice_loss 0.73656 +Epoch [3633/4000] Validation metric {'Val/mean dice_metric': 0.9510023593902588, 'Val/mean miou_metric': 0.9351751804351807, 'Val/mean f1': 0.948293924331665, 'Val/mean precision': 0.9423776268959045, 'Val/mean recall': 0.9542850255966187, 'Val/mean hd95_metric': 10.840958595275879} +Cheakpoint... +Epoch [3633/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510023593902588, 'Val/mean miou_metric': 0.9351751804351807, 'Val/mean f1': 0.948293924331665, 'Val/mean precision': 0.9423776268959045, 'Val/mean recall': 0.9542850255966187, 'Val/mean hd95_metric': 10.840958595275879} +Epoch [3634/4000] Training [1/39] Loss: 0.00670 +Epoch [3634/4000] Training [2/39] Loss: 0.12900 +Epoch [3634/4000] Training [3/39] Loss: 0.12824 +Epoch [3634/4000] Training [4/39] Loss: 0.00435 +Epoch [3634/4000] Training [5/39] Loss: 0.00446 +Epoch [3634/4000] Training [6/39] Loss: 0.00725 +Epoch [3634/4000] Training [7/39] Loss: 0.00401 +Epoch [3634/4000] Training [8/39] Loss: 0.00694 +Epoch [3634/4000] Training [9/39] Loss: 0.13089 +Epoch [3634/4000] Training [10/39] Loss: 0.00447 +Epoch [3634/4000] Training [11/39] Loss: 0.00379 +Epoch [3634/4000] Training [12/39] Loss: 0.13009 +Epoch [3634/4000] Training [13/39] Loss: 0.00419 +Epoch [3634/4000] Training [14/39] Loss: 0.00451 +Epoch [3634/4000] Training [15/39] Loss: 0.00599 +Epoch [3634/4000] Training [16/39] Loss: 0.00555 +Epoch [3634/4000] Training [17/39] Loss: 0.12783 +Epoch [3634/4000] Training [18/39] Loss: 0.00404 +Epoch [3634/4000] Training [19/39] Loss: 0.00406 +Epoch [3634/4000] Training [20/39] Loss: 0.00324 +Epoch [3634/4000] Training [21/39] Loss: 0.12842 +Epoch [3634/4000] Training [22/39] Loss: 0.00486 +Epoch [3634/4000] Training [23/39] Loss: 0.12841 +Epoch [3634/4000] Training [24/39] Loss: 0.00359 +Epoch [3634/4000] Training [25/39] Loss: 0.12912 +Epoch [3634/4000] Training [26/39] Loss: 0.00487 +Epoch [3634/4000] Training [27/39] Loss: 0.00308 +Epoch [3634/4000] Training [28/39] Loss: 0.00521 +Epoch [3634/4000] Training [29/39] Loss: 0.12867 +Epoch [3634/4000] Training [30/39] Loss: 0.12816 +Epoch [3634/4000] Training [31/39] Loss: 0.13034 +Epoch [3634/4000] Training [32/39] Loss: 0.12856 +Epoch [3634/4000] Training [33/39] Loss: 0.00664 +Epoch [3634/4000] Training [34/39] Loss: 0.00430 +Epoch [3634/4000] Training [35/39] Loss: 0.00319 +Epoch [3634/4000] Training [36/39] Loss: 0.00427 +Epoch [3634/4000] Training [37/39] Loss: 0.00365 +Epoch [3634/4000] Training [38/39] Loss: 0.00489 +Epoch [3634/4000] Training [39/39] Loss: 0.00351 +Epoch [3634/4000] Training metric {'Train/mean dice_metric': 0.9962660074234009, 'Train/mean miou_metric': 0.9930320382118225, 'Train/mean f1': 0.9968387484550476, 'Train/mean precision': 0.9964009523391724, 'Train/mean recall': 0.997277021408081, 'Train/mean hd95_metric': 0.9519637227058411} +Epoch [3634/4000] Validation [1/10] Loss: 0.73582 focal_loss 0.64600 dice_loss 0.08982 +Epoch [3634/4000] Validation [2/10] Loss: 0.48310 focal_loss 0.38673 dice_loss 0.09637 +Epoch [3634/4000] Validation [3/10] Loss: 0.37946 focal_loss 0.26863 dice_loss 0.11082 +Epoch [3634/4000] Validation [4/10] Loss: 0.89096 focal_loss 0.32606 dice_loss 0.56490 +Epoch [3634/4000] Validation [5/10] Loss: 3.01329 focal_loss 2.33988 dice_loss 0.67342 +Epoch [3634/4000] Validation [6/10] Loss: 1.33554 focal_loss 0.61969 dice_loss 0.71586 +Epoch [3634/4000] Validation [7/10] Loss: 1.18633 focal_loss 0.53665 dice_loss 0.64969 +Epoch [3634/4000] Validation [8/10] Loss: 2.32212 focal_loss 1.70631 dice_loss 0.61582 +Epoch [3634/4000] Validation [9/10] Loss: 1.49777 focal_loss 0.95084 dice_loss 0.54693 +Epoch [3634/4000] Validation [10/10] Loss: 1.88028 focal_loss 1.14477 dice_loss 0.73551 +Epoch [3634/4000] Validation metric {'Val/mean dice_metric': 0.9515371322631836, 'Val/mean miou_metric': 0.9357143640518188, 'Val/mean f1': 0.9486898183822632, 'Val/mean precision': 0.9438744187355042, 'Val/mean recall': 0.9535545706748962, 'Val/mean hd95_metric': 10.602331161499023} +Cheakpoint... +Epoch [3634/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515371322631836, 'Val/mean miou_metric': 0.9357143640518188, 'Val/mean f1': 0.9486898183822632, 'Val/mean precision': 0.9438744187355042, 'Val/mean recall': 0.9535545706748962, 'Val/mean hd95_metric': 10.602331161499023} +Epoch [3635/4000] Training [1/39] Loss: 0.00504 +Epoch [3635/4000] Training [2/39] Loss: 0.00489 +Epoch [3635/4000] Training [3/39] Loss: 0.00555 +Epoch [3635/4000] Training [4/39] Loss: 0.00360 +Epoch [3635/4000] Training [5/39] Loss: 0.00491 +Epoch [3635/4000] Training [6/39] Loss: 0.00520 +Epoch [3635/4000] Training [7/39] Loss: 0.12808 +Epoch [3635/4000] Training [8/39] Loss: 0.00446 +Epoch [3635/4000] Training [9/39] Loss: 0.12798 +Epoch [3635/4000] Training [10/39] Loss: 0.00447 +Epoch [3635/4000] Training [11/39] Loss: 0.00465 +Epoch [3635/4000] Training [12/39] Loss: 0.00593 +Epoch [3635/4000] Training [13/39] Loss: 0.00355 +Epoch [3635/4000] Training [14/39] Loss: 0.04268 +Epoch [3635/4000] Training [15/39] Loss: 0.00454 +Epoch [3635/4000] Training [16/39] Loss: 0.00284 +Epoch [3635/4000] Training [17/39] Loss: 0.01021 +Epoch [3635/4000] Training [18/39] Loss: 0.00595 +Epoch [3635/4000] Training [19/39] Loss: 0.00342 +Epoch [3635/4000] Training [20/39] Loss: 0.00375 +Epoch [3635/4000] Training [21/39] Loss: 0.00585 +Epoch [3635/4000] Training [22/39] Loss: 0.00367 +Epoch [3635/4000] Training [23/39] Loss: 0.00428 +Epoch [3635/4000] Training [24/39] Loss: 0.13132 +Epoch [3635/4000] Training [25/39] Loss: 0.00584 +Epoch [3635/4000] Training [26/39] Loss: 0.00384 +Epoch [3635/4000] Training [27/39] Loss: 0.00444 +Epoch [3635/4000] Training [28/39] Loss: 0.12890 +Epoch [3635/4000] Training [29/39] Loss: 0.13131 +Epoch [3635/4000] Training [30/39] Loss: 0.12761 +Epoch [3635/4000] Training [31/39] Loss: 0.00502 +Epoch [3635/4000] Training [32/39] Loss: 0.00641 +Epoch [3635/4000] Training [33/39] Loss: 0.12883 +Epoch [3635/4000] Training [34/39] Loss: 0.00521 +Epoch [3635/4000] Training [35/39] Loss: 0.12987 +Epoch [3635/4000] Training [36/39] Loss: 0.00693 +Epoch [3635/4000] Training [37/39] Loss: 0.00482 +Epoch [3635/4000] Training [38/39] Loss: 0.00317 +Epoch [3635/4000] Training [39/39] Loss: 0.12777 +Epoch [3635/4000] Training metric {'Train/mean dice_metric': 0.9955616593360901, 'Train/mean miou_metric': 0.9923952221870422, 'Train/mean f1': 0.9969380497932434, 'Train/mean precision': 0.9964562654495239, 'Train/mean recall': 0.9974204301834106, 'Train/mean hd95_metric': 0.9590327739715576} +Epoch [3635/4000] Validation [1/10] Loss: 0.77250 focal_loss 0.68060 dice_loss 0.09189 +Epoch [3635/4000] Validation [2/10] Loss: 0.48261 focal_loss 0.38604 dice_loss 0.09657 +Epoch [3635/4000] Validation [3/10] Loss: 0.38566 focal_loss 0.27523 dice_loss 0.11043 +Epoch [3635/4000] Validation [4/10] Loss: 0.89068 focal_loss 0.32580 dice_loss 0.56488 +Epoch [3635/4000] Validation [5/10] Loss: 3.09641 focal_loss 2.42302 dice_loss 0.67339 +Epoch [3635/4000] Validation [6/10] Loss: 1.33493 focal_loss 0.61638 dice_loss 0.71855 +Epoch [3635/4000] Validation [7/10] Loss: 1.18027 focal_loss 0.53006 dice_loss 0.65021 +Epoch [3635/4000] Validation [8/10] Loss: 2.33319 focal_loss 1.71851 dice_loss 0.61467 +Epoch [3635/4000] Validation [9/10] Loss: 1.51639 focal_loss 0.97066 dice_loss 0.54573 +Epoch [3635/4000] Validation [10/10] Loss: 1.87019 focal_loss 1.13455 dice_loss 0.73564 +Epoch [3635/4000] Validation metric {'Val/mean dice_metric': 0.9507626891136169, 'Val/mean miou_metric': 0.9349298477172852, 'Val/mean f1': 0.9488741755485535, 'Val/mean precision': 0.9445087909698486, 'Val/mean recall': 0.9532800912857056, 'Val/mean hd95_metric': 10.6719388961792} +Cheakpoint... +Epoch [3635/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507626891136169, 'Val/mean miou_metric': 0.9349298477172852, 'Val/mean f1': 0.9488741755485535, 'Val/mean precision': 0.9445087909698486, 'Val/mean recall': 0.9532800912857056, 'Val/mean hd95_metric': 10.6719388961792} +Epoch [3636/4000] Training [1/39] Loss: 0.12775 +Epoch [3636/4000] Training [2/39] Loss: 0.12936 +Epoch [3636/4000] Training [3/39] Loss: 0.00955 +Epoch [3636/4000] Training [4/39] Loss: 0.13397 +Epoch [3636/4000] Training [5/39] Loss: 0.00791 +Epoch [3636/4000] Training [6/39] Loss: 0.10161 +Epoch [3636/4000] Training [7/39] Loss: 0.00589 +Epoch [3636/4000] Training [8/39] Loss: 0.00570 +Epoch [3636/4000] Training [9/39] Loss: 0.00537 +Epoch [3636/4000] Training [10/39] Loss: 0.00651 +Epoch [3636/4000] Training [11/39] Loss: 0.00592 +Epoch [3636/4000] Training [12/39] Loss: 0.12968 +Epoch [3636/4000] Training [13/39] Loss: 0.12865 +Epoch [3636/4000] Training [14/39] Loss: 0.12867 +Epoch [3636/4000] Training [15/39] Loss: 0.00630 +Epoch [3636/4000] Training [16/39] Loss: 0.00279 +Epoch [3636/4000] Training [17/39] Loss: 0.00564 +Epoch [3636/4000] Training [18/39] Loss: 0.01107 +Epoch [3636/4000] Training [19/39] Loss: 0.00432 +Epoch [3636/4000] Training [20/39] Loss: 0.00432 +Epoch [3636/4000] Training [21/39] Loss: 0.00448 +Epoch [3636/4000] Training [22/39] Loss: 0.00349 +Epoch [3636/4000] Training [23/39] Loss: 0.00471 +Epoch [3636/4000] Training [24/39] Loss: 0.00470 +Epoch [3636/4000] Training [25/39] Loss: 0.00541 +Epoch [3636/4000] Training [26/39] Loss: 0.00528 +Epoch [3636/4000] Training [27/39] Loss: 0.12834 +Epoch [3636/4000] Training [28/39] Loss: 0.00509 +Epoch [3636/4000] Training [29/39] Loss: 0.00899 +Epoch [3636/4000] Training [30/39] Loss: 0.00590 +Epoch [3636/4000] Training [31/39] Loss: 0.00432 +Epoch [3636/4000] Training [32/39] Loss: 0.00628 +Epoch [3636/4000] Training [33/39] Loss: 0.00610 +Epoch [3636/4000] Training [34/39] Loss: 0.00469 +Epoch [3636/4000] Training [35/39] Loss: 0.13067 +Epoch [3636/4000] Training [36/39] Loss: 0.00360 +Epoch [3636/4000] Training [37/39] Loss: 0.12881 +Epoch [3636/4000] Training [38/39] Loss: 0.00567 +Epoch [3636/4000] Training [39/39] Loss: 0.12882 +Epoch [3636/4000] Training metric {'Train/mean dice_metric': 0.9959402084350586, 'Train/mean miou_metric': 0.9923697113990784, 'Train/mean f1': 0.9966054558753967, 'Train/mean precision': 0.9961427450180054, 'Train/mean recall': 0.9970685839653015, 'Train/mean hd95_metric': 0.9850600957870483} +Epoch [3636/4000] Validation [1/10] Loss: 0.77330 focal_loss 0.68099 dice_loss 0.09231 +Epoch [3636/4000] Validation [2/10] Loss: 0.47728 focal_loss 0.38379 dice_loss 0.09349 +Epoch [3636/4000] Validation [3/10] Loss: 0.37665 focal_loss 0.26694 dice_loss 0.10971 +Epoch [3636/4000] Validation [4/10] Loss: 0.90188 focal_loss 0.33560 dice_loss 0.56628 +Epoch [3636/4000] Validation [5/10] Loss: 3.05273 focal_loss 2.37921 dice_loss 0.67351 +Epoch [3636/4000] Validation [6/10] Loss: 1.34201 focal_loss 0.62780 dice_loss 0.71421 +Epoch [3636/4000] Validation [7/10] Loss: 1.19345 focal_loss 0.53927 dice_loss 0.65418 +Epoch [3636/4000] Validation [8/10] Loss: 2.30239 focal_loss 1.69339 dice_loss 0.60900 +Epoch [3636/4000] Validation [9/10] Loss: 1.50584 focal_loss 0.95960 dice_loss 0.54625 +Epoch [3636/4000] Validation [10/10] Loss: 1.91148 focal_loss 1.17415 dice_loss 0.73733 +Epoch [3636/4000] Validation metric {'Val/mean dice_metric': 0.9511349201202393, 'Val/mean miou_metric': 0.9349105358123779, 'Val/mean f1': 0.948125958442688, 'Val/mean precision': 0.9425473213195801, 'Val/mean recall': 0.9537709951400757, 'Val/mean hd95_metric': 10.818233489990234} +Cheakpoint... +Epoch [3636/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511349201202393, 'Val/mean miou_metric': 0.9349105358123779, 'Val/mean f1': 0.948125958442688, 'Val/mean precision': 0.9425473213195801, 'Val/mean recall': 0.9537709951400757, 'Val/mean hd95_metric': 10.818233489990234} +Epoch [3637/4000] Training [1/39] Loss: 0.00626 +Epoch [3637/4000] Training [2/39] Loss: 0.13036 +Epoch [3637/4000] Training [3/39] Loss: 0.00441 +Epoch [3637/4000] Training [4/39] Loss: 0.12901 +Epoch [3637/4000] Training [5/39] Loss: 0.00509 +Epoch [3637/4000] Training [6/39] Loss: 0.00628 +Epoch [3637/4000] Training [7/39] Loss: 0.13410 +Epoch [3637/4000] Training [8/39] Loss: 0.00577 +Epoch [3637/4000] Training [9/39] Loss: 0.04128 +Epoch [3637/4000] Training [10/39] Loss: 0.00906 +Epoch [3637/4000] Training [11/39] Loss: 0.01037 +Epoch [3637/4000] Training [12/39] Loss: 0.25238 +Epoch [3637/4000] Training [13/39] Loss: 0.00310 +Epoch [3637/4000] Training [14/39] Loss: 0.12825 +Epoch [3637/4000] Training [15/39] Loss: 0.00895 +Epoch [3637/4000] Training [16/39] Loss: 0.12921 +Epoch [3637/4000] Training [17/39] Loss: 0.13159 +Epoch [3637/4000] Training [18/39] Loss: 0.12880 +Epoch [3637/4000] Training [19/39] Loss: 0.12993 +Epoch [3637/4000] Training [20/39] Loss: 0.00328 +Epoch [3637/4000] Training [21/39] Loss: 0.00837 +Epoch [3637/4000] Training [22/39] Loss: 0.01006 +Epoch [3637/4000] Training [23/39] Loss: 0.00298 +Epoch [3637/4000] Training [24/39] Loss: 0.00571 +Epoch [3637/4000] Training [25/39] Loss: 0.00657 +Epoch [3637/4000] Training [26/39] Loss: 0.00609 +Epoch [3637/4000] Training [27/39] Loss: 0.00578 +Epoch [3637/4000] Training [28/39] Loss: 0.13013 +Epoch [3637/4000] Training [29/39] Loss: 0.00592 +Epoch [3637/4000] Training [30/39] Loss: 0.00423 +Epoch [3637/4000] Training [31/39] Loss: 0.00368 +Epoch [3637/4000] Training [32/39] Loss: 0.00438 +Epoch [3637/4000] Training [33/39] Loss: 0.00442 +Epoch [3637/4000] Training [34/39] Loss: 0.00471 +Epoch [3637/4000] Training [35/39] Loss: 0.25290 +Epoch [3637/4000] Training [36/39] Loss: 0.00561 +Epoch [3637/4000] Training [37/39] Loss: 0.00752 +Epoch [3637/4000] Training [38/39] Loss: 0.12916 +Epoch [3637/4000] Training [39/39] Loss: 0.00388 +Epoch [3637/4000] Training metric {'Train/mean dice_metric': 0.9958517551422119, 'Train/mean miou_metric': 0.9921674132347107, 'Train/mean f1': 0.9965780377388, 'Train/mean precision': 0.996069610118866, 'Train/mean recall': 0.9970869421958923, 'Train/mean hd95_metric': 1.001217246055603} +Epoch [3637/4000] Validation [1/10] Loss: 0.75201 focal_loss 0.66197 dice_loss 0.09005 +Epoch [3637/4000] Validation [2/10] Loss: 0.49047 focal_loss 0.39502 dice_loss 0.09545 +Epoch [3637/4000] Validation [3/10] Loss: 0.38321 focal_loss 0.27314 dice_loss 0.11008 +Epoch [3637/4000] Validation [4/10] Loss: 0.90262 focal_loss 0.33763 dice_loss 0.56498 +Epoch [3637/4000] Validation [5/10] Loss: 3.05864 focal_loss 2.38511 dice_loss 0.67353 +Epoch [3637/4000] Validation [6/10] Loss: 1.34354 focal_loss 0.63044 dice_loss 0.71310 +Epoch [3637/4000] Validation [7/10] Loss: 1.19160 focal_loss 0.54067 dice_loss 0.65093 +Epoch [3637/4000] Validation [8/10] Loss: 2.41351 focal_loss 1.79519 dice_loss 0.61831 +Epoch [3637/4000] Validation [9/10] Loss: 1.50724 focal_loss 0.96149 dice_loss 0.54575 +Epoch [3637/4000] Validation [10/10] Loss: 1.91177 focal_loss 1.17604 dice_loss 0.73572 +Epoch [3637/4000] Validation metric {'Val/mean dice_metric': 0.9510903358459473, 'Val/mean miou_metric': 0.9348239898681641, 'Val/mean f1': 0.948058009147644, 'Val/mean precision': 0.943682074546814, 'Val/mean recall': 0.95247483253479, 'Val/mean hd95_metric': 10.798242568969727} +Cheakpoint... +Epoch [3637/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510903358459473, 'Val/mean miou_metric': 0.9348239898681641, 'Val/mean f1': 0.948058009147644, 'Val/mean precision': 0.943682074546814, 'Val/mean recall': 0.95247483253479, 'Val/mean hd95_metric': 10.798242568969727} +Epoch [3638/4000] Training [1/39] Loss: 0.13021 +Epoch [3638/4000] Training [2/39] Loss: 0.12878 +Epoch [3638/4000] Training [3/39] Loss: 0.13033 +Epoch [3638/4000] Training [4/39] Loss: 0.00840 +Epoch [3638/4000] Training [5/39] Loss: 0.00462 +Epoch [3638/4000] Training [6/39] Loss: 0.01035 +Epoch [3638/4000] Training [7/39] Loss: 0.00698 +Epoch [3638/4000] Training [8/39] Loss: 0.00391 +Epoch [3638/4000] Training [9/39] Loss: 0.21359 +Epoch [3638/4000] Training [10/39] Loss: 0.00806 +Epoch [3638/4000] Training [11/39] Loss: 0.12980 +Epoch [3638/4000] Training [12/39] Loss: 0.13038 +Epoch [3638/4000] Training [13/39] Loss: 0.00443 +Epoch [3638/4000] Training [14/39] Loss: 0.00570 +Epoch [3638/4000] Training [15/39] Loss: 0.00559 +Epoch [3638/4000] Training [16/39] Loss: 0.00681 +Epoch [3638/4000] Training [17/39] Loss: 0.13012 +Epoch [3638/4000] Training [18/39] Loss: 0.00712 +Epoch [3638/4000] Training [19/39] Loss: 0.12802 +Epoch [3638/4000] Training [20/39] Loss: 0.00508 +Epoch [3638/4000] Training [21/39] Loss: 0.00527 +Epoch [3638/4000] Training [22/39] Loss: 0.04914 +Epoch [3638/4000] Training [23/39] Loss: 0.00363 +Epoch [3638/4000] Training [24/39] Loss: 0.00760 +Epoch [3638/4000] Training [25/39] Loss: 0.25322 +Epoch [3638/4000] Training [26/39] Loss: 0.13242 +Epoch [3638/4000] Training [27/39] Loss: 0.00253 +Epoch [3638/4000] Training [28/39] Loss: 0.00310 +Epoch [3638/4000] Training [29/39] Loss: 0.12729 +Epoch [3638/4000] Training [30/39] Loss: 0.00579 +Epoch [3638/4000] Training [31/39] Loss: 0.00639 +Epoch [3638/4000] Training [32/39] Loss: 0.12888 +Epoch [3638/4000] Training [33/39] Loss: 0.02018 +Epoch [3638/4000] Training [34/39] Loss: 0.00673 +Epoch [3638/4000] Training [35/39] Loss: 0.00492 +Epoch [3638/4000] Training [36/39] Loss: 0.00199 +Epoch [3638/4000] Training [37/39] Loss: 0.00611 +Epoch [3638/4000] Training [38/39] Loss: 0.12861 +Epoch [3638/4000] Training [39/39] Loss: 0.00334 +Epoch [3638/4000] Training metric {'Train/mean dice_metric': 0.9958204627037048, 'Train/mean miou_metric': 0.9921268224716187, 'Train/mean f1': 0.9964653253555298, 'Train/mean precision': 0.9960874319076538, 'Train/mean recall': 0.9968434572219849, 'Train/mean hd95_metric': 0.9937649965286255} +Epoch [3638/4000] Validation [1/10] Loss: 0.76311 focal_loss 0.67226 dice_loss 0.09084 +Epoch [3638/4000] Validation [2/10] Loss: 0.48219 focal_loss 0.38975 dice_loss 0.09244 +Epoch [3638/4000] Validation [3/10] Loss: 0.37154 focal_loss 0.26250 dice_loss 0.10904 +Epoch [3638/4000] Validation [4/10] Loss: 0.91703 focal_loss 0.34630 dice_loss 0.57073 +Epoch [3638/4000] Validation [5/10] Loss: 3.09440 focal_loss 2.42127 dice_loss 0.67313 +Epoch [3638/4000] Validation [6/10] Loss: 1.36129 focal_loss 0.64788 dice_loss 0.71341 +Epoch [3638/4000] Validation [7/10] Loss: 1.20903 focal_loss 0.55916 dice_loss 0.64987 +Epoch [3638/4000] Validation [8/10] Loss: 2.18015 focal_loss 1.58933 dice_loss 0.59082 +Epoch [3638/4000] Validation [9/10] Loss: 1.63390 focal_loss 1.08940 dice_loss 0.54450 +Epoch [3638/4000] Validation [10/10] Loss: 1.97777 focal_loss 1.23595 dice_loss 0.74181 +Epoch [3638/4000] Validation metric {'Val/mean dice_metric': 0.9511273503303528, 'Val/mean miou_metric': 0.9348312020301819, 'Val/mean f1': 0.9471434354782104, 'Val/mean precision': 0.9398244619369507, 'Val/mean recall': 0.9545772671699524, 'Val/mean hd95_metric': 10.722565650939941} +Cheakpoint... +Epoch [3638/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511273503303528, 'Val/mean miou_metric': 0.9348312020301819, 'Val/mean f1': 0.9471434354782104, 'Val/mean precision': 0.9398244619369507, 'Val/mean recall': 0.9545772671699524, 'Val/mean hd95_metric': 10.722565650939941} +Epoch [3639/4000] Training [1/39] Loss: 0.00344 +Epoch [3639/4000] Training [2/39] Loss: 0.00531 +Epoch [3639/4000] Training [3/39] Loss: 0.00373 +Epoch [3639/4000] Training [4/39] Loss: 0.12889 +Epoch [3639/4000] Training [5/39] Loss: 0.12856 +Epoch [3639/4000] Training [6/39] Loss: 0.00337 +Epoch [3639/4000] Training [7/39] Loss: 0.00332 +Epoch [3639/4000] Training [8/39] Loss: 0.00535 +Epoch [3639/4000] Training [9/39] Loss: 0.12934 +Epoch [3639/4000] Training [10/39] Loss: 0.00387 +Epoch [3639/4000] Training [11/39] Loss: 0.25287 +Epoch [3639/4000] Training [12/39] Loss: 0.00518 +Epoch [3639/4000] Training [13/39] Loss: 0.00651 +Epoch [3639/4000] Training [14/39] Loss: 0.00379 +Epoch [3639/4000] Training [15/39] Loss: 0.00510 +Epoch [3639/4000] Training [16/39] Loss: 0.00379 +Epoch [3639/4000] Training [17/39] Loss: 0.00510 +Epoch [3639/4000] Training [18/39] Loss: 0.00448 +Epoch [3639/4000] Training [19/39] Loss: 0.00602 +Epoch [3639/4000] Training [20/39] Loss: 0.12882 +Epoch [3639/4000] Training [21/39] Loss: 0.00614 +Epoch [3639/4000] Training [22/39] Loss: 0.00378 +Epoch [3639/4000] Training [23/39] Loss: 0.00449 +Epoch [3639/4000] Training [24/39] Loss: 0.00646 +Epoch [3639/4000] Training [25/39] Loss: 0.00412 +Epoch [3639/4000] Training [26/39] Loss: 0.13073 +Epoch [3639/4000] Training [27/39] Loss: 0.00323 +Epoch [3639/4000] Training [28/39] Loss: 0.00510 +Epoch [3639/4000] Training [29/39] Loss: 0.00292 +Epoch [3639/4000] Training [30/39] Loss: 0.00519 +Epoch [3639/4000] Training [31/39] Loss: 0.12941 +Epoch [3639/4000] Training [32/39] Loss: 0.00623 +Epoch [3639/4000] Training [33/39] Loss: 0.00497 +Epoch [3639/4000] Training [34/39] Loss: 0.12751 +Epoch [3639/4000] Training [35/39] Loss: 0.12867 +Epoch [3639/4000] Training [36/39] Loss: 0.00414 +Epoch [3639/4000] Training [37/39] Loss: 0.00433 +Epoch [3639/4000] Training [38/39] Loss: 0.00389 +Epoch [3639/4000] Training [39/39] Loss: 0.13179 +Epoch [3639/4000] Training metric {'Train/mean dice_metric': 0.9955853819847107, 'Train/mean miou_metric': 0.9924426078796387, 'Train/mean f1': 0.9969503879547119, 'Train/mean precision': 0.9964598417282104, 'Train/mean recall': 0.9974414706230164, 'Train/mean hd95_metric': 0.9479250311851501} +Epoch [3639/4000] Validation [1/10] Loss: 0.77553 focal_loss 0.68256 dice_loss 0.09297 +Epoch [3639/4000] Validation [2/10] Loss: 0.47395 focal_loss 0.38717 dice_loss 0.08678 +Epoch [3639/4000] Validation [3/10] Loss: 0.34673 focal_loss 0.23905 dice_loss 0.10768 +Epoch [3639/4000] Validation [4/10] Loss: 0.95478 focal_loss 0.37399 dice_loss 0.58079 +Epoch [3639/4000] Validation [5/10] Loss: 3.03416 focal_loss 2.36187 dice_loss 0.67229 +Epoch [3639/4000] Validation [6/10] Loss: 1.40205 focal_loss 0.68644 dice_loss 0.71560 +Epoch [3639/4000] Validation [7/10] Loss: 1.23311 focal_loss 0.57675 dice_loss 0.65636 +Epoch [3639/4000] Validation [8/10] Loss: 2.04833 focal_loss 1.47695 dice_loss 0.57138 +Epoch [3639/4000] Validation [9/10] Loss: 1.75329 focal_loss 1.22983 dice_loss 0.52346 +Epoch [3639/4000] Validation [10/10] Loss: 2.09614 focal_loss 1.34635 dice_loss 0.74979 +Epoch [3639/4000] Validation metric {'Val/mean dice_metric': 0.9506576061248779, 'Val/mean miou_metric': 0.934645414352417, 'Val/mean f1': 0.9459695816040039, 'Val/mean precision': 0.9347630739212036, 'Val/mean recall': 0.9574480056762695, 'Val/mean hd95_metric': 11.056639671325684} +Cheakpoint... +Epoch [3639/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506576061248779, 'Val/mean miou_metric': 0.934645414352417, 'Val/mean f1': 0.9459695816040039, 'Val/mean precision': 0.9347630739212036, 'Val/mean recall': 0.9574480056762695, 'Val/mean hd95_metric': 11.056639671325684} +Epoch [3640/4000] Training [1/39] Loss: 0.00313 +Epoch [3640/4000] Training [2/39] Loss: 0.12824 +Epoch [3640/4000] Training [3/39] Loss: 0.00347 +Epoch [3640/4000] Training [4/39] Loss: 0.12794 +Epoch [3640/4000] Training [5/39] Loss: 0.25511 +Epoch [3640/4000] Training [6/39] Loss: 0.00423 +Epoch [3640/4000] Training [7/39] Loss: 0.00481 +Epoch [3640/4000] Training [8/39] Loss: 0.00854 +Epoch [3640/4000] Training [9/39] Loss: 0.00446 +Epoch [3640/4000] Training [10/39] Loss: 0.00746 +Epoch [3640/4000] Training [11/39] Loss: 0.12919 +Epoch [3640/4000] Training [12/39] Loss: 0.00315 +Epoch [3640/4000] Training [13/39] Loss: 0.00408 +Epoch [3640/4000] Training [14/39] Loss: 0.13056 +Epoch [3640/4000] Training [15/39] Loss: 0.00533 +Epoch [3640/4000] Training [16/39] Loss: 0.00248 +Epoch [3640/4000] Training [17/39] Loss: 0.00495 +Epoch [3640/4000] Training [18/39] Loss: 0.00395 +Epoch [3640/4000] Training [19/39] Loss: 0.00476 +Epoch [3640/4000] Training [20/39] Loss: 0.25352 +Epoch [3640/4000] Training [21/39] Loss: 0.00481 +Epoch [3640/4000] Training [22/39] Loss: 0.12906 +Epoch [3640/4000] Training [23/39] Loss: 0.00367 +Epoch [3640/4000] Training [24/39] Loss: 0.00608 +Epoch [3640/4000] Training [25/39] Loss: 0.00829 +Epoch [3640/4000] Training [26/39] Loss: 0.00435 +Epoch [3640/4000] Training [27/39] Loss: 0.00424 +Epoch [3640/4000] Training [28/39] Loss: 0.00357 +Epoch [3640/4000] Training [29/39] Loss: 0.00574 +Epoch [3640/4000] Training [30/39] Loss: 0.00442 +Epoch [3640/4000] Training [31/39] Loss: 0.00394 +Epoch [3640/4000] Training [32/39] Loss: 0.00413 +Epoch [3640/4000] Training [33/39] Loss: 0.00388 +Epoch [3640/4000] Training [34/39] Loss: 0.00532 +Epoch [3640/4000] Training [35/39] Loss: 0.00346 +Epoch [3640/4000] Training [36/39] Loss: 0.00339 +Epoch [3640/4000] Training [37/39] Loss: 0.12916 +Epoch [3640/4000] Training [38/39] Loss: 0.00496 +Epoch [3640/4000] Training [39/39] Loss: 0.00456 +Epoch [3640/4000] Training metric {'Train/mean dice_metric': 0.9956961274147034, 'Train/mean miou_metric': 0.992685079574585, 'Train/mean f1': 0.9970415830612183, 'Train/mean precision': 0.9965718388557434, 'Train/mean recall': 0.997511625289917, 'Train/mean hd95_metric': 1.0067312717437744} +Epoch [3640/4000] Validation [1/10] Loss: 0.74221 focal_loss 0.65281 dice_loss 0.08941 +Epoch [3640/4000] Validation [2/10] Loss: 0.47868 focal_loss 0.38369 dice_loss 0.09498 +Epoch [3640/4000] Validation [3/10] Loss: 0.37672 focal_loss 0.26650 dice_loss 0.11022 +Epoch [3640/4000] Validation [4/10] Loss: 0.91308 focal_loss 0.34217 dice_loss 0.57091 +Epoch [3640/4000] Validation [5/10] Loss: 3.11280 focal_loss 2.43954 dice_loss 0.67326 +Epoch [3640/4000] Validation [6/10] Loss: 1.34591 focal_loss 0.63438 dice_loss 0.71154 +Epoch [3640/4000] Validation [7/10] Loss: 1.19049 focal_loss 0.54042 dice_loss 0.65006 +Epoch [3640/4000] Validation [8/10] Loss: 2.26480 focal_loss 1.65927 dice_loss 0.60552 +Epoch [3640/4000] Validation [9/10] Loss: 1.63960 focal_loss 1.09105 dice_loss 0.54855 +Epoch [3640/4000] Validation [10/10] Loss: 1.94507 focal_loss 1.20243 dice_loss 0.74264 +Epoch [3640/4000] Validation metric {'Val/mean dice_metric': 0.9509686827659607, 'Val/mean miou_metric': 0.9352236986160278, 'Val/mean f1': 0.947659969329834, 'Val/mean precision': 0.9410769939422607, 'Val/mean recall': 0.9543357491493225, 'Val/mean hd95_metric': 10.678047180175781} +Cheakpoint... +Epoch [3640/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509686827659607, 'Val/mean miou_metric': 0.9352236986160278, 'Val/mean f1': 0.947659969329834, 'Val/mean precision': 0.9410769939422607, 'Val/mean recall': 0.9543357491493225, 'Val/mean hd95_metric': 10.678047180175781} +Epoch [3641/4000] Training [1/39] Loss: 0.00520 +Epoch [3641/4000] Training [2/39] Loss: 0.00347 +Epoch [3641/4000] Training [3/39] Loss: 0.00517 +Epoch [3641/4000] Training [4/39] Loss: 0.12910 +Epoch [3641/4000] Training [5/39] Loss: 0.00368 +Epoch [3641/4000] Training [6/39] Loss: 0.00799 +Epoch [3641/4000] Training [7/39] Loss: 0.13102 +Epoch [3641/4000] Training [8/39] Loss: 0.00397 +Epoch [3641/4000] Training [9/39] Loss: 0.12964 +Epoch [3641/4000] Training [10/39] Loss: 0.00498 +Epoch [3641/4000] Training [11/39] Loss: 0.00487 +Epoch [3641/4000] Training [12/39] Loss: 0.00466 +Epoch [3641/4000] Training [13/39] Loss: 0.00570 +Epoch [3641/4000] Training [14/39] Loss: 0.00763 +Epoch [3641/4000] Training [15/39] Loss: 0.00520 +Epoch [3641/4000] Training [16/39] Loss: 0.00499 +Epoch [3641/4000] Training [17/39] Loss: 0.00554 +Epoch [3641/4000] Training [18/39] Loss: 0.00674 +Epoch [3641/4000] Training [19/39] Loss: 0.00605 +Epoch [3641/4000] Training [20/39] Loss: 0.25395 +Epoch [3641/4000] Training [21/39] Loss: 0.12910 +Epoch [3641/4000] Training [22/39] Loss: 0.00372 +Epoch [3641/4000] Training [23/39] Loss: 0.12896 +Epoch [3641/4000] Training [24/39] Loss: 0.00373 +Epoch [3641/4000] Training [25/39] Loss: 0.37858 +Epoch [3641/4000] Training [26/39] Loss: 0.00330 +Epoch [3641/4000] Training [27/39] Loss: 0.00704 +Epoch [3641/4000] Training [28/39] Loss: 0.00456 +Epoch [3641/4000] Training [29/39] Loss: 0.00432 +Epoch [3641/4000] Training [30/39] Loss: 0.00604 +Epoch [3641/4000] Training [31/39] Loss: 0.12805 +Epoch [3641/4000] Training [32/39] Loss: 0.00689 +Epoch [3641/4000] Training [33/39] Loss: 0.00475 +Epoch [3641/4000] Training [34/39] Loss: 0.00362 +Epoch [3641/4000] Training [35/39] Loss: 0.12842 +Epoch [3641/4000] Training [36/39] Loss: 0.13048 +Epoch [3641/4000] Training [37/39] Loss: 0.00414 +Epoch [3641/4000] Training [38/39] Loss: 0.00541 +Epoch [3641/4000] Training [39/39] Loss: 0.00506 +Epoch [3641/4000] Training metric {'Train/mean dice_metric': 0.9961357116699219, 'Train/mean miou_metric': 0.9927257895469666, 'Train/mean f1': 0.9967676401138306, 'Train/mean precision': 0.9962811470031738, 'Train/mean recall': 0.9972544312477112, 'Train/mean hd95_metric': 0.9876425266265869} +Epoch [3641/4000] Validation [1/10] Loss: 0.73375 focal_loss 0.64321 dice_loss 0.09054 +Epoch [3641/4000] Validation [2/10] Loss: 0.47578 focal_loss 0.38192 dice_loss 0.09385 +Epoch [3641/4000] Validation [3/10] Loss: 0.36006 focal_loss 0.25037 dice_loss 0.10969 +Epoch [3641/4000] Validation [4/10] Loss: 0.91955 focal_loss 0.34546 dice_loss 0.57408 +Epoch [3641/4000] Validation [5/10] Loss: 3.02368 focal_loss 2.35099 dice_loss 0.67268 +Epoch [3641/4000] Validation [6/10] Loss: 1.35320 focal_loss 0.64099 dice_loss 0.71221 +Epoch [3641/4000] Validation [7/10] Loss: 1.19870 focal_loss 0.54571 dice_loss 0.65298 +Epoch [3641/4000] Validation [8/10] Loss: 2.14398 focal_loss 1.54965 dice_loss 0.59433 +Epoch [3641/4000] Validation [9/10] Loss: 1.62190 focal_loss 1.07341 dice_loss 0.54849 +Epoch [3641/4000] Validation [10/10] Loss: 1.95624 focal_loss 1.21291 dice_loss 0.74333 +Epoch [3641/4000] Validation metric {'Val/mean dice_metric': 0.9513722062110901, 'Val/mean miou_metric': 0.9352749586105347, 'Val/mean f1': 0.9470719695091248, 'Val/mean precision': 0.9392639994621277, 'Val/mean recall': 0.9550108909606934, 'Val/mean hd95_metric': 10.789846420288086} +Cheakpoint... +Epoch [3641/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513722062110901, 'Val/mean miou_metric': 0.9352749586105347, 'Val/mean f1': 0.9470719695091248, 'Val/mean precision': 0.9392639994621277, 'Val/mean recall': 0.9550108909606934, 'Val/mean hd95_metric': 10.789846420288086} +Epoch [3642/4000] Training [1/39] Loss: 0.00584 +Epoch [3642/4000] Training [2/39] Loss: 0.00532 +Epoch [3642/4000] Training [3/39] Loss: 0.13028 +Epoch [3642/4000] Training [4/39] Loss: 0.00440 +Epoch [3642/4000] Training [5/39] Loss: 0.00541 +Epoch [3642/4000] Training [6/39] Loss: 0.00525 +Epoch [3642/4000] Training [7/39] Loss: 0.00548 +Epoch [3642/4000] Training [8/39] Loss: 0.12965 +Epoch [3642/4000] Training [9/39] Loss: 0.00387 +Epoch [3642/4000] Training [10/39] Loss: 0.12851 +Epoch [3642/4000] Training [11/39] Loss: 0.00434 +Epoch [3642/4000] Training [12/39] Loss: 0.00683 +Epoch [3642/4000] Training [13/39] Loss: 0.00363 +Epoch [3642/4000] Training [14/39] Loss: 0.00438 +Epoch [3642/4000] Training [15/39] Loss: 0.00616 +Epoch [3642/4000] Training [16/39] Loss: 0.12967 +Epoch [3642/4000] Training [17/39] Loss: 0.00371 +Epoch [3642/4000] Training [18/39] Loss: 0.00318 +Epoch [3642/4000] Training [19/39] Loss: 0.12939 +Epoch [3642/4000] Training [20/39] Loss: 0.00553 +Epoch [3642/4000] Training [21/39] Loss: 0.00425 +Epoch [3642/4000] Training [22/39] Loss: 0.00777 +Epoch [3642/4000] Training [23/39] Loss: 0.00368 +Epoch [3642/4000] Training [24/39] Loss: 0.12933 +Epoch [3642/4000] Training [25/39] Loss: 0.00545 +Epoch [3642/4000] Training [26/39] Loss: 0.21427 +Epoch [3642/4000] Training [27/39] Loss: 0.00355 +Epoch [3642/4000] Training [28/39] Loss: 0.00473 +Epoch [3642/4000] Training [29/39] Loss: 0.25269 +Epoch [3642/4000] Training [30/39] Loss: 0.00433 +Epoch [3642/4000] Training [31/39] Loss: 0.00531 +Epoch [3642/4000] Training [32/39] Loss: 0.00416 +Epoch [3642/4000] Training [33/39] Loss: 0.00450 +Epoch [3642/4000] Training [34/39] Loss: 0.13141 +Epoch [3642/4000] Training [35/39] Loss: 0.00582 +Epoch [3642/4000] Training [36/39] Loss: 0.12962 +Epoch [3642/4000] Training [37/39] Loss: 0.00350 +Epoch [3642/4000] Training [38/39] Loss: 0.00491 +Epoch [3642/4000] Training [39/39] Loss: 0.00653 +Epoch [3642/4000] Training metric {'Train/mean dice_metric': 0.9962229132652283, 'Train/mean miou_metric': 0.9929021000862122, 'Train/mean f1': 0.9968245029449463, 'Train/mean precision': 0.9964278936386108, 'Train/mean recall': 0.9972216486930847, 'Train/mean hd95_metric': 0.9849660992622375} +Epoch [3642/4000] Validation [1/10] Loss: 0.74795 focal_loss 0.65772 dice_loss 0.09022 +Epoch [3642/4000] Validation [2/10] Loss: 0.47895 focal_loss 0.38843 dice_loss 0.09052 +Epoch [3642/4000] Validation [3/10] Loss: 0.36138 focal_loss 0.25255 dice_loss 0.10884 +Epoch [3642/4000] Validation [4/10] Loss: 0.92003 focal_loss 0.34929 dice_loss 0.57075 +Epoch [3642/4000] Validation [5/10] Loss: 3.08686 focal_loss 2.41415 dice_loss 0.67270 +Epoch [3642/4000] Validation [6/10] Loss: 1.37072 focal_loss 0.65731 dice_loss 0.71342 +Epoch [3642/4000] Validation [7/10] Loss: 1.21572 focal_loss 0.56104 dice_loss 0.65468 +Epoch [3642/4000] Validation [8/10] Loss: 2.22518 focal_loss 1.62760 dice_loss 0.59759 +Epoch [3642/4000] Validation [9/10] Loss: 1.63694 focal_loss 1.08888 dice_loss 0.54806 +Epoch [3642/4000] Validation [10/10] Loss: 1.99830 focal_loss 1.25464 dice_loss 0.74366 +Epoch [3642/4000] Validation metric {'Val/mean dice_metric': 0.9513914585113525, 'Val/mean miou_metric': 0.935330331325531, 'Val/mean f1': 0.9472299218177795, 'Val/mean precision': 0.9396147727966309, 'Val/mean recall': 0.954969584941864, 'Val/mean hd95_metric': 10.802326202392578} +Cheakpoint... +Epoch [3642/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513914585113525, 'Val/mean miou_metric': 0.935330331325531, 'Val/mean f1': 0.9472299218177795, 'Val/mean precision': 0.9396147727966309, 'Val/mean recall': 0.954969584941864, 'Val/mean hd95_metric': 10.802326202392578} +Epoch [3643/4000] Training [1/39] Loss: 0.00490 +Epoch [3643/4000] Training [2/39] Loss: 0.00502 +Epoch [3643/4000] Training [3/39] Loss: 0.12883 +Epoch [3643/4000] Training [4/39] Loss: 0.00573 +Epoch [3643/4000] Training [5/39] Loss: 0.00334 +Epoch [3643/4000] Training [6/39] Loss: 0.00507 +Epoch [3643/4000] Training [7/39] Loss: 0.00456 +Epoch [3643/4000] Training [8/39] Loss: 0.00398 +Epoch [3643/4000] Training [9/39] Loss: 0.04057 +Epoch [3643/4000] Training [10/39] Loss: 0.00540 +Epoch [3643/4000] Training [11/39] Loss: 0.00425 +Epoch [3643/4000] Training [12/39] Loss: 0.00335 +Epoch [3643/4000] Training [13/39] Loss: 0.00597 +Epoch [3643/4000] Training [14/39] Loss: 0.00612 +Epoch [3643/4000] Training [15/39] Loss: 0.12981 +Epoch [3643/4000] Training [16/39] Loss: 0.00447 +Epoch [3643/4000] Training [17/39] Loss: 0.00507 +Epoch [3643/4000] Training [18/39] Loss: 0.00438 +Epoch [3643/4000] Training [19/39] Loss: 0.00747 +Epoch [3643/4000] Training [20/39] Loss: 0.00537 +Epoch [3643/4000] Training [21/39] Loss: 0.00754 +Epoch [3643/4000] Training [22/39] Loss: 0.00662 +Epoch [3643/4000] Training [23/39] Loss: 0.12871 +Epoch [3643/4000] Training [24/39] Loss: 0.25576 +Epoch [3643/4000] Training [25/39] Loss: 0.12924 +Epoch [3643/4000] Training [26/39] Loss: 0.00520 +Epoch [3643/4000] Training [27/39] Loss: 0.00384 +Epoch [3643/4000] Training [28/39] Loss: 0.12819 +Epoch [3643/4000] Training [29/39] Loss: 0.13059 +Epoch [3643/4000] Training [30/39] Loss: 0.00451 +Epoch [3643/4000] Training [31/39] Loss: 0.00438 +Epoch [3643/4000] Training [32/39] Loss: 0.12977 +Epoch [3643/4000] Training [33/39] Loss: 0.12744 +Epoch [3643/4000] Training [34/39] Loss: 0.00502 +Epoch [3643/4000] Training [35/39] Loss: 0.00987 +Epoch [3643/4000] Training [36/39] Loss: 0.00308 +Epoch [3643/4000] Training [37/39] Loss: 0.00730 +Epoch [3643/4000] Training [38/39] Loss: 0.00743 +Epoch [3643/4000] Training [39/39] Loss: 0.12915 +Epoch [3643/4000] Training metric {'Train/mean dice_metric': 0.9962390661239624, 'Train/mean miou_metric': 0.9929450750350952, 'Train/mean f1': 0.9968369603157043, 'Train/mean precision': 0.9963396787643433, 'Train/mean recall': 0.9973345994949341, 'Train/mean hd95_metric': 0.9313094019889832} +Epoch [3643/4000] Validation [1/10] Loss: 0.75315 focal_loss 0.66303 dice_loss 0.09012 +Epoch [3643/4000] Validation [2/10] Loss: 0.48243 focal_loss 0.38782 dice_loss 0.09461 +Epoch [3643/4000] Validation [3/10] Loss: 0.37216 focal_loss 0.26219 dice_loss 0.10997 +Epoch [3643/4000] Validation [4/10] Loss: 0.90747 focal_loss 0.33844 dice_loss 0.56903 +Epoch [3643/4000] Validation [5/10] Loss: 3.07481 focal_loss 2.40233 dice_loss 0.67248 +Epoch [3643/4000] Validation [6/10] Loss: 1.34408 focal_loss 0.63168 dice_loss 0.71240 +Epoch [3643/4000] Validation [7/10] Loss: 1.19265 focal_loss 0.54255 dice_loss 0.65010 +Epoch [3643/4000] Validation [8/10] Loss: 2.29593 focal_loss 1.68646 dice_loss 0.60947 +Epoch [3643/4000] Validation [9/10] Loss: 1.59371 focal_loss 1.04648 dice_loss 0.54722 +Epoch [3643/4000] Validation [10/10] Loss: 1.96175 focal_loss 1.21926 dice_loss 0.74248 +Epoch [3643/4000] Validation metric {'Val/mean dice_metric': 0.9513303637504578, 'Val/mean miou_metric': 0.9353446960449219, 'Val/mean f1': 0.9474173188209534, 'Val/mean precision': 0.9407361149787903, 'Val/mean recall': 0.9541942477226257, 'Val/mean hd95_metric': 10.817304611206055} +Cheakpoint... +Epoch [3643/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513303637504578, 'Val/mean miou_metric': 0.9353446960449219, 'Val/mean f1': 0.9474173188209534, 'Val/mean precision': 0.9407361149787903, 'Val/mean recall': 0.9541942477226257, 'Val/mean hd95_metric': 10.817304611206055} +Epoch [3644/4000] Training [1/39] Loss: 0.00532 +Epoch [3644/4000] Training [2/39] Loss: 0.12833 +Epoch [3644/4000] Training [3/39] Loss: 0.00478 +Epoch [3644/4000] Training [4/39] Loss: 0.00800 +Epoch [3644/4000] Training [5/39] Loss: 0.00555 +Epoch [3644/4000] Training [6/39] Loss: 0.00539 +Epoch [3644/4000] Training [7/39] Loss: 0.00626 +Epoch [3644/4000] Training [8/39] Loss: 0.13366 +Epoch [3644/4000] Training [9/39] Loss: 0.00364 +Epoch [3644/4000] Training [10/39] Loss: 0.25378 +Epoch [3644/4000] Training [11/39] Loss: 0.00730 +Epoch [3644/4000] Training [12/39] Loss: 0.00654 +Epoch [3644/4000] Training [13/39] Loss: 0.00834 +Epoch [3644/4000] Training [14/39] Loss: 0.00554 +Epoch [3644/4000] Training [15/39] Loss: 0.00595 +Epoch [3644/4000] Training [16/39] Loss: 0.00470 +Epoch [3644/4000] Training [17/39] Loss: 0.12762 +Epoch [3644/4000] Training [18/39] Loss: 0.12757 +Epoch [3644/4000] Training [19/39] Loss: 0.00344 +Epoch [3644/4000] Training [20/39] Loss: 0.12710 +Epoch [3644/4000] Training [21/39] Loss: 0.00447 +Epoch [3644/4000] Training [22/39] Loss: 0.00560 +Epoch [3644/4000] Training [23/39] Loss: 0.00382 +Epoch [3644/4000] Training [24/39] Loss: 0.12840 +Epoch [3644/4000] Training [25/39] Loss: 0.12818 +Epoch [3644/4000] Training [26/39] Loss: 0.12987 +Epoch [3644/4000] Training [27/39] Loss: 0.00518 +Epoch [3644/4000] Training [28/39] Loss: 0.12985 +Epoch [3644/4000] Training [29/39] Loss: 0.00539 +Epoch [3644/4000] Training [30/39] Loss: 0.00662 +Epoch [3644/4000] Training [31/39] Loss: 0.12757 +Epoch [3644/4000] Training [32/39] Loss: 0.00544 +Epoch [3644/4000] Training [33/39] Loss: 0.00682 +Epoch [3644/4000] Training [34/39] Loss: 0.00508 +Epoch [3644/4000] Training [35/39] Loss: 0.00529 +Epoch [3644/4000] Training [36/39] Loss: 0.00362 +Epoch [3644/4000] Training [37/39] Loss: 0.00566 +Epoch [3644/4000] Training [38/39] Loss: 0.00468 +Epoch [3644/4000] Training [39/39] Loss: 0.00342 +Epoch [3644/4000] Training metric {'Train/mean dice_metric': 0.9962040185928345, 'Train/mean miou_metric': 0.9928539395332336, 'Train/mean f1': 0.9967517256736755, 'Train/mean precision': 0.9963430166244507, 'Train/mean recall': 0.9971605539321899, 'Train/mean hd95_metric': 0.9624233841896057} +Epoch [3644/4000] Validation [1/10] Loss: 0.76742 focal_loss 0.67636 dice_loss 0.09107 +Epoch [3644/4000] Validation [2/10] Loss: 0.48330 focal_loss 0.38799 dice_loss 0.09531 +Epoch [3644/4000] Validation [3/10] Loss: 0.37646 focal_loss 0.26641 dice_loss 0.11005 +Epoch [3644/4000] Validation [4/10] Loss: 0.91226 focal_loss 0.34095 dice_loss 0.57131 +Epoch [3644/4000] Validation [5/10] Loss: 3.12200 focal_loss 2.44964 dice_loss 0.67236 +Epoch [3644/4000] Validation [6/10] Loss: 1.34636 focal_loss 0.63346 dice_loss 0.71290 +Epoch [3644/4000] Validation [7/10] Loss: 1.19501 focal_loss 0.54248 dice_loss 0.65252 +Epoch [3644/4000] Validation [8/10] Loss: 2.22703 focal_loss 1.62538 dice_loss 0.60165 +Epoch [3644/4000] Validation [9/10] Loss: 1.63107 focal_loss 1.08304 dice_loss 0.54803 +Epoch [3644/4000] Validation [10/10] Loss: 1.97798 focal_loss 1.23366 dice_loss 0.74432 +Epoch [3644/4000] Validation metric {'Val/mean dice_metric': 0.9512422680854797, 'Val/mean miou_metric': 0.9351280331611633, 'Val/mean f1': 0.9472289681434631, 'Val/mean precision': 0.940120279788971, 'Val/mean recall': 0.9544458985328674, 'Val/mean hd95_metric': 10.75407600402832} +Cheakpoint... +Epoch [3644/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512422680854797, 'Val/mean miou_metric': 0.9351280331611633, 'Val/mean f1': 0.9472289681434631, 'Val/mean precision': 0.940120279788971, 'Val/mean recall': 0.9544458985328674, 'Val/mean hd95_metric': 10.75407600402832} +Epoch [3645/4000] Training [1/39] Loss: 0.00475 +Epoch [3645/4000] Training [2/39] Loss: 0.00381 +Epoch [3645/4000] Training [3/39] Loss: 0.12951 +Epoch [3645/4000] Training [4/39] Loss: 0.00483 +Epoch [3645/4000] Training [5/39] Loss: 0.13072 +Epoch [3645/4000] Training [6/39] Loss: 0.00561 +Epoch [3645/4000] Training [7/39] Loss: 0.00438 +Epoch [3645/4000] Training [8/39] Loss: 0.00350 +Epoch [3645/4000] Training [9/39] Loss: 0.13018 +Epoch [3645/4000] Training [10/39] Loss: 0.00374 +Epoch [3645/4000] Training [11/39] Loss: 0.00458 +Epoch [3645/4000] Training [12/39] Loss: 0.13030 +Epoch [3645/4000] Training [13/39] Loss: 0.00381 +Epoch [3645/4000] Training [14/39] Loss: 0.12884 +Epoch [3645/4000] Training [15/39] Loss: 0.00467 +Epoch [3645/4000] Training [16/39] Loss: 0.00591 +Epoch [3645/4000] Training [17/39] Loss: 0.00352 +Epoch [3645/4000] Training [18/39] Loss: 0.00578 +Epoch [3645/4000] Training [19/39] Loss: 0.00400 +Epoch [3645/4000] Training [20/39] Loss: 0.00350 +Epoch [3645/4000] Training [21/39] Loss: 0.00580 +Epoch [3645/4000] Training [22/39] Loss: 0.00512 +Epoch [3645/4000] Training [23/39] Loss: 0.00443 +Epoch [3645/4000] Training [24/39] Loss: 0.00467 +Epoch [3645/4000] Training [25/39] Loss: 0.00483 +Epoch [3645/4000] Training [26/39] Loss: 0.00575 +Epoch [3645/4000] Training [27/39] Loss: 0.00579 +Epoch [3645/4000] Training [28/39] Loss: 0.12859 +Epoch [3645/4000] Training [29/39] Loss: 0.00580 +Epoch [3645/4000] Training [30/39] Loss: 0.00345 +Epoch [3645/4000] Training [31/39] Loss: 0.08542 +Epoch [3645/4000] Training [32/39] Loss: 0.00548 +Epoch [3645/4000] Training [33/39] Loss: 0.00407 +Epoch [3645/4000] Training [34/39] Loss: 0.00478 +Epoch [3645/4000] Training [35/39] Loss: 0.00330 +Epoch [3645/4000] Training [36/39] Loss: 0.12954 +Epoch [3645/4000] Training [37/39] Loss: 0.12806 +Epoch [3645/4000] Training [38/39] Loss: 0.00317 +Epoch [3645/4000] Training [39/39] Loss: 0.00533 +Epoch [3645/4000] Training metric {'Train/mean dice_metric': 0.9955419898033142, 'Train/mean miou_metric': 0.9923623204231262, 'Train/mean f1': 0.9969507455825806, 'Train/mean precision': 0.9964799284934998, 'Train/mean recall': 0.9974220991134644, 'Train/mean hd95_metric': 0.9540573954582214} +Epoch [3645/4000] Validation [1/10] Loss: 0.73967 focal_loss 0.65038 dice_loss 0.08929 +Epoch [3645/4000] Validation [2/10] Loss: 0.48414 focal_loss 0.39071 dice_loss 0.09343 +Epoch [3645/4000] Validation [3/10] Loss: 0.37021 focal_loss 0.26057 dice_loss 0.10964 +Epoch [3645/4000] Validation [4/10] Loss: 0.91878 focal_loss 0.34654 dice_loss 0.57224 +Epoch [3645/4000] Validation [5/10] Loss: 3.08135 focal_loss 2.40875 dice_loss 0.67260 +Epoch [3645/4000] Validation [6/10] Loss: 1.37210 focal_loss 0.65821 dice_loss 0.71389 +Epoch [3645/4000] Validation [7/10] Loss: 1.20490 focal_loss 0.55265 dice_loss 0.65224 +Epoch [3645/4000] Validation [8/10] Loss: 2.15836 focal_loss 1.56821 dice_loss 0.59015 +Epoch [3645/4000] Validation [9/10] Loss: 1.68761 focal_loss 1.14036 dice_loss 0.54725 +Epoch [3645/4000] Validation [10/10] Loss: 2.00801 focal_loss 1.26283 dice_loss 0.74518 +Epoch [3645/4000] Validation metric {'Val/mean dice_metric': 0.9507526159286499, 'Val/mean miou_metric': 0.9348360896110535, 'Val/mean f1': 0.9470265507698059, 'Val/mean precision': 0.9390423893928528, 'Val/mean recall': 0.9551475644111633, 'Val/mean hd95_metric': 10.746200561523438} +Cheakpoint... +Epoch [3645/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507526159286499, 'Val/mean miou_metric': 0.9348360896110535, 'Val/mean f1': 0.9470265507698059, 'Val/mean precision': 0.9390423893928528, 'Val/mean recall': 0.9551475644111633, 'Val/mean hd95_metric': 10.746200561523438} +Epoch [3646/4000] Training [1/39] Loss: 0.12920 +Epoch [3646/4000] Training [2/39] Loss: 0.00773 +Epoch [3646/4000] Training [3/39] Loss: 0.00536 +Epoch [3646/4000] Training [4/39] Loss: 0.00285 +Epoch [3646/4000] Training [5/39] Loss: 0.12992 +Epoch [3646/4000] Training [6/39] Loss: 0.00435 +Epoch [3646/4000] Training [7/39] Loss: 0.00379 +Epoch [3646/4000] Training [8/39] Loss: 0.13213 +Epoch [3646/4000] Training [9/39] Loss: 0.12955 +Epoch [3646/4000] Training [10/39] Loss: 0.12838 +Epoch [3646/4000] Training [11/39] Loss: 0.00443 +Epoch [3646/4000] Training [12/39] Loss: 0.00653 +Epoch [3646/4000] Training [13/39] Loss: 0.12969 +Epoch [3646/4000] Training [14/39] Loss: 0.00498 +Epoch [3646/4000] Training [15/39] Loss: 0.00740 +Epoch [3646/4000] Training [16/39] Loss: 0.00525 +Epoch [3646/4000] Training [17/39] Loss: 0.00480 +Epoch [3646/4000] Training [18/39] Loss: 0.00510 +Epoch [3646/4000] Training [19/39] Loss: 0.00521 +Epoch [3646/4000] Training [20/39] Loss: 0.00396 +Epoch [3646/4000] Training [21/39] Loss: 0.00606 +Epoch [3646/4000] Training [22/39] Loss: 0.00340 +Epoch [3646/4000] Training [23/39] Loss: 0.00669 +Epoch [3646/4000] Training [24/39] Loss: 0.12831 +Epoch [3646/4000] Training [25/39] Loss: 0.00648 +Epoch [3646/4000] Training [26/39] Loss: 0.00596 +Epoch [3646/4000] Training [27/39] Loss: 0.00464 +Epoch [3646/4000] Training [28/39] Loss: 0.25326 +Epoch [3646/4000] Training [29/39] Loss: 0.00447 +Epoch [3646/4000] Training [30/39] Loss: 0.00312 +Epoch [3646/4000] Training [31/39] Loss: 0.25477 +Epoch [3646/4000] Training [32/39] Loss: 0.00363 +Epoch [3646/4000] Training [33/39] Loss: 0.00450 +Epoch [3646/4000] Training [34/39] Loss: 0.00482 +Epoch [3646/4000] Training [35/39] Loss: 0.12862 +Epoch [3646/4000] Training [36/39] Loss: 0.00529 +Epoch [3646/4000] Training [37/39] Loss: 0.00500 +Epoch [3646/4000] Training [38/39] Loss: 0.00350 +Epoch [3646/4000] Training [39/39] Loss: 0.00511 +Epoch [3646/4000] Training metric {'Train/mean dice_metric': 0.9962775707244873, 'Train/mean miou_metric': 0.992970883846283, 'Train/mean f1': 0.9967978596687317, 'Train/mean precision': 0.9963496327400208, 'Train/mean recall': 0.997246503829956, 'Train/mean hd95_metric': 0.961150586605072} +Epoch [3646/4000] Validation [1/10] Loss: 0.70591 focal_loss 0.61784 dice_loss 0.08807 +Epoch [3646/4000] Validation [2/10] Loss: 0.48297 focal_loss 0.38661 dice_loss 0.09636 +Epoch [3646/4000] Validation [3/10] Loss: 0.37153 focal_loss 0.26058 dice_loss 0.11095 +Epoch [3646/4000] Validation [4/10] Loss: 0.89481 focal_loss 0.32796 dice_loss 0.56685 +Epoch [3646/4000] Validation [5/10] Loss: 2.99034 focal_loss 2.31677 dice_loss 0.67357 +Epoch [3646/4000] Validation [6/10] Loss: 1.34331 focal_loss 0.63186 dice_loss 0.71144 +Epoch [3646/4000] Validation [7/10] Loss: 1.18411 focal_loss 0.53293 dice_loss 0.65119 +Epoch [3646/4000] Validation [8/10] Loss: 2.30838 focal_loss 1.69491 dice_loss 0.61347 +Epoch [3646/4000] Validation [9/10] Loss: 1.56519 focal_loss 1.01843 dice_loss 0.54676 +Epoch [3646/4000] Validation [10/10] Loss: 1.91748 focal_loss 1.17714 dice_loss 0.74034 +Epoch [3646/4000] Validation metric {'Val/mean dice_metric': 0.9515200257301331, 'Val/mean miou_metric': 0.9355717897415161, 'Val/mean f1': 0.9480067491531372, 'Val/mean precision': 0.9426759481430054, 'Val/mean recall': 0.9533980488777161, 'Val/mean hd95_metric': 10.729106903076172} +Cheakpoint... +Epoch [3646/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515200257301331, 'Val/mean miou_metric': 0.9355717897415161, 'Val/mean f1': 0.9480067491531372, 'Val/mean precision': 0.9426759481430054, 'Val/mean recall': 0.9533980488777161, 'Val/mean hd95_metric': 10.729106903076172} +Epoch [3647/4000] Training [1/39] Loss: 0.00497 +Epoch [3647/4000] Training [2/39] Loss: 0.12866 +Epoch [3647/4000] Training [3/39] Loss: 0.00632 +Epoch [3647/4000] Training [4/39] Loss: 0.12808 +Epoch [3647/4000] Training [5/39] Loss: 0.25454 +Epoch [3647/4000] Training [6/39] Loss: 0.12935 +Epoch [3647/4000] Training [7/39] Loss: 0.00517 +Epoch [3647/4000] Training [8/39] Loss: 0.00386 +Epoch [3647/4000] Training [9/39] Loss: 0.00792 +Epoch [3647/4000] Training [10/39] Loss: 0.12800 +Epoch [3647/4000] Training [11/39] Loss: 0.00421 +Epoch [3647/4000] Training [12/39] Loss: 0.12863 +Epoch [3647/4000] Training [13/39] Loss: 0.00447 +Epoch [3647/4000] Training [14/39] Loss: 0.00780 +Epoch [3647/4000] Training [15/39] Loss: 0.00570 +Epoch [3647/4000] Training [16/39] Loss: 0.00272 +Epoch [3647/4000] Training [17/39] Loss: 0.00461 +Epoch [3647/4000] Training [18/39] Loss: 0.13168 +Epoch [3647/4000] Training [19/39] Loss: 0.12830 +Epoch [3647/4000] Training [20/39] Loss: 0.00586 +Epoch [3647/4000] Training [21/39] Loss: 0.00627 +Epoch [3647/4000] Training [22/39] Loss: 0.13146 +Epoch [3647/4000] Training [23/39] Loss: 0.00367 +Epoch [3647/4000] Training [24/39] Loss: 0.00356 +Epoch [3647/4000] Training [25/39] Loss: 0.00381 +Epoch [3647/4000] Training [26/39] Loss: 0.01698 +Epoch [3647/4000] Training [27/39] Loss: 0.00465 +Epoch [3647/4000] Training [28/39] Loss: 0.00512 +Epoch [3647/4000] Training [29/39] Loss: 0.00379 +Epoch [3647/4000] Training [30/39] Loss: 0.00476 +Epoch [3647/4000] Training [31/39] Loss: 0.00430 +Epoch [3647/4000] Training [32/39] Loss: 0.12922 +Epoch [3647/4000] Training [33/39] Loss: 0.00528 +Epoch [3647/4000] Training [34/39] Loss: 0.12851 +Epoch [3647/4000] Training [35/39] Loss: 0.00860 +Epoch [3647/4000] Training [36/39] Loss: 0.00445 +Epoch [3647/4000] Training [37/39] Loss: 0.00377 +Epoch [3647/4000] Training [38/39] Loss: 0.00475 +Epoch [3647/4000] Training [39/39] Loss: 0.00487 +Epoch [3647/4000] Training metric {'Train/mean dice_metric': 0.9962080121040344, 'Train/mean miou_metric': 0.9928926229476929, 'Train/mean f1': 0.9967865943908691, 'Train/mean precision': 0.9963808655738831, 'Train/mean recall': 0.9971926212310791, 'Train/mean hd95_metric': 0.9947848320007324} +Epoch [3647/4000] Validation [1/10] Loss: 0.74718 focal_loss 0.65611 dice_loss 0.09107 +Epoch [3647/4000] Validation [2/10] Loss: 0.47907 focal_loss 0.38520 dice_loss 0.09388 +Epoch [3647/4000] Validation [3/10] Loss: 0.37802 focal_loss 0.26786 dice_loss 0.11016 +Epoch [3647/4000] Validation [4/10] Loss: 0.91477 focal_loss 0.34438 dice_loss 0.57039 +Epoch [3647/4000] Validation [5/10] Loss: 3.10079 focal_loss 2.42757 dice_loss 0.67322 +Epoch [3647/4000] Validation [6/10] Loss: 1.36344 focal_loss 0.64913 dice_loss 0.71431 +Epoch [3647/4000] Validation [7/10] Loss: 1.20278 focal_loss 0.55040 dice_loss 0.65238 +Epoch [3647/4000] Validation [8/10] Loss: 2.05448 focal_loss 1.47754 dice_loss 0.57695 +Epoch [3647/4000] Validation [9/10] Loss: 1.69150 focal_loss 1.15485 dice_loss 0.53666 +Epoch [3647/4000] Validation [10/10] Loss: 1.98300 focal_loss 1.24023 dice_loss 0.74276 +Epoch [3647/4000] Validation metric {'Val/mean dice_metric': 0.9513182044029236, 'Val/mean miou_metric': 0.9353610277175903, 'Val/mean f1': 0.9474289417266846, 'Val/mean precision': 0.9386960864067078, 'Val/mean recall': 0.9563257694244385, 'Val/mean hd95_metric': 10.63830852508545} +Cheakpoint... +Epoch [3647/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513182044029236, 'Val/mean miou_metric': 0.9353610277175903, 'Val/mean f1': 0.9474289417266846, 'Val/mean precision': 0.9386960864067078, 'Val/mean recall': 0.9563257694244385, 'Val/mean hd95_metric': 10.63830852508545} +Epoch [3648/4000] Training [1/39] Loss: 0.00884 +Epoch [3648/4000] Training [2/39] Loss: 0.12746 +Epoch [3648/4000] Training [3/39] Loss: 0.01019 +Epoch [3648/4000] Training [4/39] Loss: 0.00605 +Epoch [3648/4000] Training [5/39] Loss: 0.00507 +Epoch [3648/4000] Training [6/39] Loss: 0.12984 +Epoch [3648/4000] Training [7/39] Loss: 0.13102 +Epoch [3648/4000] Training [8/39] Loss: 0.12985 +Epoch [3648/4000] Training [9/39] Loss: 0.12998 +Epoch [3648/4000] Training [10/39] Loss: 0.00926 +Epoch [3648/4000] Training [11/39] Loss: 0.12810 +Epoch [3648/4000] Training [12/39] Loss: 0.00617 +Epoch [3648/4000] Training [13/39] Loss: 0.00798 +Epoch [3648/4000] Training [14/39] Loss: 0.00702 +Epoch [3648/4000] Training [15/39] Loss: 0.00485 +Epoch [3648/4000] Training [16/39] Loss: 0.00314 +Epoch [3648/4000] Training [17/39] Loss: 0.00479 +Epoch [3648/4000] Training [18/39] Loss: 0.00424 +Epoch [3648/4000] Training [19/39] Loss: 0.00306 +Epoch [3648/4000] Training [20/39] Loss: 0.00679 +Epoch [3648/4000] Training [21/39] Loss: 0.00668 +Epoch [3648/4000] Training [22/39] Loss: 0.00547 +Epoch [3648/4000] Training [23/39] Loss: 0.12991 +Epoch [3648/4000] Training [24/39] Loss: 0.00679 +Epoch [3648/4000] Training [25/39] Loss: 0.00521 +Epoch [3648/4000] Training [26/39] Loss: 0.00388 +Epoch [3648/4000] Training [27/39] Loss: 0.00593 +Epoch [3648/4000] Training [28/39] Loss: 0.12917 +Epoch [3648/4000] Training [29/39] Loss: 0.00376 +Epoch [3648/4000] Training [30/39] Loss: 0.00333 +Epoch [3648/4000] Training [31/39] Loss: 0.00574 +Epoch [3648/4000] Training [32/39] Loss: 0.00737 +Epoch [3648/4000] Training [33/39] Loss: 0.00713 +Epoch [3648/4000] Training [34/39] Loss: 0.25604 +Epoch [3648/4000] Training [35/39] Loss: 0.13119 +Epoch [3648/4000] Training [36/39] Loss: 0.12807 +Epoch [3648/4000] Training [37/39] Loss: 0.00487 +Epoch [3648/4000] Training [38/39] Loss: 0.00727 +Epoch [3648/4000] Training [39/39] Loss: 0.13048 +Epoch [3648/4000] Training metric {'Train/mean dice_metric': 0.996080756187439, 'Train/mean miou_metric': 0.9926248788833618, 'Train/mean f1': 0.9966670274734497, 'Train/mean precision': 0.9961925148963928, 'Train/mean recall': 0.99714195728302, 'Train/mean hd95_metric': 0.9607778191566467} +Epoch [3648/4000] Validation [1/10] Loss: 0.74349 focal_loss 0.65423 dice_loss 0.08926 +Epoch [3648/4000] Validation [2/10] Loss: 0.47382 focal_loss 0.38088 dice_loss 0.09293 +Epoch [3648/4000] Validation [3/10] Loss: 0.38876 focal_loss 0.27821 dice_loss 0.11055 +Epoch [3648/4000] Validation [4/10] Loss: 0.90783 focal_loss 0.33984 dice_loss 0.56800 +Epoch [3648/4000] Validation [5/10] Loss: 3.11405 focal_loss 2.44078 dice_loss 0.67326 +Epoch [3648/4000] Validation [6/10] Loss: 1.35516 focal_loss 0.64028 dice_loss 0.71488 +Epoch [3648/4000] Validation [7/10] Loss: 1.20024 focal_loss 0.54512 dice_loss 0.65512 +Epoch [3648/4000] Validation [8/10] Loss: 2.11576 focal_loss 1.53035 dice_loss 0.58541 +Epoch [3648/4000] Validation [9/10] Loss: 1.69095 focal_loss 1.15969 dice_loss 0.53127 +Epoch [3648/4000] Validation [10/10] Loss: 1.96592 focal_loss 1.22384 dice_loss 0.74208 +Epoch [3648/4000] Validation metric {'Val/mean dice_metric': 0.9511492848396301, 'Val/mean miou_metric': 0.9350337386131287, 'Val/mean f1': 0.947303831577301, 'Val/mean precision': 0.9396939873695374, 'Val/mean recall': 0.9550378918647766, 'Val/mean hd95_metric': 10.709836959838867} +Cheakpoint... +Epoch [3648/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511492848396301, 'Val/mean miou_metric': 0.9350337386131287, 'Val/mean f1': 0.947303831577301, 'Val/mean precision': 0.9396939873695374, 'Val/mean recall': 0.9550378918647766, 'Val/mean hd95_metric': 10.709836959838867} +Epoch [3649/4000] Training [1/39] Loss: 0.00465 +Epoch [3649/4000] Training [2/39] Loss: 0.12852 +Epoch [3649/4000] Training [3/39] Loss: 0.00321 +Epoch [3649/4000] Training [4/39] Loss: 0.00591 +Epoch [3649/4000] Training [5/39] Loss: 0.00515 +Epoch [3649/4000] Training [6/39] Loss: 0.12752 +Epoch [3649/4000] Training [7/39] Loss: 0.00430 +Epoch [3649/4000] Training [8/39] Loss: 0.00606 +Epoch [3649/4000] Training [9/39] Loss: 0.12957 +Epoch [3649/4000] Training [10/39] Loss: 0.00361 +Epoch [3649/4000] Training [11/39] Loss: 0.00393 +Epoch [3649/4000] Training [12/39] Loss: 0.00488 +Epoch [3649/4000] Training [13/39] Loss: 0.00398 +Epoch [3649/4000] Training [14/39] Loss: 0.00401 +Epoch [3649/4000] Training [15/39] Loss: 0.00487 +Epoch [3649/4000] Training [16/39] Loss: 0.00552 +Epoch [3649/4000] Training [17/39] Loss: 0.00608 +Epoch [3649/4000] Training [18/39] Loss: 0.00587 +Epoch [3649/4000] Training [19/39] Loss: 0.00665 +Epoch [3649/4000] Training [20/39] Loss: 0.00609 +Epoch [3649/4000] Training [21/39] Loss: 0.00529 +Epoch [3649/4000] Training [22/39] Loss: 0.08719 +Epoch [3649/4000] Training [23/39] Loss: 0.00860 +Epoch [3649/4000] Training [24/39] Loss: 0.00607 +Epoch [3649/4000] Training [25/39] Loss: 0.00442 +Epoch [3649/4000] Training [26/39] Loss: 0.00415 +Epoch [3649/4000] Training [27/39] Loss: 0.00425 +Epoch [3649/4000] Training [28/39] Loss: 0.00548 +Epoch [3649/4000] Training [29/39] Loss: 0.12986 +Epoch [3649/4000] Training [30/39] Loss: 0.13143 +Epoch [3649/4000] Training [31/39] Loss: 0.00242 +Epoch [3649/4000] Training [32/39] Loss: 0.00445 +Epoch [3649/4000] Training [33/39] Loss: 0.00603 +Epoch [3649/4000] Training [34/39] Loss: 0.12834 +Epoch [3649/4000] Training [35/39] Loss: 0.00887 +Epoch [3649/4000] Training [36/39] Loss: 0.00558 +Epoch [3649/4000] Training [37/39] Loss: 0.25493 +Epoch [3649/4000] Training [38/39] Loss: 0.00494 +Epoch [3649/4000] Training [39/39] Loss: 0.12944 +Epoch [3649/4000] Training metric {'Train/mean dice_metric': 0.9961023926734924, 'Train/mean miou_metric': 0.9926579594612122, 'Train/mean f1': 0.996649980545044, 'Train/mean precision': 0.9962197542190552, 'Train/mean recall': 0.9970803260803223, 'Train/mean hd95_metric': 1.1157615184783936} +Epoch [3649/4000] Validation [1/10] Loss: 0.76667 focal_loss 0.67509 dice_loss 0.09158 +Epoch [3649/4000] Validation [2/10] Loss: 0.47598 focal_loss 0.38603 dice_loss 0.08995 +Epoch [3649/4000] Validation [3/10] Loss: 0.37772 focal_loss 0.26791 dice_loss 0.10981 +Epoch [3649/4000] Validation [4/10] Loss: 0.91889 focal_loss 0.34833 dice_loss 0.57055 +Epoch [3649/4000] Validation [5/10] Loss: 3.12046 focal_loss 2.44733 dice_loss 0.67313 +Epoch [3649/4000] Validation [6/10] Loss: 1.38121 focal_loss 0.66555 dice_loss 0.71565 +Epoch [3649/4000] Validation [7/10] Loss: 1.22295 focal_loss 0.56474 dice_loss 0.65821 +Epoch [3649/4000] Validation [8/10] Loss: 2.09824 focal_loss 1.51667 dice_loss 0.58157 +Epoch [3649/4000] Validation [9/10] Loss: 1.71689 focal_loss 1.17558 dice_loss 0.54131 +Epoch [3649/4000] Validation [10/10] Loss: 2.00613 focal_loss 1.26396 dice_loss 0.74218 +Epoch [3649/4000] Validation metric {'Val/mean dice_metric': 0.9512649178504944, 'Val/mean miou_metric': 0.9350941181182861, 'Val/mean f1': 0.9467774629592896, 'Val/mean precision': 0.9381439685821533, 'Val/mean recall': 0.9555714130401611, 'Val/mean hd95_metric': 10.762285232543945} +Cheakpoint... +Epoch [3649/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512649178504944, 'Val/mean miou_metric': 0.9350941181182861, 'Val/mean f1': 0.9467774629592896, 'Val/mean precision': 0.9381439685821533, 'Val/mean recall': 0.9555714130401611, 'Val/mean hd95_metric': 10.762285232543945} +Epoch [3650/4000] Training [1/39] Loss: 0.00409 +Epoch [3650/4000] Training [2/39] Loss: 0.00483 +Epoch [3650/4000] Training [3/39] Loss: 0.00338 +Epoch [3650/4000] Training [4/39] Loss: 0.00565 +Epoch [3650/4000] Training [5/39] Loss: 0.00514 +Epoch [3650/4000] Training [6/39] Loss: 0.00550 +Epoch [3650/4000] Training [7/39] Loss: 0.00340 +Epoch [3650/4000] Training [8/39] Loss: 0.12904 +Epoch [3650/4000] Training [9/39] Loss: 0.00505 +Epoch [3650/4000] Training [10/39] Loss: 0.00457 +Epoch [3650/4000] Training [11/39] Loss: 0.00386 +Epoch [3650/4000] Training [12/39] Loss: 0.00335 +Epoch [3650/4000] Training [13/39] Loss: 0.00688 +Epoch [3650/4000] Training [14/39] Loss: 0.12972 +Epoch [3650/4000] Training [15/39] Loss: 0.12885 +Epoch [3650/4000] Training [16/39] Loss: 0.00562 +Epoch [3650/4000] Training [17/39] Loss: 0.00630 +Epoch [3650/4000] Training [18/39] Loss: 0.00507 +Epoch [3650/4000] Training [19/39] Loss: 0.00448 +Epoch [3650/4000] Training [20/39] Loss: 0.00336 +Epoch [3650/4000] Training [21/39] Loss: 0.00667 +Epoch [3650/4000] Training [22/39] Loss: 0.00445 +Epoch [3650/4000] Training [23/39] Loss: 0.00359 +Epoch [3650/4000] Training [24/39] Loss: 0.00423 +Epoch [3650/4000] Training [25/39] Loss: 0.00394 +Epoch [3650/4000] Training [26/39] Loss: 0.12853 +Epoch [3650/4000] Training [27/39] Loss: 0.00499 +Epoch [3650/4000] Training [28/39] Loss: 0.00407 +Epoch [3650/4000] Training [29/39] Loss: 0.00345 +Epoch [3650/4000] Training [30/39] Loss: 0.00731 +Epoch [3650/4000] Training [31/39] Loss: 0.00546 +Epoch [3650/4000] Training [32/39] Loss: 0.00593 +Epoch [3650/4000] Training [33/39] Loss: 0.00546 +Epoch [3650/4000] Training [34/39] Loss: 0.00771 +Epoch [3650/4000] Training [35/39] Loss: 0.00340 +Epoch [3650/4000] Training [36/39] Loss: 0.25259 +Epoch [3650/4000] Training [37/39] Loss: 0.00637 +Epoch [3650/4000] Training [38/39] Loss: 0.25382 +Epoch [3650/4000] Training [39/39] Loss: 0.00471 +Epoch [3650/4000] Training metric {'Train/mean dice_metric': 0.9963783621788025, 'Train/mean miou_metric': 0.9932032227516174, 'Train/mean f1': 0.9967485666275024, 'Train/mean precision': 0.9963155388832092, 'Train/mean recall': 0.9971819519996643, 'Train/mean hd95_metric': 0.9406139254570007} +Epoch [3650/4000] Validation [1/10] Loss: 0.72112 focal_loss 0.63211 dice_loss 0.08901 +Epoch [3650/4000] Validation [2/10] Loss: 0.47654 focal_loss 0.38164 dice_loss 0.09490 +Epoch [3650/4000] Validation [3/10] Loss: 0.37886 focal_loss 0.26797 dice_loss 0.11089 +Epoch [3650/4000] Validation [4/10] Loss: 0.90035 focal_loss 0.33337 dice_loss 0.56698 +Epoch [3650/4000] Validation [5/10] Loss: 3.04715 focal_loss 2.37383 dice_loss 0.67332 +Epoch [3650/4000] Validation [6/10] Loss: 1.34873 focal_loss 0.63568 dice_loss 0.71305 +Epoch [3650/4000] Validation [7/10] Loss: 1.19618 focal_loss 0.54232 dice_loss 0.65386 +Epoch [3650/4000] Validation [8/10] Loss: 2.20806 focal_loss 1.60486 dice_loss 0.60320 +Epoch [3650/4000] Validation [9/10] Loss: 1.61558 focal_loss 1.06808 dice_loss 0.54750 +Epoch [3650/4000] Validation [10/10] Loss: 1.91449 focal_loss 1.17572 dice_loss 0.73876 +Epoch [3650/4000] Validation metric {'Val/mean dice_metric': 0.9515508413314819, 'Val/mean miou_metric': 0.9356967806816101, 'Val/mean f1': 0.9477872848510742, 'Val/mean precision': 0.9416680335998535, 'Val/mean recall': 0.9539865255355835, 'Val/mean hd95_metric': 10.570255279541016} +Cheakpoint... +Epoch [3650/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515508413314819, 'Val/mean miou_metric': 0.9356967806816101, 'Val/mean f1': 0.9477872848510742, 'Val/mean precision': 0.9416680335998535, 'Val/mean recall': 0.9539865255355835, 'Val/mean hd95_metric': 10.570255279541016} +Epoch [3651/4000] Training [1/39] Loss: 0.12990 +Epoch [3651/4000] Training [2/39] Loss: 0.00712 +Epoch [3651/4000] Training [3/39] Loss: 0.00659 +Epoch [3651/4000] Training [4/39] Loss: 0.00361 +Epoch [3651/4000] Training [5/39] Loss: 0.00511 +Epoch [3651/4000] Training [6/39] Loss: 0.00534 +Epoch [3651/4000] Training [7/39] Loss: 0.00643 +Epoch [3651/4000] Training [8/39] Loss: 0.00848 +Epoch [3651/4000] Training [9/39] Loss: 0.00711 +Epoch [3651/4000] Training [10/39] Loss: 0.00395 +Epoch [3651/4000] Training [11/39] Loss: 0.00537 +Epoch [3651/4000] Training [12/39] Loss: 0.00378 +Epoch [3651/4000] Training [13/39] Loss: 0.00353 +Epoch [3651/4000] Training [14/39] Loss: 0.00826 +Epoch [3651/4000] Training [15/39] Loss: 0.00443 +Epoch [3651/4000] Training [16/39] Loss: 0.09461 +Epoch [3651/4000] Training [17/39] Loss: 0.12987 +Epoch [3651/4000] Training [18/39] Loss: 0.00570 +Epoch [3651/4000] Training [19/39] Loss: 0.13189 +Epoch [3651/4000] Training [20/39] Loss: 0.00355 +Epoch [3651/4000] Training [21/39] Loss: 0.00368 +Epoch [3651/4000] Training [22/39] Loss: 0.00585 +Epoch [3651/4000] Training [23/39] Loss: 0.00480 +Epoch [3651/4000] Training [24/39] Loss: 0.00315 +Epoch [3651/4000] Training [25/39] Loss: 0.00485 +Epoch [3651/4000] Training [26/39] Loss: 0.12804 +Epoch [3651/4000] Training [27/39] Loss: 0.00514 +Epoch [3651/4000] Training [28/39] Loss: 0.12917 +Epoch [3651/4000] Training [29/39] Loss: 0.00514 +Epoch [3651/4000] Training [30/39] Loss: 0.00552 +Epoch [3651/4000] Training [31/39] Loss: 0.25459 +Epoch [3651/4000] Training [32/39] Loss: 0.00737 +Epoch [3651/4000] Training [33/39] Loss: 0.12929 +Epoch [3651/4000] Training [34/39] Loss: 0.00743 +Epoch [3651/4000] Training [35/39] Loss: 0.00629 +Epoch [3651/4000] Training [36/39] Loss: 0.12777 +Epoch [3651/4000] Training [37/39] Loss: 0.00342 +Epoch [3651/4000] Training [38/39] Loss: 0.12779 +Epoch [3651/4000] Training [39/39] Loss: 0.00564 +Epoch [3651/4000] Training metric {'Train/mean dice_metric': 0.9954049587249756, 'Train/mean miou_metric': 0.992108941078186, 'Train/mean f1': 0.9968443512916565, 'Train/mean precision': 0.9963135123252869, 'Train/mean recall': 0.9973757266998291, 'Train/mean hd95_metric': 0.9722228050231934} +Epoch [3651/4000] Validation [1/10] Loss: 0.71529 focal_loss 0.62680 dice_loss 0.08849 +Epoch [3651/4000] Validation [2/10] Loss: 0.48250 focal_loss 0.38792 dice_loss 0.09458 +Epoch [3651/4000] Validation [3/10] Loss: 0.37632 focal_loss 0.26586 dice_loss 0.11045 +Epoch [3651/4000] Validation [4/10] Loss: 0.90585 focal_loss 0.33873 dice_loss 0.56712 +Epoch [3651/4000] Validation [5/10] Loss: 3.03018 focal_loss 2.35688 dice_loss 0.67330 +Epoch [3651/4000] Validation [6/10] Loss: 1.35308 focal_loss 0.64057 dice_loss 0.71251 +Epoch [3651/4000] Validation [7/10] Loss: 1.20046 focal_loss 0.54486 dice_loss 0.65559 +Epoch [3651/4000] Validation [8/10] Loss: 2.21296 focal_loss 1.61216 dice_loss 0.60080 +Epoch [3651/4000] Validation [9/10] Loss: 1.65622 focal_loss 1.10901 dice_loss 0.54721 +Epoch [3651/4000] Validation [10/10] Loss: 1.94126 focal_loss 1.20080 dice_loss 0.74046 +Epoch [3651/4000] Validation metric {'Val/mean dice_metric': 0.950607180595398, 'Val/mean miou_metric': 0.934566080570221, 'Val/mean f1': 0.9477352499961853, 'Val/mean precision': 0.9413893222808838, 'Val/mean recall': 0.9541672468185425, 'Val/mean hd95_metric': 10.680904388427734} +Cheakpoint... +Epoch [3651/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950607180595398, 'Val/mean miou_metric': 0.934566080570221, 'Val/mean f1': 0.9477352499961853, 'Val/mean precision': 0.9413893222808838, 'Val/mean recall': 0.9541672468185425, 'Val/mean hd95_metric': 10.680904388427734} +Epoch [3652/4000] Training [1/39] Loss: 0.00551 +Epoch [3652/4000] Training [2/39] Loss: 0.00426 +Epoch [3652/4000] Training [3/39] Loss: 0.00495 +Epoch [3652/4000] Training [4/39] Loss: 0.00343 +Epoch [3652/4000] Training [5/39] Loss: 0.00478 +Epoch [3652/4000] Training [6/39] Loss: 0.00407 +Epoch [3652/4000] Training [7/39] Loss: 0.00724 +Epoch [3652/4000] Training [8/39] Loss: 0.12959 +Epoch [3652/4000] Training [9/39] Loss: 0.00428 +Epoch [3652/4000] Training [10/39] Loss: 0.12903 +Epoch [3652/4000] Training [11/39] Loss: 0.12901 +Epoch [3652/4000] Training [12/39] Loss: 0.00571 +Epoch [3652/4000] Training [13/39] Loss: 0.00590 +Epoch [3652/4000] Training [14/39] Loss: 0.00463 +Epoch [3652/4000] Training [15/39] Loss: 0.00610 +Epoch [3652/4000] Training [16/39] Loss: 0.12875 +Epoch [3652/4000] Training [17/39] Loss: 0.12790 +Epoch [3652/4000] Training [18/39] Loss: 0.00445 +Epoch [3652/4000] Training [19/39] Loss: 0.00544 +Epoch [3652/4000] Training [20/39] Loss: 0.00541 +Epoch [3652/4000] Training [21/39] Loss: 0.00386 +Epoch [3652/4000] Training [22/39] Loss: 0.12710 +Epoch [3652/4000] Training [23/39] Loss: 0.00492 +Epoch [3652/4000] Training [24/39] Loss: 0.00543 +Epoch [3652/4000] Training [25/39] Loss: 0.00463 +Epoch [3652/4000] Training [26/39] Loss: 0.00544 +Epoch [3652/4000] Training [27/39] Loss: 0.37799 +Epoch [3652/4000] Training [28/39] Loss: 0.13149 +Epoch [3652/4000] Training [29/39] Loss: 0.00317 +Epoch [3652/4000] Training [30/39] Loss: 0.00641 +Epoch [3652/4000] Training [31/39] Loss: 0.00453 +Epoch [3652/4000] Training [32/39] Loss: 0.12853 +Epoch [3652/4000] Training [33/39] Loss: 0.00561 +Epoch [3652/4000] Training [34/39] Loss: 0.12781 +Epoch [3652/4000] Training [35/39] Loss: 0.00637 +Epoch [3652/4000] Training [36/39] Loss: 0.00435 +Epoch [3652/4000] Training [37/39] Loss: 0.00474 +Epoch [3652/4000] Training [38/39] Loss: 0.00376 +Epoch [3652/4000] Training [39/39] Loss: 0.00444 +Epoch [3652/4000] Training metric {'Train/mean dice_metric': 0.9964092969894409, 'Train/mean miou_metric': 0.99326491355896, 'Train/mean f1': 0.9970093369483948, 'Train/mean precision': 0.9965905547142029, 'Train/mean recall': 0.9974286556243896, 'Train/mean hd95_metric': 0.942215085029602} +Epoch [3652/4000] Validation [1/10] Loss: 0.72155 focal_loss 0.63249 dice_loss 0.08906 +Epoch [3652/4000] Validation [2/10] Loss: 0.48289 focal_loss 0.38481 dice_loss 0.09808 +Epoch [3652/4000] Validation [3/10] Loss: 0.37993 focal_loss 0.26876 dice_loss 0.11117 +Epoch [3652/4000] Validation [4/10] Loss: 0.89158 focal_loss 0.32568 dice_loss 0.56590 +Epoch [3652/4000] Validation [5/10] Loss: 3.00509 focal_loss 2.33178 dice_loss 0.67332 +Epoch [3652/4000] Validation [6/10] Loss: 1.33054 focal_loss 0.61881 dice_loss 0.71172 +Epoch [3652/4000] Validation [7/10] Loss: 1.18036 focal_loss 0.52922 dice_loss 0.65114 +Epoch [3652/4000] Validation [8/10] Loss: 2.24508 focal_loss 1.63823 dice_loss 0.60685 +Epoch [3652/4000] Validation [9/10] Loss: 1.58232 focal_loss 1.03507 dice_loss 0.54725 +Epoch [3652/4000] Validation [10/10] Loss: 1.89844 focal_loss 1.16046 dice_loss 0.73799 +Epoch [3652/4000] Validation metric {'Val/mean dice_metric': 0.9515681266784668, 'Val/mean miou_metric': 0.9357627630233765, 'Val/mean f1': 0.9480839371681213, 'Val/mean precision': 0.942320704460144, 'Val/mean recall': 0.9539179801940918, 'Val/mean hd95_metric': 10.703681945800781} +Cheakpoint... +Epoch [3652/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515681266784668, 'Val/mean miou_metric': 0.9357627630233765, 'Val/mean f1': 0.9480839371681213, 'Val/mean precision': 0.942320704460144, 'Val/mean recall': 0.9539179801940918, 'Val/mean hd95_metric': 10.703681945800781} +Epoch [3653/4000] Training [1/39] Loss: 0.00577 +Epoch [3653/4000] Training [2/39] Loss: 0.00439 +Epoch [3653/4000] Training [3/39] Loss: 0.00452 +Epoch [3653/4000] Training [4/39] Loss: 0.12819 +Epoch [3653/4000] Training [5/39] Loss: 0.12869 +Epoch [3653/4000] Training [6/39] Loss: 0.00476 +Epoch [3653/4000] Training [7/39] Loss: 0.00773 +Epoch [3653/4000] Training [8/39] Loss: 0.00489 +Epoch [3653/4000] Training [9/39] Loss: 0.00460 +Epoch [3653/4000] Training [10/39] Loss: 0.00664 +Epoch [3653/4000] Training [11/39] Loss: 0.00500 +Epoch [3653/4000] Training [12/39] Loss: 0.00366 +Epoch [3653/4000] Training [13/39] Loss: 0.00583 +Epoch [3653/4000] Training [14/39] Loss: 0.00872 +Epoch [3653/4000] Training [15/39] Loss: 0.25273 +Epoch [3653/4000] Training [16/39] Loss: 0.00407 +Epoch [3653/4000] Training [17/39] Loss: 0.00647 +Epoch [3653/4000] Training [18/39] Loss: 0.00411 +Epoch [3653/4000] Training [19/39] Loss: 0.12791 +Epoch [3653/4000] Training [20/39] Loss: 0.00444 +Epoch [3653/4000] Training [21/39] Loss: 0.00350 +Epoch [3653/4000] Training [22/39] Loss: 0.12897 +Epoch [3653/4000] Training [23/39] Loss: 0.00281 +Epoch [3653/4000] Training [24/39] Loss: 0.00671 +Epoch [3653/4000] Training [25/39] Loss: 0.13315 +Epoch [3653/4000] Training [26/39] Loss: 0.00503 +Epoch [3653/4000] Training [27/39] Loss: 0.00394 +Epoch [3653/4000] Training [28/39] Loss: 0.00395 +Epoch [3653/4000] Training [29/39] Loss: 0.00517 +Epoch [3653/4000] Training [30/39] Loss: 0.00822 +Epoch [3653/4000] Training [31/39] Loss: 0.00478 +Epoch [3653/4000] Training [32/39] Loss: 0.00464 +Epoch [3653/4000] Training [33/39] Loss: 0.00706 +Epoch [3653/4000] Training [34/39] Loss: 0.00434 +Epoch [3653/4000] Training [35/39] Loss: 0.00419 +Epoch [3653/4000] Training [36/39] Loss: 0.00559 +Epoch [3653/4000] Training [37/39] Loss: 0.13133 +Epoch [3653/4000] Training [38/39] Loss: 0.00326 +Epoch [3653/4000] Training [39/39] Loss: 0.00541 +Epoch [3653/4000] Training metric {'Train/mean dice_metric': 0.9962354898452759, 'Train/mean miou_metric': 0.9929357767105103, 'Train/mean f1': 0.9968469738960266, 'Train/mean precision': 0.9963133335113525, 'Train/mean recall': 0.9973812103271484, 'Train/mean hd95_metric': 0.9562777280807495} +Epoch [3653/4000] Validation [1/10] Loss: 0.73280 focal_loss 0.64368 dice_loss 0.08912 +Epoch [3653/4000] Validation [2/10] Loss: 0.48021 focal_loss 0.38489 dice_loss 0.09532 +Epoch [3653/4000] Validation [3/10] Loss: 0.38217 focal_loss 0.27111 dice_loss 0.11106 +Epoch [3653/4000] Validation [4/10] Loss: 0.89386 focal_loss 0.32786 dice_loss 0.56601 +Epoch [3653/4000] Validation [5/10] Loss: 3.02410 focal_loss 2.35090 dice_loss 0.67320 +Epoch [3653/4000] Validation [6/10] Loss: 1.34169 focal_loss 0.63053 dice_loss 0.71116 +Epoch [3653/4000] Validation [7/10] Loss: 1.18367 focal_loss 0.53260 dice_loss 0.65107 +Epoch [3653/4000] Validation [8/10] Loss: 2.23352 focal_loss 1.62970 dice_loss 0.60382 +Epoch [3653/4000] Validation [9/10] Loss: 1.55848 focal_loss 1.01066 dice_loss 0.54782 +Epoch [3653/4000] Validation [10/10] Loss: 1.90606 focal_loss 1.16894 dice_loss 0.73712 +Epoch [3653/4000] Validation metric {'Val/mean dice_metric': 0.9514853358268738, 'Val/mean miou_metric': 0.9355402588844299, 'Val/mean f1': 0.9480370879173279, 'Val/mean precision': 0.9420676231384277, 'Val/mean recall': 0.9540826082229614, 'Val/mean hd95_metric': 10.684102058410645} +Cheakpoint... +Epoch [3653/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514853358268738, 'Val/mean miou_metric': 0.9355402588844299, 'Val/mean f1': 0.9480370879173279, 'Val/mean precision': 0.9420676231384277, 'Val/mean recall': 0.9540826082229614, 'Val/mean hd95_metric': 10.684102058410645} +Epoch [3654/4000] Training [1/39] Loss: 0.25353 +Epoch [3654/4000] Training [2/39] Loss: 0.00448 +Epoch [3654/4000] Training [3/39] Loss: 0.12890 +Epoch [3654/4000] Training [4/39] Loss: 0.12902 +Epoch [3654/4000] Training [5/39] Loss: 0.00452 +Epoch [3654/4000] Training [6/39] Loss: 0.00554 +Epoch [3654/4000] Training [7/39] Loss: 0.00534 +Epoch [3654/4000] Training [8/39] Loss: 0.00386 +Epoch [3654/4000] Training [9/39] Loss: 0.12847 +Epoch [3654/4000] Training [10/39] Loss: 0.13544 +Epoch [3654/4000] Training [11/39] Loss: 0.12977 +Epoch [3654/4000] Training [12/39] Loss: 0.00412 +Epoch [3654/4000] Training [13/39] Loss: 0.12942 +Epoch [3654/4000] Training [14/39] Loss: 0.00516 +Epoch [3654/4000] Training [15/39] Loss: 0.00691 +Epoch [3654/4000] Training [16/39] Loss: 0.12909 +Epoch [3654/4000] Training [17/39] Loss: 0.00363 +Epoch [3654/4000] Training [18/39] Loss: 0.00494 +Epoch [3654/4000] Training [19/39] Loss: 0.00483 +Epoch [3654/4000] Training [20/39] Loss: 0.12869 +Epoch [3654/4000] Training [21/39] Loss: 0.00330 +Epoch [3654/4000] Training [22/39] Loss: 0.00446 +Epoch [3654/4000] Training [23/39] Loss: 0.00287 +Epoch [3654/4000] Training [24/39] Loss: 0.00368 +Epoch [3654/4000] Training [25/39] Loss: 0.00421 +Epoch [3654/4000] Training [26/39] Loss: 0.00522 +Epoch [3654/4000] Training [27/39] Loss: 0.00480 +Epoch [3654/4000] Training [28/39] Loss: 0.00340 +Epoch [3654/4000] Training [29/39] Loss: 0.00465 +Epoch [3654/4000] Training [30/39] Loss: 0.00449 +Epoch [3654/4000] Training [31/39] Loss: 0.00436 +Epoch [3654/4000] Training [32/39] Loss: 0.00571 +Epoch [3654/4000] Training [33/39] Loss: 0.00515 +Epoch [3654/4000] Training [34/39] Loss: 0.12805 +Epoch [3654/4000] Training [35/39] Loss: 0.00527 +Epoch [3654/4000] Training [36/39] Loss: 0.00537 +Epoch [3654/4000] Training [37/39] Loss: 0.00656 +Epoch [3654/4000] Training [38/39] Loss: 0.12882 +Epoch [3654/4000] Training [39/39] Loss: 0.00372 +Epoch [3654/4000] Training metric {'Train/mean dice_metric': 0.9963254928588867, 'Train/mean miou_metric': 0.993107795715332, 'Train/mean f1': 0.9968633651733398, 'Train/mean precision': 0.9964720010757446, 'Train/mean recall': 0.9972550272941589, 'Train/mean hd95_metric': 0.97842937707901} +Epoch [3654/4000] Validation [1/10] Loss: 0.76008 focal_loss 0.66855 dice_loss 0.09152 +Epoch [3654/4000] Validation [2/10] Loss: 0.49378 focal_loss 0.39691 dice_loss 0.09687 +Epoch [3654/4000] Validation [3/10] Loss: 0.37272 focal_loss 0.26244 dice_loss 0.11028 +Epoch [3654/4000] Validation [4/10] Loss: 0.91270 focal_loss 0.34170 dice_loss 0.57099 +Epoch [3654/4000] Validation [5/10] Loss: 3.06231 focal_loss 2.38963 dice_loss 0.67268 +Epoch [3654/4000] Validation [6/10] Loss: 1.36182 focal_loss 0.64925 dice_loss 0.71258 +Epoch [3654/4000] Validation [7/10] Loss: 1.20760 focal_loss 0.55198 dice_loss 0.65561 +Epoch [3654/4000] Validation [8/10] Loss: 2.11838 focal_loss 1.52965 dice_loss 0.58873 +Epoch [3654/4000] Validation [9/10] Loss: 1.66060 focal_loss 1.11223 dice_loss 0.54836 +Epoch [3654/4000] Validation [10/10] Loss: 1.97177 focal_loss 1.22991 dice_loss 0.74186 +Epoch [3654/4000] Validation metric {'Val/mean dice_metric': 0.9513667821884155, 'Val/mean miou_metric': 0.9353894591331482, 'Val/mean f1': 0.9468782544136047, 'Val/mean precision': 0.9389806389808655, 'Val/mean recall': 0.9549098610877991, 'Val/mean hd95_metric': 10.73051643371582} +Cheakpoint... +Epoch [3654/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513667821884155, 'Val/mean miou_metric': 0.9353894591331482, 'Val/mean f1': 0.9468782544136047, 'Val/mean precision': 0.9389806389808655, 'Val/mean recall': 0.9549098610877991, 'Val/mean hd95_metric': 10.73051643371582} +Epoch [3655/4000] Training [1/39] Loss: 0.00622 +Epoch [3655/4000] Training [2/39] Loss: 0.12815 +Epoch [3655/4000] Training [3/39] Loss: 0.00521 +Epoch [3655/4000] Training [4/39] Loss: 0.00580 +Epoch [3655/4000] Training [5/39] Loss: 0.12962 +Epoch [3655/4000] Training [6/39] Loss: 0.00421 +Epoch [3655/4000] Training [7/39] Loss: 0.00307 +Epoch [3655/4000] Training [8/39] Loss: 0.00578 +Epoch [3655/4000] Training [9/39] Loss: 0.00418 +Epoch [3655/4000] Training [10/39] Loss: 0.00559 +Epoch [3655/4000] Training [11/39] Loss: 0.25198 +Epoch [3655/4000] Training [12/39] Loss: 0.13078 +Epoch [3655/4000] Training [13/39] Loss: 0.00603 +Epoch [3655/4000] Training [14/39] Loss: 0.00516 +Epoch [3655/4000] Training [15/39] Loss: 0.12860 +Epoch [3655/4000] Training [16/39] Loss: 0.00497 +Epoch [3655/4000] Training [17/39] Loss: 0.00593 +Epoch [3655/4000] Training [18/39] Loss: 0.00445 +Epoch [3655/4000] Training [19/39] Loss: 0.12861 +Epoch [3655/4000] Training [20/39] Loss: 0.00367 +Epoch [3655/4000] Training [21/39] Loss: 0.00444 +Epoch [3655/4000] Training [22/39] Loss: 0.00346 +Epoch [3655/4000] Training [23/39] Loss: 0.12954 +Epoch [3655/4000] Training [24/39] Loss: 0.12912 +Epoch [3655/4000] Training [25/39] Loss: 0.12956 +Epoch [3655/4000] Training [26/39] Loss: 0.00493 +Epoch [3655/4000] Training [27/39] Loss: 0.00573 +Epoch [3655/4000] Training [28/39] Loss: 0.00409 +Epoch [3655/4000] Training [29/39] Loss: 0.00433 +Epoch [3655/4000] Training [30/39] Loss: 0.00519 +Epoch [3655/4000] Training [31/39] Loss: 0.00475 +Epoch [3655/4000] Training [32/39] Loss: 0.00499 +Epoch [3655/4000] Training [33/39] Loss: 0.00853 +Epoch [3655/4000] Training [34/39] Loss: 0.00608 +Epoch [3655/4000] Training [35/39] Loss: 0.13032 +Epoch [3655/4000] Training [36/39] Loss: 0.00432 +Epoch [3655/4000] Training [37/39] Loss: 0.00700 +Epoch [3655/4000] Training [38/39] Loss: 0.00383 +Epoch [3655/4000] Training [39/39] Loss: 0.12760 +Epoch [3655/4000] Training metric {'Train/mean dice_metric': 0.9961681962013245, 'Train/mean miou_metric': 0.9927970170974731, 'Train/mean f1': 0.9968279600143433, 'Train/mean precision': 0.9963871836662292, 'Train/mean recall': 0.9972691535949707, 'Train/mean hd95_metric': 1.0057129859924316} +Epoch [3655/4000] Validation [1/10] Loss: 0.73365 focal_loss 0.64405 dice_loss 0.08960 +Epoch [3655/4000] Validation [2/10] Loss: 0.49153 focal_loss 0.39323 dice_loss 0.09830 +Epoch [3655/4000] Validation [3/10] Loss: 0.37932 focal_loss 0.26853 dice_loss 0.11078 +Epoch [3655/4000] Validation [4/10] Loss: 0.90326 focal_loss 0.33601 dice_loss 0.56724 +Epoch [3655/4000] Validation [5/10] Loss: 2.99989 focal_loss 2.32675 dice_loss 0.67314 +Epoch [3655/4000] Validation [6/10] Loss: 1.34713 focal_loss 0.63769 dice_loss 0.70945 +Epoch [3655/4000] Validation [7/10] Loss: 1.19513 focal_loss 0.54368 dice_loss 0.65145 +Epoch [3655/4000] Validation [8/10] Loss: 2.28507 focal_loss 1.67414 dice_loss 0.61093 +Epoch [3655/4000] Validation [9/10] Loss: 1.60873 focal_loss 1.05994 dice_loss 0.54879 +Epoch [3655/4000] Validation [10/10] Loss: 1.93042 focal_loss 1.19165 dice_loss 0.73876 +Epoch [3655/4000] Validation metric {'Val/mean dice_metric': 0.951292097568512, 'Val/mean miou_metric': 0.9352313280105591, 'Val/mean f1': 0.9480050802230835, 'Val/mean precision': 0.9420222043991089, 'Val/mean recall': 0.9540644884109497, 'Val/mean hd95_metric': 10.84367847442627} +Cheakpoint... +Epoch [3655/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951292097568512, 'Val/mean miou_metric': 0.9352313280105591, 'Val/mean f1': 0.9480050802230835, 'Val/mean precision': 0.9420222043991089, 'Val/mean recall': 0.9540644884109497, 'Val/mean hd95_metric': 10.84367847442627} +Epoch [3656/4000] Training [1/39] Loss: 0.00287 +Epoch [3656/4000] Training [2/39] Loss: 0.00475 +Epoch [3656/4000] Training [3/39] Loss: 0.08835 +Epoch [3656/4000] Training [4/39] Loss: 0.00602 +Epoch [3656/4000] Training [5/39] Loss: 0.13071 +Epoch [3656/4000] Training [6/39] Loss: 0.00600 +Epoch [3656/4000] Training [7/39] Loss: 0.12931 +Epoch [3656/4000] Training [8/39] Loss: 0.00741 +Epoch [3656/4000] Training [9/39] Loss: 0.00449 +Epoch [3656/4000] Training [10/39] Loss: 0.00315 +Epoch [3656/4000] Training [11/39] Loss: 0.00609 +Epoch [3656/4000] Training [12/39] Loss: 0.13024 +Epoch [3656/4000] Training [13/39] Loss: 0.00511 +Epoch [3656/4000] Training [14/39] Loss: 0.25375 +Epoch [3656/4000] Training [15/39] Loss: 0.13291 +Epoch [3656/4000] Training [16/39] Loss: 0.12961 +Epoch [3656/4000] Training [17/39] Loss: 0.12733 +Epoch [3656/4000] Training [18/39] Loss: 0.13109 +Epoch [3656/4000] Training [19/39] Loss: 0.00522 +Epoch [3656/4000] Training [20/39] Loss: 0.00751 +Epoch [3656/4000] Training [21/39] Loss: 0.00554 +Epoch [3656/4000] Training [22/39] Loss: 0.00420 +Epoch [3656/4000] Training [23/39] Loss: 0.00416 +Epoch [3656/4000] Training [24/39] Loss: 0.00413 +Epoch [3656/4000] Training [25/39] Loss: 0.12895 +Epoch [3656/4000] Training [26/39] Loss: 0.00737 +Epoch [3656/4000] Training [27/39] Loss: 0.00332 +Epoch [3656/4000] Training [28/39] Loss: 0.00459 +Epoch [3656/4000] Training [29/39] Loss: 0.00751 +Epoch [3656/4000] Training [30/39] Loss: 0.04342 +Epoch [3656/4000] Training [31/39] Loss: 0.00569 +Epoch [3656/4000] Training [32/39] Loss: 0.00746 +Epoch [3656/4000] Training [33/39] Loss: 0.00572 +Epoch [3656/4000] Training [34/39] Loss: 0.00710 +Epoch [3656/4000] Training [35/39] Loss: 0.00863 +Epoch [3656/4000] Training [36/39] Loss: 0.25396 +Epoch [3656/4000] Training [37/39] Loss: 0.00355 +Epoch [3656/4000] Training [38/39] Loss: 0.00573 +Epoch [3656/4000] Training [39/39] Loss: 0.00839 +Epoch [3656/4000] Training metric {'Train/mean dice_metric': 0.9960296750068665, 'Train/mean miou_metric': 0.9925253391265869, 'Train/mean f1': 0.9966329336166382, 'Train/mean precision': 0.9961243271827698, 'Train/mean recall': 0.9971420168876648, 'Train/mean hd95_metric': 1.1946756839752197} +Epoch [3656/4000] Validation [1/10] Loss: 0.77622 focal_loss 0.68380 dice_loss 0.09242 +Epoch [3656/4000] Validation [2/10] Loss: 0.48639 focal_loss 0.39327 dice_loss 0.09312 +Epoch [3656/4000] Validation [3/10] Loss: 0.36582 focal_loss 0.25667 dice_loss 0.10916 +Epoch [3656/4000] Validation [4/10] Loss: 0.92351 focal_loss 0.35192 dice_loss 0.57159 +Epoch [3656/4000] Validation [5/10] Loss: 3.04856 focal_loss 2.37620 dice_loss 0.67236 +Epoch [3656/4000] Validation [6/10] Loss: 1.37439 focal_loss 0.66311 dice_loss 0.71128 +Epoch [3656/4000] Validation [7/10] Loss: 1.23544 focal_loss 0.57683 dice_loss 0.65861 +Epoch [3656/4000] Validation [8/10] Loss: 2.08477 focal_loss 1.50516 dice_loss 0.57962 +Epoch [3656/4000] Validation [9/10] Loss: 1.69379 focal_loss 1.14895 dice_loss 0.54484 +Epoch [3656/4000] Validation [10/10] Loss: 2.01212 focal_loss 1.26848 dice_loss 0.74364 +Epoch [3656/4000] Validation metric {'Val/mean dice_metric': 0.9509251713752747, 'Val/mean miou_metric': 0.9346560835838318, 'Val/mean f1': 0.946233332157135, 'Val/mean precision': 0.9367179274559021, 'Val/mean recall': 0.9559440016746521, 'Val/mean hd95_metric': 11.101710319519043} +Cheakpoint... +Epoch [3656/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509251713752747, 'Val/mean miou_metric': 0.9346560835838318, 'Val/mean f1': 0.946233332157135, 'Val/mean precision': 0.9367179274559021, 'Val/mean recall': 0.9559440016746521, 'Val/mean hd95_metric': 11.101710319519043} +Epoch [3657/4000] Training [1/39] Loss: 0.00337 +Epoch [3657/4000] Training [2/39] Loss: 0.13035 +Epoch [3657/4000] Training [3/39] Loss: 0.00521 +Epoch [3657/4000] Training [4/39] Loss: 0.12938 +Epoch [3657/4000] Training [5/39] Loss: 0.00606 +Epoch [3657/4000] Training [6/39] Loss: 0.04173 +Epoch [3657/4000] Training [7/39] Loss: 0.00607 +Epoch [3657/4000] Training [8/39] Loss: 0.00714 +Epoch [3657/4000] Training [9/39] Loss: 0.00622 +Epoch [3657/4000] Training [10/39] Loss: 0.00310 +Epoch [3657/4000] Training [11/39] Loss: 0.00907 +Epoch [3657/4000] Training [12/39] Loss: 0.00667 +Epoch [3657/4000] Training [13/39] Loss: 0.12933 +Epoch [3657/4000] Training [14/39] Loss: 0.00384 +Epoch [3657/4000] Training [15/39] Loss: 0.00480 +Epoch [3657/4000] Training [16/39] Loss: 0.00539 +Epoch [3657/4000] Training [17/39] Loss: 0.12943 +Epoch [3657/4000] Training [18/39] Loss: 0.00544 +Epoch [3657/4000] Training [19/39] Loss: 0.25230 +Epoch [3657/4000] Training [20/39] Loss: 0.25261 +Epoch [3657/4000] Training [21/39] Loss: 0.12832 +Epoch [3657/4000] Training [22/39] Loss: 0.00657 +Epoch [3657/4000] Training [23/39] Loss: 0.00457 +Epoch [3657/4000] Training [24/39] Loss: 0.12891 +Epoch [3657/4000] Training [25/39] Loss: 0.00544 +Epoch [3657/4000] Training [26/39] Loss: 0.00547 +Epoch [3657/4000] Training [27/39] Loss: 0.13118 +Epoch [3657/4000] Training [28/39] Loss: 0.00459 +Epoch [3657/4000] Training [29/39] Loss: 0.00419 +Epoch [3657/4000] Training [30/39] Loss: 0.00844 +Epoch [3657/4000] Training [31/39] Loss: 0.00531 +Epoch [3657/4000] Training [32/39] Loss: 0.13045 +Epoch [3657/4000] Training [33/39] Loss: 0.00613 +Epoch [3657/4000] Training [34/39] Loss: 0.00838 +Epoch [3657/4000] Training [35/39] Loss: 0.00426 +Epoch [3657/4000] Training [36/39] Loss: 0.00625 +Epoch [3657/4000] Training [37/39] Loss: 0.00469 +Epoch [3657/4000] Training [38/39] Loss: 0.00360 +Epoch [3657/4000] Training [39/39] Loss: 0.12890 +Epoch [3657/4000] Training metric {'Train/mean dice_metric': 0.9959335327148438, 'Train/mean miou_metric': 0.9923230409622192, 'Train/mean f1': 0.996587336063385, 'Train/mean precision': 0.9960997700691223, 'Train/mean recall': 0.9970754981040955, 'Train/mean hd95_metric': 0.9702286124229431} +Epoch [3657/4000] Validation [1/10] Loss: 0.75063 focal_loss 0.66017 dice_loss 0.09047 +Epoch [3657/4000] Validation [2/10] Loss: 0.48619 focal_loss 0.39210 dice_loss 0.09408 +Epoch [3657/4000] Validation [3/10] Loss: 0.36909 focal_loss 0.25947 dice_loss 0.10962 +Epoch [3657/4000] Validation [4/10] Loss: 0.91726 focal_loss 0.34834 dice_loss 0.56893 +Epoch [3657/4000] Validation [5/10] Loss: 3.03354 focal_loss 2.36059 dice_loss 0.67295 +Epoch [3657/4000] Validation [6/10] Loss: 1.35764 focal_loss 0.64778 dice_loss 0.70986 +Epoch [3657/4000] Validation [7/10] Loss: 1.21821 focal_loss 0.56196 dice_loss 0.65625 +Epoch [3657/4000] Validation [8/10] Loss: 2.12632 focal_loss 1.53826 dice_loss 0.58806 +Epoch [3657/4000] Validation [9/10] Loss: 1.67992 focal_loss 1.13490 dice_loss 0.54502 +Epoch [3657/4000] Validation [10/10] Loss: 1.99383 focal_loss 1.25077 dice_loss 0.74307 +Epoch [3657/4000] Validation metric {'Val/mean dice_metric': 0.9510383605957031, 'Val/mean miou_metric': 0.9347121715545654, 'Val/mean f1': 0.9466531276702881, 'Val/mean precision': 0.9384979009628296, 'Val/mean recall': 0.954951286315918, 'Val/mean hd95_metric': 10.7022066116333} +Cheakpoint... +Epoch [3657/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510383605957031, 'Val/mean miou_metric': 0.9347121715545654, 'Val/mean f1': 0.9466531276702881, 'Val/mean precision': 0.9384979009628296, 'Val/mean recall': 0.954951286315918, 'Val/mean hd95_metric': 10.7022066116333} +Epoch [3658/4000] Training [1/39] Loss: 0.00523 +Epoch [3658/4000] Training [2/39] Loss: 0.25465 +Epoch [3658/4000] Training [3/39] Loss: 0.00479 +Epoch [3658/4000] Training [4/39] Loss: 0.00586 +Epoch [3658/4000] Training [5/39] Loss: 0.00246 +Epoch [3658/4000] Training [6/39] Loss: 0.00427 +Epoch [3658/4000] Training [7/39] Loss: 0.00482 +Epoch [3658/4000] Training [8/39] Loss: 0.12848 +Epoch [3658/4000] Training [9/39] Loss: 0.12902 +Epoch [3658/4000] Training [10/39] Loss: 0.00335 +Epoch [3658/4000] Training [11/39] Loss: 0.00381 +Epoch [3658/4000] Training [12/39] Loss: 0.00290 +Epoch [3658/4000] Training [13/39] Loss: 0.00641 +Epoch [3658/4000] Training [14/39] Loss: 0.00643 +Epoch [3658/4000] Training [15/39] Loss: 0.00519 +Epoch [3658/4000] Training [16/39] Loss: 0.00402 +Epoch [3658/4000] Training [17/39] Loss: 0.00346 +Epoch [3658/4000] Training [18/39] Loss: 0.00415 +Epoch [3658/4000] Training [19/39] Loss: 0.00554 +Epoch [3658/4000] Training [20/39] Loss: 0.00260 +Epoch [3658/4000] Training [21/39] Loss: 0.00304 +Epoch [3658/4000] Training [22/39] Loss: 0.12842 +Epoch [3658/4000] Training [23/39] Loss: 0.13009 +Epoch [3658/4000] Training [24/39] Loss: 0.00564 +Epoch [3658/4000] Training [25/39] Loss: 0.00826 +Epoch [3658/4000] Training [26/39] Loss: 0.00326 +Epoch [3658/4000] Training [27/39] Loss: 0.00353 +Epoch [3658/4000] Training [28/39] Loss: 0.00598 +Epoch [3658/4000] Training [29/39] Loss: 0.00283 +Epoch [3658/4000] Training [30/39] Loss: 0.08811 +Epoch [3658/4000] Training [31/39] Loss: 0.00347 +Epoch [3658/4000] Training [32/39] Loss: 0.00446 +Epoch [3658/4000] Training [33/39] Loss: 0.12812 +Epoch [3658/4000] Training [34/39] Loss: 0.00377 +Epoch [3658/4000] Training [35/39] Loss: 0.13239 +Epoch [3658/4000] Training [36/39] Loss: 0.00409 +Epoch [3658/4000] Training [37/39] Loss: 0.12927 +Epoch [3658/4000] Training [38/39] Loss: 0.00289 +Epoch [3658/4000] Training [39/39] Loss: 0.00426 +Epoch [3658/4000] Training metric {'Train/mean dice_metric': 0.9965059161186218, 'Train/mean miou_metric': 0.9934648275375366, 'Train/mean f1': 0.9969606995582581, 'Train/mean precision': 0.9965459108352661, 'Train/mean recall': 0.9973757266998291, 'Train/mean hd95_metric': 0.9283129572868347} +Epoch [3658/4000] Validation [1/10] Loss: 0.75670 focal_loss 0.66718 dice_loss 0.08952 +Epoch [3658/4000] Validation [2/10] Loss: 0.48485 focal_loss 0.38962 dice_loss 0.09523 +Epoch [3658/4000] Validation [3/10] Loss: 0.38562 focal_loss 0.27504 dice_loss 0.11058 +Epoch [3658/4000] Validation [4/10] Loss: 0.90951 focal_loss 0.34239 dice_loss 0.56712 +Epoch [3658/4000] Validation [5/10] Loss: 3.09967 focal_loss 2.42640 dice_loss 0.67327 +Epoch [3658/4000] Validation [6/10] Loss: 1.34329 focal_loss 0.63475 dice_loss 0.70853 +Epoch [3658/4000] Validation [7/10] Loss: 1.20212 focal_loss 0.54800 dice_loss 0.65413 +Epoch [3658/4000] Validation [8/10] Loss: 2.18032 focal_loss 1.58421 dice_loss 0.59611 +Epoch [3658/4000] Validation [9/10] Loss: 1.66382 focal_loss 1.11661 dice_loss 0.54721 +Epoch [3658/4000] Validation [10/10] Loss: 1.96402 focal_loss 1.22271 dice_loss 0.74132 +Epoch [3658/4000] Validation metric {'Val/mean dice_metric': 0.9515756964683533, 'Val/mean miou_metric': 0.9357370138168335, 'Val/mean f1': 0.9474003314971924, 'Val/mean precision': 0.9403939247131348, 'Val/mean recall': 0.9545119404792786, 'Val/mean hd95_metric': 10.632200241088867} +Cheakpoint... +Epoch [3658/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515756964683533, 'Val/mean miou_metric': 0.9357370138168335, 'Val/mean f1': 0.9474003314971924, 'Val/mean precision': 0.9403939247131348, 'Val/mean recall': 0.9545119404792786, 'Val/mean hd95_metric': 10.632200241088867} +Epoch [3659/4000] Training [1/39] Loss: 0.12760 +Epoch [3659/4000] Training [2/39] Loss: 0.00436 +Epoch [3659/4000] Training [3/39] Loss: 0.00491 +Epoch [3659/4000] Training [4/39] Loss: 0.13023 +Epoch [3659/4000] Training [5/39] Loss: 0.00680 +Epoch [3659/4000] Training [6/39] Loss: 0.00546 +Epoch [3659/4000] Training [7/39] Loss: 0.00356 +Epoch [3659/4000] Training [8/39] Loss: 0.00513 +Epoch [3659/4000] Training [9/39] Loss: 0.00488 +Epoch [3659/4000] Training [10/39] Loss: 0.00512 +Epoch [3659/4000] Training [11/39] Loss: 0.00458 +Epoch [3659/4000] Training [12/39] Loss: 0.00572 +Epoch [3659/4000] Training [13/39] Loss: 0.00490 +Epoch [3659/4000] Training [14/39] Loss: 0.00428 +Epoch [3659/4000] Training [15/39] Loss: 0.00312 +Epoch [3659/4000] Training [16/39] Loss: 0.13069 +Epoch [3659/4000] Training [17/39] Loss: 0.12911 +Epoch [3659/4000] Training [18/39] Loss: 0.00724 +Epoch [3659/4000] Training [19/39] Loss: 0.12885 +Epoch [3659/4000] Training [20/39] Loss: 0.00545 +Epoch [3659/4000] Training [21/39] Loss: 0.12815 +Epoch [3659/4000] Training [22/39] Loss: 0.00552 +Epoch [3659/4000] Training [23/39] Loss: 0.00401 +Epoch [3659/4000] Training [24/39] Loss: 0.00530 +Epoch [3659/4000] Training [25/39] Loss: 0.00534 +Epoch [3659/4000] Training [26/39] Loss: 0.00516 +Epoch [3659/4000] Training [27/39] Loss: 0.12800 +Epoch [3659/4000] Training [28/39] Loss: 0.01744 +Epoch [3659/4000] Training [29/39] Loss: 0.00473 +Epoch [3659/4000] Training [30/39] Loss: 0.00730 +Epoch [3659/4000] Training [31/39] Loss: 0.00440 +Epoch [3659/4000] Training [32/39] Loss: 0.12939 +Epoch [3659/4000] Training [33/39] Loss: 0.00569 +Epoch [3659/4000] Training [34/39] Loss: 0.00746 +Epoch [3659/4000] Training [35/39] Loss: 0.00550 +Epoch [3659/4000] Training [36/39] Loss: 0.00409 +Epoch [3659/4000] Training [37/39] Loss: 0.12964 +Epoch [3659/4000] Training [38/39] Loss: 0.13059 +Epoch [3659/4000] Training [39/39] Loss: 0.00347 +Epoch [3659/4000] Training metric {'Train/mean dice_metric': 0.996130108833313, 'Train/mean miou_metric': 0.9927284121513367, 'Train/mean f1': 0.9967580437660217, 'Train/mean precision': 0.9962606430053711, 'Train/mean recall': 0.9972558617591858, 'Train/mean hd95_metric': 1.1413716077804565} +Epoch [3659/4000] Validation [1/10] Loss: 0.72564 focal_loss 0.63692 dice_loss 0.08871 +Epoch [3659/4000] Validation [2/10] Loss: 0.48043 focal_loss 0.38705 dice_loss 0.09337 +Epoch [3659/4000] Validation [3/10] Loss: 0.36792 focal_loss 0.25818 dice_loss 0.10975 +Epoch [3659/4000] Validation [4/10] Loss: 0.90675 focal_loss 0.33841 dice_loss 0.56834 +Epoch [3659/4000] Validation [5/10] Loss: 2.99641 focal_loss 2.32304 dice_loss 0.67337 +Epoch [3659/4000] Validation [6/10] Loss: 1.34321 focal_loss 0.63337 dice_loss 0.70983 +Epoch [3659/4000] Validation [7/10] Loss: 1.20091 focal_loss 0.54489 dice_loss 0.65602 +Epoch [3659/4000] Validation [8/10] Loss: 2.11750 focal_loss 1.52759 dice_loss 0.58992 +Epoch [3659/4000] Validation [9/10] Loss: 1.65306 focal_loss 1.10580 dice_loss 0.54726 +Epoch [3659/4000] Validation [10/10] Loss: 1.96202 focal_loss 1.22087 dice_loss 0.74115 +Epoch [3659/4000] Validation metric {'Val/mean dice_metric': 0.9512272477149963, 'Val/mean miou_metric': 0.9350436925888062, 'Val/mean f1': 0.9471455812454224, 'Val/mean precision': 0.9396514296531677, 'Val/mean recall': 0.9547602534294128, 'Val/mean hd95_metric': 10.905835151672363} +Cheakpoint... +Epoch [3659/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512272477149963, 'Val/mean miou_metric': 0.9350436925888062, 'Val/mean f1': 0.9471455812454224, 'Val/mean precision': 0.9396514296531677, 'Val/mean recall': 0.9547602534294128, 'Val/mean hd95_metric': 10.905835151672363} +Epoch [3660/4000] Training [1/39] Loss: 0.12835 +Epoch [3660/4000] Training [2/39] Loss: 0.00378 +Epoch [3660/4000] Training [3/39] Loss: 0.00712 +Epoch [3660/4000] Training [4/39] Loss: 0.12965 +Epoch [3660/4000] Training [5/39] Loss: 0.12810 +Epoch [3660/4000] Training [6/39] Loss: 0.12932 +Epoch [3660/4000] Training [7/39] Loss: 0.12947 +Epoch [3660/4000] Training [8/39] Loss: 0.00514 +Epoch [3660/4000] Training [9/39] Loss: 0.12855 +Epoch [3660/4000] Training [10/39] Loss: 0.00394 +Epoch [3660/4000] Training [11/39] Loss: 0.00367 +Epoch [3660/4000] Training [12/39] Loss: 0.00374 +Epoch [3660/4000] Training [13/39] Loss: 0.00406 +Epoch [3660/4000] Training [14/39] Loss: 0.00462 +Epoch [3660/4000] Training [15/39] Loss: 0.00662 +Epoch [3660/4000] Training [16/39] Loss: 0.00419 +Epoch [3660/4000] Training [17/39] Loss: 0.00493 +Epoch [3660/4000] Training [18/39] Loss: 0.00867 +Epoch [3660/4000] Training [19/39] Loss: 0.13033 +Epoch [3660/4000] Training [20/39] Loss: 0.00543 +Epoch [3660/4000] Training [21/39] Loss: 0.12824 +Epoch [3660/4000] Training [22/39] Loss: 0.00542 +Epoch [3660/4000] Training [23/39] Loss: 0.00862 +Epoch [3660/4000] Training [24/39] Loss: 0.13045 +Epoch [3660/4000] Training [25/39] Loss: 0.00266 +Epoch [3660/4000] Training [26/39] Loss: 0.00535 +Epoch [3660/4000] Training [27/39] Loss: 0.00490 +Epoch [3660/4000] Training [28/39] Loss: 0.00324 +Epoch [3660/4000] Training [29/39] Loss: 0.00415 +Epoch [3660/4000] Training [30/39] Loss: 0.00466 +Epoch [3660/4000] Training [31/39] Loss: 0.13228 +Epoch [3660/4000] Training [32/39] Loss: 0.13015 +Epoch [3660/4000] Training [33/39] Loss: 0.00305 +Epoch [3660/4000] Training [34/39] Loss: 0.13264 +Epoch [3660/4000] Training [35/39] Loss: 0.00759 +Epoch [3660/4000] Training [36/39] Loss: 0.00480 +Epoch [3660/4000] Training [37/39] Loss: 0.00320 +Epoch [3660/4000] Training [38/39] Loss: 0.12835 +Epoch [3660/4000] Training [39/39] Loss: 0.00634 +Epoch [3660/4000] Training metric {'Train/mean dice_metric': 0.9957860708236694, 'Train/mean miou_metric': 0.9924034476280212, 'Train/mean f1': 0.9964484572410583, 'Train/mean precision': 0.9956924319267273, 'Train/mean recall': 0.9972056746482849, 'Train/mean hd95_metric': 1.1167572736740112} +Epoch [3660/4000] Validation [1/10] Loss: 0.72393 focal_loss 0.63546 dice_loss 0.08846 +Epoch [3660/4000] Validation [2/10] Loss: 0.48455 focal_loss 0.38874 dice_loss 0.09581 +Epoch [3660/4000] Validation [3/10] Loss: 0.37725 focal_loss 0.26690 dice_loss 0.11034 +Epoch [3660/4000] Validation [4/10] Loss: 0.89523 focal_loss 0.32866 dice_loss 0.56657 +Epoch [3660/4000] Validation [5/10] Loss: 3.02411 focal_loss 2.35086 dice_loss 0.67325 +Epoch [3660/4000] Validation [6/10] Loss: 1.33219 focal_loss 0.62161 dice_loss 0.71058 +Epoch [3660/4000] Validation [7/10] Loss: 1.19209 focal_loss 0.53591 dice_loss 0.65618 +Epoch [3660/4000] Validation [8/10] Loss: 2.17400 focal_loss 1.57350 dice_loss 0.60051 +Epoch [3660/4000] Validation [9/10] Loss: 1.60571 focal_loss 1.05853 dice_loss 0.54718 +Epoch [3660/4000] Validation [10/10] Loss: 1.92278 focal_loss 1.18382 dice_loss 0.73897 +Epoch [3660/4000] Validation metric {'Val/mean dice_metric': 0.9508757591247559, 'Val/mean miou_metric': 0.9347860217094421, 'Val/mean f1': 0.9471976161003113, 'Val/mean precision': 0.9404104351997375, 'Val/mean recall': 0.9540835022926331, 'Val/mean hd95_metric': 10.879266738891602} +Cheakpoint... +Epoch [3660/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508757591247559, 'Val/mean miou_metric': 0.9347860217094421, 'Val/mean f1': 0.9471976161003113, 'Val/mean precision': 0.9404104351997375, 'Val/mean recall': 0.9540835022926331, 'Val/mean hd95_metric': 10.879266738891602} +Epoch [3661/4000] Training [1/39] Loss: 0.00791 +Epoch [3661/4000] Training [2/39] Loss: 0.00323 +Epoch [3661/4000] Training [3/39] Loss: 0.12837 +Epoch [3661/4000] Training [4/39] Loss: 0.00764 +Epoch [3661/4000] Training [5/39] Loss: 0.12862 +Epoch [3661/4000] Training [6/39] Loss: 0.00725 +Epoch [3661/4000] Training [7/39] Loss: 0.00411 +Epoch [3661/4000] Training [8/39] Loss: 0.00633 +Epoch [3661/4000] Training [9/39] Loss: 0.12769 +Epoch [3661/4000] Training [10/39] Loss: 0.00400 +Epoch [3661/4000] Training [11/39] Loss: 0.00379 +Epoch [3661/4000] Training [12/39] Loss: 0.12737 +Epoch [3661/4000] Training [13/39] Loss: 0.00366 +Epoch [3661/4000] Training [14/39] Loss: 0.00458 +Epoch [3661/4000] Training [15/39] Loss: 0.04483 +Epoch [3661/4000] Training [16/39] Loss: 0.00487 +Epoch [3661/4000] Training [17/39] Loss: 0.12780 +Epoch [3661/4000] Training [18/39] Loss: 0.00557 +Epoch [3661/4000] Training [19/39] Loss: 0.00362 +Epoch [3661/4000] Training [20/39] Loss: 0.00672 +Epoch [3661/4000] Training [21/39] Loss: 0.00779 +Epoch [3661/4000] Training [22/39] Loss: 0.00542 +Epoch [3661/4000] Training [23/39] Loss: 0.00773 +Epoch [3661/4000] Training [24/39] Loss: 0.00409 +Epoch [3661/4000] Training [25/39] Loss: 0.00757 +Epoch [3661/4000] Training [26/39] Loss: 0.00484 +Epoch [3661/4000] Training [27/39] Loss: 0.12983 +Epoch [3661/4000] Training [28/39] Loss: 0.00444 +Epoch [3661/4000] Training [29/39] Loss: 0.00450 +Epoch [3661/4000] Training [30/39] Loss: 0.00566 +Epoch [3661/4000] Training [31/39] Loss: 0.12965 +Epoch [3661/4000] Training [32/39] Loss: 0.00626 +Epoch [3661/4000] Training [33/39] Loss: 0.00701 +Epoch [3661/4000] Training [34/39] Loss: 0.00583 +Epoch [3661/4000] Training [35/39] Loss: 0.00388 +Epoch [3661/4000] Training [36/39] Loss: 0.00417 +Epoch [3661/4000] Training [37/39] Loss: 0.00445 +Epoch [3661/4000] Training [38/39] Loss: 0.00410 +Epoch [3661/4000] Training [39/39] Loss: 0.12911 +Epoch [3661/4000] Training metric {'Train/mean dice_metric': 0.9962745904922485, 'Train/mean miou_metric': 0.9929794073104858, 'Train/mean f1': 0.9969361424446106, 'Train/mean precision': 0.9965148568153381, 'Train/mean recall': 0.9973579049110413, 'Train/mean hd95_metric': 1.1254241466522217} +Epoch [3661/4000] Validation [1/10] Loss: 0.72816 focal_loss 0.63901 dice_loss 0.08915 +Epoch [3661/4000] Validation [2/10] Loss: 0.48498 focal_loss 0.38903 dice_loss 0.09595 +Epoch [3661/4000] Validation [3/10] Loss: 0.37657 focal_loss 0.26599 dice_loss 0.11058 +Epoch [3661/4000] Validation [4/10] Loss: 0.90305 focal_loss 0.33472 dice_loss 0.56833 +Epoch [3661/4000] Validation [5/10] Loss: 3.02648 focal_loss 2.35336 dice_loss 0.67312 +Epoch [3661/4000] Validation [6/10] Loss: 1.34063 focal_loss 0.62995 dice_loss 0.71068 +Epoch [3661/4000] Validation [7/10] Loss: 1.19698 focal_loss 0.54146 dice_loss 0.65552 +Epoch [3661/4000] Validation [8/10] Loss: 2.14608 focal_loss 1.54857 dice_loss 0.59751 +Epoch [3661/4000] Validation [9/10] Loss: 1.60304 focal_loss 1.05531 dice_loss 0.54773 +Epoch [3661/4000] Validation [10/10] Loss: 1.92998 focal_loss 1.19007 dice_loss 0.73992 +Epoch [3661/4000] Validation metric {'Val/mean dice_metric': 0.9513324499130249, 'Val/mean miou_metric': 0.9352951049804688, 'Val/mean f1': 0.9475436806678772, 'Val/mean precision': 0.940218448638916, 'Val/mean recall': 0.9549838900566101, 'Val/mean hd95_metric': 10.938943862915039} +Cheakpoint... +Epoch [3661/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513324499130249, 'Val/mean miou_metric': 0.9352951049804688, 'Val/mean f1': 0.9475436806678772, 'Val/mean precision': 0.940218448638916, 'Val/mean recall': 0.9549838900566101, 'Val/mean hd95_metric': 10.938943862915039} +Epoch [3662/4000] Training [1/39] Loss: 0.13110 +Epoch [3662/4000] Training [2/39] Loss: 0.00792 +Epoch [3662/4000] Training [3/39] Loss: 0.00363 +Epoch [3662/4000] Training [4/39] Loss: 0.12863 +Epoch [3662/4000] Training [5/39] Loss: 0.00450 +Epoch [3662/4000] Training [6/39] Loss: 0.00403 +Epoch [3662/4000] Training [7/39] Loss: 0.12903 +Epoch [3662/4000] Training [8/39] Loss: 0.00419 +Epoch [3662/4000] Training [9/39] Loss: 0.00463 +Epoch [3662/4000] Training [10/39] Loss: 0.00469 +Epoch [3662/4000] Training [11/39] Loss: 0.00585 +Epoch [3662/4000] Training [12/39] Loss: 0.00625 +Epoch [3662/4000] Training [13/39] Loss: 0.00270 +Epoch [3662/4000] Training [14/39] Loss: 0.00990 +Epoch [3662/4000] Training [15/39] Loss: 0.00532 +Epoch [3662/4000] Training [16/39] Loss: 0.12836 +Epoch [3662/4000] Training [17/39] Loss: 0.00367 +Epoch [3662/4000] Training [18/39] Loss: 0.00520 +Epoch [3662/4000] Training [19/39] Loss: 0.00605 +Epoch [3662/4000] Training [20/39] Loss: 0.00513 +Epoch [3662/4000] Training [21/39] Loss: 0.00418 +Epoch [3662/4000] Training [22/39] Loss: 0.25240 +Epoch [3662/4000] Training [23/39] Loss: 0.00584 +Epoch [3662/4000] Training [24/39] Loss: 0.00543 +Epoch [3662/4000] Training [25/39] Loss: 0.00509 +Epoch [3662/4000] Training [26/39] Loss: 0.12921 +Epoch [3662/4000] Training [27/39] Loss: 0.00421 +Epoch [3662/4000] Training [28/39] Loss: 0.25313 +Epoch [3662/4000] Training [29/39] Loss: 0.00378 +Epoch [3662/4000] Training [30/39] Loss: 0.00397 +Epoch [3662/4000] Training [31/39] Loss: 0.12917 +Epoch [3662/4000] Training [32/39] Loss: 0.00504 +Epoch [3662/4000] Training [33/39] Loss: 0.00460 +Epoch [3662/4000] Training [34/39] Loss: 0.00419 +Epoch [3662/4000] Training [35/39] Loss: 0.00437 +Epoch [3662/4000] Training [36/39] Loss: 0.12881 +Epoch [3662/4000] Training [37/39] Loss: 0.00605 +Epoch [3662/4000] Training [38/39] Loss: 0.00372 +Epoch [3662/4000] Training [39/39] Loss: 0.00513 +Epoch [3662/4000] Training metric {'Train/mean dice_metric': 0.9961833357810974, 'Train/mean miou_metric': 0.9928291440010071, 'Train/mean f1': 0.9967217445373535, 'Train/mean precision': 0.9962872862815857, 'Train/mean recall': 0.9971567392349243, 'Train/mean hd95_metric': 0.9591381549835205} +Epoch [3662/4000] Validation [1/10] Loss: 0.73784 focal_loss 0.64811 dice_loss 0.08973 +Epoch [3662/4000] Validation [2/10] Loss: 0.48254 focal_loss 0.39187 dice_loss 0.09068 +Epoch [3662/4000] Validation [3/10] Loss: 0.36244 focal_loss 0.25360 dice_loss 0.10884 +Epoch [3662/4000] Validation [4/10] Loss: 0.91788 focal_loss 0.34668 dice_loss 0.57120 +Epoch [3662/4000] Validation [5/10] Loss: 3.04260 focal_loss 2.36962 dice_loss 0.67298 +Epoch [3662/4000] Validation [6/10] Loss: 1.37333 focal_loss 0.65944 dice_loss 0.71389 +Epoch [3662/4000] Validation [7/10] Loss: 1.22746 focal_loss 0.56883 dice_loss 0.65863 +Epoch [3662/4000] Validation [8/10] Loss: 2.08707 focal_loss 1.50683 dice_loss 0.58024 +Epoch [3662/4000] Validation [9/10] Loss: 1.69968 focal_loss 1.15769 dice_loss 0.54199 +Epoch [3662/4000] Validation [10/10] Loss: 2.01211 focal_loss 1.26997 dice_loss 0.74214 +Epoch [3662/4000] Validation metric {'Val/mean dice_metric': 0.9511806964874268, 'Val/mean miou_metric': 0.9350189566612244, 'Val/mean f1': 0.9463345408439636, 'Val/mean precision': 0.9371091723442078, 'Val/mean recall': 0.9557434320449829, 'Val/mean hd95_metric': 10.892071723937988} +Cheakpoint... +Epoch [3662/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511806964874268, 'Val/mean miou_metric': 0.9350189566612244, 'Val/mean f1': 0.9463345408439636, 'Val/mean precision': 0.9371091723442078, 'Val/mean recall': 0.9557434320449829, 'Val/mean hd95_metric': 10.892071723937988} +Epoch [3663/4000] Training [1/39] Loss: 0.00582 +Epoch [3663/4000] Training [2/39] Loss: 0.01157 +Epoch [3663/4000] Training [3/39] Loss: 0.00492 +Epoch [3663/4000] Training [4/39] Loss: 0.00638 +Epoch [3663/4000] Training [5/39] Loss: 0.00691 +Epoch [3663/4000] Training [6/39] Loss: 0.00594 +Epoch [3663/4000] Training [7/39] Loss: 0.25672 +Epoch [3663/4000] Training [8/39] Loss: 0.00739 +Epoch [3663/4000] Training [9/39] Loss: 0.25212 +Epoch [3663/4000] Training [10/39] Loss: 0.00452 +Epoch [3663/4000] Training [11/39] Loss: 0.00582 +Epoch [3663/4000] Training [12/39] Loss: 0.12921 +Epoch [3663/4000] Training [13/39] Loss: 0.25440 +Epoch [3663/4000] Training [14/39] Loss: 0.00722 +Epoch [3663/4000] Training [15/39] Loss: 0.12999 +Epoch [3663/4000] Training [16/39] Loss: 0.25354 +Epoch [3663/4000] Training [17/39] Loss: 0.00527 +Epoch [3663/4000] Training [18/39] Loss: 0.12981 +Epoch [3663/4000] Training [19/39] Loss: 0.00601 +Epoch [3663/4000] Training [20/39] Loss: 0.00608 +Epoch [3663/4000] Training [21/39] Loss: 0.12908 +Epoch [3663/4000] Training [22/39] Loss: 0.00465 +Epoch [3663/4000] Training [23/39] Loss: 0.00761 +Epoch [3663/4000] Training [24/39] Loss: 0.00502 +Epoch [3663/4000] Training [25/39] Loss: 0.00514 +Epoch [3663/4000] Training [26/39] Loss: 0.00468 +Epoch [3663/4000] Training [27/39] Loss: 0.00621 +Epoch [3663/4000] Training [28/39] Loss: 0.00398 +Epoch [3663/4000] Training [29/39] Loss: 0.25236 +Epoch [3663/4000] Training [30/39] Loss: 0.00580 +Epoch [3663/4000] Training [31/39] Loss: 0.00640 +Epoch [3663/4000] Training [32/39] Loss: 0.00647 +Epoch [3663/4000] Training [33/39] Loss: 0.00624 +Epoch [3663/4000] Training [34/39] Loss: 0.12779 +Epoch [3663/4000] Training [35/39] Loss: 0.00421 +Epoch [3663/4000] Training [36/39] Loss: 0.13137 +Epoch [3663/4000] Training [37/39] Loss: 0.00372 +Epoch [3663/4000] Training [38/39] Loss: 0.00347 +Epoch [3663/4000] Training [39/39] Loss: 0.00441 +Epoch [3663/4000] Training metric {'Train/mean dice_metric': 0.9961711168289185, 'Train/mean miou_metric': 0.9927913546562195, 'Train/mean f1': 0.9968218207359314, 'Train/mean precision': 0.9963567852973938, 'Train/mean recall': 0.9972874522209167, 'Train/mean hd95_metric': 0.950204610824585} +Epoch [3663/4000] Validation [1/10] Loss: 0.70717 focal_loss 0.62052 dice_loss 0.08665 +Epoch [3663/4000] Validation [2/10] Loss: 0.48703 focal_loss 0.39057 dice_loss 0.09646 +Epoch [3663/4000] Validation [3/10] Loss: 0.37377 focal_loss 0.26340 dice_loss 0.11037 +Epoch [3663/4000] Validation [4/10] Loss: 0.90405 focal_loss 0.33646 dice_loss 0.56758 +Epoch [3663/4000] Validation [5/10] Loss: 3.02503 focal_loss 2.35179 dice_loss 0.67324 +Epoch [3663/4000] Validation [6/10] Loss: 1.35579 focal_loss 0.64280 dice_loss 0.71299 +Epoch [3663/4000] Validation [7/10] Loss: 1.20021 focal_loss 0.54309 dice_loss 0.65712 +Epoch [3663/4000] Validation [8/10] Loss: 2.16930 focal_loss 1.57318 dice_loss 0.59612 +Epoch [3663/4000] Validation [9/10] Loss: 1.66004 focal_loss 1.11133 dice_loss 0.54871 +Epoch [3663/4000] Validation [10/10] Loss: 1.94145 focal_loss 1.20175 dice_loss 0.73971 +Epoch [3663/4000] Validation metric {'Val/mean dice_metric': 0.9512624144554138, 'Val/mean miou_metric': 0.9351565837860107, 'Val/mean f1': 0.9474674463272095, 'Val/mean precision': 0.9404973983764648, 'Val/mean recall': 0.9545414447784424, 'Val/mean hd95_metric': 10.782527923583984} +Cheakpoint... +Epoch [3663/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512624144554138, 'Val/mean miou_metric': 0.9351565837860107, 'Val/mean f1': 0.9474674463272095, 'Val/mean precision': 0.9404973983764648, 'Val/mean recall': 0.9545414447784424, 'Val/mean hd95_metric': 10.782527923583984} +Epoch [3664/4000] Training [1/39] Loss: 0.00308 +Epoch [3664/4000] Training [2/39] Loss: 0.12751 +Epoch [3664/4000] Training [3/39] Loss: 0.00548 +Epoch [3664/4000] Training [4/39] Loss: 0.12937 +Epoch [3664/4000] Training [5/39] Loss: 0.12956 +Epoch [3664/4000] Training [6/39] Loss: 0.00527 +Epoch [3664/4000] Training [7/39] Loss: 0.00358 +Epoch [3664/4000] Training [8/39] Loss: 0.00386 +Epoch [3664/4000] Training [9/39] Loss: 0.12921 +Epoch [3664/4000] Training [10/39] Loss: 0.12768 +Epoch [3664/4000] Training [11/39] Loss: 0.00929 +Epoch [3664/4000] Training [12/39] Loss: 0.00563 +Epoch [3664/4000] Training [13/39] Loss: 0.12922 +Epoch [3664/4000] Training [14/39] Loss: 0.12860 +Epoch [3664/4000] Training [15/39] Loss: 0.00489 +Epoch [3664/4000] Training [16/39] Loss: 0.12886 +Epoch [3664/4000] Training [17/39] Loss: 0.00408 +Epoch [3664/4000] Training [18/39] Loss: 0.00582 +Epoch [3664/4000] Training [19/39] Loss: 0.00398 +Epoch [3664/4000] Training [20/39] Loss: 0.00476 +Epoch [3664/4000] Training [21/39] Loss: 0.00442 +Epoch [3664/4000] Training [22/39] Loss: 0.00383 +Epoch [3664/4000] Training [23/39] Loss: 0.25286 +Epoch [3664/4000] Training [24/39] Loss: 0.00778 +Epoch [3664/4000] Training [25/39] Loss: 0.00446 +Epoch [3664/4000] Training [26/39] Loss: 0.00601 +Epoch [3664/4000] Training [27/39] Loss: 0.00415 +Epoch [3664/4000] Training [28/39] Loss: 0.13161 +Epoch [3664/4000] Training [29/39] Loss: 0.00447 +Epoch [3664/4000] Training [30/39] Loss: 0.00697 +Epoch [3664/4000] Training [31/39] Loss: 0.00388 +Epoch [3664/4000] Training [32/39] Loss: 0.12937 +Epoch [3664/4000] Training [33/39] Loss: 0.00619 +Epoch [3664/4000] Training [34/39] Loss: 0.00576 +Epoch [3664/4000] Training [35/39] Loss: 0.00710 +Epoch [3664/4000] Training [36/39] Loss: 0.00350 +Epoch [3664/4000] Training [37/39] Loss: 0.12754 +Epoch [3664/4000] Training [38/39] Loss: 0.00300 +Epoch [3664/4000] Training [39/39] Loss: 0.00382 +Epoch [3664/4000] Training metric {'Train/mean dice_metric': 0.9963392019271851, 'Train/mean miou_metric': 0.9931595921516418, 'Train/mean f1': 0.9968591332435608, 'Train/mean precision': 0.9964089393615723, 'Train/mean recall': 0.9973098039627075, 'Train/mean hd95_metric': 1.2498724460601807} +Epoch [3664/4000] Validation [1/10] Loss: 0.70098 focal_loss 0.61485 dice_loss 0.08613 +Epoch [3664/4000] Validation [2/10] Loss: 0.48274 focal_loss 0.38547 dice_loss 0.09727 +Epoch [3664/4000] Validation [3/10] Loss: 0.38523 focal_loss 0.27381 dice_loss 0.11142 +Epoch [3664/4000] Validation [4/10] Loss: 0.89972 focal_loss 0.33354 dice_loss 0.56618 +Epoch [3664/4000] Validation [5/10] Loss: 3.04906 focal_loss 2.37577 dice_loss 0.67329 +Epoch [3664/4000] Validation [6/10] Loss: 1.33130 focal_loss 0.62105 dice_loss 0.71025 +Epoch [3664/4000] Validation [7/10] Loss: 1.18839 focal_loss 0.53523 dice_loss 0.65316 +Epoch [3664/4000] Validation [8/10] Loss: 2.18468 focal_loss 1.58328 dice_loss 0.60140 +Epoch [3664/4000] Validation [9/10] Loss: 1.62908 focal_loss 1.08084 dice_loss 0.54825 +Epoch [3664/4000] Validation [10/10] Loss: 1.89627 focal_loss 1.15951 dice_loss 0.73676 +Epoch [3664/4000] Validation metric {'Val/mean dice_metric': 0.9514207243919373, 'Val/mean miou_metric': 0.9355325102806091, 'Val/mean f1': 0.948029100894928, 'Val/mean precision': 0.9417943358421326, 'Val/mean recall': 0.9543468356132507, 'Val/mean hd95_metric': 10.991816520690918} +Cheakpoint... +Epoch [3664/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514207243919373, 'Val/mean miou_metric': 0.9355325102806091, 'Val/mean f1': 0.948029100894928, 'Val/mean precision': 0.9417943358421326, 'Val/mean recall': 0.9543468356132507, 'Val/mean hd95_metric': 10.991816520690918} +Epoch [3665/4000] Training [1/39] Loss: 0.00936 +Epoch [3665/4000] Training [2/39] Loss: 0.13068 +Epoch [3665/4000] Training [3/39] Loss: 0.13062 +Epoch [3665/4000] Training [4/39] Loss: 0.00539 +Epoch [3665/4000] Training [5/39] Loss: 0.00428 +Epoch [3665/4000] Training [6/39] Loss: 0.00347 +Epoch [3665/4000] Training [7/39] Loss: 0.00380 +Epoch [3665/4000] Training [8/39] Loss: 0.00379 +Epoch [3665/4000] Training [9/39] Loss: 0.00384 +Epoch [3665/4000] Training [10/39] Loss: 0.00514 +Epoch [3665/4000] Training [11/39] Loss: 0.12856 +Epoch [3665/4000] Training [12/39] Loss: 0.00897 +Epoch [3665/4000] Training [13/39] Loss: 0.00679 +Epoch [3665/4000] Training [14/39] Loss: 0.00587 +Epoch [3665/4000] Training [15/39] Loss: 0.00599 +Epoch [3665/4000] Training [16/39] Loss: 0.00516 +Epoch [3665/4000] Training [17/39] Loss: 0.25256 +Epoch [3665/4000] Training [18/39] Loss: 0.00544 +Epoch [3665/4000] Training [19/39] Loss: 0.12951 +Epoch [3665/4000] Training [20/39] Loss: 0.00565 +Epoch [3665/4000] Training [21/39] Loss: 0.00561 +Epoch [3665/4000] Training [22/39] Loss: 0.00608 +Epoch [3665/4000] Training [23/39] Loss: 0.00565 +Epoch [3665/4000] Training [24/39] Loss: 0.25327 +Epoch [3665/4000] Training [25/39] Loss: 0.00391 +Epoch [3665/4000] Training [26/39] Loss: 0.00344 +Epoch [3665/4000] Training [27/39] Loss: 0.12950 +Epoch [3665/4000] Training [28/39] Loss: 0.00515 +Epoch [3665/4000] Training [29/39] Loss: 0.00537 +Epoch [3665/4000] Training [30/39] Loss: 0.00339 +Epoch [3665/4000] Training [31/39] Loss: 0.00755 +Epoch [3665/4000] Training [32/39] Loss: 0.00449 +Epoch [3665/4000] Training [33/39] Loss: 0.13037 +Epoch [3665/4000] Training [34/39] Loss: 0.00511 +Epoch [3665/4000] Training [35/39] Loss: 0.00757 +Epoch [3665/4000] Training [36/39] Loss: 0.12992 +Epoch [3665/4000] Training [37/39] Loss: 0.08753 +Epoch [3665/4000] Training [38/39] Loss: 0.00335 +Epoch [3665/4000] Training [39/39] Loss: 0.00446 +Epoch [3665/4000] Training metric {'Train/mean dice_metric': 0.9952245354652405, 'Train/mean miou_metric': 0.991736888885498, 'Train/mean f1': 0.9965619444847107, 'Train/mean precision': 0.9961332082748413, 'Train/mean recall': 0.9969910383224487, 'Train/mean hd95_metric': 0.9670060873031616} +Epoch [3665/4000] Validation [1/10] Loss: 0.71870 focal_loss 0.63124 dice_loss 0.08745 +Epoch [3665/4000] Validation [2/10] Loss: 0.47869 focal_loss 0.38530 dice_loss 0.09340 +Epoch [3665/4000] Validation [3/10] Loss: 0.38222 focal_loss 0.27164 dice_loss 0.11058 +Epoch [3665/4000] Validation [4/10] Loss: 0.90257 focal_loss 0.33542 dice_loss 0.56715 +Epoch [3665/4000] Validation [5/10] Loss: 3.08639 focal_loss 2.41300 dice_loss 0.67339 +Epoch [3665/4000] Validation [6/10] Loss: 1.34704 focal_loss 0.63611 dice_loss 0.71093 +Epoch [3665/4000] Validation [7/10] Loss: 1.19560 focal_loss 0.53910 dice_loss 0.65650 +Epoch [3665/4000] Validation [8/10] Loss: 2.22509 focal_loss 1.62452 dice_loss 0.60056 +Epoch [3665/4000] Validation [9/10] Loss: 1.62607 focal_loss 1.07834 dice_loss 0.54773 +Epoch [3665/4000] Validation [10/10] Loss: 1.93074 focal_loss 1.19291 dice_loss 0.73783 +Epoch [3665/4000] Validation metric {'Val/mean dice_metric': 0.9504852294921875, 'Val/mean miou_metric': 0.9343141317367554, 'Val/mean f1': 0.94735187292099, 'Val/mean precision': 0.9408828020095825, 'Val/mean recall': 0.9539106488227844, 'Val/mean hd95_metric': 10.622064590454102} +Cheakpoint... +Epoch [3665/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9504852294921875, 'Val/mean miou_metric': 0.9343141317367554, 'Val/mean f1': 0.94735187292099, 'Val/mean precision': 0.9408828020095825, 'Val/mean recall': 0.9539106488227844, 'Val/mean hd95_metric': 10.622064590454102} +Epoch [3666/4000] Training [1/39] Loss: 0.00513 +Epoch [3666/4000] Training [2/39] Loss: 0.00537 +Epoch [3666/4000] Training [3/39] Loss: 0.00524 +Epoch [3666/4000] Training [4/39] Loss: 0.00526 +Epoch [3666/4000] Training [5/39] Loss: 0.00619 +Epoch [3666/4000] Training [6/39] Loss: 0.00879 +Epoch [3666/4000] Training [7/39] Loss: 0.00570 +Epoch [3666/4000] Training [8/39] Loss: 0.00842 +Epoch [3666/4000] Training [9/39] Loss: 0.00321 +Epoch [3666/4000] Training [10/39] Loss: 0.00255 +Epoch [3666/4000] Training [11/39] Loss: 0.00309 +Epoch [3666/4000] Training [12/39] Loss: 0.00510 +Epoch [3666/4000] Training [13/39] Loss: 0.12788 +Epoch [3666/4000] Training [14/39] Loss: 0.12788 +Epoch [3666/4000] Training [15/39] Loss: 0.00499 +Epoch [3666/4000] Training [16/39] Loss: 0.00298 +Epoch [3666/4000] Training [17/39] Loss: 0.00601 +Epoch [3666/4000] Training [18/39] Loss: 0.00473 +Epoch [3666/4000] Training [19/39] Loss: 0.00671 +Epoch [3666/4000] Training [20/39] Loss: 0.13373 +Epoch [3666/4000] Training [21/39] Loss: 0.12908 +Epoch [3666/4000] Training [22/39] Loss: 0.00570 +Epoch [3666/4000] Training [23/39] Loss: 0.00465 +Epoch [3666/4000] Training [24/39] Loss: 0.00475 +Epoch [3666/4000] Training [25/39] Loss: 0.12869 +Epoch [3666/4000] Training [26/39] Loss: 0.00406 +Epoch [3666/4000] Training [27/39] Loss: 0.00514 +Epoch [3666/4000] Training [28/39] Loss: 0.00359 +Epoch [3666/4000] Training [29/39] Loss: 0.00518 +Epoch [3666/4000] Training [30/39] Loss: 0.13164 +Epoch [3666/4000] Training [31/39] Loss: 0.12767 +Epoch [3666/4000] Training [32/39] Loss: 0.00435 +Epoch [3666/4000] Training [33/39] Loss: 0.00308 +Epoch [3666/4000] Training [34/39] Loss: 0.00403 +Epoch [3666/4000] Training [35/39] Loss: 0.12823 +Epoch [3666/4000] Training [36/39] Loss: 0.25395 +Epoch [3666/4000] Training [37/39] Loss: 0.12926 +Epoch [3666/4000] Training [38/39] Loss: 0.00914 +Epoch [3666/4000] Training [39/39] Loss: 0.00469 +Epoch [3666/4000] Training metric {'Train/mean dice_metric': 0.9961299300193787, 'Train/mean miou_metric': 0.9927109479904175, 'Train/mean f1': 0.9968040585517883, 'Train/mean precision': 0.996338963508606, 'Train/mean recall': 0.9972696304321289, 'Train/mean hd95_metric': 0.9496416449546814} +Epoch [3666/4000] Validation [1/10] Loss: 0.71984 focal_loss 0.63207 dice_loss 0.08778 +Epoch [3666/4000] Validation [2/10] Loss: 0.48066 focal_loss 0.38604 dice_loss 0.09462 +Epoch [3666/4000] Validation [3/10] Loss: 0.38259 focal_loss 0.27170 dice_loss 0.11089 +Epoch [3666/4000] Validation [4/10] Loss: 0.91037 focal_loss 0.34291 dice_loss 0.56746 +Epoch [3666/4000] Validation [5/10] Loss: 3.05535 focal_loss 2.38208 dice_loss 0.67326 +Epoch [3666/4000] Validation [6/10] Loss: 1.34889 focal_loss 0.63723 dice_loss 0.71166 +Epoch [3666/4000] Validation [7/10] Loss: 1.19803 focal_loss 0.54199 dice_loss 0.65605 +Epoch [3666/4000] Validation [8/10] Loss: 2.26189 focal_loss 1.65618 dice_loss 0.60571 +Epoch [3666/4000] Validation [9/10] Loss: 1.63081 focal_loss 1.08305 dice_loss 0.54775 +Epoch [3666/4000] Validation [10/10] Loss: 1.93805 focal_loss 1.19967 dice_loss 0.73838 +Epoch [3666/4000] Validation metric {'Val/mean dice_metric': 0.9512600898742676, 'Val/mean miou_metric': 0.9351611137390137, 'Val/mean f1': 0.9475683569908142, 'Val/mean precision': 0.9410492777824402, 'Val/mean recall': 0.9541783332824707, 'Val/mean hd95_metric': 10.600783348083496} +Cheakpoint... +Epoch [3666/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512600898742676, 'Val/mean miou_metric': 0.9351611137390137, 'Val/mean f1': 0.9475683569908142, 'Val/mean precision': 0.9410492777824402, 'Val/mean recall': 0.9541783332824707, 'Val/mean hd95_metric': 10.600783348083496} +Epoch [3667/4000] Training [1/39] Loss: 0.00569 +Epoch [3667/4000] Training [2/39] Loss: 0.00652 +Epoch [3667/4000] Training [3/39] Loss: 0.00586 +Epoch [3667/4000] Training [4/39] Loss: 0.00451 +Epoch [3667/4000] Training [5/39] Loss: 0.00669 +Epoch [3667/4000] Training [6/39] Loss: 0.00744 +Epoch [3667/4000] Training [7/39] Loss: 0.00802 +Epoch [3667/4000] Training [8/39] Loss: 0.00564 +Epoch [3667/4000] Training [9/39] Loss: 0.00546 +Epoch [3667/4000] Training [10/39] Loss: 0.00308 +Epoch [3667/4000] Training [11/39] Loss: 0.12868 +Epoch [3667/4000] Training [12/39] Loss: 0.00493 +Epoch [3667/4000] Training [13/39] Loss: 0.12882 +Epoch [3667/4000] Training [14/39] Loss: 0.00558 +Epoch [3667/4000] Training [15/39] Loss: 0.00666 +Epoch [3667/4000] Training [16/39] Loss: 0.00520 +Epoch [3667/4000] Training [17/39] Loss: 0.00661 +Epoch [3667/4000] Training [18/39] Loss: 0.00604 +Epoch [3667/4000] Training [19/39] Loss: 0.00390 +Epoch [3667/4000] Training [20/39] Loss: 0.00638 +Epoch [3667/4000] Training [21/39] Loss: 0.00497 +Epoch [3667/4000] Training [22/39] Loss: 0.13093 +Epoch [3667/4000] Training [23/39] Loss: 0.00463 +Epoch [3667/4000] Training [24/39] Loss: 0.00329 +Epoch [3667/4000] Training [25/39] Loss: 0.00895 +Epoch [3667/4000] Training [26/39] Loss: 0.00507 +Epoch [3667/4000] Training [27/39] Loss: 0.00343 +Epoch [3667/4000] Training [28/39] Loss: 0.13099 +Epoch [3667/4000] Training [29/39] Loss: 0.00451 +Epoch [3667/4000] Training [30/39] Loss: 0.00392 +Epoch [3667/4000] Training [31/39] Loss: 0.13345 +Epoch [3667/4000] Training [32/39] Loss: 0.00617 +Epoch [3667/4000] Training [33/39] Loss: 0.00742 +Epoch [3667/4000] Training [34/39] Loss: 0.00478 +Epoch [3667/4000] Training [35/39] Loss: 0.00574 +Epoch [3667/4000] Training [36/39] Loss: 0.25388 +Epoch [3667/4000] Training [37/39] Loss: 0.12932 +Epoch [3667/4000] Training [38/39] Loss: 0.00348 +Epoch [3667/4000] Training [39/39] Loss: 0.00605 +Epoch [3667/4000] Training metric {'Train/mean dice_metric': 0.995768129825592, 'Train/mean miou_metric': 0.9919794797897339, 'Train/mean f1': 0.9964350461959839, 'Train/mean precision': 0.995943546295166, 'Train/mean recall': 0.9969269633293152, 'Train/mean hd95_metric': 1.0289514064788818} +Epoch [3667/4000] Validation [1/10] Loss: 0.72699 focal_loss 0.63833 dice_loss 0.08867 +Epoch [3667/4000] Validation [2/10] Loss: 0.47250 focal_loss 0.38002 dice_loss 0.09248 +Epoch [3667/4000] Validation [3/10] Loss: 0.37464 focal_loss 0.26458 dice_loss 0.11005 +Epoch [3667/4000] Validation [4/10] Loss: 0.91234 focal_loss 0.34427 dice_loss 0.56807 +Epoch [3667/4000] Validation [5/10] Loss: 3.02745 focal_loss 2.35435 dice_loss 0.67310 +Epoch [3667/4000] Validation [6/10] Loss: 1.34394 focal_loss 0.63200 dice_loss 0.71194 +Epoch [3667/4000] Validation [7/10] Loss: 1.20794 focal_loss 0.55009 dice_loss 0.65785 +Epoch [3667/4000] Validation [8/10] Loss: 2.18053 focal_loss 1.58316 dice_loss 0.59737 +Epoch [3667/4000] Validation [9/10] Loss: 1.66577 focal_loss 1.11895 dice_loss 0.54682 +Epoch [3667/4000] Validation [10/10] Loss: 1.95691 focal_loss 1.21644 dice_loss 0.74047 +Epoch [3667/4000] Validation metric {'Val/mean dice_metric': 0.9508513808250427, 'Val/mean miou_metric': 0.9343573451042175, 'Val/mean f1': 0.9472417831420898, 'Val/mean precision': 0.9397080540657043, 'Val/mean recall': 0.9548972249031067, 'Val/mean hd95_metric': 10.795661926269531} +Cheakpoint... +Epoch [3667/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508513808250427, 'Val/mean miou_metric': 0.9343573451042175, 'Val/mean f1': 0.9472417831420898, 'Val/mean precision': 0.9397080540657043, 'Val/mean recall': 0.9548972249031067, 'Val/mean hd95_metric': 10.795661926269531} +Epoch [3668/4000] Training [1/39] Loss: 0.00575 +Epoch [3668/4000] Training [2/39] Loss: 0.00637 +Epoch [3668/4000] Training [3/39] Loss: 0.00434 +Epoch [3668/4000] Training [4/39] Loss: 0.00342 +Epoch [3668/4000] Training [5/39] Loss: 0.00341 +Epoch [3668/4000] Training [6/39] Loss: 0.00608 +Epoch [3668/4000] Training [7/39] Loss: 0.12785 +Epoch [3668/4000] Training [8/39] Loss: 0.12884 +Epoch [3668/4000] Training [9/39] Loss: 0.13186 +Epoch [3668/4000] Training [10/39] Loss: 0.00397 +Epoch [3668/4000] Training [11/39] Loss: 0.00637 +Epoch [3668/4000] Training [12/39] Loss: 0.00503 +Epoch [3668/4000] Training [13/39] Loss: 0.00520 +Epoch [3668/4000] Training [14/39] Loss: 0.00536 +Epoch [3668/4000] Training [15/39] Loss: 0.12844 +Epoch [3668/4000] Training [16/39] Loss: 0.00540 +Epoch [3668/4000] Training [17/39] Loss: 0.12790 +Epoch [3668/4000] Training [18/39] Loss: 0.00421 +Epoch [3668/4000] Training [19/39] Loss: 0.00727 +Epoch [3668/4000] Training [20/39] Loss: 0.00568 +Epoch [3668/4000] Training [21/39] Loss: 0.00432 +Epoch [3668/4000] Training [22/39] Loss: 0.00293 +Epoch [3668/4000] Training [23/39] Loss: 0.04063 +Epoch [3668/4000] Training [24/39] Loss: 0.12850 +Epoch [3668/4000] Training [25/39] Loss: 0.00501 +Epoch [3668/4000] Training [26/39] Loss: 0.00344 +Epoch [3668/4000] Training [27/39] Loss: 0.12985 +Epoch [3668/4000] Training [28/39] Loss: 0.00373 +Epoch [3668/4000] Training [29/39] Loss: 0.12824 +Epoch [3668/4000] Training [30/39] Loss: 0.00601 +Epoch [3668/4000] Training [31/39] Loss: 0.12989 +Epoch [3668/4000] Training [32/39] Loss: 0.00407 +Epoch [3668/4000] Training [33/39] Loss: 0.00590 +Epoch [3668/4000] Training [34/39] Loss: 0.00412 +Epoch [3668/4000] Training [35/39] Loss: 0.12924 +Epoch [3668/4000] Training [36/39] Loss: 0.00675 +Epoch [3668/4000] Training [37/39] Loss: 0.00558 +Epoch [3668/4000] Training [38/39] Loss: 0.00498 +Epoch [3668/4000] Training [39/39] Loss: 0.00309 +Epoch [3668/4000] Training metric {'Train/mean dice_metric': 0.9963597655296326, 'Train/mean miou_metric': 0.993171751499176, 'Train/mean f1': 0.996988832950592, 'Train/mean precision': 0.996555745601654, 'Train/mean recall': 0.9974223971366882, 'Train/mean hd95_metric': 0.9421305656433105} +Epoch [3668/4000] Validation [1/10] Loss: 0.69081 focal_loss 0.60575 dice_loss 0.08506 +Epoch [3668/4000] Validation [2/10] Loss: 0.47276 focal_loss 0.37773 dice_loss 0.09504 +Epoch [3668/4000] Validation [3/10] Loss: 0.37706 focal_loss 0.26634 dice_loss 0.11072 +Epoch [3668/4000] Validation [4/10] Loss: 0.90407 focal_loss 0.33729 dice_loss 0.56678 +Epoch [3668/4000] Validation [5/10] Loss: 2.99688 focal_loss 2.32322 dice_loss 0.67366 +Epoch [3668/4000] Validation [6/10] Loss: 1.33437 focal_loss 0.62370 dice_loss 0.71067 +Epoch [3668/4000] Validation [7/10] Loss: 1.18392 focal_loss 0.53028 dice_loss 0.65364 +Epoch [3668/4000] Validation [8/10] Loss: 2.28916 focal_loss 1.67798 dice_loss 0.61118 +Epoch [3668/4000] Validation [9/10] Loss: 1.65526 focal_loss 1.10791 dice_loss 0.54734 +Epoch [3668/4000] Validation [10/10] Loss: 1.92294 focal_loss 1.18360 dice_loss 0.73935 +Epoch [3668/4000] Validation metric {'Val/mean dice_metric': 0.9513442516326904, 'Val/mean miou_metric': 0.935398519039154, 'Val/mean f1': 0.9475284814834595, 'Val/mean precision': 0.941390335559845, 'Val/mean recall': 0.9537472724914551, 'Val/mean hd95_metric': 10.767353057861328} +Cheakpoint... +Epoch [3668/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513442516326904, 'Val/mean miou_metric': 0.935398519039154, 'Val/mean f1': 0.9475284814834595, 'Val/mean precision': 0.941390335559845, 'Val/mean recall': 0.9537472724914551, 'Val/mean hd95_metric': 10.767353057861328} +Epoch [3669/4000] Training [1/39] Loss: 0.00519 +Epoch [3669/4000] Training [2/39] Loss: 0.00643 +Epoch [3669/4000] Training [3/39] Loss: 0.00440 +Epoch [3669/4000] Training [4/39] Loss: 0.00418 +Epoch [3669/4000] Training [5/39] Loss: 0.00353 +Epoch [3669/4000] Training [6/39] Loss: 0.00307 +Epoch [3669/4000] Training [7/39] Loss: 0.00473 +Epoch [3669/4000] Training [8/39] Loss: 0.00453 +Epoch [3669/4000] Training [9/39] Loss: 0.00504 +Epoch [3669/4000] Training [10/39] Loss: 0.12937 +Epoch [3669/4000] Training [11/39] Loss: 0.00374 +Epoch [3669/4000] Training [12/39] Loss: 0.00516 +Epoch [3669/4000] Training [13/39] Loss: 0.00665 +Epoch [3669/4000] Training [14/39] Loss: 0.00750 +Epoch [3669/4000] Training [15/39] Loss: 0.00711 +Epoch [3669/4000] Training [16/39] Loss: 0.00363 +Epoch [3669/4000] Training [17/39] Loss: 0.25336 +Epoch [3669/4000] Training [18/39] Loss: 0.03508 +Epoch [3669/4000] Training [19/39] Loss: 0.00519 +Epoch [3669/4000] Training [20/39] Loss: 0.00314 +Epoch [3669/4000] Training [21/39] Loss: 0.00505 +Epoch [3669/4000] Training [22/39] Loss: 0.25426 +Epoch [3669/4000] Training [23/39] Loss: 0.00530 +Epoch [3669/4000] Training [24/39] Loss: 0.12749 +Epoch [3669/4000] Training [25/39] Loss: 0.13593 +Epoch [3669/4000] Training [26/39] Loss: 0.00573 +Epoch [3669/4000] Training [27/39] Loss: 0.12939 +Epoch [3669/4000] Training [28/39] Loss: 0.12873 +Epoch [3669/4000] Training [29/39] Loss: 0.00661 +Epoch [3669/4000] Training [30/39] Loss: 0.00363 +Epoch [3669/4000] Training [31/39] Loss: 0.25427 +Epoch [3669/4000] Training [32/39] Loss: 0.13065 +Epoch [3669/4000] Training [33/39] Loss: 0.00684 +Epoch [3669/4000] Training [34/39] Loss: 0.00402 +Epoch [3669/4000] Training [35/39] Loss: 0.00407 +Epoch [3669/4000] Training [36/39] Loss: 0.00732 +Epoch [3669/4000] Training [37/39] Loss: 0.00287 +Epoch [3669/4000] Training [38/39] Loss: 0.00666 +Epoch [3669/4000] Training [39/39] Loss: 0.00288 +Epoch [3669/4000] Training metric {'Train/mean dice_metric': 0.9962772727012634, 'Train/mean miou_metric': 0.9930000305175781, 'Train/mean f1': 0.9968118071556091, 'Train/mean precision': 0.9963579773902893, 'Train/mean recall': 0.9972660541534424, 'Train/mean hd95_metric': 0.9443716406822205} +Epoch [3669/4000] Validation [1/10] Loss: 0.68394 focal_loss 0.59917 dice_loss 0.08478 +Epoch [3669/4000] Validation [2/10] Loss: 0.47422 focal_loss 0.37929 dice_loss 0.09492 +Epoch [3669/4000] Validation [3/10] Loss: 0.37595 focal_loss 0.26541 dice_loss 0.11055 +Epoch [3669/4000] Validation [4/10] Loss: 0.90138 focal_loss 0.33496 dice_loss 0.56642 +Epoch [3669/4000] Validation [5/10] Loss: 2.97979 focal_loss 2.30633 dice_loss 0.67346 +Epoch [3669/4000] Validation [6/10] Loss: 1.33815 focal_loss 0.62681 dice_loss 0.71133 +Epoch [3669/4000] Validation [7/10] Loss: 1.19376 focal_loss 0.54016 dice_loss 0.65360 +Epoch [3669/4000] Validation [8/10] Loss: 2.22256 focal_loss 1.61796 dice_loss 0.60460 +Epoch [3669/4000] Validation [9/10] Loss: 1.65536 focal_loss 1.10765 dice_loss 0.54771 +Epoch [3669/4000] Validation [10/10] Loss: 1.92372 focal_loss 1.18419 dice_loss 0.73953 +Epoch [3669/4000] Validation metric {'Val/mean dice_metric': 0.9513526558876038, 'Val/mean miou_metric': 0.9353673458099365, 'Val/mean f1': 0.9476193189620972, 'Val/mean precision': 0.9410276412963867, 'Val/mean recall': 0.954304039478302, 'Val/mean hd95_metric': 10.849382400512695} +Cheakpoint... +Epoch [3669/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513526558876038, 'Val/mean miou_metric': 0.9353673458099365, 'Val/mean f1': 0.9476193189620972, 'Val/mean precision': 0.9410276412963867, 'Val/mean recall': 0.954304039478302, 'Val/mean hd95_metric': 10.849382400512695} +Epoch [3670/4000] Training [1/39] Loss: 0.00511 +Epoch [3670/4000] Training [2/39] Loss: 0.12845 +Epoch [3670/4000] Training [3/39] Loss: 0.00428 +Epoch [3670/4000] Training [4/39] Loss: 0.13064 +Epoch [3670/4000] Training [5/39] Loss: 0.00523 +Epoch [3670/4000] Training [6/39] Loss: 0.00388 +Epoch [3670/4000] Training [7/39] Loss: 0.00356 +Epoch [3670/4000] Training [8/39] Loss: 0.00479 +Epoch [3670/4000] Training [9/39] Loss: 0.00295 +Epoch [3670/4000] Training [10/39] Loss: 0.00418 +Epoch [3670/4000] Training [11/39] Loss: 0.08680 +Epoch [3670/4000] Training [12/39] Loss: 0.00429 +Epoch [3670/4000] Training [13/39] Loss: 0.13033 +Epoch [3670/4000] Training [14/39] Loss: 0.00458 +Epoch [3670/4000] Training [15/39] Loss: 0.12835 +Epoch [3670/4000] Training [16/39] Loss: 0.00476 +Epoch [3670/4000] Training [17/39] Loss: 0.00457 +Epoch [3670/4000] Training [18/39] Loss: 0.00354 +Epoch [3670/4000] Training [19/39] Loss: 0.00478 +Epoch [3670/4000] Training [20/39] Loss: 0.00445 +Epoch [3670/4000] Training [21/39] Loss: 0.00412 +Epoch [3670/4000] Training [22/39] Loss: 0.00563 +Epoch [3670/4000] Training [23/39] Loss: 0.00440 +Epoch [3670/4000] Training [24/39] Loss: 0.00615 +Epoch [3670/4000] Training [25/39] Loss: 0.00462 +Epoch [3670/4000] Training [26/39] Loss: 0.00397 +Epoch [3670/4000] Training [27/39] Loss: 0.00886 +Epoch [3670/4000] Training [28/39] Loss: 0.00397 +Epoch [3670/4000] Training [29/39] Loss: 0.00338 +Epoch [3670/4000] Training [30/39] Loss: 0.25499 +Epoch [3670/4000] Training [31/39] Loss: 0.00573 +Epoch [3670/4000] Training [32/39] Loss: 0.00443 +Epoch [3670/4000] Training [33/39] Loss: 0.12827 +Epoch [3670/4000] Training [34/39] Loss: 0.00761 +Epoch [3670/4000] Training [35/39] Loss: 0.00398 +Epoch [3670/4000] Training [36/39] Loss: 0.00421 +Epoch [3670/4000] Training [37/39] Loss: 0.00394 +Epoch [3670/4000] Training [38/39] Loss: 0.00375 +Epoch [3670/4000] Training [39/39] Loss: 0.00690 +Epoch [3670/4000] Training metric {'Train/mean dice_metric': 0.9963377118110657, 'Train/mean miou_metric': 0.9931247234344482, 'Train/mean f1': 0.9968915581703186, 'Train/mean precision': 0.9964418411254883, 'Train/mean recall': 0.9973415732383728, 'Train/mean hd95_metric': 0.9408851265907288} +Epoch [3670/4000] Validation [1/10] Loss: 0.71969 focal_loss 0.63211 dice_loss 0.08758 +Epoch [3670/4000] Validation [2/10] Loss: 0.48059 focal_loss 0.38818 dice_loss 0.09241 +Epoch [3670/4000] Validation [3/10] Loss: 0.37232 focal_loss 0.26264 dice_loss 0.10969 +Epoch [3670/4000] Validation [4/10] Loss: 0.91640 focal_loss 0.34797 dice_loss 0.56843 +Epoch [3670/4000] Validation [5/10] Loss: 3.03245 focal_loss 2.35921 dice_loss 0.67324 +Epoch [3670/4000] Validation [6/10] Loss: 1.37048 focal_loss 0.65765 dice_loss 0.71283 +Epoch [3670/4000] Validation [7/10] Loss: 1.21331 focal_loss 0.55709 dice_loss 0.65622 +Epoch [3670/4000] Validation [8/10] Loss: 2.18029 focal_loss 1.58463 dice_loss 0.59566 +Epoch [3670/4000] Validation [9/10] Loss: 1.71370 focal_loss 1.16696 dice_loss 0.54674 +Epoch [3670/4000] Validation [10/10] Loss: 1.98987 focal_loss 1.24753 dice_loss 0.74234 +Epoch [3670/4000] Validation metric {'Val/mean dice_metric': 0.9511999487876892, 'Val/mean miou_metric': 0.9352236986160278, 'Val/mean f1': 0.9469952583312988, 'Val/mean precision': 0.9390398859977722, 'Val/mean recall': 0.9550867080688477, 'Val/mean hd95_metric': 10.759171485900879} +Cheakpoint... +Epoch [3670/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511999487876892, 'Val/mean miou_metric': 0.9352236986160278, 'Val/mean f1': 0.9469952583312988, 'Val/mean precision': 0.9390398859977722, 'Val/mean recall': 0.9550867080688477, 'Val/mean hd95_metric': 10.759171485900879} +Epoch [3671/4000] Training [1/39] Loss: 0.00588 +Epoch [3671/4000] Training [2/39] Loss: 0.00541 +Epoch [3671/4000] Training [3/39] Loss: 0.00365 +Epoch [3671/4000] Training [4/39] Loss: 0.00466 +Epoch [3671/4000] Training [5/39] Loss: 0.00499 +Epoch [3671/4000] Training [6/39] Loss: 0.00486 +Epoch [3671/4000] Training [7/39] Loss: 0.00721 +Epoch [3671/4000] Training [8/39] Loss: 0.00391 +Epoch [3671/4000] Training [9/39] Loss: 0.00359 +Epoch [3671/4000] Training [10/39] Loss: 0.13083 +Epoch [3671/4000] Training [11/39] Loss: 0.12916 +Epoch [3671/4000] Training [12/39] Loss: 0.25220 +Epoch [3671/4000] Training [13/39] Loss: 0.00424 +Epoch [3671/4000] Training [14/39] Loss: 0.00310 +Epoch [3671/4000] Training [15/39] Loss: 0.00836 +Epoch [3671/4000] Training [16/39] Loss: 0.00332 +Epoch [3671/4000] Training [17/39] Loss: 0.00431 +Epoch [3671/4000] Training [18/39] Loss: 0.00641 +Epoch [3671/4000] Training [19/39] Loss: 0.12833 +Epoch [3671/4000] Training [20/39] Loss: 0.00617 +Epoch [3671/4000] Training [21/39] Loss: 0.00298 +Epoch [3671/4000] Training [22/39] Loss: 0.00493 +Epoch [3671/4000] Training [23/39] Loss: 0.13296 +Epoch [3671/4000] Training [24/39] Loss: 0.25608 +Epoch [3671/4000] Training [25/39] Loss: 0.00577 +Epoch [3671/4000] Training [26/39] Loss: 0.12887 +Epoch [3671/4000] Training [27/39] Loss: 0.00514 +Epoch [3671/4000] Training [28/39] Loss: 0.00483 +Epoch [3671/4000] Training [29/39] Loss: 0.00678 +Epoch [3671/4000] Training [30/39] Loss: 0.00426 +Epoch [3671/4000] Training [31/39] Loss: 0.00571 +Epoch [3671/4000] Training [32/39] Loss: 0.13039 +Epoch [3671/4000] Training [33/39] Loss: 0.00215 +Epoch [3671/4000] Training [34/39] Loss: 0.00336 +Epoch [3671/4000] Training [35/39] Loss: 0.00333 +Epoch [3671/4000] Training [36/39] Loss: 0.00437 +Epoch [3671/4000] Training [37/39] Loss: 0.00300 +Epoch [3671/4000] Training [38/39] Loss: 0.00457 +Epoch [3671/4000] Training [39/39] Loss: 0.00505 +Epoch [3671/4000] Training metric {'Train/mean dice_metric': 0.995449423789978, 'Train/mean miou_metric': 0.9921972155570984, 'Train/mean f1': 0.9968084692955017, 'Train/mean precision': 0.9963325262069702, 'Train/mean recall': 0.9972848296165466, 'Train/mean hd95_metric': 1.008880376815796} +Epoch [3671/4000] Validation [1/10] Loss: 0.72383 focal_loss 0.63608 dice_loss 0.08776 +Epoch [3671/4000] Validation [2/10] Loss: 0.47831 focal_loss 0.38424 dice_loss 0.09406 +Epoch [3671/4000] Validation [3/10] Loss: 0.37898 focal_loss 0.26860 dice_loss 0.11039 +Epoch [3671/4000] Validation [4/10] Loss: 0.90700 focal_loss 0.33951 dice_loss 0.56750 +Epoch [3671/4000] Validation [5/10] Loss: 3.06831 focal_loss 2.39486 dice_loss 0.67346 +Epoch [3671/4000] Validation [6/10] Loss: 1.35950 focal_loss 0.64766 dice_loss 0.71184 +Epoch [3671/4000] Validation [7/10] Loss: 1.20114 focal_loss 0.54457 dice_loss 0.65656 +Epoch [3671/4000] Validation [8/10] Loss: 2.20714 focal_loss 1.60868 dice_loss 0.59846 +Epoch [3671/4000] Validation [9/10] Loss: 1.68454 focal_loss 1.13844 dice_loss 0.54610 +Epoch [3671/4000] Validation [10/10] Loss: 1.96747 focal_loss 1.22570 dice_loss 0.74177 +Epoch [3671/4000] Validation metric {'Val/mean dice_metric': 0.9505078196525574, 'Val/mean miou_metric': 0.9344956874847412, 'Val/mean f1': 0.9469407200813293, 'Val/mean precision': 0.9394661784172058, 'Val/mean recall': 0.9545351266860962, 'Val/mean hd95_metric': 10.785932540893555} +Cheakpoint... +Epoch [3671/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505078196525574, 'Val/mean miou_metric': 0.9344956874847412, 'Val/mean f1': 0.9469407200813293, 'Val/mean precision': 0.9394661784172058, 'Val/mean recall': 0.9545351266860962, 'Val/mean hd95_metric': 10.785932540893555} +Epoch [3672/4000] Training [1/39] Loss: 0.00413 +Epoch [3672/4000] Training [2/39] Loss: 0.25360 +Epoch [3672/4000] Training [3/39] Loss: 0.00467 +Epoch [3672/4000] Training [4/39] Loss: 0.00383 +Epoch [3672/4000] Training [5/39] Loss: 0.00468 +Epoch [3672/4000] Training [6/39] Loss: 0.00245 +Epoch [3672/4000] Training [7/39] Loss: 0.00530 +Epoch [3672/4000] Training [8/39] Loss: 0.00540 +Epoch [3672/4000] Training [9/39] Loss: 0.00577 +Epoch [3672/4000] Training [10/39] Loss: 0.00436 +Epoch [3672/4000] Training [11/39] Loss: 0.00474 +Epoch [3672/4000] Training [12/39] Loss: 0.13377 +Epoch [3672/4000] Training [13/39] Loss: 0.00521 +Epoch [3672/4000] Training [14/39] Loss: 0.00385 +Epoch [3672/4000] Training [15/39] Loss: 0.00379 +Epoch [3672/4000] Training [16/39] Loss: 0.00405 +Epoch [3672/4000] Training [17/39] Loss: 0.12933 +Epoch [3672/4000] Training [18/39] Loss: 0.00410 +Epoch [3672/4000] Training [19/39] Loss: 0.00396 +Epoch [3672/4000] Training [20/39] Loss: 0.00686 +Epoch [3672/4000] Training [21/39] Loss: 0.00619 +Epoch [3672/4000] Training [22/39] Loss: 0.00500 +Epoch [3672/4000] Training [23/39] Loss: 0.00372 +Epoch [3672/4000] Training [24/39] Loss: 0.12866 +Epoch [3672/4000] Training [25/39] Loss: 0.00478 +Epoch [3672/4000] Training [26/39] Loss: 0.00423 +Epoch [3672/4000] Training [27/39] Loss: 0.00488 +Epoch [3672/4000] Training [28/39] Loss: 0.00466 +Epoch [3672/4000] Training [29/39] Loss: 0.00406 +Epoch [3672/4000] Training [30/39] Loss: 0.00399 +Epoch [3672/4000] Training [31/39] Loss: 0.00579 +Epoch [3672/4000] Training [32/39] Loss: 0.12994 +Epoch [3672/4000] Training [33/39] Loss: 0.00651 +Epoch [3672/4000] Training [34/39] Loss: 0.00270 +Epoch [3672/4000] Training [35/39] Loss: 0.12947 +Epoch [3672/4000] Training [36/39] Loss: 0.00552 +Epoch [3672/4000] Training [37/39] Loss: 0.00371 +Epoch [3672/4000] Training [38/39] Loss: 0.12806 +Epoch [3672/4000] Training [39/39] Loss: 0.00390 +Epoch [3672/4000] Training metric {'Train/mean dice_metric': 0.9964188933372498, 'Train/mean miou_metric': 0.9932816624641418, 'Train/mean f1': 0.9969415068626404, 'Train/mean precision': 0.9964948296546936, 'Train/mean recall': 0.9973885416984558, 'Train/mean hd95_metric': 0.9480539560317993} +Epoch [3672/4000] Validation [1/10] Loss: 0.70631 focal_loss 0.62082 dice_loss 0.08548 +Epoch [3672/4000] Validation [2/10] Loss: 0.48618 focal_loss 0.38890 dice_loss 0.09728 +Epoch [3672/4000] Validation [3/10] Loss: 0.38927 focal_loss 0.27813 dice_loss 0.11114 +Epoch [3672/4000] Validation [4/10] Loss: 0.90089 focal_loss 0.33391 dice_loss 0.56698 +Epoch [3672/4000] Validation [5/10] Loss: 3.06574 focal_loss 2.39211 dice_loss 0.67363 +Epoch [3672/4000] Validation [6/10] Loss: 1.34528 focal_loss 0.63421 dice_loss 0.71107 +Epoch [3672/4000] Validation [7/10] Loss: 1.19416 focal_loss 0.54015 dice_loss 0.65401 +Epoch [3672/4000] Validation [8/10] Loss: 2.27903 focal_loss 1.67090 dice_loss 0.60813 +Epoch [3672/4000] Validation [9/10] Loss: 1.63029 focal_loss 1.08432 dice_loss 0.54597 +Epoch [3672/4000] Validation [10/10] Loss: 1.93286 focal_loss 1.19288 dice_loss 0.73998 +Epoch [3672/4000] Validation metric {'Val/mean dice_metric': 0.9513890743255615, 'Val/mean miou_metric': 0.9354586601257324, 'Val/mean f1': 0.9474983811378479, 'Val/mean precision': 0.9416614174842834, 'Val/mean recall': 0.953408420085907, 'Val/mean hd95_metric': 10.798406600952148} +Cheakpoint... +Epoch [3672/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513890743255615, 'Val/mean miou_metric': 0.9354586601257324, 'Val/mean f1': 0.9474983811378479, 'Val/mean precision': 0.9416614174842834, 'Val/mean recall': 0.953408420085907, 'Val/mean hd95_metric': 10.798406600952148} +Epoch [3673/4000] Training [1/39] Loss: 0.00444 +Epoch [3673/4000] Training [2/39] Loss: 0.00391 +Epoch [3673/4000] Training [3/39] Loss: 0.12953 +Epoch [3673/4000] Training [4/39] Loss: 0.00619 +Epoch [3673/4000] Training [5/39] Loss: 0.13015 +Epoch [3673/4000] Training [6/39] Loss: 0.12922 +Epoch [3673/4000] Training [7/39] Loss: 0.00654 +Epoch [3673/4000] Training [8/39] Loss: 0.00728 +Epoch [3673/4000] Training [9/39] Loss: 0.12768 +Epoch [3673/4000] Training [10/39] Loss: 0.00576 +Epoch [3673/4000] Training [11/39] Loss: 0.00350 +Epoch [3673/4000] Training [12/39] Loss: 0.13249 +Epoch [3673/4000] Training [13/39] Loss: 0.00534 +Epoch [3673/4000] Training [14/39] Loss: 0.00518 +Epoch [3673/4000] Training [15/39] Loss: 0.00514 +Epoch [3673/4000] Training [16/39] Loss: 0.00821 +Epoch [3673/4000] Training [17/39] Loss: 0.00490 +Epoch [3673/4000] Training [18/39] Loss: 0.00358 +Epoch [3673/4000] Training [19/39] Loss: 0.00493 +Epoch [3673/4000] Training [20/39] Loss: 0.00540 +Epoch [3673/4000] Training [21/39] Loss: 0.12908 +Epoch [3673/4000] Training [22/39] Loss: 0.00496 +Epoch [3673/4000] Training [23/39] Loss: 0.00290 +Epoch [3673/4000] Training [24/39] Loss: 0.00472 +Epoch [3673/4000] Training [25/39] Loss: 0.00401 +Epoch [3673/4000] Training [26/39] Loss: 0.12942 +Epoch [3673/4000] Training [27/39] Loss: 0.00747 +Epoch [3673/4000] Training [28/39] Loss: 0.12892 +Epoch [3673/4000] Training [29/39] Loss: 0.00478 +Epoch [3673/4000] Training [30/39] Loss: 0.00640 +Epoch [3673/4000] Training [31/39] Loss: 0.00517 +Epoch [3673/4000] Training [32/39] Loss: 0.00396 +Epoch [3673/4000] Training [33/39] Loss: 0.00718 +Epoch [3673/4000] Training [34/39] Loss: 0.00327 +Epoch [3673/4000] Training [35/39] Loss: 0.16318 +Epoch [3673/4000] Training [36/39] Loss: 0.00431 +Epoch [3673/4000] Training [37/39] Loss: 0.00394 +Epoch [3673/4000] Training [38/39] Loss: 0.00427 +Epoch [3673/4000] Training [39/39] Loss: 0.25208 +Epoch [3673/4000] Training metric {'Train/mean dice_metric': 0.996165931224823, 'Train/mean miou_metric': 0.9927822351455688, 'Train/mean f1': 0.9965083003044128, 'Train/mean precision': 0.9958197474479675, 'Train/mean recall': 0.9971979856491089, 'Train/mean hd95_metric': 1.079270839691162} +Epoch [3673/4000] Validation [1/10] Loss: 0.69984 focal_loss 0.61559 dice_loss 0.08424 +Epoch [3673/4000] Validation [2/10] Loss: 0.48727 focal_loss 0.38763 dice_loss 0.09964 +Epoch [3673/4000] Validation [3/10] Loss: 0.40809 focal_loss 0.29474 dice_loss 0.11335 +Epoch [3673/4000] Validation [4/10] Loss: 0.88177 focal_loss 0.31737 dice_loss 0.56439 +Epoch [3673/4000] Validation [5/10] Loss: 3.07499 focal_loss 2.40061 dice_loss 0.67438 +Epoch [3673/4000] Validation [6/10] Loss: 1.28282 focal_loss 0.57532 dice_loss 0.70750 +Epoch [3673/4000] Validation [7/10] Loss: 1.15560 focal_loss 0.50456 dice_loss 0.65104 +Epoch [3673/4000] Validation [8/10] Loss: 2.62307 focal_loss 1.98228 dice_loss 0.64079 +Epoch [3673/4000] Validation [9/10] Loss: 1.48368 focal_loss 0.93966 dice_loss 0.54403 +Epoch [3673/4000] Validation [10/10] Loss: 1.82541 focal_loss 1.09261 dice_loss 0.73281 +Epoch [3673/4000] Validation metric {'Val/mean dice_metric': 0.9513598680496216, 'Val/mean miou_metric': 0.935327410697937, 'Val/mean f1': 0.9485722184181213, 'Val/mean precision': 0.9466756582260132, 'Val/mean recall': 0.9504764080047607, 'Val/mean hd95_metric': 10.584575653076172} +Cheakpoint... +Epoch [3673/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513598680496216, 'Val/mean miou_metric': 0.935327410697937, 'Val/mean f1': 0.9485722184181213, 'Val/mean precision': 0.9466756582260132, 'Val/mean recall': 0.9504764080047607, 'Val/mean hd95_metric': 10.584575653076172} +Epoch [3674/4000] Training [1/39] Loss: 0.13060 +Epoch [3674/4000] Training [2/39] Loss: 0.12861 +Epoch [3674/4000] Training [3/39] Loss: 0.00788 +Epoch [3674/4000] Training [4/39] Loss: 0.00484 +Epoch [3674/4000] Training [5/39] Loss: 0.00591 +Epoch [3674/4000] Training [6/39] Loss: 0.00279 +Epoch [3674/4000] Training [7/39] Loss: 0.00701 +Epoch [3674/4000] Training [8/39] Loss: 0.12945 +Epoch [3674/4000] Training [9/39] Loss: 0.00497 +Epoch [3674/4000] Training [10/39] Loss: 0.13036 +Epoch [3674/4000] Training [11/39] Loss: 0.00665 +Epoch [3674/4000] Training [12/39] Loss: 0.25664 +Epoch [3674/4000] Training [13/39] Loss: 0.12999 +Epoch [3674/4000] Training [14/39] Loss: 0.08624 +Epoch [3674/4000] Training [15/39] Loss: 0.00387 +Epoch [3674/4000] Training [16/39] Loss: 0.00463 +Epoch [3674/4000] Training [17/39] Loss: 0.00470 +Epoch [3674/4000] Training [18/39] Loss: 0.12846 +Epoch [3674/4000] Training [19/39] Loss: 0.00413 +Epoch [3674/4000] Training [20/39] Loss: 0.01041 +Epoch [3674/4000] Training [21/39] Loss: 0.12781 +Epoch [3674/4000] Training [22/39] Loss: 0.00492 +Epoch [3674/4000] Training [23/39] Loss: 0.13175 +Epoch [3674/4000] Training [24/39] Loss: 0.00435 +Epoch [3674/4000] Training [25/39] Loss: 0.00314 +Epoch [3674/4000] Training [26/39] Loss: 0.00258 +Epoch [3674/4000] Training [27/39] Loss: 0.12922 +Epoch [3674/4000] Training [28/39] Loss: 0.00408 +Epoch [3674/4000] Training [29/39] Loss: 0.00338 +Epoch [3674/4000] Training [30/39] Loss: 0.12857 +Epoch [3674/4000] Training [31/39] Loss: 0.00527 +Epoch [3674/4000] Training [32/39] Loss: 0.00470 +Epoch [3674/4000] Training [33/39] Loss: 0.00540 +Epoch [3674/4000] Training [34/39] Loss: 0.12929 +Epoch [3674/4000] Training [35/39] Loss: 0.00792 +Epoch [3674/4000] Training [36/39] Loss: 0.12837 +Epoch [3674/4000] Training [37/39] Loss: 0.00521 +Epoch [3674/4000] Training [38/39] Loss: 0.00635 +Epoch [3674/4000] Training [39/39] Loss: 0.00530 +Epoch [3674/4000] Training metric {'Train/mean dice_metric': 0.9961588978767395, 'Train/mean miou_metric': 0.9927711486816406, 'Train/mean f1': 0.996785581111908, 'Train/mean precision': 0.9963446259498596, 'Train/mean recall': 0.9972270727157593, 'Train/mean hd95_metric': 0.9684707522392273} +Epoch [3674/4000] Validation [1/10] Loss: 0.70670 focal_loss 0.62145 dice_loss 0.08525 +Epoch [3674/4000] Validation [2/10] Loss: 0.47886 focal_loss 0.38482 dice_loss 0.09404 +Epoch [3674/4000] Validation [3/10] Loss: 0.38879 focal_loss 0.27755 dice_loss 0.11124 +Epoch [3674/4000] Validation [4/10] Loss: 0.89067 focal_loss 0.32554 dice_loss 0.56512 +Epoch [3674/4000] Validation [5/10] Loss: 3.09026 focal_loss 2.41616 dice_loss 0.67410 +Epoch [3674/4000] Validation [6/10] Loss: 1.31416 focal_loss 0.60590 dice_loss 0.70825 +Epoch [3674/4000] Validation [7/10] Loss: 1.17669 focal_loss 0.52527 dice_loss 0.65142 +Epoch [3674/4000] Validation [8/10] Loss: 2.50351 focal_loss 1.87749 dice_loss 0.62602 +Epoch [3674/4000] Validation [9/10] Loss: 1.52836 focal_loss 0.98292 dice_loss 0.54544 +Epoch [3674/4000] Validation [10/10] Loss: 1.90472 focal_loss 1.16847 dice_loss 0.73625 +Epoch [3674/4000] Validation metric {'Val/mean dice_metric': 0.9514021873474121, 'Val/mean miou_metric': 0.9353504776954651, 'Val/mean f1': 0.9484364986419678, 'Val/mean precision': 0.9446345567703247, 'Val/mean recall': 0.9522691369056702, 'Val/mean hd95_metric': 10.592288970947266} +Cheakpoint... +Epoch [3674/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514021873474121, 'Val/mean miou_metric': 0.9353504776954651, 'Val/mean f1': 0.9484364986419678, 'Val/mean precision': 0.9446345567703247, 'Val/mean recall': 0.9522691369056702, 'Val/mean hd95_metric': 10.592288970947266} +Epoch [3675/4000] Training [1/39] Loss: 0.00445 +Epoch [3675/4000] Training [2/39] Loss: 0.00566 +Epoch [3675/4000] Training [3/39] Loss: 0.00301 +Epoch [3675/4000] Training [4/39] Loss: 0.00696 +Epoch [3675/4000] Training [5/39] Loss: 0.00651 +Epoch [3675/4000] Training [6/39] Loss: 0.00405 +Epoch [3675/4000] Training [7/39] Loss: 0.00418 +Epoch [3675/4000] Training [8/39] Loss: 0.00403 +Epoch [3675/4000] Training [9/39] Loss: 0.00546 +Epoch [3675/4000] Training [10/39] Loss: 0.00377 +Epoch [3675/4000] Training [11/39] Loss: 0.00474 +Epoch [3675/4000] Training [12/39] Loss: 0.00571 +Epoch [3675/4000] Training [13/39] Loss: 0.00399 +Epoch [3675/4000] Training [14/39] Loss: 0.13360 +Epoch [3675/4000] Training [15/39] Loss: 0.00914 +Epoch [3675/4000] Training [16/39] Loss: 0.00988 +Epoch [3675/4000] Training [17/39] Loss: 0.00442 +Epoch [3675/4000] Training [18/39] Loss: 0.12889 +Epoch [3675/4000] Training [19/39] Loss: 0.00413 +Epoch [3675/4000] Training [20/39] Loss: 0.00960 +Epoch [3675/4000] Training [21/39] Loss: 0.12968 +Epoch [3675/4000] Training [22/39] Loss: 0.00575 +Epoch [3675/4000] Training [23/39] Loss: 0.00471 +Epoch [3675/4000] Training [24/39] Loss: 0.12833 +Epoch [3675/4000] Training [25/39] Loss: 0.00562 +Epoch [3675/4000] Training [26/39] Loss: 0.12823 +Epoch [3675/4000] Training [27/39] Loss: 0.00857 +Epoch [3675/4000] Training [28/39] Loss: 0.00472 +Epoch [3675/4000] Training [29/39] Loss: 0.12986 +Epoch [3675/4000] Training [30/39] Loss: 0.13128 +Epoch [3675/4000] Training [31/39] Loss: 0.00621 +Epoch [3675/4000] Training [32/39] Loss: 0.12871 +Epoch [3675/4000] Training [33/39] Loss: 0.00607 +Epoch [3675/4000] Training [34/39] Loss: 0.13036 +Epoch [3675/4000] Training [35/39] Loss: 0.00349 +Epoch [3675/4000] Training [36/39] Loss: 0.12951 +Epoch [3675/4000] Training [37/39] Loss: 0.12945 +Epoch [3675/4000] Training [38/39] Loss: 0.12892 +Epoch [3675/4000] Training [39/39] Loss: 0.00215 +Epoch [3675/4000] Training metric {'Train/mean dice_metric': 0.9950461387634277, 'Train/mean miou_metric': 0.9913859963417053, 'Train/mean f1': 0.9964607357978821, 'Train/mean precision': 0.9959982633590698, 'Train/mean recall': 0.9969238042831421, 'Train/mean hd95_metric': 1.014176607131958} +Epoch [3675/4000] Validation [1/10] Loss: 0.70539 focal_loss 0.62043 dice_loss 0.08496 +Epoch [3675/4000] Validation [2/10] Loss: 0.49220 focal_loss 0.39343 dice_loss 0.09877 +Epoch [3675/4000] Validation [3/10] Loss: 0.39565 focal_loss 0.28373 dice_loss 0.11191 +Epoch [3675/4000] Validation [4/10] Loss: 0.88890 focal_loss 0.32519 dice_loss 0.56371 +Epoch [3675/4000] Validation [5/10] Loss: 3.05832 focal_loss 2.38438 dice_loss 0.67393 +Epoch [3675/4000] Validation [6/10] Loss: 1.30660 focal_loss 0.59767 dice_loss 0.70893 +Epoch [3675/4000] Validation [7/10] Loss: 1.17278 focal_loss 0.52199 dice_loss 0.65079 +Epoch [3675/4000] Validation [8/10] Loss: 2.45287 focal_loss 1.82611 dice_loss 0.62676 +Epoch [3675/4000] Validation [9/10] Loss: 1.50412 focal_loss 0.95858 dice_loss 0.54554 +Epoch [3675/4000] Validation [10/10] Loss: 1.86793 focal_loss 1.13354 dice_loss 0.73439 +Epoch [3675/4000] Validation metric {'Val/mean dice_metric': 0.9505276679992676, 'Val/mean miou_metric': 0.9342582821846008, 'Val/mean f1': 0.9483447074890137, 'Val/mean precision': 0.9450686573982239, 'Val/mean recall': 0.9516435265541077, 'Val/mean hd95_metric': 10.67020320892334} +Cheakpoint... +Epoch [3675/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9505], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505276679992676, 'Val/mean miou_metric': 0.9342582821846008, 'Val/mean f1': 0.9483447074890137, 'Val/mean precision': 0.9450686573982239, 'Val/mean recall': 0.9516435265541077, 'Val/mean hd95_metric': 10.67020320892334} +Epoch [3676/4000] Training [1/39] Loss: 0.00350 +Epoch [3676/4000] Training [2/39] Loss: 0.00423 +Epoch [3676/4000] Training [3/39] Loss: 0.00573 +Epoch [3676/4000] Training [4/39] Loss: 0.00623 +Epoch [3676/4000] Training [5/39] Loss: 0.00594 +Epoch [3676/4000] Training [6/39] Loss: 0.00493 +Epoch [3676/4000] Training [7/39] Loss: 0.00573 +Epoch [3676/4000] Training [8/39] Loss: 0.00387 +Epoch [3676/4000] Training [9/39] Loss: 0.00378 +Epoch [3676/4000] Training [10/39] Loss: 0.00242 +Epoch [3676/4000] Training [11/39] Loss: 0.00483 +Epoch [3676/4000] Training [12/39] Loss: 0.00288 +Epoch [3676/4000] Training [13/39] Loss: 0.00548 +Epoch [3676/4000] Training [14/39] Loss: 0.12777 +Epoch [3676/4000] Training [15/39] Loss: 0.00657 +Epoch [3676/4000] Training [16/39] Loss: 0.00472 +Epoch [3676/4000] Training [17/39] Loss: 0.00373 +Epoch [3676/4000] Training [18/39] Loss: 0.13016 +Epoch [3676/4000] Training [19/39] Loss: 0.00447 +Epoch [3676/4000] Training [20/39] Loss: 0.12793 +Epoch [3676/4000] Training [21/39] Loss: 0.00307 +Epoch [3676/4000] Training [22/39] Loss: 0.00499 +Epoch [3676/4000] Training [23/39] Loss: 0.00582 +Epoch [3676/4000] Training [24/39] Loss: 0.12831 +Epoch [3676/4000] Training [25/39] Loss: 0.00527 +Epoch [3676/4000] Training [26/39] Loss: 0.17100 +Epoch [3676/4000] Training [27/39] Loss: 0.12863 +Epoch [3676/4000] Training [28/39] Loss: 0.00390 +Epoch [3676/4000] Training [29/39] Loss: 0.00755 +Epoch [3676/4000] Training [30/39] Loss: 0.00445 +Epoch [3676/4000] Training [31/39] Loss: 0.13163 +Epoch [3676/4000] Training [32/39] Loss: 0.00703 +Epoch [3676/4000] Training [33/39] Loss: 0.00557 +Epoch [3676/4000] Training [34/39] Loss: 0.00324 +Epoch [3676/4000] Training [35/39] Loss: 0.00497 +Epoch [3676/4000] Training [36/39] Loss: 0.00314 +Epoch [3676/4000] Training [37/39] Loss: 0.00658 +Epoch [3676/4000] Training [38/39] Loss: 0.00839 +Epoch [3676/4000] Training [39/39] Loss: 0.00840 +Epoch [3676/4000] Training metric {'Train/mean dice_metric': 0.9962169528007507, 'Train/mean miou_metric': 0.9928820729255676, 'Train/mean f1': 0.9967566132545471, 'Train/mean precision': 0.9963309168815613, 'Train/mean recall': 0.9971825480461121, 'Train/mean hd95_metric': 0.9600443243980408} +Epoch [3676/4000] Validation [1/10] Loss: 0.72994 focal_loss 0.64190 dice_loss 0.08804 +Epoch [3676/4000] Validation [2/10] Loss: 0.48436 focal_loss 0.38964 dice_loss 0.09472 +Epoch [3676/4000] Validation [3/10] Loss: 0.38154 focal_loss 0.27098 dice_loss 0.11056 +Epoch [3676/4000] Validation [4/10] Loss: 0.90239 focal_loss 0.33562 dice_loss 0.56677 +Epoch [3676/4000] Validation [5/10] Loss: 3.08633 focal_loss 2.41274 dice_loss 0.67360 +Epoch [3676/4000] Validation [6/10] Loss: 1.34161 focal_loss 0.62894 dice_loss 0.71267 +Epoch [3676/4000] Validation [7/10] Loss: 1.18854 focal_loss 0.53548 dice_loss 0.65306 +Epoch [3676/4000] Validation [8/10] Loss: 2.29253 focal_loss 1.68535 dice_loss 0.60718 +Epoch [3676/4000] Validation [9/10] Loss: 1.56181 focal_loss 1.01636 dice_loss 0.54545 +Epoch [3676/4000] Validation [10/10] Loss: 1.92811 focal_loss 1.19089 dice_loss 0.73722 +Epoch [3676/4000] Validation metric {'Val/mean dice_metric': 0.9514691829681396, 'Val/mean miou_metric': 0.9354395270347595, 'Val/mean f1': 0.9477925896644592, 'Val/mean precision': 0.9419304728507996, 'Val/mean recall': 0.9537279605865479, 'Val/mean hd95_metric': 10.62807559967041} +Cheakpoint... +Epoch [3676/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514691829681396, 'Val/mean miou_metric': 0.9354395270347595, 'Val/mean f1': 0.9477925896644592, 'Val/mean precision': 0.9419304728507996, 'Val/mean recall': 0.9537279605865479, 'Val/mean hd95_metric': 10.62807559967041} +Epoch [3677/4000] Training [1/39] Loss: 0.00534 +Epoch [3677/4000] Training [2/39] Loss: 0.00455 +Epoch [3677/4000] Training [3/39] Loss: 0.25433 +Epoch [3677/4000] Training [4/39] Loss: 0.00421 +Epoch [3677/4000] Training [5/39] Loss: 0.00379 +Epoch [3677/4000] Training [6/39] Loss: 0.00399 +Epoch [3677/4000] Training [7/39] Loss: 0.00459 +Epoch [3677/4000] Training [8/39] Loss: 0.00600 +Epoch [3677/4000] Training [9/39] Loss: 0.00306 +Epoch [3677/4000] Training [10/39] Loss: 0.00775 +Epoch [3677/4000] Training [11/39] Loss: 0.00975 +Epoch [3677/4000] Training [12/39] Loss: 0.00632 +Epoch [3677/4000] Training [13/39] Loss: 0.00363 +Epoch [3677/4000] Training [14/39] Loss: 0.12942 +Epoch [3677/4000] Training [15/39] Loss: 0.00547 +Epoch [3677/4000] Training [16/39] Loss: 0.00429 +Epoch [3677/4000] Training [17/39] Loss: 0.12940 +Epoch [3677/4000] Training [18/39] Loss: 0.12750 +Epoch [3677/4000] Training [19/39] Loss: 0.00472 +Epoch [3677/4000] Training [20/39] Loss: 0.00415 +Epoch [3677/4000] Training [21/39] Loss: 0.00245 +Epoch [3677/4000] Training [22/39] Loss: 0.00307 +Epoch [3677/4000] Training [23/39] Loss: 0.00467 +Epoch [3677/4000] Training [24/39] Loss: 0.00441 +Epoch [3677/4000] Training [25/39] Loss: 0.00390 +Epoch [3677/4000] Training [26/39] Loss: 0.00386 +Epoch [3677/4000] Training [27/39] Loss: 0.12814 +Epoch [3677/4000] Training [28/39] Loss: 0.00469 +Epoch [3677/4000] Training [29/39] Loss: 0.00277 +Epoch [3677/4000] Training [30/39] Loss: 0.00478 +Epoch [3677/4000] Training [31/39] Loss: 0.00433 +Epoch [3677/4000] Training [32/39] Loss: 0.00472 +Epoch [3677/4000] Training [33/39] Loss: 0.00439 +Epoch [3677/4000] Training [34/39] Loss: 0.13038 +Epoch [3677/4000] Training [35/39] Loss: 0.13004 +Epoch [3677/4000] Training [36/39] Loss: 0.00376 +Epoch [3677/4000] Training [37/39] Loss: 0.12817 +Epoch [3677/4000] Training [38/39] Loss: 0.12992 +Epoch [3677/4000] Training [39/39] Loss: 0.12838 +Epoch [3677/4000] Training metric {'Train/mean dice_metric': 0.9955886006355286, 'Train/mean miou_metric': 0.9924501180648804, 'Train/mean f1': 0.9969199299812317, 'Train/mean precision': 0.9964196681976318, 'Train/mean recall': 0.9974207878112793, 'Train/mean hd95_metric': 0.9411766529083252} +Epoch [3677/4000] Validation [1/10] Loss: 0.71897 focal_loss 0.63162 dice_loss 0.08735 +Epoch [3677/4000] Validation [2/10] Loss: 0.48777 focal_loss 0.39351 dice_loss 0.09426 +Epoch [3677/4000] Validation [3/10] Loss: 0.37513 focal_loss 0.26488 dice_loss 0.11025 +Epoch [3677/4000] Validation [4/10] Loss: 0.90704 focal_loss 0.34026 dice_loss 0.56678 +Epoch [3677/4000] Validation [5/10] Loss: 3.04200 focal_loss 2.36859 dice_loss 0.67341 +Epoch [3677/4000] Validation [6/10] Loss: 1.35129 focal_loss 0.63929 dice_loss 0.71200 +Epoch [3677/4000] Validation [7/10] Loss: 1.19768 focal_loss 0.54364 dice_loss 0.65404 +Epoch [3677/4000] Validation [8/10] Loss: 2.29991 focal_loss 1.69216 dice_loss 0.60775 +Epoch [3677/4000] Validation [9/10] Loss: 1.53862 focal_loss 0.99251 dice_loss 0.54611 +Epoch [3677/4000] Validation [10/10] Loss: 1.94204 focal_loss 1.20398 dice_loss 0.73806 +Epoch [3677/4000] Validation metric {'Val/mean dice_metric': 0.9509727954864502, 'Val/mean miou_metric': 0.9351124167442322, 'Val/mean f1': 0.9478967189788818, 'Val/mean precision': 0.9420347809791565, 'Val/mean recall': 0.9538322687149048, 'Val/mean hd95_metric': 10.5899019241333} +Cheakpoint... +Epoch [3677/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509727954864502, 'Val/mean miou_metric': 0.9351124167442322, 'Val/mean f1': 0.9478967189788818, 'Val/mean precision': 0.9420347809791565, 'Val/mean recall': 0.9538322687149048, 'Val/mean hd95_metric': 10.5899019241333} +Epoch [3678/4000] Training [1/39] Loss: 0.00742 +Epoch [3678/4000] Training [2/39] Loss: 0.00409 +Epoch [3678/4000] Training [3/39] Loss: 0.00296 +Epoch [3678/4000] Training [4/39] Loss: 0.00782 +Epoch [3678/4000] Training [5/39] Loss: 0.00317 +Epoch [3678/4000] Training [6/39] Loss: 0.00468 +Epoch [3678/4000] Training [7/39] Loss: 0.00454 +Epoch [3678/4000] Training [8/39] Loss: 0.00230 +Epoch [3678/4000] Training [9/39] Loss: 0.12950 +Epoch [3678/4000] Training [10/39] Loss: 0.00700 +Epoch [3678/4000] Training [11/39] Loss: 0.13041 +Epoch [3678/4000] Training [12/39] Loss: 0.00360 +Epoch [3678/4000] Training [13/39] Loss: 0.00400 +Epoch [3678/4000] Training [14/39] Loss: 0.13001 +Epoch [3678/4000] Training [15/39] Loss: 0.00604 +Epoch [3678/4000] Training [16/39] Loss: 0.00444 +Epoch [3678/4000] Training [17/39] Loss: 0.00376 +Epoch [3678/4000] Training [18/39] Loss: 0.00613 +Epoch [3678/4000] Training [19/39] Loss: 0.13646 +Epoch [3678/4000] Training [20/39] Loss: 0.00449 +Epoch [3678/4000] Training [21/39] Loss: 0.25450 +Epoch [3678/4000] Training [22/39] Loss: 0.00467 +Epoch [3678/4000] Training [23/39] Loss: 0.00488 +Epoch [3678/4000] Training [24/39] Loss: 0.00665 +Epoch [3678/4000] Training [25/39] Loss: 0.12801 +Epoch [3678/4000] Training [26/39] Loss: 0.00513 +Epoch [3678/4000] Training [27/39] Loss: 0.00485 +Epoch [3678/4000] Training [28/39] Loss: 0.00364 +Epoch [3678/4000] Training [29/39] Loss: 0.00536 +Epoch [3678/4000] Training [30/39] Loss: 0.00376 +Epoch [3678/4000] Training [31/39] Loss: 0.13136 +Epoch [3678/4000] Training [32/39] Loss: 0.00565 +Epoch [3678/4000] Training [33/39] Loss: 0.00430 +Epoch [3678/4000] Training [34/39] Loss: 0.00393 +Epoch [3678/4000] Training [35/39] Loss: 0.13049 +Epoch [3678/4000] Training [36/39] Loss: 0.00449 +Epoch [3678/4000] Training [37/39] Loss: 0.12955 +Epoch [3678/4000] Training [38/39] Loss: 0.25293 +Epoch [3678/4000] Training [39/39] Loss: 0.03668 +Epoch [3678/4000] Training metric {'Train/mean dice_metric': 0.996349573135376, 'Train/mean miou_metric': 0.9931695461273193, 'Train/mean f1': 0.9969689846038818, 'Train/mean precision': 0.9965495467185974, 'Train/mean recall': 0.9973887205123901, 'Train/mean hd95_metric': 0.9255326390266418} +Epoch [3678/4000] Validation [1/10] Loss: 0.74029 focal_loss 0.65147 dice_loss 0.08882 +Epoch [3678/4000] Validation [2/10] Loss: 0.49367 focal_loss 0.39985 dice_loss 0.09381 +Epoch [3678/4000] Validation [3/10] Loss: 0.37066 focal_loss 0.26098 dice_loss 0.10968 +Epoch [3678/4000] Validation [4/10] Loss: 0.91630 focal_loss 0.34807 dice_loss 0.56822 +Epoch [3678/4000] Validation [5/10] Loss: 3.05381 focal_loss 2.38068 dice_loss 0.67312 +Epoch [3678/4000] Validation [6/10] Loss: 1.36974 focal_loss 0.65671 dice_loss 0.71303 +Epoch [3678/4000] Validation [7/10] Loss: 1.20935 focal_loss 0.55382 dice_loss 0.65553 +Epoch [3678/4000] Validation [8/10] Loss: 2.23444 focal_loss 1.63603 dice_loss 0.59841 +Epoch [3678/4000] Validation [9/10] Loss: 1.62538 focal_loss 1.07918 dice_loss 0.54620 +Epoch [3678/4000] Validation [10/10] Loss: 1.98726 focal_loss 1.24748 dice_loss 0.73978 +Epoch [3678/4000] Validation metric {'Val/mean dice_metric': 0.9515435099601746, 'Val/mean miou_metric': 0.9356073141098022, 'Val/mean f1': 0.9478786587715149, 'Val/mean precision': 0.9407687783241272, 'Val/mean recall': 0.9550969004631042, 'Val/mean hd95_metric': 10.757861137390137} +Cheakpoint... +Epoch [3678/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515435099601746, 'Val/mean miou_metric': 0.9356073141098022, 'Val/mean f1': 0.9478786587715149, 'Val/mean precision': 0.9407687783241272, 'Val/mean recall': 0.9550969004631042, 'Val/mean hd95_metric': 10.757861137390137} +Epoch [3679/4000] Training [1/39] Loss: 0.12819 +Epoch [3679/4000] Training [2/39] Loss: 0.12995 +Epoch [3679/4000] Training [3/39] Loss: 0.12780 +Epoch [3679/4000] Training [4/39] Loss: 0.00377 +Epoch [3679/4000] Training [5/39] Loss: 0.00620 +Epoch [3679/4000] Training [6/39] Loss: 0.00795 +Epoch [3679/4000] Training [7/39] Loss: 0.25307 +Epoch [3679/4000] Training [8/39] Loss: 0.25568 +Epoch [3679/4000] Training [9/39] Loss: 0.00550 +Epoch [3679/4000] Training [10/39] Loss: 0.00398 +Epoch [3679/4000] Training [11/39] Loss: 0.00635 +Epoch [3679/4000] Training [12/39] Loss: 0.00706 +Epoch [3679/4000] Training [13/39] Loss: 0.12932 +Epoch [3679/4000] Training [14/39] Loss: 0.12866 +Epoch [3679/4000] Training [15/39] Loss: 0.00542 +Epoch [3679/4000] Training [16/39] Loss: 0.00391 +Epoch [3679/4000] Training [17/39] Loss: 0.00737 +Epoch [3679/4000] Training [18/39] Loss: 0.00364 +Epoch [3679/4000] Training [19/39] Loss: 0.00653 +Epoch [3679/4000] Training [20/39] Loss: 0.00583 +Epoch [3679/4000] Training [21/39] Loss: 0.12912 +Epoch [3679/4000] Training [22/39] Loss: 0.00456 +Epoch [3679/4000] Training [23/39] Loss: 0.00335 +Epoch [3679/4000] Training [24/39] Loss: 0.13086 +Epoch [3679/4000] Training [25/39] Loss: 0.20923 +Epoch [3679/4000] Training [26/39] Loss: 0.12839 +Epoch [3679/4000] Training [27/39] Loss: 0.00433 +Epoch [3679/4000] Training [28/39] Loss: 0.12770 +Epoch [3679/4000] Training [29/39] Loss: 0.00395 +Epoch [3679/4000] Training [30/39] Loss: 0.00585 +Epoch [3679/4000] Training [31/39] Loss: 0.00424 +Epoch [3679/4000] Training [32/39] Loss: 0.00368 +Epoch [3679/4000] Training [33/39] Loss: 0.00551 +Epoch [3679/4000] Training [34/39] Loss: 0.00505 +Epoch [3679/4000] Training [35/39] Loss: 0.00399 +Epoch [3679/4000] Training [36/39] Loss: 0.00514 +Epoch [3679/4000] Training [37/39] Loss: 0.00594 +Epoch [3679/4000] Training [38/39] Loss: 0.00449 +Epoch [3679/4000] Training [39/39] Loss: 0.00590 +Epoch [3679/4000] Training metric {'Train/mean dice_metric': 0.9963563680648804, 'Train/mean miou_metric': 0.9931578636169434, 'Train/mean f1': 0.9969250559806824, 'Train/mean precision': 0.996494472026825, 'Train/mean recall': 0.997356116771698, 'Train/mean hd95_metric': 0.9350610375404358} +Epoch [3679/4000] Validation [1/10] Loss: 0.73059 focal_loss 0.64271 dice_loss 0.08788 +Epoch [3679/4000] Validation [2/10] Loss: 0.49151 focal_loss 0.39299 dice_loss 0.09852 +Epoch [3679/4000] Validation [3/10] Loss: 0.38748 focal_loss 0.27608 dice_loss 0.11139 +Epoch [3679/4000] Validation [4/10] Loss: 0.88743 focal_loss 0.32234 dice_loss 0.56509 +Epoch [3679/4000] Validation [5/10] Loss: 3.07282 focal_loss 2.39934 dice_loss 0.67348 +Epoch [3679/4000] Validation [6/10] Loss: 1.32257 focal_loss 0.60982 dice_loss 0.71275 +Epoch [3679/4000] Validation [7/10] Loss: 1.17601 focal_loss 0.52477 dice_loss 0.65124 +Epoch [3679/4000] Validation [8/10] Loss: 2.29936 focal_loss 1.68761 dice_loss 0.61174 +Epoch [3679/4000] Validation [9/10] Loss: 1.54277 focal_loss 0.99761 dice_loss 0.54515 +Epoch [3679/4000] Validation [10/10] Loss: 1.88291 focal_loss 1.14766 dice_loss 0.73525 +Epoch [3679/4000] Validation metric {'Val/mean dice_metric': 0.9515277743339539, 'Val/mean miou_metric': 0.9356459975242615, 'Val/mean f1': 0.9484345316886902, 'Val/mean precision': 0.9435808658599854, 'Val/mean recall': 0.9533385038375854, 'Val/mean hd95_metric': 10.673020362854004} +Cheakpoint... +Epoch [3679/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515277743339539, 'Val/mean miou_metric': 0.9356459975242615, 'Val/mean f1': 0.9484345316886902, 'Val/mean precision': 0.9435808658599854, 'Val/mean recall': 0.9533385038375854, 'Val/mean hd95_metric': 10.673020362854004} +Epoch [3680/4000] Training [1/39] Loss: 0.00788 +Epoch [3680/4000] Training [2/39] Loss: 0.00507 +Epoch [3680/4000] Training [3/39] Loss: 0.00451 +Epoch [3680/4000] Training [4/39] Loss: 0.00990 +Epoch [3680/4000] Training [5/39] Loss: 0.13488 +Epoch [3680/4000] Training [6/39] Loss: 0.12970 +Epoch [3680/4000] Training [7/39] Loss: 0.00382 +Epoch [3680/4000] Training [8/39] Loss: 0.12967 +Epoch [3680/4000] Training [9/39] Loss: 0.08781 +Epoch [3680/4000] Training [10/39] Loss: 0.12908 +Epoch [3680/4000] Training [11/39] Loss: 0.00378 +Epoch [3680/4000] Training [12/39] Loss: 0.12960 +Epoch [3680/4000] Training [13/39] Loss: 0.00371 +Epoch [3680/4000] Training [14/39] Loss: 0.00571 +Epoch [3680/4000] Training [15/39] Loss: 0.00501 +Epoch [3680/4000] Training [16/39] Loss: 0.12924 +Epoch [3680/4000] Training [17/39] Loss: 0.12787 +Epoch [3680/4000] Training [18/39] Loss: 0.00416 +Epoch [3680/4000] Training [19/39] Loss: 0.00272 +Epoch [3680/4000] Training [20/39] Loss: 0.00350 +Epoch [3680/4000] Training [21/39] Loss: 0.12807 +Epoch [3680/4000] Training [22/39] Loss: 0.00641 +Epoch [3680/4000] Training [23/39] Loss: 0.13115 +Epoch [3680/4000] Training [24/39] Loss: 0.00696 +Epoch [3680/4000] Training [25/39] Loss: 0.00715 +Epoch [3680/4000] Training [26/39] Loss: 0.00444 +Epoch [3680/4000] Training [27/39] Loss: 0.00778 +Epoch [3680/4000] Training [28/39] Loss: 0.12908 +Epoch [3680/4000] Training [29/39] Loss: 0.13164 +Epoch [3680/4000] Training [30/39] Loss: 0.00383 +Epoch [3680/4000] Training [31/39] Loss: 0.00437 +Epoch [3680/4000] Training [32/39] Loss: 0.12942 +Epoch [3680/4000] Training [33/39] Loss: 0.00973 +Epoch [3680/4000] Training [34/39] Loss: 0.12888 +Epoch [3680/4000] Training [35/39] Loss: 0.12994 +Epoch [3680/4000] Training [36/39] Loss: 0.00660 +Epoch [3680/4000] Training [37/39] Loss: 0.00602 +Epoch [3680/4000] Training [38/39] Loss: 0.00386 +Epoch [3680/4000] Training [39/39] Loss: 0.12863 +Epoch [3680/4000] Training metric {'Train/mean dice_metric': 0.9959548711776733, 'Train/mean miou_metric': 0.9923626184463501, 'Train/mean f1': 0.9965901374816895, 'Train/mean precision': 0.9961494207382202, 'Train/mean recall': 0.9970314502716064, 'Train/mean hd95_metric': 0.9653213024139404} +Epoch [3680/4000] Validation [1/10] Loss: 0.70314 focal_loss 0.61712 dice_loss 0.08602 +Epoch [3680/4000] Validation [2/10] Loss: 0.48558 focal_loss 0.38950 dice_loss 0.09608 +Epoch [3680/4000] Validation [3/10] Loss: 0.37499 focal_loss 0.26459 dice_loss 0.11040 +Epoch [3680/4000] Validation [4/10] Loss: 0.89343 focal_loss 0.32864 dice_loss 0.56479 +Epoch [3680/4000] Validation [5/10] Loss: 3.01641 focal_loss 2.34268 dice_loss 0.67373 +Epoch [3680/4000] Validation [6/10] Loss: 1.32989 focal_loss 0.61857 dice_loss 0.71132 +Epoch [3680/4000] Validation [7/10] Loss: 1.17662 focal_loss 0.52578 dice_loss 0.65084 +Epoch [3680/4000] Validation [8/10] Loss: 2.27974 focal_loss 1.67006 dice_loss 0.60968 +Epoch [3680/4000] Validation [9/10] Loss: 1.52856 focal_loss 0.98298 dice_loss 0.54558 +Epoch [3680/4000] Validation [10/10] Loss: 1.88459 focal_loss 1.15008 dice_loss 0.73451 +Epoch [3680/4000] Validation metric {'Val/mean dice_metric': 0.9513925909996033, 'Val/mean miou_metric': 0.9352115988731384, 'Val/mean f1': 0.9480615258216858, 'Val/mean precision': 0.9431721568107605, 'Val/mean recall': 0.9530019164085388, 'Val/mean hd95_metric': 10.708992958068848} +Cheakpoint... +Epoch [3680/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513925909996033, 'Val/mean miou_metric': 0.9352115988731384, 'Val/mean f1': 0.9480615258216858, 'Val/mean precision': 0.9431721568107605, 'Val/mean recall': 0.9530019164085388, 'Val/mean hd95_metric': 10.708992958068848} +Epoch [3681/4000] Training [1/39] Loss: 0.00756 +Epoch [3681/4000] Training [2/39] Loss: 0.00366 +Epoch [3681/4000] Training [3/39] Loss: 0.00501 +Epoch [3681/4000] Training [4/39] Loss: 0.12794 +Epoch [3681/4000] Training [5/39] Loss: 0.00403 +Epoch [3681/4000] Training [6/39] Loss: 0.00482 +Epoch [3681/4000] Training [7/39] Loss: 0.12795 +Epoch [3681/4000] Training [8/39] Loss: 0.00443 +Epoch [3681/4000] Training [9/39] Loss: 0.00615 +Epoch [3681/4000] Training [10/39] Loss: 0.13037 +Epoch [3681/4000] Training [11/39] Loss: 0.00454 +Epoch [3681/4000] Training [12/39] Loss: 0.00370 +Epoch [3681/4000] Training [13/39] Loss: 0.25181 +Epoch [3681/4000] Training [14/39] Loss: 0.00626 +Epoch [3681/4000] Training [15/39] Loss: 0.00381 +Epoch [3681/4000] Training [16/39] Loss: 0.00516 +Epoch [3681/4000] Training [17/39] Loss: 0.00816 +Epoch [3681/4000] Training [18/39] Loss: 0.00411 +Epoch [3681/4000] Training [19/39] Loss: 0.00496 +Epoch [3681/4000] Training [20/39] Loss: 0.00809 +Epoch [3681/4000] Training [21/39] Loss: 0.00274 +Epoch [3681/4000] Training [22/39] Loss: 0.00409 +Epoch [3681/4000] Training [23/39] Loss: 0.12845 +Epoch [3681/4000] Training [24/39] Loss: 0.25592 +Epoch [3681/4000] Training [25/39] Loss: 0.12890 +Epoch [3681/4000] Training [26/39] Loss: 0.12740 +Epoch [3681/4000] Training [27/39] Loss: 0.00518 +Epoch [3681/4000] Training [28/39] Loss: 0.00460 +Epoch [3681/4000] Training [29/39] Loss: 0.00400 +Epoch [3681/4000] Training [30/39] Loss: 0.13240 +Epoch [3681/4000] Training [31/39] Loss: 0.00515 +Epoch [3681/4000] Training [32/39] Loss: 0.00628 +Epoch [3681/4000] Training [33/39] Loss: 0.13027 +Epoch [3681/4000] Training [34/39] Loss: 0.00682 +Epoch [3681/4000] Training [35/39] Loss: 0.00418 +Epoch [3681/4000] Training [36/39] Loss: 0.00550 +Epoch [3681/4000] Training [37/39] Loss: 0.00733 +Epoch [3681/4000] Training [38/39] Loss: 0.00626 +Epoch [3681/4000] Training [39/39] Loss: 0.00478 +Epoch [3681/4000] Training metric {'Train/mean dice_metric': 0.9962389469146729, 'Train/mean miou_metric': 0.9929431676864624, 'Train/mean f1': 0.9968075156211853, 'Train/mean precision': 0.996329128742218, 'Train/mean recall': 0.997286319732666, 'Train/mean hd95_metric': 1.0000768899917603} +Epoch [3681/4000] Validation [1/10] Loss: 0.72363 focal_loss 0.63623 dice_loss 0.08740 +Epoch [3681/4000] Validation [2/10] Loss: 0.48647 focal_loss 0.39022 dice_loss 0.09626 +Epoch [3681/4000] Validation [3/10] Loss: 0.38131 focal_loss 0.27068 dice_loss 0.11063 +Epoch [3681/4000] Validation [4/10] Loss: 0.89941 focal_loss 0.33419 dice_loss 0.56522 +Epoch [3681/4000] Validation [5/10] Loss: 3.04057 focal_loss 2.36696 dice_loss 0.67360 +Epoch [3681/4000] Validation [6/10] Loss: 1.33531 focal_loss 0.62474 dice_loss 0.71057 +Epoch [3681/4000] Validation [7/10] Loss: 1.17862 focal_loss 0.52655 dice_loss 0.65207 +Epoch [3681/4000] Validation [8/10] Loss: 2.32628 focal_loss 1.71343 dice_loss 0.61286 +Epoch [3681/4000] Validation [9/10] Loss: 1.56043 focal_loss 1.01592 dice_loss 0.54451 +Epoch [3681/4000] Validation [10/10] Loss: 1.89799 focal_loss 1.16250 dice_loss 0.73550 +Epoch [3681/4000] Validation metric {'Val/mean dice_metric': 0.9514928460121155, 'Val/mean miou_metric': 0.9355257153511047, 'Val/mean f1': 0.948321521282196, 'Val/mean precision': 0.9433467984199524, 'Val/mean recall': 0.9533490538597107, 'Val/mean hd95_metric': 10.571548461914062} +Cheakpoint... +Epoch [3681/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514928460121155, 'Val/mean miou_metric': 0.9355257153511047, 'Val/mean f1': 0.948321521282196, 'Val/mean precision': 0.9433467984199524, 'Val/mean recall': 0.9533490538597107, 'Val/mean hd95_metric': 10.571548461914062} +Epoch [3682/4000] Training [1/39] Loss: 0.00447 +Epoch [3682/4000] Training [2/39] Loss: 0.00485 +Epoch [3682/4000] Training [3/39] Loss: 0.00539 +Epoch [3682/4000] Training [4/39] Loss: 0.13173 +Epoch [3682/4000] Training [5/39] Loss: 0.00408 +Epoch [3682/4000] Training [6/39] Loss: 0.12914 +Epoch [3682/4000] Training [7/39] Loss: 0.00374 +Epoch [3682/4000] Training [8/39] Loss: 0.00393 +Epoch [3682/4000] Training [9/39] Loss: 0.13010 +Epoch [3682/4000] Training [10/39] Loss: 0.00453 +Epoch [3682/4000] Training [11/39] Loss: 0.12929 +Epoch [3682/4000] Training [12/39] Loss: 0.00519 +Epoch [3682/4000] Training [13/39] Loss: 0.00642 +Epoch [3682/4000] Training [14/39] Loss: 0.00523 +Epoch [3682/4000] Training [15/39] Loss: 0.12846 +Epoch [3682/4000] Training [16/39] Loss: 0.00417 +Epoch [3682/4000] Training [17/39] Loss: 0.00305 +Epoch [3682/4000] Training [18/39] Loss: 0.12769 +Epoch [3682/4000] Training [19/39] Loss: 0.00541 +Epoch [3682/4000] Training [20/39] Loss: 0.00602 +Epoch [3682/4000] Training [21/39] Loss: 0.00545 +Epoch [3682/4000] Training [22/39] Loss: 0.00616 +Epoch [3682/4000] Training [23/39] Loss: 0.00437 +Epoch [3682/4000] Training [24/39] Loss: 0.00357 +Epoch [3682/4000] Training [25/39] Loss: 0.00429 +Epoch [3682/4000] Training [26/39] Loss: 0.00305 +Epoch [3682/4000] Training [27/39] Loss: 0.04510 +Epoch [3682/4000] Training [28/39] Loss: 0.12867 +Epoch [3682/4000] Training [29/39] Loss: 0.00513 +Epoch [3682/4000] Training [30/39] Loss: 0.00462 +Epoch [3682/4000] Training [31/39] Loss: 0.00846 +Epoch [3682/4000] Training [32/39] Loss: 0.12948 +Epoch [3682/4000] Training [33/39] Loss: 0.00814 +Epoch [3682/4000] Training [34/39] Loss: 0.00384 +Epoch [3682/4000] Training [35/39] Loss: 0.00359 +Epoch [3682/4000] Training [36/39] Loss: 0.00408 +Epoch [3682/4000] Training [37/39] Loss: 0.00709 +Epoch [3682/4000] Training [38/39] Loss: 0.12762 +Epoch [3682/4000] Training [39/39] Loss: 0.00409 +Epoch [3682/4000] Training metric {'Train/mean dice_metric': 0.9954736828804016, 'Train/mean miou_metric': 0.9922148585319519, 'Train/mean f1': 0.9969707727432251, 'Train/mean precision': 0.9965335726737976, 'Train/mean recall': 0.9974083304405212, 'Train/mean hd95_metric': 0.9567112326622009} +Epoch [3682/4000] Validation [1/10] Loss: 0.69789 focal_loss 0.61213 dice_loss 0.08576 +Epoch [3682/4000] Validation [2/10] Loss: 0.48946 focal_loss 0.39254 dice_loss 0.09692 +Epoch [3682/4000] Validation [3/10] Loss: 0.37793 focal_loss 0.26689 dice_loss 0.11104 +Epoch [3682/4000] Validation [4/10] Loss: 0.90074 focal_loss 0.33473 dice_loss 0.56601 +Epoch [3682/4000] Validation [5/10] Loss: 2.98239 focal_loss 2.30871 dice_loss 0.67369 +Epoch [3682/4000] Validation [6/10] Loss: 1.34473 focal_loss 0.63296 dice_loss 0.71177 +Epoch [3682/4000] Validation [7/10] Loss: 1.18766 focal_loss 0.53368 dice_loss 0.65398 +Epoch [3682/4000] Validation [8/10] Loss: 2.27194 focal_loss 1.66400 dice_loss 0.60794 +Epoch [3682/4000] Validation [9/10] Loss: 1.58315 focal_loss 1.03774 dice_loss 0.54541 +Epoch [3682/4000] Validation [10/10] Loss: 1.90840 focal_loss 1.17254 dice_loss 0.73586 +Epoch [3682/4000] Validation metric {'Val/mean dice_metric': 0.9508311152458191, 'Val/mean miou_metric': 0.9348773956298828, 'Val/mean f1': 0.9484500885009766, 'Val/mean precision': 0.943040132522583, 'Val/mean recall': 0.9539225697517395, 'Val/mean hd95_metric': 10.832550048828125} +Cheakpoint... +Epoch [3682/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508311152458191, 'Val/mean miou_metric': 0.9348773956298828, 'Val/mean f1': 0.9484500885009766, 'Val/mean precision': 0.943040132522583, 'Val/mean recall': 0.9539225697517395, 'Val/mean hd95_metric': 10.832550048828125} +Epoch [3683/4000] Training [1/39] Loss: 0.00493 +Epoch [3683/4000] Training [2/39] Loss: 0.08504 +Epoch [3683/4000] Training [3/39] Loss: 0.00522 +Epoch [3683/4000] Training [4/39] Loss: 0.00414 +Epoch [3683/4000] Training [5/39] Loss: 0.12958 +Epoch [3683/4000] Training [6/39] Loss: 0.00357 +Epoch [3683/4000] Training [7/39] Loss: 0.00295 +Epoch [3683/4000] Training [8/39] Loss: 0.00515 +Epoch [3683/4000] Training [9/39] Loss: 0.12830 +Epoch [3683/4000] Training [10/39] Loss: 0.00653 +Epoch [3683/4000] Training [11/39] Loss: 0.00439 +Epoch [3683/4000] Training [12/39] Loss: 0.00412 +Epoch [3683/4000] Training [13/39] Loss: 0.13104 +Epoch [3683/4000] Training [14/39] Loss: 0.00337 +Epoch [3683/4000] Training [15/39] Loss: 0.00375 +Epoch [3683/4000] Training [16/39] Loss: 0.12925 +Epoch [3683/4000] Training [17/39] Loss: 0.00584 +Epoch [3683/4000] Training [18/39] Loss: 0.00410 +Epoch [3683/4000] Training [19/39] Loss: 0.00362 +Epoch [3683/4000] Training [20/39] Loss: 0.00408 +Epoch [3683/4000] Training [21/39] Loss: 0.00533 +Epoch [3683/4000] Training [22/39] Loss: 0.00487 +Epoch [3683/4000] Training [23/39] Loss: 0.00520 +Epoch [3683/4000] Training [24/39] Loss: 0.12764 +Epoch [3683/4000] Training [25/39] Loss: 0.12930 +Epoch [3683/4000] Training [26/39] Loss: 0.00601 +Epoch [3683/4000] Training [27/39] Loss: 0.00510 +Epoch [3683/4000] Training [28/39] Loss: 0.00682 +Epoch [3683/4000] Training [29/39] Loss: 0.12849 +Epoch [3683/4000] Training [30/39] Loss: 0.00442 +Epoch [3683/4000] Training [31/39] Loss: 0.00346 +Epoch [3683/4000] Training [32/39] Loss: 0.00635 +Epoch [3683/4000] Training [33/39] Loss: 0.00431 +Epoch [3683/4000] Training [34/39] Loss: 0.00439 +Epoch [3683/4000] Training [35/39] Loss: 0.00437 +Epoch [3683/4000] Training [36/39] Loss: 0.00672 +Epoch [3683/4000] Training [37/39] Loss: 0.00793 +Epoch [3683/4000] Training [38/39] Loss: 0.12896 +Epoch [3683/4000] Training [39/39] Loss: 0.00526 +Epoch [3683/4000] Training metric {'Train/mean dice_metric': 0.9963465332984924, 'Train/mean miou_metric': 0.9931348562240601, 'Train/mean f1': 0.9968385696411133, 'Train/mean precision': 0.9963931441307068, 'Train/mean recall': 0.9972844123840332, 'Train/mean hd95_metric': 0.992130696773529} +Epoch [3683/4000] Validation [1/10] Loss: 0.70700 focal_loss 0.62121 dice_loss 0.08579 +Epoch [3683/4000] Validation [2/10] Loss: 0.49146 focal_loss 0.39279 dice_loss 0.09868 +Epoch [3683/4000] Validation [3/10] Loss: 0.38692 focal_loss 0.27561 dice_loss 0.11130 +Epoch [3683/4000] Validation [4/10] Loss: 0.89805 focal_loss 0.33299 dice_loss 0.56506 +Epoch [3683/4000] Validation [5/10] Loss: 3.02235 focal_loss 2.34865 dice_loss 0.67370 +Epoch [3683/4000] Validation [6/10] Loss: 1.32270 focal_loss 0.61052 dice_loss 0.71219 +Epoch [3683/4000] Validation [7/10] Loss: 1.17846 focal_loss 0.52605 dice_loss 0.65241 +Epoch [3683/4000] Validation [8/10] Loss: 2.24982 focal_loss 1.64296 dice_loss 0.60686 +Epoch [3683/4000] Validation [9/10] Loss: 1.57485 focal_loss 1.03115 dice_loss 0.54370 +Epoch [3683/4000] Validation [10/10] Loss: 1.88027 focal_loss 1.14581 dice_loss 0.73446 +Epoch [3683/4000] Validation metric {'Val/mean dice_metric': 0.9515752196311951, 'Val/mean miou_metric': 0.9356914758682251, 'Val/mean f1': 0.9484796524047852, 'Val/mean precision': 0.9431906938552856, 'Val/mean recall': 0.9538280963897705, 'Val/mean hd95_metric': 10.815384864807129} +Cheakpoint... +Epoch [3683/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515752196311951, 'Val/mean miou_metric': 0.9356914758682251, 'Val/mean f1': 0.9484796524047852, 'Val/mean precision': 0.9431906938552856, 'Val/mean recall': 0.9538280963897705, 'Val/mean hd95_metric': 10.815384864807129} +Epoch [3684/4000] Training [1/39] Loss: 0.00330 +Epoch [3684/4000] Training [2/39] Loss: 0.12887 +Epoch [3684/4000] Training [3/39] Loss: 0.00357 +Epoch [3684/4000] Training [4/39] Loss: 0.00614 +Epoch [3684/4000] Training [5/39] Loss: 0.00662 +Epoch [3684/4000] Training [6/39] Loss: 0.00400 +Epoch [3684/4000] Training [7/39] Loss: 0.00312 +Epoch [3684/4000] Training [8/39] Loss: 0.00398 +Epoch [3684/4000] Training [9/39] Loss: 0.00787 +Epoch [3684/4000] Training [10/39] Loss: 0.00829 +Epoch [3684/4000] Training [11/39] Loss: 0.12800 +Epoch [3684/4000] Training [12/39] Loss: 0.00763 +Epoch [3684/4000] Training [13/39] Loss: 0.00506 +Epoch [3684/4000] Training [14/39] Loss: 0.00308 +Epoch [3684/4000] Training [15/39] Loss: 0.00477 +Epoch [3684/4000] Training [16/39] Loss: 0.00514 +Epoch [3684/4000] Training [17/39] Loss: 0.00543 +Epoch [3684/4000] Training [18/39] Loss: 0.00540 +Epoch [3684/4000] Training [19/39] Loss: 0.00459 +Epoch [3684/4000] Training [20/39] Loss: 0.00396 +Epoch [3684/4000] Training [21/39] Loss: 0.00291 +Epoch [3684/4000] Training [22/39] Loss: 0.00616 +Epoch [3684/4000] Training [23/39] Loss: 0.00531 +Epoch [3684/4000] Training [24/39] Loss: 0.25357 +Epoch [3684/4000] Training [25/39] Loss: 0.00508 +Epoch [3684/4000] Training [26/39] Loss: 0.00636 +Epoch [3684/4000] Training [27/39] Loss: 0.00774 +Epoch [3684/4000] Training [28/39] Loss: 0.13149 +Epoch [3684/4000] Training [29/39] Loss: 0.00483 +Epoch [3684/4000] Training [30/39] Loss: 0.00335 +Epoch [3684/4000] Training [31/39] Loss: 0.00579 +Epoch [3684/4000] Training [32/39] Loss: 0.00381 +Epoch [3684/4000] Training [33/39] Loss: 0.00478 +Epoch [3684/4000] Training [34/39] Loss: 0.00582 +Epoch [3684/4000] Training [35/39] Loss: 0.00505 +Epoch [3684/4000] Training [36/39] Loss: 0.12940 +Epoch [3684/4000] Training [37/39] Loss: 0.00295 +Epoch [3684/4000] Training [38/39] Loss: 0.12943 +Epoch [3684/4000] Training [39/39] Loss: 0.00449 +Epoch [3684/4000] Training metric {'Train/mean dice_metric': 0.9962703585624695, 'Train/mean miou_metric': 0.992987334728241, 'Train/mean f1': 0.9969116449356079, 'Train/mean precision': 0.9964550137519836, 'Train/mean recall': 0.9973686933517456, 'Train/mean hd95_metric': 0.9531062245368958} +Epoch [3684/4000] Validation [1/10] Loss: 0.70461 focal_loss 0.61892 dice_loss 0.08570 +Epoch [3684/4000] Validation [2/10] Loss: 0.49460 focal_loss 0.39524 dice_loss 0.09936 +Epoch [3684/4000] Validation [3/10] Loss: 0.38489 focal_loss 0.27375 dice_loss 0.11113 +Epoch [3684/4000] Validation [4/10] Loss: 0.89782 focal_loss 0.33251 dice_loss 0.56531 +Epoch [3684/4000] Validation [5/10] Loss: 3.02995 focal_loss 2.35605 dice_loss 0.67390 +Epoch [3684/4000] Validation [6/10] Loss: 1.32073 focal_loss 0.60949 dice_loss 0.71124 +Epoch [3684/4000] Validation [7/10] Loss: 1.17477 focal_loss 0.52314 dice_loss 0.65163 +Epoch [3684/4000] Validation [8/10] Loss: 2.28410 focal_loss 1.67379 dice_loss 0.61031 +Epoch [3684/4000] Validation [9/10] Loss: 1.54233 focal_loss 0.99778 dice_loss 0.54455 +Epoch [3684/4000] Validation [10/10] Loss: 1.87290 focal_loss 1.13959 dice_loss 0.73331 +Epoch [3684/4000] Validation metric {'Val/mean dice_metric': 0.9515852332115173, 'Val/mean miou_metric': 0.9356549978256226, 'Val/mean f1': 0.948230504989624, 'Val/mean precision': 0.9432909488677979, 'Val/mean recall': 0.9532220363616943, 'Val/mean hd95_metric': 10.617875099182129} +Cheakpoint... +Epoch [3684/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515852332115173, 'Val/mean miou_metric': 0.9356549978256226, 'Val/mean f1': 0.948230504989624, 'Val/mean precision': 0.9432909488677979, 'Val/mean recall': 0.9532220363616943, 'Val/mean hd95_metric': 10.617875099182129} +Epoch [3685/4000] Training [1/39] Loss: 0.00356 +Epoch [3685/4000] Training [2/39] Loss: 0.00419 +Epoch [3685/4000] Training [3/39] Loss: 0.12867 +Epoch [3685/4000] Training [4/39] Loss: 0.00439 +Epoch [3685/4000] Training [5/39] Loss: 0.00435 +Epoch [3685/4000] Training [6/39] Loss: 0.25468 +Epoch [3685/4000] Training [7/39] Loss: 0.25327 +Epoch [3685/4000] Training [8/39] Loss: 0.00889 +Epoch [3685/4000] Training [9/39] Loss: 0.00571 +Epoch [3685/4000] Training [10/39] Loss: 0.00339 +Epoch [3685/4000] Training [11/39] Loss: 0.00492 +Epoch [3685/4000] Training [12/39] Loss: 0.00684 +Epoch [3685/4000] Training [13/39] Loss: 0.00427 +Epoch [3685/4000] Training [14/39] Loss: 0.04556 +Epoch [3685/4000] Training [15/39] Loss: 0.00386 +Epoch [3685/4000] Training [16/39] Loss: 0.25367 +Epoch [3685/4000] Training [17/39] Loss: 0.00425 +Epoch [3685/4000] Training [18/39] Loss: 0.00588 +Epoch [3685/4000] Training [19/39] Loss: 0.00388 +Epoch [3685/4000] Training [20/39] Loss: 0.00412 +Epoch [3685/4000] Training [21/39] Loss: 0.12850 +Epoch [3685/4000] Training [22/39] Loss: 0.00394 +Epoch [3685/4000] Training [23/39] Loss: 0.13185 +Epoch [3685/4000] Training [24/39] Loss: 0.00439 +Epoch [3685/4000] Training [25/39] Loss: 0.12960 +Epoch [3685/4000] Training [26/39] Loss: 0.00396 +Epoch [3685/4000] Training [27/39] Loss: 0.00265 +Epoch [3685/4000] Training [28/39] Loss: 0.00487 +Epoch [3685/4000] Training [29/39] Loss: 0.00819 +Epoch [3685/4000] Training [30/39] Loss: 0.00382 +Epoch [3685/4000] Training [31/39] Loss: 0.00503 +Epoch [3685/4000] Training [32/39] Loss: 0.00573 +Epoch [3685/4000] Training [33/39] Loss: 0.12822 +Epoch [3685/4000] Training [34/39] Loss: 0.00362 +Epoch [3685/4000] Training [35/39] Loss: 0.13215 +Epoch [3685/4000] Training [36/39] Loss: 0.00480 +Epoch [3685/4000] Training [37/39] Loss: 0.00773 +Epoch [3685/4000] Training [38/39] Loss: 0.00362 +Epoch [3685/4000] Training [39/39] Loss: 0.00425 +Epoch [3685/4000] Training metric {'Train/mean dice_metric': 0.9962583780288696, 'Train/mean miou_metric': 0.9929592609405518, 'Train/mean f1': 0.9967320561408997, 'Train/mean precision': 0.9962782859802246, 'Train/mean recall': 0.9971862435340881, 'Train/mean hd95_metric': 0.9393996000289917} +Epoch [3685/4000] Validation [1/10] Loss: 0.71748 focal_loss 0.63076 dice_loss 0.08671 +Epoch [3685/4000] Validation [2/10] Loss: 0.48955 focal_loss 0.39355 dice_loss 0.09600 +Epoch [3685/4000] Validation [3/10] Loss: 0.38293 focal_loss 0.27215 dice_loss 0.11077 +Epoch [3685/4000] Validation [4/10] Loss: 0.90240 focal_loss 0.33639 dice_loss 0.56601 +Epoch [3685/4000] Validation [5/10] Loss: 3.04942 focal_loss 2.37573 dice_loss 0.67369 +Epoch [3685/4000] Validation [6/10] Loss: 1.33668 focal_loss 0.62494 dice_loss 0.71174 +Epoch [3685/4000] Validation [7/10] Loss: 1.18238 focal_loss 0.52980 dice_loss 0.65258 +Epoch [3685/4000] Validation [8/10] Loss: 2.23688 focal_loss 1.63311 dice_loss 0.60377 +Epoch [3685/4000] Validation [9/10] Loss: 1.59127 focal_loss 1.04715 dice_loss 0.54412 +Epoch [3685/4000] Validation [10/10] Loss: 1.91406 focal_loss 1.17744 dice_loss 0.73661 +Epoch [3685/4000] Validation metric {'Val/mean dice_metric': 0.9514703154563904, 'Val/mean miou_metric': 0.9355022311210632, 'Val/mean f1': 0.9475900530815125, 'Val/mean precision': 0.9415155053138733, 'Val/mean recall': 0.9537435173988342, 'Val/mean hd95_metric': 10.639223098754883} +Cheakpoint... +Epoch [3685/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514703154563904, 'Val/mean miou_metric': 0.9355022311210632, 'Val/mean f1': 0.9475900530815125, 'Val/mean precision': 0.9415155053138733, 'Val/mean recall': 0.9537435173988342, 'Val/mean hd95_metric': 10.639223098754883} +Epoch [3686/4000] Training [1/39] Loss: 0.00302 +Epoch [3686/4000] Training [2/39] Loss: 0.12876 +Epoch [3686/4000] Training [3/39] Loss: 0.00777 +Epoch [3686/4000] Training [4/39] Loss: 0.13455 +Epoch [3686/4000] Training [5/39] Loss: 0.12771 +Epoch [3686/4000] Training [6/39] Loss: 0.00481 +Epoch [3686/4000] Training [7/39] Loss: 0.00416 +Epoch [3686/4000] Training [8/39] Loss: 0.12991 +Epoch [3686/4000] Training [9/39] Loss: 0.00311 +Epoch [3686/4000] Training [10/39] Loss: 0.00352 +Epoch [3686/4000] Training [11/39] Loss: 0.12789 +Epoch [3686/4000] Training [12/39] Loss: 0.00675 +Epoch [3686/4000] Training [13/39] Loss: 0.00505 +Epoch [3686/4000] Training [14/39] Loss: 0.13049 +Epoch [3686/4000] Training [15/39] Loss: 0.00366 +Epoch [3686/4000] Training [16/39] Loss: 0.00394 +Epoch [3686/4000] Training [17/39] Loss: 0.00517 +Epoch [3686/4000] Training [18/39] Loss: 0.09432 +Epoch [3686/4000] Training [19/39] Loss: 0.00373 +Epoch [3686/4000] Training [20/39] Loss: 0.00710 +Epoch [3686/4000] Training [21/39] Loss: 0.00493 +Epoch [3686/4000] Training [22/39] Loss: 0.00370 +Epoch [3686/4000] Training [23/39] Loss: 0.00569 +Epoch [3686/4000] Training [24/39] Loss: 0.00420 +Epoch [3686/4000] Training [25/39] Loss: 0.00810 +Epoch [3686/4000] Training [26/39] Loss: 0.00295 +Epoch [3686/4000] Training [27/39] Loss: 0.00522 +Epoch [3686/4000] Training [28/39] Loss: 0.00625 +Epoch [3686/4000] Training [29/39] Loss: 0.00434 +Epoch [3686/4000] Training [30/39] Loss: 0.12759 +Epoch [3686/4000] Training [31/39] Loss: 0.00501 +Epoch [3686/4000] Training [32/39] Loss: 0.00520 +Epoch [3686/4000] Training [33/39] Loss: 0.00758 +Epoch [3686/4000] Training [34/39] Loss: 0.00287 +Epoch [3686/4000] Training [35/39] Loss: 0.00462 +Epoch [3686/4000] Training [36/39] Loss: 0.13100 +Epoch [3686/4000] Training [37/39] Loss: 0.00337 +Epoch [3686/4000] Training [38/39] Loss: 0.00399 +Epoch [3686/4000] Training [39/39] Loss: 0.12930 +Epoch [3686/4000] Training metric {'Train/mean dice_metric': 0.9963280558586121, 'Train/mean miou_metric': 0.9931364059448242, 'Train/mean f1': 0.9968375563621521, 'Train/mean precision': 0.9963756799697876, 'Train/mean recall': 0.9972999095916748, 'Train/mean hd95_metric': 1.0429421663284302} +Epoch [3686/4000] Validation [1/10] Loss: 0.70099 focal_loss 0.61593 dice_loss 0.08506 +Epoch [3686/4000] Validation [2/10] Loss: 0.49359 focal_loss 0.39668 dice_loss 0.09691 +Epoch [3686/4000] Validation [3/10] Loss: 0.39398 focal_loss 0.28251 dice_loss 0.11147 +Epoch [3686/4000] Validation [4/10] Loss: 0.90118 focal_loss 0.33594 dice_loss 0.56524 +Epoch [3686/4000] Validation [5/10] Loss: 3.07706 focal_loss 2.40319 dice_loss 0.67388 +Epoch [3686/4000] Validation [6/10] Loss: 1.32966 focal_loss 0.61861 dice_loss 0.71105 +Epoch [3686/4000] Validation [7/10] Loss: 1.17845 focal_loss 0.52696 dice_loss 0.65149 +Epoch [3686/4000] Validation [8/10] Loss: 2.31149 focal_loss 1.70068 dice_loss 0.61080 +Epoch [3686/4000] Validation [9/10] Loss: 1.58245 focal_loss 1.03890 dice_loss 0.54355 +Epoch [3686/4000] Validation [10/10] Loss: 1.91037 focal_loss 1.17436 dice_loss 0.73601 +Epoch [3686/4000] Validation metric {'Val/mean dice_metric': 0.9515527486801147, 'Val/mean miou_metric': 0.9356669783592224, 'Val/mean f1': 0.9481188654899597, 'Val/mean precision': 0.9430704116821289, 'Val/mean recall': 0.9532215595245361, 'Val/mean hd95_metric': 10.706507682800293} +Cheakpoint... +Epoch [3686/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515527486801147, 'Val/mean miou_metric': 0.9356669783592224, 'Val/mean f1': 0.9481188654899597, 'Val/mean precision': 0.9430704116821289, 'Val/mean recall': 0.9532215595245361, 'Val/mean hd95_metric': 10.706507682800293} +Epoch [3687/4000] Training [1/39] Loss: 0.00793 +Epoch [3687/4000] Training [2/39] Loss: 0.00688 +Epoch [3687/4000] Training [3/39] Loss: 0.00797 +Epoch [3687/4000] Training [4/39] Loss: 0.00633 +Epoch [3687/4000] Training [5/39] Loss: 0.00630 +Epoch [3687/4000] Training [6/39] Loss: 0.00541 +Epoch [3687/4000] Training [7/39] Loss: 0.00726 +Epoch [3687/4000] Training [8/39] Loss: 0.12900 +Epoch [3687/4000] Training [9/39] Loss: 0.00468 +Epoch [3687/4000] Training [10/39] Loss: 0.00386 +Epoch [3687/4000] Training [11/39] Loss: 0.01062 +Epoch [3687/4000] Training [12/39] Loss: 0.00493 +Epoch [3687/4000] Training [13/39] Loss: 0.12947 +Epoch [3687/4000] Training [14/39] Loss: 0.00394 +Epoch [3687/4000] Training [15/39] Loss: 0.00649 +Epoch [3687/4000] Training [16/39] Loss: 0.00473 +Epoch [3687/4000] Training [17/39] Loss: 0.00375 +Epoch [3687/4000] Training [18/39] Loss: 0.00412 +Epoch [3687/4000] Training [19/39] Loss: 0.00448 +Epoch [3687/4000] Training [20/39] Loss: 0.00366 +Epoch [3687/4000] Training [21/39] Loss: 0.00445 +Epoch [3687/4000] Training [22/39] Loss: 0.12845 +Epoch [3687/4000] Training [23/39] Loss: 0.00495 +Epoch [3687/4000] Training [24/39] Loss: 0.00440 +Epoch [3687/4000] Training [25/39] Loss: 0.00429 +Epoch [3687/4000] Training [26/39] Loss: 0.00357 +Epoch [3687/4000] Training [27/39] Loss: 0.12850 +Epoch [3687/4000] Training [28/39] Loss: 0.00574 +Epoch [3687/4000] Training [29/39] Loss: 0.00352 +Epoch [3687/4000] Training [30/39] Loss: 0.00325 +Epoch [3687/4000] Training [31/39] Loss: 0.13066 +Epoch [3687/4000] Training [32/39] Loss: 0.00416 +Epoch [3687/4000] Training [33/39] Loss: 0.00289 +Epoch [3687/4000] Training [34/39] Loss: 0.00355 +Epoch [3687/4000] Training [35/39] Loss: 0.00318 +Epoch [3687/4000] Training [36/39] Loss: 0.00634 +Epoch [3687/4000] Training [37/39] Loss: 0.12889 +Epoch [3687/4000] Training [38/39] Loss: 0.25249 +Epoch [3687/4000] Training [39/39] Loss: 0.00398 +Epoch [3687/4000] Training metric {'Train/mean dice_metric': 0.9962603449821472, 'Train/mean miou_metric': 0.9929625988006592, 'Train/mean f1': 0.9968380928039551, 'Train/mean precision': 0.9963921308517456, 'Train/mean recall': 0.9972844123840332, 'Train/mean hd95_metric': 1.0757968425750732} +Epoch [3687/4000] Validation [1/10] Loss: 0.69290 focal_loss 0.60820 dice_loss 0.08470 +Epoch [3687/4000] Validation [2/10] Loss: 0.48876 focal_loss 0.38997 dice_loss 0.09879 +Epoch [3687/4000] Validation [3/10] Loss: 0.39764 focal_loss 0.28518 dice_loss 0.11247 +Epoch [3687/4000] Validation [4/10] Loss: 0.89173 focal_loss 0.32730 dice_loss 0.56443 +Epoch [3687/4000] Validation [5/10] Loss: 3.03326 focal_loss 2.35943 dice_loss 0.67383 +Epoch [3687/4000] Validation [6/10] Loss: 1.32016 focal_loss 0.60693 dice_loss 0.71323 +Epoch [3687/4000] Validation [7/10] Loss: 1.16468 focal_loss 0.51455 dice_loss 0.65014 +Epoch [3687/4000] Validation [8/10] Loss: 2.30162 focal_loss 1.68722 dice_loss 0.61440 +Epoch [3687/4000] Validation [9/10] Loss: 1.52442 focal_loss 0.98035 dice_loss 0.54407 +Epoch [3687/4000] Validation [10/10] Loss: 1.87407 focal_loss 1.13842 dice_loss 0.73565 +Epoch [3687/4000] Validation metric {'Val/mean dice_metric': 0.9514769911766052, 'Val/mean miou_metric': 0.9355478882789612, 'Val/mean f1': 0.9482470750808716, 'Val/mean precision': 0.94367516040802, 'Val/mean recall': 0.9528635740280151, 'Val/mean hd95_metric': 10.803791999816895} +Cheakpoint... +Epoch [3687/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514769911766052, 'Val/mean miou_metric': 0.9355478882789612, 'Val/mean f1': 0.9482470750808716, 'Val/mean precision': 0.94367516040802, 'Val/mean recall': 0.9528635740280151, 'Val/mean hd95_metric': 10.803791999816895} +Epoch [3688/4000] Training [1/39] Loss: 0.00395 +Epoch [3688/4000] Training [2/39] Loss: 0.00376 +Epoch [3688/4000] Training [3/39] Loss: 0.00463 +Epoch [3688/4000] Training [4/39] Loss: 0.12754 +Epoch [3688/4000] Training [5/39] Loss: 0.00683 +Epoch [3688/4000] Training [6/39] Loss: 0.00536 +Epoch [3688/4000] Training [7/39] Loss: 0.00501 +Epoch [3688/4000] Training [8/39] Loss: 0.00485 +Epoch [3688/4000] Training [9/39] Loss: 0.00339 +Epoch [3688/4000] Training [10/39] Loss: 0.00521 +Epoch [3688/4000] Training [11/39] Loss: 0.00349 +Epoch [3688/4000] Training [12/39] Loss: 0.00468 +Epoch [3688/4000] Training [13/39] Loss: 0.12742 +Epoch [3688/4000] Training [14/39] Loss: 0.00434 +Epoch [3688/4000] Training [15/39] Loss: 0.00707 +Epoch [3688/4000] Training [16/39] Loss: 0.00560 +Epoch [3688/4000] Training [17/39] Loss: 0.00514 +Epoch [3688/4000] Training [18/39] Loss: 0.12737 +Epoch [3688/4000] Training [19/39] Loss: 0.00372 +Epoch [3688/4000] Training [20/39] Loss: 0.12907 +Epoch [3688/4000] Training [21/39] Loss: 0.00690 +Epoch [3688/4000] Training [22/39] Loss: 0.00829 +Epoch [3688/4000] Training [23/39] Loss: 0.00586 +Epoch [3688/4000] Training [24/39] Loss: 0.00355 +Epoch [3688/4000] Training [25/39] Loss: 0.12854 +Epoch [3688/4000] Training [26/39] Loss: 0.00565 +Epoch [3688/4000] Training [27/39] Loss: 0.00743 +Epoch [3688/4000] Training [28/39] Loss: 0.12800 +Epoch [3688/4000] Training [29/39] Loss: 0.00527 +Epoch [3688/4000] Training [30/39] Loss: 0.00420 +Epoch [3688/4000] Training [31/39] Loss: 0.00671 +Epoch [3688/4000] Training [32/39] Loss: 0.00520 +Epoch [3688/4000] Training [33/39] Loss: 0.12994 +Epoch [3688/4000] Training [34/39] Loss: 0.00574 +Epoch [3688/4000] Training [35/39] Loss: 0.12832 +Epoch [3688/4000] Training [36/39] Loss: 0.25214 +Epoch [3688/4000] Training [37/39] Loss: 0.00468 +Epoch [3688/4000] Training [38/39] Loss: 0.08411 +Epoch [3688/4000] Training [39/39] Loss: 0.12826 +Epoch [3688/4000] Training metric {'Train/mean dice_metric': 0.9961979985237122, 'Train/mean miou_metric': 0.9928386211395264, 'Train/mean f1': 0.9967802166938782, 'Train/mean precision': 0.9962797164916992, 'Train/mean recall': 0.9972812533378601, 'Train/mean hd95_metric': 0.9989730715751648} +Epoch [3688/4000] Validation [1/10] Loss: 0.72450 focal_loss 0.63700 dice_loss 0.08750 +Epoch [3688/4000] Validation [2/10] Loss: 0.49325 focal_loss 0.39842 dice_loss 0.09483 +Epoch [3688/4000] Validation [3/10] Loss: 0.38234 focal_loss 0.27150 dice_loss 0.11083 +Epoch [3688/4000] Validation [4/10] Loss: 0.91632 focal_loss 0.34834 dice_loss 0.56798 +Epoch [3688/4000] Validation [5/10] Loss: 3.02772 focal_loss 2.35413 dice_loss 0.67359 +Epoch [3688/4000] Validation [6/10] Loss: 1.36140 focal_loss 0.65043 dice_loss 0.71096 +Epoch [3688/4000] Validation [7/10] Loss: 1.20095 focal_loss 0.54629 dice_loss 0.65466 +Epoch [3688/4000] Validation [8/10] Loss: 2.16127 focal_loss 1.56904 dice_loss 0.59223 +Epoch [3688/4000] Validation [9/10] Loss: 1.59266 focal_loss 1.06067 dice_loss 0.53199 +Epoch [3688/4000] Validation [10/10] Loss: 1.94982 focal_loss 1.21303 dice_loss 0.73679 +Epoch [3688/4000] Validation metric {'Val/mean dice_metric': 0.951568603515625, 'Val/mean miou_metric': 0.9354714155197144, 'Val/mean f1': 0.9472871422767639, 'Val/mean precision': 0.9399442672729492, 'Val/mean recall': 0.9547455906867981, 'Val/mean hd95_metric': 10.838092803955078} +Cheakpoint... +Epoch [3688/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951568603515625, 'Val/mean miou_metric': 0.9354714155197144, 'Val/mean f1': 0.9472871422767639, 'Val/mean precision': 0.9399442672729492, 'Val/mean recall': 0.9547455906867981, 'Val/mean hd95_metric': 10.838092803955078} +Epoch [3689/4000] Training [1/39] Loss: 0.00546 +Epoch [3689/4000] Training [2/39] Loss: 0.12904 +Epoch [3689/4000] Training [3/39] Loss: 0.00462 +Epoch [3689/4000] Training [4/39] Loss: 0.00554 +Epoch [3689/4000] Training [5/39] Loss: 0.17097 +Epoch [3689/4000] Training [6/39] Loss: 0.12846 +Epoch [3689/4000] Training [7/39] Loss: 0.13047 +Epoch [3689/4000] Training [8/39] Loss: 0.00393 +Epoch [3689/4000] Training [9/39] Loss: 0.00533 +Epoch [3689/4000] Training [10/39] Loss: 0.00236 +Epoch [3689/4000] Training [11/39] Loss: 0.13039 +Epoch [3689/4000] Training [12/39] Loss: 0.00447 +Epoch [3689/4000] Training [13/39] Loss: 0.13133 +Epoch [3689/4000] Training [14/39] Loss: 0.00616 +Epoch [3689/4000] Training [15/39] Loss: 0.00462 +Epoch [3689/4000] Training [16/39] Loss: 0.13193 +Epoch [3689/4000] Training [17/39] Loss: 0.00570 +Epoch [3689/4000] Training [18/39] Loss: 0.00539 +Epoch [3689/4000] Training [19/39] Loss: 0.00919 +Epoch [3689/4000] Training [20/39] Loss: 0.00758 +Epoch [3689/4000] Training [21/39] Loss: 0.12782 +Epoch [3689/4000] Training [22/39] Loss: 0.12820 +Epoch [3689/4000] Training [23/39] Loss: 0.00595 +Epoch [3689/4000] Training [24/39] Loss: 0.00346 +Epoch [3689/4000] Training [25/39] Loss: 0.00494 +Epoch [3689/4000] Training [26/39] Loss: 0.00664 +Epoch [3689/4000] Training [27/39] Loss: 0.00459 +Epoch [3689/4000] Training [28/39] Loss: 0.00662 +Epoch [3689/4000] Training [29/39] Loss: 0.12772 +Epoch [3689/4000] Training [30/39] Loss: 0.00532 +Epoch [3689/4000] Training [31/39] Loss: 0.12981 +Epoch [3689/4000] Training [32/39] Loss: 0.00279 +Epoch [3689/4000] Training [33/39] Loss: 0.12738 +Epoch [3689/4000] Training [34/39] Loss: 0.00701 +Epoch [3689/4000] Training [35/39] Loss: 0.00476 +Epoch [3689/4000] Training [36/39] Loss: 0.00478 +Epoch [3689/4000] Training [37/39] Loss: 0.00521 +Epoch [3689/4000] Training [38/39] Loss: 0.00628 +Epoch [3689/4000] Training [39/39] Loss: 0.00553 +Epoch [3689/4000] Training metric {'Train/mean dice_metric': 0.9962835311889648, 'Train/mean miou_metric': 0.993013858795166, 'Train/mean f1': 0.9968935251235962, 'Train/mean precision': 0.9964259266853333, 'Train/mean recall': 0.9973616003990173, 'Train/mean hd95_metric': 1.00472891330719} +Epoch [3689/4000] Validation [1/10] Loss: 0.71402 focal_loss 0.62664 dice_loss 0.08739 +Epoch [3689/4000] Validation [2/10] Loss: 0.48474 focal_loss 0.39097 dice_loss 0.09377 +Epoch [3689/4000] Validation [3/10] Loss: 0.37942 focal_loss 0.26877 dice_loss 0.11065 +Epoch [3689/4000] Validation [4/10] Loss: 0.90393 focal_loss 0.33557 dice_loss 0.56835 +Epoch [3689/4000] Validation [5/10] Loss: 3.00184 focal_loss 2.32831 dice_loss 0.67353 +Epoch [3689/4000] Validation [6/10] Loss: 1.33728 focal_loss 0.62511 dice_loss 0.71217 +Epoch [3689/4000] Validation [7/10] Loss: 1.18727 focal_loss 0.53566 dice_loss 0.65161 +Epoch [3689/4000] Validation [8/10] Loss: 2.14467 focal_loss 1.54899 dice_loss 0.59569 +Epoch [3689/4000] Validation [9/10] Loss: 1.55441 focal_loss 1.01572 dice_loss 0.53868 +Epoch [3689/4000] Validation [10/10] Loss: 1.91253 focal_loss 1.17656 dice_loss 0.73597 +Epoch [3689/4000] Validation metric {'Val/mean dice_metric': 0.9516167044639587, 'Val/mean miou_metric': 0.9356496334075928, 'Val/mean f1': 0.9477986097335815, 'Val/mean precision': 0.9409130215644836, 'Val/mean recall': 0.9547855854034424, 'Val/mean hd95_metric': 10.837847709655762} +Cheakpoint... +Epoch [3689/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516167044639587, 'Val/mean miou_metric': 0.9356496334075928, 'Val/mean f1': 0.9477986097335815, 'Val/mean precision': 0.9409130215644836, 'Val/mean recall': 0.9547855854034424, 'Val/mean hd95_metric': 10.837847709655762} +Epoch [3690/4000] Training [1/39] Loss: 0.00567 +Epoch [3690/4000] Training [2/39] Loss: 0.00374 +Epoch [3690/4000] Training [3/39] Loss: 0.13132 +Epoch [3690/4000] Training [4/39] Loss: 0.13002 +Epoch [3690/4000] Training [5/39] Loss: 0.00455 +Epoch [3690/4000] Training [6/39] Loss: 0.25294 +Epoch [3690/4000] Training [7/39] Loss: 0.00505 +Epoch [3690/4000] Training [8/39] Loss: 0.00479 +Epoch [3690/4000] Training [9/39] Loss: 0.00577 +Epoch [3690/4000] Training [10/39] Loss: 0.00426 +Epoch [3690/4000] Training [11/39] Loss: 0.00810 +Epoch [3690/4000] Training [12/39] Loss: 0.00469 +Epoch [3690/4000] Training [13/39] Loss: 0.00410 +Epoch [3690/4000] Training [14/39] Loss: 0.00469 +Epoch [3690/4000] Training [15/39] Loss: 0.00572 +Epoch [3690/4000] Training [16/39] Loss: 0.00456 +Epoch [3690/4000] Training [17/39] Loss: 0.12853 +Epoch [3690/4000] Training [18/39] Loss: 0.00416 +Epoch [3690/4000] Training [19/39] Loss: 0.00337 +Epoch [3690/4000] Training [20/39] Loss: 0.00418 +Epoch [3690/4000] Training [21/39] Loss: 0.00474 +Epoch [3690/4000] Training [22/39] Loss: 0.12947 +Epoch [3690/4000] Training [23/39] Loss: 0.03548 +Epoch [3690/4000] Training [24/39] Loss: 0.00522 +Epoch [3690/4000] Training [25/39] Loss: 0.13074 +Epoch [3690/4000] Training [26/39] Loss: 0.00667 +Epoch [3690/4000] Training [27/39] Loss: 0.13133 +Epoch [3690/4000] Training [28/39] Loss: 0.00436 +Epoch [3690/4000] Training [29/39] Loss: 0.00505 +Epoch [3690/4000] Training [30/39] Loss: 0.25364 +Epoch [3690/4000] Training [31/39] Loss: 0.00377 +Epoch [3690/4000] Training [32/39] Loss: 0.00458 +Epoch [3690/4000] Training [33/39] Loss: 0.00438 +Epoch [3690/4000] Training [34/39] Loss: 0.00446 +Epoch [3690/4000] Training [35/39] Loss: 0.00474 +Epoch [3690/4000] Training [36/39] Loss: 0.00534 +Epoch [3690/4000] Training [37/39] Loss: 0.00334 +Epoch [3690/4000] Training [38/39] Loss: 0.12813 +Epoch [3690/4000] Training [39/39] Loss: 0.00405 +Epoch [3690/4000] Training metric {'Train/mean dice_metric': 0.9960783123970032, 'Train/mean miou_metric': 0.9925995469093323, 'Train/mean f1': 0.9967393279075623, 'Train/mean precision': 0.9963395595550537, 'Train/mean recall': 0.9971395134925842, 'Train/mean hd95_metric': 0.9771371483802795} +Epoch [3690/4000] Validation [1/10] Loss: 0.72369 focal_loss 0.63615 dice_loss 0.08755 +Epoch [3690/4000] Validation [2/10] Loss: 0.49083 focal_loss 0.39445 dice_loss 0.09638 +Epoch [3690/4000] Validation [3/10] Loss: 0.39368 focal_loss 0.28207 dice_loss 0.11161 +Epoch [3690/4000] Validation [4/10] Loss: 0.90516 focal_loss 0.33666 dice_loss 0.56849 +Epoch [3690/4000] Validation [5/10] Loss: 3.04316 focal_loss 2.36951 dice_loss 0.67365 +Epoch [3690/4000] Validation [6/10] Loss: 1.33738 focal_loss 0.62585 dice_loss 0.71153 +Epoch [3690/4000] Validation [7/10] Loss: 1.18783 focal_loss 0.53505 dice_loss 0.65278 +Epoch [3690/4000] Validation [8/10] Loss: 2.23422 focal_loss 1.63178 dice_loss 0.60244 +Epoch [3690/4000] Validation [9/10] Loss: 1.55628 focal_loss 1.01371 dice_loss 0.54257 +Epoch [3690/4000] Validation [10/10] Loss: 1.90240 focal_loss 1.16826 dice_loss 0.73414 +Epoch [3690/4000] Validation metric {'Val/mean dice_metric': 0.9514699578285217, 'Val/mean miou_metric': 0.9353966116905212, 'Val/mean f1': 0.948154866695404, 'Val/mean precision': 0.9420598149299622, 'Val/mean recall': 0.9543291330337524, 'Val/mean hd95_metric': 10.671102523803711} +Cheakpoint... +Epoch [3690/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514699578285217, 'Val/mean miou_metric': 0.9353966116905212, 'Val/mean f1': 0.948154866695404, 'Val/mean precision': 0.9420598149299622, 'Val/mean recall': 0.9543291330337524, 'Val/mean hd95_metric': 10.671102523803711} +Epoch [3691/4000] Training [1/39] Loss: 0.00470 +Epoch [3691/4000] Training [2/39] Loss: 0.12759 +Epoch [3691/4000] Training [3/39] Loss: 0.00514 +Epoch [3691/4000] Training [4/39] Loss: 0.00401 +Epoch [3691/4000] Training [5/39] Loss: 0.12993 +Epoch [3691/4000] Training [6/39] Loss: 0.00309 +Epoch [3691/4000] Training [7/39] Loss: 0.00376 +Epoch [3691/4000] Training [8/39] Loss: 0.00481 +Epoch [3691/4000] Training [9/39] Loss: 0.00695 +Epoch [3691/4000] Training [10/39] Loss: 0.00386 +Epoch [3691/4000] Training [11/39] Loss: 0.13203 +Epoch [3691/4000] Training [12/39] Loss: 0.00582 +Epoch [3691/4000] Training [13/39] Loss: 0.12866 +Epoch [3691/4000] Training [14/39] Loss: 0.00489 +Epoch [3691/4000] Training [15/39] Loss: 0.00429 +Epoch [3691/4000] Training [16/39] Loss: 0.00621 +Epoch [3691/4000] Training [17/39] Loss: 0.00560 +Epoch [3691/4000] Training [18/39] Loss: 0.00650 +Epoch [3691/4000] Training [19/39] Loss: 0.00399 +Epoch [3691/4000] Training [20/39] Loss: 0.00353 +Epoch [3691/4000] Training [21/39] Loss: 0.12887 +Epoch [3691/4000] Training [22/39] Loss: 0.00409 +Epoch [3691/4000] Training [23/39] Loss: 0.00459 +Epoch [3691/4000] Training [24/39] Loss: 0.13119 +Epoch [3691/4000] Training [25/39] Loss: 0.00521 +Epoch [3691/4000] Training [26/39] Loss: 0.00373 +Epoch [3691/4000] Training [27/39] Loss: 0.00682 +Epoch [3691/4000] Training [28/39] Loss: 0.00482 +Epoch [3691/4000] Training [29/39] Loss: 0.00397 +Epoch [3691/4000] Training [30/39] Loss: 0.00441 +Epoch [3691/4000] Training [31/39] Loss: 0.00455 +Epoch [3691/4000] Training [32/39] Loss: 0.00584 +Epoch [3691/4000] Training [33/39] Loss: 0.25424 +Epoch [3691/4000] Training [34/39] Loss: 0.25428 +Epoch [3691/4000] Training [35/39] Loss: 0.00878 +Epoch [3691/4000] Training [36/39] Loss: 0.00523 +Epoch [3691/4000] Training [37/39] Loss: 0.00605 +Epoch [3691/4000] Training [38/39] Loss: 0.00471 +Epoch [3691/4000] Training [39/39] Loss: 0.12865 +Epoch [3691/4000] Training metric {'Train/mean dice_metric': 0.9953292608261108, 'Train/mean miou_metric': 0.9919469952583313, 'Train/mean f1': 0.9967477917671204, 'Train/mean precision': 0.9962894916534424, 'Train/mean recall': 0.9972065687179565, 'Train/mean hd95_metric': 0.9386932849884033} +Epoch [3691/4000] Validation [1/10] Loss: 0.70260 focal_loss 0.61564 dice_loss 0.08696 +Epoch [3691/4000] Validation [2/10] Loss: 0.49293 focal_loss 0.39622 dice_loss 0.09672 +Epoch [3691/4000] Validation [3/10] Loss: 0.37752 focal_loss 0.26658 dice_loss 0.11093 +Epoch [3691/4000] Validation [4/10] Loss: 0.90502 focal_loss 0.33685 dice_loss 0.56817 +Epoch [3691/4000] Validation [5/10] Loss: 2.97601 focal_loss 2.30246 dice_loss 0.67355 +Epoch [3691/4000] Validation [6/10] Loss: 1.34219 focal_loss 0.63107 dice_loss 0.71112 +Epoch [3691/4000] Validation [7/10] Loss: 1.18656 focal_loss 0.53463 dice_loss 0.65194 +Epoch [3691/4000] Validation [8/10] Loss: 2.23419 focal_loss 1.62891 dice_loss 0.60529 +Epoch [3691/4000] Validation [9/10] Loss: 1.52726 focal_loss 0.98267 dice_loss 0.54459 +Epoch [3691/4000] Validation [10/10] Loss: 1.89466 focal_loss 1.16013 dice_loss 0.73453 +Epoch [3691/4000] Validation metric {'Val/mean dice_metric': 0.9508247971534729, 'Val/mean miou_metric': 0.9348331689834595, 'Val/mean f1': 0.9482208490371704, 'Val/mean precision': 0.9422498345375061, 'Val/mean recall': 0.9542680382728577, 'Val/mean hd95_metric': 10.726003646850586} +Cheakpoint... +Epoch [3691/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508247971534729, 'Val/mean miou_metric': 0.9348331689834595, 'Val/mean f1': 0.9482208490371704, 'Val/mean precision': 0.9422498345375061, 'Val/mean recall': 0.9542680382728577, 'Val/mean hd95_metric': 10.726003646850586} +Epoch [3692/4000] Training [1/39] Loss: 0.25364 +Epoch [3692/4000] Training [2/39] Loss: 0.00482 +Epoch [3692/4000] Training [3/39] Loss: 0.00741 +Epoch [3692/4000] Training [4/39] Loss: 0.00511 +Epoch [3692/4000] Training [5/39] Loss: 0.12927 +Epoch [3692/4000] Training [6/39] Loss: 0.00597 +Epoch [3692/4000] Training [7/39] Loss: 0.00552 +Epoch [3692/4000] Training [8/39] Loss: 0.00320 +Epoch [3692/4000] Training [9/39] Loss: 0.12832 +Epoch [3692/4000] Training [10/39] Loss: 0.00589 +Epoch [3692/4000] Training [11/39] Loss: 0.25205 +Epoch [3692/4000] Training [12/39] Loss: 0.13036 +Epoch [3692/4000] Training [13/39] Loss: 0.00601 +Epoch [3692/4000] Training [14/39] Loss: 0.00361 +Epoch [3692/4000] Training [15/39] Loss: 0.00475 +Epoch [3692/4000] Training [16/39] Loss: 0.12960 +Epoch [3692/4000] Training [17/39] Loss: 0.01236 +Epoch [3692/4000] Training [18/39] Loss: 0.12827 +Epoch [3692/4000] Training [19/39] Loss: 0.00393 +Epoch [3692/4000] Training [20/39] Loss: 0.00235 +Epoch [3692/4000] Training [21/39] Loss: 0.13135 +Epoch [3692/4000] Training [22/39] Loss: 0.04548 +Epoch [3692/4000] Training [23/39] Loss: 0.12978 +Epoch [3692/4000] Training [24/39] Loss: 0.20959 +Epoch [3692/4000] Training [25/39] Loss: 0.12816 +Epoch [3692/4000] Training [26/39] Loss: 0.00477 +Epoch [3692/4000] Training [27/39] Loss: 0.00619 +Epoch [3692/4000] Training [28/39] Loss: 0.12853 +Epoch [3692/4000] Training [29/39] Loss: 0.00375 +Epoch [3692/4000] Training [30/39] Loss: 0.00347 +Epoch [3692/4000] Training [31/39] Loss: 0.00367 +Epoch [3692/4000] Training [32/39] Loss: 0.00776 +Epoch [3692/4000] Training [33/39] Loss: 0.00744 +Epoch [3692/4000] Training [34/39] Loss: 0.12854 +Epoch [3692/4000] Training [35/39] Loss: 0.00559 +Epoch [3692/4000] Training [36/39] Loss: 0.00869 +Epoch [3692/4000] Training [37/39] Loss: 0.00676 +Epoch [3692/4000] Training [38/39] Loss: 0.00565 +Epoch [3692/4000] Training [39/39] Loss: 0.00368 +Epoch [3692/4000] Training metric {'Train/mean dice_metric': 0.996082603931427, 'Train/mean miou_metric': 0.9926400184631348, 'Train/mean f1': 0.996727705001831, 'Train/mean precision': 0.9962425231933594, 'Train/mean recall': 0.9972132444381714, 'Train/mean hd95_metric': 0.9859089255332947} +Epoch [3692/4000] Validation [1/10] Loss: 0.72075 focal_loss 0.63439 dice_loss 0.08636 +Epoch [3692/4000] Validation [2/10] Loss: 0.50634 focal_loss 0.40682 dice_loss 0.09952 +Epoch [3692/4000] Validation [3/10] Loss: 0.40291 focal_loss 0.29088 dice_loss 0.11203 +Epoch [3692/4000] Validation [4/10] Loss: 0.88953 focal_loss 0.32392 dice_loss 0.56561 +Epoch [3692/4000] Validation [5/10] Loss: 3.05851 focal_loss 2.38455 dice_loss 0.67395 +Epoch [3692/4000] Validation [6/10] Loss: 1.32809 focal_loss 0.61608 dice_loss 0.71201 +Epoch [3692/4000] Validation [7/10] Loss: 1.17362 focal_loss 0.52318 dice_loss 0.65044 +Epoch [3692/4000] Validation [8/10] Loss: 2.44539 focal_loss 1.82185 dice_loss 0.62354 +Epoch [3692/4000] Validation [9/10] Loss: 1.47636 focal_loss 0.93253 dice_loss 0.54383 +Epoch [3692/4000] Validation [10/10] Loss: 1.84828 focal_loss 1.11879 dice_loss 0.72949 +Epoch [3692/4000] Validation metric {'Val/mean dice_metric': 0.9513750076293945, 'Val/mean miou_metric': 0.9353242516517639, 'Val/mean f1': 0.9485798478126526, 'Val/mean precision': 0.945223331451416, 'Val/mean recall': 0.951960027217865, 'Val/mean hd95_metric': 10.55510425567627} +Cheakpoint... +Epoch [3692/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513750076293945, 'Val/mean miou_metric': 0.9353242516517639, 'Val/mean f1': 0.9485798478126526, 'Val/mean precision': 0.945223331451416, 'Val/mean recall': 0.951960027217865, 'Val/mean hd95_metric': 10.55510425567627} +Epoch [3693/4000] Training [1/39] Loss: 0.00474 +Epoch [3693/4000] Training [2/39] Loss: 0.00395 +Epoch [3693/4000] Training [3/39] Loss: 0.12903 +Epoch [3693/4000] Training [4/39] Loss: 0.00506 +Epoch [3693/4000] Training [5/39] Loss: 0.00357 +Epoch [3693/4000] Training [6/39] Loss: 0.00700 +Epoch [3693/4000] Training [7/39] Loss: 0.25287 +Epoch [3693/4000] Training [8/39] Loss: 0.00361 +Epoch [3693/4000] Training [9/39] Loss: 0.00626 +Epoch [3693/4000] Training [10/39] Loss: 0.00645 +Epoch [3693/4000] Training [11/39] Loss: 0.00613 +Epoch [3693/4000] Training [12/39] Loss: 0.00406 +Epoch [3693/4000] Training [13/39] Loss: 0.00292 +Epoch [3693/4000] Training [14/39] Loss: 0.00562 +Epoch [3693/4000] Training [15/39] Loss: 0.00316 +Epoch [3693/4000] Training [16/39] Loss: 0.00609 +Epoch [3693/4000] Training [17/39] Loss: 0.00581 +Epoch [3693/4000] Training [18/39] Loss: 0.00284 +Epoch [3693/4000] Training [19/39] Loss: 0.00620 +Epoch [3693/4000] Training [20/39] Loss: 0.13054 +Epoch [3693/4000] Training [21/39] Loss: 0.00584 +Epoch [3693/4000] Training [22/39] Loss: 0.12801 +Epoch [3693/4000] Training [23/39] Loss: 0.12998 +Epoch [3693/4000] Training [24/39] Loss: 0.00848 +Epoch [3693/4000] Training [25/39] Loss: 0.00406 +Epoch [3693/4000] Training [26/39] Loss: 0.13017 +Epoch [3693/4000] Training [27/39] Loss: 0.12944 +Epoch [3693/4000] Training [28/39] Loss: 0.00706 +Epoch [3693/4000] Training [29/39] Loss: 0.00603 +Epoch [3693/4000] Training [30/39] Loss: 0.00681 +Epoch [3693/4000] Training [31/39] Loss: 0.00534 +Epoch [3693/4000] Training [32/39] Loss: 0.13035 +Epoch [3693/4000] Training [33/39] Loss: 0.12813 +Epoch [3693/4000] Training [34/39] Loss: 0.12780 +Epoch [3693/4000] Training [35/39] Loss: 0.00354 +Epoch [3693/4000] Training [36/39] Loss: 0.00459 +Epoch [3693/4000] Training [37/39] Loss: 0.00398 +Epoch [3693/4000] Training [38/39] Loss: 0.12898 +Epoch [3693/4000] Training [39/39] Loss: 0.12939 +Epoch [3693/4000] Training metric {'Train/mean dice_metric': 0.9962279796600342, 'Train/mean miou_metric': 0.9929140210151672, 'Train/mean f1': 0.996898889541626, 'Train/mean precision': 0.9964991211891174, 'Train/mean recall': 0.9972988963127136, 'Train/mean hd95_metric': 1.1282835006713867} +Epoch [3693/4000] Validation [1/10] Loss: 0.73525 focal_loss 0.64782 dice_loss 0.08743 +Epoch [3693/4000] Validation [2/10] Loss: 0.49932 focal_loss 0.40216 dice_loss 0.09716 +Epoch [3693/4000] Validation [3/10] Loss: 0.40369 focal_loss 0.29179 dice_loss 0.11189 +Epoch [3693/4000] Validation [4/10] Loss: 0.89295 focal_loss 0.32715 dice_loss 0.56580 +Epoch [3693/4000] Validation [5/10] Loss: 3.08708 focal_loss 2.41318 dice_loss 0.67390 +Epoch [3693/4000] Validation [6/10] Loss: 1.33500 focal_loss 0.62238 dice_loss 0.71263 +Epoch [3693/4000] Validation [7/10] Loss: 1.17200 focal_loss 0.52102 dice_loss 0.65098 +Epoch [3693/4000] Validation [8/10] Loss: 2.40958 focal_loss 1.79021 dice_loss 0.61937 +Epoch [3693/4000] Validation [9/10] Loss: 1.51080 focal_loss 0.96668 dice_loss 0.54412 +Epoch [3693/4000] Validation [10/10] Loss: 1.86766 focal_loss 1.13639 dice_loss 0.73127 +Epoch [3693/4000] Validation metric {'Val/mean dice_metric': 0.9515525698661804, 'Val/mean miou_metric': 0.9356396198272705, 'Val/mean f1': 0.9486720561981201, 'Val/mean precision': 0.9446606636047363, 'Val/mean recall': 0.9527174830436707, 'Val/mean hd95_metric': 10.648319244384766} +Cheakpoint... +Epoch [3693/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515525698661804, 'Val/mean miou_metric': 0.9356396198272705, 'Val/mean f1': 0.9486720561981201, 'Val/mean precision': 0.9446606636047363, 'Val/mean recall': 0.9527174830436707, 'Val/mean hd95_metric': 10.648319244384766} +Epoch [3694/4000] Training [1/39] Loss: 0.00507 +Epoch [3694/4000] Training [2/39] Loss: 0.00483 +Epoch [3694/4000] Training [3/39] Loss: 0.00803 +Epoch [3694/4000] Training [4/39] Loss: 0.13156 +Epoch [3694/4000] Training [5/39] Loss: 0.00458 +Epoch [3694/4000] Training [6/39] Loss: 0.00579 +Epoch [3694/4000] Training [7/39] Loss: 0.00375 +Epoch [3694/4000] Training [8/39] Loss: 0.12877 +Epoch [3694/4000] Training [9/39] Loss: 0.25529 +Epoch [3694/4000] Training [10/39] Loss: 0.00454 +Epoch [3694/4000] Training [11/39] Loss: 0.00453 +Epoch [3694/4000] Training [12/39] Loss: 0.00473 +Epoch [3694/4000] Training [13/39] Loss: 0.00429 +Epoch [3694/4000] Training [14/39] Loss: 0.00294 +Epoch [3694/4000] Training [15/39] Loss: 0.12973 +Epoch [3694/4000] Training [16/39] Loss: 0.00551 +Epoch [3694/4000] Training [17/39] Loss: 0.00456 +Epoch [3694/4000] Training [18/39] Loss: 0.00386 +Epoch [3694/4000] Training [19/39] Loss: 0.12883 +Epoch [3694/4000] Training [20/39] Loss: 0.00515 +Epoch [3694/4000] Training [21/39] Loss: 0.00360 +Epoch [3694/4000] Training [22/39] Loss: 0.03094 +Epoch [3694/4000] Training [23/39] Loss: 0.12880 +Epoch [3694/4000] Training [24/39] Loss: 0.12870 +Epoch [3694/4000] Training [25/39] Loss: 0.00695 +Epoch [3694/4000] Training [26/39] Loss: 0.00561 +Epoch [3694/4000] Training [27/39] Loss: 0.00363 +Epoch [3694/4000] Training [28/39] Loss: 0.25284 +Epoch [3694/4000] Training [29/39] Loss: 0.00333 +Epoch [3694/4000] Training [30/39] Loss: 0.00397 +Epoch [3694/4000] Training [31/39] Loss: 0.12994 +Epoch [3694/4000] Training [32/39] Loss: 0.12999 +Epoch [3694/4000] Training [33/39] Loss: 0.13011 +Epoch [3694/4000] Training [34/39] Loss: 0.00706 +Epoch [3694/4000] Training [35/39] Loss: 0.00503 +Epoch [3694/4000] Training [36/39] Loss: 0.00453 +Epoch [3694/4000] Training [37/39] Loss: 0.00566 +Epoch [3694/4000] Training [38/39] Loss: 0.00628 +Epoch [3694/4000] Training [39/39] Loss: 0.00412 +Epoch [3694/4000] Training metric {'Train/mean dice_metric': 0.996226966381073, 'Train/mean miou_metric': 0.992893636226654, 'Train/mean f1': 0.9967896938323975, 'Train/mean precision': 0.9963494539260864, 'Train/mean recall': 0.9972304105758667, 'Train/mean hd95_metric': 0.9539602398872375} +Epoch [3694/4000] Validation [1/10] Loss: 0.74836 focal_loss 0.65887 dice_loss 0.08949 +Epoch [3694/4000] Validation [2/10] Loss: 0.49641 focal_loss 0.40051 dice_loss 0.09590 +Epoch [3694/4000] Validation [3/10] Loss: 0.39280 focal_loss 0.28175 dice_loss 0.11106 +Epoch [3694/4000] Validation [4/10] Loss: 0.90309 focal_loss 0.33587 dice_loss 0.56723 +Epoch [3694/4000] Validation [5/10] Loss: 3.06683 focal_loss 2.39319 dice_loss 0.67364 +Epoch [3694/4000] Validation [6/10] Loss: 1.34543 focal_loss 0.63371 dice_loss 0.71172 +Epoch [3694/4000] Validation [7/10] Loss: 1.18581 focal_loss 0.53317 dice_loss 0.65263 +Epoch [3694/4000] Validation [8/10] Loss: 2.32519 focal_loss 1.71415 dice_loss 0.61105 +Epoch [3694/4000] Validation [9/10] Loss: 1.55421 focal_loss 1.00956 dice_loss 0.54464 +Epoch [3694/4000] Validation [10/10] Loss: 1.89604 focal_loss 1.16174 dice_loss 0.73430 +Epoch [3694/4000] Validation metric {'Val/mean dice_metric': 0.9514943361282349, 'Val/mean miou_metric': 0.9355329871177673, 'Val/mean f1': 0.9483258128166199, 'Val/mean precision': 0.942671000957489, 'Val/mean recall': 0.9540488719940186, 'Val/mean hd95_metric': 10.690333366394043} +Cheakpoint... +Epoch [3694/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514943361282349, 'Val/mean miou_metric': 0.9355329871177673, 'Val/mean f1': 0.9483258128166199, 'Val/mean precision': 0.942671000957489, 'Val/mean recall': 0.9540488719940186, 'Val/mean hd95_metric': 10.690333366394043} +Epoch [3695/4000] Training [1/39] Loss: 0.00382 +Epoch [3695/4000] Training [2/39] Loss: 0.00583 +Epoch [3695/4000] Training [3/39] Loss: 0.00513 +Epoch [3695/4000] Training [4/39] Loss: 0.00761 +Epoch [3695/4000] Training [5/39] Loss: 0.00339 +Epoch [3695/4000] Training [6/39] Loss: 0.00413 +Epoch [3695/4000] Training [7/39] Loss: 0.00428 +Epoch [3695/4000] Training [8/39] Loss: 0.00536 +Epoch [3695/4000] Training [9/39] Loss: 0.00833 +Epoch [3695/4000] Training [10/39] Loss: 0.00535 +Epoch [3695/4000] Training [11/39] Loss: 0.08151 +Epoch [3695/4000] Training [12/39] Loss: 0.12994 +Epoch [3695/4000] Training [13/39] Loss: 0.00409 +Epoch [3695/4000] Training [14/39] Loss: 0.13040 +Epoch [3695/4000] Training [15/39] Loss: 0.00294 +Epoch [3695/4000] Training [16/39] Loss: 0.12874 +Epoch [3695/4000] Training [17/39] Loss: 0.00440 +Epoch [3695/4000] Training [18/39] Loss: 0.13335 +Epoch [3695/4000] Training [19/39] Loss: 0.00292 +Epoch [3695/4000] Training [20/39] Loss: 0.12885 +Epoch [3695/4000] Training [21/39] Loss: 0.25560 +Epoch [3695/4000] Training [22/39] Loss: 0.00458 +Epoch [3695/4000] Training [23/39] Loss: 0.00832 +Epoch [3695/4000] Training [24/39] Loss: 0.00518 +Epoch [3695/4000] Training [25/39] Loss: 0.00513 +Epoch [3695/4000] Training [26/39] Loss: 0.00343 +Epoch [3695/4000] Training [27/39] Loss: 0.00563 +Epoch [3695/4000] Training [28/39] Loss: 0.00503 +Epoch [3695/4000] Training [29/39] Loss: 0.00585 +Epoch [3695/4000] Training [30/39] Loss: 0.25603 +Epoch [3695/4000] Training [31/39] Loss: 0.00408 +Epoch [3695/4000] Training [32/39] Loss: 0.00548 +Epoch [3695/4000] Training [33/39] Loss: 0.00411 +Epoch [3695/4000] Training [34/39] Loss: 0.00431 +Epoch [3695/4000] Training [35/39] Loss: 0.12786 +Epoch [3695/4000] Training [36/39] Loss: 0.00406 +Epoch [3695/4000] Training [37/39] Loss: 0.12952 +Epoch [3695/4000] Training [38/39] Loss: 0.00623 +Epoch [3695/4000] Training [39/39] Loss: 0.00319 +Epoch [3695/4000] Training metric {'Train/mean dice_metric': 0.9961633086204529, 'Train/mean miou_metric': 0.9927722811698914, 'Train/mean f1': 0.9968824982643127, 'Train/mean precision': 0.99647057056427, 'Train/mean recall': 0.9972947239875793, 'Train/mean hd95_metric': 0.9583871960639954} +Epoch [3695/4000] Validation [1/10] Loss: 0.73934 focal_loss 0.65178 dice_loss 0.08755 +Epoch [3695/4000] Validation [2/10] Loss: 0.49362 focal_loss 0.39432 dice_loss 0.09930 +Epoch [3695/4000] Validation [3/10] Loss: 0.41734 focal_loss 0.30422 dice_loss 0.11312 +Epoch [3695/4000] Validation [4/10] Loss: 0.88173 focal_loss 0.31751 dice_loss 0.56422 +Epoch [3695/4000] Validation [5/10] Loss: 3.11442 focal_loss 2.44026 dice_loss 0.67416 +Epoch [3695/4000] Validation [6/10] Loss: 1.29758 focal_loss 0.58615 dice_loss 0.71144 +Epoch [3695/4000] Validation [7/10] Loss: 1.15856 focal_loss 0.50847 dice_loss 0.65009 +Epoch [3695/4000] Validation [8/10] Loss: 2.41712 focal_loss 1.79234 dice_loss 0.62478 +Epoch [3695/4000] Validation [9/10] Loss: 1.52089 focal_loss 0.97771 dice_loss 0.54318 +Epoch [3695/4000] Validation [10/10] Loss: 1.81427 focal_loss 1.08217 dice_loss 0.73210 +Epoch [3695/4000] Validation metric {'Val/mean dice_metric': 0.9513615369796753, 'Val/mean miou_metric': 0.93538498878479, 'Val/mean f1': 0.9489163756370544, 'Val/mean precision': 0.9453805088996887, 'Val/mean recall': 0.9524787068367004, 'Val/mean hd95_metric': 10.562710762023926} +Cheakpoint... +Epoch [3695/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513615369796753, 'Val/mean miou_metric': 0.93538498878479, 'Val/mean f1': 0.9489163756370544, 'Val/mean precision': 0.9453805088996887, 'Val/mean recall': 0.9524787068367004, 'Val/mean hd95_metric': 10.562710762023926} +Epoch [3696/4000] Training [1/39] Loss: 0.00574 +Epoch [3696/4000] Training [2/39] Loss: 0.25392 +Epoch [3696/4000] Training [3/39] Loss: 0.12875 +Epoch [3696/4000] Training [4/39] Loss: 0.00453 +Epoch [3696/4000] Training [5/39] Loss: 0.00565 +Epoch [3696/4000] Training [6/39] Loss: 0.12986 +Epoch [3696/4000] Training [7/39] Loss: 0.12955 +Epoch [3696/4000] Training [8/39] Loss: 0.00454 +Epoch [3696/4000] Training [9/39] Loss: 0.00486 +Epoch [3696/4000] Training [10/39] Loss: 0.00542 +Epoch [3696/4000] Training [11/39] Loss: 0.00757 +Epoch [3696/4000] Training [12/39] Loss: 0.00348 +Epoch [3696/4000] Training [13/39] Loss: 0.00616 +Epoch [3696/4000] Training [14/39] Loss: 0.00396 +Epoch [3696/4000] Training [15/39] Loss: 0.00599 +Epoch [3696/4000] Training [16/39] Loss: 0.00603 +Epoch [3696/4000] Training [17/39] Loss: 0.00682 +Epoch [3696/4000] Training [18/39] Loss: 0.00578 +Epoch [3696/4000] Training [19/39] Loss: 0.00611 +Epoch [3696/4000] Training [20/39] Loss: 0.00704 +Epoch [3696/4000] Training [21/39] Loss: 0.00262 +Epoch [3696/4000] Training [22/39] Loss: 0.13099 +Epoch [3696/4000] Training [23/39] Loss: 0.00321 +Epoch [3696/4000] Training [24/39] Loss: 0.12907 +Epoch [3696/4000] Training [25/39] Loss: 0.00504 +Epoch [3696/4000] Training [26/39] Loss: 0.12657 +Epoch [3696/4000] Training [27/39] Loss: 0.00409 +Epoch [3696/4000] Training [28/39] Loss: 0.00454 +Epoch [3696/4000] Training [29/39] Loss: 0.12927 +Epoch [3696/4000] Training [30/39] Loss: 0.00388 +Epoch [3696/4000] Training [31/39] Loss: 0.00265 +Epoch [3696/4000] Training [32/39] Loss: 0.00410 +Epoch [3696/4000] Training [33/39] Loss: 0.00411 +Epoch [3696/4000] Training [34/39] Loss: 0.13034 +Epoch [3696/4000] Training [35/39] Loss: 0.00458 +Epoch [3696/4000] Training [36/39] Loss: 0.13099 +Epoch [3696/4000] Training [37/39] Loss: 0.00301 +Epoch [3696/4000] Training [38/39] Loss: 0.12917 +Epoch [3696/4000] Training [39/39] Loss: 0.00546 +Epoch [3696/4000] Training metric {'Train/mean dice_metric': 0.9963533878326416, 'Train/mean miou_metric': 0.9931319952011108, 'Train/mean f1': 0.9968799352645874, 'Train/mean precision': 0.9964207410812378, 'Train/mean recall': 0.99733966588974, 'Train/mean hd95_metric': 0.9351514577865601} +Epoch [3696/4000] Validation [1/10] Loss: 0.73470 focal_loss 0.64625 dice_loss 0.08845 +Epoch [3696/4000] Validation [2/10] Loss: 0.48769 focal_loss 0.39297 dice_loss 0.09472 +Epoch [3696/4000] Validation [3/10] Loss: 0.38648 focal_loss 0.27565 dice_loss 0.11083 +Epoch [3696/4000] Validation [4/10] Loss: 0.89725 focal_loss 0.33156 dice_loss 0.56569 +Epoch [3696/4000] Validation [5/10] Loss: 3.02364 focal_loss 2.34951 dice_loss 0.67413 +Epoch [3696/4000] Validation [6/10] Loss: 1.34597 focal_loss 0.63373 dice_loss 0.71223 +Epoch [3696/4000] Validation [7/10] Loss: 1.18881 focal_loss 0.53786 dice_loss 0.65095 +Epoch [3696/4000] Validation [8/10] Loss: 2.34868 focal_loss 1.73488 dice_loss 0.61380 +Epoch [3696/4000] Validation [9/10] Loss: 1.56104 focal_loss 1.01686 dice_loss 0.54417 +Epoch [3696/4000] Validation [10/10] Loss: 1.90508 focal_loss 1.16997 dice_loss 0.73511 +Epoch [3696/4000] Validation metric {'Val/mean dice_metric': 0.9516744613647461, 'Val/mean miou_metric': 0.9358075857162476, 'Val/mean f1': 0.948141872882843, 'Val/mean precision': 0.9428222179412842, 'Val/mean recall': 0.9535218477249146, 'Val/mean hd95_metric': 10.689613342285156} +Cheakpoint... +Epoch [3696/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516744613647461, 'Val/mean miou_metric': 0.9358075857162476, 'Val/mean f1': 0.948141872882843, 'Val/mean precision': 0.9428222179412842, 'Val/mean recall': 0.9535218477249146, 'Val/mean hd95_metric': 10.689613342285156} +Epoch [3697/4000] Training [1/39] Loss: 0.00648 +Epoch [3697/4000] Training [2/39] Loss: 0.00527 +Epoch [3697/4000] Training [3/39] Loss: 0.00762 +Epoch [3697/4000] Training [4/39] Loss: 0.37679 +Epoch [3697/4000] Training [5/39] Loss: 0.00336 +Epoch [3697/4000] Training [6/39] Loss: 0.00609 +Epoch [3697/4000] Training [7/39] Loss: 0.00412 +Epoch [3697/4000] Training [8/39] Loss: 0.00607 +Epoch [3697/4000] Training [9/39] Loss: 0.00529 +Epoch [3697/4000] Training [10/39] Loss: 0.00359 +Epoch [3697/4000] Training [11/39] Loss: 0.00575 +Epoch [3697/4000] Training [12/39] Loss: 0.12802 +Epoch [3697/4000] Training [13/39] Loss: 0.12975 +Epoch [3697/4000] Training [14/39] Loss: 0.00603 +Epoch [3697/4000] Training [15/39] Loss: 0.12798 +Epoch [3697/4000] Training [16/39] Loss: 0.00368 +Epoch [3697/4000] Training [17/39] Loss: 0.00625 +Epoch [3697/4000] Training [18/39] Loss: 0.25442 +Epoch [3697/4000] Training [19/39] Loss: 0.04878 +Epoch [3697/4000] Training [20/39] Loss: 0.12893 +Epoch [3697/4000] Training [21/39] Loss: 0.00358 +Epoch [3697/4000] Training [22/39] Loss: 0.25281 +Epoch [3697/4000] Training [23/39] Loss: 0.00312 +Epoch [3697/4000] Training [24/39] Loss: 0.12960 +Epoch [3697/4000] Training [25/39] Loss: 0.00457 +Epoch [3697/4000] Training [26/39] Loss: 0.00358 +Epoch [3697/4000] Training [27/39] Loss: 0.01045 +Epoch [3697/4000] Training [28/39] Loss: 0.00876 +Epoch [3697/4000] Training [29/39] Loss: 0.00465 +Epoch [3697/4000] Training [30/39] Loss: 0.00407 +Epoch [3697/4000] Training [31/39] Loss: 0.00645 +Epoch [3697/4000] Training [32/39] Loss: 0.12919 +Epoch [3697/4000] Training [33/39] Loss: 0.00532 +Epoch [3697/4000] Training [34/39] Loss: 0.00428 +Epoch [3697/4000] Training [35/39] Loss: 0.12908 +Epoch [3697/4000] Training [36/39] Loss: 0.00342 +Epoch [3697/4000] Training [37/39] Loss: 0.00518 +Epoch [3697/4000] Training [38/39] Loss: 0.00391 +Epoch [3697/4000] Training [39/39] Loss: 0.00437 +Epoch [3697/4000] Training metric {'Train/mean dice_metric': 0.9961586594581604, 'Train/mean miou_metric': 0.9927689433097839, 'Train/mean f1': 0.9967419505119324, 'Train/mean precision': 0.9963366389274597, 'Train/mean recall': 0.9971476793289185, 'Train/mean hd95_metric': 1.001876950263977} +Epoch [3697/4000] Validation [1/10] Loss: 0.73395 focal_loss 0.64501 dice_loss 0.08894 +Epoch [3697/4000] Validation [2/10] Loss: 0.48960 focal_loss 0.39102 dice_loss 0.09858 +Epoch [3697/4000] Validation [3/10] Loss: 0.38491 focal_loss 0.27361 dice_loss 0.11130 +Epoch [3697/4000] Validation [4/10] Loss: 0.89240 focal_loss 0.32743 dice_loss 0.56498 +Epoch [3697/4000] Validation [5/10] Loss: 2.99886 focal_loss 2.32479 dice_loss 0.67407 +Epoch [3697/4000] Validation [6/10] Loss: 1.33590 focal_loss 0.62355 dice_loss 0.71235 +Epoch [3697/4000] Validation [7/10] Loss: 1.17857 focal_loss 0.52767 dice_loss 0.65089 +Epoch [3697/4000] Validation [8/10] Loss: 2.28954 focal_loss 1.67928 dice_loss 0.61026 +Epoch [3697/4000] Validation [9/10] Loss: 1.55378 focal_loss 1.01586 dice_loss 0.53792 +Epoch [3697/4000] Validation [10/10] Loss: 1.88728 focal_loss 1.15181 dice_loss 0.73547 +Epoch [3697/4000] Validation metric {'Val/mean dice_metric': 0.9515107870101929, 'Val/mean miou_metric': 0.935483455657959, 'Val/mean f1': 0.948316752910614, 'Val/mean precision': 0.9428339004516602, 'Val/mean recall': 0.9538637399673462, 'Val/mean hd95_metric': 10.986682891845703} +Cheakpoint... +Epoch [3697/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515107870101929, 'Val/mean miou_metric': 0.935483455657959, 'Val/mean f1': 0.948316752910614, 'Val/mean precision': 0.9428339004516602, 'Val/mean recall': 0.9538637399673462, 'Val/mean hd95_metric': 10.986682891845703} +Epoch [3698/4000] Training [1/39] Loss: 0.00593 +Epoch [3698/4000] Training [2/39] Loss: 0.12815 +Epoch [3698/4000] Training [3/39] Loss: 0.00379 +Epoch [3698/4000] Training [4/39] Loss: 0.00566 +Epoch [3698/4000] Training [5/39] Loss: 0.00603 +Epoch [3698/4000] Training [6/39] Loss: 0.00703 +Epoch [3698/4000] Training [7/39] Loss: 0.00435 +Epoch [3698/4000] Training [8/39] Loss: 0.00617 +Epoch [3698/4000] Training [9/39] Loss: 0.12900 +Epoch [3698/4000] Training [10/39] Loss: 0.00705 +Epoch [3698/4000] Training [11/39] Loss: 0.00562 +Epoch [3698/4000] Training [12/39] Loss: 0.00291 +Epoch [3698/4000] Training [13/39] Loss: 0.00462 +Epoch [3698/4000] Training [14/39] Loss: 0.00659 +Epoch [3698/4000] Training [15/39] Loss: 0.01124 +Epoch [3698/4000] Training [16/39] Loss: 0.00742 +Epoch [3698/4000] Training [17/39] Loss: 0.00395 +Epoch [3698/4000] Training [18/39] Loss: 0.12776 +Epoch [3698/4000] Training [19/39] Loss: 0.13298 +Epoch [3698/4000] Training [20/39] Loss: 0.00339 +Epoch [3698/4000] Training [21/39] Loss: 0.12884 +Epoch [3698/4000] Training [22/39] Loss: 0.00416 +Epoch [3698/4000] Training [23/39] Loss: 0.12873 +Epoch [3698/4000] Training [24/39] Loss: 0.12739 +Epoch [3698/4000] Training [25/39] Loss: 0.00407 +Epoch [3698/4000] Training [26/39] Loss: 0.00332 +Epoch [3698/4000] Training [27/39] Loss: 0.00372 +Epoch [3698/4000] Training [28/39] Loss: 0.25426 +Epoch [3698/4000] Training [29/39] Loss: 0.13053 +Epoch [3698/4000] Training [30/39] Loss: 0.00537 +Epoch [3698/4000] Training [31/39] Loss: 0.00439 +Epoch [3698/4000] Training [32/39] Loss: 0.12796 +Epoch [3698/4000] Training [33/39] Loss: 0.12820 +Epoch [3698/4000] Training [34/39] Loss: 0.00299 +Epoch [3698/4000] Training [35/39] Loss: 0.00532 +Epoch [3698/4000] Training [36/39] Loss: 0.00529 +Epoch [3698/4000] Training [37/39] Loss: 0.01380 +Epoch [3698/4000] Training [38/39] Loss: 0.00309 +Epoch [3698/4000] Training [39/39] Loss: 0.00500 +Epoch [3698/4000] Training metric {'Train/mean dice_metric': 0.9953978061676025, 'Train/mean miou_metric': 0.9920761585235596, 'Train/mean f1': 0.996809184551239, 'Train/mean precision': 0.9963638782501221, 'Train/mean recall': 0.9972548484802246, 'Train/mean hd95_metric': 1.0531991720199585} +Epoch [3698/4000] Validation [1/10] Loss: 0.75098 focal_loss 0.66240 dice_loss 0.08858 +Epoch [3698/4000] Validation [2/10] Loss: 0.49930 focal_loss 0.39919 dice_loss 0.10011 +Epoch [3698/4000] Validation [3/10] Loss: 0.40337 focal_loss 0.29125 dice_loss 0.11211 +Epoch [3698/4000] Validation [4/10] Loss: 0.89290 focal_loss 0.32764 dice_loss 0.56526 +Epoch [3698/4000] Validation [5/10] Loss: 3.09554 focal_loss 2.42137 dice_loss 0.67418 +Epoch [3698/4000] Validation [6/10] Loss: 1.32217 focal_loss 0.61093 dice_loss 0.71124 +Epoch [3698/4000] Validation [7/10] Loss: 1.17036 focal_loss 0.51997 dice_loss 0.65039 +Epoch [3698/4000] Validation [8/10] Loss: 2.35049 focal_loss 1.73470 dice_loss 0.61579 +Epoch [3698/4000] Validation [9/10] Loss: 1.56069 focal_loss 1.01837 dice_loss 0.54233 +Epoch [3698/4000] Validation [10/10] Loss: 1.86663 focal_loss 1.13264 dice_loss 0.73399 +Epoch [3698/4000] Validation metric {'Val/mean dice_metric': 0.9508373141288757, 'Val/mean miou_metric': 0.9348967671394348, 'Val/mean f1': 0.9484313130378723, 'Val/mean precision': 0.9437742829322815, 'Val/mean recall': 0.9531345367431641, 'Val/mean hd95_metric': 10.674522399902344} +Cheakpoint... +Epoch [3698/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508373141288757, 'Val/mean miou_metric': 0.9348967671394348, 'Val/mean f1': 0.9484313130378723, 'Val/mean precision': 0.9437742829322815, 'Val/mean recall': 0.9531345367431641, 'Val/mean hd95_metric': 10.674522399902344} +Epoch [3699/4000] Training [1/39] Loss: 0.00450 +Epoch [3699/4000] Training [2/39] Loss: 0.00475 +Epoch [3699/4000] Training [3/39] Loss: 0.00551 +Epoch [3699/4000] Training [4/39] Loss: 0.00382 +Epoch [3699/4000] Training [5/39] Loss: 0.13006 +Epoch [3699/4000] Training [6/39] Loss: 0.12806 +Epoch [3699/4000] Training [7/39] Loss: 0.00719 +Epoch [3699/4000] Training [8/39] Loss: 0.00781 +Epoch [3699/4000] Training [9/39] Loss: 0.00356 +Epoch [3699/4000] Training [10/39] Loss: 0.00503 +Epoch [3699/4000] Training [11/39] Loss: 0.25319 +Epoch [3699/4000] Training [12/39] Loss: 0.12851 +Epoch [3699/4000] Training [13/39] Loss: 0.00514 +Epoch [3699/4000] Training [14/39] Loss: 0.00360 +Epoch [3699/4000] Training [15/39] Loss: 0.00254 +Epoch [3699/4000] Training [16/39] Loss: 0.12813 +Epoch [3699/4000] Training [17/39] Loss: 0.00456 +Epoch [3699/4000] Training [18/39] Loss: 0.00603 +Epoch [3699/4000] Training [19/39] Loss: 0.00455 +Epoch [3699/4000] Training [20/39] Loss: 0.00443 +Epoch [3699/4000] Training [21/39] Loss: 0.00435 +Epoch [3699/4000] Training [22/39] Loss: 0.12725 +Epoch [3699/4000] Training [23/39] Loss: 0.00354 +Epoch [3699/4000] Training [24/39] Loss: 0.13394 +Epoch [3699/4000] Training [25/39] Loss: 0.12994 +Epoch [3699/4000] Training [26/39] Loss: 0.00530 +Epoch [3699/4000] Training [27/39] Loss: 0.04548 +Epoch [3699/4000] Training [28/39] Loss: 0.00408 +Epoch [3699/4000] Training [29/39] Loss: 0.00603 +Epoch [3699/4000] Training [30/39] Loss: 0.13098 +Epoch [3699/4000] Training [31/39] Loss: 0.00463 +Epoch [3699/4000] Training [32/39] Loss: 0.00419 +Epoch [3699/4000] Training [33/39] Loss: 0.13143 +Epoch [3699/4000] Training [34/39] Loss: 0.00547 +Epoch [3699/4000] Training [35/39] Loss: 0.00472 +Epoch [3699/4000] Training [36/39] Loss: 0.00524 +Epoch [3699/4000] Training [37/39] Loss: 0.00495 +Epoch [3699/4000] Training [38/39] Loss: 0.12844 +Epoch [3699/4000] Training [39/39] Loss: 0.00357 +Epoch [3699/4000] Training metric {'Train/mean dice_metric': 0.996258556842804, 'Train/mean miou_metric': 0.9929817914962769, 'Train/mean f1': 0.9969149827957153, 'Train/mean precision': 0.9964519143104553, 'Train/mean recall': 0.997378408908844, 'Train/mean hd95_metric': 1.0111374855041504} +Epoch [3699/4000] Validation [1/10] Loss: 0.74950 focal_loss 0.65996 dice_loss 0.08954 +Epoch [3699/4000] Validation [2/10] Loss: 0.49627 focal_loss 0.39472 dice_loss 0.10155 +Epoch [3699/4000] Validation [3/10] Loss: 0.40224 focal_loss 0.28966 dice_loss 0.11258 +Epoch [3699/4000] Validation [4/10] Loss: 0.89020 focal_loss 0.32495 dice_loss 0.56525 +Epoch [3699/4000] Validation [5/10] Loss: 3.04071 focal_loss 2.36678 dice_loss 0.67393 +Epoch [3699/4000] Validation [6/10] Loss: 1.31165 focal_loss 0.60110 dice_loss 0.71055 +Epoch [3699/4000] Validation [7/10] Loss: 1.16835 focal_loss 0.51782 dice_loss 0.65053 +Epoch [3699/4000] Validation [8/10] Loss: 2.39607 focal_loss 1.77399 dice_loss 0.62208 +Epoch [3699/4000] Validation [9/10] Loss: 1.50699 focal_loss 0.96419 dice_loss 0.54280 +Epoch [3699/4000] Validation [10/10] Loss: 1.84563 focal_loss 1.11467 dice_loss 0.73096 +Epoch [3699/4000] Validation metric {'Val/mean dice_metric': 0.951512336730957, 'Val/mean miou_metric': 0.9356034398078918, 'Val/mean f1': 0.9490272998809814, 'Val/mean precision': 0.9452622532844543, 'Val/mean recall': 0.9528224468231201, 'Val/mean hd95_metric': 10.607321739196777} +Cheakpoint... +Epoch [3699/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951512336730957, 'Val/mean miou_metric': 0.9356034398078918, 'Val/mean f1': 0.9490272998809814, 'Val/mean precision': 0.9452622532844543, 'Val/mean recall': 0.9528224468231201, 'Val/mean hd95_metric': 10.607321739196777} +Epoch [3700/4000] Training [1/39] Loss: 0.00440 +Epoch [3700/4000] Training [2/39] Loss: 0.00344 +Epoch [3700/4000] Training [3/39] Loss: 0.00511 +Epoch [3700/4000] Training [4/39] Loss: 0.25335 +Epoch [3700/4000] Training [5/39] Loss: 0.00405 +Epoch [3700/4000] Training [6/39] Loss: 0.13145 +Epoch [3700/4000] Training [7/39] Loss: 0.00321 +Epoch [3700/4000] Training [8/39] Loss: 0.01272 +Epoch [3700/4000] Training [9/39] Loss: 0.00387 +Epoch [3700/4000] Training [10/39] Loss: 0.00531 +Epoch [3700/4000] Training [11/39] Loss: 0.00652 +Epoch [3700/4000] Training [12/39] Loss: 0.00364 +Epoch [3700/4000] Training [13/39] Loss: 0.00485 +Epoch [3700/4000] Training [14/39] Loss: 0.00721 +Epoch [3700/4000] Training [15/39] Loss: 0.00445 +Epoch [3700/4000] Training [16/39] Loss: 0.00627 +Epoch [3700/4000] Training [17/39] Loss: 0.00441 +Epoch [3700/4000] Training [18/39] Loss: 0.00304 +Epoch [3700/4000] Training [19/39] Loss: 0.00409 +Epoch [3700/4000] Training [20/39] Loss: 0.00556 +Epoch [3700/4000] Training [21/39] Loss: 0.00694 +Epoch [3700/4000] Training [22/39] Loss: 0.00345 +Epoch [3700/4000] Training [23/39] Loss: 0.12883 +Epoch [3700/4000] Training [24/39] Loss: 0.00348 +Epoch [3700/4000] Training [25/39] Loss: 0.08923 +Epoch [3700/4000] Training [26/39] Loss: 0.00678 +Epoch [3700/4000] Training [27/39] Loss: 0.00618 +Epoch [3700/4000] Training [28/39] Loss: 0.00922 +Epoch [3700/4000] Training [29/39] Loss: 0.00349 +Epoch [3700/4000] Training [30/39] Loss: 0.12931 +Epoch [3700/4000] Training [31/39] Loss: 0.00936 +Epoch [3700/4000] Training [32/39] Loss: 0.00444 +Epoch [3700/4000] Training [33/39] Loss: 0.00627 +Epoch [3700/4000] Training [34/39] Loss: 0.00370 +Epoch [3700/4000] Training [35/39] Loss: 0.12833 +Epoch [3700/4000] Training [36/39] Loss: 0.00480 +Epoch [3700/4000] Training [37/39] Loss: 0.12844 +Epoch [3700/4000] Training [38/39] Loss: 0.00497 +Epoch [3700/4000] Training [39/39] Loss: 0.00368 +Epoch [3700/4000] Training metric {'Train/mean dice_metric': 0.9961929321289062, 'Train/mean miou_metric': 0.992855429649353, 'Train/mean f1': 0.9968039393424988, 'Train/mean precision': 0.9963340759277344, 'Train/mean recall': 0.9972741603851318, 'Train/mean hd95_metric': 0.9782513976097107} +Epoch [3700/4000] Validation [1/10] Loss: 0.75758 focal_loss 0.66901 dice_loss 0.08857 +Epoch [3700/4000] Validation [2/10] Loss: 0.50139 focal_loss 0.40113 dice_loss 0.10026 +Epoch [3700/4000] Validation [3/10] Loss: 0.41533 focal_loss 0.30265 dice_loss 0.11268 +Epoch [3700/4000] Validation [4/10] Loss: 0.89953 focal_loss 0.33410 dice_loss 0.56543 +Epoch [3700/4000] Validation [5/10] Loss: 3.15426 focal_loss 2.48012 dice_loss 0.67415 +Epoch [3700/4000] Validation [6/10] Loss: 1.32676 focal_loss 0.61660 dice_loss 0.71016 +Epoch [3700/4000] Validation [7/10] Loss: 1.18477 focal_loss 0.53428 dice_loss 0.65049 +Epoch [3700/4000] Validation [8/10] Loss: 2.48541 focal_loss 1.86179 dice_loss 0.62362 +Epoch [3700/4000] Validation [9/10] Loss: 1.54210 focal_loss 0.99901 dice_loss 0.54309 +Epoch [3700/4000] Validation [10/10] Loss: 1.87404 focal_loss 1.14260 dice_loss 0.73144 +Epoch [3700/4000] Validation metric {'Val/mean dice_metric': 0.951457142829895, 'Val/mean miou_metric': 0.9354962706565857, 'Val/mean f1': 0.9487943053245544, 'Val/mean precision': 0.9452349543571472, 'Val/mean recall': 0.9523806571960449, 'Val/mean hd95_metric': 10.530288696289062} +Cheakpoint... +Epoch [3700/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951457142829895, 'Val/mean miou_metric': 0.9354962706565857, 'Val/mean f1': 0.9487943053245544, 'Val/mean precision': 0.9452349543571472, 'Val/mean recall': 0.9523806571960449, 'Val/mean hd95_metric': 10.530288696289062} +Epoch [3701/4000] Training [1/39] Loss: 0.12748 +Epoch [3701/4000] Training [2/39] Loss: 0.00438 +Epoch [3701/4000] Training [3/39] Loss: 0.12914 +Epoch [3701/4000] Training [4/39] Loss: 0.00528 +Epoch [3701/4000] Training [5/39] Loss: 0.00473 +Epoch [3701/4000] Training [6/39] Loss: 0.12815 +Epoch [3701/4000] Training [7/39] Loss: 0.38086 +Epoch [3701/4000] Training [8/39] Loss: 0.00470 +Epoch [3701/4000] Training [9/39] Loss: 0.00438 +Epoch [3701/4000] Training [10/39] Loss: 0.00539 +Epoch [3701/4000] Training [11/39] Loss: 0.00494 +Epoch [3701/4000] Training [12/39] Loss: 0.00473 +Epoch [3701/4000] Training [13/39] Loss: 0.00743 +Epoch [3701/4000] Training [14/39] Loss: 0.25567 +Epoch [3701/4000] Training [15/39] Loss: 0.00539 +Epoch [3701/4000] Training [16/39] Loss: 0.00507 +Epoch [3701/4000] Training [17/39] Loss: 0.00474 +Epoch [3701/4000] Training [18/39] Loss: 0.00552 +Epoch [3701/4000] Training [19/39] Loss: 0.00481 +Epoch [3701/4000] Training [20/39] Loss: 0.00336 +Epoch [3701/4000] Training [21/39] Loss: 0.00482 +Epoch [3701/4000] Training [22/39] Loss: 0.00476 +Epoch [3701/4000] Training [23/39] Loss: 0.00412 +Epoch [3701/4000] Training [24/39] Loss: 0.00492 +Epoch [3701/4000] Training [25/39] Loss: 0.00323 +Epoch [3701/4000] Training [26/39] Loss: 0.00343 +Epoch [3701/4000] Training [27/39] Loss: 0.00635 +Epoch [3701/4000] Training [28/39] Loss: 0.12974 +Epoch [3701/4000] Training [29/39] Loss: 0.12781 +Epoch [3701/4000] Training [30/39] Loss: 0.13142 +Epoch [3701/4000] Training [31/39] Loss: 0.12811 +Epoch [3701/4000] Training [32/39] Loss: 0.13066 +Epoch [3701/4000] Training [33/39] Loss: 0.00374 +Epoch [3701/4000] Training [34/39] Loss: 0.00347 +Epoch [3701/4000] Training [35/39] Loss: 0.00584 +Epoch [3701/4000] Training [36/39] Loss: 0.00753 +Epoch [3701/4000] Training [37/39] Loss: 0.00439 +Epoch [3701/4000] Training [38/39] Loss: 0.00416 +Epoch [3701/4000] Training [39/39] Loss: 0.25348 +Epoch [3701/4000] Training metric {'Train/mean dice_metric': 0.9962674975395203, 'Train/mean miou_metric': 0.9930238127708435, 'Train/mean f1': 0.9969249963760376, 'Train/mean precision': 0.996459424495697, 'Train/mean recall': 0.9973909258842468, 'Train/mean hd95_metric': 0.9468753337860107} +Epoch [3701/4000] Validation [1/10] Loss: 0.72974 focal_loss 0.64266 dice_loss 0.08708 +Epoch [3701/4000] Validation [2/10] Loss: 0.49576 focal_loss 0.39428 dice_loss 0.10148 +Epoch [3701/4000] Validation [3/10] Loss: 0.41064 focal_loss 0.29775 dice_loss 0.11288 +Epoch [3701/4000] Validation [4/10] Loss: 0.89515 focal_loss 0.33036 dice_loss 0.56478 +Epoch [3701/4000] Validation [5/10] Loss: 3.08174 focal_loss 2.40760 dice_loss 0.67414 +Epoch [3701/4000] Validation [6/10] Loss: 1.31508 focal_loss 0.60359 dice_loss 0.71149 +Epoch [3701/4000] Validation [7/10] Loss: 1.17673 focal_loss 0.52578 dice_loss 0.65095 +Epoch [3701/4000] Validation [8/10] Loss: 2.41446 focal_loss 1.79200 dice_loss 0.62245 +Epoch [3701/4000] Validation [9/10] Loss: 1.54252 focal_loss 0.99901 dice_loss 0.54351 +Epoch [3701/4000] Validation [10/10] Loss: 1.84926 focal_loss 1.11789 dice_loss 0.73137 +Epoch [3701/4000] Validation metric {'Val/mean dice_metric': 0.951464831829071, 'Val/mean miou_metric': 0.935562014579773, 'Val/mean f1': 0.9489511251449585, 'Val/mean precision': 0.9454891085624695, 'Val/mean recall': 0.9524385929107666, 'Val/mean hd95_metric': 10.508406639099121} +Cheakpoint... +Epoch [3701/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951464831829071, 'Val/mean miou_metric': 0.935562014579773, 'Val/mean f1': 0.9489511251449585, 'Val/mean precision': 0.9454891085624695, 'Val/mean recall': 0.9524385929107666, 'Val/mean hd95_metric': 10.508406639099121} +Epoch [3702/4000] Training [1/39] Loss: 0.12903 +Epoch [3702/4000] Training [2/39] Loss: 0.00422 +Epoch [3702/4000] Training [3/39] Loss: 0.00358 +Epoch [3702/4000] Training [4/39] Loss: 0.00452 +Epoch [3702/4000] Training [5/39] Loss: 0.00588 +Epoch [3702/4000] Training [6/39] Loss: 0.00446 +Epoch [3702/4000] Training [7/39] Loss: 0.00368 +Epoch [3702/4000] Training [8/39] Loss: 0.00380 +Epoch [3702/4000] Training [9/39] Loss: 0.13154 +Epoch [3702/4000] Training [10/39] Loss: 0.12849 +Epoch [3702/4000] Training [11/39] Loss: 0.00386 +Epoch [3702/4000] Training [12/39] Loss: 0.01018 +Epoch [3702/4000] Training [13/39] Loss: 0.00394 +Epoch [3702/4000] Training [14/39] Loss: 0.00541 +Epoch [3702/4000] Training [15/39] Loss: 0.00571 +Epoch [3702/4000] Training [16/39] Loss: 0.00431 +Epoch [3702/4000] Training [17/39] Loss: 0.00533 +Epoch [3702/4000] Training [18/39] Loss: 0.00800 +Epoch [3702/4000] Training [19/39] Loss: 0.12967 +Epoch [3702/4000] Training [20/39] Loss: 0.25313 +Epoch [3702/4000] Training [21/39] Loss: 0.00494 +Epoch [3702/4000] Training [22/39] Loss: 0.00432 +Epoch [3702/4000] Training [23/39] Loss: 0.00315 +Epoch [3702/4000] Training [24/39] Loss: 0.00480 +Epoch [3702/4000] Training [25/39] Loss: 0.12882 +Epoch [3702/4000] Training [26/39] Loss: 0.12819 +Epoch [3702/4000] Training [27/39] Loss: 0.00610 +Epoch [3702/4000] Training [28/39] Loss: 0.01095 +Epoch [3702/4000] Training [29/39] Loss: 0.00360 +Epoch [3702/4000] Training [30/39] Loss: 0.00303 +Epoch [3702/4000] Training [31/39] Loss: 0.00621 +Epoch [3702/4000] Training [32/39] Loss: 0.12949 +Epoch [3702/4000] Training [33/39] Loss: 0.12982 +Epoch [3702/4000] Training [34/39] Loss: 0.00391 +Epoch [3702/4000] Training [35/39] Loss: 0.00465 +Epoch [3702/4000] Training [36/39] Loss: 0.00473 +Epoch [3702/4000] Training [37/39] Loss: 0.00526 +Epoch [3702/4000] Training [38/39] Loss: 0.12854 +Epoch [3702/4000] Training [39/39] Loss: 0.25510 +Epoch [3702/4000] Training metric {'Train/mean dice_metric': 0.9954392313957214, 'Train/mean miou_metric': 0.9921755194664001, 'Train/mean f1': 0.9968958497047424, 'Train/mean precision': 0.9964833855628967, 'Train/mean recall': 0.9973086714744568, 'Train/mean hd95_metric': 0.9560383558273315} +Epoch [3702/4000] Validation [1/10] Loss: 0.74113 focal_loss 0.65316 dice_loss 0.08798 +Epoch [3702/4000] Validation [2/10] Loss: 0.49207 focal_loss 0.39428 dice_loss 0.09778 +Epoch [3702/4000] Validation [3/10] Loss: 0.40155 focal_loss 0.28959 dice_loss 0.11196 +Epoch [3702/4000] Validation [4/10] Loss: 0.90237 focal_loss 0.33621 dice_loss 0.56616 +Epoch [3702/4000] Validation [5/10] Loss: 3.08557 focal_loss 2.41156 dice_loss 0.67402 +Epoch [3702/4000] Validation [6/10] Loss: 1.33012 focal_loss 0.61946 dice_loss 0.71066 +Epoch [3702/4000] Validation [7/10] Loss: 1.19064 focal_loss 0.53873 dice_loss 0.65191 +Epoch [3702/4000] Validation [8/10] Loss: 2.39879 focal_loss 1.78296 dice_loss 0.61583 +Epoch [3702/4000] Validation [9/10] Loss: 1.55359 focal_loss 1.00997 dice_loss 0.54361 +Epoch [3702/4000] Validation [10/10] Loss: 1.88686 focal_loss 1.15388 dice_loss 0.73298 +Epoch [3702/4000] Validation metric {'Val/mean dice_metric': 0.9508528113365173, 'Val/mean miou_metric': 0.9349521994590759, 'Val/mean f1': 0.9485651850700378, 'Val/mean precision': 0.9441097378730774, 'Val/mean recall': 0.9530629515647888, 'Val/mean hd95_metric': 10.573518753051758} +Cheakpoint... +Epoch [3702/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508528113365173, 'Val/mean miou_metric': 0.9349521994590759, 'Val/mean f1': 0.9485651850700378, 'Val/mean precision': 0.9441097378730774, 'Val/mean recall': 0.9530629515647888, 'Val/mean hd95_metric': 10.573518753051758} +Epoch [3703/4000] Training [1/39] Loss: 0.25673 +Epoch [3703/4000] Training [2/39] Loss: 0.01275 +Epoch [3703/4000] Training [3/39] Loss: 0.00707 +Epoch [3703/4000] Training [4/39] Loss: 0.00629 +Epoch [3703/4000] Training [5/39] Loss: 0.00402 +Epoch [3703/4000] Training [6/39] Loss: 0.00437 +Epoch [3703/4000] Training [7/39] Loss: 0.00665 +Epoch [3703/4000] Training [8/39] Loss: 0.12801 +Epoch [3703/4000] Training [9/39] Loss: 0.00247 +Epoch [3703/4000] Training [10/39] Loss: 0.12963 +Epoch [3703/4000] Training [11/39] Loss: 0.12895 +Epoch [3703/4000] Training [12/39] Loss: 0.00482 +Epoch [3703/4000] Training [13/39] Loss: 0.12962 +Epoch [3703/4000] Training [14/39] Loss: 0.00455 +Epoch [3703/4000] Training [15/39] Loss: 0.00479 +Epoch [3703/4000] Training [16/39] Loss: 0.13000 +Epoch [3703/4000] Training [17/39] Loss: 0.13108 +Epoch [3703/4000] Training [18/39] Loss: 0.12833 +Epoch [3703/4000] Training [19/39] Loss: 0.00327 +Epoch [3703/4000] Training [20/39] Loss: 0.00479 +Epoch [3703/4000] Training [21/39] Loss: 0.00431 +Epoch [3703/4000] Training [22/39] Loss: 0.00441 +Epoch [3703/4000] Training [23/39] Loss: 0.25500 +Epoch [3703/4000] Training [24/39] Loss: 0.00548 +Epoch [3703/4000] Training [25/39] Loss: 0.00570 +Epoch [3703/4000] Training [26/39] Loss: 0.00649 +Epoch [3703/4000] Training [27/39] Loss: 0.00282 +Epoch [3703/4000] Training [28/39] Loss: 0.00578 +Epoch [3703/4000] Training [29/39] Loss: 0.00626 +Epoch [3703/4000] Training [30/39] Loss: 0.00385 +Epoch [3703/4000] Training [31/39] Loss: 0.04676 +Epoch [3703/4000] Training [32/39] Loss: 0.00449 +Epoch [3703/4000] Training [33/39] Loss: 0.00514 +Epoch [3703/4000] Training [34/39] Loss: 0.00488 +Epoch [3703/4000] Training [35/39] Loss: 0.00584 +Epoch [3703/4000] Training [36/39] Loss: 0.00469 +Epoch [3703/4000] Training [37/39] Loss: 0.00271 +Epoch [3703/4000] Training [38/39] Loss: 0.00347 +Epoch [3703/4000] Training [39/39] Loss: 0.13008 +Epoch [3703/4000] Training metric {'Train/mean dice_metric': 0.9960943460464478, 'Train/mean miou_metric': 0.992647111415863, 'Train/mean f1': 0.9967573285102844, 'Train/mean precision': 0.9962642192840576, 'Train/mean recall': 0.9972508549690247, 'Train/mean hd95_metric': 1.0204828977584839} +Epoch [3703/4000] Validation [1/10] Loss: 0.73967 focal_loss 0.65195 dice_loss 0.08772 +Epoch [3703/4000] Validation [2/10] Loss: 0.49902 focal_loss 0.39842 dice_loss 0.10060 +Epoch [3703/4000] Validation [3/10] Loss: 0.41572 focal_loss 0.30261 dice_loss 0.11310 +Epoch [3703/4000] Validation [4/10] Loss: 0.90479 focal_loss 0.33862 dice_loss 0.56617 +Epoch [3703/4000] Validation [5/10] Loss: 3.08497 focal_loss 2.41105 dice_loss 0.67392 +Epoch [3703/4000] Validation [6/10] Loss: 1.32889 focal_loss 0.61983 dice_loss 0.70906 +Epoch [3703/4000] Validation [7/10] Loss: 1.17901 focal_loss 0.52803 dice_loss 0.65099 +Epoch [3703/4000] Validation [8/10] Loss: 2.50566 focal_loss 1.88030 dice_loss 0.62537 +Epoch [3703/4000] Validation [9/10] Loss: 1.55056 focal_loss 1.00648 dice_loss 0.54407 +Epoch [3703/4000] Validation [10/10] Loss: 1.86662 focal_loss 1.13436 dice_loss 0.73226 +Epoch [3703/4000] Validation metric {'Val/mean dice_metric': 0.951206624507904, 'Val/mean miou_metric': 0.9350839257240295, 'Val/mean f1': 0.9486242532730103, 'Val/mean precision': 0.9450085759162903, 'Val/mean recall': 0.952267587184906, 'Val/mean hd95_metric': 10.779035568237305} +Cheakpoint... +Epoch [3703/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951206624507904, 'Val/mean miou_metric': 0.9350839257240295, 'Val/mean f1': 0.9486242532730103, 'Val/mean precision': 0.9450085759162903, 'Val/mean recall': 0.952267587184906, 'Val/mean hd95_metric': 10.779035568237305} +Epoch [3704/4000] Training [1/39] Loss: 0.00441 +Epoch [3704/4000] Training [2/39] Loss: 0.00483 +Epoch [3704/4000] Training [3/39] Loss: 0.00549 +Epoch [3704/4000] Training [4/39] Loss: 0.12888 +Epoch [3704/4000] Training [5/39] Loss: 0.00503 +Epoch [3704/4000] Training [6/39] Loss: 0.00386 +Epoch [3704/4000] Training [7/39] Loss: 0.00438 +Epoch [3704/4000] Training [8/39] Loss: 0.12809 +Epoch [3704/4000] Training [9/39] Loss: 0.00403 +Epoch [3704/4000] Training [10/39] Loss: 0.00428 +Epoch [3704/4000] Training [11/39] Loss: 0.00449 +Epoch [3704/4000] Training [12/39] Loss: 0.12909 +Epoch [3704/4000] Training [13/39] Loss: 0.00728 +Epoch [3704/4000] Training [14/39] Loss: 0.00481 +Epoch [3704/4000] Training [15/39] Loss: 0.00397 +Epoch [3704/4000] Training [16/39] Loss: 0.12887 +Epoch [3704/4000] Training [17/39] Loss: 0.00311 +Epoch [3704/4000] Training [18/39] Loss: 0.00446 +Epoch [3704/4000] Training [19/39] Loss: 0.00540 +Epoch [3704/4000] Training [20/39] Loss: 0.13004 +Epoch [3704/4000] Training [21/39] Loss: 0.12967 +Epoch [3704/4000] Training [22/39] Loss: 0.00504 +Epoch [3704/4000] Training [23/39] Loss: 0.12923 +Epoch [3704/4000] Training [24/39] Loss: 0.00358 +Epoch [3704/4000] Training [25/39] Loss: 0.00430 +Epoch [3704/4000] Training [26/39] Loss: 0.25338 +Epoch [3704/4000] Training [27/39] Loss: 0.12937 +Epoch [3704/4000] Training [28/39] Loss: 0.00590 +Epoch [3704/4000] Training [29/39] Loss: 0.00335 +Epoch [3704/4000] Training [30/39] Loss: 0.00585 +Epoch [3704/4000] Training [31/39] Loss: 0.00672 +Epoch [3704/4000] Training [32/39] Loss: 0.12953 +Epoch [3704/4000] Training [33/39] Loss: 0.00471 +Epoch [3704/4000] Training [34/39] Loss: 0.12753 +Epoch [3704/4000] Training [35/39] Loss: 0.00356 +Epoch [3704/4000] Training [36/39] Loss: 0.00471 +Epoch [3704/4000] Training [37/39] Loss: 0.00342 +Epoch [3704/4000] Training [38/39] Loss: 0.00367 +Epoch [3704/4000] Training [39/39] Loss: 0.00570 +Epoch [3704/4000] Training metric {'Train/mean dice_metric': 0.9964295029640198, 'Train/mean miou_metric': 0.9933150410652161, 'Train/mean f1': 0.9969614744186401, 'Train/mean precision': 0.9964860677719116, 'Train/mean recall': 0.9974372982978821, 'Train/mean hd95_metric': 1.0340120792388916} +Epoch [3704/4000] Validation [1/10] Loss: 0.73897 focal_loss 0.64989 dice_loss 0.08908 +Epoch [3704/4000] Validation [2/10] Loss: 0.49192 focal_loss 0.39521 dice_loss 0.09671 +Epoch [3704/4000] Validation [3/10] Loss: 0.39231 focal_loss 0.28050 dice_loss 0.11181 +Epoch [3704/4000] Validation [4/10] Loss: 0.91035 focal_loss 0.34295 dice_loss 0.56740 +Epoch [3704/4000] Validation [5/10] Loss: 3.04812 focal_loss 2.37427 dice_loss 0.67385 +Epoch [3704/4000] Validation [6/10] Loss: 1.34847 focal_loss 0.63836 dice_loss 0.71011 +Epoch [3704/4000] Validation [7/10] Loss: 1.19405 focal_loss 0.54012 dice_loss 0.65393 +Epoch [3704/4000] Validation [8/10] Loss: 2.34457 focal_loss 1.73507 dice_loss 0.60951 +Epoch [3704/4000] Validation [9/10] Loss: 1.55356 focal_loss 1.00911 dice_loss 0.54445 +Epoch [3704/4000] Validation [10/10] Loss: 1.92721 focal_loss 1.19174 dice_loss 0.73547 +Epoch [3704/4000] Validation metric {'Val/mean dice_metric': 0.9516245722770691, 'Val/mean miou_metric': 0.9357861280441284, 'Val/mean f1': 0.948155403137207, 'Val/mean precision': 0.9428090453147888, 'Val/mean recall': 0.9535627365112305, 'Val/mean hd95_metric': 10.748010635375977} +Cheakpoint... +Epoch [3704/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516245722770691, 'Val/mean miou_metric': 0.9357861280441284, 'Val/mean f1': 0.948155403137207, 'Val/mean precision': 0.9428090453147888, 'Val/mean recall': 0.9535627365112305, 'Val/mean hd95_metric': 10.748010635375977} +Epoch [3705/4000] Training [1/39] Loss: 0.12864 +Epoch [3705/4000] Training [2/39] Loss: 0.12877 +Epoch [3705/4000] Training [3/39] Loss: 0.12800 +Epoch [3705/4000] Training [4/39] Loss: 0.00657 +Epoch [3705/4000] Training [5/39] Loss: 0.12861 +Epoch [3705/4000] Training [6/39] Loss: 0.13125 +Epoch [3705/4000] Training [7/39] Loss: 0.12930 +Epoch [3705/4000] Training [8/39] Loss: 0.00384 +Epoch [3705/4000] Training [9/39] Loss: 0.00419 +Epoch [3705/4000] Training [10/39] Loss: 0.00387 +Epoch [3705/4000] Training [11/39] Loss: 0.25713 +Epoch [3705/4000] Training [12/39] Loss: 0.00461 +Epoch [3705/4000] Training [13/39] Loss: 0.00475 +Epoch [3705/4000] Training [14/39] Loss: 0.00564 +Epoch [3705/4000] Training [15/39] Loss: 0.00364 +Epoch [3705/4000] Training [16/39] Loss: 0.00455 +Epoch [3705/4000] Training [17/39] Loss: 0.00348 +Epoch [3705/4000] Training [18/39] Loss: 0.00296 +Epoch [3705/4000] Training [19/39] Loss: 0.00325 +Epoch [3705/4000] Training [20/39] Loss: 0.00374 +Epoch [3705/4000] Training [21/39] Loss: 0.00499 +Epoch [3705/4000] Training [22/39] Loss: 0.12718 +Epoch [3705/4000] Training [23/39] Loss: 0.00277 +Epoch [3705/4000] Training [24/39] Loss: 0.00559 +Epoch [3705/4000] Training [25/39] Loss: 0.12999 +Epoch [3705/4000] Training [26/39] Loss: 0.00429 +Epoch [3705/4000] Training [27/39] Loss: 0.00380 +Epoch [3705/4000] Training [28/39] Loss: 0.00567 +Epoch [3705/4000] Training [29/39] Loss: 0.00595 +Epoch [3705/4000] Training [30/39] Loss: 0.00304 +Epoch [3705/4000] Training [31/39] Loss: 0.00387 +Epoch [3705/4000] Training [32/39] Loss: 0.00549 +Epoch [3705/4000] Training [33/39] Loss: 0.00655 +Epoch [3705/4000] Training [34/39] Loss: 0.00289 +Epoch [3705/4000] Training [35/39] Loss: 0.12777 +Epoch [3705/4000] Training [36/39] Loss: 0.00350 +Epoch [3705/4000] Training [37/39] Loss: 0.12882 +Epoch [3705/4000] Training [38/39] Loss: 0.00300 +Epoch [3705/4000] Training [39/39] Loss: 0.00641 +Epoch [3705/4000] Training metric {'Train/mean dice_metric': 0.9957183003425598, 'Train/mean miou_metric': 0.9927112460136414, 'Train/mean f1': 0.9970251321792603, 'Train/mean precision': 0.9965692758560181, 'Train/mean recall': 0.9974814653396606, 'Train/mean hd95_metric': 0.9245957732200623} +Epoch [3705/4000] Validation [1/10] Loss: 0.74024 focal_loss 0.65222 dice_loss 0.08801 +Epoch [3705/4000] Validation [2/10] Loss: 0.49250 focal_loss 0.39482 dice_loss 0.09768 +Epoch [3705/4000] Validation [3/10] Loss: 0.40311 focal_loss 0.29116 dice_loss 0.11195 +Epoch [3705/4000] Validation [4/10] Loss: 0.90882 focal_loss 0.34174 dice_loss 0.56708 +Epoch [3705/4000] Validation [5/10] Loss: 3.09371 focal_loss 2.41992 dice_loss 0.67379 +Epoch [3705/4000] Validation [6/10] Loss: 1.33868 focal_loss 0.62790 dice_loss 0.71078 +Epoch [3705/4000] Validation [7/10] Loss: 1.18835 focal_loss 0.53424 dice_loss 0.65411 +Epoch [3705/4000] Validation [8/10] Loss: 2.33968 focal_loss 1.72906 dice_loss 0.61062 +Epoch [3705/4000] Validation [9/10] Loss: 1.58540 focal_loss 1.04070 dice_loss 0.54470 +Epoch [3705/4000] Validation [10/10] Loss: 1.91853 focal_loss 1.18249 dice_loss 0.73603 +Epoch [3705/4000] Validation metric {'Val/mean dice_metric': 0.9509036540985107, 'Val/mean miou_metric': 0.9351294636726379, 'Val/mean f1': 0.9478989243507385, 'Val/mean precision': 0.9426661729812622, 'Val/mean recall': 0.95319002866745, 'Val/mean hd95_metric': 10.682165145874023} +Cheakpoint... +Epoch [3705/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509036540985107, 'Val/mean miou_metric': 0.9351294636726379, 'Val/mean f1': 0.9478989243507385, 'Val/mean precision': 0.9426661729812622, 'Val/mean recall': 0.95319002866745, 'Val/mean hd95_metric': 10.682165145874023} +Epoch [3706/4000] Training [1/39] Loss: 0.00527 +Epoch [3706/4000] Training [2/39] Loss: 0.00508 +Epoch [3706/4000] Training [3/39] Loss: 0.12833 +Epoch [3706/4000] Training [4/39] Loss: 0.00335 +Epoch [3706/4000] Training [5/39] Loss: 0.00363 +Epoch [3706/4000] Training [6/39] Loss: 0.00616 +Epoch [3706/4000] Training [7/39] Loss: 0.00657 +Epoch [3706/4000] Training [8/39] Loss: 0.00465 +Epoch [3706/4000] Training [9/39] Loss: 0.00530 +Epoch [3706/4000] Training [10/39] Loss: 0.00458 +Epoch [3706/4000] Training [11/39] Loss: 0.00254 +Epoch [3706/4000] Training [12/39] Loss: 0.00510 +Epoch [3706/4000] Training [13/39] Loss: 0.00499 +Epoch [3706/4000] Training [14/39] Loss: 0.00594 +Epoch [3706/4000] Training [15/39] Loss: 0.12985 +Epoch [3706/4000] Training [16/39] Loss: 0.00599 +Epoch [3706/4000] Training [17/39] Loss: 0.25620 +Epoch [3706/4000] Training [18/39] Loss: 0.00629 +Epoch [3706/4000] Training [19/39] Loss: 0.12923 +Epoch [3706/4000] Training [20/39] Loss: 0.00863 +Epoch [3706/4000] Training [21/39] Loss: 0.00561 +Epoch [3706/4000] Training [22/39] Loss: 0.12885 +Epoch [3706/4000] Training [23/39] Loss: 0.00909 +Epoch [3706/4000] Training [24/39] Loss: 0.00453 +Epoch [3706/4000] Training [25/39] Loss: 0.00481 +Epoch [3706/4000] Training [26/39] Loss: 0.00376 +Epoch [3706/4000] Training [27/39] Loss: 0.00498 +Epoch [3706/4000] Training [28/39] Loss: 0.12823 +Epoch [3706/4000] Training [29/39] Loss: 0.00566 +Epoch [3706/4000] Training [30/39] Loss: 0.00629 +Epoch [3706/4000] Training [31/39] Loss: 0.13101 +Epoch [3706/4000] Training [32/39] Loss: 0.00583 +Epoch [3706/4000] Training [33/39] Loss: 0.00533 +Epoch [3706/4000] Training [34/39] Loss: 0.12941 +Epoch [3706/4000] Training [35/39] Loss: 0.00323 +Epoch [3706/4000] Training [36/39] Loss: 0.00420 +Epoch [3706/4000] Training [37/39] Loss: 0.00573 +Epoch [3706/4000] Training [38/39] Loss: 0.00601 +Epoch [3706/4000] Training [39/39] Loss: 0.00322 +Epoch [3706/4000] Training metric {'Train/mean dice_metric': 0.9956395626068115, 'Train/mean miou_metric': 0.9919669032096863, 'Train/mean f1': 0.9962968826293945, 'Train/mean precision': 0.9957398176193237, 'Train/mean recall': 0.9968544840812683, 'Train/mean hd95_metric': 1.1231458187103271} +Epoch [3706/4000] Validation [1/10] Loss: 0.74410 focal_loss 0.65596 dice_loss 0.08813 +Epoch [3706/4000] Validation [2/10] Loss: 0.49560 focal_loss 0.39759 dice_loss 0.09801 +Epoch [3706/4000] Validation [3/10] Loss: 0.40260 focal_loss 0.29070 dice_loss 0.11190 +Epoch [3706/4000] Validation [4/10] Loss: 0.90462 focal_loss 0.33800 dice_loss 0.56663 +Epoch [3706/4000] Validation [5/10] Loss: 3.10348 focal_loss 2.42969 dice_loss 0.67379 +Epoch [3706/4000] Validation [6/10] Loss: 1.34125 focal_loss 0.63103 dice_loss 0.71022 +Epoch [3706/4000] Validation [7/10] Loss: 1.19041 focal_loss 0.53520 dice_loss 0.65521 +Epoch [3706/4000] Validation [8/10] Loss: 2.32408 focal_loss 1.71445 dice_loss 0.60963 +Epoch [3706/4000] Validation [9/10] Loss: 1.62868 focal_loss 1.08431 dice_loss 0.54437 +Epoch [3706/4000] Validation [10/10] Loss: 1.92183 focal_loss 1.18591 dice_loss 0.73593 +Epoch [3706/4000] Validation metric {'Val/mean dice_metric': 0.9509644508361816, 'Val/mean miou_metric': 0.9346343278884888, 'Val/mean f1': 0.9476360082626343, 'Val/mean precision': 0.9423266649246216, 'Val/mean recall': 0.9530056118965149, 'Val/mean hd95_metric': 10.734156608581543} +Cheakpoint... +Epoch [3706/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509644508361816, 'Val/mean miou_metric': 0.9346343278884888, 'Val/mean f1': 0.9476360082626343, 'Val/mean precision': 0.9423266649246216, 'Val/mean recall': 0.9530056118965149, 'Val/mean hd95_metric': 10.734156608581543} +Epoch [3707/4000] Training [1/39] Loss: 0.00555 +Epoch [3707/4000] Training [2/39] Loss: 0.12875 +Epoch [3707/4000] Training [3/39] Loss: 0.00657 +Epoch [3707/4000] Training [4/39] Loss: 0.12888 +Epoch [3707/4000] Training [5/39] Loss: 0.00722 +Epoch [3707/4000] Training [6/39] Loss: 0.00709 +Epoch [3707/4000] Training [7/39] Loss: 0.12990 +Epoch [3707/4000] Training [8/39] Loss: 0.00464 +Epoch [3707/4000] Training [9/39] Loss: 0.00406 +Epoch [3707/4000] Training [10/39] Loss: 0.00441 +Epoch [3707/4000] Training [11/39] Loss: 0.00475 +Epoch [3707/4000] Training [12/39] Loss: 0.00466 +Epoch [3707/4000] Training [13/39] Loss: 0.25430 +Epoch [3707/4000] Training [14/39] Loss: 0.00771 +Epoch [3707/4000] Training [15/39] Loss: 0.00313 +Epoch [3707/4000] Training [16/39] Loss: 0.00423 +Epoch [3707/4000] Training [17/39] Loss: 0.12813 +Epoch [3707/4000] Training [18/39] Loss: 0.00548 +Epoch [3707/4000] Training [19/39] Loss: 0.00569 +Epoch [3707/4000] Training [20/39] Loss: 0.00495 +Epoch [3707/4000] Training [21/39] Loss: 0.12971 +Epoch [3707/4000] Training [22/39] Loss: 0.00408 +Epoch [3707/4000] Training [23/39] Loss: 0.00346 +Epoch [3707/4000] Training [24/39] Loss: 0.00408 +Epoch [3707/4000] Training [25/39] Loss: 0.00390 +Epoch [3707/4000] Training [26/39] Loss: 0.00428 +Epoch [3707/4000] Training [27/39] Loss: 0.12924 +Epoch [3707/4000] Training [28/39] Loss: 0.12982 +Epoch [3707/4000] Training [29/39] Loss: 0.00369 +Epoch [3707/4000] Training [30/39] Loss: 0.00488 +Epoch [3707/4000] Training [31/39] Loss: 0.00436 +Epoch [3707/4000] Training [32/39] Loss: 0.12998 +Epoch [3707/4000] Training [33/39] Loss: 0.00346 +Epoch [3707/4000] Training [34/39] Loss: 0.13288 +Epoch [3707/4000] Training [35/39] Loss: 0.13198 +Epoch [3707/4000] Training [36/39] Loss: 0.13032 +Epoch [3707/4000] Training [37/39] Loss: 0.00523 +Epoch [3707/4000] Training [38/39] Loss: 0.00668 +Epoch [3707/4000] Training [39/39] Loss: 0.00397 +Epoch [3707/4000] Training metric {'Train/mean dice_metric': 0.9962245225906372, 'Train/mean miou_metric': 0.9928998351097107, 'Train/mean f1': 0.9968516826629639, 'Train/mean precision': 0.9964443445205688, 'Train/mean recall': 0.9972593188285828, 'Train/mean hd95_metric': 0.945703387260437} +Epoch [3707/4000] Validation [1/10] Loss: 0.73820 focal_loss 0.65022 dice_loss 0.08798 +Epoch [3707/4000] Validation [2/10] Loss: 0.48745 focal_loss 0.39211 dice_loss 0.09534 +Epoch [3707/4000] Validation [3/10] Loss: 0.39258 focal_loss 0.28148 dice_loss 0.11110 +Epoch [3707/4000] Validation [4/10] Loss: 0.90607 focal_loss 0.33896 dice_loss 0.56711 +Epoch [3707/4000] Validation [5/10] Loss: 3.10065 focal_loss 2.42687 dice_loss 0.67377 +Epoch [3707/4000] Validation [6/10] Loss: 1.34497 focal_loss 0.63468 dice_loss 0.71028 +Epoch [3707/4000] Validation [7/10] Loss: 1.19404 focal_loss 0.53897 dice_loss 0.65508 +Epoch [3707/4000] Validation [8/10] Loss: 2.30274 focal_loss 1.69574 dice_loss 0.60701 +Epoch [3707/4000] Validation [9/10] Loss: 1.64583 focal_loss 1.10105 dice_loss 0.54478 +Epoch [3707/4000] Validation [10/10] Loss: 1.94250 focal_loss 1.20532 dice_loss 0.73718 +Epoch [3707/4000] Validation metric {'Val/mean dice_metric': 0.9514615535736084, 'Val/mean miou_metric': 0.9354159235954285, 'Val/mean f1': 0.9480776190757751, 'Val/mean precision': 0.9422547817230225, 'Val/mean recall': 0.9539729952812195, 'Val/mean hd95_metric': 10.675570487976074} +Cheakpoint... +Epoch [3707/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514615535736084, 'Val/mean miou_metric': 0.9354159235954285, 'Val/mean f1': 0.9480776190757751, 'Val/mean precision': 0.9422547817230225, 'Val/mean recall': 0.9539729952812195, 'Val/mean hd95_metric': 10.675570487976074} +Epoch [3708/4000] Training [1/39] Loss: 0.12791 +Epoch [3708/4000] Training [2/39] Loss: 0.00368 +Epoch [3708/4000] Training [3/39] Loss: 0.00295 +Epoch [3708/4000] Training [4/39] Loss: 0.01048 +Epoch [3708/4000] Training [5/39] Loss: 0.04201 +Epoch [3708/4000] Training [6/39] Loss: 0.00450 +Epoch [3708/4000] Training [7/39] Loss: 0.00351 +Epoch [3708/4000] Training [8/39] Loss: 0.00843 +Epoch [3708/4000] Training [9/39] Loss: 0.00699 +Epoch [3708/4000] Training [10/39] Loss: 0.00337 +Epoch [3708/4000] Training [11/39] Loss: 0.00614 +Epoch [3708/4000] Training [12/39] Loss: 0.00447 +Epoch [3708/4000] Training [13/39] Loss: 0.00567 +Epoch [3708/4000] Training [14/39] Loss: 0.00427 +Epoch [3708/4000] Training [15/39] Loss: 0.00415 +Epoch [3708/4000] Training [16/39] Loss: 0.00397 +Epoch [3708/4000] Training [17/39] Loss: 0.08607 +Epoch [3708/4000] Training [18/39] Loss: 0.12854 +Epoch [3708/4000] Training [19/39] Loss: 0.00754 +Epoch [3708/4000] Training [20/39] Loss: 0.00470 +Epoch [3708/4000] Training [21/39] Loss: 0.00449 +Epoch [3708/4000] Training [22/39] Loss: 0.00670 +Epoch [3708/4000] Training [23/39] Loss: 0.12873 +Epoch [3708/4000] Training [24/39] Loss: 0.00437 +Epoch [3708/4000] Training [25/39] Loss: 0.00424 +Epoch [3708/4000] Training [26/39] Loss: 0.12875 +Epoch [3708/4000] Training [27/39] Loss: 0.00649 +Epoch [3708/4000] Training [28/39] Loss: 0.00334 +Epoch [3708/4000] Training [29/39] Loss: 0.00444 +Epoch [3708/4000] Training [30/39] Loss: 0.00447 +Epoch [3708/4000] Training [31/39] Loss: 0.00502 +Epoch [3708/4000] Training [32/39] Loss: 0.12961 +Epoch [3708/4000] Training [33/39] Loss: 0.00427 +Epoch [3708/4000] Training [34/39] Loss: 0.00393 +Epoch [3708/4000] Training [35/39] Loss: 0.00694 +Epoch [3708/4000] Training [36/39] Loss: 0.12851 +Epoch [3708/4000] Training [37/39] Loss: 0.00362 +Epoch [3708/4000] Training [38/39] Loss: 0.00470 +Epoch [3708/4000] Training [39/39] Loss: 0.12893 +Epoch [3708/4000] Training metric {'Train/mean dice_metric': 0.9964056611061096, 'Train/mean miou_metric': 0.9932651519775391, 'Train/mean f1': 0.9969849586486816, 'Train/mean precision': 0.9965490698814392, 'Train/mean recall': 0.997421145439148, 'Train/mean hd95_metric': 0.9245683550834656} +Epoch [3708/4000] Validation [1/10] Loss: 0.72890 focal_loss 0.64126 dice_loss 0.08765 +Epoch [3708/4000] Validation [2/10] Loss: 0.48944 focal_loss 0.39340 dice_loss 0.09605 +Epoch [3708/4000] Validation [3/10] Loss: 0.39186 focal_loss 0.28048 dice_loss 0.11139 +Epoch [3708/4000] Validation [4/10] Loss: 0.90512 focal_loss 0.33843 dice_loss 0.56670 +Epoch [3708/4000] Validation [5/10] Loss: 3.06736 focal_loss 2.39356 dice_loss 0.67380 +Epoch [3708/4000] Validation [6/10] Loss: 1.34706 focal_loss 0.63683 dice_loss 0.71023 +Epoch [3708/4000] Validation [7/10] Loss: 1.19235 focal_loss 0.53736 dice_loss 0.65498 +Epoch [3708/4000] Validation [8/10] Loss: 2.33022 focal_loss 1.71975 dice_loss 0.61047 +Epoch [3708/4000] Validation [9/10] Loss: 1.61169 focal_loss 1.06644 dice_loss 0.54525 +Epoch [3708/4000] Validation [10/10] Loss: 1.93900 focal_loss 1.20182 dice_loss 0.73719 +Epoch [3708/4000] Validation metric {'Val/mean dice_metric': 0.9515780210494995, 'Val/mean miou_metric': 0.9357112050056458, 'Val/mean f1': 0.9480048418045044, 'Val/mean precision': 0.9424399137496948, 'Val/mean recall': 0.9536359310150146, 'Val/mean hd95_metric': 10.6462984085083} +Cheakpoint... +Epoch [3708/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515780210494995, 'Val/mean miou_metric': 0.9357112050056458, 'Val/mean f1': 0.9480048418045044, 'Val/mean precision': 0.9424399137496948, 'Val/mean recall': 0.9536359310150146, 'Val/mean hd95_metric': 10.6462984085083} +Epoch [3709/4000] Training [1/39] Loss: 0.00299 +Epoch [3709/4000] Training [2/39] Loss: 0.12872 +Epoch [3709/4000] Training [3/39] Loss: 0.25218 +Epoch [3709/4000] Training [4/39] Loss: 0.00592 +Epoch [3709/4000] Training [5/39] Loss: 0.00642 +Epoch [3709/4000] Training [6/39] Loss: 0.00282 +Epoch [3709/4000] Training [7/39] Loss: 0.00699 +Epoch [3709/4000] Training [8/39] Loss: 0.00722 +Epoch [3709/4000] Training [9/39] Loss: 0.00284 +Epoch [3709/4000] Training [10/39] Loss: 0.00359 +Epoch [3709/4000] Training [11/39] Loss: 0.00499 +Epoch [3709/4000] Training [12/39] Loss: 0.00489 +Epoch [3709/4000] Training [13/39] Loss: 0.00300 +Epoch [3709/4000] Training [14/39] Loss: 0.00482 +Epoch [3709/4000] Training [15/39] Loss: 0.10240 +Epoch [3709/4000] Training [16/39] Loss: 0.00383 +Epoch [3709/4000] Training [17/39] Loss: 0.00323 +Epoch [3709/4000] Training [18/39] Loss: 0.00479 +Epoch [3709/4000] Training [19/39] Loss: 0.00260 +Epoch [3709/4000] Training [20/39] Loss: 0.00269 +Epoch [3709/4000] Training [21/39] Loss: 0.00428 +Epoch [3709/4000] Training [22/39] Loss: 0.00493 +Epoch [3709/4000] Training [23/39] Loss: 0.13061 +Epoch [3709/4000] Training [24/39] Loss: 0.12828 +Epoch [3709/4000] Training [25/39] Loss: 0.00469 +Epoch [3709/4000] Training [26/39] Loss: 0.00413 +Epoch [3709/4000] Training [27/39] Loss: 0.00839 +Epoch [3709/4000] Training [28/39] Loss: 0.00625 +Epoch [3709/4000] Training [29/39] Loss: 0.00680 +Epoch [3709/4000] Training [30/39] Loss: 0.00513 +Epoch [3709/4000] Training [31/39] Loss: 0.12825 +Epoch [3709/4000] Training [32/39] Loss: 0.00378 +Epoch [3709/4000] Training [33/39] Loss: 0.00528 +Epoch [3709/4000] Training [34/39] Loss: 0.00355 +Epoch [3709/4000] Training [35/39] Loss: 0.00467 +Epoch [3709/4000] Training [36/39] Loss: 0.00417 +Epoch [3709/4000] Training [37/39] Loss: 0.00409 +Epoch [3709/4000] Training [38/39] Loss: 0.00596 +Epoch [3709/4000] Training [39/39] Loss: 0.12876 +Epoch [3709/4000] Training metric {'Train/mean dice_metric': 0.9962981343269348, 'Train/mean miou_metric': 0.9930882453918457, 'Train/mean f1': 0.9968245029449463, 'Train/mean precision': 0.9963263273239136, 'Train/mean recall': 0.997323215007782, 'Train/mean hd95_metric': 1.0338748693466187} +Epoch [3709/4000] Validation [1/10] Loss: 0.71658 focal_loss 0.62962 dice_loss 0.08696 +Epoch [3709/4000] Validation [2/10] Loss: 0.48874 focal_loss 0.39066 dice_loss 0.09807 +Epoch [3709/4000] Validation [3/10] Loss: 0.39645 focal_loss 0.28454 dice_loss 0.11191 +Epoch [3709/4000] Validation [4/10] Loss: 0.88304 focal_loss 0.31812 dice_loss 0.56492 +Epoch [3709/4000] Validation [5/10] Loss: 3.06636 focal_loss 2.39236 dice_loss 0.67400 +Epoch [3709/4000] Validation [6/10] Loss: 1.32844 focal_loss 0.61670 dice_loss 0.71173 +Epoch [3709/4000] Validation [7/10] Loss: 1.17905 focal_loss 0.52513 dice_loss 0.65392 +Epoch [3709/4000] Validation [8/10] Loss: 2.31218 focal_loss 1.70125 dice_loss 0.61093 +Epoch [3709/4000] Validation [9/10] Loss: 1.58893 focal_loss 1.04448 dice_loss 0.54445 +Epoch [3709/4000] Validation [10/10] Loss: 1.88782 focal_loss 1.15222 dice_loss 0.73560 +Epoch [3709/4000] Validation metric {'Val/mean dice_metric': 0.9514368176460266, 'Val/mean miou_metric': 0.9355303645133972, 'Val/mean f1': 0.9480205774307251, 'Val/mean precision': 0.9430393576622009, 'Val/mean recall': 0.9530547857284546, 'Val/mean hd95_metric': 10.865462303161621} +Cheakpoint... +Epoch [3709/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514368176460266, 'Val/mean miou_metric': 0.9355303645133972, 'Val/mean f1': 0.9480205774307251, 'Val/mean precision': 0.9430393576622009, 'Val/mean recall': 0.9530547857284546, 'Val/mean hd95_metric': 10.865462303161621} +Epoch [3710/4000] Training [1/39] Loss: 0.12828 +Epoch [3710/4000] Training [2/39] Loss: 0.13175 +Epoch [3710/4000] Training [3/39] Loss: 0.12718 +Epoch [3710/4000] Training [4/39] Loss: 0.00476 +Epoch [3710/4000] Training [5/39] Loss: 0.12881 +Epoch [3710/4000] Training [6/39] Loss: 0.00335 +Epoch [3710/4000] Training [7/39] Loss: 0.12822 +Epoch [3710/4000] Training [8/39] Loss: 0.09750 +Epoch [3710/4000] Training [9/39] Loss: 0.00366 +Epoch [3710/4000] Training [10/39] Loss: 0.00531 +Epoch [3710/4000] Training [11/39] Loss: 0.00350 +Epoch [3710/4000] Training [12/39] Loss: 0.12840 +Epoch [3710/4000] Training [13/39] Loss: 0.12880 +Epoch [3710/4000] Training [14/39] Loss: 0.12766 +Epoch [3710/4000] Training [15/39] Loss: 0.00350 +Epoch [3710/4000] Training [16/39] Loss: 0.12830 +Epoch [3710/4000] Training [17/39] Loss: 0.00439 +Epoch [3710/4000] Training [18/39] Loss: 0.00458 +Epoch [3710/4000] Training [19/39] Loss: 0.00512 +Epoch [3710/4000] Training [20/39] Loss: 0.00321 +Epoch [3710/4000] Training [21/39] Loss: 0.00457 +Epoch [3710/4000] Training [22/39] Loss: 0.00389 +Epoch [3710/4000] Training [23/39] Loss: 0.00376 +Epoch [3710/4000] Training [24/39] Loss: 0.00641 +Epoch [3710/4000] Training [25/39] Loss: 0.12829 +Epoch [3710/4000] Training [26/39] Loss: 0.25757 +Epoch [3710/4000] Training [27/39] Loss: 0.00457 +Epoch [3710/4000] Training [28/39] Loss: 0.00392 +Epoch [3710/4000] Training [29/39] Loss: 0.00578 +Epoch [3710/4000] Training [30/39] Loss: 0.00340 +Epoch [3710/4000] Training [31/39] Loss: 0.00268 +Epoch [3710/4000] Training [32/39] Loss: 0.00682 +Epoch [3710/4000] Training [33/39] Loss: 0.00544 +Epoch [3710/4000] Training [34/39] Loss: 0.00487 +Epoch [3710/4000] Training [35/39] Loss: 0.12922 +Epoch [3710/4000] Training [36/39] Loss: 0.00638 +Epoch [3710/4000] Training [37/39] Loss: 0.00506 +Epoch [3710/4000] Training [38/39] Loss: 0.00431 +Epoch [3710/4000] Training [39/39] Loss: 0.12783 +Epoch [3710/4000] Training metric {'Train/mean dice_metric': 0.9965990781784058, 'Train/mean miou_metric': 0.9936766028404236, 'Train/mean f1': 0.997105062007904, 'Train/mean precision': 0.9966410398483276, 'Train/mean recall': 0.9975695013999939, 'Train/mean hd95_metric': 0.9228411912918091} +Epoch [3710/4000] Validation [1/10] Loss: 0.74600 focal_loss 0.65719 dice_loss 0.08880 +Epoch [3710/4000] Validation [2/10] Loss: 0.49213 focal_loss 0.39477 dice_loss 0.09735 +Epoch [3710/4000] Validation [3/10] Loss: 0.39525 focal_loss 0.28410 dice_loss 0.11115 +Epoch [3710/4000] Validation [4/10] Loss: 0.89246 focal_loss 0.32613 dice_loss 0.56633 +Epoch [3710/4000] Validation [5/10] Loss: 3.13465 focal_loss 2.46060 dice_loss 0.67405 +Epoch [3710/4000] Validation [6/10] Loss: 1.33272 focal_loss 0.62211 dice_loss 0.71061 +Epoch [3710/4000] Validation [7/10] Loss: 1.19071 focal_loss 0.53491 dice_loss 0.65581 +Epoch [3710/4000] Validation [8/10] Loss: 2.28232 focal_loss 1.67658 dice_loss 0.60574 +Epoch [3710/4000] Validation [9/10] Loss: 1.60604 focal_loss 1.06153 dice_loss 0.54451 +Epoch [3710/4000] Validation [10/10] Loss: 1.90604 focal_loss 1.17020 dice_loss 0.73584 +Epoch [3710/4000] Validation metric {'Val/mean dice_metric': 0.9516410231590271, 'Val/mean miou_metric': 0.935949981212616, 'Val/mean f1': 0.9482685923576355, 'Val/mean precision': 0.9425898194313049, 'Val/mean recall': 0.9540164470672607, 'Val/mean hd95_metric': 10.685635566711426} +Cheakpoint... +Epoch [3710/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516410231590271, 'Val/mean miou_metric': 0.935949981212616, 'Val/mean f1': 0.9482685923576355, 'Val/mean precision': 0.9425898194313049, 'Val/mean recall': 0.9540164470672607, 'Val/mean hd95_metric': 10.685635566711426} +Epoch [3711/4000] Training [1/39] Loss: 0.00429 +Epoch [3711/4000] Training [2/39] Loss: 0.12821 +Epoch [3711/4000] Training [3/39] Loss: 0.00580 +Epoch [3711/4000] Training [4/39] Loss: 0.00412 +Epoch [3711/4000] Training [5/39] Loss: 0.00494 +Epoch [3711/4000] Training [6/39] Loss: 0.13005 +Epoch [3711/4000] Training [7/39] Loss: 0.12870 +Epoch [3711/4000] Training [8/39] Loss: 0.00341 +Epoch [3711/4000] Training [9/39] Loss: 0.25249 +Epoch [3711/4000] Training [10/39] Loss: 0.00422 +Epoch [3711/4000] Training [11/39] Loss: 0.12936 +Epoch [3711/4000] Training [12/39] Loss: 0.00615 +Epoch [3711/4000] Training [13/39] Loss: 0.00494 +Epoch [3711/4000] Training [14/39] Loss: 0.00573 +Epoch [3711/4000] Training [15/39] Loss: 0.12711 +Epoch [3711/4000] Training [16/39] Loss: 0.00410 +Epoch [3711/4000] Training [17/39] Loss: 0.00401 +Epoch [3711/4000] Training [18/39] Loss: 0.00350 +Epoch [3711/4000] Training [19/39] Loss: 0.00366 +Epoch [3711/4000] Training [20/39] Loss: 0.00609 +Epoch [3711/4000] Training [21/39] Loss: 0.00550 +Epoch [3711/4000] Training [22/39] Loss: 0.00559 +Epoch [3711/4000] Training [23/39] Loss: 0.12724 +Epoch [3711/4000] Training [24/39] Loss: 0.12956 +Epoch [3711/4000] Training [25/39] Loss: 0.00435 +Epoch [3711/4000] Training [26/39] Loss: 0.00648 +Epoch [3711/4000] Training [27/39] Loss: 0.00382 +Epoch [3711/4000] Training [28/39] Loss: 0.00496 +Epoch [3711/4000] Training [29/39] Loss: 0.00268 +Epoch [3711/4000] Training [30/39] Loss: 0.00298 +Epoch [3711/4000] Training [31/39] Loss: 0.00626 +Epoch [3711/4000] Training [32/39] Loss: 0.13043 +Epoch [3711/4000] Training [33/39] Loss: 0.00553 +Epoch [3711/4000] Training [34/39] Loss: 0.12877 +Epoch [3711/4000] Training [35/39] Loss: 0.00549 +Epoch [3711/4000] Training [36/39] Loss: 0.00541 +Epoch [3711/4000] Training [37/39] Loss: 0.13143 +Epoch [3711/4000] Training [38/39] Loss: 0.00420 +Epoch [3711/4000] Training [39/39] Loss: 0.00371 +Epoch [3711/4000] Training metric {'Train/mean dice_metric': 0.9962819218635559, 'Train/mean miou_metric': 0.993008553981781, 'Train/mean f1': 0.9968732595443726, 'Train/mean precision': 0.9964081048965454, 'Train/mean recall': 0.9973387718200684, 'Train/mean hd95_metric': 0.9519251585006714} +Epoch [3711/4000] Validation [1/10] Loss: 0.74053 focal_loss 0.65132 dice_loss 0.08921 +Epoch [3711/4000] Validation [2/10] Loss: 0.49231 focal_loss 0.39414 dice_loss 0.09817 +Epoch [3711/4000] Validation [3/10] Loss: 0.38670 focal_loss 0.27575 dice_loss 0.11095 +Epoch [3711/4000] Validation [4/10] Loss: 0.89260 focal_loss 0.32652 dice_loss 0.56609 +Epoch [3711/4000] Validation [5/10] Loss: 3.04780 focal_loss 2.37387 dice_loss 0.67393 +Epoch [3711/4000] Validation [6/10] Loss: 1.33429 focal_loss 0.62322 dice_loss 0.71107 +Epoch [3711/4000] Validation [7/10] Loss: 1.18686 focal_loss 0.53243 dice_loss 0.65444 +Epoch [3711/4000] Validation [8/10] Loss: 2.29395 focal_loss 1.68600 dice_loss 0.60795 +Epoch [3711/4000] Validation [9/10] Loss: 1.60816 focal_loss 1.06387 dice_loss 0.54428 +Epoch [3711/4000] Validation [10/10] Loss: 1.89759 focal_loss 1.16218 dice_loss 0.73541 +Epoch [3711/4000] Validation metric {'Val/mean dice_metric': 0.9514013528823853, 'Val/mean miou_metric': 0.9354302883148193, 'Val/mean f1': 0.9480219483375549, 'Val/mean precision': 0.9424606561660767, 'Val/mean recall': 0.9536492824554443, 'Val/mean hd95_metric': 10.675362586975098} +Cheakpoint... +Epoch [3711/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514013528823853, 'Val/mean miou_metric': 0.9354302883148193, 'Val/mean f1': 0.9480219483375549, 'Val/mean precision': 0.9424606561660767, 'Val/mean recall': 0.9536492824554443, 'Val/mean hd95_metric': 10.675362586975098} +Epoch [3712/4000] Training [1/39] Loss: 0.01044 +Epoch [3712/4000] Training [2/39] Loss: 0.13337 +Epoch [3712/4000] Training [3/39] Loss: 0.00522 +Epoch [3712/4000] Training [4/39] Loss: 0.00484 +Epoch [3712/4000] Training [5/39] Loss: 0.00394 +Epoch [3712/4000] Training [6/39] Loss: 0.00511 +Epoch [3712/4000] Training [7/39] Loss: 0.00463 +Epoch [3712/4000] Training [8/39] Loss: 0.00328 +Epoch [3712/4000] Training [9/39] Loss: 0.00549 +Epoch [3712/4000] Training [10/39] Loss: 0.00486 +Epoch [3712/4000] Training [11/39] Loss: 0.00464 +Epoch [3712/4000] Training [12/39] Loss: 0.00460 +Epoch [3712/4000] Training [13/39] Loss: 0.00785 +Epoch [3712/4000] Training [14/39] Loss: 0.00275 +Epoch [3712/4000] Training [15/39] Loss: 0.00384 +Epoch [3712/4000] Training [16/39] Loss: 0.12801 +Epoch [3712/4000] Training [17/39] Loss: 0.00396 +Epoch [3712/4000] Training [18/39] Loss: 0.12959 +Epoch [3712/4000] Training [19/39] Loss: 0.00684 +Epoch [3712/4000] Training [20/39] Loss: 0.12838 +Epoch [3712/4000] Training [21/39] Loss: 0.00621 +Epoch [3712/4000] Training [22/39] Loss: 0.00380 +Epoch [3712/4000] Training [23/39] Loss: 0.12824 +Epoch [3712/4000] Training [24/39] Loss: 0.00766 +Epoch [3712/4000] Training [25/39] Loss: 0.00613 +Epoch [3712/4000] Training [26/39] Loss: 0.12838 +Epoch [3712/4000] Training [27/39] Loss: 0.12859 +Epoch [3712/4000] Training [28/39] Loss: 0.12888 +Epoch [3712/4000] Training [29/39] Loss: 0.00390 +Epoch [3712/4000] Training [30/39] Loss: 0.00375 +Epoch [3712/4000] Training [31/39] Loss: 0.00624 +Epoch [3712/4000] Training [32/39] Loss: 0.00457 +Epoch [3712/4000] Training [33/39] Loss: 0.12993 +Epoch [3712/4000] Training [34/39] Loss: 0.00362 +Epoch [3712/4000] Training [35/39] Loss: 0.00240 +Epoch [3712/4000] Training [36/39] Loss: 0.00408 +Epoch [3712/4000] Training [37/39] Loss: 0.13073 +Epoch [3712/4000] Training [38/39] Loss: 0.00780 +Epoch [3712/4000] Training [39/39] Loss: 0.00973 +Epoch [3712/4000] Training metric {'Train/mean dice_metric': 0.9955143332481384, 'Train/mean miou_metric': 0.9923262000083923, 'Train/mean f1': 0.9969133734703064, 'Train/mean precision': 0.9965119361877441, 'Train/mean recall': 0.9973150491714478, 'Train/mean hd95_metric': 0.9223692417144775} +Epoch [3712/4000] Validation [1/10] Loss: 0.75329 focal_loss 0.66276 dice_loss 0.09053 +Epoch [3712/4000] Validation [2/10] Loss: 0.49327 focal_loss 0.39875 dice_loss 0.09452 +Epoch [3712/4000] Validation [3/10] Loss: 0.37523 focal_loss 0.26529 dice_loss 0.10994 +Epoch [3712/4000] Validation [4/10] Loss: 0.90447 focal_loss 0.33734 dice_loss 0.56713 +Epoch [3712/4000] Validation [5/10] Loss: 3.04546 focal_loss 2.37178 dice_loss 0.67368 +Epoch [3712/4000] Validation [6/10] Loss: 1.36424 focal_loss 0.65202 dice_loss 0.71222 +Epoch [3712/4000] Validation [7/10] Loss: 1.21353 focal_loss 0.55696 dice_loss 0.65657 +Epoch [3712/4000] Validation [8/10] Loss: 2.31570 focal_loss 1.71221 dice_loss 0.60349 +Epoch [3712/4000] Validation [9/10] Loss: 1.61850 focal_loss 1.07318 dice_loss 0.54533 +Epoch [3712/4000] Validation [10/10] Loss: 1.95874 focal_loss 1.22241 dice_loss 0.73633 +Epoch [3712/4000] Validation metric {'Val/mean dice_metric': 0.9508155584335327, 'Val/mean miou_metric': 0.9349282383918762, 'Val/mean f1': 0.9477657079696655, 'Val/mean precision': 0.9410821795463562, 'Val/mean recall': 0.9545448422431946, 'Val/mean hd95_metric': 10.846233367919922} +Cheakpoint... +Epoch [3712/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508155584335327, 'Val/mean miou_metric': 0.9349282383918762, 'Val/mean f1': 0.9477657079696655, 'Val/mean precision': 0.9410821795463562, 'Val/mean recall': 0.9545448422431946, 'Val/mean hd95_metric': 10.846233367919922} +Epoch [3713/4000] Training [1/39] Loss: 0.00351 +Epoch [3713/4000] Training [2/39] Loss: 0.00554 +Epoch [3713/4000] Training [3/39] Loss: 0.00550 +Epoch [3713/4000] Training [4/39] Loss: 0.00379 +Epoch [3713/4000] Training [5/39] Loss: 0.00551 +Epoch [3713/4000] Training [6/39] Loss: 0.13038 +Epoch [3713/4000] Training [7/39] Loss: 0.12796 +Epoch [3713/4000] Training [8/39] Loss: 0.00515 +Epoch [3713/4000] Training [9/39] Loss: 0.12862 +Epoch [3713/4000] Training [10/39] Loss: 0.00444 +Epoch [3713/4000] Training [11/39] Loss: 0.12782 +Epoch [3713/4000] Training [12/39] Loss: 0.00464 +Epoch [3713/4000] Training [13/39] Loss: 0.00401 +Epoch [3713/4000] Training [14/39] Loss: 0.00448 +Epoch [3713/4000] Training [15/39] Loss: 0.00451 +Epoch [3713/4000] Training [16/39] Loss: 0.00396 +Epoch [3713/4000] Training [17/39] Loss: 0.00524 +Epoch [3713/4000] Training [18/39] Loss: 0.12861 +Epoch [3713/4000] Training [19/39] Loss: 0.00501 +Epoch [3713/4000] Training [20/39] Loss: 0.00589 +Epoch [3713/4000] Training [21/39] Loss: 0.00302 +Epoch [3713/4000] Training [22/39] Loss: 0.00925 +Epoch [3713/4000] Training [23/39] Loss: 0.00273 +Epoch [3713/4000] Training [24/39] Loss: 0.00488 +Epoch [3713/4000] Training [25/39] Loss: 0.00384 +Epoch [3713/4000] Training [26/39] Loss: 0.12898 +Epoch [3713/4000] Training [27/39] Loss: 0.00315 +Epoch [3713/4000] Training [28/39] Loss: 0.00448 +Epoch [3713/4000] Training [29/39] Loss: 0.00370 +Epoch [3713/4000] Training [30/39] Loss: 0.00473 +Epoch [3713/4000] Training [31/39] Loss: 0.00317 +Epoch [3713/4000] Training [32/39] Loss: 0.12917 +Epoch [3713/4000] Training [33/39] Loss: 0.00388 +Epoch [3713/4000] Training [34/39] Loss: 0.00447 +Epoch [3713/4000] Training [35/39] Loss: 0.00583 +Epoch [3713/4000] Training [36/39] Loss: 0.00524 +Epoch [3713/4000] Training [37/39] Loss: 0.12938 +Epoch [3713/4000] Training [38/39] Loss: 0.00712 +Epoch [3713/4000] Training [39/39] Loss: 0.00329 +Epoch [3713/4000] Training metric {'Train/mean dice_metric': 0.9964505434036255, 'Train/mean miou_metric': 0.9933754801750183, 'Train/mean f1': 0.9970697164535522, 'Train/mean precision': 0.996605396270752, 'Train/mean recall': 0.9975345134735107, 'Train/mean hd95_metric': 0.9256371855735779} +Epoch [3713/4000] Validation [1/10] Loss: 0.74687 focal_loss 0.65835 dice_loss 0.08852 +Epoch [3713/4000] Validation [2/10] Loss: 0.49366 focal_loss 0.39587 dice_loss 0.09780 +Epoch [3713/4000] Validation [3/10] Loss: 0.40068 focal_loss 0.28896 dice_loss 0.11172 +Epoch [3713/4000] Validation [4/10] Loss: 0.88993 focal_loss 0.32496 dice_loss 0.56497 +Epoch [3713/4000] Validation [5/10] Loss: 3.10839 focal_loss 2.43443 dice_loss 0.67396 +Epoch [3713/4000] Validation [6/10] Loss: 1.32960 focal_loss 0.61887 dice_loss 0.71073 +Epoch [3713/4000] Validation [7/10] Loss: 1.18782 focal_loss 0.53203 dice_loss 0.65579 +Epoch [3713/4000] Validation [8/10] Loss: 2.38079 focal_loss 1.76715 dice_loss 0.61364 +Epoch [3713/4000] Validation [9/10] Loss: 1.58603 focal_loss 1.04232 dice_loss 0.54371 +Epoch [3713/4000] Validation [10/10] Loss: 1.88360 focal_loss 1.14989 dice_loss 0.73371 +Epoch [3713/4000] Validation metric {'Val/mean dice_metric': 0.951545000076294, 'Val/mean miou_metric': 0.9357473850250244, 'Val/mean f1': 0.9483386278152466, 'Val/mean precision': 0.9435781240463257, 'Val/mean recall': 0.953147292137146, 'Val/mean hd95_metric': 10.670426368713379} +Cheakpoint... +Epoch [3713/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951545000076294, 'Val/mean miou_metric': 0.9357473850250244, 'Val/mean f1': 0.9483386278152466, 'Val/mean precision': 0.9435781240463257, 'Val/mean recall': 0.953147292137146, 'Val/mean hd95_metric': 10.670426368713379} +Epoch [3714/4000] Training [1/39] Loss: 0.00261 +Epoch [3714/4000] Training [2/39] Loss: 0.00380 +Epoch [3714/4000] Training [3/39] Loss: 0.00397 +Epoch [3714/4000] Training [4/39] Loss: 0.00837 +Epoch [3714/4000] Training [5/39] Loss: 0.00803 +Epoch [3714/4000] Training [6/39] Loss: 0.00580 +Epoch [3714/4000] Training [7/39] Loss: 0.00389 +Epoch [3714/4000] Training [8/39] Loss: 0.12937 +Epoch [3714/4000] Training [9/39] Loss: 0.10007 +Epoch [3714/4000] Training [10/39] Loss: 0.00779 +Epoch [3714/4000] Training [11/39] Loss: 0.00500 +Epoch [3714/4000] Training [12/39] Loss: 0.00465 +Epoch [3714/4000] Training [13/39] Loss: 0.00462 +Epoch [3714/4000] Training [14/39] Loss: 0.13187 +Epoch [3714/4000] Training [15/39] Loss: 0.00433 +Epoch [3714/4000] Training [16/39] Loss: 0.00589 +Epoch [3714/4000] Training [17/39] Loss: 0.00347 +Epoch [3714/4000] Training [18/39] Loss: 0.00423 +Epoch [3714/4000] Training [19/39] Loss: 0.00562 +Epoch [3714/4000] Training [20/39] Loss: 0.00598 +Epoch [3714/4000] Training [21/39] Loss: 0.13035 +Epoch [3714/4000] Training [22/39] Loss: 0.00494 +Epoch [3714/4000] Training [23/39] Loss: 0.00781 +Epoch [3714/4000] Training [24/39] Loss: 0.00344 +Epoch [3714/4000] Training [25/39] Loss: 0.00345 +Epoch [3714/4000] Training [26/39] Loss: 0.00520 +Epoch [3714/4000] Training [27/39] Loss: 0.00371 +Epoch [3714/4000] Training [28/39] Loss: 0.00346 +Epoch [3714/4000] Training [29/39] Loss: 0.00493 +Epoch [3714/4000] Training [30/39] Loss: 0.00365 +Epoch [3714/4000] Training [31/39] Loss: 0.12896 +Epoch [3714/4000] Training [32/39] Loss: 0.12968 +Epoch [3714/4000] Training [33/39] Loss: 0.00543 +Epoch [3714/4000] Training [34/39] Loss: 0.00486 +Epoch [3714/4000] Training [35/39] Loss: 0.01320 +Epoch [3714/4000] Training [36/39] Loss: 0.00454 +Epoch [3714/4000] Training [37/39] Loss: 0.00408 +Epoch [3714/4000] Training [38/39] Loss: 0.00277 +Epoch [3714/4000] Training [39/39] Loss: 0.12964 +Epoch [3714/4000] Training metric {'Train/mean dice_metric': 0.9962943196296692, 'Train/mean miou_metric': 0.9930728673934937, 'Train/mean f1': 0.9968237280845642, 'Train/mean precision': 0.9963549971580505, 'Train/mean recall': 0.9972931742668152, 'Train/mean hd95_metric': 0.9639195799827576} +Epoch [3714/4000] Validation [1/10] Loss: 0.71874 focal_loss 0.63226 dice_loss 0.08648 +Epoch [3714/4000] Validation [2/10] Loss: 0.49437 focal_loss 0.39582 dice_loss 0.09855 +Epoch [3714/4000] Validation [3/10] Loss: 0.39364 focal_loss 0.28204 dice_loss 0.11160 +Epoch [3714/4000] Validation [4/10] Loss: 0.89315 focal_loss 0.32791 dice_loss 0.56524 +Epoch [3714/4000] Validation [5/10] Loss: 3.05253 focal_loss 2.37837 dice_loss 0.67417 +Epoch [3714/4000] Validation [6/10] Loss: 1.32861 focal_loss 0.61798 dice_loss 0.71064 +Epoch [3714/4000] Validation [7/10] Loss: 1.18822 focal_loss 0.53423 dice_loss 0.65398 +Epoch [3714/4000] Validation [8/10] Loss: 2.36337 focal_loss 1.74938 dice_loss 0.61399 +Epoch [3714/4000] Validation [9/10] Loss: 1.58456 focal_loss 1.04008 dice_loss 0.54448 +Epoch [3714/4000] Validation [10/10] Loss: 1.89362 focal_loss 1.15835 dice_loss 0.73527 +Epoch [3714/4000] Validation metric {'Val/mean dice_metric': 0.9513955116271973, 'Val/mean miou_metric': 0.9354706406593323, 'Val/mean f1': 0.9480708837509155, 'Val/mean precision': 0.9432539939880371, 'Val/mean recall': 0.952937126159668, 'Val/mean hd95_metric': 10.736775398254395} +Cheakpoint... +Epoch [3714/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513955116271973, 'Val/mean miou_metric': 0.9354706406593323, 'Val/mean f1': 0.9480708837509155, 'Val/mean precision': 0.9432539939880371, 'Val/mean recall': 0.952937126159668, 'Val/mean hd95_metric': 10.736775398254395} +Epoch [3715/4000] Training [1/39] Loss: 0.00523 +Epoch [3715/4000] Training [2/39] Loss: 0.00513 +Epoch [3715/4000] Training [3/39] Loss: 0.00398 +Epoch [3715/4000] Training [4/39] Loss: 0.00657 +Epoch [3715/4000] Training [5/39] Loss: 0.13099 +Epoch [3715/4000] Training [6/39] Loss: 0.00451 +Epoch [3715/4000] Training [7/39] Loss: 0.00502 +Epoch [3715/4000] Training [8/39] Loss: 0.00660 +Epoch [3715/4000] Training [9/39] Loss: 0.12804 +Epoch [3715/4000] Training [10/39] Loss: 0.00482 +Epoch [3715/4000] Training [11/39] Loss: 0.13009 +Epoch [3715/4000] Training [12/39] Loss: 0.00881 +Epoch [3715/4000] Training [13/39] Loss: 0.00343 +Epoch [3715/4000] Training [14/39] Loss: 0.00434 +Epoch [3715/4000] Training [15/39] Loss: 0.00623 +Epoch [3715/4000] Training [16/39] Loss: 0.13317 +Epoch [3715/4000] Training [17/39] Loss: 0.00442 +Epoch [3715/4000] Training [18/39] Loss: 0.00829 +Epoch [3715/4000] Training [19/39] Loss: 0.00516 +Epoch [3715/4000] Training [20/39] Loss: 0.00729 +Epoch [3715/4000] Training [21/39] Loss: 0.00559 +Epoch [3715/4000] Training [22/39] Loss: 0.00652 +Epoch [3715/4000] Training [23/39] Loss: 0.00473 +Epoch [3715/4000] Training [24/39] Loss: 0.12766 +Epoch [3715/4000] Training [25/39] Loss: 0.00290 +Epoch [3715/4000] Training [26/39] Loss: 0.00676 +Epoch [3715/4000] Training [27/39] Loss: 0.00608 +Epoch [3715/4000] Training [28/39] Loss: 0.00348 +Epoch [3715/4000] Training [29/39] Loss: 0.13233 +Epoch [3715/4000] Training [30/39] Loss: 0.00542 +Epoch [3715/4000] Training [31/39] Loss: 0.13167 +Epoch [3715/4000] Training [32/39] Loss: 0.13074 +Epoch [3715/4000] Training [33/39] Loss: 0.00505 +Epoch [3715/4000] Training [34/39] Loss: 0.12777 +Epoch [3715/4000] Training [35/39] Loss: 0.13116 +Epoch [3715/4000] Training [36/39] Loss: 0.00570 +Epoch [3715/4000] Training [37/39] Loss: 0.00414 +Epoch [3715/4000] Training [38/39] Loss: 0.25298 +Epoch [3715/4000] Training [39/39] Loss: 0.00369 +Epoch [3715/4000] Training metric {'Train/mean dice_metric': 0.9961465001106262, 'Train/mean miou_metric': 0.9927398562431335, 'Train/mean f1': 0.9967853426933289, 'Train/mean precision': 0.9962562918663025, 'Train/mean recall': 0.9973149299621582, 'Train/mean hd95_metric': 0.9742143750190735} +Epoch [3715/4000] Validation [1/10] Loss: 0.70401 focal_loss 0.61836 dice_loss 0.08565 +Epoch [3715/4000] Validation [2/10] Loss: 0.50047 focal_loss 0.39977 dice_loss 0.10070 +Epoch [3715/4000] Validation [3/10] Loss: 0.39065 focal_loss 0.27894 dice_loss 0.11171 +Epoch [3715/4000] Validation [4/10] Loss: 0.88696 focal_loss 0.32228 dice_loss 0.56468 +Epoch [3715/4000] Validation [5/10] Loss: 3.02197 focal_loss 2.34791 dice_loss 0.67407 +Epoch [3715/4000] Validation [6/10] Loss: 1.32500 focal_loss 0.61095 dice_loss 0.71405 +Epoch [3715/4000] Validation [7/10] Loss: 1.17705 focal_loss 0.52582 dice_loss 0.65124 +Epoch [3715/4000] Validation [8/10] Loss: 2.37156 focal_loss 1.75313 dice_loss 0.61843 +Epoch [3715/4000] Validation [9/10] Loss: 1.53801 focal_loss 0.99327 dice_loss 0.54474 +Epoch [3715/4000] Validation [10/10] Loss: 1.86400 focal_loss 1.13030 dice_loss 0.73371 +Epoch [3715/4000] Validation metric {'Val/mean dice_metric': 0.9513028264045715, 'Val/mean miou_metric': 0.9352673888206482, 'Val/mean f1': 0.9482631087303162, 'Val/mean precision': 0.9441372752189636, 'Val/mean recall': 0.9524251222610474, 'Val/mean hd95_metric': 10.789556503295898} +Cheakpoint... +Epoch [3715/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513028264045715, 'Val/mean miou_metric': 0.9352673888206482, 'Val/mean f1': 0.9482631087303162, 'Val/mean precision': 0.9441372752189636, 'Val/mean recall': 0.9524251222610474, 'Val/mean hd95_metric': 10.789556503295898} +Epoch [3716/4000] Training [1/39] Loss: 0.00337 +Epoch [3716/4000] Training [2/39] Loss: 0.00442 +Epoch [3716/4000] Training [3/39] Loss: 0.12843 +Epoch [3716/4000] Training [4/39] Loss: 0.00428 +Epoch [3716/4000] Training [5/39] Loss: 0.00552 +Epoch [3716/4000] Training [6/39] Loss: 0.00623 +Epoch [3716/4000] Training [7/39] Loss: 0.13037 +Epoch [3716/4000] Training [8/39] Loss: 0.00565 +Epoch [3716/4000] Training [9/39] Loss: 0.00498 +Epoch [3716/4000] Training [10/39] Loss: 0.00357 +Epoch [3716/4000] Training [11/39] Loss: 0.12912 +Epoch [3716/4000] Training [12/39] Loss: 0.12841 +Epoch [3716/4000] Training [13/39] Loss: 0.12866 +Epoch [3716/4000] Training [14/39] Loss: 0.00254 +Epoch [3716/4000] Training [15/39] Loss: 0.00390 +Epoch [3716/4000] Training [16/39] Loss: 0.00437 +Epoch [3716/4000] Training [17/39] Loss: 0.00541 +Epoch [3716/4000] Training [18/39] Loss: 0.12927 +Epoch [3716/4000] Training [19/39] Loss: 0.12839 +Epoch [3716/4000] Training [20/39] Loss: 0.12994 +Epoch [3716/4000] Training [21/39] Loss: 0.12979 +Epoch [3716/4000] Training [22/39] Loss: 0.00304 +Epoch [3716/4000] Training [23/39] Loss: 0.00551 +Epoch [3716/4000] Training [24/39] Loss: 0.00508 +Epoch [3716/4000] Training [25/39] Loss: 0.00514 +Epoch [3716/4000] Training [26/39] Loss: 0.00375 +Epoch [3716/4000] Training [27/39] Loss: 0.00617 +Epoch [3716/4000] Training [28/39] Loss: 0.00808 +Epoch [3716/4000] Training [29/39] Loss: 0.12702 +Epoch [3716/4000] Training [30/39] Loss: 0.00637 +Epoch [3716/4000] Training [31/39] Loss: 0.00510 +Epoch [3716/4000] Training [32/39] Loss: 0.00769 +Epoch [3716/4000] Training [33/39] Loss: 0.12803 +Epoch [3716/4000] Training [34/39] Loss: 0.25332 +Epoch [3716/4000] Training [35/39] Loss: 0.12836 +Epoch [3716/4000] Training [36/39] Loss: 0.13073 +Epoch [3716/4000] Training [37/39] Loss: 0.00565 +Epoch [3716/4000] Training [38/39] Loss: 0.12790 +Epoch [3716/4000] Training [39/39] Loss: 0.13255 +Epoch [3716/4000] Training metric {'Train/mean dice_metric': 0.9962887167930603, 'Train/mean miou_metric': 0.9930287003517151, 'Train/mean f1': 0.9968729019165039, 'Train/mean precision': 0.9964472651481628, 'Train/mean recall': 0.9972990155220032, 'Train/mean hd95_metric': 0.9750099778175354} +Epoch [3716/4000] Validation [1/10] Loss: 0.71960 focal_loss 0.63331 dice_loss 0.08629 +Epoch [3716/4000] Validation [2/10] Loss: 0.49763 focal_loss 0.40028 dice_loss 0.09735 +Epoch [3716/4000] Validation [3/10] Loss: 0.39193 focal_loss 0.28082 dice_loss 0.11111 +Epoch [3716/4000] Validation [4/10] Loss: 0.89578 focal_loss 0.33037 dice_loss 0.56541 +Epoch [3716/4000] Validation [5/10] Loss: 3.06837 focal_loss 2.39420 dice_loss 0.67417 +Epoch [3716/4000] Validation [6/10] Loss: 1.34454 focal_loss 0.62979 dice_loss 0.71476 +Epoch [3716/4000] Validation [7/10] Loss: 1.18940 focal_loss 0.53766 dice_loss 0.65175 +Epoch [3716/4000] Validation [8/10] Loss: 2.35435 focal_loss 1.74282 dice_loss 0.61153 +Epoch [3716/4000] Validation [9/10] Loss: 1.57125 focal_loss 1.02668 dice_loss 0.54457 +Epoch [3716/4000] Validation [10/10] Loss: 1.90498 focal_loss 1.17047 dice_loss 0.73451 +Epoch [3716/4000] Validation metric {'Val/mean dice_metric': 0.9514951705932617, 'Val/mean miou_metric': 0.9355977773666382, 'Val/mean f1': 0.9481852650642395, 'Val/mean precision': 0.9432130455970764, 'Val/mean recall': 0.9532101154327393, 'Val/mean hd95_metric': 10.807235717773438} +Cheakpoint... +Epoch [3716/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514951705932617, 'Val/mean miou_metric': 0.9355977773666382, 'Val/mean f1': 0.9481852650642395, 'Val/mean precision': 0.9432130455970764, 'Val/mean recall': 0.9532101154327393, 'Val/mean hd95_metric': 10.807235717773438} +Epoch [3717/4000] Training [1/39] Loss: 0.00435 +Epoch [3717/4000] Training [2/39] Loss: 0.00423 +Epoch [3717/4000] Training [3/39] Loss: 0.00592 +Epoch [3717/4000] Training [4/39] Loss: 0.00385 +Epoch [3717/4000] Training [5/39] Loss: 0.00378 +Epoch [3717/4000] Training [6/39] Loss: 0.00486 +Epoch [3717/4000] Training [7/39] Loss: 0.13124 +Epoch [3717/4000] Training [8/39] Loss: 0.00500 +Epoch [3717/4000] Training [9/39] Loss: 0.00379 +Epoch [3717/4000] Training [10/39] Loss: 0.00809 +Epoch [3717/4000] Training [11/39] Loss: 0.00590 +Epoch [3717/4000] Training [12/39] Loss: 0.00386 +Epoch [3717/4000] Training [13/39] Loss: 0.00580 +Epoch [3717/4000] Training [14/39] Loss: 0.00428 +Epoch [3717/4000] Training [15/39] Loss: 0.00499 +Epoch [3717/4000] Training [16/39] Loss: 0.00820 +Epoch [3717/4000] Training [17/39] Loss: 0.00398 +Epoch [3717/4000] Training [18/39] Loss: 0.00535 +Epoch [3717/4000] Training [19/39] Loss: 0.00374 +Epoch [3717/4000] Training [20/39] Loss: 0.00353 +Epoch [3717/4000] Training [21/39] Loss: 0.00578 +Epoch [3717/4000] Training [22/39] Loss: 0.00517 +Epoch [3717/4000] Training [23/39] Loss: 0.00513 +Epoch [3717/4000] Training [24/39] Loss: 0.00391 +Epoch [3717/4000] Training [25/39] Loss: 0.12842 +Epoch [3717/4000] Training [26/39] Loss: 0.00453 +Epoch [3717/4000] Training [27/39] Loss: 0.00479 +Epoch [3717/4000] Training [28/39] Loss: 0.12882 +Epoch [3717/4000] Training [29/39] Loss: 0.12896 +Epoch [3717/4000] Training [30/39] Loss: 0.00492 +Epoch [3717/4000] Training [31/39] Loss: 0.00632 +Epoch [3717/4000] Training [32/39] Loss: 0.00552 +Epoch [3717/4000] Training [33/39] Loss: 0.12893 +Epoch [3717/4000] Training [34/39] Loss: 0.00420 +Epoch [3717/4000] Training [35/39] Loss: 0.12905 +Epoch [3717/4000] Training [36/39] Loss: 0.00293 +Epoch [3717/4000] Training [37/39] Loss: 0.00579 +Epoch [3717/4000] Training [38/39] Loss: 0.00373 +Epoch [3717/4000] Training [39/39] Loss: 0.12876 +Epoch [3717/4000] Training metric {'Train/mean dice_metric': 0.9963312149047852, 'Train/mean miou_metric': 0.9931095242500305, 'Train/mean f1': 0.9968333840370178, 'Train/mean precision': 0.9963342547416687, 'Train/mean recall': 0.9973330497741699, 'Train/mean hd95_metric': 0.940372109413147} +Epoch [3717/4000] Validation [1/10] Loss: 0.72196 focal_loss 0.63472 dice_loss 0.08724 +Epoch [3717/4000] Validation [2/10] Loss: 0.49710 focal_loss 0.40154 dice_loss 0.09556 +Epoch [3717/4000] Validation [3/10] Loss: 0.38048 focal_loss 0.26994 dice_loss 0.11054 +Epoch [3717/4000] Validation [4/10] Loss: 0.90433 focal_loss 0.33812 dice_loss 0.56620 +Epoch [3717/4000] Validation [5/10] Loss: 3.04865 focal_loss 2.37460 dice_loss 0.67405 +Epoch [3717/4000] Validation [6/10] Loss: 1.35694 focal_loss 0.64212 dice_loss 0.71481 +Epoch [3717/4000] Validation [7/10] Loss: 1.19216 focal_loss 0.53793 dice_loss 0.65424 +Epoch [3717/4000] Validation [8/10] Loss: 2.31162 focal_loss 1.70315 dice_loss 0.60847 +Epoch [3717/4000] Validation [9/10] Loss: 1.60765 focal_loss 1.06178 dice_loss 0.54588 +Epoch [3717/4000] Validation [10/10] Loss: 1.92892 focal_loss 1.19250 dice_loss 0.73641 +Epoch [3717/4000] Validation metric {'Val/mean dice_metric': 0.9514763355255127, 'Val/mean miou_metric': 0.9355448484420776, 'Val/mean f1': 0.9478340744972229, 'Val/mean precision': 0.9422613382339478, 'Val/mean recall': 0.9534730315208435, 'Val/mean hd95_metric': 10.807629585266113} +Cheakpoint... +Epoch [3717/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514763355255127, 'Val/mean miou_metric': 0.9355448484420776, 'Val/mean f1': 0.9478340744972229, 'Val/mean precision': 0.9422613382339478, 'Val/mean recall': 0.9534730315208435, 'Val/mean hd95_metric': 10.807629585266113} +Epoch [3718/4000] Training [1/39] Loss: 0.01047 +Epoch [3718/4000] Training [2/39] Loss: 0.00536 +Epoch [3718/4000] Training [3/39] Loss: 0.00560 +Epoch [3718/4000] Training [4/39] Loss: 0.00457 +Epoch [3718/4000] Training [5/39] Loss: 0.12872 +Epoch [3718/4000] Training [6/39] Loss: 0.12786 +Epoch [3718/4000] Training [7/39] Loss: 0.00538 +Epoch [3718/4000] Training [8/39] Loss: 0.00522 +Epoch [3718/4000] Training [9/39] Loss: 0.00511 +Epoch [3718/4000] Training [10/39] Loss: 0.00663 +Epoch [3718/4000] Training [11/39] Loss: 0.00335 +Epoch [3718/4000] Training [12/39] Loss: 0.00581 +Epoch [3718/4000] Training [13/39] Loss: 0.00645 +Epoch [3718/4000] Training [14/39] Loss: 0.00334 +Epoch [3718/4000] Training [15/39] Loss: 0.00637 +Epoch [3718/4000] Training [16/39] Loss: 0.00689 +Epoch [3718/4000] Training [17/39] Loss: 0.00461 +Epoch [3718/4000] Training [18/39] Loss: 0.00534 +Epoch [3718/4000] Training [19/39] Loss: 0.00727 +Epoch [3718/4000] Training [20/39] Loss: 0.00565 +Epoch [3718/4000] Training [21/39] Loss: 0.12937 +Epoch [3718/4000] Training [22/39] Loss: 0.00751 +Epoch [3718/4000] Training [23/39] Loss: 0.00344 +Epoch [3718/4000] Training [24/39] Loss: 0.00814 +Epoch [3718/4000] Training [25/39] Loss: 0.00573 +Epoch [3718/4000] Training [26/39] Loss: 0.00447 +Epoch [3718/4000] Training [27/39] Loss: 0.12871 +Epoch [3718/4000] Training [28/39] Loss: 0.00342 +Epoch [3718/4000] Training [29/39] Loss: 0.00707 +Epoch [3718/4000] Training [30/39] Loss: 0.00391 +Epoch [3718/4000] Training [31/39] Loss: 0.00393 +Epoch [3718/4000] Training [32/39] Loss: 0.00530 +Epoch [3718/4000] Training [33/39] Loss: 0.00622 +Epoch [3718/4000] Training [34/39] Loss: 0.00444 +Epoch [3718/4000] Training [35/39] Loss: 0.00431 +Epoch [3718/4000] Training [36/39] Loss: 0.00575 +Epoch [3718/4000] Training [37/39] Loss: 0.12942 +Epoch [3718/4000] Training [38/39] Loss: 0.00385 +Epoch [3718/4000] Training [39/39] Loss: 0.00480 +Epoch [3718/4000] Training metric {'Train/mean dice_metric': 0.996060848236084, 'Train/mean miou_metric': 0.992577075958252, 'Train/mean f1': 0.9966891407966614, 'Train/mean precision': 0.9962771534919739, 'Train/mean recall': 0.9971014261245728, 'Train/mean hd95_metric': 0.9674704670906067} +Epoch [3718/4000] Validation [1/10] Loss: 0.72548 focal_loss 0.63790 dice_loss 0.08757 +Epoch [3718/4000] Validation [2/10] Loss: 0.49862 focal_loss 0.40341 dice_loss 0.09521 +Epoch [3718/4000] Validation [3/10] Loss: 0.37618 focal_loss 0.26607 dice_loss 0.11011 +Epoch [3718/4000] Validation [4/10] Loss: 0.90837 focal_loss 0.34140 dice_loss 0.56697 +Epoch [3718/4000] Validation [5/10] Loss: 3.04056 focal_loss 2.36678 dice_loss 0.67377 +Epoch [3718/4000] Validation [6/10] Loss: 1.35660 focal_loss 0.64146 dice_loss 0.71513 +Epoch [3718/4000] Validation [7/10] Loss: 1.20029 focal_loss 0.54332 dice_loss 0.65697 +Epoch [3718/4000] Validation [8/10] Loss: 2.27951 focal_loss 1.67407 dice_loss 0.60544 +Epoch [3718/4000] Validation [9/10] Loss: 1.63433 focal_loss 1.09272 dice_loss 0.54162 +Epoch [3718/4000] Validation [10/10] Loss: 1.94800 focal_loss 1.21034 dice_loss 0.73767 +Epoch [3718/4000] Validation metric {'Val/mean dice_metric': 0.9511257410049438, 'Val/mean miou_metric': 0.9349517822265625, 'Val/mean f1': 0.9473873376846313, 'Val/mean precision': 0.9410792589187622, 'Val/mean recall': 0.9537806510925293, 'Val/mean hd95_metric': 10.847965240478516} +Cheakpoint... +Epoch [3718/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511257410049438, 'Val/mean miou_metric': 0.9349517822265625, 'Val/mean f1': 0.9473873376846313, 'Val/mean precision': 0.9410792589187622, 'Val/mean recall': 0.9537806510925293, 'Val/mean hd95_metric': 10.847965240478516} +Epoch [3719/4000] Training [1/39] Loss: 0.00514 +Epoch [3719/4000] Training [2/39] Loss: 0.00609 +Epoch [3719/4000] Training [3/39] Loss: 0.12829 +Epoch [3719/4000] Training [4/39] Loss: 0.00785 +Epoch [3719/4000] Training [5/39] Loss: 0.00417 +Epoch [3719/4000] Training [6/39] Loss: 0.12810 +Epoch [3719/4000] Training [7/39] Loss: 0.00282 +Epoch [3719/4000] Training [8/39] Loss: 0.00629 +Epoch [3719/4000] Training [9/39] Loss: 0.00328 +Epoch [3719/4000] Training [10/39] Loss: 0.00407 +Epoch [3719/4000] Training [11/39] Loss: 0.00287 +Epoch [3719/4000] Training [12/39] Loss: 0.12826 +Epoch [3719/4000] Training [13/39] Loss: 0.12825 +Epoch [3719/4000] Training [14/39] Loss: 0.13092 +Epoch [3719/4000] Training [15/39] Loss: 0.00372 +Epoch [3719/4000] Training [16/39] Loss: 0.12848 +Epoch [3719/4000] Training [17/39] Loss: 0.00397 +Epoch [3719/4000] Training [18/39] Loss: 0.12940 +Epoch [3719/4000] Training [19/39] Loss: 0.00292 +Epoch [3719/4000] Training [20/39] Loss: 0.00673 +Epoch [3719/4000] Training [21/39] Loss: 0.00628 +Epoch [3719/4000] Training [22/39] Loss: 0.00474 +Epoch [3719/4000] Training [23/39] Loss: 0.12841 +Epoch [3719/4000] Training [24/39] Loss: 0.00668 +Epoch [3719/4000] Training [25/39] Loss: 0.00467 +Epoch [3719/4000] Training [26/39] Loss: 0.00474 +Epoch [3719/4000] Training [27/39] Loss: 0.00306 +Epoch [3719/4000] Training [28/39] Loss: 0.12876 +Epoch [3719/4000] Training [29/39] Loss: 0.12925 +Epoch [3719/4000] Training [30/39] Loss: 0.00524 +Epoch [3719/4000] Training [31/39] Loss: 0.00283 +Epoch [3719/4000] Training [32/39] Loss: 0.12754 +Epoch [3719/4000] Training [33/39] Loss: 0.12795 +Epoch [3719/4000] Training [34/39] Loss: 0.00387 +Epoch [3719/4000] Training [35/39] Loss: 0.00441 +Epoch [3719/4000] Training [36/39] Loss: 0.12759 +Epoch [3719/4000] Training [37/39] Loss: 0.00456 +Epoch [3719/4000] Training [38/39] Loss: 0.00400 +Epoch [3719/4000] Training [39/39] Loss: 0.00433 +Epoch [3719/4000] Training metric {'Train/mean dice_metric': 0.9964483976364136, 'Train/mean miou_metric': 0.9933438301086426, 'Train/mean f1': 0.9969902038574219, 'Train/mean precision': 0.9965490698814392, 'Train/mean recall': 0.9974316954612732, 'Train/mean hd95_metric': 0.9643097519874573} +Epoch [3719/4000] Validation [1/10] Loss: 0.73533 focal_loss 0.64724 dice_loss 0.08810 +Epoch [3719/4000] Validation [2/10] Loss: 0.49862 focal_loss 0.40257 dice_loss 0.09605 +Epoch [3719/4000] Validation [3/10] Loss: 0.38813 focal_loss 0.27717 dice_loss 0.11096 +Epoch [3719/4000] Validation [4/10] Loss: 0.90317 focal_loss 0.33675 dice_loss 0.56642 +Epoch [3719/4000] Validation [5/10] Loss: 3.06412 focal_loss 2.39006 dice_loss 0.67406 +Epoch [3719/4000] Validation [6/10] Loss: 1.35433 focal_loss 0.64081 dice_loss 0.71352 +Epoch [3719/4000] Validation [7/10] Loss: 1.19383 focal_loss 0.53736 dice_loss 0.65647 +Epoch [3719/4000] Validation [8/10] Loss: 2.32315 focal_loss 1.71296 dice_loss 0.61019 +Epoch [3719/4000] Validation [9/10] Loss: 1.62557 focal_loss 1.08188 dice_loss 0.54368 +Epoch [3719/4000] Validation [10/10] Loss: 1.92422 focal_loss 1.18738 dice_loss 0.73684 +Epoch [3719/4000] Validation metric {'Val/mean dice_metric': 0.9513667225837708, 'Val/mean miou_metric': 0.9354944229125977, 'Val/mean f1': 0.9477208256721497, 'Val/mean precision': 0.9416576027870178, 'Val/mean recall': 0.9538627862930298, 'Val/mean hd95_metric': 10.80778980255127} +Cheakpoint... +Epoch [3719/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513667225837708, 'Val/mean miou_metric': 0.9354944229125977, 'Val/mean f1': 0.9477208256721497, 'Val/mean precision': 0.9416576027870178, 'Val/mean recall': 0.9538627862930298, 'Val/mean hd95_metric': 10.80778980255127} +Epoch [3720/4000] Training [1/39] Loss: 0.25307 +Epoch [3720/4000] Training [2/39] Loss: 0.00365 +Epoch [3720/4000] Training [3/39] Loss: 0.00613 +Epoch [3720/4000] Training [4/39] Loss: 0.00468 +Epoch [3720/4000] Training [5/39] Loss: 0.00361 +Epoch [3720/4000] Training [6/39] Loss: 0.00437 +Epoch [3720/4000] Training [7/39] Loss: 0.00464 +Epoch [3720/4000] Training [8/39] Loss: 0.00345 +Epoch [3720/4000] Training [9/39] Loss: 0.00525 +Epoch [3720/4000] Training [10/39] Loss: 0.12829 +Epoch [3720/4000] Training [11/39] Loss: 0.00415 +Epoch [3720/4000] Training [12/39] Loss: 0.00383 +Epoch [3720/4000] Training [13/39] Loss: 0.00352 +Epoch [3720/4000] Training [14/39] Loss: 0.00574 +Epoch [3720/4000] Training [15/39] Loss: 0.00447 +Epoch [3720/4000] Training [16/39] Loss: 0.00574 +Epoch [3720/4000] Training [17/39] Loss: 0.00467 +Epoch [3720/4000] Training [18/39] Loss: 0.00488 +Epoch [3720/4000] Training [19/39] Loss: 0.12833 +Epoch [3720/4000] Training [20/39] Loss: 0.00417 +Epoch [3720/4000] Training [21/39] Loss: 0.00632 +Epoch [3720/4000] Training [22/39] Loss: 0.00467 +Epoch [3720/4000] Training [23/39] Loss: 0.00598 +Epoch [3720/4000] Training [24/39] Loss: 0.00518 +Epoch [3720/4000] Training [25/39] Loss: 0.00351 +Epoch [3720/4000] Training [26/39] Loss: 0.00352 +Epoch [3720/4000] Training [27/39] Loss: 0.00407 +Epoch [3720/4000] Training [28/39] Loss: 0.00512 +Epoch [3720/4000] Training [29/39] Loss: 0.12846 +Epoch [3720/4000] Training [30/39] Loss: 0.00326 +Epoch [3720/4000] Training [31/39] Loss: 0.00425 +Epoch [3720/4000] Training [32/39] Loss: 0.00282 +Epoch [3720/4000] Training [33/39] Loss: 0.00340 +Epoch [3720/4000] Training [34/39] Loss: 0.00447 +Epoch [3720/4000] Training [35/39] Loss: 0.00747 +Epoch [3720/4000] Training [36/39] Loss: 0.00575 +Epoch [3720/4000] Training [37/39] Loss: 0.12998 +Epoch [3720/4000] Training [38/39] Loss: 0.13050 +Epoch [3720/4000] Training [39/39] Loss: 0.00565 +Epoch [3720/4000] Training metric {'Train/mean dice_metric': 0.9963829517364502, 'Train/mean miou_metric': 0.9932223558425903, 'Train/mean f1': 0.996901273727417, 'Train/mean precision': 0.9964502453804016, 'Train/mean recall': 0.9973528385162354, 'Train/mean hd95_metric': 0.9259764552116394} +Epoch [3720/4000] Validation [1/10] Loss: 0.74905 focal_loss 0.66041 dice_loss 0.08865 +Epoch [3720/4000] Validation [2/10] Loss: 0.50544 focal_loss 0.41100 dice_loss 0.09443 +Epoch [3720/4000] Validation [3/10] Loss: 0.38209 focal_loss 0.27212 dice_loss 0.10997 +Epoch [3720/4000] Validation [4/10] Loss: 0.91483 focal_loss 0.34712 dice_loss 0.56770 +Epoch [3720/4000] Validation [5/10] Loss: 3.09066 focal_loss 2.41699 dice_loss 0.67367 +Epoch [3720/4000] Validation [6/10] Loss: 1.37672 focal_loss 0.66284 dice_loss 0.71389 +Epoch [3720/4000] Validation [7/10] Loss: 1.21061 focal_loss 0.55316 dice_loss 0.65745 +Epoch [3720/4000] Validation [8/10] Loss: 2.32200 focal_loss 1.71557 dice_loss 0.60642 +Epoch [3720/4000] Validation [9/10] Loss: 1.65654 focal_loss 1.11058 dice_loss 0.54597 +Epoch [3720/4000] Validation [10/10] Loss: 1.96386 focal_loss 1.22624 dice_loss 0.73762 +Epoch [3720/4000] Validation metric {'Val/mean dice_metric': 0.9513848423957825, 'Val/mean miou_metric': 0.9354826807975769, 'Val/mean f1': 0.9476556181907654, 'Val/mean precision': 0.940942108631134, 'Val/mean recall': 0.9544655680656433, 'Val/mean hd95_metric': 10.71906566619873} +Cheakpoint... +Epoch [3720/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513848423957825, 'Val/mean miou_metric': 0.9354826807975769, 'Val/mean f1': 0.9476556181907654, 'Val/mean precision': 0.940942108631134, 'Val/mean recall': 0.9544655680656433, 'Val/mean hd95_metric': 10.71906566619873} +Epoch [3721/4000] Training [1/39] Loss: 0.00474 +Epoch [3721/4000] Training [2/39] Loss: 0.00622 +Epoch [3721/4000] Training [3/39] Loss: 0.25234 +Epoch [3721/4000] Training [4/39] Loss: 0.00802 +Epoch [3721/4000] Training [5/39] Loss: 0.00632 +Epoch [3721/4000] Training [6/39] Loss: 0.00537 +Epoch [3721/4000] Training [7/39] Loss: 0.16698 +Epoch [3721/4000] Training [8/39] Loss: 0.00623 +Epoch [3721/4000] Training [9/39] Loss: 0.00540 +Epoch [3721/4000] Training [10/39] Loss: 0.12777 +Epoch [3721/4000] Training [11/39] Loss: 0.00430 +Epoch [3721/4000] Training [12/39] Loss: 0.00241 +Epoch [3721/4000] Training [13/39] Loss: 0.00782 +Epoch [3721/4000] Training [14/39] Loss: 0.00525 +Epoch [3721/4000] Training [15/39] Loss: 0.08517 +Epoch [3721/4000] Training [16/39] Loss: 0.00433 +Epoch [3721/4000] Training [17/39] Loss: 0.13076 +Epoch [3721/4000] Training [18/39] Loss: 0.12792 +Epoch [3721/4000] Training [19/39] Loss: 0.00568 +Epoch [3721/4000] Training [20/39] Loss: 0.00633 +Epoch [3721/4000] Training [21/39] Loss: 0.00490 +Epoch [3721/4000] Training [22/39] Loss: 0.00546 +Epoch [3721/4000] Training [23/39] Loss: 0.00856 +Epoch [3721/4000] Training [24/39] Loss: 0.00512 +Epoch [3721/4000] Training [25/39] Loss: 0.00629 +Epoch [3721/4000] Training [26/39] Loss: 0.12816 +Epoch [3721/4000] Training [27/39] Loss: 0.00532 +Epoch [3721/4000] Training [28/39] Loss: 0.13042 +Epoch [3721/4000] Training [29/39] Loss: 0.00717 +Epoch [3721/4000] Training [30/39] Loss: 0.12851 +Epoch [3721/4000] Training [31/39] Loss: 0.00499 +Epoch [3721/4000] Training [32/39] Loss: 0.12722 +Epoch [3721/4000] Training [33/39] Loss: 0.00486 +Epoch [3721/4000] Training [34/39] Loss: 0.13034 +Epoch [3721/4000] Training [35/39] Loss: 0.00400 +Epoch [3721/4000] Training [36/39] Loss: 0.00339 +Epoch [3721/4000] Training [37/39] Loss: 0.13148 +Epoch [3721/4000] Training [38/39] Loss: 0.00453 +Epoch [3721/4000] Training [39/39] Loss: 0.00473 +Epoch [3721/4000] Training metric {'Train/mean dice_metric': 0.9961889386177063, 'Train/mean miou_metric': 0.9928232431411743, 'Train/mean f1': 0.9967782497406006, 'Train/mean precision': 0.9962990283966064, 'Train/mean recall': 0.9972580671310425, 'Train/mean hd95_metric': 0.9495773315429688} +Epoch [3721/4000] Validation [1/10] Loss: 0.72919 focal_loss 0.64116 dice_loss 0.08803 +Epoch [3721/4000] Validation [2/10] Loss: 0.50405 focal_loss 0.40636 dice_loss 0.09769 +Epoch [3721/4000] Validation [3/10] Loss: 0.38660 focal_loss 0.27566 dice_loss 0.11094 +Epoch [3721/4000] Validation [4/10] Loss: 0.90069 focal_loss 0.33471 dice_loss 0.56598 +Epoch [3721/4000] Validation [5/10] Loss: 3.04063 focal_loss 2.36702 dice_loss 0.67361 +Epoch [3721/4000] Validation [6/10] Loss: 1.34518 focal_loss 0.63129 dice_loss 0.71390 +Epoch [3721/4000] Validation [7/10] Loss: 1.19159 focal_loss 0.53531 dice_loss 0.65627 +Epoch [3721/4000] Validation [8/10] Loss: 2.34343 focal_loss 1.72939 dice_loss 0.61403 +Epoch [3721/4000] Validation [9/10] Loss: 1.58594 focal_loss 1.03954 dice_loss 0.54640 +Epoch [3721/4000] Validation [10/10] Loss: 1.91472 focal_loss 1.17804 dice_loss 0.73668 +Epoch [3721/4000] Validation metric {'Val/mean dice_metric': 0.9512476325035095, 'Val/mean miou_metric': 0.9352183938026428, 'Val/mean f1': 0.9478712677955627, 'Val/mean precision': 0.9422913789749146, 'Val/mean recall': 0.9535176753997803, 'Val/mean hd95_metric': 10.786375045776367} +Cheakpoint... +Epoch [3721/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512476325035095, 'Val/mean miou_metric': 0.9352183938026428, 'Val/mean f1': 0.9478712677955627, 'Val/mean precision': 0.9422913789749146, 'Val/mean recall': 0.9535176753997803, 'Val/mean hd95_metric': 10.786375045776367} +Epoch [3722/4000] Training [1/39] Loss: 0.25254 +Epoch [3722/4000] Training [2/39] Loss: 0.00586 +Epoch [3722/4000] Training [3/39] Loss: 0.12745 +Epoch [3722/4000] Training [4/39] Loss: 0.25330 +Epoch [3722/4000] Training [5/39] Loss: 0.00436 +Epoch [3722/4000] Training [6/39] Loss: 0.00565 +Epoch [3722/4000] Training [7/39] Loss: 0.00316 +Epoch [3722/4000] Training [8/39] Loss: 0.00691 +Epoch [3722/4000] Training [9/39] Loss: 0.00538 +Epoch [3722/4000] Training [10/39] Loss: 0.08384 +Epoch [3722/4000] Training [11/39] Loss: 0.12963 +Epoch [3722/4000] Training [12/39] Loss: 0.00829 +Epoch [3722/4000] Training [13/39] Loss: 0.00737 +Epoch [3722/4000] Training [14/39] Loss: 0.00680 +Epoch [3722/4000] Training [15/39] Loss: 0.00304 +Epoch [3722/4000] Training [16/39] Loss: 0.00539 +Epoch [3722/4000] Training [17/39] Loss: 0.00492 +Epoch [3722/4000] Training [18/39] Loss: 0.12885 +Epoch [3722/4000] Training [19/39] Loss: 0.00729 +Epoch [3722/4000] Training [20/39] Loss: 0.12894 +Epoch [3722/4000] Training [21/39] Loss: 0.00391 +Epoch [3722/4000] Training [22/39] Loss: 0.00591 +Epoch [3722/4000] Training [23/39] Loss: 0.00430 +Epoch [3722/4000] Training [24/39] Loss: 0.00259 +Epoch [3722/4000] Training [25/39] Loss: 0.13114 +Epoch [3722/4000] Training [26/39] Loss: 0.12854 +Epoch [3722/4000] Training [27/39] Loss: 0.13331 +Epoch [3722/4000] Training [28/39] Loss: 0.00304 +Epoch [3722/4000] Training [29/39] Loss: 0.00539 +Epoch [3722/4000] Training [30/39] Loss: 0.00606 +Epoch [3722/4000] Training [31/39] Loss: 0.00510 +Epoch [3722/4000] Training [32/39] Loss: 0.37763 +Epoch [3722/4000] Training [33/39] Loss: 0.12886 +Epoch [3722/4000] Training [34/39] Loss: 0.12908 +Epoch [3722/4000] Training [35/39] Loss: 0.00367 +Epoch [3722/4000] Training [36/39] Loss: 0.00522 +Epoch [3722/4000] Training [37/39] Loss: 0.13080 +Epoch [3722/4000] Training [38/39] Loss: 0.00547 +Epoch [3722/4000] Training [39/39] Loss: 0.00494 +Epoch [3722/4000] Training metric {'Train/mean dice_metric': 0.996121883392334, 'Train/mean miou_metric': 0.992695689201355, 'Train/mean f1': 0.9966898560523987, 'Train/mean precision': 0.9962438344955444, 'Train/mean recall': 0.9971364140510559, 'Train/mean hd95_metric': 0.9647302031517029} +Epoch [3722/4000] Validation [1/10] Loss: 0.74447 focal_loss 0.65585 dice_loss 0.08862 +Epoch [3722/4000] Validation [2/10] Loss: 0.50219 focal_loss 0.40649 dice_loss 0.09570 +Epoch [3722/4000] Validation [3/10] Loss: 0.37945 focal_loss 0.26958 dice_loss 0.10987 +Epoch [3722/4000] Validation [4/10] Loss: 0.91440 focal_loss 0.34635 dice_loss 0.56805 +Epoch [3722/4000] Validation [5/10] Loss: 3.08476 focal_loss 2.41109 dice_loss 0.67367 +Epoch [3722/4000] Validation [6/10] Loss: 1.36317 focal_loss 0.65090 dice_loss 0.71227 +Epoch [3722/4000] Validation [7/10] Loss: 1.20661 focal_loss 0.55077 dice_loss 0.65584 +Epoch [3722/4000] Validation [8/10] Loss: 2.28216 focal_loss 1.67817 dice_loss 0.60399 +Epoch [3722/4000] Validation [9/10] Loss: 1.67451 focal_loss 1.12823 dice_loss 0.54628 +Epoch [3722/4000] Validation [10/10] Loss: 1.96963 focal_loss 1.23053 dice_loss 0.73910 +Epoch [3722/4000] Validation metric {'Val/mean dice_metric': 0.9511467814445496, 'Val/mean miou_metric': 0.9349854588508606, 'Val/mean f1': 0.9472310543060303, 'Val/mean precision': 0.9403674006462097, 'Val/mean recall': 0.954195499420166, 'Val/mean hd95_metric': 10.722777366638184} +Cheakpoint... +Epoch [3722/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511467814445496, 'Val/mean miou_metric': 0.9349854588508606, 'Val/mean f1': 0.9472310543060303, 'Val/mean precision': 0.9403674006462097, 'Val/mean recall': 0.954195499420166, 'Val/mean hd95_metric': 10.722777366638184} +Epoch [3723/4000] Training [1/39] Loss: 0.12962 +Epoch [3723/4000] Training [2/39] Loss: 0.00472 +Epoch [3723/4000] Training [3/39] Loss: 0.00552 +Epoch [3723/4000] Training [4/39] Loss: 0.00594 +Epoch [3723/4000] Training [5/39] Loss: 0.25437 +Epoch [3723/4000] Training [6/39] Loss: 0.00394 +Epoch [3723/4000] Training [7/39] Loss: 0.25413 +Epoch [3723/4000] Training [8/39] Loss: 0.00545 +Epoch [3723/4000] Training [9/39] Loss: 0.00637 +Epoch [3723/4000] Training [10/39] Loss: 0.00329 +Epoch [3723/4000] Training [11/39] Loss: 0.00510 +Epoch [3723/4000] Training [12/39] Loss: 0.00430 +Epoch [3723/4000] Training [13/39] Loss: 0.12903 +Epoch [3723/4000] Training [14/39] Loss: 0.00502 +Epoch [3723/4000] Training [15/39] Loss: 0.12949 +Epoch [3723/4000] Training [16/39] Loss: 0.00461 +Epoch [3723/4000] Training [17/39] Loss: 0.00991 +Epoch [3723/4000] Training [18/39] Loss: 0.12849 +Epoch [3723/4000] Training [19/39] Loss: 0.00555 +Epoch [3723/4000] Training [20/39] Loss: 0.00535 +Epoch [3723/4000] Training [21/39] Loss: 0.25288 +Epoch [3723/4000] Training [22/39] Loss: 0.00312 +Epoch [3723/4000] Training [23/39] Loss: 0.00534 +Epoch [3723/4000] Training [24/39] Loss: 0.12760 +Epoch [3723/4000] Training [25/39] Loss: 0.00565 +Epoch [3723/4000] Training [26/39] Loss: 0.00387 +Epoch [3723/4000] Training [27/39] Loss: 0.12873 +Epoch [3723/4000] Training [28/39] Loss: 0.00370 +Epoch [3723/4000] Training [29/39] Loss: 0.12932 +Epoch [3723/4000] Training [30/39] Loss: 0.12911 +Epoch [3723/4000] Training [31/39] Loss: 0.00712 +Epoch [3723/4000] Training [32/39] Loss: 0.00580 +Epoch [3723/4000] Training [33/39] Loss: 0.12906 +Epoch [3723/4000] Training [34/39] Loss: 0.00404 +Epoch [3723/4000] Training [35/39] Loss: 0.00671 +Epoch [3723/4000] Training [36/39] Loss: 0.12690 +Epoch [3723/4000] Training [37/39] Loss: 0.00430 +Epoch [3723/4000] Training [38/39] Loss: 0.00586 +Epoch [3723/4000] Training [39/39] Loss: 0.00802 +Epoch [3723/4000] Training metric {'Train/mean dice_metric': 0.9963072538375854, 'Train/mean miou_metric': 0.9930465817451477, 'Train/mean f1': 0.9968872666358948, 'Train/mean precision': 0.9964165687561035, 'Train/mean recall': 0.9973586201667786, 'Train/mean hd95_metric': 1.099913239479065} +Epoch [3723/4000] Validation [1/10] Loss: 0.72246 focal_loss 0.63500 dice_loss 0.08745 +Epoch [3723/4000] Validation [2/10] Loss: 0.49549 focal_loss 0.39973 dice_loss 0.09576 +Epoch [3723/4000] Validation [3/10] Loss: 0.38254 focal_loss 0.27218 dice_loss 0.11036 +Epoch [3723/4000] Validation [4/10] Loss: 0.90655 focal_loss 0.34011 dice_loss 0.56644 +Epoch [3723/4000] Validation [5/10] Loss: 3.04574 focal_loss 2.37172 dice_loss 0.67402 +Epoch [3723/4000] Validation [6/10] Loss: 1.35173 focal_loss 0.64033 dice_loss 0.71140 +Epoch [3723/4000] Validation [7/10] Loss: 1.19724 focal_loss 0.54118 dice_loss 0.65606 +Epoch [3723/4000] Validation [8/10] Loss: 2.36664 focal_loss 1.75314 dice_loss 0.61351 +Epoch [3723/4000] Validation [9/10] Loss: 1.60908 focal_loss 1.06242 dice_loss 0.54666 +Epoch [3723/4000] Validation [10/10] Loss: 1.92502 focal_loss 1.18817 dice_loss 0.73684 +Epoch [3723/4000] Validation metric {'Val/mean dice_metric': 0.9513595700263977, 'Val/mean miou_metric': 0.9353429675102234, 'Val/mean f1': 0.9476709365844727, 'Val/mean precision': 0.9419425129890442, 'Val/mean recall': 0.953469455242157, 'Val/mean hd95_metric': 10.851473808288574} +Cheakpoint... +Epoch [3723/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513595700263977, 'Val/mean miou_metric': 0.9353429675102234, 'Val/mean f1': 0.9476709365844727, 'Val/mean precision': 0.9419425129890442, 'Val/mean recall': 0.953469455242157, 'Val/mean hd95_metric': 10.851473808288574} +Epoch [3724/4000] Training [1/39] Loss: 0.00693 +Epoch [3724/4000] Training [2/39] Loss: 0.00422 +Epoch [3724/4000] Training [3/39] Loss: 0.00573 +Epoch [3724/4000] Training [4/39] Loss: 0.00340 +Epoch [3724/4000] Training [5/39] Loss: 0.00657 +Epoch [3724/4000] Training [6/39] Loss: 0.00426 +Epoch [3724/4000] Training [7/39] Loss: 0.12960 +Epoch [3724/4000] Training [8/39] Loss: 0.00624 +Epoch [3724/4000] Training [9/39] Loss: 0.00492 +Epoch [3724/4000] Training [10/39] Loss: 0.00628 +Epoch [3724/4000] Training [11/39] Loss: 0.00357 +Epoch [3724/4000] Training [12/39] Loss: 0.00252 +Epoch [3724/4000] Training [13/39] Loss: 0.12867 +Epoch [3724/4000] Training [14/39] Loss: 0.00499 +Epoch [3724/4000] Training [15/39] Loss: 0.00610 +Epoch [3724/4000] Training [16/39] Loss: 0.00619 +Epoch [3724/4000] Training [17/39] Loss: 0.00740 +Epoch [3724/4000] Training [18/39] Loss: 0.00499 +Epoch [3724/4000] Training [19/39] Loss: 0.00505 +Epoch [3724/4000] Training [20/39] Loss: 0.00670 +Epoch [3724/4000] Training [21/39] Loss: 0.00449 +Epoch [3724/4000] Training [22/39] Loss: 0.12787 +Epoch [3724/4000] Training [23/39] Loss: 0.00495 +Epoch [3724/4000] Training [24/39] Loss: 0.12935 +Epoch [3724/4000] Training [25/39] Loss: 0.00529 +Epoch [3724/4000] Training [26/39] Loss: 0.13010 +Epoch [3724/4000] Training [27/39] Loss: 0.00429 +Epoch [3724/4000] Training [28/39] Loss: 0.12865 +Epoch [3724/4000] Training [29/39] Loss: 0.00435 +Epoch [3724/4000] Training [30/39] Loss: 0.00379 +Epoch [3724/4000] Training [31/39] Loss: 0.00274 +Epoch [3724/4000] Training [32/39] Loss: 0.00421 +Epoch [3724/4000] Training [33/39] Loss: 0.12873 +Epoch [3724/4000] Training [34/39] Loss: 0.00391 +Epoch [3724/4000] Training [35/39] Loss: 0.00365 +Epoch [3724/4000] Training [36/39] Loss: 0.00586 +Epoch [3724/4000] Training [37/39] Loss: 0.00537 +Epoch [3724/4000] Training [38/39] Loss: 0.00300 +Epoch [3724/4000] Training [39/39] Loss: 0.25292 +Epoch [3724/4000] Training metric {'Train/mean dice_metric': 0.9964854121208191, 'Train/mean miou_metric': 0.9934055805206299, 'Train/mean f1': 0.9969486594200134, 'Train/mean precision': 0.9964907765388489, 'Train/mean recall': 0.9974070191383362, 'Train/mean hd95_metric': 0.9161472320556641} +Epoch [3724/4000] Validation [1/10] Loss: 0.72951 focal_loss 0.64173 dice_loss 0.08779 +Epoch [3724/4000] Validation [2/10] Loss: 0.49120 focal_loss 0.39757 dice_loss 0.09363 +Epoch [3724/4000] Validation [3/10] Loss: 0.38183 focal_loss 0.27147 dice_loss 0.11036 +Epoch [3724/4000] Validation [4/10] Loss: 0.91045 focal_loss 0.34383 dice_loss 0.56662 +Epoch [3724/4000] Validation [5/10] Loss: 3.03542 focal_loss 2.36149 dice_loss 0.67392 +Epoch [3724/4000] Validation [6/10] Loss: 1.36810 focal_loss 0.65539 dice_loss 0.71270 +Epoch [3724/4000] Validation [7/10] Loss: 1.20540 focal_loss 0.54938 dice_loss 0.65602 +Epoch [3724/4000] Validation [8/10] Loss: 2.33367 focal_loss 1.72535 dice_loss 0.60832 +Epoch [3724/4000] Validation [9/10] Loss: 1.64038 focal_loss 1.09344 dice_loss 0.54693 +Epoch [3724/4000] Validation [10/10] Loss: 1.95922 focal_loss 1.22185 dice_loss 0.73736 +Epoch [3724/4000] Validation metric {'Val/mean dice_metric': 0.9514864683151245, 'Val/mean miou_metric': 0.9356392621994019, 'Val/mean f1': 0.9478598833084106, 'Val/mean precision': 0.9415538311004639, 'Val/mean recall': 0.9542511105537415, 'Val/mean hd95_metric': 10.882694244384766} +Cheakpoint... +Epoch [3724/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514864683151245, 'Val/mean miou_metric': 0.9356392621994019, 'Val/mean f1': 0.9478598833084106, 'Val/mean precision': 0.9415538311004639, 'Val/mean recall': 0.9542511105537415, 'Val/mean hd95_metric': 10.882694244384766} +Epoch [3725/4000] Training [1/39] Loss: 0.00525 +Epoch [3725/4000] Training [2/39] Loss: 0.00617 +Epoch [3725/4000] Training [3/39] Loss: 0.00303 +Epoch [3725/4000] Training [4/39] Loss: 0.00538 +Epoch [3725/4000] Training [5/39] Loss: 0.00309 +Epoch [3725/4000] Training [6/39] Loss: 0.25332 +Epoch [3725/4000] Training [7/39] Loss: 0.00386 +Epoch [3725/4000] Training [8/39] Loss: 0.00650 +Epoch [3725/4000] Training [9/39] Loss: 0.00563 +Epoch [3725/4000] Training [10/39] Loss: 0.00402 +Epoch [3725/4000] Training [11/39] Loss: 0.00920 +Epoch [3725/4000] Training [12/39] Loss: 0.00456 +Epoch [3725/4000] Training [13/39] Loss: 0.00426 +Epoch [3725/4000] Training [14/39] Loss: 0.00560 +Epoch [3725/4000] Training [15/39] Loss: 0.00348 +Epoch [3725/4000] Training [16/39] Loss: 0.00443 +Epoch [3725/4000] Training [17/39] Loss: 0.13038 +Epoch [3725/4000] Training [18/39] Loss: 0.00978 +Epoch [3725/4000] Training [19/39] Loss: 0.00601 +Epoch [3725/4000] Training [20/39] Loss: 0.00301 +Epoch [3725/4000] Training [21/39] Loss: 0.00556 +Epoch [3725/4000] Training [22/39] Loss: 0.00845 +Epoch [3725/4000] Training [23/39] Loss: 0.00969 +Epoch [3725/4000] Training [24/39] Loss: 0.00905 +Epoch [3725/4000] Training [25/39] Loss: 0.00475 +Epoch [3725/4000] Training [26/39] Loss: 0.00627 +Epoch [3725/4000] Training [27/39] Loss: 0.00546 +Epoch [3725/4000] Training [28/39] Loss: 0.12896 +Epoch [3725/4000] Training [29/39] Loss: 0.00297 +Epoch [3725/4000] Training [30/39] Loss: 0.00424 +Epoch [3725/4000] Training [31/39] Loss: 0.12902 +Epoch [3725/4000] Training [32/39] Loss: 0.00493 +Epoch [3725/4000] Training [33/39] Loss: 0.37905 +Epoch [3725/4000] Training [34/39] Loss: 0.00560 +Epoch [3725/4000] Training [35/39] Loss: 0.00386 +Epoch [3725/4000] Training [36/39] Loss: 0.00429 +Epoch [3725/4000] Training [37/39] Loss: 0.00385 +Epoch [3725/4000] Training [38/39] Loss: 0.00412 +Epoch [3725/4000] Training [39/39] Loss: 0.12849 +Epoch [3725/4000] Training metric {'Train/mean dice_metric': 0.996159553527832, 'Train/mean miou_metric': 0.9927619695663452, 'Train/mean f1': 0.9967440366744995, 'Train/mean precision': 0.9962465167045593, 'Train/mean recall': 0.9972421526908875, 'Train/mean hd95_metric': 1.0958691835403442} +Epoch [3725/4000] Validation [1/10] Loss: 0.73210 focal_loss 0.64458 dice_loss 0.08752 +Epoch [3725/4000] Validation [2/10] Loss: 0.49231 focal_loss 0.39743 dice_loss 0.09488 +Epoch [3725/4000] Validation [3/10] Loss: 0.39362 focal_loss 0.28259 dice_loss 0.11104 +Epoch [3725/4000] Validation [4/10] Loss: 0.90085 focal_loss 0.33512 dice_loss 0.56573 +Epoch [3725/4000] Validation [5/10] Loss: 3.06537 focal_loss 2.39134 dice_loss 0.67404 +Epoch [3725/4000] Validation [6/10] Loss: 1.34493 focal_loss 0.63260 dice_loss 0.71233 +Epoch [3725/4000] Validation [7/10] Loss: 1.19224 focal_loss 0.53663 dice_loss 0.65561 +Epoch [3725/4000] Validation [8/10] Loss: 2.41109 focal_loss 1.79410 dice_loss 0.61699 +Epoch [3725/4000] Validation [9/10] Loss: 1.59798 focal_loss 1.05150 dice_loss 0.54648 +Epoch [3725/4000] Validation [10/10] Loss: 1.91168 focal_loss 1.17604 dice_loss 0.73564 +Epoch [3725/4000] Validation metric {'Val/mean dice_metric': 0.9511880874633789, 'Val/mean miou_metric': 0.9350946545600891, 'Val/mean f1': 0.947929859161377, 'Val/mean precision': 0.942787230014801, 'Val/mean recall': 0.9531288146972656, 'Val/mean hd95_metric': 10.874906539916992} +Cheakpoint... +Epoch [3725/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511880874633789, 'Val/mean miou_metric': 0.9350946545600891, 'Val/mean f1': 0.947929859161377, 'Val/mean precision': 0.942787230014801, 'Val/mean recall': 0.9531288146972656, 'Val/mean hd95_metric': 10.874906539916992} +Epoch [3726/4000] Training [1/39] Loss: 0.00538 +Epoch [3726/4000] Training [2/39] Loss: 0.13179 +Epoch [3726/4000] Training [3/39] Loss: 0.00744 +Epoch [3726/4000] Training [4/39] Loss: 0.12871 +Epoch [3726/4000] Training [5/39] Loss: 0.00463 +Epoch [3726/4000] Training [6/39] Loss: 0.13069 +Epoch [3726/4000] Training [7/39] Loss: 0.00565 +Epoch [3726/4000] Training [8/39] Loss: 0.00425 +Epoch [3726/4000] Training [9/39] Loss: 0.00859 +Epoch [3726/4000] Training [10/39] Loss: 0.00453 +Epoch [3726/4000] Training [11/39] Loss: 0.12858 +Epoch [3726/4000] Training [12/39] Loss: 0.12904 +Epoch [3726/4000] Training [13/39] Loss: 0.00447 +Epoch [3726/4000] Training [14/39] Loss: 0.13009 +Epoch [3726/4000] Training [15/39] Loss: 0.12948 +Epoch [3726/4000] Training [16/39] Loss: 0.00510 +Epoch [3726/4000] Training [17/39] Loss: 0.00829 +Epoch [3726/4000] Training [18/39] Loss: 0.00703 +Epoch [3726/4000] Training [19/39] Loss: 0.13031 +Epoch [3726/4000] Training [20/39] Loss: 0.00413 +Epoch [3726/4000] Training [21/39] Loss: 0.12867 +Epoch [3726/4000] Training [22/39] Loss: 0.00635 +Epoch [3726/4000] Training [23/39] Loss: 0.00383 +Epoch [3726/4000] Training [24/39] Loss: 0.00527 +Epoch [3726/4000] Training [25/39] Loss: 0.00287 +Epoch [3726/4000] Training [26/39] Loss: 0.00429 +Epoch [3726/4000] Training [27/39] Loss: 0.00490 +Epoch [3726/4000] Training [28/39] Loss: 0.00405 +Epoch [3726/4000] Training [29/39] Loss: 0.00571 +Epoch [3726/4000] Training [30/39] Loss: 0.00376 +Epoch [3726/4000] Training [31/39] Loss: 0.00347 +Epoch [3726/4000] Training [32/39] Loss: 0.00556 +Epoch [3726/4000] Training [33/39] Loss: 0.12956 +Epoch [3726/4000] Training [34/39] Loss: 0.00689 +Epoch [3726/4000] Training [35/39] Loss: 0.00402 +Epoch [3726/4000] Training [36/39] Loss: 0.00389 +Epoch [3726/4000] Training [37/39] Loss: 0.00593 +Epoch [3726/4000] Training [38/39] Loss: 0.00406 +Epoch [3726/4000] Training [39/39] Loss: 0.12790 +Epoch [3726/4000] Training metric {'Train/mean dice_metric': 0.9955216646194458, 'Train/mean miou_metric': 0.9923273324966431, 'Train/mean f1': 0.996850311756134, 'Train/mean precision': 0.9964095950126648, 'Train/mean recall': 0.9972913265228271, 'Train/mean hd95_metric': 0.928749144077301} +Epoch [3726/4000] Validation [1/10] Loss: 0.70806 focal_loss 0.62194 dice_loss 0.08612 +Epoch [3726/4000] Validation [2/10] Loss: 0.48848 focal_loss 0.39062 dice_loss 0.09786 +Epoch [3726/4000] Validation [3/10] Loss: 0.39454 focal_loss 0.28259 dice_loss 0.11195 +Epoch [3726/4000] Validation [4/10] Loss: 0.88717 focal_loss 0.32266 dice_loss 0.56451 +Epoch [3726/4000] Validation [5/10] Loss: 3.02811 focal_loss 2.35393 dice_loss 0.67417 +Epoch [3726/4000] Validation [6/10] Loss: 1.32385 focal_loss 0.61022 dice_loss 0.71363 +Epoch [3726/4000] Validation [7/10] Loss: 1.17129 focal_loss 0.51788 dice_loss 0.65342 +Epoch [3726/4000] Validation [8/10] Loss: 2.38611 focal_loss 1.76567 dice_loss 0.62044 +Epoch [3726/4000] Validation [9/10] Loss: 1.56178 focal_loss 1.01589 dice_loss 0.54589 +Epoch [3726/4000] Validation [10/10] Loss: 1.85906 focal_loss 1.12481 dice_loss 0.73425 +Epoch [3726/4000] Validation metric {'Val/mean dice_metric': 0.9506364464759827, 'Val/mean miou_metric': 0.9347248673439026, 'Val/mean f1': 0.9481325149536133, 'Val/mean precision': 0.9440136551856995, 'Val/mean recall': 0.9522874355316162, 'Val/mean hd95_metric': 10.706587791442871} +Cheakpoint... +Epoch [3726/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506364464759827, 'Val/mean miou_metric': 0.9347248673439026, 'Val/mean f1': 0.9481325149536133, 'Val/mean precision': 0.9440136551856995, 'Val/mean recall': 0.9522874355316162, 'Val/mean hd95_metric': 10.706587791442871} +Epoch [3727/4000] Training [1/39] Loss: 0.13286 +Epoch [3727/4000] Training [2/39] Loss: 0.00479 +Epoch [3727/4000] Training [3/39] Loss: 0.00456 +Epoch [3727/4000] Training [4/39] Loss: 0.22062 +Epoch [3727/4000] Training [5/39] Loss: 0.12949 +Epoch [3727/4000] Training [6/39] Loss: 0.00437 +Epoch [3727/4000] Training [7/39] Loss: 0.00362 +Epoch [3727/4000] Training [8/39] Loss: 0.00437 +Epoch [3727/4000] Training [9/39] Loss: 0.00373 +Epoch [3727/4000] Training [10/39] Loss: 0.12837 +Epoch [3727/4000] Training [11/39] Loss: 0.25275 +Epoch [3727/4000] Training [12/39] Loss: 0.13211 +Epoch [3727/4000] Training [13/39] Loss: 0.00403 +Epoch [3727/4000] Training [14/39] Loss: 0.00560 +Epoch [3727/4000] Training [15/39] Loss: 0.00593 +Epoch [3727/4000] Training [16/39] Loss: 0.00401 +Epoch [3727/4000] Training [17/39] Loss: 0.00466 +Epoch [3727/4000] Training [18/39] Loss: 0.00653 +Epoch [3727/4000] Training [19/39] Loss: 0.00483 +Epoch [3727/4000] Training [20/39] Loss: 0.00421 +Epoch [3727/4000] Training [21/39] Loss: 0.12928 +Epoch [3727/4000] Training [22/39] Loss: 0.00440 +Epoch [3727/4000] Training [23/39] Loss: 0.00510 +Epoch [3727/4000] Training [24/39] Loss: 0.00554 +Epoch [3727/4000] Training [25/39] Loss: 0.01082 +Epoch [3727/4000] Training [26/39] Loss: 0.00462 +Epoch [3727/4000] Training [27/39] Loss: 0.00785 +Epoch [3727/4000] Training [28/39] Loss: 0.13199 +Epoch [3727/4000] Training [29/39] Loss: 0.00752 +Epoch [3727/4000] Training [30/39] Loss: 0.00525 +Epoch [3727/4000] Training [31/39] Loss: 0.00488 +Epoch [3727/4000] Training [32/39] Loss: 0.00543 +Epoch [3727/4000] Training [33/39] Loss: 0.25405 +Epoch [3727/4000] Training [34/39] Loss: 0.00432 +Epoch [3727/4000] Training [35/39] Loss: 0.00625 +Epoch [3727/4000] Training [36/39] Loss: 0.00388 +Epoch [3727/4000] Training [37/39] Loss: 0.00462 +Epoch [3727/4000] Training [38/39] Loss: 0.00506 +Epoch [3727/4000] Training [39/39] Loss: 0.00550 +Epoch [3727/4000] Training metric {'Train/mean dice_metric': 0.9961206316947937, 'Train/mean miou_metric': 0.9927305579185486, 'Train/mean f1': 0.9967092871665955, 'Train/mean precision': 0.996295154094696, 'Train/mean recall': 0.9971235990524292, 'Train/mean hd95_metric': 0.9626947045326233} +Epoch [3727/4000] Validation [1/10] Loss: 0.75638 focal_loss 0.66635 dice_loss 0.09004 +Epoch [3727/4000] Validation [2/10] Loss: 0.49775 focal_loss 0.40224 dice_loss 0.09551 +Epoch [3727/4000] Validation [3/10] Loss: 0.38591 focal_loss 0.27536 dice_loss 0.11055 +Epoch [3727/4000] Validation [4/10] Loss: 0.90808 focal_loss 0.34129 dice_loss 0.56679 +Epoch [3727/4000] Validation [5/10] Loss: 3.06413 focal_loss 2.39035 dice_loss 0.67377 +Epoch [3727/4000] Validation [6/10] Loss: 1.36408 focal_loss 0.65101 dice_loss 0.71307 +Epoch [3727/4000] Validation [7/10] Loss: 1.20605 focal_loss 0.54982 dice_loss 0.65623 +Epoch [3727/4000] Validation [8/10] Loss: 2.32831 focal_loss 1.71947 dice_loss 0.60884 +Epoch [3727/4000] Validation [9/10] Loss: 1.65376 focal_loss 1.10783 dice_loss 0.54594 +Epoch [3727/4000] Validation [10/10] Loss: 1.95174 focal_loss 1.21459 dice_loss 0.73715 +Epoch [3727/4000] Validation metric {'Val/mean dice_metric': 0.9511030316352844, 'Val/mean miou_metric': 0.9349899291992188, 'Val/mean f1': 0.947277843952179, 'Val/mean precision': 0.9410354495048523, 'Val/mean recall': 0.9536036849021912, 'Val/mean hd95_metric': 10.759622573852539} +Cheakpoint... +Epoch [3727/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511030316352844, 'Val/mean miou_metric': 0.9349899291992188, 'Val/mean f1': 0.947277843952179, 'Val/mean precision': 0.9410354495048523, 'Val/mean recall': 0.9536036849021912, 'Val/mean hd95_metric': 10.759622573852539} +Epoch [3728/4000] Training [1/39] Loss: 0.00697 +Epoch [3728/4000] Training [2/39] Loss: 0.00609 +Epoch [3728/4000] Training [3/39] Loss: 0.00554 +Epoch [3728/4000] Training [4/39] Loss: 0.00314 +Epoch [3728/4000] Training [5/39] Loss: 0.25507 +Epoch [3728/4000] Training [6/39] Loss: 0.12872 +Epoch [3728/4000] Training [7/39] Loss: 0.00243 +Epoch [3728/4000] Training [8/39] Loss: 0.12763 +Epoch [3728/4000] Training [9/39] Loss: 0.25364 +Epoch [3728/4000] Training [10/39] Loss: 0.12852 +Epoch [3728/4000] Training [11/39] Loss: 0.00502 +Epoch [3728/4000] Training [12/39] Loss: 0.12917 +Epoch [3728/4000] Training [13/39] Loss: 0.00398 +Epoch [3728/4000] Training [14/39] Loss: 0.00665 +Epoch [3728/4000] Training [15/39] Loss: 0.00368 +Epoch [3728/4000] Training [16/39] Loss: 0.12829 +Epoch [3728/4000] Training [17/39] Loss: 0.00374 +Epoch [3728/4000] Training [18/39] Loss: 0.00606 +Epoch [3728/4000] Training [19/39] Loss: 0.00492 +Epoch [3728/4000] Training [20/39] Loss: 0.00522 +Epoch [3728/4000] Training [21/39] Loss: 0.00368 +Epoch [3728/4000] Training [22/39] Loss: 0.00448 +Epoch [3728/4000] Training [23/39] Loss: 0.00604 +Epoch [3728/4000] Training [24/39] Loss: 0.16668 +Epoch [3728/4000] Training [25/39] Loss: 0.00324 +Epoch [3728/4000] Training [26/39] Loss: 0.00845 +Epoch [3728/4000] Training [27/39] Loss: 0.00381 +Epoch [3728/4000] Training [28/39] Loss: 0.00649 +Epoch [3728/4000] Training [29/39] Loss: 0.00552 +Epoch [3728/4000] Training [30/39] Loss: 0.00617 +Epoch [3728/4000] Training [31/39] Loss: 0.12919 +Epoch [3728/4000] Training [32/39] Loss: 0.08452 +Epoch [3728/4000] Training [33/39] Loss: 0.25204 +Epoch [3728/4000] Training [34/39] Loss: 0.00497 +Epoch [3728/4000] Training [35/39] Loss: 0.00680 +Epoch [3728/4000] Training [36/39] Loss: 0.13074 +Epoch [3728/4000] Training [37/39] Loss: 0.12947 +Epoch [3728/4000] Training [38/39] Loss: 0.13154 +Epoch [3728/4000] Training [39/39] Loss: 0.00441 +Epoch [3728/4000] Training metric {'Train/mean dice_metric': 0.9964261054992676, 'Train/mean miou_metric': 0.9932948350906372, 'Train/mean f1': 0.9968963861465454, 'Train/mean precision': 0.9964317679405212, 'Train/mean recall': 0.9973613619804382, 'Train/mean hd95_metric': 0.9308446049690247} +Epoch [3728/4000] Validation [1/10] Loss: 0.75160 focal_loss 0.66195 dice_loss 0.08965 +Epoch [3728/4000] Validation [2/10] Loss: 0.49345 focal_loss 0.39932 dice_loss 0.09414 +Epoch [3728/4000] Validation [3/10] Loss: 0.38301 focal_loss 0.27260 dice_loss 0.11041 +Epoch [3728/4000] Validation [4/10] Loss: 0.90999 focal_loss 0.34345 dice_loss 0.56654 +Epoch [3728/4000] Validation [5/10] Loss: 3.06792 focal_loss 2.39415 dice_loss 0.67377 +Epoch [3728/4000] Validation [6/10] Loss: 1.36249 focal_loss 0.65074 dice_loss 0.71174 +Epoch [3728/4000] Validation [7/10] Loss: 1.20506 focal_loss 0.54978 dice_loss 0.65528 +Epoch [3728/4000] Validation [8/10] Loss: 2.34929 focal_loss 1.73849 dice_loss 0.61080 +Epoch [3728/4000] Validation [9/10] Loss: 1.62079 focal_loss 1.07455 dice_loss 0.54624 +Epoch [3728/4000] Validation [10/10] Loss: 1.96292 focal_loss 1.22515 dice_loss 0.73777 +Epoch [3728/4000] Validation metric {'Val/mean dice_metric': 0.9513877630233765, 'Val/mean miou_metric': 0.9355044364929199, 'Val/mean f1': 0.9479690790176392, 'Val/mean precision': 0.9417983293533325, 'Val/mean recall': 0.9542211294174194, 'Val/mean hd95_metric': 10.721147537231445} +Cheakpoint... +Epoch [3728/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513877630233765, 'Val/mean miou_metric': 0.9355044364929199, 'Val/mean f1': 0.9479690790176392, 'Val/mean precision': 0.9417983293533325, 'Val/mean recall': 0.9542211294174194, 'Val/mean hd95_metric': 10.721147537231445} +Epoch [3729/4000] Training [1/39] Loss: 0.12872 +Epoch [3729/4000] Training [2/39] Loss: 0.12765 +Epoch [3729/4000] Training [3/39] Loss: 0.12770 +Epoch [3729/4000] Training [4/39] Loss: 0.00261 +Epoch [3729/4000] Training [5/39] Loss: 0.00439 +Epoch [3729/4000] Training [6/39] Loss: 0.00337 +Epoch [3729/4000] Training [7/39] Loss: 0.12902 +Epoch [3729/4000] Training [8/39] Loss: 0.00530 +Epoch [3729/4000] Training [9/39] Loss: 0.12885 +Epoch [3729/4000] Training [10/39] Loss: 0.00423 +Epoch [3729/4000] Training [11/39] Loss: 0.00485 +Epoch [3729/4000] Training [12/39] Loss: 0.00495 +Epoch [3729/4000] Training [13/39] Loss: 0.00449 +Epoch [3729/4000] Training [14/39] Loss: 0.00833 +Epoch [3729/4000] Training [15/39] Loss: 0.00404 +Epoch [3729/4000] Training [16/39] Loss: 0.00479 +Epoch [3729/4000] Training [17/39] Loss: 0.00716 +Epoch [3729/4000] Training [18/39] Loss: 0.04368 +Epoch [3729/4000] Training [19/39] Loss: 0.00607 +Epoch [3729/4000] Training [20/39] Loss: 0.00778 +Epoch [3729/4000] Training [21/39] Loss: 0.25358 +Epoch [3729/4000] Training [22/39] Loss: 0.00315 +Epoch [3729/4000] Training [23/39] Loss: 0.00482 +Epoch [3729/4000] Training [24/39] Loss: 0.00784 +Epoch [3729/4000] Training [25/39] Loss: 0.00487 +Epoch [3729/4000] Training [26/39] Loss: 0.00773 +Epoch [3729/4000] Training [27/39] Loss: 0.00563 +Epoch [3729/4000] Training [28/39] Loss: 0.00550 +Epoch [3729/4000] Training [29/39] Loss: 0.00373 +Epoch [3729/4000] Training [30/39] Loss: 0.12752 +Epoch [3729/4000] Training [31/39] Loss: 0.00633 +Epoch [3729/4000] Training [32/39] Loss: 0.00385 +Epoch [3729/4000] Training [33/39] Loss: 0.00539 +Epoch [3729/4000] Training [34/39] Loss: 0.00355 +Epoch [3729/4000] Training [35/39] Loss: 0.12999 +Epoch [3729/4000] Training [36/39] Loss: 0.00551 +Epoch [3729/4000] Training [37/39] Loss: 0.00793 +Epoch [3729/4000] Training [38/39] Loss: 0.00375 +Epoch [3729/4000] Training [39/39] Loss: 0.00667 +Epoch [3729/4000] Training metric {'Train/mean dice_metric': 0.9961402416229248, 'Train/mean miou_metric': 0.9927321672439575, 'Train/mean f1': 0.9967418909072876, 'Train/mean precision': 0.996278703212738, 'Train/mean recall': 0.9972056746482849, 'Train/mean hd95_metric': 1.0648841857910156} +Epoch [3729/4000] Validation [1/10] Loss: 0.75058 focal_loss 0.66104 dice_loss 0.08954 +Epoch [3729/4000] Validation [2/10] Loss: 0.48635 focal_loss 0.39232 dice_loss 0.09404 +Epoch [3729/4000] Validation [3/10] Loss: 0.38417 focal_loss 0.27370 dice_loss 0.11047 +Epoch [3729/4000] Validation [4/10] Loss: 0.90076 focal_loss 0.33448 dice_loss 0.56628 +Epoch [3729/4000] Validation [5/10] Loss: 3.06477 focal_loss 2.39096 dice_loss 0.67382 +Epoch [3729/4000] Validation [6/10] Loss: 1.34417 focal_loss 0.63352 dice_loss 0.71065 +Epoch [3729/4000] Validation [7/10] Loss: 1.19673 focal_loss 0.54156 dice_loss 0.65517 +Epoch [3729/4000] Validation [8/10] Loss: 2.30749 focal_loss 1.69808 dice_loss 0.60941 +Epoch [3729/4000] Validation [9/10] Loss: 1.61437 focal_loss 1.06873 dice_loss 0.54564 +Epoch [3729/4000] Validation [10/10] Loss: 1.94743 focal_loss 1.20887 dice_loss 0.73856 +Epoch [3729/4000] Validation metric {'Val/mean dice_metric': 0.9510789513587952, 'Val/mean miou_metric': 0.9349329471588135, 'Val/mean f1': 0.947784960269928, 'Val/mean precision': 0.9418253898620605, 'Val/mean recall': 0.9538203477859497, 'Val/mean hd95_metric': 10.845074653625488} +Cheakpoint... +Epoch [3729/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510789513587952, 'Val/mean miou_metric': 0.9349329471588135, 'Val/mean f1': 0.947784960269928, 'Val/mean precision': 0.9418253898620605, 'Val/mean recall': 0.9538203477859497, 'Val/mean hd95_metric': 10.845074653625488} +Epoch [3730/4000] Training [1/39] Loss: 0.25292 +Epoch [3730/4000] Training [2/39] Loss: 0.00348 +Epoch [3730/4000] Training [3/39] Loss: 0.00515 +Epoch [3730/4000] Training [4/39] Loss: 0.13275 +Epoch [3730/4000] Training [5/39] Loss: 0.12954 +Epoch [3730/4000] Training [6/39] Loss: 0.00429 +Epoch [3730/4000] Training [7/39] Loss: 0.12792 +Epoch [3730/4000] Training [8/39] Loss: 0.00580 +Epoch [3730/4000] Training [9/39] Loss: 0.00436 +Epoch [3730/4000] Training [10/39] Loss: 0.00798 +Epoch [3730/4000] Training [11/39] Loss: 0.13104 +Epoch [3730/4000] Training [12/39] Loss: 0.25342 +Epoch [3730/4000] Training [13/39] Loss: 0.12893 +Epoch [3730/4000] Training [14/39] Loss: 0.00674 +Epoch [3730/4000] Training [15/39] Loss: 0.00594 +Epoch [3730/4000] Training [16/39] Loss: 0.00647 +Epoch [3730/4000] Training [17/39] Loss: 0.00367 +Epoch [3730/4000] Training [18/39] Loss: 0.00443 +Epoch [3730/4000] Training [19/39] Loss: 0.00586 +Epoch [3730/4000] Training [20/39] Loss: 0.08105 +Epoch [3730/4000] Training [21/39] Loss: 0.00616 +Epoch [3730/4000] Training [22/39] Loss: 0.00844 +Epoch [3730/4000] Training [23/39] Loss: 0.25327 +Epoch [3730/4000] Training [24/39] Loss: 0.00517 +Epoch [3730/4000] Training [25/39] Loss: 0.00486 +Epoch [3730/4000] Training [26/39] Loss: 0.00725 +Epoch [3730/4000] Training [27/39] Loss: 0.00276 +Epoch [3730/4000] Training [28/39] Loss: 0.00588 +Epoch [3730/4000] Training [29/39] Loss: 0.00297 +Epoch [3730/4000] Training [30/39] Loss: 0.13016 +Epoch [3730/4000] Training [31/39] Loss: 0.00500 +Epoch [3730/4000] Training [32/39] Loss: 0.01018 +Epoch [3730/4000] Training [33/39] Loss: 0.12838 +Epoch [3730/4000] Training [34/39] Loss: 0.04445 +Epoch [3730/4000] Training [35/39] Loss: 0.00432 +Epoch [3730/4000] Training [36/39] Loss: 0.00317 +Epoch [3730/4000] Training [37/39] Loss: 0.00705 +Epoch [3730/4000] Training [38/39] Loss: 0.00368 +Epoch [3730/4000] Training [39/39] Loss: 0.13200 +Epoch [3730/4000] Training metric {'Train/mean dice_metric': 0.9962592124938965, 'Train/mean miou_metric': 0.9929624795913696, 'Train/mean f1': 0.996824324131012, 'Train/mean precision': 0.9963771104812622, 'Train/mean recall': 0.9972718954086304, 'Train/mean hd95_metric': 0.9652396440505981} +Epoch [3730/4000] Validation [1/10] Loss: 0.74811 focal_loss 0.65928 dice_loss 0.08883 +Epoch [3730/4000] Validation [2/10] Loss: 0.48926 focal_loss 0.39638 dice_loss 0.09288 +Epoch [3730/4000] Validation [3/10] Loss: 0.38119 focal_loss 0.27114 dice_loss 0.11005 +Epoch [3730/4000] Validation [4/10] Loss: 0.91414 focal_loss 0.34628 dice_loss 0.56787 +Epoch [3730/4000] Validation [5/10] Loss: 3.08857 focal_loss 2.41507 dice_loss 0.67350 +Epoch [3730/4000] Validation [6/10] Loss: 1.37011 focal_loss 0.65815 dice_loss 0.71196 +Epoch [3730/4000] Validation [7/10] Loss: 1.21331 focal_loss 0.55735 dice_loss 0.65596 +Epoch [3730/4000] Validation [8/10] Loss: 2.25282 focal_loss 1.65279 dice_loss 0.60003 +Epoch [3730/4000] Validation [9/10] Loss: 1.68729 focal_loss 1.14115 dice_loss 0.54614 +Epoch [3730/4000] Validation [10/10] Loss: 2.00373 focal_loss 1.26271 dice_loss 0.74102 +Epoch [3730/4000] Validation metric {'Val/mean dice_metric': 0.9512470960617065, 'Val/mean miou_metric': 0.9351596832275391, 'Val/mean f1': 0.9475720524787903, 'Val/mean precision': 0.9404832720756531, 'Val/mean recall': 0.954768717288971, 'Val/mean hd95_metric': 10.737784385681152} +Cheakpoint... +Epoch [3730/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512470960617065, 'Val/mean miou_metric': 0.9351596832275391, 'Val/mean f1': 0.9475720524787903, 'Val/mean precision': 0.9404832720756531, 'Val/mean recall': 0.954768717288971, 'Val/mean hd95_metric': 10.737784385681152} +Epoch [3731/4000] Training [1/39] Loss: 0.25271 +Epoch [3731/4000] Training [2/39] Loss: 0.00571 +Epoch [3731/4000] Training [3/39] Loss: 0.00281 +Epoch [3731/4000] Training [4/39] Loss: 0.12947 +Epoch [3731/4000] Training [5/39] Loss: 0.00521 +Epoch [3731/4000] Training [6/39] Loss: 0.00355 +Epoch [3731/4000] Training [7/39] Loss: 0.13150 +Epoch [3731/4000] Training [8/39] Loss: 0.00553 +Epoch [3731/4000] Training [9/39] Loss: 0.12830 +Epoch [3731/4000] Training [10/39] Loss: 0.00425 +Epoch [3731/4000] Training [11/39] Loss: 0.00492 +Epoch [3731/4000] Training [12/39] Loss: 0.00574 +Epoch [3731/4000] Training [13/39] Loss: 0.00353 +Epoch [3731/4000] Training [14/39] Loss: 0.00382 +Epoch [3731/4000] Training [15/39] Loss: 0.00460 +Epoch [3731/4000] Training [16/39] Loss: 0.04146 +Epoch [3731/4000] Training [17/39] Loss: 0.13152 +Epoch [3731/4000] Training [18/39] Loss: 0.12841 +Epoch [3731/4000] Training [19/39] Loss: 0.00361 +Epoch [3731/4000] Training [20/39] Loss: 0.25283 +Epoch [3731/4000] Training [21/39] Loss: 0.00382 +Epoch [3731/4000] Training [22/39] Loss: 0.00719 +Epoch [3731/4000] Training [23/39] Loss: 0.00373 +Epoch [3731/4000] Training [24/39] Loss: 0.00444 +Epoch [3731/4000] Training [25/39] Loss: 0.00465 +Epoch [3731/4000] Training [26/39] Loss: 0.13217 +Epoch [3731/4000] Training [27/39] Loss: 0.00542 +Epoch [3731/4000] Training [28/39] Loss: 0.12968 +Epoch [3731/4000] Training [29/39] Loss: 0.00504 +Epoch [3731/4000] Training [30/39] Loss: 0.00409 +Epoch [3731/4000] Training [31/39] Loss: 0.00414 +Epoch [3731/4000] Training [32/39] Loss: 0.00658 +Epoch [3731/4000] Training [33/39] Loss: 0.12927 +Epoch [3731/4000] Training [34/39] Loss: 0.00423 +Epoch [3731/4000] Training [35/39] Loss: 0.00424 +Epoch [3731/4000] Training [36/39] Loss: 0.00542 +Epoch [3731/4000] Training [37/39] Loss: 0.00369 +Epoch [3731/4000] Training [38/39] Loss: 0.00309 +Epoch [3731/4000] Training [39/39] Loss: 0.00552 +Epoch [3731/4000] Training metric {'Train/mean dice_metric': 0.9965814352035522, 'Train/mean miou_metric': 0.9936110973358154, 'Train/mean f1': 0.9970604181289673, 'Train/mean precision': 0.9966273903846741, 'Train/mean recall': 0.9974938035011292, 'Train/mean hd95_metric': 0.9180017709732056} +Epoch [3731/4000] Validation [1/10] Loss: 0.73814 focal_loss 0.64910 dice_loss 0.08904 +Epoch [3731/4000] Validation [2/10] Loss: 0.48584 focal_loss 0.38972 dice_loss 0.09613 +Epoch [3731/4000] Validation [3/10] Loss: 0.38867 focal_loss 0.27740 dice_loss 0.11127 +Epoch [3731/4000] Validation [4/10] Loss: 0.90189 focal_loss 0.33560 dice_loss 0.56629 +Epoch [3731/4000] Validation [5/10] Loss: 3.06665 focal_loss 2.39310 dice_loss 0.67355 +Epoch [3731/4000] Validation [6/10] Loss: 1.34281 focal_loss 0.63098 dice_loss 0.71183 +Epoch [3731/4000] Validation [7/10] Loss: 1.18423 focal_loss 0.52991 dice_loss 0.65432 +Epoch [3731/4000] Validation [8/10] Loss: 2.26676 focal_loss 1.65993 dice_loss 0.60683 +Epoch [3731/4000] Validation [9/10] Loss: 1.62513 focal_loss 1.07864 dice_loss 0.54649 +Epoch [3731/4000] Validation [10/10] Loss: 1.94062 focal_loss 1.20170 dice_loss 0.73891 +Epoch [3731/4000] Validation metric {'Val/mean dice_metric': 0.9515730142593384, 'Val/mean miou_metric': 0.9357931613922119, 'Val/mean f1': 0.9477921724319458, 'Val/mean precision': 0.9415123462677002, 'Val/mean recall': 0.954156219959259, 'Val/mean hd95_metric': 10.720473289489746} +Cheakpoint... +Epoch [3731/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515730142593384, 'Val/mean miou_metric': 0.9357931613922119, 'Val/mean f1': 0.9477921724319458, 'Val/mean precision': 0.9415123462677002, 'Val/mean recall': 0.954156219959259, 'Val/mean hd95_metric': 10.720473289489746} +Epoch [3732/4000] Training [1/39] Loss: 0.00441 +Epoch [3732/4000] Training [2/39] Loss: 0.00583 +Epoch [3732/4000] Training [3/39] Loss: 0.00351 +Epoch [3732/4000] Training [4/39] Loss: 0.12882 +Epoch [3732/4000] Training [5/39] Loss: 0.00688 +Epoch [3732/4000] Training [6/39] Loss: 0.01020 +Epoch [3732/4000] Training [7/39] Loss: 0.00430 +Epoch [3732/4000] Training [8/39] Loss: 0.12941 +Epoch [3732/4000] Training [9/39] Loss: 0.00301 +Epoch [3732/4000] Training [10/39] Loss: 0.12839 +Epoch [3732/4000] Training [11/39] Loss: 0.00569 +Epoch [3732/4000] Training [12/39] Loss: 0.00437 +Epoch [3732/4000] Training [13/39] Loss: 0.00529 +Epoch [3732/4000] Training [14/39] Loss: 0.00379 +Epoch [3732/4000] Training [15/39] Loss: 0.00296 +Epoch [3732/4000] Training [16/39] Loss: 0.00415 +Epoch [3732/4000] Training [17/39] Loss: 0.13142 +Epoch [3732/4000] Training [18/39] Loss: 0.00355 +Epoch [3732/4000] Training [19/39] Loss: 0.00590 +Epoch [3732/4000] Training [20/39] Loss: 0.00443 +Epoch [3732/4000] Training [21/39] Loss: 0.00362 +Epoch [3732/4000] Training [22/39] Loss: 0.00423 +Epoch [3732/4000] Training [23/39] Loss: 0.00317 +Epoch [3732/4000] Training [24/39] Loss: 0.00523 +Epoch [3732/4000] Training [25/39] Loss: 0.12909 +Epoch [3732/4000] Training [26/39] Loss: 0.13010 +Epoch [3732/4000] Training [27/39] Loss: 0.13101 +Epoch [3732/4000] Training [28/39] Loss: 0.00551 +Epoch [3732/4000] Training [29/39] Loss: 0.00662 +Epoch [3732/4000] Training [30/39] Loss: 0.00302 +Epoch [3732/4000] Training [31/39] Loss: 0.13004 +Epoch [3732/4000] Training [32/39] Loss: 0.13077 +Epoch [3732/4000] Training [33/39] Loss: 0.04993 +Epoch [3732/4000] Training [34/39] Loss: 0.00514 +Epoch [3732/4000] Training [35/39] Loss: 0.00985 +Epoch [3732/4000] Training [36/39] Loss: 0.00379 +Epoch [3732/4000] Training [37/39] Loss: 0.00458 +Epoch [3732/4000] Training [38/39] Loss: 0.00458 +Epoch [3732/4000] Training [39/39] Loss: 0.00415 +Epoch [3732/4000] Training metric {'Train/mean dice_metric': 0.9959471225738525, 'Train/mean miou_metric': 0.992367148399353, 'Train/mean f1': 0.9966382384300232, 'Train/mean precision': 0.9961342215538025, 'Train/mean recall': 0.9971427321434021, 'Train/mean hd95_metric': 1.279492735862732} +Epoch [3732/4000] Validation [1/10] Loss: 0.73093 focal_loss 0.64329 dice_loss 0.08764 +Epoch [3732/4000] Validation [2/10] Loss: 0.49696 focal_loss 0.39713 dice_loss 0.09983 +Epoch [3732/4000] Validation [3/10] Loss: 0.40039 focal_loss 0.28844 dice_loss 0.11195 +Epoch [3732/4000] Validation [4/10] Loss: 0.89313 focal_loss 0.32870 dice_loss 0.56442 +Epoch [3732/4000] Validation [5/10] Loss: 3.08140 focal_loss 2.40748 dice_loss 0.67392 +Epoch [3732/4000] Validation [6/10] Loss: 1.33081 focal_loss 0.61874 dice_loss 0.71207 +Epoch [3732/4000] Validation [7/10] Loss: 1.17365 focal_loss 0.52058 dice_loss 0.65306 +Epoch [3732/4000] Validation [8/10] Loss: 2.39490 focal_loss 1.77591 dice_loss 0.61899 +Epoch [3732/4000] Validation [9/10] Loss: 1.57272 focal_loss 1.02711 dice_loss 0.54561 +Epoch [3732/4000] Validation [10/10] Loss: 1.90688 focal_loss 1.17042 dice_loss 0.73646 +Epoch [3732/4000] Validation metric {'Val/mean dice_metric': 0.9511241912841797, 'Val/mean miou_metric': 0.9349201917648315, 'Val/mean f1': 0.9483997225761414, 'Val/mean precision': 0.9437946677207947, 'Val/mean recall': 0.9530498385429382, 'Val/mean hd95_metric': 10.863351821899414} +Cheakpoint... +Epoch [3732/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511241912841797, 'Val/mean miou_metric': 0.9349201917648315, 'Val/mean f1': 0.9483997225761414, 'Val/mean precision': 0.9437946677207947, 'Val/mean recall': 0.9530498385429382, 'Val/mean hd95_metric': 10.863351821899414} +Epoch [3733/4000] Training [1/39] Loss: 0.00500 +Epoch [3733/4000] Training [2/39] Loss: 0.25417 +Epoch [3733/4000] Training [3/39] Loss: 0.00360 +Epoch [3733/4000] Training [4/39] Loss: 0.12953 +Epoch [3733/4000] Training [5/39] Loss: 0.12826 +Epoch [3733/4000] Training [6/39] Loss: 0.12879 +Epoch [3733/4000] Training [7/39] Loss: 0.00418 +Epoch [3733/4000] Training [8/39] Loss: 0.00344 +Epoch [3733/4000] Training [9/39] Loss: 0.00441 +Epoch [3733/4000] Training [10/39] Loss: 0.00472 +Epoch [3733/4000] Training [11/39] Loss: 0.00831 +Epoch [3733/4000] Training [12/39] Loss: 0.12828 +Epoch [3733/4000] Training [13/39] Loss: 0.13032 +Epoch [3733/4000] Training [14/39] Loss: 0.00650 +Epoch [3733/4000] Training [15/39] Loss: 0.00406 +Epoch [3733/4000] Training [16/39] Loss: 0.00443 +Epoch [3733/4000] Training [17/39] Loss: 0.00423 +Epoch [3733/4000] Training [18/39] Loss: 0.00401 +Epoch [3733/4000] Training [19/39] Loss: 0.13241 +Epoch [3733/4000] Training [20/39] Loss: 0.12849 +Epoch [3733/4000] Training [21/39] Loss: 0.12771 +Epoch [3733/4000] Training [22/39] Loss: 0.00468 +Epoch [3733/4000] Training [23/39] Loss: 0.00418 +Epoch [3733/4000] Training [24/39] Loss: 0.12888 +Epoch [3733/4000] Training [25/39] Loss: 0.12837 +Epoch [3733/4000] Training [26/39] Loss: 0.00519 +Epoch [3733/4000] Training [27/39] Loss: 0.00563 +Epoch [3733/4000] Training [28/39] Loss: 0.13067 +Epoch [3733/4000] Training [29/39] Loss: 0.00454 +Epoch [3733/4000] Training [30/39] Loss: 0.00499 +Epoch [3733/4000] Training [31/39] Loss: 0.00400 +Epoch [3733/4000] Training [32/39] Loss: 0.00349 +Epoch [3733/4000] Training [33/39] Loss: 0.00501 +Epoch [3733/4000] Training [34/39] Loss: 0.00342 +Epoch [3733/4000] Training [35/39] Loss: 0.00333 +Epoch [3733/4000] Training [36/39] Loss: 0.12848 +Epoch [3733/4000] Training [37/39] Loss: 0.00590 +Epoch [3733/4000] Training [38/39] Loss: 0.00559 +Epoch [3733/4000] Training [39/39] Loss: 0.00371 +Epoch [3733/4000] Training metric {'Train/mean dice_metric': 0.9956375956535339, 'Train/mean miou_metric': 0.9925562739372253, 'Train/mean f1': 0.9969688653945923, 'Train/mean precision': 0.9965245723724365, 'Train/mean recall': 0.997413694858551, 'Train/mean hd95_metric': 0.9213836789131165} +Epoch [3733/4000] Validation [1/10] Loss: 0.74158 focal_loss 0.65357 dice_loss 0.08800 +Epoch [3733/4000] Validation [2/10] Loss: 0.49383 focal_loss 0.39582 dice_loss 0.09801 +Epoch [3733/4000] Validation [3/10] Loss: 0.39977 focal_loss 0.28830 dice_loss 0.11146 +Epoch [3733/4000] Validation [4/10] Loss: 0.89977 focal_loss 0.33451 dice_loss 0.56526 +Epoch [3733/4000] Validation [5/10] Loss: 3.12760 focal_loss 2.45374 dice_loss 0.67386 +Epoch [3733/4000] Validation [6/10] Loss: 1.33572 focal_loss 0.62422 dice_loss 0.71150 +Epoch [3733/4000] Validation [7/10] Loss: 1.18397 focal_loss 0.52914 dice_loss 0.65483 +Epoch [3733/4000] Validation [8/10] Loss: 2.34458 focal_loss 1.73146 dice_loss 0.61312 +Epoch [3733/4000] Validation [9/10] Loss: 1.62589 focal_loss 1.08029 dice_loss 0.54560 +Epoch [3733/4000] Validation [10/10] Loss: 1.92490 focal_loss 1.18762 dice_loss 0.73728 +Epoch [3733/4000] Validation metric {'Val/mean dice_metric': 0.9508048892021179, 'Val/mean miou_metric': 0.9349899291992188, 'Val/mean f1': 0.9484759569168091, 'Val/mean precision': 0.9432498812675476, 'Val/mean recall': 0.9537602066993713, 'Val/mean hd95_metric': 10.671960830688477} +Cheakpoint... +Epoch [3733/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508048892021179, 'Val/mean miou_metric': 0.9349899291992188, 'Val/mean f1': 0.9484759569168091, 'Val/mean precision': 0.9432498812675476, 'Val/mean recall': 0.9537602066993713, 'Val/mean hd95_metric': 10.671960830688477} +Epoch [3734/4000] Training [1/39] Loss: 0.12880 +Epoch [3734/4000] Training [2/39] Loss: 0.25327 +Epoch [3734/4000] Training [3/39] Loss: 0.00689 +Epoch [3734/4000] Training [4/39] Loss: 0.00606 +Epoch [3734/4000] Training [5/39] Loss: 0.00391 +Epoch [3734/4000] Training [6/39] Loss: 0.00533 +Epoch [3734/4000] Training [7/39] Loss: 0.00404 +Epoch [3734/4000] Training [8/39] Loss: 0.00862 +Epoch [3734/4000] Training [9/39] Loss: 0.00409 +Epoch [3734/4000] Training [10/39] Loss: 0.00452 +Epoch [3734/4000] Training [11/39] Loss: 0.00430 +Epoch [3734/4000] Training [12/39] Loss: 0.00532 +Epoch [3734/4000] Training [13/39] Loss: 0.12840 +Epoch [3734/4000] Training [14/39] Loss: 0.00444 +Epoch [3734/4000] Training [15/39] Loss: 0.00448 +Epoch [3734/4000] Training [16/39] Loss: 0.00526 +Epoch [3734/4000] Training [17/39] Loss: 0.00359 +Epoch [3734/4000] Training [18/39] Loss: 0.00476 +Epoch [3734/4000] Training [19/39] Loss: 0.00499 +Epoch [3734/4000] Training [20/39] Loss: 0.00548 +Epoch [3734/4000] Training [21/39] Loss: 0.00387 +Epoch [3734/4000] Training [22/39] Loss: 0.00605 +Epoch [3734/4000] Training [23/39] Loss: 0.00750 +Epoch [3734/4000] Training [24/39] Loss: 0.00428 +Epoch [3734/4000] Training [25/39] Loss: 0.12892 +Epoch [3734/4000] Training [26/39] Loss: 0.00548 +Epoch [3734/4000] Training [27/39] Loss: 0.00291 +Epoch [3734/4000] Training [28/39] Loss: 0.12845 +Epoch [3734/4000] Training [29/39] Loss: 0.00476 +Epoch [3734/4000] Training [30/39] Loss: 0.00660 +Epoch [3734/4000] Training [31/39] Loss: 0.00517 +Epoch [3734/4000] Training [32/39] Loss: 0.00833 +Epoch [3734/4000] Training [33/39] Loss: 0.00575 +Epoch [3734/4000] Training [34/39] Loss: 0.13038 +Epoch [3734/4000] Training [35/39] Loss: 0.00520 +Epoch [3734/4000] Training [36/39] Loss: 0.00474 +Epoch [3734/4000] Training [37/39] Loss: 0.12919 +Epoch [3734/4000] Training [38/39] Loss: 0.00572 +Epoch [3734/4000] Training [39/39] Loss: 0.00310 +Epoch [3734/4000] Training metric {'Train/mean dice_metric': 0.9962854385375977, 'Train/mean miou_metric': 0.9930244088172913, 'Train/mean f1': 0.9968188405036926, 'Train/mean precision': 0.9963295459747314, 'Train/mean recall': 0.9973087310791016, 'Train/mean hd95_metric': 0.9420055150985718} +Epoch [3734/4000] Validation [1/10] Loss: 0.72690 focal_loss 0.63935 dice_loss 0.08756 +Epoch [3734/4000] Validation [2/10] Loss: 0.48915 focal_loss 0.39097 dice_loss 0.09819 +Epoch [3734/4000] Validation [3/10] Loss: 0.39093 focal_loss 0.27961 dice_loss 0.11132 +Epoch [3734/4000] Validation [4/10] Loss: 0.89547 focal_loss 0.33028 dice_loss 0.56518 +Epoch [3734/4000] Validation [5/10] Loss: 3.05365 focal_loss 2.37985 dice_loss 0.67381 +Epoch [3734/4000] Validation [6/10] Loss: 1.32896 focal_loss 0.61757 dice_loss 0.71139 +Epoch [3734/4000] Validation [7/10] Loss: 1.18134 focal_loss 0.52734 dice_loss 0.65400 +Epoch [3734/4000] Validation [8/10] Loss: 2.30974 focal_loss 1.69714 dice_loss 0.61260 +Epoch [3734/4000] Validation [9/10] Loss: 1.61790 focal_loss 1.07266 dice_loss 0.54524 +Epoch [3734/4000] Validation [10/10] Loss: 1.91510 focal_loss 1.17776 dice_loss 0.73733 +Epoch [3734/4000] Validation metric {'Val/mean dice_metric': 0.9514034986495972, 'Val/mean miou_metric': 0.9354209303855896, 'Val/mean f1': 0.9481531381607056, 'Val/mean precision': 0.9427930116653442, 'Val/mean recall': 0.9535745978355408, 'Val/mean hd95_metric': 10.616270065307617} +Cheakpoint... +Epoch [3734/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514034986495972, 'Val/mean miou_metric': 0.9354209303855896, 'Val/mean f1': 0.9481531381607056, 'Val/mean precision': 0.9427930116653442, 'Val/mean recall': 0.9535745978355408, 'Val/mean hd95_metric': 10.616270065307617} +Epoch [3735/4000] Training [1/39] Loss: 0.00559 +Epoch [3735/4000] Training [2/39] Loss: 0.00488 +Epoch [3735/4000] Training [3/39] Loss: 0.12954 +Epoch [3735/4000] Training [4/39] Loss: 0.00579 +Epoch [3735/4000] Training [5/39] Loss: 0.13008 +Epoch [3735/4000] Training [6/39] Loss: 0.00330 +Epoch [3735/4000] Training [7/39] Loss: 0.00443 +Epoch [3735/4000] Training [8/39] Loss: 0.12879 +Epoch [3735/4000] Training [9/39] Loss: 0.00647 +Epoch [3735/4000] Training [10/39] Loss: 0.00346 +Epoch [3735/4000] Training [11/39] Loss: 0.12710 +Epoch [3735/4000] Training [12/39] Loss: 0.12770 +Epoch [3735/4000] Training [13/39] Loss: 0.00626 +Epoch [3735/4000] Training [14/39] Loss: 0.00668 +Epoch [3735/4000] Training [15/39] Loss: 0.13005 +Epoch [3735/4000] Training [16/39] Loss: 0.13047 +Epoch [3735/4000] Training [17/39] Loss: 0.00513 +Epoch [3735/4000] Training [18/39] Loss: 0.13067 +Epoch [3735/4000] Training [19/39] Loss: 0.00480 +Epoch [3735/4000] Training [20/39] Loss: 0.12836 +Epoch [3735/4000] Training [21/39] Loss: 0.00402 +Epoch [3735/4000] Training [22/39] Loss: 0.00494 +Epoch [3735/4000] Training [23/39] Loss: 0.00599 +Epoch [3735/4000] Training [24/39] Loss: 0.13003 +Epoch [3735/4000] Training [25/39] Loss: 0.00487 +Epoch [3735/4000] Training [26/39] Loss: 0.00810 +Epoch [3735/4000] Training [27/39] Loss: 0.13169 +Epoch [3735/4000] Training [28/39] Loss: 0.00348 +Epoch [3735/4000] Training [29/39] Loss: 0.00450 +Epoch [3735/4000] Training [30/39] Loss: 0.12754 +Epoch [3735/4000] Training [31/39] Loss: 0.13044 +Epoch [3735/4000] Training [32/39] Loss: 0.00664 +Epoch [3735/4000] Training [33/39] Loss: 0.12925 +Epoch [3735/4000] Training [34/39] Loss: 0.13025 +Epoch [3735/4000] Training [35/39] Loss: 0.00419 +Epoch [3735/4000] Training [36/39] Loss: 0.00499 +Epoch [3735/4000] Training [37/39] Loss: 0.00518 +Epoch [3735/4000] Training [38/39] Loss: 0.00371 +Epoch [3735/4000] Training [39/39] Loss: 0.00309 +Epoch [3735/4000] Training metric {'Train/mean dice_metric': 0.9963216781616211, 'Train/mean miou_metric': 0.9930884838104248, 'Train/mean f1': 0.9969745874404907, 'Train/mean precision': 0.9965420961380005, 'Train/mean recall': 0.9974073171615601, 'Train/mean hd95_metric': 0.9269421100616455} +Epoch [3735/4000] Validation [1/10] Loss: 0.73027 focal_loss 0.64338 dice_loss 0.08689 +Epoch [3735/4000] Validation [2/10] Loss: 0.49927 focal_loss 0.39897 dice_loss 0.10031 +Epoch [3735/4000] Validation [3/10] Loss: 0.40121 focal_loss 0.28932 dice_loss 0.11189 +Epoch [3735/4000] Validation [4/10] Loss: 0.89381 focal_loss 0.32939 dice_loss 0.56443 +Epoch [3735/4000] Validation [5/10] Loss: 3.08118 focal_loss 2.40702 dice_loss 0.67417 +Epoch [3735/4000] Validation [6/10] Loss: 1.31936 focal_loss 0.60894 dice_loss 0.71042 +Epoch [3735/4000] Validation [7/10] Loss: 1.17592 focal_loss 0.52326 dice_loss 0.65266 +Epoch [3735/4000] Validation [8/10] Loss: 2.33008 focal_loss 1.71542 dice_loss 0.61466 +Epoch [3735/4000] Validation [9/10] Loss: 1.59580 focal_loss 1.05109 dice_loss 0.54471 +Epoch [3735/4000] Validation [10/10] Loss: 1.90463 focal_loss 1.16805 dice_loss 0.73658 +Epoch [3735/4000] Validation metric {'Val/mean dice_metric': 0.9514675736427307, 'Val/mean miou_metric': 0.9355358481407166, 'Val/mean f1': 0.9481269121170044, 'Val/mean precision': 0.9432967305183411, 'Val/mean recall': 0.9530068635940552, 'Val/mean hd95_metric': 10.586929321289062} +Cheakpoint... +Epoch [3735/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514675736427307, 'Val/mean miou_metric': 0.9355358481407166, 'Val/mean f1': 0.9481269121170044, 'Val/mean precision': 0.9432967305183411, 'Val/mean recall': 0.9530068635940552, 'Val/mean hd95_metric': 10.586929321289062} +Epoch [3736/4000] Training [1/39] Loss: 0.00518 +Epoch [3736/4000] Training [2/39] Loss: 0.13096 +Epoch [3736/4000] Training [3/39] Loss: 0.12864 +Epoch [3736/4000] Training [4/39] Loss: 0.00299 +Epoch [3736/4000] Training [5/39] Loss: 0.00420 +Epoch [3736/4000] Training [6/39] Loss: 0.00758 +Epoch [3736/4000] Training [7/39] Loss: 0.00491 +Epoch [3736/4000] Training [8/39] Loss: 0.13042 +Epoch [3736/4000] Training [9/39] Loss: 0.00588 +Epoch [3736/4000] Training [10/39] Loss: 0.13380 +Epoch [3736/4000] Training [11/39] Loss: 0.00483 +Epoch [3736/4000] Training [12/39] Loss: 0.00605 +Epoch [3736/4000] Training [13/39] Loss: 0.00316 +Epoch [3736/4000] Training [14/39] Loss: 0.00394 +Epoch [3736/4000] Training [15/39] Loss: 0.12764 +Epoch [3736/4000] Training [16/39] Loss: 0.00417 +Epoch [3736/4000] Training [17/39] Loss: 0.00487 +Epoch [3736/4000] Training [18/39] Loss: 0.13034 +Epoch [3736/4000] Training [19/39] Loss: 0.00361 +Epoch [3736/4000] Training [20/39] Loss: 0.00428 +Epoch [3736/4000] Training [21/39] Loss: 0.00681 +Epoch [3736/4000] Training [22/39] Loss: 0.00666 +Epoch [3736/4000] Training [23/39] Loss: 0.00603 +Epoch [3736/4000] Training [24/39] Loss: 0.12798 +Epoch [3736/4000] Training [25/39] Loss: 0.00368 +Epoch [3736/4000] Training [26/39] Loss: 0.00691 +Epoch [3736/4000] Training [27/39] Loss: 0.00527 +Epoch [3736/4000] Training [28/39] Loss: 0.00429 +Epoch [3736/4000] Training [29/39] Loss: 0.00810 +Epoch [3736/4000] Training [30/39] Loss: 0.25468 +Epoch [3736/4000] Training [31/39] Loss: 0.00600 +Epoch [3736/4000] Training [32/39] Loss: 0.00447 +Epoch [3736/4000] Training [33/39] Loss: 0.12826 +Epoch [3736/4000] Training [34/39] Loss: 0.00433 +Epoch [3736/4000] Training [35/39] Loss: 0.00688 +Epoch [3736/4000] Training [36/39] Loss: 0.00354 +Epoch [3736/4000] Training [37/39] Loss: 0.12332 +Epoch [3736/4000] Training [38/39] Loss: 0.12937 +Epoch [3736/4000] Training [39/39] Loss: 0.00402 +Epoch [3736/4000] Training metric {'Train/mean dice_metric': 0.9962807297706604, 'Train/mean miou_metric': 0.9930105805397034, 'Train/mean f1': 0.996771514415741, 'Train/mean precision': 0.9962990880012512, 'Train/mean recall': 0.9972444772720337, 'Train/mean hd95_metric': 0.9379459023475647} +Epoch [3736/4000] Validation [1/10] Loss: 0.73079 focal_loss 0.64348 dice_loss 0.08731 +Epoch [3736/4000] Validation [2/10] Loss: 0.49670 focal_loss 0.40022 dice_loss 0.09648 +Epoch [3736/4000] Validation [3/10] Loss: 0.39188 focal_loss 0.28080 dice_loss 0.11109 +Epoch [3736/4000] Validation [4/10] Loss: 0.90561 focal_loss 0.33944 dice_loss 0.56617 +Epoch [3736/4000] Validation [5/10] Loss: 3.07922 focal_loss 2.40539 dice_loss 0.67383 +Epoch [3736/4000] Validation [6/10] Loss: 1.35396 focal_loss 0.64135 dice_loss 0.71261 +Epoch [3736/4000] Validation [7/10] Loss: 1.20028 focal_loss 0.54477 dice_loss 0.65552 +Epoch [3736/4000] Validation [8/10] Loss: 2.28540 focal_loss 1.67940 dice_loss 0.60600 +Epoch [3736/4000] Validation [9/10] Loss: 1.63945 focal_loss 1.09453 dice_loss 0.54492 +Epoch [3736/4000] Validation [10/10] Loss: 1.96871 focal_loss 1.22914 dice_loss 0.73957 +Epoch [3736/4000] Validation metric {'Val/mean dice_metric': 0.9514503479003906, 'Val/mean miou_metric': 0.9354639053344727, 'Val/mean f1': 0.9480603337287903, 'Val/mean precision': 0.9417799115180969, 'Val/mean recall': 0.9544251561164856, 'Val/mean hd95_metric': 10.621777534484863} +Cheakpoint... +Epoch [3736/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514503479003906, 'Val/mean miou_metric': 0.9354639053344727, 'Val/mean f1': 0.9480603337287903, 'Val/mean precision': 0.9417799115180969, 'Val/mean recall': 0.9544251561164856, 'Val/mean hd95_metric': 10.621777534484863} +Epoch [3737/4000] Training [1/39] Loss: 0.00558 +Epoch [3737/4000] Training [2/39] Loss: 0.00338 +Epoch [3737/4000] Training [3/39] Loss: 0.00598 +Epoch [3737/4000] Training [4/39] Loss: 0.00352 +Epoch [3737/4000] Training [5/39] Loss: 0.13329 +Epoch [3737/4000] Training [6/39] Loss: 0.13091 +Epoch [3737/4000] Training [7/39] Loss: 0.01016 +Epoch [3737/4000] Training [8/39] Loss: 0.13050 +Epoch [3737/4000] Training [9/39] Loss: 0.00348 +Epoch [3737/4000] Training [10/39] Loss: 0.00475 +Epoch [3737/4000] Training [11/39] Loss: 0.00553 +Epoch [3737/4000] Training [12/39] Loss: 0.00514 +Epoch [3737/4000] Training [13/39] Loss: 0.12997 +Epoch [3737/4000] Training [14/39] Loss: 0.12882 +Epoch [3737/4000] Training [15/39] Loss: 0.12826 +Epoch [3737/4000] Training [16/39] Loss: 0.25473 +Epoch [3737/4000] Training [17/39] Loss: 0.00594 +Epoch [3737/4000] Training [18/39] Loss: 0.00461 +Epoch [3737/4000] Training [19/39] Loss: 0.00602 +Epoch [3737/4000] Training [20/39] Loss: 0.00314 +Epoch [3737/4000] Training [21/39] Loss: 0.00401 +Epoch [3737/4000] Training [22/39] Loss: 0.12847 +Epoch [3737/4000] Training [23/39] Loss: 0.00496 +Epoch [3737/4000] Training [24/39] Loss: 0.12985 +Epoch [3737/4000] Training [25/39] Loss: 0.13045 +Epoch [3737/4000] Training [26/39] Loss: 0.12721 +Epoch [3737/4000] Training [27/39] Loss: 0.00500 +Epoch [3737/4000] Training [28/39] Loss: 0.12869 +Epoch [3737/4000] Training [29/39] Loss: 0.00391 +Epoch [3737/4000] Training [30/39] Loss: 0.00429 +Epoch [3737/4000] Training [31/39] Loss: 0.00646 +Epoch [3737/4000] Training [32/39] Loss: 0.00354 +Epoch [3737/4000] Training [33/39] Loss: 0.00577 +Epoch [3737/4000] Training [34/39] Loss: 0.00606 +Epoch [3737/4000] Training [35/39] Loss: 0.00723 +Epoch [3737/4000] Training [36/39] Loss: 0.00526 +Epoch [3737/4000] Training [37/39] Loss: 0.12995 +Epoch [3737/4000] Training [38/39] Loss: 0.00692 +Epoch [3737/4000] Training [39/39] Loss: 0.00280 +Epoch [3737/4000] Training metric {'Train/mean dice_metric': 0.996360182762146, 'Train/mean miou_metric': 0.9931782484054565, 'Train/mean f1': 0.9968991875648499, 'Train/mean precision': 0.9964759945869446, 'Train/mean recall': 0.9973227977752686, 'Train/mean hd95_metric': 0.9415203928947449} +Epoch [3737/4000] Validation [1/10] Loss: 0.71695 focal_loss 0.63077 dice_loss 0.08618 +Epoch [3737/4000] Validation [2/10] Loss: 0.49971 focal_loss 0.39944 dice_loss 0.10028 +Epoch [3737/4000] Validation [3/10] Loss: 0.40410 focal_loss 0.29180 dice_loss 0.11231 +Epoch [3737/4000] Validation [4/10] Loss: 0.88620 focal_loss 0.32166 dice_loss 0.56454 +Epoch [3737/4000] Validation [5/10] Loss: 3.05942 focal_loss 2.38528 dice_loss 0.67414 +Epoch [3737/4000] Validation [6/10] Loss: 1.32083 focal_loss 0.60860 dice_loss 0.71224 +Epoch [3737/4000] Validation [7/10] Loss: 1.17405 focal_loss 0.52021 dice_loss 0.65384 +Epoch [3737/4000] Validation [8/10] Loss: 2.37285 focal_loss 1.75454 dice_loss 0.61831 +Epoch [3737/4000] Validation [9/10] Loss: 1.55036 focal_loss 1.00611 dice_loss 0.54424 +Epoch [3737/4000] Validation [10/10] Loss: 1.89350 focal_loss 1.15684 dice_loss 0.73666 +Epoch [3737/4000] Validation metric {'Val/mean dice_metric': 0.9515430927276611, 'Val/mean miou_metric': 0.9356905221939087, 'Val/mean f1': 0.948849081993103, 'Val/mean precision': 0.9443516135215759, 'Val/mean recall': 0.9533895254135132, 'Val/mean hd95_metric': 10.6727294921875} +Cheakpoint... +Epoch [3737/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515430927276611, 'Val/mean miou_metric': 0.9356905221939087, 'Val/mean f1': 0.948849081993103, 'Val/mean precision': 0.9443516135215759, 'Val/mean recall': 0.9533895254135132, 'Val/mean hd95_metric': 10.6727294921875} +Epoch [3738/4000] Training [1/39] Loss: 0.00267 +Epoch [3738/4000] Training [2/39] Loss: 0.00434 +Epoch [3738/4000] Training [3/39] Loss: 0.00340 +Epoch [3738/4000] Training [4/39] Loss: 0.00475 +Epoch [3738/4000] Training [5/39] Loss: 0.00330 +Epoch [3738/4000] Training [6/39] Loss: 0.12966 +Epoch [3738/4000] Training [7/39] Loss: 0.00338 +Epoch [3738/4000] Training [8/39] Loss: 0.12812 +Epoch [3738/4000] Training [9/39] Loss: 0.00313 +Epoch [3738/4000] Training [10/39] Loss: 0.00534 +Epoch [3738/4000] Training [11/39] Loss: 0.00509 +Epoch [3738/4000] Training [12/39] Loss: 0.12784 +Epoch [3738/4000] Training [13/39] Loss: 0.00549 +Epoch [3738/4000] Training [14/39] Loss: 0.00468 +Epoch [3738/4000] Training [15/39] Loss: 0.13288 +Epoch [3738/4000] Training [16/39] Loss: 0.00512 +Epoch [3738/4000] Training [17/39] Loss: 0.00604 +Epoch [3738/4000] Training [18/39] Loss: 0.00414 +Epoch [3738/4000] Training [19/39] Loss: 0.25386 +Epoch [3738/4000] Training [20/39] Loss: 0.13120 +Epoch [3738/4000] Training [21/39] Loss: 0.12890 +Epoch [3738/4000] Training [22/39] Loss: 0.00468 +Epoch [3738/4000] Training [23/39] Loss: 0.00549 +Epoch [3738/4000] Training [24/39] Loss: 0.00798 +Epoch [3738/4000] Training [25/39] Loss: 0.00405 +Epoch [3738/4000] Training [26/39] Loss: 0.00323 +Epoch [3738/4000] Training [27/39] Loss: 0.12851 +Epoch [3738/4000] Training [28/39] Loss: 0.12847 +Epoch [3738/4000] Training [29/39] Loss: 0.00406 +Epoch [3738/4000] Training [30/39] Loss: 0.12877 +Epoch [3738/4000] Training [31/39] Loss: 0.00644 +Epoch [3738/4000] Training [32/39] Loss: 0.00452 +Epoch [3738/4000] Training [33/39] Loss: 0.00872 +Epoch [3738/4000] Training [34/39] Loss: 0.00691 +Epoch [3738/4000] Training [35/39] Loss: 0.00510 +Epoch [3738/4000] Training [36/39] Loss: 0.00489 +Epoch [3738/4000] Training [37/39] Loss: 0.00489 +Epoch [3738/4000] Training [38/39] Loss: 0.00578 +Epoch [3738/4000] Training [39/39] Loss: 0.12875 +Epoch [3738/4000] Training metric {'Train/mean dice_metric': 0.9963806867599487, 'Train/mean miou_metric': 0.9932029843330383, 'Train/mean f1': 0.9968538880348206, 'Train/mean precision': 0.9963907599449158, 'Train/mean recall': 0.9973174333572388, 'Train/mean hd95_metric': 0.9467049241065979} +Epoch [3738/4000] Validation [1/10] Loss: 0.72245 focal_loss 0.63582 dice_loss 0.08663 +Epoch [3738/4000] Validation [2/10] Loss: 0.49520 focal_loss 0.39757 dice_loss 0.09763 +Epoch [3738/4000] Validation [3/10] Loss: 0.39537 focal_loss 0.28418 dice_loss 0.11119 +Epoch [3738/4000] Validation [4/10] Loss: 0.89628 focal_loss 0.33062 dice_loss 0.56565 +Epoch [3738/4000] Validation [5/10] Loss: 3.08995 focal_loss 2.41581 dice_loss 0.67414 +Epoch [3738/4000] Validation [6/10] Loss: 1.33966 focal_loss 0.62746 dice_loss 0.71220 +Epoch [3738/4000] Validation [7/10] Loss: 1.18456 focal_loss 0.53063 dice_loss 0.65393 +Epoch [3738/4000] Validation [8/10] Loss: 2.35902 focal_loss 1.74471 dice_loss 0.61431 +Epoch [3738/4000] Validation [9/10] Loss: 1.59043 focal_loss 1.04568 dice_loss 0.54475 +Epoch [3738/4000] Validation [10/10] Loss: 1.92650 focal_loss 1.18914 dice_loss 0.73736 +Epoch [3738/4000] Validation metric {'Val/mean dice_metric': 0.9515923261642456, 'Val/mean miou_metric': 0.9357255101203918, 'Val/mean f1': 0.9482571482658386, 'Val/mean precision': 0.9429442882537842, 'Val/mean recall': 0.9536302089691162, 'Val/mean hd95_metric': 10.655503273010254} +Cheakpoint... +Epoch [3738/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515923261642456, 'Val/mean miou_metric': 0.9357255101203918, 'Val/mean f1': 0.9482571482658386, 'Val/mean precision': 0.9429442882537842, 'Val/mean recall': 0.9536302089691162, 'Val/mean hd95_metric': 10.655503273010254} +Epoch [3739/4000] Training [1/39] Loss: 0.12927 +Epoch [3739/4000] Training [2/39] Loss: 0.12881 +Epoch [3739/4000] Training [3/39] Loss: 0.00372 +Epoch [3739/4000] Training [4/39] Loss: 0.00497 +Epoch [3739/4000] Training [5/39] Loss: 0.00405 +Epoch [3739/4000] Training [6/39] Loss: 0.00333 +Epoch [3739/4000] Training [7/39] Loss: 0.00445 +Epoch [3739/4000] Training [8/39] Loss: 0.00552 +Epoch [3739/4000] Training [9/39] Loss: 0.00409 +Epoch [3739/4000] Training [10/39] Loss: 0.00577 +Epoch [3739/4000] Training [11/39] Loss: 0.25429 +Epoch [3739/4000] Training [12/39] Loss: 0.00436 +Epoch [3739/4000] Training [13/39] Loss: 0.00308 +Epoch [3739/4000] Training [14/39] Loss: 0.12779 +Epoch [3739/4000] Training [15/39] Loss: 0.00742 +Epoch [3739/4000] Training [16/39] Loss: 0.00734 +Epoch [3739/4000] Training [17/39] Loss: 0.00393 +Epoch [3739/4000] Training [18/39] Loss: 0.01070 +Epoch [3739/4000] Training [19/39] Loss: 0.00785 +Epoch [3739/4000] Training [20/39] Loss: 0.00499 +Epoch [3739/4000] Training [21/39] Loss: 0.13106 +Epoch [3739/4000] Training [22/39] Loss: 0.00339 +Epoch [3739/4000] Training [23/39] Loss: 0.25508 +Epoch [3739/4000] Training [24/39] Loss: 0.00756 +Epoch [3739/4000] Training [25/39] Loss: 0.00626 +Epoch [3739/4000] Training [26/39] Loss: 0.12991 +Epoch [3739/4000] Training [27/39] Loss: 0.00502 +Epoch [3739/4000] Training [28/39] Loss: 0.00305 +Epoch [3739/4000] Training [29/39] Loss: 0.00638 +Epoch [3739/4000] Training [30/39] Loss: 0.12762 +Epoch [3739/4000] Training [31/39] Loss: 0.00259 +Epoch [3739/4000] Training [32/39] Loss: 0.12813 +Epoch [3739/4000] Training [33/39] Loss: 0.00494 +Epoch [3739/4000] Training [34/39] Loss: 0.12887 +Epoch [3739/4000] Training [35/39] Loss: 0.00356 +Epoch [3739/4000] Training [36/39] Loss: 0.00870 +Epoch [3739/4000] Training [37/39] Loss: 0.00299 +Epoch [3739/4000] Training [38/39] Loss: 0.00629 +Epoch [3739/4000] Training [39/39] Loss: 0.00621 +Epoch [3739/4000] Training metric {'Train/mean dice_metric': 0.99619060754776, 'Train/mean miou_metric': 0.9928610324859619, 'Train/mean f1': 0.9968042969703674, 'Train/mean precision': 0.9963445067405701, 'Train/mean recall': 0.9972643852233887, 'Train/mean hd95_metric': 0.9374779462814331} +Epoch [3739/4000] Validation [1/10] Loss: 0.71342 focal_loss 0.62679 dice_loss 0.08664 +Epoch [3739/4000] Validation [2/10] Loss: 0.49761 focal_loss 0.40144 dice_loss 0.09616 +Epoch [3739/4000] Validation [3/10] Loss: 0.38685 focal_loss 0.27606 dice_loss 0.11080 +Epoch [3739/4000] Validation [4/10] Loss: 0.90152 focal_loss 0.33514 dice_loss 0.56638 +Epoch [3739/4000] Validation [5/10] Loss: 3.06265 focal_loss 2.38865 dice_loss 0.67400 +Epoch [3739/4000] Validation [6/10] Loss: 1.34810 focal_loss 0.63624 dice_loss 0.71186 +Epoch [3739/4000] Validation [7/10] Loss: 1.19351 focal_loss 0.53729 dice_loss 0.65621 +Epoch [3739/4000] Validation [8/10] Loss: 2.30092 focal_loss 1.69344 dice_loss 0.60748 +Epoch [3739/4000] Validation [9/10] Loss: 1.57607 focal_loss 1.03115 dice_loss 0.54492 +Epoch [3739/4000] Validation [10/10] Loss: 1.94776 focal_loss 1.20976 dice_loss 0.73799 +Epoch [3739/4000] Validation metric {'Val/mean dice_metric': 0.9513885974884033, 'Val/mean miou_metric': 0.9353630542755127, 'Val/mean f1': 0.947738528251648, 'Val/mean precision': 0.9416691660881042, 'Val/mean recall': 0.9538865685462952, 'Val/mean hd95_metric': 10.721328735351562} +Cheakpoint... +Epoch [3739/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513885974884033, 'Val/mean miou_metric': 0.9353630542755127, 'Val/mean f1': 0.947738528251648, 'Val/mean precision': 0.9416691660881042, 'Val/mean recall': 0.9538865685462952, 'Val/mean hd95_metric': 10.721328735351562} +Epoch [3740/4000] Training [1/39] Loss: 0.00397 +Epoch [3740/4000] Training [2/39] Loss: 0.00805 +Epoch [3740/4000] Training [3/39] Loss: 0.12787 +Epoch [3740/4000] Training [4/39] Loss: 0.00550 +Epoch [3740/4000] Training [5/39] Loss: 0.00462 +Epoch [3740/4000] Training [6/39] Loss: 0.12825 +Epoch [3740/4000] Training [7/39] Loss: 0.00625 +Epoch [3740/4000] Training [8/39] Loss: 0.00664 +Epoch [3740/4000] Training [9/39] Loss: 0.00384 +Epoch [3740/4000] Training [10/39] Loss: 0.00525 +Epoch [3740/4000] Training [11/39] Loss: 0.00383 +Epoch [3740/4000] Training [12/39] Loss: 0.25276 +Epoch [3740/4000] Training [13/39] Loss: 0.00600 +Epoch [3740/4000] Training [14/39] Loss: 0.00518 +Epoch [3740/4000] Training [15/39] Loss: 0.21114 +Epoch [3740/4000] Training [16/39] Loss: 0.12777 +Epoch [3740/4000] Training [17/39] Loss: 0.00433 +Epoch [3740/4000] Training [18/39] Loss: 0.12812 +Epoch [3740/4000] Training [19/39] Loss: 0.12783 +Epoch [3740/4000] Training [20/39] Loss: 0.12801 +Epoch [3740/4000] Training [21/39] Loss: 0.12908 +Epoch [3740/4000] Training [22/39] Loss: 0.00447 +Epoch [3740/4000] Training [23/39] Loss: 0.00511 +Epoch [3740/4000] Training [24/39] Loss: 0.00373 +Epoch [3740/4000] Training [25/39] Loss: 0.00560 +Epoch [3740/4000] Training [26/39] Loss: 0.13019 +Epoch [3740/4000] Training [27/39] Loss: 0.00320 +Epoch [3740/4000] Training [28/39] Loss: 0.00304 +Epoch [3740/4000] Training [29/39] Loss: 0.00706 +Epoch [3740/4000] Training [30/39] Loss: 0.00382 +Epoch [3740/4000] Training [31/39] Loss: 0.00609 +Epoch [3740/4000] Training [32/39] Loss: 0.00417 +Epoch [3740/4000] Training [33/39] Loss: 0.12860 +Epoch [3740/4000] Training [34/39] Loss: 0.00572 +Epoch [3740/4000] Training [35/39] Loss: 0.13067 +Epoch [3740/4000] Training [36/39] Loss: 0.12798 +Epoch [3740/4000] Training [37/39] Loss: 0.00385 +Epoch [3740/4000] Training [38/39] Loss: 0.00306 +Epoch [3740/4000] Training [39/39] Loss: 0.00459 +Epoch [3740/4000] Training metric {'Train/mean dice_metric': 0.9964204430580139, 'Train/mean miou_metric': 0.993293046951294, 'Train/mean f1': 0.9969729781150818, 'Train/mean precision': 0.9964916110038757, 'Train/mean recall': 0.9974549412727356, 'Train/mean hd95_metric': 0.917803168296814} +Epoch [3740/4000] Validation [1/10] Loss: 0.71163 focal_loss 0.62473 dice_loss 0.08689 +Epoch [3740/4000] Validation [2/10] Loss: 0.49821 focal_loss 0.39984 dice_loss 0.09837 +Epoch [3740/4000] Validation [3/10] Loss: 0.38839 focal_loss 0.27707 dice_loss 0.11132 +Epoch [3740/4000] Validation [4/10] Loss: 0.90006 focal_loss 0.33428 dice_loss 0.56578 +Epoch [3740/4000] Validation [5/10] Loss: 3.04515 focal_loss 2.37120 dice_loss 0.67396 +Epoch [3740/4000] Validation [6/10] Loss: 1.33733 focal_loss 0.62686 dice_loss 0.71047 +Epoch [3740/4000] Validation [7/10] Loss: 1.18863 focal_loss 0.53220 dice_loss 0.65642 +Epoch [3740/4000] Validation [8/10] Loss: 2.28199 focal_loss 1.67342 dice_loss 0.60857 +Epoch [3740/4000] Validation [9/10] Loss: 1.56064 focal_loss 1.01541 dice_loss 0.54523 +Epoch [3740/4000] Validation [10/10] Loss: 1.93013 focal_loss 1.19233 dice_loss 0.73779 +Epoch [3740/4000] Validation metric {'Val/mean dice_metric': 0.9515894055366516, 'Val/mean miou_metric': 0.9357640147209167, 'Val/mean f1': 0.9482882618904114, 'Val/mean precision': 0.9424748420715332, 'Val/mean recall': 0.9541738629341125, 'Val/mean hd95_metric': 10.655712127685547} +Cheakpoint... +Epoch [3740/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515894055366516, 'Val/mean miou_metric': 0.9357640147209167, 'Val/mean f1': 0.9482882618904114, 'Val/mean precision': 0.9424748420715332, 'Val/mean recall': 0.9541738629341125, 'Val/mean hd95_metric': 10.655712127685547} +Epoch [3741/4000] Training [1/39] Loss: 0.00682 +Epoch [3741/4000] Training [2/39] Loss: 0.00396 +Epoch [3741/4000] Training [3/39] Loss: 0.00315 +Epoch [3741/4000] Training [4/39] Loss: 0.00469 +Epoch [3741/4000] Training [5/39] Loss: 0.00387 +Epoch [3741/4000] Training [6/39] Loss: 0.00397 +Epoch [3741/4000] Training [7/39] Loss: 0.12972 +Epoch [3741/4000] Training [8/39] Loss: 0.00843 +Epoch [3741/4000] Training [9/39] Loss: 0.12969 +Epoch [3741/4000] Training [10/39] Loss: 0.00860 +Epoch [3741/4000] Training [11/39] Loss: 0.00428 +Epoch [3741/4000] Training [12/39] Loss: 0.12913 +Epoch [3741/4000] Training [13/39] Loss: 0.00377 +Epoch [3741/4000] Training [14/39] Loss: 0.13028 +Epoch [3741/4000] Training [15/39] Loss: 0.00495 +Epoch [3741/4000] Training [16/39] Loss: 0.00281 +Epoch [3741/4000] Training [17/39] Loss: 0.00583 +Epoch [3741/4000] Training [18/39] Loss: 0.00311 +Epoch [3741/4000] Training [19/39] Loss: 0.12804 +Epoch [3741/4000] Training [20/39] Loss: 0.12839 +Epoch [3741/4000] Training [21/39] Loss: 0.00426 +Epoch [3741/4000] Training [22/39] Loss: 0.12865 +Epoch [3741/4000] Training [23/39] Loss: 0.00475 +Epoch [3741/4000] Training [24/39] Loss: 0.00515 +Epoch [3741/4000] Training [25/39] Loss: 0.00356 +Epoch [3741/4000] Training [26/39] Loss: 0.00513 +Epoch [3741/4000] Training [27/39] Loss: 0.00497 +Epoch [3741/4000] Training [28/39] Loss: 0.00485 +Epoch [3741/4000] Training [29/39] Loss: 0.00530 +Epoch [3741/4000] Training [30/39] Loss: 0.00396 +Epoch [3741/4000] Training [31/39] Loss: 0.12902 +Epoch [3741/4000] Training [32/39] Loss: 0.00352 +Epoch [3741/4000] Training [33/39] Loss: 0.00667 +Epoch [3741/4000] Training [34/39] Loss: 0.00523 +Epoch [3741/4000] Training [35/39] Loss: 0.13565 +Epoch [3741/4000] Training [36/39] Loss: 0.00714 +Epoch [3741/4000] Training [37/39] Loss: 0.00539 +Epoch [3741/4000] Training [38/39] Loss: 0.00703 +Epoch [3741/4000] Training [39/39] Loss: 0.00635 +Epoch [3741/4000] Training metric {'Train/mean dice_metric': 0.9963855147361755, 'Train/mean miou_metric': 0.9932052493095398, 'Train/mean f1': 0.9968294501304626, 'Train/mean precision': 0.9963915944099426, 'Train/mean recall': 0.9972676634788513, 'Train/mean hd95_metric': 0.914821982383728} +Epoch [3741/4000] Validation [1/10] Loss: 0.74235 focal_loss 0.65448 dice_loss 0.08788 +Epoch [3741/4000] Validation [2/10] Loss: 0.49739 focal_loss 0.40288 dice_loss 0.09451 +Epoch [3741/4000] Validation [3/10] Loss: 0.39384 focal_loss 0.28306 dice_loss 0.11078 +Epoch [3741/4000] Validation [4/10] Loss: 0.90486 focal_loss 0.33836 dice_loss 0.56650 +Epoch [3741/4000] Validation [5/10] Loss: 3.12529 focal_loss 2.45116 dice_loss 0.67414 +Epoch [3741/4000] Validation [6/10] Loss: 1.35897 focal_loss 0.64819 dice_loss 0.71077 +Epoch [3741/4000] Validation [7/10] Loss: 1.19843 focal_loss 0.54230 dice_loss 0.65613 +Epoch [3741/4000] Validation [8/10] Loss: 2.35361 focal_loss 1.74603 dice_loss 0.60757 +Epoch [3741/4000] Validation [9/10] Loss: 1.61978 focal_loss 1.07428 dice_loss 0.54551 +Epoch [3741/4000] Validation [10/10] Loss: 1.96387 focal_loss 1.22613 dice_loss 0.73774 +Epoch [3741/4000] Validation metric {'Val/mean dice_metric': 0.951617956161499, 'Val/mean miou_metric': 0.9357282519340515, 'Val/mean f1': 0.9477556347846985, 'Val/mean precision': 0.9418215155601501, 'Val/mean recall': 0.9537649154663086, 'Val/mean hd95_metric': 10.611456871032715} +Cheakpoint... +Epoch [3741/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951617956161499, 'Val/mean miou_metric': 0.9357282519340515, 'Val/mean f1': 0.9477556347846985, 'Val/mean precision': 0.9418215155601501, 'Val/mean recall': 0.9537649154663086, 'Val/mean hd95_metric': 10.611456871032715} +Epoch [3742/4000] Training [1/39] Loss: 0.12933 +Epoch [3742/4000] Training [2/39] Loss: 0.00589 +Epoch [3742/4000] Training [3/39] Loss: 0.00511 +Epoch [3742/4000] Training [4/39] Loss: 0.00526 +Epoch [3742/4000] Training [5/39] Loss: 0.00397 +Epoch [3742/4000] Training [6/39] Loss: 0.00824 +Epoch [3742/4000] Training [7/39] Loss: 0.00330 +Epoch [3742/4000] Training [8/39] Loss: 0.00479 +Epoch [3742/4000] Training [9/39] Loss: 0.13082 +Epoch [3742/4000] Training [10/39] Loss: 0.12786 +Epoch [3742/4000] Training [11/39] Loss: 0.12778 +Epoch [3742/4000] Training [12/39] Loss: 0.00439 +Epoch [3742/4000] Training [13/39] Loss: 0.00405 +Epoch [3742/4000] Training [14/39] Loss: 0.00392 +Epoch [3742/4000] Training [15/39] Loss: 0.00359 +Epoch [3742/4000] Training [16/39] Loss: 0.00472 +Epoch [3742/4000] Training [17/39] Loss: 0.00467 +Epoch [3742/4000] Training [18/39] Loss: 0.00449 +Epoch [3742/4000] Training [19/39] Loss: 0.00282 +Epoch [3742/4000] Training [20/39] Loss: 0.12964 +Epoch [3742/4000] Training [21/39] Loss: 0.12933 +Epoch [3742/4000] Training [22/39] Loss: 0.00399 +Epoch [3742/4000] Training [23/39] Loss: 0.00704 +Epoch [3742/4000] Training [24/39] Loss: 0.12857 +Epoch [3742/4000] Training [25/39] Loss: 0.13036 +Epoch [3742/4000] Training [26/39] Loss: 0.00284 +Epoch [3742/4000] Training [27/39] Loss: 0.13014 +Epoch [3742/4000] Training [28/39] Loss: 0.00412 +Epoch [3742/4000] Training [29/39] Loss: 0.00535 +Epoch [3742/4000] Training [30/39] Loss: 0.00536 +Epoch [3742/4000] Training [31/39] Loss: 0.00337 +Epoch [3742/4000] Training [32/39] Loss: 0.00563 +Epoch [3742/4000] Training [33/39] Loss: 0.00430 +Epoch [3742/4000] Training [34/39] Loss: 0.00650 +Epoch [3742/4000] Training [35/39] Loss: 0.13100 +Epoch [3742/4000] Training [36/39] Loss: 0.00306 +Epoch [3742/4000] Training [37/39] Loss: 0.00581 +Epoch [3742/4000] Training [38/39] Loss: 0.12858 +Epoch [3742/4000] Training [39/39] Loss: 0.00647 +Epoch [3742/4000] Training metric {'Train/mean dice_metric': 0.9962517619132996, 'Train/mean miou_metric': 0.992973268032074, 'Train/mean f1': 0.9968419671058655, 'Train/mean precision': 0.9963719844818115, 'Train/mean recall': 0.9973123073577881, 'Train/mean hd95_metric': 1.0910474061965942} +Epoch [3742/4000] Validation [1/10] Loss: 0.72432 focal_loss 0.63782 dice_loss 0.08650 +Epoch [3742/4000] Validation [2/10] Loss: 0.50278 focal_loss 0.40428 dice_loss 0.09849 +Epoch [3742/4000] Validation [3/10] Loss: 0.40054 focal_loss 0.28893 dice_loss 0.11161 +Epoch [3742/4000] Validation [4/10] Loss: 0.89870 focal_loss 0.33303 dice_loss 0.56567 +Epoch [3742/4000] Validation [5/10] Loss: 3.09038 focal_loss 2.41624 dice_loss 0.67414 +Epoch [3742/4000] Validation [6/10] Loss: 1.33785 focal_loss 0.62653 dice_loss 0.71132 +Epoch [3742/4000] Validation [7/10] Loss: 1.18769 focal_loss 0.53429 dice_loss 0.65340 +Epoch [3742/4000] Validation [8/10] Loss: 2.35665 focal_loss 1.74379 dice_loss 0.61286 +Epoch [3742/4000] Validation [9/10] Loss: 1.56589 focal_loss 1.02061 dice_loss 0.54528 +Epoch [3742/4000] Validation [10/10] Loss: 1.91954 focal_loss 1.18394 dice_loss 0.73560 +Epoch [3742/4000] Validation metric {'Val/mean dice_metric': 0.9515232443809509, 'Val/mean miou_metric': 0.9355790615081787, 'Val/mean f1': 0.948300302028656, 'Val/mean precision': 0.9435004591941833, 'Val/mean recall': 0.9531491994857788, 'Val/mean hd95_metric': 10.78453254699707} +Cheakpoint... +Epoch [3742/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515232443809509, 'Val/mean miou_metric': 0.9355790615081787, 'Val/mean f1': 0.948300302028656, 'Val/mean precision': 0.9435004591941833, 'Val/mean recall': 0.9531491994857788, 'Val/mean hd95_metric': 10.78453254699707} +Epoch [3743/4000] Training [1/39] Loss: 0.13054 +Epoch [3743/4000] Training [2/39] Loss: 0.00455 +Epoch [3743/4000] Training [3/39] Loss: 0.00464 +Epoch [3743/4000] Training [4/39] Loss: 0.00378 +Epoch [3743/4000] Training [5/39] Loss: 0.25157 +Epoch [3743/4000] Training [6/39] Loss: 0.00499 +Epoch [3743/4000] Training [7/39] Loss: 0.00614 +Epoch [3743/4000] Training [8/39] Loss: 0.00462 +Epoch [3743/4000] Training [9/39] Loss: 0.00500 +Epoch [3743/4000] Training [10/39] Loss: 0.00462 +Epoch [3743/4000] Training [11/39] Loss: 0.00332 +Epoch [3743/4000] Training [12/39] Loss: 0.00305 +Epoch [3743/4000] Training [13/39] Loss: 0.00528 +Epoch [3743/4000] Training [14/39] Loss: 0.00468 +Epoch [3743/4000] Training [15/39] Loss: 0.00620 +Epoch [3743/4000] Training [16/39] Loss: 0.00694 +Epoch [3743/4000] Training [17/39] Loss: 0.00507 +Epoch [3743/4000] Training [18/39] Loss: 0.00452 +Epoch [3743/4000] Training [19/39] Loss: 0.00519 +Epoch [3743/4000] Training [20/39] Loss: 0.00453 +Epoch [3743/4000] Training [21/39] Loss: 0.37897 +Epoch [3743/4000] Training [22/39] Loss: 0.00217 +Epoch [3743/4000] Training [23/39] Loss: 0.00339 +Epoch [3743/4000] Training [24/39] Loss: 0.12948 +Epoch [3743/4000] Training [25/39] Loss: 0.00451 +Epoch [3743/4000] Training [26/39] Loss: 0.00435 +Epoch [3743/4000] Training [27/39] Loss: 0.00544 +Epoch [3743/4000] Training [28/39] Loss: 0.00294 +Epoch [3743/4000] Training [29/39] Loss: 0.13039 +Epoch [3743/4000] Training [30/39] Loss: 0.13220 +Epoch [3743/4000] Training [31/39] Loss: 0.00509 +Epoch [3743/4000] Training [32/39] Loss: 0.13093 +Epoch [3743/4000] Training [33/39] Loss: 0.12900 +Epoch [3743/4000] Training [34/39] Loss: 0.00317 +Epoch [3743/4000] Training [35/39] Loss: 0.13220 +Epoch [3743/4000] Training [36/39] Loss: 0.00616 +Epoch [3743/4000] Training [37/39] Loss: 0.00579 +Epoch [3743/4000] Training [38/39] Loss: 0.00512 +Epoch [3743/4000] Training [39/39] Loss: 0.12968 +Epoch [3743/4000] Training metric {'Train/mean dice_metric': 0.9964439272880554, 'Train/mean miou_metric': 0.9933356642723083, 'Train/mean f1': 0.9969735145568848, 'Train/mean precision': 0.9965366125106812, 'Train/mean recall': 0.997410774230957, 'Train/mean hd95_metric': 0.9133813977241516} +Epoch [3743/4000] Validation [1/10] Loss: 0.73054 focal_loss 0.64409 dice_loss 0.08645 +Epoch [3743/4000] Validation [2/10] Loss: 0.50433 focal_loss 0.40512 dice_loss 0.09922 +Epoch [3743/4000] Validation [3/10] Loss: 0.40622 focal_loss 0.29421 dice_loss 0.11202 +Epoch [3743/4000] Validation [4/10] Loss: 0.89671 focal_loss 0.33164 dice_loss 0.56508 +Epoch [3743/4000] Validation [5/10] Loss: 3.12251 focal_loss 2.44842 dice_loss 0.67409 +Epoch [3743/4000] Validation [6/10] Loss: 1.33716 focal_loss 0.62484 dice_loss 0.71232 +Epoch [3743/4000] Validation [7/10] Loss: 1.19017 focal_loss 0.53647 dice_loss 0.65369 +Epoch [3743/4000] Validation [8/10] Loss: 2.35664 focal_loss 1.74455 dice_loss 0.61209 +Epoch [3743/4000] Validation [9/10] Loss: 1.58146 focal_loss 1.03686 dice_loss 0.54461 +Epoch [3743/4000] Validation [10/10] Loss: 1.92521 focal_loss 1.18905 dice_loss 0.73617 +Epoch [3743/4000] Validation metric {'Val/mean dice_metric': 0.9515983462333679, 'Val/mean miou_metric': 0.9358312487602234, 'Val/mean f1': 0.9483310580253601, 'Val/mean precision': 0.9432902336120605, 'Val/mean recall': 0.9534258246421814, 'Val/mean hd95_metric': 10.765049934387207} +Cheakpoint... +Epoch [3743/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515983462333679, 'Val/mean miou_metric': 0.9358312487602234, 'Val/mean f1': 0.9483310580253601, 'Val/mean precision': 0.9432902336120605, 'Val/mean recall': 0.9534258246421814, 'Val/mean hd95_metric': 10.765049934387207} +Epoch [3744/4000] Training [1/39] Loss: 0.00291 +Epoch [3744/4000] Training [2/39] Loss: 0.12783 +Epoch [3744/4000] Training [3/39] Loss: 0.12818 +Epoch [3744/4000] Training [4/39] Loss: 0.00580 +Epoch [3744/4000] Training [5/39] Loss: 0.00670 +Epoch [3744/4000] Training [6/39] Loss: 0.00364 +Epoch [3744/4000] Training [7/39] Loss: 0.00369 +Epoch [3744/4000] Training [8/39] Loss: 0.00360 +Epoch [3744/4000] Training [9/39] Loss: 0.00562 +Epoch [3744/4000] Training [10/39] Loss: 0.00294 +Epoch [3744/4000] Training [11/39] Loss: 0.00454 +Epoch [3744/4000] Training [12/39] Loss: 0.00559 +Epoch [3744/4000] Training [13/39] Loss: 0.00431 +Epoch [3744/4000] Training [14/39] Loss: 0.00428 +Epoch [3744/4000] Training [15/39] Loss: 0.12787 +Epoch [3744/4000] Training [16/39] Loss: 0.12825 +Epoch [3744/4000] Training [17/39] Loss: 0.12962 +Epoch [3744/4000] Training [18/39] Loss: 0.00258 +Epoch [3744/4000] Training [19/39] Loss: 0.00445 +Epoch [3744/4000] Training [20/39] Loss: 0.13030 +Epoch [3744/4000] Training [21/39] Loss: 0.00579 +Epoch [3744/4000] Training [22/39] Loss: 0.00458 +Epoch [3744/4000] Training [23/39] Loss: 0.13112 +Epoch [3744/4000] Training [24/39] Loss: 0.00398 +Epoch [3744/4000] Training [25/39] Loss: 0.00419 +Epoch [3744/4000] Training [26/39] Loss: 0.00523 +Epoch [3744/4000] Training [27/39] Loss: 0.12806 +Epoch [3744/4000] Training [28/39] Loss: 0.00545 +Epoch [3744/4000] Training [29/39] Loss: 0.13055 +Epoch [3744/4000] Training [30/39] Loss: 0.00394 +Epoch [3744/4000] Training [31/39] Loss: 0.12990 +Epoch [3744/4000] Training [32/39] Loss: 0.00753 +Epoch [3744/4000] Training [33/39] Loss: 0.00539 +Epoch [3744/4000] Training [34/39] Loss: 0.00580 +Epoch [3744/4000] Training [35/39] Loss: 0.00506 +Epoch [3744/4000] Training [36/39] Loss: 0.12891 +Epoch [3744/4000] Training [37/39] Loss: 0.00394 +Epoch [3744/4000] Training [38/39] Loss: 0.00828 +Epoch [3744/4000] Training [39/39] Loss: 0.00417 +Epoch [3744/4000] Training metric {'Train/mean dice_metric': 0.99638432264328, 'Train/mean miou_metric': 0.9932126998901367, 'Train/mean f1': 0.9969556927680969, 'Train/mean precision': 0.9965017437934875, 'Train/mean recall': 0.9974098801612854, 'Train/mean hd95_metric': 1.0185589790344238} +Epoch [3744/4000] Validation [1/10] Loss: 0.71679 focal_loss 0.63043 dice_loss 0.08636 +Epoch [3744/4000] Validation [2/10] Loss: 0.50270 focal_loss 0.40451 dice_loss 0.09818 +Epoch [3744/4000] Validation [3/10] Loss: 0.39103 focal_loss 0.27996 dice_loss 0.11106 +Epoch [3744/4000] Validation [4/10] Loss: 0.89948 focal_loss 0.33368 dice_loss 0.56580 +Epoch [3744/4000] Validation [5/10] Loss: 3.03409 focal_loss 2.36016 dice_loss 0.67392 +Epoch [3744/4000] Validation [6/10] Loss: 1.34583 focal_loss 0.63344 dice_loss 0.71239 +Epoch [3744/4000] Validation [7/10] Loss: 1.19150 focal_loss 0.53510 dice_loss 0.65640 +Epoch [3744/4000] Validation [8/10] Loss: 2.31254 focal_loss 1.70256 dice_loss 0.60998 +Epoch [3744/4000] Validation [9/10] Loss: 1.60005 focal_loss 1.05472 dice_loss 0.54532 +Epoch [3744/4000] Validation [10/10] Loss: 1.94158 focal_loss 1.20363 dice_loss 0.73795 +Epoch [3744/4000] Validation metric {'Val/mean dice_metric': 0.951573371887207, 'Val/mean miou_metric': 0.935665488243103, 'Val/mean f1': 0.9481715559959412, 'Val/mean precision': 0.9426757097244263, 'Val/mean recall': 0.9537318348884583, 'Val/mean hd95_metric': 10.772972106933594} +Cheakpoint... +Epoch [3744/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951573371887207, 'Val/mean miou_metric': 0.935665488243103, 'Val/mean f1': 0.9481715559959412, 'Val/mean precision': 0.9426757097244263, 'Val/mean recall': 0.9537318348884583, 'Val/mean hd95_metric': 10.772972106933594} +Epoch [3745/4000] Training [1/39] Loss: 0.00471 +Epoch [3745/4000] Training [2/39] Loss: 0.00556 +Epoch [3745/4000] Training [3/39] Loss: 0.00546 +Epoch [3745/4000] Training [4/39] Loss: 0.13116 +Epoch [3745/4000] Training [5/39] Loss: 0.00498 +Epoch [3745/4000] Training [6/39] Loss: 0.00612 +Epoch [3745/4000] Training [7/39] Loss: 0.00458 +Epoch [3745/4000] Training [8/39] Loss: 0.00347 +Epoch [3745/4000] Training [9/39] Loss: 0.00736 +Epoch [3745/4000] Training [10/39] Loss: 0.25342 +Epoch [3745/4000] Training [11/39] Loss: 0.12933 +Epoch [3745/4000] Training [12/39] Loss: 0.00524 +Epoch [3745/4000] Training [13/39] Loss: 0.00433 +Epoch [3745/4000] Training [14/39] Loss: 0.00317 +Epoch [3745/4000] Training [15/39] Loss: 0.00359 +Epoch [3745/4000] Training [16/39] Loss: 0.00535 +Epoch [3745/4000] Training [17/39] Loss: 0.00599 +Epoch [3745/4000] Training [18/39] Loss: 0.00620 +Epoch [3745/4000] Training [19/39] Loss: 0.25357 +Epoch [3745/4000] Training [20/39] Loss: 0.00382 +Epoch [3745/4000] Training [21/39] Loss: 0.00468 +Epoch [3745/4000] Training [22/39] Loss: 0.00478 +Epoch [3745/4000] Training [23/39] Loss: 0.00630 +Epoch [3745/4000] Training [24/39] Loss: 0.12890 +Epoch [3745/4000] Training [25/39] Loss: 0.00275 +Epoch [3745/4000] Training [26/39] Loss: 0.00335 +Epoch [3745/4000] Training [27/39] Loss: 0.00675 +Epoch [3745/4000] Training [28/39] Loss: 0.00413 +Epoch [3745/4000] Training [29/39] Loss: 0.00475 +Epoch [3745/4000] Training [30/39] Loss: 0.00422 +Epoch [3745/4000] Training [31/39] Loss: 0.00374 +Epoch [3745/4000] Training [32/39] Loss: 0.00534 +Epoch [3745/4000] Training [33/39] Loss: 0.00551 +Epoch [3745/4000] Training [34/39] Loss: 0.00322 +Epoch [3745/4000] Training [35/39] Loss: 0.00541 +Epoch [3745/4000] Training [36/39] Loss: 0.00525 +Epoch [3745/4000] Training [37/39] Loss: 0.12952 +Epoch [3745/4000] Training [38/39] Loss: 0.00700 +Epoch [3745/4000] Training [39/39] Loss: 0.00447 +Epoch [3745/4000] Training metric {'Train/mean dice_metric': 0.9964712262153625, 'Train/mean miou_metric': 0.9933901429176331, 'Train/mean f1': 0.9969604015350342, 'Train/mean precision': 0.9964767694473267, 'Train/mean recall': 0.9974445700645447, 'Train/mean hd95_metric': 0.9105533957481384} +Epoch [3745/4000] Validation [1/10] Loss: 0.71159 focal_loss 0.62559 dice_loss 0.08601 +Epoch [3745/4000] Validation [2/10] Loss: 0.50325 focal_loss 0.40457 dice_loss 0.09868 +Epoch [3745/4000] Validation [3/10] Loss: 0.39292 focal_loss 0.28211 dice_loss 0.11081 +Epoch [3745/4000] Validation [4/10] Loss: 0.89494 focal_loss 0.32894 dice_loss 0.56600 +Epoch [3745/4000] Validation [5/10] Loss: 3.07839 focal_loss 2.40434 dice_loss 0.67404 +Epoch [3745/4000] Validation [6/10] Loss: 1.33911 focal_loss 0.62787 dice_loss 0.71124 +Epoch [3745/4000] Validation [7/10] Loss: 1.19056 focal_loss 0.53474 dice_loss 0.65583 +Epoch [3745/4000] Validation [8/10] Loss: 2.32122 focal_loss 1.71129 dice_loss 0.60994 +Epoch [3745/4000] Validation [9/10] Loss: 1.57273 focal_loss 1.02792 dice_loss 0.54481 +Epoch [3745/4000] Validation [10/10] Loss: 1.92099 focal_loss 1.18396 dice_loss 0.73703 +Epoch [3745/4000] Validation metric {'Val/mean dice_metric': 0.9516968727111816, 'Val/mean miou_metric': 0.9359086155891418, 'Val/mean f1': 0.9484596848487854, 'Val/mean precision': 0.9430500268936157, 'Val/mean recall': 0.9539318084716797, 'Val/mean hd95_metric': 10.600783348083496} +Cheakpoint... +Epoch [3745/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516968727111816, 'Val/mean miou_metric': 0.9359086155891418, 'Val/mean f1': 0.9484596848487854, 'Val/mean precision': 0.9430500268936157, 'Val/mean recall': 0.9539318084716797, 'Val/mean hd95_metric': 10.600783348083496} +Epoch [3746/4000] Training [1/39] Loss: 0.00753 +Epoch [3746/4000] Training [2/39] Loss: 0.00487 +Epoch [3746/4000] Training [3/39] Loss: 0.12900 +Epoch [3746/4000] Training [4/39] Loss: 0.12876 +Epoch [3746/4000] Training [5/39] Loss: 0.12943 +Epoch [3746/4000] Training [6/39] Loss: 0.12923 +Epoch [3746/4000] Training [7/39] Loss: 0.00323 +Epoch [3746/4000] Training [8/39] Loss: 0.13035 +Epoch [3746/4000] Training [9/39] Loss: 0.00380 +Epoch [3746/4000] Training [10/39] Loss: 0.12856 +Epoch [3746/4000] Training [11/39] Loss: 0.37978 +Epoch [3746/4000] Training [12/39] Loss: 0.00390 +Epoch [3746/4000] Training [13/39] Loss: 0.00733 +Epoch [3746/4000] Training [14/39] Loss: 0.00719 +Epoch [3746/4000] Training [15/39] Loss: 0.00405 +Epoch [3746/4000] Training [16/39] Loss: 0.00513 +Epoch [3746/4000] Training [17/39] Loss: 0.00465 +Epoch [3746/4000] Training [18/39] Loss: 0.00308 +Epoch [3746/4000] Training [19/39] Loss: 0.00395 +Epoch [3746/4000] Training [20/39] Loss: 0.12979 +Epoch [3746/4000] Training [21/39] Loss: 0.00860 +Epoch [3746/4000] Training [22/39] Loss: 0.12792 +Epoch [3746/4000] Training [23/39] Loss: 0.00579 +Epoch [3746/4000] Training [24/39] Loss: 0.00554 +Epoch [3746/4000] Training [25/39] Loss: 0.00533 +Epoch [3746/4000] Training [26/39] Loss: 0.00660 +Epoch [3746/4000] Training [27/39] Loss: 0.00442 +Epoch [3746/4000] Training [28/39] Loss: 0.00657 +Epoch [3746/4000] Training [29/39] Loss: 0.00398 +Epoch [3746/4000] Training [30/39] Loss: 0.00594 +Epoch [3746/4000] Training [31/39] Loss: 0.01086 +Epoch [3746/4000] Training [32/39] Loss: 0.00346 +Epoch [3746/4000] Training [33/39] Loss: 0.12810 +Epoch [3746/4000] Training [34/39] Loss: 0.00429 +Epoch [3746/4000] Training [35/39] Loss: 0.00402 +Epoch [3746/4000] Training [36/39] Loss: 0.00402 +Epoch [3746/4000] Training [37/39] Loss: 0.00700 +Epoch [3746/4000] Training [38/39] Loss: 0.00509 +Epoch [3746/4000] Training [39/39] Loss: 0.00490 +Epoch [3746/4000] Training metric {'Train/mean dice_metric': 0.9961251616477966, 'Train/mean miou_metric': 0.9927276968955994, 'Train/mean f1': 0.996692955493927, 'Train/mean precision': 0.9962796568870544, 'Train/mean recall': 0.9971065521240234, 'Train/mean hd95_metric': 0.9814817905426025} +Epoch [3746/4000] Validation [1/10] Loss: 0.70815 focal_loss 0.62199 dice_loss 0.08616 +Epoch [3746/4000] Validation [2/10] Loss: 0.49486 focal_loss 0.39684 dice_loss 0.09803 +Epoch [3746/4000] Validation [3/10] Loss: 0.38808 focal_loss 0.27709 dice_loss 0.11099 +Epoch [3746/4000] Validation [4/10] Loss: 0.89737 focal_loss 0.33066 dice_loss 0.56671 +Epoch [3746/4000] Validation [5/10] Loss: 3.04506 focal_loss 2.37109 dice_loss 0.67397 +Epoch [3746/4000] Validation [6/10] Loss: 1.34358 focal_loss 0.63213 dice_loss 0.71145 +Epoch [3746/4000] Validation [7/10] Loss: 1.18456 focal_loss 0.52927 dice_loss 0.65529 +Epoch [3746/4000] Validation [8/10] Loss: 2.30104 focal_loss 1.69176 dice_loss 0.60928 +Epoch [3746/4000] Validation [9/10] Loss: 1.59168 focal_loss 1.04679 dice_loss 0.54489 +Epoch [3746/4000] Validation [10/10] Loss: 1.91493 focal_loss 1.17755 dice_loss 0.73737 +Epoch [3746/4000] Validation metric {'Val/mean dice_metric': 0.9514083862304688, 'Val/mean miou_metric': 0.9353616833686829, 'Val/mean f1': 0.9476871490478516, 'Val/mean precision': 0.9419993758201599, 'Val/mean recall': 0.9534440636634827, 'Val/mean hd95_metric': 10.779645919799805} +Cheakpoint... +Epoch [3746/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514083862304688, 'Val/mean miou_metric': 0.9353616833686829, 'Val/mean f1': 0.9476871490478516, 'Val/mean precision': 0.9419993758201599, 'Val/mean recall': 0.9534440636634827, 'Val/mean hd95_metric': 10.779645919799805} +Epoch [3747/4000] Training [1/39] Loss: 0.12792 +Epoch [3747/4000] Training [2/39] Loss: 0.00424 +Epoch [3747/4000] Training [3/39] Loss: 0.00277 +Epoch [3747/4000] Training [4/39] Loss: 0.00804 +Epoch [3747/4000] Training [5/39] Loss: 0.00443 +Epoch [3747/4000] Training [6/39] Loss: 0.00310 +Epoch [3747/4000] Training [7/39] Loss: 0.00354 +Epoch [3747/4000] Training [8/39] Loss: 0.00342 +Epoch [3747/4000] Training [9/39] Loss: 0.00708 +Epoch [3747/4000] Training [10/39] Loss: 0.00328 +Epoch [3747/4000] Training [11/39] Loss: 0.00485 +Epoch [3747/4000] Training [12/39] Loss: 0.25283 +Epoch [3747/4000] Training [13/39] Loss: 0.25344 +Epoch [3747/4000] Training [14/39] Loss: 0.00451 +Epoch [3747/4000] Training [15/39] Loss: 0.00510 +Epoch [3747/4000] Training [16/39] Loss: 0.12937 +Epoch [3747/4000] Training [17/39] Loss: 0.00436 +Epoch [3747/4000] Training [18/39] Loss: 0.00562 +Epoch [3747/4000] Training [19/39] Loss: 0.00323 +Epoch [3747/4000] Training [20/39] Loss: 0.00412 +Epoch [3747/4000] Training [21/39] Loss: 0.00301 +Epoch [3747/4000] Training [22/39] Loss: 0.00584 +Epoch [3747/4000] Training [23/39] Loss: 0.00342 +Epoch [3747/4000] Training [24/39] Loss: 0.00592 +Epoch [3747/4000] Training [25/39] Loss: 0.12824 +Epoch [3747/4000] Training [26/39] Loss: 0.00532 +Epoch [3747/4000] Training [27/39] Loss: 0.00403 +Epoch [3747/4000] Training [28/39] Loss: 0.00920 +Epoch [3747/4000] Training [29/39] Loss: 0.13056 +Epoch [3747/4000] Training [30/39] Loss: 0.00417 +Epoch [3747/4000] Training [31/39] Loss: 0.12770 +Epoch [3747/4000] Training [32/39] Loss: 0.12808 +Epoch [3747/4000] Training [33/39] Loss: 0.00317 +Epoch [3747/4000] Training [34/39] Loss: 0.00719 +Epoch [3747/4000] Training [35/39] Loss: 0.00631 +Epoch [3747/4000] Training [36/39] Loss: 0.00483 +Epoch [3747/4000] Training [37/39] Loss: 0.00531 +Epoch [3747/4000] Training [38/39] Loss: 0.00566 +Epoch [3747/4000] Training [39/39] Loss: 0.00410 +Epoch [3747/4000] Training metric {'Train/mean dice_metric': 0.996443510055542, 'Train/mean miou_metric': 0.9933302998542786, 'Train/mean f1': 0.9969485402107239, 'Train/mean precision': 0.9964300394058228, 'Train/mean recall': 0.9974676370620728, 'Train/mean hd95_metric': 0.9205495119094849} +Epoch [3747/4000] Validation [1/10] Loss: 0.71912 focal_loss 0.63289 dice_loss 0.08624 +Epoch [3747/4000] Validation [2/10] Loss: 0.50706 focal_loss 0.40776 dice_loss 0.09930 +Epoch [3747/4000] Validation [3/10] Loss: 0.39405 focal_loss 0.28280 dice_loss 0.11125 +Epoch [3747/4000] Validation [4/10] Loss: 0.90270 focal_loss 0.33646 dice_loss 0.56624 +Epoch [3747/4000] Validation [5/10] Loss: 3.07178 focal_loss 2.39769 dice_loss 0.67409 +Epoch [3747/4000] Validation [6/10] Loss: 1.35288 focal_loss 0.64199 dice_loss 0.71089 +Epoch [3747/4000] Validation [7/10] Loss: 1.19420 focal_loss 0.53839 dice_loss 0.65580 +Epoch [3747/4000] Validation [8/10] Loss: 2.36838 focal_loss 1.75635 dice_loss 0.61203 +Epoch [3747/4000] Validation [9/10] Loss: 1.59795 focal_loss 1.05296 dice_loss 0.54498 +Epoch [3747/4000] Validation [10/10] Loss: 1.93805 focal_loss 1.20085 dice_loss 0.73721 +Epoch [3747/4000] Validation metric {'Val/mean dice_metric': 0.9515997767448425, 'Val/mean miou_metric': 0.9357706904411316, 'Val/mean f1': 0.9485555291175842, 'Val/mean precision': 0.9432519674301147, 'Val/mean recall': 0.9539191126823425, 'Val/mean hd95_metric': 10.648516654968262} +Cheakpoint... +Epoch [3747/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515997767448425, 'Val/mean miou_metric': 0.9357706904411316, 'Val/mean f1': 0.9485555291175842, 'Val/mean precision': 0.9432519674301147, 'Val/mean recall': 0.9539191126823425, 'Val/mean hd95_metric': 10.648516654968262} +Epoch [3748/4000] Training [1/39] Loss: 0.00389 +Epoch [3748/4000] Training [2/39] Loss: 0.00506 +Epoch [3748/4000] Training [3/39] Loss: 0.00569 +Epoch [3748/4000] Training [4/39] Loss: 0.12861 +Epoch [3748/4000] Training [5/39] Loss: 0.00490 +Epoch [3748/4000] Training [6/39] Loss: 0.00431 +Epoch [3748/4000] Training [7/39] Loss: 0.00717 +Epoch [3748/4000] Training [8/39] Loss: 0.00452 +Epoch [3748/4000] Training [9/39] Loss: 0.12854 +Epoch [3748/4000] Training [10/39] Loss: 0.25466 +Epoch [3748/4000] Training [11/39] Loss: 0.00349 +Epoch [3748/4000] Training [12/39] Loss: 0.00801 +Epoch [3748/4000] Training [13/39] Loss: 0.00361 +Epoch [3748/4000] Training [14/39] Loss: 0.00379 +Epoch [3748/4000] Training [15/39] Loss: 0.00414 +Epoch [3748/4000] Training [16/39] Loss: 0.00305 +Epoch [3748/4000] Training [17/39] Loss: 0.00471 +Epoch [3748/4000] Training [18/39] Loss: 0.13123 +Epoch [3748/4000] Training [19/39] Loss: 0.00445 +Epoch [3748/4000] Training [20/39] Loss: 0.00298 +Epoch [3748/4000] Training [21/39] Loss: 0.12915 +Epoch [3748/4000] Training [22/39] Loss: 0.25241 +Epoch [3748/4000] Training [23/39] Loss: 0.12827 +Epoch [3748/4000] Training [24/39] Loss: 0.00656 +Epoch [3748/4000] Training [25/39] Loss: 0.00547 +Epoch [3748/4000] Training [26/39] Loss: 0.12876 +Epoch [3748/4000] Training [27/39] Loss: 0.00408 +Epoch [3748/4000] Training [28/39] Loss: 0.00828 +Epoch [3748/4000] Training [29/39] Loss: 0.00599 +Epoch [3748/4000] Training [30/39] Loss: 0.12988 +Epoch [3748/4000] Training [31/39] Loss: 0.12834 +Epoch [3748/4000] Training [32/39] Loss: 0.37798 +Epoch [3748/4000] Training [33/39] Loss: 0.12923 +Epoch [3748/4000] Training [34/39] Loss: 0.00554 +Epoch [3748/4000] Training [35/39] Loss: 0.00339 +Epoch [3748/4000] Training [36/39] Loss: 0.00475 +Epoch [3748/4000] Training [37/39] Loss: 0.00508 +Epoch [3748/4000] Training [38/39] Loss: 0.12830 +Epoch [3748/4000] Training [39/39] Loss: 0.00361 +Epoch [3748/4000] Training metric {'Train/mean dice_metric': 0.9963885545730591, 'Train/mean miou_metric': 0.9932228922843933, 'Train/mean f1': 0.9969119429588318, 'Train/mean precision': 0.9965270757675171, 'Train/mean recall': 0.9972973465919495, 'Train/mean hd95_metric': 0.9770953059196472} +Epoch [3748/4000] Validation [1/10] Loss: 0.71523 focal_loss 0.62854 dice_loss 0.08669 +Epoch [3748/4000] Validation [2/10] Loss: 0.50281 focal_loss 0.40410 dice_loss 0.09871 +Epoch [3748/4000] Validation [3/10] Loss: 0.38499 focal_loss 0.27411 dice_loss 0.11088 +Epoch [3748/4000] Validation [4/10] Loss: 0.90806 focal_loss 0.34099 dice_loss 0.56707 +Epoch [3748/4000] Validation [5/10] Loss: 3.04086 focal_loss 2.36685 dice_loss 0.67402 +Epoch [3748/4000] Validation [6/10] Loss: 1.35533 focal_loss 0.64427 dice_loss 0.71106 +Epoch [3748/4000] Validation [7/10] Loss: 1.19671 focal_loss 0.54034 dice_loss 0.65637 +Epoch [3748/4000] Validation [8/10] Loss: 2.29736 focal_loss 1.69137 dice_loss 0.60598 +Epoch [3748/4000] Validation [9/10] Loss: 1.59338 focal_loss 1.04840 dice_loss 0.54498 +Epoch [3748/4000] Validation [10/10] Loss: 1.95010 focal_loss 1.21214 dice_loss 0.73796 +Epoch [3748/4000] Validation metric {'Val/mean dice_metric': 0.9516472816467285, 'Val/mean miou_metric': 0.9358148574829102, 'Val/mean f1': 0.9484474062919617, 'Val/mean precision': 0.9425144195556641, 'Val/mean recall': 0.9544554948806763, 'Val/mean hd95_metric': 10.653434753417969} +Cheakpoint... +Epoch [3748/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516472816467285, 'Val/mean miou_metric': 0.9358148574829102, 'Val/mean f1': 0.9484474062919617, 'Val/mean precision': 0.9425144195556641, 'Val/mean recall': 0.9544554948806763, 'Val/mean hd95_metric': 10.653434753417969} +Epoch [3749/4000] Training [1/39] Loss: 0.00272 +Epoch [3749/4000] Training [2/39] Loss: 0.00552 +Epoch [3749/4000] Training [3/39] Loss: 0.25346 +Epoch [3749/4000] Training [4/39] Loss: 0.00268 +Epoch [3749/4000] Training [5/39] Loss: 0.12894 +Epoch [3749/4000] Training [6/39] Loss: 0.00477 +Epoch [3749/4000] Training [7/39] Loss: 0.00519 +Epoch [3749/4000] Training [8/39] Loss: 0.00377 +Epoch [3749/4000] Training [9/39] Loss: 0.00652 +Epoch [3749/4000] Training [10/39] Loss: 0.00503 +Epoch [3749/4000] Training [11/39] Loss: 0.00536 +Epoch [3749/4000] Training [12/39] Loss: 0.00554 +Epoch [3749/4000] Training [13/39] Loss: 0.00499 +Epoch [3749/4000] Training [14/39] Loss: 0.25319 +Epoch [3749/4000] Training [15/39] Loss: 0.12839 +Epoch [3749/4000] Training [16/39] Loss: 0.00651 +Epoch [3749/4000] Training [17/39] Loss: 0.00650 +Epoch [3749/4000] Training [18/39] Loss: 0.00436 +Epoch [3749/4000] Training [19/39] Loss: 0.00525 +Epoch [3749/4000] Training [20/39] Loss: 0.12984 +Epoch [3749/4000] Training [21/39] Loss: 0.00395 +Epoch [3749/4000] Training [22/39] Loss: 0.00521 +Epoch [3749/4000] Training [23/39] Loss: 0.00407 +Epoch [3749/4000] Training [24/39] Loss: 0.00632 +Epoch [3749/4000] Training [25/39] Loss: 0.00366 +Epoch [3749/4000] Training [26/39] Loss: 0.00615 +Epoch [3749/4000] Training [27/39] Loss: 0.00559 +Epoch [3749/4000] Training [28/39] Loss: 0.08447 +Epoch [3749/4000] Training [29/39] Loss: 0.00554 +Epoch [3749/4000] Training [30/39] Loss: 0.12771 +Epoch [3749/4000] Training [31/39] Loss: 0.00475 +Epoch [3749/4000] Training [32/39] Loss: 0.12839 +Epoch [3749/4000] Training [33/39] Loss: 0.25333 +Epoch [3749/4000] Training [34/39] Loss: 0.00475 +Epoch [3749/4000] Training [35/39] Loss: 0.00721 +Epoch [3749/4000] Training [36/39] Loss: 0.12844 +Epoch [3749/4000] Training [37/39] Loss: 0.00554 +Epoch [3749/4000] Training [38/39] Loss: 0.12776 +Epoch [3749/4000] Training [39/39] Loss: 0.00370 +Epoch [3749/4000] Training metric {'Train/mean dice_metric': 0.9964534640312195, 'Train/mean miou_metric': 0.9933510422706604, 'Train/mean f1': 0.9969085454940796, 'Train/mean precision': 0.9964603781700134, 'Train/mean recall': 0.9973569512367249, 'Train/mean hd95_metric': 1.014779806137085} +Epoch [3749/4000] Validation [1/10] Loss: 0.69192 focal_loss 0.60697 dice_loss 0.08495 +Epoch [3749/4000] Validation [2/10] Loss: 0.49929 focal_loss 0.40089 dice_loss 0.09840 +Epoch [3749/4000] Validation [3/10] Loss: 0.38659 focal_loss 0.27564 dice_loss 0.11096 +Epoch [3749/4000] Validation [4/10] Loss: 0.89905 focal_loss 0.33407 dice_loss 0.56498 +Epoch [3749/4000] Validation [5/10] Loss: 3.02612 focal_loss 2.35206 dice_loss 0.67406 +Epoch [3749/4000] Validation [6/10] Loss: 1.34197 focal_loss 0.63084 dice_loss 0.71113 +Epoch [3749/4000] Validation [7/10] Loss: 1.18636 focal_loss 0.53317 dice_loss 0.65320 +Epoch [3749/4000] Validation [8/10] Loss: 2.37665 focal_loss 1.76187 dice_loss 0.61478 +Epoch [3749/4000] Validation [9/10] Loss: 1.54790 focal_loss 1.00262 dice_loss 0.54528 +Epoch [3749/4000] Validation [10/10] Loss: 1.92550 focal_loss 1.18893 dice_loss 0.73656 +Epoch [3749/4000] Validation metric {'Val/mean dice_metric': 0.9517071843147278, 'Val/mean miou_metric': 0.9359309077262878, 'Val/mean f1': 0.948533833026886, 'Val/mean precision': 0.9438472390174866, 'Val/mean recall': 0.9532673358917236, 'Val/mean hd95_metric': 10.705045700073242} +Cheakpoint... +Epoch [3749/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9517071843147278, 'Val/mean miou_metric': 0.9359309077262878, 'Val/mean f1': 0.948533833026886, 'Val/mean precision': 0.9438472390174866, 'Val/mean recall': 0.9532673358917236, 'Val/mean hd95_metric': 10.705045700073242} +Epoch [3750/4000] Training [1/39] Loss: 0.00945 +Epoch [3750/4000] Training [2/39] Loss: 0.00610 +Epoch [3750/4000] Training [3/39] Loss: 0.13500 +Epoch [3750/4000] Training [4/39] Loss: 0.00556 +Epoch [3750/4000] Training [5/39] Loss: 0.00681 +Epoch [3750/4000] Training [6/39] Loss: 0.13041 +Epoch [3750/4000] Training [7/39] Loss: 0.00413 +Epoch [3750/4000] Training [8/39] Loss: 0.00351 +Epoch [3750/4000] Training [9/39] Loss: 0.12888 +Epoch [3750/4000] Training [10/39] Loss: 0.13004 +Epoch [3750/4000] Training [11/39] Loss: 0.12839 +Epoch [3750/4000] Training [12/39] Loss: 0.12931 +Epoch [3750/4000] Training [13/39] Loss: 0.13029 +Epoch [3750/4000] Training [14/39] Loss: 0.13045 +Epoch [3750/4000] Training [15/39] Loss: 0.12950 +Epoch [3750/4000] Training [16/39] Loss: 0.00564 +Epoch [3750/4000] Training [17/39] Loss: 0.00671 +Epoch [3750/4000] Training [18/39] Loss: 0.00320 +Epoch [3750/4000] Training [19/39] Loss: 0.00555 +Epoch [3750/4000] Training [20/39] Loss: 0.13324 +Epoch [3750/4000] Training [21/39] Loss: 0.00357 +Epoch [3750/4000] Training [22/39] Loss: 0.00470 +Epoch [3750/4000] Training [23/39] Loss: 0.00323 +Epoch [3750/4000] Training [24/39] Loss: 0.12866 +Epoch [3750/4000] Training [25/39] Loss: 0.00525 +Epoch [3750/4000] Training [26/39] Loss: 0.00368 +Epoch [3750/4000] Training [27/39] Loss: 0.00369 +Epoch [3750/4000] Training [28/39] Loss: 0.00783 +Epoch [3750/4000] Training [29/39] Loss: 0.00487 +Epoch [3750/4000] Training [30/39] Loss: 0.12874 +Epoch [3750/4000] Training [31/39] Loss: 0.12844 +Epoch [3750/4000] Training [32/39] Loss: 0.00542 +Epoch [3750/4000] Training [33/39] Loss: 0.00326 +Epoch [3750/4000] Training [34/39] Loss: 0.00425 +Epoch [3750/4000] Training [35/39] Loss: 0.00592 +Epoch [3750/4000] Training [36/39] Loss: 0.00532 +Epoch [3750/4000] Training [37/39] Loss: 0.13018 +Epoch [3750/4000] Training [38/39] Loss: 0.00343 +Epoch [3750/4000] Training [39/39] Loss: 0.00359 +Epoch [3750/4000] Training metric {'Train/mean dice_metric': 0.9963092803955078, 'Train/mean miou_metric': 0.9930667281150818, 'Train/mean f1': 0.9967992305755615, 'Train/mean precision': 0.9963074922561646, 'Train/mean recall': 0.9972915053367615, 'Train/mean hd95_metric': 0.9977765679359436} +Epoch [3750/4000] Validation [1/10] Loss: 0.70760 focal_loss 0.62179 dice_loss 0.08581 +Epoch [3750/4000] Validation [2/10] Loss: 0.50072 focal_loss 0.40250 dice_loss 0.09821 +Epoch [3750/4000] Validation [3/10] Loss: 0.39097 focal_loss 0.27981 dice_loss 0.11116 +Epoch [3750/4000] Validation [4/10] Loss: 0.90254 focal_loss 0.33694 dice_loss 0.56559 +Epoch [3750/4000] Validation [5/10] Loss: 3.05539 focal_loss 2.38146 dice_loss 0.67393 +Epoch [3750/4000] Validation [6/10] Loss: 1.34316 focal_loss 0.63270 dice_loss 0.71045 +Epoch [3750/4000] Validation [7/10] Loss: 1.18565 focal_loss 0.53076 dice_loss 0.65489 +Epoch [3750/4000] Validation [8/10] Loss: 2.33449 focal_loss 1.72377 dice_loss 0.61072 +Epoch [3750/4000] Validation [9/10] Loss: 1.57572 focal_loss 1.03083 dice_loss 0.54490 +Epoch [3750/4000] Validation [10/10] Loss: 1.92974 focal_loss 1.19261 dice_loss 0.73714 +Epoch [3750/4000] Validation metric {'Val/mean dice_metric': 0.9515286087989807, 'Val/mean miou_metric': 0.935590386390686, 'Val/mean f1': 0.9481654167175293, 'Val/mean precision': 0.9430021047592163, 'Val/mean recall': 0.9533857107162476, 'Val/mean hd95_metric': 10.755969047546387} +Cheakpoint... +Epoch [3750/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515286087989807, 'Val/mean miou_metric': 0.935590386390686, 'Val/mean f1': 0.9481654167175293, 'Val/mean precision': 0.9430021047592163, 'Val/mean recall': 0.9533857107162476, 'Val/mean hd95_metric': 10.755969047546387} +Epoch [3751/4000] Training [1/39] Loss: 0.00433 +Epoch [3751/4000] Training [2/39] Loss: 0.00421 +Epoch [3751/4000] Training [3/39] Loss: 0.00399 +Epoch [3751/4000] Training [4/39] Loss: 0.00491 +Epoch [3751/4000] Training [5/39] Loss: 0.00567 +Epoch [3751/4000] Training [6/39] Loss: 0.12923 +Epoch [3751/4000] Training [7/39] Loss: 0.00477 +Epoch [3751/4000] Training [8/39] Loss: 0.00551 +Epoch [3751/4000] Training [9/39] Loss: 0.13173 +Epoch [3751/4000] Training [10/39] Loss: 0.00564 +Epoch [3751/4000] Training [11/39] Loss: 0.00442 +Epoch [3751/4000] Training [12/39] Loss: 0.00366 +Epoch [3751/4000] Training [13/39] Loss: 0.00450 +Epoch [3751/4000] Training [14/39] Loss: 0.00407 +Epoch [3751/4000] Training [15/39] Loss: 0.00349 +Epoch [3751/4000] Training [16/39] Loss: 0.12941 +Epoch [3751/4000] Training [17/39] Loss: 0.00746 +Epoch [3751/4000] Training [18/39] Loss: 0.00381 +Epoch [3751/4000] Training [19/39] Loss: 0.00553 +Epoch [3751/4000] Training [20/39] Loss: 0.00598 +Epoch [3751/4000] Training [21/39] Loss: 0.00377 +Epoch [3751/4000] Training [22/39] Loss: 0.00574 +Epoch [3751/4000] Training [23/39] Loss: 0.00704 +Epoch [3751/4000] Training [24/39] Loss: 0.12960 +Epoch [3751/4000] Training [25/39] Loss: 0.00492 +Epoch [3751/4000] Training [26/39] Loss: 0.12788 +Epoch [3751/4000] Training [27/39] Loss: 0.12954 +Epoch [3751/4000] Training [28/39] Loss: 0.00472 +Epoch [3751/4000] Training [29/39] Loss: 0.00428 +Epoch [3751/4000] Training [30/39] Loss: 0.09333 +Epoch [3751/4000] Training [31/39] Loss: 0.00363 +Epoch [3751/4000] Training [32/39] Loss: 0.00847 +Epoch [3751/4000] Training [33/39] Loss: 0.12796 +Epoch [3751/4000] Training [34/39] Loss: 0.12969 +Epoch [3751/4000] Training [35/39] Loss: 0.00594 +Epoch [3751/4000] Training [36/39] Loss: 0.00365 +Epoch [3751/4000] Training [37/39] Loss: 0.12864 +Epoch [3751/4000] Training [38/39] Loss: 0.00521 +Epoch [3751/4000] Training [39/39] Loss: 0.12980 +Epoch [3751/4000] Training metric {'Train/mean dice_metric': 0.9963157773017883, 'Train/mean miou_metric': 0.9931020140647888, 'Train/mean f1': 0.9968479871749878, 'Train/mean precision': 0.9963197708129883, 'Train/mean recall': 0.9973767995834351, 'Train/mean hd95_metric': 0.9203119874000549} +Epoch [3751/4000] Validation [1/10] Loss: 0.71768 focal_loss 0.63146 dice_loss 0.08622 +Epoch [3751/4000] Validation [2/10] Loss: 0.50084 focal_loss 0.40132 dice_loss 0.09953 +Epoch [3751/4000] Validation [3/10] Loss: 0.39765 focal_loss 0.28581 dice_loss 0.11184 +Epoch [3751/4000] Validation [4/10] Loss: 0.89580 focal_loss 0.33069 dice_loss 0.56511 +Epoch [3751/4000] Validation [5/10] Loss: 3.07786 focal_loss 2.40394 dice_loss 0.67392 +Epoch [3751/4000] Validation [6/10] Loss: 1.33130 focal_loss 0.62119 dice_loss 0.71010 +Epoch [3751/4000] Validation [7/10] Loss: 1.17629 focal_loss 0.52085 dice_loss 0.65545 +Epoch [3751/4000] Validation [8/10] Loss: 2.34223 focal_loss 1.72864 dice_loss 0.61359 +Epoch [3751/4000] Validation [9/10] Loss: 1.58191 focal_loss 1.03739 dice_loss 0.54452 +Epoch [3751/4000] Validation [10/10] Loss: 1.90189 focal_loss 1.16526 dice_loss 0.73663 +Epoch [3751/4000] Validation metric {'Val/mean dice_metric': 0.9514819979667664, 'Val/mean miou_metric': 0.9355810284614563, 'Val/mean f1': 0.947790801525116, 'Val/mean precision': 0.9428948163986206, 'Val/mean recall': 0.9527378678321838, 'Val/mean hd95_metric': 10.719172477722168} +Cheakpoint... +Epoch [3751/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514819979667664, 'Val/mean miou_metric': 0.9355810284614563, 'Val/mean f1': 0.947790801525116, 'Val/mean precision': 0.9428948163986206, 'Val/mean recall': 0.9527378678321838, 'Val/mean hd95_metric': 10.719172477722168} +Epoch [3752/4000] Training [1/39] Loss: 0.00432 +Epoch [3752/4000] Training [2/39] Loss: 0.00465 +Epoch [3752/4000] Training [3/39] Loss: 0.12993 +Epoch [3752/4000] Training [4/39] Loss: 0.00476 +Epoch [3752/4000] Training [5/39] Loss: 0.00558 +Epoch [3752/4000] Training [6/39] Loss: 0.00538 +Epoch [3752/4000] Training [7/39] Loss: 0.12931 +Epoch [3752/4000] Training [8/39] Loss: 0.00443 +Epoch [3752/4000] Training [9/39] Loss: 0.00621 +Epoch [3752/4000] Training [10/39] Loss: 0.00341 +Epoch [3752/4000] Training [11/39] Loss: 0.00253 +Epoch [3752/4000] Training [12/39] Loss: 0.00502 +Epoch [3752/4000] Training [13/39] Loss: 0.00631 +Epoch [3752/4000] Training [14/39] Loss: 0.00589 +Epoch [3752/4000] Training [15/39] Loss: 0.00410 +Epoch [3752/4000] Training [16/39] Loss: 0.00352 +Epoch [3752/4000] Training [17/39] Loss: 0.00824 +Epoch [3752/4000] Training [18/39] Loss: 0.00723 +Epoch [3752/4000] Training [19/39] Loss: 0.00400 +Epoch [3752/4000] Training [20/39] Loss: 0.00547 +Epoch [3752/4000] Training [21/39] Loss: 0.00473 +Epoch [3752/4000] Training [22/39] Loss: 0.00215 +Epoch [3752/4000] Training [23/39] Loss: 0.00416 +Epoch [3752/4000] Training [24/39] Loss: 0.00505 +Epoch [3752/4000] Training [25/39] Loss: 0.00633 +Epoch [3752/4000] Training [26/39] Loss: 0.00431 +Epoch [3752/4000] Training [27/39] Loss: 0.00326 +Epoch [3752/4000] Training [28/39] Loss: 0.00454 +Epoch [3752/4000] Training [29/39] Loss: 0.00287 +Epoch [3752/4000] Training [30/39] Loss: 0.13058 +Epoch [3752/4000] Training [31/39] Loss: 0.00233 +Epoch [3752/4000] Training [32/39] Loss: 0.00623 +Epoch [3752/4000] Training [33/39] Loss: 0.13208 +Epoch [3752/4000] Training [34/39] Loss: 0.00506 +Epoch [3752/4000] Training [35/39] Loss: 0.13191 +Epoch [3752/4000] Training [36/39] Loss: 0.00734 +Epoch [3752/4000] Training [37/39] Loss: 0.12821 +Epoch [3752/4000] Training [38/39] Loss: 0.13104 +Epoch [3752/4000] Training [39/39] Loss: 0.00419 +Epoch [3752/4000] Training metric {'Train/mean dice_metric': 0.9962977170944214, 'Train/mean miou_metric': 0.9930509924888611, 'Train/mean f1': 0.9969736933708191, 'Train/mean precision': 0.9965081810951233, 'Train/mean recall': 0.9974396824836731, 'Train/mean hd95_metric': 0.9248302578926086} +Epoch [3752/4000] Validation [1/10] Loss: 0.73095 focal_loss 0.64327 dice_loss 0.08768 +Epoch [3752/4000] Validation [2/10] Loss: 0.49974 focal_loss 0.40352 dice_loss 0.09621 +Epoch [3752/4000] Validation [3/10] Loss: 0.38659 focal_loss 0.27586 dice_loss 0.11074 +Epoch [3752/4000] Validation [4/10] Loss: 0.90614 focal_loss 0.33917 dice_loss 0.56698 +Epoch [3752/4000] Validation [5/10] Loss: 3.07099 focal_loss 2.39736 dice_loss 0.67363 +Epoch [3752/4000] Validation [6/10] Loss: 1.35889 focal_loss 0.64664 dice_loss 0.71224 +Epoch [3752/4000] Validation [7/10] Loss: 1.19684 focal_loss 0.53977 dice_loss 0.65708 +Epoch [3752/4000] Validation [8/10] Loss: 2.25352 focal_loss 1.65215 dice_loss 0.60137 +Epoch [3752/4000] Validation [9/10] Loss: 1.63821 focal_loss 1.09253 dice_loss 0.54568 +Epoch [3752/4000] Validation [10/10] Loss: 1.95400 focal_loss 1.21584 dice_loss 0.73815 +Epoch [3752/4000] Validation metric {'Val/mean dice_metric': 0.9514693021774292, 'Val/mean miou_metric': 0.9355188012123108, 'Val/mean f1': 0.9479418396949768, 'Val/mean precision': 0.9415025115013123, 'Val/mean recall': 0.9544698596000671, 'Val/mean hd95_metric': 10.717534065246582} +Cheakpoint... +Epoch [3752/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514693021774292, 'Val/mean miou_metric': 0.9355188012123108, 'Val/mean f1': 0.9479418396949768, 'Val/mean precision': 0.9415025115013123, 'Val/mean recall': 0.9544698596000671, 'Val/mean hd95_metric': 10.717534065246582} +Epoch [3753/4000] Training [1/39] Loss: 0.00576 +Epoch [3753/4000] Training [2/39] Loss: 0.00630 +Epoch [3753/4000] Training [3/39] Loss: 0.12914 +Epoch [3753/4000] Training [4/39] Loss: 0.00358 +Epoch [3753/4000] Training [5/39] Loss: 0.00341 +Epoch [3753/4000] Training [6/39] Loss: 0.00504 +Epoch [3753/4000] Training [7/39] Loss: 0.00391 +Epoch [3753/4000] Training [8/39] Loss: 0.00488 +Epoch [3753/4000] Training [9/39] Loss: 0.00366 +Epoch [3753/4000] Training [10/39] Loss: 0.00410 +Epoch [3753/4000] Training [11/39] Loss: 0.13179 +Epoch [3753/4000] Training [12/39] Loss: 0.12908 +Epoch [3753/4000] Training [13/39] Loss: 0.12853 +Epoch [3753/4000] Training [14/39] Loss: 0.12988 +Epoch [3753/4000] Training [15/39] Loss: 0.12744 +Epoch [3753/4000] Training [16/39] Loss: 0.13366 +Epoch [3753/4000] Training [17/39] Loss: 0.13034 +Epoch [3753/4000] Training [18/39] Loss: 0.00404 +Epoch [3753/4000] Training [19/39] Loss: 0.00390 +Epoch [3753/4000] Training [20/39] Loss: 0.12860 +Epoch [3753/4000] Training [21/39] Loss: 0.00646 +Epoch [3753/4000] Training [22/39] Loss: 0.00517 +Epoch [3753/4000] Training [23/39] Loss: 0.12939 +Epoch [3753/4000] Training [24/39] Loss: 0.00529 +Epoch [3753/4000] Training [25/39] Loss: 0.04365 +Epoch [3753/4000] Training [26/39] Loss: 0.00461 +Epoch [3753/4000] Training [27/39] Loss: 0.00705 +Epoch [3753/4000] Training [28/39] Loss: 0.00366 +Epoch [3753/4000] Training [29/39] Loss: 0.00437 +Epoch [3753/4000] Training [30/39] Loss: 0.00616 +Epoch [3753/4000] Training [31/39] Loss: 0.00597 +Epoch [3753/4000] Training [32/39] Loss: 0.00510 +Epoch [3753/4000] Training [33/39] Loss: 0.00867 +Epoch [3753/4000] Training [34/39] Loss: 0.12818 +Epoch [3753/4000] Training [35/39] Loss: 0.12938 +Epoch [3753/4000] Training [36/39] Loss: 0.00573 +Epoch [3753/4000] Training [37/39] Loss: 0.00650 +Epoch [3753/4000] Training [38/39] Loss: 0.00660 +Epoch [3753/4000] Training [39/39] Loss: 0.25296 +Epoch [3753/4000] Training metric {'Train/mean dice_metric': 0.99531489610672, 'Train/mean miou_metric': 0.9919171929359436, 'Train/mean f1': 0.9967333674430847, 'Train/mean precision': 0.9962815642356873, 'Train/mean recall': 0.997185468673706, 'Train/mean hd95_metric': 0.9568425416946411} +Epoch [3753/4000] Validation [1/10] Loss: 0.73582 focal_loss 0.64887 dice_loss 0.08695 +Epoch [3753/4000] Validation [2/10] Loss: 0.50496 focal_loss 0.40774 dice_loss 0.09723 +Epoch [3753/4000] Validation [3/10] Loss: 0.39623 focal_loss 0.28513 dice_loss 0.11110 +Epoch [3753/4000] Validation [4/10] Loss: 0.90475 focal_loss 0.33846 dice_loss 0.56629 +Epoch [3753/4000] Validation [5/10] Loss: 3.10058 focal_loss 2.42686 dice_loss 0.67372 +Epoch [3753/4000] Validation [6/10] Loss: 1.35170 focal_loss 0.64102 dice_loss 0.71068 +Epoch [3753/4000] Validation [7/10] Loss: 1.20073 focal_loss 0.54359 dice_loss 0.65714 +Epoch [3753/4000] Validation [8/10] Loss: 2.32914 focal_loss 1.72082 dice_loss 0.60833 +Epoch [3753/4000] Validation [9/10] Loss: 1.62257 focal_loss 1.07658 dice_loss 0.54599 +Epoch [3753/4000] Validation [10/10] Loss: 1.94116 focal_loss 1.20421 dice_loss 0.73695 +Epoch [3753/4000] Validation metric {'Val/mean dice_metric': 0.9506713151931763, 'Val/mean miou_metric': 0.9346305727958679, 'Val/mean f1': 0.9477461576461792, 'Val/mean precision': 0.9421851634979248, 'Val/mean recall': 0.9533730745315552, 'Val/mean hd95_metric': 10.715986251831055} +Cheakpoint... +Epoch [3753/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506713151931763, 'Val/mean miou_metric': 0.9346305727958679, 'Val/mean f1': 0.9477461576461792, 'Val/mean precision': 0.9421851634979248, 'Val/mean recall': 0.9533730745315552, 'Val/mean hd95_metric': 10.715986251831055} +Epoch [3754/4000] Training [1/39] Loss: 0.00788 +Epoch [3754/4000] Training [2/39] Loss: 0.00601 +Epoch [3754/4000] Training [3/39] Loss: 0.00373 +Epoch [3754/4000] Training [4/39] Loss: 0.00289 +Epoch [3754/4000] Training [5/39] Loss: 0.12919 +Epoch [3754/4000] Training [6/39] Loss: 0.12804 +Epoch [3754/4000] Training [7/39] Loss: 0.00405 +Epoch [3754/4000] Training [8/39] Loss: 0.00480 +Epoch [3754/4000] Training [9/39] Loss: 0.00318 +Epoch [3754/4000] Training [10/39] Loss: 0.00766 +Epoch [3754/4000] Training [11/39] Loss: 0.00572 +Epoch [3754/4000] Training [12/39] Loss: 0.00563 +Epoch [3754/4000] Training [13/39] Loss: 0.00374 +Epoch [3754/4000] Training [14/39] Loss: 0.00281 +Epoch [3754/4000] Training [15/39] Loss: 0.00242 +Epoch [3754/4000] Training [16/39] Loss: 0.00226 +Epoch [3754/4000] Training [17/39] Loss: 0.00393 +Epoch [3754/4000] Training [18/39] Loss: 0.12964 +Epoch [3754/4000] Training [19/39] Loss: 0.00407 +Epoch [3754/4000] Training [20/39] Loss: 0.12740 +Epoch [3754/4000] Training [21/39] Loss: 0.00571 +Epoch [3754/4000] Training [22/39] Loss: 0.00550 +Epoch [3754/4000] Training [23/39] Loss: 0.00326 +Epoch [3754/4000] Training [24/39] Loss: 0.12849 +Epoch [3754/4000] Training [25/39] Loss: 0.12803 +Epoch [3754/4000] Training [26/39] Loss: 0.00480 +Epoch [3754/4000] Training [27/39] Loss: 0.00435 +Epoch [3754/4000] Training [28/39] Loss: 0.00627 +Epoch [3754/4000] Training [29/39] Loss: 0.13265 +Epoch [3754/4000] Training [30/39] Loss: 0.00840 +Epoch [3754/4000] Training [31/39] Loss: 0.00415 +Epoch [3754/4000] Training [32/39] Loss: 0.00441 +Epoch [3754/4000] Training [33/39] Loss: 0.00701 +Epoch [3754/4000] Training [34/39] Loss: 0.25266 +Epoch [3754/4000] Training [35/39] Loss: 0.16823 +Epoch [3754/4000] Training [36/39] Loss: 0.00492 +Epoch [3754/4000] Training [37/39] Loss: 0.00620 +Epoch [3754/4000] Training [38/39] Loss: 0.00583 +Epoch [3754/4000] Training [39/39] Loss: 0.00672 +Epoch [3754/4000] Training metric {'Train/mean dice_metric': 0.9953382015228271, 'Train/mean miou_metric': 0.9920010566711426, 'Train/mean f1': 0.9966488480567932, 'Train/mean precision': 0.996060311794281, 'Train/mean recall': 0.9972379803657532, 'Train/mean hd95_metric': 1.0344228744506836} +Epoch [3754/4000] Validation [1/10] Loss: 0.72760 focal_loss 0.64063 dice_loss 0.08697 +Epoch [3754/4000] Validation [2/10] Loss: 0.50591 focal_loss 0.40906 dice_loss 0.09685 +Epoch [3754/4000] Validation [3/10] Loss: 0.39023 focal_loss 0.27949 dice_loss 0.11074 +Epoch [3754/4000] Validation [4/10] Loss: 0.90635 focal_loss 0.33971 dice_loss 0.56664 +Epoch [3754/4000] Validation [5/10] Loss: 3.05867 focal_loss 2.38495 dice_loss 0.67373 +Epoch [3754/4000] Validation [6/10] Loss: 1.35672 focal_loss 0.64471 dice_loss 0.71201 +Epoch [3754/4000] Validation [7/10] Loss: 1.19959 focal_loss 0.54298 dice_loss 0.65661 +Epoch [3754/4000] Validation [8/10] Loss: 2.34457 focal_loss 1.73766 dice_loss 0.60690 +Epoch [3754/4000] Validation [9/10] Loss: 1.60555 focal_loss 1.06024 dice_loss 0.54531 +Epoch [3754/4000] Validation [10/10] Loss: 1.94746 focal_loss 1.21101 dice_loss 0.73644 +Epoch [3754/4000] Validation metric {'Val/mean dice_metric': 0.9507797956466675, 'Val/mean miou_metric': 0.9347779154777527, 'Val/mean f1': 0.9479508996009827, 'Val/mean precision': 0.9422362446784973, 'Val/mean recall': 0.9537351727485657, 'Val/mean hd95_metric': 10.811445236206055} +Cheakpoint... +Epoch [3754/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507797956466675, 'Val/mean miou_metric': 0.9347779154777527, 'Val/mean f1': 0.9479508996009827, 'Val/mean precision': 0.9422362446784973, 'Val/mean recall': 0.9537351727485657, 'Val/mean hd95_metric': 10.811445236206055} +Epoch [3755/4000] Training [1/39] Loss: 0.00383 +Epoch [3755/4000] Training [2/39] Loss: 0.00510 +Epoch [3755/4000] Training [3/39] Loss: 0.00448 +Epoch [3755/4000] Training [4/39] Loss: 0.00480 +Epoch [3755/4000] Training [5/39] Loss: 0.00513 +Epoch [3755/4000] Training [6/39] Loss: 0.00538 +Epoch [3755/4000] Training [7/39] Loss: 0.12968 +Epoch [3755/4000] Training [8/39] Loss: 0.00340 +Epoch [3755/4000] Training [9/39] Loss: 0.00334 +Epoch [3755/4000] Training [10/39] Loss: 0.16857 +Epoch [3755/4000] Training [11/39] Loss: 0.00338 +Epoch [3755/4000] Training [12/39] Loss: 0.25300 +Epoch [3755/4000] Training [13/39] Loss: 0.00572 +Epoch [3755/4000] Training [14/39] Loss: 0.00646 +Epoch [3755/4000] Training [15/39] Loss: 0.00426 +Epoch [3755/4000] Training [16/39] Loss: 0.13171 +Epoch [3755/4000] Training [17/39] Loss: 0.00713 +Epoch [3755/4000] Training [18/39] Loss: 0.25276 +Epoch [3755/4000] Training [19/39] Loss: 0.25335 +Epoch [3755/4000] Training [20/39] Loss: 0.12940 +Epoch [3755/4000] Training [21/39] Loss: 0.00513 +Epoch [3755/4000] Training [22/39] Loss: 0.00387 +Epoch [3755/4000] Training [23/39] Loss: 0.00875 +Epoch [3755/4000] Training [24/39] Loss: 0.12842 +Epoch [3755/4000] Training [25/39] Loss: 0.00376 +Epoch [3755/4000] Training [26/39] Loss: 0.00396 +Epoch [3755/4000] Training [27/39] Loss: 0.00363 +Epoch [3755/4000] Training [28/39] Loss: 0.00756 +Epoch [3755/4000] Training [29/39] Loss: 0.00606 +Epoch [3755/4000] Training [30/39] Loss: 0.12895 +Epoch [3755/4000] Training [31/39] Loss: 0.00405 +Epoch [3755/4000] Training [32/39] Loss: 0.00854 +Epoch [3755/4000] Training [33/39] Loss: 0.00733 +Epoch [3755/4000] Training [34/39] Loss: 0.00444 +Epoch [3755/4000] Training [35/39] Loss: 0.00963 +Epoch [3755/4000] Training [36/39] Loss: 0.13091 +Epoch [3755/4000] Training [37/39] Loss: 0.00530 +Epoch [3755/4000] Training [38/39] Loss: 0.00408 +Epoch [3755/4000] Training [39/39] Loss: 0.00441 +Epoch [3755/4000] Training metric {'Train/mean dice_metric': 0.9954051375389099, 'Train/mean miou_metric': 0.9920885562896729, 'Train/mean f1': 0.9968071579933167, 'Train/mean precision': 0.9963411092758179, 'Train/mean recall': 0.9972737431526184, 'Train/mean hd95_metric': 0.961998462677002} +Epoch [3755/4000] Validation [1/10] Loss: 0.72196 focal_loss 0.63481 dice_loss 0.08714 +Epoch [3755/4000] Validation [2/10] Loss: 0.50557 focal_loss 0.40722 dice_loss 0.09835 +Epoch [3755/4000] Validation [3/10] Loss: 0.39044 focal_loss 0.27927 dice_loss 0.11117 +Epoch [3755/4000] Validation [4/10] Loss: 0.90517 focal_loss 0.33879 dice_loss 0.56638 +Epoch [3755/4000] Validation [5/10] Loss: 3.04239 focal_loss 2.36874 dice_loss 0.67365 +Epoch [3755/4000] Validation [6/10] Loss: 1.35105 focal_loss 0.63824 dice_loss 0.71281 +Epoch [3755/4000] Validation [7/10] Loss: 1.19312 focal_loss 0.53595 dice_loss 0.65717 +Epoch [3755/4000] Validation [8/10] Loss: 2.32448 focal_loss 1.71700 dice_loss 0.60748 +Epoch [3755/4000] Validation [9/10] Loss: 1.58859 focal_loss 1.04340 dice_loss 0.54519 +Epoch [3755/4000] Validation [10/10] Loss: 1.93265 focal_loss 1.19638 dice_loss 0.73627 +Epoch [3755/4000] Validation metric {'Val/mean dice_metric': 0.9507594704627991, 'Val/mean miou_metric': 0.9347887635231018, 'Val/mean f1': 0.947941243648529, 'Val/mean precision': 0.9422762393951416, 'Val/mean recall': 0.9536746740341187, 'Val/mean hd95_metric': 10.759977340698242} +Cheakpoint... +Epoch [3755/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507594704627991, 'Val/mean miou_metric': 0.9347887635231018, 'Val/mean f1': 0.947941243648529, 'Val/mean precision': 0.9422762393951416, 'Val/mean recall': 0.9536746740341187, 'Val/mean hd95_metric': 10.759977340698242} +Epoch [3756/4000] Training [1/39] Loss: 0.00388 +Epoch [3756/4000] Training [2/39] Loss: 0.00494 +Epoch [3756/4000] Training [3/39] Loss: 0.00499 +Epoch [3756/4000] Training [4/39] Loss: 0.00531 +Epoch [3756/4000] Training [5/39] Loss: 0.00354 +Epoch [3756/4000] Training [6/39] Loss: 0.00393 +Epoch [3756/4000] Training [7/39] Loss: 0.00527 +Epoch [3756/4000] Training [8/39] Loss: 0.00362 +Epoch [3756/4000] Training [9/39] Loss: 0.00400 +Epoch [3756/4000] Training [10/39] Loss: 0.00349 +Epoch [3756/4000] Training [11/39] Loss: 0.12727 +Epoch [3756/4000] Training [12/39] Loss: 0.00661 +Epoch [3756/4000] Training [13/39] Loss: 0.00412 +Epoch [3756/4000] Training [14/39] Loss: 0.00694 +Epoch [3756/4000] Training [15/39] Loss: 0.00229 +Epoch [3756/4000] Training [16/39] Loss: 0.13184 +Epoch [3756/4000] Training [17/39] Loss: 0.12926 +Epoch [3756/4000] Training [18/39] Loss: 0.00535 +Epoch [3756/4000] Training [19/39] Loss: 0.00534 +Epoch [3756/4000] Training [20/39] Loss: 0.00461 +Epoch [3756/4000] Training [21/39] Loss: 0.00321 +Epoch [3756/4000] Training [22/39] Loss: 0.12826 +Epoch [3756/4000] Training [23/39] Loss: 0.00477 +Epoch [3756/4000] Training [24/39] Loss: 0.00852 +Epoch [3756/4000] Training [25/39] Loss: 0.00384 +Epoch [3756/4000] Training [26/39] Loss: 0.12937 +Epoch [3756/4000] Training [27/39] Loss: 0.00392 +Epoch [3756/4000] Training [28/39] Loss: 0.00566 +Epoch [3756/4000] Training [29/39] Loss: 0.12814 +Epoch [3756/4000] Training [30/39] Loss: 0.00433 +Epoch [3756/4000] Training [31/39] Loss: 0.12915 +Epoch [3756/4000] Training [32/39] Loss: 0.00319 +Epoch [3756/4000] Training [33/39] Loss: 0.12746 +Epoch [3756/4000] Training [34/39] Loss: 0.00415 +Epoch [3756/4000] Training [35/39] Loss: 0.00520 +Epoch [3756/4000] Training [36/39] Loss: 0.00436 +Epoch [3756/4000] Training [37/39] Loss: 0.00731 +Epoch [3756/4000] Training [38/39] Loss: 0.12824 +Epoch [3756/4000] Training [39/39] Loss: 0.12957 +Epoch [3756/4000] Training metric {'Train/mean dice_metric': 0.9965210556983948, 'Train/mean miou_metric': 0.9934883713722229, 'Train/mean f1': 0.9971002340316772, 'Train/mean precision': 0.9966264963150024, 'Train/mean recall': 0.9975745677947998, 'Train/mean hd95_metric': 0.97267085313797} +Epoch [3756/4000] Validation [1/10] Loss: 0.72294 focal_loss 0.63632 dice_loss 0.08662 +Epoch [3756/4000] Validation [2/10] Loss: 0.52188 focal_loss 0.42047 dice_loss 0.10141 +Epoch [3756/4000] Validation [3/10] Loss: 0.39777 focal_loss 0.28593 dice_loss 0.11184 +Epoch [3756/4000] Validation [4/10] Loss: 0.90612 focal_loss 0.34089 dice_loss 0.56523 +Epoch [3756/4000] Validation [5/10] Loss: 3.06816 focal_loss 2.39453 dice_loss 0.67363 +Epoch [3756/4000] Validation [6/10] Loss: 1.35842 focal_loss 0.64295 dice_loss 0.71547 +Epoch [3756/4000] Validation [7/10] Loss: 1.19665 focal_loss 0.54122 dice_loss 0.65544 +Epoch [3756/4000] Validation [8/10] Loss: 2.39940 focal_loss 1.78534 dice_loss 0.61405 +Epoch [3756/4000] Validation [9/10] Loss: 1.58518 focal_loss 1.04020 dice_loss 0.54498 +Epoch [3756/4000] Validation [10/10] Loss: 1.93226 focal_loss 1.19757 dice_loss 0.73469 +Epoch [3756/4000] Validation metric {'Val/mean dice_metric': 0.9515685439109802, 'Val/mean miou_metric': 0.9358760118484497, 'Val/mean f1': 0.9484210014343262, 'Val/mean precision': 0.9434518218040466, 'Val/mean recall': 0.9534427523612976, 'Val/mean hd95_metric': 10.776287078857422} +Cheakpoint... +Epoch [3756/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515685439109802, 'Val/mean miou_metric': 0.9358760118484497, 'Val/mean f1': 0.9484210014343262, 'Val/mean precision': 0.9434518218040466, 'Val/mean recall': 0.9534427523612976, 'Val/mean hd95_metric': 10.776287078857422} +Epoch [3757/4000] Training [1/39] Loss: 0.00366 +Epoch [3757/4000] Training [2/39] Loss: 0.00389 +Epoch [3757/4000] Training [3/39] Loss: 0.00537 +Epoch [3757/4000] Training [4/39] Loss: 0.00385 +Epoch [3757/4000] Training [5/39] Loss: 0.12850 +Epoch [3757/4000] Training [6/39] Loss: 0.00269 +Epoch [3757/4000] Training [7/39] Loss: 0.12791 +Epoch [3757/4000] Training [8/39] Loss: 0.00496 +Epoch [3757/4000] Training [9/39] Loss: 0.00357 +Epoch [3757/4000] Training [10/39] Loss: 0.00564 +Epoch [3757/4000] Training [11/39] Loss: 0.12842 +Epoch [3757/4000] Training [12/39] Loss: 0.25388 +Epoch [3757/4000] Training [13/39] Loss: 0.12896 +Epoch [3757/4000] Training [14/39] Loss: 0.00461 +Epoch [3757/4000] Training [15/39] Loss: 0.00556 +Epoch [3757/4000] Training [16/39] Loss: 0.00614 +Epoch [3757/4000] Training [17/39] Loss: 0.00301 +Epoch [3757/4000] Training [18/39] Loss: 0.12910 +Epoch [3757/4000] Training [19/39] Loss: 0.00375 +Epoch [3757/4000] Training [20/39] Loss: 0.13048 +Epoch [3757/4000] Training [21/39] Loss: 0.13026 +Epoch [3757/4000] Training [22/39] Loss: 0.13237 +Epoch [3757/4000] Training [23/39] Loss: 0.00476 +Epoch [3757/4000] Training [24/39] Loss: 0.12958 +Epoch [3757/4000] Training [25/39] Loss: 0.00447 +Epoch [3757/4000] Training [26/39] Loss: 0.00283 +Epoch [3757/4000] Training [27/39] Loss: 0.00793 +Epoch [3757/4000] Training [28/39] Loss: 0.00385 +Epoch [3757/4000] Training [29/39] Loss: 0.12895 +Epoch [3757/4000] Training [30/39] Loss: 0.00270 +Epoch [3757/4000] Training [31/39] Loss: 0.00417 +Epoch [3757/4000] Training [32/39] Loss: 0.00286 +Epoch [3757/4000] Training [33/39] Loss: 0.12857 +Epoch [3757/4000] Training [34/39] Loss: 0.00422 +Epoch [3757/4000] Training [35/39] Loss: 0.00404 +Epoch [3757/4000] Training [36/39] Loss: 0.12969 +Epoch [3757/4000] Training [37/39] Loss: 0.13250 +Epoch [3757/4000] Training [38/39] Loss: 0.13019 +Epoch [3757/4000] Training [39/39] Loss: 0.00372 +Epoch [3757/4000] Training metric {'Train/mean dice_metric': 0.9960746169090271, 'Train/mean miou_metric': 0.9929555058479309, 'Train/mean f1': 0.9967442154884338, 'Train/mean precision': 0.9959896802902222, 'Train/mean recall': 0.9974998831748962, 'Train/mean hd95_metric': 1.070828914642334} +Epoch [3757/4000] Validation [1/10] Loss: 0.73354 focal_loss 0.64641 dice_loss 0.08712 +Epoch [3757/4000] Validation [2/10] Loss: 0.51542 focal_loss 0.41441 dice_loss 0.10101 +Epoch [3757/4000] Validation [3/10] Loss: 0.40086 focal_loss 0.28880 dice_loss 0.11206 +Epoch [3757/4000] Validation [4/10] Loss: 0.89897 focal_loss 0.33408 dice_loss 0.56489 +Epoch [3757/4000] Validation [5/10] Loss: 3.08865 focal_loss 2.41492 dice_loss 0.67372 +Epoch [3757/4000] Validation [6/10] Loss: 1.34592 focal_loss 0.62864 dice_loss 0.71727 +Epoch [3757/4000] Validation [7/10] Loss: 1.18434 focal_loss 0.52884 dice_loss 0.65550 +Epoch [3757/4000] Validation [8/10] Loss: 2.38167 focal_loss 1.76764 dice_loss 0.61403 +Epoch [3757/4000] Validation [9/10] Loss: 1.57317 focal_loss 1.02893 dice_loss 0.54424 +Epoch [3757/4000] Validation [10/10] Loss: 1.90196 focal_loss 1.16813 dice_loss 0.73383 +Epoch [3757/4000] Validation metric {'Val/mean dice_metric': 0.9512057900428772, 'Val/mean miou_metric': 0.9354535937309265, 'Val/mean f1': 0.9482036828994751, 'Val/mean precision': 0.9431760311126709, 'Val/mean recall': 0.953285276889801, 'Val/mean hd95_metric': 10.85549545288086} +Cheakpoint... +Epoch [3757/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512057900428772, 'Val/mean miou_metric': 0.9354535937309265, 'Val/mean f1': 0.9482036828994751, 'Val/mean precision': 0.9431760311126709, 'Val/mean recall': 0.953285276889801, 'Val/mean hd95_metric': 10.85549545288086} +Epoch [3758/4000] Training [1/39] Loss: 0.01026 +Epoch [3758/4000] Training [2/39] Loss: 0.00424 +Epoch [3758/4000] Training [3/39] Loss: 0.00654 +Epoch [3758/4000] Training [4/39] Loss: 0.12992 +Epoch [3758/4000] Training [5/39] Loss: 0.13090 +Epoch [3758/4000] Training [6/39] Loss: 0.04336 +Epoch [3758/4000] Training [7/39] Loss: 0.12790 +Epoch [3758/4000] Training [8/39] Loss: 0.12842 +Epoch [3758/4000] Training [9/39] Loss: 0.00593 +Epoch [3758/4000] Training [10/39] Loss: 0.00415 +Epoch [3758/4000] Training [11/39] Loss: 0.00518 +Epoch [3758/4000] Training [12/39] Loss: 0.03977 +Epoch [3758/4000] Training [13/39] Loss: 0.12839 +Epoch [3758/4000] Training [14/39] Loss: 0.12819 +Epoch [3758/4000] Training [15/39] Loss: 0.13202 +Epoch [3758/4000] Training [16/39] Loss: 0.12834 +Epoch [3758/4000] Training [17/39] Loss: 0.00318 +Epoch [3758/4000] Training [18/39] Loss: 0.00351 +Epoch [3758/4000] Training [19/39] Loss: 0.00643 +Epoch [3758/4000] Training [20/39] Loss: 0.12911 +Epoch [3758/4000] Training [21/39] Loss: 0.00572 +Epoch [3758/4000] Training [22/39] Loss: 0.12816 +Epoch [3758/4000] Training [23/39] Loss: 0.00348 +Epoch [3758/4000] Training [24/39] Loss: 0.00436 +Epoch [3758/4000] Training [25/39] Loss: 0.00421 +Epoch [3758/4000] Training [26/39] Loss: 0.12823 +Epoch [3758/4000] Training [27/39] Loss: 0.00343 +Epoch [3758/4000] Training [28/39] Loss: 0.00428 +Epoch [3758/4000] Training [29/39] Loss: 0.00343 +Epoch [3758/4000] Training [30/39] Loss: 0.13003 +Epoch [3758/4000] Training [31/39] Loss: 0.00413 +Epoch [3758/4000] Training [32/39] Loss: 0.00512 +Epoch [3758/4000] Training [33/39] Loss: 0.00498 +Epoch [3758/4000] Training [34/39] Loss: 0.00452 +Epoch [3758/4000] Training [35/39] Loss: 0.00275 +Epoch [3758/4000] Training [36/39] Loss: 0.00633 +Epoch [3758/4000] Training [37/39] Loss: 0.00399 +Epoch [3758/4000] Training [38/39] Loss: 0.00379 +Epoch [3758/4000] Training [39/39] Loss: 0.00319 +Epoch [3758/4000] Training metric {'Train/mean dice_metric': 0.9960147738456726, 'Train/mean miou_metric': 0.9925251007080078, 'Train/mean f1': 0.9965927600860596, 'Train/mean precision': 0.9962417483329773, 'Train/mean recall': 0.9969441294670105, 'Train/mean hd95_metric': 1.0955421924591064} +Epoch [3758/4000] Validation [1/10] Loss: 0.71378 focal_loss 0.62692 dice_loss 0.08686 +Epoch [3758/4000] Validation [2/10] Loss: 0.50874 focal_loss 0.40730 dice_loss 0.10144 +Epoch [3758/4000] Validation [3/10] Loss: 0.39660 focal_loss 0.28423 dice_loss 0.11237 +Epoch [3758/4000] Validation [4/10] Loss: 0.88914 focal_loss 0.32502 dice_loss 0.56412 +Epoch [3758/4000] Validation [5/10] Loss: 3.03902 focal_loss 2.36521 dice_loss 0.67381 +Epoch [3758/4000] Validation [6/10] Loss: 1.33103 focal_loss 0.61560 dice_loss 0.71544 +Epoch [3758/4000] Validation [7/10] Loss: 1.17452 focal_loss 0.52120 dice_loss 0.65332 +Epoch [3758/4000] Validation [8/10] Loss: 2.33339 focal_loss 1.71920 dice_loss 0.61419 +Epoch [3758/4000] Validation [9/10] Loss: 1.55730 focal_loss 1.01283 dice_loss 0.54447 +Epoch [3758/4000] Validation [10/10] Loss: 1.87953 focal_loss 1.14553 dice_loss 0.73400 +Epoch [3758/4000] Validation metric {'Val/mean dice_metric': 0.951211154460907, 'Val/mean miou_metric': 0.9351828694343567, 'Val/mean f1': 0.9484432339668274, 'Val/mean precision': 0.943710207939148, 'Val/mean recall': 0.9532239437103271, 'Val/mean hd95_metric': 10.85085391998291} +Cheakpoint... +Epoch [3758/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951211154460907, 'Val/mean miou_metric': 0.9351828694343567, 'Val/mean f1': 0.9484432339668274, 'Val/mean precision': 0.943710207939148, 'Val/mean recall': 0.9532239437103271, 'Val/mean hd95_metric': 10.85085391998291} +Epoch [3759/4000] Training [1/39] Loss: 0.00468 +Epoch [3759/4000] Training [2/39] Loss: 0.12817 +Epoch [3759/4000] Training [3/39] Loss: 0.00449 +Epoch [3759/4000] Training [4/39] Loss: 0.00386 +Epoch [3759/4000] Training [5/39] Loss: 0.00694 +Epoch [3759/4000] Training [6/39] Loss: 0.00393 +Epoch [3759/4000] Training [7/39] Loss: 0.00467 +Epoch [3759/4000] Training [8/39] Loss: 0.00493 +Epoch [3759/4000] Training [9/39] Loss: 0.00372 +Epoch [3759/4000] Training [10/39] Loss: 0.12857 +Epoch [3759/4000] Training [11/39] Loss: 0.00406 +Epoch [3759/4000] Training [12/39] Loss: 0.00428 +Epoch [3759/4000] Training [13/39] Loss: 0.13036 +Epoch [3759/4000] Training [14/39] Loss: 0.00441 +Epoch [3759/4000] Training [15/39] Loss: 0.25478 +Epoch [3759/4000] Training [16/39] Loss: 0.12804 +Epoch [3759/4000] Training [17/39] Loss: 0.00294 +Epoch [3759/4000] Training [18/39] Loss: 0.00417 +Epoch [3759/4000] Training [19/39] Loss: 0.00355 +Epoch [3759/4000] Training [20/39] Loss: 0.00418 +Epoch [3759/4000] Training [21/39] Loss: 0.00585 +Epoch [3759/4000] Training [22/39] Loss: 0.00554 +Epoch [3759/4000] Training [23/39] Loss: 0.25352 +Epoch [3759/4000] Training [24/39] Loss: 0.12974 +Epoch [3759/4000] Training [25/39] Loss: 0.00877 +Epoch [3759/4000] Training [26/39] Loss: 0.00472 +Epoch [3759/4000] Training [27/39] Loss: 0.12970 +Epoch [3759/4000] Training [28/39] Loss: 0.25334 +Epoch [3759/4000] Training [29/39] Loss: 0.00287 +Epoch [3759/4000] Training [30/39] Loss: 0.00439 +Epoch [3759/4000] Training [31/39] Loss: 0.00588 +Epoch [3759/4000] Training [32/39] Loss: 0.00437 +Epoch [3759/4000] Training [33/39] Loss: 0.00774 +Epoch [3759/4000] Training [34/39] Loss: 0.00365 +Epoch [3759/4000] Training [35/39] Loss: 0.00554 +Epoch [3759/4000] Training [36/39] Loss: 0.00443 +Epoch [3759/4000] Training [37/39] Loss: 0.00406 +Epoch [3759/4000] Training [38/39] Loss: 0.00381 +Epoch [3759/4000] Training [39/39] Loss: 0.12835 +Epoch [3759/4000] Training metric {'Train/mean dice_metric': 0.9964972734451294, 'Train/mean miou_metric': 0.993432343006134, 'Train/mean f1': 0.9969826340675354, 'Train/mean precision': 0.9964998364448547, 'Train/mean recall': 0.9974656701087952, 'Train/mean hd95_metric': 0.9229898452758789} +Epoch [3759/4000] Validation [1/10] Loss: 0.72553 focal_loss 0.63858 dice_loss 0.08694 +Epoch [3759/4000] Validation [2/10] Loss: 0.51279 focal_loss 0.41166 dice_loss 0.10113 +Epoch [3759/4000] Validation [3/10] Loss: 0.39985 focal_loss 0.28791 dice_loss 0.11194 +Epoch [3759/4000] Validation [4/10] Loss: 0.89525 focal_loss 0.33045 dice_loss 0.56479 +Epoch [3759/4000] Validation [5/10] Loss: 3.08990 focal_loss 2.41619 dice_loss 0.67371 +Epoch [3759/4000] Validation [6/10] Loss: 1.33366 focal_loss 0.61943 dice_loss 0.71422 +Epoch [3759/4000] Validation [7/10] Loss: 1.17916 focal_loss 0.52523 dice_loss 0.65393 +Epoch [3759/4000] Validation [8/10] Loss: 2.35507 focal_loss 1.74123 dice_loss 0.61384 +Epoch [3759/4000] Validation [9/10] Loss: 1.58295 focal_loss 1.03864 dice_loss 0.54431 +Epoch [3759/4000] Validation [10/10] Loss: 1.89530 focal_loss 1.16025 dice_loss 0.73505 +Epoch [3759/4000] Validation metric {'Val/mean dice_metric': 0.9515436887741089, 'Val/mean miou_metric': 0.9358246922492981, 'Val/mean f1': 0.9482129216194153, 'Val/mean precision': 0.943321168422699, 'Val/mean recall': 0.953155517578125, 'Val/mean hd95_metric': 10.750622749328613} +Cheakpoint... +Epoch [3759/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515436887741089, 'Val/mean miou_metric': 0.9358246922492981, 'Val/mean f1': 0.9482129216194153, 'Val/mean precision': 0.943321168422699, 'Val/mean recall': 0.953155517578125, 'Val/mean hd95_metric': 10.750622749328613} +Epoch [3760/4000] Training [1/39] Loss: 0.00365 +Epoch [3760/4000] Training [2/39] Loss: 0.00264 +Epoch [3760/4000] Training [3/39] Loss: 0.00752 +Epoch [3760/4000] Training [4/39] Loss: 0.00392 +Epoch [3760/4000] Training [5/39] Loss: 0.00386 +Epoch [3760/4000] Training [6/39] Loss: 0.00456 +Epoch [3760/4000] Training [7/39] Loss: 0.12948 +Epoch [3760/4000] Training [8/39] Loss: 0.13254 +Epoch [3760/4000] Training [9/39] Loss: 0.13145 +Epoch [3760/4000] Training [10/39] Loss: 0.12997 +Epoch [3760/4000] Training [11/39] Loss: 0.00326 +Epoch [3760/4000] Training [12/39] Loss: 0.12732 +Epoch [3760/4000] Training [13/39] Loss: 0.25425 +Epoch [3760/4000] Training [14/39] Loss: 0.12838 +Epoch [3760/4000] Training [15/39] Loss: 0.00418 +Epoch [3760/4000] Training [16/39] Loss: 0.00890 +Epoch [3760/4000] Training [17/39] Loss: 0.12855 +Epoch [3760/4000] Training [18/39] Loss: 0.00334 +Epoch [3760/4000] Training [19/39] Loss: 0.00640 +Epoch [3760/4000] Training [20/39] Loss: 0.00486 +Epoch [3760/4000] Training [21/39] Loss: 0.12866 +Epoch [3760/4000] Training [22/39] Loss: 0.00480 +Epoch [3760/4000] Training [23/39] Loss: 0.00413 +Epoch [3760/4000] Training [24/39] Loss: 0.12959 +Epoch [3760/4000] Training [25/39] Loss: 0.09417 +Epoch [3760/4000] Training [26/39] Loss: 0.00423 +Epoch [3760/4000] Training [27/39] Loss: 0.00470 +Epoch [3760/4000] Training [28/39] Loss: 0.00391 +Epoch [3760/4000] Training [29/39] Loss: 0.00429 +Epoch [3760/4000] Training [30/39] Loss: 0.00550 +Epoch [3760/4000] Training [31/39] Loss: 0.00355 +Epoch [3760/4000] Training [32/39] Loss: 0.00636 +Epoch [3760/4000] Training [33/39] Loss: 0.00597 +Epoch [3760/4000] Training [34/39] Loss: 0.25244 +Epoch [3760/4000] Training [35/39] Loss: 0.12967 +Epoch [3760/4000] Training [36/39] Loss: 0.00433 +Epoch [3760/4000] Training [37/39] Loss: 0.00584 +Epoch [3760/4000] Training [38/39] Loss: 0.00690 +Epoch [3760/4000] Training [39/39] Loss: 0.15923 +Epoch [3760/4000] Training metric {'Train/mean dice_metric': 0.9962435960769653, 'Train/mean miou_metric': 0.9929572343826294, 'Train/mean f1': 0.9968119859695435, 'Train/mean precision': 0.9963687658309937, 'Train/mean recall': 0.9972557425498962, 'Train/mean hd95_metric': 1.020397663116455} +Epoch [3760/4000] Validation [1/10] Loss: 0.74754 focal_loss 0.65938 dice_loss 0.08816 +Epoch [3760/4000] Validation [2/10] Loss: 0.51794 focal_loss 0.41905 dice_loss 0.09889 +Epoch [3760/4000] Validation [3/10] Loss: 0.39791 focal_loss 0.28678 dice_loss 0.11113 +Epoch [3760/4000] Validation [4/10] Loss: 0.91052 focal_loss 0.34423 dice_loss 0.56630 +Epoch [3760/4000] Validation [5/10] Loss: 3.12629 focal_loss 2.45299 dice_loss 0.67331 +Epoch [3760/4000] Validation [6/10] Loss: 1.36562 focal_loss 0.65194 dice_loss 0.71368 +Epoch [3760/4000] Validation [7/10] Loss: 1.20661 focal_loss 0.54967 dice_loss 0.65694 +Epoch [3760/4000] Validation [8/10] Loss: 2.30389 focal_loss 1.70076 dice_loss 0.60312 +Epoch [3760/4000] Validation [9/10] Loss: 1.68157 focal_loss 1.13617 dice_loss 0.54540 +Epoch [3760/4000] Validation [10/10] Loss: 1.97457 focal_loss 1.23617 dice_loss 0.73839 +Epoch [3760/4000] Validation metric {'Val/mean dice_metric': 0.9513107538223267, 'Val/mean miou_metric': 0.9353217482566833, 'Val/mean f1': 0.9476671814918518, 'Val/mean precision': 0.9412379860877991, 'Val/mean recall': 0.9541848301887512, 'Val/mean hd95_metric': 10.913487434387207} +Cheakpoint... +Epoch [3760/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513107538223267, 'Val/mean miou_metric': 0.9353217482566833, 'Val/mean f1': 0.9476671814918518, 'Val/mean precision': 0.9412379860877991, 'Val/mean recall': 0.9541848301887512, 'Val/mean hd95_metric': 10.913487434387207} +Epoch [3761/4000] Training [1/39] Loss: 0.12884 +Epoch [3761/4000] Training [2/39] Loss: 0.25220 +Epoch [3761/4000] Training [3/39] Loss: 0.12865 +Epoch [3761/4000] Training [4/39] Loss: 0.00448 +Epoch [3761/4000] Training [5/39] Loss: 0.13152 +Epoch [3761/4000] Training [6/39] Loss: 0.00294 +Epoch [3761/4000] Training [7/39] Loss: 0.00349 +Epoch [3761/4000] Training [8/39] Loss: 0.13137 +Epoch [3761/4000] Training [9/39] Loss: 0.12784 +Epoch [3761/4000] Training [10/39] Loss: 0.00452 +Epoch [3761/4000] Training [11/39] Loss: 0.00603 +Epoch [3761/4000] Training [12/39] Loss: 0.00599 +Epoch [3761/4000] Training [13/39] Loss: 0.00503 +Epoch [3761/4000] Training [14/39] Loss: 0.12778 +Epoch [3761/4000] Training [15/39] Loss: 0.12869 +Epoch [3761/4000] Training [16/39] Loss: 0.12732 +Epoch [3761/4000] Training [17/39] Loss: 0.00705 +Epoch [3761/4000] Training [18/39] Loss: 0.00462 +Epoch [3761/4000] Training [19/39] Loss: 0.00491 +Epoch [3761/4000] Training [20/39] Loss: 0.00448 +Epoch [3761/4000] Training [21/39] Loss: 0.00429 +Epoch [3761/4000] Training [22/39] Loss: 0.00505 +Epoch [3761/4000] Training [23/39] Loss: 0.00371 +Epoch [3761/4000] Training [24/39] Loss: 0.12938 +Epoch [3761/4000] Training [25/39] Loss: 0.00431 +Epoch [3761/4000] Training [26/39] Loss: 0.12976 +Epoch [3761/4000] Training [27/39] Loss: 0.00420 +Epoch [3761/4000] Training [28/39] Loss: 0.12777 +Epoch [3761/4000] Training [29/39] Loss: 0.00625 +Epoch [3761/4000] Training [30/39] Loss: 0.00449 +Epoch [3761/4000] Training [31/39] Loss: 0.00432 +Epoch [3761/4000] Training [32/39] Loss: 0.00581 +Epoch [3761/4000] Training [33/39] Loss: 0.00567 +Epoch [3761/4000] Training [34/39] Loss: 0.12724 +Epoch [3761/4000] Training [35/39] Loss: 0.00608 +Epoch [3761/4000] Training [36/39] Loss: 0.00534 +Epoch [3761/4000] Training [37/39] Loss: 0.00571 +Epoch [3761/4000] Training [38/39] Loss: 0.00491 +Epoch [3761/4000] Training [39/39] Loss: 0.00611 +Epoch [3761/4000] Training metric {'Train/mean dice_metric': 0.9964713454246521, 'Train/mean miou_metric': 0.9934093952178955, 'Train/mean f1': 0.9969819784164429, 'Train/mean precision': 0.9964954853057861, 'Train/mean recall': 0.9974690675735474, 'Train/mean hd95_metric': 1.0465240478515625} +Epoch [3761/4000] Validation [1/10] Loss: 0.71804 focal_loss 0.63096 dice_loss 0.08708 +Epoch [3761/4000] Validation [2/10] Loss: 0.50307 focal_loss 0.40526 dice_loss 0.09780 +Epoch [3761/4000] Validation [3/10] Loss: 0.38689 focal_loss 0.27577 dice_loss 0.11112 +Epoch [3761/4000] Validation [4/10] Loss: 0.90043 focal_loss 0.33491 dice_loss 0.56552 +Epoch [3761/4000] Validation [5/10] Loss: 3.05593 focal_loss 2.38245 dice_loss 0.67348 +Epoch [3761/4000] Validation [6/10] Loss: 1.34891 focal_loss 0.63551 dice_loss 0.71340 +Epoch [3761/4000] Validation [7/10] Loss: 1.18736 focal_loss 0.53175 dice_loss 0.65561 +Epoch [3761/4000] Validation [8/10] Loss: 2.29673 focal_loss 1.68819 dice_loss 0.60854 +Epoch [3761/4000] Validation [9/10] Loss: 1.61481 focal_loss 1.06977 dice_loss 0.54504 +Epoch [3761/4000] Validation [10/10] Loss: 1.93262 focal_loss 1.19487 dice_loss 0.73775 +Epoch [3761/4000] Validation metric {'Val/mean dice_metric': 0.9514715671539307, 'Val/mean miou_metric': 0.9356761574745178, 'Val/mean f1': 0.9480236172676086, 'Val/mean precision': 0.9420854449272156, 'Val/mean recall': 0.9540371298789978, 'Val/mean hd95_metric': 10.754584312438965} +Cheakpoint... +Epoch [3761/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514715671539307, 'Val/mean miou_metric': 0.9356761574745178, 'Val/mean f1': 0.9480236172676086, 'Val/mean precision': 0.9420854449272156, 'Val/mean recall': 0.9540371298789978, 'Val/mean hd95_metric': 10.754584312438965} +Epoch [3762/4000] Training [1/39] Loss: 0.12848 +Epoch [3762/4000] Training [2/39] Loss: 0.00257 +Epoch [3762/4000] Training [3/39] Loss: 0.00673 +Epoch [3762/4000] Training [4/39] Loss: 0.00385 +Epoch [3762/4000] Training [5/39] Loss: 0.00495 +Epoch [3762/4000] Training [6/39] Loss: 0.00394 +Epoch [3762/4000] Training [7/39] Loss: 0.00576 +Epoch [3762/4000] Training [8/39] Loss: 0.00620 +Epoch [3762/4000] Training [9/39] Loss: 0.00537 +Epoch [3762/4000] Training [10/39] Loss: 0.00481 +Epoch [3762/4000] Training [11/39] Loss: 0.00572 +Epoch [3762/4000] Training [12/39] Loss: 0.00371 +Epoch [3762/4000] Training [13/39] Loss: 0.13238 +Epoch [3762/4000] Training [14/39] Loss: 0.12796 +Epoch [3762/4000] Training [15/39] Loss: 0.12817 +Epoch [3762/4000] Training [16/39] Loss: 0.00365 +Epoch [3762/4000] Training [17/39] Loss: 0.00365 +Epoch [3762/4000] Training [18/39] Loss: 0.00552 +Epoch [3762/4000] Training [19/39] Loss: 0.00342 +Epoch [3762/4000] Training [20/39] Loss: 0.12830 +Epoch [3762/4000] Training [21/39] Loss: 0.12939 +Epoch [3762/4000] Training [22/39] Loss: 0.00548 +Epoch [3762/4000] Training [23/39] Loss: 0.13076 +Epoch [3762/4000] Training [24/39] Loss: 0.00597 +Epoch [3762/4000] Training [25/39] Loss: 0.00866 +Epoch [3762/4000] Training [26/39] Loss: 0.00620 +Epoch [3762/4000] Training [27/39] Loss: 0.00655 +Epoch [3762/4000] Training [28/39] Loss: 0.12962 +Epoch [3762/4000] Training [29/39] Loss: 0.00485 +Epoch [3762/4000] Training [30/39] Loss: 0.00458 +Epoch [3762/4000] Training [31/39] Loss: 0.12988 +Epoch [3762/4000] Training [32/39] Loss: 0.00509 +Epoch [3762/4000] Training [33/39] Loss: 0.00467 +Epoch [3762/4000] Training [34/39] Loss: 0.00400 +Epoch [3762/4000] Training [35/39] Loss: 0.12727 +Epoch [3762/4000] Training [36/39] Loss: 0.13228 +Epoch [3762/4000] Training [37/39] Loss: 0.00519 +Epoch [3762/4000] Training [38/39] Loss: 0.00366 +Epoch [3762/4000] Training [39/39] Loss: 0.00304 +Epoch [3762/4000] Training metric {'Train/mean dice_metric': 0.9963953495025635, 'Train/mean miou_metric': 0.9932371377944946, 'Train/mean f1': 0.9968189001083374, 'Train/mean precision': 0.9963667392730713, 'Train/mean recall': 0.9972715973854065, 'Train/mean hd95_metric': 1.0289859771728516} +Epoch [3762/4000] Validation [1/10] Loss: 0.73169 focal_loss 0.64486 dice_loss 0.08683 +Epoch [3762/4000] Validation [2/10] Loss: 0.50980 focal_loss 0.41034 dice_loss 0.09946 +Epoch [3762/4000] Validation [3/10] Loss: 0.40106 focal_loss 0.28928 dice_loss 0.11178 +Epoch [3762/4000] Validation [4/10] Loss: 0.90006 focal_loss 0.33519 dice_loss 0.56488 +Epoch [3762/4000] Validation [5/10] Loss: 3.10133 focal_loss 2.42768 dice_loss 0.67365 +Epoch [3762/4000] Validation [6/10] Loss: 1.34078 focal_loss 0.62752 dice_loss 0.71326 +Epoch [3762/4000] Validation [7/10] Loss: 1.18769 focal_loss 0.53298 dice_loss 0.65471 +Epoch [3762/4000] Validation [8/10] Loss: 2.31782 focal_loss 1.70800 dice_loss 0.60983 +Epoch [3762/4000] Validation [9/10] Loss: 1.60542 focal_loss 1.06109 dice_loss 0.54433 +Epoch [3762/4000] Validation [10/10] Loss: 1.92085 focal_loss 1.18501 dice_loss 0.73583 +Epoch [3762/4000] Validation metric {'Val/mean dice_metric': 0.951451301574707, 'Val/mean miou_metric': 0.935612678527832, 'Val/mean f1': 0.9481642246246338, 'Val/mean precision': 0.9427505135536194, 'Val/mean recall': 0.953640341758728, 'Val/mean hd95_metric': 10.873026847839355} +Cheakpoint... +Epoch [3762/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951451301574707, 'Val/mean miou_metric': 0.935612678527832, 'Val/mean f1': 0.9481642246246338, 'Val/mean precision': 0.9427505135536194, 'Val/mean recall': 0.953640341758728, 'Val/mean hd95_metric': 10.873026847839355} +Epoch [3763/4000] Training [1/39] Loss: 0.00618 +Epoch [3763/4000] Training [2/39] Loss: 0.00442 +Epoch [3763/4000] Training [3/39] Loss: 0.00589 +Epoch [3763/4000] Training [4/39] Loss: 0.00604 +Epoch [3763/4000] Training [5/39] Loss: 0.16667 +Epoch [3763/4000] Training [6/39] Loss: 0.00482 +Epoch [3763/4000] Training [7/39] Loss: 0.00602 +Epoch [3763/4000] Training [8/39] Loss: 0.00423 +Epoch [3763/4000] Training [9/39] Loss: 0.00421 +Epoch [3763/4000] Training [10/39] Loss: 0.00547 +Epoch [3763/4000] Training [11/39] Loss: 0.00274 +Epoch [3763/4000] Training [12/39] Loss: 0.00367 +Epoch [3763/4000] Training [13/39] Loss: 0.09387 +Epoch [3763/4000] Training [14/39] Loss: 0.13152 +Epoch [3763/4000] Training [15/39] Loss: 0.00295 +Epoch [3763/4000] Training [16/39] Loss: 0.00385 +Epoch [3763/4000] Training [17/39] Loss: 0.00605 +Epoch [3763/4000] Training [18/39] Loss: 0.00564 +Epoch [3763/4000] Training [19/39] Loss: 0.13126 +Epoch [3763/4000] Training [20/39] Loss: 0.00335 +Epoch [3763/4000] Training [21/39] Loss: 0.13086 +Epoch [3763/4000] Training [22/39] Loss: 0.00647 +Epoch [3763/4000] Training [23/39] Loss: 0.00428 +Epoch [3763/4000] Training [24/39] Loss: 0.12954 +Epoch [3763/4000] Training [25/39] Loss: 0.12847 +Epoch [3763/4000] Training [26/39] Loss: 0.00546 +Epoch [3763/4000] Training [27/39] Loss: 0.00715 +Epoch [3763/4000] Training [28/39] Loss: 0.00419 +Epoch [3763/4000] Training [29/39] Loss: 0.00465 +Epoch [3763/4000] Training [30/39] Loss: 0.00387 +Epoch [3763/4000] Training [31/39] Loss: 0.00472 +Epoch [3763/4000] Training [32/39] Loss: 0.00453 +Epoch [3763/4000] Training [33/39] Loss: 0.13000 +Epoch [3763/4000] Training [34/39] Loss: 0.12938 +Epoch [3763/4000] Training [35/39] Loss: 0.25337 +Epoch [3763/4000] Training [36/39] Loss: 0.12817 +Epoch [3763/4000] Training [37/39] Loss: 0.00529 +Epoch [3763/4000] Training [38/39] Loss: 0.00324 +Epoch [3763/4000] Training [39/39] Loss: 0.00498 +Epoch [3763/4000] Training metric {'Train/mean dice_metric': 0.9963228702545166, 'Train/mean miou_metric': 0.9931111335754395, 'Train/mean f1': 0.9968599677085876, 'Train/mean precision': 0.9964094161987305, 'Train/mean recall': 0.9973108768463135, 'Train/mean hd95_metric': 0.9252773523330688} +Epoch [3763/4000] Validation [1/10] Loss: 0.74088 focal_loss 0.65414 dice_loss 0.08673 +Epoch [3763/4000] Validation [2/10] Loss: 0.51461 focal_loss 0.41420 dice_loss 0.10041 +Epoch [3763/4000] Validation [3/10] Loss: 0.41352 focal_loss 0.30128 dice_loss 0.11224 +Epoch [3763/4000] Validation [4/10] Loss: 0.89639 focal_loss 0.33194 dice_loss 0.56445 +Epoch [3763/4000] Validation [5/10] Loss: 3.15182 focal_loss 2.47787 dice_loss 0.67394 +Epoch [3763/4000] Validation [6/10] Loss: 1.33345 focal_loss 0.62014 dice_loss 0.71331 +Epoch [3763/4000] Validation [7/10] Loss: 1.18342 focal_loss 0.52789 dice_loss 0.65552 +Epoch [3763/4000] Validation [8/10] Loss: 2.37491 focal_loss 1.76147 dice_loss 0.61344 +Epoch [3763/4000] Validation [9/10] Loss: 1.59745 focal_loss 1.05235 dice_loss 0.54510 +Epoch [3763/4000] Validation [10/10] Loss: 1.89721 focal_loss 1.16317 dice_loss 0.73404 +Epoch [3763/4000] Validation metric {'Val/mean dice_metric': 0.951434314250946, 'Val/mean miou_metric': 0.9355707168579102, 'Val/mean f1': 0.9480186700820923, 'Val/mean precision': 0.9431436061859131, 'Val/mean recall': 0.9529443979263306, 'Val/mean hd95_metric': 10.69493293762207} +Cheakpoint... +Epoch [3763/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951434314250946, 'Val/mean miou_metric': 0.9355707168579102, 'Val/mean f1': 0.9480186700820923, 'Val/mean precision': 0.9431436061859131, 'Val/mean recall': 0.9529443979263306, 'Val/mean hd95_metric': 10.69493293762207} +Epoch [3764/4000] Training [1/39] Loss: 0.00459 +Epoch [3764/4000] Training [2/39] Loss: 0.00412 +Epoch [3764/4000] Training [3/39] Loss: 0.00527 +Epoch [3764/4000] Training [4/39] Loss: 0.00457 +Epoch [3764/4000] Training [5/39] Loss: 0.13003 +Epoch [3764/4000] Training [6/39] Loss: 0.00616 +Epoch [3764/4000] Training [7/39] Loss: 0.00506 +Epoch [3764/4000] Training [8/39] Loss: 0.00805 +Epoch [3764/4000] Training [9/39] Loss: 0.00520 +Epoch [3764/4000] Training [10/39] Loss: 0.12701 +Epoch [3764/4000] Training [11/39] Loss: 0.00378 +Epoch [3764/4000] Training [12/39] Loss: 0.00467 +Epoch [3764/4000] Training [13/39] Loss: 0.00457 +Epoch [3764/4000] Training [14/39] Loss: 0.00537 +Epoch [3764/4000] Training [15/39] Loss: 0.00539 +Epoch [3764/4000] Training [16/39] Loss: 0.00421 +Epoch [3764/4000] Training [17/39] Loss: 0.00687 +Epoch [3764/4000] Training [18/39] Loss: 0.00479 +Epoch [3764/4000] Training [19/39] Loss: 0.00565 +Epoch [3764/4000] Training [20/39] Loss: 0.00288 +Epoch [3764/4000] Training [21/39] Loss: 0.00633 +Epoch [3764/4000] Training [22/39] Loss: 0.00639 +Epoch [3764/4000] Training [23/39] Loss: 0.00633 +Epoch [3764/4000] Training [24/39] Loss: 0.00366 +Epoch [3764/4000] Training [25/39] Loss: 0.00355 +Epoch [3764/4000] Training [26/39] Loss: 0.12880 +Epoch [3764/4000] Training [27/39] Loss: 0.13201 +Epoch [3764/4000] Training [28/39] Loss: 0.00472 +Epoch [3764/4000] Training [29/39] Loss: 0.00525 +Epoch [3764/4000] Training [30/39] Loss: 0.00476 +Epoch [3764/4000] Training [31/39] Loss: 0.00298 +Epoch [3764/4000] Training [32/39] Loss: 0.12883 +Epoch [3764/4000] Training [33/39] Loss: 0.00450 +Epoch [3764/4000] Training [34/39] Loss: 0.00553 +Epoch [3764/4000] Training [35/39] Loss: 0.12940 +Epoch [3764/4000] Training [36/39] Loss: 0.00489 +Epoch [3764/4000] Training [37/39] Loss: 0.00626 +Epoch [3764/4000] Training [38/39] Loss: 0.00418 +Epoch [3764/4000] Training [39/39] Loss: 0.00556 +Epoch [3764/4000] Training metric {'Train/mean dice_metric': 0.9963454008102417, 'Train/mean miou_metric': 0.9931303858757019, 'Train/mean f1': 0.9970025420188904, 'Train/mean precision': 0.9965563416481018, 'Train/mean recall': 0.9974491000175476, 'Train/mean hd95_metric': 0.9294883608818054} +Epoch [3764/4000] Validation [1/10] Loss: 0.74536 focal_loss 0.65851 dice_loss 0.08685 +Epoch [3764/4000] Validation [2/10] Loss: 0.52050 focal_loss 0.42019 dice_loss 0.10031 +Epoch [3764/4000] Validation [3/10] Loss: 0.40749 focal_loss 0.29587 dice_loss 0.11162 +Epoch [3764/4000] Validation [4/10] Loss: 0.90328 focal_loss 0.33850 dice_loss 0.56478 +Epoch [3764/4000] Validation [5/10] Loss: 3.16452 focal_loss 2.49055 dice_loss 0.67397 +Epoch [3764/4000] Validation [6/10] Loss: 1.34697 focal_loss 0.63466 dice_loss 0.71231 +Epoch [3764/4000] Validation [7/10] Loss: 1.19342 focal_loss 0.53760 dice_loss 0.65581 +Epoch [3764/4000] Validation [8/10] Loss: 2.37181 focal_loss 1.76009 dice_loss 0.61173 +Epoch [3764/4000] Validation [9/10] Loss: 1.63353 focal_loss 1.08834 dice_loss 0.54519 +Epoch [3764/4000] Validation [10/10] Loss: 1.92636 focal_loss 1.19090 dice_loss 0.73546 +Epoch [3764/4000] Validation metric {'Val/mean dice_metric': 0.9514713287353516, 'Val/mean miou_metric': 0.9355952739715576, 'Val/mean f1': 0.9482334852218628, 'Val/mean precision': 0.943079948425293, 'Val/mean recall': 0.9534434676170349, 'Val/mean hd95_metric': 10.553716659545898} +Cheakpoint... +Epoch [3764/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514713287353516, 'Val/mean miou_metric': 0.9355952739715576, 'Val/mean f1': 0.9482334852218628, 'Val/mean precision': 0.943079948425293, 'Val/mean recall': 0.9534434676170349, 'Val/mean hd95_metric': 10.553716659545898} +Epoch [3765/4000] Training [1/39] Loss: 0.00460 +Epoch [3765/4000] Training [2/39] Loss: 0.00511 +Epoch [3765/4000] Training [3/39] Loss: 0.00635 +Epoch [3765/4000] Training [4/39] Loss: 0.00506 +Epoch [3765/4000] Training [5/39] Loss: 0.00442 +Epoch [3765/4000] Training [6/39] Loss: 0.13002 +Epoch [3765/4000] Training [7/39] Loss: 0.00488 +Epoch [3765/4000] Training [8/39] Loss: 0.00342 +Epoch [3765/4000] Training [9/39] Loss: 0.00480 +Epoch [3765/4000] Training [10/39] Loss: 0.12878 +Epoch [3765/4000] Training [11/39] Loss: 0.25525 +Epoch [3765/4000] Training [12/39] Loss: 0.00583 +Epoch [3765/4000] Training [13/39] Loss: 0.00467 +Epoch [3765/4000] Training [14/39] Loss: 0.13007 +Epoch [3765/4000] Training [15/39] Loss: 0.12912 +Epoch [3765/4000] Training [16/39] Loss: 0.00351 +Epoch [3765/4000] Training [17/39] Loss: 0.00645 +Epoch [3765/4000] Training [18/39] Loss: 0.00334 +Epoch [3765/4000] Training [19/39] Loss: 0.00398 +Epoch [3765/4000] Training [20/39] Loss: 0.00571 +Epoch [3765/4000] Training [21/39] Loss: 0.00611 +Epoch [3765/4000] Training [22/39] Loss: 0.37930 +Epoch [3765/4000] Training [23/39] Loss: 0.13073 +Epoch [3765/4000] Training [24/39] Loss: 0.13236 +Epoch [3765/4000] Training [25/39] Loss: 0.00405 +Epoch [3765/4000] Training [26/39] Loss: 0.00792 +Epoch [3765/4000] Training [27/39] Loss: 0.00360 +Epoch [3765/4000] Training [28/39] Loss: 0.00432 +Epoch [3765/4000] Training [29/39] Loss: 0.12901 +Epoch [3765/4000] Training [30/39] Loss: 0.00372 +Epoch [3765/4000] Training [31/39] Loss: 0.00683 +Epoch [3765/4000] Training [32/39] Loss: 0.00716 +Epoch [3765/4000] Training [33/39] Loss: 0.00430 +Epoch [3765/4000] Training [34/39] Loss: 0.08605 +Epoch [3765/4000] Training [35/39] Loss: 0.12978 +Epoch [3765/4000] Training [36/39] Loss: 0.00615 +Epoch [3765/4000] Training [37/39] Loss: 0.00550 +Epoch [3765/4000] Training [38/39] Loss: 0.00442 +Epoch [3765/4000] Training [39/39] Loss: 0.00492 +Epoch [3765/4000] Training metric {'Train/mean dice_metric': 0.9953358173370361, 'Train/mean miou_metric': 0.9919476509094238, 'Train/mean f1': 0.9966985583305359, 'Train/mean precision': 0.9962890148162842, 'Train/mean recall': 0.9971084594726562, 'Train/mean hd95_metric': 0.9610736966133118} +Epoch [3765/4000] Validation [1/10] Loss: 0.74358 focal_loss 0.65561 dice_loss 0.08797 +Epoch [3765/4000] Validation [2/10] Loss: 0.51331 focal_loss 0.41380 dice_loss 0.09952 +Epoch [3765/4000] Validation [3/10] Loss: 0.39520 focal_loss 0.28405 dice_loss 0.11115 +Epoch [3765/4000] Validation [4/10] Loss: 0.90375 focal_loss 0.33814 dice_loss 0.56561 +Epoch [3765/4000] Validation [5/10] Loss: 3.10241 focal_loss 2.42842 dice_loss 0.67398 +Epoch [3765/4000] Validation [6/10] Loss: 1.34960 focal_loss 0.63801 dice_loss 0.71159 +Epoch [3765/4000] Validation [7/10] Loss: 1.18993 focal_loss 0.53282 dice_loss 0.65711 +Epoch [3765/4000] Validation [8/10] Loss: 2.32873 focal_loss 1.71963 dice_loss 0.60911 +Epoch [3765/4000] Validation [9/10] Loss: 1.62626 focal_loss 1.08069 dice_loss 0.54557 +Epoch [3765/4000] Validation [10/10] Loss: 1.91429 focal_loss 1.17918 dice_loss 0.73512 +Epoch [3765/4000] Validation metric {'Val/mean dice_metric': 0.9505854249000549, 'Val/mean miou_metric': 0.934536874294281, 'Val/mean f1': 0.9477418661117554, 'Val/mean precision': 0.9419041872024536, 'Val/mean recall': 0.9536522626876831, 'Val/mean hd95_metric': 10.738153457641602} +Cheakpoint... +Epoch [3765/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505854249000549, 'Val/mean miou_metric': 0.934536874294281, 'Val/mean f1': 0.9477418661117554, 'Val/mean precision': 0.9419041872024536, 'Val/mean recall': 0.9536522626876831, 'Val/mean hd95_metric': 10.738153457641602} +Epoch [3766/4000] Training [1/39] Loss: 0.00292 +Epoch [3766/4000] Training [2/39] Loss: 0.00426 +Epoch [3766/4000] Training [3/39] Loss: 0.13045 +Epoch [3766/4000] Training [4/39] Loss: 0.12886 +Epoch [3766/4000] Training [5/39] Loss: 0.00968 +Epoch [3766/4000] Training [6/39] Loss: 0.00517 +Epoch [3766/4000] Training [7/39] Loss: 0.12911 +Epoch [3766/4000] Training [8/39] Loss: 0.00524 +Epoch [3766/4000] Training [9/39] Loss: 0.00340 +Epoch [3766/4000] Training [10/39] Loss: 0.00324 +Epoch [3766/4000] Training [11/39] Loss: 0.00536 +Epoch [3766/4000] Training [12/39] Loss: 0.00328 +Epoch [3766/4000] Training [13/39] Loss: 0.00403 +Epoch [3766/4000] Training [14/39] Loss: 0.12944 +Epoch [3766/4000] Training [15/39] Loss: 0.00747 +Epoch [3766/4000] Training [16/39] Loss: 0.00656 +Epoch [3766/4000] Training [17/39] Loss: 0.00319 +Epoch [3766/4000] Training [18/39] Loss: 0.00850 +Epoch [3766/4000] Training [19/39] Loss: 0.00476 +Epoch [3766/4000] Training [20/39] Loss: 0.25336 +Epoch [3766/4000] Training [21/39] Loss: 0.00385 +Epoch [3766/4000] Training [22/39] Loss: 0.00488 +Epoch [3766/4000] Training [23/39] Loss: 0.25416 +Epoch [3766/4000] Training [24/39] Loss: 0.00514 +Epoch [3766/4000] Training [25/39] Loss: 0.00378 +Epoch [3766/4000] Training [26/39] Loss: 0.00558 +Epoch [3766/4000] Training [27/39] Loss: 0.12876 +Epoch [3766/4000] Training [28/39] Loss: 0.00601 +Epoch [3766/4000] Training [29/39] Loss: 0.00509 +Epoch [3766/4000] Training [30/39] Loss: 0.00654 +Epoch [3766/4000] Training [31/39] Loss: 0.00609 +Epoch [3766/4000] Training [32/39] Loss: 0.00431 +Epoch [3766/4000] Training [33/39] Loss: 0.00497 +Epoch [3766/4000] Training [34/39] Loss: 0.00305 +Epoch [3766/4000] Training [35/39] Loss: 0.00499 +Epoch [3766/4000] Training [36/39] Loss: 0.00687 +Epoch [3766/4000] Training [37/39] Loss: 0.00526 +Epoch [3766/4000] Training [38/39] Loss: 0.00559 +Epoch [3766/4000] Training [39/39] Loss: 0.00487 +Epoch [3766/4000] Training metric {'Train/mean dice_metric': 0.9963048100471497, 'Train/mean miou_metric': 0.9930555820465088, 'Train/mean f1': 0.9969111084938049, 'Train/mean precision': 0.9963971376419067, 'Train/mean recall': 0.9974256753921509, 'Train/mean hd95_metric': 0.9419578909873962} +Epoch [3766/4000] Validation [1/10] Loss: 0.74429 focal_loss 0.65596 dice_loss 0.08834 +Epoch [3766/4000] Validation [2/10] Loss: 0.51343 focal_loss 0.41239 dice_loss 0.10104 +Epoch [3766/4000] Validation [3/10] Loss: 0.40079 focal_loss 0.28887 dice_loss 0.11192 +Epoch [3766/4000] Validation [4/10] Loss: 0.89908 focal_loss 0.33377 dice_loss 0.56531 +Epoch [3766/4000] Validation [5/10] Loss: 3.09069 focal_loss 2.41659 dice_loss 0.67410 +Epoch [3766/4000] Validation [6/10] Loss: 1.33753 focal_loss 0.62596 dice_loss 0.71157 +Epoch [3766/4000] Validation [7/10] Loss: 1.17820 focal_loss 0.52147 dice_loss 0.65673 +Epoch [3766/4000] Validation [8/10] Loss: 2.36710 focal_loss 1.75136 dice_loss 0.61575 +Epoch [3766/4000] Validation [9/10] Loss: 1.60684 focal_loss 1.06178 dice_loss 0.54506 +Epoch [3766/4000] Validation [10/10] Loss: 1.88771 focal_loss 1.15356 dice_loss 0.73415 +Epoch [3766/4000] Validation metric {'Val/mean dice_metric': 0.9513730406761169, 'Val/mean miou_metric': 0.9354215860366821, 'Val/mean f1': 0.948222279548645, 'Val/mean precision': 0.9431352019309998, 'Val/mean recall': 0.9533644914627075, 'Val/mean hd95_metric': 10.690289497375488} +Cheakpoint... +Epoch [3766/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513730406761169, 'Val/mean miou_metric': 0.9354215860366821, 'Val/mean f1': 0.948222279548645, 'Val/mean precision': 0.9431352019309998, 'Val/mean recall': 0.9533644914627075, 'Val/mean hd95_metric': 10.690289497375488} +Epoch [3767/4000] Training [1/39] Loss: 0.00450 +Epoch [3767/4000] Training [2/39] Loss: 0.00577 +Epoch [3767/4000] Training [3/39] Loss: 0.00503 +Epoch [3767/4000] Training [4/39] Loss: 0.00464 +Epoch [3767/4000] Training [5/39] Loss: 0.25385 +Epoch [3767/4000] Training [6/39] Loss: 0.00399 +Epoch [3767/4000] Training [7/39] Loss: 0.00795 +Epoch [3767/4000] Training [8/39] Loss: 0.00888 +Epoch [3767/4000] Training [9/39] Loss: 0.00604 +Epoch [3767/4000] Training [10/39] Loss: 0.00693 +Epoch [3767/4000] Training [11/39] Loss: 0.00663 +Epoch [3767/4000] Training [12/39] Loss: 0.00500 +Epoch [3767/4000] Training [13/39] Loss: 0.00639 +Epoch [3767/4000] Training [14/39] Loss: 0.00768 +Epoch [3767/4000] Training [15/39] Loss: 0.00395 +Epoch [3767/4000] Training [16/39] Loss: 0.00544 +Epoch [3767/4000] Training [17/39] Loss: 0.00675 +Epoch [3767/4000] Training [18/39] Loss: 0.00491 +Epoch [3767/4000] Training [19/39] Loss: 0.00632 +Epoch [3767/4000] Training [20/39] Loss: 0.00729 +Epoch [3767/4000] Training [21/39] Loss: 0.12801 +Epoch [3767/4000] Training [22/39] Loss: 0.12953 +Epoch [3767/4000] Training [23/39] Loss: 0.00401 +Epoch [3767/4000] Training [24/39] Loss: 0.00322 +Epoch [3767/4000] Training [25/39] Loss: 0.00406 +Epoch [3767/4000] Training [26/39] Loss: 0.00408 +Epoch [3767/4000] Training [27/39] Loss: 0.01060 +Epoch [3767/4000] Training [28/39] Loss: 0.00434 +Epoch [3767/4000] Training [29/39] Loss: 0.00468 +Epoch [3767/4000] Training [30/39] Loss: 0.00430 +Epoch [3767/4000] Training [31/39] Loss: 0.00429 +Epoch [3767/4000] Training [32/39] Loss: 0.00353 +Epoch [3767/4000] Training [33/39] Loss: 0.00716 +Epoch [3767/4000] Training [34/39] Loss: 0.00624 +Epoch [3767/4000] Training [35/39] Loss: 0.00541 +Epoch [3767/4000] Training [36/39] Loss: 0.12948 +Epoch [3767/4000] Training [37/39] Loss: 0.00486 +Epoch [3767/4000] Training [38/39] Loss: 0.00713 +Epoch [3767/4000] Training [39/39] Loss: 0.13024 +Epoch [3767/4000] Training metric {'Train/mean dice_metric': 0.9960899949073792, 'Train/mean miou_metric': 0.9926262497901917, 'Train/mean f1': 0.9966528415679932, 'Train/mean precision': 0.9962334632873535, 'Train/mean recall': 0.9970725774765015, 'Train/mean hd95_metric': 1.2161264419555664} +Epoch [3767/4000] Validation [1/10] Loss: 0.74577 focal_loss 0.65705 dice_loss 0.08872 +Epoch [3767/4000] Validation [2/10] Loss: 0.51002 focal_loss 0.41222 dice_loss 0.09780 +Epoch [3767/4000] Validation [3/10] Loss: 0.38814 focal_loss 0.27748 dice_loss 0.11065 +Epoch [3767/4000] Validation [4/10] Loss: 0.90250 focal_loss 0.33719 dice_loss 0.56532 +Epoch [3767/4000] Validation [5/10] Loss: 3.08863 focal_loss 2.41488 dice_loss 0.67375 +Epoch [3767/4000] Validation [6/10] Loss: 1.35692 focal_loss 0.64251 dice_loss 0.71441 +Epoch [3767/4000] Validation [7/10] Loss: 1.19019 focal_loss 0.53367 dice_loss 0.65651 +Epoch [3767/4000] Validation [8/10] Loss: 2.36541 focal_loss 1.75201 dice_loss 0.61340 +Epoch [3767/4000] Validation [9/10] Loss: 1.61392 focal_loss 1.06854 dice_loss 0.54538 +Epoch [3767/4000] Validation [10/10] Loss: 1.92553 focal_loss 1.19047 dice_loss 0.73506 +Epoch [3767/4000] Validation metric {'Val/mean dice_metric': 0.9511730670928955, 'Val/mean miou_metric': 0.9350587725639343, 'Val/mean f1': 0.9475050568580627, 'Val/mean precision': 0.9416331052780151, 'Val/mean recall': 0.9534505605697632, 'Val/mean hd95_metric': 11.055594444274902} +Cheakpoint... +Epoch [3767/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511730670928955, 'Val/mean miou_metric': 0.9350587725639343, 'Val/mean f1': 0.9475050568580627, 'Val/mean precision': 0.9416331052780151, 'Val/mean recall': 0.9534505605697632, 'Val/mean hd95_metric': 11.055594444274902} +Epoch [3768/4000] Training [1/39] Loss: 0.25327 +Epoch [3768/4000] Training [2/39] Loss: 0.00520 +Epoch [3768/4000] Training [3/39] Loss: 0.00486 +Epoch [3768/4000] Training [4/39] Loss: 0.00556 +Epoch [3768/4000] Training [5/39] Loss: 0.00544 +Epoch [3768/4000] Training [6/39] Loss: 0.00326 +Epoch [3768/4000] Training [7/39] Loss: 0.00263 +Epoch [3768/4000] Training [8/39] Loss: 0.12855 +Epoch [3768/4000] Training [9/39] Loss: 0.00649 +Epoch [3768/4000] Training [10/39] Loss: 0.00380 +Epoch [3768/4000] Training [11/39] Loss: 0.00516 +Epoch [3768/4000] Training [12/39] Loss: 0.00522 +Epoch [3768/4000] Training [13/39] Loss: 0.00510 +Epoch [3768/4000] Training [14/39] Loss: 0.13031 +Epoch [3768/4000] Training [15/39] Loss: 0.00757 +Epoch [3768/4000] Training [16/39] Loss: 0.12878 +Epoch [3768/4000] Training [17/39] Loss: 0.00533 +Epoch [3768/4000] Training [18/39] Loss: 0.00886 +Epoch [3768/4000] Training [19/39] Loss: 0.00449 +Epoch [3768/4000] Training [20/39] Loss: 0.00373 +Epoch [3768/4000] Training [21/39] Loss: 0.12873 +Epoch [3768/4000] Training [22/39] Loss: 0.00414 +Epoch [3768/4000] Training [23/39] Loss: 0.00538 +Epoch [3768/4000] Training [24/39] Loss: 0.00360 +Epoch [3768/4000] Training [25/39] Loss: 0.12991 +Epoch [3768/4000] Training [26/39] Loss: 0.00218 +Epoch [3768/4000] Training [27/39] Loss: 0.00490 +Epoch [3768/4000] Training [28/39] Loss: 0.12771 +Epoch [3768/4000] Training [29/39] Loss: 0.13038 +Epoch [3768/4000] Training [30/39] Loss: 0.00608 +Epoch [3768/4000] Training [31/39] Loss: 0.00475 +Epoch [3768/4000] Training [32/39] Loss: 0.00455 +Epoch [3768/4000] Training [33/39] Loss: 0.12826 +Epoch [3768/4000] Training [34/39] Loss: 0.13006 +Epoch [3768/4000] Training [35/39] Loss: 0.00310 +Epoch [3768/4000] Training [36/39] Loss: 0.13009 +Epoch [3768/4000] Training [37/39] Loss: 0.00659 +Epoch [3768/4000] Training [38/39] Loss: 0.25300 +Epoch [3768/4000] Training [39/39] Loss: 0.00655 +Epoch [3768/4000] Training metric {'Train/mean dice_metric': 0.9964635968208313, 'Train/mean miou_metric': 0.9933776259422302, 'Train/mean f1': 0.9969448447227478, 'Train/mean precision': 0.9965124130249023, 'Train/mean recall': 0.9973776936531067, 'Train/mean hd95_metric': 0.9944814443588257} +Epoch [3768/4000] Validation [1/10] Loss: 0.75358 focal_loss 0.66521 dice_loss 0.08837 +Epoch [3768/4000] Validation [2/10] Loss: 0.51808 focal_loss 0.41837 dice_loss 0.09972 +Epoch [3768/4000] Validation [3/10] Loss: 0.39926 focal_loss 0.28803 dice_loss 0.11123 +Epoch [3768/4000] Validation [4/10] Loss: 0.89906 focal_loss 0.33484 dice_loss 0.56421 +Epoch [3768/4000] Validation [5/10] Loss: 3.11216 focal_loss 2.43825 dice_loss 0.67391 +Epoch [3768/4000] Validation [6/10] Loss: 1.35498 focal_loss 0.63967 dice_loss 0.71531 +Epoch [3768/4000] Validation [7/10] Loss: 1.18828 focal_loss 0.53363 dice_loss 0.65465 +Epoch [3768/4000] Validation [8/10] Loss: 2.46517 focal_loss 1.84429 dice_loss 0.62089 +Epoch [3768/4000] Validation [9/10] Loss: 1.60131 focal_loss 1.05633 dice_loss 0.54498 +Epoch [3768/4000] Validation [10/10] Loss: 1.91149 focal_loss 1.17874 dice_loss 0.73275 +Epoch [3768/4000] Validation metric {'Val/mean dice_metric': 0.9514006972312927, 'Val/mean miou_metric': 0.935596764087677, 'Val/mean f1': 0.9479687809944153, 'Val/mean precision': 0.943160355091095, 'Val/mean recall': 0.9528264999389648, 'Val/mean hd95_metric': 10.811835289001465} +Cheakpoint... +Epoch [3768/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514006972312927, 'Val/mean miou_metric': 0.935596764087677, 'Val/mean f1': 0.9479687809944153, 'Val/mean precision': 0.943160355091095, 'Val/mean recall': 0.9528264999389648, 'Val/mean hd95_metric': 10.811835289001465} +Epoch [3769/4000] Training [1/39] Loss: 0.25729 +Epoch [3769/4000] Training [2/39] Loss: 0.12909 +Epoch [3769/4000] Training [3/39] Loss: 0.00539 +Epoch [3769/4000] Training [4/39] Loss: 0.00373 +Epoch [3769/4000] Training [5/39] Loss: 0.00441 +Epoch [3769/4000] Training [6/39] Loss: 0.00251 +Epoch [3769/4000] Training [7/39] Loss: 0.00472 +Epoch [3769/4000] Training [8/39] Loss: 0.00666 +Epoch [3769/4000] Training [9/39] Loss: 0.00714 +Epoch [3769/4000] Training [10/39] Loss: 0.00349 +Epoch [3769/4000] Training [11/39] Loss: 0.12968 +Epoch [3769/4000] Training [12/39] Loss: 0.00476 +Epoch [3769/4000] Training [13/39] Loss: 0.00302 +Epoch [3769/4000] Training [14/39] Loss: 0.12892 +Epoch [3769/4000] Training [15/39] Loss: 0.00445 +Epoch [3769/4000] Training [16/39] Loss: 0.13037 +Epoch [3769/4000] Training [17/39] Loss: 0.00465 +Epoch [3769/4000] Training [18/39] Loss: 0.00349 +Epoch [3769/4000] Training [19/39] Loss: 0.00390 +Epoch [3769/4000] Training [20/39] Loss: 0.00460 +Epoch [3769/4000] Training [21/39] Loss: 0.00498 +Epoch [3769/4000] Training [22/39] Loss: 0.00349 +Epoch [3769/4000] Training [23/39] Loss: 0.00384 +Epoch [3769/4000] Training [24/39] Loss: 0.12858 +Epoch [3769/4000] Training [25/39] Loss: 0.00539 +Epoch [3769/4000] Training [26/39] Loss: 0.00385 +Epoch [3769/4000] Training [27/39] Loss: 0.21100 +Epoch [3769/4000] Training [28/39] Loss: 0.00606 +Epoch [3769/4000] Training [29/39] Loss: 0.13015 +Epoch [3769/4000] Training [30/39] Loss: 0.25384 +Epoch [3769/4000] Training [31/39] Loss: 0.00422 +Epoch [3769/4000] Training [32/39] Loss: 0.13070 +Epoch [3769/4000] Training [33/39] Loss: 0.12832 +Epoch [3769/4000] Training [34/39] Loss: 0.00303 +Epoch [3769/4000] Training [35/39] Loss: 0.00413 +Epoch [3769/4000] Training [36/39] Loss: 0.00533 +Epoch [3769/4000] Training [37/39] Loss: 0.00389 +Epoch [3769/4000] Training [38/39] Loss: 0.13033 +Epoch [3769/4000] Training [39/39] Loss: 0.00387 +Epoch [3769/4000] Training metric {'Train/mean dice_metric': 0.9965115785598755, 'Train/mean miou_metric': 0.9934625625610352, 'Train/mean f1': 0.9970874786376953, 'Train/mean precision': 0.9966475367546082, 'Train/mean recall': 0.9975278377532959, 'Train/mean hd95_metric': 0.9223035573959351} +Epoch [3769/4000] Validation [1/10] Loss: 0.73552 focal_loss 0.64793 dice_loss 0.08759 +Epoch [3769/4000] Validation [2/10] Loss: 0.51283 focal_loss 0.41201 dice_loss 0.10082 +Epoch [3769/4000] Validation [3/10] Loss: 0.39950 focal_loss 0.28782 dice_loss 0.11168 +Epoch [3769/4000] Validation [4/10] Loss: 0.89176 focal_loss 0.32809 dice_loss 0.56367 +Epoch [3769/4000] Validation [5/10] Loss: 3.07800 focal_loss 2.40401 dice_loss 0.67399 +Epoch [3769/4000] Validation [6/10] Loss: 1.33567 focal_loss 0.62124 dice_loss 0.71444 +Epoch [3769/4000] Validation [7/10] Loss: 1.17202 focal_loss 0.52104 dice_loss 0.65098 +Epoch [3769/4000] Validation [8/10] Loss: 2.46720 focal_loss 1.84241 dice_loss 0.62479 +Epoch [3769/4000] Validation [9/10] Loss: 1.57100 focal_loss 1.02631 dice_loss 0.54469 +Epoch [3769/4000] Validation [10/10] Loss: 1.87735 focal_loss 1.14482 dice_loss 0.73253 +Epoch [3769/4000] Validation metric {'Val/mean dice_metric': 0.9514520764350891, 'Val/mean miou_metric': 0.9357089996337891, 'Val/mean f1': 0.948675274848938, 'Val/mean precision': 0.9446142315864563, 'Val/mean recall': 0.9527713060379028, 'Val/mean hd95_metric': 10.765149116516113} +Cheakpoint... +Epoch [3769/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514520764350891, 'Val/mean miou_metric': 0.9357089996337891, 'Val/mean f1': 0.948675274848938, 'Val/mean precision': 0.9446142315864563, 'Val/mean recall': 0.9527713060379028, 'Val/mean hd95_metric': 10.765149116516113} +Epoch [3770/4000] Training [1/39] Loss: 0.37961 +Epoch [3770/4000] Training [2/39] Loss: 0.00807 +Epoch [3770/4000] Training [3/39] Loss: 0.00387 +Epoch [3770/4000] Training [4/39] Loss: 0.00283 +Epoch [3770/4000] Training [5/39] Loss: 0.00753 +Epoch [3770/4000] Training [6/39] Loss: 0.17046 +Epoch [3770/4000] Training [7/39] Loss: 0.00966 +Epoch [3770/4000] Training [8/39] Loss: 0.00683 +Epoch [3770/4000] Training [9/39] Loss: 0.00305 +Epoch [3770/4000] Training [10/39] Loss: 0.00491 +Epoch [3770/4000] Training [11/39] Loss: 0.00610 +Epoch [3770/4000] Training [12/39] Loss: 0.00448 +Epoch [3770/4000] Training [13/39] Loss: 0.00392 +Epoch [3770/4000] Training [14/39] Loss: 0.00588 +Epoch [3770/4000] Training [15/39] Loss: 0.00412 +Epoch [3770/4000] Training [16/39] Loss: 0.00326 +Epoch [3770/4000] Training [17/39] Loss: 0.00556 +Epoch [3770/4000] Training [18/39] Loss: 0.00789 +Epoch [3770/4000] Training [19/39] Loss: 0.13113 +Epoch [3770/4000] Training [20/39] Loss: 0.00344 +Epoch [3770/4000] Training [21/39] Loss: 0.00597 +Epoch [3770/4000] Training [22/39] Loss: 0.12920 +Epoch [3770/4000] Training [23/39] Loss: 0.00396 +Epoch [3770/4000] Training [24/39] Loss: 0.00464 +Epoch [3770/4000] Training [25/39] Loss: 0.00599 +Epoch [3770/4000] Training [26/39] Loss: 0.00662 +Epoch [3770/4000] Training [27/39] Loss: 0.00520 +Epoch [3770/4000] Training [28/39] Loss: 0.00467 +Epoch [3770/4000] Training [29/39] Loss: 0.00515 +Epoch [3770/4000] Training [30/39] Loss: 0.00452 +Epoch [3770/4000] Training [31/39] Loss: 0.00286 +Epoch [3770/4000] Training [32/39] Loss: 0.00328 +Epoch [3770/4000] Training [33/39] Loss: 0.00454 +Epoch [3770/4000] Training [34/39] Loss: 0.00596 +Epoch [3770/4000] Training [35/39] Loss: 0.12820 +Epoch [3770/4000] Training [36/39] Loss: 0.00339 +Epoch [3770/4000] Training [37/39] Loss: 0.00524 +Epoch [3770/4000] Training [38/39] Loss: 0.00406 +Epoch [3770/4000] Training [39/39] Loss: 0.00321 +Epoch [3770/4000] Training metric {'Train/mean dice_metric': 0.9964963793754578, 'Train/mean miou_metric': 0.9934384226799011, 'Train/mean f1': 0.9969678521156311, 'Train/mean precision': 0.9965412020683289, 'Train/mean recall': 0.9973948001861572, 'Train/mean hd95_metric': 0.9243541359901428} +Epoch [3770/4000] Validation [1/10] Loss: 0.72485 focal_loss 0.63805 dice_loss 0.08680 +Epoch [3770/4000] Validation [2/10] Loss: 0.51604 focal_loss 0.41223 dice_loss 0.10381 +Epoch [3770/4000] Validation [3/10] Loss: 0.41023 focal_loss 0.29750 dice_loss 0.11272 +Epoch [3770/4000] Validation [4/10] Loss: 0.88661 focal_loss 0.32337 dice_loss 0.56323 +Epoch [3770/4000] Validation [5/10] Loss: 3.07726 focal_loss 2.40309 dice_loss 0.67417 +Epoch [3770/4000] Validation [6/10] Loss: 1.31685 focal_loss 0.60211 dice_loss 0.71473 +Epoch [3770/4000] Validation [7/10] Loss: 1.16043 focal_loss 0.50982 dice_loss 0.65061 +Epoch [3770/4000] Validation [8/10] Loss: 2.48165 focal_loss 1.85236 dice_loss 0.62929 +Epoch [3770/4000] Validation [9/10] Loss: 1.55925 focal_loss 1.01483 dice_loss 0.54442 +Epoch [3770/4000] Validation [10/10] Loss: 1.84321 focal_loss 1.11052 dice_loss 0.73269 +Epoch [3770/4000] Validation metric {'Val/mean dice_metric': 0.951356828212738, 'Val/mean miou_metric': 0.9355989694595337, 'Val/mean f1': 0.9480929970741272, 'Val/mean precision': 0.9447105526924133, 'Val/mean recall': 0.9514995813369751, 'Val/mean hd95_metric': 10.752302169799805} +Cheakpoint... +Epoch [3770/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951356828212738, 'Val/mean miou_metric': 0.9355989694595337, 'Val/mean f1': 0.9480929970741272, 'Val/mean precision': 0.9447105526924133, 'Val/mean recall': 0.9514995813369751, 'Val/mean hd95_metric': 10.752302169799805} +Epoch [3771/4000] Training [1/39] Loss: 0.12865 +Epoch [3771/4000] Training [2/39] Loss: 0.12863 +Epoch [3771/4000] Training [3/39] Loss: 0.00625 +Epoch [3771/4000] Training [4/39] Loss: 0.12750 +Epoch [3771/4000] Training [5/39] Loss: 0.00434 +Epoch [3771/4000] Training [6/39] Loss: 0.00386 +Epoch [3771/4000] Training [7/39] Loss: 0.12825 +Epoch [3771/4000] Training [8/39] Loss: 0.00274 +Epoch [3771/4000] Training [9/39] Loss: 0.00377 +Epoch [3771/4000] Training [10/39] Loss: 0.00538 +Epoch [3771/4000] Training [11/39] Loss: 0.12894 +Epoch [3771/4000] Training [12/39] Loss: 0.00671 +Epoch [3771/4000] Training [13/39] Loss: 0.04394 +Epoch [3771/4000] Training [14/39] Loss: 0.00290 +Epoch [3771/4000] Training [15/39] Loss: 0.00382 +Epoch [3771/4000] Training [16/39] Loss: 0.00441 +Epoch [3771/4000] Training [17/39] Loss: 0.00385 +Epoch [3771/4000] Training [18/39] Loss: 0.00870 +Epoch [3771/4000] Training [19/39] Loss: 0.00697 +Epoch [3771/4000] Training [20/39] Loss: 0.00298 +Epoch [3771/4000] Training [21/39] Loss: 0.13003 +Epoch [3771/4000] Training [22/39] Loss: 0.13018 +Epoch [3771/4000] Training [23/39] Loss: 0.00428 +Epoch [3771/4000] Training [24/39] Loss: 0.00358 +Epoch [3771/4000] Training [25/39] Loss: 0.00472 +Epoch [3771/4000] Training [26/39] Loss: 0.00406 +Epoch [3771/4000] Training [27/39] Loss: 0.00491 +Epoch [3771/4000] Training [28/39] Loss: 0.00590 +Epoch [3771/4000] Training [29/39] Loss: 0.00438 +Epoch [3771/4000] Training [30/39] Loss: 0.00453 +Epoch [3771/4000] Training [31/39] Loss: 0.00565 +Epoch [3771/4000] Training [32/39] Loss: 0.00498 +Epoch [3771/4000] Training [33/39] Loss: 0.00460 +Epoch [3771/4000] Training [34/39] Loss: 0.00347 +Epoch [3771/4000] Training [35/39] Loss: 0.12878 +Epoch [3771/4000] Training [36/39] Loss: 0.00480 +Epoch [3771/4000] Training [37/39] Loss: 0.00570 +Epoch [3771/4000] Training [38/39] Loss: 0.12968 +Epoch [3771/4000] Training [39/39] Loss: 0.00492 +Epoch [3771/4000] Training metric {'Train/mean dice_metric': 0.9956595301628113, 'Train/mean miou_metric': 0.9925956130027771, 'Train/mean f1': 0.9969494938850403, 'Train/mean precision': 0.9964789748191833, 'Train/mean recall': 0.9974205493927002, 'Train/mean hd95_metric': 0.9383859038352966} +Epoch [3771/4000] Validation [1/10] Loss: 0.73671 focal_loss 0.64862 dice_loss 0.08809 +Epoch [3771/4000] Validation [2/10] Loss: 0.50458 focal_loss 0.40634 dice_loss 0.09824 +Epoch [3771/4000] Validation [3/10] Loss: 0.39380 focal_loss 0.28257 dice_loss 0.11123 +Epoch [3771/4000] Validation [4/10] Loss: 0.89983 focal_loss 0.33434 dice_loss 0.56548 +Epoch [3771/4000] Validation [5/10] Loss: 3.08515 focal_loss 2.41135 dice_loss 0.67381 +Epoch [3771/4000] Validation [6/10] Loss: 1.34617 focal_loss 0.63238 dice_loss 0.71379 +Epoch [3771/4000] Validation [7/10] Loss: 1.18487 focal_loss 0.53109 dice_loss 0.65378 +Epoch [3771/4000] Validation [8/10] Loss: 2.34057 focal_loss 1.72880 dice_loss 0.61177 +Epoch [3771/4000] Validation [9/10] Loss: 1.61981 focal_loss 1.07472 dice_loss 0.54509 +Epoch [3771/4000] Validation [10/10] Loss: 1.91080 focal_loss 1.17484 dice_loss 0.73596 +Epoch [3771/4000] Validation metric {'Val/mean dice_metric': 0.9507763385772705, 'Val/mean miou_metric': 0.9349595904350281, 'Val/mean f1': 0.9479057788848877, 'Val/mean precision': 0.9423863291740417, 'Val/mean recall': 0.9534902572631836, 'Val/mean hd95_metric': 10.720722198486328} +Cheakpoint... +Epoch [3771/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507763385772705, 'Val/mean miou_metric': 0.9349595904350281, 'Val/mean f1': 0.9479057788848877, 'Val/mean precision': 0.9423863291740417, 'Val/mean recall': 0.9534902572631836, 'Val/mean hd95_metric': 10.720722198486328} +Epoch [3772/4000] Training [1/39] Loss: 0.13017 +Epoch [3772/4000] Training [2/39] Loss: 0.00459 +Epoch [3772/4000] Training [3/39] Loss: 0.00412 +Epoch [3772/4000] Training [4/39] Loss: 0.00409 +Epoch [3772/4000] Training [5/39] Loss: 0.00399 +Epoch [3772/4000] Training [6/39] Loss: 0.00350 +Epoch [3772/4000] Training [7/39] Loss: 0.12822 +Epoch [3772/4000] Training [8/39] Loss: 0.00401 +Epoch [3772/4000] Training [9/39] Loss: 0.12871 +Epoch [3772/4000] Training [10/39] Loss: 0.00513 +Epoch [3772/4000] Training [11/39] Loss: 0.00384 +Epoch [3772/4000] Training [12/39] Loss: 0.00363 +Epoch [3772/4000] Training [13/39] Loss: 0.00327 +Epoch [3772/4000] Training [14/39] Loss: 0.12916 +Epoch [3772/4000] Training [15/39] Loss: 0.13014 +Epoch [3772/4000] Training [16/39] Loss: 0.12790 +Epoch [3772/4000] Training [17/39] Loss: 0.00538 +Epoch [3772/4000] Training [18/39] Loss: 0.00659 +Epoch [3772/4000] Training [19/39] Loss: 0.00333 +Epoch [3772/4000] Training [20/39] Loss: 0.00427 +Epoch [3772/4000] Training [21/39] Loss: 0.00872 +Epoch [3772/4000] Training [22/39] Loss: 0.00726 +Epoch [3772/4000] Training [23/39] Loss: 0.12688 +Epoch [3772/4000] Training [24/39] Loss: 0.00562 +Epoch [3772/4000] Training [25/39] Loss: 0.13028 +Epoch [3772/4000] Training [26/39] Loss: 0.00446 +Epoch [3772/4000] Training [27/39] Loss: 0.00390 +Epoch [3772/4000] Training [28/39] Loss: 0.00645 +Epoch [3772/4000] Training [29/39] Loss: 0.00700 +Epoch [3772/4000] Training [30/39] Loss: 0.00594 +Epoch [3772/4000] Training [31/39] Loss: 0.00480 +Epoch [3772/4000] Training [32/39] Loss: 0.00964 +Epoch [3772/4000] Training [33/39] Loss: 0.00693 +Epoch [3772/4000] Training [34/39] Loss: 0.00403 +Epoch [3772/4000] Training [35/39] Loss: 0.00916 +Epoch [3772/4000] Training [36/39] Loss: 0.00280 +Epoch [3772/4000] Training [37/39] Loss: 0.12850 +Epoch [3772/4000] Training [38/39] Loss: 0.00617 +Epoch [3772/4000] Training [39/39] Loss: 0.00492 +Epoch [3772/4000] Training metric {'Train/mean dice_metric': 0.9964101910591125, 'Train/mean miou_metric': 0.9932615756988525, 'Train/mean f1': 0.9969442486763, 'Train/mean precision': 0.9964675307273865, 'Train/mean recall': 0.9974215030670166, 'Train/mean hd95_metric': 0.909434974193573} +Epoch [3772/4000] Validation [1/10] Loss: 0.73108 focal_loss 0.64329 dice_loss 0.08779 +Epoch [3772/4000] Validation [2/10] Loss: 0.50518 focal_loss 0.40785 dice_loss 0.09734 +Epoch [3772/4000] Validation [3/10] Loss: 0.39303 focal_loss 0.28170 dice_loss 0.11133 +Epoch [3772/4000] Validation [4/10] Loss: 0.90085 focal_loss 0.33555 dice_loss 0.56530 +Epoch [3772/4000] Validation [5/10] Loss: 3.07327 focal_loss 2.39934 dice_loss 0.67393 +Epoch [3772/4000] Validation [6/10] Loss: 1.35199 focal_loss 0.63901 dice_loss 0.71298 +Epoch [3772/4000] Validation [7/10] Loss: 1.18813 focal_loss 0.53416 dice_loss 0.65397 +Epoch [3772/4000] Validation [8/10] Loss: 2.40172 focal_loss 1.78464 dice_loss 0.61708 +Epoch [3772/4000] Validation [9/10] Loss: 1.60392 focal_loss 1.05872 dice_loss 0.54520 +Epoch [3772/4000] Validation [10/10] Loss: 1.91512 focal_loss 1.17992 dice_loss 0.73520 +Epoch [3772/4000] Validation metric {'Val/mean dice_metric': 0.9514143466949463, 'Val/mean miou_metric': 0.9355611205101013, 'Val/mean f1': 0.9478790163993835, 'Val/mean precision': 0.9427040219306946, 'Val/mean recall': 0.9531110525131226, 'Val/mean hd95_metric': 10.755356788635254} +Cheakpoint... +Epoch [3772/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514143466949463, 'Val/mean miou_metric': 0.9355611205101013, 'Val/mean f1': 0.9478790163993835, 'Val/mean precision': 0.9427040219306946, 'Val/mean recall': 0.9531110525131226, 'Val/mean hd95_metric': 10.755356788635254} +Epoch [3773/4000] Training [1/39] Loss: 0.12789 +Epoch [3773/4000] Training [2/39] Loss: 0.00574 +Epoch [3773/4000] Training [3/39] Loss: 0.00321 +Epoch [3773/4000] Training [4/39] Loss: 0.00277 +Epoch [3773/4000] Training [5/39] Loss: 0.12788 +Epoch [3773/4000] Training [6/39] Loss: 0.12967 +Epoch [3773/4000] Training [7/39] Loss: 0.00306 +Epoch [3773/4000] Training [8/39] Loss: 0.00559 +Epoch [3773/4000] Training [9/39] Loss: 0.00561 +Epoch [3773/4000] Training [10/39] Loss: 0.00324 +Epoch [3773/4000] Training [11/39] Loss: 0.00550 +Epoch [3773/4000] Training [12/39] Loss: 0.00283 +Epoch [3773/4000] Training [13/39] Loss: 0.00424 +Epoch [3773/4000] Training [14/39] Loss: 0.12898 +Epoch [3773/4000] Training [15/39] Loss: 0.00598 +Epoch [3773/4000] Training [16/39] Loss: 0.08547 +Epoch [3773/4000] Training [17/39] Loss: 0.25245 +Epoch [3773/4000] Training [18/39] Loss: 0.00694 +Epoch [3773/4000] Training [19/39] Loss: 0.12800 +Epoch [3773/4000] Training [20/39] Loss: 0.00408 +Epoch [3773/4000] Training [21/39] Loss: 0.00436 +Epoch [3773/4000] Training [22/39] Loss: 0.00478 +Epoch [3773/4000] Training [23/39] Loss: 0.12990 +Epoch [3773/4000] Training [24/39] Loss: 0.00664 +Epoch [3773/4000] Training [25/39] Loss: 0.12818 +Epoch [3773/4000] Training [26/39] Loss: 0.00770 +Epoch [3773/4000] Training [27/39] Loss: 0.00414 +Epoch [3773/4000] Training [28/39] Loss: 0.00523 +Epoch [3773/4000] Training [29/39] Loss: 0.00877 +Epoch [3773/4000] Training [30/39] Loss: 0.00244 +Epoch [3773/4000] Training [31/39] Loss: 0.00456 +Epoch [3773/4000] Training [32/39] Loss: 0.12882 +Epoch [3773/4000] Training [33/39] Loss: 0.12765 +Epoch [3773/4000] Training [34/39] Loss: 0.00348 +Epoch [3773/4000] Training [35/39] Loss: 0.00546 +Epoch [3773/4000] Training [36/39] Loss: 0.12742 +Epoch [3773/4000] Training [37/39] Loss: 0.00399 +Epoch [3773/4000] Training [38/39] Loss: 0.00625 +Epoch [3773/4000] Training [39/39] Loss: 0.00325 +Epoch [3773/4000] Training metric {'Train/mean dice_metric': 0.9966439008712769, 'Train/mean miou_metric': 0.9937332272529602, 'Train/mean f1': 0.9971200823783875, 'Train/mean precision': 0.9966667890548706, 'Train/mean recall': 0.9975738525390625, 'Train/mean hd95_metric': 0.9152984023094177} +Epoch [3773/4000] Validation [1/10] Loss: 0.71744 focal_loss 0.63046 dice_loss 0.08697 +Epoch [3773/4000] Validation [2/10] Loss: 0.50703 focal_loss 0.40569 dice_loss 0.10134 +Epoch [3773/4000] Validation [3/10] Loss: 0.39874 focal_loss 0.28645 dice_loss 0.11229 +Epoch [3773/4000] Validation [4/10] Loss: 0.89210 focal_loss 0.32803 dice_loss 0.56407 +Epoch [3773/4000] Validation [5/10] Loss: 3.04094 focal_loss 2.36702 dice_loss 0.67392 +Epoch [3773/4000] Validation [6/10] Loss: 1.33290 focal_loss 0.61924 dice_loss 0.71366 +Epoch [3773/4000] Validation [7/10] Loss: 1.17468 focal_loss 0.52154 dice_loss 0.65314 +Epoch [3773/4000] Validation [8/10] Loss: 2.39596 focal_loss 1.77412 dice_loss 0.62184 +Epoch [3773/4000] Validation [9/10] Loss: 1.57470 focal_loss 1.02945 dice_loss 0.54524 +Epoch [3773/4000] Validation [10/10] Loss: 1.87742 focal_loss 1.14271 dice_loss 0.73471 +Epoch [3773/4000] Validation metric {'Val/mean dice_metric': 0.9516164660453796, 'Val/mean miou_metric': 0.9359906315803528, 'Val/mean f1': 0.9483963251113892, 'Val/mean precision': 0.9438231587409973, 'Val/mean recall': 0.9530139565467834, 'Val/mean hd95_metric': 10.758706092834473} +Cheakpoint... +Epoch [3773/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516164660453796, 'Val/mean miou_metric': 0.9359906315803528, 'Val/mean f1': 0.9483963251113892, 'Val/mean precision': 0.9438231587409973, 'Val/mean recall': 0.9530139565467834, 'Val/mean hd95_metric': 10.758706092834473} +Epoch [3774/4000] Training [1/39] Loss: 0.25278 +Epoch [3774/4000] Training [2/39] Loss: 0.12979 +Epoch [3774/4000] Training [3/39] Loss: 0.00846 +Epoch [3774/4000] Training [4/39] Loss: 0.13091 +Epoch [3774/4000] Training [5/39] Loss: 0.00348 +Epoch [3774/4000] Training [6/39] Loss: 0.00394 +Epoch [3774/4000] Training [7/39] Loss: 0.00549 +Epoch [3774/4000] Training [8/39] Loss: 0.00472 +Epoch [3774/4000] Training [9/39] Loss: 0.00506 +Epoch [3774/4000] Training [10/39] Loss: 0.00750 +Epoch [3774/4000] Training [11/39] Loss: 0.00455 +Epoch [3774/4000] Training [12/39] Loss: 0.00333 +Epoch [3774/4000] Training [13/39] Loss: 0.00375 +Epoch [3774/4000] Training [14/39] Loss: 0.12779 +Epoch [3774/4000] Training [15/39] Loss: 0.00322 +Epoch [3774/4000] Training [16/39] Loss: 0.00429 +Epoch [3774/4000] Training [17/39] Loss: 0.00325 +Epoch [3774/4000] Training [18/39] Loss: 0.25341 +Epoch [3774/4000] Training [19/39] Loss: 0.12812 +Epoch [3774/4000] Training [20/39] Loss: 0.00468 +Epoch [3774/4000] Training [21/39] Loss: 0.00951 +Epoch [3774/4000] Training [22/39] Loss: 0.00625 +Epoch [3774/4000] Training [23/39] Loss: 0.00534 +Epoch [3774/4000] Training [24/39] Loss: 0.12863 +Epoch [3774/4000] Training [25/39] Loss: 0.12831 +Epoch [3774/4000] Training [26/39] Loss: 0.12964 +Epoch [3774/4000] Training [27/39] Loss: 0.00349 +Epoch [3774/4000] Training [28/39] Loss: 0.00444 +Epoch [3774/4000] Training [29/39] Loss: 0.00429 +Epoch [3774/4000] Training [30/39] Loss: 0.00628 +Epoch [3774/4000] Training [31/39] Loss: 0.00662 +Epoch [3774/4000] Training [32/39] Loss: 0.00493 +Epoch [3774/4000] Training [33/39] Loss: 0.00541 +Epoch [3774/4000] Training [34/39] Loss: 0.00495 +Epoch [3774/4000] Training [35/39] Loss: 0.00511 +Epoch [3774/4000] Training [36/39] Loss: 0.13562 +Epoch [3774/4000] Training [37/39] Loss: 0.00639 +Epoch [3774/4000] Training [38/39] Loss: 0.12973 +Epoch [3774/4000] Training [39/39] Loss: 0.12881 +Epoch [3774/4000] Training metric {'Train/mean dice_metric': 0.9954264163970947, 'Train/mean miou_metric': 0.9921544194221497, 'Train/mean f1': 0.9968125820159912, 'Train/mean precision': 0.9963216781616211, 'Train/mean recall': 0.9973037838935852, 'Train/mean hd95_metric': 0.9471729397773743} +Epoch [3774/4000] Validation [1/10] Loss: 0.73920 focal_loss 0.65107 dice_loss 0.08813 +Epoch [3774/4000] Validation [2/10] Loss: 0.50645 focal_loss 0.40848 dice_loss 0.09797 +Epoch [3774/4000] Validation [3/10] Loss: 0.39070 focal_loss 0.27950 dice_loss 0.11121 +Epoch [3774/4000] Validation [4/10] Loss: 0.90572 focal_loss 0.34002 dice_loss 0.56570 +Epoch [3774/4000] Validation [5/10] Loss: 3.08603 focal_loss 2.41229 dice_loss 0.67373 +Epoch [3774/4000] Validation [6/10] Loss: 1.35257 focal_loss 0.63958 dice_loss 0.71299 +Epoch [3774/4000] Validation [7/10] Loss: 1.19314 focal_loss 0.53702 dice_loss 0.65613 +Epoch [3774/4000] Validation [8/10] Loss: 2.33987 focal_loss 1.72814 dice_loss 0.61173 +Epoch [3774/4000] Validation [9/10] Loss: 1.61581 focal_loss 1.06996 dice_loss 0.54585 +Epoch [3774/4000] Validation [10/10] Loss: 1.92813 focal_loss 1.19151 dice_loss 0.73662 +Epoch [3774/4000] Validation metric {'Val/mean dice_metric': 0.9505894184112549, 'Val/mean miou_metric': 0.9346160292625427, 'Val/mean f1': 0.947682797908783, 'Val/mean precision': 0.9418870210647583, 'Val/mean recall': 0.9535504579544067, 'Val/mean hd95_metric': 10.727474212646484} +Cheakpoint... +Epoch [3774/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505894184112549, 'Val/mean miou_metric': 0.9346160292625427, 'Val/mean f1': 0.947682797908783, 'Val/mean precision': 0.9418870210647583, 'Val/mean recall': 0.9535504579544067, 'Val/mean hd95_metric': 10.727474212646484} +Epoch [3775/4000] Training [1/39] Loss: 0.00396 +Epoch [3775/4000] Training [2/39] Loss: 0.00406 +Epoch [3775/4000] Training [3/39] Loss: 0.12828 +Epoch [3775/4000] Training [4/39] Loss: 0.12719 +Epoch [3775/4000] Training [5/39] Loss: 0.00513 +Epoch [3775/4000] Training [6/39] Loss: 0.00376 +Epoch [3775/4000] Training [7/39] Loss: 0.00297 +Epoch [3775/4000] Training [8/39] Loss: 0.00396 +Epoch [3775/4000] Training [9/39] Loss: 0.12910 +Epoch [3775/4000] Training [10/39] Loss: 0.00798 +Epoch [3775/4000] Training [11/39] Loss: 0.00421 +Epoch [3775/4000] Training [12/39] Loss: 0.12976 +Epoch [3775/4000] Training [13/39] Loss: 0.25545 +Epoch [3775/4000] Training [14/39] Loss: 0.25279 +Epoch [3775/4000] Training [15/39] Loss: 0.00378 +Epoch [3775/4000] Training [16/39] Loss: 0.00388 +Epoch [3775/4000] Training [17/39] Loss: 0.12874 +Epoch [3775/4000] Training [18/39] Loss: 0.00505 +Epoch [3775/4000] Training [19/39] Loss: 0.00257 +Epoch [3775/4000] Training [20/39] Loss: 0.00385 +Epoch [3775/4000] Training [21/39] Loss: 0.00665 +Epoch [3775/4000] Training [22/39] Loss: 0.00566 +Epoch [3775/4000] Training [23/39] Loss: 0.12809 +Epoch [3775/4000] Training [24/39] Loss: 0.00633 +Epoch [3775/4000] Training [25/39] Loss: 0.00355 +Epoch [3775/4000] Training [26/39] Loss: 0.12952 +Epoch [3775/4000] Training [27/39] Loss: 0.00546 +Epoch [3775/4000] Training [28/39] Loss: 0.00639 +Epoch [3775/4000] Training [29/39] Loss: 0.12927 +Epoch [3775/4000] Training [30/39] Loss: 0.00362 +Epoch [3775/4000] Training [31/39] Loss: 0.00810 +Epoch [3775/4000] Training [32/39] Loss: 0.00349 +Epoch [3775/4000] Training [33/39] Loss: 0.00479 +Epoch [3775/4000] Training [34/39] Loss: 0.00365 +Epoch [3775/4000] Training [35/39] Loss: 0.00638 +Epoch [3775/4000] Training [36/39] Loss: 0.00582 +Epoch [3775/4000] Training [37/39] Loss: 0.00362 +Epoch [3775/4000] Training [38/39] Loss: 0.00319 +Epoch [3775/4000] Training [39/39] Loss: 0.00628 +Epoch [3775/4000] Training metric {'Train/mean dice_metric': 0.9965049624443054, 'Train/mean miou_metric': 0.9934497475624084, 'Train/mean f1': 0.9969296455383301, 'Train/mean precision': 0.9965241551399231, 'Train/mean recall': 0.9973354339599609, 'Train/mean hd95_metric': 0.9227730631828308} +Epoch [3775/4000] Validation [1/10] Loss: 0.73193 focal_loss 0.64502 dice_loss 0.08691 +Epoch [3775/4000] Validation [2/10] Loss: 0.50605 focal_loss 0.40533 dice_loss 0.10072 +Epoch [3775/4000] Validation [3/10] Loss: 0.41102 focal_loss 0.29841 dice_loss 0.11261 +Epoch [3775/4000] Validation [4/10] Loss: 0.89116 focal_loss 0.32695 dice_loss 0.56421 +Epoch [3775/4000] Validation [5/10] Loss: 3.11354 focal_loss 2.43942 dice_loss 0.67411 +Epoch [3775/4000] Validation [6/10] Loss: 1.31567 focal_loss 0.60416 dice_loss 0.71151 +Epoch [3775/4000] Validation [7/10] Loss: 1.16925 focal_loss 0.51626 dice_loss 0.65299 +Epoch [3775/4000] Validation [8/10] Loss: 2.44221 focal_loss 1.81870 dice_loss 0.62351 +Epoch [3775/4000] Validation [9/10] Loss: 1.58304 focal_loss 1.03851 dice_loss 0.54454 +Epoch [3775/4000] Validation [10/10] Loss: 1.87008 focal_loss 1.13551 dice_loss 0.73457 +Epoch [3775/4000] Validation metric {'Val/mean dice_metric': 0.9514833688735962, 'Val/mean miou_metric': 0.9357213377952576, 'Val/mean f1': 0.9480956792831421, 'Val/mean precision': 0.9439568519592285, 'Val/mean recall': 0.952271044254303, 'Val/mean hd95_metric': 10.753706932067871} +Cheakpoint... +Epoch [3775/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514833688735962, 'Val/mean miou_metric': 0.9357213377952576, 'Val/mean f1': 0.9480956792831421, 'Val/mean precision': 0.9439568519592285, 'Val/mean recall': 0.952271044254303, 'Val/mean hd95_metric': 10.753706932067871} +Epoch [3776/4000] Training [1/39] Loss: 0.00361 +Epoch [3776/4000] Training [2/39] Loss: 0.12952 +Epoch [3776/4000] Training [3/39] Loss: 0.13005 +Epoch [3776/4000] Training [4/39] Loss: 0.00397 +Epoch [3776/4000] Training [5/39] Loss: 0.12944 +Epoch [3776/4000] Training [6/39] Loss: 0.00513 +Epoch [3776/4000] Training [7/39] Loss: 0.12898 +Epoch [3776/4000] Training [8/39] Loss: 0.00484 +Epoch [3776/4000] Training [9/39] Loss: 0.00607 +Epoch [3776/4000] Training [10/39] Loss: 0.00797 +Epoch [3776/4000] Training [11/39] Loss: 0.00493 +Epoch [3776/4000] Training [12/39] Loss: 0.00538 +Epoch [3776/4000] Training [13/39] Loss: 0.00479 +Epoch [3776/4000] Training [14/39] Loss: 0.00394 +Epoch [3776/4000] Training [15/39] Loss: 0.00453 +Epoch [3776/4000] Training [16/39] Loss: 0.00442 +Epoch [3776/4000] Training [17/39] Loss: 0.08314 +Epoch [3776/4000] Training [18/39] Loss: 0.01159 +Epoch [3776/4000] Training [19/39] Loss: 0.00317 +Epoch [3776/4000] Training [20/39] Loss: 0.00590 +Epoch [3776/4000] Training [21/39] Loss: 0.00428 +Epoch [3776/4000] Training [22/39] Loss: 0.00533 +Epoch [3776/4000] Training [23/39] Loss: 0.00377 +Epoch [3776/4000] Training [24/39] Loss: 0.12796 +Epoch [3776/4000] Training [25/39] Loss: 0.00371 +Epoch [3776/4000] Training [26/39] Loss: 0.00634 +Epoch [3776/4000] Training [27/39] Loss: 0.00430 +Epoch [3776/4000] Training [28/39] Loss: 0.00336 +Epoch [3776/4000] Training [29/39] Loss: 0.13119 +Epoch [3776/4000] Training [30/39] Loss: 0.00397 +Epoch [3776/4000] Training [31/39] Loss: 0.00382 +Epoch [3776/4000] Training [32/39] Loss: 0.00511 +Epoch [3776/4000] Training [33/39] Loss: 0.00719 +Epoch [3776/4000] Training [34/39] Loss: 0.00549 +Epoch [3776/4000] Training [35/39] Loss: 0.00714 +Epoch [3776/4000] Training [36/39] Loss: 0.12847 +Epoch [3776/4000] Training [37/39] Loss: 0.00422 +Epoch [3776/4000] Training [38/39] Loss: 0.01116 +Epoch [3776/4000] Training [39/39] Loss: 0.00520 +Epoch [3776/4000] Training metric {'Train/mean dice_metric': 0.9960619211196899, 'Train/mean miou_metric': 0.9925800561904907, 'Train/mean f1': 0.9967001080513, 'Train/mean precision': 0.9961529970169067, 'Train/mean recall': 0.9972477555274963, 'Train/mean hd95_metric': 1.0594236850738525} +Epoch [3776/4000] Validation [1/10] Loss: 0.72415 focal_loss 0.63656 dice_loss 0.08759 +Epoch [3776/4000] Validation [2/10] Loss: 0.49545 focal_loss 0.39760 dice_loss 0.09785 +Epoch [3776/4000] Validation [3/10] Loss: 0.39105 focal_loss 0.27962 dice_loss 0.11143 +Epoch [3776/4000] Validation [4/10] Loss: 0.89363 focal_loss 0.32867 dice_loss 0.56496 +Epoch [3776/4000] Validation [5/10] Loss: 3.05951 focal_loss 2.38551 dice_loss 0.67400 +Epoch [3776/4000] Validation [6/10] Loss: 1.32672 focal_loss 0.61472 dice_loss 0.71200 +Epoch [3776/4000] Validation [7/10] Loss: 1.17377 focal_loss 0.51949 dice_loss 0.65428 +Epoch [3776/4000] Validation [8/10] Loss: 2.40237 focal_loss 1.78192 dice_loss 0.62046 +Epoch [3776/4000] Validation [9/10] Loss: 1.56773 focal_loss 1.02292 dice_loss 0.54481 +Epoch [3776/4000] Validation [10/10] Loss: 1.88719 focal_loss 1.15195 dice_loss 0.73523 +Epoch [3776/4000] Validation metric {'Val/mean dice_metric': 0.9511825442314148, 'Val/mean miou_metric': 0.9350599646568298, 'Val/mean f1': 0.9480948448181152, 'Val/mean precision': 0.9432820677757263, 'Val/mean recall': 0.9529569149017334, 'Val/mean hd95_metric': 10.879081726074219} +Cheakpoint... +Epoch [3776/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511825442314148, 'Val/mean miou_metric': 0.9350599646568298, 'Val/mean f1': 0.9480948448181152, 'Val/mean precision': 0.9432820677757263, 'Val/mean recall': 0.9529569149017334, 'Val/mean hd95_metric': 10.879081726074219} +Epoch [3777/4000] Training [1/39] Loss: 0.00467 +Epoch [3777/4000] Training [2/39] Loss: 0.00467 +Epoch [3777/4000] Training [3/39] Loss: 0.00363 +Epoch [3777/4000] Training [4/39] Loss: 0.00404 +Epoch [3777/4000] Training [5/39] Loss: 0.00535 +Epoch [3777/4000] Training [6/39] Loss: 0.00293 +Epoch [3777/4000] Training [7/39] Loss: 0.00343 +Epoch [3777/4000] Training [8/39] Loss: 0.12889 +Epoch [3777/4000] Training [9/39] Loss: 0.12965 +Epoch [3777/4000] Training [10/39] Loss: 0.13013 +Epoch [3777/4000] Training [11/39] Loss: 0.12923 +Epoch [3777/4000] Training [12/39] Loss: 0.00443 +Epoch [3777/4000] Training [13/39] Loss: 0.12928 +Epoch [3777/4000] Training [14/39] Loss: 0.00450 +Epoch [3777/4000] Training [15/39] Loss: 0.00346 +Epoch [3777/4000] Training [16/39] Loss: 0.00570 +Epoch [3777/4000] Training [17/39] Loss: 0.00445 +Epoch [3777/4000] Training [18/39] Loss: 0.00465 +Epoch [3777/4000] Training [19/39] Loss: 0.00932 +Epoch [3777/4000] Training [20/39] Loss: 0.12957 +Epoch [3777/4000] Training [21/39] Loss: 0.00529 +Epoch [3777/4000] Training [22/39] Loss: 0.00328 +Epoch [3777/4000] Training [23/39] Loss: 0.12921 +Epoch [3777/4000] Training [24/39] Loss: 0.00596 +Epoch [3777/4000] Training [25/39] Loss: 0.00649 +Epoch [3777/4000] Training [26/39] Loss: 0.00602 +Epoch [3777/4000] Training [27/39] Loss: 0.25351 +Epoch [3777/4000] Training [28/39] Loss: 0.00557 +Epoch [3777/4000] Training [29/39] Loss: 0.12802 +Epoch [3777/4000] Training [30/39] Loss: 0.00310 +Epoch [3777/4000] Training [31/39] Loss: 0.00402 +Epoch [3777/4000] Training [32/39] Loss: 0.00609 +Epoch [3777/4000] Training [33/39] Loss: 0.00859 +Epoch [3777/4000] Training [34/39] Loss: 0.00535 +Epoch [3777/4000] Training [35/39] Loss: 0.00507 +Epoch [3777/4000] Training [36/39] Loss: 0.13013 +Epoch [3777/4000] Training [37/39] Loss: 0.00535 +Epoch [3777/4000] Training [38/39] Loss: 0.00288 +Epoch [3777/4000] Training [39/39] Loss: 0.00420 +Epoch [3777/4000] Training metric {'Train/mean dice_metric': 0.9964411854743958, 'Train/mean miou_metric': 0.9933295845985413, 'Train/mean f1': 0.9969221949577332, 'Train/mean precision': 0.9964680075645447, 'Train/mean recall': 0.9973768591880798, 'Train/mean hd95_metric': 0.9907180070877075} +Epoch [3777/4000] Validation [1/10] Loss: 0.72383 focal_loss 0.63816 dice_loss 0.08567 +Epoch [3777/4000] Validation [2/10] Loss: 0.50469 focal_loss 0.40329 dice_loss 0.10141 +Epoch [3777/4000] Validation [3/10] Loss: 0.41596 focal_loss 0.30316 dice_loss 0.11281 +Epoch [3777/4000] Validation [4/10] Loss: 0.88639 focal_loss 0.32258 dice_loss 0.56381 +Epoch [3777/4000] Validation [5/10] Loss: 3.13276 focal_loss 2.45840 dice_loss 0.67436 +Epoch [3777/4000] Validation [6/10] Loss: 1.31049 focal_loss 0.59927 dice_loss 0.71122 +Epoch [3777/4000] Validation [7/10] Loss: 1.16352 focal_loss 0.51267 dice_loss 0.65085 +Epoch [3777/4000] Validation [8/10] Loss: 2.54000 focal_loss 1.90788 dice_loss 0.63213 +Epoch [3777/4000] Validation [9/10] Loss: 1.54998 focal_loss 1.00634 dice_loss 0.54364 +Epoch [3777/4000] Validation [10/10] Loss: 1.85481 focal_loss 1.12193 dice_loss 0.73288 +Epoch [3777/4000] Validation metric {'Val/mean dice_metric': 0.9514578580856323, 'Val/mean miou_metric': 0.9356619119644165, 'Val/mean f1': 0.9486818313598633, 'Val/mean precision': 0.9457554221153259, 'Val/mean recall': 0.9516262412071228, 'Val/mean hd95_metric': 10.7570219039917} +Cheakpoint... +Epoch [3777/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514578580856323, 'Val/mean miou_metric': 0.9356619119644165, 'Val/mean f1': 0.9486818313598633, 'Val/mean precision': 0.9457554221153259, 'Val/mean recall': 0.9516262412071228, 'Val/mean hd95_metric': 10.7570219039917} +Epoch [3778/4000] Training [1/39] Loss: 0.00590 +Epoch [3778/4000] Training [2/39] Loss: 0.00466 +Epoch [3778/4000] Training [3/39] Loss: 0.00438 +Epoch [3778/4000] Training [4/39] Loss: 0.00452 +Epoch [3778/4000] Training [5/39] Loss: 0.00507 +Epoch [3778/4000] Training [6/39] Loss: 0.00261 +Epoch [3778/4000] Training [7/39] Loss: 0.13338 +Epoch [3778/4000] Training [8/39] Loss: 0.00445 +Epoch [3778/4000] Training [9/39] Loss: 0.00425 +Epoch [3778/4000] Training [10/39] Loss: 0.00309 +Epoch [3778/4000] Training [11/39] Loss: 0.00518 +Epoch [3778/4000] Training [12/39] Loss: 0.12788 +Epoch [3778/4000] Training [13/39] Loss: 0.00586 +Epoch [3778/4000] Training [14/39] Loss: 0.00300 +Epoch [3778/4000] Training [15/39] Loss: 0.00602 +Epoch [3778/4000] Training [16/39] Loss: 0.12951 +Epoch [3778/4000] Training [17/39] Loss: 0.00431 +Epoch [3778/4000] Training [18/39] Loss: 0.12951 +Epoch [3778/4000] Training [19/39] Loss: 0.00343 +Epoch [3778/4000] Training [20/39] Loss: 0.13361 +Epoch [3778/4000] Training [21/39] Loss: 0.00498 +Epoch [3778/4000] Training [22/39] Loss: 0.08820 +Epoch [3778/4000] Training [23/39] Loss: 0.13103 +Epoch [3778/4000] Training [24/39] Loss: 0.12876 +Epoch [3778/4000] Training [25/39] Loss: 0.12821 +Epoch [3778/4000] Training [26/39] Loss: 0.00423 +Epoch [3778/4000] Training [27/39] Loss: 0.12836 +Epoch [3778/4000] Training [28/39] Loss: 0.00289 +Epoch [3778/4000] Training [29/39] Loss: 0.00396 +Epoch [3778/4000] Training [30/39] Loss: 0.00447 +Epoch [3778/4000] Training [31/39] Loss: 0.00645 +Epoch [3778/4000] Training [32/39] Loss: 0.00361 +Epoch [3778/4000] Training [33/39] Loss: 0.00524 +Epoch [3778/4000] Training [34/39] Loss: 0.00578 +Epoch [3778/4000] Training [35/39] Loss: 0.12869 +Epoch [3778/4000] Training [36/39] Loss: 0.00602 +Epoch [3778/4000] Training [37/39] Loss: 0.00521 +Epoch [3778/4000] Training [38/39] Loss: 0.12752 +Epoch [3778/4000] Training [39/39] Loss: 0.12911 +Epoch [3778/4000] Training metric {'Train/mean dice_metric': 0.9964970946311951, 'Train/mean miou_metric': 0.993443489074707, 'Train/mean f1': 0.9969132542610168, 'Train/mean precision': 0.9964661598205566, 'Train/mean recall': 0.9973608255386353, 'Train/mean hd95_metric': 0.9147158265113831} +Epoch [3778/4000] Validation [1/10] Loss: 0.70937 focal_loss 0.62359 dice_loss 0.08577 +Epoch [3778/4000] Validation [2/10] Loss: 0.50396 focal_loss 0.40423 dice_loss 0.09973 +Epoch [3778/4000] Validation [3/10] Loss: 0.39574 focal_loss 0.28412 dice_loss 0.11162 +Epoch [3778/4000] Validation [4/10] Loss: 0.89198 focal_loss 0.32792 dice_loss 0.56406 +Epoch [3778/4000] Validation [5/10] Loss: 3.07792 focal_loss 2.40377 dice_loss 0.67416 +Epoch [3778/4000] Validation [6/10] Loss: 1.32842 focal_loss 0.61622 dice_loss 0.71220 +Epoch [3778/4000] Validation [7/10] Loss: 1.17684 focal_loss 0.52523 dice_loss 0.65161 +Epoch [3778/4000] Validation [8/10] Loss: 2.44869 focal_loss 1.82408 dice_loss 0.62461 +Epoch [3778/4000] Validation [9/10] Loss: 1.55853 focal_loss 1.01371 dice_loss 0.54482 +Epoch [3778/4000] Validation [10/10] Loss: 1.88714 focal_loss 1.15272 dice_loss 0.73442 +Epoch [3778/4000] Validation metric {'Val/mean dice_metric': 0.9515913128852844, 'Val/mean miou_metric': 0.9358541369438171, 'Val/mean f1': 0.9482941627502441, 'Val/mean precision': 0.9442867636680603, 'Val/mean recall': 0.9523356556892395, 'Val/mean hd95_metric': 10.665139198303223} +Cheakpoint... +Epoch [3778/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515913128852844, 'Val/mean miou_metric': 0.9358541369438171, 'Val/mean f1': 0.9482941627502441, 'Val/mean precision': 0.9442867636680603, 'Val/mean recall': 0.9523356556892395, 'Val/mean hd95_metric': 10.665139198303223} +Epoch [3779/4000] Training [1/39] Loss: 0.00457 +Epoch [3779/4000] Training [2/39] Loss: 0.00461 +Epoch [3779/4000] Training [3/39] Loss: 0.00446 +Epoch [3779/4000] Training [4/39] Loss: 0.00457 +Epoch [3779/4000] Training [5/39] Loss: 0.00922 +Epoch [3779/4000] Training [6/39] Loss: 0.00548 +Epoch [3779/4000] Training [7/39] Loss: 0.00765 +Epoch [3779/4000] Training [8/39] Loss: 0.00509 +Epoch [3779/4000] Training [9/39] Loss: 0.00502 +Epoch [3779/4000] Training [10/39] Loss: 0.00372 +Epoch [3779/4000] Training [11/39] Loss: 0.00489 +Epoch [3779/4000] Training [12/39] Loss: 0.12938 +Epoch [3779/4000] Training [13/39] Loss: 0.00427 +Epoch [3779/4000] Training [14/39] Loss: 0.00520 +Epoch [3779/4000] Training [15/39] Loss: 0.00469 +Epoch [3779/4000] Training [16/39] Loss: 0.12806 +Epoch [3779/4000] Training [17/39] Loss: 0.00619 +Epoch [3779/4000] Training [18/39] Loss: 0.00484 +Epoch [3779/4000] Training [19/39] Loss: 0.00639 +Epoch [3779/4000] Training [20/39] Loss: 0.13138 +Epoch [3779/4000] Training [21/39] Loss: 0.00466 +Epoch [3779/4000] Training [22/39] Loss: 0.00527 +Epoch [3779/4000] Training [23/39] Loss: 0.12782 +Epoch [3779/4000] Training [24/39] Loss: 0.00594 +Epoch [3779/4000] Training [25/39] Loss: 0.00339 +Epoch [3779/4000] Training [26/39] Loss: 0.00416 +Epoch [3779/4000] Training [27/39] Loss: 0.00402 +Epoch [3779/4000] Training [28/39] Loss: 0.12894 +Epoch [3779/4000] Training [29/39] Loss: 0.13043 +Epoch [3779/4000] Training [30/39] Loss: 0.00474 +Epoch [3779/4000] Training [31/39] Loss: 0.12906 +Epoch [3779/4000] Training [32/39] Loss: 0.00303 +Epoch [3779/4000] Training [33/39] Loss: 0.00603 +Epoch [3779/4000] Training [34/39] Loss: 0.00371 +Epoch [3779/4000] Training [35/39] Loss: 0.00501 +Epoch [3779/4000] Training [36/39] Loss: 0.00381 +Epoch [3779/4000] Training [37/39] Loss: 0.00475 +Epoch [3779/4000] Training [38/39] Loss: 0.00380 +Epoch [3779/4000] Training [39/39] Loss: 0.12981 +Epoch [3779/4000] Training metric {'Train/mean dice_metric': 0.9962148070335388, 'Train/mean miou_metric': 0.9928821921348572, 'Train/mean f1': 0.996720552444458, 'Train/mean precision': 0.996263861656189, 'Train/mean recall': 0.99717777967453, 'Train/mean hd95_metric': 1.0396534204483032} +Epoch [3779/4000] Validation [1/10] Loss: 0.71585 focal_loss 0.62947 dice_loss 0.08637 +Epoch [3779/4000] Validation [2/10] Loss: 0.50256 focal_loss 0.40446 dice_loss 0.09810 +Epoch [3779/4000] Validation [3/10] Loss: 0.39563 focal_loss 0.28418 dice_loss 0.11144 +Epoch [3779/4000] Validation [4/10] Loss: 0.90044 focal_loss 0.33535 dice_loss 0.56510 +Epoch [3779/4000] Validation [5/10] Loss: 3.06111 focal_loss 2.38709 dice_loss 0.67402 +Epoch [3779/4000] Validation [6/10] Loss: 1.33679 focal_loss 0.62561 dice_loss 0.71118 +Epoch [3779/4000] Validation [7/10] Loss: 1.18129 focal_loss 0.52850 dice_loss 0.65279 +Epoch [3779/4000] Validation [8/10] Loss: 2.43283 focal_loss 1.81089 dice_loss 0.62193 +Epoch [3779/4000] Validation [9/10] Loss: 1.58305 focal_loss 1.03832 dice_loss 0.54474 +Epoch [3779/4000] Validation [10/10] Loss: 1.91020 focal_loss 1.17473 dice_loss 0.73547 +Epoch [3779/4000] Validation metric {'Val/mean dice_metric': 0.9513700008392334, 'Val/mean miou_metric': 0.9353417158126831, 'Val/mean f1': 0.9479146003723145, 'Val/mean precision': 0.9433573484420776, 'Val/mean recall': 0.9525161385536194, 'Val/mean hd95_metric': 10.906062126159668} +Cheakpoint... +Epoch [3779/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513700008392334, 'Val/mean miou_metric': 0.9353417158126831, 'Val/mean f1': 0.9479146003723145, 'Val/mean precision': 0.9433573484420776, 'Val/mean recall': 0.9525161385536194, 'Val/mean hd95_metric': 10.906062126159668} +Epoch [3780/4000] Training [1/39] Loss: 0.00523 +Epoch [3780/4000] Training [2/39] Loss: 0.12887 +Epoch [3780/4000] Training [3/39] Loss: 0.00503 +Epoch [3780/4000] Training [4/39] Loss: 0.00753 +Epoch [3780/4000] Training [5/39] Loss: 0.00458 +Epoch [3780/4000] Training [6/39] Loss: 0.00602 +Epoch [3780/4000] Training [7/39] Loss: 0.12825 +Epoch [3780/4000] Training [8/39] Loss: 0.00428 +Epoch [3780/4000] Training [9/39] Loss: 0.00427 +Epoch [3780/4000] Training [10/39] Loss: 0.00301 +Epoch [3780/4000] Training [11/39] Loss: 0.00600 +Epoch [3780/4000] Training [12/39] Loss: 0.12761 +Epoch [3780/4000] Training [13/39] Loss: 0.25233 +Epoch [3780/4000] Training [14/39] Loss: 0.00343 +Epoch [3780/4000] Training [15/39] Loss: 0.00358 +Epoch [3780/4000] Training [16/39] Loss: 0.00464 +Epoch [3780/4000] Training [17/39] Loss: 0.13128 +Epoch [3780/4000] Training [18/39] Loss: 0.00535 +Epoch [3780/4000] Training [19/39] Loss: 0.00451 +Epoch [3780/4000] Training [20/39] Loss: 0.00298 +Epoch [3780/4000] Training [21/39] Loss: 0.00589 +Epoch [3780/4000] Training [22/39] Loss: 0.12982 +Epoch [3780/4000] Training [23/39] Loss: 0.00659 +Epoch [3780/4000] Training [24/39] Loss: 0.00385 +Epoch [3780/4000] Training [25/39] Loss: 0.00381 +Epoch [3780/4000] Training [26/39] Loss: 0.12819 +Epoch [3780/4000] Training [27/39] Loss: 0.00476 +Epoch [3780/4000] Training [28/39] Loss: 0.13030 +Epoch [3780/4000] Training [29/39] Loss: 0.00436 +Epoch [3780/4000] Training [30/39] Loss: 0.00409 +Epoch [3780/4000] Training [31/39] Loss: 0.12841 +Epoch [3780/4000] Training [32/39] Loss: 0.12832 +Epoch [3780/4000] Training [33/39] Loss: 0.13349 +Epoch [3780/4000] Training [34/39] Loss: 0.12925 +Epoch [3780/4000] Training [35/39] Loss: 0.00496 +Epoch [3780/4000] Training [36/39] Loss: 0.00383 +Epoch [3780/4000] Training [37/39] Loss: 0.00473 +Epoch [3780/4000] Training [38/39] Loss: 0.00777 +Epoch [3780/4000] Training [39/39] Loss: 0.08716 +Epoch [3780/4000] Training metric {'Train/mean dice_metric': 0.9956314563751221, 'Train/mean miou_metric': 0.9925388693809509, 'Train/mean f1': 0.9969992637634277, 'Train/mean precision': 0.9965048432350159, 'Train/mean recall': 0.9974943995475769, 'Train/mean hd95_metric': 0.9786638021469116} +Epoch [3780/4000] Validation [1/10] Loss: 0.72129 focal_loss 0.63541 dice_loss 0.08588 +Epoch [3780/4000] Validation [2/10] Loss: 0.51052 focal_loss 0.41021 dice_loss 0.10031 +Epoch [3780/4000] Validation [3/10] Loss: 0.40193 focal_loss 0.29038 dice_loss 0.11155 +Epoch [3780/4000] Validation [4/10] Loss: 0.89346 focal_loss 0.32917 dice_loss 0.56429 +Epoch [3780/4000] Validation [5/10] Loss: 3.10419 focal_loss 2.43015 dice_loss 0.67404 +Epoch [3780/4000] Validation [6/10] Loss: 1.33922 focal_loss 0.62616 dice_loss 0.71305 +Epoch [3780/4000] Validation [7/10] Loss: 1.18013 focal_loss 0.52867 dice_loss 0.65146 +Epoch [3780/4000] Validation [8/10] Loss: 2.45899 focal_loss 1.83673 dice_loss 0.62226 +Epoch [3780/4000] Validation [9/10] Loss: 1.58598 focal_loss 1.04177 dice_loss 0.54421 +Epoch [3780/4000] Validation [10/10] Loss: 1.90604 focal_loss 1.17051 dice_loss 0.73552 +Epoch [3780/4000] Validation metric {'Val/mean dice_metric': 0.950751543045044, 'Val/mean miou_metric': 0.9349385499954224, 'Val/mean f1': 0.9483694434165955, 'Val/mean precision': 0.9442490935325623, 'Val/mean recall': 0.9525259733200073, 'Val/mean hd95_metric': 10.712718963623047} +Cheakpoint... +Epoch [3780/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950751543045044, 'Val/mean miou_metric': 0.9349385499954224, 'Val/mean f1': 0.9483694434165955, 'Val/mean precision': 0.9442490935325623, 'Val/mean recall': 0.9525259733200073, 'Val/mean hd95_metric': 10.712718963623047} +Epoch [3781/4000] Training [1/39] Loss: 0.00344 +Epoch [3781/4000] Training [2/39] Loss: 0.00445 +Epoch [3781/4000] Training [3/39] Loss: 0.00358 +Epoch [3781/4000] Training [4/39] Loss: 0.00444 +Epoch [3781/4000] Training [5/39] Loss: 0.12882 +Epoch [3781/4000] Training [6/39] Loss: 0.00526 +Epoch [3781/4000] Training [7/39] Loss: 0.00263 +Epoch [3781/4000] Training [8/39] Loss: 0.00550 +Epoch [3781/4000] Training [9/39] Loss: 0.00497 +Epoch [3781/4000] Training [10/39] Loss: 0.00392 +Epoch [3781/4000] Training [11/39] Loss: 0.00597 +Epoch [3781/4000] Training [12/39] Loss: 0.00612 +Epoch [3781/4000] Training [13/39] Loss: 0.01025 +Epoch [3781/4000] Training [14/39] Loss: 0.00411 +Epoch [3781/4000] Training [15/39] Loss: 0.00508 +Epoch [3781/4000] Training [16/39] Loss: 0.00391 +Epoch [3781/4000] Training [17/39] Loss: 0.00608 +Epoch [3781/4000] Training [18/39] Loss: 0.12754 +Epoch [3781/4000] Training [19/39] Loss: 0.12794 +Epoch [3781/4000] Training [20/39] Loss: 0.00387 +Epoch [3781/4000] Training [21/39] Loss: 0.00618 +Epoch [3781/4000] Training [22/39] Loss: 0.13026 +Epoch [3781/4000] Training [23/39] Loss: 0.12871 +Epoch [3781/4000] Training [24/39] Loss: 0.00449 +Epoch [3781/4000] Training [25/39] Loss: 0.00589 +Epoch [3781/4000] Training [26/39] Loss: 0.25744 +Epoch [3781/4000] Training [27/39] Loss: 0.00495 +Epoch [3781/4000] Training [28/39] Loss: 0.00546 +Epoch [3781/4000] Training [29/39] Loss: 0.00232 +Epoch [3781/4000] Training [30/39] Loss: 0.00662 +Epoch [3781/4000] Training [31/39] Loss: 0.13169 +Epoch [3781/4000] Training [32/39] Loss: 0.00623 +Epoch [3781/4000] Training [33/39] Loss: 0.00391 +Epoch [3781/4000] Training [34/39] Loss: 0.00443 +Epoch [3781/4000] Training [35/39] Loss: 0.00437 +Epoch [3781/4000] Training [36/39] Loss: 0.00464 +Epoch [3781/4000] Training [37/39] Loss: 0.13051 +Epoch [3781/4000] Training [38/39] Loss: 0.00497 +Epoch [3781/4000] Training [39/39] Loss: 0.12828 +Epoch [3781/4000] Training metric {'Train/mean dice_metric': 0.9953907132148743, 'Train/mean miou_metric': 0.9920594692230225, 'Train/mean f1': 0.9966671466827393, 'Train/mean precision': 0.9963054060935974, 'Train/mean recall': 0.9970292448997498, 'Train/mean hd95_metric': 1.089394450187683} +Epoch [3781/4000] Validation [1/10] Loss: 0.73006 focal_loss 0.64403 dice_loss 0.08603 +Epoch [3781/4000] Validation [2/10] Loss: 0.50416 focal_loss 0.40542 dice_loss 0.09874 +Epoch [3781/4000] Validation [3/10] Loss: 0.40257 focal_loss 0.29130 dice_loss 0.11127 +Epoch [3781/4000] Validation [4/10] Loss: 0.89201 focal_loss 0.32759 dice_loss 0.56442 +Epoch [3781/4000] Validation [5/10] Loss: 3.14367 focal_loss 2.46978 dice_loss 0.67389 +Epoch [3781/4000] Validation [6/10] Loss: 1.34053 focal_loss 0.62655 dice_loss 0.71398 +Epoch [3781/4000] Validation [7/10] Loss: 1.17660 focal_loss 0.52629 dice_loss 0.65031 +Epoch [3781/4000] Validation [8/10] Loss: 2.46570 focal_loss 1.84402 dice_loss 0.62168 +Epoch [3781/4000] Validation [9/10] Loss: 1.59404 focal_loss 1.04979 dice_loss 0.54425 +Epoch [3781/4000] Validation [10/10] Loss: 1.90981 focal_loss 1.17368 dice_loss 0.73613 +Epoch [3781/4000] Validation metric {'Val/mean dice_metric': 0.9506035447120667, 'Val/mean miou_metric': 0.9346145391464233, 'Val/mean f1': 0.9483432173728943, 'Val/mean precision': 0.9442131519317627, 'Val/mean recall': 0.9525095224380493, 'Val/mean hd95_metric': 10.751276016235352} +Cheakpoint... +Epoch [3781/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506035447120667, 'Val/mean miou_metric': 0.9346145391464233, 'Val/mean f1': 0.9483432173728943, 'Val/mean precision': 0.9442131519317627, 'Val/mean recall': 0.9525095224380493, 'Val/mean hd95_metric': 10.751276016235352} +Epoch [3782/4000] Training [1/39] Loss: 0.25596 +Epoch [3782/4000] Training [2/39] Loss: 0.00535 +Epoch [3782/4000] Training [3/39] Loss: 0.00313 +Epoch [3782/4000] Training [4/39] Loss: 0.00433 +Epoch [3782/4000] Training [5/39] Loss: 0.00494 +Epoch [3782/4000] Training [6/39] Loss: 0.00412 +Epoch [3782/4000] Training [7/39] Loss: 0.12868 +Epoch [3782/4000] Training [8/39] Loss: 0.00492 +Epoch [3782/4000] Training [9/39] Loss: 0.00460 +Epoch [3782/4000] Training [10/39] Loss: 0.00472 +Epoch [3782/4000] Training [11/39] Loss: 0.12776 +Epoch [3782/4000] Training [12/39] Loss: 0.00662 +Epoch [3782/4000] Training [13/39] Loss: 0.13089 +Epoch [3782/4000] Training [14/39] Loss: 0.00425 +Epoch [3782/4000] Training [15/39] Loss: 0.00291 +Epoch [3782/4000] Training [16/39] Loss: 0.00490 +Epoch [3782/4000] Training [17/39] Loss: 0.00705 +Epoch [3782/4000] Training [18/39] Loss: 0.00627 +Epoch [3782/4000] Training [19/39] Loss: 0.12847 +Epoch [3782/4000] Training [20/39] Loss: 0.00545 +Epoch [3782/4000] Training [21/39] Loss: 0.00472 +Epoch [3782/4000] Training [22/39] Loss: 0.00405 +Epoch [3782/4000] Training [23/39] Loss: 0.00372 +Epoch [3782/4000] Training [24/39] Loss: 0.00523 +Epoch [3782/4000] Training [25/39] Loss: 0.00542 +Epoch [3782/4000] Training [26/39] Loss: 0.12921 +Epoch [3782/4000] Training [27/39] Loss: 0.00678 +Epoch [3782/4000] Training [28/39] Loss: 0.00723 +Epoch [3782/4000] Training [29/39] Loss: 0.00494 +Epoch [3782/4000] Training [30/39] Loss: 0.12797 +Epoch [3782/4000] Training [31/39] Loss: 0.00305 +Epoch [3782/4000] Training [32/39] Loss: 0.12906 +Epoch [3782/4000] Training [33/39] Loss: 0.00299 +Epoch [3782/4000] Training [34/39] Loss: 0.12842 +Epoch [3782/4000] Training [35/39] Loss: 0.00267 +Epoch [3782/4000] Training [36/39] Loss: 0.00395 +Epoch [3782/4000] Training [37/39] Loss: 0.13055 +Epoch [3782/4000] Training [38/39] Loss: 0.25312 +Epoch [3782/4000] Training [39/39] Loss: 0.00336 +Epoch [3782/4000] Training metric {'Train/mean dice_metric': 0.9964193105697632, 'Train/mean miou_metric': 0.9932832717895508, 'Train/mean f1': 0.9970189929008484, 'Train/mean precision': 0.9965068697929382, 'Train/mean recall': 0.997531533241272, 'Train/mean hd95_metric': 0.9296468496322632} +Epoch [3782/4000] Validation [1/10] Loss: 0.71171 focal_loss 0.62704 dice_loss 0.08466 +Epoch [3782/4000] Validation [2/10] Loss: 0.51073 focal_loss 0.40720 dice_loss 0.10353 +Epoch [3782/4000] Validation [3/10] Loss: 0.41519 focal_loss 0.30242 dice_loss 0.11277 +Epoch [3782/4000] Validation [4/10] Loss: 0.88182 focal_loss 0.31916 dice_loss 0.56266 +Epoch [3782/4000] Validation [5/10] Loss: 3.11210 focal_loss 2.43808 dice_loss 0.67401 +Epoch [3782/4000] Validation [6/10] Loss: 1.31073 focal_loss 0.59640 dice_loss 0.71433 +Epoch [3782/4000] Validation [7/10] Loss: 1.16217 focal_loss 0.51211 dice_loss 0.65006 +Epoch [3782/4000] Validation [8/10] Loss: 2.49106 focal_loss 1.86058 dice_loss 0.63048 +Epoch [3782/4000] Validation [9/10] Loss: 1.55675 focal_loss 1.01312 dice_loss 0.54363 +Epoch [3782/4000] Validation [10/10] Loss: 1.85922 focal_loss 1.12417 dice_loss 0.73505 +Epoch [3782/4000] Validation metric {'Val/mean dice_metric': 0.9513647556304932, 'Val/mean miou_metric': 0.9355589151382446, 'Val/mean f1': 0.948459804058075, 'Val/mean precision': 0.945336103439331, 'Val/mean recall': 0.9516041278839111, 'Val/mean hd95_metric': 10.5881986618042} +Cheakpoint... +Epoch [3782/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513647556304932, 'Val/mean miou_metric': 0.9355589151382446, 'Val/mean f1': 0.948459804058075, 'Val/mean precision': 0.945336103439331, 'Val/mean recall': 0.9516041278839111, 'Val/mean hd95_metric': 10.5881986618042} +Epoch [3783/4000] Training [1/39] Loss: 0.00488 +Epoch [3783/4000] Training [2/39] Loss: 0.00400 +Epoch [3783/4000] Training [3/39] Loss: 0.25395 +Epoch [3783/4000] Training [4/39] Loss: 0.00346 +Epoch [3783/4000] Training [5/39] Loss: 0.00662 +Epoch [3783/4000] Training [6/39] Loss: 0.12859 +Epoch [3783/4000] Training [7/39] Loss: 0.00364 +Epoch [3783/4000] Training [8/39] Loss: 0.00488 +Epoch [3783/4000] Training [9/39] Loss: 0.00331 +Epoch [3783/4000] Training [10/39] Loss: 0.00360 +Epoch [3783/4000] Training [11/39] Loss: 0.12919 +Epoch [3783/4000] Training [12/39] Loss: 0.00308 +Epoch [3783/4000] Training [13/39] Loss: 0.12831 +Epoch [3783/4000] Training [14/39] Loss: 0.00501 +Epoch [3783/4000] Training [15/39] Loss: 0.00488 +Epoch [3783/4000] Training [16/39] Loss: 0.00369 +Epoch [3783/4000] Training [17/39] Loss: 0.00283 +Epoch [3783/4000] Training [18/39] Loss: 0.00379 +Epoch [3783/4000] Training [19/39] Loss: 0.12744 +Epoch [3783/4000] Training [20/39] Loss: 0.00331 +Epoch [3783/4000] Training [21/39] Loss: 0.13141 +Epoch [3783/4000] Training [22/39] Loss: 0.00535 +Epoch [3783/4000] Training [23/39] Loss: 0.00896 +Epoch [3783/4000] Training [24/39] Loss: 0.00657 +Epoch [3783/4000] Training [25/39] Loss: 0.00491 +Epoch [3783/4000] Training [26/39] Loss: 0.00466 +Epoch [3783/4000] Training [27/39] Loss: 0.12892 +Epoch [3783/4000] Training [28/39] Loss: 0.00402 +Epoch [3783/4000] Training [29/39] Loss: 0.00391 +Epoch [3783/4000] Training [30/39] Loss: 0.00467 +Epoch [3783/4000] Training [31/39] Loss: 0.12921 +Epoch [3783/4000] Training [32/39] Loss: 0.12889 +Epoch [3783/4000] Training [33/39] Loss: 0.00501 +Epoch [3783/4000] Training [34/39] Loss: 0.00392 +Epoch [3783/4000] Training [35/39] Loss: 0.00398 +Epoch [3783/4000] Training [36/39] Loss: 0.00719 +Epoch [3783/4000] Training [37/39] Loss: 0.00496 +Epoch [3783/4000] Training [38/39] Loss: 0.12985 +Epoch [3783/4000] Training [39/39] Loss: 0.00640 +Epoch [3783/4000] Training metric {'Train/mean dice_metric': 0.9964177012443542, 'Train/mean miou_metric': 0.993278443813324, 'Train/mean f1': 0.9969131350517273, 'Train/mean precision': 0.9964728355407715, 'Train/mean recall': 0.9973537921905518, 'Train/mean hd95_metric': 0.9407743811607361} +Epoch [3783/4000] Validation [1/10] Loss: 0.71081 focal_loss 0.62480 dice_loss 0.08601 +Epoch [3783/4000] Validation [2/10] Loss: 0.49718 focal_loss 0.39785 dice_loss 0.09933 +Epoch [3783/4000] Validation [3/10] Loss: 0.39533 focal_loss 0.28382 dice_loss 0.11151 +Epoch [3783/4000] Validation [4/10] Loss: 0.88825 focal_loss 0.32394 dice_loss 0.56431 +Epoch [3783/4000] Validation [5/10] Loss: 3.07766 focal_loss 2.40364 dice_loss 0.67402 +Epoch [3783/4000] Validation [6/10] Loss: 1.32481 focal_loss 0.61158 dice_loss 0.71323 +Epoch [3783/4000] Validation [7/10] Loss: 1.16966 focal_loss 0.51872 dice_loss 0.65094 +Epoch [3783/4000] Validation [8/10] Loss: 2.39698 focal_loss 1.77645 dice_loss 0.62054 +Epoch [3783/4000] Validation [9/10] Loss: 1.56534 focal_loss 1.02036 dice_loss 0.54498 +Epoch [3783/4000] Validation [10/10] Loss: 1.89165 focal_loss 1.15524 dice_loss 0.73641 +Epoch [3783/4000] Validation metric {'Val/mean dice_metric': 0.9515213966369629, 'Val/mean miou_metric': 0.9357165694236755, 'Val/mean f1': 0.9481581449508667, 'Val/mean precision': 0.9436768293380737, 'Val/mean recall': 0.9526822566986084, 'Val/mean hd95_metric': 10.652945518493652} +Cheakpoint... +Epoch [3783/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515213966369629, 'Val/mean miou_metric': 0.9357165694236755, 'Val/mean f1': 0.9481581449508667, 'Val/mean precision': 0.9436768293380737, 'Val/mean recall': 0.9526822566986084, 'Val/mean hd95_metric': 10.652945518493652} +Epoch [3784/4000] Training [1/39] Loss: 0.00344 +Epoch [3784/4000] Training [2/39] Loss: 0.12844 +Epoch [3784/4000] Training [3/39] Loss: 0.00447 +Epoch [3784/4000] Training [4/39] Loss: 0.12924 +Epoch [3784/4000] Training [5/39] Loss: 0.00420 +Epoch [3784/4000] Training [6/39] Loss: 0.12923 +Epoch [3784/4000] Training [7/39] Loss: 0.00392 +Epoch [3784/4000] Training [8/39] Loss: 0.01108 +Epoch [3784/4000] Training [9/39] Loss: 0.00553 +Epoch [3784/4000] Training [10/39] Loss: 0.12781 +Epoch [3784/4000] Training [11/39] Loss: 0.12761 +Epoch [3784/4000] Training [12/39] Loss: 0.00419 +Epoch [3784/4000] Training [13/39] Loss: 0.00413 +Epoch [3784/4000] Training [14/39] Loss: 0.09050 +Epoch [3784/4000] Training [15/39] Loss: 0.00365 +Epoch [3784/4000] Training [16/39] Loss: 0.12941 +Epoch [3784/4000] Training [17/39] Loss: 0.00478 +Epoch [3784/4000] Training [18/39] Loss: 0.25453 +Epoch [3784/4000] Training [19/39] Loss: 0.00268 +Epoch [3784/4000] Training [20/39] Loss: 0.00419 +Epoch [3784/4000] Training [21/39] Loss: 0.00459 +Epoch [3784/4000] Training [22/39] Loss: 0.00277 +Epoch [3784/4000] Training [23/39] Loss: 0.00666 +Epoch [3784/4000] Training [24/39] Loss: 0.12946 +Epoch [3784/4000] Training [25/39] Loss: 0.00870 +Epoch [3784/4000] Training [26/39] Loss: 0.00661 +Epoch [3784/4000] Training [27/39] Loss: 0.00459 +Epoch [3784/4000] Training [28/39] Loss: 0.00407 +Epoch [3784/4000] Training [29/39] Loss: 0.00484 +Epoch [3784/4000] Training [30/39] Loss: 0.00495 +Epoch [3784/4000] Training [31/39] Loss: 0.13120 +Epoch [3784/4000] Training [32/39] Loss: 0.00352 +Epoch [3784/4000] Training [33/39] Loss: 0.00521 +Epoch [3784/4000] Training [34/39] Loss: 0.00492 +Epoch [3784/4000] Training [35/39] Loss: 0.00465 +Epoch [3784/4000] Training [36/39] Loss: 0.12842 +Epoch [3784/4000] Training [37/39] Loss: 0.12934 +Epoch [3784/4000] Training [38/39] Loss: 0.00549 +Epoch [3784/4000] Training [39/39] Loss: 0.00464 +Epoch [3784/4000] Training metric {'Train/mean dice_metric': 0.9964547157287598, 'Train/mean miou_metric': 0.9933682680130005, 'Train/mean f1': 0.9970003366470337, 'Train/mean precision': 0.9965658187866211, 'Train/mean recall': 0.9974352717399597, 'Train/mean hd95_metric': 0.9154257774353027} +Epoch [3784/4000] Validation [1/10] Loss: 0.72268 focal_loss 0.63577 dice_loss 0.08691 +Epoch [3784/4000] Validation [2/10] Loss: 0.49612 focal_loss 0.39980 dice_loss 0.09632 +Epoch [3784/4000] Validation [3/10] Loss: 0.38955 focal_loss 0.27869 dice_loss 0.11086 +Epoch [3784/4000] Validation [4/10] Loss: 0.90076 focal_loss 0.33511 dice_loss 0.56565 +Epoch [3784/4000] Validation [5/10] Loss: 3.08458 focal_loss 2.41067 dice_loss 0.67391 +Epoch [3784/4000] Validation [6/10] Loss: 1.34711 focal_loss 0.63458 dice_loss 0.71253 +Epoch [3784/4000] Validation [7/10] Loss: 1.18534 focal_loss 0.53179 dice_loss 0.65354 +Epoch [3784/4000] Validation [8/10] Loss: 2.37933 focal_loss 1.76437 dice_loss 0.61496 +Epoch [3784/4000] Validation [9/10] Loss: 1.59151 focal_loss 1.04635 dice_loss 0.54516 +Epoch [3784/4000] Validation [10/10] Loss: 1.93018 focal_loss 1.19301 dice_loss 0.73716 +Epoch [3784/4000] Validation metric {'Val/mean dice_metric': 0.9516062140464783, 'Val/mean miou_metric': 0.9358073472976685, 'Val/mean f1': 0.9484416842460632, 'Val/mean precision': 0.9431399703025818, 'Val/mean recall': 0.9538034200668335, 'Val/mean hd95_metric': 10.719398498535156} +Cheakpoint... +Epoch [3784/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516062140464783, 'Val/mean miou_metric': 0.9358073472976685, 'Val/mean f1': 0.9484416842460632, 'Val/mean precision': 0.9431399703025818, 'Val/mean recall': 0.9538034200668335, 'Val/mean hd95_metric': 10.719398498535156} +Epoch [3785/4000] Training [1/39] Loss: 0.00592 +Epoch [3785/4000] Training [2/39] Loss: 0.00442 +Epoch [3785/4000] Training [3/39] Loss: 0.00536 +Epoch [3785/4000] Training [4/39] Loss: 0.00348 +Epoch [3785/4000] Training [5/39] Loss: 0.00242 +Epoch [3785/4000] Training [6/39] Loss: 0.00902 +Epoch [3785/4000] Training [7/39] Loss: 0.00598 +Epoch [3785/4000] Training [8/39] Loss: 0.00409 +Epoch [3785/4000] Training [9/39] Loss: 0.00667 +Epoch [3785/4000] Training [10/39] Loss: 0.12889 +Epoch [3785/4000] Training [11/39] Loss: 0.12787 +Epoch [3785/4000] Training [12/39] Loss: 0.00294 +Epoch [3785/4000] Training [13/39] Loss: 0.00439 +Epoch [3785/4000] Training [14/39] Loss: 0.00325 +Epoch [3785/4000] Training [15/39] Loss: 0.00309 +Epoch [3785/4000] Training [16/39] Loss: 0.00506 +Epoch [3785/4000] Training [17/39] Loss: 0.00464 +Epoch [3785/4000] Training [18/39] Loss: 0.00595 +Epoch [3785/4000] Training [19/39] Loss: 0.25487 +Epoch [3785/4000] Training [20/39] Loss: 0.00483 +Epoch [3785/4000] Training [21/39] Loss: 0.00764 +Epoch [3785/4000] Training [22/39] Loss: 0.25793 +Epoch [3785/4000] Training [23/39] Loss: 0.00401 +Epoch [3785/4000] Training [24/39] Loss: 0.12879 +Epoch [3785/4000] Training [25/39] Loss: 0.13126 +Epoch [3785/4000] Training [26/39] Loss: 0.12973 +Epoch [3785/4000] Training [27/39] Loss: 0.00508 +Epoch [3785/4000] Training [28/39] Loss: 0.00394 +Epoch [3785/4000] Training [29/39] Loss: 0.00320 +Epoch [3785/4000] Training [30/39] Loss: 0.12989 +Epoch [3785/4000] Training [31/39] Loss: 0.12956 +Epoch [3785/4000] Training [32/39] Loss: 0.12874 +Epoch [3785/4000] Training [33/39] Loss: 0.00962 +Epoch [3785/4000] Training [34/39] Loss: 0.13584 +Epoch [3785/4000] Training [35/39] Loss: 0.13215 +Epoch [3785/4000] Training [36/39] Loss: 0.00461 +Epoch [3785/4000] Training [37/39] Loss: 0.00425 +Epoch [3785/4000] Training [38/39] Loss: 0.25424 +Epoch [3785/4000] Training [39/39] Loss: 0.00469 +Epoch [3785/4000] Training metric {'Train/mean dice_metric': 0.9960842132568359, 'Train/mean miou_metric': 0.9926517605781555, 'Train/mean f1': 0.9966902136802673, 'Train/mean precision': 0.996220588684082, 'Train/mean recall': 0.9971600770950317, 'Train/mean hd95_metric': 0.9675722122192383} +Epoch [3785/4000] Validation [1/10] Loss: 0.72291 focal_loss 0.63502 dice_loss 0.08789 +Epoch [3785/4000] Validation [2/10] Loss: 0.49191 focal_loss 0.39890 dice_loss 0.09301 +Epoch [3785/4000] Validation [3/10] Loss: 0.37868 focal_loss 0.26867 dice_loss 0.11001 +Epoch [3785/4000] Validation [4/10] Loss: 0.90731 focal_loss 0.34048 dice_loss 0.56683 +Epoch [3785/4000] Validation [5/10] Loss: 3.04983 focal_loss 2.37619 dice_loss 0.67364 +Epoch [3785/4000] Validation [6/10] Loss: 1.35970 focal_loss 0.64701 dice_loss 0.71269 +Epoch [3785/4000] Validation [7/10] Loss: 1.19990 focal_loss 0.54291 dice_loss 0.65699 +Epoch [3785/4000] Validation [8/10] Loss: 2.29684 focal_loss 1.69113 dice_loss 0.60571 +Epoch [3785/4000] Validation [9/10] Loss: 1.61308 focal_loss 1.06754 dice_loss 0.54554 +Epoch [3785/4000] Validation [10/10] Loss: 1.96039 focal_loss 1.22216 dice_loss 0.73823 +Epoch [3785/4000] Validation metric {'Val/mean dice_metric': 0.9513042569160461, 'Val/mean miou_metric': 0.9351711273193359, 'Val/mean f1': 0.947624683380127, 'Val/mean precision': 0.941094696521759, 'Val/mean recall': 0.9542460441589355, 'Val/mean hd95_metric': 10.976624488830566} +Cheakpoint... +Epoch [3785/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513042569160461, 'Val/mean miou_metric': 0.9351711273193359, 'Val/mean f1': 0.947624683380127, 'Val/mean precision': 0.941094696521759, 'Val/mean recall': 0.9542460441589355, 'Val/mean hd95_metric': 10.976624488830566} +Epoch [3786/4000] Training [1/39] Loss: 0.00493 +Epoch [3786/4000] Training [2/39] Loss: 0.12827 +Epoch [3786/4000] Training [3/39] Loss: 0.00537 +Epoch [3786/4000] Training [4/39] Loss: 0.00311 +Epoch [3786/4000] Training [5/39] Loss: 0.12958 +Epoch [3786/4000] Training [6/39] Loss: 0.00487 +Epoch [3786/4000] Training [7/39] Loss: 0.12700 +Epoch [3786/4000] Training [8/39] Loss: 0.00236 +Epoch [3786/4000] Training [9/39] Loss: 0.00710 +Epoch [3786/4000] Training [10/39] Loss: 0.00541 +Epoch [3786/4000] Training [11/39] Loss: 0.00355 +Epoch [3786/4000] Training [12/39] Loss: 0.00371 +Epoch [3786/4000] Training [13/39] Loss: 0.00676 +Epoch [3786/4000] Training [14/39] Loss: 0.00581 +Epoch [3786/4000] Training [15/39] Loss: 0.00695 +Epoch [3786/4000] Training [16/39] Loss: 0.00436 +Epoch [3786/4000] Training [17/39] Loss: 0.00518 +Epoch [3786/4000] Training [18/39] Loss: 0.12902 +Epoch [3786/4000] Training [19/39] Loss: 0.12822 +Epoch [3786/4000] Training [20/39] Loss: 0.00349 +Epoch [3786/4000] Training [21/39] Loss: 0.00494 +Epoch [3786/4000] Training [22/39] Loss: 0.25236 +Epoch [3786/4000] Training [23/39] Loss: 0.00516 +Epoch [3786/4000] Training [24/39] Loss: 0.00422 +Epoch [3786/4000] Training [25/39] Loss: 0.00739 +Epoch [3786/4000] Training [26/39] Loss: 0.00402 +Epoch [3786/4000] Training [27/39] Loss: 0.00847 +Epoch [3786/4000] Training [28/39] Loss: 0.00769 +Epoch [3786/4000] Training [29/39] Loss: 0.00338 +Epoch [3786/4000] Training [30/39] Loss: 0.00651 +Epoch [3786/4000] Training [31/39] Loss: 0.00327 +Epoch [3786/4000] Training [32/39] Loss: 0.00395 +Epoch [3786/4000] Training [33/39] Loss: 0.12888 +Epoch [3786/4000] Training [34/39] Loss: 0.00623 +Epoch [3786/4000] Training [35/39] Loss: 0.00335 +Epoch [3786/4000] Training [36/39] Loss: 0.12971 +Epoch [3786/4000] Training [37/39] Loss: 0.00596 +Epoch [3786/4000] Training [38/39] Loss: 0.13157 +Epoch [3786/4000] Training [39/39] Loss: 0.00432 +Epoch [3786/4000] Training metric {'Train/mean dice_metric': 0.9954524636268616, 'Train/mean miou_metric': 0.9922035336494446, 'Train/mean f1': 0.9968531727790833, 'Train/mean precision': 0.9963839054107666, 'Train/mean recall': 0.9973229765892029, 'Train/mean hd95_metric': 0.9387788772583008} +Epoch [3786/4000] Validation [1/10] Loss: 0.72750 focal_loss 0.64092 dice_loss 0.08658 +Epoch [3786/4000] Validation [2/10] Loss: 0.49919 focal_loss 0.40218 dice_loss 0.09701 +Epoch [3786/4000] Validation [3/10] Loss: 0.40146 focal_loss 0.29009 dice_loss 0.11137 +Epoch [3786/4000] Validation [4/10] Loss: 0.89668 focal_loss 0.33210 dice_loss 0.56458 +Epoch [3786/4000] Validation [5/10] Loss: 3.12600 focal_loss 2.45210 dice_loss 0.67390 +Epoch [3786/4000] Validation [6/10] Loss: 1.33193 focal_loss 0.62011 dice_loss 0.71182 +Epoch [3786/4000] Validation [7/10] Loss: 1.18225 focal_loss 0.52864 dice_loss 0.65361 +Epoch [3786/4000] Validation [8/10] Loss: 2.41864 focal_loss 1.79999 dice_loss 0.61864 +Epoch [3786/4000] Validation [9/10] Loss: 1.57964 focal_loss 1.03522 dice_loss 0.54442 +Epoch [3786/4000] Validation [10/10] Loss: 1.91550 focal_loss 1.17925 dice_loss 0.73625 +Epoch [3786/4000] Validation metric {'Val/mean dice_metric': 0.9506543874740601, 'Val/mean miou_metric': 0.934719979763031, 'Val/mean f1': 0.9482791423797607, 'Val/mean precision': 0.9436084628105164, 'Val/mean recall': 0.9529962539672852, 'Val/mean hd95_metric': 10.694215774536133} +Cheakpoint... +Epoch [3786/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506543874740601, 'Val/mean miou_metric': 0.934719979763031, 'Val/mean f1': 0.9482791423797607, 'Val/mean precision': 0.9436084628105164, 'Val/mean recall': 0.9529962539672852, 'Val/mean hd95_metric': 10.694215774536133} +Epoch [3787/4000] Training [1/39] Loss: 0.00294 +Epoch [3787/4000] Training [2/39] Loss: 0.00338 +Epoch [3787/4000] Training [3/39] Loss: 0.00655 +Epoch [3787/4000] Training [4/39] Loss: 0.00367 +Epoch [3787/4000] Training [5/39] Loss: 0.12886 +Epoch [3787/4000] Training [6/39] Loss: 0.00483 +Epoch [3787/4000] Training [7/39] Loss: 0.25385 +Epoch [3787/4000] Training [8/39] Loss: 0.00554 +Epoch [3787/4000] Training [9/39] Loss: 0.00381 +Epoch [3787/4000] Training [10/39] Loss: 0.00633 +Epoch [3787/4000] Training [11/39] Loss: 0.00600 +Epoch [3787/4000] Training [12/39] Loss: 0.12785 +Epoch [3787/4000] Training [13/39] Loss: 0.12784 +Epoch [3787/4000] Training [14/39] Loss: 0.00350 +Epoch [3787/4000] Training [15/39] Loss: 0.12847 +Epoch [3787/4000] Training [16/39] Loss: 0.00467 +Epoch [3787/4000] Training [17/39] Loss: 0.00623 +Epoch [3787/4000] Training [18/39] Loss: 0.25285 +Epoch [3787/4000] Training [19/39] Loss: 0.00570 +Epoch [3787/4000] Training [20/39] Loss: 0.00356 +Epoch [3787/4000] Training [21/39] Loss: 0.12864 +Epoch [3787/4000] Training [22/39] Loss: 0.12988 +Epoch [3787/4000] Training [23/39] Loss: 0.00782 +Epoch [3787/4000] Training [24/39] Loss: 0.13002 +Epoch [3787/4000] Training [25/39] Loss: 0.12887 +Epoch [3787/4000] Training [26/39] Loss: 0.00436 +Epoch [3787/4000] Training [27/39] Loss: 0.00384 +Epoch [3787/4000] Training [28/39] Loss: 0.00396 +Epoch [3787/4000] Training [29/39] Loss: 0.00421 +Epoch [3787/4000] Training [30/39] Loss: 0.00355 +Epoch [3787/4000] Training [31/39] Loss: 0.12992 +Epoch [3787/4000] Training [32/39] Loss: 0.00577 +Epoch [3787/4000] Training [33/39] Loss: 0.00407 +Epoch [3787/4000] Training [34/39] Loss: 0.00529 +Epoch [3787/4000] Training [35/39] Loss: 0.00564 +Epoch [3787/4000] Training [36/39] Loss: 0.00631 +Epoch [3787/4000] Training [37/39] Loss: 0.00625 +Epoch [3787/4000] Training [38/39] Loss: 0.00602 +Epoch [3787/4000] Training [39/39] Loss: 0.00408 +Epoch [3787/4000] Training metric {'Train/mean dice_metric': 0.9955146312713623, 'Train/mean miou_metric': 0.9923036694526672, 'Train/mean f1': 0.9968459010124207, 'Train/mean precision': 0.9964046478271484, 'Train/mean recall': 0.9972875118255615, 'Train/mean hd95_metric': 0.9130404591560364} +Epoch [3787/4000] Validation [1/10] Loss: 0.71977 focal_loss 0.63270 dice_loss 0.08706 +Epoch [3787/4000] Validation [2/10] Loss: 0.49492 focal_loss 0.39893 dice_loss 0.09599 +Epoch [3787/4000] Validation [3/10] Loss: 0.38929 focal_loss 0.27840 dice_loss 0.11089 +Epoch [3787/4000] Validation [4/10] Loss: 0.89859 focal_loss 0.33317 dice_loss 0.56542 +Epoch [3787/4000] Validation [5/10] Loss: 3.08217 focal_loss 2.40831 dice_loss 0.67385 +Epoch [3787/4000] Validation [6/10] Loss: 1.33596 focal_loss 0.62455 dice_loss 0.71141 +Epoch [3787/4000] Validation [7/10] Loss: 1.18713 focal_loss 0.53248 dice_loss 0.65465 +Epoch [3787/4000] Validation [8/10] Loss: 2.34349 focal_loss 1.73181 dice_loss 0.61168 +Epoch [3787/4000] Validation [9/10] Loss: 1.59041 focal_loss 1.04594 dice_loss 0.54446 +Epoch [3787/4000] Validation [10/10] Loss: 1.92404 focal_loss 1.18662 dice_loss 0.73741 +Epoch [3787/4000] Validation metric {'Val/mean dice_metric': 0.9507682919502258, 'Val/mean miou_metric': 0.9348628520965576, 'Val/mean f1': 0.9482777714729309, 'Val/mean precision': 0.9428603649139404, 'Val/mean recall': 0.9537579417228699, 'Val/mean hd95_metric': 10.769922256469727} +Cheakpoint... +Epoch [3787/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507682919502258, 'Val/mean miou_metric': 0.9348628520965576, 'Val/mean f1': 0.9482777714729309, 'Val/mean precision': 0.9428603649139404, 'Val/mean recall': 0.9537579417228699, 'Val/mean hd95_metric': 10.769922256469727} +Epoch [3788/4000] Training [1/39] Loss: 0.00386 +Epoch [3788/4000] Training [2/39] Loss: 0.00794 +Epoch [3788/4000] Training [3/39] Loss: 0.00542 +Epoch [3788/4000] Training [4/39] Loss: 0.00310 +Epoch [3788/4000] Training [5/39] Loss: 0.00412 +Epoch [3788/4000] Training [6/39] Loss: 0.00520 +Epoch [3788/4000] Training [7/39] Loss: 0.00516 +Epoch [3788/4000] Training [8/39] Loss: 0.12867 +Epoch [3788/4000] Training [9/39] Loss: 0.00257 +Epoch [3788/4000] Training [10/39] Loss: 0.00424 +Epoch [3788/4000] Training [11/39] Loss: 0.12953 +Epoch [3788/4000] Training [12/39] Loss: 0.00618 +Epoch [3788/4000] Training [13/39] Loss: 0.00717 +Epoch [3788/4000] Training [14/39] Loss: 0.00689 +Epoch [3788/4000] Training [15/39] Loss: 0.12986 +Epoch [3788/4000] Training [16/39] Loss: 0.12922 +Epoch [3788/4000] Training [17/39] Loss: 0.00610 +Epoch [3788/4000] Training [18/39] Loss: 0.12884 +Epoch [3788/4000] Training [19/39] Loss: 0.00614 +Epoch [3788/4000] Training [20/39] Loss: 0.12750 +Epoch [3788/4000] Training [21/39] Loss: 0.12975 +Epoch [3788/4000] Training [22/39] Loss: 0.13120 +Epoch [3788/4000] Training [23/39] Loss: 0.00428 +Epoch [3788/4000] Training [24/39] Loss: 0.00249 +Epoch [3788/4000] Training [25/39] Loss: 0.00458 +Epoch [3788/4000] Training [26/39] Loss: 0.00407 +Epoch [3788/4000] Training [27/39] Loss: 0.00444 +Epoch [3788/4000] Training [28/39] Loss: 0.00657 +Epoch [3788/4000] Training [29/39] Loss: 0.00351 +Epoch [3788/4000] Training [30/39] Loss: 0.00517 +Epoch [3788/4000] Training [31/39] Loss: 0.00353 +Epoch [3788/4000] Training [32/39] Loss: 0.00358 +Epoch [3788/4000] Training [33/39] Loss: 0.00454 +Epoch [3788/4000] Training [34/39] Loss: 0.00589 +Epoch [3788/4000] Training [35/39] Loss: 0.00458 +Epoch [3788/4000] Training [36/39] Loss: 0.00834 +Epoch [3788/4000] Training [37/39] Loss: 0.00664 +Epoch [3788/4000] Training [38/39] Loss: 0.00365 +Epoch [3788/4000] Training [39/39] Loss: 0.00439 +Epoch [3788/4000] Training metric {'Train/mean dice_metric': 0.996450662612915, 'Train/mean miou_metric': 0.9933451414108276, 'Train/mean f1': 0.9968060255050659, 'Train/mean precision': 0.9963212013244629, 'Train/mean recall': 0.9972912669181824, 'Train/mean hd95_metric': 0.9419748187065125} +Epoch [3788/4000] Validation [1/10] Loss: 0.72558 focal_loss 0.63891 dice_loss 0.08668 +Epoch [3788/4000] Validation [2/10] Loss: 0.49773 focal_loss 0.39876 dice_loss 0.09897 +Epoch [3788/4000] Validation [3/10] Loss: 0.40091 focal_loss 0.28941 dice_loss 0.11150 +Epoch [3788/4000] Validation [4/10] Loss: 0.89085 focal_loss 0.32630 dice_loss 0.56455 +Epoch [3788/4000] Validation [5/10] Loss: 3.11411 focal_loss 2.44015 dice_loss 0.67396 +Epoch [3788/4000] Validation [6/10] Loss: 1.31551 focal_loss 0.60385 dice_loss 0.71166 +Epoch [3788/4000] Validation [7/10] Loss: 1.17473 focal_loss 0.52284 dice_loss 0.65189 +Epoch [3788/4000] Validation [8/10] Loss: 2.32865 focal_loss 1.71629 dice_loss 0.61235 +Epoch [3788/4000] Validation [9/10] Loss: 1.58800 focal_loss 1.04406 dice_loss 0.54394 +Epoch [3788/4000] Validation [10/10] Loss: 1.89279 focal_loss 1.15627 dice_loss 0.73652 +Epoch [3788/4000] Validation metric {'Val/mean dice_metric': 0.951580286026001, 'Val/mean miou_metric': 0.935781717300415, 'Val/mean f1': 0.9479848742485046, 'Val/mean precision': 0.9429322481155396, 'Val/mean recall': 0.9530919790267944, 'Val/mean hd95_metric': 10.816287994384766} +Cheakpoint... +Epoch [3788/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951580286026001, 'Val/mean miou_metric': 0.935781717300415, 'Val/mean f1': 0.9479848742485046, 'Val/mean precision': 0.9429322481155396, 'Val/mean recall': 0.9530919790267944, 'Val/mean hd95_metric': 10.816287994384766} +Epoch [3789/4000] Training [1/39] Loss: 0.00518 +Epoch [3789/4000] Training [2/39] Loss: 0.00413 +Epoch [3789/4000] Training [3/39] Loss: 0.12859 +Epoch [3789/4000] Training [4/39] Loss: 0.00333 +Epoch [3789/4000] Training [5/39] Loss: 0.13095 +Epoch [3789/4000] Training [6/39] Loss: 0.00427 +Epoch [3789/4000] Training [7/39] Loss: 0.00331 +Epoch [3789/4000] Training [8/39] Loss: 0.00289 +Epoch [3789/4000] Training [9/39] Loss: 0.00420 +Epoch [3789/4000] Training [10/39] Loss: 0.00395 +Epoch [3789/4000] Training [11/39] Loss: 0.12855 +Epoch [3789/4000] Training [12/39] Loss: 0.00447 +Epoch [3789/4000] Training [13/39] Loss: 0.00360 +Epoch [3789/4000] Training [14/39] Loss: 0.00414 +Epoch [3789/4000] Training [15/39] Loss: 0.00458 +Epoch [3789/4000] Training [16/39] Loss: 0.12933 +Epoch [3789/4000] Training [17/39] Loss: 0.00491 +Epoch [3789/4000] Training [18/39] Loss: 0.12861 +Epoch [3789/4000] Training [19/39] Loss: 0.12963 +Epoch [3789/4000] Training [20/39] Loss: 0.00461 +Epoch [3789/4000] Training [21/39] Loss: 0.25353 +Epoch [3789/4000] Training [22/39] Loss: 0.12792 +Epoch [3789/4000] Training [23/39] Loss: 0.00457 +Epoch [3789/4000] Training [24/39] Loss: 0.00743 +Epoch [3789/4000] Training [25/39] Loss: 0.13107 +Epoch [3789/4000] Training [26/39] Loss: 0.00362 +Epoch [3789/4000] Training [27/39] Loss: 0.00627 +Epoch [3789/4000] Training [28/39] Loss: 0.00473 +Epoch [3789/4000] Training [29/39] Loss: 0.00468 +Epoch [3789/4000] Training [30/39] Loss: 0.00683 +Epoch [3789/4000] Training [31/39] Loss: 0.00376 +Epoch [3789/4000] Training [32/39] Loss: 0.25497 +Epoch [3789/4000] Training [33/39] Loss: 0.37980 +Epoch [3789/4000] Training [34/39] Loss: 0.00303 +Epoch [3789/4000] Training [35/39] Loss: 0.00398 +Epoch [3789/4000] Training [36/39] Loss: 0.00350 +Epoch [3789/4000] Training [37/39] Loss: 0.13047 +Epoch [3789/4000] Training [38/39] Loss: 0.00403 +Epoch [3789/4000] Training [39/39] Loss: 0.12839 +Epoch [3789/4000] Training metric {'Train/mean dice_metric': 0.99648118019104, 'Train/mean miou_metric': 0.9934080243110657, 'Train/mean f1': 0.9969767928123474, 'Train/mean precision': 0.996514618396759, 'Train/mean recall': 0.9974393248558044, 'Train/mean hd95_metric': 0.913658082485199} +Epoch [3789/4000] Validation [1/10] Loss: 0.71350 focal_loss 0.62668 dice_loss 0.08683 +Epoch [3789/4000] Validation [2/10] Loss: 0.49966 focal_loss 0.40240 dice_loss 0.09726 +Epoch [3789/4000] Validation [3/10] Loss: 0.38402 focal_loss 0.27324 dice_loss 0.11078 +Epoch [3789/4000] Validation [4/10] Loss: 0.90092 focal_loss 0.33573 dice_loss 0.56519 +Epoch [3789/4000] Validation [5/10] Loss: 3.04500 focal_loss 2.37124 dice_loss 0.67376 +Epoch [3789/4000] Validation [6/10] Loss: 1.34313 focal_loss 0.63122 dice_loss 0.71191 +Epoch [3789/4000] Validation [7/10] Loss: 1.18649 focal_loss 0.53415 dice_loss 0.65234 +Epoch [3789/4000] Validation [8/10] Loss: 2.29314 focal_loss 1.68367 dice_loss 0.60947 +Epoch [3789/4000] Validation [9/10] Loss: 1.61881 focal_loss 1.07398 dice_loss 0.54483 +Epoch [3789/4000] Validation [10/10] Loss: 1.93223 focal_loss 1.19481 dice_loss 0.73741 +Epoch [3789/4000] Validation metric {'Val/mean dice_metric': 0.9516060948371887, 'Val/mean miou_metric': 0.9358416199684143, 'Val/mean f1': 0.9485813975334167, 'Val/mean precision': 0.9427675008773804, 'Val/mean recall': 0.9544675350189209, 'Val/mean hd95_metric': 10.681585311889648} +Cheakpoint... +Epoch [3789/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516060948371887, 'Val/mean miou_metric': 0.9358416199684143, 'Val/mean f1': 0.9485813975334167, 'Val/mean precision': 0.9427675008773804, 'Val/mean recall': 0.9544675350189209, 'Val/mean hd95_metric': 10.681585311889648} +Epoch [3790/4000] Training [1/39] Loss: 0.00343 +Epoch [3790/4000] Training [2/39] Loss: 0.00275 +Epoch [3790/4000] Training [3/39] Loss: 0.00310 +Epoch [3790/4000] Training [4/39] Loss: 0.12940 +Epoch [3790/4000] Training [5/39] Loss: 0.00740 +Epoch [3790/4000] Training [6/39] Loss: 0.00521 +Epoch [3790/4000] Training [7/39] Loss: 0.00460 +Epoch [3790/4000] Training [8/39] Loss: 0.00733 +Epoch [3790/4000] Training [9/39] Loss: 0.00379 +Epoch [3790/4000] Training [10/39] Loss: 0.00586 +Epoch [3790/4000] Training [11/39] Loss: 0.00384 +Epoch [3790/4000] Training [12/39] Loss: 0.13008 +Epoch [3790/4000] Training [13/39] Loss: 0.00489 +Epoch [3790/4000] Training [14/39] Loss: 0.00735 +Epoch [3790/4000] Training [15/39] Loss: 0.00438 +Epoch [3790/4000] Training [16/39] Loss: 0.00566 +Epoch [3790/4000] Training [17/39] Loss: 0.00895 +Epoch [3790/4000] Training [18/39] Loss: 0.12719 +Epoch [3790/4000] Training [19/39] Loss: 0.00626 +Epoch [3790/4000] Training [20/39] Loss: 0.00778 +Epoch [3790/4000] Training [21/39] Loss: 0.00491 +Epoch [3790/4000] Training [22/39] Loss: 0.00753 +Epoch [3790/4000] Training [23/39] Loss: 0.00607 +Epoch [3790/4000] Training [24/39] Loss: 0.13056 +Epoch [3790/4000] Training [25/39] Loss: 0.00519 +Epoch [3790/4000] Training [26/39] Loss: 0.00365 +Epoch [3790/4000] Training [27/39] Loss: 0.13030 +Epoch [3790/4000] Training [28/39] Loss: 0.13069 +Epoch [3790/4000] Training [29/39] Loss: 0.12799 +Epoch [3790/4000] Training [30/39] Loss: 0.00334 +Epoch [3790/4000] Training [31/39] Loss: 0.12818 +Epoch [3790/4000] Training [32/39] Loss: 0.00336 +Epoch [3790/4000] Training [33/39] Loss: 0.00581 +Epoch [3790/4000] Training [34/39] Loss: 0.12946 +Epoch [3790/4000] Training [35/39] Loss: 0.00671 +Epoch [3790/4000] Training [36/39] Loss: 0.00429 +Epoch [3790/4000] Training [37/39] Loss: 0.00575 +Epoch [3790/4000] Training [38/39] Loss: 0.00451 +Epoch [3790/4000] Training [39/39] Loss: 0.00369 +Epoch [3790/4000] Training metric {'Train/mean dice_metric': 0.9962827563285828, 'Train/mean miou_metric': 0.9930113554000854, 'Train/mean f1': 0.9967551827430725, 'Train/mean precision': 0.9963253140449524, 'Train/mean recall': 0.9971856474876404, 'Train/mean hd95_metric': 0.9494280219078064} +Epoch [3790/4000] Validation [1/10] Loss: 0.72463 focal_loss 0.63758 dice_loss 0.08705 +Epoch [3790/4000] Validation [2/10] Loss: 0.50017 focal_loss 0.40222 dice_loss 0.09794 +Epoch [3790/4000] Validation [3/10] Loss: 0.39295 focal_loss 0.28177 dice_loss 0.11118 +Epoch [3790/4000] Validation [4/10] Loss: 0.89853 focal_loss 0.33336 dice_loss 0.56517 +Epoch [3790/4000] Validation [5/10] Loss: 3.10243 focal_loss 2.42856 dice_loss 0.67388 +Epoch [3790/4000] Validation [6/10] Loss: 1.33310 focal_loss 0.62069 dice_loss 0.71241 +Epoch [3790/4000] Validation [7/10] Loss: 1.17659 focal_loss 0.52285 dice_loss 0.65374 +Epoch [3790/4000] Validation [8/10] Loss: 2.33457 focal_loss 1.72260 dice_loss 0.61197 +Epoch [3790/4000] Validation [9/10] Loss: 1.60186 focal_loss 1.05784 dice_loss 0.54401 +Epoch [3790/4000] Validation [10/10] Loss: 1.91568 focal_loss 1.17902 dice_loss 0.73666 +Epoch [3790/4000] Validation metric {'Val/mean dice_metric': 0.9513526558876038, 'Val/mean miou_metric': 0.9354231953620911, 'Val/mean f1': 0.948245108127594, 'Val/mean precision': 0.9428848028182983, 'Val/mean recall': 0.9536666870117188, 'Val/mean hd95_metric': 10.741950988769531} +Cheakpoint... +Epoch [3790/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513526558876038, 'Val/mean miou_metric': 0.9354231953620911, 'Val/mean f1': 0.948245108127594, 'Val/mean precision': 0.9428848028182983, 'Val/mean recall': 0.9536666870117188, 'Val/mean hd95_metric': 10.741950988769531} +Epoch [3791/4000] Training [1/39] Loss: 0.00575 +Epoch [3791/4000] Training [2/39] Loss: 0.00670 +Epoch [3791/4000] Training [3/39] Loss: 0.00464 +Epoch [3791/4000] Training [4/39] Loss: 0.13007 +Epoch [3791/4000] Training [5/39] Loss: 0.12763 +Epoch [3791/4000] Training [6/39] Loss: 0.00478 +Epoch [3791/4000] Training [7/39] Loss: 0.00510 +Epoch [3791/4000] Training [8/39] Loss: 0.12867 +Epoch [3791/4000] Training [9/39] Loss: 0.09143 +Epoch [3791/4000] Training [10/39] Loss: 0.00713 +Epoch [3791/4000] Training [11/39] Loss: 0.00559 +Epoch [3791/4000] Training [12/39] Loss: 0.00692 +Epoch [3791/4000] Training [13/39] Loss: 0.00449 +Epoch [3791/4000] Training [14/39] Loss: 0.00471 +Epoch [3791/4000] Training [15/39] Loss: 0.00638 +Epoch [3791/4000] Training [16/39] Loss: 0.00449 +Epoch [3791/4000] Training [17/39] Loss: 0.00442 +Epoch [3791/4000] Training [18/39] Loss: 0.00346 +Epoch [3791/4000] Training [19/39] Loss: 0.00681 +Epoch [3791/4000] Training [20/39] Loss: 0.13007 +Epoch [3791/4000] Training [21/39] Loss: 0.00370 +Epoch [3791/4000] Training [22/39] Loss: 0.00696 +Epoch [3791/4000] Training [23/39] Loss: 0.04264 +Epoch [3791/4000] Training [24/39] Loss: 0.00361 +Epoch [3791/4000] Training [25/39] Loss: 0.00428 +Epoch [3791/4000] Training [26/39] Loss: 0.25312 +Epoch [3791/4000] Training [27/39] Loss: 0.00305 +Epoch [3791/4000] Training [28/39] Loss: 0.00452 +Epoch [3791/4000] Training [29/39] Loss: 0.25694 +Epoch [3791/4000] Training [30/39] Loss: 0.00551 +Epoch [3791/4000] Training [31/39] Loss: 0.13121 +Epoch [3791/4000] Training [32/39] Loss: 0.00485 +Epoch [3791/4000] Training [33/39] Loss: 0.00375 +Epoch [3791/4000] Training [34/39] Loss: 0.12856 +Epoch [3791/4000] Training [35/39] Loss: 0.00360 +Epoch [3791/4000] Training [36/39] Loss: 0.00599 +Epoch [3791/4000] Training [37/39] Loss: 0.00606 +Epoch [3791/4000] Training [38/39] Loss: 0.00366 +Epoch [3791/4000] Training [39/39] Loss: 0.00297 +Epoch [3791/4000] Training metric {'Train/mean dice_metric': 0.9955567121505737, 'Train/mean miou_metric': 0.9923953413963318, 'Train/mean f1': 0.9969494342803955, 'Train/mean precision': 0.996488630771637, 'Train/mean recall': 0.997410774230957, 'Train/mean hd95_metric': 1.0165691375732422} +Epoch [3791/4000] Validation [1/10] Loss: 0.70408 focal_loss 0.61876 dice_loss 0.08531 +Epoch [3791/4000] Validation [2/10] Loss: 0.50537 focal_loss 0.40467 dice_loss 0.10070 +Epoch [3791/4000] Validation [3/10] Loss: 0.40020 focal_loss 0.28817 dice_loss 0.11203 +Epoch [3791/4000] Validation [4/10] Loss: 0.88940 focal_loss 0.32535 dice_loss 0.56406 +Epoch [3791/4000] Validation [5/10] Loss: 3.07454 focal_loss 2.40030 dice_loss 0.67424 +Epoch [3791/4000] Validation [6/10] Loss: 1.32189 focal_loss 0.60962 dice_loss 0.71227 +Epoch [3791/4000] Validation [7/10] Loss: 1.16572 focal_loss 0.51517 dice_loss 0.65055 +Epoch [3791/4000] Validation [8/10] Loss: 2.43099 focal_loss 1.80843 dice_loss 0.62256 +Epoch [3791/4000] Validation [9/10] Loss: 1.55294 focal_loss 1.00907 dice_loss 0.54387 +Epoch [3791/4000] Validation [10/10] Loss: 1.87098 focal_loss 1.13728 dice_loss 0.73370 +Epoch [3791/4000] Validation metric {'Val/mean dice_metric': 0.9507893323898315, 'Val/mean miou_metric': 0.934983491897583, 'Val/mean f1': 0.9485695958137512, 'Val/mean precision': 0.9447494149208069, 'Val/mean recall': 0.9524208307266235, 'Val/mean hd95_metric': 10.842228889465332} +Cheakpoint... +Epoch [3791/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507893323898315, 'Val/mean miou_metric': 0.934983491897583, 'Val/mean f1': 0.9485695958137512, 'Val/mean precision': 0.9447494149208069, 'Val/mean recall': 0.9524208307266235, 'Val/mean hd95_metric': 10.842228889465332} +Epoch [3792/4000] Training [1/39] Loss: 0.00464 +Epoch [3792/4000] Training [2/39] Loss: 0.00691 +Epoch [3792/4000] Training [3/39] Loss: 0.00435 +Epoch [3792/4000] Training [4/39] Loss: 0.13082 +Epoch [3792/4000] Training [5/39] Loss: 0.00437 +Epoch [3792/4000] Training [6/39] Loss: 0.00495 +Epoch [3792/4000] Training [7/39] Loss: 0.00695 +Epoch [3792/4000] Training [8/39] Loss: 0.00436 +Epoch [3792/4000] Training [9/39] Loss: 0.00506 +Epoch [3792/4000] Training [10/39] Loss: 0.00432 +Epoch [3792/4000] Training [11/39] Loss: 0.00308 +Epoch [3792/4000] Training [12/39] Loss: 0.00499 +Epoch [3792/4000] Training [13/39] Loss: 0.00409 +Epoch [3792/4000] Training [14/39] Loss: 0.00457 +Epoch [3792/4000] Training [15/39] Loss: 0.25304 +Epoch [3792/4000] Training [16/39] Loss: 0.00519 +Epoch [3792/4000] Training [17/39] Loss: 0.00311 +Epoch [3792/4000] Training [18/39] Loss: 0.25519 +Epoch [3792/4000] Training [19/39] Loss: 0.20478 +Epoch [3792/4000] Training [20/39] Loss: 0.00352 +Epoch [3792/4000] Training [21/39] Loss: 0.00409 +Epoch [3792/4000] Training [22/39] Loss: 0.00598 +Epoch [3792/4000] Training [23/39] Loss: 0.12724 +Epoch [3792/4000] Training [24/39] Loss: 0.00406 +Epoch [3792/4000] Training [25/39] Loss: 0.00465 +Epoch [3792/4000] Training [26/39] Loss: 0.00270 +Epoch [3792/4000] Training [27/39] Loss: 0.00486 +Epoch [3792/4000] Training [28/39] Loss: 0.00853 +Epoch [3792/4000] Training [29/39] Loss: 0.00607 +Epoch [3792/4000] Training [30/39] Loss: 0.00527 +Epoch [3792/4000] Training [31/39] Loss: 0.00485 +Epoch [3792/4000] Training [32/39] Loss: 0.00476 +Epoch [3792/4000] Training [33/39] Loss: 0.00945 +Epoch [3792/4000] Training [34/39] Loss: 0.12799 +Epoch [3792/4000] Training [35/39] Loss: 0.00304 +Epoch [3792/4000] Training [36/39] Loss: 0.00412 +Epoch [3792/4000] Training [37/39] Loss: 0.00362 +Epoch [3792/4000] Training [38/39] Loss: 0.00504 +Epoch [3792/4000] Training [39/39] Loss: 0.12907 +Epoch [3792/4000] Training metric {'Train/mean dice_metric': 0.995638370513916, 'Train/mean miou_metric': 0.9925310611724854, 'Train/mean f1': 0.9970632195472717, 'Train/mean precision': 0.9966579675674438, 'Train/mean recall': 0.9974687695503235, 'Train/mean hd95_metric': 0.9299395680427551} +Epoch [3792/4000] Validation [1/10] Loss: 0.70111 focal_loss 0.61573 dice_loss 0.08538 +Epoch [3792/4000] Validation [2/10] Loss: 0.50157 focal_loss 0.40240 dice_loss 0.09917 +Epoch [3792/4000] Validation [3/10] Loss: 0.39458 focal_loss 0.28307 dice_loss 0.11150 +Epoch [3792/4000] Validation [4/10] Loss: 0.89631 focal_loss 0.33190 dice_loss 0.56441 +Epoch [3792/4000] Validation [5/10] Loss: 3.05779 focal_loss 2.38367 dice_loss 0.67412 +Epoch [3792/4000] Validation [6/10] Loss: 1.33272 focal_loss 0.62106 dice_loss 0.71166 +Epoch [3792/4000] Validation [7/10] Loss: 1.17790 focal_loss 0.52724 dice_loss 0.65066 +Epoch [3792/4000] Validation [8/10] Loss: 2.40657 focal_loss 1.78728 dice_loss 0.61929 +Epoch [3792/4000] Validation [9/10] Loss: 1.56824 focal_loss 1.02411 dice_loss 0.54413 +Epoch [3792/4000] Validation [10/10] Loss: 1.89273 focal_loss 1.15860 dice_loss 0.73413 +Epoch [3792/4000] Validation metric {'Val/mean dice_metric': 0.9509077668190002, 'Val/mean miou_metric': 0.9351363182067871, 'Val/mean f1': 0.9484590888023376, 'Val/mean precision': 0.9440761208534241, 'Val/mean recall': 0.9528830051422119, 'Val/mean hd95_metric': 10.671585083007812} +Cheakpoint... +Epoch [3792/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509077668190002, 'Val/mean miou_metric': 0.9351363182067871, 'Val/mean f1': 0.9484590888023376, 'Val/mean precision': 0.9440761208534241, 'Val/mean recall': 0.9528830051422119, 'Val/mean hd95_metric': 10.671585083007812} +Epoch [3793/4000] Training [1/39] Loss: 0.13035 +Epoch [3793/4000] Training [2/39] Loss: 0.12824 +Epoch [3793/4000] Training [3/39] Loss: 0.00303 +Epoch [3793/4000] Training [4/39] Loss: 0.00588 +Epoch [3793/4000] Training [5/39] Loss: 0.00336 +Epoch [3793/4000] Training [6/39] Loss: 0.12991 +Epoch [3793/4000] Training [7/39] Loss: 0.12877 +Epoch [3793/4000] Training [8/39] Loss: 0.00551 +Epoch [3793/4000] Training [9/39] Loss: 0.00581 +Epoch [3793/4000] Training [10/39] Loss: 0.00471 +Epoch [3793/4000] Training [11/39] Loss: 0.13071 +Epoch [3793/4000] Training [12/39] Loss: 0.00900 +Epoch [3793/4000] Training [13/39] Loss: 0.00485 +Epoch [3793/4000] Training [14/39] Loss: 0.00292 +Epoch [3793/4000] Training [15/39] Loss: 0.00451 +Epoch [3793/4000] Training [16/39] Loss: 0.00590 +Epoch [3793/4000] Training [17/39] Loss: 0.00388 +Epoch [3793/4000] Training [18/39] Loss: 0.00582 +Epoch [3793/4000] Training [19/39] Loss: 0.00448 +Epoch [3793/4000] Training [20/39] Loss: 0.00589 +Epoch [3793/4000] Training [21/39] Loss: 0.00360 +Epoch [3793/4000] Training [22/39] Loss: 0.00319 +Epoch [3793/4000] Training [23/39] Loss: 0.00416 +Epoch [3793/4000] Training [24/39] Loss: 0.00547 +Epoch [3793/4000] Training [25/39] Loss: 0.00454 +Epoch [3793/4000] Training [26/39] Loss: 0.13134 +Epoch [3793/4000] Training [27/39] Loss: 0.00577 +Epoch [3793/4000] Training [28/39] Loss: 0.13053 +Epoch [3793/4000] Training [29/39] Loss: 0.00302 +Epoch [3793/4000] Training [30/39] Loss: 0.00355 +Epoch [3793/4000] Training [31/39] Loss: 0.00453 +Epoch [3793/4000] Training [32/39] Loss: 0.00707 +Epoch [3793/4000] Training [33/39] Loss: 0.00397 +Epoch [3793/4000] Training [34/39] Loss: 0.00405 +Epoch [3793/4000] Training [35/39] Loss: 0.00452 +Epoch [3793/4000] Training [36/39] Loss: 0.12872 +Epoch [3793/4000] Training [37/39] Loss: 0.00377 +Epoch [3793/4000] Training [38/39] Loss: 0.12954 +Epoch [3793/4000] Training [39/39] Loss: 0.00418 +Epoch [3793/4000] Training metric {'Train/mean dice_metric': 0.9963752627372742, 'Train/mean miou_metric': 0.9932274222373962, 'Train/mean f1': 0.9969085454940796, 'Train/mean precision': 0.9964897632598877, 'Train/mean recall': 0.9973277449607849, 'Train/mean hd95_metric': 0.9265949130058289} +Epoch [3793/4000] Validation [1/10] Loss: 0.70370 focal_loss 0.61719 dice_loss 0.08651 +Epoch [3793/4000] Validation [2/10] Loss: 0.49629 focal_loss 0.39896 dice_loss 0.09734 +Epoch [3793/4000] Validation [3/10] Loss: 0.38310 focal_loss 0.27210 dice_loss 0.11100 +Epoch [3793/4000] Validation [4/10] Loss: 0.89803 focal_loss 0.33240 dice_loss 0.56563 +Epoch [3793/4000] Validation [5/10] Loss: 3.03398 focal_loss 2.35997 dice_loss 0.67401 +Epoch [3793/4000] Validation [6/10] Loss: 1.34558 focal_loss 0.63261 dice_loss 0.71296 +Epoch [3793/4000] Validation [7/10] Loss: 1.18074 focal_loss 0.52957 dice_loss 0.65118 +Epoch [3793/4000] Validation [8/10] Loss: 2.31289 focal_loss 1.70196 dice_loss 0.61094 +Epoch [3793/4000] Validation [9/10] Loss: 1.58184 focal_loss 1.03733 dice_loss 0.54451 +Epoch [3793/4000] Validation [10/10] Loss: 1.91421 focal_loss 1.17834 dice_loss 0.73588 +Epoch [3793/4000] Validation metric {'Val/mean dice_metric': 0.9515167474746704, 'Val/mean miou_metric': 0.9357033967971802, 'Val/mean f1': 0.9480165243148804, 'Val/mean precision': 0.9424529671669006, 'Val/mean recall': 0.9536461234092712, 'Val/mean hd95_metric': 10.76639175415039} +Cheakpoint... +Epoch [3793/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515167474746704, 'Val/mean miou_metric': 0.9357033967971802, 'Val/mean f1': 0.9480165243148804, 'Val/mean precision': 0.9424529671669006, 'Val/mean recall': 0.9536461234092712, 'Val/mean hd95_metric': 10.76639175415039} +Epoch [3794/4000] Training [1/39] Loss: 0.00416 +Epoch [3794/4000] Training [2/39] Loss: 0.00777 +Epoch [3794/4000] Training [3/39] Loss: 0.00327 +Epoch [3794/4000] Training [4/39] Loss: 0.00511 +Epoch [3794/4000] Training [5/39] Loss: 0.00798 +Epoch [3794/4000] Training [6/39] Loss: 0.12715 +Epoch [3794/4000] Training [7/39] Loss: 0.00793 +Epoch [3794/4000] Training [8/39] Loss: 0.00337 +Epoch [3794/4000] Training [9/39] Loss: 0.12745 +Epoch [3794/4000] Training [10/39] Loss: 0.00369 +Epoch [3794/4000] Training [11/39] Loss: 0.00399 +Epoch [3794/4000] Training [12/39] Loss: 0.25500 +Epoch [3794/4000] Training [13/39] Loss: 0.00551 +Epoch [3794/4000] Training [14/39] Loss: 0.12816 +Epoch [3794/4000] Training [15/39] Loss: 0.00417 +Epoch [3794/4000] Training [16/39] Loss: 0.00563 +Epoch [3794/4000] Training [17/39] Loss: 0.00448 +Epoch [3794/4000] Training [18/39] Loss: 0.00250 +Epoch [3794/4000] Training [19/39] Loss: 0.00600 +Epoch [3794/4000] Training [20/39] Loss: 0.00362 +Epoch [3794/4000] Training [21/39] Loss: 0.00726 +Epoch [3794/4000] Training [22/39] Loss: 0.25273 +Epoch [3794/4000] Training [23/39] Loss: 0.00389 +Epoch [3794/4000] Training [24/39] Loss: 0.00438 +Epoch [3794/4000] Training [25/39] Loss: 0.00604 +Epoch [3794/4000] Training [26/39] Loss: 0.12964 +Epoch [3794/4000] Training [27/39] Loss: 0.00473 +Epoch [3794/4000] Training [28/39] Loss: 0.00607 +Epoch [3794/4000] Training [29/39] Loss: 0.08372 +Epoch [3794/4000] Training [30/39] Loss: 0.00487 +Epoch [3794/4000] Training [31/39] Loss: 0.00491 +Epoch [3794/4000] Training [32/39] Loss: 0.00501 +Epoch [3794/4000] Training [33/39] Loss: 0.00731 +Epoch [3794/4000] Training [34/39] Loss: 0.00583 +Epoch [3794/4000] Training [35/39] Loss: 0.00622 +Epoch [3794/4000] Training [36/39] Loss: 0.00448 +Epoch [3794/4000] Training [37/39] Loss: 0.12935 +Epoch [3794/4000] Training [38/39] Loss: 0.00455 +Epoch [3794/4000] Training [39/39] Loss: 0.00469 +Epoch [3794/4000] Training metric {'Train/mean dice_metric': 0.9964315891265869, 'Train/mean miou_metric': 0.9933128952980042, 'Train/mean f1': 0.9969612956047058, 'Train/mean precision': 0.9964848160743713, 'Train/mean recall': 0.9974383115768433, 'Train/mean hd95_metric': 0.9277450442314148} +Epoch [3794/4000] Validation [1/10] Loss: 0.71648 focal_loss 0.62967 dice_loss 0.08680 +Epoch [3794/4000] Validation [2/10] Loss: 0.50175 focal_loss 0.40463 dice_loss 0.09712 +Epoch [3794/4000] Validation [3/10] Loss: 0.38968 focal_loss 0.27875 dice_loss 0.11093 +Epoch [3794/4000] Validation [4/10] Loss: 0.90505 focal_loss 0.33890 dice_loss 0.56615 +Epoch [3794/4000] Validation [5/10] Loss: 3.08837 focal_loss 2.41434 dice_loss 0.67402 +Epoch [3794/4000] Validation [6/10] Loss: 1.35348 focal_loss 0.64095 dice_loss 0.71253 +Epoch [3794/4000] Validation [7/10] Loss: 1.18720 focal_loss 0.53512 dice_loss 0.65208 +Epoch [3794/4000] Validation [8/10] Loss: 2.31572 focal_loss 1.70678 dice_loss 0.60895 +Epoch [3794/4000] Validation [9/10] Loss: 1.61136 focal_loss 1.06707 dice_loss 0.54429 +Epoch [3794/4000] Validation [10/10] Loss: 1.93343 focal_loss 1.19710 dice_loss 0.73634 +Epoch [3794/4000] Validation metric {'Val/mean dice_metric': 0.9515764117240906, 'Val/mean miou_metric': 0.9357759356498718, 'Val/mean f1': 0.9479926824569702, 'Val/mean precision': 0.9422246217727661, 'Val/mean recall': 0.9538317322731018, 'Val/mean hd95_metric': 10.834918022155762} +Cheakpoint... +Epoch [3794/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515764117240906, 'Val/mean miou_metric': 0.9357759356498718, 'Val/mean f1': 0.9479926824569702, 'Val/mean precision': 0.9422246217727661, 'Val/mean recall': 0.9538317322731018, 'Val/mean hd95_metric': 10.834918022155762} +Epoch [3795/4000] Training [1/39] Loss: 0.00334 +Epoch [3795/4000] Training [2/39] Loss: 0.12879 +Epoch [3795/4000] Training [3/39] Loss: 0.00563 +Epoch [3795/4000] Training [4/39] Loss: 0.00327 +Epoch [3795/4000] Training [5/39] Loss: 0.00379 +Epoch [3795/4000] Training [6/39] Loss: 0.00805 +Epoch [3795/4000] Training [7/39] Loss: 0.00724 +Epoch [3795/4000] Training [8/39] Loss: 0.00351 +Epoch [3795/4000] Training [9/39] Loss: 0.00465 +Epoch [3795/4000] Training [10/39] Loss: 0.00359 +Epoch [3795/4000] Training [11/39] Loss: 0.25251 +Epoch [3795/4000] Training [12/39] Loss: 0.13286 +Epoch [3795/4000] Training [13/39] Loss: 0.00408 +Epoch [3795/4000] Training [14/39] Loss: 0.00586 +Epoch [3795/4000] Training [15/39] Loss: 0.00356 +Epoch [3795/4000] Training [16/39] Loss: 0.00560 +Epoch [3795/4000] Training [17/39] Loss: 0.00501 +Epoch [3795/4000] Training [18/39] Loss: 0.00434 +Epoch [3795/4000] Training [19/39] Loss: 0.00478 +Epoch [3795/4000] Training [20/39] Loss: 0.00606 +Epoch [3795/4000] Training [21/39] Loss: 0.00417 +Epoch [3795/4000] Training [22/39] Loss: 0.00750 +Epoch [3795/4000] Training [23/39] Loss: 0.00588 +Epoch [3795/4000] Training [24/39] Loss: 0.00654 +Epoch [3795/4000] Training [25/39] Loss: 0.12906 +Epoch [3795/4000] Training [26/39] Loss: 0.00705 +Epoch [3795/4000] Training [27/39] Loss: 0.25219 +Epoch [3795/4000] Training [28/39] Loss: 0.00385 +Epoch [3795/4000] Training [29/39] Loss: 0.00289 +Epoch [3795/4000] Training [30/39] Loss: 0.12941 +Epoch [3795/4000] Training [31/39] Loss: 0.00702 +Epoch [3795/4000] Training [32/39] Loss: 0.12820 +Epoch [3795/4000] Training [33/39] Loss: 0.12696 +Epoch [3795/4000] Training [34/39] Loss: 0.00261 +Epoch [3795/4000] Training [35/39] Loss: 0.00507 +Epoch [3795/4000] Training [36/39] Loss: 0.12874 +Epoch [3795/4000] Training [37/39] Loss: 0.12967 +Epoch [3795/4000] Training [38/39] Loss: 0.25196 +Epoch [3795/4000] Training [39/39] Loss: 0.00771 +Epoch [3795/4000] Training metric {'Train/mean dice_metric': 0.9964467883110046, 'Train/mean miou_metric': 0.9933419227600098, 'Train/mean f1': 0.9970102310180664, 'Train/mean precision': 0.9965705871582031, 'Train/mean recall': 0.9974501729011536, 'Train/mean hd95_metric': 0.9660285711288452} +Epoch [3795/4000] Validation [1/10] Loss: 0.71026 focal_loss 0.62434 dice_loss 0.08592 +Epoch [3795/4000] Validation [2/10] Loss: 0.50654 focal_loss 0.40923 dice_loss 0.09731 +Epoch [3795/4000] Validation [3/10] Loss: 0.39028 focal_loss 0.27965 dice_loss 0.11063 +Epoch [3795/4000] Validation [4/10] Loss: 0.90593 focal_loss 0.34056 dice_loss 0.56537 +Epoch [3795/4000] Validation [5/10] Loss: 3.07670 focal_loss 2.40262 dice_loss 0.67408 +Epoch [3795/4000] Validation [6/10] Loss: 1.35630 focal_loss 0.64333 dice_loss 0.71297 +Epoch [3795/4000] Validation [7/10] Loss: 1.19303 focal_loss 0.54165 dice_loss 0.65138 +Epoch [3795/4000] Validation [8/10] Loss: 2.36777 focal_loss 1.75536 dice_loss 0.61241 +Epoch [3795/4000] Validation [9/10] Loss: 1.59989 focal_loss 1.05594 dice_loss 0.54395 +Epoch [3795/4000] Validation [10/10] Loss: 1.93654 focal_loss 1.20086 dice_loss 0.73569 +Epoch [3795/4000] Validation metric {'Val/mean dice_metric': 0.9516092538833618, 'Val/mean miou_metric': 0.9358287453651428, 'Val/mean f1': 0.9484322667121887, 'Val/mean precision': 0.9430966377258301, 'Val/mean recall': 0.9538285136222839, 'Val/mean hd95_metric': 10.81207275390625} +Cheakpoint... +Epoch [3795/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516092538833618, 'Val/mean miou_metric': 0.9358287453651428, 'Val/mean f1': 0.9484322667121887, 'Val/mean precision': 0.9430966377258301, 'Val/mean recall': 0.9538285136222839, 'Val/mean hd95_metric': 10.81207275390625} +Epoch [3796/4000] Training [1/39] Loss: 0.12783 +Epoch [3796/4000] Training [2/39] Loss: 0.12809 +Epoch [3796/4000] Training [3/39] Loss: 0.12842 +Epoch [3796/4000] Training [4/39] Loss: 0.00351 +Epoch [3796/4000] Training [5/39] Loss: 0.00706 +Epoch [3796/4000] Training [6/39] Loss: 0.00834 +Epoch [3796/4000] Training [7/39] Loss: 0.00394 +Epoch [3796/4000] Training [8/39] Loss: 0.00601 +Epoch [3796/4000] Training [9/39] Loss: 0.00634 +Epoch [3796/4000] Training [10/39] Loss: 0.25349 +Epoch [3796/4000] Training [11/39] Loss: 0.00426 +Epoch [3796/4000] Training [12/39] Loss: 0.00473 +Epoch [3796/4000] Training [13/39] Loss: 0.00559 +Epoch [3796/4000] Training [14/39] Loss: 0.00678 +Epoch [3796/4000] Training [15/39] Loss: 0.25347 +Epoch [3796/4000] Training [16/39] Loss: 0.00869 +Epoch [3796/4000] Training [17/39] Loss: 0.08557 +Epoch [3796/4000] Training [18/39] Loss: 0.00333 +Epoch [3796/4000] Training [19/39] Loss: 0.00299 +Epoch [3796/4000] Training [20/39] Loss: 0.00497 +Epoch [3796/4000] Training [21/39] Loss: 0.12826 +Epoch [3796/4000] Training [22/39] Loss: 0.12921 +Epoch [3796/4000] Training [23/39] Loss: 0.00411 +Epoch [3796/4000] Training [24/39] Loss: 0.00545 +Epoch [3796/4000] Training [25/39] Loss: 0.00363 +Epoch [3796/4000] Training [26/39] Loss: 0.00402 +Epoch [3796/4000] Training [27/39] Loss: 0.00398 +Epoch [3796/4000] Training [28/39] Loss: 0.13050 +Epoch [3796/4000] Training [29/39] Loss: 0.00322 +Epoch [3796/4000] Training [30/39] Loss: 0.13365 +Epoch [3796/4000] Training [31/39] Loss: 0.13033 +Epoch [3796/4000] Training [32/39] Loss: 0.00419 +Epoch [3796/4000] Training [33/39] Loss: 0.13141 +Epoch [3796/4000] Training [34/39] Loss: 0.00663 +Epoch [3796/4000] Training [35/39] Loss: 0.00636 +Epoch [3796/4000] Training [36/39] Loss: 0.00585 +Epoch [3796/4000] Training [37/39] Loss: 0.00450 +Epoch [3796/4000] Training [38/39] Loss: 0.12782 +Epoch [3796/4000] Training [39/39] Loss: 0.12877 +Epoch [3796/4000] Training metric {'Train/mean dice_metric': 0.9963277578353882, 'Train/mean miou_metric': 0.9931095838546753, 'Train/mean f1': 0.9968449473381042, 'Train/mean precision': 0.9963408708572388, 'Train/mean recall': 0.9973495006561279, 'Train/mean hd95_metric': 0.9982221126556396} +Epoch [3796/4000] Validation [1/10] Loss: 0.71037 focal_loss 0.62589 dice_loss 0.08448 +Epoch [3796/4000] Validation [2/10] Loss: 0.50905 focal_loss 0.40844 dice_loss 0.10061 +Epoch [3796/4000] Validation [3/10] Loss: 0.40616 focal_loss 0.29439 dice_loss 0.11176 +Epoch [3796/4000] Validation [4/10] Loss: 0.88937 focal_loss 0.32559 dice_loss 0.56377 +Epoch [3796/4000] Validation [5/10] Loss: 3.11871 focal_loss 2.44438 dice_loss 0.67433 +Epoch [3796/4000] Validation [6/10] Loss: 1.31659 focal_loss 0.60372 dice_loss 0.71288 +Epoch [3796/4000] Validation [7/10] Loss: 1.17248 focal_loss 0.52249 dice_loss 0.65000 +Epoch [3796/4000] Validation [8/10] Loss: 2.41702 focal_loss 1.79466 dice_loss 0.62236 +Epoch [3796/4000] Validation [9/10] Loss: 1.55912 focal_loss 1.01641 dice_loss 0.54271 +Epoch [3796/4000] Validation [10/10] Loss: 1.87104 focal_loss 1.13748 dice_loss 0.73356 +Epoch [3796/4000] Validation metric {'Val/mean dice_metric': 0.9514835476875305, 'Val/mean miou_metric': 0.9356276988983154, 'Val/mean f1': 0.9483421444892883, 'Val/mean precision': 0.9447156190872192, 'Val/mean recall': 0.9519966244697571, 'Val/mean hd95_metric': 10.735315322875977} +Cheakpoint... +Epoch [3796/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514835476875305, 'Val/mean miou_metric': 0.9356276988983154, 'Val/mean f1': 0.9483421444892883, 'Val/mean precision': 0.9447156190872192, 'Val/mean recall': 0.9519966244697571, 'Val/mean hd95_metric': 10.735315322875977} +Epoch [3797/4000] Training [1/39] Loss: 0.00449 +Epoch [3797/4000] Training [2/39] Loss: 0.00502 +Epoch [3797/4000] Training [3/39] Loss: 0.00618 +Epoch [3797/4000] Training [4/39] Loss: 0.12876 +Epoch [3797/4000] Training [5/39] Loss: 0.00562 +Epoch [3797/4000] Training [6/39] Loss: 0.12952 +Epoch [3797/4000] Training [7/39] Loss: 0.00626 +Epoch [3797/4000] Training [8/39] Loss: 0.00655 +Epoch [3797/4000] Training [9/39] Loss: 0.00327 +Epoch [3797/4000] Training [10/39] Loss: 0.12948 +Epoch [3797/4000] Training [11/39] Loss: 0.00326 +Epoch [3797/4000] Training [12/39] Loss: 0.13428 +Epoch [3797/4000] Training [13/39] Loss: 0.00494 +Epoch [3797/4000] Training [14/39] Loss: 0.00799 +Epoch [3797/4000] Training [15/39] Loss: 0.13027 +Epoch [3797/4000] Training [16/39] Loss: 0.00727 +Epoch [3797/4000] Training [17/39] Loss: 0.00327 +Epoch [3797/4000] Training [18/39] Loss: 0.00749 +Epoch [3797/4000] Training [19/39] Loss: 0.12757 +Epoch [3797/4000] Training [20/39] Loss: 0.13067 +Epoch [3797/4000] Training [21/39] Loss: 0.00325 +Epoch [3797/4000] Training [22/39] Loss: 0.00344 +Epoch [3797/4000] Training [23/39] Loss: 0.00927 +Epoch [3797/4000] Training [24/39] Loss: 0.00471 +Epoch [3797/4000] Training [25/39] Loss: 0.00496 +Epoch [3797/4000] Training [26/39] Loss: 0.00486 +Epoch [3797/4000] Training [27/39] Loss: 0.13106 +Epoch [3797/4000] Training [28/39] Loss: 0.00446 +Epoch [3797/4000] Training [29/39] Loss: 0.00401 +Epoch [3797/4000] Training [30/39] Loss: 0.00310 +Epoch [3797/4000] Training [31/39] Loss: 0.00822 +Epoch [3797/4000] Training [32/39] Loss: 0.00608 +Epoch [3797/4000] Training [33/39] Loss: 0.13164 +Epoch [3797/4000] Training [34/39] Loss: 0.12856 +Epoch [3797/4000] Training [35/39] Loss: 0.00353 +Epoch [3797/4000] Training [36/39] Loss: 0.25451 +Epoch [3797/4000] Training [37/39] Loss: 0.00590 +Epoch [3797/4000] Training [38/39] Loss: 0.00489 +Epoch [3797/4000] Training [39/39] Loss: 0.00577 +Epoch [3797/4000] Training metric {'Train/mean dice_metric': 0.9960376620292664, 'Train/mean miou_metric': 0.9925479292869568, 'Train/mean f1': 0.9965681433677673, 'Train/mean precision': 0.9961425065994263, 'Train/mean recall': 0.9969939589500427, 'Train/mean hd95_metric': 1.0725427865982056} +Epoch [3797/4000] Validation [1/10] Loss: 0.70536 focal_loss 0.61930 dice_loss 0.08606 +Epoch [3797/4000] Validation [2/10] Loss: 0.50099 focal_loss 0.40507 dice_loss 0.09592 +Epoch [3797/4000] Validation [3/10] Loss: 0.37970 focal_loss 0.26963 dice_loss 0.11007 +Epoch [3797/4000] Validation [4/10] Loss: 0.90156 focal_loss 0.33603 dice_loss 0.56552 +Epoch [3797/4000] Validation [5/10] Loss: 3.05439 focal_loss 2.38050 dice_loss 0.67389 +Epoch [3797/4000] Validation [6/10] Loss: 1.35009 focal_loss 0.63610 dice_loss 0.71399 +Epoch [3797/4000] Validation [7/10] Loss: 1.19347 focal_loss 0.54079 dice_loss 0.65268 +Epoch [3797/4000] Validation [8/10] Loss: 2.31190 focal_loss 1.70249 dice_loss 0.60941 +Epoch [3797/4000] Validation [9/10] Loss: 1.59442 focal_loss 1.05050 dice_loss 0.54392 +Epoch [3797/4000] Validation [10/10] Loss: 1.93285 focal_loss 1.19707 dice_loss 0.73577 +Epoch [3797/4000] Validation metric {'Val/mean dice_metric': 0.9513118863105774, 'Val/mean miou_metric': 0.93522047996521, 'Val/mean f1': 0.9479460120201111, 'Val/mean precision': 0.9424207210540771, 'Val/mean recall': 0.9535364508628845, 'Val/mean hd95_metric': 10.94606876373291} +Cheakpoint... +Epoch [3797/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513118863105774, 'Val/mean miou_metric': 0.93522047996521, 'Val/mean f1': 0.9479460120201111, 'Val/mean precision': 0.9424207210540771, 'Val/mean recall': 0.9535364508628845, 'Val/mean hd95_metric': 10.94606876373291} +Epoch [3798/4000] Training [1/39] Loss: 0.00551 +Epoch [3798/4000] Training [2/39] Loss: 0.00380 +Epoch [3798/4000] Training [3/39] Loss: 0.00362 +Epoch [3798/4000] Training [4/39] Loss: 0.00620 +Epoch [3798/4000] Training [5/39] Loss: 0.25253 +Epoch [3798/4000] Training [6/39] Loss: 0.12957 +Epoch [3798/4000] Training [7/39] Loss: 0.00419 +Epoch [3798/4000] Training [8/39] Loss: 0.00490 +Epoch [3798/4000] Training [9/39] Loss: 0.00330 +Epoch [3798/4000] Training [10/39] Loss: 0.12773 +Epoch [3798/4000] Training [11/39] Loss: 0.00370 +Epoch [3798/4000] Training [12/39] Loss: 0.12946 +Epoch [3798/4000] Training [13/39] Loss: 0.00495 +Epoch [3798/4000] Training [14/39] Loss: 0.00380 +Epoch [3798/4000] Training [15/39] Loss: 0.20749 +Epoch [3798/4000] Training [16/39] Loss: 0.00345 +Epoch [3798/4000] Training [17/39] Loss: 0.00509 +Epoch [3798/4000] Training [18/39] Loss: 0.00769 +Epoch [3798/4000] Training [19/39] Loss: 0.00418 +Epoch [3798/4000] Training [20/39] Loss: 0.00475 +Epoch [3798/4000] Training [21/39] Loss: 0.00535 +Epoch [3798/4000] Training [22/39] Loss: 0.00442 +Epoch [3798/4000] Training [23/39] Loss: 0.00429 +Epoch [3798/4000] Training [24/39] Loss: 0.00584 +Epoch [3798/4000] Training [25/39] Loss: 0.00420 +Epoch [3798/4000] Training [26/39] Loss: 0.00268 +Epoch [3798/4000] Training [27/39] Loss: 0.00926 +Epoch [3798/4000] Training [28/39] Loss: 0.13092 +Epoch [3798/4000] Training [29/39] Loss: 0.00386 +Epoch [3798/4000] Training [30/39] Loss: 0.12785 +Epoch [3798/4000] Training [31/39] Loss: 0.25223 +Epoch [3798/4000] Training [32/39] Loss: 0.00642 +Epoch [3798/4000] Training [33/39] Loss: 0.12812 +Epoch [3798/4000] Training [34/39] Loss: 0.00360 +Epoch [3798/4000] Training [35/39] Loss: 0.00498 +Epoch [3798/4000] Training [36/39] Loss: 0.00488 +Epoch [3798/4000] Training [37/39] Loss: 0.00475 +Epoch [3798/4000] Training [38/39] Loss: 0.00244 +Epoch [3798/4000] Training [39/39] Loss: 0.00723 +Epoch [3798/4000] Training metric {'Train/mean dice_metric': 0.9964562058448792, 'Train/mean miou_metric': 0.9933568835258484, 'Train/mean f1': 0.996989369392395, 'Train/mean precision': 0.9965309500694275, 'Train/mean recall': 0.9974480271339417, 'Train/mean hd95_metric': 0.9570367336273193} +Epoch [3798/4000] Validation [1/10] Loss: 0.70781 focal_loss 0.62258 dice_loss 0.08523 +Epoch [3798/4000] Validation [2/10] Loss: 0.50463 focal_loss 0.40559 dice_loss 0.09903 +Epoch [3798/4000] Validation [3/10] Loss: 0.39147 focal_loss 0.28047 dice_loss 0.11100 +Epoch [3798/4000] Validation [4/10] Loss: 0.89613 focal_loss 0.33131 dice_loss 0.56482 +Epoch [3798/4000] Validation [5/10] Loss: 3.10115 focal_loss 2.42712 dice_loss 0.67403 +Epoch [3798/4000] Validation [6/10] Loss: 1.33571 focal_loss 0.62119 dice_loss 0.71452 +Epoch [3798/4000] Validation [7/10] Loss: 1.17967 focal_loss 0.52826 dice_loss 0.65140 +Epoch [3798/4000] Validation [8/10] Loss: 2.34760 focal_loss 1.73419 dice_loss 0.61341 +Epoch [3798/4000] Validation [9/10] Loss: 1.59038 focal_loss 1.04717 dice_loss 0.54321 +Epoch [3798/4000] Validation [10/10] Loss: 1.89702 focal_loss 1.16223 dice_loss 0.73479 +Epoch [3798/4000] Validation metric {'Val/mean dice_metric': 0.951646089553833, 'Val/mean miou_metric': 0.9358929991722107, 'Val/mean f1': 0.9482499957084656, 'Val/mean precision': 0.9433762431144714, 'Val/mean recall': 0.9531745314598083, 'Val/mean hd95_metric': 10.701498031616211} +Cheakpoint... +Epoch [3798/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951646089553833, 'Val/mean miou_metric': 0.9358929991722107, 'Val/mean f1': 0.9482499957084656, 'Val/mean precision': 0.9433762431144714, 'Val/mean recall': 0.9531745314598083, 'Val/mean hd95_metric': 10.701498031616211} +Epoch [3799/4000] Training [1/39] Loss: 0.00608 +Epoch [3799/4000] Training [2/39] Loss: 0.13023 +Epoch [3799/4000] Training [3/39] Loss: 0.00359 +Epoch [3799/4000] Training [4/39] Loss: 0.16177 +Epoch [3799/4000] Training [5/39] Loss: 0.00514 +Epoch [3799/4000] Training [6/39] Loss: 0.14054 +Epoch [3799/4000] Training [7/39] Loss: 0.00489 +Epoch [3799/4000] Training [8/39] Loss: 0.00361 +Epoch [3799/4000] Training [9/39] Loss: 0.00443 +Epoch [3799/4000] Training [10/39] Loss: 0.00443 +Epoch [3799/4000] Training [11/39] Loss: 0.00467 +Epoch [3799/4000] Training [12/39] Loss: 0.12840 +Epoch [3799/4000] Training [13/39] Loss: 0.00380 +Epoch [3799/4000] Training [14/39] Loss: 0.00350 +Epoch [3799/4000] Training [15/39] Loss: 0.08869 +Epoch [3799/4000] Training [16/39] Loss: 0.00527 +Epoch [3799/4000] Training [17/39] Loss: 0.25371 +Epoch [3799/4000] Training [18/39] Loss: 0.00288 +Epoch [3799/4000] Training [19/39] Loss: 0.00666 +Epoch [3799/4000] Training [20/39] Loss: 0.00828 +Epoch [3799/4000] Training [21/39] Loss: 0.00439 +Epoch [3799/4000] Training [22/39] Loss: 0.00544 +Epoch [3799/4000] Training [23/39] Loss: 0.00502 +Epoch [3799/4000] Training [24/39] Loss: 0.00442 +Epoch [3799/4000] Training [25/39] Loss: 0.00447 +Epoch [3799/4000] Training [26/39] Loss: 0.00546 +Epoch [3799/4000] Training [27/39] Loss: 0.12945 +Epoch [3799/4000] Training [28/39] Loss: 0.13034 +Epoch [3799/4000] Training [29/39] Loss: 0.00312 +Epoch [3799/4000] Training [30/39] Loss: 0.00362 +Epoch [3799/4000] Training [31/39] Loss: 0.00379 +Epoch [3799/4000] Training [32/39] Loss: 0.00399 +Epoch [3799/4000] Training [33/39] Loss: 0.00417 +Epoch [3799/4000] Training [34/39] Loss: 0.00480 +Epoch [3799/4000] Training [35/39] Loss: 0.13194 +Epoch [3799/4000] Training [36/39] Loss: 0.00435 +Epoch [3799/4000] Training [37/39] Loss: 0.12793 +Epoch [3799/4000] Training [38/39] Loss: 0.00646 +Epoch [3799/4000] Training [39/39] Loss: 0.00533 +Epoch [3799/4000] Training metric {'Train/mean dice_metric': 0.9962502717971802, 'Train/mean miou_metric': 0.9929651618003845, 'Train/mean f1': 0.9969091415405273, 'Train/mean precision': 0.9964886903762817, 'Train/mean recall': 0.997329831123352, 'Train/mean hd95_metric': 1.068331003189087} +Epoch [3799/4000] Validation [1/10] Loss: 0.69982 focal_loss 0.61575 dice_loss 0.08407 +Epoch [3799/4000] Validation [2/10] Loss: 0.50629 focal_loss 0.40388 dice_loss 0.10242 +Epoch [3799/4000] Validation [3/10] Loss: 0.40876 focal_loss 0.29623 dice_loss 0.11254 +Epoch [3799/4000] Validation [4/10] Loss: 0.87845 focal_loss 0.31566 dice_loss 0.56279 +Epoch [3799/4000] Validation [5/10] Loss: 3.10631 focal_loss 2.43206 dice_loss 0.67425 +Epoch [3799/4000] Validation [6/10] Loss: 1.30231 focal_loss 0.58618 dice_loss 0.71613 +Epoch [3799/4000] Validation [7/10] Loss: 1.15904 focal_loss 0.50893 dice_loss 0.65011 +Epoch [3799/4000] Validation [8/10] Loss: 2.42182 focal_loss 1.79650 dice_loss 0.62532 +Epoch [3799/4000] Validation [9/10] Loss: 1.54627 focal_loss 1.00447 dice_loss 0.54180 +Epoch [3799/4000] Validation [10/10] Loss: 1.83235 focal_loss 1.10085 dice_loss 0.73149 +Epoch [3799/4000] Validation metric {'Val/mean dice_metric': 0.9514239430427551, 'Val/mean miou_metric': 0.9355495572090149, 'Val/mean f1': 0.9490219950675964, 'Val/mean precision': 0.9459764361381531, 'Val/mean recall': 0.9520872831344604, 'Val/mean hd95_metric': 10.609912872314453} +Cheakpoint... +Epoch [3799/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514239430427551, 'Val/mean miou_metric': 0.9355495572090149, 'Val/mean f1': 0.9490219950675964, 'Val/mean precision': 0.9459764361381531, 'Val/mean recall': 0.9520872831344604, 'Val/mean hd95_metric': 10.609912872314453} +Epoch [3800/4000] Training [1/39] Loss: 0.00416 +Epoch [3800/4000] Training [2/39] Loss: 0.00501 +Epoch [3800/4000] Training [3/39] Loss: 0.00399 +Epoch [3800/4000] Training [4/39] Loss: 0.00326 +Epoch [3800/4000] Training [5/39] Loss: 0.12935 +Epoch [3800/4000] Training [6/39] Loss: 0.00424 +Epoch [3800/4000] Training [7/39] Loss: 0.00514 +Epoch [3800/4000] Training [8/39] Loss: 0.00790 +Epoch [3800/4000] Training [9/39] Loss: 0.00446 +Epoch [3800/4000] Training [10/39] Loss: 0.12912 +Epoch [3800/4000] Training [11/39] Loss: 0.00413 +Epoch [3800/4000] Training [12/39] Loss: 0.00395 +Epoch [3800/4000] Training [13/39] Loss: 0.00407 +Epoch [3800/4000] Training [14/39] Loss: 0.00480 +Epoch [3800/4000] Training [15/39] Loss: 0.00635 +Epoch [3800/4000] Training [16/39] Loss: 0.12849 +Epoch [3800/4000] Training [17/39] Loss: 0.00545 +Epoch [3800/4000] Training [18/39] Loss: 0.00587 +Epoch [3800/4000] Training [19/39] Loss: 0.00466 +Epoch [3800/4000] Training [20/39] Loss: 0.00376 +Epoch [3800/4000] Training [21/39] Loss: 0.00312 +Epoch [3800/4000] Training [22/39] Loss: 0.00700 +Epoch [3800/4000] Training [23/39] Loss: 0.25267 +Epoch [3800/4000] Training [24/39] Loss: 0.00387 +Epoch [3800/4000] Training [25/39] Loss: 0.00286 +Epoch [3800/4000] Training [26/39] Loss: 0.00423 +Epoch [3800/4000] Training [27/39] Loss: 0.00233 +Epoch [3800/4000] Training [28/39] Loss: 0.00611 +Epoch [3800/4000] Training [29/39] Loss: 0.13026 +Epoch [3800/4000] Training [30/39] Loss: 0.00359 +Epoch [3800/4000] Training [31/39] Loss: 0.00498 +Epoch [3800/4000] Training [32/39] Loss: 0.00481 +Epoch [3800/4000] Training [33/39] Loss: 0.00431 +Epoch [3800/4000] Training [34/39] Loss: 0.00509 +Epoch [3800/4000] Training [35/39] Loss: 0.00426 +Epoch [3800/4000] Training [36/39] Loss: 0.00461 +Epoch [3800/4000] Training [37/39] Loss: 0.00528 +Epoch [3800/4000] Training [38/39] Loss: 0.00723 +Epoch [3800/4000] Training [39/39] Loss: 0.00561 +Epoch [3800/4000] Training metric {'Train/mean dice_metric': 0.9964228272438049, 'Train/mean miou_metric': 0.9932949542999268, 'Train/mean f1': 0.9969736337661743, 'Train/mean precision': 0.9964708685874939, 'Train/mean recall': 0.9974768757820129, 'Train/mean hd95_metric': 1.0921880006790161} +Epoch [3800/4000] Validation [1/10] Loss: 0.70178 focal_loss 0.61643 dice_loss 0.08536 +Epoch [3800/4000] Validation [2/10] Loss: 0.49594 focal_loss 0.39734 dice_loss 0.09860 +Epoch [3800/4000] Validation [3/10] Loss: 0.39249 focal_loss 0.28112 dice_loss 0.11137 +Epoch [3800/4000] Validation [4/10] Loss: 0.88376 focal_loss 0.31961 dice_loss 0.56415 +Epoch [3800/4000] Validation [5/10] Loss: 3.07340 focal_loss 2.39935 dice_loss 0.67405 +Epoch [3800/4000] Validation [6/10] Loss: 1.31899 focal_loss 0.60383 dice_loss 0.71516 +Epoch [3800/4000] Validation [7/10] Loss: 1.16623 focal_loss 0.51466 dice_loss 0.65157 +Epoch [3800/4000] Validation [8/10] Loss: 2.33425 focal_loss 1.71899 dice_loss 0.61525 +Epoch [3800/4000] Validation [9/10] Loss: 1.55007 focal_loss 1.00746 dice_loss 0.54261 +Epoch [3800/4000] Validation [10/10] Loss: 1.86687 focal_loss 1.13353 dice_loss 0.73335 +Epoch [3800/4000] Validation metric {'Val/mean dice_metric': 0.9516701102256775, 'Val/mean miou_metric': 0.9359237551689148, 'Val/mean f1': 0.9487991333007812, 'Val/mean precision': 0.9443671703338623, 'Val/mean recall': 0.953272819519043, 'Val/mean hd95_metric': 10.864911079406738} +Cheakpoint... +Epoch [3800/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516701102256775, 'Val/mean miou_metric': 0.9359237551689148, 'Val/mean f1': 0.9487991333007812, 'Val/mean precision': 0.9443671703338623, 'Val/mean recall': 0.953272819519043, 'Val/mean hd95_metric': 10.864911079406738} +Epoch [3801/4000] Training [1/39] Loss: 0.12948 +Epoch [3801/4000] Training [2/39] Loss: 0.00335 +Epoch [3801/4000] Training [3/39] Loss: 0.00519 +Epoch [3801/4000] Training [4/39] Loss: 0.12947 +Epoch [3801/4000] Training [5/39] Loss: 0.00443 +Epoch [3801/4000] Training [6/39] Loss: 0.00378 +Epoch [3801/4000] Training [7/39] Loss: 0.00517 +Epoch [3801/4000] Training [8/39] Loss: 0.13001 +Epoch [3801/4000] Training [9/39] Loss: 0.12870 +Epoch [3801/4000] Training [10/39] Loss: 0.12937 +Epoch [3801/4000] Training [11/39] Loss: 0.00309 +Epoch [3801/4000] Training [12/39] Loss: 0.00472 +Epoch [3801/4000] Training [13/39] Loss: 0.00551 +Epoch [3801/4000] Training [14/39] Loss: 0.00866 +Epoch [3801/4000] Training [15/39] Loss: 0.00397 +Epoch [3801/4000] Training [16/39] Loss: 0.00551 +Epoch [3801/4000] Training [17/39] Loss: 0.00881 +Epoch [3801/4000] Training [18/39] Loss: 0.00477 +Epoch [3801/4000] Training [19/39] Loss: 0.00519 +Epoch [3801/4000] Training [20/39] Loss: 0.00408 +Epoch [3801/4000] Training [21/39] Loss: 0.12966 +Epoch [3801/4000] Training [22/39] Loss: 0.00543 +Epoch [3801/4000] Training [23/39] Loss: 0.12908 +Epoch [3801/4000] Training [24/39] Loss: 0.12824 +Epoch [3801/4000] Training [25/39] Loss: 0.00274 +Epoch [3801/4000] Training [26/39] Loss: 0.12825 +Epoch [3801/4000] Training [27/39] Loss: 0.00573 +Epoch [3801/4000] Training [28/39] Loss: 0.00299 +Epoch [3801/4000] Training [29/39] Loss: 0.00289 +Epoch [3801/4000] Training [30/39] Loss: 0.00413 +Epoch [3801/4000] Training [31/39] Loss: 0.00426 +Epoch [3801/4000] Training [32/39] Loss: 0.00505 +Epoch [3801/4000] Training [33/39] Loss: 0.00285 +Epoch [3801/4000] Training [34/39] Loss: 0.00465 +Epoch [3801/4000] Training [35/39] Loss: 0.00529 +Epoch [3801/4000] Training [36/39] Loss: 0.00490 +Epoch [3801/4000] Training [37/39] Loss: 0.12869 +Epoch [3801/4000] Training [38/39] Loss: 0.00617 +Epoch [3801/4000] Training [39/39] Loss: 0.13051 +Epoch [3801/4000] Training metric {'Train/mean dice_metric': 0.9964327216148376, 'Train/mean miou_metric': 0.9933054447174072, 'Train/mean f1': 0.9970327615737915, 'Train/mean precision': 0.9965999722480774, 'Train/mean recall': 0.9974658489227295, 'Train/mean hd95_metric': 0.9278846979141235} +Epoch [3801/4000] Validation [1/10] Loss: 0.72494 focal_loss 0.63982 dice_loss 0.08512 +Epoch [3801/4000] Validation [2/10] Loss: 0.50567 focal_loss 0.40576 dice_loss 0.09990 +Epoch [3801/4000] Validation [3/10] Loss: 0.41584 focal_loss 0.30342 dice_loss 0.11242 +Epoch [3801/4000] Validation [4/10] Loss: 0.88895 focal_loss 0.32528 dice_loss 0.56366 +Epoch [3801/4000] Validation [5/10] Loss: 3.15609 focal_loss 2.48199 dice_loss 0.67410 +Epoch [3801/4000] Validation [6/10] Loss: 1.32005 focal_loss 0.60652 dice_loss 0.71353 +Epoch [3801/4000] Validation [7/10] Loss: 1.16716 focal_loss 0.51644 dice_loss 0.65072 +Epoch [3801/4000] Validation [8/10] Loss: 2.44672 focal_loss 1.82514 dice_loss 0.62158 +Epoch [3801/4000] Validation [9/10] Loss: 1.58385 focal_loss 1.04155 dice_loss 0.54230 +Epoch [3801/4000] Validation [10/10] Loss: 1.87144 focal_loss 1.13902 dice_loss 0.73242 +Epoch [3801/4000] Validation metric {'Val/mean dice_metric': 0.9515478014945984, 'Val/mean miou_metric': 0.9357739090919495, 'Val/mean f1': 0.9487075209617615, 'Val/mean precision': 0.944995641708374, 'Val/mean recall': 0.9524486064910889, 'Val/mean hd95_metric': 10.702255249023438} +Cheakpoint... +Epoch [3801/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515478014945984, 'Val/mean miou_metric': 0.9357739090919495, 'Val/mean f1': 0.9487075209617615, 'Val/mean precision': 0.944995641708374, 'Val/mean recall': 0.9524486064910889, 'Val/mean hd95_metric': 10.702255249023438} +Epoch [3802/4000] Training [1/39] Loss: 0.00557 +Epoch [3802/4000] Training [2/39] Loss: 0.12894 +Epoch [3802/4000] Training [3/39] Loss: 0.12907 +Epoch [3802/4000] Training [4/39] Loss: 0.00458 +Epoch [3802/4000] Training [5/39] Loss: 0.00678 +Epoch [3802/4000] Training [6/39] Loss: 0.12974 +Epoch [3802/4000] Training [7/39] Loss: 0.00421 +Epoch [3802/4000] Training [8/39] Loss: 0.00369 +Epoch [3802/4000] Training [9/39] Loss: 0.00394 +Epoch [3802/4000] Training [10/39] Loss: 0.00357 +Epoch [3802/4000] Training [11/39] Loss: 0.00406 +Epoch [3802/4000] Training [12/39] Loss: 0.00388 +Epoch [3802/4000] Training [13/39] Loss: 0.13255 +Epoch [3802/4000] Training [14/39] Loss: 0.00394 +Epoch [3802/4000] Training [15/39] Loss: 0.00605 +Epoch [3802/4000] Training [16/39] Loss: 0.12840 +Epoch [3802/4000] Training [17/39] Loss: 0.12983 +Epoch [3802/4000] Training [18/39] Loss: 0.12902 +Epoch [3802/4000] Training [19/39] Loss: 0.25513 +Epoch [3802/4000] Training [20/39] Loss: 0.00685 +Epoch [3802/4000] Training [21/39] Loss: 0.13209 +Epoch [3802/4000] Training [22/39] Loss: 0.00333 +Epoch [3802/4000] Training [23/39] Loss: 0.00467 +Epoch [3802/4000] Training [24/39] Loss: 0.00923 +Epoch [3802/4000] Training [25/39] Loss: 0.00478 +Epoch [3802/4000] Training [26/39] Loss: 0.00375 +Epoch [3802/4000] Training [27/39] Loss: 0.00429 +Epoch [3802/4000] Training [28/39] Loss: 0.00432 +Epoch [3802/4000] Training [29/39] Loss: 0.12780 +Epoch [3802/4000] Training [30/39] Loss: 0.00789 +Epoch [3802/4000] Training [31/39] Loss: 0.00459 +Epoch [3802/4000] Training [32/39] Loss: 0.00405 +Epoch [3802/4000] Training [33/39] Loss: 0.25238 +Epoch [3802/4000] Training [34/39] Loss: 0.00244 +Epoch [3802/4000] Training [35/39] Loss: 0.12908 +Epoch [3802/4000] Training [36/39] Loss: 0.00568 +Epoch [3802/4000] Training [37/39] Loss: 0.00468 +Epoch [3802/4000] Training [38/39] Loss: 0.00216 +Epoch [3802/4000] Training [39/39] Loss: 0.04278 +Epoch [3802/4000] Training metric {'Train/mean dice_metric': 0.9955902099609375, 'Train/mean miou_metric': 0.9924560189247131, 'Train/mean f1': 0.9969005584716797, 'Train/mean precision': 0.9964653849601746, 'Train/mean recall': 0.997336208820343, 'Train/mean hd95_metric': 0.9714459180831909} +Epoch [3802/4000] Validation [1/10] Loss: 0.70665 focal_loss 0.62250 dice_loss 0.08415 +Epoch [3802/4000] Validation [2/10] Loss: 0.50743 focal_loss 0.40601 dice_loss 0.10141 +Epoch [3802/4000] Validation [3/10] Loss: 0.41402 focal_loss 0.30117 dice_loss 0.11284 +Epoch [3802/4000] Validation [4/10] Loss: 0.88339 focal_loss 0.32029 dice_loss 0.56310 +Epoch [3802/4000] Validation [5/10] Loss: 3.11525 focal_loss 2.44100 dice_loss 0.67425 +Epoch [3802/4000] Validation [6/10] Loss: 1.30868 focal_loss 0.59551 dice_loss 0.71317 +Epoch [3802/4000] Validation [7/10] Loss: 1.16195 focal_loss 0.51199 dice_loss 0.64995 +Epoch [3802/4000] Validation [8/10] Loss: 2.47084 focal_loss 1.84484 dice_loss 0.62601 +Epoch [3802/4000] Validation [9/10] Loss: 1.55595 focal_loss 1.01334 dice_loss 0.54261 +Epoch [3802/4000] Validation [10/10] Loss: 1.85292 focal_loss 1.12082 dice_loss 0.73210 +Epoch [3802/4000] Validation metric {'Val/mean dice_metric': 0.9508389830589294, 'Val/mean miou_metric': 0.9350753426551819, 'Val/mean f1': 0.9488263726234436, 'Val/mean precision': 0.9456396102905273, 'Val/mean recall': 0.9520347714424133, 'Val/mean hd95_metric': 10.606877326965332} +Cheakpoint... +Epoch [3802/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508389830589294, 'Val/mean miou_metric': 0.9350753426551819, 'Val/mean f1': 0.9488263726234436, 'Val/mean precision': 0.9456396102905273, 'Val/mean recall': 0.9520347714424133, 'Val/mean hd95_metric': 10.606877326965332} +Epoch [3803/4000] Training [1/39] Loss: 0.00310 +Epoch [3803/4000] Training [2/39] Loss: 0.12899 +Epoch [3803/4000] Training [3/39] Loss: 0.00610 +Epoch [3803/4000] Training [4/39] Loss: 0.00537 +Epoch [3803/4000] Training [5/39] Loss: 0.00282 +Epoch [3803/4000] Training [6/39] Loss: 0.00464 +Epoch [3803/4000] Training [7/39] Loss: 0.00688 +Epoch [3803/4000] Training [8/39] Loss: 0.00376 +Epoch [3803/4000] Training [9/39] Loss: 0.00409 +Epoch [3803/4000] Training [10/39] Loss: 0.00527 +Epoch [3803/4000] Training [11/39] Loss: 0.12858 +Epoch [3803/4000] Training [12/39] Loss: 0.00422 +Epoch [3803/4000] Training [13/39] Loss: 0.12833 +Epoch [3803/4000] Training [14/39] Loss: 0.00458 +Epoch [3803/4000] Training [15/39] Loss: 0.00407 +Epoch [3803/4000] Training [16/39] Loss: 0.00623 +Epoch [3803/4000] Training [17/39] Loss: 0.00385 +Epoch [3803/4000] Training [18/39] Loss: 0.00895 +Epoch [3803/4000] Training [19/39] Loss: 0.00675 +Epoch [3803/4000] Training [20/39] Loss: 0.00522 +Epoch [3803/4000] Training [21/39] Loss: 0.00408 +Epoch [3803/4000] Training [22/39] Loss: 0.00284 +Epoch [3803/4000] Training [23/39] Loss: 0.00459 +Epoch [3803/4000] Training [24/39] Loss: 0.00380 +Epoch [3803/4000] Training [25/39] Loss: 0.00457 +Epoch [3803/4000] Training [26/39] Loss: 0.12803 +Epoch [3803/4000] Training [27/39] Loss: 0.00345 +Epoch [3803/4000] Training [28/39] Loss: 0.12806 +Epoch [3803/4000] Training [29/39] Loss: 0.12704 +Epoch [3803/4000] Training [30/39] Loss: 0.13023 +Epoch [3803/4000] Training [31/39] Loss: 0.00582 +Epoch [3803/4000] Training [32/39] Loss: 0.13218 +Epoch [3803/4000] Training [33/39] Loss: 0.00467 +Epoch [3803/4000] Training [34/39] Loss: 0.12879 +Epoch [3803/4000] Training [35/39] Loss: 0.12822 +Epoch [3803/4000] Training [36/39] Loss: 0.00413 +Epoch [3803/4000] Training [37/39] Loss: 0.00624 +Epoch [3803/4000] Training [38/39] Loss: 0.00546 +Epoch [3803/4000] Training [39/39] Loss: 0.13098 +Epoch [3803/4000] Training metric {'Train/mean dice_metric': 0.9956585168838501, 'Train/mean miou_metric': 0.992588996887207, 'Train/mean f1': 0.996967077255249, 'Train/mean precision': 0.9964749217033386, 'Train/mean recall': 0.9974597096443176, 'Train/mean hd95_metric': 1.04983389377594} +Epoch [3803/4000] Validation [1/10] Loss: 0.69896 focal_loss 0.61399 dice_loss 0.08497 +Epoch [3803/4000] Validation [2/10] Loss: 0.50519 focal_loss 0.40646 dice_loss 0.09873 +Epoch [3803/4000] Validation [3/10] Loss: 0.39303 focal_loss 0.28174 dice_loss 0.11129 +Epoch [3803/4000] Validation [4/10] Loss: 0.89436 focal_loss 0.33000 dice_loss 0.56436 +Epoch [3803/4000] Validation [5/10] Loss: 3.06716 focal_loss 2.39320 dice_loss 0.67395 +Epoch [3803/4000] Validation [6/10] Loss: 1.33903 focal_loss 0.62545 dice_loss 0.71358 +Epoch [3803/4000] Validation [7/10] Loss: 1.17995 focal_loss 0.52934 dice_loss 0.65061 +Epoch [3803/4000] Validation [8/10] Loss: 2.36483 focal_loss 1.74986 dice_loss 0.61497 +Epoch [3803/4000] Validation [9/10] Loss: 1.57357 focal_loss 1.02966 dice_loss 0.54392 +Epoch [3803/4000] Validation [10/10] Loss: 1.91215 focal_loss 1.17743 dice_loss 0.73473 +Epoch [3803/4000] Validation metric {'Val/mean dice_metric': 0.951030969619751, 'Val/mean miou_metric': 0.9353424310684204, 'Val/mean f1': 0.9484488368034363, 'Val/mean precision': 0.9436094164848328, 'Val/mean recall': 0.953338086605072, 'Val/mean hd95_metric': 10.806961059570312} +Cheakpoint... +Epoch [3803/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951030969619751, 'Val/mean miou_metric': 0.9353424310684204, 'Val/mean f1': 0.9484488368034363, 'Val/mean precision': 0.9436094164848328, 'Val/mean recall': 0.953338086605072, 'Val/mean hd95_metric': 10.806961059570312} +Epoch [3804/4000] Training [1/39] Loss: 0.12738 +Epoch [3804/4000] Training [2/39] Loss: 0.00442 +Epoch [3804/4000] Training [3/39] Loss: 0.00676 +Epoch [3804/4000] Training [4/39] Loss: 0.00296 +Epoch [3804/4000] Training [5/39] Loss: 0.00309 +Epoch [3804/4000] Training [6/39] Loss: 0.12798 +Epoch [3804/4000] Training [7/39] Loss: 0.00422 +Epoch [3804/4000] Training [8/39] Loss: 0.12804 +Epoch [3804/4000] Training [9/39] Loss: 0.00413 +Epoch [3804/4000] Training [10/39] Loss: 0.00609 +Epoch [3804/4000] Training [11/39] Loss: 0.12821 +Epoch [3804/4000] Training [12/39] Loss: 0.00404 +Epoch [3804/4000] Training [13/39] Loss: 0.00462 +Epoch [3804/4000] Training [14/39] Loss: 0.12864 +Epoch [3804/4000] Training [15/39] Loss: 0.12775 +Epoch [3804/4000] Training [16/39] Loss: 0.00309 +Epoch [3804/4000] Training [17/39] Loss: 0.00491 +Epoch [3804/4000] Training [18/39] Loss: 0.00530 +Epoch [3804/4000] Training [19/39] Loss: 0.00576 +Epoch [3804/4000] Training [20/39] Loss: 0.00477 +Epoch [3804/4000] Training [21/39] Loss: 0.00415 +Epoch [3804/4000] Training [22/39] Loss: 0.13011 +Epoch [3804/4000] Training [23/39] Loss: 0.00478 +Epoch [3804/4000] Training [24/39] Loss: 0.00451 +Epoch [3804/4000] Training [25/39] Loss: 0.00470 +Epoch [3804/4000] Training [26/39] Loss: 0.00666 +Epoch [3804/4000] Training [27/39] Loss: 0.00366 +Epoch [3804/4000] Training [28/39] Loss: 0.00442 +Epoch [3804/4000] Training [29/39] Loss: 0.00360 +Epoch [3804/4000] Training [30/39] Loss: 0.00886 +Epoch [3804/4000] Training [31/39] Loss: 0.12872 +Epoch [3804/4000] Training [32/39] Loss: 0.00437 +Epoch [3804/4000] Training [33/39] Loss: 0.00289 +Epoch [3804/4000] Training [34/39] Loss: 0.00525 +Epoch [3804/4000] Training [35/39] Loss: 0.00393 +Epoch [3804/4000] Training [36/39] Loss: 0.00604 +Epoch [3804/4000] Training [37/39] Loss: 0.00461 +Epoch [3804/4000] Training [38/39] Loss: 0.00439 +Epoch [3804/4000] Training [39/39] Loss: 0.00421 +Epoch [3804/4000] Training metric {'Train/mean dice_metric': 0.9957019686698914, 'Train/mean miou_metric': 0.9926977753639221, 'Train/mean f1': 0.9970241785049438, 'Train/mean precision': 0.9965695142745972, 'Train/mean recall': 0.997479259967804, 'Train/mean hd95_metric': 0.9057546257972717} +Epoch [3804/4000] Validation [1/10] Loss: 0.68885 focal_loss 0.60415 dice_loss 0.08470 +Epoch [3804/4000] Validation [2/10] Loss: 0.49540 focal_loss 0.39692 dice_loss 0.09848 +Epoch [3804/4000] Validation [3/10] Loss: 0.38963 focal_loss 0.27811 dice_loss 0.11153 +Epoch [3804/4000] Validation [4/10] Loss: 0.88837 focal_loss 0.32357 dice_loss 0.56480 +Epoch [3804/4000] Validation [5/10] Loss: 3.03836 focal_loss 2.36431 dice_loss 0.67404 +Epoch [3804/4000] Validation [6/10] Loss: 1.32592 focal_loss 0.61215 dice_loss 0.71377 +Epoch [3804/4000] Validation [7/10] Loss: 1.16771 focal_loss 0.51710 dice_loss 0.65061 +Epoch [3804/4000] Validation [8/10] Loss: 2.35719 focal_loss 1.74117 dice_loss 0.61602 +Epoch [3804/4000] Validation [9/10] Loss: 1.57077 focal_loss 1.02720 dice_loss 0.54357 +Epoch [3804/4000] Validation [10/10] Loss: 1.88842 focal_loss 1.15330 dice_loss 0.73511 +Epoch [3804/4000] Validation metric {'Val/mean dice_metric': 0.9510296583175659, 'Val/mean miou_metric': 0.9353726506233215, 'Val/mean f1': 0.9486621618270874, 'Val/mean precision': 0.9438491463661194, 'Val/mean recall': 0.9535245895385742, 'Val/mean hd95_metric': 10.637439727783203} +Cheakpoint... +Epoch [3804/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510296583175659, 'Val/mean miou_metric': 0.9353726506233215, 'Val/mean f1': 0.9486621618270874, 'Val/mean precision': 0.9438491463661194, 'Val/mean recall': 0.9535245895385742, 'Val/mean hd95_metric': 10.637439727783203} +Epoch [3805/4000] Training [1/39] Loss: 0.00405 +Epoch [3805/4000] Training [2/39] Loss: 0.00401 +Epoch [3805/4000] Training [3/39] Loss: 0.00455 +Epoch [3805/4000] Training [4/39] Loss: 0.00675 +Epoch [3805/4000] Training [5/39] Loss: 0.00508 +Epoch [3805/4000] Training [6/39] Loss: 0.00394 +Epoch [3805/4000] Training [7/39] Loss: 0.00593 +Epoch [3805/4000] Training [8/39] Loss: 0.00344 +Epoch [3805/4000] Training [9/39] Loss: 0.00464 +Epoch [3805/4000] Training [10/39] Loss: 0.00460 +Epoch [3805/4000] Training [11/39] Loss: 0.00472 +Epoch [3805/4000] Training [12/39] Loss: 0.00481 +Epoch [3805/4000] Training [13/39] Loss: 0.00549 +Epoch [3805/4000] Training [14/39] Loss: 0.00502 +Epoch [3805/4000] Training [15/39] Loss: 0.00353 +Epoch [3805/4000] Training [16/39] Loss: 0.00705 +Epoch [3805/4000] Training [17/39] Loss: 0.00821 +Epoch [3805/4000] Training [18/39] Loss: 0.00602 +Epoch [3805/4000] Training [19/39] Loss: 0.00456 +Epoch [3805/4000] Training [20/39] Loss: 0.00530 +Epoch [3805/4000] Training [21/39] Loss: 0.25421 +Epoch [3805/4000] Training [22/39] Loss: 0.00858 +Epoch [3805/4000] Training [23/39] Loss: 0.00296 +Epoch [3805/4000] Training [24/39] Loss: 0.12928 +Epoch [3805/4000] Training [25/39] Loss: 0.12941 +Epoch [3805/4000] Training [26/39] Loss: 0.00666 +Epoch [3805/4000] Training [27/39] Loss: 0.00477 +Epoch [3805/4000] Training [28/39] Loss: 0.00467 +Epoch [3805/4000] Training [29/39] Loss: 0.00537 +Epoch [3805/4000] Training [30/39] Loss: 0.00479 +Epoch [3805/4000] Training [31/39] Loss: 0.00383 +Epoch [3805/4000] Training [32/39] Loss: 0.13266 +Epoch [3805/4000] Training [33/39] Loss: 0.12756 +Epoch [3805/4000] Training [34/39] Loss: 0.00868 +Epoch [3805/4000] Training [35/39] Loss: 0.13455 +Epoch [3805/4000] Training [36/39] Loss: 0.00472 +Epoch [3805/4000] Training [37/39] Loss: 0.00436 +Epoch [3805/4000] Training [38/39] Loss: 0.00589 +Epoch [3805/4000] Training [39/39] Loss: 0.00708 +Epoch [3805/4000] Training metric {'Train/mean dice_metric': 0.9961856007575989, 'Train/mean miou_metric': 0.9928464293479919, 'Train/mean f1': 0.9967398047447205, 'Train/mean precision': 0.9962632656097412, 'Train/mean recall': 0.9972167611122131, 'Train/mean hd95_metric': 1.0494128465652466} +Epoch [3805/4000] Validation [1/10] Loss: 0.70530 focal_loss 0.62130 dice_loss 0.08400 +Epoch [3805/4000] Validation [2/10] Loss: 0.50721 focal_loss 0.40759 dice_loss 0.09962 +Epoch [3805/4000] Validation [3/10] Loss: 0.40344 focal_loss 0.29168 dice_loss 0.11176 +Epoch [3805/4000] Validation [4/10] Loss: 0.89366 focal_loss 0.32966 dice_loss 0.56400 +Epoch [3805/4000] Validation [5/10] Loss: 3.11831 focal_loss 2.44423 dice_loss 0.67408 +Epoch [3805/4000] Validation [6/10] Loss: 1.32942 focal_loss 0.61686 dice_loss 0.71256 +Epoch [3805/4000] Validation [7/10] Loss: 1.17895 focal_loss 0.52850 dice_loss 0.65046 +Epoch [3805/4000] Validation [8/10] Loss: 2.45127 focal_loss 1.83011 dice_loss 0.62115 +Epoch [3805/4000] Validation [9/10] Loss: 1.59894 focal_loss 1.05569 dice_loss 0.54325 +Epoch [3805/4000] Validation [10/10] Loss: 1.90574 focal_loss 1.17166 dice_loss 0.73408 +Epoch [3805/4000] Validation metric {'Val/mean dice_metric': 0.9513869881629944, 'Val/mean miou_metric': 0.9354550242424011, 'Val/mean f1': 0.9481541514396667, 'Val/mean precision': 0.9439948797225952, 'Val/mean recall': 0.9523501992225647, 'Val/mean hd95_metric': 10.750799179077148} +Cheakpoint... +Epoch [3805/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513869881629944, 'Val/mean miou_metric': 0.9354550242424011, 'Val/mean f1': 0.9481541514396667, 'Val/mean precision': 0.9439948797225952, 'Val/mean recall': 0.9523501992225647, 'Val/mean hd95_metric': 10.750799179077148} +Epoch [3806/4000] Training [1/39] Loss: 0.00538 +Epoch [3806/4000] Training [2/39] Loss: 0.00423 +Epoch [3806/4000] Training [3/39] Loss: 0.00310 +Epoch [3806/4000] Training [4/39] Loss: 0.00472 +Epoch [3806/4000] Training [5/39] Loss: 0.00390 +Epoch [3806/4000] Training [6/39] Loss: 0.00514 +Epoch [3806/4000] Training [7/39] Loss: 0.00630 +Epoch [3806/4000] Training [8/39] Loss: 0.12997 +Epoch [3806/4000] Training [9/39] Loss: 0.00692 +Epoch [3806/4000] Training [10/39] Loss: 0.00486 +Epoch [3806/4000] Training [11/39] Loss: 0.00515 +Epoch [3806/4000] Training [12/39] Loss: 0.00550 +Epoch [3806/4000] Training [13/39] Loss: 0.12974 +Epoch [3806/4000] Training [14/39] Loss: 0.00392 +Epoch [3806/4000] Training [15/39] Loss: 0.12704 +Epoch [3806/4000] Training [16/39] Loss: 0.00287 +Epoch [3806/4000] Training [17/39] Loss: 0.12798 +Epoch [3806/4000] Training [18/39] Loss: 0.00489 +Epoch [3806/4000] Training [19/39] Loss: 0.12935 +Epoch [3806/4000] Training [20/39] Loss: 0.00534 +Epoch [3806/4000] Training [21/39] Loss: 0.00486 +Epoch [3806/4000] Training [22/39] Loss: 0.00502 +Epoch [3806/4000] Training [23/39] Loss: 0.00305 +Epoch [3806/4000] Training [24/39] Loss: 0.00497 +Epoch [3806/4000] Training [25/39] Loss: 0.12915 +Epoch [3806/4000] Training [26/39] Loss: 0.12916 +Epoch [3806/4000] Training [27/39] Loss: 0.00344 +Epoch [3806/4000] Training [28/39] Loss: 0.00447 +Epoch [3806/4000] Training [29/39] Loss: 0.00454 +Epoch [3806/4000] Training [30/39] Loss: 0.00644 +Epoch [3806/4000] Training [31/39] Loss: 0.00297 +Epoch [3806/4000] Training [32/39] Loss: 0.12981 +Epoch [3806/4000] Training [33/39] Loss: 0.13115 +Epoch [3806/4000] Training [34/39] Loss: 0.13073 +Epoch [3806/4000] Training [35/39] Loss: 0.00446 +Epoch [3806/4000] Training [36/39] Loss: 0.00384 +Epoch [3806/4000] Training [37/39] Loss: 0.00416 +Epoch [3806/4000] Training [38/39] Loss: 0.12793 +Epoch [3806/4000] Training [39/39] Loss: 0.00469 +Epoch [3806/4000] Training metric {'Train/mean dice_metric': 0.9958919882774353, 'Train/mean miou_metric': 0.9930652379989624, 'Train/mean f1': 0.9971421360969543, 'Train/mean precision': 0.996701717376709, 'Train/mean recall': 0.9975829124450684, 'Train/mean hd95_metric': 0.8910459876060486} +Epoch [3806/4000] Validation [1/10] Loss: 0.69828 focal_loss 0.61387 dice_loss 0.08441 +Epoch [3806/4000] Validation [2/10] Loss: 0.50603 focal_loss 0.40548 dice_loss 0.10055 +Epoch [3806/4000] Validation [3/10] Loss: 0.39878 focal_loss 0.28703 dice_loss 0.11175 +Epoch [3806/4000] Validation [4/10] Loss: 0.88943 focal_loss 0.32542 dice_loss 0.56402 +Epoch [3806/4000] Validation [5/10] Loss: 3.07561 focal_loss 2.40149 dice_loss 0.67412 +Epoch [3806/4000] Validation [6/10] Loss: 1.31795 focal_loss 0.60614 dice_loss 0.71181 +Epoch [3806/4000] Validation [7/10] Loss: 1.16990 focal_loss 0.51944 dice_loss 0.65046 +Epoch [3806/4000] Validation [8/10] Loss: 2.45868 focal_loss 1.83455 dice_loss 0.62414 +Epoch [3806/4000] Validation [9/10] Loss: 1.55793 focal_loss 1.01494 dice_loss 0.54298 +Epoch [3806/4000] Validation [10/10] Loss: 1.88081 focal_loss 1.14803 dice_loss 0.73278 +Epoch [3806/4000] Validation metric {'Val/mean dice_metric': 0.9511404037475586, 'Val/mean miou_metric': 0.9356394410133362, 'Val/mean f1': 0.9488241672515869, 'Val/mean precision': 0.9449201822280884, 'Val/mean recall': 0.952760636806488, 'Val/mean hd95_metric': 10.604424476623535} +Cheakpoint... +Epoch [3806/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511404037475586, 'Val/mean miou_metric': 0.9356394410133362, 'Val/mean f1': 0.9488241672515869, 'Val/mean precision': 0.9449201822280884, 'Val/mean recall': 0.952760636806488, 'Val/mean hd95_metric': 10.604424476623535} +Epoch [3807/4000] Training [1/39] Loss: 0.13016 +Epoch [3807/4000] Training [2/39] Loss: 0.00456 +Epoch [3807/4000] Training [3/39] Loss: 0.00403 +Epoch [3807/4000] Training [4/39] Loss: 0.01056 +Epoch [3807/4000] Training [5/39] Loss: 0.00381 +Epoch [3807/4000] Training [6/39] Loss: 0.13342 +Epoch [3807/4000] Training [7/39] Loss: 0.12803 +Epoch [3807/4000] Training [8/39] Loss: 0.00553 +Epoch [3807/4000] Training [9/39] Loss: 0.00437 +Epoch [3807/4000] Training [10/39] Loss: 0.00310 +Epoch [3807/4000] Training [11/39] Loss: 0.00450 +Epoch [3807/4000] Training [12/39] Loss: 0.00644 +Epoch [3807/4000] Training [13/39] Loss: 0.00427 +Epoch [3807/4000] Training [14/39] Loss: 0.25397 +Epoch [3807/4000] Training [15/39] Loss: 0.12754 +Epoch [3807/4000] Training [16/39] Loss: 0.25306 +Epoch [3807/4000] Training [17/39] Loss: 0.00324 +Epoch [3807/4000] Training [18/39] Loss: 0.00527 +Epoch [3807/4000] Training [19/39] Loss: 0.00472 +Epoch [3807/4000] Training [20/39] Loss: 0.00274 +Epoch [3807/4000] Training [21/39] Loss: 0.00568 +Epoch [3807/4000] Training [22/39] Loss: 0.00363 +Epoch [3807/4000] Training [23/39] Loss: 0.12799 +Epoch [3807/4000] Training [24/39] Loss: 0.12967 +Epoch [3807/4000] Training [25/39] Loss: 0.00581 +Epoch [3807/4000] Training [26/39] Loss: 0.00763 +Epoch [3807/4000] Training [27/39] Loss: 0.00367 +Epoch [3807/4000] Training [28/39] Loss: 0.00279 +Epoch [3807/4000] Training [29/39] Loss: 0.00295 +Epoch [3807/4000] Training [30/39] Loss: 0.00601 +Epoch [3807/4000] Training [31/39] Loss: 0.00426 +Epoch [3807/4000] Training [32/39] Loss: 0.00670 +Epoch [3807/4000] Training [33/39] Loss: 0.00478 +Epoch [3807/4000] Training [34/39] Loss: 0.13170 +Epoch [3807/4000] Training [35/39] Loss: 0.00307 +Epoch [3807/4000] Training [36/39] Loss: 0.00787 +Epoch [3807/4000] Training [37/39] Loss: 0.00316 +Epoch [3807/4000] Training [38/39] Loss: 0.00517 +Epoch [3807/4000] Training [39/39] Loss: 0.00495 +Epoch [3807/4000] Training metric {'Train/mean dice_metric': 0.9964269995689392, 'Train/mean miou_metric': 0.9933034777641296, 'Train/mean f1': 0.9968594908714294, 'Train/mean precision': 0.996430516242981, 'Train/mean recall': 0.9972887635231018, 'Train/mean hd95_metric': 0.9138206839561462} +Epoch [3807/4000] Validation [1/10] Loss: 0.70771 focal_loss 0.62276 dice_loss 0.08495 +Epoch [3807/4000] Validation [2/10] Loss: 0.50347 focal_loss 0.40313 dice_loss 0.10034 +Epoch [3807/4000] Validation [3/10] Loss: 0.40020 focal_loss 0.28860 dice_loss 0.11160 +Epoch [3807/4000] Validation [4/10] Loss: 0.88845 focal_loss 0.32405 dice_loss 0.56440 +Epoch [3807/4000] Validation [5/10] Loss: 3.09801 focal_loss 2.42403 dice_loss 0.67397 +Epoch [3807/4000] Validation [6/10] Loss: 1.30890 focal_loss 0.59717 dice_loss 0.71173 +Epoch [3807/4000] Validation [7/10] Loss: 1.16854 focal_loss 0.51782 dice_loss 0.65072 +Epoch [3807/4000] Validation [8/10] Loss: 2.38617 focal_loss 1.76739 dice_loss 0.61878 +Epoch [3807/4000] Validation [9/10] Loss: 1.56887 focal_loss 1.02568 dice_loss 0.54319 +Epoch [3807/4000] Validation [10/10] Loss: 1.87434 focal_loss 1.14095 dice_loss 0.73340 +Epoch [3807/4000] Validation metric {'Val/mean dice_metric': 0.9515954256057739, 'Val/mean miou_metric': 0.9358382225036621, 'Val/mean f1': 0.948427140712738, 'Val/mean precision': 0.9441730380058289, 'Val/mean recall': 0.9527196288108826, 'Val/mean hd95_metric': 10.646480560302734} +Cheakpoint... +Epoch [3807/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515954256057739, 'Val/mean miou_metric': 0.9358382225036621, 'Val/mean f1': 0.948427140712738, 'Val/mean precision': 0.9441730380058289, 'Val/mean recall': 0.9527196288108826, 'Val/mean hd95_metric': 10.646480560302734} +Epoch [3808/4000] Training [1/39] Loss: 0.00403 +Epoch [3808/4000] Training [2/39] Loss: 0.00462 +Epoch [3808/4000] Training [3/39] Loss: 0.00325 +Epoch [3808/4000] Training [4/39] Loss: 0.00394 +Epoch [3808/4000] Training [5/39] Loss: 0.00458 +Epoch [3808/4000] Training [6/39] Loss: 0.00762 +Epoch [3808/4000] Training [7/39] Loss: 0.12922 +Epoch [3808/4000] Training [8/39] Loss: 0.00402 +Epoch [3808/4000] Training [9/39] Loss: 0.00394 +Epoch [3808/4000] Training [10/39] Loss: 0.00750 +Epoch [3808/4000] Training [11/39] Loss: 0.00497 +Epoch [3808/4000] Training [12/39] Loss: 0.00328 +Epoch [3808/4000] Training [13/39] Loss: 0.00444 +Epoch [3808/4000] Training [14/39] Loss: 0.12895 +Epoch [3808/4000] Training [15/39] Loss: 0.00330 +Epoch [3808/4000] Training [16/39] Loss: 0.00413 +Epoch [3808/4000] Training [17/39] Loss: 0.12913 +Epoch [3808/4000] Training [18/39] Loss: 0.00449 +Epoch [3808/4000] Training [19/39] Loss: 0.00375 +Epoch [3808/4000] Training [20/39] Loss: 0.00546 +Epoch [3808/4000] Training [21/39] Loss: 0.00698 +Epoch [3808/4000] Training [22/39] Loss: 0.00315 +Epoch [3808/4000] Training [23/39] Loss: 0.00361 +Epoch [3808/4000] Training [24/39] Loss: 0.00953 +Epoch [3808/4000] Training [25/39] Loss: 0.00409 +Epoch [3808/4000] Training [26/39] Loss: 0.00835 +Epoch [3808/4000] Training [27/39] Loss: 0.00409 +Epoch [3808/4000] Training [28/39] Loss: 0.00423 +Epoch [3808/4000] Training [29/39] Loss: 0.00708 +Epoch [3808/4000] Training [30/39] Loss: 0.00626 +Epoch [3808/4000] Training [31/39] Loss: 0.12995 +Epoch [3808/4000] Training [32/39] Loss: 0.12833 +Epoch [3808/4000] Training [33/39] Loss: 0.00583 +Epoch [3808/4000] Training [34/39] Loss: 0.00624 +Epoch [3808/4000] Training [35/39] Loss: 0.12969 +Epoch [3808/4000] Training [36/39] Loss: 0.00528 +Epoch [3808/4000] Training [37/39] Loss: 0.12874 +Epoch [3808/4000] Training [38/39] Loss: 0.00809 +Epoch [3808/4000] Training [39/39] Loss: 0.00632 +Epoch [3808/4000] Training metric {'Train/mean dice_metric': 0.9962701797485352, 'Train/mean miou_metric': 0.9929900765419006, 'Train/mean f1': 0.9968651533126831, 'Train/mean precision': 0.9963940382003784, 'Train/mean recall': 0.9973365664482117, 'Train/mean hd95_metric': 0.9768826961517334} +Epoch [3808/4000] Validation [1/10] Loss: 0.71145 focal_loss 0.62547 dice_loss 0.08598 +Epoch [3808/4000] Validation [2/10] Loss: 0.50085 focal_loss 0.40462 dice_loss 0.09622 +Epoch [3808/4000] Validation [3/10] Loss: 0.38837 focal_loss 0.27816 dice_loss 0.11021 +Epoch [3808/4000] Validation [4/10] Loss: 0.90065 focal_loss 0.33482 dice_loss 0.56583 +Epoch [3808/4000] Validation [5/10] Loss: 3.09989 focal_loss 2.42614 dice_loss 0.67375 +Epoch [3808/4000] Validation [6/10] Loss: 1.33713 focal_loss 0.62411 dice_loss 0.71303 +Epoch [3808/4000] Validation [7/10] Loss: 1.18870 focal_loss 0.53481 dice_loss 0.65389 +Epoch [3808/4000] Validation [8/10] Loss: 2.30146 focal_loss 1.69455 dice_loss 0.60691 +Epoch [3808/4000] Validation [9/10] Loss: 1.60481 focal_loss 1.06088 dice_loss 0.54393 +Epoch [3808/4000] Validation [10/10] Loss: 1.93219 focal_loss 1.19637 dice_loss 0.73582 +Epoch [3808/4000] Validation metric {'Val/mean dice_metric': 0.9514841437339783, 'Val/mean miou_metric': 0.9355648159980774, 'Val/mean f1': 0.9481717348098755, 'Val/mean precision': 0.9423547387123108, 'Val/mean recall': 0.9540608525276184, 'Val/mean hd95_metric': 10.926916122436523} +Cheakpoint... +Epoch [3808/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514841437339783, 'Val/mean miou_metric': 0.9355648159980774, 'Val/mean f1': 0.9481717348098755, 'Val/mean precision': 0.9423547387123108, 'Val/mean recall': 0.9540608525276184, 'Val/mean hd95_metric': 10.926916122436523} +Epoch [3809/4000] Training [1/39] Loss: 0.00748 +Epoch [3809/4000] Training [2/39] Loss: 0.00461 +Epoch [3809/4000] Training [3/39] Loss: 0.00374 +Epoch [3809/4000] Training [4/39] Loss: 0.00434 +Epoch [3809/4000] Training [5/39] Loss: 0.00337 +Epoch [3809/4000] Training [6/39] Loss: 0.00384 +Epoch [3809/4000] Training [7/39] Loss: 0.00630 +Epoch [3809/4000] Training [8/39] Loss: 0.00605 +Epoch [3809/4000] Training [9/39] Loss: 0.00335 +Epoch [3809/4000] Training [10/39] Loss: 0.00394 +Epoch [3809/4000] Training [11/39] Loss: 0.13011 +Epoch [3809/4000] Training [12/39] Loss: 0.12767 +Epoch [3809/4000] Training [13/39] Loss: 0.00470 +Epoch [3809/4000] Training [14/39] Loss: 0.00474 +Epoch [3809/4000] Training [15/39] Loss: 0.00365 +Epoch [3809/4000] Training [16/39] Loss: 0.00500 +Epoch [3809/4000] Training [17/39] Loss: 0.00753 +Epoch [3809/4000] Training [18/39] Loss: 0.00437 +Epoch [3809/4000] Training [19/39] Loss: 0.00370 +Epoch [3809/4000] Training [20/39] Loss: 0.00480 +Epoch [3809/4000] Training [21/39] Loss: 0.12964 +Epoch [3809/4000] Training [22/39] Loss: 0.12805 +Epoch [3809/4000] Training [23/39] Loss: 0.00376 +Epoch [3809/4000] Training [24/39] Loss: 0.12858 +Epoch [3809/4000] Training [25/39] Loss: 0.13226 +Epoch [3809/4000] Training [26/39] Loss: 0.00291 +Epoch [3809/4000] Training [27/39] Loss: 0.25575 +Epoch [3809/4000] Training [28/39] Loss: 0.00399 +Epoch [3809/4000] Training [29/39] Loss: 0.00462 +Epoch [3809/4000] Training [30/39] Loss: 0.00517 +Epoch [3809/4000] Training [31/39] Loss: 0.12818 +Epoch [3809/4000] Training [32/39] Loss: 0.00463 +Epoch [3809/4000] Training [33/39] Loss: 0.12983 +Epoch [3809/4000] Training [34/39] Loss: 0.00328 +Epoch [3809/4000] Training [35/39] Loss: 0.13115 +Epoch [3809/4000] Training [36/39] Loss: 0.12892 +Epoch [3809/4000] Training [37/39] Loss: 0.00395 +Epoch [3809/4000] Training [38/39] Loss: 0.12862 +Epoch [3809/4000] Training [39/39] Loss: 0.00635 +Epoch [3809/4000] Training metric {'Train/mean dice_metric': 0.9964672327041626, 'Train/mean miou_metric': 0.9933990836143494, 'Train/mean f1': 0.9970002770423889, 'Train/mean precision': 0.9965686202049255, 'Train/mean recall': 0.9974322319030762, 'Train/mean hd95_metric': 0.9508377313613892} +Epoch [3809/4000] Validation [1/10] Loss: 0.71139 focal_loss 0.62635 dice_loss 0.08504 +Epoch [3809/4000] Validation [2/10] Loss: 0.50618 focal_loss 0.40739 dice_loss 0.09879 +Epoch [3809/4000] Validation [3/10] Loss: 0.39989 focal_loss 0.28873 dice_loss 0.11116 +Epoch [3809/4000] Validation [4/10] Loss: 0.89812 focal_loss 0.33330 dice_loss 0.56482 +Epoch [3809/4000] Validation [5/10] Loss: 3.11705 focal_loss 2.44318 dice_loss 0.67387 +Epoch [3809/4000] Validation [6/10] Loss: 1.33213 focal_loss 0.61922 dice_loss 0.71290 +Epoch [3809/4000] Validation [7/10] Loss: 1.18221 focal_loss 0.53019 dice_loss 0.65202 +Epoch [3809/4000] Validation [8/10] Loss: 2.38594 focal_loss 1.77077 dice_loss 0.61517 +Epoch [3809/4000] Validation [9/10] Loss: 1.59868 focal_loss 1.05495 dice_loss 0.54373 +Epoch [3809/4000] Validation [10/10] Loss: 1.91557 focal_loss 1.18106 dice_loss 0.73451 +Epoch [3809/4000] Validation metric {'Val/mean dice_metric': 0.9516198635101318, 'Val/mean miou_metric': 0.9359086155891418, 'Val/mean f1': 0.9484681487083435, 'Val/mean precision': 0.943596601486206, 'Val/mean recall': 0.9533904790878296, 'Val/mean hd95_metric': 10.678625106811523} +Cheakpoint... +Epoch [3809/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516198635101318, 'Val/mean miou_metric': 0.9359086155891418, 'Val/mean f1': 0.9484681487083435, 'Val/mean precision': 0.943596601486206, 'Val/mean recall': 0.9533904790878296, 'Val/mean hd95_metric': 10.678625106811523} +Epoch [3810/4000] Training [1/39] Loss: 0.00428 +Epoch [3810/4000] Training [2/39] Loss: 0.00752 +Epoch [3810/4000] Training [3/39] Loss: 0.12940 +Epoch [3810/4000] Training [4/39] Loss: 0.00586 +Epoch [3810/4000] Training [5/39] Loss: 0.12962 +Epoch [3810/4000] Training [6/39] Loss: 0.12882 +Epoch [3810/4000] Training [7/39] Loss: 0.00295 +Epoch [3810/4000] Training [8/39] Loss: 0.12924 +Epoch [3810/4000] Training [9/39] Loss: 0.00443 +Epoch [3810/4000] Training [10/39] Loss: 0.12725 +Epoch [3810/4000] Training [11/39] Loss: 0.00409 +Epoch [3810/4000] Training [12/39] Loss: 0.00313 +Epoch [3810/4000] Training [13/39] Loss: 0.13396 +Epoch [3810/4000] Training [14/39] Loss: 0.00322 +Epoch [3810/4000] Training [15/39] Loss: 0.00440 +Epoch [3810/4000] Training [16/39] Loss: 0.00439 +Epoch [3810/4000] Training [17/39] Loss: 0.00454 +Epoch [3810/4000] Training [18/39] Loss: 0.00545 +Epoch [3810/4000] Training [19/39] Loss: 0.00488 +Epoch [3810/4000] Training [20/39] Loss: 0.37800 +Epoch [3810/4000] Training [21/39] Loss: 0.13475 +Epoch [3810/4000] Training [22/39] Loss: 0.00506 +Epoch [3810/4000] Training [23/39] Loss: 0.13136 +Epoch [3810/4000] Training [24/39] Loss: 0.00426 +Epoch [3810/4000] Training [25/39] Loss: 0.00407 +Epoch [3810/4000] Training [26/39] Loss: 0.00853 +Epoch [3810/4000] Training [27/39] Loss: 0.00509 +Epoch [3810/4000] Training [28/39] Loss: 0.13052 +Epoch [3810/4000] Training [29/39] Loss: 0.00549 +Epoch [3810/4000] Training [30/39] Loss: 0.00450 +Epoch [3810/4000] Training [31/39] Loss: 0.00460 +Epoch [3810/4000] Training [32/39] Loss: 0.00421 +Epoch [3810/4000] Training [33/39] Loss: 0.12805 +Epoch [3810/4000] Training [34/39] Loss: 0.00498 +Epoch [3810/4000] Training [35/39] Loss: 0.00418 +Epoch [3810/4000] Training [36/39] Loss: 0.00478 +Epoch [3810/4000] Training [37/39] Loss: 0.00318 +Epoch [3810/4000] Training [38/39] Loss: 0.00387 +Epoch [3810/4000] Training [39/39] Loss: 0.00770 +Epoch [3810/4000] Training metric {'Train/mean dice_metric': 0.9965013265609741, 'Train/mean miou_metric': 0.9934509992599487, 'Train/mean f1': 0.9969769716262817, 'Train/mean precision': 0.9965060353279114, 'Train/mean recall': 0.9974483847618103, 'Train/mean hd95_metric': 0.9510279297828674} +Epoch [3810/4000] Validation [1/10] Loss: 0.69308 focal_loss 0.60825 dice_loss 0.08483 +Epoch [3810/4000] Validation [2/10] Loss: 0.50046 focal_loss 0.40060 dice_loss 0.09986 +Epoch [3810/4000] Validation [3/10] Loss: 0.39512 focal_loss 0.28344 dice_loss 0.11168 +Epoch [3810/4000] Validation [4/10] Loss: 0.88869 focal_loss 0.32428 dice_loss 0.56441 +Epoch [3810/4000] Validation [5/10] Loss: 3.04722 focal_loss 2.37333 dice_loss 0.67389 +Epoch [3810/4000] Validation [6/10] Loss: 1.32015 focal_loss 0.60688 dice_loss 0.71328 +Epoch [3810/4000] Validation [7/10] Loss: 1.16925 focal_loss 0.51764 dice_loss 0.65161 +Epoch [3810/4000] Validation [8/10] Loss: 2.33950 focal_loss 1.72406 dice_loss 0.61545 +Epoch [3810/4000] Validation [9/10] Loss: 1.57410 focal_loss 1.03004 dice_loss 0.54406 +Epoch [3810/4000] Validation [10/10] Loss: 1.88351 focal_loss 1.14962 dice_loss 0.73389 +Epoch [3810/4000] Validation metric {'Val/mean dice_metric': 0.9516462683677673, 'Val/mean miou_metric': 0.9359608888626099, 'Val/mean f1': 0.9484272003173828, 'Val/mean precision': 0.9437305331230164, 'Val/mean recall': 0.953170657157898, 'Val/mean hd95_metric': 10.777710914611816} +Cheakpoint... +Epoch [3810/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516462683677673, 'Val/mean miou_metric': 0.9359608888626099, 'Val/mean f1': 0.9484272003173828, 'Val/mean precision': 0.9437305331230164, 'Val/mean recall': 0.953170657157898, 'Val/mean hd95_metric': 10.777710914611816} +Epoch [3811/4000] Training [1/39] Loss: 0.12892 +Epoch [3811/4000] Training [2/39] Loss: 0.00395 +Epoch [3811/4000] Training [3/39] Loss: 0.25234 +Epoch [3811/4000] Training [4/39] Loss: 0.00302 +Epoch [3811/4000] Training [5/39] Loss: 0.12982 +Epoch [3811/4000] Training [6/39] Loss: 0.12792 +Epoch [3811/4000] Training [7/39] Loss: 0.00458 +Epoch [3811/4000] Training [8/39] Loss: 0.00373 +Epoch [3811/4000] Training [9/39] Loss: 0.00425 +Epoch [3811/4000] Training [10/39] Loss: 0.00381 +Epoch [3811/4000] Training [11/39] Loss: 0.00452 +Epoch [3811/4000] Training [12/39] Loss: 0.00431 +Epoch [3811/4000] Training [13/39] Loss: 0.13062 +Epoch [3811/4000] Training [14/39] Loss: 0.00753 +Epoch [3811/4000] Training [15/39] Loss: 0.00455 +Epoch [3811/4000] Training [16/39] Loss: 0.12987 +Epoch [3811/4000] Training [17/39] Loss: 0.13059 +Epoch [3811/4000] Training [18/39] Loss: 0.00493 +Epoch [3811/4000] Training [19/39] Loss: 0.00466 +Epoch [3811/4000] Training [20/39] Loss: 0.00491 +Epoch [3811/4000] Training [21/39] Loss: 0.00838 +Epoch [3811/4000] Training [22/39] Loss: 0.00558 +Epoch [3811/4000] Training [23/39] Loss: 0.12863 +Epoch [3811/4000] Training [24/39] Loss: 0.00459 +Epoch [3811/4000] Training [25/39] Loss: 0.00308 +Epoch [3811/4000] Training [26/39] Loss: 0.12851 +Epoch [3811/4000] Training [27/39] Loss: 0.00526 +Epoch [3811/4000] Training [28/39] Loss: 0.25490 +Epoch [3811/4000] Training [29/39] Loss: 0.12910 +Epoch [3811/4000] Training [30/39] Loss: 0.00363 +Epoch [3811/4000] Training [31/39] Loss: 0.00612 +Epoch [3811/4000] Training [32/39] Loss: 0.00454 +Epoch [3811/4000] Training [33/39] Loss: 0.00789 +Epoch [3811/4000] Training [34/39] Loss: 0.12942 +Epoch [3811/4000] Training [35/39] Loss: 0.12857 +Epoch [3811/4000] Training [36/39] Loss: 0.00597 +Epoch [3811/4000] Training [37/39] Loss: 0.00364 +Epoch [3811/4000] Training [38/39] Loss: 0.00587 +Epoch [3811/4000] Training [39/39] Loss: 0.00494 +Epoch [3811/4000] Training metric {'Train/mean dice_metric': 0.9964386224746704, 'Train/mean miou_metric': 0.993341863155365, 'Train/mean f1': 0.9969034790992737, 'Train/mean precision': 0.9964377880096436, 'Train/mean recall': 0.9973694086074829, 'Train/mean hd95_metric': 0.9305116534233093} +Epoch [3811/4000] Validation [1/10] Loss: 0.69997 focal_loss 0.61506 dice_loss 0.08491 +Epoch [3811/4000] Validation [2/10] Loss: 0.50671 focal_loss 0.40786 dice_loss 0.09885 +Epoch [3811/4000] Validation [3/10] Loss: 0.38961 focal_loss 0.27868 dice_loss 0.11094 +Epoch [3811/4000] Validation [4/10] Loss: 0.89795 focal_loss 0.33309 dice_loss 0.56485 +Epoch [3811/4000] Validation [5/10] Loss: 3.05775 focal_loss 2.38391 dice_loss 0.67384 +Epoch [3811/4000] Validation [6/10] Loss: 1.34011 focal_loss 0.62685 dice_loss 0.71326 +Epoch [3811/4000] Validation [7/10] Loss: 1.18745 focal_loss 0.53388 dice_loss 0.65357 +Epoch [3811/4000] Validation [8/10] Loss: 2.35614 focal_loss 1.74270 dice_loss 0.61344 +Epoch [3811/4000] Validation [9/10] Loss: 1.58135 focal_loss 1.03739 dice_loss 0.54396 +Epoch [3811/4000] Validation [10/10] Loss: 1.92404 focal_loss 1.18839 dice_loss 0.73566 +Epoch [3811/4000] Validation metric {'Val/mean dice_metric': 0.9515282511711121, 'Val/mean miou_metric': 0.9357523918151855, 'Val/mean f1': 0.9484049677848816, 'Val/mean precision': 0.9434505701065063, 'Val/mean recall': 0.9534116387367249, 'Val/mean hd95_metric': 10.66526985168457} +Cheakpoint... +Epoch [3811/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515282511711121, 'Val/mean miou_metric': 0.9357523918151855, 'Val/mean f1': 0.9484049677848816, 'Val/mean precision': 0.9434505701065063, 'Val/mean recall': 0.9534116387367249, 'Val/mean hd95_metric': 10.66526985168457} +Epoch [3812/4000] Training [1/39] Loss: 0.12872 +Epoch [3812/4000] Training [2/39] Loss: 0.00537 +Epoch [3812/4000] Training [3/39] Loss: 0.00655 +Epoch [3812/4000] Training [4/39] Loss: 0.00589 +Epoch [3812/4000] Training [5/39] Loss: 0.00339 +Epoch [3812/4000] Training [6/39] Loss: 0.00461 +Epoch [3812/4000] Training [7/39] Loss: 0.00468 +Epoch [3812/4000] Training [8/39] Loss: 0.00662 +Epoch [3812/4000] Training [9/39] Loss: 0.00347 +Epoch [3812/4000] Training [10/39] Loss: 0.13114 +Epoch [3812/4000] Training [11/39] Loss: 0.00337 +Epoch [3812/4000] Training [12/39] Loss: 0.00302 +Epoch [3812/4000] Training [13/39] Loss: 0.00828 +Epoch [3812/4000] Training [14/39] Loss: 0.00661 +Epoch [3812/4000] Training [15/39] Loss: 0.12972 +Epoch [3812/4000] Training [16/39] Loss: 0.00308 +Epoch [3812/4000] Training [17/39] Loss: 0.12875 +Epoch [3812/4000] Training [18/39] Loss: 0.00527 +Epoch [3812/4000] Training [19/39] Loss: 0.12923 +Epoch [3812/4000] Training [20/39] Loss: 0.12849 +Epoch [3812/4000] Training [21/39] Loss: 0.00577 +Epoch [3812/4000] Training [22/39] Loss: 0.00513 +Epoch [3812/4000] Training [23/39] Loss: 0.00429 +Epoch [3812/4000] Training [24/39] Loss: 0.00420 +Epoch [3812/4000] Training [25/39] Loss: 0.12869 +Epoch [3812/4000] Training [26/39] Loss: 0.12770 +Epoch [3812/4000] Training [27/39] Loss: 0.13026 +Epoch [3812/4000] Training [28/39] Loss: 0.25378 +Epoch [3812/4000] Training [29/39] Loss: 0.12821 +Epoch [3812/4000] Training [30/39] Loss: 0.00339 +Epoch [3812/4000] Training [31/39] Loss: 0.00371 +Epoch [3812/4000] Training [32/39] Loss: 0.00536 +Epoch [3812/4000] Training [33/39] Loss: 0.00658 +Epoch [3812/4000] Training [34/39] Loss: 0.00325 +Epoch [3812/4000] Training [35/39] Loss: 0.12826 +Epoch [3812/4000] Training [36/39] Loss: 0.25395 +Epoch [3812/4000] Training [37/39] Loss: 0.25418 +Epoch [3812/4000] Training [38/39] Loss: 0.00581 +Epoch [3812/4000] Training [39/39] Loss: 0.00657 +Epoch [3812/4000] Training metric {'Train/mean dice_metric': 0.9963955283164978, 'Train/mean miou_metric': 0.9932377338409424, 'Train/mean f1': 0.996853232383728, 'Train/mean precision': 0.9964616894721985, 'Train/mean recall': 0.9972449541091919, 'Train/mean hd95_metric': 0.9670313596725464} +Epoch [3812/4000] Validation [1/10] Loss: 0.72900 focal_loss 0.64385 dice_loss 0.08515 +Epoch [3812/4000] Validation [2/10] Loss: 0.51701 focal_loss 0.41739 dice_loss 0.09962 +Epoch [3812/4000] Validation [3/10] Loss: 0.40498 focal_loss 0.29394 dice_loss 0.11103 +Epoch [3812/4000] Validation [4/10] Loss: 0.89644 focal_loss 0.33237 dice_loss 0.56406 +Epoch [3812/4000] Validation [5/10] Loss: 3.17185 focal_loss 2.49787 dice_loss 0.67398 +Epoch [3812/4000] Validation [6/10] Loss: 1.34459 focal_loss 0.63033 dice_loss 0.71426 +Epoch [3812/4000] Validation [7/10] Loss: 1.19344 focal_loss 0.53781 dice_loss 0.65563 +Epoch [3812/4000] Validation [8/10] Loss: 2.41397 focal_loss 1.80004 dice_loss 0.61393 +Epoch [3812/4000] Validation [9/10] Loss: 1.59892 focal_loss 1.05566 dice_loss 0.54325 +Epoch [3812/4000] Validation [10/10] Loss: 1.92301 focal_loss 1.18909 dice_loss 0.73392 +Epoch [3812/4000] Validation metric {'Val/mean dice_metric': 0.9514746069908142, 'Val/mean miou_metric': 0.9356579184532166, 'Val/mean f1': 0.9482807517051697, 'Val/mean precision': 0.943785548210144, 'Val/mean recall': 0.9528189897537231, 'Val/mean hd95_metric': 10.840761184692383} +Cheakpoint... +Epoch [3812/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514746069908142, 'Val/mean miou_metric': 0.9356579184532166, 'Val/mean f1': 0.9482807517051697, 'Val/mean precision': 0.943785548210144, 'Val/mean recall': 0.9528189897537231, 'Val/mean hd95_metric': 10.840761184692383} +Epoch [3813/4000] Training [1/39] Loss: 0.00503 +Epoch [3813/4000] Training [2/39] Loss: 0.13059 +Epoch [3813/4000] Training [3/39] Loss: 0.00501 +Epoch [3813/4000] Training [4/39] Loss: 0.13093 +Epoch [3813/4000] Training [5/39] Loss: 0.00577 +Epoch [3813/4000] Training [6/39] Loss: 0.12908 +Epoch [3813/4000] Training [7/39] Loss: 0.13130 +Epoch [3813/4000] Training [8/39] Loss: 0.00477 +Epoch [3813/4000] Training [9/39] Loss: 0.12948 +Epoch [3813/4000] Training [10/39] Loss: 0.00477 +Epoch [3813/4000] Training [11/39] Loss: 0.00751 +Epoch [3813/4000] Training [12/39] Loss: 0.12883 +Epoch [3813/4000] Training [13/39] Loss: 0.00361 +Epoch [3813/4000] Training [14/39] Loss: 0.00318 +Epoch [3813/4000] Training [15/39] Loss: 0.00709 +Epoch [3813/4000] Training [16/39] Loss: 0.00377 +Epoch [3813/4000] Training [17/39] Loss: 0.00901 +Epoch [3813/4000] Training [18/39] Loss: 0.00468 +Epoch [3813/4000] Training [19/39] Loss: 0.25457 +Epoch [3813/4000] Training [20/39] Loss: 0.12940 +Epoch [3813/4000] Training [21/39] Loss: 0.00349 +Epoch [3813/4000] Training [22/39] Loss: 0.00457 +Epoch [3813/4000] Training [23/39] Loss: 0.00491 +Epoch [3813/4000] Training [24/39] Loss: 0.00466 +Epoch [3813/4000] Training [25/39] Loss: 0.00453 +Epoch [3813/4000] Training [26/39] Loss: 0.00404 +Epoch [3813/4000] Training [27/39] Loss: 0.00553 +Epoch [3813/4000] Training [28/39] Loss: 0.00417 +Epoch [3813/4000] Training [29/39] Loss: 0.00380 +Epoch [3813/4000] Training [30/39] Loss: 0.00430 +Epoch [3813/4000] Training [31/39] Loss: 0.00331 +Epoch [3813/4000] Training [32/39] Loss: 0.12950 +Epoch [3813/4000] Training [33/39] Loss: 0.12746 +Epoch [3813/4000] Training [34/39] Loss: 0.13277 +Epoch [3813/4000] Training [35/39] Loss: 0.00467 +Epoch [3813/4000] Training [36/39] Loss: 0.00614 +Epoch [3813/4000] Training [37/39] Loss: 0.00900 +Epoch [3813/4000] Training [38/39] Loss: 0.00555 +Epoch [3813/4000] Training [39/39] Loss: 0.00643 +Epoch [3813/4000] Training metric {'Train/mean dice_metric': 0.9963789582252502, 'Train/mean miou_metric': 0.9932085871696472, 'Train/mean f1': 0.9968792200088501, 'Train/mean precision': 0.9964508414268494, 'Train/mean recall': 0.9973078966140747, 'Train/mean hd95_metric': 1.1127625703811646} +Epoch [3813/4000] Validation [1/10] Loss: 0.72649 focal_loss 0.64153 dice_loss 0.08496 +Epoch [3813/4000] Validation [2/10] Loss: 0.51902 focal_loss 0.41762 dice_loss 0.10140 +Epoch [3813/4000] Validation [3/10] Loss: 0.40963 focal_loss 0.29819 dice_loss 0.11144 +Epoch [3813/4000] Validation [4/10] Loss: 0.89062 focal_loss 0.32746 dice_loss 0.56315 +Epoch [3813/4000] Validation [5/10] Loss: 3.18016 focal_loss 2.50601 dice_loss 0.67416 +Epoch [3813/4000] Validation [6/10] Loss: 1.33167 focal_loss 0.61684 dice_loss 0.71483 +Epoch [3813/4000] Validation [7/10] Loss: 1.18985 focal_loss 0.53600 dice_loss 0.65385 +Epoch [3813/4000] Validation [8/10] Loss: 2.41683 focal_loss 1.80111 dice_loss 0.61573 +Epoch [3813/4000] Validation [9/10] Loss: 1.57825 focal_loss 1.03555 dice_loss 0.54270 +Epoch [3813/4000] Validation [10/10] Loss: 1.90110 focal_loss 1.16815 dice_loss 0.73295 +Epoch [3813/4000] Validation metric {'Val/mean dice_metric': 0.9514556527137756, 'Val/mean miou_metric': 0.9356524348258972, 'Val/mean f1': 0.9487629532814026, 'Val/mean precision': 0.9445945024490356, 'Val/mean recall': 0.952968418598175, 'Val/mean hd95_metric': 10.764408111572266} +Cheakpoint... +Epoch [3813/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514556527137756, 'Val/mean miou_metric': 0.9356524348258972, 'Val/mean f1': 0.9487629532814026, 'Val/mean precision': 0.9445945024490356, 'Val/mean recall': 0.952968418598175, 'Val/mean hd95_metric': 10.764408111572266} +Epoch [3814/4000] Training [1/39] Loss: 0.00290 +Epoch [3814/4000] Training [2/39] Loss: 0.00353 +Epoch [3814/4000] Training [3/39] Loss: 0.00511 +Epoch [3814/4000] Training [4/39] Loss: 0.12908 +Epoch [3814/4000] Training [5/39] Loss: 0.00335 +Epoch [3814/4000] Training [6/39] Loss: 0.00592 +Epoch [3814/4000] Training [7/39] Loss: 0.00351 +Epoch [3814/4000] Training [8/39] Loss: 0.00663 +Epoch [3814/4000] Training [9/39] Loss: 0.00397 +Epoch [3814/4000] Training [10/39] Loss: 0.12724 +Epoch [3814/4000] Training [11/39] Loss: 0.12870 +Epoch [3814/4000] Training [12/39] Loss: 0.00448 +Epoch [3814/4000] Training [13/39] Loss: 0.12915 +Epoch [3814/4000] Training [14/39] Loss: 0.00393 +Epoch [3814/4000] Training [15/39] Loss: 0.00634 +Epoch [3814/4000] Training [16/39] Loss: 0.01082 +Epoch [3814/4000] Training [17/39] Loss: 0.12818 +Epoch [3814/4000] Training [18/39] Loss: 0.12809 +Epoch [3814/4000] Training [19/39] Loss: 0.12983 +Epoch [3814/4000] Training [20/39] Loss: 0.00476 +Epoch [3814/4000] Training [21/39] Loss: 0.00655 +Epoch [3814/4000] Training [22/39] Loss: 0.00684 +Epoch [3814/4000] Training [23/39] Loss: 0.00348 +Epoch [3814/4000] Training [24/39] Loss: 0.12992 +Epoch [3814/4000] Training [25/39] Loss: 0.00577 +Epoch [3814/4000] Training [26/39] Loss: 0.00737 +Epoch [3814/4000] Training [27/39] Loss: 0.12873 +Epoch [3814/4000] Training [28/39] Loss: 0.00551 +Epoch [3814/4000] Training [29/39] Loss: 0.25320 +Epoch [3814/4000] Training [30/39] Loss: 0.00696 +Epoch [3814/4000] Training [31/39] Loss: 0.00307 +Epoch [3814/4000] Training [32/39] Loss: 0.00578 +Epoch [3814/4000] Training [33/39] Loss: 0.00550 +Epoch [3814/4000] Training [34/39] Loss: 0.13216 +Epoch [3814/4000] Training [35/39] Loss: 0.00496 +Epoch [3814/4000] Training [36/39] Loss: 0.00380 +Epoch [3814/4000] Training [37/39] Loss: 0.00738 +Epoch [3814/4000] Training [38/39] Loss: 0.00422 +Epoch [3814/4000] Training [39/39] Loss: 0.00434 +Epoch [3814/4000] Training metric {'Train/mean dice_metric': 0.9954582452774048, 'Train/mean miou_metric': 0.9922453165054321, 'Train/mean f1': 0.996799111366272, 'Train/mean precision': 0.9962528347969055, 'Train/mean recall': 0.9973459243774414, 'Train/mean hd95_metric': 1.147837519645691} +Epoch [3814/4000] Validation [1/10] Loss: 0.71161 focal_loss 0.62704 dice_loss 0.08458 +Epoch [3814/4000] Validation [2/10] Loss: 0.51212 focal_loss 0.41076 dice_loss 0.10136 +Epoch [3814/4000] Validation [3/10] Loss: 0.40584 focal_loss 0.29407 dice_loss 0.11178 +Epoch [3814/4000] Validation [4/10] Loss: 0.88583 focal_loss 0.32265 dice_loss 0.56318 +Epoch [3814/4000] Validation [5/10] Loss: 3.12478 focal_loss 2.45057 dice_loss 0.67421 +Epoch [3814/4000] Validation [6/10] Loss: 1.32145 focal_loss 0.60754 dice_loss 0.71391 +Epoch [3814/4000] Validation [7/10] Loss: 1.17510 focal_loss 0.52227 dice_loss 0.65283 +Epoch [3814/4000] Validation [8/10] Loss: 2.42458 focal_loss 1.80499 dice_loss 0.61959 +Epoch [3814/4000] Validation [9/10] Loss: 1.54613 focal_loss 1.00316 dice_loss 0.54297 +Epoch [3814/4000] Validation [10/10] Loss: 1.87368 focal_loss 1.14115 dice_loss 0.73253 +Epoch [3814/4000] Validation metric {'Val/mean dice_metric': 0.9507162570953369, 'Val/mean miou_metric': 0.934883713722229, 'Val/mean f1': 0.9483970403671265, 'Val/mean precision': 0.9444762468338013, 'Val/mean recall': 0.9523506164550781, 'Val/mean hd95_metric': 10.916515350341797} +Cheakpoint... +Epoch [3814/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507162570953369, 'Val/mean miou_metric': 0.934883713722229, 'Val/mean f1': 0.9483970403671265, 'Val/mean precision': 0.9444762468338013, 'Val/mean recall': 0.9523506164550781, 'Val/mean hd95_metric': 10.916515350341797} +Epoch [3815/4000] Training [1/39] Loss: 0.12970 +Epoch [3815/4000] Training [2/39] Loss: 0.00627 +Epoch [3815/4000] Training [3/39] Loss: 0.13068 +Epoch [3815/4000] Training [4/39] Loss: 0.00551 +Epoch [3815/4000] Training [5/39] Loss: 0.00521 +Epoch [3815/4000] Training [6/39] Loss: 0.00342 +Epoch [3815/4000] Training [7/39] Loss: 0.12875 +Epoch [3815/4000] Training [8/39] Loss: 0.00671 +Epoch [3815/4000] Training [9/39] Loss: 0.00587 +Epoch [3815/4000] Training [10/39] Loss: 0.00499 +Epoch [3815/4000] Training [11/39] Loss: 0.13033 +Epoch [3815/4000] Training [12/39] Loss: 0.00453 +Epoch [3815/4000] Training [13/39] Loss: 0.00412 +Epoch [3815/4000] Training [14/39] Loss: 0.04113 +Epoch [3815/4000] Training [15/39] Loss: 0.00534 +Epoch [3815/4000] Training [16/39] Loss: 0.13004 +Epoch [3815/4000] Training [17/39] Loss: 0.12852 +Epoch [3815/4000] Training [18/39] Loss: 0.00525 +Epoch [3815/4000] Training [19/39] Loss: 0.13048 +Epoch [3815/4000] Training [20/39] Loss: 0.00500 +Epoch [3815/4000] Training [21/39] Loss: 0.00639 +Epoch [3815/4000] Training [22/39] Loss: 0.12834 +Epoch [3815/4000] Training [23/39] Loss: 0.00411 +Epoch [3815/4000] Training [24/39] Loss: 0.00588 +Epoch [3815/4000] Training [25/39] Loss: 0.00565 +Epoch [3815/4000] Training [26/39] Loss: 0.00723 +Epoch [3815/4000] Training [27/39] Loss: 0.00405 +Epoch [3815/4000] Training [28/39] Loss: 0.00268 +Epoch [3815/4000] Training [29/39] Loss: 0.12936 +Epoch [3815/4000] Training [30/39] Loss: 0.00506 +Epoch [3815/4000] Training [31/39] Loss: 0.13165 +Epoch [3815/4000] Training [32/39] Loss: 0.00705 +Epoch [3815/4000] Training [33/39] Loss: 0.00532 +Epoch [3815/4000] Training [34/39] Loss: 0.00316 +Epoch [3815/4000] Training [35/39] Loss: 0.00519 +Epoch [3815/4000] Training [36/39] Loss: 0.00400 +Epoch [3815/4000] Training [37/39] Loss: 0.00353 +Epoch [3815/4000] Training [38/39] Loss: 0.00609 +Epoch [3815/4000] Training [39/39] Loss: 0.12771 +Epoch [3815/4000] Training metric {'Train/mean dice_metric': 0.9963756203651428, 'Train/mean miou_metric': 0.9932103157043457, 'Train/mean f1': 0.9968549013137817, 'Train/mean precision': 0.9964054226875305, 'Train/mean recall': 0.9973048567771912, 'Train/mean hd95_metric': 0.9279517531394958} +Epoch [3815/4000] Validation [1/10] Loss: 0.71882 focal_loss 0.63395 dice_loss 0.08488 +Epoch [3815/4000] Validation [2/10] Loss: 0.51855 focal_loss 0.41512 dice_loss 0.10343 +Epoch [3815/4000] Validation [3/10] Loss: 0.41739 focal_loss 0.30493 dice_loss 0.11246 +Epoch [3815/4000] Validation [4/10] Loss: 0.88366 focal_loss 0.32086 dice_loss 0.56280 +Epoch [3815/4000] Validation [5/10] Loss: 3.13997 focal_loss 2.46572 dice_loss 0.67425 +Epoch [3815/4000] Validation [6/10] Loss: 1.31322 focal_loss 0.59949 dice_loss 0.71373 +Epoch [3815/4000] Validation [7/10] Loss: 1.17118 focal_loss 0.51963 dice_loss 0.65155 +Epoch [3815/4000] Validation [8/10] Loss: 2.44638 focal_loss 1.82459 dice_loss 0.62179 +Epoch [3815/4000] Validation [9/10] Loss: 1.55183 focal_loss 1.00926 dice_loss 0.54258 +Epoch [3815/4000] Validation [10/10] Loss: 1.86681 focal_loss 1.13390 dice_loss 0.73291 +Epoch [3815/4000] Validation metric {'Val/mean dice_metric': 0.9513745307922363, 'Val/mean miou_metric': 0.9355568885803223, 'Val/mean f1': 0.9484859704971313, 'Val/mean precision': 0.9450766444206238, 'Val/mean recall': 0.9519198536872864, 'Val/mean hd95_metric': 10.632196426391602} +Cheakpoint... +Epoch [3815/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513745307922363, 'Val/mean miou_metric': 0.9355568885803223, 'Val/mean f1': 0.9484859704971313, 'Val/mean precision': 0.9450766444206238, 'Val/mean recall': 0.9519198536872864, 'Val/mean hd95_metric': 10.632196426391602} +Epoch [3816/4000] Training [1/39] Loss: 0.00590 +Epoch [3816/4000] Training [2/39] Loss: 0.25354 +Epoch [3816/4000] Training [3/39] Loss: 0.00711 +Epoch [3816/4000] Training [4/39] Loss: 0.00397 +Epoch [3816/4000] Training [5/39] Loss: 0.00278 +Epoch [3816/4000] Training [6/39] Loss: 0.00334 +Epoch [3816/4000] Training [7/39] Loss: 0.00377 +Epoch [3816/4000] Training [8/39] Loss: 0.00486 +Epoch [3816/4000] Training [9/39] Loss: 0.00424 +Epoch [3816/4000] Training [10/39] Loss: 0.00572 +Epoch [3816/4000] Training [11/39] Loss: 0.00451 +Epoch [3816/4000] Training [12/39] Loss: 0.00437 +Epoch [3816/4000] Training [13/39] Loss: 0.00535 +Epoch [3816/4000] Training [14/39] Loss: 0.00548 +Epoch [3816/4000] Training [15/39] Loss: 0.12806 +Epoch [3816/4000] Training [16/39] Loss: 0.00452 +Epoch [3816/4000] Training [17/39] Loss: 0.00470 +Epoch [3816/4000] Training [18/39] Loss: 0.00321 +Epoch [3816/4000] Training [19/39] Loss: 0.00577 +Epoch [3816/4000] Training [20/39] Loss: 0.00649 +Epoch [3816/4000] Training [21/39] Loss: 0.00528 +Epoch [3816/4000] Training [22/39] Loss: 0.00439 +Epoch [3816/4000] Training [23/39] Loss: 0.00272 +Epoch [3816/4000] Training [24/39] Loss: 0.00462 +Epoch [3816/4000] Training [25/39] Loss: 0.00422 +Epoch [3816/4000] Training [26/39] Loss: 0.00786 +Epoch [3816/4000] Training [27/39] Loss: 0.00502 +Epoch [3816/4000] Training [28/39] Loss: 0.12716 +Epoch [3816/4000] Training [29/39] Loss: 0.00961 +Epoch [3816/4000] Training [30/39] Loss: 0.12810 +Epoch [3816/4000] Training [31/39] Loss: 0.00266 +Epoch [3816/4000] Training [32/39] Loss: 0.00513 +Epoch [3816/4000] Training [33/39] Loss: 0.08650 +Epoch [3816/4000] Training [34/39] Loss: 0.00405 +Epoch [3816/4000] Training [35/39] Loss: 0.00510 +Epoch [3816/4000] Training [36/39] Loss: 0.25356 +Epoch [3816/4000] Training [37/39] Loss: 0.12986 +Epoch [3816/4000] Training [38/39] Loss: 0.13035 +Epoch [3816/4000] Training [39/39] Loss: 0.00403 +Epoch [3816/4000] Training metric {'Train/mean dice_metric': 0.9963746070861816, 'Train/mean miou_metric': 0.9931977391242981, 'Train/mean f1': 0.9969699382781982, 'Train/mean precision': 0.9965012073516846, 'Train/mean recall': 0.9974390864372253, 'Train/mean hd95_metric': 0.9130697250366211} +Epoch [3816/4000] Validation [1/10] Loss: 0.71501 focal_loss 0.62972 dice_loss 0.08529 +Epoch [3816/4000] Validation [2/10] Loss: 0.51507 focal_loss 0.41370 dice_loss 0.10137 +Epoch [3816/4000] Validation [3/10] Loss: 0.40353 focal_loss 0.29207 dice_loss 0.11146 +Epoch [3816/4000] Validation [4/10] Loss: 0.89099 focal_loss 0.32756 dice_loss 0.56343 +Epoch [3816/4000] Validation [5/10] Loss: 3.11009 focal_loss 2.43594 dice_loss 0.67415 +Epoch [3816/4000] Validation [6/10] Loss: 1.32904 focal_loss 0.61555 dice_loss 0.71349 +Epoch [3816/4000] Validation [7/10] Loss: 1.17920 focal_loss 0.52658 dice_loss 0.65262 +Epoch [3816/4000] Validation [8/10] Loss: 2.40939 focal_loss 1.79047 dice_loss 0.61892 +Epoch [3816/4000] Validation [9/10] Loss: 1.56784 focal_loss 1.02477 dice_loss 0.54307 +Epoch [3816/4000] Validation [10/10] Loss: 1.88868 focal_loss 1.15488 dice_loss 0.73379 +Epoch [3816/4000] Validation metric {'Val/mean dice_metric': 0.9514150619506836, 'Val/mean miou_metric': 0.9355791807174683, 'Val/mean f1': 0.9485554099082947, 'Val/mean precision': 0.9444411396980286, 'Val/mean recall': 0.9527056813240051, 'Val/mean hd95_metric': 10.730478286743164} +Cheakpoint... +Epoch [3816/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514150619506836, 'Val/mean miou_metric': 0.9355791807174683, 'Val/mean f1': 0.9485554099082947, 'Val/mean precision': 0.9444411396980286, 'Val/mean recall': 0.9527056813240051, 'Val/mean hd95_metric': 10.730478286743164} +Epoch [3817/4000] Training [1/39] Loss: 0.12955 +Epoch [3817/4000] Training [2/39] Loss: 0.00665 +Epoch [3817/4000] Training [3/39] Loss: 0.12892 +Epoch [3817/4000] Training [4/39] Loss: 0.00464 +Epoch [3817/4000] Training [5/39] Loss: 0.00400 +Epoch [3817/4000] Training [6/39] Loss: 0.00310 +Epoch [3817/4000] Training [7/39] Loss: 0.00561 +Epoch [3817/4000] Training [8/39] Loss: 0.00472 +Epoch [3817/4000] Training [9/39] Loss: 0.00475 +Epoch [3817/4000] Training [10/39] Loss: 0.00350 +Epoch [3817/4000] Training [11/39] Loss: 0.12885 +Epoch [3817/4000] Training [12/39] Loss: 0.00516 +Epoch [3817/4000] Training [13/39] Loss: 0.00444 +Epoch [3817/4000] Training [14/39] Loss: 0.00270 +Epoch [3817/4000] Training [15/39] Loss: 0.01088 +Epoch [3817/4000] Training [16/39] Loss: 0.00520 +Epoch [3817/4000] Training [17/39] Loss: 0.00539 +Epoch [3817/4000] Training [18/39] Loss: 0.00654 +Epoch [3817/4000] Training [19/39] Loss: 0.00457 +Epoch [3817/4000] Training [20/39] Loss: 0.00344 +Epoch [3817/4000] Training [21/39] Loss: 0.00560 +Epoch [3817/4000] Training [22/39] Loss: 0.00688 +Epoch [3817/4000] Training [23/39] Loss: 0.12742 +Epoch [3817/4000] Training [24/39] Loss: 0.12914 +Epoch [3817/4000] Training [25/39] Loss: 0.25312 +Epoch [3817/4000] Training [26/39] Loss: 0.00535 +Epoch [3817/4000] Training [27/39] Loss: 0.00310 +Epoch [3817/4000] Training [28/39] Loss: 0.12842 +Epoch [3817/4000] Training [29/39] Loss: 0.00507 +Epoch [3817/4000] Training [30/39] Loss: 0.00872 +Epoch [3817/4000] Training [31/39] Loss: 0.00466 +Epoch [3817/4000] Training [32/39] Loss: 0.00676 +Epoch [3817/4000] Training [33/39] Loss: 0.00316 +Epoch [3817/4000] Training [34/39] Loss: 0.00361 +Epoch [3817/4000] Training [35/39] Loss: 0.00556 +Epoch [3817/4000] Training [36/39] Loss: 0.25214 +Epoch [3817/4000] Training [37/39] Loss: 0.00310 +Epoch [3817/4000] Training [38/39] Loss: 0.00293 +Epoch [3817/4000] Training [39/39] Loss: 0.00328 +Epoch [3817/4000] Training metric {'Train/mean dice_metric': 0.9964156150817871, 'Train/mean miou_metric': 0.9933016300201416, 'Train/mean f1': 0.9969778060913086, 'Train/mean precision': 0.9965599179267883, 'Train/mean recall': 0.9973960518836975, 'Train/mean hd95_metric': 0.9274571537971497} +Epoch [3817/4000] Validation [1/10] Loss: 0.69924 focal_loss 0.61435 dice_loss 0.08489 +Epoch [3817/4000] Validation [2/10] Loss: 0.50897 focal_loss 0.40741 dice_loss 0.10156 +Epoch [3817/4000] Validation [3/10] Loss: 0.39967 focal_loss 0.28795 dice_loss 0.11171 +Epoch [3817/4000] Validation [4/10] Loss: 0.88829 focal_loss 0.32434 dice_loss 0.56395 +Epoch [3817/4000] Validation [5/10] Loss: 3.07352 focal_loss 2.39935 dice_loss 0.67417 +Epoch [3817/4000] Validation [6/10] Loss: 1.31744 focal_loss 0.60494 dice_loss 0.71249 +Epoch [3817/4000] Validation [7/10] Loss: 1.16999 focal_loss 0.51860 dice_loss 0.65138 +Epoch [3817/4000] Validation [8/10] Loss: 2.37580 focal_loss 1.75670 dice_loss 0.61910 +Epoch [3817/4000] Validation [9/10] Loss: 1.54606 focal_loss 1.00269 dice_loss 0.54337 +Epoch [3817/4000] Validation [10/10] Loss: 1.86592 focal_loss 1.13223 dice_loss 0.73369 +Epoch [3817/4000] Validation metric {'Val/mean dice_metric': 0.9514507055282593, 'Val/mean miou_metric': 0.9356768131256104, 'Val/mean f1': 0.9486304521560669, 'Val/mean precision': 0.9445768594741821, 'Val/mean recall': 0.9527188539505005, 'Val/mean hd95_metric': 10.796141624450684} +Cheakpoint... +Epoch [3817/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514507055282593, 'Val/mean miou_metric': 0.9356768131256104, 'Val/mean f1': 0.9486304521560669, 'Val/mean precision': 0.9445768594741821, 'Val/mean recall': 0.9527188539505005, 'Val/mean hd95_metric': 10.796141624450684} +Epoch [3818/4000] Training [1/39] Loss: 0.00429 +Epoch [3818/4000] Training [2/39] Loss: 0.00425 +Epoch [3818/4000] Training [3/39] Loss: 0.00427 +Epoch [3818/4000] Training [4/39] Loss: 0.00327 +Epoch [3818/4000] Training [5/39] Loss: 0.00395 +Epoch [3818/4000] Training [6/39] Loss: 0.00401 +Epoch [3818/4000] Training [7/39] Loss: 0.00279 +Epoch [3818/4000] Training [8/39] Loss: 0.25303 +Epoch [3818/4000] Training [9/39] Loss: 0.00552 +Epoch [3818/4000] Training [10/39] Loss: 0.00426 +Epoch [3818/4000] Training [11/39] Loss: 0.13006 +Epoch [3818/4000] Training [12/39] Loss: 0.01204 +Epoch [3818/4000] Training [13/39] Loss: 0.00445 +Epoch [3818/4000] Training [14/39] Loss: 0.00505 +Epoch [3818/4000] Training [15/39] Loss: 0.13218 +Epoch [3818/4000] Training [16/39] Loss: 0.12880 +Epoch [3818/4000] Training [17/39] Loss: 0.00533 +Epoch [3818/4000] Training [18/39] Loss: 0.00345 +Epoch [3818/4000] Training [19/39] Loss: 0.00404 +Epoch [3818/4000] Training [20/39] Loss: 0.00585 +Epoch [3818/4000] Training [21/39] Loss: 0.00371 +Epoch [3818/4000] Training [22/39] Loss: 0.12889 +Epoch [3818/4000] Training [23/39] Loss: 0.25329 +Epoch [3818/4000] Training [24/39] Loss: 0.00420 +Epoch [3818/4000] Training [25/39] Loss: 0.00661 +Epoch [3818/4000] Training [26/39] Loss: 0.13016 +Epoch [3818/4000] Training [27/39] Loss: 0.00528 +Epoch [3818/4000] Training [28/39] Loss: 0.12798 +Epoch [3818/4000] Training [29/39] Loss: 0.00752 +Epoch [3818/4000] Training [30/39] Loss: 0.00553 +Epoch [3818/4000] Training [31/39] Loss: 0.12830 +Epoch [3818/4000] Training [32/39] Loss: 0.00460 +Epoch [3818/4000] Training [33/39] Loss: 0.00515 +Epoch [3818/4000] Training [34/39] Loss: 0.12976 +Epoch [3818/4000] Training [35/39] Loss: 0.00402 +Epoch [3818/4000] Training [36/39] Loss: 0.12732 +Epoch [3818/4000] Training [37/39] Loss: 0.12891 +Epoch [3818/4000] Training [38/39] Loss: 0.00383 +Epoch [3818/4000] Training [39/39] Loss: 0.12836 +Epoch [3818/4000] Training metric {'Train/mean dice_metric': 0.9954401850700378, 'Train/mean miou_metric': 0.9921678304672241, 'Train/mean f1': 0.9969149231910706, 'Train/mean precision': 0.9963939189910889, 'Train/mean recall': 0.9974364042282104, 'Train/mean hd95_metric': 1.3996387720108032} +Epoch [3818/4000] Validation [1/10] Loss: 0.71205 focal_loss 0.62663 dice_loss 0.08543 +Epoch [3818/4000] Validation [2/10] Loss: 0.51028 focal_loss 0.41060 dice_loss 0.09968 +Epoch [3818/4000] Validation [3/10] Loss: 0.39958 focal_loss 0.28818 dice_loss 0.11139 +Epoch [3818/4000] Validation [4/10] Loss: 0.89511 focal_loss 0.33090 dice_loss 0.56421 +Epoch [3818/4000] Validation [5/10] Loss: 3.08537 focal_loss 2.41126 dice_loss 0.67411 +Epoch [3818/4000] Validation [6/10] Loss: 1.33080 focal_loss 0.61842 dice_loss 0.71238 +Epoch [3818/4000] Validation [7/10] Loss: 1.17990 focal_loss 0.52689 dice_loss 0.65301 +Epoch [3818/4000] Validation [8/10] Loss: 2.41556 focal_loss 1.79647 dice_loss 0.61909 +Epoch [3818/4000] Validation [9/10] Loss: 1.56301 focal_loss 1.01974 dice_loss 0.54327 +Epoch [3818/4000] Validation [10/10] Loss: 1.89960 focal_loss 1.16555 dice_loss 0.73404 +Epoch [3818/4000] Validation metric {'Val/mean dice_metric': 0.9506396055221558, 'Val/mean miou_metric': 0.9347113370895386, 'Val/mean f1': 0.9485607147216797, 'Val/mean precision': 0.9443021416664124, 'Val/mean recall': 0.9528577923774719, 'Val/mean hd95_metric': 11.212780952453613} +Cheakpoint... +Epoch [3818/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506396055221558, 'Val/mean miou_metric': 0.9347113370895386, 'Val/mean f1': 0.9485607147216797, 'Val/mean precision': 0.9443021416664124, 'Val/mean recall': 0.9528577923774719, 'Val/mean hd95_metric': 11.212780952453613} +Epoch [3819/4000] Training [1/39] Loss: 0.00515 +Epoch [3819/4000] Training [2/39] Loss: 0.12790 +Epoch [3819/4000] Training [3/39] Loss: 0.00481 +Epoch [3819/4000] Training [4/39] Loss: 0.00423 +Epoch [3819/4000] Training [5/39] Loss: 0.00409 +Epoch [3819/4000] Training [6/39] Loss: 0.04097 +Epoch [3819/4000] Training [7/39] Loss: 0.00722 +Epoch [3819/4000] Training [8/39] Loss: 0.00436 +Epoch [3819/4000] Training [9/39] Loss: 0.01043 +Epoch [3819/4000] Training [10/39] Loss: 0.12874 +Epoch [3819/4000] Training [11/39] Loss: 0.12767 +Epoch [3819/4000] Training [12/39] Loss: 0.12787 +Epoch [3819/4000] Training [13/39] Loss: 0.00553 +Epoch [3819/4000] Training [14/39] Loss: 0.00574 +Epoch [3819/4000] Training [15/39] Loss: 0.12899 +Epoch [3819/4000] Training [16/39] Loss: 0.00376 +Epoch [3819/4000] Training [17/39] Loss: 0.00636 +Epoch [3819/4000] Training [18/39] Loss: 0.00534 +Epoch [3819/4000] Training [19/39] Loss: 0.12829 +Epoch [3819/4000] Training [20/39] Loss: 0.00338 +Epoch [3819/4000] Training [21/39] Loss: 0.00402 +Epoch [3819/4000] Training [22/39] Loss: 0.00479 +Epoch [3819/4000] Training [23/39] Loss: 0.00389 +Epoch [3819/4000] Training [24/39] Loss: 0.00324 +Epoch [3819/4000] Training [25/39] Loss: 0.08482 +Epoch [3819/4000] Training [26/39] Loss: 0.00489 +Epoch [3819/4000] Training [27/39] Loss: 0.00764 +Epoch [3819/4000] Training [28/39] Loss: 0.00593 +Epoch [3819/4000] Training [29/39] Loss: 0.12791 +Epoch [3819/4000] Training [30/39] Loss: 0.00604 +Epoch [3819/4000] Training [31/39] Loss: 0.00306 +Epoch [3819/4000] Training [32/39] Loss: 0.00444 +Epoch [3819/4000] Training [33/39] Loss: 0.00574 +Epoch [3819/4000] Training [34/39] Loss: 0.00434 +Epoch [3819/4000] Training [35/39] Loss: 0.00304 +Epoch [3819/4000] Training [36/39] Loss: 0.00304 +Epoch [3819/4000] Training [37/39] Loss: 0.13155 +Epoch [3819/4000] Training [38/39] Loss: 0.12834 +Epoch [3819/4000] Training [39/39] Loss: 0.12958 +Epoch [3819/4000] Training metric {'Train/mean dice_metric': 0.9963087439537048, 'Train/mean miou_metric': 0.9930658340454102, 'Train/mean f1': 0.99688720703125, 'Train/mean precision': 0.9964631795883179, 'Train/mean recall': 0.997311532497406, 'Train/mean hd95_metric': 0.9358106851577759} +Epoch [3819/4000] Validation [1/10] Loss: 0.71983 focal_loss 0.63337 dice_loss 0.08645 +Epoch [3819/4000] Validation [2/10] Loss: 0.50562 focal_loss 0.40624 dice_loss 0.09938 +Epoch [3819/4000] Validation [3/10] Loss: 0.40018 focal_loss 0.28881 dice_loss 0.11137 +Epoch [3819/4000] Validation [4/10] Loss: 0.89016 focal_loss 0.32593 dice_loss 0.56423 +Epoch [3819/4000] Validation [5/10] Loss: 3.10883 focal_loss 2.43464 dice_loss 0.67418 +Epoch [3819/4000] Validation [6/10] Loss: 1.32380 focal_loss 0.61074 dice_loss 0.71305 +Epoch [3819/4000] Validation [7/10] Loss: 1.17346 focal_loss 0.51891 dice_loss 0.65455 +Epoch [3819/4000] Validation [8/10] Loss: 2.40678 focal_loss 1.78845 dice_loss 0.61833 +Epoch [3819/4000] Validation [9/10] Loss: 1.55959 focal_loss 1.01651 dice_loss 0.54308 +Epoch [3819/4000] Validation [10/10] Loss: 1.88672 focal_loss 1.15277 dice_loss 0.73395 +Epoch [3819/4000] Validation metric {'Val/mean dice_metric': 0.9512917399406433, 'Val/mean miou_metric': 0.9353724718093872, 'Val/mean f1': 0.9479857087135315, 'Val/mean precision': 0.9435302019119263, 'Val/mean recall': 0.9524835348129272, 'Val/mean hd95_metric': 10.710441589355469} +Cheakpoint... +Epoch [3819/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512917399406433, 'Val/mean miou_metric': 0.9353724718093872, 'Val/mean f1': 0.9479857087135315, 'Val/mean precision': 0.9435302019119263, 'Val/mean recall': 0.9524835348129272, 'Val/mean hd95_metric': 10.710441589355469} +Epoch [3820/4000] Training [1/39] Loss: 0.12787 +Epoch [3820/4000] Training [2/39] Loss: 0.00612 +Epoch [3820/4000] Training [3/39] Loss: 0.12877 +Epoch [3820/4000] Training [4/39] Loss: 0.00449 +Epoch [3820/4000] Training [5/39] Loss: 0.00380 +Epoch [3820/4000] Training [6/39] Loss: 0.00561 +Epoch [3820/4000] Training [7/39] Loss: 0.00393 +Epoch [3820/4000] Training [8/39] Loss: 0.00376 +Epoch [3820/4000] Training [9/39] Loss: 0.00753 +Epoch [3820/4000] Training [10/39] Loss: 0.00383 +Epoch [3820/4000] Training [11/39] Loss: 0.12940 +Epoch [3820/4000] Training [12/39] Loss: 0.00455 +Epoch [3820/4000] Training [13/39] Loss: 0.00522 +Epoch [3820/4000] Training [14/39] Loss: 0.00396 +Epoch [3820/4000] Training [15/39] Loss: 0.12855 +Epoch [3820/4000] Training [16/39] Loss: 0.00520 +Epoch [3820/4000] Training [17/39] Loss: 0.00923 +Epoch [3820/4000] Training [18/39] Loss: 0.25391 +Epoch [3820/4000] Training [19/39] Loss: 0.00362 +Epoch [3820/4000] Training [20/39] Loss: 0.00462 +Epoch [3820/4000] Training [21/39] Loss: 0.01047 +Epoch [3820/4000] Training [22/39] Loss: 0.00645 +Epoch [3820/4000] Training [23/39] Loss: 0.00643 +Epoch [3820/4000] Training [24/39] Loss: 0.00390 +Epoch [3820/4000] Training [25/39] Loss: 0.12867 +Epoch [3820/4000] Training [26/39] Loss: 0.00523 +Epoch [3820/4000] Training [27/39] Loss: 0.12777 +Epoch [3820/4000] Training [28/39] Loss: 0.00542 +Epoch [3820/4000] Training [29/39] Loss: 0.00522 +Epoch [3820/4000] Training [30/39] Loss: 0.00477 +Epoch [3820/4000] Training [31/39] Loss: 0.00637 +Epoch [3820/4000] Training [32/39] Loss: 0.12879 +Epoch [3820/4000] Training [33/39] Loss: 0.00499 +Epoch [3820/4000] Training [34/39] Loss: 0.00532 +Epoch [3820/4000] Training [35/39] Loss: 0.00430 +Epoch [3820/4000] Training [36/39] Loss: 0.00357 +Epoch [3820/4000] Training [37/39] Loss: 0.00508 +Epoch [3820/4000] Training [38/39] Loss: 0.13175 +Epoch [3820/4000] Training [39/39] Loss: 0.00698 +Epoch [3820/4000] Training metric {'Train/mean dice_metric': 0.9952174425125122, 'Train/mean miou_metric': 0.991746187210083, 'Train/mean f1': 0.996711254119873, 'Train/mean precision': 0.9962561130523682, 'Train/mean recall': 0.9971668124198914, 'Train/mean hd95_metric': 0.973096489906311} +Epoch [3820/4000] Validation [1/10] Loss: 0.72302 focal_loss 0.63626 dice_loss 0.08676 +Epoch [3820/4000] Validation [2/10] Loss: 0.50896 focal_loss 0.41131 dice_loss 0.09765 +Epoch [3820/4000] Validation [3/10] Loss: 0.39274 focal_loss 0.28203 dice_loss 0.11071 +Epoch [3820/4000] Validation [4/10] Loss: 0.90226 focal_loss 0.33662 dice_loss 0.56564 +Epoch [3820/4000] Validation [5/10] Loss: 3.10358 focal_loss 2.42966 dice_loss 0.67391 +Epoch [3820/4000] Validation [6/10] Loss: 1.34332 focal_loss 0.63009 dice_loss 0.71323 +Epoch [3820/4000] Validation [7/10] Loss: 1.18814 focal_loss 0.53203 dice_loss 0.65611 +Epoch [3820/4000] Validation [8/10] Loss: 2.35133 focal_loss 1.74005 dice_loss 0.61128 +Epoch [3820/4000] Validation [9/10] Loss: 1.57611 focal_loss 1.03230 dice_loss 0.54380 +Epoch [3820/4000] Validation [10/10] Loss: 1.92375 focal_loss 1.18847 dice_loss 0.73528 +Epoch [3820/4000] Validation metric {'Val/mean dice_metric': 0.9503616690635681, 'Val/mean miou_metric': 0.9342184066772461, 'Val/mean f1': 0.9479265809059143, 'Val/mean precision': 0.9426573514938354, 'Val/mean recall': 0.9532551765441895, 'Val/mean hd95_metric': 10.871736526489258} +Cheakpoint... +Epoch [3820/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9504], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9503616690635681, 'Val/mean miou_metric': 0.9342184066772461, 'Val/mean f1': 0.9479265809059143, 'Val/mean precision': 0.9426573514938354, 'Val/mean recall': 0.9532551765441895, 'Val/mean hd95_metric': 10.871736526489258} +Epoch [3821/4000] Training [1/39] Loss: 0.00226 +Epoch [3821/4000] Training [2/39] Loss: 0.12961 +Epoch [3821/4000] Training [3/39] Loss: 0.00488 +Epoch [3821/4000] Training [4/39] Loss: 0.12880 +Epoch [3821/4000] Training [5/39] Loss: 0.00433 +Epoch [3821/4000] Training [6/39] Loss: 0.00460 +Epoch [3821/4000] Training [7/39] Loss: 0.00515 +Epoch [3821/4000] Training [8/39] Loss: 0.00580 +Epoch [3821/4000] Training [9/39] Loss: 0.00459 +Epoch [3821/4000] Training [10/39] Loss: 0.00380 +Epoch [3821/4000] Training [11/39] Loss: 0.13309 +Epoch [3821/4000] Training [12/39] Loss: 0.12818 +Epoch [3821/4000] Training [13/39] Loss: 0.25289 +Epoch [3821/4000] Training [14/39] Loss: 0.00464 +Epoch [3821/4000] Training [15/39] Loss: 0.12892 +Epoch [3821/4000] Training [16/39] Loss: 0.00577 +Epoch [3821/4000] Training [17/39] Loss: 0.00531 +Epoch [3821/4000] Training [18/39] Loss: 0.00416 +Epoch [3821/4000] Training [19/39] Loss: 0.00498 +Epoch [3821/4000] Training [20/39] Loss: 0.13173 +Epoch [3821/4000] Training [21/39] Loss: 0.00438 +Epoch [3821/4000] Training [22/39] Loss: 0.12846 +Epoch [3821/4000] Training [23/39] Loss: 0.00264 +Epoch [3821/4000] Training [24/39] Loss: 0.00562 +Epoch [3821/4000] Training [25/39] Loss: 0.00450 +Epoch [3821/4000] Training [26/39] Loss: 0.00617 +Epoch [3821/4000] Training [27/39] Loss: 0.00336 +Epoch [3821/4000] Training [28/39] Loss: 0.12753 +Epoch [3821/4000] Training [29/39] Loss: 0.00534 +Epoch [3821/4000] Training [30/39] Loss: 0.00513 +Epoch [3821/4000] Training [31/39] Loss: 0.00487 +Epoch [3821/4000] Training [32/39] Loss: 0.12941 +Epoch [3821/4000] Training [33/39] Loss: 0.12884 +Epoch [3821/4000] Training [34/39] Loss: 0.00521 +Epoch [3821/4000] Training [35/39] Loss: 0.00294 +Epoch [3821/4000] Training [36/39] Loss: 0.00490 +Epoch [3821/4000] Training [37/39] Loss: 0.00524 +Epoch [3821/4000] Training [38/39] Loss: 0.00480 +Epoch [3821/4000] Training [39/39] Loss: 0.00573 +Epoch [3821/4000] Training metric {'Train/mean dice_metric': 0.9964855909347534, 'Train/mean miou_metric': 0.993424654006958, 'Train/mean f1': 0.9970648288726807, 'Train/mean precision': 0.9966229200363159, 'Train/mean recall': 0.9975070953369141, 'Train/mean hd95_metric': 0.9263672828674316} +Epoch [3821/4000] Validation [1/10] Loss: 0.71771 focal_loss 0.63141 dice_loss 0.08630 +Epoch [3821/4000] Validation [2/10] Loss: 0.50346 focal_loss 0.40576 dice_loss 0.09770 +Epoch [3821/4000] Validation [3/10] Loss: 0.39650 focal_loss 0.28542 dice_loss 0.11108 +Epoch [3821/4000] Validation [4/10] Loss: 0.89657 focal_loss 0.33144 dice_loss 0.56513 +Epoch [3821/4000] Validation [5/10] Loss: 3.11553 focal_loss 2.44154 dice_loss 0.67399 +Epoch [3821/4000] Validation [6/10] Loss: 1.33547 focal_loss 0.62259 dice_loss 0.71288 +Epoch [3821/4000] Validation [7/10] Loss: 1.17765 focal_loss 0.52221 dice_loss 0.65544 +Epoch [3821/4000] Validation [8/10] Loss: 2.35636 focal_loss 1.74337 dice_loss 0.61300 +Epoch [3821/4000] Validation [9/10] Loss: 1.58528 focal_loss 1.04148 dice_loss 0.54380 +Epoch [3821/4000] Validation [10/10] Loss: 1.90639 focal_loss 1.17088 dice_loss 0.73551 +Epoch [3821/4000] Validation metric {'Val/mean dice_metric': 0.9514226317405701, 'Val/mean miou_metric': 0.9356279373168945, 'Val/mean f1': 0.9481770992279053, 'Val/mean precision': 0.9432067275047302, 'Val/mean recall': 0.9532000422477722, 'Val/mean hd95_metric': 10.865464210510254} +Cheakpoint... +Epoch [3821/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514226317405701, 'Val/mean miou_metric': 0.9356279373168945, 'Val/mean f1': 0.9481770992279053, 'Val/mean precision': 0.9432067275047302, 'Val/mean recall': 0.9532000422477722, 'Val/mean hd95_metric': 10.865464210510254} +Epoch [3822/4000] Training [1/39] Loss: 0.12777 +Epoch [3822/4000] Training [2/39] Loss: 0.00297 +Epoch [3822/4000] Training [3/39] Loss: 0.00474 +Epoch [3822/4000] Training [4/39] Loss: 0.00291 +Epoch [3822/4000] Training [5/39] Loss: 0.12906 +Epoch [3822/4000] Training [6/39] Loss: 0.00688 +Epoch [3822/4000] Training [7/39] Loss: 0.00728 +Epoch [3822/4000] Training [8/39] Loss: 0.00371 +Epoch [3822/4000] Training [9/39] Loss: 0.00433 +Epoch [3822/4000] Training [10/39] Loss: 0.13109 +Epoch [3822/4000] Training [11/39] Loss: 0.13098 +Epoch [3822/4000] Training [12/39] Loss: 0.12793 +Epoch [3822/4000] Training [13/39] Loss: 0.00346 +Epoch [3822/4000] Training [14/39] Loss: 0.00466 +Epoch [3822/4000] Training [15/39] Loss: 0.00752 +Epoch [3822/4000] Training [16/39] Loss: 0.00329 +Epoch [3822/4000] Training [17/39] Loss: 0.00586 +Epoch [3822/4000] Training [18/39] Loss: 0.00583 +Epoch [3822/4000] Training [19/39] Loss: 0.00588 +Epoch [3822/4000] Training [20/39] Loss: 0.13017 +Epoch [3822/4000] Training [21/39] Loss: 0.12877 +Epoch [3822/4000] Training [22/39] Loss: 0.12868 +Epoch [3822/4000] Training [23/39] Loss: 0.00429 +Epoch [3822/4000] Training [24/39] Loss: 0.00495 +Epoch [3822/4000] Training [25/39] Loss: 0.00275 +Epoch [3822/4000] Training [26/39] Loss: 0.00534 +Epoch [3822/4000] Training [27/39] Loss: 0.00608 +Epoch [3822/4000] Training [28/39] Loss: 0.00425 +Epoch [3822/4000] Training [29/39] Loss: 0.00405 +Epoch [3822/4000] Training [30/39] Loss: 0.00425 +Epoch [3822/4000] Training [31/39] Loss: 0.00503 +Epoch [3822/4000] Training [32/39] Loss: 0.00599 +Epoch [3822/4000] Training [33/39] Loss: 0.00498 +Epoch [3822/4000] Training [34/39] Loss: 0.00402 +Epoch [3822/4000] Training [35/39] Loss: 0.12984 +Epoch [3822/4000] Training [36/39] Loss: 0.00403 +Epoch [3822/4000] Training [37/39] Loss: 0.00552 +Epoch [3822/4000] Training [38/39] Loss: 0.00716 +Epoch [3822/4000] Training [39/39] Loss: 0.00580 +Epoch [3822/4000] Training metric {'Train/mean dice_metric': 0.9962292909622192, 'Train/mean miou_metric': 0.9929333925247192, 'Train/mean f1': 0.9968078136444092, 'Train/mean precision': 0.9963688850402832, 'Train/mean recall': 0.9972471594810486, 'Train/mean hd95_metric': 1.1105382442474365} +Epoch [3822/4000] Validation [1/10] Loss: 0.70309 focal_loss 0.61721 dice_loss 0.08588 +Epoch [3822/4000] Validation [2/10] Loss: 0.50306 focal_loss 0.40474 dice_loss 0.09832 +Epoch [3822/4000] Validation [3/10] Loss: 0.39136 focal_loss 0.28014 dice_loss 0.11122 +Epoch [3822/4000] Validation [4/10] Loss: 0.89590 focal_loss 0.33041 dice_loss 0.56549 +Epoch [3822/4000] Validation [5/10] Loss: 3.07273 focal_loss 2.39877 dice_loss 0.67396 +Epoch [3822/4000] Validation [6/10] Loss: 1.33196 focal_loss 0.61925 dice_loss 0.71272 +Epoch [3822/4000] Validation [7/10] Loss: 1.17603 focal_loss 0.52152 dice_loss 0.65451 +Epoch [3822/4000] Validation [8/10] Loss: 2.35232 focal_loss 1.73782 dice_loss 0.61450 +Epoch [3822/4000] Validation [9/10] Loss: 1.57253 focal_loss 1.02853 dice_loss 0.54400 +Epoch [3822/4000] Validation [10/10] Loss: 1.90169 focal_loss 1.16611 dice_loss 0.73558 +Epoch [3822/4000] Validation metric {'Val/mean dice_metric': 0.9512091279029846, 'Val/mean miou_metric': 0.9352219104766846, 'Val/mean f1': 0.9479576349258423, 'Val/mean precision': 0.943038284778595, 'Val/mean recall': 0.9529288411140442, 'Val/mean hd95_metric': 10.932766914367676} +Cheakpoint... +Epoch [3822/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512091279029846, 'Val/mean miou_metric': 0.9352219104766846, 'Val/mean f1': 0.9479576349258423, 'Val/mean precision': 0.943038284778595, 'Val/mean recall': 0.9529288411140442, 'Val/mean hd95_metric': 10.932766914367676} +Epoch [3823/4000] Training [1/39] Loss: 0.12945 +Epoch [3823/4000] Training [2/39] Loss: 0.00390 +Epoch [3823/4000] Training [3/39] Loss: 0.00273 +Epoch [3823/4000] Training [4/39] Loss: 0.16678 +Epoch [3823/4000] Training [5/39] Loss: 0.00442 +Epoch [3823/4000] Training [6/39] Loss: 0.00350 +Epoch [3823/4000] Training [7/39] Loss: 0.00335 +Epoch [3823/4000] Training [8/39] Loss: 0.00666 +Epoch [3823/4000] Training [9/39] Loss: 0.13097 +Epoch [3823/4000] Training [10/39] Loss: 0.00398 +Epoch [3823/4000] Training [11/39] Loss: 0.00483 +Epoch [3823/4000] Training [12/39] Loss: 0.12772 +Epoch [3823/4000] Training [13/39] Loss: 0.12845 +Epoch [3823/4000] Training [14/39] Loss: 0.25413 +Epoch [3823/4000] Training [15/39] Loss: 0.00503 +Epoch [3823/4000] Training [16/39] Loss: 0.13115 +Epoch [3823/4000] Training [17/39] Loss: 0.00506 +Epoch [3823/4000] Training [18/39] Loss: 0.00550 +Epoch [3823/4000] Training [19/39] Loss: 0.00523 +Epoch [3823/4000] Training [20/39] Loss: 0.00426 +Epoch [3823/4000] Training [21/39] Loss: 0.00419 +Epoch [3823/4000] Training [22/39] Loss: 0.00352 +Epoch [3823/4000] Training [23/39] Loss: 0.00460 +Epoch [3823/4000] Training [24/39] Loss: 0.12892 +Epoch [3823/4000] Training [25/39] Loss: 0.00556 +Epoch [3823/4000] Training [26/39] Loss: 0.00546 +Epoch [3823/4000] Training [27/39] Loss: 0.00469 +Epoch [3823/4000] Training [28/39] Loss: 0.01131 +Epoch [3823/4000] Training [29/39] Loss: 0.00403 +Epoch [3823/4000] Training [30/39] Loss: 0.00450 +Epoch [3823/4000] Training [31/39] Loss: 0.00728 +Epoch [3823/4000] Training [32/39] Loss: 0.12829 +Epoch [3823/4000] Training [33/39] Loss: 0.12891 +Epoch [3823/4000] Training [34/39] Loss: 0.12826 +Epoch [3823/4000] Training [35/39] Loss: 0.00341 +Epoch [3823/4000] Training [36/39] Loss: 0.00417 +Epoch [3823/4000] Training [37/39] Loss: 0.00435 +Epoch [3823/4000] Training [38/39] Loss: 0.00620 +Epoch [3823/4000] Training [39/39] Loss: 0.00389 +Epoch [3823/4000] Training metric {'Train/mean dice_metric': 0.9955815076828003, 'Train/mean miou_metric': 0.9924461245536804, 'Train/mean f1': 0.9968793392181396, 'Train/mean precision': 0.9963454604148865, 'Train/mean recall': 0.9974137544631958, 'Train/mean hd95_metric': 0.9254475235939026} +Epoch [3823/4000] Validation [1/10] Loss: 0.69245 focal_loss 0.60858 dice_loss 0.08387 +Epoch [3823/4000] Validation [2/10] Loss: 0.50391 focal_loss 0.40439 dice_loss 0.09952 +Epoch [3823/4000] Validation [3/10] Loss: 0.40166 focal_loss 0.28967 dice_loss 0.11200 +Epoch [3823/4000] Validation [4/10] Loss: 0.89112 focal_loss 0.32679 dice_loss 0.56433 +Epoch [3823/4000] Validation [5/10] Loss: 3.07851 focal_loss 2.40435 dice_loss 0.67416 +Epoch [3823/4000] Validation [6/10] Loss: 1.32115 focal_loss 0.60845 dice_loss 0.71270 +Epoch [3823/4000] Validation [7/10] Loss: 1.16520 focal_loss 0.51407 dice_loss 0.65113 +Epoch [3823/4000] Validation [8/10] Loss: 2.44116 focal_loss 1.81781 dice_loss 0.62335 +Epoch [3823/4000] Validation [9/10] Loss: 1.54083 focal_loss 0.99731 dice_loss 0.54352 +Epoch [3823/4000] Validation [10/10] Loss: 1.87797 focal_loss 1.14420 dice_loss 0.73377 +Epoch [3823/4000] Validation metric {'Val/mean dice_metric': 0.9507059454917908, 'Val/mean miou_metric': 0.9348867535591125, 'Val/mean f1': 0.9483259916305542, 'Val/mean precision': 0.9447253346443176, 'Val/mean recall': 0.9519543647766113, 'Val/mean hd95_metric': 10.783374786376953} +Cheakpoint... +Epoch [3823/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507059454917908, 'Val/mean miou_metric': 0.9348867535591125, 'Val/mean f1': 0.9483259916305542, 'Val/mean precision': 0.9447253346443176, 'Val/mean recall': 0.9519543647766113, 'Val/mean hd95_metric': 10.783374786376953} +Epoch [3824/4000] Training [1/39] Loss: 0.00555 +Epoch [3824/4000] Training [2/39] Loss: 0.00449 +Epoch [3824/4000] Training [3/39] Loss: 0.00714 +Epoch [3824/4000] Training [4/39] Loss: 0.00379 +Epoch [3824/4000] Training [5/39] Loss: 0.00434 +Epoch [3824/4000] Training [6/39] Loss: 0.00570 +Epoch [3824/4000] Training [7/39] Loss: 0.00465 +Epoch [3824/4000] Training [8/39] Loss: 0.00447 +Epoch [3824/4000] Training [9/39] Loss: 0.25484 +Epoch [3824/4000] Training [10/39] Loss: 0.00462 +Epoch [3824/4000] Training [11/39] Loss: 0.00393 +Epoch [3824/4000] Training [12/39] Loss: 0.00447 +Epoch [3824/4000] Training [13/39] Loss: 0.00780 +Epoch [3824/4000] Training [14/39] Loss: 0.00547 +Epoch [3824/4000] Training [15/39] Loss: 0.09276 +Epoch [3824/4000] Training [16/39] Loss: 0.00379 +Epoch [3824/4000] Training [17/39] Loss: 0.00379 +Epoch [3824/4000] Training [18/39] Loss: 0.00434 +Epoch [3824/4000] Training [19/39] Loss: 0.00313 +Epoch [3824/4000] Training [20/39] Loss: 0.00411 +Epoch [3824/4000] Training [21/39] Loss: 0.12842 +Epoch [3824/4000] Training [22/39] Loss: 0.00576 +Epoch [3824/4000] Training [23/39] Loss: 0.00572 +Epoch [3824/4000] Training [24/39] Loss: 0.00341 +Epoch [3824/4000] Training [25/39] Loss: 0.00642 +Epoch [3824/4000] Training [26/39] Loss: 0.00477 +Epoch [3824/4000] Training [27/39] Loss: 0.00384 +Epoch [3824/4000] Training [28/39] Loss: 0.00466 +Epoch [3824/4000] Training [29/39] Loss: 0.00268 +Epoch [3824/4000] Training [30/39] Loss: 0.00364 +Epoch [3824/4000] Training [31/39] Loss: 0.00378 +Epoch [3824/4000] Training [32/39] Loss: 0.00632 +Epoch [3824/4000] Training [33/39] Loss: 0.00368 +Epoch [3824/4000] Training [34/39] Loss: 0.12910 +Epoch [3824/4000] Training [35/39] Loss: 0.00418 +Epoch [3824/4000] Training [36/39] Loss: 0.00879 +Epoch [3824/4000] Training [37/39] Loss: 0.12802 +Epoch [3824/4000] Training [38/39] Loss: 0.00691 +Epoch [3824/4000] Training [39/39] Loss: 0.00418 +Epoch [3824/4000] Training metric {'Train/mean dice_metric': 0.996353030204773, 'Train/mean miou_metric': 0.9931654930114746, 'Train/mean f1': 0.9968584179878235, 'Train/mean precision': 0.9963999390602112, 'Train/mean recall': 0.9973173141479492, 'Train/mean hd95_metric': 0.9785696864128113} +Epoch [3824/4000] Validation [1/10] Loss: 0.71412 focal_loss 0.62979 dice_loss 0.08433 +Epoch [3824/4000] Validation [2/10] Loss: 0.50582 focal_loss 0.40609 dice_loss 0.09973 +Epoch [3824/4000] Validation [3/10] Loss: 0.41361 focal_loss 0.30143 dice_loss 0.11218 +Epoch [3824/4000] Validation [4/10] Loss: 0.89094 focal_loss 0.32696 dice_loss 0.56398 +Epoch [3824/4000] Validation [5/10] Loss: 3.15514 focal_loss 2.48093 dice_loss 0.67421 +Epoch [3824/4000] Validation [6/10] Loss: 1.31592 focal_loss 0.60293 dice_loss 0.71299 +Epoch [3824/4000] Validation [7/10] Loss: 1.16840 focal_loss 0.51730 dice_loss 0.65110 +Epoch [3824/4000] Validation [8/10] Loss: 2.47958 focal_loss 1.85530 dice_loss 0.62428 +Epoch [3824/4000] Validation [9/10] Loss: 1.56000 focal_loss 1.01691 dice_loss 0.54309 +Epoch [3824/4000] Validation [10/10] Loss: 1.87466 focal_loss 1.14158 dice_loss 0.73308 +Epoch [3824/4000] Validation metric {'Val/mean dice_metric': 0.9513547420501709, 'Val/mean miou_metric': 0.9355118870735168, 'Val/mean f1': 0.9484803676605225, 'Val/mean precision': 0.9450522065162659, 'Val/mean recall': 0.9519335031509399, 'Val/mean hd95_metric': 10.723734855651855} +Cheakpoint... +Epoch [3824/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513547420501709, 'Val/mean miou_metric': 0.9355118870735168, 'Val/mean f1': 0.9484803676605225, 'Val/mean precision': 0.9450522065162659, 'Val/mean recall': 0.9519335031509399, 'Val/mean hd95_metric': 10.723734855651855} +Epoch [3825/4000] Training [1/39] Loss: 0.00324 +Epoch [3825/4000] Training [2/39] Loss: 0.00718 +Epoch [3825/4000] Training [3/39] Loss: 0.12961 +Epoch [3825/4000] Training [4/39] Loss: 0.13109 +Epoch [3825/4000] Training [5/39] Loss: 0.00448 +Epoch [3825/4000] Training [6/39] Loss: 0.00462 +Epoch [3825/4000] Training [7/39] Loss: 0.00375 +Epoch [3825/4000] Training [8/39] Loss: 0.00348 +Epoch [3825/4000] Training [9/39] Loss: 0.12900 +Epoch [3825/4000] Training [10/39] Loss: 0.00432 +Epoch [3825/4000] Training [11/39] Loss: 0.00258 +Epoch [3825/4000] Training [12/39] Loss: 0.00473 +Epoch [3825/4000] Training [13/39] Loss: 0.00487 +Epoch [3825/4000] Training [14/39] Loss: 0.00741 +Epoch [3825/4000] Training [15/39] Loss: 0.00658 +Epoch [3825/4000] Training [16/39] Loss: 0.00400 +Epoch [3825/4000] Training [17/39] Loss: 0.00453 +Epoch [3825/4000] Training [18/39] Loss: 0.00791 +Epoch [3825/4000] Training [19/39] Loss: 0.00494 +Epoch [3825/4000] Training [20/39] Loss: 0.13093 +Epoch [3825/4000] Training [21/39] Loss: 0.00339 +Epoch [3825/4000] Training [22/39] Loss: 0.00462 +Epoch [3825/4000] Training [23/39] Loss: 0.00441 +Epoch [3825/4000] Training [24/39] Loss: 0.00376 +Epoch [3825/4000] Training [25/39] Loss: 0.00605 +Epoch [3825/4000] Training [26/39] Loss: 0.00597 +Epoch [3825/4000] Training [27/39] Loss: 0.00495 +Epoch [3825/4000] Training [28/39] Loss: 0.00505 +Epoch [3825/4000] Training [29/39] Loss: 0.00520 +Epoch [3825/4000] Training [30/39] Loss: 0.00440 +Epoch [3825/4000] Training [31/39] Loss: 0.12867 +Epoch [3825/4000] Training [32/39] Loss: 0.12747 +Epoch [3825/4000] Training [33/39] Loss: 0.12929 +Epoch [3825/4000] Training [34/39] Loss: 0.00631 +Epoch [3825/4000] Training [35/39] Loss: 0.00445 +Epoch [3825/4000] Training [36/39] Loss: 0.00454 +Epoch [3825/4000] Training [37/39] Loss: 0.37921 +Epoch [3825/4000] Training [38/39] Loss: 0.12781 +Epoch [3825/4000] Training [39/39] Loss: 0.12752 +Epoch [3825/4000] Training metric {'Train/mean dice_metric': 0.9963750839233398, 'Train/mean miou_metric': 0.9932100176811218, 'Train/mean f1': 0.9969350695610046, 'Train/mean precision': 0.9964925646781921, 'Train/mean recall': 0.9973779916763306, 'Train/mean hd95_metric': 0.9590575695037842} +Epoch [3825/4000] Validation [1/10] Loss: 0.69456 focal_loss 0.61049 dice_loss 0.08407 +Epoch [3825/4000] Validation [2/10] Loss: 0.50507 focal_loss 0.40539 dice_loss 0.09968 +Epoch [3825/4000] Validation [3/10] Loss: 0.40160 focal_loss 0.28962 dice_loss 0.11199 +Epoch [3825/4000] Validation [4/10] Loss: 0.89219 focal_loss 0.32812 dice_loss 0.56408 +Epoch [3825/4000] Validation [5/10] Loss: 3.07540 focal_loss 2.40127 dice_loss 0.67413 +Epoch [3825/4000] Validation [6/10] Loss: 1.31798 focal_loss 0.60490 dice_loss 0.71308 +Epoch [3825/4000] Validation [7/10] Loss: 1.16651 focal_loss 0.51560 dice_loss 0.65091 +Epoch [3825/4000] Validation [8/10] Loss: 2.44727 focal_loss 1.82267 dice_loss 0.62460 +Epoch [3825/4000] Validation [9/10] Loss: 1.53974 focal_loss 0.99655 dice_loss 0.54319 +Epoch [3825/4000] Validation [10/10] Loss: 1.87077 focal_loss 1.13812 dice_loss 0.73265 +Epoch [3825/4000] Validation metric {'Val/mean dice_metric': 0.9514259099960327, 'Val/mean miou_metric': 0.9356096386909485, 'Val/mean f1': 0.9487462043762207, 'Val/mean precision': 0.9451411366462708, 'Val/mean recall': 0.9523788690567017, 'Val/mean hd95_metric': 10.837172508239746} +Cheakpoint... +Epoch [3825/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514259099960327, 'Val/mean miou_metric': 0.9356096386909485, 'Val/mean f1': 0.9487462043762207, 'Val/mean precision': 0.9451411366462708, 'Val/mean recall': 0.9523788690567017, 'Val/mean hd95_metric': 10.837172508239746} +Epoch [3826/4000] Training [1/39] Loss: 0.00389 +Epoch [3826/4000] Training [2/39] Loss: 0.00555 +Epoch [3826/4000] Training [3/39] Loss: 0.12990 +Epoch [3826/4000] Training [4/39] Loss: 0.00628 +Epoch [3826/4000] Training [5/39] Loss: 0.13030 +Epoch [3826/4000] Training [6/39] Loss: 0.13074 +Epoch [3826/4000] Training [7/39] Loss: 0.00357 +Epoch [3826/4000] Training [8/39] Loss: 0.00397 +Epoch [3826/4000] Training [9/39] Loss: 0.00737 +Epoch [3826/4000] Training [10/39] Loss: 0.00439 +Epoch [3826/4000] Training [11/39] Loss: 0.25234 +Epoch [3826/4000] Training [12/39] Loss: 0.04410 +Epoch [3826/4000] Training [13/39] Loss: 0.00486 +Epoch [3826/4000] Training [14/39] Loss: 0.12914 +Epoch [3826/4000] Training [15/39] Loss: 0.00385 +Epoch [3826/4000] Training [16/39] Loss: 0.00372 +Epoch [3826/4000] Training [17/39] Loss: 0.00340 +Epoch [3826/4000] Training [18/39] Loss: 0.00470 +Epoch [3826/4000] Training [19/39] Loss: 0.12706 +Epoch [3826/4000] Training [20/39] Loss: 0.00395 +Epoch [3826/4000] Training [21/39] Loss: 0.00537 +Epoch [3826/4000] Training [22/39] Loss: 0.00480 +Epoch [3826/4000] Training [23/39] Loss: 0.00545 +Epoch [3826/4000] Training [24/39] Loss: 0.25505 +Epoch [3826/4000] Training [25/39] Loss: 0.00633 +Epoch [3826/4000] Training [26/39] Loss: 0.12946 +Epoch [3826/4000] Training [27/39] Loss: 0.00542 +Epoch [3826/4000] Training [28/39] Loss: 0.00423 +Epoch [3826/4000] Training [29/39] Loss: 0.00744 +Epoch [3826/4000] Training [30/39] Loss: 0.00640 +Epoch [3826/4000] Training [31/39] Loss: 0.00448 +Epoch [3826/4000] Training [32/39] Loss: 0.00728 +Epoch [3826/4000] Training [33/39] Loss: 0.13018 +Epoch [3826/4000] Training [34/39] Loss: 0.00389 +Epoch [3826/4000] Training [35/39] Loss: 0.12723 +Epoch [3826/4000] Training [36/39] Loss: 0.12886 +Epoch [3826/4000] Training [37/39] Loss: 0.00568 +Epoch [3826/4000] Training [38/39] Loss: 0.00313 +Epoch [3826/4000] Training [39/39] Loss: 0.00369 +Epoch [3826/4000] Training metric {'Train/mean dice_metric': 0.9963070750236511, 'Train/mean miou_metric': 0.9930758476257324, 'Train/mean f1': 0.9968996047973633, 'Train/mean precision': 0.9964792728424072, 'Train/mean recall': 0.9973202347755432, 'Train/mean hd95_metric': 0.9305764436721802} +Epoch [3826/4000] Validation [1/10] Loss: 0.69551 focal_loss 0.61191 dice_loss 0.08360 +Epoch [3826/4000] Validation [2/10] Loss: 0.50571 focal_loss 0.40431 dice_loss 0.10141 +Epoch [3826/4000] Validation [3/10] Loss: 0.41166 focal_loss 0.29880 dice_loss 0.11286 +Epoch [3826/4000] Validation [4/10] Loss: 0.88468 focal_loss 0.32158 dice_loss 0.56310 +Epoch [3826/4000] Validation [5/10] Loss: 3.08017 focal_loss 2.40582 dice_loss 0.67434 +Epoch [3826/4000] Validation [6/10] Loss: 1.30365 focal_loss 0.59065 dice_loss 0.71300 +Epoch [3826/4000] Validation [7/10] Loss: 1.15694 focal_loss 0.50696 dice_loss 0.64998 +Epoch [3826/4000] Validation [8/10] Loss: 2.48385 focal_loss 1.85396 dice_loss 0.62989 +Epoch [3826/4000] Validation [9/10] Loss: 1.51714 focal_loss 0.97460 dice_loss 0.54254 +Epoch [3826/4000] Validation [10/10] Loss: 1.84094 focal_loss 1.10927 dice_loss 0.73168 +Epoch [3826/4000] Validation metric {'Val/mean dice_metric': 0.9513298869132996, 'Val/mean miou_metric': 0.9354656934738159, 'Val/mean f1': 0.9488449692726135, 'Val/mean precision': 0.946110725402832, 'Val/mean recall': 0.9515951871871948, 'Val/mean hd95_metric': 10.701327323913574} +Cheakpoint... +Epoch [3826/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513298869132996, 'Val/mean miou_metric': 0.9354656934738159, 'Val/mean f1': 0.9488449692726135, 'Val/mean precision': 0.946110725402832, 'Val/mean recall': 0.9515951871871948, 'Val/mean hd95_metric': 10.701327323913574} +Epoch [3827/4000] Training [1/39] Loss: 0.00541 +Epoch [3827/4000] Training [2/39] Loss: 0.00350 +Epoch [3827/4000] Training [3/39] Loss: 0.12890 +Epoch [3827/4000] Training [4/39] Loss: 0.00492 +Epoch [3827/4000] Training [5/39] Loss: 0.00595 +Epoch [3827/4000] Training [6/39] Loss: 0.00282 +Epoch [3827/4000] Training [7/39] Loss: 0.00540 +Epoch [3827/4000] Training [8/39] Loss: 0.12891 +Epoch [3827/4000] Training [9/39] Loss: 0.00364 +Epoch [3827/4000] Training [10/39] Loss: 0.00520 +Epoch [3827/4000] Training [11/39] Loss: 0.13224 +Epoch [3827/4000] Training [12/39] Loss: 0.00316 +Epoch [3827/4000] Training [13/39] Loss: 0.00340 +Epoch [3827/4000] Training [14/39] Loss: 0.00436 +Epoch [3827/4000] Training [15/39] Loss: 0.00415 +Epoch [3827/4000] Training [16/39] Loss: 0.00413 +Epoch [3827/4000] Training [17/39] Loss: 0.12843 +Epoch [3827/4000] Training [18/39] Loss: 0.00561 +Epoch [3827/4000] Training [19/39] Loss: 0.00465 +Epoch [3827/4000] Training [20/39] Loss: 0.00569 +Epoch [3827/4000] Training [21/39] Loss: 0.00448 +Epoch [3827/4000] Training [22/39] Loss: 0.00466 +Epoch [3827/4000] Training [23/39] Loss: 0.00518 +Epoch [3827/4000] Training [24/39] Loss: 0.00682 +Epoch [3827/4000] Training [25/39] Loss: 0.00689 +Epoch [3827/4000] Training [26/39] Loss: 0.00312 +Epoch [3827/4000] Training [27/39] Loss: 0.00276 +Epoch [3827/4000] Training [28/39] Loss: 0.00460 +Epoch [3827/4000] Training [29/39] Loss: 0.00467 +Epoch [3827/4000] Training [30/39] Loss: 0.00445 +Epoch [3827/4000] Training [31/39] Loss: 0.00241 +Epoch [3827/4000] Training [32/39] Loss: 0.00391 +Epoch [3827/4000] Training [33/39] Loss: 0.12831 +Epoch [3827/4000] Training [34/39] Loss: 0.00495 +Epoch [3827/4000] Training [35/39] Loss: 0.00696 +Epoch [3827/4000] Training [36/39] Loss: 0.00468 +Epoch [3827/4000] Training [37/39] Loss: 0.00478 +Epoch [3827/4000] Training [38/39] Loss: 0.00547 +Epoch [3827/4000] Training [39/39] Loss: 0.00367 +Epoch [3827/4000] Training metric {'Train/mean dice_metric': 0.9964824914932251, 'Train/mean miou_metric': 0.9934067726135254, 'Train/mean f1': 0.9969618916511536, 'Train/mean precision': 0.9964762330055237, 'Train/mean recall': 0.9974479079246521, 'Train/mean hd95_metric': 0.9029229879379272} +Epoch [3827/4000] Validation [1/10] Loss: 0.69938 focal_loss 0.61469 dice_loss 0.08469 +Epoch [3827/4000] Validation [2/10] Loss: 0.50011 focal_loss 0.40213 dice_loss 0.09798 +Epoch [3827/4000] Validation [3/10] Loss: 0.39710 focal_loss 0.28541 dice_loss 0.11169 +Epoch [3827/4000] Validation [4/10] Loss: 0.89398 focal_loss 0.32952 dice_loss 0.56446 +Epoch [3827/4000] Validation [5/10] Loss: 3.08124 focal_loss 2.40711 dice_loss 0.67413 +Epoch [3827/4000] Validation [6/10] Loss: 1.32538 focal_loss 0.61211 dice_loss 0.71327 +Epoch [3827/4000] Validation [7/10] Loss: 1.16591 focal_loss 0.51490 dice_loss 0.65101 +Epoch [3827/4000] Validation [8/10] Loss: 2.43781 focal_loss 1.81502 dice_loss 0.62279 +Epoch [3827/4000] Validation [9/10] Loss: 1.52252 focal_loss 0.97926 dice_loss 0.54326 +Epoch [3827/4000] Validation [10/10] Loss: 1.88198 focal_loss 1.14849 dice_loss 0.73349 +Epoch [3827/4000] Validation metric {'Val/mean dice_metric': 0.9515202641487122, 'Val/mean miou_metric': 0.9357700347900391, 'Val/mean f1': 0.9483559131622314, 'Val/mean precision': 0.9444611668586731, 'Val/mean recall': 0.9522829055786133, 'Val/mean hd95_metric': 10.685774803161621} +Cheakpoint... +Epoch [3827/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515202641487122, 'Val/mean miou_metric': 0.9357700347900391, 'Val/mean f1': 0.9483559131622314, 'Val/mean precision': 0.9444611668586731, 'Val/mean recall': 0.9522829055786133, 'Val/mean hd95_metric': 10.685774803161621} +Epoch [3828/4000] Training [1/39] Loss: 0.00386 +Epoch [3828/4000] Training [2/39] Loss: 0.00479 +Epoch [3828/4000] Training [3/39] Loss: 0.00725 +Epoch [3828/4000] Training [4/39] Loss: 0.00450 +Epoch [3828/4000] Training [5/39] Loss: 0.12825 +Epoch [3828/4000] Training [6/39] Loss: 0.03864 +Epoch [3828/4000] Training [7/39] Loss: 0.00544 +Epoch [3828/4000] Training [8/39] Loss: 0.00368 +Epoch [3828/4000] Training [9/39] Loss: 0.00475 +Epoch [3828/4000] Training [10/39] Loss: 0.00549 +Epoch [3828/4000] Training [11/39] Loss: 0.00476 +Epoch [3828/4000] Training [12/39] Loss: 0.00843 +Epoch [3828/4000] Training [13/39] Loss: 0.00419 +Epoch [3828/4000] Training [14/39] Loss: 0.00490 +Epoch [3828/4000] Training [15/39] Loss: 0.00618 +Epoch [3828/4000] Training [16/39] Loss: 0.00454 +Epoch [3828/4000] Training [17/39] Loss: 0.00422 +Epoch [3828/4000] Training [18/39] Loss: 0.00473 +Epoch [3828/4000] Training [19/39] Loss: 0.00443 +Epoch [3828/4000] Training [20/39] Loss: 0.12917 +Epoch [3828/4000] Training [21/39] Loss: 0.00351 +Epoch [3828/4000] Training [22/39] Loss: 0.12828 +Epoch [3828/4000] Training [23/39] Loss: 0.00284 +Epoch [3828/4000] Training [24/39] Loss: 0.00322 +Epoch [3828/4000] Training [25/39] Loss: 0.00490 +Epoch [3828/4000] Training [26/39] Loss: 0.00483 +Epoch [3828/4000] Training [27/39] Loss: 0.00441 +Epoch [3828/4000] Training [28/39] Loss: 0.00442 +Epoch [3828/4000] Training [29/39] Loss: 0.12880 +Epoch [3828/4000] Training [30/39] Loss: 0.12831 +Epoch [3828/4000] Training [31/39] Loss: 0.00529 +Epoch [3828/4000] Training [32/39] Loss: 0.00478 +Epoch [3828/4000] Training [33/39] Loss: 0.00433 +Epoch [3828/4000] Training [34/39] Loss: 0.00480 +Epoch [3828/4000] Training [35/39] Loss: 0.00411 +Epoch [3828/4000] Training [36/39] Loss: 0.00413 +Epoch [3828/4000] Training [37/39] Loss: 0.00460 +Epoch [3828/4000] Training [38/39] Loss: 0.00305 +Epoch [3828/4000] Training [39/39] Loss: 0.00556 +Epoch [3828/4000] Training metric {'Train/mean dice_metric': 0.9966112375259399, 'Train/mean miou_metric': 0.9936577081680298, 'Train/mean f1': 0.9970698952674866, 'Train/mean precision': 0.9966343641281128, 'Train/mean recall': 0.9975056052207947, 'Train/mean hd95_metric': 0.9170417785644531} +Epoch [3828/4000] Validation [1/10] Loss: 0.70167 focal_loss 0.61644 dice_loss 0.08524 +Epoch [3828/4000] Validation [2/10] Loss: 0.49885 focal_loss 0.40014 dice_loss 0.09871 +Epoch [3828/4000] Validation [3/10] Loss: 0.39844 focal_loss 0.28655 dice_loss 0.11189 +Epoch [3828/4000] Validation [4/10] Loss: 0.89109 focal_loss 0.32661 dice_loss 0.56448 +Epoch [3828/4000] Validation [5/10] Loss: 3.06097 focal_loss 2.38670 dice_loss 0.67428 +Epoch [3828/4000] Validation [6/10] Loss: 1.31956 focal_loss 0.60669 dice_loss 0.71287 +Epoch [3828/4000] Validation [7/10] Loss: 1.16184 focal_loss 0.50995 dice_loss 0.65188 +Epoch [3828/4000] Validation [8/10] Loss: 2.44298 focal_loss 1.81908 dice_loss 0.62390 +Epoch [3828/4000] Validation [9/10] Loss: 1.52419 focal_loss 0.98082 dice_loss 0.54336 +Epoch [3828/4000] Validation [10/10] Loss: 1.87061 focal_loss 1.13753 dice_loss 0.73308 +Epoch [3828/4000] Validation metric {'Val/mean dice_metric': 0.9515398740768433, 'Val/mean miou_metric': 0.9358676075935364, 'Val/mean f1': 0.9483430981636047, 'Val/mean precision': 0.9445266723632812, 'Val/mean recall': 0.9521903395652771, 'Val/mean hd95_metric': 10.705970764160156} +Cheakpoint... +Epoch [3828/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515398740768433, 'Val/mean miou_metric': 0.9358676075935364, 'Val/mean f1': 0.9483430981636047, 'Val/mean precision': 0.9445266723632812, 'Val/mean recall': 0.9521903395652771, 'Val/mean hd95_metric': 10.705970764160156} +Epoch [3829/4000] Training [1/39] Loss: 0.25411 +Epoch [3829/4000] Training [2/39] Loss: 0.00615 +Epoch [3829/4000] Training [3/39] Loss: 0.12943 +Epoch [3829/4000] Training [4/39] Loss: 0.00657 +Epoch [3829/4000] Training [5/39] Loss: 0.00783 +Epoch [3829/4000] Training [6/39] Loss: 0.12899 +Epoch [3829/4000] Training [7/39] Loss: 0.00540 +Epoch [3829/4000] Training [8/39] Loss: 0.00421 +Epoch [3829/4000] Training [9/39] Loss: 0.00906 +Epoch [3829/4000] Training [10/39] Loss: 0.00335 +Epoch [3829/4000] Training [11/39] Loss: 0.00419 +Epoch [3829/4000] Training [12/39] Loss: 0.00674 +Epoch [3829/4000] Training [13/39] Loss: 0.00292 +Epoch [3829/4000] Training [14/39] Loss: 0.13167 +Epoch [3829/4000] Training [15/39] Loss: 0.00410 +Epoch [3829/4000] Training [16/39] Loss: 0.00545 +Epoch [3829/4000] Training [17/39] Loss: 0.12910 +Epoch [3829/4000] Training [18/39] Loss: 0.25283 +Epoch [3829/4000] Training [19/39] Loss: 0.00547 +Epoch [3829/4000] Training [20/39] Loss: 0.12807 +Epoch [3829/4000] Training [21/39] Loss: 0.00349 +Epoch [3829/4000] Training [22/39] Loss: 0.00391 +Epoch [3829/4000] Training [23/39] Loss: 0.00336 +Epoch [3829/4000] Training [24/39] Loss: 0.12874 +Epoch [3829/4000] Training [25/39] Loss: 0.00751 +Epoch [3829/4000] Training [26/39] Loss: 0.00712 +Epoch [3829/4000] Training [27/39] Loss: 0.00434 +Epoch [3829/4000] Training [28/39] Loss: 0.00325 +Epoch [3829/4000] Training [29/39] Loss: 0.00539 +Epoch [3829/4000] Training [30/39] Loss: 0.00323 +Epoch [3829/4000] Training [31/39] Loss: 0.00642 +Epoch [3829/4000] Training [32/39] Loss: 0.00420 +Epoch [3829/4000] Training [33/39] Loss: 0.00838 +Epoch [3829/4000] Training [34/39] Loss: 0.00326 +Epoch [3829/4000] Training [35/39] Loss: 0.12844 +Epoch [3829/4000] Training [36/39] Loss: 0.00373 +Epoch [3829/4000] Training [37/39] Loss: 0.12964 +Epoch [3829/4000] Training [38/39] Loss: 0.00694 +Epoch [3829/4000] Training [39/39] Loss: 0.00503 +Epoch [3829/4000] Training metric {'Train/mean dice_metric': 0.9962139129638672, 'Train/mean miou_metric': 0.9928720593452454, 'Train/mean f1': 0.9968488812446594, 'Train/mean precision': 0.9964301586151123, 'Train/mean recall': 0.9972679615020752, 'Train/mean hd95_metric': 0.9408335089683533} +Epoch [3829/4000] Validation [1/10] Loss: 0.70259 focal_loss 0.61743 dice_loss 0.08516 +Epoch [3829/4000] Validation [2/10] Loss: 0.49903 focal_loss 0.39989 dice_loss 0.09914 +Epoch [3829/4000] Validation [3/10] Loss: 0.39745 focal_loss 0.28569 dice_loss 0.11177 +Epoch [3829/4000] Validation [4/10] Loss: 0.89107 focal_loss 0.32660 dice_loss 0.56447 +Epoch [3829/4000] Validation [5/10] Loss: 3.05732 focal_loss 2.38319 dice_loss 0.67413 +Epoch [3829/4000] Validation [6/10] Loss: 1.31722 focal_loss 0.60396 dice_loss 0.71326 +Epoch [3829/4000] Validation [7/10] Loss: 1.16194 focal_loss 0.50948 dice_loss 0.65247 +Epoch [3829/4000] Validation [8/10] Loss: 2.41436 focal_loss 1.79218 dice_loss 0.62218 +Epoch [3829/4000] Validation [9/10] Loss: 1.53567 focal_loss 0.99240 dice_loss 0.54327 +Epoch [3829/4000] Validation [10/10] Loss: 1.86219 focal_loss 1.12934 dice_loss 0.73285 +Epoch [3829/4000] Validation metric {'Val/mean dice_metric': 0.9511435031890869, 'Val/mean miou_metric': 0.9351335167884827, 'Val/mean f1': 0.9483262300491333, 'Val/mean precision': 0.9443657994270325, 'Val/mean recall': 0.9523201584815979, 'Val/mean hd95_metric': 10.763282775878906} +Cheakpoint... +Epoch [3829/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511435031890869, 'Val/mean miou_metric': 0.9351335167884827, 'Val/mean f1': 0.9483262300491333, 'Val/mean precision': 0.9443657994270325, 'Val/mean recall': 0.9523201584815979, 'Val/mean hd95_metric': 10.763282775878906} +Epoch [3830/4000] Training [1/39] Loss: 0.12861 +Epoch [3830/4000] Training [2/39] Loss: 0.00637 +Epoch [3830/4000] Training [3/39] Loss: 0.00834 +Epoch [3830/4000] Training [4/39] Loss: 0.00463 +Epoch [3830/4000] Training [5/39] Loss: 0.00527 +Epoch [3830/4000] Training [6/39] Loss: 0.12978 +Epoch [3830/4000] Training [7/39] Loss: 0.00430 +Epoch [3830/4000] Training [8/39] Loss: 0.12815 +Epoch [3830/4000] Training [9/39] Loss: 0.00328 +Epoch [3830/4000] Training [10/39] Loss: 0.00503 +Epoch [3830/4000] Training [11/39] Loss: 0.12936 +Epoch [3830/4000] Training [12/39] Loss: 0.12866 +Epoch [3830/4000] Training [13/39] Loss: 0.12888 +Epoch [3830/4000] Training [14/39] Loss: 0.00490 +Epoch [3830/4000] Training [15/39] Loss: 0.00578 +Epoch [3830/4000] Training [16/39] Loss: 0.12805 +Epoch [3830/4000] Training [17/39] Loss: 0.00461 +Epoch [3830/4000] Training [18/39] Loss: 0.00464 +Epoch [3830/4000] Training [19/39] Loss: 0.00347 +Epoch [3830/4000] Training [20/39] Loss: 0.13094 +Epoch [3830/4000] Training [21/39] Loss: 0.00428 +Epoch [3830/4000] Training [22/39] Loss: 0.00411 +Epoch [3830/4000] Training [23/39] Loss: 0.12954 +Epoch [3830/4000] Training [24/39] Loss: 0.00717 +Epoch [3830/4000] Training [25/39] Loss: 0.00607 +Epoch [3830/4000] Training [26/39] Loss: 0.00334 +Epoch [3830/4000] Training [27/39] Loss: 0.12806 +Epoch [3830/4000] Training [28/39] Loss: 0.00295 +Epoch [3830/4000] Training [29/39] Loss: 0.00455 +Epoch [3830/4000] Training [30/39] Loss: 0.00489 +Epoch [3830/4000] Training [31/39] Loss: 0.00476 +Epoch [3830/4000] Training [32/39] Loss: 0.00503 +Epoch [3830/4000] Training [33/39] Loss: 0.00426 +Epoch [3830/4000] Training [34/39] Loss: 0.00407 +Epoch [3830/4000] Training [35/39] Loss: 0.00440 +Epoch [3830/4000] Training [36/39] Loss: 0.00600 +Epoch [3830/4000] Training [37/39] Loss: 0.00363 +Epoch [3830/4000] Training [38/39] Loss: 0.00337 +Epoch [3830/4000] Training [39/39] Loss: 0.00615 +Epoch [3830/4000] Training metric {'Train/mean dice_metric': 0.9954591393470764, 'Train/mean miou_metric': 0.992194414138794, 'Train/mean f1': 0.9968284368515015, 'Train/mean precision': 0.9963634014129639, 'Train/mean recall': 0.9972939491271973, 'Train/mean hd95_metric': 0.9463952779769897} +Epoch [3830/4000] Validation [1/10] Loss: 0.71924 focal_loss 0.63298 dice_loss 0.08627 +Epoch [3830/4000] Validation [2/10] Loss: 0.50086 focal_loss 0.40343 dice_loss 0.09743 +Epoch [3830/4000] Validation [3/10] Loss: 0.39625 focal_loss 0.28509 dice_loss 0.11116 +Epoch [3830/4000] Validation [4/10] Loss: 0.89762 focal_loss 0.33257 dice_loss 0.56505 +Epoch [3830/4000] Validation [5/10] Loss: 3.07971 focal_loss 2.40560 dice_loss 0.67410 +Epoch [3830/4000] Validation [6/10] Loss: 1.33125 focal_loss 0.61753 dice_loss 0.71372 +Epoch [3830/4000] Validation [7/10] Loss: 1.17395 focal_loss 0.52011 dice_loss 0.65384 +Epoch [3830/4000] Validation [8/10] Loss: 2.40364 focal_loss 1.78594 dice_loss 0.61770 +Epoch [3830/4000] Validation [9/10] Loss: 1.56051 focal_loss 1.01721 dice_loss 0.54330 +Epoch [3830/4000] Validation [10/10] Loss: 1.89320 focal_loss 1.15927 dice_loss 0.73393 +Epoch [3830/4000] Validation metric {'Val/mean dice_metric': 0.9505612850189209, 'Val/mean miou_metric': 0.9346153736114502, 'Val/mean f1': 0.9482636451721191, 'Val/mean precision': 0.9435533881187439, 'Val/mean recall': 0.9530211091041565, 'Val/mean hd95_metric': 10.844836235046387} +Cheakpoint... +Epoch [3830/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505612850189209, 'Val/mean miou_metric': 0.9346153736114502, 'Val/mean f1': 0.9482636451721191, 'Val/mean precision': 0.9435533881187439, 'Val/mean recall': 0.9530211091041565, 'Val/mean hd95_metric': 10.844836235046387} +Epoch [3831/4000] Training [1/39] Loss: 0.00450 +Epoch [3831/4000] Training [2/39] Loss: 0.12785 +Epoch [3831/4000] Training [3/39] Loss: 0.13008 +Epoch [3831/4000] Training [4/39] Loss: 0.00644 +Epoch [3831/4000] Training [5/39] Loss: 0.12955 +Epoch [3831/4000] Training [6/39] Loss: 0.00674 +Epoch [3831/4000] Training [7/39] Loss: 0.12773 +Epoch [3831/4000] Training [8/39] Loss: 0.12898 +Epoch [3831/4000] Training [9/39] Loss: 0.00271 +Epoch [3831/4000] Training [10/39] Loss: 0.13123 +Epoch [3831/4000] Training [11/39] Loss: 0.00293 +Epoch [3831/4000] Training [12/39] Loss: 0.00452 +Epoch [3831/4000] Training [13/39] Loss: 0.00739 +Epoch [3831/4000] Training [14/39] Loss: 0.00325 +Epoch [3831/4000] Training [15/39] Loss: 0.12915 +Epoch [3831/4000] Training [16/39] Loss: 0.00668 +Epoch [3831/4000] Training [17/39] Loss: 0.00784 +Epoch [3831/4000] Training [18/39] Loss: 0.00611 +Epoch [3831/4000] Training [19/39] Loss: 0.00474 +Epoch [3831/4000] Training [20/39] Loss: 0.12985 +Epoch [3831/4000] Training [21/39] Loss: 0.13113 +Epoch [3831/4000] Training [22/39] Loss: 0.00450 +Epoch [3831/4000] Training [23/39] Loss: 0.00464 +Epoch [3831/4000] Training [24/39] Loss: 0.12842 +Epoch [3831/4000] Training [25/39] Loss: 0.12827 +Epoch [3831/4000] Training [26/39] Loss: 0.00441 +Epoch [3831/4000] Training [27/39] Loss: 0.12839 +Epoch [3831/4000] Training [28/39] Loss: 0.12907 +Epoch [3831/4000] Training [29/39] Loss: 0.13092 +Epoch [3831/4000] Training [30/39] Loss: 0.00679 +Epoch [3831/4000] Training [31/39] Loss: 0.00360 +Epoch [3831/4000] Training [32/39] Loss: 0.12833 +Epoch [3831/4000] Training [33/39] Loss: 0.13049 +Epoch [3831/4000] Training [34/39] Loss: 0.00587 +Epoch [3831/4000] Training [35/39] Loss: 0.00307 +Epoch [3831/4000] Training [36/39] Loss: 0.00408 +Epoch [3831/4000] Training [37/39] Loss: 0.00305 +Epoch [3831/4000] Training [38/39] Loss: 0.00547 +Epoch [3831/4000] Training [39/39] Loss: 0.12737 +Epoch [3831/4000] Training metric {'Train/mean dice_metric': 0.9962928891181946, 'Train/mean miou_metric': 0.9930434226989746, 'Train/mean f1': 0.9967050552368164, 'Train/mean precision': 0.9962283372879028, 'Train/mean recall': 0.9971821904182434, 'Train/mean hd95_metric': 0.9740734696388245} +Epoch [3831/4000] Validation [1/10] Loss: 0.70386 focal_loss 0.61895 dice_loss 0.08492 +Epoch [3831/4000] Validation [2/10] Loss: 0.50801 focal_loss 0.40816 dice_loss 0.09985 +Epoch [3831/4000] Validation [3/10] Loss: 0.40206 focal_loss 0.29017 dice_loss 0.11190 +Epoch [3831/4000] Validation [4/10] Loss: 0.89557 focal_loss 0.33103 dice_loss 0.56454 +Epoch [3831/4000] Validation [5/10] Loss: 3.06572 focal_loss 2.39163 dice_loss 0.67409 +Epoch [3831/4000] Validation [6/10] Loss: 1.32943 focal_loss 0.61615 dice_loss 0.71328 +Epoch [3831/4000] Validation [7/10] Loss: 1.17313 focal_loss 0.52142 dice_loss 0.65170 +Epoch [3831/4000] Validation [8/10] Loss: 2.41493 focal_loss 1.79375 dice_loss 0.62119 +Epoch [3831/4000] Validation [9/10] Loss: 1.53880 focal_loss 0.99520 dice_loss 0.54360 +Epoch [3831/4000] Validation [10/10] Loss: 1.87994 focal_loss 1.14657 dice_loss 0.73337 +Epoch [3831/4000] Validation metric {'Val/mean dice_metric': 0.9513183832168579, 'Val/mean miou_metric': 0.9354179501533508, 'Val/mean f1': 0.9481221437454224, 'Val/mean precision': 0.9439781904220581, 'Val/mean recall': 0.9523025751113892, 'Val/mean hd95_metric': 10.846041679382324} +Cheakpoint... +Epoch [3831/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513183832168579, 'Val/mean miou_metric': 0.9354179501533508, 'Val/mean f1': 0.9481221437454224, 'Val/mean precision': 0.9439781904220581, 'Val/mean recall': 0.9523025751113892, 'Val/mean hd95_metric': 10.846041679382324} +Epoch [3832/4000] Training [1/39] Loss: 0.00548 +Epoch [3832/4000] Training [2/39] Loss: 0.00358 +Epoch [3832/4000] Training [3/39] Loss: 0.00563 +Epoch [3832/4000] Training [4/39] Loss: 0.12926 +Epoch [3832/4000] Training [5/39] Loss: 0.25317 +Epoch [3832/4000] Training [6/39] Loss: 0.12770 +Epoch [3832/4000] Training [7/39] Loss: 0.12819 +Epoch [3832/4000] Training [8/39] Loss: 0.00491 +Epoch [3832/4000] Training [9/39] Loss: 0.00556 +Epoch [3832/4000] Training [10/39] Loss: 0.00562 +Epoch [3832/4000] Training [11/39] Loss: 0.00382 +Epoch [3832/4000] Training [12/39] Loss: 0.00853 +Epoch [3832/4000] Training [13/39] Loss: 0.00496 +Epoch [3832/4000] Training [14/39] Loss: 0.00449 +Epoch [3832/4000] Training [15/39] Loss: 0.25282 +Epoch [3832/4000] Training [16/39] Loss: 0.12893 +Epoch [3832/4000] Training [17/39] Loss: 0.12866 +Epoch [3832/4000] Training [18/39] Loss: 0.00492 +Epoch [3832/4000] Training [19/39] Loss: 0.00426 +Epoch [3832/4000] Training [20/39] Loss: 0.00351 +Epoch [3832/4000] Training [21/39] Loss: 0.00622 +Epoch [3832/4000] Training [22/39] Loss: 0.00625 +Epoch [3832/4000] Training [23/39] Loss: 0.00508 +Epoch [3832/4000] Training [24/39] Loss: 0.00383 +Epoch [3832/4000] Training [25/39] Loss: 0.00515 +Epoch [3832/4000] Training [26/39] Loss: 0.00667 +Epoch [3832/4000] Training [27/39] Loss: 0.00468 +Epoch [3832/4000] Training [28/39] Loss: 0.00865 +Epoch [3832/4000] Training [29/39] Loss: 0.12917 +Epoch [3832/4000] Training [30/39] Loss: 0.00437 +Epoch [3832/4000] Training [31/39] Loss: 0.12909 +Epoch [3832/4000] Training [32/39] Loss: 0.00344 +Epoch [3832/4000] Training [33/39] Loss: 0.00436 +Epoch [3832/4000] Training [34/39] Loss: 0.00363 +Epoch [3832/4000] Training [35/39] Loss: 0.00494 +Epoch [3832/4000] Training [36/39] Loss: 0.00369 +Epoch [3832/4000] Training [37/39] Loss: 0.13160 +Epoch [3832/4000] Training [38/39] Loss: 0.00417 +Epoch [3832/4000] Training [39/39] Loss: 0.00416 +Epoch [3832/4000] Training metric {'Train/mean dice_metric': 0.9964383244514465, 'Train/mean miou_metric': 0.993328869342804, 'Train/mean f1': 0.9969345927238464, 'Train/mean precision': 0.9964966177940369, 'Train/mean recall': 0.997373104095459, 'Train/mean hd95_metric': 1.0145845413208008} +Epoch [3832/4000] Validation [1/10] Loss: 0.71526 focal_loss 0.62929 dice_loss 0.08598 +Epoch [3832/4000] Validation [2/10] Loss: 0.50395 focal_loss 0.40581 dice_loss 0.09814 +Epoch [3832/4000] Validation [3/10] Loss: 0.39824 focal_loss 0.28687 dice_loss 0.11137 +Epoch [3832/4000] Validation [4/10] Loss: 0.89748 focal_loss 0.33237 dice_loss 0.56511 +Epoch [3832/4000] Validation [5/10] Loss: 3.09376 focal_loss 2.41968 dice_loss 0.67408 +Epoch [3832/4000] Validation [6/10] Loss: 1.33454 focal_loss 0.62080 dice_loss 0.71373 +Epoch [3832/4000] Validation [7/10] Loss: 1.17766 focal_loss 0.52413 dice_loss 0.65353 +Epoch [3832/4000] Validation [8/10] Loss: 2.37676 focal_loss 1.76126 dice_loss 0.61551 +Epoch [3832/4000] Validation [9/10] Loss: 1.55130 focal_loss 1.00783 dice_loss 0.54347 +Epoch [3832/4000] Validation [10/10] Loss: 1.89783 focal_loss 1.16341 dice_loss 0.73441 +Epoch [3832/4000] Validation metric {'Val/mean dice_metric': 0.9514197111129761, 'Val/mean miou_metric': 0.9356171488761902, 'Val/mean f1': 0.9485330581665039, 'Val/mean precision': 0.9437993764877319, 'Val/mean recall': 0.9533146023750305, 'Val/mean hd95_metric': 10.773517608642578} +Cheakpoint... +Epoch [3832/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514197111129761, 'Val/mean miou_metric': 0.9356171488761902, 'Val/mean f1': 0.9485330581665039, 'Val/mean precision': 0.9437993764877319, 'Val/mean recall': 0.9533146023750305, 'Val/mean hd95_metric': 10.773517608642578} +Epoch [3833/4000] Training [1/39] Loss: 0.12796 +Epoch [3833/4000] Training [2/39] Loss: 0.12841 +Epoch [3833/4000] Training [3/39] Loss: 0.00341 +Epoch [3833/4000] Training [4/39] Loss: 0.00706 +Epoch [3833/4000] Training [5/39] Loss: 0.00500 +Epoch [3833/4000] Training [6/39] Loss: 0.00388 +Epoch [3833/4000] Training [7/39] Loss: 0.12989 +Epoch [3833/4000] Training [8/39] Loss: 0.00399 +Epoch [3833/4000] Training [9/39] Loss: 0.00331 +Epoch [3833/4000] Training [10/39] Loss: 0.00552 +Epoch [3833/4000] Training [11/39] Loss: 0.00420 +Epoch [3833/4000] Training [12/39] Loss: 0.00407 +Epoch [3833/4000] Training [13/39] Loss: 0.00386 +Epoch [3833/4000] Training [14/39] Loss: 0.00243 +Epoch [3833/4000] Training [15/39] Loss: 0.12807 +Epoch [3833/4000] Training [16/39] Loss: 0.00465 +Epoch [3833/4000] Training [17/39] Loss: 0.00343 +Epoch [3833/4000] Training [18/39] Loss: 0.00412 +Epoch [3833/4000] Training [19/39] Loss: 0.00327 +Epoch [3833/4000] Training [20/39] Loss: 0.25329 +Epoch [3833/4000] Training [21/39] Loss: 0.25386 +Epoch [3833/4000] Training [22/39] Loss: 0.00409 +Epoch [3833/4000] Training [23/39] Loss: 0.00454 +Epoch [3833/4000] Training [24/39] Loss: 0.12927 +Epoch [3833/4000] Training [25/39] Loss: 0.00454 +Epoch [3833/4000] Training [26/39] Loss: 0.00480 +Epoch [3833/4000] Training [27/39] Loss: 0.00421 +Epoch [3833/4000] Training [28/39] Loss: 0.13147 +Epoch [3833/4000] Training [29/39] Loss: 0.13152 +Epoch [3833/4000] Training [30/39] Loss: 0.25245 +Epoch [3833/4000] Training [31/39] Loss: 0.00459 +Epoch [3833/4000] Training [32/39] Loss: 0.13264 +Epoch [3833/4000] Training [33/39] Loss: 0.00435 +Epoch [3833/4000] Training [34/39] Loss: 0.00591 +Epoch [3833/4000] Training [35/39] Loss: 0.00327 +Epoch [3833/4000] Training [36/39] Loss: 0.00289 +Epoch [3833/4000] Training [37/39] Loss: 0.00401 +Epoch [3833/4000] Training [38/39] Loss: 0.00420 +Epoch [3833/4000] Training [39/39] Loss: 0.00562 +Epoch [3833/4000] Training metric {'Train/mean dice_metric': 0.996532142162323, 'Train/mean miou_metric': 0.9935216307640076, 'Train/mean f1': 0.9970661997795105, 'Train/mean precision': 0.996604859828949, 'Train/mean recall': 0.9975280165672302, 'Train/mean hd95_metric': 0.9276638627052307} +Epoch [3833/4000] Validation [1/10] Loss: 0.71142 focal_loss 0.62561 dice_loss 0.08581 +Epoch [3833/4000] Validation [2/10] Loss: 0.49954 focal_loss 0.39992 dice_loss 0.09962 +Epoch [3833/4000] Validation [3/10] Loss: 0.40212 focal_loss 0.29031 dice_loss 0.11181 +Epoch [3833/4000] Validation [4/10] Loss: 0.89102 focal_loss 0.32641 dice_loss 0.56461 +Epoch [3833/4000] Validation [5/10] Loss: 3.07835 focal_loss 2.40426 dice_loss 0.67409 +Epoch [3833/4000] Validation [6/10] Loss: 1.31595 focal_loss 0.60284 dice_loss 0.71311 +Epoch [3833/4000] Validation [7/10] Loss: 1.16982 focal_loss 0.51565 dice_loss 0.65417 +Epoch [3833/4000] Validation [8/10] Loss: 2.37751 focal_loss 1.75901 dice_loss 0.61851 +Epoch [3833/4000] Validation [9/10] Loss: 1.54700 focal_loss 1.00396 dice_loss 0.54304 +Epoch [3833/4000] Validation [10/10] Loss: 1.86972 focal_loss 1.13563 dice_loss 0.73409 +Epoch [3833/4000] Validation metric {'Val/mean dice_metric': 0.9513811469078064, 'Val/mean miou_metric': 0.9356297850608826, 'Val/mean f1': 0.948491632938385, 'Val/mean precision': 0.9441183805465698, 'Val/mean recall': 0.9529056549072266, 'Val/mean hd95_metric': 10.852741241455078} +Cheakpoint... +Epoch [3833/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513811469078064, 'Val/mean miou_metric': 0.9356297850608826, 'Val/mean f1': 0.948491632938385, 'Val/mean precision': 0.9441183805465698, 'Val/mean recall': 0.9529056549072266, 'Val/mean hd95_metric': 10.852741241455078} +Epoch [3834/4000] Training [1/39] Loss: 0.13098 +Epoch [3834/4000] Training [2/39] Loss: 0.00645 +Epoch [3834/4000] Training [3/39] Loss: 0.00508 +Epoch [3834/4000] Training [4/39] Loss: 0.00499 +Epoch [3834/4000] Training [5/39] Loss: 0.00349 +Epoch [3834/4000] Training [6/39] Loss: 0.00428 +Epoch [3834/4000] Training [7/39] Loss: 0.00363 +Epoch [3834/4000] Training [8/39] Loss: 0.13021 +Epoch [3834/4000] Training [9/39] Loss: 0.00323 +Epoch [3834/4000] Training [10/39] Loss: 0.00363 +Epoch [3834/4000] Training [11/39] Loss: 0.00658 +Epoch [3834/4000] Training [12/39] Loss: 0.00600 +Epoch [3834/4000] Training [13/39] Loss: 0.00416 +Epoch [3834/4000] Training [14/39] Loss: 0.00583 +Epoch [3834/4000] Training [15/39] Loss: 0.00480 +Epoch [3834/4000] Training [16/39] Loss: 0.00453 +Epoch [3834/4000] Training [17/39] Loss: 0.00356 +Epoch [3834/4000] Training [18/39] Loss: 0.12939 +Epoch [3834/4000] Training [19/39] Loss: 0.12892 +Epoch [3834/4000] Training [20/39] Loss: 0.00369 +Epoch [3834/4000] Training [21/39] Loss: 0.00419 +Epoch [3834/4000] Training [22/39] Loss: 0.00468 +Epoch [3834/4000] Training [23/39] Loss: 0.00383 +Epoch [3834/4000] Training [24/39] Loss: 0.25248 +Epoch [3834/4000] Training [25/39] Loss: 0.00623 +Epoch [3834/4000] Training [26/39] Loss: 0.12906 +Epoch [3834/4000] Training [27/39] Loss: 0.00583 +Epoch [3834/4000] Training [28/39] Loss: 0.00623 +Epoch [3834/4000] Training [29/39] Loss: 0.00312 +Epoch [3834/4000] Training [30/39] Loss: 0.12862 +Epoch [3834/4000] Training [31/39] Loss: 0.12887 +Epoch [3834/4000] Training [32/39] Loss: 0.12938 +Epoch [3834/4000] Training [33/39] Loss: 0.00536 +Epoch [3834/4000] Training [34/39] Loss: 0.00300 +Epoch [3834/4000] Training [35/39] Loss: 0.13159 +Epoch [3834/4000] Training [36/39] Loss: 0.12788 +Epoch [3834/4000] Training [37/39] Loss: 0.00388 +Epoch [3834/4000] Training [38/39] Loss: 0.00337 +Epoch [3834/4000] Training [39/39] Loss: 0.00419 +Epoch [3834/4000] Training metric {'Train/mean dice_metric': 0.9964510202407837, 'Train/mean miou_metric': 0.993344247341156, 'Train/mean f1': 0.9969173073768616, 'Train/mean precision': 0.9964574575424194, 'Train/mean recall': 0.9973775744438171, 'Train/mean hd95_metric': 0.923481285572052} +Epoch [3834/4000] Validation [1/10] Loss: 0.71026 focal_loss 0.62519 dice_loss 0.08507 +Epoch [3834/4000] Validation [2/10] Loss: 0.50711 focal_loss 0.40652 dice_loss 0.10060 +Epoch [3834/4000] Validation [3/10] Loss: 0.40972 focal_loss 0.29757 dice_loss 0.11214 +Epoch [3834/4000] Validation [4/10] Loss: 0.89523 focal_loss 0.33065 dice_loss 0.56458 +Epoch [3834/4000] Validation [5/10] Loss: 3.09862 focal_loss 2.42448 dice_loss 0.67414 +Epoch [3834/4000] Validation [6/10] Loss: 1.32089 focal_loss 0.60855 dice_loss 0.71234 +Epoch [3834/4000] Validation [7/10] Loss: 1.17468 focal_loss 0.52241 dice_loss 0.65227 +Epoch [3834/4000] Validation [8/10] Loss: 2.42866 focal_loss 1.80739 dice_loss 0.62127 +Epoch [3834/4000] Validation [9/10] Loss: 1.56100 focal_loss 1.01771 dice_loss 0.54329 +Epoch [3834/4000] Validation [10/10] Loss: 1.88225 focal_loss 1.14800 dice_loss 0.73424 +Epoch [3834/4000] Validation metric {'Val/mean dice_metric': 0.9513746500015259, 'Val/mean miou_metric': 0.9355719685554504, 'Val/mean f1': 0.9482529163360596, 'Val/mean precision': 0.9440640807151794, 'Val/mean recall': 0.9524790644645691, 'Val/mean hd95_metric': 10.837621688842773} +Cheakpoint... +Epoch [3834/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513746500015259, 'Val/mean miou_metric': 0.9355719685554504, 'Val/mean f1': 0.9482529163360596, 'Val/mean precision': 0.9440640807151794, 'Val/mean recall': 0.9524790644645691, 'Val/mean hd95_metric': 10.837621688842773} +Epoch [3835/4000] Training [1/39] Loss: 0.00334 +Epoch [3835/4000] Training [2/39] Loss: 0.13029 +Epoch [3835/4000] Training [3/39] Loss: 0.00433 +Epoch [3835/4000] Training [4/39] Loss: 0.00638 +Epoch [3835/4000] Training [5/39] Loss: 0.00259 +Epoch [3835/4000] Training [6/39] Loss: 0.00503 +Epoch [3835/4000] Training [7/39] Loss: 0.00297 +Epoch [3835/4000] Training [8/39] Loss: 0.00420 +Epoch [3835/4000] Training [9/39] Loss: 0.00411 +Epoch [3835/4000] Training [10/39] Loss: 0.00438 +Epoch [3835/4000] Training [11/39] Loss: 0.00450 +Epoch [3835/4000] Training [12/39] Loss: 0.00420 +Epoch [3835/4000] Training [13/39] Loss: 0.00547 +Epoch [3835/4000] Training [14/39] Loss: 0.12995 +Epoch [3835/4000] Training [15/39] Loss: 0.00546 +Epoch [3835/4000] Training [16/39] Loss: 0.00811 +Epoch [3835/4000] Training [17/39] Loss: 0.12763 +Epoch [3835/4000] Training [18/39] Loss: 0.00359 +Epoch [3835/4000] Training [19/39] Loss: 0.00522 +Epoch [3835/4000] Training [20/39] Loss: 0.00606 +Epoch [3835/4000] Training [21/39] Loss: 0.00376 +Epoch [3835/4000] Training [22/39] Loss: 0.00308 +Epoch [3835/4000] Training [23/39] Loss: 0.00344 +Epoch [3835/4000] Training [24/39] Loss: 0.00335 +Epoch [3835/4000] Training [25/39] Loss: 0.04561 +Epoch [3835/4000] Training [26/39] Loss: 0.12668 +Epoch [3835/4000] Training [27/39] Loss: 0.12997 +Epoch [3835/4000] Training [28/39] Loss: 0.12826 +Epoch [3835/4000] Training [29/39] Loss: 0.12915 +Epoch [3835/4000] Training [30/39] Loss: 0.00393 +Epoch [3835/4000] Training [31/39] Loss: 0.00424 +Epoch [3835/4000] Training [32/39] Loss: 0.00587 +Epoch [3835/4000] Training [33/39] Loss: 0.00609 +Epoch [3835/4000] Training [34/39] Loss: 0.00731 +Epoch [3835/4000] Training [35/39] Loss: 0.00510 +Epoch [3835/4000] Training [36/39] Loss: 0.12877 +Epoch [3835/4000] Training [37/39] Loss: 0.00551 +Epoch [3835/4000] Training [38/39] Loss: 0.00370 +Epoch [3835/4000] Training [39/39] Loss: 0.00608 +Epoch [3835/4000] Training metric {'Train/mean dice_metric': 0.9966030120849609, 'Train/mean miou_metric': 0.9936434030532837, 'Train/mean f1': 0.9970317482948303, 'Train/mean precision': 0.9965789318084717, 'Train/mean recall': 0.9974851608276367, 'Train/mean hd95_metric': 0.902886152267456} +Epoch [3835/4000] Validation [1/10] Loss: 0.70638 focal_loss 0.62020 dice_loss 0.08618 +Epoch [3835/4000] Validation [2/10] Loss: 0.49736 focal_loss 0.40063 dice_loss 0.09673 +Epoch [3835/4000] Validation [3/10] Loss: 0.38716 focal_loss 0.27635 dice_loss 0.11081 +Epoch [3835/4000] Validation [4/10] Loss: 0.90024 focal_loss 0.33458 dice_loss 0.56566 +Epoch [3835/4000] Validation [5/10] Loss: 3.04594 focal_loss 2.37194 dice_loss 0.67400 +Epoch [3835/4000] Validation [6/10] Loss: 1.33882 focal_loss 0.62572 dice_loss 0.71311 +Epoch [3835/4000] Validation [7/10] Loss: 1.18354 focal_loss 0.52769 dice_loss 0.65585 +Epoch [3835/4000] Validation [8/10] Loss: 2.33724 focal_loss 1.72558 dice_loss 0.61166 +Epoch [3835/4000] Validation [9/10] Loss: 1.56738 focal_loss 1.02358 dice_loss 0.54380 +Epoch [3835/4000] Validation [10/10] Loss: 1.91621 focal_loss 1.18051 dice_loss 0.73571 +Epoch [3835/4000] Validation metric {'Val/mean dice_metric': 0.9515669345855713, 'Val/mean miou_metric': 0.9358741641044617, 'Val/mean f1': 0.9480214715003967, 'Val/mean precision': 0.9425201416015625, 'Val/mean recall': 0.9535873532295227, 'Val/mean hd95_metric': 10.766544342041016} +Cheakpoint... +Epoch [3835/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515669345855713, 'Val/mean miou_metric': 0.9358741641044617, 'Val/mean f1': 0.9480214715003967, 'Val/mean precision': 0.9425201416015625, 'Val/mean recall': 0.9535873532295227, 'Val/mean hd95_metric': 10.766544342041016} +Epoch [3836/4000] Training [1/39] Loss: 0.00480 +Epoch [3836/4000] Training [2/39] Loss: 0.13019 +Epoch [3836/4000] Training [3/39] Loss: 0.13394 +Epoch [3836/4000] Training [4/39] Loss: 0.00383 +Epoch [3836/4000] Training [5/39] Loss: 0.12912 +Epoch [3836/4000] Training [6/39] Loss: 0.13085 +Epoch [3836/4000] Training [7/39] Loss: 0.00555 +Epoch [3836/4000] Training [8/39] Loss: 0.12826 +Epoch [3836/4000] Training [9/39] Loss: 0.00694 +Epoch [3836/4000] Training [10/39] Loss: 0.00869 +Epoch [3836/4000] Training [11/39] Loss: 0.01651 +Epoch [3836/4000] Training [12/39] Loss: 0.00439 +Epoch [3836/4000] Training [13/39] Loss: 0.00350 +Epoch [3836/4000] Training [14/39] Loss: 0.00634 +Epoch [3836/4000] Training [15/39] Loss: 0.00566 +Epoch [3836/4000] Training [16/39] Loss: 0.00402 +Epoch [3836/4000] Training [17/39] Loss: 0.13003 +Epoch [3836/4000] Training [18/39] Loss: 0.00650 +Epoch [3836/4000] Training [19/39] Loss: 0.00340 +Epoch [3836/4000] Training [20/39] Loss: 0.00540 +Epoch [3836/4000] Training [21/39] Loss: 0.00435 +Epoch [3836/4000] Training [22/39] Loss: 0.00381 +Epoch [3836/4000] Training [23/39] Loss: 0.13126 +Epoch [3836/4000] Training [24/39] Loss: 0.00419 +Epoch [3836/4000] Training [25/39] Loss: 0.00525 +Epoch [3836/4000] Training [26/39] Loss: 0.12900 +Epoch [3836/4000] Training [27/39] Loss: 0.00542 +Epoch [3836/4000] Training [28/39] Loss: 0.00446 +Epoch [3836/4000] Training [29/39] Loss: 0.00541 +Epoch [3836/4000] Training [30/39] Loss: 0.00407 +Epoch [3836/4000] Training [31/39] Loss: 0.00340 +Epoch [3836/4000] Training [32/39] Loss: 0.13135 +Epoch [3836/4000] Training [33/39] Loss: 0.12918 +Epoch [3836/4000] Training [34/39] Loss: 0.00505 +Epoch [3836/4000] Training [35/39] Loss: 0.13092 +Epoch [3836/4000] Training [36/39] Loss: 0.12967 +Epoch [3836/4000] Training [37/39] Loss: 0.00306 +Epoch [3836/4000] Training [38/39] Loss: 0.00609 +Epoch [3836/4000] Training [39/39] Loss: 0.00340 +Epoch [3836/4000] Training metric {'Train/mean dice_metric': 0.9954781532287598, 'Train/mean miou_metric': 0.9918027520179749, 'Train/mean f1': 0.9964380264282227, 'Train/mean precision': 0.9956364631652832, 'Train/mean recall': 0.9972410202026367, 'Train/mean hd95_metric': 1.0187710523605347} +Epoch [3836/4000] Validation [1/10] Loss: 0.69302 focal_loss 0.60863 dice_loss 0.08439 +Epoch [3836/4000] Validation [2/10] Loss: 0.50030 focal_loss 0.39936 dice_loss 0.10094 +Epoch [3836/4000] Validation [3/10] Loss: 0.40383 focal_loss 0.29143 dice_loss 0.11240 +Epoch [3836/4000] Validation [4/10] Loss: 0.88625 focal_loss 0.32201 dice_loss 0.56423 +Epoch [3836/4000] Validation [5/10] Loss: 3.03984 focal_loss 2.36566 dice_loss 0.67418 +Epoch [3836/4000] Validation [6/10] Loss: 1.31280 focal_loss 0.60039 dice_loss 0.71242 +Epoch [3836/4000] Validation [7/10] Loss: 1.16167 focal_loss 0.50832 dice_loss 0.65335 +Epoch [3836/4000] Validation [8/10] Loss: 2.42623 focal_loss 1.80128 dice_loss 0.62495 +Epoch [3836/4000] Validation [9/10] Loss: 1.52759 focal_loss 0.98431 dice_loss 0.54329 +Epoch [3836/4000] Validation [10/10] Loss: 1.85114 focal_loss 1.11794 dice_loss 0.73320 +Epoch [3836/4000] Validation metric {'Val/mean dice_metric': 0.9506165981292725, 'Val/mean miou_metric': 0.9343403577804565, 'Val/mean f1': 0.9480022192001343, 'Val/mean precision': 0.9441259503364563, 'Val/mean recall': 0.9519103765487671, 'Val/mean hd95_metric': 10.891054153442383} +Cheakpoint... +Epoch [3836/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506165981292725, 'Val/mean miou_metric': 0.9343403577804565, 'Val/mean f1': 0.9480022192001343, 'Val/mean precision': 0.9441259503364563, 'Val/mean recall': 0.9519103765487671, 'Val/mean hd95_metric': 10.891054153442383} +Epoch [3837/4000] Training [1/39] Loss: 0.00582 +Epoch [3837/4000] Training [2/39] Loss: 0.00546 +Epoch [3837/4000] Training [3/39] Loss: 0.00596 +Epoch [3837/4000] Training [4/39] Loss: 0.00387 +Epoch [3837/4000] Training [5/39] Loss: 0.00428 +Epoch [3837/4000] Training [6/39] Loss: 0.00458 +Epoch [3837/4000] Training [7/39] Loss: 0.00538 +Epoch [3837/4000] Training [8/39] Loss: 0.13026 +Epoch [3837/4000] Training [9/39] Loss: 0.12844 +Epoch [3837/4000] Training [10/39] Loss: 0.00641 +Epoch [3837/4000] Training [11/39] Loss: 0.00495 +Epoch [3837/4000] Training [12/39] Loss: 0.00386 +Epoch [3837/4000] Training [13/39] Loss: 0.00408 +Epoch [3837/4000] Training [14/39] Loss: 0.03473 +Epoch [3837/4000] Training [15/39] Loss: 0.00690 +Epoch [3837/4000] Training [16/39] Loss: 0.12822 +Epoch [3837/4000] Training [17/39] Loss: 0.00484 +Epoch [3837/4000] Training [18/39] Loss: 0.00636 +Epoch [3837/4000] Training [19/39] Loss: 0.00381 +Epoch [3837/4000] Training [20/39] Loss: 0.12925 +Epoch [3837/4000] Training [21/39] Loss: 0.00496 +Epoch [3837/4000] Training [22/39] Loss: 0.00685 +Epoch [3837/4000] Training [23/39] Loss: 0.00298 +Epoch [3837/4000] Training [24/39] Loss: 0.00496 +Epoch [3837/4000] Training [25/39] Loss: 0.00440 +Epoch [3837/4000] Training [26/39] Loss: 0.00606 +Epoch [3837/4000] Training [27/39] Loss: 0.00514 +Epoch [3837/4000] Training [28/39] Loss: 0.00504 +Epoch [3837/4000] Training [29/39] Loss: 0.00264 +Epoch [3837/4000] Training [30/39] Loss: 0.00430 +Epoch [3837/4000] Training [31/39] Loss: 0.00372 +Epoch [3837/4000] Training [32/39] Loss: 0.00537 +Epoch [3837/4000] Training [33/39] Loss: 0.00335 +Epoch [3837/4000] Training [34/39] Loss: 0.00548 +Epoch [3837/4000] Training [35/39] Loss: 0.00358 +Epoch [3837/4000] Training [36/39] Loss: 0.12863 +Epoch [3837/4000] Training [37/39] Loss: 0.00422 +Epoch [3837/4000] Training [38/39] Loss: 0.00769 +Epoch [3837/4000] Training [39/39] Loss: 0.00353 +Epoch [3837/4000] Training metric {'Train/mean dice_metric': 0.9954701066017151, 'Train/mean miou_metric': 0.9922007918357849, 'Train/mean f1': 0.9968705773353577, 'Train/mean precision': 0.9964638948440552, 'Train/mean recall': 0.9972774386405945, 'Train/mean hd95_metric': 0.9190633296966553} +Epoch [3837/4000] Validation [1/10] Loss: 0.70702 focal_loss 0.62185 dice_loss 0.08517 +Epoch [3837/4000] Validation [2/10] Loss: 0.50054 focal_loss 0.40072 dice_loss 0.09983 +Epoch [3837/4000] Validation [3/10] Loss: 0.40323 focal_loss 0.29121 dice_loss 0.11202 +Epoch [3837/4000] Validation [4/10] Loss: 0.89135 focal_loss 0.32666 dice_loss 0.56469 +Epoch [3837/4000] Validation [5/10] Loss: 3.08802 focal_loss 2.41391 dice_loss 0.67410 +Epoch [3837/4000] Validation [6/10] Loss: 1.32080 focal_loss 0.60824 dice_loss 0.71256 +Epoch [3837/4000] Validation [7/10] Loss: 1.17016 focal_loss 0.51617 dice_loss 0.65399 +Epoch [3837/4000] Validation [8/10] Loss: 2.39721 focal_loss 1.77749 dice_loss 0.61972 +Epoch [3837/4000] Validation [9/10] Loss: 1.54785 focal_loss 1.00428 dice_loss 0.54357 +Epoch [3837/4000] Validation [10/10] Loss: 1.87232 focal_loss 1.13798 dice_loss 0.73434 +Epoch [3837/4000] Validation metric {'Val/mean dice_metric': 0.9505971074104309, 'Val/mean miou_metric': 0.9346446394920349, 'Val/mean f1': 0.9484721422195435, 'Val/mean precision': 0.944295346736908, 'Val/mean recall': 0.9526860117912292, 'Val/mean hd95_metric': 10.865689277648926} +Cheakpoint... +Epoch [3837/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505971074104309, 'Val/mean miou_metric': 0.9346446394920349, 'Val/mean f1': 0.9484721422195435, 'Val/mean precision': 0.944295346736908, 'Val/mean recall': 0.9526860117912292, 'Val/mean hd95_metric': 10.865689277648926} +Epoch [3838/4000] Training [1/39] Loss: 0.00388 +Epoch [3838/4000] Training [2/39] Loss: 0.00368 +Epoch [3838/4000] Training [3/39] Loss: 0.12742 +Epoch [3838/4000] Training [4/39] Loss: 0.00490 +Epoch [3838/4000] Training [5/39] Loss: 0.00477 +Epoch [3838/4000] Training [6/39] Loss: 0.00416 +Epoch [3838/4000] Training [7/39] Loss: 0.12810 +Epoch [3838/4000] Training [8/39] Loss: 0.00280 +Epoch [3838/4000] Training [9/39] Loss: 0.00557 +Epoch [3838/4000] Training [10/39] Loss: 0.00468 +Epoch [3838/4000] Training [11/39] Loss: 0.00524 +Epoch [3838/4000] Training [12/39] Loss: 0.00457 +Epoch [3838/4000] Training [13/39] Loss: 0.00421 +Epoch [3838/4000] Training [14/39] Loss: 0.00561 +Epoch [3838/4000] Training [15/39] Loss: 0.00578 +Epoch [3838/4000] Training [16/39] Loss: 0.00407 +Epoch [3838/4000] Training [17/39] Loss: 0.00444 +Epoch [3838/4000] Training [18/39] Loss: 0.00335 +Epoch [3838/4000] Training [19/39] Loss: 0.00635 +Epoch [3838/4000] Training [20/39] Loss: 0.00480 +Epoch [3838/4000] Training [21/39] Loss: 0.12913 +Epoch [3838/4000] Training [22/39] Loss: 0.00356 +Epoch [3838/4000] Training [23/39] Loss: 0.12928 +Epoch [3838/4000] Training [24/39] Loss: 0.00293 +Epoch [3838/4000] Training [25/39] Loss: 0.00620 +Epoch [3838/4000] Training [26/39] Loss: 0.00659 +Epoch [3838/4000] Training [27/39] Loss: 0.00494 +Epoch [3838/4000] Training [28/39] Loss: 0.12981 +Epoch [3838/4000] Training [29/39] Loss: 0.00606 +Epoch [3838/4000] Training [30/39] Loss: 0.00862 +Epoch [3838/4000] Training [31/39] Loss: 0.00633 +Epoch [3838/4000] Training [32/39] Loss: 0.00341 +Epoch [3838/4000] Training [33/39] Loss: 0.00433 +Epoch [3838/4000] Training [34/39] Loss: 0.00445 +Epoch [3838/4000] Training [35/39] Loss: 0.00684 +Epoch [3838/4000] Training [36/39] Loss: 0.00485 +Epoch [3838/4000] Training [37/39] Loss: 0.00273 +Epoch [3838/4000] Training [38/39] Loss: 0.00458 +Epoch [3838/4000] Training [39/39] Loss: 0.13075 +Epoch [3838/4000] Training metric {'Train/mean dice_metric': 0.9963629245758057, 'Train/mean miou_metric': 0.9931836128234863, 'Train/mean f1': 0.9968905448913574, 'Train/mean precision': 0.9964015483856201, 'Train/mean recall': 0.9973799586296082, 'Train/mean hd95_metric': 0.9314109086990356} +Epoch [3838/4000] Validation [1/10] Loss: 0.70677 focal_loss 0.62124 dice_loss 0.08553 +Epoch [3838/4000] Validation [2/10] Loss: 0.49725 focal_loss 0.39947 dice_loss 0.09778 +Epoch [3838/4000] Validation [3/10] Loss: 0.39413 focal_loss 0.28272 dice_loss 0.11141 +Epoch [3838/4000] Validation [4/10] Loss: 0.89760 focal_loss 0.33217 dice_loss 0.56543 +Epoch [3838/4000] Validation [5/10] Loss: 3.07508 focal_loss 2.40103 dice_loss 0.67406 +Epoch [3838/4000] Validation [6/10] Loss: 1.33420 focal_loss 0.62207 dice_loss 0.71213 +Epoch [3838/4000] Validation [7/10] Loss: 1.17683 focal_loss 0.52101 dice_loss 0.65582 +Epoch [3838/4000] Validation [8/10] Loss: 2.38078 focal_loss 1.76534 dice_loss 0.61545 +Epoch [3838/4000] Validation [9/10] Loss: 1.56206 focal_loss 1.01813 dice_loss 0.54393 +Epoch [3838/4000] Validation [10/10] Loss: 1.90034 focal_loss 1.16517 dice_loss 0.73517 +Epoch [3838/4000] Validation metric {'Val/mean dice_metric': 0.9514119625091553, 'Val/mean miou_metric': 0.9355400800704956, 'Val/mean f1': 0.9484325051307678, 'Val/mean precision': 0.9435409307479858, 'Val/mean recall': 0.9533752202987671, 'Val/mean hd95_metric': 10.78570556640625} +Cheakpoint... +Epoch [3838/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514119625091553, 'Val/mean miou_metric': 0.9355400800704956, 'Val/mean f1': 0.9484325051307678, 'Val/mean precision': 0.9435409307479858, 'Val/mean recall': 0.9533752202987671, 'Val/mean hd95_metric': 10.78570556640625} +Epoch [3839/4000] Training [1/39] Loss: 0.00642 +Epoch [3839/4000] Training [2/39] Loss: 0.00461 +Epoch [3839/4000] Training [3/39] Loss: 0.00540 +Epoch [3839/4000] Training [4/39] Loss: 0.00491 +Epoch [3839/4000] Training [5/39] Loss: 0.00505 +Epoch [3839/4000] Training [6/39] Loss: 0.00428 +Epoch [3839/4000] Training [7/39] Loss: 0.00623 +Epoch [3839/4000] Training [8/39] Loss: 0.00575 +Epoch [3839/4000] Training [9/39] Loss: 0.00690 +Epoch [3839/4000] Training [10/39] Loss: 0.00701 +Epoch [3839/4000] Training [11/39] Loss: 0.00384 +Epoch [3839/4000] Training [12/39] Loss: 0.00362 +Epoch [3839/4000] Training [13/39] Loss: 0.00340 +Epoch [3839/4000] Training [14/39] Loss: 0.00431 +Epoch [3839/4000] Training [15/39] Loss: 0.00548 +Epoch [3839/4000] Training [16/39] Loss: 0.00339 +Epoch [3839/4000] Training [17/39] Loss: 0.00318 +Epoch [3839/4000] Training [18/39] Loss: 0.00360 +Epoch [3839/4000] Training [19/39] Loss: 0.00305 +Epoch [3839/4000] Training [20/39] Loss: 0.00191 +Epoch [3839/4000] Training [21/39] Loss: 0.13057 +Epoch [3839/4000] Training [22/39] Loss: 0.00412 +Epoch [3839/4000] Training [23/39] Loss: 0.00493 +Epoch [3839/4000] Training [24/39] Loss: 0.00362 +Epoch [3839/4000] Training [25/39] Loss: 0.12920 +Epoch [3839/4000] Training [26/39] Loss: 0.00486 +Epoch [3839/4000] Training [27/39] Loss: 0.00382 +Epoch [3839/4000] Training [28/39] Loss: 0.12825 +Epoch [3839/4000] Training [29/39] Loss: 0.00426 +Epoch [3839/4000] Training [30/39] Loss: 0.12859 +Epoch [3839/4000] Training [31/39] Loss: 0.12780 +Epoch [3839/4000] Training [32/39] Loss: 0.12802 +Epoch [3839/4000] Training [33/39] Loss: 0.12922 +Epoch [3839/4000] Training [34/39] Loss: 0.21009 +Epoch [3839/4000] Training [35/39] Loss: 0.00478 +Epoch [3839/4000] Training [36/39] Loss: 0.00570 +Epoch [3839/4000] Training [37/39] Loss: 0.00442 +Epoch [3839/4000] Training [38/39] Loss: 0.13239 +Epoch [3839/4000] Training [39/39] Loss: 0.00371 +Epoch [3839/4000] Training metric {'Train/mean dice_metric': 0.9965019226074219, 'Train/mean miou_metric': 0.993453323841095, 'Train/mean f1': 0.9970390200614929, 'Train/mean precision': 0.9966019988059998, 'Train/mean recall': 0.9974763989448547, 'Train/mean hd95_metric': 0.9754016995429993} +Epoch [3839/4000] Validation [1/10] Loss: 0.70418 focal_loss 0.61901 dice_loss 0.08518 +Epoch [3839/4000] Validation [2/10] Loss: 0.50447 focal_loss 0.40454 dice_loss 0.09993 +Epoch [3839/4000] Validation [3/10] Loss: 0.40171 focal_loss 0.28990 dice_loss 0.11180 +Epoch [3839/4000] Validation [4/10] Loss: 0.89699 focal_loss 0.33203 dice_loss 0.56496 +Epoch [3839/4000] Validation [5/10] Loss: 3.07678 focal_loss 2.40267 dice_loss 0.67411 +Epoch [3839/4000] Validation [6/10] Loss: 1.33316 focal_loss 0.62060 dice_loss 0.71256 +Epoch [3839/4000] Validation [7/10] Loss: 1.17877 focal_loss 0.52450 dice_loss 0.65427 +Epoch [3839/4000] Validation [8/10] Loss: 2.38605 focal_loss 1.76900 dice_loss 0.61705 +Epoch [3839/4000] Validation [9/10] Loss: 1.56822 focal_loss 1.02419 dice_loss 0.54403 +Epoch [3839/4000] Validation [10/10] Loss: 1.89404 focal_loss 1.15947 dice_loss 0.73457 +Epoch [3839/4000] Validation metric {'Val/mean dice_metric': 0.9515219926834106, 'Val/mean miou_metric': 0.9357677698135376, 'Val/mean f1': 0.9481616616249084, 'Val/mean precision': 0.9436039924621582, 'Val/mean recall': 0.9527636170387268, 'Val/mean hd95_metric': 10.836872100830078} +Cheakpoint... +Epoch [3839/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515219926834106, 'Val/mean miou_metric': 0.9357677698135376, 'Val/mean f1': 0.9481616616249084, 'Val/mean precision': 0.9436039924621582, 'Val/mean recall': 0.9527636170387268, 'Val/mean hd95_metric': 10.836872100830078} +Epoch [3840/4000] Training [1/39] Loss: 0.12843 +Epoch [3840/4000] Training [2/39] Loss: 0.00357 +Epoch [3840/4000] Training [3/39] Loss: 0.00297 +Epoch [3840/4000] Training [4/39] Loss: 0.00468 +Epoch [3840/4000] Training [5/39] Loss: 0.00342 +Epoch [3840/4000] Training [6/39] Loss: 0.00516 +Epoch [3840/4000] Training [7/39] Loss: 0.13207 +Epoch [3840/4000] Training [8/39] Loss: 0.03634 +Epoch [3840/4000] Training [9/39] Loss: 0.00604 +Epoch [3840/4000] Training [10/39] Loss: 0.00360 +Epoch [3840/4000] Training [11/39] Loss: 0.12941 +Epoch [3840/4000] Training [12/39] Loss: 0.00683 +Epoch [3840/4000] Training [13/39] Loss: 0.00526 +Epoch [3840/4000] Training [14/39] Loss: 0.00467 +Epoch [3840/4000] Training [15/39] Loss: 0.12895 +Epoch [3840/4000] Training [16/39] Loss: 0.00380 +Epoch [3840/4000] Training [17/39] Loss: 0.12977 +Epoch [3840/4000] Training [18/39] Loss: 0.00385 +Epoch [3840/4000] Training [19/39] Loss: 0.12716 +Epoch [3840/4000] Training [20/39] Loss: 0.00772 +Epoch [3840/4000] Training [21/39] Loss: 0.00521 +Epoch [3840/4000] Training [22/39] Loss: 0.00386 +Epoch [3840/4000] Training [23/39] Loss: 0.00539 +Epoch [3840/4000] Training [24/39] Loss: 0.00530 +Epoch [3840/4000] Training [25/39] Loss: 0.00861 +Epoch [3840/4000] Training [26/39] Loss: 0.00529 +Epoch [3840/4000] Training [27/39] Loss: 0.12772 +Epoch [3840/4000] Training [28/39] Loss: 0.12937 +Epoch [3840/4000] Training [29/39] Loss: 0.00468 +Epoch [3840/4000] Training [30/39] Loss: 0.25210 +Epoch [3840/4000] Training [31/39] Loss: 0.00314 +Epoch [3840/4000] Training [32/39] Loss: 0.00487 +Epoch [3840/4000] Training [33/39] Loss: 0.00471 +Epoch [3840/4000] Training [34/39] Loss: 0.00687 +Epoch [3840/4000] Training [35/39] Loss: 0.12959 +Epoch [3840/4000] Training [36/39] Loss: 0.00511 +Epoch [3840/4000] Training [37/39] Loss: 0.12898 +Epoch [3840/4000] Training [38/39] Loss: 0.12877 +Epoch [3840/4000] Training [39/39] Loss: 0.00550 +Epoch [3840/4000] Training metric {'Train/mean dice_metric': 0.9954455494880676, 'Train/mean miou_metric': 0.9921791553497314, 'Train/mean f1': 0.9967560768127441, 'Train/mean precision': 0.996258020401001, 'Train/mean recall': 0.9972546696662903, 'Train/mean hd95_metric': 1.0569565296173096} +Epoch [3840/4000] Validation [1/10] Loss: 0.71522 focal_loss 0.62949 dice_loss 0.08573 +Epoch [3840/4000] Validation [2/10] Loss: 0.50007 focal_loss 0.40111 dice_loss 0.09896 +Epoch [3840/4000] Validation [3/10] Loss: 0.40186 focal_loss 0.29034 dice_loss 0.11152 +Epoch [3840/4000] Validation [4/10] Loss: 0.89548 focal_loss 0.33063 dice_loss 0.56485 +Epoch [3840/4000] Validation [5/10] Loss: 3.08707 focal_loss 2.41299 dice_loss 0.67408 +Epoch [3840/4000] Validation [6/10] Loss: 1.32702 focal_loss 0.61373 dice_loss 0.71329 +Epoch [3840/4000] Validation [7/10] Loss: 1.17554 focal_loss 0.52078 dice_loss 0.65475 +Epoch [3840/4000] Validation [8/10] Loss: 2.37389 focal_loss 1.75812 dice_loss 0.61576 +Epoch [3840/4000] Validation [9/10] Loss: 1.55315 focal_loss 1.00966 dice_loss 0.54349 +Epoch [3840/4000] Validation [10/10] Loss: 1.88222 focal_loss 1.14796 dice_loss 0.73426 +Epoch [3840/4000] Validation metric {'Val/mean dice_metric': 0.9506086707115173, 'Val/mean miou_metric': 0.9346591234207153, 'Val/mean f1': 0.9484624862670898, 'Val/mean precision': 0.9439420700073242, 'Val/mean recall': 0.9530262351036072, 'Val/mean hd95_metric': 10.9140043258667} +Cheakpoint... +Epoch [3840/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506086707115173, 'Val/mean miou_metric': 0.9346591234207153, 'Val/mean f1': 0.9484624862670898, 'Val/mean precision': 0.9439420700073242, 'Val/mean recall': 0.9530262351036072, 'Val/mean hd95_metric': 10.9140043258667} +Epoch [3841/4000] Training [1/39] Loss: 0.00316 +Epoch [3841/4000] Training [2/39] Loss: 0.00411 +Epoch [3841/4000] Training [3/39] Loss: 0.00606 +Epoch [3841/4000] Training [4/39] Loss: 0.13004 +Epoch [3841/4000] Training [5/39] Loss: 0.13029 +Epoch [3841/4000] Training [6/39] Loss: 0.00756 +Epoch [3841/4000] Training [7/39] Loss: 0.00539 +Epoch [3841/4000] Training [8/39] Loss: 0.00557 +Epoch [3841/4000] Training [9/39] Loss: 0.00382 +Epoch [3841/4000] Training [10/39] Loss: 0.00584 +Epoch [3841/4000] Training [11/39] Loss: 0.00501 +Epoch [3841/4000] Training [12/39] Loss: 0.00405 +Epoch [3841/4000] Training [13/39] Loss: 0.00354 +Epoch [3841/4000] Training [14/39] Loss: 0.00633 +Epoch [3841/4000] Training [15/39] Loss: 0.00689 +Epoch [3841/4000] Training [16/39] Loss: 0.00488 +Epoch [3841/4000] Training [17/39] Loss: 0.00699 +Epoch [3841/4000] Training [18/39] Loss: 0.12941 +Epoch [3841/4000] Training [19/39] Loss: 0.16810 +Epoch [3841/4000] Training [20/39] Loss: 0.12900 +Epoch [3841/4000] Training [21/39] Loss: 0.00517 +Epoch [3841/4000] Training [22/39] Loss: 0.00459 +Epoch [3841/4000] Training [23/39] Loss: 0.12885 +Epoch [3841/4000] Training [24/39] Loss: 0.00410 +Epoch [3841/4000] Training [25/39] Loss: 0.12882 +Epoch [3841/4000] Training [26/39] Loss: 0.00526 +Epoch [3841/4000] Training [27/39] Loss: 0.00279 +Epoch [3841/4000] Training [28/39] Loss: 0.00502 +Epoch [3841/4000] Training [29/39] Loss: 0.12813 +Epoch [3841/4000] Training [30/39] Loss: 0.00880 +Epoch [3841/4000] Training [31/39] Loss: 0.00653 +Epoch [3841/4000] Training [32/39] Loss: 0.00508 +Epoch [3841/4000] Training [33/39] Loss: 0.12916 +Epoch [3841/4000] Training [34/39] Loss: 0.00596 +Epoch [3841/4000] Training [35/39] Loss: 0.00291 +Epoch [3841/4000] Training [36/39] Loss: 0.00600 +Epoch [3841/4000] Training [37/39] Loss: 0.25324 +Epoch [3841/4000] Training [38/39] Loss: 0.00898 +Epoch [3841/4000] Training [39/39] Loss: 0.00598 +Epoch [3841/4000] Training metric {'Train/mean dice_metric': 0.9963486194610596, 'Train/mean miou_metric': 0.9931474328041077, 'Train/mean f1': 0.996889591217041, 'Train/mean precision': 0.9964638352394104, 'Train/mean recall': 0.9973157048225403, 'Train/mean hd95_metric': 0.9722653031349182} +Epoch [3841/4000] Validation [1/10] Loss: 0.71412 focal_loss 0.62852 dice_loss 0.08560 +Epoch [3841/4000] Validation [2/10] Loss: 0.50660 focal_loss 0.40882 dice_loss 0.09779 +Epoch [3841/4000] Validation [3/10] Loss: 0.39445 focal_loss 0.28365 dice_loss 0.11080 +Epoch [3841/4000] Validation [4/10] Loss: 0.90565 focal_loss 0.34028 dice_loss 0.56538 +Epoch [3841/4000] Validation [5/10] Loss: 3.07587 focal_loss 2.40191 dice_loss 0.67396 +Epoch [3841/4000] Validation [6/10] Loss: 1.35110 focal_loss 0.63721 dice_loss 0.71389 +Epoch [3841/4000] Validation [7/10] Loss: 1.19566 focal_loss 0.54033 dice_loss 0.65533 +Epoch [3841/4000] Validation [8/10] Loss: 2.35751 focal_loss 1.74561 dice_loss 0.61191 +Epoch [3841/4000] Validation [9/10] Loss: 1.58386 focal_loss 1.04001 dice_loss 0.54385 +Epoch [3841/4000] Validation [10/10] Loss: 1.92641 focal_loss 1.19142 dice_loss 0.73499 +Epoch [3841/4000] Validation metric {'Val/mean dice_metric': 0.9514004588127136, 'Val/mean miou_metric': 0.9355128407478333, 'Val/mean f1': 0.9480386972427368, 'Val/mean precision': 0.9429303407669067, 'Val/mean recall': 0.9532026052474976, 'Val/mean hd95_metric': 10.84084415435791} +Cheakpoint... +Epoch [3841/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514004588127136, 'Val/mean miou_metric': 0.9355128407478333, 'Val/mean f1': 0.9480386972427368, 'Val/mean precision': 0.9429303407669067, 'Val/mean recall': 0.9532026052474976, 'Val/mean hd95_metric': 10.84084415435791} +Epoch [3842/4000] Training [1/39] Loss: 0.00608 +Epoch [3842/4000] Training [2/39] Loss: 0.00446 +Epoch [3842/4000] Training [3/39] Loss: 0.12728 +Epoch [3842/4000] Training [4/39] Loss: 0.00544 +Epoch [3842/4000] Training [5/39] Loss: 0.00340 +Epoch [3842/4000] Training [6/39] Loss: 0.00665 +Epoch [3842/4000] Training [7/39] Loss: 0.00411 +Epoch [3842/4000] Training [8/39] Loss: 0.00473 +Epoch [3842/4000] Training [9/39] Loss: 0.13144 +Epoch [3842/4000] Training [10/39] Loss: 0.00643 +Epoch [3842/4000] Training [11/39] Loss: 0.12793 +Epoch [3842/4000] Training [12/39] Loss: 0.00486 +Epoch [3842/4000] Training [13/39] Loss: 0.12904 +Epoch [3842/4000] Training [14/39] Loss: 0.12841 +Epoch [3842/4000] Training [15/39] Loss: 0.00607 +Epoch [3842/4000] Training [16/39] Loss: 0.13010 +Epoch [3842/4000] Training [17/39] Loss: 0.12835 +Epoch [3842/4000] Training [18/39] Loss: 0.13206 +Epoch [3842/4000] Training [19/39] Loss: 0.00384 +Epoch [3842/4000] Training [20/39] Loss: 0.12966 +Epoch [3842/4000] Training [21/39] Loss: 0.12884 +Epoch [3842/4000] Training [22/39] Loss: 0.00462 +Epoch [3842/4000] Training [23/39] Loss: 0.13018 +Epoch [3842/4000] Training [24/39] Loss: 0.04346 +Epoch [3842/4000] Training [25/39] Loss: 0.00623 +Epoch [3842/4000] Training [26/39] Loss: 0.00515 +Epoch [3842/4000] Training [27/39] Loss: 0.00316 +Epoch [3842/4000] Training [28/39] Loss: 0.00482 +Epoch [3842/4000] Training [29/39] Loss: 0.00438 +Epoch [3842/4000] Training [30/39] Loss: 0.00493 +Epoch [3842/4000] Training [31/39] Loss: 0.12781 +Epoch [3842/4000] Training [32/39] Loss: 0.00471 +Epoch [3842/4000] Training [33/39] Loss: 0.00414 +Epoch [3842/4000] Training [34/39] Loss: 0.12815 +Epoch [3842/4000] Training [35/39] Loss: 0.13032 +Epoch [3842/4000] Training [36/39] Loss: 0.00400 +Epoch [3842/4000] Training [37/39] Loss: 0.00299 +Epoch [3842/4000] Training [38/39] Loss: 0.00448 +Epoch [3842/4000] Training [39/39] Loss: 0.12792 +Epoch [3842/4000] Training metric {'Train/mean dice_metric': 0.9963483214378357, 'Train/mean miou_metric': 0.9931499361991882, 'Train/mean f1': 0.9968830943107605, 'Train/mean precision': 0.9964876174926758, 'Train/mean recall': 0.9972789287567139, 'Train/mean hd95_metric': 1.0581703186035156} +Epoch [3842/4000] Validation [1/10] Loss: 0.69840 focal_loss 0.61393 dice_loss 0.08447 +Epoch [3842/4000] Validation [2/10] Loss: 0.49901 focal_loss 0.40054 dice_loss 0.09847 +Epoch [3842/4000] Validation [3/10] Loss: 0.40006 focal_loss 0.28826 dice_loss 0.11179 +Epoch [3842/4000] Validation [4/10] Loss: 0.89417 focal_loss 0.32918 dice_loss 0.56499 +Epoch [3842/4000] Validation [5/10] Loss: 3.07847 focal_loss 2.40433 dice_loss 0.67414 +Epoch [3842/4000] Validation [6/10] Loss: 1.32621 focal_loss 0.61358 dice_loss 0.71263 +Epoch [3842/4000] Validation [7/10] Loss: 1.17519 focal_loss 0.52263 dice_loss 0.65257 +Epoch [3842/4000] Validation [8/10] Loss: 2.38052 focal_loss 1.76352 dice_loss 0.61700 +Epoch [3842/4000] Validation [9/10] Loss: 1.53894 focal_loss 0.99481 dice_loss 0.54413 +Epoch [3842/4000] Validation [10/10] Loss: 1.87719 focal_loss 1.14405 dice_loss 0.73314 +Epoch [3842/4000] Validation metric {'Val/mean dice_metric': 0.951461911201477, 'Val/mean miou_metric': 0.9356176257133484, 'Val/mean f1': 0.9484317898750305, 'Val/mean precision': 0.9440535306930542, 'Val/mean recall': 0.9528507590293884, 'Val/mean hd95_metric': 10.890356063842773} +Cheakpoint... +Epoch [3842/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951461911201477, 'Val/mean miou_metric': 0.9356176257133484, 'Val/mean f1': 0.9484317898750305, 'Val/mean precision': 0.9440535306930542, 'Val/mean recall': 0.9528507590293884, 'Val/mean hd95_metric': 10.890356063842773} +Epoch [3843/4000] Training [1/39] Loss: 0.00436 +Epoch [3843/4000] Training [2/39] Loss: 0.00568 +Epoch [3843/4000] Training [3/39] Loss: 0.00918 +Epoch [3843/4000] Training [4/39] Loss: 0.00389 +Epoch [3843/4000] Training [5/39] Loss: 0.00518 +Epoch [3843/4000] Training [6/39] Loss: 0.12909 +Epoch [3843/4000] Training [7/39] Loss: 0.00432 +Epoch [3843/4000] Training [8/39] Loss: 0.00438 +Epoch [3843/4000] Training [9/39] Loss: 0.00425 +Epoch [3843/4000] Training [10/39] Loss: 0.00401 +Epoch [3843/4000] Training [11/39] Loss: 0.00502 +Epoch [3843/4000] Training [12/39] Loss: 0.12870 +Epoch [3843/4000] Training [13/39] Loss: 0.00587 +Epoch [3843/4000] Training [14/39] Loss: 0.00611 +Epoch [3843/4000] Training [15/39] Loss: 0.00605 +Epoch [3843/4000] Training [16/39] Loss: 0.12958 +Epoch [3843/4000] Training [17/39] Loss: 0.00418 +Epoch [3843/4000] Training [18/39] Loss: 0.00415 +Epoch [3843/4000] Training [19/39] Loss: 0.00586 +Epoch [3843/4000] Training [20/39] Loss: 0.00485 +Epoch [3843/4000] Training [21/39] Loss: 0.00427 +Epoch [3843/4000] Training [22/39] Loss: 0.00359 +Epoch [3843/4000] Training [23/39] Loss: 0.25224 +Epoch [3843/4000] Training [24/39] Loss: 0.13046 +Epoch [3843/4000] Training [25/39] Loss: 0.00562 +Epoch [3843/4000] Training [26/39] Loss: 0.12777 +Epoch [3843/4000] Training [27/39] Loss: 0.00270 +Epoch [3843/4000] Training [28/39] Loss: 0.00396 +Epoch [3843/4000] Training [29/39] Loss: 0.00584 +Epoch [3843/4000] Training [30/39] Loss: 0.00389 +Epoch [3843/4000] Training [31/39] Loss: 0.12999 +Epoch [3843/4000] Training [32/39] Loss: 0.08331 +Epoch [3843/4000] Training [33/39] Loss: 0.00620 +Epoch [3843/4000] Training [34/39] Loss: 0.13001 +Epoch [3843/4000] Training [35/39] Loss: 0.12925 +Epoch [3843/4000] Training [36/39] Loss: 0.00490 +Epoch [3843/4000] Training [37/39] Loss: 0.00595 +Epoch [3843/4000] Training [38/39] Loss: 0.12736 +Epoch [3843/4000] Training [39/39] Loss: 0.00421 +Epoch [3843/4000] Training metric {'Train/mean dice_metric': 0.9955679774284363, 'Train/mean miou_metric': 0.9924123883247375, 'Train/mean f1': 0.9969348311424255, 'Train/mean precision': 0.9964661002159119, 'Train/mean recall': 0.9974038600921631, 'Train/mean hd95_metric': 1.0130789279937744} +Epoch [3843/4000] Validation [1/10] Loss: 0.69731 focal_loss 0.61226 dice_loss 0.08505 +Epoch [3843/4000] Validation [2/10] Loss: 0.49676 focal_loss 0.39938 dice_loss 0.09738 +Epoch [3843/4000] Validation [3/10] Loss: 0.39222 focal_loss 0.28080 dice_loss 0.11142 +Epoch [3843/4000] Validation [4/10] Loss: 0.89686 focal_loss 0.33154 dice_loss 0.56531 +Epoch [3843/4000] Validation [5/10] Loss: 3.03672 focal_loss 2.36270 dice_loss 0.67401 +Epoch [3843/4000] Validation [6/10] Loss: 1.33126 focal_loss 0.61909 dice_loss 0.71218 +Epoch [3843/4000] Validation [7/10] Loss: 1.17763 focal_loss 0.52253 dice_loss 0.65510 +Epoch [3843/4000] Validation [8/10] Loss: 2.35044 focal_loss 1.73489 dice_loss 0.61554 +Epoch [3843/4000] Validation [9/10] Loss: 1.53035 focal_loss 0.98611 dice_loss 0.54424 +Epoch [3843/4000] Validation [10/10] Loss: 1.88545 focal_loss 1.15144 dice_loss 0.73401 +Epoch [3843/4000] Validation metric {'Val/mean dice_metric': 0.9508199691772461, 'Val/mean miou_metric': 0.9349895715713501, 'Val/mean f1': 0.9485288262367249, 'Val/mean precision': 0.9438313841819763, 'Val/mean recall': 0.9532731175422668, 'Val/mean hd95_metric': 10.856229782104492} +Cheakpoint... +Epoch [3843/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508199691772461, 'Val/mean miou_metric': 0.9349895715713501, 'Val/mean f1': 0.9485288262367249, 'Val/mean precision': 0.9438313841819763, 'Val/mean recall': 0.9532731175422668, 'Val/mean hd95_metric': 10.856229782104492} +Epoch [3844/4000] Training [1/39] Loss: 0.00585 +Epoch [3844/4000] Training [2/39] Loss: 0.12862 +Epoch [3844/4000] Training [3/39] Loss: 0.00341 +Epoch [3844/4000] Training [4/39] Loss: 0.00440 +Epoch [3844/4000] Training [5/39] Loss: 0.00487 +Epoch [3844/4000] Training [6/39] Loss: 0.00451 +Epoch [3844/4000] Training [7/39] Loss: 0.00334 +Epoch [3844/4000] Training [8/39] Loss: 0.25508 +Epoch [3844/4000] Training [9/39] Loss: 0.13090 +Epoch [3844/4000] Training [10/39] Loss: 0.00431 +Epoch [3844/4000] Training [11/39] Loss: 0.12821 +Epoch [3844/4000] Training [12/39] Loss: 0.00395 +Epoch [3844/4000] Training [13/39] Loss: 0.00634 +Epoch [3844/4000] Training [14/39] Loss: 0.00452 +Epoch [3844/4000] Training [15/39] Loss: 0.00927 +Epoch [3844/4000] Training [16/39] Loss: 0.00480 +Epoch [3844/4000] Training [17/39] Loss: 0.25574 +Epoch [3844/4000] Training [18/39] Loss: 0.00527 +Epoch [3844/4000] Training [19/39] Loss: 0.00385 +Epoch [3844/4000] Training [20/39] Loss: 0.12910 +Epoch [3844/4000] Training [21/39] Loss: 0.00373 +Epoch [3844/4000] Training [22/39] Loss: 0.00407 +Epoch [3844/4000] Training [23/39] Loss: 0.12713 +Epoch [3844/4000] Training [24/39] Loss: 0.12742 +Epoch [3844/4000] Training [25/39] Loss: 0.00693 +Epoch [3844/4000] Training [26/39] Loss: 0.25380 +Epoch [3844/4000] Training [27/39] Loss: 0.00412 +Epoch [3844/4000] Training [28/39] Loss: 0.00524 +Epoch [3844/4000] Training [29/39] Loss: 0.00711 +Epoch [3844/4000] Training [30/39] Loss: 0.00444 +Epoch [3844/4000] Training [31/39] Loss: 0.12897 +Epoch [3844/4000] Training [32/39] Loss: 0.00406 +Epoch [3844/4000] Training [33/39] Loss: 0.00539 +Epoch [3844/4000] Training [34/39] Loss: 0.00453 +Epoch [3844/4000] Training [35/39] Loss: 0.00716 +Epoch [3844/4000] Training [36/39] Loss: 0.00351 +Epoch [3844/4000] Training [37/39] Loss: 0.00559 +Epoch [3844/4000] Training [38/39] Loss: 0.00450 +Epoch [3844/4000] Training [39/39] Loss: 0.00569 +Epoch [3844/4000] Training metric {'Train/mean dice_metric': 0.9964421391487122, 'Train/mean miou_metric': 0.9933311343193054, 'Train/mean f1': 0.9970964789390564, 'Train/mean precision': 0.9966211318969727, 'Train/mean recall': 0.9975722432136536, 'Train/mean hd95_metric': 0.9175183773040771} +Epoch [3844/4000] Validation [1/10] Loss: 0.70488 focal_loss 0.62082 dice_loss 0.08407 +Epoch [3844/4000] Validation [2/10] Loss: 0.50839 focal_loss 0.40696 dice_loss 0.10143 +Epoch [3844/4000] Validation [3/10] Loss: 0.41172 focal_loss 0.29912 dice_loss 0.11261 +Epoch [3844/4000] Validation [4/10] Loss: 0.89056 focal_loss 0.32654 dice_loss 0.56402 +Epoch [3844/4000] Validation [5/10] Loss: 3.10022 focal_loss 2.42605 dice_loss 0.67417 +Epoch [3844/4000] Validation [6/10] Loss: 1.31409 focal_loss 0.60190 dice_loss 0.71220 +Epoch [3844/4000] Validation [7/10] Loss: 1.16847 focal_loss 0.51598 dice_loss 0.65250 +Epoch [3844/4000] Validation [8/10] Loss: 2.45186 focal_loss 1.82651 dice_loss 0.62535 +Epoch [3844/4000] Validation [9/10] Loss: 1.52886 focal_loss 0.98523 dice_loss 0.54363 +Epoch [3844/4000] Validation [10/10] Loss: 1.85245 focal_loss 1.11994 dice_loss 0.73251 +Epoch [3844/4000] Validation metric {'Val/mean dice_metric': 0.9514757394790649, 'Val/mean miou_metric': 0.9356980323791504, 'Val/mean f1': 0.9491244554519653, 'Val/mean precision': 0.9458029270172119, 'Val/mean recall': 0.9524694681167603, 'Val/mean hd95_metric': 10.762742042541504} +Cheakpoint... +Epoch [3844/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514757394790649, 'Val/mean miou_metric': 0.9356980323791504, 'Val/mean f1': 0.9491244554519653, 'Val/mean precision': 0.9458029270172119, 'Val/mean recall': 0.9524694681167603, 'Val/mean hd95_metric': 10.762742042541504} +Epoch [3845/4000] Training [1/39] Loss: 0.00538 +Epoch [3845/4000] Training [2/39] Loss: 0.00366 +Epoch [3845/4000] Training [3/39] Loss: 0.00416 +Epoch [3845/4000] Training [4/39] Loss: 0.00444 +Epoch [3845/4000] Training [5/39] Loss: 0.00381 +Epoch [3845/4000] Training [6/39] Loss: 0.00463 +Epoch [3845/4000] Training [7/39] Loss: 0.00399 +Epoch [3845/4000] Training [8/39] Loss: 0.00425 +Epoch [3845/4000] Training [9/39] Loss: 0.25278 +Epoch [3845/4000] Training [10/39] Loss: 0.00373 +Epoch [3845/4000] Training [11/39] Loss: 0.00284 +Epoch [3845/4000] Training [12/39] Loss: 0.00429 +Epoch [3845/4000] Training [13/39] Loss: 0.00500 +Epoch [3845/4000] Training [14/39] Loss: 0.00496 +Epoch [3845/4000] Training [15/39] Loss: 0.00577 +Epoch [3845/4000] Training [16/39] Loss: 0.00570 +Epoch [3845/4000] Training [17/39] Loss: 0.00912 +Epoch [3845/4000] Training [18/39] Loss: 0.13008 +Epoch [3845/4000] Training [19/39] Loss: 0.00312 +Epoch [3845/4000] Training [20/39] Loss: 0.00488 +Epoch [3845/4000] Training [21/39] Loss: 0.00476 +Epoch [3845/4000] Training [22/39] Loss: 0.12994 +Epoch [3845/4000] Training [23/39] Loss: 0.00395 +Epoch [3845/4000] Training [24/39] Loss: 0.00314 +Epoch [3845/4000] Training [25/39] Loss: 0.00494 +Epoch [3845/4000] Training [26/39] Loss: 0.00495 +Epoch [3845/4000] Training [27/39] Loss: 0.00576 +Epoch [3845/4000] Training [28/39] Loss: 0.00452 +Epoch [3845/4000] Training [29/39] Loss: 0.00354 +Epoch [3845/4000] Training [30/39] Loss: 0.12817 +Epoch [3845/4000] Training [31/39] Loss: 0.00702 +Epoch [3845/4000] Training [32/39] Loss: 0.00752 +Epoch [3845/4000] Training [33/39] Loss: 0.00445 +Epoch [3845/4000] Training [34/39] Loss: 0.00378 +Epoch [3845/4000] Training [35/39] Loss: 0.00766 +Epoch [3845/4000] Training [36/39] Loss: 0.13099 +Epoch [3845/4000] Training [37/39] Loss: 0.00446 +Epoch [3845/4000] Training [38/39] Loss: 0.13079 +Epoch [3845/4000] Training [39/39] Loss: 0.00448 +Epoch [3845/4000] Training metric {'Train/mean dice_metric': 0.9955292344093323, 'Train/mean miou_metric': 0.9923402667045593, 'Train/mean f1': 0.9969584345817566, 'Train/mean precision': 0.9965699911117554, 'Train/mean recall': 0.9973472356796265, 'Train/mean hd95_metric': 0.9530760645866394} +Epoch [3845/4000] Validation [1/10] Loss: 0.71561 focal_loss 0.63005 dice_loss 0.08556 +Epoch [3845/4000] Validation [2/10] Loss: 0.50616 focal_loss 0.40809 dice_loss 0.09807 +Epoch [3845/4000] Validation [3/10] Loss: 0.39868 focal_loss 0.28742 dice_loss 0.11125 +Epoch [3845/4000] Validation [4/10] Loss: 0.90087 focal_loss 0.33524 dice_loss 0.56563 +Epoch [3845/4000] Validation [5/10] Loss: 3.11090 focal_loss 2.43690 dice_loss 0.67401 +Epoch [3845/4000] Validation [6/10] Loss: 1.33842 focal_loss 0.62592 dice_loss 0.71251 +Epoch [3845/4000] Validation [7/10] Loss: 1.18307 focal_loss 0.52813 dice_loss 0.65494 +Epoch [3845/4000] Validation [8/10] Loss: 2.40683 focal_loss 1.78932 dice_loss 0.61751 +Epoch [3845/4000] Validation [9/10] Loss: 1.54428 focal_loss 1.00017 dice_loss 0.54411 +Epoch [3845/4000] Validation [10/10] Loss: 1.90124 focal_loss 1.16710 dice_loss 0.73414 +Epoch [3845/4000] Validation metric {'Val/mean dice_metric': 0.9507102966308594, 'Val/mean miou_metric': 0.9348344206809998, 'Val/mean f1': 0.94917231798172, 'Val/mean precision': 0.9448482990264893, 'Val/mean recall': 0.9535360932350159, 'Val/mean hd95_metric': 10.72012710571289} +Cheakpoint... +Epoch [3845/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507102966308594, 'Val/mean miou_metric': 0.9348344206809998, 'Val/mean f1': 0.94917231798172, 'Val/mean precision': 0.9448482990264893, 'Val/mean recall': 0.9535360932350159, 'Val/mean hd95_metric': 10.72012710571289} +Epoch [3846/4000] Training [1/39] Loss: 0.00609 +Epoch [3846/4000] Training [2/39] Loss: 0.00364 +Epoch [3846/4000] Training [3/39] Loss: 0.00436 +Epoch [3846/4000] Training [4/39] Loss: 0.00394 +Epoch [3846/4000] Training [5/39] Loss: 0.00690 +Epoch [3846/4000] Training [6/39] Loss: 0.00421 +Epoch [3846/4000] Training [7/39] Loss: 0.00339 +Epoch [3846/4000] Training [8/39] Loss: 0.12702 +Epoch [3846/4000] Training [9/39] Loss: 0.00505 +Epoch [3846/4000] Training [10/39] Loss: 0.12896 +Epoch [3846/4000] Training [11/39] Loss: 0.00387 +Epoch [3846/4000] Training [12/39] Loss: 0.12842 +Epoch [3846/4000] Training [13/39] Loss: 0.00363 +Epoch [3846/4000] Training [14/39] Loss: 0.00258 +Epoch [3846/4000] Training [15/39] Loss: 0.00596 +Epoch [3846/4000] Training [16/39] Loss: 0.00574 +Epoch [3846/4000] Training [17/39] Loss: 0.12852 +Epoch [3846/4000] Training [18/39] Loss: 0.00419 +Epoch [3846/4000] Training [19/39] Loss: 0.00288 +Epoch [3846/4000] Training [20/39] Loss: 0.00475 +Epoch [3846/4000] Training [21/39] Loss: 0.00535 +Epoch [3846/4000] Training [22/39] Loss: 0.00373 +Epoch [3846/4000] Training [23/39] Loss: 0.00444 +Epoch [3846/4000] Training [24/39] Loss: 0.00461 +Epoch [3846/4000] Training [25/39] Loss: 0.00418 +Epoch [3846/4000] Training [26/39] Loss: 0.12921 +Epoch [3846/4000] Training [27/39] Loss: 0.00688 +Epoch [3846/4000] Training [28/39] Loss: 0.12874 +Epoch [3846/4000] Training [29/39] Loss: 0.00410 +Epoch [3846/4000] Training [30/39] Loss: 0.00399 +Epoch [3846/4000] Training [31/39] Loss: 0.12884 +Epoch [3846/4000] Training [32/39] Loss: 0.12950 +Epoch [3846/4000] Training [33/39] Loss: 0.00316 +Epoch [3846/4000] Training [34/39] Loss: 0.00584 +Epoch [3846/4000] Training [35/39] Loss: 0.00473 +Epoch [3846/4000] Training [36/39] Loss: 0.12867 +Epoch [3846/4000] Training [37/39] Loss: 0.00232 +Epoch [3846/4000] Training [38/39] Loss: 0.12848 +Epoch [3846/4000] Training [39/39] Loss: 0.00383 +Epoch [3846/4000] Training metric {'Train/mean dice_metric': 0.9967262148857117, 'Train/mean miou_metric': 0.9939025640487671, 'Train/mean f1': 0.9971895217895508, 'Train/mean precision': 0.9967294931411743, 'Train/mean recall': 0.9976499676704407, 'Train/mean hd95_metric': 0.901240348815918} +Epoch [3846/4000] Validation [1/10] Loss: 0.69240 focal_loss 0.60782 dice_loss 0.08458 +Epoch [3846/4000] Validation [2/10] Loss: 0.50490 focal_loss 0.40420 dice_loss 0.10070 +Epoch [3846/4000] Validation [3/10] Loss: 0.39543 focal_loss 0.28366 dice_loss 0.11177 +Epoch [3846/4000] Validation [4/10] Loss: 0.89255 focal_loss 0.32819 dice_loss 0.56436 +Epoch [3846/4000] Validation [5/10] Loss: 3.04320 focal_loss 2.36904 dice_loss 0.67417 +Epoch [3846/4000] Validation [6/10] Loss: 1.32333 focal_loss 0.61081 dice_loss 0.71252 +Epoch [3846/4000] Validation [7/10] Loss: 1.17087 focal_loss 0.51910 dice_loss 0.65178 +Epoch [3846/4000] Validation [8/10] Loss: 2.41410 focal_loss 1.79109 dice_loss 0.62301 +Epoch [3846/4000] Validation [9/10] Loss: 1.51997 focal_loss 0.97616 dice_loss 0.54381 +Epoch [3846/4000] Validation [10/10] Loss: 1.86491 focal_loss 1.13224 dice_loss 0.73268 +Epoch [3846/4000] Validation metric {'Val/mean dice_metric': 0.951724648475647, 'Val/mean miou_metric': 0.9361873865127563, 'Val/mean f1': 0.9488959312438965, 'Val/mean precision': 0.9450657963752747, 'Val/mean recall': 0.9527571201324463, 'Val/mean hd95_metric': 10.769438743591309} +Cheakpoint... +Epoch [3846/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951724648475647, 'Val/mean miou_metric': 0.9361873865127563, 'Val/mean f1': 0.9488959312438965, 'Val/mean precision': 0.9450657963752747, 'Val/mean recall': 0.9527571201324463, 'Val/mean hd95_metric': 10.769438743591309} +Epoch [3847/4000] Training [1/39] Loss: 0.00465 +Epoch [3847/4000] Training [2/39] Loss: 0.00450 +Epoch [3847/4000] Training [3/39] Loss: 0.00454 +Epoch [3847/4000] Training [4/39] Loss: 0.12913 +Epoch [3847/4000] Training [5/39] Loss: 0.00401 +Epoch [3847/4000] Training [6/39] Loss: 0.25320 +Epoch [3847/4000] Training [7/39] Loss: 0.00451 +Epoch [3847/4000] Training [8/39] Loss: 0.00561 +Epoch [3847/4000] Training [9/39] Loss: 0.12758 +Epoch [3847/4000] Training [10/39] Loss: 0.00548 +Epoch [3847/4000] Training [11/39] Loss: 0.25453 +Epoch [3847/4000] Training [12/39] Loss: 0.12921 +Epoch [3847/4000] Training [13/39] Loss: 0.00645 +Epoch [3847/4000] Training [14/39] Loss: 0.12884 +Epoch [3847/4000] Training [15/39] Loss: 0.00342 +Epoch [3847/4000] Training [16/39] Loss: 0.00511 +Epoch [3847/4000] Training [17/39] Loss: 0.00235 +Epoch [3847/4000] Training [18/39] Loss: 0.00328 +Epoch [3847/4000] Training [19/39] Loss: 0.00491 +Epoch [3847/4000] Training [20/39] Loss: 0.00367 +Epoch [3847/4000] Training [21/39] Loss: 0.01374 +Epoch [3847/4000] Training [22/39] Loss: 0.00581 +Epoch [3847/4000] Training [23/39] Loss: 0.50183 +Epoch [3847/4000] Training [24/39] Loss: 0.00525 +Epoch [3847/4000] Training [25/39] Loss: 0.00543 +Epoch [3847/4000] Training [26/39] Loss: 0.13084 +Epoch [3847/4000] Training [27/39] Loss: 0.00327 +Epoch [3847/4000] Training [28/39] Loss: 0.00570 +Epoch [3847/4000] Training [29/39] Loss: 0.00866 +Epoch [3847/4000] Training [30/39] Loss: 0.00388 +Epoch [3847/4000] Training [31/39] Loss: 0.00282 +Epoch [3847/4000] Training [32/39] Loss: 0.00488 +Epoch [3847/4000] Training [33/39] Loss: 0.12917 +Epoch [3847/4000] Training [34/39] Loss: 0.00367 +Epoch [3847/4000] Training [35/39] Loss: 0.00390 +Epoch [3847/4000] Training [36/39] Loss: 0.00935 +Epoch [3847/4000] Training [37/39] Loss: 0.20098 +Epoch [3847/4000] Training [38/39] Loss: 0.00580 +Epoch [3847/4000] Training [39/39] Loss: 0.12842 +Epoch [3847/4000] Training metric {'Train/mean dice_metric': 0.9962926506996155, 'Train/mean miou_metric': 0.9930360317230225, 'Train/mean f1': 0.9969323873519897, 'Train/mean precision': 0.9965147972106934, 'Train/mean recall': 0.9973503947257996, 'Train/mean hd95_metric': 1.1036134958267212} +Epoch [3847/4000] Validation [1/10] Loss: 0.72621 focal_loss 0.64026 dice_loss 0.08595 +Epoch [3847/4000] Validation [2/10] Loss: 0.50876 focal_loss 0.40979 dice_loss 0.09897 +Epoch [3847/4000] Validation [3/10] Loss: 0.40221 focal_loss 0.29073 dice_loss 0.11148 +Epoch [3847/4000] Validation [4/10] Loss: 0.90081 focal_loss 0.33584 dice_loss 0.56497 +Epoch [3847/4000] Validation [5/10] Loss: 3.12566 focal_loss 2.45162 dice_loss 0.67404 +Epoch [3847/4000] Validation [6/10] Loss: 1.33577 focal_loss 0.62351 dice_loss 0.71226 +Epoch [3847/4000] Validation [7/10] Loss: 1.18096 focal_loss 0.52660 dice_loss 0.65436 +Epoch [3847/4000] Validation [8/10] Loss: 2.45465 focal_loss 1.83369 dice_loss 0.62096 +Epoch [3847/4000] Validation [9/10] Loss: 1.53856 focal_loss 0.99491 dice_loss 0.54365 +Epoch [3847/4000] Validation [10/10] Loss: 1.89050 focal_loss 1.15786 dice_loss 0.73264 +Epoch [3847/4000] Validation metric {'Val/mean dice_metric': 0.9513056874275208, 'Val/mean miou_metric': 0.9353748559951782, 'Val/mean f1': 0.948576807975769, 'Val/mean precision': 0.9445443153381348, 'Val/mean recall': 0.9526439905166626, 'Val/mean hd95_metric': 10.848773002624512} +Cheakpoint... +Epoch [3847/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513056874275208, 'Val/mean miou_metric': 0.9353748559951782, 'Val/mean f1': 0.948576807975769, 'Val/mean precision': 0.9445443153381348, 'Val/mean recall': 0.9526439905166626, 'Val/mean hd95_metric': 10.848773002624512} +Epoch [3848/4000] Training [1/39] Loss: 0.01174 +Epoch [3848/4000] Training [2/39] Loss: 0.00517 +Epoch [3848/4000] Training [3/39] Loss: 0.13171 +Epoch [3848/4000] Training [4/39] Loss: 0.12800 +Epoch [3848/4000] Training [5/39] Loss: 0.00362 +Epoch [3848/4000] Training [6/39] Loss: 0.25546 +Epoch [3848/4000] Training [7/39] Loss: 0.00606 +Epoch [3848/4000] Training [8/39] Loss: 0.00439 +Epoch [3848/4000] Training [9/39] Loss: 0.00442 +Epoch [3848/4000] Training [10/39] Loss: 0.12735 +Epoch [3848/4000] Training [11/39] Loss: 0.00415 +Epoch [3848/4000] Training [12/39] Loss: 0.12806 +Epoch [3848/4000] Training [13/39] Loss: 0.12853 +Epoch [3848/4000] Training [14/39] Loss: 0.00355 +Epoch [3848/4000] Training [15/39] Loss: 0.00510 +Epoch [3848/4000] Training [16/39] Loss: 0.00580 +Epoch [3848/4000] Training [17/39] Loss: 0.00633 +Epoch [3848/4000] Training [18/39] Loss: 0.00711 +Epoch [3848/4000] Training [19/39] Loss: 0.12870 +Epoch [3848/4000] Training [20/39] Loss: 0.00405 +Epoch [3848/4000] Training [21/39] Loss: 0.00455 +Epoch [3848/4000] Training [22/39] Loss: 0.00579 +Epoch [3848/4000] Training [23/39] Loss: 0.12947 +Epoch [3848/4000] Training [24/39] Loss: 0.00262 +Epoch [3848/4000] Training [25/39] Loss: 0.00312 +Epoch [3848/4000] Training [26/39] Loss: 0.00437 +Epoch [3848/4000] Training [27/39] Loss: 0.12829 +Epoch [3848/4000] Training [28/39] Loss: 0.00574 +Epoch [3848/4000] Training [29/39] Loss: 0.00413 +Epoch [3848/4000] Training [30/39] Loss: 0.00441 +Epoch [3848/4000] Training [31/39] Loss: 0.00833 +Epoch [3848/4000] Training [32/39] Loss: 0.00619 +Epoch [3848/4000] Training [33/39] Loss: 0.00325 +Epoch [3848/4000] Training [34/39] Loss: 0.12751 +Epoch [3848/4000] Training [35/39] Loss: 0.00362 +Epoch [3848/4000] Training [36/39] Loss: 0.25444 +Epoch [3848/4000] Training [37/39] Loss: 0.12782 +Epoch [3848/4000] Training [38/39] Loss: 0.13214 +Epoch [3848/4000] Training [39/39] Loss: 0.00564 +Epoch [3848/4000] Training metric {'Train/mean dice_metric': 0.9963798522949219, 'Train/mean miou_metric': 0.9932148456573486, 'Train/mean f1': 0.9969912767410278, 'Train/mean precision': 0.9965628981590271, 'Train/mean recall': 0.9974201321601868, 'Train/mean hd95_metric': 0.9099041819572449} +Epoch [3848/4000] Validation [1/10] Loss: 0.73557 focal_loss 0.64886 dice_loss 0.08671 +Epoch [3848/4000] Validation [2/10] Loss: 0.50979 focal_loss 0.41210 dice_loss 0.09769 +Epoch [3848/4000] Validation [3/10] Loss: 0.39945 focal_loss 0.28834 dice_loss 0.11111 +Epoch [3848/4000] Validation [4/10] Loss: 0.90697 focal_loss 0.34135 dice_loss 0.56562 +Epoch [3848/4000] Validation [5/10] Loss: 3.13516 focal_loss 2.46116 dice_loss 0.67400 +Epoch [3848/4000] Validation [6/10] Loss: 1.35107 focal_loss 0.63903 dice_loss 0.71204 +Epoch [3848/4000] Validation [7/10] Loss: 1.19166 focal_loss 0.53649 dice_loss 0.65517 +Epoch [3848/4000] Validation [8/10] Loss: 2.43250 focal_loss 1.81681 dice_loss 0.61568 +Epoch [3848/4000] Validation [9/10] Loss: 1.55452 focal_loss 1.01042 dice_loss 0.54410 +Epoch [3848/4000] Validation [10/10] Loss: 1.92316 focal_loss 1.18935 dice_loss 0.73382 +Epoch [3848/4000] Validation metric {'Val/mean dice_metric': 0.9514430165290833, 'Val/mean miou_metric': 0.9355983138084412, 'Val/mean f1': 0.9484679698944092, 'Val/mean precision': 0.9436647295951843, 'Val/mean recall': 0.9533204436302185, 'Val/mean hd95_metric': 10.655613899230957} +Cheakpoint... +Epoch [3848/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514430165290833, 'Val/mean miou_metric': 0.9355983138084412, 'Val/mean f1': 0.9484679698944092, 'Val/mean precision': 0.9436647295951843, 'Val/mean recall': 0.9533204436302185, 'Val/mean hd95_metric': 10.655613899230957} +Epoch [3849/4000] Training [1/39] Loss: 0.12781 +Epoch [3849/4000] Training [2/39] Loss: 0.00327 +Epoch [3849/4000] Training [3/39] Loss: 0.08548 +Epoch [3849/4000] Training [4/39] Loss: 0.00239 +Epoch [3849/4000] Training [5/39] Loss: 0.00401 +Epoch [3849/4000] Training [6/39] Loss: 0.12961 +Epoch [3849/4000] Training [7/39] Loss: 0.00366 +Epoch [3849/4000] Training [8/39] Loss: 0.12954 +Epoch [3849/4000] Training [9/39] Loss: 0.00615 +Epoch [3849/4000] Training [10/39] Loss: 0.00580 +Epoch [3849/4000] Training [11/39] Loss: 0.00366 +Epoch [3849/4000] Training [12/39] Loss: 0.12867 +Epoch [3849/4000] Training [13/39] Loss: 0.25476 +Epoch [3849/4000] Training [14/39] Loss: 0.00430 +Epoch [3849/4000] Training [15/39] Loss: 0.00642 +Epoch [3849/4000] Training [16/39] Loss: 0.00298 +Epoch [3849/4000] Training [17/39] Loss: 0.00468 +Epoch [3849/4000] Training [18/39] Loss: 0.12800 +Epoch [3849/4000] Training [19/39] Loss: 0.00298 +Epoch [3849/4000] Training [20/39] Loss: 0.00357 +Epoch [3849/4000] Training [21/39] Loss: 0.12913 +Epoch [3849/4000] Training [22/39] Loss: 0.00504 +Epoch [3849/4000] Training [23/39] Loss: 0.00329 +Epoch [3849/4000] Training [24/39] Loss: 0.00524 +Epoch [3849/4000] Training [25/39] Loss: 0.00636 +Epoch [3849/4000] Training [26/39] Loss: 0.00765 +Epoch [3849/4000] Training [27/39] Loss: 0.12878 +Epoch [3849/4000] Training [28/39] Loss: 0.00509 +Epoch [3849/4000] Training [29/39] Loss: 0.00623 +Epoch [3849/4000] Training [30/39] Loss: 0.00393 +Epoch [3849/4000] Training [31/39] Loss: 0.00491 +Epoch [3849/4000] Training [32/39] Loss: 0.25388 +Epoch [3849/4000] Training [33/39] Loss: 0.12747 +Epoch [3849/4000] Training [34/39] Loss: 0.00454 +Epoch [3849/4000] Training [35/39] Loss: 0.00365 +Epoch [3849/4000] Training [36/39] Loss: 0.00269 +Epoch [3849/4000] Training [37/39] Loss: 0.00314 +Epoch [3849/4000] Training [38/39] Loss: 0.00538 +Epoch [3849/4000] Training [39/39] Loss: 0.00363 +Epoch [3849/4000] Training metric {'Train/mean dice_metric': 0.9966109991073608, 'Train/mean miou_metric': 0.9936638474464417, 'Train/mean f1': 0.9971145391464233, 'Train/mean precision': 0.9966846704483032, 'Train/mean recall': 0.9975450038909912, 'Train/mean hd95_metric': 0.9173245429992676} +Epoch [3849/4000] Validation [1/10] Loss: 0.71160 focal_loss 0.62582 dice_loss 0.08577 +Epoch [3849/4000] Validation [2/10] Loss: 0.50312 focal_loss 0.40263 dice_loss 0.10049 +Epoch [3849/4000] Validation [3/10] Loss: 0.40393 focal_loss 0.29167 dice_loss 0.11226 +Epoch [3849/4000] Validation [4/10] Loss: 0.89079 focal_loss 0.32624 dice_loss 0.56456 +Epoch [3849/4000] Validation [5/10] Loss: 3.07739 focal_loss 2.40326 dice_loss 0.67413 +Epoch [3849/4000] Validation [6/10] Loss: 1.31838 focal_loss 0.60634 dice_loss 0.71204 +Epoch [3849/4000] Validation [7/10] Loss: 1.16545 focal_loss 0.51202 dice_loss 0.65343 +Epoch [3849/4000] Validation [8/10] Loss: 2.41593 focal_loss 1.79427 dice_loss 0.62166 +Epoch [3849/4000] Validation [9/10] Loss: 1.52215 focal_loss 0.97838 dice_loss 0.54377 +Epoch [3849/4000] Validation [10/10] Loss: 1.85745 focal_loss 1.12465 dice_loss 0.73280 +Epoch [3849/4000] Validation metric {'Val/mean dice_metric': 0.9515308141708374, 'Val/mean miou_metric': 0.9358604550361633, 'Val/mean f1': 0.9484601020812988, 'Val/mean precision': 0.9444443583488464, 'Val/mean recall': 0.9525102972984314, 'Val/mean hd95_metric': 10.803728103637695} +Cheakpoint... +Epoch [3849/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515308141708374, 'Val/mean miou_metric': 0.9358604550361633, 'Val/mean f1': 0.9484601020812988, 'Val/mean precision': 0.9444443583488464, 'Val/mean recall': 0.9525102972984314, 'Val/mean hd95_metric': 10.803728103637695} +Epoch [3850/4000] Training [1/39] Loss: 0.00822 +Epoch [3850/4000] Training [2/39] Loss: 0.00316 +Epoch [3850/4000] Training [3/39] Loss: 0.00681 +Epoch [3850/4000] Training [4/39] Loss: 0.00374 +Epoch [3850/4000] Training [5/39] Loss: 0.00362 +Epoch [3850/4000] Training [6/39] Loss: 0.12847 +Epoch [3850/4000] Training [7/39] Loss: 0.00495 +Epoch [3850/4000] Training [8/39] Loss: 0.12902 +Epoch [3850/4000] Training [9/39] Loss: 0.00399 +Epoch [3850/4000] Training [10/39] Loss: 0.00308 +Epoch [3850/4000] Training [11/39] Loss: 0.00367 +Epoch [3850/4000] Training [12/39] Loss: 0.00486 +Epoch [3850/4000] Training [13/39] Loss: 0.00636 +Epoch [3850/4000] Training [14/39] Loss: 0.13034 +Epoch [3850/4000] Training [15/39] Loss: 0.00439 +Epoch [3850/4000] Training [16/39] Loss: 0.00451 +Epoch [3850/4000] Training [17/39] Loss: 0.00532 +Epoch [3850/4000] Training [18/39] Loss: 0.00627 +Epoch [3850/4000] Training [19/39] Loss: 0.00499 +Epoch [3850/4000] Training [20/39] Loss: 0.12820 +Epoch [3850/4000] Training [21/39] Loss: 0.00418 +Epoch [3850/4000] Training [22/39] Loss: 0.12872 +Epoch [3850/4000] Training [23/39] Loss: 0.00468 +Epoch [3850/4000] Training [24/39] Loss: 0.00291 +Epoch [3850/4000] Training [25/39] Loss: 0.00521 +Epoch [3850/4000] Training [26/39] Loss: 0.00372 +Epoch [3850/4000] Training [27/39] Loss: 0.00412 +Epoch [3850/4000] Training [28/39] Loss: 0.00472 +Epoch [3850/4000] Training [29/39] Loss: 0.00316 +Epoch [3850/4000] Training [30/39] Loss: 0.00376 +Epoch [3850/4000] Training [31/39] Loss: 0.00319 +Epoch [3850/4000] Training [32/39] Loss: 0.12819 +Epoch [3850/4000] Training [33/39] Loss: 0.00446 +Epoch [3850/4000] Training [34/39] Loss: 0.12836 +Epoch [3850/4000] Training [35/39] Loss: 0.12856 +Epoch [3850/4000] Training [36/39] Loss: 0.12925 +Epoch [3850/4000] Training [37/39] Loss: 0.12870 +Epoch [3850/4000] Training [38/39] Loss: 0.25261 +Epoch [3850/4000] Training [39/39] Loss: 0.00437 +Epoch [3850/4000] Training metric {'Train/mean dice_metric': 0.9965227246284485, 'Train/mean miou_metric': 0.9934848546981812, 'Train/mean f1': 0.996989369392395, 'Train/mean precision': 0.9965114593505859, 'Train/mean recall': 0.997467577457428, 'Train/mean hd95_metric': 0.9356973171234131} +Epoch [3850/4000] Validation [1/10] Loss: 0.70095 focal_loss 0.61547 dice_loss 0.08548 +Epoch [3850/4000] Validation [2/10] Loss: 0.50575 focal_loss 0.40666 dice_loss 0.09909 +Epoch [3850/4000] Validation [3/10] Loss: 0.39155 focal_loss 0.28004 dice_loss 0.11151 +Epoch [3850/4000] Validation [4/10] Loss: 0.89885 focal_loss 0.33365 dice_loss 0.56519 +Epoch [3850/4000] Validation [5/10] Loss: 3.03090 focal_loss 2.35695 dice_loss 0.67395 +Epoch [3850/4000] Validation [6/10] Loss: 1.33867 focal_loss 0.62613 dice_loss 0.71255 +Epoch [3850/4000] Validation [7/10] Loss: 1.17929 focal_loss 0.52484 dice_loss 0.65445 +Epoch [3850/4000] Validation [8/10] Loss: 2.37153 focal_loss 1.75397 dice_loss 0.61757 +Epoch [3850/4000] Validation [9/10] Loss: 1.52340 focal_loss 0.97924 dice_loss 0.54416 +Epoch [3850/4000] Validation [10/10] Loss: 1.89300 focal_loss 1.15893 dice_loss 0.73407 +Epoch [3850/4000] Validation metric {'Val/mean dice_metric': 0.9515830874443054, 'Val/mean miou_metric': 0.9358605742454529, 'Val/mean f1': 0.9485873579978943, 'Val/mean precision': 0.9439760446548462, 'Val/mean recall': 0.9532438516616821, 'Val/mean hd95_metric': 10.785455703735352} +Cheakpoint... +Epoch [3850/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515830874443054, 'Val/mean miou_metric': 0.9358605742454529, 'Val/mean f1': 0.9485873579978943, 'Val/mean precision': 0.9439760446548462, 'Val/mean recall': 0.9532438516616821, 'Val/mean hd95_metric': 10.785455703735352} +Epoch [3851/4000] Training [1/39] Loss: 0.00796 +Epoch [3851/4000] Training [2/39] Loss: 0.00294 +Epoch [3851/4000] Training [3/39] Loss: 0.00558 +Epoch [3851/4000] Training [4/39] Loss: 0.12803 +Epoch [3851/4000] Training [5/39] Loss: 0.00400 +Epoch [3851/4000] Training [6/39] Loss: 0.13379 +Epoch [3851/4000] Training [7/39] Loss: 0.12872 +Epoch [3851/4000] Training [8/39] Loss: 0.00416 +Epoch [3851/4000] Training [9/39] Loss: 0.00592 +Epoch [3851/4000] Training [10/39] Loss: 0.00380 +Epoch [3851/4000] Training [11/39] Loss: 0.00693 +Epoch [3851/4000] Training [12/39] Loss: 0.25490 +Epoch [3851/4000] Training [13/39] Loss: 0.00418 +Epoch [3851/4000] Training [14/39] Loss: 0.00341 +Epoch [3851/4000] Training [15/39] Loss: 0.12745 +Epoch [3851/4000] Training [16/39] Loss: 0.00415 +Epoch [3851/4000] Training [17/39] Loss: 0.00395 +Epoch [3851/4000] Training [18/39] Loss: 0.00455 +Epoch [3851/4000] Training [19/39] Loss: 0.00375 +Epoch [3851/4000] Training [20/39] Loss: 0.12802 +Epoch [3851/4000] Training [21/39] Loss: 0.25282 +Epoch [3851/4000] Training [22/39] Loss: 0.00488 +Epoch [3851/4000] Training [23/39] Loss: 0.00466 +Epoch [3851/4000] Training [24/39] Loss: 0.00340 +Epoch [3851/4000] Training [25/39] Loss: 0.12833 +Epoch [3851/4000] Training [26/39] Loss: 0.12812 +Epoch [3851/4000] Training [27/39] Loss: 0.25512 +Epoch [3851/4000] Training [28/39] Loss: 0.12933 +Epoch [3851/4000] Training [29/39] Loss: 0.00387 +Epoch [3851/4000] Training [30/39] Loss: 0.00421 +Epoch [3851/4000] Training [31/39] Loss: 0.12818 +Epoch [3851/4000] Training [32/39] Loss: 0.13063 +Epoch [3851/4000] Training [33/39] Loss: 0.00553 +Epoch [3851/4000] Training [34/39] Loss: 0.00442 +Epoch [3851/4000] Training [35/39] Loss: 0.08599 +Epoch [3851/4000] Training [36/39] Loss: 0.00581 +Epoch [3851/4000] Training [37/39] Loss: 0.00500 +Epoch [3851/4000] Training [38/39] Loss: 0.00570 +Epoch [3851/4000] Training [39/39] Loss: 0.13073 +Epoch [3851/4000] Training metric {'Train/mean dice_metric': 0.9955089688301086, 'Train/mean miou_metric': 0.9923020601272583, 'Train/mean f1': 0.9968072175979614, 'Train/mean precision': 0.996381402015686, 'Train/mean recall': 0.997233510017395, 'Train/mean hd95_metric': 0.935425341129303} +Epoch [3851/4000] Validation [1/10] Loss: 0.72652 focal_loss 0.64022 dice_loss 0.08630 +Epoch [3851/4000] Validation [2/10] Loss: 0.50568 focal_loss 0.40810 dice_loss 0.09758 +Epoch [3851/4000] Validation [3/10] Loss: 0.39907 focal_loss 0.28797 dice_loss 0.11110 +Epoch [3851/4000] Validation [4/10] Loss: 0.90046 focal_loss 0.33501 dice_loss 0.56545 +Epoch [3851/4000] Validation [5/10] Loss: 3.12502 focal_loss 2.45109 dice_loss 0.67394 +Epoch [3851/4000] Validation [6/10] Loss: 1.34120 focal_loss 0.62887 dice_loss 0.71233 +Epoch [3851/4000] Validation [7/10] Loss: 1.18476 focal_loss 0.52984 dice_loss 0.65492 +Epoch [3851/4000] Validation [8/10] Loss: 2.39788 focal_loss 1.78287 dice_loss 0.61502 +Epoch [3851/4000] Validation [9/10] Loss: 1.54884 focal_loss 1.00505 dice_loss 0.54379 +Epoch [3851/4000] Validation [10/10] Loss: 1.90998 focal_loss 1.17537 dice_loss 0.73461 +Epoch [3851/4000] Validation metric {'Val/mean dice_metric': 0.9507461190223694, 'Val/mean miou_metric': 0.9348626136779785, 'Val/mean f1': 0.9482827186584473, 'Val/mean precision': 0.943534255027771, 'Val/mean recall': 0.9530792832374573, 'Val/mean hd95_metric': 10.676346778869629} +Cheakpoint... +Epoch [3851/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507461190223694, 'Val/mean miou_metric': 0.9348626136779785, 'Val/mean f1': 0.9482827186584473, 'Val/mean precision': 0.943534255027771, 'Val/mean recall': 0.9530792832374573, 'Val/mean hd95_metric': 10.676346778869629} +Epoch [3852/4000] Training [1/39] Loss: 0.00432 +Epoch [3852/4000] Training [2/39] Loss: 0.00565 +Epoch [3852/4000] Training [3/39] Loss: 0.00455 +Epoch [3852/4000] Training [4/39] Loss: 0.12948 +Epoch [3852/4000] Training [5/39] Loss: 0.00898 +Epoch [3852/4000] Training [6/39] Loss: 0.00568 +Epoch [3852/4000] Training [7/39] Loss: 0.12912 +Epoch [3852/4000] Training [8/39] Loss: 0.12802 +Epoch [3852/4000] Training [9/39] Loss: 0.00445 +Epoch [3852/4000] Training [10/39] Loss: 0.00565 +Epoch [3852/4000] Training [11/39] Loss: 0.00472 +Epoch [3852/4000] Training [12/39] Loss: 0.00494 +Epoch [3852/4000] Training [13/39] Loss: 0.00369 +Epoch [3852/4000] Training [14/39] Loss: 0.25292 +Epoch [3852/4000] Training [15/39] Loss: 0.00482 +Epoch [3852/4000] Training [16/39] Loss: 0.12794 +Epoch [3852/4000] Training [17/39] Loss: 0.13052 +Epoch [3852/4000] Training [18/39] Loss: 0.00443 +Epoch [3852/4000] Training [19/39] Loss: 0.00570 +Epoch [3852/4000] Training [20/39] Loss: 0.12957 +Epoch [3852/4000] Training [21/39] Loss: 0.00404 +Epoch [3852/4000] Training [22/39] Loss: 0.00558 +Epoch [3852/4000] Training [23/39] Loss: 0.00380 +Epoch [3852/4000] Training [24/39] Loss: 0.00332 +Epoch [3852/4000] Training [25/39] Loss: 0.00520 +Epoch [3852/4000] Training [26/39] Loss: 0.00388 +Epoch [3852/4000] Training [27/39] Loss: 0.12950 +Epoch [3852/4000] Training [28/39] Loss: 0.12954 +Epoch [3852/4000] Training [29/39] Loss: 0.00603 +Epoch [3852/4000] Training [30/39] Loss: 0.00490 +Epoch [3852/4000] Training [31/39] Loss: 0.00371 +Epoch [3852/4000] Training [32/39] Loss: 0.25394 +Epoch [3852/4000] Training [33/39] Loss: 0.00714 +Epoch [3852/4000] Training [34/39] Loss: 0.00415 +Epoch [3852/4000] Training [35/39] Loss: 0.00465 +Epoch [3852/4000] Training [36/39] Loss: 0.00784 +Epoch [3852/4000] Training [37/39] Loss: 0.00497 +Epoch [3852/4000] Training [38/39] Loss: 0.12830 +Epoch [3852/4000] Training [39/39] Loss: 0.00419 +Epoch [3852/4000] Training metric {'Train/mean dice_metric': 0.9964195489883423, 'Train/mean miou_metric': 0.9932959675788879, 'Train/mean f1': 0.9968957304954529, 'Train/mean precision': 0.996427059173584, 'Train/mean recall': 0.9973649382591248, 'Train/mean hd95_metric': 0.9148551821708679} +Epoch [3852/4000] Validation [1/10] Loss: 0.71137 focal_loss 0.62583 dice_loss 0.08554 +Epoch [3852/4000] Validation [2/10] Loss: 0.51013 focal_loss 0.41013 dice_loss 0.10000 +Epoch [3852/4000] Validation [3/10] Loss: 0.39884 focal_loss 0.28728 dice_loss 0.11156 +Epoch [3852/4000] Validation [4/10] Loss: 0.89875 focal_loss 0.33388 dice_loss 0.56487 +Epoch [3852/4000] Validation [5/10] Loss: 3.09090 focal_loss 2.41681 dice_loss 0.67409 +Epoch [3852/4000] Validation [6/10] Loss: 1.33608 focal_loss 0.62391 dice_loss 0.71216 +Epoch [3852/4000] Validation [7/10] Loss: 1.18124 focal_loss 0.52792 dice_loss 0.65332 +Epoch [3852/4000] Validation [8/10] Loss: 2.43219 focal_loss 1.81128 dice_loss 0.62091 +Epoch [3852/4000] Validation [9/10] Loss: 1.53167 focal_loss 0.98751 dice_loss 0.54416 +Epoch [3852/4000] Validation [10/10] Loss: 1.89672 focal_loss 1.16238 dice_loss 0.73434 +Epoch [3852/4000] Validation metric {'Val/mean dice_metric': 0.9514607191085815, 'Val/mean miou_metric': 0.9356560707092285, 'Val/mean f1': 0.9485698938369751, 'Val/mean precision': 0.9443355202674866, 'Val/mean recall': 0.9528424739837646, 'Val/mean hd95_metric': 10.784621238708496} +Cheakpoint... +Epoch [3852/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514607191085815, 'Val/mean miou_metric': 0.9356560707092285, 'Val/mean f1': 0.9485698938369751, 'Val/mean precision': 0.9443355202674866, 'Val/mean recall': 0.9528424739837646, 'Val/mean hd95_metric': 10.784621238708496} +Epoch [3853/4000] Training [1/39] Loss: 0.12904 +Epoch [3853/4000] Training [2/39] Loss: 0.13038 +Epoch [3853/4000] Training [3/39] Loss: 0.12889 +Epoch [3853/4000] Training [4/39] Loss: 0.00427 +Epoch [3853/4000] Training [5/39] Loss: 0.00403 +Epoch [3853/4000] Training [6/39] Loss: 0.25222 +Epoch [3853/4000] Training [7/39] Loss: 0.00613 +Epoch [3853/4000] Training [8/39] Loss: 0.00596 +Epoch [3853/4000] Training [9/39] Loss: 0.12933 +Epoch [3853/4000] Training [10/39] Loss: 0.00292 +Epoch [3853/4000] Training [11/39] Loss: 0.12870 +Epoch [3853/4000] Training [12/39] Loss: 0.00329 +Epoch [3853/4000] Training [13/39] Loss: 0.00348 +Epoch [3853/4000] Training [14/39] Loss: 0.00370 +Epoch [3853/4000] Training [15/39] Loss: 0.00541 +Epoch [3853/4000] Training [16/39] Loss: 0.00355 +Epoch [3853/4000] Training [17/39] Loss: 0.00427 +Epoch [3853/4000] Training [18/39] Loss: 0.00599 +Epoch [3853/4000] Training [19/39] Loss: 0.12835 +Epoch [3853/4000] Training [20/39] Loss: 0.00412 +Epoch [3853/4000] Training [21/39] Loss: 0.00414 +Epoch [3853/4000] Training [22/39] Loss: 0.13237 +Epoch [3853/4000] Training [23/39] Loss: 0.13019 +Epoch [3853/4000] Training [24/39] Loss: 0.00503 +Epoch [3853/4000] Training [25/39] Loss: 0.13057 +Epoch [3853/4000] Training [26/39] Loss: 0.00533 +Epoch [3853/4000] Training [27/39] Loss: 0.00510 +Epoch [3853/4000] Training [28/39] Loss: 0.00343 +Epoch [3853/4000] Training [29/39] Loss: 0.00280 +Epoch [3853/4000] Training [30/39] Loss: 0.00389 +Epoch [3853/4000] Training [31/39] Loss: 0.00381 +Epoch [3853/4000] Training [32/39] Loss: 0.00354 +Epoch [3853/4000] Training [33/39] Loss: 0.00385 +Epoch [3853/4000] Training [34/39] Loss: 0.00683 +Epoch [3853/4000] Training [35/39] Loss: 0.00433 +Epoch [3853/4000] Training [36/39] Loss: 0.00703 +Epoch [3853/4000] Training [37/39] Loss: 0.12973 +Epoch [3853/4000] Training [38/39] Loss: 0.13081 +Epoch [3853/4000] Training [39/39] Loss: 0.00520 +Epoch [3853/4000] Training metric {'Train/mean dice_metric': 0.996424674987793, 'Train/mean miou_metric': 0.9933070540428162, 'Train/mean f1': 0.9969502091407776, 'Train/mean precision': 0.9964991211891174, 'Train/mean recall': 0.9974015951156616, 'Train/mean hd95_metric': 0.9232550263404846} +Epoch [3853/4000] Validation [1/10] Loss: 0.70882 focal_loss 0.62351 dice_loss 0.08531 +Epoch [3853/4000] Validation [2/10] Loss: 0.50649 focal_loss 0.40707 dice_loss 0.09942 +Epoch [3853/4000] Validation [3/10] Loss: 0.39903 focal_loss 0.28726 dice_loss 0.11177 +Epoch [3853/4000] Validation [4/10] Loss: 0.89600 focal_loss 0.33140 dice_loss 0.56460 +Epoch [3853/4000] Validation [5/10] Loss: 3.06969 focal_loss 2.39563 dice_loss 0.67406 +Epoch [3853/4000] Validation [6/10] Loss: 1.33482 focal_loss 0.62206 dice_loss 0.71276 +Epoch [3853/4000] Validation [7/10] Loss: 1.17792 focal_loss 0.52544 dice_loss 0.65248 +Epoch [3853/4000] Validation [8/10] Loss: 2.41988 focal_loss 1.79926 dice_loss 0.62062 +Epoch [3853/4000] Validation [9/10] Loss: 1.53621 focal_loss 0.99205 dice_loss 0.54416 +Epoch [3853/4000] Validation [10/10] Loss: 1.88948 focal_loss 1.15543 dice_loss 0.73405 +Epoch [3853/4000] Validation metric {'Val/mean dice_metric': 0.9514683485031128, 'Val/mean miou_metric': 0.9356747269630432, 'Val/mean f1': 0.9484586715698242, 'Val/mean precision': 0.944241464138031, 'Val/mean recall': 0.9527137279510498, 'Val/mean hd95_metric': 10.784236907958984} +Cheakpoint... +Epoch [3853/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514683485031128, 'Val/mean miou_metric': 0.9356747269630432, 'Val/mean f1': 0.9484586715698242, 'Val/mean precision': 0.944241464138031, 'Val/mean recall': 0.9527137279510498, 'Val/mean hd95_metric': 10.784236907958984} +Epoch [3854/4000] Training [1/39] Loss: 0.00361 +Epoch [3854/4000] Training [2/39] Loss: 0.25265 +Epoch [3854/4000] Training [3/39] Loss: 0.25431 +Epoch [3854/4000] Training [4/39] Loss: 0.25232 +Epoch [3854/4000] Training [5/39] Loss: 0.00435 +Epoch [3854/4000] Training [6/39] Loss: 0.00412 +Epoch [3854/4000] Training [7/39] Loss: 0.00399 +Epoch [3854/4000] Training [8/39] Loss: 0.00408 +Epoch [3854/4000] Training [9/39] Loss: 0.12961 +Epoch [3854/4000] Training [10/39] Loss: 0.12703 +Epoch [3854/4000] Training [11/39] Loss: 0.00237 +Epoch [3854/4000] Training [12/39] Loss: 0.12912 +Epoch [3854/4000] Training [13/39] Loss: 0.12897 +Epoch [3854/4000] Training [14/39] Loss: 0.12847 +Epoch [3854/4000] Training [15/39] Loss: 0.00462 +Epoch [3854/4000] Training [16/39] Loss: 0.00357 +Epoch [3854/4000] Training [17/39] Loss: 0.25265 +Epoch [3854/4000] Training [18/39] Loss: 0.00525 +Epoch [3854/4000] Training [19/39] Loss: 0.00673 +Epoch [3854/4000] Training [20/39] Loss: 0.00568 +Epoch [3854/4000] Training [21/39] Loss: 0.00434 +Epoch [3854/4000] Training [22/39] Loss: 0.00314 +Epoch [3854/4000] Training [23/39] Loss: 0.00308 +Epoch [3854/4000] Training [24/39] Loss: 0.00286 +Epoch [3854/4000] Training [25/39] Loss: 0.12787 +Epoch [3854/4000] Training [26/39] Loss: 0.00499 +Epoch [3854/4000] Training [27/39] Loss: 0.12891 +Epoch [3854/4000] Training [28/39] Loss: 0.00337 +Epoch [3854/4000] Training [29/39] Loss: 0.00725 +Epoch [3854/4000] Training [30/39] Loss: 0.00309 +Epoch [3854/4000] Training [31/39] Loss: 0.12860 +Epoch [3854/4000] Training [32/39] Loss: 0.00328 +Epoch [3854/4000] Training [33/39] Loss: 0.00619 +Epoch [3854/4000] Training [34/39] Loss: 0.00283 +Epoch [3854/4000] Training [35/39] Loss: 0.00404 +Epoch [3854/4000] Training [36/39] Loss: 0.00438 +Epoch [3854/4000] Training [37/39] Loss: 0.12883 +Epoch [3854/4000] Training [38/39] Loss: 0.13111 +Epoch [3854/4000] Training [39/39] Loss: 0.00556 +Epoch [3854/4000] Training metric {'Train/mean dice_metric': 0.9966092109680176, 'Train/mean miou_metric': 0.993657112121582, 'Train/mean f1': 0.9970428347587585, 'Train/mean precision': 0.9965479373931885, 'Train/mean recall': 0.9975380301475525, 'Train/mean hd95_metric': 0.9138656854629517} +Epoch [3854/4000] Validation [1/10] Loss: 0.72329 focal_loss 0.63745 dice_loss 0.08584 +Epoch [3854/4000] Validation [2/10] Loss: 0.50566 focal_loss 0.40671 dice_loss 0.09895 +Epoch [3854/4000] Validation [3/10] Loss: 0.40502 focal_loss 0.29333 dice_loss 0.11170 +Epoch [3854/4000] Validation [4/10] Loss: 0.89607 focal_loss 0.33136 dice_loss 0.56471 +Epoch [3854/4000] Validation [5/10] Loss: 3.13606 focal_loss 2.46201 dice_loss 0.67405 +Epoch [3854/4000] Validation [6/10] Loss: 1.33295 focal_loss 0.62055 dice_loss 0.71240 +Epoch [3854/4000] Validation [7/10] Loss: 1.17712 focal_loss 0.52356 dice_loss 0.65356 +Epoch [3854/4000] Validation [8/10] Loss: 2.42268 focal_loss 1.80367 dice_loss 0.61901 +Epoch [3854/4000] Validation [9/10] Loss: 1.54451 focal_loss 1.00061 dice_loss 0.54391 +Epoch [3854/4000] Validation [10/10] Loss: 1.89027 focal_loss 1.15596 dice_loss 0.73431 +Epoch [3854/4000] Validation metric {'Val/mean dice_metric': 0.9516294598579407, 'Val/mean miou_metric': 0.9359707832336426, 'Val/mean f1': 0.9484891295433044, 'Val/mean precision': 0.9441069960594177, 'Val/mean recall': 0.9529120922088623, 'Val/mean hd95_metric': 10.797260284423828} +Cheakpoint... +Epoch [3854/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516294598579407, 'Val/mean miou_metric': 0.9359707832336426, 'Val/mean f1': 0.9484891295433044, 'Val/mean precision': 0.9441069960594177, 'Val/mean recall': 0.9529120922088623, 'Val/mean hd95_metric': 10.797260284423828} +Epoch [3855/4000] Training [1/39] Loss: 0.00313 +Epoch [3855/4000] Training [2/39] Loss: 0.00369 +Epoch [3855/4000] Training [3/39] Loss: 0.12825 +Epoch [3855/4000] Training [4/39] Loss: 0.00514 +Epoch [3855/4000] Training [5/39] Loss: 0.00515 +Epoch [3855/4000] Training [6/39] Loss: 0.12962 +Epoch [3855/4000] Training [7/39] Loss: 0.12845 +Epoch [3855/4000] Training [8/39] Loss: 0.00398 +Epoch [3855/4000] Training [9/39] Loss: 0.00455 +Epoch [3855/4000] Training [10/39] Loss: 0.00471 +Epoch [3855/4000] Training [11/39] Loss: 0.00422 +Epoch [3855/4000] Training [12/39] Loss: 0.00778 +Epoch [3855/4000] Training [13/39] Loss: 0.00671 +Epoch [3855/4000] Training [14/39] Loss: 0.00320 +Epoch [3855/4000] Training [15/39] Loss: 0.00584 +Epoch [3855/4000] Training [16/39] Loss: 0.12968 +Epoch [3855/4000] Training [17/39] Loss: 0.00558 +Epoch [3855/4000] Training [18/39] Loss: 0.12941 +Epoch [3855/4000] Training [19/39] Loss: 0.12942 +Epoch [3855/4000] Training [20/39] Loss: 0.00600 +Epoch [3855/4000] Training [21/39] Loss: 0.00493 +Epoch [3855/4000] Training [22/39] Loss: 0.13161 +Epoch [3855/4000] Training [23/39] Loss: 0.13238 +Epoch [3855/4000] Training [24/39] Loss: 0.12873 +Epoch [3855/4000] Training [25/39] Loss: 0.00523 +Epoch [3855/4000] Training [26/39] Loss: 0.00307 +Epoch [3855/4000] Training [27/39] Loss: 0.12930 +Epoch [3855/4000] Training [28/39] Loss: 0.00339 +Epoch [3855/4000] Training [29/39] Loss: 0.00408 +Epoch [3855/4000] Training [30/39] Loss: 0.25273 +Epoch [3855/4000] Training [31/39] Loss: 0.25590 +Epoch [3855/4000] Training [32/39] Loss: 0.00496 +Epoch [3855/4000] Training [33/39] Loss: 0.12864 +Epoch [3855/4000] Training [34/39] Loss: 0.00479 +Epoch [3855/4000] Training [35/39] Loss: 0.00365 +Epoch [3855/4000] Training [36/39] Loss: 0.00362 +Epoch [3855/4000] Training [37/39] Loss: 0.00575 +Epoch [3855/4000] Training [38/39] Loss: 0.00395 +Epoch [3855/4000] Training [39/39] Loss: 0.00394 +Epoch [3855/4000] Training metric {'Train/mean dice_metric': 0.9963015913963318, 'Train/mean miou_metric': 0.9930540323257446, 'Train/mean f1': 0.99690181016922, 'Train/mean precision': 0.996464729309082, 'Train/mean recall': 0.9973392486572266, 'Train/mean hd95_metric': 0.9136634469032288} +Epoch [3855/4000] Validation [1/10] Loss: 0.70025 focal_loss 0.61406 dice_loss 0.08619 +Epoch [3855/4000] Validation [2/10] Loss: 0.49760 focal_loss 0.40071 dice_loss 0.09690 +Epoch [3855/4000] Validation [3/10] Loss: 0.38271 focal_loss 0.27177 dice_loss 0.11094 +Epoch [3855/4000] Validation [4/10] Loss: 0.90040 focal_loss 0.33459 dice_loss 0.56581 +Epoch [3855/4000] Validation [5/10] Loss: 3.02053 focal_loss 2.34667 dice_loss 0.67386 +Epoch [3855/4000] Validation [6/10] Loss: 1.34790 focal_loss 0.63500 dice_loss 0.71290 +Epoch [3855/4000] Validation [7/10] Loss: 1.18381 focal_loss 0.52934 dice_loss 0.65447 +Epoch [3855/4000] Validation [8/10] Loss: 2.33639 focal_loss 1.72296 dice_loss 0.61343 +Epoch [3855/4000] Validation [9/10] Loss: 1.54082 focal_loss 0.99616 dice_loss 0.54466 +Epoch [3855/4000] Validation [10/10] Loss: 1.91472 focal_loss 1.17881 dice_loss 0.73591 +Epoch [3855/4000] Validation metric {'Val/mean dice_metric': 0.9513943791389465, 'Val/mean miou_metric': 0.9354733824729919, 'Val/mean f1': 0.9479994177818298, 'Val/mean precision': 0.9427351951599121, 'Val/mean recall': 0.9533228278160095, 'Val/mean hd95_metric': 10.891133308410645} +Cheakpoint... +Epoch [3855/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513943791389465, 'Val/mean miou_metric': 0.9354733824729919, 'Val/mean f1': 0.9479994177818298, 'Val/mean precision': 0.9427351951599121, 'Val/mean recall': 0.9533228278160095, 'Val/mean hd95_metric': 10.891133308410645} +Epoch [3856/4000] Training [1/39] Loss: 0.00713 +Epoch [3856/4000] Training [2/39] Loss: 0.00388 +Epoch [3856/4000] Training [3/39] Loss: 0.04330 +Epoch [3856/4000] Training [4/39] Loss: 0.00541 +Epoch [3856/4000] Training [5/39] Loss: 0.00352 +Epoch [3856/4000] Training [6/39] Loss: 0.00436 +Epoch [3856/4000] Training [7/39] Loss: 0.00486 +Epoch [3856/4000] Training [8/39] Loss: 0.00504 +Epoch [3856/4000] Training [9/39] Loss: 0.12717 +Epoch [3856/4000] Training [10/39] Loss: 0.00345 +Epoch [3856/4000] Training [11/39] Loss: 0.00479 +Epoch [3856/4000] Training [12/39] Loss: 0.00374 +Epoch [3856/4000] Training [13/39] Loss: 0.00360 +Epoch [3856/4000] Training [14/39] Loss: 0.00607 +Epoch [3856/4000] Training [15/39] Loss: 0.00691 +Epoch [3856/4000] Training [16/39] Loss: 0.00444 +Epoch [3856/4000] Training [17/39] Loss: 0.00580 +Epoch [3856/4000] Training [18/39] Loss: 0.00398 +Epoch [3856/4000] Training [19/39] Loss: 0.00631 +Epoch [3856/4000] Training [20/39] Loss: 0.00355 +Epoch [3856/4000] Training [21/39] Loss: 0.12944 +Epoch [3856/4000] Training [22/39] Loss: 0.00893 +Epoch [3856/4000] Training [23/39] Loss: 0.12956 +Epoch [3856/4000] Training [24/39] Loss: 0.00365 +Epoch [3856/4000] Training [25/39] Loss: 0.00378 +Epoch [3856/4000] Training [26/39] Loss: 0.00263 +Epoch [3856/4000] Training [27/39] Loss: 0.00809 +Epoch [3856/4000] Training [28/39] Loss: 0.00549 +Epoch [3856/4000] Training [29/39] Loss: 0.12905 +Epoch [3856/4000] Training [30/39] Loss: 0.12763 +Epoch [3856/4000] Training [31/39] Loss: 0.12783 +Epoch [3856/4000] Training [32/39] Loss: 0.12891 +Epoch [3856/4000] Training [33/39] Loss: 0.00464 +Epoch [3856/4000] Training [34/39] Loss: 0.00588 +Epoch [3856/4000] Training [35/39] Loss: 0.00417 +Epoch [3856/4000] Training [36/39] Loss: 0.12933 +Epoch [3856/4000] Training [37/39] Loss: 0.00506 +Epoch [3856/4000] Training [38/39] Loss: 0.00493 +Epoch [3856/4000] Training [39/39] Loss: 0.00483 +Epoch [3856/4000] Training metric {'Train/mean dice_metric': 0.996671736240387, 'Train/mean miou_metric': 0.9937664866447449, 'Train/mean f1': 0.9971240162849426, 'Train/mean precision': 0.9966942667961121, 'Train/mean recall': 0.9975541830062866, 'Train/mean hd95_metric': 0.9218928217887878} +Epoch [3856/4000] Validation [1/10] Loss: 0.70736 focal_loss 0.62176 dice_loss 0.08560 +Epoch [3856/4000] Validation [2/10] Loss: 0.50087 focal_loss 0.40145 dice_loss 0.09942 +Epoch [3856/4000] Validation [3/10] Loss: 0.39856 focal_loss 0.28675 dice_loss 0.11181 +Epoch [3856/4000] Validation [4/10] Loss: 0.89267 focal_loss 0.32802 dice_loss 0.56465 +Epoch [3856/4000] Validation [5/10] Loss: 3.06805 focal_loss 2.39405 dice_loss 0.67400 +Epoch [3856/4000] Validation [6/10] Loss: 1.32827 focal_loss 0.61562 dice_loss 0.71265 +Epoch [3856/4000] Validation [7/10] Loss: 1.17135 focal_loss 0.51895 dice_loss 0.65240 +Epoch [3856/4000] Validation [8/10] Loss: 2.38022 focal_loss 1.76170 dice_loss 0.61853 +Epoch [3856/4000] Validation [9/10] Loss: 1.54260 focal_loss 0.99860 dice_loss 0.54400 +Epoch [3856/4000] Validation [10/10] Loss: 1.88126 focal_loss 1.14625 dice_loss 0.73501 +Epoch [3856/4000] Validation metric {'Val/mean dice_metric': 0.9516851305961609, 'Val/mean miou_metric': 0.9360597133636475, 'Val/mean f1': 0.9486393928527832, 'Val/mean precision': 0.9442811012268066, 'Val/mean recall': 0.9530379176139832, 'Val/mean hd95_metric': 10.834478378295898} +Cheakpoint... +Epoch [3856/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516851305961609, 'Val/mean miou_metric': 0.9360597133636475, 'Val/mean f1': 0.9486393928527832, 'Val/mean precision': 0.9442811012268066, 'Val/mean recall': 0.9530379176139832, 'Val/mean hd95_metric': 10.834478378295898} +Epoch [3857/4000] Training [1/39] Loss: 0.12948 +Epoch [3857/4000] Training [2/39] Loss: 0.12763 +Epoch [3857/4000] Training [3/39] Loss: 0.00485 +Epoch [3857/4000] Training [4/39] Loss: 0.00560 +Epoch [3857/4000] Training [5/39] Loss: 0.00581 +Epoch [3857/4000] Training [6/39] Loss: 0.00768 +Epoch [3857/4000] Training [7/39] Loss: 0.00771 +Epoch [3857/4000] Training [8/39] Loss: 0.08464 +Epoch [3857/4000] Training [9/39] Loss: 0.12787 +Epoch [3857/4000] Training [10/39] Loss: 0.00390 +Epoch [3857/4000] Training [11/39] Loss: 0.00442 +Epoch [3857/4000] Training [12/39] Loss: 0.00361 +Epoch [3857/4000] Training [13/39] Loss: 0.13414 +Epoch [3857/4000] Training [14/39] Loss: 0.00478 +Epoch [3857/4000] Training [15/39] Loss: 0.00348 +Epoch [3857/4000] Training [16/39] Loss: 0.00445 +Epoch [3857/4000] Training [17/39] Loss: 0.00492 +Epoch [3857/4000] Training [18/39] Loss: 0.00530 +Epoch [3857/4000] Training [19/39] Loss: 0.00522 +Epoch [3857/4000] Training [20/39] Loss: 0.25336 +Epoch [3857/4000] Training [21/39] Loss: 0.00331 +Epoch [3857/4000] Training [22/39] Loss: 0.00432 +Epoch [3857/4000] Training [23/39] Loss: 0.00562 +Epoch [3857/4000] Training [24/39] Loss: 0.00361 +Epoch [3857/4000] Training [25/39] Loss: 0.00607 +Epoch [3857/4000] Training [26/39] Loss: 0.12728 +Epoch [3857/4000] Training [27/39] Loss: 0.00594 +Epoch [3857/4000] Training [28/39] Loss: 0.13035 +Epoch [3857/4000] Training [29/39] Loss: 0.00361 +Epoch [3857/4000] Training [30/39] Loss: 0.00468 +Epoch [3857/4000] Training [31/39] Loss: 0.04484 +Epoch [3857/4000] Training [32/39] Loss: 0.00561 +Epoch [3857/4000] Training [33/39] Loss: 0.00432 +Epoch [3857/4000] Training [34/39] Loss: 0.00781 +Epoch [3857/4000] Training [35/39] Loss: 0.00599 +Epoch [3857/4000] Training [36/39] Loss: 0.00586 +Epoch [3857/4000] Training [37/39] Loss: 0.12784 +Epoch [3857/4000] Training [38/39] Loss: 0.13241 +Epoch [3857/4000] Training [39/39] Loss: 0.12789 +Epoch [3857/4000] Training metric {'Train/mean dice_metric': 0.9953925609588623, 'Train/mean miou_metric': 0.9920707941055298, 'Train/mean f1': 0.9968223571777344, 'Train/mean precision': 0.9963554739952087, 'Train/mean recall': 0.997289776802063, 'Train/mean hd95_metric': 0.9302984476089478} +Epoch [3857/4000] Validation [1/10] Loss: 0.71937 focal_loss 0.63375 dice_loss 0.08562 +Epoch [3857/4000] Validation [2/10] Loss: 0.50832 focal_loss 0.40835 dice_loss 0.09996 +Epoch [3857/4000] Validation [3/10] Loss: 0.40508 focal_loss 0.29324 dice_loss 0.11184 +Epoch [3857/4000] Validation [4/10] Loss: 0.89659 focal_loss 0.33211 dice_loss 0.56448 +Epoch [3857/4000] Validation [5/10] Loss: 3.11348 focal_loss 2.43951 dice_loss 0.67397 +Epoch [3857/4000] Validation [6/10] Loss: 1.33018 focal_loss 0.61849 dice_loss 0.71169 +Epoch [3857/4000] Validation [7/10] Loss: 1.17961 focal_loss 0.52702 dice_loss 0.65259 +Epoch [3857/4000] Validation [8/10] Loss: 2.41035 focal_loss 1.79051 dice_loss 0.61984 +Epoch [3857/4000] Validation [9/10] Loss: 1.55169 focal_loss 1.00787 dice_loss 0.54381 +Epoch [3857/4000] Validation [10/10] Loss: 1.89183 focal_loss 1.15726 dice_loss 0.73458 +Epoch [3857/4000] Validation metric {'Val/mean dice_metric': 0.9505646824836731, 'Val/mean miou_metric': 0.9345804452896118, 'Val/mean f1': 0.9482519030570984, 'Val/mean precision': 0.9440219402313232, 'Val/mean recall': 0.9525200128555298, 'Val/mean hd95_metric': 10.83353042602539} +Cheakpoint... +Epoch [3857/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505646824836731, 'Val/mean miou_metric': 0.9345804452896118, 'Val/mean f1': 0.9482519030570984, 'Val/mean precision': 0.9440219402313232, 'Val/mean recall': 0.9525200128555298, 'Val/mean hd95_metric': 10.83353042602539} +Epoch [3858/4000] Training [1/39] Loss: 0.00398 +Epoch [3858/4000] Training [2/39] Loss: 0.00271 +Epoch [3858/4000] Training [3/39] Loss: 0.00354 +Epoch [3858/4000] Training [4/39] Loss: 0.00466 +Epoch [3858/4000] Training [5/39] Loss: 0.00449 +Epoch [3858/4000] Training [6/39] Loss: 0.00643 +Epoch [3858/4000] Training [7/39] Loss: 0.12819 +Epoch [3858/4000] Training [8/39] Loss: 0.12862 +Epoch [3858/4000] Training [9/39] Loss: 0.00375 +Epoch [3858/4000] Training [10/39] Loss: 0.00389 +Epoch [3858/4000] Training [11/39] Loss: 0.00483 +Epoch [3858/4000] Training [12/39] Loss: 0.00525 +Epoch [3858/4000] Training [13/39] Loss: 0.00456 +Epoch [3858/4000] Training [14/39] Loss: 0.00611 +Epoch [3858/4000] Training [15/39] Loss: 0.00341 +Epoch [3858/4000] Training [16/39] Loss: 0.00850 +Epoch [3858/4000] Training [17/39] Loss: 0.25200 +Epoch [3858/4000] Training [18/39] Loss: 0.00422 +Epoch [3858/4000] Training [19/39] Loss: 0.00264 +Epoch [3858/4000] Training [20/39] Loss: 0.00589 +Epoch [3858/4000] Training [21/39] Loss: 0.00369 +Epoch [3858/4000] Training [22/39] Loss: 0.00387 +Epoch [3858/4000] Training [23/39] Loss: 0.12916 +Epoch [3858/4000] Training [24/39] Loss: 0.12871 +Epoch [3858/4000] Training [25/39] Loss: 0.00655 +Epoch [3858/4000] Training [26/39] Loss: 0.00519 +Epoch [3858/4000] Training [27/39] Loss: 0.00538 +Epoch [3858/4000] Training [28/39] Loss: 0.00333 +Epoch [3858/4000] Training [29/39] Loss: 0.00564 +Epoch [3858/4000] Training [30/39] Loss: 0.00400 +Epoch [3858/4000] Training [31/39] Loss: 0.00391 +Epoch [3858/4000] Training [32/39] Loss: 0.00294 +Epoch [3858/4000] Training [33/39] Loss: 0.00375 +Epoch [3858/4000] Training [34/39] Loss: 0.00560 +Epoch [3858/4000] Training [35/39] Loss: 0.12799 +Epoch [3858/4000] Training [36/39] Loss: 0.00440 +Epoch [3858/4000] Training [37/39] Loss: 0.00559 +Epoch [3858/4000] Training [38/39] Loss: 0.00560 +Epoch [3858/4000] Training [39/39] Loss: 0.12985 +Epoch [3858/4000] Training metric {'Train/mean dice_metric': 0.9966769218444824, 'Train/mean miou_metric': 0.9937738180160522, 'Train/mean f1': 0.9971629977226257, 'Train/mean precision': 0.9967195391654968, 'Train/mean recall': 0.9976069331169128, 'Train/mean hd95_metric': 0.8878019452095032} +Epoch [3858/4000] Validation [1/10] Loss: 0.72386 focal_loss 0.63730 dice_loss 0.08656 +Epoch [3858/4000] Validation [2/10] Loss: 0.50371 focal_loss 0.40652 dice_loss 0.09719 +Epoch [3858/4000] Validation [3/10] Loss: 0.39847 focal_loss 0.28736 dice_loss 0.11112 +Epoch [3858/4000] Validation [4/10] Loss: 0.90164 focal_loss 0.33605 dice_loss 0.56559 +Epoch [3858/4000] Validation [5/10] Loss: 3.12169 focal_loss 2.44766 dice_loss 0.67402 +Epoch [3858/4000] Validation [6/10] Loss: 1.34523 focal_loss 0.63344 dice_loss 0.71179 +Epoch [3858/4000] Validation [7/10] Loss: 1.18688 focal_loss 0.53309 dice_loss 0.65379 +Epoch [3858/4000] Validation [8/10] Loss: 2.38807 focal_loss 1.77479 dice_loss 0.61328 +Epoch [3858/4000] Validation [9/10] Loss: 1.56131 focal_loss 1.01718 dice_loss 0.54414 +Epoch [3858/4000] Validation [10/10] Loss: 1.92190 focal_loss 1.18614 dice_loss 0.73576 +Epoch [3858/4000] Validation metric {'Val/mean dice_metric': 0.951682984828949, 'Val/mean miou_metric': 0.936032772064209, 'Val/mean f1': 0.9484338760375977, 'Val/mean precision': 0.9434298276901245, 'Val/mean recall': 0.9534912705421448, 'Val/mean hd95_metric': 10.662400245666504} +Cheakpoint... +Epoch [3858/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951682984828949, 'Val/mean miou_metric': 0.936032772064209, 'Val/mean f1': 0.9484338760375977, 'Val/mean precision': 0.9434298276901245, 'Val/mean recall': 0.9534912705421448, 'Val/mean hd95_metric': 10.662400245666504} +Epoch [3859/4000] Training [1/39] Loss: 0.00345 +Epoch [3859/4000] Training [2/39] Loss: 0.00733 +Epoch [3859/4000] Training [3/39] Loss: 0.00682 +Epoch [3859/4000] Training [4/39] Loss: 0.00273 +Epoch [3859/4000] Training [5/39] Loss: 0.00713 +Epoch [3859/4000] Training [6/39] Loss: 0.00437 +Epoch [3859/4000] Training [7/39] Loss: 0.12793 +Epoch [3859/4000] Training [8/39] Loss: 0.00387 +Epoch [3859/4000] Training [9/39] Loss: 0.00450 +Epoch [3859/4000] Training [10/39] Loss: 0.00556 +Epoch [3859/4000] Training [11/39] Loss: 0.00392 +Epoch [3859/4000] Training [12/39] Loss: 0.25283 +Epoch [3859/4000] Training [13/39] Loss: 0.00533 +Epoch [3859/4000] Training [14/39] Loss: 0.00454 +Epoch [3859/4000] Training [15/39] Loss: 0.00499 +Epoch [3859/4000] Training [16/39] Loss: 0.00485 +Epoch [3859/4000] Training [17/39] Loss: 0.00701 +Epoch [3859/4000] Training [18/39] Loss: 0.00444 +Epoch [3859/4000] Training [19/39] Loss: 0.37710 +Epoch [3859/4000] Training [20/39] Loss: 0.00687 +Epoch [3859/4000] Training [21/39] Loss: 0.00395 +Epoch [3859/4000] Training [22/39] Loss: 0.00667 +Epoch [3859/4000] Training [23/39] Loss: 0.00339 +Epoch [3859/4000] Training [24/39] Loss: 0.12852 +Epoch [3859/4000] Training [25/39] Loss: 0.00702 +Epoch [3859/4000] Training [26/39] Loss: 0.00286 +Epoch [3859/4000] Training [27/39] Loss: 0.12821 +Epoch [3859/4000] Training [28/39] Loss: 0.00401 +Epoch [3859/4000] Training [29/39] Loss: 0.12960 +Epoch [3859/4000] Training [30/39] Loss: 0.00584 +Epoch [3859/4000] Training [31/39] Loss: 0.00578 +Epoch [3859/4000] Training [32/39] Loss: 0.00422 +Epoch [3859/4000] Training [33/39] Loss: 0.00273 +Epoch [3859/4000] Training [34/39] Loss: 0.00467 +Epoch [3859/4000] Training [35/39] Loss: 0.00520 +Epoch [3859/4000] Training [36/39] Loss: 0.00417 +Epoch [3859/4000] Training [37/39] Loss: 0.00465 +Epoch [3859/4000] Training [38/39] Loss: 0.13233 +Epoch [3859/4000] Training [39/39] Loss: 0.12987 +Epoch [3859/4000] Training metric {'Train/mean dice_metric': 0.9955034852027893, 'Train/mean miou_metric': 0.9923149347305298, 'Train/mean f1': 0.9968980550765991, 'Train/mean precision': 0.9963608980178833, 'Train/mean recall': 0.9974359273910522, 'Train/mean hd95_metric': 1.0109589099884033} +Epoch [3859/4000] Validation [1/10] Loss: 0.70397 focal_loss 0.61798 dice_loss 0.08599 +Epoch [3859/4000] Validation [2/10] Loss: 0.49948 focal_loss 0.40257 dice_loss 0.09691 +Epoch [3859/4000] Validation [3/10] Loss: 0.38662 focal_loss 0.27578 dice_loss 0.11084 +Epoch [3859/4000] Validation [4/10] Loss: 0.89982 focal_loss 0.33444 dice_loss 0.56538 +Epoch [3859/4000] Validation [5/10] Loss: 3.04103 focal_loss 2.36713 dice_loss 0.67390 +Epoch [3859/4000] Validation [6/10] Loss: 1.34682 focal_loss 0.63418 dice_loss 0.71264 +Epoch [3859/4000] Validation [7/10] Loss: 1.18563 focal_loss 0.53198 dice_loss 0.65365 +Epoch [3859/4000] Validation [8/10] Loss: 2.35681 focal_loss 1.74409 dice_loss 0.61272 +Epoch [3859/4000] Validation [9/10] Loss: 1.53723 focal_loss 0.99260 dice_loss 0.54463 +Epoch [3859/4000] Validation [10/10] Loss: 1.91703 focal_loss 1.18127 dice_loss 0.73576 +Epoch [3859/4000] Validation metric {'Val/mean dice_metric': 0.9507465958595276, 'Val/mean miou_metric': 0.934870719909668, 'Val/mean f1': 0.9483710527420044, 'Val/mean precision': 0.9432108402252197, 'Val/mean recall': 0.9535880088806152, 'Val/mean hd95_metric': 10.872700691223145} +Cheakpoint... +Epoch [3859/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507465958595276, 'Val/mean miou_metric': 0.934870719909668, 'Val/mean f1': 0.9483710527420044, 'Val/mean precision': 0.9432108402252197, 'Val/mean recall': 0.9535880088806152, 'Val/mean hd95_metric': 10.872700691223145} +Epoch [3860/4000] Training [1/39] Loss: 0.00504 +Epoch [3860/4000] Training [2/39] Loss: 0.00600 +Epoch [3860/4000] Training [3/39] Loss: 0.00652 +Epoch [3860/4000] Training [4/39] Loss: 0.00281 +Epoch [3860/4000] Training [5/39] Loss: 0.13035 +Epoch [3860/4000] Training [6/39] Loss: 0.00552 +Epoch [3860/4000] Training [7/39] Loss: 0.00506 +Epoch [3860/4000] Training [8/39] Loss: 0.00400 +Epoch [3860/4000] Training [9/39] Loss: 0.00460 +Epoch [3860/4000] Training [10/39] Loss: 0.12795 +Epoch [3860/4000] Training [11/39] Loss: 0.01259 +Epoch [3860/4000] Training [12/39] Loss: 0.00601 +Epoch [3860/4000] Training [13/39] Loss: 0.25206 +Epoch [3860/4000] Training [14/39] Loss: 0.00376 +Epoch [3860/4000] Training [15/39] Loss: 0.13167 +Epoch [3860/4000] Training [16/39] Loss: 0.12751 +Epoch [3860/4000] Training [17/39] Loss: 0.00534 +Epoch [3860/4000] Training [18/39] Loss: 0.00408 +Epoch [3860/4000] Training [19/39] Loss: 0.00456 +Epoch [3860/4000] Training [20/39] Loss: 0.00466 +Epoch [3860/4000] Training [21/39] Loss: 0.00357 +Epoch [3860/4000] Training [22/39] Loss: 0.00585 +Epoch [3860/4000] Training [23/39] Loss: 0.00670 +Epoch [3860/4000] Training [24/39] Loss: 0.00617 +Epoch [3860/4000] Training [25/39] Loss: 0.00544 +Epoch [3860/4000] Training [26/39] Loss: 0.00478 +Epoch [3860/4000] Training [27/39] Loss: 0.12794 +Epoch [3860/4000] Training [28/39] Loss: 0.00365 +Epoch [3860/4000] Training [29/39] Loss: 0.00557 +Epoch [3860/4000] Training [30/39] Loss: 0.12797 +Epoch [3860/4000] Training [31/39] Loss: 0.00411 +Epoch [3860/4000] Training [32/39] Loss: 0.00596 +Epoch [3860/4000] Training [33/39] Loss: 0.00475 +Epoch [3860/4000] Training [34/39] Loss: 0.12826 +Epoch [3860/4000] Training [35/39] Loss: 0.00497 +Epoch [3860/4000] Training [36/39] Loss: 0.00466 +Epoch [3860/4000] Training [37/39] Loss: 0.00467 +Epoch [3860/4000] Training [38/39] Loss: 0.00538 +Epoch [3860/4000] Training [39/39] Loss: 0.00390 +Epoch [3860/4000] Training metric {'Train/mean dice_metric': 0.9963776469230652, 'Train/mean miou_metric': 0.9932182431221008, 'Train/mean f1': 0.9968377947807312, 'Train/mean precision': 0.996399462223053, 'Train/mean recall': 0.9972766637802124, 'Train/mean hd95_metric': 0.9292694330215454} +Epoch [3860/4000] Validation [1/10] Loss: 0.70472 focal_loss 0.61848 dice_loss 0.08624 +Epoch [3860/4000] Validation [2/10] Loss: 0.49854 focal_loss 0.40118 dice_loss 0.09736 +Epoch [3860/4000] Validation [3/10] Loss: 0.38678 focal_loss 0.27571 dice_loss 0.11108 +Epoch [3860/4000] Validation [4/10] Loss: 0.89709 focal_loss 0.33189 dice_loss 0.56520 +Epoch [3860/4000] Validation [5/10] Loss: 3.03419 focal_loss 2.36019 dice_loss 0.67401 +Epoch [3860/4000] Validation [6/10] Loss: 1.33626 focal_loss 0.62418 dice_loss 0.71208 +Epoch [3860/4000] Validation [7/10] Loss: 1.17655 focal_loss 0.52358 dice_loss 0.65298 +Epoch [3860/4000] Validation [8/10] Loss: 2.33581 focal_loss 1.72205 dice_loss 0.61376 +Epoch [3860/4000] Validation [9/10] Loss: 1.53362 focal_loss 0.98922 dice_loss 0.54440 +Epoch [3860/4000] Validation [10/10] Loss: 1.89865 focal_loss 1.16345 dice_loss 0.73520 +Epoch [3860/4000] Validation metric {'Val/mean dice_metric': 0.9514346122741699, 'Val/mean miou_metric': 0.9355800747871399, 'Val/mean f1': 0.948419988155365, 'Val/mean precision': 0.9435178637504578, 'Val/mean recall': 0.953373372554779, 'Val/mean hd95_metric': 10.706903457641602} +Cheakpoint... +Epoch [3860/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514346122741699, 'Val/mean miou_metric': 0.9355800747871399, 'Val/mean f1': 0.948419988155365, 'Val/mean precision': 0.9435178637504578, 'Val/mean recall': 0.953373372554779, 'Val/mean hd95_metric': 10.706903457641602} +Epoch [3861/4000] Training [1/39] Loss: 0.13260 +Epoch [3861/4000] Training [2/39] Loss: 0.00507 +Epoch [3861/4000] Training [3/39] Loss: 0.00575 +Epoch [3861/4000] Training [4/39] Loss: 0.12927 +Epoch [3861/4000] Training [5/39] Loss: 0.00335 +Epoch [3861/4000] Training [6/39] Loss: 0.12808 +Epoch [3861/4000] Training [7/39] Loss: 0.00571 +Epoch [3861/4000] Training [8/39] Loss: 0.12851 +Epoch [3861/4000] Training [9/39] Loss: 0.00663 +Epoch [3861/4000] Training [10/39] Loss: 0.00600 +Epoch [3861/4000] Training [11/39] Loss: 0.12977 +Epoch [3861/4000] Training [12/39] Loss: 0.00309 +Epoch [3861/4000] Training [13/39] Loss: 0.12865 +Epoch [3861/4000] Training [14/39] Loss: 0.00354 +Epoch [3861/4000] Training [15/39] Loss: 0.16722 +Epoch [3861/4000] Training [16/39] Loss: 0.00467 +Epoch [3861/4000] Training [17/39] Loss: 0.13029 +Epoch [3861/4000] Training [18/39] Loss: 0.12911 +Epoch [3861/4000] Training [19/39] Loss: 0.00288 +Epoch [3861/4000] Training [20/39] Loss: 0.00330 +Epoch [3861/4000] Training [21/39] Loss: 0.00357 +Epoch [3861/4000] Training [22/39] Loss: 0.12847 +Epoch [3861/4000] Training [23/39] Loss: 0.00465 +Epoch [3861/4000] Training [24/39] Loss: 0.00468 +Epoch [3861/4000] Training [25/39] Loss: 0.00509 +Epoch [3861/4000] Training [26/39] Loss: 0.00397 +Epoch [3861/4000] Training [27/39] Loss: 0.13006 +Epoch [3861/4000] Training [28/39] Loss: 0.00346 +Epoch [3861/4000] Training [29/39] Loss: 0.00674 +Epoch [3861/4000] Training [30/39] Loss: 0.00463 +Epoch [3861/4000] Training [31/39] Loss: 0.00439 +Epoch [3861/4000] Training [32/39] Loss: 0.12847 +Epoch [3861/4000] Training [33/39] Loss: 0.00344 +Epoch [3861/4000] Training [34/39] Loss: 0.00284 +Epoch [3861/4000] Training [35/39] Loss: 0.00436 +Epoch [3861/4000] Training [36/39] Loss: 0.00326 +Epoch [3861/4000] Training [37/39] Loss: 0.00460 +Epoch [3861/4000] Training [38/39] Loss: 0.00336 +Epoch [3861/4000] Training [39/39] Loss: 0.00494 +Epoch [3861/4000] Training metric {'Train/mean dice_metric': 0.9965419173240662, 'Train/mean miou_metric': 0.9935263395309448, 'Train/mean f1': 0.9969649910926819, 'Train/mean precision': 0.9964959621429443, 'Train/mean recall': 0.9974344968795776, 'Train/mean hd95_metric': 0.9116900563240051} +Epoch [3861/4000] Validation [1/10] Loss: 0.71015 focal_loss 0.62389 dice_loss 0.08627 +Epoch [3861/4000] Validation [2/10] Loss: 0.50303 focal_loss 0.40419 dice_loss 0.09884 +Epoch [3861/4000] Validation [3/10] Loss: 0.39225 focal_loss 0.28103 dice_loss 0.11123 +Epoch [3861/4000] Validation [4/10] Loss: 0.89589 focal_loss 0.33105 dice_loss 0.56485 +Epoch [3861/4000] Validation [5/10] Loss: 3.05296 focal_loss 2.37887 dice_loss 0.67409 +Epoch [3861/4000] Validation [6/10] Loss: 1.33245 focal_loss 0.61998 dice_loss 0.71247 +Epoch [3861/4000] Validation [7/10] Loss: 1.17928 focal_loss 0.52717 dice_loss 0.65210 +Epoch [3861/4000] Validation [8/10] Loss: 2.35941 focal_loss 1.74451 dice_loss 0.61491 +Epoch [3861/4000] Validation [9/10] Loss: 1.54790 focal_loss 1.00362 dice_loss 0.54428 +Epoch [3861/4000] Validation [10/10] Loss: 1.90157 focal_loss 1.16625 dice_loss 0.73532 +Epoch [3861/4000] Validation metric {'Val/mean dice_metric': 0.9515477418899536, 'Val/mean miou_metric': 0.9358243942260742, 'Val/mean f1': 0.9484979510307312, 'Val/mean precision': 0.9436559081077576, 'Val/mean recall': 0.9533899426460266, 'Val/mean hd95_metric': 10.788007736206055} +Cheakpoint... +Epoch [3861/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515477418899536, 'Val/mean miou_metric': 0.9358243942260742, 'Val/mean f1': 0.9484979510307312, 'Val/mean precision': 0.9436559081077576, 'Val/mean recall': 0.9533899426460266, 'Val/mean hd95_metric': 10.788007736206055} +Epoch [3862/4000] Training [1/39] Loss: 0.00480 +Epoch [3862/4000] Training [2/39] Loss: 0.00478 +Epoch [3862/4000] Training [3/39] Loss: 0.00716 +Epoch [3862/4000] Training [4/39] Loss: 0.00381 +Epoch [3862/4000] Training [5/39] Loss: 0.00241 +Epoch [3862/4000] Training [6/39] Loss: 0.00501 +Epoch [3862/4000] Training [7/39] Loss: 0.12911 +Epoch [3862/4000] Training [8/39] Loss: 0.00349 +Epoch [3862/4000] Training [9/39] Loss: 0.13052 +Epoch [3862/4000] Training [10/39] Loss: 0.00367 +Epoch [3862/4000] Training [11/39] Loss: 0.00674 +Epoch [3862/4000] Training [12/39] Loss: 0.00592 +Epoch [3862/4000] Training [13/39] Loss: 0.00663 +Epoch [3862/4000] Training [14/39] Loss: 0.00671 +Epoch [3862/4000] Training [15/39] Loss: 0.00538 +Epoch [3862/4000] Training [16/39] Loss: 0.00465 +Epoch [3862/4000] Training [17/39] Loss: 0.00356 +Epoch [3862/4000] Training [18/39] Loss: 0.00401 +Epoch [3862/4000] Training [19/39] Loss: 0.00424 +Epoch [3862/4000] Training [20/39] Loss: 0.00471 +Epoch [3862/4000] Training [21/39] Loss: 0.12873 +Epoch [3862/4000] Training [22/39] Loss: 0.12930 +Epoch [3862/4000] Training [23/39] Loss: 0.00643 +Epoch [3862/4000] Training [24/39] Loss: 0.12815 +Epoch [3862/4000] Training [25/39] Loss: 0.00327 +Epoch [3862/4000] Training [26/39] Loss: 0.00475 +Epoch [3862/4000] Training [27/39] Loss: 0.04185 +Epoch [3862/4000] Training [28/39] Loss: 0.00664 +Epoch [3862/4000] Training [29/39] Loss: 0.12835 +Epoch [3862/4000] Training [30/39] Loss: 0.12798 +Epoch [3862/4000] Training [31/39] Loss: 0.00624 +Epoch [3862/4000] Training [32/39] Loss: 0.00602 +Epoch [3862/4000] Training [33/39] Loss: 0.13118 +Epoch [3862/4000] Training [34/39] Loss: 0.00547 +Epoch [3862/4000] Training [35/39] Loss: 0.00636 +Epoch [3862/4000] Training [36/39] Loss: 0.12745 +Epoch [3862/4000] Training [37/39] Loss: 0.00571 +Epoch [3862/4000] Training [38/39] Loss: 0.00329 +Epoch [3862/4000] Training [39/39] Loss: 0.00748 +Epoch [3862/4000] Training metric {'Train/mean dice_metric': 0.9953607320785522, 'Train/mean miou_metric': 0.9920076131820679, 'Train/mean f1': 0.9968120455741882, 'Train/mean precision': 0.996328592300415, 'Train/mean recall': 0.9972957968711853, 'Train/mean hd95_metric': 1.002266526222229} +Epoch [3862/4000] Validation [1/10] Loss: 0.72807 focal_loss 0.64110 dice_loss 0.08697 +Epoch [3862/4000] Validation [2/10] Loss: 0.50289 focal_loss 0.40647 dice_loss 0.09641 +Epoch [3862/4000] Validation [3/10] Loss: 0.38965 focal_loss 0.27910 dice_loss 0.11056 +Epoch [3862/4000] Validation [4/10] Loss: 0.90350 focal_loss 0.33795 dice_loss 0.56555 +Epoch [3862/4000] Validation [5/10] Loss: 3.08893 focal_loss 2.41511 dice_loss 0.67382 +Epoch [3862/4000] Validation [6/10] Loss: 1.35051 focal_loss 0.63729 dice_loss 0.71322 +Epoch [3862/4000] Validation [7/10] Loss: 1.18939 focal_loss 0.53400 dice_loss 0.65539 +Epoch [3862/4000] Validation [8/10] Loss: 2.35135 focal_loss 1.74019 dice_loss 0.61117 +Epoch [3862/4000] Validation [9/10] Loss: 1.56558 focal_loss 1.02132 dice_loss 0.54426 +Epoch [3862/4000] Validation [10/10] Loss: 1.92798 focal_loss 1.19189 dice_loss 0.73609 +Epoch [3862/4000] Validation metric {'Val/mean dice_metric': 0.9506077170372009, 'Val/mean miou_metric': 0.9345763921737671, 'Val/mean f1': 0.9481568932533264, 'Val/mean precision': 0.9426678419113159, 'Val/mean recall': 0.9537104368209839, 'Val/mean hd95_metric': 10.805889129638672} +Cheakpoint... +Epoch [3862/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506077170372009, 'Val/mean miou_metric': 0.9345763921737671, 'Val/mean f1': 0.9481568932533264, 'Val/mean precision': 0.9426678419113159, 'Val/mean recall': 0.9537104368209839, 'Val/mean hd95_metric': 10.805889129638672} +Epoch [3863/4000] Training [1/39] Loss: 0.00513 +Epoch [3863/4000] Training [2/39] Loss: 0.00287 +Epoch [3863/4000] Training [3/39] Loss: 0.00399 +Epoch [3863/4000] Training [4/39] Loss: 0.00377 +Epoch [3863/4000] Training [5/39] Loss: 0.00561 +Epoch [3863/4000] Training [6/39] Loss: 0.00532 +Epoch [3863/4000] Training [7/39] Loss: 0.00340 +Epoch [3863/4000] Training [8/39] Loss: 0.00406 +Epoch [3863/4000] Training [9/39] Loss: 0.01117 +Epoch [3863/4000] Training [10/39] Loss: 0.12839 +Epoch [3863/4000] Training [11/39] Loss: 0.04026 +Epoch [3863/4000] Training [12/39] Loss: 0.12962 +Epoch [3863/4000] Training [13/39] Loss: 0.00422 +Epoch [3863/4000] Training [14/39] Loss: 0.00513 +Epoch [3863/4000] Training [15/39] Loss: 0.00463 +Epoch [3863/4000] Training [16/39] Loss: 0.00420 +Epoch [3863/4000] Training [17/39] Loss: 0.00422 +Epoch [3863/4000] Training [18/39] Loss: 0.00436 +Epoch [3863/4000] Training [19/39] Loss: 0.12864 +Epoch [3863/4000] Training [20/39] Loss: 0.00581 +Epoch [3863/4000] Training [21/39] Loss: 0.00460 +Epoch [3863/4000] Training [22/39] Loss: 0.00356 +Epoch [3863/4000] Training [23/39] Loss: 0.00500 +Epoch [3863/4000] Training [24/39] Loss: 0.12874 +Epoch [3863/4000] Training [25/39] Loss: 0.00346 +Epoch [3863/4000] Training [26/39] Loss: 0.00418 +Epoch [3863/4000] Training [27/39] Loss: 0.00543 +Epoch [3863/4000] Training [28/39] Loss: 0.00531 +Epoch [3863/4000] Training [29/39] Loss: 0.12971 +Epoch [3863/4000] Training [30/39] Loss: 0.00500 +Epoch [3863/4000] Training [31/39] Loss: 0.08553 +Epoch [3863/4000] Training [32/39] Loss: 0.00374 +Epoch [3863/4000] Training [33/39] Loss: 0.12875 +Epoch [3863/4000] Training [34/39] Loss: 0.12868 +Epoch [3863/4000] Training [35/39] Loss: 0.12951 +Epoch [3863/4000] Training [36/39] Loss: 0.00306 +Epoch [3863/4000] Training [37/39] Loss: 0.00437 +Epoch [3863/4000] Training [38/39] Loss: 0.00712 +Epoch [3863/4000] Training [39/39] Loss: 0.12986 +Epoch [3863/4000] Training metric {'Train/mean dice_metric': 0.996522843837738, 'Train/mean miou_metric': 0.9934873580932617, 'Train/mean f1': 0.9970100522041321, 'Train/mean precision': 0.9965500235557556, 'Train/mean recall': 0.9974703788757324, 'Train/mean hd95_metric': 0.9330804347991943} +Epoch [3863/4000] Validation [1/10] Loss: 0.71023 focal_loss 0.62375 dice_loss 0.08649 +Epoch [3863/4000] Validation [2/10] Loss: 0.50605 focal_loss 0.40801 dice_loss 0.09804 +Epoch [3863/4000] Validation [3/10] Loss: 0.38452 focal_loss 0.27375 dice_loss 0.11077 +Epoch [3863/4000] Validation [4/10] Loss: 0.90268 focal_loss 0.33735 dice_loss 0.56533 +Epoch [3863/4000] Validation [5/10] Loss: 3.02516 focal_loss 2.35127 dice_loss 0.67389 +Epoch [3863/4000] Validation [6/10] Loss: 1.34919 focal_loss 0.63621 dice_loss 0.71299 +Epoch [3863/4000] Validation [7/10] Loss: 1.18989 focal_loss 0.53514 dice_loss 0.65475 +Epoch [3863/4000] Validation [8/10] Loss: 2.34471 focal_loss 1.73124 dice_loss 0.61347 +Epoch [3863/4000] Validation [9/10] Loss: 1.55130 focal_loss 1.00717 dice_loss 0.54413 +Epoch [3863/4000] Validation [10/10] Loss: 1.92376 focal_loss 1.18805 dice_loss 0.73571 +Epoch [3863/4000] Validation metric {'Val/mean dice_metric': 0.9515872597694397, 'Val/mean miou_metric': 0.9358341693878174, 'Val/mean f1': 0.9483867287635803, 'Val/mean precision': 0.9430937767028809, 'Val/mean recall': 0.9537394642829895, 'Val/mean hd95_metric': 10.770623207092285} +Cheakpoint... +Epoch [3863/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515872597694397, 'Val/mean miou_metric': 0.9358341693878174, 'Val/mean f1': 0.9483867287635803, 'Val/mean precision': 0.9430937767028809, 'Val/mean recall': 0.9537394642829895, 'Val/mean hd95_metric': 10.770623207092285} +Epoch [3864/4000] Training [1/39] Loss: 0.00651 +Epoch [3864/4000] Training [2/39] Loss: 0.00333 +Epoch [3864/4000] Training [3/39] Loss: 0.00479 +Epoch [3864/4000] Training [4/39] Loss: 0.12902 +Epoch [3864/4000] Training [5/39] Loss: 0.00414 +Epoch [3864/4000] Training [6/39] Loss: 0.00757 +Epoch [3864/4000] Training [7/39] Loss: 0.12883 +Epoch [3864/4000] Training [8/39] Loss: 0.00287 +Epoch [3864/4000] Training [9/39] Loss: 0.00372 +Epoch [3864/4000] Training [10/39] Loss: 0.00534 +Epoch [3864/4000] Training [11/39] Loss: 0.00386 +Epoch [3864/4000] Training [12/39] Loss: 0.00709 +Epoch [3864/4000] Training [13/39] Loss: 0.12977 +Epoch [3864/4000] Training [14/39] Loss: 0.12829 +Epoch [3864/4000] Training [15/39] Loss: 0.12867 +Epoch [3864/4000] Training [16/39] Loss: 0.12921 +Epoch [3864/4000] Training [17/39] Loss: 0.13363 +Epoch [3864/4000] Training [18/39] Loss: 0.00443 +Epoch [3864/4000] Training [19/39] Loss: 0.00469 +Epoch [3864/4000] Training [20/39] Loss: 0.00413 +Epoch [3864/4000] Training [21/39] Loss: 0.00411 +Epoch [3864/4000] Training [22/39] Loss: 0.00475 +Epoch [3864/4000] Training [23/39] Loss: 0.13103 +Epoch [3864/4000] Training [24/39] Loss: 0.12967 +Epoch [3864/4000] Training [25/39] Loss: 0.00451 +Epoch [3864/4000] Training [26/39] Loss: 0.00354 +Epoch [3864/4000] Training [27/39] Loss: 0.00428 +Epoch [3864/4000] Training [28/39] Loss: 0.00388 +Epoch [3864/4000] Training [29/39] Loss: 0.12920 +Epoch [3864/4000] Training [30/39] Loss: 0.12847 +Epoch [3864/4000] Training [31/39] Loss: 0.00566 +Epoch [3864/4000] Training [32/39] Loss: 0.00595 +Epoch [3864/4000] Training [33/39] Loss: 0.13199 +Epoch [3864/4000] Training [34/39] Loss: 0.00336 +Epoch [3864/4000] Training [35/39] Loss: 0.12787 +Epoch [3864/4000] Training [36/39] Loss: 0.12892 +Epoch [3864/4000] Training [37/39] Loss: 0.00370 +Epoch [3864/4000] Training [38/39] Loss: 0.00597 +Epoch [3864/4000] Training [39/39] Loss: 0.00704 +Epoch [3864/4000] Training metric {'Train/mean dice_metric': 0.996518075466156, 'Train/mean miou_metric': 0.9934768080711365, 'Train/mean f1': 0.9969280362129211, 'Train/mean precision': 0.9964466691017151, 'Train/mean recall': 0.9974099397659302, 'Train/mean hd95_metric': 0.8960829377174377} +Epoch [3864/4000] Validation [1/10] Loss: 0.72130 focal_loss 0.63484 dice_loss 0.08645 +Epoch [3864/4000] Validation [2/10] Loss: 0.50707 focal_loss 0.40863 dice_loss 0.09843 +Epoch [3864/4000] Validation [3/10] Loss: 0.39348 focal_loss 0.28250 dice_loss 0.11098 +Epoch [3864/4000] Validation [4/10] Loss: 0.89903 focal_loss 0.33395 dice_loss 0.56508 +Epoch [3864/4000] Validation [5/10] Loss: 3.08001 focal_loss 2.40597 dice_loss 0.67404 +Epoch [3864/4000] Validation [6/10] Loss: 1.34306 focal_loss 0.63076 dice_loss 0.71230 +Epoch [3864/4000] Validation [7/10] Loss: 1.18770 focal_loss 0.53296 dice_loss 0.65475 +Epoch [3864/4000] Validation [8/10] Loss: 2.38831 focal_loss 1.77337 dice_loss 0.61494 +Epoch [3864/4000] Validation [9/10] Loss: 1.55188 focal_loss 1.00779 dice_loss 0.54408 +Epoch [3864/4000] Validation [10/10] Loss: 1.91529 focal_loss 1.17984 dice_loss 0.73545 +Epoch [3864/4000] Validation metric {'Val/mean dice_metric': 0.9515445828437805, 'Val/mean miou_metric': 0.9357813596725464, 'Val/mean f1': 0.9482409358024597, 'Val/mean precision': 0.9433293342590332, 'Val/mean recall': 0.9532039761543274, 'Val/mean hd95_metric': 10.640740394592285} +Cheakpoint... +Epoch [3864/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515445828437805, 'Val/mean miou_metric': 0.9357813596725464, 'Val/mean f1': 0.9482409358024597, 'Val/mean precision': 0.9433293342590332, 'Val/mean recall': 0.9532039761543274, 'Val/mean hd95_metric': 10.640740394592285} +Epoch [3865/4000] Training [1/39] Loss: 0.00521 +Epoch [3865/4000] Training [2/39] Loss: 0.00580 +Epoch [3865/4000] Training [3/39] Loss: 0.00650 +Epoch [3865/4000] Training [4/39] Loss: 0.00339 +Epoch [3865/4000] Training [5/39] Loss: 0.00420 +Epoch [3865/4000] Training [6/39] Loss: 0.00457 +Epoch [3865/4000] Training [7/39] Loss: 0.00460 +Epoch [3865/4000] Training [8/39] Loss: 0.00520 +Epoch [3865/4000] Training [9/39] Loss: 0.00444 +Epoch [3865/4000] Training [10/39] Loss: 0.00728 +Epoch [3865/4000] Training [11/39] Loss: 0.12879 +Epoch [3865/4000] Training [12/39] Loss: 0.12826 +Epoch [3865/4000] Training [13/39] Loss: 0.00270 +Epoch [3865/4000] Training [14/39] Loss: 0.00329 +Epoch [3865/4000] Training [15/39] Loss: 0.00501 +Epoch [3865/4000] Training [16/39] Loss: 0.25541 +Epoch [3865/4000] Training [17/39] Loss: 0.00238 +Epoch [3865/4000] Training [18/39] Loss: 0.00379 +Epoch [3865/4000] Training [19/39] Loss: 0.12778 +Epoch [3865/4000] Training [20/39] Loss: 0.00563 +Epoch [3865/4000] Training [21/39] Loss: 0.25375 +Epoch [3865/4000] Training [22/39] Loss: 0.00593 +Epoch [3865/4000] Training [23/39] Loss: 0.12893 +Epoch [3865/4000] Training [24/39] Loss: 0.00469 +Epoch [3865/4000] Training [25/39] Loss: 0.00381 +Epoch [3865/4000] Training [26/39] Loss: 0.00478 +Epoch [3865/4000] Training [27/39] Loss: 0.00717 +Epoch [3865/4000] Training [28/39] Loss: 0.00495 +Epoch [3865/4000] Training [29/39] Loss: 0.13032 +Epoch [3865/4000] Training [30/39] Loss: 0.00406 +Epoch [3865/4000] Training [31/39] Loss: 0.00399 +Epoch [3865/4000] Training [32/39] Loss: 0.00341 +Epoch [3865/4000] Training [33/39] Loss: 0.00373 +Epoch [3865/4000] Training [34/39] Loss: 0.12875 +Epoch [3865/4000] Training [35/39] Loss: 0.12862 +Epoch [3865/4000] Training [36/39] Loss: 0.00662 +Epoch [3865/4000] Training [37/39] Loss: 0.00483 +Epoch [3865/4000] Training [38/39] Loss: 0.00627 +Epoch [3865/4000] Training [39/39] Loss: 0.37928 +Epoch [3865/4000] Training metric {'Train/mean dice_metric': 0.9957080483436584, 'Train/mean miou_metric': 0.9926890730857849, 'Train/mean f1': 0.9970919489860535, 'Train/mean precision': 0.9966919422149658, 'Train/mean recall': 0.9974921941757202, 'Train/mean hd95_metric': 0.9167324304580688} +Epoch [3865/4000] Validation [1/10] Loss: 0.71902 focal_loss 0.63284 dice_loss 0.08618 +Epoch [3865/4000] Validation [2/10] Loss: 0.51044 focal_loss 0.41235 dice_loss 0.09809 +Epoch [3865/4000] Validation [3/10] Loss: 0.39305 focal_loss 0.28207 dice_loss 0.11098 +Epoch [3865/4000] Validation [4/10] Loss: 0.90432 focal_loss 0.33896 dice_loss 0.56536 +Epoch [3865/4000] Validation [5/10] Loss: 3.07225 focal_loss 2.39828 dice_loss 0.67397 +Epoch [3865/4000] Validation [6/10] Loss: 1.35187 focal_loss 0.63979 dice_loss 0.71208 +Epoch [3865/4000] Validation [7/10] Loss: 1.19072 focal_loss 0.53601 dice_loss 0.65470 +Epoch [3865/4000] Validation [8/10] Loss: 2.38255 focal_loss 1.76809 dice_loss 0.61446 +Epoch [3865/4000] Validation [9/10] Loss: 1.55553 focal_loss 1.01137 dice_loss 0.54416 +Epoch [3865/4000] Validation [10/10] Loss: 1.92678 focal_loss 1.19135 dice_loss 0.73543 +Epoch [3865/4000] Validation metric {'Val/mean dice_metric': 0.9509065747261047, 'Val/mean miou_metric': 0.9351707100868225, 'Val/mean f1': 0.9486551880836487, 'Val/mean precision': 0.9437504410743713, 'Val/mean recall': 0.9536111354827881, 'Val/mean hd95_metric': 10.685340881347656} +Cheakpoint... +Epoch [3865/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509065747261047, 'Val/mean miou_metric': 0.9351707100868225, 'Val/mean f1': 0.9486551880836487, 'Val/mean precision': 0.9437504410743713, 'Val/mean recall': 0.9536111354827881, 'Val/mean hd95_metric': 10.685340881347656} +Epoch [3866/4000] Training [1/39] Loss: 0.00429 +Epoch [3866/4000] Training [2/39] Loss: 0.00303 +Epoch [3866/4000] Training [3/39] Loss: 0.00340 +Epoch [3866/4000] Training [4/39] Loss: 0.00563 +Epoch [3866/4000] Training [5/39] Loss: 0.00494 +Epoch [3866/4000] Training [6/39] Loss: 0.00452 +Epoch [3866/4000] Training [7/39] Loss: 0.00587 +Epoch [3866/4000] Training [8/39] Loss: 0.00615 +Epoch [3866/4000] Training [9/39] Loss: 0.12957 +Epoch [3866/4000] Training [10/39] Loss: 0.00424 +Epoch [3866/4000] Training [11/39] Loss: 0.00405 +Epoch [3866/4000] Training [12/39] Loss: 0.00624 +Epoch [3866/4000] Training [13/39] Loss: 0.00366 +Epoch [3866/4000] Training [14/39] Loss: 0.00336 +Epoch [3866/4000] Training [15/39] Loss: 0.00618 +Epoch [3866/4000] Training [16/39] Loss: 0.12759 +Epoch [3866/4000] Training [17/39] Loss: 0.00383 +Epoch [3866/4000] Training [18/39] Loss: 0.12860 +Epoch [3866/4000] Training [19/39] Loss: 0.00534 +Epoch [3866/4000] Training [20/39] Loss: 0.13372 +Epoch [3866/4000] Training [21/39] Loss: 0.12955 +Epoch [3866/4000] Training [22/39] Loss: 0.25423 +Epoch [3866/4000] Training [23/39] Loss: 0.00589 +Epoch [3866/4000] Training [24/39] Loss: 0.00584 +Epoch [3866/4000] Training [25/39] Loss: 0.00409 +Epoch [3866/4000] Training [26/39] Loss: 0.12821 +Epoch [3866/4000] Training [27/39] Loss: 0.12772 +Epoch [3866/4000] Training [28/39] Loss: 0.00402 +Epoch [3866/4000] Training [29/39] Loss: 0.00601 +Epoch [3866/4000] Training [30/39] Loss: 0.00488 +Epoch [3866/4000] Training [31/39] Loss: 0.00497 +Epoch [3866/4000] Training [32/39] Loss: 0.13078 +Epoch [3866/4000] Training [33/39] Loss: 0.00346 +Epoch [3866/4000] Training [34/39] Loss: 0.00246 +Epoch [3866/4000] Training [35/39] Loss: 0.00486 +Epoch [3866/4000] Training [36/39] Loss: 0.00514 +Epoch [3866/4000] Training [37/39] Loss: 0.01049 +Epoch [3866/4000] Training [38/39] Loss: 0.00686 +Epoch [3866/4000] Training [39/39] Loss: 0.00450 +Epoch [3866/4000] Training metric {'Train/mean dice_metric': 0.9964312314987183, 'Train/mean miou_metric': 0.9933157563209534, 'Train/mean f1': 0.9968128204345703, 'Train/mean precision': 0.9963123798370361, 'Train/mean recall': 0.9973137974739075, 'Train/mean hd95_metric': 0.906731367111206} +Epoch [3866/4000] Validation [1/10] Loss: 0.73690 focal_loss 0.65062 dice_loss 0.08628 +Epoch [3866/4000] Validation [2/10] Loss: 0.50714 focal_loss 0.40683 dice_loss 0.10031 +Epoch [3866/4000] Validation [3/10] Loss: 0.41598 focal_loss 0.30384 dice_loss 0.11214 +Epoch [3866/4000] Validation [4/10] Loss: 0.88907 focal_loss 0.32498 dice_loss 0.56408 +Epoch [3866/4000] Validation [5/10] Loss: 3.16212 focal_loss 2.48790 dice_loss 0.67422 +Epoch [3866/4000] Validation [6/10] Loss: 1.31871 focal_loss 0.60676 dice_loss 0.71194 +Epoch [3866/4000] Validation [7/10] Loss: 1.17278 focal_loss 0.51951 dice_loss 0.65326 +Epoch [3866/4000] Validation [8/10] Loss: 2.44164 focal_loss 1.82185 dice_loss 0.61979 +Epoch [3866/4000] Validation [9/10] Loss: 1.55694 focal_loss 1.01396 dice_loss 0.54298 +Epoch [3866/4000] Validation [10/10] Loss: 1.87536 focal_loss 1.14129 dice_loss 0.73408 +Epoch [3866/4000] Validation metric {'Val/mean dice_metric': 0.9514156579971313, 'Val/mean miou_metric': 0.9355975985527039, 'Val/mean f1': 0.9483739137649536, 'Val/mean precision': 0.9443957805633545, 'Val/mean recall': 0.9523856043815613, 'Val/mean hd95_metric': 10.754836082458496} +Cheakpoint... +Epoch [3866/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514156579971313, 'Val/mean miou_metric': 0.9355975985527039, 'Val/mean f1': 0.9483739137649536, 'Val/mean precision': 0.9443957805633545, 'Val/mean recall': 0.9523856043815613, 'Val/mean hd95_metric': 10.754836082458496} +Epoch [3867/4000] Training [1/39] Loss: 0.00386 +Epoch [3867/4000] Training [2/39] Loss: 0.16347 +Epoch [3867/4000] Training [3/39] Loss: 0.00401 +Epoch [3867/4000] Training [4/39] Loss: 0.00409 +Epoch [3867/4000] Training [5/39] Loss: 0.00362 +Epoch [3867/4000] Training [6/39] Loss: 0.00730 +Epoch [3867/4000] Training [7/39] Loss: 0.00249 +Epoch [3867/4000] Training [8/39] Loss: 0.00402 +Epoch [3867/4000] Training [9/39] Loss: 0.00380 +Epoch [3867/4000] Training [10/39] Loss: 0.00520 +Epoch [3867/4000] Training [11/39] Loss: 0.12801 +Epoch [3867/4000] Training [12/39] Loss: 0.12902 +Epoch [3867/4000] Training [13/39] Loss: 0.00327 +Epoch [3867/4000] Training [14/39] Loss: 0.00445 +Epoch [3867/4000] Training [15/39] Loss: 0.00344 +Epoch [3867/4000] Training [16/39] Loss: 0.12857 +Epoch [3867/4000] Training [17/39] Loss: 0.25437 +Epoch [3867/4000] Training [18/39] Loss: 0.00598 +Epoch [3867/4000] Training [19/39] Loss: 0.12915 +Epoch [3867/4000] Training [20/39] Loss: 0.00277 +Epoch [3867/4000] Training [21/39] Loss: 0.00377 +Epoch [3867/4000] Training [22/39] Loss: 0.00418 +Epoch [3867/4000] Training [23/39] Loss: 0.00331 +Epoch [3867/4000] Training [24/39] Loss: 0.12855 +Epoch [3867/4000] Training [25/39] Loss: 0.00498 +Epoch [3867/4000] Training [26/39] Loss: 0.00483 +Epoch [3867/4000] Training [27/39] Loss: 0.00791 +Epoch [3867/4000] Training [28/39] Loss: 0.00544 +Epoch [3867/4000] Training [29/39] Loss: 0.00315 +Epoch [3867/4000] Training [30/39] Loss: 0.00432 +Epoch [3867/4000] Training [31/39] Loss: 0.00406 +Epoch [3867/4000] Training [32/39] Loss: 0.12874 +Epoch [3867/4000] Training [33/39] Loss: 0.00657 +Epoch [3867/4000] Training [34/39] Loss: 0.00627 +Epoch [3867/4000] Training [35/39] Loss: 0.00560 +Epoch [3867/4000] Training [36/39] Loss: 0.00290 +Epoch [3867/4000] Training [37/39] Loss: 0.00618 +Epoch [3867/4000] Training [38/39] Loss: 0.00431 +Epoch [3867/4000] Training [39/39] Loss: 0.00490 +Epoch [3867/4000] Training metric {'Train/mean dice_metric': 0.9965188503265381, 'Train/mean miou_metric': 0.9934825301170349, 'Train/mean f1': 0.9970659613609314, 'Train/mean precision': 0.9966158866882324, 'Train/mean recall': 0.9975164532661438, 'Train/mean hd95_metric': 0.9476020336151123} +Epoch [3867/4000] Validation [1/10] Loss: 0.72527 focal_loss 0.63884 dice_loss 0.08643 +Epoch [3867/4000] Validation [2/10] Loss: 0.50513 focal_loss 0.40637 dice_loss 0.09877 +Epoch [3867/4000] Validation [3/10] Loss: 0.39939 focal_loss 0.28787 dice_loss 0.11153 +Epoch [3867/4000] Validation [4/10] Loss: 0.89501 focal_loss 0.33008 dice_loss 0.56493 +Epoch [3867/4000] Validation [5/10] Loss: 3.10652 focal_loss 2.43235 dice_loss 0.67416 +Epoch [3867/4000] Validation [6/10] Loss: 1.33305 focal_loss 0.62078 dice_loss 0.71226 +Epoch [3867/4000] Validation [7/10] Loss: 1.17708 focal_loss 0.52296 dice_loss 0.65412 +Epoch [3867/4000] Validation [8/10] Loss: 2.40545 focal_loss 1.78758 dice_loss 0.61787 +Epoch [3867/4000] Validation [9/10] Loss: 1.53931 focal_loss 0.99601 dice_loss 0.54330 +Epoch [3867/4000] Validation [10/10] Loss: 1.89215 focal_loss 1.15743 dice_loss 0.73472 +Epoch [3867/4000] Validation metric {'Val/mean dice_metric': 0.9515317678451538, 'Val/mean miou_metric': 0.935783326625824, 'Val/mean f1': 0.9484249949455261, 'Val/mean precision': 0.9440113306045532, 'Val/mean recall': 0.9528800845146179, 'Val/mean hd95_metric': 10.70018482208252} +Cheakpoint... +Epoch [3867/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515317678451538, 'Val/mean miou_metric': 0.935783326625824, 'Val/mean f1': 0.9484249949455261, 'Val/mean precision': 0.9440113306045532, 'Val/mean recall': 0.9528800845146179, 'Val/mean hd95_metric': 10.70018482208252} +Epoch [3868/4000] Training [1/39] Loss: 0.12824 +Epoch [3868/4000] Training [2/39] Loss: 0.00656 +Epoch [3868/4000] Training [3/39] Loss: 0.12782 +Epoch [3868/4000] Training [4/39] Loss: 0.00431 +Epoch [3868/4000] Training [5/39] Loss: 0.25402 +Epoch [3868/4000] Training [6/39] Loss: 0.12901 +Epoch [3868/4000] Training [7/39] Loss: 0.13056 +Epoch [3868/4000] Training [8/39] Loss: 0.00445 +Epoch [3868/4000] Training [9/39] Loss: 0.00763 +Epoch [3868/4000] Training [10/39] Loss: 0.00456 +Epoch [3868/4000] Training [11/39] Loss: 0.00466 +Epoch [3868/4000] Training [12/39] Loss: 0.00519 +Epoch [3868/4000] Training [13/39] Loss: 0.00487 +Epoch [3868/4000] Training [14/39] Loss: 0.00492 +Epoch [3868/4000] Training [15/39] Loss: 0.00532 +Epoch [3868/4000] Training [16/39] Loss: 0.00503 +Epoch [3868/4000] Training [17/39] Loss: 0.00632 +Epoch [3868/4000] Training [18/39] Loss: 0.00371 +Epoch [3868/4000] Training [19/39] Loss: 0.00674 +Epoch [3868/4000] Training [20/39] Loss: 0.12787 +Epoch [3868/4000] Training [21/39] Loss: 0.00376 +Epoch [3868/4000] Training [22/39] Loss: 0.00294 +Epoch [3868/4000] Training [23/39] Loss: 0.00853 +Epoch [3868/4000] Training [24/39] Loss: 0.12937 +Epoch [3868/4000] Training [25/39] Loss: 0.00517 +Epoch [3868/4000] Training [26/39] Loss: 0.12802 +Epoch [3868/4000] Training [27/39] Loss: 0.00360 +Epoch [3868/4000] Training [28/39] Loss: 0.00463 +Epoch [3868/4000] Training [29/39] Loss: 0.00297 +Epoch [3868/4000] Training [30/39] Loss: 0.00448 +Epoch [3868/4000] Training [31/39] Loss: 0.00477 +Epoch [3868/4000] Training [32/39] Loss: 0.12885 +Epoch [3868/4000] Training [33/39] Loss: 0.13164 +Epoch [3868/4000] Training [34/39] Loss: 0.00515 +Epoch [3868/4000] Training [35/39] Loss: 0.00380 +Epoch [3868/4000] Training [36/39] Loss: 0.00516 +Epoch [3868/4000] Training [37/39] Loss: 0.00443 +Epoch [3868/4000] Training [38/39] Loss: 0.00503 +Epoch [3868/4000] Training [39/39] Loss: 0.00598 +Epoch [3868/4000] Training metric {'Train/mean dice_metric': 0.9956319332122803, 'Train/mean miou_metric': 0.9925392866134644, 'Train/mean f1': 0.9970089197158813, 'Train/mean precision': 0.9965691566467285, 'Train/mean recall': 0.9974489808082581, 'Train/mean hd95_metric': 0.9215197563171387} +Epoch [3868/4000] Validation [1/10] Loss: 0.72475 focal_loss 0.63818 dice_loss 0.08658 +Epoch [3868/4000] Validation [2/10] Loss: 0.50454 focal_loss 0.40573 dice_loss 0.09881 +Epoch [3868/4000] Validation [3/10] Loss: 0.39972 focal_loss 0.28825 dice_loss 0.11147 +Epoch [3868/4000] Validation [4/10] Loss: 0.89512 focal_loss 0.33016 dice_loss 0.56495 +Epoch [3868/4000] Validation [5/10] Loss: 3.10286 focal_loss 2.42875 dice_loss 0.67411 +Epoch [3868/4000] Validation [6/10] Loss: 1.33444 focal_loss 0.62194 dice_loss 0.71250 +Epoch [3868/4000] Validation [7/10] Loss: 1.18080 focal_loss 0.52624 dice_loss 0.65456 +Epoch [3868/4000] Validation [8/10] Loss: 2.38274 focal_loss 1.76706 dice_loss 0.61568 +Epoch [3868/4000] Validation [9/10] Loss: 1.54824 focal_loss 1.00479 dice_loss 0.54344 +Epoch [3868/4000] Validation [10/10] Loss: 1.89508 focal_loss 1.16033 dice_loss 0.73475 +Epoch [3868/4000] Validation metric {'Val/mean dice_metric': 0.9507861733436584, 'Val/mean miou_metric': 0.9349907636642456, 'Val/mean f1': 0.9484187364578247, 'Val/mean precision': 0.943796694278717, 'Val/mean recall': 0.9530861973762512, 'Val/mean hd95_metric': 10.64991569519043} +Cheakpoint... +Epoch [3868/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507861733436584, 'Val/mean miou_metric': 0.9349907636642456, 'Val/mean f1': 0.9484187364578247, 'Val/mean precision': 0.943796694278717, 'Val/mean recall': 0.9530861973762512, 'Val/mean hd95_metric': 10.64991569519043} +Epoch [3869/4000] Training [1/39] Loss: 0.00321 +Epoch [3869/4000] Training [2/39] Loss: 0.00407 +Epoch [3869/4000] Training [3/39] Loss: 0.00578 +Epoch [3869/4000] Training [4/39] Loss: 0.00366 +Epoch [3869/4000] Training [5/39] Loss: 0.00454 +Epoch [3869/4000] Training [6/39] Loss: 0.00571 +Epoch [3869/4000] Training [7/39] Loss: 0.00685 +Epoch [3869/4000] Training [8/39] Loss: 0.00553 +Epoch [3869/4000] Training [9/39] Loss: 0.00460 +Epoch [3869/4000] Training [10/39] Loss: 0.13025 +Epoch [3869/4000] Training [11/39] Loss: 0.00499 +Epoch [3869/4000] Training [12/39] Loss: 0.00515 +Epoch [3869/4000] Training [13/39] Loss: 0.00418 +Epoch [3869/4000] Training [14/39] Loss: 0.00353 +Epoch [3869/4000] Training [15/39] Loss: 0.00512 +Epoch [3869/4000] Training [16/39] Loss: 0.00358 +Epoch [3869/4000] Training [17/39] Loss: 0.00270 +Epoch [3869/4000] Training [18/39] Loss: 0.12738 +Epoch [3869/4000] Training [19/39] Loss: 0.00612 +Epoch [3869/4000] Training [20/39] Loss: 0.01332 +Epoch [3869/4000] Training [21/39] Loss: 0.00545 +Epoch [3869/4000] Training [22/39] Loss: 0.00498 +Epoch [3869/4000] Training [23/39] Loss: 0.12869 +Epoch [3869/4000] Training [24/39] Loss: 0.00582 +Epoch [3869/4000] Training [25/39] Loss: 0.12993 +Epoch [3869/4000] Training [26/39] Loss: 0.00336 +Epoch [3869/4000] Training [27/39] Loss: 0.12801 +Epoch [3869/4000] Training [28/39] Loss: 0.13069 +Epoch [3869/4000] Training [29/39] Loss: 0.00491 +Epoch [3869/4000] Training [30/39] Loss: 0.00318 +Epoch [3869/4000] Training [31/39] Loss: 0.12800 +Epoch [3869/4000] Training [32/39] Loss: 0.03688 +Epoch [3869/4000] Training [33/39] Loss: 0.00661 +Epoch [3869/4000] Training [34/39] Loss: 0.00308 +Epoch [3869/4000] Training [35/39] Loss: 0.00665 +Epoch [3869/4000] Training [36/39] Loss: 0.00359 +Epoch [3869/4000] Training [37/39] Loss: 0.00619 +Epoch [3869/4000] Training [38/39] Loss: 0.00456 +Epoch [3869/4000] Training [39/39] Loss: 0.00399 +Epoch [3869/4000] Training metric {'Train/mean dice_metric': 0.9963967204093933, 'Train/mean miou_metric': 0.993238091468811, 'Train/mean f1': 0.9968527555465698, 'Train/mean precision': 0.9963817000389099, 'Train/mean recall': 0.9973243474960327, 'Train/mean hd95_metric': 0.941102921962738} +Epoch [3869/4000] Validation [1/10] Loss: 0.72092 focal_loss 0.63479 dice_loss 0.08612 +Epoch [3869/4000] Validation [2/10] Loss: 0.50169 focal_loss 0.40220 dice_loss 0.09950 +Epoch [3869/4000] Validation [3/10] Loss: 0.40193 focal_loss 0.29019 dice_loss 0.11174 +Epoch [3869/4000] Validation [4/10] Loss: 0.89135 focal_loss 0.32655 dice_loss 0.56480 +Epoch [3869/4000] Validation [5/10] Loss: 3.10521 focal_loss 2.43108 dice_loss 0.67413 +Epoch [3869/4000] Validation [6/10] Loss: 1.32484 focal_loss 0.61248 dice_loss 0.71235 +Epoch [3869/4000] Validation [7/10] Loss: 1.16944 focal_loss 0.51607 dice_loss 0.65337 +Epoch [3869/4000] Validation [8/10] Loss: 2.40155 focal_loss 1.78261 dice_loss 0.61894 +Epoch [3869/4000] Validation [9/10] Loss: 1.53243 focal_loss 0.98907 dice_loss 0.54335 +Epoch [3869/4000] Validation [10/10] Loss: 1.87325 focal_loss 1.13879 dice_loss 0.73446 +Epoch [3869/4000] Validation metric {'Val/mean dice_metric': 0.9514610767364502, 'Val/mean miou_metric': 0.9356259107589722, 'Val/mean f1': 0.9484423398971558, 'Val/mean precision': 0.9442062973976135, 'Val/mean recall': 0.9527167677879333, 'Val/mean hd95_metric': 10.701314926147461} +Cheakpoint... +Epoch [3869/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514610767364502, 'Val/mean miou_metric': 0.9356259107589722, 'Val/mean f1': 0.9484423398971558, 'Val/mean precision': 0.9442062973976135, 'Val/mean recall': 0.9527167677879333, 'Val/mean hd95_metric': 10.701314926147461} +Epoch [3870/4000] Training [1/39] Loss: 0.00829 +Epoch [3870/4000] Training [2/39] Loss: 0.00632 +Epoch [3870/4000] Training [3/39] Loss: 0.12830 +Epoch [3870/4000] Training [4/39] Loss: 0.00537 +Epoch [3870/4000] Training [5/39] Loss: 0.00377 +Epoch [3870/4000] Training [6/39] Loss: 0.08458 +Epoch [3870/4000] Training [7/39] Loss: 0.00544 +Epoch [3870/4000] Training [8/39] Loss: 0.00525 +Epoch [3870/4000] Training [9/39] Loss: 0.12887 +Epoch [3870/4000] Training [10/39] Loss: 0.12863 +Epoch [3870/4000] Training [11/39] Loss: 0.12769 +Epoch [3870/4000] Training [12/39] Loss: 0.00410 +Epoch [3870/4000] Training [13/39] Loss: 0.00389 +Epoch [3870/4000] Training [14/39] Loss: 0.00458 +Epoch [3870/4000] Training [15/39] Loss: 0.12774 +Epoch [3870/4000] Training [16/39] Loss: 0.00398 +Epoch [3870/4000] Training [17/39] Loss: 0.12792 +Epoch [3870/4000] Training [18/39] Loss: 0.00601 +Epoch [3870/4000] Training [19/39] Loss: 0.13181 +Epoch [3870/4000] Training [20/39] Loss: 0.00505 +Epoch [3870/4000] Training [21/39] Loss: 0.00594 +Epoch [3870/4000] Training [22/39] Loss: 0.00380 +Epoch [3870/4000] Training [23/39] Loss: 0.12835 +Epoch [3870/4000] Training [24/39] Loss: 0.00280 +Epoch [3870/4000] Training [25/39] Loss: 0.12782 +Epoch [3870/4000] Training [26/39] Loss: 0.00388 +Epoch [3870/4000] Training [27/39] Loss: 0.00590 +Epoch [3870/4000] Training [28/39] Loss: 0.00569 +Epoch [3870/4000] Training [29/39] Loss: 0.00579 +Epoch [3870/4000] Training [30/39] Loss: 0.00395 +Epoch [3870/4000] Training [31/39] Loss: 0.00363 +Epoch [3870/4000] Training [32/39] Loss: 0.12861 +Epoch [3870/4000] Training [33/39] Loss: 0.00934 +Epoch [3870/4000] Training [34/39] Loss: 0.25464 +Epoch [3870/4000] Training [35/39] Loss: 0.00465 +Epoch [3870/4000] Training [36/39] Loss: 0.00378 +Epoch [3870/4000] Training [37/39] Loss: 0.01075 +Epoch [3870/4000] Training [38/39] Loss: 0.12790 +Epoch [3870/4000] Training [39/39] Loss: 0.12750 +Epoch [3870/4000] Training metric {'Train/mean dice_metric': 0.9965082406997681, 'Train/mean miou_metric': 0.9934585094451904, 'Train/mean f1': 0.9969620704650879, 'Train/mean precision': 0.9965270757675171, 'Train/mean recall': 0.9973973035812378, 'Train/mean hd95_metric': 1.0316790342330933} +Epoch [3870/4000] Validation [1/10] Loss: 0.71541 focal_loss 0.63051 dice_loss 0.08490 +Epoch [3870/4000] Validation [2/10] Loss: 0.51370 focal_loss 0.41147 dice_loss 0.10223 +Epoch [3870/4000] Validation [3/10] Loss: 0.41248 focal_loss 0.29993 dice_loss 0.11255 +Epoch [3870/4000] Validation [4/10] Loss: 0.89119 focal_loss 0.32754 dice_loss 0.56365 +Epoch [3870/4000] Validation [5/10] Loss: 3.10600 focal_loss 2.43174 dice_loss 0.67426 +Epoch [3870/4000] Validation [6/10] Loss: 1.32217 focal_loss 0.60950 dice_loss 0.71267 +Epoch [3870/4000] Validation [7/10] Loss: 1.17042 focal_loss 0.51897 dice_loss 0.65146 +Epoch [3870/4000] Validation [8/10] Loss: 2.45726 focal_loss 1.83245 dice_loss 0.62481 +Epoch [3870/4000] Validation [9/10] Loss: 1.53254 focal_loss 0.98927 dice_loss 0.54327 +Epoch [3870/4000] Validation [10/10] Loss: 1.86831 focal_loss 1.13475 dice_loss 0.73356 +Epoch [3870/4000] Validation metric {'Val/mean dice_metric': 0.9515441060066223, 'Val/mean miou_metric': 0.935822069644928, 'Val/mean f1': 0.9489355683326721, 'Val/mean precision': 0.9455293416976929, 'Val/mean recall': 0.9523664712905884, 'Val/mean hd95_metric': 10.858940124511719} +Cheakpoint... +Epoch [3870/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515441060066223, 'Val/mean miou_metric': 0.935822069644928, 'Val/mean f1': 0.9489355683326721, 'Val/mean precision': 0.9455293416976929, 'Val/mean recall': 0.9523664712905884, 'Val/mean hd95_metric': 10.858940124511719} +Epoch [3871/4000] Training [1/39] Loss: 0.00624 +Epoch [3871/4000] Training [2/39] Loss: 0.00342 +Epoch [3871/4000] Training [3/39] Loss: 0.12854 +Epoch [3871/4000] Training [4/39] Loss: 0.00575 +Epoch [3871/4000] Training [5/39] Loss: 0.00470 +Epoch [3871/4000] Training [6/39] Loss: 0.00590 +Epoch [3871/4000] Training [7/39] Loss: 0.00396 +Epoch [3871/4000] Training [8/39] Loss: 0.00505 +Epoch [3871/4000] Training [9/39] Loss: 0.13073 +Epoch [3871/4000] Training [10/39] Loss: 0.08514 +Epoch [3871/4000] Training [11/39] Loss: 0.00332 +Epoch [3871/4000] Training [12/39] Loss: 0.12980 +Epoch [3871/4000] Training [13/39] Loss: 0.00535 +Epoch [3871/4000] Training [14/39] Loss: 0.00317 +Epoch [3871/4000] Training [15/39] Loss: 0.00421 +Epoch [3871/4000] Training [16/39] Loss: 0.12826 +Epoch [3871/4000] Training [17/39] Loss: 0.00521 +Epoch [3871/4000] Training [18/39] Loss: 0.00260 +Epoch [3871/4000] Training [19/39] Loss: 0.00498 +Epoch [3871/4000] Training [20/39] Loss: 0.00388 +Epoch [3871/4000] Training [21/39] Loss: 0.12783 +Epoch [3871/4000] Training [22/39] Loss: 0.12835 +Epoch [3871/4000] Training [23/39] Loss: 0.00456 +Epoch [3871/4000] Training [24/39] Loss: 0.00394 +Epoch [3871/4000] Training [25/39] Loss: 0.00490 +Epoch [3871/4000] Training [26/39] Loss: 0.00562 +Epoch [3871/4000] Training [27/39] Loss: 0.00608 +Epoch [3871/4000] Training [28/39] Loss: 0.00340 +Epoch [3871/4000] Training [29/39] Loss: 0.25368 +Epoch [3871/4000] Training [30/39] Loss: 0.00445 +Epoch [3871/4000] Training [31/39] Loss: 0.00470 +Epoch [3871/4000] Training [32/39] Loss: 0.00326 +Epoch [3871/4000] Training [33/39] Loss: 0.00353 +Epoch [3871/4000] Training [34/39] Loss: 0.00313 +Epoch [3871/4000] Training [35/39] Loss: 0.00385 +Epoch [3871/4000] Training [36/39] Loss: 0.00301 +Epoch [3871/4000] Training [37/39] Loss: 0.00332 +Epoch [3871/4000] Training [38/39] Loss: 0.00580 +Epoch [3871/4000] Training [39/39] Loss: 0.00497 +Epoch [3871/4000] Training metric {'Train/mean dice_metric': 0.9966180324554443, 'Train/mean miou_metric': 0.993663489818573, 'Train/mean f1': 0.99704909324646, 'Train/mean precision': 0.9965570569038391, 'Train/mean recall': 0.9975418448448181, 'Train/mean hd95_metric': 0.9072590470314026} +Epoch [3871/4000] Validation [1/10] Loss: 0.72588 focal_loss 0.63905 dice_loss 0.08683 +Epoch [3871/4000] Validation [2/10] Loss: 0.49824 focal_loss 0.40168 dice_loss 0.09656 +Epoch [3871/4000] Validation [3/10] Loss: 0.39333 focal_loss 0.28235 dice_loss 0.11098 +Epoch [3871/4000] Validation [4/10] Loss: 0.89967 focal_loss 0.33389 dice_loss 0.56578 +Epoch [3871/4000] Validation [5/10] Loss: 3.08815 focal_loss 2.41411 dice_loss 0.67404 +Epoch [3871/4000] Validation [6/10] Loss: 1.34344 focal_loss 0.63094 dice_loss 0.71249 +Epoch [3871/4000] Validation [7/10] Loss: 1.18355 focal_loss 0.52752 dice_loss 0.65603 +Epoch [3871/4000] Validation [8/10] Loss: 2.35751 focal_loss 1.74577 dice_loss 0.61174 +Epoch [3871/4000] Validation [9/10] Loss: 1.55625 focal_loss 1.01240 dice_loss 0.54386 +Epoch [3871/4000] Validation [10/10] Loss: 1.91577 focal_loss 1.17994 dice_loss 0.73583 +Epoch [3871/4000] Validation metric {'Val/mean dice_metric': 0.9516124129295349, 'Val/mean miou_metric': 0.935906708240509, 'Val/mean f1': 0.948183000087738, 'Val/mean precision': 0.942983865737915, 'Val/mean recall': 0.9534398317337036, 'Val/mean hd95_metric': 10.69332504272461} +Cheakpoint... +Epoch [3871/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516124129295349, 'Val/mean miou_metric': 0.935906708240509, 'Val/mean f1': 0.948183000087738, 'Val/mean precision': 0.942983865737915, 'Val/mean recall': 0.9534398317337036, 'Val/mean hd95_metric': 10.69332504272461} +Epoch [3872/4000] Training [1/39] Loss: 0.00342 +Epoch [3872/4000] Training [2/39] Loss: 0.00438 +Epoch [3872/4000] Training [3/39] Loss: 0.00552 +Epoch [3872/4000] Training [4/39] Loss: 0.12893 +Epoch [3872/4000] Training [5/39] Loss: 0.00506 +Epoch [3872/4000] Training [6/39] Loss: 0.00302 +Epoch [3872/4000] Training [7/39] Loss: 0.00587 +Epoch [3872/4000] Training [8/39] Loss: 0.00511 +Epoch [3872/4000] Training [9/39] Loss: 0.12835 +Epoch [3872/4000] Training [10/39] Loss: 0.00569 +Epoch [3872/4000] Training [11/39] Loss: 0.00447 +Epoch [3872/4000] Training [12/39] Loss: 0.00330 +Epoch [3872/4000] Training [13/39] Loss: 0.00671 +Epoch [3872/4000] Training [14/39] Loss: 0.00402 +Epoch [3872/4000] Training [15/39] Loss: 0.00441 +Epoch [3872/4000] Training [16/39] Loss: 0.00416 +Epoch [3872/4000] Training [17/39] Loss: 0.13189 +Epoch [3872/4000] Training [18/39] Loss: 0.00644 +Epoch [3872/4000] Training [19/39] Loss: 0.00524 +Epoch [3872/4000] Training [20/39] Loss: 0.00332 +Epoch [3872/4000] Training [21/39] Loss: 0.00494 +Epoch [3872/4000] Training [22/39] Loss: 0.00410 +Epoch [3872/4000] Training [23/39] Loss: 0.12807 +Epoch [3872/4000] Training [24/39] Loss: 0.12903 +Epoch [3872/4000] Training [25/39] Loss: 0.00422 +Epoch [3872/4000] Training [26/39] Loss: 0.00567 +Epoch [3872/4000] Training [27/39] Loss: 0.04165 +Epoch [3872/4000] Training [28/39] Loss: 0.00517 +Epoch [3872/4000] Training [29/39] Loss: 0.00637 +Epoch [3872/4000] Training [30/39] Loss: 0.12792 +Epoch [3872/4000] Training [31/39] Loss: 0.25470 +Epoch [3872/4000] Training [32/39] Loss: 0.00550 +Epoch [3872/4000] Training [33/39] Loss: 0.00414 +Epoch [3872/4000] Training [34/39] Loss: 0.00420 +Epoch [3872/4000] Training [35/39] Loss: 0.00660 +Epoch [3872/4000] Training [36/39] Loss: 0.12982 +Epoch [3872/4000] Training [37/39] Loss: 0.13067 +Epoch [3872/4000] Training [38/39] Loss: 0.25291 +Epoch [3872/4000] Training [39/39] Loss: 0.00500 +Epoch [3872/4000] Training metric {'Train/mean dice_metric': 0.9960114359855652, 'Train/mean miou_metric': 0.9928488731384277, 'Train/mean f1': 0.9967625141143799, 'Train/mean precision': 0.9959912300109863, 'Train/mean recall': 0.9975349307060242, 'Train/mean hd95_metric': 0.9663351774215698} +Epoch [3872/4000] Validation [1/10] Loss: 0.72377 focal_loss 0.63691 dice_loss 0.08686 +Epoch [3872/4000] Validation [2/10] Loss: 0.50362 focal_loss 0.40818 dice_loss 0.09544 +Epoch [3872/4000] Validation [3/10] Loss: 0.38379 focal_loss 0.27352 dice_loss 0.11027 +Epoch [3872/4000] Validation [4/10] Loss: 0.90807 focal_loss 0.34204 dice_loss 0.56603 +Epoch [3872/4000] Validation [5/10] Loss: 3.06409 focal_loss 2.39019 dice_loss 0.67390 +Epoch [3872/4000] Validation [6/10] Loss: 1.36214 focal_loss 0.64943 dice_loss 0.71271 +Epoch [3872/4000] Validation [7/10] Loss: 1.19648 focal_loss 0.53988 dice_loss 0.65660 +Epoch [3872/4000] Validation [8/10] Loss: 2.36304 focal_loss 1.75181 dice_loss 0.61123 +Epoch [3872/4000] Validation [9/10] Loss: 1.54734 focal_loss 1.00317 dice_loss 0.54417 +Epoch [3872/4000] Validation [10/10] Loss: 1.94270 focal_loss 1.20687 dice_loss 0.73582 +Epoch [3872/4000] Validation metric {'Val/mean dice_metric': 0.9511424899101257, 'Val/mean miou_metric': 0.9352652430534363, 'Val/mean f1': 0.9485132098197937, 'Val/mean precision': 0.9428333044052124, 'Val/mean recall': 0.9542617797851562, 'Val/mean hd95_metric': 10.909356117248535} +Cheakpoint... +Epoch [3872/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9511], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511424899101257, 'Val/mean miou_metric': 0.9352652430534363, 'Val/mean f1': 0.9485132098197937, 'Val/mean precision': 0.9428333044052124, 'Val/mean recall': 0.9542617797851562, 'Val/mean hd95_metric': 10.909356117248535} +Epoch [3873/4000] Training [1/39] Loss: 0.00547 +Epoch [3873/4000] Training [2/39] Loss: 0.13309 +Epoch [3873/4000] Training [3/39] Loss: 0.00525 +Epoch [3873/4000] Training [4/39] Loss: 0.25350 +Epoch [3873/4000] Training [5/39] Loss: 0.00377 +Epoch [3873/4000] Training [6/39] Loss: 0.01032 +Epoch [3873/4000] Training [7/39] Loss: 0.00447 +Epoch [3873/4000] Training [8/39] Loss: 0.12866 +Epoch [3873/4000] Training [9/39] Loss: 0.00483 +Epoch [3873/4000] Training [10/39] Loss: 0.00457 +Epoch [3873/4000] Training [11/39] Loss: 0.00354 +Epoch [3873/4000] Training [12/39] Loss: 0.00696 +Epoch [3873/4000] Training [13/39] Loss: 0.00454 +Epoch [3873/4000] Training [14/39] Loss: 0.00393 +Epoch [3873/4000] Training [15/39] Loss: 0.00443 +Epoch [3873/4000] Training [16/39] Loss: 0.25630 +Epoch [3873/4000] Training [17/39] Loss: 0.00435 +Epoch [3873/4000] Training [18/39] Loss: 0.00621 +Epoch [3873/4000] Training [19/39] Loss: 0.12821 +Epoch [3873/4000] Training [20/39] Loss: 0.00577 +Epoch [3873/4000] Training [21/39] Loss: 0.25273 +Epoch [3873/4000] Training [22/39] Loss: 0.00741 +Epoch [3873/4000] Training [23/39] Loss: 0.00528 +Epoch [3873/4000] Training [24/39] Loss: 0.12783 +Epoch [3873/4000] Training [25/39] Loss: 0.00374 +Epoch [3873/4000] Training [26/39] Loss: 0.00445 +Epoch [3873/4000] Training [27/39] Loss: 0.00544 +Epoch [3873/4000] Training [28/39] Loss: 0.00514 +Epoch [3873/4000] Training [29/39] Loss: 0.12884 +Epoch [3873/4000] Training [30/39] Loss: 0.00490 +Epoch [3873/4000] Training [31/39] Loss: 0.00354 +Epoch [3873/4000] Training [32/39] Loss: 0.00472 +Epoch [3873/4000] Training [33/39] Loss: 0.00417 +Epoch [3873/4000] Training [34/39] Loss: 0.00559 +Epoch [3873/4000] Training [35/39] Loss: 0.00628 +Epoch [3873/4000] Training [36/39] Loss: 0.12968 +Epoch [3873/4000] Training [37/39] Loss: 0.00433 +Epoch [3873/4000] Training [38/39] Loss: 0.12838 +Epoch [3873/4000] Training [39/39] Loss: 0.00650 +Epoch [3873/4000] Training metric {'Train/mean dice_metric': 0.9962924718856812, 'Train/mean miou_metric': 0.9930347800254822, 'Train/mean f1': 0.9967803359031677, 'Train/mean precision': 0.9963809847831726, 'Train/mean recall': 0.9971800446510315, 'Train/mean hd95_metric': 0.9398903250694275} +Epoch [3873/4000] Validation [1/10] Loss: 0.71694 focal_loss 0.63029 dice_loss 0.08665 +Epoch [3873/4000] Validation [2/10] Loss: 0.49719 focal_loss 0.39941 dice_loss 0.09778 +Epoch [3873/4000] Validation [3/10] Loss: 0.39209 focal_loss 0.28084 dice_loss 0.11125 +Epoch [3873/4000] Validation [4/10] Loss: 0.89227 focal_loss 0.32709 dice_loss 0.56518 +Epoch [3873/4000] Validation [5/10] Loss: 3.06283 focal_loss 2.38882 dice_loss 0.67401 +Epoch [3873/4000] Validation [6/10] Loss: 1.32987 focal_loss 0.61736 dice_loss 0.71252 +Epoch [3873/4000] Validation [7/10] Loss: 1.17199 focal_loss 0.51687 dice_loss 0.65512 +Epoch [3873/4000] Validation [8/10] Loss: 2.35954 focal_loss 1.74387 dice_loss 0.61568 +Epoch [3873/4000] Validation [9/10] Loss: 1.51377 focal_loss 0.97005 dice_loss 0.54372 +Epoch [3873/4000] Validation [10/10] Loss: 1.88638 focal_loss 1.15153 dice_loss 0.73485 +Epoch [3873/4000] Validation metric {'Val/mean dice_metric': 0.9513774514198303, 'Val/mean miou_metric': 0.9354454278945923, 'Val/mean f1': 0.9483639001846313, 'Val/mean precision': 0.9437562227249146, 'Val/mean recall': 0.9530168175697327, 'Val/mean hd95_metric': 10.708381652832031} +Cheakpoint... +Epoch [3873/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513774514198303, 'Val/mean miou_metric': 0.9354454278945923, 'Val/mean f1': 0.9483639001846313, 'Val/mean precision': 0.9437562227249146, 'Val/mean recall': 0.9530168175697327, 'Val/mean hd95_metric': 10.708381652832031} +Epoch [3874/4000] Training [1/39] Loss: 0.12834 +Epoch [3874/4000] Training [2/39] Loss: 0.00303 +Epoch [3874/4000] Training [3/39] Loss: 0.00355 +Epoch [3874/4000] Training [4/39] Loss: 0.00443 +Epoch [3874/4000] Training [5/39] Loss: 0.00302 +Epoch [3874/4000] Training [6/39] Loss: 0.13061 +Epoch [3874/4000] Training [7/39] Loss: 0.00622 +Epoch [3874/4000] Training [8/39] Loss: 0.00395 +Epoch [3874/4000] Training [9/39] Loss: 0.00440 +Epoch [3874/4000] Training [10/39] Loss: 0.00370 +Epoch [3874/4000] Training [11/39] Loss: 0.00421 +Epoch [3874/4000] Training [12/39] Loss: 0.00654 +Epoch [3874/4000] Training [13/39] Loss: 0.01022 +Epoch [3874/4000] Training [14/39] Loss: 0.00480 +Epoch [3874/4000] Training [15/39] Loss: 0.00649 +Epoch [3874/4000] Training [16/39] Loss: 0.00381 +Epoch [3874/4000] Training [17/39] Loss: 0.12792 +Epoch [3874/4000] Training [18/39] Loss: 0.01062 +Epoch [3874/4000] Training [19/39] Loss: 0.00363 +Epoch [3874/4000] Training [20/39] Loss: 0.12833 +Epoch [3874/4000] Training [21/39] Loss: 0.00455 +Epoch [3874/4000] Training [22/39] Loss: 0.00365 +Epoch [3874/4000] Training [23/39] Loss: 0.00319 +Epoch [3874/4000] Training [24/39] Loss: 0.12903 +Epoch [3874/4000] Training [25/39] Loss: 0.00578 +Epoch [3874/4000] Training [26/39] Loss: 0.00443 +Epoch [3874/4000] Training [27/39] Loss: 0.00419 +Epoch [3874/4000] Training [28/39] Loss: 0.00614 +Epoch [3874/4000] Training [29/39] Loss: 0.00280 +Epoch [3874/4000] Training [30/39] Loss: 0.00508 +Epoch [3874/4000] Training [31/39] Loss: 0.00506 +Epoch [3874/4000] Training [32/39] Loss: 0.00472 +Epoch [3874/4000] Training [33/39] Loss: 0.00822 +Epoch [3874/4000] Training [34/39] Loss: 0.00683 +Epoch [3874/4000] Training [35/39] Loss: 0.00398 +Epoch [3874/4000] Training [36/39] Loss: 0.00637 +Epoch [3874/4000] Training [37/39] Loss: 0.00393 +Epoch [3874/4000] Training [38/39] Loss: 0.12957 +Epoch [3874/4000] Training [39/39] Loss: 0.00409 +Epoch [3874/4000] Training metric {'Train/mean dice_metric': 0.9964445233345032, 'Train/mean miou_metric': 0.9933393597602844, 'Train/mean f1': 0.9969208836555481, 'Train/mean precision': 0.9965093731880188, 'Train/mean recall': 0.9973328113555908, 'Train/mean hd95_metric': 1.0673564672470093} +Epoch [3874/4000] Validation [1/10] Loss: 0.72431 focal_loss 0.63771 dice_loss 0.08660 +Epoch [3874/4000] Validation [2/10] Loss: 0.49952 focal_loss 0.40120 dice_loss 0.09831 +Epoch [3874/4000] Validation [3/10] Loss: 0.40031 focal_loss 0.28870 dice_loss 0.11161 +Epoch [3874/4000] Validation [4/10] Loss: 0.89284 focal_loss 0.32784 dice_loss 0.56500 +Epoch [3874/4000] Validation [5/10] Loss: 3.09993 focal_loss 2.42589 dice_loss 0.67404 +Epoch [3874/4000] Validation [6/10] Loss: 1.32959 focal_loss 0.61690 dice_loss 0.71269 +Epoch [3874/4000] Validation [7/10] Loss: 1.17425 focal_loss 0.51973 dice_loss 0.65452 +Epoch [3874/4000] Validation [8/10] Loss: 2.38528 focal_loss 1.76825 dice_loss 0.61703 +Epoch [3874/4000] Validation [9/10] Loss: 1.53681 focal_loss 0.99323 dice_loss 0.54357 +Epoch [3874/4000] Validation [10/10] Loss: 1.88770 focal_loss 1.15276 dice_loss 0.73493 +Epoch [3874/4000] Validation metric {'Val/mean dice_metric': 0.9514154195785522, 'Val/mean miou_metric': 0.9355922937393188, 'Val/mean f1': 0.9482952356338501, 'Val/mean precision': 0.94377601146698, 'Val/mean recall': 0.9528578519821167, 'Val/mean hd95_metric': 10.789023399353027} +Cheakpoint... +Epoch [3874/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514154195785522, 'Val/mean miou_metric': 0.9355922937393188, 'Val/mean f1': 0.9482952356338501, 'Val/mean precision': 0.94377601146698, 'Val/mean recall': 0.9528578519821167, 'Val/mean hd95_metric': 10.789023399353027} +Epoch [3875/4000] Training [1/39] Loss: 0.00526 +Epoch [3875/4000] Training [2/39] Loss: 0.12756 +Epoch [3875/4000] Training [3/39] Loss: 0.00457 +Epoch [3875/4000] Training [4/39] Loss: 0.00599 +Epoch [3875/4000] Training [5/39] Loss: 0.12758 +Epoch [3875/4000] Training [6/39] Loss: 0.13070 +Epoch [3875/4000] Training [7/39] Loss: 0.12973 +Epoch [3875/4000] Training [8/39] Loss: 0.00664 +Epoch [3875/4000] Training [9/39] Loss: 0.00395 +Epoch [3875/4000] Training [10/39] Loss: 0.00426 +Epoch [3875/4000] Training [11/39] Loss: 0.00644 +Epoch [3875/4000] Training [12/39] Loss: 0.00487 +Epoch [3875/4000] Training [13/39] Loss: 0.00520 +Epoch [3875/4000] Training [14/39] Loss: 0.00443 +Epoch [3875/4000] Training [15/39] Loss: 0.00478 +Epoch [3875/4000] Training [16/39] Loss: 0.00878 +Epoch [3875/4000] Training [17/39] Loss: 0.12899 +Epoch [3875/4000] Training [18/39] Loss: 0.12883 +Epoch [3875/4000] Training [19/39] Loss: 0.00489 +Epoch [3875/4000] Training [20/39] Loss: 0.00426 +Epoch [3875/4000] Training [21/39] Loss: 0.00650 +Epoch [3875/4000] Training [22/39] Loss: 0.00825 +Epoch [3875/4000] Training [23/39] Loss: 0.00340 +Epoch [3875/4000] Training [24/39] Loss: 0.13154 +Epoch [3875/4000] Training [25/39] Loss: 0.13158 +Epoch [3875/4000] Training [26/39] Loss: 0.12848 +Epoch [3875/4000] Training [27/39] Loss: 0.00365 +Epoch [3875/4000] Training [28/39] Loss: 0.00406 +Epoch [3875/4000] Training [29/39] Loss: 0.00642 +Epoch [3875/4000] Training [30/39] Loss: 0.12866 +Epoch [3875/4000] Training [31/39] Loss: 0.12932 +Epoch [3875/4000] Training [32/39] Loss: 0.00478 +Epoch [3875/4000] Training [33/39] Loss: 0.00760 +Epoch [3875/4000] Training [34/39] Loss: 0.12965 +Epoch [3875/4000] Training [35/39] Loss: 0.12943 +Epoch [3875/4000] Training [36/39] Loss: 0.00429 +Epoch [3875/4000] Training [37/39] Loss: 0.00454 +Epoch [3875/4000] Training [38/39] Loss: 0.12875 +Epoch [3875/4000] Training [39/39] Loss: 0.00531 +Epoch [3875/4000] Training metric {'Train/mean dice_metric': 0.9954479932785034, 'Train/mean miou_metric': 0.9921815991401672, 'Train/mean f1': 0.9968062043190002, 'Train/mean precision': 0.9962600469589233, 'Train/mean recall': 0.9973529577255249, 'Train/mean hd95_metric': 0.9533642530441284} +Epoch [3875/4000] Validation [1/10] Loss: 0.72132 focal_loss 0.63455 dice_loss 0.08677 +Epoch [3875/4000] Validation [2/10] Loss: 0.50080 focal_loss 0.40377 dice_loss 0.09703 +Epoch [3875/4000] Validation [3/10] Loss: 0.38940 focal_loss 0.27860 dice_loss 0.11080 +Epoch [3875/4000] Validation [4/10] Loss: 0.90028 focal_loss 0.33458 dice_loss 0.56570 +Epoch [3875/4000] Validation [5/10] Loss: 3.06542 focal_loss 2.39156 dice_loss 0.67386 +Epoch [3875/4000] Validation [6/10] Loss: 1.34874 focal_loss 0.63573 dice_loss 0.71302 +Epoch [3875/4000] Validation [7/10] Loss: 1.18678 focal_loss 0.53066 dice_loss 0.65612 +Epoch [3875/4000] Validation [8/10] Loss: 2.34624 focal_loss 1.73318 dice_loss 0.61306 +Epoch [3875/4000] Validation [9/10] Loss: 1.54830 focal_loss 1.00410 dice_loss 0.54420 +Epoch [3875/4000] Validation [10/10] Loss: 1.91523 focal_loss 1.17950 dice_loss 0.73573 +Epoch [3875/4000] Validation metric {'Val/mean dice_metric': 0.9506096839904785, 'Val/mean miou_metric': 0.934638261795044, 'Val/mean f1': 0.9482778310775757, 'Val/mean precision': 0.9430981874465942, 'Val/mean recall': 0.953514814376831, 'Val/mean hd95_metric': 10.732572555541992} +Cheakpoint... +Epoch [3875/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506096839904785, 'Val/mean miou_metric': 0.934638261795044, 'Val/mean f1': 0.9482778310775757, 'Val/mean precision': 0.9430981874465942, 'Val/mean recall': 0.953514814376831, 'Val/mean hd95_metric': 10.732572555541992} +Epoch [3876/4000] Training [1/39] Loss: 0.00268 +Epoch [3876/4000] Training [2/39] Loss: 0.00601 +Epoch [3876/4000] Training [3/39] Loss: 0.12937 +Epoch [3876/4000] Training [4/39] Loss: 0.00346 +Epoch [3876/4000] Training [5/39] Loss: 0.00413 +Epoch [3876/4000] Training [6/39] Loss: 0.00370 +Epoch [3876/4000] Training [7/39] Loss: 0.00368 +Epoch [3876/4000] Training [8/39] Loss: 0.00520 +Epoch [3876/4000] Training [9/39] Loss: 0.00630 +Epoch [3876/4000] Training [10/39] Loss: 0.00450 +Epoch [3876/4000] Training [11/39] Loss: 0.00621 +Epoch [3876/4000] Training [12/39] Loss: 0.00453 +Epoch [3876/4000] Training [13/39] Loss: 0.00522 +Epoch [3876/4000] Training [14/39] Loss: 0.00734 +Epoch [3876/4000] Training [15/39] Loss: 0.25257 +Epoch [3876/4000] Training [16/39] Loss: 0.00542 +Epoch [3876/4000] Training [17/39] Loss: 0.12855 +Epoch [3876/4000] Training [18/39] Loss: 0.12770 +Epoch [3876/4000] Training [19/39] Loss: 0.00444 +Epoch [3876/4000] Training [20/39] Loss: 0.12852 +Epoch [3876/4000] Training [21/39] Loss: 0.12907 +Epoch [3876/4000] Training [22/39] Loss: 0.13195 +Epoch [3876/4000] Training [23/39] Loss: 0.00337 +Epoch [3876/4000] Training [24/39] Loss: 0.00744 +Epoch [3876/4000] Training [25/39] Loss: 0.00326 +Epoch [3876/4000] Training [26/39] Loss: 0.00378 +Epoch [3876/4000] Training [27/39] Loss: 0.00317 +Epoch [3876/4000] Training [28/39] Loss: 0.00523 +Epoch [3876/4000] Training [29/39] Loss: 0.00419 +Epoch [3876/4000] Training [30/39] Loss: 0.00539 +Epoch [3876/4000] Training [31/39] Loss: 0.00695 +Epoch [3876/4000] Training [32/39] Loss: 0.00398 +Epoch [3876/4000] Training [33/39] Loss: 0.13009 +Epoch [3876/4000] Training [34/39] Loss: 0.00822 +Epoch [3876/4000] Training [35/39] Loss: 0.12766 +Epoch [3876/4000] Training [36/39] Loss: 0.12902 +Epoch [3876/4000] Training [37/39] Loss: 0.00474 +Epoch [3876/4000] Training [38/39] Loss: 0.00406 +Epoch [3876/4000] Training [39/39] Loss: 0.00481 +Epoch [3876/4000] Training metric {'Train/mean dice_metric': 0.9964941740036011, 'Train/mean miou_metric': 0.9934356212615967, 'Train/mean f1': 0.9969238042831421, 'Train/mean precision': 0.9964550137519836, 'Train/mean recall': 0.997393012046814, 'Train/mean hd95_metric': 1.0307420492172241} +Epoch [3876/4000] Validation [1/10] Loss: 0.72096 focal_loss 0.63388 dice_loss 0.08708 +Epoch [3876/4000] Validation [2/10] Loss: 0.49605 focal_loss 0.39954 dice_loss 0.09652 +Epoch [3876/4000] Validation [3/10] Loss: 0.38774 focal_loss 0.27686 dice_loss 0.11088 +Epoch [3876/4000] Validation [4/10] Loss: 0.89557 focal_loss 0.32991 dice_loss 0.56567 +Epoch [3876/4000] Validation [5/10] Loss: 3.05333 focal_loss 2.37938 dice_loss 0.67396 +Epoch [3876/4000] Validation [6/10] Loss: 1.34191 focal_loss 0.62916 dice_loss 0.71274 +Epoch [3876/4000] Validation [7/10] Loss: 1.18091 focal_loss 0.52472 dice_loss 0.65619 +Epoch [3876/4000] Validation [8/10] Loss: 2.33875 focal_loss 1.72652 dice_loss 0.61223 +Epoch [3876/4000] Validation [9/10] Loss: 1.54014 focal_loss 0.99615 dice_loss 0.54400 +Epoch [3876/4000] Validation [10/10] Loss: 1.90410 focal_loss 1.16830 dice_loss 0.73580 +Epoch [3876/4000] Validation metric {'Val/mean dice_metric': 0.9514505863189697, 'Val/mean miou_metric': 0.9356377720832825, 'Val/mean f1': 0.9481601715087891, 'Val/mean precision': 0.9429689645767212, 'Val/mean recall': 0.95340895652771, 'Val/mean hd95_metric': 10.899930000305176} +Cheakpoint... +Epoch [3876/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514505863189697, 'Val/mean miou_metric': 0.9356377720832825, 'Val/mean f1': 0.9481601715087891, 'Val/mean precision': 0.9429689645767212, 'Val/mean recall': 0.95340895652771, 'Val/mean hd95_metric': 10.899930000305176} +Epoch [3877/4000] Training [1/39] Loss: 0.12956 +Epoch [3877/4000] Training [2/39] Loss: 0.00332 +Epoch [3877/4000] Training [3/39] Loss: 0.12799 +Epoch [3877/4000] Training [4/39] Loss: 0.00430 +Epoch [3877/4000] Training [5/39] Loss: 0.00349 +Epoch [3877/4000] Training [6/39] Loss: 0.00487 +Epoch [3877/4000] Training [7/39] Loss: 0.00615 +Epoch [3877/4000] Training [8/39] Loss: 0.00566 +Epoch [3877/4000] Training [9/39] Loss: 0.12903 +Epoch [3877/4000] Training [10/39] Loss: 0.00418 +Epoch [3877/4000] Training [11/39] Loss: 0.00504 +Epoch [3877/4000] Training [12/39] Loss: 0.00533 +Epoch [3877/4000] Training [13/39] Loss: 0.00595 +Epoch [3877/4000] Training [14/39] Loss: 0.00314 +Epoch [3877/4000] Training [15/39] Loss: 0.00392 +Epoch [3877/4000] Training [16/39] Loss: 0.00476 +Epoch [3877/4000] Training [17/39] Loss: 0.00546 +Epoch [3877/4000] Training [18/39] Loss: 0.00293 +Epoch [3877/4000] Training [19/39] Loss: 0.25440 +Epoch [3877/4000] Training [20/39] Loss: 0.00462 +Epoch [3877/4000] Training [21/39] Loss: 0.00393 +Epoch [3877/4000] Training [22/39] Loss: 0.12734 +Epoch [3877/4000] Training [23/39] Loss: 0.00519 +Epoch [3877/4000] Training [24/39] Loss: 0.00346 +Epoch [3877/4000] Training [25/39] Loss: 0.00454 +Epoch [3877/4000] Training [26/39] Loss: 0.00424 +Epoch [3877/4000] Training [27/39] Loss: 0.12889 +Epoch [3877/4000] Training [28/39] Loss: 0.00478 +Epoch [3877/4000] Training [29/39] Loss: 0.00478 +Epoch [3877/4000] Training [30/39] Loss: 0.00534 +Epoch [3877/4000] Training [31/39] Loss: 0.25417 +Epoch [3877/4000] Training [32/39] Loss: 0.12897 +Epoch [3877/4000] Training [33/39] Loss: 0.00557 +Epoch [3877/4000] Training [34/39] Loss: 0.00311 +Epoch [3877/4000] Training [35/39] Loss: 0.00676 +Epoch [3877/4000] Training [36/39] Loss: 0.25334 +Epoch [3877/4000] Training [37/39] Loss: 0.01329 +Epoch [3877/4000] Training [38/39] Loss: 0.00260 +Epoch [3877/4000] Training [39/39] Loss: 0.00649 +Epoch [3877/4000] Training metric {'Train/mean dice_metric': 0.9963691830635071, 'Train/mean miou_metric': 0.9931859970092773, 'Train/mean f1': 0.9968710541725159, 'Train/mean precision': 0.9964214563369751, 'Train/mean recall': 0.9973210692405701, 'Train/mean hd95_metric': 0.9272273778915405} +Epoch [3877/4000] Validation [1/10] Loss: 0.73043 focal_loss 0.64370 dice_loss 0.08673 +Epoch [3877/4000] Validation [2/10] Loss: 0.50413 focal_loss 0.40646 dice_loss 0.09767 +Epoch [3877/4000] Validation [3/10] Loss: 0.39785 focal_loss 0.28680 dice_loss 0.11105 +Epoch [3877/4000] Validation [4/10] Loss: 0.90082 focal_loss 0.33516 dice_loss 0.56566 +Epoch [3877/4000] Validation [5/10] Loss: 3.10973 focal_loss 2.43578 dice_loss 0.67394 +Epoch [3877/4000] Validation [6/10] Loss: 1.34727 focal_loss 0.63480 dice_loss 0.71247 +Epoch [3877/4000] Validation [7/10] Loss: 1.18781 focal_loss 0.53222 dice_loss 0.65560 +Epoch [3877/4000] Validation [8/10] Loss: 2.37996 focal_loss 1.76499 dice_loss 0.61497 +Epoch [3877/4000] Validation [9/10] Loss: 1.55765 focal_loss 1.01357 dice_loss 0.54408 +Epoch [3877/4000] Validation [10/10] Loss: 1.91240 focal_loss 1.17697 dice_loss 0.73543 +Epoch [3877/4000] Validation metric {'Val/mean dice_metric': 0.9513363242149353, 'Val/mean miou_metric': 0.935417115688324, 'Val/mean f1': 0.9480909109115601, 'Val/mean precision': 0.94324791431427, 'Val/mean recall': 0.9529838562011719, 'Val/mean hd95_metric': 10.685086250305176} +Cheakpoint... +Epoch [3877/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513363242149353, 'Val/mean miou_metric': 0.935417115688324, 'Val/mean f1': 0.9480909109115601, 'Val/mean precision': 0.94324791431427, 'Val/mean recall': 0.9529838562011719, 'Val/mean hd95_metric': 10.685086250305176} +Epoch [3878/4000] Training [1/39] Loss: 0.00668 +Epoch [3878/4000] Training [2/39] Loss: 0.00381 +Epoch [3878/4000] Training [3/39] Loss: 0.00493 +Epoch [3878/4000] Training [4/39] Loss: 0.00316 +Epoch [3878/4000] Training [5/39] Loss: 0.04147 +Epoch [3878/4000] Training [6/39] Loss: 0.12885 +Epoch [3878/4000] Training [7/39] Loss: 0.00355 +Epoch [3878/4000] Training [8/39] Loss: 0.12767 +Epoch [3878/4000] Training [9/39] Loss: 0.25670 +Epoch [3878/4000] Training [10/39] Loss: 0.00442 +Epoch [3878/4000] Training [11/39] Loss: 0.00395 +Epoch [3878/4000] Training [12/39] Loss: 0.00261 +Epoch [3878/4000] Training [13/39] Loss: 0.00440 +Epoch [3878/4000] Training [14/39] Loss: 0.12851 +Epoch [3878/4000] Training [15/39] Loss: 0.00460 +Epoch [3878/4000] Training [16/39] Loss: 0.00636 +Epoch [3878/4000] Training [17/39] Loss: 0.13026 +Epoch [3878/4000] Training [18/39] Loss: 0.00503 +Epoch [3878/4000] Training [19/39] Loss: 0.12936 +Epoch [3878/4000] Training [20/39] Loss: 0.12789 +Epoch [3878/4000] Training [21/39] Loss: 0.00417 +Epoch [3878/4000] Training [22/39] Loss: 0.00678 +Epoch [3878/4000] Training [23/39] Loss: 0.00529 +Epoch [3878/4000] Training [24/39] Loss: 0.00473 +Epoch [3878/4000] Training [25/39] Loss: 0.00396 +Epoch [3878/4000] Training [26/39] Loss: 0.00417 +Epoch [3878/4000] Training [27/39] Loss: 0.00484 +Epoch [3878/4000] Training [28/39] Loss: 0.00524 +Epoch [3878/4000] Training [29/39] Loss: 0.00471 +Epoch [3878/4000] Training [30/39] Loss: 0.00642 +Epoch [3878/4000] Training [31/39] Loss: 0.00441 +Epoch [3878/4000] Training [32/39] Loss: 0.00505 +Epoch [3878/4000] Training [33/39] Loss: 0.13028 +Epoch [3878/4000] Training [34/39] Loss: 0.00572 +Epoch [3878/4000] Training [35/39] Loss: 0.00463 +Epoch [3878/4000] Training [36/39] Loss: 0.00464 +Epoch [3878/4000] Training [37/39] Loss: 0.00309 +Epoch [3878/4000] Training [38/39] Loss: 0.00524 +Epoch [3878/4000] Training [39/39] Loss: 0.00419 +Epoch [3878/4000] Training metric {'Train/mean dice_metric': 0.9957965612411499, 'Train/mean miou_metric': 0.9928381443023682, 'Train/mean f1': 0.9971054792404175, 'Train/mean precision': 0.9966595768928528, 'Train/mean recall': 0.9975517988204956, 'Train/mean hd95_metric': 0.9172611236572266} +Epoch [3878/4000] Validation [1/10] Loss: 0.71697 focal_loss 0.62960 dice_loss 0.08737 +Epoch [3878/4000] Validation [2/10] Loss: 0.49620 focal_loss 0.40049 dice_loss 0.09572 +Epoch [3878/4000] Validation [3/10] Loss: 0.38214 focal_loss 0.27167 dice_loss 0.11047 +Epoch [3878/4000] Validation [4/10] Loss: 0.90270 focal_loss 0.33608 dice_loss 0.56663 +Epoch [3878/4000] Validation [5/10] Loss: 3.02512 focal_loss 2.35133 dice_loss 0.67379 +Epoch [3878/4000] Validation [6/10] Loss: 1.35324 focal_loss 0.64017 dice_loss 0.71307 +Epoch [3878/4000] Validation [7/10] Loss: 1.18982 focal_loss 0.53370 dice_loss 0.65612 +Epoch [3878/4000] Validation [8/10] Loss: 2.30015 focal_loss 1.69183 dice_loss 0.60833 +Epoch [3878/4000] Validation [9/10] Loss: 1.56521 focal_loss 1.02056 dice_loss 0.54465 +Epoch [3878/4000] Validation [10/10] Loss: 1.92888 focal_loss 1.19204 dice_loss 0.73684 +Epoch [3878/4000] Validation metric {'Val/mean dice_metric': 0.9509022831916809, 'Val/mean miou_metric': 0.9351637363433838, 'Val/mean f1': 0.9482299089431763, 'Val/mean precision': 0.9423525333404541, 'Val/mean recall': 0.9541809558868408, 'Val/mean hd95_metric': 10.847057342529297} +Cheakpoint... +Epoch [3878/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509022831916809, 'Val/mean miou_metric': 0.9351637363433838, 'Val/mean f1': 0.9482299089431763, 'Val/mean precision': 0.9423525333404541, 'Val/mean recall': 0.9541809558868408, 'Val/mean hd95_metric': 10.847057342529297} +Epoch [3879/4000] Training [1/39] Loss: 0.00955 +Epoch [3879/4000] Training [2/39] Loss: 0.13037 +Epoch [3879/4000] Training [3/39] Loss: 0.12879 +Epoch [3879/4000] Training [4/39] Loss: 0.00401 +Epoch [3879/4000] Training [5/39] Loss: 0.12858 +Epoch [3879/4000] Training [6/39] Loss: 0.00479 +Epoch [3879/4000] Training [7/39] Loss: 0.00541 +Epoch [3879/4000] Training [8/39] Loss: 0.00614 +Epoch [3879/4000] Training [9/39] Loss: 0.00506 +Epoch [3879/4000] Training [10/39] Loss: 0.00478 +Epoch [3879/4000] Training [11/39] Loss: 0.13023 +Epoch [3879/4000] Training [12/39] Loss: 0.12855 +Epoch [3879/4000] Training [13/39] Loss: 0.00292 +Epoch [3879/4000] Training [14/39] Loss: 0.00500 +Epoch [3879/4000] Training [15/39] Loss: 0.00344 +Epoch [3879/4000] Training [16/39] Loss: 0.00373 +Epoch [3879/4000] Training [17/39] Loss: 0.00412 +Epoch [3879/4000] Training [18/39] Loss: 0.12921 +Epoch [3879/4000] Training [19/39] Loss: 0.00618 +Epoch [3879/4000] Training [20/39] Loss: 0.00542 +Epoch [3879/4000] Training [21/39] Loss: 0.00430 +Epoch [3879/4000] Training [22/39] Loss: 0.00398 +Epoch [3879/4000] Training [23/39] Loss: 0.00520 +Epoch [3879/4000] Training [24/39] Loss: 0.00734 +Epoch [3879/4000] Training [25/39] Loss: 0.12924 +Epoch [3879/4000] Training [26/39] Loss: 0.00401 +Epoch [3879/4000] Training [27/39] Loss: 0.00603 +Epoch [3879/4000] Training [28/39] Loss: 0.00478 +Epoch [3879/4000] Training [29/39] Loss: 0.13053 +Epoch [3879/4000] Training [30/39] Loss: 0.12853 +Epoch [3879/4000] Training [31/39] Loss: 0.25311 +Epoch [3879/4000] Training [32/39] Loss: 0.00410 +Epoch [3879/4000] Training [33/39] Loss: 0.00590 +Epoch [3879/4000] Training [34/39] Loss: 0.00499 +Epoch [3879/4000] Training [35/39] Loss: 0.00636 +Epoch [3879/4000] Training [36/39] Loss: 0.12984 +Epoch [3879/4000] Training [37/39] Loss: 0.25463 +Epoch [3879/4000] Training [38/39] Loss: 0.00928 +Epoch [3879/4000] Training [39/39] Loss: 0.08750 +Epoch [3879/4000] Training metric {'Train/mean dice_metric': 0.9954431653022766, 'Train/mean miou_metric': 0.9922024011611938, 'Train/mean f1': 0.9967880249023438, 'Train/mean precision': 0.9963223338127136, 'Train/mean recall': 0.9972541332244873, 'Train/mean hd95_metric': 0.9262982606887817} +Epoch [3879/4000] Validation [1/10] Loss: 0.73970 focal_loss 0.65231 dice_loss 0.08738 +Epoch [3879/4000] Validation [2/10] Loss: 0.50242 focal_loss 0.40706 dice_loss 0.09536 +Epoch [3879/4000] Validation [3/10] Loss: 0.39043 focal_loss 0.28017 dice_loss 0.11026 +Epoch [3879/4000] Validation [4/10] Loss: 0.90709 focal_loss 0.34093 dice_loss 0.56616 +Epoch [3879/4000] Validation [5/10] Loss: 3.11213 focal_loss 2.43829 dice_loss 0.67384 +Epoch [3879/4000] Validation [6/10] Loss: 1.36345 focal_loss 0.65020 dice_loss 0.71325 +Epoch [3879/4000] Validation [7/10] Loss: 1.19896 focal_loss 0.54263 dice_loss 0.65633 +Epoch [3879/4000] Validation [8/10] Loss: 2.35409 focal_loss 1.74510 dice_loss 0.60899 +Epoch [3879/4000] Validation [9/10] Loss: 1.59427 focal_loss 1.04979 dice_loss 0.54448 +Epoch [3879/4000] Validation [10/10] Loss: 1.95066 focal_loss 1.21377 dice_loss 0.73689 +Epoch [3879/4000] Validation metric {'Val/mean dice_metric': 0.9506315588951111, 'Val/mean miou_metric': 0.9346792697906494, 'Val/mean f1': 0.9478076100349426, 'Val/mean precision': 0.9420285224914551, 'Val/mean recall': 0.9536581039428711, 'Val/mean hd95_metric': 10.755626678466797} +Cheakpoint... +Epoch [3879/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506315588951111, 'Val/mean miou_metric': 0.9346792697906494, 'Val/mean f1': 0.9478076100349426, 'Val/mean precision': 0.9420285224914551, 'Val/mean recall': 0.9536581039428711, 'Val/mean hd95_metric': 10.755626678466797} +Epoch [3880/4000] Training [1/39] Loss: 0.00352 +Epoch [3880/4000] Training [2/39] Loss: 0.00415 +Epoch [3880/4000] Training [3/39] Loss: 0.12717 +Epoch [3880/4000] Training [4/39] Loss: 0.00471 +Epoch [3880/4000] Training [5/39] Loss: 0.00486 +Epoch [3880/4000] Training [6/39] Loss: 0.00593 +Epoch [3880/4000] Training [7/39] Loss: 0.09337 +Epoch [3880/4000] Training [8/39] Loss: 0.00616 +Epoch [3880/4000] Training [9/39] Loss: 0.00380 +Epoch [3880/4000] Training [10/39] Loss: 0.25350 +Epoch [3880/4000] Training [11/39] Loss: 0.13084 +Epoch [3880/4000] Training [12/39] Loss: 0.00402 +Epoch [3880/4000] Training [13/39] Loss: 0.12995 +Epoch [3880/4000] Training [14/39] Loss: 0.00422 +Epoch [3880/4000] Training [15/39] Loss: 0.00226 +Epoch [3880/4000] Training [16/39] Loss: 0.00383 +Epoch [3880/4000] Training [17/39] Loss: 0.00480 +Epoch [3880/4000] Training [18/39] Loss: 0.00609 +Epoch [3880/4000] Training [19/39] Loss: 0.00429 +Epoch [3880/4000] Training [20/39] Loss: 0.00267 +Epoch [3880/4000] Training [21/39] Loss: 0.00377 +Epoch [3880/4000] Training [22/39] Loss: 0.00403 +Epoch [3880/4000] Training [23/39] Loss: 0.00475 +Epoch [3880/4000] Training [24/39] Loss: 0.00572 +Epoch [3880/4000] Training [25/39] Loss: 0.00458 +Epoch [3880/4000] Training [26/39] Loss: 0.12816 +Epoch [3880/4000] Training [27/39] Loss: 0.00403 +Epoch [3880/4000] Training [28/39] Loss: 0.00545 +Epoch [3880/4000] Training [29/39] Loss: 0.12876 +Epoch [3880/4000] Training [30/39] Loss: 0.12742 +Epoch [3880/4000] Training [31/39] Loss: 0.12754 +Epoch [3880/4000] Training [32/39] Loss: 0.13216 +Epoch [3880/4000] Training [33/39] Loss: 0.12959 +Epoch [3880/4000] Training [34/39] Loss: 0.00728 +Epoch [3880/4000] Training [35/39] Loss: 0.00483 +Epoch [3880/4000] Training [36/39] Loss: 0.00609 +Epoch [3880/4000] Training [37/39] Loss: 0.12855 +Epoch [3880/4000] Training [38/39] Loss: 0.16817 +Epoch [3880/4000] Training [39/39] Loss: 0.12999 +Epoch [3880/4000] Training metric {'Train/mean dice_metric': 0.9955725073814392, 'Train/mean miou_metric': 0.9924330711364746, 'Train/mean f1': 0.996950089931488, 'Train/mean precision': 0.9964791536331177, 'Train/mean recall': 0.9974213242530823, 'Train/mean hd95_metric': 0.9297695159912109} +Epoch [3880/4000] Validation [1/10] Loss: 0.73887 focal_loss 0.65180 dice_loss 0.08707 +Epoch [3880/4000] Validation [2/10] Loss: 0.51166 focal_loss 0.41570 dice_loss 0.09596 +Epoch [3880/4000] Validation [3/10] Loss: 0.39172 focal_loss 0.28138 dice_loss 0.11034 +Epoch [3880/4000] Validation [4/10] Loss: 0.91412 focal_loss 0.34787 dice_loss 0.56624 +Epoch [3880/4000] Validation [5/10] Loss: 3.10558 focal_loss 2.43172 dice_loss 0.67386 +Epoch [3880/4000] Validation [6/10] Loss: 1.37618 focal_loss 0.66310 dice_loss 0.71308 +Epoch [3880/4000] Validation [7/10] Loss: 1.20766 focal_loss 0.55122 dice_loss 0.65644 +Epoch [3880/4000] Validation [8/10] Loss: 2.38460 focal_loss 1.77346 dice_loss 0.61113 +Epoch [3880/4000] Validation [9/10] Loss: 1.59947 focal_loss 1.05500 dice_loss 0.54448 +Epoch [3880/4000] Validation [10/10] Loss: 1.96835 focal_loss 1.23192 dice_loss 0.73643 +Epoch [3880/4000] Validation metric {'Val/mean dice_metric': 0.9507313966751099, 'Val/mean miou_metric': 0.9348500967025757, 'Val/mean f1': 0.9480479955673218, 'Val/mean precision': 0.9424931406974792, 'Val/mean recall': 0.9536686539649963, 'Val/mean hd95_metric': 10.742444038391113} +Cheakpoint... +Epoch [3880/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507313966751099, 'Val/mean miou_metric': 0.9348500967025757, 'Val/mean f1': 0.9480479955673218, 'Val/mean precision': 0.9424931406974792, 'Val/mean recall': 0.9536686539649963, 'Val/mean hd95_metric': 10.742444038391113} +Epoch [3881/4000] Training [1/39] Loss: 0.00579 +Epoch [3881/4000] Training [2/39] Loss: 0.00527 +Epoch [3881/4000] Training [3/39] Loss: 0.00420 +Epoch [3881/4000] Training [4/39] Loss: 0.00615 +Epoch [3881/4000] Training [5/39] Loss: 0.00548 +Epoch [3881/4000] Training [6/39] Loss: 0.25423 +Epoch [3881/4000] Training [7/39] Loss: 0.12968 +Epoch [3881/4000] Training [8/39] Loss: 0.00325 +Epoch [3881/4000] Training [9/39] Loss: 0.13171 +Epoch [3881/4000] Training [10/39] Loss: 0.00580 +Epoch [3881/4000] Training [11/39] Loss: 0.00581 +Epoch [3881/4000] Training [12/39] Loss: 0.00366 +Epoch [3881/4000] Training [13/39] Loss: 0.00296 +Epoch [3881/4000] Training [14/39] Loss: 0.13116 +Epoch [3881/4000] Training [15/39] Loss: 0.00455 +Epoch [3881/4000] Training [16/39] Loss: 0.00651 +Epoch [3881/4000] Training [17/39] Loss: 0.00615 +Epoch [3881/4000] Training [18/39] Loss: 0.00708 +Epoch [3881/4000] Training [19/39] Loss: 0.00543 +Epoch [3881/4000] Training [20/39] Loss: 0.12871 +Epoch [3881/4000] Training [21/39] Loss: 0.00408 +Epoch [3881/4000] Training [22/39] Loss: 0.00418 +Epoch [3881/4000] Training [23/39] Loss: 0.12766 +Epoch [3881/4000] Training [24/39] Loss: 0.12813 +Epoch [3881/4000] Training [25/39] Loss: 0.00732 +Epoch [3881/4000] Training [26/39] Loss: 0.00375 +Epoch [3881/4000] Training [27/39] Loss: 0.12778 +Epoch [3881/4000] Training [28/39] Loss: 0.12850 +Epoch [3881/4000] Training [29/39] Loss: 0.00460 +Epoch [3881/4000] Training [30/39] Loss: 0.00532 +Epoch [3881/4000] Training [31/39] Loss: 0.00730 +Epoch [3881/4000] Training [32/39] Loss: 0.00523 +Epoch [3881/4000] Training [33/39] Loss: 0.00509 +Epoch [3881/4000] Training [34/39] Loss: 0.00307 +Epoch [3881/4000] Training [35/39] Loss: 0.00677 +Epoch [3881/4000] Training [36/39] Loss: 0.00334 +Epoch [3881/4000] Training [37/39] Loss: 0.00517 +Epoch [3881/4000] Training [38/39] Loss: 0.25322 +Epoch [3881/4000] Training [39/39] Loss: 0.00389 +Epoch [3881/4000] Training metric {'Train/mean dice_metric': 0.9955191016197205, 'Train/mean miou_metric': 0.9923166632652283, 'Train/mean f1': 0.9968528747558594, 'Train/mean precision': 0.9964485764503479, 'Train/mean recall': 0.9972575902938843, 'Train/mean hd95_metric': 0.9388440847396851} +Epoch [3881/4000] Validation [1/10] Loss: 0.71423 focal_loss 0.62754 dice_loss 0.08670 +Epoch [3881/4000] Validation [2/10] Loss: 0.50085 focal_loss 0.40336 dice_loss 0.09749 +Epoch [3881/4000] Validation [3/10] Loss: 0.38865 focal_loss 0.27778 dice_loss 0.11088 +Epoch [3881/4000] Validation [4/10] Loss: 0.89950 focal_loss 0.33388 dice_loss 0.56562 +Epoch [3881/4000] Validation [5/10] Loss: 3.05087 focal_loss 2.37689 dice_loss 0.67398 +Epoch [3881/4000] Validation [6/10] Loss: 1.34745 focal_loss 0.63460 dice_loss 0.71285 +Epoch [3881/4000] Validation [7/10] Loss: 1.18653 focal_loss 0.53174 dice_loss 0.65479 +Epoch [3881/4000] Validation [8/10] Loss: 2.34856 focal_loss 1.73587 dice_loss 0.61268 +Epoch [3881/4000] Validation [9/10] Loss: 1.55469 focal_loss 1.01021 dice_loss 0.54448 +Epoch [3881/4000] Validation [10/10] Loss: 1.91816 focal_loss 1.18229 dice_loss 0.73587 +Epoch [3881/4000] Validation metric {'Val/mean dice_metric': 0.9506924748420715, 'Val/mean miou_metric': 0.934791088104248, 'Val/mean f1': 0.9482514262199402, 'Val/mean precision': 0.9430541396141052, 'Val/mean recall': 0.9535061717033386, 'Val/mean hd95_metric': 10.704707145690918} +Cheakpoint... +Epoch [3881/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506924748420715, 'Val/mean miou_metric': 0.934791088104248, 'Val/mean f1': 0.9482514262199402, 'Val/mean precision': 0.9430541396141052, 'Val/mean recall': 0.9535061717033386, 'Val/mean hd95_metric': 10.704707145690918} +Epoch [3882/4000] Training [1/39] Loss: 0.00427 +Epoch [3882/4000] Training [2/39] Loss: 0.00295 +Epoch [3882/4000] Training [3/39] Loss: 0.00438 +Epoch [3882/4000] Training [4/39] Loss: 0.00367 +Epoch [3882/4000] Training [5/39] Loss: 0.00408 +Epoch [3882/4000] Training [6/39] Loss: 0.00458 +Epoch [3882/4000] Training [7/39] Loss: 0.12989 +Epoch [3882/4000] Training [8/39] Loss: 0.13098 +Epoch [3882/4000] Training [9/39] Loss: 0.00735 +Epoch [3882/4000] Training [10/39] Loss: 0.00355 +Epoch [3882/4000] Training [11/39] Loss: 0.00674 +Epoch [3882/4000] Training [12/39] Loss: 0.08620 +Epoch [3882/4000] Training [13/39] Loss: 0.00495 +Epoch [3882/4000] Training [14/39] Loss: 0.00609 +Epoch [3882/4000] Training [15/39] Loss: 0.25237 +Epoch [3882/4000] Training [16/39] Loss: 0.12819 +Epoch [3882/4000] Training [17/39] Loss: 0.00376 +Epoch [3882/4000] Training [18/39] Loss: 0.12807 +Epoch [3882/4000] Training [19/39] Loss: 0.00549 +Epoch [3882/4000] Training [20/39] Loss: 0.00500 +Epoch [3882/4000] Training [21/39] Loss: 0.00521 +Epoch [3882/4000] Training [22/39] Loss: 0.12894 +Epoch [3882/4000] Training [23/39] Loss: 0.00717 +Epoch [3882/4000] Training [24/39] Loss: 0.00392 +Epoch [3882/4000] Training [25/39] Loss: 0.00301 +Epoch [3882/4000] Training [26/39] Loss: 0.12771 +Epoch [3882/4000] Training [27/39] Loss: 0.00637 +Epoch [3882/4000] Training [28/39] Loss: 0.00442 +Epoch [3882/4000] Training [29/39] Loss: 0.12749 +Epoch [3882/4000] Training [30/39] Loss: 0.12925 +Epoch [3882/4000] Training [31/39] Loss: 0.00553 +Epoch [3882/4000] Training [32/39] Loss: 0.00467 +Epoch [3882/4000] Training [33/39] Loss: 0.12817 +Epoch [3882/4000] Training [34/39] Loss: 0.00315 +Epoch [3882/4000] Training [35/39] Loss: 0.00553 +Epoch [3882/4000] Training [36/39] Loss: 0.12876 +Epoch [3882/4000] Training [37/39] Loss: 0.00488 +Epoch [3882/4000] Training [38/39] Loss: 0.12831 +Epoch [3882/4000] Training [39/39] Loss: 0.13097 +Epoch [3882/4000] Training metric {'Train/mean dice_metric': 0.996401846408844, 'Train/mean miou_metric': 0.9932529330253601, 'Train/mean f1': 0.9970796704292297, 'Train/mean precision': 0.9966302514076233, 'Train/mean recall': 0.9975293874740601, 'Train/mean hd95_metric': 0.939666748046875} +Epoch [3882/4000] Validation [1/10] Loss: 0.72597 focal_loss 0.63930 dice_loss 0.08667 +Epoch [3882/4000] Validation [2/10] Loss: 0.50638 focal_loss 0.40904 dice_loss 0.09734 +Epoch [3882/4000] Validation [3/10] Loss: 0.39366 focal_loss 0.28264 dice_loss 0.11102 +Epoch [3882/4000] Validation [4/10] Loss: 0.90467 focal_loss 0.33888 dice_loss 0.56579 +Epoch [3882/4000] Validation [5/10] Loss: 3.08808 focal_loss 2.41407 dice_loss 0.67401 +Epoch [3882/4000] Validation [6/10] Loss: 1.35625 focal_loss 0.64327 dice_loss 0.71299 +Epoch [3882/4000] Validation [7/10] Loss: 1.19144 focal_loss 0.53578 dice_loss 0.65566 +Epoch [3882/4000] Validation [8/10] Loss: 2.38441 focal_loss 1.76982 dice_loss 0.61459 +Epoch [3882/4000] Validation [9/10] Loss: 1.56711 focal_loss 1.02288 dice_loss 0.54423 +Epoch [3882/4000] Validation [10/10] Loss: 1.93234 focal_loss 1.19642 dice_loss 0.73592 +Epoch [3882/4000] Validation metric {'Val/mean dice_metric': 0.9513411521911621, 'Val/mean miou_metric': 0.9354584813117981, 'Val/mean f1': 0.9482468366622925, 'Val/mean precision': 0.9431827664375305, 'Val/mean recall': 0.9533656239509583, 'Val/mean hd95_metric': 10.73257064819336} +Cheakpoint... +Epoch [3882/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513411521911621, 'Val/mean miou_metric': 0.9354584813117981, 'Val/mean f1': 0.9482468366622925, 'Val/mean precision': 0.9431827664375305, 'Val/mean recall': 0.9533656239509583, 'Val/mean hd95_metric': 10.73257064819336} +Epoch [3883/4000] Training [1/39] Loss: 0.00451 +Epoch [3883/4000] Training [2/39] Loss: 0.00407 +Epoch [3883/4000] Training [3/39] Loss: 0.00409 +Epoch [3883/4000] Training [4/39] Loss: 0.00601 +Epoch [3883/4000] Training [5/39] Loss: 0.08894 +Epoch [3883/4000] Training [6/39] Loss: 0.12996 +Epoch [3883/4000] Training [7/39] Loss: 0.00496 +Epoch [3883/4000] Training [8/39] Loss: 0.00873 +Epoch [3883/4000] Training [9/39] Loss: 0.00714 +Epoch [3883/4000] Training [10/39] Loss: 0.12854 +Epoch [3883/4000] Training [11/39] Loss: 0.12798 +Epoch [3883/4000] Training [12/39] Loss: 0.00347 +Epoch [3883/4000] Training [13/39] Loss: 0.00331 +Epoch [3883/4000] Training [14/39] Loss: 0.00358 +Epoch [3883/4000] Training [15/39] Loss: 0.00583 +Epoch [3883/4000] Training [16/39] Loss: 0.00379 +Epoch [3883/4000] Training [17/39] Loss: 0.00363 +Epoch [3883/4000] Training [18/39] Loss: 0.00365 +Epoch [3883/4000] Training [19/39] Loss: 0.00378 +Epoch [3883/4000] Training [20/39] Loss: 0.00495 +Epoch [3883/4000] Training [21/39] Loss: 0.00526 +Epoch [3883/4000] Training [22/39] Loss: 0.00586 +Epoch [3883/4000] Training [23/39] Loss: 0.00558 +Epoch [3883/4000] Training [24/39] Loss: 0.00417 +Epoch [3883/4000] Training [25/39] Loss: 0.25330 +Epoch [3883/4000] Training [26/39] Loss: 0.25354 +Epoch [3883/4000] Training [27/39] Loss: 0.00532 +Epoch [3883/4000] Training [28/39] Loss: 0.12858 +Epoch [3883/4000] Training [29/39] Loss: 0.00474 +Epoch [3883/4000] Training [30/39] Loss: 0.12772 +Epoch [3883/4000] Training [31/39] Loss: 0.00552 +Epoch [3883/4000] Training [32/39] Loss: 0.00495 +Epoch [3883/4000] Training [33/39] Loss: 0.00464 +Epoch [3883/4000] Training [34/39] Loss: 0.00292 +Epoch [3883/4000] Training [35/39] Loss: 0.00408 +Epoch [3883/4000] Training [36/39] Loss: 0.00340 +Epoch [3883/4000] Training [37/39] Loss: 0.13041 +Epoch [3883/4000] Training [38/39] Loss: 0.00432 +Epoch [3883/4000] Training [39/39] Loss: 0.00660 +Epoch [3883/4000] Training metric {'Train/mean dice_metric': 0.9963538646697998, 'Train/mean miou_metric': 0.9931573867797852, 'Train/mean f1': 0.9968349933624268, 'Train/mean precision': 0.9963774681091309, 'Train/mean recall': 0.9972929358482361, 'Train/mean hd95_metric': 0.933029294013977} +Epoch [3883/4000] Validation [1/10] Loss: 0.70770 focal_loss 0.62163 dice_loss 0.08607 +Epoch [3883/4000] Validation [2/10] Loss: 0.49927 focal_loss 0.39947 dice_loss 0.09980 +Epoch [3883/4000] Validation [3/10] Loss: 0.39635 focal_loss 0.28439 dice_loss 0.11196 +Epoch [3883/4000] Validation [4/10] Loss: 0.88900 focal_loss 0.32408 dice_loss 0.56493 +Epoch [3883/4000] Validation [5/10] Loss: 3.04379 focal_loss 2.36968 dice_loss 0.67411 +Epoch [3883/4000] Validation [6/10] Loss: 1.32331 focal_loss 0.61030 dice_loss 0.71301 +Epoch [3883/4000] Validation [7/10] Loss: 1.16532 focal_loss 0.51138 dice_loss 0.65394 +Epoch [3883/4000] Validation [8/10] Loss: 2.36809 focal_loss 1.74872 dice_loss 0.61936 +Epoch [3883/4000] Validation [9/10] Loss: 1.52013 focal_loss 0.97636 dice_loss 0.54377 +Epoch [3883/4000] Validation [10/10] Loss: 1.86444 focal_loss 1.12962 dice_loss 0.73482 +Epoch [3883/4000] Validation metric {'Val/mean dice_metric': 0.9512718319892883, 'Val/mean miou_metric': 0.9353532791137695, 'Val/mean f1': 0.9484890103340149, 'Val/mean precision': 0.9443622827529907, 'Val/mean recall': 0.9526519775390625, 'Val/mean hd95_metric': 10.846176147460938} +Cheakpoint... +Epoch [3883/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512718319892883, 'Val/mean miou_metric': 0.9353532791137695, 'Val/mean f1': 0.9484890103340149, 'Val/mean precision': 0.9443622827529907, 'Val/mean recall': 0.9526519775390625, 'Val/mean hd95_metric': 10.846176147460938} +Epoch [3884/4000] Training [1/39] Loss: 0.00463 +Epoch [3884/4000] Training [2/39] Loss: 0.13269 +Epoch [3884/4000] Training [3/39] Loss: 0.00621 +Epoch [3884/4000] Training [4/39] Loss: 0.00319 +Epoch [3884/4000] Training [5/39] Loss: 0.00437 +Epoch [3884/4000] Training [6/39] Loss: 0.00292 +Epoch [3884/4000] Training [7/39] Loss: 0.00347 +Epoch [3884/4000] Training [8/39] Loss: 0.00331 +Epoch [3884/4000] Training [9/39] Loss: 0.00755 +Epoch [3884/4000] Training [10/39] Loss: 0.12916 +Epoch [3884/4000] Training [11/39] Loss: 0.00387 +Epoch [3884/4000] Training [12/39] Loss: 0.00329 +Epoch [3884/4000] Training [13/39] Loss: 0.00295 +Epoch [3884/4000] Training [14/39] Loss: 0.13228 +Epoch [3884/4000] Training [15/39] Loss: 0.00418 +Epoch [3884/4000] Training [16/39] Loss: 0.12787 +Epoch [3884/4000] Training [17/39] Loss: 0.13048 +Epoch [3884/4000] Training [18/39] Loss: 0.00445 +Epoch [3884/4000] Training [19/39] Loss: 0.12775 +Epoch [3884/4000] Training [20/39] Loss: 0.12952 +Epoch [3884/4000] Training [21/39] Loss: 0.13264 +Epoch [3884/4000] Training [22/39] Loss: 0.00482 +Epoch [3884/4000] Training [23/39] Loss: 0.00728 +Epoch [3884/4000] Training [24/39] Loss: 0.00378 +Epoch [3884/4000] Training [25/39] Loss: 0.00437 +Epoch [3884/4000] Training [26/39] Loss: 0.00647 +Epoch [3884/4000] Training [27/39] Loss: 0.00309 +Epoch [3884/4000] Training [28/39] Loss: 0.12836 +Epoch [3884/4000] Training [29/39] Loss: 0.00546 +Epoch [3884/4000] Training [30/39] Loss: 0.00473 +Epoch [3884/4000] Training [31/39] Loss: 0.00606 +Epoch [3884/4000] Training [32/39] Loss: 0.00325 +Epoch [3884/4000] Training [33/39] Loss: 0.00570 +Epoch [3884/4000] Training [34/39] Loss: 0.00514 +Epoch [3884/4000] Training [35/39] Loss: 0.00362 +Epoch [3884/4000] Training [36/39] Loss: 0.00462 +Epoch [3884/4000] Training [37/39] Loss: 0.00436 +Epoch [3884/4000] Training [38/39] Loss: 0.00334 +Epoch [3884/4000] Training [39/39] Loss: 0.12944 +Epoch [3884/4000] Training metric {'Train/mean dice_metric': 0.9965179562568665, 'Train/mean miou_metric': 0.9934908151626587, 'Train/mean f1': 0.9970736503601074, 'Train/mean precision': 0.9966654181480408, 'Train/mean recall': 0.997482180595398, 'Train/mean hd95_metric': 1.0212751626968384} +Epoch [3884/4000] Validation [1/10] Loss: 0.72746 focal_loss 0.64142 dice_loss 0.08604 +Epoch [3884/4000] Validation [2/10] Loss: 0.50899 focal_loss 0.40818 dice_loss 0.10081 +Epoch [3884/4000] Validation [3/10] Loss: 0.41335 focal_loss 0.30090 dice_loss 0.11245 +Epoch [3884/4000] Validation [4/10] Loss: 0.89172 focal_loss 0.32718 dice_loss 0.56454 +Epoch [3884/4000] Validation [5/10] Loss: 3.12930 focal_loss 2.45517 dice_loss 0.67413 +Epoch [3884/4000] Validation [6/10] Loss: 1.32663 focal_loss 0.61408 dice_loss 0.71255 +Epoch [3884/4000] Validation [7/10] Loss: 1.16988 focal_loss 0.51616 dice_loss 0.65372 +Epoch [3884/4000] Validation [8/10] Loss: 2.45315 focal_loss 1.82955 dice_loss 0.62360 +Epoch [3884/4000] Validation [9/10] Loss: 1.54504 focal_loss 1.00157 dice_loss 0.54347 +Epoch [3884/4000] Validation [10/10] Loss: 1.87122 focal_loss 1.13713 dice_loss 0.73409 +Epoch [3884/4000] Validation metric {'Val/mean dice_metric': 0.9514222741127014, 'Val/mean miou_metric': 0.9356586933135986, 'Val/mean f1': 0.9485340714454651, 'Val/mean precision': 0.9449388384819031, 'Val/mean recall': 0.952156662940979, 'Val/mean hd95_metric': 10.871835708618164} +Cheakpoint... +Epoch [3884/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514222741127014, 'Val/mean miou_metric': 0.9356586933135986, 'Val/mean f1': 0.9485340714454651, 'Val/mean precision': 0.9449388384819031, 'Val/mean recall': 0.952156662940979, 'Val/mean hd95_metric': 10.871835708618164} +Epoch [3885/4000] Training [1/39] Loss: 0.00458 +Epoch [3885/4000] Training [2/39] Loss: 0.00561 +Epoch [3885/4000] Training [3/39] Loss: 0.13303 +Epoch [3885/4000] Training [4/39] Loss: 0.00414 +Epoch [3885/4000] Training [5/39] Loss: 0.00369 +Epoch [3885/4000] Training [6/39] Loss: 0.13248 +Epoch [3885/4000] Training [7/39] Loss: 0.00605 +Epoch [3885/4000] Training [8/39] Loss: 0.00450 +Epoch [3885/4000] Training [9/39] Loss: 0.25218 +Epoch [3885/4000] Training [10/39] Loss: 0.00719 +Epoch [3885/4000] Training [11/39] Loss: 0.12845 +Epoch [3885/4000] Training [12/39] Loss: 0.12883 +Epoch [3885/4000] Training [13/39] Loss: 0.00648 +Epoch [3885/4000] Training [14/39] Loss: 0.13096 +Epoch [3885/4000] Training [15/39] Loss: 0.00426 +Epoch [3885/4000] Training [16/39] Loss: 0.00603 +Epoch [3885/4000] Training [17/39] Loss: 0.00566 +Epoch [3885/4000] Training [18/39] Loss: 0.00272 +Epoch [3885/4000] Training [19/39] Loss: 0.00793 +Epoch [3885/4000] Training [20/39] Loss: 0.00423 +Epoch [3885/4000] Training [21/39] Loss: 0.00341 +Epoch [3885/4000] Training [22/39] Loss: 0.00361 +Epoch [3885/4000] Training [23/39] Loss: 0.00544 +Epoch [3885/4000] Training [24/39] Loss: 0.00451 +Epoch [3885/4000] Training [25/39] Loss: 0.12823 +Epoch [3885/4000] Training [26/39] Loss: 0.12914 +Epoch [3885/4000] Training [27/39] Loss: 0.00446 +Epoch [3885/4000] Training [28/39] Loss: 0.00301 +Epoch [3885/4000] Training [29/39] Loss: 0.00483 +Epoch [3885/4000] Training [30/39] Loss: 0.00329 +Epoch [3885/4000] Training [31/39] Loss: 0.00434 +Epoch [3885/4000] Training [32/39] Loss: 0.25371 +Epoch [3885/4000] Training [33/39] Loss: 0.08507 +Epoch [3885/4000] Training [34/39] Loss: 0.00497 +Epoch [3885/4000] Training [35/39] Loss: 0.00381 +Epoch [3885/4000] Training [36/39] Loss: 0.00689 +Epoch [3885/4000] Training [37/39] Loss: 0.00433 +Epoch [3885/4000] Training [38/39] Loss: 0.00482 +Epoch [3885/4000] Training [39/39] Loss: 0.00501 +Epoch [3885/4000] Training metric {'Train/mean dice_metric': 0.9959550499916077, 'Train/mean miou_metric': 0.9923965334892273, 'Train/mean f1': 0.996654748916626, 'Train/mean precision': 0.9961873292922974, 'Train/mean recall': 0.9971225261688232, 'Train/mean hd95_metric': 0.9846757650375366} +Epoch [3885/4000] Validation [1/10] Loss: 0.71860 focal_loss 0.63199 dice_loss 0.08661 +Epoch [3885/4000] Validation [2/10] Loss: 0.50383 focal_loss 0.40597 dice_loss 0.09786 +Epoch [3885/4000] Validation [3/10] Loss: 0.39156 focal_loss 0.28049 dice_loss 0.11107 +Epoch [3885/4000] Validation [4/10] Loss: 0.89973 focal_loss 0.33406 dice_loss 0.56566 +Epoch [3885/4000] Validation [5/10] Loss: 3.06699 focal_loss 2.39299 dice_loss 0.67400 +Epoch [3885/4000] Validation [6/10] Loss: 1.34905 focal_loss 0.63619 dice_loss 0.71286 +Epoch [3885/4000] Validation [7/10] Loss: 1.18755 focal_loss 0.53174 dice_loss 0.65580 +Epoch [3885/4000] Validation [8/10] Loss: 2.37640 focal_loss 1.76185 dice_loss 0.61455 +Epoch [3885/4000] Validation [9/10] Loss: 1.55361 focal_loss 1.00948 dice_loss 0.54412 +Epoch [3885/4000] Validation [10/10] Loss: 1.91503 focal_loss 1.17917 dice_loss 0.73586 +Epoch [3885/4000] Validation metric {'Val/mean dice_metric': 0.9509774446487427, 'Val/mean miou_metric': 0.9347501993179321, 'Val/mean f1': 0.9481368660926819, 'Val/mean precision': 0.9431998133659363, 'Val/mean recall': 0.9531258940696716, 'Val/mean hd95_metric': 10.766573905944824} +Cheakpoint... +Epoch [3885/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509774446487427, 'Val/mean miou_metric': 0.9347501993179321, 'Val/mean f1': 0.9481368660926819, 'Val/mean precision': 0.9431998133659363, 'Val/mean recall': 0.9531258940696716, 'Val/mean hd95_metric': 10.766573905944824} +Epoch [3886/4000] Training [1/39] Loss: 0.00564 +Epoch [3886/4000] Training [2/39] Loss: 0.00579 +Epoch [3886/4000] Training [3/39] Loss: 0.00267 +Epoch [3886/4000] Training [4/39] Loss: 0.12949 +Epoch [3886/4000] Training [5/39] Loss: 0.12919 +Epoch [3886/4000] Training [6/39] Loss: 0.00654 +Epoch [3886/4000] Training [7/39] Loss: 0.00453 +Epoch [3886/4000] Training [8/39] Loss: 0.25198 +Epoch [3886/4000] Training [9/39] Loss: 0.00304 +Epoch [3886/4000] Training [10/39] Loss: 0.12839 +Epoch [3886/4000] Training [11/39] Loss: 0.13275 +Epoch [3886/4000] Training [12/39] Loss: 0.00665 +Epoch [3886/4000] Training [13/39] Loss: 0.12781 +Epoch [3886/4000] Training [14/39] Loss: 0.00437 +Epoch [3886/4000] Training [15/39] Loss: 0.00428 +Epoch [3886/4000] Training [16/39] Loss: 0.00515 +Epoch [3886/4000] Training [17/39] Loss: 0.13042 +Epoch [3886/4000] Training [18/39] Loss: 0.00303 +Epoch [3886/4000] Training [19/39] Loss: 0.00563 +Epoch [3886/4000] Training [20/39] Loss: 0.00466 +Epoch [3886/4000] Training [21/39] Loss: 0.00448 +Epoch [3886/4000] Training [22/39] Loss: 0.00482 +Epoch [3886/4000] Training [23/39] Loss: 0.00351 +Epoch [3886/4000] Training [24/39] Loss: 0.00455 +Epoch [3886/4000] Training [25/39] Loss: 0.00590 +Epoch [3886/4000] Training [26/39] Loss: 0.12811 +Epoch [3886/4000] Training [27/39] Loss: 0.00514 +Epoch [3886/4000] Training [28/39] Loss: 0.12741 +Epoch [3886/4000] Training [29/39] Loss: 0.00524 +Epoch [3886/4000] Training [30/39] Loss: 0.25364 +Epoch [3886/4000] Training [31/39] Loss: 0.50331 +Epoch [3886/4000] Training [32/39] Loss: 0.00418 +Epoch [3886/4000] Training [33/39] Loss: 0.00407 +Epoch [3886/4000] Training [34/39] Loss: 0.00351 +Epoch [3886/4000] Training [35/39] Loss: 0.00510 +Epoch [3886/4000] Training [36/39] Loss: 0.00489 +Epoch [3886/4000] Training [37/39] Loss: 0.00387 +Epoch [3886/4000] Training [38/39] Loss: 0.25312 +Epoch [3886/4000] Training [39/39] Loss: 0.12862 +Epoch [3886/4000] Training metric {'Train/mean dice_metric': 0.9963944554328918, 'Train/mean miou_metric': 0.9932382702827454, 'Train/mean f1': 0.9968327879905701, 'Train/mean precision': 0.9963555932044983, 'Train/mean recall': 0.9973104000091553, 'Train/mean hd95_metric': 0.9838023781776428} +Epoch [3886/4000] Validation [1/10] Loss: 0.72886 focal_loss 0.64217 dice_loss 0.08669 +Epoch [3886/4000] Validation [2/10] Loss: 0.50787 focal_loss 0.40911 dice_loss 0.09876 +Epoch [3886/4000] Validation [3/10] Loss: 0.40152 focal_loss 0.29007 dice_loss 0.11146 +Epoch [3886/4000] Validation [4/10] Loss: 0.89993 focal_loss 0.33437 dice_loss 0.56556 +Epoch [3886/4000] Validation [5/10] Loss: 3.11194 focal_loss 2.43792 dice_loss 0.67402 +Epoch [3886/4000] Validation [6/10] Loss: 1.34583 focal_loss 0.63324 dice_loss 0.71259 +Epoch [3886/4000] Validation [7/10] Loss: 1.18439 focal_loss 0.52941 dice_loss 0.65498 +Epoch [3886/4000] Validation [8/10] Loss: 2.41309 focal_loss 1.79550 dice_loss 0.61759 +Epoch [3886/4000] Validation [9/10] Loss: 1.55170 focal_loss 1.00767 dice_loss 0.54403 +Epoch [3886/4000] Validation [10/10] Loss: 1.90583 focal_loss 1.17051 dice_loss 0.73531 +Epoch [3886/4000] Validation metric {'Val/mean dice_metric': 0.9513665437698364, 'Val/mean miou_metric': 0.9354856014251709, 'Val/mean f1': 0.9482508301734924, 'Val/mean precision': 0.9436696767807007, 'Val/mean recall': 0.9528768062591553, 'Val/mean hd95_metric': 10.838543891906738} +Cheakpoint... +Epoch [3886/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513665437698364, 'Val/mean miou_metric': 0.9354856014251709, 'Val/mean f1': 0.9482508301734924, 'Val/mean precision': 0.9436696767807007, 'Val/mean recall': 0.9528768062591553, 'Val/mean hd95_metric': 10.838543891906738} +Epoch [3887/4000] Training [1/39] Loss: 0.00459 +Epoch [3887/4000] Training [2/39] Loss: 0.00327 +Epoch [3887/4000] Training [3/39] Loss: 0.00328 +Epoch [3887/4000] Training [4/39] Loss: 0.13186 +Epoch [3887/4000] Training [5/39] Loss: 0.00528 +Epoch [3887/4000] Training [6/39] Loss: 0.00350 +Epoch [3887/4000] Training [7/39] Loss: 0.00357 +Epoch [3887/4000] Training [8/39] Loss: 0.12800 +Epoch [3887/4000] Training [9/39] Loss: 0.00407 +Epoch [3887/4000] Training [10/39] Loss: 0.12835 +Epoch [3887/4000] Training [11/39] Loss: 0.00328 +Epoch [3887/4000] Training [12/39] Loss: 0.00501 +Epoch [3887/4000] Training [13/39] Loss: 0.00386 +Epoch [3887/4000] Training [14/39] Loss: 0.00679 +Epoch [3887/4000] Training [15/39] Loss: 0.13062 +Epoch [3887/4000] Training [16/39] Loss: 0.00402 +Epoch [3887/4000] Training [17/39] Loss: 0.00371 +Epoch [3887/4000] Training [18/39] Loss: 0.12790 +Epoch [3887/4000] Training [19/39] Loss: 0.12955 +Epoch [3887/4000] Training [20/39] Loss: 0.12979 +Epoch [3887/4000] Training [21/39] Loss: 0.00547 +Epoch [3887/4000] Training [22/39] Loss: 0.00492 +Epoch [3887/4000] Training [23/39] Loss: 0.12831 +Epoch [3887/4000] Training [24/39] Loss: 0.00429 +Epoch [3887/4000] Training [25/39] Loss: 0.12717 +Epoch [3887/4000] Training [26/39] Loss: 0.00281 +Epoch [3887/4000] Training [27/39] Loss: 0.00449 +Epoch [3887/4000] Training [28/39] Loss: 0.00595 +Epoch [3887/4000] Training [29/39] Loss: 0.00469 +Epoch [3887/4000] Training [30/39] Loss: 0.00512 +Epoch [3887/4000] Training [31/39] Loss: 0.00381 +Epoch [3887/4000] Training [32/39] Loss: 0.00335 +Epoch [3887/4000] Training [33/39] Loss: 0.00470 +Epoch [3887/4000] Training [34/39] Loss: 0.00401 +Epoch [3887/4000] Training [35/39] Loss: 0.00403 +Epoch [3887/4000] Training [36/39] Loss: 0.00534 +Epoch [3887/4000] Training [37/39] Loss: 0.12798 +Epoch [3887/4000] Training [38/39] Loss: 0.00538 +Epoch [3887/4000] Training [39/39] Loss: 0.00491 +Epoch [3887/4000] Training metric {'Train/mean dice_metric': 0.9959125518798828, 'Train/mean miou_metric': 0.993093729019165, 'Train/mean f1': 0.9971596598625183, 'Train/mean precision': 0.9967156052589417, 'Train/mean recall': 0.997604250907898, 'Train/mean hd95_metric': 0.8864781856536865} +Epoch [3887/4000] Validation [1/10] Loss: 0.71821 focal_loss 0.63204 dice_loss 0.08617 +Epoch [3887/4000] Validation [2/10] Loss: 0.50731 focal_loss 0.40748 dice_loss 0.09983 +Epoch [3887/4000] Validation [3/10] Loss: 0.40263 focal_loss 0.29077 dice_loss 0.11186 +Epoch [3887/4000] Validation [4/10] Loss: 0.89534 focal_loss 0.33056 dice_loss 0.56478 +Epoch [3887/4000] Validation [5/10] Loss: 3.07853 focal_loss 2.40441 dice_loss 0.67412 +Epoch [3887/4000] Validation [6/10] Loss: 1.33569 focal_loss 0.62316 dice_loss 0.71254 +Epoch [3887/4000] Validation [7/10] Loss: 1.17826 focal_loss 0.52396 dice_loss 0.65431 +Epoch [3887/4000] Validation [8/10] Loss: 2.44021 focal_loss 1.81864 dice_loss 0.62158 +Epoch [3887/4000] Validation [9/10] Loss: 1.54152 focal_loss 0.99769 dice_loss 0.54384 +Epoch [3887/4000] Validation [10/10] Loss: 1.89048 focal_loss 1.15584 dice_loss 0.73464 +Epoch [3887/4000] Validation metric {'Val/mean dice_metric': 0.9509593844413757, 'Val/mean miou_metric': 0.9353764057159424, 'Val/mean f1': 0.9487837553024292, 'Val/mean precision': 0.9447082281112671, 'Val/mean recall': 0.9528945684432983, 'Val/mean hd95_metric': 10.761428833007812} +Cheakpoint... +Epoch [3887/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509593844413757, 'Val/mean miou_metric': 0.9353764057159424, 'Val/mean f1': 0.9487837553024292, 'Val/mean precision': 0.9447082281112671, 'Val/mean recall': 0.9528945684432983, 'Val/mean hd95_metric': 10.761428833007812} +Epoch [3888/4000] Training [1/39] Loss: 0.00655 +Epoch [3888/4000] Training [2/39] Loss: 0.00351 +Epoch [3888/4000] Training [3/39] Loss: 0.00389 +Epoch [3888/4000] Training [4/39] Loss: 0.00348 +Epoch [3888/4000] Training [5/39] Loss: 0.12804 +Epoch [3888/4000] Training [6/39] Loss: 0.00803 +Epoch [3888/4000] Training [7/39] Loss: 0.12823 +Epoch [3888/4000] Training [8/39] Loss: 0.00535 +Epoch [3888/4000] Training [9/39] Loss: 0.00421 +Epoch [3888/4000] Training [10/39] Loss: 0.13322 +Epoch [3888/4000] Training [11/39] Loss: 0.00444 +Epoch [3888/4000] Training [12/39] Loss: 0.00586 +Epoch [3888/4000] Training [13/39] Loss: 0.12755 +Epoch [3888/4000] Training [14/39] Loss: 0.12890 +Epoch [3888/4000] Training [15/39] Loss: 0.13066 +Epoch [3888/4000] Training [16/39] Loss: 0.00495 +Epoch [3888/4000] Training [17/39] Loss: 0.00371 +Epoch [3888/4000] Training [18/39] Loss: 0.00805 +Epoch [3888/4000] Training [19/39] Loss: 0.00377 +Epoch [3888/4000] Training [20/39] Loss: 0.00326 +Epoch [3888/4000] Training [21/39] Loss: 0.25433 +Epoch [3888/4000] Training [22/39] Loss: 0.00379 +Epoch [3888/4000] Training [23/39] Loss: 0.00582 +Epoch [3888/4000] Training [24/39] Loss: 0.13089 +Epoch [3888/4000] Training [25/39] Loss: 0.12943 +Epoch [3888/4000] Training [26/39] Loss: 0.00485 +Epoch [3888/4000] Training [27/39] Loss: 0.00396 +Epoch [3888/4000] Training [28/39] Loss: 0.12876 +Epoch [3888/4000] Training [29/39] Loss: 0.12972 +Epoch [3888/4000] Training [30/39] Loss: 0.00497 +Epoch [3888/4000] Training [31/39] Loss: 0.25227 +Epoch [3888/4000] Training [32/39] Loss: 0.00602 +Epoch [3888/4000] Training [33/39] Loss: 0.00437 +Epoch [3888/4000] Training [34/39] Loss: 0.00757 +Epoch [3888/4000] Training [35/39] Loss: 0.00629 +Epoch [3888/4000] Training [36/39] Loss: 0.00365 +Epoch [3888/4000] Training [37/39] Loss: 0.00302 +Epoch [3888/4000] Training [38/39] Loss: 0.00750 +Epoch [3888/4000] Training [39/39] Loss: 0.00438 +Epoch [3888/4000] Training metric {'Train/mean dice_metric': 0.9963428378105164, 'Train/mean miou_metric': 0.993144690990448, 'Train/mean f1': 0.9968830943107605, 'Train/mean precision': 0.9964133501052856, 'Train/mean recall': 0.9973532557487488, 'Train/mean hd95_metric': 0.9340435862541199} +Epoch [3888/4000] Validation [1/10] Loss: 0.71249 focal_loss 0.62556 dice_loss 0.08693 +Epoch [3888/4000] Validation [2/10] Loss: 0.50112 focal_loss 0.40304 dice_loss 0.09808 +Epoch [3888/4000] Validation [3/10] Loss: 0.38899 focal_loss 0.27775 dice_loss 0.11125 +Epoch [3888/4000] Validation [4/10] Loss: 0.89771 focal_loss 0.33226 dice_loss 0.56545 +Epoch [3888/4000] Validation [5/10] Loss: 3.03748 focal_loss 2.36354 dice_loss 0.67394 +Epoch [3888/4000] Validation [6/10] Loss: 1.34377 focal_loss 0.63024 dice_loss 0.71354 +Epoch [3888/4000] Validation [7/10] Loss: 1.17981 focal_loss 0.52465 dice_loss 0.65516 +Epoch [3888/4000] Validation [8/10] Loss: 2.37922 focal_loss 1.76187 dice_loss 0.61734 +Epoch [3888/4000] Validation [9/10] Loss: 1.53522 focal_loss 0.99103 dice_loss 0.54419 +Epoch [3888/4000] Validation [10/10] Loss: 1.89974 focal_loss 1.16442 dice_loss 0.73531 +Epoch [3888/4000] Validation metric {'Val/mean dice_metric': 0.9513336420059204, 'Val/mean miou_metric': 0.9354280233383179, 'Val/mean f1': 0.9480366110801697, 'Val/mean precision': 0.9432446360588074, 'Val/mean recall': 0.9528776407241821, 'Val/mean hd95_metric': 10.693830490112305} +Cheakpoint... +Epoch [3888/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513336420059204, 'Val/mean miou_metric': 0.9354280233383179, 'Val/mean f1': 0.9480366110801697, 'Val/mean precision': 0.9432446360588074, 'Val/mean recall': 0.9528776407241821, 'Val/mean hd95_metric': 10.693830490112305} +Epoch [3889/4000] Training [1/39] Loss: 0.00366 +Epoch [3889/4000] Training [2/39] Loss: 0.00442 +Epoch [3889/4000] Training [3/39] Loss: 0.00674 +Epoch [3889/4000] Training [4/39] Loss: 0.00592 +Epoch [3889/4000] Training [5/39] Loss: 0.00343 +Epoch [3889/4000] Training [6/39] Loss: 0.00377 +Epoch [3889/4000] Training [7/39] Loss: 0.25289 +Epoch [3889/4000] Training [8/39] Loss: 0.00543 +Epoch [3889/4000] Training [9/39] Loss: 0.00582 +Epoch [3889/4000] Training [10/39] Loss: 0.12862 +Epoch [3889/4000] Training [11/39] Loss: 0.12953 +Epoch [3889/4000] Training [12/39] Loss: 0.25389 +Epoch [3889/4000] Training [13/39] Loss: 0.00427 +Epoch [3889/4000] Training [14/39] Loss: 0.00234 +Epoch [3889/4000] Training [15/39] Loss: 0.00422 +Epoch [3889/4000] Training [16/39] Loss: 0.00350 +Epoch [3889/4000] Training [17/39] Loss: 0.12810 +Epoch [3889/4000] Training [18/39] Loss: 0.00680 +Epoch [3889/4000] Training [19/39] Loss: 0.00725 +Epoch [3889/4000] Training [20/39] Loss: 0.00407 +Epoch [3889/4000] Training [21/39] Loss: 0.00371 +Epoch [3889/4000] Training [22/39] Loss: 0.00637 +Epoch [3889/4000] Training [23/39] Loss: 0.00533 +Epoch [3889/4000] Training [24/39] Loss: 0.00508 +Epoch [3889/4000] Training [25/39] Loss: 0.00589 +Epoch [3889/4000] Training [26/39] Loss: 0.00329 +Epoch [3889/4000] Training [27/39] Loss: 0.00566 +Epoch [3889/4000] Training [28/39] Loss: 0.00556 +Epoch [3889/4000] Training [29/39] Loss: 0.00892 +Epoch [3889/4000] Training [30/39] Loss: 0.00517 +Epoch [3889/4000] Training [31/39] Loss: 0.00697 +Epoch [3889/4000] Training [32/39] Loss: 0.00516 +Epoch [3889/4000] Training [33/39] Loss: 0.25238 +Epoch [3889/4000] Training [34/39] Loss: 0.00454 +Epoch [3889/4000] Training [35/39] Loss: 0.12819 +Epoch [3889/4000] Training [36/39] Loss: 0.00754 +Epoch [3889/4000] Training [37/39] Loss: 0.00488 +Epoch [3889/4000] Training [38/39] Loss: 0.00345 +Epoch [3889/4000] Training [39/39] Loss: 0.12839 +Epoch [3889/4000] Training metric {'Train/mean dice_metric': 0.9963616132736206, 'Train/mean miou_metric': 0.9931827187538147, 'Train/mean f1': 0.9968664050102234, 'Train/mean precision': 0.9964048862457275, 'Train/mean recall': 0.9973284602165222, 'Train/mean hd95_metric': 0.9530404806137085} +Epoch [3889/4000] Validation [1/10] Loss: 0.71724 focal_loss 0.63076 dice_loss 0.08648 +Epoch [3889/4000] Validation [2/10] Loss: 0.50169 focal_loss 0.40235 dice_loss 0.09934 +Epoch [3889/4000] Validation [3/10] Loss: 0.39995 focal_loss 0.28814 dice_loss 0.11181 +Epoch [3889/4000] Validation [4/10] Loss: 0.89328 focal_loss 0.32826 dice_loss 0.56502 +Epoch [3889/4000] Validation [5/10] Loss: 3.07471 focal_loss 2.40071 dice_loss 0.67400 +Epoch [3889/4000] Validation [6/10] Loss: 1.33360 focal_loss 0.62045 dice_loss 0.71315 +Epoch [3889/4000] Validation [7/10] Loss: 1.17264 focal_loss 0.51823 dice_loss 0.65442 +Epoch [3889/4000] Validation [8/10] Loss: 2.42049 focal_loss 1.79895 dice_loss 0.62153 +Epoch [3889/4000] Validation [9/10] Loss: 1.52756 focal_loss 0.98381 dice_loss 0.54375 +Epoch [3889/4000] Validation [10/10] Loss: 1.87539 focal_loss 1.14084 dice_loss 0.73455 +Epoch [3889/4000] Validation metric {'Val/mean dice_metric': 0.951322615146637, 'Val/mean miou_metric': 0.9354379177093506, 'Val/mean f1': 0.9481652975082397, 'Val/mean precision': 0.9440028071403503, 'Val/mean recall': 0.9523645639419556, 'Val/mean hd95_metric': 10.850226402282715} +Cheakpoint... +Epoch [3889/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951322615146637, 'Val/mean miou_metric': 0.9354379177093506, 'Val/mean f1': 0.9481652975082397, 'Val/mean precision': 0.9440028071403503, 'Val/mean recall': 0.9523645639419556, 'Val/mean hd95_metric': 10.850226402282715} +Epoch [3890/4000] Training [1/39] Loss: 0.00459 +Epoch [3890/4000] Training [2/39] Loss: 0.00282 +Epoch [3890/4000] Training [3/39] Loss: 0.00493 +Epoch [3890/4000] Training [4/39] Loss: 0.00489 +Epoch [3890/4000] Training [5/39] Loss: 0.00530 +Epoch [3890/4000] Training [6/39] Loss: 0.00423 +Epoch [3890/4000] Training [7/39] Loss: 0.00374 +Epoch [3890/4000] Training [8/39] Loss: 0.12706 +Epoch [3890/4000] Training [9/39] Loss: 0.12800 +Epoch [3890/4000] Training [10/39] Loss: 0.00334 +Epoch [3890/4000] Training [11/39] Loss: 0.00351 +Epoch [3890/4000] Training [12/39] Loss: 0.00904 +Epoch [3890/4000] Training [13/39] Loss: 0.00780 +Epoch [3890/4000] Training [14/39] Loss: 0.00380 +Epoch [3890/4000] Training [15/39] Loss: 0.12930 +Epoch [3890/4000] Training [16/39] Loss: 0.00489 +Epoch [3890/4000] Training [17/39] Loss: 0.00399 +Epoch [3890/4000] Training [18/39] Loss: 0.13031 +Epoch [3890/4000] Training [19/39] Loss: 0.12751 +Epoch [3890/4000] Training [20/39] Loss: 0.00466 +Epoch [3890/4000] Training [21/39] Loss: 0.00790 +Epoch [3890/4000] Training [22/39] Loss: 0.00289 +Epoch [3890/4000] Training [23/39] Loss: 0.00362 +Epoch [3890/4000] Training [24/39] Loss: 0.00415 +Epoch [3890/4000] Training [25/39] Loss: 0.12810 +Epoch [3890/4000] Training [26/39] Loss: 0.00431 +Epoch [3890/4000] Training [27/39] Loss: 0.12940 +Epoch [3890/4000] Training [28/39] Loss: 0.12999 +Epoch [3890/4000] Training [29/39] Loss: 0.00520 +Epoch [3890/4000] Training [30/39] Loss: 0.12989 +Epoch [3890/4000] Training [31/39] Loss: 0.00446 +Epoch [3890/4000] Training [32/39] Loss: 0.00243 +Epoch [3890/4000] Training [33/39] Loss: 0.13052 +Epoch [3890/4000] Training [34/39] Loss: 0.00466 +Epoch [3890/4000] Training [35/39] Loss: 0.00471 +Epoch [3890/4000] Training [36/39] Loss: 0.00469 +Epoch [3890/4000] Training [37/39] Loss: 0.00338 +Epoch [3890/4000] Training [38/39] Loss: 0.12919 +Epoch [3890/4000] Training [39/39] Loss: 0.01027 +Epoch [3890/4000] Training metric {'Train/mean dice_metric': 0.9964743852615356, 'Train/mean miou_metric': 0.9933986663818359, 'Train/mean f1': 0.9969910979270935, 'Train/mean precision': 0.9965370893478394, 'Train/mean recall': 0.9974454641342163, 'Train/mean hd95_metric': 0.9129648804664612} +Epoch [3890/4000] Validation [1/10] Loss: 0.71682 focal_loss 0.63015 dice_loss 0.08667 +Epoch [3890/4000] Validation [2/10] Loss: 0.50318 focal_loss 0.40523 dice_loss 0.09795 +Epoch [3890/4000] Validation [3/10] Loss: 0.39294 focal_loss 0.28172 dice_loss 0.11122 +Epoch [3890/4000] Validation [4/10] Loss: 0.89958 focal_loss 0.33408 dice_loss 0.56550 +Epoch [3890/4000] Validation [5/10] Loss: 3.06527 focal_loss 2.39128 dice_loss 0.67399 +Epoch [3890/4000] Validation [6/10] Loss: 1.34785 focal_loss 0.63498 dice_loss 0.71287 +Epoch [3890/4000] Validation [7/10] Loss: 1.18466 focal_loss 0.52946 dice_loss 0.65520 +Epoch [3890/4000] Validation [8/10] Loss: 2.41064 focal_loss 1.79235 dice_loss 0.61829 +Epoch [3890/4000] Validation [9/10] Loss: 1.53587 focal_loss 0.99179 dice_loss 0.54407 +Epoch [3890/4000] Validation [10/10] Loss: 1.90914 focal_loss 1.17390 dice_loss 0.73524 +Epoch [3890/4000] Validation metric {'Val/mean dice_metric': 0.9514848589897156, 'Val/mean miou_metric': 0.9356940984725952, 'Val/mean f1': 0.9484058618545532, 'Val/mean precision': 0.943720817565918, 'Val/mean recall': 0.9531376361846924, 'Val/mean hd95_metric': 10.659821510314941} +Cheakpoint... +Epoch [3890/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514848589897156, 'Val/mean miou_metric': 0.9356940984725952, 'Val/mean f1': 0.9484058618545532, 'Val/mean precision': 0.943720817565918, 'Val/mean recall': 0.9531376361846924, 'Val/mean hd95_metric': 10.659821510314941} +Epoch [3891/4000] Training [1/39] Loss: 0.00332 +Epoch [3891/4000] Training [2/39] Loss: 0.12925 +Epoch [3891/4000] Training [3/39] Loss: 0.00328 +Epoch [3891/4000] Training [4/39] Loss: 0.00628 +Epoch [3891/4000] Training [5/39] Loss: 0.00388 +Epoch [3891/4000] Training [6/39] Loss: 0.00603 +Epoch [3891/4000] Training [7/39] Loss: 0.00296 +Epoch [3891/4000] Training [8/39] Loss: 0.12897 +Epoch [3891/4000] Training [9/39] Loss: 0.00335 +Epoch [3891/4000] Training [10/39] Loss: 0.00391 +Epoch [3891/4000] Training [11/39] Loss: 0.00647 +Epoch [3891/4000] Training [12/39] Loss: 0.00385 +Epoch [3891/4000] Training [13/39] Loss: 0.25379 +Epoch [3891/4000] Training [14/39] Loss: 0.12934 +Epoch [3891/4000] Training [15/39] Loss: 0.00624 +Epoch [3891/4000] Training [16/39] Loss: 0.00552 +Epoch [3891/4000] Training [17/39] Loss: 0.13081 +Epoch [3891/4000] Training [18/39] Loss: 0.00543 +Epoch [3891/4000] Training [19/39] Loss: 0.00294 +Epoch [3891/4000] Training [20/39] Loss: 0.13028 +Epoch [3891/4000] Training [21/39] Loss: 0.00546 +Epoch [3891/4000] Training [22/39] Loss: 0.00543 +Epoch [3891/4000] Training [23/39] Loss: 0.00725 +Epoch [3891/4000] Training [24/39] Loss: 0.00442 +Epoch [3891/4000] Training [25/39] Loss: 0.12867 +Epoch [3891/4000] Training [26/39] Loss: 0.12837 +Epoch [3891/4000] Training [27/39] Loss: 0.12907 +Epoch [3891/4000] Training [28/39] Loss: 0.00498 +Epoch [3891/4000] Training [29/39] Loss: 0.00382 +Epoch [3891/4000] Training [30/39] Loss: 0.12865 +Epoch [3891/4000] Training [31/39] Loss: 0.00403 +Epoch [3891/4000] Training [32/39] Loss: 0.00920 +Epoch [3891/4000] Training [33/39] Loss: 0.00446 +Epoch [3891/4000] Training [34/39] Loss: 0.00492 +Epoch [3891/4000] Training [35/39] Loss: 0.00429 +Epoch [3891/4000] Training [36/39] Loss: 0.00513 +Epoch [3891/4000] Training [37/39] Loss: 0.00765 +Epoch [3891/4000] Training [38/39] Loss: 0.12888 +Epoch [3891/4000] Training [39/39] Loss: 0.00541 +Epoch [3891/4000] Training metric {'Train/mean dice_metric': 0.9956180453300476, 'Train/mean miou_metric': 0.992505669593811, 'Train/mean f1': 0.9969148635864258, 'Train/mean precision': 0.9965007901191711, 'Train/mean recall': 0.9973292946815491, 'Train/mean hd95_metric': 0.9606488347053528} +Epoch [3891/4000] Validation [1/10] Loss: 0.74520 focal_loss 0.65838 dice_loss 0.08682 +Epoch [3891/4000] Validation [2/10] Loss: 0.51303 focal_loss 0.41396 dice_loss 0.09907 +Epoch [3891/4000] Validation [3/10] Loss: 0.41373 focal_loss 0.30183 dice_loss 0.11190 +Epoch [3891/4000] Validation [4/10] Loss: 0.90296 focal_loss 0.33761 dice_loss 0.56535 +Epoch [3891/4000] Validation [5/10] Loss: 3.17236 focal_loss 2.49829 dice_loss 0.67407 +Epoch [3891/4000] Validation [6/10] Loss: 1.34928 focal_loss 0.63679 dice_loss 0.71249 +Epoch [3891/4000] Validation [7/10] Loss: 1.18721 focal_loss 0.53182 dice_loss 0.65539 +Epoch [3891/4000] Validation [8/10] Loss: 2.46953 focal_loss 1.84942 dice_loss 0.62011 +Epoch [3891/4000] Validation [9/10] Loss: 1.56350 focal_loss 1.01978 dice_loss 0.54372 +Epoch [3891/4000] Validation [10/10] Loss: 1.91258 focal_loss 1.17781 dice_loss 0.73477 +Epoch [3891/4000] Validation metric {'Val/mean dice_metric': 0.9507155418395996, 'Val/mean miou_metric': 0.9348822236061096, 'Val/mean f1': 0.9484189748764038, 'Val/mean precision': 0.9441912770271301, 'Val/mean recall': 0.952684760093689, 'Val/mean hd95_metric': 10.845166206359863} +Cheakpoint... +Epoch [3891/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507155418395996, 'Val/mean miou_metric': 0.9348822236061096, 'Val/mean f1': 0.9484189748764038, 'Val/mean precision': 0.9441912770271301, 'Val/mean recall': 0.952684760093689, 'Val/mean hd95_metric': 10.845166206359863} +Epoch [3892/4000] Training [1/39] Loss: 0.12841 +Epoch [3892/4000] Training [2/39] Loss: 0.00489 +Epoch [3892/4000] Training [3/39] Loss: 0.25456 +Epoch [3892/4000] Training [4/39] Loss: 0.12981 +Epoch [3892/4000] Training [5/39] Loss: 0.00706 +Epoch [3892/4000] Training [6/39] Loss: 0.00678 +Epoch [3892/4000] Training [7/39] Loss: 0.00345 +Epoch [3892/4000] Training [8/39] Loss: 0.12951 +Epoch [3892/4000] Training [9/39] Loss: 0.00404 +Epoch [3892/4000] Training [10/39] Loss: 0.00295 +Epoch [3892/4000] Training [11/39] Loss: 0.00417 +Epoch [3892/4000] Training [12/39] Loss: 0.00516 +Epoch [3892/4000] Training [13/39] Loss: 0.00411 +Epoch [3892/4000] Training [14/39] Loss: 0.00497 +Epoch [3892/4000] Training [15/39] Loss: 0.00893 +Epoch [3892/4000] Training [16/39] Loss: 0.00544 +Epoch [3892/4000] Training [17/39] Loss: 0.00469 +Epoch [3892/4000] Training [18/39] Loss: 0.12814 +Epoch [3892/4000] Training [19/39] Loss: 0.00614 +Epoch [3892/4000] Training [20/39] Loss: 0.00543 +Epoch [3892/4000] Training [21/39] Loss: 0.00392 +Epoch [3892/4000] Training [22/39] Loss: 0.00470 +Epoch [3892/4000] Training [23/39] Loss: 0.00401 +Epoch [3892/4000] Training [24/39] Loss: 0.12800 +Epoch [3892/4000] Training [25/39] Loss: 0.00541 +Epoch [3892/4000] Training [26/39] Loss: 0.00651 +Epoch [3892/4000] Training [27/39] Loss: 0.13191 +Epoch [3892/4000] Training [28/39] Loss: 0.12815 +Epoch [3892/4000] Training [29/39] Loss: 0.12847 +Epoch [3892/4000] Training [30/39] Loss: 0.00546 +Epoch [3892/4000] Training [31/39] Loss: 0.00801 +Epoch [3892/4000] Training [32/39] Loss: 0.00653 +Epoch [3892/4000] Training [33/39] Loss: 0.00586 +Epoch [3892/4000] Training [34/39] Loss: 0.00535 +Epoch [3892/4000] Training [35/39] Loss: 0.12762 +Epoch [3892/4000] Training [36/39] Loss: 0.00342 +Epoch [3892/4000] Training [37/39] Loss: 0.00626 +Epoch [3892/4000] Training [38/39] Loss: 0.00378 +Epoch [3892/4000] Training [39/39] Loss: 0.00498 +Epoch [3892/4000] Training metric {'Train/mean dice_metric': 0.9963196516036987, 'Train/mean miou_metric': 0.9931009411811829, 'Train/mean f1': 0.9968445897102356, 'Train/mean precision': 0.9963646531105042, 'Train/mean recall': 0.9973248243331909, 'Train/mean hd95_metric': 0.9220858812332153} +Epoch [3892/4000] Validation [1/10] Loss: 0.71687 focal_loss 0.62992 dice_loss 0.08696 +Epoch [3892/4000] Validation [2/10] Loss: 0.50094 focal_loss 0.40331 dice_loss 0.09764 +Epoch [3892/4000] Validation [3/10] Loss: 0.39093 focal_loss 0.27981 dice_loss 0.11112 +Epoch [3892/4000] Validation [4/10] Loss: 0.89763 focal_loss 0.33204 dice_loss 0.56559 +Epoch [3892/4000] Validation [5/10] Loss: 3.05421 focal_loss 2.38030 dice_loss 0.67391 +Epoch [3892/4000] Validation [6/10] Loss: 1.34634 focal_loss 0.63341 dice_loss 0.71293 +Epoch [3892/4000] Validation [7/10] Loss: 1.18373 focal_loss 0.52795 dice_loss 0.65578 +Epoch [3892/4000] Validation [8/10] Loss: 2.37612 focal_loss 1.76127 dice_loss 0.61485 +Epoch [3892/4000] Validation [9/10] Loss: 1.53234 focal_loss 0.98810 dice_loss 0.54424 +Epoch [3892/4000] Validation [10/10] Loss: 1.90685 focal_loss 1.17124 dice_loss 0.73561 +Epoch [3892/4000] Validation metric {'Val/mean dice_metric': 0.9513454437255859, 'Val/mean miou_metric': 0.9354258179664612, 'Val/mean f1': 0.9480140209197998, 'Val/mean precision': 0.9429143667221069, 'Val/mean recall': 0.953169047832489, 'Val/mean hd95_metric': 10.666837692260742} +Cheakpoint... +Epoch [3892/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513454437255859, 'Val/mean miou_metric': 0.9354258179664612, 'Val/mean f1': 0.9480140209197998, 'Val/mean precision': 0.9429143667221069, 'Val/mean recall': 0.953169047832489, 'Val/mean hd95_metric': 10.666837692260742} +Epoch [3893/4000] Training [1/39] Loss: 0.00511 +Epoch [3893/4000] Training [2/39] Loss: 0.12933 +Epoch [3893/4000] Training [3/39] Loss: 0.00289 +Epoch [3893/4000] Training [4/39] Loss: 0.00490 +Epoch [3893/4000] Training [5/39] Loss: 0.12993 +Epoch [3893/4000] Training [6/39] Loss: 0.00492 +Epoch [3893/4000] Training [7/39] Loss: 0.00304 +Epoch [3893/4000] Training [8/39] Loss: 0.00700 +Epoch [3893/4000] Training [9/39] Loss: 0.12855 +Epoch [3893/4000] Training [10/39] Loss: 0.00427 +Epoch [3893/4000] Training [11/39] Loss: 0.00421 +Epoch [3893/4000] Training [12/39] Loss: 0.00408 +Epoch [3893/4000] Training [13/39] Loss: 0.00382 +Epoch [3893/4000] Training [14/39] Loss: 0.00429 +Epoch [3893/4000] Training [15/39] Loss: 0.00477 +Epoch [3893/4000] Training [16/39] Loss: 0.00454 +Epoch [3893/4000] Training [17/39] Loss: 0.00501 +Epoch [3893/4000] Training [18/39] Loss: 0.13120 +Epoch [3893/4000] Training [19/39] Loss: 0.12926 +Epoch [3893/4000] Training [20/39] Loss: 0.00588 +Epoch [3893/4000] Training [21/39] Loss: 0.13132 +Epoch [3893/4000] Training [22/39] Loss: 0.00498 +Epoch [3893/4000] Training [23/39] Loss: 0.00769 +Epoch [3893/4000] Training [24/39] Loss: 0.00532 +Epoch [3893/4000] Training [25/39] Loss: 0.00477 +Epoch [3893/4000] Training [26/39] Loss: 0.00421 +Epoch [3893/4000] Training [27/39] Loss: 0.00580 +Epoch [3893/4000] Training [28/39] Loss: 0.00427 +Epoch [3893/4000] Training [29/39] Loss: 0.00526 +Epoch [3893/4000] Training [30/39] Loss: 0.00712 +Epoch [3893/4000] Training [31/39] Loss: 0.00348 +Epoch [3893/4000] Training [32/39] Loss: 0.00381 +Epoch [3893/4000] Training [33/39] Loss: 0.00853 +Epoch [3893/4000] Training [34/39] Loss: 0.00371 +Epoch [3893/4000] Training [35/39] Loss: 0.00304 +Epoch [3893/4000] Training [36/39] Loss: 0.12935 +Epoch [3893/4000] Training [37/39] Loss: 0.00544 +Epoch [3893/4000] Training [38/39] Loss: 0.12851 +Epoch [3893/4000] Training [39/39] Loss: 0.12859 +Epoch [3893/4000] Training metric {'Train/mean dice_metric': 0.9964107275009155, 'Train/mean miou_metric': 0.9932785034179688, 'Train/mean f1': 0.9969372749328613, 'Train/mean precision': 0.9964529275894165, 'Train/mean recall': 0.9974220395088196, 'Train/mean hd95_metric': 1.0284216403961182} +Epoch [3893/4000] Validation [1/10] Loss: 0.70685 focal_loss 0.62093 dice_loss 0.08591 +Epoch [3893/4000] Validation [2/10] Loss: 0.50107 focal_loss 0.40138 dice_loss 0.09970 +Epoch [3893/4000] Validation [3/10] Loss: 0.39911 focal_loss 0.28716 dice_loss 0.11195 +Epoch [3893/4000] Validation [4/10] Loss: 0.88905 focal_loss 0.32468 dice_loss 0.56438 +Epoch [3893/4000] Validation [5/10] Loss: 3.04939 focal_loss 2.37533 dice_loss 0.67406 +Epoch [3893/4000] Validation [6/10] Loss: 1.32916 focal_loss 0.61650 dice_loss 0.71266 +Epoch [3893/4000] Validation [7/10] Loss: 1.16839 focal_loss 0.51613 dice_loss 0.65225 +Epoch [3893/4000] Validation [8/10] Loss: 2.42445 focal_loss 1.80168 dice_loss 0.62276 +Epoch [3893/4000] Validation [9/10] Loss: 1.50270 focal_loss 0.95905 dice_loss 0.54365 +Epoch [3893/4000] Validation [10/10] Loss: 1.87008 focal_loss 1.13607 dice_loss 0.73401 +Epoch [3893/4000] Validation metric {'Val/mean dice_metric': 0.9514604806900024, 'Val/mean miou_metric': 0.9356521964073181, 'Val/mean f1': 0.94863361120224, 'Val/mean precision': 0.9446048140525818, 'Val/mean recall': 0.9526969790458679, 'Val/mean hd95_metric': 10.828142166137695} +Cheakpoint... +Epoch [3893/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514604806900024, 'Val/mean miou_metric': 0.9356521964073181, 'Val/mean f1': 0.94863361120224, 'Val/mean precision': 0.9446048140525818, 'Val/mean recall': 0.9526969790458679, 'Val/mean hd95_metric': 10.828142166137695} +Epoch [3894/4000] Training [1/39] Loss: 0.00299 +Epoch [3894/4000] Training [2/39] Loss: 0.00246 +Epoch [3894/4000] Training [3/39] Loss: 0.00543 +Epoch [3894/4000] Training [4/39] Loss: 0.00486 +Epoch [3894/4000] Training [5/39] Loss: 0.00298 +Epoch [3894/4000] Training [6/39] Loss: 0.00292 +Epoch [3894/4000] Training [7/39] Loss: 0.00385 +Epoch [3894/4000] Training [8/39] Loss: 0.00486 +Epoch [3894/4000] Training [9/39] Loss: 0.00399 +Epoch [3894/4000] Training [10/39] Loss: 0.00456 +Epoch [3894/4000] Training [11/39] Loss: 0.00404 +Epoch [3894/4000] Training [12/39] Loss: 0.00554 +Epoch [3894/4000] Training [13/39] Loss: 0.00378 +Epoch [3894/4000] Training [14/39] Loss: 0.00339 +Epoch [3894/4000] Training [15/39] Loss: 0.00456 +Epoch [3894/4000] Training [16/39] Loss: 0.00293 +Epoch [3894/4000] Training [17/39] Loss: 0.12890 +Epoch [3894/4000] Training [18/39] Loss: 0.00580 +Epoch [3894/4000] Training [19/39] Loss: 0.37925 +Epoch [3894/4000] Training [20/39] Loss: 0.12799 +Epoch [3894/4000] Training [21/39] Loss: 0.00295 +Epoch [3894/4000] Training [22/39] Loss: 0.00597 +Epoch [3894/4000] Training [23/39] Loss: 0.00784 +Epoch [3894/4000] Training [24/39] Loss: 0.00591 +Epoch [3894/4000] Training [25/39] Loss: 0.00583 +Epoch [3894/4000] Training [26/39] Loss: 0.25271 +Epoch [3894/4000] Training [27/39] Loss: 0.12924 +Epoch [3894/4000] Training [28/39] Loss: 0.00404 +Epoch [3894/4000] Training [29/39] Loss: 0.00364 +Epoch [3894/4000] Training [30/39] Loss: 0.00363 +Epoch [3894/4000] Training [31/39] Loss: 0.00507 +Epoch [3894/4000] Training [32/39] Loss: 0.12757 +Epoch [3894/4000] Training [33/39] Loss: 0.00569 +Epoch [3894/4000] Training [34/39] Loss: 0.00509 +Epoch [3894/4000] Training [35/39] Loss: 0.00352 +Epoch [3894/4000] Training [36/39] Loss: 0.12934 +Epoch [3894/4000] Training [37/39] Loss: 0.00820 +Epoch [3894/4000] Training [38/39] Loss: 0.00638 +Epoch [3894/4000] Training [39/39] Loss: 0.00651 +Epoch [3894/4000] Training metric {'Train/mean dice_metric': 0.9966599941253662, 'Train/mean miou_metric': 0.9937681555747986, 'Train/mean f1': 0.9971290230751038, 'Train/mean precision': 0.9967098236083984, 'Train/mean recall': 0.9975486397743225, 'Train/mean hd95_metric': 0.9077633023262024} +Epoch [3894/4000] Validation [1/10] Loss: 0.71959 focal_loss 0.63295 dice_loss 0.08663 +Epoch [3894/4000] Validation [2/10] Loss: 0.50342 focal_loss 0.40489 dice_loss 0.09853 +Epoch [3894/4000] Validation [3/10] Loss: 0.40123 focal_loss 0.28936 dice_loss 0.11187 +Epoch [3894/4000] Validation [4/10] Loss: 0.89535 focal_loss 0.33004 dice_loss 0.56530 +Epoch [3894/4000] Validation [5/10] Loss: 3.06753 focal_loss 2.39352 dice_loss 0.67401 +Epoch [3894/4000] Validation [6/10] Loss: 1.33840 focal_loss 0.62590 dice_loss 0.71250 +Epoch [3894/4000] Validation [7/10] Loss: 1.17399 focal_loss 0.52052 dice_loss 0.65347 +Epoch [3894/4000] Validation [8/10] Loss: 2.42687 focal_loss 1.80570 dice_loss 0.62116 +Epoch [3894/4000] Validation [9/10] Loss: 1.52018 focal_loss 0.97642 dice_loss 0.54376 +Epoch [3894/4000] Validation [10/10] Loss: 1.89164 focal_loss 1.15725 dice_loss 0.73439 +Epoch [3894/4000] Validation metric {'Val/mean dice_metric': 0.9516434073448181, 'Val/mean miou_metric': 0.9360133409500122, 'Val/mean f1': 0.948909342288971, 'Val/mean precision': 0.9447532892227173, 'Val/mean recall': 0.9531022310256958, 'Val/mean hd95_metric': 10.792106628417969} +Cheakpoint... +Epoch [3894/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516434073448181, 'Val/mean miou_metric': 0.9360133409500122, 'Val/mean f1': 0.948909342288971, 'Val/mean precision': 0.9447532892227173, 'Val/mean recall': 0.9531022310256958, 'Val/mean hd95_metric': 10.792106628417969} +Epoch [3895/4000] Training [1/39] Loss: 0.12861 +Epoch [3895/4000] Training [2/39] Loss: 0.12757 +Epoch [3895/4000] Training [3/39] Loss: 0.00606 +Epoch [3895/4000] Training [4/39] Loss: 0.00464 +Epoch [3895/4000] Training [5/39] Loss: 0.00437 +Epoch [3895/4000] Training [6/39] Loss: 0.00402 +Epoch [3895/4000] Training [7/39] Loss: 0.00510 +Epoch [3895/4000] Training [8/39] Loss: 0.00587 +Epoch [3895/4000] Training [9/39] Loss: 0.00312 +Epoch [3895/4000] Training [10/39] Loss: 0.00333 +Epoch [3895/4000] Training [11/39] Loss: 0.00378 +Epoch [3895/4000] Training [12/39] Loss: 0.00691 +Epoch [3895/4000] Training [13/39] Loss: 0.00579 +Epoch [3895/4000] Training [14/39] Loss: 0.13102 +Epoch [3895/4000] Training [15/39] Loss: 0.00468 +Epoch [3895/4000] Training [16/39] Loss: 0.00252 +Epoch [3895/4000] Training [17/39] Loss: 0.12807 +Epoch [3895/4000] Training [18/39] Loss: 0.00644 +Epoch [3895/4000] Training [19/39] Loss: 0.00635 +Epoch [3895/4000] Training [20/39] Loss: 0.25341 +Epoch [3895/4000] Training [21/39] Loss: 0.00385 +Epoch [3895/4000] Training [22/39] Loss: 0.25290 +Epoch [3895/4000] Training [23/39] Loss: 0.00951 +Epoch [3895/4000] Training [24/39] Loss: 0.00354 +Epoch [3895/4000] Training [25/39] Loss: 0.00565 +Epoch [3895/4000] Training [26/39] Loss: 0.00357 +Epoch [3895/4000] Training [27/39] Loss: 0.12995 +Epoch [3895/4000] Training [28/39] Loss: 0.00686 +Epoch [3895/4000] Training [29/39] Loss: 0.00469 +Epoch [3895/4000] Training [30/39] Loss: 0.00509 +Epoch [3895/4000] Training [31/39] Loss: 0.00592 +Epoch [3895/4000] Training [32/39] Loss: 0.25479 +Epoch [3895/4000] Training [33/39] Loss: 0.00405 +Epoch [3895/4000] Training [34/39] Loss: 0.00741 +Epoch [3895/4000] Training [35/39] Loss: 0.00543 +Epoch [3895/4000] Training [36/39] Loss: 0.00321 +Epoch [3895/4000] Training [37/39] Loss: 0.00304 +Epoch [3895/4000] Training [38/39] Loss: 0.00349 +Epoch [3895/4000] Training [39/39] Loss: 0.00638 +Epoch [3895/4000] Training metric {'Train/mean dice_metric': 0.9964175224304199, 'Train/mean miou_metric': 0.9932894706726074, 'Train/mean f1': 0.9970240592956543, 'Train/mean precision': 0.9965538382530212, 'Train/mean recall': 0.9974946975708008, 'Train/mean hd95_metric': 0.9113974571228027} +Epoch [3895/4000] Validation [1/10] Loss: 0.72177 focal_loss 0.63563 dice_loss 0.08614 +Epoch [3895/4000] Validation [2/10] Loss: 0.50275 focal_loss 0.40257 dice_loss 0.10017 +Epoch [3895/4000] Validation [3/10] Loss: 0.41007 focal_loss 0.29764 dice_loss 0.11243 +Epoch [3895/4000] Validation [4/10] Loss: 0.88754 focal_loss 0.32323 dice_loss 0.56431 +Epoch [3895/4000] Validation [5/10] Loss: 3.09855 focal_loss 2.42444 dice_loss 0.67411 +Epoch [3895/4000] Validation [6/10] Loss: 1.32214 focal_loss 0.60966 dice_loss 0.71248 +Epoch [3895/4000] Validation [7/10] Loss: 1.16358 focal_loss 0.51138 dice_loss 0.65220 +Epoch [3895/4000] Validation [8/10] Loss: 2.45849 focal_loss 1.83410 dice_loss 0.62438 +Epoch [3895/4000] Validation [9/10] Loss: 1.50850 focal_loss 0.96526 dice_loss 0.54323 +Epoch [3895/4000] Validation [10/10] Loss: 1.85836 focal_loss 1.12498 dice_loss 0.73338 +Epoch [3895/4000] Validation metric {'Val/mean dice_metric': 0.9513896703720093, 'Val/mean miou_metric': 0.9355673789978027, 'Val/mean f1': 0.9487650990486145, 'Val/mean precision': 0.9451451301574707, 'Val/mean recall': 0.9524128437042236, 'Val/mean hd95_metric': 10.749943733215332} +Cheakpoint... +Epoch [3895/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513896703720093, 'Val/mean miou_metric': 0.9355673789978027, 'Val/mean f1': 0.9487650990486145, 'Val/mean precision': 0.9451451301574707, 'Val/mean recall': 0.9524128437042236, 'Val/mean hd95_metric': 10.749943733215332} +Epoch [3896/4000] Training [1/39] Loss: 0.25215 +Epoch [3896/4000] Training [2/39] Loss: 0.00256 +Epoch [3896/4000] Training [3/39] Loss: 0.00451 +Epoch [3896/4000] Training [4/39] Loss: 0.13367 +Epoch [3896/4000] Training [5/39] Loss: 0.00371 +Epoch [3896/4000] Training [6/39] Loss: 0.00344 +Epoch [3896/4000] Training [7/39] Loss: 0.00501 +Epoch [3896/4000] Training [8/39] Loss: 0.00395 +Epoch [3896/4000] Training [9/39] Loss: 0.00343 +Epoch [3896/4000] Training [10/39] Loss: 0.00548 +Epoch [3896/4000] Training [11/39] Loss: 0.00572 +Epoch [3896/4000] Training [12/39] Loss: 0.00300 +Epoch [3896/4000] Training [13/39] Loss: 0.00584 +Epoch [3896/4000] Training [14/39] Loss: 0.00515 +Epoch [3896/4000] Training [15/39] Loss: 0.00759 +Epoch [3896/4000] Training [16/39] Loss: 0.00423 +Epoch [3896/4000] Training [17/39] Loss: 0.12911 +Epoch [3896/4000] Training [18/39] Loss: 0.13127 +Epoch [3896/4000] Training [19/39] Loss: 0.00454 +Epoch [3896/4000] Training [20/39] Loss: 0.00994 +Epoch [3896/4000] Training [21/39] Loss: 0.00476 +Epoch [3896/4000] Training [22/39] Loss: 0.00547 +Epoch [3896/4000] Training [23/39] Loss: 0.12845 +Epoch [3896/4000] Training [24/39] Loss: 0.00488 +Epoch [3896/4000] Training [25/39] Loss: 0.00768 +Epoch [3896/4000] Training [26/39] Loss: 0.00436 +Epoch [3896/4000] Training [27/39] Loss: 0.00397 +Epoch [3896/4000] Training [28/39] Loss: 0.00436 +Epoch [3896/4000] Training [29/39] Loss: 0.00572 +Epoch [3896/4000] Training [30/39] Loss: 0.00656 +Epoch [3896/4000] Training [31/39] Loss: 0.13235 +Epoch [3896/4000] Training [32/39] Loss: 0.00557 +Epoch [3896/4000] Training [33/39] Loss: 0.00338 +Epoch [3896/4000] Training [34/39] Loss: 0.00350 +Epoch [3896/4000] Training [35/39] Loss: 0.00514 +Epoch [3896/4000] Training [36/39] Loss: 0.00544 +Epoch [3896/4000] Training [37/39] Loss: 0.00656 +Epoch [3896/4000] Training [38/39] Loss: 0.12826 +Epoch [3896/4000] Training [39/39] Loss: 0.00480 +Epoch [3896/4000] Training metric {'Train/mean dice_metric': 0.9963230490684509, 'Train/mean miou_metric': 0.9931012988090515, 'Train/mean f1': 0.9969326257705688, 'Train/mean precision': 0.9964742064476013, 'Train/mean recall': 0.9973915219306946, 'Train/mean hd95_metric': 0.9651691317558289} +Epoch [3896/4000] Validation [1/10] Loss: 0.69751 focal_loss 0.61146 dice_loss 0.08605 +Epoch [3896/4000] Validation [2/10] Loss: 0.50008 focal_loss 0.40032 dice_loss 0.09977 +Epoch [3896/4000] Validation [3/10] Loss: 0.39235 focal_loss 0.28047 dice_loss 0.11187 +Epoch [3896/4000] Validation [4/10] Loss: 0.88908 focal_loss 0.32423 dice_loss 0.56485 +Epoch [3896/4000] Validation [5/10] Loss: 3.00262 focal_loss 2.32863 dice_loss 0.67399 +Epoch [3896/4000] Validation [6/10] Loss: 1.32620 focal_loss 0.61357 dice_loss 0.71262 +Epoch [3896/4000] Validation [7/10] Loss: 1.16693 focal_loss 0.51433 dice_loss 0.65260 +Epoch [3896/4000] Validation [8/10] Loss: 2.37461 focal_loss 1.75366 dice_loss 0.62095 +Epoch [3896/4000] Validation [9/10] Loss: 1.49087 focal_loss 0.94704 dice_loss 0.54384 +Epoch [3896/4000] Validation [10/10] Loss: 1.86508 focal_loss 1.13122 dice_loss 0.73386 +Epoch [3896/4000] Validation metric {'Val/mean dice_metric': 0.9513558745384216, 'Val/mean miou_metric': 0.9354589581489563, 'Val/mean f1': 0.9486165046691895, 'Val/mean precision': 0.9444361925125122, 'Val/mean recall': 0.9528339505195618, 'Val/mean hd95_metric': 10.776117324829102} +Cheakpoint... +Epoch [3896/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513558745384216, 'Val/mean miou_metric': 0.9354589581489563, 'Val/mean f1': 0.9486165046691895, 'Val/mean precision': 0.9444361925125122, 'Val/mean recall': 0.9528339505195618, 'Val/mean hd95_metric': 10.776117324829102} +Epoch [3897/4000] Training [1/39] Loss: 0.12901 +Epoch [3897/4000] Training [2/39] Loss: 0.00353 +Epoch [3897/4000] Training [3/39] Loss: 0.00415 +Epoch [3897/4000] Training [4/39] Loss: 0.00496 +Epoch [3897/4000] Training [5/39] Loss: 0.04185 +Epoch [3897/4000] Training [6/39] Loss: 0.00315 +Epoch [3897/4000] Training [7/39] Loss: 0.25357 +Epoch [3897/4000] Training [8/39] Loss: 0.00722 +Epoch [3897/4000] Training [9/39] Loss: 0.00422 +Epoch [3897/4000] Training [10/39] Loss: 0.00510 +Epoch [3897/4000] Training [11/39] Loss: 0.25378 +Epoch [3897/4000] Training [12/39] Loss: 0.00310 +Epoch [3897/4000] Training [13/39] Loss: 0.13061 +Epoch [3897/4000] Training [14/39] Loss: 0.00274 +Epoch [3897/4000] Training [15/39] Loss: 0.25460 +Epoch [3897/4000] Training [16/39] Loss: 0.00494 +Epoch [3897/4000] Training [17/39] Loss: 0.00495 +Epoch [3897/4000] Training [18/39] Loss: 0.00405 +Epoch [3897/4000] Training [19/39] Loss: 0.00398 +Epoch [3897/4000] Training [20/39] Loss: 0.00347 +Epoch [3897/4000] Training [21/39] Loss: 0.00553 +Epoch [3897/4000] Training [22/39] Loss: 0.00521 +Epoch [3897/4000] Training [23/39] Loss: 0.00648 +Epoch [3897/4000] Training [24/39] Loss: 0.00271 +Epoch [3897/4000] Training [25/39] Loss: 0.00430 +Epoch [3897/4000] Training [26/39] Loss: 0.00413 +Epoch [3897/4000] Training [27/39] Loss: 0.13062 +Epoch [3897/4000] Training [28/39] Loss: 0.00466 +Epoch [3897/4000] Training [29/39] Loss: 0.00318 +Epoch [3897/4000] Training [30/39] Loss: 0.00273 +Epoch [3897/4000] Training [31/39] Loss: 0.00446 +Epoch [3897/4000] Training [32/39] Loss: 0.00383 +Epoch [3897/4000] Training [33/39] Loss: 0.00295 +Epoch [3897/4000] Training [34/39] Loss: 0.00292 +Epoch [3897/4000] Training [35/39] Loss: 0.12884 +Epoch [3897/4000] Training [36/39] Loss: 0.00501 +Epoch [3897/4000] Training [37/39] Loss: 0.00756 +Epoch [3897/4000] Training [38/39] Loss: 0.00402 +Epoch [3897/4000] Training [39/39] Loss: 0.00324 +Epoch [3897/4000] Training metric {'Train/mean dice_metric': 0.9965875148773193, 'Train/mean miou_metric': 0.993621289730072, 'Train/mean f1': 0.9969890713691711, 'Train/mean precision': 0.9965452551841736, 'Train/mean recall': 0.9974332451820374, 'Train/mean hd95_metric': 0.8963702321052551} +Epoch [3897/4000] Validation [1/10] Loss: 0.71369 focal_loss 0.62730 dice_loss 0.08639 +Epoch [3897/4000] Validation [2/10] Loss: 0.50469 focal_loss 0.40512 dice_loss 0.09957 +Epoch [3897/4000] Validation [3/10] Loss: 0.40092 focal_loss 0.28890 dice_loss 0.11202 +Epoch [3897/4000] Validation [4/10] Loss: 0.89283 focal_loss 0.32766 dice_loss 0.56517 +Epoch [3897/4000] Validation [5/10] Loss: 3.06138 focal_loss 2.38728 dice_loss 0.67411 +Epoch [3897/4000] Validation [6/10] Loss: 1.33069 focal_loss 0.61845 dice_loss 0.71223 +Epoch [3897/4000] Validation [7/10] Loss: 1.17271 focal_loss 0.51974 dice_loss 0.65297 +Epoch [3897/4000] Validation [8/10] Loss: 2.43238 focal_loss 1.81033 dice_loss 0.62205 +Epoch [3897/4000] Validation [9/10] Loss: 1.50457 focal_loss 0.96112 dice_loss 0.54345 +Epoch [3897/4000] Validation [10/10] Loss: 1.87729 focal_loss 1.14342 dice_loss 0.73387 +Epoch [3897/4000] Validation metric {'Val/mean dice_metric': 0.9515793323516846, 'Val/mean miou_metric': 0.9358887672424316, 'Val/mean f1': 0.9486255645751953, 'Val/mean precision': 0.9446083903312683, 'Val/mean recall': 0.9526771306991577, 'Val/mean hd95_metric': 10.78252124786377} +Cheakpoint... +Epoch [3897/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515793323516846, 'Val/mean miou_metric': 0.9358887672424316, 'Val/mean f1': 0.9486255645751953, 'Val/mean precision': 0.9446083903312683, 'Val/mean recall': 0.9526771306991577, 'Val/mean hd95_metric': 10.78252124786377} +Epoch [3898/4000] Training [1/39] Loss: 0.00471 +Epoch [3898/4000] Training [2/39] Loss: 0.00384 +Epoch [3898/4000] Training [3/39] Loss: 0.13033 +Epoch [3898/4000] Training [4/39] Loss: 0.00495 +Epoch [3898/4000] Training [5/39] Loss: 0.00375 +Epoch [3898/4000] Training [6/39] Loss: 0.00486 +Epoch [3898/4000] Training [7/39] Loss: 0.00770 +Epoch [3898/4000] Training [8/39] Loss: 0.00378 +Epoch [3898/4000] Training [9/39] Loss: 0.00530 +Epoch [3898/4000] Training [10/39] Loss: 0.00292 +Epoch [3898/4000] Training [11/39] Loss: 0.00734 +Epoch [3898/4000] Training [12/39] Loss: 0.00396 +Epoch [3898/4000] Training [13/39] Loss: 0.00476 +Epoch [3898/4000] Training [14/39] Loss: 0.00445 +Epoch [3898/4000] Training [15/39] Loss: 0.00544 +Epoch [3898/4000] Training [16/39] Loss: 0.12938 +Epoch [3898/4000] Training [17/39] Loss: 0.00552 +Epoch [3898/4000] Training [18/39] Loss: 0.00436 +Epoch [3898/4000] Training [19/39] Loss: 0.00561 +Epoch [3898/4000] Training [20/39] Loss: 0.00730 +Epoch [3898/4000] Training [21/39] Loss: 0.00391 +Epoch [3898/4000] Training [22/39] Loss: 0.00300 +Epoch [3898/4000] Training [23/39] Loss: 0.00524 +Epoch [3898/4000] Training [24/39] Loss: 0.00488 +Epoch [3898/4000] Training [25/39] Loss: 0.00339 +Epoch [3898/4000] Training [26/39] Loss: 0.00305 +Epoch [3898/4000] Training [27/39] Loss: 0.12851 +Epoch [3898/4000] Training [28/39] Loss: 0.00468 +Epoch [3898/4000] Training [29/39] Loss: 0.00380 +Epoch [3898/4000] Training [30/39] Loss: 0.00414 +Epoch [3898/4000] Training [31/39] Loss: 0.00583 +Epoch [3898/4000] Training [32/39] Loss: 0.00320 +Epoch [3898/4000] Training [33/39] Loss: 0.00507 +Epoch [3898/4000] Training [34/39] Loss: 0.12791 +Epoch [3898/4000] Training [35/39] Loss: 0.00626 +Epoch [3898/4000] Training [36/39] Loss: 0.00297 +Epoch [3898/4000] Training [37/39] Loss: 0.08799 +Epoch [3898/4000] Training [38/39] Loss: 0.00482 +Epoch [3898/4000] Training [39/39] Loss: 0.12951 +Epoch [3898/4000] Training metric {'Train/mean dice_metric': 0.9965664744377136, 'Train/mean miou_metric': 0.9935709834098816, 'Train/mean f1': 0.9969976544380188, 'Train/mean precision': 0.9965304136276245, 'Train/mean recall': 0.9974653124809265, 'Train/mean hd95_metric': 0.9189749956130981} +Epoch [3898/4000] Validation [1/10] Loss: 0.73572 focal_loss 0.64864 dice_loss 0.08708 +Epoch [3898/4000] Validation [2/10] Loss: 0.50743 focal_loss 0.40939 dice_loss 0.09804 +Epoch [3898/4000] Validation [3/10] Loss: 0.40641 focal_loss 0.29476 dice_loss 0.11165 +Epoch [3898/4000] Validation [4/10] Loss: 0.89929 focal_loss 0.33381 dice_loss 0.56548 +Epoch [3898/4000] Validation [5/10] Loss: 3.14547 focal_loss 2.47132 dice_loss 0.67415 +Epoch [3898/4000] Validation [6/10] Loss: 1.34399 focal_loss 0.63148 dice_loss 0.71251 +Epoch [3898/4000] Validation [7/10] Loss: 1.18312 focal_loss 0.52942 dice_loss 0.65370 +Epoch [3898/4000] Validation [8/10] Loss: 2.43972 focal_loss 1.82177 dice_loss 0.61796 +Epoch [3898/4000] Validation [9/10] Loss: 1.53723 focal_loss 0.99367 dice_loss 0.54357 +Epoch [3898/4000] Validation [10/10] Loss: 1.90665 focal_loss 1.17203 dice_loss 0.73462 +Epoch [3898/4000] Validation metric {'Val/mean dice_metric': 0.9515416622161865, 'Val/mean miou_metric': 0.9358147382736206, 'Val/mean f1': 0.9483765959739685, 'Val/mean precision': 0.9438572525978088, 'Val/mean recall': 0.9529393911361694, 'Val/mean hd95_metric': 10.705806732177734} +Cheakpoint... +Epoch [3898/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515416622161865, 'Val/mean miou_metric': 0.9358147382736206, 'Val/mean f1': 0.9483765959739685, 'Val/mean precision': 0.9438572525978088, 'Val/mean recall': 0.9529393911361694, 'Val/mean hd95_metric': 10.705806732177734} +Epoch [3899/4000] Training [1/39] Loss: 0.00349 +Epoch [3899/4000] Training [2/39] Loss: 0.12839 +Epoch [3899/4000] Training [3/39] Loss: 0.12819 +Epoch [3899/4000] Training [4/39] Loss: 0.04331 +Epoch [3899/4000] Training [5/39] Loss: 0.00410 +Epoch [3899/4000] Training [6/39] Loss: 0.00684 +Epoch [3899/4000] Training [7/39] Loss: 0.12852 +Epoch [3899/4000] Training [8/39] Loss: 0.00415 +Epoch [3899/4000] Training [9/39] Loss: 0.00305 +Epoch [3899/4000] Training [10/39] Loss: 0.00711 +Epoch [3899/4000] Training [11/39] Loss: 0.00465 +Epoch [3899/4000] Training [12/39] Loss: 0.00532 +Epoch [3899/4000] Training [13/39] Loss: 0.00426 +Epoch [3899/4000] Training [14/39] Loss: 0.12933 +Epoch [3899/4000] Training [15/39] Loss: 0.00513 +Epoch [3899/4000] Training [16/39] Loss: 0.25297 +Epoch [3899/4000] Training [17/39] Loss: 0.00428 +Epoch [3899/4000] Training [18/39] Loss: 0.00470 +Epoch [3899/4000] Training [19/39] Loss: 0.00343 +Epoch [3899/4000] Training [20/39] Loss: 0.00385 +Epoch [3899/4000] Training [21/39] Loss: 0.00651 +Epoch [3899/4000] Training [22/39] Loss: 0.00566 +Epoch [3899/4000] Training [23/39] Loss: 0.00443 +Epoch [3899/4000] Training [24/39] Loss: 0.00441 +Epoch [3899/4000] Training [25/39] Loss: 0.00578 +Epoch [3899/4000] Training [26/39] Loss: 0.00546 +Epoch [3899/4000] Training [27/39] Loss: 0.00435 +Epoch [3899/4000] Training [28/39] Loss: 0.00310 +Epoch [3899/4000] Training [29/39] Loss: 0.00393 +Epoch [3899/4000] Training [30/39] Loss: 0.13147 +Epoch [3899/4000] Training [31/39] Loss: 0.12825 +Epoch [3899/4000] Training [32/39] Loss: 0.00338 +Epoch [3899/4000] Training [33/39] Loss: 0.00396 +Epoch [3899/4000] Training [34/39] Loss: 0.13092 +Epoch [3899/4000] Training [35/39] Loss: 0.00470 +Epoch [3899/4000] Training [36/39] Loss: 0.00418 +Epoch [3899/4000] Training [37/39] Loss: 0.12954 +Epoch [3899/4000] Training [38/39] Loss: 0.00338 +Epoch [3899/4000] Training [39/39] Loss: 0.12987 +Epoch [3899/4000] Training metric {'Train/mean dice_metric': 0.9957645535469055, 'Train/mean miou_metric': 0.9928119778633118, 'Train/mean f1': 0.9971343874931335, 'Train/mean precision': 0.9966685175895691, 'Train/mean recall': 0.9976004362106323, 'Train/mean hd95_metric': 0.9089264273643494} +Epoch [3899/4000] Validation [1/10] Loss: 0.70668 focal_loss 0.62063 dice_loss 0.08605 +Epoch [3899/4000] Validation [2/10] Loss: 0.50223 focal_loss 0.40279 dice_loss 0.09944 +Epoch [3899/4000] Validation [3/10] Loss: 0.39712 focal_loss 0.28527 dice_loss 0.11185 +Epoch [3899/4000] Validation [4/10] Loss: 0.89186 focal_loss 0.32687 dice_loss 0.56498 +Epoch [3899/4000] Validation [5/10] Loss: 3.04794 focal_loss 2.37388 dice_loss 0.67406 +Epoch [3899/4000] Validation [6/10] Loss: 1.33261 focal_loss 0.61993 dice_loss 0.71268 +Epoch [3899/4000] Validation [7/10] Loss: 1.17003 focal_loss 0.51709 dice_loss 0.65294 +Epoch [3899/4000] Validation [8/10] Loss: 2.40896 focal_loss 1.78875 dice_loss 0.62021 +Epoch [3899/4000] Validation [9/10] Loss: 1.50627 focal_loss 0.96242 dice_loss 0.54384 +Epoch [3899/4000] Validation [10/10] Loss: 1.87442 focal_loss 1.14040 dice_loss 0.73402 +Epoch [3899/4000] Validation metric {'Val/mean dice_metric': 0.9509168863296509, 'Val/mean miou_metric': 0.9352477192878723, 'Val/mean f1': 0.9487658143043518, 'Val/mean precision': 0.9444910287857056, 'Val/mean recall': 0.9530794620513916, 'Val/mean hd95_metric': 10.805176734924316} +Cheakpoint... +Epoch [3899/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509168863296509, 'Val/mean miou_metric': 0.9352477192878723, 'Val/mean f1': 0.9487658143043518, 'Val/mean precision': 0.9444910287857056, 'Val/mean recall': 0.9530794620513916, 'Val/mean hd95_metric': 10.805176734924316} +Epoch [3900/4000] Training [1/39] Loss: 0.00381 +Epoch [3900/4000] Training [2/39] Loss: 0.00435 +Epoch [3900/4000] Training [3/39] Loss: 0.00434 +Epoch [3900/4000] Training [4/39] Loss: 0.00401 +Epoch [3900/4000] Training [5/39] Loss: 0.00373 +Epoch [3900/4000] Training [6/39] Loss: 0.00595 +Epoch [3900/4000] Training [7/39] Loss: 0.00510 +Epoch [3900/4000] Training [8/39] Loss: 0.00633 +Epoch [3900/4000] Training [9/39] Loss: 0.00743 +Epoch [3900/4000] Training [10/39] Loss: 0.00395 +Epoch [3900/4000] Training [11/39] Loss: 0.00497 +Epoch [3900/4000] Training [12/39] Loss: 0.00470 +Epoch [3900/4000] Training [13/39] Loss: 0.12810 +Epoch [3900/4000] Training [14/39] Loss: 0.00466 +Epoch [3900/4000] Training [15/39] Loss: 0.00398 +Epoch [3900/4000] Training [16/39] Loss: 0.00323 +Epoch [3900/4000] Training [17/39] Loss: 0.13078 +Epoch [3900/4000] Training [18/39] Loss: 0.00316 +Epoch [3900/4000] Training [19/39] Loss: 0.13099 +Epoch [3900/4000] Training [20/39] Loss: 0.00434 +Epoch [3900/4000] Training [21/39] Loss: 0.00519 +Epoch [3900/4000] Training [22/39] Loss: 0.00444 +Epoch [3900/4000] Training [23/39] Loss: 0.00724 +Epoch [3900/4000] Training [24/39] Loss: 0.00566 +Epoch [3900/4000] Training [25/39] Loss: 0.00287 +Epoch [3900/4000] Training [26/39] Loss: 0.00379 +Epoch [3900/4000] Training [27/39] Loss: 0.00691 +Epoch [3900/4000] Training [28/39] Loss: 0.00374 +Epoch [3900/4000] Training [29/39] Loss: 0.00475 +Epoch [3900/4000] Training [30/39] Loss: 0.00806 +Epoch [3900/4000] Training [31/39] Loss: 0.00370 +Epoch [3900/4000] Training [32/39] Loss: 0.00299 +Epoch [3900/4000] Training [33/39] Loss: 0.25608 +Epoch [3900/4000] Training [34/39] Loss: 0.00356 +Epoch [3900/4000] Training [35/39] Loss: 0.00418 +Epoch [3900/4000] Training [36/39] Loss: 0.00280 +Epoch [3900/4000] Training [37/39] Loss: 0.00661 +Epoch [3900/4000] Training [38/39] Loss: 0.00391 +Epoch [3900/4000] Training [39/39] Loss: 0.00357 +Epoch [3900/4000] Training metric {'Train/mean dice_metric': 0.9965031743049622, 'Train/mean miou_metric': 0.9934439063072205, 'Train/mean f1': 0.9969277381896973, 'Train/mean precision': 0.9965400695800781, 'Train/mean recall': 0.9973156452178955, 'Train/mean hd95_metric': 0.9201499819755554} +Epoch [3900/4000] Validation [1/10] Loss: 0.70342 focal_loss 0.61753 dice_loss 0.08589 +Epoch [3900/4000] Validation [2/10] Loss: 0.50406 focal_loss 0.40379 dice_loss 0.10027 +Epoch [3900/4000] Validation [3/10] Loss: 0.40092 focal_loss 0.28891 dice_loss 0.11202 +Epoch [3900/4000] Validation [4/10] Loss: 0.89175 focal_loss 0.32681 dice_loss 0.56494 +Epoch [3900/4000] Validation [5/10] Loss: 3.03751 focal_loss 2.36343 dice_loss 0.67408 +Epoch [3900/4000] Validation [6/10] Loss: 1.32919 focal_loss 0.61679 dice_loss 0.71240 +Epoch [3900/4000] Validation [7/10] Loss: 1.17160 focal_loss 0.51918 dice_loss 0.65243 +Epoch [3900/4000] Validation [8/10] Loss: 2.41703 focal_loss 1.79563 dice_loss 0.62141 +Epoch [3900/4000] Validation [9/10] Loss: 1.50718 focal_loss 0.96353 dice_loss 0.54365 +Epoch [3900/4000] Validation [10/10] Loss: 1.87516 focal_loss 1.14108 dice_loss 0.73409 +Epoch [3900/4000] Validation metric {'Val/mean dice_metric': 0.9515592455863953, 'Val/mean miou_metric': 0.9358108043670654, 'Val/mean f1': 0.9487135410308838, 'Val/mean precision': 0.9446813464164734, 'Val/mean recall': 0.9527802467346191, 'Val/mean hd95_metric': 10.787188529968262} +Cheakpoint... +Epoch [3900/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515592455863953, 'Val/mean miou_metric': 0.9358108043670654, 'Val/mean f1': 0.9487135410308838, 'Val/mean precision': 0.9446813464164734, 'Val/mean recall': 0.9527802467346191, 'Val/mean hd95_metric': 10.787188529968262} +Epoch [3901/4000] Training [1/39] Loss: 0.00325 +Epoch [3901/4000] Training [2/39] Loss: 0.25540 +Epoch [3901/4000] Training [3/39] Loss: 0.00907 +Epoch [3901/4000] Training [4/39] Loss: 0.00395 +Epoch [3901/4000] Training [5/39] Loss: 0.00353 +Epoch [3901/4000] Training [6/39] Loss: 0.00424 +Epoch [3901/4000] Training [7/39] Loss: 0.00418 +Epoch [3901/4000] Training [8/39] Loss: 0.00497 +Epoch [3901/4000] Training [9/39] Loss: 0.00563 +Epoch [3901/4000] Training [10/39] Loss: 0.00331 +Epoch [3901/4000] Training [11/39] Loss: 0.00361 +Epoch [3901/4000] Training [12/39] Loss: 0.00312 +Epoch [3901/4000] Training [13/39] Loss: 0.00291 +Epoch [3901/4000] Training [14/39] Loss: 0.13064 +Epoch [3901/4000] Training [15/39] Loss: 0.25204 +Epoch [3901/4000] Training [16/39] Loss: 0.00425 +Epoch [3901/4000] Training [17/39] Loss: 0.00568 +Epoch [3901/4000] Training [18/39] Loss: 0.00449 +Epoch [3901/4000] Training [19/39] Loss: 0.00483 +Epoch [3901/4000] Training [20/39] Loss: 0.25437 +Epoch [3901/4000] Training [21/39] Loss: 0.25272 +Epoch [3901/4000] Training [22/39] Loss: 0.00313 +Epoch [3901/4000] Training [23/39] Loss: 0.00505 +Epoch [3901/4000] Training [24/39] Loss: 0.00356 +Epoch [3901/4000] Training [25/39] Loss: 0.00772 +Epoch [3901/4000] Training [26/39] Loss: 0.12849 +Epoch [3901/4000] Training [27/39] Loss: 0.00304 +Epoch [3901/4000] Training [28/39] Loss: 0.00300 +Epoch [3901/4000] Training [29/39] Loss: 0.13027 +Epoch [3901/4000] Training [30/39] Loss: 0.12856 +Epoch [3901/4000] Training [31/39] Loss: 0.00306 +Epoch [3901/4000] Training [32/39] Loss: 0.25330 +Epoch [3901/4000] Training [33/39] Loss: 0.00387 +Epoch [3901/4000] Training [34/39] Loss: 0.00438 +Epoch [3901/4000] Training [35/39] Loss: 0.00559 +Epoch [3901/4000] Training [36/39] Loss: 0.13075 +Epoch [3901/4000] Training [37/39] Loss: 0.00560 +Epoch [3901/4000] Training [38/39] Loss: 0.12735 +Epoch [3901/4000] Training [39/39] Loss: 0.00390 +Epoch [3901/4000] Training metric {'Train/mean dice_metric': 0.9966788291931152, 'Train/mean miou_metric': 0.9938001036643982, 'Train/mean f1': 0.997107982635498, 'Train/mean precision': 0.9966099858283997, 'Train/mean recall': 0.9976065158843994, 'Train/mean hd95_metric': 0.9258516430854797} +Epoch [3901/4000] Validation [1/10] Loss: 0.70241 focal_loss 0.61693 dice_loss 0.08548 +Epoch [3901/4000] Validation [2/10] Loss: 0.50970 focal_loss 0.40918 dice_loss 0.10052 +Epoch [3901/4000] Validation [3/10] Loss: 0.39821 focal_loss 0.28635 dice_loss 0.11186 +Epoch [3901/4000] Validation [4/10] Loss: 0.89467 focal_loss 0.33033 dice_loss 0.56434 +Epoch [3901/4000] Validation [5/10] Loss: 3.03351 focal_loss 2.35942 dice_loss 0.67409 +Epoch [3901/4000] Validation [6/10] Loss: 1.33827 focal_loss 0.62529 dice_loss 0.71299 +Epoch [3901/4000] Validation [7/10] Loss: 1.17730 focal_loss 0.52561 dice_loss 0.65169 +Epoch [3901/4000] Validation [8/10] Loss: 2.44333 focal_loss 1.82002 dice_loss 0.62332 +Epoch [3901/4000] Validation [9/10] Loss: 1.51199 focal_loss 0.96810 dice_loss 0.54389 +Epoch [3901/4000] Validation [10/10] Loss: 1.88688 focal_loss 1.15310 dice_loss 0.73378 +Epoch [3901/4000] Validation metric {'Val/mean dice_metric': 0.9516623616218567, 'Val/mean miou_metric': 0.9360671639442444, 'Val/mean f1': 0.9487695693969727, 'Val/mean precision': 0.9448192715644836, 'Val/mean recall': 0.9527530670166016, 'Val/mean hd95_metric': 10.732696533203125} +Cheakpoint... +Epoch [3901/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516623616218567, 'Val/mean miou_metric': 0.9360671639442444, 'Val/mean f1': 0.9487695693969727, 'Val/mean precision': 0.9448192715644836, 'Val/mean recall': 0.9527530670166016, 'Val/mean hd95_metric': 10.732696533203125} +Epoch [3902/4000] Training [1/39] Loss: 0.00339 +Epoch [3902/4000] Training [2/39] Loss: 0.00496 +Epoch [3902/4000] Training [3/39] Loss: 0.00410 +Epoch [3902/4000] Training [4/39] Loss: 0.00258 +Epoch [3902/4000] Training [5/39] Loss: 0.12769 +Epoch [3902/4000] Training [6/39] Loss: 0.00458 +Epoch [3902/4000] Training [7/39] Loss: 0.00433 +Epoch [3902/4000] Training [8/39] Loss: 0.12912 +Epoch [3902/4000] Training [9/39] Loss: 0.00596 +Epoch [3902/4000] Training [10/39] Loss: 0.00425 +Epoch [3902/4000] Training [11/39] Loss: 0.00533 +Epoch [3902/4000] Training [12/39] Loss: 0.00340 +Epoch [3902/4000] Training [13/39] Loss: 0.00425 +Epoch [3902/4000] Training [14/39] Loss: 0.12813 +Epoch [3902/4000] Training [15/39] Loss: 0.13019 +Epoch [3902/4000] Training [16/39] Loss: 0.00593 +Epoch [3902/4000] Training [17/39] Loss: 0.00499 +Epoch [3902/4000] Training [18/39] Loss: 0.00456 +Epoch [3902/4000] Training [19/39] Loss: 0.00357 +Epoch [3902/4000] Training [20/39] Loss: 0.00447 +Epoch [3902/4000] Training [21/39] Loss: 0.00422 +Epoch [3902/4000] Training [22/39] Loss: 0.00320 +Epoch [3902/4000] Training [23/39] Loss: 0.00565 +Epoch [3902/4000] Training [24/39] Loss: 0.12853 +Epoch [3902/4000] Training [25/39] Loss: 0.00385 +Epoch [3902/4000] Training [26/39] Loss: 0.00581 +Epoch [3902/4000] Training [27/39] Loss: 0.00461 +Epoch [3902/4000] Training [28/39] Loss: 0.12769 +Epoch [3902/4000] Training [29/39] Loss: 0.13101 +Epoch [3902/4000] Training [30/39] Loss: 0.00612 +Epoch [3902/4000] Training [31/39] Loss: 0.00529 +Epoch [3902/4000] Training [32/39] Loss: 0.00495 +Epoch [3902/4000] Training [33/39] Loss: 0.00584 +Epoch [3902/4000] Training [34/39] Loss: 0.00406 +Epoch [3902/4000] Training [35/39] Loss: 0.00418 +Epoch [3902/4000] Training [36/39] Loss: 0.00445 +Epoch [3902/4000] Training [37/39] Loss: 0.12996 +Epoch [3902/4000] Training [38/39] Loss: 0.00566 +Epoch [3902/4000] Training [39/39] Loss: 0.00605 +Epoch [3902/4000] Training metric {'Train/mean dice_metric': 0.9956756234169006, 'Train/mean miou_metric': 0.9926226735115051, 'Train/mean f1': 0.9969837069511414, 'Train/mean precision': 0.996564507484436, 'Train/mean recall': 0.9974032640457153, 'Train/mean hd95_metric': 0.947826087474823} +Epoch [3902/4000] Validation [1/10] Loss: 0.71057 focal_loss 0.62425 dice_loss 0.08631 +Epoch [3902/4000] Validation [2/10] Loss: 0.50158 focal_loss 0.40280 dice_loss 0.09879 +Epoch [3902/4000] Validation [3/10] Loss: 0.39608 focal_loss 0.28440 dice_loss 0.11168 +Epoch [3902/4000] Validation [4/10] Loss: 0.89269 focal_loss 0.32764 dice_loss 0.56504 +Epoch [3902/4000] Validation [5/10] Loss: 3.04836 focal_loss 2.37434 dice_loss 0.67402 +Epoch [3902/4000] Validation [6/10] Loss: 1.33514 focal_loss 0.62234 dice_loss 0.71280 +Epoch [3902/4000] Validation [7/10] Loss: 1.17501 focal_loss 0.52140 dice_loss 0.65361 +Epoch [3902/4000] Validation [8/10] Loss: 2.40001 focal_loss 1.78109 dice_loss 0.61892 +Epoch [3902/4000] Validation [9/10] Loss: 1.51200 focal_loss 0.96806 dice_loss 0.54394 +Epoch [3902/4000] Validation [10/10] Loss: 1.88494 focal_loss 1.15056 dice_loss 0.73438 +Epoch [3902/4000] Validation metric {'Val/mean dice_metric': 0.9508111476898193, 'Val/mean miou_metric': 0.9350435137748718, 'Val/mean f1': 0.9487335681915283, 'Val/mean precision': 0.9445416927337646, 'Val/mean recall': 0.9529627561569214, 'Val/mean hd95_metric': 10.807804107666016} +Cheakpoint... +Epoch [3902/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508111476898193, 'Val/mean miou_metric': 0.9350435137748718, 'Val/mean f1': 0.9487335681915283, 'Val/mean precision': 0.9445416927337646, 'Val/mean recall': 0.9529627561569214, 'Val/mean hd95_metric': 10.807804107666016} +Epoch [3903/4000] Training [1/39] Loss: 0.13167 +Epoch [3903/4000] Training [2/39] Loss: 0.00399 +Epoch [3903/4000] Training [3/39] Loss: 0.12850 +Epoch [3903/4000] Training [4/39] Loss: 0.12782 +Epoch [3903/4000] Training [5/39] Loss: 0.00589 +Epoch [3903/4000] Training [6/39] Loss: 0.00497 +Epoch [3903/4000] Training [7/39] Loss: 0.00514 +Epoch [3903/4000] Training [8/39] Loss: 0.00485 +Epoch [3903/4000] Training [9/39] Loss: 0.00496 +Epoch [3903/4000] Training [10/39] Loss: 0.00517 +Epoch [3903/4000] Training [11/39] Loss: 0.00466 +Epoch [3903/4000] Training [12/39] Loss: 0.00491 +Epoch [3903/4000] Training [13/39] Loss: 0.00304 +Epoch [3903/4000] Training [14/39] Loss: 0.00425 +Epoch [3903/4000] Training [15/39] Loss: 0.12992 +Epoch [3903/4000] Training [16/39] Loss: 0.00643 +Epoch [3903/4000] Training [17/39] Loss: 0.00311 +Epoch [3903/4000] Training [18/39] Loss: 0.00446 +Epoch [3903/4000] Training [19/39] Loss: 0.00452 +Epoch [3903/4000] Training [20/39] Loss: 0.13040 +Epoch [3903/4000] Training [21/39] Loss: 0.12746 +Epoch [3903/4000] Training [22/39] Loss: 0.00490 +Epoch [3903/4000] Training [23/39] Loss: 0.00342 +Epoch [3903/4000] Training [24/39] Loss: 0.00636 +Epoch [3903/4000] Training [25/39] Loss: 0.00645 +Epoch [3903/4000] Training [26/39] Loss: 0.00420 +Epoch [3903/4000] Training [27/39] Loss: 0.00440 +Epoch [3903/4000] Training [28/39] Loss: 0.25304 +Epoch [3903/4000] Training [29/39] Loss: 0.00431 +Epoch [3903/4000] Training [30/39] Loss: 0.00523 +Epoch [3903/4000] Training [31/39] Loss: 0.00342 +Epoch [3903/4000] Training [32/39] Loss: 0.00337 +Epoch [3903/4000] Training [33/39] Loss: 0.12913 +Epoch [3903/4000] Training [34/39] Loss: 0.00277 +Epoch [3903/4000] Training [35/39] Loss: 0.00625 +Epoch [3903/4000] Training [36/39] Loss: 0.12908 +Epoch [3903/4000] Training [37/39] Loss: 0.12792 +Epoch [3903/4000] Training [38/39] Loss: 0.12949 +Epoch [3903/4000] Training [39/39] Loss: 0.00386 +Epoch [3903/4000] Training metric {'Train/mean dice_metric': 0.9965993762016296, 'Train/mean miou_metric': 0.9936410784721375, 'Train/mean f1': 0.9971303343772888, 'Train/mean precision': 0.9966386556625366, 'Train/mean recall': 0.9976224899291992, 'Train/mean hd95_metric': 0.9119217395782471} +Epoch [3903/4000] Validation [1/10] Loss: 0.71707 focal_loss 0.63050 dice_loss 0.08657 +Epoch [3903/4000] Validation [2/10] Loss: 0.50671 focal_loss 0.40848 dice_loss 0.09823 +Epoch [3903/4000] Validation [3/10] Loss: 0.39493 focal_loss 0.28357 dice_loss 0.11136 +Epoch [3903/4000] Validation [4/10] Loss: 0.90096 focal_loss 0.33520 dice_loss 0.56576 +Epoch [3903/4000] Validation [5/10] Loss: 3.05920 focal_loss 2.38528 dice_loss 0.67392 +Epoch [3903/4000] Validation [6/10] Loss: 1.34729 focal_loss 0.63438 dice_loss 0.71291 +Epoch [3903/4000] Validation [7/10] Loss: 1.18267 focal_loss 0.52835 dice_loss 0.65432 +Epoch [3903/4000] Validation [8/10] Loss: 2.39311 focal_loss 1.77554 dice_loss 0.61757 +Epoch [3903/4000] Validation [9/10] Loss: 1.52400 focal_loss 0.97992 dice_loss 0.54407 +Epoch [3903/4000] Validation [10/10] Loss: 1.90437 focal_loss 1.16965 dice_loss 0.73472 +Epoch [3903/4000] Validation metric {'Val/mean dice_metric': 0.9515730738639832, 'Val/mean miou_metric': 0.9358727931976318, 'Val/mean f1': 0.9483810663223267, 'Val/mean precision': 0.9437811374664307, 'Val/mean recall': 0.9530260562896729, 'Val/mean hd95_metric': 10.67045783996582} +Cheakpoint... +Epoch [3903/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515730738639832, 'Val/mean miou_metric': 0.9358727931976318, 'Val/mean f1': 0.9483810663223267, 'Val/mean precision': 0.9437811374664307, 'Val/mean recall': 0.9530260562896729, 'Val/mean hd95_metric': 10.67045783996582} +Epoch [3904/4000] Training [1/39] Loss: 0.13094 +Epoch [3904/4000] Training [2/39] Loss: 0.12901 +Epoch [3904/4000] Training [3/39] Loss: 0.12884 +Epoch [3904/4000] Training [4/39] Loss: 0.12813 +Epoch [3904/4000] Training [5/39] Loss: 0.00333 +Epoch [3904/4000] Training [6/39] Loss: 0.00506 +Epoch [3904/4000] Training [7/39] Loss: 0.12952 +Epoch [3904/4000] Training [8/39] Loss: 0.00437 +Epoch [3904/4000] Training [9/39] Loss: 0.00452 +Epoch [3904/4000] Training [10/39] Loss: 0.00343 +Epoch [3904/4000] Training [11/39] Loss: 0.12957 +Epoch [3904/4000] Training [12/39] Loss: 0.00679 +Epoch [3904/4000] Training [13/39] Loss: 0.12808 +Epoch [3904/4000] Training [14/39] Loss: 0.00317 +Epoch [3904/4000] Training [15/39] Loss: 0.00474 +Epoch [3904/4000] Training [16/39] Loss: 0.12786 +Epoch [3904/4000] Training [17/39] Loss: 0.00427 +Epoch [3904/4000] Training [18/39] Loss: 0.00343 +Epoch [3904/4000] Training [19/39] Loss: 0.00545 +Epoch [3904/4000] Training [20/39] Loss: 0.00377 +Epoch [3904/4000] Training [21/39] Loss: 0.00485 +Epoch [3904/4000] Training [22/39] Loss: 0.00534 +Epoch [3904/4000] Training [23/39] Loss: 0.00448 +Epoch [3904/4000] Training [24/39] Loss: 0.00445 +Epoch [3904/4000] Training [25/39] Loss: 0.00316 +Epoch [3904/4000] Training [26/39] Loss: 0.12757 +Epoch [3904/4000] Training [27/39] Loss: 0.00342 +Epoch [3904/4000] Training [28/39] Loss: 0.00406 +Epoch [3904/4000] Training [29/39] Loss: 0.13028 +Epoch [3904/4000] Training [30/39] Loss: 0.00356 +Epoch [3904/4000] Training [31/39] Loss: 0.13311 +Epoch [3904/4000] Training [32/39] Loss: 0.00403 +Epoch [3904/4000] Training [33/39] Loss: 0.00504 +Epoch [3904/4000] Training [34/39] Loss: 0.00372 +Epoch [3904/4000] Training [35/39] Loss: 0.00367 +Epoch [3904/4000] Training [36/39] Loss: 0.00566 +Epoch [3904/4000] Training [37/39] Loss: 0.00587 +Epoch [3904/4000] Training [38/39] Loss: 0.00468 +Epoch [3904/4000] Training [39/39] Loss: 0.00424 +Epoch [3904/4000] Training metric {'Train/mean dice_metric': 0.9958951473236084, 'Train/mean miou_metric': 0.9930656552314758, 'Train/mean f1': 0.9971948862075806, 'Train/mean precision': 0.9967474937438965, 'Train/mean recall': 0.9976428151130676, 'Train/mean hd95_metric': 0.8805056810379028} +Epoch [3904/4000] Validation [1/10] Loss: 0.70336 focal_loss 0.61781 dice_loss 0.08555 +Epoch [3904/4000] Validation [2/10] Loss: 0.51000 focal_loss 0.40837 dice_loss 0.10164 +Epoch [3904/4000] Validation [3/10] Loss: 0.40334 focal_loss 0.29099 dice_loss 0.11235 +Epoch [3904/4000] Validation [4/10] Loss: 0.89064 focal_loss 0.32634 dice_loss 0.56430 +Epoch [3904/4000] Validation [5/10] Loss: 3.04214 focal_loss 2.36799 dice_loss 0.67415 +Epoch [3904/4000] Validation [6/10] Loss: 1.32610 focal_loss 0.61337 dice_loss 0.71273 +Epoch [3904/4000] Validation [7/10] Loss: 1.16983 focal_loss 0.51860 dice_loss 0.65123 +Epoch [3904/4000] Validation [8/10] Loss: 2.43786 focal_loss 1.81264 dice_loss 0.62522 +Epoch [3904/4000] Validation [9/10] Loss: 1.50842 focal_loss 0.96487 dice_loss 0.54356 +Epoch [3904/4000] Validation [10/10] Loss: 1.86529 focal_loss 1.13206 dice_loss 0.73323 +Epoch [3904/4000] Validation metric {'Val/mean dice_metric': 0.9510016441345215, 'Val/mean miou_metric': 0.9354426860809326, 'Val/mean f1': 0.9490829706192017, 'Val/mean precision': 0.9456033110618591, 'Val/mean recall': 0.9525883197784424, 'Val/mean hd95_metric': 10.744361877441406} +Cheakpoint... +Epoch [3904/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9510016441345215, 'Val/mean miou_metric': 0.9354426860809326, 'Val/mean f1': 0.9490829706192017, 'Val/mean precision': 0.9456033110618591, 'Val/mean recall': 0.9525883197784424, 'Val/mean hd95_metric': 10.744361877441406} +Epoch [3905/4000] Training [1/39] Loss: 0.00358 +Epoch [3905/4000] Training [2/39] Loss: 0.00525 +Epoch [3905/4000] Training [3/39] Loss: 0.00372 +Epoch [3905/4000] Training [4/39] Loss: 0.00278 +Epoch [3905/4000] Training [5/39] Loss: 0.00474 +Epoch [3905/4000] Training [6/39] Loss: 0.00578 +Epoch [3905/4000] Training [7/39] Loss: 0.00335 +Epoch [3905/4000] Training [8/39] Loss: 0.00796 +Epoch [3905/4000] Training [9/39] Loss: 0.00409 +Epoch [3905/4000] Training [10/39] Loss: 0.00354 +Epoch [3905/4000] Training [11/39] Loss: 0.00731 +Epoch [3905/4000] Training [12/39] Loss: 0.00412 +Epoch [3905/4000] Training [13/39] Loss: 0.00536 +Epoch [3905/4000] Training [14/39] Loss: 0.00536 +Epoch [3905/4000] Training [15/39] Loss: 0.00493 +Epoch [3905/4000] Training [16/39] Loss: 0.00410 +Epoch [3905/4000] Training [17/39] Loss: 0.00306 +Epoch [3905/4000] Training [18/39] Loss: 0.01119 +Epoch [3905/4000] Training [19/39] Loss: 0.00444 +Epoch [3905/4000] Training [20/39] Loss: 0.00300 +Epoch [3905/4000] Training [21/39] Loss: 0.00510 +Epoch [3905/4000] Training [22/39] Loss: 0.00489 +Epoch [3905/4000] Training [23/39] Loss: 0.00469 +Epoch [3905/4000] Training [24/39] Loss: 0.12816 +Epoch [3905/4000] Training [25/39] Loss: 0.00448 +Epoch [3905/4000] Training [26/39] Loss: 0.12875 +Epoch [3905/4000] Training [27/39] Loss: 0.00430 +Epoch [3905/4000] Training [28/39] Loss: 0.12952 +Epoch [3905/4000] Training [29/39] Loss: 0.09481 +Epoch [3905/4000] Training [30/39] Loss: 0.00525 +Epoch [3905/4000] Training [31/39] Loss: 0.12930 +Epoch [3905/4000] Training [32/39] Loss: 0.13078 +Epoch [3905/4000] Training [33/39] Loss: 0.00700 +Epoch [3905/4000] Training [34/39] Loss: 0.25336 +Epoch [3905/4000] Training [35/39] Loss: 0.00579 +Epoch [3905/4000] Training [36/39] Loss: 0.00863 +Epoch [3905/4000] Training [37/39] Loss: 0.00817 +Epoch [3905/4000] Training [38/39] Loss: 0.00605 +Epoch [3905/4000] Training [39/39] Loss: 0.00388 +Epoch [3905/4000] Training metric {'Train/mean dice_metric': 0.9963374733924866, 'Train/mean miou_metric': 0.9931366443634033, 'Train/mean f1': 0.9968827962875366, 'Train/mean precision': 0.9963860511779785, 'Train/mean recall': 0.9973800778388977, 'Train/mean hd95_metric': 0.9338007569313049} +Epoch [3905/4000] Validation [1/10] Loss: 0.71877 focal_loss 0.63197 dice_loss 0.08681 +Epoch [3905/4000] Validation [2/10] Loss: 0.50329 focal_loss 0.40640 dice_loss 0.09689 +Epoch [3905/4000] Validation [3/10] Loss: 0.38867 focal_loss 0.27803 dice_loss 0.11064 +Epoch [3905/4000] Validation [4/10] Loss: 0.90022 focal_loss 0.33440 dice_loss 0.56582 +Epoch [3905/4000] Validation [5/10] Loss: 3.06850 focal_loss 2.39460 dice_loss 0.67390 +Epoch [3905/4000] Validation [6/10] Loss: 1.35289 focal_loss 0.63987 dice_loss 0.71302 +Epoch [3905/4000] Validation [7/10] Loss: 1.18959 focal_loss 0.53484 dice_loss 0.65475 +Epoch [3905/4000] Validation [8/10] Loss: 2.38270 focal_loss 1.76910 dice_loss 0.61360 +Epoch [3905/4000] Validation [9/10] Loss: 1.53218 focal_loss 0.98806 dice_loss 0.54412 +Epoch [3905/4000] Validation [10/10] Loss: 1.92147 focal_loss 1.18608 dice_loss 0.73538 +Epoch [3905/4000] Validation metric {'Val/mean dice_metric': 0.9514157772064209, 'Val/mean miou_metric': 0.9355193376541138, 'Val/mean f1': 0.9483408331871033, 'Val/mean precision': 0.9432986974716187, 'Val/mean recall': 0.9534372687339783, 'Val/mean hd95_metric': 10.665815353393555} +Cheakpoint... +Epoch [3905/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514157772064209, 'Val/mean miou_metric': 0.9355193376541138, 'Val/mean f1': 0.9483408331871033, 'Val/mean precision': 0.9432986974716187, 'Val/mean recall': 0.9534372687339783, 'Val/mean hd95_metric': 10.665815353393555} +Epoch [3906/4000] Training [1/39] Loss: 0.00460 +Epoch [3906/4000] Training [2/39] Loss: 0.00511 +Epoch [3906/4000] Training [3/39] Loss: 0.00592 +Epoch [3906/4000] Training [4/39] Loss: 0.00467 +Epoch [3906/4000] Training [5/39] Loss: 0.08454 +Epoch [3906/4000] Training [6/39] Loss: 0.00518 +Epoch [3906/4000] Training [7/39] Loss: 0.00550 +Epoch [3906/4000] Training [8/39] Loss: 0.00307 +Epoch [3906/4000] Training [9/39] Loss: 0.12731 +Epoch [3906/4000] Training [10/39] Loss: 0.00505 +Epoch [3906/4000] Training [11/39] Loss: 0.00390 +Epoch [3906/4000] Training [12/39] Loss: 0.00408 +Epoch [3906/4000] Training [13/39] Loss: 0.00530 +Epoch [3906/4000] Training [14/39] Loss: 0.12841 +Epoch [3906/4000] Training [15/39] Loss: 0.12883 +Epoch [3906/4000] Training [16/39] Loss: 0.00509 +Epoch [3906/4000] Training [17/39] Loss: 0.00486 +Epoch [3906/4000] Training [18/39] Loss: 0.12920 +Epoch [3906/4000] Training [19/39] Loss: 0.12894 +Epoch [3906/4000] Training [20/39] Loss: 0.00581 +Epoch [3906/4000] Training [21/39] Loss: 0.00765 +Epoch [3906/4000] Training [22/39] Loss: 0.00393 +Epoch [3906/4000] Training [23/39] Loss: 0.00329 +Epoch [3906/4000] Training [24/39] Loss: 0.13038 +Epoch [3906/4000] Training [25/39] Loss: 0.00496 +Epoch [3906/4000] Training [26/39] Loss: 0.00365 +Epoch [3906/4000] Training [27/39] Loss: 0.00463 +Epoch [3906/4000] Training [28/39] Loss: 0.00517 +Epoch [3906/4000] Training [29/39] Loss: 0.00299 +Epoch [3906/4000] Training [30/39] Loss: 0.00493 +Epoch [3906/4000] Training [31/39] Loss: 0.00772 +Epoch [3906/4000] Training [32/39] Loss: 0.00334 +Epoch [3906/4000] Training [33/39] Loss: 0.00672 +Epoch [3906/4000] Training [34/39] Loss: 0.00346 +Epoch [3906/4000] Training [35/39] Loss: 0.12969 +Epoch [3906/4000] Training [36/39] Loss: 0.00675 +Epoch [3906/4000] Training [37/39] Loss: 0.00361 +Epoch [3906/4000] Training [38/39] Loss: 0.12895 +Epoch [3906/4000] Training [39/39] Loss: 0.00419 +Epoch [3906/4000] Training metric {'Train/mean dice_metric': 0.9962514042854309, 'Train/mean miou_metric': 0.9929913878440857, 'Train/mean f1': 0.9968198537826538, 'Train/mean precision': 0.996286153793335, 'Train/mean recall': 0.9973542094230652, 'Train/mean hd95_metric': 0.9753174781799316} +Epoch [3906/4000] Validation [1/10] Loss: 0.71281 focal_loss 0.62615 dice_loss 0.08666 +Epoch [3906/4000] Validation [2/10] Loss: 0.50109 focal_loss 0.40324 dice_loss 0.09785 +Epoch [3906/4000] Validation [3/10] Loss: 0.38902 focal_loss 0.27804 dice_loss 0.11098 +Epoch [3906/4000] Validation [4/10] Loss: 0.89611 focal_loss 0.33074 dice_loss 0.56537 +Epoch [3906/4000] Validation [5/10] Loss: 3.05177 focal_loss 2.37774 dice_loss 0.67402 +Epoch [3906/4000] Validation [6/10] Loss: 1.34124 focal_loss 0.62851 dice_loss 0.71272 +Epoch [3906/4000] Validation [7/10] Loss: 1.17682 focal_loss 0.52288 dice_loss 0.65395 +Epoch [3906/4000] Validation [8/10] Loss: 2.38141 focal_loss 1.76460 dice_loss 0.61681 +Epoch [3906/4000] Validation [9/10] Loss: 1.51656 focal_loss 0.97268 dice_loss 0.54388 +Epoch [3906/4000] Validation [10/10] Loss: 1.89550 focal_loss 1.16080 dice_loss 0.73470 +Epoch [3906/4000] Validation metric {'Val/mean dice_metric': 0.9513643383979797, 'Val/mean miou_metric': 0.9354310035705566, 'Val/mean f1': 0.9483445882797241, 'Val/mean precision': 0.9436553716659546, 'Val/mean recall': 0.9530806541442871, 'Val/mean hd95_metric': 10.708855628967285} +Cheakpoint... +Epoch [3906/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513643383979797, 'Val/mean miou_metric': 0.9354310035705566, 'Val/mean f1': 0.9483445882797241, 'Val/mean precision': 0.9436553716659546, 'Val/mean recall': 0.9530806541442871, 'Val/mean hd95_metric': 10.708855628967285} +Epoch [3907/4000] Training [1/39] Loss: 0.12904 +Epoch [3907/4000] Training [2/39] Loss: 0.12834 +Epoch [3907/4000] Training [3/39] Loss: 0.12881 +Epoch [3907/4000] Training [4/39] Loss: 0.00544 +Epoch [3907/4000] Training [5/39] Loss: 0.12724 +Epoch [3907/4000] Training [6/39] Loss: 0.00584 +Epoch [3907/4000] Training [7/39] Loss: 0.00568 +Epoch [3907/4000] Training [8/39] Loss: 0.12918 +Epoch [3907/4000] Training [9/39] Loss: 0.00300 +Epoch [3907/4000] Training [10/39] Loss: 0.00415 +Epoch [3907/4000] Training [11/39] Loss: 0.00405 +Epoch [3907/4000] Training [12/39] Loss: 0.04182 +Epoch [3907/4000] Training [13/39] Loss: 0.00625 +Epoch [3907/4000] Training [14/39] Loss: 0.00369 +Epoch [3907/4000] Training [15/39] Loss: 0.12885 +Epoch [3907/4000] Training [16/39] Loss: 0.12834 +Epoch [3907/4000] Training [17/39] Loss: 0.25324 +Epoch [3907/4000] Training [18/39] Loss: 0.00691 +Epoch [3907/4000] Training [19/39] Loss: 0.00643 +Epoch [3907/4000] Training [20/39] Loss: 0.12880 +Epoch [3907/4000] Training [21/39] Loss: 0.00312 +Epoch [3907/4000] Training [22/39] Loss: 0.00410 +Epoch [3907/4000] Training [23/39] Loss: 0.00432 +Epoch [3907/4000] Training [24/39] Loss: 0.00572 +Epoch [3907/4000] Training [25/39] Loss: 0.13051 +Epoch [3907/4000] Training [26/39] Loss: 0.00599 +Epoch [3907/4000] Training [27/39] Loss: 0.13090 +Epoch [3907/4000] Training [28/39] Loss: 0.12968 +Epoch [3907/4000] Training [29/39] Loss: 0.25438 +Epoch [3907/4000] Training [30/39] Loss: 0.00295 +Epoch [3907/4000] Training [31/39] Loss: 0.12826 +Epoch [3907/4000] Training [32/39] Loss: 0.00323 +Epoch [3907/4000] Training [33/39] Loss: 0.00715 +Epoch [3907/4000] Training [34/39] Loss: 0.12805 +Epoch [3907/4000] Training [35/39] Loss: 0.00485 +Epoch [3907/4000] Training [36/39] Loss: 0.00635 +Epoch [3907/4000] Training [37/39] Loss: 0.00642 +Epoch [3907/4000] Training [38/39] Loss: 0.00614 +Epoch [3907/4000] Training [39/39] Loss: 0.00578 +Epoch [3907/4000] Training metric {'Train/mean dice_metric': 0.9963477253913879, 'Train/mean miou_metric': 0.9931512475013733, 'Train/mean f1': 0.996860682964325, 'Train/mean precision': 0.9963871240615845, 'Train/mean recall': 0.9973345398902893, 'Train/mean hd95_metric': 1.0296920537948608} +Epoch [3907/4000] Validation [1/10] Loss: 0.72760 focal_loss 0.64120 dice_loss 0.08639 +Epoch [3907/4000] Validation [2/10] Loss: 0.51182 focal_loss 0.41278 dice_loss 0.09904 +Epoch [3907/4000] Validation [3/10] Loss: 0.40409 focal_loss 0.29251 dice_loss 0.11158 +Epoch [3907/4000] Validation [4/10] Loss: 0.90089 focal_loss 0.33560 dice_loss 0.56529 +Epoch [3907/4000] Validation [5/10] Loss: 3.10600 focal_loss 2.43191 dice_loss 0.67408 +Epoch [3907/4000] Validation [6/10] Loss: 1.34601 focal_loss 0.63386 dice_loss 0.71215 +Epoch [3907/4000] Validation [7/10] Loss: 1.18409 focal_loss 0.53040 dice_loss 0.65369 +Epoch [3907/4000] Validation [8/10] Loss: 2.44788 focal_loss 1.82754 dice_loss 0.62034 +Epoch [3907/4000] Validation [9/10] Loss: 1.53927 focal_loss 0.99543 dice_loss 0.54384 +Epoch [3907/4000] Validation [10/10] Loss: 1.90800 focal_loss 1.17380 dice_loss 0.73419 +Epoch [3907/4000] Validation metric {'Val/mean dice_metric': 0.9513533711433411, 'Val/mean miou_metric': 0.9354544281959534, 'Val/mean f1': 0.9482600688934326, 'Val/mean precision': 0.944171667098999, 'Val/mean recall': 0.9523841142654419, 'Val/mean hd95_metric': 10.778101921081543} +Cheakpoint... +Epoch [3907/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513533711433411, 'Val/mean miou_metric': 0.9354544281959534, 'Val/mean f1': 0.9482600688934326, 'Val/mean precision': 0.944171667098999, 'Val/mean recall': 0.9523841142654419, 'Val/mean hd95_metric': 10.778101921081543} +Epoch [3908/4000] Training [1/39] Loss: 0.00318 +Epoch [3908/4000] Training [2/39] Loss: 0.00319 +Epoch [3908/4000] Training [3/39] Loss: 0.00487 +Epoch [3908/4000] Training [4/39] Loss: 0.00367 +Epoch [3908/4000] Training [5/39] Loss: 0.00789 +Epoch [3908/4000] Training [6/39] Loss: 0.00479 +Epoch [3908/4000] Training [7/39] Loss: 0.12800 +Epoch [3908/4000] Training [8/39] Loss: 0.00391 +Epoch [3908/4000] Training [9/39] Loss: 0.00331 +Epoch [3908/4000] Training [10/39] Loss: 0.12805 +Epoch [3908/4000] Training [11/39] Loss: 0.00572 +Epoch [3908/4000] Training [12/39] Loss: 0.00748 +Epoch [3908/4000] Training [13/39] Loss: 0.00478 +Epoch [3908/4000] Training [14/39] Loss: 0.00509 +Epoch [3908/4000] Training [15/39] Loss: 0.00440 +Epoch [3908/4000] Training [16/39] Loss: 0.12826 +Epoch [3908/4000] Training [17/39] Loss: 0.00663 +Epoch [3908/4000] Training [18/39] Loss: 0.00473 +Epoch [3908/4000] Training [19/39] Loss: 0.00378 +Epoch [3908/4000] Training [20/39] Loss: 0.00500 +Epoch [3908/4000] Training [21/39] Loss: 0.00397 +Epoch [3908/4000] Training [22/39] Loss: 0.00314 +Epoch [3908/4000] Training [23/39] Loss: 0.13036 +Epoch [3908/4000] Training [24/39] Loss: 0.12986 +Epoch [3908/4000] Training [25/39] Loss: 0.00592 +Epoch [3908/4000] Training [26/39] Loss: 0.00651 +Epoch [3908/4000] Training [27/39] Loss: 0.00547 +Epoch [3908/4000] Training [28/39] Loss: 0.12796 +Epoch [3908/4000] Training [29/39] Loss: 0.00590 +Epoch [3908/4000] Training [30/39] Loss: 0.00407 +Epoch [3908/4000] Training [31/39] Loss: 0.12848 +Epoch [3908/4000] Training [32/39] Loss: 0.00828 +Epoch [3908/4000] Training [33/39] Loss: 0.12964 +Epoch [3908/4000] Training [34/39] Loss: 0.00697 +Epoch [3908/4000] Training [35/39] Loss: 0.08402 +Epoch [3908/4000] Training [36/39] Loss: 0.00295 +Epoch [3908/4000] Training [37/39] Loss: 0.00450 +Epoch [3908/4000] Training [38/39] Loss: 0.12828 +Epoch [3908/4000] Training [39/39] Loss: 0.00523 +Epoch [3908/4000] Training metric {'Train/mean dice_metric': 0.9963756799697876, 'Train/mean miou_metric': 0.9932087659835815, 'Train/mean f1': 0.9968351125717163, 'Train/mean precision': 0.9963986277580261, 'Train/mean recall': 0.9972718358039856, 'Train/mean hd95_metric': 0.9321951866149902} +Epoch [3908/4000] Validation [1/10] Loss: 0.73227 focal_loss 0.64526 dice_loss 0.08700 +Epoch [3908/4000] Validation [2/10] Loss: 0.51230 focal_loss 0.41513 dice_loss 0.09717 +Epoch [3908/4000] Validation [3/10] Loss: 0.39552 focal_loss 0.28466 dice_loss 0.11086 +Epoch [3908/4000] Validation [4/10] Loss: 0.90975 focal_loss 0.34338 dice_loss 0.56637 +Epoch [3908/4000] Validation [5/10] Loss: 3.10825 focal_loss 2.43429 dice_loss 0.67396 +Epoch [3908/4000] Validation [6/10] Loss: 1.36488 focal_loss 0.65233 dice_loss 0.71255 +Epoch [3908/4000] Validation [7/10] Loss: 1.19898 focal_loss 0.54414 dice_loss 0.65484 +Epoch [3908/4000] Validation [8/10] Loss: 2.39564 focal_loss 1.78297 dice_loss 0.61267 +Epoch [3908/4000] Validation [9/10] Loss: 1.56301 focal_loss 1.01851 dice_loss 0.54450 +Epoch [3908/4000] Validation [10/10] Loss: 1.94719 focal_loss 1.21152 dice_loss 0.73567 +Epoch [3908/4000] Validation metric {'Val/mean dice_metric': 0.9514303803443909, 'Val/mean miou_metric': 0.9355553388595581, 'Val/mean f1': 0.948024570941925, 'Val/mean precision': 0.9428514242172241, 'Val/mean recall': 0.9532545804977417, 'Val/mean hd95_metric': 10.668366432189941} +Cheakpoint... +Epoch [3908/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514303803443909, 'Val/mean miou_metric': 0.9355553388595581, 'Val/mean f1': 0.948024570941925, 'Val/mean precision': 0.9428514242172241, 'Val/mean recall': 0.9532545804977417, 'Val/mean hd95_metric': 10.668366432189941} +Epoch [3909/4000] Training [1/39] Loss: 0.00363 +Epoch [3909/4000] Training [2/39] Loss: 0.00399 +Epoch [3909/4000] Training [3/39] Loss: 0.12881 +Epoch [3909/4000] Training [4/39] Loss: 0.00513 +Epoch [3909/4000] Training [5/39] Loss: 0.12984 +Epoch [3909/4000] Training [6/39] Loss: 0.00623 +Epoch [3909/4000] Training [7/39] Loss: 0.12903 +Epoch [3909/4000] Training [8/39] Loss: 0.12789 +Epoch [3909/4000] Training [9/39] Loss: 0.00526 +Epoch [3909/4000] Training [10/39] Loss: 0.13057 +Epoch [3909/4000] Training [11/39] Loss: 0.00553 +Epoch [3909/4000] Training [12/39] Loss: 0.00464 +Epoch [3909/4000] Training [13/39] Loss: 0.00632 +Epoch [3909/4000] Training [14/39] Loss: 0.00576 +Epoch [3909/4000] Training [15/39] Loss: 0.00399 +Epoch [3909/4000] Training [16/39] Loss: 0.00610 +Epoch [3909/4000] Training [17/39] Loss: 0.00452 +Epoch [3909/4000] Training [18/39] Loss: 0.12748 +Epoch [3909/4000] Training [19/39] Loss: 0.00279 +Epoch [3909/4000] Training [20/39] Loss: 0.00753 +Epoch [3909/4000] Training [21/39] Loss: 0.00407 +Epoch [3909/4000] Training [22/39] Loss: 0.00652 +Epoch [3909/4000] Training [23/39] Loss: 0.00539 +Epoch [3909/4000] Training [24/39] Loss: 0.12942 +Epoch [3909/4000] Training [25/39] Loss: 0.00626 +Epoch [3909/4000] Training [26/39] Loss: 0.00424 +Epoch [3909/4000] Training [27/39] Loss: 0.00310 +Epoch [3909/4000] Training [28/39] Loss: 0.00269 +Epoch [3909/4000] Training [29/39] Loss: 0.00356 +Epoch [3909/4000] Training [30/39] Loss: 0.00420 +Epoch [3909/4000] Training [31/39] Loss: 0.00602 +Epoch [3909/4000] Training [32/39] Loss: 0.00389 +Epoch [3909/4000] Training [33/39] Loss: 0.13378 +Epoch [3909/4000] Training [34/39] Loss: 0.00321 +Epoch [3909/4000] Training [35/39] Loss: 0.00579 +Epoch [3909/4000] Training [36/39] Loss: 0.00494 +Epoch [3909/4000] Training [37/39] Loss: 0.12801 +Epoch [3909/4000] Training [38/39] Loss: 0.00454 +Epoch [3909/4000] Training [39/39] Loss: 0.00325 +Epoch [3909/4000] Training metric {'Train/mean dice_metric': 0.9963279366493225, 'Train/mean miou_metric': 0.9931007027626038, 'Train/mean f1': 0.9967437982559204, 'Train/mean precision': 0.9963297843933105, 'Train/mean recall': 0.9971583485603333, 'Train/mean hd95_metric': 0.9295902252197266} +Epoch [3909/4000] Validation [1/10] Loss: 0.71801 focal_loss 0.63120 dice_loss 0.08681 +Epoch [3909/4000] Validation [2/10] Loss: 0.49742 focal_loss 0.39942 dice_loss 0.09800 +Epoch [3909/4000] Validation [3/10] Loss: 0.39431 focal_loss 0.28294 dice_loss 0.11137 +Epoch [3909/4000] Validation [4/10] Loss: 0.89168 focal_loss 0.32633 dice_loss 0.56535 +Epoch [3909/4000] Validation [5/10] Loss: 3.07128 focal_loss 2.39720 dice_loss 0.67409 +Epoch [3909/4000] Validation [6/10] Loss: 1.32962 focal_loss 0.61758 dice_loss 0.71204 +Epoch [3909/4000] Validation [7/10] Loss: 1.17053 focal_loss 0.51671 dice_loss 0.65382 +Epoch [3909/4000] Validation [8/10] Loss: 2.37072 focal_loss 1.75510 dice_loss 0.61562 +Epoch [3909/4000] Validation [9/10] Loss: 1.52273 focal_loss 0.97891 dice_loss 0.54382 +Epoch [3909/4000] Validation [10/10] Loss: 1.88208 focal_loss 1.14750 dice_loss 0.73458 +Epoch [3909/4000] Validation metric {'Val/mean dice_metric': 0.9513574242591858, 'Val/mean miou_metric': 0.9354310035705566, 'Val/mean f1': 0.9484770894050598, 'Val/mean precision': 0.9439619183540344, 'Val/mean recall': 0.9530356526374817, 'Val/mean hd95_metric': 10.710902214050293} +Cheakpoint... +Epoch [3909/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513574242591858, 'Val/mean miou_metric': 0.9354310035705566, 'Val/mean f1': 0.9484770894050598, 'Val/mean precision': 0.9439619183540344, 'Val/mean recall': 0.9530356526374817, 'Val/mean hd95_metric': 10.710902214050293} +Epoch [3910/4000] Training [1/39] Loss: 0.00319 +Epoch [3910/4000] Training [2/39] Loss: 0.00492 +Epoch [3910/4000] Training [3/39] Loss: 0.00326 +Epoch [3910/4000] Training [4/39] Loss: 0.00422 +Epoch [3910/4000] Training [5/39] Loss: 0.00635 +Epoch [3910/4000] Training [6/39] Loss: 0.00449 +Epoch [3910/4000] Training [7/39] Loss: 0.00478 +Epoch [3910/4000] Training [8/39] Loss: 0.00453 +Epoch [3910/4000] Training [9/39] Loss: 0.00576 +Epoch [3910/4000] Training [10/39] Loss: 0.00499 +Epoch [3910/4000] Training [11/39] Loss: 0.12867 +Epoch [3910/4000] Training [12/39] Loss: 0.12926 +Epoch [3910/4000] Training [13/39] Loss: 0.25271 +Epoch [3910/4000] Training [14/39] Loss: 0.00683 +Epoch [3910/4000] Training [15/39] Loss: 0.00500 +Epoch [3910/4000] Training [16/39] Loss: 0.00468 +Epoch [3910/4000] Training [17/39] Loss: 0.00413 +Epoch [3910/4000] Training [18/39] Loss: 0.00517 +Epoch [3910/4000] Training [19/39] Loss: 0.00440 +Epoch [3910/4000] Training [20/39] Loss: 0.00466 +Epoch [3910/4000] Training [21/39] Loss: 0.00504 +Epoch [3910/4000] Training [22/39] Loss: 0.00354 +Epoch [3910/4000] Training [23/39] Loss: 0.12798 +Epoch [3910/4000] Training [24/39] Loss: 0.00428 +Epoch [3910/4000] Training [25/39] Loss: 0.00367 +Epoch [3910/4000] Training [26/39] Loss: 0.00767 +Epoch [3910/4000] Training [27/39] Loss: 0.00604 +Epoch [3910/4000] Training [28/39] Loss: 0.00464 +Epoch [3910/4000] Training [29/39] Loss: 0.00528 +Epoch [3910/4000] Training [30/39] Loss: 0.00367 +Epoch [3910/4000] Training [31/39] Loss: 0.00406 +Epoch [3910/4000] Training [32/39] Loss: 0.00609 +Epoch [3910/4000] Training [33/39] Loss: 0.00551 +Epoch [3910/4000] Training [34/39] Loss: 0.00532 +Epoch [3910/4000] Training [35/39] Loss: 0.00351 +Epoch [3910/4000] Training [36/39] Loss: 0.00705 +Epoch [3910/4000] Training [37/39] Loss: 0.12948 +Epoch [3910/4000] Training [38/39] Loss: 0.00387 +Epoch [3910/4000] Training [39/39] Loss: 0.13410 +Epoch [3910/4000] Training metric {'Train/mean dice_metric': 0.9964445233345032, 'Train/mean miou_metric': 0.9933305382728577, 'Train/mean f1': 0.9969797134399414, 'Train/mean precision': 0.9964733719825745, 'Train/mean recall': 0.9974866509437561, 'Train/mean hd95_metric': 0.9171388149261475} +Epoch [3910/4000] Validation [1/10] Loss: 0.72274 focal_loss 0.63558 dice_loss 0.08715 +Epoch [3910/4000] Validation [2/10] Loss: 0.49908 focal_loss 0.40215 dice_loss 0.09693 +Epoch [3910/4000] Validation [3/10] Loss: 0.39147 focal_loss 0.28043 dice_loss 0.11104 +Epoch [3910/4000] Validation [4/10] Loss: 0.89695 focal_loss 0.33120 dice_loss 0.56575 +Epoch [3910/4000] Validation [5/10] Loss: 3.07919 focal_loss 2.40519 dice_loss 0.67400 +Epoch [3910/4000] Validation [6/10] Loss: 1.33900 focal_loss 0.62666 dice_loss 0.71234 +Epoch [3910/4000] Validation [7/10] Loss: 1.17823 focal_loss 0.52356 dice_loss 0.65467 +Epoch [3910/4000] Validation [8/10] Loss: 2.36841 focal_loss 1.75414 dice_loss 0.61428 +Epoch [3910/4000] Validation [9/10] Loss: 1.53005 focal_loss 0.98602 dice_loss 0.54403 +Epoch [3910/4000] Validation [10/10] Loss: 1.90073 focal_loss 1.16571 dice_loss 0.73502 +Epoch [3910/4000] Validation metric {'Val/mean dice_metric': 0.9515178799629211, 'Val/mean miou_metric': 0.9357001781463623, 'Val/mean f1': 0.9486637115478516, 'Val/mean precision': 0.9438108801841736, 'Val/mean recall': 0.95356684923172, 'Val/mean hd95_metric': 10.662042617797852} +Cheakpoint... +Epoch [3910/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515178799629211, 'Val/mean miou_metric': 0.9357001781463623, 'Val/mean f1': 0.9486637115478516, 'Val/mean precision': 0.9438108801841736, 'Val/mean recall': 0.95356684923172, 'Val/mean hd95_metric': 10.662042617797852} +Epoch [3911/4000] Training [1/39] Loss: 0.00281 +Epoch [3911/4000] Training [2/39] Loss: 0.00462 +Epoch [3911/4000] Training [3/39] Loss: 0.00457 +Epoch [3911/4000] Training [4/39] Loss: 0.00592 +Epoch [3911/4000] Training [5/39] Loss: 0.12886 +Epoch [3911/4000] Training [6/39] Loss: 0.00420 +Epoch [3911/4000] Training [7/39] Loss: 0.00327 +Epoch [3911/4000] Training [8/39] Loss: 0.00582 +Epoch [3911/4000] Training [9/39] Loss: 0.00474 +Epoch [3911/4000] Training [10/39] Loss: 0.00954 +Epoch [3911/4000] Training [11/39] Loss: 0.00361 +Epoch [3911/4000] Training [12/39] Loss: 0.00480 +Epoch [3911/4000] Training [13/39] Loss: 0.00417 +Epoch [3911/4000] Training [14/39] Loss: 0.13215 +Epoch [3911/4000] Training [15/39] Loss: 0.00465 +Epoch [3911/4000] Training [16/39] Loss: 0.13071 +Epoch [3911/4000] Training [17/39] Loss: 0.00483 +Epoch [3911/4000] Training [18/39] Loss: 0.00745 +Epoch [3911/4000] Training [19/39] Loss: 0.00554 +Epoch [3911/4000] Training [20/39] Loss: 0.00464 +Epoch [3911/4000] Training [21/39] Loss: 0.00361 +Epoch [3911/4000] Training [22/39] Loss: 0.00285 +Epoch [3911/4000] Training [23/39] Loss: 0.00453 +Epoch [3911/4000] Training [24/39] Loss: 0.00372 +Epoch [3911/4000] Training [25/39] Loss: 0.00499 +Epoch [3911/4000] Training [26/39] Loss: 0.00529 +Epoch [3911/4000] Training [27/39] Loss: 0.00393 +Epoch [3911/4000] Training [28/39] Loss: 0.00450 +Epoch [3911/4000] Training [29/39] Loss: 0.12848 +Epoch [3911/4000] Training [30/39] Loss: 0.00771 +Epoch [3911/4000] Training [31/39] Loss: 0.00598 +Epoch [3911/4000] Training [32/39] Loss: 0.00566 +Epoch [3911/4000] Training [33/39] Loss: 0.12903 +Epoch [3911/4000] Training [34/39] Loss: 0.00595 +Epoch [3911/4000] Training [35/39] Loss: 0.00392 +Epoch [3911/4000] Training [36/39] Loss: 0.00296 +Epoch [3911/4000] Training [37/39] Loss: 0.00484 +Epoch [3911/4000] Training [38/39] Loss: 0.12942 +Epoch [3911/4000] Training [39/39] Loss: 0.00471 +Epoch [3911/4000] Training metric {'Train/mean dice_metric': 0.9956440925598145, 'Train/mean miou_metric': 0.9925599098205566, 'Train/mean f1': 0.996973991394043, 'Train/mean precision': 0.9965223670005798, 'Train/mean recall': 0.9974260926246643, 'Train/mean hd95_metric': 0.9090047478675842} +Epoch [3911/4000] Validation [1/10] Loss: 0.71578 focal_loss 0.62898 dice_loss 0.08679 +Epoch [3911/4000] Validation [2/10] Loss: 0.50295 focal_loss 0.40523 dice_loss 0.09772 +Epoch [3911/4000] Validation [3/10] Loss: 0.39161 focal_loss 0.28053 dice_loss 0.11109 +Epoch [3911/4000] Validation [4/10] Loss: 0.89888 focal_loss 0.33309 dice_loss 0.56579 +Epoch [3911/4000] Validation [5/10] Loss: 3.05539 focal_loss 2.38137 dice_loss 0.67402 +Epoch [3911/4000] Validation [6/10] Loss: 1.34474 focal_loss 0.63210 dice_loss 0.71264 +Epoch [3911/4000] Validation [7/10] Loss: 1.18393 focal_loss 0.53040 dice_loss 0.65353 +Epoch [3911/4000] Validation [8/10] Loss: 2.37368 focal_loss 1.75934 dice_loss 0.61435 +Epoch [3911/4000] Validation [9/10] Loss: 1.53143 focal_loss 0.98717 dice_loss 0.54426 +Epoch [3911/4000] Validation [10/10] Loss: 1.91074 focal_loss 1.17571 dice_loss 0.73502 +Epoch [3911/4000] Validation metric {'Val/mean dice_metric': 0.9508686661720276, 'Val/mean miou_metric': 0.9350908994674683, 'Val/mean f1': 0.9485722184181213, 'Val/mean precision': 0.9436768889427185, 'Val/mean recall': 0.9535186290740967, 'Val/mean hd95_metric': 10.675860404968262} +Cheakpoint... +Epoch [3911/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508686661720276, 'Val/mean miou_metric': 0.9350908994674683, 'Val/mean f1': 0.9485722184181213, 'Val/mean precision': 0.9436768889427185, 'Val/mean recall': 0.9535186290740967, 'Val/mean hd95_metric': 10.675860404968262} +Epoch [3912/4000] Training [1/39] Loss: 0.12831 +Epoch [3912/4000] Training [2/39] Loss: 0.12936 +Epoch [3912/4000] Training [3/39] Loss: 0.00527 +Epoch [3912/4000] Training [4/39] Loss: 0.12802 +Epoch [3912/4000] Training [5/39] Loss: 0.00290 +Epoch [3912/4000] Training [6/39] Loss: 0.00455 +Epoch [3912/4000] Training [7/39] Loss: 0.12883 +Epoch [3912/4000] Training [8/39] Loss: 0.12848 +Epoch [3912/4000] Training [9/39] Loss: 0.12941 +Epoch [3912/4000] Training [10/39] Loss: 0.00476 +Epoch [3912/4000] Training [11/39] Loss: 0.00745 +Epoch [3912/4000] Training [12/39] Loss: 0.00535 +Epoch [3912/4000] Training [13/39] Loss: 0.00304 +Epoch [3912/4000] Training [14/39] Loss: 0.00380 +Epoch [3912/4000] Training [15/39] Loss: 0.00389 +Epoch [3912/4000] Training [16/39] Loss: 0.13047 +Epoch [3912/4000] Training [17/39] Loss: 0.12875 +Epoch [3912/4000] Training [18/39] Loss: 0.00461 +Epoch [3912/4000] Training [19/39] Loss: 0.00490 +Epoch [3912/4000] Training [20/39] Loss: 0.00471 +Epoch [3912/4000] Training [21/39] Loss: 0.00392 +Epoch [3912/4000] Training [22/39] Loss: 0.00590 +Epoch [3912/4000] Training [23/39] Loss: 0.00484 +Epoch [3912/4000] Training [24/39] Loss: 0.00407 +Epoch [3912/4000] Training [25/39] Loss: 0.00363 +Epoch [3912/4000] Training [26/39] Loss: 0.12996 +Epoch [3912/4000] Training [27/39] Loss: 0.12846 +Epoch [3912/4000] Training [28/39] Loss: 0.00464 +Epoch [3912/4000] Training [29/39] Loss: 0.00633 +Epoch [3912/4000] Training [30/39] Loss: 0.00817 +Epoch [3912/4000] Training [31/39] Loss: 0.13011 +Epoch [3912/4000] Training [32/39] Loss: 0.00418 +Epoch [3912/4000] Training [33/39] Loss: 0.00467 +Epoch [3912/4000] Training [34/39] Loss: 0.13003 +Epoch [3912/4000] Training [35/39] Loss: 0.12883 +Epoch [3912/4000] Training [36/39] Loss: 0.00471 +Epoch [3912/4000] Training [37/39] Loss: 0.00498 +Epoch [3912/4000] Training [38/39] Loss: 0.00621 +Epoch [3912/4000] Training [39/39] Loss: 0.12796 +Epoch [3912/4000] Training metric {'Train/mean dice_metric': 0.9965540170669556, 'Train/mean miou_metric': 0.9935471415519714, 'Train/mean f1': 0.9970207810401917, 'Train/mean precision': 0.9965445399284363, 'Train/mean recall': 0.9974974989891052, 'Train/mean hd95_metric': 0.9119542241096497} +Epoch [3912/4000] Validation [1/10] Loss: 0.71180 focal_loss 0.62558 dice_loss 0.08622 +Epoch [3912/4000] Validation [2/10] Loss: 0.50396 focal_loss 0.40456 dice_loss 0.09940 +Epoch [3912/4000] Validation [3/10] Loss: 0.39905 focal_loss 0.28738 dice_loss 0.11167 +Epoch [3912/4000] Validation [4/10] Loss: 0.89399 focal_loss 0.32884 dice_loss 0.56515 +Epoch [3912/4000] Validation [5/10] Loss: 3.05290 focal_loss 2.37887 dice_loss 0.67402 +Epoch [3912/4000] Validation [6/10] Loss: 1.33188 focal_loss 0.61968 dice_loss 0.71220 +Epoch [3912/4000] Validation [7/10] Loss: 1.17547 focal_loss 0.52220 dice_loss 0.65327 +Epoch [3912/4000] Validation [8/10] Loss: 2.39105 focal_loss 1.77239 dice_loss 0.61866 +Epoch [3912/4000] Validation [9/10] Loss: 1.52166 focal_loss 0.97769 dice_loss 0.54397 +Epoch [3912/4000] Validation [10/10] Loss: 1.88331 focal_loss 1.14939 dice_loss 0.73392 +Epoch [3912/4000] Validation metric {'Val/mean dice_metric': 0.9515355229377747, 'Val/mean miou_metric': 0.9358008503913879, 'Val/mean f1': 0.9487791061401367, 'Val/mean precision': 0.944627046585083, 'Val/mean recall': 0.9529677033424377, 'Val/mean hd95_metric': 10.793206214904785} +Cheakpoint... +Epoch [3912/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515355229377747, 'Val/mean miou_metric': 0.9358008503913879, 'Val/mean f1': 0.9487791061401367, 'Val/mean precision': 0.944627046585083, 'Val/mean recall': 0.9529677033424377, 'Val/mean hd95_metric': 10.793206214904785} +Epoch [3913/4000] Training [1/39] Loss: 0.00317 +Epoch [3913/4000] Training [2/39] Loss: 0.00508 +Epoch [3913/4000] Training [3/39] Loss: 0.00525 +Epoch [3913/4000] Training [4/39] Loss: 0.12917 +Epoch [3913/4000] Training [5/39] Loss: 0.00580 +Epoch [3913/4000] Training [6/39] Loss: 0.00444 +Epoch [3913/4000] Training [7/39] Loss: 0.12823 +Epoch [3913/4000] Training [8/39] Loss: 0.12837 +Epoch [3913/4000] Training [9/39] Loss: 0.12806 +Epoch [3913/4000] Training [10/39] Loss: 0.00432 +Epoch [3913/4000] Training [11/39] Loss: 0.00345 +Epoch [3913/4000] Training [12/39] Loss: 0.00595 +Epoch [3913/4000] Training [13/39] Loss: 0.12751 +Epoch [3913/4000] Training [14/39] Loss: 0.00441 +Epoch [3913/4000] Training [15/39] Loss: 0.00554 +Epoch [3913/4000] Training [16/39] Loss: 0.00441 +Epoch [3913/4000] Training [17/39] Loss: 0.00362 +Epoch [3913/4000] Training [18/39] Loss: 0.00436 +Epoch [3913/4000] Training [19/39] Loss: 0.12823 +Epoch [3913/4000] Training [20/39] Loss: 0.12726 +Epoch [3913/4000] Training [21/39] Loss: 0.00895 +Epoch [3913/4000] Training [22/39] Loss: 0.00521 +Epoch [3913/4000] Training [23/39] Loss: 0.00545 +Epoch [3913/4000] Training [24/39] Loss: 0.00417 +Epoch [3913/4000] Training [25/39] Loss: 0.00432 +Epoch [3913/4000] Training [26/39] Loss: 0.00302 +Epoch [3913/4000] Training [27/39] Loss: 0.12845 +Epoch [3913/4000] Training [28/39] Loss: 0.12964 +Epoch [3913/4000] Training [29/39] Loss: 0.21102 +Epoch [3913/4000] Training [30/39] Loss: 0.00392 +Epoch [3913/4000] Training [31/39] Loss: 0.00348 +Epoch [3913/4000] Training [32/39] Loss: 0.00513 +Epoch [3913/4000] Training [33/39] Loss: 0.00670 +Epoch [3913/4000] Training [34/39] Loss: 0.00298 +Epoch [3913/4000] Training [35/39] Loss: 0.00468 +Epoch [3913/4000] Training [36/39] Loss: 0.00481 +Epoch [3913/4000] Training [37/39] Loss: 0.00427 +Epoch [3913/4000] Training [38/39] Loss: 0.00375 +Epoch [3913/4000] Training [39/39] Loss: 0.12714 +Epoch [3913/4000] Training metric {'Train/mean dice_metric': 0.9966762065887451, 'Train/mean miou_metric': 0.9937968254089355, 'Train/mean f1': 0.9971350431442261, 'Train/mean precision': 0.9966540932655334, 'Train/mean recall': 0.9976165294647217, 'Train/mean hd95_metric': 0.8918753266334534} +Epoch [3913/4000] Validation [1/10] Loss: 0.71021 focal_loss 0.62431 dice_loss 0.08591 +Epoch [3913/4000] Validation [2/10] Loss: 0.50742 focal_loss 0.40769 dice_loss 0.09973 +Epoch [3913/4000] Validation [3/10] Loss: 0.39821 focal_loss 0.28658 dice_loss 0.11163 +Epoch [3913/4000] Validation [4/10] Loss: 0.89565 focal_loss 0.33061 dice_loss 0.56503 +Epoch [3913/4000] Validation [5/10] Loss: 3.06341 focal_loss 2.38928 dice_loss 0.67412 +Epoch [3913/4000] Validation [6/10] Loss: 1.33689 focal_loss 0.62473 dice_loss 0.71216 +Epoch [3913/4000] Validation [7/10] Loss: 1.17655 focal_loss 0.52406 dice_loss 0.65249 +Epoch [3913/4000] Validation [8/10] Loss: 2.40815 focal_loss 1.78900 dice_loss 0.61915 +Epoch [3913/4000] Validation [9/10] Loss: 1.52214 focal_loss 0.97809 dice_loss 0.54405 +Epoch [3913/4000] Validation [10/10] Loss: 1.88860 focal_loss 1.15454 dice_loss 0.73406 +Epoch [3913/4000] Validation metric {'Val/mean dice_metric': 0.951683521270752, 'Val/mean miou_metric': 0.9360764622688293, 'Val/mean f1': 0.9485575556755066, 'Val/mean precision': 0.9443491101264954, 'Val/mean recall': 0.9528035521507263, 'Val/mean hd95_metric': 10.717836380004883} +Cheakpoint... +Epoch [3913/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951683521270752, 'Val/mean miou_metric': 0.9360764622688293, 'Val/mean f1': 0.9485575556755066, 'Val/mean precision': 0.9443491101264954, 'Val/mean recall': 0.9528035521507263, 'Val/mean hd95_metric': 10.717836380004883} +Epoch [3914/4000] Training [1/39] Loss: 0.25480 +Epoch [3914/4000] Training [2/39] Loss: 0.00638 +Epoch [3914/4000] Training [3/39] Loss: 0.00411 +Epoch [3914/4000] Training [4/39] Loss: 0.00494 +Epoch [3914/4000] Training [5/39] Loss: 0.00274 +Epoch [3914/4000] Training [6/39] Loss: 0.00630 +Epoch [3914/4000] Training [7/39] Loss: 0.12932 +Epoch [3914/4000] Training [8/39] Loss: 0.00432 +Epoch [3914/4000] Training [9/39] Loss: 0.00295 +Epoch [3914/4000] Training [10/39] Loss: 0.00439 +Epoch [3914/4000] Training [11/39] Loss: 0.00478 +Epoch [3914/4000] Training [12/39] Loss: 0.00637 +Epoch [3914/4000] Training [13/39] Loss: 0.00293 +Epoch [3914/4000] Training [14/39] Loss: 0.00593 +Epoch [3914/4000] Training [15/39] Loss: 0.00619 +Epoch [3914/4000] Training [16/39] Loss: 0.00657 +Epoch [3914/4000] Training [17/39] Loss: 0.00676 +Epoch [3914/4000] Training [18/39] Loss: 0.00369 +Epoch [3914/4000] Training [19/39] Loss: 0.00486 +Epoch [3914/4000] Training [20/39] Loss: 0.00423 +Epoch [3914/4000] Training [21/39] Loss: 0.12743 +Epoch [3914/4000] Training [22/39] Loss: 0.00645 +Epoch [3914/4000] Training [23/39] Loss: 0.00414 +Epoch [3914/4000] Training [24/39] Loss: 0.00667 +Epoch [3914/4000] Training [25/39] Loss: 0.04392 +Epoch [3914/4000] Training [26/39] Loss: 0.00474 +Epoch [3914/4000] Training [27/39] Loss: 0.00495 +Epoch [3914/4000] Training [28/39] Loss: 0.00606 +Epoch [3914/4000] Training [29/39] Loss: 0.00417 +Epoch [3914/4000] Training [30/39] Loss: 0.00484 +Epoch [3914/4000] Training [31/39] Loss: 0.00278 +Epoch [3914/4000] Training [32/39] Loss: 0.08351 +Epoch [3914/4000] Training [33/39] Loss: 0.00496 +Epoch [3914/4000] Training [34/39] Loss: 0.00345 +Epoch [3914/4000] Training [35/39] Loss: 0.12962 +Epoch [3914/4000] Training [36/39] Loss: 0.12771 +Epoch [3914/4000] Training [37/39] Loss: 0.00655 +Epoch [3914/4000] Training [38/39] Loss: 0.00360 +Epoch [3914/4000] Training [39/39] Loss: 0.00513 +Epoch [3914/4000] Training metric {'Train/mean dice_metric': 0.9956432580947876, 'Train/mean miou_metric': 0.9925639629364014, 'Train/mean f1': 0.9970017671585083, 'Train/mean precision': 0.9965569376945496, 'Train/mean recall': 0.9974469542503357, 'Train/mean hd95_metric': 0.9244987964630127} +Epoch [3914/4000] Validation [1/10] Loss: 0.71723 focal_loss 0.63048 dice_loss 0.08676 +Epoch [3914/4000] Validation [2/10] Loss: 0.49757 focal_loss 0.39995 dice_loss 0.09762 +Epoch [3914/4000] Validation [3/10] Loss: 0.39339 focal_loss 0.28220 dice_loss 0.11119 +Epoch [3914/4000] Validation [4/10] Loss: 0.89301 focal_loss 0.32756 dice_loss 0.56545 +Epoch [3914/4000] Validation [5/10] Loss: 3.07125 focal_loss 2.39722 dice_loss 0.67404 +Epoch [3914/4000] Validation [6/10] Loss: 1.33294 focal_loss 0.62069 dice_loss 0.71225 +Epoch [3914/4000] Validation [7/10] Loss: 1.17309 focal_loss 0.51909 dice_loss 0.65400 +Epoch [3914/4000] Validation [8/10] Loss: 2.37004 focal_loss 1.75485 dice_loss 0.61519 +Epoch [3914/4000] Validation [9/10] Loss: 1.51904 focal_loss 0.97511 dice_loss 0.54393 +Epoch [3914/4000] Validation [10/10] Loss: 1.88615 focal_loss 1.15152 dice_loss 0.73463 +Epoch [3914/4000] Validation metric {'Val/mean dice_metric': 0.9507703185081482, 'Val/mean miou_metric': 0.9349663853645325, 'Val/mean f1': 0.948419988155365, 'Val/mean precision': 0.9437834024429321, 'Val/mean recall': 0.9531025886535645, 'Val/mean hd95_metric': 10.692852973937988} +Cheakpoint... +Epoch [3914/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507703185081482, 'Val/mean miou_metric': 0.9349663853645325, 'Val/mean f1': 0.948419988155365, 'Val/mean precision': 0.9437834024429321, 'Val/mean recall': 0.9531025886535645, 'Val/mean hd95_metric': 10.692852973937988} +Epoch [3915/4000] Training [1/39] Loss: 0.00717 +Epoch [3915/4000] Training [2/39] Loss: 0.00326 +Epoch [3915/4000] Training [3/39] Loss: 0.00663 +Epoch [3915/4000] Training [4/39] Loss: 0.00711 +Epoch [3915/4000] Training [5/39] Loss: 0.12943 +Epoch [3915/4000] Training [6/39] Loss: 0.12789 +Epoch [3915/4000] Training [7/39] Loss: 0.12854 +Epoch [3915/4000] Training [8/39] Loss: 0.00519 +Epoch [3915/4000] Training [9/39] Loss: 0.00503 +Epoch [3915/4000] Training [10/39] Loss: 0.00351 +Epoch [3915/4000] Training [11/39] Loss: 0.13627 +Epoch [3915/4000] Training [12/39] Loss: 0.00368 +Epoch [3915/4000] Training [13/39] Loss: 0.25300 +Epoch [3915/4000] Training [14/39] Loss: 0.12961 +Epoch [3915/4000] Training [15/39] Loss: 0.00364 +Epoch [3915/4000] Training [16/39] Loss: 0.12882 +Epoch [3915/4000] Training [17/39] Loss: 0.00647 +Epoch [3915/4000] Training [18/39] Loss: 0.00313 +Epoch [3915/4000] Training [19/39] Loss: 0.00324 +Epoch [3915/4000] Training [20/39] Loss: 0.00437 +Epoch [3915/4000] Training [21/39] Loss: 0.00454 +Epoch [3915/4000] Training [22/39] Loss: 0.00428 +Epoch [3915/4000] Training [23/39] Loss: 0.00538 +Epoch [3915/4000] Training [24/39] Loss: 0.00651 +Epoch [3915/4000] Training [25/39] Loss: 0.12955 +Epoch [3915/4000] Training [26/39] Loss: 0.12895 +Epoch [3915/4000] Training [27/39] Loss: 0.13200 +Epoch [3915/4000] Training [28/39] Loss: 0.00350 +Epoch [3915/4000] Training [29/39] Loss: 0.00443 +Epoch [3915/4000] Training [30/39] Loss: 0.00462 +Epoch [3915/4000] Training [31/39] Loss: 0.00340 +Epoch [3915/4000] Training [32/39] Loss: 0.00281 +Epoch [3915/4000] Training [33/39] Loss: 0.00422 +Epoch [3915/4000] Training [34/39] Loss: 0.00623 +Epoch [3915/4000] Training [35/39] Loss: 0.00456 +Epoch [3915/4000] Training [36/39] Loss: 0.00428 +Epoch [3915/4000] Training [37/39] Loss: 0.00485 +Epoch [3915/4000] Training [38/39] Loss: 0.00424 +Epoch [3915/4000] Training [39/39] Loss: 0.00502 +Epoch [3915/4000] Training metric {'Train/mean dice_metric': 0.9963782429695129, 'Train/mean miou_metric': 0.993213415145874, 'Train/mean f1': 0.9968748688697815, 'Train/mean precision': 0.9964362978935242, 'Train/mean recall': 0.9973139762878418, 'Train/mean hd95_metric': 0.9387620687484741} +Epoch [3915/4000] Validation [1/10] Loss: 0.71199 focal_loss 0.62557 dice_loss 0.08641 +Epoch [3915/4000] Validation [2/10] Loss: 0.50339 focal_loss 0.40436 dice_loss 0.09903 +Epoch [3915/4000] Validation [3/10] Loss: 0.39546 focal_loss 0.28394 dice_loss 0.11152 +Epoch [3915/4000] Validation [4/10] Loss: 0.89492 focal_loss 0.32939 dice_loss 0.56553 +Epoch [3915/4000] Validation [5/10] Loss: 3.06134 focal_loss 2.38726 dice_loss 0.67408 +Epoch [3915/4000] Validation [6/10] Loss: 1.33516 focal_loss 0.62292 dice_loss 0.71224 +Epoch [3915/4000] Validation [7/10] Loss: 1.17374 focal_loss 0.52060 dice_loss 0.65314 +Epoch [3915/4000] Validation [8/10] Loss: 2.37531 focal_loss 1.75873 dice_loss 0.61658 +Epoch [3915/4000] Validation [9/10] Loss: 1.52201 focal_loss 0.97785 dice_loss 0.54416 +Epoch [3915/4000] Validation [10/10] Loss: 1.88680 focal_loss 1.15222 dice_loss 0.73458 +Epoch [3915/4000] Validation metric {'Val/mean dice_metric': 0.9514517784118652, 'Val/mean miou_metric': 0.935600996017456, 'Val/mean f1': 0.9483200907707214, 'Val/mean precision': 0.9438207745552063, 'Val/mean recall': 0.9528626203536987, 'Val/mean hd95_metric': 10.813182830810547} +Cheakpoint... +Epoch [3915/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514517784118652, 'Val/mean miou_metric': 0.935600996017456, 'Val/mean f1': 0.9483200907707214, 'Val/mean precision': 0.9438207745552063, 'Val/mean recall': 0.9528626203536987, 'Val/mean hd95_metric': 10.813182830810547} +Epoch [3916/4000] Training [1/39] Loss: 0.00374 +Epoch [3916/4000] Training [2/39] Loss: 0.12793 +Epoch [3916/4000] Training [3/39] Loss: 0.00450 +Epoch [3916/4000] Training [4/39] Loss: 0.00354 +Epoch [3916/4000] Training [5/39] Loss: 0.00422 +Epoch [3916/4000] Training [6/39] Loss: 0.00447 +Epoch [3916/4000] Training [7/39] Loss: 0.00806 +Epoch [3916/4000] Training [8/39] Loss: 0.00370 +Epoch [3916/4000] Training [9/39] Loss: 0.00409 +Epoch [3916/4000] Training [10/39] Loss: 0.00303 +Epoch [3916/4000] Training [11/39] Loss: 0.00383 +Epoch [3916/4000] Training [12/39] Loss: 0.00441 +Epoch [3916/4000] Training [13/39] Loss: 0.00498 +Epoch [3916/4000] Training [14/39] Loss: 0.00530 +Epoch [3916/4000] Training [15/39] Loss: 0.00438 +Epoch [3916/4000] Training [16/39] Loss: 0.00553 +Epoch [3916/4000] Training [17/39] Loss: 0.12785 +Epoch [3916/4000] Training [18/39] Loss: 0.12880 +Epoch [3916/4000] Training [19/39] Loss: 0.00661 +Epoch [3916/4000] Training [20/39] Loss: 0.00532 +Epoch [3916/4000] Training [21/39] Loss: 0.00362 +Epoch [3916/4000] Training [22/39] Loss: 0.13030 +Epoch [3916/4000] Training [23/39] Loss: 0.00454 +Epoch [3916/4000] Training [24/39] Loss: 0.00496 +Epoch [3916/4000] Training [25/39] Loss: 0.00444 +Epoch [3916/4000] Training [26/39] Loss: 0.12860 +Epoch [3916/4000] Training [27/39] Loss: 0.00507 +Epoch [3916/4000] Training [28/39] Loss: 0.00420 +Epoch [3916/4000] Training [29/39] Loss: 0.00836 +Epoch [3916/4000] Training [30/39] Loss: 0.00512 +Epoch [3916/4000] Training [31/39] Loss: 0.00568 +Epoch [3916/4000] Training [32/39] Loss: 0.00421 +Epoch [3916/4000] Training [33/39] Loss: 0.00450 +Epoch [3916/4000] Training [34/39] Loss: 0.00334 +Epoch [3916/4000] Training [35/39] Loss: 0.00437 +Epoch [3916/4000] Training [36/39] Loss: 0.00999 +Epoch [3916/4000] Training [37/39] Loss: 0.00376 +Epoch [3916/4000] Training [38/39] Loss: 0.00498 +Epoch [3916/4000] Training [39/39] Loss: 0.12801 +Epoch [3916/4000] Training metric {'Train/mean dice_metric': 0.9965071082115173, 'Train/mean miou_metric': 0.9934714436531067, 'Train/mean f1': 0.9970360398292542, 'Train/mean precision': 0.9965884685516357, 'Train/mean recall': 0.997484028339386, 'Train/mean hd95_metric': 0.915228545665741} +Epoch [3916/4000] Validation [1/10] Loss: 0.71270 focal_loss 0.62653 dice_loss 0.08617 +Epoch [3916/4000] Validation [2/10] Loss: 0.50461 focal_loss 0.40512 dice_loss 0.09949 +Epoch [3916/4000] Validation [3/10] Loss: 0.39632 focal_loss 0.28478 dice_loss 0.11154 +Epoch [3916/4000] Validation [4/10] Loss: 0.89301 focal_loss 0.32801 dice_loss 0.56499 +Epoch [3916/4000] Validation [5/10] Loss: 3.07849 focal_loss 2.40439 dice_loss 0.67410 +Epoch [3916/4000] Validation [6/10] Loss: 1.33089 focal_loss 0.61854 dice_loss 0.71235 +Epoch [3916/4000] Validation [7/10] Loss: 1.17279 focal_loss 0.52018 dice_loss 0.65261 +Epoch [3916/4000] Validation [8/10] Loss: 2.39287 focal_loss 1.77428 dice_loss 0.61859 +Epoch [3916/4000] Validation [9/10] Loss: 1.51734 focal_loss 0.97356 dice_loss 0.54378 +Epoch [3916/4000] Validation [10/10] Loss: 1.88252 focal_loss 1.14827 dice_loss 0.73425 +Epoch [3916/4000] Validation metric {'Val/mean dice_metric': 0.9515138864517212, 'Val/mean miou_metric': 0.935768723487854, 'Val/mean f1': 0.9485089778900146, 'Val/mean precision': 0.9442628026008606, 'Val/mean recall': 0.9527934193611145, 'Val/mean hd95_metric': 10.747013092041016} +Cheakpoint... +Epoch [3916/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515138864517212, 'Val/mean miou_metric': 0.935768723487854, 'Val/mean f1': 0.9485089778900146, 'Val/mean precision': 0.9442628026008606, 'Val/mean recall': 0.9527934193611145, 'Val/mean hd95_metric': 10.747013092041016} +Epoch [3917/4000] Training [1/39] Loss: 0.00279 +Epoch [3917/4000] Training [2/39] Loss: 0.00544 +Epoch [3917/4000] Training [3/39] Loss: 0.00329 +Epoch [3917/4000] Training [4/39] Loss: 0.00322 +Epoch [3917/4000] Training [5/39] Loss: 0.00408 +Epoch [3917/4000] Training [6/39] Loss: 0.00445 +Epoch [3917/4000] Training [7/39] Loss: 0.12795 +Epoch [3917/4000] Training [8/39] Loss: 0.00382 +Epoch [3917/4000] Training [9/39] Loss: 0.00542 +Epoch [3917/4000] Training [10/39] Loss: 0.00769 +Epoch [3917/4000] Training [11/39] Loss: 0.00455 +Epoch [3917/4000] Training [12/39] Loss: 0.00580 +Epoch [3917/4000] Training [13/39] Loss: 0.00347 +Epoch [3917/4000] Training [14/39] Loss: 0.12825 +Epoch [3917/4000] Training [15/39] Loss: 0.12807 +Epoch [3917/4000] Training [16/39] Loss: 0.12935 +Epoch [3917/4000] Training [17/39] Loss: 0.12797 +Epoch [3917/4000] Training [18/39] Loss: 0.00597 +Epoch [3917/4000] Training [19/39] Loss: 0.00507 +Epoch [3917/4000] Training [20/39] Loss: 0.00464 +Epoch [3917/4000] Training [21/39] Loss: 0.00458 +Epoch [3917/4000] Training [22/39] Loss: 0.00318 +Epoch [3917/4000] Training [23/39] Loss: 0.12930 +Epoch [3917/4000] Training [24/39] Loss: 0.00722 +Epoch [3917/4000] Training [25/39] Loss: 0.00475 +Epoch [3917/4000] Training [26/39] Loss: 0.00436 +Epoch [3917/4000] Training [27/39] Loss: 0.13004 +Epoch [3917/4000] Training [28/39] Loss: 0.00360 +Epoch [3917/4000] Training [29/39] Loss: 0.00501 +Epoch [3917/4000] Training [30/39] Loss: 0.00495 +Epoch [3917/4000] Training [31/39] Loss: 0.00305 +Epoch [3917/4000] Training [32/39] Loss: 0.12809 +Epoch [3917/4000] Training [33/39] Loss: 0.00468 +Epoch [3917/4000] Training [34/39] Loss: 0.00573 +Epoch [3917/4000] Training [35/39] Loss: 0.13030 +Epoch [3917/4000] Training [36/39] Loss: 0.00395 +Epoch [3917/4000] Training [37/39] Loss: 0.00727 +Epoch [3917/4000] Training [38/39] Loss: 0.00603 +Epoch [3917/4000] Training [39/39] Loss: 0.00550 +Epoch [3917/4000] Training metric {'Train/mean dice_metric': 0.9965238571166992, 'Train/mean miou_metric': 0.993488609790802, 'Train/mean f1': 0.9969958066940308, 'Train/mean precision': 0.9965407848358154, 'Train/mean recall': 0.9974511861801147, 'Train/mean hd95_metric': 0.8837894797325134} +Epoch [3917/4000] Validation [1/10] Loss: 0.73009 focal_loss 0.64372 dice_loss 0.08637 +Epoch [3917/4000] Validation [2/10] Loss: 0.51089 focal_loss 0.41110 dice_loss 0.09979 +Epoch [3917/4000] Validation [3/10] Loss: 0.40905 focal_loss 0.29716 dice_loss 0.11189 +Epoch [3917/4000] Validation [4/10] Loss: 0.89643 focal_loss 0.33125 dice_loss 0.56518 +Epoch [3917/4000] Validation [5/10] Loss: 3.13730 focal_loss 2.46319 dice_loss 0.67411 +Epoch [3917/4000] Validation [6/10] Loss: 1.33374 focal_loss 0.62209 dice_loss 0.71165 +Epoch [3917/4000] Validation [7/10] Loss: 1.17643 focal_loss 0.52298 dice_loss 0.65345 +Epoch [3917/4000] Validation [8/10] Loss: 2.43207 focal_loss 1.81252 dice_loss 0.61954 +Epoch [3917/4000] Validation [9/10] Loss: 1.53848 focal_loss 0.99483 dice_loss 0.54365 +Epoch [3917/4000] Validation [10/10] Loss: 1.89042 focal_loss 1.15632 dice_loss 0.73410 +Epoch [3917/4000] Validation metric {'Val/mean dice_metric': 0.9515324831008911, 'Val/mean miou_metric': 0.9357778429985046, 'Val/mean f1': 0.9484711289405823, 'Val/mean precision': 0.9444752931594849, 'Val/mean recall': 0.9525010585784912, 'Val/mean hd95_metric': 10.75796127319336} +Cheakpoint... +Epoch [3917/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515324831008911, 'Val/mean miou_metric': 0.9357778429985046, 'Val/mean f1': 0.9484711289405823, 'Val/mean precision': 0.9444752931594849, 'Val/mean recall': 0.9525010585784912, 'Val/mean hd95_metric': 10.75796127319336} +Epoch [3918/4000] Training [1/39] Loss: 0.00336 +Epoch [3918/4000] Training [2/39] Loss: 0.00519 +Epoch [3918/4000] Training [3/39] Loss: 0.00503 +Epoch [3918/4000] Training [4/39] Loss: 0.13060 +Epoch [3918/4000] Training [5/39] Loss: 0.00262 +Epoch [3918/4000] Training [6/39] Loss: 0.00528 +Epoch [3918/4000] Training [7/39] Loss: 0.00391 +Epoch [3918/4000] Training [8/39] Loss: 0.12776 +Epoch [3918/4000] Training [9/39] Loss: 0.00472 +Epoch [3918/4000] Training [10/39] Loss: 0.12846 +Epoch [3918/4000] Training [11/39] Loss: 0.00361 +Epoch [3918/4000] Training [12/39] Loss: 0.00363 +Epoch [3918/4000] Training [13/39] Loss: 0.00631 +Epoch [3918/4000] Training [14/39] Loss: 0.00452 +Epoch [3918/4000] Training [15/39] Loss: 0.00270 +Epoch [3918/4000] Training [16/39] Loss: 0.00563 +Epoch [3918/4000] Training [17/39] Loss: 0.00800 +Epoch [3918/4000] Training [18/39] Loss: 0.08455 +Epoch [3918/4000] Training [19/39] Loss: 0.00661 +Epoch [3918/4000] Training [20/39] Loss: 0.12957 +Epoch [3918/4000] Training [21/39] Loss: 0.00422 +Epoch [3918/4000] Training [22/39] Loss: 0.00822 +Epoch [3918/4000] Training [23/39] Loss: 0.00854 +Epoch [3918/4000] Training [24/39] Loss: 0.00344 +Epoch [3918/4000] Training [25/39] Loss: 0.00449 +Epoch [3918/4000] Training [26/39] Loss: 0.00716 +Epoch [3918/4000] Training [27/39] Loss: 0.12766 +Epoch [3918/4000] Training [28/39] Loss: 0.00478 +Epoch [3918/4000] Training [29/39] Loss: 0.00494 +Epoch [3918/4000] Training [30/39] Loss: 0.12872 +Epoch [3918/4000] Training [31/39] Loss: 0.00556 +Epoch [3918/4000] Training [32/39] Loss: 0.13089 +Epoch [3918/4000] Training [33/39] Loss: 0.00386 +Epoch [3918/4000] Training [34/39] Loss: 0.00495 +Epoch [3918/4000] Training [35/39] Loss: 0.00325 +Epoch [3918/4000] Training [36/39] Loss: 0.00419 +Epoch [3918/4000] Training [37/39] Loss: 0.00406 +Epoch [3918/4000] Training [38/39] Loss: 0.00560 +Epoch [3918/4000] Training [39/39] Loss: 0.12838 +Epoch [3918/4000] Training metric {'Train/mean dice_metric': 0.9965078234672546, 'Train/mean miou_metric': 0.9934641718864441, 'Train/mean f1': 0.9969820380210876, 'Train/mean precision': 0.9965125322341919, 'Train/mean recall': 0.9974520206451416, 'Train/mean hd95_metric': 0.9183951020240784} +Epoch [3918/4000] Validation [1/10] Loss: 0.71031 focal_loss 0.62406 dice_loss 0.08626 +Epoch [3918/4000] Validation [2/10] Loss: 0.50429 focal_loss 0.40570 dice_loss 0.09859 +Epoch [3918/4000] Validation [3/10] Loss: 0.39291 focal_loss 0.28151 dice_loss 0.11139 +Epoch [3918/4000] Validation [4/10] Loss: 0.89539 focal_loss 0.33014 dice_loss 0.56526 +Epoch [3918/4000] Validation [5/10] Loss: 3.04214 focal_loss 2.36808 dice_loss 0.67406 +Epoch [3918/4000] Validation [6/10] Loss: 1.33783 focal_loss 0.62546 dice_loss 0.71236 +Epoch [3918/4000] Validation [7/10] Loss: 1.17838 focal_loss 0.52452 dice_loss 0.65385 +Epoch [3918/4000] Validation [8/10] Loss: 2.39093 focal_loss 1.77328 dice_loss 0.61765 +Epoch [3918/4000] Validation [9/10] Loss: 1.52227 focal_loss 0.97829 dice_loss 0.54398 +Epoch [3918/4000] Validation [10/10] Loss: 1.89475 focal_loss 1.16019 dice_loss 0.73456 +Epoch [3918/4000] Validation metric {'Val/mean dice_metric': 0.951557457447052, 'Val/mean miou_metric': 0.9358075261116028, 'Val/mean f1': 0.9484252333641052, 'Val/mean precision': 0.9439022541046143, 'Val/mean recall': 0.9529917240142822, 'Val/mean hd95_metric': 10.672521591186523} +Cheakpoint... +Epoch [3918/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951557457447052, 'Val/mean miou_metric': 0.9358075261116028, 'Val/mean f1': 0.9484252333641052, 'Val/mean precision': 0.9439022541046143, 'Val/mean recall': 0.9529917240142822, 'Val/mean hd95_metric': 10.672521591186523} +Epoch [3919/4000] Training [1/39] Loss: 0.12808 +Epoch [3919/4000] Training [2/39] Loss: 0.12857 +Epoch [3919/4000] Training [3/39] Loss: 0.00352 +Epoch [3919/4000] Training [4/39] Loss: 0.00347 +Epoch [3919/4000] Training [5/39] Loss: 0.00469 +Epoch [3919/4000] Training [6/39] Loss: 0.00242 +Epoch [3919/4000] Training [7/39] Loss: 0.00474 +Epoch [3919/4000] Training [8/39] Loss: 0.00456 +Epoch [3919/4000] Training [9/39] Loss: 0.00748 +Epoch [3919/4000] Training [10/39] Loss: 0.00391 +Epoch [3919/4000] Training [11/39] Loss: 0.00425 +Epoch [3919/4000] Training [12/39] Loss: 0.00392 +Epoch [3919/4000] Training [13/39] Loss: 0.12961 +Epoch [3919/4000] Training [14/39] Loss: 0.00670 +Epoch [3919/4000] Training [15/39] Loss: 0.00665 +Epoch [3919/4000] Training [16/39] Loss: 0.12726 +Epoch [3919/4000] Training [17/39] Loss: 0.00602 +Epoch [3919/4000] Training [18/39] Loss: 0.25298 +Epoch [3919/4000] Training [19/39] Loss: 0.00593 +Epoch [3919/4000] Training [20/39] Loss: 0.00561 +Epoch [3919/4000] Training [21/39] Loss: 0.00287 +Epoch [3919/4000] Training [22/39] Loss: 0.00902 +Epoch [3919/4000] Training [23/39] Loss: 0.00379 +Epoch [3919/4000] Training [24/39] Loss: 0.00565 +Epoch [3919/4000] Training [25/39] Loss: 0.00901 +Epoch [3919/4000] Training [26/39] Loss: 0.00365 +Epoch [3919/4000] Training [27/39] Loss: 0.00690 +Epoch [3919/4000] Training [28/39] Loss: 0.00430 +Epoch [3919/4000] Training [29/39] Loss: 0.00617 +Epoch [3919/4000] Training [30/39] Loss: 0.00515 +Epoch [3919/4000] Training [31/39] Loss: 0.00413 +Epoch [3919/4000] Training [32/39] Loss: 0.12831 +Epoch [3919/4000] Training [33/39] Loss: 0.00529 +Epoch [3919/4000] Training [34/39] Loss: 0.00402 +Epoch [3919/4000] Training [35/39] Loss: 0.00452 +Epoch [3919/4000] Training [36/39] Loss: 0.00448 +Epoch [3919/4000] Training [37/39] Loss: 0.00361 +Epoch [3919/4000] Training [38/39] Loss: 0.00327 +Epoch [3919/4000] Training [39/39] Loss: 0.00536 +Epoch [3919/4000] Training metric {'Train/mean dice_metric': 0.9964114427566528, 'Train/mean miou_metric': 0.9932747483253479, 'Train/mean f1': 0.9968361258506775, 'Train/mean precision': 0.9963749647140503, 'Train/mean recall': 0.9972978830337524, 'Train/mean hd95_metric': 1.003292441368103} +Epoch [3919/4000] Validation [1/10] Loss: 0.71903 focal_loss 0.63273 dice_loss 0.08630 +Epoch [3919/4000] Validation [2/10] Loss: 0.50379 focal_loss 0.40483 dice_loss 0.09896 +Epoch [3919/4000] Validation [3/10] Loss: 0.40001 focal_loss 0.28831 dice_loss 0.11170 +Epoch [3919/4000] Validation [4/10] Loss: 0.89413 focal_loss 0.32899 dice_loss 0.56514 +Epoch [3919/4000] Validation [5/10] Loss: 3.09017 focal_loss 2.41607 dice_loss 0.67410 +Epoch [3919/4000] Validation [6/10] Loss: 1.33197 focal_loss 0.61989 dice_loss 0.71208 +Epoch [3919/4000] Validation [7/10] Loss: 1.17521 focal_loss 0.52167 dice_loss 0.65353 +Epoch [3919/4000] Validation [8/10] Loss: 2.40457 focal_loss 1.78618 dice_loss 0.61839 +Epoch [3919/4000] Validation [9/10] Loss: 1.52568 focal_loss 0.98197 dice_loss 0.54372 +Epoch [3919/4000] Validation [10/10] Loss: 1.88537 focal_loss 1.15098 dice_loss 0.73439 +Epoch [3919/4000] Validation metric {'Val/mean dice_metric': 0.9514381885528564, 'Val/mean miou_metric': 0.9356037378311157, 'Val/mean f1': 0.9482004642486572, 'Val/mean precision': 0.9438799619674683, 'Val/mean recall': 0.9525607228279114, 'Val/mean hd95_metric': 10.875690460205078} +Cheakpoint... +Epoch [3919/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514381885528564, 'Val/mean miou_metric': 0.9356037378311157, 'Val/mean f1': 0.9482004642486572, 'Val/mean precision': 0.9438799619674683, 'Val/mean recall': 0.9525607228279114, 'Val/mean hd95_metric': 10.875690460205078} +Epoch [3920/4000] Training [1/39] Loss: 0.00767 +Epoch [3920/4000] Training [2/39] Loss: 0.00697 +Epoch [3920/4000] Training [3/39] Loss: 0.00527 +Epoch [3920/4000] Training [4/39] Loss: 0.13163 +Epoch [3920/4000] Training [5/39] Loss: 0.00649 +Epoch [3920/4000] Training [6/39] Loss: 0.00359 +Epoch [3920/4000] Training [7/39] Loss: 0.12771 +Epoch [3920/4000] Training [8/39] Loss: 0.12876 +Epoch [3920/4000] Training [9/39] Loss: 0.00753 +Epoch [3920/4000] Training [10/39] Loss: 0.00640 +Epoch [3920/4000] Training [11/39] Loss: 0.00396 +Epoch [3920/4000] Training [12/39] Loss: 0.13039 +Epoch [3920/4000] Training [13/39] Loss: 0.00450 +Epoch [3920/4000] Training [14/39] Loss: 0.00449 +Epoch [3920/4000] Training [15/39] Loss: 0.00407 +Epoch [3920/4000] Training [16/39] Loss: 0.12780 +Epoch [3920/4000] Training [17/39] Loss: 0.00451 +Epoch [3920/4000] Training [18/39] Loss: 0.01010 +Epoch [3920/4000] Training [19/39] Loss: 0.00469 +Epoch [3920/4000] Training [20/39] Loss: 0.00519 +Epoch [3920/4000] Training [21/39] Loss: 0.12834 +Epoch [3920/4000] Training [22/39] Loss: 0.25310 +Epoch [3920/4000] Training [23/39] Loss: 0.13013 +Epoch [3920/4000] Training [24/39] Loss: 0.00446 +Epoch [3920/4000] Training [25/39] Loss: 0.00579 +Epoch [3920/4000] Training [26/39] Loss: 0.00588 +Epoch [3920/4000] Training [27/39] Loss: 0.00430 +Epoch [3920/4000] Training [28/39] Loss: 0.00416 +Epoch [3920/4000] Training [29/39] Loss: 0.00368 +Epoch [3920/4000] Training [30/39] Loss: 0.00477 +Epoch [3920/4000] Training [31/39] Loss: 0.00363 +Epoch [3920/4000] Training [32/39] Loss: 0.00407 +Epoch [3920/4000] Training [33/39] Loss: 0.00627 +Epoch [3920/4000] Training [34/39] Loss: 0.00298 +Epoch [3920/4000] Training [35/39] Loss: 0.12959 +Epoch [3920/4000] Training [36/39] Loss: 0.00765 +Epoch [3920/4000] Training [37/39] Loss: 0.00450 +Epoch [3920/4000] Training [38/39] Loss: 0.00470 +Epoch [3920/4000] Training [39/39] Loss: 0.00360 +Epoch [3920/4000] Training metric {'Train/mean dice_metric': 0.9964582324028015, 'Train/mean miou_metric': 0.9933658838272095, 'Train/mean f1': 0.9970663189888, 'Train/mean precision': 0.9966058731079102, 'Train/mean recall': 0.9975271224975586, 'Train/mean hd95_metric': 0.9818463325500488} +Epoch [3920/4000] Validation [1/10] Loss: 0.70372 focal_loss 0.61794 dice_loss 0.08579 +Epoch [3920/4000] Validation [2/10] Loss: 0.50349 focal_loss 0.40368 dice_loss 0.09981 +Epoch [3920/4000] Validation [3/10] Loss: 0.39497 focal_loss 0.28319 dice_loss 0.11179 +Epoch [3920/4000] Validation [4/10] Loss: 0.89168 focal_loss 0.32683 dice_loss 0.56485 +Epoch [3920/4000] Validation [5/10] Loss: 3.03699 focal_loss 2.36292 dice_loss 0.67407 +Epoch [3920/4000] Validation [6/10] Loss: 1.32876 focal_loss 0.61663 dice_loss 0.71213 +Epoch [3920/4000] Validation [7/10] Loss: 1.17052 focal_loss 0.51742 dice_loss 0.65310 +Epoch [3920/4000] Validation [8/10] Loss: 2.38285 focal_loss 1.76344 dice_loss 0.61942 +Epoch [3920/4000] Validation [9/10] Loss: 1.50934 focal_loss 0.96546 dice_loss 0.54387 +Epoch [3920/4000] Validation [10/10] Loss: 1.87611 focal_loss 1.14190 dice_loss 0.73421 +Epoch [3920/4000] Validation metric {'Val/mean dice_metric': 0.9514784812927246, 'Val/mean miou_metric': 0.9356844425201416, 'Val/mean f1': 0.9486314654350281, 'Val/mean precision': 0.9444356560707092, 'Val/mean recall': 0.9528645873069763, 'Val/mean hd95_metric': 10.841087341308594} +Cheakpoint... +Epoch [3920/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514784812927246, 'Val/mean miou_metric': 0.9356844425201416, 'Val/mean f1': 0.9486314654350281, 'Val/mean precision': 0.9444356560707092, 'Val/mean recall': 0.9528645873069763, 'Val/mean hd95_metric': 10.841087341308594} +Epoch [3921/4000] Training [1/39] Loss: 0.00319 +Epoch [3921/4000] Training [2/39] Loss: 0.13026 +Epoch [3921/4000] Training [3/39] Loss: 0.00663 +Epoch [3921/4000] Training [4/39] Loss: 0.00477 +Epoch [3921/4000] Training [5/39] Loss: 0.00427 +Epoch [3921/4000] Training [6/39] Loss: 0.13061 +Epoch [3921/4000] Training [7/39] Loss: 0.25478 +Epoch [3921/4000] Training [8/39] Loss: 0.00442 +Epoch [3921/4000] Training [9/39] Loss: 0.00724 +Epoch [3921/4000] Training [10/39] Loss: 0.00432 +Epoch [3921/4000] Training [11/39] Loss: 0.00344 +Epoch [3921/4000] Training [12/39] Loss: 0.12791 +Epoch [3921/4000] Training [13/39] Loss: 0.00481 +Epoch [3921/4000] Training [14/39] Loss: 0.12817 +Epoch [3921/4000] Training [15/39] Loss: 0.12956 +Epoch [3921/4000] Training [16/39] Loss: 0.00425 +Epoch [3921/4000] Training [17/39] Loss: 0.13195 +Epoch [3921/4000] Training [18/39] Loss: 0.00534 +Epoch [3921/4000] Training [19/39] Loss: 0.00433 +Epoch [3921/4000] Training [20/39] Loss: 0.00654 +Epoch [3921/4000] Training [21/39] Loss: 0.12998 +Epoch [3921/4000] Training [22/39] Loss: 0.12697 +Epoch [3921/4000] Training [23/39] Loss: 0.00480 +Epoch [3921/4000] Training [24/39] Loss: 0.00395 +Epoch [3921/4000] Training [25/39] Loss: 0.00455 +Epoch [3921/4000] Training [26/39] Loss: 0.00399 +Epoch [3921/4000] Training [27/39] Loss: 0.13014 +Epoch [3921/4000] Training [28/39] Loss: 0.00482 +Epoch [3921/4000] Training [29/39] Loss: 0.00520 +Epoch [3921/4000] Training [30/39] Loss: 0.00817 +Epoch [3921/4000] Training [31/39] Loss: 0.00325 +Epoch [3921/4000] Training [32/39] Loss: 0.00497 +Epoch [3921/4000] Training [33/39] Loss: 0.00725 +Epoch [3921/4000] Training [34/39] Loss: 0.00653 +Epoch [3921/4000] Training [35/39] Loss: 0.00407 +Epoch [3921/4000] Training [36/39] Loss: 0.12914 +Epoch [3921/4000] Training [37/39] Loss: 0.00529 +Epoch [3921/4000] Training [38/39] Loss: 0.12903 +Epoch [3921/4000] Training [39/39] Loss: 0.00367 +Epoch [3921/4000] Training metric {'Train/mean dice_metric': 0.9964066743850708, 'Train/mean miou_metric': 0.9932546019554138, 'Train/mean f1': 0.9968262910842896, 'Train/mean precision': 0.9962950348854065, 'Train/mean recall': 0.9973580837249756, 'Train/mean hd95_metric': 0.9155001640319824} +Epoch [3921/4000] Validation [1/10] Loss: 0.71044 focal_loss 0.62479 dice_loss 0.08566 +Epoch [3921/4000] Validation [2/10] Loss: 0.50706 focal_loss 0.40717 dice_loss 0.09989 +Epoch [3921/4000] Validation [3/10] Loss: 0.40127 focal_loss 0.28953 dice_loss 0.11174 +Epoch [3921/4000] Validation [4/10] Loss: 0.89258 focal_loss 0.32774 dice_loss 0.56484 +Epoch [3921/4000] Validation [5/10] Loss: 3.07168 focal_loss 2.39758 dice_loss 0.67410 +Epoch [3921/4000] Validation [6/10] Loss: 1.33400 focal_loss 0.62193 dice_loss 0.71208 +Epoch [3921/4000] Validation [7/10] Loss: 1.17776 focal_loss 0.52509 dice_loss 0.65267 +Epoch [3921/4000] Validation [8/10] Loss: 2.41821 focal_loss 1.79904 dice_loss 0.61917 +Epoch [3921/4000] Validation [9/10] Loss: 1.52149 focal_loss 0.97752 dice_loss 0.54397 +Epoch [3921/4000] Validation [10/10] Loss: 1.88834 focal_loss 1.15405 dice_loss 0.73429 +Epoch [3921/4000] Validation metric {'Val/mean dice_metric': 0.9514673948287964, 'Val/mean miou_metric': 0.9356349110603333, 'Val/mean f1': 0.9483151435852051, 'Val/mean precision': 0.9440826773643494, 'Val/mean recall': 0.9525858759880066, 'Val/mean hd95_metric': 10.730753898620605} +Cheakpoint... +Epoch [3921/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514673948287964, 'Val/mean miou_metric': 0.9356349110603333, 'Val/mean f1': 0.9483151435852051, 'Val/mean precision': 0.9440826773643494, 'Val/mean recall': 0.9525858759880066, 'Val/mean hd95_metric': 10.730753898620605} +Epoch [3922/4000] Training [1/39] Loss: 0.00454 +Epoch [3922/4000] Training [2/39] Loss: 0.00537 +Epoch [3922/4000] Training [3/39] Loss: 0.00479 +Epoch [3922/4000] Training [4/39] Loss: 0.00898 +Epoch [3922/4000] Training [5/39] Loss: 0.00721 +Epoch [3922/4000] Training [6/39] Loss: 0.00415 +Epoch [3922/4000] Training [7/39] Loss: 0.00581 +Epoch [3922/4000] Training [8/39] Loss: 0.00277 +Epoch [3922/4000] Training [9/39] Loss: 0.00578 +Epoch [3922/4000] Training [10/39] Loss: 0.12851 +Epoch [3922/4000] Training [11/39] Loss: 0.00528 +Epoch [3922/4000] Training [12/39] Loss: 0.00470 +Epoch [3922/4000] Training [13/39] Loss: 0.00611 +Epoch [3922/4000] Training [14/39] Loss: 0.00572 +Epoch [3922/4000] Training [15/39] Loss: 0.00467 +Epoch [3922/4000] Training [16/39] Loss: 0.00433 +Epoch [3922/4000] Training [17/39] Loss: 0.00369 +Epoch [3922/4000] Training [18/39] Loss: 0.00290 +Epoch [3922/4000] Training [19/39] Loss: 0.00430 +Epoch [3922/4000] Training [20/39] Loss: 0.13009 +Epoch [3922/4000] Training [21/39] Loss: 0.00579 +Epoch [3922/4000] Training [22/39] Loss: 0.00315 +Epoch [3922/4000] Training [23/39] Loss: 0.00302 +Epoch [3922/4000] Training [24/39] Loss: 0.00602 +Epoch [3922/4000] Training [25/39] Loss: 0.00552 +Epoch [3922/4000] Training [26/39] Loss: 0.25477 +Epoch [3922/4000] Training [27/39] Loss: 0.00507 +Epoch [3922/4000] Training [28/39] Loss: 0.00420 +Epoch [3922/4000] Training [29/39] Loss: 0.00304 +Epoch [3922/4000] Training [30/39] Loss: 0.00716 +Epoch [3922/4000] Training [31/39] Loss: 0.00362 +Epoch [3922/4000] Training [32/39] Loss: 0.00463 +Epoch [3922/4000] Training [33/39] Loss: 0.12933 +Epoch [3922/4000] Training [34/39] Loss: 0.00413 +Epoch [3922/4000] Training [35/39] Loss: 0.13180 +Epoch [3922/4000] Training [36/39] Loss: 0.00395 +Epoch [3922/4000] Training [37/39] Loss: 0.00620 +Epoch [3922/4000] Training [38/39] Loss: 0.01105 +Epoch [3922/4000] Training [39/39] Loss: 0.00373 +Epoch [3922/4000] Training metric {'Train/mean dice_metric': 0.996183454990387, 'Train/mean miou_metric': 0.9928183555603027, 'Train/mean f1': 0.996793270111084, 'Train/mean precision': 0.9963861107826233, 'Train/mean recall': 0.9972007870674133, 'Train/mean hd95_metric': 1.0441439151763916} +Epoch [3922/4000] Validation [1/10] Loss: 0.70631 focal_loss 0.62024 dice_loss 0.08607 +Epoch [3922/4000] Validation [2/10] Loss: 0.50056 focal_loss 0.40240 dice_loss 0.09816 +Epoch [3922/4000] Validation [3/10] Loss: 0.38928 focal_loss 0.27811 dice_loss 0.11118 +Epoch [3922/4000] Validation [4/10] Loss: 0.89381 focal_loss 0.32850 dice_loss 0.56532 +Epoch [3922/4000] Validation [5/10] Loss: 3.03601 focal_loss 2.36208 dice_loss 0.67393 +Epoch [3922/4000] Validation [6/10] Loss: 1.33625 focal_loss 0.62381 dice_loss 0.71244 +Epoch [3922/4000] Validation [7/10] Loss: 1.17724 focal_loss 0.52279 dice_loss 0.65444 +Epoch [3922/4000] Validation [8/10] Loss: 2.36754 focal_loss 1.75132 dice_loss 0.61622 +Epoch [3922/4000] Validation [9/10] Loss: 1.51719 focal_loss 0.97310 dice_loss 0.54409 +Epoch [3922/4000] Validation [10/10] Loss: 1.89207 focal_loss 1.15717 dice_loss 0.73491 +Epoch [3922/4000] Validation metric {'Val/mean dice_metric': 0.9512670040130615, 'Val/mean miou_metric': 0.9352374076843262, 'Val/mean f1': 0.9488429427146912, 'Val/mean precision': 0.9442704319953918, 'Val/mean recall': 0.9534598588943481, 'Val/mean hd95_metric': 10.77137565612793} +Cheakpoint... +Epoch [3922/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512670040130615, 'Val/mean miou_metric': 0.9352374076843262, 'Val/mean f1': 0.9488429427146912, 'Val/mean precision': 0.9442704319953918, 'Val/mean recall': 0.9534598588943481, 'Val/mean hd95_metric': 10.77137565612793} +Epoch [3923/4000] Training [1/39] Loss: 0.00324 +Epoch [3923/4000] Training [2/39] Loss: 0.00479 +Epoch [3923/4000] Training [3/39] Loss: 0.00377 +Epoch [3923/4000] Training [4/39] Loss: 0.00598 +Epoch [3923/4000] Training [5/39] Loss: 0.00574 +Epoch [3923/4000] Training [6/39] Loss: 0.00517 +Epoch [3923/4000] Training [7/39] Loss: 0.00515 +Epoch [3923/4000] Training [8/39] Loss: 0.12760 +Epoch [3923/4000] Training [9/39] Loss: 0.00385 +Epoch [3923/4000] Training [10/39] Loss: 0.00552 +Epoch [3923/4000] Training [11/39] Loss: 0.00440 +Epoch [3923/4000] Training [12/39] Loss: 0.00440 +Epoch [3923/4000] Training [13/39] Loss: 0.00311 +Epoch [3923/4000] Training [14/39] Loss: 0.00552 +Epoch [3923/4000] Training [15/39] Loss: 0.00604 +Epoch [3923/4000] Training [16/39] Loss: 0.00446 +Epoch [3923/4000] Training [17/39] Loss: 0.00404 +Epoch [3923/4000] Training [18/39] Loss: 0.12783 +Epoch [3923/4000] Training [19/39] Loss: 0.25316 +Epoch [3923/4000] Training [20/39] Loss: 0.00370 +Epoch [3923/4000] Training [21/39] Loss: 0.00286 +Epoch [3923/4000] Training [22/39] Loss: 0.00507 +Epoch [3923/4000] Training [23/39] Loss: 0.00581 +Epoch [3923/4000] Training [24/39] Loss: 0.00374 +Epoch [3923/4000] Training [25/39] Loss: 0.00589 +Epoch [3923/4000] Training [26/39] Loss: 0.00482 +Epoch [3923/4000] Training [27/39] Loss: 0.00512 +Epoch [3923/4000] Training [28/39] Loss: 0.00429 +Epoch [3923/4000] Training [29/39] Loss: 0.00508 +Epoch [3923/4000] Training [30/39] Loss: 0.00500 +Epoch [3923/4000] Training [31/39] Loss: 0.00386 +Epoch [3923/4000] Training [32/39] Loss: 0.13109 +Epoch [3923/4000] Training [33/39] Loss: 0.00535 +Epoch [3923/4000] Training [34/39] Loss: 0.00418 +Epoch [3923/4000] Training [35/39] Loss: 0.12990 +Epoch [3923/4000] Training [36/39] Loss: 0.00462 +Epoch [3923/4000] Training [37/39] Loss: 0.00381 +Epoch [3923/4000] Training [38/39] Loss: 0.01120 +Epoch [3923/4000] Training [39/39] Loss: 0.00582 +Epoch [3923/4000] Training metric {'Train/mean dice_metric': 0.9955507516860962, 'Train/mean miou_metric': 0.9923832416534424, 'Train/mean f1': 0.9969554543495178, 'Train/mean precision': 0.9964459538459778, 'Train/mean recall': 0.9974654912948608, 'Train/mean hd95_metric': 0.9488115310668945} +Epoch [3923/4000] Validation [1/10] Loss: 0.71119 focal_loss 0.62504 dice_loss 0.08615 +Epoch [3923/4000] Validation [2/10] Loss: 0.50696 focal_loss 0.40819 dice_loss 0.09877 +Epoch [3923/4000] Validation [3/10] Loss: 0.39513 focal_loss 0.28376 dice_loss 0.11138 +Epoch [3923/4000] Validation [4/10] Loss: 0.89786 focal_loss 0.33241 dice_loss 0.56545 +Epoch [3923/4000] Validation [5/10] Loss: 3.05469 focal_loss 2.38066 dice_loss 0.67402 +Epoch [3923/4000] Validation [6/10] Loss: 1.34319 focal_loss 0.63115 dice_loss 0.71204 +Epoch [3923/4000] Validation [7/10] Loss: 1.18613 focal_loss 0.53182 dice_loss 0.65431 +Epoch [3923/4000] Validation [8/10] Loss: 2.39648 focal_loss 1.77992 dice_loss 0.61656 +Epoch [3923/4000] Validation [9/10] Loss: 1.53335 focal_loss 0.98902 dice_loss 0.54434 +Epoch [3923/4000] Validation [10/10] Loss: 1.90854 focal_loss 1.17366 dice_loss 0.73489 +Epoch [3923/4000] Validation metric {'Val/mean dice_metric': 0.9507216811180115, 'Val/mean miou_metric': 0.9348525404930115, 'Val/mean f1': 0.948464035987854, 'Val/mean precision': 0.9438562989234924, 'Val/mean recall': 0.9531169533729553, 'Val/mean hd95_metric': 10.798212051391602} +Cheakpoint... +Epoch [3923/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507216811180115, 'Val/mean miou_metric': 0.9348525404930115, 'Val/mean f1': 0.948464035987854, 'Val/mean precision': 0.9438562989234924, 'Val/mean recall': 0.9531169533729553, 'Val/mean hd95_metric': 10.798212051391602} +Epoch [3924/4000] Training [1/39] Loss: 0.00796 +Epoch [3924/4000] Training [2/39] Loss: 0.00428 +Epoch [3924/4000] Training [3/39] Loss: 0.00494 +Epoch [3924/4000] Training [4/39] Loss: 0.00593 +Epoch [3924/4000] Training [5/39] Loss: 0.00686 +Epoch [3924/4000] Training [6/39] Loss: 0.00725 +Epoch [3924/4000] Training [7/39] Loss: 0.00567 +Epoch [3924/4000] Training [8/39] Loss: 0.00367 +Epoch [3924/4000] Training [9/39] Loss: 0.00321 +Epoch [3924/4000] Training [10/39] Loss: 0.00281 +Epoch [3924/4000] Training [11/39] Loss: 0.12971 +Epoch [3924/4000] Training [12/39] Loss: 0.00651 +Epoch [3924/4000] Training [13/39] Loss: 0.00500 +Epoch [3924/4000] Training [14/39] Loss: 0.00824 +Epoch [3924/4000] Training [15/39] Loss: 0.12766 +Epoch [3924/4000] Training [16/39] Loss: 0.00498 +Epoch [3924/4000] Training [17/39] Loss: 0.13201 +Epoch [3924/4000] Training [18/39] Loss: 0.00431 +Epoch [3924/4000] Training [19/39] Loss: 0.00418 +Epoch [3924/4000] Training [20/39] Loss: 0.00401 +Epoch [3924/4000] Training [21/39] Loss: 0.08455 +Epoch [3924/4000] Training [22/39] Loss: 0.00308 +Epoch [3924/4000] Training [23/39] Loss: 0.12779 +Epoch [3924/4000] Training [24/39] Loss: 0.00505 +Epoch [3924/4000] Training [25/39] Loss: 0.00628 +Epoch [3924/4000] Training [26/39] Loss: 0.00503 +Epoch [3924/4000] Training [27/39] Loss: 0.00549 +Epoch [3924/4000] Training [28/39] Loss: 0.00488 +Epoch [3924/4000] Training [29/39] Loss: 0.12735 +Epoch [3924/4000] Training [30/39] Loss: 0.00312 +Epoch [3924/4000] Training [31/39] Loss: 0.00599 +Epoch [3924/4000] Training [32/39] Loss: 0.13032 +Epoch [3924/4000] Training [33/39] Loss: 0.13143 +Epoch [3924/4000] Training [34/39] Loss: 0.12815 +Epoch [3924/4000] Training [35/39] Loss: 0.13095 +Epoch [3924/4000] Training [36/39] Loss: 0.13069 +Epoch [3924/4000] Training [37/39] Loss: 0.25459 +Epoch [3924/4000] Training [38/39] Loss: 0.00344 +Epoch [3924/4000] Training [39/39] Loss: 0.00766 +Epoch [3924/4000] Training metric {'Train/mean dice_metric': 0.9953998923301697, 'Train/mean miou_metric': 0.9921059012413025, 'Train/mean f1': 0.9968823790550232, 'Train/mean precision': 0.9964873194694519, 'Train/mean recall': 0.9972777366638184, 'Train/mean hd95_metric': 1.1067036390304565} +Epoch [3924/4000] Validation [1/10] Loss: 0.72338 focal_loss 0.63690 dice_loss 0.08648 +Epoch [3924/4000] Validation [2/10] Loss: 0.50679 focal_loss 0.40940 dice_loss 0.09739 +Epoch [3924/4000] Validation [3/10] Loss: 0.39528 focal_loss 0.28416 dice_loss 0.11111 +Epoch [3924/4000] Validation [4/10] Loss: 0.90230 focal_loss 0.33673 dice_loss 0.56558 +Epoch [3924/4000] Validation [5/10] Loss: 3.10271 focal_loss 2.42875 dice_loss 0.67397 +Epoch [3924/4000] Validation [6/10] Loss: 1.35025 focal_loss 0.63757 dice_loss 0.71268 +Epoch [3924/4000] Validation [7/10] Loss: 1.18878 focal_loss 0.53391 dice_loss 0.65487 +Epoch [3924/4000] Validation [8/10] Loss: 2.40101 focal_loss 1.78532 dice_loss 0.61569 +Epoch [3924/4000] Validation [9/10] Loss: 1.54727 focal_loss 1.00310 dice_loss 0.54417 +Epoch [3924/4000] Validation [10/10] Loss: 1.92063 focal_loss 1.18550 dice_loss 0.73514 +Epoch [3924/4000] Validation metric {'Val/mean dice_metric': 0.9505871534347534, 'Val/mean miou_metric': 0.9346087574958801, 'Val/mean f1': 0.9482733607292175, 'Val/mean precision': 0.9435363411903381, 'Val/mean recall': 0.953058123588562, 'Val/mean hd95_metric': 10.821474075317383} +Cheakpoint... +Epoch [3924/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505871534347534, 'Val/mean miou_metric': 0.9346087574958801, 'Val/mean f1': 0.9482733607292175, 'Val/mean precision': 0.9435363411903381, 'Val/mean recall': 0.953058123588562, 'Val/mean hd95_metric': 10.821474075317383} +Epoch [3925/4000] Training [1/39] Loss: 0.00809 +Epoch [3925/4000] Training [2/39] Loss: 0.13009 +Epoch [3925/4000] Training [3/39] Loss: 0.04116 +Epoch [3925/4000] Training [4/39] Loss: 0.00324 +Epoch [3925/4000] Training [5/39] Loss: 0.00529 +Epoch [3925/4000] Training [6/39] Loss: 0.00447 +Epoch [3925/4000] Training [7/39] Loss: 0.00897 +Epoch [3925/4000] Training [8/39] Loss: 0.00436 +Epoch [3925/4000] Training [9/39] Loss: 0.00369 +Epoch [3925/4000] Training [10/39] Loss: 0.00298 +Epoch [3925/4000] Training [11/39] Loss: 0.00536 +Epoch [3925/4000] Training [12/39] Loss: 0.00343 +Epoch [3925/4000] Training [13/39] Loss: 0.00575 +Epoch [3925/4000] Training [14/39] Loss: 0.00504 +Epoch [3925/4000] Training [15/39] Loss: 0.00622 +Epoch [3925/4000] Training [16/39] Loss: 0.00334 +Epoch [3925/4000] Training [17/39] Loss: 0.00384 +Epoch [3925/4000] Training [18/39] Loss: 0.00428 +Epoch [3925/4000] Training [19/39] Loss: 0.00380 +Epoch [3925/4000] Training [20/39] Loss: 0.00424 +Epoch [3925/4000] Training [21/39] Loss: 0.12972 +Epoch [3925/4000] Training [22/39] Loss: 0.00526 +Epoch [3925/4000] Training [23/39] Loss: 0.00615 +Epoch [3925/4000] Training [24/39] Loss: 0.00511 +Epoch [3925/4000] Training [25/39] Loss: 0.00599 +Epoch [3925/4000] Training [26/39] Loss: 0.12831 +Epoch [3925/4000] Training [27/39] Loss: 0.00253 +Epoch [3925/4000] Training [28/39] Loss: 0.00387 +Epoch [3925/4000] Training [29/39] Loss: 0.00572 +Epoch [3925/4000] Training [30/39] Loss: 0.00428 +Epoch [3925/4000] Training [31/39] Loss: 0.12756 +Epoch [3925/4000] Training [32/39] Loss: 0.13315 +Epoch [3925/4000] Training [33/39] Loss: 0.00345 +Epoch [3925/4000] Training [34/39] Loss: 0.00563 +Epoch [3925/4000] Training [35/39] Loss: 0.00559 +Epoch [3925/4000] Training [36/39] Loss: 0.00619 +Epoch [3925/4000] Training [37/39] Loss: 0.00409 +Epoch [3925/4000] Training [38/39] Loss: 0.00545 +Epoch [3925/4000] Training [39/39] Loss: 0.00418 +Epoch [3925/4000] Training metric {'Train/mean dice_metric': 0.9964393973350525, 'Train/mean miou_metric': 0.9933232665061951, 'Train/mean f1': 0.9969336986541748, 'Train/mean precision': 0.9965041279792786, 'Train/mean recall': 0.9973635673522949, 'Train/mean hd95_metric': 0.9337823390960693} +Epoch [3925/4000] Validation [1/10] Loss: 0.72547 focal_loss 0.63895 dice_loss 0.08652 +Epoch [3925/4000] Validation [2/10] Loss: 0.50917 focal_loss 0.40979 dice_loss 0.09938 +Epoch [3925/4000] Validation [3/10] Loss: 0.40484 focal_loss 0.29311 dice_loss 0.11174 +Epoch [3925/4000] Validation [4/10] Loss: 0.89827 focal_loss 0.33286 dice_loss 0.56541 +Epoch [3925/4000] Validation [5/10] Loss: 3.11103 focal_loss 2.43696 dice_loss 0.67407 +Epoch [3925/4000] Validation [6/10] Loss: 1.33696 focal_loss 0.62498 dice_loss 0.71197 +Epoch [3925/4000] Validation [7/10] Loss: 1.17916 focal_loss 0.52551 dice_loss 0.65365 +Epoch [3925/4000] Validation [8/10] Loss: 2.41551 focal_loss 1.79762 dice_loss 0.61789 +Epoch [3925/4000] Validation [9/10] Loss: 1.54557 focal_loss 1.00153 dice_loss 0.54405 +Epoch [3925/4000] Validation [10/10] Loss: 1.89844 focal_loss 1.16376 dice_loss 0.73469 +Epoch [3925/4000] Validation metric {'Val/mean dice_metric': 0.9514525532722473, 'Val/mean miou_metric': 0.935626208782196, 'Val/mean f1': 0.9484463930130005, 'Val/mean precision': 0.9441049098968506, 'Val/mean recall': 0.952828049659729, 'Val/mean hd95_metric': 10.80132007598877} +Cheakpoint... +Epoch [3925/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514525532722473, 'Val/mean miou_metric': 0.935626208782196, 'Val/mean f1': 0.9484463930130005, 'Val/mean precision': 0.9441049098968506, 'Val/mean recall': 0.952828049659729, 'Val/mean hd95_metric': 10.80132007598877} +Epoch [3926/4000] Training [1/39] Loss: 0.00316 +Epoch [3926/4000] Training [2/39] Loss: 0.00465 +Epoch [3926/4000] Training [3/39] Loss: 0.00750 +Epoch [3926/4000] Training [4/39] Loss: 0.00417 +Epoch [3926/4000] Training [5/39] Loss: 0.00254 +Epoch [3926/4000] Training [6/39] Loss: 0.00363 +Epoch [3926/4000] Training [7/39] Loss: 0.00395 +Epoch [3926/4000] Training [8/39] Loss: 0.00416 +Epoch [3926/4000] Training [9/39] Loss: 0.00536 +Epoch [3926/4000] Training [10/39] Loss: 0.00514 +Epoch [3926/4000] Training [11/39] Loss: 0.00321 +Epoch [3926/4000] Training [12/39] Loss: 0.00279 +Epoch [3926/4000] Training [13/39] Loss: 0.00547 +Epoch [3926/4000] Training [14/39] Loss: 0.00546 +Epoch [3926/4000] Training [15/39] Loss: 0.12805 +Epoch [3926/4000] Training [16/39] Loss: 0.00426 +Epoch [3926/4000] Training [17/39] Loss: 0.00258 +Epoch [3926/4000] Training [18/39] Loss: 0.25706 +Epoch [3926/4000] Training [19/39] Loss: 0.00452 +Epoch [3926/4000] Training [20/39] Loss: 0.00636 +Epoch [3926/4000] Training [21/39] Loss: 0.00673 +Epoch [3926/4000] Training [22/39] Loss: 0.12820 +Epoch [3926/4000] Training [23/39] Loss: 0.13096 +Epoch [3926/4000] Training [24/39] Loss: 0.12968 +Epoch [3926/4000] Training [25/39] Loss: 0.00475 +Epoch [3926/4000] Training [26/39] Loss: 0.00293 +Epoch [3926/4000] Training [27/39] Loss: 0.13046 +Epoch [3926/4000] Training [28/39] Loss: 0.08298 +Epoch [3926/4000] Training [29/39] Loss: 0.00275 +Epoch [3926/4000] Training [30/39] Loss: 0.00425 +Epoch [3926/4000] Training [31/39] Loss: 0.00554 +Epoch [3926/4000] Training [32/39] Loss: 0.00496 +Epoch [3926/4000] Training [33/39] Loss: 0.00223 +Epoch [3926/4000] Training [34/39] Loss: 0.12865 +Epoch [3926/4000] Training [35/39] Loss: 0.00321 +Epoch [3926/4000] Training [36/39] Loss: 0.00572 +Epoch [3926/4000] Training [37/39] Loss: 0.00289 +Epoch [3926/4000] Training [38/39] Loss: 0.03748 +Epoch [3926/4000] Training [39/39] Loss: 0.00392 +Epoch [3926/4000] Training metric {'Train/mean dice_metric': 0.9966276288032532, 'Train/mean miou_metric': 0.9936893582344055, 'Train/mean f1': 0.9970635771751404, 'Train/mean precision': 0.9966689348220825, 'Train/mean recall': 0.9974583983421326, 'Train/mean hd95_metric': 0.9118534922599792} +Epoch [3926/4000] Validation [1/10] Loss: 0.70745 focal_loss 0.62142 dice_loss 0.08603 +Epoch [3926/4000] Validation [2/10] Loss: 0.50876 focal_loss 0.40985 dice_loss 0.09891 +Epoch [3926/4000] Validation [3/10] Loss: 0.39066 focal_loss 0.27938 dice_loss 0.11128 +Epoch [3926/4000] Validation [4/10] Loss: 0.90064 focal_loss 0.33510 dice_loss 0.56554 +Epoch [3926/4000] Validation [5/10] Loss: 3.03187 focal_loss 2.35794 dice_loss 0.67393 +Epoch [3926/4000] Validation [6/10] Loss: 1.34708 focal_loss 0.63470 dice_loss 0.71238 +Epoch [3926/4000] Validation [7/10] Loss: 1.18745 focal_loss 0.53314 dice_loss 0.65431 +Epoch [3926/4000] Validation [8/10] Loss: 2.38799 focal_loss 1.77179 dice_loss 0.61620 +Epoch [3926/4000] Validation [9/10] Loss: 1.53703 focal_loss 0.99251 dice_loss 0.54452 +Epoch [3926/4000] Validation [10/10] Loss: 1.91442 focal_loss 1.17936 dice_loss 0.73505 +Epoch [3926/4000] Validation metric {'Val/mean dice_metric': 0.951648473739624, 'Val/mean miou_metric': 0.935982882976532, 'Val/mean f1': 0.9485579133033752, 'Val/mean precision': 0.9438040256500244, 'Val/mean recall': 0.9533600211143494, 'Val/mean hd95_metric': 10.838254928588867} +Cheakpoint... +Epoch [3926/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951648473739624, 'Val/mean miou_metric': 0.935982882976532, 'Val/mean f1': 0.9485579133033752, 'Val/mean precision': 0.9438040256500244, 'Val/mean recall': 0.9533600211143494, 'Val/mean hd95_metric': 10.838254928588867} +Epoch [3927/4000] Training [1/39] Loss: 0.00291 +Epoch [3927/4000] Training [2/39] Loss: 0.00427 +Epoch [3927/4000] Training [3/39] Loss: 0.00479 +Epoch [3927/4000] Training [4/39] Loss: 0.00399 +Epoch [3927/4000] Training [5/39] Loss: 0.00473 +Epoch [3927/4000] Training [6/39] Loss: 0.00449 +Epoch [3927/4000] Training [7/39] Loss: 0.12991 +Epoch [3927/4000] Training [8/39] Loss: 0.00321 +Epoch [3927/4000] Training [9/39] Loss: 0.00478 +Epoch [3927/4000] Training [10/39] Loss: 0.00244 +Epoch [3927/4000] Training [11/39] Loss: 0.00561 +Epoch [3927/4000] Training [12/39] Loss: 0.00491 +Epoch [3927/4000] Training [13/39] Loss: 0.13003 +Epoch [3927/4000] Training [14/39] Loss: 0.00289 +Epoch [3927/4000] Training [15/39] Loss: 0.00445 +Epoch [3927/4000] Training [16/39] Loss: 0.00466 +Epoch [3927/4000] Training [17/39] Loss: 0.00334 +Epoch [3927/4000] Training [18/39] Loss: 0.12839 +Epoch [3927/4000] Training [19/39] Loss: 0.00444 +Epoch [3927/4000] Training [20/39] Loss: 0.00467 +Epoch [3927/4000] Training [21/39] Loss: 0.00378 +Epoch [3927/4000] Training [22/39] Loss: 0.00433 +Epoch [3927/4000] Training [23/39] Loss: 0.00353 +Epoch [3927/4000] Training [24/39] Loss: 0.00784 +Epoch [3927/4000] Training [25/39] Loss: 0.00331 +Epoch [3927/4000] Training [26/39] Loss: 0.00343 +Epoch [3927/4000] Training [27/39] Loss: 0.25278 +Epoch [3927/4000] Training [28/39] Loss: 0.00384 +Epoch [3927/4000] Training [29/39] Loss: 0.00622 +Epoch [3927/4000] Training [30/39] Loss: 0.00422 +Epoch [3927/4000] Training [31/39] Loss: 0.00386 +Epoch [3927/4000] Training [32/39] Loss: 0.13001 +Epoch [3927/4000] Training [33/39] Loss: 0.00366 +Epoch [3927/4000] Training [34/39] Loss: 0.13046 +Epoch [3927/4000] Training [35/39] Loss: 0.00431 +Epoch [3927/4000] Training [36/39] Loss: 0.00346 +Epoch [3927/4000] Training [37/39] Loss: 0.00901 +Epoch [3927/4000] Training [38/39] Loss: 0.12812 +Epoch [3927/4000] Training [39/39] Loss: 0.00413 +Epoch [3927/4000] Training metric {'Train/mean dice_metric': 0.9966272711753845, 'Train/mean miou_metric': 0.993716299533844, 'Train/mean f1': 0.9970794320106506, 'Train/mean precision': 0.9966790080070496, 'Train/mean recall': 0.9974802732467651, 'Train/mean hd95_metric': 0.9087518453598022} +Epoch [3927/4000] Validation [1/10] Loss: 0.70583 focal_loss 0.61968 dice_loss 0.08615 +Epoch [3927/4000] Validation [2/10] Loss: 0.50526 focal_loss 0.40577 dice_loss 0.09949 +Epoch [3927/4000] Validation [3/10] Loss: 0.39437 focal_loss 0.28271 dice_loss 0.11165 +Epoch [3927/4000] Validation [4/10] Loss: 0.89594 focal_loss 0.33047 dice_loss 0.56547 +Epoch [3927/4000] Validation [5/10] Loss: 3.04162 focal_loss 2.36761 dice_loss 0.67401 +Epoch [3927/4000] Validation [6/10] Loss: 1.33633 focal_loss 0.62393 dice_loss 0.71240 +Epoch [3927/4000] Validation [7/10] Loss: 1.17711 focal_loss 0.52301 dice_loss 0.65410 +Epoch [3927/4000] Validation [8/10] Loss: 2.37356 focal_loss 1.75605 dice_loss 0.61751 +Epoch [3927/4000] Validation [9/10] Loss: 1.52798 focal_loss 0.98377 dice_loss 0.54421 +Epoch [3927/4000] Validation [10/10] Loss: 1.89140 focal_loss 1.15673 dice_loss 0.73467 +Epoch [3927/4000] Validation metric {'Val/mean dice_metric': 0.9516286849975586, 'Val/mean miou_metric': 0.9359866976737976, 'Val/mean f1': 0.94880610704422, 'Val/mean precision': 0.9443001747131348, 'Val/mean recall': 0.953355073928833, 'Val/mean hd95_metric': 10.778151512145996} +Cheakpoint... +Epoch [3927/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516286849975586, 'Val/mean miou_metric': 0.9359866976737976, 'Val/mean f1': 0.94880610704422, 'Val/mean precision': 0.9443001747131348, 'Val/mean recall': 0.953355073928833, 'Val/mean hd95_metric': 10.778151512145996} +Epoch [3928/4000] Training [1/39] Loss: 0.13137 +Epoch [3928/4000] Training [2/39] Loss: 0.00367 +Epoch [3928/4000] Training [3/39] Loss: 0.00457 +Epoch [3928/4000] Training [4/39] Loss: 0.13074 +Epoch [3928/4000] Training [5/39] Loss: 0.00511 +Epoch [3928/4000] Training [6/39] Loss: 0.00499 +Epoch [3928/4000] Training [7/39] Loss: 0.00709 +Epoch [3928/4000] Training [8/39] Loss: 0.00588 +Epoch [3928/4000] Training [9/39] Loss: 0.12933 +Epoch [3928/4000] Training [10/39] Loss: 0.00423 +Epoch [3928/4000] Training [11/39] Loss: 0.00390 +Epoch [3928/4000] Training [12/39] Loss: 0.00605 +Epoch [3928/4000] Training [13/39] Loss: 0.12895 +Epoch [3928/4000] Training [14/39] Loss: 0.12820 +Epoch [3928/4000] Training [15/39] Loss: 0.00563 +Epoch [3928/4000] Training [16/39] Loss: 0.25275 +Epoch [3928/4000] Training [17/39] Loss: 0.00383 +Epoch [3928/4000] Training [18/39] Loss: 0.00353 +Epoch [3928/4000] Training [19/39] Loss: 0.12904 +Epoch [3928/4000] Training [20/39] Loss: 0.00482 +Epoch [3928/4000] Training [21/39] Loss: 0.00521 +Epoch [3928/4000] Training [22/39] Loss: 0.00665 +Epoch [3928/4000] Training [23/39] Loss: 0.00385 +Epoch [3928/4000] Training [24/39] Loss: 0.00387 +Epoch [3928/4000] Training [25/39] Loss: 0.00418 +Epoch [3928/4000] Training [26/39] Loss: 0.00545 +Epoch [3928/4000] Training [27/39] Loss: 0.00711 +Epoch [3928/4000] Training [28/39] Loss: 0.12952 +Epoch [3928/4000] Training [29/39] Loss: 0.12886 +Epoch [3928/4000] Training [30/39] Loss: 0.00488 +Epoch [3928/4000] Training [31/39] Loss: 0.00374 +Epoch [3928/4000] Training [32/39] Loss: 0.00408 +Epoch [3928/4000] Training [33/39] Loss: 0.00460 +Epoch [3928/4000] Training [34/39] Loss: 0.00361 +Epoch [3928/4000] Training [35/39] Loss: 0.00416 +Epoch [3928/4000] Training [36/39] Loss: 0.00384 +Epoch [3928/4000] Training [37/39] Loss: 0.25352 +Epoch [3928/4000] Training [38/39] Loss: 0.00291 +Epoch [3928/4000] Training [39/39] Loss: 0.00620 +Epoch [3928/4000] Training metric {'Train/mean dice_metric': 0.995625376701355, 'Train/mean miou_metric': 0.9925353527069092, 'Train/mean f1': 0.9970217943191528, 'Train/mean precision': 0.9965670704841614, 'Train/mean recall': 0.9974769949913025, 'Train/mean hd95_metric': 0.9141756296157837} +Epoch [3928/4000] Validation [1/10] Loss: 0.72449 focal_loss 0.63797 dice_loss 0.08653 +Epoch [3928/4000] Validation [2/10] Loss: 0.50924 focal_loss 0.41025 dice_loss 0.09898 +Epoch [3928/4000] Validation [3/10] Loss: 0.40004 focal_loss 0.28856 dice_loss 0.11148 +Epoch [3928/4000] Validation [4/10] Loss: 0.89996 focal_loss 0.33441 dice_loss 0.56555 +Epoch [3928/4000] Validation [5/10] Loss: 3.10120 focal_loss 2.42723 dice_loss 0.67397 +Epoch [3928/4000] Validation [6/10] Loss: 1.34291 focal_loss 0.63058 dice_loss 0.71233 +Epoch [3928/4000] Validation [7/10] Loss: 1.18275 focal_loss 0.52796 dice_loss 0.65479 +Epoch [3928/4000] Validation [8/10] Loss: 2.39422 focal_loss 1.77805 dice_loss 0.61617 +Epoch [3928/4000] Validation [9/10] Loss: 1.54895 focal_loss 1.00493 dice_loss 0.54402 +Epoch [3928/4000] Validation [10/10] Loss: 1.90876 focal_loss 1.17357 dice_loss 0.73519 +Epoch [3928/4000] Validation metric {'Val/mean dice_metric': 0.950750470161438, 'Val/mean miou_metric': 0.9349374771118164, 'Val/mean f1': 0.948435366153717, 'Val/mean precision': 0.9438116550445557, 'Val/mean recall': 0.953104555606842, 'Val/mean hd95_metric': 10.691716194152832} +Cheakpoint... +Epoch [3928/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950750470161438, 'Val/mean miou_metric': 0.9349374771118164, 'Val/mean f1': 0.948435366153717, 'Val/mean precision': 0.9438116550445557, 'Val/mean recall': 0.953104555606842, 'Val/mean hd95_metric': 10.691716194152832} +Epoch [3929/4000] Training [1/39] Loss: 0.12780 +Epoch [3929/4000] Training [2/39] Loss: 0.00393 +Epoch [3929/4000] Training [3/39] Loss: 0.00409 +Epoch [3929/4000] Training [4/39] Loss: 0.13201 +Epoch [3929/4000] Training [5/39] Loss: 0.00504 +Epoch [3929/4000] Training [6/39] Loss: 0.13022 +Epoch [3929/4000] Training [7/39] Loss: 0.12881 +Epoch [3929/4000] Training [8/39] Loss: 0.12875 +Epoch [3929/4000] Training [9/39] Loss: 0.00332 +Epoch [3929/4000] Training [10/39] Loss: 0.00484 +Epoch [3929/4000] Training [11/39] Loss: 0.12829 +Epoch [3929/4000] Training [12/39] Loss: 0.00627 +Epoch [3929/4000] Training [13/39] Loss: 0.00624 +Epoch [3929/4000] Training [14/39] Loss: 0.00400 +Epoch [3929/4000] Training [15/39] Loss: 0.00694 +Epoch [3929/4000] Training [16/39] Loss: 0.25258 +Epoch [3929/4000] Training [17/39] Loss: 0.00997 +Epoch [3929/4000] Training [18/39] Loss: 0.00661 +Epoch [3929/4000] Training [19/39] Loss: 0.00343 +Epoch [3929/4000] Training [20/39] Loss: 0.00718 +Epoch [3929/4000] Training [21/39] Loss: 0.00455 +Epoch [3929/4000] Training [22/39] Loss: 0.00678 +Epoch [3929/4000] Training [23/39] Loss: 0.13093 +Epoch [3929/4000] Training [24/39] Loss: 0.00453 +Epoch [3929/4000] Training [25/39] Loss: 0.00332 +Epoch [3929/4000] Training [26/39] Loss: 0.00432 +Epoch [3929/4000] Training [27/39] Loss: 0.00303 +Epoch [3929/4000] Training [28/39] Loss: 0.12783 +Epoch [3929/4000] Training [29/39] Loss: 0.00406 +Epoch [3929/4000] Training [30/39] Loss: 0.12999 +Epoch [3929/4000] Training [31/39] Loss: 0.00333 +Epoch [3929/4000] Training [32/39] Loss: 0.00379 +Epoch [3929/4000] Training [33/39] Loss: 0.12903 +Epoch [3929/4000] Training [34/39] Loss: 0.00374 +Epoch [3929/4000] Training [35/39] Loss: 0.00622 +Epoch [3929/4000] Training [36/39] Loss: 0.00304 +Epoch [3929/4000] Training [37/39] Loss: 0.00465 +Epoch [3929/4000] Training [38/39] Loss: 0.13051 +Epoch [3929/4000] Training [39/39] Loss: 0.00537 +Epoch [3929/4000] Training metric {'Train/mean dice_metric': 0.9955056309700012, 'Train/mean miou_metric': 0.9923025965690613, 'Train/mean f1': 0.9968521595001221, 'Train/mean precision': 0.9963456988334656, 'Train/mean recall': 0.9973590970039368, 'Train/mean hd95_metric': 0.9413263201713562} +Epoch [3929/4000] Validation [1/10] Loss: 0.70118 focal_loss 0.61437 dice_loss 0.08681 +Epoch [3929/4000] Validation [2/10] Loss: 0.49790 focal_loss 0.40084 dice_loss 0.09706 +Epoch [3929/4000] Validation [3/10] Loss: 0.37749 focal_loss 0.26684 dice_loss 0.11065 +Epoch [3929/4000] Validation [4/10] Loss: 0.89864 focal_loss 0.33231 dice_loss 0.56634 +Epoch [3929/4000] Validation [5/10] Loss: 2.99523 focal_loss 2.32145 dice_loss 0.67378 +Epoch [3929/4000] Validation [6/10] Loss: 1.34419 focal_loss 0.63122 dice_loss 0.71296 +Epoch [3929/4000] Validation [7/10] Loss: 1.17913 focal_loss 0.52333 dice_loss 0.65580 +Epoch [3929/4000] Validation [8/10] Loss: 2.30115 focal_loss 1.69001 dice_loss 0.61114 +Epoch [3929/4000] Validation [9/10] Loss: 1.51996 focal_loss 0.97543 dice_loss 0.54452 +Epoch [3929/4000] Validation [10/10] Loss: 1.90423 focal_loss 1.16820 dice_loss 0.73603 +Epoch [3929/4000] Validation metric {'Val/mean dice_metric': 0.950703501701355, 'Val/mean miou_metric': 0.934794545173645, 'Val/mean f1': 0.9484615325927734, 'Val/mean precision': 0.9430263042449951, 'Val/mean recall': 0.9539599418640137, 'Val/mean hd95_metric': 10.918196678161621} +Cheakpoint... +Epoch [3929/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950703501701355, 'Val/mean miou_metric': 0.934794545173645, 'Val/mean f1': 0.9484615325927734, 'Val/mean precision': 0.9430263042449951, 'Val/mean recall': 0.9539599418640137, 'Val/mean hd95_metric': 10.918196678161621} +Epoch [3930/4000] Training [1/39] Loss: 0.00439 +Epoch [3930/4000] Training [2/39] Loss: 0.00314 +Epoch [3930/4000] Training [3/39] Loss: 0.00411 +Epoch [3930/4000] Training [4/39] Loss: 0.00595 +Epoch [3930/4000] Training [5/39] Loss: 0.00355 +Epoch [3930/4000] Training [6/39] Loss: 0.00481 +Epoch [3930/4000] Training [7/39] Loss: 0.00300 +Epoch [3930/4000] Training [8/39] Loss: 0.00828 +Epoch [3930/4000] Training [9/39] Loss: 0.00449 +Epoch [3930/4000] Training [10/39] Loss: 0.00306 +Epoch [3930/4000] Training [11/39] Loss: 0.12750 +Epoch [3930/4000] Training [12/39] Loss: 0.12734 +Epoch [3930/4000] Training [13/39] Loss: 0.00321 +Epoch [3930/4000] Training [14/39] Loss: 0.00439 +Epoch [3930/4000] Training [15/39] Loss: 0.12765 +Epoch [3930/4000] Training [16/39] Loss: 0.00498 +Epoch [3930/4000] Training [17/39] Loss: 0.12829 +Epoch [3930/4000] Training [18/39] Loss: 0.00281 +Epoch [3930/4000] Training [19/39] Loss: 0.00539 +Epoch [3930/4000] Training [20/39] Loss: 0.00391 +Epoch [3930/4000] Training [21/39] Loss: 0.00569 +Epoch [3930/4000] Training [22/39] Loss: 0.00349 +Epoch [3930/4000] Training [23/39] Loss: 0.13685 +Epoch [3930/4000] Training [24/39] Loss: 0.00445 +Epoch [3930/4000] Training [25/39] Loss: 0.12878 +Epoch [3930/4000] Training [26/39] Loss: 0.00559 +Epoch [3930/4000] Training [27/39] Loss: 0.00381 +Epoch [3930/4000] Training [28/39] Loss: 0.00518 +Epoch [3930/4000] Training [29/39] Loss: 0.00485 +Epoch [3930/4000] Training [30/39] Loss: 0.00456 +Epoch [3930/4000] Training [31/39] Loss: 0.00444 +Epoch [3930/4000] Training [32/39] Loss: 0.00544 +Epoch [3930/4000] Training [33/39] Loss: 0.00459 +Epoch [3930/4000] Training [34/39] Loss: 0.00245 +Epoch [3930/4000] Training [35/39] Loss: 0.00413 +Epoch [3930/4000] Training [36/39] Loss: 0.00496 +Epoch [3930/4000] Training [37/39] Loss: 0.00536 +Epoch [3930/4000] Training [38/39] Loss: 0.00478 +Epoch [3930/4000] Training [39/39] Loss: 0.12775 +Epoch [3930/4000] Training metric {'Train/mean dice_metric': 0.9965291023254395, 'Train/mean miou_metric': 0.9935460686683655, 'Train/mean f1': 0.9970069527626038, 'Train/mean precision': 0.9964274764060974, 'Train/mean recall': 0.9975873231887817, 'Train/mean hd95_metric': 0.9595742225646973} +Epoch [3930/4000] Validation [1/10] Loss: 0.72370 focal_loss 0.63787 dice_loss 0.08584 +Epoch [3930/4000] Validation [2/10] Loss: 0.51008 focal_loss 0.40862 dice_loss 0.10146 +Epoch [3930/4000] Validation [3/10] Loss: 0.41471 focal_loss 0.30215 dice_loss 0.11256 +Epoch [3930/4000] Validation [4/10] Loss: 0.88903 focal_loss 0.32469 dice_loss 0.56434 +Epoch [3930/4000] Validation [5/10] Loss: 3.11476 focal_loss 2.44057 dice_loss 0.67419 +Epoch [3930/4000] Validation [6/10] Loss: 1.31803 focal_loss 0.60598 dice_loss 0.71206 +Epoch [3930/4000] Validation [7/10] Loss: 1.16454 focal_loss 0.51223 dice_loss 0.65230 +Epoch [3930/4000] Validation [8/10] Loss: 2.45876 focal_loss 1.83443 dice_loss 0.62433 +Epoch [3930/4000] Validation [9/10] Loss: 1.52859 focal_loss 0.98519 dice_loss 0.54340 +Epoch [3930/4000] Validation [10/10] Loss: 1.85943 focal_loss 1.12591 dice_loss 0.73352 +Epoch [3930/4000] Validation metric {'Val/mean dice_metric': 0.9515097737312317, 'Val/mean miou_metric': 0.9358135461807251, 'Val/mean f1': 0.9487931728363037, 'Val/mean precision': 0.9452682733535767, 'Val/mean recall': 0.9523442983627319, 'Val/mean hd95_metric': 10.790802001953125} +Cheakpoint... +Epoch [3930/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515097737312317, 'Val/mean miou_metric': 0.9358135461807251, 'Val/mean f1': 0.9487931728363037, 'Val/mean precision': 0.9452682733535767, 'Val/mean recall': 0.9523442983627319, 'Val/mean hd95_metric': 10.790802001953125} +Epoch [3931/4000] Training [1/39] Loss: 0.12869 +Epoch [3931/4000] Training [2/39] Loss: 0.00392 +Epoch [3931/4000] Training [3/39] Loss: 0.13112 +Epoch [3931/4000] Training [4/39] Loss: 0.00413 +Epoch [3931/4000] Training [5/39] Loss: 0.00360 +Epoch [3931/4000] Training [6/39] Loss: 0.12869 +Epoch [3931/4000] Training [7/39] Loss: 0.00626 +Epoch [3931/4000] Training [8/39] Loss: 0.37740 +Epoch [3931/4000] Training [9/39] Loss: 0.00574 +Epoch [3931/4000] Training [10/39] Loss: 0.00401 +Epoch [3931/4000] Training [11/39] Loss: 0.00445 +Epoch [3931/4000] Training [12/39] Loss: 0.00490 +Epoch [3931/4000] Training [13/39] Loss: 0.00513 +Epoch [3931/4000] Training [14/39] Loss: 0.00390 +Epoch [3931/4000] Training [15/39] Loss: 0.00369 +Epoch [3931/4000] Training [16/39] Loss: 0.00469 +Epoch [3931/4000] Training [17/39] Loss: 0.00356 +Epoch [3931/4000] Training [18/39] Loss: 0.13034 +Epoch [3931/4000] Training [19/39] Loss: 0.00426 +Epoch [3931/4000] Training [20/39] Loss: 0.00818 +Epoch [3931/4000] Training [21/39] Loss: 0.25310 +Epoch [3931/4000] Training [22/39] Loss: 0.00346 +Epoch [3931/4000] Training [23/39] Loss: 0.00613 +Epoch [3931/4000] Training [24/39] Loss: 0.00486 +Epoch [3931/4000] Training [25/39] Loss: 0.00267 +Epoch [3931/4000] Training [26/39] Loss: 0.00459 +Epoch [3931/4000] Training [27/39] Loss: 0.13143 +Epoch [3931/4000] Training [28/39] Loss: 0.00569 +Epoch [3931/4000] Training [29/39] Loss: 0.00774 +Epoch [3931/4000] Training [30/39] Loss: 0.00600 +Epoch [3931/4000] Training [31/39] Loss: 0.00511 +Epoch [3931/4000] Training [32/39] Loss: 0.00337 +Epoch [3931/4000] Training [33/39] Loss: 0.00306 +Epoch [3931/4000] Training [34/39] Loss: 0.12892 +Epoch [3931/4000] Training [35/39] Loss: 0.00468 +Epoch [3931/4000] Training [36/39] Loss: 0.00450 +Epoch [3931/4000] Training [37/39] Loss: 0.00356 +Epoch [3931/4000] Training [38/39] Loss: 0.12897 +Epoch [3931/4000] Training [39/39] Loss: 0.00487 +Epoch [3931/4000] Training metric {'Train/mean dice_metric': 0.9964592456817627, 'Train/mean miou_metric': 0.9933642745018005, 'Train/mean f1': 0.9970116019248962, 'Train/mean precision': 0.9965385794639587, 'Train/mean recall': 0.9974851608276367, 'Train/mean hd95_metric': 0.9356234073638916} +Epoch [3931/4000] Validation [1/10] Loss: 0.71987 focal_loss 0.63369 dice_loss 0.08619 +Epoch [3931/4000] Validation [2/10] Loss: 0.50833 focal_loss 0.40815 dice_loss 0.10018 +Epoch [3931/4000] Validation [3/10] Loss: 0.40417 focal_loss 0.29223 dice_loss 0.11194 +Epoch [3931/4000] Validation [4/10] Loss: 0.89390 focal_loss 0.32886 dice_loss 0.56503 +Epoch [3931/4000] Validation [5/10] Loss: 3.10991 focal_loss 2.43583 dice_loss 0.67407 +Epoch [3931/4000] Validation [6/10] Loss: 1.33021 focal_loss 0.61820 dice_loss 0.71201 +Epoch [3931/4000] Validation [7/10] Loss: 1.17199 focal_loss 0.51884 dice_loss 0.65315 +Epoch [3931/4000] Validation [8/10] Loss: 2.41655 focal_loss 1.79664 dice_loss 0.61991 +Epoch [3931/4000] Validation [9/10] Loss: 1.53351 focal_loss 0.98977 dice_loss 0.54374 +Epoch [3931/4000] Validation [10/10] Loss: 1.88204 focal_loss 1.14760 dice_loss 0.73445 +Epoch [3931/4000] Validation metric {'Val/mean dice_metric': 0.9514443874359131, 'Val/mean miou_metric': 0.9356372952461243, 'Val/mean f1': 0.9488142132759094, 'Val/mean precision': 0.9447237253189087, 'Val/mean recall': 0.9529401659965515, 'Val/mean hd95_metric': 10.806495666503906} +Cheakpoint... +Epoch [3931/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514443874359131, 'Val/mean miou_metric': 0.9356372952461243, 'Val/mean f1': 0.9488142132759094, 'Val/mean precision': 0.9447237253189087, 'Val/mean recall': 0.9529401659965515, 'Val/mean hd95_metric': 10.806495666503906} +Epoch [3932/4000] Training [1/39] Loss: 0.12843 +Epoch [3932/4000] Training [2/39] Loss: 0.00332 +Epoch [3932/4000] Training [3/39] Loss: 0.00374 +Epoch [3932/4000] Training [4/39] Loss: 0.00641 +Epoch [3932/4000] Training [5/39] Loss: 0.00525 +Epoch [3932/4000] Training [6/39] Loss: 0.00309 +Epoch [3932/4000] Training [7/39] Loss: 0.00386 +Epoch [3932/4000] Training [8/39] Loss: 0.00957 +Epoch [3932/4000] Training [9/39] Loss: 0.00484 +Epoch [3932/4000] Training [10/39] Loss: 0.13038 +Epoch [3932/4000] Training [11/39] Loss: 0.00415 +Epoch [3932/4000] Training [12/39] Loss: 0.13162 +Epoch [3932/4000] Training [13/39] Loss: 0.00375 +Epoch [3932/4000] Training [14/39] Loss: 0.00472 +Epoch [3932/4000] Training [15/39] Loss: 0.00527 +Epoch [3932/4000] Training [16/39] Loss: 0.00392 +Epoch [3932/4000] Training [17/39] Loss: 0.25358 +Epoch [3932/4000] Training [18/39] Loss: 0.12914 +Epoch [3932/4000] Training [19/39] Loss: 0.12898 +Epoch [3932/4000] Training [20/39] Loss: 0.00549 +Epoch [3932/4000] Training [21/39] Loss: 0.12986 +Epoch [3932/4000] Training [22/39] Loss: 0.00353 +Epoch [3932/4000] Training [23/39] Loss: 0.00728 +Epoch [3932/4000] Training [24/39] Loss: 0.00543 +Epoch [3932/4000] Training [25/39] Loss: 0.00364 +Epoch [3932/4000] Training [26/39] Loss: 0.00348 +Epoch [3932/4000] Training [27/39] Loss: 0.00287 +Epoch [3932/4000] Training [28/39] Loss: 0.00369 +Epoch [3932/4000] Training [29/39] Loss: 0.00412 +Epoch [3932/4000] Training [30/39] Loss: 0.00690 +Epoch [3932/4000] Training [31/39] Loss: 0.00352 +Epoch [3932/4000] Training [32/39] Loss: 0.00445 +Epoch [3932/4000] Training [33/39] Loss: 0.00470 +Epoch [3932/4000] Training [34/39] Loss: 0.00402 +Epoch [3932/4000] Training [35/39] Loss: 0.00502 +Epoch [3932/4000] Training [36/39] Loss: 0.13273 +Epoch [3932/4000] Training [37/39] Loss: 0.00506 +Epoch [3932/4000] Training [38/39] Loss: 0.12902 +Epoch [3932/4000] Training [39/39] Loss: 0.00450 +Epoch [3932/4000] Training metric {'Train/mean dice_metric': 0.9956573247909546, 'Train/mean miou_metric': 0.9925859570503235, 'Train/mean f1': 0.9970024228096008, 'Train/mean precision': 0.996559739112854, 'Train/mean recall': 0.9974454641342163, 'Train/mean hd95_metric': 0.9070181846618652} +Epoch [3932/4000] Validation [1/10] Loss: 0.71166 focal_loss 0.62557 dice_loss 0.08609 +Epoch [3932/4000] Validation [2/10] Loss: 0.50797 focal_loss 0.40842 dice_loss 0.09955 +Epoch [3932/4000] Validation [3/10] Loss: 0.39552 focal_loss 0.28409 dice_loss 0.11143 +Epoch [3932/4000] Validation [4/10] Loss: 0.89605 focal_loss 0.33092 dice_loss 0.56513 +Epoch [3932/4000] Validation [5/10] Loss: 3.06736 focal_loss 2.39332 dice_loss 0.67404 +Epoch [3932/4000] Validation [6/10] Loss: 1.34005 focal_loss 0.62747 dice_loss 0.71258 +Epoch [3932/4000] Validation [7/10] Loss: 1.18065 focal_loss 0.52723 dice_loss 0.65342 +Epoch [3932/4000] Validation [8/10] Loss: 2.39384 focal_loss 1.77684 dice_loss 0.61700 +Epoch [3932/4000] Validation [9/10] Loss: 1.53322 focal_loss 0.98904 dice_loss 0.54418 +Epoch [3932/4000] Validation [10/10] Loss: 1.90111 focal_loss 1.16616 dice_loss 0.73495 +Epoch [3932/4000] Validation metric {'Val/mean dice_metric': 0.9508242607116699, 'Val/mean miou_metric': 0.9350485801696777, 'Val/mean f1': 0.9485428333282471, 'Val/mean precision': 0.9440147280693054, 'Val/mean recall': 0.9531146287918091, 'Val/mean hd95_metric': 10.748196601867676} +Cheakpoint... +Epoch [3932/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508242607116699, 'Val/mean miou_metric': 0.9350485801696777, 'Val/mean f1': 0.9485428333282471, 'Val/mean precision': 0.9440147280693054, 'Val/mean recall': 0.9531146287918091, 'Val/mean hd95_metric': 10.748196601867676} +Epoch [3933/4000] Training [1/39] Loss: 0.00506 +Epoch [3933/4000] Training [2/39] Loss: 0.00863 +Epoch [3933/4000] Training [3/39] Loss: 0.12817 +Epoch [3933/4000] Training [4/39] Loss: 0.00532 +Epoch [3933/4000] Training [5/39] Loss: 0.13215 +Epoch [3933/4000] Training [6/39] Loss: 0.00468 +Epoch [3933/4000] Training [7/39] Loss: 0.00354 +Epoch [3933/4000] Training [8/39] Loss: 0.00419 +Epoch [3933/4000] Training [9/39] Loss: 0.01039 +Epoch [3933/4000] Training [10/39] Loss: 0.25288 +Epoch [3933/4000] Training [11/39] Loss: 0.00353 +Epoch [3933/4000] Training [12/39] Loss: 0.00722 +Epoch [3933/4000] Training [13/39] Loss: 0.00406 +Epoch [3933/4000] Training [14/39] Loss: 0.00340 +Epoch [3933/4000] Training [15/39] Loss: 0.00404 +Epoch [3933/4000] Training [16/39] Loss: 0.12894 +Epoch [3933/4000] Training [17/39] Loss: 0.00328 +Epoch [3933/4000] Training [18/39] Loss: 0.00432 +Epoch [3933/4000] Training [19/39] Loss: 0.12832 +Epoch [3933/4000] Training [20/39] Loss: 0.00490 +Epoch [3933/4000] Training [21/39] Loss: 0.12885 +Epoch [3933/4000] Training [22/39] Loss: 0.00648 +Epoch [3933/4000] Training [23/39] Loss: 0.00369 +Epoch [3933/4000] Training [24/39] Loss: 0.00747 +Epoch [3933/4000] Training [25/39] Loss: 0.01082 +Epoch [3933/4000] Training [26/39] Loss: 0.00553 +Epoch [3933/4000] Training [27/39] Loss: 0.12877 +Epoch [3933/4000] Training [28/39] Loss: 0.00584 +Epoch [3933/4000] Training [29/39] Loss: 0.00361 +Epoch [3933/4000] Training [30/39] Loss: 0.12796 +Epoch [3933/4000] Training [31/39] Loss: 0.25466 +Epoch [3933/4000] Training [32/39] Loss: 0.22135 +Epoch [3933/4000] Training [33/39] Loss: 0.12936 +Epoch [3933/4000] Training [34/39] Loss: 0.00552 +Epoch [3933/4000] Training [35/39] Loss: 0.00407 +Epoch [3933/4000] Training [36/39] Loss: 0.25318 +Epoch [3933/4000] Training [37/39] Loss: 0.00425 +Epoch [3933/4000] Training [38/39] Loss: 0.00543 +Epoch [3933/4000] Training [39/39] Loss: 0.00381 +Epoch [3933/4000] Training metric {'Train/mean dice_metric': 0.995434045791626, 'Train/mean miou_metric': 0.9922032952308655, 'Train/mean f1': 0.9967582821846008, 'Train/mean precision': 0.996332049369812, 'Train/mean recall': 0.9971848726272583, 'Train/mean hd95_metric': 1.149294376373291} +Epoch [3933/4000] Validation [1/10] Loss: 0.71730 focal_loss 0.63102 dice_loss 0.08628 +Epoch [3933/4000] Validation [2/10] Loss: 0.50898 focal_loss 0.41087 dice_loss 0.09811 +Epoch [3933/4000] Validation [3/10] Loss: 0.39270 focal_loss 0.28164 dice_loss 0.11106 +Epoch [3933/4000] Validation [4/10] Loss: 0.90123 focal_loss 0.33576 dice_loss 0.56547 +Epoch [3933/4000] Validation [5/10] Loss: 3.07268 focal_loss 2.39872 dice_loss 0.67396 +Epoch [3933/4000] Validation [6/10] Loss: 1.34966 focal_loss 0.63742 dice_loss 0.71223 +Epoch [3933/4000] Validation [7/10] Loss: 1.18752 focal_loss 0.53295 dice_loss 0.65456 +Epoch [3933/4000] Validation [8/10] Loss: 2.39481 focal_loss 1.77919 dice_loss 0.61563 +Epoch [3933/4000] Validation [9/10] Loss: 1.54301 focal_loss 0.99874 dice_loss 0.54427 +Epoch [3933/4000] Validation [10/10] Loss: 1.91891 focal_loss 1.18378 dice_loss 0.73513 +Epoch [3933/4000] Validation metric {'Val/mean dice_metric': 0.9506605863571167, 'Val/mean miou_metric': 0.9347470998764038, 'Val/mean f1': 0.9481484293937683, 'Val/mean precision': 0.9434170126914978, 'Val/mean recall': 0.9529274106025696, 'Val/mean hd95_metric': 10.851273536682129} +Cheakpoint... +Epoch [3933/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506605863571167, 'Val/mean miou_metric': 0.9347470998764038, 'Val/mean f1': 0.9481484293937683, 'Val/mean precision': 0.9434170126914978, 'Val/mean recall': 0.9529274106025696, 'Val/mean hd95_metric': 10.851273536682129} +Epoch [3934/4000] Training [1/39] Loss: 0.00429 +Epoch [3934/4000] Training [2/39] Loss: 0.00418 +Epoch [3934/4000] Training [3/39] Loss: 0.00552 +Epoch [3934/4000] Training [4/39] Loss: 0.00425 +Epoch [3934/4000] Training [5/39] Loss: 0.00346 +Epoch [3934/4000] Training [6/39] Loss: 0.00294 +Epoch [3934/4000] Training [7/39] Loss: 0.12722 +Epoch [3934/4000] Training [8/39] Loss: 0.00429 +Epoch [3934/4000] Training [9/39] Loss: 0.12732 +Epoch [3934/4000] Training [10/39] Loss: 0.00360 +Epoch [3934/4000] Training [11/39] Loss: 0.12849 +Epoch [3934/4000] Training [12/39] Loss: 0.00275 +Epoch [3934/4000] Training [13/39] Loss: 0.12822 +Epoch [3934/4000] Training [14/39] Loss: 0.00465 +Epoch [3934/4000] Training [15/39] Loss: 0.00477 +Epoch [3934/4000] Training [16/39] Loss: 0.25530 +Epoch [3934/4000] Training [17/39] Loss: 0.00571 +Epoch [3934/4000] Training [18/39] Loss: 0.25446 +Epoch [3934/4000] Training [19/39] Loss: 0.00321 +Epoch [3934/4000] Training [20/39] Loss: 0.00474 +Epoch [3934/4000] Training [21/39] Loss: 0.00674 +Epoch [3934/4000] Training [22/39] Loss: 0.00504 +Epoch [3934/4000] Training [23/39] Loss: 0.00550 +Epoch [3934/4000] Training [24/39] Loss: 0.00430 +Epoch [3934/4000] Training [25/39] Loss: 0.00482 +Epoch [3934/4000] Training [26/39] Loss: 0.25501 +Epoch [3934/4000] Training [27/39] Loss: 0.00248 +Epoch [3934/4000] Training [28/39] Loss: 0.00339 +Epoch [3934/4000] Training [29/39] Loss: 0.00424 +Epoch [3934/4000] Training [30/39] Loss: 0.00521 +Epoch [3934/4000] Training [31/39] Loss: 0.00402 +Epoch [3934/4000] Training [32/39] Loss: 0.00631 +Epoch [3934/4000] Training [33/39] Loss: 0.00665 +Epoch [3934/4000] Training [34/39] Loss: 0.00342 +Epoch [3934/4000] Training [35/39] Loss: 0.00459 +Epoch [3934/4000] Training [36/39] Loss: 0.00508 +Epoch [3934/4000] Training [37/39] Loss: 0.00598 +Epoch [3934/4000] Training [38/39] Loss: 0.00317 +Epoch [3934/4000] Training [39/39] Loss: 0.00389 +Epoch [3934/4000] Training metric {'Train/mean dice_metric': 0.9965957403182983, 'Train/mean miou_metric': 0.993644654750824, 'Train/mean f1': 0.9971162676811218, 'Train/mean precision': 0.9967167973518372, 'Train/mean recall': 0.9975160360336304, 'Train/mean hd95_metric': 0.9099137187004089} +Epoch [3934/4000] Validation [1/10] Loss: 0.70517 focal_loss 0.61949 dice_loss 0.08568 +Epoch [3934/4000] Validation [2/10] Loss: 0.50753 focal_loss 0.40712 dice_loss 0.10041 +Epoch [3934/4000] Validation [3/10] Loss: 0.39925 focal_loss 0.28721 dice_loss 0.11203 +Epoch [3934/4000] Validation [4/10] Loss: 0.89205 focal_loss 0.32721 dice_loss 0.56484 +Epoch [3934/4000] Validation [5/10] Loss: 3.05803 focal_loss 2.38391 dice_loss 0.67412 +Epoch [3934/4000] Validation [6/10] Loss: 1.33015 focal_loss 0.61798 dice_loss 0.71217 +Epoch [3934/4000] Validation [7/10] Loss: 1.17172 focal_loss 0.51939 dice_loss 0.65234 +Epoch [3934/4000] Validation [8/10] Loss: 2.40878 focal_loss 1.78812 dice_loss 0.62066 +Epoch [3934/4000] Validation [9/10] Loss: 1.52175 focal_loss 0.97755 dice_loss 0.54420 +Epoch [3934/4000] Validation [10/10] Loss: 1.87928 focal_loss 1.14514 dice_loss 0.73414 +Epoch [3934/4000] Validation metric {'Val/mean dice_metric': 0.951593279838562, 'Val/mean miou_metric': 0.9359245300292969, 'Val/mean f1': 0.9490719437599182, 'Val/mean precision': 0.945112943649292, 'Val/mean recall': 0.9530641436576843, 'Val/mean hd95_metric': 10.729214668273926} +Cheakpoint... +Epoch [3934/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951593279838562, 'Val/mean miou_metric': 0.9359245300292969, 'Val/mean f1': 0.9490719437599182, 'Val/mean precision': 0.945112943649292, 'Val/mean recall': 0.9530641436576843, 'Val/mean hd95_metric': 10.729214668273926} +Epoch [3935/4000] Training [1/39] Loss: 0.12807 +Epoch [3935/4000] Training [2/39] Loss: 0.00211 +Epoch [3935/4000] Training [3/39] Loss: 0.00374 +Epoch [3935/4000] Training [4/39] Loss: 0.04181 +Epoch [3935/4000] Training [5/39] Loss: 0.00422 +Epoch [3935/4000] Training [6/39] Loss: 0.00306 +Epoch [3935/4000] Training [7/39] Loss: 0.00399 +Epoch [3935/4000] Training [8/39] Loss: 0.00270 +Epoch [3935/4000] Training [9/39] Loss: 0.12789 +Epoch [3935/4000] Training [10/39] Loss: 0.00396 +Epoch [3935/4000] Training [11/39] Loss: 0.00383 +Epoch [3935/4000] Training [12/39] Loss: 0.12816 +Epoch [3935/4000] Training [13/39] Loss: 0.00335 +Epoch [3935/4000] Training [14/39] Loss: 0.00325 +Epoch [3935/4000] Training [15/39] Loss: 0.00375 +Epoch [3935/4000] Training [16/39] Loss: 0.00206 +Epoch [3935/4000] Training [17/39] Loss: 0.13144 +Epoch [3935/4000] Training [18/39] Loss: 0.12929 +Epoch [3935/4000] Training [19/39] Loss: 0.00333 +Epoch [3935/4000] Training [20/39] Loss: 0.12805 +Epoch [3935/4000] Training [21/39] Loss: 0.00349 +Epoch [3935/4000] Training [22/39] Loss: 0.00447 +Epoch [3935/4000] Training [23/39] Loss: 0.00527 +Epoch [3935/4000] Training [24/39] Loss: 0.00469 +Epoch [3935/4000] Training [25/39] Loss: 0.00496 +Epoch [3935/4000] Training [26/39] Loss: 0.25316 +Epoch [3935/4000] Training [27/39] Loss: 0.00611 +Epoch [3935/4000] Training [28/39] Loss: 0.12758 +Epoch [3935/4000] Training [29/39] Loss: 0.00360 +Epoch [3935/4000] Training [30/39] Loss: 0.00504 +Epoch [3935/4000] Training [31/39] Loss: 0.00607 +Epoch [3935/4000] Training [32/39] Loss: 0.00451 +Epoch [3935/4000] Training [33/39] Loss: 0.00442 +Epoch [3935/4000] Training [34/39] Loss: 0.12928 +Epoch [3935/4000] Training [35/39] Loss: 0.00413 +Epoch [3935/4000] Training [36/39] Loss: 0.00453 +Epoch [3935/4000] Training [37/39] Loss: 0.00635 +Epoch [3935/4000] Training [38/39] Loss: 0.00479 +Epoch [3935/4000] Training [39/39] Loss: 0.00415 +Epoch [3935/4000] Training metric {'Train/mean dice_metric': 0.9968171119689941, 'Train/mean miou_metric': 0.9940831065177917, 'Train/mean f1': 0.9972693920135498, 'Train/mean precision': 0.9968301653862, 'Train/mean recall': 0.9977089762687683, 'Train/mean hd95_metric': 0.9724531769752502} +Epoch [3935/4000] Validation [1/10] Loss: 0.70133 focal_loss 0.61517 dice_loss 0.08616 +Epoch [3935/4000] Validation [2/10] Loss: 0.50092 focal_loss 0.40232 dice_loss 0.09860 +Epoch [3935/4000] Validation [3/10] Loss: 0.38793 focal_loss 0.27654 dice_loss 0.11139 +Epoch [3935/4000] Validation [4/10] Loss: 0.89387 focal_loss 0.32826 dice_loss 0.56561 +Epoch [3935/4000] Validation [5/10] Loss: 3.01914 focal_loss 2.34519 dice_loss 0.67395 +Epoch [3935/4000] Validation [6/10] Loss: 1.33546 focal_loss 0.62316 dice_loss 0.71229 +Epoch [3935/4000] Validation [7/10] Loss: 1.17365 focal_loss 0.51935 dice_loss 0.65430 +Epoch [3935/4000] Validation [8/10] Loss: 2.35188 focal_loss 1.73576 dice_loss 0.61612 +Epoch [3935/4000] Validation [9/10] Loss: 1.51595 focal_loss 0.97165 dice_loss 0.54429 +Epoch [3935/4000] Validation [10/10] Loss: 1.88648 focal_loss 1.15149 dice_loss 0.73499 +Epoch [3935/4000] Validation metric {'Val/mean dice_metric': 0.9517806768417358, 'Val/mean miou_metric': 0.936271607875824, 'Val/mean f1': 0.9487989544868469, 'Val/mean precision': 0.9441555142402649, 'Val/mean recall': 0.9534881711006165, 'Val/mean hd95_metric': 10.74177360534668} +Cheakpoint... +Epoch [3935/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9518], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9517806768417358, 'Val/mean miou_metric': 0.936271607875824, 'Val/mean f1': 0.9487989544868469, 'Val/mean precision': 0.9441555142402649, 'Val/mean recall': 0.9534881711006165, 'Val/mean hd95_metric': 10.74177360534668} +Epoch [3936/4000] Training [1/39] Loss: 0.00572 +Epoch [3936/4000] Training [2/39] Loss: 0.12857 +Epoch [3936/4000] Training [3/39] Loss: 0.00450 +Epoch [3936/4000] Training [4/39] Loss: 0.00515 +Epoch [3936/4000] Training [5/39] Loss: 0.00486 +Epoch [3936/4000] Training [6/39] Loss: 0.12895 +Epoch [3936/4000] Training [7/39] Loss: 0.00556 +Epoch [3936/4000] Training [8/39] Loss: 0.00330 +Epoch [3936/4000] Training [9/39] Loss: 0.00403 +Epoch [3936/4000] Training [10/39] Loss: 0.00376 +Epoch [3936/4000] Training [11/39] Loss: 0.12795 +Epoch [3936/4000] Training [12/39] Loss: 0.00412 +Epoch [3936/4000] Training [13/39] Loss: 0.00451 +Epoch [3936/4000] Training [14/39] Loss: 0.00391 +Epoch [3936/4000] Training [15/39] Loss: 0.12997 +Epoch [3936/4000] Training [16/39] Loss: 0.00377 +Epoch [3936/4000] Training [17/39] Loss: 0.00437 +Epoch [3936/4000] Training [18/39] Loss: 0.00742 +Epoch [3936/4000] Training [19/39] Loss: 0.12787 +Epoch [3936/4000] Training [20/39] Loss: 0.12781 +Epoch [3936/4000] Training [21/39] Loss: 0.00829 +Epoch [3936/4000] Training [22/39] Loss: 0.00342 +Epoch [3936/4000] Training [23/39] Loss: 0.00332 +Epoch [3936/4000] Training [24/39] Loss: 0.00299 +Epoch [3936/4000] Training [25/39] Loss: 0.12868 +Epoch [3936/4000] Training [26/39] Loss: 0.00369 +Epoch [3936/4000] Training [27/39] Loss: 0.12806 +Epoch [3936/4000] Training [28/39] Loss: 0.00461 +Epoch [3936/4000] Training [29/39] Loss: 0.00291 +Epoch [3936/4000] Training [30/39] Loss: 0.00615 +Epoch [3936/4000] Training [31/39] Loss: 0.00417 +Epoch [3936/4000] Training [32/39] Loss: 0.00330 +Epoch [3936/4000] Training [33/39] Loss: 0.00756 +Epoch [3936/4000] Training [34/39] Loss: 0.00593 +Epoch [3936/4000] Training [35/39] Loss: 0.00388 +Epoch [3936/4000] Training [36/39] Loss: 0.00541 +Epoch [3936/4000] Training [37/39] Loss: 0.00514 +Epoch [3936/4000] Training [38/39] Loss: 0.00586 +Epoch [3936/4000] Training [39/39] Loss: 0.00294 +Epoch [3936/4000] Training metric {'Train/mean dice_metric': 0.9964897036552429, 'Train/mean miou_metric': 0.9934065937995911, 'Train/mean f1': 0.9970593452453613, 'Train/mean precision': 0.9965723156929016, 'Train/mean recall': 0.9975467324256897, 'Train/mean hd95_metric': 0.9193698763847351} +Epoch [3936/4000] Validation [1/10] Loss: 0.71437 focal_loss 0.62868 dice_loss 0.08569 +Epoch [3936/4000] Validation [2/10] Loss: 0.50682 focal_loss 0.40561 dice_loss 0.10121 +Epoch [3936/4000] Validation [3/10] Loss: 0.40939 focal_loss 0.29688 dice_loss 0.11250 +Epoch [3936/4000] Validation [4/10] Loss: 0.88672 focal_loss 0.32227 dice_loss 0.56445 +Epoch [3936/4000] Validation [5/10] Loss: 3.08224 focal_loss 2.40806 dice_loss 0.67418 +Epoch [3936/4000] Validation [6/10] Loss: 1.31496 focal_loss 0.60314 dice_loss 0.71183 +Epoch [3936/4000] Validation [7/10] Loss: 1.16150 focal_loss 0.50967 dice_loss 0.65183 +Epoch [3936/4000] Validation [8/10] Loss: 2.43896 focal_loss 1.81489 dice_loss 0.62407 +Epoch [3936/4000] Validation [9/10] Loss: 1.51772 focal_loss 0.97428 dice_loss 0.54345 +Epoch [3936/4000] Validation [10/10] Loss: 1.85310 focal_loss 1.11959 dice_loss 0.73351 +Epoch [3936/4000] Validation metric {'Val/mean dice_metric': 0.9514625668525696, 'Val/mean miou_metric': 0.9356759786605835, 'Val/mean f1': 0.948596715927124, 'Val/mean precision': 0.9450956583023071, 'Val/mean recall': 0.952123761177063, 'Val/mean hd95_metric': 10.781416893005371} +Cheakpoint... +Epoch [3936/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514625668525696, 'Val/mean miou_metric': 0.9356759786605835, 'Val/mean f1': 0.948596715927124, 'Val/mean precision': 0.9450956583023071, 'Val/mean recall': 0.952123761177063, 'Val/mean hd95_metric': 10.781416893005371} +Epoch [3937/4000] Training [1/39] Loss: 0.00671 +Epoch [3937/4000] Training [2/39] Loss: 0.12921 +Epoch [3937/4000] Training [3/39] Loss: 0.00507 +Epoch [3937/4000] Training [4/39] Loss: 0.00249 +Epoch [3937/4000] Training [5/39] Loss: 0.00359 +Epoch [3937/4000] Training [6/39] Loss: 0.00469 +Epoch [3937/4000] Training [7/39] Loss: 0.00408 +Epoch [3937/4000] Training [8/39] Loss: 0.00421 +Epoch [3937/4000] Training [9/39] Loss: 0.12745 +Epoch [3937/4000] Training [10/39] Loss: 0.00389 +Epoch [3937/4000] Training [11/39] Loss: 0.12864 +Epoch [3937/4000] Training [12/39] Loss: 0.00396 +Epoch [3937/4000] Training [13/39] Loss: 0.00502 +Epoch [3937/4000] Training [14/39] Loss: 0.13149 +Epoch [3937/4000] Training [15/39] Loss: 0.00399 +Epoch [3937/4000] Training [16/39] Loss: 0.25306 +Epoch [3937/4000] Training [17/39] Loss: 0.00373 +Epoch [3937/4000] Training [18/39] Loss: 0.00444 +Epoch [3937/4000] Training [19/39] Loss: 0.13034 +Epoch [3937/4000] Training [20/39] Loss: 0.12724 +Epoch [3937/4000] Training [21/39] Loss: 0.00388 +Epoch [3937/4000] Training [22/39] Loss: 0.00585 +Epoch [3937/4000] Training [23/39] Loss: 0.12757 +Epoch [3937/4000] Training [24/39] Loss: 0.12833 +Epoch [3937/4000] Training [25/39] Loss: 0.00390 +Epoch [3937/4000] Training [26/39] Loss: 0.00595 +Epoch [3937/4000] Training [27/39] Loss: 0.12961 +Epoch [3937/4000] Training [28/39] Loss: 0.12961 +Epoch [3937/4000] Training [29/39] Loss: 0.04101 +Epoch [3937/4000] Training [30/39] Loss: 0.00957 +Epoch [3937/4000] Training [31/39] Loss: 0.13032 +Epoch [3937/4000] Training [32/39] Loss: 0.00430 +Epoch [3937/4000] Training [33/39] Loss: 0.00485 +Epoch [3937/4000] Training [34/39] Loss: 0.00615 +Epoch [3937/4000] Training [35/39] Loss: 0.12777 +Epoch [3937/4000] Training [36/39] Loss: 0.00476 +Epoch [3937/4000] Training [37/39] Loss: 0.00467 +Epoch [3937/4000] Training [38/39] Loss: 0.00486 +Epoch [3937/4000] Training [39/39] Loss: 0.00421 +Epoch [3937/4000] Training metric {'Train/mean dice_metric': 0.9956066608428955, 'Train/mean miou_metric': 0.9924895763397217, 'Train/mean f1': 0.9968858361244202, 'Train/mean precision': 0.9963817596435547, 'Train/mean recall': 0.9973903298377991, 'Train/mean hd95_metric': 1.029236912727356} +Epoch [3937/4000] Validation [1/10] Loss: 0.72273 focal_loss 0.63624 dice_loss 0.08649 +Epoch [3937/4000] Validation [2/10] Loss: 0.50773 focal_loss 0.41006 dice_loss 0.09767 +Epoch [3937/4000] Validation [3/10] Loss: 0.39404 focal_loss 0.28308 dice_loss 0.11096 +Epoch [3937/4000] Validation [4/10] Loss: 0.90218 focal_loss 0.33649 dice_loss 0.56569 +Epoch [3937/4000] Validation [5/10] Loss: 3.08862 focal_loss 2.41471 dice_loss 0.67392 +Epoch [3937/4000] Validation [6/10] Loss: 1.35304 focal_loss 0.64035 dice_loss 0.71269 +Epoch [3937/4000] Validation [7/10] Loss: 1.18840 focal_loss 0.53354 dice_loss 0.65487 +Epoch [3937/4000] Validation [8/10] Loss: 2.39199 focal_loss 1.77754 dice_loss 0.61445 +Epoch [3937/4000] Validation [9/10] Loss: 1.54888 focal_loss 1.00457 dice_loss 0.54431 +Epoch [3937/4000] Validation [10/10] Loss: 1.92232 focal_loss 1.18688 dice_loss 0.73544 +Epoch [3937/4000] Validation metric {'Val/mean dice_metric': 0.9507809281349182, 'Val/mean miou_metric': 0.9349539875984192, 'Val/mean f1': 0.9483631253242493, 'Val/mean precision': 0.9434065222740173, 'Val/mean recall': 0.9533720016479492, 'Val/mean hd95_metric': 10.758103370666504} +Cheakpoint... +Epoch [3937/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507809281349182, 'Val/mean miou_metric': 0.9349539875984192, 'Val/mean f1': 0.9483631253242493, 'Val/mean precision': 0.9434065222740173, 'Val/mean recall': 0.9533720016479492, 'Val/mean hd95_metric': 10.758103370666504} +Epoch [3938/4000] Training [1/39] Loss: 0.00395 +Epoch [3938/4000] Training [2/39] Loss: 0.00602 +Epoch [3938/4000] Training [3/39] Loss: 0.25333 +Epoch [3938/4000] Training [4/39] Loss: 0.00760 +Epoch [3938/4000] Training [5/39] Loss: 0.13298 +Epoch [3938/4000] Training [6/39] Loss: 0.00377 +Epoch [3938/4000] Training [7/39] Loss: 0.00561 +Epoch [3938/4000] Training [8/39] Loss: 0.12794 +Epoch [3938/4000] Training [9/39] Loss: 0.00471 +Epoch [3938/4000] Training [10/39] Loss: 0.00756 +Epoch [3938/4000] Training [11/39] Loss: 0.00349 +Epoch [3938/4000] Training [12/39] Loss: 0.00447 +Epoch [3938/4000] Training [13/39] Loss: 0.00315 +Epoch [3938/4000] Training [14/39] Loss: 0.00440 +Epoch [3938/4000] Training [15/39] Loss: 0.00523 +Epoch [3938/4000] Training [16/39] Loss: 0.00488 +Epoch [3938/4000] Training [17/39] Loss: 0.12888 +Epoch [3938/4000] Training [18/39] Loss: 0.00750 +Epoch [3938/4000] Training [19/39] Loss: 0.00456 +Epoch [3938/4000] Training [20/39] Loss: 0.00516 +Epoch [3938/4000] Training [21/39] Loss: 0.00559 +Epoch [3938/4000] Training [22/39] Loss: 0.00380 +Epoch [3938/4000] Training [23/39] Loss: 0.12795 +Epoch [3938/4000] Training [24/39] Loss: 0.00387 +Epoch [3938/4000] Training [25/39] Loss: 0.00717 +Epoch [3938/4000] Training [26/39] Loss: 0.00293 +Epoch [3938/4000] Training [27/39] Loss: 0.00593 +Epoch [3938/4000] Training [28/39] Loss: 0.00447 +Epoch [3938/4000] Training [29/39] Loss: 0.00387 +Epoch [3938/4000] Training [30/39] Loss: 0.00299 +Epoch [3938/4000] Training [31/39] Loss: 0.37744 +Epoch [3938/4000] Training [32/39] Loss: 0.00652 +Epoch [3938/4000] Training [33/39] Loss: 0.00412 +Epoch [3938/4000] Training [34/39] Loss: 0.00436 +Epoch [3938/4000] Training [35/39] Loss: 0.13044 +Epoch [3938/4000] Training [36/39] Loss: 0.00573 +Epoch [3938/4000] Training [37/39] Loss: 0.00417 +Epoch [3938/4000] Training [38/39] Loss: 0.13143 +Epoch [3938/4000] Training [39/39] Loss: 0.25627 +Epoch [3938/4000] Training metric {'Train/mean dice_metric': 0.9963796734809875, 'Train/mean miou_metric': 0.9932153820991516, 'Train/mean f1': 0.9968870878219604, 'Train/mean precision': 0.9964026212692261, 'Train/mean recall': 0.9973719716072083, 'Train/mean hd95_metric': 0.979630708694458} +Epoch [3938/4000] Validation [1/10] Loss: 0.71586 focal_loss 0.62954 dice_loss 0.08631 +Epoch [3938/4000] Validation [2/10] Loss: 0.51022 focal_loss 0.41240 dice_loss 0.09782 +Epoch [3938/4000] Validation [3/10] Loss: 0.39135 focal_loss 0.28043 dice_loss 0.11092 +Epoch [3938/4000] Validation [4/10] Loss: 0.90533 focal_loss 0.33962 dice_loss 0.56572 +Epoch [3938/4000] Validation [5/10] Loss: 3.07848 focal_loss 2.40451 dice_loss 0.67397 +Epoch [3938/4000] Validation [6/10] Loss: 1.35945 focal_loss 0.64705 dice_loss 0.71240 +Epoch [3938/4000] Validation [7/10] Loss: 1.19492 focal_loss 0.54046 dice_loss 0.65446 +Epoch [3938/4000] Validation [8/10] Loss: 2.39529 focal_loss 1.78082 dice_loss 0.61448 +Epoch [3938/4000] Validation [9/10] Loss: 1.55585 focal_loss 1.01108 dice_loss 0.54477 +Epoch [3938/4000] Validation [10/10] Loss: 1.93862 focal_loss 1.20295 dice_loss 0.73567 +Epoch [3938/4000] Validation metric {'Val/mean dice_metric': 0.9514638781547546, 'Val/mean miou_metric': 0.935607373714447, 'Val/mean f1': 0.9481552243232727, 'Val/mean precision': 0.9430505633354187, 'Val/mean recall': 0.9533154964447021, 'Val/mean hd95_metric': 10.741137504577637} +Cheakpoint... +Epoch [3938/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514638781547546, 'Val/mean miou_metric': 0.935607373714447, 'Val/mean f1': 0.9481552243232727, 'Val/mean precision': 0.9430505633354187, 'Val/mean recall': 0.9533154964447021, 'Val/mean hd95_metric': 10.741137504577637} +Epoch [3939/4000] Training [1/39] Loss: 0.00491 +Epoch [3939/4000] Training [2/39] Loss: 0.12867 +Epoch [3939/4000] Training [3/39] Loss: 0.12942 +Epoch [3939/4000] Training [4/39] Loss: 0.12935 +Epoch [3939/4000] Training [5/39] Loss: 0.00480 +Epoch [3939/4000] Training [6/39] Loss: 0.12797 +Epoch [3939/4000] Training [7/39] Loss: 0.00421 +Epoch [3939/4000] Training [8/39] Loss: 0.00313 +Epoch [3939/4000] Training [9/39] Loss: 0.00618 +Epoch [3939/4000] Training [10/39] Loss: 0.12718 +Epoch [3939/4000] Training [11/39] Loss: 0.00446 +Epoch [3939/4000] Training [12/39] Loss: 0.00581 +Epoch [3939/4000] Training [13/39] Loss: 0.00381 +Epoch [3939/4000] Training [14/39] Loss: 0.13506 +Epoch [3939/4000] Training [15/39] Loss: 0.25495 +Epoch [3939/4000] Training [16/39] Loss: 0.00472 +Epoch [3939/4000] Training [17/39] Loss: 0.00459 +Epoch [3939/4000] Training [18/39] Loss: 0.00386 +Epoch [3939/4000] Training [19/39] Loss: 0.12980 +Epoch [3939/4000] Training [20/39] Loss: 0.12911 +Epoch [3939/4000] Training [21/39] Loss: 0.00410 +Epoch [3939/4000] Training [22/39] Loss: 0.00727 +Epoch [3939/4000] Training [23/39] Loss: 0.00443 +Epoch [3939/4000] Training [24/39] Loss: 0.00279 +Epoch [3939/4000] Training [25/39] Loss: 0.13119 +Epoch [3939/4000] Training [26/39] Loss: 0.12928 +Epoch [3939/4000] Training [27/39] Loss: 0.00487 +Epoch [3939/4000] Training [28/39] Loss: 0.00316 +Epoch [3939/4000] Training [29/39] Loss: 0.12920 +Epoch [3939/4000] Training [30/39] Loss: 0.13131 +Epoch [3939/4000] Training [31/39] Loss: 0.00309 +Epoch [3939/4000] Training [32/39] Loss: 0.00594 +Epoch [3939/4000] Training [33/39] Loss: 0.12865 +Epoch [3939/4000] Training [34/39] Loss: 0.00581 +Epoch [3939/4000] Training [35/39] Loss: 0.04439 +Epoch [3939/4000] Training [36/39] Loss: 0.12893 +Epoch [3939/4000] Training [37/39] Loss: 0.00408 +Epoch [3939/4000] Training [38/39] Loss: 0.12828 +Epoch [3939/4000] Training [39/39] Loss: 0.00392 +Epoch [3939/4000] Training metric {'Train/mean dice_metric': 0.9965512156486511, 'Train/mean miou_metric': 0.9935430288314819, 'Train/mean f1': 0.9970841407775879, 'Train/mean precision': 0.996671736240387, 'Train/mean recall': 0.9974967837333679, 'Train/mean hd95_metric': 0.9493820071220398} +Epoch [3939/4000] Validation [1/10] Loss: 0.72585 focal_loss 0.63929 dice_loss 0.08657 +Epoch [3939/4000] Validation [2/10] Loss: 0.50977 focal_loss 0.41144 dice_loss 0.09833 +Epoch [3939/4000] Validation [3/10] Loss: 0.40163 focal_loss 0.29025 dice_loss 0.11137 +Epoch [3939/4000] Validation [4/10] Loss: 0.90110 focal_loss 0.33541 dice_loss 0.56569 +Epoch [3939/4000] Validation [5/10] Loss: 3.11305 focal_loss 2.43905 dice_loss 0.67400 +Epoch [3939/4000] Validation [6/10] Loss: 1.34802 focal_loss 0.63612 dice_loss 0.71191 +Epoch [3939/4000] Validation [7/10] Loss: 1.18854 focal_loss 0.53392 dice_loss 0.65462 +Epoch [3939/4000] Validation [8/10] Loss: 2.42640 focal_loss 1.80961 dice_loss 0.61678 +Epoch [3939/4000] Validation [9/10] Loss: 1.55408 focal_loss 1.00989 dice_loss 0.54420 +Epoch [3939/4000] Validation [10/10] Loss: 1.92152 focal_loss 1.18626 dice_loss 0.73526 +Epoch [3939/4000] Validation metric {'Val/mean dice_metric': 0.9515684843063354, 'Val/mean miou_metric': 0.9358310103416443, 'Val/mean f1': 0.948502779006958, 'Val/mean precision': 0.9438711404800415, 'Val/mean recall': 0.9531800150871277, 'Val/mean hd95_metric': 10.700797080993652} +Cheakpoint... +Epoch [3939/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515684843063354, 'Val/mean miou_metric': 0.9358310103416443, 'Val/mean f1': 0.948502779006958, 'Val/mean precision': 0.9438711404800415, 'Val/mean recall': 0.9531800150871277, 'Val/mean hd95_metric': 10.700797080993652} +Epoch [3940/4000] Training [1/39] Loss: 0.00505 +Epoch [3940/4000] Training [2/39] Loss: 0.12990 +Epoch [3940/4000] Training [3/39] Loss: 0.00876 +Epoch [3940/4000] Training [4/39] Loss: 0.00519 +Epoch [3940/4000] Training [5/39] Loss: 0.00314 +Epoch [3940/4000] Training [6/39] Loss: 0.12933 +Epoch [3940/4000] Training [7/39] Loss: 0.00274 +Epoch [3940/4000] Training [8/39] Loss: 0.13008 +Epoch [3940/4000] Training [9/39] Loss: 0.00535 +Epoch [3940/4000] Training [10/39] Loss: 0.00352 +Epoch [3940/4000] Training [11/39] Loss: 0.00353 +Epoch [3940/4000] Training [12/39] Loss: 0.00318 +Epoch [3940/4000] Training [13/39] Loss: 0.00641 +Epoch [3940/4000] Training [14/39] Loss: 0.00501 +Epoch [3940/4000] Training [15/39] Loss: 0.13030 +Epoch [3940/4000] Training [16/39] Loss: 0.00487 +Epoch [3940/4000] Training [17/39] Loss: 0.12808 +Epoch [3940/4000] Training [18/39] Loss: 0.12707 +Epoch [3940/4000] Training [19/39] Loss: 0.00505 +Epoch [3940/4000] Training [20/39] Loss: 0.25307 +Epoch [3940/4000] Training [21/39] Loss: 0.13141 +Epoch [3940/4000] Training [22/39] Loss: 0.00458 +Epoch [3940/4000] Training [23/39] Loss: 0.00431 +Epoch [3940/4000] Training [24/39] Loss: 0.00770 +Epoch [3940/4000] Training [25/39] Loss: 0.12870 +Epoch [3940/4000] Training [26/39] Loss: 0.25282 +Epoch [3940/4000] Training [27/39] Loss: 0.00490 +Epoch [3940/4000] Training [28/39] Loss: 0.00475 +Epoch [3940/4000] Training [29/39] Loss: 0.00503 +Epoch [3940/4000] Training [30/39] Loss: 0.00328 +Epoch [3940/4000] Training [31/39] Loss: 0.00392 +Epoch [3940/4000] Training [32/39] Loss: 0.12822 +Epoch [3940/4000] Training [33/39] Loss: 0.12915 +Epoch [3940/4000] Training [34/39] Loss: 0.00479 +Epoch [3940/4000] Training [35/39] Loss: 0.00700 +Epoch [3940/4000] Training [36/39] Loss: 0.12953 +Epoch [3940/4000] Training [37/39] Loss: 0.13002 +Epoch [3940/4000] Training [38/39] Loss: 0.00650 +Epoch [3940/4000] Training [39/39] Loss: 0.00707 +Epoch [3940/4000] Training metric {'Train/mean dice_metric': 0.995517373085022, 'Train/mean miou_metric': 0.9923142194747925, 'Train/mean f1': 0.9968047738075256, 'Train/mean precision': 0.9963839650154114, 'Train/mean recall': 0.9972260594367981, 'Train/mean hd95_metric': 0.9235683679580688} +Epoch [3940/4000] Validation [1/10] Loss: 0.72454 focal_loss 0.63762 dice_loss 0.08693 +Epoch [3940/4000] Validation [2/10] Loss: 0.50657 focal_loss 0.40960 dice_loss 0.09697 +Epoch [3940/4000] Validation [3/10] Loss: 0.39442 focal_loss 0.28353 dice_loss 0.11089 +Epoch [3940/4000] Validation [4/10] Loss: 0.90473 focal_loss 0.33853 dice_loss 0.56621 +Epoch [3940/4000] Validation [5/10] Loss: 3.09766 focal_loss 2.42373 dice_loss 0.67393 +Epoch [3940/4000] Validation [6/10] Loss: 1.35543 focal_loss 0.64331 dice_loss 0.71211 +Epoch [3940/4000] Validation [7/10] Loss: 1.19268 focal_loss 0.53692 dice_loss 0.65577 +Epoch [3940/4000] Validation [8/10] Loss: 2.38878 focal_loss 1.77515 dice_loss 0.61363 +Epoch [3940/4000] Validation [9/10] Loss: 1.55684 focal_loss 1.01227 dice_loss 0.54457 +Epoch [3940/4000] Validation [10/10] Loss: 1.93286 focal_loss 1.19697 dice_loss 0.73589 +Epoch [3940/4000] Validation metric {'Val/mean dice_metric': 0.9506967067718506, 'Val/mean miou_metric': 0.9347813129425049, 'Val/mean f1': 0.948125958442688, 'Val/mean precision': 0.9430118799209595, 'Val/mean recall': 0.9532955884933472, 'Val/mean hd95_metric': 10.676236152648926} +Cheakpoint... +Epoch [3940/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506967067718506, 'Val/mean miou_metric': 0.9347813129425049, 'Val/mean f1': 0.948125958442688, 'Val/mean precision': 0.9430118799209595, 'Val/mean recall': 0.9532955884933472, 'Val/mean hd95_metric': 10.676236152648926} +Epoch [3941/4000] Training [1/39] Loss: 0.12893 +Epoch [3941/4000] Training [2/39] Loss: 0.00473 +Epoch [3941/4000] Training [3/39] Loss: 0.00374 +Epoch [3941/4000] Training [4/39] Loss: 0.00425 +Epoch [3941/4000] Training [5/39] Loss: 0.00418 +Epoch [3941/4000] Training [6/39] Loss: 0.00412 +Epoch [3941/4000] Training [7/39] Loss: 0.00462 +Epoch [3941/4000] Training [8/39] Loss: 0.00375 +Epoch [3941/4000] Training [9/39] Loss: 0.13045 +Epoch [3941/4000] Training [10/39] Loss: 0.00606 +Epoch [3941/4000] Training [11/39] Loss: 0.13024 +Epoch [3941/4000] Training [12/39] Loss: 0.00345 +Epoch [3941/4000] Training [13/39] Loss: 0.00645 +Epoch [3941/4000] Training [14/39] Loss: 0.12774 +Epoch [3941/4000] Training [15/39] Loss: 0.00489 +Epoch [3941/4000] Training [16/39] Loss: 0.00415 +Epoch [3941/4000] Training [17/39] Loss: 0.00373 +Epoch [3941/4000] Training [18/39] Loss: 0.12752 +Epoch [3941/4000] Training [19/39] Loss: 0.00439 +Epoch [3941/4000] Training [20/39] Loss: 0.08365 +Epoch [3941/4000] Training [21/39] Loss: 0.13300 +Epoch [3941/4000] Training [22/39] Loss: 0.00497 +Epoch [3941/4000] Training [23/39] Loss: 0.12816 +Epoch [3941/4000] Training [24/39] Loss: 0.00423 +Epoch [3941/4000] Training [25/39] Loss: 0.00593 +Epoch [3941/4000] Training [26/39] Loss: 0.00609 +Epoch [3941/4000] Training [27/39] Loss: 0.00352 +Epoch [3941/4000] Training [28/39] Loss: 0.00352 +Epoch [3941/4000] Training [29/39] Loss: 0.00734 +Epoch [3941/4000] Training [30/39] Loss: 0.00571 +Epoch [3941/4000] Training [31/39] Loss: 0.12803 +Epoch [3941/4000] Training [32/39] Loss: 0.13071 +Epoch [3941/4000] Training [33/39] Loss: 0.00400 +Epoch [3941/4000] Training [34/39] Loss: 0.12966 +Epoch [3941/4000] Training [35/39] Loss: 0.00734 +Epoch [3941/4000] Training [36/39] Loss: 0.00579 +Epoch [3941/4000] Training [37/39] Loss: 0.00778 +Epoch [3941/4000] Training [38/39] Loss: 0.12685 +Epoch [3941/4000] Training [39/39] Loss: 0.00697 +Epoch [3941/4000] Training metric {'Train/mean dice_metric': 0.9964304566383362, 'Train/mean miou_metric': 0.9933164715766907, 'Train/mean f1': 0.9970198273658752, 'Train/mean precision': 0.996543288230896, 'Train/mean recall': 0.9974966645240784, 'Train/mean hd95_metric': 0.9971679449081421} +Epoch [3941/4000] Validation [1/10] Loss: 0.71211 focal_loss 0.62584 dice_loss 0.08627 +Epoch [3941/4000] Validation [2/10] Loss: 0.50341 focal_loss 0.40513 dice_loss 0.09828 +Epoch [3941/4000] Validation [3/10] Loss: 0.39221 focal_loss 0.28091 dice_loss 0.11130 +Epoch [3941/4000] Validation [4/10] Loss: 0.89641 focal_loss 0.33093 dice_loss 0.56548 +Epoch [3941/4000] Validation [5/10] Loss: 3.05997 focal_loss 2.38604 dice_loss 0.67393 +Epoch [3941/4000] Validation [6/10] Loss: 1.33905 focal_loss 0.62657 dice_loss 0.71248 +Epoch [3941/4000] Validation [7/10] Loss: 1.17669 focal_loss 0.52222 dice_loss 0.65447 +Epoch [3941/4000] Validation [8/10] Loss: 2.38401 focal_loss 1.76688 dice_loss 0.61713 +Epoch [3941/4000] Validation [9/10] Loss: 1.52908 focal_loss 0.98483 dice_loss 0.54425 +Epoch [3941/4000] Validation [10/10] Loss: 1.89479 focal_loss 1.15974 dice_loss 0.73505 +Epoch [3941/4000] Validation metric {'Val/mean dice_metric': 0.9514449834823608, 'Val/mean miou_metric': 0.9356191754341125, 'Val/mean f1': 0.9482443928718567, 'Val/mean precision': 0.9435594081878662, 'Val/mean recall': 0.9529762864112854, 'Val/mean hd95_metric': 10.74556827545166} +Cheakpoint... +Epoch [3941/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514449834823608, 'Val/mean miou_metric': 0.9356191754341125, 'Val/mean f1': 0.9482443928718567, 'Val/mean precision': 0.9435594081878662, 'Val/mean recall': 0.9529762864112854, 'Val/mean hd95_metric': 10.74556827545166} +Epoch [3942/4000] Training [1/39] Loss: 0.13150 +Epoch [3942/4000] Training [2/39] Loss: 0.00370 +Epoch [3942/4000] Training [3/39] Loss: 0.00405 +Epoch [3942/4000] Training [4/39] Loss: 0.12913 +Epoch [3942/4000] Training [5/39] Loss: 0.25372 +Epoch [3942/4000] Training [6/39] Loss: 0.01795 +Epoch [3942/4000] Training [7/39] Loss: 0.00497 +Epoch [3942/4000] Training [8/39] Loss: 0.00537 +Epoch [3942/4000] Training [9/39] Loss: 0.00442 +Epoch [3942/4000] Training [10/39] Loss: 0.00396 +Epoch [3942/4000] Training [11/39] Loss: 0.00257 +Epoch [3942/4000] Training [12/39] Loss: 0.00589 +Epoch [3942/4000] Training [13/39] Loss: 0.00508 +Epoch [3942/4000] Training [14/39] Loss: 0.00360 +Epoch [3942/4000] Training [15/39] Loss: 0.00497 +Epoch [3942/4000] Training [16/39] Loss: 0.04123 +Epoch [3942/4000] Training [17/39] Loss: 0.00670 +Epoch [3942/4000] Training [18/39] Loss: 0.12817 +Epoch [3942/4000] Training [19/39] Loss: 0.12792 +Epoch [3942/4000] Training [20/39] Loss: 0.00366 +Epoch [3942/4000] Training [21/39] Loss: 0.00752 +Epoch [3942/4000] Training [22/39] Loss: 0.00593 +Epoch [3942/4000] Training [23/39] Loss: 0.00468 +Epoch [3942/4000] Training [24/39] Loss: 0.00683 +Epoch [3942/4000] Training [25/39] Loss: 0.00583 +Epoch [3942/4000] Training [26/39] Loss: 0.00560 +Epoch [3942/4000] Training [27/39] Loss: 0.13336 +Epoch [3942/4000] Training [28/39] Loss: 0.00506 +Epoch [3942/4000] Training [29/39] Loss: 0.25261 +Epoch [3942/4000] Training [30/39] Loss: 0.00533 +Epoch [3942/4000] Training [31/39] Loss: 0.00388 +Epoch [3942/4000] Training [32/39] Loss: 0.12825 +Epoch [3942/4000] Training [33/39] Loss: 0.00331 +Epoch [3942/4000] Training [34/39] Loss: 0.00572 +Epoch [3942/4000] Training [35/39] Loss: 0.00446 +Epoch [3942/4000] Training [36/39] Loss: 0.00603 +Epoch [3942/4000] Training [37/39] Loss: 0.12799 +Epoch [3942/4000] Training [38/39] Loss: 0.00524 +Epoch [3942/4000] Training [39/39] Loss: 0.13101 +Epoch [3942/4000] Training metric {'Train/mean dice_metric': 0.9961892366409302, 'Train/mean miou_metric': 0.9928342700004578, 'Train/mean f1': 0.9966580271720886, 'Train/mean precision': 0.9961833357810974, 'Train/mean recall': 0.9971333146095276, 'Train/mean hd95_metric': 0.9511688351631165} +Epoch [3942/4000] Validation [1/10] Loss: 0.73684 focal_loss 0.65018 dice_loss 0.08666 +Epoch [3942/4000] Validation [2/10] Loss: 0.51036 focal_loss 0.41157 dice_loss 0.09879 +Epoch [3942/4000] Validation [3/10] Loss: 0.40645 focal_loss 0.29496 dice_loss 0.11149 +Epoch [3942/4000] Validation [4/10] Loss: 0.89788 focal_loss 0.33261 dice_loss 0.56527 +Epoch [3942/4000] Validation [5/10] Loss: 3.14491 focal_loss 2.47092 dice_loss 0.67399 +Epoch [3942/4000] Validation [6/10] Loss: 1.34105 focal_loss 0.62926 dice_loss 0.71179 +Epoch [3942/4000] Validation [7/10] Loss: 1.18231 focal_loss 0.52732 dice_loss 0.65499 +Epoch [3942/4000] Validation [8/10] Loss: 2.44119 focal_loss 1.82379 dice_loss 0.61740 +Epoch [3942/4000] Validation [9/10] Loss: 1.55415 focal_loss 1.01046 dice_loss 0.54369 +Epoch [3942/4000] Validation [10/10] Loss: 1.90606 focal_loss 1.17113 dice_loss 0.73493 +Epoch [3942/4000] Validation metric {'Val/mean dice_metric': 0.95122891664505, 'Val/mean miou_metric': 0.9351962208747864, 'Val/mean f1': 0.9481154084205627, 'Val/mean precision': 0.9437153935432434, 'Val/mean recall': 0.9525566697120667, 'Val/mean hd95_metric': 10.718214988708496} +Cheakpoint... +Epoch [3942/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.95122891664505, 'Val/mean miou_metric': 0.9351962208747864, 'Val/mean f1': 0.9481154084205627, 'Val/mean precision': 0.9437153935432434, 'Val/mean recall': 0.9525566697120667, 'Val/mean hd95_metric': 10.718214988708496} +Epoch [3943/4000] Training [1/39] Loss: 0.12788 +Epoch [3943/4000] Training [2/39] Loss: 0.12879 +Epoch [3943/4000] Training [3/39] Loss: 0.12737 +Epoch [3943/4000] Training [4/39] Loss: 0.00429 +Epoch [3943/4000] Training [5/39] Loss: 0.13070 +Epoch [3943/4000] Training [6/39] Loss: 0.12921 +Epoch [3943/4000] Training [7/39] Loss: 0.00422 +Epoch [3943/4000] Training [8/39] Loss: 0.12801 +Epoch [3943/4000] Training [9/39] Loss: 0.12818 +Epoch [3943/4000] Training [10/39] Loss: 0.00391 +Epoch [3943/4000] Training [11/39] Loss: 0.00558 +Epoch [3943/4000] Training [12/39] Loss: 0.12885 +Epoch [3943/4000] Training [13/39] Loss: 0.00406 +Epoch [3943/4000] Training [14/39] Loss: 0.00473 +Epoch [3943/4000] Training [15/39] Loss: 0.12855 +Epoch [3943/4000] Training [16/39] Loss: 0.00389 +Epoch [3943/4000] Training [17/39] Loss: 0.00599 +Epoch [3943/4000] Training [18/39] Loss: 0.00546 +Epoch [3943/4000] Training [19/39] Loss: 0.00447 +Epoch [3943/4000] Training [20/39] Loss: 0.00886 +Epoch [3943/4000] Training [21/39] Loss: 0.12982 +Epoch [3943/4000] Training [22/39] Loss: 0.13240 +Epoch [3943/4000] Training [23/39] Loss: 0.00531 +Epoch [3943/4000] Training [24/39] Loss: 0.00801 +Epoch [3943/4000] Training [25/39] Loss: 0.00344 +Epoch [3943/4000] Training [26/39] Loss: 0.00329 +Epoch [3943/4000] Training [27/39] Loss: 0.00887 +Epoch [3943/4000] Training [28/39] Loss: 0.00441 +Epoch [3943/4000] Training [29/39] Loss: 0.00273 +Epoch [3943/4000] Training [30/39] Loss: 0.00442 +Epoch [3943/4000] Training [31/39] Loss: 0.00581 +Epoch [3943/4000] Training [32/39] Loss: 0.00573 +Epoch [3943/4000] Training [33/39] Loss: 0.00289 +Epoch [3943/4000] Training [34/39] Loss: 0.00432 +Epoch [3943/4000] Training [35/39] Loss: 0.00543 +Epoch [3943/4000] Training [36/39] Loss: 0.00536 +Epoch [3943/4000] Training [37/39] Loss: 0.12781 +Epoch [3943/4000] Training [38/39] Loss: 0.00464 +Epoch [3943/4000] Training [39/39] Loss: 0.12872 +Epoch [3943/4000] Training metric {'Train/mean dice_metric': 0.995633602142334, 'Train/mean miou_metric': 0.9925378561019897, 'Train/mean f1': 0.9969615936279297, 'Train/mean precision': 0.996461808681488, 'Train/mean recall': 0.9974618554115295, 'Train/mean hd95_metric': 0.9261027574539185} +Epoch [3943/4000] Validation [1/10] Loss: 0.72892 focal_loss 0.64255 dice_loss 0.08636 +Epoch [3943/4000] Validation [2/10] Loss: 0.51178 focal_loss 0.41203 dice_loss 0.09975 +Epoch [3943/4000] Validation [3/10] Loss: 0.40649 focal_loss 0.29479 dice_loss 0.11171 +Epoch [3943/4000] Validation [4/10] Loss: 0.89753 focal_loss 0.33235 dice_loss 0.56518 +Epoch [3943/4000] Validation [5/10] Loss: 3.13144 focal_loss 2.45734 dice_loss 0.67410 +Epoch [3943/4000] Validation [6/10] Loss: 1.33829 focal_loss 0.62592 dice_loss 0.71236 +Epoch [3943/4000] Validation [7/10] Loss: 1.18197 focal_loss 0.52819 dice_loss 0.65378 +Epoch [3943/4000] Validation [8/10] Loss: 2.44310 focal_loss 1.82358 dice_loss 0.61951 +Epoch [3943/4000] Validation [9/10] Loss: 1.54682 focal_loss 1.00295 dice_loss 0.54388 +Epoch [3943/4000] Validation [10/10] Loss: 1.90009 focal_loss 1.16542 dice_loss 0.73467 +Epoch [3943/4000] Validation metric {'Val/mean dice_metric': 0.9507216215133667, 'Val/mean miou_metric': 0.9349048137664795, 'Val/mean f1': 0.9483550190925598, 'Val/mean precision': 0.9440928101539612, 'Val/mean recall': 0.9526560306549072, 'Val/mean hd95_metric': 10.814901351928711} +Cheakpoint... +Epoch [3943/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507216215133667, 'Val/mean miou_metric': 0.9349048137664795, 'Val/mean f1': 0.9483550190925598, 'Val/mean precision': 0.9440928101539612, 'Val/mean recall': 0.9526560306549072, 'Val/mean hd95_metric': 10.814901351928711} +Epoch [3944/4000] Training [1/39] Loss: 0.00514 +Epoch [3944/4000] Training [2/39] Loss: 0.25283 +Epoch [3944/4000] Training [3/39] Loss: 0.00334 +Epoch [3944/4000] Training [4/39] Loss: 0.00368 +Epoch [3944/4000] Training [5/39] Loss: 0.12922 +Epoch [3944/4000] Training [6/39] Loss: 0.13197 +Epoch [3944/4000] Training [7/39] Loss: 0.12874 +Epoch [3944/4000] Training [8/39] Loss: 0.00590 +Epoch [3944/4000] Training [9/39] Loss: 0.00494 +Epoch [3944/4000] Training [10/39] Loss: 0.12840 +Epoch [3944/4000] Training [11/39] Loss: 0.00399 +Epoch [3944/4000] Training [12/39] Loss: 0.00399 +Epoch [3944/4000] Training [13/39] Loss: 0.00383 +Epoch [3944/4000] Training [14/39] Loss: 0.00497 +Epoch [3944/4000] Training [15/39] Loss: 0.00576 +Epoch [3944/4000] Training [16/39] Loss: 0.00864 +Epoch [3944/4000] Training [17/39] Loss: 0.00620 +Epoch [3944/4000] Training [18/39] Loss: 0.00573 +Epoch [3944/4000] Training [19/39] Loss: 0.00478 +Epoch [3944/4000] Training [20/39] Loss: 0.00443 +Epoch [3944/4000] Training [21/39] Loss: 0.12868 +Epoch [3944/4000] Training [22/39] Loss: 0.00677 +Epoch [3944/4000] Training [23/39] Loss: 0.00343 +Epoch [3944/4000] Training [24/39] Loss: 0.00437 +Epoch [3944/4000] Training [25/39] Loss: 0.00469 +Epoch [3944/4000] Training [26/39] Loss: 0.00532 +Epoch [3944/4000] Training [27/39] Loss: 0.00389 +Epoch [3944/4000] Training [28/39] Loss: 0.00414 +Epoch [3944/4000] Training [29/39] Loss: 0.00495 +Epoch [3944/4000] Training [30/39] Loss: 0.00454 +Epoch [3944/4000] Training [31/39] Loss: 0.00990 +Epoch [3944/4000] Training [32/39] Loss: 0.12703 +Epoch [3944/4000] Training [33/39] Loss: 0.00552 +Epoch [3944/4000] Training [34/39] Loss: 0.12933 +Epoch [3944/4000] Training [35/39] Loss: 0.00516 +Epoch [3944/4000] Training [36/39] Loss: 0.00415 +Epoch [3944/4000] Training [37/39] Loss: 0.12885 +Epoch [3944/4000] Training [38/39] Loss: 0.00315 +Epoch [3944/4000] Training [39/39] Loss: 0.00470 +Epoch [3944/4000] Training metric {'Train/mean dice_metric': 0.9965423941612244, 'Train/mean miou_metric': 0.9935279488563538, 'Train/mean f1': 0.9970235824584961, 'Train/mean precision': 0.9965513944625854, 'Train/mean recall': 0.9974963068962097, 'Train/mean hd95_metric': 0.9094916582107544} +Epoch [3944/4000] Validation [1/10] Loss: 0.70208 focal_loss 0.61662 dice_loss 0.08546 +Epoch [3944/4000] Validation [2/10] Loss: 0.50844 focal_loss 0.40658 dice_loss 0.10186 +Epoch [3944/4000] Validation [3/10] Loss: 0.40143 focal_loss 0.28907 dice_loss 0.11235 +Epoch [3944/4000] Validation [4/10] Loss: 0.88817 focal_loss 0.32393 dice_loss 0.56424 +Epoch [3944/4000] Validation [5/10] Loss: 3.05111 focal_loss 2.37695 dice_loss 0.67415 +Epoch [3944/4000] Validation [6/10] Loss: 1.32013 focal_loss 0.60745 dice_loss 0.71268 +Epoch [3944/4000] Validation [7/10] Loss: 1.16505 focal_loss 0.51347 dice_loss 0.65158 +Epoch [3944/4000] Validation [8/10] Loss: 2.42717 focal_loss 1.80307 dice_loss 0.62410 +Epoch [3944/4000] Validation [9/10] Loss: 1.51212 focal_loss 0.96843 dice_loss 0.54369 +Epoch [3944/4000] Validation [10/10] Loss: 1.85757 focal_loss 1.12389 dice_loss 0.73368 +Epoch [3944/4000] Validation metric {'Val/mean dice_metric': 0.9515679478645325, 'Val/mean miou_metric': 0.9358670115470886, 'Val/mean f1': 0.9488492012023926, 'Val/mean precision': 0.945162832736969, 'Val/mean recall': 0.9525643587112427, 'Val/mean hd95_metric': 10.749890327453613} +Cheakpoint... +Epoch [3944/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515679478645325, 'Val/mean miou_metric': 0.9358670115470886, 'Val/mean f1': 0.9488492012023926, 'Val/mean precision': 0.945162832736969, 'Val/mean recall': 0.9525643587112427, 'Val/mean hd95_metric': 10.749890327453613} +Epoch [3945/4000] Training [1/39] Loss: 0.00393 +Epoch [3945/4000] Training [2/39] Loss: 0.00959 +Epoch [3945/4000] Training [3/39] Loss: 0.00732 +Epoch [3945/4000] Training [4/39] Loss: 0.00547 +Epoch [3945/4000] Training [5/39] Loss: 0.00735 +Epoch [3945/4000] Training [6/39] Loss: 0.00406 +Epoch [3945/4000] Training [7/39] Loss: 0.00422 +Epoch [3945/4000] Training [8/39] Loss: 0.12804 +Epoch [3945/4000] Training [9/39] Loss: 0.00425 +Epoch [3945/4000] Training [10/39] Loss: 0.00460 +Epoch [3945/4000] Training [11/39] Loss: 0.00520 +Epoch [3945/4000] Training [12/39] Loss: 0.12998 +Epoch [3945/4000] Training [13/39] Loss: 0.12796 +Epoch [3945/4000] Training [14/39] Loss: 0.13103 +Epoch [3945/4000] Training [15/39] Loss: 0.12878 +Epoch [3945/4000] Training [16/39] Loss: 0.00712 +Epoch [3945/4000] Training [17/39] Loss: 0.00507 +Epoch [3945/4000] Training [18/39] Loss: 0.00441 +Epoch [3945/4000] Training [19/39] Loss: 0.12880 +Epoch [3945/4000] Training [20/39] Loss: 0.00442 +Epoch [3945/4000] Training [21/39] Loss: 0.00492 +Epoch [3945/4000] Training [22/39] Loss: 0.00449 +Epoch [3945/4000] Training [23/39] Loss: 0.12950 +Epoch [3945/4000] Training [24/39] Loss: 0.00401 +Epoch [3945/4000] Training [25/39] Loss: 0.00488 +Epoch [3945/4000] Training [26/39] Loss: 0.00481 +Epoch [3945/4000] Training [27/39] Loss: 0.00575 +Epoch [3945/4000] Training [28/39] Loss: 0.12883 +Epoch [3945/4000] Training [29/39] Loss: 0.12836 +Epoch [3945/4000] Training [30/39] Loss: 0.00329 +Epoch [3945/4000] Training [31/39] Loss: 0.00380 +Epoch [3945/4000] Training [32/39] Loss: 0.00355 +Epoch [3945/4000] Training [33/39] Loss: 0.00457 +Epoch [3945/4000] Training [34/39] Loss: 0.00333 +Epoch [3945/4000] Training [35/39] Loss: 0.00270 +Epoch [3945/4000] Training [36/39] Loss: 0.00616 +Epoch [3945/4000] Training [37/39] Loss: 0.00538 +Epoch [3945/4000] Training [38/39] Loss: 0.12758 +Epoch [3945/4000] Training [39/39] Loss: 0.00479 +Epoch [3945/4000] Training metric {'Train/mean dice_metric': 0.9964833855628967, 'Train/mean miou_metric': 0.9934068918228149, 'Train/mean f1': 0.9969375729560852, 'Train/mean precision': 0.9964240789413452, 'Train/mean recall': 0.9974516034126282, 'Train/mean hd95_metric': 0.907390832901001} +Epoch [3945/4000] Validation [1/10] Loss: 0.71335 focal_loss 0.62681 dice_loss 0.08654 +Epoch [3945/4000] Validation [2/10] Loss: 0.50667 focal_loss 0.40704 dice_loss 0.09963 +Epoch [3945/4000] Validation [3/10] Loss: 0.39688 focal_loss 0.28511 dice_loss 0.11177 +Epoch [3945/4000] Validation [4/10] Loss: 0.89586 focal_loss 0.33034 dice_loss 0.56553 +Epoch [3945/4000] Validation [5/10] Loss: 3.05813 focal_loss 2.38409 dice_loss 0.67403 +Epoch [3945/4000] Validation [6/10] Loss: 1.33523 focal_loss 0.62289 dice_loss 0.71234 +Epoch [3945/4000] Validation [7/10] Loss: 1.17558 focal_loss 0.52159 dice_loss 0.65399 +Epoch [3945/4000] Validation [8/10] Loss: 2.39001 focal_loss 1.77189 dice_loss 0.61813 +Epoch [3945/4000] Validation [9/10] Loss: 1.53149 focal_loss 0.98732 dice_loss 0.54417 +Epoch [3945/4000] Validation [10/10] Loss: 1.89112 focal_loss 1.15619 dice_loss 0.73492 +Epoch [3945/4000] Validation metric {'Val/mean dice_metric': 0.9514845609664917, 'Val/mean miou_metric': 0.9356915950775146, 'Val/mean f1': 0.9484069347381592, 'Val/mean precision': 0.9438787698745728, 'Val/mean recall': 0.952978789806366, 'Val/mean hd95_metric': 10.800880432128906} +Cheakpoint... +Epoch [3945/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514845609664917, 'Val/mean miou_metric': 0.9356915950775146, 'Val/mean f1': 0.9484069347381592, 'Val/mean precision': 0.9438787698745728, 'Val/mean recall': 0.952978789806366, 'Val/mean hd95_metric': 10.800880432128906} +Epoch [3946/4000] Training [1/39] Loss: 0.00301 +Epoch [3946/4000] Training [2/39] Loss: 0.00448 +Epoch [3946/4000] Training [3/39] Loss: 0.00307 +Epoch [3946/4000] Training [4/39] Loss: 0.12807 +Epoch [3946/4000] Training [5/39] Loss: 0.00336 +Epoch [3946/4000] Training [6/39] Loss: 0.00428 +Epoch [3946/4000] Training [7/39] Loss: 0.25325 +Epoch [3946/4000] Training [8/39] Loss: 0.00406 +Epoch [3946/4000] Training [9/39] Loss: 0.00329 +Epoch [3946/4000] Training [10/39] Loss: 0.00384 +Epoch [3946/4000] Training [11/39] Loss: 0.12793 +Epoch [3946/4000] Training [12/39] Loss: 0.12887 +Epoch [3946/4000] Training [13/39] Loss: 0.13287 +Epoch [3946/4000] Training [14/39] Loss: 0.00784 +Epoch [3946/4000] Training [15/39] Loss: 0.00650 +Epoch [3946/4000] Training [16/39] Loss: 0.00332 +Epoch [3946/4000] Training [17/39] Loss: 0.13400 +Epoch [3946/4000] Training [18/39] Loss: 0.25255 +Epoch [3946/4000] Training [19/39] Loss: 0.00695 +Epoch [3946/4000] Training [20/39] Loss: 0.00942 +Epoch [3946/4000] Training [21/39] Loss: 0.00474 +Epoch [3946/4000] Training [22/39] Loss: 0.00559 +Epoch [3946/4000] Training [23/39] Loss: 0.00388 +Epoch [3946/4000] Training [24/39] Loss: 0.00660 +Epoch [3946/4000] Training [25/39] Loss: 0.00665 +Epoch [3946/4000] Training [26/39] Loss: 0.12914 +Epoch [3946/4000] Training [27/39] Loss: 0.12991 +Epoch [3946/4000] Training [28/39] Loss: 0.00312 +Epoch [3946/4000] Training [29/39] Loss: 0.00361 +Epoch [3946/4000] Training [30/39] Loss: 0.00511 +Epoch [3946/4000] Training [31/39] Loss: 0.00397 +Epoch [3946/4000] Training [32/39] Loss: 0.00457 +Epoch [3946/4000] Training [33/39] Loss: 0.12873 +Epoch [3946/4000] Training [34/39] Loss: 0.00361 +Epoch [3946/4000] Training [35/39] Loss: 0.00386 +Epoch [3946/4000] Training [36/39] Loss: 0.00556 +Epoch [3946/4000] Training [37/39] Loss: 0.00483 +Epoch [3946/4000] Training [38/39] Loss: 0.00563 +Epoch [3946/4000] Training [39/39] Loss: 0.00410 +Epoch [3946/4000] Training metric {'Train/mean dice_metric': 0.9962851405143738, 'Train/mean miou_metric': 0.9930222034454346, 'Train/mean f1': 0.9968735575675964, 'Train/mean precision': 0.9963881969451904, 'Train/mean recall': 0.9973594546318054, 'Train/mean hd95_metric': 0.9146004915237427} +Epoch [3946/4000] Validation [1/10] Loss: 0.73562 focal_loss 0.64848 dice_loss 0.08713 +Epoch [3946/4000] Validation [2/10] Loss: 0.50753 focal_loss 0.40953 dice_loss 0.09800 +Epoch [3946/4000] Validation [3/10] Loss: 0.40137 focal_loss 0.29010 dice_loss 0.11127 +Epoch [3946/4000] Validation [4/10] Loss: 0.89977 focal_loss 0.33403 dice_loss 0.56574 +Epoch [3946/4000] Validation [5/10] Loss: 3.13081 focal_loss 2.45687 dice_loss 0.67395 +Epoch [3946/4000] Validation [6/10] Loss: 1.34384 focal_loss 0.63136 dice_loss 0.71248 +Epoch [3946/4000] Validation [7/10] Loss: 1.18394 focal_loss 0.52832 dice_loss 0.65562 +Epoch [3946/4000] Validation [8/10] Loss: 2.40129 focal_loss 1.78699 dice_loss 0.61430 +Epoch [3946/4000] Validation [9/10] Loss: 1.55574 focal_loss 1.01182 dice_loss 0.54392 +Epoch [3946/4000] Validation [10/10] Loss: 1.91247 focal_loss 1.17698 dice_loss 0.73549 +Epoch [3946/4000] Validation metric {'Val/mean dice_metric': 0.951299786567688, 'Val/mean miou_metric': 0.9353348016738892, 'Val/mean f1': 0.9481076002120972, 'Val/mean precision': 0.9432002902030945, 'Val/mean recall': 0.9530662894248962, 'Val/mean hd95_metric': 10.677290916442871} +Cheakpoint... +Epoch [3946/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951299786567688, 'Val/mean miou_metric': 0.9353348016738892, 'Val/mean f1': 0.9481076002120972, 'Val/mean precision': 0.9432002902030945, 'Val/mean recall': 0.9530662894248962, 'Val/mean hd95_metric': 10.677290916442871} +Epoch [3947/4000] Training [1/39] Loss: 0.25585 +Epoch [3947/4000] Training [2/39] Loss: 0.00671 +Epoch [3947/4000] Training [3/39] Loss: 0.13075 +Epoch [3947/4000] Training [4/39] Loss: 0.00583 +Epoch [3947/4000] Training [5/39] Loss: 0.00622 +Epoch [3947/4000] Training [6/39] Loss: 0.00248 +Epoch [3947/4000] Training [7/39] Loss: 0.00391 +Epoch [3947/4000] Training [8/39] Loss: 0.00442 +Epoch [3947/4000] Training [9/39] Loss: 0.00421 +Epoch [3947/4000] Training [10/39] Loss: 0.25461 +Epoch [3947/4000] Training [11/39] Loss: 0.00308 +Epoch [3947/4000] Training [12/39] Loss: 0.00535 +Epoch [3947/4000] Training [13/39] Loss: 0.12892 +Epoch [3947/4000] Training [14/39] Loss: 0.12957 +Epoch [3947/4000] Training [15/39] Loss: 0.12803 +Epoch [3947/4000] Training [16/39] Loss: 0.00584 +Epoch [3947/4000] Training [17/39] Loss: 0.00808 +Epoch [3947/4000] Training [18/39] Loss: 0.12921 +Epoch [3947/4000] Training [19/39] Loss: 0.00259 +Epoch [3947/4000] Training [20/39] Loss: 0.13131 +Epoch [3947/4000] Training [21/39] Loss: 0.00553 +Epoch [3947/4000] Training [22/39] Loss: 0.13041 +Epoch [3947/4000] Training [23/39] Loss: 0.00839 +Epoch [3947/4000] Training [24/39] Loss: 0.00362 +Epoch [3947/4000] Training [25/39] Loss: 0.00560 +Epoch [3947/4000] Training [26/39] Loss: 0.00594 +Epoch [3947/4000] Training [27/39] Loss: 0.00517 +Epoch [3947/4000] Training [28/39] Loss: 0.00399 +Epoch [3947/4000] Training [29/39] Loss: 0.12748 +Epoch [3947/4000] Training [30/39] Loss: 0.00511 +Epoch [3947/4000] Training [31/39] Loss: 0.00395 +Epoch [3947/4000] Training [32/39] Loss: 0.00436 +Epoch [3947/4000] Training [33/39] Loss: 0.00521 +Epoch [3947/4000] Training [34/39] Loss: 0.00452 +Epoch [3947/4000] Training [35/39] Loss: 0.00847 +Epoch [3947/4000] Training [36/39] Loss: 0.00564 +Epoch [3947/4000] Training [37/39] Loss: 0.00341 +Epoch [3947/4000] Training [38/39] Loss: 0.00395 +Epoch [3947/4000] Training [39/39] Loss: 0.00689 +Epoch [3947/4000] Training metric {'Train/mean dice_metric': 0.9954116940498352, 'Train/mean miou_metric': 0.992125928401947, 'Train/mean f1': 0.9967954754829407, 'Train/mean precision': 0.996339738368988, 'Train/mean recall': 0.997251570224762, 'Train/mean hd95_metric': 0.9892942905426025} +Epoch [3947/4000] Validation [1/10] Loss: 0.71662 focal_loss 0.62995 dice_loss 0.08667 +Epoch [3947/4000] Validation [2/10] Loss: 0.50294 focal_loss 0.40430 dice_loss 0.09865 +Epoch [3947/4000] Validation [3/10] Loss: 0.39325 focal_loss 0.28197 dice_loss 0.11128 +Epoch [3947/4000] Validation [4/10] Loss: 0.89537 focal_loss 0.32983 dice_loss 0.56554 +Epoch [3947/4000] Validation [5/10] Loss: 3.06644 focal_loss 2.39245 dice_loss 0.67399 +Epoch [3947/4000] Validation [6/10] Loss: 1.33568 focal_loss 0.62312 dice_loss 0.71256 +Epoch [3947/4000] Validation [7/10] Loss: 1.17577 focal_loss 0.52104 dice_loss 0.65473 +Epoch [3947/4000] Validation [8/10] Loss: 2.37499 focal_loss 1.75846 dice_loss 0.61653 +Epoch [3947/4000] Validation [9/10] Loss: 1.52810 focal_loss 0.98413 dice_loss 0.54397 +Epoch [3947/4000] Validation [10/10] Loss: 1.89066 focal_loss 1.15555 dice_loss 0.73511 +Epoch [3947/4000] Validation metric {'Val/mean dice_metric': 0.9505577087402344, 'Val/mean miou_metric': 0.9345763325691223, 'Val/mean f1': 0.9481630921363831, 'Val/mean precision': 0.9434723854064941, 'Val/mean recall': 0.9529006481170654, 'Val/mean hd95_metric': 10.76848030090332} +Cheakpoint... +Epoch [3947/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9506], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9505577087402344, 'Val/mean miou_metric': 0.9345763325691223, 'Val/mean f1': 0.9481630921363831, 'Val/mean precision': 0.9434723854064941, 'Val/mean recall': 0.9529006481170654, 'Val/mean hd95_metric': 10.76848030090332} +Epoch [3948/4000] Training [1/39] Loss: 0.00576 +Epoch [3948/4000] Training [2/39] Loss: 0.00448 +Epoch [3948/4000] Training [3/39] Loss: 0.00356 +Epoch [3948/4000] Training [4/39] Loss: 0.00602 +Epoch [3948/4000] Training [5/39] Loss: 0.12841 +Epoch [3948/4000] Training [6/39] Loss: 0.00356 +Epoch [3948/4000] Training [7/39] Loss: 0.00356 +Epoch [3948/4000] Training [8/39] Loss: 0.00988 +Epoch [3948/4000] Training [9/39] Loss: 0.00288 +Epoch [3948/4000] Training [10/39] Loss: 0.00445 +Epoch [3948/4000] Training [11/39] Loss: 0.00391 +Epoch [3948/4000] Training [12/39] Loss: 0.12898 +Epoch [3948/4000] Training [13/39] Loss: 0.00442 +Epoch [3948/4000] Training [14/39] Loss: 0.12862 +Epoch [3948/4000] Training [15/39] Loss: 0.00414 +Epoch [3948/4000] Training [16/39] Loss: 0.00227 +Epoch [3948/4000] Training [17/39] Loss: 0.00545 +Epoch [3948/4000] Training [18/39] Loss: 0.00412 +Epoch [3948/4000] Training [19/39] Loss: 0.00652 +Epoch [3948/4000] Training [20/39] Loss: 0.00324 +Epoch [3948/4000] Training [21/39] Loss: 0.00541 +Epoch [3948/4000] Training [22/39] Loss: 0.00418 +Epoch [3948/4000] Training [23/39] Loss: 0.00342 +Epoch [3948/4000] Training [24/39] Loss: 0.12889 +Epoch [3948/4000] Training [25/39] Loss: 0.00434 +Epoch [3948/4000] Training [26/39] Loss: 0.12833 +Epoch [3948/4000] Training [27/39] Loss: 0.00321 +Epoch [3948/4000] Training [28/39] Loss: 0.00505 +Epoch [3948/4000] Training [29/39] Loss: 0.00284 +Epoch [3948/4000] Training [30/39] Loss: 0.00331 +Epoch [3948/4000] Training [31/39] Loss: 0.00601 +Epoch [3948/4000] Training [32/39] Loss: 0.13027 +Epoch [3948/4000] Training [33/39] Loss: 0.25259 +Epoch [3948/4000] Training [34/39] Loss: 0.12847 +Epoch [3948/4000] Training [35/39] Loss: 0.00890 +Epoch [3948/4000] Training [36/39] Loss: 0.00522 +Epoch [3948/4000] Training [37/39] Loss: 0.00452 +Epoch [3948/4000] Training [38/39] Loss: 0.00311 +Epoch [3948/4000] Training [39/39] Loss: 0.00489 +Epoch [3948/4000] Training metric {'Train/mean dice_metric': 0.9964398145675659, 'Train/mean miou_metric': 0.993375837802887, 'Train/mean f1': 0.9970226883888245, 'Train/mean precision': 0.9965020418167114, 'Train/mean recall': 0.9975438714027405, 'Train/mean hd95_metric': 1.055147409439087} +Epoch [3948/4000] Validation [1/10] Loss: 0.71968 focal_loss 0.63277 dice_loss 0.08690 +Epoch [3948/4000] Validation [2/10] Loss: 0.50938 focal_loss 0.41169 dice_loss 0.09769 +Epoch [3948/4000] Validation [3/10] Loss: 0.39075 focal_loss 0.27985 dice_loss 0.11090 +Epoch [3948/4000] Validation [4/10] Loss: 0.90562 focal_loss 0.33933 dice_loss 0.56630 +Epoch [3948/4000] Validation [5/10] Loss: 3.06544 focal_loss 2.39151 dice_loss 0.67392 +Epoch [3948/4000] Validation [6/10] Loss: 1.35707 focal_loss 0.64450 dice_loss 0.71257 +Epoch [3948/4000] Validation [7/10] Loss: 1.19152 focal_loss 0.53587 dice_loss 0.65566 +Epoch [3948/4000] Validation [8/10] Loss: 2.37716 focal_loss 1.76321 dice_loss 0.61395 +Epoch [3948/4000] Validation [9/10] Loss: 1.55056 focal_loss 1.00606 dice_loss 0.54450 +Epoch [3948/4000] Validation [10/10] Loss: 1.93081 focal_loss 1.19502 dice_loss 0.73579 +Epoch [3948/4000] Validation metric {'Val/mean dice_metric': 0.9514660239219666, 'Val/mean miou_metric': 0.9356681704521179, 'Val/mean f1': 0.9482530951499939, 'Val/mean precision': 0.9430620670318604, 'Val/mean recall': 0.9535017013549805, 'Val/mean hd95_metric': 10.79917049407959} +Cheakpoint... +Epoch [3948/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514660239219666, 'Val/mean miou_metric': 0.9356681704521179, 'Val/mean f1': 0.9482530951499939, 'Val/mean precision': 0.9430620670318604, 'Val/mean recall': 0.9535017013549805, 'Val/mean hd95_metric': 10.79917049407959} +Epoch [3949/4000] Training [1/39] Loss: 0.00650 +Epoch [3949/4000] Training [2/39] Loss: 0.00567 +Epoch [3949/4000] Training [3/39] Loss: 0.00452 +Epoch [3949/4000] Training [4/39] Loss: 0.00381 +Epoch [3949/4000] Training [5/39] Loss: 0.00560 +Epoch [3949/4000] Training [6/39] Loss: 0.00451 +Epoch [3949/4000] Training [7/39] Loss: 0.00501 +Epoch [3949/4000] Training [8/39] Loss: 0.00423 +Epoch [3949/4000] Training [9/39] Loss: 0.12973 +Epoch [3949/4000] Training [10/39] Loss: 0.12978 +Epoch [3949/4000] Training [11/39] Loss: 0.12787 +Epoch [3949/4000] Training [12/39] Loss: 0.13056 +Epoch [3949/4000] Training [13/39] Loss: 0.00401 +Epoch [3949/4000] Training [14/39] Loss: 0.00511 +Epoch [3949/4000] Training [15/39] Loss: 0.00499 +Epoch [3949/4000] Training [16/39] Loss: 0.00515 +Epoch [3949/4000] Training [17/39] Loss: 0.00735 +Epoch [3949/4000] Training [18/39] Loss: 0.00539 +Epoch [3949/4000] Training [19/39] Loss: 0.00564 +Epoch [3949/4000] Training [20/39] Loss: 0.00555 +Epoch [3949/4000] Training [21/39] Loss: 0.00461 +Epoch [3949/4000] Training [22/39] Loss: 0.12982 +Epoch [3949/4000] Training [23/39] Loss: 0.12974 +Epoch [3949/4000] Training [24/39] Loss: 0.12910 +Epoch [3949/4000] Training [25/39] Loss: 0.00615 +Epoch [3949/4000] Training [26/39] Loss: 0.00544 +Epoch [3949/4000] Training [27/39] Loss: 0.12732 +Epoch [3949/4000] Training [28/39] Loss: 0.00441 +Epoch [3949/4000] Training [29/39] Loss: 0.00363 +Epoch [3949/4000] Training [30/39] Loss: 0.04256 +Epoch [3949/4000] Training [31/39] Loss: 0.00507 +Epoch [3949/4000] Training [32/39] Loss: 0.12880 +Epoch [3949/4000] Training [33/39] Loss: 0.00552 +Epoch [3949/4000] Training [34/39] Loss: 0.00748 +Epoch [3949/4000] Training [35/39] Loss: 0.12908 +Epoch [3949/4000] Training [36/39] Loss: 0.12895 +Epoch [3949/4000] Training [37/39] Loss: 0.00603 +Epoch [3949/4000] Training [38/39] Loss: 0.00747 +Epoch [3949/4000] Training [39/39] Loss: 0.13191 +Epoch [3949/4000] Training metric {'Train/mean dice_metric': 0.9961413741111755, 'Train/mean miou_metric': 0.9927326440811157, 'Train/mean f1': 0.9967171549797058, 'Train/mean precision': 0.9962833523750305, 'Train/mean recall': 0.997151255607605, 'Train/mean hd95_metric': 0.9991360902786255} +Epoch [3949/4000] Validation [1/10] Loss: 0.71912 focal_loss 0.63228 dice_loss 0.08684 +Epoch [3949/4000] Validation [2/10] Loss: 0.50582 focal_loss 0.40872 dice_loss 0.09709 +Epoch [3949/4000] Validation [3/10] Loss: 0.38751 focal_loss 0.27685 dice_loss 0.11066 +Epoch [3949/4000] Validation [4/10] Loss: 0.90296 focal_loss 0.33700 dice_loss 0.56595 +Epoch [3949/4000] Validation [5/10] Loss: 3.06060 focal_loss 2.38676 dice_loss 0.67383 +Epoch [3949/4000] Validation [6/10] Loss: 1.35458 focal_loss 0.64192 dice_loss 0.71267 +Epoch [3949/4000] Validation [7/10] Loss: 1.18967 focal_loss 0.53358 dice_loss 0.65608 +Epoch [3949/4000] Validation [8/10] Loss: 2.36595 focal_loss 1.75295 dice_loss 0.61299 +Epoch [3949/4000] Validation [9/10] Loss: 1.54259 focal_loss 0.99824 dice_loss 0.54435 +Epoch [3949/4000] Validation [10/10] Loss: 1.92467 focal_loss 1.18897 dice_loss 0.73570 +Epoch [3949/4000] Validation metric {'Val/mean dice_metric': 0.9512485861778259, 'Val/mean miou_metric': 0.9351707696914673, 'Val/mean f1': 0.9483054280281067, 'Val/mean precision': 0.9432046413421631, 'Val/mean recall': 0.953461766242981, 'Val/mean hd95_metric': 10.887014389038086} +Cheakpoint... +Epoch [3949/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512485861778259, 'Val/mean miou_metric': 0.9351707696914673, 'Val/mean f1': 0.9483054280281067, 'Val/mean precision': 0.9432046413421631, 'Val/mean recall': 0.953461766242981, 'Val/mean hd95_metric': 10.887014389038086} +Epoch [3950/4000] Training [1/39] Loss: 0.00573 +Epoch [3950/4000] Training [2/39] Loss: 0.00386 +Epoch [3950/4000] Training [3/39] Loss: 0.00595 +Epoch [3950/4000] Training [4/39] Loss: 0.00617 +Epoch [3950/4000] Training [5/39] Loss: 0.12802 +Epoch [3950/4000] Training [6/39] Loss: 0.12658 +Epoch [3950/4000] Training [7/39] Loss: 0.00634 +Epoch [3950/4000] Training [8/39] Loss: 0.12771 +Epoch [3950/4000] Training [9/39] Loss: 0.00686 +Epoch [3950/4000] Training [10/39] Loss: 0.04182 +Epoch [3950/4000] Training [11/39] Loss: 0.00354 +Epoch [3950/4000] Training [12/39] Loss: 0.00368 +Epoch [3950/4000] Training [13/39] Loss: 0.00489 +Epoch [3950/4000] Training [14/39] Loss: 0.12793 +Epoch [3950/4000] Training [15/39] Loss: 0.12863 +Epoch [3950/4000] Training [16/39] Loss: 0.00435 +Epoch [3950/4000] Training [17/39] Loss: 0.00674 +Epoch [3950/4000] Training [18/39] Loss: 0.00321 +Epoch [3950/4000] Training [19/39] Loss: 0.00336 +Epoch [3950/4000] Training [20/39] Loss: 0.00574 +Epoch [3950/4000] Training [21/39] Loss: 0.00369 +Epoch [3950/4000] Training [22/39] Loss: 0.00342 +Epoch [3950/4000] Training [23/39] Loss: 0.25241 +Epoch [3950/4000] Training [24/39] Loss: 0.00563 +Epoch [3950/4000] Training [25/39] Loss: 0.13129 +Epoch [3950/4000] Training [26/39] Loss: 0.00391 +Epoch [3950/4000] Training [27/39] Loss: 0.00418 +Epoch [3950/4000] Training [28/39] Loss: 0.00494 +Epoch [3950/4000] Training [29/39] Loss: 0.12821 +Epoch [3950/4000] Training [30/39] Loss: 0.00313 +Epoch [3950/4000] Training [31/39] Loss: 0.25385 +Epoch [3950/4000] Training [32/39] Loss: 0.00474 +Epoch [3950/4000] Training [33/39] Loss: 0.00322 +Epoch [3950/4000] Training [34/39] Loss: 0.12883 +Epoch [3950/4000] Training [35/39] Loss: 0.12971 +Epoch [3950/4000] Training [36/39] Loss: 0.00522 +Epoch [3950/4000] Training [37/39] Loss: 0.12910 +Epoch [3950/4000] Training [38/39] Loss: 0.00601 +Epoch [3950/4000] Training [39/39] Loss: 0.00560 +Epoch [3950/4000] Training metric {'Train/mean dice_metric': 0.9966461062431335, 'Train/mean miou_metric': 0.993743896484375, 'Train/mean f1': 0.9970802664756775, 'Train/mean precision': 0.9966341257095337, 'Train/mean recall': 0.9975268244743347, 'Train/mean hd95_metric': 0.983330488204956} +Epoch [3950/4000] Validation [1/10] Loss: 0.71281 focal_loss 0.62643 dice_loss 0.08638 +Epoch [3950/4000] Validation [2/10] Loss: 0.50697 focal_loss 0.40770 dice_loss 0.09927 +Epoch [3950/4000] Validation [3/10] Loss: 0.39550 focal_loss 0.28396 dice_loss 0.11155 +Epoch [3950/4000] Validation [4/10] Loss: 0.89610 focal_loss 0.33070 dice_loss 0.56540 +Epoch [3950/4000] Validation [5/10] Loss: 3.05972 focal_loss 2.38571 dice_loss 0.67401 +Epoch [3950/4000] Validation [6/10] Loss: 1.33872 focal_loss 0.62636 dice_loss 0.71236 +Epoch [3950/4000] Validation [7/10] Loss: 1.17943 focal_loss 0.52496 dice_loss 0.65448 +Epoch [3950/4000] Validation [8/10] Loss: 2.38719 focal_loss 1.76964 dice_loss 0.61755 +Epoch [3950/4000] Validation [9/10] Loss: 1.53266 focal_loss 0.98849 dice_loss 0.54417 +Epoch [3950/4000] Validation [10/10] Loss: 1.89731 focal_loss 1.16236 dice_loss 0.73494 +Epoch [3950/4000] Validation metric {'Val/mean dice_metric': 0.9516345858573914, 'Val/mean miou_metric': 0.9359889030456543, 'Val/mean f1': 0.9486620426177979, 'Val/mean precision': 0.9441884160041809, 'Val/mean recall': 0.9531781077384949, 'Val/mean hd95_metric': 10.73342514038086} +Cheakpoint... +Epoch [3950/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516345858573914, 'Val/mean miou_metric': 0.9359889030456543, 'Val/mean f1': 0.9486620426177979, 'Val/mean precision': 0.9441884160041809, 'Val/mean recall': 0.9531781077384949, 'Val/mean hd95_metric': 10.73342514038086} +Epoch [3951/4000] Training [1/39] Loss: 0.00614 +Epoch [3951/4000] Training [2/39] Loss: 0.00478 +Epoch [3951/4000] Training [3/39] Loss: 0.12945 +Epoch [3951/4000] Training [4/39] Loss: 0.00548 +Epoch [3951/4000] Training [5/39] Loss: 0.00425 +Epoch [3951/4000] Training [6/39] Loss: 0.12914 +Epoch [3951/4000] Training [7/39] Loss: 0.12953 +Epoch [3951/4000] Training [8/39] Loss: 0.13196 +Epoch [3951/4000] Training [9/39] Loss: 0.00525 +Epoch [3951/4000] Training [10/39] Loss: 0.00494 +Epoch [3951/4000] Training [11/39] Loss: 0.00397 +Epoch [3951/4000] Training [12/39] Loss: 0.12843 +Epoch [3951/4000] Training [13/39] Loss: 0.00422 +Epoch [3951/4000] Training [14/39] Loss: 0.00440 +Epoch [3951/4000] Training [15/39] Loss: 0.13064 +Epoch [3951/4000] Training [16/39] Loss: 0.00543 +Epoch [3951/4000] Training [17/39] Loss: 0.00456 +Epoch [3951/4000] Training [18/39] Loss: 0.12935 +Epoch [3951/4000] Training [19/39] Loss: 0.08689 +Epoch [3951/4000] Training [20/39] Loss: 0.00431 +Epoch [3951/4000] Training [21/39] Loss: 0.12777 +Epoch [3951/4000] Training [22/39] Loss: 0.00322 +Epoch [3951/4000] Training [23/39] Loss: 0.00331 +Epoch [3951/4000] Training [24/39] Loss: 0.00495 +Epoch [3951/4000] Training [25/39] Loss: 0.00330 +Epoch [3951/4000] Training [26/39] Loss: 0.00518 +Epoch [3951/4000] Training [27/39] Loss: 0.12864 +Epoch [3951/4000] Training [28/39] Loss: 0.00567 +Epoch [3951/4000] Training [29/39] Loss: 0.25411 +Epoch [3951/4000] Training [30/39] Loss: 0.12908 +Epoch [3951/4000] Training [31/39] Loss: 0.00502 +Epoch [3951/4000] Training [32/39] Loss: 0.00466 +Epoch [3951/4000] Training [33/39] Loss: 0.00390 +Epoch [3951/4000] Training [34/39] Loss: 0.00523 +Epoch [3951/4000] Training [35/39] Loss: 0.00552 +Epoch [3951/4000] Training [36/39] Loss: 0.00529 +Epoch [3951/4000] Training [37/39] Loss: 0.13043 +Epoch [3951/4000] Training [38/39] Loss: 0.00313 +Epoch [3951/4000] Training [39/39] Loss: 0.12974 +Epoch [3951/4000] Training metric {'Train/mean dice_metric': 0.996429979801178, 'Train/mean miou_metric': 0.9933040738105774, 'Train/mean f1': 0.9969543218612671, 'Train/mean precision': 0.9964213371276855, 'Train/mean recall': 0.9974879622459412, 'Train/mean hd95_metric': 0.9228469133377075} +Epoch [3951/4000] Validation [1/10] Loss: 0.71977 focal_loss 0.63327 dice_loss 0.08650 +Epoch [3951/4000] Validation [2/10] Loss: 0.50568 focal_loss 0.40699 dice_loss 0.09869 +Epoch [3951/4000] Validation [3/10] Loss: 0.39567 focal_loss 0.28443 dice_loss 0.11124 +Epoch [3951/4000] Validation [4/10] Loss: 0.89590 focal_loss 0.33061 dice_loss 0.56529 +Epoch [3951/4000] Validation [5/10] Loss: 3.08471 focal_loss 2.41075 dice_loss 0.67395 +Epoch [3951/4000] Validation [6/10] Loss: 1.34092 focal_loss 0.62846 dice_loss 0.71246 +Epoch [3951/4000] Validation [7/10] Loss: 1.17905 focal_loss 0.52428 dice_loss 0.65477 +Epoch [3951/4000] Validation [8/10] Loss: 2.38928 focal_loss 1.77319 dice_loss 0.61609 +Epoch [3951/4000] Validation [9/10] Loss: 1.53619 focal_loss 0.99227 dice_loss 0.54392 +Epoch [3951/4000] Validation [10/10] Loss: 1.90000 focal_loss 1.16496 dice_loss 0.73503 +Epoch [3951/4000] Validation metric {'Val/mean dice_metric': 0.9514835476875305, 'Val/mean miou_metric': 0.935659646987915, 'Val/mean f1': 0.948493480682373, 'Val/mean precision': 0.943810224533081, 'Val/mean recall': 0.9532235264778137, 'Val/mean hd95_metric': 10.66405200958252} +Cheakpoint... +Epoch [3951/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514835476875305, 'Val/mean miou_metric': 0.935659646987915, 'Val/mean f1': 0.948493480682373, 'Val/mean precision': 0.943810224533081, 'Val/mean recall': 0.9532235264778137, 'Val/mean hd95_metric': 10.66405200958252} +Epoch [3952/4000] Training [1/39] Loss: 0.00408 +Epoch [3952/4000] Training [2/39] Loss: 0.25541 +Epoch [3952/4000] Training [3/39] Loss: 0.12796 +Epoch [3952/4000] Training [4/39] Loss: 0.00527 +Epoch [3952/4000] Training [5/39] Loss: 0.12859 +Epoch [3952/4000] Training [6/39] Loss: 0.00249 +Epoch [3952/4000] Training [7/39] Loss: 0.00645 +Epoch [3952/4000] Training [8/39] Loss: 0.12906 +Epoch [3952/4000] Training [9/39] Loss: 0.00359 +Epoch [3952/4000] Training [10/39] Loss: 0.00443 +Epoch [3952/4000] Training [11/39] Loss: 0.00592 +Epoch [3952/4000] Training [12/39] Loss: 0.00742 +Epoch [3952/4000] Training [13/39] Loss: 0.00487 +Epoch [3952/4000] Training [14/39] Loss: 0.00474 +Epoch [3952/4000] Training [15/39] Loss: 0.00300 +Epoch [3952/4000] Training [16/39] Loss: 0.12930 +Epoch [3952/4000] Training [17/39] Loss: 0.00300 +Epoch [3952/4000] Training [18/39] Loss: 0.00693 +Epoch [3952/4000] Training [19/39] Loss: 0.00697 +Epoch [3952/4000] Training [20/39] Loss: 0.00518 +Epoch [3952/4000] Training [21/39] Loss: 0.00530 +Epoch [3952/4000] Training [22/39] Loss: 0.00489 +Epoch [3952/4000] Training [23/39] Loss: 0.00485 +Epoch [3952/4000] Training [24/39] Loss: 0.00321 +Epoch [3952/4000] Training [25/39] Loss: 0.00388 +Epoch [3952/4000] Training [26/39] Loss: 0.00422 +Epoch [3952/4000] Training [27/39] Loss: 0.00342 +Epoch [3952/4000] Training [28/39] Loss: 0.12918 +Epoch [3952/4000] Training [29/39] Loss: 0.00530 +Epoch [3952/4000] Training [30/39] Loss: 0.25648 +Epoch [3952/4000] Training [31/39] Loss: 0.00292 +Epoch [3952/4000] Training [32/39] Loss: 0.00353 +Epoch [3952/4000] Training [33/39] Loss: 0.00410 +Epoch [3952/4000] Training [34/39] Loss: 0.12945 +Epoch [3952/4000] Training [35/39] Loss: 0.00512 +Epoch [3952/4000] Training [36/39] Loss: 0.12892 +Epoch [3952/4000] Training [37/39] Loss: 0.12783 +Epoch [3952/4000] Training [38/39] Loss: 0.00594 +Epoch [3952/4000] Training [39/39] Loss: 0.00465 +Epoch [3952/4000] Training metric {'Train/mean dice_metric': 0.9957684278488159, 'Train/mean miou_metric': 0.9928040504455566, 'Train/mean f1': 0.9970576167106628, 'Train/mean precision': 0.9966110587120056, 'Train/mean recall': 0.997504472732544, 'Train/mean hd95_metric': 0.9207422733306885} +Epoch [3952/4000] Validation [1/10] Loss: 0.71586 focal_loss 0.62943 dice_loss 0.08643 +Epoch [3952/4000] Validation [2/10] Loss: 0.50464 focal_loss 0.40583 dice_loss 0.09881 +Epoch [3952/4000] Validation [3/10] Loss: 0.39517 focal_loss 0.28373 dice_loss 0.11143 +Epoch [3952/4000] Validation [4/10] Loss: 0.89574 focal_loss 0.33036 dice_loss 0.56538 +Epoch [3952/4000] Validation [5/10] Loss: 3.07765 focal_loss 2.40367 dice_loss 0.67398 +Epoch [3952/4000] Validation [6/10] Loss: 1.33774 focal_loss 0.62517 dice_loss 0.71257 +Epoch [3952/4000] Validation [7/10] Loss: 1.17680 focal_loss 0.52252 dice_loss 0.65428 +Epoch [3952/4000] Validation [8/10] Loss: 2.39090 focal_loss 1.77367 dice_loss 0.61722 +Epoch [3952/4000] Validation [9/10] Loss: 1.53031 focal_loss 0.98630 dice_loss 0.54401 +Epoch [3952/4000] Validation [10/10] Loss: 1.89441 focal_loss 1.15939 dice_loss 0.73502 +Epoch [3952/4000] Validation metric {'Val/mean dice_metric': 0.9508967399597168, 'Val/mean miou_metric': 0.9351999759674072, 'Val/mean f1': 0.9486442804336548, 'Val/mean precision': 0.9440861940383911, 'Val/mean recall': 0.953246533870697, 'Val/mean hd95_metric': 10.691730499267578} +Cheakpoint... +Epoch [3952/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9509], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508967399597168, 'Val/mean miou_metric': 0.9351999759674072, 'Val/mean f1': 0.9486442804336548, 'Val/mean precision': 0.9440861940383911, 'Val/mean recall': 0.953246533870697, 'Val/mean hd95_metric': 10.691730499267578} +Epoch [3953/4000] Training [1/39] Loss: 0.00299 +Epoch [3953/4000] Training [2/39] Loss: 0.00337 +Epoch [3953/4000] Training [3/39] Loss: 0.00334 +Epoch [3953/4000] Training [4/39] Loss: 0.00320 +Epoch [3953/4000] Training [5/39] Loss: 0.00284 +Epoch [3953/4000] Training [6/39] Loss: 0.00428 +Epoch [3953/4000] Training [7/39] Loss: 0.00344 +Epoch [3953/4000] Training [8/39] Loss: 0.00411 +Epoch [3953/4000] Training [9/39] Loss: 0.00341 +Epoch [3953/4000] Training [10/39] Loss: 0.00593 +Epoch [3953/4000] Training [11/39] Loss: 0.00733 +Epoch [3953/4000] Training [12/39] Loss: 0.00768 +Epoch [3953/4000] Training [13/39] Loss: 0.00472 +Epoch [3953/4000] Training [14/39] Loss: 0.00403 +Epoch [3953/4000] Training [15/39] Loss: 0.00360 +Epoch [3953/4000] Training [16/39] Loss: 0.00422 +Epoch [3953/4000] Training [17/39] Loss: 0.00725 +Epoch [3953/4000] Training [18/39] Loss: 0.00373 +Epoch [3953/4000] Training [19/39] Loss: 0.00423 +Epoch [3953/4000] Training [20/39] Loss: 0.00410 +Epoch [3953/4000] Training [21/39] Loss: 0.00530 +Epoch [3953/4000] Training [22/39] Loss: 0.12703 +Epoch [3953/4000] Training [23/39] Loss: 0.13032 +Epoch [3953/4000] Training [24/39] Loss: 0.00909 +Epoch [3953/4000] Training [25/39] Loss: 0.00466 +Epoch [3953/4000] Training [26/39] Loss: 0.00412 +Epoch [3953/4000] Training [27/39] Loss: 0.00466 +Epoch [3953/4000] Training [28/39] Loss: 0.12844 +Epoch [3953/4000] Training [29/39] Loss: 0.00528 +Epoch [3953/4000] Training [30/39] Loss: 0.00308 +Epoch [3953/4000] Training [31/39] Loss: 0.12967 +Epoch [3953/4000] Training [32/39] Loss: 0.00471 +Epoch [3953/4000] Training [33/39] Loss: 0.00641 +Epoch [3953/4000] Training [34/39] Loss: 0.12781 +Epoch [3953/4000] Training [35/39] Loss: 0.12946 +Epoch [3953/4000] Training [36/39] Loss: 0.00413 +Epoch [3953/4000] Training [37/39] Loss: 0.00482 +Epoch [3953/4000] Training [38/39] Loss: 0.00582 +Epoch [3953/4000] Training [39/39] Loss: 0.37934 +Epoch [3953/4000] Training metric {'Train/mean dice_metric': 0.9966655969619751, 'Train/mean miou_metric': 0.9937763214111328, 'Train/mean f1': 0.9970937371253967, 'Train/mean precision': 0.9965925812721252, 'Train/mean recall': 0.997595489025116, 'Train/mean hd95_metric': 0.8857912421226501} +Epoch [3953/4000] Validation [1/10] Loss: 0.73203 focal_loss 0.64559 dice_loss 0.08644 +Epoch [3953/4000] Validation [2/10] Loss: 0.51127 focal_loss 0.41210 dice_loss 0.09917 +Epoch [3953/4000] Validation [3/10] Loss: 0.40672 focal_loss 0.29510 dice_loss 0.11162 +Epoch [3953/4000] Validation [4/10] Loss: 0.89756 focal_loss 0.33248 dice_loss 0.56507 +Epoch [3953/4000] Validation [5/10] Loss: 3.14163 focal_loss 2.46764 dice_loss 0.67400 +Epoch [3953/4000] Validation [6/10] Loss: 1.34100 focal_loss 0.62869 dice_loss 0.71231 +Epoch [3953/4000] Validation [7/10] Loss: 1.18295 focal_loss 0.52823 dice_loss 0.65473 +Epoch [3953/4000] Validation [8/10] Loss: 2.44800 focal_loss 1.82917 dice_loss 0.61883 +Epoch [3953/4000] Validation [9/10] Loss: 1.55161 focal_loss 1.00783 dice_loss 0.54378 +Epoch [3953/4000] Validation [10/10] Loss: 1.90315 focal_loss 1.16860 dice_loss 0.73455 +Epoch [3953/4000] Validation metric {'Val/mean dice_metric': 0.951653242111206, 'Val/mean miou_metric': 0.936021625995636, 'Val/mean f1': 0.9483360648155212, 'Val/mean precision': 0.9440078735351562, 'Val/mean recall': 0.952704131603241, 'Val/mean hd95_metric': 10.654555320739746} +Cheakpoint... +Epoch [3953/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9517], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951653242111206, 'Val/mean miou_metric': 0.936021625995636, 'Val/mean f1': 0.9483360648155212, 'Val/mean precision': 0.9440078735351562, 'Val/mean recall': 0.952704131603241, 'Val/mean hd95_metric': 10.654555320739746} +Epoch [3954/4000] Training [1/39] Loss: 0.00579 +Epoch [3954/4000] Training [2/39] Loss: 0.00402 +Epoch [3954/4000] Training [3/39] Loss: 0.00379 +Epoch [3954/4000] Training [4/39] Loss: 0.13277 +Epoch [3954/4000] Training [5/39] Loss: 0.00308 +Epoch [3954/4000] Training [6/39] Loss: 0.13005 +Epoch [3954/4000] Training [7/39] Loss: 0.00480 +Epoch [3954/4000] Training [8/39] Loss: 0.00527 +Epoch [3954/4000] Training [9/39] Loss: 0.00548 +Epoch [3954/4000] Training [10/39] Loss: 0.00378 +Epoch [3954/4000] Training [11/39] Loss: 0.00430 +Epoch [3954/4000] Training [12/39] Loss: 0.00483 +Epoch [3954/4000] Training [13/39] Loss: 0.00415 +Epoch [3954/4000] Training [14/39] Loss: 0.00498 +Epoch [3954/4000] Training [15/39] Loss: 0.00634 +Epoch [3954/4000] Training [16/39] Loss: 0.00396 +Epoch [3954/4000] Training [17/39] Loss: 0.00468 +Epoch [3954/4000] Training [18/39] Loss: 0.00458 +Epoch [3954/4000] Training [19/39] Loss: 0.12837 +Epoch [3954/4000] Training [20/39] Loss: 0.00823 +Epoch [3954/4000] Training [21/39] Loss: 0.25214 +Epoch [3954/4000] Training [22/39] Loss: 0.00520 +Epoch [3954/4000] Training [23/39] Loss: 0.00442 +Epoch [3954/4000] Training [24/39] Loss: 0.12823 +Epoch [3954/4000] Training [25/39] Loss: 0.00639 +Epoch [3954/4000] Training [26/39] Loss: 0.01069 +Epoch [3954/4000] Training [27/39] Loss: 0.00347 +Epoch [3954/4000] Training [28/39] Loss: 0.12887 +Epoch [3954/4000] Training [29/39] Loss: 0.00448 +Epoch [3954/4000] Training [30/39] Loss: 0.00321 +Epoch [3954/4000] Training [31/39] Loss: 0.00302 +Epoch [3954/4000] Training [32/39] Loss: 0.13029 +Epoch [3954/4000] Training [33/39] Loss: 0.12776 +Epoch [3954/4000] Training [34/39] Loss: 0.12911 +Epoch [3954/4000] Training [35/39] Loss: 0.00757 +Epoch [3954/4000] Training [36/39] Loss: 0.12896 +Epoch [3954/4000] Training [37/39] Loss: 0.00417 +Epoch [3954/4000] Training [38/39] Loss: 0.00302 +Epoch [3954/4000] Training [39/39] Loss: 0.00321 +Epoch [3954/4000] Training metric {'Train/mean dice_metric': 0.9955592155456543, 'Train/mean miou_metric': 0.9924073219299316, 'Train/mean f1': 0.996944010257721, 'Train/mean precision': 0.9964215755462646, 'Train/mean recall': 0.9974669218063354, 'Train/mean hd95_metric': 0.9249054193496704} +Epoch [3954/4000] Validation [1/10] Loss: 0.70614 focal_loss 0.62078 dice_loss 0.08537 +Epoch [3954/4000] Validation [2/10] Loss: 0.50695 focal_loss 0.40539 dice_loss 0.10156 +Epoch [3954/4000] Validation [3/10] Loss: 0.40426 focal_loss 0.29184 dice_loss 0.11242 +Epoch [3954/4000] Validation [4/10] Loss: 0.88725 focal_loss 0.32288 dice_loss 0.56437 +Epoch [3954/4000] Validation [5/10] Loss: 3.07738 focal_loss 2.40326 dice_loss 0.67412 +Epoch [3954/4000] Validation [6/10] Loss: 1.31765 focal_loss 0.60546 dice_loss 0.71219 +Epoch [3954/4000] Validation [7/10] Loss: 1.16345 focal_loss 0.51154 dice_loss 0.65190 +Epoch [3954/4000] Validation [8/10] Loss: 2.42916 focal_loss 1.80517 dice_loss 0.62399 +Epoch [3954/4000] Validation [9/10] Loss: 1.51204 focal_loss 0.96828 dice_loss 0.54376 +Epoch [3954/4000] Validation [10/10] Loss: 1.85255 focal_loss 1.11892 dice_loss 0.73363 +Epoch [3954/4000] Validation metric {'Val/mean dice_metric': 0.9507301449775696, 'Val/mean miou_metric': 0.9349032640457153, 'Val/mean f1': 0.948540449142456, 'Val/mean precision': 0.9448995590209961, 'Val/mean recall': 0.9522094130516052, 'Val/mean hd95_metric': 10.767314910888672} +Cheakpoint... +Epoch [3954/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507301449775696, 'Val/mean miou_metric': 0.9349032640457153, 'Val/mean f1': 0.948540449142456, 'Val/mean precision': 0.9448995590209961, 'Val/mean recall': 0.9522094130516052, 'Val/mean hd95_metric': 10.767314910888672} +Epoch [3955/4000] Training [1/39] Loss: 0.00534 +Epoch [3955/4000] Training [2/39] Loss: 0.00391 +Epoch [3955/4000] Training [3/39] Loss: 0.12867 +Epoch [3955/4000] Training [4/39] Loss: 0.00618 +Epoch [3955/4000] Training [5/39] Loss: 0.00515 +Epoch [3955/4000] Training [6/39] Loss: 0.12819 +Epoch [3955/4000] Training [7/39] Loss: 0.00304 +Epoch [3955/4000] Training [8/39] Loss: 0.12825 +Epoch [3955/4000] Training [9/39] Loss: 0.00547 +Epoch [3955/4000] Training [10/39] Loss: 0.00521 +Epoch [3955/4000] Training [11/39] Loss: 0.00450 +Epoch [3955/4000] Training [12/39] Loss: 0.00464 +Epoch [3955/4000] Training [13/39] Loss: 0.00468 +Epoch [3955/4000] Training [14/39] Loss: 0.00686 +Epoch [3955/4000] Training [15/39] Loss: 0.00348 +Epoch [3955/4000] Training [16/39] Loss: 0.00659 +Epoch [3955/4000] Training [17/39] Loss: 0.12899 +Epoch [3955/4000] Training [18/39] Loss: 0.00423 +Epoch [3955/4000] Training [19/39] Loss: 0.00704 +Epoch [3955/4000] Training [20/39] Loss: 0.00334 +Epoch [3955/4000] Training [21/39] Loss: 0.21163 +Epoch [3955/4000] Training [22/39] Loss: 0.00459 +Epoch [3955/4000] Training [23/39] Loss: 0.00420 +Epoch [3955/4000] Training [24/39] Loss: 0.00406 +Epoch [3955/4000] Training [25/39] Loss: 0.00580 +Epoch [3955/4000] Training [26/39] Loss: 0.00349 +Epoch [3955/4000] Training [27/39] Loss: 0.00357 +Epoch [3955/4000] Training [28/39] Loss: 0.00360 +Epoch [3955/4000] Training [29/39] Loss: 0.00531 +Epoch [3955/4000] Training [30/39] Loss: 0.00328 +Epoch [3955/4000] Training [31/39] Loss: 0.12784 +Epoch [3955/4000] Training [32/39] Loss: 0.03758 +Epoch [3955/4000] Training [33/39] Loss: 0.00552 +Epoch [3955/4000] Training [34/39] Loss: 0.00426 +Epoch [3955/4000] Training [35/39] Loss: 0.12718 +Epoch [3955/4000] Training [36/39] Loss: 0.00516 +Epoch [3955/4000] Training [37/39] Loss: 0.12995 +Epoch [3955/4000] Training [38/39] Loss: 0.25639 +Epoch [3955/4000] Training [39/39] Loss: 0.13107 +Epoch [3955/4000] Training metric {'Train/mean dice_metric': 0.9966142773628235, 'Train/mean miou_metric': 0.9936724305152893, 'Train/mean f1': 0.9970576167106628, 'Train/mean precision': 0.9966174364089966, 'Train/mean recall': 0.9974981546401978, 'Train/mean hd95_metric': 0.8895066976547241} +Epoch [3955/4000] Validation [1/10] Loss: 0.72749 focal_loss 0.64128 dice_loss 0.08621 +Epoch [3955/4000] Validation [2/10] Loss: 0.51818 focal_loss 0.41928 dice_loss 0.09889 +Epoch [3955/4000] Validation [3/10] Loss: 0.40058 focal_loss 0.28931 dice_loss 0.11127 +Epoch [3955/4000] Validation [4/10] Loss: 0.90744 focal_loss 0.34198 dice_loss 0.56547 +Epoch [3955/4000] Validation [5/10] Loss: 3.11965 focal_loss 2.44567 dice_loss 0.67399 +Epoch [3955/4000] Validation [6/10] Loss: 1.36190 focal_loss 0.64961 dice_loss 0.71230 +Epoch [3955/4000] Validation [7/10] Loss: 1.19770 focal_loss 0.54333 dice_loss 0.65437 +Epoch [3955/4000] Validation [8/10] Loss: 2.44481 focal_loss 1.82725 dice_loss 0.61756 +Epoch [3955/4000] Validation [9/10] Loss: 1.56501 focal_loss 1.02078 dice_loss 0.54423 +Epoch [3955/4000] Validation [10/10] Loss: 1.94012 focal_loss 1.20497 dice_loss 0.73514 +Epoch [3955/4000] Validation metric {'Val/mean dice_metric': 0.951626718044281, 'Val/mean miou_metric': 0.9359542727470398, 'Val/mean f1': 0.9483941197395325, 'Val/mean precision': 0.9437779188156128, 'Val/mean recall': 0.9530559182167053, 'Val/mean hd95_metric': 10.642062187194824} +Cheakpoint... +Epoch [3955/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951626718044281, 'Val/mean miou_metric': 0.9359542727470398, 'Val/mean f1': 0.9483941197395325, 'Val/mean precision': 0.9437779188156128, 'Val/mean recall': 0.9530559182167053, 'Val/mean hd95_metric': 10.642062187194824} +Epoch [3956/4000] Training [1/39] Loss: 0.00351 +Epoch [3956/4000] Training [2/39] Loss: 0.00507 +Epoch [3956/4000] Training [3/39] Loss: 0.00657 +Epoch [3956/4000] Training [4/39] Loss: 0.00387 +Epoch [3956/4000] Training [5/39] Loss: 0.12856 +Epoch [3956/4000] Training [6/39] Loss: 0.00390 +Epoch [3956/4000] Training [7/39] Loss: 0.00581 +Epoch [3956/4000] Training [8/39] Loss: 0.00249 +Epoch [3956/4000] Training [9/39] Loss: 0.13032 +Epoch [3956/4000] Training [10/39] Loss: 0.12753 +Epoch [3956/4000] Training [11/39] Loss: 0.12784 +Epoch [3956/4000] Training [12/39] Loss: 0.00489 +Epoch [3956/4000] Training [13/39] Loss: 0.00596 +Epoch [3956/4000] Training [14/39] Loss: 0.12782 +Epoch [3956/4000] Training [15/39] Loss: 0.00342 +Epoch [3956/4000] Training [16/39] Loss: 0.00347 +Epoch [3956/4000] Training [17/39] Loss: 0.00469 +Epoch [3956/4000] Training [18/39] Loss: 0.00540 +Epoch [3956/4000] Training [19/39] Loss: 0.00476 +Epoch [3956/4000] Training [20/39] Loss: 0.00852 +Epoch [3956/4000] Training [21/39] Loss: 0.00608 +Epoch [3956/4000] Training [22/39] Loss: 0.25406 +Epoch [3956/4000] Training [23/39] Loss: 0.00270 +Epoch [3956/4000] Training [24/39] Loss: 0.00650 +Epoch [3956/4000] Training [25/39] Loss: 0.00612 +Epoch [3956/4000] Training [26/39] Loss: 0.00413 +Epoch [3956/4000] Training [27/39] Loss: 0.00289 +Epoch [3956/4000] Training [28/39] Loss: 0.00451 +Epoch [3956/4000] Training [29/39] Loss: 0.00397 +Epoch [3956/4000] Training [30/39] Loss: 0.00494 +Epoch [3956/4000] Training [31/39] Loss: 0.12885 +Epoch [3956/4000] Training [32/39] Loss: 0.00553 +Epoch [3956/4000] Training [33/39] Loss: 0.08477 +Epoch [3956/4000] Training [34/39] Loss: 0.00569 +Epoch [3956/4000] Training [35/39] Loss: 0.00612 +Epoch [3956/4000] Training [36/39] Loss: 0.12973 +Epoch [3956/4000] Training [37/39] Loss: 0.13190 +Epoch [3956/4000] Training [38/39] Loss: 0.00410 +Epoch [3956/4000] Training [39/39] Loss: 0.00393 +Epoch [3956/4000] Training metric {'Train/mean dice_metric': 0.9965194463729858, 'Train/mean miou_metric': 0.9934836626052856, 'Train/mean f1': 0.9970482587814331, 'Train/mean precision': 0.9965676665306091, 'Train/mean recall': 0.997529149055481, 'Train/mean hd95_metric': 0.935849130153656} +Epoch [3956/4000] Validation [1/10] Loss: 0.70728 focal_loss 0.62155 dice_loss 0.08573 +Epoch [3956/4000] Validation [2/10] Loss: 0.50876 focal_loss 0.40875 dice_loss 0.10001 +Epoch [3956/4000] Validation [3/10] Loss: 0.39525 focal_loss 0.28365 dice_loss 0.11160 +Epoch [3956/4000] Validation [4/10] Loss: 0.89514 focal_loss 0.33032 dice_loss 0.56482 +Epoch [3956/4000] Validation [5/10] Loss: 3.05286 focal_loss 2.37887 dice_loss 0.67399 +Epoch [3956/4000] Validation [6/10] Loss: 1.33741 focal_loss 0.62486 dice_loss 0.71255 +Epoch [3956/4000] Validation [7/10] Loss: 1.17788 focal_loss 0.52437 dice_loss 0.65352 +Epoch [3956/4000] Validation [8/10] Loss: 2.40883 focal_loss 1.78884 dice_loss 0.62000 +Epoch [3956/4000] Validation [9/10] Loss: 1.52589 focal_loss 0.98185 dice_loss 0.54404 +Epoch [3956/4000] Validation [10/10] Loss: 1.89019 focal_loss 1.15571 dice_loss 0.73448 +Epoch [3956/4000] Validation metric {'Val/mean dice_metric': 0.9515629410743713, 'Val/mean miou_metric': 0.9358261823654175, 'Val/mean f1': 0.9484542012214661, 'Val/mean precision': 0.9441729187965393, 'Val/mean recall': 0.9527743458747864, 'Val/mean hd95_metric': 10.768534660339355} +Cheakpoint... +Epoch [3956/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515629410743713, 'Val/mean miou_metric': 0.9358261823654175, 'Val/mean f1': 0.9484542012214661, 'Val/mean precision': 0.9441729187965393, 'Val/mean recall': 0.9527743458747864, 'Val/mean hd95_metric': 10.768534660339355} +Epoch [3957/4000] Training [1/39] Loss: 0.12730 +Epoch [3957/4000] Training [2/39] Loss: 0.00424 +Epoch [3957/4000] Training [3/39] Loss: 0.12993 +Epoch [3957/4000] Training [4/39] Loss: 0.00372 +Epoch [3957/4000] Training [5/39] Loss: 0.12842 +Epoch [3957/4000] Training [6/39] Loss: 0.01131 +Epoch [3957/4000] Training [7/39] Loss: 0.00709 +Epoch [3957/4000] Training [8/39] Loss: 0.00412 +Epoch [3957/4000] Training [9/39] Loss: 0.00375 +Epoch [3957/4000] Training [10/39] Loss: 0.12904 +Epoch [3957/4000] Training [11/39] Loss: 0.00399 +Epoch [3957/4000] Training [12/39] Loss: 0.00536 +Epoch [3957/4000] Training [13/39] Loss: 0.00389 +Epoch [3957/4000] Training [14/39] Loss: 0.00368 +Epoch [3957/4000] Training [15/39] Loss: 0.00616 +Epoch [3957/4000] Training [16/39] Loss: 0.12913 +Epoch [3957/4000] Training [17/39] Loss: 0.12861 +Epoch [3957/4000] Training [18/39] Loss: 0.00583 +Epoch [3957/4000] Training [19/39] Loss: 0.00602 +Epoch [3957/4000] Training [20/39] Loss: 0.00559 +Epoch [3957/4000] Training [21/39] Loss: 0.00433 +Epoch [3957/4000] Training [22/39] Loss: 0.00362 +Epoch [3957/4000] Training [23/39] Loss: 0.00466 +Epoch [3957/4000] Training [24/39] Loss: 0.00331 +Epoch [3957/4000] Training [25/39] Loss: 0.25257 +Epoch [3957/4000] Training [26/39] Loss: 0.12905 +Epoch [3957/4000] Training [27/39] Loss: 0.00456 +Epoch [3957/4000] Training [28/39] Loss: 0.00252 +Epoch [3957/4000] Training [29/39] Loss: 0.00824 +Epoch [3957/4000] Training [30/39] Loss: 0.12902 +Epoch [3957/4000] Training [31/39] Loss: 0.00466 +Epoch [3957/4000] Training [32/39] Loss: 0.00574 +Epoch [3957/4000] Training [33/39] Loss: 0.00387 +Epoch [3957/4000] Training [34/39] Loss: 0.12798 +Epoch [3957/4000] Training [35/39] Loss: 0.12951 +Epoch [3957/4000] Training [36/39] Loss: 0.00281 +Epoch [3957/4000] Training [37/39] Loss: 0.00371 +Epoch [3957/4000] Training [38/39] Loss: 0.00282 +Epoch [3957/4000] Training [39/39] Loss: 0.00440 +Epoch [3957/4000] Training metric {'Train/mean dice_metric': 0.9965372681617737, 'Train/mean miou_metric': 0.9935253858566284, 'Train/mean f1': 0.9970917701721191, 'Train/mean precision': 0.9966535568237305, 'Train/mean recall': 0.9975303411483765, 'Train/mean hd95_metric': 1.0518038272857666} +Epoch [3957/4000] Validation [1/10] Loss: 0.70864 focal_loss 0.62294 dice_loss 0.08570 +Epoch [3957/4000] Validation [2/10] Loss: 0.50584 focal_loss 0.40478 dice_loss 0.10106 +Epoch [3957/4000] Validation [3/10] Loss: 0.40334 focal_loss 0.29104 dice_loss 0.11230 +Epoch [3957/4000] Validation [4/10] Loss: 0.88838 focal_loss 0.32385 dice_loss 0.56453 +Epoch [3957/4000] Validation [5/10] Loss: 3.07742 focal_loss 2.40329 dice_loss 0.67412 +Epoch [3957/4000] Validation [6/10] Loss: 1.32028 focal_loss 0.60799 dice_loss 0.71229 +Epoch [3957/4000] Validation [7/10] Loss: 1.16366 focal_loss 0.51143 dice_loss 0.65223 +Epoch [3957/4000] Validation [8/10] Loss: 2.42143 focal_loss 1.79817 dice_loss 0.62326 +Epoch [3957/4000] Validation [9/10] Loss: 1.51569 focal_loss 0.97198 dice_loss 0.54371 +Epoch [3957/4000] Validation [10/10] Loss: 1.85902 focal_loss 1.12516 dice_loss 0.73386 +Epoch [3957/4000] Validation metric {'Val/mean dice_metric': 0.9515117406845093, 'Val/mean miou_metric': 0.9357900023460388, 'Val/mean f1': 0.9488130211830139, 'Val/mean precision': 0.9450809955596924, 'Val/mean recall': 0.9525747895240784, 'Val/mean hd95_metric': 10.887044906616211} +Cheakpoint... +Epoch [3957/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515117406845093, 'Val/mean miou_metric': 0.9357900023460388, 'Val/mean f1': 0.9488130211830139, 'Val/mean precision': 0.9450809955596924, 'Val/mean recall': 0.9525747895240784, 'Val/mean hd95_metric': 10.887044906616211} +Epoch [3958/4000] Training [1/39] Loss: 0.00478 +Epoch [3958/4000] Training [2/39] Loss: 0.00506 +Epoch [3958/4000] Training [3/39] Loss: 0.12838 +Epoch [3958/4000] Training [4/39] Loss: 0.00465 +Epoch [3958/4000] Training [5/39] Loss: 0.13316 +Epoch [3958/4000] Training [6/39] Loss: 0.00371 +Epoch [3958/4000] Training [7/39] Loss: 0.12944 +Epoch [3958/4000] Training [8/39] Loss: 0.00355 +Epoch [3958/4000] Training [9/39] Loss: 0.00504 +Epoch [3958/4000] Training [10/39] Loss: 0.00513 +Epoch [3958/4000] Training [11/39] Loss: 0.00996 +Epoch [3958/4000] Training [12/39] Loss: 0.00411 +Epoch [3958/4000] Training [13/39] Loss: 0.00504 +Epoch [3958/4000] Training [14/39] Loss: 0.13028 +Epoch [3958/4000] Training [15/39] Loss: 0.00326 +Epoch [3958/4000] Training [16/39] Loss: 0.00438 +Epoch [3958/4000] Training [17/39] Loss: 0.00304 +Epoch [3958/4000] Training [18/39] Loss: 0.00484 +Epoch [3958/4000] Training [19/39] Loss: 0.12742 +Epoch [3958/4000] Training [20/39] Loss: 0.00571 +Epoch [3958/4000] Training [21/39] Loss: 0.16689 +Epoch [3958/4000] Training [22/39] Loss: 0.13069 +Epoch [3958/4000] Training [23/39] Loss: 0.00298 +Epoch [3958/4000] Training [24/39] Loss: 0.00285 +Epoch [3958/4000] Training [25/39] Loss: 0.00372 +Epoch [3958/4000] Training [26/39] Loss: 0.25486 +Epoch [3958/4000] Training [27/39] Loss: 0.00484 +Epoch [3958/4000] Training [28/39] Loss: 0.00776 +Epoch [3958/4000] Training [29/39] Loss: 0.00334 +Epoch [3958/4000] Training [30/39] Loss: 0.00560 +Epoch [3958/4000] Training [31/39] Loss: 0.00537 +Epoch [3958/4000] Training [32/39] Loss: 0.00640 +Epoch [3958/4000] Training [33/39] Loss: 0.00399 +Epoch [3958/4000] Training [34/39] Loss: 0.00649 +Epoch [3958/4000] Training [35/39] Loss: 0.25455 +Epoch [3958/4000] Training [36/39] Loss: 0.00656 +Epoch [3958/4000] Training [37/39] Loss: 0.12806 +Epoch [3958/4000] Training [38/39] Loss: 0.00449 +Epoch [3958/4000] Training [39/39] Loss: 0.12774 +Epoch [3958/4000] Training metric {'Train/mean dice_metric': 0.996434211730957, 'Train/mean miou_metric': 0.9933240413665771, 'Train/mean f1': 0.9969135522842407, 'Train/mean precision': 0.9965015649795532, 'Train/mean recall': 0.9973259568214417, 'Train/mean hd95_metric': 1.043599009513855} +Epoch [3958/4000] Validation [1/10] Loss: 0.71154 focal_loss 0.62522 dice_loss 0.08632 +Epoch [3958/4000] Validation [2/10] Loss: 0.50739 focal_loss 0.40896 dice_loss 0.09844 +Epoch [3958/4000] Validation [3/10] Loss: 0.38901 focal_loss 0.27797 dice_loss 0.11104 +Epoch [3958/4000] Validation [4/10] Loss: 0.89880 focal_loss 0.33343 dice_loss 0.56536 +Epoch [3958/4000] Validation [5/10] Loss: 3.05124 focal_loss 2.37729 dice_loss 0.67395 +Epoch [3958/4000] Validation [6/10] Loss: 1.34837 focal_loss 0.63521 dice_loss 0.71316 +Epoch [3958/4000] Validation [7/10] Loss: 1.18651 focal_loss 0.53225 dice_loss 0.65426 +Epoch [3958/4000] Validation [8/10] Loss: 2.38884 focal_loss 1.77308 dice_loss 0.61576 +Epoch [3958/4000] Validation [9/10] Loss: 1.53639 focal_loss 0.99203 dice_loss 0.54436 +Epoch [3958/4000] Validation [10/10] Loss: 1.91397 focal_loss 1.17871 dice_loss 0.73526 +Epoch [3958/4000] Validation metric {'Val/mean dice_metric': 0.9515068531036377, 'Val/mean miou_metric': 0.9357029795646667, 'Val/mean f1': 0.9485005140304565, 'Val/mean precision': 0.9437403082847595, 'Val/mean recall': 0.9533089995384216, 'Val/mean hd95_metric': 10.782036781311035} +Cheakpoint... +Epoch [3958/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515068531036377, 'Val/mean miou_metric': 0.9357029795646667, 'Val/mean f1': 0.9485005140304565, 'Val/mean precision': 0.9437403082847595, 'Val/mean recall': 0.9533089995384216, 'Val/mean hd95_metric': 10.782036781311035} +Epoch [3959/4000] Training [1/39] Loss: 0.00499 +Epoch [3959/4000] Training [2/39] Loss: 0.00455 +Epoch [3959/4000] Training [3/39] Loss: 0.00683 +Epoch [3959/4000] Training [4/39] Loss: 0.00439 +Epoch [3959/4000] Training [5/39] Loss: 0.00393 +Epoch [3959/4000] Training [6/39] Loss: 0.12946 +Epoch [3959/4000] Training [7/39] Loss: 0.00341 +Epoch [3959/4000] Training [8/39] Loss: 0.00335 +Epoch [3959/4000] Training [9/39] Loss: 0.00400 +Epoch [3959/4000] Training [10/39] Loss: 0.13027 +Epoch [3959/4000] Training [11/39] Loss: 0.00289 +Epoch [3959/4000] Training [12/39] Loss: 0.00967 +Epoch [3959/4000] Training [13/39] Loss: 0.13043 +Epoch [3959/4000] Training [14/39] Loss: 0.00730 +Epoch [3959/4000] Training [15/39] Loss: 0.00270 +Epoch [3959/4000] Training [16/39] Loss: 0.00595 +Epoch [3959/4000] Training [17/39] Loss: 0.00315 +Epoch [3959/4000] Training [18/39] Loss: 0.00454 +Epoch [3959/4000] Training [19/39] Loss: 0.00438 +Epoch [3959/4000] Training [20/39] Loss: 0.00498 +Epoch [3959/4000] Training [21/39] Loss: 0.13096 +Epoch [3959/4000] Training [22/39] Loss: 0.00474 +Epoch [3959/4000] Training [23/39] Loss: 0.12930 +Epoch [3959/4000] Training [24/39] Loss: 0.25329 +Epoch [3959/4000] Training [25/39] Loss: 0.00562 +Epoch [3959/4000] Training [26/39] Loss: 0.00539 +Epoch [3959/4000] Training [27/39] Loss: 0.00508 +Epoch [3959/4000] Training [28/39] Loss: 0.00394 +Epoch [3959/4000] Training [29/39] Loss: 0.00570 +Epoch [3959/4000] Training [30/39] Loss: 0.00412 +Epoch [3959/4000] Training [31/39] Loss: 0.12774 +Epoch [3959/4000] Training [32/39] Loss: 0.00657 +Epoch [3959/4000] Training [33/39] Loss: 0.13203 +Epoch [3959/4000] Training [34/39] Loss: 0.12905 +Epoch [3959/4000] Training [35/39] Loss: 0.00534 +Epoch [3959/4000] Training [36/39] Loss: 0.00311 +Epoch [3959/4000] Training [37/39] Loss: 0.00395 +Epoch [3959/4000] Training [38/39] Loss: 0.00702 +Epoch [3959/4000] Training [39/39] Loss: 0.12761 +Epoch [3959/4000] Training metric {'Train/mean dice_metric': 0.9964239597320557, 'Train/mean miou_metric': 0.9932937622070312, 'Train/mean f1': 0.9969519376754761, 'Train/mean precision': 0.9965787529945374, 'Train/mean recall': 0.9973254203796387, 'Train/mean hd95_metric': 0.9088382124900818} +Epoch [3959/4000] Validation [1/10] Loss: 0.71473 focal_loss 0.62845 dice_loss 0.08628 +Epoch [3959/4000] Validation [2/10] Loss: 0.50639 focal_loss 0.40720 dice_loss 0.09919 +Epoch [3959/4000] Validation [3/10] Loss: 0.39689 focal_loss 0.28537 dice_loss 0.11152 +Epoch [3959/4000] Validation [4/10] Loss: 0.89624 focal_loss 0.33081 dice_loss 0.56543 +Epoch [3959/4000] Validation [5/10] Loss: 3.06853 focal_loss 2.39458 dice_loss 0.67396 +Epoch [3959/4000] Validation [6/10] Loss: 1.33799 focal_loss 0.62543 dice_loss 0.71255 +Epoch [3959/4000] Validation [7/10] Loss: 1.17727 focal_loss 0.52319 dice_loss 0.65407 +Epoch [3959/4000] Validation [8/10] Loss: 2.39331 focal_loss 1.77557 dice_loss 0.61775 +Epoch [3959/4000] Validation [9/10] Loss: 1.53020 focal_loss 0.98614 dice_loss 0.54407 +Epoch [3959/4000] Validation [10/10] Loss: 1.89305 focal_loss 1.15817 dice_loss 0.73488 +Epoch [3959/4000] Validation metric {'Val/mean dice_metric': 0.9514612555503845, 'Val/mean miou_metric': 0.9356303811073303, 'Val/mean f1': 0.9486592411994934, 'Val/mean precision': 0.9442541003227234, 'Val/mean recall': 0.9531056880950928, 'Val/mean hd95_metric': 10.781286239624023} +Cheakpoint... +Epoch [3959/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514612555503845, 'Val/mean miou_metric': 0.9356303811073303, 'Val/mean f1': 0.9486592411994934, 'Val/mean precision': 0.9442541003227234, 'Val/mean recall': 0.9531056880950928, 'Val/mean hd95_metric': 10.781286239624023} +Epoch [3960/4000] Training [1/39] Loss: 0.00833 +Epoch [3960/4000] Training [2/39] Loss: 0.25466 +Epoch [3960/4000] Training [3/39] Loss: 0.00381 +Epoch [3960/4000] Training [4/39] Loss: 0.00341 +Epoch [3960/4000] Training [5/39] Loss: 0.00347 +Epoch [3960/4000] Training [6/39] Loss: 0.00416 +Epoch [3960/4000] Training [7/39] Loss: 0.00630 +Epoch [3960/4000] Training [8/39] Loss: 0.00405 +Epoch [3960/4000] Training [9/39] Loss: 0.00439 +Epoch [3960/4000] Training [10/39] Loss: 0.12808 +Epoch [3960/4000] Training [11/39] Loss: 0.12945 +Epoch [3960/4000] Training [12/39] Loss: 0.00507 +Epoch [3960/4000] Training [13/39] Loss: 0.00510 +Epoch [3960/4000] Training [14/39] Loss: 0.12852 +Epoch [3960/4000] Training [15/39] Loss: 0.00340 +Epoch [3960/4000] Training [16/39] Loss: 0.12799 +Epoch [3960/4000] Training [17/39] Loss: 0.00468 +Epoch [3960/4000] Training [18/39] Loss: 0.00658 +Epoch [3960/4000] Training [19/39] Loss: 0.00581 +Epoch [3960/4000] Training [20/39] Loss: 0.12872 +Epoch [3960/4000] Training [21/39] Loss: 0.00674 +Epoch [3960/4000] Training [22/39] Loss: 0.12865 +Epoch [3960/4000] Training [23/39] Loss: 0.00610 +Epoch [3960/4000] Training [24/39] Loss: 0.13000 +Epoch [3960/4000] Training [25/39] Loss: 0.00485 +Epoch [3960/4000] Training [26/39] Loss: 0.00696 +Epoch [3960/4000] Training [27/39] Loss: 0.00431 +Epoch [3960/4000] Training [28/39] Loss: 0.00398 +Epoch [3960/4000] Training [29/39] Loss: 0.12949 +Epoch [3960/4000] Training [30/39] Loss: 0.00331 +Epoch [3960/4000] Training [31/39] Loss: 0.00303 +Epoch [3960/4000] Training [32/39] Loss: 0.00419 +Epoch [3960/4000] Training [33/39] Loss: 0.00238 +Epoch [3960/4000] Training [34/39] Loss: 0.00364 +Epoch [3960/4000] Training [35/39] Loss: 0.00671 +Epoch [3960/4000] Training [36/39] Loss: 0.12920 +Epoch [3960/4000] Training [37/39] Loss: 0.00343 +Epoch [3960/4000] Training [38/39] Loss: 0.00403 +Epoch [3960/4000] Training [39/39] Loss: 0.00642 +Epoch [3960/4000] Training metric {'Train/mean dice_metric': 0.9963424801826477, 'Train/mean miou_metric': 0.9931383728981018, 'Train/mean f1': 0.996851921081543, 'Train/mean precision': 0.9963772892951965, 'Train/mean recall': 0.9973269104957581, 'Train/mean hd95_metric': 0.9241752624511719} +Epoch [3960/4000] Validation [1/10] Loss: 0.72530 focal_loss 0.63823 dice_loss 0.08707 +Epoch [3960/4000] Validation [2/10] Loss: 0.50658 focal_loss 0.40876 dice_loss 0.09781 +Epoch [3960/4000] Validation [3/10] Loss: 0.39581 focal_loss 0.28457 dice_loss 0.11124 +Epoch [3960/4000] Validation [4/10] Loss: 0.90020 focal_loss 0.33422 dice_loss 0.56598 +Epoch [3960/4000] Validation [5/10] Loss: 3.08637 focal_loss 2.41246 dice_loss 0.67391 +Epoch [3960/4000] Validation [6/10] Loss: 1.34618 focal_loss 0.63349 dice_loss 0.71269 +Epoch [3960/4000] Validation [7/10] Loss: 1.18386 focal_loss 0.52849 dice_loss 0.65537 +Epoch [3960/4000] Validation [8/10] Loss: 2.38642 focal_loss 1.77150 dice_loss 0.61492 +Epoch [3960/4000] Validation [9/10] Loss: 1.54703 focal_loss 1.00293 dice_loss 0.54410 +Epoch [3960/4000] Validation [10/10] Loss: 1.91333 focal_loss 1.17784 dice_loss 0.73549 +Epoch [3960/4000] Validation metric {'Val/mean dice_metric': 0.9513118863105774, 'Val/mean miou_metric': 0.9353845715522766, 'Val/mean f1': 0.9482281804084778, 'Val/mean precision': 0.9433177709579468, 'Val/mean recall': 0.9531898498535156, 'Val/mean hd95_metric': 10.69095230102539} +Cheakpoint... +Epoch [3960/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513118863105774, 'Val/mean miou_metric': 0.9353845715522766, 'Val/mean f1': 0.9482281804084778, 'Val/mean precision': 0.9433177709579468, 'Val/mean recall': 0.9531898498535156, 'Val/mean hd95_metric': 10.69095230102539} +Epoch [3961/4000] Training [1/39] Loss: 0.00526 +Epoch [3961/4000] Training [2/39] Loss: 0.00844 +Epoch [3961/4000] Training [3/39] Loss: 0.00821 +Epoch [3961/4000] Training [4/39] Loss: 0.00332 +Epoch [3961/4000] Training [5/39] Loss: 0.25381 +Epoch [3961/4000] Training [6/39] Loss: 0.00364 +Epoch [3961/4000] Training [7/39] Loss: 0.00322 +Epoch [3961/4000] Training [8/39] Loss: 0.00558 +Epoch [3961/4000] Training [9/39] Loss: 0.00430 +Epoch [3961/4000] Training [10/39] Loss: 0.00425 +Epoch [3961/4000] Training [11/39] Loss: 0.12812 +Epoch [3961/4000] Training [12/39] Loss: 0.12777 +Epoch [3961/4000] Training [13/39] Loss: 0.00716 +Epoch [3961/4000] Training [14/39] Loss: 0.00611 +Epoch [3961/4000] Training [15/39] Loss: 0.00369 +Epoch [3961/4000] Training [16/39] Loss: 0.00373 +Epoch [3961/4000] Training [17/39] Loss: 0.00586 +Epoch [3961/4000] Training [18/39] Loss: 0.12799 +Epoch [3961/4000] Training [19/39] Loss: 0.12921 +Epoch [3961/4000] Training [20/39] Loss: 0.12945 +Epoch [3961/4000] Training [21/39] Loss: 0.00405 +Epoch [3961/4000] Training [22/39] Loss: 0.00536 +Epoch [3961/4000] Training [23/39] Loss: 0.00615 +Epoch [3961/4000] Training [24/39] Loss: 0.00629 +Epoch [3961/4000] Training [25/39] Loss: 0.25461 +Epoch [3961/4000] Training [26/39] Loss: 0.12906 +Epoch [3961/4000] Training [27/39] Loss: 0.00367 +Epoch [3961/4000] Training [28/39] Loss: 0.12752 +Epoch [3961/4000] Training [29/39] Loss: 0.00348 +Epoch [3961/4000] Training [30/39] Loss: 0.00466 +Epoch [3961/4000] Training [31/39] Loss: 0.00562 +Epoch [3961/4000] Training [32/39] Loss: 0.12839 +Epoch [3961/4000] Training [33/39] Loss: 0.25332 +Epoch [3961/4000] Training [34/39] Loss: 0.00546 +Epoch [3961/4000] Training [35/39] Loss: 0.12794 +Epoch [3961/4000] Training [36/39] Loss: 0.00450 +Epoch [3961/4000] Training [37/39] Loss: 0.01004 +Epoch [3961/4000] Training [38/39] Loss: 0.00498 +Epoch [3961/4000] Training [39/39] Loss: 0.00411 +Epoch [3961/4000] Training metric {'Train/mean dice_metric': 0.9955782890319824, 'Train/mean miou_metric': 0.992433488368988, 'Train/mean f1': 0.9968997836112976, 'Train/mean precision': 0.9964122772216797, 'Train/mean recall': 0.9973875880241394, 'Train/mean hd95_metric': 1.0266135931015015} +Epoch [3961/4000] Validation [1/10] Loss: 0.73281 focal_loss 0.64601 dice_loss 0.08680 +Epoch [3961/4000] Validation [2/10] Loss: 0.51251 focal_loss 0.41431 dice_loss 0.09820 +Epoch [3961/4000] Validation [3/10] Loss: 0.40123 focal_loss 0.29001 dice_loss 0.11122 +Epoch [3961/4000] Validation [4/10] Loss: 0.90386 focal_loss 0.33814 dice_loss 0.56572 +Epoch [3961/4000] Validation [5/10] Loss: 3.12808 focal_loss 2.45413 dice_loss 0.67395 +Epoch [3961/4000] Validation [6/10] Loss: 1.35237 focal_loss 0.64001 dice_loss 0.71236 +Epoch [3961/4000] Validation [7/10] Loss: 1.19173 focal_loss 0.53666 dice_loss 0.65507 +Epoch [3961/4000] Validation [8/10] Loss: 2.41744 focal_loss 1.80099 dice_loss 0.61645 +Epoch [3961/4000] Validation [9/10] Loss: 1.55810 focal_loss 1.01394 dice_loss 0.54416 +Epoch [3961/4000] Validation [10/10] Loss: 1.92674 focal_loss 1.19149 dice_loss 0.73525 +Epoch [3961/4000] Validation metric {'Val/mean dice_metric': 0.95070481300354, 'Val/mean miou_metric': 0.9348424673080444, 'Val/mean f1': 0.9481300115585327, 'Val/mean precision': 0.9433944821357727, 'Val/mean recall': 0.9529131054878235, 'Val/mean hd95_metric': 10.768318176269531} +Cheakpoint... +Epoch [3961/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.95070481300354, 'Val/mean miou_metric': 0.9348424673080444, 'Val/mean f1': 0.9481300115585327, 'Val/mean precision': 0.9433944821357727, 'Val/mean recall': 0.9529131054878235, 'Val/mean hd95_metric': 10.768318176269531} +Epoch [3962/4000] Training [1/39] Loss: 0.00376 +Epoch [3962/4000] Training [2/39] Loss: 0.12951 +Epoch [3962/4000] Training [3/39] Loss: 0.00399 +Epoch [3962/4000] Training [4/39] Loss: 0.00858 +Epoch [3962/4000] Training [5/39] Loss: 0.00399 +Epoch [3962/4000] Training [6/39] Loss: 0.00580 +Epoch [3962/4000] Training [7/39] Loss: 0.13068 +Epoch [3962/4000] Training [8/39] Loss: 0.12812 +Epoch [3962/4000] Training [9/39] Loss: 0.00462 +Epoch [3962/4000] Training [10/39] Loss: 0.00476 +Epoch [3962/4000] Training [11/39] Loss: 0.12815 +Epoch [3962/4000] Training [12/39] Loss: 0.00466 +Epoch [3962/4000] Training [13/39] Loss: 0.12890 +Epoch [3962/4000] Training [14/39] Loss: 0.00349 +Epoch [3962/4000] Training [15/39] Loss: 0.00374 +Epoch [3962/4000] Training [16/39] Loss: 0.12728 +Epoch [3962/4000] Training [17/39] Loss: 0.00369 +Epoch [3962/4000] Training [18/39] Loss: 0.00385 +Epoch [3962/4000] Training [19/39] Loss: 0.00810 +Epoch [3962/4000] Training [20/39] Loss: 0.25332 +Epoch [3962/4000] Training [21/39] Loss: 0.00418 +Epoch [3962/4000] Training [22/39] Loss: 0.13123 +Epoch [3962/4000] Training [23/39] Loss: 0.00328 +Epoch [3962/4000] Training [24/39] Loss: 0.12838 +Epoch [3962/4000] Training [25/39] Loss: 0.12868 +Epoch [3962/4000] Training [26/39] Loss: 0.00497 +Epoch [3962/4000] Training [27/39] Loss: 0.00346 +Epoch [3962/4000] Training [28/39] Loss: 0.00328 +Epoch [3962/4000] Training [29/39] Loss: 0.00596 +Epoch [3962/4000] Training [30/39] Loss: 0.00446 +Epoch [3962/4000] Training [31/39] Loss: 0.00655 +Epoch [3962/4000] Training [32/39] Loss: 0.00452 +Epoch [3962/4000] Training [33/39] Loss: 0.00770 +Epoch [3962/4000] Training [34/39] Loss: 0.13010 +Epoch [3962/4000] Training [35/39] Loss: 0.00606 +Epoch [3962/4000] Training [36/39] Loss: 0.00610 +Epoch [3962/4000] Training [37/39] Loss: 0.00519 +Epoch [3962/4000] Training [38/39] Loss: 0.00320 +Epoch [3962/4000] Training [39/39] Loss: 0.00380 +Epoch [3962/4000] Training metric {'Train/mean dice_metric': 0.9955691695213318, 'Train/mean miou_metric': 0.9924135804176331, 'Train/mean f1': 0.9969332218170166, 'Train/mean precision': 0.9964565634727478, 'Train/mean recall': 0.997410237789154, 'Train/mean hd95_metric': 0.9121035933494568} +Epoch [3962/4000] Validation [1/10] Loss: 0.71402 focal_loss 0.62783 dice_loss 0.08620 +Epoch [3962/4000] Validation [2/10] Loss: 0.50801 focal_loss 0.40853 dice_loss 0.09948 +Epoch [3962/4000] Validation [3/10] Loss: 0.39850 focal_loss 0.28688 dice_loss 0.11162 +Epoch [3962/4000] Validation [4/10] Loss: 0.89540 focal_loss 0.33015 dice_loss 0.56525 +Epoch [3962/4000] Validation [5/10] Loss: 3.06588 focal_loss 2.39188 dice_loss 0.67400 +Epoch [3962/4000] Validation [6/10] Loss: 1.33655 focal_loss 0.62396 dice_loss 0.71258 +Epoch [3962/4000] Validation [7/10] Loss: 1.17841 focal_loss 0.52452 dice_loss 0.65389 +Epoch [3962/4000] Validation [8/10] Loss: 2.40923 focal_loss 1.79013 dice_loss 0.61910 +Epoch [3962/4000] Validation [9/10] Loss: 1.53206 focal_loss 0.98788 dice_loss 0.54418 +Epoch [3962/4000] Validation [10/10] Loss: 1.89363 focal_loss 1.15894 dice_loss 0.73469 +Epoch [3962/4000] Validation metric {'Val/mean dice_metric': 0.9507339000701904, 'Val/mean miou_metric': 0.9348819851875305, 'Val/mean f1': 0.9486122727394104, 'Val/mean precision': 0.9442951083183289, 'Val/mean recall': 0.9529691338539124, 'Val/mean hd95_metric': 10.780744552612305} +Cheakpoint... +Epoch [3962/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507339000701904, 'Val/mean miou_metric': 0.9348819851875305, 'Val/mean f1': 0.9486122727394104, 'Val/mean precision': 0.9442951083183289, 'Val/mean recall': 0.9529691338539124, 'Val/mean hd95_metric': 10.780744552612305} +Epoch [3963/4000] Training [1/39] Loss: 0.00421 +Epoch [3963/4000] Training [2/39] Loss: 0.00387 +Epoch [3963/4000] Training [3/39] Loss: 0.12886 +Epoch [3963/4000] Training [4/39] Loss: 0.00855 +Epoch [3963/4000] Training [5/39] Loss: 0.00539 +Epoch [3963/4000] Training [6/39] Loss: 0.12731 +Epoch [3963/4000] Training [7/39] Loss: 0.00367 +Epoch [3963/4000] Training [8/39] Loss: 0.00421 +Epoch [3963/4000] Training [9/39] Loss: 0.00542 +Epoch [3963/4000] Training [10/39] Loss: 0.13084 +Epoch [3963/4000] Training [11/39] Loss: 0.00455 +Epoch [3963/4000] Training [12/39] Loss: 0.00270 +Epoch [3963/4000] Training [13/39] Loss: 0.00416 +Epoch [3963/4000] Training [14/39] Loss: 0.00564 +Epoch [3963/4000] Training [15/39] Loss: 0.00514 +Epoch [3963/4000] Training [16/39] Loss: 0.00465 +Epoch [3963/4000] Training [17/39] Loss: 0.13170 +Epoch [3963/4000] Training [18/39] Loss: 0.05108 +Epoch [3963/4000] Training [19/39] Loss: 0.00332 +Epoch [3963/4000] Training [20/39] Loss: 0.00335 +Epoch [3963/4000] Training [21/39] Loss: 0.00403 +Epoch [3963/4000] Training [22/39] Loss: 0.00390 +Epoch [3963/4000] Training [23/39] Loss: 0.00550 +Epoch [3963/4000] Training [24/39] Loss: 0.00487 +Epoch [3963/4000] Training [25/39] Loss: 0.12963 +Epoch [3963/4000] Training [26/39] Loss: 0.00592 +Epoch [3963/4000] Training [27/39] Loss: 0.12939 +Epoch [3963/4000] Training [28/39] Loss: 0.00613 +Epoch [3963/4000] Training [29/39] Loss: 0.00626 +Epoch [3963/4000] Training [30/39] Loss: 0.00583 +Epoch [3963/4000] Training [31/39] Loss: 0.00276 +Epoch [3963/4000] Training [32/39] Loss: 0.00546 +Epoch [3963/4000] Training [33/39] Loss: 0.00520 +Epoch [3963/4000] Training [34/39] Loss: 0.00865 +Epoch [3963/4000] Training [35/39] Loss: 0.00387 +Epoch [3963/4000] Training [36/39] Loss: 0.12926 +Epoch [3963/4000] Training [37/39] Loss: 0.12865 +Epoch [3963/4000] Training [38/39] Loss: 0.21574 +Epoch [3963/4000] Training [39/39] Loss: 0.00358 +Epoch [3963/4000] Training metric {'Train/mean dice_metric': 0.9955610036849976, 'Train/mean miou_metric': 0.9924231171607971, 'Train/mean f1': 0.9968448281288147, 'Train/mean precision': 0.9963998794555664, 'Train/mean recall': 0.9972901940345764, 'Train/mean hd95_metric': 0.9376846551895142} +Epoch [3963/4000] Validation [1/10] Loss: 0.72478 focal_loss 0.63902 dice_loss 0.08576 +Epoch [3963/4000] Validation [2/10] Loss: 0.51283 focal_loss 0.41188 dice_loss 0.10095 +Epoch [3963/4000] Validation [3/10] Loss: 0.41192 focal_loss 0.29962 dice_loss 0.11230 +Epoch [3963/4000] Validation [4/10] Loss: 0.89231 focal_loss 0.32785 dice_loss 0.56446 +Epoch [3963/4000] Validation [5/10] Loss: 3.12170 focal_loss 2.44763 dice_loss 0.67407 +Epoch [3963/4000] Validation [6/10] Loss: 1.32920 focal_loss 0.61687 dice_loss 0.71233 +Epoch [3963/4000] Validation [7/10] Loss: 1.17413 focal_loss 0.52109 dice_loss 0.65304 +Epoch [3963/4000] Validation [8/10] Loss: 2.46698 focal_loss 1.84354 dice_loss 0.62344 +Epoch [3963/4000] Validation [9/10] Loss: 1.53659 focal_loss 0.99305 dice_loss 0.54355 +Epoch [3963/4000] Validation [10/10] Loss: 1.87686 focal_loss 1.14315 dice_loss 0.73370 +Epoch [3963/4000] Validation metric {'Val/mean dice_metric': 0.9506845474243164, 'Val/mean miou_metric': 0.9348493218421936, 'Val/mean f1': 0.9486320614814758, 'Val/mean precision': 0.9450034499168396, 'Val/mean recall': 0.9522887468338013, 'Val/mean hd95_metric': 10.789008140563965} +Cheakpoint... +Epoch [3963/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506845474243164, 'Val/mean miou_metric': 0.9348493218421936, 'Val/mean f1': 0.9486320614814758, 'Val/mean precision': 0.9450034499168396, 'Val/mean recall': 0.9522887468338013, 'Val/mean hd95_metric': 10.789008140563965} +Epoch [3964/4000] Training [1/39] Loss: 0.00316 +Epoch [3964/4000] Training [2/39] Loss: 0.21380 +Epoch [3964/4000] Training [3/39] Loss: 0.00346 +Epoch [3964/4000] Training [4/39] Loss: 0.00430 +Epoch [3964/4000] Training [5/39] Loss: 0.00463 +Epoch [3964/4000] Training [6/39] Loss: 0.00411 +Epoch [3964/4000] Training [7/39] Loss: 0.00337 +Epoch [3964/4000] Training [8/39] Loss: 0.00522 +Epoch [3964/4000] Training [9/39] Loss: 0.00397 +Epoch [3964/4000] Training [10/39] Loss: 0.00539 +Epoch [3964/4000] Training [11/39] Loss: 0.00380 +Epoch [3964/4000] Training [12/39] Loss: 0.12802 +Epoch [3964/4000] Training [13/39] Loss: 0.00275 +Epoch [3964/4000] Training [14/39] Loss: 0.00635 +Epoch [3964/4000] Training [15/39] Loss: 0.00760 +Epoch [3964/4000] Training [16/39] Loss: 0.13066 +Epoch [3964/4000] Training [17/39] Loss: 0.25242 +Epoch [3964/4000] Training [18/39] Loss: 0.12771 +Epoch [3964/4000] Training [19/39] Loss: 0.12868 +Epoch [3964/4000] Training [20/39] Loss: 0.00364 +Epoch [3964/4000] Training [21/39] Loss: 0.25295 +Epoch [3964/4000] Training [22/39] Loss: 0.12983 +Epoch [3964/4000] Training [23/39] Loss: 0.12783 +Epoch [3964/4000] Training [24/39] Loss: 0.00411 +Epoch [3964/4000] Training [25/39] Loss: 0.00300 +Epoch [3964/4000] Training [26/39] Loss: 0.00286 +Epoch [3964/4000] Training [27/39] Loss: 0.12790 +Epoch [3964/4000] Training [28/39] Loss: 0.00561 +Epoch [3964/4000] Training [29/39] Loss: 0.00442 +Epoch [3964/4000] Training [30/39] Loss: 0.00357 +Epoch [3964/4000] Training [31/39] Loss: 0.00355 +Epoch [3964/4000] Training [32/39] Loss: 0.00448 +Epoch [3964/4000] Training [33/39] Loss: 0.00440 +Epoch [3964/4000] Training [34/39] Loss: 0.00570 +Epoch [3964/4000] Training [35/39] Loss: 0.00405 +Epoch [3964/4000] Training [36/39] Loss: 0.00466 +Epoch [3964/4000] Training [37/39] Loss: 0.12771 +Epoch [3964/4000] Training [38/39] Loss: 0.13234 +Epoch [3964/4000] Training [39/39] Loss: 0.00453 +Epoch [3964/4000] Training metric {'Train/mean dice_metric': 0.9965581297874451, 'Train/mean miou_metric': 0.9935684204101562, 'Train/mean f1': 0.9971058368682861, 'Train/mean precision': 0.9966367483139038, 'Train/mean recall': 0.9975753426551819, 'Train/mean hd95_metric': 0.9053119421005249} +Epoch [3964/4000] Validation [1/10] Loss: 0.71137 focal_loss 0.62471 dice_loss 0.08666 +Epoch [3964/4000] Validation [2/10] Loss: 0.50656 focal_loss 0.40910 dice_loss 0.09746 +Epoch [3964/4000] Validation [3/10] Loss: 0.38668 focal_loss 0.27583 dice_loss 0.11086 +Epoch [3964/4000] Validation [4/10] Loss: 0.90370 focal_loss 0.33765 dice_loss 0.56605 +Epoch [3964/4000] Validation [5/10] Loss: 3.04081 focal_loss 2.36698 dice_loss 0.67383 +Epoch [3964/4000] Validation [6/10] Loss: 1.35385 focal_loss 0.64089 dice_loss 0.71296 +Epoch [3964/4000] Validation [7/10] Loss: 1.18857 focal_loss 0.53340 dice_loss 0.65517 +Epoch [3964/4000] Validation [8/10] Loss: 2.36568 focal_loss 1.75077 dice_loss 0.61491 +Epoch [3964/4000] Validation [9/10] Loss: 1.54111 focal_loss 0.99654 dice_loss 0.54458 +Epoch [3964/4000] Validation [10/10] Loss: 1.92508 focal_loss 1.18946 dice_loss 0.73561 +Epoch [3964/4000] Validation metric {'Val/mean dice_metric': 0.95156329870224, 'Val/mean miou_metric': 0.935839056968689, 'Val/mean f1': 0.9484897255897522, 'Val/mean precision': 0.9434077143669128, 'Val/mean recall': 0.9536266326904297, 'Val/mean hd95_metric': 10.798111915588379} +Cheakpoint... +Epoch [3964/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.95156329870224, 'Val/mean miou_metric': 0.935839056968689, 'Val/mean f1': 0.9484897255897522, 'Val/mean precision': 0.9434077143669128, 'Val/mean recall': 0.9536266326904297, 'Val/mean hd95_metric': 10.798111915588379} +Epoch [3965/4000] Training [1/39] Loss: 0.00714 +Epoch [3965/4000] Training [2/39] Loss: 0.00397 +Epoch [3965/4000] Training [3/39] Loss: 0.00391 +Epoch [3965/4000] Training [4/39] Loss: 0.12867 +Epoch [3965/4000] Training [5/39] Loss: 0.00468 +Epoch [3965/4000] Training [6/39] Loss: 0.00391 +Epoch [3965/4000] Training [7/39] Loss: 0.25360 +Epoch [3965/4000] Training [8/39] Loss: 0.25454 +Epoch [3965/4000] Training [9/39] Loss: 0.00483 +Epoch [3965/4000] Training [10/39] Loss: 0.08444 +Epoch [3965/4000] Training [11/39] Loss: 0.00505 +Epoch [3965/4000] Training [12/39] Loss: 0.00374 +Epoch [3965/4000] Training [13/39] Loss: 0.00821 +Epoch [3965/4000] Training [14/39] Loss: 0.00837 +Epoch [3965/4000] Training [15/39] Loss: 0.00299 +Epoch [3965/4000] Training [16/39] Loss: 0.00320 +Epoch [3965/4000] Training [17/39] Loss: 0.00569 +Epoch [3965/4000] Training [18/39] Loss: 0.00601 +Epoch [3965/4000] Training [19/39] Loss: 0.00560 +Epoch [3965/4000] Training [20/39] Loss: 0.00378 +Epoch [3965/4000] Training [21/39] Loss: 0.00525 +Epoch [3965/4000] Training [22/39] Loss: 0.00492 +Epoch [3965/4000] Training [23/39] Loss: 0.12746 +Epoch [3965/4000] Training [24/39] Loss: 0.00351 +Epoch [3965/4000] Training [25/39] Loss: 0.25261 +Epoch [3965/4000] Training [26/39] Loss: 0.00398 +Epoch [3965/4000] Training [27/39] Loss: 0.12917 +Epoch [3965/4000] Training [28/39] Loss: 0.00865 +Epoch [3965/4000] Training [29/39] Loss: 0.00562 +Epoch [3965/4000] Training [30/39] Loss: 0.00362 +Epoch [3965/4000] Training [31/39] Loss: 0.13026 +Epoch [3965/4000] Training [32/39] Loss: 0.00314 +Epoch [3965/4000] Training [33/39] Loss: 0.12955 +Epoch [3965/4000] Training [34/39] Loss: 0.00387 +Epoch [3965/4000] Training [35/39] Loss: 0.00340 +Epoch [3965/4000] Training [36/39] Loss: 0.00625 +Epoch [3965/4000] Training [37/39] Loss: 0.12906 +Epoch [3965/4000] Training [38/39] Loss: 0.25442 +Epoch [3965/4000] Training [39/39] Loss: 0.12876 +Epoch [3965/4000] Training metric {'Train/mean dice_metric': 0.9954572319984436, 'Train/mean miou_metric': 0.9921959042549133, 'Train/mean f1': 0.996819257736206, 'Train/mean precision': 0.9963886141777039, 'Train/mean recall': 0.9972503185272217, 'Train/mean hd95_metric': 0.930600643157959} +Epoch [3965/4000] Validation [1/10] Loss: 0.70841 focal_loss 0.62180 dice_loss 0.08661 +Epoch [3965/4000] Validation [2/10] Loss: 0.50767 focal_loss 0.41050 dice_loss 0.09716 +Epoch [3965/4000] Validation [3/10] Loss: 0.38416 focal_loss 0.27347 dice_loss 0.11069 +Epoch [3965/4000] Validation [4/10] Loss: 0.90421 focal_loss 0.33808 dice_loss 0.56613 +Epoch [3965/4000] Validation [5/10] Loss: 3.02807 focal_loss 2.35420 dice_loss 0.67387 +Epoch [3965/4000] Validation [6/10] Loss: 1.36052 focal_loss 0.64751 dice_loss 0.71301 +Epoch [3965/4000] Validation [7/10] Loss: 1.19624 focal_loss 0.54080 dice_loss 0.65545 +Epoch [3965/4000] Validation [8/10] Loss: 2.36877 focal_loss 1.75570 dice_loss 0.61307 +Epoch [3965/4000] Validation [9/10] Loss: 1.54469 focal_loss 0.99994 dice_loss 0.54476 +Epoch [3965/4000] Validation [10/10] Loss: 1.93779 focal_loss 1.20201 dice_loss 0.73578 +Epoch [3965/4000] Validation metric {'Val/mean dice_metric': 0.950683057308197, 'Val/mean miou_metric': 0.9347386360168457, 'Val/mean f1': 0.9480009078979492, 'Val/mean precision': 0.9427485466003418, 'Val/mean recall': 0.95331209897995, 'Val/mean hd95_metric': 10.911884307861328} +Cheakpoint... +Epoch [3965/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.950683057308197, 'Val/mean miou_metric': 0.9347386360168457, 'Val/mean f1': 0.9480009078979492, 'Val/mean precision': 0.9427485466003418, 'Val/mean recall': 0.95331209897995, 'Val/mean hd95_metric': 10.911884307861328} +Epoch [3966/4000] Training [1/39] Loss: 0.25340 +Epoch [3966/4000] Training [2/39] Loss: 0.00353 +Epoch [3966/4000] Training [3/39] Loss: 0.00483 +Epoch [3966/4000] Training [4/39] Loss: 0.00337 +Epoch [3966/4000] Training [5/39] Loss: 0.00383 +Epoch [3966/4000] Training [6/39] Loss: 0.00446 +Epoch [3966/4000] Training [7/39] Loss: 0.00535 +Epoch [3966/4000] Training [8/39] Loss: 0.00345 +Epoch [3966/4000] Training [9/39] Loss: 0.00367 +Epoch [3966/4000] Training [10/39] Loss: 0.00409 +Epoch [3966/4000] Training [11/39] Loss: 0.00465 +Epoch [3966/4000] Training [12/39] Loss: 0.00326 +Epoch [3966/4000] Training [13/39] Loss: 0.00492 +Epoch [3966/4000] Training [14/39] Loss: 0.12899 +Epoch [3966/4000] Training [15/39] Loss: 0.00492 +Epoch [3966/4000] Training [16/39] Loss: 0.00691 +Epoch [3966/4000] Training [17/39] Loss: 0.12921 +Epoch [3966/4000] Training [18/39] Loss: 0.13048 +Epoch [3966/4000] Training [19/39] Loss: 0.12832 +Epoch [3966/4000] Training [20/39] Loss: 0.00330 +Epoch [3966/4000] Training [21/39] Loss: 0.00389 +Epoch [3966/4000] Training [22/39] Loss: 0.00507 +Epoch [3966/4000] Training [23/39] Loss: 0.00406 +Epoch [3966/4000] Training [24/39] Loss: 0.13050 +Epoch [3966/4000] Training [25/39] Loss: 0.00386 +Epoch [3966/4000] Training [26/39] Loss: 0.00306 +Epoch [3966/4000] Training [27/39] Loss: 0.00411 +Epoch [3966/4000] Training [28/39] Loss: 0.00532 +Epoch [3966/4000] Training [29/39] Loss: 0.00451 +Epoch [3966/4000] Training [30/39] Loss: 0.12834 +Epoch [3966/4000] Training [31/39] Loss: 0.00432 +Epoch [3966/4000] Training [32/39] Loss: 0.00499 +Epoch [3966/4000] Training [33/39] Loss: 0.00446 +Epoch [3966/4000] Training [34/39] Loss: 0.12942 +Epoch [3966/4000] Training [35/39] Loss: 0.12988 +Epoch [3966/4000] Training [36/39] Loss: 0.00408 +Epoch [3966/4000] Training [37/39] Loss: 0.00773 +Epoch [3966/4000] Training [38/39] Loss: 0.00449 +Epoch [3966/4000] Training [39/39] Loss: 0.00394 +Epoch [3966/4000] Training metric {'Train/mean dice_metric': 0.9966326355934143, 'Train/mean miou_metric': 0.9937068223953247, 'Train/mean f1': 0.9971486330032349, 'Train/mean precision': 0.9967012405395508, 'Train/mean recall': 0.9975966215133667, 'Train/mean hd95_metric': 0.9799853563308716} +Epoch [3966/4000] Validation [1/10] Loss: 0.71172 focal_loss 0.62604 dice_loss 0.08567 +Epoch [3966/4000] Validation [2/10] Loss: 0.50747 focal_loss 0.40649 dice_loss 0.10098 +Epoch [3966/4000] Validation [3/10] Loss: 0.40424 focal_loss 0.29209 dice_loss 0.11216 +Epoch [3966/4000] Validation [4/10] Loss: 0.88780 focal_loss 0.32341 dice_loss 0.56439 +Epoch [3966/4000] Validation [5/10] Loss: 3.07948 focal_loss 2.40540 dice_loss 0.67408 +Epoch [3966/4000] Validation [6/10] Loss: 1.32210 focal_loss 0.60939 dice_loss 0.71271 +Epoch [3966/4000] Validation [7/10] Loss: 1.16697 focal_loss 0.51475 dice_loss 0.65222 +Epoch [3966/4000] Validation [8/10] Loss: 2.42664 focal_loss 1.80424 dice_loss 0.62240 +Epoch [3966/4000] Validation [9/10] Loss: 1.51682 focal_loss 0.97321 dice_loss 0.54360 +Epoch [3966/4000] Validation [10/10] Loss: 1.86329 focal_loss 1.12944 dice_loss 0.73385 +Epoch [3966/4000] Validation metric {'Val/mean dice_metric': 0.9515577554702759, 'Val/mean miou_metric': 0.9358998537063599, 'Val/mean f1': 0.948917031288147, 'Val/mean precision': 0.9451377987861633, 'Val/mean recall': 0.9527267813682556, 'Val/mean hd95_metric': 10.80716609954834} +Cheakpoint... +Epoch [3966/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515577554702759, 'Val/mean miou_metric': 0.9358998537063599, 'Val/mean f1': 0.948917031288147, 'Val/mean precision': 0.9451377987861633, 'Val/mean recall': 0.9527267813682556, 'Val/mean hd95_metric': 10.80716609954834} +Epoch [3967/4000] Training [1/39] Loss: 0.00421 +Epoch [3967/4000] Training [2/39] Loss: 0.12950 +Epoch [3967/4000] Training [3/39] Loss: 0.00497 +Epoch [3967/4000] Training [4/39] Loss: 0.12842 +Epoch [3967/4000] Training [5/39] Loss: 0.00711 +Epoch [3967/4000] Training [6/39] Loss: 0.00346 +Epoch [3967/4000] Training [7/39] Loss: 0.12797 +Epoch [3967/4000] Training [8/39] Loss: 0.00305 +Epoch [3967/4000] Training [9/39] Loss: 0.25419 +Epoch [3967/4000] Training [10/39] Loss: 0.12882 +Epoch [3967/4000] Training [11/39] Loss: 0.12785 +Epoch [3967/4000] Training [12/39] Loss: 0.00435 +Epoch [3967/4000] Training [13/39] Loss: 0.00579 +Epoch [3967/4000] Training [14/39] Loss: 0.00340 +Epoch [3967/4000] Training [15/39] Loss: 0.00360 +Epoch [3967/4000] Training [16/39] Loss: 0.13202 +Epoch [3967/4000] Training [17/39] Loss: 0.00450 +Epoch [3967/4000] Training [18/39] Loss: 0.00355 +Epoch [3967/4000] Training [19/39] Loss: 0.00438 +Epoch [3967/4000] Training [20/39] Loss: 0.00395 +Epoch [3967/4000] Training [21/39] Loss: 0.00419 +Epoch [3967/4000] Training [22/39] Loss: 0.00289 +Epoch [3967/4000] Training [23/39] Loss: 0.00412 +Epoch [3967/4000] Training [24/39] Loss: 0.00430 +Epoch [3967/4000] Training [25/39] Loss: 0.00460 +Epoch [3967/4000] Training [26/39] Loss: 0.00735 +Epoch [3967/4000] Training [27/39] Loss: 0.00341 +Epoch [3967/4000] Training [28/39] Loss: 0.13110 +Epoch [3967/4000] Training [29/39] Loss: 0.12829 +Epoch [3967/4000] Training [30/39] Loss: 0.00557 +Epoch [3967/4000] Training [31/39] Loss: 0.12752 +Epoch [3967/4000] Training [32/39] Loss: 0.00397 +Epoch [3967/4000] Training [33/39] Loss: 0.01070 +Epoch [3967/4000] Training [34/39] Loss: 0.05298 +Epoch [3967/4000] Training [35/39] Loss: 0.13067 +Epoch [3967/4000] Training [36/39] Loss: 0.00527 +Epoch [3967/4000] Training [37/39] Loss: 0.12827 +Epoch [3967/4000] Training [38/39] Loss: 0.13320 +Epoch [3967/4000] Training [39/39] Loss: 0.00745 +Epoch [3967/4000] Training metric {'Train/mean dice_metric': 0.996277928352356, 'Train/mean miou_metric': 0.9930433034896851, 'Train/mean f1': 0.9968694448471069, 'Train/mean precision': 0.9965090155601501, 'Train/mean recall': 0.9972299933433533, 'Train/mean hd95_metric': 0.9196673631668091} +Epoch [3967/4000] Validation [1/10] Loss: 0.73833 focal_loss 0.65087 dice_loss 0.08746 +Epoch [3967/4000] Validation [2/10] Loss: 0.51069 focal_loss 0.41511 dice_loss 0.09558 +Epoch [3967/4000] Validation [3/10] Loss: 0.39080 focal_loss 0.28056 dice_loss 0.11024 +Epoch [3967/4000] Validation [4/10] Loss: 0.90968 focal_loss 0.34325 dice_loss 0.56643 +Epoch [3967/4000] Validation [5/10] Loss: 3.11014 focal_loss 2.43631 dice_loss 0.67383 +Epoch [3967/4000] Validation [6/10] Loss: 1.36951 focal_loss 0.65623 dice_loss 0.71328 +Epoch [3967/4000] Validation [7/10] Loss: 1.20398 focal_loss 0.54755 dice_loss 0.65643 +Epoch [3967/4000] Validation [8/10] Loss: 2.38728 focal_loss 1.77763 dice_loss 0.60966 +Epoch [3967/4000] Validation [9/10] Loss: 1.57528 focal_loss 1.03075 dice_loss 0.54453 +Epoch [3967/4000] Validation [10/10] Loss: 1.96175 focal_loss 1.22533 dice_loss 0.73642 +Epoch [3967/4000] Validation metric {'Val/mean dice_metric': 0.9513244032859802, 'Val/mean miou_metric': 0.9353744983673096, 'Val/mean f1': 0.9480994343757629, 'Val/mean precision': 0.9425179958343506, 'Val/mean recall': 0.9537473320960999, 'Val/mean hd95_metric': 10.64666748046875} +Cheakpoint... +Epoch [3967/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513244032859802, 'Val/mean miou_metric': 0.9353744983673096, 'Val/mean f1': 0.9480994343757629, 'Val/mean precision': 0.9425179958343506, 'Val/mean recall': 0.9537473320960999, 'Val/mean hd95_metric': 10.64666748046875} +Epoch [3968/4000] Training [1/39] Loss: 0.00541 +Epoch [3968/4000] Training [2/39] Loss: 0.12789 +Epoch [3968/4000] Training [3/39] Loss: 0.00508 +Epoch [3968/4000] Training [4/39] Loss: 0.00453 +Epoch [3968/4000] Training [5/39] Loss: 0.00490 +Epoch [3968/4000] Training [6/39] Loss: 0.12963 +Epoch [3968/4000] Training [7/39] Loss: 0.00363 +Epoch [3968/4000] Training [8/39] Loss: 0.00348 +Epoch [3968/4000] Training [9/39] Loss: 0.00608 +Epoch [3968/4000] Training [10/39] Loss: 0.00591 +Epoch [3968/4000] Training [11/39] Loss: 0.00417 +Epoch [3968/4000] Training [12/39] Loss: 0.00375 +Epoch [3968/4000] Training [13/39] Loss: 0.00499 +Epoch [3968/4000] Training [14/39] Loss: 0.00400 +Epoch [3968/4000] Training [15/39] Loss: 0.00383 +Epoch [3968/4000] Training [16/39] Loss: 0.00474 +Epoch [3968/4000] Training [17/39] Loss: 0.28892 +Epoch [3968/4000] Training [18/39] Loss: 0.00569 +Epoch [3968/4000] Training [19/39] Loss: 0.00540 +Epoch [3968/4000] Training [20/39] Loss: 0.00537 +Epoch [3968/4000] Training [21/39] Loss: 0.00539 +Epoch [3968/4000] Training [22/39] Loss: 0.00589 +Epoch [3968/4000] Training [23/39] Loss: 0.25355 +Epoch [3968/4000] Training [24/39] Loss: 0.00518 +Epoch [3968/4000] Training [25/39] Loss: 0.00331 +Epoch [3968/4000] Training [26/39] Loss: 0.00465 +Epoch [3968/4000] Training [27/39] Loss: 0.00744 +Epoch [3968/4000] Training [28/39] Loss: 0.00449 +Epoch [3968/4000] Training [29/39] Loss: 0.00425 +Epoch [3968/4000] Training [30/39] Loss: 0.00342 +Epoch [3968/4000] Training [31/39] Loss: 0.00556 +Epoch [3968/4000] Training [32/39] Loss: 0.00433 +Epoch [3968/4000] Training [33/39] Loss: 0.00329 +Epoch [3968/4000] Training [34/39] Loss: 0.00427 +Epoch [3968/4000] Training [35/39] Loss: 0.12839 +Epoch [3968/4000] Training [36/39] Loss: 0.00606 +Epoch [3968/4000] Training [37/39] Loss: 0.00317 +Epoch [3968/4000] Training [38/39] Loss: 0.00511 +Epoch [3968/4000] Training [39/39] Loss: 0.00461 +Epoch [3968/4000] Training metric {'Train/mean dice_metric': 0.9964476823806763, 'Train/mean miou_metric': 0.9933369755744934, 'Train/mean f1': 0.9968921542167664, 'Train/mean precision': 0.9964130520820618, 'Train/mean recall': 0.9973717927932739, 'Train/mean hd95_metric': 0.9276644587516785} +Epoch [3968/4000] Validation [1/10] Loss: 0.73111 focal_loss 0.64426 dice_loss 0.08685 +Epoch [3968/4000] Validation [2/10] Loss: 0.50415 focal_loss 0.40566 dice_loss 0.09849 +Epoch [3968/4000] Validation [3/10] Loss: 0.40392 focal_loss 0.29229 dice_loss 0.11163 +Epoch [3968/4000] Validation [4/10] Loss: 0.89335 focal_loss 0.32801 dice_loss 0.56534 +Epoch [3968/4000] Validation [5/10] Loss: 3.12881 focal_loss 2.45479 dice_loss 0.67402 +Epoch [3968/4000] Validation [6/10] Loss: 1.33205 focal_loss 0.61961 dice_loss 0.71244 +Epoch [3968/4000] Validation [7/10] Loss: 1.17433 focal_loss 0.51967 dice_loss 0.65466 +Epoch [3968/4000] Validation [8/10] Loss: 2.41119 focal_loss 1.79385 dice_loss 0.61734 +Epoch [3968/4000] Validation [9/10] Loss: 1.53944 focal_loss 0.99577 dice_loss 0.54367 +Epoch [3968/4000] Validation [10/10] Loss: 1.88810 focal_loss 1.15332 dice_loss 0.73478 +Epoch [3968/4000] Validation metric {'Val/mean dice_metric': 0.9514283537864685, 'Val/mean miou_metric': 0.9355976581573486, 'Val/mean f1': 0.9482281804084778, 'Val/mean precision': 0.9438095688819885, 'Val/mean recall': 0.9526882767677307, 'Val/mean hd95_metric': 10.713394165039062} +Cheakpoint... +Epoch [3968/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514283537864685, 'Val/mean miou_metric': 0.9355976581573486, 'Val/mean f1': 0.9482281804084778, 'Val/mean precision': 0.9438095688819885, 'Val/mean recall': 0.9526882767677307, 'Val/mean hd95_metric': 10.713394165039062} +Epoch [3969/4000] Training [1/39] Loss: 0.12815 +Epoch [3969/4000] Training [2/39] Loss: 0.00502 +Epoch [3969/4000] Training [3/39] Loss: 0.00380 +Epoch [3969/4000] Training [4/39] Loss: 0.12959 +Epoch [3969/4000] Training [5/39] Loss: 0.12925 +Epoch [3969/4000] Training [6/39] Loss: 0.12922 +Epoch [3969/4000] Training [7/39] Loss: 0.00397 +Epoch [3969/4000] Training [8/39] Loss: 0.13019 +Epoch [3969/4000] Training [9/39] Loss: 0.00367 +Epoch [3969/4000] Training [10/39] Loss: 0.00656 +Epoch [3969/4000] Training [11/39] Loss: 0.13358 +Epoch [3969/4000] Training [12/39] Loss: 0.12867 +Epoch [3969/4000] Training [13/39] Loss: 0.00408 +Epoch [3969/4000] Training [14/39] Loss: 0.00522 +Epoch [3969/4000] Training [15/39] Loss: 0.00506 +Epoch [3969/4000] Training [16/39] Loss: 0.00500 +Epoch [3969/4000] Training [17/39] Loss: 0.00518 +Epoch [3969/4000] Training [18/39] Loss: 0.00633 +Epoch [3969/4000] Training [19/39] Loss: 0.00295 +Epoch [3969/4000] Training [20/39] Loss: 0.00588 +Epoch [3969/4000] Training [21/39] Loss: 0.00481 +Epoch [3969/4000] Training [22/39] Loss: 0.00429 +Epoch [3969/4000] Training [23/39] Loss: 0.00515 +Epoch [3969/4000] Training [24/39] Loss: 0.00434 +Epoch [3969/4000] Training [25/39] Loss: 0.00268 +Epoch [3969/4000] Training [26/39] Loss: 0.00300 +Epoch [3969/4000] Training [27/39] Loss: 0.13112 +Epoch [3969/4000] Training [28/39] Loss: 0.00343 +Epoch [3969/4000] Training [29/39] Loss: 0.00412 +Epoch [3969/4000] Training [30/39] Loss: 0.00521 +Epoch [3969/4000] Training [31/39] Loss: 0.00492 +Epoch [3969/4000] Training [32/39] Loss: 0.00353 +Epoch [3969/4000] Training [33/39] Loss: 0.00428 +Epoch [3969/4000] Training [34/39] Loss: 0.12817 +Epoch [3969/4000] Training [35/39] Loss: 0.00405 +Epoch [3969/4000] Training [36/39] Loss: 0.12854 +Epoch [3969/4000] Training [37/39] Loss: 0.12873 +Epoch [3969/4000] Training [38/39] Loss: 0.00395 +Epoch [3969/4000] Training [39/39] Loss: 0.12900 +Epoch [3969/4000] Training metric {'Train/mean dice_metric': 0.9964072704315186, 'Train/mean miou_metric': 0.9932551980018616, 'Train/mean f1': 0.9968698620796204, 'Train/mean precision': 0.9964075684547424, 'Train/mean recall': 0.9973328113555908, 'Train/mean hd95_metric': 0.9199087023735046} +Epoch [3969/4000] Validation [1/10] Loss: 0.72191 focal_loss 0.63617 dice_loss 0.08574 +Epoch [3969/4000] Validation [2/10] Loss: 0.51143 focal_loss 0.40940 dice_loss 0.10203 +Epoch [3969/4000] Validation [3/10] Loss: 0.41576 focal_loss 0.30298 dice_loss 0.11278 +Epoch [3969/4000] Validation [4/10] Loss: 0.88776 focal_loss 0.32341 dice_loss 0.56435 +Epoch [3969/4000] Validation [5/10] Loss: 3.12932 focal_loss 2.45516 dice_loss 0.67416 +Epoch [3969/4000] Validation [6/10] Loss: 1.31559 focal_loss 0.60342 dice_loss 0.71217 +Epoch [3969/4000] Validation [7/10] Loss: 1.16288 focal_loss 0.51082 dice_loss 0.65205 +Epoch [3969/4000] Validation [8/10] Loss: 2.46160 focal_loss 1.83579 dice_loss 0.62582 +Epoch [3969/4000] Validation [9/10] Loss: 1.52568 focal_loss 0.98226 dice_loss 0.54342 +Epoch [3969/4000] Validation [10/10] Loss: 1.85235 focal_loss 1.11904 dice_loss 0.73331 +Epoch [3969/4000] Validation metric {'Val/mean dice_metric': 0.9513520002365112, 'Val/mean miou_metric': 0.935503363609314, 'Val/mean f1': 0.9485343098640442, 'Val/mean precision': 0.945284366607666, 'Val/mean recall': 0.9518067240715027, 'Val/mean hd95_metric': 10.707608222961426} +Cheakpoint... +Epoch [3969/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513520002365112, 'Val/mean miou_metric': 0.935503363609314, 'Val/mean f1': 0.9485343098640442, 'Val/mean precision': 0.945284366607666, 'Val/mean recall': 0.9518067240715027, 'Val/mean hd95_metric': 10.707608222961426} +Epoch [3970/4000] Training [1/39] Loss: 0.00614 +Epoch [3970/4000] Training [2/39] Loss: 0.00586 +Epoch [3970/4000] Training [3/39] Loss: 0.00618 +Epoch [3970/4000] Training [4/39] Loss: 0.12865 +Epoch [3970/4000] Training [5/39] Loss: 0.00536 +Epoch [3970/4000] Training [6/39] Loss: 0.00298 +Epoch [3970/4000] Training [7/39] Loss: 0.00747 +Epoch [3970/4000] Training [8/39] Loss: 0.00517 +Epoch [3970/4000] Training [9/39] Loss: 0.00696 +Epoch [3970/4000] Training [10/39] Loss: 0.00509 +Epoch [3970/4000] Training [11/39] Loss: 0.00565 +Epoch [3970/4000] Training [12/39] Loss: 0.00333 +Epoch [3970/4000] Training [13/39] Loss: 0.00418 +Epoch [3970/4000] Training [14/39] Loss: 0.00562 +Epoch [3970/4000] Training [15/39] Loss: 0.03925 +Epoch [3970/4000] Training [16/39] Loss: 0.00617 +Epoch [3970/4000] Training [17/39] Loss: 0.25357 +Epoch [3970/4000] Training [18/39] Loss: 0.00558 +Epoch [3970/4000] Training [19/39] Loss: 0.00319 +Epoch [3970/4000] Training [20/39] Loss: 0.00547 +Epoch [3970/4000] Training [21/39] Loss: 0.00376 +Epoch [3970/4000] Training [22/39] Loss: 0.12823 +Epoch [3970/4000] Training [23/39] Loss: 0.00672 +Epoch [3970/4000] Training [24/39] Loss: 0.00355 +Epoch [3970/4000] Training [25/39] Loss: 0.25229 +Epoch [3970/4000] Training [26/39] Loss: 0.00627 +Epoch [3970/4000] Training [27/39] Loss: 0.00385 +Epoch [3970/4000] Training [28/39] Loss: 0.12730 +Epoch [3970/4000] Training [29/39] Loss: 0.00347 +Epoch [3970/4000] Training [30/39] Loss: 0.00616 +Epoch [3970/4000] Training [31/39] Loss: 0.00408 +Epoch [3970/4000] Training [32/39] Loss: 0.12947 +Epoch [3970/4000] Training [33/39] Loss: 0.00634 +Epoch [3970/4000] Training [34/39] Loss: 0.00649 +Epoch [3970/4000] Training [35/39] Loss: 0.00495 +Epoch [3970/4000] Training [36/39] Loss: 0.12877 +Epoch [3970/4000] Training [37/39] Loss: 0.00539 +Epoch [3970/4000] Training [38/39] Loss: 0.00269 +Epoch [3970/4000] Training [39/39] Loss: 0.00340 +Epoch [3970/4000] Training metric {'Train/mean dice_metric': 0.9962591528892517, 'Train/mean miou_metric': 0.9929739236831665, 'Train/mean f1': 0.9968140125274658, 'Train/mean precision': 0.9963940978050232, 'Train/mean recall': 0.9972343444824219, 'Train/mean hd95_metric': 1.1480368375778198} +Epoch [3970/4000] Validation [1/10] Loss: 0.71117 focal_loss 0.62474 dice_loss 0.08643 +Epoch [3970/4000] Validation [2/10] Loss: 0.50804 focal_loss 0.40948 dice_loss 0.09856 +Epoch [3970/4000] Validation [3/10] Loss: 0.39000 focal_loss 0.27888 dice_loss 0.11111 +Epoch [3970/4000] Validation [4/10] Loss: 0.89943 focal_loss 0.33384 dice_loss 0.56560 +Epoch [3970/4000] Validation [5/10] Loss: 3.04987 focal_loss 2.37595 dice_loss 0.67392 +Epoch [3970/4000] Validation [6/10] Loss: 1.34799 focal_loss 0.63524 dice_loss 0.71276 +Epoch [3970/4000] Validation [7/10] Loss: 1.18635 focal_loss 0.53193 dice_loss 0.65442 +Epoch [3970/4000] Validation [8/10] Loss: 2.38489 focal_loss 1.76918 dice_loss 0.61571 +Epoch [3970/4000] Validation [9/10] Loss: 1.53668 focal_loss 0.99222 dice_loss 0.54445 +Epoch [3970/4000] Validation [10/10] Loss: 1.91443 focal_loss 1.17914 dice_loss 0.73529 +Epoch [3970/4000] Validation metric {'Val/mean dice_metric': 0.9513203501701355, 'Val/mean miou_metric': 0.9353569746017456, 'Val/mean f1': 0.9484182000160217, 'Val/mean precision': 0.9436295032501221, 'Val/mean recall': 0.953255832195282, 'Val/mean hd95_metric': 10.956143379211426} +Cheakpoint... +Epoch [3970/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513203501701355, 'Val/mean miou_metric': 0.9353569746017456, 'Val/mean f1': 0.9484182000160217, 'Val/mean precision': 0.9436295032501221, 'Val/mean recall': 0.953255832195282, 'Val/mean hd95_metric': 10.956143379211426} +Epoch [3971/4000] Training [1/39] Loss: 0.00348 +Epoch [3971/4000] Training [2/39] Loss: 0.25285 +Epoch [3971/4000] Training [3/39] Loss: 0.00430 +Epoch [3971/4000] Training [4/39] Loss: 0.00486 +Epoch [3971/4000] Training [5/39] Loss: 0.00599 +Epoch [3971/4000] Training [6/39] Loss: 0.00558 +Epoch [3971/4000] Training [7/39] Loss: 0.00697 +Epoch [3971/4000] Training [8/39] Loss: 0.00633 +Epoch [3971/4000] Training [9/39] Loss: 0.00864 +Epoch [3971/4000] Training [10/39] Loss: 0.00435 +Epoch [3971/4000] Training [11/39] Loss: 0.00481 +Epoch [3971/4000] Training [12/39] Loss: 0.00328 +Epoch [3971/4000] Training [13/39] Loss: 0.13062 +Epoch [3971/4000] Training [14/39] Loss: 0.00467 +Epoch [3971/4000] Training [15/39] Loss: 0.00429 +Epoch [3971/4000] Training [16/39] Loss: 0.00505 +Epoch [3971/4000] Training [17/39] Loss: 0.00451 +Epoch [3971/4000] Training [18/39] Loss: 0.00396 +Epoch [3971/4000] Training [19/39] Loss: 0.00414 +Epoch [3971/4000] Training [20/39] Loss: 0.00319 +Epoch [3971/4000] Training [21/39] Loss: 0.00327 +Epoch [3971/4000] Training [22/39] Loss: 0.00387 +Epoch [3971/4000] Training [23/39] Loss: 0.00598 +Epoch [3971/4000] Training [24/39] Loss: 0.00451 +Epoch [3971/4000] Training [25/39] Loss: 0.00460 +Epoch [3971/4000] Training [26/39] Loss: 0.00440 +Epoch [3971/4000] Training [27/39] Loss: 0.37737 +Epoch [3971/4000] Training [28/39] Loss: 0.00605 +Epoch [3971/4000] Training [29/39] Loss: 0.00340 +Epoch [3971/4000] Training [30/39] Loss: 0.00514 +Epoch [3971/4000] Training [31/39] Loss: 0.00384 +Epoch [3971/4000] Training [32/39] Loss: 0.00368 +Epoch [3971/4000] Training [33/39] Loss: 0.00291 +Epoch [3971/4000] Training [34/39] Loss: 0.00464 +Epoch [3971/4000] Training [35/39] Loss: 0.00536 +Epoch [3971/4000] Training [36/39] Loss: 0.00438 +Epoch [3971/4000] Training [37/39] Loss: 0.00534 +Epoch [3971/4000] Training [38/39] Loss: 0.00795 +Epoch [3971/4000] Training [39/39] Loss: 0.00656 +Epoch [3971/4000] Training metric {'Train/mean dice_metric': 0.996519148349762, 'Train/mean miou_metric': 0.9934865832328796, 'Train/mean f1': 0.9969840049743652, 'Train/mean precision': 0.9965659976005554, 'Train/mean recall': 0.9974021315574646, 'Train/mean hd95_metric': 0.9045440554618835} +Epoch [3971/4000] Validation [1/10] Loss: 0.72306 focal_loss 0.63671 dice_loss 0.08635 +Epoch [3971/4000] Validation [2/10] Loss: 0.51395 focal_loss 0.41449 dice_loss 0.09946 +Epoch [3971/4000] Validation [3/10] Loss: 0.40075 focal_loss 0.28924 dice_loss 0.11151 +Epoch [3971/4000] Validation [4/10] Loss: 0.90094 focal_loss 0.33555 dice_loss 0.56539 +Epoch [3971/4000] Validation [5/10] Loss: 3.11185 focal_loss 2.43778 dice_loss 0.67407 +Epoch [3971/4000] Validation [6/10] Loss: 1.34784 focal_loss 0.63518 dice_loss 0.71266 +Epoch [3971/4000] Validation [7/10] Loss: 1.18903 focal_loss 0.53556 dice_loss 0.65347 +Epoch [3971/4000] Validation [8/10] Loss: 2.43304 focal_loss 1.81487 dice_loss 0.61817 +Epoch [3971/4000] Validation [9/10] Loss: 1.55359 focal_loss 1.00926 dice_loss 0.54433 +Epoch [3971/4000] Validation [10/10] Loss: 1.91873 focal_loss 1.18372 dice_loss 0.73501 +Epoch [3971/4000] Validation metric {'Val/mean dice_metric': 0.9515421390533447, 'Val/mean miou_metric': 0.9358018636703491, 'Val/mean f1': 0.9484611749649048, 'Val/mean precision': 0.9439648389816284, 'Val/mean recall': 0.953000545501709, 'Val/mean hd95_metric': 10.75775146484375} +Cheakpoint... +Epoch [3971/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515421390533447, 'Val/mean miou_metric': 0.9358018636703491, 'Val/mean f1': 0.9484611749649048, 'Val/mean precision': 0.9439648389816284, 'Val/mean recall': 0.953000545501709, 'Val/mean hd95_metric': 10.75775146484375} +Epoch [3972/4000] Training [1/39] Loss: 0.01131 +Epoch [3972/4000] Training [2/39] Loss: 0.00319 +Epoch [3972/4000] Training [3/39] Loss: 0.25228 +Epoch [3972/4000] Training [4/39] Loss: 0.00358 +Epoch [3972/4000] Training [5/39] Loss: 0.13069 +Epoch [3972/4000] Training [6/39] Loss: 0.00620 +Epoch [3972/4000] Training [7/39] Loss: 0.00650 +Epoch [3972/4000] Training [8/39] Loss: 0.00475 +Epoch [3972/4000] Training [9/39] Loss: 0.00583 +Epoch [3972/4000] Training [10/39] Loss: 0.12839 +Epoch [3972/4000] Training [11/39] Loss: 0.12835 +Epoch [3972/4000] Training [12/39] Loss: 0.00445 +Epoch [3972/4000] Training [13/39] Loss: 0.00407 +Epoch [3972/4000] Training [14/39] Loss: 0.00383 +Epoch [3972/4000] Training [15/39] Loss: 0.00258 +Epoch [3972/4000] Training [16/39] Loss: 0.00387 +Epoch [3972/4000] Training [17/39] Loss: 0.00435 +Epoch [3972/4000] Training [18/39] Loss: 0.00453 +Epoch [3972/4000] Training [19/39] Loss: 0.00322 +Epoch [3972/4000] Training [20/39] Loss: 0.13031 +Epoch [3972/4000] Training [21/39] Loss: 0.12869 +Epoch [3972/4000] Training [22/39] Loss: 0.13013 +Epoch [3972/4000] Training [23/39] Loss: 0.00802 +Epoch [3972/4000] Training [24/39] Loss: 0.00546 +Epoch [3972/4000] Training [25/39] Loss: 0.00491 +Epoch [3972/4000] Training [26/39] Loss: 0.12904 +Epoch [3972/4000] Training [27/39] Loss: 0.00457 +Epoch [3972/4000] Training [28/39] Loss: 0.00464 +Epoch [3972/4000] Training [29/39] Loss: 0.00429 +Epoch [3972/4000] Training [30/39] Loss: 0.00454 +Epoch [3972/4000] Training [31/39] Loss: 0.00360 +Epoch [3972/4000] Training [32/39] Loss: 0.00807 +Epoch [3972/4000] Training [33/39] Loss: 0.00446 +Epoch [3972/4000] Training [34/39] Loss: 0.00486 +Epoch [3972/4000] Training [35/39] Loss: 0.00629 +Epoch [3972/4000] Training [36/39] Loss: 0.00486 +Epoch [3972/4000] Training [37/39] Loss: 0.00587 +Epoch [3972/4000] Training [38/39] Loss: 0.00697 +Epoch [3972/4000] Training [39/39] Loss: 0.13011 +Epoch [3972/4000] Training metric {'Train/mean dice_metric': 0.9961596727371216, 'Train/mean miou_metric': 0.9927871823310852, 'Train/mean f1': 0.996850848197937, 'Train/mean precision': 0.9964179992675781, 'Train/mean recall': 0.9972841143608093, 'Train/mean hd95_metric': 1.0608673095703125} +Epoch [3972/4000] Validation [1/10] Loss: 0.72676 focal_loss 0.64000 dice_loss 0.08676 +Epoch [3972/4000] Validation [2/10] Loss: 0.50554 focal_loss 0.40690 dice_loss 0.09864 +Epoch [3972/4000] Validation [3/10] Loss: 0.40005 focal_loss 0.28870 dice_loss 0.11135 +Epoch [3972/4000] Validation [4/10] Loss: 0.89518 focal_loss 0.32990 dice_loss 0.56529 +Epoch [3972/4000] Validation [5/10] Loss: 3.12292 focal_loss 2.44893 dice_loss 0.67400 +Epoch [3972/4000] Validation [6/10] Loss: 1.33758 focal_loss 0.62489 dice_loss 0.71269 +Epoch [3972/4000] Validation [7/10] Loss: 1.17945 focal_loss 0.52496 dice_loss 0.65449 +Epoch [3972/4000] Validation [8/10] Loss: 2.40285 focal_loss 1.78641 dice_loss 0.61643 +Epoch [3972/4000] Validation [9/10] Loss: 1.54082 focal_loss 0.99693 dice_loss 0.54390 +Epoch [3972/4000] Validation [10/10] Loss: 1.89840 focal_loss 1.16341 dice_loss 0.73499 +Epoch [3972/4000] Validation metric {'Val/mean dice_metric': 0.9512562155723572, 'Val/mean miou_metric': 0.9352272152900696, 'Val/mean f1': 0.9483963251113892, 'Val/mean precision': 0.9438405632972717, 'Val/mean recall': 0.9529962539672852, 'Val/mean hd95_metric': 10.794987678527832} +Cheakpoint... +Epoch [3972/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9512562155723572, 'Val/mean miou_metric': 0.9352272152900696, 'Val/mean f1': 0.9483963251113892, 'Val/mean precision': 0.9438405632972717, 'Val/mean recall': 0.9529962539672852, 'Val/mean hd95_metric': 10.794987678527832} +Epoch [3973/4000] Training [1/39] Loss: 0.12933 +Epoch [3973/4000] Training [2/39] Loss: 0.12778 +Epoch [3973/4000] Training [3/39] Loss: 0.25350 +Epoch [3973/4000] Training [4/39] Loss: 0.00286 +Epoch [3973/4000] Training [5/39] Loss: 0.00336 +Epoch [3973/4000] Training [6/39] Loss: 0.00623 +Epoch [3973/4000] Training [7/39] Loss: 0.12994 +Epoch [3973/4000] Training [8/39] Loss: 0.00730 +Epoch [3973/4000] Training [9/39] Loss: 0.00508 +Epoch [3973/4000] Training [10/39] Loss: 0.00417 +Epoch [3973/4000] Training [11/39] Loss: 0.12883 +Epoch [3973/4000] Training [12/39] Loss: 0.25506 +Epoch [3973/4000] Training [13/39] Loss: 0.00690 +Epoch [3973/4000] Training [14/39] Loss: 0.00308 +Epoch [3973/4000] Training [15/39] Loss: 0.00587 +Epoch [3973/4000] Training [16/39] Loss: 0.00414 +Epoch [3973/4000] Training [17/39] Loss: 0.13103 +Epoch [3973/4000] Training [18/39] Loss: 0.00379 +Epoch [3973/4000] Training [19/39] Loss: 0.00401 +Epoch [3973/4000] Training [20/39] Loss: 0.00410 +Epoch [3973/4000] Training [21/39] Loss: 0.00536 +Epoch [3973/4000] Training [22/39] Loss: 0.00551 +Epoch [3973/4000] Training [23/39] Loss: 0.00359 +Epoch [3973/4000] Training [24/39] Loss: 0.00637 +Epoch [3973/4000] Training [25/39] Loss: 0.00489 +Epoch [3973/4000] Training [26/39] Loss: 0.00402 +Epoch [3973/4000] Training [27/39] Loss: 0.00272 +Epoch [3973/4000] Training [28/39] Loss: 0.00484 +Epoch [3973/4000] Training [29/39] Loss: 0.00742 +Epoch [3973/4000] Training [30/39] Loss: 0.00435 +Epoch [3973/4000] Training [31/39] Loss: 0.00307 +Epoch [3973/4000] Training [32/39] Loss: 0.00448 +Epoch [3973/4000] Training [33/39] Loss: 0.00624 +Epoch [3973/4000] Training [34/39] Loss: 0.00786 +Epoch [3973/4000] Training [35/39] Loss: 0.12875 +Epoch [3973/4000] Training [36/39] Loss: 0.00220 +Epoch [3973/4000] Training [37/39] Loss: 0.00259 +Epoch [3973/4000] Training [38/39] Loss: 0.00489 +Epoch [3973/4000] Training [39/39] Loss: 0.00392 +Epoch [3973/4000] Training metric {'Train/mean dice_metric': 0.9965221881866455, 'Train/mean miou_metric': 0.9934967160224915, 'Train/mean f1': 0.9969724416732788, 'Train/mean precision': 0.996544361114502, 'Train/mean recall': 0.9974008798599243, 'Train/mean hd95_metric': 0.8990517258644104} +Epoch [3973/4000] Validation [1/10] Loss: 0.71587 focal_loss 0.62983 dice_loss 0.08604 +Epoch [3973/4000] Validation [2/10] Loss: 0.50869 focal_loss 0.40844 dice_loss 0.10025 +Epoch [3973/4000] Validation [3/10] Loss: 0.40434 focal_loss 0.29242 dice_loss 0.11192 +Epoch [3973/4000] Validation [4/10] Loss: 0.89263 focal_loss 0.32772 dice_loss 0.56491 +Epoch [3973/4000] Validation [5/10] Loss: 3.09542 focal_loss 2.42137 dice_loss 0.67405 +Epoch [3973/4000] Validation [6/10] Loss: 1.33144 focal_loss 0.61895 dice_loss 0.71249 +Epoch [3973/4000] Validation [7/10] Loss: 1.17670 focal_loss 0.52417 dice_loss 0.65253 +Epoch [3973/4000] Validation [8/10] Loss: 2.43163 focal_loss 1.81173 dice_loss 0.61990 +Epoch [3973/4000] Validation [9/10] Loss: 1.53366 focal_loss 0.98966 dice_loss 0.54399 +Epoch [3973/4000] Validation [10/10] Loss: 1.88821 focal_loss 1.15361 dice_loss 0.73460 +Epoch [3973/4000] Validation metric {'Val/mean dice_metric': 0.9515265822410583, 'Val/mean miou_metric': 0.9357926845550537, 'Val/mean f1': 0.9485284090042114, 'Val/mean precision': 0.944316565990448, 'Val/mean recall': 0.9527779817581177, 'Val/mean hd95_metric': 10.761516571044922} +Cheakpoint... +Epoch [3973/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515265822410583, 'Val/mean miou_metric': 0.9357926845550537, 'Val/mean f1': 0.9485284090042114, 'Val/mean precision': 0.944316565990448, 'Val/mean recall': 0.9527779817581177, 'Val/mean hd95_metric': 10.761516571044922} +Epoch [3974/4000] Training [1/39] Loss: 0.12922 +Epoch [3974/4000] Training [2/39] Loss: 0.00334 +Epoch [3974/4000] Training [3/39] Loss: 0.00437 +Epoch [3974/4000] Training [4/39] Loss: 0.13018 +Epoch [3974/4000] Training [5/39] Loss: 0.12802 +Epoch [3974/4000] Training [6/39] Loss: 0.00530 +Epoch [3974/4000] Training [7/39] Loss: 0.00725 +Epoch [3974/4000] Training [8/39] Loss: 0.00342 +Epoch [3974/4000] Training [9/39] Loss: 0.00606 +Epoch [3974/4000] Training [10/39] Loss: 0.00403 +Epoch [3974/4000] Training [11/39] Loss: 0.12785 +Epoch [3974/4000] Training [12/39] Loss: 0.00376 +Epoch [3974/4000] Training [13/39] Loss: 0.12824 +Epoch [3974/4000] Training [14/39] Loss: 0.00578 +Epoch [3974/4000] Training [15/39] Loss: 0.13053 +Epoch [3974/4000] Training [16/39] Loss: 0.00509 +Epoch [3974/4000] Training [17/39] Loss: 0.00331 +Epoch [3974/4000] Training [18/39] Loss: 0.00476 +Epoch [3974/4000] Training [19/39] Loss: 0.00426 +Epoch [3974/4000] Training [20/39] Loss: 0.00343 +Epoch [3974/4000] Training [21/39] Loss: 0.00275 +Epoch [3974/4000] Training [22/39] Loss: 0.00536 +Epoch [3974/4000] Training [23/39] Loss: 0.00565 +Epoch [3974/4000] Training [24/39] Loss: 0.00524 +Epoch [3974/4000] Training [25/39] Loss: 0.12729 +Epoch [3974/4000] Training [26/39] Loss: 0.00400 +Epoch [3974/4000] Training [27/39] Loss: 0.00316 +Epoch [3974/4000] Training [28/39] Loss: 0.00556 +Epoch [3974/4000] Training [29/39] Loss: 0.04089 +Epoch [3974/4000] Training [30/39] Loss: 0.00565 +Epoch [3974/4000] Training [31/39] Loss: 0.00841 +Epoch [3974/4000] Training [32/39] Loss: 0.00351 +Epoch [3974/4000] Training [33/39] Loss: 0.00346 +Epoch [3974/4000] Training [34/39] Loss: 0.12999 +Epoch [3974/4000] Training [35/39] Loss: 0.00346 +Epoch [3974/4000] Training [36/39] Loss: 0.00521 +Epoch [3974/4000] Training [37/39] Loss: 0.00396 +Epoch [3974/4000] Training [38/39] Loss: 0.00322 +Epoch [3974/4000] Training [39/39] Loss: 0.13046 +Epoch [3974/4000] Training metric {'Train/mean dice_metric': 0.9964624047279358, 'Train/mean miou_metric': 0.9933691024780273, 'Train/mean f1': 0.9970324039459229, 'Train/mean precision': 0.9965947866439819, 'Train/mean recall': 0.9974703788757324, 'Train/mean hd95_metric': 0.9103644490242004} +Epoch [3974/4000] Validation [1/10] Loss: 0.73343 focal_loss 0.64739 dice_loss 0.08603 +Epoch [3974/4000] Validation [2/10] Loss: 0.51425 focal_loss 0.41311 dice_loss 0.10113 +Epoch [3974/4000] Validation [3/10] Loss: 0.41742 focal_loss 0.30503 dice_loss 0.11240 +Epoch [3974/4000] Validation [4/10] Loss: 0.89156 focal_loss 0.32716 dice_loss 0.56440 +Epoch [3974/4000] Validation [5/10] Loss: 3.15098 focal_loss 2.47685 dice_loss 0.67413 +Epoch [3974/4000] Validation [6/10] Loss: 1.32667 focal_loss 0.61422 dice_loss 0.71245 +Epoch [3974/4000] Validation [7/10] Loss: 1.17483 focal_loss 0.52227 dice_loss 0.65256 +Epoch [3974/4000] Validation [8/10] Loss: 2.48305 focal_loss 1.85991 dice_loss 0.62313 +Epoch [3974/4000] Validation [9/10] Loss: 1.54488 focal_loss 1.00150 dice_loss 0.54338 +Epoch [3974/4000] Validation [10/10] Loss: 1.87895 focal_loss 1.14518 dice_loss 0.73377 +Epoch [3974/4000] Validation metric {'Val/mean dice_metric': 0.9514034986495972, 'Val/mean miou_metric': 0.9355995059013367, 'Val/mean f1': 0.94864422082901, 'Val/mean precision': 0.9450367093086243, 'Val/mean recall': 0.9522793889045715, 'Val/mean hd95_metric': 10.776679039001465} +Cheakpoint... +Epoch [3974/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514034986495972, 'Val/mean miou_metric': 0.9355995059013367, 'Val/mean f1': 0.94864422082901, 'Val/mean precision': 0.9450367093086243, 'Val/mean recall': 0.9522793889045715, 'Val/mean hd95_metric': 10.776679039001465} +Epoch [3975/4000] Training [1/39] Loss: 0.13225 +Epoch [3975/4000] Training [2/39] Loss: 0.00545 +Epoch [3975/4000] Training [3/39] Loss: 0.00408 +Epoch [3975/4000] Training [4/39] Loss: 0.13024 +Epoch [3975/4000] Training [5/39] Loss: 0.00307 +Epoch [3975/4000] Training [6/39] Loss: 0.00567 +Epoch [3975/4000] Training [7/39] Loss: 0.00319 +Epoch [3975/4000] Training [8/39] Loss: 0.12791 +Epoch [3975/4000] Training [9/39] Loss: 0.12862 +Epoch [3975/4000] Training [10/39] Loss: 0.00483 +Epoch [3975/4000] Training [11/39] Loss: 0.00496 +Epoch [3975/4000] Training [12/39] Loss: 0.00622 +Epoch [3975/4000] Training [13/39] Loss: 0.00547 +Epoch [3975/4000] Training [14/39] Loss: 0.00550 +Epoch [3975/4000] Training [15/39] Loss: 0.00635 +Epoch [3975/4000] Training [16/39] Loss: 0.00380 +Epoch [3975/4000] Training [17/39] Loss: 0.00323 +Epoch [3975/4000] Training [18/39] Loss: 0.00607 +Epoch [3975/4000] Training [19/39] Loss: 0.12969 +Epoch [3975/4000] Training [20/39] Loss: 0.00567 +Epoch [3975/4000] Training [21/39] Loss: 0.00648 +Epoch [3975/4000] Training [22/39] Loss: 0.00293 +Epoch [3975/4000] Training [23/39] Loss: 0.12926 +Epoch [3975/4000] Training [24/39] Loss: 0.00602 +Epoch [3975/4000] Training [25/39] Loss: 0.00383 +Epoch [3975/4000] Training [26/39] Loss: 0.00318 +Epoch [3975/4000] Training [27/39] Loss: 0.00678 +Epoch [3975/4000] Training [28/39] Loss: 0.00614 +Epoch [3975/4000] Training [29/39] Loss: 0.00563 +Epoch [3975/4000] Training [30/39] Loss: 0.12966 +Epoch [3975/4000] Training [31/39] Loss: 0.00792 +Epoch [3975/4000] Training [32/39] Loss: 0.00396 +Epoch [3975/4000] Training [33/39] Loss: 0.12971 +Epoch [3975/4000] Training [34/39] Loss: 0.00634 +Epoch [3975/4000] Training [35/39] Loss: 0.00714 +Epoch [3975/4000] Training [36/39] Loss: 0.00461 +Epoch [3975/4000] Training [37/39] Loss: 0.00264 +Epoch [3975/4000] Training [38/39] Loss: 0.00614 +Epoch [3975/4000] Training [39/39] Loss: 0.00414 +Epoch [3975/4000] Training metric {'Train/mean dice_metric': 0.9963535070419312, 'Train/mean miou_metric': 0.9931589961051941, 'Train/mean f1': 0.9967435598373413, 'Train/mean precision': 0.996195375919342, 'Train/mean recall': 0.9972923994064331, 'Train/mean hd95_metric': 0.9281355142593384} +Epoch [3975/4000] Validation [1/10] Loss: 0.71933 focal_loss 0.63346 dice_loss 0.08587 +Epoch [3975/4000] Validation [2/10] Loss: 0.50890 focal_loss 0.40826 dice_loss 0.10064 +Epoch [3975/4000] Validation [3/10] Loss: 0.40813 focal_loss 0.29594 dice_loss 0.11219 +Epoch [3975/4000] Validation [4/10] Loss: 0.89012 focal_loss 0.32562 dice_loss 0.56450 +Epoch [3975/4000] Validation [5/10] Loss: 3.09682 focal_loss 2.42278 dice_loss 0.67404 +Epoch [3975/4000] Validation [6/10] Loss: 1.32443 focal_loss 0.61191 dice_loss 0.71252 +Epoch [3975/4000] Validation [7/10] Loss: 1.17066 focal_loss 0.51778 dice_loss 0.65287 +Epoch [3975/4000] Validation [8/10] Loss: 2.44194 focal_loss 1.81944 dice_loss 0.62250 +Epoch [3975/4000] Validation [9/10] Loss: 1.53020 focal_loss 0.98640 dice_loss 0.54380 +Epoch [3975/4000] Validation [10/10] Loss: 1.87192 focal_loss 1.13795 dice_loss 0.73396 +Epoch [3975/4000] Validation metric {'Val/mean dice_metric': 0.9513212442398071, 'Val/mean miou_metric': 0.9354302883148193, 'Val/mean f1': 0.9484615325927734, 'Val/mean precision': 0.944648265838623, 'Val/mean recall': 0.9523056745529175, 'Val/mean hd95_metric': 10.768309593200684} +Cheakpoint... +Epoch [3975/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9513], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513212442398071, 'Val/mean miou_metric': 0.9354302883148193, 'Val/mean f1': 0.9484615325927734, 'Val/mean precision': 0.944648265838623, 'Val/mean recall': 0.9523056745529175, 'Val/mean hd95_metric': 10.768309593200684} +Epoch [3976/4000] Training [1/39] Loss: 0.00320 +Epoch [3976/4000] Training [2/39] Loss: 0.00432 +Epoch [3976/4000] Training [3/39] Loss: 0.00476 +Epoch [3976/4000] Training [4/39] Loss: 0.12946 +Epoch [3976/4000] Training [5/39] Loss: 0.00361 +Epoch [3976/4000] Training [6/39] Loss: 0.00284 +Epoch [3976/4000] Training [7/39] Loss: 0.13044 +Epoch [3976/4000] Training [8/39] Loss: 0.00649 +Epoch [3976/4000] Training [9/39] Loss: 0.00293 +Epoch [3976/4000] Training [10/39] Loss: 0.00627 +Epoch [3976/4000] Training [11/39] Loss: 0.00297 +Epoch [3976/4000] Training [12/39] Loss: 0.13276 +Epoch [3976/4000] Training [13/39] Loss: 0.00478 +Epoch [3976/4000] Training [14/39] Loss: 0.00384 +Epoch [3976/4000] Training [15/39] Loss: 0.00471 +Epoch [3976/4000] Training [16/39] Loss: 0.00746 +Epoch [3976/4000] Training [17/39] Loss: 0.13416 +Epoch [3976/4000] Training [18/39] Loss: 0.00359 +Epoch [3976/4000] Training [19/39] Loss: 0.00314 +Epoch [3976/4000] Training [20/39] Loss: 0.00855 +Epoch [3976/4000] Training [21/39] Loss: 0.00427 +Epoch [3976/4000] Training [22/39] Loss: 0.00369 +Epoch [3976/4000] Training [23/39] Loss: 0.00304 +Epoch [3976/4000] Training [24/39] Loss: 0.00500 +Epoch [3976/4000] Training [25/39] Loss: 0.00527 +Epoch [3976/4000] Training [26/39] Loss: 0.00568 +Epoch [3976/4000] Training [27/39] Loss: 0.12973 +Epoch [3976/4000] Training [28/39] Loss: 0.00857 +Epoch [3976/4000] Training [29/39] Loss: 0.00433 +Epoch [3976/4000] Training [30/39] Loss: 0.25309 +Epoch [3976/4000] Training [31/39] Loss: 0.00329 +Epoch [3976/4000] Training [32/39] Loss: 0.00341 +Epoch [3976/4000] Training [33/39] Loss: 0.00710 +Epoch [3976/4000] Training [34/39] Loss: 0.00349 +Epoch [3976/4000] Training [35/39] Loss: 0.00435 +Epoch [3976/4000] Training [36/39] Loss: 0.00493 +Epoch [3976/4000] Training [37/39] Loss: 0.13035 +Epoch [3976/4000] Training [38/39] Loss: 0.00463 +Epoch [3976/4000] Training [39/39] Loss: 0.12736 +Epoch [3976/4000] Training metric {'Train/mean dice_metric': 0.9955238699913025, 'Train/mean miou_metric': 0.9923281669616699, 'Train/mean f1': 0.9967984557151794, 'Train/mean precision': 0.9963279962539673, 'Train/mean recall': 0.997269332408905, 'Train/mean hd95_metric': 0.9570372700691223} +Epoch [3976/4000] Validation [1/10] Loss: 0.71853 focal_loss 0.63203 dice_loss 0.08649 +Epoch [3976/4000] Validation [2/10] Loss: 0.50518 focal_loss 0.40635 dice_loss 0.09882 +Epoch [3976/4000] Validation [3/10] Loss: 0.39855 focal_loss 0.28706 dice_loss 0.11149 +Epoch [3976/4000] Validation [4/10] Loss: 0.89553 focal_loss 0.33005 dice_loss 0.56548 +Epoch [3976/4000] Validation [5/10] Loss: 3.08779 focal_loss 2.41384 dice_loss 0.67395 +Epoch [3976/4000] Validation [6/10] Loss: 1.33713 focal_loss 0.62462 dice_loss 0.71251 +Epoch [3976/4000] Validation [7/10] Loss: 1.17969 focal_loss 0.52538 dice_loss 0.65431 +Epoch [3976/4000] Validation [8/10] Loss: 2.39955 focal_loss 1.78237 dice_loss 0.61718 +Epoch [3976/4000] Validation [9/10] Loss: 1.53709 focal_loss 0.99300 dice_loss 0.54409 +Epoch [3976/4000] Validation [10/10] Loss: 1.89786 focal_loss 1.16289 dice_loss 0.73497 +Epoch [3976/4000] Validation metric {'Val/mean dice_metric': 0.9506747126579285, 'Val/mean miou_metric': 0.9347769618034363, 'Val/mean f1': 0.9484384059906006, 'Val/mean precision': 0.9439040422439575, 'Val/mean recall': 0.9530165791511536, 'Val/mean hd95_metric': 10.734021186828613} +Cheakpoint... +Epoch [3976/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506747126579285, 'Val/mean miou_metric': 0.9347769618034363, 'Val/mean f1': 0.9484384059906006, 'Val/mean precision': 0.9439040422439575, 'Val/mean recall': 0.9530165791511536, 'Val/mean hd95_metric': 10.734021186828613} +Epoch [3977/4000] Training [1/39] Loss: 0.12962 +Epoch [3977/4000] Training [2/39] Loss: 0.00530 +Epoch [3977/4000] Training [3/39] Loss: 0.00552 +Epoch [3977/4000] Training [4/39] Loss: 0.00383 +Epoch [3977/4000] Training [5/39] Loss: 0.00409 +Epoch [3977/4000] Training [6/39] Loss: 0.00300 +Epoch [3977/4000] Training [7/39] Loss: 0.25192 +Epoch [3977/4000] Training [8/39] Loss: 0.00472 +Epoch [3977/4000] Training [9/39] Loss: 0.00558 +Epoch [3977/4000] Training [10/39] Loss: 0.00522 +Epoch [3977/4000] Training [11/39] Loss: 0.25573 +Epoch [3977/4000] Training [12/39] Loss: 0.00228 +Epoch [3977/4000] Training [13/39] Loss: 0.12897 +Epoch [3977/4000] Training [14/39] Loss: 0.00433 +Epoch [3977/4000] Training [15/39] Loss: 0.00906 +Epoch [3977/4000] Training [16/39] Loss: 0.12974 +Epoch [3977/4000] Training [17/39] Loss: 0.00359 +Epoch [3977/4000] Training [18/39] Loss: 0.00615 +Epoch [3977/4000] Training [19/39] Loss: 0.00347 +Epoch [3977/4000] Training [20/39] Loss: 0.00421 +Epoch [3977/4000] Training [21/39] Loss: 0.00444 +Epoch [3977/4000] Training [22/39] Loss: 0.00315 +Epoch [3977/4000] Training [23/39] Loss: 0.00659 +Epoch [3977/4000] Training [24/39] Loss: 0.00471 +Epoch [3977/4000] Training [25/39] Loss: 0.00339 +Epoch [3977/4000] Training [26/39] Loss: 0.12793 +Epoch [3977/4000] Training [27/39] Loss: 0.12799 +Epoch [3977/4000] Training [28/39] Loss: 0.00687 +Epoch [3977/4000] Training [29/39] Loss: 0.00385 +Epoch [3977/4000] Training [30/39] Loss: 0.00430 +Epoch [3977/4000] Training [31/39] Loss: 0.00418 +Epoch [3977/4000] Training [32/39] Loss: 0.00357 +Epoch [3977/4000] Training [33/39] Loss: 0.13753 +Epoch [3977/4000] Training [34/39] Loss: 0.00442 +Epoch [3977/4000] Training [35/39] Loss: 0.13071 +Epoch [3977/4000] Training [36/39] Loss: 0.00289 +Epoch [3977/4000] Training [37/39] Loss: 0.12875 +Epoch [3977/4000] Training [38/39] Loss: 0.00394 +Epoch [3977/4000] Training [39/39] Loss: 0.00431 +Epoch [3977/4000] Training metric {'Train/mean dice_metric': 0.9964967966079712, 'Train/mean miou_metric': 0.9934380650520325, 'Train/mean f1': 0.9969919919967651, 'Train/mean precision': 0.9965633749961853, 'Train/mean recall': 0.9974210858345032, 'Train/mean hd95_metric': 0.9112507104873657} +Epoch [3977/4000] Validation [1/10] Loss: 0.71583 focal_loss 0.63008 dice_loss 0.08575 +Epoch [3977/4000] Validation [2/10] Loss: 0.51061 focal_loss 0.40933 dice_loss 0.10128 +Epoch [3977/4000] Validation [3/10] Loss: 0.40947 focal_loss 0.29703 dice_loss 0.11244 +Epoch [3977/4000] Validation [4/10] Loss: 0.89139 focal_loss 0.32660 dice_loss 0.56479 +Epoch [3977/4000] Validation [5/10] Loss: 3.07886 focal_loss 2.40476 dice_loss 0.67410 +Epoch [3977/4000] Validation [6/10] Loss: 1.32407 focal_loss 0.61183 dice_loss 0.71224 +Epoch [3977/4000] Validation [7/10] Loss: 1.16961 focal_loss 0.51730 dice_loss 0.65231 +Epoch [3977/4000] Validation [8/10] Loss: 2.43847 focal_loss 1.81513 dice_loss 0.62334 +Epoch [3977/4000] Validation [9/10] Loss: 1.52591 focal_loss 0.98204 dice_loss 0.54387 +Epoch [3977/4000] Validation [10/10] Loss: 1.86905 focal_loss 1.13516 dice_loss 0.73390 +Epoch [3977/4000] Validation metric {'Val/mean dice_metric': 0.9514449834823608, 'Val/mean miou_metric': 0.9356706142425537, 'Val/mean f1': 0.9486854672431946, 'Val/mean precision': 0.945056140422821, 'Val/mean recall': 0.9523428678512573, 'Val/mean hd95_metric': 10.775486946105957} +Cheakpoint... +Epoch [3977/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514449834823608, 'Val/mean miou_metric': 0.9356706142425537, 'Val/mean f1': 0.9486854672431946, 'Val/mean precision': 0.945056140422821, 'Val/mean recall': 0.9523428678512573, 'Val/mean hd95_metric': 10.775486946105957} +Epoch [3978/4000] Training [1/39] Loss: 0.00581 +Epoch [3978/4000] Training [2/39] Loss: 0.00478 +Epoch [3978/4000] Training [3/39] Loss: 0.00715 +Epoch [3978/4000] Training [4/39] Loss: 0.00323 +Epoch [3978/4000] Training [5/39] Loss: 0.00654 +Epoch [3978/4000] Training [6/39] Loss: 0.00460 +Epoch [3978/4000] Training [7/39] Loss: 0.12825 +Epoch [3978/4000] Training [8/39] Loss: 0.00491 +Epoch [3978/4000] Training [9/39] Loss: 0.00509 +Epoch [3978/4000] Training [10/39] Loss: 0.13019 +Epoch [3978/4000] Training [11/39] Loss: 0.00596 +Epoch [3978/4000] Training [12/39] Loss: 0.08573 +Epoch [3978/4000] Training [13/39] Loss: 0.12976 +Epoch [3978/4000] Training [14/39] Loss: 0.00363 +Epoch [3978/4000] Training [15/39] Loss: 0.00678 +Epoch [3978/4000] Training [16/39] Loss: 0.00477 +Epoch [3978/4000] Training [17/39] Loss: 0.12795 +Epoch [3978/4000] Training [18/39] Loss: 0.00366 +Epoch [3978/4000] Training [19/39] Loss: 0.00340 +Epoch [3978/4000] Training [20/39] Loss: 0.00979 +Epoch [3978/4000] Training [21/39] Loss: 0.00345 +Epoch [3978/4000] Training [22/39] Loss: 0.00276 +Epoch [3978/4000] Training [23/39] Loss: 0.01068 +Epoch [3978/4000] Training [24/39] Loss: 0.00400 +Epoch [3978/4000] Training [25/39] Loss: 0.00371 +Epoch [3978/4000] Training [26/39] Loss: 0.00772 +Epoch [3978/4000] Training [27/39] Loss: 0.12861 +Epoch [3978/4000] Training [28/39] Loss: 0.12819 +Epoch [3978/4000] Training [29/39] Loss: 0.00444 +Epoch [3978/4000] Training [30/39] Loss: 0.00473 +Epoch [3978/4000] Training [31/39] Loss: 0.12981 +Epoch [3978/4000] Training [32/39] Loss: 0.12880 +Epoch [3978/4000] Training [33/39] Loss: 0.00432 +Epoch [3978/4000] Training [34/39] Loss: 0.00268 +Epoch [3978/4000] Training [35/39] Loss: 0.00419 +Epoch [3978/4000] Training [36/39] Loss: 0.00489 +Epoch [3978/4000] Training [37/39] Loss: 0.12830 +Epoch [3978/4000] Training [38/39] Loss: 0.12850 +Epoch [3978/4000] Training [39/39] Loss: 0.00542 +Epoch [3978/4000] Training metric {'Train/mean dice_metric': 0.9964591264724731, 'Train/mean miou_metric': 0.9933724403381348, 'Train/mean f1': 0.996939480304718, 'Train/mean precision': 0.9964593052864075, 'Train/mean recall': 0.9974200129508972, 'Train/mean hd95_metric': 0.9351546168327332} +Epoch [3978/4000] Validation [1/10] Loss: 0.71340 focal_loss 0.62726 dice_loss 0.08614 +Epoch [3978/4000] Validation [2/10] Loss: 0.50443 focal_loss 0.40472 dice_loss 0.09971 +Epoch [3978/4000] Validation [3/10] Loss: 0.39900 focal_loss 0.28721 dice_loss 0.11179 +Epoch [3978/4000] Validation [4/10] Loss: 0.89287 focal_loss 0.32775 dice_loss 0.56511 +Epoch [3978/4000] Validation [5/10] Loss: 3.07292 focal_loss 2.39894 dice_loss 0.67398 +Epoch [3978/4000] Validation [6/10] Loss: 1.33008 focal_loss 0.61748 dice_loss 0.71260 +Epoch [3978/4000] Validation [7/10] Loss: 1.17074 focal_loss 0.51711 dice_loss 0.65363 +Epoch [3978/4000] Validation [8/10] Loss: 2.39388 focal_loss 1.77434 dice_loss 0.61954 +Epoch [3978/4000] Validation [9/10] Loss: 1.52477 focal_loss 0.98086 dice_loss 0.54390 +Epoch [3978/4000] Validation [10/10] Loss: 1.87852 focal_loss 1.14396 dice_loss 0.73456 +Epoch [3978/4000] Validation metric {'Val/mean dice_metric': 0.9514858722686768, 'Val/mean miou_metric': 0.9356988072395325, 'Val/mean f1': 0.9483630657196045, 'Val/mean precision': 0.9440770149230957, 'Val/mean recall': 0.9526880383491516, 'Val/mean hd95_metric': 10.832995414733887} +Cheakpoint... +Epoch [3978/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514858722686768, 'Val/mean miou_metric': 0.9356988072395325, 'Val/mean f1': 0.9483630657196045, 'Val/mean precision': 0.9440770149230957, 'Val/mean recall': 0.9526880383491516, 'Val/mean hd95_metric': 10.832995414733887} +Epoch [3979/4000] Training [1/39] Loss: 0.12863 +Epoch [3979/4000] Training [2/39] Loss: 0.00812 +Epoch [3979/4000] Training [3/39] Loss: 0.00538 +Epoch [3979/4000] Training [4/39] Loss: 0.12796 +Epoch [3979/4000] Training [5/39] Loss: 0.00484 +Epoch [3979/4000] Training [6/39] Loss: 0.00381 +Epoch [3979/4000] Training [7/39] Loss: 0.00334 +Epoch [3979/4000] Training [8/39] Loss: 0.00462 +Epoch [3979/4000] Training [9/39] Loss: 0.12869 +Epoch [3979/4000] Training [10/39] Loss: 0.13156 +Epoch [3979/4000] Training [11/39] Loss: 0.00450 +Epoch [3979/4000] Training [12/39] Loss: 0.12923 +Epoch [3979/4000] Training [13/39] Loss: 0.00448 +Epoch [3979/4000] Training [14/39] Loss: 0.00408 +Epoch [3979/4000] Training [15/39] Loss: 0.00541 +Epoch [3979/4000] Training [16/39] Loss: 0.00481 +Epoch [3979/4000] Training [17/39] Loss: 0.00663 +Epoch [3979/4000] Training [18/39] Loss: 0.00543 +Epoch [3979/4000] Training [19/39] Loss: 0.00516 +Epoch [3979/4000] Training [20/39] Loss: 0.00406 +Epoch [3979/4000] Training [21/39] Loss: 0.00382 +Epoch [3979/4000] Training [22/39] Loss: 0.12855 +Epoch [3979/4000] Training [23/39] Loss: 0.00427 +Epoch [3979/4000] Training [24/39] Loss: 0.00625 +Epoch [3979/4000] Training [25/39] Loss: 0.00390 +Epoch [3979/4000] Training [26/39] Loss: 0.12827 +Epoch [3979/4000] Training [27/39] Loss: 0.00375 +Epoch [3979/4000] Training [28/39] Loss: 0.00674 +Epoch [3979/4000] Training [29/39] Loss: 0.00370 +Epoch [3979/4000] Training [30/39] Loss: 0.00388 +Epoch [3979/4000] Training [31/39] Loss: 0.00239 +Epoch [3979/4000] Training [32/39] Loss: 0.00579 +Epoch [3979/4000] Training [33/39] Loss: 0.00405 +Epoch [3979/4000] Training [34/39] Loss: 0.00495 +Epoch [3979/4000] Training [35/39] Loss: 0.00378 +Epoch [3979/4000] Training [36/39] Loss: 0.00758 +Epoch [3979/4000] Training [37/39] Loss: 0.00387 +Epoch [3979/4000] Training [38/39] Loss: 0.00444 +Epoch [3979/4000] Training [39/39] Loss: 0.00550 +Epoch [3979/4000] Training metric {'Train/mean dice_metric': 0.9965099692344666, 'Train/mean miou_metric': 0.9934646487236023, 'Train/mean f1': 0.9970319271087646, 'Train/mean precision': 0.996566891670227, 'Train/mean recall': 0.99749755859375, 'Train/mean hd95_metric': 0.9185495972633362} +Epoch [3979/4000] Validation [1/10] Loss: 0.71798 focal_loss 0.63109 dice_loss 0.08689 +Epoch [3979/4000] Validation [2/10] Loss: 0.50140 focal_loss 0.40338 dice_loss 0.09802 +Epoch [3979/4000] Validation [3/10] Loss: 0.39282 focal_loss 0.28150 dice_loss 0.11133 +Epoch [3979/4000] Validation [4/10] Loss: 0.89563 focal_loss 0.32968 dice_loss 0.56595 +Epoch [3979/4000] Validation [5/10] Loss: 3.07765 focal_loss 2.40373 dice_loss 0.67392 +Epoch [3979/4000] Validation [6/10] Loss: 1.33617 focal_loss 0.62334 dice_loss 0.71283 +Epoch [3979/4000] Validation [7/10] Loss: 1.17604 focal_loss 0.52138 dice_loss 0.65466 +Epoch [3979/4000] Validation [8/10] Loss: 2.36311 focal_loss 1.74825 dice_loss 0.61485 +Epoch [3979/4000] Validation [9/10] Loss: 1.53186 focal_loss 0.98777 dice_loss 0.54409 +Epoch [3979/4000] Validation [10/10] Loss: 1.89520 focal_loss 1.15972 dice_loss 0.73549 +Epoch [3979/4000] Validation metric {'Val/mean dice_metric': 0.9514954090118408, 'Val/mean miou_metric': 0.9357209205627441, 'Val/mean f1': 0.9482972621917725, 'Val/mean precision': 0.9433940649032593, 'Val/mean recall': 0.9532516002655029, 'Val/mean hd95_metric': 10.688865661621094} +Cheakpoint... +Epoch [3979/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514954090118408, 'Val/mean miou_metric': 0.9357209205627441, 'Val/mean f1': 0.9482972621917725, 'Val/mean precision': 0.9433940649032593, 'Val/mean recall': 0.9532516002655029, 'Val/mean hd95_metric': 10.688865661621094} +Epoch [3980/4000] Training [1/39] Loss: 0.00471 +Epoch [3980/4000] Training [2/39] Loss: 0.12811 +Epoch [3980/4000] Training [3/39] Loss: 0.00523 +Epoch [3980/4000] Training [4/39] Loss: 0.00454 +Epoch [3980/4000] Training [5/39] Loss: 0.00424 +Epoch [3980/4000] Training [6/39] Loss: 0.12939 +Epoch [3980/4000] Training [7/39] Loss: 0.00353 +Epoch [3980/4000] Training [8/39] Loss: 0.12820 +Epoch [3980/4000] Training [9/39] Loss: 0.12854 +Epoch [3980/4000] Training [10/39] Loss: 0.00461 +Epoch [3980/4000] Training [11/39] Loss: 0.00842 +Epoch [3980/4000] Training [12/39] Loss: 0.00383 +Epoch [3980/4000] Training [13/39] Loss: 0.12801 +Epoch [3980/4000] Training [14/39] Loss: 0.25233 +Epoch [3980/4000] Training [15/39] Loss: 0.00485 +Epoch [3980/4000] Training [16/39] Loss: 0.00422 +Epoch [3980/4000] Training [17/39] Loss: 0.00480 +Epoch [3980/4000] Training [18/39] Loss: 0.00425 +Epoch [3980/4000] Training [19/39] Loss: 0.12813 +Epoch [3980/4000] Training [20/39] Loss: 0.00548 +Epoch [3980/4000] Training [21/39] Loss: 0.00364 +Epoch [3980/4000] Training [22/39] Loss: 0.00359 +Epoch [3980/4000] Training [23/39] Loss: 0.00625 +Epoch [3980/4000] Training [24/39] Loss: 0.00374 +Epoch [3980/4000] Training [25/39] Loss: 0.00351 +Epoch [3980/4000] Training [26/39] Loss: 0.00552 +Epoch [3980/4000] Training [27/39] Loss: 0.00450 +Epoch [3980/4000] Training [28/39] Loss: 0.00298 +Epoch [3980/4000] Training [29/39] Loss: 0.13134 +Epoch [3980/4000] Training [30/39] Loss: 0.00413 +Epoch [3980/4000] Training [31/39] Loss: 0.00580 +Epoch [3980/4000] Training [32/39] Loss: 0.00367 +Epoch [3980/4000] Training [33/39] Loss: 0.00357 +Epoch [3980/4000] Training [34/39] Loss: 0.00737 +Epoch [3980/4000] Training [35/39] Loss: 0.00378 +Epoch [3980/4000] Training [36/39] Loss: 0.25325 +Epoch [3980/4000] Training [37/39] Loss: 0.00548 +Epoch [3980/4000] Training [38/39] Loss: 0.12761 +Epoch [3980/4000] Training [39/39] Loss: 0.12771 +Epoch [3980/4000] Training metric {'Train/mean dice_metric': 0.9966546297073364, 'Train/mean miou_metric': 0.9937649965286255, 'Train/mean f1': 0.9971132278442383, 'Train/mean precision': 0.9966976046562195, 'Train/mean recall': 0.9975293278694153, 'Train/mean hd95_metric': 0.8868130445480347} +Epoch [3980/4000] Validation [1/10] Loss: 0.70421 focal_loss 0.61917 dice_loss 0.08504 +Epoch [3980/4000] Validation [2/10] Loss: 0.51250 focal_loss 0.41003 dice_loss 0.10247 +Epoch [3980/4000] Validation [3/10] Loss: 0.40710 focal_loss 0.29444 dice_loss 0.11265 +Epoch [3980/4000] Validation [4/10] Loss: 0.88833 focal_loss 0.32442 dice_loss 0.56391 +Epoch [3980/4000] Validation [5/10] Loss: 3.06342 focal_loss 2.38927 dice_loss 0.67415 +Epoch [3980/4000] Validation [6/10] Loss: 1.32052 focal_loss 0.60793 dice_loss 0.71259 +Epoch [3980/4000] Validation [7/10] Loss: 1.16453 focal_loss 0.51314 dice_loss 0.65139 +Epoch [3980/4000] Validation [8/10] Loss: 2.45106 focal_loss 1.82491 dice_loss 0.62615 +Epoch [3980/4000] Validation [9/10] Loss: 1.51653 focal_loss 0.97286 dice_loss 0.54367 +Epoch [3980/4000] Validation [10/10] Loss: 1.85563 focal_loss 1.12233 dice_loss 0.73331 +Epoch [3980/4000] Validation metric {'Val/mean dice_metric': 0.9516398310661316, 'Val/mean miou_metric': 0.9360411763191223, 'Val/mean f1': 0.948961079120636, 'Val/mean precision': 0.9456232786178589, 'Val/mean recall': 0.9523224234580994, 'Val/mean hd95_metric': 10.725062370300293} +Cheakpoint... +Epoch [3980/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516398310661316, 'Val/mean miou_metric': 0.9360411763191223, 'Val/mean f1': 0.948961079120636, 'Val/mean precision': 0.9456232786178589, 'Val/mean recall': 0.9523224234580994, 'Val/mean hd95_metric': 10.725062370300293} +Epoch [3981/4000] Training [1/39] Loss: 0.00465 +Epoch [3981/4000] Training [2/39] Loss: 0.00519 +Epoch [3981/4000] Training [3/39] Loss: 0.04231 +Epoch [3981/4000] Training [4/39] Loss: 0.00459 +Epoch [3981/4000] Training [5/39] Loss: 0.00401 +Epoch [3981/4000] Training [6/39] Loss: 0.00586 +Epoch [3981/4000] Training [7/39] Loss: 0.25307 +Epoch [3981/4000] Training [8/39] Loss: 0.00846 +Epoch [3981/4000] Training [9/39] Loss: 0.00473 +Epoch [3981/4000] Training [10/39] Loss: 0.25374 +Epoch [3981/4000] Training [11/39] Loss: 0.00438 +Epoch [3981/4000] Training [12/39] Loss: 0.00378 +Epoch [3981/4000] Training [13/39] Loss: 0.00329 +Epoch [3981/4000] Training [14/39] Loss: 0.01033 +Epoch [3981/4000] Training [15/39] Loss: 0.00563 +Epoch [3981/4000] Training [16/39] Loss: 0.00305 +Epoch [3981/4000] Training [17/39] Loss: 0.00374 +Epoch [3981/4000] Training [18/39] Loss: 0.13093 +Epoch [3981/4000] Training [19/39] Loss: 0.00541 +Epoch [3981/4000] Training [20/39] Loss: 0.00430 +Epoch [3981/4000] Training [21/39] Loss: 0.00237 +Epoch [3981/4000] Training [22/39] Loss: 0.00571 +Epoch [3981/4000] Training [23/39] Loss: 0.13113 +Epoch [3981/4000] Training [24/39] Loss: 0.00313 +Epoch [3981/4000] Training [25/39] Loss: 0.13042 +Epoch [3981/4000] Training [26/39] Loss: 0.00553 +Epoch [3981/4000] Training [27/39] Loss: 0.00730 +Epoch [3981/4000] Training [28/39] Loss: 0.00630 +Epoch [3981/4000] Training [29/39] Loss: 0.00459 +Epoch [3981/4000] Training [30/39] Loss: 0.00460 +Epoch [3981/4000] Training [31/39] Loss: 0.12985 +Epoch [3981/4000] Training [32/39] Loss: 0.37723 +Epoch [3981/4000] Training [33/39] Loss: 0.00391 +Epoch [3981/4000] Training [34/39] Loss: 0.00503 +Epoch [3981/4000] Training [35/39] Loss: 0.00503 +Epoch [3981/4000] Training [36/39] Loss: 0.00469 +Epoch [3981/4000] Training [37/39] Loss: 0.00388 +Epoch [3981/4000] Training [38/39] Loss: 0.13156 +Epoch [3981/4000] Training [39/39] Loss: 0.12796 +Epoch [3981/4000] Training metric {'Train/mean dice_metric': 0.9963037371635437, 'Train/mean miou_metric': 0.9930706024169922, 'Train/mean f1': 0.9969000816345215, 'Train/mean precision': 0.9965354800224304, 'Train/mean recall': 0.9972650408744812, 'Train/mean hd95_metric': 1.1223093271255493} +Epoch [3981/4000] Validation [1/10] Loss: 0.70520 focal_loss 0.61882 dice_loss 0.08639 +Epoch [3981/4000] Validation [2/10] Loss: 0.50395 focal_loss 0.40611 dice_loss 0.09784 +Epoch [3981/4000] Validation [3/10] Loss: 0.38396 focal_loss 0.27310 dice_loss 0.11086 +Epoch [3981/4000] Validation [4/10] Loss: 0.89852 focal_loss 0.33289 dice_loss 0.56563 +Epoch [3981/4000] Validation [5/10] Loss: 3.02739 focal_loss 2.35354 dice_loss 0.67385 +Epoch [3981/4000] Validation [6/10] Loss: 1.34756 focal_loss 0.63466 dice_loss 0.71290 +Epoch [3981/4000] Validation [7/10] Loss: 1.18385 focal_loss 0.52864 dice_loss 0.65521 +Epoch [3981/4000] Validation [8/10] Loss: 2.35630 focal_loss 1.74184 dice_loss 0.61446 +Epoch [3981/4000] Validation [9/10] Loss: 1.53033 focal_loss 0.98591 dice_loss 0.54442 +Epoch [3981/4000] Validation [10/10] Loss: 1.91091 focal_loss 1.17542 dice_loss 0.73548 +Epoch [3981/4000] Validation metric {'Val/mean dice_metric': 0.9513919949531555, 'Val/mean miou_metric': 0.935477077960968, 'Val/mean f1': 0.9484029412269592, 'Val/mean precision': 0.9434581398963928, 'Val/mean recall': 0.9533998370170593, 'Val/mean hd95_metric': 10.96980094909668} +Cheakpoint... +Epoch [3981/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513919949531555, 'Val/mean miou_metric': 0.935477077960968, 'Val/mean f1': 0.9484029412269592, 'Val/mean precision': 0.9434581398963928, 'Val/mean recall': 0.9533998370170593, 'Val/mean hd95_metric': 10.96980094909668} +Epoch [3982/4000] Training [1/39] Loss: 0.00675 +Epoch [3982/4000] Training [2/39] Loss: 0.00232 +Epoch [3982/4000] Training [3/39] Loss: 0.12968 +Epoch [3982/4000] Training [4/39] Loss: 0.12842 +Epoch [3982/4000] Training [5/39] Loss: 0.00551 +Epoch [3982/4000] Training [6/39] Loss: 0.00353 +Epoch [3982/4000] Training [7/39] Loss: 0.00288 +Epoch [3982/4000] Training [8/39] Loss: 0.00604 +Epoch [3982/4000] Training [9/39] Loss: 0.00606 +Epoch [3982/4000] Training [10/39] Loss: 0.00600 +Epoch [3982/4000] Training [11/39] Loss: 0.00344 +Epoch [3982/4000] Training [12/39] Loss: 0.00417 +Epoch [3982/4000] Training [13/39] Loss: 0.00378 +Epoch [3982/4000] Training [14/39] Loss: 0.00508 +Epoch [3982/4000] Training [15/39] Loss: 0.37798 +Epoch [3982/4000] Training [16/39] Loss: 0.12993 +Epoch [3982/4000] Training [17/39] Loss: 0.00357 +Epoch [3982/4000] Training [18/39] Loss: 0.00610 +Epoch [3982/4000] Training [19/39] Loss: 0.00455 +Epoch [3982/4000] Training [20/39] Loss: 0.00800 +Epoch [3982/4000] Training [21/39] Loss: 0.00472 +Epoch [3982/4000] Training [22/39] Loss: 0.00439 +Epoch [3982/4000] Training [23/39] Loss: 0.00522 +Epoch [3982/4000] Training [24/39] Loss: 0.00336 +Epoch [3982/4000] Training [25/39] Loss: 0.12941 +Epoch [3982/4000] Training [26/39] Loss: 0.12860 +Epoch [3982/4000] Training [27/39] Loss: 0.00547 +Epoch [3982/4000] Training [28/39] Loss: 0.13020 +Epoch [3982/4000] Training [29/39] Loss: 0.00296 +Epoch [3982/4000] Training [30/39] Loss: 0.25802 +Epoch [3982/4000] Training [31/39] Loss: 0.00596 +Epoch [3982/4000] Training [32/39] Loss: 0.25394 +Epoch [3982/4000] Training [33/39] Loss: 0.00444 +Epoch [3982/4000] Training [34/39] Loss: 0.00285 +Epoch [3982/4000] Training [35/39] Loss: 0.00503 +Epoch [3982/4000] Training [36/39] Loss: 0.00616 +Epoch [3982/4000] Training [37/39] Loss: 0.00568 +Epoch [3982/4000] Training [38/39] Loss: 0.00406 +Epoch [3982/4000] Training [39/39] Loss: 0.00502 +Epoch [3982/4000] Training metric {'Train/mean dice_metric': 0.996652364730835, 'Train/mean miou_metric': 0.9937475323677063, 'Train/mean f1': 0.9970958828926086, 'Train/mean precision': 0.9965922236442566, 'Train/mean recall': 0.9976000189781189, 'Train/mean hd95_metric': 0.9091235399246216} +Epoch [3982/4000] Validation [1/10] Loss: 0.70733 focal_loss 0.62216 dice_loss 0.08517 +Epoch [3982/4000] Validation [2/10] Loss: 0.51051 focal_loss 0.40826 dice_loss 0.10224 +Epoch [3982/4000] Validation [3/10] Loss: 0.40748 focal_loss 0.29491 dice_loss 0.11258 +Epoch [3982/4000] Validation [4/10] Loss: 0.88699 focal_loss 0.32302 dice_loss 0.56397 +Epoch [3982/4000] Validation [5/10] Loss: 3.08178 focal_loss 2.40763 dice_loss 0.67415 +Epoch [3982/4000] Validation [6/10] Loss: 1.31856 focal_loss 0.60591 dice_loss 0.71266 +Epoch [3982/4000] Validation [7/10] Loss: 1.16388 focal_loss 0.51241 dice_loss 0.65147 +Epoch [3982/4000] Validation [8/10] Loss: 2.44449 focal_loss 1.81929 dice_loss 0.62519 +Epoch [3982/4000] Validation [9/10] Loss: 1.51640 focal_loss 0.97275 dice_loss 0.54365 +Epoch [3982/4000] Validation [10/10] Loss: 1.85289 focal_loss 1.11942 dice_loss 0.73347 +Epoch [3982/4000] Validation metric {'Val/mean dice_metric': 0.9516406059265137, 'Val/mean miou_metric': 0.9360313415527344, 'Val/mean f1': 0.9488359093666077, 'Val/mean precision': 0.9453608393669128, 'Val/mean recall': 0.9523365497589111, 'Val/mean hd95_metric': 10.747234344482422} +Cheakpoint... +Epoch [3982/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9516406059265137, 'Val/mean miou_metric': 0.9360313415527344, 'Val/mean f1': 0.9488359093666077, 'Val/mean precision': 0.9453608393669128, 'Val/mean recall': 0.9523365497589111, 'Val/mean hd95_metric': 10.747234344482422} +Epoch [3983/4000] Training [1/39] Loss: 0.00489 +Epoch [3983/4000] Training [2/39] Loss: 0.13253 +Epoch [3983/4000] Training [3/39] Loss: 0.00324 +Epoch [3983/4000] Training [4/39] Loss: 0.00531 +Epoch [3983/4000] Training [5/39] Loss: 0.12982 +Epoch [3983/4000] Training [6/39] Loss: 0.00346 +Epoch [3983/4000] Training [7/39] Loss: 0.00433 +Epoch [3983/4000] Training [8/39] Loss: 0.00401 +Epoch [3983/4000] Training [9/39] Loss: 0.00394 +Epoch [3983/4000] Training [10/39] Loss: 0.00439 +Epoch [3983/4000] Training [11/39] Loss: 0.25369 +Epoch [3983/4000] Training [12/39] Loss: 0.01068 +Epoch [3983/4000] Training [13/39] Loss: 0.00817 +Epoch [3983/4000] Training [14/39] Loss: 0.13055 +Epoch [3983/4000] Training [15/39] Loss: 0.00324 +Epoch [3983/4000] Training [16/39] Loss: 0.13020 +Epoch [3983/4000] Training [17/39] Loss: 0.00431 +Epoch [3983/4000] Training [18/39] Loss: 0.00743 +Epoch [3983/4000] Training [19/39] Loss: 0.12728 +Epoch [3983/4000] Training [20/39] Loss: 0.00424 +Epoch [3983/4000] Training [21/39] Loss: 0.00481 +Epoch [3983/4000] Training [22/39] Loss: 0.12744 +Epoch [3983/4000] Training [23/39] Loss: 0.12818 +Epoch [3983/4000] Training [24/39] Loss: 0.00328 +Epoch [3983/4000] Training [25/39] Loss: 0.00425 +Epoch [3983/4000] Training [26/39] Loss: 0.00490 +Epoch [3983/4000] Training [27/39] Loss: 0.25335 +Epoch [3983/4000] Training [28/39] Loss: 0.00573 +Epoch [3983/4000] Training [29/39] Loss: 0.00419 +Epoch [3983/4000] Training [30/39] Loss: 0.12893 +Epoch [3983/4000] Training [31/39] Loss: 0.00525 +Epoch [3983/4000] Training [32/39] Loss: 0.13235 +Epoch [3983/4000] Training [33/39] Loss: 0.12886 +Epoch [3983/4000] Training [34/39] Loss: 0.00438 +Epoch [3983/4000] Training [35/39] Loss: 0.00591 +Epoch [3983/4000] Training [36/39] Loss: 0.12766 +Epoch [3983/4000] Training [37/39] Loss: 0.00643 +Epoch [3983/4000] Training [38/39] Loss: 0.00485 +Epoch [3983/4000] Training [39/39] Loss: 0.00600 +Epoch [3983/4000] Training metric {'Train/mean dice_metric': 0.9963988065719604, 'Train/mean miou_metric': 0.9932408928871155, 'Train/mean f1': 0.996854841709137, 'Train/mean precision': 0.996383786201477, 'Train/mean recall': 0.9973264932632446, 'Train/mean hd95_metric': 0.9150975346565247} +Epoch [3983/4000] Validation [1/10] Loss: 0.72117 focal_loss 0.63447 dice_loss 0.08670 +Epoch [3983/4000] Validation [2/10] Loss: 0.50871 focal_loss 0.41136 dice_loss 0.09734 +Epoch [3983/4000] Validation [3/10] Loss: 0.39139 focal_loss 0.28052 dice_loss 0.11087 +Epoch [3983/4000] Validation [4/10] Loss: 0.90543 focal_loss 0.33935 dice_loss 0.56608 +Epoch [3983/4000] Validation [5/10] Loss: 3.08026 focal_loss 2.40642 dice_loss 0.67384 +Epoch [3983/4000] Validation [6/10] Loss: 1.35773 focal_loss 0.64482 dice_loss 0.71291 +Epoch [3983/4000] Validation [7/10] Loss: 1.19244 focal_loss 0.53693 dice_loss 0.65551 +Epoch [3983/4000] Validation [8/10] Loss: 2.37559 focal_loss 1.76213 dice_loss 0.61346 +Epoch [3983/4000] Validation [9/10] Loss: 1.55272 focal_loss 1.00821 dice_loss 0.54451 +Epoch [3983/4000] Validation [10/10] Loss: 1.93388 focal_loss 1.19807 dice_loss 0.73580 +Epoch [3983/4000] Validation metric {'Val/mean dice_metric': 0.9514400959014893, 'Val/mean miou_metric': 0.9355754852294922, 'Val/mean f1': 0.9481130838394165, 'Val/mean precision': 0.9429463744163513, 'Val/mean recall': 0.9533367156982422, 'Val/mean hd95_metric': 10.65640640258789} +Cheakpoint... +Epoch [3983/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514400959014893, 'Val/mean miou_metric': 0.9355754852294922, 'Val/mean f1': 0.9481130838394165, 'Val/mean precision': 0.9429463744163513, 'Val/mean recall': 0.9533367156982422, 'Val/mean hd95_metric': 10.65640640258789} +Epoch [3984/4000] Training [1/39] Loss: 0.00289 +Epoch [3984/4000] Training [2/39] Loss: 0.00356 +Epoch [3984/4000] Training [3/39] Loss: 0.12941 +Epoch [3984/4000] Training [4/39] Loss: 0.00692 +Epoch [3984/4000] Training [5/39] Loss: 0.00614 +Epoch [3984/4000] Training [6/39] Loss: 0.00625 +Epoch [3984/4000] Training [7/39] Loss: 0.00539 +Epoch [3984/4000] Training [8/39] Loss: 0.13336 +Epoch [3984/4000] Training [9/39] Loss: 0.00684 +Epoch [3984/4000] Training [10/39] Loss: 0.00499 +Epoch [3984/4000] Training [11/39] Loss: 0.00408 +Epoch [3984/4000] Training [12/39] Loss: 0.12690 +Epoch [3984/4000] Training [13/39] Loss: 0.00331 +Epoch [3984/4000] Training [14/39] Loss: 0.00789 +Epoch [3984/4000] Training [15/39] Loss: 0.12763 +Epoch [3984/4000] Training [16/39] Loss: 0.00686 +Epoch [3984/4000] Training [17/39] Loss: 0.00263 +Epoch [3984/4000] Training [18/39] Loss: 0.00459 +Epoch [3984/4000] Training [19/39] Loss: 0.00378 +Epoch [3984/4000] Training [20/39] Loss: 0.00388 +Epoch [3984/4000] Training [21/39] Loss: 0.00347 +Epoch [3984/4000] Training [22/39] Loss: 0.00560 +Epoch [3984/4000] Training [23/39] Loss: 0.00685 +Epoch [3984/4000] Training [24/39] Loss: 0.00477 +Epoch [3984/4000] Training [25/39] Loss: 0.12921 +Epoch [3984/4000] Training [26/39] Loss: 0.00340 +Epoch [3984/4000] Training [27/39] Loss: 0.25286 +Epoch [3984/4000] Training [28/39] Loss: 0.00654 +Epoch [3984/4000] Training [29/39] Loss: 0.12878 +Epoch [3984/4000] Training [30/39] Loss: 0.12991 +Epoch [3984/4000] Training [31/39] Loss: 0.00415 +Epoch [3984/4000] Training [32/39] Loss: 0.00507 +Epoch [3984/4000] Training [33/39] Loss: 0.00557 +Epoch [3984/4000] Training [34/39] Loss: 0.25294 +Epoch [3984/4000] Training [35/39] Loss: 0.00342 +Epoch [3984/4000] Training [36/39] Loss: 0.00326 +Epoch [3984/4000] Training [37/39] Loss: 0.00521 +Epoch [3984/4000] Training [38/39] Loss: 0.12992 +Epoch [3984/4000] Training [39/39] Loss: 0.12869 +Epoch [3984/4000] Training metric {'Train/mean dice_metric': 0.9955120086669922, 'Train/mean miou_metric': 0.992303192615509, 'Train/mean f1': 0.9968554973602295, 'Train/mean precision': 0.996411144733429, 'Train/mean recall': 0.9973002672195435, 'Train/mean hd95_metric': 0.9214502573013306} +Epoch [3984/4000] Validation [1/10] Loss: 0.71194 focal_loss 0.62566 dice_loss 0.08628 +Epoch [3984/4000] Validation [2/10] Loss: 0.50790 focal_loss 0.40929 dice_loss 0.09861 +Epoch [3984/4000] Validation [3/10] Loss: 0.39233 focal_loss 0.28118 dice_loss 0.11115 +Epoch [3984/4000] Validation [4/10] Loss: 0.90001 focal_loss 0.33452 dice_loss 0.56549 +Epoch [3984/4000] Validation [5/10] Loss: 3.06382 focal_loss 2.38988 dice_loss 0.67394 +Epoch [3984/4000] Validation [6/10] Loss: 1.34890 focal_loss 0.63620 dice_loss 0.71270 +Epoch [3984/4000] Validation [7/10] Loss: 1.18548 focal_loss 0.53090 dice_loss 0.65458 +Epoch [3984/4000] Validation [8/10] Loss: 2.38644 focal_loss 1.77085 dice_loss 0.61559 +Epoch [3984/4000] Validation [9/10] Loss: 1.53956 focal_loss 0.99506 dice_loss 0.54451 +Epoch [3984/4000] Validation [10/10] Loss: 1.91426 focal_loss 1.17888 dice_loss 0.73538 +Epoch [3984/4000] Validation metric {'Val/mean dice_metric': 0.9506701827049255, 'Val/mean miou_metric': 0.9347633123397827, 'Val/mean f1': 0.9484257698059082, 'Val/mean precision': 0.9435746669769287, 'Val/mean recall': 0.9533268809318542, 'Val/mean hd95_metric': 10.679938316345215} +Cheakpoint... +Epoch [3984/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9506701827049255, 'Val/mean miou_metric': 0.9347633123397827, 'Val/mean f1': 0.9484257698059082, 'Val/mean precision': 0.9435746669769287, 'Val/mean recall': 0.9533268809318542, 'Val/mean hd95_metric': 10.679938316345215} +Epoch [3985/4000] Training [1/39] Loss: 0.00488 +Epoch [3985/4000] Training [2/39] Loss: 0.00591 +Epoch [3985/4000] Training [3/39] Loss: 0.00346 +Epoch [3985/4000] Training [4/39] Loss: 0.00545 +Epoch [3985/4000] Training [5/39] Loss: 0.00539 +Epoch [3985/4000] Training [6/39] Loss: 0.00521 +Epoch [3985/4000] Training [7/39] Loss: 0.00379 +Epoch [3985/4000] Training [8/39] Loss: 0.00423 +Epoch [3985/4000] Training [9/39] Loss: 0.00499 +Epoch [3985/4000] Training [10/39] Loss: 0.00696 +Epoch [3985/4000] Training [11/39] Loss: 0.25297 +Epoch [3985/4000] Training [12/39] Loss: 0.00522 +Epoch [3985/4000] Training [13/39] Loss: 0.00463 +Epoch [3985/4000] Training [14/39] Loss: 0.00328 +Epoch [3985/4000] Training [15/39] Loss: 0.00844 +Epoch [3985/4000] Training [16/39] Loss: 0.13404 +Epoch [3985/4000] Training [17/39] Loss: 0.00477 +Epoch [3985/4000] Training [18/39] Loss: 0.00994 +Epoch [3985/4000] Training [19/39] Loss: 0.12775 +Epoch [3985/4000] Training [20/39] Loss: 0.00443 +Epoch [3985/4000] Training [21/39] Loss: 0.00432 +Epoch [3985/4000] Training [22/39] Loss: 0.00404 +Epoch [3985/4000] Training [23/39] Loss: 0.00454 +Epoch [3985/4000] Training [24/39] Loss: 0.00397 +Epoch [3985/4000] Training [25/39] Loss: 0.00510 +Epoch [3985/4000] Training [26/39] Loss: 0.00463 +Epoch [3985/4000] Training [27/39] Loss: 0.00436 +Epoch [3985/4000] Training [28/39] Loss: 0.12924 +Epoch [3985/4000] Training [29/39] Loss: 0.00359 +Epoch [3985/4000] Training [30/39] Loss: 0.00444 +Epoch [3985/4000] Training [31/39] Loss: 0.12931 +Epoch [3985/4000] Training [32/39] Loss: 0.00288 +Epoch [3985/4000] Training [33/39] Loss: 0.00557 +Epoch [3985/4000] Training [34/39] Loss: 0.12819 +Epoch [3985/4000] Training [35/39] Loss: 0.00433 +Epoch [3985/4000] Training [36/39] Loss: 0.00400 +Epoch [3985/4000] Training [37/39] Loss: 0.00485 +Epoch [3985/4000] Training [38/39] Loss: 0.00546 +Epoch [3985/4000] Training [39/39] Loss: 0.00482 +Epoch [3985/4000] Training metric {'Train/mean dice_metric': 0.9965145587921143, 'Train/mean miou_metric': 0.9934873580932617, 'Train/mean f1': 0.9970108270645142, 'Train/mean precision': 0.996548593044281, 'Train/mean recall': 0.9974734783172607, 'Train/mean hd95_metric': 0.9057403802871704} +Epoch [3985/4000] Validation [1/10] Loss: 0.71896 focal_loss 0.63237 dice_loss 0.08659 +Epoch [3985/4000] Validation [2/10] Loss: 0.50621 focal_loss 0.40741 dice_loss 0.09880 +Epoch [3985/4000] Validation [3/10] Loss: 0.39660 focal_loss 0.28516 dice_loss 0.11145 +Epoch [3985/4000] Validation [4/10] Loss: 0.89658 focal_loss 0.33092 dice_loss 0.56566 +Epoch [3985/4000] Validation [5/10] Loss: 3.08675 focal_loss 2.41280 dice_loss 0.67395 +Epoch [3985/4000] Validation [6/10] Loss: 1.33900 focal_loss 0.62648 dice_loss 0.71252 +Epoch [3985/4000] Validation [7/10] Loss: 1.18092 focal_loss 0.52635 dice_loss 0.65457 +Epoch [3985/4000] Validation [8/10] Loss: 2.38936 focal_loss 1.77329 dice_loss 0.61607 +Epoch [3985/4000] Validation [9/10] Loss: 1.53898 focal_loss 0.99486 dice_loss 0.54412 +Epoch [3985/4000] Validation [10/10] Loss: 1.90234 focal_loss 1.16708 dice_loss 0.73526 +Epoch [3985/4000] Validation metric {'Val/mean dice_metric': 0.951500415802002, 'Val/mean miou_metric': 0.9357439279556274, 'Val/mean f1': 0.9485009908676147, 'Val/mean precision': 0.9438090920448303, 'Val/mean recall': 0.9532398581504822, 'Val/mean hd95_metric': 10.665477752685547} +Cheakpoint... +Epoch [3985/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951500415802002, 'Val/mean miou_metric': 0.9357439279556274, 'Val/mean f1': 0.9485009908676147, 'Val/mean precision': 0.9438090920448303, 'Val/mean recall': 0.9532398581504822, 'Val/mean hd95_metric': 10.665477752685547} +Epoch [3986/4000] Training [1/39] Loss: 0.00545 +Epoch [3986/4000] Training [2/39] Loss: 0.00724 +Epoch [3986/4000] Training [3/39] Loss: 0.12710 +Epoch [3986/4000] Training [4/39] Loss: 0.00389 +Epoch [3986/4000] Training [5/39] Loss: 0.12868 +Epoch [3986/4000] Training [6/39] Loss: 0.25417 +Epoch [3986/4000] Training [7/39] Loss: 0.00510 +Epoch [3986/4000] Training [8/39] Loss: 0.00593 +Epoch [3986/4000] Training [9/39] Loss: 0.01137 +Epoch [3986/4000] Training [10/39] Loss: 0.00416 +Epoch [3986/4000] Training [11/39] Loss: 0.00268 +Epoch [3986/4000] Training [12/39] Loss: 0.00390 +Epoch [3986/4000] Training [13/39] Loss: 0.12966 +Epoch [3986/4000] Training [14/39] Loss: 0.00541 +Epoch [3986/4000] Training [15/39] Loss: 0.12862 +Epoch [3986/4000] Training [16/39] Loss: 0.00253 +Epoch [3986/4000] Training [17/39] Loss: 0.00503 +Epoch [3986/4000] Training [18/39] Loss: 0.00404 +Epoch [3986/4000] Training [19/39] Loss: 0.12808 +Epoch [3986/4000] Training [20/39] Loss: 0.00312 +Epoch [3986/4000] Training [21/39] Loss: 0.00446 +Epoch [3986/4000] Training [22/39] Loss: 0.12802 +Epoch [3986/4000] Training [23/39] Loss: 0.00414 +Epoch [3986/4000] Training [24/39] Loss: 0.00668 +Epoch [3986/4000] Training [25/39] Loss: 0.00309 +Epoch [3986/4000] Training [26/39] Loss: 0.00410 +Epoch [3986/4000] Training [27/39] Loss: 0.00348 +Epoch [3986/4000] Training [28/39] Loss: 0.12786 +Epoch [3986/4000] Training [29/39] Loss: 0.00524 +Epoch [3986/4000] Training [30/39] Loss: 0.13036 +Epoch [3986/4000] Training [31/39] Loss: 0.12752 +Epoch [3986/4000] Training [32/39] Loss: 0.00411 +Epoch [3986/4000] Training [33/39] Loss: 0.13065 +Epoch [3986/4000] Training [34/39] Loss: 0.00540 +Epoch [3986/4000] Training [35/39] Loss: 0.00251 +Epoch [3986/4000] Training [36/39] Loss: 0.00396 +Epoch [3986/4000] Training [37/39] Loss: 0.00468 +Epoch [3986/4000] Training [38/39] Loss: 0.00851 +Epoch [3986/4000] Training [39/39] Loss: 0.00458 +Epoch [3986/4000] Training metric {'Train/mean dice_metric': 0.9955959320068359, 'Train/mean miou_metric': 0.992466926574707, 'Train/mean f1': 0.9969676733016968, 'Train/mean precision': 0.9965552091598511, 'Train/mean recall': 0.9973803758621216, 'Train/mean hd95_metric': 0.9357001781463623} +Epoch [3986/4000] Validation [1/10] Loss: 0.71609 focal_loss 0.62943 dice_loss 0.08666 +Epoch [3986/4000] Validation [2/10] Loss: 0.50471 focal_loss 0.40667 dice_loss 0.09804 +Epoch [3986/4000] Validation [3/10] Loss: 0.39241 focal_loss 0.28122 dice_loss 0.11119 +Epoch [3986/4000] Validation [4/10] Loss: 0.89870 focal_loss 0.33298 dice_loss 0.56572 +Epoch [3986/4000] Validation [5/10] Loss: 3.06669 focal_loss 2.39278 dice_loss 0.67391 +Epoch [3986/4000] Validation [6/10] Loss: 1.34435 focal_loss 0.63184 dice_loss 0.71251 +Epoch [3986/4000] Validation [7/10] Loss: 1.18233 focal_loss 0.52743 dice_loss 0.65490 +Epoch [3986/4000] Validation [8/10] Loss: 2.37456 focal_loss 1.75933 dice_loss 0.61523 +Epoch [3986/4000] Validation [9/10] Loss: 1.53938 focal_loss 0.99502 dice_loss 0.54436 +Epoch [3986/4000] Validation [10/10] Loss: 1.91046 focal_loss 1.17510 dice_loss 0.73536 +Epoch [3986/4000] Validation metric {'Val/mean dice_metric': 0.9507654905319214, 'Val/mean miou_metric': 0.9349279999732971, 'Val/mean f1': 0.9482911825180054, 'Val/mean precision': 0.9434458017349243, 'Val/mean recall': 0.9531866312026978, 'Val/mean hd95_metric': 10.703707695007324} +Cheakpoint... +Epoch [3986/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507654905319214, 'Val/mean miou_metric': 0.9349279999732971, 'Val/mean f1': 0.9482911825180054, 'Val/mean precision': 0.9434458017349243, 'Val/mean recall': 0.9531866312026978, 'Val/mean hd95_metric': 10.703707695007324} +Epoch [3987/4000] Training [1/39] Loss: 0.00331 +Epoch [3987/4000] Training [2/39] Loss: 0.00619 +Epoch [3987/4000] Training [3/39] Loss: 0.00600 +Epoch [3987/4000] Training [4/39] Loss: 0.00398 +Epoch [3987/4000] Training [5/39] Loss: 0.00680 +Epoch [3987/4000] Training [6/39] Loss: 0.00456 +Epoch [3987/4000] Training [7/39] Loss: 0.00455 +Epoch [3987/4000] Training [8/39] Loss: 0.00354 +Epoch [3987/4000] Training [9/39] Loss: 0.00319 +Epoch [3987/4000] Training [10/39] Loss: 0.00418 +Epoch [3987/4000] Training [11/39] Loss: 0.13054 +Epoch [3987/4000] Training [12/39] Loss: 0.00561 +Epoch [3987/4000] Training [13/39] Loss: 0.00341 +Epoch [3987/4000] Training [14/39] Loss: 0.00368 +Epoch [3987/4000] Training [15/39] Loss: 0.00404 +Epoch [3987/4000] Training [16/39] Loss: 0.00494 +Epoch [3987/4000] Training [17/39] Loss: 0.00857 +Epoch [3987/4000] Training [18/39] Loss: 0.00471 +Epoch [3987/4000] Training [19/39] Loss: 0.12910 +Epoch [3987/4000] Training [20/39] Loss: 0.00311 +Epoch [3987/4000] Training [21/39] Loss: 0.00447 +Epoch [3987/4000] Training [22/39] Loss: 0.00467 +Epoch [3987/4000] Training [23/39] Loss: 0.00522 +Epoch [3987/4000] Training [24/39] Loss: 0.04166 +Epoch [3987/4000] Training [25/39] Loss: 0.00495 +Epoch [3987/4000] Training [26/39] Loss: 0.00505 +Epoch [3987/4000] Training [27/39] Loss: 0.00620 +Epoch [3987/4000] Training [28/39] Loss: 0.00581 +Epoch [3987/4000] Training [29/39] Loss: 0.12877 +Epoch [3987/4000] Training [30/39] Loss: 0.00410 +Epoch [3987/4000] Training [31/39] Loss: 0.25263 +Epoch [3987/4000] Training [32/39] Loss: 0.00281 +Epoch [3987/4000] Training [33/39] Loss: 0.13061 +Epoch [3987/4000] Training [34/39] Loss: 0.00531 +Epoch [3987/4000] Training [35/39] Loss: 0.12957 +Epoch [3987/4000] Training [36/39] Loss: 0.12951 +Epoch [3987/4000] Training [37/39] Loss: 0.00572 +Epoch [3987/4000] Training [38/39] Loss: 0.00415 +Epoch [3987/4000] Training [39/39] Loss: 0.00511 +Epoch [3987/4000] Training metric {'Train/mean dice_metric': 0.9958585500717163, 'Train/mean miou_metric': 0.9926296472549438, 'Train/mean f1': 0.9968960881233215, 'Train/mean precision': 0.9963797926902771, 'Train/mean recall': 0.997412919998169, 'Train/mean hd95_metric': 1.047087550163269} +Epoch [3987/4000] Validation [1/10] Loss: 0.72094 focal_loss 0.63460 dice_loss 0.08633 +Epoch [3987/4000] Validation [2/10] Loss: 0.50866 focal_loss 0.41003 dice_loss 0.09863 +Epoch [3987/4000] Validation [3/10] Loss: 0.39749 focal_loss 0.28616 dice_loss 0.11133 +Epoch [3987/4000] Validation [4/10] Loss: 0.89908 focal_loss 0.33372 dice_loss 0.56535 +Epoch [3987/4000] Validation [5/10] Loss: 3.09614 focal_loss 2.42220 dice_loss 0.67394 +Epoch [3987/4000] Validation [6/10] Loss: 1.34591 focal_loss 0.63307 dice_loss 0.71283 +Epoch [3987/4000] Validation [7/10] Loss: 1.18585 focal_loss 0.53146 dice_loss 0.65439 +Epoch [3987/4000] Validation [8/10] Loss: 2.40714 focal_loss 1.79055 dice_loss 0.61658 +Epoch [3987/4000] Validation [9/10] Loss: 1.54612 focal_loss 1.00181 dice_loss 0.54431 +Epoch [3987/4000] Validation [10/10] Loss: 1.91452 focal_loss 1.17941 dice_loss 0.73511 +Epoch [3987/4000] Validation metric {'Val/mean dice_metric': 0.9509983658790588, 'Val/mean miou_metric': 0.9350905418395996, 'Val/mean f1': 0.9484060406684875, 'Val/mean precision': 0.9437035918235779, 'Val/mean recall': 0.953155517578125, 'Val/mean hd95_metric': 10.770635604858398} +Cheakpoint... +Epoch [3987/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9510], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9509983658790588, 'Val/mean miou_metric': 0.9350905418395996, 'Val/mean f1': 0.9484060406684875, 'Val/mean precision': 0.9437035918235779, 'Val/mean recall': 0.953155517578125, 'Val/mean hd95_metric': 10.770635604858398} +Epoch [3988/4000] Training [1/39] Loss: 0.00391 +Epoch [3988/4000] Training [2/39] Loss: 0.12989 +Epoch [3988/4000] Training [3/39] Loss: 0.12803 +Epoch [3988/4000] Training [4/39] Loss: 0.12923 +Epoch [3988/4000] Training [5/39] Loss: 0.16365 +Epoch [3988/4000] Training [6/39] Loss: 0.00617 +Epoch [3988/4000] Training [7/39] Loss: 0.00388 +Epoch [3988/4000] Training [8/39] Loss: 0.00557 +Epoch [3988/4000] Training [9/39] Loss: 0.00494 +Epoch [3988/4000] Training [10/39] Loss: 0.00659 +Epoch [3988/4000] Training [11/39] Loss: 0.00423 +Epoch [3988/4000] Training [12/39] Loss: 0.00312 +Epoch [3988/4000] Training [13/39] Loss: 0.12930 +Epoch [3988/4000] Training [14/39] Loss: 0.00752 +Epoch [3988/4000] Training [15/39] Loss: 0.00448 +Epoch [3988/4000] Training [16/39] Loss: 0.00383 +Epoch [3988/4000] Training [17/39] Loss: 0.00463 +Epoch [3988/4000] Training [18/39] Loss: 0.00479 +Epoch [3988/4000] Training [19/39] Loss: 0.00576 +Epoch [3988/4000] Training [20/39] Loss: 0.00534 +Epoch [3988/4000] Training [21/39] Loss: 0.00304 +Epoch [3988/4000] Training [22/39] Loss: 0.12840 +Epoch [3988/4000] Training [23/39] Loss: 0.00476 +Epoch [3988/4000] Training [24/39] Loss: 0.13185 +Epoch [3988/4000] Training [25/39] Loss: 0.00380 +Epoch [3988/4000] Training [26/39] Loss: 0.00408 +Epoch [3988/4000] Training [27/39] Loss: 0.00426 +Epoch [3988/4000] Training [28/39] Loss: 0.00369 +Epoch [3988/4000] Training [29/39] Loss: 0.00438 +Epoch [3988/4000] Training [30/39] Loss: 0.12852 +Epoch [3988/4000] Training [31/39] Loss: 0.00355 +Epoch [3988/4000] Training [32/39] Loss: 0.25454 +Epoch [3988/4000] Training [33/39] Loss: 0.00411 +Epoch [3988/4000] Training [34/39] Loss: 0.00272 +Epoch [3988/4000] Training [35/39] Loss: 0.00655 +Epoch [3988/4000] Training [36/39] Loss: 0.25447 +Epoch [3988/4000] Training [37/39] Loss: 0.00499 +Epoch [3988/4000] Training [38/39] Loss: 0.25390 +Epoch [3988/4000] Training [39/39] Loss: 0.12818 +Epoch [3988/4000] Training metric {'Train/mean dice_metric': 0.9964123964309692, 'Train/mean miou_metric': 0.9932743906974792, 'Train/mean f1': 0.9969427585601807, 'Train/mean precision': 0.9965370893478394, 'Train/mean recall': 0.9973488450050354, 'Train/mean hd95_metric': 0.9278735518455505} +Epoch [3988/4000] Validation [1/10] Loss: 0.70814 focal_loss 0.62214 dice_loss 0.08600 +Epoch [3988/4000] Validation [2/10] Loss: 0.51241 focal_loss 0.41279 dice_loss 0.09962 +Epoch [3988/4000] Validation [3/10] Loss: 0.39651 focal_loss 0.28491 dice_loss 0.11160 +Epoch [3988/4000] Validation [4/10] Loss: 0.90094 focal_loss 0.33549 dice_loss 0.56544 +Epoch [3988/4000] Validation [5/10] Loss: 3.05389 focal_loss 2.37989 dice_loss 0.67400 +Epoch [3988/4000] Validation [6/10] Loss: 1.34781 focal_loss 0.63549 dice_loss 0.71233 +Epoch [3988/4000] Validation [7/10] Loss: 1.18700 focal_loss 0.53317 dice_loss 0.65383 +Epoch [3988/4000] Validation [8/10] Loss: 2.40476 focal_loss 1.78578 dice_loss 0.61898 +Epoch [3988/4000] Validation [9/10] Loss: 1.54060 focal_loss 0.99599 dice_loss 0.54461 +Epoch [3988/4000] Validation [10/10] Loss: 1.91306 focal_loss 1.17816 dice_loss 0.73490 +Epoch [3988/4000] Validation metric {'Val/mean dice_metric': 0.9514386057853699, 'Val/mean miou_metric': 0.9356039762496948, 'Val/mean f1': 0.9485144019126892, 'Val/mean precision': 0.9441059231758118, 'Val/mean recall': 0.952964186668396, 'Val/mean hd95_metric': 10.793667793273926} +Cheakpoint... +Epoch [3988/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514386057853699, 'Val/mean miou_metric': 0.9356039762496948, 'Val/mean f1': 0.9485144019126892, 'Val/mean precision': 0.9441059231758118, 'Val/mean recall': 0.952964186668396, 'Val/mean hd95_metric': 10.793667793273926} +Epoch [3989/4000] Training [1/39] Loss: 0.00329 +Epoch [3989/4000] Training [2/39] Loss: 0.00490 +Epoch [3989/4000] Training [3/39] Loss: 0.00472 +Epoch [3989/4000] Training [4/39] Loss: 0.00441 +Epoch [3989/4000] Training [5/39] Loss: 0.37870 +Epoch [3989/4000] Training [6/39] Loss: 0.00559 +Epoch [3989/4000] Training [7/39] Loss: 0.25241 +Epoch [3989/4000] Training [8/39] Loss: 0.12968 +Epoch [3989/4000] Training [9/39] Loss: 0.00330 +Epoch [3989/4000] Training [10/39] Loss: 0.00368 +Epoch [3989/4000] Training [11/39] Loss: 0.09011 +Epoch [3989/4000] Training [12/39] Loss: 0.00294 +Epoch [3989/4000] Training [13/39] Loss: 0.00441 +Epoch [3989/4000] Training [14/39] Loss: 0.00501 +Epoch [3989/4000] Training [15/39] Loss: 0.00463 +Epoch [3989/4000] Training [16/39] Loss: 0.00474 +Epoch [3989/4000] Training [17/39] Loss: 0.00573 +Epoch [3989/4000] Training [18/39] Loss: 0.12743 +Epoch [3989/4000] Training [19/39] Loss: 0.00838 +Epoch [3989/4000] Training [20/39] Loss: 0.00607 +Epoch [3989/4000] Training [21/39] Loss: 0.00390 +Epoch [3989/4000] Training [22/39] Loss: 0.00429 +Epoch [3989/4000] Training [23/39] Loss: 0.00608 +Epoch [3989/4000] Training [24/39] Loss: 0.00536 +Epoch [3989/4000] Training [25/39] Loss: 0.00337 +Epoch [3989/4000] Training [26/39] Loss: 0.00579 +Epoch [3989/4000] Training [27/39] Loss: 0.00420 +Epoch [3989/4000] Training [28/39] Loss: 0.00254 +Epoch [3989/4000] Training [29/39] Loss: 0.00604 +Epoch [3989/4000] Training [30/39] Loss: 0.00618 +Epoch [3989/4000] Training [31/39] Loss: 0.00535 +Epoch [3989/4000] Training [32/39] Loss: 0.00750 +Epoch [3989/4000] Training [33/39] Loss: 0.00375 +Epoch [3989/4000] Training [34/39] Loss: 0.00418 +Epoch [3989/4000] Training [35/39] Loss: 0.00399 +Epoch [3989/4000] Training [36/39] Loss: 0.00526 +Epoch [3989/4000] Training [37/39] Loss: 0.12968 +Epoch [3989/4000] Training [38/39] Loss: 0.13245 +Epoch [3989/4000] Training [39/39] Loss: 0.12894 +Epoch [3989/4000] Training metric {'Train/mean dice_metric': 0.9955600500106812, 'Train/mean miou_metric': 0.9924150109291077, 'Train/mean f1': 0.9968662858009338, 'Train/mean precision': 0.9964161515235901, 'Train/mean recall': 0.9973167777061462, 'Train/mean hd95_metric': 0.971290647983551} +Epoch [3989/4000] Validation [1/10] Loss: 0.72561 focal_loss 0.63975 dice_loss 0.08586 +Epoch [3989/4000] Validation [2/10] Loss: 0.51120 focal_loss 0.41084 dice_loss 0.10036 +Epoch [3989/4000] Validation [3/10] Loss: 0.40932 focal_loss 0.29730 dice_loss 0.11201 +Epoch [3989/4000] Validation [4/10] Loss: 0.89433 focal_loss 0.32964 dice_loss 0.56469 +Epoch [3989/4000] Validation [5/10] Loss: 3.13951 focal_loss 2.46542 dice_loss 0.67409 +Epoch [3989/4000] Validation [6/10] Loss: 1.33213 focal_loss 0.61953 dice_loss 0.71260 +Epoch [3989/4000] Validation [7/10] Loss: 1.17513 focal_loss 0.52232 dice_loss 0.65281 +Epoch [3989/4000] Validation [8/10] Loss: 2.44833 focal_loss 1.82680 dice_loss 0.62153 +Epoch [3989/4000] Validation [9/10] Loss: 1.54034 focal_loss 0.99655 dice_loss 0.54380 +Epoch [3989/4000] Validation [10/10] Loss: 1.88463 focal_loss 1.15045 dice_loss 0.73417 +Epoch [3989/4000] Validation metric {'Val/mean dice_metric': 0.9507386088371277, 'Val/mean miou_metric': 0.934916079044342, 'Val/mean f1': 0.9486272931098938, 'Val/mean precision': 0.9446978569030762, 'Val/mean recall': 0.9525894522666931, 'Val/mean hd95_metric': 10.797626495361328} +Cheakpoint... +Epoch [3989/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9507], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9507386088371277, 'Val/mean miou_metric': 0.934916079044342, 'Val/mean f1': 0.9486272931098938, 'Val/mean precision': 0.9446978569030762, 'Val/mean recall': 0.9525894522666931, 'Val/mean hd95_metric': 10.797626495361328} +Epoch [3990/4000] Training [1/39] Loss: 0.00440 +Epoch [3990/4000] Training [2/39] Loss: 0.29117 +Epoch [3990/4000] Training [3/39] Loss: 0.12849 +Epoch [3990/4000] Training [4/39] Loss: 0.25272 +Epoch [3990/4000] Training [5/39] Loss: 0.13079 +Epoch [3990/4000] Training [6/39] Loss: 0.00446 +Epoch [3990/4000] Training [7/39] Loss: 0.12896 +Epoch [3990/4000] Training [8/39] Loss: 0.00579 +Epoch [3990/4000] Training [9/39] Loss: 0.12907 +Epoch [3990/4000] Training [10/39] Loss: 0.00342 +Epoch [3990/4000] Training [11/39] Loss: 0.12903 +Epoch [3990/4000] Training [12/39] Loss: 0.00349 +Epoch [3990/4000] Training [13/39] Loss: 0.00447 +Epoch [3990/4000] Training [14/39] Loss: 0.00476 +Epoch [3990/4000] Training [15/39] Loss: 0.00517 +Epoch [3990/4000] Training [16/39] Loss: 0.00645 +Epoch [3990/4000] Training [17/39] Loss: 0.13136 +Epoch [3990/4000] Training [18/39] Loss: 0.00531 +Epoch [3990/4000] Training [19/39] Loss: 0.00557 +Epoch [3990/4000] Training [20/39] Loss: 0.12972 +Epoch [3990/4000] Training [21/39] Loss: 0.00725 +Epoch [3990/4000] Training [22/39] Loss: 0.00454 +Epoch [3990/4000] Training [23/39] Loss: 0.13142 +Epoch [3990/4000] Training [24/39] Loss: 0.12873 +Epoch [3990/4000] Training [25/39] Loss: 0.12982 +Epoch [3990/4000] Training [26/39] Loss: 0.13354 +Epoch [3990/4000] Training [27/39] Loss: 0.00374 +Epoch [3990/4000] Training [28/39] Loss: 0.00618 +Epoch [3990/4000] Training [29/39] Loss: 0.08998 +Epoch [3990/4000] Training [30/39] Loss: 0.25422 +Epoch [3990/4000] Training [31/39] Loss: 0.00298 +Epoch [3990/4000] Training [32/39] Loss: 0.00509 +Epoch [3990/4000] Training [33/39] Loss: 0.00389 +Epoch [3990/4000] Training [34/39] Loss: 0.00426 +Epoch [3990/4000] Training [35/39] Loss: 0.00392 +Epoch [3990/4000] Training [36/39] Loss: 0.25301 +Epoch [3990/4000] Training [37/39] Loss: 0.13128 +Epoch [3990/4000] Training [38/39] Loss: 0.00308 +Epoch [3990/4000] Training [39/39] Loss: 0.00541 +Epoch [3990/4000] Training metric {'Train/mean dice_metric': 0.9956530928611755, 'Train/mean miou_metric': 0.9925903081893921, 'Train/mean f1': 0.9969794750213623, 'Train/mean precision': 0.9965636134147644, 'Train/mean recall': 0.9973956942558289, 'Train/mean hd95_metric': 0.9452795386314392} +Epoch [3990/4000] Validation [1/10] Loss: 0.72307 focal_loss 0.63710 dice_loss 0.08597 +Epoch [3990/4000] Validation [2/10] Loss: 0.51381 focal_loss 0.41345 dice_loss 0.10035 +Epoch [3990/4000] Validation [3/10] Loss: 0.40763 focal_loss 0.29569 dice_loss 0.11194 +Epoch [3990/4000] Validation [4/10] Loss: 0.89708 focal_loss 0.33227 dice_loss 0.56481 +Epoch [3990/4000] Validation [5/10] Loss: 3.12327 focal_loss 2.44921 dice_loss 0.67406 +Epoch [3990/4000] Validation [6/10] Loss: 1.33878 focal_loss 0.62650 dice_loss 0.71228 +Epoch [3990/4000] Validation [7/10] Loss: 1.18033 focal_loss 0.52707 dice_loss 0.65326 +Epoch [3990/4000] Validation [8/10] Loss: 2.44949 focal_loss 1.82852 dice_loss 0.62097 +Epoch [3990/4000] Validation [9/10] Loss: 1.54477 focal_loss 1.00081 dice_loss 0.54396 +Epoch [3990/4000] Validation [10/10] Loss: 1.89552 focal_loss 1.16116 dice_loss 0.73436 +Epoch [3990/4000] Validation metric {'Val/mean dice_metric': 0.9508114457130432, 'Val/mean miou_metric': 0.9350475668907166, 'Val/mean f1': 0.9486283659934998, 'Val/mean precision': 0.9446151852607727, 'Val/mean recall': 0.9526758790016174, 'Val/mean hd95_metric': 10.804949760437012} +Cheakpoint... +Epoch [3990/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9508], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9508114457130432, 'Val/mean miou_metric': 0.9350475668907166, 'Val/mean f1': 0.9486283659934998, 'Val/mean precision': 0.9446151852607727, 'Val/mean recall': 0.9526758790016174, 'Val/mean hd95_metric': 10.804949760437012} +Epoch [3991/4000] Training [1/39] Loss: 0.00344 +Epoch [3991/4000] Training [2/39] Loss: 0.00428 +Epoch [3991/4000] Training [3/39] Loss: 0.12912 +Epoch [3991/4000] Training [4/39] Loss: 0.00663 +Epoch [3991/4000] Training [5/39] Loss: 0.12977 +Epoch [3991/4000] Training [6/39] Loss: 0.12868 +Epoch [3991/4000] Training [7/39] Loss: 0.00334 +Epoch [3991/4000] Training [8/39] Loss: 0.00676 +Epoch [3991/4000] Training [9/39] Loss: 0.00462 +Epoch [3991/4000] Training [10/39] Loss: 0.01392 +Epoch [3991/4000] Training [11/39] Loss: 0.00521 +Epoch [3991/4000] Training [12/39] Loss: 0.00410 +Epoch [3991/4000] Training [13/39] Loss: 0.00480 +Epoch [3991/4000] Training [14/39] Loss: 0.13050 +Epoch [3991/4000] Training [15/39] Loss: 0.00448 +Epoch [3991/4000] Training [16/39] Loss: 0.12787 +Epoch [3991/4000] Training [17/39] Loss: 0.00366 +Epoch [3991/4000] Training [18/39] Loss: 0.25221 +Epoch [3991/4000] Training [19/39] Loss: 0.00622 +Epoch [3991/4000] Training [20/39] Loss: 0.12908 +Epoch [3991/4000] Training [21/39] Loss: 0.12828 +Epoch [3991/4000] Training [22/39] Loss: 0.00390 +Epoch [3991/4000] Training [23/39] Loss: 0.00528 +Epoch [3991/4000] Training [24/39] Loss: 0.00315 +Epoch [3991/4000] Training [25/39] Loss: 0.00420 +Epoch [3991/4000] Training [26/39] Loss: 0.12894 +Epoch [3991/4000] Training [27/39] Loss: 0.25426 +Epoch [3991/4000] Training [28/39] Loss: 0.12879 +Epoch [3991/4000] Training [29/39] Loss: 0.00417 +Epoch [3991/4000] Training [30/39] Loss: 0.00827 +Epoch [3991/4000] Training [31/39] Loss: 0.12867 +Epoch [3991/4000] Training [32/39] Loss: 0.12821 +Epoch [3991/4000] Training [33/39] Loss: 0.00487 +Epoch [3991/4000] Training [34/39] Loss: 0.25356 +Epoch [3991/4000] Training [35/39] Loss: 0.00394 +Epoch [3991/4000] Training [36/39] Loss: 0.00592 +Epoch [3991/4000] Training [37/39] Loss: 0.00629 +Epoch [3991/4000] Training [38/39] Loss: 0.00510 +Epoch [3991/4000] Training [39/39] Loss: 0.00329 +Epoch [3991/4000] Training metric {'Train/mean dice_metric': 0.9964377284049988, 'Train/mean miou_metric': 0.9933249354362488, 'Train/mean f1': 0.9970770478248596, 'Train/mean precision': 0.9966553449630737, 'Train/mean recall': 0.9974991679191589, 'Train/mean hd95_metric': 0.8945761919021606} +Epoch [3991/4000] Validation [1/10] Loss: 0.70715 focal_loss 0.62116 dice_loss 0.08600 +Epoch [3991/4000] Validation [2/10] Loss: 0.51024 focal_loss 0.41091 dice_loss 0.09933 +Epoch [3991/4000] Validation [3/10] Loss: 0.39403 focal_loss 0.28260 dice_loss 0.11142 +Epoch [3991/4000] Validation [4/10] Loss: 0.90038 focal_loss 0.33497 dice_loss 0.56541 +Epoch [3991/4000] Validation [5/10] Loss: 3.04593 focal_loss 2.37200 dice_loss 0.67393 +Epoch [3991/4000] Validation [6/10] Loss: 1.34781 focal_loss 0.63504 dice_loss 0.71277 +Epoch [3991/4000] Validation [7/10] Loss: 1.18520 focal_loss 0.53149 dice_loss 0.65370 +Epoch [3991/4000] Validation [8/10] Loss: 2.39589 focal_loss 1.77790 dice_loss 0.61799 +Epoch [3991/4000] Validation [9/10] Loss: 1.53699 focal_loss 0.99255 dice_loss 0.54444 +Epoch [3991/4000] Validation [10/10] Loss: 1.91156 focal_loss 1.17658 dice_loss 0.73498 +Epoch [3991/4000] Validation metric {'Val/mean dice_metric': 0.9514814019203186, 'Val/mean miou_metric': 0.9356716275215149, 'Val/mean f1': 0.9484362602233887, 'Val/mean precision': 0.9438483119010925, 'Val/mean recall': 0.9530689716339111, 'Val/mean hd95_metric': 10.753154754638672} +Cheakpoint... +Epoch [3991/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9514814019203186, 'Val/mean miou_metric': 0.9356716275215149, 'Val/mean f1': 0.9484362602233887, 'Val/mean precision': 0.9438483119010925, 'Val/mean recall': 0.9530689716339111, 'Val/mean hd95_metric': 10.753154754638672} +Epoch [3992/4000] Training [1/39] Loss: 0.25427 +Epoch [3992/4000] Training [2/39] Loss: 0.00292 +Epoch [3992/4000] Training [3/39] Loss: 0.00780 +Epoch [3992/4000] Training [4/39] Loss: 0.00428 +Epoch [3992/4000] Training [5/39] Loss: 0.00546 +Epoch [3992/4000] Training [6/39] Loss: 0.00359 +Epoch [3992/4000] Training [7/39] Loss: 0.13002 +Epoch [3992/4000] Training [8/39] Loss: 0.00298 +Epoch [3992/4000] Training [9/39] Loss: 0.00718 +Epoch [3992/4000] Training [10/39] Loss: 0.13072 +Epoch [3992/4000] Training [11/39] Loss: 0.00379 +Epoch [3992/4000] Training [12/39] Loss: 0.25572 +Epoch [3992/4000] Training [13/39] Loss: 0.00569 +Epoch [3992/4000] Training [14/39] Loss: 0.00613 +Epoch [3992/4000] Training [15/39] Loss: 0.00506 +Epoch [3992/4000] Training [16/39] Loss: 0.00368 +Epoch [3992/4000] Training [17/39] Loss: 0.01085 +Epoch [3992/4000] Training [18/39] Loss: 0.12882 +Epoch [3992/4000] Training [19/39] Loss: 0.00455 +Epoch [3992/4000] Training [20/39] Loss: 0.00514 +Epoch [3992/4000] Training [21/39] Loss: 0.12832 +Epoch [3992/4000] Training [22/39] Loss: 0.00322 +Epoch [3992/4000] Training [23/39] Loss: 0.00444 +Epoch [3992/4000] Training [24/39] Loss: 0.00665 +Epoch [3992/4000] Training [25/39] Loss: 0.00624 +Epoch [3992/4000] Training [26/39] Loss: 0.00523 +Epoch [3992/4000] Training [27/39] Loss: 0.00228 +Epoch [3992/4000] Training [28/39] Loss: 0.00714 +Epoch [3992/4000] Training [29/39] Loss: 0.00376 +Epoch [3992/4000] Training [30/39] Loss: 0.12763 +Epoch [3992/4000] Training [31/39] Loss: 0.00681 +Epoch [3992/4000] Training [32/39] Loss: 0.00593 +Epoch [3992/4000] Training [33/39] Loss: 0.00356 +Epoch [3992/4000] Training [34/39] Loss: 0.00382 +Epoch [3992/4000] Training [35/39] Loss: 0.37773 +Epoch [3992/4000] Training [36/39] Loss: 0.00564 +Epoch [3992/4000] Training [37/39] Loss: 0.00626 +Epoch [3992/4000] Training [38/39] Loss: 0.21000 +Epoch [3992/4000] Training [39/39] Loss: 0.12809 +Epoch [3992/4000] Training metric {'Train/mean dice_metric': 0.9964674711227417, 'Train/mean miou_metric': 0.9933779239654541, 'Train/mean f1': 0.9969679117202759, 'Train/mean precision': 0.9965741634368896, 'Train/mean recall': 0.9973620176315308, 'Train/mean hd95_metric': 0.9204289317131042} +Epoch [3992/4000] Validation [1/10] Loss: 0.71639 focal_loss 0.63018 dice_loss 0.08621 +Epoch [3992/4000] Validation [2/10] Loss: 0.50723 focal_loss 0.40867 dice_loss 0.09857 +Epoch [3992/4000] Validation [3/10] Loss: 0.39506 focal_loss 0.28382 dice_loss 0.11124 +Epoch [3992/4000] Validation [4/10] Loss: 0.89877 focal_loss 0.33336 dice_loss 0.56541 +Epoch [3992/4000] Validation [5/10] Loss: 3.08500 focal_loss 2.41109 dice_loss 0.67392 +Epoch [3992/4000] Validation [6/10] Loss: 1.34487 focal_loss 0.63214 dice_loss 0.71273 +Epoch [3992/4000] Validation [7/10] Loss: 1.18339 focal_loss 0.52874 dice_loss 0.65465 +Epoch [3992/4000] Validation [8/10] Loss: 2.39663 focal_loss 1.77994 dice_loss 0.61669 +Epoch [3992/4000] Validation [9/10] Loss: 1.54020 focal_loss 0.99585 dice_loss 0.54435 +Epoch [3992/4000] Validation [10/10] Loss: 1.90656 focal_loss 1.17145 dice_loss 0.73511 +Epoch [3992/4000] Validation metric {'Val/mean dice_metric': 0.951445996761322, 'Val/mean miou_metric': 0.935634970664978, 'Val/mean f1': 0.9485483765602112, 'Val/mean precision': 0.943895161151886, 'Val/mean recall': 0.9532476663589478, 'Val/mean hd95_metric': 10.679703712463379} +Cheakpoint... +Epoch [3992/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951445996761322, 'Val/mean miou_metric': 0.935634970664978, 'Val/mean f1': 0.9485483765602112, 'Val/mean precision': 0.943895161151886, 'Val/mean recall': 0.9532476663589478, 'Val/mean hd95_metric': 10.679703712463379} +Epoch [3993/4000] Training [1/39] Loss: 0.00475 +Epoch [3993/4000] Training [2/39] Loss: 0.13102 +Epoch [3993/4000] Training [3/39] Loss: 0.00547 +Epoch [3993/4000] Training [4/39] Loss: 0.00534 +Epoch [3993/4000] Training [5/39] Loss: 0.00278 +Epoch [3993/4000] Training [6/39] Loss: 0.00497 +Epoch [3993/4000] Training [7/39] Loss: 0.00375 +Epoch [3993/4000] Training [8/39] Loss: 0.00500 +Epoch [3993/4000] Training [9/39] Loss: 0.00283 +Epoch [3993/4000] Training [10/39] Loss: 0.13145 +Epoch [3993/4000] Training [11/39] Loss: 0.00502 +Epoch [3993/4000] Training [12/39] Loss: 0.00671 +Epoch [3993/4000] Training [13/39] Loss: 0.00491 +Epoch [3993/4000] Training [14/39] Loss: 0.12814 +Epoch [3993/4000] Training [15/39] Loss: 0.12891 +Epoch [3993/4000] Training [16/39] Loss: 0.12966 +Epoch [3993/4000] Training [17/39] Loss: 0.00465 +Epoch [3993/4000] Training [18/39] Loss: 0.00424 +Epoch [3993/4000] Training [19/39] Loss: 0.00654 +Epoch [3993/4000] Training [20/39] Loss: 0.12748 +Epoch [3993/4000] Training [21/39] Loss: 0.00509 +Epoch [3993/4000] Training [22/39] Loss: 0.00417 +Epoch [3993/4000] Training [23/39] Loss: 0.00459 +Epoch [3993/4000] Training [24/39] Loss: 0.00593 +Epoch [3993/4000] Training [25/39] Loss: 0.00489 +Epoch [3993/4000] Training [26/39] Loss: 0.12729 +Epoch [3993/4000] Training [27/39] Loss: 0.13260 +Epoch [3993/4000] Training [28/39] Loss: 0.12833 +Epoch [3993/4000] Training [29/39] Loss: 0.12917 +Epoch [3993/4000] Training [30/39] Loss: 0.00417 +Epoch [3993/4000] Training [31/39] Loss: 0.12904 +Epoch [3993/4000] Training [32/39] Loss: 0.25387 +Epoch [3993/4000] Training [33/39] Loss: 0.01103 +Epoch [3993/4000] Training [34/39] Loss: 0.12939 +Epoch [3993/4000] Training [35/39] Loss: 0.00301 +Epoch [3993/4000] Training [36/39] Loss: 0.12853 +Epoch [3993/4000] Training [37/39] Loss: 0.00517 +Epoch [3993/4000] Training [38/39] Loss: 0.12896 +Epoch [3993/4000] Training [39/39] Loss: 0.00421 +Epoch [3993/4000] Training metric {'Train/mean dice_metric': 0.9961506128311157, 'Train/mean miou_metric': 0.9927492141723633, 'Train/mean f1': 0.99672532081604, 'Train/mean precision': 0.9961809515953064, 'Train/mean recall': 0.9972703456878662, 'Train/mean hd95_metric': 0.9392375946044922} +Epoch [3993/4000] Validation [1/10] Loss: 0.71957 focal_loss 0.63311 dice_loss 0.08646 +Epoch [3993/4000] Validation [2/10] Loss: 0.51159 focal_loss 0.41389 dice_loss 0.09770 +Epoch [3993/4000] Validation [3/10] Loss: 0.39145 focal_loss 0.28063 dice_loss 0.11082 +Epoch [3993/4000] Validation [4/10] Loss: 0.90679 focal_loss 0.34092 dice_loss 0.56586 +Epoch [3993/4000] Validation [5/10] Loss: 3.07812 focal_loss 2.40429 dice_loss 0.67383 +Epoch [3993/4000] Validation [6/10] Loss: 1.36133 focal_loss 0.64825 dice_loss 0.71308 +Epoch [3993/4000] Validation [7/10] Loss: 1.19713 focal_loss 0.54207 dice_loss 0.65506 +Epoch [3993/4000] Validation [8/10] Loss: 2.39147 focal_loss 1.77682 dice_loss 0.61464 +Epoch [3993/4000] Validation [9/10] Loss: 1.55538 focal_loss 1.01081 dice_loss 0.54457 +Epoch [3993/4000] Validation [10/10] Loss: 1.94063 focal_loss 1.20498 dice_loss 0.73565 +Epoch [3993/4000] Validation metric {'Val/mean dice_metric': 0.9511778950691223, 'Val/mean miou_metric': 0.9351009726524353, 'Val/mean f1': 0.948315441608429, 'Val/mean precision': 0.9431887269020081, 'Val/mean recall': 0.9534981846809387, 'Val/mean hd95_metric': 10.75287914276123} +Cheakpoint... +Epoch [3993/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511778950691223, 'Val/mean miou_metric': 0.9351009726524353, 'Val/mean f1': 0.948315441608429, 'Val/mean precision': 0.9431887269020081, 'Val/mean recall': 0.9534981846809387, 'Val/mean hd95_metric': 10.75287914276123} +Epoch [3994/4000] Training [1/39] Loss: 0.08914 +Epoch [3994/4000] Training [2/39] Loss: 0.00444 +Epoch [3994/4000] Training [3/39] Loss: 0.25448 +Epoch [3994/4000] Training [4/39] Loss: 0.00452 +Epoch [3994/4000] Training [5/39] Loss: 0.12750 +Epoch [3994/4000] Training [6/39] Loss: 0.12935 +Epoch [3994/4000] Training [7/39] Loss: 0.00459 +Epoch [3994/4000] Training [8/39] Loss: 0.00448 +Epoch [3994/4000] Training [9/39] Loss: 0.00503 +Epoch [3994/4000] Training [10/39] Loss: 0.00561 +Epoch [3994/4000] Training [11/39] Loss: 0.12780 +Epoch [3994/4000] Training [12/39] Loss: 0.00396 +Epoch [3994/4000] Training [13/39] Loss: 0.00517 +Epoch [3994/4000] Training [14/39] Loss: 0.00570 +Epoch [3994/4000] Training [15/39] Loss: 0.12916 +Epoch [3994/4000] Training [16/39] Loss: 0.00334 +Epoch [3994/4000] Training [17/39] Loss: 0.00517 +Epoch [3994/4000] Training [18/39] Loss: 0.00360 +Epoch [3994/4000] Training [19/39] Loss: 0.00352 +Epoch [3994/4000] Training [20/39] Loss: 0.13073 +Epoch [3994/4000] Training [21/39] Loss: 0.00531 +Epoch [3994/4000] Training [22/39] Loss: 0.00408 +Epoch [3994/4000] Training [23/39] Loss: 0.12911 +Epoch [3994/4000] Training [24/39] Loss: 0.12954 +Epoch [3994/4000] Training [25/39] Loss: 0.00350 +Epoch [3994/4000] Training [26/39] Loss: 0.12935 +Epoch [3994/4000] Training [27/39] Loss: 0.00573 +Epoch [3994/4000] Training [28/39] Loss: 0.00406 +Epoch [3994/4000] Training [29/39] Loss: 0.13010 +Epoch [3994/4000] Training [30/39] Loss: 0.12896 +Epoch [3994/4000] Training [31/39] Loss: 0.00532 +Epoch [3994/4000] Training [32/39] Loss: 0.12891 +Epoch [3994/4000] Training [33/39] Loss: 0.00258 +Epoch [3994/4000] Training [34/39] Loss: 0.00430 +Epoch [3994/4000] Training [35/39] Loss: 0.00389 +Epoch [3994/4000] Training [36/39] Loss: 0.00460 +Epoch [3994/4000] Training [37/39] Loss: 0.00272 +Epoch [3994/4000] Training [38/39] Loss: 0.00733 +Epoch [3994/4000] Training [39/39] Loss: 0.00416 +Epoch [3994/4000] Training metric {'Train/mean dice_metric': 0.9961121082305908, 'Train/mean miou_metric': 0.9930456280708313, 'Train/mean f1': 0.9967263340950012, 'Train/mean precision': 0.9958958625793457, 'Train/mean recall': 0.9975581169128418, 'Train/mean hd95_metric': 0.9498743414878845} +Epoch [3994/4000] Validation [1/10] Loss: 0.71635 focal_loss 0.62987 dice_loss 0.08648 +Epoch [3994/4000] Validation [2/10] Loss: 0.50549 focal_loss 0.40664 dice_loss 0.09885 +Epoch [3994/4000] Validation [3/10] Loss: 0.39675 focal_loss 0.28521 dice_loss 0.11155 +Epoch [3994/4000] Validation [4/10] Loss: 0.89707 focal_loss 0.33147 dice_loss 0.56560 +Epoch [3994/4000] Validation [5/10] Loss: 3.06668 focal_loss 2.39273 dice_loss 0.67395 +Epoch [3994/4000] Validation [6/10] Loss: 1.33790 focal_loss 0.62544 dice_loss 0.71246 +Epoch [3994/4000] Validation [7/10] Loss: 1.17696 focal_loss 0.52243 dice_loss 0.65453 +Epoch [3994/4000] Validation [8/10] Loss: 2.39177 focal_loss 1.77424 dice_loss 0.61753 +Epoch [3994/4000] Validation [9/10] Loss: 1.53480 focal_loss 0.99060 dice_loss 0.54420 +Epoch [3994/4000] Validation [10/10] Loss: 1.89613 focal_loss 1.16103 dice_loss 0.73510 +Epoch [3994/4000] Validation metric {'Val/mean dice_metric': 0.9511771202087402, 'Val/mean miou_metric': 0.9353905320167542, 'Val/mean f1': 0.9480502009391785, 'Val/mean precision': 0.9431713819503784, 'Val/mean recall': 0.9529798030853271, 'Val/mean hd95_metric': 10.720407485961914} +Cheakpoint... +Epoch [3994/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9512], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9511771202087402, 'Val/mean miou_metric': 0.9353905320167542, 'Val/mean f1': 0.9480502009391785, 'Val/mean precision': 0.9431713819503784, 'Val/mean recall': 0.9529798030853271, 'Val/mean hd95_metric': 10.720407485961914} +Epoch [3995/4000] Training [1/39] Loss: 0.13110 +Epoch [3995/4000] Training [2/39] Loss: 0.00566 +Epoch [3995/4000] Training [3/39] Loss: 0.00401 +Epoch [3995/4000] Training [4/39] Loss: 0.00387 +Epoch [3995/4000] Training [5/39] Loss: 0.25436 +Epoch [3995/4000] Training [6/39] Loss: 0.00345 +Epoch [3995/4000] Training [7/39] Loss: 0.00495 +Epoch [3995/4000] Training [8/39] Loss: 0.00438 +Epoch [3995/4000] Training [9/39] Loss: 0.12794 +Epoch [3995/4000] Training [10/39] Loss: 0.00448 +Epoch [3995/4000] Training [11/39] Loss: 0.12813 +Epoch [3995/4000] Training [12/39] Loss: 0.00377 +Epoch [3995/4000] Training [13/39] Loss: 0.00542 +Epoch [3995/4000] Training [14/39] Loss: 0.00493 +Epoch [3995/4000] Training [15/39] Loss: 0.12792 +Epoch [3995/4000] Training [16/39] Loss: 0.00506 +Epoch [3995/4000] Training [17/39] Loss: 0.00355 +Epoch [3995/4000] Training [18/39] Loss: 0.00702 +Epoch [3995/4000] Training [19/39] Loss: 0.13045 +Epoch [3995/4000] Training [20/39] Loss: 0.13190 +Epoch [3995/4000] Training [21/39] Loss: 0.12864 +Epoch [3995/4000] Training [22/39] Loss: 0.00465 +Epoch [3995/4000] Training [23/39] Loss: 0.00513 +Epoch [3995/4000] Training [24/39] Loss: 0.12818 +Epoch [3995/4000] Training [25/39] Loss: 0.12818 +Epoch [3995/4000] Training [26/39] Loss: 0.00845 +Epoch [3995/4000] Training [27/39] Loss: 0.00482 +Epoch [3995/4000] Training [28/39] Loss: 0.00371 +Epoch [3995/4000] Training [29/39] Loss: 0.00614 +Epoch [3995/4000] Training [30/39] Loss: 0.12737 +Epoch [3995/4000] Training [31/39] Loss: 0.00312 +Epoch [3995/4000] Training [32/39] Loss: 0.00595 +Epoch [3995/4000] Training [33/39] Loss: 0.00369 +Epoch [3995/4000] Training [34/39] Loss: 0.00583 +Epoch [3995/4000] Training [35/39] Loss: 0.00332 +Epoch [3995/4000] Training [36/39] Loss: 0.00388 +Epoch [3995/4000] Training [37/39] Loss: 0.12995 +Epoch [3995/4000] Training [38/39] Loss: 0.00646 +Epoch [3995/4000] Training [39/39] Loss: 0.00368 +Epoch [3995/4000] Training metric {'Train/mean dice_metric': 0.9965876936912537, 'Train/mean miou_metric': 0.9936205744743347, 'Train/mean f1': 0.9970299601554871, 'Train/mean precision': 0.996593177318573, 'Train/mean recall': 0.9974671602249146, 'Train/mean hd95_metric': 0.9941830635070801} +Epoch [3995/4000] Validation [1/10] Loss: 0.72072 focal_loss 0.63447 dice_loss 0.08625 +Epoch [3995/4000] Validation [2/10] Loss: 0.50882 focal_loss 0.40887 dice_loss 0.09995 +Epoch [3995/4000] Validation [3/10] Loss: 0.40542 focal_loss 0.29341 dice_loss 0.11201 +Epoch [3995/4000] Validation [4/10] Loss: 0.89479 focal_loss 0.32968 dice_loss 0.56511 +Epoch [3995/4000] Validation [5/10] Loss: 3.10070 focal_loss 2.42664 dice_loss 0.67406 +Epoch [3995/4000] Validation [6/10] Loss: 1.33270 focal_loss 0.62021 dice_loss 0.71249 +Epoch [3995/4000] Validation [7/10] Loss: 1.17519 focal_loss 0.52179 dice_loss 0.65341 +Epoch [3995/4000] Validation [8/10] Loss: 2.42409 focal_loss 1.80382 dice_loss 0.62027 +Epoch [3995/4000] Validation [9/10] Loss: 1.53725 focal_loss 0.99340 dice_loss 0.54385 +Epoch [3995/4000] Validation [10/10] Loss: 1.88684 focal_loss 1.15243 dice_loss 0.73441 +Epoch [3995/4000] Validation metric {'Val/mean dice_metric': 0.951538622379303, 'Val/mean miou_metric': 0.9358341693878174, 'Val/mean f1': 0.9486770033836365, 'Val/mean precision': 0.9445986747741699, 'Val/mean recall': 0.9527905583381653, 'Val/mean hd95_metric': 10.896280288696289} +Cheakpoint... +Epoch [3995/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.951538622379303, 'Val/mean miou_metric': 0.9358341693878174, 'Val/mean f1': 0.9486770033836365, 'Val/mean precision': 0.9445986747741699, 'Val/mean recall': 0.9527905583381653, 'Val/mean hd95_metric': 10.896280288696289} +Epoch [3996/4000] Training [1/39] Loss: 0.12801 +Epoch [3996/4000] Training [2/39] Loss: 0.00789 +Epoch [3996/4000] Training [3/39] Loss: 0.00533 +Epoch [3996/4000] Training [4/39] Loss: 0.12996 +Epoch [3996/4000] Training [5/39] Loss: 0.00399 +Epoch [3996/4000] Training [6/39] Loss: 0.00373 +Epoch [3996/4000] Training [7/39] Loss: 0.04125 +Epoch [3996/4000] Training [8/39] Loss: 0.00626 +Epoch [3996/4000] Training [9/39] Loss: 0.00547 +Epoch [3996/4000] Training [10/39] Loss: 0.12891 +Epoch [3996/4000] Training [11/39] Loss: 0.00410 +Epoch [3996/4000] Training [12/39] Loss: 0.00410 +Epoch [3996/4000] Training [13/39] Loss: 0.00915 +Epoch [3996/4000] Training [14/39] Loss: 0.00535 +Epoch [3996/4000] Training [15/39] Loss: 0.13026 +Epoch [3996/4000] Training [16/39] Loss: 0.00374 +Epoch [3996/4000] Training [17/39] Loss: 0.12820 +Epoch [3996/4000] Training [18/39] Loss: 0.12954 +Epoch [3996/4000] Training [19/39] Loss: 0.00742 +Epoch [3996/4000] Training [20/39] Loss: 0.25485 +Epoch [3996/4000] Training [21/39] Loss: 0.00678 +Epoch [3996/4000] Training [22/39] Loss: 0.00391 +Epoch [3996/4000] Training [23/39] Loss: 0.00332 +Epoch [3996/4000] Training [24/39] Loss: 0.00474 +Epoch [3996/4000] Training [25/39] Loss: 0.00364 +Epoch [3996/4000] Training [26/39] Loss: 0.12934 +Epoch [3996/4000] Training [27/39] Loss: 0.00370 +Epoch [3996/4000] Training [28/39] Loss: 0.00514 +Epoch [3996/4000] Training [29/39] Loss: 0.00292 +Epoch [3996/4000] Training [30/39] Loss: 0.00615 +Epoch [3996/4000] Training [31/39] Loss: 0.00526 +Epoch [3996/4000] Training [32/39] Loss: 0.00435 +Epoch [3996/4000] Training [33/39] Loss: 0.12909 +Epoch [3996/4000] Training [34/39] Loss: 0.12723 +Epoch [3996/4000] Training [35/39] Loss: 0.12787 +Epoch [3996/4000] Training [36/39] Loss: 0.00448 +Epoch [3996/4000] Training [37/39] Loss: 0.37799 +Epoch [3996/4000] Training [38/39] Loss: 0.00378 +Epoch [3996/4000] Training [39/39] Loss: 0.12875 +Epoch [3996/4000] Training metric {'Train/mean dice_metric': 0.996570348739624, 'Train/mean miou_metric': 0.9935848712921143, 'Train/mean f1': 0.9970390796661377, 'Train/mean precision': 0.9966249465942383, 'Train/mean recall': 0.9974536299705505, 'Train/mean hd95_metric': 0.895564615726471} +Epoch [3996/4000] Validation [1/10] Loss: 0.71762 focal_loss 0.63176 dice_loss 0.08586 +Epoch [3996/4000] Validation [2/10] Loss: 0.51518 focal_loss 0.41497 dice_loss 0.10021 +Epoch [3996/4000] Validation [3/10] Loss: 0.40235 focal_loss 0.29059 dice_loss 0.11176 +Epoch [3996/4000] Validation [4/10] Loss: 0.89947 focal_loss 0.33472 dice_loss 0.56475 +Epoch [3996/4000] Validation [5/10] Loss: 3.09310 focal_loss 2.41908 dice_loss 0.67402 +Epoch [3996/4000] Validation [6/10] Loss: 1.34428 focal_loss 0.63196 dice_loss 0.71231 +Epoch [3996/4000] Validation [7/10] Loss: 1.18489 focal_loss 0.53171 dice_loss 0.65318 +Epoch [3996/4000] Validation [8/10] Loss: 2.43433 focal_loss 1.81427 dice_loss 0.62007 +Epoch [3996/4000] Validation [9/10] Loss: 1.54306 focal_loss 0.99902 dice_loss 0.54404 +Epoch [3996/4000] Validation [10/10] Loss: 1.90762 focal_loss 1.17310 dice_loss 0.73452 +Epoch [3996/4000] Validation metric {'Val/mean dice_metric': 0.9515618085861206, 'Val/mean miou_metric': 0.9358590841293335, 'Val/mean f1': 0.9484784007072449, 'Val/mean precision': 0.9442960023880005, 'Val/mean recall': 0.9526981115341187, 'Val/mean hd95_metric': 10.747416496276855} +Cheakpoint... +Epoch [3996/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9516], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515618085861206, 'Val/mean miou_metric': 0.9358590841293335, 'Val/mean f1': 0.9484784007072449, 'Val/mean precision': 0.9442960023880005, 'Val/mean recall': 0.9526981115341187, 'Val/mean hd95_metric': 10.747416496276855} +Epoch [3997/4000] Training [1/39] Loss: 0.00546 +Epoch [3997/4000] Training [2/39] Loss: 0.00429 +Epoch [3997/4000] Training [3/39] Loss: 0.12861 +Epoch [3997/4000] Training [4/39] Loss: 0.12967 +Epoch [3997/4000] Training [5/39] Loss: 0.12951 +Epoch [3997/4000] Training [6/39] Loss: 0.12943 +Epoch [3997/4000] Training [7/39] Loss: 0.00443 +Epoch [3997/4000] Training [8/39] Loss: 0.00272 +Epoch [3997/4000] Training [9/39] Loss: 0.25403 +Epoch [3997/4000] Training [10/39] Loss: 0.00538 +Epoch [3997/4000] Training [11/39] Loss: 0.00462 +Epoch [3997/4000] Training [12/39] Loss: 0.00460 +Epoch [3997/4000] Training [13/39] Loss: 0.00861 +Epoch [3997/4000] Training [14/39] Loss: 0.00250 +Epoch [3997/4000] Training [15/39] Loss: 0.00626 +Epoch [3997/4000] Training [16/39] Loss: 0.00460 +Epoch [3997/4000] Training [17/39] Loss: 0.00385 +Epoch [3997/4000] Training [18/39] Loss: 0.00620 +Epoch [3997/4000] Training [19/39] Loss: 0.00532 +Epoch [3997/4000] Training [20/39] Loss: 0.00392 +Epoch [3997/4000] Training [21/39] Loss: 0.00463 +Epoch [3997/4000] Training [22/39] Loss: 0.25288 +Epoch [3997/4000] Training [23/39] Loss: 0.13000 +Epoch [3997/4000] Training [24/39] Loss: 0.00530 +Epoch [3997/4000] Training [25/39] Loss: 0.00619 +Epoch [3997/4000] Training [26/39] Loss: 0.00324 +Epoch [3997/4000] Training [27/39] Loss: 0.00434 +Epoch [3997/4000] Training [28/39] Loss: 0.12928 +Epoch [3997/4000] Training [29/39] Loss: 0.00417 +Epoch [3997/4000] Training [30/39] Loss: 0.00681 +Epoch [3997/4000] Training [31/39] Loss: 0.12811 +Epoch [3997/4000] Training [32/39] Loss: 0.00542 +Epoch [3997/4000] Training [33/39] Loss: 0.00718 +Epoch [3997/4000] Training [34/39] Loss: 0.00233 +Epoch [3997/4000] Training [35/39] Loss: 0.00446 +Epoch [3997/4000] Training [36/39] Loss: 0.00507 +Epoch [3997/4000] Training [37/39] Loss: 0.00690 +Epoch [3997/4000] Training [38/39] Loss: 0.00374 +Epoch [3997/4000] Training [39/39] Loss: 0.00328 +Epoch [3997/4000] Training metric {'Train/mean dice_metric': 0.9963251948356628, 'Train/mean miou_metric': 0.99311363697052, 'Train/mean f1': 0.9969838857650757, 'Train/mean precision': 0.9965196847915649, 'Train/mean recall': 0.9974485039710999, 'Train/mean hd95_metric': 0.9212594628334045} +Epoch [3997/4000] Validation [1/10] Loss: 0.70795 focal_loss 0.62197 dice_loss 0.08598 +Epoch [3997/4000] Validation [2/10] Loss: 0.50645 focal_loss 0.40687 dice_loss 0.09958 +Epoch [3997/4000] Validation [3/10] Loss: 0.39439 focal_loss 0.28283 dice_loss 0.11156 +Epoch [3997/4000] Validation [4/10] Loss: 0.89412 focal_loss 0.32900 dice_loss 0.56512 +Epoch [3997/4000] Validation [5/10] Loss: 3.05452 focal_loss 2.38052 dice_loss 0.67400 +Epoch [3997/4000] Validation [6/10] Loss: 1.33665 focal_loss 0.62376 dice_loss 0.71288 +Epoch [3997/4000] Validation [7/10] Loss: 1.17627 focal_loss 0.52307 dice_loss 0.65320 +Epoch [3997/4000] Validation [8/10] Loss: 2.39637 focal_loss 1.77785 dice_loss 0.61852 +Epoch [3997/4000] Validation [9/10] Loss: 1.52779 focal_loss 0.98358 dice_loss 0.54421 +Epoch [3997/4000] Validation [10/10] Loss: 1.89216 focal_loss 1.15732 dice_loss 0.73484 +Epoch [3997/4000] Validation metric {'Val/mean dice_metric': 0.9513711333274841, 'Val/mean miou_metric': 0.9354825615882874, 'Val/mean f1': 0.948322057723999, 'Val/mean precision': 0.9438548684120178, 'Val/mean recall': 0.9528318047523499, 'Val/mean hd95_metric': 10.760894775390625} +Cheakpoint... +Epoch [3997/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513711333274841, 'Val/mean miou_metric': 0.9354825615882874, 'Val/mean f1': 0.948322057723999, 'Val/mean precision': 0.9438548684120178, 'Val/mean recall': 0.9528318047523499, 'Val/mean hd95_metric': 10.760894775390625} +Epoch [3998/4000] Training [1/39] Loss: 0.12892 +Epoch [3998/4000] Training [2/39] Loss: 0.00367 +Epoch [3998/4000] Training [3/39] Loss: 0.00498 +Epoch [3998/4000] Training [4/39] Loss: 0.00402 +Epoch [3998/4000] Training [5/39] Loss: 0.00712 +Epoch [3998/4000] Training [6/39] Loss: 0.00468 +Epoch [3998/4000] Training [7/39] Loss: 0.12861 +Epoch [3998/4000] Training [8/39] Loss: 0.12844 +Epoch [3998/4000] Training [9/39] Loss: 0.00830 +Epoch [3998/4000] Training [10/39] Loss: 0.00430 +Epoch [3998/4000] Training [11/39] Loss: 0.00226 +Epoch [3998/4000] Training [12/39] Loss: 0.12776 +Epoch [3998/4000] Training [13/39] Loss: 0.13223 +Epoch [3998/4000] Training [14/39] Loss: 0.00281 +Epoch [3998/4000] Training [15/39] Loss: 0.00704 +Epoch [3998/4000] Training [16/39] Loss: 0.00630 +Epoch [3998/4000] Training [17/39] Loss: 0.00325 +Epoch [3998/4000] Training [18/39] Loss: 0.13283 +Epoch [3998/4000] Training [19/39] Loss: 0.12790 +Epoch [3998/4000] Training [20/39] Loss: 0.00508 +Epoch [3998/4000] Training [21/39] Loss: 0.00269 +Epoch [3998/4000] Training [22/39] Loss: 0.00513 +Epoch [3998/4000] Training [23/39] Loss: 0.00358 +Epoch [3998/4000] Training [24/39] Loss: 0.00602 +Epoch [3998/4000] Training [25/39] Loss: 0.00587 +Epoch [3998/4000] Training [26/39] Loss: 0.00510 +Epoch [3998/4000] Training [27/39] Loss: 0.00496 +Epoch [3998/4000] Training [28/39] Loss: 0.00269 +Epoch [3998/4000] Training [29/39] Loss: 0.00660 +Epoch [3998/4000] Training [30/39] Loss: 0.00820 +Epoch [3998/4000] Training [31/39] Loss: 0.12906 +Epoch [3998/4000] Training [32/39] Loss: 0.12940 +Epoch [3998/4000] Training [33/39] Loss: 0.12960 +Epoch [3998/4000] Training [34/39] Loss: 0.00558 +Epoch [3998/4000] Training [35/39] Loss: 0.00569 +Epoch [3998/4000] Training [36/39] Loss: 0.00427 +Epoch [3998/4000] Training [37/39] Loss: 0.12951 +Epoch [3998/4000] Training [38/39] Loss: 0.00521 +Epoch [3998/4000] Training [39/39] Loss: 0.00383 +Epoch [3998/4000] Training metric {'Train/mean dice_metric': 0.9963563680648804, 'Train/mean miou_metric': 0.9931626319885254, 'Train/mean f1': 0.9968892931938171, 'Train/mean precision': 0.9964853525161743, 'Train/mean recall': 0.9972937703132629, 'Train/mean hd95_metric': 1.003840446472168} +Epoch [3998/4000] Validation [1/10] Loss: 0.71638 focal_loss 0.62974 dice_loss 0.08664 +Epoch [3998/4000] Validation [2/10] Loss: 0.50323 focal_loss 0.40549 dice_loss 0.09774 +Epoch [3998/4000] Validation [3/10] Loss: 0.39124 focal_loss 0.28022 dice_loss 0.11102 +Epoch [3998/4000] Validation [4/10] Loss: 0.89860 focal_loss 0.33285 dice_loss 0.56575 +Epoch [3998/4000] Validation [5/10] Loss: 3.07188 focal_loss 2.39797 dice_loss 0.67391 +Epoch [3998/4000] Validation [6/10] Loss: 1.34435 focal_loss 0.63148 dice_loss 0.71286 +Epoch [3998/4000] Validation [7/10] Loss: 1.18217 focal_loss 0.52725 dice_loss 0.65491 +Epoch [3998/4000] Validation [8/10] Loss: 2.36973 focal_loss 1.75542 dice_loss 0.61432 +Epoch [3998/4000] Validation [9/10] Loss: 1.53679 focal_loss 0.99250 dice_loss 0.54429 +Epoch [3998/4000] Validation [10/10] Loss: 1.90798 focal_loss 1.17241 dice_loss 0.73557 +Epoch [3998/4000] Validation metric {'Val/mean dice_metric': 0.9513578414916992, 'Val/mean miou_metric': 0.9354561567306519, 'Val/mean f1': 0.9481204152107239, 'Val/mean precision': 0.94313645362854, 'Val/mean recall': 0.9531571269035339, 'Val/mean hd95_metric': 10.740544319152832} +Cheakpoint... +Epoch [3998/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9514], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9513578414916992, 'Val/mean miou_metric': 0.9354561567306519, 'Val/mean f1': 0.9481204152107239, 'Val/mean precision': 0.94313645362854, 'Val/mean recall': 0.9531571269035339, 'Val/mean hd95_metric': 10.740544319152832} +Epoch [3999/4000] Training [1/39] Loss: 0.12905 +Epoch [3999/4000] Training [2/39] Loss: 0.00531 +Epoch [3999/4000] Training [3/39] Loss: 0.00503 +Epoch [3999/4000] Training [4/39] Loss: 0.04546 +Epoch [3999/4000] Training [5/39] Loss: 0.00319 +Epoch [3999/4000] Training [6/39] Loss: 0.00564 +Epoch [3999/4000] Training [7/39] Loss: 0.00636 +Epoch [3999/4000] Training [8/39] Loss: 0.00526 +Epoch [3999/4000] Training [9/39] Loss: 0.00312 +Epoch [3999/4000] Training [10/39] Loss: 0.13089 +Epoch [3999/4000] Training [11/39] Loss: 0.00384 +Epoch [3999/4000] Training [12/39] Loss: 0.00495 +Epoch [3999/4000] Training [13/39] Loss: 0.12885 +Epoch [3999/4000] Training [14/39] Loss: 0.00418 +Epoch [3999/4000] Training [15/39] Loss: 0.00561 +Epoch [3999/4000] Training [16/39] Loss: 0.12936 +Epoch [3999/4000] Training [17/39] Loss: 0.00494 +Epoch [3999/4000] Training [18/39] Loss: 0.12863 +Epoch [3999/4000] Training [19/39] Loss: 0.00305 +Epoch [3999/4000] Training [20/39] Loss: 0.00493 +Epoch [3999/4000] Training [21/39] Loss: 0.00494 +Epoch [3999/4000] Training [22/39] Loss: 0.00486 +Epoch [3999/4000] Training [23/39] Loss: 0.12957 +Epoch [3999/4000] Training [24/39] Loss: 0.00398 +Epoch [3999/4000] Training [25/39] Loss: 0.00542 +Epoch [3999/4000] Training [26/39] Loss: 0.00585 +Epoch [3999/4000] Training [27/39] Loss: 0.12917 +Epoch [3999/4000] Training [28/39] Loss: 0.12869 +Epoch [3999/4000] Training [29/39] Loss: 0.00475 +Epoch [3999/4000] Training [30/39] Loss: 0.00417 +Epoch [3999/4000] Training [31/39] Loss: 0.00394 +Epoch [3999/4000] Training [32/39] Loss: 0.25311 +Epoch [3999/4000] Training [33/39] Loss: 0.00407 +Epoch [3999/4000] Training [34/39] Loss: 0.00368 +Epoch [3999/4000] Training [35/39] Loss: 0.00600 +Epoch [3999/4000] Training [36/39] Loss: 0.00559 +Epoch [3999/4000] Training [37/39] Loss: 0.00263 +Epoch [3999/4000] Training [38/39] Loss: 0.00394 +Epoch [3999/4000] Training [39/39] Loss: 0.00357 +Epoch [3999/4000] Training metric {'Train/mean dice_metric': 0.9965664148330688, 'Train/mean miou_metric': 0.9935750365257263, 'Train/mean f1': 0.9970805644989014, 'Train/mean precision': 0.9966333508491516, 'Train/mean recall': 0.9975281357765198, 'Train/mean hd95_metric': 0.905732274055481} +Epoch [3999/4000] Validation [1/10] Loss: 0.70298 focal_loss 0.61732 dice_loss 0.08567 +Epoch [3999/4000] Validation [2/10] Loss: 0.50895 focal_loss 0.40824 dice_loss 0.10071 +Epoch [3999/4000] Validation [3/10] Loss: 0.39763 focal_loss 0.28561 dice_loss 0.11201 +Epoch [3999/4000] Validation [4/10] Loss: 0.89370 focal_loss 0.32865 dice_loss 0.56505 +Epoch [3999/4000] Validation [5/10] Loss: 3.04198 focal_loss 2.36795 dice_loss 0.67403 +Epoch [3999/4000] Validation [6/10] Loss: 1.33185 focal_loss 0.61932 dice_loss 0.71253 +Epoch [3999/4000] Validation [7/10] Loss: 1.17254 focal_loss 0.51977 dice_loss 0.65277 +Epoch [3999/4000] Validation [8/10] Loss: 2.39947 focal_loss 1.77845 dice_loss 0.62102 +Epoch [3999/4000] Validation [9/10] Loss: 1.52222 focal_loss 0.97806 dice_loss 0.54416 +Epoch [3999/4000] Validation [10/10] Loss: 1.87996 focal_loss 1.14556 dice_loss 0.73441 +Epoch [3999/4000] Validation metric {'Val/mean dice_metric': 0.9515435099601746, 'Val/mean miou_metric': 0.9358320832252502, 'Val/mean f1': 0.9486624598503113, 'Val/mean precision': 0.9445801973342896, 'Val/mean recall': 0.9527801275253296, 'Val/mean hd95_metric': 10.76945686340332} +Cheakpoint... +Epoch [3999/4000] best acc:tensor([0.9518], device='cuda:0'), Now : mean acc: tensor([0.9515], device='cuda:0'), mean class: {'Val/mean dice_metric': 0.9515435099601746, 'Val/mean miou_metric': 0.9358320832252502, 'Val/mean f1': 0.9486624598503113, 'Val/mean precision': 0.9445801973342896, 'Val/mean recall': 0.9527801275253296, 'Val/mean hd95_metric': 10.76945686340332} +Epoch [4000/4000] Training [1/39] Loss: 0.00660 +Epoch [4000/4000] Training [2/39] Loss: 0.00497 +Epoch [4000/4000] Training [3/39] Loss: 0.00526 +Epoch [4000/4000] Training [4/39] Loss: 0.12930 +Epoch [4000/4000] Training [5/39] Loss: 0.00350 +Epoch [4000/4000] Training [6/39] Loss: 0.00921 +Epoch [4000/4000] Training [7/39] Loss: 0.00414 +Epoch [4000/4000] Training [8/39] Loss: 0.00756 +Epoch [4000/4000] Training [9/39] Loss: 0.00549 +Epoch [4000/4000] Training [10/39] Loss: 0.00311 +Epoch [4000/4000] Training [11/39] Loss: 0.12829 +Epoch [4000/4000] Training [12/39] Loss: 0.00502 +Epoch [4000/4000] Training [13/39] Loss: 0.12877 +Epoch [4000/4000] Training [14/39] Loss: 0.25261 +Epoch [4000/4000] Training [15/39] Loss: 0.00462 +Epoch [4000/4000] Training [16/39] Loss: 0.00798 +Epoch [4000/4000] Training [17/39] Loss: 0.12910 +Epoch [4000/4000] Training [18/39] Loss: 0.00760 +Epoch [4000/4000] Training [19/39] Loss: 0.00378 +Epoch [4000/4000] Training [20/39] Loss: 0.00436 +Epoch [4000/4000] Training [21/39] Loss: 0.00412 +Epoch [4000/4000] Training [22/39] Loss: 0.00616 +Epoch [4000/4000] Training [23/39] Loss: 0.12808 +Epoch [4000/4000] Training [24/39] Loss: 0.00486 +Epoch [4000/4000] Training [25/39] Loss: 0.00498 +Epoch [4000/4000] Training [26/39] Loss: 0.00426 +Epoch [4000/4000] Training [27/39] Loss: 0.00461 +Epoch [4000/4000] Training [28/39] Loss: 0.00537 +Epoch [4000/4000] Training [29/39] Loss: 0.00576 +Epoch [4000/4000] Training [30/39] Loss: 0.00475 +Epoch [4000/4000] Training [31/39] Loss: 0.00378 +Epoch [4000/4000] Training [32/39] Loss: 0.00504 +Epoch [4000/4000] Training [33/39] Loss: 0.00520 +Epoch [4000/4000] Training [34/39] Loss: 0.00397 +Epoch [4000/4000] Training [35/39] Loss: 0.00611 +Epoch [4000/4000] Training [36/39] Loss: 0.00381 +Epoch [4000/4000] Training [37/39] Loss: 0.00401 +Epoch [4000/4000] Training [38/39] Loss: 0.12805 +Epoch [4000/4000] Training [39/39] Loss: 0.12918 +Epoch [4000/4000] Training metric {'Train/mean dice_metric': 0.9962573051452637, 'Train/mean miou_metric': 0.9929651021957397, 'Train/mean f1': 0.9967523813247681, 'Train/mean precision': 0.9963333010673523, 'Train/mean recall': 0.9971717000007629, 'Train/mean hd95_metric': 0.9439378380775452} +Epoch [4000/4000] Validation [1/10] Loss: 0.72153 focal_loss 0.63524 dice_loss 0.08630 +Epoch [4000/4000] Validation [2/10] Loss: 0.50695 focal_loss 0.40719 dice_loss 0.09976 +Epoch [4000/4000] Validation [3/10] Loss: 0.40386 focal_loss 0.29196 dice_loss 0.11190 +Epoch [4000/4000] Validation [4/10] Loss: 0.89278 focal_loss 0.32754 dice_loss 0.56524 +Epoch [4000/4000] Validation [5/10] Loss: 3.09615 focal_loss 2.42213 dice_loss 0.67402 +Epoch [4000/4000] Validation [6/10] Loss: 1.33045 focal_loss 0.61812 dice_loss 0.71233 +Epoch [4000/4000] Validation [7/10] Loss: 1.17315 focal_loss 0.51937 dice_loss 0.65378 +Epoch [4000/4000] Validation [8/10] Loss: 2.40706 focal_loss 1.78836 dice_loss 0.61869 +Epoch [4000/4000] Validation [9/10] Loss: 1.53197 focal_loss 0.98810 dice_loss 0.54388 +Epoch [4000/4000] Validation [10/10] Loss: 1.88366 focal_loss 1.14904 dice_loss 0.73462