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-
-#######################################################################
-Please cite the following paper when using nnU-Net:
-Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
-#######################################################################
- 
-
-This is the configuration used by this training:
-Configuration name: 3d_fullres
- {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} 
- 
-These are the global plan.json settings:
- {'dataset_name': 'Dataset722_TSPrimeCTVN', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1399.0, 'mean': -13.421175003051758, 'median': -38.0, 'min': -956.0, 'percentile_00_5': -119.0, 'percentile_99_5': 213.0, 'std': 80.46653747558594}}} 
- 
-2023-11-01 18:43:13.253533: unpacking dataset... 
-2023-11-01 18:43:20.560333: unpacking done... 
-2023-11-01 18:43:20.561652: do_dummy_2d_data_aug: False 
-2023-11-01 18:43:20.562053: Creating new 5-fold cross-validation split... 
-2023-11-01 18:43:20.562627: Desired fold for training: 0 
-2023-11-01 18:43:20.562653: This split has 48 training and 12 validation cases. 
-2023-11-01 18:43:20.565820: Unable to plot network architecture: 
-2023-11-01 18:43:20.565846: No module named 'hiddenlayer' 
-2023-11-01 18:43:20.568311:  
-2023-11-01 18:43:20.568338: Epoch 0 
-2023-11-01 18:43:20.568374: Current learning rate: 0.01 
-2023-11-01 18:45:18.034654: train_loss -0.216 
-2023-11-01 18:45:18.034859: val_loss -0.5438 
-2023-11-01 18:45:18.034900: Pseudo dice [0.6387] 
-2023-11-01 18:45:18.034937: Epoch time: 117.47 s 
-2023-11-01 18:45:18.034971: Yayy! New best EMA pseudo Dice: 0.6387 
-2023-11-01 18:45:18.744715:  
-2023-11-01 18:45:18.744777: Epoch 1 
-2023-11-01 18:45:18.744830: Current learning rate: 0.00999 
-2023-11-01 18:47:08.230496: train_loss -0.5977 
-2023-11-01 18:47:08.230624: val_loss -0.6399 
-2023-11-01 18:47:08.230675: Pseudo dice [0.7106] 
-2023-11-01 18:47:08.230702: Epoch time: 109.49 s 
-2023-11-01 18:47:08.230721: Yayy! New best EMA pseudo Dice: 0.6459 
-2023-11-01 18:47:08.960224:  
-2023-11-01 18:47:08.960295: Epoch 2 
-2023-11-01 18:47:08.960373: Current learning rate: 0.00998 
-2023-11-01 18:48:57.019761: train_loss -0.6433 
-2023-11-01 18:48:57.019889: val_loss -0.6502 
-2023-11-01 18:48:57.019948: Pseudo dice [0.7171] 
-2023-11-01 18:48:57.019976: Epoch time: 108.06 s 
-2023-11-01 18:48:57.019996: Yayy! New best EMA pseudo Dice: 0.653 
-2023-11-01 18:48:57.784118:  
-2023-11-01 18:48:57.784189: Epoch 3 
-2023-11-01 18:48:57.784245: Current learning rate: 0.00997 
-2023-11-01 18:50:47.001720: train_loss -0.69 
-2023-11-01 18:50:47.001870: val_loss -0.7104 
-2023-11-01 18:50:47.001898: Pseudo dice [0.761] 
-2023-11-01 18:50:47.001925: Epoch time: 109.22 s 
-2023-11-01 18:50:47.001943: Yayy! New best EMA pseudo Dice: 0.6638 
-2023-11-01 18:50:47.744853:  
-2023-11-01 18:50:47.744927: Epoch 4 
-2023-11-01 18:50:47.744980: Current learning rate: 0.00996 
-2023-11-01 18:52:37.109966: train_loss -0.7091 
-2023-11-01 18:52:37.110098: val_loss -0.7068 
-2023-11-01 18:52:37.110127: Pseudo dice [0.7594] 
-2023-11-01 18:52:37.110155: Epoch time: 109.37 s 
-2023-11-01 18:52:37.110179: Yayy! New best EMA pseudo Dice: 0.6733 
-2023-11-01 18:52:37.859092:  
-2023-11-01 18:52:37.859156: Epoch 5 
-2023-11-01 18:52:37.859234: Current learning rate: 0.00995 
-2023-11-01 18:54:26.754580: train_loss -0.717 
-2023-11-01 18:54:26.754704: val_loss -0.7193 
-2023-11-01 18:54:26.754762: Pseudo dice [0.7768] 
-2023-11-01 18:54:26.754792: Epoch time: 108.9 s 
-2023-11-01 18:54:26.754814: Yayy! New best EMA pseudo Dice: 0.6837 
-2023-11-01 18:54:27.583947:  
-2023-11-01 18:54:27.584052: Epoch 6 
-2023-11-01 18:54:27.584107: Current learning rate: 0.00995 
-2023-11-01 18:56:16.939491: train_loss -0.7312 
-2023-11-01 18:56:16.939623: val_loss -0.7384 
-2023-11-01 18:56:16.939672: Pseudo dice [0.7884] 
-2023-11-01 18:56:16.939698: Epoch time: 109.36 s 
-2023-11-01 18:56:16.939716: Yayy! New best EMA pseudo Dice: 0.6942 
-2023-11-01 18:56:17.690640:  
-2023-11-01 18:56:17.690716: Epoch 7 
-2023-11-01 18:56:17.690787: Current learning rate: 0.00994 
-2023-11-01 18:58:05.257473: train_loss -0.7412 
-2023-11-01 18:58:05.257619: val_loss -0.7438 
-2023-11-01 18:58:05.257646: Pseudo dice [0.7921] 
-2023-11-01 18:58:05.257673: Epoch time: 107.57 s 
-2023-11-01 18:58:05.257693: Yayy! New best EMA pseudo Dice: 0.704 
-2023-11-01 18:58:06.015668:  
-2023-11-01 18:58:06.015738: Epoch 8 
-2023-11-01 18:58:06.015815: Current learning rate: 0.00993 
-2023-11-01 18:59:54.142199: train_loss -0.7456 
-2023-11-01 18:59:54.142325: val_loss -0.7389 
-2023-11-01 18:59:54.142349: Pseudo dice [0.7882] 
-2023-11-01 18:59:54.142376: Epoch time: 108.13 s 
-2023-11-01 18:59:54.142393: Yayy! New best EMA pseudo Dice: 0.7124 
-2023-11-01 18:59:54.905008:  
-2023-11-01 18:59:54.905077: Epoch 9 
-2023-11-01 18:59:54.905144: Current learning rate: 0.00992 
-2023-11-01 19:01:43.094768: train_loss -0.752 
-2023-11-01 19:01:43.094899: val_loss -0.7523 
-2023-11-01 19:01:43.094931: Pseudo dice [0.7997] 
-2023-11-01 19:01:43.094961: Epoch time: 108.19 s 
-2023-11-01 19:01:43.094980: Yayy! New best EMA pseudo Dice: 0.7211 
-2023-11-01 19:01:43.828020:  
-2023-11-01 19:01:43.828094: Epoch 10 
-2023-11-01 19:01:43.828186: Current learning rate: 0.00991 
-2023-11-01 19:03:30.785204: train_loss -0.7737 
-2023-11-01 19:03:30.785334: val_loss -0.7651 
-2023-11-01 19:03:30.785388: Pseudo dice [0.8079] 
-2023-11-01 19:03:30.785416: Epoch time: 106.96 s 
-2023-11-01 19:03:30.785435: Yayy! New best EMA pseudo Dice: 0.7298 
-2023-11-01 19:03:31.533154:  
-2023-11-01 19:03:31.533228: Epoch 11 
-2023-11-01 19:03:31.533282: Current learning rate: 0.0099 
-2023-11-01 19:05:18.944713: train_loss -0.7717 
-2023-11-01 19:05:18.944870: val_loss -0.7801 
-2023-11-01 19:05:18.944894: Pseudo dice [0.8227] 
-2023-11-01 19:05:18.944921: Epoch time: 107.41 s 
-2023-11-01 19:05:18.944937: Yayy! New best EMA pseudo Dice: 0.7391 
-2023-11-01 19:05:19.785975:  
-2023-11-01 19:05:19.786060: Epoch 12 
-2023-11-01 19:05:19.786116: Current learning rate: 0.00989 
-2023-11-01 19:07:09.496158: train_loss -0.7726 
-2023-11-01 19:07:09.496295: val_loss -0.7854 
-2023-11-01 19:07:09.496346: Pseudo dice [0.8238] 
-2023-11-01 19:07:09.496373: Epoch time: 109.71 s 
-2023-11-01 19:07:09.496392: Yayy! New best EMA pseudo Dice: 0.7476 
-2023-11-01 19:07:10.263956:  
-2023-11-01 19:07:10.264048: Epoch 13 
-2023-11-01 19:07:10.264163: Current learning rate: 0.00988 
-2023-11-01 19:09:00.287227: train_loss -0.7808 
-2023-11-01 19:09:00.287359: val_loss -0.7716 
-2023-11-01 19:09:00.287385: Pseudo dice [0.8174] 
-2023-11-01 19:09:00.287413: Epoch time: 110.02 s 
-2023-11-01 19:09:00.287435: Yayy! New best EMA pseudo Dice: 0.7545 
-2023-11-01 19:09:01.055712:  
-2023-11-01 19:09:01.055792: Epoch 14 
-2023-11-01 19:09:01.055847: Current learning rate: 0.00987 
-2023-11-01 19:10:57.253103: train_loss -0.7806 
-2023-11-01 19:10:57.253237: val_loss -0.7781 
-2023-11-01 19:10:57.253266: Pseudo dice [0.8208] 
-2023-11-01 19:10:57.253297: Epoch time: 116.2 s 
-2023-11-01 19:10:57.253318: Yayy! New best EMA pseudo Dice: 0.7612 
-2023-11-01 19:10:58.010948:  
-2023-11-01 19:10:58.011027: Epoch 15 
-2023-11-01 19:10:58.011367: Current learning rate: 0.00986 
-2023-11-01 19:12:46.215935: train_loss -0.7902 
-2023-11-01 19:12:46.216079: val_loss -0.7745 
-2023-11-01 19:12:46.216109: Pseudo dice [0.8185] 
-2023-11-01 19:12:46.216145: Epoch time: 108.21 s 
-2023-11-01 19:12:46.216168: Yayy! New best EMA pseudo Dice: 0.7669 
-2023-11-01 19:12:46.988793:  
-2023-11-01 19:12:46.988914: Epoch 16 
-2023-11-01 19:12:46.988984: Current learning rate: 0.00986 
-2023-11-01 19:14:35.776715: train_loss -0.7909 
-2023-11-01 19:14:35.776868: val_loss -0.7669 
-2023-11-01 19:14:35.776896: Pseudo dice [0.8126] 
-2023-11-01 19:14:35.776922: Epoch time: 108.79 s 
-2023-11-01 19:14:35.776940: Yayy! New best EMA pseudo Dice: 0.7715 
-2023-11-01 19:14:36.568859:  
-2023-11-01 19:14:36.568927: Epoch 17 
-2023-11-01 19:14:36.568982: Current learning rate: 0.00985 
-2023-11-01 19:16:23.283410: train_loss -0.7878 
-2023-11-01 19:16:23.283572: val_loss -0.7786 
-2023-11-01 19:16:23.283598: Pseudo dice [0.8213] 
-2023-11-01 19:16:23.283625: Epoch time: 106.71 s 
-2023-11-01 19:16:23.283643: Yayy! New best EMA pseudo Dice: 0.7765 
-2023-11-01 19:16:24.133961:  
-2023-11-01 19:16:24.134048: Epoch 18 
-2023-11-01 19:16:24.134134: Current learning rate: 0.00984 
-2023-11-01 19:18:10.688354: train_loss -0.7977 
-2023-11-01 19:18:10.688475: val_loss -0.7544 
-2023-11-01 19:18:10.688500: Pseudo dice [0.8018] 
-2023-11-01 19:18:10.688529: Epoch time: 106.55 s 
-2023-11-01 19:18:10.688547: Yayy! New best EMA pseudo Dice: 0.779 
-2023-11-01 19:18:11.461069:  
-2023-11-01 19:18:11.461163: Epoch 19 
-2023-11-01 19:18:11.461216: Current learning rate: 0.00983 
-2023-11-01 19:19:57.869036: train_loss -0.8003 
-2023-11-01 19:19:57.869190: val_loss -0.8023 
-2023-11-01 19:19:57.869216: Pseudo dice [0.839] 
-2023-11-01 19:19:57.869263: Epoch time: 106.41 s 
-2023-11-01 19:19:57.869282: Yayy! New best EMA pseudo Dice: 0.785 
-2023-11-01 19:19:58.631590:  
-2023-11-01 19:19:58.631689: Epoch 20 
-2023-11-01 19:19:58.631745: Current learning rate: 0.00982 
-2023-11-01 19:21:45.088861: train_loss -0.8044 
-2023-11-01 19:21:45.088993: val_loss -0.7789 
-2023-11-01 19:21:45.089032: Pseudo dice [0.8197] 
-2023-11-01 19:21:45.089058: Epoch time: 106.46 s 
-2023-11-01 19:21:45.089076: Yayy! New best EMA pseudo Dice: 0.7885 
-2023-11-01 19:21:45.850080:  
-2023-11-01 19:21:45.850142: Epoch 21 
-2023-11-01 19:21:45.850194: Current learning rate: 0.00981 
-2023-11-01 19:23:32.411395: train_loss -0.7996 
-2023-11-01 19:23:32.411550: val_loss -0.7992 
-2023-11-01 19:23:32.411576: Pseudo dice [0.8352] 
-2023-11-01 19:23:32.411603: Epoch time: 106.56 s 
-2023-11-01 19:23:32.411620: Yayy! New best EMA pseudo Dice: 0.7931 
-2023-11-01 19:23:33.161994:  
-2023-11-01 19:23:33.162071: Epoch 22 
-2023-11-01 19:23:33.162346: Current learning rate: 0.0098 
-2023-11-01 19:25:19.657690: train_loss -0.8016 
-2023-11-01 19:25:19.657834: val_loss -0.7606 
-2023-11-01 19:25:19.657882: Pseudo dice [0.8023] 
-2023-11-01 19:25:19.657909: Epoch time: 106.5 s 
-2023-11-01 19:25:19.657927: Yayy! New best EMA pseudo Dice: 0.794 
-2023-11-01 19:25:20.407161:  
-2023-11-01 19:25:20.407228: Epoch 23 
-2023-11-01 19:25:20.407295: Current learning rate: 0.00979 
-2023-11-01 19:27:07.119793: train_loss -0.7791 
-2023-11-01 19:27:07.119914: val_loss -0.7422 
-2023-11-01 19:27:07.119971: Pseudo dice [0.7955] 
-2023-11-01 19:27:07.119997: Epoch time: 106.71 s 
-2023-11-01 19:27:07.120015: Yayy! New best EMA pseudo Dice: 0.7942 
-2023-11-01 19:27:07.942127:  
-2023-11-01 19:27:07.942219: Epoch 24 
-2023-11-01 19:27:07.942351: Current learning rate: 0.00978 
-2023-11-01 19:28:54.585142: train_loss -0.7926 
-2023-11-01 19:28:54.585271: val_loss -0.7707 
-2023-11-01 19:28:54.585320: Pseudo dice [0.8156] 
-2023-11-01 19:28:54.585346: Epoch time: 106.64 s 
-2023-11-01 19:28:54.585368: Yayy! New best EMA pseudo Dice: 0.7963 
-2023-11-01 19:28:55.326258:  
-2023-11-01 19:28:55.326327: Epoch 25 
-2023-11-01 19:28:55.326391: Current learning rate: 0.00977 
-2023-11-01 19:30:41.865790: train_loss -0.798 
-2023-11-01 19:30:41.865953: val_loss -0.78 
-2023-11-01 19:30:41.865980: Pseudo dice [0.8192] 
-2023-11-01 19:30:41.866007: Epoch time: 106.54 s 
-2023-11-01 19:30:41.866025: Yayy! New best EMA pseudo Dice: 0.7986 
-2023-11-01 19:30:42.603333:  
-2023-11-01 19:30:42.603406: Epoch 26 
-2023-11-01 19:30:42.603485: Current learning rate: 0.00977 
-2023-11-01 19:32:29.015298: train_loss -0.8062 
-2023-11-01 19:32:29.015431: val_loss -0.7799 
-2023-11-01 19:32:29.015481: Pseudo dice [0.823] 
-2023-11-01 19:32:29.015506: Epoch time: 106.41 s 
-2023-11-01 19:32:29.015524: Yayy! New best EMA pseudo Dice: 0.8011 
-2023-11-01 19:32:29.756229:  
-2023-11-01 19:32:29.756306: Epoch 27 
-2023-11-01 19:32:29.756392: Current learning rate: 0.00976 
-2023-11-01 19:34:16.238780: train_loss -0.8103 
-2023-11-01 19:34:16.238942: val_loss -0.7959 
-2023-11-01 19:34:16.238972: Pseudo dice [0.8326] 
-2023-11-01 19:34:16.239003: Epoch time: 106.48 s 
-2023-11-01 19:34:16.239019: Yayy! New best EMA pseudo Dice: 0.8042 
-2023-11-01 19:34:16.991383:  
-2023-11-01 19:34:16.991451: Epoch 28 
-2023-11-01 19:34:16.991502: Current learning rate: 0.00975 
-2023-11-01 19:36:03.410838: train_loss -0.8094 
-2023-11-01 19:36:03.410971: val_loss -0.8023 
-2023-11-01 19:36:03.410996: Pseudo dice [0.8409] 
-2023-11-01 19:36:03.411024: Epoch time: 106.42 s 
-2023-11-01 19:36:03.411048: Yayy! New best EMA pseudo Dice: 0.8079 
-2023-11-01 19:36:04.150906:  
-2023-11-01 19:36:04.150975: Epoch 29 
-2023-11-01 19:36:04.151051: Current learning rate: 0.00974 
-2023-11-01 19:37:50.734898: train_loss -0.815 
-2023-11-01 19:37:50.735055: val_loss -0.7829 
-2023-11-01 19:37:50.735084: Pseudo dice [0.8238] 
-2023-11-01 19:37:50.735110: Epoch time: 106.58 s 
-2023-11-01 19:37:50.735127: Yayy! New best EMA pseudo Dice: 0.8095 
-2023-11-01 19:37:51.574360:  
-2023-11-01 19:37:51.574431: Epoch 30 
-2023-11-01 19:37:51.574485: Current learning rate: 0.00973 
-2023-11-01 19:39:38.182249: train_loss -0.813 
-2023-11-01 19:39:38.182375: val_loss -0.8059 
-2023-11-01 19:39:38.182426: Pseudo dice [0.84] 
-2023-11-01 19:39:38.182453: Epoch time: 106.61 s 
-2023-11-01 19:39:38.182471: Yayy! New best EMA pseudo Dice: 0.8125 
-2023-11-01 19:39:38.923961:  
-2023-11-01 19:39:38.924059: Epoch 31 
-2023-11-01 19:39:38.924113: Current learning rate: 0.00972 
-2023-11-01 19:41:25.532835: train_loss -0.8078 
-2023-11-01 19:41:25.532974: val_loss -0.7843 
-2023-11-01 19:41:25.533005: Pseudo dice [0.8239] 
-2023-11-01 19:41:25.533036: Epoch time: 106.61 s 
-2023-11-01 19:41:25.533058: Yayy! New best EMA pseudo Dice: 0.8137 
-2023-11-01 19:41:26.299329:  
-2023-11-01 19:41:26.299400: Epoch 32 
-2023-11-01 19:41:26.299464: Current learning rate: 0.00971 
-2023-11-01 19:43:12.900869: train_loss -0.8152 
-2023-11-01 19:43:12.901024: val_loss -0.8023 
-2023-11-01 19:43:12.901052: Pseudo dice [0.8383] 
-2023-11-01 19:43:12.901078: Epoch time: 106.6 s 
-2023-11-01 19:43:12.901095: Yayy! New best EMA pseudo Dice: 0.8161 
-2023-11-01 19:43:13.670655:  
-2023-11-01 19:43:13.670734: Epoch 33 
-2023-11-01 19:43:13.670847: Current learning rate: 0.0097 
-2023-11-01 19:45:00.187120: train_loss -0.821 
-2023-11-01 19:45:00.187259: val_loss -0.8034 
-2023-11-01 19:45:00.187312: Pseudo dice [0.8377] 
-2023-11-01 19:45:00.187341: Epoch time: 106.52 s 
-2023-11-01 19:45:00.187359: Yayy! New best EMA pseudo Dice: 0.8183 
-2023-11-01 19:45:00.950562:  
-2023-11-01 19:45:00.950631: Epoch 34 
-2023-11-01 19:45:00.950696: Current learning rate: 0.00969 
-2023-11-01 19:46:47.443700: train_loss -0.821 
-2023-11-01 19:46:47.443818: val_loss -0.792 
-2023-11-01 19:46:47.443868: Pseudo dice [0.829] 
-2023-11-01 19:46:47.443893: Epoch time: 106.49 s 
-2023-11-01 19:46:47.443917: Yayy! New best EMA pseudo Dice: 0.8194 
-2023-11-01 19:46:48.206014:  
-2023-11-01 19:46:48.206082: Epoch 35 
-2023-11-01 19:46:48.206183: Current learning rate: 0.00968 
-2023-11-01 19:48:34.693099: train_loss -0.8191 
-2023-11-01 19:48:34.693225: val_loss -0.8043 
-2023-11-01 19:48:34.693252: Pseudo dice [0.8385] 
-2023-11-01 19:48:34.693280: Epoch time: 106.49 s 
-2023-11-01 19:48:34.693299: Yayy! New best EMA pseudo Dice: 0.8213 
-2023-11-01 19:48:35.455973:  
-2023-11-01 19:48:35.456095: Epoch 36 
-2023-11-01 19:48:35.456150: Current learning rate: 0.00968 
-2023-11-01 19:50:21.940097: train_loss -0.8269 
-2023-11-01 19:50:21.940224: val_loss -0.8093 
-2023-11-01 19:50:21.940249: Pseudo dice [0.8456] 
-2023-11-01 19:50:21.940274: Epoch time: 106.48 s 
-2023-11-01 19:50:21.940291: Yayy! New best EMA pseudo Dice: 0.8237 
-2023-11-01 19:50:22.689226:  
-2023-11-01 19:50:22.689298: Epoch 37 
-2023-11-01 19:50:22.689395: Current learning rate: 0.00967 
-2023-11-01 19:52:09.150033: train_loss -0.8271 
-2023-11-01 19:52:09.150162: val_loss -0.8109 
-2023-11-01 19:52:09.150203: Pseudo dice [0.8453] 
-2023-11-01 19:52:09.150234: Epoch time: 106.46 s 
-2023-11-01 19:52:09.150256: Yayy! New best EMA pseudo Dice: 0.8259 
-2023-11-01 19:52:09.916923:  
-2023-11-01 19:52:09.916996: Epoch 38 
-2023-11-01 19:52:09.917047: Current learning rate: 0.00966 
-2023-11-01 19:53:56.384883: train_loss -0.8182 
-2023-11-01 19:53:56.385023: val_loss -0.7965 
-2023-11-01 19:53:56.385047: Pseudo dice [0.8317] 
-2023-11-01 19:53:56.385073: Epoch time: 106.47 s 
-2023-11-01 19:53:56.385091: Yayy! New best EMA pseudo Dice: 0.8264 
-2023-11-01 19:53:57.138546:  
-2023-11-01 19:53:57.138611: Epoch 39 
-2023-11-01 19:53:57.138661: Current learning rate: 0.00965 
-2023-11-01 19:55:43.623169: train_loss -0.8252 
-2023-11-01 19:55:43.623328: val_loss -0.7868 
-2023-11-01 19:55:43.623357: Pseudo dice [0.8254] 
-2023-11-01 19:55:43.623388: Epoch time: 106.49 s 
-2023-11-01 19:55:44.160403:  
-2023-11-01 19:55:44.160475: Epoch 40 
-2023-11-01 19:55:44.160529: Current learning rate: 0.00964 
-2023-11-01 19:57:30.771449: train_loss -0.818 
-2023-11-01 19:57:30.771571: val_loss -0.7957 
-2023-11-01 19:57:30.771609: Pseudo dice [0.8333] 
-2023-11-01 19:57:30.771636: Epoch time: 106.61 s 
-2023-11-01 19:57:30.771655: Yayy! New best EMA pseudo Dice: 0.827 
-2023-11-01 19:57:31.608534:  
-2023-11-01 19:57:31.608608: Epoch 41 
-2023-11-01 19:57:31.608661: Current learning rate: 0.00963 
-2023-11-01 19:59:18.175242: train_loss -0.828 
-2023-11-01 19:59:18.175385: val_loss -0.8151 
-2023-11-01 19:59:18.175441: Pseudo dice [0.8475] 
-2023-11-01 19:59:18.175468: Epoch time: 106.57 s 
-2023-11-01 19:59:18.175485: Yayy! New best EMA pseudo Dice: 0.8291 
-2023-11-01 19:59:18.909770:  
-2023-11-01 19:59:18.909837: Epoch 42 
-2023-11-01 19:59:18.909913: Current learning rate: 0.00962 
-2023-11-01 20:01:05.490677: train_loss -0.8282 
-2023-11-01 20:01:05.490898: val_loss -0.8145 
-2023-11-01 20:01:05.490950: Pseudo dice [0.8485] 
-2023-11-01 20:01:05.490980: Epoch time: 106.58 s 
-2023-11-01 20:01:05.490998: Yayy! New best EMA pseudo Dice: 0.831 
-2023-11-01 20:01:06.231412:  
-2023-11-01 20:01:06.231485: Epoch 43 
-2023-11-01 20:01:06.231537: Current learning rate: 0.00961 
-2023-11-01 20:02:52.925582: train_loss -0.83 
-2023-11-01 20:02:52.925709: val_loss -0.8107 
-2023-11-01 20:02:52.925758: Pseudo dice [0.8458] 
-2023-11-01 20:02:52.925785: Epoch time: 106.69 s 
-2023-11-01 20:02:52.925802: Yayy! New best EMA pseudo Dice: 0.8325 
-2023-11-01 20:02:53.671937:  
-2023-11-01 20:02:53.672033: Epoch 44 
-2023-11-01 20:02:53.672086: Current learning rate: 0.0096 
-2023-11-01 20:04:40.275544: train_loss -0.8312 
-2023-11-01 20:04:40.275679: val_loss -0.7987 
-2023-11-01 20:04:40.275717: Pseudo dice [0.8373] 
-2023-11-01 20:04:40.275745: Epoch time: 106.6 s 
-2023-11-01 20:04:40.275764: Yayy! New best EMA pseudo Dice: 0.833 
-2023-11-01 20:04:41.023169:  
-2023-11-01 20:04:41.023230: Epoch 45 
-2023-11-01 20:04:41.023279: Current learning rate: 0.00959 
-2023-11-01 20:06:27.605026: train_loss -0.8326 
-2023-11-01 20:06:27.605136: val_loss -0.8036 
-2023-11-01 20:06:27.605194: Pseudo dice [0.8408] 
-2023-11-01 20:06:27.605223: Epoch time: 106.58 s 
-2023-11-01 20:06:27.605240: Yayy! New best EMA pseudo Dice: 0.8338 
-2023-11-01 20:06:28.344873:  
-2023-11-01 20:06:28.344973: Epoch 46 
-2023-11-01 20:06:28.345025: Current learning rate: 0.00959 
-2023-11-01 20:08:14.967579: train_loss -0.8349 
-2023-11-01 20:08:14.967710: val_loss -0.8101 
-2023-11-01 20:08:14.967734: Pseudo dice [0.8422] 
-2023-11-01 20:08:14.967761: Epoch time: 106.62 s 
-2023-11-01 20:08:14.967778: Yayy! New best EMA pseudo Dice: 0.8346 
-2023-11-01 20:08:15.694230:  
-2023-11-01 20:08:15.694297: Epoch 47 
-2023-11-01 20:08:15.694348: Current learning rate: 0.00958 
-2023-11-01 20:10:04.108287: train_loss -0.8336 
-2023-11-01 20:10:04.108416: val_loss -0.8041 
-2023-11-01 20:10:04.108455: Pseudo dice [0.8379] 
-2023-11-01 20:10:04.108480: Epoch time: 108.41 s 
-2023-11-01 20:10:04.108497: Yayy! New best EMA pseudo Dice: 0.8349 
-2023-11-01 20:10:04.834979:  
-2023-11-01 20:10:04.835048: Epoch 48 
-2023-11-01 20:10:04.835101: Current learning rate: 0.00957 
-2023-11-01 20:11:54.682631: train_loss -0.8343 
-2023-11-01 20:11:54.682791: val_loss -0.8045 
-2023-11-01 20:11:54.682818: Pseudo dice [0.8411] 
-2023-11-01 20:11:54.682845: Epoch time: 109.85 s 
-2023-11-01 20:11:54.682862: Yayy! New best EMA pseudo Dice: 0.8355 
-2023-11-01 20:11:55.433455:  
-2023-11-01 20:11:55.433533: Epoch 49 
-2023-11-01 20:11:55.433586: Current learning rate: 0.00956 
-2023-11-01 20:13:46.525921: train_loss -0.8407 
-2023-11-01 20:13:46.526053: val_loss -0.8013 
-2023-11-01 20:13:46.526092: Pseudo dice [0.8386] 
-2023-11-01 20:13:46.526119: Epoch time: 111.09 s 
-2023-11-01 20:13:46.693618: Yayy! New best EMA pseudo Dice: 0.8358 
-2023-11-01 20:13:47.439823:  
-2023-11-01 20:13:47.439897: Epoch 50 
-2023-11-01 20:13:47.439953: Current learning rate: 0.00955 
-2023-11-01 20:15:37.232013: train_loss -0.8372 
-2023-11-01 20:15:37.232159: val_loss -0.8014 
-2023-11-01 20:15:37.232190: Pseudo dice [0.8403] 
-2023-11-01 20:15:37.232224: Epoch time: 109.79 s 
-2023-11-01 20:15:37.232247: Yayy! New best EMA pseudo Dice: 0.8363 
-2023-11-01 20:15:37.971898:  
-2023-11-01 20:15:37.972138: Epoch 51 
-2023-11-01 20:15:37.972208: Current learning rate: 0.00954 
-2023-11-01 20:17:27.146282: train_loss -0.8369 
-2023-11-01 20:17:27.146411: val_loss -0.805 
-2023-11-01 20:17:27.146449: Pseudo dice [0.8445] 
-2023-11-01 20:17:27.146476: Epoch time: 109.17 s 
-2023-11-01 20:17:27.146520: Yayy! New best EMA pseudo Dice: 0.8371 
-2023-11-01 20:17:27.890123:  
-2023-11-01 20:17:27.890346: Epoch 52 
-2023-11-01 20:17:27.890430: Current learning rate: 0.00953 
-2023-11-01 20:19:17.691799: train_loss -0.8389 
-2023-11-01 20:19:17.691921: val_loss -0.8194 
-2023-11-01 20:19:17.691953: Pseudo dice [0.8507] 
-2023-11-01 20:19:17.692006: Epoch time: 109.8 s 
-2023-11-01 20:19:17.692026: Yayy! New best EMA pseudo Dice: 0.8385 
-2023-11-01 20:19:18.461071:  
-2023-11-01 20:19:18.461136: Epoch 53 
-2023-11-01 20:19:18.461201: Current learning rate: 0.00952 
-2023-11-01 20:21:08.690364: train_loss -0.8401 
-2023-11-01 20:21:08.690522: val_loss -0.8064 
-2023-11-01 20:21:08.690547: Pseudo dice [0.8439] 
-2023-11-01 20:21:08.690573: Epoch time: 110.23 s 
-2023-11-01 20:21:08.690589: Yayy! New best EMA pseudo Dice: 0.839 
-2023-11-01 20:21:09.525604:  
-2023-11-01 20:21:09.525714: Epoch 54 
-2023-11-01 20:21:09.525811: Current learning rate: 0.00951 
-2023-11-01 20:22:59.171300: train_loss -0.8385 
-2023-11-01 20:22:59.171443: val_loss -0.8041 
-2023-11-01 20:22:59.171499: Pseudo dice [0.8418] 
-2023-11-01 20:22:59.171525: Epoch time: 109.65 s 
-2023-11-01 20:22:59.171541: Yayy! New best EMA pseudo Dice: 0.8393 
-2023-11-01 20:22:59.909955:  
-2023-11-01 20:22:59.910179: Epoch 55 
-2023-11-01 20:22:59.910282: Current learning rate: 0.0095 
-2023-11-01 20:24:49.814560: train_loss -0.8427 
-2023-11-01 20:24:49.814703: val_loss -0.8076 
-2023-11-01 20:24:49.814730: Pseudo dice [0.8438] 
-2023-11-01 20:24:49.814756: Epoch time: 109.9 s 
-2023-11-01 20:24:49.814773: Yayy! New best EMA pseudo Dice: 0.8397 
-2023-11-01 20:24:50.571825:  
-2023-11-01 20:24:50.571902: Epoch 56 
-2023-11-01 20:24:50.571967: Current learning rate: 0.00949 
-2023-11-01 20:26:39.647761: train_loss -0.8405 
-2023-11-01 20:26:39.647927: val_loss -0.8074 
-2023-11-01 20:26:39.647970: Pseudo dice [0.8409] 
-2023-11-01 20:26:39.648005: Epoch time: 109.08 s 
-2023-11-01 20:26:39.648027: Yayy! New best EMA pseudo Dice: 0.8399 
-2023-11-01 20:26:40.393765:  
-2023-11-01 20:26:40.393831: Epoch 57 
-2023-11-01 20:26:40.393894: Current learning rate: 0.00949 
-2023-11-01 20:28:28.105114: train_loss -0.8382 
-2023-11-01 20:28:28.105246: val_loss -0.8201 
-2023-11-01 20:28:28.105296: Pseudo dice [0.8514] 
-2023-11-01 20:28:28.105324: Epoch time: 107.71 s 
-2023-11-01 20:28:28.105341: Yayy! New best EMA pseudo Dice: 0.841 
-2023-11-01 20:28:28.867011:  
-2023-11-01 20:28:28.867079: Epoch 58 
-2023-11-01 20:28:28.867149: Current learning rate: 0.00948 
-2023-11-01 20:30:17.447812: train_loss -0.8384 
-2023-11-01 20:30:17.447934: val_loss -0.8115 
-2023-11-01 20:30:17.447964: Pseudo dice [0.8467] 
-2023-11-01 20:30:17.447989: Epoch time: 108.58 s 
-2023-11-01 20:30:17.448008: Yayy! New best EMA pseudo Dice: 0.8416 
-2023-11-01 20:30:18.206266:  
-2023-11-01 20:30:18.206330: Epoch 59 
-2023-11-01 20:30:18.206380: Current learning rate: 0.00947 
-2023-11-01 20:32:06.209670: train_loss -0.8407 
-2023-11-01 20:32:06.209800: val_loss -0.8364 
-2023-11-01 20:32:06.209850: Pseudo dice [0.8633] 
-2023-11-01 20:32:06.209875: Epoch time: 108.0 s 
-2023-11-01 20:32:06.209893: Yayy! New best EMA pseudo Dice: 0.8437 
-2023-11-01 20:32:07.043803:  
-2023-11-01 20:32:07.043883: Epoch 60 
-2023-11-01 20:32:07.043935: Current learning rate: 0.00946 
-2023-11-01 20:33:57.152250: train_loss -0.8433 
-2023-11-01 20:33:57.152389: val_loss -0.8046 
-2023-11-01 20:33:57.152421: Pseudo dice [0.8439] 
-2023-11-01 20:33:57.152451: Epoch time: 110.11 s 
-2023-11-01 20:33:57.152468: Yayy! New best EMA pseudo Dice: 0.8438 
-2023-11-01 20:33:57.900627:  
-2023-11-01 20:33:57.900701: Epoch 61 
-2023-11-01 20:33:57.900774: Current learning rate: 0.00945 
-2023-11-01 20:35:47.796391: train_loss -0.8431 
-2023-11-01 20:35:47.796570: val_loss -0.8046 
-2023-11-01 20:35:47.796604: Pseudo dice [0.8414] 
-2023-11-01 20:35:47.796659: Epoch time: 109.9 s 
-2023-11-01 20:35:48.317517:  
-2023-11-01 20:35:48.317595: Epoch 62 
-2023-11-01 20:35:48.317648: Current learning rate: 0.00944 
-2023-11-01 20:37:38.778349: train_loss -0.8482 
-2023-11-01 20:37:38.778473: val_loss -0.8106 
-2023-11-01 20:37:38.778523: Pseudo dice [0.8438] 
-2023-11-01 20:37:38.778552: Epoch time: 110.46 s 
-2023-11-01 20:37:39.309955:  
-2023-11-01 20:37:39.310050: Epoch 63 
-2023-11-01 20:37:39.310114: Current learning rate: 0.00943 
-2023-11-01 20:39:27.809885: train_loss -0.8487 
-2023-11-01 20:39:27.810040: val_loss -0.8143 
-2023-11-01 20:39:27.810066: Pseudo dice [0.8455] 
-2023-11-01 20:39:27.810093: Epoch time: 108.5 s 
-2023-11-01 20:39:28.328039:  
-2023-11-01 20:39:28.328114: Epoch 64 
-2023-11-01 20:39:28.328167: Current learning rate: 0.00942 
-2023-11-01 20:41:20.048426: train_loss -0.847 
-2023-11-01 20:41:20.048546: val_loss -0.7931 
-2023-11-01 20:41:20.048594: Pseudo dice [0.8329] 
-2023-11-01 20:41:20.048620: Epoch time: 111.72 s 
-2023-11-01 20:41:20.578288:  
-2023-11-01 20:41:20.578358: Epoch 65 
-2023-11-01 20:41:20.578424: Current learning rate: 0.00941 
-2023-11-01 20:43:11.375023: train_loss -0.8482 
-2023-11-01 20:43:11.375185: val_loss -0.8115 
-2023-11-01 20:43:11.375212: Pseudo dice [0.846] 
-2023-11-01 20:43:11.375240: Epoch time: 110.8 s 
-2023-11-01 20:43:11.995972:  
-2023-11-01 20:43:11.996050: Epoch 66 
-2023-11-01 20:43:11.996110: Current learning rate: 0.0094 
-2023-11-01 20:45:00.620399: train_loss -0.8469 
-2023-11-01 20:45:00.620534: val_loss -0.8106 
-2023-11-01 20:45:00.620561: Pseudo dice [0.8457] 
-2023-11-01 20:45:00.620589: Epoch time: 108.62 s 
-2023-11-01 20:45:01.151438:  
-2023-11-01 20:45:01.151519: Epoch 67 
-2023-11-01 20:45:01.151595: Current learning rate: 0.00939 
-2023-11-01 20:46:50.294003: train_loss -0.8451 
-2023-11-01 20:46:50.294130: val_loss -0.8144 
-2023-11-01 20:46:50.294157: Pseudo dice [0.8503] 
-2023-11-01 20:46:50.294191: Epoch time: 109.14 s 
-2023-11-01 20:46:50.294210: Yayy! New best EMA pseudo Dice: 0.844 
-2023-11-01 20:46:51.064024:  
-2023-11-01 20:46:51.064109: Epoch 68 
-2023-11-01 20:46:51.064165: Current learning rate: 0.00939 
-2023-11-01 20:48:39.452909: train_loss -0.8471 
-2023-11-01 20:48:39.453043: val_loss -0.7882 
-2023-11-01 20:48:39.453074: Pseudo dice [0.8298] 
-2023-11-01 20:48:39.453104: Epoch time: 108.39 s 
-2023-11-01 20:48:39.994886:  
-2023-11-01 20:48:39.994986: Epoch 69 
-2023-11-01 20:48:39.995070: Current learning rate: 0.00938 
-2023-11-01 20:50:29.455110: train_loss -0.8504 
-2023-11-01 20:50:29.455252: val_loss -0.8112 
-2023-11-01 20:50:29.455278: Pseudo dice [0.8483] 
-2023-11-01 20:50:29.455304: Epoch time: 109.46 s 
-2023-11-01 20:50:29.982386:  
-2023-11-01 20:50:29.982503: Epoch 70 
-2023-11-01 20:50:29.982785: Current learning rate: 0.00937 
-2023-11-01 20:52:17.093593: train_loss -0.8464 
-2023-11-01 20:52:17.093711: val_loss -0.8079 
-2023-11-01 20:52:17.093775: Pseudo dice [0.8429] 
-2023-11-01 20:52:17.093804: Epoch time: 107.11 s 
-2023-11-01 20:52:17.625109:  
-2023-11-01 20:52:17.625178: Epoch 71 
-2023-11-01 20:52:17.625231: Current learning rate: 0.00936 
-2023-11-01 20:54:05.324485: train_loss -0.8465 
-2023-11-01 20:54:05.324621: val_loss -0.8093 
-2023-11-01 20:54:05.324664: Pseudo dice [0.8466] 
-2023-11-01 20:54:05.324693: Epoch time: 107.7 s 
-2023-11-01 20:54:05.946991:  
-2023-11-01 20:54:05.947068: Epoch 72 
-2023-11-01 20:54:05.947120: Current learning rate: 0.00935 
-2023-11-01 20:55:53.042433: train_loss -0.8397 
-2023-11-01 20:55:53.042573: val_loss -0.8105 
-2023-11-01 20:55:53.042628: Pseudo dice [0.8466] 
-2023-11-01 20:55:53.042654: Epoch time: 107.1 s 
-2023-11-01 20:55:53.573358:  
-2023-11-01 20:55:53.573436: Epoch 73 
-2023-11-01 20:55:53.573511: Current learning rate: 0.00934 
-2023-11-01 20:57:44.394509: train_loss -0.851 
-2023-11-01 20:57:44.394649: val_loss -0.8276 
-2023-11-01 20:57:44.394680: Pseudo dice [0.857] 
-2023-11-01 20:57:44.394708: Epoch time: 110.82 s 
-2023-11-01 20:57:44.394727: Yayy! New best EMA pseudo Dice: 0.8451 
-2023-11-01 20:57:45.170772:  
-2023-11-01 20:57:45.170853: Epoch 74 
-2023-11-01 20:57:45.170906: Current learning rate: 0.00933 
-2023-11-01 20:59:36.202818: train_loss -0.847 
-2023-11-01 20:59:36.202944: val_loss -0.8112 
-2023-11-01 20:59:36.202987: Pseudo dice [0.8452] 
-2023-11-01 20:59:36.203012: Epoch time: 111.03 s 
-2023-11-01 20:59:36.203056: Yayy! New best EMA pseudo Dice: 0.8451 
-2023-11-01 20:59:36.958668:  
-2023-11-01 20:59:36.958744: Epoch 75 
-2023-11-01 20:59:36.958809: Current learning rate: 0.00932 
-2023-11-01 21:01:25.595872: train_loss -0.8494 
-2023-11-01 21:01:25.596043: val_loss -0.8059 
-2023-11-01 21:01:25.596071: Pseudo dice [0.8415] 
-2023-11-01 21:01:25.596099: Epoch time: 108.64 s 
-2023-11-01 21:01:26.127660:  
-2023-11-01 21:01:26.127728: Epoch 76 
-2023-11-01 21:01:26.127805: Current learning rate: 0.00931 
-2023-11-01 21:03:14.394264: train_loss -0.8442 
-2023-11-01 21:03:14.394404: val_loss -0.8072 
-2023-11-01 21:03:14.394436: Pseudo dice [0.8415] 
-2023-11-01 21:03:14.394471: Epoch time: 108.27 s 
-2023-11-01 21:03:14.940296:  
-2023-11-01 21:03:14.940382: Epoch 77 
-2023-11-01 21:03:14.940475: Current learning rate: 0.0093 
-2023-11-01 21:05:05.609533: train_loss -0.8491 
-2023-11-01 21:05:05.609677: val_loss -0.8139 
-2023-11-01 21:05:05.609730: Pseudo dice [0.8451] 
-2023-11-01 21:05:05.609763: Epoch time: 110.67 s 
-2023-11-01 21:05:06.157824:  
-2023-11-01 21:05:06.157945: Epoch 78 
-2023-11-01 21:05:06.157999: Current learning rate: 0.0093 
-2023-11-01 21:06:59.006934: train_loss -0.8467 
-2023-11-01 21:06:59.007062: val_loss -0.8179 
-2023-11-01 21:06:59.007113: Pseudo dice [0.8504] 
-2023-11-01 21:06:59.007142: Epoch time: 112.85 s 
-2023-11-01 21:06:59.666527:  
-2023-11-01 21:06:59.666610: Epoch 79 
-2023-11-01 21:06:59.666688: Current learning rate: 0.00929 
-2023-11-01 21:08:52.398765: train_loss -0.8475 
-2023-11-01 21:08:52.398912: val_loss -0.8063 
-2023-11-01 21:08:52.398935: Pseudo dice [0.8394] 
-2023-11-01 21:08:52.398968: Epoch time: 112.73 s 
-2023-11-01 21:08:52.947913:  
-2023-11-01 21:08:52.948043: Epoch 80 
-2023-11-01 21:08:52.948125: Current learning rate: 0.00928 
-2023-11-01 21:10:45.491596: train_loss -0.8538 
-2023-11-01 21:10:45.491725: val_loss -0.8236 
-2023-11-01 21:10:45.491755: Pseudo dice [0.8548] 
-2023-11-01 21:10:45.491783: Epoch time: 112.54 s 
-2023-11-01 21:10:45.491802: Yayy! New best EMA pseudo Dice: 0.8455 
-2023-11-01 21:10:46.270866:  
-2023-11-01 21:10:46.270941: Epoch 81 
-2023-11-01 21:10:46.270996: Current learning rate: 0.00927 
-2023-11-01 21:12:39.461020: train_loss -0.8514 
-2023-11-01 21:12:39.461151: val_loss -0.8049 
-2023-11-01 21:12:39.461200: Pseudo dice [0.8442] 
-2023-11-01 21:12:39.461227: Epoch time: 113.19 s 
-2023-11-01 21:12:40.010263:  
-2023-11-01 21:12:40.010363: Epoch 82 
-2023-11-01 21:12:40.010423: Current learning rate: 0.00926 
-2023-11-01 21:14:33.893663: train_loss -0.8554 
-2023-11-01 21:14:33.893803: val_loss -0.82 
-2023-11-01 21:14:33.893830: Pseudo dice [0.8529] 
-2023-11-01 21:14:33.893858: Epoch time: 113.88 s 
-2023-11-01 21:14:33.893877: Yayy! New best EMA pseudo Dice: 0.8461 
-2023-11-01 21:14:34.659175:  
-2023-11-01 21:14:34.659250: Epoch 83 
-2023-11-01 21:14:34.659307: Current learning rate: 0.00925 
-2023-11-01 21:16:30.278013: train_loss -0.8547 
-2023-11-01 21:16:30.278154: val_loss -0.8289 
-2023-11-01 21:16:30.278185: Pseudo dice [0.8573] 
-2023-11-01 21:16:30.278221: Epoch time: 115.62 s 
-2023-11-01 21:16:30.278239: Yayy! New best EMA pseudo Dice: 0.8473 
-2023-11-01 21:16:31.036788:  
-2023-11-01 21:16:31.036860: Epoch 84 
-2023-11-01 21:16:31.036915: Current learning rate: 0.00924 
-2023-11-01 21:18:24.795647: train_loss -0.8548 
-2023-11-01 21:18:24.795776: val_loss -0.8132 
-2023-11-01 21:18:24.795803: Pseudo dice [0.8449] 
-2023-11-01 21:18:24.795831: Epoch time: 113.76 s 
-2023-11-01 21:18:25.421642:  
-2023-11-01 21:18:25.421849: Epoch 85 
-2023-11-01 21:18:25.421910: Current learning rate: 0.00923 
-2023-11-01 21:20:18.894744: train_loss -0.8538 
-2023-11-01 21:20:18.894873: val_loss -0.8174 
-2023-11-01 21:20:18.894900: Pseudo dice [0.8487] 
-2023-11-01 21:20:18.894928: Epoch time: 113.47 s 
-2023-11-01 21:20:19.429407:  
-2023-11-01 21:20:19.429497: Epoch 86 
-2023-11-01 21:20:19.429555: Current learning rate: 0.00922 
-2023-11-01 21:22:08.124179: train_loss -0.8504 
-2023-11-01 21:22:08.124311: val_loss -0.8125 
-2023-11-01 21:22:08.124377: Pseudo dice [0.8477] 
-2023-11-01 21:22:08.124406: Epoch time: 108.7 s 
-2023-11-01 21:22:08.640013:  
-2023-11-01 21:22:08.640095: Epoch 87 
-2023-11-01 21:22:08.640148: Current learning rate: 0.00921 
-2023-11-01 21:23:57.915165: train_loss -0.8512 
-2023-11-01 21:23:57.915284: val_loss -0.8158 
-2023-11-01 21:23:57.915311: Pseudo dice [0.8484] 
-2023-11-01 21:23:57.915337: Epoch time: 109.28 s 
-2023-11-01 21:23:57.915354: Yayy! New best EMA pseudo Dice: 0.8474 
-2023-11-01 21:23:58.671387:  
-2023-11-01 21:23:58.671471: Epoch 88 
-2023-11-01 21:23:58.671525: Current learning rate: 0.0092 
-2023-11-01 21:25:51.602859: train_loss -0.853 
-2023-11-01 21:25:51.602996: val_loss -0.8089 
-2023-11-01 21:25:51.603045: Pseudo dice [0.8427] 
-2023-11-01 21:25:51.603072: Epoch time: 112.93 s 
-2023-11-01 21:25:52.123028:  
-2023-11-01 21:25:52.123103: Epoch 89 
-2023-11-01 21:25:52.123180: Current learning rate: 0.0092 
-2023-11-01 21:27:43.731836: train_loss -0.8484 
-2023-11-01 21:27:43.731970: val_loss -0.8175 
-2023-11-01 21:27:43.731996: Pseudo dice [0.8506] 
-2023-11-01 21:27:43.732024: Epoch time: 111.61 s 
-2023-11-01 21:27:44.244365:  
-2023-11-01 21:27:44.244437: Epoch 90 
-2023-11-01 21:27:44.244515: Current learning rate: 0.00919 
-2023-11-01 21:29:35.599632: train_loss -0.8559 
-2023-11-01 21:29:35.599782: val_loss -0.8189 
-2023-11-01 21:29:35.599829: Pseudo dice [0.8511] 
-2023-11-01 21:29:35.599857: Epoch time: 111.36 s 
-2023-11-01 21:29:35.599875: Yayy! New best EMA pseudo Dice: 0.8476 
-2023-11-01 21:29:36.339420:  
-2023-11-01 21:29:36.339487: Epoch 91 
-2023-11-01 21:29:36.339561: Current learning rate: 0.00918 
-2023-11-01 21:31:24.799200: train_loss -0.8565 
-2023-11-01 21:31:24.799349: val_loss -0.8052 
-2023-11-01 21:31:24.799403: Pseudo dice [0.8435] 
-2023-11-01 21:31:24.799430: Epoch time: 108.46 s 
-2023-11-01 21:31:25.305162:  
-2023-11-01 21:31:25.305273: Epoch 92 
-2023-11-01 21:31:25.305376: Current learning rate: 0.00917 
-2023-11-01 21:33:14.419265: train_loss -0.857 
-2023-11-01 21:33:14.419388: val_loss -0.8231 
-2023-11-01 21:33:14.419418: Pseudo dice [0.8524] 
-2023-11-01 21:33:14.419447: Epoch time: 109.11 s 
-2023-11-01 21:33:14.419466: Yayy! New best EMA pseudo Dice: 0.8477 
-2023-11-01 21:33:15.176303:  
-2023-11-01 21:33:15.176380: Epoch 93 
-2023-11-01 21:33:15.176448: Current learning rate: 0.00916 
-2023-11-01 21:35:08.980935: train_loss -0.8559 
-2023-11-01 21:35:08.981075: val_loss -0.813 
-2023-11-01 21:35:08.981101: Pseudo dice [0.8454] 
-2023-11-01 21:35:08.981128: Epoch time: 113.81 s 
-2023-11-01 21:35:09.499855:  
-2023-11-01 21:35:09.499945: Epoch 94 
-2023-11-01 21:35:09.500003: Current learning rate: 0.00915 
-2023-11-01 21:37:02.170302: train_loss -0.8536 
-2023-11-01 21:37:02.170453: val_loss -0.8214 
-2023-11-01 21:37:02.170482: Pseudo dice [0.8518] 
-2023-11-01 21:37:02.170513: Epoch time: 112.67 s 
-2023-11-01 21:37:02.170531: Yayy! New best EMA pseudo Dice: 0.8479 
-2023-11-01 21:37:02.915385:  
-2023-11-01 21:37:02.915461: Epoch 95 
-2023-11-01 21:37:02.915516: Current learning rate: 0.00914 
-2023-11-01 21:38:52.768006: train_loss -0.8558 
-2023-11-01 21:38:52.768145: val_loss -0.8168 
-2023-11-01 21:38:52.768171: Pseudo dice [0.8493] 
-2023-11-01 21:38:52.768198: Epoch time: 109.85 s 
-2023-11-01 21:38:52.768215: Yayy! New best EMA pseudo Dice: 0.8481 
-2023-11-01 21:38:53.517664:  
-2023-11-01 21:38:53.517739: Epoch 96 
-2023-11-01 21:38:53.517793: Current learning rate: 0.00913 
-2023-11-01 21:40:45.887296: train_loss -0.8523 
-2023-11-01 21:40:45.887452: val_loss -0.8066 
-2023-11-01 21:40:45.887485: Pseudo dice [0.8428] 
-2023-11-01 21:40:45.887519: Epoch time: 112.37 s 
-2023-11-01 21:40:46.424934:  
-2023-11-01 21:40:46.425066: Epoch 97 
-2023-11-01 21:40:46.425127: Current learning rate: 0.00912 
-2023-11-01 21:42:39.965027: train_loss -0.8587 
-2023-11-01 21:42:39.965162: val_loss -0.7931 
-2023-11-01 21:42:39.965193: Pseudo dice [0.8336] 
-2023-11-01 21:42:39.965226: Epoch time: 113.54 s 
-2023-11-01 21:42:40.598002:  
-2023-11-01 21:42:40.598090: Epoch 98 
-2023-11-01 21:42:40.598173: Current learning rate: 0.00911 
-2023-11-01 21:44:34.380399: train_loss -0.8608 
-2023-11-01 21:44:34.380555: val_loss -0.8232 
-2023-11-01 21:44:34.380586: Pseudo dice [0.8549] 
-2023-11-01 21:44:34.380616: Epoch time: 113.78 s 
-2023-11-01 21:44:34.910179:  
-2023-11-01 21:44:34.910263: Epoch 99 
-2023-11-01 21:44:34.910319: Current learning rate: 0.0091 
-2023-11-01 21:46:26.980966: train_loss -0.8578 
-2023-11-01 21:46:26.981107: val_loss -0.7974 
-2023-11-01 21:46:26.981136: Pseudo dice [0.8353] 
-2023-11-01 21:46:26.981164: Epoch time: 112.07 s 
-2023-11-01 21:46:27.729262:  
-2023-11-01 21:46:27.729338: Epoch 100 
-2023-11-01 21:46:27.729405: Current learning rate: 0.0091 
-2023-11-01 21:48:20.101368: train_loss -0.8565 
-2023-11-01 21:48:20.101494: val_loss -0.8138 
-2023-11-01 21:48:20.101520: Pseudo dice [0.8471] 
-2023-11-01 21:48:20.101548: Epoch time: 112.37 s 
-2023-11-01 21:48:20.627272:  
-2023-11-01 21:48:20.627359: Epoch 101 
-2023-11-01 21:48:20.627414: Current learning rate: 0.00909 
-2023-11-01 21:50:12.469646: train_loss -0.8616 
-2023-11-01 21:50:12.469795: val_loss -0.8126 
-2023-11-01 21:50:12.469826: Pseudo dice [0.8458] 
-2023-11-01 21:50:12.469856: Epoch time: 111.84 s 
-2023-11-01 21:50:12.986461:  
-2023-11-01 21:50:12.986536: Epoch 102 
-2023-11-01 21:50:12.986589: Current learning rate: 0.00908 
-2023-11-01 21:52:06.271267: train_loss -0.8557 
-2023-11-01 21:52:06.271392: val_loss -0.8103 
-2023-11-01 21:52:06.271419: Pseudo dice [0.8445] 
-2023-11-01 21:52:06.271447: Epoch time: 113.29 s 
-2023-11-01 21:52:06.802034:  
-2023-11-01 21:52:06.802114: Epoch 103 
-2023-11-01 21:52:06.802208: Current learning rate: 0.00907 
-2023-11-01 21:53:57.430863: train_loss -0.8574 
-2023-11-01 21:53:57.430983: val_loss -0.8211 
-2023-11-01 21:53:57.431014: Pseudo dice [0.853] 
-2023-11-01 21:53:57.431040: Epoch time: 110.63 s 
-2023-11-01 21:53:58.050946:  
-2023-11-01 21:53:58.051024: Epoch 104 
-2023-11-01 21:53:58.051107: Current learning rate: 0.00906 
-2023-11-01 21:55:49.203145: train_loss -0.8622 
-2023-11-01 21:55:49.203284: val_loss -0.8181 
-2023-11-01 21:55:49.203313: Pseudo dice [0.8505] 
-2023-11-01 21:55:49.203341: Epoch time: 111.15 s 
-2023-11-01 21:55:49.731467:  
-2023-11-01 21:55:49.731547: Epoch 105 
-2023-11-01 21:55:49.731609: Current learning rate: 0.00905 
-2023-11-01 21:57:42.748731: train_loss -0.8604 
-2023-11-01 21:57:42.756913: val_loss -0.8083 
-2023-11-01 21:57:42.756946: Pseudo dice [0.8436] 
-2023-11-01 21:57:42.756974: Epoch time: 113.02 s 
-2023-11-01 21:57:43.282550:  
-2023-11-01 21:57:43.282629: Epoch 106 
-2023-11-01 21:57:43.282708: Current learning rate: 0.00904 
-2023-11-01 21:59:36.310881: train_loss -0.8657 
-2023-11-01 21:59:36.311016: val_loss -0.809 
-2023-11-01 21:59:36.311041: Pseudo dice [0.8462] 
-2023-11-01 21:59:36.311069: Epoch time: 113.03 s 
-2023-11-01 21:59:36.833644:  
-2023-11-01 21:59:36.833725: Epoch 107 
-2023-11-01 21:59:36.833802: Current learning rate: 0.00903 
-2023-11-01 22:01:28.863890: train_loss -0.865 
-2023-11-01 22:01:28.864024: val_loss -0.8214 
-2023-11-01 22:01:28.864054: Pseudo dice [0.8505] 
-2023-11-01 22:01:28.864084: Epoch time: 112.03 s 
-2023-11-01 22:01:29.397965:  
-2023-11-01 22:01:29.398234: Epoch 108 
-2023-11-01 22:01:29.398293: Current learning rate: 0.00902 
-2023-11-01 22:03:20.894591: train_loss -0.8576 
-2023-11-01 22:03:20.894696: val_loss -0.8023 
-2023-11-01 22:03:20.894725: Pseudo dice [0.8383] 
-2023-11-01 22:03:20.894801: Epoch time: 111.5 s 
-2023-11-01 22:03:21.424237:  
-2023-11-01 22:03:21.424327: Epoch 109 
-2023-11-01 22:03:21.424431: Current learning rate: 0.00901 
-2023-11-01 22:05:12.525877: train_loss -0.8498 
-2023-11-01 22:05:12.526009: val_loss -0.8237 
-2023-11-01 22:05:12.526054: Pseudo dice [0.8535] 
-2023-11-01 22:05:12.526081: Epoch time: 111.1 s 
-2023-11-01 22:05:13.048152:  
-2023-11-01 22:05:13.048222: Epoch 110 
-2023-11-01 22:05:13.048288: Current learning rate: 0.009 
-2023-11-01 22:07:04.042364: train_loss -0.8559 
-2023-11-01 22:07:04.042562: val_loss -0.8269 
-2023-11-01 22:07:04.042595: Pseudo dice [0.8566] 
-2023-11-01 22:07:04.042670: Epoch time: 110.99 s 
-2023-11-01 22:07:04.666220:  
-2023-11-01 22:07:04.666302: Epoch 111 
-2023-11-01 22:07:04.666360: Current learning rate: 0.009 
-2023-11-01 22:08:54.726235: train_loss -0.86 
-2023-11-01 22:08:54.726371: val_loss -0.8164 
-2023-11-01 22:08:54.726398: Pseudo dice [0.8479] 
-2023-11-01 22:08:54.726446: Epoch time: 110.06 s 
-2023-11-01 22:08:55.250589:  
-2023-11-01 22:08:55.250679: Epoch 112 
-2023-11-01 22:08:55.250736: Current learning rate: 0.00899 
-2023-11-01 22:10:49.529035: train_loss -0.8611 
-2023-11-01 22:10:49.529181: val_loss -0.819 
-2023-11-01 22:10:49.529221: Pseudo dice [0.8537] 
-2023-11-01 22:10:49.529252: Epoch time: 114.28 s 
-2023-11-01 22:10:49.529273: Yayy! New best EMA pseudo Dice: 0.8484 
-2023-11-01 22:10:50.273398:  
-2023-11-01 22:10:50.273488: Epoch 113 
-2023-11-01 22:10:50.273542: Current learning rate: 0.00898 
-2023-11-01 22:12:41.885647: train_loss -0.8622 
-2023-11-01 22:12:41.885786: val_loss -0.7982 
-2023-11-01 22:12:41.885820: Pseudo dice [0.8358] 
-2023-11-01 22:12:41.885851: Epoch time: 111.61 s 
-2023-11-01 22:12:42.413475:  
-2023-11-01 22:12:42.413558: Epoch 114 
-2023-11-01 22:12:42.413617: Current learning rate: 0.00897 
-2023-11-01 22:14:34.314267: train_loss -0.8609 
-2023-11-01 22:14:34.314389: val_loss -0.82 
-2023-11-01 22:14:34.314440: Pseudo dice [0.8524] 
-2023-11-01 22:14:34.314466: Epoch time: 111.9 s 
-2023-11-01 22:14:34.842762:  
-2023-11-01 22:14:34.842843: Epoch 115 
-2023-11-01 22:14:34.842942: Current learning rate: 0.00896 
-2023-11-01 22:16:27.092913: train_loss -0.8661 
-2023-11-01 22:16:27.093065: val_loss -0.8099 
-2023-11-01 22:16:27.093120: Pseudo dice [0.8436] 
-2023-11-01 22:16:27.093149: Epoch time: 112.25 s 
-2023-11-01 22:16:27.640975:  
-2023-11-01 22:16:27.641047: Epoch 116 
-2023-11-01 22:16:27.641134: Current learning rate: 0.00895 
-2023-11-01 22:18:19.318524: train_loss -0.8619 
-2023-11-01 22:18:19.318660: val_loss -0.8156 
-2023-11-01 22:18:19.318690: Pseudo dice [0.8477] 
-2023-11-01 22:18:19.318717: Epoch time: 111.68 s 
-2023-11-01 22:18:19.941560:  
-2023-11-01 22:18:19.941639: Epoch 117 
-2023-11-01 22:18:19.941692: Current learning rate: 0.00894 
-2023-11-01 22:20:10.836186: train_loss -0.8627 
-2023-11-01 22:20:10.836322: val_loss -0.8267 
-2023-11-01 22:20:10.836346: Pseudo dice [0.8567] 
-2023-11-01 22:20:10.836375: Epoch time: 110.9 s 
-2023-11-01 22:20:11.378020:  
-2023-11-01 22:20:11.378102: Epoch 118 
-2023-11-01 22:20:11.378157: Current learning rate: 0.00893 
-2023-11-01 22:22:02.489258: train_loss -0.8629 
-2023-11-01 22:22:02.489400: val_loss -0.8214 
-2023-11-01 22:22:02.489450: Pseudo dice [0.8525] 
-2023-11-01 22:22:02.489477: Epoch time: 111.11 s 
-2023-11-01 22:22:02.489496: Yayy! New best EMA pseudo Dice: 0.8487 
-2023-11-01 22:22:03.255751:  
-2023-11-01 22:22:03.255823: Epoch 119 
-2023-11-01 22:22:03.255899: Current learning rate: 0.00892 
-2023-11-01 22:23:54.207838: train_loss -0.8633 
-2023-11-01 22:23:54.207979: val_loss -0.804 
-2023-11-01 22:23:54.208005: Pseudo dice [0.8381] 
-2023-11-01 22:23:54.208031: Epoch time: 110.95 s 
-2023-11-01 22:23:54.730730:  
-2023-11-01 22:23:54.730801: Epoch 120 
-2023-11-01 22:23:54.730853: Current learning rate: 0.00891 
-2023-11-01 22:25:43.036373: train_loss -0.8619 
-2023-11-01 22:25:43.036534: val_loss -0.8074 
-2023-11-01 22:25:43.036559: Pseudo dice [0.8417] 
-2023-11-01 22:25:43.036587: Epoch time: 108.31 s 
-2023-11-01 22:25:43.560846:  
-2023-11-01 22:25:43.560920: Epoch 121 
-2023-11-01 22:25:43.560996: Current learning rate: 0.0089 
-2023-11-01 22:27:31.804255: train_loss -0.8634 
-2023-11-01 22:27:31.804380: val_loss -0.8117 
-2023-11-01 22:27:31.804429: Pseudo dice [0.846] 
-2023-11-01 22:27:31.804456: Epoch time: 108.24 s 
-2023-11-01 22:27:32.331002:  
-2023-11-01 22:27:32.331071: Epoch 122 
-2023-11-01 22:27:32.331169: Current learning rate: 0.00889 
-2023-11-01 22:29:20.853316: train_loss -0.8659 
-2023-11-01 22:29:20.853435: val_loss -0.8154 
-2023-11-01 22:29:20.853483: Pseudo dice [0.8487] 
-2023-11-01 22:29:20.853509: Epoch time: 108.52 s 
-2023-11-01 22:29:21.375186:  
-2023-11-01 22:29:21.375281: Epoch 123 
-2023-11-01 22:29:21.375333: Current learning rate: 0.00889 
-2023-11-01 22:31:09.833831: train_loss -0.8634 
-2023-11-01 22:31:09.833952: val_loss -0.805 
-2023-11-01 22:31:09.834000: Pseudo dice [0.8387] 
-2023-11-01 22:31:09.834026: Epoch time: 108.46 s 
-2023-11-01 22:31:10.460764:  
-2023-11-01 22:31:10.460849: Epoch 124 
-2023-11-01 22:31:10.460950: Current learning rate: 0.00888 
-2023-11-01 22:32:59.032597: train_loss -0.8624 
-2023-11-01 22:32:59.032736: val_loss -0.8099 
-2023-11-01 22:32:59.032776: Pseudo dice [0.8459] 
-2023-11-01 22:32:59.032804: Epoch time: 108.57 s 
-2023-11-01 22:32:59.560919:  
-2023-11-01 22:32:59.561126: Epoch 125 
-2023-11-01 22:32:59.561196: Current learning rate: 0.00887 
-2023-11-01 22:34:48.057177: train_loss -0.8645 
-2023-11-01 22:34:48.057354: val_loss -0.8293 
-2023-11-01 22:34:48.057379: Pseudo dice [0.858] 
-2023-11-01 22:34:48.057407: Epoch time: 108.5 s 
-2023-11-01 22:34:48.580138:  
-2023-11-01 22:34:48.580217: Epoch 126 
-2023-11-01 22:34:48.580271: Current learning rate: 0.00886 
-2023-11-01 22:36:36.987437: train_loss -0.867 
-2023-11-01 22:36:36.987573: val_loss -0.8183 
-2023-11-01 22:36:36.987624: Pseudo dice [0.8511] 
-2023-11-01 22:36:36.987654: Epoch time: 108.41 s 
-2023-11-01 22:36:37.514796:  
-2023-11-01 22:36:37.514871: Epoch 127 
-2023-11-01 22:36:37.514923: Current learning rate: 0.00885 
-2023-11-01 22:38:25.971317: train_loss -0.8603 
-2023-11-01 22:38:25.971457: val_loss -0.818 
-2023-11-01 22:38:25.971482: Pseudo dice [0.8512] 
-2023-11-01 22:38:25.971509: Epoch time: 108.46 s 
-2023-11-01 22:38:26.492882:  
-2023-11-01 22:38:26.492951: Epoch 128 
-2023-11-01 22:38:26.493004: Current learning rate: 0.00884 
-2023-11-01 22:40:15.024412: train_loss -0.8649 
-2023-11-01 22:40:15.024521: val_loss -0.8093 
-2023-11-01 22:40:15.024558: Pseudo dice [0.8437] 
-2023-11-01 22:40:15.024587: Epoch time: 108.53 s 
-2023-11-01 22:40:15.551515:  
-2023-11-01 22:40:15.551586: Epoch 129 
-2023-11-01 22:40:15.551668: Current learning rate: 0.00883 
-2023-11-01 22:42:04.031046: train_loss -0.87 
-2023-11-01 22:42:04.031173: val_loss -0.8216 
-2023-11-01 22:42:04.031217: Pseudo dice [0.8547] 
-2023-11-01 22:42:04.031246: Epoch time: 108.48 s 
-2023-11-01 22:42:04.659872:  
-2023-11-01 22:42:04.659953: Epoch 130 
-2023-11-01 22:42:04.660030: Current learning rate: 0.00882 
-2023-11-01 22:43:53.219147: train_loss -0.8715 
-2023-11-01 22:43:53.219282: val_loss -0.8188 
-2023-11-01 22:43:53.219326: Pseudo dice [0.849] 
-2023-11-01 22:43:53.219356: Epoch time: 108.56 s 
-2023-11-01 22:43:53.752056:  
-2023-11-01 22:43:53.752141: Epoch 131 
-2023-11-01 22:43:53.752218: Current learning rate: 0.00881 
-2023-11-01 22:45:42.264135: train_loss -0.8638 
-2023-11-01 22:45:42.264302: val_loss -0.8199 
-2023-11-01 22:45:42.264331: Pseudo dice [0.8517] 
-2023-11-01 22:45:42.264358: Epoch time: 108.51 s 
-2023-11-01 22:45:42.264376: Yayy! New best EMA pseudo Dice: 0.8488 
-2023-11-01 22:45:43.017870:  
-2023-11-01 22:45:43.017951: Epoch 132 
-2023-11-01 22:45:43.018060: Current learning rate: 0.0088 
-2023-11-01 22:47:31.516578: train_loss -0.8654 
-2023-11-01 22:47:31.516732: val_loss -0.8217 
-2023-11-01 22:47:31.516757: Pseudo dice [0.8523] 
-2023-11-01 22:47:31.516783: Epoch time: 108.5 s 
-2023-11-01 22:47:31.516800: Yayy! New best EMA pseudo Dice: 0.8491 
-2023-11-01 22:47:32.272169:  
-2023-11-01 22:47:32.272242: Epoch 133 
-2023-11-01 22:47:32.272298: Current learning rate: 0.00879 
-2023-11-01 22:49:20.747290: train_loss -0.8711 
-2023-11-01 22:49:20.747423: val_loss -0.8173 
-2023-11-01 22:49:20.747453: Pseudo dice [0.8518] 
-2023-11-01 22:49:20.747485: Epoch time: 108.48 s 
-2023-11-01 22:49:20.747506: Yayy! New best EMA pseudo Dice: 0.8494 
-2023-11-01 22:49:21.508657:  
-2023-11-01 22:49:21.508762: Epoch 134 
-2023-11-01 22:49:21.508815: Current learning rate: 0.00879 
-2023-11-01 22:51:09.862574: train_loss -0.8726 
-2023-11-01 22:51:09.862707: val_loss -0.8218 
-2023-11-01 22:51:09.862738: Pseudo dice [0.852] 
-2023-11-01 22:51:09.862770: Epoch time: 108.35 s 
-2023-11-01 22:51:09.862815: Yayy! New best EMA pseudo Dice: 0.8497 
-2023-11-01 22:51:10.627866:  
-2023-11-01 22:51:10.627944: Epoch 135 
-2023-11-01 22:51:10.628001: Current learning rate: 0.00878 
-2023-11-01 22:52:58.994873: train_loss -0.8732 
-2023-11-01 22:52:58.995000: val_loss -0.8114 
-2023-11-01 22:52:58.995026: Pseudo dice [0.8469] 
-2023-11-01 22:52:58.995055: Epoch time: 108.37 s 
-2023-11-01 22:52:59.626553:  
-2023-11-01 22:52:59.626625: Epoch 136 
-2023-11-01 22:52:59.626709: Current learning rate: 0.00877 
-2023-11-01 22:54:48.071572: train_loss -0.8728 
-2023-11-01 22:54:48.071735: val_loss -0.8056 
-2023-11-01 22:54:48.071764: Pseudo dice [0.8392] 
-2023-11-01 22:54:48.071792: Epoch time: 108.45 s 
-2023-11-01 22:54:48.607602:  
-2023-11-01 22:54:48.607704: Epoch 137 
-2023-11-01 22:54:48.607792: Current learning rate: 0.00876 
-2023-11-01 22:56:37.064523: train_loss -0.8732 
-2023-11-01 22:56:37.064649: val_loss -0.8045 
-2023-11-01 22:56:37.064674: Pseudo dice [0.8426] 
-2023-11-01 22:56:37.064703: Epoch time: 108.46 s 
-2023-11-01 22:56:37.601932:  
-2023-11-01 22:56:37.602012: Epoch 138 
-2023-11-01 22:56:37.602064: Current learning rate: 0.00875 
-2023-11-01 22:58:26.001857: train_loss -0.8735 
-2023-11-01 22:58:26.001980: val_loss -0.8217 
-2023-11-01 22:58:26.002028: Pseudo dice [0.8516] 
-2023-11-01 22:58:26.002056: Epoch time: 108.4 s 
-2023-11-01 22:58:26.538335:  
-2023-11-01 22:58:26.538410: Epoch 139 
-2023-11-01 22:58:26.538492: Current learning rate: 0.00874 
-2023-11-01 23:00:15.001801: train_loss -0.87 
-2023-11-01 23:00:15.001922: val_loss -0.8092 
-2023-11-01 23:00:15.001972: Pseudo dice [0.8452] 
-2023-11-01 23:00:15.001999: Epoch time: 108.46 s 
-2023-11-01 23:00:15.539119:  
-2023-11-01 23:00:15.539193: Epoch 140 
-2023-11-01 23:00:15.539267: Current learning rate: 0.00873 
-2023-11-01 23:02:03.982489: train_loss -0.8718 
-2023-11-01 23:02:03.982663: val_loss -0.8069 
-2023-11-01 23:02:03.982693: Pseudo dice [0.8437] 
-2023-11-01 23:02:03.982722: Epoch time: 108.44 s 
-2023-11-01 23:02:04.519164:  
-2023-11-01 23:02:04.519231: Epoch 141 
-2023-11-01 23:02:04.519305: Current learning rate: 0.00872 
-2023-11-01 23:03:53.080774: train_loss -0.875 
-2023-11-01 23:03:53.080899: val_loss -0.7967 
-2023-11-01 23:03:53.080925: Pseudo dice [0.8354] 
-2023-11-01 23:03:53.080951: Epoch time: 108.56 s 
-2023-11-01 23:03:53.719037:  
-2023-11-01 23:03:53.719111: Epoch 142 
-2023-11-01 23:03:53.719192: Current learning rate: 0.00871 
-2023-11-01 23:05:42.277385: train_loss -0.8778 
-2023-11-01 23:05:42.277539: val_loss -0.8208 
-2023-11-01 23:05:42.277567: Pseudo dice [0.8523] 
-2023-11-01 23:05:42.277594: Epoch time: 108.56 s 
-2023-11-01 23:05:42.811805:  
-2023-11-01 23:05:42.811881: Epoch 143 
-2023-11-01 23:05:42.811958: Current learning rate: 0.0087 
-2023-11-01 23:07:31.393494: train_loss -0.8723 
-2023-11-01 23:07:31.393656: val_loss -0.8237 
-2023-11-01 23:07:31.393686: Pseudo dice [0.856] 
-2023-11-01 23:07:31.393718: Epoch time: 108.58 s 
-2023-11-01 23:07:31.933407:  
-2023-11-01 23:07:31.933479: Epoch 144 
-2023-11-01 23:07:31.933558: Current learning rate: 0.00869 
-2023-11-01 23:09:20.524274: train_loss -0.8727 
-2023-11-01 23:09:20.524484: val_loss -0.8229 
-2023-11-01 23:09:20.524516: Pseudo dice [0.8519] 
-2023-11-01 23:09:20.524545: Epoch time: 108.59 s 
-2023-11-01 23:09:21.060789:  
-2023-11-01 23:09:21.060862: Epoch 145 
-2023-11-01 23:09:21.060913: Current learning rate: 0.00868 
-2023-11-01 23:11:09.602975: train_loss -0.8697 
-2023-11-01 23:11:09.603104: val_loss -0.8115 
-2023-11-01 23:11:09.603157: Pseudo dice [0.8457] 
-2023-11-01 23:11:09.603183: Epoch time: 108.54 s 
-2023-11-01 23:11:10.150643:  
-2023-11-01 23:11:10.150718: Epoch 146 
-2023-11-01 23:11:10.150769: Current learning rate: 0.00868 
-2023-11-01 23:12:58.679421: train_loss -0.8641 
-2023-11-01 23:12:58.679544: val_loss -0.8219 
-2023-11-01 23:12:58.679581: Pseudo dice [0.8531] 
-2023-11-01 23:12:58.679606: Epoch time: 108.53 s 
-2023-11-01 23:12:59.209478:  
-2023-11-01 23:12:59.209546: Epoch 147 
-2023-11-01 23:12:59.209600: Current learning rate: 0.00867 
-2023-11-01 23:14:47.666432: train_loss -0.8627 
-2023-11-01 23:14:47.666591: val_loss -0.8023 
-2023-11-01 23:14:47.666616: Pseudo dice [0.8366] 
-2023-11-01 23:14:47.666644: Epoch time: 108.46 s 
-2023-11-01 23:14:48.304126:  
-2023-11-01 23:14:48.304206: Epoch 148 
-2023-11-01 23:14:48.304260: Current learning rate: 0.00866 
-2023-11-01 23:16:36.734396: train_loss -0.8593 
-2023-11-01 23:16:36.734509: val_loss -0.8139 
-2023-11-01 23:16:36.734540: Pseudo dice [0.8455] 
-2023-11-01 23:16:36.734573: Epoch time: 108.43 s 
-2023-11-01 23:16:37.274932:  
-2023-11-01 23:16:37.275024: Epoch 149 
-2023-11-01 23:16:37.275101: Current learning rate: 0.00865 
-2023-11-01 23:18:25.712272: train_loss -0.8638 
-2023-11-01 23:18:25.712404: val_loss -0.8054 
-2023-11-01 23:18:25.712454: Pseudo dice [0.8422] 
-2023-11-01 23:18:25.712480: Epoch time: 108.44 s 
-2023-11-01 23:18:26.470326:  
-2023-11-01 23:18:26.470402: Epoch 150 
-2023-11-01 23:18:26.470467: Current learning rate: 0.00864 
-2023-11-01 23:20:14.970460: train_loss -0.8695 
-2023-11-01 23:20:14.970626: val_loss -0.8186 
-2023-11-01 23:20:14.970649: Pseudo dice [0.8507] 
-2023-11-01 23:20:14.970677: Epoch time: 108.5 s 
-2023-11-01 23:20:15.517531:  
-2023-11-01 23:20:15.517637: Epoch 151 
-2023-11-01 23:20:15.517712: Current learning rate: 0.00863 
-2023-11-01 23:22:04.043018: train_loss -0.8701 
-2023-11-01 23:22:04.043160: val_loss -0.8075 
-2023-11-01 23:22:04.043209: Pseudo dice [0.8406] 
-2023-11-01 23:22:04.043236: Epoch time: 108.53 s 
-2023-11-01 23:22:04.579104:  
-2023-11-01 23:22:04.579173: Epoch 152 
-2023-11-01 23:22:04.579251: Current learning rate: 0.00862 
-2023-11-01 23:23:52.983931: train_loss -0.873 
-2023-11-01 23:23:52.984069: val_loss -0.8127 
-2023-11-01 23:23:52.984094: Pseudo dice [0.8445] 
-2023-11-01 23:23:52.984121: Epoch time: 108.41 s 
-2023-11-01 23:23:53.520195:  
-2023-11-01 23:23:53.520262: Epoch 153 
-2023-11-01 23:23:53.520341: Current learning rate: 0.00861 
-2023-11-01 23:25:41.927995: train_loss -0.8693 
-2023-11-01 23:25:41.928120: val_loss -0.8155 
-2023-11-01 23:25:41.928146: Pseudo dice [0.848] 
-2023-11-01 23:25:41.928172: Epoch time: 108.41 s 
-2023-11-01 23:25:42.569481:  
-2023-11-01 23:25:42.569561: Epoch 154 
-2023-11-01 23:25:42.569663: Current learning rate: 0.0086 
-2023-11-01 23:27:31.078620: train_loss -0.872 
-2023-11-01 23:27:31.078744: val_loss -0.8024 
-2023-11-01 23:27:31.078795: Pseudo dice [0.8428] 
-2023-11-01 23:27:31.078821: Epoch time: 108.51 s 
-2023-11-01 23:27:31.621279:  
-2023-11-01 23:27:31.621351: Epoch 155 
-2023-11-01 23:27:31.621424: Current learning rate: 0.00859 
-2023-11-01 23:29:20.214144: train_loss -0.8701 
-2023-11-01 23:29:20.214274: val_loss -0.824 
-2023-11-01 23:29:20.214329: Pseudo dice [0.8541] 
-2023-11-01 23:29:20.214363: Epoch time: 108.59 s 
-2023-11-01 23:29:20.757985:  
-2023-11-01 23:29:20.758075: Epoch 156 
-2023-11-01 23:29:20.758179: Current learning rate: 0.00858 
-2023-11-01 23:31:09.249047: train_loss -0.875 
-2023-11-01 23:31:09.249189: val_loss -0.8172 
-2023-11-01 23:31:09.249214: Pseudo dice [0.8502] 
-2023-11-01 23:31:09.249266: Epoch time: 108.49 s 
-2023-11-01 23:31:09.792506:  
-2023-11-01 23:31:09.792587: Epoch 157 
-2023-11-01 23:31:09.792641: Current learning rate: 0.00858 
-2023-11-01 23:32:58.303877: train_loss -0.8726 
-2023-11-01 23:32:58.304003: val_loss -0.8191 
-2023-11-01 23:32:58.304028: Pseudo dice [0.8504] 
-2023-11-01 23:32:58.304056: Epoch time: 108.51 s 
-2023-11-01 23:32:58.848232:  
-2023-11-01 23:32:58.848300: Epoch 158 
-2023-11-01 23:32:58.848377: Current learning rate: 0.00857 
-2023-11-01 23:34:47.438391: train_loss -0.8773 
-2023-11-01 23:34:47.438540: val_loss -0.8148 
-2023-11-01 23:34:47.438582: Pseudo dice [0.8485] 
-2023-11-01 23:34:47.438610: Epoch time: 108.59 s 
-2023-11-01 23:34:47.983235:  
-2023-11-01 23:34:47.983309: Epoch 159 
-2023-11-01 23:34:47.983360: Current learning rate: 0.00856 
-2023-11-01 23:36:36.578091: train_loss -0.8745 
-2023-11-01 23:36:36.578231: val_loss -0.8296 
-2023-11-01 23:36:36.578270: Pseudo dice [0.8603] 
-2023-11-01 23:36:36.578321: Epoch time: 108.6 s 
-2023-11-01 23:36:37.223032:  
-2023-11-01 23:36:37.223118: Epoch 160 
-2023-11-01 23:36:37.223194: Current learning rate: 0.00855 
-2023-11-01 23:38:25.714025: train_loss -0.8782 
-2023-11-01 23:38:25.714161: val_loss -0.8246 
-2023-11-01 23:38:25.714202: Pseudo dice [0.8532] 
-2023-11-01 23:38:25.714230: Epoch time: 108.49 s 
-2023-11-01 23:38:26.257719:  
-2023-11-01 23:38:26.257798: Epoch 161 
-2023-11-01 23:38:26.257850: Current learning rate: 0.00854 
-2023-11-01 23:40:14.924929: train_loss -0.8736 
-2023-11-01 23:40:14.925159: val_loss -0.81 
-2023-11-01 23:40:14.925189: Pseudo dice [0.8437] 
-2023-11-01 23:40:14.925217: Epoch time: 108.67 s 
-2023-11-01 23:40:15.473290:  
-2023-11-01 23:40:15.473368: Epoch 162 
-2023-11-01 23:40:15.473419: Current learning rate: 0.00853 
-2023-11-01 23:42:04.112934: train_loss -0.8773 
-2023-11-01 23:42:04.113059: val_loss -0.8243 
-2023-11-01 23:42:04.113111: Pseudo dice [0.8541] 
-2023-11-01 23:42:04.113137: Epoch time: 108.64 s 
-2023-11-01 23:42:04.658542:  
-2023-11-01 23:42:04.658621: Epoch 163 
-2023-11-01 23:42:04.658695: Current learning rate: 0.00852 
-2023-11-01 23:43:53.254811: train_loss -0.8762 
-2023-11-01 23:43:53.254935: val_loss -0.823 
-2023-11-01 23:43:53.254986: Pseudo dice [0.8556] 
-2023-11-01 23:43:53.255013: Epoch time: 108.6 s 
-2023-11-01 23:43:53.255031: Yayy! New best EMA pseudo Dice: 0.8499 
-2023-11-01 23:43:54.041637:  
-2023-11-01 23:43:54.041705: Epoch 164 
-2023-11-01 23:43:54.041758: Current learning rate: 0.00851 
-2023-11-01 23:45:42.551781: train_loss -0.8738 
-2023-11-01 23:45:42.551907: val_loss -0.816 
-2023-11-01 23:45:42.551954: Pseudo dice [0.8483] 
-2023-11-01 23:45:42.552014: Epoch time: 108.51 s 
-2023-11-01 23:45:43.083080:  
-2023-11-01 23:45:43.083177: Epoch 165 
-2023-11-01 23:45:43.083231: Current learning rate: 0.0085 
-2023-11-01 23:47:31.579648: train_loss -0.8769 
-2023-11-01 23:47:31.579771: val_loss -0.8188 
-2023-11-01 23:47:31.579829: Pseudo dice [0.8502] 
-2023-11-01 23:47:31.579857: Epoch time: 108.5 s 
-2023-11-01 23:47:32.203992:  
-2023-11-01 23:47:32.204076: Epoch 166 
-2023-11-01 23:47:32.204129: Current learning rate: 0.00849 
-2023-11-01 23:49:20.698542: train_loss -0.8782 
-2023-11-01 23:49:20.698680: val_loss -0.8206 
-2023-11-01 23:49:20.698719: Pseudo dice [0.8519] 
-2023-11-01 23:49:20.698769: Epoch time: 108.49 s 
-2023-11-01 23:49:20.698786: Yayy! New best EMA pseudo Dice: 0.85 
-2023-11-01 23:49:21.459555:  
-2023-11-01 23:49:21.459629: Epoch 167 
-2023-11-01 23:49:21.459723: Current learning rate: 0.00848 
-2023-11-01 23:51:09.885404: train_loss -0.8805 
-2023-11-01 23:51:09.885541: val_loss -0.8124 
-2023-11-01 23:51:09.885580: Pseudo dice [0.847] 
-2023-11-01 23:51:09.885605: Epoch time: 108.43 s 
-2023-11-01 23:51:10.427336:  
-2023-11-01 23:51:10.427421: Epoch 168 
-2023-11-01 23:51:10.427474: Current learning rate: 0.00847 
-2023-11-01 23:52:58.815440: train_loss -0.877 
-2023-11-01 23:52:58.815598: val_loss -0.8239 
-2023-11-01 23:52:58.815630: Pseudo dice [0.8544] 
-2023-11-01 23:52:58.815657: Epoch time: 108.39 s 
-2023-11-01 23:52:58.815676: Yayy! New best EMA pseudo Dice: 0.8502 
-2023-11-01 23:52:59.593683:  
-2023-11-01 23:52:59.593749: Epoch 169 
-2023-11-01 23:52:59.593801: Current learning rate: 0.00847 
-2023-11-01 23:54:48.096281: train_loss -0.877 
-2023-11-01 23:54:48.096447: val_loss -0.8185 
-2023-11-01 23:54:48.096472: Pseudo dice [0.8518] 
-2023-11-01 23:54:48.096500: Epoch time: 108.5 s 
-2023-11-01 23:54:48.096518: Yayy! New best EMA pseudo Dice: 0.8503 
-2023-11-01 23:54:48.869476:  
-2023-11-01 23:54:48.869546: Epoch 170 
-2023-11-01 23:54:48.869597: Current learning rate: 0.00846 
-2023-11-01 23:56:37.377544: train_loss -0.8755 
-2023-11-01 23:56:37.377668: val_loss -0.8135 
-2023-11-01 23:56:37.377718: Pseudo dice [0.8463] 
-2023-11-01 23:56:37.377745: Epoch time: 108.51 s 
-2023-11-01 23:56:37.918033:  
-2023-11-01 23:56:37.918103: Epoch 171 
-2023-11-01 23:56:37.918157: Current learning rate: 0.00845 
-2023-11-01 23:58:26.412528: train_loss -0.8775 
-2023-11-01 23:58:26.412654: val_loss -0.814 
-2023-11-01 23:58:26.412696: Pseudo dice [0.8472] 
-2023-11-01 23:58:26.412729: Epoch time: 108.49 s 
-2023-11-01 23:58:27.062915:  
-2023-11-01 23:58:27.062997: Epoch 172 
-2023-11-01 23:58:27.063102: Current learning rate: 0.00844 
-2023-11-02 00:00:15.649734: train_loss -0.8782 
-2023-11-02 00:00:15.649842: val_loss -0.8202 
-2023-11-02 00:00:15.649880: Pseudo dice [0.8508] 
-2023-11-02 00:00:15.649907: Epoch time: 108.59 s 
-2023-11-02 00:00:16.192179:  
-2023-11-02 00:00:16.192298: Epoch 173 
-2023-11-02 00:00:16.192352: Current learning rate: 0.00843 
-2023-11-02 00:02:04.860390: train_loss -0.8765 
-2023-11-02 00:02:04.860523: val_loss -0.8221 
-2023-11-02 00:02:04.860547: Pseudo dice [0.8541] 
-2023-11-02 00:02:04.860574: Epoch time: 108.67 s 
-2023-11-02 00:02:05.399107:  
-2023-11-02 00:02:05.399182: Epoch 174 
-2023-11-02 00:02:05.399233: Current learning rate: 0.00842 
-2023-11-02 00:03:53.984104: train_loss -0.8773 
-2023-11-02 00:03:53.984251: val_loss -0.8195 
-2023-11-02 00:03:53.984277: Pseudo dice [0.8522] 
-2023-11-02 00:03:53.984303: Epoch time: 108.59 s 
-2023-11-02 00:03:53.984321: Yayy! New best EMA pseudo Dice: 0.8504 
-2023-11-02 00:03:54.763606:  
-2023-11-02 00:03:54.763684: Epoch 175 
-2023-11-02 00:03:54.763733: Current learning rate: 0.00841 
-2023-11-02 00:05:43.310889: train_loss -0.878 
-2023-11-02 00:05:43.311012: val_loss -0.8045 
-2023-11-02 00:05:43.311062: Pseudo dice [0.8416] 
-2023-11-02 00:05:43.311089: Epoch time: 108.55 s 
-2023-11-02 00:05:43.855167:  
-2023-11-02 00:05:43.855237: Epoch 176 
-2023-11-02 00:05:43.855314: Current learning rate: 0.0084 
-2023-11-02 00:07:32.421733: train_loss -0.8787 
-2023-11-02 00:07:32.421853: val_loss -0.8116 
-2023-11-02 00:07:32.421902: Pseudo dice [0.8463] 
-2023-11-02 00:07:32.421930: Epoch time: 108.57 s 
-2023-11-02 00:07:32.962184:  
-2023-11-02 00:07:32.962254: Epoch 177 
-2023-11-02 00:07:32.962333: Current learning rate: 0.00839 
-2023-11-02 00:09:21.567136: train_loss -0.8804 
-2023-11-02 00:09:21.567291: val_loss -0.8111 
-2023-11-02 00:09:21.567317: Pseudo dice [0.8448] 
-2023-11-02 00:09:21.567343: Epoch time: 108.61 s 
-2023-11-02 00:09:22.202337:  
-2023-11-02 00:09:22.202417: Epoch 178 
-2023-11-02 00:09:22.202472: Current learning rate: 0.00838 
-2023-11-02 00:11:10.839632: train_loss -0.8809 
-2023-11-02 00:11:10.839782: val_loss -0.8097 
-2023-11-02 00:11:10.839807: Pseudo dice [0.8444] 
-2023-11-02 00:11:10.839834: Epoch time: 108.64 s 
-2023-11-02 00:11:11.378489:  
-2023-11-02 00:11:11.378566: Epoch 179 
-2023-11-02 00:11:11.378644: Current learning rate: 0.00837 
-2023-11-02 00:12:59.988153: train_loss -0.8806 
-2023-11-02 00:12:59.988313: val_loss -0.8079 
-2023-11-02 00:12:59.988342: Pseudo dice [0.8403] 
-2023-11-02 00:12:59.988372: Epoch time: 108.61 s 
-2023-11-02 00:13:00.527389:  
-2023-11-02 00:13:00.527468: Epoch 180 
-2023-11-02 00:13:00.527520: Current learning rate: 0.00836 
-2023-11-02 00:14:49.079710: train_loss -0.8741 
-2023-11-02 00:14:49.079851: val_loss -0.8136 
-2023-11-02 00:14:49.079879: Pseudo dice [0.8483] 
-2023-11-02 00:14:49.079921: Epoch time: 108.55 s 
-2023-11-02 00:14:49.618013:  
-2023-11-02 00:14:49.618089: Epoch 181 
-2023-11-02 00:14:49.618165: Current learning rate: 0.00836 
-2023-11-02 00:16:38.117772: train_loss -0.8748 
-2023-11-02 00:16:38.117898: val_loss -0.8133 
-2023-11-02 00:16:38.117949: Pseudo dice [0.8465] 
-2023-11-02 00:16:38.117975: Epoch time: 108.5 s 
-2023-11-02 00:16:38.656082:  
-2023-11-02 00:16:38.656159: Epoch 182 
-2023-11-02 00:16:38.656237: Current learning rate: 0.00835 
-2023-11-02 00:18:27.115348: train_loss -0.8788 
-2023-11-02 00:18:27.115481: val_loss -0.8202 
-2023-11-02 00:18:27.115535: Pseudo dice [0.8516] 
-2023-11-02 00:18:27.115562: Epoch time: 108.46 s 
-2023-11-02 00:18:27.650372:  
-2023-11-02 00:18:27.650438: Epoch 183 
-2023-11-02 00:18:27.650515: Current learning rate: 0.00834 
-2023-11-02 00:20:16.123714: train_loss -0.8752 
-2023-11-02 00:20:16.123897: val_loss -0.8173 
-2023-11-02 00:20:16.123945: Pseudo dice [0.8493] 
-2023-11-02 00:20:16.123973: Epoch time: 108.47 s 
-2023-11-02 00:20:16.754467:  
-2023-11-02 00:20:16.754538: Epoch 184 
-2023-11-02 00:20:16.754616: Current learning rate: 0.00833 
-2023-11-02 00:22:05.261498: train_loss -0.8779 
-2023-11-02 00:22:05.261645: val_loss -0.8265 
-2023-11-02 00:22:05.261676: Pseudo dice [0.8568] 
-2023-11-02 00:22:05.261708: Epoch time: 108.51 s 
-2023-11-02 00:22:05.799170:  
-2023-11-02 00:22:05.799246: Epoch 185 
-2023-11-02 00:22:05.799300: Current learning rate: 0.00832 
-2023-11-02 00:23:54.252124: train_loss -0.8808 
-2023-11-02 00:23:54.252266: val_loss -0.8247 
-2023-11-02 00:23:54.252309: Pseudo dice [0.8543] 
-2023-11-02 00:23:54.252338: Epoch time: 108.45 s 
-2023-11-02 00:23:54.791202:  
-2023-11-02 00:23:54.791283: Epoch 186 
-2023-11-02 00:23:54.791337: Current learning rate: 0.00831 
-2023-11-02 00:25:43.258136: train_loss -0.8819 
-2023-11-02 00:25:43.258293: val_loss -0.8285 
-2023-11-02 00:25:43.258322: Pseudo dice [0.8601] 
-2023-11-02 00:25:43.258348: Epoch time: 108.47 s 
-2023-11-02 00:25:43.258365: Yayy! New best EMA pseudo Dice: 0.8505 
-2023-11-02 00:25:44.037056:  
-2023-11-02 00:25:44.037132: Epoch 187 
-2023-11-02 00:25:44.037184: Current learning rate: 0.0083 
-2023-11-02 00:27:32.509010: train_loss -0.8811 
-2023-11-02 00:27:32.509135: val_loss -0.8232 
-2023-11-02 00:27:32.509173: Pseudo dice [0.8519] 
-2023-11-02 00:27:32.509201: Epoch time: 108.47 s 
-2023-11-02 00:27:32.509219: Yayy! New best EMA pseudo Dice: 0.8507 
-2023-11-02 00:27:33.280956:  
-2023-11-02 00:27:33.281024: Epoch 188 
-2023-11-02 00:27:33.281075: Current learning rate: 0.00829 
-2023-11-02 00:29:21.761898: train_loss -0.8835 
-2023-11-02 00:29:21.762023: val_loss -0.8163 
-2023-11-02 00:29:21.762061: Pseudo dice [0.8475] 
-2023-11-02 00:29:21.762087: Epoch time: 108.48 s 
-2023-11-02 00:29:22.301314:  
-2023-11-02 00:29:22.301381: Epoch 189 
-2023-11-02 00:29:22.301433: Current learning rate: 0.00828 
-2023-11-02 00:31:10.866282: train_loss -0.8794 
-2023-11-02 00:31:10.866414: val_loss -0.8128 
-2023-11-02 00:31:10.866464: Pseudo dice [0.8466] 
-2023-11-02 00:31:10.866497: Epoch time: 108.57 s 
-2023-11-02 00:31:11.403575:  
-2023-11-02 00:31:11.403643: Epoch 190 
-2023-11-02 00:31:11.403715: Current learning rate: 0.00827 
-2023-11-02 00:32:59.892313: train_loss -0.8813 
-2023-11-02 00:32:59.892462: val_loss -0.8207 
-2023-11-02 00:32:59.892491: Pseudo dice [0.8533] 
-2023-11-02 00:32:59.892522: Epoch time: 108.49 s 
-2023-11-02 00:33:00.532142:  
-2023-11-02 00:33:00.532214: Epoch 191 
-2023-11-02 00:33:00.532297: Current learning rate: 0.00826 
-2023-11-02 00:34:49.107778: train_loss -0.8793 
-2023-11-02 00:34:49.107911: val_loss -0.8084 
-2023-11-02 00:34:49.107963: Pseudo dice [0.8433] 
-2023-11-02 00:34:49.108018: Epoch time: 108.58 s 
-2023-11-02 00:34:49.653573:  
-2023-11-02 00:34:49.653650: Epoch 192 
-2023-11-02 00:34:49.653702: Current learning rate: 0.00825 
-2023-11-02 00:36:38.188107: train_loss -0.8803 
-2023-11-02 00:36:38.188287: val_loss -0.8198 
-2023-11-02 00:36:38.188315: Pseudo dice [0.8512] 
-2023-11-02 00:36:38.188365: Epoch time: 108.53 s 
-2023-11-02 00:36:38.733083:  
-2023-11-02 00:36:38.733160: Epoch 193 
-2023-11-02 00:36:38.733238: Current learning rate: 0.00824 
-2023-11-02 00:38:27.259314: train_loss -0.8819 
-2023-11-02 00:38:27.259468: val_loss -0.8113 
-2023-11-02 00:38:27.259497: Pseudo dice [0.844] 
-2023-11-02 00:38:27.259531: Epoch time: 108.53 s 
-2023-11-02 00:38:27.804239:  
-2023-11-02 00:38:27.804317: Epoch 194 
-2023-11-02 00:38:27.804397: Current learning rate: 0.00824 
-2023-11-02 00:40:16.270155: train_loss -0.879 
-2023-11-02 00:40:16.270308: val_loss -0.8185 
-2023-11-02 00:40:16.270339: Pseudo dice [0.8518] 
-2023-11-02 00:40:16.270370: Epoch time: 108.47 s 
-2023-11-02 00:40:16.814682:  
-2023-11-02 00:40:16.814749: Epoch 195 
-2023-11-02 00:40:16.814823: Current learning rate: 0.00823 
-2023-11-02 00:42:05.266238: train_loss -0.8773 
-2023-11-02 00:42:05.266402: val_loss -0.8315 
-2023-11-02 00:42:05.266428: Pseudo dice [0.8598] 
-2023-11-02 00:42:05.266456: Epoch time: 108.45 s 
-2023-11-02 00:42:05.811833:  
-2023-11-02 00:42:05.812093: Epoch 196 
-2023-11-02 00:42:05.812225: Current learning rate: 0.00822 
-2023-11-02 00:43:54.309413: train_loss -0.8798 
-2023-11-02 00:43:54.309544: val_loss -0.8178 
-2023-11-02 00:43:54.309612: Pseudo dice [0.8518] 
-2023-11-02 00:43:54.309641: Epoch time: 108.5 s 
-2023-11-02 00:43:54.854802:  
-2023-11-02 00:43:54.854879: Epoch 197 
-2023-11-02 00:43:54.854931: Current learning rate: 0.00821 
-2023-11-02 00:45:43.329835: train_loss -0.8839 
-2023-11-02 00:45:43.329966: val_loss -0.8193 
-2023-11-02 00:45:43.330004: Pseudo dice [0.8528] 
-2023-11-02 00:45:43.330031: Epoch time: 108.48 s 
-2023-11-02 00:45:43.330074: Yayy! New best EMA pseudo Dice: 0.8508 
-2023-11-02 00:45:44.104403:  
-2023-11-02 00:45:44.104485: Epoch 198 
-2023-11-02 00:45:44.104539: Current learning rate: 0.0082 
-2023-11-02 00:47:32.559901: train_loss -0.8814 
-2023-11-02 00:47:32.560066: val_loss -0.8144 
-2023-11-02 00:47:32.560091: Pseudo dice [0.8472] 
-2023-11-02 00:47:32.560119: Epoch time: 108.46 s 
-2023-11-02 00:47:33.105464:  
-2023-11-02 00:47:33.105533: Epoch 199 
-2023-11-02 00:47:33.105611: Current learning rate: 0.00819 
-2023-11-02 00:49:21.566056: train_loss -0.8786 
-2023-11-02 00:49:21.566186: val_loss -0.7974 
-2023-11-02 00:49:21.566224: Pseudo dice [0.836] 
-2023-11-02 00:49:21.566251: Epoch time: 108.46 s 
-2023-11-02 00:49:22.365209:  
-2023-11-02 00:49:22.365275: Epoch 200 
-2023-11-02 00:49:22.365327: Current learning rate: 0.00818 
-2023-11-02 00:51:10.817586: train_loss -0.8753 
-2023-11-02 00:51:10.817695: val_loss -0.8212 
-2023-11-02 00:51:10.817744: Pseudo dice [0.8531] 
-2023-11-02 00:51:10.817771: Epoch time: 108.45 s 
-2023-11-02 00:51:11.352070:  
-2023-11-02 00:51:11.352167: Epoch 201 
-2023-11-02 00:51:11.352221: Current learning rate: 0.00817 
-2023-11-02 00:52:59.676356: train_loss -0.8819 
-2023-11-02 00:52:59.676480: val_loss -0.8188 
-2023-11-02 00:52:59.676517: Pseudo dice [0.8509] 
-2023-11-02 00:52:59.676570: Epoch time: 108.32 s 
-2023-11-02 00:53:00.317217:  
-2023-11-02 00:53:00.317317: Epoch 202 
-2023-11-02 00:53:00.317410: Current learning rate: 0.00816 
-2023-11-02 00:54:48.778375: train_loss -0.8785 
-2023-11-02 00:54:48.778500: val_loss -0.815 
-2023-11-02 00:54:48.778529: Pseudo dice [0.8497] 
-2023-11-02 00:54:48.778557: Epoch time: 108.46 s 
-2023-11-02 00:54:49.331059:  
-2023-11-02 00:54:49.331136: Epoch 203 
-2023-11-02 00:54:49.331190: Current learning rate: 0.00815 
-2023-11-02 00:56:37.782737: train_loss -0.8802 
-2023-11-02 00:56:37.782861: val_loss -0.8243 
-2023-11-02 00:56:37.782913: Pseudo dice [0.8543] 
-2023-11-02 00:56:37.782940: Epoch time: 108.45 s 
-2023-11-02 00:56:38.330607:  
-2023-11-02 00:56:38.330681: Epoch 204 
-2023-11-02 00:56:38.330736: Current learning rate: 0.00814 
-2023-11-02 00:58:26.679420: train_loss -0.8819 
-2023-11-02 00:58:26.679543: val_loss -0.8172 
-2023-11-02 00:58:26.679574: Pseudo dice [0.8511] 
-2023-11-02 00:58:26.679603: Epoch time: 108.35 s 
-2023-11-02 00:58:27.232910:  
-2023-11-02 00:58:27.232988: Epoch 205 
-2023-11-02 00:58:27.233043: Current learning rate: 0.00813 
-2023-11-02 01:00:15.584338: train_loss -0.8803 
-2023-11-02 01:00:15.584464: val_loss -0.8117 
-2023-11-02 01:00:15.584490: Pseudo dice [0.8486] 
-2023-11-02 01:00:15.584517: Epoch time: 108.35 s 
-2023-11-02 01:00:16.100263:  
-2023-11-02 01:00:16.100330: Epoch 206 
-2023-11-02 01:00:16.100408: Current learning rate: 0.00813 
-2023-11-02 01:02:04.398890: train_loss -0.8822 
-2023-11-02 01:02:04.399023: val_loss -0.8143 
-2023-11-02 01:02:04.399053: Pseudo dice [0.8465] 
-2023-11-02 01:02:04.399087: Epoch time: 108.3 s 
-2023-11-02 01:02:04.912579:  
-2023-11-02 01:02:04.912674: Epoch 207 
-2023-11-02 01:02:04.912737: Current learning rate: 0.00812 
-2023-11-02 01:03:53.347075: train_loss -0.882 
-2023-11-02 01:03:53.347220: val_loss -0.8107 
-2023-11-02 01:03:53.347269: Pseudo dice [0.8445] 
-2023-11-02 01:03:53.347296: Epoch time: 108.43 s 
-2023-11-02 01:03:53.862412:  
-2023-11-02 01:03:53.862478: Epoch 208 
-2023-11-02 01:03:53.862554: Current learning rate: 0.00811 
-2023-11-02 01:05:42.214276: train_loss -0.882 
-2023-11-02 01:05:42.214405: val_loss -0.8288 
-2023-11-02 01:05:42.214454: Pseudo dice [0.8581] 
-2023-11-02 01:05:42.214479: Epoch time: 108.35 s 
-2023-11-02 01:05:42.725121:  
-2023-11-02 01:05:42.725204: Epoch 209 
-2023-11-02 01:05:42.725259: Current learning rate: 0.0081 
-2023-11-02 01:07:31.057391: train_loss -0.881 
-2023-11-02 01:07:31.057512: val_loss -0.81 
-2023-11-02 01:07:31.057563: Pseudo dice [0.8445] 
-2023-11-02 01:07:31.057590: Epoch time: 108.33 s 
-2023-11-02 01:07:31.570970:  
-2023-11-02 01:07:31.571051: Epoch 210 
-2023-11-02 01:07:31.571126: Current learning rate: 0.00809 
-2023-11-02 01:09:19.993937: train_loss -0.8851 
-2023-11-02 01:09:19.994082: val_loss -0.8157 
-2023-11-02 01:09:19.994107: Pseudo dice [0.8492] 
-2023-11-02 01:09:19.994133: Epoch time: 108.42 s 
-2023-11-02 01:09:20.512244:  
-2023-11-02 01:09:20.512316: Epoch 211 
-2023-11-02 01:09:20.512392: Current learning rate: 0.00808 
-2023-11-02 01:11:09.014393: train_loss -0.8849 
-2023-11-02 01:11:09.014539: val_loss -0.8329 
-2023-11-02 01:11:09.014593: Pseudo dice [0.8626] 
-2023-11-02 01:11:09.014623: Epoch time: 108.5 s 
-2023-11-02 01:11:09.533705:  
-2023-11-02 01:11:09.533772: Epoch 212 
-2023-11-02 01:11:09.533847: Current learning rate: 0.00807 
-2023-11-02 01:12:58.047812: train_loss -0.8818 
-2023-11-02 01:12:58.047945: val_loss -0.8168 
-2023-11-02 01:12:58.047999: Pseudo dice [0.8502] 
-2023-11-02 01:12:58.048029: Epoch time: 108.51 s 
-2023-11-02 01:12:58.565228:  
-2023-11-02 01:12:58.565297: Epoch 213 
-2023-11-02 01:12:58.565373: Current learning rate: 0.00806 
-2023-11-02 01:14:47.102195: train_loss -0.8834 
-2023-11-02 01:14:47.102345: val_loss -0.828 
-2023-11-02 01:14:47.102398: Pseudo dice [0.8572] 
-2023-11-02 01:14:47.102426: Epoch time: 108.54 s 
-2023-11-02 01:14:47.102446: Yayy! New best EMA pseudo Dice: 0.8514 
-2023-11-02 01:14:47.847679:  
-2023-11-02 01:14:47.847748: Epoch 214 
-2023-11-02 01:14:47.847800: Current learning rate: 0.00805 
-2023-11-02 01:16:36.330593: train_loss -0.8857 
-2023-11-02 01:16:36.330726: val_loss -0.8276 
-2023-11-02 01:16:36.330755: Pseudo dice [0.8567] 
-2023-11-02 01:16:36.330795: Epoch time: 108.48 s 
-2023-11-02 01:16:36.330822: Yayy! New best EMA pseudo Dice: 0.8519 
-2023-11-02 01:16:37.176963:  
-2023-11-02 01:16:37.177037: Epoch 215 
-2023-11-02 01:16:37.177089: Current learning rate: 0.00804 
-2023-11-02 01:18:25.762121: train_loss -0.8829 
-2023-11-02 01:18:25.762248: val_loss -0.8167 
-2023-11-02 01:18:25.762287: Pseudo dice [0.8483] 
-2023-11-02 01:18:25.762314: Epoch time: 108.59 s 
-2023-11-02 01:18:26.283104:  
-2023-11-02 01:18:26.283174: Epoch 216 
-2023-11-02 01:18:26.283227: Current learning rate: 0.00803 
-2023-11-02 01:20:14.878641: train_loss -0.8827 
-2023-11-02 01:20:14.878810: val_loss -0.8201 
-2023-11-02 01:20:14.878838: Pseudo dice [0.853] 
-2023-11-02 01:20:14.878864: Epoch time: 108.6 s 
-2023-11-02 01:20:15.399037:  
-2023-11-02 01:20:15.399112: Epoch 217 
-2023-11-02 01:20:15.399186: Current learning rate: 0.00802 
-2023-11-02 01:22:04.007740: train_loss -0.8793 
-2023-11-02 01:22:04.007876: val_loss -0.8206 
-2023-11-02 01:22:04.007909: Pseudo dice [0.8529] 
-2023-11-02 01:22:04.007970: Epoch time: 108.61 s 
-2023-11-02 01:22:04.524377:  
-2023-11-02 01:22:04.524447: Epoch 218 
-2023-11-02 01:22:04.524499: Current learning rate: 0.00801 
-2023-11-02 01:23:53.122594: train_loss -0.8894 
-2023-11-02 01:23:53.122721: val_loss -0.8286 
-2023-11-02 01:23:53.122757: Pseudo dice [0.8591] 
-2023-11-02 01:23:53.122784: Epoch time: 108.6 s 
-2023-11-02 01:23:53.122802: Yayy! New best EMA pseudo Dice: 0.8525 
-2023-11-02 01:23:53.877418:  
-2023-11-02 01:23:53.877492: Epoch 219 
-2023-11-02 01:23:53.877547: Current learning rate: 0.00801 
-2023-11-02 01:25:42.462288: train_loss -0.8857 
-2023-11-02 01:25:42.462425: val_loss -0.8116 
-2023-11-02 01:25:42.462480: Pseudo dice [0.8482] 
-2023-11-02 01:25:42.462508: Epoch time: 108.59 s 
-2023-11-02 01:25:42.978337:  
-2023-11-02 01:25:42.978403: Epoch 220 
-2023-11-02 01:25:42.978452: Current learning rate: 0.008 
-2023-11-02 01:27:31.567199: train_loss -0.8875 
-2023-11-02 01:27:31.567352: val_loss -0.8106 
-2023-11-02 01:27:31.567381: Pseudo dice [0.8453] 
-2023-11-02 01:27:31.567408: Epoch time: 108.59 s 
-2023-11-02 01:27:32.177442:  
-2023-11-02 01:27:32.177515: Epoch 221 
-2023-11-02 01:27:32.177593: Current learning rate: 0.00799 
-2023-11-02 01:29:20.779187: train_loss -0.8879 
-2023-11-02 01:29:20.779334: val_loss -0.8165 
-2023-11-02 01:29:20.779386: Pseudo dice [0.8502] 
-2023-11-02 01:29:20.779413: Epoch time: 108.6 s 
-2023-11-02 01:29:21.292313:  
-2023-11-02 01:29:21.292387: Epoch 222 
-2023-11-02 01:29:21.292459: Current learning rate: 0.00798 
-2023-11-02 01:31:09.852803: train_loss -0.8854 
-2023-11-02 01:31:09.852933: val_loss -0.8148 
-2023-11-02 01:31:09.852983: Pseudo dice [0.8488] 
-2023-11-02 01:31:09.853010: Epoch time: 108.56 s 
-2023-11-02 01:31:10.375665:  
-2023-11-02 01:31:10.375739: Epoch 223 
-2023-11-02 01:31:10.375815: Current learning rate: 0.00797 
-2023-11-02 01:32:58.954169: train_loss -0.8838 
-2023-11-02 01:32:58.954301: val_loss -0.8259 
-2023-11-02 01:32:58.954344: Pseudo dice [0.8569] 
-2023-11-02 01:32:58.954370: Epoch time: 108.58 s 
-2023-11-02 01:32:59.471659:  
-2023-11-02 01:32:59.471735: Epoch 224 
-2023-11-02 01:32:59.471788: Current learning rate: 0.00796 
-2023-11-02 01:34:47.993019: train_loss -0.8877 
-2023-11-02 01:34:47.993177: val_loss -0.798 
-2023-11-02 01:34:47.993205: Pseudo dice [0.8363] 
-2023-11-02 01:34:47.993232: Epoch time: 108.52 s 
-2023-11-02 01:34:48.507842:  
-2023-11-02 01:34:48.507911: Epoch 225 
-2023-11-02 01:34:48.507990: Current learning rate: 0.00795 
-2023-11-02 01:36:36.858521: train_loss -0.8845 
-2023-11-02 01:36:36.858654: val_loss -0.8201 
-2023-11-02 01:36:36.858683: Pseudo dice [0.8503] 
-2023-11-02 01:36:36.858713: Epoch time: 108.35 s 
-2023-11-02 01:36:37.368695:  
-2023-11-02 01:36:37.368789: Epoch 226 
-2023-11-02 01:36:37.368843: Current learning rate: 0.00794 
-2023-11-02 01:38:25.613933: train_loss -0.8865 
-2023-11-02 01:38:25.614054: val_loss -0.8143 
-2023-11-02 01:38:25.614079: Pseudo dice [0.8478] 
-2023-11-02 01:38:25.614107: Epoch time: 108.25 s 
-2023-11-02 01:38:26.130805:  
-2023-11-02 01:38:26.130874: Epoch 227 
-2023-11-02 01:38:26.130952: Current learning rate: 0.00793 
-2023-11-02 01:40:14.434930: train_loss -0.8899 
-2023-11-02 01:40:14.435064: val_loss -0.8268 
-2023-11-02 01:40:14.435116: Pseudo dice [0.8552] 
-2023-11-02 01:40:14.435143: Epoch time: 108.3 s 
-2023-11-02 01:40:15.046403:  
-2023-11-02 01:40:15.046482: Epoch 228 
-2023-11-02 01:40:15.046536: Current learning rate: 0.00792 
-2023-11-02 01:42:03.403415: train_loss -0.8914 
-2023-11-02 01:42:03.403537: val_loss -0.8132 
-2023-11-02 01:42:03.403575: Pseudo dice [0.8453] 
-2023-11-02 01:42:03.403601: Epoch time: 108.36 s 
-2023-11-02 01:42:03.925086:  
-2023-11-02 01:42:03.925168: Epoch 229 
-2023-11-02 01:42:03.925223: Current learning rate: 0.00791 
-2023-11-02 01:43:52.340786: train_loss -0.8899 
-2023-11-02 01:43:52.340927: val_loss -0.8158 
-2023-11-02 01:43:52.340956: Pseudo dice [0.8477] 
-2023-11-02 01:43:52.340982: Epoch time: 108.42 s 
-2023-11-02 01:43:52.854464:  
-2023-11-02 01:43:52.854532: Epoch 230 
-2023-11-02 01:43:52.854585: Current learning rate: 0.0079 
-2023-11-02 01:45:41.335245: train_loss -0.8892 
-2023-11-02 01:45:41.335385: val_loss -0.8118 
-2023-11-02 01:45:41.335437: Pseudo dice [0.8463] 
-2023-11-02 01:45:41.335467: Epoch time: 108.48 s 
-2023-11-02 01:45:41.845984:  
-2023-11-02 01:45:41.846078: Epoch 231 
-2023-11-02 01:45:41.846179: Current learning rate: 0.00789 
-2023-11-02 01:47:30.251608: train_loss -0.8903 
-2023-11-02 01:47:30.251734: val_loss -0.8149 
-2023-11-02 01:47:30.251772: Pseudo dice [0.8491] 
-2023-11-02 01:47:30.251797: Epoch time: 108.41 s 
-2023-11-02 01:47:30.768380:  
-2023-11-02 01:47:30.768449: Epoch 232 
-2023-11-02 01:47:30.768516: Current learning rate: 0.00789 
-2023-11-02 01:49:19.156526: train_loss -0.8922 
-2023-11-02 01:49:19.156624: val_loss -0.827 
-2023-11-02 01:49:19.156674: Pseudo dice [0.8581] 
-2023-11-02 01:49:19.156700: Epoch time: 108.39 s 
-2023-11-02 01:49:19.669045:  
-2023-11-02 01:49:19.669113: Epoch 233 
-2023-11-02 01:49:19.669186: Current learning rate: 0.00788 
-2023-11-02 01:51:08.089065: train_loss -0.8929 
-2023-11-02 01:51:08.089192: val_loss -0.8234 
-2023-11-02 01:51:08.089220: Pseudo dice [0.8555] 
-2023-11-02 01:51:08.089248: Epoch time: 108.42 s 
-2023-11-02 01:51:08.699576:  
-2023-11-02 01:51:08.699651: Epoch 234 
-2023-11-02 01:51:08.699706: Current learning rate: 0.00787 
-2023-11-02 01:52:57.082287: train_loss -0.8912 
-2023-11-02 01:52:57.082420: val_loss -0.8219 
-2023-11-02 01:52:57.082470: Pseudo dice [0.8525] 
-2023-11-02 01:52:57.082497: Epoch time: 108.38 s 
-2023-11-02 01:52:57.597039:  
-2023-11-02 01:52:57.597116: Epoch 235 
-2023-11-02 01:52:57.597195: Current learning rate: 0.00786 
-2023-11-02 01:54:46.016975: train_loss -0.8928 
-2023-11-02 01:54:46.017090: val_loss -0.8202 
-2023-11-02 01:54:46.017139: Pseudo dice [0.8504] 
-2023-11-02 01:54:46.017166: Epoch time: 108.42 s 
-2023-11-02 01:54:46.532772:  
-2023-11-02 01:54:46.532846: Epoch 236 
-2023-11-02 01:54:46.532923: Current learning rate: 0.00785 
-2023-11-02 01:56:35.047531: train_loss -0.8887 
-2023-11-02 01:56:35.047651: val_loss -0.8171 
-2023-11-02 01:56:35.047673: Pseudo dice [0.8518] 
-2023-11-02 01:56:35.047700: Epoch time: 108.52 s 
-2023-11-02 01:56:35.559534:  
-2023-11-02 01:56:35.559640: Epoch 237 
-2023-11-02 01:56:35.559747: Current learning rate: 0.00784 
-2023-11-02 01:58:24.130658: train_loss -0.8742 
-2023-11-02 01:58:24.130805: val_loss -0.8113 
-2023-11-02 01:58:24.130831: Pseudo dice [0.8465] 
-2023-11-02 01:58:24.130858: Epoch time: 108.57 s 
-2023-11-02 01:58:24.646338:  
-2023-11-02 01:58:24.646413: Epoch 238 
-2023-11-02 01:58:24.646466: Current learning rate: 0.00783 
-2023-11-02 02:00:13.101672: train_loss -0.8832 
-2023-11-02 02:00:13.101791: val_loss -0.8265 
-2023-11-02 02:00:13.101841: Pseudo dice [0.8584] 
-2023-11-02 02:00:13.101868: Epoch time: 108.46 s 
-2023-11-02 02:00:13.620935:  
-2023-11-02 02:00:13.621008: Epoch 239 
-2023-11-02 02:00:13.621063: Current learning rate: 0.00782 
-2023-11-02 02:02:02.050931: train_loss -0.8856 
-2023-11-02 02:02:02.051059: val_loss -0.8174 
-2023-11-02 02:02:02.051101: Pseudo dice [0.8516] 
-2023-11-02 02:02:02.051143: Epoch time: 108.43 s 
-2023-11-02 02:02:02.571314:  
-2023-11-02 02:02:02.571382: Epoch 240 
-2023-11-02 02:02:02.571435: Current learning rate: 0.00781 
-2023-11-02 02:03:50.973761: train_loss -0.8878 
-2023-11-02 02:03:50.973981: val_loss -0.8201 
-2023-11-02 02:03:50.974014: Pseudo dice [0.8549] 
-2023-11-02 02:03:50.974045: Epoch time: 108.4 s 
-2023-11-02 02:03:51.604998:  
-2023-11-02 02:03:51.605077: Epoch 241 
-2023-11-02 02:03:51.605157: Current learning rate: 0.0078 
-2023-11-02 02:05:40.083244: train_loss -0.8845 
-2023-11-02 02:05:40.083381: val_loss -0.8201 
-2023-11-02 02:05:40.083409: Pseudo dice [0.8508] 
-2023-11-02 02:05:40.083435: Epoch time: 108.48 s 
-2023-11-02 02:05:40.604371:  
-2023-11-02 02:05:40.604446: Epoch 242 
-2023-11-02 02:05:40.604556: Current learning rate: 0.00779 
-2023-11-02 02:07:28.999120: train_loss -0.8891 
-2023-11-02 02:07:28.999255: val_loss -0.8187 
-2023-11-02 02:07:28.999305: Pseudo dice [0.8504] 
-2023-11-02 02:07:28.999331: Epoch time: 108.4 s 
-2023-11-02 02:07:29.517709:  
-2023-11-02 02:07:29.517781: Epoch 243 
-2023-11-02 02:07:29.517833: Current learning rate: 0.00778 
-2023-11-02 02:09:17.898141: train_loss -0.8922 
-2023-11-02 02:09:17.898271: val_loss -0.809 
-2023-11-02 02:09:17.898320: Pseudo dice [0.8447] 
-2023-11-02 02:09:17.898347: Epoch time: 108.38 s 
-2023-11-02 02:09:18.417577:  
-2023-11-02 02:09:18.417647: Epoch 244 
-2023-11-02 02:09:18.417723: Current learning rate: 0.00777 
-2023-11-02 02:11:06.867957: train_loss -0.8886 
-2023-11-02 02:11:06.868114: val_loss -0.815 
-2023-11-02 02:11:06.868148: Pseudo dice [0.8478] 
-2023-11-02 02:11:06.868174: Epoch time: 108.45 s 
-2023-11-02 02:11:07.387964:  
-2023-11-02 02:11:07.388060: Epoch 245 
-2023-11-02 02:11:07.388114: Current learning rate: 0.00777 
-2023-11-02 02:12:55.859612: train_loss -0.8853 
-2023-11-02 02:12:55.859767: val_loss -0.8246 
-2023-11-02 02:12:55.859796: Pseudo dice [0.8547] 
-2023-11-02 02:12:55.859827: Epoch time: 108.47 s 
-2023-11-02 02:12:56.377218:  
-2023-11-02 02:12:56.377287: Epoch 246 
-2023-11-02 02:12:56.377362: Current learning rate: 0.00776 
-2023-11-02 02:14:44.828462: train_loss -0.8868 
-2023-11-02 02:14:44.828587: val_loss -0.8154 
-2023-11-02 02:14:44.828612: Pseudo dice [0.8504] 
-2023-11-02 02:14:44.828638: Epoch time: 108.45 s 
-2023-11-02 02:14:45.345683:  
-2023-11-02 02:14:45.345747: Epoch 247 
-2023-11-02 02:14:45.345824: Current learning rate: 0.00775 
-2023-11-02 02:16:33.856910: train_loss -0.8891 
-2023-11-02 02:16:33.857065: val_loss -0.8065 
-2023-11-02 02:16:33.857091: Pseudo dice [0.8412] 
-2023-11-02 02:16:33.857117: Epoch time: 108.51 s 
-2023-11-02 02:16:34.374614:  
-2023-11-02 02:16:34.374687: Epoch 248 
-2023-11-02 02:16:34.374767: Current learning rate: 0.00774 
-2023-11-02 02:18:22.851471: train_loss -0.8877 
-2023-11-02 02:18:22.851600: val_loss -0.798 
-2023-11-02 02:18:22.851648: Pseudo dice [0.8373] 
-2023-11-02 02:18:22.851675: Epoch time: 108.48 s 
-2023-11-02 02:18:23.371107:  
-2023-11-02 02:18:23.371179: Epoch 249 
-2023-11-02 02:18:23.371256: Current learning rate: 0.00773 
-2023-11-02 02:20:11.814887: train_loss -0.8841 
-2023-11-02 02:20:11.815022: val_loss -0.8213 
-2023-11-02 02:20:11.815061: Pseudo dice [0.8529] 
-2023-11-02 02:20:11.815088: Epoch time: 108.44 s 
-2023-11-02 02:20:12.566232:  
-2023-11-02 02:20:12.566330: Epoch 250 
-2023-11-02 02:20:12.566384: Current learning rate: 0.00772 
-2023-11-02 02:22:01.096305: train_loss -0.8829 
-2023-11-02 02:22:01.096436: val_loss -0.8138 
-2023-11-02 02:22:01.096461: Pseudo dice [0.8476] 
-2023-11-02 02:22:01.096489: Epoch time: 108.53 s 
-2023-11-02 02:22:01.612485:  
-2023-11-02 02:22:01.612560: Epoch 251 
-2023-11-02 02:22:01.612639: Current learning rate: 0.00771 
-2023-11-02 02:23:50.103537: train_loss -0.886 
-2023-11-02 02:23:50.103667: val_loss -0.83 
-2023-11-02 02:23:50.103698: Pseudo dice [0.8571] 
-2023-11-02 02:23:50.103731: Epoch time: 108.49 s 
-2023-11-02 02:23:50.622338:  
-2023-11-02 02:23:50.622406: Epoch 252 
-2023-11-02 02:23:50.622479: Current learning rate: 0.0077 
-2023-11-02 02:25:39.111003: train_loss -0.8873 
-2023-11-02 02:25:39.111213: val_loss -0.8213 
-2023-11-02 02:25:39.111240: Pseudo dice [0.8522] 
-2023-11-02 02:25:39.111294: Epoch time: 108.49 s 
-2023-11-02 02:25:39.625703:  
-2023-11-02 02:25:39.625772: Epoch 253 
-2023-11-02 02:25:39.625822: Current learning rate: 0.00769 
-2023-11-02 02:27:28.091243: train_loss -0.8826 
-2023-11-02 02:27:28.091374: val_loss -0.8012 
-2023-11-02 02:27:28.091424: Pseudo dice [0.8367] 
-2023-11-02 02:27:28.091449: Epoch time: 108.47 s 
-2023-11-02 02:27:28.707119:  
-2023-11-02 02:27:28.707196: Epoch 254 
-2023-11-02 02:27:28.707278: Current learning rate: 0.00768 
-2023-11-02 02:29:17.258857: train_loss -0.8735 
-2023-11-02 02:29:17.258979: val_loss -0.8204 
-2023-11-02 02:29:17.259017: Pseudo dice [0.8505] 
-2023-11-02 02:29:17.259043: Epoch time: 108.55 s 
-2023-11-02 02:29:17.782259:  
-2023-11-02 02:29:17.782336: Epoch 255 
-2023-11-02 02:29:17.782389: Current learning rate: 0.00767 
-2023-11-02 02:31:06.370835: train_loss -0.8854 
-2023-11-02 02:31:06.370970: val_loss -0.8161 
-2023-11-02 02:31:06.371000: Pseudo dice [0.8506] 
-2023-11-02 02:31:06.371027: Epoch time: 108.59 s 
-2023-11-02 02:31:06.896803:  
-2023-11-02 02:31:06.896879: Epoch 256 
-2023-11-02 02:31:06.896930: Current learning rate: 0.00766 
-2023-11-02 02:32:55.409951: train_loss -0.8867 
-2023-11-02 02:32:55.410084: val_loss -0.8258 
-2023-11-02 02:32:55.410108: Pseudo dice [0.8573] 
-2023-11-02 02:32:55.410137: Epoch time: 108.51 s 
-2023-11-02 02:32:55.930543:  
-2023-11-02 02:32:55.930616: Epoch 257 
-2023-11-02 02:32:55.930668: Current learning rate: 0.00765 
-2023-11-02 02:34:44.451066: train_loss -0.8872 
-2023-11-02 02:34:44.451217: val_loss -0.8177 
-2023-11-02 02:34:44.451242: Pseudo dice [0.8513] 
-2023-11-02 02:34:44.451268: Epoch time: 108.52 s 
-2023-11-02 02:34:44.969431:  
-2023-11-02 02:34:44.969499: Epoch 258 
-2023-11-02 02:34:44.969575: Current learning rate: 0.00764 
-2023-11-02 02:36:33.592894: train_loss -0.8887 
-2023-11-02 02:36:33.593023: val_loss -0.8077 
-2023-11-02 02:36:33.593050: Pseudo dice [0.842] 
-2023-11-02 02:36:33.593079: Epoch time: 108.62 s 
-2023-11-02 02:36:34.112084:  
-2023-11-02 02:36:34.112177: Epoch 259 
-2023-11-02 02:36:34.112228: Current learning rate: 0.00764 
-2023-11-02 02:38:22.679979: train_loss -0.89 
-2023-11-02 02:38:22.680144: val_loss -0.8191 
-2023-11-02 02:38:22.680168: Pseudo dice [0.8529] 
-2023-11-02 02:38:22.680196: Epoch time: 108.57 s 
-2023-11-02 02:38:23.294737:  
-2023-11-02 02:38:23.294861: Epoch 260 
-2023-11-02 02:38:23.294918: Current learning rate: 0.00763 
-2023-11-02 02:40:11.929649: train_loss -0.8922 
-2023-11-02 02:40:11.929796: val_loss -0.826 
-2023-11-02 02:40:11.929846: Pseudo dice [0.8551] 
-2023-11-02 02:40:11.929872: Epoch time: 108.64 s 
-2023-11-02 02:40:12.451093:  
-2023-11-02 02:40:12.451170: Epoch 261 
-2023-11-02 02:40:12.451249: Current learning rate: 0.00762 
-2023-11-02 02:42:01.072782: train_loss -0.8919 
-2023-11-02 02:42:01.072912: val_loss -0.8179 
-2023-11-02 02:42:01.072961: Pseudo dice [0.8512] 
-2023-11-02 02:42:01.072988: Epoch time: 108.62 s 
-2023-11-02 02:42:01.595150:  
-2023-11-02 02:42:01.595229: Epoch 262 
-2023-11-02 02:42:01.595308: Current learning rate: 0.00761 
-2023-11-02 02:43:50.209628: train_loss -0.8932 
-2023-11-02 02:43:50.209791: val_loss -0.8235 
-2023-11-02 02:43:50.209881: Pseudo dice [0.8556] 
-2023-11-02 02:43:50.209910: Epoch time: 108.61 s 
-2023-11-02 02:43:50.731419:  
-2023-11-02 02:43:50.731494: Epoch 263 
-2023-11-02 02:43:50.731567: Current learning rate: 0.0076 
-2023-11-02 02:45:39.302936: train_loss -0.8933 
-2023-11-02 02:45:39.303046: val_loss -0.8159 
-2023-11-02 02:45:39.303096: Pseudo dice [0.8519] 
-2023-11-02 02:45:39.303123: Epoch time: 108.57 s 
-2023-11-02 02:45:39.826591:  
-2023-11-02 02:45:39.826664: Epoch 264 
-2023-11-02 02:45:39.826769: Current learning rate: 0.00759 
-2023-11-02 02:47:28.356943: train_loss -0.8936 
-2023-11-02 02:47:28.357085: val_loss -0.8181 
-2023-11-02 02:47:28.357116: Pseudo dice [0.8504] 
-2023-11-02 02:47:28.357149: Epoch time: 108.53 s 
-2023-11-02 02:47:28.878896:  
-2023-11-02 02:47:28.878979: Epoch 265 
-2023-11-02 02:47:28.879039: Current learning rate: 0.00758 
-2023-11-02 02:49:17.536768: train_loss -0.8943 
-2023-11-02 02:49:17.536894: val_loss -0.8098 
-2023-11-02 02:49:17.536942: Pseudo dice [0.8514] 
-2023-11-02 02:49:17.536968: Epoch time: 108.66 s 
-2023-11-02 02:49:18.056411:  
-2023-11-02 02:49:18.056491: Epoch 266 
-2023-11-02 02:49:18.056572: Current learning rate: 0.00757 
-2023-11-02 02:51:06.657683: train_loss -0.891 
-2023-11-02 02:51:06.657810: val_loss -0.8091 
-2023-11-02 02:51:06.657867: Pseudo dice [0.8464] 
-2023-11-02 02:51:06.657894: Epoch time: 108.6 s 
-2023-11-02 02:51:07.282881:  
-2023-11-02 02:51:07.282980: Epoch 267 
-2023-11-02 02:51:07.283033: Current learning rate: 0.00756 
-2023-11-02 02:52:55.801507: train_loss -0.8926 
-2023-11-02 02:52:55.801638: val_loss -0.8191 
-2023-11-02 02:52:55.801662: Pseudo dice [0.853] 
-2023-11-02 02:52:55.801689: Epoch time: 108.52 s 
-2023-11-02 02:52:56.324778:  
-2023-11-02 02:52:56.324859: Epoch 268 
-2023-11-02 02:52:56.324967: Current learning rate: 0.00755 
-2023-11-02 02:54:44.788031: train_loss -0.8916 
-2023-11-02 02:54:44.788182: val_loss -0.8178 
-2023-11-02 02:54:44.788234: Pseudo dice [0.8508] 
-2023-11-02 02:54:44.788260: Epoch time: 108.46 s 
-2023-11-02 02:54:45.313042:  
-2023-11-02 02:54:45.313122: Epoch 269 
-2023-11-02 02:54:45.313175: Current learning rate: 0.00754 
-2023-11-02 02:56:33.734466: train_loss -0.8937 
-2023-11-02 02:56:33.734586: val_loss -0.8126 
-2023-11-02 02:56:33.734637: Pseudo dice [0.8478] 
-2023-11-02 02:56:33.734664: Epoch time: 108.42 s 
-2023-11-02 02:56:34.257509:  
-2023-11-02 02:56:34.257630: Epoch 270 
-2023-11-02 02:56:34.257687: Current learning rate: 0.00753 
-2023-11-02 02:58:22.705815: train_loss -0.8948 
-2023-11-02 02:58:22.705967: val_loss -0.8124 
-2023-11-02 02:58:22.705993: Pseudo dice [0.8472] 
-2023-11-02 02:58:22.706020: Epoch time: 108.45 s 
-2023-11-02 02:58:23.226118:  
-2023-11-02 02:58:23.226187: Epoch 271 
-2023-11-02 02:58:23.226264: Current learning rate: 0.00752 
-2023-11-02 03:00:11.715910: train_loss -0.8929 
-2023-11-02 03:00:11.716083: val_loss -0.8082 
-2023-11-02 03:00:11.716111: Pseudo dice [0.8465] 
-2023-11-02 03:00:11.716141: Epoch time: 108.49 s 
-2023-11-02 03:00:12.236891:  
-2023-11-02 03:00:12.236956: Epoch 272 
-2023-11-02 03:00:12.237032: Current learning rate: 0.00751 
-2023-11-02 03:02:00.640975: train_loss -0.8917 
-2023-11-02 03:02:00.641101: val_loss -0.8182 
-2023-11-02 03:02:00.641140: Pseudo dice [0.8509] 
-2023-11-02 03:02:00.641166: Epoch time: 108.4 s 
-2023-11-02 03:02:01.159235:  
-2023-11-02 03:02:01.159305: Epoch 273 
-2023-11-02 03:02:01.159358: Current learning rate: 0.00751 
-2023-11-02 03:03:49.556300: train_loss -0.8951 
-2023-11-02 03:03:49.556444: val_loss -0.8179 
-2023-11-02 03:03:49.556472: Pseudo dice [0.8497] 
-2023-11-02 03:03:49.556498: Epoch time: 108.4 s 
-2023-11-02 03:03:50.078813:  
-2023-11-02 03:03:50.078882: Epoch 274 
-2023-11-02 03:03:50.078947: Current learning rate: 0.0075 
-2023-11-02 03:05:38.503753: train_loss -0.896 
-2023-11-02 03:05:38.503880: val_loss -0.8246 
-2023-11-02 03:05:38.503905: Pseudo dice [0.8568] 
-2023-11-02 03:05:38.503931: Epoch time: 108.43 s 
-2023-11-02 03:05:39.035689:  
-2023-11-02 03:05:39.035772: Epoch 275 
-2023-11-02 03:05:39.035851: Current learning rate: 0.00749 
-2023-11-02 03:07:27.510633: train_loss -0.8947 
-2023-11-02 03:07:27.510763: val_loss -0.8038 
-2023-11-02 03:07:27.510813: Pseudo dice [0.8398] 
-2023-11-02 03:07:27.510840: Epoch time: 108.48 s 
-2023-11-02 03:07:28.030913:  
-2023-11-02 03:07:28.030991: Epoch 276 
-2023-11-02 03:07:28.031067: Current learning rate: 0.00748 
-2023-11-02 03:09:16.557901: train_loss -0.895 
-2023-11-02 03:09:16.558021: val_loss -0.8164 
-2023-11-02 03:09:16.558068: Pseudo dice [0.851] 
-2023-11-02 03:09:16.558094: Epoch time: 108.53 s 
-2023-11-02 03:09:17.079537:  
-2023-11-02 03:09:17.079608: Epoch 277 
-2023-11-02 03:09:17.079674: Current learning rate: 0.00747 
-2023-11-02 03:11:05.772901: train_loss -0.8895 
-2023-11-02 03:11:05.773026: val_loss -0.824 
-2023-11-02 03:11:05.773057: Pseudo dice [0.8567] 
-2023-11-02 03:11:05.773085: Epoch time: 108.69 s 
-2023-11-02 03:11:06.296264:  
-2023-11-02 03:11:06.296334: Epoch 278 
-2023-11-02 03:11:06.296385: Current learning rate: 0.00746 
-2023-11-02 03:12:54.801620: train_loss -0.8871 
-2023-11-02 03:12:54.801751: val_loss -0.8156 
-2023-11-02 03:12:54.801778: Pseudo dice [0.848] 
-2023-11-02 03:12:54.801805: Epoch time: 108.51 s 
-2023-11-02 03:12:55.324717:  
-2023-11-02 03:12:55.324785: Epoch 279 
-2023-11-02 03:12:55.324866: Current learning rate: 0.00745 
-2023-11-02 03:14:43.826348: train_loss -0.8912 
-2023-11-02 03:14:43.826476: val_loss -0.8226 
-2023-11-02 03:14:43.826519: Pseudo dice [0.8525] 
-2023-11-02 03:14:43.826545: Epoch time: 108.5 s 
-2023-11-02 03:14:44.448284:  
-2023-11-02 03:14:44.448360: Epoch 280 
-2023-11-02 03:14:44.448411: Current learning rate: 0.00744 
-2023-11-02 03:16:33.054097: train_loss -0.8919 
-2023-11-02 03:16:33.054256: val_loss -0.8177 
-2023-11-02 03:16:33.054287: Pseudo dice [0.8491] 
-2023-11-02 03:16:33.054315: Epoch time: 108.61 s 
-2023-11-02 03:16:33.573986:  
-2023-11-02 03:16:33.574067: Epoch 281 
-2023-11-02 03:16:33.574122: Current learning rate: 0.00743 
-2023-11-02 03:18:22.092015: train_loss -0.892 
-2023-11-02 03:18:22.092151: val_loss -0.8178 
-2023-11-02 03:18:22.092209: Pseudo dice [0.8506] 
-2023-11-02 03:18:22.092235: Epoch time: 108.52 s 
-2023-11-02 03:18:22.616439:  
-2023-11-02 03:18:22.616510: Epoch 282 
-2023-11-02 03:18:22.616588: Current learning rate: 0.00742 
-2023-11-02 03:20:11.111100: train_loss -0.8886 
-2023-11-02 03:20:11.111253: val_loss -0.8114 
-2023-11-02 03:20:11.111277: Pseudo dice [0.8466] 
-2023-11-02 03:20:11.111305: Epoch time: 108.5 s 
-2023-11-02 03:20:11.631791:  
-2023-11-02 03:20:11.631877: Epoch 283 
-2023-11-02 03:20:11.631928: Current learning rate: 0.00741 
-2023-11-02 03:22:00.172333: train_loss -0.8911 
-2023-11-02 03:22:00.172458: val_loss -0.8216 
-2023-11-02 03:22:00.172507: Pseudo dice [0.854] 
-2023-11-02 03:22:00.172533: Epoch time: 108.54 s 
-2023-11-02 03:22:00.699066:  
-2023-11-02 03:22:00.699139: Epoch 284 
-2023-11-02 03:22:00.699191: Current learning rate: 0.0074 
-2023-11-02 03:23:49.187288: train_loss -0.8937 
-2023-11-02 03:23:49.187422: val_loss -0.8165 
-2023-11-02 03:23:49.187461: Pseudo dice [0.8513] 
-2023-11-02 03:23:49.187490: Epoch time: 108.49 s 
-2023-11-02 03:23:49.711168:  
-2023-11-02 03:23:49.711237: Epoch 285 
-2023-11-02 03:23:49.711304: Current learning rate: 0.00739 
-2023-11-02 03:25:38.167849: train_loss -0.8945 
-2023-11-02 03:25:38.167982: val_loss -0.8211 
-2023-11-02 03:25:38.168009: Pseudo dice [0.8523] 
-2023-11-02 03:25:38.168058: Epoch time: 108.46 s 
-2023-11-02 03:25:38.692192:  
-2023-11-02 03:25:38.692263: Epoch 286 
-2023-11-02 03:25:38.692318: Current learning rate: 0.00738 
-2023-11-02 03:27:27.170542: train_loss -0.892 
-2023-11-02 03:27:27.170719: val_loss -0.8272 
-2023-11-02 03:27:27.170747: Pseudo dice [0.8589] 
-2023-11-02 03:27:27.170775: Epoch time: 108.48 s 
-2023-11-02 03:27:27.700269:  
-2023-11-02 03:27:27.700347: Epoch 287 
-2023-11-02 03:27:27.700402: Current learning rate: 0.00738 
-2023-11-02 03:29:16.169558: train_loss -0.8913 
-2023-11-02 03:29:16.169699: val_loss -0.8285 
-2023-11-02 03:29:16.169747: Pseudo dice [0.8587] 
-2023-11-02 03:29:16.169774: Epoch time: 108.47 s 
-2023-11-02 03:29:16.701684:  
-2023-11-02 03:29:16.701758: Epoch 288 
-2023-11-02 03:29:16.701837: Current learning rate: 0.00737 
-2023-11-02 03:31:05.308617: train_loss -0.8859 
-2023-11-02 03:31:05.308745: val_loss -0.8181 
-2023-11-02 03:31:05.308796: Pseudo dice [0.8515] 
-2023-11-02 03:31:05.308821: Epoch time: 108.61 s 
-2023-11-02 03:31:05.841534:  
-2023-11-02 03:31:05.841602: Epoch 289 
-2023-11-02 03:31:05.841678: Current learning rate: 0.00736 
-2023-11-02 03:32:54.348739: train_loss -0.8805 
-2023-11-02 03:32:54.348887: val_loss -0.8271 
-2023-11-02 03:32:54.348937: Pseudo dice [0.8568] 
-2023-11-02 03:32:54.348965: Epoch time: 108.51 s 
-2023-11-02 03:32:54.348983: Yayy! New best EMA pseudo Dice: 0.8526 
-2023-11-02 03:32:55.113837:  
-2023-11-02 03:32:55.113910: Epoch 290 
-2023-11-02 03:32:55.113977: Current learning rate: 0.00735 
-2023-11-02 03:34:43.652987: train_loss -0.887 
-2023-11-02 03:34:43.653115: val_loss -0.8211 
-2023-11-02 03:34:43.653166: Pseudo dice [0.8542] 
-2023-11-02 03:34:43.653193: Epoch time: 108.54 s 
-2023-11-02 03:34:43.653210: Yayy! New best EMA pseudo Dice: 0.8527 
-2023-11-02 03:34:44.413526:  
-2023-11-02 03:34:44.413621: Epoch 291 
-2023-11-02 03:34:44.413676: Current learning rate: 0.00734 
-2023-11-02 03:36:33.024887: train_loss -0.8886 
-2023-11-02 03:36:33.025013: val_loss -0.82 
-2023-11-02 03:36:33.025062: Pseudo dice [0.8537] 
-2023-11-02 03:36:33.025089: Epoch time: 108.61 s 
-2023-11-02 03:36:33.025108: Yayy! New best EMA pseudo Dice: 0.8528 
-2023-11-02 03:36:33.886277:  
-2023-11-02 03:36:33.886353: Epoch 292 
-2023-11-02 03:36:33.886408: Current learning rate: 0.00733 
-2023-11-02 03:38:22.416989: train_loss -0.8967 
-2023-11-02 03:38:22.417140: val_loss -0.8303 
-2023-11-02 03:38:22.417166: Pseudo dice [0.8572] 
-2023-11-02 03:38:22.417192: Epoch time: 108.53 s 
-2023-11-02 03:38:22.417209: Yayy! New best EMA pseudo Dice: 0.8533 
-2023-11-02 03:38:23.187676:  
-2023-11-02 03:38:23.187754: Epoch 293 
-2023-11-02 03:38:23.187806: Current learning rate: 0.00732 
-2023-11-02 03:40:11.672152: train_loss -0.8956 
-2023-11-02 03:40:11.672275: val_loss -0.8189 
-2023-11-02 03:40:11.672326: Pseudo dice [0.8514] 
-2023-11-02 03:40:11.672352: Epoch time: 108.48 s 
-2023-11-02 03:40:12.199555:  
-2023-11-02 03:40:12.199632: Epoch 294 
-2023-11-02 03:40:12.199711: Current learning rate: 0.00731 
-2023-11-02 03:42:00.823078: train_loss -0.8941 
-2023-11-02 03:42:00.823206: val_loss -0.817 
-2023-11-02 03:42:00.823232: Pseudo dice [0.8513] 
-2023-11-02 03:42:00.823260: Epoch time: 108.62 s 
-2023-11-02 03:42:01.350564:  
-2023-11-02 03:42:01.350641: Epoch 295 
-2023-11-02 03:42:01.350694: Current learning rate: 0.0073 
-2023-11-02 03:43:49.945811: train_loss -0.8937 
-2023-11-02 03:43:49.945956: val_loss -0.8209 
-2023-11-02 03:43:49.945982: Pseudo dice [0.8545] 
-2023-11-02 03:43:49.946021: Epoch time: 108.6 s 
-2023-11-02 03:43:50.476429:  
-2023-11-02 03:43:50.476498: Epoch 296 
-2023-11-02 03:43:50.476552: Current learning rate: 0.00729 
-2023-11-02 03:45:39.052551: train_loss -0.8946 
-2023-11-02 03:45:39.052685: val_loss -0.8229 
-2023-11-02 03:45:39.052710: Pseudo dice [0.8535] 
-2023-11-02 03:45:39.052737: Epoch time: 108.58 s 
-2023-11-02 03:45:39.579168:  
-2023-11-02 03:45:39.579234: Epoch 297 
-2023-11-02 03:45:39.579288: Current learning rate: 0.00728 
-2023-11-02 03:47:28.108362: train_loss -0.8939 
-2023-11-02 03:47:28.108513: val_loss -0.8143 
-2023-11-02 03:47:28.108538: Pseudo dice [0.8472] 
-2023-11-02 03:47:28.108566: Epoch time: 108.53 s 
-2023-11-02 03:47:28.638801:  
-2023-11-02 03:47:28.638868: Epoch 298 
-2023-11-02 03:47:28.638942: Current learning rate: 0.00727 
-2023-11-02 03:49:17.129270: train_loss -0.8993 
-2023-11-02 03:49:17.129416: val_loss -0.8167 
-2023-11-02 03:49:17.129441: Pseudo dice [0.8496] 
-2023-11-02 03:49:17.129469: Epoch time: 108.49 s 
-2023-11-02 03:49:17.765069:  
-2023-11-02 03:49:17.765153: Epoch 299 
-2023-11-02 03:49:17.765231: Current learning rate: 0.00726 
-2023-11-02 03:51:06.318035: train_loss -0.8965 
-2023-11-02 03:51:06.318172: val_loss -0.814 
-2023-11-02 03:51:06.318221: Pseudo dice [0.848] 
-2023-11-02 03:51:06.318247: Epoch time: 108.55 s 
-2023-11-02 03:51:07.077519:  
-2023-11-02 03:51:07.077600: Epoch 300 
-2023-11-02 03:51:07.077693: Current learning rate: 0.00725 
-2023-11-02 03:52:55.745102: train_loss -0.8924 
-2023-11-02 03:52:55.745248: val_loss -0.8263 
-2023-11-02 03:52:55.745276: Pseudo dice [0.8593] 
-2023-11-02 03:52:55.745327: Epoch time: 108.67 s 
-2023-11-02 03:52:56.278171:  
-2023-11-02 03:52:56.278248: Epoch 301 
-2023-11-02 03:52:56.278300: Current learning rate: 0.00724 
-2023-11-02 03:54:44.917311: train_loss -0.893 
-2023-11-02 03:54:44.917467: val_loss -0.8174 
-2023-11-02 03:54:44.917494: Pseudo dice [0.8482] 
-2023-11-02 03:54:44.917521: Epoch time: 108.64 s 
-2023-11-02 03:54:45.449735:  
-2023-11-02 03:54:45.449811: Epoch 302 
-2023-11-02 03:54:45.449862: Current learning rate: 0.00724 
-2023-11-02 03:56:34.075675: train_loss -0.8939 
-2023-11-02 03:56:34.075811: val_loss -0.8147 
-2023-11-02 03:56:34.075836: Pseudo dice [0.8493] 
-2023-11-02 03:56:34.075864: Epoch time: 108.63 s 
-2023-11-02 03:56:34.607928:  
-2023-11-02 03:56:34.608029: Epoch 303 
-2023-11-02 03:56:34.608087: Current learning rate: 0.00723 
-2023-11-02 03:58:23.210663: train_loss -0.8918 
-2023-11-02 03:58:23.210786: val_loss -0.8271 
-2023-11-02 03:58:23.210823: Pseudo dice [0.8579] 
-2023-11-02 03:58:23.210849: Epoch time: 108.6 s 
-2023-11-02 03:58:23.739066:  
-2023-11-02 03:58:23.739133: Epoch 304 
-2023-11-02 03:58:23.739184: Current learning rate: 0.00722 
-2023-11-02 04:00:12.366936: train_loss -0.8912 
-2023-11-02 04:00:12.367062: val_loss -0.8135 
-2023-11-02 04:00:12.367112: Pseudo dice [0.8497] 
-2023-11-02 04:00:12.367140: Epoch time: 108.63 s 
-2023-11-02 04:00:12.997158:  
-2023-11-02 04:00:12.997231: Epoch 305 
-2023-11-02 04:00:12.997310: Current learning rate: 0.00721 
-2023-11-02 04:02:01.659050: train_loss -0.8911 
-2023-11-02 04:02:01.659182: val_loss -0.815 
-2023-11-02 04:02:01.659232: Pseudo dice [0.8493] 
-2023-11-02 04:02:01.659259: Epoch time: 108.66 s 
-2023-11-02 04:02:02.185170:  
-2023-11-02 04:02:02.185273: Epoch 306 
-2023-11-02 04:02:02.185325: Current learning rate: 0.0072 
-2023-11-02 04:03:50.767678: train_loss -0.893 
-2023-11-02 04:03:50.767821: val_loss -0.8143 
-2023-11-02 04:03:50.767871: Pseudo dice [0.8476] 
-2023-11-02 04:03:50.767897: Epoch time: 108.58 s 
-2023-11-02 04:03:51.300298:  
-2023-11-02 04:03:51.300381: Epoch 307 
-2023-11-02 04:03:51.300435: Current learning rate: 0.00719 
-2023-11-02 04:05:39.887552: train_loss -0.8965 
-2023-11-02 04:05:39.887675: val_loss -0.8204 
-2023-11-02 04:05:39.887700: Pseudo dice [0.8568] 
-2023-11-02 04:05:39.887727: Epoch time: 108.59 s 
-2023-11-02 04:05:40.420962:  
-2023-11-02 04:05:40.421053: Epoch 308 
-2023-11-02 04:05:40.421105: Current learning rate: 0.00718 
-2023-11-02 04:07:28.988318: train_loss -0.899 
-2023-11-02 04:07:28.988418: val_loss -0.8155 
-2023-11-02 04:07:28.988454: Pseudo dice [0.8479] 
-2023-11-02 04:07:28.988480: Epoch time: 108.57 s 
-2023-11-02 04:07:29.520088:  
-2023-11-02 04:07:29.520163: Epoch 309 
-2023-11-02 04:07:29.520218: Current learning rate: 0.00717 
-2023-11-02 04:09:18.196171: train_loss -0.896 
-2023-11-02 04:09:18.196299: val_loss -0.8277 
-2023-11-02 04:09:18.196349: Pseudo dice [0.8564] 
-2023-11-02 04:09:18.196377: Epoch time: 108.68 s 
-2023-11-02 04:09:18.723401:  
-2023-11-02 04:09:18.723469: Epoch 310 
-2023-11-02 04:09:18.723570: Current learning rate: 0.00716 
-2023-11-02 04:11:07.060856: train_loss -0.8914 
-2023-11-02 04:11:07.060999: val_loss -0.8171 
-2023-11-02 04:11:07.061027: Pseudo dice [0.8526] 
-2023-11-02 04:11:07.061078: Epoch time: 108.34 s 
-2023-11-02 04:11:07.691279:  
-2023-11-02 04:11:07.691359: Epoch 311 
-2023-11-02 04:11:07.691411: Current learning rate: 0.00715 
-2023-11-02 04:12:56.079233: train_loss -0.8834 
-2023-11-02 04:12:56.079394: val_loss -0.8161 
-2023-11-02 04:12:56.079424: Pseudo dice [0.8517] 
-2023-11-02 04:12:56.079452: Epoch time: 108.39 s 
-2023-11-02 04:12:56.611926:  
-2023-11-02 04:12:56.612006: Epoch 312 
-2023-11-02 04:12:56.612060: Current learning rate: 0.00714 
-2023-11-02 04:14:45.216667: train_loss -0.887 
-2023-11-02 04:14:45.216799: val_loss -0.8209 
-2023-11-02 04:14:45.216849: Pseudo dice [0.853] 
-2023-11-02 04:14:45.216875: Epoch time: 108.61 s 
-2023-11-02 04:14:45.756166:  
-2023-11-02 04:14:45.756239: Epoch 313 
-2023-11-02 04:14:45.756317: Current learning rate: 0.00713 
-2023-11-02 04:16:34.401178: train_loss -0.8896 
-2023-11-02 04:16:34.401327: val_loss -0.8196 
-2023-11-02 04:16:34.401356: Pseudo dice [0.8519] 
-2023-11-02 04:16:34.401386: Epoch time: 108.65 s 
-2023-11-02 04:16:34.944667:  
-2023-11-02 04:16:34.944753: Epoch 314 
-2023-11-02 04:16:34.944821: Current learning rate: 0.00712 
-2023-11-02 04:18:23.493184: train_loss -0.8955 
-2023-11-02 04:18:23.493330: val_loss -0.8131 
-2023-11-02 04:18:23.493390: Pseudo dice [0.8482] 
-2023-11-02 04:18:23.493422: Epoch time: 108.55 s 
-2023-11-02 04:18:24.023214:  
-2023-11-02 04:18:24.023279: Epoch 315 
-2023-11-02 04:18:24.023361: Current learning rate: 0.00711 
-2023-11-02 04:20:12.543013: train_loss -0.8943 
-2023-11-02 04:20:12.543135: val_loss -0.8153 
-2023-11-02 04:20:12.543184: Pseudo dice [0.8505] 
-2023-11-02 04:20:12.543210: Epoch time: 108.52 s 
-2023-11-02 04:20:13.075199:  
-2023-11-02 04:20:13.075269: Epoch 316 
-2023-11-02 04:20:13.075346: Current learning rate: 0.0071 
-2023-11-02 04:22:01.510234: train_loss -0.8954 
-2023-11-02 04:22:01.510365: val_loss -0.802 
-2023-11-02 04:22:01.510401: Pseudo dice [0.8407] 
-2023-11-02 04:22:01.510428: Epoch time: 108.44 s 
-2023-11-02 04:22:02.043087:  
-2023-11-02 04:22:02.043155: Epoch 317 
-2023-11-02 04:22:02.043221: Current learning rate: 0.0071 
-2023-11-02 04:23:50.586563: train_loss -0.8987 
-2023-11-02 04:23:50.586681: val_loss -0.8235 
-2023-11-02 04:23:50.586730: Pseudo dice [0.8549] 
-2023-11-02 04:23:50.586755: Epoch time: 108.54 s 
-2023-11-02 04:23:51.219139:  
-2023-11-02 04:23:51.219215: Epoch 318 
-2023-11-02 04:23:51.219291: Current learning rate: 0.00709 
-2023-11-02 04:25:39.762149: train_loss -0.894 
-2023-11-02 04:25:39.762276: val_loss -0.8117 
-2023-11-02 04:25:39.762326: Pseudo dice [0.8449] 
-2023-11-02 04:25:39.762352: Epoch time: 108.54 s 
-2023-11-02 04:25:40.297568:  
-2023-11-02 04:25:40.297805: Epoch 319 
-2023-11-02 04:25:40.297886: Current learning rate: 0.00708 
-2023-11-02 04:27:28.761603: train_loss -0.8869 
-2023-11-02 04:27:28.761757: val_loss -0.8168 
-2023-11-02 04:27:28.761783: Pseudo dice [0.851] 
-2023-11-02 04:27:28.761809: Epoch time: 108.46 s 
-2023-11-02 04:27:29.292605:  
-2023-11-02 04:27:29.292683: Epoch 320 
-2023-11-02 04:27:29.292761: Current learning rate: 0.00707 
-2023-11-02 04:29:17.720560: train_loss -0.8875 
-2023-11-02 04:29:17.720697: val_loss -0.8233 
-2023-11-02 04:29:17.720726: Pseudo dice [0.8533] 
-2023-11-02 04:29:17.720783: Epoch time: 108.43 s 
-2023-11-02 04:29:18.252424:  
-2023-11-02 04:29:18.252496: Epoch 321 
-2023-11-02 04:29:18.252549: Current learning rate: 0.00706 
-2023-11-02 04:31:06.726846: train_loss -0.8891 
-2023-11-02 04:31:06.726995: val_loss -0.8235 
-2023-11-02 04:31:06.727026: Pseudo dice [0.8531] 
-2023-11-02 04:31:06.727057: Epoch time: 108.47 s 
-2023-11-02 04:31:07.258575:  
-2023-11-02 04:31:07.258644: Epoch 322 
-2023-11-02 04:31:07.258721: Current learning rate: 0.00705 
-2023-11-02 04:32:55.634292: train_loss -0.8861 
-2023-11-02 04:32:55.634428: val_loss -0.802 
-2023-11-02 04:32:55.634468: Pseudo dice [0.8421] 
-2023-11-02 04:32:55.634496: Epoch time: 108.38 s 
-2023-11-02 04:32:56.168769:  
-2023-11-02 04:32:56.168884: Epoch 323 
-2023-11-02 04:32:56.168976: Current learning rate: 0.00704 
-2023-11-02 04:34:44.622413: train_loss -0.8787 
-2023-11-02 04:34:44.622528: val_loss -0.8133 
-2023-11-02 04:34:44.622579: Pseudo dice [0.8486] 
-2023-11-02 04:34:44.622606: Epoch time: 108.45 s 
-2023-11-02 04:34:45.254264:  
-2023-11-02 04:34:45.254339: Epoch 324 
-2023-11-02 04:34:45.254420: Current learning rate: 0.00703 
-2023-11-02 04:36:33.695875: train_loss -0.8836 
-2023-11-02 04:36:33.696048: val_loss -0.8155 
-2023-11-02 04:36:33.696075: Pseudo dice [0.8465] 
-2023-11-02 04:36:33.696103: Epoch time: 108.44 s 
-2023-11-02 04:36:34.229080:  
-2023-11-02 04:36:34.229155: Epoch 325 
-2023-11-02 04:36:34.229232: Current learning rate: 0.00702 
-2023-11-02 04:38:22.589266: train_loss -0.8873 
-2023-11-02 04:38:22.589421: val_loss -0.8162 
-2023-11-02 04:38:22.589473: Pseudo dice [0.8496] 
-2023-11-02 04:38:22.589501: Epoch time: 108.36 s 
-2023-11-02 04:38:23.129999:  
-2023-11-02 04:38:23.130102: Epoch 326 
-2023-11-02 04:38:23.130158: Current learning rate: 0.00701 
-2023-11-02 04:40:11.559723: train_loss -0.891 
-2023-11-02 04:40:11.559848: val_loss -0.828 
-2023-11-02 04:40:11.559902: Pseudo dice [0.858] 
-2023-11-02 04:40:11.559930: Epoch time: 108.43 s 
-2023-11-02 04:40:12.091032:  
-2023-11-02 04:40:12.091103: Epoch 327 
-2023-11-02 04:40:12.091179: Current learning rate: 0.007 
-2023-11-02 04:42:00.613202: train_loss -0.8952 
-2023-11-02 04:42:00.613333: val_loss -0.826 
-2023-11-02 04:42:00.613358: Pseudo dice [0.8589] 
-2023-11-02 04:42:00.613385: Epoch time: 108.52 s 
-2023-11-02 04:42:01.143385:  
-2023-11-02 04:42:01.143456: Epoch 328 
-2023-11-02 04:42:01.143508: Current learning rate: 0.00699 
-2023-11-02 04:43:49.641814: train_loss -0.8912 
-2023-11-02 04:43:49.641972: val_loss -0.8085 
-2023-11-02 04:43:49.642001: Pseudo dice [0.8428] 
-2023-11-02 04:43:49.642028: Epoch time: 108.5 s 
-2023-11-02 04:43:50.174150:  
-2023-11-02 04:43:50.174218: Epoch 329 
-2023-11-02 04:43:50.174296: Current learning rate: 0.00698 
-2023-11-02 04:45:38.635013: train_loss -0.8946 
-2023-11-02 04:45:38.635139: val_loss -0.8301 
-2023-11-02 04:45:38.635190: Pseudo dice [0.8573] 
-2023-11-02 04:45:38.635219: Epoch time: 108.46 s 
-2023-11-02 04:45:39.289404:  
-2023-11-02 04:45:39.289482: Epoch 330 
-2023-11-02 04:45:39.289562: Current learning rate: 0.00697 
-2023-11-02 04:47:27.712913: train_loss -0.8979 
-2023-11-02 04:47:27.713048: val_loss -0.817 
-2023-11-02 04:47:27.713075: Pseudo dice [0.8508] 
-2023-11-02 04:47:27.713103: Epoch time: 108.42 s 
-2023-11-02 04:47:28.240799:  
-2023-11-02 04:47:28.240871: Epoch 331 
-2023-11-02 04:47:28.240945: Current learning rate: 0.00696 
-2023-11-02 04:49:16.739392: train_loss -0.8981 
-2023-11-02 04:49:16.739519: val_loss -0.8008 
-2023-11-02 04:49:16.739556: Pseudo dice [0.8377] 
-2023-11-02 04:49:16.739583: Epoch time: 108.5 s 
-2023-11-02 04:49:17.269850:  
-2023-11-02 04:49:17.269925: Epoch 332 
-2023-11-02 04:49:17.269983: Current learning rate: 0.00696 
-2023-11-02 04:51:05.776536: train_loss -0.8988 
-2023-11-02 04:51:05.776664: val_loss -0.8222 
-2023-11-02 04:51:05.776715: Pseudo dice [0.8525] 
-2023-11-02 04:51:05.776742: Epoch time: 108.51 s 
-2023-11-02 04:51:06.307668:  
-2023-11-02 04:51:06.307765: Epoch 333 
-2023-11-02 04:51:06.307819: Current learning rate: 0.00695 
-2023-11-02 04:52:54.782239: train_loss -0.8977 
-2023-11-02 04:52:54.782408: val_loss -0.8137 
-2023-11-02 04:52:54.782438: Pseudo dice [0.8478] 
-2023-11-02 04:52:54.782469: Epoch time: 108.47 s 
-2023-11-02 04:52:55.313976:  
-2023-11-02 04:52:55.314047: Epoch 334 
-2023-11-02 04:52:55.314126: Current learning rate: 0.00694 
-2023-11-02 04:54:43.798989: train_loss -0.895 
-2023-11-02 04:54:43.799148: val_loss -0.8207 
-2023-11-02 04:54:43.799172: Pseudo dice [0.8531] 
-2023-11-02 04:54:43.799199: Epoch time: 108.49 s 
-2023-11-02 04:54:44.338089:  
-2023-11-02 04:54:44.338157: Epoch 335 
-2023-11-02 04:54:44.338210: Current learning rate: 0.00693 
-2023-11-02 04:56:32.809758: train_loss -0.8919 
-2023-11-02 04:56:32.809894: val_loss -0.8241 
-2023-11-02 04:56:32.809945: Pseudo dice [0.8543] 
-2023-11-02 04:56:32.809973: Epoch time: 108.47 s 
-2023-11-02 04:56:33.444357:  
-2023-11-02 04:56:33.444437: Epoch 336 
-2023-11-02 04:56:33.444489: Current learning rate: 0.00692 
-2023-11-02 04:58:21.848118: train_loss -0.8972 
-2023-11-02 04:58:21.848253: val_loss -0.8135 
-2023-11-02 04:58:21.848293: Pseudo dice [0.8486] 
-2023-11-02 04:58:21.848320: Epoch time: 108.4 s 
-2023-11-02 04:58:22.391128:  
-2023-11-02 04:58:22.391205: Epoch 337 
-2023-11-02 04:58:22.391257: Current learning rate: 0.00691 
-2023-11-02 05:00:10.755661: train_loss -0.8985 
-2023-11-02 05:00:10.755818: val_loss -0.8238 
-2023-11-02 05:00:10.755843: Pseudo dice [0.8545] 
-2023-11-02 05:00:10.755869: Epoch time: 108.36 s 
-2023-11-02 05:00:11.292828:  
-2023-11-02 05:00:11.292904: Epoch 338 
-2023-11-02 05:00:11.292976: Current learning rate: 0.0069 
-2023-11-02 05:01:59.630020: train_loss -0.8958 
-2023-11-02 05:01:59.630156: val_loss -0.811 
-2023-11-02 05:01:59.630205: Pseudo dice [0.8461] 
-2023-11-02 05:01:59.630234: Epoch time: 108.34 s 
-2023-11-02 05:02:00.166168:  
-2023-11-02 05:02:00.166247: Epoch 339 
-2023-11-02 05:02:00.166322: Current learning rate: 0.00689 
-2023-11-02 05:03:48.621671: train_loss -0.8978 
-2023-11-02 05:03:48.621792: val_loss -0.7946 
-2023-11-02 05:03:48.621842: Pseudo dice [0.8328] 
-2023-11-02 05:03:48.621868: Epoch time: 108.46 s 
-2023-11-02 05:03:49.157447:  
-2023-11-02 05:03:49.157518: Epoch 340 
-2023-11-02 05:03:49.157570: Current learning rate: 0.00688 
-2023-11-02 05:05:37.618785: train_loss -0.9004 
-2023-11-02 05:05:37.618916: val_loss -0.8226 
-2023-11-02 05:05:37.618974: Pseudo dice [0.8526] 
-2023-11-02 05:05:37.619002: Epoch time: 108.46 s 
-2023-11-02 05:05:38.154626:  
-2023-11-02 05:05:38.154696: Epoch 341 
-2023-11-02 05:05:38.154776: Current learning rate: 0.00687 
-2023-11-02 05:07:26.595835: train_loss -0.8989 
-2023-11-02 05:07:26.595975: val_loss -0.8056 
-2023-11-02 05:07:26.596001: Pseudo dice [0.8415] 
-2023-11-02 05:07:26.596028: Epoch time: 108.44 s 
-2023-11-02 05:07:27.236015:  
-2023-11-02 05:07:27.236100: Epoch 342 
-2023-11-02 05:07:27.236155: Current learning rate: 0.00686 
-2023-11-02 05:09:15.713023: train_loss -0.896 
-2023-11-02 05:09:15.713207: val_loss -0.8126 
-2023-11-02 05:09:15.713282: Pseudo dice [0.8452] 
-2023-11-02 05:09:15.713313: Epoch time: 108.48 s 
-2023-11-02 05:09:16.249133:  
-2023-11-02 05:09:16.249210: Epoch 343 
-2023-11-02 05:09:16.249288: Current learning rate: 0.00685 
-2023-11-02 05:11:04.756174: train_loss -0.8991 
-2023-11-02 05:11:04.756314: val_loss -0.8227 
-2023-11-02 05:11:04.756338: Pseudo dice [0.8554] 
-2023-11-02 05:11:04.756365: Epoch time: 108.51 s 
-2023-11-02 05:11:05.296974:  
-2023-11-02 05:11:05.297047: Epoch 344 
-2023-11-02 05:11:05.297100: Current learning rate: 0.00684 
-2023-11-02 05:12:53.726189: train_loss -0.8973 
-2023-11-02 05:12:53.726325: val_loss -0.8136 
-2023-11-02 05:12:53.726366: Pseudo dice [0.8483] 
-2023-11-02 05:12:53.726398: Epoch time: 108.43 s 
-2023-11-02 05:12:54.271640:  
-2023-11-02 05:12:54.271726: Epoch 345 
-2023-11-02 05:12:54.271800: Current learning rate: 0.00683 
-2023-11-02 05:14:42.793862: train_loss -0.8975 
-2023-11-02 05:14:42.793990: val_loss -0.8027 
-2023-11-02 05:14:42.794042: Pseudo dice [0.8394] 
-2023-11-02 05:14:42.794070: Epoch time: 108.52 s 
-2023-11-02 05:14:43.345264:  
-2023-11-02 05:14:43.345330: Epoch 346 
-2023-11-02 05:14:43.345409: Current learning rate: 0.00682 
-2023-11-02 05:16:31.794741: train_loss -0.8925 
-2023-11-02 05:16:31.794879: val_loss -0.8092 
-2023-11-02 05:16:31.794943: Pseudo dice [0.8465] 
-2023-11-02 05:16:31.794990: Epoch time: 108.45 s 
-2023-11-02 05:16:32.334799:  
-2023-11-02 05:16:32.334871: Epoch 347 
-2023-11-02 05:16:32.334950: Current learning rate: 0.00681 
-2023-11-02 05:18:20.699584: train_loss -0.8973 
-2023-11-02 05:18:20.699709: val_loss -0.8169 
-2023-11-02 05:18:20.699734: Pseudo dice [0.8516] 
-2023-11-02 05:18:20.699762: Epoch time: 108.37 s 
-2023-11-02 05:18:21.239833:  
-2023-11-02 05:18:21.239903: Epoch 348 
-2023-11-02 05:18:21.239959: Current learning rate: 0.0068 
-2023-11-02 05:20:09.708446: train_loss -0.8981 
-2023-11-02 05:20:09.708568: val_loss -0.814 
-2023-11-02 05:20:09.708592: Pseudo dice [0.849] 
-2023-11-02 05:20:09.708618: Epoch time: 108.47 s 
-2023-11-02 05:20:10.347877:  
-2023-11-02 05:20:10.347959: Epoch 349 
-2023-11-02 05:20:10.348045: Current learning rate: 0.0068 
-2023-11-02 05:21:58.800762: train_loss -0.9006 
-2023-11-02 05:21:58.800906: val_loss -0.8206 
-2023-11-02 05:21:58.800933: Pseudo dice [0.8525] 
-2023-11-02 05:21:58.800963: Epoch time: 108.45 s 
-2023-11-02 05:21:59.571315:  
-2023-11-02 05:21:59.571391: Epoch 350 
-2023-11-02 05:21:59.571443: Current learning rate: 0.00679 
-2023-11-02 05:23:47.969881: train_loss -0.8983 
-2023-11-02 05:23:47.970008: val_loss -0.8127 
-2023-11-02 05:23:47.970056: Pseudo dice [0.8486] 
-2023-11-02 05:23:47.970082: Epoch time: 108.4 s 
-2023-11-02 05:23:48.510621:  
-2023-11-02 05:23:48.510692: Epoch 351 
-2023-11-02 05:23:48.510768: Current learning rate: 0.00678 
-2023-11-02 05:25:36.970259: train_loss -0.9021 
-2023-11-02 05:25:36.970410: val_loss -0.8121 
-2023-11-02 05:25:36.970437: Pseudo dice [0.8493] 
-2023-11-02 05:25:36.970463: Epoch time: 108.46 s 
-2023-11-02 05:25:37.511685:  
-2023-11-02 05:25:37.511753: Epoch 352 
-2023-11-02 05:25:37.511804: Current learning rate: 0.00677 
-2023-11-02 05:27:25.921412: train_loss -0.9006 
-2023-11-02 05:27:25.921526: val_loss -0.8154 
-2023-11-02 05:27:25.921582: Pseudo dice [0.8488] 
-2023-11-02 05:27:25.921613: Epoch time: 108.41 s 
-2023-11-02 05:27:26.460352:  
-2023-11-02 05:27:26.460428: Epoch 353 
-2023-11-02 05:27:26.460483: Current learning rate: 0.00676 
-2023-11-02 05:29:14.855006: train_loss -0.9047 
-2023-11-02 05:29:14.855153: val_loss -0.8186 
-2023-11-02 05:29:14.855179: Pseudo dice [0.8504] 
-2023-11-02 05:29:14.855205: Epoch time: 108.4 s 
-2023-11-02 05:29:15.394811:  
-2023-11-02 05:29:15.394877: Epoch 354 
-2023-11-02 05:29:15.394953: Current learning rate: 0.00675 
-2023-11-02 05:31:03.850738: train_loss -0.9006 
-2023-11-02 05:31:03.850859: val_loss -0.8287 
-2023-11-02 05:31:03.850919: Pseudo dice [0.8584] 
-2023-11-02 05:31:03.850947: Epoch time: 108.46 s 
-2023-11-02 05:31:04.481566:  
-2023-11-02 05:31:04.481644: Epoch 355 
-2023-11-02 05:31:04.481728: Current learning rate: 0.00674 
-2023-11-02 05:32:52.866313: train_loss -0.9028 
-2023-11-02 05:32:52.866444: val_loss -0.8237 
-2023-11-02 05:32:52.866469: Pseudo dice [0.856] 
-2023-11-02 05:32:52.866497: Epoch time: 108.39 s 
-2023-11-02 05:32:53.403121:  
-2023-11-02 05:32:53.403200: Epoch 356 
-2023-11-02 05:32:53.403251: Current learning rate: 0.00673 
-2023-11-02 05:34:41.858078: train_loss -0.9036 
-2023-11-02 05:34:41.858238: val_loss -0.8131 
-2023-11-02 05:34:41.858263: Pseudo dice [0.8471] 
-2023-11-02 05:34:41.858291: Epoch time: 108.46 s 
-2023-11-02 05:34:42.394041:  
-2023-11-02 05:34:42.394116: Epoch 357 
-2023-11-02 05:34:42.394192: Current learning rate: 0.00672 
-2023-11-02 05:36:30.946017: train_loss -0.8955 
-2023-11-02 05:36:30.946139: val_loss -0.8146 
-2023-11-02 05:36:30.946189: Pseudo dice [0.8478] 
-2023-11-02 05:36:30.946214: Epoch time: 108.55 s 
-2023-11-02 05:36:31.486606:  
-2023-11-02 05:36:31.486678: Epoch 358 
-2023-11-02 05:36:31.486755: Current learning rate: 0.00671 
-2023-11-02 05:38:20.048103: train_loss -0.8962 
-2023-11-02 05:38:20.048229: val_loss -0.823 
-2023-11-02 05:38:20.048280: Pseudo dice [0.8547] 
-2023-11-02 05:38:20.048307: Epoch time: 108.56 s 
-2023-11-02 05:38:20.591966:  
-2023-11-02 05:38:20.592096: Epoch 359 
-2023-11-02 05:38:20.592162: Current learning rate: 0.0067 
-2023-11-02 05:40:09.163344: train_loss -0.8982 
-2023-11-02 05:40:09.163476: val_loss -0.8211 
-2023-11-02 05:40:09.163501: Pseudo dice [0.8526] 
-2023-11-02 05:40:09.163527: Epoch time: 108.57 s 
-2023-11-02 05:40:09.701056:  
-2023-11-02 05:40:09.701128: Epoch 360 
-2023-11-02 05:40:09.701182: Current learning rate: 0.00669 
-2023-11-02 05:41:58.173879: train_loss -0.9016 
-2023-11-02 05:41:58.174044: val_loss -0.8258 
-2023-11-02 05:41:58.174094: Pseudo dice [0.8541] 
-2023-11-02 05:41:58.174133: Epoch time: 108.47 s 
-2023-11-02 05:41:58.808119:  
-2023-11-02 05:41:58.808205: Epoch 361 
-2023-11-02 05:41:58.808273: Current learning rate: 0.00668 
-2023-11-02 05:43:47.328703: train_loss -0.9008 
-2023-11-02 05:43:47.328838: val_loss -0.8124 
-2023-11-02 05:43:47.328888: Pseudo dice [0.8461] 
-2023-11-02 05:43:47.328915: Epoch time: 108.52 s 
-2023-11-02 05:43:47.865439:  
-2023-11-02 05:43:47.865520: Epoch 362 
-2023-11-02 05:43:47.865574: Current learning rate: 0.00667 
-2023-11-02 05:45:36.449244: train_loss -0.9019 
-2023-11-02 05:45:36.449384: val_loss -0.8157 
-2023-11-02 05:45:36.449432: Pseudo dice [0.8524] 
-2023-11-02 05:45:36.449459: Epoch time: 108.58 s 
-2023-11-02 05:45:36.987491:  
-2023-11-02 05:45:36.987565: Epoch 363 
-2023-11-02 05:45:36.987641: Current learning rate: 0.00666 
-2023-11-02 05:47:25.500100: train_loss -0.9016 
-2023-11-02 05:47:25.500240: val_loss -0.8188 
-2023-11-02 05:47:25.500292: Pseudo dice [0.8509] 
-2023-11-02 05:47:25.500323: Epoch time: 108.51 s 
-2023-11-02 05:47:26.045413:  
-2023-11-02 05:47:26.045486: Epoch 364 
-2023-11-02 05:47:26.045539: Current learning rate: 0.00665 
-2023-11-02 05:49:14.521257: train_loss -0.9026 
-2023-11-02 05:49:14.521414: val_loss -0.8152 
-2023-11-02 05:49:14.521438: Pseudo dice [0.8493] 
-2023-11-02 05:49:14.521465: Epoch time: 108.48 s 
-2023-11-02 05:49:15.057749:  
-2023-11-02 05:49:15.057844: Epoch 365 
-2023-11-02 05:49:15.057896: Current learning rate: 0.00665 
-2023-11-02 05:51:03.642447: train_loss -0.8984 
-2023-11-02 05:51:03.642583: val_loss -0.8029 
-2023-11-02 05:51:03.642609: Pseudo dice [0.8406] 
-2023-11-02 05:51:03.642638: Epoch time: 108.59 s 
-2023-11-02 05:51:04.180627:  
-2023-11-02 05:51:04.180691: Epoch 366 
-2023-11-02 05:51:04.180770: Current learning rate: 0.00664 
-2023-11-02 05:52:52.580559: train_loss -0.9019 
-2023-11-02 05:52:52.580683: val_loss -0.8271 
-2023-11-02 05:52:52.580737: Pseudo dice [0.8565] 
-2023-11-02 05:52:52.580763: Epoch time: 108.4 s 
-2023-11-02 05:52:53.213190:  
-2023-11-02 05:52:53.213263: Epoch 367 
-2023-11-02 05:52:53.213341: Current learning rate: 0.00663 
-2023-11-02 05:54:41.738963: train_loss -0.9053 
-2023-11-02 05:54:41.739094: val_loss -0.8178 
-2023-11-02 05:54:41.739145: Pseudo dice [0.8521] 
-2023-11-02 05:54:41.739172: Epoch time: 108.53 s 
-2023-11-02 05:54:42.274783:  
-2023-11-02 05:54:42.274854: Epoch 368 
-2023-11-02 05:54:42.274925: Current learning rate: 0.00662 
-2023-11-02 05:56:30.886298: train_loss -0.9024 
-2023-11-02 05:56:30.886426: val_loss -0.8202 
-2023-11-02 05:56:30.886464: Pseudo dice [0.8535] 
-2023-11-02 05:56:30.886490: Epoch time: 108.61 s 
-2023-11-02 05:56:31.435929:  
-2023-11-02 05:56:31.436033: Epoch 369 
-2023-11-02 05:56:31.436085: Current learning rate: 0.00661 
-2023-11-02 05:58:19.959612: train_loss -0.9008 
-2023-11-02 05:58:19.959745: val_loss -0.8147 
-2023-11-02 05:58:19.959794: Pseudo dice [0.8503] 
-2023-11-02 05:58:19.959820: Epoch time: 108.52 s 
-2023-11-02 05:58:20.501114:  
-2023-11-02 05:58:20.501181: Epoch 370 
-2023-11-02 05:58:20.501254: Current learning rate: 0.0066 
-2023-11-02 06:00:08.960913: train_loss -0.9015 
-2023-11-02 06:00:08.961077: val_loss -0.8194 
-2023-11-02 06:00:08.961103: Pseudo dice [0.851] 
-2023-11-02 06:00:08.961130: Epoch time: 108.46 s 
-2023-11-02 06:00:09.501328:  
-2023-11-02 06:00:09.501402: Epoch 371 
-2023-11-02 06:00:09.501454: Current learning rate: 0.00659 
-2023-11-02 06:01:58.033525: train_loss -0.8984 
-2023-11-02 06:01:58.033648: val_loss -0.8218 
-2023-11-02 06:01:58.033697: Pseudo dice [0.854] 
-2023-11-02 06:01:58.033724: Epoch time: 108.53 s 
-2023-11-02 06:01:58.574472:  
-2023-11-02 06:01:58.574537: Epoch 372 
-2023-11-02 06:01:58.574614: Current learning rate: 0.00658 
-2023-11-02 06:03:47.080052: train_loss -0.9019 
-2023-11-02 06:03:47.080175: val_loss -0.8235 
-2023-11-02 06:03:47.080200: Pseudo dice [0.8535] 
-2023-11-02 06:03:47.080226: Epoch time: 108.51 s 
-2023-11-02 06:03:47.713297:  
-2023-11-02 06:03:47.713373: Epoch 373 
-2023-11-02 06:03:47.713425: Current learning rate: 0.00657 
-2023-11-02 06:05:36.258302: train_loss -0.9035 
-2023-11-02 06:05:36.258458: val_loss -0.8218 
-2023-11-02 06:05:36.258501: Pseudo dice [0.8541] 
-2023-11-02 06:05:36.258528: Epoch time: 108.55 s 
-2023-11-02 06:05:36.797151:  
-2023-11-02 06:05:36.797231: Epoch 374 
-2023-11-02 06:05:36.797281: Current learning rate: 0.00656 
-2023-11-02 06:07:25.230219: train_loss -0.9025 
-2023-11-02 06:07:25.230367: val_loss -0.8152 
-2023-11-02 06:07:25.230421: Pseudo dice [0.8491] 
-2023-11-02 06:07:25.230450: Epoch time: 108.43 s 
-2023-11-02 06:07:25.770301:  
-2023-11-02 06:07:25.770377: Epoch 375 
-2023-11-02 06:07:25.770454: Current learning rate: 0.00655 
-2023-11-02 06:09:14.310429: train_loss -0.9027 
-2023-11-02 06:09:14.310563: val_loss -0.8227 
-2023-11-02 06:09:14.310596: Pseudo dice [0.8552] 
-2023-11-02 06:09:14.310631: Epoch time: 108.54 s 
-2023-11-02 06:09:14.855183:  
-2023-11-02 06:09:14.855256: Epoch 376 
-2023-11-02 06:09:14.855309: Current learning rate: 0.00654 
-2023-11-02 06:11:03.410057: train_loss -0.9018 
-2023-11-02 06:11:03.410184: val_loss -0.81 
-2023-11-02 06:11:03.410236: Pseudo dice [0.8467] 
-2023-11-02 06:11:03.410268: Epoch time: 108.56 s 
-2023-11-02 06:11:03.954718:  
-2023-11-02 06:11:03.954786: Epoch 377 
-2023-11-02 06:11:03.954839: Current learning rate: 0.00653 
-2023-11-02 06:12:52.393541: train_loss -0.9002 
-2023-11-02 06:12:52.393673: val_loss -0.8176 
-2023-11-02 06:12:52.393716: Pseudo dice [0.8502] 
-2023-11-02 06:12:52.393745: Epoch time: 108.44 s 
-2023-11-02 06:12:52.930362:  
-2023-11-02 06:12:52.930431: Epoch 378 
-2023-11-02 06:12:52.930483: Current learning rate: 0.00652 
-2023-11-02 06:14:41.444067: train_loss -0.8965 
-2023-11-02 06:14:41.444204: val_loss -0.8219 
-2023-11-02 06:14:41.444248: Pseudo dice [0.8541] 
-2023-11-02 06:14:41.444273: Epoch time: 108.51 s 
-2023-11-02 06:14:42.085837:  
-2023-11-02 06:14:42.085914: Epoch 379 
-2023-11-02 06:14:42.085979: Current learning rate: 0.00651 
-2023-11-02 06:16:30.458949: train_loss -0.8959 
-2023-11-02 06:16:30.459090: val_loss -0.8181 
-2023-11-02 06:16:30.459140: Pseudo dice [0.8499] 
-2023-11-02 06:16:30.459167: Epoch time: 108.37 s 
-2023-11-02 06:16:30.996831:  
-2023-11-02 06:16:30.996911: Epoch 380 
-2023-11-02 06:16:30.996963: Current learning rate: 0.0065 
-2023-11-02 06:18:19.656646: train_loss -0.8914 
-2023-11-02 06:18:19.656803: val_loss -0.8165 
-2023-11-02 06:18:19.656828: Pseudo dice [0.8487] 
-2023-11-02 06:18:19.656856: Epoch time: 108.66 s 
-2023-11-02 06:18:20.203255:  
-2023-11-02 06:18:20.203336: Epoch 381 
-2023-11-02 06:18:20.203411: Current learning rate: 0.00649 
-2023-11-02 06:20:08.800107: train_loss -0.8966 
-2023-11-02 06:20:08.800236: val_loss -0.8173 
-2023-11-02 06:20:08.800263: Pseudo dice [0.8517] 
-2023-11-02 06:20:08.800291: Epoch time: 108.6 s 
-2023-11-02 06:20:09.352800:  
-2023-11-02 06:20:09.352902: Epoch 382 
-2023-11-02 06:20:09.352956: Current learning rate: 0.00648 
-2023-11-02 06:21:57.899096: train_loss -0.8936 
-2023-11-02 06:21:57.899264: val_loss -0.822 
-2023-11-02 06:21:57.899297: Pseudo dice [0.8545] 
-2023-11-02 06:21:57.899331: Epoch time: 108.55 s 
-2023-11-02 06:21:58.445826:  
-2023-11-02 06:21:58.445900: Epoch 383 
-2023-11-02 06:21:58.445984: Current learning rate: 0.00648 
-2023-11-02 06:23:46.949891: train_loss -0.8972 
-2023-11-02 06:23:46.950029: val_loss -0.8155 
-2023-11-02 06:23:46.950081: Pseudo dice [0.8488] 
-2023-11-02 06:23:46.950110: Epoch time: 108.5 s 
-2023-11-02 06:23:47.504030:  
-2023-11-02 06:23:47.504100: Epoch 384 
-2023-11-02 06:23:47.504156: Current learning rate: 0.00647 
-2023-11-02 06:25:35.901163: train_loss -0.899 
-2023-11-02 06:25:35.901287: val_loss -0.8129 
-2023-11-02 06:25:35.901335: Pseudo dice [0.8466] 
-2023-11-02 06:25:35.901362: Epoch time: 108.4 s 
-2023-11-02 06:25:36.551692:  
-2023-11-02 06:25:36.551786: Epoch 385 
-2023-11-02 06:25:36.551907: Current learning rate: 0.00646 
-2023-11-02 06:27:24.965599: train_loss -0.901 
-2023-11-02 06:27:24.965740: val_loss -0.8131 
-2023-11-02 06:27:24.965765: Pseudo dice [0.848] 
-2023-11-02 06:27:24.965793: Epoch time: 108.41 s 
-2023-11-02 06:27:25.517356:  
-2023-11-02 06:27:25.517438: Epoch 386 
-2023-11-02 06:27:25.517492: Current learning rate: 0.00645 
-2023-11-02 06:29:14.069834: train_loss -0.8997 
-2023-11-02 06:29:14.069959: val_loss -0.8169 
-2023-11-02 06:29:14.070010: Pseudo dice [0.8506] 
-2023-11-02 06:29:14.070036: Epoch time: 108.55 s 
-2023-11-02 06:29:14.618293:  
-2023-11-02 06:29:14.618369: Epoch 387 
-2023-11-02 06:29:14.618446: Current learning rate: 0.00644 
-2023-11-02 06:31:03.211075: train_loss -0.8999 
-2023-11-02 06:31:03.211249: val_loss -0.805 
-2023-11-02 06:31:03.211277: Pseudo dice [0.8435] 
-2023-11-02 06:31:03.211304: Epoch time: 108.59 s 
-2023-11-02 06:31:03.761907:  
-2023-11-02 06:31:03.761997: Epoch 388 
-2023-11-02 06:31:03.762089: Current learning rate: 0.00643 
-2023-11-02 06:32:52.144840: train_loss -0.8981 
-2023-11-02 06:32:52.144969: val_loss -0.8231 
-2023-11-02 06:32:52.145024: Pseudo dice [0.8529] 
-2023-11-02 06:32:52.145052: Epoch time: 108.38 s 
-2023-11-02 06:32:52.691905:  
-2023-11-02 06:32:52.691992: Epoch 389 
-2023-11-02 06:32:52.692060: Current learning rate: 0.00642 
-2023-11-02 06:34:41.205628: train_loss -0.8965 
-2023-11-02 06:34:41.205746: val_loss -0.808 
-2023-11-02 06:34:41.205796: Pseudo dice [0.8416] 
-2023-11-02 06:34:41.205823: Epoch time: 108.51 s 
-2023-11-02 06:34:41.751263:  
-2023-11-02 06:34:41.751339: Epoch 390 
-2023-11-02 06:34:41.751394: Current learning rate: 0.00641 
-2023-11-02 06:36:30.206649: train_loss -0.895 
-2023-11-02 06:36:30.206780: val_loss -0.8213 
-2023-11-02 06:36:30.206830: Pseudo dice [0.8526] 
-2023-11-02 06:36:30.206856: Epoch time: 108.46 s 
-2023-11-02 06:36:30.851243:  
-2023-11-02 06:36:30.851314: Epoch 391 
-2023-11-02 06:36:30.851396: Current learning rate: 0.0064 
-2023-11-02 06:38:19.366167: train_loss -0.8898 
-2023-11-02 06:38:19.366296: val_loss -0.8128 
-2023-11-02 06:38:19.366322: Pseudo dice [0.8471] 
-2023-11-02 06:38:19.366349: Epoch time: 108.52 s 
-2023-11-02 06:38:19.916702:  
-2023-11-02 06:38:19.916775: Epoch 392 
-2023-11-02 06:38:19.916828: Current learning rate: 0.00639 
-2023-11-02 06:40:08.374161: train_loss -0.8967 
-2023-11-02 06:40:08.374301: val_loss -0.8238 
-2023-11-02 06:40:08.374351: Pseudo dice [0.8543] 
-2023-11-02 06:40:08.374380: Epoch time: 108.46 s 
-2023-11-02 06:40:08.926088:  
-2023-11-02 06:40:08.926161: Epoch 393 
-2023-11-02 06:40:08.926270: Current learning rate: 0.00638 
-2023-11-02 06:41:57.397796: train_loss -0.9 
-2023-11-02 06:41:57.397926: val_loss -0.8265 
-2023-11-02 06:41:57.397977: Pseudo dice [0.8563] 
-2023-11-02 06:41:57.398010: Epoch time: 108.47 s 
-2023-11-02 06:41:57.945234:  
-2023-11-02 06:41:57.945312: Epoch 394 
-2023-11-02 06:41:57.945383: Current learning rate: 0.00637 
-2023-11-02 06:43:46.467703: train_loss -0.9004 
-2023-11-02 06:43:46.467828: val_loss -0.8191 
-2023-11-02 06:43:46.467855: Pseudo dice [0.8533] 
-2023-11-02 06:43:46.467881: Epoch time: 108.52 s 
-2023-11-02 06:43:47.039870:  
-2023-11-02 06:43:47.039950: Epoch 395 
-2023-11-02 06:43:47.040028: Current learning rate: 0.00636 
-2023-11-02 06:45:35.576965: train_loss -0.902 
-2023-11-02 06:45:35.577092: val_loss -0.8179 
-2023-11-02 06:45:35.577141: Pseudo dice [0.8517] 
-2023-11-02 06:45:35.577168: Epoch time: 108.54 s 
-2023-11-02 06:45:36.126074:  
-2023-11-02 06:45:36.126140: Epoch 396 
-2023-11-02 06:45:36.126207: Current learning rate: 0.00635 
-2023-11-02 06:47:24.680298: train_loss -0.9039 
-2023-11-02 06:47:24.680445: val_loss -0.8212 
-2023-11-02 06:47:24.680475: Pseudo dice [0.8534] 
-2023-11-02 06:47:24.680504: Epoch time: 108.55 s 
-2023-11-02 06:47:25.323432:  
-2023-11-02 06:47:25.323538: Epoch 397 
-2023-11-02 06:47:25.323640: Current learning rate: 0.00634 
-2023-11-02 06:49:13.691832: train_loss -0.9011 
-2023-11-02 06:49:13.691961: val_loss -0.8171 
-2023-11-02 06:49:13.691992: Pseudo dice [0.8508] 
-2023-11-02 06:49:13.692021: Epoch time: 108.37 s 
-2023-11-02 06:49:14.242656:  
-2023-11-02 06:49:14.242763: Epoch 398 
-2023-11-02 06:49:14.242838: Current learning rate: 0.00633 
-2023-11-02 06:51:02.609484: train_loss -0.9031 
-2023-11-02 06:51:02.609626: val_loss -0.8227 
-2023-11-02 06:51:02.609676: Pseudo dice [0.854] 
-2023-11-02 06:51:02.609701: Epoch time: 108.37 s 
-2023-11-02 06:51:03.154739:  
-2023-11-02 06:51:03.154819: Epoch 399 
-2023-11-02 06:51:03.154923: Current learning rate: 0.00632 
-2023-11-02 06:52:51.662697: train_loss -0.9029 
-2023-11-02 06:52:51.662830: val_loss -0.8113 
-2023-11-02 06:52:51.662862: Pseudo dice [0.8474] 
-2023-11-02 06:52:51.662889: Epoch time: 108.51 s 
-2023-11-02 06:52:52.453133:  
-2023-11-02 06:52:52.453206: Epoch 400 
-2023-11-02 06:52:52.453283: Current learning rate: 0.00631 
-2023-11-02 06:54:41.080914: train_loss -0.8991 
-2023-11-02 06:54:41.081010: val_loss -0.814 
-2023-11-02 06:54:41.081048: Pseudo dice [0.8489] 
-2023-11-02 06:54:41.081100: Epoch time: 108.63 s 
-2023-11-02 06:54:41.630459:  
-2023-11-02 06:54:41.630528: Epoch 401 
-2023-11-02 06:54:41.630604: Current learning rate: 0.0063 
-2023-11-02 06:56:30.293590: train_loss -0.8936 
-2023-11-02 06:56:30.293732: val_loss -0.8207 
-2023-11-02 06:56:30.293784: Pseudo dice [0.8513] 
-2023-11-02 06:56:30.293813: Epoch time: 108.66 s 
-2023-11-02 06:56:30.842736:  
-2023-11-02 06:56:30.842803: Epoch 402 
-2023-11-02 06:56:30.842874: Current learning rate: 0.0063 
-2023-11-02 06:58:19.329934: train_loss -0.9016 
-2023-11-02 06:58:19.330096: val_loss -0.8134 
-2023-11-02 06:58:19.330123: Pseudo dice [0.8479] 
-2023-11-02 06:58:19.330151: Epoch time: 108.49 s 
-2023-11-02 06:58:19.978227:  
-2023-11-02 06:58:19.978322: Epoch 403 
-2023-11-02 06:58:19.978408: Current learning rate: 0.00629 
-2023-11-02 07:00:08.432308: train_loss -0.9014 
-2023-11-02 07:00:08.432443: val_loss -0.8197 
-2023-11-02 07:00:08.432475: Pseudo dice [0.8546] 
-2023-11-02 07:00:08.432505: Epoch time: 108.45 s 
-2023-11-02 07:00:08.979288:  
-2023-11-02 07:00:08.979368: Epoch 404 
-2023-11-02 07:00:08.979424: Current learning rate: 0.00628 
-2023-11-02 07:01:57.509092: train_loss -0.8966 
-2023-11-02 07:01:57.509223: val_loss -0.8036 
-2023-11-02 07:01:57.509272: Pseudo dice [0.8412] 
-2023-11-02 07:01:57.509299: Epoch time: 108.53 s 
-2023-11-02 07:01:58.058924:  
-2023-11-02 07:01:58.059013: Epoch 405 
-2023-11-02 07:01:58.059132: Current learning rate: 0.00627 
-2023-11-02 07:03:46.619359: train_loss -0.8995 
-2023-11-02 07:03:46.619489: val_loss -0.8159 
-2023-11-02 07:03:46.619519: Pseudo dice [0.8499] 
-2023-11-02 07:03:46.619551: Epoch time: 108.56 s 
-2023-11-02 07:03:47.178223:  
-2023-11-02 07:03:47.178301: Epoch 406 
-2023-11-02 07:03:47.178355: Current learning rate: 0.00626 
-2023-11-02 07:05:35.584463: train_loss -0.8991 
-2023-11-02 07:05:35.584633: val_loss -0.8261 
-2023-11-02 07:05:35.584662: Pseudo dice [0.8545] 
-2023-11-02 07:05:35.584691: Epoch time: 108.41 s 
-2023-11-02 07:05:36.134786:  
-2023-11-02 07:05:36.134856: Epoch 407 
-2023-11-02 07:05:36.134934: Current learning rate: 0.00625 
-2023-11-02 07:07:24.624991: train_loss -0.8999 
-2023-11-02 07:07:24.625126: val_loss -0.7949 
-2023-11-02 07:07:24.625169: Pseudo dice [0.8355] 
-2023-11-02 07:07:24.625195: Epoch time: 108.49 s 
-2023-11-02 07:07:25.171861:  
-2023-11-02 07:07:25.171930: Epoch 408 
-2023-11-02 07:07:25.171987: Current learning rate: 0.00624 
-2023-11-02 07:09:13.918825: train_loss -0.9029 
-2023-11-02 07:09:13.918955: val_loss -0.8055 
-2023-11-02 07:09:13.919008: Pseudo dice [0.8425] 
-2023-11-02 07:09:13.919039: Epoch time: 108.75 s 
-2023-11-02 07:09:14.564171:  
-2023-11-02 07:09:14.564255: Epoch 409 
-2023-11-02 07:09:14.564334: Current learning rate: 0.00623 
-2023-11-02 07:11:03.083565: train_loss -0.9032 
-2023-11-02 07:11:03.083695: val_loss -0.8105 
-2023-11-02 07:11:03.083745: Pseudo dice [0.8458] 
-2023-11-02 07:11:03.083771: Epoch time: 108.52 s 
-2023-11-02 07:11:03.637700:  
-2023-11-02 07:11:03.637805: Epoch 410 
-2023-11-02 07:11:03.637898: Current learning rate: 0.00622 
-2023-11-02 07:12:52.146138: train_loss -0.9026 
-2023-11-02 07:12:52.146278: val_loss -0.8189 
-2023-11-02 07:12:52.146306: Pseudo dice [0.8517] 
-2023-11-02 07:12:52.146332: Epoch time: 108.51 s 
-2023-11-02 07:12:52.663494:  
-2023-11-02 07:12:52.663567: Epoch 411 
-2023-11-02 07:12:52.663618: Current learning rate: 0.00621 
-2023-11-02 07:14:41.189263: train_loss -0.9017 
-2023-11-02 07:14:41.189427: val_loss -0.807 
-2023-11-02 07:14:41.189490: Pseudo dice [0.8437] 
-2023-11-02 07:14:41.189520: Epoch time: 108.53 s 
-2023-11-02 07:14:41.704755:  
-2023-11-02 07:14:41.704826: Epoch 412 
-2023-11-02 07:14:41.704902: Current learning rate: 0.0062 
-2023-11-02 07:16:30.161490: train_loss -0.9012 
-2023-11-02 07:16:30.161611: val_loss -0.8196 
-2023-11-02 07:16:30.161647: Pseudo dice [0.8523] 
-2023-11-02 07:16:30.161673: Epoch time: 108.46 s 
-2023-11-02 07:16:30.675191:  
-2023-11-02 07:16:30.675260: Epoch 413 
-2023-11-02 07:16:30.675337: Current learning rate: 0.00619 
-2023-11-02 07:18:19.146106: train_loss -0.9022 
-2023-11-02 07:18:19.146262: val_loss -0.8146 
-2023-11-02 07:18:19.146291: Pseudo dice [0.8479] 
-2023-11-02 07:18:19.146323: Epoch time: 108.47 s 
-2023-11-02 07:18:19.662955:  
-2023-11-02 07:18:19.663025: Epoch 414 
-2023-11-02 07:18:19.663078: Current learning rate: 0.00618 
-2023-11-02 07:20:08.222184: train_loss -0.9037 
-2023-11-02 07:20:08.222315: val_loss -0.8244 
-2023-11-02 07:20:08.222358: Pseudo dice [0.8549] 
-2023-11-02 07:20:08.222386: Epoch time: 108.56 s 
-2023-11-02 07:20:08.839842:  
-2023-11-02 07:20:08.839923: Epoch 415 
-2023-11-02 07:20:08.839981: Current learning rate: 0.00617 
-2023-11-02 07:21:57.262905: train_loss -0.9075 
-2023-11-02 07:21:57.263058: val_loss -0.8092 
-2023-11-02 07:21:57.263087: Pseudo dice [0.8449] 
-2023-11-02 07:21:57.263114: Epoch time: 108.42 s 
-2023-11-02 07:21:57.785800:  
-2023-11-02 07:21:57.785883: Epoch 416 
-2023-11-02 07:21:57.785936: Current learning rate: 0.00616 
-2023-11-02 07:23:46.348379: train_loss -0.9026 
-2023-11-02 07:23:46.348540: val_loss -0.816 
-2023-11-02 07:23:46.348567: Pseudo dice [0.8501] 
-2023-11-02 07:23:46.348601: Epoch time: 108.56 s 
-2023-11-02 07:23:46.875499:  
-2023-11-02 07:23:46.875592: Epoch 417 
-2023-11-02 07:23:46.875652: Current learning rate: 0.00615 
-2023-11-02 07:25:35.392289: train_loss -0.9048 
-2023-11-02 07:25:35.392421: val_loss -0.8174 
-2023-11-02 07:25:35.392462: Pseudo dice [0.8505] 
-2023-11-02 07:25:35.392490: Epoch time: 108.52 s 
-2023-11-02 07:25:35.912546:  
-2023-11-02 07:25:35.912622: Epoch 418 
-2023-11-02 07:25:35.912674: Current learning rate: 0.00614 
-2023-11-02 07:27:24.320582: train_loss -0.9017 
-2023-11-02 07:27:24.320706: val_loss -0.823 
-2023-11-02 07:27:24.320756: Pseudo dice [0.8542] 
-2023-11-02 07:27:24.320783: Epoch time: 108.41 s 
-2023-11-02 07:27:24.838224:  
-2023-11-02 07:27:24.838298: Epoch 419 
-2023-11-02 07:27:24.838368: Current learning rate: 0.00613 
-2023-11-02 07:29:13.346714: train_loss -0.9033 
-2023-11-02 07:29:13.346862: val_loss -0.8139 
-2023-11-02 07:29:13.346887: Pseudo dice [0.8475] 
-2023-11-02 07:29:13.346914: Epoch time: 108.51 s 
-2023-11-02 07:29:13.876117:  
-2023-11-02 07:29:13.876190: Epoch 420 
-2023-11-02 07:29:13.876269: Current learning rate: 0.00612 
-2023-11-02 07:31:02.412688: train_loss -0.9054 
-2023-11-02 07:31:02.412817: val_loss -0.8193 
-2023-11-02 07:31:02.412867: Pseudo dice [0.8511] 
-2023-11-02 07:31:02.412892: Epoch time: 108.54 s 
-2023-11-02 07:31:02.933717:  
-2023-11-02 07:31:02.933784: Epoch 421 
-2023-11-02 07:31:02.933861: Current learning rate: 0.00612 
-2023-11-02 07:32:51.419912: train_loss -0.9032 
-2023-11-02 07:32:51.420076: val_loss -0.8113 
-2023-11-02 07:32:51.420107: Pseudo dice [0.8443] 
-2023-11-02 07:32:51.420137: Epoch time: 108.49 s 
-2023-11-02 07:32:52.044505:  
-2023-11-02 07:32:52.044612: Epoch 422 
-2023-11-02 07:32:52.044669: Current learning rate: 0.00611 
-2023-11-02 07:34:40.636944: train_loss -0.8984 
-2023-11-02 07:34:40.637082: val_loss -0.8199 
-2023-11-02 07:34:40.637110: Pseudo dice [0.8518] 
-2023-11-02 07:34:40.637138: Epoch time: 108.59 s 
-2023-11-02 07:34:41.156208:  
-2023-11-02 07:34:41.156287: Epoch 423 
-2023-11-02 07:34:41.156352: Current learning rate: 0.0061 
-2023-11-02 07:36:29.734816: train_loss -0.9003 
-2023-11-02 07:36:29.734980: val_loss -0.8081 
-2023-11-02 07:36:29.735007: Pseudo dice [0.8454] 
-2023-11-02 07:36:29.735033: Epoch time: 108.58 s 
-2023-11-02 07:36:30.254835:  
-2023-11-02 07:36:30.254913: Epoch 424 
-2023-11-02 07:36:30.254984: Current learning rate: 0.00609 
-2023-11-02 07:38:18.673002: train_loss -0.9025 
-2023-11-02 07:38:18.673139: val_loss -0.8111 
-2023-11-02 07:38:18.673190: Pseudo dice [0.8452] 
-2023-11-02 07:38:18.673217: Epoch time: 108.42 s 
-2023-11-02 07:38:19.192494:  
-2023-11-02 07:38:19.192569: Epoch 425 
-2023-11-02 07:38:19.192643: Current learning rate: 0.00608 
-2023-11-02 07:40:07.666169: train_loss -0.9039 
-2023-11-02 07:40:07.666299: val_loss -0.8161 
-2023-11-02 07:40:07.666337: Pseudo dice [0.8494] 
-2023-11-02 07:40:07.666364: Epoch time: 108.47 s 
-2023-11-02 07:40:08.183875:  
-2023-11-02 07:40:08.183945: Epoch 426 
-2023-11-02 07:40:08.184024: Current learning rate: 0.00607 
-2023-11-02 07:41:56.749605: train_loss -0.9057 
-2023-11-02 07:41:56.749740: val_loss -0.823 
-2023-11-02 07:41:56.749770: Pseudo dice [0.8531] 
-2023-11-02 07:41:56.749802: Epoch time: 108.57 s 
-2023-11-02 07:41:57.268219:  
-2023-11-02 07:41:57.268286: Epoch 427 
-2023-11-02 07:41:57.268364: Current learning rate: 0.00606 
-2023-11-02 07:43:45.920558: train_loss -0.9036 
-2023-11-02 07:43:45.920692: val_loss -0.8091 
-2023-11-02 07:43:45.920717: Pseudo dice [0.8444] 
-2023-11-02 07:43:45.920744: Epoch time: 108.65 s 
-2023-11-02 07:43:46.537718:  
-2023-11-02 07:43:46.537803: Epoch 428 
-2023-11-02 07:43:46.537878: Current learning rate: 0.00605 
-2023-11-02 07:45:35.189379: train_loss -0.9059 
-2023-11-02 07:45:35.189503: val_loss -0.8144 
-2023-11-02 07:45:35.189541: Pseudo dice [0.8469] 
-2023-11-02 07:45:35.189566: Epoch time: 108.65 s 
-2023-11-02 07:45:35.710505:  
-2023-11-02 07:45:35.710580: Epoch 429 
-2023-11-02 07:45:35.710655: Current learning rate: 0.00604 
-2023-11-02 07:47:24.210968: train_loss -0.9065 
-2023-11-02 07:47:24.211135: val_loss -0.8223 
-2023-11-02 07:47:24.211160: Pseudo dice [0.8543] 
-2023-11-02 07:47:24.211188: Epoch time: 108.5 s 
-2023-11-02 07:47:24.731911:  
-2023-11-02 07:47:24.732022: Epoch 430 
-2023-11-02 07:47:24.732077: Current learning rate: 0.00603 
-2023-11-02 07:49:13.228437: train_loss -0.9083 
-2023-11-02 07:49:13.228596: val_loss -0.8216 
-2023-11-02 07:49:13.228624: Pseudo dice [0.8533] 
-2023-11-02 07:49:13.228652: Epoch time: 108.5 s 
-2023-11-02 07:49:13.757758:  
-2023-11-02 07:49:13.757842: Epoch 431 
-2023-11-02 07:49:13.757896: Current learning rate: 0.00602 
-2023-11-02 07:51:02.306360: train_loss -0.9027 
-2023-11-02 07:51:02.306513: val_loss -0.8031 
-2023-11-02 07:51:02.306542: Pseudo dice [0.8408] 
-2023-11-02 07:51:02.306575: Epoch time: 108.55 s 
-2023-11-02 07:51:02.825514:  
-2023-11-02 07:51:02.825585: Epoch 432 
-2023-11-02 07:51:02.825662: Current learning rate: 0.00601 
-2023-11-02 07:52:51.316957: train_loss -0.9055 
-2023-11-02 07:52:51.317084: val_loss -0.8148 
-2023-11-02 07:52:51.317135: Pseudo dice [0.8482] 
-2023-11-02 07:52:51.317162: Epoch time: 108.49 s 
-2023-11-02 07:52:51.844865:  
-2023-11-02 07:52:51.844933: Epoch 433 
-2023-11-02 07:52:51.845010: Current learning rate: 0.006 
-2023-11-02 07:54:40.289245: train_loss -0.9071 
-2023-11-02 07:54:40.289375: val_loss -0.8063 
-2023-11-02 07:54:40.289412: Pseudo dice [0.8431] 
-2023-11-02 07:54:40.289439: Epoch time: 108.44 s 
-2023-11-02 07:54:40.807835:  
-2023-11-02 07:54:40.807905: Epoch 434 
-2023-11-02 07:54:40.807961: Current learning rate: 0.00599 
-2023-11-02 07:56:29.256669: train_loss -0.9061 
-2023-11-02 07:56:29.256820: val_loss -0.8087 
-2023-11-02 07:56:29.256848: Pseudo dice [0.8446] 
-2023-11-02 07:56:29.256876: Epoch time: 108.45 s 
-2023-11-02 07:56:29.880051:  
-2023-11-02 07:56:29.880136: Epoch 435 
-2023-11-02 07:56:29.880213: Current learning rate: 0.00598 
-2023-11-02 07:58:18.361155: train_loss -0.9041 
-2023-11-02 07:58:18.361290: val_loss -0.8129 
-2023-11-02 07:58:18.361340: Pseudo dice [0.8458] 
-2023-11-02 07:58:18.361367: Epoch time: 108.48 s 
-2023-11-02 07:58:18.882308:  
-2023-11-02 07:58:18.882389: Epoch 436 
-2023-11-02 07:58:18.882462: Current learning rate: 0.00597 
-2023-11-02 08:00:07.575595: train_loss -0.9044 
-2023-11-02 08:00:07.575740: val_loss -0.8128 
-2023-11-02 08:00:07.575772: Pseudo dice [0.8483] 
-2023-11-02 08:00:07.575802: Epoch time: 108.69 s 
-2023-11-02 08:00:08.096346:  
-2023-11-02 08:00:08.096423: Epoch 437 
-2023-11-02 08:00:08.096476: Current learning rate: 0.00596 
-2023-11-02 08:01:56.680033: train_loss -0.9026 
-2023-11-02 08:01:56.680176: val_loss -0.8092 
-2023-11-02 08:01:56.680213: Pseudo dice [0.8453] 
-2023-11-02 08:01:56.680239: Epoch time: 108.58 s 
-2023-11-02 08:01:57.201543:  
-2023-11-02 08:01:57.201617: Epoch 438 
-2023-11-02 08:01:57.201668: Current learning rate: 0.00595 
-2023-11-02 08:03:45.740966: train_loss -0.9022 
-2023-11-02 08:03:45.741091: val_loss -0.8192 
-2023-11-02 08:03:45.741115: Pseudo dice [0.8516] 
-2023-11-02 08:03:45.741154: Epoch time: 108.54 s 
-2023-11-02 08:03:46.258677:  
-2023-11-02 08:03:46.258747: Epoch 439 
-2023-11-02 08:03:46.258799: Current learning rate: 0.00594 
-2023-11-02 08:05:34.833827: train_loss -0.9031 
-2023-11-02 08:05:34.833987: val_loss -0.8183 
-2023-11-02 08:05:34.834014: Pseudo dice [0.8516] 
-2023-11-02 08:05:34.834048: Epoch time: 108.58 s 
-2023-11-02 08:05:35.352171:  
-2023-11-02 08:05:35.352240: Epoch 440 
-2023-11-02 08:05:35.352315: Current learning rate: 0.00593 
-2023-11-02 08:07:23.921430: train_loss -0.9024 
-2023-11-02 08:07:23.921556: val_loss -0.8074 
-2023-11-02 08:07:23.921606: Pseudo dice [0.8436] 
-2023-11-02 08:07:23.921633: Epoch time: 108.57 s 
-2023-11-02 08:07:24.441025:  
-2023-11-02 08:07:24.441091: Epoch 441 
-2023-11-02 08:07:24.441165: Current learning rate: 0.00592 
-2023-11-02 08:09:13.058722: train_loss -0.9041 
-2023-11-02 08:09:13.058853: val_loss -0.8285 
-2023-11-02 08:09:13.058896: Pseudo dice [0.8589] 
-2023-11-02 08:09:13.058927: Epoch time: 108.62 s 
-2023-11-02 08:09:13.582255:  
-2023-11-02 08:09:13.582332: Epoch 442 
-2023-11-02 08:09:13.582434: Current learning rate: 0.00592 
-2023-11-02 08:11:02.167343: train_loss -0.9071 
-2023-11-02 08:11:02.167516: val_loss -0.8207 
-2023-11-02 08:11:02.167544: Pseudo dice [0.8535] 
-2023-11-02 08:11:02.167572: Epoch time: 108.59 s 
-2023-11-02 08:11:02.688515:  
-2023-11-02 08:11:02.688594: Epoch 443 
-2023-11-02 08:11:02.688645: Current learning rate: 0.00591 
-2023-11-02 08:12:51.279654: train_loss -0.905 
-2023-11-02 08:12:51.279785: val_loss -0.8306 
-2023-11-02 08:12:51.279811: Pseudo dice [0.8583] 
-2023-11-02 08:12:51.279839: Epoch time: 108.59 s 
-2023-11-02 08:12:51.795781:  
-2023-11-02 08:12:51.795866: Epoch 444 
-2023-11-02 08:12:51.795949: Current learning rate: 0.0059 
-2023-11-02 08:14:40.479637: train_loss -0.9061 
-2023-11-02 08:14:40.479776: val_loss -0.8146 
-2023-11-02 08:14:40.479806: Pseudo dice [0.8509] 
-2023-11-02 08:14:40.479838: Epoch time: 108.68 s 
-2023-11-02 08:14:40.996198:  
-2023-11-02 08:14:40.996268: Epoch 445 
-2023-11-02 08:14:40.996320: Current learning rate: 0.00589 
-2023-11-02 08:16:29.484320: train_loss -0.9037 
-2023-11-02 08:16:29.484453: val_loss -0.8074 
-2023-11-02 08:16:29.484494: Pseudo dice [0.8447] 
-2023-11-02 08:16:29.484542: Epoch time: 108.49 s 
-2023-11-02 08:16:29.996424:  
-2023-11-02 08:16:29.996493: Epoch 446 
-2023-11-02 08:16:29.996585: Current learning rate: 0.00588 
-2023-11-02 08:18:18.414216: train_loss -0.9066 
-2023-11-02 08:18:18.414412: val_loss -0.8123 
-2023-11-02 08:18:18.414440: Pseudo dice [0.847] 
-2023-11-02 08:18:18.414467: Epoch time: 108.42 s 
-2023-11-02 08:18:18.930113:  
-2023-11-02 08:18:18.930181: Epoch 447 
-2023-11-02 08:18:18.930235: Current learning rate: 0.00587 
-2023-11-02 08:20:07.392921: train_loss -0.9076 
-2023-11-02 08:20:07.393046: val_loss -0.809 
-2023-11-02 08:20:07.393082: Pseudo dice [0.8459] 
-2023-11-02 08:20:07.393108: Epoch time: 108.46 s 
-2023-11-02 08:20:08.003901:  
-2023-11-02 08:20:08.003983: Epoch 448 
-2023-11-02 08:20:08.004037: Current learning rate: 0.00586 
-2023-11-02 08:21:56.632093: train_loss -0.9059 
-2023-11-02 08:21:56.632231: val_loss -0.8173 
-2023-11-02 08:21:56.632259: Pseudo dice [0.8495] 
-2023-11-02 08:21:56.632287: Epoch time: 108.63 s 
-2023-11-02 08:21:57.148539:  
-2023-11-02 08:21:57.148612: Epoch 449 
-2023-11-02 08:21:57.148692: Current learning rate: 0.00585 
-2023-11-02 08:23:45.702026: train_loss -0.9053 
-2023-11-02 08:23:45.702159: val_loss -0.8178 
-2023-11-02 08:23:45.702209: Pseudo dice [0.8517] 
-2023-11-02 08:23:45.702236: Epoch time: 108.55 s 
-2023-11-02 08:23:46.451875:  
-2023-11-02 08:23:46.451977: Epoch 450 
-2023-11-02 08:23:46.452034: Current learning rate: 0.00584 
-2023-11-02 08:25:34.978013: train_loss -0.9103 
-2023-11-02 08:25:34.978172: val_loss -0.8246 
-2023-11-02 08:25:34.978203: Pseudo dice [0.8554] 
-2023-11-02 08:25:34.978237: Epoch time: 108.53 s 
-2023-11-02 08:25:35.493381:  
-2023-11-02 08:25:35.493451: Epoch 451 
-2023-11-02 08:25:35.493527: Current learning rate: 0.00583 
-2023-11-02 08:27:23.934613: train_loss -0.9107 
-2023-11-02 08:27:23.934745: val_loss -0.8204 
-2023-11-02 08:27:23.934784: Pseudo dice [0.8524] 
-2023-11-02 08:27:23.934810: Epoch time: 108.44 s 
-2023-11-02 08:27:24.452346:  
-2023-11-02 08:27:24.452419: Epoch 452 
-2023-11-02 08:27:24.452473: Current learning rate: 0.00582 
-2023-11-02 08:29:13.026576: train_loss -0.9077 
-2023-11-02 08:29:13.026699: val_loss -0.8165 
-2023-11-02 08:29:13.026738: Pseudo dice [0.8513] 
-2023-11-02 08:29:13.026766: Epoch time: 108.57 s 
-2023-11-02 08:29:13.545831:  
-2023-11-02 08:29:13.545905: Epoch 453 
-2023-11-02 08:29:13.545959: Current learning rate: 0.00581 
-2023-11-02 08:31:02.074641: train_loss -0.9096 
-2023-11-02 08:31:02.074772: val_loss -0.8004 
-2023-11-02 08:31:02.074821: Pseudo dice [0.838] 
-2023-11-02 08:31:02.074848: Epoch time: 108.53 s 
-2023-11-02 08:31:02.588521:  
-2023-11-02 08:31:02.588595: Epoch 454 
-2023-11-02 08:31:02.588676: Current learning rate: 0.0058 
-2023-11-02 08:32:51.226854: train_loss -0.9077 
-2023-11-02 08:32:51.226979: val_loss -0.8163 
-2023-11-02 08:32:51.227017: Pseudo dice [0.8504] 
-2023-11-02 08:32:51.227045: Epoch time: 108.64 s 
-2023-11-02 08:32:51.843631:  
-2023-11-02 08:32:51.843710: Epoch 455 
-2023-11-02 08:32:51.843793: Current learning rate: 0.00579 
-2023-11-02 08:34:40.412489: train_loss -0.9071 
-2023-11-02 08:34:40.412622: val_loss -0.8212 
-2023-11-02 08:34:40.412647: Pseudo dice [0.8525] 
-2023-11-02 08:34:40.412673: Epoch time: 108.57 s 
-2023-11-02 08:34:40.930286:  
-2023-11-02 08:34:40.930368: Epoch 456 
-2023-11-02 08:34:40.930421: Current learning rate: 0.00578 
-2023-11-02 08:36:29.527611: train_loss -0.9068 
-2023-11-02 08:36:29.527723: val_loss -0.8191 
-2023-11-02 08:36:29.527750: Pseudo dice [0.8512] 
-2023-11-02 08:36:29.527778: Epoch time: 108.6 s 
-2023-11-02 08:36:30.044943:  
-2023-11-02 08:36:30.045023: Epoch 457 
-2023-11-02 08:36:30.045108: Current learning rate: 0.00577 
-2023-11-02 08:38:18.632788: train_loss -0.907 
-2023-11-02 08:38:18.632954: val_loss -0.823 
-2023-11-02 08:38:18.632982: Pseudo dice [0.8532] 
-2023-11-02 08:38:18.633016: Epoch time: 108.59 s 
-2023-11-02 08:38:19.146082:  
-2023-11-02 08:38:19.146155: Epoch 458 
-2023-11-02 08:38:19.146229: Current learning rate: 0.00576 
-2023-11-02 08:40:07.681627: train_loss -0.9086 
-2023-11-02 08:40:07.681759: val_loss -0.8106 
-2023-11-02 08:40:07.681783: Pseudo dice [0.8472] 
-2023-11-02 08:40:07.681811: Epoch time: 108.54 s 
-2023-11-02 08:40:08.203396:  
-2023-11-02 08:40:08.203462: Epoch 459 
-2023-11-02 08:40:08.203514: Current learning rate: 0.00575 
-2023-11-02 08:41:56.686742: train_loss -0.9093 
-2023-11-02 08:41:56.686902: val_loss -0.8192 
-2023-11-02 08:41:56.686928: Pseudo dice [0.8503] 
-2023-11-02 08:41:56.686955: Epoch time: 108.48 s 
-2023-11-02 08:41:57.201500:  
-2023-11-02 08:41:57.201569: Epoch 460 
-2023-11-02 08:41:57.201647: Current learning rate: 0.00574 
-2023-11-02 08:43:45.711470: train_loss -0.908 
-2023-11-02 08:43:45.711602: val_loss -0.8307 
-2023-11-02 08:43:45.711656: Pseudo dice [0.8601] 
-2023-11-02 08:43:45.711681: Epoch time: 108.51 s 
-2023-11-02 08:43:46.223126:  
-2023-11-02 08:43:46.223222: Epoch 461 
-2023-11-02 08:43:46.223274: Current learning rate: 0.00573 
-2023-11-02 08:45:34.827512: train_loss -0.91 
-2023-11-02 08:45:34.827662: val_loss -0.8159 
-2023-11-02 08:45:34.827692: Pseudo dice [0.8499] 
-2023-11-02 08:45:34.827724: Epoch time: 108.6 s 
-2023-11-02 08:45:35.442422:  
-2023-11-02 08:45:35.442504: Epoch 462 
-2023-11-02 08:45:35.442577: Current learning rate: 0.00572 
-2023-11-02 08:47:24.020596: train_loss -0.9084 
-2023-11-02 08:47:24.020722: val_loss -0.819 
-2023-11-02 08:47:24.020749: Pseudo dice [0.854] 
-2023-11-02 08:47:24.020775: Epoch time: 108.58 s 
-2023-11-02 08:47:24.533209:  
-2023-11-02 08:47:24.533285: Epoch 463 
-2023-11-02 08:47:24.533358: Current learning rate: 0.00571 
-2023-11-02 08:49:13.094226: train_loss -0.9039 
-2023-11-02 08:49:13.094355: val_loss -0.8115 
-2023-11-02 08:49:13.094404: Pseudo dice [0.8474] 
-2023-11-02 08:49:13.094430: Epoch time: 108.56 s 
-2023-11-02 08:49:13.614628:  
-2023-11-02 08:49:13.614704: Epoch 464 
-2023-11-02 08:49:13.614756: Current learning rate: 0.0057 
-2023-11-02 08:51:02.203397: train_loss -0.9084 
-2023-11-02 08:51:02.203528: val_loss -0.8228 
-2023-11-02 08:51:02.203578: Pseudo dice [0.8553] 
-2023-11-02 08:51:02.203605: Epoch time: 108.59 s 
-2023-11-02 08:51:02.725704:  
-2023-11-02 08:51:02.725798: Epoch 465 
-2023-11-02 08:51:02.725850: Current learning rate: 0.0057 
-2023-11-02 08:52:51.132130: train_loss -0.9079 
-2023-11-02 08:52:51.132269: val_loss -0.8151 
-2023-11-02 08:52:51.132322: Pseudo dice [0.8484] 
-2023-11-02 08:52:51.132348: Epoch time: 108.41 s 
-2023-11-02 08:52:51.649644:  
-2023-11-02 08:52:51.649712: Epoch 466 
-2023-11-02 08:52:51.649790: Current learning rate: 0.00569 
-2023-11-02 08:54:40.058595: train_loss -0.9069 
-2023-11-02 08:54:40.058719: val_loss -0.8041 
-2023-11-02 08:54:40.058746: Pseudo dice [0.8426] 
-2023-11-02 08:54:40.058773: Epoch time: 108.41 s 
-2023-11-02 08:54:40.574839:  
-2023-11-02 08:54:40.574910: Epoch 467 
-2023-11-02 08:54:40.574988: Current learning rate: 0.00568 
-2023-11-02 08:56:29.101209: train_loss -0.9041 
-2023-11-02 08:56:29.101351: val_loss -0.8144 
-2023-11-02 08:56:29.101411: Pseudo dice [0.8485] 
-2023-11-02 08:56:29.101439: Epoch time: 108.53 s 
-2023-11-02 08:56:29.716188:  
-2023-11-02 08:56:29.716269: Epoch 468 
-2023-11-02 08:56:29.716344: Current learning rate: 0.00567 
-2023-11-02 08:58:18.281340: train_loss -0.9041 
-2023-11-02 08:58:18.281487: val_loss -0.8195 
-2023-11-02 08:58:18.281514: Pseudo dice [0.8531] 
-2023-11-02 08:58:18.281541: Epoch time: 108.57 s 
-2023-11-02 08:58:18.796136:  
-2023-11-02 08:58:18.796242: Epoch 469 
-2023-11-02 08:58:18.796295: Current learning rate: 0.00566 
-2023-11-02 09:00:07.492617: train_loss -0.9058 
-2023-11-02 09:00:07.492752: val_loss -0.8124 
-2023-11-02 09:00:07.492793: Pseudo dice [0.8494] 
-2023-11-02 09:00:07.492820: Epoch time: 108.7 s 
-2023-11-02 09:00:08.007833:  
-2023-11-02 09:00:08.007910: Epoch 470 
-2023-11-02 09:00:08.007965: Current learning rate: 0.00565 
-2023-11-02 09:01:56.546088: train_loss -0.9074 
-2023-11-02 09:01:56.546226: val_loss -0.8188 
-2023-11-02 09:01:56.546256: Pseudo dice [0.8517] 
-2023-11-02 09:01:56.546287: Epoch time: 108.54 s 
-2023-11-02 09:01:57.064479:  
-2023-11-02 09:01:57.064554: Epoch 471 
-2023-11-02 09:01:57.064633: Current learning rate: 0.00564 
-2023-11-02 09:03:45.678844: train_loss -0.9059 
-2023-11-02 09:03:45.678988: val_loss -0.8241 
-2023-11-02 09:03:45.679012: Pseudo dice [0.8542] 
-2023-11-02 09:03:45.679040: Epoch time: 108.61 s 
-2023-11-02 09:03:46.193893:  
-2023-11-02 09:03:46.193962: Epoch 472 
-2023-11-02 09:03:46.194013: Current learning rate: 0.00563 
-2023-11-02 09:05:34.832153: train_loss -0.9045 
-2023-11-02 09:05:34.832282: val_loss -0.8148 
-2023-11-02 09:05:34.832334: Pseudo dice [0.8485] 
-2023-11-02 09:05:34.832361: Epoch time: 108.64 s 
-2023-11-02 09:05:35.346941:  
-2023-11-02 09:05:35.347009: Epoch 473 
-2023-11-02 09:05:35.347082: Current learning rate: 0.00562 
-2023-11-02 09:07:23.953652: train_loss -0.9068 
-2023-11-02 09:07:23.953798: val_loss -0.8166 
-2023-11-02 09:07:23.953823: Pseudo dice [0.8495] 
-2023-11-02 09:07:23.953848: Epoch time: 108.61 s 
-2023-11-02 09:07:24.470055:  
-2023-11-02 09:07:24.470120: Epoch 474 
-2023-11-02 09:07:24.470197: Current learning rate: 0.00561 
-2023-11-02 09:09:13.119420: train_loss -0.9093 
-2023-11-02 09:09:13.119541: val_loss -0.8121 
-2023-11-02 09:09:13.119580: Pseudo dice [0.8493] 
-2023-11-02 09:09:13.119607: Epoch time: 108.65 s 
-2023-11-02 09:09:13.742063:  
-2023-11-02 09:09:13.742186: Epoch 475 
-2023-11-02 09:09:13.742301: Current learning rate: 0.0056 
-2023-11-02 09:11:02.420782: train_loss -0.9067 
-2023-11-02 09:11:02.420910: val_loss -0.8184 
-2023-11-02 09:11:02.420946: Pseudo dice [0.8518] 
-2023-11-02 09:11:02.420974: Epoch time: 108.68 s 
-2023-11-02 09:11:02.937721:  
-2023-11-02 09:11:02.937799: Epoch 476 
-2023-11-02 09:11:02.937875: Current learning rate: 0.00559 
-2023-11-02 09:12:51.505769: train_loss -0.909 
-2023-11-02 09:12:51.505911: val_loss -0.8208 
-2023-11-02 09:12:51.505942: Pseudo dice [0.8524] 
-2023-11-02 09:12:51.505970: Epoch time: 108.57 s 
-2023-11-02 09:12:52.020736:  
-2023-11-02 09:12:52.020812: Epoch 477 
-2023-11-02 09:12:52.020876: Current learning rate: 0.00558 
-2023-11-02 09:14:40.657981: train_loss -0.9077 
-2023-11-02 09:14:40.658104: val_loss -0.8195 
-2023-11-02 09:14:40.658144: Pseudo dice [0.8527] 
-2023-11-02 09:14:40.658170: Epoch time: 108.64 s 
-2023-11-02 09:14:41.180063:  
-2023-11-02 09:14:41.180138: Epoch 478 
-2023-11-02 09:14:41.180190: Current learning rate: 0.00557 
-2023-11-02 09:16:29.645044: train_loss -0.9079 
-2023-11-02 09:16:29.645191: val_loss -0.8146 
-2023-11-02 09:16:29.645216: Pseudo dice [0.8483] 
-2023-11-02 09:16:29.645243: Epoch time: 108.47 s 
-2023-11-02 09:16:30.165831:  
-2023-11-02 09:16:30.165904: Epoch 479 
-2023-11-02 09:16:30.165986: Current learning rate: 0.00556 
-2023-11-02 09:18:18.593886: train_loss -0.9054 
-2023-11-02 09:18:18.594017: val_loss -0.8219 
-2023-11-02 09:18:18.594043: Pseudo dice [0.8529] 
-2023-11-02 09:18:18.594069: Epoch time: 108.43 s 
-2023-11-02 09:18:19.120049:  
-2023-11-02 09:18:19.120120: Epoch 480 
-2023-11-02 09:18:19.120178: Current learning rate: 0.00555 
-2023-11-02 09:20:07.948193: train_loss -0.9076 
-2023-11-02 09:20:07.948330: val_loss -0.8251 
-2023-11-02 09:20:07.948381: Pseudo dice [0.8551] 
-2023-11-02 09:20:07.948410: Epoch time: 108.83 s 
-2023-11-02 09:20:08.573747:  
-2023-11-02 09:20:08.573820: Epoch 481 
-2023-11-02 09:20:08.573900: Current learning rate: 0.00554 
-2023-11-02 09:21:57.199922: train_loss -0.909 
-2023-11-02 09:21:57.200069: val_loss -0.8057 
-2023-11-02 09:21:57.200099: Pseudo dice [0.8425] 
-2023-11-02 09:21:57.200131: Epoch time: 108.63 s 
-2023-11-02 09:21:57.722358:  
-2023-11-02 09:21:57.722434: Epoch 482 
-2023-11-02 09:21:57.722514: Current learning rate: 0.00553 
-2023-11-02 09:23:46.227494: train_loss -0.9058 
-2023-11-02 09:23:46.227622: val_loss -0.816 
-2023-11-02 09:23:46.227660: Pseudo dice [0.8484] 
-2023-11-02 09:23:46.227686: Epoch time: 108.51 s 
-2023-11-02 09:23:46.757027:  
-2023-11-02 09:23:46.757153: Epoch 483 
-2023-11-02 09:23:46.757211: Current learning rate: 0.00552 
-2023-11-02 09:25:35.303539: train_loss -0.9088 
-2023-11-02 09:25:35.303660: val_loss -0.8124 
-2023-11-02 09:25:35.303699: Pseudo dice [0.849] 
-2023-11-02 09:25:35.303725: Epoch time: 108.55 s 
-2023-11-02 09:25:35.824344:  
-2023-11-02 09:25:35.824416: Epoch 484 
-2023-11-02 09:25:35.824482: Current learning rate: 0.00551 
-2023-11-02 09:27:24.418068: train_loss -0.9068 
-2023-11-02 09:27:24.418233: val_loss -0.8141 
-2023-11-02 09:27:24.418258: Pseudo dice [0.8491] 
-2023-11-02 09:27:24.418286: Epoch time: 108.59 s 
-2023-11-02 09:27:24.942194:  
-2023-11-02 09:27:24.942263: Epoch 485 
-2023-11-02 09:27:24.942340: Current learning rate: 0.0055 
-2023-11-02 09:29:13.514560: train_loss -0.9075 
-2023-11-02 09:29:13.514693: val_loss -0.8146 
-2023-11-02 09:29:13.514724: Pseudo dice [0.8483] 
-2023-11-02 09:29:13.514755: Epoch time: 108.57 s 
-2023-11-02 09:29:14.045496:  
-2023-11-02 09:29:14.045593: Epoch 486 
-2023-11-02 09:29:14.045678: Current learning rate: 0.00549 
-2023-11-02 09:31:02.748057: train_loss -0.9062 
-2023-11-02 09:31:02.748192: val_loss -0.8117 
-2023-11-02 09:31:02.748241: Pseudo dice [0.8474] 
-2023-11-02 09:31:02.748269: Epoch time: 108.7 s 
-2023-11-02 09:31:03.272446:  
-2023-11-02 09:31:03.272519: Epoch 487 
-2023-11-02 09:31:03.272601: Current learning rate: 0.00548 
-2023-11-02 09:32:51.795773: train_loss -0.9043 
-2023-11-02 09:32:51.795920: val_loss -0.8171 
-2023-11-02 09:32:51.795974: Pseudo dice [0.8508] 
-2023-11-02 09:32:51.796002: Epoch time: 108.52 s 
-2023-11-02 09:32:52.418832:  
-2023-11-02 09:32:52.418911: Epoch 488 
-2023-11-02 09:32:52.418988: Current learning rate: 0.00547 
-2023-11-02 09:34:40.888750: train_loss -0.9019 
-2023-11-02 09:34:40.888913: val_loss -0.8172 
-2023-11-02 09:34:40.888965: Pseudo dice [0.8486] 
-2023-11-02 09:34:40.888995: Epoch time: 108.47 s 
-2023-11-02 09:34:41.412387:  
-2023-11-02 09:34:41.412489: Epoch 489 
-2023-11-02 09:34:41.412542: Current learning rate: 0.00546 
-2023-11-02 09:36:30.004287: train_loss -0.8968 
-2023-11-02 09:36:30.004431: val_loss -0.8147 
-2023-11-02 09:36:30.004472: Pseudo dice [0.8498] 
-2023-11-02 09:36:30.004503: Epoch time: 108.59 s 
-2023-11-02 09:36:30.534081:  
-2023-11-02 09:36:30.534157: Epoch 490 
-2023-11-02 09:36:30.534222: Current learning rate: 0.00546 
-2023-11-02 09:38:19.070379: train_loss -0.9003 
-2023-11-02 09:38:19.070535: val_loss -0.8195 
-2023-11-02 09:38:19.070564: Pseudo dice [0.8524] 
-2023-11-02 09:38:19.070591: Epoch time: 108.54 s 
-2023-11-02 09:38:19.596486:  
-2023-11-02 09:38:19.596565: Epoch 491 
-2023-11-02 09:38:19.596641: Current learning rate: 0.00545 
-2023-11-02 09:40:08.146126: train_loss -0.9052 
-2023-11-02 09:40:08.146284: val_loss -0.8072 
-2023-11-02 09:40:08.146308: Pseudo dice [0.8447] 
-2023-11-02 09:40:08.146336: Epoch time: 108.55 s 
-2023-11-02 09:40:08.667935:  
-2023-11-02 09:40:08.668038: Epoch 492 
-2023-11-02 09:40:08.668093: Current learning rate: 0.00544 
-2023-11-02 09:41:57.112732: train_loss -0.9052 
-2023-11-02 09:41:57.112871: val_loss -0.8163 
-2023-11-02 09:41:57.112909: Pseudo dice [0.852] 
-2023-11-02 09:41:57.112938: Epoch time: 108.45 s 
-2023-11-02 09:41:57.640617:  
-2023-11-02 09:41:57.640692: Epoch 493 
-2023-11-02 09:41:57.640746: Current learning rate: 0.00543 
-2023-11-02 09:43:46.175621: train_loss -0.9078 
-2023-11-02 09:43:46.175757: val_loss -0.8215 
-2023-11-02 09:43:46.175808: Pseudo dice [0.8546] 
-2023-11-02 09:43:46.175835: Epoch time: 108.54 s 
-2023-11-02 09:43:46.707499:  
-2023-11-02 09:43:46.707586: Epoch 494 
-2023-11-02 09:43:46.707717: Current learning rate: 0.00542 
-2023-11-02 09:45:35.343756: train_loss -0.9041 
-2023-11-02 09:45:35.343886: val_loss -0.8091 
-2023-11-02 09:45:35.343937: Pseudo dice [0.8461] 
-2023-11-02 09:45:35.343969: Epoch time: 108.64 s 
-2023-11-02 09:45:35.866150:  
-2023-11-02 09:45:35.866228: Epoch 495 
-2023-11-02 09:45:35.866305: Current learning rate: 0.00541 
-2023-11-02 09:47:24.292794: train_loss -0.9055 
-2023-11-02 09:47:24.292991: val_loss -0.8131 
-2023-11-02 09:47:24.293048: Pseudo dice [0.8469] 
-2023-11-02 09:47:24.293087: Epoch time: 108.43 s 
-2023-11-02 09:47:24.818497:  
-2023-11-02 09:47:24.818575: Epoch 496 
-2023-11-02 09:47:24.818651: Current learning rate: 0.0054 
-2023-11-02 09:49:13.246707: train_loss -0.9052 
-2023-11-02 09:49:13.246847: val_loss -0.8126 
-2023-11-02 09:49:13.246887: Pseudo dice [0.8465] 
-2023-11-02 09:49:13.246940: Epoch time: 108.43 s 
-2023-11-02 09:49:13.779119:  
-2023-11-02 09:49:13.779344: Epoch 497 
-2023-11-02 09:49:13.779446: Current learning rate: 0.00539 
-2023-11-02 09:51:02.580036: train_loss -0.9062 
-2023-11-02 09:51:02.580176: val_loss -0.8134 
-2023-11-02 09:51:02.580202: Pseudo dice [0.8483] 
-2023-11-02 09:51:02.580229: Epoch time: 108.8 s 
-2023-11-02 09:51:03.098695:  
-2023-11-02 09:51:03.098764: Epoch 498 
-2023-11-02 09:51:03.098843: Current learning rate: 0.00538 
-2023-11-02 09:52:51.745525: train_loss -0.9077 
-2023-11-02 09:52:51.745679: val_loss -0.8012 
-2023-11-02 09:52:51.745710: Pseudo dice [0.8412] 
-2023-11-02 09:52:51.745742: Epoch time: 108.65 s 
-2023-11-02 09:52:52.272762:  
-2023-11-02 09:52:52.272836: Epoch 499 
-2023-11-02 09:52:52.272911: Current learning rate: 0.00537 
-2023-11-02 09:54:40.970174: train_loss -0.9091 
-2023-11-02 09:54:40.970299: val_loss -0.8131 
-2023-11-02 09:54:40.970349: Pseudo dice [0.8491] 
-2023-11-02 09:54:40.970375: Epoch time: 108.7 s 
-2023-11-02 09:54:41.717831:  
-2023-11-02 09:54:41.717900: Epoch 500 
-2023-11-02 09:54:41.717975: Current learning rate: 0.00536 
-2023-11-02 09:56:30.201923: train_loss -0.9105 
-2023-11-02 09:56:30.202036: val_loss -0.8169 
-2023-11-02 09:56:30.202088: Pseudo dice [0.8522] 
-2023-11-02 09:56:30.202116: Epoch time: 108.48 s 
-2023-11-02 09:56:30.820618:  
-2023-11-02 09:56:30.820697: Epoch 501 
-2023-11-02 09:56:30.820750: Current learning rate: 0.00535 
-2023-11-02 09:58:19.309041: train_loss -0.9106 
-2023-11-02 09:58:19.309202: val_loss -0.8152 
-2023-11-02 09:58:19.309230: Pseudo dice [0.8491] 
-2023-11-02 09:58:19.309255: Epoch time: 108.49 s 
-2023-11-02 09:58:19.831093:  
-2023-11-02 09:58:19.831167: Epoch 502 
-2023-11-02 09:58:19.831244: Current learning rate: 0.00534 
-2023-11-02 10:00:08.587421: train_loss -0.9109 
-2023-11-02 10:00:08.587548: val_loss -0.8179 
-2023-11-02 10:00:08.587598: Pseudo dice [0.8528] 
-2023-11-02 10:00:08.587625: Epoch time: 108.76 s 
-2023-11-02 10:00:09.112915:  
-2023-11-02 10:00:09.113004: Epoch 503 
-2023-11-02 10:00:09.113083: Current learning rate: 0.00533 
-2023-11-02 10:01:57.652414: train_loss -0.9105 
-2023-11-02 10:01:57.652564: val_loss -0.8252 
-2023-11-02 10:01:57.652589: Pseudo dice [0.856] 
-2023-11-02 10:01:57.652618: Epoch time: 108.54 s 
-2023-11-02 10:01:58.178555:  
-2023-11-02 10:01:58.178625: Epoch 504 
-2023-11-02 10:01:58.178705: Current learning rate: 0.00532 
-2023-11-02 10:03:46.682833: train_loss -0.9113 
-2023-11-02 10:03:46.682959: val_loss -0.8141 
-2023-11-02 10:03:46.682986: Pseudo dice [0.8483] 
-2023-11-02 10:03:46.683014: Epoch time: 108.5 s 
-2023-11-02 10:03:47.224952:  
-2023-11-02 10:03:47.225028: Epoch 505 
-2023-11-02 10:03:47.225080: Current learning rate: 0.00531 
-2023-11-02 10:05:35.735346: train_loss -0.9061 
-2023-11-02 10:05:35.735470: val_loss -0.8192 
-2023-11-02 10:05:35.735515: Pseudo dice [0.8533] 
-2023-11-02 10:05:35.735542: Epoch time: 108.51 s 
-2023-11-02 10:05:36.261239:  
-2023-11-02 10:05:36.261311: Epoch 506 
-2023-11-02 10:05:36.261389: Current learning rate: 0.0053 
-2023-11-02 10:07:24.744298: train_loss -0.9033 
-2023-11-02 10:07:24.744453: val_loss -0.7962 
-2023-11-02 10:07:24.744483: Pseudo dice [0.8355] 
-2023-11-02 10:07:24.744510: Epoch time: 108.48 s 
-2023-11-02 10:07:25.364774:  
-2023-11-02 10:07:25.364851: Epoch 507 
-2023-11-02 10:07:25.364907: Current learning rate: 0.00529 
-2023-11-02 10:09:13.810924: train_loss -0.9063 
-2023-11-02 10:09:13.811084: val_loss -0.8215 
-2023-11-02 10:09:13.811110: Pseudo dice [0.854] 
-2023-11-02 10:09:13.811139: Epoch time: 108.45 s 
-2023-11-02 10:09:14.331771:  
-2023-11-02 10:09:14.331848: Epoch 508 
-2023-11-02 10:09:14.331924: Current learning rate: 0.00528 
-2023-11-02 10:11:02.786325: train_loss -0.901 
-2023-11-02 10:11:02.786458: val_loss -0.8196 
-2023-11-02 10:11:02.786509: Pseudo dice [0.8512] 
-2023-11-02 10:11:02.786536: Epoch time: 108.45 s 
-2023-11-02 10:11:03.310228:  
-2023-11-02 10:11:03.310308: Epoch 509 
-2023-11-02 10:11:03.310385: Current learning rate: 0.00527 
-2023-11-02 10:12:51.791116: train_loss -0.9058 
-2023-11-02 10:12:51.791245: val_loss -0.8214 
-2023-11-02 10:12:51.791294: Pseudo dice [0.8542] 
-2023-11-02 10:12:51.791319: Epoch time: 108.48 s 
-2023-11-02 10:12:52.315834:  
-2023-11-02 10:12:52.315909: Epoch 510 
-2023-11-02 10:12:52.315990: Current learning rate: 0.00526 
-2023-11-02 10:14:40.842171: train_loss -0.9053 
-2023-11-02 10:14:40.842316: val_loss -0.8158 
-2023-11-02 10:14:40.842366: Pseudo dice [0.8496] 
-2023-11-02 10:14:40.842395: Epoch time: 108.53 s 
-2023-11-02 10:14:41.362912:  
-2023-11-02 10:14:41.362980: Epoch 511 
-2023-11-02 10:14:41.363050: Current learning rate: 0.00525 
-2023-11-02 10:16:29.862911: train_loss -0.907 
-2023-11-02 10:16:29.863039: val_loss -0.8172 
-2023-11-02 10:16:29.863062: Pseudo dice [0.851] 
-2023-11-02 10:16:29.863090: Epoch time: 108.5 s 
-2023-11-02 10:16:30.382947:  
-2023-11-02 10:16:30.383015: Epoch 512 
-2023-11-02 10:16:30.383078: Current learning rate: 0.00524 
-2023-11-02 10:18:18.917135: train_loss -0.9067 
-2023-11-02 10:18:18.917262: val_loss -0.8244 
-2023-11-02 10:18:18.917310: Pseudo dice [0.8561] 
-2023-11-02 10:18:18.917336: Epoch time: 108.53 s 
-2023-11-02 10:18:19.441478:  
-2023-11-02 10:18:19.441547: Epoch 513 
-2023-11-02 10:18:19.441625: Current learning rate: 0.00523 
-2023-11-02 10:20:08.022870: train_loss -0.9066 
-2023-11-02 10:20:08.023011: val_loss -0.8176 
-2023-11-02 10:20:08.023049: Pseudo dice [0.8516] 
-2023-11-02 10:20:08.023078: Epoch time: 108.58 s 
-2023-11-02 10:20:08.654694:  
-2023-11-02 10:20:08.654772: Epoch 514 
-2023-11-02 10:20:08.654825: Current learning rate: 0.00522 
-2023-11-02 10:21:57.317604: train_loss -0.9078 
-2023-11-02 10:21:57.317746: val_loss -0.8226 
-2023-11-02 10:21:57.317793: Pseudo dice [0.8539] 
-2023-11-02 10:21:57.317825: Epoch time: 108.66 s 
-2023-11-02 10:21:57.848904:  
-2023-11-02 10:21:57.848984: Epoch 515 
-2023-11-02 10:21:57.849037: Current learning rate: 0.00521 
-2023-11-02 10:23:46.305581: train_loss -0.9092 
-2023-11-02 10:23:46.305713: val_loss -0.8209 
-2023-11-02 10:23:46.305760: Pseudo dice [0.8527] 
-2023-11-02 10:23:46.305785: Epoch time: 108.46 s 
-2023-11-02 10:23:46.842716:  
-2023-11-02 10:23:46.842797: Epoch 516 
-2023-11-02 10:23:46.842864: Current learning rate: 0.0052 
-2023-11-02 10:25:35.606430: train_loss -0.9093 
-2023-11-02 10:25:35.606584: val_loss -0.8281 
-2023-11-02 10:25:35.606611: Pseudo dice [0.8581] 
-2023-11-02 10:25:35.606647: Epoch time: 108.76 s 
-2023-11-02 10:25:36.129673:  
-2023-11-02 10:25:36.129747: Epoch 517 
-2023-11-02 10:25:36.129798: Current learning rate: 0.00519 
-2023-11-02 10:27:24.628509: train_loss -0.9053 
-2023-11-02 10:27:24.628700: val_loss -0.8031 
-2023-11-02 10:27:24.628746: Pseudo dice [0.8412] 
-2023-11-02 10:27:24.628793: Epoch time: 108.5 s 
-2023-11-02 10:27:25.154779:  
-2023-11-02 10:27:25.154848: Epoch 518 
-2023-11-02 10:27:25.154899: Current learning rate: 0.00518 
-2023-11-02 10:29:13.673324: train_loss -0.908 
-2023-11-02 10:29:13.673451: val_loss -0.8282 
-2023-11-02 10:29:13.673474: Pseudo dice [0.8588] 
-2023-11-02 10:29:13.673500: Epoch time: 108.52 s 
-2023-11-02 10:29:14.198766:  
-2023-11-02 10:29:14.198833: Epoch 519 
-2023-11-02 10:29:14.198885: Current learning rate: 0.00518 
-2023-11-02 10:31:04.236366: train_loss -0.8998 
-2023-11-02 10:31:04.236494: val_loss -0.8152 
-2023-11-02 10:31:04.236547: Pseudo dice [0.8486] 
-2023-11-02 10:31:04.236579: Epoch time: 110.04 s 
-2023-11-02 10:31:04.865516:  
-2023-11-02 10:31:04.865623: Epoch 520 
-2023-11-02 10:31:04.865702: Current learning rate: 0.00517 
-2023-11-02 10:32:56.618752: train_loss -0.9036 
-2023-11-02 10:32:56.618880: val_loss -0.8136 
-2023-11-02 10:32:56.618910: Pseudo dice [0.8488] 
-2023-11-02 10:32:56.618941: Epoch time: 111.75 s 
-2023-11-02 10:32:57.142778:  
-2023-11-02 10:32:57.142861: Epoch 521 
-2023-11-02 10:32:57.142917: Current learning rate: 0.00516 
-2023-11-02 10:34:47.014642: train_loss -0.9041 
-2023-11-02 10:34:47.014772: val_loss -0.8085 
-2023-11-02 10:34:47.014800: Pseudo dice [0.8449] 
-2023-11-02 10:34:47.014829: Epoch time: 109.87 s 
-2023-11-02 10:34:47.544263:  
-2023-11-02 10:34:47.544347: Epoch 522 
-2023-11-02 10:34:47.544402: Current learning rate: 0.00515 
-2023-11-02 10:36:36.484765: train_loss -0.9045 
-2023-11-02 10:36:36.484898: val_loss -0.824 
-2023-11-02 10:36:36.484926: Pseudo dice [0.8545] 
-2023-11-02 10:36:36.484954: Epoch time: 108.94 s 
-2023-11-02 10:36:37.008872:  
-2023-11-02 10:36:37.008954: Epoch 523 
-2023-11-02 10:36:37.009007: Current learning rate: 0.00514 
-2023-11-02 10:38:26.092483: train_loss -0.9003 
-2023-11-02 10:38:26.092676: val_loss -0.8274 
-2023-11-02 10:38:26.092706: Pseudo dice [0.8568] 
-2023-11-02 10:38:26.092733: Epoch time: 109.08 s 
-2023-11-02 10:38:26.631596:  
-2023-11-02 10:38:26.631670: Epoch 524 
-2023-11-02 10:38:26.631726: Current learning rate: 0.00513 
-2023-11-02 10:40:17.000780: train_loss -0.8984 
-2023-11-02 10:40:17.001448: val_loss -0.8175 
-2023-11-02 10:40:17.001480: Pseudo dice [0.8506] 
-2023-11-02 10:40:17.001723: Epoch time: 110.37 s 
-2023-11-02 10:40:17.536833:  
-2023-11-02 10:40:17.536903: Epoch 525 
-2023-11-02 10:40:17.536976: Current learning rate: 0.00512 
-2023-11-02 10:42:03.532036: train_loss -0.9005 
-2023-11-02 10:42:03.532199: val_loss -0.8153 
-2023-11-02 10:42:03.532229: Pseudo dice [0.8483] 
-2023-11-02 10:42:03.532255: Epoch time: 106.0 s 
-2023-11-02 10:42:04.056428:  
-2023-11-02 10:42:04.056487: Epoch 526 
-2023-11-02 10:42:04.056555: Current learning rate: 0.00511 
-2023-11-02 10:43:50.061446: train_loss -0.9058 
-2023-11-02 10:43:50.061717: val_loss -0.8214 
-2023-11-02 10:43:50.068521: Pseudo dice [0.8533] 
-2023-11-02 10:43:50.068551: Epoch time: 106.01 s 
-2023-11-02 10:43:50.689447:  
-2023-11-02 10:43:50.689521: Epoch 527 
-2023-11-02 10:43:50.689601: Current learning rate: 0.0051 
-2023-11-02 10:45:36.650610: train_loss -0.9034 
-2023-11-02 10:45:36.650746: val_loss -0.814 
-2023-11-02 10:45:36.650782: Pseudo dice [0.8493] 
-2023-11-02 10:45:36.650808: Epoch time: 105.96 s 
-2023-11-02 10:45:37.172217:  
-2023-11-02 10:45:37.172284: Epoch 528 
-2023-11-02 10:45:37.172333: Current learning rate: 0.00509 
-2023-11-02 10:47:23.196808: train_loss -0.9043 
-2023-11-02 10:47:23.196941: val_loss -0.8136 
-2023-11-02 10:47:23.196990: Pseudo dice [0.8479] 
-2023-11-02 10:47:23.197018: Epoch time: 106.03 s 
-2023-11-02 10:47:23.726490:  
-2023-11-02 10:47:23.726561: Epoch 529 
-2023-11-02 10:47:23.726639: Current learning rate: 0.00508 
-2023-11-02 10:49:11.665159: train_loss -0.9095 
-2023-11-02 10:49:11.665314: val_loss -0.8174 
-2023-11-02 10:49:11.665343: Pseudo dice [0.85] 
-2023-11-02 10:49:11.665376: Epoch time: 107.94 s 
-2023-11-02 10:49:12.193665:  
-2023-11-02 10:49:12.193738: Epoch 530 
-2023-11-02 10:49:12.193788: Current learning rate: 0.00507 
-2023-11-02 10:51:01.467658: train_loss -0.9062 
-2023-11-02 10:51:01.467784: val_loss -0.8238 
-2023-11-02 10:51:01.467809: Pseudo dice [0.8545] 
-2023-11-02 10:51:01.467836: Epoch time: 109.27 s 
-2023-11-02 10:51:02.001364:  
-2023-11-02 10:51:02.001450: Epoch 531 
-2023-11-02 10:51:02.001515: Current learning rate: 0.00506 
-2023-11-02 10:52:50.547579: train_loss -0.9044 
-2023-11-02 10:52:50.547713: val_loss -0.8196 
-2023-11-02 10:52:50.547741: Pseudo dice [0.8529] 
-2023-11-02 10:52:50.547772: Epoch time: 108.55 s 
-2023-11-02 10:52:51.068314:  
-2023-11-02 10:52:51.068385: Epoch 532 
-2023-11-02 10:52:51.068438: Current learning rate: 0.00505 
-2023-11-02 10:54:40.865603: train_loss -0.9078 
-2023-11-02 10:54:40.865736: val_loss -0.8103 
-2023-11-02 10:54:40.865785: Pseudo dice [0.8477] 
-2023-11-02 10:54:40.865811: Epoch time: 109.8 s 
-2023-11-02 10:54:41.394416:  
-2023-11-02 10:54:41.394481: Epoch 533 
-2023-11-02 10:54:41.394554: Current learning rate: 0.00504 
-2023-11-02 10:56:30.636372: train_loss -0.9083 
-2023-11-02 10:56:30.636503: val_loss -0.8141 
-2023-11-02 10:56:30.636545: Pseudo dice [0.8475] 
-2023-11-02 10:56:30.636575: Epoch time: 109.24 s 
-2023-11-02 10:56:31.164201:  
-2023-11-02 10:56:31.164284: Epoch 534 
-2023-11-02 10:56:31.164340: Current learning rate: 0.00503 
-2023-11-02 10:58:20.101808: train_loss -0.9089 
-2023-11-02 10:58:20.101949: val_loss -0.821 
-2023-11-02 10:58:20.101988: Pseudo dice [0.8561] 
-2023-11-02 10:58:20.102015: Epoch time: 108.94 s 
-2023-11-02 10:58:20.622919:  
-2023-11-02 10:58:20.622993: Epoch 535 
-2023-11-02 10:58:20.623043: Current learning rate: 0.00502 
-2023-11-02 11:00:06.476618: train_loss -0.9065 
-2023-11-02 11:00:06.476741: val_loss -0.8113 
-2023-11-02 11:00:06.476778: Pseudo dice [0.8455] 
-2023-11-02 11:00:06.476804: Epoch time: 105.85 s 
-2023-11-02 11:00:07.001871:  
-2023-11-02 11:00:07.001940: Epoch 536 
-2023-11-02 11:00:07.001988: Current learning rate: 0.00501 
-2023-11-02 11:01:52.772956: train_loss -0.9108 
-2023-11-02 11:01:52.773090: val_loss -0.8194 
-2023-11-02 11:01:52.773115: Pseudo dice [0.8531] 
-2023-11-02 11:01:52.773143: Epoch time: 105.77 s 
-2023-11-02 11:01:53.293260:  
-2023-11-02 11:01:53.293329: Epoch 537 
-2023-11-02 11:01:53.293403: Current learning rate: 0.005 
-2023-11-02 11:03:39.230626: train_loss -0.9097 
-2023-11-02 11:03:39.230743: val_loss -0.8116 
-2023-11-02 11:03:39.230780: Pseudo dice [0.8467] 
-2023-11-02 11:03:39.230807: Epoch time: 105.94 s 
-2023-11-02 11:03:39.755628:  
-2023-11-02 11:03:39.755696: Epoch 538 
-2023-11-02 11:03:39.755761: Current learning rate: 0.00499 
-2023-11-02 11:05:25.842558: train_loss -0.9104 
-2023-11-02 11:05:25.842687: val_loss -0.8177 
-2023-11-02 11:05:25.842728: Pseudo dice [0.851] 
-2023-11-02 11:05:25.842760: Epoch time: 106.09 s 
-2023-11-02 11:05:26.364403:  
-2023-11-02 11:05:26.364465: Epoch 539 
-2023-11-02 11:05:26.364511: Current learning rate: 0.00498 
-2023-11-02 11:07:12.478393: train_loss -0.9128 
-2023-11-02 11:07:12.478521: val_loss -0.8165 
-2023-11-02 11:07:12.478570: Pseudo dice [0.8504] 
-2023-11-02 11:07:12.478595: Epoch time: 106.11 s 
-2023-11-02 11:07:13.097917:  
-2023-11-02 11:07:13.097992: Epoch 540 
-2023-11-02 11:07:13.098065: Current learning rate: 0.00497 
-2023-11-02 11:08:59.277869: train_loss -0.907 
-2023-11-02 11:08:59.277996: val_loss -0.8206 
-2023-11-02 11:08:59.278040: Pseudo dice [0.8538] 
-2023-11-02 11:08:59.278068: Epoch time: 106.18 s 
-2023-11-02 11:08:59.801918:  
-2023-11-02 11:08:59.801984: Epoch 541 
-2023-11-02 11:08:59.802030: Current learning rate: 0.00496 
-2023-11-02 11:10:45.959854: train_loss -0.9081 
-2023-11-02 11:10:45.960012: val_loss -0.8146 
-2023-11-02 11:10:45.960038: Pseudo dice [0.8485] 
-2023-11-02 11:10:45.960065: Epoch time: 106.16 s 
-2023-11-02 11:10:46.485097:  
-2023-11-02 11:10:46.485170: Epoch 542 
-2023-11-02 11:10:46.485248: Current learning rate: 0.00495 
-2023-11-02 11:12:32.595481: train_loss -0.9086 
-2023-11-02 11:12:32.595639: val_loss -0.8161 
-2023-11-02 11:12:32.595664: Pseudo dice [0.8516] 
-2023-11-02 11:12:32.595690: Epoch time: 106.11 s 
-2023-11-02 11:12:33.119627:  
-2023-11-02 11:12:33.119692: Epoch 543 
-2023-11-02 11:12:33.119767: Current learning rate: 0.00494 
-2023-11-02 11:14:19.189136: train_loss -0.9103 
-2023-11-02 11:14:19.189264: val_loss -0.8053 
-2023-11-02 11:14:19.189312: Pseudo dice [0.8421] 
-2023-11-02 11:14:19.189344: Epoch time: 106.07 s 
-2023-11-02 11:14:19.712853:  
-2023-11-02 11:14:19.712922: Epoch 544 
-2023-11-02 11:14:19.712998: Current learning rate: 0.00493 
-2023-11-02 11:16:05.722886: train_loss -0.908 
-2023-11-02 11:16:05.723044: val_loss -0.8143 
-2023-11-02 11:16:05.723096: Pseudo dice [0.8485] 
-2023-11-02 11:16:05.723123: Epoch time: 106.01 s 
-2023-11-02 11:16:06.246098:  
-2023-11-02 11:16:06.246163: Epoch 545 
-2023-11-02 11:16:06.246237: Current learning rate: 0.00492 
-2023-11-02 11:17:52.156400: train_loss -0.9094 
-2023-11-02 11:17:52.156537: val_loss -0.8205 
-2023-11-02 11:17:52.156594: Pseudo dice [0.8567] 
-2023-11-02 11:17:52.156627: Epoch time: 105.91 s 
-2023-11-02 11:17:52.680299:  
-2023-11-02 11:17:52.680364: Epoch 546 
-2023-11-02 11:17:52.680441: Current learning rate: 0.00491 
-2023-11-02 11:19:38.600208: train_loss -0.9112 
-2023-11-02 11:19:38.600338: val_loss -0.8223 
-2023-11-02 11:19:38.600377: Pseudo dice [0.8555] 
-2023-11-02 11:19:38.600404: Epoch time: 105.92 s 
-2023-11-02 11:19:39.121282:  
-2023-11-02 11:19:39.121355: Epoch 547 
-2023-11-02 11:19:39.121406: Current learning rate: 0.0049 
-2023-11-02 11:21:25.190036: train_loss -0.9108 
-2023-11-02 11:21:25.190166: val_loss -0.8015 
-2023-11-02 11:21:25.190205: Pseudo dice [0.8383] 
-2023-11-02 11:21:25.190232: Epoch time: 106.07 s 
-2023-11-02 11:21:25.713506:  
-2023-11-02 11:21:25.713577: Epoch 548 
-2023-11-02 11:21:25.713628: Current learning rate: 0.00489 
-2023-11-02 11:23:11.639998: train_loss -0.9112 
-2023-11-02 11:23:11.640143: val_loss -0.8247 
-2023-11-02 11:23:11.640193: Pseudo dice [0.8572] 
-2023-11-02 11:23:11.640220: Epoch time: 105.93 s 
-2023-11-02 11:23:12.161531:  
-2023-11-02 11:23:12.161605: Epoch 549 
-2023-11-02 11:23:12.161682: Current learning rate: 0.00488 
-2023-11-02 11:24:58.068094: train_loss -0.9112 
-2023-11-02 11:24:58.068251: val_loss -0.8237 
-2023-11-02 11:24:58.068303: Pseudo dice [0.8555] 
-2023-11-02 11:24:58.068332: Epoch time: 105.91 s 
-2023-11-02 11:24:58.812050:  
-2023-11-02 11:24:58.812116: Epoch 550 
-2023-11-02 11:24:58.812182: Current learning rate: 0.00487 
-2023-11-02 11:26:46.790815: train_loss -0.9095 
-2023-11-02 11:26:46.790948: val_loss -0.8149 
-2023-11-02 11:26:46.790974: Pseudo dice [0.8483] 
-2023-11-02 11:26:46.791001: Epoch time: 107.98 s 
-2023-11-02 11:26:47.315035:  
-2023-11-02 11:26:47.315108: Epoch 551 
-2023-11-02 11:26:47.315161: Current learning rate: 0.00486 
-2023-11-02 11:28:36.783817: train_loss -0.9101 
-2023-11-02 11:28:36.783937: val_loss -0.8145 
-2023-11-02 11:28:36.783967: Pseudo dice [0.8491] 
-2023-11-02 11:28:36.783996: Epoch time: 109.47 s 
-2023-11-02 11:28:37.311115:  
-2023-11-02 11:28:37.311186: Epoch 552 
-2023-11-02 11:28:37.311238: Current learning rate: 0.00485 
-2023-11-02 11:30:26.791569: train_loss -0.9105 
-2023-11-02 11:30:26.791701: val_loss -0.8167 
-2023-11-02 11:30:26.791728: Pseudo dice [0.8522] 
-2023-11-02 11:30:26.791755: Epoch time: 109.48 s 
-2023-11-02 11:30:27.409017:  
-2023-11-02 11:30:27.409091: Epoch 553 
-2023-11-02 11:30:27.409169: Current learning rate: 0.00484 
-2023-11-02 11:32:17.984037: train_loss -0.9093 
-2023-11-02 11:32:17.984175: val_loss -0.8143 
-2023-11-02 11:32:17.984226: Pseudo dice [0.8473] 
-2023-11-02 11:32:17.984253: Epoch time: 110.58 s 
-2023-11-02 11:32:18.513118:  
-2023-11-02 11:32:18.513205: Epoch 554 
-2023-11-02 11:32:18.513295: Current learning rate: 0.00484 
-2023-11-02 11:34:07.920856: train_loss -0.9072 
-2023-11-02 11:34:07.921010: val_loss -0.8093 
-2023-11-02 11:34:07.921035: Pseudo dice [0.8464] 
-2023-11-02 11:34:07.921061: Epoch time: 109.41 s 
-2023-11-02 11:34:08.444619:  
-2023-11-02 11:34:08.444698: Epoch 555 
-2023-11-02 11:34:08.444776: Current learning rate: 0.00483 
-2023-11-02 11:35:59.343758: train_loss -0.9073 
-2023-11-02 11:35:59.343888: val_loss -0.8108 
-2023-11-02 11:35:59.343925: Pseudo dice [0.8452] 
-2023-11-02 11:35:59.343955: Epoch time: 110.9 s 
-2023-11-02 11:35:59.869420:  
-2023-11-02 11:35:59.869488: Epoch 556 
-2023-11-02 11:35:59.869539: Current learning rate: 0.00482 
-2023-11-02 11:37:49.305909: train_loss -0.9036 
-2023-11-02 11:37:49.306048: val_loss -0.8059 
-2023-11-02 11:37:49.306074: Pseudo dice [0.8432] 
-2023-11-02 11:37:49.306100: Epoch time: 109.44 s 
-2023-11-02 11:37:49.830580:  
-2023-11-02 11:37:49.830654: Epoch 557 
-2023-11-02 11:37:49.830727: Current learning rate: 0.00481 
-2023-11-02 11:39:39.201988: train_loss -0.9076 
-2023-11-02 11:39:39.202119: val_loss -0.8181 
-2023-11-02 11:39:39.202146: Pseudo dice [0.8529] 
-2023-11-02 11:39:39.202171: Epoch time: 109.37 s 
-2023-11-02 11:39:39.732008:  
-2023-11-02 11:39:39.732081: Epoch 558 
-2023-11-02 11:39:39.732138: Current learning rate: 0.0048 
-2023-11-02 11:41:29.001642: train_loss -0.9112 
-2023-11-02 11:41:29.001798: val_loss -0.8163 
-2023-11-02 11:41:29.001827: Pseudo dice [0.8487] 
-2023-11-02 11:41:29.001861: Epoch time: 109.27 s 
-2023-11-02 11:41:29.658471:  
-2023-11-02 11:41:29.658558: Epoch 559 
-2023-11-02 11:41:29.658615: Current learning rate: 0.00479 
-2023-11-02 11:43:18.019646: train_loss -0.9115 
-2023-11-02 11:43:18.019788: val_loss -0.8168 
-2023-11-02 11:43:18.019828: Pseudo dice [0.8529] 
-2023-11-02 11:43:18.019855: Epoch time: 108.36 s 
-2023-11-02 11:43:18.544183:  
-2023-11-02 11:43:18.544260: Epoch 560 
-2023-11-02 11:43:18.544337: Current learning rate: 0.00478 
-2023-11-02 11:45:04.857539: train_loss -0.9126 
-2023-11-02 11:45:04.857663: val_loss -0.8306 
-2023-11-02 11:45:04.857713: Pseudo dice [0.8591] 
-2023-11-02 11:45:04.857738: Epoch time: 106.31 s 
-2023-11-02 11:45:05.393964:  
-2023-11-02 11:45:05.394043: Epoch 561 
-2023-11-02 11:45:05.394153: Current learning rate: 0.00477 
-2023-11-02 11:46:51.895699: train_loss -0.9103 
-2023-11-02 11:46:51.895833: val_loss -0.8226 
-2023-11-02 11:46:51.895858: Pseudo dice [0.8579] 
-2023-11-02 11:46:51.895884: Epoch time: 106.5 s 
-2023-11-02 11:46:52.417176:  
-2023-11-02 11:46:52.417244: Epoch 562 
-2023-11-02 11:46:52.417314: Current learning rate: 0.00476 
-2023-11-02 11:48:38.356170: train_loss -0.9114 
-2023-11-02 11:48:38.356326: val_loss -0.8209 
-2023-11-02 11:48:38.356351: Pseudo dice [0.8536] 
-2023-11-02 11:48:38.356379: Epoch time: 105.94 s 
-2023-11-02 11:48:38.879645:  
-2023-11-02 11:48:38.879709: Epoch 563 
-2023-11-02 11:48:38.879758: Current learning rate: 0.00475 
-2023-11-02 11:50:24.809865: train_loss -0.9117 
-2023-11-02 11:50:24.809996: val_loss -0.8245 
-2023-11-02 11:50:24.810046: Pseudo dice [0.8534] 
-2023-11-02 11:50:24.810073: Epoch time: 105.93 s 
-2023-11-02 11:50:25.331780:  
-2023-11-02 11:50:25.331843: Epoch 564 
-2023-11-02 11:50:25.331908: Current learning rate: 0.00474 
-2023-11-02 11:52:11.325713: train_loss -0.9115 
-2023-11-02 11:52:11.325860: val_loss -0.8195 
-2023-11-02 11:52:11.325911: Pseudo dice [0.8531] 
-2023-11-02 11:52:11.325939: Epoch time: 105.99 s 
-2023-11-02 11:52:11.853163:  
-2023-11-02 11:52:11.853224: Epoch 565 
-2023-11-02 11:52:11.853298: Current learning rate: 0.00473 
-2023-11-02 11:53:57.790581: train_loss -0.9112 
-2023-11-02 11:53:57.790733: val_loss -0.819 
-2023-11-02 11:53:57.790763: Pseudo dice [0.853] 
-2023-11-02 11:53:57.790796: Epoch time: 105.94 s 
-2023-11-02 11:53:58.413043:  
-2023-11-02 11:53:58.413115: Epoch 566 
-2023-11-02 11:53:58.413190: Current learning rate: 0.00472 
-2023-11-02 11:55:44.266198: train_loss -0.9115 
-2023-11-02 11:55:44.266335: val_loss -0.8121 
-2023-11-02 11:55:44.266397: Pseudo dice [0.8487] 
-2023-11-02 11:55:44.266426: Epoch time: 105.85 s 
-2023-11-02 11:55:44.787905:  
-2023-11-02 11:55:44.788008: Epoch 567 
-2023-11-02 11:55:44.788056: Current learning rate: 0.00471 
-2023-11-02 11:57:30.734611: train_loss -0.909 
-2023-11-02 11:57:30.734783: val_loss -0.8106 
-2023-11-02 11:57:30.734811: Pseudo dice [0.8455] 
-2023-11-02 11:57:30.734839: Epoch time: 105.95 s 
-2023-11-02 11:57:31.254207:  
-2023-11-02 11:57:31.254278: Epoch 568 
-2023-11-02 11:57:31.254346: Current learning rate: 0.0047 
-2023-11-02 11:59:17.199268: train_loss -0.9111 
-2023-11-02 11:59:17.199438: val_loss -0.8239 
-2023-11-02 11:59:17.199465: Pseudo dice [0.8562] 
-2023-11-02 11:59:17.199492: Epoch time: 105.95 s 
-2023-11-02 11:59:17.720896:  
-2023-11-02 11:59:17.720962: Epoch 569 
-2023-11-02 11:59:17.721039: Current learning rate: 0.00469 
-2023-11-02 12:01:03.569227: train_loss -0.911 
-2023-11-02 12:01:03.569353: val_loss -0.8228 
-2023-11-02 12:01:03.569400: Pseudo dice [0.8548] 
-2023-11-02 12:01:03.569426: Epoch time: 105.85 s 
-2023-11-02 12:01:04.093070:  
-2023-11-02 12:01:04.093135: Epoch 570 
-2023-11-02 12:01:04.093205: Current learning rate: 0.00468 
-2023-11-02 12:02:53.753610: train_loss -0.9066 
-2023-11-02 12:02:53.753733: val_loss -0.8183 
-2023-11-02 12:02:53.753782: Pseudo dice [0.8528] 
-2023-11-02 12:02:53.753808: Epoch time: 109.66 s 
-2023-11-02 12:02:54.280994:  
-2023-11-02 12:02:54.281061: Epoch 571 
-2023-11-02 12:02:54.281138: Current learning rate: 0.00467 
-2023-11-02 12:04:45.121698: train_loss -0.9112 
-2023-11-02 12:04:45.121852: val_loss -0.8103 
-2023-11-02 12:04:45.121876: Pseudo dice [0.8476] 
-2023-11-02 12:04:45.121911: Epoch time: 110.84 s 
-2023-11-02 12:04:45.743427:  
-2023-11-02 12:04:45.743509: Epoch 572 
-2023-11-02 12:04:45.743590: Current learning rate: 0.00466 
-2023-11-02 12:06:34.737634: train_loss -0.9115 
-2023-11-02 12:06:34.737802: val_loss -0.8377 
-2023-11-02 12:06:34.737827: Pseudo dice [0.865] 
-2023-11-02 12:06:34.737854: Epoch time: 108.99 s 
-2023-11-02 12:06:35.279977:  
-2023-11-02 12:06:35.280060: Epoch 573 
-2023-11-02 12:06:35.280131: Current learning rate: 0.00465 
-2023-11-02 12:08:25.398245: train_loss -0.9095 
-2023-11-02 12:08:25.398380: val_loss -0.7948 
-2023-11-02 12:08:25.398404: Pseudo dice [0.8363] 
-2023-11-02 12:08:25.398432: Epoch time: 110.12 s 
-2023-11-02 12:08:25.931808:  
-2023-11-02 12:08:25.931894: Epoch 574 
-2023-11-02 12:08:25.931951: Current learning rate: 0.00464 
-2023-11-02 12:10:16.573066: train_loss -0.9037 
-2023-11-02 12:10:16.573190: val_loss -0.8059 
-2023-11-02 12:10:16.573216: Pseudo dice [0.8468] 
-2023-11-02 12:10:16.573243: Epoch time: 110.64 s 
-2023-11-02 12:10:17.125063:  
-2023-11-02 12:10:17.125142: Epoch 575 
-2023-11-02 12:10:17.125216: Current learning rate: 0.00463 
-2023-11-02 12:12:07.470307: train_loss -0.9078 
-2023-11-02 12:12:07.470506: val_loss -0.8136 
-2023-11-02 12:12:07.470539: Pseudo dice [0.8491] 
-2023-11-02 12:12:07.470565: Epoch time: 110.35 s 
-2023-11-02 12:12:08.003767:  
-2023-11-02 12:12:08.003842: Epoch 576 
-2023-11-02 12:12:08.003907: Current learning rate: 0.00462 
-2023-11-02 12:13:58.022239: train_loss -0.9122 
-2023-11-02 12:13:58.022378: val_loss -0.8136 
-2023-11-02 12:13:58.022419: Pseudo dice [0.8486] 
-2023-11-02 12:13:58.022446: Epoch time: 110.02 s 
-2023-11-02 12:13:58.558507:  
-2023-11-02 12:13:58.558580: Epoch 577 
-2023-11-02 12:13:58.558646: Current learning rate: 0.00461 
-2023-11-02 12:15:47.514325: train_loss -0.9132 
-2023-11-02 12:15:47.514456: val_loss -0.8212 
-2023-11-02 12:15:47.514487: Pseudo dice [0.8561] 
-2023-11-02 12:15:47.514518: Epoch time: 108.96 s 
-2023-11-02 12:15:48.050271:  
-2023-11-02 12:15:48.050342: Epoch 578 
-2023-11-02 12:15:48.050459: Current learning rate: 0.0046 
-2023-11-02 12:17:39.132934: train_loss -0.9121 
-2023-11-02 12:17:39.133085: val_loss -0.83 
-2023-11-02 12:17:39.133112: Pseudo dice [0.8598] 
-2023-11-02 12:17:39.133139: Epoch time: 111.08 s 
-2023-11-02 12:17:39.776131:  
-2023-11-02 12:17:39.776211: Epoch 579 
-2023-11-02 12:17:39.776290: Current learning rate: 0.00459 
-2023-11-02 12:19:29.808201: train_loss -0.9123 
-2023-11-02 12:19:29.808325: val_loss -0.8056 
-2023-11-02 12:19:29.808351: Pseudo dice [0.8423] 
-2023-11-02 12:19:29.808380: Epoch time: 110.03 s 
-2023-11-02 12:19:30.351552:  
-2023-11-02 12:19:30.351645: Epoch 580 
-2023-11-02 12:19:30.351726: Current learning rate: 0.00458 
-2023-11-02 12:21:20.787438: train_loss -0.9115 
-2023-11-02 12:21:20.787599: val_loss -0.8176 
-2023-11-02 12:21:20.787630: Pseudo dice [0.8521] 
-2023-11-02 12:21:20.787663: Epoch time: 110.44 s 
-2023-11-02 12:21:21.318126:  
-2023-11-02 12:21:21.318212: Epoch 581 
-2023-11-02 12:21:21.318263: Current learning rate: 0.00457 
-2023-11-02 12:23:10.885081: train_loss -0.9125 
-2023-11-02 12:23:10.885229: val_loss -0.8149 
-2023-11-02 12:23:10.885267: Pseudo dice [0.8477] 
-2023-11-02 12:23:10.885293: Epoch time: 109.57 s 
-2023-11-02 12:23:11.415109:  
-2023-11-02 12:23:11.415177: Epoch 582 
-2023-11-02 12:23:11.415254: Current learning rate: 0.00456 
-2023-11-02 12:25:00.225337: train_loss -0.9139 
-2023-11-02 12:25:00.225478: val_loss -0.8121 
-2023-11-02 12:25:00.225504: Pseudo dice [0.8456] 
-2023-11-02 12:25:00.225534: Epoch time: 108.81 s 
-2023-11-02 12:25:00.758213:  
-2023-11-02 12:25:00.758282: Epoch 583 
-2023-11-02 12:25:00.758334: Current learning rate: 0.00455 
-2023-11-02 12:26:50.401746: train_loss -0.911 
-2023-11-02 12:26:50.401865: val_loss -0.8288 
-2023-11-02 12:26:50.401913: Pseudo dice [0.8586] 
-2023-11-02 12:26:50.401939: Epoch time: 109.64 s 
-2023-11-02 12:26:51.023499:  
-2023-11-02 12:26:51.023585: Epoch 584 
-2023-11-02 12:26:51.023665: Current learning rate: 0.00454 
-2023-11-02 12:28:39.977746: train_loss -0.9141 
-2023-11-02 12:28:39.977868: val_loss -0.8274 
-2023-11-02 12:28:39.977916: Pseudo dice [0.8591] 
-2023-11-02 12:28:39.977943: Epoch time: 108.95 s 
-2023-11-02 12:28:40.515241:  
-2023-11-02 12:28:40.515326: Epoch 585 
-2023-11-02 12:28:40.515470: Current learning rate: 0.00453 
-2023-11-02 12:30:29.446366: train_loss -0.9139 
-2023-11-02 12:30:29.446513: val_loss -0.814 
-2023-11-02 12:30:29.446546: Pseudo dice [0.8501] 
-2023-11-02 12:30:29.446581: Epoch time: 108.93 s 
-2023-11-02 12:30:29.981564:  
-2023-11-02 12:30:29.981639: Epoch 586 
-2023-11-02 12:30:29.981693: Current learning rate: 0.00452 
-2023-11-02 12:32:17.176368: train_loss -0.913 
-2023-11-02 12:32:17.176508: val_loss -0.8135 
-2023-11-02 12:32:17.176547: Pseudo dice [0.8479] 
-2023-11-02 12:32:17.176579: Epoch time: 107.2 s 
-2023-11-02 12:32:17.711863:  
-2023-11-02 12:32:17.711984: Epoch 587 
-2023-11-02 12:32:17.712051: Current learning rate: 0.00451 
-2023-11-02 12:34:03.657502: train_loss -0.9123 
-2023-11-02 12:34:03.657651: val_loss -0.8186 
-2023-11-02 12:34:03.657677: Pseudo dice [0.8507] 
-2023-11-02 12:34:03.657704: Epoch time: 105.95 s 
-2023-11-02 12:34:04.194079:  
-2023-11-02 12:34:04.194141: Epoch 588 
-2023-11-02 12:34:04.194191: Current learning rate: 0.0045 
-2023-11-02 12:35:50.066108: train_loss -0.911 
-2023-11-02 12:35:50.066294: val_loss -0.8153 
-2023-11-02 12:35:50.066321: Pseudo dice [0.8502] 
-2023-11-02 12:35:50.066347: Epoch time: 105.87 s 
-2023-11-02 12:35:50.597901:  
-2023-11-02 12:35:50.597967: Epoch 589 
-2023-11-02 12:35:50.598019: Current learning rate: 0.00449 
-2023-11-02 12:37:38.087614: train_loss -0.913 
-2023-11-02 12:37:38.087771: val_loss -0.8106 
-2023-11-02 12:37:38.087802: Pseudo dice [0.8455] 
-2023-11-02 12:37:38.087835: Epoch time: 107.49 s 
-2023-11-02 12:37:38.617418:  
-2023-11-02 12:37:38.617486: Epoch 590 
-2023-11-02 12:37:38.617539: Current learning rate: 0.00448 
-2023-11-02 12:39:28.189661: train_loss -0.913 
-2023-11-02 12:39:28.189782: val_loss -0.8136 
-2023-11-02 12:39:28.189829: Pseudo dice [0.8511] 
-2023-11-02 12:39:28.189856: Epoch time: 109.57 s 
-2023-11-02 12:39:28.824355:  
-2023-11-02 12:39:28.824437: Epoch 591 
-2023-11-02 12:39:28.824495: Current learning rate: 0.00447 
-2023-11-02 12:41:17.563045: train_loss -0.9147 
-2023-11-02 12:41:17.563169: val_loss -0.8164 
-2023-11-02 12:41:17.563217: Pseudo dice [0.8518] 
-2023-11-02 12:41:17.563244: Epoch time: 108.74 s 
-2023-11-02 12:41:18.101876:  
-2023-11-02 12:41:18.101961: Epoch 592 
-2023-11-02 12:41:18.102064: Current learning rate: 0.00446 
-2023-11-02 12:43:04.572462: train_loss -0.9122 
-2023-11-02 12:43:04.572567: val_loss -0.8172 
-2023-11-02 12:43:04.572617: Pseudo dice [0.8484] 
-2023-11-02 12:43:04.572644: Epoch time: 106.47 s 
-2023-11-02 12:43:05.123646:  
-2023-11-02 12:43:05.123728: Epoch 593 
-2023-11-02 12:43:05.123785: Current learning rate: 0.00445 
-2023-11-02 12:44:51.246178: train_loss -0.9111 
-2023-11-02 12:44:51.246309: val_loss -0.8165 
-2023-11-02 12:44:51.246340: Pseudo dice [0.8514] 
-2023-11-02 12:44:51.246370: Epoch time: 106.12 s 
-2023-11-02 12:44:51.779657:  
-2023-11-02 12:44:51.779725: Epoch 594 
-2023-11-02 12:44:51.779779: Current learning rate: 0.00444 
-2023-11-02 12:46:38.030976: train_loss -0.9099 
-2023-11-02 12:46:38.031125: val_loss -0.8228 
-2023-11-02 12:46:38.031160: Pseudo dice [0.8567] 
-2023-11-02 12:46:38.031188: Epoch time: 106.25 s 
-2023-11-02 12:46:38.563780:  
-2023-11-02 12:46:38.563848: Epoch 595 
-2023-11-02 12:46:38.563901: Current learning rate: 0.00443 
-2023-11-02 12:48:24.637711: train_loss -0.9131 
-2023-11-02 12:48:24.637859: val_loss -0.8171 
-2023-11-02 12:48:24.637888: Pseudo dice [0.8508] 
-2023-11-02 12:48:24.637915: Epoch time: 106.07 s 
-2023-11-02 12:48:25.182235:  
-2023-11-02 12:48:25.182306: Epoch 596 
-2023-11-02 12:48:25.182381: Current learning rate: 0.00442 
-2023-11-02 12:50:11.155483: train_loss -0.9116 
-2023-11-02 12:50:11.155650: val_loss -0.8222 
-2023-11-02 12:50:11.155706: Pseudo dice [0.8549] 
-2023-11-02 12:50:11.155737: Epoch time: 105.97 s 
-2023-11-02 12:50:11.782298:  
-2023-11-02 12:50:11.782378: Epoch 597 
-2023-11-02 12:50:11.782426: Current learning rate: 0.00441 
-2023-11-02 12:51:57.790228: train_loss -0.9108 
-2023-11-02 12:51:57.790373: val_loss -0.8127 
-2023-11-02 12:51:57.790424: Pseudo dice [0.8494] 
-2023-11-02 12:51:57.790452: Epoch time: 106.01 s 
-2023-11-02 12:51:58.324040:  
-2023-11-02 12:51:58.324129: Epoch 598 
-2023-11-02 12:51:58.324182: Current learning rate: 0.0044 
-2023-11-02 12:53:44.346343: train_loss -0.9127 
-2023-11-02 12:53:44.346486: val_loss -0.8238 
-2023-11-02 12:53:44.346516: Pseudo dice [0.857] 
-2023-11-02 12:53:44.346547: Epoch time: 106.02 s 
-2023-11-02 12:53:44.888808:  
-2023-11-02 12:53:44.888892: Epoch 599 
-2023-11-02 12:53:44.888947: Current learning rate: 0.00439 
-2023-11-02 12:55:30.868809: train_loss -0.9138 
-2023-11-02 12:55:30.868938: val_loss -0.8125 
-2023-11-02 12:55:30.868982: Pseudo dice [0.8462] 
-2023-11-02 12:55:30.869009: Epoch time: 105.98 s 
-2023-11-02 12:55:31.632638:  
-2023-11-02 12:55:31.632712: Epoch 600 
-2023-11-02 12:55:31.632762: Current learning rate: 0.00438 
-2023-11-02 12:57:17.556647: train_loss -0.9111 
-2023-11-02 12:57:17.556779: val_loss -0.821 
-2023-11-02 12:57:17.556815: Pseudo dice [0.8525] 
-2023-11-02 12:57:17.556841: Epoch time: 105.92 s 
-2023-11-02 12:57:18.089011:  
-2023-11-02 12:57:18.089078: Epoch 601 
-2023-11-02 12:57:18.089129: Current learning rate: 0.00437 
-2023-11-02 12:59:04.891429: train_loss -0.915 
-2023-11-02 12:59:04.891556: val_loss -0.817 
-2023-11-02 12:59:04.891606: Pseudo dice [0.8518] 
-2023-11-02 12:59:04.891635: Epoch time: 106.8 s 
-2023-11-02 12:59:05.433454:  
-2023-11-02 12:59:05.433523: Epoch 602 
-2023-11-02 12:59:05.433599: Current learning rate: 0.00436 
-2023-11-02 13:00:51.393582: train_loss -0.9139 
-2023-11-02 13:00:51.393709: val_loss -0.8209 
-2023-11-02 13:00:51.393758: Pseudo dice [0.8539] 
-2023-11-02 13:00:51.393784: Epoch time: 105.96 s 
-2023-11-02 13:00:52.019663:  
-2023-11-02 13:00:52.019741: Epoch 603 
-2023-11-02 13:00:52.019821: Current learning rate: 0.00435 
-2023-11-02 13:02:38.054785: train_loss -0.9137 
-2023-11-02 13:02:38.054924: val_loss -0.8158 
-2023-11-02 13:02:38.054967: Pseudo dice [0.8485] 
-2023-11-02 13:02:38.054994: Epoch time: 106.04 s 
-2023-11-02 13:02:38.586928:  
-2023-11-02 13:02:38.587005: Epoch 604 
-2023-11-02 13:02:38.587056: Current learning rate: 0.00434 
-2023-11-02 13:04:24.546110: train_loss -0.9051 
-2023-11-02 13:04:24.546239: val_loss -0.8137 
-2023-11-02 13:04:24.546265: Pseudo dice [0.8472] 
-2023-11-02 13:04:24.546292: Epoch time: 105.96 s 
-2023-11-02 13:04:25.094263:  
-2023-11-02 13:04:25.094374: Epoch 605 
-2023-11-02 13:04:25.094430: Current learning rate: 0.00433 
-2023-11-02 13:06:11.083900: train_loss -0.9106 
-2023-11-02 13:06:11.084043: val_loss -0.807 
-2023-11-02 13:06:11.084070: Pseudo dice [0.8435] 
-2023-11-02 13:06:11.084097: Epoch time: 105.99 s 
-2023-11-02 13:06:11.613482:  
-2023-11-02 13:06:11.613552: Epoch 606 
-2023-11-02 13:06:11.613620: Current learning rate: 0.00432 
-2023-11-02 13:07:57.757718: train_loss -0.9116 
-2023-11-02 13:07:57.757869: val_loss -0.8192 
-2023-11-02 13:07:57.757897: Pseudo dice [0.8527] 
-2023-11-02 13:07:57.757928: Epoch time: 106.14 s 
-2023-11-02 13:07:58.289168:  
-2023-11-02 13:07:58.289254: Epoch 607 
-2023-11-02 13:07:58.289335: Current learning rate: 0.00431 
-2023-11-02 13:09:44.375035: train_loss -0.9142 
-2023-11-02 13:09:44.375162: val_loss -0.8195 
-2023-11-02 13:09:44.375191: Pseudo dice [0.8541] 
-2023-11-02 13:09:44.375222: Epoch time: 106.09 s 
-2023-11-02 13:09:44.919209:  
-2023-11-02 13:09:44.919303: Epoch 608 
-2023-11-02 13:09:44.919400: Current learning rate: 0.0043 
-2023-11-02 13:11:30.964046: train_loss -0.9125 
-2023-11-02 13:11:30.964191: val_loss -0.82 
-2023-11-02 13:11:30.964224: Pseudo dice [0.8535] 
-2023-11-02 13:11:30.964256: Epoch time: 106.05 s 
-2023-11-02 13:11:31.496676:  
-2023-11-02 13:11:31.496750: Epoch 609 
-2023-11-02 13:11:31.496828: Current learning rate: 0.00429 
-2023-11-02 13:13:17.434798: train_loss -0.9144 
-2023-11-02 13:13:17.434926: val_loss -0.8119 
-2023-11-02 13:13:17.434975: Pseudo dice [0.8483] 
-2023-11-02 13:13:17.435001: Epoch time: 105.94 s 
-2023-11-02 13:13:18.070225:  
-2023-11-02 13:13:18.070300: Epoch 610 
-2023-11-02 13:13:18.070377: Current learning rate: 0.00429 
-2023-11-02 13:15:04.022752: train_loss -0.9148 
-2023-11-02 13:15:04.022878: val_loss -0.8087 
-2023-11-02 13:15:04.022906: Pseudo dice [0.847] 
-2023-11-02 13:15:04.022934: Epoch time: 105.95 s 
-2023-11-02 13:15:04.560542:  
-2023-11-02 13:15:04.560624: Epoch 611 
-2023-11-02 13:15:04.560705: Current learning rate: 0.00428 
-2023-11-02 13:16:50.653021: train_loss -0.9126 
-2023-11-02 13:16:50.653157: val_loss -0.8086 
-2023-11-02 13:16:50.653196: Pseudo dice [0.8451] 
-2023-11-02 13:16:50.653222: Epoch time: 106.09 s 
-2023-11-02 13:16:51.189398:  
-2023-11-02 13:16:51.189482: Epoch 612 
-2023-11-02 13:16:51.189547: Current learning rate: 0.00427 
-2023-11-02 13:18:37.253227: train_loss -0.9132 
-2023-11-02 13:18:37.253359: val_loss -0.8093 
-2023-11-02 13:18:37.253397: Pseudo dice [0.845] 
-2023-11-02 13:18:37.253434: Epoch time: 106.06 s 
-2023-11-02 13:18:37.791231:  
-2023-11-02 13:18:37.791303: Epoch 613 
-2023-11-02 13:18:37.791378: Current learning rate: 0.00426 
-2023-11-02 13:20:23.791240: train_loss -0.9106 
-2023-11-02 13:20:23.791377: val_loss -0.8124 
-2023-11-02 13:20:23.791408: Pseudo dice [0.8484] 
-2023-11-02 13:20:23.791439: Epoch time: 106.0 s 
-2023-11-02 13:20:24.323159:  
-2023-11-02 13:20:24.323225: Epoch 614 
-2023-11-02 13:20:24.323301: Current learning rate: 0.00425 
-2023-11-02 13:22:10.331806: train_loss -0.9154 
-2023-11-02 13:22:10.331964: val_loss -0.8132 
-2023-11-02 13:22:10.331997: Pseudo dice [0.8466] 
-2023-11-02 13:22:10.332028: Epoch time: 106.01 s 
-2023-11-02 13:22:10.864886:  
-2023-11-02 13:22:10.864976: Epoch 615 
-2023-11-02 13:22:10.865027: Current learning rate: 0.00424 
-2023-11-02 13:23:56.848100: train_loss -0.9139 
-2023-11-02 13:23:56.848237: val_loss -0.8092 
-2023-11-02 13:23:56.848288: Pseudo dice [0.8459] 
-2023-11-02 13:23:56.848314: Epoch time: 105.98 s 
-2023-11-02 13:23:57.480972:  
-2023-11-02 13:23:57.481053: Epoch 616 
-2023-11-02 13:23:57.481151: Current learning rate: 0.00423 
-2023-11-02 13:25:44.306259: train_loss -0.9142 
-2023-11-02 13:25:44.306386: val_loss -0.8093 
-2023-11-02 13:25:44.306424: Pseudo dice [0.845] 
-2023-11-02 13:25:44.306476: Epoch time: 106.83 s 
-2023-11-02 13:25:44.851685:  
-2023-11-02 13:25:44.851805: Epoch 617 
-2023-11-02 13:25:44.851896: Current learning rate: 0.00422 
-2023-11-02 13:27:30.905056: train_loss -0.9114 
-2023-11-02 13:27:30.905190: val_loss -0.8164 
-2023-11-02 13:27:30.905241: Pseudo dice [0.8495] 
-2023-11-02 13:27:30.905270: Epoch time: 106.05 s 
-2023-11-02 13:27:31.440939:  
-2023-11-02 13:27:31.441040: Epoch 618 
-2023-11-02 13:27:31.441091: Current learning rate: 0.00421 
-2023-11-02 13:29:18.095880: train_loss -0.9155 
-2023-11-02 13:29:18.096020: val_loss -0.8135 
-2023-11-02 13:29:18.096045: Pseudo dice [0.8464] 
-2023-11-02 13:29:18.096073: Epoch time: 106.66 s 
-2023-11-02 13:29:18.633286:  
-2023-11-02 13:29:18.633358: Epoch 619 
-2023-11-02 13:29:18.633437: Current learning rate: 0.0042 
-2023-11-02 13:31:08.335246: train_loss -0.9122 
-2023-11-02 13:31:08.335367: val_loss -0.8162 
-2023-11-02 13:31:08.335397: Pseudo dice [0.8516] 
-2023-11-02 13:31:08.335426: Epoch time: 109.7 s 
-2023-11-02 13:31:08.889721:  
-2023-11-02 13:31:08.889794: Epoch 620 
-2023-11-02 13:31:08.889851: Current learning rate: 0.00419 
-2023-11-02 13:32:57.503532: train_loss -0.9152 
-2023-11-02 13:32:57.503666: val_loss -0.8124 
-2023-11-02 13:32:57.503716: Pseudo dice [0.8494] 
-2023-11-02 13:32:57.503743: Epoch time: 108.61 s 
-2023-11-02 13:32:58.034570:  
-2023-11-02 13:32:58.034638: Epoch 621 
-2023-11-02 13:32:58.034717: Current learning rate: 0.00418 
-2023-11-02 13:34:47.824118: train_loss -0.9168 
-2023-11-02 13:34:47.824248: val_loss -0.7987 
-2023-11-02 13:34:47.824274: Pseudo dice [0.841] 
-2023-11-02 13:34:47.824301: Epoch time: 109.79 s 
-2023-11-02 13:34:48.474393:  
-2023-11-02 13:34:48.474481: Epoch 622 
-2023-11-02 13:34:48.474558: Current learning rate: 0.00417 
-2023-11-02 13:36:39.036782: train_loss -0.9157 
-2023-11-02 13:36:39.036925: val_loss -0.8224 
-2023-11-02 13:36:39.036957: Pseudo dice [0.8556] 
-2023-11-02 13:36:39.036989: Epoch time: 110.56 s 
-2023-11-02 13:36:39.627870:  
-2023-11-02 13:36:39.627968: Epoch 623 
-2023-11-02 13:36:39.628023: Current learning rate: 0.00416 
-2023-11-02 13:38:38.558495: train_loss -0.9145 
-2023-11-02 13:38:38.558626: val_loss -0.8165 
-2023-11-02 13:38:38.558651: Pseudo dice [0.8496] 
-2023-11-02 13:38:38.558678: Epoch time: 118.93 s 
-2023-11-02 13:38:39.122407:  
-2023-11-02 13:38:39.122496: Epoch 624 
-2023-11-02 13:38:39.122553: Current learning rate: 0.00415 
-2023-11-02 13:40:35.208705: train_loss -0.9171 
-2023-11-02 13:40:35.208840: val_loss -0.8184 
-2023-11-02 13:40:35.208873: Pseudo dice [0.8537] 
-2023-11-02 13:40:35.208907: Epoch time: 116.09 s 
-2023-11-02 13:40:35.774257:  
-2023-11-02 13:40:35.774346: Epoch 625 
-2023-11-02 13:40:35.774402: Current learning rate: 0.00414 
-2023-11-02 13:42:27.021127: train_loss -0.9152 
-2023-11-02 13:42:27.021247: val_loss -0.8181 
-2023-11-02 13:42:27.021297: Pseudo dice [0.8537] 
-2023-11-02 13:42:27.021323: Epoch time: 111.25 s 
-2023-11-02 13:42:27.568386:  
-2023-11-02 13:42:27.568460: Epoch 626 
-2023-11-02 13:42:27.568534: Current learning rate: 0.00413 
-2023-11-02 13:44:17.880230: train_loss -0.9147 
-2023-11-02 13:44:17.880374: val_loss -0.8245 
-2023-11-02 13:44:17.880424: Pseudo dice [0.856] 
-2023-11-02 13:44:17.880452: Epoch time: 110.31 s 
-2023-11-02 13:44:18.425204:  
-2023-11-02 13:44:18.425273: Epoch 627 
-2023-11-02 13:44:18.425351: Current learning rate: 0.00412 
-2023-11-02 13:46:08.737256: train_loss -0.9146 
-2023-11-02 13:46:08.737391: val_loss -0.8046 
-2023-11-02 13:46:08.737424: Pseudo dice [0.8439] 
-2023-11-02 13:46:08.737459: Epoch time: 110.31 s 
-2023-11-02 13:46:09.291307:  
-2023-11-02 13:46:09.291378: Epoch 628 
-2023-11-02 13:46:09.291433: Current learning rate: 0.00411 
-2023-11-02 13:48:05.657697: train_loss -0.9155 
-2023-11-02 13:48:05.657854: val_loss -0.8239 
-2023-11-02 13:48:05.657888: Pseudo dice [0.8547] 
-2023-11-02 13:48:05.657918: Epoch time: 116.37 s 
-2023-11-02 13:48:06.241820:  
-2023-11-02 13:48:06.241934: Epoch 629 
-2023-11-02 13:48:06.241990: Current learning rate: 0.0041 
-2023-11-02 13:49:56.534776: train_loss -0.9157 
-2023-11-02 13:49:56.534940: val_loss -0.8146 
-2023-11-02 13:49:56.534965: Pseudo dice [0.8518] 
-2023-11-02 13:49:56.534992: Epoch time: 110.29 s 
-2023-11-02 13:49:57.085222:  
-2023-11-02 13:49:57.085310: Epoch 630 
-2023-11-02 13:49:57.085365: Current learning rate: 0.00409 
-2023-11-02 13:51:46.542644: train_loss -0.9133 
-2023-11-02 13:51:46.542797: val_loss -0.8025 
-2023-11-02 13:51:46.542823: Pseudo dice [0.8429] 
-2023-11-02 13:51:46.542852: Epoch time: 109.46 s 
-2023-11-02 13:51:47.083126:  
-2023-11-02 13:51:47.083231: Epoch 631 
-2023-11-02 13:51:47.083342: Current learning rate: 0.00408 
-2023-11-02 13:53:38.270948: train_loss -0.9141 
-2023-11-02 13:53:38.271109: val_loss -0.8189 
-2023-11-02 13:53:38.271145: Pseudo dice [0.8527] 
-2023-11-02 13:53:38.271182: Epoch time: 111.19 s 
-2023-11-02 13:53:38.945429:  
-2023-11-02 13:53:38.945520: Epoch 632 
-2023-11-02 13:53:38.945581: Current learning rate: 0.00407 
-2023-11-02 13:55:31.443484: train_loss -0.9178 
-2023-11-02 13:55:31.443616: val_loss -0.8176 
-2023-11-02 13:55:31.443649: Pseudo dice [0.8519] 
-2023-11-02 13:55:31.443676: Epoch time: 112.5 s 
-2023-11-02 13:55:31.988176:  
-2023-11-02 13:55:31.988249: Epoch 633 
-2023-11-02 13:55:31.988301: Current learning rate: 0.00406 
-2023-11-02 13:57:22.805507: train_loss -0.9148 
-2023-11-02 13:57:22.805634: val_loss -0.8217 
-2023-11-02 13:57:22.805664: Pseudo dice [0.8529] 
-2023-11-02 13:57:22.805696: Epoch time: 110.82 s 
-2023-11-02 13:57:23.359913:  
-2023-11-02 13:57:23.359996: Epoch 634 
-2023-11-02 13:57:23.360056: Current learning rate: 0.00405 
-2023-11-02 13:59:13.417481: train_loss -0.9154 
-2023-11-02 13:59:13.417614: val_loss -0.8126 
-2023-11-02 13:59:13.417643: Pseudo dice [0.849] 
-2023-11-02 13:59:13.417674: Epoch time: 110.06 s 
-2023-11-02 13:59:14.063313:  
-2023-11-02 13:59:14.063395: Epoch 635 
-2023-11-02 13:59:14.063447: Current learning rate: 0.00404 
-2023-11-02 14:01:05.695471: train_loss -0.9164 
-2023-11-02 14:01:05.695618: val_loss -0.8176 
-2023-11-02 14:01:05.695648: Pseudo dice [0.8508] 
-2023-11-02 14:01:05.695677: Epoch time: 111.63 s 
-2023-11-02 14:01:06.252317:  
-2023-11-02 14:01:06.252400: Epoch 636 
-2023-11-02 14:01:06.252455: Current learning rate: 0.00403 
-2023-11-02 14:02:56.853829: train_loss -0.9157 
-2023-11-02 14:02:56.853970: val_loss -0.8084 
-2023-11-02 14:02:56.854002: Pseudo dice [0.8453] 
-2023-11-02 14:02:56.854030: Epoch time: 110.6 s 
-2023-11-02 14:02:57.396671:  
-2023-11-02 14:02:57.396744: Epoch 637 
-2023-11-02 14:02:57.396856: Current learning rate: 0.00402 
-2023-11-02 14:04:46.774649: train_loss -0.9185 
-2023-11-02 14:04:46.774802: val_loss -0.8037 
-2023-11-02 14:04:46.774830: Pseudo dice [0.8394] 
-2023-11-02 14:04:46.774858: Epoch time: 109.38 s 
-2023-11-02 14:04:47.328734:  
-2023-11-02 14:04:47.328809: Epoch 638 
-2023-11-02 14:04:47.328885: Current learning rate: 0.00401 
-2023-11-02 14:06:38.992091: train_loss -0.9142 
-2023-11-02 14:06:38.992218: val_loss -0.8012 
-2023-11-02 14:06:38.992256: Pseudo dice [0.8399] 
-2023-11-02 14:06:38.992284: Epoch time: 111.66 s 
-2023-11-02 14:06:39.529433:  
-2023-11-02 14:06:39.529535: Epoch 639 
-2023-11-02 14:06:39.529631: Current learning rate: 0.004 
-2023-11-02 14:08:29.015418: train_loss -0.9136 
-2023-11-02 14:08:29.015532: val_loss -0.8148 
-2023-11-02 14:08:29.015581: Pseudo dice [0.8486] 
-2023-11-02 14:08:29.015609: Epoch time: 109.49 s 
-2023-11-02 14:08:29.557682:  
-2023-11-02 14:08:29.557757: Epoch 640 
-2023-11-02 14:08:29.557812: Current learning rate: 0.00399 
-2023-11-02 14:10:18.944202: train_loss -0.9157 
-2023-11-02 14:10:18.944332: val_loss -0.8167 
-2023-11-02 14:10:18.944381: Pseudo dice [0.8514] 
-2023-11-02 14:10:18.944407: Epoch time: 109.39 s 
-2023-11-02 14:10:19.585613:  
-2023-11-02 14:10:19.585694: Epoch 641 
-2023-11-02 14:10:19.585767: Current learning rate: 0.00398 
-2023-11-02 14:12:09.483460: train_loss -0.916 
-2023-11-02 14:12:09.483639: val_loss -0.8203 
-2023-11-02 14:12:09.483666: Pseudo dice [0.8531] 
-2023-11-02 14:12:09.483695: Epoch time: 109.9 s 
-2023-11-02 14:12:10.024308:  
-2023-11-02 14:12:10.024399: Epoch 642 
-2023-11-02 14:12:10.024477: Current learning rate: 0.00397 
-2023-11-02 14:14:00.110183: train_loss -0.9164 
-2023-11-02 14:14:00.110325: val_loss -0.8204 
-2023-11-02 14:14:00.110350: Pseudo dice [0.8517] 
-2023-11-02 14:14:00.110376: Epoch time: 110.09 s 
-2023-11-02 14:14:00.651719:  
-2023-11-02 14:14:00.651844: Epoch 643 
-2023-11-02 14:14:00.651923: Current learning rate: 0.00396 
-2023-11-02 14:15:50.283432: train_loss -0.916 
-2023-11-02 14:15:50.283560: val_loss -0.8142 
-2023-11-02 14:15:50.283609: Pseudo dice [0.8492] 
-2023-11-02 14:15:50.283635: Epoch time: 109.63 s 
-2023-11-02 14:15:50.824536:  
-2023-11-02 14:15:50.824656: Epoch 644 
-2023-11-02 14:15:50.824710: Current learning rate: 0.00395 
-2023-11-02 14:17:39.702869: train_loss -0.9165 
-2023-11-02 14:17:39.703010: val_loss -0.8312 
-2023-11-02 14:17:39.703041: Pseudo dice [0.8619] 
-2023-11-02 14:17:39.703070: Epoch time: 108.88 s 
-2023-11-02 14:17:40.250251:  
-2023-11-02 14:17:40.250323: Epoch 645 
-2023-11-02 14:17:40.250391: Current learning rate: 0.00394 
-2023-11-02 14:19:30.704073: train_loss -0.9154 
-2023-11-02 14:19:30.704201: val_loss -0.8101 
-2023-11-02 14:19:30.704226: Pseudo dice [0.8473] 
-2023-11-02 14:19:30.704252: Epoch time: 110.45 s 
-2023-11-02 14:19:31.238500:  
-2023-11-02 14:19:31.238571: Epoch 646 
-2023-11-02 14:19:31.238649: Current learning rate: 0.00393 
-2023-11-02 14:21:19.974872: train_loss -0.9177 
-2023-11-02 14:21:19.975023: val_loss -0.8077 
-2023-11-02 14:21:19.975075: Pseudo dice [0.8442] 
-2023-11-02 14:21:19.975102: Epoch time: 108.74 s 
-2023-11-02 14:21:20.524405:  
-2023-11-02 14:21:20.524473: Epoch 647 
-2023-11-02 14:21:20.524550: Current learning rate: 0.00392 
-2023-11-02 14:23:09.842182: train_loss -0.914 
-2023-11-02 14:23:09.842315: val_loss -0.8093 
-2023-11-02 14:23:09.842366: Pseudo dice [0.8472] 
-2023-11-02 14:23:09.842394: Epoch time: 109.32 s 
-2023-11-02 14:23:10.507849:  
-2023-11-02 14:23:10.507945: Epoch 648 
-2023-11-02 14:23:10.508005: Current learning rate: 0.00391 
-2023-11-02 14:24:59.827662: train_loss -0.9156 
-2023-11-02 14:24:59.827792: val_loss -0.8149 
-2023-11-02 14:24:59.827817: Pseudo dice [0.8485] 
-2023-11-02 14:24:59.827844: Epoch time: 109.32 s 
-2023-11-02 14:25:00.367839:  
-2023-11-02 14:25:00.367928: Epoch 649 
-2023-11-02 14:25:00.367985: Current learning rate: 0.0039 
-2023-11-02 14:26:50.135918: train_loss -0.917 
-2023-11-02 14:26:50.136087: val_loss -0.8054 
-2023-11-02 14:26:50.136114: Pseudo dice [0.8445] 
-2023-11-02 14:26:50.136141: Epoch time: 109.77 s 
-2023-11-02 14:26:50.913046:  
-2023-11-02 14:26:50.913213: Epoch 650 
-2023-11-02 14:26:50.913291: Current learning rate: 0.00389 
-2023-11-02 14:28:39.770302: train_loss -0.9109 
-2023-11-02 14:28:39.770442: val_loss -0.8047 
-2023-11-02 14:28:39.770475: Pseudo dice [0.8405] 
-2023-11-02 14:28:39.770509: Epoch time: 108.86 s 
-2023-11-02 14:28:40.322821:  
-2023-11-02 14:28:40.322926: Epoch 651 
-2023-11-02 14:28:40.322982: Current learning rate: 0.00388 
-2023-11-02 14:30:33.359925: train_loss -0.9135 
-2023-11-02 14:30:33.360072: val_loss -0.807 
-2023-11-02 14:30:33.360097: Pseudo dice [0.8443] 
-2023-11-02 14:30:33.360123: Epoch time: 113.04 s 
-2023-11-02 14:30:33.922751:  
-2023-11-02 14:30:33.922845: Epoch 652 
-2023-11-02 14:30:33.922902: Current learning rate: 0.00387 
-2023-11-02 14:32:26.749856: train_loss -0.9154 
-2023-11-02 14:32:26.749991: val_loss -0.8218 
-2023-11-02 14:32:26.750018: Pseudo dice [0.8531] 
-2023-11-02 14:32:26.750045: Epoch time: 112.83 s 
-2023-11-02 14:32:27.308146:  
-2023-11-02 14:32:27.308234: Epoch 653 
-2023-11-02 14:32:27.308291: Current learning rate: 0.00386 
-2023-11-02 14:34:22.627194: train_loss -0.9137 
-2023-11-02 14:34:22.627325: val_loss -0.8151 
-2023-11-02 14:34:22.627350: Pseudo dice [0.8492] 
-2023-11-02 14:34:22.627377: Epoch time: 115.32 s 
-2023-11-02 14:34:23.284157:  
-2023-11-02 14:34:23.284247: Epoch 654 
-2023-11-02 14:34:23.284308: Current learning rate: 0.00385 
-2023-11-02 14:36:14.626942: train_loss -0.9076 
-2023-11-02 14:36:14.627086: val_loss -0.8088 
-2023-11-02 14:36:14.627140: Pseudo dice [0.8453] 
-2023-11-02 14:36:14.627167: Epoch time: 111.34 s 
-2023-11-02 14:36:15.187240:  
-2023-11-02 14:36:15.187336: Epoch 655 
-2023-11-02 14:36:15.187391: Current learning rate: 0.00384 
-2023-11-02 14:38:02.786920: train_loss -0.9081 
-2023-11-02 14:38:02.787066: val_loss -0.8261 
-2023-11-02 14:38:02.787096: Pseudo dice [0.8564] 
-2023-11-02 14:38:02.787124: Epoch time: 107.6 s 
-2023-11-02 14:38:03.340541:  
-2023-11-02 14:38:03.340627: Epoch 656 
-2023-11-02 14:38:03.340708: Current learning rate: 0.00383 
-2023-11-02 14:39:57.313269: train_loss -0.9138 
-2023-11-02 14:39:57.313405: val_loss -0.8016 
-2023-11-02 14:39:57.313433: Pseudo dice [0.8418] 
-2023-11-02 14:39:57.313461: Epoch time: 113.97 s 
-2023-11-02 14:39:57.883917:  
-2023-11-02 14:39:57.883996: Epoch 657 
-2023-11-02 14:39:57.884055: Current learning rate: 0.00382 
-2023-11-02 14:41:52.352665: train_loss -0.915 
-2023-11-02 14:41:52.352803: val_loss -0.8196 
-2023-11-02 14:41:52.352837: Pseudo dice [0.8521] 
-2023-11-02 14:41:52.352869: Epoch time: 114.47 s 
-2023-11-02 14:41:52.919734:  
-2023-11-02 14:41:52.919900: Epoch 658 
-2023-11-02 14:41:52.919966: Current learning rate: 0.00381 
-2023-11-02 14:43:47.120544: train_loss -0.9133 
-2023-11-02 14:43:47.120682: val_loss -0.8151 
-2023-11-02 14:43:47.120708: Pseudo dice [0.8486] 
-2023-11-02 14:43:47.120736: Epoch time: 114.2 s 
-2023-11-02 14:43:47.777141:  
-2023-11-02 14:43:47.777228: Epoch 659 
-2023-11-02 14:43:47.777284: Current learning rate: 0.0038 
-2023-11-02 14:45:41.882719: train_loss -0.9137 
-2023-11-02 14:45:41.882853: val_loss -0.826 
-2023-11-02 14:45:41.882879: Pseudo dice [0.8577] 
-2023-11-02 14:45:41.882906: Epoch time: 114.11 s 
-2023-11-02 14:45:42.435578:  
-2023-11-02 14:45:42.435669: Epoch 660 
-2023-11-02 14:45:42.435728: Current learning rate: 0.00379 
-2023-11-02 14:47:35.729013: train_loss -0.9157 
-2023-11-02 14:47:35.729147: val_loss -0.8111 
-2023-11-02 14:47:35.729174: Pseudo dice [0.8489] 
-2023-11-02 14:47:35.729202: Epoch time: 113.29 s 
-2023-11-02 14:47:36.293516:  
-2023-11-02 14:47:36.293605: Epoch 661 
-2023-11-02 14:47:36.293660: Current learning rate: 0.00378 
-2023-11-02 14:49:30.745023: train_loss -0.9179 
-2023-11-02 14:49:30.745173: val_loss -0.8078 
-2023-11-02 14:49:30.745198: Pseudo dice [0.8458] 
-2023-11-02 14:49:30.745227: Epoch time: 114.45 s 
-2023-11-02 14:49:31.310671:  
-2023-11-02 14:49:31.310758: Epoch 662 
-2023-11-02 14:49:31.310812: Current learning rate: 0.00377 
-2023-11-02 14:51:25.698722: train_loss -0.9156 
-2023-11-02 14:51:25.698853: val_loss -0.8249 
-2023-11-02 14:51:25.698882: Pseudo dice [0.8561] 
-2023-11-02 14:51:25.698910: Epoch time: 114.39 s 
-2023-11-02 14:51:26.277736:  
-2023-11-02 14:51:26.277816: Epoch 663 
-2023-11-02 14:51:26.277896: Current learning rate: 0.00376 
-2023-11-02 14:53:20.819367: train_loss -0.919 
-2023-11-02 14:53:20.819498: val_loss -0.7955 
-2023-11-02 14:53:20.819524: Pseudo dice [0.8365] 
-2023-11-02 14:53:20.819552: Epoch time: 114.54 s 
-2023-11-02 14:53:21.381477:  
-2023-11-02 14:53:21.381552: Epoch 664 
-2023-11-02 14:53:21.381605: Current learning rate: 0.00375 
-2023-11-02 14:55:12.239523: train_loss -0.9169 
-2023-11-02 14:55:12.239652: val_loss -0.8132 
-2023-11-02 14:55:12.239691: Pseudo dice [0.8475] 
-2023-11-02 14:55:12.239717: Epoch time: 110.86 s 
-2023-11-02 14:55:12.774862:  
-2023-11-02 14:55:12.774953: Epoch 665 
-2023-11-02 14:55:12.775003: Current learning rate: 0.00374 
-2023-11-02 14:57:00.094306: train_loss -0.9142 
-2023-11-02 14:57:00.094471: val_loss -0.8067 
-2023-11-02 14:57:00.094500: Pseudo dice [0.8424] 
-2023-11-02 14:57:00.094527: Epoch time: 107.32 s 
-2023-11-02 14:57:00.730505:  
-2023-11-02 14:57:00.730614: Epoch 666 
-2023-11-02 14:57:00.730721: Current learning rate: 0.00373 
-2023-11-02 14:58:48.367150: train_loss -0.9136 
-2023-11-02 14:58:48.367277: val_loss -0.8175 
-2023-11-02 14:58:48.367326: Pseudo dice [0.8516] 
-2023-11-02 14:58:48.367354: Epoch time: 107.64 s 
-2023-11-02 14:58:48.903426:  
-2023-11-02 14:58:48.903494: Epoch 667 
-2023-11-02 14:58:48.903565: Current learning rate: 0.00372 
-2023-11-02 15:00:35.646763: train_loss -0.9163 
-2023-11-02 15:00:35.646897: val_loss -0.8202 
-2023-11-02 15:00:35.646934: Pseudo dice [0.8548] 
-2023-11-02 15:00:35.646961: Epoch time: 106.74 s 
-2023-11-02 15:00:36.184870:  
-2023-11-02 15:00:36.184955: Epoch 668 
-2023-11-02 15:00:36.185031: Current learning rate: 0.00371 
-2023-11-02 15:02:22.887032: train_loss -0.9154 
-2023-11-02 15:02:22.887154: val_loss -0.8214 
-2023-11-02 15:02:22.887204: Pseudo dice [0.8539] 
-2023-11-02 15:02:22.887231: Epoch time: 106.7 s 
-2023-11-02 15:02:23.429770:  
-2023-11-02 15:02:23.429839: Epoch 669 
-2023-11-02 15:02:23.429889: Current learning rate: 0.0037 
-2023-11-02 15:04:10.203046: train_loss -0.916 
-2023-11-02 15:04:10.203173: val_loss -0.8277 
-2023-11-02 15:04:10.203209: Pseudo dice [0.8573] 
-2023-11-02 15:04:10.203235: Epoch time: 106.77 s 
-2023-11-02 15:04:10.751717:  
-2023-11-02 15:04:10.751799: Epoch 670 
-2023-11-02 15:04:10.752024: Current learning rate: 0.00369 
-2023-11-02 15:05:57.526599: train_loss -0.916 
-2023-11-02 15:05:57.526725: val_loss -0.816 
-2023-11-02 15:05:57.526775: Pseudo dice [0.8502] 
-2023-11-02 15:05:57.526811: Epoch time: 106.78 s 
-2023-11-02 15:05:58.067971:  
-2023-11-02 15:05:58.068037: Epoch 671 
-2023-11-02 15:05:58.068107: Current learning rate: 0.00368 
-2023-11-02 15:07:44.831681: train_loss -0.9165 
-2023-11-02 15:07:44.831806: val_loss -0.8166 
-2023-11-02 15:07:44.831855: Pseudo dice [0.8499] 
-2023-11-02 15:07:44.831881: Epoch time: 106.76 s 
-2023-11-02 15:07:45.470383:  
-2023-11-02 15:07:45.470466: Epoch 672 
-2023-11-02 15:07:45.470545: Current learning rate: 0.00367 
-2023-11-02 15:09:32.115025: train_loss -0.9174 
-2023-11-02 15:09:32.115118: val_loss -0.8141 
-2023-11-02 15:09:32.115165: Pseudo dice [0.851] 
-2023-11-02 15:09:32.115190: Epoch time: 106.65 s 
-2023-11-02 15:09:32.659147:  
-2023-11-02 15:09:32.659222: Epoch 673 
-2023-11-02 15:09:32.659299: Current learning rate: 0.00366 
-2023-11-02 15:11:19.367427: train_loss -0.9161 
-2023-11-02 15:11:19.367584: val_loss -0.8158 
-2023-11-02 15:11:19.367608: Pseudo dice [0.8496] 
-2023-11-02 15:11:19.367634: Epoch time: 106.71 s 
-2023-11-02 15:11:19.909690:  
-2023-11-02 15:11:19.909764: Epoch 674 
-2023-11-02 15:11:19.909839: Current learning rate: 0.00365 
-2023-11-02 15:13:06.649073: train_loss -0.9176 
-2023-11-02 15:13:06.649221: val_loss -0.8097 
-2023-11-02 15:13:06.649273: Pseudo dice [0.8462] 
-2023-11-02 15:13:06.649299: Epoch time: 106.74 s 
-2023-11-02 15:13:07.191798:  
-2023-11-02 15:13:07.191893: Epoch 675 
-2023-11-02 15:13:07.191990: Current learning rate: 0.00364 
-2023-11-02 15:14:53.942675: train_loss -0.9196 
-2023-11-02 15:14:53.942798: val_loss -0.8068 
-2023-11-02 15:14:53.942849: Pseudo dice [0.8457] 
-2023-11-02 15:14:53.942878: Epoch time: 106.75 s 
-2023-11-02 15:14:54.493751:  
-2023-11-02 15:14:54.493816: Epoch 676 
-2023-11-02 15:14:54.493892: Current learning rate: 0.00363 
-2023-11-02 15:16:41.864333: train_loss -0.918 
-2023-11-02 15:16:41.864469: val_loss -0.8283 
-2023-11-02 15:16:41.864497: Pseudo dice [0.8583] 
-2023-11-02 15:16:41.864527: Epoch time: 107.37 s 
-2023-11-02 15:16:42.413047:  
-2023-11-02 15:16:42.413259: Epoch 677 
-2023-11-02 15:16:42.413559: Current learning rate: 0.00362 
-2023-11-02 15:18:30.440742: train_loss -0.9186 
-2023-11-02 15:18:30.440873: val_loss -0.8155 
-2023-11-02 15:18:30.440903: Pseudo dice [0.85] 
-2023-11-02 15:18:30.440957: Epoch time: 108.03 s 
-2023-11-02 15:18:31.095228:  
-2023-11-02 15:18:31.095314: Epoch 678 
-2023-11-02 15:18:31.095383: Current learning rate: 0.00361 
-2023-11-02 15:20:18.861192: train_loss -0.9171 
-2023-11-02 15:20:18.861339: val_loss -0.8184 
-2023-11-02 15:20:18.861364: Pseudo dice [0.8538] 
-2023-11-02 15:20:18.861392: Epoch time: 107.77 s 
-2023-11-02 15:20:19.405650:  
-2023-11-02 15:20:19.405863: Epoch 679 
-2023-11-02 15:20:19.406207: Current learning rate: 0.0036 
-2023-11-02 15:22:07.060197: train_loss -0.915 
-2023-11-02 15:22:07.060333: val_loss -0.7993 
-2023-11-02 15:22:07.060381: Pseudo dice [0.8398] 
-2023-11-02 15:22:07.060410: Epoch time: 107.65 s 
-2023-11-02 15:22:07.609292:  
-2023-11-02 15:22:07.609372: Epoch 680 
-2023-11-02 15:22:07.609449: Current learning rate: 0.00359 
-2023-11-02 15:23:55.270344: train_loss -0.9181 
-2023-11-02 15:23:55.270466: val_loss -0.8153 
-2023-11-02 15:23:55.270492: Pseudo dice [0.8491] 
-2023-11-02 15:23:55.270519: Epoch time: 107.66 s 
-2023-11-02 15:23:55.827918:  
-2023-11-02 15:23:55.828024: Epoch 681 
-2023-11-02 15:23:55.828079: Current learning rate: 0.00358 
-2023-11-02 15:25:43.749517: train_loss -0.917 
-2023-11-02 15:25:43.749641: val_loss -0.8152 
-2023-11-02 15:25:43.749671: Pseudo dice [0.8508] 
-2023-11-02 15:25:43.749716: Epoch time: 107.92 s 
-2023-11-02 15:25:44.298186:  
-2023-11-02 15:25:44.298258: Epoch 682 
-2023-11-02 15:25:44.298311: Current learning rate: 0.00357 
-2023-11-02 15:27:32.698722: train_loss -0.917 
-2023-11-02 15:27:32.698988: val_loss -0.8158 
-2023-11-02 15:27:32.699026: Pseudo dice [0.8501] 
-2023-11-02 15:27:32.699056: Epoch time: 108.4 s 
-2023-11-02 15:27:33.270731:  
-2023-11-02 15:27:33.270833: Epoch 683 
-2023-11-02 15:27:33.270890: Current learning rate: 0.00356 
-2023-11-02 15:29:22.885100: train_loss -0.9176 
-2023-11-02 15:29:22.885248: val_loss -0.8158 
-2023-11-02 15:29:22.885308: Pseudo dice [0.8528] 
-2023-11-02 15:29:22.885337: Epoch time: 109.61 s 
-2023-11-02 15:29:23.548834:  
-2023-11-02 15:29:23.548948: Epoch 684 
-2023-11-02 15:29:23.549003: Current learning rate: 0.00355 
-2023-11-02 15:31:13.129354: train_loss -0.9191 
-2023-11-02 15:31:13.129491: val_loss -0.8112 
-2023-11-02 15:31:13.129541: Pseudo dice [0.8486] 
-2023-11-02 15:31:13.129569: Epoch time: 109.58 s 
-2023-11-02 15:31:13.686096:  
-2023-11-02 15:31:13.686187: Epoch 685 
-2023-11-02 15:31:13.686246: Current learning rate: 0.00354 
-2023-11-02 15:33:02.081291: train_loss -0.9188 
-2023-11-02 15:33:02.081433: val_loss -0.819 
-2023-11-02 15:33:02.081481: Pseudo dice [0.8523] 
-2023-11-02 15:33:02.081507: Epoch time: 108.4 s 
-2023-11-02 15:33:02.625156:  
-2023-11-02 15:33:02.625243: Epoch 686 
-2023-11-02 15:33:02.625322: Current learning rate: 0.00353 
-2023-11-02 15:34:49.155752: train_loss -0.9161 
-2023-11-02 15:34:49.155883: val_loss -0.8244 
-2023-11-02 15:34:49.155938: Pseudo dice [0.8553] 
-2023-11-02 15:34:49.155970: Epoch time: 106.53 s 
-2023-11-02 15:34:49.710514:  
-2023-11-02 15:34:49.710589: Epoch 687 
-2023-11-02 15:34:49.710666: Current learning rate: 0.00352 
-2023-11-02 15:36:36.854670: train_loss -0.9177 
-2023-11-02 15:36:36.854833: val_loss -0.8245 
-2023-11-02 15:36:36.854859: Pseudo dice [0.8558] 
-2023-11-02 15:36:36.854886: Epoch time: 107.14 s 
-2023-11-02 15:36:37.407913:  
-2023-11-02 15:36:37.407989: Epoch 688 
-2023-11-02 15:36:37.408042: Current learning rate: 0.00351 
-2023-11-02 15:38:26.249476: train_loss -0.9181 
-2023-11-02 15:38:26.249634: val_loss -0.8226 
-2023-11-02 15:38:26.249660: Pseudo dice [0.8554] 
-2023-11-02 15:38:26.249687: Epoch time: 108.84 s 
-2023-11-02 15:38:26.818367:  
-2023-11-02 15:38:26.818443: Epoch 689 
-2023-11-02 15:38:26.818522: Current learning rate: 0.0035 
-2023-11-02 15:40:15.940699: train_loss -0.9185 
-2023-11-02 15:40:15.940838: val_loss -0.8089 
-2023-11-02 15:40:15.940865: Pseudo dice [0.8468] 
-2023-11-02 15:40:15.940892: Epoch time: 109.12 s 
-2023-11-02 15:40:16.603651:  
-2023-11-02 15:40:16.603771: Epoch 690 
-2023-11-02 15:40:16.603831: Current learning rate: 0.00349 
-2023-11-02 15:42:06.435734: train_loss -0.9179 
-2023-11-02 15:42:06.435880: val_loss -0.8141 
-2023-11-02 15:42:06.435913: Pseudo dice [0.8506] 
-2023-11-02 15:42:06.435953: Epoch time: 109.83 s 
-2023-11-02 15:42:06.998638:  
-2023-11-02 15:42:06.998721: Epoch 691 
-2023-11-02 15:42:06.998822: Current learning rate: 0.00348 
-2023-11-02 15:43:56.808384: train_loss -0.9169 
-2023-11-02 15:43:56.808519: val_loss -0.8078 
-2023-11-02 15:43:56.808548: Pseudo dice [0.8442] 
-2023-11-02 15:43:56.808575: Epoch time: 109.81 s 
-2023-11-02 15:43:57.357321:  
-2023-11-02 15:43:57.357406: Epoch 692 
-2023-11-02 15:43:57.357461: Current learning rate: 0.00346 
-2023-11-02 15:45:50.856749: train_loss -0.9133 
-2023-11-02 15:45:50.856885: val_loss -0.826 
-2023-11-02 15:45:50.856936: Pseudo dice [0.857] 
-2023-11-02 15:45:50.856965: Epoch time: 113.5 s 
-2023-11-02 15:45:51.401255:  
-2023-11-02 15:45:51.401329: Epoch 693 
-2023-11-02 15:45:51.401407: Current learning rate: 0.00345 
-2023-11-02 15:47:42.306048: train_loss -0.9139 
-2023-11-02 15:47:42.306338: val_loss -0.8148 
-2023-11-02 15:47:42.306376: Pseudo dice [0.8483] 
-2023-11-02 15:47:42.306407: Epoch time: 110.91 s 
-2023-11-02 15:47:42.859712:  
-2023-11-02 15:47:42.859783: Epoch 694 
-2023-11-02 15:47:42.859837: Current learning rate: 0.00344 
-2023-11-02 15:49:33.682489: train_loss -0.9117 
-2023-11-02 15:49:33.682605: val_loss -0.8169 
-2023-11-02 15:49:33.682655: Pseudo dice [0.8505] 
-2023-11-02 15:49:33.682683: Epoch time: 110.82 s 
-2023-11-02 15:49:34.235137:  
-2023-11-02 15:49:34.235204: Epoch 695 
-2023-11-02 15:49:34.235276: Current learning rate: 0.00343 
-2023-11-02 15:51:21.023720: train_loss -0.9116 
-2023-11-02 15:51:21.023856: val_loss -0.8181 
-2023-11-02 15:51:21.023883: Pseudo dice [0.8508] 
-2023-11-02 15:51:21.023910: Epoch time: 106.79 s 
-2023-11-02 15:51:21.568119:  
-2023-11-02 15:51:21.568185: Epoch 696 
-2023-11-02 15:51:21.568239: Current learning rate: 0.00342 
-2023-11-02 15:53:08.281879: train_loss -0.9179 
-2023-11-02 15:53:08.282002: val_loss -0.8242 
-2023-11-02 15:53:08.282045: Pseudo dice [0.856] 
-2023-11-02 15:53:08.282071: Epoch time: 106.71 s 
-2023-11-02 15:53:08.823492:  
-2023-11-02 15:53:08.823720: Epoch 697 
-2023-11-02 15:53:08.823770: Current learning rate: 0.00341 
-2023-11-02 15:54:55.519873: train_loss -0.9155 
-2023-11-02 15:54:55.520034: val_loss -0.8142 
-2023-11-02 15:54:55.520060: Pseudo dice [0.8484] 
-2023-11-02 15:54:55.520087: Epoch time: 106.7 s 
-2023-11-02 15:54:56.062463:  
-2023-11-02 15:54:56.062529: Epoch 698 
-2023-11-02 15:54:56.062580: Current learning rate: 0.0034 
-2023-11-02 15:56:42.722382: train_loss -0.9137 
-2023-11-02 15:56:42.722539: val_loss -0.8213 
-2023-11-02 15:56:42.722564: Pseudo dice [0.8574] 
-2023-11-02 15:56:42.722592: Epoch time: 106.66 s 
-2023-11-02 15:56:43.265703:  
-2023-11-02 15:56:43.265935: Epoch 699 
-2023-11-02 15:56:43.266082: Current learning rate: 0.00339 
-2023-11-02 15:58:31.073863: train_loss -0.9133 
-2023-11-02 15:58:31.073998: val_loss -0.8054 
-2023-11-02 15:58:31.074046: Pseudo dice [0.8425] 
-2023-11-02 15:58:31.074073: Epoch time: 107.81 s 
-2023-11-02 15:58:31.864853:  
-2023-11-02 15:58:31.864922: Epoch 700 
-2023-11-02 15:58:31.864975: Current learning rate: 0.00338 
-2023-11-02 16:00:22.110829: train_loss -0.9161 
-2023-11-02 16:00:22.110970: val_loss -0.8115 
-2023-11-02 16:00:22.110996: Pseudo dice [0.8458] 
-2023-11-02 16:00:22.111025: Epoch time: 110.25 s 
-2023-11-02 16:00:22.656462:  
-2023-11-02 16:00:22.656533: Epoch 701 
-2023-11-02 16:00:22.656591: Current learning rate: 0.00337 
-2023-11-02 16:02:12.276189: train_loss -0.9175 
-2023-11-02 16:02:12.276343: val_loss -0.8264 
-2023-11-02 16:02:12.276367: Pseudo dice [0.859] 
-2023-11-02 16:02:12.276395: Epoch time: 109.62 s 
-2023-11-02 16:02:12.911166:  
-2023-11-02 16:02:12.911245: Epoch 702 
-2023-11-02 16:02:12.911327: Current learning rate: 0.00336 
-2023-11-02 16:04:02.755465: train_loss -0.916 
-2023-11-02 16:04:02.755597: val_loss -0.8158 
-2023-11-02 16:04:02.755646: Pseudo dice [0.8496] 
-2023-11-02 16:04:02.755672: Epoch time: 109.84 s 
-2023-11-02 16:04:03.305109:  
-2023-11-02 16:04:03.309508: Epoch 703 
-2023-11-02 16:04:03.309577: Current learning rate: 0.00335 
-2023-11-02 16:05:51.799527: train_loss -0.9175 
-2023-11-02 16:05:51.799675: val_loss -0.8222 
-2023-11-02 16:05:51.799702: Pseudo dice [0.8546] 
-2023-11-02 16:05:51.799728: Epoch time: 108.49 s 
-2023-11-02 16:05:52.341245:  
-2023-11-02 16:05:52.341307: Epoch 704 
-2023-11-02 16:05:52.341376: Current learning rate: 0.00334 
-2023-11-02 16:07:39.215075: train_loss -0.9185 
-2023-11-02 16:07:39.215229: val_loss -0.8147 
-2023-11-02 16:07:39.215279: Pseudo dice [0.8507] 
-2023-11-02 16:07:39.215306: Epoch time: 106.87 s 
-2023-11-02 16:07:39.760773:  
-2023-11-02 16:07:39.760849: Epoch 705 
-2023-11-02 16:07:39.760960: Current learning rate: 0.00333 
-2023-11-02 16:09:26.604456: train_loss -0.9157 
-2023-11-02 16:09:26.604574: val_loss -0.8215 
-2023-11-02 16:09:26.604623: Pseudo dice [0.8544] 
-2023-11-02 16:09:26.604650: Epoch time: 106.84 s 
-2023-11-02 16:09:27.148017:  
-2023-11-02 16:09:27.148086: Epoch 706 
-2023-11-02 16:09:27.148140: Current learning rate: 0.00332 
-2023-11-02 16:11:13.901036: train_loss -0.9175 
-2023-11-02 16:11:13.901166: val_loss -0.8108 
-2023-11-02 16:11:13.901190: Pseudo dice [0.8487] 
-2023-11-02 16:11:13.901218: Epoch time: 106.75 s 
-2023-11-02 16:11:14.449795:  
-2023-11-02 16:11:14.449887: Epoch 707 
-2023-11-02 16:11:14.449939: Current learning rate: 0.00331 
-2023-11-02 16:13:01.162035: train_loss -0.919 
-2023-11-02 16:13:01.162163: val_loss -0.8178 
-2023-11-02 16:13:01.162226: Pseudo dice [0.8526] 
-2023-11-02 16:13:01.162253: Epoch time: 106.71 s 
-2023-11-02 16:13:01.814280:  
-2023-11-02 16:13:01.814359: Epoch 708 
-2023-11-02 16:13:01.814432: Current learning rate: 0.0033 
-2023-11-02 16:14:48.494069: train_loss -0.9194 
-2023-11-02 16:14:48.494191: val_loss -0.8161 
-2023-11-02 16:14:48.494239: Pseudo dice [0.8503] 
-2023-11-02 16:14:48.494269: Epoch time: 106.68 s 
-2023-11-02 16:14:49.042901:  
-2023-11-02 16:14:49.042992: Epoch 709 
-2023-11-02 16:14:49.043252: Current learning rate: 0.00329 
-2023-11-02 16:16:35.722537: train_loss -0.9181 
-2023-11-02 16:16:35.722691: val_loss -0.8059 
-2023-11-02 16:16:35.722718: Pseudo dice [0.8433] 
-2023-11-02 16:16:35.722744: Epoch time: 106.68 s 
-2023-11-02 16:16:36.269103:  
-2023-11-02 16:16:36.269190: Epoch 710 
-2023-11-02 16:16:36.269437: Current learning rate: 0.00328 
-2023-11-02 16:18:22.995933: train_loss -0.9189 
-2023-11-02 16:18:22.996072: val_loss -0.8141 
-2023-11-02 16:18:22.996121: Pseudo dice [0.8483] 
-2023-11-02 16:18:22.996147: Epoch time: 106.73 s 
-2023-11-02 16:18:23.541127:  
-2023-11-02 16:18:23.541229: Epoch 711 
-2023-11-02 16:18:23.541292: Current learning rate: 0.00327 
-2023-11-02 16:20:10.317406: train_loss -0.918 
-2023-11-02 16:20:10.317546: val_loss -0.8247 
-2023-11-02 16:20:10.317584: Pseudo dice [0.8563] 
-2023-11-02 16:20:10.317611: Epoch time: 106.78 s 
-2023-11-02 16:20:10.865630:  
-2023-11-02 16:20:10.865712: Epoch 712 
-2023-11-02 16:20:10.865806: Current learning rate: 0.00326 
-2023-11-02 16:21:57.541352: train_loss -0.9198 
-2023-11-02 16:21:57.541477: val_loss -0.8119 
-2023-11-02 16:21:57.541517: Pseudo dice [0.8476] 
-2023-11-02 16:21:57.541546: Epoch time: 106.68 s 
-2023-11-02 16:21:58.087365:  
-2023-11-02 16:21:58.087428: Epoch 713 
-2023-11-02 16:21:58.087492: Current learning rate: 0.00325 
-2023-11-02 16:23:44.711676: train_loss -0.9185 
-2023-11-02 16:23:44.711816: val_loss -0.8192 
-2023-11-02 16:23:44.711859: Pseudo dice [0.8531] 
-2023-11-02 16:23:44.711889: Epoch time: 106.62 s 
-2023-11-02 16:23:45.354509:  
-2023-11-02 16:23:45.354738: Epoch 714 
-2023-11-02 16:23:45.354792: Current learning rate: 0.00324 
-2023-11-02 16:25:31.991447: train_loss -0.9204 
-2023-11-02 16:25:31.991596: val_loss -0.8165 
-2023-11-02 16:25:31.991643: Pseudo dice [0.8524] 
-2023-11-02 16:25:31.991669: Epoch time: 106.64 s 
-2023-11-02 16:25:32.536440:  
-2023-11-02 16:25:32.536508: Epoch 715 
-2023-11-02 16:25:32.536586: Current learning rate: 0.00323 
-2023-11-02 16:27:19.323308: train_loss -0.9165 
-2023-11-02 16:27:19.323433: val_loss -0.8052 
-2023-11-02 16:27:19.323456: Pseudo dice [0.842] 
-2023-11-02 16:27:19.323482: Epoch time: 106.79 s 
-2023-11-02 16:27:19.870375:  
-2023-11-02 16:27:19.870452: Epoch 716 
-2023-11-02 16:27:19.870628: Current learning rate: 0.00322 
-2023-11-02 16:29:06.621298: train_loss -0.9197 
-2023-11-02 16:29:06.621460: val_loss -0.8213 
-2023-11-02 16:29:06.621490: Pseudo dice [0.8534] 
-2023-11-02 16:29:06.621516: Epoch time: 106.75 s 
-2023-11-02 16:29:07.166245:  
-2023-11-02 16:29:07.166313: Epoch 717 
-2023-11-02 16:29:07.166381: Current learning rate: 0.00321 
-2023-11-02 16:30:54.002459: train_loss -0.9191 
-2023-11-02 16:30:54.002583: val_loss -0.8204 
-2023-11-02 16:30:54.002632: Pseudo dice [0.8539] 
-2023-11-02 16:30:54.002660: Epoch time: 106.84 s 
-2023-11-02 16:30:54.554961:  
-2023-11-02 16:30:54.555042: Epoch 718 
-2023-11-02 16:30:54.555288: Current learning rate: 0.0032 
-2023-11-02 16:32:41.377345: train_loss -0.9172 
-2023-11-02 16:32:41.377474: val_loss -0.8199 
-2023-11-02 16:32:41.377526: Pseudo dice [0.8537] 
-2023-11-02 16:32:41.377552: Epoch time: 106.82 s 
-2023-11-02 16:32:41.923073:  
-2023-11-02 16:32:41.923141: Epoch 719 
-2023-11-02 16:32:41.923194: Current learning rate: 0.00319 
-2023-11-02 16:34:28.570610: train_loss -0.9163 
-2023-11-02 16:34:28.570735: val_loss -0.8163 
-2023-11-02 16:34:28.570773: Pseudo dice [0.8511] 
-2023-11-02 16:34:28.570799: Epoch time: 106.65 s 
-2023-11-02 16:34:29.117054:  
-2023-11-02 16:34:29.117270: Epoch 720 
-2023-11-02 16:34:29.117348: Current learning rate: 0.00318 
-2023-11-02 16:36:15.824997: train_loss -0.9168 
-2023-11-02 16:36:15.825118: val_loss -0.8086 
-2023-11-02 16:36:15.825167: Pseudo dice [0.8463] 
-2023-11-02 16:36:15.825194: Epoch time: 106.71 s 
-2023-11-02 16:36:16.476351:  
-2023-11-02 16:36:16.476431: Epoch 721 
-2023-11-02 16:36:16.476482: Current learning rate: 0.00317 
-2023-11-02 16:38:03.171798: train_loss -0.9143 
-2023-11-02 16:38:03.171939: val_loss -0.8213 
-2023-11-02 16:38:03.172007: Pseudo dice [0.8542] 
-2023-11-02 16:38:03.172037: Epoch time: 106.7 s 
-2023-11-02 16:38:03.721543:  
-2023-11-02 16:38:03.721637: Epoch 722 
-2023-11-02 16:38:03.721724: Current learning rate: 0.00316 
-2023-11-02 16:39:50.193680: train_loss -0.9172 
-2023-11-02 16:39:50.193809: val_loss -0.81 
-2023-11-02 16:39:50.193860: Pseudo dice [0.8483] 
-2023-11-02 16:39:50.193887: Epoch time: 106.47 s 
-2023-11-02 16:39:50.741593:  
-2023-11-02 16:39:50.741680: Epoch 723 
-2023-11-02 16:39:50.741778: Current learning rate: 0.00315 
-2023-11-02 16:41:38.034202: train_loss -0.9177 
-2023-11-02 16:41:38.034355: val_loss -0.8187 
-2023-11-02 16:41:38.034386: Pseudo dice [0.8517] 
-2023-11-02 16:41:38.034414: Epoch time: 107.29 s 
-2023-11-02 16:41:38.578764:  
-2023-11-02 16:41:38.578859: Epoch 724 
-2023-11-02 16:41:38.579036: Current learning rate: 0.00314 
-2023-11-02 16:43:25.332085: train_loss -0.9171 
-2023-11-02 16:43:25.332262: val_loss -0.807 
-2023-11-02 16:43:25.332292: Pseudo dice [0.8462] 
-2023-11-02 16:43:25.332323: Epoch time: 106.75 s 
-2023-11-02 16:43:25.888250:  
-2023-11-02 16:43:25.888324: Epoch 725 
-2023-11-02 16:43:25.888404: Current learning rate: 0.00313 
-2023-11-02 16:45:15.168745: train_loss -0.919 
-2023-11-02 16:45:15.168881: val_loss -0.8195 
-2023-11-02 16:45:15.168911: Pseudo dice [0.853] 
-2023-11-02 16:45:15.168943: Epoch time: 109.28 s 
-2023-11-02 16:45:15.721565:  
-2023-11-02 16:45:15.721635: Epoch 726 
-2023-11-02 16:45:15.721713: Current learning rate: 0.00312 
-2023-11-02 16:47:04.521225: train_loss -0.9201 
-2023-11-02 16:47:04.521396: val_loss -0.8245 
-2023-11-02 16:47:04.521426: Pseudo dice [0.8569] 
-2023-11-02 16:47:04.521455: Epoch time: 108.8 s 
-2023-11-02 16:47:05.170724:  
-2023-11-02 16:47:05.170795: Epoch 727 
-2023-11-02 16:47:05.170852: Current learning rate: 0.00311 
-2023-11-02 16:48:54.854742: train_loss -0.9208 
-2023-11-02 16:48:54.854868: val_loss -0.8213 
-2023-11-02 16:48:54.854917: Pseudo dice [0.8552] 
-2023-11-02 16:48:54.854943: Epoch time: 109.68 s 
-2023-11-02 16:48:55.404753:  
-2023-11-02 16:48:55.404828: Epoch 728 
-2023-11-02 16:48:55.404905: Current learning rate: 0.0031 
-2023-11-02 16:50:46.151611: train_loss -0.9203 
-2023-11-02 16:50:46.151769: val_loss -0.8132 
-2023-11-02 16:50:46.151796: Pseudo dice [0.8494] 
-2023-11-02 16:50:46.151826: Epoch time: 110.75 s 
-2023-11-02 16:50:46.714656:  
-2023-11-02 16:50:46.714748: Epoch 729 
-2023-11-02 16:50:46.714802: Current learning rate: 0.00309 
-2023-11-02 16:52:37.809516: train_loss -0.9205 
-2023-11-02 16:52:37.809645: val_loss -0.8248 
-2023-11-02 16:52:37.809685: Pseudo dice [0.8593] 
-2023-11-02 16:52:37.809733: Epoch time: 111.1 s 
-2023-11-02 16:52:38.358774:  
-2023-11-02 16:52:38.358846: Epoch 730 
-2023-11-02 16:52:38.358912: Current learning rate: 0.00308 
-2023-11-02 16:54:27.513616: train_loss -0.919 
-2023-11-02 16:54:27.513760: val_loss -0.8097 
-2023-11-02 16:54:27.513787: Pseudo dice [0.849] 
-2023-11-02 16:54:27.513814: Epoch time: 109.16 s 
-2023-11-02 16:54:28.062596:  
-2023-11-02 16:54:28.062672: Epoch 731 
-2023-11-02 16:54:28.062753: Current learning rate: 0.00307 
-2023-11-02 16:56:17.168456: train_loss -0.917 
-2023-11-02 16:56:17.168580: val_loss -0.8223 
-2023-11-02 16:56:17.168634: Pseudo dice [0.8546] 
-2023-11-02 16:56:17.168660: Epoch time: 109.11 s 
-2023-11-02 16:56:17.723827:  
-2023-11-02 16:56:17.723899: Epoch 732 
-2023-11-02 16:56:17.723960: Current learning rate: 0.00306 
-2023-11-02 16:58:04.611362: train_loss -0.9198 
-2023-11-02 16:58:04.611490: val_loss -0.8138 
-2023-11-02 16:58:04.611539: Pseudo dice [0.8481] 
-2023-11-02 16:58:04.611565: Epoch time: 106.89 s 
-2023-11-02 16:58:05.247684:  
-2023-11-02 16:58:05.247753: Epoch 733 
-2023-11-02 16:58:05.247805: Current learning rate: 0.00305 
-2023-11-02 16:59:51.957421: train_loss -0.9192 
-2023-11-02 16:59:51.957554: val_loss -0.8221 
-2023-11-02 16:59:51.957608: Pseudo dice [0.855] 
-2023-11-02 16:59:51.957633: Epoch time: 106.71 s 
-2023-11-02 16:59:52.505972:  
-2023-11-02 16:59:52.506050: Epoch 734 
-2023-11-02 16:59:52.506128: Current learning rate: 0.00304 
-2023-11-02 17:01:39.277510: train_loss -0.9199 
-2023-11-02 17:01:39.277647: val_loss -0.8118 
-2023-11-02 17:01:39.277671: Pseudo dice [0.849] 
-2023-11-02 17:01:39.277698: Epoch time: 106.77 s 
-2023-11-02 17:01:39.821694:  
-2023-11-02 17:01:39.821811: Epoch 735 
-2023-11-02 17:01:39.821913: Current learning rate: 0.00303 
-2023-11-02 17:03:28.339771: train_loss -0.9185 
-2023-11-02 17:03:28.339913: val_loss -0.8207 
-2023-11-02 17:03:28.339956: Pseudo dice [0.8528] 
-2023-11-02 17:03:28.339993: Epoch time: 108.52 s 
-2023-11-02 17:03:28.891827:  
-2023-11-02 17:03:28.892009: Epoch 736 
-2023-11-02 17:03:28.892078: Current learning rate: 0.00302 
-2023-11-02 17:05:17.688006: train_loss -0.918 
-2023-11-02 17:05:17.688133: val_loss -0.806 
-2023-11-02 17:05:17.688159: Pseudo dice [0.8448] 
-2023-11-02 17:05:17.688190: Epoch time: 108.8 s 
-2023-11-02 17:05:18.248747:  
-2023-11-02 17:05:18.248820: Epoch 737 
-2023-11-02 17:05:18.248897: Current learning rate: 0.00301 
-2023-11-02 17:07:06.291668: train_loss -0.9177 
-2023-11-02 17:07:06.291803: val_loss -0.8219 
-2023-11-02 17:07:06.291845: Pseudo dice [0.8544] 
-2023-11-02 17:07:06.291878: Epoch time: 108.04 s 
-2023-11-02 17:07:06.841104:  
-2023-11-02 17:07:06.841185: Epoch 738 
-2023-11-02 17:07:06.841276: Current learning rate: 0.003 
-2023-11-02 17:08:53.728135: train_loss -0.9132 
-2023-11-02 17:08:53.728302: val_loss -0.8226 
-2023-11-02 17:08:53.728332: Pseudo dice [0.8546] 
-2023-11-02 17:08:53.728360: Epoch time: 106.89 s 
-2023-11-02 17:08:54.370777:  
-2023-11-02 17:08:54.370852: Epoch 739 
-2023-11-02 17:08:54.370920: Current learning rate: 0.00299 
-2023-11-02 17:10:41.151730: train_loss -0.9183 
-2023-11-02 17:10:41.151877: val_loss -0.8158 
-2023-11-02 17:10:41.151902: Pseudo dice [0.8493] 
-2023-11-02 17:10:41.151927: Epoch time: 106.78 s 
-2023-11-02 17:10:41.699267:  
-2023-11-02 17:10:41.699383: Epoch 740 
-2023-11-02 17:10:41.699510: Current learning rate: 0.00297 
-2023-11-02 17:12:28.519437: train_loss -0.9187 
-2023-11-02 17:12:28.519586: val_loss -0.8171 
-2023-11-02 17:12:28.519639: Pseudo dice [0.8539] 
-2023-11-02 17:12:28.519667: Epoch time: 106.82 s 
-2023-11-02 17:12:29.064383:  
-2023-11-02 17:12:29.064454: Epoch 741 
-2023-11-02 17:12:29.064506: Current learning rate: 0.00296 
-2023-11-02 17:14:15.840978: train_loss -0.9199 
-2023-11-02 17:14:15.841131: val_loss -0.8207 
-2023-11-02 17:14:15.841157: Pseudo dice [0.8534] 
-2023-11-02 17:14:15.841184: Epoch time: 106.78 s 
-2023-11-02 17:14:16.390591:  
-2023-11-02 17:14:16.390672: Epoch 742 
-2023-11-02 17:14:16.390769: Current learning rate: 0.00295 
-2023-11-02 17:16:03.214409: train_loss -0.9198 
-2023-11-02 17:16:03.214560: val_loss -0.809 
-2023-11-02 17:16:03.214589: Pseudo dice [0.8459] 
-2023-11-02 17:16:03.214615: Epoch time: 106.82 s 
-2023-11-02 17:16:03.771281:  
-2023-11-02 17:16:03.771374: Epoch 743 
-2023-11-02 17:16:03.771430: Current learning rate: 0.00294 
-2023-11-02 17:17:50.617698: train_loss -0.9201 
-2023-11-02 17:17:50.617815: val_loss -0.816 
-2023-11-02 17:17:50.617851: Pseudo dice [0.8518] 
-2023-11-02 17:17:50.617878: Epoch time: 106.85 s 
-2023-11-02 17:17:51.161262:  
-2023-11-02 17:17:51.161478: Epoch 744 
-2023-11-02 17:17:51.161534: Current learning rate: 0.00293 
-2023-11-02 17:19:38.012418: train_loss -0.9195 
-2023-11-02 17:19:38.012551: val_loss -0.8177 
-2023-11-02 17:19:38.012601: Pseudo dice [0.8527] 
-2023-11-02 17:19:38.012628: Epoch time: 106.85 s 
-2023-11-02 17:19:38.655286:  
-2023-11-02 17:19:38.655376: Epoch 745 
-2023-11-02 17:19:38.655531: Current learning rate: 0.00292 
-2023-11-02 17:21:25.545075: train_loss -0.919 
-2023-11-02 17:21:25.545207: val_loss -0.8154 
-2023-11-02 17:21:25.545249: Pseudo dice [0.8506] 
-2023-11-02 17:21:25.545275: Epoch time: 106.89 s 
-2023-11-02 17:21:26.089420:  
-2023-11-02 17:21:26.089499: Epoch 746 
-2023-11-02 17:21:26.089576: Current learning rate: 0.00291 
-2023-11-02 17:23:12.878277: train_loss -0.9212 
-2023-11-02 17:23:12.878400: val_loss -0.8187 
-2023-11-02 17:23:12.878449: Pseudo dice [0.8542] 
-2023-11-02 17:23:12.878475: Epoch time: 106.79 s 
-2023-11-02 17:23:13.422431:  
-2023-11-02 17:23:13.422501: Epoch 747 
-2023-11-02 17:23:13.422577: Current learning rate: 0.0029 
-2023-11-02 17:25:00.158300: train_loss -0.9225 
-2023-11-02 17:25:00.158467: val_loss -0.8063 
-2023-11-02 17:25:00.158499: Pseudo dice [0.8453] 
-2023-11-02 17:25:00.158539: Epoch time: 106.74 s 
-2023-11-02 17:25:00.702866:  
-2023-11-02 17:25:00.703005: Epoch 748 
-2023-11-02 17:25:00.703061: Current learning rate: 0.00289 
-2023-11-02 17:26:47.507017: train_loss -0.9209 
-2023-11-02 17:26:47.507173: val_loss -0.8242 
-2023-11-02 17:26:47.507197: Pseudo dice [0.8556] 
-2023-11-02 17:26:47.507222: Epoch time: 106.8 s 
-2023-11-02 17:26:48.054548:  
-2023-11-02 17:26:48.054626: Epoch 749 
-2023-11-02 17:26:48.054913: Current learning rate: 0.00288 
-2023-11-02 17:28:34.953548: train_loss -0.9208 
-2023-11-02 17:28:34.953673: val_loss -0.8114 
-2023-11-02 17:28:34.953721: Pseudo dice [0.8489] 
-2023-11-02 17:28:34.953748: Epoch time: 106.9 s 
-2023-11-02 17:28:35.739180:  
-2023-11-02 17:28:35.739253: Epoch 750 
-2023-11-02 17:28:35.739307: Current learning rate: 0.00287 
-2023-11-02 17:30:22.568347: train_loss -0.9207 
-2023-11-02 17:30:22.568475: val_loss -0.8059 
-2023-11-02 17:30:22.568518: Pseudo dice [0.8446] 
-2023-11-02 17:30:22.568545: Epoch time: 106.83 s 
-2023-11-02 17:30:23.213083:  
-2023-11-02 17:30:23.213151: Epoch 751 
-2023-11-02 17:30:23.213202: Current learning rate: 0.00286 
-2023-11-02 17:32:10.062894: train_loss -0.9204 
-2023-11-02 17:32:10.063055: val_loss -0.8094 
-2023-11-02 17:32:10.063081: Pseudo dice [0.8468] 
-2023-11-02 17:32:10.063113: Epoch time: 106.85 s 
-2023-11-02 17:32:10.611548:  
-2023-11-02 17:32:10.611740: Epoch 752 
-2023-11-02 17:32:10.611862: Current learning rate: 0.00285 
-2023-11-02 17:33:57.373429: train_loss -0.9205 
-2023-11-02 17:33:57.373553: val_loss -0.8202 
-2023-11-02 17:33:57.373602: Pseudo dice [0.8559] 
-2023-11-02 17:33:57.373629: Epoch time: 106.76 s 
-2023-11-02 17:33:57.930233:  
-2023-11-02 17:33:57.930299: Epoch 753 
-2023-11-02 17:33:57.930369: Current learning rate: 0.00284 
-2023-11-02 17:35:44.746042: train_loss -0.9237 
-2023-11-02 17:35:44.746175: val_loss -0.8138 
-2023-11-02 17:35:44.746198: Pseudo dice [0.8478] 
-2023-11-02 17:35:44.746225: Epoch time: 106.82 s 
-2023-11-02 17:35:45.291203:  
-2023-11-02 17:35:45.291336: Epoch 754 
-2023-11-02 17:35:45.291395: Current learning rate: 0.00283 
-2023-11-02 17:37:32.128008: train_loss -0.9226 
-2023-11-02 17:37:32.128128: val_loss -0.8169 
-2023-11-02 17:37:32.128178: Pseudo dice [0.8538] 
-2023-11-02 17:37:32.128204: Epoch time: 106.84 s 
-2023-11-02 17:37:32.681675:  
-2023-11-02 17:37:32.681744: Epoch 755 
-2023-11-02 17:37:32.681819: Current learning rate: 0.00282 
-2023-11-02 17:39:20.461143: train_loss -0.9229 
-2023-11-02 17:39:20.461282: val_loss -0.8204 
-2023-11-02 17:39:20.461310: Pseudo dice [0.854] 
-2023-11-02 17:39:20.461337: Epoch time: 107.78 s 
-2023-11-02 17:39:21.004233:  
-2023-11-02 17:39:21.004302: Epoch 756 
-2023-11-02 17:39:21.004511: Current learning rate: 0.00281 
-2023-11-02 17:41:07.815863: train_loss -0.9224 
-2023-11-02 17:41:07.815996: val_loss -0.8155 
-2023-11-02 17:41:07.816021: Pseudo dice [0.8527] 
-2023-11-02 17:41:07.816049: Epoch time: 106.81 s 
-2023-11-02 17:41:08.461349:  
-2023-11-02 17:41:08.461441: Epoch 757 
-2023-11-02 17:41:08.461503: Current learning rate: 0.0028 
-2023-11-02 17:42:55.349739: train_loss -0.9228 
-2023-11-02 17:42:55.349875: val_loss -0.818 
-2023-11-02 17:42:55.349930: Pseudo dice [0.8517] 
-2023-11-02 17:42:55.349962: Epoch time: 106.89 s 
-2023-11-02 17:42:55.898189:  
-2023-11-02 17:42:55.898449: Epoch 758 
-2023-11-02 17:42:55.898505: Current learning rate: 0.00279 
-2023-11-02 17:44:42.707302: train_loss -0.921 
-2023-11-02 17:44:42.707445: val_loss -0.8218 
-2023-11-02 17:44:42.707471: Pseudo dice [0.8556] 
-2023-11-02 17:44:42.707499: Epoch time: 106.81 s 
-2023-11-02 17:44:43.250572:  
-2023-11-02 17:44:43.250665: Epoch 759 
-2023-11-02 17:44:43.250714: Current learning rate: 0.00278 
-2023-11-02 17:46:30.124510: train_loss -0.9219 
-2023-11-02 17:46:30.124639: val_loss -0.8207 
-2023-11-02 17:46:30.124670: Pseudo dice [0.8539] 
-2023-11-02 17:46:30.124721: Epoch time: 106.87 s 
-2023-11-02 17:46:30.670001:  
-2023-11-02 17:46:30.670345: Epoch 760 
-2023-11-02 17:46:30.670419: Current learning rate: 0.00277 
-2023-11-02 17:48:17.489015: train_loss -0.9238 
-2023-11-02 17:48:17.489123: val_loss -0.8131 
-2023-11-02 17:48:17.489173: Pseudo dice [0.8485] 
-2023-11-02 17:48:17.489200: Epoch time: 106.82 s 
-2023-11-02 17:48:18.035715:  
-2023-11-02 17:48:18.035822: Epoch 761 
-2023-11-02 17:48:18.035901: Current learning rate: 0.00276 
-2023-11-02 17:50:04.789895: train_loss -0.9206 
-2023-11-02 17:50:04.790044: val_loss -0.818 
-2023-11-02 17:50:04.790070: Pseudo dice [0.8513] 
-2023-11-02 17:50:04.790096: Epoch time: 106.75 s 
-2023-11-02 17:50:05.338796:  
-2023-11-02 17:50:05.338877: Epoch 762 
-2023-11-02 17:50:05.338946: Current learning rate: 0.00275 
-2023-11-02 17:51:52.113432: train_loss -0.9231 
-2023-11-02 17:51:52.113564: val_loss -0.8267 
-2023-11-02 17:51:52.113617: Pseudo dice [0.8586] 
-2023-11-02 17:51:52.113643: Epoch time: 106.78 s 
-2023-11-02 17:51:52.758771:  
-2023-11-02 17:51:52.758845: Epoch 763 
-2023-11-02 17:51:52.758925: Current learning rate: 0.00274 
-2023-11-02 17:53:39.540658: train_loss -0.921 
-2023-11-02 17:53:39.540811: val_loss -0.8171 
-2023-11-02 17:53:39.540852: Pseudo dice [0.8505] 
-2023-11-02 17:53:39.540881: Epoch time: 106.78 s 
-2023-11-02 17:53:40.097728:  
-2023-11-02 17:53:40.097803: Epoch 764 
-2023-11-02 17:53:40.097878: Current learning rate: 0.00273 
-2023-11-02 17:55:26.888289: train_loss -0.9208 
-2023-11-02 17:55:26.888437: val_loss -0.8251 
-2023-11-02 17:55:26.888488: Pseudo dice [0.8569] 
-2023-11-02 17:55:26.888516: Epoch time: 106.79 s 
-2023-11-02 17:55:27.440266:  
-2023-11-02 17:55:27.440331: Epoch 765 
-2023-11-02 17:55:27.440402: Current learning rate: 0.00272 
-2023-11-02 17:57:14.321396: train_loss -0.9248 
-2023-11-02 17:57:14.321522: val_loss -0.8241 
-2023-11-02 17:57:14.321571: Pseudo dice [0.8575] 
-2023-11-02 17:57:14.321597: Epoch time: 106.88 s 
-2023-11-02 17:57:14.878633:  
-2023-11-02 17:57:14.878701: Epoch 766 
-2023-11-02 17:57:14.878776: Current learning rate: 0.00271 
-2023-11-02 17:59:01.685909: train_loss -0.9224 
-2023-11-02 17:59:01.686038: val_loss -0.8367 
-2023-11-02 17:59:01.686078: Pseudo dice [0.864] 
-2023-11-02 17:59:01.686108: Epoch time: 106.81 s 
-2023-11-02 17:59:01.686131: Yayy! New best EMA pseudo Dice: 0.8542 
-2023-11-02 17:59:02.458567:  
-2023-11-02 17:59:02.458636: Epoch 767 
-2023-11-02 17:59:02.458688: Current learning rate: 0.0027 
-2023-11-02 18:00:49.073850: train_loss -0.9227 
-2023-11-02 18:00:49.073996: val_loss -0.8134 
-2023-11-02 18:00:49.074021: Pseudo dice [0.8496] 
-2023-11-02 18:00:49.074063: Epoch time: 106.62 s 
-2023-11-02 18:00:49.625233:  
-2023-11-02 18:00:49.625438: Epoch 768 
-2023-11-02 18:00:49.625495: Current learning rate: 0.00268 
-2023-11-02 18:02:36.234642: train_loss -0.9222 
-2023-11-02 18:02:36.234810: val_loss -0.82 
-2023-11-02 18:02:36.234835: Pseudo dice [0.8547] 
-2023-11-02 18:02:36.234861: Epoch time: 106.61 s 
-2023-11-02 18:02:36.882469:  
-2023-11-02 18:02:36.882538: Epoch 769 
-2023-11-02 18:02:36.882619: Current learning rate: 0.00267 
-2023-11-02 18:04:23.520766: train_loss -0.9228 
-2023-11-02 18:04:23.520900: val_loss -0.8112 
-2023-11-02 18:04:23.520926: Pseudo dice [0.8488] 
-2023-11-02 18:04:23.520953: Epoch time: 106.64 s 
-2023-11-02 18:04:24.072067:  
-2023-11-02 18:04:24.072149: Epoch 770 
-2023-11-02 18:04:24.072200: Current learning rate: 0.00266 
-2023-11-02 18:06:10.745467: train_loss -0.9236 
-2023-11-02 18:06:10.745610: val_loss -0.8212 
-2023-11-02 18:06:10.745661: Pseudo dice [0.8547] 
-2023-11-02 18:06:10.745688: Epoch time: 106.67 s 
-2023-11-02 18:06:11.299265:  
-2023-11-02 18:06:11.299334: Epoch 771 
-2023-11-02 18:06:11.299409: Current learning rate: 0.00265 
-2023-11-02 18:07:57.981726: train_loss -0.9215 
-2023-11-02 18:07:57.981861: val_loss -0.8138 
-2023-11-02 18:07:57.981886: Pseudo dice [0.8497] 
-2023-11-02 18:07:57.981915: Epoch time: 106.68 s 
-2023-11-02 18:07:58.539009:  
-2023-11-02 18:07:58.539086: Epoch 772 
-2023-11-02 18:07:58.539138: Current learning rate: 0.00264 
-2023-11-02 18:09:45.205100: train_loss -0.9193 
-2023-11-02 18:09:45.205251: val_loss -0.8231 
-2023-11-02 18:09:45.205277: Pseudo dice [0.8553] 
-2023-11-02 18:09:45.205304: Epoch time: 106.67 s 
-2023-11-02 18:09:45.757857:  
-2023-11-02 18:09:45.757928: Epoch 773 
-2023-11-02 18:09:45.758005: Current learning rate: 0.00263 
-2023-11-02 18:11:32.398316: train_loss -0.9201 
-2023-11-02 18:11:32.398436: val_loss -0.813 
-2023-11-02 18:11:32.398484: Pseudo dice [0.8501] 
-2023-11-02 18:11:32.398510: Epoch time: 106.64 s 
-2023-11-02 18:11:32.953726:  
-2023-11-02 18:11:32.953794: Epoch 774 
-2023-11-02 18:11:32.953865: Current learning rate: 0.00262 
-2023-11-02 18:13:19.458244: train_loss -0.922 
-2023-11-02 18:13:19.458378: val_loss -0.8167 
-2023-11-02 18:13:19.458410: Pseudo dice [0.8507] 
-2023-11-02 18:13:19.458444: Epoch time: 106.5 s 
-2023-11-02 18:13:20.108876:  
-2023-11-02 18:13:20.108951: Epoch 775 
-2023-11-02 18:13:20.109056: Current learning rate: 0.00261 
-2023-11-02 18:15:06.746029: train_loss -0.921 
-2023-11-02 18:15:06.746186: val_loss -0.8167 
-2023-11-02 18:15:06.746212: Pseudo dice [0.8506] 
-2023-11-02 18:15:06.746238: Epoch time: 106.64 s 
-2023-11-02 18:15:07.301719:  
-2023-11-02 18:15:07.301797: Epoch 776 
-2023-11-02 18:15:07.301848: Current learning rate: 0.0026 
-2023-11-02 18:16:54.108271: train_loss -0.9215 
-2023-11-02 18:16:54.108422: val_loss -0.8209 
-2023-11-02 18:16:54.108453: Pseudo dice [0.8557] 
-2023-11-02 18:16:54.108481: Epoch time: 106.81 s 
-2023-11-02 18:16:54.661495:  
-2023-11-02 18:16:54.661571: Epoch 777 
-2023-11-02 18:16:54.661650: Current learning rate: 0.00259 
-2023-11-02 18:18:41.497952: train_loss -0.9219 
-2023-11-02 18:18:41.498127: val_loss -0.8154 
-2023-11-02 18:18:41.498152: Pseudo dice [0.8505] 
-2023-11-02 18:18:41.498180: Epoch time: 106.84 s 
-2023-11-02 18:18:42.048903:  
-2023-11-02 18:18:42.048973: Epoch 778 
-2023-11-02 18:18:42.049023: Current learning rate: 0.00258 
-2023-11-02 18:20:28.788254: train_loss -0.9215 
-2023-11-02 18:20:28.788401: val_loss -0.8145 
-2023-11-02 18:20:28.788457: Pseudo dice [0.8505] 
-2023-11-02 18:20:28.788484: Epoch time: 106.74 s 
-2023-11-02 18:20:29.340780:  
-2023-11-02 18:20:29.340845: Epoch 779 
-2023-11-02 18:20:29.340908: Current learning rate: 0.00257 
-2023-11-02 18:22:15.971144: train_loss -0.9225 
-2023-11-02 18:22:15.971266: val_loss -0.808 
-2023-11-02 18:22:15.971302: Pseudo dice [0.8455] 
-2023-11-02 18:22:15.971328: Epoch time: 106.63 s 
-2023-11-02 18:22:16.526874:  
-2023-11-02 18:22:16.526939: Epoch 780 
-2023-11-02 18:22:16.527015: Current learning rate: 0.00256 
-2023-11-02 18:24:03.158162: train_loss -0.9182 
-2023-11-02 18:24:03.158278: val_loss -0.8243 
-2023-11-02 18:24:03.158327: Pseudo dice [0.8575] 
-2023-11-02 18:24:03.158353: Epoch time: 106.63 s 
-2023-11-02 18:24:03.711166:  
-2023-11-02 18:24:03.711260: Epoch 781 
-2023-11-02 18:24:03.711488: Current learning rate: 0.00255 
-2023-11-02 18:25:50.466321: train_loss -0.9199 
-2023-11-02 18:25:50.466451: val_loss -0.806 
-2023-11-02 18:25:50.466474: Pseudo dice [0.8436] 
-2023-11-02 18:25:50.466500: Epoch time: 106.76 s 
-2023-11-02 18:25:51.118710:  
-2023-11-02 18:25:51.118837: Epoch 782 
-2023-11-02 18:25:51.118947: Current learning rate: 0.00254 
-2023-11-02 18:27:37.936440: train_loss -0.9219 
-2023-11-02 18:27:37.936573: val_loss -0.8238 
-2023-11-02 18:27:37.936622: Pseudo dice [0.8568] 
-2023-11-02 18:27:37.936648: Epoch time: 106.82 s 
-2023-11-02 18:27:38.491147:  
-2023-11-02 18:27:38.491247: Epoch 783 
-2023-11-02 18:27:38.491331: Current learning rate: 0.00253 
-2023-11-02 18:29:25.247769: train_loss -0.9199 
-2023-11-02 18:29:25.247920: val_loss -0.8095 
-2023-11-02 18:29:25.247957: Pseudo dice [0.8477] 
-2023-11-02 18:29:25.247996: Epoch time: 106.76 s 
-2023-11-02 18:29:25.801031:  
-2023-11-02 18:29:25.801109: Epoch 784 
-2023-11-02 18:29:25.801213: Current learning rate: 0.00252 
-2023-11-02 18:31:12.681633: train_loss -0.9224 
-2023-11-02 18:31:12.681790: val_loss -0.8252 
-2023-11-02 18:31:12.681815: Pseudo dice [0.8579] 
-2023-11-02 18:31:12.681842: Epoch time: 106.88 s 
-2023-11-02 18:31:13.240524:  
-2023-11-02 18:31:13.240593: Epoch 785 
-2023-11-02 18:31:13.240658: Current learning rate: 0.00251 
-2023-11-02 18:33:00.099735: train_loss -0.9235 
-2023-11-02 18:33:00.099891: val_loss -0.8271 
-2023-11-02 18:33:00.099924: Pseudo dice [0.8587] 
-2023-11-02 18:33:00.099957: Epoch time: 106.86 s 
-2023-11-02 18:33:00.650844:  
-2023-11-02 18:33:00.650916: Epoch 786 
-2023-11-02 18:33:00.650990: Current learning rate: 0.0025 
-2023-11-02 18:34:48.336146: train_loss -0.923 
-2023-11-02 18:34:48.336280: val_loss -0.8164 
-2023-11-02 18:34:48.336307: Pseudo dice [0.8514] 
-2023-11-02 18:34:48.336335: Epoch time: 107.69 s 
-2023-11-02 18:34:48.888609:  
-2023-11-02 18:34:48.888672: Epoch 787 
-2023-11-02 18:34:48.888744: Current learning rate: 0.00249 
-2023-11-02 18:36:36.305824: train_loss -0.9234 
-2023-11-02 18:36:36.305959: val_loss -0.8145 
-2023-11-02 18:36:36.306009: Pseudo dice [0.8496] 
-2023-11-02 18:36:36.306036: Epoch time: 107.42 s 
-2023-11-02 18:36:36.966299:  
-2023-11-02 18:36:36.966381: Epoch 788 
-2023-11-02 18:36:36.966438: Current learning rate: 0.00248 
-2023-11-02 18:38:26.198688: train_loss -0.9207 
-2023-11-02 18:38:26.198837: val_loss -0.8175 
-2023-11-02 18:38:26.198862: Pseudo dice [0.8517] 
-2023-11-02 18:38:26.198887: Epoch time: 109.23 s 
-2023-11-02 18:38:26.763219:  
-2023-11-02 18:38:26.763299: Epoch 789 
-2023-11-02 18:38:26.763365: Current learning rate: 0.00247 
-2023-11-02 18:40:15.760223: train_loss -0.9229 
-2023-11-02 18:40:15.760367: val_loss -0.8178 
-2023-11-02 18:40:15.760392: Pseudo dice [0.8523] 
-2023-11-02 18:40:15.760417: Epoch time: 109.0 s 
-2023-11-02 18:40:16.312386:  
-2023-11-02 18:40:16.312604: Epoch 790 
-2023-11-02 18:40:16.312671: Current learning rate: 0.00245 
-2023-11-02 18:42:05.206544: train_loss -0.9226 
-2023-11-02 18:42:05.206676: val_loss -0.8146 
-2023-11-02 18:42:05.206714: Pseudo dice [0.8491] 
-2023-11-02 18:42:05.206740: Epoch time: 108.89 s 
-2023-11-02 18:42:05.772770:  
-2023-11-02 18:42:05.773106: Epoch 791 
-2023-11-02 18:42:05.773192: Current learning rate: 0.00244 
-2023-11-02 18:43:54.773646: train_loss -0.9239 
-2023-11-02 18:43:54.773773: val_loss -0.8135 
-2023-11-02 18:43:54.773802: Pseudo dice [0.8496] 
-2023-11-02 18:43:54.773841: Epoch time: 109.0 s 
-2023-11-02 18:43:55.329510:  
-2023-11-02 18:43:55.329603: Epoch 792 
-2023-11-02 18:43:55.329831: Current learning rate: 0.00243 
-2023-11-02 18:45:44.296037: train_loss -0.9214 
-2023-11-02 18:45:44.296175: val_loss -0.8098 
-2023-11-02 18:45:44.296226: Pseudo dice [0.8466] 
-2023-11-02 18:45:44.296254: Epoch time: 108.97 s 
-2023-11-02 18:45:44.849051:  
-2023-11-02 18:45:44.849121: Epoch 793 
-2023-11-02 18:45:44.849203: Current learning rate: 0.00242 
-2023-11-02 18:47:33.831482: train_loss -0.9211 
-2023-11-02 18:47:33.831608: val_loss -0.8258 
-2023-11-02 18:47:33.831634: Pseudo dice [0.8569] 
-2023-11-02 18:47:33.831660: Epoch time: 108.98 s 
-2023-11-02 18:47:34.386501:  
-2023-11-02 18:47:34.386579: Epoch 794 
-2023-11-02 18:47:34.386657: Current learning rate: 0.00241 
-2023-11-02 18:49:23.281384: train_loss -0.9238 
-2023-11-02 18:49:23.281548: val_loss -0.8193 
-2023-11-02 18:49:23.281574: Pseudo dice [0.8527] 
-2023-11-02 18:49:23.281627: Epoch time: 108.9 s 
-2023-11-02 18:49:23.840712:  
-2023-11-02 18:49:23.840791: Epoch 795 
-2023-11-02 18:49:23.840845: Current learning rate: 0.0024 
-2023-11-02 18:51:12.970086: train_loss -0.9251 
-2023-11-02 18:51:12.970231: val_loss -0.8098 
-2023-11-02 18:51:12.970282: Pseudo dice [0.8473] 
-2023-11-02 18:51:12.970309: Epoch time: 109.13 s 
-2023-11-02 18:51:13.530050:  
-2023-11-02 18:51:13.530124: Epoch 796 
-2023-11-02 18:51:13.530177: Current learning rate: 0.00239 
-2023-11-02 18:53:01.205717: train_loss -0.9229 
-2023-11-02 18:53:01.205867: val_loss -0.821 
-2023-11-02 18:53:01.205899: Pseudo dice [0.854] 
-2023-11-02 18:53:01.205929: Epoch time: 107.68 s 
-2023-11-02 18:53:01.763373:  
-2023-11-02 18:53:01.763447: Epoch 797 
-2023-11-02 18:53:01.763526: Current learning rate: 0.00238 
-2023-11-02 18:54:49.303061: train_loss -0.9228 
-2023-11-02 18:54:49.303188: val_loss -0.8152 
-2023-11-02 18:54:49.303249: Pseudo dice [0.8506] 
-2023-11-02 18:54:49.303280: Epoch time: 107.54 s 
-2023-11-02 18:54:49.863846:  
-2023-11-02 18:54:49.863914: Epoch 798 
-2023-11-02 18:54:49.863992: Current learning rate: 0.00237 
-2023-11-02 18:56:38.633018: train_loss -0.9226 
-2023-11-02 18:56:38.633141: val_loss -0.8168 
-2023-11-02 18:56:38.633180: Pseudo dice [0.8528] 
-2023-11-02 18:56:38.633207: Epoch time: 108.77 s 
-2023-11-02 18:56:39.286407:  
-2023-11-02 18:56:39.286487: Epoch 799 
-2023-11-02 18:56:39.286540: Current learning rate: 0.00236 
-2023-11-02 18:58:27.154934: train_loss -0.9232 
-2023-11-02 18:58:27.155092: val_loss -0.818 
-2023-11-02 18:58:27.155116: Pseudo dice [0.8535] 
-2023-11-02 18:58:27.155144: Epoch time: 107.87 s 
-2023-11-02 18:58:27.953631:  
-2023-11-02 18:58:27.953703: Epoch 800 
-2023-11-02 18:58:27.953756: Current learning rate: 0.00235 
-2023-11-02 19:00:14.899807: train_loss -0.9246 
-2023-11-02 19:00:14.899959: val_loss -0.8139 
-2023-11-02 19:00:14.900019: Pseudo dice [0.8506] 
-2023-11-02 19:00:14.900049: Epoch time: 106.95 s 
-2023-11-02 19:00:15.455197:  
-2023-11-02 19:00:15.455448: Epoch 801 
-2023-11-02 19:00:15.455548: Current learning rate: 0.00234 
-2023-11-02 19:02:02.381448: train_loss -0.9254 
-2023-11-02 19:02:02.381576: val_loss -0.8224 
-2023-11-02 19:02:02.381614: Pseudo dice [0.8567] 
-2023-11-02 19:02:02.381640: Epoch time: 106.93 s 
-2023-11-02 19:02:02.937157:  
-2023-11-02 19:02:02.937257: Epoch 802 
-2023-11-02 19:02:02.937346: Current learning rate: 0.00233 
-2023-11-02 19:03:49.853355: train_loss -0.9242 
-2023-11-02 19:03:49.853486: val_loss -0.8233 
-2023-11-02 19:03:49.853511: Pseudo dice [0.8561] 
-2023-11-02 19:03:49.853538: Epoch time: 106.92 s 
-2023-11-02 19:03:50.411508:  
-2023-11-02 19:03:50.411571: Epoch 803 
-2023-11-02 19:03:50.411618: Current learning rate: 0.00232 
-2023-11-02 19:05:37.250426: train_loss -0.923 
-2023-11-02 19:05:37.250551: val_loss -0.8109 
-2023-11-02 19:05:37.250601: Pseudo dice [0.849] 
-2023-11-02 19:05:37.250626: Epoch time: 106.84 s 
-2023-11-02 19:05:37.807739:  
-2023-11-02 19:05:37.807805: Epoch 804 
-2023-11-02 19:05:37.807874: Current learning rate: 0.00231 
-2023-11-02 19:07:26.400414: train_loss -0.922 
-2023-11-02 19:07:26.400568: val_loss -0.8209 
-2023-11-02 19:07:26.400596: Pseudo dice [0.854] 
-2023-11-02 19:07:26.400633: Epoch time: 108.59 s 
-2023-11-02 19:07:27.060564:  
-2023-11-02 19:07:27.060644: Epoch 805 
-2023-11-02 19:07:27.060701: Current learning rate: 0.0023 
-2023-11-02 19:09:15.978962: train_loss -0.9266 
-2023-11-02 19:09:15.979101: val_loss -0.809 
-2023-11-02 19:09:15.979124: Pseudo dice [0.8465] 
-2023-11-02 19:09:15.979150: Epoch time: 108.92 s 
-2023-11-02 19:09:16.535819:  
-2023-11-02 19:09:16.535903: Epoch 806 
-2023-11-02 19:09:16.536003: Current learning rate: 0.00229 
-2023-11-02 19:11:05.500846: train_loss -0.9248 
-2023-11-02 19:11:05.501002: val_loss -0.8182 
-2023-11-02 19:11:05.501037: Pseudo dice [0.8527] 
-2023-11-02 19:11:05.501069: Epoch time: 108.97 s 
-2023-11-02 19:11:06.062006:  
-2023-11-02 19:11:06.062090: Epoch 807 
-2023-11-02 19:11:06.062148: Current learning rate: 0.00228 
-2023-11-02 19:12:55.254526: train_loss -0.9267 
-2023-11-02 19:12:55.254651: val_loss -0.8125 
-2023-11-02 19:12:55.254689: Pseudo dice [0.8482] 
-2023-11-02 19:12:55.254714: Epoch time: 109.19 s 
-2023-11-02 19:12:55.812957:  
-2023-11-02 19:12:55.813036: Epoch 808 
-2023-11-02 19:12:55.813088: Current learning rate: 0.00226 
-2023-11-02 19:14:45.343929: train_loss -0.9238 
-2023-11-02 19:14:45.344065: val_loss -0.8224 
-2023-11-02 19:14:45.344095: Pseudo dice [0.8563] 
-2023-11-02 19:14:45.344127: Epoch time: 109.53 s 
-2023-11-02 19:14:45.901070:  
-2023-11-02 19:14:45.901170: Epoch 809 
-2023-11-02 19:14:45.901258: Current learning rate: 0.00225 
-2023-11-02 19:16:35.424884: train_loss -0.9234 
-2023-11-02 19:16:35.425044: val_loss -0.8243 
-2023-11-02 19:16:35.425072: Pseudo dice [0.8558] 
-2023-11-02 19:16:35.425099: Epoch time: 109.52 s 
-2023-11-02 19:16:35.982902:  
-2023-11-02 19:16:35.983110: Epoch 810 
-2023-11-02 19:16:35.983166: Current learning rate: 0.00224 
-2023-11-02 19:18:26.001843: train_loss -0.9251 
-2023-11-02 19:18:26.001974: val_loss -0.817 
-2023-11-02 19:18:26.002028: Pseudo dice [0.8515] 
-2023-11-02 19:18:26.002061: Epoch time: 110.02 s 
-2023-11-02 19:18:26.679272:  
-2023-11-02 19:18:26.679421: Epoch 811 
-2023-11-02 19:18:26.679507: Current learning rate: 0.00223 
-2023-11-02 19:20:16.805053: train_loss -0.9238 
-2023-11-02 19:20:16.805238: val_loss -0.8249 
-2023-11-02 19:20:16.805268: Pseudo dice [0.8553] 
-2023-11-02 19:20:16.805298: Epoch time: 110.13 s 
-2023-11-02 19:20:17.399806:  
-2023-11-02 19:20:17.399909: Epoch 812 
-2023-11-02 19:20:17.399995: Current learning rate: 0.00222 
-2023-11-02 19:22:07.165398: train_loss -0.9252 
-2023-11-02 19:22:07.165591: val_loss -0.8125 
-2023-11-02 19:22:07.165620: Pseudo dice [0.8489] 
-2023-11-02 19:22:07.165649: Epoch time: 109.77 s 
-2023-11-02 19:22:07.741978:  
-2023-11-02 19:22:07.742069: Epoch 813 
-2023-11-02 19:22:07.742123: Current learning rate: 0.00221 
-2023-11-02 19:23:57.267210: train_loss -0.9251 
-2023-11-02 19:23:57.267338: val_loss -0.8208 
-2023-11-02 19:23:57.267375: Pseudo dice [0.8542] 
-2023-11-02 19:23:57.267401: Epoch time: 109.53 s 
-2023-11-02 19:23:57.830137:  
-2023-11-02 19:23:57.830231: Epoch 814 
-2023-11-02 19:23:57.830346: Current learning rate: 0.0022 
-2023-11-02 19:25:47.322510: train_loss -0.9247 
-2023-11-02 19:25:47.322628: val_loss -0.8254 
-2023-11-02 19:25:47.322666: Pseudo dice [0.8573] 
-2023-11-02 19:25:47.322693: Epoch time: 109.49 s 
-2023-11-02 19:25:47.880984:  
-2023-11-02 19:25:47.881083: Epoch 815 
-2023-11-02 19:25:47.881135: Current learning rate: 0.00219 
-2023-11-02 19:27:37.349321: train_loss -0.9262 
-2023-11-02 19:27:37.349472: val_loss -0.8174 
-2023-11-02 19:27:37.349523: Pseudo dice [0.8513] 
-2023-11-02 19:27:37.349550: Epoch time: 109.47 s 
-2023-11-02 19:27:37.909055:  
-2023-11-02 19:27:37.909124: Epoch 816 
-2023-11-02 19:27:37.909199: Current learning rate: 0.00218 
-2023-11-02 19:29:27.410959: train_loss -0.9241 
-2023-11-02 19:29:27.411102: val_loss -0.8309 
-2023-11-02 19:29:27.411153: Pseudo dice [0.8622] 
-2023-11-02 19:29:27.411183: Epoch time: 109.5 s 
-2023-11-02 19:29:28.077487:  
-2023-11-02 19:29:28.077574: Epoch 817 
-2023-11-02 19:29:28.077653: Current learning rate: 0.00217 
-2023-11-02 19:31:17.420184: train_loss -0.9242 
-2023-11-02 19:31:17.420348: val_loss -0.8144 
-2023-11-02 19:31:17.420376: Pseudo dice [0.8493] 
-2023-11-02 19:31:17.420404: Epoch time: 109.34 s 
-2023-11-02 19:31:17.994751:  
-2023-11-02 19:31:17.994853: Epoch 818 
-2023-11-02 19:31:17.994932: Current learning rate: 0.00216 
-2023-11-02 19:33:07.082632: train_loss -0.9232 
-2023-11-02 19:33:07.082769: val_loss -0.8186 
-2023-11-02 19:33:07.082796: Pseudo dice [0.8544] 
-2023-11-02 19:33:07.082822: Epoch time: 109.09 s 
-2023-11-02 19:33:07.641772:  
-2023-11-02 19:33:07.641853: Epoch 819 
-2023-11-02 19:33:07.641928: Current learning rate: 0.00215 
-2023-11-02 19:34:56.395552: train_loss -0.9256 
-2023-11-02 19:34:56.395677: val_loss -0.8162 
-2023-11-02 19:34:56.395703: Pseudo dice [0.8527] 
-2023-11-02 19:34:56.395730: Epoch time: 108.75 s 
-2023-11-02 19:34:56.930246:  
-2023-11-02 19:34:56.930322: Epoch 820 
-2023-11-02 19:34:56.930396: Current learning rate: 0.00214 
-2023-11-02 19:36:45.768486: train_loss -0.9237 
-2023-11-02 19:36:45.768624: val_loss -0.8092 
-2023-11-02 19:36:45.768650: Pseudo dice [0.8466] 
-2023-11-02 19:36:45.768703: Epoch time: 108.84 s 
-2023-11-02 19:36:46.303331:  
-2023-11-02 19:36:46.303426: Epoch 821 
-2023-11-02 19:36:46.303479: Current learning rate: 0.00213 
-2023-11-02 19:38:35.185334: train_loss -0.9231 
-2023-11-02 19:38:35.185458: val_loss -0.8277 
-2023-11-02 19:38:35.185509: Pseudo dice [0.8578] 
-2023-11-02 19:38:35.185535: Epoch time: 108.88 s 
-2023-11-02 19:38:35.716229:  
-2023-11-02 19:38:35.716314: Epoch 822 
-2023-11-02 19:38:35.716391: Current learning rate: 0.00212 
-2023-11-02 19:40:24.575097: train_loss -0.9259 
-2023-11-02 19:40:24.575235: val_loss -0.8103 
-2023-11-02 19:40:24.575288: Pseudo dice [0.8472] 
-2023-11-02 19:40:24.575315: Epoch time: 108.86 s 
-2023-11-02 19:40:25.101450:  
-2023-11-02 19:40:25.101523: Epoch 823 
-2023-11-02 19:40:25.101636: Current learning rate: 0.0021 
-2023-11-02 19:42:13.944741: train_loss -0.9251 
-2023-11-02 19:42:13.944871: val_loss -0.8024 
-2023-11-02 19:42:13.944902: Pseudo dice [0.8405] 
-2023-11-02 19:42:13.944933: Epoch time: 108.84 s 
-2023-11-02 19:42:14.580709:  
-2023-11-02 19:42:14.580853: Epoch 824 
-2023-11-02 19:42:14.580926: Current learning rate: 0.00209 
-2023-11-02 19:44:03.825652: train_loss -0.9204 
-2023-11-02 19:44:03.825792: val_loss -0.8126 
-2023-11-02 19:44:03.825837: Pseudo dice [0.8476] 
-2023-11-02 19:44:03.825869: Epoch time: 109.25 s 
-2023-11-02 19:44:04.350635:  
-2023-11-02 19:44:04.350711: Epoch 825 
-2023-11-02 19:44:04.350765: Current learning rate: 0.00208 
-2023-11-02 19:45:55.316341: train_loss -0.9251 
-2023-11-02 19:45:55.316498: val_loss -0.8131 
-2023-11-02 19:45:55.316528: Pseudo dice [0.8512] 
-2023-11-02 19:45:55.316558: Epoch time: 110.97 s 
-2023-11-02 19:45:55.867090:  
-2023-11-02 19:45:55.867213: Epoch 826 
-2023-11-02 19:45:55.867302: Current learning rate: 0.00207 
-2023-11-02 19:47:49.326232: train_loss -0.9227 
-2023-11-02 19:47:49.326387: val_loss -0.8218 
-2023-11-02 19:47:49.326416: Pseudo dice [0.8551] 
-2023-11-02 19:47:49.326470: Epoch time: 113.46 s 
-2023-11-02 19:47:49.857418:  
-2023-11-02 19:47:49.857507: Epoch 827 
-2023-11-02 19:47:49.857874: Current learning rate: 0.00206 
-2023-11-02 19:49:40.693825: train_loss -0.9219 
-2023-11-02 19:49:40.693940: val_loss -0.815 
-2023-11-02 19:49:40.693963: Pseudo dice [0.8515] 
-2023-11-02 19:49:40.693989: Epoch time: 110.84 s 
-2023-11-02 19:49:41.224450:  
-2023-11-02 19:49:41.224554: Epoch 828 
-2023-11-02 19:49:41.224611: Current learning rate: 0.00205 
-2023-11-02 19:51:30.784500: train_loss -0.9232 
-2023-11-02 19:51:30.784640: val_loss -0.813 
-2023-11-02 19:51:30.784670: Pseudo dice [0.8482] 
-2023-11-02 19:51:30.784698: Epoch time: 109.56 s 
-2023-11-02 19:51:31.314885:  
-2023-11-02 19:51:31.314952: Epoch 829 
-2023-11-02 19:51:31.315063: Current learning rate: 0.00204 
-2023-11-02 19:53:20.868694: train_loss -0.9235 
-2023-11-02 19:53:20.868815: val_loss -0.8164 
-2023-11-02 19:53:20.868865: Pseudo dice [0.8513] 
-2023-11-02 19:53:20.868891: Epoch time: 109.55 s 
-2023-11-02 19:53:21.491722:  
-2023-11-02 19:53:21.491812: Epoch 830 
-2023-11-02 19:53:21.491866: Current learning rate: 0.00203 
-2023-11-02 19:55:09.486638: train_loss -0.9229 
-2023-11-02 19:55:09.486801: val_loss -0.8052 
-2023-11-02 19:55:09.486834: Pseudo dice [0.8433] 
-2023-11-02 19:55:09.486866: Epoch time: 108.0 s 
-2023-11-02 19:55:10.015095:  
-2023-11-02 19:55:10.015167: Epoch 831 
-2023-11-02 19:55:10.015215: Current learning rate: 0.00202 
-2023-11-02 19:56:56.716532: train_loss -0.9244 
-2023-11-02 19:56:56.716660: val_loss -0.8129 
-2023-11-02 19:56:56.716713: Pseudo dice [0.8494] 
-2023-11-02 19:56:56.716738: Epoch time: 106.7 s 
-2023-11-02 19:56:57.245450:  
-2023-11-02 19:56:57.245543: Epoch 832 
-2023-11-02 19:56:57.245638: Current learning rate: 0.00201 
-2023-11-02 19:58:44.069121: train_loss -0.9215 
-2023-11-02 19:58:44.069310: val_loss -0.8271 
-2023-11-02 19:58:44.069368: Pseudo dice [0.8568] 
-2023-11-02 19:58:44.069396: Epoch time: 106.82 s 
-2023-11-02 19:58:44.600330:  
-2023-11-02 19:58:44.600407: Epoch 833 
-2023-11-02 19:58:44.600487: Current learning rate: 0.002 
-2023-11-02 20:00:32.223364: train_loss -0.9282 
-2023-11-02 20:00:32.223482: val_loss -0.8125 
-2023-11-02 20:00:32.223531: Pseudo dice [0.8482] 
-2023-11-02 20:00:32.223559: Epoch time: 107.62 s 
-2023-11-02 20:00:32.752503:  
-2023-11-02 20:00:32.752721: Epoch 834 
-2023-11-02 20:00:32.752967: Current learning rate: 0.00199 
-2023-11-02 20:02:23.329994: train_loss -0.9246 
-2023-11-02 20:02:23.330114: val_loss -0.8209 
-2023-11-02 20:02:23.330166: Pseudo dice [0.8543] 
-2023-11-02 20:02:23.330193: Epoch time: 110.58 s 
-2023-11-02 20:02:23.864913:  
-2023-11-02 20:02:23.864986: Epoch 835 
-2023-11-02 20:02:23.865067: Current learning rate: 0.00198 
-2023-11-02 20:04:15.154133: train_loss -0.9262 
-2023-11-02 20:04:15.154268: val_loss -0.8125 
-2023-11-02 20:04:15.154310: Pseudo dice [0.8501] 
-2023-11-02 20:04:15.154353: Epoch time: 111.29 s 
-2023-11-02 20:04:15.682241:  
-2023-11-02 20:04:15.682369: Epoch 836 
-2023-11-02 20:04:15.682428: Current learning rate: 0.00196 
-2023-11-02 20:06:07.141429: train_loss -0.9261 
-2023-11-02 20:06:07.141578: val_loss -0.8127 
-2023-11-02 20:06:07.141603: Pseudo dice [0.8532] 
-2023-11-02 20:06:07.141629: Epoch time: 111.46 s 
-2023-11-02 20:06:07.773931:  
-2023-11-02 20:06:07.774012: Epoch 837 
-2023-11-02 20:06:07.774067: Current learning rate: 0.00195 
-2023-11-02 20:07:59.439167: train_loss -0.9251 
-2023-11-02 20:07:59.439332: val_loss -0.816 
-2023-11-02 20:07:59.439363: Pseudo dice [0.8514] 
-2023-11-02 20:07:59.439393: Epoch time: 111.67 s 
-2023-11-02 20:07:59.968368:  
-2023-11-02 20:07:59.968451: Epoch 838 
-2023-11-02 20:07:59.968535: Current learning rate: 0.00194 
-2023-11-02 20:09:51.659310: train_loss -0.9226 
-2023-11-02 20:09:51.659434: val_loss -0.8178 
-2023-11-02 20:09:51.659472: Pseudo dice [0.8531] 
-2023-11-02 20:09:51.659498: Epoch time: 111.69 s 
-2023-11-02 20:09:52.190380:  
-2023-11-02 20:09:52.190483: Epoch 839 
-2023-11-02 20:09:52.190574: Current learning rate: 0.00193 
-2023-11-02 20:11:43.640873: train_loss -0.9253 
-2023-11-02 20:11:43.641020: val_loss -0.8098 
-2023-11-02 20:11:43.641058: Pseudo dice [0.8453] 
-2023-11-02 20:11:43.641097: Epoch time: 111.45 s 
-2023-11-02 20:11:44.167418:  
-2023-11-02 20:11:44.167492: Epoch 840 
-2023-11-02 20:11:44.167594: Current learning rate: 0.00192 
-2023-11-02 20:13:34.301193: train_loss -0.9263 
-2023-11-02 20:13:34.301325: val_loss -0.8147 
-2023-11-02 20:13:34.301362: Pseudo dice [0.8513] 
-2023-11-02 20:13:34.301389: Epoch time: 110.13 s 
-2023-11-02 20:13:34.829534:  
-2023-11-02 20:13:34.829615: Epoch 841 
-2023-11-02 20:13:34.829726: Current learning rate: 0.00191 
-2023-11-02 20:15:25.029933: train_loss -0.926 
-2023-11-02 20:15:25.030062: val_loss -0.8275 
-2023-11-02 20:15:25.030109: Pseudo dice [0.8599] 
-2023-11-02 20:15:25.030136: Epoch time: 110.2 s 
-2023-11-02 20:15:25.559794:  
-2023-11-02 20:15:25.559917: Epoch 842 
-2023-11-02 20:15:25.559978: Current learning rate: 0.0019 
-2023-11-02 20:17:16.537094: train_loss -0.9283 
-2023-11-02 20:17:16.537209: val_loss -0.8097 
-2023-11-02 20:17:16.537259: Pseudo dice [0.847] 
-2023-11-02 20:17:16.537286: Epoch time: 110.98 s 
-2023-11-02 20:17:17.176395:  
-2023-11-02 20:17:17.176480: Epoch 843 
-2023-11-02 20:17:17.176541: Current learning rate: 0.00189 
-2023-11-02 20:19:07.400262: train_loss -0.9274 
-2023-11-02 20:19:07.400411: val_loss -0.8182 
-2023-11-02 20:19:07.400436: Pseudo dice [0.8523] 
-2023-11-02 20:19:07.400462: Epoch time: 110.22 s 
-2023-11-02 20:19:07.926893:  
-2023-11-02 20:19:07.926971: Epoch 844 
-2023-11-02 20:19:07.927064: Current learning rate: 0.00188 
-2023-11-02 20:20:59.915067: train_loss -0.9274 
-2023-11-02 20:20:59.915214: val_loss -0.8264 
-2023-11-02 20:20:59.915243: Pseudo dice [0.858] 
-2023-11-02 20:20:59.915276: Epoch time: 111.99 s 
-2023-11-02 20:21:00.455603:  
-2023-11-02 20:21:00.455696: Epoch 845 
-2023-11-02 20:21:00.455753: Current learning rate: 0.00187 
-2023-11-02 20:22:49.018112: train_loss -0.9253 
-2023-11-02 20:22:49.018227: val_loss -0.8181 
-2023-11-02 20:22:49.018276: Pseudo dice [0.8529] 
-2023-11-02 20:22:49.018305: Epoch time: 108.56 s 
-2023-11-02 20:22:49.540668:  
-2023-11-02 20:22:49.540741: Epoch 846 
-2023-11-02 20:22:49.540818: Current learning rate: 0.00186 
-2023-11-02 20:24:36.259271: train_loss -0.9244 
-2023-11-02 20:24:36.259400: val_loss -0.8337 
-2023-11-02 20:24:36.259449: Pseudo dice [0.8633] 
-2023-11-02 20:24:36.259476: Epoch time: 106.72 s 
-2023-11-02 20:24:36.788914:  
-2023-11-02 20:24:36.789138: Epoch 847 
-2023-11-02 20:24:36.789313: Current learning rate: 0.00185 
-2023-11-02 20:26:23.560867: train_loss -0.9261 
-2023-11-02 20:26:23.561007: val_loss -0.8172 
-2023-11-02 20:26:23.561058: Pseudo dice [0.851] 
-2023-11-02 20:26:23.561084: Epoch time: 106.77 s 
-2023-11-02 20:26:24.087085:  
-2023-11-02 20:26:24.087151: Epoch 848 
-2023-11-02 20:26:24.087227: Current learning rate: 0.00184 
-2023-11-02 20:28:10.976760: train_loss -0.9231 
-2023-11-02 20:28:10.976896: val_loss -0.8229 
-2023-11-02 20:28:10.976949: Pseudo dice [0.8556] 
-2023-11-02 20:28:10.976974: Epoch time: 106.89 s 
-2023-11-02 20:28:11.509999:  
-2023-11-02 20:28:11.510066: Epoch 849 
-2023-11-02 20:28:11.510168: Current learning rate: 0.00182 
-2023-11-02 20:29:58.406573: train_loss -0.9247 
-2023-11-02 20:29:58.406753: val_loss -0.8168 
-2023-11-02 20:29:58.406810: Pseudo dice [0.8527] 
-2023-11-02 20:29:58.406837: Epoch time: 106.9 s 
-2023-11-02 20:29:59.277013:  
-2023-11-02 20:29:59.277093: Epoch 850 
-2023-11-02 20:29:59.277147: Current learning rate: 0.00181 
-2023-11-02 20:31:46.033954: train_loss -0.9245 
-2023-11-02 20:31:46.034075: val_loss -0.8222 
-2023-11-02 20:31:46.034110: Pseudo dice [0.8542] 
-2023-11-02 20:31:46.034136: Epoch time: 106.76 s 
-2023-11-02 20:31:46.552584:  
-2023-11-02 20:31:46.552659: Epoch 851 
-2023-11-02 20:31:46.552712: Current learning rate: 0.0018 
-2023-11-02 20:33:33.346589: train_loss -0.9241 
-2023-11-02 20:33:33.346767: val_loss -0.8204 
-2023-11-02 20:33:33.346796: Pseudo dice [0.8541] 
-2023-11-02 20:33:33.346830: Epoch time: 106.79 s 
-2023-11-02 20:33:33.869775:  
-2023-11-02 20:33:33.869844: Epoch 852 
-2023-11-02 20:33:33.869921: Current learning rate: 0.00179 
-2023-11-02 20:35:20.522542: train_loss -0.9243 
-2023-11-02 20:35:20.522679: val_loss -0.8101 
-2023-11-02 20:35:20.522732: Pseudo dice [0.8469] 
-2023-11-02 20:35:20.522757: Epoch time: 106.65 s 
-2023-11-02 20:35:21.043129:  
-2023-11-02 20:35:21.043198: Epoch 853 
-2023-11-02 20:35:21.043269: Current learning rate: 0.00178 
-2023-11-02 20:37:07.764131: train_loss -0.9264 
-2023-11-02 20:37:07.764267: val_loss -0.8095 
-2023-11-02 20:37:07.764291: Pseudo dice [0.8466] 
-2023-11-02 20:37:07.764318: Epoch time: 106.72 s 
-2023-11-02 20:37:08.284318:  
-2023-11-02 20:37:08.284404: Epoch 854 
-2023-11-02 20:37:08.284477: Current learning rate: 0.00177 
-2023-11-02 20:38:55.004075: train_loss -0.9261 
-2023-11-02 20:38:55.004200: val_loss -0.8213 
-2023-11-02 20:38:55.004225: Pseudo dice [0.8557] 
-2023-11-02 20:38:55.004255: Epoch time: 106.72 s 
-2023-11-02 20:38:55.527547:  
-2023-11-02 20:38:55.527619: Epoch 855 
-2023-11-02 20:38:55.527695: Current learning rate: 0.00176 
-2023-11-02 20:40:42.339645: train_loss -0.9263 
-2023-11-02 20:40:42.339772: val_loss -0.8236 
-2023-11-02 20:40:42.339797: Pseudo dice [0.8572] 
-2023-11-02 20:40:42.339822: Epoch time: 106.81 s 
-2023-11-02 20:40:42.965500:  
-2023-11-02 20:40:42.965596: Epoch 856 
-2023-11-02 20:40:42.965815: Current learning rate: 0.00175 
-2023-11-02 20:42:30.607063: train_loss -0.9292 
-2023-11-02 20:42:30.607203: val_loss -0.8208 
-2023-11-02 20:42:30.607259: Pseudo dice [0.8532] 
-2023-11-02 20:42:30.607290: Epoch time: 107.64 s 
-2023-11-02 20:42:31.130811:  
-2023-11-02 20:42:31.130892: Epoch 857 
-2023-11-02 20:42:31.131002: Current learning rate: 0.00174 
-2023-11-02 20:44:17.805860: train_loss -0.9256 
-2023-11-02 20:44:17.805994: val_loss -0.8025 
-2023-11-02 20:44:17.806043: Pseudo dice [0.8401] 
-2023-11-02 20:44:17.806067: Epoch time: 106.68 s 
-2023-11-02 20:44:18.329465:  
-2023-11-02 20:44:18.329587: Epoch 858 
-2023-11-02 20:44:18.329844: Current learning rate: 0.00173 
-2023-11-02 20:46:05.046454: train_loss -0.9293 
-2023-11-02 20:46:05.046583: val_loss -0.8142 
-2023-11-02 20:46:05.046632: Pseudo dice [0.851] 
-2023-11-02 20:46:05.046659: Epoch time: 106.72 s 
-2023-11-02 20:46:05.564000:  
-2023-11-02 20:46:05.564075: Epoch 859 
-2023-11-02 20:46:05.564129: Current learning rate: 0.00172 
-2023-11-02 20:47:52.391752: train_loss -0.9244 
-2023-11-02 20:47:52.391901: val_loss -0.8152 
-2023-11-02 20:47:52.391931: Pseudo dice [0.8495] 
-2023-11-02 20:47:52.391993: Epoch time: 106.83 s 
-2023-11-02 20:47:52.912358:  
-2023-11-02 20:47:52.912420: Epoch 860 
-2023-11-02 20:47:52.912468: Current learning rate: 0.0017 
-2023-11-02 20:49:39.621082: train_loss -0.9276 
-2023-11-02 20:49:39.621182: val_loss -0.8201 
-2023-11-02 20:49:39.621218: Pseudo dice [0.8542] 
-2023-11-02 20:49:39.621245: Epoch time: 106.71 s 
-2023-11-02 20:49:40.138858:  
-2023-11-02 20:49:40.138928: Epoch 861 
-2023-11-02 20:49:40.139011: Current learning rate: 0.00169 
-2023-11-02 20:51:26.817839: train_loss -0.9288 
-2023-11-02 20:51:26.817965: val_loss -0.8151 
-2023-11-02 20:51:26.818014: Pseudo dice [0.8507] 
-2023-11-02 20:51:26.818041: Epoch time: 106.68 s 
-2023-11-02 20:51:27.334603:  
-2023-11-02 20:51:27.334668: Epoch 862 
-2023-11-02 20:51:27.334745: Current learning rate: 0.00168 
-2023-11-02 20:53:13.947071: train_loss -0.9283 
-2023-11-02 20:53:13.947203: val_loss -0.818 
-2023-11-02 20:53:13.947252: Pseudo dice [0.8525] 
-2023-11-02 20:53:13.947278: Epoch time: 106.61 s 
-2023-11-02 20:53:14.568300:  
-2023-11-02 20:53:14.568376: Epoch 863 
-2023-11-02 20:53:14.568446: Current learning rate: 0.00167 
-2023-11-02 20:55:01.141519: train_loss -0.9285 
-2023-11-02 20:55:01.141700: val_loss -0.8239 
-2023-11-02 20:55:01.141728: Pseudo dice [0.8584] 
-2023-11-02 20:55:01.141755: Epoch time: 106.57 s 
-2023-11-02 20:55:01.660688:  
-2023-11-02 20:55:01.660771: Epoch 864 
-2023-11-02 20:55:01.660827: Current learning rate: 0.00166 
-2023-11-02 20:56:48.350348: train_loss -0.926 
-2023-11-02 20:56:48.350472: val_loss -0.8173 
-2023-11-02 20:56:48.350512: Pseudo dice [0.8524] 
-2023-11-02 20:56:48.350538: Epoch time: 106.69 s 
-2023-11-02 20:56:48.871448:  
-2023-11-02 20:56:48.871516: Epoch 865 
-2023-11-02 20:56:48.871568: Current learning rate: 0.00165 
-2023-11-02 20:58:35.566061: train_loss -0.9247 
-2023-11-02 20:58:35.566201: val_loss -0.8254 
-2023-11-02 20:58:35.566244: Pseudo dice [0.8577] 
-2023-11-02 20:58:35.566277: Epoch time: 106.69 s 
-2023-11-02 20:58:36.082787:  
-2023-11-02 20:58:36.082858: Epoch 866 
-2023-11-02 20:58:36.082905: Current learning rate: 0.00164 
-2023-11-02 21:00:22.698232: train_loss -0.9282 
-2023-11-02 21:00:22.698387: val_loss -0.8262 
-2023-11-02 21:00:22.698418: Pseudo dice [0.8565] 
-2023-11-02 21:00:22.698448: Epoch time: 106.62 s 
-2023-11-02 21:00:23.221330:  
-2023-11-02 21:00:23.221400: Epoch 867 
-2023-11-02 21:00:23.221451: Current learning rate: 0.00163 
-2023-11-02 21:02:09.927276: train_loss -0.9275 
-2023-11-02 21:02:09.927406: val_loss -0.8158 
-2023-11-02 21:02:09.927455: Pseudo dice [0.851] 
-2023-11-02 21:02:09.927481: Epoch time: 106.71 s 
-2023-11-02 21:02:10.449363:  
-2023-11-02 21:02:10.449439: Epoch 868 
-2023-11-02 21:02:10.449513: Current learning rate: 0.00162 
-2023-11-02 21:03:57.118117: train_loss -0.9266 
-2023-11-02 21:03:57.118272: val_loss -0.8095 
-2023-11-02 21:03:57.118299: Pseudo dice [0.846] 
-2023-11-02 21:03:57.118327: Epoch time: 106.67 s 
-2023-11-02 21:03:57.636343:  
-2023-11-02 21:03:57.636412: Epoch 869 
-2023-11-02 21:03:57.636465: Current learning rate: 0.00161 
-2023-11-02 21:05:44.386833: train_loss -0.9271 
-2023-11-02 21:05:44.386968: val_loss -0.8074 
-2023-11-02 21:05:44.387006: Pseudo dice [0.8437] 
-2023-11-02 21:05:44.387033: Epoch time: 106.75 s 
-2023-11-02 21:05:45.010665:  
-2023-11-02 21:05:45.010741: Epoch 870 
-2023-11-02 21:05:45.010821: Current learning rate: 0.00159 
-2023-11-02 21:07:31.801463: train_loss -0.9272 
-2023-11-02 21:07:31.801593: val_loss -0.8143 
-2023-11-02 21:07:31.801636: Pseudo dice [0.8499] 
-2023-11-02 21:07:31.801665: Epoch time: 106.79 s 
-2023-11-02 21:07:32.326635:  
-2023-11-02 21:07:32.326721: Epoch 871 
-2023-11-02 21:07:32.326950: Current learning rate: 0.00158 
-2023-11-02 21:09:19.082681: train_loss -0.9266 
-2023-11-02 21:09:19.082846: val_loss -0.8331 
-2023-11-02 21:09:19.082877: Pseudo dice [0.864] 
-2023-11-02 21:09:19.082907: Epoch time: 106.76 s 
-2023-11-02 21:09:19.604421:  
-2023-11-02 21:09:19.604492: Epoch 872 
-2023-11-02 21:09:19.604546: Current learning rate: 0.00157 
-2023-11-02 21:11:06.324407: train_loss -0.9269 
-2023-11-02 21:11:06.324550: val_loss -0.8088 
-2023-11-02 21:11:06.324589: Pseudo dice [0.8471] 
-2023-11-02 21:11:06.324615: Epoch time: 106.72 s 
-2023-11-02 21:11:06.847170:  
-2023-11-02 21:11:06.847240: Epoch 873 
-2023-11-02 21:11:06.847304: Current learning rate: 0.00156 
-2023-11-02 21:12:53.616704: train_loss -0.9272 
-2023-11-02 21:12:53.616833: val_loss -0.8234 
-2023-11-02 21:12:53.616859: Pseudo dice [0.858] 
-2023-11-02 21:12:53.616912: Epoch time: 106.77 s 
-2023-11-02 21:12:54.139572:  
-2023-11-02 21:12:54.139655: Epoch 874 
-2023-11-02 21:12:54.139731: Current learning rate: 0.00155 
-2023-11-02 21:14:40.822383: train_loss -0.9276 
-2023-11-02 21:14:40.822510: val_loss -0.8157 
-2023-11-02 21:14:40.822548: Pseudo dice [0.8511] 
-2023-11-02 21:14:40.822574: Epoch time: 106.68 s 
-2023-11-02 21:14:41.343226:  
-2023-11-02 21:14:41.343294: Epoch 875 
-2023-11-02 21:14:41.343348: Current learning rate: 0.00154 
-2023-11-02 21:16:28.067977: train_loss -0.9282 
-2023-11-02 21:16:28.068125: val_loss -0.8196 
-2023-11-02 21:16:28.068150: Pseudo dice [0.8552] 
-2023-11-02 21:16:28.068176: Epoch time: 106.73 s 
-2023-11-02 21:16:28.685661:  
-2023-11-02 21:16:28.685766: Epoch 876 
-2023-11-02 21:16:28.685847: Current learning rate: 0.00153 
-2023-11-02 21:18:15.533091: train_loss -0.9266 
-2023-11-02 21:18:15.533229: val_loss -0.8169 
-2023-11-02 21:18:15.533281: Pseudo dice [0.8516] 
-2023-11-02 21:18:15.533306: Epoch time: 106.85 s 
-2023-11-02 21:18:16.053483:  
-2023-11-02 21:18:16.053556: Epoch 877 
-2023-11-02 21:18:16.053632: Current learning rate: 0.00152 
-2023-11-02 21:20:02.826361: train_loss -0.927 
-2023-11-02 21:20:02.826484: val_loss -0.8057 
-2023-11-02 21:20:02.826533: Pseudo dice [0.8458] 
-2023-11-02 21:20:02.826558: Epoch time: 106.77 s 
-2023-11-02 21:20:03.348824:  
-2023-11-02 21:20:03.348901: Epoch 878 
-2023-11-02 21:20:03.348979: Current learning rate: 0.00151 
-2023-11-02 21:21:50.132939: train_loss -0.9297 
-2023-11-02 21:21:50.133095: val_loss -0.8194 
-2023-11-02 21:21:50.133119: Pseudo dice [0.8547] 
-2023-11-02 21:21:50.133143: Epoch time: 106.78 s 
-2023-11-02 21:21:50.656090:  
-2023-11-02 21:21:50.656181: Epoch 879 
-2023-11-02 21:21:50.656265: Current learning rate: 0.00149 
-2023-11-02 21:23:37.457763: train_loss -0.9279 
-2023-11-02 21:23:37.457876: val_loss -0.814 
-2023-11-02 21:23:37.457925: Pseudo dice [0.8519] 
-2023-11-02 21:23:37.457952: Epoch time: 106.8 s 
-2023-11-02 21:23:37.980063:  
-2023-11-02 21:23:37.980134: Epoch 880 
-2023-11-02 21:23:37.980205: Current learning rate: 0.00148 
-2023-11-02 21:25:24.814791: train_loss -0.9285 
-2023-11-02 21:25:24.814909: val_loss -0.8222 
-2023-11-02 21:25:24.814958: Pseudo dice [0.8546] 
-2023-11-02 21:25:24.814985: Epoch time: 106.84 s 
-2023-11-02 21:25:25.351218:  
-2023-11-02 21:25:25.351286: Epoch 881 
-2023-11-02 21:25:25.351360: Current learning rate: 0.00147 
-2023-11-02 21:27:13.222595: train_loss -0.9266 
-2023-11-02 21:27:13.222719: val_loss -0.824 
-2023-11-02 21:27:13.222755: Pseudo dice [0.8568] 
-2023-11-02 21:27:13.222782: Epoch time: 107.87 s 
-2023-11-02 21:27:13.750845:  
-2023-11-02 21:27:13.750915: Epoch 882 
-2023-11-02 21:27:13.750970: Current learning rate: 0.00146 
-2023-11-02 21:29:00.665035: train_loss -0.9284 
-2023-11-02 21:29:00.665177: val_loss -0.8154 
-2023-11-02 21:29:00.665226: Pseudo dice [0.8503] 
-2023-11-02 21:29:00.665253: Epoch time: 106.91 s 
-2023-11-02 21:29:01.290644:  
-2023-11-02 21:29:01.290726: Epoch 883 
-2023-11-02 21:29:01.290802: Current learning rate: 0.00145 
-2023-11-02 21:30:48.095298: train_loss -0.9309 
-2023-11-02 21:30:48.095460: val_loss -0.8209 
-2023-11-02 21:30:48.095490: Pseudo dice [0.8567] 
-2023-11-02 21:30:48.095518: Epoch time: 106.81 s 
-2023-11-02 21:30:48.616592:  
-2023-11-02 21:30:48.616669: Epoch 884 
-2023-11-02 21:30:48.616722: Current learning rate: 0.00144 
-2023-11-02 21:32:35.241947: train_loss -0.9291 
-2023-11-02 21:32:35.242085: val_loss -0.8194 
-2023-11-02 21:32:35.242134: Pseudo dice [0.8539] 
-2023-11-02 21:32:35.242161: Epoch time: 106.63 s 
-2023-11-02 21:32:35.761840:  
-2023-11-02 21:32:35.761913: Epoch 885 
-2023-11-02 21:32:35.761986: Current learning rate: 0.00143 
-2023-11-02 21:34:22.430320: train_loss -0.9274 
-2023-11-02 21:34:22.430449: val_loss -0.8332 
-2023-11-02 21:34:22.430487: Pseudo dice [0.8639] 
-2023-11-02 21:34:22.430514: Epoch time: 106.67 s 
-2023-11-02 21:34:22.430534: Yayy! New best EMA pseudo Dice: 0.8542 
-2023-11-02 21:34:23.185954:  
-2023-11-02 21:34:23.186025: Epoch 886 
-2023-11-02 21:34:23.186079: Current learning rate: 0.00142 
-2023-11-02 21:36:09.647168: train_loss -0.9252 
-2023-11-02 21:36:09.647291: val_loss -0.8158 
-2023-11-02 21:36:09.647328: Pseudo dice [0.85] 
-2023-11-02 21:36:09.647354: Epoch time: 106.46 s 
-2023-11-02 21:36:10.166351:  
-2023-11-02 21:36:10.166423: Epoch 887 
-2023-11-02 21:36:10.166500: Current learning rate: 0.00141 
-2023-11-02 21:37:56.675110: train_loss -0.9294 
-2023-11-02 21:37:56.675236: val_loss -0.819 
-2023-11-02 21:37:56.675263: Pseudo dice [0.8538] 
-2023-11-02 21:37:56.675291: Epoch time: 106.51 s 
-2023-11-02 21:37:57.212862:  
-2023-11-02 21:37:57.212960: Epoch 888 
-2023-11-02 21:37:57.213058: Current learning rate: 0.00139 
-2023-11-02 21:39:43.837251: train_loss -0.9289 
-2023-11-02 21:39:43.837423: val_loss -0.819 
-2023-11-02 21:39:43.837453: Pseudo dice [0.8549] 
-2023-11-02 21:39:43.837485: Epoch time: 106.62 s 
-2023-11-02 21:39:44.356345:  
-2023-11-02 21:39:44.356408: Epoch 889 
-2023-11-02 21:39:44.356479: Current learning rate: 0.00138 
-2023-11-02 21:41:31.001314: train_loss -0.9272 
-2023-11-02 21:41:31.001445: val_loss -0.827 
-2023-11-02 21:41:31.001485: Pseudo dice [0.8597] 
-2023-11-02 21:41:31.001513: Epoch time: 106.65 s 
-2023-11-02 21:41:31.001556: Yayy! New best EMA pseudo Dice: 0.8545 
-2023-11-02 21:41:31.871379:  
-2023-11-02 21:41:31.871454: Epoch 890 
-2023-11-02 21:41:31.871506: Current learning rate: 0.00137 
-2023-11-02 21:43:18.458302: train_loss -0.931 
-2023-11-02 21:43:18.458452: val_loss -0.8237 
-2023-11-02 21:43:18.458481: Pseudo dice [0.8563] 
-2023-11-02 21:43:18.458508: Epoch time: 106.59 s 
-2023-11-02 21:43:18.458527: Yayy! New best EMA pseudo Dice: 0.8546 
-2023-11-02 21:43:19.221729:  
-2023-11-02 21:43:19.221805: Epoch 891 
-2023-11-02 21:43:19.221875: Current learning rate: 0.00136 
-2023-11-02 21:45:05.872715: train_loss -0.9298 
-2023-11-02 21:45:05.872843: val_loss -0.7984 
-2023-11-02 21:45:05.872895: Pseudo dice [0.8397] 
-2023-11-02 21:45:05.872921: Epoch time: 106.65 s 
-2023-11-02 21:45:06.394135:  
-2023-11-02 21:45:06.394204: Epoch 892 
-2023-11-02 21:45:06.394279: Current learning rate: 0.00135 
-2023-11-02 21:46:53.126603: train_loss -0.9269 
-2023-11-02 21:46:53.126738: val_loss -0.8158 
-2023-11-02 21:46:53.126768: Pseudo dice [0.8511] 
-2023-11-02 21:46:53.126795: Epoch time: 106.73 s 
-2023-11-02 21:46:53.648838:  
-2023-11-02 21:46:53.648923: Epoch 893 
-2023-11-02 21:46:53.648997: Current learning rate: 0.00134 
-2023-11-02 21:48:40.359756: train_loss -0.9269 
-2023-11-02 21:48:40.359881: val_loss -0.8138 
-2023-11-02 21:48:40.359919: Pseudo dice [0.8491] 
-2023-11-02 21:48:40.359955: Epoch time: 106.71 s 
-2023-11-02 21:48:40.881133:  
-2023-11-02 21:48:40.881204: Epoch 894 
-2023-11-02 21:48:40.881256: Current learning rate: 0.00133 
-2023-11-02 21:50:32.602060: train_loss -0.9289 
-2023-11-02 21:50:32.602198: val_loss -0.8222 
-2023-11-02 21:50:32.602223: Pseudo dice [0.8557] 
-2023-11-02 21:50:32.602251: Epoch time: 111.72 s 
-2023-11-02 21:50:33.125544:  
-2023-11-02 21:50:33.125646: Epoch 895 
-2023-11-02 21:50:33.125700: Current learning rate: 0.00132 
-2023-11-02 21:52:24.778487: train_loss -0.9289 
-2023-11-02 21:52:24.778636: val_loss -0.8256 
-2023-11-02 21:52:24.778666: Pseudo dice [0.8585] 
-2023-11-02 21:52:24.778692: Epoch time: 111.65 s 
-2023-11-02 21:52:25.407807:  
-2023-11-02 21:52:25.407892: Epoch 896 
-2023-11-02 21:52:25.407954: Current learning rate: 0.0013 
-2023-11-02 21:54:17.702766: train_loss -0.9297 
-2023-11-02 21:54:17.702902: val_loss -0.8073 
-2023-11-02 21:54:17.702928: Pseudo dice [0.8465] 
-2023-11-02 21:54:17.702955: Epoch time: 112.3 s 
-2023-11-02 21:54:18.239348:  
-2023-11-02 21:54:18.239427: Epoch 897 
-2023-11-02 21:54:18.239496: Current learning rate: 0.00129 
-2023-11-02 21:56:09.909327: train_loss -0.9298 
-2023-11-02 21:56:09.909463: val_loss -0.809 
-2023-11-02 21:56:09.909503: Pseudo dice [0.8487] 
-2023-11-02 21:56:09.909532: Epoch time: 111.67 s 
-2023-11-02 21:56:10.451478:  
-2023-11-02 21:56:10.451557: Epoch 898 
-2023-11-02 21:56:10.451611: Current learning rate: 0.00128 
-2023-11-02 21:58:02.962977: train_loss -0.9297 
-2023-11-02 21:58:02.963106: val_loss -0.8125 
-2023-11-02 21:58:02.963137: Pseudo dice [0.8488] 
-2023-11-02 21:58:02.963163: Epoch time: 112.51 s 
-2023-11-02 21:58:03.502796:  
-2023-11-02 21:58:03.502879: Epoch 899 
-2023-11-02 21:58:03.502933: Current learning rate: 0.00127 
-2023-11-02 21:59:54.586379: train_loss -0.928 
-2023-11-02 21:59:54.586508: val_loss -0.8189 
-2023-11-02 21:59:54.586532: Pseudo dice [0.8539] 
-2023-11-02 21:59:54.586560: Epoch time: 111.08 s 
-2023-11-02 21:59:55.353074:  
-2023-11-02 21:59:55.353150: Epoch 900 
-2023-11-02 21:59:55.353203: Current learning rate: 0.00126 
-2023-11-02 22:01:47.391350: train_loss -0.9305 
-2023-11-02 22:01:47.391479: val_loss -0.8274 
-2023-11-02 22:01:47.391506: Pseudo dice [0.8608] 
-2023-11-02 22:01:47.391534: Epoch time: 112.04 s 
-2023-11-02 22:01:47.925383:  
-2023-11-02 22:01:47.925465: Epoch 901 
-2023-11-02 22:01:47.925524: Current learning rate: 0.00125 
-2023-11-02 22:03:39.007405: train_loss -0.9308 
-2023-11-02 22:03:39.007539: val_loss -0.8238 
-2023-11-02 22:03:39.007565: Pseudo dice [0.856] 
-2023-11-02 22:03:39.007592: Epoch time: 111.08 s 
-2023-11-02 22:03:39.535646:  
-2023-11-02 22:03:39.535719: Epoch 902 
-2023-11-02 22:03:39.535773: Current learning rate: 0.00124 
-2023-11-02 22:05:29.667114: train_loss -0.9288 
-2023-11-02 22:05:29.667270: val_loss -0.8105 
-2023-11-02 22:05:29.667300: Pseudo dice [0.8478] 
-2023-11-02 22:05:29.667332: Epoch time: 110.13 s 
-2023-11-02 22:05:30.187802:  
-2023-11-02 22:05:30.187891: Epoch 903 
-2023-11-02 22:05:30.187963: Current learning rate: 0.00122 
-2023-11-02 22:07:16.761024: train_loss -0.9296 
-2023-11-02 22:07:16.761170: val_loss -0.8227 
-2023-11-02 22:07:16.761200: Pseudo dice [0.8557] 
-2023-11-02 22:07:16.761230: Epoch time: 106.57 s 
-2023-11-02 22:07:17.297672:  
-2023-11-02 22:07:17.297744: Epoch 904 
-2023-11-02 22:07:17.297821: Current learning rate: 0.00121 
-2023-11-02 22:09:08.018031: train_loss -0.9309 
-2023-11-02 22:09:08.018189: val_loss -0.814 
-2023-11-02 22:09:08.018217: Pseudo dice [0.8495] 
-2023-11-02 22:09:08.018243: Epoch time: 110.72 s 
-2023-11-02 22:09:08.559595:  
-2023-11-02 22:09:08.559674: Epoch 905 
-2023-11-02 22:09:08.559726: Current learning rate: 0.0012 
-2023-11-02 22:10:59.176376: train_loss -0.9286 
-2023-11-02 22:10:59.176550: val_loss -0.8137 
-2023-11-02 22:10:59.176590: Pseudo dice [0.8489] 
-2023-11-02 22:10:59.176619: Epoch time: 110.62 s 
-2023-11-02 22:10:59.697232:  
-2023-11-02 22:10:59.697303: Epoch 906 
-2023-11-02 22:10:59.697358: Current learning rate: 0.00119 
-2023-11-02 22:12:49.489517: train_loss -0.9302 
-2023-11-02 22:12:49.489667: val_loss -0.82 
-2023-11-02 22:12:49.489692: Pseudo dice [0.8548] 
-2023-11-02 22:12:49.489719: Epoch time: 109.79 s 
-2023-11-02 22:12:50.011285:  
-2023-11-02 22:12:50.011354: Epoch 907 
-2023-11-02 22:12:50.011430: Current learning rate: 0.00118 
-2023-11-02 22:14:38.062193: train_loss -0.9301 
-2023-11-02 22:14:38.062316: val_loss -0.8206 
-2023-11-02 22:14:38.062364: Pseudo dice [0.8559] 
-2023-11-02 22:14:38.062390: Epoch time: 108.05 s 
-2023-11-02 22:14:38.581383:  
-2023-11-02 22:14:38.581453: Epoch 908 
-2023-11-02 22:14:38.581505: Current learning rate: 0.00117 
-2023-11-02 22:16:25.141128: train_loss -0.9318 
-2023-11-02 22:16:25.141271: val_loss -0.8138 
-2023-11-02 22:16:25.141325: Pseudo dice [0.8499] 
-2023-11-02 22:16:25.141352: Epoch time: 106.56 s 
-2023-11-02 22:16:25.756521:  
-2023-11-02 22:16:25.756591: Epoch 909 
-2023-11-02 22:16:25.756672: Current learning rate: 0.00116 
-2023-11-02 22:18:12.412958: train_loss -0.9292 
-2023-11-02 22:18:12.413086: val_loss -0.8091 
-2023-11-02 22:18:12.413124: Pseudo dice [0.8474] 
-2023-11-02 22:18:12.413151: Epoch time: 106.66 s 
-2023-11-02 22:18:12.945387:  
-2023-11-02 22:18:12.945513: Epoch 910 
-2023-11-02 22:18:12.945624: Current learning rate: 0.00115 
-2023-11-02 22:19:59.531117: train_loss -0.9309 
-2023-11-02 22:19:59.531266: val_loss -0.8175 
-2023-11-02 22:19:59.531291: Pseudo dice [0.8522] 
-2023-11-02 22:19:59.531318: Epoch time: 106.59 s 
-2023-11-02 22:20:00.051129:  
-2023-11-02 22:20:00.051203: Epoch 911 
-2023-11-02 22:20:00.051253: Current learning rate: 0.00113 
-2023-11-02 22:21:46.676616: train_loss -0.932 
-2023-11-02 22:21:46.676750: val_loss -0.8193 
-2023-11-02 22:21:46.676800: Pseudo dice [0.8535] 
-2023-11-02 22:21:46.676825: Epoch time: 106.63 s 
-2023-11-02 22:21:47.196763:  
-2023-11-02 22:21:47.196826: Epoch 912 
-2023-11-02 22:21:47.196893: Current learning rate: 0.00112 
-2023-11-02 22:23:33.816692: train_loss -0.9285 
-2023-11-02 22:23:33.816813: val_loss -0.8127 
-2023-11-02 22:23:33.816863: Pseudo dice [0.8492] 
-2023-11-02 22:23:33.816891: Epoch time: 106.62 s 
-2023-11-02 22:23:34.346267:  
-2023-11-02 22:23:34.346333: Epoch 913 
-2023-11-02 22:23:34.346385: Current learning rate: 0.00111 
-2023-11-02 22:25:20.867850: train_loss -0.9301 
-2023-11-02 22:25:20.867987: val_loss -0.8144 
-2023-11-02 22:25:20.868013: Pseudo dice [0.8516] 
-2023-11-02 22:25:20.868042: Epoch time: 106.52 s 
-2023-11-02 22:25:21.392122:  
-2023-11-02 22:25:21.392194: Epoch 914 
-2023-11-02 22:25:21.392270: Current learning rate: 0.0011 
-2023-11-02 22:27:07.971131: train_loss -0.9294 
-2023-11-02 22:27:07.971296: val_loss -0.8137 
-2023-11-02 22:27:07.971347: Pseudo dice [0.8492] 
-2023-11-02 22:27:07.971375: Epoch time: 106.58 s 
-2023-11-02 22:27:08.491639:  
-2023-11-02 22:27:08.491704: Epoch 915 
-2023-11-02 22:27:08.491766: Current learning rate: 0.00109 
-2023-11-02 22:28:55.059726: train_loss -0.9311 
-2023-11-02 22:28:55.059867: val_loss -0.8214 
-2023-11-02 22:28:55.059905: Pseudo dice [0.8552] 
-2023-11-02 22:28:55.059931: Epoch time: 106.57 s 
-2023-11-02 22:28:55.678992:  
-2023-11-02 22:28:55.679070: Epoch 916 
-2023-11-02 22:28:55.679123: Current learning rate: 0.00108 
-2023-11-02 22:30:42.294352: train_loss -0.9296 
-2023-11-02 22:30:42.294474: val_loss -0.8108 
-2023-11-02 22:30:42.294524: Pseudo dice [0.8486] 
-2023-11-02 22:30:42.294550: Epoch time: 106.62 s 
-2023-11-02 22:30:42.814121:  
-2023-11-02 22:30:42.814187: Epoch 917 
-2023-11-02 22:30:42.814232: Current learning rate: 0.00106 
-2023-11-02 22:32:29.321111: train_loss -0.9292 
-2023-11-02 22:32:29.321233: val_loss -0.8244 
-2023-11-02 22:32:29.321281: Pseudo dice [0.8575] 
-2023-11-02 22:32:29.321308: Epoch time: 106.51 s 
-2023-11-02 22:32:29.844333:  
-2023-11-02 22:32:29.844429: Epoch 918 
-2023-11-02 22:32:29.844509: Current learning rate: 0.00105 
-2023-11-02 22:34:16.287550: train_loss -0.9296 
-2023-11-02 22:34:16.287682: val_loss -0.8195 
-2023-11-02 22:34:16.287734: Pseudo dice [0.8518] 
-2023-11-02 22:34:16.287761: Epoch time: 106.44 s 
-2023-11-02 22:34:16.807497:  
-2023-11-02 22:34:16.807566: Epoch 919 
-2023-11-02 22:34:16.807642: Current learning rate: 0.00104 
-2023-11-02 22:36:03.278799: train_loss -0.9309 
-2023-11-02 22:36:03.278928: val_loss -0.813 
-2023-11-02 22:36:03.278970: Pseudo dice [0.8504] 
-2023-11-02 22:36:03.279003: Epoch time: 106.47 s 
-2023-11-02 22:36:03.800122:  
-2023-11-02 22:36:03.800189: Epoch 920 
-2023-11-02 22:36:03.800265: Current learning rate: 0.00103 
-2023-11-02 22:37:50.474650: train_loss -0.9309 
-2023-11-02 22:37:50.474775: val_loss -0.8126 
-2023-11-02 22:37:50.474816: Pseudo dice [0.8494] 
-2023-11-02 22:37:50.474846: Epoch time: 106.67 s 
-2023-11-02 22:37:50.991616:  
-2023-11-02 22:37:50.991679: Epoch 921 
-2023-11-02 22:37:50.991728: Current learning rate: 0.00102 
-2023-11-02 22:39:37.722655: train_loss -0.9299 
-2023-11-02 22:39:37.722787: val_loss -0.8154 
-2023-11-02 22:39:37.722817: Pseudo dice [0.8507] 
-2023-11-02 22:39:37.722850: Epoch time: 106.73 s 
-2023-11-02 22:39:38.339239:  
-2023-11-02 22:39:38.339308: Epoch 922 
-2023-11-02 22:39:38.339387: Current learning rate: 0.00101 
-2023-11-02 22:41:25.086127: train_loss -0.9315 
-2023-11-02 22:41:25.086278: val_loss -0.8299 
-2023-11-02 22:41:25.086330: Pseudo dice [0.8602] 
-2023-11-02 22:41:25.086358: Epoch time: 106.75 s 
-2023-11-02 22:41:25.607876:  
-2023-11-02 22:41:25.607946: Epoch 923 
-2023-11-02 22:41:25.608030: Current learning rate: 0.001 
-2023-11-02 22:43:12.291312: train_loss -0.9306 
-2023-11-02 22:43:12.291443: val_loss -0.8214 
-2023-11-02 22:43:12.291482: Pseudo dice [0.8553] 
-2023-11-02 22:43:12.291509: Epoch time: 106.68 s 
-2023-11-02 22:43:12.815562:  
-2023-11-02 22:43:12.815627: Epoch 924 
-2023-11-02 22:43:12.815674: Current learning rate: 0.00098 
-2023-11-02 22:44:59.320940: train_loss -0.9306 
-2023-11-02 22:44:59.321077: val_loss -0.8231 
-2023-11-02 22:44:59.321117: Pseudo dice [0.8571] 
-2023-11-02 22:44:59.321143: Epoch time: 106.51 s 
-2023-11-02 22:44:59.842869:  
-2023-11-02 22:44:59.842936: Epoch 925 
-2023-11-02 22:44:59.842999: Current learning rate: 0.00097 
-2023-11-02 22:46:46.361664: train_loss -0.9324 
-2023-11-02 22:46:46.361788: val_loss -0.8243 
-2023-11-02 22:46:46.361813: Pseudo dice [0.857] 
-2023-11-02 22:46:46.361840: Epoch time: 106.52 s 
-2023-11-02 22:46:46.881494:  
-2023-11-02 22:46:46.881558: Epoch 926 
-2023-11-02 22:46:46.881629: Current learning rate: 0.00096 
-2023-11-02 22:48:33.519586: train_loss -0.9328 
-2023-11-02 22:48:33.519715: val_loss -0.8178 
-2023-11-02 22:48:33.519770: Pseudo dice [0.8559] 
-2023-11-02 22:48:33.519801: Epoch time: 106.64 s 
-2023-11-02 22:48:34.040163:  
-2023-11-02 22:48:34.040231: Epoch 927 
-2023-11-02 22:48:34.040307: Current learning rate: 0.00095 
-2023-11-02 22:50:20.638681: train_loss -0.9299 
-2023-11-02 22:50:20.638808: val_loss -0.8169 
-2023-11-02 22:50:20.638846: Pseudo dice [0.8518] 
-2023-11-02 22:50:20.638874: Epoch time: 106.6 s 
-2023-11-02 22:50:21.161483:  
-2023-11-02 22:50:21.161549: Epoch 928 
-2023-11-02 22:50:21.161599: Current learning rate: 0.00094 
-2023-11-02 22:52:07.761314: train_loss -0.9304 
-2023-11-02 22:52:07.761436: val_loss -0.8194 
-2023-11-02 22:52:07.761475: Pseudo dice [0.8558] 
-2023-11-02 22:52:07.761502: Epoch time: 106.6 s 
-2023-11-02 22:52:08.383637:  
-2023-11-02 22:52:08.383706: Epoch 929 
-2023-11-02 22:52:08.383756: Current learning rate: 0.00092 
-2023-11-02 22:53:54.994493: train_loss -0.9304 
-2023-11-02 22:53:54.994643: val_loss -0.8106 
-2023-11-02 22:53:54.994669: Pseudo dice [0.8477] 
-2023-11-02 22:53:54.994696: Epoch time: 106.61 s 
-2023-11-02 22:53:55.517551:  
-2023-11-02 22:53:55.517622: Epoch 930 
-2023-11-02 22:53:55.517698: Current learning rate: 0.00091 
-2023-11-02 22:55:42.181490: train_loss -0.9314 
-2023-11-02 22:55:42.181643: val_loss -0.8228 
-2023-11-02 22:55:42.181692: Pseudo dice [0.8559] 
-2023-11-02 22:55:42.181719: Epoch time: 106.66 s 
-2023-11-02 22:55:42.701458:  
-2023-11-02 22:55:42.701530: Epoch 931 
-2023-11-02 22:55:42.701602: Current learning rate: 0.0009 
-2023-11-02 22:57:29.394408: train_loss -0.9296 
-2023-11-02 22:57:29.394537: val_loss -0.8192 
-2023-11-02 22:57:29.394579: Pseudo dice [0.8543] 
-2023-11-02 22:57:29.394609: Epoch time: 106.69 s 
-2023-11-02 22:57:29.914710:  
-2023-11-02 22:57:29.914780: Epoch 932 
-2023-11-02 22:57:29.914852: Current learning rate: 0.00089 
-2023-11-02 22:59:16.485787: train_loss -0.9333 
-2023-11-02 22:59:16.485926: val_loss -0.8275 
-2023-11-02 22:59:16.485982: Pseudo dice [0.8607] 
-2023-11-02 22:59:16.486015: Epoch time: 106.57 s 
-2023-11-02 22:59:17.022373:  
-2023-11-02 22:59:17.022445: Epoch 933 
-2023-11-02 22:59:17.022524: Current learning rate: 0.00088 
-2023-11-02 23:01:03.506000: train_loss -0.9318 
-2023-11-02 23:01:03.506133: val_loss -0.8082 
-2023-11-02 23:01:03.506189: Pseudo dice [0.8472] 
-2023-11-02 23:01:03.506223: Epoch time: 106.48 s 
-2023-11-02 23:01:04.022294:  
-2023-11-02 23:01:04.022361: Epoch 934 
-2023-11-02 23:01:04.022437: Current learning rate: 0.00087 
-2023-11-02 23:02:50.538528: train_loss -0.9335 
-2023-11-02 23:02:50.538660: val_loss -0.8241 
-2023-11-02 23:02:50.538713: Pseudo dice [0.858] 
-2023-11-02 23:02:50.538739: Epoch time: 106.52 s 
-2023-11-02 23:02:51.062519:  
-2023-11-02 23:02:51.062585: Epoch 935 
-2023-11-02 23:02:51.062661: Current learning rate: 0.00085 
-2023-11-02 23:04:37.619624: train_loss -0.931 
-2023-11-02 23:04:37.619776: val_loss -0.8159 
-2023-11-02 23:04:37.619800: Pseudo dice [0.8528] 
-2023-11-02 23:04:37.619826: Epoch time: 106.56 s 
-2023-11-02 23:04:38.241562:  
-2023-11-02 23:04:38.241632: Epoch 936 
-2023-11-02 23:04:38.241708: Current learning rate: 0.00084 
-2023-11-02 23:06:24.819246: train_loss -0.9302 
-2023-11-02 23:06:24.819404: val_loss -0.8115 
-2023-11-02 23:06:24.819443: Pseudo dice [0.8487] 
-2023-11-02 23:06:24.819470: Epoch time: 106.58 s 
-2023-11-02 23:06:25.341020:  
-2023-11-02 23:06:25.341089: Epoch 937 
-2023-11-02 23:06:25.341138: Current learning rate: 0.00083 
-2023-11-02 23:08:11.944698: train_loss -0.9315 
-2023-11-02 23:08:11.944845: val_loss -0.8207 
-2023-11-02 23:08:11.944894: Pseudo dice [0.8536] 
-2023-11-02 23:08:11.944922: Epoch time: 106.6 s 
-2023-11-02 23:08:12.464903:  
-2023-11-02 23:08:12.464982: Epoch 938 
-2023-11-02 23:08:12.465034: Current learning rate: 0.00082 
-2023-11-02 23:09:59.030084: train_loss -0.9312 
-2023-11-02 23:09:59.030221: val_loss -0.8235 
-2023-11-02 23:09:59.030248: Pseudo dice [0.8575] 
-2023-11-02 23:09:59.030274: Epoch time: 106.57 s 
-2023-11-02 23:09:59.548981:  
-2023-11-02 23:09:59.549055: Epoch 939 
-2023-11-02 23:09:59.549133: Current learning rate: 0.00081 
-2023-11-02 23:11:46.191521: train_loss -0.9326 
-2023-11-02 23:11:46.191651: val_loss -0.8149 
-2023-11-02 23:11:46.191719: Pseudo dice [0.8513] 
-2023-11-02 23:11:46.191749: Epoch time: 106.64 s 
-2023-11-02 23:11:46.710364:  
-2023-11-02 23:11:46.710431: Epoch 940 
-2023-11-02 23:11:46.710511: Current learning rate: 0.00079 
-2023-11-02 23:13:33.452772: train_loss -0.93 
-2023-11-02 23:13:33.452914: val_loss -0.815 
-2023-11-02 23:13:33.452940: Pseudo dice [0.8505] 
-2023-11-02 23:13:33.452969: Epoch time: 106.74 s 
-2023-11-02 23:13:33.970401:  
-2023-11-02 23:13:33.970464: Epoch 941 
-2023-11-02 23:13:33.970536: Current learning rate: 0.00078 
-2023-11-02 23:15:20.641752: train_loss -0.9332 
-2023-11-02 23:15:20.641899: val_loss -0.8269 
-2023-11-02 23:15:20.641936: Pseudo dice [0.8598] 
-2023-11-02 23:15:20.641963: Epoch time: 106.67 s 
-2023-11-02 23:15:21.257118:  
-2023-11-02 23:15:21.257191: Epoch 942 
-2023-11-02 23:15:21.257244: Current learning rate: 0.00077 
-2023-11-02 23:17:07.824325: train_loss -0.9309 
-2023-11-02 23:17:07.824462: val_loss -0.813 
-2023-11-02 23:17:07.824508: Pseudo dice [0.8512] 
-2023-11-02 23:17:07.824534: Epoch time: 106.57 s 
-2023-11-02 23:17:08.345634:  
-2023-11-02 23:17:08.345702: Epoch 943 
-2023-11-02 23:17:08.345769: Current learning rate: 0.00076 
-2023-11-02 23:18:55.037412: train_loss -0.9304 
-2023-11-02 23:18:55.037554: val_loss -0.809 
-2023-11-02 23:18:55.037581: Pseudo dice [0.8444] 
-2023-11-02 23:18:55.037610: Epoch time: 106.69 s 
-2023-11-02 23:18:55.570186:  
-2023-11-02 23:18:55.570280: Epoch 944 
-2023-11-02 23:18:55.570507: Current learning rate: 0.00075 
-2023-11-02 23:20:42.103886: train_loss -0.9341 
-2023-11-02 23:20:42.104053: val_loss -0.8203 
-2023-11-02 23:20:42.104080: Pseudo dice [0.8541] 
-2023-11-02 23:20:42.104106: Epoch time: 106.53 s 
-2023-11-02 23:20:42.635409:  
-2023-11-02 23:20:42.635481: Epoch 945 
-2023-11-02 23:20:42.635533: Current learning rate: 0.00074 
-2023-11-02 23:22:29.171399: train_loss -0.9312 
-2023-11-02 23:22:29.171521: val_loss -0.8088 
-2023-11-02 23:22:29.171560: Pseudo dice [0.8452] 
-2023-11-02 23:22:29.171585: Epoch time: 106.54 s 
-2023-11-02 23:22:29.692312:  
-2023-11-02 23:22:29.692383: Epoch 946 
-2023-11-02 23:22:29.692436: Current learning rate: 0.00072 
-2023-11-02 23:24:16.207089: train_loss -0.9308 
-2023-11-02 23:24:16.207221: val_loss -0.8202 
-2023-11-02 23:24:16.207277: Pseudo dice [0.8542] 
-2023-11-02 23:24:16.207311: Epoch time: 106.52 s 
-2023-11-02 23:24:16.732711:  
-2023-11-02 23:24:16.732777: Epoch 947 
-2023-11-02 23:24:16.732829: Current learning rate: 0.00071 
-2023-11-02 23:26:03.318631: train_loss -0.9346 
-2023-11-02 23:26:03.318756: val_loss -0.808 
-2023-11-02 23:26:03.318794: Pseudo dice [0.8452] 
-2023-11-02 23:26:03.318821: Epoch time: 106.59 s 
-2023-11-02 23:26:03.839522:  
-2023-11-02 23:26:03.839584: Epoch 948 
-2023-11-02 23:26:03.839650: Current learning rate: 0.0007 
-2023-11-02 23:27:50.513179: train_loss -0.934 
-2023-11-02 23:27:50.513312: val_loss -0.8119 
-2023-11-02 23:27:50.513350: Pseudo dice [0.8492] 
-2023-11-02 23:27:50.513376: Epoch time: 106.67 s 
-2023-11-02 23:27:51.130898:  
-2023-11-02 23:27:51.130975: Epoch 949 
-2023-11-02 23:27:51.131041: Current learning rate: 0.00069 
-2023-11-02 23:29:37.804967: train_loss -0.9304 
-2023-11-02 23:29:37.805118: val_loss -0.8196 
-2023-11-02 23:29:37.805156: Pseudo dice [0.855] 
-2023-11-02 23:29:37.805183: Epoch time: 106.67 s 
-2023-11-02 23:29:38.565592:  
-2023-11-02 23:29:38.565663: Epoch 950 
-2023-11-02 23:29:38.565716: Current learning rate: 0.00067 
-2023-11-02 23:31:25.277879: train_loss -0.9322 
-2023-11-02 23:31:25.278019: val_loss -0.8208 
-2023-11-02 23:31:25.278070: Pseudo dice [0.8546] 
-2023-11-02 23:31:25.278098: Epoch time: 106.71 s 
-2023-11-02 23:31:25.801915:  
-2023-11-02 23:31:25.802001: Epoch 951 
-2023-11-02 23:31:25.802102: Current learning rate: 0.00066 
-2023-11-02 23:33:13.306561: train_loss -0.9325 
-2023-11-02 23:33:13.306691: val_loss -0.8233 
-2023-11-02 23:33:13.306715: Pseudo dice [0.8567] 
-2023-11-02 23:33:13.306741: Epoch time: 107.51 s 
-2023-11-02 23:33:13.843434:  
-2023-11-02 23:33:13.843518: Epoch 952 
-2023-11-02 23:33:13.843575: Current learning rate: 0.00065 
-2023-11-02 23:35:00.457344: train_loss -0.9308 
-2023-11-02 23:35:00.457478: val_loss -0.8175 
-2023-11-02 23:35:00.457508: Pseudo dice [0.8545] 
-2023-11-02 23:35:00.457540: Epoch time: 106.61 s 
-2023-11-02 23:35:00.989164:  
-2023-11-02 23:35:00.989264: Epoch 953 
-2023-11-02 23:35:00.989357: Current learning rate: 0.00064 
-2023-11-02 23:36:47.629650: train_loss -0.9327 
-2023-11-02 23:36:47.629796: val_loss -0.8252 
-2023-11-02 23:36:47.629820: Pseudo dice [0.858] 
-2023-11-02 23:36:47.629846: Epoch time: 106.64 s 
-2023-11-02 23:36:48.156843:  
-2023-11-02 23:36:48.156922: Epoch 954 
-2023-11-02 23:36:48.157006: Current learning rate: 0.00063 
-2023-11-02 23:38:34.824220: train_loss -0.9296 
-2023-11-02 23:38:34.824371: val_loss -0.8207 
-2023-11-02 23:38:34.824396: Pseudo dice [0.8547] 
-2023-11-02 23:38:34.824424: Epoch time: 106.67 s 
-2023-11-02 23:38:35.448748:  
-2023-11-02 23:38:35.448821: Epoch 955 
-2023-11-02 23:38:35.448874: Current learning rate: 0.00061 
-2023-11-02 23:40:22.077465: train_loss -0.9327 
-2023-11-02 23:40:22.077635: val_loss -0.8143 
-2023-11-02 23:40:22.077661: Pseudo dice [0.8503] 
-2023-11-02 23:40:22.077688: Epoch time: 106.63 s 
-2023-11-02 23:40:22.604620:  
-2023-11-02 23:40:22.604696: Epoch 956 
-2023-11-02 23:40:22.604775: Current learning rate: 0.0006 
-2023-11-02 23:42:09.307881: train_loss -0.9315 
-2023-11-02 23:42:09.308024: val_loss -0.8025 
-2023-11-02 23:42:09.308057: Pseudo dice [0.8442] 
-2023-11-02 23:42:09.308087: Epoch time: 106.7 s 
-2023-11-02 23:42:09.841584:  
-2023-11-02 23:42:09.841727: Epoch 957 
-2023-11-02 23:42:09.841782: Current learning rate: 0.00059 
-2023-11-02 23:43:56.507473: train_loss -0.932 
-2023-11-02 23:43:56.507602: val_loss -0.824 
-2023-11-02 23:43:56.507654: Pseudo dice [0.8557] 
-2023-11-02 23:43:56.507681: Epoch time: 106.67 s 
-2023-11-02 23:43:57.037574:  
-2023-11-02 23:43:57.037662: Epoch 958 
-2023-11-02 23:43:57.037908: Current learning rate: 0.00058 
-2023-11-02 23:45:44.616183: train_loss -0.9318 
-2023-11-02 23:45:44.616309: val_loss -0.8104 
-2023-11-02 23:45:44.616361: Pseudo dice [0.8465] 
-2023-11-02 23:45:44.616388: Epoch time: 107.58 s 
-2023-11-02 23:45:45.142511:  
-2023-11-02 23:45:45.142580: Epoch 959 
-2023-11-02 23:45:45.142658: Current learning rate: 0.00056 
-2023-11-02 23:47:31.859879: train_loss -0.9302 
-2023-11-02 23:47:31.860010: val_loss -0.8163 
-2023-11-02 23:47:31.860036: Pseudo dice [0.8513] 
-2023-11-02 23:47:31.860062: Epoch time: 106.72 s 
-2023-11-02 23:47:32.389937:  
-2023-11-02 23:47:32.390020: Epoch 960 
-2023-11-02 23:47:32.390097: Current learning rate: 0.00055 
-2023-11-02 23:49:18.824567: train_loss -0.933 
-2023-11-02 23:49:18.824689: val_loss -0.8107 
-2023-11-02 23:49:18.824738: Pseudo dice [0.8476] 
-2023-11-02 23:49:18.824764: Epoch time: 106.44 s 
-2023-11-02 23:49:19.355610:  
-2023-11-02 23:49:19.355856: Epoch 961 
-2023-11-02 23:49:19.355935: Current learning rate: 0.00054 
-2023-11-02 23:51:05.758890: train_loss -0.9319 
-2023-11-02 23:51:05.759021: val_loss -0.811 
-2023-11-02 23:51:05.759064: Pseudo dice [0.8503] 
-2023-11-02 23:51:05.759097: Epoch time: 106.4 s 
-2023-11-02 23:51:06.390645:  
-2023-11-02 23:51:06.390720: Epoch 962 
-2023-11-02 23:51:06.390774: Current learning rate: 0.00053 
-2023-11-02 23:52:53.005969: train_loss -0.9321 
-2023-11-02 23:52:53.006129: val_loss -0.8179 
-2023-11-02 23:52:53.006156: Pseudo dice [0.8523] 
-2023-11-02 23:52:53.006185: Epoch time: 106.62 s 
-2023-11-02 23:52:53.539514:  
-2023-11-02 23:52:53.539588: Epoch 963 
-2023-11-02 23:52:53.539668: Current learning rate: 0.00051 
-2023-11-02 23:54:40.204570: train_loss -0.9321 
-2023-11-02 23:54:40.204710: val_loss -0.8221 
-2023-11-02 23:54:40.204759: Pseudo dice [0.8554] 
-2023-11-02 23:54:40.204785: Epoch time: 106.67 s 
-2023-11-02 23:54:40.732928:  
-2023-11-02 23:54:40.733000: Epoch 964 
-2023-11-02 23:54:40.733064: Current learning rate: 0.0005 
-2023-11-02 23:56:27.271410: train_loss -0.9333 
-2023-11-02 23:56:27.271561: val_loss -0.8046 
-2023-11-02 23:56:27.271588: Pseudo dice [0.8442] 
-2023-11-02 23:56:27.271615: Epoch time: 106.54 s 
-2023-11-02 23:56:27.799762:  
-2023-11-02 23:56:27.799827: Epoch 965 
-2023-11-02 23:56:27.799902: Current learning rate: 0.00049 
-2023-11-02 23:58:14.260666: train_loss -0.9342 
-2023-11-02 23:58:14.260828: val_loss -0.8147 
-2023-11-02 23:58:14.260859: Pseudo dice [0.8501] 
-2023-11-02 23:58:14.260891: Epoch time: 106.46 s 
-2023-11-02 23:58:14.790864:  
-2023-11-02 23:58:14.790948: Epoch 966 
-2023-11-02 23:58:14.791028: Current learning rate: 0.00048 
-2023-11-03 00:00:01.217238: train_loss -0.9317 
-2023-11-03 00:00:01.217365: val_loss -0.8154 
-2023-11-03 00:00:01.217403: Pseudo dice [0.8502] 
-2023-11-03 00:00:01.217457: Epoch time: 106.43 s 
-2023-11-03 00:00:01.747188:  
-2023-11-03 00:00:01.747300: Epoch 967 
-2023-11-03 00:00:01.747355: Current learning rate: 0.00046 
-2023-11-03 00:01:48.322920: train_loss -0.9338 
-2023-11-03 00:01:48.323085: val_loss -0.8235 
-2023-11-03 00:01:48.323116: Pseudo dice [0.8587] 
-2023-11-03 00:01:48.323149: Epoch time: 106.58 s 
-2023-11-03 00:01:48.949700:  
-2023-11-03 00:01:48.949777: Epoch 968 
-2023-11-03 00:01:48.949849: Current learning rate: 0.00045 
-2023-11-03 00:03:35.639553: train_loss -0.9336 
-2023-11-03 00:03:35.639678: val_loss -0.81 
-2023-11-03 00:03:35.639702: Pseudo dice [0.8496] 
-2023-11-03 00:03:35.639729: Epoch time: 106.69 s 
-2023-11-03 00:03:36.173887:  
-2023-11-03 00:03:36.173959: Epoch 969 
-2023-11-03 00:03:36.174033: Current learning rate: 0.00044 
-2023-11-03 00:05:22.766460: train_loss -0.9327 
-2023-11-03 00:05:22.766601: val_loss -0.8072 
-2023-11-03 00:05:22.766627: Pseudo dice [0.845] 
-2023-11-03 00:05:22.766655: Epoch time: 106.59 s 
-2023-11-03 00:05:23.299620:  
-2023-11-03 00:05:23.299690: Epoch 970 
-2023-11-03 00:05:23.299753: Current learning rate: 0.00043 
-2023-11-03 00:07:09.864407: train_loss -0.9327 
-2023-11-03 00:07:09.864531: val_loss -0.8196 
-2023-11-03 00:07:09.864581: Pseudo dice [0.8557] 
-2023-11-03 00:07:09.864606: Epoch time: 106.57 s 
-2023-11-03 00:07:10.395443:  
-2023-11-03 00:07:10.395653: Epoch 971 
-2023-11-03 00:07:10.395738: Current learning rate: 0.00041 
-2023-11-03 00:08:57.008729: train_loss -0.9325 
-2023-11-03 00:08:57.008854: val_loss -0.8163 
-2023-11-03 00:08:57.008902: Pseudo dice [0.8522] 
-2023-11-03 00:08:57.008928: Epoch time: 106.61 s 
-2023-11-03 00:08:57.538175:  
-2023-11-03 00:08:57.538241: Epoch 972 
-2023-11-03 00:08:57.538296: Current learning rate: 0.0004 
-2023-11-03 00:10:44.231426: train_loss -0.933 
-2023-11-03 00:10:44.231532: val_loss -0.8172 
-2023-11-03 00:10:44.231560: Pseudo dice [0.8543] 
-2023-11-03 00:10:44.231594: Epoch time: 106.69 s 
-2023-11-03 00:10:44.769300:  
-2023-11-03 00:10:44.769369: Epoch 973 
-2023-11-03 00:10:44.769447: Current learning rate: 0.00039 
-2023-11-03 00:12:31.422402: train_loss -0.9328 
-2023-11-03 00:12:31.422531: val_loss -0.8129 
-2023-11-03 00:12:31.422583: Pseudo dice [0.8502] 
-2023-11-03 00:12:31.422610: Epoch time: 106.65 s 
-2023-11-03 00:12:31.953635:  
-2023-11-03 00:12:31.953696: Epoch 974 
-2023-11-03 00:12:31.953779: Current learning rate: 0.00037 
-2023-11-03 00:14:18.560899: train_loss -0.9338 
-2023-11-03 00:14:18.561020: val_loss -0.8211 
-2023-11-03 00:14:18.561068: Pseudo dice [0.8553] 
-2023-11-03 00:14:18.561100: Epoch time: 106.61 s 
-2023-11-03 00:14:19.193029:  
-2023-11-03 00:14:19.193240: Epoch 975 
-2023-11-03 00:14:19.193661: Current learning rate: 0.00036 
-2023-11-03 00:16:05.807399: train_loss -0.9313 
-2023-11-03 00:16:05.807554: val_loss -0.8215 
-2023-11-03 00:16:05.807580: Pseudo dice [0.8568] 
-2023-11-03 00:16:05.807606: Epoch time: 106.61 s 
-2023-11-03 00:16:06.341502:  
-2023-11-03 00:16:06.341598: Epoch 976 
-2023-11-03 00:16:06.341647: Current learning rate: 0.00035 
-2023-11-03 00:17:52.923401: train_loss -0.9347 
-2023-11-03 00:17:52.923560: val_loss -0.8283 
-2023-11-03 00:17:52.923585: Pseudo dice [0.8605] 
-2023-11-03 00:17:52.923611: Epoch time: 106.58 s 
-2023-11-03 00:17:53.467086:  
-2023-11-03 00:17:53.467158: Epoch 977 
-2023-11-03 00:17:53.467212: Current learning rate: 0.00034 
-2023-11-03 00:19:40.049464: train_loss -0.9359 
-2023-11-03 00:19:40.049618: val_loss -0.8195 
-2023-11-03 00:19:40.049652: Pseudo dice [0.8551] 
-2023-11-03 00:19:40.049687: Epoch time: 106.58 s 
-2023-11-03 00:19:40.581973:  
-2023-11-03 00:19:40.582040: Epoch 978 
-2023-11-03 00:19:40.582092: Current learning rate: 0.00032 
-2023-11-03 00:21:27.250496: train_loss -0.934 
-2023-11-03 00:21:27.250629: val_loss -0.8205 
-2023-11-03 00:21:27.250679: Pseudo dice [0.8578] 
-2023-11-03 00:21:27.250708: Epoch time: 106.67 s 
-2023-11-03 00:21:27.782955:  
-2023-11-03 00:21:27.783018: Epoch 979 
-2023-11-03 00:21:27.783106: Current learning rate: 0.00031 
-2023-11-03 00:23:14.419619: train_loss -0.9313 
-2023-11-03 00:23:14.419745: val_loss -0.8218 
-2023-11-03 00:23:14.419793: Pseudo dice [0.856] 
-2023-11-03 00:23:14.419819: Epoch time: 106.64 s 
-2023-11-03 00:23:14.955403:  
-2023-11-03 00:23:14.955482: Epoch 980 
-2023-11-03 00:23:14.955552: Current learning rate: 0.0003 
-2023-11-03 00:25:01.534269: train_loss -0.9331 
-2023-11-03 00:25:01.534399: val_loss -0.8078 
-2023-11-03 00:25:01.534428: Pseudo dice [0.8474] 
-2023-11-03 00:25:01.534457: Epoch time: 106.58 s 
-2023-11-03 00:25:02.161182:  
-2023-11-03 00:25:02.161256: Epoch 981 
-2023-11-03 00:25:02.161386: Current learning rate: 0.00028 
-2023-11-03 00:26:48.749900: train_loss -0.9343 
-2023-11-03 00:26:48.750036: val_loss -0.8215 
-2023-11-03 00:26:48.750078: Pseudo dice [0.8552] 
-2023-11-03 00:26:48.750109: Epoch time: 106.59 s 
-2023-11-03 00:26:49.278095:  
-2023-11-03 00:26:49.278172: Epoch 982 
-2023-11-03 00:26:49.278251: Current learning rate: 0.00027 
-2023-11-03 00:28:36.751260: train_loss -0.9346 
-2023-11-03 00:28:36.751415: val_loss -0.8126 
-2023-11-03 00:28:36.751443: Pseudo dice [0.8493] 
-2023-11-03 00:28:36.751470: Epoch time: 107.47 s 
-2023-11-03 00:28:37.294576:  
-2023-11-03 00:28:37.294676: Epoch 983 
-2023-11-03 00:28:37.294732: Current learning rate: 0.00026 
-2023-11-03 00:30:23.862229: train_loss -0.9345 
-2023-11-03 00:30:23.862352: val_loss -0.8163 
-2023-11-03 00:30:23.862390: Pseudo dice [0.8524] 
-2023-11-03 00:30:23.862417: Epoch time: 106.57 s 
-2023-11-03 00:30:24.393611:  
-2023-11-03 00:30:24.393721: Epoch 984 
-2023-11-03 00:30:24.393793: Current learning rate: 0.00024 
-2023-11-03 00:32:11.040336: train_loss -0.9326 
-2023-11-03 00:32:11.040470: val_loss -0.8155 
-2023-11-03 00:32:11.040493: Pseudo dice [0.8517] 
-2023-11-03 00:32:11.040519: Epoch time: 106.65 s 
-2023-11-03 00:32:11.572686:  
-2023-11-03 00:32:11.572754: Epoch 985 
-2023-11-03 00:32:11.572823: Current learning rate: 0.00023 
-2023-11-03 00:33:58.046430: train_loss -0.9318 
-2023-11-03 00:33:58.046563: val_loss -0.8024 
-2023-11-03 00:33:58.046615: Pseudo dice [0.8428] 
-2023-11-03 00:33:58.046642: Epoch time: 106.47 s 
-2023-11-03 00:33:58.575716:  
-2023-11-03 00:33:58.575882: Epoch 986 
-2023-11-03 00:33:58.575963: Current learning rate: 0.00021 
-2023-11-03 00:35:45.020925: train_loss -0.9334 
-2023-11-03 00:35:45.021088: val_loss -0.8092 
-2023-11-03 00:35:45.021117: Pseudo dice [0.8471] 
-2023-11-03 00:35:45.021149: Epoch time: 106.45 s 
-2023-11-03 00:35:45.551717:  
-2023-11-03 00:35:45.551782: Epoch 987 
-2023-11-03 00:35:45.551858: Current learning rate: 0.0002 
-2023-11-03 00:37:31.963296: train_loss -0.9345 
-2023-11-03 00:37:31.963430: val_loss -0.8175 
-2023-11-03 00:37:31.963469: Pseudo dice [0.853] 
-2023-11-03 00:37:31.963495: Epoch time: 106.41 s 
-2023-11-03 00:37:32.596337:  
-2023-11-03 00:37:32.596427: Epoch 988 
-2023-11-03 00:37:32.596631: Current learning rate: 0.00019 
-2023-11-03 00:39:19.072401: train_loss -0.9342 
-2023-11-03 00:39:19.072547: val_loss -0.8076 
-2023-11-03 00:39:19.072579: Pseudo dice [0.8484] 
-2023-11-03 00:39:19.072608: Epoch time: 106.48 s 
-2023-11-03 00:39:19.602591:  
-2023-11-03 00:39:19.602667: Epoch 989 
-2023-11-03 00:39:19.602718: Current learning rate: 0.00017 
-2023-11-03 00:41:06.010952: train_loss -0.9339 
-2023-11-03 00:41:06.011108: val_loss -0.8148 
-2023-11-03 00:41:06.011133: Pseudo dice [0.8518] 
-2023-11-03 00:41:06.011160: Epoch time: 106.41 s 
-2023-11-03 00:41:06.544967:  
-2023-11-03 00:41:06.545037: Epoch 990 
-2023-11-03 00:41:06.545115: Current learning rate: 0.00016 
-2023-11-03 00:42:53.079529: train_loss -0.9359 
-2023-11-03 00:42:53.079658: val_loss -0.8253 
-2023-11-03 00:42:53.079707: Pseudo dice [0.8607] 
-2023-11-03 00:42:53.079734: Epoch time: 106.53 s 
-2023-11-03 00:42:53.611370:  
-2023-11-03 00:42:53.611456: Epoch 991 
-2023-11-03 00:42:53.611603: Current learning rate: 0.00014 
-2023-11-03 00:44:40.171468: train_loss -0.9343 
-2023-11-03 00:44:40.171600: val_loss -0.8125 
-2023-11-03 00:44:40.171628: Pseudo dice [0.848] 
-2023-11-03 00:44:40.171654: Epoch time: 106.56 s 
-2023-11-03 00:44:40.706387:  
-2023-11-03 00:44:40.706472: Epoch 992 
-2023-11-03 00:44:40.706785: Current learning rate: 0.00013 
-2023-11-03 00:46:27.224413: train_loss -0.9316 
-2023-11-03 00:46:27.224549: val_loss -0.8142 
-2023-11-03 00:46:27.224602: Pseudo dice [0.8488] 
-2023-11-03 00:46:27.224628: Epoch time: 106.52 s 
-2023-11-03 00:46:27.761183:  
-2023-11-03 00:46:27.761255: Epoch 993 
-2023-11-03 00:46:27.761308: Current learning rate: 0.00011 
-2023-11-03 00:48:14.269547: train_loss -0.9353 
-2023-11-03 00:48:14.269674: val_loss -0.8242 
-2023-11-03 00:48:14.269723: Pseudo dice [0.8568] 
-2023-11-03 00:48:14.269749: Epoch time: 106.51 s 
-2023-11-03 00:48:14.891938:  
-2023-11-03 00:48:14.892044: Epoch 994 
-2023-11-03 00:48:14.892108: Current learning rate: 0.0001 
-2023-11-03 00:50:01.420262: train_loss -0.9312 
-2023-11-03 00:50:01.420394: val_loss -0.8097 
-2023-11-03 00:50:01.420426: Pseudo dice [0.8458] 
-2023-11-03 00:50:01.420458: Epoch time: 106.53 s 
-2023-11-03 00:50:01.953067:  
-2023-11-03 00:50:01.953166: Epoch 995 
-2023-11-03 00:50:01.953218: Current learning rate: 8e-05 
-2023-11-03 00:51:48.444643: train_loss -0.9353 
-2023-11-03 00:51:48.444769: val_loss -0.8173 
-2023-11-03 00:51:48.444796: Pseudo dice [0.8525] 
-2023-11-03 00:51:48.444823: Epoch time: 106.49 s 
-2023-11-03 00:51:48.988503:  
-2023-11-03 00:51:48.988590: Epoch 996 
-2023-11-03 00:51:48.988669: Current learning rate: 7e-05 
-2023-11-03 00:53:35.589220: train_loss -0.9348 
-2023-11-03 00:53:35.589373: val_loss -0.8226 
-2023-11-03 00:53:35.589402: Pseudo dice [0.8556] 
-2023-11-03 00:53:35.589450: Epoch time: 106.6 s 
-2023-11-03 00:53:36.121492:  
-2023-11-03 00:53:36.121588: Epoch 997 
-2023-11-03 00:53:36.121638: Current learning rate: 5e-05 
-2023-11-03 00:55:22.672446: train_loss -0.934 
-2023-11-03 00:55:22.672569: val_loss -0.8212 
-2023-11-03 00:55:22.672617: Pseudo dice [0.857] 
-2023-11-03 00:55:22.672644: Epoch time: 106.55 s 
-2023-11-03 00:55:23.206603:  
-2023-11-03 00:55:23.206671: Epoch 998 
-2023-11-03 00:55:23.206723: Current learning rate: 4e-05 
-2023-11-03 00:57:09.755674: train_loss -0.9352 
-2023-11-03 00:57:09.755797: val_loss -0.8215 
-2023-11-03 00:57:09.755836: Pseudo dice [0.8564] 
-2023-11-03 00:57:09.755862: Epoch time: 106.55 s 
-2023-11-03 00:57:10.290334:  
-2023-11-03 00:57:10.290407: Epoch 999 
-2023-11-03 00:57:10.290460: Current learning rate: 2e-05 
-2023-11-03 00:58:56.942530: train_loss -0.9324 
-2023-11-03 00:58:56.942665: val_loss -0.82 
-2023-11-03 00:58:56.942688: Pseudo dice [0.8544] 
-2023-11-03 00:58:56.942717: Epoch time: 106.65 s 
-2023-11-03 00:58:57.849090: Training done. 
-2023-11-03 00:58:57.855299: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset722_TSPrimeCTVN/splits_final.json 
-2023-11-03 00:58:57.855579: The split file contains 5 splits. 
-2023-11-03 00:58:57.855632: Desired fold for training: 0 
-2023-11-03 00:58:57.855670: This split has 48 training and 12 validation cases. 
-2023-11-03 00:58:57.855783: predicting seg_006 
-2023-11-03 01:00:13.321881: predicting seg_021 
-2023-11-03 01:01:42.220691: predicting seg_022 
-2023-11-03 01:03:11.089087: predicting seg_030 
-2023-11-03 01:04:25.176621: predicting seg_032 
-2023-11-03 01:05:54.105508: predicting seg_035 
-2023-11-03 01:07:08.190406: predicting seg_042 
-2023-11-03 01:08:22.279825: predicting seg_044 
-2023-11-03 01:09:51.119307: predicting seg_045 
-2023-11-03 01:11:20.040565: predicting seg_047 
-2023-11-03 01:12:34.124866: predicting seg_050 
-2023-11-03 01:13:48.243557: predicting seg_053 
-2023-11-03 01:15:25.167216: Validation complete 
-2023-11-03 01:15:25.167301: Mean Validation Dice:  0.8522019724789348 
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