diff --git a/.gitattributes b/.gitattributes
index 49c7bb1c533c9fad5134c19722f1de3bd53460e7..d36eb9fcc2e52586acaccd80b237dbfb588046eb 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset.json b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/dataset.json
old mode 100644
new mode 100755
similarity index 95%
rename from MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset.json
rename to MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/dataset.json
index 5b00c40539f29b1ede22db694e80f5a59faf8e76..aa6d182671eb6c8c54193c359a902dc0d82b5e09
--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset.json
+++ b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/dataset.json
@@ -1,5 +1,5 @@
 {
-    "name": "Dataset145_Fast_organs",
+    "name": "Dataset123_Organs",
     "description": "",
     "reference": "",
     "licence": "hands off!",
diff --git a/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
new file mode 100755
index 0000000000000000000000000000000000000000..4cf7c9300d46d3b7cc6b1b684526e64928d6a790
--- /dev/null
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+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset123_Organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [649, 318, 318], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [318.0, 318.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [319, 156, 156], 'spacing': [3.049191159690605, 3.049191159690605, 3.049191159690605], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3033.969970703125, 'mean': -300.0020751953125, 'median': -1.0779765844345093, 'min': -1331.90234375, 'percentile_00_5': -959.2193603515625, 'percentile_99_5': 195.26037063598665, 'std': 406.9311828613281}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'name': 'Dataset123_Organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "2000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_results/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all",
+    "output_folder_base": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_results/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset123_Organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [649, 318, 318], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [318.0, 318.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [319, 156, 156], 'spacing': [3.049191159690605, 3.049191159690605, 3.049191159690605], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3033.969970703125, 'mean': -300.0020751953125, 'median': -1.0779765844345093, 'min': -1331.90234375, 'percentile_00_5': -959.2193603515625, 'percentile_99_5': 195.26037063598665, 'std': 406.9311828613281}}}",
+    "preprocessed_dataset_folder": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_preprocessed/Dataset123_Organs/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_preprocessed/Dataset123_Organs",
+    "save_every": "50",
+    "torch_version": "2.5.1+cu124",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
diff --git a/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/progress.png b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/progress.png
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diff --git a/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/training_log_2025_5_5_01_25_42.txt b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/training_log_2025_5_5_01_25_42.txt
new file mode 100755
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+++ b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/training_log_2025_5_5_01_25_42.txt
@@ -0,0 +1,16173 @@
+
+#######################################################################
+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.
+#######################################################################
+ 
+2025-05-05 01:25:42.603417: do_dummy_2d_data_aug: False 
+2025-05-05 01:26:13.939238: Using torch.compile... 
+
+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': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset123_Organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [649, 318, 318], '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': 3033.969970703125, 'mean': -300.0020751953125, 'median': -1.0779765844345093, 'min': -1331.90234375, 'percentile_00_5': -959.2193603515625, 'percentile_99_5': 195.26037063598665, 'std': 406.9311828613281}}} 
+ 
+2025-05-05 01:26:15.788649: unpacking dataset... 
+2025-05-05 01:51:19.073314: unpacking done... 
+2025-05-05 01:51:19.091247: Unable to plot network architecture: nnUNet_compile is enabled! 
+2025-05-05 01:51:19.117254:  
+2025-05-05 01:51:19.117688: Epoch 0 
+2025-05-05 01:51:19.118134: Current learning rate: 0.01 
+2025-05-05 01:54:04.359560: train_loss 0.9183 
+2025-05-05 01:54:04.415314: val_loss 0.5943 
+2025-05-05 01:54:04.423846: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1742), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 01:54:04.436351: Epoch time: 165.24 s 
+2025-05-05 01:54:04.448202: Yayy! New best EMA pseudo Dice: 0.009200000204145908 
+2025-05-05 01:54:06.941091:  
+2025-05-05 01:54:06.959191: Epoch 1 
+2025-05-05 01:54:06.959950: Current learning rate: 0.01 
+2025-05-05 01:55:36.630761: train_loss 0.5498 
+2025-05-05 01:55:36.745755: val_loss 0.5104 
+2025-05-05 01:55:36.777557: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0453), np.float32(0.2729), np.float32(0.0), np.float32(0.1446), np.float32(0.0004), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 01:55:36.804930: Epoch time: 89.69 s 
+2025-05-05 01:55:36.823495: Yayy! New best EMA pseudo Dice: 0.010700000450015068 
+2025-05-05 01:55:39.065977:  
+2025-05-05 01:55:39.111328: Epoch 2 
+2025-05-05 01:55:39.135747: Current learning rate: 0.00999 
+2025-05-05 01:57:09.915730: train_loss 0.4749 
+2025-05-05 01:57:10.086441: val_loss 0.414 
+2025-05-05 01:57:10.123843: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5236), np.float32(0.0), np.float32(0.2533), np.float32(0.0), np.float32(0.0864), np.float32(0.1045), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 01:57:10.153362: Epoch time: 90.85 s 
+2025-05-05 01:57:10.164453: Yayy! New best EMA pseudo Dice: 0.014700000174343586 
+2025-05-05 01:57:12.964996:  
+2025-05-05 01:57:12.969723: Epoch 3 
+2025-05-05 01:57:12.970193: Current learning rate: 0.00999 
+2025-05-05 01:58:53.309402: train_loss 0.4224 
+2025-05-05 01:58:53.419293: val_loss 0.3298 
+2025-05-05 01:58:53.445462: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6212), np.float32(0.0), np.float32(0.29), np.float32(0.0278), np.float32(0.0), np.float32(0.2666), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 01:58:53.473984: Epoch time: 100.35 s 
+2025-05-05 01:58:53.495571: Yayy! New best EMA pseudo Dice: 0.019600000232458115 
+2025-05-05 01:58:55.811032:  
+2025-05-05 01:58:55.897972: Epoch 4 
+2025-05-05 01:58:56.042116: Current learning rate: 0.00998 
+2025-05-05 02:01:11.819145: train_loss 0.396 
+2025-05-05 02:01:11.896239: val_loss 0.3295 
+2025-05-05 02:01:11.897211: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6676), np.float32(0.0), np.float32(0.3774), np.float32(0.0), np.float32(0.0004), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:01:11.897926: Epoch time: 136.01 s 
+2025-05-05 02:01:11.907923: Yayy! New best EMA pseudo Dice: 0.023099999874830246 
+2025-05-05 02:01:14.259308:  
+2025-05-05 02:01:14.348145: Epoch 5 
+2025-05-05 02:01:14.401510: Current learning rate: 0.00998 
+2025-05-05 02:03:02.659546: train_loss 0.3204 
+2025-05-05 02:03:02.759950: val_loss 0.2755 
+2025-05-05 02:03:02.771540: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7216), np.float32(0.0), np.float32(0.3418), np.float32(0.1408), np.float32(0.0084), np.float32(0.4317), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:03:02.780367: Epoch time: 108.4 s 
+2025-05-05 02:03:02.787914: Yayy! New best EMA pseudo Dice: 0.029500000178813934 
+2025-05-05 02:03:05.196387:  
+2025-05-05 02:03:05.211990: Epoch 6 
+2025-05-05 02:03:05.212842: Current learning rate: 0.00997 
+2025-05-05 02:04:52.488499: train_loss 0.266 
+2025-05-05 02:04:52.561436: val_loss 0.1881 
+2025-05-05 02:04:52.576545: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7548), np.float32(0.0254), np.float32(0.5894), np.float32(0.4654), np.float32(0.628), np.float32(0.6906), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:04:52.607349: Epoch time: 107.29 s 
+2025-05-05 02:04:52.664278: Yayy! New best EMA pseudo Dice: 0.04309999942779541 
+2025-05-05 02:04:54.845515:  
+2025-05-05 02:04:54.887560: Epoch 7 
+2025-05-05 02:04:54.913224: Current learning rate: 0.00997 
+2025-05-05 02:06:22.507650: train_loss 0.2369 
+2025-05-05 02:06:22.632957: val_loss 0.2159 
+2025-05-05 02:06:22.666408: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7601), np.float32(0.5311), np.float32(0.6544), np.float32(0.5646), np.float32(0.6421), np.float32(0.7302), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:06:22.696735: Epoch time: 87.66 s 
+2025-05-05 02:06:22.741972: Yayy! New best EMA pseudo Dice: 0.05920000001788139 
+2025-05-05 02:06:25.027199:  
+2025-05-05 02:06:25.081162: Epoch 8 
+2025-05-05 02:06:25.106986: Current learning rate: 0.00996 
+2025-05-05 02:07:55.385432: train_loss 0.1674 
+2025-05-05 02:07:55.463323: val_loss 0.1088 
+2025-05-05 02:07:55.507991: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8294), np.float32(0.5098), np.float32(0.6568), np.float32(0.5082), np.float32(0.7026), np.float32(0.8068), np.float32(0.0), np.float32(0.0), np.float32(0.1293), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:07:55.541555: Epoch time: 90.36 s 
+2025-05-05 02:07:55.567377: Yayy! New best EMA pseudo Dice: 0.07509999722242355 
+2025-05-05 02:07:57.911408:  
+2025-05-05 02:07:57.962079: Epoch 9 
+2025-05-05 02:07:57.980765: Current learning rate: 0.00996 
+2025-05-05 02:09:30.619249: train_loss 0.1052 
+2025-05-05 02:09:30.715100: val_loss 0.068 
+2025-05-05 02:09:30.722500: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0024), np.float32(0.0), np.float32(0.1461), np.float32(1e-04), np.float32(0.7536), np.float32(0.8236), np.float32(0.8158), np.float32(0.6208), np.float32(0.8374), np.float32(0.7917), np.float32(0.0), np.float32(0.0), np.float32(0.4475), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:09:30.723264: Epoch time: 92.71 s 
+2025-05-05 02:09:30.723951: Yayy! New best EMA pseudo Dice: 0.09520000219345093 
+2025-05-05 02:09:32.586849:  
+2025-05-05 02:09:32.651729: Epoch 10 
+2025-05-05 02:09:32.710909: Current learning rate: 0.00995 
+2025-05-05 02:11:04.976849: train_loss 0.0892 
+2025-05-05 02:11:05.105960: val_loss 0.0324 
+2025-05-05 02:11:05.166496: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.448), np.float32(0.0), np.float32(0.0), np.float32(0.2509), np.float32(0.7703), np.float32(0.8248), np.float32(0.8028), np.float32(0.6371), np.float32(0.8688), np.float32(0.8253), np.float32(0.0), np.float32(0.298), np.float32(0.5332), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:11:05.218738: Epoch time: 92.39 s 
+2025-05-05 02:11:05.260882: Yayy! New best EMA pseudo Dice: 0.11860000342130661 
+2025-05-05 02:11:07.971493:  
+2025-05-05 02:11:07.976825: Epoch 11 
+2025-05-05 02:11:07.977762: Current learning rate: 0.00995 
+2025-05-05 02:12:44.045180: train_loss 0.0156 
+2025-05-05 02:12:44.141550: val_loss -0.021 
+2025-05-05 02:12:44.180443: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4834), np.float32(0.0), np.float32(0.5184), np.float32(0.0), np.float32(0.8412), np.float32(0.7849), np.float32(0.8231), np.float32(0.7247), np.float32(0.8598), np.float32(0.8537), np.float32(0.0), np.float32(0.7165), np.float32(0.5914), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-05-05 02:12:44.211119: Epoch time: 96.07 s 
+2025-05-05 02:12:44.236481: Yayy! New best EMA pseudo Dice: 0.1446000039577484 
+2025-05-05 02:12:46.301873:  
+2025-05-05 02:12:46.391757: Epoch 12 
+2025-05-05 02:12:46.424484: Current learning rate: 0.00995 
+2025-05-05 02:14:16.297538: train_loss -0.0031 
+2025-05-05 02:14:16.368358: val_loss -0.0222 
+2025-05-05 02:14:16.376011: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6107), np.float32(0.0), np.float32(0.2772), np.float32(0.3498), np.float32(0.8782), np.float32(0.8292), np.float32(0.8263), np.float32(0.7028), np.float32(0.855), np.float32(0.8332), np.float32(0.004), np.float32(0.5732), np.float32(0.5482), np.float32(0.0), np.float32(0.0), np.float32(0.1087)] 
+2025-05-05 02:14:16.376728: Epoch time: 90.0 s 
+2025-05-05 02:14:16.377245: Yayy! New best EMA pseudo Dice: 0.16910000145435333 
+2025-05-05 02:14:18.425286:  
+2025-05-05 02:14:18.450369: Epoch 13 
+2025-05-05 02:14:18.456485: Current learning rate: 0.00994 
+2025-05-05 02:15:55.631636: train_loss -0.0184 
+2025-05-05 02:15:55.717810: val_loss -0.0575 
+2025-05-05 02:15:55.726807: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6909), np.float32(0.0), np.float32(0.67), np.float32(0.6663), np.float32(0.8743), np.float32(0.737), np.float32(0.8139), np.float32(0.6611), np.float32(0.8808), np.float32(0.8363), np.float32(0.0348), np.float32(0.7789), np.float32(0.6081), np.float32(0.0), np.float32(0.0), np.float32(0.5216)] 
+2025-05-05 02:15:55.741148: Epoch time: 97.21 s 
+2025-05-05 02:15:55.749951: Yayy! New best EMA pseudo Dice: 0.19840000569820404 
+2025-05-05 02:15:58.321788:  
+2025-05-05 02:15:58.333947: Epoch 14 
+2025-05-05 02:15:58.334609: Current learning rate: 0.00994 
+2025-05-05 02:17:30.597138: train_loss -0.0573 
+2025-05-05 02:17:30.742681: val_loss -0.0433 
+2025-05-05 02:17:30.776655: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6373), np.float32(0.0), np.float32(0.5397), np.float32(0.6174), np.float32(0.889), np.float32(0.8078), np.float32(0.7719), np.float32(0.6919), np.float32(0.841), np.float32(0.8247), np.float32(0.0911), np.float32(0.7633), np.float32(0.627), np.float32(0.0), np.float32(0.0), np.float32(0.6744)] 
+2025-05-05 02:17:30.789262: Epoch time: 92.28 s 
+2025-05-05 02:17:30.805770: Yayy! New best EMA pseudo Dice: 0.22470000386238098 
+2025-05-05 02:17:32.962499:  
+2025-05-05 02:17:33.037978: Epoch 15 
+2025-05-05 02:17:33.079365: Current learning rate: 0.00993 
+2025-05-05 02:19:04.165320: train_loss -0.078 
+2025-05-05 02:19:04.240548: val_loss -0.1429 
+2025-05-05 02:19:04.259108: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6872), np.float32(0.0), np.float32(0.7652), np.float32(0.7063), np.float32(0.8941), np.float32(0.8586), np.float32(0.886), np.float32(0.7284), np.float32(0.8968), np.float32(0.84), np.float32(0.3467), np.float32(0.827), np.float32(0.6598), np.float32(0.0), np.float32(0.0), np.float32(0.7402)] 
+2025-05-05 02:19:04.263137: Epoch time: 91.2 s 
+2025-05-05 02:19:04.268260: Yayy! New best EMA pseudo Dice: 0.2540000081062317 
+2025-05-05 02:19:06.627665:  
+2025-05-05 02:19:06.716509: Epoch 16 
+2025-05-05 02:19:06.739822: Current learning rate: 0.00993 
+2025-05-05 02:20:44.079238: train_loss -0.1089 
+2025-05-05 02:20:44.182233: val_loss -0.1832 
+2025-05-05 02:20:44.189899: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7734), np.float32(0.0), np.float32(0.8092), np.float32(0.7878), np.float32(0.9171), np.float32(0.8668), np.float32(0.8942), np.float32(0.8136), np.float32(0.8961), np.float32(0.8924), np.float32(0.5042), np.float32(0.8775), np.float32(0.6759), np.float32(0.0), np.float32(0.0), np.float32(0.7881)] 
+2025-05-05 02:20:44.203216: Epoch time: 97.45 s 
+2025-05-05 02:20:44.205451: Yayy! New best EMA pseudo Dice: 0.2838999927043915 
+2025-05-05 02:20:46.255178:  
+2025-05-05 02:20:46.371639: Epoch 17 
+2025-05-05 02:20:46.372435: Current learning rate: 0.00992 
+2025-05-05 02:22:17.099128: train_loss -0.1459 
+2025-05-05 02:22:17.174523: val_loss -0.1784 
+2025-05-05 02:22:17.204991: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.9117), np.float32(0.0), np.float32(0.8212), np.float32(0.791), np.float32(0.9156), np.float32(0.8574), np.float32(0.862), np.float32(0.7442), np.float32(0.8542), np.float32(0.7988), np.float32(0.471), np.float32(0.8586), np.float32(0.7014), np.float32(0.0), np.float32(0.0), np.float32(0.7911)] 
+2025-05-05 02:22:17.222609: Epoch time: 90.85 s 
+2025-05-05 02:22:17.237647: Yayy! New best EMA pseudo Dice: 0.3100999891757965 
+2025-05-05 02:22:19.705527:  
+2025-05-05 02:22:19.708988: Epoch 18 
+2025-05-05 02:22:19.709714: Current learning rate: 0.00992 
+2025-05-05 02:24:05.412633: train_loss -0.1582 
+2025-05-05 02:24:05.511623: val_loss -0.1716 
+2025-05-05 02:24:05.535939: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8554), np.float32(0.0), np.float32(0.6646), np.float32(0.7561), np.float32(0.9037), np.float32(0.8595), np.float32(0.8556), np.float32(0.7268), np.float32(0.8823), np.float32(0.8458), np.float32(0.5495), np.float32(0.8481), np.float32(0.7378), np.float32(0.0), np.float32(0.0), np.float32(0.8019)] 
+2025-05-05 02:24:05.557901: Epoch time: 105.71 s 
+2025-05-05 02:24:05.574472: Yayy! New best EMA pseudo Dice: 0.33320000767707825 
+2025-05-05 02:24:07.826065:  
+2025-05-05 02:24:07.847232: Epoch 19 
+2025-05-05 02:24:07.848104: Current learning rate: 0.00991 
+2025-05-05 02:25:37.039411: train_loss -0.1952 
+2025-05-05 02:25:37.125680: val_loss -0.2189 
+2025-05-05 02:25:37.166214: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.909), np.float32(0.0), np.float32(0.8363), np.float32(0.8276), np.float32(0.9307), np.float32(0.8913), np.float32(0.8777), np.float32(0.7757), np.float32(0.8883), np.float32(0.8828), np.float32(0.5425), np.float32(0.8693), np.float32(0.805), np.float32(0.0), np.float32(0.0), np.float32(0.8252)] 
+2025-05-05 02:25:37.203062: Epoch time: 89.21 s 
+2025-05-05 02:25:37.240101: Yayy! New best EMA pseudo Dice: 0.3571000099182129 
+2025-05-05 02:25:39.344309:  
+2025-05-05 02:25:39.423556: Epoch 20 
+2025-05-05 02:25:39.457336: Current learning rate: 0.00991 
+2025-05-05 02:27:11.973441: train_loss -0.1835 
+2025-05-05 02:27:12.001245: val_loss -0.2261 
+2025-05-05 02:27:12.002136: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7819), np.float32(0.0), np.float32(0.8293), np.float32(0.7914), np.float32(0.9306), np.float32(0.8788), np.float32(0.8827), np.float32(0.7917), np.float32(0.8929), np.float32(0.8798), np.float32(0.6037), np.float32(0.8765), np.float32(0.745), np.float32(0.0), np.float32(0.0), np.float32(0.8327)] 
+2025-05-05 02:27:12.009727: Epoch time: 92.63 s 
+2025-05-05 02:27:12.010276: Yayy! New best EMA pseudo Dice: 0.37779998779296875 
+2025-05-05 02:27:14.147223:  
+2025-05-05 02:27:14.233614: Epoch 21 
+2025-05-05 02:27:14.274705: Current learning rate: 0.00991 
+2025-05-05 02:28:42.466727: train_loss -0.172 
+2025-05-05 02:28:42.561055: val_loss -0.2107 
+2025-05-05 02:28:42.585562: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8491), np.float32(0.0), np.float32(0.7968), np.float32(0.824), np.float32(0.9399), np.float32(0.8387), np.float32(0.8896), np.float32(0.7929), np.float32(0.8949), np.float32(0.8682), np.float32(0.6238), np.float32(0.8479), np.float32(0.7304), np.float32(0.0), np.float32(0.0), np.float32(0.7992)] 
+2025-05-05 02:28:42.615612: Epoch time: 88.32 s 
+2025-05-05 02:28:42.631903: Yayy! New best EMA pseudo Dice: 0.39629998803138733 
+2025-05-05 02:28:44.939397:  
+2025-05-05 02:28:44.958122: Epoch 22 
+2025-05-05 02:28:44.959223: Current learning rate: 0.0099 
+2025-05-05 02:30:27.802723: train_loss -0.1977 
+2025-05-05 02:30:27.989227: val_loss -0.2262 
+2025-05-05 02:30:28.005884: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.1953), np.float32(0.8911), np.float32(0.0), np.float32(0.7946), np.float32(0.8602), np.float32(0.937), np.float32(0.8918), np.float32(0.8723), np.float32(0.7931), np.float32(0.9163), np.float32(0.8899), np.float32(0.6301), np.float32(0.8549), np.float32(0.7425), np.float32(0.0019), np.float32(0.0), np.float32(0.8276)] 
+2025-05-05 02:30:28.031894: Epoch time: 102.86 s 
+2025-05-05 02:30:28.052688: Yayy! New best EMA pseudo Dice: 0.41510000824928284 
+2025-05-05 02:30:30.196377:  
+2025-05-05 02:30:30.318996: Epoch 23 
+2025-05-05 02:30:30.365697: Current learning rate: 0.0099 
+2025-05-05 02:32:04.634463: train_loss -0.1711 
+2025-05-05 02:32:04.768111: val_loss -0.1948 
+2025-05-05 02:32:04.777834: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.3832), np.float32(0.7857), np.float32(0.0), np.float32(0.79), np.float32(0.8176), np.float32(0.9196), np.float32(0.8801), np.float32(0.8514), np.float32(0.7086), np.float32(0.8909), np.float32(0.8502), np.float32(0.5517), np.float32(0.8782), np.float32(0.7914), np.float32(0.0007), np.float32(0.0), np.float32(0.8145)] 
+2025-05-05 02:32:04.778728: Epoch time: 94.44 s 
+2025-05-05 02:32:04.793625: Yayy! New best EMA pseudo Dice: 0.4309999942779541 
+2025-05-05 02:32:06.680592:  
+2025-05-05 02:32:06.757434: Epoch 24 
+2025-05-05 02:32:06.778215: Current learning rate: 0.00989 
+2025-05-05 02:33:57.993630: train_loss -0.1781 
+2025-05-05 02:33:58.100767: val_loss -0.2257 
+2025-05-05 02:33:58.114344: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.3882), np.float32(0.9083), np.float32(0.0), np.float32(0.8469), np.float32(0.8831), np.float32(0.9319), np.float32(0.8788), np.float32(0.8894), np.float32(0.7881), np.float32(0.8885), np.float32(0.8825), np.float32(0.6622), np.float32(0.9106), np.float32(0.7658), np.float32(0.0325), np.float32(0.0), np.float32(0.812)] 
+2025-05-05 02:33:58.121713: Epoch time: 111.31 s 
+2025-05-05 02:33:58.143992: Yayy! New best EMA pseudo Dice: 0.44830000400543213 
+2025-05-05 02:34:00.914515:  
+2025-05-05 02:34:00.926797: Epoch 25 
+2025-05-05 02:34:00.933803: Current learning rate: 0.00989 
+2025-05-05 02:36:03.623972: train_loss -0.2208 
+2025-05-05 02:36:03.667769: val_loss -0.2337 
+2025-05-05 02:36:03.668902: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.7876), np.float32(0.9132), np.float32(0.4778), np.float32(0.9035), np.float32(0.8606), np.float32(0.9245), np.float32(0.8857), np.float32(0.8569), np.float32(0.7824), np.float32(0.9084), np.float32(0.896), np.float32(0.6205), np.float32(0.9244), np.float32(0.8363), np.float32(0.4875), np.float32(0.0), np.float32(0.8534)] 
+2025-05-05 02:36:03.672691: Epoch time: 122.71 s 
+2025-05-05 02:36:03.678307: Yayy! New best EMA pseudo Dice: 0.4713999927043915 
+2025-05-05 02:36:05.757294:  
+2025-05-05 02:36:05.791011: Epoch 26 
+2025-05-05 02:36:05.806103: Current learning rate: 0.00988 
+2025-05-05 02:37:35.384787: train_loss -0.2278 
+2025-05-05 02:37:35.483586: val_loss -0.2404 
+2025-05-05 02:37:35.500906: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.7186), np.float32(0.9328), np.float32(0.36), np.float32(0.8239), np.float32(0.9071), np.float32(0.9481), np.float32(0.8388), np.float32(0.8789), np.float32(0.7861), np.float32(0.9151), np.float32(0.8855), np.float32(0.6304), np.float32(0.8614), np.float32(0.7607), np.float32(0.4228), np.float32(0.0), np.float32(0.8596)] 
+2025-05-05 02:37:35.519617: Epoch time: 89.63 s 
+2025-05-05 02:37:35.527444: Yayy! New best EMA pseudo Dice: 0.490200012922287 
+2025-05-05 02:37:37.701199:  
+2025-05-05 02:37:37.708099: Epoch 27 
+2025-05-05 02:37:37.709313: Current learning rate: 0.00988 
+2025-05-05 02:39:08.031541: train_loss -0.2259 
+2025-05-05 02:39:08.170652: val_loss -0.3014 
+2025-05-05 02:39:08.218383: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.5981), np.float32(0.932), np.float32(0.58), np.float32(0.8468), np.float32(0.8305), np.float32(0.943), np.float32(0.8853), np.float32(0.8931), np.float32(0.8312), np.float32(0.9125), np.float32(0.9117), np.float32(0.5835), np.float32(0.913), np.float32(0.7902), np.float32(0.5709), np.float32(0.017), np.float32(0.8579)] 
+2025-05-05 02:39:08.270620: Epoch time: 90.33 s 
+2025-05-05 02:39:08.326314: Yayy! New best EMA pseudo Dice: 0.5091000199317932 
+2025-05-05 02:39:10.661264:  
+2025-05-05 02:39:10.695913: Epoch 28 
+2025-05-05 02:39:10.717753: Current learning rate: 0.00987 
+2025-05-05 02:40:38.096733: train_loss -0.2403 
+2025-05-05 02:40:38.186277: val_loss -0.2762 
+2025-05-05 02:40:38.224702: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.4175), np.float32(0.8375), np.float32(0.5813), np.float32(0.9004), np.float32(0.8878), np.float32(0.9416), np.float32(0.9134), np.float32(0.9127), np.float32(0.7907), np.float32(0.9181), np.float32(0.9058), np.float32(0.7014), np.float32(0.8491), np.float32(0.7964), np.float32(0.5489), np.float32(0.0253), np.float32(0.8318)] 
+2025-05-05 02:40:38.256275: Epoch time: 87.44 s 
+2025-05-05 02:40:38.260079: Yayy! New best EMA pseudo Dice: 0.5253000259399414 
+2025-05-05 02:40:41.051476:  
+2025-05-05 02:40:41.056269: Epoch 29 
+2025-05-05 02:40:41.056619: Current learning rate: 0.00987 
+2025-05-05 02:42:09.922490: train_loss -0.2672 
+2025-05-05 02:42:09.951898: val_loss -0.306 
+2025-05-05 02:42:09.952789: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.7119), np.float32(0.8735), np.float32(0.6958), np.float32(0.8867), np.float32(0.8588), np.float32(0.9285), np.float32(0.9166), np.float32(0.9213), np.float32(0.8259), np.float32(0.9281), np.float32(0.8962), np.float32(0.7182), np.float32(0.9242), np.float32(0.778), np.float32(0.7099), np.float32(0.4446), np.float32(0.8543)] 
+2025-05-05 02:42:09.965220: Epoch time: 88.87 s 
+2025-05-05 02:42:09.986919: Yayy! New best EMA pseudo Dice: 0.545799970626831 
+2025-05-05 02:42:12.020289:  
+2025-05-05 02:42:12.077716: Epoch 30 
+2025-05-05 02:42:12.078429: Current learning rate: 0.00986 
+2025-05-05 02:43:50.678435: train_loss -0.262 
+2025-05-05 02:43:50.741215: val_loss -0.3014 
+2025-05-05 02:43:50.742079: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.7121), np.float32(0.9064), np.float32(0.5036), np.float32(0.8993), np.float32(0.8788), np.float32(0.9266), np.float32(0.9027), np.float32(0.922), np.float32(0.8121), np.float32(0.9165), np.float32(0.9152), np.float32(0.7064), np.float32(0.9032), np.float32(0.819), np.float32(0.6438), np.float32(0.6091), np.float32(0.8569)] 
+2025-05-05 02:43:50.749506: Epoch time: 98.66 s 
+2025-05-05 02:43:50.749995: Yayy! New best EMA pseudo Dice: 0.5640000104904175 
+2025-05-05 02:43:53.131005:  
+2025-05-05 02:43:53.138168: Epoch 31 
+2025-05-05 02:43:53.138829: Current learning rate: 0.00986 
+2025-05-05 02:45:22.790325: train_loss -0.2953 
+2025-05-05 02:45:22.837246: val_loss -0.2976 
+2025-05-05 02:45:22.863423: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.6645), np.float32(0.9235), np.float32(0.6253), np.float32(0.8629), np.float32(0.8264), np.float32(0.9431), np.float32(0.9217), np.float32(0.8958), np.float32(0.8513), np.float32(0.9322), np.float32(0.9127), np.float32(0.7072), np.float32(0.9264), np.float32(0.8316), np.float32(0.6756), np.float32(0.5888), np.float32(0.8546)] 
+2025-05-05 02:45:22.899584: Epoch time: 89.66 s 
+2025-05-05 02:45:22.925448: Yayy! New best EMA pseudo Dice: 0.5809999704360962 
+2025-05-05 02:45:25.006912:  
+2025-05-05 02:45:25.011860: Epoch 32 
+2025-05-05 02:45:25.012376: Current learning rate: 0.00986 
+2025-05-05 02:46:53.631892: train_loss -0.2945 
+2025-05-05 02:46:53.722230: val_loss -0.3107 
+2025-05-05 02:46:53.746002: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.813), np.float32(0.9375), np.float32(0.6419), np.float32(0.9305), np.float32(0.9015), np.float32(0.9556), np.float32(0.931), np.float32(0.8944), np.float32(0.8051), np.float32(0.9384), np.float32(0.8846), np.float32(0.7266), np.float32(0.9496), np.float32(0.8797), np.float32(0.711), np.float32(0.6632), np.float32(0.8681)] 
+2025-05-05 02:46:53.775017: Epoch time: 88.63 s 
+2025-05-05 02:46:53.824611: Yayy! New best EMA pseudo Dice: 0.5989000201225281 
+2025-05-05 02:46:56.074925:  
+2025-05-05 02:46:56.076497: Epoch 33 
+2025-05-05 02:46:56.076917: Current learning rate: 0.00985 
+2025-05-05 02:48:29.164418: train_loss -0.303 
+2025-05-05 02:48:29.275743: val_loss -0.315 
+2025-05-05 02:48:29.291312: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.6765), np.float32(0.9531), np.float32(0.7115), np.float32(0.892), np.float32(0.8872), np.float32(0.9544), np.float32(0.8936), np.float32(0.9015), np.float32(0.8158), np.float32(0.9116), np.float32(0.8893), np.float32(0.7304), np.float32(0.9135), np.float32(0.8152), np.float32(0.6779), np.float32(0.5921), np.float32(0.8725)] 
+2025-05-05 02:48:29.318957: Epoch time: 93.09 s 
+2025-05-05 02:48:29.351973: Yayy! New best EMA pseudo Dice: 0.613099992275238 
+2025-05-05 02:48:32.167067:  
+2025-05-05 02:48:32.307766: Epoch 34 
+2025-05-05 02:48:32.344622: Current learning rate: 0.00985 
+2025-05-05 02:50:00.801875: train_loss -0.3128 
+2025-05-05 02:50:00.830189: val_loss -0.3317 
+2025-05-05 02:50:00.843374: Pseudo dice [np.float32(0.2953), np.float32(0.0), np.float32(0.7479), np.float32(0.8989), np.float32(0.6449), np.float32(0.9023), np.float32(0.9141), np.float32(0.95), np.float32(0.9074), np.float32(0.9332), np.float32(0.8902), np.float32(0.9276), np.float32(0.9441), np.float32(0.7033), np.float32(0.9212), np.float32(0.8305), np.float32(0.7164), np.float32(0.6754), np.float32(0.867)] 
+2025-05-05 02:50:00.854347: Epoch time: 88.64 s 
+2025-05-05 02:50:00.865550: Yayy! New best EMA pseudo Dice: 0.6290000081062317 
+2025-05-05 02:50:02.888913:  
+2025-05-05 02:50:02.932893: Epoch 35 
+2025-05-05 02:50:02.943991: Current learning rate: 0.00984 
+2025-05-05 02:51:35.065765: train_loss -0.3243 
+2025-05-05 02:51:35.154431: val_loss -0.3439 
+2025-05-05 02:51:35.196926: Pseudo dice [np.float32(0.4176), np.float32(0.0002), np.float32(0.3037), np.float32(0.9703), np.float32(0.6792), np.float32(0.9099), np.float32(0.9348), np.float32(0.9419), np.float32(0.9281), np.float32(0.9302), np.float32(0.8829), np.float32(0.9175), np.float32(0.9412), np.float32(0.7732), np.float32(0.9056), np.float32(0.8632), np.float32(0.6879), np.float32(0.6597), np.float32(0.8874)] 
+2025-05-05 02:51:35.225594: Epoch time: 92.18 s 
+2025-05-05 02:51:35.268810: Yayy! New best EMA pseudo Dice: 0.6425999999046326 
+2025-05-05 02:51:37.419974:  
+2025-05-05 02:51:37.424623: Epoch 36 
+2025-05-05 02:51:37.425088: Current learning rate: 0.00984 
+2025-05-05 02:53:07.164303: train_loss -0.2867 
+2025-05-05 02:53:07.218947: val_loss -0.3047 
+2025-05-05 02:53:07.244866: Pseudo dice [np.float32(0.2545), np.float32(0.1387), np.float32(0.6681), np.float32(0.8456), np.float32(0.5904), np.float32(0.8551), np.float32(0.8563), np.float32(0.9358), np.float32(0.9305), np.float32(0.8964), np.float32(0.8169), np.float32(0.9319), np.float32(0.9095), np.float32(0.7067), np.float32(0.8538), np.float32(0.797), np.float32(0.607), np.float32(0.6171), np.float32(0.8641)] 
+2025-05-05 02:53:07.287232: Epoch time: 89.75 s 
+2025-05-05 02:53:07.320705: Yayy! New best EMA pseudo Dice: 0.652400016784668 
+2025-05-05 02:53:09.717401:  
+2025-05-05 02:53:09.722352: Epoch 37 
+2025-05-05 02:53:09.722834: Current learning rate: 0.00983 
+2025-05-05 02:54:49.204546: train_loss -0.2999 
+2025-05-05 02:54:49.301277: val_loss -0.3205 
+2025-05-05 02:54:49.316360: Pseudo dice [np.float32(0.3974), np.float32(0.4768), np.float32(0.6874), np.float32(0.928), np.float32(0.5993), np.float32(0.9148), np.float32(0.867), np.float32(0.9472), np.float32(0.9272), np.float32(0.912), np.float32(0.8677), np.float32(0.9369), np.float32(0.9347), np.float32(0.6455), np.float32(0.9224), np.float32(0.8887), np.float32(0.7154), np.float32(0.6797), np.float32(0.8749)] 
+2025-05-05 02:54:49.336030: Epoch time: 99.49 s 
+2025-05-05 02:54:49.359442: Yayy! New best EMA pseudo Dice: 0.6668000221252441 
+2025-05-05 02:54:51.824888:  
+2025-05-05 02:54:51.829676: Epoch 38 
+2025-05-05 02:54:51.830114: Current learning rate: 0.00983 
+2025-05-05 02:56:19.321785: train_loss -0.3283 
+2025-05-05 02:56:19.386456: val_loss -0.364 
+2025-05-05 02:56:19.411815: Pseudo dice [np.float32(0.5951), np.float32(0.5026), np.float32(0.7129), np.float32(0.955), np.float32(0.759), np.float32(0.8945), np.float32(0.892), np.float32(0.9553), np.float32(0.9134), np.float32(0.9286), np.float32(0.8879), np.float32(0.9366), np.float32(0.9268), np.float32(0.7388), np.float32(0.9243), np.float32(0.8422), np.float32(0.7172), np.float32(0.6685), np.float32(0.8857)] 
+2025-05-05 02:56:19.442386: Epoch time: 87.5 s 
+2025-05-05 02:56:19.489371: Yayy! New best EMA pseudo Dice: 0.6823999881744385 
+2025-05-05 02:56:21.590521:  
+2025-05-05 02:56:21.642052: Epoch 39 
+2025-05-05 02:56:21.644405: Current learning rate: 0.00982 
+2025-05-05 02:57:50.463310: train_loss -0.3238 
+2025-05-05 02:57:50.493025: val_loss -0.3211 
+2025-05-05 02:57:50.498154: Pseudo dice [np.float32(0.5574), np.float32(0.4657), np.float32(0.6781), np.float32(0.8558), np.float32(0.7543), np.float32(0.898), np.float32(0.9226), np.float32(0.9406), np.float32(0.9363), np.float32(0.9191), np.float32(0.8335), np.float32(0.9398), np.float32(0.9277), np.float32(0.7336), np.float32(0.9159), np.float32(0.8701), np.float32(0.7479), np.float32(0.6813), np.float32(0.8357)] 
+2025-05-05 02:57:50.502766: Epoch time: 88.87 s 
+2025-05-05 02:57:50.508443: Yayy! New best EMA pseudo Dice: 0.6952999830245972 
+2025-05-05 02:57:53.048534:  
+2025-05-05 02:57:53.133529: Epoch 40 
+2025-05-05 02:57:53.166511: Current learning rate: 0.00982 
+2025-05-05 02:59:34.525471: train_loss -0.3512 
+2025-05-05 02:59:34.537467: val_loss -0.3565 
+2025-05-05 02:59:34.538114: Pseudo dice [np.float32(0.5969), np.float32(0.5513), np.float32(0.5376), np.float32(0.9216), np.float32(0.7454), np.float32(0.9113), np.float32(0.8885), np.float32(0.9448), np.float32(0.9171), np.float32(0.9284), np.float32(0.8334), np.float32(0.935), np.float32(0.9061), np.float32(0.7461), np.float32(0.9435), np.float32(0.8552), np.float32(0.547), np.float32(0.513), np.float32(0.8535)] 
+2025-05-05 02:59:34.538605: Epoch time: 101.48 s 
+2025-05-05 02:59:34.549952: Yayy! New best EMA pseudo Dice: 0.7050999999046326 
+2025-05-05 02:59:36.996564:  
+2025-05-05 02:59:37.088819: Epoch 41 
+2025-05-05 02:59:37.113324: Current learning rate: 0.00982 
+2025-05-05 03:01:04.394757: train_loss -0.3249 
+2025-05-05 03:01:04.486988: val_loss -0.3494 
+2025-05-05 03:01:04.521602: Pseudo dice [np.float32(0.5745), np.float32(0.5964), np.float32(0.3509), np.float32(0.9308), np.float32(0.6449), np.float32(0.9218), np.float32(0.919), np.float32(0.9584), np.float32(0.9249), np.float32(0.8863), np.float32(0.8), np.float32(0.933), np.float32(0.9032), np.float32(0.7726), np.float32(0.9351), np.float32(0.883), np.float32(0.7194), np.float32(0.7152), np.float32(0.8755)] 
+2025-05-05 03:01:04.545592: Epoch time: 87.4 s 
+2025-05-05 03:01:04.571230: Yayy! New best EMA pseudo Dice: 0.7148000001907349 
+2025-05-05 03:01:06.660908:  
+2025-05-05 03:01:06.715518: Epoch 42 
+2025-05-05 03:01:06.716532: Current learning rate: 0.00981 
+2025-05-05 03:02:46.892148: train_loss -0.351 
+2025-05-05 03:02:47.077774: val_loss -0.3921 
+2025-05-05 03:02:47.111758: Pseudo dice [np.float32(0.6108), np.float32(0.6105), np.float32(0.834), np.float32(0.9432), np.float32(0.7011), np.float32(0.9116), np.float32(0.9246), np.float32(0.9442), np.float32(0.9526), np.float32(0.9323), np.float32(0.8873), np.float32(0.9528), np.float32(0.9415), np.float32(0.7856), np.float32(0.9158), np.float32(0.8829), np.float32(0.7552), np.float32(0.7263), np.float32(0.8898)] 
+2025-05-05 03:02:47.175056: Epoch time: 100.23 s 
+2025-05-05 03:02:47.218597: Yayy! New best EMA pseudo Dice: 0.7281000018119812 
+2025-05-05 03:02:50.318919:  
+2025-05-05 03:02:50.326202: Epoch 43 
+2025-05-05 03:02:50.330210: Current learning rate: 0.00981 
+2025-05-05 03:04:22.765122: train_loss -0.3492 
+2025-05-05 03:04:22.788554: val_loss -0.3785 
+2025-05-05 03:04:22.822022: Pseudo dice [np.float32(0.5844), np.float32(0.5894), np.float32(0.7575), np.float32(0.9485), np.float32(0.7008), np.float32(0.9056), np.float32(0.9211), np.float32(0.961), np.float32(0.9401), np.float32(0.9399), np.float32(0.8888), np.float32(0.952), np.float32(0.9413), np.float32(0.7049), np.float32(0.9352), np.float32(0.8701), np.float32(0.7472), np.float32(0.7001), np.float32(0.8703)] 
+2025-05-05 03:04:22.840264: Epoch time: 92.45 s 
+2025-05-05 03:04:22.852187: Yayy! New best EMA pseudo Dice: 0.7387999892234802 
+2025-05-05 03:04:25.156870:  
+2025-05-05 03:04:25.193161: Epoch 44 
+2025-05-05 03:04:25.211490: Current learning rate: 0.0098 
+2025-05-05 03:05:54.642654: train_loss -0.3545 
+2025-05-05 03:05:54.854137: val_loss -0.3859 
+2025-05-05 03:05:54.881923: Pseudo dice [np.float32(0.6939), np.float32(0.66), np.float32(0.6514), np.float32(0.965), np.float32(0.7116), np.float32(0.915), np.float32(0.8977), np.float32(0.9561), np.float32(0.9395), np.float32(0.9212), np.float32(0.8681), np.float32(0.9405), np.float32(0.9427), np.float32(0.7652), np.float32(0.9529), np.float32(0.8027), np.float32(0.7618), np.float32(0.7573), np.float32(0.8983)] 
+2025-05-05 03:05:54.888499: Epoch time: 89.49 s 
+2025-05-05 03:05:54.889020: Yayy! New best EMA pseudo Dice: 0.7491000294685364 
+2025-05-05 03:05:58.872443:  
+2025-05-05 03:05:58.875334: Epoch 45 
+2025-05-05 03:05:58.875944: Current learning rate: 0.0098 
+2025-05-05 03:07:31.448857: train_loss -0.3753 
+2025-05-05 03:07:31.616183: val_loss -0.3746 
+2025-05-05 03:07:31.649810: Pseudo dice [np.float32(0.7408), np.float32(0.7093), np.float32(0.7516), np.float32(0.9447), np.float32(0.5341), np.float32(0.9037), np.float32(0.925), np.float32(0.9643), np.float32(0.9266), np.float32(0.9446), np.float32(0.8994), np.float32(0.9267), np.float32(0.9202), np.float32(0.7663), np.float32(0.9386), np.float32(0.8932), np.float32(0.7625), np.float32(0.7445), np.float32(0.8694)] 
+2025-05-05 03:07:31.662278: Epoch time: 92.58 s 
+2025-05-05 03:07:31.662855: Yayy! New best EMA pseudo Dice: 0.7587000131607056 
+2025-05-05 03:07:33.868713:  
+2025-05-05 03:07:33.893865: Epoch 46 
+2025-05-05 03:07:33.925935: Current learning rate: 0.00979 
+2025-05-05 03:09:16.858586: train_loss -0.3782 
+2025-05-05 03:09:17.069689: val_loss -0.3988 
+2025-05-05 03:09:17.134753: Pseudo dice [np.float32(0.7215), np.float32(0.7457), np.float32(0.7612), np.float32(0.9466), np.float32(0.7388), np.float32(0.9343), np.float32(0.8969), np.float32(0.9595), np.float32(0.9432), np.float32(0.938), np.float32(0.9051), np.float32(0.9504), np.float32(0.9373), np.float32(0.7857), np.float32(0.952), np.float32(0.9037), np.float32(0.7739), np.float32(0.7614), np.float32(0.8847)] 
+2025-05-05 03:09:17.172428: Epoch time: 102.99 s 
+2025-05-05 03:09:17.190284: Yayy! New best EMA pseudo Dice: 0.7694000005722046 
+2025-05-05 03:09:19.466143:  
+2025-05-05 03:09:19.474916: Epoch 47 
+2025-05-05 03:09:19.515056: Current learning rate: 0.00979 
+2025-05-05 03:10:53.637358: train_loss -0.3721 
+2025-05-05 03:10:53.757009: val_loss -0.4219 
+2025-05-05 03:10:53.782962: Pseudo dice [np.float32(0.7447), np.float32(0.7136), np.float32(0.6483), np.float32(0.9035), np.float32(0.6948), np.float32(0.9356), np.float32(0.9326), np.float32(0.9562), np.float32(0.9289), np.float32(0.9331), np.float32(0.8737), np.float32(0.9485), np.float32(0.9385), np.float32(0.7871), np.float32(0.9248), np.float32(0.8837), np.float32(0.7607), np.float32(0.7446), np.float32(0.8665)] 
+2025-05-05 03:10:53.804000: Epoch time: 94.17 s 
+2025-05-05 03:10:53.822071: Yayy! New best EMA pseudo Dice: 0.7773000001907349 
+2025-05-05 03:10:56.993379:  
+2025-05-05 03:10:57.072208: Epoch 48 
+2025-05-05 03:10:57.098863: Current learning rate: 0.00978 
+2025-05-05 03:12:35.040192: train_loss -0.3644 
+2025-05-05 03:12:35.121534: val_loss -0.3489 
+2025-05-05 03:12:35.138208: Pseudo dice [np.float32(0.6159), np.float32(0.5348), np.float32(0.8208), np.float32(0.9519), np.float32(0.7475), np.float32(0.8409), np.float32(0.8725), np.float32(0.9337), np.float32(0.9203), np.float32(0.9192), np.float32(0.8776), np.float32(0.9073), np.float32(0.9379), np.float32(0.7641), np.float32(0.8462), np.float32(0.8523), np.float32(0.7336), np.float32(0.6876), np.float32(0.8399)] 
+2025-05-05 03:12:35.150626: Epoch time: 98.05 s 
+2025-05-05 03:12:35.151169: Yayy! New best EMA pseudo Dice: 0.7817000150680542 
+2025-05-05 03:12:37.779997:  
+2025-05-05 03:12:37.865552: Epoch 49 
+2025-05-05 03:12:37.933186: Current learning rate: 0.00978 
+2025-05-05 03:14:09.699088: train_loss -0.3674 
+2025-05-05 03:14:09.803128: val_loss -0.3927 
+2025-05-05 03:14:09.830225: Pseudo dice [np.float32(0.6981), np.float32(0.7049), np.float32(0.7442), np.float32(0.9493), np.float32(0.7673), np.float32(0.9347), np.float32(0.9247), np.float32(0.9482), np.float32(0.9096), np.float32(0.9172), np.float32(0.8185), np.float32(0.9212), np.float32(0.9298), np.float32(0.7803), np.float32(0.947), np.float32(0.8804), np.float32(0.7082), np.float32(0.7468), np.float32(0.8598)] 
+2025-05-05 03:14:09.835995: Epoch time: 91.92 s 
+2025-05-05 03:14:11.121355: Yayy! New best EMA pseudo Dice: 0.7882000207901001 
+2025-05-05 03:14:13.205822:  
+2025-05-05 03:14:13.236989: Epoch 50 
+2025-05-05 03:14:13.241068: Current learning rate: 0.00977 
+2025-05-05 03:16:05.089773: train_loss -0.3822 
+2025-05-05 03:16:05.220974: val_loss -0.4102 
+2025-05-05 03:16:05.249617: Pseudo dice [np.float32(0.6096), np.float32(0.7522), np.float32(0.6893), np.float32(0.9729), np.float32(0.7931), np.float32(0.9322), np.float32(0.9453), np.float32(0.9658), np.float32(0.9254), np.float32(0.9304), np.float32(0.9019), np.float32(0.9518), np.float32(0.9527), np.float32(0.773), np.float32(0.9389), np.float32(0.8954), np.float32(0.7648), np.float32(0.7142), np.float32(0.8737)] 
+2025-05-05 03:16:05.270053: Epoch time: 111.89 s 
+2025-05-05 03:16:05.270617: Yayy! New best EMA pseudo Dice: 0.7950999736785889 
+2025-05-05 03:16:07.214303:  
+2025-05-05 03:16:07.238958: Epoch 51 
+2025-05-05 03:16:07.242964: Current learning rate: 0.00977 
+2025-05-05 03:17:41.137206: train_loss -0.3703 
+2025-05-05 03:17:41.241975: val_loss -0.4154 
+2025-05-05 03:17:41.252475: Pseudo dice [np.float32(0.7152), np.float32(0.7305), np.float32(0.6989), np.float32(0.9642), np.float32(0.6459), np.float32(0.9108), np.float32(0.9158), np.float32(0.9651), np.float32(0.9092), np.float32(0.9514), np.float32(0.9096), np.float32(0.9444), np.float32(0.949), np.float32(0.7804), np.float32(0.9297), np.float32(0.8504), np.float32(0.6829), np.float32(0.7075), np.float32(0.8878)] 
+2025-05-05 03:17:41.271181: Epoch time: 93.92 s 
+2025-05-05 03:17:41.281991: Yayy! New best EMA pseudo Dice: 0.800000011920929 
+2025-05-05 03:17:43.363841:  
+2025-05-05 03:17:43.446196: Epoch 52 
+2025-05-05 03:17:43.448735: Current learning rate: 0.00977 
+2025-05-05 03:19:12.573872: train_loss -0.3806 
+2025-05-05 03:19:12.622024: val_loss -0.4254 
+2025-05-05 03:19:12.632277: Pseudo dice [np.float32(0.5907), np.float32(0.6904), np.float32(0.7404), np.float32(0.9105), np.float32(0.7986), np.float32(0.9152), np.float32(0.9237), np.float32(0.9646), np.float32(0.9415), np.float32(0.9376), np.float32(0.9124), np.float32(0.9409), np.float32(0.9411), np.float32(0.789), np.float32(0.9411), np.float32(0.8739), np.float32(0.7924), np.float32(0.7824), np.float32(0.8897)] 
+2025-05-05 03:19:12.632951: Epoch time: 89.21 s 
+2025-05-05 03:19:12.633450: Yayy! New best EMA pseudo Dice: 0.8057000041007996 
+2025-05-05 03:19:14.796162:  
+2025-05-05 03:19:14.861882: Epoch 53 
+2025-05-05 03:19:14.864511: Current learning rate: 0.00976 
+2025-05-05 03:21:02.588184: train_loss -0.3895 
+2025-05-05 03:21:02.717274: val_loss -0.4227 
+2025-05-05 03:21:02.736589: Pseudo dice [np.float32(0.7495), np.float32(0.7134), np.float32(0.7075), np.float32(0.9576), np.float32(0.7968), np.float32(0.944), np.float32(0.9315), np.float32(0.964), np.float32(0.93), np.float32(0.9236), np.float32(0.9109), np.float32(0.9485), np.float32(0.93), np.float32(0.8087), np.float32(0.9485), np.float32(0.9165), np.float32(0.7556), np.float32(0.7568), np.float32(0.8828)] 
+2025-05-05 03:21:02.752831: Epoch time: 107.79 s 
+2025-05-05 03:21:02.753312: Yayy! New best EMA pseudo Dice: 0.8118000030517578 
+2025-05-05 03:21:04.741436:  
+2025-05-05 03:21:04.841394: Epoch 54 
+2025-05-05 03:21:04.900499: Current learning rate: 0.00976 
+2025-05-05 03:22:35.550418: train_loss -0.3564 
+2025-05-05 03:22:35.670984: val_loss -0.4095 
+2025-05-05 03:22:35.693122: Pseudo dice [np.float32(0.6855), np.float32(0.7352), np.float32(0.8483), np.float32(0.8943), np.float32(0.7729), np.float32(0.9281), np.float32(0.9473), np.float32(0.9576), np.float32(0.9472), np.float32(0.9505), np.float32(0.9088), np.float32(0.9524), np.float32(0.9505), np.float32(0.74), np.float32(0.9447), np.float32(0.8347), np.float32(0.7657), np.float32(0.7307), np.float32(0.8944)] 
+2025-05-05 03:22:35.704397: Epoch time: 90.81 s 
+2025-05-05 03:22:35.717563: Yayy! New best EMA pseudo Dice: 0.8169000148773193 
+2025-05-05 03:22:37.968504:  
+2025-05-05 03:22:37.977355: Epoch 55 
+2025-05-05 03:22:37.977876: Current learning rate: 0.00975 
+2025-05-05 03:24:10.361437: train_loss -0.3921 
+2025-05-05 03:24:10.527282: val_loss -0.4368 
+2025-05-05 03:24:10.559753: Pseudo dice [np.float32(0.7362), np.float32(0.7391), np.float32(0.3685), np.float32(0.9522), np.float32(0.8283), np.float32(0.9133), np.float32(0.9378), np.float32(0.9617), np.float32(0.9441), np.float32(0.9367), np.float32(0.8963), np.float32(0.9518), np.float32(0.9456), np.float32(0.8093), np.float32(0.9259), np.float32(0.8931), np.float32(0.7915), np.float32(0.7968), np.float32(0.8845)] 
+2025-05-05 03:24:10.588225: Epoch time: 92.39 s 
+2025-05-05 03:24:10.610609: Yayy! New best EMA pseudo Dice: 0.8205999732017517 
+2025-05-05 03:24:12.887396:  
+2025-05-05 03:24:12.912898: Epoch 56 
+2025-05-05 03:24:12.917707: Current learning rate: 0.00975 
+2025-05-05 03:25:56.298425: train_loss -0.395 
+2025-05-05 03:25:56.472301: val_loss -0.415 
+2025-05-05 03:25:56.473453: Pseudo dice [np.float32(0.6823), np.float32(0.711), np.float32(0.8138), np.float32(0.9638), np.float32(0.7268), np.float32(0.9303), np.float32(0.9143), np.float32(0.9506), np.float32(0.952), np.float32(0.9309), np.float32(0.8785), np.float32(0.9576), np.float32(0.9463), np.float32(0.7948), np.float32(0.9517), np.float32(0.922), np.float32(0.6852), np.float32(0.7518), np.float32(0.8794)] 
+2025-05-05 03:25:56.474114: Epoch time: 103.41 s 
+2025-05-05 03:25:56.474940: Yayy! New best EMA pseudo Dice: 0.8245000243186951 
+2025-05-05 03:25:59.823725:  
+2025-05-05 03:25:59.832046: Epoch 57 
+2025-05-05 03:25:59.839619: Current learning rate: 0.00974 
+2025-05-05 03:27:33.125672: train_loss -0.3933 
+2025-05-05 03:27:33.248299: val_loss -0.3687 
+2025-05-05 03:27:33.277595: Pseudo dice [np.float32(0.6477), np.float32(0.7352), np.float32(0.8585), np.float32(0.931), np.float32(0.8106), np.float32(0.8856), np.float32(0.9258), np.float32(0.9541), np.float32(0.9152), np.float32(0.9063), np.float32(0.893), np.float32(0.9327), np.float32(0.93), np.float32(0.8089), np.float32(0.9468), np.float32(0.8887), np.float32(0.7856), np.float32(0.7739), np.float32(0.8993)] 
+2025-05-05 03:27:33.284986: Epoch time: 93.3 s 
+2025-05-05 03:27:33.313368: Yayy! New best EMA pseudo Dice: 0.828499972820282 
+2025-05-05 03:27:35.807445:  
+2025-05-05 03:27:35.885805: Epoch 58 
+2025-05-05 03:27:35.891909: Current learning rate: 0.00974 
+2025-05-05 03:29:11.548271: train_loss -0.3883 
+2025-05-05 03:29:11.657979: val_loss -0.4302 
+2025-05-05 03:29:11.685759: Pseudo dice [np.float32(0.7911), np.float32(0.7493), np.float32(0.7681), np.float32(0.9639), np.float32(0.7986), np.float32(0.9352), np.float32(0.9211), np.float32(0.9619), np.float32(0.942), np.float32(0.9416), np.float32(0.8958), np.float32(0.9528), np.float32(0.9471), np.float32(0.8053), np.float32(0.9358), np.float32(0.9138), np.float32(0.8287), np.float32(0.7918), np.float32(0.8828)] 
+2025-05-05 03:29:11.726071: Epoch time: 95.74 s 
+2025-05-05 03:29:11.755504: Yayy! New best EMA pseudo Dice: 0.8337000012397766 
+2025-05-05 03:29:14.183447:  
+2025-05-05 03:29:14.281729: Epoch 59 
+2025-05-05 03:29:14.333745: Current learning rate: 0.00973 
+2025-05-05 03:31:09.764433: train_loss -0.3802 
+2025-05-05 03:31:09.895420: val_loss -0.3958 
+2025-05-05 03:31:09.923764: Pseudo dice [np.float32(0.7454), np.float32(0.7323), np.float32(0.8365), np.float32(0.9216), np.float32(0.6809), np.float32(0.9142), np.float32(0.935), np.float32(0.9632), np.float32(0.9279), np.float32(0.9483), np.float32(0.9137), np.float32(0.9318), np.float32(0.9447), np.float32(0.8117), np.float32(0.9492), np.float32(0.9158), np.float32(0.6428), np.float32(0.5872), np.float32(0.8798)] 
+2025-05-05 03:31:09.932198: Epoch time: 115.58 s 
+2025-05-05 03:31:09.940394: Yayy! New best EMA pseudo Dice: 0.8355000019073486 
+2025-05-05 03:31:12.652061:  
+2025-05-05 03:31:12.760725: Epoch 60 
+2025-05-05 03:31:12.777185: Current learning rate: 0.00973 
+2025-05-05 03:32:45.654692: train_loss -0.3998 
+2025-05-05 03:32:45.763926: val_loss -0.3925 
+2025-05-05 03:32:45.803005: Pseudo dice [np.float32(0.6518), np.float32(0.7009), np.float32(0.6888), np.float32(0.9387), np.float32(0.7895), np.float32(0.9361), np.float32(0.9313), np.float32(0.9648), np.float32(0.9084), np.float32(0.936), np.float32(0.8652), np.float32(0.9343), np.float32(0.9449), np.float32(0.8022), np.float32(0.9376), np.float32(0.9112), np.float32(0.815), np.float32(0.8164), np.float32(0.8835)] 
+2025-05-05 03:32:45.821396: Epoch time: 93.0 s 
+2025-05-05 03:32:45.837901: Yayy! New best EMA pseudo Dice: 0.8379999995231628 
+2025-05-05 03:32:48.224073:  
+2025-05-05 03:32:48.244844: Epoch 61 
+2025-05-05 03:32:48.256185: Current learning rate: 0.00973 
+2025-05-05 03:34:18.782098: train_loss -0.3915 
+2025-05-05 03:34:18.884839: val_loss -0.4189 
+2025-05-05 03:34:18.885968: Pseudo dice [np.float32(0.7446), np.float32(0.7297), np.float32(0.7411), np.float32(0.9227), np.float32(0.7842), np.float32(0.9398), np.float32(0.9246), np.float32(0.9594), np.float32(0.9339), np.float32(0.9489), np.float32(0.9026), np.float32(0.9499), np.float32(0.9415), np.float32(0.8146), np.float32(0.9576), np.float32(0.885), np.float32(0.8259), np.float32(0.8257), np.float32(0.883)] 
+2025-05-05 03:34:18.886621: Epoch time: 90.56 s 
+2025-05-05 03:34:18.887011: Yayy! New best EMA pseudo Dice: 0.84170001745224 
+2025-05-05 03:34:22.005934:  
+2025-05-05 03:34:22.011691: Epoch 62 
+2025-05-05 03:34:22.012247: Current learning rate: 0.00972 
+2025-05-05 03:35:52.714188: train_loss -0.4089 
+2025-05-05 03:35:52.912354: val_loss -0.4421 
+2025-05-05 03:35:52.948791: Pseudo dice [np.float32(0.7162), np.float32(0.7603), np.float32(0.6607), np.float32(0.9601), np.float32(0.7689), np.float32(0.9476), np.float32(0.9386), np.float32(0.964), np.float32(0.9413), np.float32(0.9384), np.float32(0.8914), np.float32(0.9431), np.float32(0.95), np.float32(0.8226), np.float32(0.9524), np.float32(0.9016), np.float32(0.8081), np.float32(0.7883), np.float32(0.895)] 
+2025-05-05 03:35:52.972448: Epoch time: 90.71 s 
+2025-05-05 03:35:52.983464: Yayy! New best EMA pseudo Dice: 0.8446000218391418 
+2025-05-05 03:35:55.164396:  
+2025-05-05 03:35:55.207333: Epoch 63 
+2025-05-05 03:35:55.227830: Current learning rate: 0.00972 
+2025-05-05 03:37:29.268934: train_loss -0.4164 
+2025-05-05 03:37:29.410556: val_loss -0.4217 
+2025-05-05 03:37:29.435766: Pseudo dice [np.float32(0.7462), np.float32(0.7976), np.float32(0.7986), np.float32(0.9739), np.float32(0.7543), np.float32(0.9426), np.float32(0.9409), np.float32(0.9663), np.float32(0.9479), np.float32(0.9408), np.float32(0.8969), np.float32(0.9628), np.float32(0.9424), np.float32(0.8044), np.float32(0.9518), np.float32(0.9027), np.float32(0.805), np.float32(0.793), np.float32(0.8881)] 
+2025-05-05 03:37:29.461499: Epoch time: 94.11 s 
+2025-05-05 03:37:29.486933: Yayy! New best EMA pseudo Dice: 0.8482999801635742 
+2025-05-05 03:37:31.744239:  
+2025-05-05 03:37:31.791406: Epoch 64 
+2025-05-05 03:37:31.802416: Current learning rate: 0.00971 
+2025-05-05 03:39:04.059959: train_loss -0.405 
+2025-05-05 03:39:04.181503: val_loss -0.4301 
+2025-05-05 03:39:04.218419: Pseudo dice [np.float32(0.7843), np.float32(0.747), np.float32(0.7746), np.float32(0.9488), np.float32(0.7974), np.float32(0.9419), np.float32(0.9322), np.float32(0.937), np.float32(0.9322), np.float32(0.9392), np.float32(0.8863), np.float32(0.9385), np.float32(0.9426), np.float32(0.8267), np.float32(0.9445), np.float32(0.8744), np.float32(0.8059), np.float32(0.7973), np.float32(0.9155)] 
+2025-05-05 03:39:04.226447: Epoch time: 92.32 s 
+2025-05-05 03:39:04.244500: Yayy! New best EMA pseudo Dice: 0.8511999845504761 
+2025-05-05 03:39:06.877163:  
+2025-05-05 03:39:06.960780: Epoch 65 
+2025-05-05 03:39:07.008092: Current learning rate: 0.00971 
+2025-05-05 03:40:57.355784: train_loss -0.3919 
+2025-05-05 03:40:57.488448: val_loss -0.3974 
+2025-05-05 03:40:57.503168: Pseudo dice [np.float32(0.775), np.float32(0.7716), np.float32(0.823), np.float32(0.9683), np.float32(0.5925), np.float32(0.9413), np.float32(0.9438), np.float32(0.9643), np.float32(0.932), np.float32(0.9361), np.float32(0.8856), np.float32(0.9485), np.float32(0.9521), np.float32(0.8251), np.float32(0.9497), np.float32(0.925), np.float32(0.7942), np.float32(0.7752), np.float32(0.9006)] 
+2025-05-05 03:40:57.505441: Epoch time: 110.48 s 
+2025-05-05 03:40:57.506392: Yayy! New best EMA pseudo Dice: 0.8535000085830688 
+2025-05-05 03:40:59.996625:  
+2025-05-05 03:41:00.048492: Epoch 66 
+2025-05-05 03:41:00.071952: Current learning rate: 0.0097 
+2025-05-05 03:42:47.927534: train_loss -0.4265 
+2025-05-05 03:42:48.034520: val_loss -0.4632 
+2025-05-05 03:42:48.050568: Pseudo dice [np.float32(0.7705), np.float32(0.7759), np.float32(0.8234), np.float32(0.9685), np.float32(0.8141), np.float32(0.9364), np.float32(0.9252), np.float32(0.9663), np.float32(0.9547), np.float32(0.9482), np.float32(0.9039), np.float32(0.9617), np.float32(0.9401), np.float32(0.8306), np.float32(0.9496), np.float32(0.9244), np.float32(0.7993), np.float32(0.7923), np.float32(0.9014)] 
+2025-05-05 03:42:48.064187: Epoch time: 107.93 s 
+2025-05-05 03:42:48.097172: Yayy! New best EMA pseudo Dice: 0.8569999933242798 
+2025-05-05 03:42:50.717724:  
+2025-05-05 03:42:50.762542: Epoch 67 
+2025-05-05 03:42:50.771511: Current learning rate: 0.0097 
+2025-05-05 03:45:02.727283: train_loss -0.4115 
+2025-05-05 03:45:02.917017: val_loss -0.398 
+2025-05-05 03:45:02.948314: Pseudo dice [np.float32(0.7081), np.float32(0.7242), np.float32(0.7417), np.float32(0.9689), np.float32(0.7996), np.float32(0.9222), np.float32(0.9486), np.float32(0.9576), np.float32(0.9453), np.float32(0.9418), np.float32(0.8965), np.float32(0.9546), np.float32(0.936), np.float32(0.8093), np.float32(0.9586), np.float32(0.9056), np.float32(0.8291), np.float32(0.7684), np.float32(0.8882)] 
+2025-05-05 03:45:02.983778: Epoch time: 132.01 s 
+2025-05-05 03:45:03.015951: Yayy! New best EMA pseudo Dice: 0.8586999773979187 
+2025-05-05 03:45:05.414048:  
+2025-05-05 03:45:05.503897: Epoch 68 
+2025-05-05 03:45:05.529847: Current learning rate: 0.00969 
+2025-05-05 03:46:49.967816: train_loss -0.4116 
+2025-05-05 03:46:50.102226: val_loss -0.4037 
+2025-05-05 03:46:50.107208: Pseudo dice [np.float32(0.7532), np.float32(0.749), np.float32(0.8821), np.float32(0.9442), np.float32(0.7701), np.float32(0.9432), np.float32(0.9223), np.float32(0.956), np.float32(0.9377), np.float32(0.9483), np.float32(0.892), np.float32(0.9474), np.float32(0.944), np.float32(0.8164), np.float32(0.9531), np.float32(0.9001), np.float32(0.7634), np.float32(0.7771), np.float32(0.8765)] 
+2025-05-05 03:46:50.125507: Epoch time: 104.56 s 
+2025-05-05 03:46:50.132967: Yayy! New best EMA pseudo Dice: 0.8605999946594238 
+2025-05-05 03:46:52.730939:  
+2025-05-05 03:46:52.759532: Epoch 69 
+2025-05-05 03:46:52.761288: Current learning rate: 0.00969 
+2025-05-05 03:48:34.158360: train_loss -0.402 
+2025-05-05 03:48:34.359300: val_loss -0.4424 
+2025-05-05 03:48:34.367111: Pseudo dice [np.float32(0.6714), np.float32(0.6687), np.float32(0.835), np.float32(0.9713), np.float32(0.7407), np.float32(0.9248), np.float32(0.9416), np.float32(0.9672), np.float32(0.9389), np.float32(0.9462), np.float32(0.9163), np.float32(0.9484), np.float32(0.9325), np.float32(0.8228), np.float32(0.9357), np.float32(0.9009), np.float32(0.7264), np.float32(0.7954), np.float32(0.8864)] 
+2025-05-05 03:48:34.372580: Epoch time: 101.43 s 
+2025-05-05 03:48:34.376638: Yayy! New best EMA pseudo Dice: 0.8611999750137329 
+2025-05-05 03:48:37.288593:  
+2025-05-05 03:48:37.300925: Epoch 70 
+2025-05-05 03:48:37.301630: Current learning rate: 0.00968 
+2025-05-05 03:50:08.009186: train_loss -0.3884 
+2025-05-05 03:50:08.195464: val_loss -0.4175 
+2025-05-05 03:50:08.205822: Pseudo dice [np.float32(0.7057), np.float32(0.7372), np.float32(0.5949), np.float32(0.9644), np.float32(0.8429), np.float32(0.9242), np.float32(0.9493), np.float32(0.9623), np.float32(0.9349), np.float32(0.9231), np.float32(0.8738), np.float32(0.947), np.float32(0.9407), np.float32(0.839), np.float32(0.9223), np.float32(0.9097), np.float32(0.8257), np.float32(0.7948), np.float32(0.8983)] 
+2025-05-05 03:50:08.211995: Epoch time: 90.72 s 
+2025-05-05 03:50:08.226261: Yayy! New best EMA pseudo Dice: 0.8618999719619751 
+2025-05-05 03:50:10.712739:  
+2025-05-05 03:50:10.729925: Epoch 71 
+2025-05-05 03:50:10.730599: Current learning rate: 0.00968 
+2025-05-05 03:51:43.373380: train_loss -0.4057 
+2025-05-05 03:51:43.584391: val_loss -0.4228 
+2025-05-05 03:51:43.625333: Pseudo dice [np.float32(0.7789), np.float32(0.7996), np.float32(0.8457), np.float32(0.9605), np.float32(0.692), np.float32(0.9188), np.float32(0.9451), np.float32(0.9619), np.float32(0.9491), np.float32(0.9326), np.float32(0.9023), np.float32(0.9504), np.float32(0.9431), np.float32(0.8528), np.float32(0.9458), np.float32(0.9048), np.float32(0.7891), np.float32(0.8263), np.float32(0.8977)] 
+2025-05-05 03:51:43.654427: Epoch time: 92.66 s 
+2025-05-05 03:51:43.696513: Yayy! New best EMA pseudo Dice: 0.8640999794006348 
+2025-05-05 03:51:45.940112:  
+2025-05-05 03:51:46.011002: Epoch 72 
+2025-05-05 03:51:46.047728: Current learning rate: 0.00968 
+2025-05-05 03:53:21.297657: train_loss -0.418 
+2025-05-05 03:53:21.436900: val_loss -0.4273 
+2025-05-05 03:53:21.461524: Pseudo dice [np.float32(0.7213), np.float32(0.6637), np.float32(0.7776), np.float32(0.9643), np.float32(0.8425), np.float32(0.9355), np.float32(0.9231), np.float32(0.9378), np.float32(0.9284), np.float32(0.931), np.float32(0.9012), np.float32(0.9454), np.float32(0.9483), np.float32(0.8021), np.float32(0.8293), np.float32(0.8912), np.float32(0.8088), np.float32(0.7156), np.float32(0.8875)] 
+2025-05-05 03:53:21.472143: Epoch time: 95.36 s 
+2025-05-05 03:53:22.969150:  
+2025-05-05 03:53:23.000551: Epoch 73 
+2025-05-05 03:53:23.004206: Current learning rate: 0.00967 
+2025-05-05 03:54:58.639719: train_loss -0.418 
+2025-05-05 03:54:58.737343: val_loss -0.4425 
+2025-05-05 03:54:58.776011: Pseudo dice [np.float32(0.7512), np.float32(0.7903), np.float32(0.8349), np.float32(0.9643), np.float32(0.7787), np.float32(0.9311), np.float32(0.9416), np.float32(0.9659), np.float32(0.9492), np.float32(0.9348), np.float32(0.9018), np.float32(0.9537), np.float32(0.9539), np.float32(0.8366), np.float32(0.946), np.float32(0.9263), np.float32(0.7338), np.float32(0.806), np.float32(0.9043)] 
+2025-05-05 03:54:58.812677: Epoch time: 95.67 s 
+2025-05-05 03:54:58.847602: Yayy! New best EMA pseudo Dice: 0.8658000230789185 
+2025-05-05 03:55:01.241830:  
+2025-05-05 03:55:01.347157: Epoch 74 
+2025-05-05 03:55:01.381669: Current learning rate: 0.00967 
+2025-05-05 03:56:34.808369: train_loss -0.4083 
+2025-05-05 03:56:34.941053: val_loss -0.4423 
+2025-05-05 03:56:34.994328: Pseudo dice [np.float32(0.7554), np.float32(0.7698), np.float32(0.8801), np.float32(0.9368), np.float32(0.8119), np.float32(0.9437), np.float32(0.9352), np.float32(0.9646), np.float32(0.9525), np.float32(0.9492), np.float32(0.9058), np.float32(0.9559), np.float32(0.9545), np.float32(0.8343), np.float32(0.9486), np.float32(0.9061), np.float32(0.8069), np.float32(0.7729), np.float32(0.878)] 
+2025-05-05 03:56:35.008966: Epoch time: 93.57 s 
+2025-05-05 03:56:35.064318: Yayy! New best EMA pseudo Dice: 0.8679999709129333 
+2025-05-05 03:56:37.105975:  
+2025-05-05 03:56:37.177554: Epoch 75 
+2025-05-05 03:56:37.192457: Current learning rate: 0.00966 
+2025-05-05 03:58:13.902010: train_loss -0.4224 
+2025-05-05 03:58:14.034751: val_loss -0.4261 
+2025-05-05 03:58:14.071922: Pseudo dice [np.float32(0.7197), np.float32(0.7934), np.float32(0.85), np.float32(0.9689), np.float32(0.7881), np.float32(0.9285), np.float32(0.9272), np.float32(0.9605), np.float32(0.9465), np.float32(0.9501), np.float32(0.9182), np.float32(0.9542), np.float32(0.9544), np.float32(0.8252), np.float32(0.9443), np.float32(0.9078), np.float32(0.783), np.float32(0.7838), np.float32(0.8923)] 
+2025-05-05 03:58:14.097817: Epoch time: 96.8 s 
+2025-05-05 03:58:14.123200: Yayy! New best EMA pseudo Dice: 0.8695999979972839 
+2025-05-05 03:58:16.658719:  
+2025-05-05 03:58:16.676728: Epoch 76 
+2025-05-05 03:58:16.680968: Current learning rate: 0.00966 
+2025-05-05 03:59:54.002270: train_loss -0.4263 
+2025-05-05 03:59:54.349199: val_loss -0.4644 
+2025-05-05 03:59:54.389985: Pseudo dice [np.float32(0.7617), np.float32(0.8051), np.float32(0.6726), np.float32(0.944), np.float32(0.8222), np.float32(0.953), np.float32(0.9416), np.float32(0.9719), np.float32(0.9395), np.float32(0.9442), np.float32(0.9001), np.float32(0.9516), np.float32(0.95), np.float32(0.839), np.float32(0.9595), np.float32(0.9346), np.float32(0.7787), np.float32(0.7658), np.float32(0.8992)] 
+2025-05-05 03:59:54.401004: Epoch time: 97.34 s 
+2025-05-05 03:59:54.401507: Yayy! New best EMA pseudo Dice: 0.8707000017166138 
+2025-05-05 03:59:56.742709:  
+2025-05-05 03:59:56.751211: Epoch 77 
+2025-05-05 03:59:56.752516: Current learning rate: 0.00965 
+2025-05-05 04:01:30.198027: train_loss -0.4182 
+2025-05-05 04:01:30.417728: val_loss -0.4061 
+2025-05-05 04:01:30.441469: Pseudo dice [np.float32(0.7421), np.float32(0.7778), np.float32(0.835), np.float32(0.97), np.float32(0.846), np.float32(0.9046), np.float32(0.9359), np.float32(0.9638), np.float32(0.9099), np.float32(0.9512), np.float32(0.9226), np.float32(0.9498), np.float32(0.9617), np.float32(0.8102), np.float32(0.9257), np.float32(0.9086), np.float32(0.7972), np.float32(0.8201), np.float32(0.9027)] 
+2025-05-05 04:01:30.470441: Epoch time: 93.46 s 
+2025-05-05 04:01:30.488735: Yayy! New best EMA pseudo Dice: 0.8723000288009644 
+2025-05-05 04:01:33.242816:  
+2025-05-05 04:01:33.329575: Epoch 78 
+2025-05-05 04:01:33.408576: Current learning rate: 0.00965 
+2025-05-05 04:03:06.611375: train_loss -0.4096 
+2025-05-05 04:03:06.702244: val_loss -0.4368 
+2025-05-05 04:03:06.722892: Pseudo dice [np.float32(0.7331), np.float32(0.7733), np.float32(0.7707), np.float32(0.9687), np.float32(0.8525), np.float32(0.9171), np.float32(0.9357), np.float32(0.9492), np.float32(0.9231), np.float32(0.927), np.float32(0.9251), np.float32(0.9542), np.float32(0.9505), np.float32(0.773), np.float32(0.9491), np.float32(0.9179), np.float32(0.7911), np.float32(0.8351), np.float32(0.912)] 
+2025-05-05 04:03:06.752884: Epoch time: 93.37 s 
+2025-05-05 04:03:06.771320: Yayy! New best EMA pseudo Dice: 0.873199999332428 
+2025-05-05 04:03:09.314052:  
+2025-05-05 04:03:09.370123: Epoch 79 
+2025-05-05 04:03:09.397135: Current learning rate: 0.00964 
+2025-05-05 04:05:00.812022: train_loss -0.4152 
+2025-05-05 04:05:00.978983: val_loss -0.4273 
+2025-05-05 04:05:01.009177: Pseudo dice [np.float32(0.7677), np.float32(0.799), np.float32(0.7373), np.float32(0.9728), np.float32(0.853), np.float32(0.9262), np.float32(0.9395), np.float32(0.9696), np.float32(0.9374), np.float32(0.9475), np.float32(0.8886), np.float32(0.9508), np.float32(0.9424), np.float32(0.8461), np.float32(0.915), np.float32(0.8997), np.float32(0.8278), np.float32(0.8214), np.float32(0.8852)] 
+2025-05-05 04:05:01.009898: Epoch time: 111.5 s 
+2025-05-05 04:05:01.010359: Yayy! New best EMA pseudo Dice: 0.8744999766349792 
+2025-05-05 04:05:03.449416:  
+2025-05-05 04:05:03.540064: Epoch 80 
+2025-05-05 04:05:03.544385: Current learning rate: 0.00964 
+2025-05-05 04:06:36.000801: train_loss -0.4269 
+2025-05-05 04:06:36.067297: val_loss -0.3925 
+2025-05-05 04:06:36.068375: Pseudo dice [np.float32(0.7761), np.float32(0.79), np.float32(0.8282), np.float32(0.9584), np.float32(0.7772), np.float32(0.9358), np.float32(0.9351), np.float32(0.9624), np.float32(0.9577), np.float32(0.9468), np.float32(0.9137), np.float32(0.9519), np.float32(0.958), np.float32(0.8448), np.float32(0.9088), np.float32(0.9301), np.float32(0.8209), np.float32(0.7859), np.float32(0.8921)] 
+2025-05-05 04:06:36.068928: Epoch time: 92.55 s 
+2025-05-05 04:06:36.074317: Yayy! New best EMA pseudo Dice: 0.8758000135421753 
+2025-05-05 04:06:38.156553:  
+2025-05-05 04:06:38.198351: Epoch 81 
+2025-05-05 04:06:38.229820: Current learning rate: 0.00963 
+2025-05-05 04:08:09.477559: train_loss -0.4337 
+2025-05-05 04:08:09.731761: val_loss -0.4779 
+2025-05-05 04:08:09.763738: Pseudo dice [np.float32(0.8119), np.float32(0.7708), np.float32(0.7631), np.float32(0.9566), np.float32(0.8188), np.float32(0.9455), np.float32(0.9562), np.float32(0.9681), np.float32(0.943), np.float32(0.9333), np.float32(0.9082), np.float32(0.9588), np.float32(0.9547), np.float32(0.8557), np.float32(0.9508), np.float32(0.9153), np.float32(0.8274), np.float32(0.8066), np.float32(0.8939)] 
+2025-05-05 04:08:09.784524: Epoch time: 91.32 s 
+2025-05-05 04:08:09.818952: Yayy! New best EMA pseudo Dice: 0.8773999810218811 
+2025-05-05 04:08:12.340549:  
+2025-05-05 04:08:12.375349: Epoch 82 
+2025-05-05 04:08:12.380781: Current learning rate: 0.00963 
+2025-05-05 04:09:53.169826: train_loss -0.4273 
+2025-05-05 04:09:53.357297: val_loss -0.47 
+2025-05-05 04:09:53.369214: Pseudo dice [np.float32(0.7577), np.float32(0.7889), np.float32(0.8328), np.float32(0.929), np.float32(0.8239), np.float32(0.9385), np.float32(0.9473), np.float32(0.9703), np.float32(0.9489), np.float32(0.9513), np.float32(0.9177), np.float32(0.94), np.float32(0.9509), np.float32(0.8507), np.float32(0.9451), np.float32(0.8839), np.float32(0.8092), np.float32(0.7442), np.float32(0.9055)] 
+2025-05-05 04:09:53.405414: Epoch time: 100.83 s 
+2025-05-05 04:09:53.431291: Yayy! New best EMA pseudo Dice: 0.8783000111579895 
+2025-05-05 04:09:55.916502:  
+2025-05-05 04:09:55.928367: Epoch 83 
+2025-05-05 04:09:55.929653: Current learning rate: 0.00963 
+2025-05-05 04:11:37.943244: train_loss -0.412 
+2025-05-05 04:11:38.087418: val_loss -0.4388 
+2025-05-05 04:11:38.110105: Pseudo dice [np.float32(0.6657), np.float32(0.7541), np.float32(0.888), np.float32(0.9491), np.float32(0.7556), np.float32(0.928), np.float32(0.9393), np.float32(0.9625), np.float32(0.9434), np.float32(0.9217), np.float32(0.8577), np.float32(0.9457), np.float32(0.9267), np.float32(0.8161), np.float32(0.9434), np.float32(0.8988), np.float32(0.8097), np.float32(0.7455), np.float32(0.9015)] 
+2025-05-05 04:11:38.117340: Epoch time: 102.03 s 
+2025-05-05 04:11:39.802634:  
+2025-05-05 04:11:39.839238: Epoch 84 
+2025-05-05 04:11:39.854392: Current learning rate: 0.00962 
+2025-05-05 04:13:11.385668: train_loss -0.4064 
+2025-05-05 04:13:11.496123: val_loss -0.4286 
+2025-05-05 04:13:11.562601: Pseudo dice [np.float32(0.7317), np.float32(0.7725), np.float32(0.4727), np.float32(0.9346), np.float32(0.805), np.float32(0.9405), np.float32(0.9403), np.float32(0.9579), np.float32(0.9368), np.float32(0.9223), np.float32(0.8918), np.float32(0.9589), np.float32(0.9379), np.float32(0.8197), np.float32(0.9546), np.float32(0.9034), np.float32(0.772), np.float32(0.815), np.float32(0.9061)] 
+2025-05-05 04:13:11.602684: Epoch time: 91.58 s 
+2025-05-05 04:13:13.223482:  
+2025-05-05 04:13:13.359355: Epoch 85 
+2025-05-05 04:13:13.416947: Current learning rate: 0.00962 
+2025-05-05 04:14:46.771284: train_loss -0.4251 
+2025-05-05 04:14:46.825012: val_loss -0.4685 
+2025-05-05 04:14:46.829113: Pseudo dice [np.float32(0.7704), np.float32(0.771), np.float32(0.903), np.float32(0.9688), np.float32(0.839), np.float32(0.9316), np.float32(0.9414), np.float32(0.9566), np.float32(0.9506), np.float32(0.9505), np.float32(0.9005), np.float32(0.9571), np.float32(0.9491), np.float32(0.8216), np.float32(0.9206), np.float32(0.8872), np.float32(0.8457), np.float32(0.8272), np.float32(0.9018)] 
+2025-05-05 04:14:46.829648: Epoch time: 93.55 s 
+2025-05-05 04:14:48.433305:  
+2025-05-05 04:14:48.488564: Epoch 86 
+2025-05-05 04:14:48.491315: Current learning rate: 0.00961 
+2025-05-05 04:16:19.971246: train_loss -0.4398 
+2025-05-05 04:16:20.045866: val_loss -0.435 
+2025-05-05 04:16:20.068099: Pseudo dice [np.float32(0.7815), np.float32(0.789), np.float32(0.9053), np.float32(0.9707), np.float32(0.8409), np.float32(0.9496), np.float32(0.9532), np.float32(0.975), np.float32(0.9488), np.float32(0.955), np.float32(0.9381), np.float32(0.963), np.float32(0.9557), np.float32(0.8468), np.float32(0.9355), np.float32(0.9371), np.float32(0.7522), np.float32(0.7263), np.float32(0.8854)] 
+2025-05-05 04:16:20.071435: Epoch time: 91.54 s 
+2025-05-05 04:16:20.079059: Yayy! New best EMA pseudo Dice: 0.8795999884605408 
+2025-05-05 04:16:22.771356:  
+2025-05-05 04:16:22.817366: Epoch 87 
+2025-05-05 04:16:22.824658: Current learning rate: 0.00961 
+2025-05-05 04:18:40.874598: train_loss -0.4213 
+2025-05-05 04:18:41.083384: val_loss -0.4397 
+2025-05-05 04:18:41.093786: Pseudo dice [np.float32(0.7589), np.float32(0.7811), np.float32(0.7001), np.float32(0.9431), np.float32(0.765), np.float32(0.945), np.float32(0.9525), np.float32(0.9715), np.float32(0.9442), np.float32(0.9418), np.float32(0.8843), np.float32(0.9459), np.float32(0.9443), np.float32(0.8442), np.float32(0.9551), np.float32(0.9229), np.float32(0.8274), np.float32(0.7947), np.float32(0.8808)] 
+2025-05-05 04:18:41.094617: Epoch time: 138.11 s 
+2025-05-05 04:18:42.674924:  
+2025-05-05 04:18:42.677617: Epoch 88 
+2025-05-05 04:18:42.677985: Current learning rate: 0.0096 
+2025-05-05 04:20:44.322529: train_loss -0.4275 
+2025-05-05 04:20:44.466816: val_loss -0.4579 
+2025-05-05 04:20:44.493307: Pseudo dice [np.float32(0.7436), np.float32(0.766), np.float32(0.7775), np.float32(0.971), np.float32(0.814), np.float32(0.9249), np.float32(0.9534), np.float32(0.9693), np.float32(0.9378), np.float32(0.9517), np.float32(0.9241), np.float32(0.9512), np.float32(0.9567), np.float32(0.8229), np.float32(0.9425), np.float32(0.8986), np.float32(0.842), np.float32(0.8453), np.float32(0.9073)] 
+2025-05-05 04:20:44.506006: Epoch time: 121.65 s 
+2025-05-05 04:20:44.525107: Yayy! New best EMA pseudo Dice: 0.8805000185966492 
+2025-05-05 04:20:46.856633:  
+2025-05-05 04:20:46.881497: Epoch 89 
+2025-05-05 04:20:46.897650: Current learning rate: 0.0096 
+2025-05-05 04:22:48.260337: train_loss -0.4246 
+2025-05-05 04:22:48.388695: val_loss -0.4209 
+2025-05-05 04:22:48.421761: Pseudo dice [np.float32(0.7866), np.float32(0.7258), np.float32(0.8368), np.float32(0.9652), np.float32(0.8314), np.float32(0.9483), np.float32(0.9397), np.float32(0.9709), np.float32(0.9517), np.float32(0.9652), np.float32(0.9244), np.float32(0.9617), np.float32(0.952), np.float32(0.8428), np.float32(0.9612), np.float32(0.938), np.float32(0.7015), np.float32(0.6812), np.float32(0.901)] 
+2025-05-05 04:22:48.439782: Epoch time: 121.41 s 
+2025-05-05 04:22:48.458407: Yayy! New best EMA pseudo Dice: 0.8808000087738037 
+2025-05-05 04:22:51.261010:  
+2025-05-05 04:22:51.288980: Epoch 90 
+2025-05-05 04:22:51.309355: Current learning rate: 0.00959 
+2025-05-05 04:24:55.134761: train_loss -0.4294 
+2025-05-05 04:24:55.247387: val_loss -0.4269 
+2025-05-05 04:24:55.251859: Pseudo dice [np.float32(0.778), np.float32(0.7043), np.float32(0.7643), np.float32(0.9572), np.float32(0.8215), np.float32(0.9421), np.float32(0.9268), np.float32(0.9564), np.float32(0.9389), np.float32(0.9424), np.float32(0.9002), np.float32(0.9504), np.float32(0.9511), np.float32(0.8433), np.float32(0.948), np.float32(0.9082), np.float32(0.8078), np.float32(0.8055), np.float32(0.9168)] 
+2025-05-05 04:24:55.252582: Epoch time: 123.87 s 
+2025-05-05 04:24:55.253204: Yayy! New best EMA pseudo Dice: 0.8809999823570251 
+2025-05-05 04:24:57.399473:  
+2025-05-05 04:24:57.448649: Epoch 91 
+2025-05-05 04:24:57.450526: Current learning rate: 0.00959 
+2025-05-05 04:26:32.430711: train_loss -0.4386 
+2025-05-05 04:26:32.510634: val_loss -0.4387 
+2025-05-05 04:26:32.520113: Pseudo dice [np.float32(0.8057), np.float32(0.7746), np.float32(0.8201), np.float32(0.9678), np.float32(0.7975), np.float32(0.9431), np.float32(0.9358), np.float32(0.965), np.float32(0.9489), np.float32(0.9523), np.float32(0.9261), np.float32(0.9529), np.float32(0.9559), np.float32(0.8182), np.float32(0.9361), np.float32(0.9197), np.float32(0.7323), np.float32(0.8284), np.float32(0.9054)] 
+2025-05-05 04:26:32.545938: Epoch time: 95.03 s 
+2025-05-05 04:26:32.596226: Yayy! New best EMA pseudo Dice: 0.8816999793052673 
+2025-05-05 04:26:35.085313:  
+2025-05-05 04:26:35.149787: Epoch 92 
+2025-05-05 04:26:35.150527: Current learning rate: 0.00959 
+2025-05-05 04:28:09.992717: train_loss -0.4286 
+2025-05-05 04:28:10.098525: val_loss -0.4468 
+2025-05-05 04:28:10.136183: Pseudo dice [np.float32(0.7729), np.float32(0.7822), np.float32(0.812), np.float32(0.9484), np.float32(0.8385), np.float32(0.9177), np.float32(0.9326), np.float32(0.9661), np.float32(0.9425), np.float32(0.9384), np.float32(0.8516), np.float32(0.9478), np.float32(0.9404), np.float32(0.844), np.float32(0.9288), np.float32(0.9252), np.float32(0.8263), np.float32(0.8035), np.float32(0.9028)] 
+2025-05-05 04:28:10.150513: Epoch time: 94.91 s 
+2025-05-05 04:28:10.151220: Yayy! New best EMA pseudo Dice: 0.882099986076355 
+2025-05-05 04:28:12.193125:  
+2025-05-05 04:28:12.229736: Epoch 93 
+2025-05-05 04:28:12.241048: Current learning rate: 0.00958 
+2025-05-05 04:29:44.967849: train_loss -0.4138 
+2025-05-05 04:29:45.067121: val_loss -0.4317 
+2025-05-05 04:29:45.083593: Pseudo dice [np.float32(0.7788), np.float32(0.7582), np.float32(0.6754), np.float32(0.9665), np.float32(0.7918), np.float32(0.9457), np.float32(0.947), np.float32(0.9618), np.float32(0.9558), np.float32(0.9528), np.float32(0.9106), np.float32(0.9589), np.float32(0.9283), np.float32(0.8415), np.float32(0.9543), np.float32(0.9182), np.float32(0.8188), np.float32(0.801), np.float32(0.894)] 
+2025-05-05 04:29:45.097115: Epoch time: 92.78 s 
+2025-05-05 04:29:46.694356:  
+2025-05-05 04:29:46.751953: Epoch 94 
+2025-05-05 04:29:46.784000: Current learning rate: 0.00958 
+2025-05-05 04:31:18.049000: train_loss -0.4044 
+2025-05-05 04:31:18.127209: val_loss -0.4705 
+2025-05-05 04:31:18.165163: Pseudo dice [np.float32(0.7833), np.float32(0.7476), np.float32(0.8404), np.float32(0.9581), np.float32(0.8515), np.float32(0.9114), np.float32(0.909), np.float32(0.9686), np.float32(0.9404), np.float32(0.9396), np.float32(0.9165), np.float32(0.947), np.float32(0.953), np.float32(0.8469), np.float32(0.9534), np.float32(0.9263), np.float32(0.849), np.float32(0.8227), np.float32(0.8828)] 
+2025-05-05 04:31:18.205666: Epoch time: 91.36 s 
+2025-05-05 04:31:18.256428: Yayy! New best EMA pseudo Dice: 0.8830999732017517 
+2025-05-05 04:31:20.951883:  
+2025-05-05 04:31:21.002325: Epoch 95 
+2025-05-05 04:31:21.057782: Current learning rate: 0.00957 
+2025-05-05 04:32:52.443676: train_loss -0.4379 
+2025-05-05 04:32:52.545961: val_loss -0.4538 
+2025-05-05 04:32:52.591347: Pseudo dice [np.float32(0.7814), np.float32(0.8008), np.float32(0.7873), np.float32(0.9736), np.float32(0.756), np.float32(0.9514), np.float32(0.934), np.float32(0.9656), np.float32(0.9547), np.float32(0.9371), np.float32(0.8955), np.float32(0.9604), np.float32(0.9372), np.float32(0.8496), np.float32(0.9633), np.float32(0.933), np.float32(0.855), np.float32(0.8245), np.float32(0.9034)] 
+2025-05-05 04:32:52.635950: Epoch time: 91.49 s 
+2025-05-05 04:32:52.670840: Yayy! New best EMA pseudo Dice: 0.8841000199317932 
+2025-05-05 04:32:56.048256:  
+2025-05-05 04:32:56.083412: Epoch 96 
+2025-05-05 04:32:56.087636: Current learning rate: 0.00957 
+2025-05-05 04:34:28.604025: train_loss -0.4187 
+2025-05-05 04:34:28.705559: val_loss -0.4108 
+2025-05-05 04:34:28.731419: Pseudo dice [np.float32(0.7911), np.float32(0.7191), np.float32(0.8543), np.float32(0.9288), np.float32(0.7674), np.float32(0.9392), np.float32(0.9311), np.float32(0.9628), np.float32(0.9389), np.float32(0.9279), np.float32(0.8947), np.float32(0.9582), np.float32(0.9408), np.float32(0.8505), np.float32(0.9546), np.float32(0.8935), np.float32(0.8048), np.float32(0.829), np.float32(0.9031)] 
+2025-05-05 04:34:28.767815: Epoch time: 92.56 s 
+2025-05-05 04:34:29.952254:  
+2025-05-05 04:34:30.054611: Epoch 97 
+2025-05-05 04:34:30.138515: Current learning rate: 0.00956 
+2025-05-05 04:35:59.794452: train_loss -0.417 
+2025-05-05 04:35:59.879867: val_loss -0.4065 
+2025-05-05 04:35:59.880778: Pseudo dice [np.float32(0.7672), np.float32(0.7905), np.float32(0.787), np.float32(0.8867), np.float32(0.8292), np.float32(0.9359), np.float32(0.934), np.float32(0.9676), np.float32(0.9576), np.float32(0.9403), np.float32(0.9058), np.float32(0.9652), np.float32(0.9479), np.float32(0.8298), np.float32(0.9493), np.float32(0.9159), np.float32(0.8115), np.float32(0.8053), np.float32(0.9183)] 
+2025-05-05 04:35:59.881310: Epoch time: 89.84 s 
+2025-05-05 04:35:59.882539: Yayy! New best EMA pseudo Dice: 0.8842999935150146 
+2025-05-05 04:36:02.449490:  
+2025-05-05 04:36:02.498467: Epoch 98 
+2025-05-05 04:36:02.502627: Current learning rate: 0.00956 
+2025-05-05 04:37:36.586411: train_loss -0.41 
+2025-05-05 04:37:36.858415: val_loss -0.3955 
+2025-05-05 04:37:36.859462: Pseudo dice [np.float32(0.686), np.float32(0.7326), np.float32(0.8095), np.float32(0.9598), np.float32(0.8685), np.float32(0.9064), np.float32(0.9025), np.float32(0.9517), np.float32(0.9157), np.float32(0.9132), np.float32(0.8785), np.float32(0.9417), np.float32(0.9533), np.float32(0.7869), np.float32(0.9419), np.float32(0.8872), np.float32(0.7033), np.float32(0.5555), np.float32(0.9127)] 
+2025-05-05 04:37:36.860474: Epoch time: 94.14 s 
+2025-05-05 04:37:38.095518:  
+2025-05-05 04:37:38.200019: Epoch 99 
+2025-05-05 04:37:38.230901: Current learning rate: 0.00955 
+2025-05-05 04:39:05.720643: train_loss -0.4296 
+2025-05-05 04:39:05.844564: val_loss -0.4261 
+2025-05-05 04:39:05.863177: Pseudo dice [np.float32(0.7473), np.float32(0.7516), np.float32(0.9101), np.float32(0.965), np.float32(0.8635), np.float32(0.9307), np.float32(0.9465), np.float32(0.9703), np.float32(0.9481), np.float32(0.9519), np.float32(0.9198), np.float32(0.9593), np.float32(0.9475), np.float32(0.8311), np.float32(0.9311), np.float32(0.9222), np.float32(0.8231), np.float32(0.8036), np.float32(0.8945)] 
+2025-05-05 04:39:05.877405: Epoch time: 87.63 s 
+2025-05-05 04:39:08.435859:  
+2025-05-05 04:39:08.549159: Epoch 100 
+2025-05-05 04:39:08.550901: Current learning rate: 0.00955 
+2025-05-05 04:40:39.506192: train_loss -0.4377 
+2025-05-05 04:40:39.603164: val_loss -0.472 
+2025-05-05 04:40:39.620534: Pseudo dice [np.float32(0.7678), np.float32(0.8034), np.float32(0.8246), np.float32(0.9632), np.float32(0.8778), np.float32(0.9185), np.float32(0.9429), np.float32(0.9661), np.float32(0.9273), np.float32(0.9573), np.float32(0.923), np.float32(0.9518), np.float32(0.9571), np.float32(0.8467), np.float32(0.9446), np.float32(0.9165), np.float32(0.8344), np.float32(0.8493), np.float32(0.919)] 
+2025-05-05 04:40:39.627865: Epoch time: 91.07 s 
+2025-05-05 04:40:39.674777: Yayy! New best EMA pseudo Dice: 0.8842999935150146 
+2025-05-05 04:40:42.668268:  
+2025-05-05 04:40:42.730480: Epoch 101 
+2025-05-05 04:40:42.787241: Current learning rate: 0.00954 
+2025-05-05 04:42:14.424182: train_loss -0.4359 
+2025-05-05 04:42:14.584188: val_loss -0.462 
+2025-05-05 04:42:14.605506: Pseudo dice [np.float32(0.8159), np.float32(0.8082), np.float32(0.8783), np.float32(0.9661), np.float32(0.6974), np.float32(0.9456), np.float32(0.9507), np.float32(0.9673), np.float32(0.9619), np.float32(0.9461), np.float32(0.914), np.float32(0.9672), np.float32(0.9604), np.float32(0.8796), np.float32(0.9589), np.float32(0.9368), np.float32(0.8509), np.float32(0.8392), np.float32(0.8842)] 
+2025-05-05 04:42:14.614908: Epoch time: 91.76 s 
+2025-05-05 04:42:14.644121: Yayy! New best EMA pseudo Dice: 0.8859999775886536 
+2025-05-05 04:42:16.896394:  
+2025-05-05 04:42:16.922781: Epoch 102 
+2025-05-05 04:42:16.963610: Current learning rate: 0.00954 
+2025-05-05 04:43:50.081530: train_loss -0.4182 
+2025-05-05 04:43:50.115440: val_loss -0.4526 
+2025-05-05 04:43:50.153240: Pseudo dice [np.float32(0.8021), np.float32(0.7923), np.float32(0.9067), np.float32(0.9637), np.float32(0.7486), np.float32(0.9535), np.float32(0.9386), np.float32(0.9716), np.float32(0.9478), np.float32(0.9283), np.float32(0.9191), np.float32(0.9571), np.float32(0.9428), np.float32(0.8491), np.float32(0.9434), np.float32(0.9274), np.float32(0.7906), np.float32(0.8319), np.float32(0.885)] 
+2025-05-05 04:43:50.167663: Epoch time: 93.19 s 
+2025-05-05 04:43:50.178647: Yayy! New best EMA pseudo Dice: 0.886900007724762 
+2025-05-05 04:43:52.380349:  
+2025-05-05 04:43:52.454652: Epoch 103 
+2025-05-05 04:43:52.456042: Current learning rate: 0.00954 
+2025-05-05 04:45:24.192574: train_loss -0.4197 
+2025-05-05 04:45:24.300193: val_loss -0.4454 
+2025-05-05 04:45:24.331287: Pseudo dice [np.float32(0.8154), np.float32(0.7915), np.float32(0.823), np.float32(0.961), np.float32(0.8145), np.float32(0.9424), np.float32(0.9448), np.float32(0.969), np.float32(0.9536), np.float32(0.9131), np.float32(0.8597), np.float32(0.9623), np.float32(0.9415), np.float32(0.8604), np.float32(0.94), np.float32(0.9316), np.float32(0.7841), np.float32(0.848), np.float32(0.9093)] 
+2025-05-05 04:45:24.365857: Epoch time: 91.81 s 
+2025-05-05 04:45:24.387408: Yayy! New best EMA pseudo Dice: 0.887499988079071 
+2025-05-05 04:45:26.597869:  
+2025-05-05 04:45:26.623670: Epoch 104 
+2025-05-05 04:45:26.655351: Current learning rate: 0.00953 
+2025-05-05 04:47:00.413102: train_loss -0.4371 
+2025-05-05 04:47:00.495289: val_loss -0.485 
+2025-05-05 04:47:00.495789: Pseudo dice [np.float32(0.7905), np.float32(0.775), np.float32(0.8265), np.float32(0.9674), np.float32(0.8517), np.float32(0.9518), np.float32(0.9574), np.float32(0.9699), np.float32(0.9459), np.float32(0.9576), np.float32(0.9294), np.float32(0.9497), np.float32(0.9624), np.float32(0.858), np.float32(0.9579), np.float32(0.9368), np.float32(0.7942), np.float32(0.8409), np.float32(0.9093)] 
+2025-05-05 04:47:00.496168: Epoch time: 93.82 s 
+2025-05-05 04:47:00.496670: Yayy! New best EMA pseudo Dice: 0.8888999819755554 
+2025-05-05 04:47:02.655031:  
+2025-05-05 04:47:02.714611: Epoch 105 
+2025-05-05 04:47:02.715508: Current learning rate: 0.00953 
+2025-05-05 04:48:42.867136: train_loss -0.4304 
+2025-05-05 04:48:42.992090: val_loss -0.4378 
+2025-05-05 04:48:43.018794: Pseudo dice [np.float32(0.7804), np.float32(0.7555), np.float32(0.8197), np.float32(0.952), np.float32(0.835), np.float32(0.9521), np.float32(0.9419), np.float32(0.9558), np.float32(0.9564), np.float32(0.9448), np.float32(0.9212), np.float32(0.9611), np.float32(0.9533), np.float32(0.8249), np.float32(0.9597), np.float32(0.9122), np.float32(0.8513), np.float32(0.8247), np.float32(0.9117)] 
+2025-05-05 04:48:43.019461: Epoch time: 100.21 s 
+2025-05-05 04:48:43.019863: Yayy! New best EMA pseudo Dice: 0.8895999789237976 
+2025-05-05 04:48:45.084143:  
+2025-05-05 04:48:45.150518: Epoch 106 
+2025-05-05 04:48:45.177044: Current learning rate: 0.00952 
+2025-05-05 04:50:17.069803: train_loss -0.4215 
+2025-05-05 04:50:17.220953: val_loss -0.4235 
+2025-05-05 04:50:17.251435: Pseudo dice [np.float32(0.7824), np.float32(0.7949), np.float32(0.6433), np.float32(0.9621), np.float32(0.869), np.float32(0.9473), np.float32(0.9518), np.float32(0.9691), np.float32(0.9541), np.float32(0.9509), np.float32(0.9263), np.float32(0.962), np.float32(0.9556), np.float32(0.8384), np.float32(0.9603), np.float32(0.9275), np.float32(0.7634), np.float32(0.7241), np.float32(0.9142)] 
+2025-05-05 04:50:17.269221: Epoch time: 91.99 s 
+2025-05-05 04:50:18.508726:  
+2025-05-05 04:50:18.546843: Epoch 107 
+2025-05-05 04:50:18.576458: Current learning rate: 0.00952 
+2025-05-05 04:51:47.559234: train_loss -0.4213 
+2025-05-05 04:51:47.646288: val_loss -0.4234 
+2025-05-05 04:51:47.676953: Pseudo dice [np.float32(0.8055), np.float32(0.8049), np.float32(0.8267), np.float32(0.9632), np.float32(0.852), np.float32(0.9499), np.float32(0.9472), np.float32(0.9665), np.float32(0.9489), np.float32(0.9623), np.float32(0.9307), np.float32(0.935), np.float32(0.9597), np.float32(0.8422), np.float32(0.9506), np.float32(0.9288), np.float32(0.7992), np.float32(0.8083), np.float32(0.8893)] 
+2025-05-05 04:51:47.704299: Epoch time: 89.05 s 
+2025-05-05 04:51:47.705332: Yayy! New best EMA pseudo Dice: 0.8899999856948853 
+2025-05-05 04:51:49.628154:  
+2025-05-05 04:51:49.649949: Epoch 108 
+2025-05-05 04:51:49.650665: Current learning rate: 0.00951 
+2025-05-05 04:53:18.758772: train_loss -0.4196 
+2025-05-05 04:53:18.875525: val_loss -0.4015 
+2025-05-05 04:53:18.913548: Pseudo dice [np.float32(0.78), np.float32(0.7348), np.float32(0.7322), np.float32(0.9648), np.float32(0.8611), np.float32(0.9126), np.float32(0.9329), np.float32(0.9661), np.float32(0.8168), np.float32(0.8305), np.float32(0.9179), np.float32(0.9484), np.float32(0.949), np.float32(0.8228), np.float32(0.9021), np.float32(0.9092), np.float32(0.8128), np.float32(0.8397), np.float32(0.8949)] 
+2025-05-05 04:53:18.949471: Epoch time: 89.13 s 
+2025-05-05 04:53:20.233654:  
+2025-05-05 04:53:20.348673: Epoch 109 
+2025-05-05 04:53:20.380068: Current learning rate: 0.00951 
+2025-05-05 04:54:56.460158: train_loss -0.4206 
+2025-05-05 04:54:56.602964: val_loss -0.4569 
+2025-05-05 04:54:56.614081: Pseudo dice [np.float32(0.781), np.float32(0.7553), np.float32(0.8964), np.float32(0.9686), np.float32(0.8033), np.float32(0.9399), np.float32(0.9201), np.float32(0.9518), np.float32(0.947), np.float32(0.9533), np.float32(0.916), np.float32(0.9558), np.float32(0.9552), np.float32(0.844), np.float32(0.948), np.float32(0.9263), np.float32(0.8022), np.float32(0.8011), np.float32(0.8891)] 
+2025-05-05 04:54:56.617533: Epoch time: 96.23 s 
+2025-05-05 04:54:57.941964:  
+2025-05-05 04:54:58.032361: Epoch 110 
+2025-05-05 04:54:58.039975: Current learning rate: 0.0095 
+2025-05-05 04:56:40.310894: train_loss -0.4417 
+2025-05-05 04:56:40.504194: val_loss -0.4483 
+2025-05-05 04:56:40.536654: Pseudo dice [np.float32(0.7427), np.float32(0.7684), np.float32(0.7504), np.float32(0.9601), np.float32(0.8409), np.float32(0.9414), np.float32(0.9277), np.float32(0.9688), np.float32(0.958), np.float32(0.9314), np.float32(0.919), np.float32(0.9606), np.float32(0.9584), np.float32(0.8394), np.float32(0.9596), np.float32(0.935), np.float32(0.8221), np.float32(0.8191), np.float32(0.8989)] 
+2025-05-05 04:56:40.567564: Epoch time: 102.37 s 
+2025-05-05 04:56:42.266900:  
+2025-05-05 04:56:42.420102: Epoch 111 
+2025-05-05 04:56:42.443551: Current learning rate: 0.0095 
+2025-05-05 04:58:21.261034: train_loss -0.3748 
+2025-05-05 04:58:21.310958: val_loss -0.4023 
+2025-05-05 04:58:21.311831: Pseudo dice [np.float32(0.7402), np.float32(0.7691), np.float32(0.8195), np.float32(0.9535), np.float32(0.7951), np.float32(0.9164), np.float32(0.9361), np.float32(0.9628), np.float32(0.917), np.float32(0.9274), np.float32(0.8927), np.float32(0.9568), np.float32(0.9401), np.float32(0.81), np.float32(0.952), np.float32(0.9187), np.float32(0.8286), np.float32(0.7547), np.float32(0.8904)] 
+2025-05-05 04:58:21.312352: Epoch time: 99.0 s 
+2025-05-05 04:58:22.766270:  
+2025-05-05 04:58:22.919497: Epoch 112 
+2025-05-05 04:58:22.938706: Current learning rate: 0.00949 
+2025-05-05 04:59:52.219970: train_loss -0.4045 
+2025-05-05 04:59:52.347779: val_loss -0.4296 
+2025-05-05 04:59:52.365997: Pseudo dice [np.float32(0.7785), np.float32(0.7096), np.float32(0.8817), np.float32(0.9546), np.float32(0.8123), np.float32(0.946), np.float32(0.9137), np.float32(0.9407), np.float32(0.9408), np.float32(0.9294), np.float32(0.9031), np.float32(0.951), np.float32(0.9359), np.float32(0.8559), np.float32(0.9472), np.float32(0.9075), np.float32(0.8408), np.float32(0.7766), np.float32(0.9093)] 
+2025-05-05 04:59:52.376721: Epoch time: 89.46 s 
+2025-05-05 04:59:54.450830:  
+2025-05-05 04:59:54.488549: Epoch 113 
+2025-05-05 04:59:54.503403: Current learning rate: 0.00949 
+2025-05-05 05:01:17.234654: train_loss -0.4273 
+2025-05-05 05:01:17.374451: val_loss -0.4582 
+2025-05-05 05:01:17.411816: Pseudo dice [np.float32(0.7765), np.float32(0.7963), np.float32(0.8635), np.float32(0.9659), np.float32(0.8286), np.float32(0.9398), np.float32(0.9496), np.float32(0.9647), np.float32(0.9546), np.float32(0.9505), np.float32(0.9001), np.float32(0.9606), np.float32(0.957), np.float32(0.8416), np.float32(0.9525), np.float32(0.8793), np.float32(0.7853), np.float32(0.8291), np.float32(0.9043)] 
+2025-05-05 05:01:17.440024: Epoch time: 82.78 s 
+2025-05-05 05:01:19.085296:  
+2025-05-05 05:01:19.134317: Epoch 114 
+2025-05-05 05:01:19.169179: Current learning rate: 0.00949 
+2025-05-05 05:02:44.925289: train_loss -0.4234 
+2025-05-05 05:02:45.003184: val_loss -0.4337 
+2025-05-05 05:02:45.045694: Pseudo dice [np.float32(0.8066), np.float32(0.7891), np.float32(0.8257), np.float32(0.9301), np.float32(0.8518), np.float32(0.9262), np.float32(0.9399), np.float32(0.9707), np.float32(0.9545), np.float32(0.9522), np.float32(0.9208), np.float32(0.9669), np.float32(0.9515), np.float32(0.8341), np.float32(0.8671), np.float32(0.9023), np.float32(0.7601), np.float32(0.8), np.float32(0.9047)] 
+2025-05-05 05:02:45.080478: Epoch time: 85.84 s 
+2025-05-05 05:02:46.794371:  
+2025-05-05 05:02:46.913448: Epoch 115 
+2025-05-05 05:02:46.928485: Current learning rate: 0.00948 
+2025-05-05 05:04:12.359863: train_loss -0.4001 
+2025-05-05 05:04:12.471122: val_loss -0.4099 
+2025-05-05 05:04:12.475305: Pseudo dice [np.float32(0.7521), np.float32(0.7805), np.float32(0.4685), np.float32(0.922), np.float32(0.8458), np.float32(0.8954), np.float32(0.9372), np.float32(0.9647), np.float32(0.949), np.float32(0.9521), np.float32(0.9233), np.float32(0.9571), np.float32(0.948), np.float32(0.8512), np.float32(0.949), np.float32(0.9089), np.float32(0.7778), np.float32(0.8074), np.float32(0.8931)] 
+2025-05-05 05:04:12.491792: Epoch time: 85.57 s 
+2025-05-05 05:04:14.135135:  
+2025-05-05 05:04:14.236127: Epoch 116 
+2025-05-05 05:04:14.281102: Current learning rate: 0.00948 
+2025-05-05 05:05:39.976964: train_loss -0.4307 
+2025-05-05 05:05:40.039541: val_loss -0.4371 
+2025-05-05 05:05:40.054414: Pseudo dice [np.float32(0.7271), np.float32(0.7977), np.float32(0.8758), np.float32(0.9706), np.float32(0.8559), np.float32(0.9372), np.float32(0.95), np.float32(0.963), np.float32(0.9301), np.float32(0.9537), np.float32(0.9322), np.float32(0.9336), np.float32(0.9566), np.float32(0.8314), np.float32(0.9147), np.float32(0.9081), np.float32(0.8256), np.float32(0.8565), np.float32(0.8973)] 
+2025-05-05 05:05:40.067539: Epoch time: 85.84 s 
+2025-05-05 05:05:41.763767:  
+2025-05-05 05:05:41.787466: Epoch 117 
+2025-05-05 05:05:41.788424: Current learning rate: 0.00947 
+2025-05-05 05:07:07.221365: train_loss -0.439 
+2025-05-05 05:07:07.312471: val_loss -0.4246 
+2025-05-05 05:07:07.334847: Pseudo dice [np.float32(0.8146), np.float32(0.8112), np.float32(0.8593), np.float32(0.9666), np.float32(0.8732), np.float32(0.9496), np.float32(0.959), np.float32(0.9631), np.float32(0.9569), np.float32(0.9616), np.float32(0.9228), np.float32(0.9628), np.float32(0.955), np.float32(0.8774), np.float32(0.9344), np.float32(0.9382), np.float32(0.8334), np.float32(0.8153), np.float32(0.9091)] 
+2025-05-05 05:07:07.358612: Epoch time: 85.46 s 
+2025-05-05 05:07:09.151186:  
+2025-05-05 05:07:09.199304: Epoch 118 
+2025-05-05 05:07:09.200274: Current learning rate: 0.00947 
+2025-05-05 05:08:34.642161: train_loss -0.4318 
+2025-05-05 05:08:34.699995: val_loss -0.4392 
+2025-05-05 05:08:34.701053: Pseudo dice [np.float32(0.781), np.float32(0.8139), np.float32(0.8669), np.float32(0.9649), np.float32(0.8362), np.float32(0.9457), np.float32(0.9524), np.float32(0.969), np.float32(0.9585), np.float32(0.9429), np.float32(0.9159), np.float32(0.9546), np.float32(0.9484), np.float32(0.8519), np.float32(0.9587), np.float32(0.9346), np.float32(0.7851), np.float32(0.8574), np.float32(0.9032)] 
+2025-05-05 05:08:34.701723: Epoch time: 85.49 s 
+2025-05-05 05:08:34.702284: Yayy! New best EMA pseudo Dice: 0.8903999924659729 
+2025-05-05 05:08:36.711900:  
+2025-05-05 05:08:36.714183: Epoch 119 
+2025-05-05 05:08:36.715606: Current learning rate: 0.00946 
+2025-05-05 05:10:06.192995: train_loss -0.429 
+2025-05-05 05:10:06.317044: val_loss -0.4625 
+2025-05-05 05:10:06.354954: Pseudo dice [np.float32(0.8095), np.float32(0.7769), np.float32(0.7302), np.float32(0.9649), np.float32(0.7874), np.float32(0.9216), np.float32(0.9409), np.float32(0.9623), np.float32(0.9497), np.float32(0.9486), np.float32(0.9181), np.float32(0.9609), np.float32(0.9455), np.float32(0.8387), np.float32(0.9464), np.float32(0.9163), np.float32(0.7368), np.float32(0.764), np.float32(0.9004)] 
+2025-05-05 05:10:06.401689: Epoch time: 89.48 s 
+2025-05-05 05:10:08.205021:  
+2025-05-05 05:10:08.265837: Epoch 120 
+2025-05-05 05:10:08.291711: Current learning rate: 0.00946 
+2025-05-05 05:11:33.777525: train_loss -0.4287 
+2025-05-05 05:11:33.863279: val_loss -0.4491 
+2025-05-05 05:11:33.878092: Pseudo dice [np.float32(0.7534), np.float32(0.8057), np.float32(0.9121), np.float32(0.9609), np.float32(0.8587), np.float32(0.9333), np.float32(0.9577), np.float32(0.9661), np.float32(0.9422), np.float32(0.9465), np.float32(0.9116), np.float32(0.956), np.float32(0.9405), np.float32(0.8429), np.float32(0.946), np.float32(0.9079), np.float32(0.7475), np.float32(0.7024), np.float32(0.9082)] 
+2025-05-05 05:11:33.923817: Epoch time: 85.57 s 
+2025-05-05 05:11:35.102800:  
+2025-05-05 05:11:35.234108: Epoch 121 
+2025-05-05 05:11:35.267561: Current learning rate: 0.00945 
+2025-05-05 05:13:00.346773: train_loss -0.4259 
+2025-05-05 05:13:00.428628: val_loss -0.466 
+2025-05-05 05:13:00.440478: Pseudo dice [np.float32(0.7827), np.float32(0.7656), np.float32(0.8956), np.float32(0.9588), np.float32(0.7389), np.float32(0.9217), np.float32(0.9456), np.float32(0.9667), np.float32(0.953), np.float32(0.952), np.float32(0.9196), np.float32(0.9616), np.float32(0.9568), np.float32(0.8627), np.float32(0.9365), np.float32(0.8815), np.float32(0.8535), np.float32(0.8274), np.float32(0.8906)] 
+2025-05-05 05:13:00.456294: Epoch time: 85.25 s 
+2025-05-05 05:13:02.198364:  
+2025-05-05 05:13:02.232136: Epoch 122 
+2025-05-05 05:13:02.245624: Current learning rate: 0.00945 
+2025-05-05 05:14:28.713866: train_loss -0.4311 
+2025-05-05 05:14:28.768055: val_loss -0.4463 
+2025-05-05 05:14:28.793799: Pseudo dice [np.float32(0.776), np.float32(0.7962), np.float32(0.8005), np.float32(0.9601), np.float32(0.6693), np.float32(0.9264), np.float32(0.9378), np.float32(0.966), np.float32(0.9637), np.float32(0.9349), np.float32(0.9029), np.float32(0.9664), np.float32(0.9446), np.float32(0.8609), np.float32(0.9228), np.float32(0.9209), np.float32(0.8483), np.float32(0.8313), np.float32(0.8912)] 
+2025-05-05 05:14:28.835630: Epoch time: 86.52 s 
+2025-05-05 05:14:30.596656:  
+2025-05-05 05:14:30.696890: Epoch 123 
+2025-05-05 05:14:30.728418: Current learning rate: 0.00944 
+2025-05-05 05:15:57.291662: train_loss -0.4554 
+2025-05-05 05:15:57.444574: val_loss -0.4553 
+2025-05-05 05:15:57.506412: Pseudo dice [np.float32(0.8), np.float32(0.7933), np.float32(0.8013), np.float32(0.9561), np.float32(0.8716), np.float32(0.9517), np.float32(0.9445), np.float32(0.9686), np.float32(0.9281), np.float32(0.9621), np.float32(0.9218), np.float32(0.9557), np.float32(0.9541), np.float32(0.8728), np.float32(0.9576), np.float32(0.925), np.float32(0.8246), np.float32(0.7997), np.float32(0.8992)] 
+2025-05-05 05:15:57.561444: Epoch time: 86.7 s 
+2025-05-05 05:15:59.330567:  
+2025-05-05 05:15:59.418894: Epoch 124 
+2025-05-05 05:15:59.442997: Current learning rate: 0.00944 
+2025-05-05 05:17:24.586104: train_loss -0.4456 
+2025-05-05 05:17:24.674706: val_loss -0.4344 
+2025-05-05 05:17:24.689638: Pseudo dice [np.float32(0.7855), np.float32(0.8171), np.float32(0.8374), np.float32(0.971), np.float32(0.8616), np.float32(0.9187), np.float32(0.9538), np.float32(0.9738), np.float32(0.9556), np.float32(0.9554), np.float32(0.9323), np.float32(0.9659), np.float32(0.9482), np.float32(0.8827), np.float32(0.9419), np.float32(0.9187), np.float32(0.8423), np.float32(0.8301), np.float32(0.9067)] 
+2025-05-05 05:17:24.717703: Epoch time: 85.26 s 
+2025-05-05 05:17:24.740830: Yayy! New best EMA pseudo Dice: 0.8917999863624573 
+2025-05-05 05:17:26.906267:  
+2025-05-05 05:17:26.950037: Epoch 125 
+2025-05-05 05:17:26.977827: Current learning rate: 0.00944 
+2025-05-05 05:18:56.138978: train_loss -0.449 
+2025-05-05 05:18:56.160514: val_loss -0.4692 
+2025-05-05 05:18:56.174020: Pseudo dice [np.float32(0.7436), np.float32(0.8161), np.float32(0.7995), np.float32(0.9526), np.float32(0.8834), np.float32(0.9543), np.float32(0.9544), np.float32(0.9762), np.float32(0.9546), np.float32(0.9522), np.float32(0.9339), np.float32(0.9593), np.float32(0.9553), np.float32(0.8705), np.float32(0.9569), np.float32(0.9355), np.float32(0.8147), np.float32(0.8017), np.float32(0.9058)] 
+2025-05-05 05:18:56.194289: Epoch time: 89.23 s 
+2025-05-05 05:18:56.198243: Yayy! New best EMA pseudo Dice: 0.8927000164985657 
+2025-05-05 05:18:58.995259:  
+2025-05-05 05:18:59.016088: Epoch 126 
+2025-05-05 05:18:59.028073: Current learning rate: 0.00943 
+2025-05-05 05:20:27.882062: train_loss -0.4473 
+2025-05-05 05:20:28.050328: val_loss -0.4078 
+2025-05-05 05:20:28.064920: Pseudo dice [np.float32(0.7724), np.float32(0.7352), np.float32(0.7426), np.float32(0.9763), np.float32(0.7948), np.float32(0.9516), np.float32(0.9063), np.float32(0.959), np.float32(0.9584), np.float32(0.954), np.float32(0.9234), np.float32(0.9639), np.float32(0.9605), np.float32(0.8227), np.float32(0.9515), np.float32(0.909), np.float32(0.7951), np.float32(0.785), np.float32(0.9036)] 
+2025-05-05 05:20:28.081047: Epoch time: 88.89 s 
+2025-05-05 05:20:29.807844:  
+2025-05-05 05:20:29.825687: Epoch 127 
+2025-05-05 05:20:29.871294: Current learning rate: 0.00943 
+2025-05-05 05:21:56.965533: train_loss -0.4316 
+2025-05-05 05:21:57.022501: val_loss -0.4435 
+2025-05-05 05:21:57.026588: Pseudo dice [np.float32(0.8047), np.float32(0.7957), np.float32(0.883), np.float32(0.9636), np.float32(0.8529), np.float32(0.948), np.float32(0.9371), np.float32(0.9682), np.float32(0.9621), np.float32(0.9462), np.float32(0.9345), np.float32(0.9653), np.float32(0.9618), np.float32(0.8652), np.float32(0.9601), np.float32(0.9335), np.float32(0.8156), np.float32(0.8101), np.float32(0.9124)] 
+2025-05-05 05:21:57.027037: Epoch time: 87.16 s 
+2025-05-05 05:21:57.027440: Yayy! New best EMA pseudo Dice: 0.8931000232696533 
+2025-05-05 05:21:59.670887:  
+2025-05-05 05:21:59.845985: Epoch 128 
+2025-05-05 05:21:59.872528: Current learning rate: 0.00942 
+2025-05-05 05:23:27.150432: train_loss -0.4491 
+2025-05-05 05:23:27.211975: val_loss -0.435 
+2025-05-05 05:23:27.214243: Pseudo dice [np.float32(0.7945), np.float32(0.7917), np.float32(0.9001), np.float32(0.9748), np.float32(0.8201), np.float32(0.9161), np.float32(0.9223), np.float32(0.9711), np.float32(0.9566), np.float32(0.9515), np.float32(0.9093), np.float32(0.9639), np.float32(0.9373), np.float32(0.8268), np.float32(0.9395), np.float32(0.9264), np.float32(0.866), np.float32(0.8554), np.float32(0.9041)] 
+2025-05-05 05:23:27.215202: Epoch time: 87.48 s 
+2025-05-05 05:23:27.219395: Yayy! New best EMA pseudo Dice: 0.8939999938011169 
+2025-05-05 05:23:29.264508:  
+2025-05-05 05:23:29.335816: Epoch 129 
+2025-05-05 05:23:29.383076: Current learning rate: 0.00942 
+2025-05-05 05:24:56.952194: train_loss -0.4432 
+2025-05-05 05:24:57.049987: val_loss -0.492 
+2025-05-05 05:24:57.087448: Pseudo dice [np.float32(0.804), np.float32(0.7955), np.float32(0.8724), np.float32(0.9713), np.float32(0.8342), np.float32(0.9319), np.float32(0.9424), np.float32(0.9698), np.float32(0.9354), np.float32(0.9534), np.float32(0.9217), np.float32(0.9554), np.float32(0.9584), np.float32(0.8723), np.float32(0.9462), np.float32(0.9282), np.float32(0.8556), np.float32(0.8809), np.float32(0.9188)] 
+2025-05-05 05:24:57.127708: Epoch time: 87.69 s 
+2025-05-05 05:24:57.175737: Yayy! New best EMA pseudo Dice: 0.8953999876976013 
+2025-05-05 05:25:00.290237:  
+2025-05-05 05:25:00.295418: Epoch 130 
+2025-05-05 05:25:00.295846: Current learning rate: 0.00941 
+2025-05-05 05:26:29.630103: train_loss -0.4553 
+2025-05-05 05:26:29.648592: val_loss -0.4815 
+2025-05-05 05:26:29.649413: Pseudo dice [np.float32(0.8124), np.float32(0.8173), np.float32(0.7699), np.float32(0.9682), np.float32(0.8738), np.float32(0.9537), np.float32(0.9482), np.float32(0.9633), np.float32(0.9446), np.float32(0.9385), np.float32(0.9035), np.float32(0.9453), np.float32(0.9348), np.float32(0.869), np.float32(0.939), np.float32(0.933), np.float32(0.8693), np.float32(0.8586), np.float32(0.9061)] 
+2025-05-05 05:26:29.653887: Epoch time: 89.34 s 
+2025-05-05 05:26:29.654459: Yayy! New best EMA pseudo Dice: 0.8960999846458435 
+2025-05-05 05:26:32.240959:  
+2025-05-05 05:26:32.274472: Epoch 131 
+2025-05-05 05:26:32.277328: Current learning rate: 0.00941 
+2025-05-05 05:28:00.893505: train_loss -0.4367 
+2025-05-05 05:28:00.935309: val_loss -0.469 
+2025-05-05 05:28:00.944075: Pseudo dice [np.float32(0.7835), np.float32(0.7593), np.float32(0.8551), np.float32(0.9512), np.float32(0.8379), np.float32(0.9555), np.float32(0.9573), np.float32(0.9752), np.float32(0.9326), np.float32(0.9409), np.float32(0.9189), np.float32(0.9575), np.float32(0.9531), np.float32(0.8754), np.float32(0.9629), np.float32(0.9268), np.float32(0.879), np.float32(0.8335), np.float32(0.9061)] 
+2025-05-05 05:28:00.959518: Epoch time: 88.65 s 
+2025-05-05 05:28:00.995188: Yayy! New best EMA pseudo Dice: 0.8967999815940857 
+2025-05-05 05:28:02.857323:  
+2025-05-05 05:28:02.893197: Epoch 132 
+2025-05-05 05:28:02.898905: Current learning rate: 0.0094 
+2025-05-05 05:29:31.242371: train_loss -0.4495 
+2025-05-05 05:29:31.273384: val_loss -0.4601 
+2025-05-05 05:29:31.273956: Pseudo dice [np.float32(0.8203), np.float32(0.8176), np.float32(0.8087), np.float32(0.9598), np.float32(0.8687), np.float32(0.955), np.float32(0.955), np.float32(0.9644), np.float32(0.9595), np.float32(0.9345), np.float32(0.8806), np.float32(0.9653), np.float32(0.9495), np.float32(0.8673), np.float32(0.9396), np.float32(0.935), np.float32(0.8507), np.float32(0.8246), np.float32(0.9189)] 
+2025-05-05 05:29:31.274529: Epoch time: 88.39 s 
+2025-05-05 05:29:31.274988: Yayy! New best EMA pseudo Dice: 0.8974999785423279 
+2025-05-05 05:29:33.196138:  
+2025-05-05 05:29:33.240194: Epoch 133 
+2025-05-05 05:29:33.241077: Current learning rate: 0.0094 
+2025-05-05 05:31:00.823092: train_loss -0.4457 
+2025-05-05 05:31:00.870038: val_loss -0.4225 
+2025-05-05 05:31:00.874850: Pseudo dice [np.float32(0.7574), np.float32(0.816), np.float32(0.8235), np.float32(0.9698), np.float32(0.8474), np.float32(0.9486), np.float32(0.9476), np.float32(0.964), np.float32(0.9452), np.float32(0.9328), np.float32(0.9032), np.float32(0.9589), np.float32(0.9555), np.float32(0.8486), np.float32(0.954), np.float32(0.937), np.float32(0.8422), np.float32(0.7255), np.float32(0.8929)] 
+2025-05-05 05:31:00.881836: Epoch time: 87.63 s 
+2025-05-05 05:31:02.536222:  
+2025-05-05 05:31:02.545993: Epoch 134 
+2025-05-05 05:31:02.551379: Current learning rate: 0.00939 
+2025-05-05 05:32:27.702675: train_loss -0.4469 
+2025-05-05 05:32:27.740275: val_loss -0.4493 
+2025-05-05 05:32:27.740793: Pseudo dice [np.float32(0.7597), np.float32(0.7635), np.float32(0.8925), np.float32(0.9649), np.float32(0.8362), np.float32(0.9517), np.float32(0.9503), np.float32(0.975), np.float32(0.94), np.float32(0.9549), np.float32(0.8849), np.float32(0.9533), np.float32(0.9649), np.float32(0.8556), np.float32(0.9497), np.float32(0.943), np.float32(0.6921), np.float32(0.6162), np.float32(0.8939)] 
+2025-05-05 05:32:27.756743: Epoch time: 85.17 s 
+2025-05-05 05:32:29.474820:  
+2025-05-05 05:32:29.561238: Epoch 135 
+2025-05-05 05:32:29.609774: Current learning rate: 0.00939 
+2025-05-05 05:33:54.513678: train_loss -0.4553 
+2025-05-05 05:33:54.593870: val_loss -0.4728 
+2025-05-05 05:33:54.632783: Pseudo dice [np.float32(0.7847), np.float32(0.8233), np.float32(0.8799), np.float32(0.9673), np.float32(0.8799), np.float32(0.931), np.float32(0.9352), np.float32(0.958), np.float32(0.9322), np.float32(0.9631), np.float32(0.9212), np.float32(0.9513), np.float32(0.9629), np.float32(0.8668), np.float32(0.9122), np.float32(0.8492), np.float32(0.8633), np.float32(0.8636), np.float32(0.9086)] 
+2025-05-05 05:33:54.672404: Epoch time: 85.04 s 
+2025-05-05 05:33:56.375470:  
+2025-05-05 05:33:56.449088: Epoch 136 
+2025-05-05 05:33:56.462087: Current learning rate: 0.00939 
+2025-05-05 05:35:21.925762: train_loss -0.4374 
+2025-05-05 05:35:21.999062: val_loss -0.4329 
+2025-05-05 05:35:22.005112: Pseudo dice [np.float32(0.8005), np.float32(0.7744), np.float32(0.8464), np.float32(0.9272), np.float32(0.8854), np.float32(0.9447), np.float32(0.9522), np.float32(0.9624), np.float32(0.9431), np.float32(0.9514), np.float32(0.9286), np.float32(0.9578), np.float32(0.9609), np.float32(0.8309), np.float32(0.9552), np.float32(0.9248), np.float32(0.8375), np.float32(0.8478), np.float32(0.908)] 
+2025-05-05 05:35:22.005805: Epoch time: 85.55 s 
+2025-05-05 05:35:23.210279:  
+2025-05-05 05:35:23.318782: Epoch 137 
+2025-05-05 05:35:23.344764: Current learning rate: 0.00938 
+2025-05-05 05:36:49.005352: train_loss -0.455 
+2025-05-05 05:36:49.096502: val_loss -0.4375 
+2025-05-05 05:36:49.129299: Pseudo dice [np.float32(0.8176), np.float32(0.7759), np.float32(0.8582), np.float32(0.9702), np.float32(0.8927), np.float32(0.9572), np.float32(0.9546), np.float32(0.9699), np.float32(0.9461), np.float32(0.9362), np.float32(0.9282), np.float32(0.9534), np.float32(0.9544), np.float32(0.868), np.float32(0.9615), np.float32(0.9179), np.float32(0.7992), np.float32(0.7741), np.float32(0.9054)] 
+2025-05-05 05:36:49.151306: Epoch time: 85.8 s 
+2025-05-05 05:36:50.402763:  
+2025-05-05 05:36:50.467217: Epoch 138 
+2025-05-05 05:36:50.496204: Current learning rate: 0.00938 
+2025-05-05 05:38:16.220705: train_loss -0.4276 
+2025-05-05 05:38:16.339100: val_loss -0.4404 
+2025-05-05 05:38:16.365045: Pseudo dice [np.float32(0.7656), np.float32(0.7247), np.float32(0.9274), np.float32(0.9608), np.float32(0.8668), np.float32(0.9375), np.float32(0.9396), np.float32(0.9713), np.float32(0.9462), np.float32(0.9481), np.float32(0.9295), np.float32(0.9409), np.float32(0.9481), np.float32(0.8403), np.float32(0.9367), np.float32(0.9304), np.float32(0.7022), np.float32(0.6391), np.float32(0.9134)] 
+2025-05-05 05:38:16.384807: Epoch time: 85.82 s 
+2025-05-05 05:38:18.127836:  
+2025-05-05 05:38:18.253431: Epoch 139 
+2025-05-05 05:38:18.278929: Current learning rate: 0.00937 
+2025-05-05 05:39:42.378998: train_loss -0.4365 
+2025-05-05 05:39:42.517707: val_loss -0.4619 
+2025-05-05 05:39:42.522149: Pseudo dice [np.float32(0.7352), np.float32(0.7851), np.float32(0.9011), np.float32(0.9717), np.float32(0.8123), np.float32(0.9428), np.float32(0.9161), np.float32(0.9706), np.float32(0.9443), np.float32(0.9497), np.float32(0.916), np.float32(0.9572), np.float32(0.9544), np.float32(0.8664), np.float32(0.9587), np.float32(0.9252), np.float32(0.8561), np.float32(0.8286), np.float32(0.8966)] 
+2025-05-05 05:39:42.547778: Epoch time: 84.25 s 
+2025-05-05 05:39:43.719715:  
+2025-05-05 05:39:43.779905: Epoch 140 
+2025-05-05 05:39:43.809436: Current learning rate: 0.00937 
+2025-05-05 05:41:08.167559: train_loss -0.4361 
+2025-05-05 05:41:08.214917: val_loss -0.4411 
+2025-05-05 05:41:08.222516: Pseudo dice [np.float32(0.8124), np.float32(0.8025), np.float32(0.8709), np.float32(0.9764), np.float32(0.8777), np.float32(0.9531), np.float32(0.9568), np.float32(0.9724), np.float32(0.953), np.float32(0.948), np.float32(0.9093), np.float32(0.9644), np.float32(0.9561), np.float32(0.8773), np.float32(0.9542), np.float32(0.946), np.float32(0.8572), np.float32(0.8644), np.float32(0.8989)] 
+2025-05-05 05:41:08.238639: Epoch time: 84.45 s 
+2025-05-05 05:41:08.260186: Yayy! New best EMA pseudo Dice: 0.8978999853134155 
+2025-05-05 05:41:10.548313:  
+2025-05-05 05:41:10.726758: Epoch 141 
+2025-05-05 05:41:10.777940: Current learning rate: 0.00936 
+2025-05-05 05:42:39.074550: train_loss -0.4374 
+2025-05-05 05:42:39.171756: val_loss -0.4438 
+2025-05-05 05:42:39.172502: Pseudo dice [np.float32(0.7961), np.float32(0.7713), np.float32(0.6294), np.float32(0.8947), np.float32(0.8254), np.float32(0.953), np.float32(0.9402), np.float32(0.9586), np.float32(0.9403), np.float32(0.9558), np.float32(0.9207), np.float32(0.9521), np.float32(0.951), np.float32(0.8624), np.float32(0.9624), np.float32(0.9198), np.float32(0.8491), np.float32(0.8609), np.float32(0.8855)] 
+2025-05-05 05:42:39.192364: Epoch time: 88.53 s 
+2025-05-05 05:42:40.550295:  
+2025-05-05 05:42:40.649028: Epoch 142 
+2025-05-05 05:42:40.681776: Current learning rate: 0.00936 
+2025-05-05 05:44:04.702393: train_loss -0.4059 
+2025-05-05 05:44:04.771060: val_loss -0.5066 
+2025-05-05 05:44:04.789595: Pseudo dice [np.float32(0.7907), np.float32(0.8177), np.float32(0.8318), np.float32(0.9707), np.float32(0.8721), np.float32(0.9512), np.float32(0.9525), np.float32(0.9645), np.float32(0.9643), np.float32(0.9407), np.float32(0.932), np.float32(0.9544), np.float32(0.9585), np.float32(0.8581), np.float32(0.9502), np.float32(0.9178), np.float32(0.825), np.float32(0.8417), np.float32(0.9156)] 
+2025-05-05 05:44:04.814995: Epoch time: 84.16 s 
+2025-05-05 05:44:06.525311:  
+2025-05-05 05:44:06.601703: Epoch 143 
+2025-05-05 05:44:06.631400: Current learning rate: 0.00935 
+2025-05-05 05:45:32.027102: train_loss -0.4329 
+2025-05-05 05:45:32.170727: val_loss -0.4642 
+2025-05-05 05:45:32.198571: Pseudo dice [np.float32(0.7759), np.float32(0.7702), np.float32(0.8645), np.float32(0.9508), np.float32(0.8882), np.float32(0.9327), np.float32(0.9466), np.float32(0.9729), np.float32(0.9423), np.float32(0.9553), np.float32(0.9262), np.float32(0.9456), np.float32(0.959), np.float32(0.8409), np.float32(0.9337), np.float32(0.9149), np.float32(0.8615), np.float32(0.8364), np.float32(0.9006)] 
+2025-05-05 05:45:32.202341: Epoch time: 85.5 s 
+2025-05-05 05:45:32.202989: Yayy! New best EMA pseudo Dice: 0.8978999853134155 
+2025-05-05 05:45:34.849538:  
+2025-05-05 05:45:34.937981: Epoch 144 
+2025-05-05 05:45:34.945837: Current learning rate: 0.00935 
+2025-05-05 05:47:01.413007: train_loss -0.4514 
+2025-05-05 05:47:01.550688: val_loss -0.4519 
+2025-05-05 05:47:01.584546: Pseudo dice [np.float32(0.8075), np.float32(0.8128), np.float32(0.8853), np.float32(0.9568), np.float32(0.85), np.float32(0.9408), np.float32(0.9518), np.float32(0.9672), np.float32(0.939), np.float32(0.9391), np.float32(0.9091), np.float32(0.9519), np.float32(0.9523), np.float32(0.8707), np.float32(0.9515), np.float32(0.9237), np.float32(0.8684), np.float32(0.8845), np.float32(0.9227)] 
+2025-05-05 05:47:01.623095: Epoch time: 86.57 s 
+2025-05-05 05:47:01.637739: Yayy! New best EMA pseudo Dice: 0.8991000056266785 
+2025-05-05 05:47:03.822551:  
+2025-05-05 05:47:03.852200: Epoch 145 
+2025-05-05 05:47:03.856033: Current learning rate: 0.00935 
+2025-05-05 05:48:31.668333: train_loss -0.4523 
+2025-05-05 05:48:31.793682: val_loss -0.425 
+2025-05-05 05:48:31.806456: Pseudo dice [np.float32(0.8233), np.float32(0.842), np.float32(0.89), np.float32(0.9722), np.float32(0.8995), np.float32(0.9382), np.float32(0.9367), np.float32(0.9671), np.float32(0.9273), np.float32(0.9534), np.float32(0.9383), np.float32(0.9612), np.float32(0.9613), np.float32(0.8566), np.float32(0.9305), np.float32(0.931), np.float32(0.7608), np.float32(0.7122), np.float32(0.9199)] 
+2025-05-05 05:48:31.816058: Epoch time: 87.85 s 
+2025-05-05 05:48:31.817065: Yayy! New best EMA pseudo Dice: 0.8992999792098999 
+2025-05-05 05:48:33.767525:  
+2025-05-05 05:48:33.881384: Epoch 146 
+2025-05-05 05:48:33.953765: Current learning rate: 0.00934 
+2025-05-05 05:49:59.087962: train_loss -0.4542 
+2025-05-05 05:49:59.206966: val_loss -0.4724 
+2025-05-05 05:49:59.239621: Pseudo dice [np.float32(0.8035), np.float32(0.8505), np.float32(0.8704), np.float32(0.969), np.float32(0.8806), np.float32(0.9424), np.float32(0.9631), np.float32(0.9727), np.float32(0.9508), np.float32(0.9517), np.float32(0.9343), np.float32(0.9602), np.float32(0.9638), np.float32(0.8553), np.float32(0.9392), np.float32(0.9218), np.float32(0.8174), np.float32(0.8324), np.float32(0.9132)] 
+2025-05-05 05:49:59.255914: Epoch time: 85.32 s 
+2025-05-05 05:49:59.270724: Yayy! New best EMA pseudo Dice: 0.9003999829292297 
+2025-05-05 05:50:02.366619:  
+2025-05-05 05:50:02.398876: Epoch 147 
+2025-05-05 05:50:02.399966: Current learning rate: 0.00934 
+2025-05-05 05:51:30.426744: train_loss -0.4425 
+2025-05-05 05:51:30.511992: val_loss -0.4695 
+2025-05-05 05:51:30.534299: Pseudo dice [np.float32(0.8349), np.float32(0.7887), np.float32(0.8249), np.float32(0.9594), np.float32(0.851), np.float32(0.9501), np.float32(0.9548), np.float32(0.9733), np.float32(0.9591), np.float32(0.9445), np.float32(0.9431), np.float32(0.9676), np.float32(0.9628), np.float32(0.8627), np.float32(0.9494), np.float32(0.908), np.float32(0.8792), np.float32(0.8639), np.float32(0.9158)] 
+2025-05-05 05:51:30.557349: Epoch time: 88.06 s 
+2025-05-05 05:51:30.595378: Yayy! New best EMA pseudo Dice: 0.9014000296592712 
+2025-05-05 05:51:33.059848:  
+2025-05-05 05:51:33.179461: Epoch 148 
+2025-05-05 05:51:33.198807: Current learning rate: 0.00933 
+2025-05-05 05:53:00.683585: train_loss -0.4372 
+2025-05-05 05:53:00.824799: val_loss -0.4368 
+2025-05-05 05:53:00.871670: Pseudo dice [np.float32(0.8193), np.float32(0.813), np.float32(0.8269), np.float32(0.9739), np.float32(0.8648), np.float32(0.9503), np.float32(0.9516), np.float32(0.9733), np.float32(0.9592), np.float32(0.9613), np.float32(0.929), np.float32(0.9613), np.float32(0.9512), np.float32(0.8577), np.float32(0.9558), np.float32(0.9443), np.float32(0.797), np.float32(0.8091), np.float32(0.8904)] 
+2025-05-05 05:53:00.915098: Epoch time: 87.63 s 
+2025-05-05 05:53:00.960878: Yayy! New best EMA pseudo Dice: 0.9017000198364258 
+2025-05-05 05:53:03.761299:  
+2025-05-05 05:53:03.770178: Epoch 149 
+2025-05-05 05:53:03.770685: Current learning rate: 0.00933 
+2025-05-05 05:54:32.022851: train_loss -0.4484 
+2025-05-05 05:54:32.120223: val_loss -0.4349 
+2025-05-05 05:54:32.150232: Pseudo dice [np.float32(0.7653), np.float32(0.7923), np.float32(0.9236), np.float32(0.9558), np.float32(0.8994), np.float32(0.945), np.float32(0.9417), np.float32(0.9597), np.float32(0.9507), np.float32(0.9489), np.float32(0.9231), np.float32(0.9555), np.float32(0.9627), np.float32(0.8425), np.float32(0.9531), np.float32(0.9333), np.float32(0.824), np.float32(0.8501), np.float32(0.8957)] 
+2025-05-05 05:54:32.175635: Epoch time: 88.26 s 
+2025-05-05 05:54:33.108224: Yayy! New best EMA pseudo Dice: 0.9021999835968018 
+2025-05-05 05:54:36.063454:  
+2025-05-05 05:54:36.069550: Epoch 150 
+2025-05-05 05:54:36.088899: Current learning rate: 0.00932 
+2025-05-05 05:56:07.807942: train_loss -0.4411 
+2025-05-05 05:56:07.916345: val_loss -0.4642 
+2025-05-05 05:56:07.962796: Pseudo dice [np.float32(0.7784), np.float32(0.8004), np.float32(0.7705), np.float32(0.9684), np.float32(0.8425), np.float32(0.9525), np.float32(0.9515), np.float32(0.9664), np.float32(0.9156), np.float32(0.9449), np.float32(0.9139), np.float32(0.9616), np.float32(0.9489), np.float32(0.8611), np.float32(0.9549), np.float32(0.9061), np.float32(0.8156), np.float32(0.8412), np.float32(0.8993)] 
+2025-05-05 05:56:07.986779: Epoch time: 91.75 s 
+2025-05-05 05:56:09.694847:  
+2025-05-05 05:56:09.815118: Epoch 151 
+2025-05-05 05:56:09.838152: Current learning rate: 0.00932 
+2025-05-05 05:57:35.088285: train_loss -0.458 
+2025-05-05 05:57:35.180863: val_loss -0.4732 
+2025-05-05 05:57:35.203270: Pseudo dice [np.float32(0.7862), np.float32(0.8126), np.float32(0.9057), np.float32(0.932), np.float32(0.8311), np.float32(0.9405), np.float32(0.9524), np.float32(0.963), np.float32(0.9469), np.float32(0.956), np.float32(0.9129), np.float32(0.9628), np.float32(0.9549), np.float32(0.864), np.float32(0.9488), np.float32(0.9319), np.float32(0.8226), np.float32(0.8168), np.float32(0.9102)] 
+2025-05-05 05:57:35.230588: Epoch time: 85.4 s 
+2025-05-05 05:57:36.935755:  
+2025-05-05 05:57:36.990089: Epoch 152 
+2025-05-05 05:57:36.997942: Current learning rate: 0.00931 
+2025-05-05 05:59:00.302674: train_loss -0.4556 
+2025-05-05 05:59:00.405520: val_loss -0.4573 
+2025-05-05 05:59:00.436535: Pseudo dice [np.float32(0.7992), np.float32(0.8164), np.float32(0.8405), np.float32(0.9686), np.float32(0.825), np.float32(0.9493), np.float32(0.9562), np.float32(0.9643), np.float32(0.9363), np.float32(0.9629), np.float32(0.9152), np.float32(0.96), np.float32(0.9567), np.float32(0.8542), np.float32(0.907), np.float32(0.9155), np.float32(0.8534), np.float32(0.8831), np.float32(0.888)] 
+2025-05-05 05:59:00.473933: Epoch time: 83.37 s 
+2025-05-05 05:59:02.239058:  
+2025-05-05 05:59:02.343075: Epoch 153 
+2025-05-05 05:59:02.369492: Current learning rate: 0.00931 
+2025-05-05 06:00:28.550986: train_loss -0.4498 
+2025-05-05 06:00:28.652201: val_loss -0.5154 
+2025-05-05 06:00:28.686732: Pseudo dice [np.float32(0.8016), np.float32(0.8089), np.float32(0.8288), np.float32(0.9294), np.float32(0.8441), np.float32(0.955), np.float32(0.9615), np.float32(0.9724), np.float32(0.9519), np.float32(0.9641), np.float32(0.9266), np.float32(0.9578), np.float32(0.9548), np.float32(0.8748), np.float32(0.9617), np.float32(0.9367), np.float32(0.834), np.float32(0.8183), np.float32(0.8872)] 
+2025-05-05 06:00:28.725596: Epoch time: 86.31 s 
+2025-05-05 06:00:30.014995:  
+2025-05-05 06:00:30.120366: Epoch 154 
+2025-05-05 06:00:30.151846: Current learning rate: 0.0093 
+2025-05-05 06:01:55.780015: train_loss -0.4587 
+2025-05-05 06:01:55.875389: val_loss -0.485 
+2025-05-05 06:01:55.879782: Pseudo dice [np.float32(0.7927), np.float32(0.8311), np.float32(0.8193), np.float32(0.9665), np.float32(0.87), np.float32(0.9316), np.float32(0.9512), np.float32(0.9702), np.float32(0.9477), np.float32(0.9555), np.float32(0.9266), np.float32(0.9552), np.float32(0.962), np.float32(0.8396), np.float32(0.9237), np.float32(0.937), np.float32(0.8674), np.float32(0.8424), np.float32(0.9163)] 
+2025-05-05 06:01:55.885178: Epoch time: 85.77 s 
+2025-05-05 06:01:55.895770: Yayy! New best EMA pseudo Dice: 0.9021999835968018 
+2025-05-05 06:01:58.184666:  
+2025-05-05 06:01:58.236614: Epoch 155 
+2025-05-05 06:01:58.265854: Current learning rate: 0.0093 
+2025-05-05 06:03:26.416123: train_loss -0.4438 
+2025-05-05 06:03:26.464632: val_loss -0.4824 
+2025-05-05 06:03:26.501231: Pseudo dice [np.float32(0.7924), np.float32(0.8163), np.float32(0.8414), np.float32(0.9602), np.float32(0.8364), np.float32(0.9455), np.float32(0.9539), np.float32(0.9721), np.float32(0.9526), np.float32(0.9424), np.float32(0.8997), np.float32(0.9634), np.float32(0.9392), np.float32(0.8638), np.float32(0.9603), np.float32(0.9298), np.float32(0.854), np.float32(0.8724), np.float32(0.9168)] 
+2025-05-05 06:03:26.543033: Epoch time: 88.23 s 
+2025-05-05 06:03:26.581398: Yayy! New best EMA pseudo Dice: 0.9025999903678894 
+2025-05-05 06:03:28.507591:  
+2025-05-05 06:03:28.555985: Epoch 156 
+2025-05-05 06:03:28.557287: Current learning rate: 0.0093 
+2025-05-05 06:04:57.147623: train_loss -0.4509 
+2025-05-05 06:04:57.232539: val_loss -0.4815 
+2025-05-05 06:04:57.248799: Pseudo dice [np.float32(0.791), np.float32(0.8218), np.float32(0.9135), np.float32(0.9681), np.float32(0.8711), np.float32(0.9509), np.float32(0.9524), np.float32(0.9693), np.float32(0.9605), np.float32(0.9401), np.float32(0.8989), np.float32(0.9642), np.float32(0.957), np.float32(0.8554), np.float32(0.9535), np.float32(0.9415), np.float32(0.8888), np.float32(0.8617), np.float32(0.9102)] 
+2025-05-05 06:04:57.264167: Epoch time: 88.64 s 
+2025-05-05 06:04:57.280722: Yayy! New best EMA pseudo Dice: 0.9038000106811523 
+2025-05-05 06:05:00.218623:  
+2025-05-05 06:05:00.226428: Epoch 157 
+2025-05-05 06:05:00.227047: Current learning rate: 0.00929 
+2025-05-05 06:06:27.188903: train_loss -0.4694 
+2025-05-05 06:06:27.250626: val_loss -0.4454 
+2025-05-05 06:06:27.276336: Pseudo dice [np.float32(0.8055), np.float32(0.7957), np.float32(0.8441), np.float32(0.9698), np.float32(0.8003), np.float32(0.9413), np.float32(0.9586), np.float32(0.9453), np.float32(0.9647), np.float32(0.9639), np.float32(0.9372), np.float32(0.968), np.float32(0.9618), np.float32(0.8619), np.float32(0.9589), np.float32(0.9367), np.float32(0.85), np.float32(0.8585), np.float32(0.9069)] 
+2025-05-05 06:06:27.297969: Epoch time: 86.97 s 
+2025-05-05 06:06:27.319777: Yayy! New best EMA pseudo Dice: 0.9041000008583069 
+2025-05-05 06:06:29.982337:  
+2025-05-05 06:06:30.049604: Epoch 158 
+2025-05-05 06:06:30.060069: Current learning rate: 0.00929 
+2025-05-05 06:07:58.502352: train_loss -0.4525 
+2025-05-05 06:07:58.565947: val_loss -0.4742 
+2025-05-05 06:07:58.579076: Pseudo dice [np.float32(0.7869), np.float32(0.7964), np.float32(0.8432), np.float32(0.9668), np.float32(0.8904), np.float32(0.9553), np.float32(0.9582), np.float32(0.9773), np.float32(0.9551), np.float32(0.955), np.float32(0.9281), np.float32(0.9648), np.float32(0.9586), np.float32(0.8401), np.float32(0.9634), np.float32(0.9368), np.float32(0.8628), np.float32(0.8622), np.float32(0.9084)] 
+2025-05-05 06:07:58.596261: Epoch time: 88.52 s 
+2025-05-05 06:07:58.609347: Yayy! New best EMA pseudo Dice: 0.9047999978065491 
+2025-05-05 06:08:01.069182:  
+2025-05-05 06:08:01.142550: Epoch 159 
+2025-05-05 06:08:01.157859: Current learning rate: 0.00928 
+2025-05-05 06:09:31.819772: train_loss -0.4505 
+2025-05-05 06:09:31.875095: val_loss -0.4878 
+2025-05-05 06:09:31.891930: Pseudo dice [np.float32(0.8041), np.float32(0.7677), np.float32(0.9135), np.float32(0.9689), np.float32(0.8186), np.float32(0.9465), np.float32(0.9446), np.float32(0.9584), np.float32(0.9467), np.float32(0.9597), np.float32(0.9212), np.float32(0.9631), np.float32(0.9548), np.float32(0.8752), np.float32(0.9631), np.float32(0.9256), np.float32(0.8476), np.float32(0.8335), np.float32(0.8892)] 
+2025-05-05 06:09:31.909851: Epoch time: 90.75 s 
+2025-05-05 06:09:31.935362: Yayy! New best EMA pseudo Dice: 0.9047999978065491 
+2025-05-05 06:09:34.760093:  
+2025-05-05 06:09:34.818710: Epoch 160 
+2025-05-05 06:09:34.833216: Current learning rate: 0.00928 
+2025-05-05 06:11:03.355615: train_loss -0.4501 
+2025-05-05 06:11:03.414711: val_loss -0.4872 
+2025-05-05 06:11:03.440859: Pseudo dice [np.float32(0.798), np.float32(0.8126), np.float32(0.7978), np.float32(0.9609), np.float32(0.8913), np.float32(0.9515), np.float32(0.96), np.float32(0.9777), np.float32(0.9517), np.float32(0.9495), np.float32(0.9226), np.float32(0.9439), np.float32(0.9574), np.float32(0.8601), np.float32(0.9606), np.float32(0.9455), np.float32(0.8558), np.float32(0.859), np.float32(0.9135)] 
+2025-05-05 06:11:03.476457: Epoch time: 88.6 s 
+2025-05-05 06:11:03.507628: Yayy! New best EMA pseudo Dice: 0.9052000045776367 
+2025-05-05 06:11:06.189959:  
+2025-05-05 06:11:06.329849: Epoch 161 
+2025-05-05 06:11:06.381193: Current learning rate: 0.00927 
+2025-05-05 06:12:35.210999: train_loss -0.436 
+2025-05-05 06:12:35.331023: val_loss -0.4499 
+2025-05-05 06:12:35.389703: Pseudo dice [np.float32(0.8089), np.float32(0.7364), np.float32(0.8845), np.float32(0.9513), np.float32(0.8619), np.float32(0.947), np.float32(0.9353), np.float32(0.9575), np.float32(0.9515), np.float32(0.9459), np.float32(0.8951), np.float32(0.9641), np.float32(0.9471), np.float32(0.8574), np.float32(0.9586), np.float32(0.9182), np.float32(0.8565), np.float32(0.8649), np.float32(0.907)] 
+2025-05-05 06:12:35.434744: Epoch time: 89.02 s 
+2025-05-05 06:12:37.198276:  
+2025-05-05 06:12:37.289397: Epoch 162 
+2025-05-05 06:12:37.312039: Current learning rate: 0.00927 
+2025-05-05 06:14:01.857124: train_loss -0.4448 
+2025-05-05 06:14:01.976401: val_loss -0.45 
+2025-05-05 06:14:01.998326: Pseudo dice [np.float32(0.7656), np.float32(0.8248), np.float32(0.8832), np.float32(0.9642), np.float32(0.7393), np.float32(0.915), np.float32(0.964), np.float32(0.9606), np.float32(0.9451), np.float32(0.9594), np.float32(0.9354), np.float32(0.9618), np.float32(0.9622), np.float32(0.8542), np.float32(0.9187), np.float32(0.9301), np.float32(0.8185), np.float32(0.7564), np.float32(0.8825)] 
+2025-05-05 06:14:02.012709: Epoch time: 84.66 s 
+2025-05-05 06:14:04.533078:  
+2025-05-05 06:14:04.541697: Epoch 163 
+2025-05-05 06:14:04.542475: Current learning rate: 0.00926 
+2025-05-05 06:15:28.847883: train_loss -0.4288 
+2025-05-05 06:15:28.897686: val_loss -0.4089 
+2025-05-05 06:15:28.905816: Pseudo dice [np.float32(0.8263), np.float32(0.7868), np.float32(0.83), np.float32(0.9254), np.float32(0.8011), np.float32(0.9454), np.float32(0.9425), np.float32(0.9622), np.float32(0.9392), np.float32(0.943), np.float32(0.9123), np.float32(0.9564), np.float32(0.9554), np.float32(0.8581), np.float32(0.9295), np.float32(0.8965), np.float32(0.8408), np.float32(0.83), np.float32(0.8995)] 
+2025-05-05 06:15:28.906718: Epoch time: 84.32 s 
+2025-05-05 06:15:30.639418:  
+2025-05-05 06:15:30.783230: Epoch 164 
+2025-05-05 06:15:30.806133: Current learning rate: 0.00926 
+2025-05-05 06:16:59.193966: train_loss -0.4139 
+2025-05-05 06:16:59.275334: val_loss -0.4463 
+2025-05-05 06:16:59.311616: Pseudo dice [np.float32(0.7958), np.float32(0.7912), np.float32(0.7861), np.float32(0.9513), np.float32(0.8108), np.float32(0.9022), np.float32(0.94), np.float32(0.9582), np.float32(0.9477), np.float32(0.941), np.float32(0.854), np.float32(0.9588), np.float32(0.9404), np.float32(0.8696), np.float32(0.861), np.float32(0.9316), np.float32(0.8481), np.float32(0.8498), np.float32(0.9118)] 
+2025-05-05 06:16:59.344957: Epoch time: 88.56 s 
+2025-05-05 06:17:00.984290:  
+2025-05-05 06:17:01.050077: Epoch 165 
+2025-05-05 06:17:01.069170: Current learning rate: 0.00925 
+2025-05-05 06:18:26.077151: train_loss -0.4407 
+2025-05-05 06:18:26.184304: val_loss -0.4223 
+2025-05-05 06:18:26.199357: Pseudo dice [np.float32(0.7966), np.float32(0.8115), np.float32(0.6726), np.float32(0.9671), np.float32(0.852), np.float32(0.938), np.float32(0.9545), np.float32(0.9643), np.float32(0.963), np.float32(0.9349), np.float32(0.9112), np.float32(0.9581), np.float32(0.9521), np.float32(0.8562), np.float32(0.956), np.float32(0.9213), np.float32(0.847), np.float32(0.7673), np.float32(0.9015)] 
+2025-05-05 06:18:26.215754: Epoch time: 85.09 s 
+2025-05-05 06:18:27.873512:  
+2025-05-05 06:18:27.981535: Epoch 166 
+2025-05-05 06:18:27.989815: Current learning rate: 0.00925 
+2025-05-05 06:19:54.261961: train_loss -0.4298 
+2025-05-05 06:19:54.307964: val_loss -0.4726 
+2025-05-05 06:19:54.329896: Pseudo dice [np.float32(0.7586), np.float32(0.7676), np.float32(0.8119), np.float32(0.9712), np.float32(0.8492), np.float32(0.9464), np.float32(0.9361), np.float32(0.9653), np.float32(0.9507), np.float32(0.9441), np.float32(0.9184), np.float32(0.9569), np.float32(0.9547), np.float32(0.865), np.float32(0.9559), np.float32(0.9234), np.float32(0.8358), np.float32(0.8269), np.float32(0.9114)] 
+2025-05-05 06:19:54.338048: Epoch time: 86.39 s 
+2025-05-05 06:19:56.084125:  
+2025-05-05 06:19:56.138733: Epoch 167 
+2025-05-05 06:19:56.159142: Current learning rate: 0.00925 
+2025-05-05 06:21:23.176179: train_loss -0.439 
+2025-05-05 06:21:23.277935: val_loss -0.4247 
+2025-05-05 06:21:23.317023: Pseudo dice [np.float32(0.7827), np.float32(0.7863), np.float32(0.8205), np.float32(0.9723), np.float32(0.8756), np.float32(0.9296), np.float32(0.9564), np.float32(0.9711), np.float32(0.9294), np.float32(0.953), np.float32(0.9181), np.float32(0.9542), np.float32(0.9634), np.float32(0.8644), np.float32(0.956), np.float32(0.9339), np.float32(0.8154), np.float32(0.8518), np.float32(0.8941)] 
+2025-05-05 06:21:23.362614: Epoch time: 87.09 s 
+2025-05-05 06:21:25.117897:  
+2025-05-05 06:21:25.302459: Epoch 168 
+2025-05-05 06:21:25.336225: Current learning rate: 0.00924 
+2025-05-05 06:22:49.165655: train_loss -0.432 
+2025-05-05 06:22:49.216422: val_loss -0.4683 
+2025-05-05 06:22:49.249435: Pseudo dice [np.float32(0.744), np.float32(0.7446), np.float32(0.886), np.float32(0.9718), np.float32(0.8334), np.float32(0.9221), np.float32(0.9528), np.float32(0.9674), np.float32(0.9613), np.float32(0.9482), np.float32(0.9319), np.float32(0.9667), np.float32(0.9522), np.float32(0.8566), np.float32(0.9477), np.float32(0.935), np.float32(0.8557), np.float32(0.7875), np.float32(0.9132)] 
+2025-05-05 06:22:49.272880: Epoch time: 84.05 s 
+2025-05-05 06:22:50.969795:  
+2025-05-05 06:22:51.005059: Epoch 169 
+2025-05-05 06:22:51.015434: Current learning rate: 0.00924 
+2025-05-05 06:24:16.175930: train_loss -0.4472 
+2025-05-05 06:24:16.230823: val_loss -0.4462 
+2025-05-05 06:24:16.232249: Pseudo dice [np.float32(0.7888), np.float32(0.7971), np.float32(0.7954), np.float32(0.9604), np.float32(0.8653), np.float32(0.951), np.float32(0.9546), np.float32(0.9689), np.float32(0.9623), np.float32(0.9489), np.float32(0.9053), np.float32(0.9535), np.float32(0.9512), np.float32(0.8565), np.float32(0.9602), np.float32(0.9172), np.float32(0.8047), np.float32(0.8307), np.float32(0.891)] 
+2025-05-05 06:24:16.237109: Epoch time: 85.21 s 
+2025-05-05 06:24:17.936267:  
+2025-05-05 06:24:18.118923: Epoch 170 
+2025-05-05 06:24:18.191600: Current learning rate: 0.00923 
+2025-05-05 06:25:47.321000: train_loss -0.4643 
+2025-05-05 06:25:47.438981: val_loss -0.4769 
+2025-05-05 06:25:47.453909: Pseudo dice [np.float32(0.8165), np.float32(0.8008), np.float32(0.8357), np.float32(0.9582), np.float32(0.8441), np.float32(0.9518), np.float32(0.9627), np.float32(0.9676), np.float32(0.9475), np.float32(0.9487), np.float32(0.9193), np.float32(0.9571), np.float32(0.9606), np.float32(0.8613), np.float32(0.9519), np.float32(0.9385), np.float32(0.8495), np.float32(0.8408), np.float32(0.904)] 
+2025-05-05 06:25:47.504579: Epoch time: 89.39 s 
+2025-05-05 06:25:48.860172:  
+2025-05-05 06:25:48.959919: Epoch 171 
+2025-05-05 06:25:48.982643: Current learning rate: 0.00923 
+2025-05-05 06:27:15.425469: train_loss -0.4457 
+2025-05-05 06:27:15.503163: val_loss -0.4427 
+2025-05-05 06:27:15.520356: Pseudo dice [np.float32(0.7977), np.float32(0.8195), np.float32(0.7188), np.float32(0.9725), np.float32(0.8547), np.float32(0.9516), np.float32(0.9525), np.float32(0.9666), np.float32(0.9431), np.float32(0.9508), np.float32(0.8897), np.float32(0.9571), np.float32(0.9384), np.float32(0.8767), np.float32(0.9529), np.float32(0.9275), np.float32(0.8071), np.float32(0.8372), np.float32(0.9069)] 
+2025-05-05 06:27:15.533029: Epoch time: 86.57 s 
+2025-05-05 06:27:17.166137:  
+2025-05-05 06:27:17.278156: Epoch 172 
+2025-05-05 06:27:17.322715: Current learning rate: 0.00922 
+2025-05-05 06:28:43.147872: train_loss -0.4568 
+2025-05-05 06:28:43.278173: val_loss -0.4619 
+2025-05-05 06:28:43.309350: Pseudo dice [np.float32(0.8047), np.float32(0.7655), np.float32(0.8926), np.float32(0.9722), np.float32(0.87), np.float32(0.9392), np.float32(0.9537), np.float32(0.973), np.float32(0.9368), np.float32(0.95), np.float32(0.898), np.float32(0.9435), np.float32(0.9521), np.float32(0.8713), np.float32(0.9609), np.float32(0.9357), np.float32(0.8225), np.float32(0.8775), np.float32(0.914)] 
+2025-05-05 06:28:43.330897: Epoch time: 85.98 s 
+2025-05-05 06:28:45.110764:  
+2025-05-05 06:28:45.144178: Epoch 173 
+2025-05-05 06:28:45.173562: Current learning rate: 0.00922 
+2025-05-05 06:30:10.409195: train_loss -0.4622 
+2025-05-05 06:30:10.559154: val_loss -0.4279 
+2025-05-05 06:30:10.594668: Pseudo dice [np.float32(0.7547), np.float32(0.8071), np.float32(0.8845), np.float32(0.9656), np.float32(0.8308), np.float32(0.9442), np.float32(0.9491), np.float32(0.9756), np.float32(0.9471), np.float32(0.9581), np.float32(0.9334), np.float32(0.9559), np.float32(0.9599), np.float32(0.8433), np.float32(0.959), np.float32(0.911), np.float32(0.823), np.float32(0.8081), np.float32(0.9078)] 
+2025-05-05 06:30:10.626744: Epoch time: 85.3 s 
+2025-05-05 06:30:12.312903:  
+2025-05-05 06:30:12.371272: Epoch 174 
+2025-05-05 06:30:12.372542: Current learning rate: 0.00921 
+2025-05-05 06:31:36.750011: train_loss -0.4632 
+2025-05-05 06:31:36.877190: val_loss -0.4635 
+2025-05-05 06:31:36.907521: Pseudo dice [np.float32(0.8266), np.float32(0.8189), np.float32(0.7938), np.float32(0.9662), np.float32(0.8801), np.float32(0.9542), np.float32(0.9598), np.float32(0.961), np.float32(0.9591), np.float32(0.9596), np.float32(0.9279), np.float32(0.9618), np.float32(0.9682), np.float32(0.8912), np.float32(0.9147), np.float32(0.9217), np.float32(0.841), np.float32(0.8005), np.float32(0.8965)] 
+2025-05-05 06:31:36.930934: Epoch time: 84.44 s 
+2025-05-05 06:31:38.638901:  
+2025-05-05 06:31:38.739335: Epoch 175 
+2025-05-05 06:31:38.764898: Current learning rate: 0.00921 
+2025-05-05 06:33:03.197887: train_loss -0.4285 
+2025-05-05 06:33:03.281304: val_loss -0.4806 
+2025-05-05 06:33:03.294191: Pseudo dice [np.float32(0.8211), np.float32(0.7986), np.float32(0.913), np.float32(0.9573), np.float32(0.8745), np.float32(0.9581), np.float32(0.957), np.float32(0.9785), np.float32(0.9362), np.float32(0.9396), np.float32(0.9107), np.float32(0.9469), np.float32(0.9442), np.float32(0.8743), np.float32(0.9496), np.float32(0.9403), np.float32(0.8483), np.float32(0.8229), np.float32(0.9044)] 
+2025-05-05 06:33:03.312690: Epoch time: 84.56 s 
+2025-05-05 06:33:04.552585:  
+2025-05-05 06:33:04.611015: Epoch 176 
+2025-05-05 06:33:04.643794: Current learning rate: 0.0092 
+2025-05-05 06:34:29.733230: train_loss -0.4401 
+2025-05-05 06:34:29.826771: val_loss -0.4634 
+2025-05-05 06:34:29.847404: Pseudo dice [np.float32(0.7795), np.float32(0.8197), np.float32(0.8507), np.float32(0.9529), np.float32(0.808), np.float32(0.949), np.float32(0.956), np.float32(0.9475), np.float32(0.9517), np.float32(0.9597), np.float32(0.9259), np.float32(0.9663), np.float32(0.9675), np.float32(0.8908), np.float32(0.9503), np.float32(0.9283), np.float32(0.7067), np.float32(0.7236), np.float32(0.9)] 
+2025-05-05 06:34:29.862146: Epoch time: 85.18 s 
+2025-05-05 06:34:31.609941:  
+2025-05-05 06:34:31.796569: Epoch 177 
+2025-05-05 06:34:31.846826: Current learning rate: 0.0092 
+2025-05-05 06:35:56.922770: train_loss -0.4704 
+2025-05-05 06:35:57.018105: val_loss -0.4715 
+2025-05-05 06:35:57.043888: Pseudo dice [np.float32(0.8138), np.float32(0.8297), np.float32(0.9127), np.float32(0.9652), np.float32(0.8404), np.float32(0.9569), np.float32(0.9517), np.float32(0.9753), np.float32(0.9581), np.float32(0.9603), np.float32(0.9313), np.float32(0.9617), np.float32(0.9598), np.float32(0.872), np.float32(0.9579), np.float32(0.944), np.float32(0.8491), np.float32(0.8455), np.float32(0.9106)] 
+2025-05-05 06:35:57.084194: Epoch time: 85.31 s 
+2025-05-05 06:35:58.230574:  
+2025-05-05 06:35:58.340579: Epoch 178 
+2025-05-05 06:35:58.371422: Current learning rate: 0.0092 
+2025-05-05 06:37:26.826691: train_loss -0.4445 
+2025-05-05 06:37:26.887066: val_loss -0.4405 
+2025-05-05 06:37:26.903162: Pseudo dice [np.float32(0.7863), np.float32(0.7868), np.float32(0.9241), np.float32(0.9786), np.float32(0.8819), np.float32(0.9452), np.float32(0.9524), np.float32(0.9665), np.float32(0.9422), np.float32(0.9484), np.float32(0.9169), np.float32(0.9395), np.float32(0.9289), np.float32(0.8702), np.float32(0.9638), np.float32(0.9492), np.float32(0.8569), np.float32(0.881), np.float32(0.9049)] 
+2025-05-05 06:37:26.915535: Epoch time: 88.6 s 
+2025-05-05 06:37:28.583886:  
+2025-05-05 06:37:28.740155: Epoch 179 
+2025-05-05 06:37:28.765414: Current learning rate: 0.00919 
+2025-05-05 06:38:55.302960: train_loss -0.442 
+2025-05-05 06:38:55.421391: val_loss -0.4557 
+2025-05-05 06:38:55.430801: Pseudo dice [np.float32(0.7933), np.float32(0.8039), np.float32(0.8689), np.float32(0.9638), np.float32(0.8735), np.float32(0.9327), np.float32(0.9548), np.float32(0.9425), np.float32(0.9473), np.float32(0.9481), np.float32(0.9113), np.float32(0.9552), np.float32(0.9452), np.float32(0.842), np.float32(0.9581), np.float32(0.9268), np.float32(0.856), np.float32(0.8771), np.float32(0.9015)] 
+2025-05-05 06:38:55.435895: Epoch time: 86.72 s 
+2025-05-05 06:38:57.872015:  
+2025-05-05 06:38:57.920879: Epoch 180 
+2025-05-05 06:38:57.922498: Current learning rate: 0.00919 
+2025-05-05 06:40:22.749939: train_loss -0.4406 
+2025-05-05 06:40:22.840048: val_loss -0.4183 
+2025-05-05 06:40:22.864215: Pseudo dice [np.float32(0.7814), np.float32(0.813), np.float32(0.8732), np.float32(0.9517), np.float32(0.8928), np.float32(0.9385), np.float32(0.9451), np.float32(0.9748), np.float32(0.9352), np.float32(0.9541), np.float32(0.9228), np.float32(0.9572), np.float32(0.9534), np.float32(0.8644), np.float32(0.9264), np.float32(0.9081), np.float32(0.8063), np.float32(0.8168), np.float32(0.8756)] 
+2025-05-05 06:40:22.882486: Epoch time: 84.88 s 
+2025-05-05 06:40:24.592735:  
+2025-05-05 06:40:24.682729: Epoch 181 
+2025-05-05 06:40:24.723783: Current learning rate: 0.00918 
+2025-05-05 06:41:48.886225: train_loss -0.4249 
+2025-05-05 06:41:48.955601: val_loss -0.4806 
+2025-05-05 06:41:48.972425: Pseudo dice [np.float32(0.7808), np.float32(0.7959), np.float32(0.8906), np.float32(0.9719), np.float32(0.8846), np.float32(0.9369), np.float32(0.9476), np.float32(0.9621), np.float32(0.9479), np.float32(0.9268), np.float32(0.8521), np.float32(0.961), np.float32(0.9447), np.float32(0.8666), np.float32(0.9445), np.float32(0.9331), np.float32(0.8523), np.float32(0.8204), np.float32(0.9086)] 
+2025-05-05 06:41:48.987391: Epoch time: 84.3 s 
+2025-05-05 06:41:50.675502:  
+2025-05-05 06:41:50.700222: Epoch 182 
+2025-05-05 06:41:50.724483: Current learning rate: 0.00918 
+2025-05-05 06:43:15.704690: train_loss -0.4365 
+2025-05-05 06:43:15.819350: val_loss -0.4864 
+2025-05-05 06:43:15.844629: Pseudo dice [np.float32(0.7903), np.float32(0.7823), np.float32(0.8365), np.float32(0.976), np.float32(0.888), np.float32(0.9571), np.float32(0.9512), np.float32(0.9758), np.float32(0.9583), np.float32(0.9383), np.float32(0.905), np.float32(0.9638), np.float32(0.9556), np.float32(0.8551), np.float32(0.9673), np.float32(0.9374), np.float32(0.8378), np.float32(0.8246), np.float32(0.9183)] 
+2025-05-05 06:43:15.876912: Epoch time: 85.03 s 
+2025-05-05 06:43:17.655122:  
+2025-05-05 06:43:17.726076: Epoch 183 
+2025-05-05 06:43:17.742565: Current learning rate: 0.00917 
+2025-05-05 06:44:43.578208: train_loss -0.4561 
+2025-05-05 06:44:43.624536: val_loss -0.4544 
+2025-05-05 06:44:43.650105: Pseudo dice [np.float32(0.759), np.float32(0.811), np.float32(0.9038), np.float32(0.9497), np.float32(0.8742), np.float32(0.9577), np.float32(0.9545), np.float32(0.9709), np.float32(0.9542), np.float32(0.945), np.float32(0.9409), np.float32(0.964), np.float32(0.955), np.float32(0.8763), np.float32(0.9574), np.float32(0.9331), np.float32(0.8723), np.float32(0.8571), np.float32(0.9113)] 
+2025-05-05 06:44:43.679058: Epoch time: 85.92 s 
+2025-05-05 06:44:45.158539:  
+2025-05-05 06:44:45.245775: Epoch 184 
+2025-05-05 06:44:45.281327: Current learning rate: 0.00917 
+2025-05-05 06:46:08.394432: train_loss -0.4514 
+2025-05-05 06:46:08.501698: val_loss -0.4977 
+2025-05-05 06:46:08.567323: Pseudo dice [np.float32(0.7838), np.float32(0.8115), np.float32(0.8719), np.float32(0.969), np.float32(0.8559), np.float32(0.953), np.float32(0.957), np.float32(0.9681), np.float32(0.924), np.float32(0.9524), np.float32(0.9078), np.float32(0.923), np.float32(0.9536), np.float32(0.8659), np.float32(0.9506), np.float32(0.9084), np.float32(0.842), np.float32(0.839), np.float32(0.8987)] 
+2025-05-05 06:46:08.633679: Epoch time: 83.24 s 
+2025-05-05 06:46:10.338093:  
+2025-05-05 06:46:10.393367: Epoch 185 
+2025-05-05 06:46:10.404622: Current learning rate: 0.00916 
+2025-05-05 06:47:37.200313: train_loss -0.4526 
+2025-05-05 06:47:37.239657: val_loss -0.4711 
+2025-05-05 06:47:37.262014: Pseudo dice [np.float32(0.7133), np.float32(0.73), np.float32(0.8666), np.float32(0.9614), np.float32(0.8926), np.float32(0.9497), np.float32(0.9429), np.float32(0.9725), np.float32(0.9333), np.float32(0.9573), np.float32(0.9336), np.float32(0.9603), np.float32(0.9594), np.float32(0.8451), np.float32(0.9571), np.float32(0.9334), np.float32(0.8608), np.float32(0.808), np.float32(0.9036)] 
+2025-05-05 06:47:37.279824: Epoch time: 86.86 s 
+2025-05-05 06:47:38.610220:  
+2025-05-05 06:47:38.695019: Epoch 186 
+2025-05-05 06:47:38.712433: Current learning rate: 0.00916 
+2025-05-05 06:49:05.484919: train_loss -0.4638 
+2025-05-05 06:49:05.554326: val_loss -0.4824 
+2025-05-05 06:49:05.556059: Pseudo dice [np.float32(0.8271), np.float32(0.8202), np.float32(0.905), np.float32(0.9736), np.float32(0.8386), np.float32(0.9543), np.float32(0.9586), np.float32(0.976), np.float32(0.9509), np.float32(0.9547), np.float32(0.9311), np.float32(0.959), np.float32(0.9363), np.float32(0.8757), np.float32(0.9512), np.float32(0.9448), np.float32(0.8468), np.float32(0.8465), np.float32(0.9136)] 
+2025-05-05 06:49:05.562185: Epoch time: 86.88 s 
+2025-05-05 06:49:07.250768:  
+2025-05-05 06:49:07.391878: Epoch 187 
+2025-05-05 06:49:07.440669: Current learning rate: 0.00915 
+2025-05-05 06:50:32.448334: train_loss -0.4813 
+2025-05-05 06:50:32.549385: val_loss -0.4986 
+2025-05-05 06:50:32.593866: Pseudo dice [np.float32(0.7949), np.float32(0.8133), np.float32(0.848), np.float32(0.9521), np.float32(0.8353), np.float32(0.9175), np.float32(0.9539), np.float32(0.9764), np.float32(0.9577), np.float32(0.9511), np.float32(0.8958), np.float32(0.9558), np.float32(0.9503), np.float32(0.8568), np.float32(0.9551), np.float32(0.9353), np.float32(0.8581), np.float32(0.8636), np.float32(0.9054)] 
+2025-05-05 06:50:32.621713: Epoch time: 85.2 s 
+2025-05-05 06:50:33.839901:  
+2025-05-05 06:50:33.914707: Epoch 188 
+2025-05-05 06:50:33.934000: Current learning rate: 0.00915 
+2025-05-05 06:52:00.325109: train_loss -0.4723 
+2025-05-05 06:52:00.432460: val_loss -0.5062 
+2025-05-05 06:52:00.433893: Pseudo dice [np.float32(0.8168), np.float32(0.7817), np.float32(0.8605), np.float32(0.9717), np.float32(0.8608), np.float32(0.9419), np.float32(0.9446), np.float32(0.9784), np.float32(0.9594), np.float32(0.9574), np.float32(0.9368), np.float32(0.9555), np.float32(0.9646), np.float32(0.8916), np.float32(0.9675), np.float32(0.939), np.float32(0.8792), np.float32(0.8184), np.float32(0.9217)] 
+2025-05-05 06:52:00.441694: Epoch time: 86.49 s 
+2025-05-05 06:52:00.456861: Yayy! New best EMA pseudo Dice: 0.9053000211715698 
+2025-05-05 06:52:02.988520:  
+2025-05-05 06:52:02.993754: Epoch 189 
+2025-05-05 06:52:02.994197: Current learning rate: 0.00915 
+2025-05-05 06:53:34.391863: train_loss -0.4573 
+2025-05-05 06:53:34.446116: val_loss -0.4851 
+2025-05-05 06:53:34.458086: Pseudo dice [np.float32(0.8166), np.float32(0.8236), np.float32(0.752), np.float32(0.9757), np.float32(0.8677), np.float32(0.952), np.float32(0.9543), np.float32(0.9738), np.float32(0.9646), np.float32(0.963), np.float32(0.9341), np.float32(0.9667), np.float32(0.9529), np.float32(0.8653), np.float32(0.9648), np.float32(0.9441), np.float32(0.6972), np.float32(0.7741), np.float32(0.9095)] 
+2025-05-05 06:53:34.492379: Epoch time: 91.4 s 
+2025-05-05 06:53:35.790598:  
+2025-05-05 06:53:35.853137: Epoch 190 
+2025-05-05 06:53:35.896410: Current learning rate: 0.00914 
+2025-05-05 06:55:00.279489: train_loss -0.4587 
+2025-05-05 06:55:00.305082: val_loss -0.4722 
+2025-05-05 06:55:00.335065: Pseudo dice [np.float32(0.8123), np.float32(0.8238), np.float32(0.8561), np.float32(0.965), np.float32(0.8492), np.float32(0.9542), np.float32(0.9619), np.float32(0.9767), np.float32(0.9331), np.float32(0.9493), np.float32(0.9239), np.float32(0.9479), np.float32(0.9541), np.float32(0.8663), np.float32(0.9595), np.float32(0.9268), np.float32(0.8506), np.float32(0.8574), np.float32(0.9159)] 
+2025-05-05 06:55:00.382139: Epoch time: 84.49 s 
+2025-05-05 06:55:02.131675:  
+2025-05-05 06:55:02.168325: Epoch 191 
+2025-05-05 06:55:02.170051: Current learning rate: 0.00914 
+2025-05-05 06:56:27.456572: train_loss -0.4509 
+2025-05-05 06:56:27.491914: val_loss -0.4552 
+2025-05-05 06:56:27.506754: Pseudo dice [np.float32(0.7589), np.float32(0.824), np.float32(0.8588), np.float32(0.969), np.float32(0.8086), np.float32(0.9474), np.float32(0.9546), np.float32(0.9709), np.float32(0.947), np.float32(0.9585), np.float32(0.9274), np.float32(0.9589), np.float32(0.9536), np.float32(0.8578), np.float32(0.9564), np.float32(0.9191), np.float32(0.8733), np.float32(0.8741), np.float32(0.9221)] 
+2025-05-05 06:56:27.531929: Epoch time: 85.33 s 
+2025-05-05 06:56:29.328070:  
+2025-05-05 06:56:29.418487: Epoch 192 
+2025-05-05 06:56:29.437201: Current learning rate: 0.00913 
+2025-05-05 06:57:55.383124: train_loss -0.4657 
+2025-05-05 06:57:55.542731: val_loss -0.4541 
+2025-05-05 06:57:55.571469: Pseudo dice [np.float32(0.7957), np.float32(0.8352), np.float32(0.9401), np.float32(0.9456), np.float32(0.8645), np.float32(0.9307), np.float32(0.9497), np.float32(0.9743), np.float32(0.9475), np.float32(0.949), np.float32(0.9375), np.float32(0.9576), np.float32(0.9627), np.float32(0.8749), np.float32(0.9639), np.float32(0.934), np.float32(0.8799), np.float32(0.8488), np.float32(0.9034)] 
+2025-05-05 06:57:55.602325: Epoch time: 86.06 s 
+2025-05-05 06:57:55.615076: Yayy! New best EMA pseudo Dice: 0.9063000082969666 
+2025-05-05 06:57:57.598952:  
+2025-05-05 06:57:57.661272: Epoch 193 
+2025-05-05 06:57:57.686656: Current learning rate: 0.00913 
+2025-05-05 06:59:27.517246: train_loss -0.4443 
+2025-05-05 06:59:27.628310: val_loss -0.4661 
+2025-05-05 06:59:27.654848: Pseudo dice [np.float32(0.8068), np.float32(0.7782), np.float32(0.8997), np.float32(0.9654), np.float32(0.8726), np.float32(0.9422), np.float32(0.9439), np.float32(0.9704), np.float32(0.9427), np.float32(0.9552), np.float32(0.9229), np.float32(0.9534), np.float32(0.9599), np.float32(0.8749), np.float32(0.9436), np.float32(0.9418), np.float32(0.8635), np.float32(0.8748), np.float32(0.9157)] 
+2025-05-05 06:59:27.705833: Epoch time: 89.92 s 
+2025-05-05 06:59:27.748482: Yayy! New best EMA pseudo Dice: 0.9068999886512756 
+2025-05-05 06:59:29.763171:  
+2025-05-05 06:59:29.765526: Epoch 194 
+2025-05-05 06:59:29.766354: Current learning rate: 0.00912 
+2025-05-05 07:00:57.251552: train_loss -0.4568 
+2025-05-05 07:00:57.322564: val_loss -0.4729 
+2025-05-05 07:00:57.323644: Pseudo dice [np.float32(0.8227), np.float32(0.7671), np.float32(0.5885), np.float32(0.9452), np.float32(0.8834), np.float32(0.9572), np.float32(0.9565), np.float32(0.9657), np.float32(0.9591), np.float32(0.9615), np.float32(0.9378), np.float32(0.9595), np.float32(0.9622), np.float32(0.8723), np.float32(0.8847), np.float32(0.9288), np.float32(0.8333), np.float32(0.8638), np.float32(0.9027)] 
+2025-05-05 07:00:57.324291: Epoch time: 87.49 s 
+2025-05-05 07:00:58.965212:  
+2025-05-05 07:00:59.045793: Epoch 195 
+2025-05-05 07:00:59.062759: Current learning rate: 0.00912 
+2025-05-05 07:02:23.872559: train_loss -0.4478 
+2025-05-05 07:02:23.973184: val_loss -0.4585 
+2025-05-05 07:02:24.004858: Pseudo dice [np.float32(0.819), np.float32(0.8517), np.float32(0.8451), np.float32(0.9061), np.float32(0.8832), np.float32(0.9472), np.float32(0.9595), np.float32(0.9682), np.float32(0.9531), np.float32(0.9547), np.float32(0.9068), np.float32(0.9607), np.float32(0.9574), np.float32(0.8611), np.float32(0.9566), np.float32(0.9141), np.float32(0.791), np.float32(0.6932), np.float32(0.913)] 
+2025-05-05 07:02:24.023086: Epoch time: 84.91 s 
+2025-05-05 07:02:25.695954:  
+2025-05-05 07:02:25.765584: Epoch 196 
+2025-05-05 07:02:25.767530: Current learning rate: 0.00911 
+2025-05-05 07:03:50.809004: train_loss -0.4613 
+2025-05-05 07:03:50.894787: val_loss -0.4185 
+2025-05-05 07:03:50.924150: Pseudo dice [np.float32(0.8122), np.float32(0.8284), np.float32(0.9005), np.float32(0.9543), np.float32(0.8936), np.float32(0.9432), np.float32(0.956), np.float32(0.978), np.float32(0.9499), np.float32(0.9578), np.float32(0.942), np.float32(0.96), np.float32(0.9642), np.float32(0.8773), np.float32(0.9581), np.float32(0.9408), np.float32(0.8966), np.float32(0.8868), np.float32(0.926)] 
+2025-05-05 07:03:50.951028: Epoch time: 85.12 s 
+2025-05-05 07:03:52.641345:  
+2025-05-05 07:03:52.693478: Epoch 197 
+2025-05-05 07:03:52.709995: Current learning rate: 0.00911 
+2025-05-05 07:05:19.596761: train_loss -0.4575 
+2025-05-05 07:05:19.640326: val_loss -0.4814 
+2025-05-05 07:05:19.641357: Pseudo dice [np.float32(0.8309), np.float32(0.8364), np.float32(0.8983), np.float32(0.9739), np.float32(0.9044), np.float32(0.9561), np.float32(0.9559), np.float32(0.969), np.float32(0.9654), np.float32(0.952), np.float32(0.9217), np.float32(0.9666), np.float32(0.9537), np.float32(0.8791), np.float32(0.964), np.float32(0.9243), np.float32(0.8255), np.float32(0.822), np.float32(0.8974)] 
+2025-05-05 07:05:19.654495: Epoch time: 86.96 s 
+2025-05-05 07:05:19.673356: Yayy! New best EMA pseudo Dice: 0.9072999954223633 
+2025-05-05 07:05:23.113256:  
+2025-05-05 07:05:23.117914: Epoch 198 
+2025-05-05 07:05:23.118333: Current learning rate: 0.0091 
+2025-05-05 07:06:51.224949: train_loss -0.4576 
+2025-05-05 07:06:51.313677: val_loss -0.4841 
+2025-05-05 07:06:51.330139: Pseudo dice [np.float32(0.8029), np.float32(0.8414), np.float32(0.8671), np.float32(0.9765), np.float32(0.8639), np.float32(0.9488), np.float32(0.9491), np.float32(0.9756), np.float32(0.9634), np.float32(0.9491), np.float32(0.9037), np.float32(0.9654), np.float32(0.958), np.float32(0.8794), np.float32(0.9499), np.float32(0.9283), np.float32(0.7647), np.float32(0.8106), np.float32(0.9183)] 
+2025-05-05 07:06:51.348679: Epoch time: 88.11 s 
+2025-05-05 07:06:52.550177:  
+2025-05-05 07:06:52.644312: Epoch 199 
+2025-05-05 07:06:52.694547: Current learning rate: 0.0091 
+2025-05-05 07:08:19.493730: train_loss -0.4591 
+2025-05-05 07:08:19.596612: val_loss -0.4621 
+2025-05-05 07:08:19.631419: Pseudo dice [np.float32(0.7972), np.float32(0.8277), np.float32(0.9265), np.float32(0.9666), np.float32(0.8519), np.float32(0.9571), np.float32(0.9555), np.float32(0.9757), np.float32(0.9572), np.float32(0.9696), np.float32(0.9305), np.float32(0.9626), np.float32(0.9563), np.float32(0.8449), np.float32(0.9468), np.float32(0.9304), np.float32(0.8539), np.float32(0.8158), np.float32(0.9235)] 
+2025-05-05 07:08:19.667890: Epoch time: 86.95 s 
+2025-05-05 07:08:20.502877: Yayy! New best EMA pseudo Dice: 0.907800018787384 
+2025-05-05 07:08:22.854018:  
+2025-05-05 07:08:22.892333: Epoch 200 
+2025-05-05 07:08:22.903682: Current learning rate: 0.0091 
+2025-05-05 07:09:55.248204: train_loss -0.4653 
+2025-05-05 07:09:55.292927: val_loss -0.5171 
+2025-05-05 07:09:55.326428: Pseudo dice [np.float32(0.8117), np.float32(0.7999), np.float32(0.8357), np.float32(0.9683), np.float32(0.8522), np.float32(0.9547), np.float32(0.9589), np.float32(0.9684), np.float32(0.9364), np.float32(0.9558), np.float32(0.9277), np.float32(0.958), np.float32(0.9623), np.float32(0.8643), np.float32(0.9554), np.float32(0.9357), np.float32(0.8704), np.float32(0.8677), np.float32(0.8985)] 
+2025-05-05 07:09:55.362669: Epoch time: 92.4 s 
+2025-05-05 07:09:55.403082: Yayy! New best EMA pseudo Dice: 0.9078999757766724 
+2025-05-05 07:09:58.506310:  
+2025-05-05 07:09:58.611823: Epoch 201 
+2025-05-05 07:09:58.710268: Current learning rate: 0.00909 
+2025-05-05 07:11:27.971377: train_loss -0.471 
+2025-05-05 07:11:28.117522: val_loss -0.4912 
+2025-05-05 07:11:28.156544: Pseudo dice [np.float32(0.8321), np.float32(0.8002), np.float32(0.9231), np.float32(0.974), np.float32(0.8748), np.float32(0.9524), np.float32(0.9401), np.float32(0.963), np.float32(0.9636), np.float32(0.9201), np.float32(0.8723), np.float32(0.9662), np.float32(0.9553), np.float32(0.8602), np.float32(0.9585), np.float32(0.9496), np.float32(0.8692), np.float32(0.8686), np.float32(0.911)] 
+2025-05-05 07:11:28.193493: Epoch time: 89.47 s 
+2025-05-05 07:11:28.209651: Yayy! New best EMA pseudo Dice: 0.9085000157356262 
+2025-05-05 07:11:30.644396:  
+2025-05-05 07:11:30.658918: Epoch 202 
+2025-05-05 07:11:30.663030: Current learning rate: 0.00909 
+2025-05-05 07:12:59.872190: train_loss -0.4644 
+2025-05-05 07:13:00.018020: val_loss -0.4716 
+2025-05-05 07:13:00.050043: Pseudo dice [np.float32(0.7955), np.float32(0.8255), np.float32(0.9118), np.float32(0.9769), np.float32(0.8653), np.float32(0.9388), np.float32(0.9583), np.float32(0.9756), np.float32(0.9359), np.float32(0.9534), np.float32(0.9423), np.float32(0.9668), np.float32(0.9641), np.float32(0.8773), np.float32(0.9417), np.float32(0.9277), np.float32(0.8576), np.float32(0.8845), np.float32(0.9176)] 
+2025-05-05 07:13:00.070452: Epoch time: 89.23 s 
+2025-05-05 07:13:00.097064: Yayy! New best EMA pseudo Dice: 0.9093000292778015 
+2025-05-05 07:13:01.896326:  
+2025-05-05 07:13:01.909877: Epoch 203 
+2025-05-05 07:13:01.923781: Current learning rate: 0.00908 
+2025-05-05 07:14:27.787206: train_loss -0.4743 
+2025-05-05 07:14:27.898962: val_loss -0.4707 
+2025-05-05 07:14:27.932200: Pseudo dice [np.float32(0.8341), np.float32(0.8395), np.float32(0.8948), np.float32(0.9564), np.float32(0.8823), np.float32(0.9528), np.float32(0.9452), np.float32(0.9721), np.float32(0.9517), np.float32(0.9549), np.float32(0.9294), np.float32(0.9555), np.float32(0.9549), np.float32(0.8807), np.float32(0.9632), np.float32(0.9436), np.float32(0.8066), np.float32(0.8554), np.float32(0.9121)] 
+2025-05-05 07:14:27.981739: Epoch time: 85.89 s 
+2025-05-05 07:14:28.019067: Yayy! New best EMA pseudo Dice: 0.9099000096321106 
+2025-05-05 07:14:30.289042:  
+2025-05-05 07:14:30.345891: Epoch 204 
+2025-05-05 07:14:30.408782: Current learning rate: 0.00908 
+2025-05-05 07:15:57.830982: train_loss -0.461 
+2025-05-05 07:15:57.927539: val_loss -0.4257 
+2025-05-05 07:15:57.953025: Pseudo dice [np.float32(0.8327), np.float32(0.851), np.float32(0.8937), np.float32(0.9762), np.float32(0.8553), np.float32(0.9148), np.float32(0.946), np.float32(0.9691), np.float32(0.945), np.float32(0.9423), np.float32(0.9079), np.float32(0.9507), np.float32(0.9538), np.float32(0.8648), np.float32(0.9464), np.float32(0.9251), np.float32(0.8868), np.float32(0.88), np.float32(0.9195)] 
+2025-05-05 07:15:57.978423: Epoch time: 87.54 s 
+2025-05-05 07:15:58.003660: Yayy! New best EMA pseudo Dice: 0.9103000164031982 
+2025-05-05 07:16:00.385837:  
+2025-05-05 07:16:00.445721: Epoch 205 
+2025-05-05 07:16:00.475899: Current learning rate: 0.00907 
+2025-05-05 07:17:28.467691: train_loss -0.459 
+2025-05-05 07:17:28.529302: val_loss -0.4991 
+2025-05-05 07:17:28.530185: Pseudo dice [np.float32(0.7734), np.float32(0.7905), np.float32(0.8727), np.float32(0.9707), np.float32(0.8253), np.float32(0.9383), np.float32(0.9521), np.float32(0.9717), np.float32(0.9615), np.float32(0.9604), np.float32(0.9404), np.float32(0.9649), np.float32(0.9565), np.float32(0.8614), np.float32(0.956), np.float32(0.9276), np.float32(0.8642), np.float32(0.8584), np.float32(0.9015)] 
+2025-05-05 07:17:28.534619: Epoch time: 88.08 s 
+2025-05-05 07:17:30.153154:  
+2025-05-05 07:17:30.208863: Epoch 206 
+2025-05-05 07:17:30.239128: Current learning rate: 0.00907 
+2025-05-05 07:18:55.921828: train_loss -0.4703 
+2025-05-05 07:18:55.958622: val_loss -0.4489 
+2025-05-05 07:18:55.959594: Pseudo dice [np.float32(0.8032), np.float32(0.8216), np.float32(0.9287), np.float32(0.9703), np.float32(0.9055), np.float32(0.96), np.float32(0.9601), np.float32(0.9751), np.float32(0.9314), np.float32(0.9608), np.float32(0.9173), np.float32(0.953), np.float32(0.9604), np.float32(0.8977), np.float32(0.96), np.float32(0.9453), np.float32(0.8524), np.float32(0.869), np.float32(0.903)] 
+2025-05-05 07:18:55.964539: Epoch time: 85.77 s 
+2025-05-05 07:18:55.965161: Yayy! New best EMA pseudo Dice: 0.9110000133514404 
+2025-05-05 07:18:57.926300:  
+2025-05-05 07:18:57.931050: Epoch 207 
+2025-05-05 07:18:57.931469: Current learning rate: 0.00906 
+2025-05-05 07:20:28.828441: train_loss -0.4645 
+2025-05-05 07:20:28.924456: val_loss -0.4893 
+2025-05-05 07:20:28.985768: Pseudo dice [np.float32(0.83), np.float32(0.7878), np.float32(0.7657), np.float32(0.9578), np.float32(0.8836), np.float32(0.9573), np.float32(0.9453), np.float32(0.9767), np.float32(0.9515), np.float32(0.9561), np.float32(0.9262), np.float32(0.965), np.float32(0.9641), np.float32(0.8859), np.float32(0.9646), np.float32(0.9401), np.float32(0.8757), np.float32(0.8754), np.float32(0.908)] 
+2025-05-05 07:20:29.019227: Epoch time: 90.9 s 
+2025-05-05 07:20:29.054674: Yayy! New best EMA pseudo Dice: 0.9110000133514404 
+2025-05-05 07:20:31.414454:  
+2025-05-05 07:20:31.425496: Epoch 208 
+2025-05-05 07:20:31.426247: Current learning rate: 0.00906 
+2025-05-05 07:21:59.713374: train_loss -0.4574 
+2025-05-05 07:21:59.845097: val_loss -0.4706 
+2025-05-05 07:21:59.867240: Pseudo dice [np.float32(0.7617), np.float32(0.7803), np.float32(0.1956), np.float32(0.9724), np.float32(0.8514), np.float32(0.9531), np.float32(0.9606), np.float32(0.9728), np.float32(0.9643), np.float32(0.9631), np.float32(0.9459), np.float32(0.9647), np.float32(0.9699), np.float32(0.8671), np.float32(0.9669), np.float32(0.9388), np.float32(0.8768), np.float32(0.8766), np.float32(0.915)] 
+2025-05-05 07:21:59.892398: Epoch time: 88.3 s 
+2025-05-05 07:22:01.559239:  
+2025-05-05 07:22:01.625794: Epoch 209 
+2025-05-05 07:22:01.653229: Current learning rate: 0.00905 
+2025-05-05 07:23:27.368362: train_loss -0.4732 
+2025-05-05 07:23:27.441215: val_loss -0.4639 
+2025-05-05 07:23:27.450787: Pseudo dice [np.float32(0.826), np.float32(0.8125), np.float32(0.8952), np.float32(0.9626), np.float32(0.8833), np.float32(0.9516), np.float32(0.9403), np.float32(0.9691), np.float32(0.9636), np.float32(0.9579), np.float32(0.9226), np.float32(0.9621), np.float32(0.9569), np.float32(0.8785), np.float32(0.9521), np.float32(0.9317), np.float32(0.8542), np.float32(0.8623), np.float32(0.9224)] 
+2025-05-05 07:23:27.465813: Epoch time: 85.81 s 
+2025-05-05 07:23:28.588259:  
+2025-05-05 07:23:28.712448: Epoch 210 
+2025-05-05 07:23:28.791933: Current learning rate: 0.00905 
+2025-05-05 07:24:54.583563: train_loss -0.46 
+2025-05-05 07:24:54.707821: val_loss -0.4464 
+2025-05-05 07:24:54.751365: Pseudo dice [np.float32(0.833), np.float32(0.7832), np.float32(0.9143), np.float32(0.9174), np.float32(0.8719), np.float32(0.9583), np.float32(0.9421), np.float32(0.9723), np.float32(0.9572), np.float32(0.9616), np.float32(0.9214), np.float32(0.9579), np.float32(0.9494), np.float32(0.8591), np.float32(0.9627), np.float32(0.9321), np.float32(0.8972), np.float32(0.8904), np.float32(0.9263)] 
+2025-05-05 07:24:54.797037: Epoch time: 86.0 s 
+2025-05-05 07:24:55.966138:  
+2025-05-05 07:24:56.079794: Epoch 211 
+2025-05-05 07:24:56.107684: Current learning rate: 0.00905 
+2025-05-05 07:26:20.266275: train_loss -0.4608 
+2025-05-05 07:26:20.362902: val_loss -0.4492 
+2025-05-05 07:26:20.375501: Pseudo dice [np.float32(0.7974), np.float32(0.8213), np.float32(0.7764), np.float32(0.9737), np.float32(0.8499), np.float32(0.9444), np.float32(0.9453), np.float32(0.9637), np.float32(0.9555), np.float32(0.9435), np.float32(0.9233), np.float32(0.9533), np.float32(0.9545), np.float32(0.8734), np.float32(0.9361), np.float32(0.9321), np.float32(0.8518), np.float32(0.8512), np.float32(0.902)] 
+2025-05-05 07:26:20.386996: Epoch time: 84.3 s 
+2025-05-05 07:26:21.960480:  
+2025-05-05 07:26:22.035666: Epoch 212 
+2025-05-05 07:26:22.038241: Current learning rate: 0.00904 
+2025-05-05 07:27:47.787737: train_loss -0.4615 
+2025-05-05 07:27:47.881698: val_loss -0.4782 
+2025-05-05 07:27:47.899857: Pseudo dice [np.float32(0.8126), np.float32(0.8269), np.float32(0.8868), np.float32(0.9705), np.float32(0.7743), np.float32(0.9611), np.float32(0.9606), np.float32(0.9702), np.float32(0.9576), np.float32(0.9538), np.float32(0.9373), np.float32(0.9573), np.float32(0.9672), np.float32(0.8849), np.float32(0.9571), np.float32(0.9292), np.float32(0.842), np.float32(0.8473), np.float32(0.9105)] 
+2025-05-05 07:27:47.909036: Epoch time: 85.83 s 
+2025-05-05 07:27:49.534282:  
+2025-05-05 07:27:49.660893: Epoch 213 
+2025-05-05 07:27:49.692657: Current learning rate: 0.00904 
+2025-05-05 07:29:18.875294: train_loss -0.448 
+2025-05-05 07:29:19.043669: val_loss -0.4633 
+2025-05-05 07:29:19.064829: Pseudo dice [np.float32(0.8209), np.float32(0.817), np.float32(0.9135), np.float32(0.9672), np.float32(0.8644), np.float32(0.9573), np.float32(0.9519), np.float32(0.9729), np.float32(0.9579), np.float32(0.9588), np.float32(0.9192), np.float32(0.9587), np.float32(0.9632), np.float32(0.8659), np.float32(0.946), np.float32(0.9354), np.float32(0.8253), np.float32(0.8353), np.float32(0.907)] 
+2025-05-05 07:29:19.065562: Epoch time: 89.34 s 
+2025-05-05 07:29:20.910210:  
+2025-05-05 07:29:20.936176: Epoch 214 
+2025-05-05 07:29:20.947617: Current learning rate: 0.00903 
+2025-05-05 07:30:47.375193: train_loss -0.4511 
+2025-05-05 07:30:47.490752: val_loss -0.4468 
+2025-05-05 07:30:47.509530: Pseudo dice [np.float32(0.7681), np.float32(0.8159), np.float32(0.8923), np.float32(0.9626), np.float32(0.7931), np.float32(0.9424), np.float32(0.9475), np.float32(0.9664), np.float32(0.9639), np.float32(0.9393), np.float32(0.9277), np.float32(0.9624), np.float32(0.9623), np.float32(0.8551), np.float32(0.9326), np.float32(0.9289), np.float32(0.7867), np.float32(0.8417), np.float32(0.9004)] 
+2025-05-05 07:30:47.533514: Epoch time: 86.47 s 
+2025-05-05 07:30:49.188888:  
+2025-05-05 07:30:49.279974: Epoch 215 
+2025-05-05 07:30:49.294673: Current learning rate: 0.00903 
+2025-05-05 07:32:13.929281: train_loss -0.4649 
+2025-05-05 07:32:14.038082: val_loss -0.4566 
+2025-05-05 07:32:14.056839: Pseudo dice [np.float32(0.8465), np.float32(0.7807), np.float32(0.8995), np.float32(0.9663), np.float32(0.8586), np.float32(0.9368), np.float32(0.9626), np.float32(0.9515), np.float32(0.9432), np.float32(0.9413), np.float32(0.8937), np.float32(0.9564), np.float32(0.9551), np.float32(0.877), np.float32(0.9513), np.float32(0.9332), np.float32(0.8168), np.float32(0.8151), np.float32(0.9107)] 
+2025-05-05 07:32:14.086397: Epoch time: 84.74 s 
+2025-05-05 07:32:15.792144:  
+2025-05-05 07:32:15.807421: Epoch 216 
+2025-05-05 07:32:15.807961: Current learning rate: 0.00902 
+2025-05-05 07:33:41.348900: train_loss -0.4569 
+2025-05-05 07:33:41.431012: val_loss -0.4642 
+2025-05-05 07:33:41.453143: Pseudo dice [np.float32(0.8104), np.float32(0.7856), np.float32(0.9426), np.float32(0.9579), np.float32(0.8981), np.float32(0.9564), np.float32(0.9558), np.float32(0.9575), np.float32(0.9531), np.float32(0.941), np.float32(0.9382), np.float32(0.9709), np.float32(0.9613), np.float32(0.8676), np.float32(0.9518), np.float32(0.9305), np.float32(0.8376), np.float32(0.8206), np.float32(0.9027)] 
+2025-05-05 07:33:41.470534: Epoch time: 85.56 s 
+2025-05-05 07:33:43.071397:  
+2025-05-05 07:33:43.145119: Epoch 217 
+2025-05-05 07:33:43.181218: Current learning rate: 0.00902 
+2025-05-05 07:35:10.022984: train_loss -0.4536 
+2025-05-05 07:35:10.222399: val_loss -0.4773 
+2025-05-05 07:35:10.249796: Pseudo dice [np.float32(0.7394), np.float32(0.7907), np.float32(0.8411), np.float32(0.9692), np.float32(0.8624), np.float32(0.9478), np.float32(0.959), np.float32(0.9722), np.float32(0.9559), np.float32(0.954), np.float32(0.9166), np.float32(0.9566), np.float32(0.957), np.float32(0.8749), np.float32(0.9552), np.float32(0.9284), np.float32(0.8719), np.float32(0.8731), np.float32(0.9147)] 
+2025-05-05 07:35:10.267847: Epoch time: 86.95 s 
+2025-05-05 07:35:11.906744:  
+2025-05-05 07:35:12.018337: Epoch 218 
+2025-05-05 07:35:12.050149: Current learning rate: 0.00901 
+2025-05-05 07:36:38.754766: train_loss -0.4557 
+2025-05-05 07:36:38.868974: val_loss -0.4765 
+2025-05-05 07:36:38.903723: Pseudo dice [np.float32(0.7931), np.float32(0.8306), np.float32(0.369), np.float32(0.919), np.float32(0.8341), np.float32(0.9527), np.float32(0.959), np.float32(0.972), np.float32(0.9382), np.float32(0.9563), np.float32(0.9345), np.float32(0.9522), np.float32(0.9631), np.float32(0.8855), np.float32(0.9539), np.float32(0.9442), np.float32(0.827), np.float32(0.8485), np.float32(0.8964)] 
+2025-05-05 07:36:38.941354: Epoch time: 86.85 s 
+2025-05-05 07:36:40.577068:  
+2025-05-05 07:36:40.700817: Epoch 219 
+2025-05-05 07:36:40.724793: Current learning rate: 0.00901 
+2025-05-05 07:38:05.344735: train_loss -0.452 
+2025-05-05 07:38:05.450392: val_loss -0.4689 
+2025-05-05 07:38:05.463550: Pseudo dice [np.float32(0.8049), np.float32(0.7535), np.float32(0.9018), np.float32(0.9667), np.float32(0.8823), np.float32(0.9644), np.float32(0.9507), np.float32(0.969), np.float32(0.9454), np.float32(0.958), np.float32(0.9427), np.float32(0.9627), np.float32(0.9607), np.float32(0.8755), np.float32(0.949), np.float32(0.9461), np.float32(0.8645), np.float32(0.8713), np.float32(0.9039)] 
+2025-05-05 07:38:05.486545: Epoch time: 84.77 s 
+2025-05-05 07:38:07.090967:  
+2025-05-05 07:38:07.169855: Epoch 220 
+2025-05-05 07:38:07.199137: Current learning rate: 0.009 
+2025-05-05 07:39:33.389461: train_loss -0.4626 
+2025-05-05 07:39:33.437784: val_loss -0.4737 
+2025-05-05 07:39:33.443149: Pseudo dice [np.float32(0.8158), np.float32(0.8078), np.float32(0.792), np.float32(0.934), np.float32(0.847), np.float32(0.9467), np.float32(0.9409), np.float32(0.9604), np.float32(0.9582), np.float32(0.9297), np.float32(0.9211), np.float32(0.9635), np.float32(0.9555), np.float32(0.874), np.float32(0.9645), np.float32(0.9449), np.float32(0.816), np.float32(0.8183), np.float32(0.8967)] 
+2025-05-05 07:39:33.444028: Epoch time: 86.3 s 
+2025-05-05 07:39:35.141118:  
+2025-05-05 07:39:35.231092: Epoch 221 
+2025-05-05 07:39:35.243222: Current learning rate: 0.009 
+2025-05-05 07:41:00.238560: train_loss -0.4637 
+2025-05-05 07:41:00.326375: val_loss -0.4374 
+2025-05-05 07:41:00.348412: Pseudo dice [np.float32(0.8137), np.float32(0.792), np.float32(0.9307), np.float32(0.9747), np.float32(0.8325), np.float32(0.9581), np.float32(0.939), np.float32(0.9724), np.float32(0.9459), np.float32(0.9521), np.float32(0.9098), np.float32(0.9545), np.float32(0.9585), np.float32(0.8581), np.float32(0.958), np.float32(0.9284), np.float32(0.842), np.float32(0.8441), np.float32(0.9129)] 
+2025-05-05 07:41:00.364584: Epoch time: 85.1 s 
+2025-05-05 07:41:02.042037:  
+2025-05-05 07:41:02.117188: Epoch 222 
+2025-05-05 07:41:02.132555: Current learning rate: 0.009 
+2025-05-05 07:42:26.842225: train_loss -0.4582 
+2025-05-05 07:42:26.863965: val_loss -0.4614 
+2025-05-05 07:42:26.865286: Pseudo dice [np.float32(0.817), np.float32(0.7854), np.float32(0.8375), np.float32(0.9777), np.float32(0.8789), np.float32(0.9331), np.float32(0.9582), np.float32(0.9724), np.float32(0.9177), np.float32(0.9488), np.float32(0.9304), np.float32(0.9498), np.float32(0.9511), np.float32(0.8668), np.float32(0.93), np.float32(0.9373), np.float32(0.89), np.float32(0.8708), np.float32(0.9121)] 
+2025-05-05 07:42:26.865855: Epoch time: 84.8 s 
+2025-05-05 07:42:28.392152:  
+2025-05-05 07:42:28.459520: Epoch 223 
+2025-05-05 07:42:28.496768: Current learning rate: 0.00899 
+2025-05-05 07:43:53.691485: train_loss -0.4617 
+2025-05-05 07:43:53.770808: val_loss -0.4497 
+2025-05-05 07:43:53.778624: Pseudo dice [np.float32(0.7781), np.float32(0.7864), np.float32(0.6731), np.float32(0.9739), np.float32(0.8744), np.float32(0.9592), np.float32(0.917), np.float32(0.9589), np.float32(0.9527), np.float32(0.9609), np.float32(0.9376), np.float32(0.9563), np.float32(0.9604), np.float32(0.8775), np.float32(0.9571), np.float32(0.9364), np.float32(0.7597), np.float32(0.7151), np.float32(0.9183)] 
+2025-05-05 07:43:53.806073: Epoch time: 85.3 s 
+2025-05-05 07:43:55.407223:  
+2025-05-05 07:43:55.564175: Epoch 224 
+2025-05-05 07:43:55.600088: Current learning rate: 0.00899 
+2025-05-05 07:45:21.741950: train_loss -0.4342 
+2025-05-05 07:45:21.781163: val_loss -0.4646 
+2025-05-05 07:45:21.797361: Pseudo dice [np.float32(0.8049), np.float32(0.7771), np.float32(0.7072), np.float32(0.9729), np.float32(0.7174), np.float32(0.9384), np.float32(0.9532), np.float32(0.9657), np.float32(0.9293), np.float32(0.9627), np.float32(0.9384), np.float32(0.9525), np.float32(0.9615), np.float32(0.8609), np.float32(0.9512), np.float32(0.9273), np.float32(0.864), np.float32(0.8119), np.float32(0.9093)] 
+2025-05-05 07:45:21.832536: Epoch time: 86.34 s 
+2025-05-05 07:45:23.415525:  
+2025-05-05 07:45:23.564322: Epoch 225 
+2025-05-05 07:45:23.597769: Current learning rate: 0.00898 
+2025-05-05 07:46:55.444827: train_loss -0.4488 
+2025-05-05 07:46:55.549382: val_loss -0.4534 
+2025-05-05 07:46:55.559458: Pseudo dice [np.float32(0.8188), np.float32(0.8116), np.float32(0.8772), np.float32(0.9692), np.float32(0.8957), np.float32(0.953), np.float32(0.95), np.float32(0.9713), np.float32(0.9549), np.float32(0.9507), np.float32(0.9337), np.float32(0.96), np.float32(0.9571), np.float32(0.8687), np.float32(0.9337), np.float32(0.9382), np.float32(0.8228), np.float32(0.8335), np.float32(0.8944)] 
+2025-05-05 07:46:55.572383: Epoch time: 92.03 s 
+2025-05-05 07:46:57.236159:  
+2025-05-05 07:46:57.358503: Epoch 226 
+2025-05-05 07:46:57.407938: Current learning rate: 0.00898 
+2025-05-05 07:48:21.718339: train_loss -0.4551 
+2025-05-05 07:48:21.817359: val_loss -0.4373 
+2025-05-05 07:48:21.839568: Pseudo dice [np.float32(0.781), np.float32(0.8134), np.float32(0.7193), np.float32(0.9691), np.float32(0.8763), np.float32(0.9445), np.float32(0.9315), np.float32(0.9414), np.float32(0.946), np.float32(0.9335), np.float32(0.8975), np.float32(0.9596), np.float32(0.946), np.float32(0.8602), np.float32(0.9371), np.float32(0.9189), np.float32(0.8049), np.float32(0.8103), np.float32(0.9052)] 
+2025-05-05 07:48:21.884837: Epoch time: 84.48 s 
+2025-05-05 07:48:23.523177:  
+2025-05-05 07:48:23.613767: Epoch 227 
+2025-05-05 07:48:23.636516: Current learning rate: 0.00897 
+2025-05-05 07:49:49.401934: train_loss -0.4344 
+2025-05-05 07:49:49.474716: val_loss -0.4542 
+2025-05-05 07:49:49.486755: Pseudo dice [np.float32(0.8012), np.float32(0.8261), np.float32(0.885), np.float32(0.9717), np.float32(0.8861), np.float32(0.9528), np.float32(0.9533), np.float32(0.974), np.float32(0.9428), np.float32(0.9558), np.float32(0.9388), np.float32(0.9521), np.float32(0.9588), np.float32(0.8739), np.float32(0.943), np.float32(0.921), np.float32(0.8416), np.float32(0.7853), np.float32(0.9064)] 
+2025-05-05 07:49:49.504756: Epoch time: 85.88 s 
+2025-05-05 07:49:51.125132:  
+2025-05-05 07:49:51.223152: Epoch 228 
+2025-05-05 07:49:51.253455: Current learning rate: 0.00897 
+2025-05-05 07:51:18.712291: train_loss -0.45 
+2025-05-05 07:51:18.825218: val_loss -0.4597 
+2025-05-05 07:51:18.847084: Pseudo dice [np.float32(0.8127), np.float32(0.8223), np.float32(0.7944), np.float32(0.9634), np.float32(0.8694), np.float32(0.9479), np.float32(0.9402), np.float32(0.9742), np.float32(0.9574), np.float32(0.9596), np.float32(0.9261), np.float32(0.9649), np.float32(0.9532), np.float32(0.8677), np.float32(0.9577), np.float32(0.9295), np.float32(0.8045), np.float32(0.8172), np.float32(0.8904)] 
+2025-05-05 07:51:18.858435: Epoch time: 87.59 s 
+2025-05-05 07:51:20.486711:  
+2025-05-05 07:51:20.535954: Epoch 229 
+2025-05-05 07:51:20.554449: Current learning rate: 0.00896 
+2025-05-05 07:52:45.950605: train_loss -0.456 
+2025-05-05 07:52:46.030725: val_loss -0.4489 
+2025-05-05 07:52:46.039968: Pseudo dice [np.float32(0.8111), np.float32(0.8302), np.float32(0.8754), np.float32(0.9434), np.float32(0.8436), np.float32(0.9518), np.float32(0.9481), np.float32(0.9451), np.float32(0.9526), np.float32(0.9489), np.float32(0.9284), np.float32(0.9599), np.float32(0.9398), np.float32(0.847), np.float32(0.9635), np.float32(0.9376), np.float32(0.8826), np.float32(0.8784), np.float32(0.8833)] 
+2025-05-05 07:52:46.042670: Epoch time: 85.47 s 
+2025-05-05 07:52:47.133759:  
+2025-05-05 07:52:47.181163: Epoch 230 
+2025-05-05 07:52:47.202305: Current learning rate: 0.00896 
+2025-05-05 07:54:15.629483: train_loss -0.4495 
+2025-05-05 07:54:15.718976: val_loss -0.4524 
+2025-05-05 07:54:15.744142: Pseudo dice [np.float32(0.7805), np.float32(0.8363), np.float32(0.8571), np.float32(0.9709), np.float32(0.8197), np.float32(0.9512), np.float32(0.945), np.float32(0.9726), np.float32(0.9615), np.float32(0.9663), np.float32(0.922), np.float32(0.9665), np.float32(0.9472), np.float32(0.869), np.float32(0.9584), np.float32(0.9394), np.float32(0.8063), np.float32(0.8365), np.float32(0.9139)] 
+2025-05-05 07:54:15.776198: Epoch time: 88.5 s 
+2025-05-05 07:54:17.365843:  
+2025-05-05 07:54:17.468534: Epoch 231 
+2025-05-05 07:54:17.509964: Current learning rate: 0.00895 
+2025-05-05 07:55:44.043435: train_loss -0.4467 
+2025-05-05 07:55:44.150696: val_loss -0.4478 
+2025-05-05 07:55:44.188968: Pseudo dice [np.float32(0.8068), np.float32(0.7766), np.float32(0.857), np.float32(0.97), np.float32(0.9001), np.float32(0.9435), np.float32(0.9524), np.float32(0.9735), np.float32(0.9491), np.float32(0.9608), np.float32(0.9245), np.float32(0.9537), np.float32(0.9595), np.float32(0.8608), np.float32(0.9384), np.float32(0.9264), np.float32(0.8411), np.float32(0.8532), np.float32(0.9035)] 
+2025-05-05 07:55:44.210337: Epoch time: 86.68 s 
+2025-05-05 07:55:45.855936:  
+2025-05-05 07:55:46.008846: Epoch 232 
+2025-05-05 07:55:46.036768: Current learning rate: 0.00895 
+2025-05-05 07:57:09.498926: train_loss -0.4579 
+2025-05-05 07:57:09.592812: val_loss -0.4568 
+2025-05-05 07:57:09.608005: Pseudo dice [np.float32(0.7983), np.float32(0.8028), np.float32(0.8634), np.float32(0.9282), np.float32(0.8192), np.float32(0.9469), np.float32(0.9358), np.float32(0.8946), np.float32(0.9622), np.float32(0.9456), np.float32(0.9238), np.float32(0.9647), np.float32(0.9589), np.float32(0.8724), np.float32(0.9436), np.float32(0.9405), np.float32(0.8066), np.float32(0.8131), np.float32(0.904)] 
+2025-05-05 07:57:09.617440: Epoch time: 83.65 s 
+2025-05-05 07:57:12.007503:  
+2025-05-05 07:57:12.009510: Epoch 233 
+2025-05-05 07:57:12.010048: Current learning rate: 0.00895 
+2025-05-05 07:58:35.775938: train_loss -0.4385 
+2025-05-05 07:58:35.871436: val_loss -0.4667 
+2025-05-05 07:58:35.893529: Pseudo dice [np.float32(0.8363), np.float32(0.7604), np.float32(0.8587), np.float32(0.9613), np.float32(0.8184), np.float32(0.9584), np.float32(0.9436), np.float32(0.9678), np.float32(0.941), np.float32(0.9442), np.float32(0.93), np.float32(0.9572), np.float32(0.9501), np.float32(0.885), np.float32(0.9512), np.float32(0.9408), np.float32(0.8556), np.float32(0.8513), np.float32(0.8919)] 
+2025-05-05 07:58:35.913891: Epoch time: 83.77 s 
+2025-05-05 07:58:37.547415:  
+2025-05-05 07:58:37.644320: Epoch 234 
+2025-05-05 07:58:37.673846: Current learning rate: 0.00894 
+2025-05-05 08:00:04.137397: train_loss -0.4623 
+2025-05-05 08:00:04.224528: val_loss -0.4597 
+2025-05-05 08:00:04.247397: Pseudo dice [np.float32(0.7811), np.float32(0.8198), np.float32(0.8837), np.float32(0.9712), np.float32(0.8624), np.float32(0.9404), np.float32(0.94), np.float32(0.972), np.float32(0.9527), np.float32(0.9567), np.float32(0.9234), np.float32(0.9607), np.float32(0.9616), np.float32(0.8834), np.float32(0.9459), np.float32(0.9216), np.float32(0.7553), np.float32(0.7043), np.float32(0.9208)] 
+2025-05-05 08:00:04.258373: Epoch time: 86.59 s 
+2025-05-05 08:00:06.013782:  
+2025-05-05 08:00:06.049434: Epoch 235 
+2025-05-05 08:00:06.097009: Current learning rate: 0.00894 
+2025-05-05 08:01:31.003986: train_loss -0.4648 
+2025-05-05 08:01:31.112724: val_loss -0.4741 
+2025-05-05 08:01:31.144587: Pseudo dice [np.float32(0.8271), np.float32(0.8215), np.float32(0.9238), np.float32(0.9716), np.float32(0.8757), np.float32(0.9605), np.float32(0.9443), np.float32(0.9638), np.float32(0.9567), np.float32(0.9636), np.float32(0.9398), np.float32(0.9667), np.float32(0.965), np.float32(0.8679), np.float32(0.963), np.float32(0.9379), np.float32(0.8461), np.float32(0.8551), np.float32(0.9069)] 
+2025-05-05 08:01:31.183484: Epoch time: 84.99 s 
+2025-05-05 08:01:32.833690:  
+2025-05-05 08:01:32.951559: Epoch 236 
+2025-05-05 08:01:32.987648: Current learning rate: 0.00893 
+2025-05-05 08:02:59.666732: train_loss -0.4581 
+2025-05-05 08:02:59.749321: val_loss -0.4834 
+2025-05-05 08:02:59.751093: Pseudo dice [np.float32(0.8435), np.float32(0.8202), np.float32(0.7848), np.float32(0.9643), np.float32(0.8871), np.float32(0.9445), np.float32(0.9589), np.float32(0.9694), np.float32(0.9642), np.float32(0.9513), np.float32(0.9066), np.float32(0.9651), np.float32(0.946), np.float32(0.8767), np.float32(0.9476), np.float32(0.946), np.float32(0.7546), np.float32(0.8226), np.float32(0.8806)] 
+2025-05-05 08:02:59.756279: Epoch time: 86.84 s 
+2025-05-05 08:03:01.307741:  
+2025-05-05 08:03:01.395523: Epoch 237 
+2025-05-05 08:03:01.405449: Current learning rate: 0.00893 
+2025-05-05 08:04:26.900714: train_loss -0.4559 
+2025-05-05 08:04:26.959647: val_loss -0.398 
+2025-05-05 08:04:26.960405: Pseudo dice [np.float32(0.7804), np.float32(0.8563), np.float32(0.8015), np.float32(0.966), np.float32(0.8835), np.float32(0.9547), np.float32(0.9533), np.float32(0.9724), np.float32(0.9326), np.float32(0.9344), np.float32(0.8718), np.float32(0.9334), np.float32(0.9479), np.float32(0.8727), np.float32(0.9202), np.float32(0.9416), np.float32(0.8666), np.float32(0.7583), np.float32(0.9275)] 
+2025-05-05 08:04:26.960880: Epoch time: 85.59 s 
+2025-05-05 08:04:28.577348:  
+2025-05-05 08:04:28.610925: Epoch 238 
+2025-05-05 08:04:28.615203: Current learning rate: 0.00892 
+2025-05-05 08:05:55.695211: train_loss -0.4642 
+2025-05-05 08:05:55.787083: val_loss -0.434 
+2025-05-05 08:05:55.791448: Pseudo dice [np.float32(0.8186), np.float32(0.8041), np.float32(0.8643), np.float32(0.9464), np.float32(0.8751), np.float32(0.9389), np.float32(0.9433), np.float32(0.9733), np.float32(0.9581), np.float32(0.9331), np.float32(0.9316), np.float32(0.9656), np.float32(0.9516), np.float32(0.8867), np.float32(0.9565), np.float32(0.9268), np.float32(0.852), np.float32(0.8593), np.float32(0.9148)] 
+2025-05-05 08:05:55.817189: Epoch time: 87.12 s 
+2025-05-05 08:05:57.459309:  
+2025-05-05 08:05:57.513172: Epoch 239 
+2025-05-05 08:05:57.537199: Current learning rate: 0.00892 
+2025-05-05 08:07:21.749225: train_loss -0.4714 
+2025-05-05 08:07:21.825417: val_loss -0.5134 
+2025-05-05 08:07:21.844296: Pseudo dice [np.float32(0.841), np.float32(0.7944), np.float32(0.8401), np.float32(0.9555), np.float32(0.8612), np.float32(0.9555), np.float32(0.9507), np.float32(0.9706), np.float32(0.9488), np.float32(0.9615), np.float32(0.9434), np.float32(0.9595), np.float32(0.9637), np.float32(0.8603), np.float32(0.9663), np.float32(0.9418), np.float32(0.8325), np.float32(0.842), np.float32(0.9051)] 
+2025-05-05 08:07:21.876983: Epoch time: 84.29 s 
+2025-05-05 08:07:23.515574:  
+2025-05-05 08:07:23.639658: Epoch 240 
+2025-05-05 08:07:23.670474: Current learning rate: 0.00891 
+2025-05-05 08:08:46.997136: train_loss -0.4526 
+2025-05-05 08:08:47.090306: val_loss -0.4914 
+2025-05-05 08:08:47.119122: Pseudo dice [np.float32(0.8207), np.float32(0.8205), np.float32(0.8629), np.float32(0.9726), np.float32(0.8997), np.float32(0.9497), np.float32(0.9597), np.float32(0.9716), np.float32(0.961), np.float32(0.9577), np.float32(0.9414), np.float32(0.9638), np.float32(0.9628), np.float32(0.8778), np.float32(0.9553), np.float32(0.9486), np.float32(0.8625), np.float32(0.8863), np.float32(0.9273)] 
+2025-05-05 08:08:47.141576: Epoch time: 83.48 s 
+2025-05-05 08:08:48.750312:  
+2025-05-05 08:08:48.791360: Epoch 241 
+2025-05-05 08:08:48.793301: Current learning rate: 0.00891 
+2025-05-05 08:10:13.547915: train_loss -0.4659 
+2025-05-05 08:10:13.626555: val_loss -0.4906 
+2025-05-05 08:10:13.638460: Pseudo dice [np.float32(0.8117), np.float32(0.8221), np.float32(0.8153), np.float32(0.9763), np.float32(0.9087), np.float32(0.9495), np.float32(0.9527), np.float32(0.9718), np.float32(0.9531), np.float32(0.9536), np.float32(0.9326), np.float32(0.9532), np.float32(0.9677), np.float32(0.8893), np.float32(0.9651), np.float32(0.9482), np.float32(0.8549), np.float32(0.831), np.float32(0.8992)] 
+2025-05-05 08:10:13.655410: Epoch time: 84.8 s 
+2025-05-05 08:10:15.284514:  
+2025-05-05 08:10:15.484318: Epoch 242 
+2025-05-05 08:10:15.484974: Current learning rate: 0.0089 
+2025-05-05 08:11:39.155555: train_loss -0.4715 
+2025-05-05 08:11:39.235752: val_loss -0.4583 
+2025-05-05 08:11:39.247639: Pseudo dice [np.float32(0.8203), np.float32(0.8171), np.float32(0.9047), np.float32(0.9656), np.float32(0.8588), np.float32(0.9404), np.float32(0.9549), np.float32(0.9575), np.float32(0.9554), np.float32(0.9511), np.float32(0.9251), np.float32(0.9618), np.float32(0.9554), np.float32(0.8808), np.float32(0.9563), np.float32(0.9424), np.float32(0.8658), np.float32(0.8836), np.float32(0.9147)] 
+2025-05-05 08:11:39.267811: Epoch time: 83.87 s 
+2025-05-05 08:11:40.943248:  
+2025-05-05 08:11:41.056525: Epoch 243 
+2025-05-05 08:11:41.097600: Current learning rate: 0.0089 
+2025-05-05 08:13:06.049479: train_loss -0.4646 
+2025-05-05 08:13:06.148011: val_loss -0.4954 
+2025-05-05 08:13:06.162483: Pseudo dice [np.float32(0.7998), np.float32(0.8289), np.float32(0.8823), np.float32(0.9622), np.float32(0.9017), np.float32(0.9553), np.float32(0.9468), np.float32(0.9709), np.float32(0.9604), np.float32(0.9657), np.float32(0.9227), np.float32(0.9658), np.float32(0.9447), np.float32(0.8584), np.float32(0.959), np.float32(0.934), np.float32(0.768), np.float32(0.8328), np.float32(0.8911)] 
+2025-05-05 08:13:06.183203: Epoch time: 85.11 s 
+2025-05-05 08:13:07.828963:  
+2025-05-05 08:13:07.872450: Epoch 244 
+2025-05-05 08:13:07.873164: Current learning rate: 0.00889 
+2025-05-05 08:14:33.734155: train_loss -0.4488 
+2025-05-05 08:14:33.821986: val_loss -0.4727 
+2025-05-05 08:14:33.831295: Pseudo dice [np.float32(0.8502), np.float32(0.8099), np.float32(0.8843), np.float32(0.9724), np.float32(0.8526), np.float32(0.9409), np.float32(0.9523), np.float32(0.969), np.float32(0.9327), np.float32(0.9598), np.float32(0.9285), np.float32(0.9425), np.float32(0.9584), np.float32(0.8797), np.float32(0.9062), np.float32(0.9431), np.float32(0.8661), np.float32(0.8242), np.float32(0.892)] 
+2025-05-05 08:14:33.832168: Epoch time: 85.91 s 
+2025-05-05 08:14:35.533782:  
+2025-05-05 08:14:35.661568: Epoch 245 
+2025-05-05 08:14:35.687769: Current learning rate: 0.00889 
+2025-05-05 08:16:02.228044: train_loss -0.438 
+2025-05-05 08:16:02.337312: val_loss -0.4572 
+2025-05-05 08:16:02.352775: Pseudo dice [np.float32(0.8253), np.float32(0.8215), np.float32(0.8733), np.float32(0.9624), np.float32(0.8612), np.float32(0.9489), np.float32(0.9511), np.float32(0.9739), np.float32(0.9566), np.float32(0.9472), np.float32(0.9298), np.float32(0.9539), np.float32(0.9291), np.float32(0.8621), np.float32(0.956), np.float32(0.9315), np.float32(0.8503), np.float32(0.8484), np.float32(0.892)] 
+2025-05-05 08:16:02.385633: Epoch time: 86.7 s 
+2025-05-05 08:16:03.975280:  
+2025-05-05 08:16:04.002285: Epoch 246 
+2025-05-05 08:16:04.003219: Current learning rate: 0.00889 
+2025-05-05 08:17:30.815937: train_loss -0.4656 
+2025-05-05 08:17:30.826151: val_loss -0.479 
+2025-05-05 08:17:30.826625: Pseudo dice [np.float32(0.8225), np.float32(0.8032), np.float32(0.8422), np.float32(0.9765), np.float32(0.8261), np.float32(0.9435), np.float32(0.9567), np.float32(0.9724), np.float32(0.9477), np.float32(0.9636), np.float32(0.9351), np.float32(0.9568), np.float32(0.9649), np.float32(0.872), np.float32(0.9371), np.float32(0.9338), np.float32(0.8559), np.float32(0.8729), np.float32(0.9064)] 
+2025-05-05 08:17:30.827047: Epoch time: 86.84 s 
+2025-05-05 08:17:32.261098:  
+2025-05-05 08:17:32.430274: Epoch 247 
+2025-05-05 08:17:32.477950: Current learning rate: 0.00888 
+2025-05-05 08:19:01.142084: train_loss -0.4523 
+2025-05-05 08:19:01.199536: val_loss -0.4662 
+2025-05-05 08:19:01.221794: Pseudo dice [np.float32(0.8108), np.float32(0.8281), np.float32(0.8678), np.float32(0.9783), np.float32(0.8777), np.float32(0.9514), np.float32(0.9264), np.float32(0.9677), np.float32(0.9628), np.float32(0.9527), np.float32(0.9328), np.float32(0.9657), np.float32(0.9635), np.float32(0.8342), np.float32(0.9579), np.float32(0.9318), np.float32(0.8527), np.float32(0.8795), np.float32(0.914)] 
+2025-05-05 08:19:01.295578: Epoch time: 88.88 s 
+2025-05-05 08:19:03.005624:  
+2025-05-05 08:19:03.030369: Epoch 248 
+2025-05-05 08:19:03.030955: Current learning rate: 0.00888 
+2025-05-05 08:20:28.356588: train_loss -0.4582 
+2025-05-05 08:20:28.472583: val_loss -0.4928 
+2025-05-05 08:20:28.507645: Pseudo dice [np.float32(0.822), np.float32(0.8164), np.float32(0.8332), np.float32(0.9697), np.float32(0.8966), np.float32(0.9527), np.float32(0.9643), np.float32(0.9711), np.float32(0.9554), np.float32(0.936), np.float32(0.927), np.float32(0.9644), np.float32(0.9497), np.float32(0.8982), np.float32(0.961), np.float32(0.949), np.float32(0.842), np.float32(0.8667), np.float32(0.9087)] 
+2025-05-05 08:20:28.553858: Epoch time: 85.35 s 
+2025-05-05 08:20:30.218987:  
+2025-05-05 08:20:30.250738: Epoch 249 
+2025-05-05 08:20:30.291182: Current learning rate: 0.00887 
+2025-05-05 08:21:55.529245: train_loss -0.4525 
+2025-05-05 08:21:55.599055: val_loss -0.4847 
+2025-05-05 08:21:55.625045: Pseudo dice [np.float32(0.822), np.float32(0.8323), np.float32(0.8533), np.float32(0.9586), np.float32(0.8859), np.float32(0.9246), np.float32(0.9556), np.float32(0.9715), np.float32(0.9456), np.float32(0.9498), np.float32(0.9371), np.float32(0.9636), np.float32(0.9594), np.float32(0.865), np.float32(0.9316), np.float32(0.9265), np.float32(0.8837), np.float32(0.8708), np.float32(0.8892)] 
+2025-05-05 08:21:55.648701: Epoch time: 85.31 s 
+2025-05-05 08:21:57.730732:  
+2025-05-05 08:21:57.808772: Epoch 250 
+2025-05-05 08:21:57.844222: Current learning rate: 0.00887 
+2025-05-05 08:23:24.836998: train_loss -0.4684 
+2025-05-05 08:23:24.928046: val_loss -0.488 
+2025-05-05 08:23:24.938777: Pseudo dice [np.float32(0.8141), np.float32(0.7838), np.float32(0.7995), np.float32(0.9716), np.float32(0.8735), np.float32(0.9463), np.float32(0.9473), np.float32(0.96), np.float32(0.9411), np.float32(0.9453), np.float32(0.9178), np.float32(0.9527), np.float32(0.9605), np.float32(0.8671), np.float32(0.9628), np.float32(0.932), np.float32(0.8777), np.float32(0.8777), np.float32(0.9113)] 
+2025-05-05 08:23:24.963948: Epoch time: 87.11 s 
+2025-05-05 08:23:27.375148:  
+2025-05-05 08:23:27.379128: Epoch 251 
+2025-05-05 08:23:27.379821: Current learning rate: 0.00886 
+2025-05-05 08:24:52.122657: train_loss -0.4518 
+2025-05-05 08:24:52.222119: val_loss -0.4665 
+2025-05-05 08:24:52.246451: Pseudo dice [np.float32(0.8083), np.float32(0.7885), np.float32(0.8817), np.float32(0.9606), np.float32(0.8725), np.float32(0.9423), np.float32(0.9617), np.float32(0.9655), np.float32(0.9657), np.float32(0.9516), np.float32(0.9045), np.float32(0.9692), np.float32(0.9645), np.float32(0.8803), np.float32(0.9581), np.float32(0.9362), np.float32(0.8567), np.float32(0.8628), np.float32(0.9011)] 
+2025-05-05 08:24:52.291158: Epoch time: 84.75 s 
+2025-05-05 08:24:53.889604:  
+2025-05-05 08:24:53.940774: Epoch 252 
+2025-05-05 08:24:53.963026: Current learning rate: 0.00886 
+2025-05-05 08:26:19.282881: train_loss -0.4622 
+2025-05-05 08:26:19.311429: val_loss -0.4776 
+2025-05-05 08:26:19.316015: Pseudo dice [np.float32(0.8224), np.float32(0.8047), np.float32(0.8365), np.float32(0.9587), np.float32(0.8842), np.float32(0.9448), np.float32(0.9483), np.float32(0.9726), np.float32(0.9541), np.float32(0.9528), np.float32(0.9118), np.float32(0.9657), np.float32(0.9437), np.float32(0.8786), np.float32(0.9599), np.float32(0.9522), np.float32(0.8553), np.float32(0.8695), np.float32(0.9041)] 
+2025-05-05 08:26:19.322667: Epoch time: 85.39 s 
+2025-05-05 08:26:21.036674:  
+2025-05-05 08:26:21.173188: Epoch 253 
+2025-05-05 08:26:21.195009: Current learning rate: 0.00885 
+2025-05-05 08:27:46.103390: train_loss -0.4684 
+2025-05-05 08:27:46.120245: val_loss -0.4867 
+2025-05-05 08:27:46.120976: Pseudo dice [np.float32(0.7895), np.float32(0.8484), np.float32(0.8478), np.float32(0.9785), np.float32(0.8745), np.float32(0.951), np.float32(0.9633), np.float32(0.9744), np.float32(0.9557), np.float32(0.9647), np.float32(0.9387), np.float32(0.9686), np.float32(0.9585), np.float32(0.8802), np.float32(0.9615), np.float32(0.9197), np.float32(0.8664), np.float32(0.8803), np.float32(0.9046)] 
+2025-05-05 08:27:46.143378: Epoch time: 85.07 s 
+2025-05-05 08:27:47.779152:  
+2025-05-05 08:27:47.895922: Epoch 254 
+2025-05-05 08:27:47.936609: Current learning rate: 0.00885 
+2025-05-05 08:29:13.757841: train_loss -0.4708 
+2025-05-05 08:29:13.797240: val_loss -0.4816 
+2025-05-05 08:29:13.798187: Pseudo dice [np.float32(0.8072), np.float32(0.8296), np.float32(0.9112), np.float32(0.9667), np.float32(0.8949), np.float32(0.9606), np.float32(0.9589), np.float32(0.9734), np.float32(0.9444), np.float32(0.9649), np.float32(0.938), np.float32(0.9447), np.float32(0.9633), np.float32(0.8869), np.float32(0.9559), np.float32(0.9494), np.float32(0.8522), np.float32(0.8456), np.float32(0.9108)] 
+2025-05-05 08:29:13.798674: Epoch time: 85.98 s 
+2025-05-05 08:29:13.803941: Yayy! New best EMA pseudo Dice: 0.9114999771118164 
+2025-05-05 08:29:16.346971:  
+2025-05-05 08:29:16.400826: Epoch 255 
+2025-05-05 08:29:16.461763: Current learning rate: 0.00884 
+2025-05-05 08:30:44.998025: train_loss -0.4603 
+2025-05-05 08:30:45.073760: val_loss -0.4675 
+2025-05-05 08:30:45.084897: Pseudo dice [np.float32(0.8114), np.float32(0.8231), np.float32(0.8559), np.float32(0.9615), np.float32(0.8677), np.float32(0.9603), np.float32(0.9584), np.float32(0.9746), np.float32(0.9524), np.float32(0.9561), np.float32(0.9175), np.float32(0.9632), np.float32(0.969), np.float32(0.8841), np.float32(0.9636), np.float32(0.9382), np.float32(0.7868), np.float32(0.8565), np.float32(0.9207)] 
+2025-05-05 08:30:45.092693: Epoch time: 88.65 s 
+2025-05-05 08:30:45.104399: Yayy! New best EMA pseudo Dice: 0.9115999937057495 
+2025-05-05 08:30:47.390879:  
+2025-05-05 08:30:47.449270: Epoch 256 
+2025-05-05 08:30:47.453529: Current learning rate: 0.00884 
+2025-05-05 08:32:15.768442: train_loss -0.4625 
+2025-05-05 08:32:15.820575: val_loss -0.4634 
+2025-05-05 08:32:15.828475: Pseudo dice [np.float32(0.8273), np.float32(0.8243), np.float32(0.7968), np.float32(0.9635), np.float32(0.8335), np.float32(0.9609), np.float32(0.9618), np.float32(0.9747), np.float32(0.9669), np.float32(0.9469), np.float32(0.9389), np.float32(0.9657), np.float32(0.9558), np.float32(0.8798), np.float32(0.9327), np.float32(0.9425), np.float32(0.8631), np.float32(0.8349), np.float32(0.8864)] 
+2025-05-05 08:32:15.850924: Epoch time: 88.38 s 
+2025-05-05 08:32:17.548321:  
+2025-05-05 08:32:17.575959: Epoch 257 
+2025-05-05 08:32:17.600059: Current learning rate: 0.00884 
+2025-05-05 08:33:39.671685: train_loss -0.4835 
+2025-05-05 08:33:39.753333: val_loss -0.4447 
+2025-05-05 08:33:39.781150: Pseudo dice [np.float32(0.841), np.float32(0.8456), np.float32(0.8648), np.float32(0.9648), np.float32(0.8247), np.float32(0.9572), np.float32(0.9634), np.float32(0.9744), np.float32(0.9643), np.float32(0.9443), np.float32(0.9218), np.float32(0.9753), np.float32(0.9472), np.float32(0.893), np.float32(0.9631), np.float32(0.9492), np.float32(0.8488), np.float32(0.8847), np.float32(0.904)] 
+2025-05-05 08:33:39.803050: Epoch time: 82.12 s 
+2025-05-05 08:33:39.823331: Yayy! New best EMA pseudo Dice: 0.9118000268936157 
+2025-05-05 08:33:42.315679:  
+2025-05-05 08:33:42.464624: Epoch 258 
+2025-05-05 08:33:42.477713: Current learning rate: 0.00883 
+2025-05-05 08:35:10.201884: train_loss -0.4687 
+2025-05-05 08:35:10.296311: val_loss -0.4819 
+2025-05-05 08:35:10.309863: Pseudo dice [np.float32(0.8205), np.float32(0.8296), np.float32(0.9024), np.float32(0.9701), np.float32(0.8954), np.float32(0.9518), np.float32(0.9678), np.float32(0.9659), np.float32(0.9362), np.float32(0.9579), np.float32(0.9396), np.float32(0.9468), np.float32(0.9575), np.float32(0.8894), np.float32(0.9662), np.float32(0.9371), np.float32(0.8708), np.float32(0.8933), np.float32(0.9119)] 
+2025-05-05 08:35:10.329828: Epoch time: 87.89 s 
+2025-05-05 08:35:10.367903: Yayy! New best EMA pseudo Dice: 0.9128000140190125 
+2025-05-05 08:35:12.603555:  
+2025-05-05 08:35:12.654554: Epoch 259 
+2025-05-05 08:35:12.680840: Current learning rate: 0.00883 
+2025-05-05 08:36:38.854963: train_loss -0.4599 
+2025-05-05 08:36:38.988443: val_loss -0.4426 
+2025-05-05 08:36:39.018317: Pseudo dice [np.float32(0.7577), np.float32(0.8243), np.float32(0.9183), np.float32(0.9664), np.float32(0.8871), np.float32(0.9485), np.float32(0.952), np.float32(0.9713), np.float32(0.9507), np.float32(0.9485), np.float32(0.9291), np.float32(0.954), np.float32(0.9632), np.float32(0.8611), np.float32(0.9531), np.float32(0.9355), np.float32(0.6759), np.float32(0.7679), np.float32(0.9066)] 
+2025-05-05 08:36:39.078118: Epoch time: 86.25 s 
+2025-05-05 08:36:40.622443:  
+2025-05-05 08:36:40.798174: Epoch 260 
+2025-05-05 08:36:40.827775: Current learning rate: 0.00882 
+2025-05-05 08:38:03.421418: train_loss -0.4542 
+2025-05-05 08:38:03.537860: val_loss -0.4574 
+2025-05-05 08:38:03.558598: Pseudo dice [np.float32(0.8237), np.float32(0.8339), np.float32(0.9106), np.float32(0.9413), np.float32(0.868), np.float32(0.9551), np.float32(0.9407), np.float32(0.9621), np.float32(0.9525), np.float32(0.958), np.float32(0.9063), np.float32(0.9463), np.float32(0.9565), np.float32(0.8524), np.float32(0.9469), np.float32(0.9272), np.float32(0.8543), np.float32(0.8464), np.float32(0.896)] 
+2025-05-05 08:38:03.576853: Epoch time: 82.8 s 
+2025-05-05 08:38:05.213299:  
+2025-05-05 08:38:05.311713: Epoch 261 
+2025-05-05 08:38:05.322971: Current learning rate: 0.00882 
+2025-05-05 08:39:30.124208: train_loss -0.4554 
+2025-05-05 08:39:30.163674: val_loss -0.4059 
+2025-05-05 08:39:30.164352: Pseudo dice [np.float32(0.8081), np.float32(0.8049), np.float32(0.8312), np.float32(0.8265), np.float32(0.8614), np.float32(0.954), np.float32(0.9395), np.float32(0.9616), np.float32(0.9333), np.float32(0.9459), np.float32(0.8575), np.float32(0.942), np.float32(0.9357), np.float32(0.8541), np.float32(0.9644), np.float32(0.9417), np.float32(0.8804), np.float32(0.8742), np.float32(0.9134)] 
+2025-05-05 08:39:30.164847: Epoch time: 84.91 s 
+2025-05-05 08:39:31.609001:  
+2025-05-05 08:39:31.735941: Epoch 262 
+2025-05-05 08:39:31.782093: Current learning rate: 0.00881 
+2025-05-05 08:40:55.901231: train_loss -0.453 
+2025-05-05 08:40:56.038520: val_loss -0.4528 
+2025-05-05 08:40:56.044641: Pseudo dice [np.float32(0.8243), np.float32(0.7845), np.float32(0.5318), np.float32(0.971), np.float32(0.8063), np.float32(0.9571), np.float32(0.9488), np.float32(0.9726), np.float32(0.9593), np.float32(0.9581), np.float32(0.9279), np.float32(0.9497), np.float32(0.9533), np.float32(0.8716), np.float32(0.9611), np.float32(0.9452), np.float32(0.8481), np.float32(0.8724), np.float32(0.8979)] 
+2025-05-05 08:40:56.050424: Epoch time: 84.29 s 
+2025-05-05 08:40:57.672039:  
+2025-05-05 08:40:57.738202: Epoch 263 
+2025-05-05 08:40:57.774538: Current learning rate: 0.00881 
+2025-05-05 08:42:24.327445: train_loss -0.4599 
+2025-05-05 08:42:24.424038: val_loss -0.4544 
+2025-05-05 08:42:24.454995: Pseudo dice [np.float32(0.7944), np.float32(0.8291), np.float32(0.9044), np.float32(0.9732), np.float32(0.9178), np.float32(0.9177), np.float32(0.9619), np.float32(0.9754), np.float32(0.9503), np.float32(0.9583), np.float32(0.9372), np.float32(0.967), np.float32(0.9623), np.float32(0.88), np.float32(0.9498), np.float32(0.9308), np.float32(0.864), np.float32(0.8007), np.float32(0.9074)] 
+2025-05-05 08:42:24.483536: Epoch time: 86.66 s 
+2025-05-05 08:42:26.070744:  
+2025-05-05 08:42:26.118249: Epoch 264 
+2025-05-05 08:42:26.138800: Current learning rate: 0.0088 
+2025-05-05 08:43:52.057115: train_loss -0.4836 
+2025-05-05 08:43:52.169697: val_loss -0.4826 
+2025-05-05 08:43:52.174108: Pseudo dice [np.float32(0.8261), np.float32(0.8239), np.float32(0.8407), np.float32(0.9711), np.float32(0.908), np.float32(0.9597), np.float32(0.9623), np.float32(0.9748), np.float32(0.9615), np.float32(0.9619), np.float32(0.9379), np.float32(0.9702), np.float32(0.9594), np.float32(0.8824), np.float32(0.96), np.float32(0.9409), np.float32(0.8212), np.float32(0.8521), np.float32(0.9195)] 
+2025-05-05 08:43:52.175111: Epoch time: 85.99 s 
+2025-05-05 08:43:53.804486:  
+2025-05-05 08:43:53.828929: Epoch 265 
+2025-05-05 08:43:53.833663: Current learning rate: 0.0088 
+2025-05-05 08:45:19.636248: train_loss -0.4716 
+2025-05-05 08:45:19.679591: val_loss -0.447 
+2025-05-05 08:45:19.738443: Pseudo dice [np.float32(0.8294), np.float32(0.812), np.float32(0.926), np.float32(0.9765), np.float32(0.8125), np.float32(0.9558), np.float32(0.9397), np.float32(0.9646), np.float32(0.9631), np.float32(0.9584), np.float32(0.9221), np.float32(0.9689), np.float32(0.9529), np.float32(0.8913), np.float32(0.9383), np.float32(0.9426), np.float32(0.8632), np.float32(0.8667), np.float32(0.9032)] 
+2025-05-05 08:45:19.763732: Epoch time: 85.83 s 
+2025-05-05 08:45:20.919919:  
+2025-05-05 08:45:21.029518: Epoch 266 
+2025-05-05 08:45:21.056134: Current learning rate: 0.00879 
+2025-05-05 08:46:48.072615: train_loss -0.4731 
+2025-05-05 08:46:48.151801: val_loss -0.481 
+2025-05-05 08:46:48.152436: Pseudo dice [np.float32(0.809), np.float32(0.8153), np.float32(0.8123), np.float32(0.9725), np.float32(0.897), np.float32(0.9382), np.float32(0.9505), np.float32(0.9731), np.float32(0.9637), np.float32(0.9421), np.float32(0.9365), np.float32(0.9676), np.float32(0.9483), np.float32(0.8763), np.float32(0.9594), np.float32(0.9403), np.float32(0.8747), np.float32(0.8681), np.float32(0.8973)] 
+2025-05-05 08:46:48.152913: Epoch time: 87.15 s 
+2025-05-05 08:46:49.709457:  
+2025-05-05 08:46:49.854162: Epoch 267 
+2025-05-05 08:46:49.943125: Current learning rate: 0.00879 
+2025-05-05 08:48:15.403289: train_loss -0.4721 
+2025-05-05 08:48:15.465190: val_loss -0.4736 
+2025-05-05 08:48:15.480289: Pseudo dice [np.float32(0.7798), np.float32(0.8386), np.float32(0.7485), np.float32(0.9686), np.float32(0.8156), np.float32(0.9538), np.float32(0.955), np.float32(0.9702), np.float32(0.9434), np.float32(0.9412), np.float32(0.9292), np.float32(0.9562), np.float32(0.9476), np.float32(0.8445), np.float32(0.9591), np.float32(0.9437), np.float32(0.8532), np.float32(0.844), np.float32(0.9049)] 
+2025-05-05 08:48:15.510916: Epoch time: 85.7 s 
+2025-05-05 08:48:17.341245:  
+2025-05-05 08:48:17.376846: Epoch 268 
+2025-05-05 08:48:17.377715: Current learning rate: 0.00879 
+2025-05-05 08:49:42.083315: train_loss -0.4607 
+2025-05-05 08:49:42.248720: val_loss -0.4639 
+2025-05-05 08:49:42.279603: Pseudo dice [np.float32(0.807), np.float32(0.8402), np.float32(0.5915), np.float32(0.9645), np.float32(0.8999), np.float32(0.9542), np.float32(0.9587), np.float32(0.9742), np.float32(0.9402), np.float32(0.96), np.float32(0.9464), np.float32(0.96), np.float32(0.9657), np.float32(0.8941), np.float32(0.9619), np.float32(0.9326), np.float32(0.8633), np.float32(0.8511), np.float32(0.9103)] 
+2025-05-05 08:49:42.321639: Epoch time: 84.74 s 
+2025-05-05 08:49:44.740066:  
+2025-05-05 08:49:44.741759: Epoch 269 
+2025-05-05 08:49:44.742910: Current learning rate: 0.00878 
+2025-05-05 08:51:10.054118: train_loss -0.4609 
+2025-05-05 08:51:10.171458: val_loss -0.4772 
+2025-05-05 08:51:10.226930: Pseudo dice [np.float32(0.8296), np.float32(0.836), np.float32(0.9186), np.float32(0.9616), np.float32(0.8752), np.float32(0.9562), np.float32(0.9657), np.float32(0.9725), np.float32(0.9303), np.float32(0.9679), np.float32(0.9357), np.float32(0.9423), np.float32(0.9583), np.float32(0.8663), np.float32(0.944), np.float32(0.9331), np.float32(0.8368), np.float32(0.8798), np.float32(0.8932)] 
+2025-05-05 08:51:10.263850: Epoch time: 85.32 s 
+2025-05-05 08:51:11.869194:  
+2025-05-05 08:51:12.030665: Epoch 270 
+2025-05-05 08:51:12.059850: Current learning rate: 0.00878 
+2025-05-05 08:52:37.847852: train_loss -0.4707 
+2025-05-05 08:52:37.947649: val_loss -0.448 
+2025-05-05 08:52:37.970528: Pseudo dice [np.float32(0.7751), np.float32(0.8018), np.float32(0.8643), np.float32(0.9753), np.float32(0.8743), np.float32(0.9436), np.float32(0.9551), np.float32(0.9706), np.float32(0.9562), np.float32(0.9631), np.float32(0.938), np.float32(0.9355), np.float32(0.9641), np.float32(0.848), np.float32(0.9479), np.float32(0.8672), np.float32(0.8503), np.float32(0.8544), np.float32(0.9076)] 
+2025-05-05 08:52:38.013310: Epoch time: 85.98 s 
+2025-05-05 08:52:39.754091:  
+2025-05-05 08:52:39.828660: Epoch 271 
+2025-05-05 08:52:39.885978: Current learning rate: 0.00877 
+2025-05-05 08:54:03.691111: train_loss -0.4603 
+2025-05-05 08:54:03.793897: val_loss -0.4885 
+2025-05-05 08:54:03.843315: Pseudo dice [np.float32(0.8275), np.float32(0.8561), np.float32(0.8966), np.float32(0.9639), np.float32(0.8727), np.float32(0.9489), np.float32(0.9641), np.float32(0.9746), np.float32(0.9562), np.float32(0.9636), np.float32(0.9387), np.float32(0.9613), np.float32(0.9652), np.float32(0.883), np.float32(0.9581), np.float32(0.9451), np.float32(0.8675), np.float32(0.7859), np.float32(0.9136)] 
+2025-05-05 08:54:03.879360: Epoch time: 83.94 s 
+2025-05-05 08:54:05.103070:  
+2025-05-05 08:54:05.245344: Epoch 272 
+2025-05-05 08:54:05.249861: Current learning rate: 0.00877 
+2025-05-05 08:55:33.078963: train_loss -0.4706 
+2025-05-05 08:55:33.231291: val_loss -0.4444 
+2025-05-05 08:55:33.261663: Pseudo dice [np.float32(0.8082), np.float32(0.7982), np.float32(0.9084), np.float32(0.9738), np.float32(0.8158), np.float32(0.9591), np.float32(0.961), np.float32(0.9731), np.float32(0.9636), np.float32(0.9612), np.float32(0.9352), np.float32(0.9636), np.float32(0.9651), np.float32(0.8563), np.float32(0.9607), np.float32(0.9501), np.float32(0.8625), np.float32(0.8389), np.float32(0.8915)] 
+2025-05-05 08:55:33.277672: Epoch time: 87.98 s 
+2025-05-05 08:55:34.894295:  
+2025-05-05 08:55:34.946285: Epoch 273 
+2025-05-05 08:55:34.963480: Current learning rate: 0.00876 
+2025-05-05 08:57:00.916537: train_loss -0.4633 
+2025-05-05 08:57:01.029403: val_loss -0.4266 
+2025-05-05 08:57:01.080564: Pseudo dice [np.float32(0.8289), np.float32(0.8269), np.float32(0.909), np.float32(0.9671), np.float32(0.8275), np.float32(0.9504), np.float32(0.9538), np.float32(0.9722), np.float32(0.9463), np.float32(0.9252), np.float32(0.9203), np.float32(0.9544), np.float32(0.9583), np.float32(0.8958), np.float32(0.9573), np.float32(0.9363), np.float32(0.8406), np.float32(0.8294), np.float32(0.8865)] 
+2025-05-05 08:57:01.105532: Epoch time: 86.02 s 
+2025-05-05 08:57:02.693631:  
+2025-05-05 08:57:02.848782: Epoch 274 
+2025-05-05 08:57:02.878063: Current learning rate: 0.00876 
+2025-05-05 08:58:28.924407: train_loss -0.4501 
+2025-05-05 08:58:29.011858: val_loss -0.4923 
+2025-05-05 08:58:29.049165: Pseudo dice [np.float32(0.8227), np.float32(0.8178), np.float32(0.8326), np.float32(0.9653), np.float32(0.8681), np.float32(0.9553), np.float32(0.9607), np.float32(0.9644), np.float32(0.948), np.float32(0.9445), np.float32(0.9202), np.float32(0.9627), np.float32(0.9604), np.float32(0.8893), np.float32(0.9641), np.float32(0.9377), np.float32(0.7099), np.float32(0.6759), np.float32(0.9109)] 
+2025-05-05 08:58:29.075454: Epoch time: 86.23 s 
+2025-05-05 08:58:30.778634:  
+2025-05-05 08:58:30.785666: Epoch 275 
+2025-05-05 08:58:30.786249: Current learning rate: 0.00875 
+2025-05-05 08:59:55.792533: train_loss -0.4754 
+2025-05-05 08:59:55.881423: val_loss -0.4941 
+2025-05-05 08:59:55.904270: Pseudo dice [np.float32(0.8293), np.float32(0.8285), np.float32(0.7955), np.float32(0.9639), np.float32(0.8241), np.float32(0.9543), np.float32(0.9434), np.float32(0.9633), np.float32(0.9673), np.float32(0.9501), np.float32(0.9295), np.float32(0.9685), np.float32(0.959), np.float32(0.8842), np.float32(0.9282), np.float32(0.9445), np.float32(0.8537), np.float32(0.8562), np.float32(0.8975)] 
+2025-05-05 08:59:55.929938: Epoch time: 85.02 s 
+2025-05-05 08:59:57.607889:  
+2025-05-05 08:59:57.674833: Epoch 276 
+2025-05-05 08:59:57.716861: Current learning rate: 0.00875 
+2025-05-05 09:01:22.283366: train_loss -0.4385 
+2025-05-05 09:01:22.376484: val_loss -0.4784 
+2025-05-05 09:01:22.384731: Pseudo dice [np.float32(0.8328), np.float32(0.8438), np.float32(0.8693), np.float32(0.9736), np.float32(0.7861), np.float32(0.9415), np.float32(0.962), np.float32(0.9489), np.float32(0.9565), np.float32(0.9541), np.float32(0.9015), np.float32(0.9643), np.float32(0.953), np.float32(0.8778), np.float32(0.9373), np.float32(0.9511), np.float32(0.8574), np.float32(0.8289), np.float32(0.9024)] 
+2025-05-05 09:01:22.388713: Epoch time: 84.68 s 
+2025-05-05 09:01:24.125582:  
+2025-05-05 09:01:24.235176: Epoch 277 
+2025-05-05 09:01:24.275293: Current learning rate: 0.00874 
+2025-05-05 09:02:48.439009: train_loss -0.4557 
+2025-05-05 09:02:48.533421: val_loss -0.5009 
+2025-05-05 09:02:48.556268: Pseudo dice [np.float32(0.8297), np.float32(0.8362), np.float32(0.9126), np.float32(0.8995), np.float32(0.8651), np.float32(0.9474), np.float32(0.9476), np.float32(0.9763), np.float32(0.9597), np.float32(0.9615), np.float32(0.9271), np.float32(0.9647), np.float32(0.9598), np.float32(0.8849), np.float32(0.9199), np.float32(0.9461), np.float32(0.8825), np.float32(0.8607), np.float32(0.9154)] 
+2025-05-05 09:02:48.572795: Epoch time: 84.32 s 
+2025-05-05 09:02:50.270355:  
+2025-05-05 09:02:50.436132: Epoch 278 
+2025-05-05 09:02:50.513558: Current learning rate: 0.00874 
+2025-05-05 09:04:18.793584: train_loss -0.4516 
+2025-05-05 09:04:18.868024: val_loss -0.5066 
+2025-05-05 09:04:18.915711: Pseudo dice [np.float32(0.8318), np.float32(0.8232), np.float32(0.8821), np.float32(0.9697), np.float32(0.8894), np.float32(0.9554), np.float32(0.9464), np.float32(0.9676), np.float32(0.9523), np.float32(0.95), np.float32(0.9373), np.float32(0.966), np.float32(0.9616), np.float32(0.8912), np.float32(0.9326), np.float32(0.9363), np.float32(0.8674), np.float32(0.7647), np.float32(0.8897)] 
+2025-05-05 09:04:18.967794: Epoch time: 88.52 s 
+2025-05-05 09:04:20.662689:  
+2025-05-05 09:04:20.782941: Epoch 279 
+2025-05-05 09:04:20.808155: Current learning rate: 0.00874 
+2025-05-05 09:05:45.207299: train_loss -0.4382 
+2025-05-05 09:05:45.288971: val_loss -0.4849 
+2025-05-05 09:05:45.300338: Pseudo dice [np.float32(0.8261), np.float32(0.802), np.float32(0.8448), np.float32(0.971), np.float32(0.9004), np.float32(0.9526), np.float32(0.9511), np.float32(0.9698), np.float32(0.9456), np.float32(0.9508), np.float32(0.9355), np.float32(0.9519), np.float32(0.9557), np.float32(0.875), np.float32(0.9387), np.float32(0.9492), np.float32(0.8744), np.float32(0.8578), np.float32(0.9142)] 
+2025-05-05 09:05:45.313478: Epoch time: 84.55 s 
+2025-05-05 09:05:46.407174:  
+2025-05-05 09:05:46.482489: Epoch 280 
+2025-05-05 09:05:46.506855: Current learning rate: 0.00873 
+2025-05-05 09:07:12.958613: train_loss -0.4838 
+2025-05-05 09:07:13.076665: val_loss -0.4932 
+2025-05-05 09:07:13.110911: Pseudo dice [np.float32(0.7681), np.float32(0.7785), np.float32(0.7805), np.float32(0.9657), np.float32(0.7937), np.float32(0.9518), np.float32(0.9525), np.float32(0.9704), np.float32(0.9591), np.float32(0.9567), np.float32(0.9221), np.float32(0.9643), np.float32(0.955), np.float32(0.8786), np.float32(0.9469), np.float32(0.9473), np.float32(0.8035), np.float32(0.8057), np.float32(0.8987)] 
+2025-05-05 09:07:13.144528: Epoch time: 86.55 s 
+2025-05-05 09:07:14.789151:  
+2025-05-05 09:07:14.890658: Epoch 281 
+2025-05-05 09:07:14.925152: Current learning rate: 0.00873 
+2025-05-05 09:08:38.259862: train_loss -0.4589 
+2025-05-05 09:08:38.327850: val_loss -0.4705 
+2025-05-05 09:08:38.339425: Pseudo dice [np.float32(0.8328), np.float32(0.8131), np.float32(0.9204), np.float32(0.9604), np.float32(0.9012), np.float32(0.9514), np.float32(0.9641), np.float32(0.9746), np.float32(0.9469), np.float32(0.9465), np.float32(0.9271), np.float32(0.9641), np.float32(0.952), np.float32(0.8895), np.float32(0.9284), np.float32(0.935), np.float32(0.887), np.float32(0.8592), np.float32(0.9096)] 
+2025-05-05 09:08:38.375920: Epoch time: 83.47 s 
+2025-05-05 09:08:40.015833:  
+2025-05-05 09:08:40.118096: Epoch 282 
+2025-05-05 09:08:40.177742: Current learning rate: 0.00872 
+2025-05-05 09:10:06.458357: train_loss -0.4731 
+2025-05-05 09:10:06.540093: val_loss -0.4579 
+2025-05-05 09:10:06.569874: Pseudo dice [np.float32(0.8074), np.float32(0.8106), np.float32(0.6526), np.float32(0.9635), np.float32(0.887), np.float32(0.9551), np.float32(0.9568), np.float32(0.9666), np.float32(0.9625), np.float32(0.9347), np.float32(0.8894), np.float32(0.9685), np.float32(0.949), np.float32(0.8753), np.float32(0.9561), np.float32(0.9461), np.float32(0.8882), np.float32(0.876), np.float32(0.8969)] 
+2025-05-05 09:10:06.615637: Epoch time: 86.44 s 
+2025-05-05 09:10:08.304021:  
+2025-05-05 09:10:08.340312: Epoch 283 
+2025-05-05 09:10:08.364439: Current learning rate: 0.00872 
+2025-05-05 09:11:31.806489: train_loss -0.4603 
+2025-05-05 09:11:31.919166: val_loss -0.4883 
+2025-05-05 09:11:31.932295: Pseudo dice [np.float32(0.8423), np.float32(0.819), np.float32(0.8312), np.float32(0.9613), np.float32(0.867), np.float32(0.9466), np.float32(0.9565), np.float32(0.975), np.float32(0.962), np.float32(0.9526), np.float32(0.9142), np.float32(0.9678), np.float32(0.9588), np.float32(0.8881), np.float32(0.9642), np.float32(0.9349), np.float32(0.8709), np.float32(0.8653), np.float32(0.903)] 
+2025-05-05 09:11:31.954874: Epoch time: 83.5 s 
+2025-05-05 09:11:33.516910:  
+2025-05-05 09:11:33.622910: Epoch 284 
+2025-05-05 09:11:33.654202: Current learning rate: 0.00871 
+2025-05-05 09:12:56.789414: train_loss -0.476 
+2025-05-05 09:12:56.917770: val_loss -0.4606 
+2025-05-05 09:12:56.938472: Pseudo dice [np.float32(0.8253), np.float32(0.8253), np.float32(0.8753), np.float32(0.9779), np.float32(0.7525), np.float32(0.9498), np.float32(0.9575), np.float32(0.9573), np.float32(0.9516), np.float32(0.9378), np.float32(0.8848), np.float32(0.9419), np.float32(0.9483), np.float32(0.9004), np.float32(0.9589), np.float32(0.9476), np.float32(0.8338), np.float32(0.8047), np.float32(0.9141)] 
+2025-05-05 09:12:56.952372: Epoch time: 83.27 s 
+2025-05-05 09:12:58.706990:  
+2025-05-05 09:12:58.887631: Epoch 285 
+2025-05-05 09:12:58.889501: Current learning rate: 0.00871 
+2025-05-05 09:14:23.905015: train_loss -0.4631 
+2025-05-05 09:14:24.012935: val_loss -0.472 
+2025-05-05 09:14:24.038904: Pseudo dice [np.float32(0.8159), np.float32(0.8068), np.float32(0.8644), np.float32(0.971), np.float32(0.8758), np.float32(0.9396), np.float32(0.9497), np.float32(0.9729), np.float32(0.9628), np.float32(0.9615), np.float32(0.9455), np.float32(0.9605), np.float32(0.9671), np.float32(0.8821), np.float32(0.9525), np.float32(0.9322), np.float32(0.8589), np.float32(0.8738), np.float32(0.9091)] 
+2025-05-05 09:14:24.057056: Epoch time: 85.2 s 
+2025-05-05 09:14:25.666541:  
+2025-05-05 09:14:25.765944: Epoch 286 
+2025-05-05 09:14:25.799748: Current learning rate: 0.0087 
+2025-05-05 09:15:49.210487: train_loss -0.469 
+2025-05-05 09:15:49.321486: val_loss -0.4787 
+2025-05-05 09:15:49.346278: Pseudo dice [np.float32(0.7739), np.float32(0.8146), np.float32(0.9095), np.float32(0.9761), np.float32(0.8728), np.float32(0.9652), np.float32(0.9511), np.float32(0.9715), np.float32(0.9407), np.float32(0.962), np.float32(0.9317), np.float32(0.9547), np.float32(0.9638), np.float32(0.871), np.float32(0.957), np.float32(0.9461), np.float32(0.8172), np.float32(0.8495), np.float32(0.927)] 
+2025-05-05 09:15:49.357529: Epoch time: 83.54 s 
+2025-05-05 09:15:51.863667:  
+2025-05-05 09:15:51.887038: Epoch 287 
+2025-05-05 09:15:51.887673: Current learning rate: 0.0087 
+2025-05-05 09:17:15.595917: train_loss -0.4772 
+2025-05-05 09:17:15.671104: val_loss -0.5078 
+2025-05-05 09:17:15.684423: Pseudo dice [np.float32(0.8372), np.float32(0.8431), np.float32(0.9087), np.float32(0.9709), np.float32(0.8049), np.float32(0.956), np.float32(0.9557), np.float32(0.9753), np.float32(0.9544), np.float32(0.9674), np.float32(0.9347), np.float32(0.9673), np.float32(0.9582), np.float32(0.8919), np.float32(0.9658), np.float32(0.9429), np.float32(0.8997), np.float32(0.9073), np.float32(0.9107)] 
+2025-05-05 09:17:15.698837: Epoch time: 83.73 s 
+2025-05-05 09:17:16.913319:  
+2025-05-05 09:17:16.989922: Epoch 288 
+2025-05-05 09:17:17.001971: Current learning rate: 0.00869 
+2025-05-05 09:18:42.810668: train_loss -0.4599 
+2025-05-05 09:18:42.928986: val_loss -0.5026 
+2025-05-05 09:18:42.944187: Pseudo dice [np.float32(0.7957), np.float32(0.8136), np.float32(0.8788), np.float32(0.9602), np.float32(0.8909), np.float32(0.9441), np.float32(0.9604), np.float32(0.9749), np.float32(0.9324), np.float32(0.9501), np.float32(0.9158), np.float32(0.9476), np.float32(0.9491), np.float32(0.8898), np.float32(0.9627), np.float32(0.9485), np.float32(0.8425), np.float32(0.8571), np.float32(0.9015)] 
+2025-05-05 09:18:42.963894: Epoch time: 85.9 s 
+2025-05-05 09:18:44.614741:  
+2025-05-05 09:18:44.759698: Epoch 289 
+2025-05-05 09:18:44.815901: Current learning rate: 0.00869 
+2025-05-05 09:20:09.298818: train_loss -0.464 
+2025-05-05 09:20:09.420094: val_loss -0.4979 
+2025-05-05 09:20:09.453060: Pseudo dice [np.float32(0.8423), np.float32(0.817), np.float32(0.8356), np.float32(0.9765), np.float32(0.8673), np.float32(0.9508), np.float32(0.9425), np.float32(0.971), np.float32(0.9518), np.float32(0.9464), np.float32(0.9351), np.float32(0.9613), np.float32(0.952), np.float32(0.8738), np.float32(0.9618), np.float32(0.9399), np.float32(0.8385), np.float32(0.8399), np.float32(0.9172)] 
+2025-05-05 09:20:09.478772: Epoch time: 84.69 s 
+2025-05-05 09:20:11.145654:  
+2025-05-05 09:20:11.214101: Epoch 290 
+2025-05-05 09:20:11.245197: Current learning rate: 0.00868 
+2025-05-05 09:21:37.953497: train_loss -0.4667 
+2025-05-05 09:21:38.079885: val_loss -0.4601 
+2025-05-05 09:21:38.103502: Pseudo dice [np.float32(0.8451), np.float32(0.8188), np.float32(0.9172), np.float32(0.9451), np.float32(0.767), np.float32(0.963), np.float32(0.9371), np.float32(0.9688), np.float32(0.9421), np.float32(0.9562), np.float32(0.929), np.float32(0.9587), np.float32(0.9594), np.float32(0.8882), np.float32(0.9505), np.float32(0.9362), np.float32(0.8534), np.float32(0.8744), np.float32(0.9213)] 
+2025-05-05 09:21:38.123424: Epoch time: 86.81 s 
+2025-05-05 09:21:39.813514:  
+2025-05-05 09:21:39.876941: Epoch 291 
+2025-05-05 09:21:39.884979: Current learning rate: 0.00868 
+2025-05-05 09:23:05.026995: train_loss -0.4783 
+2025-05-05 09:23:05.130278: val_loss -0.4561 
+2025-05-05 09:23:05.150964: Pseudo dice [np.float32(0.8381), np.float32(0.7839), np.float32(0.8617), np.float32(0.9399), np.float32(0.8606), np.float32(0.9489), np.float32(0.9587), np.float32(0.8975), np.float32(0.9477), np.float32(0.9568), np.float32(0.9231), np.float32(0.9421), np.float32(0.9608), np.float32(0.8847), np.float32(0.9382), np.float32(0.9481), np.float32(0.797), np.float32(0.8352), np.float32(0.9067)] 
+2025-05-05 09:23:05.195654: Epoch time: 85.22 s 
+2025-05-05 09:23:06.847012:  
+2025-05-05 09:23:06.948201: Epoch 292 
+2025-05-05 09:23:06.970998: Current learning rate: 0.00868 
+2025-05-05 09:24:31.221618: train_loss -0.4619 
+2025-05-05 09:24:31.348773: val_loss -0.4792 
+2025-05-05 09:24:31.371071: Pseudo dice [np.float32(0.8071), np.float32(0.7964), np.float32(0.876), np.float32(0.9692), np.float32(0.8492), np.float32(0.9442), np.float32(0.953), np.float32(0.9705), np.float32(0.9564), np.float32(0.9497), np.float32(0.9372), np.float32(0.97), np.float32(0.9686), np.float32(0.8351), np.float32(0.9621), np.float32(0.9331), np.float32(0.8196), np.float32(0.8496), np.float32(0.8893)] 
+2025-05-05 09:24:31.389172: Epoch time: 84.38 s 
+2025-05-05 09:24:32.601120:  
+2025-05-05 09:24:32.771181: Epoch 293 
+2025-05-05 09:24:32.807293: Current learning rate: 0.00867 
+2025-05-05 09:26:01.816188: train_loss -0.4683 
+2025-05-05 09:26:01.838502: val_loss -0.4961 
+2025-05-05 09:26:01.839428: Pseudo dice [np.float32(0.7986), np.float32(0.8376), np.float32(0.8995), np.float32(0.9597), np.float32(0.8901), np.float32(0.9548), np.float32(0.9552), np.float32(0.9662), np.float32(0.9618), np.float32(0.9464), np.float32(0.9034), np.float32(0.9634), np.float32(0.9579), np.float32(0.8543), np.float32(0.9623), np.float32(0.9421), np.float32(0.8866), np.float32(0.8235), np.float32(0.9002)] 
+2025-05-05 09:26:01.839988: Epoch time: 89.22 s 
+2025-05-05 09:26:03.016112:  
+2025-05-05 09:26:03.112741: Epoch 294 
+2025-05-05 09:26:03.149269: Current learning rate: 0.00867 
+2025-05-05 09:27:29.637146: train_loss -0.4594 
+2025-05-05 09:27:29.729383: val_loss -0.427 
+2025-05-05 09:27:29.756350: Pseudo dice [np.float32(0.7825), np.float32(0.8064), np.float32(0.8899), np.float32(0.9447), np.float32(0.8526), np.float32(0.954), np.float32(0.9509), np.float32(0.9741), np.float32(0.9568), np.float32(0.9438), np.float32(0.9161), np.float32(0.9641), np.float32(0.9558), np.float32(0.8663), np.float32(0.9452), np.float32(0.9314), np.float32(0.8807), np.float32(0.8697), np.float32(0.9115)] 
+2025-05-05 09:27:29.780115: Epoch time: 86.62 s 
+2025-05-05 09:27:31.019096:  
+2025-05-05 09:27:31.117527: Epoch 295 
+2025-05-05 09:27:31.157804: Current learning rate: 0.00866 
+2025-05-05 09:28:57.466838: train_loss -0.4588 
+2025-05-05 09:28:57.541607: val_loss -0.4839 
+2025-05-05 09:28:57.543290: Pseudo dice [np.float32(0.7786), np.float32(0.8319), np.float32(0.9136), np.float32(0.9565), np.float32(0.8589), np.float32(0.9531), np.float32(0.9513), np.float32(0.9701), np.float32(0.9405), np.float32(0.9548), np.float32(0.9352), np.float32(0.9572), np.float32(0.9646), np.float32(0.879), np.float32(0.9658), np.float32(0.9218), np.float32(0.8803), np.float32(0.8592), np.float32(0.9138)] 
+2025-05-05 09:28:57.544814: Epoch time: 86.45 s 
+2025-05-05 09:28:59.013026:  
+2025-05-05 09:28:59.102471: Epoch 296 
+2025-05-05 09:28:59.110040: Current learning rate: 0.00866 
+2025-05-05 09:30:25.066581: train_loss -0.4812 
+2025-05-05 09:30:25.129462: val_loss -0.4472 
+2025-05-05 09:30:25.150596: Pseudo dice [np.float32(0.8026), np.float32(0.8192), np.float32(0.8314), np.float32(0.9748), np.float32(0.8768), np.float32(0.9389), np.float32(0.9611), np.float32(0.9658), np.float32(0.9431), np.float32(0.9523), np.float32(0.9342), np.float32(0.9626), np.float32(0.9542), np.float32(0.8844), np.float32(0.9657), np.float32(0.9443), np.float32(0.7959), np.float32(0.817), np.float32(0.9126)] 
+2025-05-05 09:30:25.179145: Epoch time: 86.06 s 
+2025-05-05 09:30:26.821631:  
+2025-05-05 09:30:26.919192: Epoch 297 
+2025-05-05 09:30:26.937904: Current learning rate: 0.00865 
+2025-05-05 09:31:53.443236: train_loss -0.48 
+2025-05-05 09:31:53.579409: val_loss -0.4628 
+2025-05-05 09:31:53.655217: Pseudo dice [np.float32(0.8148), np.float32(0.8275), np.float32(0.7883), np.float32(0.9612), np.float32(0.885), np.float32(0.9523), np.float32(0.9637), np.float32(0.9753), np.float32(0.9317), np.float32(0.9429), np.float32(0.9151), np.float32(0.956), np.float32(0.9543), np.float32(0.8994), np.float32(0.9475), np.float32(0.9356), np.float32(0.8154), np.float32(0.8332), np.float32(0.9075)] 
+2025-05-05 09:31:53.693008: Epoch time: 86.62 s 
+2025-05-05 09:31:55.102440:  
+2025-05-05 09:31:55.226616: Epoch 298 
+2025-05-05 09:31:55.254792: Current learning rate: 0.00865 
+2025-05-05 09:33:19.359188: train_loss -0.4693 
+2025-05-05 09:33:19.444417: val_loss -0.4595 
+2025-05-05 09:33:19.481176: Pseudo dice [np.float32(0.838), np.float32(0.8217), np.float32(0.8996), np.float32(0.9745), np.float32(0.9005), np.float32(0.9598), np.float32(0.9472), np.float32(0.9681), np.float32(0.9572), np.float32(0.9683), np.float32(0.9453), np.float32(0.9661), np.float32(0.9673), np.float32(0.8995), np.float32(0.9423), np.float32(0.9472), np.float32(0.8681), np.float32(0.8434), np.float32(0.9171)] 
+2025-05-05 09:33:19.512427: Epoch time: 84.26 s 
+2025-05-05 09:33:21.199719:  
+2025-05-05 09:33:21.288436: Epoch 299 
+2025-05-05 09:33:21.306972: Current learning rate: 0.00864 
+2025-05-05 09:34:46.454697: train_loss -0.4556 
+2025-05-05 09:34:46.521170: val_loss -0.489 
+2025-05-05 09:34:46.540159: Pseudo dice [np.float32(0.833), np.float32(0.8063), np.float32(0.9018), np.float32(0.9695), np.float32(0.8647), np.float32(0.9599), np.float32(0.9529), np.float32(0.9699), np.float32(0.9652), np.float32(0.9564), np.float32(0.9386), np.float32(0.9691), np.float32(0.9659), np.float32(0.8853), np.float32(0.9503), np.float32(0.9448), np.float32(0.8147), np.float32(0.8212), np.float32(0.9149)] 
+2025-05-05 09:34:46.582226: Epoch time: 85.26 s 
+2025-05-05 09:34:49.023490:  
+2025-05-05 09:34:49.102407: Epoch 300 
+2025-05-05 09:34:49.117825: Current learning rate: 0.00864 
+2025-05-05 09:36:19.407447: train_loss -0.4768 
+2025-05-05 09:36:19.526007: val_loss -0.4586 
+2025-05-05 09:36:19.546617: Pseudo dice [np.float32(0.8321), np.float32(0.8382), np.float32(0.8891), np.float32(0.9637), np.float32(0.8609), np.float32(0.9561), np.float32(0.9564), np.float32(0.9734), np.float32(0.9618), np.float32(0.9409), np.float32(0.9136), np.float32(0.9646), np.float32(0.9259), np.float32(0.8795), np.float32(0.9609), np.float32(0.9467), np.float32(0.852), np.float32(0.8056), np.float32(0.8973)] 
+2025-05-05 09:36:19.559703: Epoch time: 90.38 s 
+2025-05-05 09:36:21.432776:  
+2025-05-05 09:36:21.450062: Epoch 301 
+2025-05-05 09:36:21.466502: Current learning rate: 0.00863 
+2025-05-05 09:37:47.049542: train_loss -0.4691 
+2025-05-05 09:37:47.128382: val_loss -0.4592 
+2025-05-05 09:37:47.133334: Pseudo dice [np.float32(0.8394), np.float32(0.7676), np.float32(0.915), np.float32(0.9544), np.float32(0.8783), np.float32(0.9484), np.float32(0.9479), np.float32(0.9696), np.float32(0.9594), np.float32(0.959), np.float32(0.9305), np.float32(0.967), np.float32(0.9664), np.float32(0.8818), np.float32(0.9455), np.float32(0.9484), np.float32(0.8437), np.float32(0.8513), np.float32(0.9222)] 
+2025-05-05 09:37:47.141410: Epoch time: 85.62 s 
+2025-05-05 09:37:48.941015:  
+2025-05-05 09:37:49.003210: Epoch 302 
+2025-05-05 09:37:49.021444: Current learning rate: 0.00863 
+2025-05-05 09:39:14.983776: train_loss -0.4626 
+2025-05-05 09:39:15.004483: val_loss -0.4832 
+2025-05-05 09:39:15.005669: Pseudo dice [np.float32(0.8297), np.float32(0.7975), np.float32(0.9034), np.float32(0.9508), np.float32(0.8887), np.float32(0.9519), np.float32(0.9576), np.float32(0.9775), np.float32(0.9479), np.float32(0.9692), np.float32(0.933), np.float32(0.9623), np.float32(0.9574), np.float32(0.8727), np.float32(0.9463), np.float32(0.925), np.float32(0.8756), np.float32(0.8278), np.float32(0.9198)] 
+2025-05-05 09:39:15.011111: Epoch time: 86.04 s 
+2025-05-05 09:39:16.657959:  
+2025-05-05 09:39:16.704086: Epoch 303 
+2025-05-05 09:39:16.708422: Current learning rate: 0.00863 
+2025-05-05 09:40:41.050570: train_loss -0.4623 
+2025-05-05 09:40:41.077825: val_loss -0.5037 
+2025-05-05 09:40:41.087156: Pseudo dice [np.float32(0.8489), np.float32(0.8106), np.float32(0.9078), np.float32(0.9755), np.float32(0.854), np.float32(0.9441), np.float32(0.9469), np.float32(0.9746), np.float32(0.9627), np.float32(0.9492), np.float32(0.9231), np.float32(0.9686), np.float32(0.9583), np.float32(0.8974), np.float32(0.9191), np.float32(0.9485), np.float32(0.8149), np.float32(0.7986), np.float32(0.8839)] 
+2025-05-05 09:40:41.089348: Epoch time: 84.39 s 
+2025-05-05 09:40:42.762768:  
+2025-05-05 09:40:42.880776: Epoch 304 
+2025-05-05 09:40:42.926319: Current learning rate: 0.00862 
+2025-05-05 09:42:11.122519: train_loss -0.4547 
+2025-05-05 09:42:11.214410: val_loss -0.4723 
+2025-05-05 09:42:11.252204: Pseudo dice [np.float32(0.8171), np.float32(0.8088), np.float32(0.9286), np.float32(0.9752), np.float32(0.9074), np.float32(0.9536), np.float32(0.9532), np.float32(0.9716), np.float32(0.9459), np.float32(0.953), np.float32(0.9295), np.float32(0.9543), np.float32(0.964), np.float32(0.8885), np.float32(0.965), np.float32(0.9456), np.float32(0.8607), np.float32(0.8714), np.float32(0.9192)] 
+2025-05-05 09:42:11.292892: Epoch time: 88.36 s 
+2025-05-05 09:42:11.350897: Yayy! New best EMA pseudo Dice: 0.913100004196167 
+2025-05-05 09:42:15.061841:  
+2025-05-05 09:42:15.068944: Epoch 305 
+2025-05-05 09:42:15.069703: Current learning rate: 0.00862 
+2025-05-05 09:43:43.118423: train_loss -0.4558 
+2025-05-05 09:43:43.215315: val_loss -0.5057 
+2025-05-05 09:43:43.225094: Pseudo dice [np.float32(0.8387), np.float32(0.8441), np.float32(0.8065), np.float32(0.9698), np.float32(0.9176), np.float32(0.9538), np.float32(0.9597), np.float32(0.9737), np.float32(0.9569), np.float32(0.9419), np.float32(0.9187), np.float32(0.9666), np.float32(0.9625), np.float32(0.8888), np.float32(0.9645), np.float32(0.9436), np.float32(0.8369), np.float32(0.8623), np.float32(0.9088)] 
+2025-05-05 09:43:43.230331: Epoch time: 88.06 s 
+2025-05-05 09:43:43.231496: Yayy! New best EMA pseudo Dice: 0.9133999943733215 
+2025-05-05 09:43:45.697901:  
+2025-05-05 09:43:45.740083: Epoch 306 
+2025-05-05 09:43:45.745141: Current learning rate: 0.00861 
+2025-05-05 09:45:14.506951: train_loss -0.4756 
+2025-05-05 09:45:14.576600: val_loss -0.4707 
+2025-05-05 09:45:14.614515: Pseudo dice [np.float32(0.8164), np.float32(0.8045), np.float32(0.8619), np.float32(0.9728), np.float32(0.8119), np.float32(0.9398), np.float32(0.9572), np.float32(0.9745), np.float32(0.9601), np.float32(0.94), np.float32(0.9293), np.float32(0.9408), np.float32(0.9607), np.float32(0.8771), np.float32(0.9595), np.float32(0.9235), np.float32(0.8846), np.float32(0.8563), np.float32(0.902)] 
+2025-05-05 09:45:14.645350: Epoch time: 88.81 s 
+2025-05-05 09:45:16.415850:  
+2025-05-05 09:45:16.457577: Epoch 307 
+2025-05-05 09:45:16.470633: Current learning rate: 0.00861 
+2025-05-05 09:46:42.065675: train_loss -0.4573 
+2025-05-05 09:46:42.181457: val_loss -0.458 
+2025-05-05 09:46:42.200989: Pseudo dice [np.float32(0.8056), np.float32(0.8237), np.float32(0.9051), np.float32(0.9655), np.float32(0.8646), np.float32(0.9395), np.float32(0.9525), np.float32(0.974), np.float32(0.966), np.float32(0.951), np.float32(0.9127), np.float32(0.9629), np.float32(0.9605), np.float32(0.8846), np.float32(0.9497), np.float32(0.9356), np.float32(0.8539), np.float32(0.8645), np.float32(0.9213)] 
+2025-05-05 09:46:42.227072: Epoch time: 85.65 s 
+2025-05-05 09:46:43.412496:  
+2025-05-05 09:46:43.528006: Epoch 308 
+2025-05-05 09:46:43.557429: Current learning rate: 0.0086 
+2025-05-05 09:48:08.968780: train_loss -0.474 
+2025-05-05 09:48:09.032489: val_loss -0.4853 
+2025-05-05 09:48:09.036766: Pseudo dice [np.float32(0.826), np.float32(0.7739), np.float32(0.8989), np.float32(0.975), np.float32(0.8871), np.float32(0.9537), np.float32(0.961), np.float32(0.9745), np.float32(0.9577), np.float32(0.9653), np.float32(0.9402), np.float32(0.9667), np.float32(0.9662), np.float32(0.8823), np.float32(0.9555), np.float32(0.9414), np.float32(0.8976), np.float32(0.8468), np.float32(0.9232)] 
+2025-05-05 09:48:09.037861: Epoch time: 85.56 s 
+2025-05-05 09:48:09.040411: Yayy! New best EMA pseudo Dice: 0.9139999747276306 
+2025-05-05 09:48:11.350784:  
+2025-05-05 09:48:11.475426: Epoch 309 
+2025-05-05 09:48:11.508915: Current learning rate: 0.0086 
+2025-05-05 09:49:38.638418: train_loss -0.4744 
+2025-05-05 09:49:38.790642: val_loss -0.4884 
+2025-05-05 09:49:38.799055: Pseudo dice [np.float32(0.7982), np.float32(0.8345), np.float32(0.7338), np.float32(0.9658), np.float32(0.8447), np.float32(0.9476), np.float32(0.9502), np.float32(0.9724), np.float32(0.9607), np.float32(0.9534), np.float32(0.932), np.float32(0.9614), np.float32(0.9723), np.float32(0.8793), np.float32(0.9378), np.float32(0.9325), np.float32(0.8837), np.float32(0.8757), np.float32(0.9124)] 
+2025-05-05 09:49:38.806895: Epoch time: 87.29 s 
+2025-05-05 09:49:40.507961:  
+2025-05-05 09:49:40.675469: Epoch 310 
+2025-05-05 09:49:40.686514: Current learning rate: 0.00859 
+2025-05-05 09:51:06.817920: train_loss -0.4778 
+2025-05-05 09:51:06.888195: val_loss -0.4971 
+2025-05-05 09:51:06.899299: Pseudo dice [np.float32(0.8258), np.float32(0.8362), np.float32(0.9168), np.float32(0.9659), np.float32(0.9179), np.float32(0.9531), np.float32(0.9432), np.float32(0.969), np.float32(0.9461), np.float32(0.9665), np.float32(0.9447), np.float32(0.9609), np.float32(0.9643), np.float32(0.8901), np.float32(0.9594), np.float32(0.9512), np.float32(0.8956), np.float32(0.8791), np.float32(0.9193)] 
+2025-05-05 09:51:06.910016: Epoch time: 86.31 s 
+2025-05-05 09:51:06.938888: Yayy! New best EMA pseudo Dice: 0.9146999716758728 
+2025-05-05 09:51:10.084808:  
+2025-05-05 09:51:10.087938: Epoch 311 
+2025-05-05 09:51:10.088714: Current learning rate: 0.00859 
+2025-05-05 09:52:35.838222: train_loss -0.4693 
+2025-05-05 09:52:35.939285: val_loss -0.5065 
+2025-05-05 09:52:35.985792: Pseudo dice [np.float32(0.8432), np.float32(0.8495), np.float32(0.9061), np.float32(0.9751), np.float32(0.9018), np.float32(0.953), np.float32(0.965), np.float32(0.9731), np.float32(0.929), np.float32(0.9635), np.float32(0.9493), np.float32(0.9366), np.float32(0.9684), np.float32(0.8875), np.float32(0.9315), np.float32(0.9477), np.float32(0.8994), np.float32(0.8827), np.float32(0.9014)] 
+2025-05-05 09:52:35.987218: Epoch time: 85.75 s 
+2025-05-05 09:52:35.988609: Yayy! New best EMA pseudo Dice: 0.9157000184059143 
+2025-05-05 09:52:37.985928:  
+2025-05-05 09:52:38.006614: Epoch 312 
+2025-05-05 09:52:38.007670: Current learning rate: 0.00858 
+2025-05-05 09:54:07.291804: train_loss -0.4672 
+2025-05-05 09:54:07.342718: val_loss -0.455 
+2025-05-05 09:54:07.353337: Pseudo dice [np.float32(0.8417), np.float32(0.7995), np.float32(0.8241), np.float32(0.9616), np.float32(0.8873), np.float32(0.9508), np.float32(0.9568), np.float32(0.9719), np.float32(0.9561), np.float32(0.9668), np.float32(0.9395), np.float32(0.9703), np.float32(0.9609), np.float32(0.8898), np.float32(0.9629), np.float32(0.9388), np.float32(0.8722), np.float32(0.8962), np.float32(0.9195)] 
+2025-05-05 09:54:07.378699: Epoch time: 89.31 s 
+2025-05-05 09:54:07.414798: Yayy! New best EMA pseudo Dice: 0.9160000085830688 
+2025-05-05 09:54:09.239856:  
+2025-05-05 09:54:09.329124: Epoch 313 
+2025-05-05 09:54:09.330862: Current learning rate: 0.00858 
+2025-05-05 09:55:41.178526: train_loss -0.4569 
+2025-05-05 09:55:41.259443: val_loss -0.4314 
+2025-05-05 09:55:41.283849: Pseudo dice [np.float32(0.8343), np.float32(0.7985), np.float32(0.8809), np.float32(0.9705), np.float32(0.8271), np.float32(0.9566), np.float32(0.9435), np.float32(0.9677), np.float32(0.9315), np.float32(0.954), np.float32(0.9214), np.float32(0.9457), np.float32(0.9607), np.float32(0.878), np.float32(0.925), np.float32(0.9258), np.float32(0.8052), np.float32(0.8424), np.float32(0.926)] 
+2025-05-05 09:55:41.294484: Epoch time: 91.94 s 
+2025-05-05 09:55:43.020951:  
+2025-05-05 09:55:43.072299: Epoch 314 
+2025-05-05 09:55:43.091743: Current learning rate: 0.00858 
+2025-05-05 09:57:08.868340: train_loss -0.4633 
+2025-05-05 09:57:08.914721: val_loss -0.4944 
+2025-05-05 09:57:08.932214: Pseudo dice [np.float32(0.8459), np.float32(0.8013), np.float32(0.9083), np.float32(0.9735), np.float32(0.9069), np.float32(0.9571), np.float32(0.9524), np.float32(0.9705), np.float32(0.9532), np.float32(0.9499), np.float32(0.9295), np.float32(0.9434), np.float32(0.9616), np.float32(0.8816), np.float32(0.9607), np.float32(0.9452), np.float32(0.87), np.float32(0.8516), np.float32(0.8979)] 
+2025-05-05 09:57:08.957940: Epoch time: 85.85 s 
+2025-05-05 09:57:10.163550:  
+2025-05-05 09:57:10.284977: Epoch 315 
+2025-05-05 09:57:10.306834: Current learning rate: 0.00857 
+2025-05-05 09:58:34.498409: train_loss -0.4669 
+2025-05-05 09:58:34.629696: val_loss -0.4625 
+2025-05-05 09:58:34.673177: Pseudo dice [np.float32(0.7683), np.float32(0.7601), np.float32(0.8633), np.float32(0.9667), np.float32(0.8883), np.float32(0.9447), np.float32(0.9633), np.float32(0.9729), np.float32(0.9617), np.float32(0.9449), np.float32(0.9286), np.float32(0.9685), np.float32(0.9655), np.float32(0.8814), np.float32(0.9517), np.float32(0.9364), np.float32(0.8452), np.float32(0.7698), np.float32(0.9093)] 
+2025-05-05 09:58:34.715243: Epoch time: 84.34 s 
+2025-05-05 09:58:36.486780:  
+2025-05-05 09:58:36.565539: Epoch 316 
+2025-05-05 09:58:36.590268: Current learning rate: 0.00857 
+2025-05-05 10:00:00.843907: train_loss -0.4408 
+2025-05-05 10:00:00.948609: val_loss -0.4764 
+2025-05-05 10:00:00.959792: Pseudo dice [np.float32(0.8102), np.float32(0.8053), np.float32(0.8922), np.float32(0.966), np.float32(0.8704), np.float32(0.9541), np.float32(0.9582), np.float32(0.9727), np.float32(0.9474), np.float32(0.9544), np.float32(0.9102), np.float32(0.9532), np.float32(0.9488), np.float32(0.889), np.float32(0.9669), np.float32(0.9558), np.float32(0.8409), np.float32(0.8487), np.float32(0.9026)] 
+2025-05-05 10:00:00.984054: Epoch time: 84.36 s 
+2025-05-05 10:00:02.688749:  
+2025-05-05 10:00:02.788208: Epoch 317 
+2025-05-05 10:00:02.799340: Current learning rate: 0.00856 
+2025-05-05 10:01:27.433732: train_loss -0.4695 
+2025-05-05 10:01:27.553625: val_loss -0.4906 
+2025-05-05 10:01:27.581747: Pseudo dice [np.float32(0.8224), np.float32(0.8097), np.float32(0.8971), np.float32(0.9698), np.float32(0.8517), np.float32(0.9554), np.float32(0.9467), np.float32(0.9717), np.float32(0.9543), np.float32(0.9431), np.float32(0.9367), np.float32(0.9643), np.float32(0.9596), np.float32(0.8694), np.float32(0.9596), np.float32(0.9422), np.float32(0.8576), np.float32(0.8666), np.float32(0.8953)] 
+2025-05-05 10:01:27.615096: Epoch time: 84.75 s 
+2025-05-05 10:01:29.376766:  
+2025-05-05 10:01:29.401293: Epoch 318 
+2025-05-05 10:01:29.407852: Current learning rate: 0.00856 
+2025-05-05 10:02:54.854054: train_loss -0.4558 
+2025-05-05 10:02:54.938673: val_loss -0.4961 
+2025-05-05 10:02:54.939653: Pseudo dice [np.float32(0.838), np.float32(0.8462), np.float32(0.9041), np.float32(0.9699), np.float32(0.8964), np.float32(0.9485), np.float32(0.9619), np.float32(0.9722), np.float32(0.9603), np.float32(0.9587), np.float32(0.9328), np.float32(0.9597), np.float32(0.9628), np.float32(0.8908), np.float32(0.9579), np.float32(0.9335), np.float32(0.855), np.float32(0.8653), np.float32(0.8954)] 
+2025-05-05 10:02:54.945001: Epoch time: 85.48 s 
+2025-05-05 10:02:56.647421:  
+2025-05-05 10:02:56.728400: Epoch 319 
+2025-05-05 10:02:56.744131: Current learning rate: 0.00855 
+2025-05-05 10:04:23.863823: train_loss -0.4739 
+2025-05-05 10:04:24.000546: val_loss -0.4728 
+2025-05-05 10:04:24.025113: Pseudo dice [np.float32(0.8508), np.float32(0.8447), np.float32(0.9336), np.float32(0.9682), np.float32(0.8386), np.float32(0.9459), np.float32(0.9659), np.float32(0.9727), np.float32(0.952), np.float32(0.9441), np.float32(0.941), np.float32(0.9585), np.float32(0.9543), np.float32(0.893), np.float32(0.958), np.float32(0.9544), np.float32(0.7505), np.float32(0.7376), np.float32(0.9041)] 
+2025-05-05 10:04:24.049343: Epoch time: 87.22 s 
+2025-05-05 10:04:25.778202:  
+2025-05-05 10:04:25.872163: Epoch 320 
+2025-05-05 10:04:25.894002: Current learning rate: 0.00855 
+2025-05-05 10:05:51.280001: train_loss -0.4738 
+2025-05-05 10:05:51.332273: val_loss -0.4731 
+2025-05-05 10:05:51.358184: Pseudo dice [np.float32(0.8091), np.float32(0.8117), np.float32(0.9107), np.float32(0.9759), np.float32(0.8914), np.float32(0.9521), np.float32(0.9605), np.float32(0.9746), np.float32(0.9367), np.float32(0.953), np.float32(0.9346), np.float32(0.9603), np.float32(0.9592), np.float32(0.8994), np.float32(0.9646), np.float32(0.936), np.float32(0.862), np.float32(0.8772), np.float32(0.9119)] 
+2025-05-05 10:05:51.402212: Epoch time: 85.5 s 
+2025-05-05 10:05:53.071193:  
+2025-05-05 10:05:53.234560: Epoch 321 
+2025-05-05 10:05:53.258424: Current learning rate: 0.00854 
+2025-05-05 10:07:20.627260: train_loss -0.4641 
+2025-05-05 10:07:20.754068: val_loss -0.4972 
+2025-05-05 10:07:20.779784: Pseudo dice [np.float32(0.8454), np.float32(0.833), np.float32(0.9306), np.float32(0.9725), np.float32(0.8979), np.float32(0.9548), np.float32(0.9617), np.float32(0.9754), np.float32(0.9594), np.float32(0.955), np.float32(0.9368), np.float32(0.9634), np.float32(0.9606), np.float32(0.8851), np.float32(0.96), np.float32(0.9492), np.float32(0.8665), np.float32(0.8069), np.float32(0.9085)] 
+2025-05-05 10:07:20.818940: Epoch time: 87.56 s 
+2025-05-05 10:07:23.075103:  
+2025-05-05 10:07:23.143682: Epoch 322 
+2025-05-05 10:07:23.169214: Current learning rate: 0.00854 
+2025-05-05 10:08:56.040654: train_loss -0.4683 
+2025-05-05 10:08:56.119289: val_loss -0.4088 
+2025-05-05 10:08:56.133902: Pseudo dice [np.float32(0.829), np.float32(0.7995), np.float32(0.8819), np.float32(0.974), np.float32(0.8808), np.float32(0.9595), np.float32(0.9422), np.float32(0.9605), np.float32(0.9463), np.float32(0.923), np.float32(0.9284), np.float32(0.9583), np.float32(0.965), np.float32(0.8779), np.float32(0.9043), np.float32(0.9354), np.float32(0.861), np.float32(0.8848), np.float32(0.9039)] 
+2025-05-05 10:08:56.153264: Epoch time: 92.97 s 
+2025-05-05 10:08:57.860575:  
+2025-05-05 10:08:57.908846: Epoch 323 
+2025-05-05 10:08:57.935240: Current learning rate: 0.00853 
+2025-05-05 10:10:28.524006: train_loss -0.4449 
+2025-05-05 10:10:28.638622: val_loss -0.5001 
+2025-05-05 10:10:28.657370: Pseudo dice [np.float32(0.8117), np.float32(0.7941), np.float32(0.8488), np.float32(0.8915), np.float32(0.8636), np.float32(0.953), np.float32(0.9591), np.float32(0.9741), np.float32(0.9569), np.float32(0.9483), np.float32(0.9335), np.float32(0.9636), np.float32(0.9591), np.float32(0.8903), np.float32(0.9632), np.float32(0.9381), np.float32(0.8393), np.float32(0.7934), np.float32(0.9094)] 
+2025-05-05 10:10:28.683120: Epoch time: 90.66 s 
+2025-05-05 10:10:30.107457:  
+2025-05-05 10:10:30.212424: Epoch 324 
+2025-05-05 10:10:30.234706: Current learning rate: 0.00853 
+2025-05-05 10:12:00.149028: train_loss -0.4519 
+2025-05-05 10:12:00.280388: val_loss -0.5103 
+2025-05-05 10:12:00.339083: Pseudo dice [np.float32(0.8457), np.float32(0.8367), np.float32(0.8226), np.float32(0.9721), np.float32(0.9225), np.float32(0.955), np.float32(0.9515), np.float32(0.9724), np.float32(0.9544), np.float32(0.9652), np.float32(0.9363), np.float32(0.9531), np.float32(0.9653), np.float32(0.8934), np.float32(0.9311), np.float32(0.9522), np.float32(0.8683), np.float32(0.8373), np.float32(0.9062)] 
+2025-05-05 10:12:00.365949: Epoch time: 90.04 s 
+2025-05-05 10:12:02.134980:  
+2025-05-05 10:12:02.180437: Epoch 325 
+2025-05-05 10:12:02.191632: Current learning rate: 0.00852 
+2025-05-05 10:13:33.309236: train_loss -0.4611 
+2025-05-05 10:13:33.364679: val_loss -0.4938 
+2025-05-05 10:13:33.374613: Pseudo dice [np.float32(0.8446), np.float32(0.8447), np.float32(0.9288), np.float32(0.9783), np.float32(0.8914), np.float32(0.9606), np.float32(0.9526), np.float32(0.976), np.float32(0.9578), np.float32(0.9589), np.float32(0.9413), np.float32(0.9666), np.float32(0.9661), np.float32(0.8958), np.float32(0.9637), np.float32(0.9385), np.float32(0.8692), np.float32(0.8546), np.float32(0.906)] 
+2025-05-05 10:13:33.385827: Epoch time: 91.18 s 
+2025-05-05 10:13:35.216966:  
+2025-05-05 10:13:35.285738: Epoch 326 
+2025-05-05 10:13:35.308321: Current learning rate: 0.00852 
+2025-05-05 10:15:03.605976: train_loss -0.4641 
+2025-05-05 10:15:03.669477: val_loss -0.4966 
+2025-05-05 10:15:03.698266: Pseudo dice [np.float32(0.8466), np.float32(0.8582), np.float32(0.9319), np.float32(0.9638), np.float32(0.8211), np.float32(0.9613), np.float32(0.9638), np.float32(0.9545), np.float32(0.9631), np.float32(0.9557), np.float32(0.9341), np.float32(0.967), np.float32(0.9644), np.float32(0.8995), np.float32(0.961), np.float32(0.9406), np.float32(0.903), np.float32(0.865), np.float32(0.92)] 
+2025-05-05 10:15:03.733469: Epoch time: 88.39 s 
+2025-05-05 10:15:03.755893: Yayy! New best EMA pseudo Dice: 0.9165999889373779 
+2025-05-05 10:15:06.247239:  
+2025-05-05 10:15:06.260574: Epoch 327 
+2025-05-05 10:15:06.267265: Current learning rate: 0.00852 
+2025-05-05 10:16:41.159988: train_loss -0.4761 
+2025-05-05 10:16:41.247275: val_loss -0.4693 
+2025-05-05 10:16:41.248812: Pseudo dice [np.float32(0.8189), np.float32(0.8316), np.float32(0.7975), np.float32(0.9649), np.float32(0.8733), np.float32(0.9549), np.float32(0.9597), np.float32(0.9765), np.float32(0.9684), np.float32(0.9646), np.float32(0.9234), np.float32(0.9614), np.float32(0.9558), np.float32(0.8841), np.float32(0.9617), np.float32(0.9494), np.float32(0.8678), np.float32(0.8724), np.float32(0.9167)] 
+2025-05-05 10:16:41.254729: Epoch time: 94.91 s 
+2025-05-05 10:16:42.882512:  
+2025-05-05 10:16:42.976502: Epoch 328 
+2025-05-05 10:16:42.995460: Current learning rate: 0.00851 
+2025-05-05 10:18:16.238255: train_loss -0.4669 
+2025-05-05 10:18:16.309272: val_loss -0.473 
+2025-05-05 10:18:16.320756: Pseudo dice [np.float32(0.7813), np.float32(0.7683), np.float32(0.9385), np.float32(0.9518), np.float32(0.9149), np.float32(0.9575), np.float32(0.9491), np.float32(0.9612), np.float32(0.9548), np.float32(0.9466), np.float32(0.9278), np.float32(0.9594), np.float32(0.9551), np.float32(0.876), np.float32(0.938), np.float32(0.9465), np.float32(0.804), np.float32(0.8235), np.float32(0.8993)] 
+2025-05-05 10:18:16.332094: Epoch time: 93.36 s 
+2025-05-05 10:18:18.040351:  
+2025-05-05 10:18:18.152575: Epoch 329 
+2025-05-05 10:18:18.197058: Current learning rate: 0.00851 
+2025-05-05 10:19:49.199892: train_loss -0.4773 
+2025-05-05 10:19:49.279953: val_loss -0.5094 
+2025-05-05 10:19:49.313107: Pseudo dice [np.float32(0.7981), np.float32(0.8142), np.float32(0.8494), np.float32(0.9674), np.float32(0.8629), np.float32(0.9533), np.float32(0.9568), np.float32(0.9641), np.float32(0.9538), np.float32(0.9646), np.float32(0.9333), np.float32(0.9512), np.float32(0.962), np.float32(0.882), np.float32(0.9539), np.float32(0.9381), np.float32(0.8634), np.float32(0.8555), np.float32(0.9016)] 
+2025-05-05 10:19:49.349532: Epoch time: 91.16 s 
+2025-05-05 10:19:51.030502:  
+2025-05-05 10:19:51.125670: Epoch 330 
+2025-05-05 10:19:51.155589: Current learning rate: 0.0085 
+2025-05-05 10:21:21.691641: train_loss -0.4721 
+2025-05-05 10:21:21.818068: val_loss -0.4661 
+2025-05-05 10:21:21.852644: Pseudo dice [np.float32(0.8109), np.float32(0.8251), np.float32(0.9126), np.float32(0.9708), np.float32(0.8728), np.float32(0.9521), np.float32(0.9603), np.float32(0.9586), np.float32(0.965), np.float32(0.9662), np.float32(0.9413), np.float32(0.9693), np.float32(0.964), np.float32(0.8981), np.float32(0.9579), np.float32(0.9406), np.float32(0.8798), np.float32(0.8996), np.float32(0.9159)] 
+2025-05-05 10:21:21.856074: Epoch time: 90.66 s 
+2025-05-05 10:21:23.526325:  
+2025-05-05 10:21:23.697307: Epoch 331 
+2025-05-05 10:21:23.699301: Current learning rate: 0.0085 
+2025-05-05 10:22:56.452627: train_loss -0.4629 
+2025-05-05 10:22:56.633206: val_loss -0.4442 
+2025-05-05 10:22:56.670908: Pseudo dice [np.float32(0.8335), np.float32(0.8345), np.float32(0.8051), np.float32(0.9586), np.float32(0.9052), np.float32(0.9439), np.float32(0.9589), np.float32(0.9759), np.float32(0.9514), np.float32(0.962), np.float32(0.9388), np.float32(0.9624), np.float32(0.9683), np.float32(0.881), np.float32(0.9582), np.float32(0.9411), np.float32(0.8161), np.float32(0.8667), np.float32(0.9183)] 
+2025-05-05 10:22:56.686019: Epoch time: 92.93 s 
+2025-05-05 10:22:58.369575:  
+2025-05-05 10:22:58.456751: Epoch 332 
+2025-05-05 10:22:58.496894: Current learning rate: 0.00849 
+2025-05-05 10:24:28.253711: train_loss -0.487 
+2025-05-05 10:24:28.423790: val_loss -0.4896 
+2025-05-05 10:24:28.463705: Pseudo dice [np.float32(0.813), np.float32(0.8643), np.float32(0.8979), np.float32(0.9714), np.float32(0.8635), np.float32(0.9613), np.float32(0.9657), np.float32(0.9659), np.float32(0.957), np.float32(0.9492), np.float32(0.9172), np.float32(0.9597), np.float32(0.9629), np.float32(0.9), np.float32(0.9421), np.float32(0.9539), np.float32(0.8721), np.float32(0.8791), np.float32(0.9147)] 
+2025-05-05 10:24:28.507642: Epoch time: 89.89 s 
+2025-05-05 10:24:28.557107: Yayy! New best EMA pseudo Dice: 0.9165999889373779 
+2025-05-05 10:24:31.181573:  
+2025-05-05 10:24:31.194716: Epoch 333 
+2025-05-05 10:24:31.206824: Current learning rate: 0.00849 
+2025-05-05 10:26:05.679437: train_loss -0.4752 
+2025-05-05 10:26:05.791610: val_loss -0.4799 
+2025-05-05 10:26:05.830642: Pseudo dice [np.float32(0.7636), np.float32(0.807), np.float32(0.8941), np.float32(0.9697), np.float32(0.8818), np.float32(0.9454), np.float32(0.9384), np.float32(0.9728), np.float32(0.9429), np.float32(0.9633), np.float32(0.935), np.float32(0.9562), np.float32(0.9595), np.float32(0.8805), np.float32(0.953), np.float32(0.934), np.float32(0.8716), np.float32(0.8552), np.float32(0.9038)] 
+2025-05-05 10:26:05.865621: Epoch time: 94.5 s 
+2025-05-05 10:26:07.611934:  
+2025-05-05 10:26:07.753886: Epoch 334 
+2025-05-05 10:26:07.793543: Current learning rate: 0.00848 
+2025-05-05 10:27:39.896509: train_loss -0.4552 
+2025-05-05 10:27:39.998282: val_loss -0.4907 
+2025-05-05 10:27:40.016002: Pseudo dice [np.float32(0.7775), np.float32(0.8486), np.float32(0.7485), np.float32(0.961), np.float32(0.9148), np.float32(0.9419), np.float32(0.9643), np.float32(0.9739), np.float32(0.9495), np.float32(0.9574), np.float32(0.9256), np.float32(0.9583), np.float32(0.9625), np.float32(0.8814), np.float32(0.963), np.float32(0.9298), np.float32(0.8846), np.float32(0.8796), np.float32(0.9204)] 
+2025-05-05 10:27:40.055762: Epoch time: 92.29 s 
+2025-05-05 10:27:41.477737:  
+2025-05-05 10:27:41.492195: Epoch 335 
+2025-05-05 10:27:41.503238: Current learning rate: 0.00848 
+2025-05-05 10:29:16.132568: train_loss -0.4763 
+2025-05-05 10:29:16.239393: val_loss -0.4444 
+2025-05-05 10:29:16.265682: Pseudo dice [np.float32(0.8225), np.float32(0.8287), np.float32(0.8641), np.float32(0.9574), np.float32(0.8623), np.float32(0.9581), np.float32(0.966), np.float32(0.9748), np.float32(0.9479), np.float32(0.9653), np.float32(0.9303), np.float32(0.9639), np.float32(0.957), np.float32(0.8535), np.float32(0.9608), np.float32(0.9267), np.float32(0.8922), np.float32(0.8778), np.float32(0.9039)] 
+2025-05-05 10:29:16.289732: Epoch time: 94.66 s 
+2025-05-05 10:29:18.076463:  
+2025-05-05 10:29:18.159053: Epoch 336 
+2025-05-05 10:29:18.183118: Current learning rate: 0.00847 
+2025-05-05 10:30:55.640459: train_loss -0.4497 
+2025-05-05 10:30:55.671642: val_loss -0.5176 
+2025-05-05 10:30:55.677199: Pseudo dice [np.float32(0.8316), np.float32(0.8105), np.float32(0.948), np.float32(0.9485), np.float32(0.8971), np.float32(0.9446), np.float32(0.9605), np.float32(0.9662), np.float32(0.9655), np.float32(0.9628), np.float32(0.9415), np.float32(0.9633), np.float32(0.9665), np.float32(0.8717), np.float32(0.9546), np.float32(0.9404), np.float32(0.8677), np.float32(0.8582), np.float32(0.9219)] 
+2025-05-05 10:30:55.693340: Epoch time: 97.57 s 
+2025-05-05 10:30:57.229353:  
+2025-05-05 10:30:57.294733: Epoch 337 
+2025-05-05 10:30:57.317327: Current learning rate: 0.00847 
+2025-05-05 10:32:28.220610: train_loss -0.4766 
+2025-05-05 10:32:28.317008: val_loss -0.4918 
+2025-05-05 10:32:28.348193: Pseudo dice [np.float32(0.8324), np.float32(0.835), np.float32(0.7593), np.float32(0.9206), np.float32(0.9153), np.float32(0.9532), np.float32(0.9579), np.float32(0.9643), np.float32(0.9628), np.float32(0.9585), np.float32(0.9218), np.float32(0.9679), np.float32(0.9586), np.float32(0.8879), np.float32(0.9562), np.float32(0.9423), np.float32(0.8699), np.float32(0.8472), np.float32(0.9214)] 
+2025-05-05 10:32:28.399113: Epoch time: 90.99 s 
+2025-05-05 10:32:29.927859:  
+2025-05-05 10:32:29.931463: Epoch 338 
+2025-05-05 10:32:29.932060: Current learning rate: 0.00847 
+2025-05-05 10:34:04.184728: train_loss -0.4365 
+2025-05-05 10:34:04.311074: val_loss -0.4329 
+2025-05-05 10:34:04.351463: Pseudo dice [np.float32(0.8255), np.float32(0.797), np.float32(0.792), np.float32(0.9411), np.float32(0.7587), np.float32(0.9426), np.float32(0.9521), np.float32(0.9598), np.float32(0.9465), np.float32(0.9318), np.float32(0.916), np.float32(0.9422), np.float32(0.9477), np.float32(0.8605), np.float32(0.931), np.float32(0.913), np.float32(0.7919), np.float32(0.7183), np.float32(0.8759)] 
+2025-05-05 10:34:04.359401: Epoch time: 94.26 s 
+2025-05-05 10:34:06.682860:  
+2025-05-05 10:34:06.747352: Epoch 339 
+2025-05-05 10:34:06.773076: Current learning rate: 0.00846 
+2025-05-05 10:35:36.071123: train_loss -0.4481 
+2025-05-05 10:35:36.127187: val_loss -0.4679 
+2025-05-05 10:35:36.203535: Pseudo dice [np.float32(0.8187), np.float32(0.8088), np.float32(0.8305), np.float32(0.9235), np.float32(0.8953), np.float32(0.9424), np.float32(0.9298), np.float32(0.9693), np.float32(0.9514), np.float32(0.9422), np.float32(0.9253), np.float32(0.9599), np.float32(0.9547), np.float32(0.8785), np.float32(0.9563), np.float32(0.9306), np.float32(0.8114), np.float32(0.8396), np.float32(0.8975)] 
+2025-05-05 10:35:36.251534: Epoch time: 89.39 s 
+2025-05-05 10:35:37.717604:  
+2025-05-05 10:35:37.743187: Epoch 340 
+2025-05-05 10:35:37.758667: Current learning rate: 0.00846 
+2025-05-05 10:37:07.113044: train_loss -0.4571 
+2025-05-05 10:37:07.214203: val_loss -0.4616 
+2025-05-05 10:37:07.260039: Pseudo dice [np.float32(0.8193), np.float32(0.845), np.float32(0.9153), np.float32(0.9761), np.float32(0.915), np.float32(0.9306), np.float32(0.9539), np.float32(0.9763), np.float32(0.9357), np.float32(0.9539), np.float32(0.9449), np.float32(0.9461), np.float32(0.9633), np.float32(0.8729), np.float32(0.9126), np.float32(0.9512), np.float32(0.8546), np.float32(0.7885), np.float32(0.921)] 
+2025-05-05 10:37:07.338689: Epoch time: 89.4 s 
+2025-05-05 10:37:09.094084:  
+2025-05-05 10:37:09.100166: Epoch 341 
+2025-05-05 10:37:09.100744: Current learning rate: 0.00845 
+2025-05-05 10:38:39.727430: train_loss -0.4478 
+2025-05-05 10:38:39.851506: val_loss -0.4629 
+2025-05-05 10:38:39.868960: Pseudo dice [np.float32(0.822), np.float32(0.8362), np.float32(0.8687), np.float32(0.9634), np.float32(0.8848), np.float32(0.96), np.float32(0.9478), np.float32(0.9745), np.float32(0.938), np.float32(0.9588), np.float32(0.9398), np.float32(0.9453), np.float32(0.9692), np.float32(0.8687), np.float32(0.9526), np.float32(0.9281), np.float32(0.8386), np.float32(0.8763), np.float32(0.9215)] 
+2025-05-05 10:38:39.889201: Epoch time: 90.63 s 
+2025-05-05 10:38:41.584975:  
+2025-05-05 10:38:41.631274: Epoch 342 
+2025-05-05 10:38:41.651502: Current learning rate: 0.00845 
+2025-05-05 10:40:13.182464: train_loss -0.4507 
+2025-05-05 10:40:13.252306: val_loss -0.4999 
+2025-05-05 10:40:13.256726: Pseudo dice [np.float32(0.829), np.float32(0.7984), np.float32(0.9253), np.float32(0.9723), np.float32(0.8898), np.float32(0.9393), np.float32(0.9592), np.float32(0.9751), np.float32(0.9592), np.float32(0.9629), np.float32(0.9482), np.float32(0.9672), np.float32(0.9652), np.float32(0.894), np.float32(0.9572), np.float32(0.9452), np.float32(0.8503), np.float32(0.8611), np.float32(0.9202)] 
+2025-05-05 10:40:13.263629: Epoch time: 91.6 s 
+2025-05-05 10:40:14.850426:  
+2025-05-05 10:40:14.953077: Epoch 343 
+2025-05-05 10:40:14.994726: Current learning rate: 0.00844 
+2025-05-05 10:41:51.801341: train_loss -0.4743 
+2025-05-05 10:41:51.842357: val_loss -0.4557 
+2025-05-05 10:41:51.874705: Pseudo dice [np.float32(0.8201), np.float32(0.8363), np.float32(0.8586), np.float32(0.9662), np.float32(0.8854), np.float32(0.9578), np.float32(0.9547), np.float32(0.9767), np.float32(0.9611), np.float32(0.9548), np.float32(0.9153), np.float32(0.9622), np.float32(0.9551), np.float32(0.8771), np.float32(0.9605), np.float32(0.9153), np.float32(0.8342), np.float32(0.7823), np.float32(0.9209)] 
+2025-05-05 10:41:51.879551: Epoch time: 96.95 s 
+2025-05-05 10:41:53.347719:  
+2025-05-05 10:41:53.471141: Epoch 344 
+2025-05-05 10:41:53.513545: Current learning rate: 0.00844 
+2025-05-05 10:43:25.318801: train_loss -0.4805 
+2025-05-05 10:43:25.387851: val_loss -0.5283 
+2025-05-05 10:43:25.392043: Pseudo dice [np.float32(0.8194), np.float32(0.8222), np.float32(0.7598), np.float32(0.9661), np.float32(0.8985), np.float32(0.9524), np.float32(0.9595), np.float32(0.9754), np.float32(0.9546), np.float32(0.9599), np.float32(0.9459), np.float32(0.9668), np.float32(0.968), np.float32(0.8925), np.float32(0.9651), np.float32(0.9531), np.float32(0.8837), np.float32(0.8243), np.float32(0.9216)] 
+2025-05-05 10:43:25.392619: Epoch time: 91.97 s 
+2025-05-05 10:43:27.118380:  
+2025-05-05 10:43:27.148227: Epoch 345 
+2025-05-05 10:43:27.163059: Current learning rate: 0.00843 
+2025-05-05 10:45:01.318614: train_loss -0.4658 
+2025-05-05 10:45:01.400894: val_loss -0.4573 
+2025-05-05 10:45:01.431620: Pseudo dice [np.float32(0.8083), np.float32(0.8387), np.float32(0.8494), np.float32(0.971), np.float32(0.7172), np.float32(0.9158), np.float32(0.9648), np.float32(0.9734), np.float32(0.958), np.float32(0.9671), np.float32(0.9348), np.float32(0.9614), np.float32(0.9677), np.float32(0.8872), np.float32(0.9422), np.float32(0.9509), np.float32(0.8653), np.float32(0.8757), np.float32(0.9102)] 
+2025-05-05 10:45:01.462823: Epoch time: 94.2 s 
+2025-05-05 10:45:03.409000:  
+2025-05-05 10:45:03.488103: Epoch 346 
+2025-05-05 10:45:03.518535: Current learning rate: 0.00843 
+2025-05-05 10:46:36.024545: train_loss -0.4676 
+2025-05-05 10:46:36.129238: val_loss -0.4527 
+2025-05-05 10:46:36.164296: Pseudo dice [np.float32(0.7784), np.float32(0.7998), np.float32(0.9152), np.float32(0.9713), np.float32(0.8712), np.float32(0.9461), np.float32(0.9696), np.float32(0.9731), np.float32(0.955), np.float32(0.9596), np.float32(0.9496), np.float32(0.9574), np.float32(0.967), np.float32(0.8646), np.float32(0.944), np.float32(0.8945), np.float32(0.8499), np.float32(0.8867), np.float32(0.9233)] 
+2025-05-05 10:46:36.204427: Epoch time: 92.62 s 
+2025-05-05 10:46:37.708613:  
+2025-05-05 10:46:37.826203: Epoch 347 
+2025-05-05 10:46:37.879650: Current learning rate: 0.00842 
+2025-05-05 10:48:09.339360: train_loss -0.4605 
+2025-05-05 10:48:09.449567: val_loss -0.4282 
+2025-05-05 10:48:09.492042: Pseudo dice [np.float32(0.8165), np.float32(0.8402), np.float32(0.8946), np.float32(0.9556), np.float32(0.8889), np.float32(0.9386), np.float32(0.9544), np.float32(0.9701), np.float32(0.931), np.float32(0.9592), np.float32(0.9325), np.float32(0.9299), np.float32(0.9493), np.float32(0.8546), np.float32(0.9581), np.float32(0.9403), np.float32(0.8415), np.float32(0.8189), np.float32(0.9178)] 
+2025-05-05 10:48:09.515891: Epoch time: 91.63 s 
+2025-05-05 10:48:11.291778:  
+2025-05-05 10:48:11.450479: Epoch 348 
+2025-05-05 10:48:11.489583: Current learning rate: 0.00842 
+2025-05-05 10:49:47.222407: train_loss -0.4761 
+2025-05-05 10:49:47.315329: val_loss -0.4423 
+2025-05-05 10:49:47.335161: Pseudo dice [np.float32(0.8195), np.float32(0.831), np.float32(0.8938), np.float32(0.9631), np.float32(0.8687), np.float32(0.9516), np.float32(0.9617), np.float32(0.9756), np.float32(0.9356), np.float32(0.9596), np.float32(0.9344), np.float32(0.9499), np.float32(0.9616), np.float32(0.8667), np.float32(0.9551), np.float32(0.9475), np.float32(0.8357), np.float32(0.8833), np.float32(0.904)] 
+2025-05-05 10:49:47.357072: Epoch time: 95.93 s 
+2025-05-05 10:49:48.741266:  
+2025-05-05 10:49:48.796163: Epoch 349 
+2025-05-05 10:49:48.818552: Current learning rate: 0.00841 
+2025-05-05 10:51:25.373327: train_loss -0.4511 
+2025-05-05 10:51:25.445239: val_loss -0.4964 
+2025-05-05 10:51:25.463955: Pseudo dice [np.float32(0.8448), np.float32(0.8445), np.float32(0.8894), np.float32(0.9577), np.float32(0.8573), np.float32(0.9463), np.float32(0.9658), np.float32(0.978), np.float32(0.9579), np.float32(0.9584), np.float32(0.9365), np.float32(0.9693), np.float32(0.9438), np.float32(0.8835), np.float32(0.9484), np.float32(0.9546), np.float32(0.8373), np.float32(0.8825), np.float32(0.9056)] 
+2025-05-05 10:51:25.491555: Epoch time: 96.63 s 
+2025-05-05 10:51:28.153733:  
+2025-05-05 10:51:28.174223: Epoch 350 
+2025-05-05 10:51:28.185603: Current learning rate: 0.00841 
+2025-05-05 10:53:02.583592: train_loss -0.4617 
+2025-05-05 10:53:02.644563: val_loss -0.4801 
+2025-05-05 10:53:02.645716: Pseudo dice [np.float32(0.8248), np.float32(0.7503), np.float32(0.8958), np.float32(0.9715), np.float32(0.8765), np.float32(0.9526), np.float32(0.9609), np.float32(0.9715), np.float32(0.9409), np.float32(0.9577), np.float32(0.9322), np.float32(0.9658), np.float32(0.9587), np.float32(0.8888), np.float32(0.9566), np.float32(0.9412), np.float32(0.882), np.float32(0.861), np.float32(0.8832)] 
+2025-05-05 10:53:02.646332: Epoch time: 94.43 s 
+2025-05-05 10:53:04.029874:  
+2025-05-05 10:53:04.111468: Epoch 351 
+2025-05-05 10:53:04.173135: Current learning rate: 0.00841 
+2025-05-05 10:54:35.158551: train_loss -0.4703 
+2025-05-05 10:54:35.251519: val_loss -0.4988 
+2025-05-05 10:54:35.274141: Pseudo dice [np.float32(0.8351), np.float32(0.831), np.float32(0.8125), np.float32(0.9622), np.float32(0.8106), np.float32(0.944), np.float32(0.9554), np.float32(0.972), np.float32(0.9557), np.float32(0.9603), np.float32(0.9501), np.float32(0.9637), np.float32(0.9661), np.float32(0.8862), np.float32(0.9621), np.float32(0.9321), np.float32(0.869), np.float32(0.8645), np.float32(0.9231)] 
+2025-05-05 10:54:35.314593: Epoch time: 91.13 s 
+2025-05-05 10:54:37.288023:  
+2025-05-05 10:54:37.380798: Epoch 352 
+2025-05-05 10:54:37.407416: Current learning rate: 0.0084 
+2025-05-05 10:56:37.064486: train_loss -0.466 
+2025-05-05 10:56:37.134892: val_loss -0.4921 
+2025-05-05 10:56:37.184275: Pseudo dice [np.float32(0.7775), np.float32(0.7948), np.float32(0.5971), np.float32(0.9746), np.float32(0.8625), np.float32(0.9514), np.float32(0.9555), np.float32(0.963), np.float32(0.9561), np.float32(0.9663), np.float32(0.919), np.float32(0.9311), np.float32(0.963), np.float32(0.8708), np.float32(0.9643), np.float32(0.9246), np.float32(0.8613), np.float32(0.8583), np.float32(0.9181)] 
+2025-05-05 10:56:37.204476: Epoch time: 119.78 s 
+2025-05-05 10:56:38.764800:  
+2025-05-05 10:56:38.872928: Epoch 353 
+2025-05-05 10:56:38.910570: Current learning rate: 0.0084 
+2025-05-05 10:58:21.937916: train_loss -0.462 
+2025-05-05 10:58:22.058372: val_loss -0.4846 
+2025-05-05 10:58:22.084142: Pseudo dice [np.float32(0.7978), np.float32(0.8212), np.float32(0.903), np.float32(0.9736), np.float32(0.8641), np.float32(0.9535), np.float32(0.9339), np.float32(0.9734), np.float32(0.9584), np.float32(0.9314), np.float32(0.8834), np.float32(0.9622), np.float32(0.9486), np.float32(0.8828), np.float32(0.9701), np.float32(0.9495), np.float32(0.8721), np.float32(0.8894), np.float32(0.9079)] 
+2025-05-05 10:58:22.120532: Epoch time: 103.17 s 
+2025-05-05 10:58:23.839859:  
+2025-05-05 10:58:23.889722: Epoch 354 
+2025-05-05 10:58:23.914086: Current learning rate: 0.00839 
+2025-05-05 11:00:04.196297: train_loss -0.4533 
+2025-05-05 11:00:04.264518: val_loss -0.4824 
+2025-05-05 11:00:04.300827: Pseudo dice [np.float32(0.8298), np.float32(0.8078), np.float32(0.9011), np.float32(0.9721), np.float32(0.8363), np.float32(0.9489), np.float32(0.951), np.float32(0.967), np.float32(0.9618), np.float32(0.9695), np.float32(0.9314), np.float32(0.9688), np.float32(0.9683), np.float32(0.855), np.float32(0.9641), np.float32(0.9478), np.float32(0.8675), np.float32(0.8701), np.float32(0.8883)] 
+2025-05-05 11:00:04.308705: Epoch time: 100.36 s 
+2025-05-05 11:00:05.989417:  
+2025-05-05 11:00:06.095739: Epoch 355 
+2025-05-05 11:00:06.122964: Current learning rate: 0.00839 
+2025-05-05 11:01:45.919234: train_loss -0.4708 
+2025-05-05 11:01:45.949218: val_loss -0.5143 
+2025-05-05 11:01:45.966352: Pseudo dice [np.float32(0.8514), np.float32(0.8183), np.float32(0.9056), np.float32(0.9601), np.float32(0.9014), np.float32(0.9584), np.float32(0.9592), np.float32(0.9725), np.float32(0.947), np.float32(0.9462), np.float32(0.9235), np.float32(0.9562), np.float32(0.9565), np.float32(0.8931), np.float32(0.9573), np.float32(0.9517), np.float32(0.8897), np.float32(0.8928), np.float32(0.911)] 
+2025-05-05 11:01:45.995750: Epoch time: 99.93 s 
+2025-05-05 11:01:48.846800:  
+2025-05-05 11:01:48.851950: Epoch 356 
+2025-05-05 11:01:48.852385: Current learning rate: 0.00838 
+2025-05-05 11:03:26.143539: train_loss -0.466 
+2025-05-05 11:03:26.201241: val_loss -0.463 
+2025-05-05 11:03:26.202620: Pseudo dice [np.float32(0.8157), np.float32(0.8236), np.float32(0.9334), np.float32(0.9743), np.float32(0.8399), np.float32(0.959), np.float32(0.9517), np.float32(0.9718), np.float32(0.961), np.float32(0.9634), np.float32(0.938), np.float32(0.9664), np.float32(0.9681), np.float32(0.8924), np.float32(0.9655), np.float32(0.949), np.float32(0.8583), np.float32(0.8589), np.float32(0.9333)] 
+2025-05-05 11:03:26.203236: Epoch time: 97.3 s 
+2025-05-05 11:03:27.727702:  
+2025-05-05 11:03:27.829112: Epoch 357 
+2025-05-05 11:03:27.844013: Current learning rate: 0.00838 
+2025-05-05 11:05:05.943274: train_loss -0.46 
+2025-05-05 11:05:06.017673: val_loss -0.4532 
+2025-05-05 11:05:06.044024: Pseudo dice [np.float32(0.8166), np.float32(0.8354), np.float32(0.8364), np.float32(0.9661), np.float32(0.8177), np.float32(0.9472), np.float32(0.934), np.float32(0.9596), np.float32(0.9361), np.float32(0.9642), np.float32(0.9298), np.float32(0.9551), np.float32(0.9647), np.float32(0.8675), np.float32(0.9323), np.float32(0.9466), np.float32(0.8485), np.float32(0.8285), np.float32(0.9095)] 
+2025-05-05 11:05:06.075638: Epoch time: 98.22 s 
+2025-05-05 11:05:07.567417:  
+2025-05-05 11:05:07.593681: Epoch 358 
+2025-05-05 11:05:07.595006: Current learning rate: 0.00837 
+2025-05-05 11:06:47.399978: train_loss -0.4495 
+2025-05-05 11:06:47.486399: val_loss -0.4522 
+2025-05-05 11:06:47.508782: Pseudo dice [np.float32(0.7875), np.float32(0.7677), np.float32(0.8912), np.float32(0.9563), np.float32(0.8468), np.float32(0.9465), np.float32(0.9598), np.float32(0.9688), np.float32(0.9555), np.float32(0.8851), np.float32(0.8697), np.float32(0.9654), np.float32(0.9438), np.float32(0.8182), np.float32(0.9599), np.float32(0.9352), np.float32(0.841), np.float32(0.7989), np.float32(0.8953)] 
+2025-05-05 11:06:47.536003: Epoch time: 99.83 s 
+2025-05-05 11:06:49.175218:  
+2025-05-05 11:06:49.313111: Epoch 359 
+2025-05-05 11:06:49.347945: Current learning rate: 0.00837 
+2025-05-05 11:08:33.288080: train_loss -0.4738 
+2025-05-05 11:08:33.410184: val_loss -0.4958 
+2025-05-05 11:08:33.423002: Pseudo dice [np.float32(0.8211), np.float32(0.842), np.float32(0.9013), np.float32(0.9719), np.float32(0.6987), np.float32(0.9495), np.float32(0.9616), np.float32(0.9647), np.float32(0.9557), np.float32(0.9597), np.float32(0.9299), np.float32(0.9658), np.float32(0.9633), np.float32(0.8813), np.float32(0.964), np.float32(0.932), np.float32(0.8823), np.float32(0.8928), np.float32(0.9088)] 
+2025-05-05 11:08:33.428759: Epoch time: 104.11 s 
+2025-05-05 11:08:34.900273:  
+2025-05-05 11:08:35.010865: Epoch 360 
+2025-05-05 11:08:35.044750: Current learning rate: 0.00836 
+2025-05-05 11:10:17.060007: train_loss -0.4436 
+2025-05-05 11:10:17.150761: val_loss -0.4533 
+2025-05-05 11:10:17.171578: Pseudo dice [np.float32(0.7764), np.float32(0.796), np.float32(0.8537), np.float32(0.9666), np.float32(0.8695), np.float32(0.9473), np.float32(0.9419), np.float32(0.9679), np.float32(0.9478), np.float32(0.956), np.float32(0.9343), np.float32(0.9507), np.float32(0.9629), np.float32(0.8804), np.float32(0.9626), np.float32(0.9148), np.float32(0.8792), np.float32(0.8768), np.float32(0.8873)] 
+2025-05-05 11:10:17.194145: Epoch time: 102.16 s 
+2025-05-05 11:10:19.172231:  
+2025-05-05 11:10:19.286718: Epoch 361 
+2025-05-05 11:10:19.318874: Current learning rate: 0.00836 
+2025-05-05 11:11:52.337708: train_loss -0.4466 
+2025-05-05 11:11:52.415978: val_loss -0.4771 
+2025-05-05 11:11:52.433217: Pseudo dice [np.float32(0.7916), np.float32(0.8208), np.float32(0.9139), np.float32(0.9709), np.float32(0.8323), np.float32(0.9403), np.float32(0.9568), np.float32(0.9709), np.float32(0.9574), np.float32(0.9611), np.float32(0.9227), np.float32(0.9573), np.float32(0.9527), np.float32(0.8953), np.float32(0.9487), np.float32(0.9484), np.float32(0.8785), np.float32(0.8888), np.float32(0.8788)] 
+2025-05-05 11:11:52.480732: Epoch time: 93.17 s 
+2025-05-05 11:11:53.932390:  
+2025-05-05 11:11:53.959375: Epoch 362 
+2025-05-05 11:11:53.959871: Current learning rate: 0.00836 
+2025-05-05 11:13:29.272059: train_loss -0.4698 
+2025-05-05 11:13:29.325672: val_loss -0.4613 
+2025-05-05 11:13:29.346033: Pseudo dice [np.float32(0.8313), np.float32(0.816), np.float32(0.9328), np.float32(0.9642), np.float32(0.9089), np.float32(0.9562), np.float32(0.9591), np.float32(0.9769), np.float32(0.9552), np.float32(0.9472), np.float32(0.9007), np.float32(0.9659), np.float32(0.9522), np.float32(0.9005), np.float32(0.9663), np.float32(0.9345), np.float32(0.8164), np.float32(0.8814), np.float32(0.9163)] 
+2025-05-05 11:13:29.360749: Epoch time: 95.34 s 
+2025-05-05 11:13:31.062466:  
+2025-05-05 11:13:31.171815: Epoch 363 
+2025-05-05 11:13:31.216049: Current learning rate: 0.00835 
+2025-05-05 11:15:06.017944: train_loss -0.4584 
+2025-05-05 11:15:06.081174: val_loss -0.4731 
+2025-05-05 11:15:06.085919: Pseudo dice [np.float32(0.829), np.float32(0.7946), np.float32(0.8688), np.float32(0.9275), np.float32(0.8769), np.float32(0.9545), np.float32(0.9381), np.float32(0.962), np.float32(0.9432), np.float32(0.9572), np.float32(0.9353), np.float32(0.9603), np.float32(0.9649), np.float32(0.8523), np.float32(0.93), np.float32(0.9147), np.float32(0.855), np.float32(0.8256), np.float32(0.8982)] 
+2025-05-05 11:15:06.086529: Epoch time: 94.96 s 
+2025-05-05 11:15:07.620113:  
+2025-05-05 11:15:07.752285: Epoch 364 
+2025-05-05 11:15:07.802506: Current learning rate: 0.00835 
+2025-05-05 11:16:45.175745: train_loss -0.4537 
+2025-05-05 11:16:45.354758: val_loss -0.4742 
+2025-05-05 11:16:45.391469: Pseudo dice [np.float32(0.8156), np.float32(0.7593), np.float32(0.8703), np.float32(0.9643), np.float32(0.8676), np.float32(0.9576), np.float32(0.9524), np.float32(0.9432), np.float32(0.951), np.float32(0.9406), np.float32(0.9205), np.float32(0.9605), np.float32(0.9593), np.float32(0.8732), np.float32(0.9588), np.float32(0.9322), np.float32(0.8423), np.float32(0.8614), np.float32(0.8907)] 
+2025-05-05 11:16:45.419336: Epoch time: 97.56 s 
+2025-05-05 11:16:47.102846:  
+2025-05-05 11:16:47.165368: Epoch 365 
+2025-05-05 11:16:47.172855: Current learning rate: 0.00834 
+2025-05-05 11:18:23.639959: train_loss -0.4497 
+2025-05-05 11:18:23.762305: val_loss -0.506 
+2025-05-05 11:18:23.825709: Pseudo dice [np.float32(0.8466), np.float32(0.7856), np.float32(0.8802), np.float32(0.9647), np.float32(0.8384), np.float32(0.9565), np.float32(0.9562), np.float32(0.9736), np.float32(0.9568), np.float32(0.9585), np.float32(0.9299), np.float32(0.9652), np.float32(0.9566), np.float32(0.8934), np.float32(0.9641), np.float32(0.9461), np.float32(0.8097), np.float32(0.8271), np.float32(0.8988)] 
+2025-05-05 11:18:23.858019: Epoch time: 96.54 s 
+2025-05-05 11:18:25.399916:  
+2025-05-05 11:18:25.460866: Epoch 366 
+2025-05-05 11:18:25.470868: Current learning rate: 0.00834 
+2025-05-05 11:20:02.104862: train_loss -0.4703 
+2025-05-05 11:20:02.178020: val_loss -0.4738 
+2025-05-05 11:20:02.198783: Pseudo dice [np.float32(0.834), np.float32(0.8303), np.float32(0.8821), np.float32(0.9761), np.float32(0.7955), np.float32(0.935), np.float32(0.9638), np.float32(0.9726), np.float32(0.9599), np.float32(0.9698), np.float32(0.9398), np.float32(0.9657), np.float32(0.9678), np.float32(0.8705), np.float32(0.9291), np.float32(0.9372), np.float32(0.8538), np.float32(0.8677), np.float32(0.9099)] 
+2025-05-05 11:20:02.215297: Epoch time: 96.71 s 
+2025-05-05 11:20:03.790891:  
+2025-05-05 11:20:03.838460: Epoch 367 
+2025-05-05 11:20:03.847086: Current learning rate: 0.00833 
+2025-05-05 11:21:40.407803: train_loss -0.4611 
+2025-05-05 11:21:40.516534: val_loss -0.4702 
+2025-05-05 11:21:40.540002: Pseudo dice [np.float32(0.8313), np.float32(0.8388), np.float32(0.9036), np.float32(0.9572), np.float32(0.8921), np.float32(0.9583), np.float32(0.9545), np.float32(0.9647), np.float32(0.965), np.float32(0.9449), np.float32(0.9261), np.float32(0.9334), np.float32(0.9483), np.float32(0.8854), np.float32(0.9633), np.float32(0.9542), np.float32(0.8752), np.float32(0.8501), np.float32(0.9026)] 
+2025-05-05 11:21:40.559757: Epoch time: 96.62 s 
+2025-05-05 11:21:41.970646:  
+2025-05-05 11:21:41.986737: Epoch 368 
+2025-05-05 11:21:41.987318: Current learning rate: 0.00833 
+2025-05-05 11:23:15.921084: train_loss -0.4617 
+2025-05-05 11:23:15.991108: val_loss -0.4706 
+2025-05-05 11:23:16.003094: Pseudo dice [np.float32(0.8466), np.float32(0.7992), np.float32(0.8873), np.float32(0.9736), np.float32(0.903), np.float32(0.9538), np.float32(0.9529), np.float32(0.971), np.float32(0.961), np.float32(0.9505), np.float32(0.8918), np.float32(0.9675), np.float32(0.9487), np.float32(0.886), np.float32(0.9462), np.float32(0.9507), np.float32(0.828), np.float32(0.8559), np.float32(0.9096)] 
+2025-05-05 11:23:16.018134: Epoch time: 93.95 s 
+2025-05-05 11:23:17.532721:  
+2025-05-05 11:23:17.618485: Epoch 369 
+2025-05-05 11:23:17.631627: Current learning rate: 0.00832 
+2025-05-05 11:24:52.612165: train_loss -0.4454 
+2025-05-05 11:24:52.733308: val_loss -0.4863 
+2025-05-05 11:24:52.766388: Pseudo dice [np.float32(0.8239), np.float32(0.7986), np.float32(0.9038), np.float32(0.96), np.float32(0.8562), np.float32(0.9496), np.float32(0.9402), np.float32(0.9723), np.float32(0.9422), np.float32(0.9497), np.float32(0.932), np.float32(0.9591), np.float32(0.9599), np.float32(0.8844), np.float32(0.9531), np.float32(0.9239), np.float32(0.8487), np.float32(0.8205), np.float32(0.8972)] 
+2025-05-05 11:24:52.805086: Epoch time: 95.08 s 
+2025-05-05 11:24:54.384478:  
+2025-05-05 11:24:54.460793: Epoch 370 
+2025-05-05 11:24:54.540814: Current learning rate: 0.00832 
+2025-05-05 11:26:30.768387: train_loss -0.4608 
+2025-05-05 11:26:30.806577: val_loss -0.5111 
+2025-05-05 11:26:30.849695: Pseudo dice [np.float32(0.8316), np.float32(0.8342), np.float32(0.8695), np.float32(0.9655), np.float32(0.8967), np.float32(0.9583), np.float32(0.9494), np.float32(0.9744), np.float32(0.9583), np.float32(0.9656), np.float32(0.9399), np.float32(0.9618), np.float32(0.967), np.float32(0.892), np.float32(0.9483), np.float32(0.9439), np.float32(0.8551), np.float32(0.7846), np.float32(0.9125)] 
+2025-05-05 11:26:30.890703: Epoch time: 96.39 s 
+2025-05-05 11:26:32.547801:  
+2025-05-05 11:26:32.702145: Epoch 371 
+2025-05-05 11:26:32.733831: Current learning rate: 0.00831 
+2025-05-05 11:28:10.649662: train_loss -0.4744 
+2025-05-05 11:28:10.735598: val_loss -0.4649 
+2025-05-05 11:28:10.758186: Pseudo dice [np.float32(0.819), np.float32(0.8218), np.float32(0.8283), np.float32(0.9744), np.float32(0.8001), np.float32(0.9398), np.float32(0.9603), np.float32(0.9711), np.float32(0.962), np.float32(0.9554), np.float32(0.926), np.float32(0.9668), np.float32(0.9563), np.float32(0.8885), np.float32(0.9629), np.float32(0.9293), np.float32(0.824), np.float32(0.8558), np.float32(0.9229)] 
+2025-05-05 11:28:10.785857: Epoch time: 98.1 s 
+2025-05-05 11:28:12.466298:  
+2025-05-05 11:28:12.560542: Epoch 372 
+2025-05-05 11:28:12.592695: Current learning rate: 0.00831 
+2025-05-05 11:29:46.879438: train_loss -0.4797 
+2025-05-05 11:29:47.000080: val_loss -0.4741 
+2025-05-05 11:29:47.038561: Pseudo dice [np.float32(0.8372), np.float32(0.8092), np.float32(0.8913), np.float32(0.9676), np.float32(0.8654), np.float32(0.9577), np.float32(0.938), np.float32(0.9681), np.float32(0.9579), np.float32(0.9607), np.float32(0.9242), np.float32(0.9604), np.float32(0.9517), np.float32(0.8705), np.float32(0.96), np.float32(0.9229), np.float32(0.8668), np.float32(0.8951), np.float32(0.9045)] 
+2025-05-05 11:29:47.057631: Epoch time: 94.41 s 
+2025-05-05 11:29:48.671455:  
+2025-05-05 11:29:48.763611: Epoch 373 
+2025-05-05 11:29:48.781231: Current learning rate: 0.0083 
+2025-05-05 11:31:23.048355: train_loss -0.469 
+2025-05-05 11:31:23.090578: val_loss -0.4583 
+2025-05-05 11:31:23.129160: Pseudo dice [np.float32(0.8251), np.float32(0.8033), np.float32(0.8672), np.float32(0.97), np.float32(0.8972), np.float32(0.9456), np.float32(0.9514), np.float32(0.9696), np.float32(0.9213), np.float32(0.9428), np.float32(0.9325), np.float32(0.9651), np.float32(0.9506), np.float32(0.8812), np.float32(0.9651), np.float32(0.9328), np.float32(0.7921), np.float32(0.7256), np.float32(0.9056)] 
+2025-05-05 11:31:23.218747: Epoch time: 94.38 s 
+2025-05-05 11:31:26.602261:  
+2025-05-05 11:31:26.604726: Epoch 374 
+2025-05-05 11:31:26.605129: Current learning rate: 0.0083 
+2025-05-05 11:33:01.865549: train_loss -0.4672 
+2025-05-05 11:33:01.883402: val_loss -0.4975 
+2025-05-05 11:33:01.884158: Pseudo dice [np.float32(0.8331), np.float32(0.8128), np.float32(0.8514), np.float32(0.9724), np.float32(0.9067), np.float32(0.9343), np.float32(0.9487), np.float32(0.9664), np.float32(0.957), np.float32(0.9619), np.float32(0.9383), np.float32(0.9665), np.float32(0.9613), np.float32(0.886), np.float32(0.9547), np.float32(0.9411), np.float32(0.8678), np.float32(0.889), np.float32(0.9066)] 
+2025-05-05 11:33:01.884667: Epoch time: 95.26 s 
+2025-05-05 11:33:03.333429:  
+2025-05-05 11:33:03.468892: Epoch 375 
+2025-05-05 11:33:03.485625: Current learning rate: 0.0083 
+2025-05-05 11:34:44.250700: train_loss -0.4546 
+2025-05-05 11:34:44.397608: val_loss -0.4765 
+2025-05-05 11:34:44.422323: Pseudo dice [np.float32(0.8407), np.float32(0.7639), np.float32(0.8419), np.float32(0.9701), np.float32(0.889), np.float32(0.9534), np.float32(0.9577), np.float32(0.9746), np.float32(0.9628), np.float32(0.9615), np.float32(0.9439), np.float32(0.9564), np.float32(0.9678), np.float32(0.8703), np.float32(0.9645), np.float32(0.9409), np.float32(0.8783), np.float32(0.8457), np.float32(0.8975)] 
+2025-05-05 11:34:44.457418: Epoch time: 100.92 s 
+2025-05-05 11:34:46.317090:  
+2025-05-05 11:34:46.406435: Epoch 376 
+2025-05-05 11:34:46.410488: Current learning rate: 0.00829 
+2025-05-05 11:36:23.125346: train_loss -0.454 
+2025-05-05 11:36:23.182206: val_loss -0.485 
+2025-05-05 11:36:23.247544: Pseudo dice [np.float32(0.8497), np.float32(0.804), np.float32(0.8908), np.float32(0.9734), np.float32(0.891), np.float32(0.954), np.float32(0.9615), np.float32(0.9653), np.float32(0.9534), np.float32(0.956), np.float32(0.9402), np.float32(0.9605), np.float32(0.9579), np.float32(0.9036), np.float32(0.9608), np.float32(0.9379), np.float32(0.8596), np.float32(0.8537), np.float32(0.9162)] 
+2025-05-05 11:36:23.314693: Epoch time: 96.81 s 
+2025-05-05 11:36:24.912392:  
+2025-05-05 11:36:25.041800: Epoch 377 
+2025-05-05 11:36:25.076481: Current learning rate: 0.00829 
+2025-05-05 11:37:57.748525: train_loss -0.475 
+2025-05-05 11:37:57.787720: val_loss -0.4991 
+2025-05-05 11:37:57.788725: Pseudo dice [np.float32(0.8108), np.float32(0.8159), np.float32(0.9095), np.float32(0.9722), np.float32(0.8763), np.float32(0.9521), np.float32(0.9513), np.float32(0.9665), np.float32(0.9425), np.float32(0.9555), np.float32(0.8948), np.float32(0.962), np.float32(0.9564), np.float32(0.8817), np.float32(0.9519), np.float32(0.9405), np.float32(0.8435), np.float32(0.8489), np.float32(0.9213)] 
+2025-05-05 11:37:57.793919: Epoch time: 92.84 s 
+2025-05-05 11:37:59.272799:  
+2025-05-05 11:37:59.349012: Epoch 378 
+2025-05-05 11:37:59.373365: Current learning rate: 0.00828 
+2025-05-05 11:39:33.752029: train_loss -0.4501 
+2025-05-05 11:39:33.843784: val_loss -0.4664 
+2025-05-05 11:39:33.863760: Pseudo dice [np.float32(0.7987), np.float32(0.8105), np.float32(0.7656), np.float32(0.9739), np.float32(0.8925), np.float32(0.9554), np.float32(0.9399), np.float32(0.9693), np.float32(0.9583), np.float32(0.9645), np.float32(0.9403), np.float32(0.9634), np.float32(0.9616), np.float32(0.8923), np.float32(0.9575), np.float32(0.9455), np.float32(0.8902), np.float32(0.8808), np.float32(0.9198)] 
+2025-05-05 11:39:33.867383: Epoch time: 94.48 s 
+2025-05-05 11:39:35.428625:  
+2025-05-05 11:39:35.529527: Epoch 379 
+2025-05-05 11:39:35.582475: Current learning rate: 0.00828 
+2025-05-05 11:41:10.255009: train_loss -0.4518 
+2025-05-05 11:41:10.318559: val_loss -0.4664 
+2025-05-05 11:41:10.341811: Pseudo dice [np.float32(0.8146), np.float32(0.7937), np.float32(0.8708), np.float32(0.9737), np.float32(0.8887), np.float32(0.9511), np.float32(0.9519), np.float32(0.9701), np.float32(0.9287), np.float32(0.9612), np.float32(0.9416), np.float32(0.957), np.float32(0.9636), np.float32(0.8876), np.float32(0.9579), np.float32(0.9453), np.float32(0.8623), np.float32(0.8558), np.float32(0.9104)] 
+2025-05-05 11:41:10.354850: Epoch time: 94.83 s 
+2025-05-05 11:41:11.978844:  
+2025-05-05 11:41:12.093562: Epoch 380 
+2025-05-05 11:41:12.113751: Current learning rate: 0.00827 
+2025-05-05 11:42:45.356562: train_loss -0.4546 
+2025-05-05 11:42:45.432766: val_loss -0.4514 
+2025-05-05 11:42:45.463851: Pseudo dice [np.float32(0.8213), np.float32(0.8224), np.float32(0.9235), np.float32(0.956), np.float32(0.8944), np.float32(0.9555), np.float32(0.9492), np.float32(0.9536), np.float32(0.9653), np.float32(0.9609), np.float32(0.9367), np.float32(0.9671), np.float32(0.9604), np.float32(0.8912), np.float32(0.9645), np.float32(0.9415), np.float32(0.8872), np.float32(0.8971), np.float32(0.9063)] 
+2025-05-05 11:42:45.489193: Epoch time: 93.38 s 
+2025-05-05 11:42:47.057558:  
+2025-05-05 11:42:47.217214: Epoch 381 
+2025-05-05 11:42:47.266743: Current learning rate: 0.00827 
+2025-05-05 11:44:19.795089: train_loss -0.4617 
+2025-05-05 11:44:19.879321: val_loss -0.4685 
+2025-05-05 11:44:19.884635: Pseudo dice [np.float32(0.8403), np.float32(0.8477), np.float32(0.7992), np.float32(0.9729), np.float32(0.9203), np.float32(0.9291), np.float32(0.9599), np.float32(0.9766), np.float32(0.9419), np.float32(0.9632), np.float32(0.9207), np.float32(0.9558), np.float32(0.9615), np.float32(0.9005), np.float32(0.9583), np.float32(0.9555), np.float32(0.8281), np.float32(0.8279), np.float32(0.9082)] 
+2025-05-05 11:44:19.891957: Epoch time: 92.74 s 
+2025-05-05 11:44:21.436644:  
+2025-05-05 11:44:21.560926: Epoch 382 
+2025-05-05 11:44:21.572385: Current learning rate: 0.00826 
+2025-05-05 11:45:54.331054: train_loss -0.4591 
+2025-05-05 11:45:54.392516: val_loss -0.4504 
+2025-05-05 11:45:54.405696: Pseudo dice [np.float32(0.7989), np.float32(0.8231), np.float32(0.855), np.float32(0.9752), np.float32(0.8774), np.float32(0.9585), np.float32(0.962), np.float32(0.9601), np.float32(0.9613), np.float32(0.9621), np.float32(0.9375), np.float32(0.964), np.float32(0.9636), np.float32(0.8756), np.float32(0.9565), np.float32(0.9382), np.float32(0.8707), np.float32(0.8721), np.float32(0.9013)] 
+2025-05-05 11:45:54.428122: Epoch time: 92.9 s 
+2025-05-05 11:45:55.857647:  
+2025-05-05 11:45:55.968776: Epoch 383 
+2025-05-05 11:45:55.986569: Current learning rate: 0.00826 
+2025-05-05 11:47:30.895408: train_loss -0.4713 
+2025-05-05 11:47:30.961868: val_loss -0.4804 
+2025-05-05 11:47:30.977007: Pseudo dice [np.float32(0.8481), np.float32(0.8432), np.float32(0.8253), np.float32(0.9776), np.float32(0.9197), np.float32(0.9417), np.float32(0.9647), np.float32(0.9725), np.float32(0.9639), np.float32(0.9611), np.float32(0.9387), np.float32(0.966), np.float32(0.9652), np.float32(0.8928), np.float32(0.9676), np.float32(0.9163), np.float32(0.8129), np.float32(0.8385), np.float32(0.9133)] 
+2025-05-05 11:47:30.986161: Epoch time: 95.04 s 
+2025-05-05 11:47:32.483134:  
+2025-05-05 11:47:32.675417: Epoch 384 
+2025-05-05 11:47:32.724937: Current learning rate: 0.00825 
+2025-05-05 11:49:10.661480: train_loss -0.4792 
+2025-05-05 11:49:10.835534: val_loss -0.4446 
+2025-05-05 11:49:10.889184: Pseudo dice [np.float32(0.8276), np.float32(0.8184), np.float32(0.9046), np.float32(0.9806), np.float32(0.8961), np.float32(0.9511), np.float32(0.9573), np.float32(0.9705), np.float32(0.9491), np.float32(0.9613), np.float32(0.949), np.float32(0.9591), np.float32(0.964), np.float32(0.8895), np.float32(0.9624), np.float32(0.9224), np.float32(0.8625), np.float32(0.8573), np.float32(0.9237)] 
+2025-05-05 11:49:10.922011: Epoch time: 98.18 s 
+2025-05-05 11:49:12.404392:  
+2025-05-05 11:49:12.438586: Epoch 385 
+2025-05-05 11:49:12.439135: Current learning rate: 0.00825 
+2025-05-05 11:50:47.822041: train_loss -0.4724 
+2025-05-05 11:50:47.914820: val_loss -0.4693 
+2025-05-05 11:50:47.939106: Pseudo dice [np.float32(0.8207), np.float32(0.84), np.float32(0.9137), np.float32(0.9749), np.float32(0.8991), np.float32(0.9465), np.float32(0.9534), np.float32(0.9759), np.float32(0.969), np.float32(0.9574), np.float32(0.9242), np.float32(0.9697), np.float32(0.9606), np.float32(0.872), np.float32(0.9622), np.float32(0.9551), np.float32(0.88), np.float32(0.876), np.float32(0.9148)] 
+2025-05-05 11:50:47.963031: Epoch time: 95.42 s 
+2025-05-05 11:50:49.471057:  
+2025-05-05 11:50:49.594119: Epoch 386 
+2025-05-05 11:50:49.627622: Current learning rate: 0.00824 
+2025-05-05 11:52:23.946212: train_loss -0.4672 
+2025-05-05 11:52:24.063633: val_loss -0.4622 
+2025-05-05 11:52:24.078778: Pseudo dice [np.float32(0.8082), np.float32(0.835), np.float32(0.9224), np.float32(0.9758), np.float32(0.9173), np.float32(0.9297), np.float32(0.9512), np.float32(0.9758), np.float32(0.947), np.float32(0.957), np.float32(0.9203), np.float32(0.9466), np.float32(0.9565), np.float32(0.8989), np.float32(0.9177), np.float32(0.9328), np.float32(0.8827), np.float32(0.8797), np.float32(0.8969)] 
+2025-05-05 11:52:24.092339: Epoch time: 94.48 s 
+2025-05-05 11:52:24.109349: Yayy! New best EMA pseudo Dice: 0.916700005531311 
+2025-05-05 11:52:26.395757:  
+2025-05-05 11:52:26.454841: Epoch 387 
+2025-05-05 11:52:26.495401: Current learning rate: 0.00824 
+2025-05-05 11:54:09.105120: train_loss -0.4793 
+2025-05-05 11:54:09.123229: val_loss -0.4874 
+2025-05-05 11:54:09.124476: Pseudo dice [np.float32(0.8147), np.float32(0.806), np.float32(0.8646), np.float32(0.9724), np.float32(0.8539), np.float32(0.9496), np.float32(0.9554), np.float32(0.9718), np.float32(0.9431), np.float32(0.9502), np.float32(0.9248), np.float32(0.9487), np.float32(0.9576), np.float32(0.8801), np.float32(0.9599), np.float32(0.9329), np.float32(0.8272), np.float32(0.8637), np.float32(0.9045)] 
+2025-05-05 11:54:09.125043: Epoch time: 102.71 s 
+2025-05-05 11:54:10.781704:  
+2025-05-05 11:54:10.915468: Epoch 388 
+2025-05-05 11:54:10.939946: Current learning rate: 0.00824 
+2025-05-05 11:55:45.687697: train_loss -0.4661 
+2025-05-05 11:55:45.803448: val_loss -0.5009 
+2025-05-05 11:55:45.810937: Pseudo dice [np.float32(0.8434), np.float32(0.8351), np.float32(0.9302), np.float32(0.9682), np.float32(0.9122), np.float32(0.9602), np.float32(0.9626), np.float32(0.9672), np.float32(0.9639), np.float32(0.9592), np.float32(0.9369), np.float32(0.9666), np.float32(0.9644), np.float32(0.8887), np.float32(0.9658), np.float32(0.9346), np.float32(0.8554), np.float32(0.8664), np.float32(0.9203)] 
+2025-05-05 11:55:45.831192: Epoch time: 94.91 s 
+2025-05-05 11:55:45.838019: Yayy! New best EMA pseudo Dice: 0.9171000123023987 
+2025-05-05 11:55:48.337427:  
+2025-05-05 11:55:48.368011: Epoch 389 
+2025-05-05 11:55:48.368824: Current learning rate: 0.00823 
+2025-05-05 11:57:30.462145: train_loss -0.4607 
+2025-05-05 11:57:30.539528: val_loss -0.4881 
+2025-05-05 11:57:30.555274: Pseudo dice [np.float32(0.8176), np.float32(0.8445), np.float32(0.8743), np.float32(0.9716), np.float32(0.8704), np.float32(0.9463), np.float32(0.9537), np.float32(0.9721), np.float32(0.9576), np.float32(0.9453), np.float32(0.9315), np.float32(0.9656), np.float32(0.9501), np.float32(0.8416), np.float32(0.9393), np.float32(0.9334), np.float32(0.8515), np.float32(0.8497), np.float32(0.906)] 
+2025-05-05 11:57:30.556201: Epoch time: 102.13 s 
+2025-05-05 11:57:32.043736:  
+2025-05-05 11:57:32.116270: Epoch 390 
+2025-05-05 11:57:32.142278: Current learning rate: 0.00823 
+2025-05-05 11:59:13.959471: train_loss -0.4779 
+2025-05-05 11:59:14.056386: val_loss -0.479 
+2025-05-05 11:59:14.092537: Pseudo dice [np.float32(0.8186), np.float32(0.8279), np.float32(0.9087), np.float32(0.9779), np.float32(0.8372), np.float32(0.9453), np.float32(0.943), np.float32(0.9729), np.float32(0.9638), np.float32(0.9587), np.float32(0.924), np.float32(0.9522), np.float32(0.9337), np.float32(0.8698), np.float32(0.9539), np.float32(0.9265), np.float32(0.8804), np.float32(0.8701), np.float32(0.9133)] 
+2025-05-05 11:59:14.126791: Epoch time: 101.92 s 
+2025-05-05 11:59:18.319928:  
+2025-05-05 11:59:18.324743: Epoch 391 
+2025-05-05 11:59:18.325387: Current learning rate: 0.00822 
+2025-05-05 12:00:55.890007: train_loss -0.4903 
+2025-05-05 12:00:55.934278: val_loss -0.4814 
+2025-05-05 12:00:55.947667: Pseudo dice [np.float32(0.8032), np.float32(0.8235), np.float32(0.9159), np.float32(0.9631), np.float32(0.8606), np.float32(0.9172), np.float32(0.9311), np.float32(0.9735), np.float32(0.962), np.float32(0.9639), np.float32(0.9371), np.float32(0.9677), np.float32(0.9588), np.float32(0.8839), np.float32(0.9393), np.float32(0.9467), np.float32(0.8856), np.float32(0.8626), np.float32(0.8931)] 
+2025-05-05 12:00:55.977396: Epoch time: 97.57 s 
+2025-05-05 12:00:57.513380:  
+2025-05-05 12:00:57.587741: Epoch 392 
+2025-05-05 12:00:57.600970: Current learning rate: 0.00822 
+2025-05-05 12:02:37.157947: train_loss -0.4707 
+2025-05-05 12:02:37.221955: val_loss -0.4868 
+2025-05-05 12:02:37.233534: Pseudo dice [np.float32(0.8453), np.float32(0.8464), np.float32(0.7138), np.float32(0.9487), np.float32(0.8894), np.float32(0.9431), np.float32(0.9644), np.float32(0.9674), np.float32(0.9513), np.float32(0.9504), np.float32(0.9373), np.float32(0.9563), np.float32(0.96), np.float32(0.8746), np.float32(0.961), np.float32(0.9269), np.float32(0.8597), np.float32(0.8044), np.float32(0.8976)] 
+2025-05-05 12:02:37.259235: Epoch time: 99.65 s 
+2025-05-05 12:02:38.677124:  
+2025-05-05 12:02:38.758396: Epoch 393 
+2025-05-05 12:02:38.795450: Current learning rate: 0.00821 
+2025-05-05 12:04:12.752948: train_loss -0.4741 
+2025-05-05 12:04:12.800956: val_loss -0.4801 
+2025-05-05 12:04:12.822257: Pseudo dice [np.float32(0.843), np.float32(0.8253), np.float32(0.8998), np.float32(0.9709), np.float32(0.8792), np.float32(0.9564), np.float32(0.9505), np.float32(0.9577), np.float32(0.9634), np.float32(0.9494), np.float32(0.9387), np.float32(0.9705), np.float32(0.9642), np.float32(0.8826), np.float32(0.9566), np.float32(0.9269), np.float32(0.8653), np.float32(0.8947), np.float32(0.9209)] 
+2025-05-05 12:04:12.828081: Epoch time: 94.08 s 
+2025-05-05 12:04:14.205461:  
+2025-05-05 12:04:14.320836: Epoch 394 
+2025-05-05 12:04:14.354471: Current learning rate: 0.00821 
+2025-05-05 12:05:51.707480: train_loss -0.4471 
+2025-05-05 12:05:51.817188: val_loss -0.492 
+2025-05-05 12:05:51.844022: Pseudo dice [np.float32(0.8348), np.float32(0.8405), np.float32(0.9222), np.float32(0.9782), np.float32(0.9093), np.float32(0.9514), np.float32(0.9643), np.float32(0.9697), np.float32(0.9503), np.float32(0.944), np.float32(0.9245), np.float32(0.9637), np.float32(0.9603), np.float32(0.8882), np.float32(0.9556), np.float32(0.9406), np.float32(0.861), np.float32(0.8794), np.float32(0.9203)] 
+2025-05-05 12:05:51.860487: Epoch time: 97.5 s 
+2025-05-05 12:05:53.577482:  
+2025-05-05 12:05:53.628517: Epoch 395 
+2025-05-05 12:05:53.639571: Current learning rate: 0.0082 
+2025-05-05 12:07:26.919775: train_loss -0.479 
+2025-05-05 12:07:26.969412: val_loss -0.5137 
+2025-05-05 12:07:26.995457: Pseudo dice [np.float32(0.8234), np.float32(0.8418), np.float32(0.8944), np.float32(0.9748), np.float32(0.8284), np.float32(0.9419), np.float32(0.9534), np.float32(0.9753), np.float32(0.9643), np.float32(0.9662), np.float32(0.9418), np.float32(0.967), np.float32(0.9684), np.float32(0.8861), np.float32(0.9646), np.float32(0.9435), np.float32(0.8295), np.float32(0.8811), np.float32(0.9093)] 
+2025-05-05 12:07:27.010383: Epoch time: 93.34 s 
+2025-05-05 12:07:28.586691:  
+2025-05-05 12:07:28.656643: Epoch 396 
+2025-05-05 12:07:28.682529: Current learning rate: 0.0082 
+2025-05-05 12:09:08.467201: train_loss -0.4752 
+2025-05-05 12:09:08.526292: val_loss -0.444 
+2025-05-05 12:09:08.534479: Pseudo dice [np.float32(0.8377), np.float32(0.862), np.float32(0.9186), np.float32(0.9694), np.float32(0.8945), np.float32(0.9523), np.float32(0.9552), np.float32(0.9778), np.float32(0.9662), np.float32(0.9617), np.float32(0.944), np.float32(0.9625), np.float32(0.9659), np.float32(0.9014), np.float32(0.9652), np.float32(0.9345), np.float32(0.863), np.float32(0.8652), np.float32(0.899)] 
+2025-05-05 12:09:08.535703: Epoch time: 99.88 s 
+2025-05-05 12:09:08.540741: Yayy! New best EMA pseudo Dice: 0.9178000092506409 
+2025-05-05 12:09:10.929917:  
+2025-05-05 12:09:10.973813: Epoch 397 
+2025-05-05 12:09:10.985809: Current learning rate: 0.00819 
+2025-05-05 12:10:48.057158: train_loss -0.4666 
+2025-05-05 12:10:48.159040: val_loss -0.4854 
+2025-05-05 12:10:48.177655: Pseudo dice [np.float32(0.8217), np.float32(0.8291), np.float32(0.935), np.float32(0.9728), np.float32(0.8472), np.float32(0.9538), np.float32(0.9451), np.float32(0.9713), np.float32(0.9617), np.float32(0.9625), np.float32(0.9393), np.float32(0.9637), np.float32(0.9578), np.float32(0.883), np.float32(0.9651), np.float32(0.9428), np.float32(0.8482), np.float32(0.8592), np.float32(0.9128)] 
+2025-05-05 12:10:48.199908: Epoch time: 97.13 s 
+2025-05-05 12:10:48.230822: Yayy! New best EMA pseudo Dice: 0.917900025844574 
+2025-05-05 12:10:51.012550:  
+2025-05-05 12:10:51.015214: Epoch 398 
+2025-05-05 12:10:51.015674: Current learning rate: 0.00819 
+2025-05-05 12:12:33.364561: train_loss -0.4616 
+2025-05-05 12:12:33.479448: val_loss -0.4281 
+2025-05-05 12:12:33.501322: Pseudo dice [np.float32(0.7979), np.float32(0.8119), np.float32(0.8207), np.float32(0.9774), np.float32(0.8706), np.float32(0.9267), np.float32(0.9602), np.float32(0.9668), np.float32(0.9577), np.float32(0.9639), np.float32(0.9278), np.float32(0.9601), np.float32(0.9615), np.float32(0.8976), np.float32(0.9617), np.float32(0.9544), np.float32(0.8447), np.float32(0.875), np.float32(0.9045)] 
+2025-05-05 12:12:33.508992: Epoch time: 102.35 s 
+2025-05-05 12:12:35.031184:  
+2025-05-05 12:12:35.065447: Epoch 399 
+2025-05-05 12:12:35.069623: Current learning rate: 0.00819 
+2025-05-05 12:14:09.996355: train_loss -0.4737 
+2025-05-05 12:14:10.088518: val_loss -0.4808 
+2025-05-05 12:14:10.108775: Pseudo dice [np.float32(0.8335), np.float32(0.8214), np.float32(0.9223), np.float32(0.9784), np.float32(0.8857), np.float32(0.9481), np.float32(0.9646), np.float32(0.9752), np.float32(0.9615), np.float32(0.9646), np.float32(0.945), np.float32(0.9661), np.float32(0.9679), np.float32(0.8937), np.float32(0.9652), np.float32(0.9554), np.float32(0.8243), np.float32(0.8475), np.float32(0.8979)] 
+2025-05-05 12:14:10.136835: Epoch time: 94.97 s 
+2025-05-05 12:14:12.484797:  
+2025-05-05 12:14:12.571708: Epoch 400 
+2025-05-05 12:14:12.613923: Current learning rate: 0.00818 
+2025-05-05 12:15:49.528370: train_loss -0.466 
+2025-05-05 12:15:49.577537: val_loss -0.4228 
+2025-05-05 12:15:49.589823: Pseudo dice [np.float32(0.8237), np.float32(0.8443), np.float32(0.8422), np.float32(0.9718), np.float32(0.9101), np.float32(0.9431), np.float32(0.9608), np.float32(0.979), np.float32(0.9557), np.float32(0.9563), np.float32(0.9469), np.float32(0.9474), np.float32(0.9595), np.float32(0.8674), np.float32(0.9515), np.float32(0.9363), np.float32(0.8603), np.float32(0.8605), np.float32(0.8983)] 
+2025-05-05 12:15:49.602061: Epoch time: 97.04 s 
+2025-05-05 12:15:51.152087:  
+2025-05-05 12:15:51.274727: Epoch 401 
+2025-05-05 12:15:51.279236: Current learning rate: 0.00818 
+2025-05-05 12:17:24.727353: train_loss -0.4816 
+2025-05-05 12:17:24.782724: val_loss -0.437 
+2025-05-05 12:17:24.804683: Pseudo dice [np.float32(0.8104), np.float32(0.7998), np.float32(0.9254), np.float32(0.9786), np.float32(0.792), np.float32(0.9566), np.float32(0.959), np.float32(0.9634), np.float32(0.9437), np.float32(0.9546), np.float32(0.9422), np.float32(0.9638), np.float32(0.9617), np.float32(0.8496), np.float32(0.9607), np.float32(0.9226), np.float32(0.8745), np.float32(0.8855), np.float32(0.9216)] 
+2025-05-05 12:17:24.853554: Epoch time: 93.58 s 
+2025-05-05 12:17:26.386507:  
+2025-05-05 12:17:26.533526: Epoch 402 
+2025-05-05 12:17:26.583248: Current learning rate: 0.00817 
+2025-05-05 12:19:00.466137: train_loss -0.4665 
+2025-05-05 12:19:00.540131: val_loss -0.5041 
+2025-05-05 12:19:00.544798: Pseudo dice [np.float32(0.8169), np.float32(0.7919), np.float32(0.94), np.float32(0.9667), np.float32(0.8673), np.float32(0.9567), np.float32(0.9616), np.float32(0.9718), np.float32(0.9588), np.float32(0.9644), np.float32(0.9303), np.float32(0.9637), np.float32(0.9595), np.float32(0.8838), np.float32(0.9234), np.float32(0.9285), np.float32(0.8165), np.float32(0.8638), np.float32(0.9138)] 
+2025-05-05 12:19:00.570512: Epoch time: 94.08 s 
+2025-05-05 12:19:02.143328:  
+2025-05-05 12:19:02.303883: Epoch 403 
+2025-05-05 12:19:02.357234: Current learning rate: 0.00817 
+2025-05-05 12:20:37.720382: train_loss -0.4737 
+2025-05-05 12:20:37.801947: val_loss -0.4896 
+2025-05-05 12:20:37.816120: Pseudo dice [np.float32(0.8109), np.float32(0.8347), np.float32(0.8783), np.float32(0.9701), np.float32(0.8943), np.float32(0.9585), np.float32(0.9612), np.float32(0.9717), np.float32(0.9603), np.float32(0.9677), np.float32(0.9447), np.float32(0.9691), np.float32(0.9667), np.float32(0.8961), np.float32(0.9683), np.float32(0.9472), np.float32(0.8114), np.float32(0.8396), np.float32(0.9101)] 
+2025-05-05 12:20:37.816683: Epoch time: 95.58 s 
+2025-05-05 12:20:39.264530:  
+2025-05-05 12:20:39.401141: Epoch 404 
+2025-05-05 12:20:39.454993: Current learning rate: 0.00816 
+2025-05-05 12:22:14.201292: train_loss -0.4674 
+2025-05-05 12:22:14.347997: val_loss -0.4757 
+2025-05-05 12:22:14.386195: Pseudo dice [np.float32(0.8272), np.float32(0.8392), np.float32(0.8881), np.float32(0.9713), np.float32(0.876), np.float32(0.96), np.float32(0.9399), np.float32(0.976), np.float32(0.9422), np.float32(0.9696), np.float32(0.9505), np.float32(0.9665), np.float32(0.9694), np.float32(0.8853), np.float32(0.9535), np.float32(0.9405), np.float32(0.8642), np.float32(0.8386), np.float32(0.9009)] 
+2025-05-05 12:22:14.438844: Epoch time: 94.94 s 
+2025-05-05 12:22:16.066914:  
+2025-05-05 12:22:16.200665: Epoch 405 
+2025-05-05 12:22:16.253779: Current learning rate: 0.00816 
+2025-05-05 12:23:51.747233: train_loss -0.4704 
+2025-05-05 12:23:51.889811: val_loss -0.475 
+2025-05-05 12:23:51.922938: Pseudo dice [np.float32(0.8251), np.float32(0.8093), np.float32(0.9101), np.float32(0.977), np.float32(0.7714), np.float32(0.9532), np.float32(0.9546), np.float32(0.9732), np.float32(0.9438), np.float32(0.9562), np.float32(0.9409), np.float32(0.9507), np.float32(0.9658), np.float32(0.8691), np.float32(0.958), np.float32(0.9187), np.float32(0.8908), np.float32(0.8711), np.float32(0.904)] 
+2025-05-05 12:23:51.949494: Epoch time: 95.68 s 
+2025-05-05 12:23:53.509007:  
+2025-05-05 12:23:53.567044: Epoch 406 
+2025-05-05 12:23:53.597613: Current learning rate: 0.00815 
+2025-05-05 12:25:28.931892: train_loss -0.4583 
+2025-05-05 12:25:28.997569: val_loss -0.4783 
+2025-05-05 12:25:29.024107: Pseudo dice [np.float32(0.8297), np.float32(0.8523), np.float32(0.9207), np.float32(0.9763), np.float32(0.8927), np.float32(0.9383), np.float32(0.9542), np.float32(0.9576), np.float32(0.9469), np.float32(0.958), np.float32(0.9461), np.float32(0.9566), np.float32(0.9667), np.float32(0.8553), np.float32(0.9383), np.float32(0.9217), np.float32(0.827), np.float32(0.8325), np.float32(0.9341)] 
+2025-05-05 12:25:29.055981: Epoch time: 95.42 s 
+2025-05-05 12:25:33.300430:  
+2025-05-05 12:25:33.306376: Epoch 407 
+2025-05-05 12:25:33.306907: Current learning rate: 0.00815 
+2025-05-05 12:27:08.577555: train_loss -0.443 
+2025-05-05 12:27:08.697679: val_loss -0.4563 
+2025-05-05 12:27:08.716343: Pseudo dice [np.float32(0.8166), np.float32(0.8104), np.float32(0.6924), np.float32(0.9693), np.float32(0.8746), np.float32(0.9449), np.float32(0.8984), np.float32(0.9521), np.float32(0.9535), np.float32(0.9637), np.float32(0.9274), np.float32(0.965), np.float32(0.958), np.float32(0.8626), np.float32(0.9643), np.float32(0.935), np.float32(0.8607), np.float32(0.8529), np.float32(0.9044)] 
+2025-05-05 12:27:08.736502: Epoch time: 95.28 s 
+2025-05-05 12:27:10.262733:  
+2025-05-05 12:27:10.436793: Epoch 408 
+2025-05-05 12:27:10.437511: Current learning rate: 0.00814 
+2025-05-05 12:28:46.880483: train_loss -0.4788 
+2025-05-05 12:28:46.953691: val_loss -0.4425 
+2025-05-05 12:28:46.972098: Pseudo dice [np.float32(0.7313), np.float32(0.7629), np.float32(0.9348), np.float32(0.9613), np.float32(0.8895), np.float32(0.9358), np.float32(0.9545), np.float32(0.9722), np.float32(0.9629), np.float32(0.9579), np.float32(0.9394), np.float32(0.9622), np.float32(0.9627), np.float32(0.8577), np.float32(0.9278), np.float32(0.9274), np.float32(0.8316), np.float32(0.8509), np.float32(0.9034)] 
+2025-05-05 12:28:46.980980: Epoch time: 96.62 s 
+2025-05-05 12:28:48.787943:  
+2025-05-05 12:28:48.955591: Epoch 409 
+2025-05-05 12:28:48.997636: Current learning rate: 0.00814 
+2025-05-05 12:30:23.701690: train_loss -0.4714 
+2025-05-05 12:30:23.824694: val_loss -0.4408 
+2025-05-05 12:30:23.860698: Pseudo dice [np.float32(0.8355), np.float32(0.8069), np.float32(0.8797), np.float32(0.9605), np.float32(0.8847), np.float32(0.9558), np.float32(0.9685), np.float32(0.9776), np.float32(0.9523), np.float32(0.943), np.float32(0.921), np.float32(0.9583), np.float32(0.9314), np.float32(0.8807), np.float32(0.9615), np.float32(0.9484), np.float32(0.8696), np.float32(0.8675), np.float32(0.9205)] 
+2025-05-05 12:30:23.897615: Epoch time: 94.92 s 
+2025-05-05 12:30:25.457980:  
+2025-05-05 12:30:25.468951: Epoch 410 
+2025-05-05 12:30:25.469398: Current learning rate: 0.00813 
+2025-05-05 12:32:03.185118: train_loss -0.4576 
+2025-05-05 12:32:03.222506: val_loss -0.4902 
+2025-05-05 12:32:03.234041: Pseudo dice [np.float32(0.8194), np.float32(0.792), np.float32(0.9203), np.float32(0.9662), np.float32(0.8798), np.float32(0.9591), np.float32(0.9546), np.float32(0.9729), np.float32(0.9624), np.float32(0.9568), np.float32(0.9261), np.float32(0.965), np.float32(0.9561), np.float32(0.8766), np.float32(0.9651), np.float32(0.9387), np.float32(0.8823), np.float32(0.8626), np.float32(0.9112)] 
+2025-05-05 12:32:03.246916: Epoch time: 97.73 s 
+2025-05-05 12:32:04.883655:  
+2025-05-05 12:32:04.899312: Epoch 411 
+2025-05-05 12:32:04.900184: Current learning rate: 0.00813 
+2025-05-05 12:33:38.335122: train_loss -0.4611 
+2025-05-05 12:33:38.368983: val_loss -0.4762 
+2025-05-05 12:33:38.378845: Pseudo dice [np.float32(0.832), np.float32(0.8232), np.float32(0.8801), np.float32(0.9654), np.float32(0.8683), np.float32(0.9507), np.float32(0.9298), np.float32(0.9755), np.float32(0.9658), np.float32(0.9574), np.float32(0.9488), np.float32(0.9641), np.float32(0.9611), np.float32(0.8798), np.float32(0.9557), np.float32(0.9485), np.float32(0.8736), np.float32(0.8971), np.float32(0.9254)] 
+2025-05-05 12:33:38.382215: Epoch time: 93.45 s 
+2025-05-05 12:33:39.857667:  
+2025-05-05 12:33:39.988842: Epoch 412 
+2025-05-05 12:33:40.022161: Current learning rate: 0.00813 
+2025-05-05 12:35:14.528605: train_loss -0.4727 
+2025-05-05 12:35:14.724212: val_loss -0.4872 
+2025-05-05 12:35:14.778843: Pseudo dice [np.float32(0.8269), np.float32(0.8412), np.float32(0.8746), np.float32(0.8238), np.float32(0.8801), np.float32(0.9557), np.float32(0.958), np.float32(0.9645), np.float32(0.937), np.float32(0.9515), np.float32(0.9338), np.float32(0.9574), np.float32(0.9605), np.float32(0.8784), np.float32(0.9494), np.float32(0.9409), np.float32(0.8497), np.float32(0.7765), np.float32(0.9192)] 
+2025-05-05 12:35:14.823185: Epoch time: 94.67 s 
+2025-05-05 12:35:16.303925:  
+2025-05-05 12:35:16.388657: Epoch 413 
+2025-05-05 12:35:16.415034: Current learning rate: 0.00812 
+2025-05-05 12:36:50.673307: train_loss -0.4527 
+2025-05-05 12:36:50.784454: val_loss -0.437 
+2025-05-05 12:36:50.811106: Pseudo dice [np.float32(0.809), np.float32(0.8194), np.float32(0.8702), np.float32(0.9645), np.float32(0.8405), np.float32(0.9383), np.float32(0.9616), np.float32(0.9691), np.float32(0.9426), np.float32(0.9569), np.float32(0.9331), np.float32(0.9639), np.float32(0.9637), np.float32(0.8716), np.float32(0.8581), np.float32(0.9256), np.float32(0.8527), np.float32(0.8705), np.float32(0.9081)] 
+2025-05-05 12:36:50.830993: Epoch time: 94.37 s 
+2025-05-05 12:36:52.287510:  
+2025-05-05 12:36:52.403986: Epoch 414 
+2025-05-05 12:36:52.462049: Current learning rate: 0.00812 
+2025-05-05 12:38:22.665845: train_loss -0.4587 
+2025-05-05 12:38:22.793542: val_loss -0.4823 
+2025-05-05 12:38:22.815080: Pseudo dice [np.float32(0.8318), np.float32(0.8231), np.float32(0.8404), np.float32(0.9673), np.float32(0.8942), np.float32(0.9542), np.float32(0.9306), np.float32(0.9762), np.float32(0.9635), np.float32(0.9571), np.float32(0.9287), np.float32(0.9652), np.float32(0.9495), np.float32(0.8955), np.float32(0.9676), np.float32(0.9471), np.float32(0.835), np.float32(0.9028), np.float32(0.8937)] 
+2025-05-05 12:38:22.872457: Epoch time: 90.38 s 
+2025-05-05 12:38:24.350497:  
+2025-05-05 12:38:24.409387: Epoch 415 
+2025-05-05 12:38:24.431046: Current learning rate: 0.00811 
+2025-05-05 12:40:01.344883: train_loss -0.4818 
+2025-05-05 12:40:01.475401: val_loss -0.5005 
+2025-05-05 12:40:01.495893: Pseudo dice [np.float32(0.8368), np.float32(0.8004), np.float32(0.9149), np.float32(0.9722), np.float32(0.8822), np.float32(0.9524), np.float32(0.9564), np.float32(0.9687), np.float32(0.9574), np.float32(0.9624), np.float32(0.9442), np.float32(0.9628), np.float32(0.9634), np.float32(0.8934), np.float32(0.96), np.float32(0.9375), np.float32(0.8739), np.float32(0.8602), np.float32(0.9136)] 
+2025-05-05 12:40:01.527845: Epoch time: 97.0 s 
+2025-05-05 12:40:03.000835:  
+2025-05-05 12:40:03.076695: Epoch 416 
+2025-05-05 12:40:03.080992: Current learning rate: 0.00811 
+2025-05-05 12:41:35.550790: train_loss -0.4426 
+2025-05-05 12:41:35.633700: val_loss -0.4672 
+2025-05-05 12:41:35.635696: Pseudo dice [np.float32(0.8158), np.float32(0.7589), np.float32(0.8653), np.float32(0.9595), np.float32(0.8469), np.float32(0.9467), np.float32(0.9381), np.float32(0.9622), np.float32(0.9629), np.float32(0.9484), np.float32(0.9393), np.float32(0.9663), np.float32(0.9646), np.float32(0.8839), np.float32(0.9605), np.float32(0.9515), np.float32(0.8516), np.float32(0.8615), np.float32(0.9025)] 
+2025-05-05 12:41:35.636134: Epoch time: 92.55 s 
+2025-05-05 12:41:37.060321:  
+2025-05-05 12:41:37.163798: Epoch 417 
+2025-05-05 12:41:37.189219: Current learning rate: 0.0081 
+2025-05-05 12:43:14.226399: train_loss -0.4616 
+2025-05-05 12:43:14.248004: val_loss -0.5032 
+2025-05-05 12:43:14.248821: Pseudo dice [np.float32(0.8345), np.float32(0.7936), np.float32(0.9306), np.float32(0.9674), np.float32(0.8805), np.float32(0.9452), np.float32(0.9545), np.float32(0.9723), np.float32(0.9557), np.float32(0.949), np.float32(0.926), np.float32(0.9607), np.float32(0.9599), np.float32(0.8923), np.float32(0.9453), np.float32(0.9406), np.float32(0.8441), np.float32(0.8695), np.float32(0.9036)] 
+2025-05-05 12:43:14.249404: Epoch time: 97.17 s 
+2025-05-05 12:43:15.693575:  
+2025-05-05 12:43:15.819051: Epoch 418 
+2025-05-05 12:43:15.853365: Current learning rate: 0.0081 
+2025-05-05 12:44:51.342963: train_loss -0.4602 
+2025-05-05 12:44:51.404704: val_loss -0.4853 
+2025-05-05 12:44:51.427480: Pseudo dice [np.float32(0.8292), np.float32(0.8318), np.float32(0.8591), np.float32(0.9667), np.float32(0.9017), np.float32(0.9515), np.float32(0.9458), np.float32(0.9684), np.float32(0.9484), np.float32(0.9542), np.float32(0.9264), np.float32(0.9532), np.float32(0.9603), np.float32(0.8904), np.float32(0.9594), np.float32(0.9347), np.float32(0.8014), np.float32(0.8703), np.float32(0.9123)] 
+2025-05-05 12:44:51.438039: Epoch time: 95.65 s 
+2025-05-05 12:44:52.870257:  
+2025-05-05 12:44:53.073956: Epoch 419 
+2025-05-05 12:44:53.132118: Current learning rate: 0.00809 
+2025-05-05 12:46:28.041836: train_loss -0.4487 
+2025-05-05 12:46:28.167384: val_loss -0.435 
+2025-05-05 12:46:28.218317: Pseudo dice [np.float32(0.829), np.float32(0.8363), np.float32(0.9373), np.float32(0.9541), np.float32(0.888), np.float32(0.9288), np.float32(0.9542), np.float32(0.9587), np.float32(0.9427), np.float32(0.9363), np.float32(0.8945), np.float32(0.9531), np.float32(0.9495), np.float32(0.8547), np.float32(0.9457), np.float32(0.9116), np.float32(0.8621), np.float32(0.8545), np.float32(0.9091)] 
+2025-05-05 12:46:28.312130: Epoch time: 95.17 s 
+2025-05-05 12:46:29.949008:  
+2025-05-05 12:46:30.043162: Epoch 420 
+2025-05-05 12:46:30.071276: Current learning rate: 0.00809 
+2025-05-05 12:48:11.631125: train_loss -0.4619 
+2025-05-05 12:48:11.708219: val_loss -0.4647 
+2025-05-05 12:48:11.730116: Pseudo dice [np.float32(0.8233), np.float32(0.8366), np.float32(0.8841), np.float32(0.9629), np.float32(0.8385), np.float32(0.9396), np.float32(0.9305), np.float32(0.9536), np.float32(0.955), np.float32(0.962), np.float32(0.9418), np.float32(0.9658), np.float32(0.9621), np.float32(0.8501), np.float32(0.9327), np.float32(0.9398), np.float32(0.8421), np.float32(0.8628), np.float32(0.9044)] 
+2025-05-05 12:48:11.731329: Epoch time: 101.68 s 
+2025-05-05 12:48:13.227215:  
+2025-05-05 12:48:13.348157: Epoch 421 
+2025-05-05 12:48:13.379191: Current learning rate: 0.00808 
+2025-05-05 12:49:47.856297: train_loss -0.4508 
+2025-05-05 12:49:47.961090: val_loss -0.4639 
+2025-05-05 12:49:47.986902: Pseudo dice [np.float32(0.8275), np.float32(0.8257), np.float32(0.8635), np.float32(0.9686), np.float32(0.9134), np.float32(0.9591), np.float32(0.9535), np.float32(0.9764), np.float32(0.9408), np.float32(0.9544), np.float32(0.9399), np.float32(0.965), np.float32(0.9607), np.float32(0.8996), np.float32(0.9449), np.float32(0.9524), np.float32(0.8951), np.float32(0.896), np.float32(0.9178)] 
+2025-05-05 12:49:48.020520: Epoch time: 94.63 s 
+2025-05-05 12:49:49.537317:  
+2025-05-05 12:49:49.565269: Epoch 422 
+2025-05-05 12:49:49.565936: Current learning rate: 0.00808 
+2025-05-05 12:51:26.581064: train_loss -0.463 
+2025-05-05 12:51:26.766938: val_loss -0.4893 
+2025-05-05 12:51:26.806821: Pseudo dice [np.float32(0.8081), np.float32(0.8157), np.float32(0.8946), np.float32(0.9735), np.float32(0.875), np.float32(0.9594), np.float32(0.9547), np.float32(0.9692), np.float32(0.9499), np.float32(0.9665), np.float32(0.9461), np.float32(0.9517), np.float32(0.9677), np.float32(0.8851), np.float32(0.9127), np.float32(0.9403), np.float32(0.8805), np.float32(0.8506), np.float32(0.8966)] 
+2025-05-05 12:51:26.839533: Epoch time: 97.05 s 
+2025-05-05 12:51:28.348324:  
+2025-05-05 12:51:28.454630: Epoch 423 
+2025-05-05 12:51:28.484344: Current learning rate: 0.00807 
+2025-05-05 12:53:01.135452: train_loss -0.4659 
+2025-05-05 12:53:01.178557: val_loss -0.4714 
+2025-05-05 12:53:01.179759: Pseudo dice [np.float32(0.8108), np.float32(0.811), np.float32(0.7223), np.float32(0.9748), np.float32(0.8913), np.float32(0.9438), np.float32(0.9613), np.float32(0.9547), np.float32(0.9493), np.float32(0.9591), np.float32(0.9094), np.float32(0.9547), np.float32(0.9598), np.float32(0.8768), np.float32(0.8703), np.float32(0.9361), np.float32(0.7991), np.float32(0.8416), np.float32(0.9078)] 
+2025-05-05 12:53:01.180754: Epoch time: 92.79 s 
+2025-05-05 12:53:02.649836:  
+2025-05-05 12:53:02.774507: Epoch 424 
+2025-05-05 12:53:02.809035: Current learning rate: 0.00807 
+2025-05-05 12:54:42.562593: train_loss -0.453 
+2025-05-05 12:54:42.650215: val_loss -0.4786 
+2025-05-05 12:54:42.663462: Pseudo dice [np.float32(0.8169), np.float32(0.8311), np.float32(0.8858), np.float32(0.9564), np.float32(0.8555), np.float32(0.9596), np.float32(0.9364), np.float32(0.9701), np.float32(0.9463), np.float32(0.9628), np.float32(0.9352), np.float32(0.965), np.float32(0.9661), np.float32(0.8874), np.float32(0.9467), np.float32(0.9418), np.float32(0.8403), np.float32(0.8588), np.float32(0.9092)] 
+2025-05-05 12:54:42.674641: Epoch time: 99.91 s 
+2025-05-05 12:54:47.531013:  
+2025-05-05 12:54:47.536522: Epoch 425 
+2025-05-05 12:54:47.536871: Current learning rate: 0.00807 
+2025-05-05 12:56:24.154844: train_loss -0.46 
+2025-05-05 12:56:24.268159: val_loss -0.4677 
+2025-05-05 12:56:24.288854: Pseudo dice [np.float32(0.8024), np.float32(0.8033), np.float32(0.8968), np.float32(0.9746), np.float32(0.8395), np.float32(0.945), np.float32(0.9419), np.float32(0.9531), np.float32(0.9556), np.float32(0.938), np.float32(0.9293), np.float32(0.9632), np.float32(0.9582), np.float32(0.8861), np.float32(0.9478), np.float32(0.939), np.float32(0.867), np.float32(0.8728), np.float32(0.9064)] 
+2025-05-05 12:56:24.305136: Epoch time: 96.62 s 
+2025-05-05 12:56:25.869620:  
+2025-05-05 12:56:26.011449: Epoch 426 
+2025-05-05 12:56:26.044431: Current learning rate: 0.00806 
+2025-05-05 12:58:02.038396: train_loss -0.4675 
+2025-05-05 12:58:02.065681: val_loss -0.464 
+2025-05-05 12:58:02.070798: Pseudo dice [np.float32(0.8283), np.float32(0.8208), np.float32(0.8741), np.float32(0.9741), np.float32(0.8934), np.float32(0.9543), np.float32(0.9479), np.float32(0.9734), np.float32(0.9667), np.float32(0.9552), np.float32(0.9326), np.float32(0.9678), np.float32(0.9649), np.float32(0.8952), np.float32(0.9367), np.float32(0.9344), np.float32(0.8193), np.float32(0.8397), np.float32(0.8984)] 
+2025-05-05 12:58:02.086215: Epoch time: 96.17 s 
+2025-05-05 12:58:03.744550:  
+2025-05-05 12:58:03.835020: Epoch 427 
+2025-05-05 12:58:03.869332: Current learning rate: 0.00806 
+2025-05-05 12:59:36.842560: train_loss -0.4513 
+2025-05-05 12:59:36.876330: val_loss -0.4627 
+2025-05-05 12:59:36.877329: Pseudo dice [np.float32(0.837), np.float32(0.8191), np.float32(0.8762), np.float32(0.9664), np.float32(0.8854), np.float32(0.9577), np.float32(0.9394), np.float32(0.9626), np.float32(0.964), np.float32(0.9631), np.float32(0.9553), np.float32(0.9701), np.float32(0.9705), np.float32(0.8729), np.float32(0.9487), np.float32(0.9307), np.float32(0.8494), np.float32(0.8942), np.float32(0.9076)] 
+2025-05-05 12:59:36.877816: Epoch time: 93.1 s 
+2025-05-05 12:59:38.326916:  
+2025-05-05 12:59:38.335808: Epoch 428 
+2025-05-05 12:59:38.336274: Current learning rate: 0.00805 
+2025-05-05 13:01:10.867774: train_loss -0.4588 
+2025-05-05 13:01:10.992718: val_loss -0.4665 
+2025-05-05 13:01:11.050691: Pseudo dice [np.float32(0.8347), np.float32(0.8412), np.float32(0.7206), np.float32(0.9692), np.float32(0.8693), np.float32(0.9566), np.float32(0.9343), np.float32(0.956), np.float32(0.9525), np.float32(0.9398), np.float32(0.9289), np.float32(0.9607), np.float32(0.9505), np.float32(0.8757), np.float32(0.9406), np.float32(0.9252), np.float32(0.8644), np.float32(0.8226), np.float32(0.9021)] 
+2025-05-05 13:01:11.087303: Epoch time: 92.54 s 
+2025-05-05 13:01:12.474698:  
+2025-05-05 13:01:12.635895: Epoch 429 
+2025-05-05 13:01:12.665734: Current learning rate: 0.00805 
+2025-05-05 13:02:49.588066: train_loss -0.4687 
+2025-05-05 13:02:49.628376: val_loss -0.4946 
+2025-05-05 13:02:49.629381: Pseudo dice [np.float32(0.8336), np.float32(0.7889), np.float32(0.9182), np.float32(0.9757), np.float32(0.9005), np.float32(0.9463), np.float32(0.9571), np.float32(0.9721), np.float32(0.9356), np.float32(0.9503), np.float32(0.8769), np.float32(0.9625), np.float32(0.9603), np.float32(0.8971), np.float32(0.9547), np.float32(0.9474), np.float32(0.8527), np.float32(0.8735), np.float32(0.9156)] 
+2025-05-05 13:02:49.630291: Epoch time: 97.11 s 
+2025-05-05 13:02:50.996181:  
+2025-05-05 13:02:51.099849: Epoch 430 
+2025-05-05 13:02:51.131105: Current learning rate: 0.00804 
+2025-05-05 13:04:30.926999: train_loss -0.4695 
+2025-05-05 13:04:31.015645: val_loss -0.4666 
+2025-05-05 13:04:31.022137: Pseudo dice [np.float32(0.8202), np.float32(0.828), np.float32(0.7779), np.float32(0.9765), np.float32(0.8661), np.float32(0.9554), np.float32(0.945), np.float32(0.9759), np.float32(0.9633), np.float32(0.9497), np.float32(0.9213), np.float32(0.9641), np.float32(0.9474), np.float32(0.8914), np.float32(0.9632), np.float32(0.9515), np.float32(0.8705), np.float32(0.8436), np.float32(0.9174)] 
+2025-05-05 13:04:31.034503: Epoch time: 99.93 s 
+2025-05-05 13:04:32.583241:  
+2025-05-05 13:04:32.710448: Epoch 431 
+2025-05-05 13:04:32.727856: Current learning rate: 0.00804 
+2025-05-05 13:06:06.351058: train_loss -0.4557 
+2025-05-05 13:06:06.464658: val_loss -0.4707 
+2025-05-05 13:06:06.492510: Pseudo dice [np.float32(0.8019), np.float32(0.8282), np.float32(0.8822), np.float32(0.9734), np.float32(0.8952), np.float32(0.9464), np.float32(0.9299), np.float32(0.9728), np.float32(0.9572), np.float32(0.9692), np.float32(0.9515), np.float32(0.9697), np.float32(0.969), np.float32(0.8808), np.float32(0.9524), np.float32(0.9524), np.float32(0.8648), np.float32(0.8471), np.float32(0.9171)] 
+2025-05-05 13:06:06.511419: Epoch time: 93.77 s 
+2025-05-05 13:06:08.060399:  
+2025-05-05 13:06:08.185219: Epoch 432 
+2025-05-05 13:06:08.215318: Current learning rate: 0.00803 
+2025-05-05 13:07:42.668305: train_loss -0.4682 
+2025-05-05 13:07:42.804302: val_loss -0.4878 
+2025-05-05 13:07:42.821406: Pseudo dice [np.float32(0.8466), np.float32(0.7913), np.float32(0.7535), np.float32(0.9619), np.float32(0.8929), np.float32(0.9457), np.float32(0.9573), np.float32(0.9747), np.float32(0.9683), np.float32(0.9673), np.float32(0.9513), np.float32(0.9702), np.float32(0.9711), np.float32(0.8818), np.float32(0.944), np.float32(0.9453), np.float32(0.9003), np.float32(0.9062), np.float32(0.9169)] 
+2025-05-05 13:07:42.837989: Epoch time: 94.61 s 
+2025-05-05 13:07:44.257523:  
+2025-05-05 13:07:44.295650: Epoch 433 
+2025-05-05 13:07:44.296299: Current learning rate: 0.00803 
+2025-05-05 13:09:20.556520: train_loss -0.4865 
+2025-05-05 13:09:20.577445: val_loss -0.4656 
+2025-05-05 13:09:20.578477: Pseudo dice [np.float32(0.8193), np.float32(0.8532), np.float32(0.7848), np.float32(0.9706), np.float32(0.9018), np.float32(0.9573), np.float32(0.9639), np.float32(0.9753), np.float32(0.958), np.float32(0.9626), np.float32(0.9334), np.float32(0.9662), np.float32(0.9643), np.float32(0.9019), np.float32(0.9616), np.float32(0.9471), np.float32(0.8119), np.float32(0.7605), np.float32(0.908)] 
+2025-05-05 13:09:20.579190: Epoch time: 96.3 s 
+2025-05-05 13:09:21.922982:  
+2025-05-05 13:09:21.971648: Epoch 434 
+2025-05-05 13:09:21.976048: Current learning rate: 0.00802 
+2025-05-05 13:11:01.415528: train_loss -0.4544 
+2025-05-05 13:11:01.484945: val_loss -0.4958 
+2025-05-05 13:11:01.492891: Pseudo dice [np.float32(0.8529), np.float32(0.8125), np.float32(0.9207), np.float32(0.9724), np.float32(0.9032), np.float32(0.9591), np.float32(0.9591), np.float32(0.9689), np.float32(0.9609), np.float32(0.9469), np.float32(0.8909), np.float32(0.9636), np.float32(0.9479), np.float32(0.8748), np.float32(0.9669), np.float32(0.9467), np.float32(0.9115), np.float32(0.8961), np.float32(0.9075)] 
+2025-05-05 13:11:01.493419: Epoch time: 99.49 s 
+2025-05-05 13:11:02.956258:  
+2025-05-05 13:11:02.998658: Epoch 435 
+2025-05-05 13:11:03.006135: Current learning rate: 0.00802 
+2025-05-05 13:12:37.381491: train_loss -0.4693 
+2025-05-05 13:12:37.454414: val_loss -0.4774 
+2025-05-05 13:12:37.457620: Pseudo dice [np.float32(0.8152), np.float32(0.8155), np.float32(0.9021), np.float32(0.9679), np.float32(0.9092), np.float32(0.9631), np.float32(0.9618), np.float32(0.9706), np.float32(0.9516), np.float32(0.9653), np.float32(0.932), np.float32(0.9685), np.float32(0.959), np.float32(0.8823), np.float32(0.9341), np.float32(0.9448), np.float32(0.8821), np.float32(0.8588), np.float32(0.9141)] 
+2025-05-05 13:12:37.468734: Epoch time: 94.43 s 
+2025-05-05 13:12:38.872640:  
+2025-05-05 13:12:38.950377: Epoch 436 
+2025-05-05 13:12:38.973177: Current learning rate: 0.00801 
+2025-05-05 13:14:15.111996: train_loss -0.4743 
+2025-05-05 13:14:15.230472: val_loss -0.5025 
+2025-05-05 13:14:15.256234: Pseudo dice [np.float32(0.8417), np.float32(0.8314), np.float32(0.8595), np.float32(0.9676), np.float32(0.8905), np.float32(0.9582), np.float32(0.9595), np.float32(0.9773), np.float32(0.9592), np.float32(0.9635), np.float32(0.9453), np.float32(0.9696), np.float32(0.969), np.float32(0.914), np.float32(0.9532), np.float32(0.9498), np.float32(0.8895), np.float32(0.8782), np.float32(0.93)] 
+2025-05-05 13:14:15.284011: Epoch time: 96.24 s 
+2025-05-05 13:14:16.731385:  
+2025-05-05 13:14:16.745009: Epoch 437 
+2025-05-05 13:14:16.745647: Current learning rate: 0.00801 
+2025-05-05 13:15:50.683842: train_loss -0.4517 
+2025-05-05 13:15:50.797281: val_loss -0.513 
+2025-05-05 13:15:50.835794: Pseudo dice [np.float32(0.8352), np.float32(0.8303), np.float32(0.9379), np.float32(0.9609), np.float32(0.8895), np.float32(0.9546), np.float32(0.9335), np.float32(0.9723), np.float32(0.9426), np.float32(0.9602), np.float32(0.941), np.float32(0.943), np.float32(0.9642), np.float32(0.9022), np.float32(0.9347), np.float32(0.9434), np.float32(0.8539), np.float32(0.8769), np.float32(0.9072)] 
+2025-05-05 13:15:50.871720: Epoch time: 93.95 s 
+2025-05-05 13:15:52.470088:  
+2025-05-05 13:15:52.541310: Epoch 438 
+2025-05-05 13:15:52.561571: Current learning rate: 0.00801 
+2025-05-05 13:17:28.175116: train_loss -0.4872 
+2025-05-05 13:17:28.292743: val_loss -0.4747 
+2025-05-05 13:17:28.331729: Pseudo dice [np.float32(0.8211), np.float32(0.8339), np.float32(0.9223), np.float32(0.9643), np.float32(0.9003), np.float32(0.9609), np.float32(0.945), np.float32(0.9746), np.float32(0.9579), np.float32(0.9679), np.float32(0.9423), np.float32(0.9628), np.float32(0.9642), np.float32(0.8683), np.float32(0.9673), np.float32(0.937), np.float32(0.8661), np.float32(0.8691), np.float32(0.9191)] 
+2025-05-05 13:17:28.382493: Epoch time: 95.71 s 
+2025-05-05 13:17:29.930575:  
+2025-05-05 13:17:29.963390: Epoch 439 
+2025-05-05 13:17:29.988550: Current learning rate: 0.008 
+2025-05-05 13:19:03.119338: train_loss -0.4822 
+2025-05-05 13:19:03.178618: val_loss -0.4927 
+2025-05-05 13:19:03.182914: Pseudo dice [np.float32(0.8377), np.float32(0.8395), np.float32(0.8527), np.float32(0.9563), np.float32(0.8684), np.float32(0.9601), np.float32(0.9549), np.float32(0.9787), np.float32(0.9462), np.float32(0.9689), np.float32(0.9356), np.float32(0.9504), np.float32(0.9591), np.float32(0.8903), np.float32(0.9401), np.float32(0.9457), np.float32(0.8588), np.float32(0.8674), np.float32(0.9218)] 
+2025-05-05 13:19:03.208817: Epoch time: 93.19 s 
+2025-05-05 13:19:04.879386:  
+2025-05-05 13:19:05.059294: Epoch 440 
+2025-05-05 13:19:05.133111: Current learning rate: 0.008 
+2025-05-05 13:20:43.134846: train_loss -0.4857 
+2025-05-05 13:20:43.243155: val_loss -0.4766 
+2025-05-05 13:20:43.299750: Pseudo dice [np.float32(0.8449), np.float32(0.7984), np.float32(0.9167), np.float32(0.9758), np.float32(0.8935), np.float32(0.9574), np.float32(0.9644), np.float32(0.969), np.float32(0.946), np.float32(0.9674), np.float32(0.9276), np.float32(0.9589), np.float32(0.9631), np.float32(0.8879), np.float32(0.9532), np.float32(0.9417), np.float32(0.8839), np.float32(0.9), np.float32(0.9174)] 
+2025-05-05 13:20:43.358011: Epoch time: 98.26 s 
+2025-05-05 13:20:43.423655: Yayy! New best EMA pseudo Dice: 0.9182000160217285 
+2025-05-05 13:20:46.075583:  
+2025-05-05 13:20:46.077514: Epoch 441 
+2025-05-05 13:20:46.083106: Current learning rate: 0.00799 
+2025-05-05 13:22:24.409084: train_loss -0.4772 
+2025-05-05 13:22:24.557377: val_loss -0.4779 
+2025-05-05 13:22:24.586444: Pseudo dice [np.float32(0.8479), np.float32(0.7953), np.float32(0.9139), np.float32(0.9699), np.float32(0.8606), np.float32(0.9608), np.float32(0.9658), np.float32(0.9779), np.float32(0.9637), np.float32(0.9704), np.float32(0.9474), np.float32(0.9684), np.float32(0.967), np.float32(0.8683), np.float32(0.9659), np.float32(0.9537), np.float32(0.8296), np.float32(0.8599), np.float32(0.9156)] 
+2025-05-05 13:22:24.624209: Epoch time: 98.33 s 
+2025-05-05 13:22:24.653205: Yayy! New best EMA pseudo Dice: 0.9185000061988831 
+2025-05-05 13:22:27.034267:  
+2025-05-05 13:22:27.042142: Epoch 442 
+2025-05-05 13:22:27.042642: Current learning rate: 0.00799 
+2025-05-05 13:24:06.220557: train_loss -0.4678 
+2025-05-05 13:24:06.316379: val_loss -0.4126 
+2025-05-05 13:24:06.320828: Pseudo dice [np.float32(0.8388), np.float32(0.8134), np.float32(0.6295), np.float32(0.9491), np.float32(0.9051), np.float32(0.9551), np.float32(0.9656), np.float32(0.9715), np.float32(0.9441), np.float32(0.952), np.float32(0.9278), np.float32(0.9467), np.float32(0.9633), np.float32(0.892), np.float32(0.9567), np.float32(0.939), np.float32(0.8238), np.float32(0.882), np.float32(0.9034)] 
+2025-05-05 13:24:06.339417: Epoch time: 99.19 s 
+2025-05-05 13:24:11.040462:  
+2025-05-05 13:24:11.049011: Epoch 443 
+2025-05-05 13:24:11.049394: Current learning rate: 0.00798 
+2025-05-05 13:25:44.534236: train_loss -0.4662 
+2025-05-05 13:25:44.646284: val_loss -0.4816 
+2025-05-05 13:25:44.669092: Pseudo dice [np.float32(0.8365), np.float32(0.8629), np.float32(0.929), np.float32(0.9709), np.float32(0.8959), np.float32(0.9576), np.float32(0.9668), np.float32(0.9736), np.float32(0.9533), np.float32(0.9595), np.float32(0.9412), np.float32(0.964), np.float32(0.955), np.float32(0.9027), np.float32(0.9704), np.float32(0.9504), np.float32(0.8108), np.float32(0.8963), np.float32(0.9233)] 
+2025-05-05 13:25:44.687903: Epoch time: 93.5 s 
+2025-05-05 13:25:46.124729:  
+2025-05-05 13:25:46.262961: Epoch 444 
+2025-05-05 13:25:46.283902: Current learning rate: 0.00798 
+2025-05-05 13:27:19.404525: train_loss -0.4738 
+2025-05-05 13:27:19.558513: val_loss -0.5082 
+2025-05-05 13:27:19.571202: Pseudo dice [np.float32(0.8108), np.float32(0.8356), np.float32(0.885), np.float32(0.967), np.float32(0.8983), np.float32(0.9581), np.float32(0.9507), np.float32(0.9777), np.float32(0.9646), np.float32(0.9589), np.float32(0.9224), np.float32(0.9651), np.float32(0.9595), np.float32(0.8825), np.float32(0.9608), np.float32(0.9371), np.float32(0.8599), np.float32(0.8852), np.float32(0.9189)] 
+2025-05-05 13:27:19.589861: Epoch time: 93.28 s 
+2025-05-05 13:27:21.255280:  
+2025-05-05 13:27:21.380177: Epoch 445 
+2025-05-05 13:27:21.414068: Current learning rate: 0.00797 
+2025-05-05 13:28:51.937933: train_loss -0.4729 
+2025-05-05 13:28:52.078369: val_loss -0.4697 
+2025-05-05 13:28:52.087932: Pseudo dice [np.float32(0.8366), np.float32(0.7991), np.float32(0.8284), np.float32(0.9773), np.float32(0.8941), np.float32(0.9615), np.float32(0.9599), np.float32(0.97), np.float32(0.9604), np.float32(0.9574), np.float32(0.9493), np.float32(0.9693), np.float32(0.971), np.float32(0.896), np.float32(0.9604), np.float32(0.9525), np.float32(0.8784), np.float32(0.9036), np.float32(0.9268)] 
+2025-05-05 13:28:52.115613: Epoch time: 90.68 s 
+2025-05-05 13:28:52.162576: Yayy! New best EMA pseudo Dice: 0.9189000129699707 
+2025-05-05 13:28:54.663211:  
+2025-05-05 13:28:54.757329: Epoch 446 
+2025-05-05 13:28:54.782553: Current learning rate: 0.00797 
+2025-05-05 13:30:36.365061: train_loss -0.478 
+2025-05-05 13:30:36.414557: val_loss -0.5217 
+2025-05-05 13:30:36.434628: Pseudo dice [np.float32(0.8029), np.float32(0.8539), np.float32(0.899), np.float32(0.9704), np.float32(0.6933), np.float32(0.9335), np.float32(0.964), np.float32(0.9766), np.float32(0.9556), np.float32(0.9695), np.float32(0.945), np.float32(0.9683), np.float32(0.9652), np.float32(0.8873), np.float32(0.9562), np.float32(0.9512), np.float32(0.9001), np.float32(0.9018), np.float32(0.915)] 
+2025-05-05 13:30:36.465389: Epoch time: 101.7 s 
+2025-05-05 13:30:38.187010:  
+2025-05-05 13:30:38.296164: Epoch 447 
+2025-05-05 13:30:38.297343: Current learning rate: 0.00796 
+2025-05-05 13:32:17.889991: train_loss -0.478 
+2025-05-05 13:32:18.074939: val_loss -0.4731 
+2025-05-05 13:32:18.106958: Pseudo dice [np.float32(0.8045), np.float32(0.7981), np.float32(0.753), np.float32(0.9492), np.float32(0.8885), np.float32(0.9489), np.float32(0.9565), np.float32(0.9763), np.float32(0.9547), np.float32(0.9629), np.float32(0.9366), np.float32(0.9698), np.float32(0.96), np.float32(0.8814), np.float32(0.9638), np.float32(0.9478), np.float32(0.8607), np.float32(0.8737), np.float32(0.9148)] 
+2025-05-05 13:32:18.129318: Epoch time: 99.7 s 
+2025-05-05 13:32:19.614062:  
+2025-05-05 13:32:19.731663: Epoch 448 
+2025-05-05 13:32:19.750937: Current learning rate: 0.00796 
+2025-05-05 13:33:58.039005: train_loss -0.4848 
+2025-05-05 13:33:58.197764: val_loss -0.3985 
+2025-05-05 13:33:58.275002: Pseudo dice [np.float32(0.8368), np.float32(0.8154), np.float32(0.8605), np.float32(0.9668), np.float32(0.7786), np.float32(0.9329), np.float32(0.9407), np.float32(0.9633), np.float32(0.9056), np.float32(0.8953), np.float32(0.9161), np.float32(0.9346), np.float32(0.9456), np.float32(0.8817), np.float32(0.9105), np.float32(0.9428), np.float32(0.8583), np.float32(0.8352), np.float32(0.9061)] 
+2025-05-05 13:33:58.304104: Epoch time: 98.43 s 
+2025-05-05 13:33:59.771492:  
+2025-05-05 13:33:59.858399: Epoch 449 
+2025-05-05 13:33:59.874030: Current learning rate: 0.00795 
+2025-05-05 13:35:35.464508: train_loss -0.4478 
+2025-05-05 13:35:35.602100: val_loss -0.4724 
+2025-05-05 13:35:35.642041: Pseudo dice [np.float32(0.8207), np.float32(0.8527), np.float32(0.702), np.float32(0.9692), np.float32(0.8403), np.float32(0.9575), np.float32(0.9384), np.float32(0.9698), np.float32(0.9475), np.float32(0.9583), np.float32(0.944), np.float32(0.9558), np.float32(0.9643), np.float32(0.876), np.float32(0.9438), np.float32(0.9469), np.float32(0.8785), np.float32(0.8633), np.float32(0.8937)] 
+2025-05-05 13:35:35.663863: Epoch time: 95.69 s 
+2025-05-05 13:35:38.003812:  
+2025-05-05 13:35:38.050558: Epoch 450 
+2025-05-05 13:35:38.067172: Current learning rate: 0.00795 
+2025-05-05 13:37:17.673426: train_loss -0.4796 
+2025-05-05 13:37:17.785926: val_loss -0.4608 
+2025-05-05 13:37:17.786866: Pseudo dice [np.float32(0.8342), np.float32(0.8187), np.float32(0.9145), np.float32(0.974), np.float32(0.8837), np.float32(0.9568), np.float32(0.9487), np.float32(0.972), np.float32(0.9589), np.float32(0.9601), np.float32(0.9283), np.float32(0.9683), np.float32(0.9525), np.float32(0.8802), np.float32(0.9541), np.float32(0.9312), np.float32(0.893), np.float32(0.8689), np.float32(0.909)] 
+2025-05-05 13:37:17.811131: Epoch time: 99.67 s 
+2025-05-05 13:37:19.267923:  
+2025-05-05 13:37:19.284700: Epoch 451 
+2025-05-05 13:37:19.286343: Current learning rate: 0.00795 
+2025-05-05 13:38:55.082443: train_loss -0.4741 
+2025-05-05 13:38:55.153946: val_loss -0.5103 
+2025-05-05 13:38:55.161979: Pseudo dice [np.float32(0.8494), np.float32(0.8267), np.float32(0.913), np.float32(0.9688), np.float32(0.8832), np.float32(0.9539), np.float32(0.9594), np.float32(0.9761), np.float32(0.9662), np.float32(0.9714), np.float32(0.9457), np.float32(0.968), np.float32(0.9713), np.float32(0.8878), np.float32(0.9344), np.float32(0.9289), np.float32(0.7809), np.float32(0.8768), np.float32(0.9058)] 
+2025-05-05 13:38:55.166403: Epoch time: 95.82 s 
+2025-05-05 13:38:56.637527:  
+2025-05-05 13:38:56.644139: Epoch 452 
+2025-05-05 13:38:56.644546: Current learning rate: 0.00794 
+2025-05-05 13:40:36.702839: train_loss -0.4802 
+2025-05-05 13:40:36.793757: val_loss -0.5178 
+2025-05-05 13:40:36.799051: Pseudo dice [np.float32(0.8183), np.float32(0.8482), np.float32(0.9135), np.float32(0.9002), np.float32(0.9283), np.float32(0.953), np.float32(0.9542), np.float32(0.9759), np.float32(0.9389), np.float32(0.9509), np.float32(0.9441), np.float32(0.9583), np.float32(0.9683), np.float32(0.8871), np.float32(0.937), np.float32(0.9454), np.float32(0.8169), np.float32(0.8915), np.float32(0.8997)] 
+2025-05-05 13:40:36.824983: Epoch time: 100.07 s 
+2025-05-05 13:40:38.352129:  
+2025-05-05 13:40:38.453391: Epoch 453 
+2025-05-05 13:40:38.479034: Current learning rate: 0.00794 
+2025-05-05 13:42:14.685023: train_loss -0.4722 
+2025-05-05 13:42:14.765323: val_loss -0.4921 
+2025-05-05 13:42:14.782917: Pseudo dice [np.float32(0.807), np.float32(0.83), np.float32(0.874), np.float32(0.9725), np.float32(0.8422), np.float32(0.9487), np.float32(0.9526), np.float32(0.9739), np.float32(0.9503), np.float32(0.9667), np.float32(0.9443), np.float32(0.9711), np.float32(0.964), np.float32(0.8784), np.float32(0.9589), np.float32(0.9378), np.float32(0.8848), np.float32(0.9009), np.float32(0.9103)] 
+2025-05-05 13:42:14.798058: Epoch time: 96.33 s 
+2025-05-05 13:42:16.209280:  
+2025-05-05 13:42:16.212291: Epoch 454 
+2025-05-05 13:42:16.212726: Current learning rate: 0.00793 
+2025-05-05 13:43:52.848145: train_loss -0.4804 
+2025-05-05 13:43:52.932699: val_loss -0.4937 
+2025-05-05 13:43:52.959570: Pseudo dice [np.float32(0.8256), np.float32(0.8405), np.float32(0.9134), np.float32(0.965), np.float32(0.8577), np.float32(0.9602), np.float32(0.9563), np.float32(0.9676), np.float32(0.9541), np.float32(0.9633), np.float32(0.9458), np.float32(0.9662), np.float32(0.9648), np.float32(0.9109), np.float32(0.9642), np.float32(0.94), np.float32(0.7595), np.float32(0.8196), np.float32(0.9092)] 
+2025-05-05 13:43:52.986721: Epoch time: 96.64 s 
+2025-05-05 13:43:54.477929:  
+2025-05-05 13:43:54.535543: Epoch 455 
+2025-05-05 13:43:54.542297: Current learning rate: 0.00793 
+2025-05-05 13:45:34.120816: train_loss -0.4776 
+2025-05-05 13:45:34.158002: val_loss -0.4847 
+2025-05-05 13:45:34.207774: Pseudo dice [np.float32(0.8174), np.float32(0.8217), np.float32(0.8816), np.float32(0.9517), np.float32(0.8977), np.float32(0.9486), np.float32(0.9642), np.float32(0.9727), np.float32(0.9564), np.float32(0.9611), np.float32(0.9303), np.float32(0.9643), np.float32(0.97), np.float32(0.8758), np.float32(0.9357), np.float32(0.9392), np.float32(0.9009), np.float32(0.873), np.float32(0.9161)] 
+2025-05-05 13:45:34.252849: Epoch time: 99.64 s 
+2025-05-05 13:45:35.743261:  
+2025-05-05 13:45:35.876979: Epoch 456 
+2025-05-05 13:45:35.933714: Current learning rate: 0.00792 
+2025-05-05 13:47:09.470017: train_loss -0.4646 
+2025-05-05 13:47:09.528227: val_loss -0.5102 
+2025-05-05 13:47:09.573371: Pseudo dice [np.float32(0.7993), np.float32(0.8108), np.float32(0.8484), np.float32(0.9649), np.float32(0.8702), np.float32(0.9347), np.float32(0.9577), np.float32(0.9765), np.float32(0.9608), np.float32(0.9431), np.float32(0.9233), np.float32(0.9679), np.float32(0.9596), np.float32(0.8861), np.float32(0.9587), np.float32(0.9446), np.float32(0.8649), np.float32(0.8373), np.float32(0.915)] 
+2025-05-05 13:47:09.611958: Epoch time: 93.73 s 
+2025-05-05 13:47:11.091055:  
+2025-05-05 13:47:11.193175: Epoch 457 
+2025-05-05 13:47:11.215869: Current learning rate: 0.00792 
+2025-05-05 13:48:44.791490: train_loss -0.4606 
+2025-05-05 13:48:44.859581: val_loss -0.4772 
+2025-05-05 13:48:44.902582: Pseudo dice [np.float32(0.8127), np.float32(0.8173), np.float32(0.8516), np.float32(0.9658), np.float32(0.8816), np.float32(0.9518), np.float32(0.964), np.float32(0.9759), np.float32(0.9426), np.float32(0.969), np.float32(0.9418), np.float32(0.9602), np.float32(0.9673), np.float32(0.8839), np.float32(0.9603), np.float32(0.9337), np.float32(0.8012), np.float32(0.8651), np.float32(0.8965)] 
+2025-05-05 13:48:44.950245: Epoch time: 93.7 s 
+2025-05-05 13:48:46.393471:  
+2025-05-05 13:48:46.525779: Epoch 458 
+2025-05-05 13:48:46.572321: Current learning rate: 0.00791 
+2025-05-05 13:50:19.308629: train_loss -0.4735 
+2025-05-05 13:50:19.483497: val_loss -0.5144 
+2025-05-05 13:50:19.509268: Pseudo dice [np.float32(0.8559), np.float32(0.8539), np.float32(0.8823), np.float32(0.9722), np.float32(0.8897), np.float32(0.9579), np.float32(0.9421), np.float32(0.9737), np.float32(0.9492), np.float32(0.9528), np.float32(0.932), np.float32(0.9687), np.float32(0.9598), np.float32(0.8917), np.float32(0.9619), np.float32(0.9358), np.float32(0.8418), np.float32(0.8751), np.float32(0.9158)] 
+2025-05-05 13:50:19.520378: Epoch time: 92.92 s 
+2025-05-05 13:50:20.984934:  
+2025-05-05 13:50:21.061065: Epoch 459 
+2025-05-05 13:50:21.089864: Current learning rate: 0.00791 
+2025-05-05 13:51:55.578955: train_loss -0.4815 
+2025-05-05 13:51:55.649895: val_loss -0.5385 
+2025-05-05 13:51:55.651638: Pseudo dice [np.float32(0.8105), np.float32(0.7787), np.float32(0.9148), np.float32(0.9739), np.float32(0.9021), np.float32(0.9579), np.float32(0.9388), np.float32(0.9663), np.float32(0.9507), np.float32(0.9513), np.float32(0.9344), np.float32(0.9682), np.float32(0.9627), np.float32(0.8965), np.float32(0.9444), np.float32(0.9541), np.float32(0.8877), np.float32(0.8656), np.float32(0.9227)] 
+2025-05-05 13:51:55.652895: Epoch time: 94.6 s 
+2025-05-05 13:51:57.391191:  
+2025-05-05 13:51:57.578840: Epoch 460 
+2025-05-05 13:51:57.598218: Current learning rate: 0.0079 
+2025-05-05 13:53:32.477773: train_loss -0.4724 
+2025-05-05 13:53:32.512781: val_loss -0.4651 
+2025-05-05 13:53:32.513936: Pseudo dice [np.float32(0.8603), np.float32(0.8249), np.float32(0.7679), np.float32(0.9705), np.float32(0.8954), np.float32(0.9638), np.float32(0.9574), np.float32(0.9747), np.float32(0.964), np.float32(0.9649), np.float32(0.9476), np.float32(0.9648), np.float32(0.9678), np.float32(0.9041), np.float32(0.9661), np.float32(0.9485), np.float32(0.857), np.float32(0.8384), np.float32(0.9227)] 
+2025-05-05 13:53:32.529624: Epoch time: 95.09 s 
+2025-05-05 13:53:37.630926:  
+2025-05-05 13:53:37.647370: Epoch 461 
+2025-05-05 13:53:37.655312: Current learning rate: 0.0079 
+2025-05-05 13:55:19.191779: train_loss -0.4634 
+2025-05-05 13:55:19.312758: val_loss -0.4755 
+2025-05-05 13:55:19.354559: Pseudo dice [np.float32(0.8276), np.float32(0.8127), np.float32(0.9159), np.float32(0.9608), np.float32(0.8888), np.float32(0.9565), np.float32(0.9511), np.float32(0.9743), np.float32(0.9515), np.float32(0.9639), np.float32(0.9409), np.float32(0.9514), np.float32(0.9636), np.float32(0.8816), np.float32(0.9577), np.float32(0.944), np.float32(0.873), np.float32(0.8672), np.float32(0.904)] 
+2025-05-05 13:55:19.390962: Epoch time: 101.56 s 
+2025-05-05 13:55:20.926732:  
+2025-05-05 13:55:20.957512: Epoch 462 
+2025-05-05 13:55:20.971597: Current learning rate: 0.00789 
+2025-05-05 13:56:59.824425: train_loss -0.4688 
+2025-05-05 13:56:59.924372: val_loss -0.5196 
+2025-05-05 13:56:59.946945: Pseudo dice [np.float32(0.837), np.float32(0.8448), np.float32(0.803), np.float32(0.9724), np.float32(0.8676), np.float32(0.9514), np.float32(0.9649), np.float32(0.9766), np.float32(0.9636), np.float32(0.9661), np.float32(0.9441), np.float32(0.9622), np.float32(0.9633), np.float32(0.8759), np.float32(0.9504), np.float32(0.9546), np.float32(0.8764), np.float32(0.8876), np.float32(0.9117)] 
+2025-05-05 13:56:59.989975: Epoch time: 98.9 s 
+2025-05-05 13:57:01.479334:  
+2025-05-05 13:57:01.596570: Epoch 463 
+2025-05-05 13:57:01.612679: Current learning rate: 0.00789 
+2025-05-05 13:58:39.528139: train_loss -0.4683 
+2025-05-05 13:58:39.612743: val_loss -0.458 
+2025-05-05 13:58:39.628436: Pseudo dice [np.float32(0.8523), np.float32(0.8284), np.float32(0.912), np.float32(0.9705), np.float32(0.8864), np.float32(0.957), np.float32(0.9536), np.float32(0.9751), np.float32(0.958), np.float32(0.9565), np.float32(0.937), np.float32(0.9687), np.float32(0.9668), np.float32(0.8463), np.float32(0.9683), np.float32(0.9481), np.float32(0.8817), np.float32(0.8961), np.float32(0.9112)] 
+2025-05-05 13:58:39.674563: Epoch time: 98.05 s 
+2025-05-05 13:58:41.024666:  
+2025-05-05 13:58:41.117768: Epoch 464 
+2025-05-05 13:58:41.154317: Current learning rate: 0.00789 
+2025-05-05 14:00:15.084990: train_loss -0.4717 
+2025-05-05 14:00:15.161900: val_loss -0.4984 
+2025-05-05 14:00:15.187424: Pseudo dice [np.float32(0.84), np.float32(0.8355), np.float32(0.8662), np.float32(0.9748), np.float32(0.9263), np.float32(0.96), np.float32(0.9547), np.float32(0.9758), np.float32(0.9654), np.float32(0.9624), np.float32(0.914), np.float32(0.9708), np.float32(0.951), np.float32(0.884), np.float32(0.9622), np.float32(0.944), np.float32(0.8405), np.float32(0.8649), np.float32(0.9142)] 
+2025-05-05 14:00:15.205908: Epoch time: 94.06 s 
+2025-05-05 14:00:16.658853:  
+2025-05-05 14:00:16.686456: Epoch 465 
+2025-05-05 14:00:16.687588: Current learning rate: 0.00788 
+2025-05-05 14:01:50.392428: train_loss -0.4647 
+2025-05-05 14:01:50.537777: val_loss -0.4692 
+2025-05-05 14:01:50.573848: Pseudo dice [np.float32(0.8191), np.float32(0.8325), np.float32(0.8953), np.float32(0.976), np.float32(0.8714), np.float32(0.9584), np.float32(0.9471), np.float32(0.9673), np.float32(0.9565), np.float32(0.9569), np.float32(0.9445), np.float32(0.9332), np.float32(0.9579), np.float32(0.8989), np.float32(0.9366), np.float32(0.9481), np.float32(0.7894), np.float32(0.8602), np.float32(0.9178)] 
+2025-05-05 14:01:50.636884: Epoch time: 93.73 s 
+2025-05-05 14:01:52.141605:  
+2025-05-05 14:01:52.286282: Epoch 466 
+2025-05-05 14:01:52.320275: Current learning rate: 0.00788 
+2025-05-05 14:03:28.347522: train_loss -0.4783 
+2025-05-05 14:03:28.459921: val_loss -0.4787 
+2025-05-05 14:03:28.486449: Pseudo dice [np.float32(0.8282), np.float32(0.834), np.float32(0.8774), np.float32(0.9726), np.float32(0.8688), np.float32(0.9451), np.float32(0.9472), np.float32(0.9777), np.float32(0.967), np.float32(0.9495), np.float32(0.9413), np.float32(0.9664), np.float32(0.9581), np.float32(0.8617), np.float32(0.9487), np.float32(0.9397), np.float32(0.8696), np.float32(0.8871), np.float32(0.9172)] 
+2025-05-05 14:03:28.502377: Epoch time: 96.21 s 
+2025-05-05 14:03:29.954280:  
+2025-05-05 14:03:30.065354: Epoch 467 
+2025-05-05 14:03:30.116032: Current learning rate: 0.00787 
+2025-05-05 14:05:04.912911: train_loss -0.4589 
+2025-05-05 14:05:05.012972: val_loss -0.4723 
+2025-05-05 14:05:05.049524: Pseudo dice [np.float32(0.8294), np.float32(0.8434), np.float32(0.8857), np.float32(0.9655), np.float32(0.9025), np.float32(0.9544), np.float32(0.965), np.float32(0.9737), np.float32(0.9561), np.float32(0.9486), np.float32(0.9186), np.float32(0.9639), np.float32(0.9345), np.float32(0.8989), np.float32(0.9462), np.float32(0.9339), np.float32(0.8833), np.float32(0.8688), np.float32(0.9083)] 
+2025-05-05 14:05:05.082800: Epoch time: 94.96 s 
+2025-05-05 14:05:06.548754:  
+2025-05-05 14:05:06.621116: Epoch 468 
+2025-05-05 14:05:06.640900: Current learning rate: 0.00787 
+2025-05-05 14:06:40.152889: train_loss -0.4634 
+2025-05-05 14:06:40.262540: val_loss -0.4699 
+2025-05-05 14:06:40.277674: Pseudo dice [np.float32(0.812), np.float32(0.8042), np.float32(0.9298), np.float32(0.9586), np.float32(0.8809), np.float32(0.9586), np.float32(0.9491), np.float32(0.9719), np.float32(0.9565), np.float32(0.9609), np.float32(0.9362), np.float32(0.9583), np.float32(0.9685), np.float32(0.8977), np.float32(0.9606), np.float32(0.9519), np.float32(0.7936), np.float32(0.8216), np.float32(0.9165)] 
+2025-05-05 14:06:40.286810: Epoch time: 93.61 s 
+2025-05-05 14:06:41.774963:  
+2025-05-05 14:06:41.829191: Epoch 469 
+2025-05-05 14:06:41.867162: Current learning rate: 0.00786 
+2025-05-05 14:08:18.957039: train_loss -0.4673 
+2025-05-05 14:08:19.077545: val_loss -0.4427 
+2025-05-05 14:08:19.081775: Pseudo dice [np.float32(0.8473), np.float32(0.8284), np.float32(0.8314), np.float32(0.9774), np.float32(0.9264), np.float32(0.9599), np.float32(0.9614), np.float32(0.9753), np.float32(0.8416), np.float32(0.9513), np.float32(0.9349), np.float32(0.9538), np.float32(0.9535), np.float32(0.8731), np.float32(0.9671), np.float32(0.9413), np.float32(0.8292), np.float32(0.8083), np.float32(0.9097)] 
+2025-05-05 14:08:19.086832: Epoch time: 97.18 s 
+2025-05-05 14:08:20.478218:  
+2025-05-05 14:08:20.591510: Epoch 470 
+2025-05-05 14:08:20.682975: Current learning rate: 0.00786 
+2025-05-05 14:09:56.154945: train_loss -0.4539 
+2025-05-05 14:09:56.201164: val_loss -0.4691 
+2025-05-05 14:09:56.203243: Pseudo dice [np.float32(0.8419), np.float32(0.8042), np.float32(0.9065), np.float32(0.9714), np.float32(0.9139), np.float32(0.9575), np.float32(0.9539), np.float32(0.9723), np.float32(0.9543), np.float32(0.9514), np.float32(0.9413), np.float32(0.946), np.float32(0.9615), np.float32(0.8933), np.float32(0.9643), np.float32(0.9318), np.float32(0.8176), np.float32(0.8083), np.float32(0.9056)] 
+2025-05-05 14:09:56.207676: Epoch time: 95.68 s 
+2025-05-05 14:09:57.627817:  
+2025-05-05 14:09:57.723065: Epoch 471 
+2025-05-05 14:09:57.738184: Current learning rate: 0.00785 
+2025-05-05 14:11:33.197047: train_loss -0.4853 
+2025-05-05 14:11:33.253020: val_loss -0.5261 
+2025-05-05 14:11:33.265257: Pseudo dice [np.float32(0.8402), np.float32(0.8535), np.float32(0.8475), np.float32(0.9652), np.float32(0.8982), np.float32(0.9508), np.float32(0.9604), np.float32(0.9705), np.float32(0.9597), np.float32(0.9607), np.float32(0.9273), np.float32(0.9636), np.float32(0.9575), np.float32(0.8968), np.float32(0.9396), np.float32(0.9476), np.float32(0.8561), np.float32(0.8671), np.float32(0.8877)] 
+2025-05-05 14:11:33.271154: Epoch time: 95.57 s 
+2025-05-05 14:11:34.624069:  
+2025-05-05 14:11:34.708298: Epoch 472 
+2025-05-05 14:11:34.732311: Current learning rate: 0.00785 
+2025-05-05 14:13:09.103815: train_loss -0.4884 
+2025-05-05 14:13:09.199396: val_loss -0.4508 
+2025-05-05 14:13:09.213223: Pseudo dice [np.float32(0.7956), np.float32(0.7985), np.float32(0.9231), np.float32(0.9744), np.float32(0.8695), np.float32(0.9581), np.float32(0.9649), np.float32(0.9747), np.float32(0.9424), np.float32(0.9577), np.float32(0.9407), np.float32(0.9636), np.float32(0.968), np.float32(0.8859), np.float32(0.9645), np.float32(0.95), np.float32(0.8634), np.float32(0.8761), np.float32(0.9127)] 
+2025-05-05 14:13:09.219975: Epoch time: 94.48 s 
+2025-05-05 14:13:10.633899:  
+2025-05-05 14:13:10.775920: Epoch 473 
+2025-05-05 14:13:10.812828: Current learning rate: 0.00784 
+2025-05-05 14:14:44.964126: train_loss -0.4942 
+2025-05-05 14:14:45.073675: val_loss -0.5059 
+2025-05-05 14:14:45.101778: Pseudo dice [np.float32(0.7665), np.float32(0.8271), np.float32(0.9349), np.float32(0.9708), np.float32(0.8881), np.float32(0.9579), np.float32(0.9631), np.float32(0.9741), np.float32(0.9575), np.float32(0.9603), np.float32(0.9376), np.float32(0.9653), np.float32(0.97), np.float32(0.8914), np.float32(0.9616), np.float32(0.951), np.float32(0.8777), np.float32(0.8775), np.float32(0.9047)] 
+2025-05-05 14:14:45.140658: Epoch time: 94.33 s 
+2025-05-05 14:14:46.705000:  
+2025-05-05 14:14:46.760423: Epoch 474 
+2025-05-05 14:14:46.771380: Current learning rate: 0.00784 
+2025-05-05 14:16:21.136884: train_loss -0.4839 
+2025-05-05 14:16:21.311217: val_loss -0.4818 
+2025-05-05 14:16:21.351155: Pseudo dice [np.float32(0.7642), np.float32(0.7715), np.float32(0.8545), np.float32(0.9729), np.float32(0.8701), np.float32(0.9553), np.float32(0.9535), np.float32(0.9724), np.float32(0.9632), np.float32(0.9611), np.float32(0.9368), np.float32(0.9557), np.float32(0.9635), np.float32(0.8765), np.float32(0.9624), np.float32(0.952), np.float32(0.898), np.float32(0.8883), np.float32(0.9104)] 
+2025-05-05 14:16:21.378145: Epoch time: 94.43 s 
+2025-05-05 14:16:22.812900:  
+2025-05-05 14:16:22.894687: Epoch 475 
+2025-05-05 14:16:22.927915: Current learning rate: 0.00783 
+2025-05-05 14:17:57.336303: train_loss -0.4764 
+2025-05-05 14:17:57.453967: val_loss -0.479 
+2025-05-05 14:17:57.473094: Pseudo dice [np.float32(0.7998), np.float32(0.8551), np.float32(0.8884), np.float32(0.9446), np.float32(0.9217), np.float32(0.9508), np.float32(0.9621), np.float32(0.9767), np.float32(0.9465), np.float32(0.9502), np.float32(0.9343), np.float32(0.9519), np.float32(0.9555), np.float32(0.8825), np.float32(0.9598), np.float32(0.9448), np.float32(0.8579), np.float32(0.8559), np.float32(0.9192)] 
+2025-05-05 14:17:57.494238: Epoch time: 94.52 s 
+2025-05-05 14:17:58.938480:  
+2025-05-05 14:17:59.019752: Epoch 476 
+2025-05-05 14:17:59.048112: Current learning rate: 0.00783 
+2025-05-05 14:19:33.255070: train_loss -0.4858 
+2025-05-05 14:19:33.374124: val_loss -0.45 
+2025-05-05 14:19:33.387588: Pseudo dice [np.float32(0.8344), np.float32(0.8085), np.float32(0.9213), np.float32(0.9596), np.float32(0.8875), np.float32(0.9468), np.float32(0.9539), np.float32(0.9655), np.float32(0.9574), np.float32(0.9668), np.float32(0.9448), np.float32(0.9682), np.float32(0.964), np.float32(0.9), np.float32(0.9241), np.float32(0.9417), np.float32(0.8786), np.float32(0.8852), np.float32(0.9102)] 
+2025-05-05 14:19:33.427216: Epoch time: 94.32 s 
+2025-05-05 14:19:34.790525:  
+2025-05-05 14:19:34.942496: Epoch 477 
+2025-05-05 14:19:35.000324: Current learning rate: 0.00783 
+2025-05-05 14:21:08.625154: train_loss -0.4838 
+2025-05-05 14:21:08.650064: val_loss -0.4625 
+2025-05-05 14:21:08.689854: Pseudo dice [np.float32(0.8151), np.float32(0.7833), np.float32(0.9116), np.float32(0.9746), np.float32(0.8613), np.float32(0.9561), np.float32(0.9523), np.float32(0.95), np.float32(0.9647), np.float32(0.9519), np.float32(0.8433), np.float32(0.9689), np.float32(0.9536), np.float32(0.8958), np.float32(0.9676), np.float32(0.9379), np.float32(0.89), np.float32(0.9022), np.float32(0.929)] 
+2025-05-05 14:21:08.704792: Epoch time: 93.84 s 
+2025-05-05 14:21:10.220116:  
+2025-05-05 14:21:10.324867: Epoch 478 
+2025-05-05 14:21:10.345481: Current learning rate: 0.00782 
+2025-05-05 14:22:46.851188: train_loss -0.4739 
+2025-05-05 14:22:46.989135: val_loss -0.4836 
+2025-05-05 14:22:47.045655: Pseudo dice [np.float32(0.8608), np.float32(0.834), np.float32(0.8952), np.float32(0.9717), np.float32(0.8509), np.float32(0.958), np.float32(0.9506), np.float32(0.9651), np.float32(0.9667), np.float32(0.9686), np.float32(0.9382), np.float32(0.9664), np.float32(0.9637), np.float32(0.888), np.float32(0.9387), np.float32(0.925), np.float32(0.8766), np.float32(0.893), np.float32(0.9193)] 
+2025-05-05 14:22:47.076159: Epoch time: 96.63 s 
+2025-05-05 14:22:48.551815:  
+2025-05-05 14:22:48.610112: Epoch 479 
+2025-05-05 14:22:48.629396: Current learning rate: 0.00782 
+2025-05-05 14:24:23.281466: train_loss -0.4637 
+2025-05-05 14:24:23.413706: val_loss -0.4794 
+2025-05-05 14:24:23.452509: Pseudo dice [np.float32(0.8404), np.float32(0.8499), np.float32(0.9425), np.float32(0.9702), np.float32(0.9117), np.float32(0.9407), np.float32(0.964), np.float32(0.9737), np.float32(0.9565), np.float32(0.9576), np.float32(0.9278), np.float32(0.9437), np.float32(0.9559), np.float32(0.8917), np.float32(0.9417), np.float32(0.9127), np.float32(0.8557), np.float32(0.8904), np.float32(0.9012)] 
+2025-05-05 14:24:23.496058: Epoch time: 94.73 s 
+2025-05-05 14:24:23.527047: Yayy! New best EMA pseudo Dice: 0.9189000129699707 
+2025-05-05 14:24:29.617138:  
+2025-05-05 14:24:29.623528: Epoch 480 
+2025-05-05 14:24:29.624330: Current learning rate: 0.00781 
+2025-05-05 14:26:07.565526: train_loss -0.4785 
+2025-05-05 14:26:07.658063: val_loss -0.4916 
+2025-05-05 14:26:07.673721: Pseudo dice [np.float32(0.8052), np.float32(0.8618), np.float32(0.898), np.float32(0.9632), np.float32(0.8299), np.float32(0.942), np.float32(0.9586), np.float32(0.972), np.float32(0.9454), np.float32(0.9492), np.float32(0.9374), np.float32(0.9663), np.float32(0.9525), np.float32(0.8985), np.float32(0.8986), np.float32(0.9429), np.float32(0.863), np.float32(0.881), np.float32(0.9197)] 
+2025-05-05 14:26:07.695245: Epoch time: 97.95 s 
+2025-05-05 14:26:09.104095:  
+2025-05-05 14:26:09.166988: Epoch 481 
+2025-05-05 14:26:09.189380: Current learning rate: 0.00781 
+2025-05-05 14:27:45.638723: train_loss -0.4618 
+2025-05-05 14:27:45.702139: val_loss -0.4545 
+2025-05-05 14:27:45.722523: Pseudo dice [np.float32(0.8155), np.float32(0.8078), np.float32(0.905), np.float32(0.9743), np.float32(0.9195), np.float32(0.9533), np.float32(0.9578), np.float32(0.969), np.float32(0.9588), np.float32(0.9589), np.float32(0.9353), np.float32(0.9573), np.float32(0.9661), np.float32(0.8753), np.float32(0.9671), np.float32(0.9428), np.float32(0.862), np.float32(0.8827), np.float32(0.9135)] 
+2025-05-05 14:27:45.730460: Epoch time: 96.54 s 
+2025-05-05 14:27:47.122547:  
+2025-05-05 14:27:47.188892: Epoch 482 
+2025-05-05 14:27:47.205361: Current learning rate: 0.0078 
+2025-05-05 14:29:26.563043: train_loss -0.4582 
+2025-05-05 14:29:26.631183: val_loss -0.4706 
+2025-05-05 14:29:26.660916: Pseudo dice [np.float32(0.8299), np.float32(0.8534), np.float32(0.9274), np.float32(0.9712), np.float32(0.8536), np.float32(0.954), np.float32(0.9588), np.float32(0.9647), np.float32(0.9611), np.float32(0.9639), np.float32(0.941), np.float32(0.9512), np.float32(0.9625), np.float32(0.8922), np.float32(0.9646), np.float32(0.9295), np.float32(0.8099), np.float32(0.7894), np.float32(0.9168)] 
+2025-05-05 14:29:26.696131: Epoch time: 99.44 s 
+2025-05-05 14:29:28.395883:  
+2025-05-05 14:29:28.452461: Epoch 483 
+2025-05-05 14:29:28.458049: Current learning rate: 0.0078 
+2025-05-05 14:31:02.424396: train_loss -0.4731 
+2025-05-05 14:31:02.484833: val_loss -0.4419 
+2025-05-05 14:31:02.489050: Pseudo dice [np.float32(0.83), np.float32(0.8173), np.float32(0.8987), np.float32(0.9684), np.float32(0.7807), np.float32(0.9504), np.float32(0.943), np.float32(0.9776), np.float32(0.9499), np.float32(0.966), np.float32(0.9368), np.float32(0.9643), np.float32(0.9604), np.float32(0.8811), np.float32(0.9414), np.float32(0.9498), np.float32(0.8673), np.float32(0.8708), np.float32(0.9148)] 
+2025-05-05 14:31:02.490025: Epoch time: 94.03 s 
+2025-05-05 14:31:04.133752:  
+2025-05-05 14:31:04.240541: Epoch 484 
+2025-05-05 14:31:04.266761: Current learning rate: 0.00779 
+2025-05-05 14:32:37.743555: train_loss -0.4714 
+2025-05-05 14:32:37.830708: val_loss -0.4779 
+2025-05-05 14:32:37.858881: Pseudo dice [np.float32(0.8032), np.float32(0.7734), np.float32(0.8929), np.float32(0.9495), np.float32(0.8474), np.float32(0.9584), np.float32(0.9642), np.float32(0.9754), np.float32(0.962), np.float32(0.9476), np.float32(0.9428), np.float32(0.9687), np.float32(0.9637), np.float32(0.8869), np.float32(0.9525), np.float32(0.9439), np.float32(0.8845), np.float32(0.8981), np.float32(0.897)] 
+2025-05-05 14:32:37.891661: Epoch time: 93.61 s 
+2025-05-05 14:32:39.344998:  
+2025-05-05 14:32:39.435382: Epoch 485 
+2025-05-05 14:32:39.488160: Current learning rate: 0.00779 
+2025-05-05 14:34:13.051187: train_loss -0.474 
+2025-05-05 14:34:13.124934: val_loss -0.5103 
+2025-05-05 14:34:13.126652: Pseudo dice [np.float32(0.8553), np.float32(0.8092), np.float32(0.9043), np.float32(0.9674), np.float32(0.9), np.float32(0.9654), np.float32(0.959), np.float32(0.9513), np.float32(0.954), np.float32(0.963), np.float32(0.9496), np.float32(0.9617), np.float32(0.9573), np.float32(0.9028), np.float32(0.9644), np.float32(0.9501), np.float32(0.8659), np.float32(0.8833), np.float32(0.8969)] 
+2025-05-05 14:34:13.127237: Epoch time: 93.71 s 
+2025-05-05 14:34:14.635337:  
+2025-05-05 14:34:14.661290: Epoch 486 
+2025-05-05 14:34:14.662310: Current learning rate: 0.00778 
+2025-05-05 14:35:51.265046: train_loss -0.458 
+2025-05-05 14:35:51.391140: val_loss -0.4951 
+2025-05-05 14:35:51.443949: Pseudo dice [np.float32(0.8267), np.float32(0.8299), np.float32(0.8668), np.float32(0.9743), np.float32(0.9055), np.float32(0.9557), np.float32(0.9638), np.float32(0.9652), np.float32(0.9553), np.float32(0.9595), np.float32(0.9356), np.float32(0.9621), np.float32(0.9669), np.float32(0.9062), np.float32(0.9614), np.float32(0.9429), np.float32(0.8844), np.float32(0.8873), np.float32(0.9089)] 
+2025-05-05 14:35:51.471019: Epoch time: 96.63 s 
+2025-05-05 14:35:51.474997: Yayy! New best EMA pseudo Dice: 0.9190999865531921 
+2025-05-05 14:35:54.225169:  
+2025-05-05 14:35:54.243188: Epoch 487 
+2025-05-05 14:35:54.247551: Current learning rate: 0.00778 
+2025-05-05 14:37:36.599312: train_loss -0.4749 
+2025-05-05 14:37:36.675470: val_loss -0.4591 
+2025-05-05 14:37:36.688271: Pseudo dice [np.float32(0.7575), np.float32(0.84), np.float32(0.9058), np.float32(0.976), np.float32(0.8733), np.float32(0.9592), np.float32(0.9625), np.float32(0.9701), np.float32(0.9642), np.float32(0.957), np.float32(0.9254), np.float32(0.9652), np.float32(0.9567), np.float32(0.8967), np.float32(0.9616), np.float32(0.947), np.float32(0.8325), np.float32(0.8232), np.float32(0.9007)] 
+2025-05-05 14:37:36.689354: Epoch time: 102.38 s 
+2025-05-05 14:37:38.240557:  
+2025-05-05 14:37:38.317305: Epoch 488 
+2025-05-05 14:37:38.335997: Current learning rate: 0.00777 
+2025-05-05 14:39:15.735267: train_loss -0.4503 
+2025-05-05 14:39:15.859943: val_loss -0.4649 
+2025-05-05 14:39:15.874412: Pseudo dice [np.float32(0.7978), np.float32(0.8224), np.float32(0.8784), np.float32(0.9613), np.float32(0.9097), np.float32(0.9529), np.float32(0.9613), np.float32(0.9719), np.float32(0.9507), np.float32(0.9526), np.float32(0.9001), np.float32(0.9612), np.float32(0.9665), np.float32(0.881), np.float32(0.9594), np.float32(0.9447), np.float32(0.8454), np.float32(0.8584), np.float32(0.9079)] 
+2025-05-05 14:39:15.893230: Epoch time: 97.5 s 
+2025-05-05 14:39:17.505531:  
+2025-05-05 14:39:17.541077: Epoch 489 
+2025-05-05 14:39:17.542073: Current learning rate: 0.00777 
+2025-05-05 14:40:55.793159: train_loss -0.4693 
+2025-05-05 14:40:55.838357: val_loss -0.501 
+2025-05-05 14:40:55.870147: Pseudo dice [np.float32(0.8581), np.float32(0.847), np.float32(0.87), np.float32(0.9678), np.float32(0.88), np.float32(0.9414), np.float32(0.9515), np.float32(0.9739), np.float32(0.9643), np.float32(0.9595), np.float32(0.9434), np.float32(0.9672), np.float32(0.9646), np.float32(0.9008), np.float32(0.9595), np.float32(0.9583), np.float32(0.8487), np.float32(0.8645), np.float32(0.9035)] 
+2025-05-05 14:40:55.893846: Epoch time: 98.29 s 
+2025-05-05 14:40:57.423387:  
+2025-05-05 14:40:57.467828: Epoch 490 
+2025-05-05 14:40:57.487495: Current learning rate: 0.00777 
+2025-05-05 14:42:37.433913: train_loss -0.4529 
+2025-05-05 14:42:37.559143: val_loss -0.5021 
+2025-05-05 14:42:37.588931: Pseudo dice [np.float32(0.847), np.float32(0.8278), np.float32(0.8673), np.float32(0.9676), np.float32(0.8755), np.float32(0.9495), np.float32(0.9547), np.float32(0.9733), np.float32(0.9463), np.float32(0.9603), np.float32(0.9374), np.float32(0.9633), np.float32(0.9628), np.float32(0.8926), np.float32(0.9619), np.float32(0.9399), np.float32(0.8489), np.float32(0.872), np.float32(0.9109)] 
+2025-05-05 14:42:37.606296: Epoch time: 100.01 s 
+2025-05-05 14:42:39.126984:  
+2025-05-05 14:42:39.217650: Epoch 491 
+2025-05-05 14:42:39.266762: Current learning rate: 0.00776 
+2025-05-05 14:44:14.845422: train_loss -0.4769 
+2025-05-05 14:44:14.974369: val_loss -0.4934 
+2025-05-05 14:44:15.005970: Pseudo dice [np.float32(0.8407), np.float32(0.8118), np.float32(0.8796), np.float32(0.9707), np.float32(0.8822), np.float32(0.9405), np.float32(0.9611), np.float32(0.9704), np.float32(0.9692), np.float32(0.9504), np.float32(0.8995), np.float32(0.9726), np.float32(0.9554), np.float32(0.8883), np.float32(0.9564), np.float32(0.9356), np.float32(0.8343), np.float32(0.8244), np.float32(0.914)] 
+2025-05-05 14:44:15.025279: Epoch time: 95.72 s 
+2025-05-05 14:44:16.601973:  
+2025-05-05 14:44:16.688895: Epoch 492 
+2025-05-05 14:44:16.724720: Current learning rate: 0.00776 
+2025-05-05 14:45:53.287883: train_loss -0.4719 
+2025-05-05 14:45:53.421402: val_loss -0.4723 
+2025-05-05 14:45:53.441354: Pseudo dice [np.float32(0.8152), np.float32(0.8403), np.float32(0.8297), np.float32(0.9756), np.float32(0.8416), np.float32(0.9605), np.float32(0.961), np.float32(0.9747), np.float32(0.9462), np.float32(0.9662), np.float32(0.9403), np.float32(0.957), np.float32(0.964), np.float32(0.8893), np.float32(0.9667), np.float32(0.9481), np.float32(0.8627), np.float32(0.8433), np.float32(0.9099)] 
+2025-05-05 14:45:53.445945: Epoch time: 96.69 s 
+2025-05-05 14:45:54.950502:  
+2025-05-05 14:45:54.952958: Epoch 493 
+2025-05-05 14:45:54.953417: Current learning rate: 0.00775 
+2025-05-05 14:47:32.492152: train_loss -0.4594 
+2025-05-05 14:47:32.551594: val_loss -0.511 
+2025-05-05 14:47:32.552209: Pseudo dice [np.float32(0.8095), np.float32(0.8459), np.float32(0.9472), np.float32(0.9711), np.float32(0.913), np.float32(0.9533), np.float32(0.9647), np.float32(0.9767), np.float32(0.9577), np.float32(0.9625), np.float32(0.9454), np.float32(0.9642), np.float32(0.9672), np.float32(0.8924), np.float32(0.9479), np.float32(0.9462), np.float32(0.8723), np.float32(0.896), np.float32(0.9177)] 
+2025-05-05 14:47:32.552623: Epoch time: 97.54 s 
+2025-05-05 14:47:33.969164:  
+2025-05-05 14:47:34.045060: Epoch 494 
+2025-05-05 14:47:34.056536: Current learning rate: 0.00775 
+2025-05-05 14:49:10.566833: train_loss -0.4789 
+2025-05-05 14:49:10.675776: val_loss -0.4512 
+2025-05-05 14:49:10.713124: Pseudo dice [np.float32(0.8241), np.float32(0.8584), np.float32(0.9174), np.float32(0.9697), np.float32(0.9006), np.float32(0.9566), np.float32(0.9665), np.float32(0.9796), np.float32(0.962), np.float32(0.9661), np.float32(0.9415), np.float32(0.9648), np.float32(0.953), np.float32(0.8953), np.float32(0.959), np.float32(0.946), np.float32(0.854), np.float32(0.8615), np.float32(0.9098)] 
+2025-05-05 14:49:10.745810: Epoch time: 96.6 s 
+2025-05-05 14:49:10.804455: Yayy! New best EMA pseudo Dice: 0.919700026512146 
+2025-05-05 14:49:13.145849:  
+2025-05-05 14:49:13.150615: Epoch 495 
+2025-05-05 14:49:13.151455: Current learning rate: 0.00774 
+2025-05-05 14:50:53.559347: train_loss -0.484 
+2025-05-05 14:50:53.620843: val_loss -0.4636 
+2025-05-05 14:50:53.639470: Pseudo dice [np.float32(0.8433), np.float32(0.8492), np.float32(0.9327), np.float32(0.9751), np.float32(0.8642), np.float32(0.9583), np.float32(0.9629), np.float32(0.9759), np.float32(0.9529), np.float32(0.9638), np.float32(0.95), np.float32(0.9628), np.float32(0.9594), np.float32(0.8946), np.float32(0.9632), np.float32(0.9455), np.float32(0.8571), np.float32(0.8127), np.float32(0.9093)] 
+2025-05-05 14:50:53.650576: Epoch time: 100.41 s 
+2025-05-05 14:50:53.676490: Yayy! New best EMA pseudo Dice: 0.9200000166893005 
+2025-05-05 14:50:56.372670:  
+2025-05-05 14:50:56.432959: Epoch 496 
+2025-05-05 14:50:56.463137: Current learning rate: 0.00774 
+2025-05-05 14:52:31.561604: train_loss -0.475 
+2025-05-05 14:52:31.640748: val_loss -0.4973 
+2025-05-05 14:52:31.641752: Pseudo dice [np.float32(0.7788), np.float32(0.8491), np.float32(0.737), np.float32(0.9777), np.float32(0.901), np.float32(0.9616), np.float32(0.9635), np.float32(0.9697), np.float32(0.9622), np.float32(0.9564), np.float32(0.944), np.float32(0.9708), np.float32(0.9669), np.float32(0.8896), np.float32(0.9624), np.float32(0.9424), np.float32(0.8497), np.float32(0.8284), np.float32(0.9092)] 
+2025-05-05 14:52:31.642466: Epoch time: 95.19 s 
+2025-05-05 14:52:36.870548:  
+2025-05-05 14:52:36.876390: Epoch 497 
+2025-05-05 14:52:36.877046: Current learning rate: 0.00773 
+2025-05-05 14:54:09.494370: train_loss -0.4671 
+2025-05-05 14:54:09.575388: val_loss -0.4448 
+2025-05-05 14:54:09.603372: Pseudo dice [np.float32(0.8215), np.float32(0.8202), np.float32(0.9331), np.float32(0.9745), np.float32(0.8668), np.float32(0.9344), np.float32(0.9534), np.float32(0.9689), np.float32(0.9645), np.float32(0.9535), np.float32(0.9438), np.float32(0.9677), np.float32(0.9599), np.float32(0.879), np.float32(0.9532), np.float32(0.9482), np.float32(0.8619), np.float32(0.7902), np.float32(0.8871)] 
+2025-05-05 14:54:09.618307: Epoch time: 92.62 s 
+2025-05-05 14:54:11.196851:  
+2025-05-05 14:54:11.316292: Epoch 498 
+2025-05-05 14:54:11.333931: Current learning rate: 0.00773 
+2025-05-05 14:55:47.629230: train_loss -0.4802 
+2025-05-05 14:55:47.746936: val_loss -0.4999 
+2025-05-05 14:55:47.769372: Pseudo dice [np.float32(0.8403), np.float32(0.8106), np.float32(0.8937), np.float32(0.9733), np.float32(0.8822), np.float32(0.9578), np.float32(0.9613), np.float32(0.9685), np.float32(0.959), np.float32(0.9631), np.float32(0.9364), np.float32(0.9667), np.float32(0.962), np.float32(0.902), np.float32(0.9557), np.float32(0.939), np.float32(0.8982), np.float32(0.8532), np.float32(0.9123)] 
+2025-05-05 14:55:47.776764: Epoch time: 96.43 s 
+2025-05-05 14:55:49.420824:  
+2025-05-05 14:55:49.544930: Epoch 499 
+2025-05-05 14:55:49.571196: Current learning rate: 0.00772 
+2025-05-05 14:57:30.681043: train_loss -0.4715 
+2025-05-05 14:57:30.761794: val_loss -0.5187 
+2025-05-05 14:57:30.786822: Pseudo dice [np.float32(0.8359), np.float32(0.8445), np.float32(0.8312), np.float32(0.9684), np.float32(0.9207), np.float32(0.9518), np.float32(0.9612), np.float32(0.9768), np.float32(0.9659), np.float32(0.9554), np.float32(0.9446), np.float32(0.9655), np.float32(0.9612), np.float32(0.8897), np.float32(0.9581), np.float32(0.9387), np.float32(0.8288), np.float32(0.8644), np.float32(0.9126)] 
+2025-05-05 14:57:30.805312: Epoch time: 101.26 s 
+2025-05-05 14:57:33.175085:  
+2025-05-05 14:57:33.301855: Epoch 500 
+2025-05-05 14:57:33.349910: Current learning rate: 0.00772 
+2025-05-05 14:59:08.415092: train_loss -0.4765 
+2025-05-05 14:59:08.479694: val_loss -0.4952 
+2025-05-05 14:59:08.491314: Pseudo dice [np.float32(0.8409), np.float32(0.8184), np.float32(0.8041), np.float32(0.9692), np.float32(0.9278), np.float32(0.9564), np.float32(0.9675), np.float32(0.978), np.float32(0.9594), np.float32(0.9658), np.float32(0.9468), np.float32(0.9671), np.float32(0.9606), np.float32(0.8997), np.float32(0.9459), np.float32(0.9492), np.float32(0.8712), np.float32(0.8819), np.float32(0.9041)] 
+2025-05-05 14:59:08.506026: Epoch time: 95.24 s 
+2025-05-05 14:59:10.013577:  
+2025-05-05 14:59:10.089855: Epoch 501 
+2025-05-05 14:59:10.122580: Current learning rate: 0.00771 
+2025-05-05 15:00:48.063636: train_loss -0.4754 
+2025-05-05 15:00:48.158509: val_loss -0.4892 
+2025-05-05 15:00:48.173405: Pseudo dice [np.float32(0.8467), np.float32(0.8323), np.float32(0.8161), np.float32(0.9766), np.float32(0.9137), np.float32(0.9568), np.float32(0.9514), np.float32(0.9757), np.float32(0.959), np.float32(0.9637), np.float32(0.9259), np.float32(0.9653), np.float32(0.9569), np.float32(0.8961), np.float32(0.9605), np.float32(0.9487), np.float32(0.8524), np.float32(0.7104), np.float32(0.9163)] 
+2025-05-05 15:00:48.187029: Epoch time: 98.05 s 
+2025-05-05 15:00:49.716717:  
+2025-05-05 15:00:49.752127: Epoch 502 
+2025-05-05 15:00:49.775110: Current learning rate: 0.00771 
+2025-05-05 15:02:23.709167: train_loss -0.4741 
+2025-05-05 15:02:23.756657: val_loss -0.4877 
+2025-05-05 15:02:23.757147: Pseudo dice [np.float32(0.8282), np.float32(0.8348), np.float32(0.8941), np.float32(0.9661), np.float32(0.8825), np.float32(0.9517), np.float32(0.9633), np.float32(0.9713), np.float32(0.9604), np.float32(0.9632), np.float32(0.9481), np.float32(0.9678), np.float32(0.9695), np.float32(0.8895), np.float32(0.9593), np.float32(0.9455), np.float32(0.8686), np.float32(0.8813), np.float32(0.9101)] 
+2025-05-05 15:02:23.757525: Epoch time: 93.99 s 
+2025-05-05 15:02:25.247311:  
+2025-05-05 15:02:25.392125: Epoch 503 
+2025-05-05 15:02:25.438577: Current learning rate: 0.0077 
+2025-05-05 15:04:00.352225: train_loss -0.4712 
+2025-05-05 15:04:00.437931: val_loss -0.502 
+2025-05-05 15:04:00.462204: Pseudo dice [np.float32(0.8401), np.float32(0.8465), np.float32(0.6289), np.float32(0.9593), np.float32(0.8205), np.float32(0.9621), np.float32(0.9591), np.float32(0.9721), np.float32(0.9558), np.float32(0.9478), np.float32(0.9309), np.float32(0.9623), np.float32(0.9635), np.float32(0.9013), np.float32(0.9636), np.float32(0.9395), np.float32(0.8594), np.float32(0.8328), np.float32(0.922)] 
+2025-05-05 15:04:00.486184: Epoch time: 95.11 s 
+2025-05-05 15:04:01.995825:  
+2025-05-05 15:04:02.126390: Epoch 504 
+2025-05-05 15:04:02.172034: Current learning rate: 0.0077 
+2025-05-05 15:05:35.165844: train_loss -0.4869 
+2025-05-05 15:05:35.262045: val_loss -0.4828 
+2025-05-05 15:05:35.284378: Pseudo dice [np.float32(0.8459), np.float32(0.8474), np.float32(0.8833), np.float32(0.9758), np.float32(0.819), np.float32(0.9578), np.float32(0.9646), np.float32(0.9668), np.float32(0.9465), np.float32(0.9686), np.float32(0.9429), np.float32(0.9587), np.float32(0.9641), np.float32(0.908), np.float32(0.9558), np.float32(0.9525), np.float32(0.8923), np.float32(0.8916), np.float32(0.9212)] 
+2025-05-05 15:05:35.322943: Epoch time: 93.17 s 
+2025-05-05 15:05:36.873570:  
+2025-05-05 15:05:36.960013: Epoch 505 
+2025-05-05 15:05:37.015025: Current learning rate: 0.0077 
+2025-05-05 15:07:12.270526: train_loss -0.4652 
+2025-05-05 15:07:12.456347: val_loss -0.4997 
+2025-05-05 15:07:12.493243: Pseudo dice [np.float32(0.8539), np.float32(0.8415), np.float32(0.8687), np.float32(0.9739), np.float32(0.8193), np.float32(0.9532), np.float32(0.9516), np.float32(0.9782), np.float32(0.9595), np.float32(0.9624), np.float32(0.9367), np.float32(0.9637), np.float32(0.962), np.float32(0.8994), np.float32(0.9677), np.float32(0.9557), np.float32(0.8617), np.float32(0.8632), np.float32(0.907)] 
+2025-05-05 15:07:12.520889: Epoch time: 95.4 s 
+2025-05-05 15:07:13.971236:  
+2025-05-05 15:07:14.076399: Epoch 506 
+2025-05-05 15:07:14.108070: Current learning rate: 0.00769 
+2025-05-05 15:08:51.013474: train_loss -0.4529 
+2025-05-05 15:08:51.072432: val_loss -0.466 
+2025-05-05 15:08:51.091432: Pseudo dice [np.float32(0.8087), np.float32(0.8315), np.float32(0.9231), np.float32(0.9542), np.float32(0.891), np.float32(0.9499), np.float32(0.9513), np.float32(0.9707), np.float32(0.9509), np.float32(0.9607), np.float32(0.9267), np.float32(0.9612), np.float32(0.9555), np.float32(0.8938), np.float32(0.9559), np.float32(0.9115), np.float32(0.8504), np.float32(0.8788), np.float32(0.9044)] 
+2025-05-05 15:08:51.104397: Epoch time: 97.04 s 
+2025-05-05 15:08:52.566158:  
+2025-05-05 15:08:52.627542: Epoch 507 
+2025-05-05 15:08:52.647960: Current learning rate: 0.00769 
+2025-05-05 15:10:27.864990: train_loss -0.4647 
+2025-05-05 15:10:27.961646: val_loss -0.5148 
+2025-05-05 15:10:27.963481: Pseudo dice [np.float32(0.8277), np.float32(0.8264), np.float32(0.8877), np.float32(0.9682), np.float32(0.9219), np.float32(0.9553), np.float32(0.9561), np.float32(0.9755), np.float32(0.9529), np.float32(0.9647), np.float32(0.9361), np.float32(0.9605), np.float32(0.9655), np.float32(0.8849), np.float32(0.9417), np.float32(0.9412), np.float32(0.8766), np.float32(0.8978), np.float32(0.9077)] 
+2025-05-05 15:10:27.969638: Epoch time: 95.3 s 
+2025-05-05 15:10:29.552584:  
+2025-05-05 15:10:29.558871: Epoch 508 
+2025-05-05 15:10:29.588801: Current learning rate: 0.00768 
+2025-05-05 15:12:02.332809: train_loss -0.4867 
+2025-05-05 15:12:02.435294: val_loss -0.4767 
+2025-05-05 15:12:02.475208: Pseudo dice [np.float32(0.7871), np.float32(0.8266), np.float32(0.8667), np.float32(0.9784), np.float32(0.882), np.float32(0.9546), np.float32(0.9631), np.float32(0.9721), np.float32(0.9619), np.float32(0.9592), np.float32(0.947), np.float32(0.9686), np.float32(0.9651), np.float32(0.8933), np.float32(0.9629), np.float32(0.9403), np.float32(0.8221), np.float32(0.8238), np.float32(0.9115)] 
+2025-05-05 15:12:02.498452: Epoch time: 92.78 s 
+2025-05-05 15:12:04.035302:  
+2025-05-05 15:12:04.038366: Epoch 509 
+2025-05-05 15:12:04.042725: Current learning rate: 0.00768 
+2025-05-05 15:13:40.934428: train_loss -0.4915 
+2025-05-05 15:13:41.033139: val_loss -0.4995 
+2025-05-05 15:13:41.062679: Pseudo dice [np.float32(0.8504), np.float32(0.8241), np.float32(0.8537), np.float32(0.9637), np.float32(0.8077), np.float32(0.9452), np.float32(0.9682), np.float32(0.9672), np.float32(0.9655), np.float32(0.9591), np.float32(0.942), np.float32(0.9673), np.float32(0.9632), np.float32(0.9008), np.float32(0.9649), np.float32(0.9295), np.float32(0.8683), np.float32(0.8765), np.float32(0.9098)] 
+2025-05-05 15:13:41.104884: Epoch time: 96.9 s 
+2025-05-05 15:13:42.591839:  
+2025-05-05 15:13:42.652232: Epoch 510 
+2025-05-05 15:13:42.653023: Current learning rate: 0.00767 
+2025-05-05 15:15:21.354881: train_loss -0.4746 
+2025-05-05 15:15:21.393921: val_loss -0.4691 
+2025-05-05 15:15:21.394991: Pseudo dice [np.float32(0.8608), np.float32(0.8343), np.float32(0.8935), np.float32(0.9714), np.float32(0.878), np.float32(0.9611), np.float32(0.9647), np.float32(0.9762), np.float32(0.9679), np.float32(0.9573), np.float32(0.9475), np.float32(0.9706), np.float32(0.9646), np.float32(0.8893), np.float32(0.9707), np.float32(0.9562), np.float32(0.8297), np.float32(0.8744), np.float32(0.9201)] 
+2025-05-05 15:15:21.407331: Epoch time: 98.76 s 
+2025-05-05 15:15:22.851147:  
+2025-05-05 15:15:22.872184: Epoch 511 
+2025-05-05 15:15:22.873118: Current learning rate: 0.00767 
+2025-05-05 15:16:58.376394: train_loss -0.4697 
+2025-05-05 15:16:58.496476: val_loss -0.4896 
+2025-05-05 15:16:58.532539: Pseudo dice [np.float32(0.8277), np.float32(0.799), np.float32(0.9159), np.float32(0.9699), np.float32(0.8746), np.float32(0.9602), np.float32(0.9534), np.float32(0.9725), np.float32(0.9612), np.float32(0.9657), np.float32(0.9305), np.float32(0.9727), np.float32(0.9663), np.float32(0.9002), np.float32(0.9543), np.float32(0.9494), np.float32(0.8866), np.float32(0.8916), np.float32(0.9305)] 
+2025-05-05 15:16:58.568206: Epoch time: 95.53 s 
+2025-05-05 15:17:00.104942:  
+2025-05-05 15:17:00.195010: Epoch 512 
+2025-05-05 15:17:00.218466: Current learning rate: 0.00766 
+2025-05-05 15:18:33.466694: train_loss -0.4619 
+2025-05-05 15:18:33.532180: val_loss -0.4504 
+2025-05-05 15:18:33.627723: Pseudo dice [np.float32(0.8414), np.float32(0.8422), np.float32(0.846), np.float32(0.976), np.float32(0.8732), np.float32(0.9579), np.float32(0.9628), np.float32(0.9792), np.float32(0.958), np.float32(0.9688), np.float32(0.8918), np.float32(0.9556), np.float32(0.9282), np.float32(0.8938), np.float32(0.9656), np.float32(0.942), np.float32(0.8377), np.float32(0.8625), np.float32(0.92)] 
+2025-05-05 15:18:33.691686: Epoch time: 93.36 s 
+2025-05-05 15:18:35.204977:  
+2025-05-05 15:18:35.325569: Epoch 513 
+2025-05-05 15:18:35.351897: Current learning rate: 0.00766 
+2025-05-05 15:20:12.208823: train_loss -0.4691 
+2025-05-05 15:20:12.319859: val_loss -0.4924 
+2025-05-05 15:20:12.351766: Pseudo dice [np.float32(0.8309), np.float32(0.8138), np.float32(0.8439), np.float32(0.9689), np.float32(0.8911), np.float32(0.9556), np.float32(0.9597), np.float32(0.9634), np.float32(0.9615), np.float32(0.9413), np.float32(0.936), np.float32(0.9638), np.float32(0.9668), np.float32(0.8561), np.float32(0.9454), np.float32(0.9246), np.float32(0.7662), np.float32(0.7294), np.float32(0.9162)] 
+2025-05-05 15:20:12.373223: Epoch time: 97.01 s 
+2025-05-05 15:20:13.869898:  
+2025-05-05 15:20:13.885349: Epoch 514 
+2025-05-05 15:20:13.889846: Current learning rate: 0.00765 
+2025-05-05 15:21:49.447635: train_loss -0.4589 
+2025-05-05 15:21:49.505593: val_loss -0.4728 
+2025-05-05 15:21:49.520866: Pseudo dice [np.float32(0.8103), np.float32(0.8313), np.float32(0.9314), np.float32(0.9775), np.float32(0.8991), np.float32(0.9512), np.float32(0.9582), np.float32(0.9764), np.float32(0.9645), np.float32(0.956), np.float32(0.9291), np.float32(0.9647), np.float32(0.9582), np.float32(0.8982), np.float32(0.9619), np.float32(0.9496), np.float32(0.8637), np.float32(0.8954), np.float32(0.9008)] 
+2025-05-05 15:21:49.528849: Epoch time: 95.58 s 
+2025-05-05 15:21:54.641301:  
+2025-05-05 15:21:54.647009: Epoch 515 
+2025-05-05 15:21:54.647791: Current learning rate: 0.00765 
+2025-05-05 15:23:30.416827: train_loss -0.4689 
+2025-05-05 15:23:30.525150: val_loss -0.4832 
+2025-05-05 15:23:30.540162: Pseudo dice [np.float32(0.8332), np.float32(0.8419), np.float32(0.9244), np.float32(0.9701), np.float32(0.8959), np.float32(0.9585), np.float32(0.96), np.float32(0.9775), np.float32(0.9517), np.float32(0.9316), np.float32(0.9135), np.float32(0.965), np.float32(0.9603), np.float32(0.8855), np.float32(0.9645), np.float32(0.9481), np.float32(0.8448), np.float32(0.8495), np.float32(0.8836)] 
+2025-05-05 15:23:30.565903: Epoch time: 95.78 s 
+2025-05-05 15:23:32.064888:  
+2025-05-05 15:23:32.182740: Epoch 516 
+2025-05-05 15:23:32.232559: Current learning rate: 0.00764 
+2025-05-05 15:25:07.880546: train_loss -0.4494 
+2025-05-05 15:25:07.969917: val_loss -0.4922 
+2025-05-05 15:25:07.976480: Pseudo dice [np.float32(0.7889), np.float32(0.7984), np.float32(0.7447), np.float32(0.9669), np.float32(0.8959), np.float32(0.9533), np.float32(0.9623), np.float32(0.9664), np.float32(0.9555), np.float32(0.9508), np.float32(0.9429), np.float32(0.9695), np.float32(0.9587), np.float32(0.8956), np.float32(0.9093), np.float32(0.9345), np.float32(0.8596), np.float32(0.8819), np.float32(0.8978)] 
+2025-05-05 15:25:07.977225: Epoch time: 95.82 s 
+2025-05-05 15:25:09.409653:  
+2025-05-05 15:25:09.486347: Epoch 517 
+2025-05-05 15:25:09.506625: Current learning rate: 0.00764 
+2025-05-05 15:26:47.790425: train_loss -0.4705 
+2025-05-05 15:26:47.892810: val_loss -0.499 
+2025-05-05 15:26:47.977113: Pseudo dice [np.float32(0.8374), np.float32(0.8234), np.float32(0.8668), np.float32(0.9731), np.float32(0.8578), np.float32(0.9539), np.float32(0.9559), np.float32(0.9696), np.float32(0.9436), np.float32(0.9666), np.float32(0.9393), np.float32(0.938), np.float32(0.9673), np.float32(0.8827), np.float32(0.9458), np.float32(0.9509), np.float32(0.8432), np.float32(0.8693), np.float32(0.9212)] 
+2025-05-05 15:26:48.011534: Epoch time: 98.38 s 
+2025-05-05 15:26:49.578599:  
+2025-05-05 15:26:49.669186: Epoch 518 
+2025-05-05 15:26:49.680564: Current learning rate: 0.00764 
+2025-05-05 15:28:26.637205: train_loss -0.4823 
+2025-05-05 15:28:26.712701: val_loss -0.4896 
+2025-05-05 15:28:26.730735: Pseudo dice [np.float32(0.8327), np.float32(0.8265), np.float32(0.9175), np.float32(0.9763), np.float32(0.912), np.float32(0.9532), np.float32(0.9592), np.float32(0.9769), np.float32(0.9402), np.float32(0.9595), np.float32(0.9303), np.float32(0.9068), np.float32(0.9525), np.float32(0.8942), np.float32(0.9641), np.float32(0.9501), np.float32(0.8438), np.float32(0.8558), np.float32(0.9198)] 
+2025-05-05 15:28:26.748895: Epoch time: 97.06 s 
+2025-05-05 15:28:28.301579:  
+2025-05-05 15:28:28.414299: Epoch 519 
+2025-05-05 15:28:28.465001: Current learning rate: 0.00763 
+2025-05-05 15:30:06.440394: train_loss -0.4833 
+2025-05-05 15:30:06.507839: val_loss -0.5007 
+2025-05-05 15:30:06.508718: Pseudo dice [np.float32(0.8465), np.float32(0.8458), np.float32(0.9268), np.float32(0.9712), np.float32(0.8669), np.float32(0.9601), np.float32(0.9324), np.float32(0.96), np.float32(0.9718), np.float32(0.9557), np.float32(0.9217), np.float32(0.9682), np.float32(0.9501), np.float32(0.8897), np.float32(0.9603), np.float32(0.951), np.float32(0.8791), np.float32(0.8814), np.float32(0.9027)] 
+2025-05-05 15:30:06.509454: Epoch time: 98.14 s 
+2025-05-05 15:30:07.923193:  
+2025-05-05 15:30:07.998072: Epoch 520 
+2025-05-05 15:30:08.001151: Current learning rate: 0.00763 
+2025-05-05 15:31:41.834598: train_loss -0.4431 
+2025-05-05 15:31:41.897317: val_loss -0.4784 
+2025-05-05 15:31:41.898077: Pseudo dice [np.float32(0.7963), np.float32(0.8397), np.float32(0.8983), np.float32(0.9677), np.float32(0.8393), np.float32(0.9608), np.float32(0.9632), np.float32(0.9721), np.float32(0.9536), np.float32(0.9679), np.float32(0.9455), np.float32(0.964), np.float32(0.9667), np.float32(0.9009), np.float32(0.9585), np.float32(0.9466), np.float32(0.7715), np.float32(0.7138), np.float32(0.9037)] 
+2025-05-05 15:31:41.898760: Epoch time: 93.91 s 
+2025-05-05 15:31:43.338335:  
+2025-05-05 15:31:43.391008: Epoch 521 
+2025-05-05 15:31:43.392468: Current learning rate: 0.00762 
+2025-05-05 15:33:21.496583: train_loss -0.4569 
+2025-05-05 15:33:21.570612: val_loss -0.5064 
+2025-05-05 15:33:21.594706: Pseudo dice [np.float32(0.8206), np.float32(0.8056), np.float32(0.5421), np.float32(0.9652), np.float32(0.8254), np.float32(0.9409), np.float32(0.9417), np.float32(0.9732), np.float32(0.9639), np.float32(0.9596), np.float32(0.9431), np.float32(0.9653), np.float32(0.9688), np.float32(0.8709), np.float32(0.9582), np.float32(0.9352), np.float32(0.8687), np.float32(0.8128), np.float32(0.894)] 
+2025-05-05 15:33:21.618325: Epoch time: 98.16 s 
+2025-05-05 15:33:23.250190:  
+2025-05-05 15:33:23.369576: Epoch 522 
+2025-05-05 15:33:23.404066: Current learning rate: 0.00762 
+2025-05-05 15:34:58.231917: train_loss -0.4531 
+2025-05-05 15:34:58.275206: val_loss -0.478 
+2025-05-05 15:34:58.295823: Pseudo dice [np.float32(0.8522), np.float32(0.8418), np.float32(0.7212), np.float32(0.9676), np.float32(0.8999), np.float32(0.9615), np.float32(0.9562), np.float32(0.9768), np.float32(0.9397), np.float32(0.9625), np.float32(0.9509), np.float32(0.948), np.float32(0.9637), np.float32(0.9092), np.float32(0.9534), np.float32(0.9479), np.float32(0.885), np.float32(0.8745), np.float32(0.8904)] 
+2025-05-05 15:34:58.313972: Epoch time: 94.98 s 
+2025-05-05 15:34:59.812959:  
+2025-05-05 15:34:59.869514: Epoch 523 
+2025-05-05 15:34:59.880747: Current learning rate: 0.00761 
+2025-05-05 15:36:38.287144: train_loss -0.4766 
+2025-05-05 15:36:38.410430: val_loss -0.4626 
+2025-05-05 15:36:38.455948: Pseudo dice [np.float32(0.8102), np.float32(0.8508), np.float32(0.7811), np.float32(0.9734), np.float32(0.9033), np.float32(0.9437), np.float32(0.9652), np.float32(0.9731), np.float32(0.9533), np.float32(0.9343), np.float32(0.9069), np.float32(0.9636), np.float32(0.9447), np.float32(0.8855), np.float32(0.9455), np.float32(0.9446), np.float32(0.8332), np.float32(0.8806), np.float32(0.9086)] 
+2025-05-05 15:36:38.489692: Epoch time: 98.48 s 
+2025-05-05 15:36:40.301169:  
+2025-05-05 15:36:40.331008: Epoch 524 
+2025-05-05 15:36:40.343661: Current learning rate: 0.00761 
+2025-05-05 15:38:16.694224: train_loss -0.4664 
+2025-05-05 15:38:16.878906: val_loss -0.4355 
+2025-05-05 15:38:16.931396: Pseudo dice [np.float32(0.8225), np.float32(0.832), np.float32(0.9153), np.float32(0.9624), np.float32(0.8141), np.float32(0.9112), np.float32(0.9362), np.float32(0.9668), np.float32(0.9515), np.float32(0.9633), np.float32(0.9464), np.float32(0.9573), np.float32(0.9644), np.float32(0.8873), np.float32(0.9583), np.float32(0.9452), np.float32(0.7992), np.float32(0.7333), np.float32(0.8958)] 
+2025-05-05 15:38:16.956580: Epoch time: 96.39 s 
+2025-05-05 15:38:18.485027:  
+2025-05-05 15:38:18.545994: Epoch 525 
+2025-05-05 15:38:18.547020: Current learning rate: 0.0076 
+2025-05-05 15:39:55.573200: train_loss -0.4319 
+2025-05-05 15:39:55.677439: val_loss -0.4288 
+2025-05-05 15:39:55.706206: Pseudo dice [np.float32(0.8214), np.float32(0.8339), np.float32(0.9059), np.float32(0.9686), np.float32(0.8855), np.float32(0.9551), np.float32(0.956), np.float32(0.9703), np.float32(0.9493), np.float32(0.9579), np.float32(0.9063), np.float32(0.9672), np.float32(0.9501), np.float32(0.8468), np.float32(0.9477), np.float32(0.9464), np.float32(0.7108), np.float32(0.8148), np.float32(0.917)] 
+2025-05-05 15:39:55.738364: Epoch time: 97.09 s 
+2025-05-05 15:39:57.283599:  
+2025-05-05 15:39:57.338080: Epoch 526 
+2025-05-05 15:39:57.342443: Current learning rate: 0.0076 
+2025-05-05 15:41:35.861971: train_loss -0.461 
+2025-05-05 15:41:35.975781: val_loss -0.4945 
+2025-05-05 15:41:36.031603: Pseudo dice [np.float32(0.8151), np.float32(0.8021), np.float32(0.8686), np.float32(0.9399), np.float32(0.8922), np.float32(0.9541), np.float32(0.9595), np.float32(0.9757), np.float32(0.9641), np.float32(0.9615), np.float32(0.9424), np.float32(0.9689), np.float32(0.9659), np.float32(0.8858), np.float32(0.9602), np.float32(0.9384), np.float32(0.8212), np.float32(0.8292), np.float32(0.9091)] 
+2025-05-05 15:41:36.067823: Epoch time: 98.58 s 
+2025-05-05 15:41:37.773852:  
+2025-05-05 15:41:37.825842: Epoch 527 
+2025-05-05 15:41:37.826679: Current learning rate: 0.00759 
+2025-05-05 15:43:11.708034: train_loss -0.4555 
+2025-05-05 15:43:11.861555: val_loss -0.497 
+2025-05-05 15:43:11.946576: Pseudo dice [np.float32(0.8017), np.float32(0.8298), np.float32(0.8676), np.float32(0.9725), np.float32(0.7877), np.float32(0.9496), np.float32(0.9582), np.float32(0.9747), np.float32(0.9548), np.float32(0.9668), np.float32(0.9452), np.float32(0.9584), np.float32(0.9622), np.float32(0.9006), np.float32(0.9402), np.float32(0.9458), np.float32(0.8484), np.float32(0.7978), np.float32(0.9167)] 
+2025-05-05 15:43:11.980147: Epoch time: 93.94 s 
+2025-05-05 15:43:13.499761:  
+2025-05-05 15:43:13.651686: Epoch 528 
+2025-05-05 15:43:13.686190: Current learning rate: 0.00759 
+2025-05-05 15:44:45.890558: train_loss -0.4794 
+2025-05-05 15:44:45.978134: val_loss -0.4616 
+2025-05-05 15:44:46.017539: Pseudo dice [np.float32(0.8321), np.float32(0.8152), np.float32(0.8307), np.float32(0.9589), np.float32(0.9117), np.float32(0.95), np.float32(0.9492), np.float32(0.9753), np.float32(0.9509), np.float32(0.922), np.float32(0.9259), np.float32(0.9585), np.float32(0.9455), np.float32(0.8872), np.float32(0.9617), np.float32(0.9483), np.float32(0.837), np.float32(0.8198), np.float32(0.9081)] 
+2025-05-05 15:44:46.056122: Epoch time: 92.39 s 
+2025-05-05 15:44:47.682107:  
+2025-05-05 15:44:47.717777: Epoch 529 
+2025-05-05 15:44:47.734672: Current learning rate: 0.00758 
+2025-05-05 15:46:30.280524: train_loss -0.461 
+2025-05-05 15:46:30.353514: val_loss -0.4855 
+2025-05-05 15:46:30.376853: Pseudo dice [np.float32(0.8492), np.float32(0.8218), np.float32(0.7823), np.float32(0.9737), np.float32(0.8734), np.float32(0.9429), np.float32(0.9542), np.float32(0.9716), np.float32(0.9629), np.float32(0.9589), np.float32(0.9337), np.float32(0.9588), np.float32(0.9495), np.float32(0.8876), np.float32(0.9565), np.float32(0.9486), np.float32(0.8789), np.float32(0.901), np.float32(0.921)] 
+2025-05-05 15:46:30.387933: Epoch time: 102.6 s 
+2025-05-05 15:46:31.905817:  
+2025-05-05 15:46:32.038141: Epoch 530 
+2025-05-05 15:46:32.078204: Current learning rate: 0.00758 
+2025-05-05 15:48:11.419096: train_loss -0.4673 
+2025-05-05 15:48:11.531810: val_loss -0.4816 
+2025-05-05 15:48:11.576847: Pseudo dice [np.float32(0.8403), np.float32(0.7886), np.float32(0.845), np.float32(0.9674), np.float32(0.8146), np.float32(0.9523), np.float32(0.9631), np.float32(0.9644), np.float32(0.9643), np.float32(0.96), np.float32(0.9393), np.float32(0.9678), np.float32(0.9633), np.float32(0.8822), np.float32(0.9479), np.float32(0.913), np.float32(0.8373), np.float32(0.8655), np.float32(0.9098)] 
+2025-05-05 15:48:11.616717: Epoch time: 99.51 s 
+2025-05-05 15:48:13.167526:  
+2025-05-05 15:48:13.221136: Epoch 531 
+2025-05-05 15:48:13.242757: Current learning rate: 0.00758 
+2025-05-05 15:49:52.142411: train_loss -0.4706 
+2025-05-05 15:49:52.271214: val_loss -0.5255 
+2025-05-05 15:49:52.286538: Pseudo dice [np.float32(0.8331), np.float32(0.836), np.float32(0.8838), np.float32(0.9664), np.float32(0.9042), np.float32(0.9489), np.float32(0.9576), np.float32(0.9779), np.float32(0.9507), np.float32(0.9552), np.float32(0.9487), np.float32(0.9605), np.float32(0.9694), np.float32(0.8738), np.float32(0.9535), np.float32(0.9169), np.float32(0.841), np.float32(0.835), np.float32(0.9033)] 
+2025-05-05 15:49:52.297954: Epoch time: 98.98 s 
+2025-05-05 15:49:53.810587:  
+2025-05-05 15:49:53.852192: Epoch 532 
+2025-05-05 15:49:53.852761: Current learning rate: 0.00757 
+2025-05-05 15:51:27.642221: train_loss -0.4824 
+2025-05-05 15:51:27.773532: val_loss -0.4913 
+2025-05-05 15:51:27.804918: Pseudo dice [np.float32(0.8083), np.float32(0.8159), np.float32(0.9133), np.float32(0.9741), np.float32(0.8544), np.float32(0.9599), np.float32(0.9548), np.float32(0.9715), np.float32(0.9356), np.float32(0.9605), np.float32(0.9477), np.float32(0.941), np.float32(0.9696), np.float32(0.8887), np.float32(0.9641), np.float32(0.9419), np.float32(0.8645), np.float32(0.8477), np.float32(0.9134)] 
+2025-05-05 15:51:27.821818: Epoch time: 93.83 s 
+2025-05-05 15:51:29.333533:  
+2025-05-05 15:51:29.427695: Epoch 533 
+2025-05-05 15:51:29.447337: Current learning rate: 0.00757 
+2025-05-05 15:53:03.845183: train_loss -0.4731 
+2025-05-05 15:53:03.865181: val_loss -0.4733 
+2025-05-05 15:53:03.877102: Pseudo dice [np.float32(0.8437), np.float32(0.8243), np.float32(0.8368), np.float32(0.971), np.float32(0.8697), np.float32(0.9559), np.float32(0.9455), np.float32(0.9725), np.float32(0.964), np.float32(0.9637), np.float32(0.9294), np.float32(0.9719), np.float32(0.9571), np.float32(0.8931), np.float32(0.9473), np.float32(0.958), np.float32(0.8405), np.float32(0.8362), np.float32(0.9034)] 
+2025-05-05 15:53:03.877917: Epoch time: 94.51 s 
+2025-05-05 15:53:08.988376:  
+2025-05-05 15:53:08.994430: Epoch 534 
+2025-05-05 15:53:08.994860: Current learning rate: 0.00756 
+2025-05-05 15:54:47.675923: train_loss -0.475 
+2025-05-05 15:54:47.710753: val_loss -0.4804 
+2025-05-05 15:54:47.729135: Pseudo dice [np.float32(0.8491), np.float32(0.8202), np.float32(0.9057), np.float32(0.9746), np.float32(0.8997), np.float32(0.9624), np.float32(0.9583), np.float32(0.979), np.float32(0.9693), np.float32(0.9629), np.float32(0.9104), np.float32(0.97), np.float32(0.9476), np.float32(0.8997), np.float32(0.9223), np.float32(0.9565), np.float32(0.8649), np.float32(0.8734), np.float32(0.9149)] 
+2025-05-05 15:54:47.755595: Epoch time: 98.69 s 
+2025-05-05 15:54:49.204461:  
+2025-05-05 15:54:49.312657: Epoch 535 
+2025-05-05 15:54:49.357884: Current learning rate: 0.00756 
+2025-05-05 15:56:25.682551: train_loss -0.4856 
+2025-05-05 15:56:25.820298: val_loss -0.5037 
+2025-05-05 15:56:25.842756: Pseudo dice [np.float32(0.831), np.float32(0.8479), np.float32(0.4723), np.float32(0.9641), np.float32(0.8406), np.float32(0.9543), np.float32(0.9587), np.float32(0.9756), np.float32(0.9646), np.float32(0.9554), np.float32(0.9368), np.float32(0.9685), np.float32(0.9521), np.float32(0.8846), np.float32(0.9607), np.float32(0.9285), np.float32(0.8721), np.float32(0.8601), np.float32(0.9058)] 
+2025-05-05 15:56:25.866175: Epoch time: 96.48 s 
+2025-05-05 15:56:27.284552:  
+2025-05-05 15:56:27.407476: Epoch 536 
+2025-05-05 15:56:27.443848: Current learning rate: 0.00755 
+2025-05-05 15:58:04.458373: train_loss -0.4877 
+2025-05-05 15:58:04.594794: val_loss -0.4914 
+2025-05-05 15:58:04.627218: Pseudo dice [np.float32(0.822), np.float32(0.8561), np.float32(0.7991), np.float32(0.9542), np.float32(0.8308), np.float32(0.9566), np.float32(0.966), np.float32(0.9743), np.float32(0.9499), np.float32(0.9604), np.float32(0.9409), np.float32(0.9618), np.float32(0.9662), np.float32(0.8973), np.float32(0.9387), np.float32(0.9516), np.float32(0.8998), np.float32(0.9023), np.float32(0.9153)] 
+2025-05-05 15:58:04.650920: Epoch time: 97.17 s 
+2025-05-05 15:58:06.607199:  
+2025-05-05 15:58:06.682173: Epoch 537 
+2025-05-05 15:58:06.717043: Current learning rate: 0.00755 
+2025-05-05 15:59:40.981970: train_loss -0.4663 
+2025-05-05 15:59:41.035943: val_loss -0.4875 
+2025-05-05 15:59:41.049674: Pseudo dice [np.float32(0.8214), np.float32(0.8409), np.float32(0.7739), np.float32(0.8611), np.float32(0.8833), np.float32(0.948), np.float32(0.9332), np.float32(0.9628), np.float32(0.9616), np.float32(0.9548), np.float32(0.9424), np.float32(0.9632), np.float32(0.9613), np.float32(0.8951), np.float32(0.959), np.float32(0.9408), np.float32(0.8747), np.float32(0.8525), np.float32(0.9042)] 
+2025-05-05 15:59:41.050494: Epoch time: 94.38 s 
+2025-05-05 15:59:42.504185:  
+2025-05-05 15:59:42.541246: Epoch 538 
+2025-05-05 15:59:42.556661: Current learning rate: 0.00754 
+2025-05-05 16:01:19.345383: train_loss -0.4698 
+2025-05-05 16:01:19.457447: val_loss -0.5184 
+2025-05-05 16:01:19.481431: Pseudo dice [np.float32(0.8377), np.float32(0.8361), np.float32(0.8546), np.float32(0.9741), np.float32(0.9043), np.float32(0.9553), np.float32(0.9622), np.float32(0.97), np.float32(0.9645), np.float32(0.9522), np.float32(0.8834), np.float32(0.9716), np.float32(0.948), np.float32(0.8932), np.float32(0.964), np.float32(0.9538), np.float32(0.856), np.float32(0.8441), np.float32(0.9071)] 
+2025-05-05 16:01:19.485233: Epoch time: 96.84 s 
+2025-05-05 16:01:20.952364:  
+2025-05-05 16:01:21.102387: Epoch 539 
+2025-05-05 16:01:21.158717: Current learning rate: 0.00754 
+2025-05-05 16:02:57.871822: train_loss -0.4705 
+2025-05-05 16:02:57.968864: val_loss -0.4662 
+2025-05-05 16:02:57.997367: Pseudo dice [np.float32(0.8093), np.float32(0.8152), np.float32(0.8613), np.float32(0.9711), np.float32(0.901), np.float32(0.9586), np.float32(0.9626), np.float32(0.9749), np.float32(0.9602), np.float32(0.953), np.float32(0.9357), np.float32(0.9695), np.float32(0.9653), np.float32(0.8777), np.float32(0.9514), np.float32(0.9149), np.float32(0.8898), np.float32(0.8645), np.float32(0.9303)] 
+2025-05-05 16:02:58.014303: Epoch time: 96.92 s 
+2025-05-05 16:02:59.494927:  
+2025-05-05 16:02:59.511448: Epoch 540 
+2025-05-05 16:02:59.517133: Current learning rate: 0.00753 
+2025-05-05 16:04:33.151970: train_loss -0.477 
+2025-05-05 16:04:33.246569: val_loss -0.445 
+2025-05-05 16:04:33.272345: Pseudo dice [np.float32(0.8222), np.float32(0.8299), np.float32(0.657), np.float32(0.8764), np.float32(0.8797), np.float32(0.9525), np.float32(0.9576), np.float32(0.9769), np.float32(0.9593), np.float32(0.9544), np.float32(0.9351), np.float32(0.9634), np.float32(0.9429), np.float32(0.8883), np.float32(0.9574), np.float32(0.9436), np.float32(0.8233), np.float32(0.7975), np.float32(0.8867)] 
+2025-05-05 16:04:33.292413: Epoch time: 93.66 s 
+2025-05-05 16:04:34.807624:  
+2025-05-05 16:04:34.873446: Epoch 541 
+2025-05-05 16:04:34.900832: Current learning rate: 0.00753 
+2025-05-05 16:06:06.850800: train_loss -0.4562 
+2025-05-05 16:06:06.948270: val_loss -0.4854 
+2025-05-05 16:06:06.965490: Pseudo dice [np.float32(0.8193), np.float32(0.8218), np.float32(0.9042), np.float32(0.9771), np.float32(0.9174), np.float32(0.9581), np.float32(0.9429), np.float32(0.9728), np.float32(0.9529), np.float32(0.9644), np.float32(0.9487), np.float32(0.958), np.float32(0.9679), np.float32(0.8913), np.float32(0.971), np.float32(0.9555), np.float32(0.8444), np.float32(0.8381), np.float32(0.9131)] 
+2025-05-05 16:06:06.979729: Epoch time: 92.04 s 
+2025-05-05 16:06:08.513125:  
+2025-05-05 16:06:08.569540: Epoch 542 
+2025-05-05 16:06:08.628044: Current learning rate: 0.00752 
+2025-05-05 16:07:44.213612: train_loss -0.506 
+2025-05-05 16:07:44.279291: val_loss -0.4977 
+2025-05-05 16:07:44.303082: Pseudo dice [np.float32(0.7876), np.float32(0.8582), np.float32(0.9061), np.float32(0.976), np.float32(0.9097), np.float32(0.9532), np.float32(0.961), np.float32(0.9765), np.float32(0.9552), np.float32(0.963), np.float32(0.9378), np.float32(0.9588), np.float32(0.9661), np.float32(0.8941), np.float32(0.9651), np.float32(0.9185), np.float32(0.8881), np.float32(0.8801), np.float32(0.9075)] 
+2025-05-05 16:07:44.332355: Epoch time: 95.7 s 
+2025-05-05 16:07:45.883549:  
+2025-05-05 16:07:45.953985: Epoch 543 
+2025-05-05 16:07:45.981032: Current learning rate: 0.00752 
+2025-05-05 16:09:23.599352: train_loss -0.4764 
+2025-05-05 16:09:23.649868: val_loss -0.4682 
+2025-05-05 16:09:23.650787: Pseudo dice [np.float32(0.798), np.float32(0.8461), np.float32(0.9008), np.float32(0.9735), np.float32(0.8862), np.float32(0.9461), np.float32(0.9655), np.float32(0.9809), np.float32(0.9564), np.float32(0.95), np.float32(0.9251), np.float32(0.9655), np.float32(0.9511), np.float32(0.887), np.float32(0.9639), np.float32(0.9442), np.float32(0.8704), np.float32(0.8823), np.float32(0.9055)] 
+2025-05-05 16:09:23.658822: Epoch time: 97.72 s 
+2025-05-05 16:09:25.155089:  
+2025-05-05 16:09:25.231360: Epoch 544 
+2025-05-05 16:09:25.272047: Current learning rate: 0.00751 
+2025-05-05 16:10:55.709800: train_loss -0.469 
+2025-05-05 16:10:55.775718: val_loss -0.4994 
+2025-05-05 16:10:55.811558: Pseudo dice [np.float32(0.7738), np.float32(0.8197), np.float32(0.9112), np.float32(0.9745), np.float32(0.8342), np.float32(0.9551), np.float32(0.9625), np.float32(0.9736), np.float32(0.9518), np.float32(0.9686), np.float32(0.9465), np.float32(0.9715), np.float32(0.9668), np.float32(0.8849), np.float32(0.9638), np.float32(0.9478), np.float32(0.8972), np.float32(0.8912), np.float32(0.9301)] 
+2025-05-05 16:10:55.897424: Epoch time: 90.56 s 
+2025-05-05 16:10:57.513059:  
+2025-05-05 16:10:57.564843: Epoch 545 
+2025-05-05 16:10:57.565663: Current learning rate: 0.00751 
+2025-05-05 16:12:30.676486: train_loss -0.465 
+2025-05-05 16:12:30.750908: val_loss -0.4847 
+2025-05-05 16:12:30.751765: Pseudo dice [np.float32(0.8334), np.float32(0.8452), np.float32(0.8607), np.float32(0.9624), np.float32(0.9052), np.float32(0.9562), np.float32(0.9576), np.float32(0.9763), np.float32(0.959), np.float32(0.9663), np.float32(0.9404), np.float32(0.9675), np.float32(0.9578), np.float32(0.882), np.float32(0.9625), np.float32(0.9524), np.float32(0.8795), np.float32(0.8349), np.float32(0.9101)] 
+2025-05-05 16:12:30.781275: Epoch time: 93.16 s 
+2025-05-05 16:12:32.308325:  
+2025-05-05 16:12:32.396601: Epoch 546 
+2025-05-05 16:12:32.424416: Current learning rate: 0.00751 
+2025-05-05 16:14:08.036025: train_loss -0.4985 
+2025-05-05 16:14:08.116331: val_loss -0.4776 
+2025-05-05 16:14:08.127528: Pseudo dice [np.float32(0.8123), np.float32(0.8225), np.float32(0.802), np.float32(0.9688), np.float32(0.9194), np.float32(0.9649), np.float32(0.9614), np.float32(0.9765), np.float32(0.9677), np.float32(0.9653), np.float32(0.9472), np.float32(0.9656), np.float32(0.969), np.float32(0.893), np.float32(0.9678), np.float32(0.9462), np.float32(0.8704), np.float32(0.8796), np.float32(0.9047)] 
+2025-05-05 16:14:08.145892: Epoch time: 95.73 s 
+2025-05-05 16:14:09.777722:  
+2025-05-05 16:14:09.845695: Epoch 547 
+2025-05-05 16:14:09.865006: Current learning rate: 0.0075 
+2025-05-05 16:15:44.109239: train_loss -0.4761 
+2025-05-05 16:15:44.176069: val_loss -0.4925 
+2025-05-05 16:15:44.191124: Pseudo dice [np.float32(0.7906), np.float32(0.8294), np.float32(0.9193), np.float32(0.975), np.float32(0.9105), np.float32(0.9604), np.float32(0.9552), np.float32(0.9737), np.float32(0.9619), np.float32(0.9566), np.float32(0.9437), np.float32(0.9647), np.float32(0.9698), np.float32(0.8979), np.float32(0.9705), np.float32(0.9504), np.float32(0.8996), np.float32(0.9049), np.float32(0.9302)] 
+2025-05-05 16:15:44.198457: Epoch time: 94.33 s 
+2025-05-05 16:15:45.753234:  
+2025-05-05 16:15:45.886064: Epoch 548 
+2025-05-05 16:15:45.940928: Current learning rate: 0.0075 
+2025-05-05 16:17:17.148787: train_loss -0.4478 
+2025-05-05 16:17:17.203280: val_loss -0.4632 
+2025-05-05 16:17:17.228495: Pseudo dice [np.float32(0.8142), np.float32(0.8101), np.float32(0.9305), np.float32(0.9659), np.float32(0.8751), np.float32(0.9388), np.float32(0.9222), np.float32(0.9762), np.float32(0.9592), np.float32(0.9464), np.float32(0.9208), np.float32(0.9711), np.float32(0.9665), np.float32(0.9004), np.float32(0.964), np.float32(0.9271), np.float32(0.8706), np.float32(0.8559), np.float32(0.9233)] 
+2025-05-05 16:17:17.268576: Epoch time: 91.4 s 
+2025-05-05 16:17:18.766917:  
+2025-05-05 16:17:18.857382: Epoch 549 
+2025-05-05 16:17:18.899662: Current learning rate: 0.00749 
+2025-05-05 16:18:54.347723: train_loss -0.4512 
+2025-05-05 16:18:54.380702: val_loss -0.4656 
+2025-05-05 16:18:54.381587: Pseudo dice [np.float32(0.8366), np.float32(0.8195), np.float32(0.7494), np.float32(0.9769), np.float32(0.9081), np.float32(0.9513), np.float32(0.9629), np.float32(0.9812), np.float32(0.9557), np.float32(0.9617), np.float32(0.9464), np.float32(0.9601), np.float32(0.9617), np.float32(0.8927), np.float32(0.9634), np.float32(0.9521), np.float32(0.8384), np.float32(0.8774), np.float32(0.9068)] 
+2025-05-05 16:18:54.396742: Epoch time: 95.58 s 
+2025-05-05 16:18:56.683986:  
+2025-05-05 16:18:56.769064: Epoch 550 
+2025-05-05 16:18:56.800420: Current learning rate: 0.00749 
+2025-05-05 16:20:39.396766: train_loss -0.4626 
+2025-05-05 16:20:39.410761: val_loss -0.4687 
+2025-05-05 16:20:39.417792: Pseudo dice [np.float32(0.7598), np.float32(0.8454), np.float32(0.87), np.float32(0.9682), np.float32(0.8626), np.float32(0.944), np.float32(0.9527), np.float32(0.9659), np.float32(0.9659), np.float32(0.9423), np.float32(0.9128), np.float32(0.9669), np.float32(0.9282), np.float32(0.8919), np.float32(0.9553), np.float32(0.934), np.float32(0.8627), np.float32(0.8692), np.float32(0.8899)] 
+2025-05-05 16:20:39.429694: Epoch time: 102.71 s 
+2025-05-05 16:20:44.229865:  
+2025-05-05 16:20:44.251985: Epoch 551 
+2025-05-05 16:20:44.262545: Current learning rate: 0.00748 
+2025-05-05 16:22:22.899518: train_loss -0.4632 
+2025-05-05 16:22:22.977371: val_loss -0.4551 
+2025-05-05 16:22:22.978544: Pseudo dice [np.float32(0.8368), np.float32(0.8396), np.float32(0.8847), np.float32(0.972), np.float32(0.8844), np.float32(0.9618), np.float32(0.9376), np.float32(0.9794), np.float32(0.9529), np.float32(0.962), np.float32(0.9335), np.float32(0.9576), np.float32(0.9623), np.float32(0.8941), np.float32(0.9587), np.float32(0.9514), np.float32(0.8253), np.float32(0.9055), np.float32(0.911)] 
+2025-05-05 16:22:23.004947: Epoch time: 98.67 s 
+2025-05-05 16:22:24.512324:  
+2025-05-05 16:22:24.607792: Epoch 552 
+2025-05-05 16:22:24.625210: Current learning rate: 0.00748 
+2025-05-05 16:24:03.095952: train_loss -0.4635 
+2025-05-05 16:24:03.215541: val_loss -0.4683 
+2025-05-05 16:24:03.234458: Pseudo dice [np.float32(0.8216), np.float32(0.7822), np.float32(0.85), np.float32(0.9715), np.float32(0.9019), np.float32(0.9561), np.float32(0.9482), np.float32(0.9481), np.float32(0.9455), np.float32(0.9462), np.float32(0.9366), np.float32(0.9645), np.float32(0.9571), np.float32(0.8883), np.float32(0.9625), np.float32(0.9323), np.float32(0.8679), np.float32(0.8635), np.float32(0.9013)] 
+2025-05-05 16:24:03.270631: Epoch time: 98.58 s 
+2025-05-05 16:24:04.778368:  
+2025-05-05 16:24:04.955650: Epoch 553 
+2025-05-05 16:24:04.989385: Current learning rate: 0.00747 
+2025-05-05 16:25:39.212704: train_loss -0.4764 
+2025-05-05 16:25:39.288653: val_loss -0.4489 
+2025-05-05 16:25:39.314889: Pseudo dice [np.float32(0.8435), np.float32(0.8321), np.float32(0.9152), np.float32(0.9754), np.float32(0.8888), np.float32(0.9526), np.float32(0.9569), np.float32(0.9738), np.float32(0.9591), np.float32(0.9521), np.float32(0.9295), np.float32(0.9665), np.float32(0.9526), np.float32(0.8859), np.float32(0.9252), np.float32(0.9328), np.float32(0.8892), np.float32(0.9072), np.float32(0.9053)] 
+2025-05-05 16:25:39.341078: Epoch time: 94.44 s 
+2025-05-05 16:25:40.835909:  
+2025-05-05 16:25:40.885234: Epoch 554 
+2025-05-05 16:25:40.901354: Current learning rate: 0.00747 
+2025-05-05 16:27:20.524098: train_loss -0.4806 
+2025-05-05 16:27:20.629876: val_loss -0.4951 
+2025-05-05 16:27:20.640281: Pseudo dice [np.float32(0.8479), np.float32(0.8518), np.float32(0.8989), np.float32(0.975), np.float32(0.8964), np.float32(0.9581), np.float32(0.9622), np.float32(0.9758), np.float32(0.9636), np.float32(0.9543), np.float32(0.9346), np.float32(0.9709), np.float32(0.9611), np.float32(0.8975), np.float32(0.9372), np.float32(0.9512), np.float32(0.8747), np.float32(0.8934), np.float32(0.928)] 
+2025-05-05 16:27:20.641062: Epoch time: 99.69 s 
+2025-05-05 16:27:22.106647:  
+2025-05-05 16:27:22.207120: Epoch 555 
+2025-05-05 16:27:22.237150: Current learning rate: 0.00746 
+2025-05-05 16:29:01.773618: train_loss -0.4647 
+2025-05-05 16:29:01.838082: val_loss -0.5177 
+2025-05-05 16:29:01.929661: Pseudo dice [np.float32(0.8161), np.float32(0.8021), np.float32(0.8565), np.float32(0.9652), np.float32(0.8849), np.float32(0.9513), np.float32(0.9614), np.float32(0.9729), np.float32(0.9612), np.float32(0.9576), np.float32(0.9289), np.float32(0.9647), np.float32(0.9601), np.float32(0.8981), np.float32(0.9655), np.float32(0.9408), np.float32(0.7856), np.float32(0.7734), np.float32(0.909)] 
+2025-05-05 16:29:01.979735: Epoch time: 99.67 s 
+2025-05-05 16:29:03.598422:  
+2025-05-05 16:29:03.646075: Epoch 556 
+2025-05-05 16:29:03.646934: Current learning rate: 0.00746 
+2025-05-05 16:30:45.448765: train_loss -0.4666 
+2025-05-05 16:30:45.617737: val_loss -0.5013 
+2025-05-05 16:30:45.677232: Pseudo dice [np.float32(0.8257), np.float32(0.8335), np.float32(0.9292), np.float32(0.9673), np.float32(0.8505), np.float32(0.9639), np.float32(0.9577), np.float32(0.9718), np.float32(0.9636), np.float32(0.9615), np.float32(0.9446), np.float32(0.9605), np.float32(0.963), np.float32(0.8966), np.float32(0.9472), np.float32(0.9508), np.float32(0.8744), np.float32(0.8322), np.float32(0.9194)] 
+2025-05-05 16:30:45.732137: Epoch time: 101.85 s 
+2025-05-05 16:30:47.223645:  
+2025-05-05 16:30:47.351134: Epoch 557 
+2025-05-05 16:30:47.427693: Current learning rate: 0.00745 
+2025-05-05 16:32:25.064939: train_loss -0.488 
+2025-05-05 16:32:25.091015: val_loss -0.479 
+2025-05-05 16:32:25.091849: Pseudo dice [np.float32(0.8209), np.float32(0.8393), np.float32(0.9034), np.float32(0.9322), np.float32(0.89), np.float32(0.9506), np.float32(0.9444), np.float32(0.9563), np.float32(0.9651), np.float32(0.9565), np.float32(0.9369), np.float32(0.9641), np.float32(0.9667), np.float32(0.8806), np.float32(0.9301), np.float32(0.9401), np.float32(0.8342), np.float32(0.8292), np.float32(0.9161)] 
+2025-05-05 16:32:25.095911: Epoch time: 97.84 s 
+2025-05-05 16:32:26.713271:  
+2025-05-05 16:32:26.890233: Epoch 558 
+2025-05-05 16:32:26.911142: Current learning rate: 0.00745 
+2025-05-05 16:34:01.913176: train_loss -0.4713 
+2025-05-05 16:34:02.002896: val_loss -0.5081 
+2025-05-05 16:34:02.013108: Pseudo dice [np.float32(0.8457), np.float32(0.841), np.float32(0.8439), np.float32(0.977), np.float32(0.923), np.float32(0.9628), np.float32(0.9614), np.float32(0.9771), np.float32(0.9569), np.float32(0.9651), np.float32(0.9468), np.float32(0.9635), np.float32(0.9636), np.float32(0.9105), np.float32(0.966), np.float32(0.934), np.float32(0.8195), np.float32(0.8412), np.float32(0.9061)] 
+2025-05-05 16:34:02.024658: Epoch time: 95.2 s 
+2025-05-05 16:34:03.587103:  
+2025-05-05 16:34:03.655419: Epoch 559 
+2025-05-05 16:34:03.687879: Current learning rate: 0.00745 
+2025-05-05 16:35:38.643155: train_loss -0.4764 
+2025-05-05 16:35:38.723019: val_loss -0.4951 
+2025-05-05 16:35:38.726723: Pseudo dice [np.float32(0.8463), np.float32(0.8252), np.float32(0.8271), np.float32(0.9634), np.float32(0.8732), np.float32(0.9541), np.float32(0.9555), np.float32(0.9695), np.float32(0.9436), np.float32(0.9615), np.float32(0.9555), np.float32(0.9522), np.float32(0.9725), np.float32(0.8992), np.float32(0.9676), np.float32(0.9551), np.float32(0.8835), np.float32(0.878), np.float32(0.9127)] 
+2025-05-05 16:35:38.727396: Epoch time: 95.06 s 
+2025-05-05 16:35:40.320139:  
+2025-05-05 16:35:40.386818: Epoch 560 
+2025-05-05 16:35:40.408319: Current learning rate: 0.00744 
+2025-05-05 16:37:19.024527: train_loss -0.4804 
+2025-05-05 16:37:19.040308: val_loss -0.453 
+2025-05-05 16:37:19.040912: Pseudo dice [np.float32(0.8445), np.float32(0.8044), np.float32(0.9241), np.float32(0.9559), np.float32(0.9082), np.float32(0.9625), np.float32(0.9658), np.float32(0.9712), np.float32(0.9498), np.float32(0.9519), np.float32(0.938), np.float32(0.9691), np.float32(0.9456), np.float32(0.8801), np.float32(0.9691), np.float32(0.9466), np.float32(0.8768), np.float32(0.8773), np.float32(0.9104)] 
+2025-05-05 16:37:19.051885: Epoch time: 98.71 s 
+2025-05-05 16:37:20.688000:  
+2025-05-05 16:37:20.696367: Epoch 561 
+2025-05-05 16:37:20.697224: Current learning rate: 0.00744 
+2025-05-05 16:38:52.796715: train_loss -0.4778 
+2025-05-05 16:38:52.925483: val_loss -0.4997 
+2025-05-05 16:38:52.946403: Pseudo dice [np.float32(0.8362), np.float32(0.8174), np.float32(0.6818), np.float32(0.9711), np.float32(0.8558), np.float32(0.9483), np.float32(0.9502), np.float32(0.9747), np.float32(0.9379), np.float32(0.9716), np.float32(0.9425), np.float32(0.9672), np.float32(0.9672), np.float32(0.8941), np.float32(0.9579), np.float32(0.9237), np.float32(0.8358), np.float32(0.839), np.float32(0.8954)] 
+2025-05-05 16:38:52.968094: Epoch time: 92.11 s 
+2025-05-05 16:38:54.424305:  
+2025-05-05 16:38:54.468148: Epoch 562 
+2025-05-05 16:38:54.480193: Current learning rate: 0.00743 
+2025-05-05 16:40:29.641798: train_loss -0.4638 
+2025-05-05 16:40:29.716385: val_loss -0.4392 
+2025-05-05 16:40:29.727008: Pseudo dice [np.float32(0.8483), np.float32(0.8227), np.float32(0.8136), np.float32(0.9734), np.float32(0.8743), np.float32(0.9593), np.float32(0.96), np.float32(0.9678), np.float32(0.9657), np.float32(0.936), np.float32(0.92), np.float32(0.9691), np.float32(0.9466), np.float32(0.8995), np.float32(0.963), np.float32(0.9521), np.float32(0.8837), np.float32(0.8669), np.float32(0.9165)] 
+2025-05-05 16:40:29.735119: Epoch time: 95.22 s 
+2025-05-05 16:40:31.174176:  
+2025-05-05 16:40:31.221803: Epoch 563 
+2025-05-05 16:40:31.222314: Current learning rate: 0.00743 
+2025-05-05 16:42:08.430957: train_loss -0.4575 
+2025-05-05 16:42:08.472563: val_loss -0.4647 
+2025-05-05 16:42:08.479010: Pseudo dice [np.float32(0.7964), np.float32(0.8214), np.float32(0.8905), np.float32(0.9633), np.float32(0.8868), np.float32(0.9591), np.float32(0.9605), np.float32(0.9737), np.float32(0.9539), np.float32(0.9707), np.float32(0.9534), np.float32(0.9556), np.float32(0.9714), np.float32(0.8958), np.float32(0.9546), np.float32(0.9512), np.float32(0.8586), np.float32(0.8602), np.float32(0.9135)] 
+2025-05-05 16:42:08.485642: Epoch time: 97.26 s 
+2025-05-05 16:42:09.942772:  
+2025-05-05 16:42:09.952666: Epoch 564 
+2025-05-05 16:42:09.956861: Current learning rate: 0.00742 
+2025-05-05 16:43:48.772657: train_loss -0.4761 
+2025-05-05 16:43:48.900146: val_loss -0.4887 
+2025-05-05 16:43:48.918999: Pseudo dice [np.float32(0.8114), np.float32(0.823), np.float32(0.9318), np.float32(0.9691), np.float32(0.8923), np.float32(0.9489), np.float32(0.9614), np.float32(0.964), np.float32(0.9421), np.float32(0.9648), np.float32(0.9492), np.float32(0.9649), np.float32(0.9603), np.float32(0.897), np.float32(0.9584), np.float32(0.9373), np.float32(0.878), np.float32(0.889), np.float32(0.9151)] 
+2025-05-05 16:43:48.945198: Epoch time: 98.83 s 
+2025-05-05 16:43:50.432433:  
+2025-05-05 16:43:50.558136: Epoch 565 
+2025-05-05 16:43:50.611615: Current learning rate: 0.00742 
+2025-05-05 16:45:25.958017: train_loss -0.485 
+2025-05-05 16:45:26.022865: val_loss -0.4994 
+2025-05-05 16:45:26.051769: Pseudo dice [np.float32(0.8519), np.float32(0.8133), np.float32(0.871), np.float32(0.9714), np.float32(0.9025), np.float32(0.9568), np.float32(0.9582), np.float32(0.9739), np.float32(0.9608), np.float32(0.9582), np.float32(0.9377), np.float32(0.9667), np.float32(0.9547), np.float32(0.8898), np.float32(0.945), np.float32(0.9435), np.float32(0.8699), np.float32(0.8591), np.float32(0.9013)] 
+2025-05-05 16:45:26.070440: Epoch time: 95.53 s 
+2025-05-05 16:45:27.583679:  
+2025-05-05 16:45:27.635247: Epoch 566 
+2025-05-05 16:45:27.647935: Current learning rate: 0.00741 
+2025-05-05 16:47:05.324264: train_loss -0.4932 
+2025-05-05 16:47:05.414676: val_loss -0.5074 
+2025-05-05 16:47:05.432804: Pseudo dice [np.float32(0.8476), np.float32(0.8228), np.float32(0.7945), np.float32(0.969), np.float32(0.9039), np.float32(0.9513), np.float32(0.9629), np.float32(0.9784), np.float32(0.9543), np.float32(0.9581), np.float32(0.9331), np.float32(0.9646), np.float32(0.9587), np.float32(0.8865), np.float32(0.9617), np.float32(0.961), np.float32(0.8687), np.float32(0.8544), np.float32(0.8973)] 
+2025-05-05 16:47:05.440996: Epoch time: 97.74 s 
+2025-05-05 16:47:06.934891:  
+2025-05-05 16:47:07.015638: Epoch 567 
+2025-05-05 16:47:07.031944: Current learning rate: 0.00741 
+2025-05-05 16:48:45.316746: train_loss -0.4891 
+2025-05-05 16:48:45.470132: val_loss -0.4734 
+2025-05-05 16:48:45.515062: Pseudo dice [np.float32(0.7994), np.float32(0.8337), np.float32(0.9212), np.float32(0.9625), np.float32(0.8921), np.float32(0.9595), np.float32(0.9378), np.float32(0.9742), np.float32(0.9691), np.float32(0.9697), np.float32(0.9504), np.float32(0.9701), np.float32(0.9695), np.float32(0.8896), np.float32(0.9665), np.float32(0.9411), np.float32(0.774), np.float32(0.8504), np.float32(0.9049)] 
+2025-05-05 16:48:45.556865: Epoch time: 98.38 s 
+2025-05-05 16:48:47.048225:  
+2025-05-05 16:48:47.092638: Epoch 568 
+2025-05-05 16:48:47.105831: Current learning rate: 0.0074 
+2025-05-05 16:50:27.067974: train_loss -0.4533 
+2025-05-05 16:50:27.164958: val_loss -0.4882 
+2025-05-05 16:50:27.180078: Pseudo dice [np.float32(0.8031), np.float32(0.7034), np.float32(0.9109), np.float32(0.9589), np.float32(0.857), np.float32(0.9478), np.float32(0.9567), np.float32(0.9747), np.float32(0.9629), np.float32(0.9668), np.float32(0.9532), np.float32(0.9626), np.float32(0.966), np.float32(0.878), np.float32(0.9506), np.float32(0.9338), np.float32(0.8513), np.float32(0.8751), np.float32(0.9087)] 
+2025-05-05 16:50:27.184091: Epoch time: 100.02 s 
+2025-05-05 16:50:28.732189:  
+2025-05-05 16:50:28.808321: Epoch 569 
+2025-05-05 16:50:28.819738: Current learning rate: 0.0074 
+2025-05-05 16:52:01.595479: train_loss -0.4616 
+2025-05-05 16:52:01.684015: val_loss -0.4606 
+2025-05-05 16:52:01.744746: Pseudo dice [np.float32(0.8386), np.float32(0.8114), np.float32(0.8459), np.float32(0.9658), np.float32(0.8308), np.float32(0.9495), np.float32(0.9602), np.float32(0.965), np.float32(0.966), np.float32(0.9444), np.float32(0.8742), np.float32(0.969), np.float32(0.9624), np.float32(0.8821), np.float32(0.9298), np.float32(0.939), np.float32(0.8421), np.float32(0.7804), np.float32(0.9018)] 
+2025-05-05 16:52:01.780924: Epoch time: 92.86 s 
+2025-05-05 16:52:06.952800:  
+2025-05-05 16:52:06.958194: Epoch 570 
+2025-05-05 16:52:06.958575: Current learning rate: 0.00739 
+2025-05-05 16:53:46.980164: train_loss -0.4634 
+2025-05-05 16:53:47.073783: val_loss -0.4799 
+2025-05-05 16:53:47.074847: Pseudo dice [np.float32(0.8236), np.float32(0.7989), np.float32(0.8674), np.float32(0.9561), np.float32(0.8957), np.float32(0.9482), np.float32(0.9546), np.float32(0.9693), np.float32(0.9582), np.float32(0.9581), np.float32(0.9205), np.float32(0.9632), np.float32(0.9447), np.float32(0.8908), np.float32(0.933), np.float32(0.9325), np.float32(0.8743), np.float32(0.8864), np.float32(0.9171)] 
+2025-05-05 16:53:47.080991: Epoch time: 100.03 s 
+2025-05-05 16:53:48.514789:  
+2025-05-05 16:53:48.588289: Epoch 571 
+2025-05-05 16:53:48.618456: Current learning rate: 0.00739 
+2025-05-05 16:55:26.794322: train_loss -0.4682 
+2025-05-05 16:55:26.910486: val_loss -0.4443 
+2025-05-05 16:55:26.922141: Pseudo dice [np.float32(0.809), np.float32(0.8166), np.float32(0.8818), np.float32(0.9675), np.float32(0.779), np.float32(0.9559), np.float32(0.9618), np.float32(0.97), np.float32(0.9669), np.float32(0.9615), np.float32(0.9385), np.float32(0.9667), np.float32(0.9633), np.float32(0.873), np.float32(0.9565), np.float32(0.9422), np.float32(0.8789), np.float32(0.8555), np.float32(0.9117)] 
+2025-05-05 16:55:26.949006: Epoch time: 98.28 s 
+2025-05-05 16:55:28.389952:  
+2025-05-05 16:55:28.466908: Epoch 572 
+2025-05-05 16:55:28.493297: Current learning rate: 0.00738 
+2025-05-05 16:57:06.282286: train_loss -0.4614 
+2025-05-05 16:57:06.377813: val_loss -0.4763 
+2025-05-05 16:57:06.382098: Pseudo dice [np.float32(0.839), np.float32(0.8156), np.float32(0.8546), np.float32(0.9797), np.float32(0.8993), np.float32(0.9496), np.float32(0.9658), np.float32(0.9757), np.float32(0.9616), np.float32(0.972), np.float32(0.9415), np.float32(0.9678), np.float32(0.9704), np.float32(0.8836), np.float32(0.9487), np.float32(0.9515), np.float32(0.886), np.float32(0.8518), np.float32(0.9055)] 
+2025-05-05 16:57:06.382742: Epoch time: 97.89 s 
+2025-05-05 16:57:07.875003:  
+2025-05-05 16:57:07.908373: Epoch 573 
+2025-05-05 16:57:07.929504: Current learning rate: 0.00738 
+2025-05-05 16:58:42.930722: train_loss -0.4819 
+2025-05-05 16:58:43.052614: val_loss -0.483 
+2025-05-05 16:58:43.097227: Pseudo dice [np.float32(0.789), np.float32(0.803), np.float32(0.8757), np.float32(0.9716), np.float32(0.8953), np.float32(0.9514), np.float32(0.9581), np.float32(0.9666), np.float32(0.9563), np.float32(0.9593), np.float32(0.9436), np.float32(0.9617), np.float32(0.9665), np.float32(0.891), np.float32(0.8884), np.float32(0.9285), np.float32(0.7898), np.float32(0.8148), np.float32(0.8979)] 
+2025-05-05 16:58:43.141059: Epoch time: 95.06 s 
+2025-05-05 16:58:44.689936:  
+2025-05-05 16:58:44.738330: Epoch 574 
+2025-05-05 16:58:44.742765: Current learning rate: 0.00738 
+2025-05-05 17:00:18.823441: train_loss -0.4617 
+2025-05-05 17:00:18.960729: val_loss -0.5061 
+2025-05-05 17:00:19.001391: Pseudo dice [np.float32(0.8132), np.float32(0.8131), np.float32(0.8498), np.float32(0.9759), np.float32(0.8529), np.float32(0.9582), np.float32(0.9574), np.float32(0.9692), np.float32(0.9608), np.float32(0.9581), np.float32(0.9375), np.float32(0.9648), np.float32(0.9622), np.float32(0.8805), np.float32(0.9657), np.float32(0.9428), np.float32(0.8535), np.float32(0.8663), np.float32(0.9066)] 
+2025-05-05 17:00:19.025689: Epoch time: 94.13 s 
+2025-05-05 17:00:20.461608:  
+2025-05-05 17:00:20.533844: Epoch 575 
+2025-05-05 17:00:20.548942: Current learning rate: 0.00737 
+2025-05-05 17:01:56.850406: train_loss -0.4602 
+2025-05-05 17:01:56.915185: val_loss -0.5041 
+2025-05-05 17:01:56.933644: Pseudo dice [np.float32(0.81), np.float32(0.8118), np.float32(0.8379), np.float32(0.9717), np.float32(0.8729), np.float32(0.93), np.float32(0.9488), np.float32(0.9779), np.float32(0.9606), np.float32(0.9661), np.float32(0.9501), np.float32(0.9706), np.float32(0.9649), np.float32(0.893), np.float32(0.9482), np.float32(0.9505), np.float32(0.8798), np.float32(0.859), np.float32(0.9174)] 
+2025-05-05 17:01:56.947980: Epoch time: 96.39 s 
+2025-05-05 17:01:58.515706:  
+2025-05-05 17:01:58.639707: Epoch 576 
+2025-05-05 17:01:58.682037: Current learning rate: 0.00737 
+2025-05-05 17:03:33.166712: train_loss -0.4756 
+2025-05-05 17:03:33.246214: val_loss -0.4811 
+2025-05-05 17:03:33.263174: Pseudo dice [np.float32(0.8295), np.float32(0.8539), np.float32(0.8049), np.float32(0.9671), np.float32(0.8847), np.float32(0.9546), np.float32(0.946), np.float32(0.9735), np.float32(0.9695), np.float32(0.9576), np.float32(0.9397), np.float32(0.9688), np.float32(0.9326), np.float32(0.8825), np.float32(0.961), np.float32(0.936), np.float32(0.8819), np.float32(0.895), np.float32(0.9111)] 
+2025-05-05 17:03:33.287368: Epoch time: 94.65 s 
+2025-05-05 17:03:34.713858:  
+2025-05-05 17:03:34.870572: Epoch 577 
+2025-05-05 17:03:34.906771: Current learning rate: 0.00736 
+2025-05-05 17:05:09.916643: train_loss -0.4825 
+2025-05-05 17:05:10.104194: val_loss -0.4466 
+2025-05-05 17:05:10.162245: Pseudo dice [np.float32(0.8459), np.float32(0.8148), np.float32(0.9043), np.float32(0.9738), np.float32(0.8787), np.float32(0.9586), np.float32(0.9557), np.float32(0.9733), np.float32(0.9641), np.float32(0.9463), np.float32(0.9321), np.float32(0.9738), np.float32(0.9553), np.float32(0.8856), np.float32(0.9628), np.float32(0.9472), np.float32(0.875), np.float32(0.8519), np.float32(0.9278)] 
+2025-05-05 17:05:10.219945: Epoch time: 95.2 s 
+2025-05-05 17:05:11.765392:  
+2025-05-05 17:05:11.829229: Epoch 578 
+2025-05-05 17:05:11.849846: Current learning rate: 0.00736 
+2025-05-05 17:06:47.853720: train_loss -0.4698 
+2025-05-05 17:06:47.909998: val_loss -0.5176 
+2025-05-05 17:06:47.917382: Pseudo dice [np.float32(0.8397), np.float32(0.8454), np.float32(0.9099), np.float32(0.9636), np.float32(0.8802), np.float32(0.9557), np.float32(0.9652), np.float32(0.9755), np.float32(0.96), np.float32(0.9694), np.float32(0.9453), np.float32(0.9684), np.float32(0.9663), np.float32(0.8876), np.float32(0.9653), np.float32(0.9361), np.float32(0.8785), np.float32(0.8866), np.float32(0.9089)] 
+2025-05-05 17:06:47.918158: Epoch time: 96.09 s 
+2025-05-05 17:06:49.436242:  
+2025-05-05 17:06:49.542619: Epoch 579 
+2025-05-05 17:06:49.573897: Current learning rate: 0.00735 
+2025-05-05 17:08:26.766897: train_loss -0.4754 
+2025-05-05 17:08:26.824002: val_loss -0.4847 
+2025-05-05 17:08:26.846542: Pseudo dice [np.float32(0.8489), np.float32(0.8291), np.float32(0.8967), np.float32(0.9776), np.float32(0.8966), np.float32(0.9611), np.float32(0.9579), np.float32(0.977), np.float32(0.9664), np.float32(0.9529), np.float32(0.9485), np.float32(0.966), np.float32(0.9623), np.float32(0.881), np.float32(0.9701), np.float32(0.9519), np.float32(0.8547), np.float32(0.8901), np.float32(0.9126)] 
+2025-05-05 17:08:26.875580: Epoch time: 97.33 s 
+2025-05-05 17:08:28.448899:  
+2025-05-05 17:08:28.602593: Epoch 580 
+2025-05-05 17:08:28.642813: Current learning rate: 0.00735 
+2025-05-05 17:10:04.280403: train_loss -0.4859 
+2025-05-05 17:10:04.381618: val_loss -0.521 
+2025-05-05 17:10:04.403962: Pseudo dice [np.float32(0.8293), np.float32(0.8069), np.float32(0.9294), np.float32(0.953), np.float32(0.9137), np.float32(0.9507), np.float32(0.9639), np.float32(0.9741), np.float32(0.9583), np.float32(0.9564), np.float32(0.9488), np.float32(0.958), np.float32(0.9634), np.float32(0.8916), np.float32(0.9647), np.float32(0.9423), np.float32(0.8716), np.float32(0.8557), np.float32(0.9053)] 
+2025-05-05 17:10:04.424060: Epoch time: 95.83 s 
+2025-05-05 17:10:05.979875:  
+2025-05-05 17:10:06.120022: Epoch 581 
+2025-05-05 17:10:06.156143: Current learning rate: 0.00734 
+2025-05-05 17:11:41.600156: train_loss -0.4722 
+2025-05-05 17:11:41.668416: val_loss -0.4917 
+2025-05-05 17:11:41.704763: Pseudo dice [np.float32(0.8263), np.float32(0.7994), np.float32(0.9089), np.float32(0.9678), np.float32(0.89), np.float32(0.9609), np.float32(0.9642), np.float32(0.9693), np.float32(0.9613), np.float32(0.9633), np.float32(0.9456), np.float32(0.9568), np.float32(0.9666), np.float32(0.8935), np.float32(0.9614), np.float32(0.9397), np.float32(0.8095), np.float32(0.8343), np.float32(0.9106)] 
+2025-05-05 17:11:41.715652: Epoch time: 95.62 s 
+2025-05-05 17:11:43.209664:  
+2025-05-05 17:11:43.282530: Epoch 582 
+2025-05-05 17:11:43.326817: Current learning rate: 0.00734 
+2025-05-05 17:13:16.146322: train_loss -0.4759 
+2025-05-05 17:13:16.229770: val_loss -0.5171 
+2025-05-05 17:13:16.270710: Pseudo dice [np.float32(0.8294), np.float32(0.8548), np.float32(0.8949), np.float32(0.9686), np.float32(0.9116), np.float32(0.9589), np.float32(0.967), np.float32(0.9754), np.float32(0.9625), np.float32(0.9631), np.float32(0.9402), np.float32(0.9621), np.float32(0.9641), np.float32(0.9012), np.float32(0.9403), np.float32(0.948), np.float32(0.8436), np.float32(0.863), np.float32(0.9105)] 
+2025-05-05 17:13:16.316815: Epoch time: 92.94 s 
+2025-05-05 17:13:17.919394:  
+2025-05-05 17:13:18.056189: Epoch 583 
+2025-05-05 17:13:18.084820: Current learning rate: 0.00733 
+2025-05-05 17:14:52.433953: train_loss -0.4836 
+2025-05-05 17:14:52.547716: val_loss -0.4991 
+2025-05-05 17:14:52.624348: Pseudo dice [np.float32(0.8124), np.float32(0.8242), np.float32(0.9133), np.float32(0.9654), np.float32(0.902), np.float32(0.9539), np.float32(0.9639), np.float32(0.9795), np.float32(0.9523), np.float32(0.9592), np.float32(0.9346), np.float32(0.9623), np.float32(0.9595), np.float32(0.9042), np.float32(0.9598), np.float32(0.9596), np.float32(0.8258), np.float32(0.8376), np.float32(0.9177)] 
+2025-05-05 17:14:52.670481: Epoch time: 94.52 s 
+2025-05-05 17:14:54.189204:  
+2025-05-05 17:14:54.308105: Epoch 584 
+2025-05-05 17:14:54.337285: Current learning rate: 0.00733 
+2025-05-05 17:16:30.589927: train_loss -0.4731 
+2025-05-05 17:16:30.663687: val_loss -0.5228 
+2025-05-05 17:16:30.664306: Pseudo dice [np.float32(0.837), np.float32(0.8081), np.float32(0.8819), np.float32(0.9729), np.float32(0.9014), np.float32(0.9479), np.float32(0.9637), np.float32(0.9732), np.float32(0.9499), np.float32(0.9591), np.float32(0.9224), np.float32(0.9669), np.float32(0.9565), np.float32(0.8777), np.float32(0.9487), np.float32(0.9264), np.float32(0.8674), np.float32(0.8808), np.float32(0.9081)] 
+2025-05-05 17:16:30.670276: Epoch time: 96.4 s 
+2025-05-05 17:16:32.183923:  
+2025-05-05 17:16:32.215506: Epoch 585 
+2025-05-05 17:16:32.216276: Current learning rate: 0.00732 
+2025-05-05 17:18:08.030491: train_loss -0.4599 
+2025-05-05 17:18:08.143692: val_loss -0.4853 
+2025-05-05 17:18:08.153369: Pseudo dice [np.float32(0.855), np.float32(0.8336), np.float32(0.8245), np.float32(0.9718), np.float32(0.8952), np.float32(0.9523), np.float32(0.9572), np.float32(0.978), np.float32(0.9626), np.float32(0.9535), np.float32(0.9321), np.float32(0.9711), np.float32(0.9604), np.float32(0.8996), np.float32(0.9709), np.float32(0.935), np.float32(0.88), np.float32(0.8538), np.float32(0.9039)] 
+2025-05-05 17:18:08.161808: Epoch time: 95.85 s 
+2025-05-05 17:18:09.720562:  
+2025-05-05 17:18:09.723868: Epoch 586 
+2025-05-05 17:18:09.724535: Current learning rate: 0.00732 
+2025-05-05 17:19:45.399063: train_loss -0.4797 
+2025-05-05 17:19:45.451062: val_loss -0.4515 
+2025-05-05 17:19:45.474704: Pseudo dice [np.float32(0.8386), np.float32(0.8443), np.float32(0.9362), np.float32(0.9804), np.float32(0.8927), np.float32(0.962), np.float32(0.9656), np.float32(0.9735), np.float32(0.9654), np.float32(0.9701), np.float32(0.9487), np.float32(0.9693), np.float32(0.9728), np.float32(0.8817), np.float32(0.9651), np.float32(0.9512), np.float32(0.8828), np.float32(0.872), np.float32(0.9027)] 
+2025-05-05 17:19:45.487380: Epoch time: 95.68 s 
+2025-05-05 17:19:45.502902: Yayy! New best EMA pseudo Dice: 0.9204999804496765 
+2025-05-05 17:19:51.617404:  
+2025-05-05 17:19:51.619195: Epoch 587 
+2025-05-05 17:19:51.619589: Current learning rate: 0.00731 
+2025-05-05 17:21:30.100656: train_loss -0.4856 
+2025-05-05 17:21:30.151484: val_loss -0.4889 
+2025-05-05 17:21:30.152270: Pseudo dice [np.float32(0.8208), np.float32(0.8309), np.float32(0.912), np.float32(0.9676), np.float32(0.9145), np.float32(0.9511), np.float32(0.9609), np.float32(0.9633), np.float32(0.9593), np.float32(0.947), np.float32(0.9273), np.float32(0.9559), np.float32(0.9662), np.float32(0.8846), np.float32(0.9629), np.float32(0.9459), np.float32(0.8181), np.float32(0.8523), np.float32(0.9226)] 
+2025-05-05 17:21:30.165702: Epoch time: 98.48 s 
+2025-05-05 17:21:31.562217:  
+2025-05-05 17:21:31.686048: Epoch 588 
+2025-05-05 17:21:31.724154: Current learning rate: 0.00731 
+2025-05-05 17:23:11.478780: train_loss -0.4796 
+2025-05-05 17:23:11.486363: val_loss -0.5064 
+2025-05-05 17:23:11.486969: Pseudo dice [np.float32(0.8601), np.float32(0.8519), np.float32(0.9107), np.float32(0.9661), np.float32(0.8518), np.float32(0.9548), np.float32(0.9516), np.float32(0.9731), np.float32(0.9519), np.float32(0.955), np.float32(0.941), np.float32(0.9577), np.float32(0.9515), np.float32(0.895), np.float32(0.9629), np.float32(0.9493), np.float32(0.8782), np.float32(0.8798), np.float32(0.9132)] 
+2025-05-05 17:23:11.492446: Epoch time: 99.92 s 
+2025-05-05 17:23:11.493189: Yayy! New best EMA pseudo Dice: 0.9207000136375427 
+2025-05-05 17:23:14.689886:  
+2025-05-05 17:23:14.725858: Epoch 589 
+2025-05-05 17:23:14.728356: Current learning rate: 0.00731 
+2025-05-05 17:24:51.939818: train_loss -0.4662 
+2025-05-05 17:24:52.002665: val_loss -0.4988 
+2025-05-05 17:24:52.017345: Pseudo dice [np.float32(0.8066), np.float32(0.8295), np.float32(0.9075), np.float32(0.9615), np.float32(0.895), np.float32(0.9509), np.float32(0.921), np.float32(0.9662), np.float32(0.951), np.float32(0.9538), np.float32(0.9301), np.float32(0.9486), np.float32(0.9586), np.float32(0.9012), np.float32(0.9609), np.float32(0.9257), np.float32(0.8517), np.float32(0.8181), np.float32(0.9074)] 
+2025-05-05 17:24:52.070122: Epoch time: 97.25 s 
+2025-05-05 17:24:53.555594:  
+2025-05-05 17:24:53.613438: Epoch 590 
+2025-05-05 17:24:53.658013: Current learning rate: 0.0073 
+2025-05-05 17:26:32.250155: train_loss -0.4793 
+2025-05-05 17:26:32.385540: val_loss -0.5164 
+2025-05-05 17:26:32.419149: Pseudo dice [np.float32(0.8423), np.float32(0.8196), np.float32(0.928), np.float32(0.9733), np.float32(0.9039), np.float32(0.9606), np.float32(0.9645), np.float32(0.9637), np.float32(0.9547), np.float32(0.9584), np.float32(0.9415), np.float32(0.9622), np.float32(0.9615), np.float32(0.8967), np.float32(0.9635), np.float32(0.9482), np.float32(0.8691), np.float32(0.8516), np.float32(0.9142)] 
+2025-05-05 17:26:32.448819: Epoch time: 98.7 s 
+2025-05-05 17:26:34.116755:  
+2025-05-05 17:26:34.208909: Epoch 591 
+2025-05-05 17:26:34.232757: Current learning rate: 0.0073 
+2025-05-05 17:28:09.019776: train_loss -0.4716 
+2025-05-05 17:28:09.146616: val_loss -0.4611 
+2025-05-05 17:28:09.184239: Pseudo dice [np.float32(0.823), np.float32(0.8328), np.float32(0.8775), np.float32(0.9719), np.float32(0.8529), np.float32(0.9548), np.float32(0.9583), np.float32(0.9635), np.float32(0.9547), np.float32(0.956), np.float32(0.9363), np.float32(0.9571), np.float32(0.9383), np.float32(0.8937), np.float32(0.9324), np.float32(0.943), np.float32(0.8688), np.float32(0.8969), np.float32(0.8899)] 
+2025-05-05 17:28:09.218863: Epoch time: 94.9 s 
+2025-05-05 17:28:10.785761:  
+2025-05-05 17:28:10.853924: Epoch 592 
+2025-05-05 17:28:10.855179: Current learning rate: 0.00729 
+2025-05-05 17:29:45.255836: train_loss -0.4762 
+2025-05-05 17:29:45.367648: val_loss -0.4905 
+2025-05-05 17:29:45.399206: Pseudo dice [np.float32(0.8019), np.float32(0.8274), np.float32(0.7116), np.float32(0.9684), np.float32(0.9207), np.float32(0.9567), np.float32(0.9624), np.float32(0.9728), np.float32(0.9617), np.float32(0.9577), np.float32(0.9389), np.float32(0.9606), np.float32(0.9671), np.float32(0.9028), np.float32(0.9215), np.float32(0.9515), np.float32(0.8521), np.float32(0.8383), np.float32(0.9094)] 
+2025-05-05 17:29:45.401868: Epoch time: 94.47 s 
+2025-05-05 17:29:46.875950:  
+2025-05-05 17:29:46.946247: Epoch 593 
+2025-05-05 17:29:46.947002: Current learning rate: 0.00729 
+2025-05-05 17:31:21.051764: train_loss -0.4674 
+2025-05-05 17:31:21.120348: val_loss -0.4841 
+2025-05-05 17:31:21.142268: Pseudo dice [np.float32(0.804), np.float32(0.8173), np.float32(0.9335), np.float32(0.9685), np.float32(0.8871), np.float32(0.9499), np.float32(0.9242), np.float32(0.9728), np.float32(0.9511), np.float32(0.9563), np.float32(0.935), np.float32(0.9711), np.float32(0.9605), np.float32(0.8898), np.float32(0.9309), np.float32(0.9296), np.float32(0.8781), np.float32(0.8661), np.float32(0.9177)] 
+2025-05-05 17:31:21.162366: Epoch time: 94.18 s 
+2025-05-05 17:31:22.815183:  
+2025-05-05 17:31:22.854527: Epoch 594 
+2025-05-05 17:31:22.865819: Current learning rate: 0.00728 
+2025-05-05 17:33:00.430288: train_loss -0.4839 
+2025-05-05 17:33:00.519890: val_loss -0.4887 
+2025-05-05 17:33:00.521176: Pseudo dice [np.float32(0.8436), np.float32(0.8512), np.float32(0.9034), np.float32(0.9665), np.float32(0.8818), np.float32(0.9604), np.float32(0.9558), np.float32(0.9744), np.float32(0.9593), np.float32(0.967), np.float32(0.9431), np.float32(0.9684), np.float32(0.9685), np.float32(0.9061), np.float32(0.9657), np.float32(0.9521), np.float32(0.9068), np.float32(0.8864), np.float32(0.912)] 
+2025-05-05 17:33:00.526469: Epoch time: 97.62 s 
+2025-05-05 17:33:02.132512:  
+2025-05-05 17:33:02.179387: Epoch 595 
+2025-05-05 17:33:02.180425: Current learning rate: 0.00728 
+2025-05-05 17:34:38.171684: train_loss -0.4929 
+2025-05-05 17:34:38.262029: val_loss -0.5314 
+2025-05-05 17:34:38.275110: Pseudo dice [np.float32(0.8411), np.float32(0.8541), np.float32(0.9044), np.float32(0.9672), np.float32(0.8935), np.float32(0.9563), np.float32(0.9625), np.float32(0.9773), np.float32(0.9691), np.float32(0.9636), np.float32(0.9264), np.float32(0.9662), np.float32(0.9624), np.float32(0.897), np.float32(0.9689), np.float32(0.9521), np.float32(0.8507), np.float32(0.8638), np.float32(0.8902)] 
+2025-05-05 17:34:38.283085: Epoch time: 96.04 s 
+2025-05-05 17:34:39.757757:  
+2025-05-05 17:34:39.843478: Epoch 596 
+2025-05-05 17:34:39.883195: Current learning rate: 0.00727 
+2025-05-05 17:36:11.952235: train_loss -0.4639 
+2025-05-05 17:36:12.019739: val_loss -0.4823 
+2025-05-05 17:36:12.036452: Pseudo dice [np.float32(0.7916), np.float32(0.8352), np.float32(0.7393), np.float32(0.9591), np.float32(0.8769), np.float32(0.9488), np.float32(0.9593), np.float32(0.9681), np.float32(0.9609), np.float32(0.9599), np.float32(0.9402), np.float32(0.9686), np.float32(0.9592), np.float32(0.8836), np.float32(0.9439), np.float32(0.9204), np.float32(0.881), np.float32(0.8491), np.float32(0.8932)] 
+2025-05-05 17:36:12.047875: Epoch time: 92.2 s 
+2025-05-05 17:36:13.601230:  
+2025-05-05 17:36:13.630034: Epoch 597 
+2025-05-05 17:36:13.630725: Current learning rate: 0.00727 
+2025-05-05 17:37:52.677982: train_loss -0.4837 
+2025-05-05 17:37:52.783476: val_loss -0.5351 
+2025-05-05 17:37:52.815633: Pseudo dice [np.float32(0.8192), np.float32(0.8317), np.float32(0.8347), np.float32(0.9681), np.float32(0.8869), np.float32(0.9521), np.float32(0.958), np.float32(0.9646), np.float32(0.9617), np.float32(0.952), np.float32(0.9459), np.float32(0.9673), np.float32(0.97), np.float32(0.9016), np.float32(0.9612), np.float32(0.9455), np.float32(0.8518), np.float32(0.8643), np.float32(0.9237)] 
+2025-05-05 17:37:52.860664: Epoch time: 99.08 s 
+2025-05-05 17:37:54.386502:  
+2025-05-05 17:37:54.495098: Epoch 598 
+2025-05-05 17:37:54.526058: Current learning rate: 0.00726 
+2025-05-05 17:39:30.316299: train_loss -0.4631 
+2025-05-05 17:39:30.397383: val_loss -0.4515 
+2025-05-05 17:39:30.435652: Pseudo dice [np.float32(0.8278), np.float32(0.8358), np.float32(0.9136), np.float32(0.9633), np.float32(0.9197), np.float32(0.9481), np.float32(0.9616), np.float32(0.9751), np.float32(0.9615), np.float32(0.9592), np.float32(0.9509), np.float32(0.9644), np.float32(0.9653), np.float32(0.8995), np.float32(0.9624), np.float32(0.9441), np.float32(0.8513), np.float32(0.8412), np.float32(0.9266)] 
+2025-05-05 17:39:30.458851: Epoch time: 95.93 s 
+2025-05-05 17:39:31.916784:  
+2025-05-05 17:39:32.033414: Epoch 599 
+2025-05-05 17:39:32.055634: Current learning rate: 0.00726 
+2025-05-05 17:41:10.709019: train_loss -0.5001 
+2025-05-05 17:41:10.780937: val_loss -0.4656 
+2025-05-05 17:41:10.819215: Pseudo dice [np.float32(0.8428), np.float32(0.8228), np.float32(0.9263), np.float32(0.9729), np.float32(0.8675), np.float32(0.9553), np.float32(0.965), np.float32(0.9701), np.float32(0.9621), np.float32(0.9592), np.float32(0.9442), np.float32(0.9672), np.float32(0.9639), np.float32(0.8894), np.float32(0.9579), np.float32(0.9389), np.float32(0.8641), np.float32(0.8696), np.float32(0.923)] 
+2025-05-05 17:41:10.835648: Epoch time: 98.79 s 
+2025-05-05 17:41:13.464441:  
+2025-05-05 17:41:13.534368: Epoch 600 
+2025-05-05 17:41:13.569961: Current learning rate: 0.00725 
+2025-05-05 17:42:51.729862: train_loss -0.4895 
+2025-05-05 17:42:51.837600: val_loss -0.4563 
+2025-05-05 17:42:51.870088: Pseudo dice [np.float32(0.807), np.float32(0.8437), np.float32(0.8703), np.float32(0.9625), np.float32(0.8198), np.float32(0.9619), np.float32(0.9556), np.float32(0.9676), np.float32(0.9449), np.float32(0.9496), np.float32(0.9256), np.float32(0.9668), np.float32(0.9584), np.float32(0.8805), np.float32(0.9662), np.float32(0.9436), np.float32(0.779), np.float32(0.7549), np.float32(0.9116)] 
+2025-05-05 17:42:51.892006: Epoch time: 98.27 s 
+2025-05-05 17:42:53.525165:  
+2025-05-05 17:42:53.581133: Epoch 601 
+2025-05-05 17:42:53.599426: Current learning rate: 0.00725 
+2025-05-05 17:44:30.380583: train_loss -0.4706 
+2025-05-05 17:44:30.439337: val_loss -0.476 
+2025-05-05 17:44:30.456184: Pseudo dice [np.float32(0.8479), np.float32(0.8321), np.float32(0.9225), np.float32(0.9758), np.float32(0.9046), np.float32(0.9261), np.float32(0.9251), np.float32(0.9637), np.float32(0.9572), np.float32(0.95), np.float32(0.9209), np.float32(0.9598), np.float32(0.9599), np.float32(0.8904), np.float32(0.9251), np.float32(0.9293), np.float32(0.8526), np.float32(0.8939), np.float32(0.9018)] 
+2025-05-05 17:44:30.462197: Epoch time: 96.86 s 
+2025-05-05 17:44:31.980189:  
+2025-05-05 17:44:32.084969: Epoch 602 
+2025-05-05 17:44:32.102648: Current learning rate: 0.00724 
+2025-05-05 17:46:06.914722: train_loss -0.4862 
+2025-05-05 17:46:07.031322: val_loss -0.4832 
+2025-05-05 17:46:07.061089: Pseudo dice [np.float32(0.8344), np.float32(0.8416), np.float32(0.9006), np.float32(0.9744), np.float32(0.8983), np.float32(0.9595), np.float32(0.9644), np.float32(0.9751), np.float32(0.961), np.float32(0.9664), np.float32(0.947), np.float32(0.957), np.float32(0.9586), np.float32(0.8957), np.float32(0.9628), np.float32(0.9617), np.float32(0.7633), np.float32(0.7781), np.float32(0.9186)] 
+2025-05-05 17:46:07.095881: Epoch time: 94.94 s 
+2025-05-05 17:46:08.578907:  
+2025-05-05 17:46:08.774629: Epoch 603 
+2025-05-05 17:46:08.817484: Current learning rate: 0.00724 
+2025-05-05 17:47:44.269504: train_loss -0.4915 
+2025-05-05 17:47:44.367356: val_loss -0.4697 
+2025-05-05 17:47:44.378399: Pseudo dice [np.float32(0.8474), np.float32(0.8273), np.float32(0.8387), np.float32(0.9796), np.float32(0.8781), np.float32(0.9612), np.float32(0.9579), np.float32(0.9734), np.float32(0.9503), np.float32(0.9529), np.float32(0.9277), np.float32(0.9629), np.float32(0.9575), np.float32(0.8925), np.float32(0.9269), np.float32(0.9453), np.float32(0.8611), np.float32(0.8681), np.float32(0.8911)] 
+2025-05-05 17:47:44.392550: Epoch time: 95.69 s 
+2025-05-05 17:47:45.922203:  
+2025-05-05 17:47:46.015852: Epoch 604 
+2025-05-05 17:47:46.053467: Current learning rate: 0.00724 
+2025-05-05 17:49:21.881838: train_loss -0.4605 
+2025-05-05 17:49:21.967111: val_loss -0.4985 
+2025-05-05 17:49:22.002025: Pseudo dice [np.float32(0.8294), np.float32(0.7921), np.float32(0.905), np.float32(0.9691), np.float32(0.8978), np.float32(0.9542), np.float32(0.9482), np.float32(0.9582), np.float32(0.9637), np.float32(0.9464), np.float32(0.9381), np.float32(0.9603), np.float32(0.9717), np.float32(0.877), np.float32(0.9423), np.float32(0.9491), np.float32(0.8623), np.float32(0.8897), np.float32(0.9198)] 
+2025-05-05 17:49:22.063883: Epoch time: 95.96 s 
+2025-05-05 17:49:27.291230:  
+2025-05-05 17:49:27.297271: Epoch 605 
+2025-05-05 17:49:27.298448: Current learning rate: 0.00723 
+2025-05-05 17:51:01.089794: train_loss -0.4549 
+2025-05-05 17:51:01.201000: val_loss -0.4977 
+2025-05-05 17:51:01.233232: Pseudo dice [np.float32(0.8486), np.float32(0.8283), np.float32(0.8656), np.float32(0.9795), np.float32(0.8874), np.float32(0.9625), np.float32(0.965), np.float32(0.9691), np.float32(0.9542), np.float32(0.9629), np.float32(0.9505), np.float32(0.9631), np.float32(0.9656), np.float32(0.8912), np.float32(0.9357), np.float32(0.9454), np.float32(0.8444), np.float32(0.8564), np.float32(0.9116)] 
+2025-05-05 17:51:01.264896: Epoch time: 93.8 s 
+2025-05-05 17:51:02.809589:  
+2025-05-05 17:51:02.853778: Epoch 606 
+2025-05-05 17:51:02.878647: Current learning rate: 0.00723 
+2025-05-05 17:52:39.554485: train_loss -0.48 
+2025-05-05 17:52:39.579162: val_loss -0.4898 
+2025-05-05 17:52:39.579699: Pseudo dice [np.float32(0.8336), np.float32(0.8284), np.float32(0.8869), np.float32(0.9617), np.float32(0.8402), np.float32(0.9529), np.float32(0.9504), np.float32(0.9673), np.float32(0.9575), np.float32(0.9573), np.float32(0.9255), np.float32(0.9674), np.float32(0.9513), np.float32(0.9035), np.float32(0.9599), np.float32(0.9448), np.float32(0.8959), np.float32(0.8761), np.float32(0.9041)] 
+2025-05-05 17:52:39.583637: Epoch time: 96.75 s 
+2025-05-05 17:52:41.017240:  
+2025-05-05 17:52:41.076701: Epoch 607 
+2025-05-05 17:52:41.091480: Current learning rate: 0.00722 
+2025-05-05 17:54:15.229916: train_loss -0.4757 
+2025-05-05 17:54:15.273408: val_loss -0.4684 
+2025-05-05 17:54:15.291848: Pseudo dice [np.float32(0.8503), np.float32(0.8272), np.float32(0.8856), np.float32(0.9763), np.float32(0.9173), np.float32(0.9591), np.float32(0.9562), np.float32(0.9747), np.float32(0.9635), np.float32(0.9561), np.float32(0.9331), np.float32(0.9673), np.float32(0.9621), np.float32(0.8902), np.float32(0.9621), np.float32(0.9557), np.float32(0.8578), np.float32(0.8632), np.float32(0.9051)] 
+2025-05-05 17:54:15.307076: Epoch time: 94.21 s 
+2025-05-05 17:54:16.755340:  
+2025-05-05 17:54:16.892961: Epoch 608 
+2025-05-05 17:54:16.948078: Current learning rate: 0.00722 
+2025-05-05 17:55:57.302755: train_loss -0.4746 
+2025-05-05 17:55:57.394794: val_loss -0.4792 
+2025-05-05 17:55:57.408340: Pseudo dice [np.float32(0.7887), np.float32(0.8331), np.float32(0.9121), np.float32(0.9796), np.float32(0.9163), np.float32(0.952), np.float32(0.9552), np.float32(0.9731), np.float32(0.9538), np.float32(0.8592), np.float32(0.9383), np.float32(0.9602), np.float32(0.9674), np.float32(0.8864), np.float32(0.9405), np.float32(0.9265), np.float32(0.8683), np.float32(0.8784), np.float32(0.9035)] 
+2025-05-05 17:55:57.421470: Epoch time: 100.55 s 
+2025-05-05 17:55:58.924468:  
+2025-05-05 17:55:58.929202: Epoch 609 
+2025-05-05 17:55:58.929682: Current learning rate: 0.00721 
+2025-05-05 17:57:32.463112: train_loss -0.4579 
+2025-05-05 17:57:32.586136: val_loss -0.4396 
+2025-05-05 17:57:32.606851: Pseudo dice [np.float32(0.8368), np.float32(0.8412), np.float32(0.8749), np.float32(0.9699), np.float32(0.8392), np.float32(0.9447), np.float32(0.9561), np.float32(0.9675), np.float32(0.938), np.float32(0.9304), np.float32(0.8746), np.float32(0.9588), np.float32(0.9541), np.float32(0.8381), np.float32(0.9653), np.float32(0.9413), np.float32(0.8065), np.float32(0.827), np.float32(0.9068)] 
+2025-05-05 17:57:32.632491: Epoch time: 93.54 s 
+2025-05-05 17:57:34.280018:  
+2025-05-05 17:57:34.387418: Epoch 610 
+2025-05-05 17:57:34.432182: Current learning rate: 0.00721 
+2025-05-05 17:59:13.813616: train_loss -0.4795 
+2025-05-05 17:59:13.887832: val_loss -0.4731 
+2025-05-05 17:59:13.913463: Pseudo dice [np.float32(0.8402), np.float32(0.8375), np.float32(0.9169), np.float32(0.9758), np.float32(0.8914), np.float32(0.9481), np.float32(0.9532), np.float32(0.9793), np.float32(0.9578), np.float32(0.9434), np.float32(0.9148), np.float32(0.9682), np.float32(0.9669), np.float32(0.8965), np.float32(0.961), np.float32(0.9475), np.float32(0.859), np.float32(0.88), np.float32(0.9148)] 
+2025-05-05 17:59:13.934019: Epoch time: 99.54 s 
+2025-05-05 17:59:15.424030:  
+2025-05-05 17:59:15.507984: Epoch 611 
+2025-05-05 17:59:15.508676: Current learning rate: 0.0072 
+2025-05-05 18:00:53.731695: train_loss -0.455 
+2025-05-05 18:00:53.824085: val_loss -0.4683 
+2025-05-05 18:00:53.834925: Pseudo dice [np.float32(0.7986), np.float32(0.8383), np.float32(0.8807), np.float32(0.9706), np.float32(0.872), np.float32(0.934), np.float32(0.9559), np.float32(0.9685), np.float32(0.9568), np.float32(0.9661), np.float32(0.9424), np.float32(0.9664), np.float32(0.9685), np.float32(0.8784), np.float32(0.9611), np.float32(0.9367), np.float32(0.8815), np.float32(0.8687), np.float32(0.8977)] 
+2025-05-05 18:00:53.838623: Epoch time: 98.31 s 
+2025-05-05 18:00:55.264461:  
+2025-05-05 18:00:55.288609: Epoch 612 
+2025-05-05 18:00:55.335247: Current learning rate: 0.0072 
+2025-05-05 18:02:39.960882: train_loss -0.4642 
+2025-05-05 18:02:40.026655: val_loss -0.4751 
+2025-05-05 18:02:40.031899: Pseudo dice [np.float32(0.8116), np.float32(0.8149), np.float32(0.4544), np.float32(0.9224), np.float32(0.861), np.float32(0.9452), np.float32(0.941), np.float32(0.9693), np.float32(0.9665), np.float32(0.9553), np.float32(0.9293), np.float32(0.966), np.float32(0.9419), np.float32(0.8616), np.float32(0.9563), np.float32(0.9384), np.float32(0.8351), np.float32(0.8452), np.float32(0.9207)] 
+2025-05-05 18:02:40.032349: Epoch time: 104.7 s 
+2025-05-05 18:02:41.501153:  
+2025-05-05 18:02:41.561599: Epoch 613 
+2025-05-05 18:02:41.580530: Current learning rate: 0.00719 
+2025-05-05 18:04:18.472365: train_loss -0.481 
+2025-05-05 18:04:18.570314: val_loss -0.4854 
+2025-05-05 18:04:18.626184: Pseudo dice [np.float32(0.8151), np.float32(0.8378), np.float32(0.9322), np.float32(0.9754), np.float32(0.8954), np.float32(0.9527), np.float32(0.9577), np.float32(0.972), np.float32(0.9609), np.float32(0.9727), np.float32(0.95), np.float32(0.9652), np.float32(0.9722), np.float32(0.902), np.float32(0.9608), np.float32(0.9431), np.float32(0.7956), np.float32(0.7782), np.float32(0.9204)] 
+2025-05-05 18:04:18.638927: Epoch time: 96.97 s 
+2025-05-05 18:04:20.304027:  
+2025-05-05 18:04:20.366222: Epoch 614 
+2025-05-05 18:04:20.366712: Current learning rate: 0.00719 
+2025-05-05 18:05:58.834437: train_loss -0.4537 
+2025-05-05 18:05:58.867615: val_loss -0.4812 
+2025-05-05 18:05:58.868381: Pseudo dice [np.float32(0.8472), np.float32(0.8636), np.float32(0.9329), np.float32(0.9665), np.float32(0.8992), np.float32(0.9595), np.float32(0.9412), np.float32(0.9783), np.float32(0.9548), np.float32(0.9655), np.float32(0.9314), np.float32(0.9625), np.float32(0.9655), np.float32(0.9028), np.float32(0.9325), np.float32(0.9411), np.float32(0.8539), np.float32(0.8529), np.float32(0.912)] 
+2025-05-05 18:05:58.868956: Epoch time: 98.53 s 
+2025-05-05 18:06:00.548648:  
+2025-05-05 18:06:00.649367: Epoch 615 
+2025-05-05 18:06:00.675701: Current learning rate: 0.00718 
+2025-05-05 18:07:37.671131: train_loss -0.4808 
+2025-05-05 18:07:37.746658: val_loss -0.4744 
+2025-05-05 18:07:37.779485: Pseudo dice [np.float32(0.8351), np.float32(0.8393), np.float32(0.91), np.float32(0.9773), np.float32(0.8288), np.float32(0.9145), np.float32(0.9291), np.float32(0.9678), np.float32(0.962), np.float32(0.9548), np.float32(0.9265), np.float32(0.9695), np.float32(0.9593), np.float32(0.8879), np.float32(0.9488), np.float32(0.944), np.float32(0.789), np.float32(0.7433), np.float32(0.9112)] 
+2025-05-05 18:07:37.797207: Epoch time: 97.12 s 
+2025-05-05 18:07:39.359798:  
+2025-05-05 18:07:39.399202: Epoch 616 
+2025-05-05 18:07:39.399933: Current learning rate: 0.00718 
+2025-05-05 18:09:16.553873: train_loss -0.4686 
+2025-05-05 18:09:16.635547: val_loss -0.4969 
+2025-05-05 18:09:16.651092: Pseudo dice [np.float32(0.8421), np.float32(0.8544), np.float32(0.9268), np.float32(0.9631), np.float32(0.8787), np.float32(0.9532), np.float32(0.9611), np.float32(0.9723), np.float32(0.9634), np.float32(0.9552), np.float32(0.9325), np.float32(0.9646), np.float32(0.9611), np.float32(0.8846), np.float32(0.9486), np.float32(0.9379), np.float32(0.8751), np.float32(0.8683), np.float32(0.9164)] 
+2025-05-05 18:09:16.663633: Epoch time: 97.2 s 
+2025-05-05 18:09:18.189736:  
+2025-05-05 18:09:18.263128: Epoch 617 
+2025-05-05 18:09:18.296689: Current learning rate: 0.00717 
+2025-05-05 18:10:55.251867: train_loss -0.4589 
+2025-05-05 18:10:55.337110: val_loss -0.4773 
+2025-05-05 18:10:55.338149: Pseudo dice [np.float32(0.8014), np.float32(0.7987), np.float32(0.9014), np.float32(0.8894), np.float32(0.8877), np.float32(0.9548), np.float32(0.9444), np.float32(0.9652), np.float32(0.9646), np.float32(0.966), np.float32(0.9319), np.float32(0.9455), np.float32(0.9608), np.float32(0.8725), np.float32(0.963), np.float32(0.9504), np.float32(0.8063), np.float32(0.8604), np.float32(0.9051)] 
+2025-05-05 18:10:55.371960: Epoch time: 97.06 s 
+2025-05-05 18:10:56.934086:  
+2025-05-05 18:10:57.029123: Epoch 618 
+2025-05-05 18:10:57.094107: Current learning rate: 0.00717 
+2025-05-05 18:12:32.922302: train_loss -0.4793 
+2025-05-05 18:12:33.060623: val_loss -0.4458 
+2025-05-05 18:12:33.088454: Pseudo dice [np.float32(0.8601), np.float32(0.8506), np.float32(0.9256), np.float32(0.9794), np.float32(0.9244), np.float32(0.9563), np.float32(0.9503), np.float32(0.9787), np.float32(0.9533), np.float32(0.9509), np.float32(0.9369), np.float32(0.9539), np.float32(0.9647), np.float32(0.896), np.float32(0.9509), np.float32(0.9524), np.float32(0.848), np.float32(0.8503), np.float32(0.9098)] 
+2025-05-05 18:12:33.112315: Epoch time: 95.99 s 
+2025-05-05 18:12:34.645037:  
+2025-05-05 18:12:34.743776: Epoch 619 
+2025-05-05 18:12:34.768491: Current learning rate: 0.00717 
+2025-05-05 18:14:10.477360: train_loss -0.478 
+2025-05-05 18:14:10.510278: val_loss -0.492 
+2025-05-05 18:14:10.513035: Pseudo dice [np.float32(0.8365), np.float32(0.8456), np.float32(0.9247), np.float32(0.9733), np.float32(0.8706), np.float32(0.9635), np.float32(0.9609), np.float32(0.9793), np.float32(0.9612), np.float32(0.9584), np.float32(0.9488), np.float32(0.9603), np.float32(0.9665), np.float32(0.8737), np.float32(0.9562), np.float32(0.9531), np.float32(0.8565), np.float32(0.8732), np.float32(0.9083)] 
+2025-05-05 18:14:10.524076: Epoch time: 95.83 s 
+2025-05-05 18:14:12.110488:  
+2025-05-05 18:14:12.221420: Epoch 620 
+2025-05-05 18:14:12.242752: Current learning rate: 0.00716 
+2025-05-05 18:15:44.541748: train_loss -0.4674 
+2025-05-05 18:15:44.658147: val_loss -0.5088 
+2025-05-05 18:15:44.692015: Pseudo dice [np.float32(0.8314), np.float32(0.8342), np.float32(0.915), np.float32(0.9785), np.float32(0.8275), np.float32(0.9559), np.float32(0.9642), np.float32(0.9774), np.float32(0.9601), np.float32(0.969), np.float32(0.9492), np.float32(0.9663), np.float32(0.9661), np.float32(0.903), np.float32(0.9371), np.float32(0.9458), np.float32(0.84), np.float32(0.8209), np.float32(0.929)] 
+2025-05-05 18:15:44.726323: Epoch time: 92.43 s 
+2025-05-05 18:15:46.287709:  
+2025-05-05 18:15:46.291101: Epoch 621 
+2025-05-05 18:15:46.291564: Current learning rate: 0.00716 
+2025-05-05 18:17:22.888445: train_loss -0.4713 
+2025-05-05 18:17:22.924786: val_loss -0.49 
+2025-05-05 18:17:22.939881: Pseudo dice [np.float32(0.8316), np.float32(0.7842), np.float32(0.915), np.float32(0.9725), np.float32(0.8919), np.float32(0.9514), np.float32(0.9534), np.float32(0.9763), np.float32(0.9631), np.float32(0.9612), np.float32(0.9527), np.float32(0.963), np.float32(0.971), np.float32(0.8838), np.float32(0.9615), np.float32(0.9388), np.float32(0.8578), np.float32(0.883), np.float32(0.9272)] 
+2025-05-05 18:17:22.955136: Epoch time: 96.6 s 
+2025-05-05 18:17:24.520498:  
+2025-05-05 18:17:24.609469: Epoch 622 
+2025-05-05 18:17:24.610318: Current learning rate: 0.00715 
+2025-05-05 18:18:59.703421: train_loss -0.4922 
+2025-05-05 18:18:59.811073: val_loss -0.4896 
+2025-05-05 18:18:59.844865: Pseudo dice [np.float32(0.844), np.float32(0.8068), np.float32(0.9026), np.float32(0.9712), np.float32(0.8466), np.float32(0.9537), np.float32(0.9634), np.float32(0.9709), np.float32(0.9556), np.float32(0.9542), np.float32(0.9404), np.float32(0.9614), np.float32(0.9662), np.float32(0.883), np.float32(0.9586), np.float32(0.9517), np.float32(0.8047), np.float32(0.8377), np.float32(0.9016)] 
+2025-05-05 18:18:59.859339: Epoch time: 95.19 s 
+2025-05-05 18:19:04.875009:  
+2025-05-05 18:19:04.879696: Epoch 623 
+2025-05-05 18:19:04.880090: Current learning rate: 0.00715 
+2025-05-05 18:20:36.686867: train_loss -0.4892 
+2025-05-05 18:20:36.872020: val_loss -0.4851 
+2025-05-05 18:20:36.923430: Pseudo dice [np.float32(0.8554), np.float32(0.83), np.float32(0.9217), np.float32(0.9799), np.float32(0.895), np.float32(0.963), np.float32(0.958), np.float32(0.9747), np.float32(0.9454), np.float32(0.9706), np.float32(0.9454), np.float32(0.9453), np.float32(0.9643), np.float32(0.8797), np.float32(0.9683), np.float32(0.9559), np.float32(0.8718), np.float32(0.8603), np.float32(0.9151)] 
+2025-05-05 18:20:36.953525: Epoch time: 91.81 s 
+2025-05-05 18:20:38.521305:  
+2025-05-05 18:20:38.587291: Epoch 624 
+2025-05-05 18:20:38.626177: Current learning rate: 0.00714 
+2025-05-05 18:22:15.652014: train_loss -0.4795 
+2025-05-05 18:22:15.690477: val_loss -0.5072 
+2025-05-05 18:22:15.708838: Pseudo dice [np.float32(0.8276), np.float32(0.8391), np.float32(0.7769), np.float32(0.9692), np.float32(0.8433), np.float32(0.9517), np.float32(0.9526), np.float32(0.9765), np.float32(0.9577), np.float32(0.9535), np.float32(0.9143), np.float32(0.964), np.float32(0.9585), np.float32(0.8811), np.float32(0.9669), np.float32(0.946), np.float32(0.8622), np.float32(0.8643), np.float32(0.9022)] 
+2025-05-05 18:22:15.723107: Epoch time: 97.13 s 
+2025-05-05 18:22:17.204091:  
+2025-05-05 18:22:17.286079: Epoch 625 
+2025-05-05 18:22:17.299330: Current learning rate: 0.00714 
+2025-05-05 18:23:48.997899: train_loss -0.4484 
+2025-05-05 18:23:49.128211: val_loss -0.4784 
+2025-05-05 18:23:49.152985: Pseudo dice [np.float32(0.8395), np.float32(0.8447), np.float32(0.8754), np.float32(0.9734), np.float32(0.9004), np.float32(0.9588), np.float32(0.9568), np.float32(0.9703), np.float32(0.9234), np.float32(0.9175), np.float32(0.9313), np.float32(0.9617), np.float32(0.962), np.float32(0.8816), np.float32(0.9666), np.float32(0.949), np.float32(0.7675), np.float32(0.7842), np.float32(0.9009)] 
+2025-05-05 18:23:49.173835: Epoch time: 91.8 s 
+2025-05-05 18:23:50.681690:  
+2025-05-05 18:23:50.704341: Epoch 626 
+2025-05-05 18:23:50.709506: Current learning rate: 0.00713 
+2025-05-05 18:25:29.119584: train_loss -0.4617 
+2025-05-05 18:25:29.200593: val_loss -0.4743 
+2025-05-05 18:25:29.237039: Pseudo dice [np.float32(0.8126), np.float32(0.8413), np.float32(0.9048), np.float32(0.9697), np.float32(0.8855), np.float32(0.9522), np.float32(0.9617), np.float32(0.9747), np.float32(0.9591), np.float32(0.963), np.float32(0.9491), np.float32(0.968), np.float32(0.9639), np.float32(0.8835), np.float32(0.9589), np.float32(0.9413), np.float32(0.8823), np.float32(0.8962), np.float32(0.9155)] 
+2025-05-05 18:25:29.242670: Epoch time: 98.44 s 
+2025-05-05 18:25:30.738497:  
+2025-05-05 18:25:30.815299: Epoch 627 
+2025-05-05 18:25:30.839506: Current learning rate: 0.00713 
+2025-05-05 18:27:11.130018: train_loss -0.4423 
+2025-05-05 18:27:11.242872: val_loss -0.5117 
+2025-05-05 18:27:11.274405: Pseudo dice [np.float32(0.8329), np.float32(0.8517), np.float32(0.9087), np.float32(0.9722), np.float32(0.9074), np.float32(0.9469), np.float32(0.9625), np.float32(0.9547), np.float32(0.9554), np.float32(0.9596), np.float32(0.9418), np.float32(0.9647), np.float32(0.9648), np.float32(0.894), np.float32(0.9644), np.float32(0.9444), np.float32(0.8751), np.float32(0.8184), np.float32(0.9046)] 
+2025-05-05 18:27:11.289563: Epoch time: 100.39 s 
+2025-05-05 18:27:12.842678:  
+2025-05-05 18:27:12.884017: Epoch 628 
+2025-05-05 18:27:12.900735: Current learning rate: 0.00712 
+2025-05-05 18:28:50.924461: train_loss -0.4827 
+2025-05-05 18:28:50.986110: val_loss -0.491 
+2025-05-05 18:28:50.997921: Pseudo dice [np.float32(0.8625), np.float32(0.8446), np.float32(0.6211), np.float32(0.9702), np.float32(0.8871), np.float32(0.9505), np.float32(0.9535), np.float32(0.9634), np.float32(0.9497), np.float32(0.9648), np.float32(0.9309), np.float32(0.9546), np.float32(0.9641), np.float32(0.8791), np.float32(0.9615), np.float32(0.9399), np.float32(0.8605), np.float32(0.8292), np.float32(0.9154)] 
+2025-05-05 18:28:51.008728: Epoch time: 98.08 s 
+2025-05-05 18:28:52.563920:  
+2025-05-05 18:28:52.680491: Epoch 629 
+2025-05-05 18:28:52.720726: Current learning rate: 0.00712 
+2025-05-05 18:30:27.853001: train_loss -0.4798 
+2025-05-05 18:30:27.925714: val_loss -0.4736 
+2025-05-05 18:30:27.932431: Pseudo dice [np.float32(0.8314), np.float32(0.8148), np.float32(0.8595), np.float32(0.9701), np.float32(0.8514), np.float32(0.9553), np.float32(0.9446), np.float32(0.9773), np.float32(0.9594), np.float32(0.97), np.float32(0.9493), np.float32(0.9521), np.float32(0.9673), np.float32(0.89), np.float32(0.9601), np.float32(0.9518), np.float32(0.834), np.float32(0.845), np.float32(0.9179)] 
+2025-05-05 18:30:27.936262: Epoch time: 95.29 s 
+2025-05-05 18:30:29.604263:  
+2025-05-05 18:30:29.682431: Epoch 630 
+2025-05-05 18:30:29.683388: Current learning rate: 0.00711 
+2025-05-05 18:32:02.454556: train_loss -0.4821 
+2025-05-05 18:32:02.543928: val_loss -0.4691 
+2025-05-05 18:32:02.557200: Pseudo dice [np.float32(0.825), np.float32(0.8251), np.float32(0.864), np.float32(0.9732), np.float32(0.8926), np.float32(0.9244), np.float32(0.9581), np.float32(0.9754), np.float32(0.9371), np.float32(0.959), np.float32(0.9299), np.float32(0.9641), np.float32(0.966), np.float32(0.8848), np.float32(0.9489), np.float32(0.9403), np.float32(0.8675), np.float32(0.8954), np.float32(0.9121)] 
+2025-05-05 18:32:02.570131: Epoch time: 92.85 s 
+2025-05-05 18:32:04.180340:  
+2025-05-05 18:32:04.216677: Epoch 631 
+2025-05-05 18:32:04.217597: Current learning rate: 0.00711 
+2025-05-05 18:33:38.992335: train_loss -0.4506 
+2025-05-05 18:33:39.158083: val_loss -0.494 
+2025-05-05 18:33:39.191235: Pseudo dice [np.float32(0.8205), np.float32(0.8074), np.float32(0.8329), np.float32(0.973), np.float32(0.893), np.float32(0.9595), np.float32(0.9431), np.float32(0.9763), np.float32(0.9695), np.float32(0.9563), np.float32(0.9386), np.float32(0.9747), np.float32(0.9673), np.float32(0.8955), np.float32(0.9614), np.float32(0.9507), np.float32(0.89), np.float32(0.8896), np.float32(0.9282)] 
+2025-05-05 18:33:39.198752: Epoch time: 94.81 s 
+2025-05-05 18:33:40.720591:  
+2025-05-05 18:33:40.853575: Epoch 632 
+2025-05-05 18:33:40.884794: Current learning rate: 0.0071 
+2025-05-05 18:35:15.294148: train_loss -0.4767 
+2025-05-05 18:35:15.429097: val_loss -0.4779 
+2025-05-05 18:35:15.463005: Pseudo dice [np.float32(0.8496), np.float32(0.852), np.float32(0.9268), np.float32(0.9723), np.float32(0.8814), np.float32(0.9613), np.float32(0.9671), np.float32(0.9697), np.float32(0.9539), np.float32(0.9668), np.float32(0.9476), np.float32(0.9607), np.float32(0.9699), np.float32(0.8829), np.float32(0.891), np.float32(0.9449), np.float32(0.86), np.float32(0.8394), np.float32(0.9125)] 
+2025-05-05 18:35:15.510918: Epoch time: 94.57 s 
+2025-05-05 18:35:17.150003:  
+2025-05-05 18:35:17.247318: Epoch 633 
+2025-05-05 18:35:17.272979: Current learning rate: 0.0071 
+2025-05-05 18:36:50.341746: train_loss -0.4742 
+2025-05-05 18:36:50.423206: val_loss -0.4529 
+2025-05-05 18:36:50.460205: Pseudo dice [np.float32(0.8311), np.float32(0.8409), np.float32(0.9162), np.float32(0.9551), np.float32(0.7747), np.float32(0.9508), np.float32(0.9634), np.float32(0.9734), np.float32(0.9644), np.float32(0.9576), np.float32(0.8951), np.float32(0.964), np.float32(0.9427), np.float32(0.906), np.float32(0.952), np.float32(0.9418), np.float32(0.9025), np.float32(0.897), np.float32(0.9093)] 
+2025-05-05 18:36:50.490935: Epoch time: 93.19 s 
+2025-05-05 18:36:51.938620:  
+2025-05-05 18:36:52.036763: Epoch 634 
+2025-05-05 18:36:52.060760: Current learning rate: 0.0071 
+2025-05-05 18:38:23.813236: train_loss -0.4702 
+2025-05-05 18:38:23.854864: val_loss -0.4975 
+2025-05-05 18:38:23.862938: Pseudo dice [np.float32(0.8472), np.float32(0.8307), np.float32(0.9113), np.float32(0.9759), np.float32(0.9059), np.float32(0.9612), np.float32(0.9577), np.float32(0.9664), np.float32(0.9597), np.float32(0.9562), np.float32(0.9424), np.float32(0.9647), np.float32(0.9664), np.float32(0.8983), np.float32(0.9575), np.float32(0.9507), np.float32(0.8636), np.float32(0.8917), np.float32(0.919)] 
+2025-05-05 18:38:23.863845: Epoch time: 91.88 s 
+2025-05-05 18:38:25.362214:  
+2025-05-05 18:38:25.476463: Epoch 635 
+2025-05-05 18:38:25.501817: Current learning rate: 0.00709 
+2025-05-05 18:40:04.498592: train_loss -0.4779 
+2025-05-05 18:40:04.534837: val_loss -0.4613 
+2025-05-05 18:40:04.580835: Pseudo dice [np.float32(0.8109), np.float32(0.8401), np.float32(0.8757), np.float32(0.9709), np.float32(0.8515), np.float32(0.9584), np.float32(0.964), np.float32(0.9757), np.float32(0.9586), np.float32(0.956), np.float32(0.9259), np.float32(0.9512), np.float32(0.9661), np.float32(0.8979), np.float32(0.9639), np.float32(0.9419), np.float32(0.8883), np.float32(0.8959), np.float32(0.9092)] 
+2025-05-05 18:40:04.599009: Epoch time: 99.14 s 
+2025-05-05 18:40:06.173731:  
+2025-05-05 18:40:06.184957: Epoch 636 
+2025-05-05 18:40:06.185498: Current learning rate: 0.00709 
+2025-05-05 18:41:43.602060: train_loss -0.4923 
+2025-05-05 18:41:43.694847: val_loss -0.4957 
+2025-05-05 18:41:43.708214: Pseudo dice [np.float32(0.8211), np.float32(0.8104), np.float32(0.8321), np.float32(0.9774), np.float32(0.9003), np.float32(0.9564), np.float32(0.9624), np.float32(0.9771), np.float32(0.9567), np.float32(0.9561), np.float32(0.935), np.float32(0.9686), np.float32(0.9531), np.float32(0.8853), np.float32(0.9525), np.float32(0.9461), np.float32(0.8492), np.float32(0.8978), np.float32(0.9189)] 
+2025-05-05 18:41:43.727130: Epoch time: 97.43 s 
+2025-05-05 18:41:45.307242:  
+2025-05-05 18:41:45.367493: Epoch 637 
+2025-05-05 18:41:45.371872: Current learning rate: 0.00708 
+2025-05-05 18:43:19.993383: train_loss -0.4947 
+2025-05-05 18:43:20.041216: val_loss -0.415 
+2025-05-05 18:43:20.041913: Pseudo dice [np.float32(0.7862), np.float32(0.8244), np.float32(0.8451), np.float32(0.9691), np.float32(0.8568), np.float32(0.943), np.float32(0.9665), np.float32(0.9637), np.float32(0.9121), np.float32(0.9567), np.float32(0.9321), np.float32(0.9067), np.float32(0.9532), np.float32(0.8705), np.float32(0.9595), np.float32(0.9314), np.float32(0.9005), np.float32(0.868), np.float32(0.9079)] 
+2025-05-05 18:43:20.042440: Epoch time: 94.69 s 
+2025-05-05 18:43:21.543562:  
+2025-05-05 18:43:21.571142: Epoch 638 
+2025-05-05 18:43:21.590142: Current learning rate: 0.00708 
+2025-05-05 18:44:57.339960: train_loss -0.4879 
+2025-05-05 18:44:57.363799: val_loss -0.4672 
+2025-05-05 18:44:57.367972: Pseudo dice [np.float32(0.8547), np.float32(0.8323), np.float32(0.8592), np.float32(0.9744), np.float32(0.8649), np.float32(0.9596), np.float32(0.9673), np.float32(0.9732), np.float32(0.9537), np.float32(0.9622), np.float32(0.9392), np.float32(0.964), np.float32(0.9688), np.float32(0.8898), np.float32(0.9624), np.float32(0.9396), np.float32(0.8787), np.float32(0.8739), np.float32(0.9187)] 
+2025-05-05 18:44:57.368528: Epoch time: 95.8 s 
+2025-05-05 18:44:58.910195:  
+2025-05-05 18:44:59.064844: Epoch 639 
+2025-05-05 18:44:59.103696: Current learning rate: 0.00707 
+2025-05-05 18:46:32.346061: train_loss -0.4748 
+2025-05-05 18:46:32.422435: val_loss -0.4974 
+2025-05-05 18:46:32.453552: Pseudo dice [np.float32(0.8401), np.float32(0.8521), np.float32(0.8394), np.float32(0.9742), np.float32(0.92), np.float32(0.9626), np.float32(0.9513), np.float32(0.9776), np.float32(0.9446), np.float32(0.9566), np.float32(0.9349), np.float32(0.9634), np.float32(0.9535), np.float32(0.9057), np.float32(0.9394), np.float32(0.9569), np.float32(0.8708), np.float32(0.8953), np.float32(0.9184)] 
+2025-05-05 18:46:32.491714: Epoch time: 93.44 s 
+2025-05-05 18:46:37.772202:  
+2025-05-05 18:46:37.778398: Epoch 640 
+2025-05-05 18:46:37.778821: Current learning rate: 0.00707 
+2025-05-05 18:48:12.505268: train_loss -0.4685 
+2025-05-05 18:48:12.617362: val_loss -0.4938 
+2025-05-05 18:48:12.660203: Pseudo dice [np.float32(0.8542), np.float32(0.8284), np.float32(0.9334), np.float32(0.9743), np.float32(0.8316), np.float32(0.9615), np.float32(0.9631), np.float32(0.9775), np.float32(0.9377), np.float32(0.9749), np.float32(0.9433), np.float32(0.969), np.float32(0.9658), np.float32(0.8867), np.float32(0.9594), np.float32(0.9456), np.float32(0.876), np.float32(0.889), np.float32(0.9202)] 
+2025-05-05 18:48:12.749564: Epoch time: 94.73 s 
+2025-05-05 18:48:14.489967:  
+2025-05-05 18:48:14.566526: Epoch 641 
+2025-05-05 18:48:14.614676: Current learning rate: 0.00706 
+2025-05-05 18:49:46.954164: train_loss -0.472 
+2025-05-05 18:49:46.985885: val_loss -0.4879 
+2025-05-05 18:49:47.000972: Pseudo dice [np.float32(0.8315), np.float32(0.8235), np.float32(0.8991), np.float32(0.9689), np.float32(0.9153), np.float32(0.9519), np.float32(0.9532), np.float32(0.9684), np.float32(0.9635), np.float32(0.9674), np.float32(0.9417), np.float32(0.9671), np.float32(0.9637), np.float32(0.8962), np.float32(0.9665), np.float32(0.9506), np.float32(0.8576), np.float32(0.7467), np.float32(0.9243)] 
+2025-05-05 18:49:47.017663: Epoch time: 92.47 s 
+2025-05-05 18:49:48.578042:  
+2025-05-05 18:49:48.632070: Epoch 642 
+2025-05-05 18:49:48.668511: Current learning rate: 0.00706 
+2025-05-05 18:51:22.927490: train_loss -0.4817 
+2025-05-05 18:51:23.075680: val_loss -0.4771 
+2025-05-05 18:51:23.090896: Pseudo dice [np.float32(0.8339), np.float32(0.8489), np.float32(0.8514), np.float32(0.9757), np.float32(0.9003), np.float32(0.9577), np.float32(0.9609), np.float32(0.9762), np.float32(0.9686), np.float32(0.9542), np.float32(0.9233), np.float32(0.9719), np.float32(0.9598), np.float32(0.8977), np.float32(0.9697), np.float32(0.9528), np.float32(0.8463), np.float32(0.8333), np.float32(0.8958)] 
+2025-05-05 18:51:23.111507: Epoch time: 94.35 s 
+2025-05-05 18:51:24.580614:  
+2025-05-05 18:51:24.621367: Epoch 643 
+2025-05-05 18:51:24.622270: Current learning rate: 0.00705 
+2025-05-05 18:53:02.484455: train_loss -0.4818 
+2025-05-05 18:53:02.613106: val_loss -0.4745 
+2025-05-05 18:53:02.653704: Pseudo dice [np.float32(0.8294), np.float32(0.8269), np.float32(0.9129), np.float32(0.9773), np.float32(0.8681), np.float32(0.956), np.float32(0.9655), np.float32(0.9757), np.float32(0.9644), np.float32(0.9528), np.float32(0.9466), np.float32(0.9716), np.float32(0.9675), np.float32(0.8745), np.float32(0.9589), np.float32(0.9486), np.float32(0.8966), np.float32(0.8714), np.float32(0.9238)] 
+2025-05-05 18:53:02.676895: Epoch time: 97.9 s 
+2025-05-05 18:53:04.124663:  
+2025-05-05 18:53:04.183337: Epoch 644 
+2025-05-05 18:53:04.184554: Current learning rate: 0.00705 
+2025-05-05 18:54:44.396912: train_loss -0.4659 
+2025-05-05 18:54:44.503572: val_loss -0.458 
+2025-05-05 18:54:44.516665: Pseudo dice [np.float32(0.7913), np.float32(0.842), np.float32(0.7735), np.float32(0.9565), np.float32(0.9064), np.float32(0.9579), np.float32(0.9593), np.float32(0.9714), np.float32(0.9678), np.float32(0.9606), np.float32(0.9142), np.float32(0.9679), np.float32(0.9418), np.float32(0.8681), np.float32(0.9509), np.float32(0.9411), np.float32(0.8615), np.float32(0.8841), np.float32(0.8988)] 
+2025-05-05 18:54:44.556851: Epoch time: 100.27 s 
+2025-05-05 18:54:46.205461:  
+2025-05-05 18:54:46.320489: Epoch 645 
+2025-05-05 18:54:46.321269: Current learning rate: 0.00704 
+2025-05-05 18:56:19.649441: train_loss -0.4633 
+2025-05-05 18:56:19.679933: val_loss -0.5385 
+2025-05-05 18:56:19.684277: Pseudo dice [np.float32(0.8406), np.float32(0.8088), np.float32(0.8361), np.float32(0.9761), np.float32(0.8644), np.float32(0.9279), np.float32(0.9374), np.float32(0.9713), np.float32(0.9631), np.float32(0.9532), np.float32(0.9367), np.float32(0.9653), np.float32(0.967), np.float32(0.892), np.float32(0.9592), np.float32(0.9442), np.float32(0.8682), np.float32(0.8749), np.float32(0.9122)] 
+2025-05-05 18:56:19.684846: Epoch time: 93.45 s 
+2025-05-05 18:56:21.141645:  
+2025-05-05 18:56:21.235354: Epoch 646 
+2025-05-05 18:56:21.259518: Current learning rate: 0.00704 
+2025-05-05 18:57:55.107518: train_loss -0.4898 
+2025-05-05 18:57:55.212196: val_loss -0.5129 
+2025-05-05 18:57:55.236508: Pseudo dice [np.float32(0.8218), np.float32(0.8394), np.float32(0.8105), np.float32(0.9695), np.float32(0.8772), np.float32(0.9587), np.float32(0.9254), np.float32(0.9663), np.float32(0.9552), np.float32(0.9523), np.float32(0.9329), np.float32(0.9655), np.float32(0.9673), np.float32(0.9003), np.float32(0.9584), np.float32(0.9494), np.float32(0.8726), np.float32(0.8387), np.float32(0.9252)] 
+2025-05-05 18:57:55.249159: Epoch time: 93.97 s 
+2025-05-05 18:57:56.731020:  
+2025-05-05 18:57:56.868722: Epoch 647 
+2025-05-05 18:57:56.915874: Current learning rate: 0.00703 
+2025-05-05 18:59:28.438622: train_loss -0.4745 
+2025-05-05 18:59:28.551188: val_loss -0.4887 
+2025-05-05 18:59:28.573261: Pseudo dice [np.float32(0.805), np.float32(0.8105), np.float32(0.8909), np.float32(0.9732), np.float32(0.8084), np.float32(0.9528), np.float32(0.955), np.float32(0.9755), np.float32(0.94), np.float32(0.9628), np.float32(0.9332), np.float32(0.9521), np.float32(0.9591), np.float32(0.8682), np.float32(0.9544), np.float32(0.9403), np.float32(0.8541), np.float32(0.8648), np.float32(0.9122)] 
+2025-05-05 18:59:28.609871: Epoch time: 91.71 s 
+2025-05-05 18:59:30.082771:  
+2025-05-05 18:59:30.138731: Epoch 648 
+2025-05-05 18:59:30.175641: Current learning rate: 0.00703 
+2025-05-05 19:01:05.876590: train_loss -0.4743 
+2025-05-05 19:01:05.918353: val_loss -0.4646 
+2025-05-05 19:01:05.919484: Pseudo dice [np.float32(0.785), np.float32(0.8328), np.float32(0.9303), np.float32(0.9738), np.float32(0.8956), np.float32(0.9574), np.float32(0.9553), np.float32(0.9772), np.float32(0.9352), np.float32(0.9371), np.float32(0.9318), np.float32(0.9597), np.float32(0.9585), np.float32(0.8792), np.float32(0.9406), np.float32(0.9314), np.float32(0.8664), np.float32(0.8375), np.float32(0.9088)] 
+2025-05-05 19:01:05.924638: Epoch time: 95.79 s 
+2025-05-05 19:01:07.508639:  
+2025-05-05 19:01:07.516962: Epoch 649 
+2025-05-05 19:01:07.533272: Current learning rate: 0.00703 
+2025-05-05 19:02:42.259781: train_loss -0.4707 
+2025-05-05 19:02:42.296486: val_loss -0.4505 
+2025-05-05 19:02:42.307101: Pseudo dice [np.float32(0.8373), np.float32(0.8272), np.float32(0.8601), np.float32(0.9735), np.float32(0.9075), np.float32(0.9504), np.float32(0.9644), np.float32(0.9802), np.float32(0.9515), np.float32(0.9652), np.float32(0.9323), np.float32(0.9554), np.float32(0.9388), np.float32(0.8854), np.float32(0.9661), np.float32(0.9469), np.float32(0.815), np.float32(0.838), np.float32(0.9157)] 
+2025-05-05 19:02:42.320881: Epoch time: 94.75 s 
+2025-05-05 19:02:44.720972:  
+2025-05-05 19:02:44.830728: Epoch 650 
+2025-05-05 19:02:44.867665: Current learning rate: 0.00702 
+2025-05-05 19:04:23.289924: train_loss -0.4837 
+2025-05-05 19:04:23.372681: val_loss -0.4897 
+2025-05-05 19:04:23.405706: Pseudo dice [np.float32(0.8025), np.float32(0.8157), np.float32(0.8697), np.float32(0.9668), np.float32(0.8562), np.float32(0.9559), np.float32(0.9661), np.float32(0.969), np.float32(0.9517), np.float32(0.9586), np.float32(0.9385), np.float32(0.9615), np.float32(0.9553), np.float32(0.8901), np.float32(0.9576), np.float32(0.9521), np.float32(0.8311), np.float32(0.8608), np.float32(0.9018)] 
+2025-05-05 19:04:23.462449: Epoch time: 98.57 s 
+2025-05-05 19:04:25.061852:  
+2025-05-05 19:04:25.111785: Epoch 651 
+2025-05-05 19:04:25.121770: Current learning rate: 0.00702 
+2025-05-05 19:05:57.901974: train_loss -0.4752 
+2025-05-05 19:05:57.967388: val_loss -0.496 
+2025-05-05 19:05:57.979455: Pseudo dice [np.float32(0.856), np.float32(0.8561), np.float32(0.4458), np.float32(0.9768), np.float32(0.8899), np.float32(0.9538), np.float32(0.9469), np.float32(0.9713), np.float32(0.9469), np.float32(0.954), np.float32(0.9391), np.float32(0.9595), np.float32(0.961), np.float32(0.8965), np.float32(0.9452), np.float32(0.9541), np.float32(0.8704), np.float32(0.8725), np.float32(0.9001)] 
+2025-05-05 19:05:57.979982: Epoch time: 92.84 s 
+2025-05-05 19:05:59.536606:  
+2025-05-05 19:05:59.638586: Epoch 652 
+2025-05-05 19:05:59.639508: Current learning rate: 0.00701 
+2025-05-05 19:07:34.993587: train_loss -0.4829 
+2025-05-05 19:07:35.043958: val_loss -0.4733 
+2025-05-05 19:07:35.061197: Pseudo dice [np.float32(0.8387), np.float32(0.8281), np.float32(0.8923), np.float32(0.9742), np.float32(0.8835), np.float32(0.9533), np.float32(0.959), np.float32(0.9663), np.float32(0.944), np.float32(0.9648), np.float32(0.9197), np.float32(0.9603), np.float32(0.9612), np.float32(0.9001), np.float32(0.9548), np.float32(0.9508), np.float32(0.8438), np.float32(0.8371), np.float32(0.9008)] 
+2025-05-05 19:07:35.080805: Epoch time: 95.46 s 
+2025-05-05 19:07:36.701111:  
+2025-05-05 19:07:36.758358: Epoch 653 
+2025-05-05 19:07:36.759197: Current learning rate: 0.00701 
+2025-05-05 19:09:09.159604: train_loss -0.4808 
+2025-05-05 19:09:09.343426: val_loss -0.499 
+2025-05-05 19:09:09.377779: Pseudo dice [np.float32(0.8521), np.float32(0.7934), np.float32(0.8927), np.float32(0.9451), np.float32(0.8863), np.float32(0.9621), np.float32(0.9475), np.float32(0.9751), np.float32(0.957), np.float32(0.9622), np.float32(0.9369), np.float32(0.9437), np.float32(0.963), np.float32(0.9201), np.float32(0.957), np.float32(0.9489), np.float32(0.8709), np.float32(0.8552), np.float32(0.9166)] 
+2025-05-05 19:09:09.418615: Epoch time: 92.46 s 
+2025-05-05 19:09:10.957176:  
+2025-05-05 19:09:11.158608: Epoch 654 
+2025-05-05 19:09:11.203943: Current learning rate: 0.007 
+2025-05-05 19:10:45.057740: train_loss -0.4681 
+2025-05-05 19:10:45.130703: val_loss -0.4987 
+2025-05-05 19:10:45.138176: Pseudo dice [np.float32(0.7804), np.float32(0.839), np.float32(0.9001), np.float32(0.9691), np.float32(0.8296), np.float32(0.9358), np.float32(0.9437), np.float32(0.9725), np.float32(0.9345), np.float32(0.9654), np.float32(0.929), np.float32(0.9601), np.float32(0.9646), np.float32(0.8885), np.float32(0.9664), np.float32(0.9524), np.float32(0.8905), np.float32(0.9034), np.float32(0.9244)] 
+2025-05-05 19:10:45.154562: Epoch time: 94.1 s 
+2025-05-05 19:10:46.738274:  
+2025-05-05 19:10:46.799676: Epoch 655 
+2025-05-05 19:10:46.829072: Current learning rate: 0.007 
+2025-05-05 19:12:16.541567: train_loss -0.4648 
+2025-05-05 19:12:16.569088: val_loss -0.4917 
+2025-05-05 19:12:16.591224: Pseudo dice [np.float32(0.81), np.float32(0.8469), np.float32(0.8884), np.float32(0.976), np.float32(0.9106), np.float32(0.9499), np.float32(0.9616), np.float32(0.9752), np.float32(0.9545), np.float32(0.9523), np.float32(0.9172), np.float32(0.9673), np.float32(0.9594), np.float32(0.8904), np.float32(0.9259), np.float32(0.9526), np.float32(0.8825), np.float32(0.8053), np.float32(0.9168)] 
+2025-05-05 19:12:16.641032: Epoch time: 89.8 s 
+2025-05-05 19:12:18.205189:  
+2025-05-05 19:12:18.282290: Epoch 656 
+2025-05-05 19:12:18.327006: Current learning rate: 0.00699 
+2025-05-05 19:13:52.971455: train_loss -0.4823 
+2025-05-05 19:13:53.079448: val_loss -0.4925 
+2025-05-05 19:13:53.098161: Pseudo dice [np.float32(0.8377), np.float32(0.8538), np.float32(0.8416), np.float32(0.9701), np.float32(0.8862), np.float32(0.9601), np.float32(0.9643), np.float32(0.9764), np.float32(0.9588), np.float32(0.9546), np.float32(0.9384), np.float32(0.959), np.float32(0.9677), np.float32(0.8915), np.float32(0.9611), np.float32(0.9441), np.float32(0.8687), np.float32(0.8838), np.float32(0.9112)] 
+2025-05-05 19:13:53.105807: Epoch time: 94.77 s 
+2025-05-05 19:13:54.589605:  
+2025-05-05 19:13:54.728627: Epoch 657 
+2025-05-05 19:13:54.768607: Current learning rate: 0.00699 
+2025-05-05 19:15:28.664631: train_loss -0.4789 
+2025-05-05 19:15:28.762403: val_loss -0.4486 
+2025-05-05 19:15:28.811923: Pseudo dice [np.float32(0.8059), np.float32(0.8532), np.float32(0.8926), np.float32(0.9756), np.float32(0.8915), np.float32(0.9425), np.float32(0.9579), np.float32(0.9716), np.float32(0.9602), np.float32(0.9584), np.float32(0.9434), np.float32(0.9617), np.float32(0.9675), np.float32(0.8986), np.float32(0.9676), np.float32(0.9486), np.float32(0.8278), np.float32(0.8775), np.float32(0.9077)] 
+2025-05-05 19:15:28.847807: Epoch time: 94.08 s 
+2025-05-05 19:15:34.104839:  
+2025-05-05 19:15:34.110718: Epoch 658 
+2025-05-05 19:15:34.111186: Current learning rate: 0.00698 
+2025-05-05 19:17:10.392230: train_loss -0.4715 
+2025-05-05 19:17:10.468659: val_loss -0.4691 
+2025-05-05 19:17:10.473238: Pseudo dice [np.float32(0.8183), np.float32(0.8342), np.float32(0.6848), np.float32(0.9716), np.float32(0.8653), np.float32(0.9586), np.float32(0.9597), np.float32(0.9703), np.float32(0.9617), np.float32(0.948), np.float32(0.9232), np.float32(0.965), np.float32(0.9284), np.float32(0.897), np.float32(0.9644), np.float32(0.9528), np.float32(0.8619), np.float32(0.8888), np.float32(0.9032)] 
+2025-05-05 19:17:10.473892: Epoch time: 96.29 s 
+2025-05-05 19:17:12.206544:  
+2025-05-05 19:17:12.323049: Epoch 659 
+2025-05-05 19:17:12.367972: Current learning rate: 0.00698 
+2025-05-05 19:18:53.001651: train_loss -0.4647 
+2025-05-05 19:18:53.061155: val_loss -0.4897 
+2025-05-05 19:18:53.062329: Pseudo dice [np.float32(0.8301), np.float32(0.8256), np.float32(0.8017), np.float32(0.9692), np.float32(0.8579), np.float32(0.9547), np.float32(0.9492), np.float32(0.9714), np.float32(0.958), np.float32(0.9533), np.float32(0.9186), np.float32(0.9659), np.float32(0.9588), np.float32(0.8739), np.float32(0.9651), np.float32(0.9375), np.float32(0.843), np.float32(0.8697), np.float32(0.9189)] 
+2025-05-05 19:18:53.097103: Epoch time: 100.8 s 
+2025-05-05 19:18:54.586320:  
+2025-05-05 19:18:54.737547: Epoch 660 
+2025-05-05 19:18:54.764761: Current learning rate: 0.00697 
+2025-05-05 19:20:29.702353: train_loss -0.4712 
+2025-05-05 19:20:29.775273: val_loss -0.5082 
+2025-05-05 19:20:29.798853: Pseudo dice [np.float32(0.828), np.float32(0.8257), np.float32(0.8754), np.float32(0.967), np.float32(0.8586), np.float32(0.9587), np.float32(0.9523), np.float32(0.969), np.float32(0.9507), np.float32(0.9539), np.float32(0.9381), np.float32(0.9698), np.float32(0.9624), np.float32(0.8979), np.float32(0.9537), np.float32(0.9585), np.float32(0.8415), np.float32(0.8509), np.float32(0.9136)] 
+2025-05-05 19:20:29.839402: Epoch time: 95.12 s 
+2025-05-05 19:20:31.367691:  
+2025-05-05 19:20:31.420768: Epoch 661 
+2025-05-05 19:20:31.421434: Current learning rate: 0.00697 
+2025-05-05 19:22:05.302012: train_loss -0.4748 
+2025-05-05 19:22:05.464639: val_loss -0.4994 
+2025-05-05 19:22:05.497996: Pseudo dice [np.float32(0.8239), np.float32(0.8327), np.float32(0.9068), np.float32(0.9762), np.float32(0.9148), np.float32(0.9605), np.float32(0.9568), np.float32(0.9745), np.float32(0.9592), np.float32(0.9657), np.float32(0.9393), np.float32(0.9664), np.float32(0.9625), np.float32(0.9023), np.float32(0.9673), np.float32(0.9428), np.float32(0.8698), np.float32(0.8394), np.float32(0.9198)] 
+2025-05-05 19:22:05.537331: Epoch time: 93.94 s 
+2025-05-05 19:22:07.066449:  
+2025-05-05 19:22:07.183175: Epoch 662 
+2025-05-05 19:22:07.198170: Current learning rate: 0.00696 
+2025-05-05 19:23:41.134429: train_loss -0.453 
+2025-05-05 19:23:41.211628: val_loss -0.5007 
+2025-05-05 19:23:41.225095: Pseudo dice [np.float32(0.8178), np.float32(0.8218), np.float32(0.8924), np.float32(0.9715), np.float32(0.9019), np.float32(0.9241), np.float32(0.9607), np.float32(0.9743), np.float32(0.9557), np.float32(0.9589), np.float32(0.9402), np.float32(0.9615), np.float32(0.9626), np.float32(0.8894), np.float32(0.938), np.float32(0.9299), np.float32(0.8599), np.float32(0.8564), np.float32(0.9152)] 
+2025-05-05 19:23:41.250794: Epoch time: 94.07 s 
+2025-05-05 19:23:42.679566:  
+2025-05-05 19:23:42.686179: Epoch 663 
+2025-05-05 19:23:42.697609: Current learning rate: 0.00696 
+2025-05-05 19:25:15.975838: train_loss -0.4576 
+2025-05-05 19:25:16.108577: val_loss -0.4916 
+2025-05-05 19:25:16.127321: Pseudo dice [np.float32(0.8409), np.float32(0.831), np.float32(0.9048), np.float32(0.9735), np.float32(0.8877), np.float32(0.9567), np.float32(0.965), np.float32(0.9744), np.float32(0.9542), np.float32(0.9602), np.float32(0.9349), np.float32(0.9652), np.float32(0.9642), np.float32(0.8915), np.float32(0.9684), np.float32(0.9384), np.float32(0.8961), np.float32(0.8696), np.float32(0.9188)] 
+2025-05-05 19:25:16.175667: Epoch time: 93.3 s 
+2025-05-05 19:25:17.736687:  
+2025-05-05 19:25:17.801089: Epoch 664 
+2025-05-05 19:25:17.826030: Current learning rate: 0.00696 
+2025-05-05 19:26:47.648514: train_loss -0.4815 
+2025-05-05 19:26:47.744502: val_loss -0.4801 
+2025-05-05 19:26:47.788524: Pseudo dice [np.float32(0.7876), np.float32(0.8315), np.float32(0.8771), np.float32(0.9537), np.float32(0.8655), np.float32(0.9552), np.float32(0.953), np.float32(0.9652), np.float32(0.9526), np.float32(0.9688), np.float32(0.9417), np.float32(0.9631), np.float32(0.9664), np.float32(0.8697), np.float32(0.9628), np.float32(0.9372), np.float32(0.889), np.float32(0.8976), np.float32(0.9059)] 
+2025-05-05 19:26:47.831539: Epoch time: 89.91 s 
+2025-05-05 19:26:49.330715:  
+2025-05-05 19:26:49.403456: Epoch 665 
+2025-05-05 19:26:49.439105: Current learning rate: 0.00695 
+2025-05-05 19:28:28.683114: train_loss -0.4722 
+2025-05-05 19:28:28.759902: val_loss -0.4617 
+2025-05-05 19:28:28.779709: Pseudo dice [np.float32(0.8449), np.float32(0.8548), np.float32(0.9284), np.float32(0.9781), np.float32(0.8773), np.float32(0.9534), np.float32(0.9592), np.float32(0.9729), np.float32(0.9631), np.float32(0.971), np.float32(0.9392), np.float32(0.9698), np.float32(0.9577), np.float32(0.9038), np.float32(0.9594), np.float32(0.9432), np.float32(0.8269), np.float32(0.8415), np.float32(0.9068)] 
+2025-05-05 19:28:28.805974: Epoch time: 99.35 s 
+2025-05-05 19:28:30.295955:  
+2025-05-05 19:28:30.353322: Epoch 666 
+2025-05-05 19:28:30.361069: Current learning rate: 0.00695 
+2025-05-05 19:30:05.447288: train_loss -0.4991 
+2025-05-05 19:30:05.564796: val_loss -0.4899 
+2025-05-05 19:30:05.626030: Pseudo dice [np.float32(0.8416), np.float32(0.8384), np.float32(0.7062), np.float32(0.9681), np.float32(0.6689), np.float32(0.9231), np.float32(0.9613), np.float32(0.9731), np.float32(0.9554), np.float32(0.9533), np.float32(0.9339), np.float32(0.943), np.float32(0.9642), np.float32(0.8903), np.float32(0.9607), np.float32(0.946), np.float32(0.8907), np.float32(0.8849), np.float32(0.9062)] 
+2025-05-05 19:30:05.644133: Epoch time: 95.15 s 
+2025-05-05 19:30:07.207462:  
+2025-05-05 19:30:07.293473: Epoch 667 
+2025-05-05 19:30:07.297874: Current learning rate: 0.00694 
+2025-05-05 19:31:43.187482: train_loss -0.4617 
+2025-05-05 19:31:43.287392: val_loss -0.5404 
+2025-05-05 19:31:43.330798: Pseudo dice [np.float32(0.7905), np.float32(0.8169), np.float32(0.9166), np.float32(0.9722), np.float32(0.8953), np.float32(0.9592), np.float32(0.9573), np.float32(0.9737), np.float32(0.9696), np.float32(0.9607), np.float32(0.9341), np.float32(0.9723), np.float32(0.9559), np.float32(0.8855), np.float32(0.9513), np.float32(0.9569), np.float32(0.8623), np.float32(0.8544), np.float32(0.9087)] 
+2025-05-05 19:31:43.365396: Epoch time: 95.98 s 
+2025-05-05 19:31:44.966554:  
+2025-05-05 19:31:45.122818: Epoch 668 
+2025-05-05 19:31:45.153272: Current learning rate: 0.00694 
+2025-05-05 19:33:16.517408: train_loss -0.4754 
+2025-05-05 19:33:16.925936: val_loss -0.4925 
+2025-05-05 19:33:16.951486: Pseudo dice [np.float32(0.8531), np.float32(0.8196), np.float32(0.9276), np.float32(0.9672), np.float32(0.878), np.float32(0.9511), np.float32(0.9516), np.float32(0.9773), np.float32(0.9558), np.float32(0.9638), np.float32(0.9391), np.float32(0.9693), np.float32(0.9649), np.float32(0.8973), np.float32(0.9102), np.float32(0.9349), np.float32(0.8575), np.float32(0.888), np.float32(0.9038)] 
+2025-05-05 19:33:16.967358: Epoch time: 91.55 s 
+2025-05-05 19:33:18.458305:  
+2025-05-05 19:33:18.533286: Epoch 669 
+2025-05-05 19:33:18.559184: Current learning rate: 0.00693 
+2025-05-05 19:34:52.484623: train_loss -0.465 
+2025-05-05 19:34:52.514262: val_loss -0.4347 
+2025-05-05 19:34:52.515680: Pseudo dice [np.float32(0.8152), np.float32(0.8301), np.float32(0.8927), np.float32(0.9716), np.float32(0.8861), np.float32(0.9539), np.float32(0.9492), np.float32(0.9773), np.float32(0.9502), np.float32(0.9506), np.float32(0.9287), np.float32(0.9621), np.float32(0.9576), np.float32(0.8712), np.float32(0.9612), np.float32(0.9411), np.float32(0.7778), np.float32(0.6873), np.float32(0.9001)] 
+2025-05-05 19:34:52.528282: Epoch time: 94.03 s 
+2025-05-05 19:34:54.007913:  
+2025-05-05 19:34:54.075862: Epoch 670 
+2025-05-05 19:34:54.101444: Current learning rate: 0.00693 
+2025-05-05 19:36:29.134069: train_loss -0.4809 
+2025-05-05 19:36:29.231339: val_loss -0.4793 
+2025-05-05 19:36:29.262737: Pseudo dice [np.float32(0.7849), np.float32(0.8412), np.float32(0.8962), np.float32(0.9755), np.float32(0.9063), np.float32(0.9543), np.float32(0.9545), np.float32(0.9744), np.float32(0.9586), np.float32(0.9647), np.float32(0.9387), np.float32(0.9641), np.float32(0.9647), np.float32(0.9008), np.float32(0.9458), np.float32(0.924), np.float32(0.8894), np.float32(0.8795), np.float32(0.9207)] 
+2025-05-05 19:36:29.292424: Epoch time: 95.13 s 
+2025-05-05 19:36:30.905798:  
+2025-05-05 19:36:30.999051: Epoch 671 
+2025-05-05 19:36:31.021879: Current learning rate: 0.00692 
+2025-05-05 19:38:05.158636: train_loss -0.4896 
+2025-05-05 19:38:05.242810: val_loss -0.5373 
+2025-05-05 19:38:05.258489: Pseudo dice [np.float32(0.8303), np.float32(0.8194), np.float32(0.8593), np.float32(0.9768), np.float32(0.8782), np.float32(0.9618), np.float32(0.9689), np.float32(0.9757), np.float32(0.9607), np.float32(0.9654), np.float32(0.9469), np.float32(0.9663), np.float32(0.9657), np.float32(0.9017), np.float32(0.9616), np.float32(0.9495), np.float32(0.8869), np.float32(0.8785), np.float32(0.9193)] 
+2025-05-05 19:38:05.276551: Epoch time: 94.25 s 
+2025-05-05 19:38:06.812094:  
+2025-05-05 19:38:06.897924: Epoch 672 
+2025-05-05 19:38:06.912374: Current learning rate: 0.00692 
+2025-05-05 19:39:45.849121: train_loss -0.4737 
+2025-05-05 19:39:45.910850: val_loss -0.4638 
+2025-05-05 19:39:45.919703: Pseudo dice [np.float32(0.843), np.float32(0.8537), np.float32(0.9287), np.float32(0.969), np.float32(0.9104), np.float32(0.9584), np.float32(0.9564), np.float32(0.9743), np.float32(0.9612), np.float32(0.9517), np.float32(0.9244), np.float32(0.967), np.float32(0.9602), np.float32(0.8991), np.float32(0.9513), np.float32(0.9384), np.float32(0.8485), np.float32(0.8669), np.float32(0.9029)] 
+2025-05-05 19:39:45.921804: Epoch time: 99.04 s 
+2025-05-05 19:39:47.471808:  
+2025-05-05 19:39:47.612690: Epoch 673 
+2025-05-05 19:39:47.696521: Current learning rate: 0.00691 
+2025-05-05 19:41:25.334423: train_loss -0.4796 
+2025-05-05 19:41:25.402130: val_loss -0.4634 
+2025-05-05 19:41:25.430410: Pseudo dice [np.float32(0.823), np.float32(0.8437), np.float32(0.9218), np.float32(0.9663), np.float32(0.8822), np.float32(0.9564), np.float32(0.9622), np.float32(0.9741), np.float32(0.9491), np.float32(0.9644), np.float32(0.9215), np.float32(0.9487), np.float32(0.9637), np.float32(0.8906), np.float32(0.9602), np.float32(0.9434), np.float32(0.8894), np.float32(0.7835), np.float32(0.9211)] 
+2025-05-05 19:41:25.438801: Epoch time: 97.86 s 
+2025-05-05 19:41:27.042316:  
+2025-05-05 19:41:27.103801: Epoch 674 
+2025-05-05 19:41:27.108323: Current learning rate: 0.00691 
+2025-05-05 19:43:07.345840: train_loss -0.494 
+2025-05-05 19:43:07.482997: val_loss -0.4916 
+2025-05-05 19:43:07.520104: Pseudo dice [np.float32(0.8323), np.float32(0.8361), np.float32(0.8481), np.float32(0.976), np.float32(0.9002), np.float32(0.9468), np.float32(0.9461), np.float32(0.9634), np.float32(0.9594), np.float32(0.9602), np.float32(0.9303), np.float32(0.9552), np.float32(0.9603), np.float32(0.8811), np.float32(0.943), np.float32(0.9382), np.float32(0.8804), np.float32(0.8692), np.float32(0.901)] 
+2025-05-05 19:43:07.555123: Epoch time: 100.3 s 
+2025-05-05 19:43:09.084468:  
+2025-05-05 19:43:09.144350: Epoch 675 
+2025-05-05 19:43:09.149011: Current learning rate: 0.0069 
+2025-05-05 19:44:40.347923: train_loss -0.4756 
+2025-05-05 19:44:40.387871: val_loss -0.4903 
+2025-05-05 19:44:40.389127: Pseudo dice [np.float32(0.8018), np.float32(0.8215), np.float32(0.8817), np.float32(0.9762), np.float32(0.9237), np.float32(0.942), np.float32(0.9645), np.float32(0.9757), np.float32(0.9601), np.float32(0.9526), np.float32(0.941), np.float32(0.9667), np.float32(0.9609), np.float32(0.917), np.float32(0.9634), np.float32(0.9595), np.float32(0.8929), np.float32(0.8793), np.float32(0.9227)] 
+2025-05-05 19:44:40.389697: Epoch time: 91.26 s 
+2025-05-05 19:44:45.677099:  
+2025-05-05 19:44:45.683226: Epoch 676 
+2025-05-05 19:44:45.683699: Current learning rate: 0.0069 
+2025-05-05 19:46:22.708030: train_loss -0.4912 
+2025-05-05 19:46:22.749748: val_loss -0.4637 
+2025-05-05 19:46:22.750549: Pseudo dice [np.float32(0.8261), np.float32(0.834), np.float32(0.8864), np.float32(0.973), np.float32(0.8261), np.float32(0.9552), np.float32(0.9599), np.float32(0.9596), np.float32(0.9518), np.float32(0.9669), np.float32(0.9272), np.float32(0.9597), np.float32(0.9654), np.float32(0.8959), np.float32(0.9434), np.float32(0.9492), np.float32(0.8838), np.float32(0.8841), np.float32(0.9233)] 
+2025-05-05 19:46:22.751162: Epoch time: 97.03 s 
+2025-05-05 19:46:24.194357:  
+2025-05-05 19:46:24.330788: Epoch 677 
+2025-05-05 19:46:24.377493: Current learning rate: 0.00689 
+2025-05-05 19:48:01.194975: train_loss -0.5067 
+2025-05-05 19:48:01.294682: val_loss -0.4866 
+2025-05-05 19:48:01.302290: Pseudo dice [np.float32(0.8037), np.float32(0.8396), np.float32(0.8877), np.float32(0.9696), np.float32(0.8835), np.float32(0.9514), np.float32(0.9603), np.float32(0.9765), np.float32(0.9563), np.float32(0.9505), np.float32(0.9289), np.float32(0.9573), np.float32(0.9543), np.float32(0.9043), np.float32(0.9602), np.float32(0.9505), np.float32(0.8338), np.float32(0.6952), np.float32(0.9195)] 
+2025-05-05 19:48:01.324248: Epoch time: 97.0 s 
+2025-05-05 19:48:02.982380:  
+2025-05-05 19:48:03.112676: Epoch 678 
+2025-05-05 19:48:03.142109: Current learning rate: 0.00689 
+2025-05-05 19:49:38.244754: train_loss -0.4698 
+2025-05-05 19:49:38.378244: val_loss -0.4865 
+2025-05-05 19:49:38.392361: Pseudo dice [np.float32(0.8396), np.float32(0.8321), np.float32(0.9236), np.float32(0.9774), np.float32(0.8885), np.float32(0.9538), np.float32(0.9538), np.float32(0.9751), np.float32(0.9692), np.float32(0.9609), np.float32(0.9467), np.float32(0.9719), np.float32(0.9646), np.float32(0.8826), np.float32(0.9519), np.float32(0.9526), np.float32(0.7521), np.float32(0.7592), np.float32(0.9128)] 
+2025-05-05 19:49:38.399968: Epoch time: 95.26 s 
+2025-05-05 19:49:40.017178:  
+2025-05-05 19:49:40.077152: Epoch 679 
+2025-05-05 19:49:40.099467: Current learning rate: 0.00688 
+2025-05-05 19:51:16.259215: train_loss -0.4803 
+2025-05-05 19:51:16.344314: val_loss -0.4781 
+2025-05-05 19:51:16.353863: Pseudo dice [np.float32(0.8461), np.float32(0.8344), np.float32(0.8239), np.float32(0.9762), np.float32(0.9053), np.float32(0.9404), np.float32(0.9619), np.float32(0.9771), np.float32(0.9539), np.float32(0.9563), np.float32(0.9366), np.float32(0.9687), np.float32(0.9635), np.float32(0.8851), np.float32(0.9487), np.float32(0.9357), np.float32(0.8249), np.float32(0.834), np.float32(0.9142)] 
+2025-05-05 19:51:16.370522: Epoch time: 96.24 s 
+2025-05-05 19:51:18.037209:  
+2025-05-05 19:51:18.177220: Epoch 680 
+2025-05-05 19:51:18.224865: Current learning rate: 0.00688 
+2025-05-05 19:52:52.082976: train_loss -0.4903 
+2025-05-05 19:52:52.195046: val_loss -0.4752 
+2025-05-05 19:52:52.209945: Pseudo dice [np.float32(0.8429), np.float32(0.8402), np.float32(0.8315), np.float32(0.9825), np.float32(0.8967), np.float32(0.9491), np.float32(0.9693), np.float32(0.9752), np.float32(0.9595), np.float32(0.9671), np.float32(0.9487), np.float32(0.9586), np.float32(0.9654), np.float32(0.8788), np.float32(0.965), np.float32(0.9357), np.float32(0.8717), np.float32(0.8689), np.float32(0.923)] 
+2025-05-05 19:52:52.218073: Epoch time: 94.05 s 
+2025-05-05 19:52:53.786964:  
+2025-05-05 19:52:53.868319: Epoch 681 
+2025-05-05 19:52:53.886431: Current learning rate: 0.00688 
+2025-05-05 19:54:33.358181: train_loss -0.4721 
+2025-05-05 19:54:33.398033: val_loss -0.5122 
+2025-05-05 19:54:33.410735: Pseudo dice [np.float32(0.86), np.float32(0.8357), np.float32(0.9418), np.float32(0.9348), np.float32(0.9073), np.float32(0.9563), np.float32(0.9667), np.float32(0.979), np.float32(0.9626), np.float32(0.9645), np.float32(0.954), np.float32(0.9713), np.float32(0.9702), np.float32(0.8991), np.float32(0.9366), np.float32(0.9434), np.float32(0.8596), np.float32(0.8988), np.float32(0.9073)] 
+2025-05-05 19:54:33.412396: Epoch time: 99.57 s 
+2025-05-05 19:54:34.901452:  
+2025-05-05 19:54:34.954172: Epoch 682 
+2025-05-05 19:54:34.966026: Current learning rate: 0.00687 
+2025-05-05 19:56:13.312237: train_loss -0.4694 
+2025-05-05 19:56:13.350760: val_loss -0.4782 
+2025-05-05 19:56:13.351486: Pseudo dice [np.float32(0.8475), np.float32(0.8472), np.float32(0.8254), np.float32(0.9724), np.float32(0.9241), np.float32(0.9506), np.float32(0.9611), np.float32(0.9782), np.float32(0.9521), np.float32(0.9671), np.float32(0.9248), np.float32(0.9653), np.float32(0.9614), np.float32(0.8937), np.float32(0.9513), np.float32(0.935), np.float32(0.8819), np.float32(0.8955), np.float32(0.9156)] 
+2025-05-05 19:56:13.351938: Epoch time: 98.41 s 
+2025-05-05 19:56:14.806255:  
+2025-05-05 19:56:14.929938: Epoch 683 
+2025-05-05 19:56:14.955664: Current learning rate: 0.00687 
+2025-05-05 19:57:53.774078: train_loss -0.494 
+2025-05-05 19:57:53.832819: val_loss -0.5116 
+2025-05-05 19:57:53.879276: Pseudo dice [np.float32(0.8327), np.float32(0.8625), np.float32(0.8514), np.float32(0.9804), np.float32(0.9301), np.float32(0.9623), np.float32(0.9667), np.float32(0.9761), np.float32(0.96), np.float32(0.9629), np.float32(0.9454), np.float32(0.9684), np.float32(0.9673), np.float32(0.8931), np.float32(0.9645), np.float32(0.957), np.float32(0.858), np.float32(0.8813), np.float32(0.918)] 
+2025-05-05 19:57:53.951377: Epoch time: 98.97 s 
+2025-05-05 19:57:55.604240:  
+2025-05-05 19:57:55.656995: Epoch 684 
+2025-05-05 19:57:55.682237: Current learning rate: 0.00686 
+2025-05-05 19:59:30.008884: train_loss -0.472 
+2025-05-05 19:59:30.039351: val_loss -0.4485 
+2025-05-05 19:59:30.040272: Pseudo dice [np.float32(0.8285), np.float32(0.8069), np.float32(0.892), np.float32(0.9687), np.float32(0.9083), np.float32(0.9355), np.float32(0.9454), np.float32(0.9658), np.float32(0.9652), np.float32(0.9581), np.float32(0.9461), np.float32(0.9712), np.float32(0.9686), np.float32(0.8922), np.float32(0.7648), np.float32(0.942), np.float32(0.8676), np.float32(0.8614), np.float32(0.9095)] 
+2025-05-05 19:59:30.040691: Epoch time: 94.41 s 
+2025-05-05 19:59:31.625959:  
+2025-05-05 19:59:31.757644: Epoch 685 
+2025-05-05 19:59:31.791629: Current learning rate: 0.00686 
+2025-05-05 20:01:09.554635: train_loss -0.4983 
+2025-05-05 20:01:09.593230: val_loss -0.4609 
+2025-05-05 20:01:09.599964: Pseudo dice [np.float32(0.8559), np.float32(0.7908), np.float32(0.8563), np.float32(0.9615), np.float32(0.8804), np.float32(0.9536), np.float32(0.9596), np.float32(0.9686), np.float32(0.9604), np.float32(0.9639), np.float32(0.9262), np.float32(0.9626), np.float32(0.9529), np.float32(0.8834), np.float32(0.9671), np.float32(0.9537), np.float32(0.769), np.float32(0.8064), np.float32(0.9011)] 
+2025-05-05 20:01:09.605232: Epoch time: 97.93 s 
+2025-05-05 20:01:11.127924:  
+2025-05-05 20:01:11.258718: Epoch 686 
+2025-05-05 20:01:11.295060: Current learning rate: 0.00685 
+2025-05-05 20:02:48.103062: train_loss -0.4785 
+2025-05-05 20:02:48.140027: val_loss -0.5156 
+2025-05-05 20:02:48.144256: Pseudo dice [np.float32(0.8251), np.float32(0.8281), np.float32(0.8597), np.float32(0.9541), np.float32(0.89), np.float32(0.9557), np.float32(0.9539), np.float32(0.9731), np.float32(0.9617), np.float32(0.9667), np.float32(0.9456), np.float32(0.9686), np.float32(0.9643), np.float32(0.9028), np.float32(0.961), np.float32(0.9432), np.float32(0.7685), np.float32(0.786), np.float32(0.9056)] 
+2025-05-05 20:02:48.158588: Epoch time: 96.98 s 
+2025-05-05 20:02:49.693333:  
+2025-05-05 20:02:49.724534: Epoch 687 
+2025-05-05 20:02:49.728787: Current learning rate: 0.00685 
+2025-05-05 20:04:24.366687: train_loss -0.4772 
+2025-05-05 20:04:24.404968: val_loss -0.5122 
+2025-05-05 20:04:24.405888: Pseudo dice [np.float32(0.8435), np.float32(0.8477), np.float32(0.8296), np.float32(0.9236), np.float32(0.8734), np.float32(0.958), np.float32(0.9651), np.float32(0.9745), np.float32(0.9631), np.float32(0.9612), np.float32(0.9471), np.float32(0.9524), np.float32(0.9625), np.float32(0.9051), np.float32(0.9578), np.float32(0.9433), np.float32(0.8767), np.float32(0.8896), np.float32(0.8966)] 
+2025-05-05 20:04:24.413562: Epoch time: 94.67 s 
+2025-05-05 20:04:25.951139:  
+2025-05-05 20:04:25.992329: Epoch 688 
+2025-05-05 20:04:25.994921: Current learning rate: 0.00684 
+2025-05-05 20:06:02.372525: train_loss -0.476 
+2025-05-05 20:06:02.450961: val_loss -0.4985 
+2025-05-05 20:06:02.455297: Pseudo dice [np.float32(0.8306), np.float32(0.8135), np.float32(0.9266), np.float32(0.9707), np.float32(0.8857), np.float32(0.9542), np.float32(0.9566), np.float32(0.9676), np.float32(0.9574), np.float32(0.9458), np.float32(0.9139), np.float32(0.965), np.float32(0.9459), np.float32(0.886), np.float32(0.9662), np.float32(0.948), np.float32(0.8699), np.float32(0.8649), np.float32(0.8896)] 
+2025-05-05 20:06:02.456061: Epoch time: 96.42 s 
+2025-05-05 20:06:03.928391:  
+2025-05-05 20:06:04.048976: Epoch 689 
+2025-05-05 20:06:04.087775: Current learning rate: 0.00684 
+2025-05-05 20:07:40.121039: train_loss -0.4845 
+2025-05-05 20:07:40.124484: val_loss -0.4884 
+2025-05-05 20:07:40.124983: Pseudo dice [np.float32(0.8455), np.float32(0.8414), np.float32(0.8989), np.float32(0.9719), np.float32(0.8471), np.float32(0.9513), np.float32(0.9572), np.float32(0.9527), np.float32(0.9643), np.float32(0.9633), np.float32(0.9381), np.float32(0.9674), np.float32(0.964), np.float32(0.9007), np.float32(0.9651), np.float32(0.9512), np.float32(0.8653), np.float32(0.8728), np.float32(0.9037)] 
+2025-05-05 20:07:40.125407: Epoch time: 96.19 s 
+2025-05-05 20:07:41.564702:  
+2025-05-05 20:07:41.626698: Epoch 690 
+2025-05-05 20:07:41.659337: Current learning rate: 0.00683 
+2025-05-05 20:09:17.385842: train_loss -0.4723 
+2025-05-05 20:09:17.409207: val_loss -0.4651 
+2025-05-05 20:09:17.410512: Pseudo dice [np.float32(0.8188), np.float32(0.8485), np.float32(0.9341), np.float32(0.9737), np.float32(0.8994), np.float32(0.9616), np.float32(0.9557), np.float32(0.9768), np.float32(0.9442), np.float32(0.9637), np.float32(0.945), np.float32(0.9504), np.float32(0.9621), np.float32(0.8776), np.float32(0.9599), np.float32(0.9472), np.float32(0.8288), np.float32(0.8891), np.float32(0.906)] 
+2025-05-05 20:09:17.410872: Epoch time: 95.82 s 
+2025-05-05 20:09:18.958179:  
+2025-05-05 20:09:19.053980: Epoch 691 
+2025-05-05 20:09:19.090833: Current learning rate: 0.00683 
+2025-05-05 20:10:59.732831: train_loss -0.4896 
+2025-05-05 20:10:59.797610: val_loss -0.4881 
+2025-05-05 20:10:59.805074: Pseudo dice [np.float32(0.82), np.float32(0.8573), np.float32(0.8629), np.float32(0.9612), np.float32(0.9024), np.float32(0.9554), np.float32(0.9648), np.float32(0.9811), np.float32(0.9683), np.float32(0.9701), np.float32(0.9489), np.float32(0.9664), np.float32(0.9663), np.float32(0.8933), np.float32(0.9565), np.float32(0.9488), np.float32(0.8052), np.float32(0.8547), np.float32(0.9107)] 
+2025-05-05 20:10:59.817966: Epoch time: 100.78 s 
+2025-05-05 20:11:01.404318:  
+2025-05-05 20:11:01.537173: Epoch 692 
+2025-05-05 20:11:01.573099: Current learning rate: 0.00682 
+2025-05-05 20:12:36.180378: train_loss -0.4877 
+2025-05-05 20:12:36.321784: val_loss -0.4913 
+2025-05-05 20:12:36.374722: Pseudo dice [np.float32(0.8489), np.float32(0.8148), np.float32(0.9046), np.float32(0.9677), np.float32(0.857), np.float32(0.9576), np.float32(0.9594), np.float32(0.9746), np.float32(0.9564), np.float32(0.953), np.float32(0.9353), np.float32(0.9636), np.float32(0.9508), np.float32(0.8904), np.float32(0.9668), np.float32(0.9554), np.float32(0.8808), np.float32(0.8918), np.float32(0.9038)] 
+2025-05-05 20:12:36.415240: Epoch time: 94.78 s 
+2025-05-05 20:12:41.610397:  
+2025-05-05 20:12:41.616172: Epoch 693 
+2025-05-05 20:12:41.616883: Current learning rate: 0.00682 
+2025-05-05 20:14:18.120202: train_loss -0.4796 
+2025-05-05 20:14:18.226259: val_loss -0.4649 
+2025-05-05 20:14:18.242610: Pseudo dice [np.float32(0.8421), np.float32(0.8375), np.float32(0.9185), np.float32(0.9756), np.float32(0.9007), np.float32(0.9595), np.float32(0.9613), np.float32(0.9644), np.float32(0.9627), np.float32(0.967), np.float32(0.9447), np.float32(0.9687), np.float32(0.9637), np.float32(0.8872), np.float32(0.9579), np.float32(0.9473), np.float32(0.8667), np.float32(0.8832), np.float32(0.909)] 
+2025-05-05 20:14:18.243268: Epoch time: 96.51 s 
+2025-05-05 20:14:19.677259:  
+2025-05-05 20:14:19.712813: Epoch 694 
+2025-05-05 20:14:19.725057: Current learning rate: 0.00681 
+2025-05-05 20:16:00.517185: train_loss -0.4756 
+2025-05-05 20:16:00.556996: val_loss -0.5262 
+2025-05-05 20:16:00.564882: Pseudo dice [np.float32(0.821), np.float32(0.826), np.float32(0.8655), np.float32(0.9684), np.float32(0.907), np.float32(0.954), np.float32(0.9539), np.float32(0.977), np.float32(0.9482), np.float32(0.9564), np.float32(0.9402), np.float32(0.9557), np.float32(0.9638), np.float32(0.8912), np.float32(0.9571), np.float32(0.9371), np.float32(0.8895), np.float32(0.9073), np.float32(0.9227)] 
+2025-05-05 20:16:00.595825: Epoch time: 100.84 s 
+2025-05-05 20:16:02.141581:  
+2025-05-05 20:16:02.168616: Epoch 695 
+2025-05-05 20:16:02.172852: Current learning rate: 0.00681 
+2025-05-05 20:17:40.298825: train_loss -0.4798 
+2025-05-05 20:17:40.390971: val_loss -0.4696 
+2025-05-05 20:17:40.420499: Pseudo dice [np.float32(0.8056), np.float32(0.8507), np.float32(0.9391), np.float32(0.978), np.float32(0.9198), np.float32(0.9518), np.float32(0.9674), np.float32(0.9746), np.float32(0.9518), np.float32(0.9552), np.float32(0.9289), np.float32(0.9652), np.float32(0.9722), np.float32(0.8988), np.float32(0.9648), np.float32(0.9257), np.float32(0.8509), np.float32(0.7739), np.float32(0.9188)] 
+2025-05-05 20:17:40.454825: Epoch time: 98.16 s 
+2025-05-05 20:17:42.013092:  
+2025-05-05 20:17:42.064341: Epoch 696 
+2025-05-05 20:17:42.075636: Current learning rate: 0.0068 
+2025-05-05 20:19:15.444088: train_loss -0.4676 
+2025-05-05 20:19:15.537842: val_loss -0.5087 
+2025-05-05 20:19:15.552578: Pseudo dice [np.float32(0.8441), np.float32(0.8165), np.float32(0.9143), np.float32(0.9713), np.float32(0.8845), np.float32(0.9534), np.float32(0.9503), np.float32(0.9773), np.float32(0.9562), np.float32(0.9574), np.float32(0.9374), np.float32(0.9666), np.float32(0.9687), np.float32(0.9001), np.float32(0.9586), np.float32(0.9498), np.float32(0.881), np.float32(0.8736), np.float32(0.9103)] 
+2025-05-05 20:19:15.573227: Epoch time: 93.43 s 
+2025-05-05 20:19:15.593354: Yayy! New best EMA pseudo Dice: 0.9210000038146973 
+2025-05-05 20:19:17.756482:  
+2025-05-05 20:19:17.839371: Epoch 697 
+2025-05-05 20:19:17.865099: Current learning rate: 0.0068 
+2025-05-05 20:21:00.371087: train_loss -0.4766 
+2025-05-05 20:21:00.475650: val_loss -0.4588 
+2025-05-05 20:21:00.526330: Pseudo dice [np.float32(0.8319), np.float32(0.8127), np.float32(0.9), np.float32(0.9525), np.float32(0.8779), np.float32(0.9352), np.float32(0.9398), np.float32(0.9634), np.float32(0.9282), np.float32(0.9486), np.float32(0.9373), np.float32(0.9593), np.float32(0.9596), np.float32(0.8711), np.float32(0.9481), np.float32(0.9322), np.float32(0.8335), np.float32(0.8647), np.float32(0.898)] 
+2025-05-05 20:21:00.580471: Epoch time: 102.62 s 
+2025-05-05 20:21:02.168211:  
+2025-05-05 20:21:02.194463: Epoch 698 
+2025-05-05 20:21:02.195224: Current learning rate: 0.0068 
+2025-05-05 20:22:36.605136: train_loss -0.4685 
+2025-05-05 20:22:36.733953: val_loss -0.4722 
+2025-05-05 20:22:36.770567: Pseudo dice [np.float32(0.8454), np.float32(0.85), np.float32(0.9048), np.float32(0.9698), np.float32(0.8573), np.float32(0.9585), np.float32(0.9406), np.float32(0.9756), np.float32(0.9378), np.float32(0.9541), np.float32(0.8978), np.float32(0.9627), np.float32(0.9606), np.float32(0.9054), np.float32(0.9614), np.float32(0.947), np.float32(0.8623), np.float32(0.8718), np.float32(0.8904)] 
+2025-05-05 20:22:36.786358: Epoch time: 94.44 s 
+2025-05-05 20:22:38.352686:  
+2025-05-05 20:22:38.477160: Epoch 699 
+2025-05-05 20:22:38.504570: Current learning rate: 0.00679 
+2025-05-05 20:24:13.890279: train_loss -0.4819 
+2025-05-05 20:24:13.970993: val_loss -0.4584 
+2025-05-05 20:24:13.989984: Pseudo dice [np.float32(0.8311), np.float32(0.8048), np.float32(0.9255), np.float32(0.9755), np.float32(0.9067), np.float32(0.9565), np.float32(0.9581), np.float32(0.9682), np.float32(0.965), np.float32(0.9563), np.float32(0.9253), np.float32(0.9645), np.float32(0.963), np.float32(0.8915), np.float32(0.9623), np.float32(0.9446), np.float32(0.8558), np.float32(0.875), np.float32(0.9068)] 
+2025-05-05 20:24:14.001708: Epoch time: 95.54 s 
+2025-05-05 20:24:16.291502:  
+2025-05-05 20:24:16.330551: Epoch 700 
+2025-05-05 20:24:16.335024: Current learning rate: 0.00679 
+2025-05-05 20:25:52.057765: train_loss -0.4672 
+2025-05-05 20:25:52.185219: val_loss -0.472 
+2025-05-05 20:25:52.209127: Pseudo dice [np.float32(0.8244), np.float32(0.8348), np.float32(0.9278), np.float32(0.9749), np.float32(0.9142), np.float32(0.9481), np.float32(0.9659), np.float32(0.9735), np.float32(0.9655), np.float32(0.9661), np.float32(0.9536), np.float32(0.9691), np.float32(0.9707), np.float32(0.8573), np.float32(0.9427), np.float32(0.9427), np.float32(0.8654), np.float32(0.8792), np.float32(0.9164)] 
+2025-05-05 20:25:52.240898: Epoch time: 95.77 s 
+2025-05-05 20:25:53.782704:  
+2025-05-05 20:25:53.796018: Epoch 701 
+2025-05-05 20:25:53.796520: Current learning rate: 0.00678 
+2025-05-05 20:27:28.273851: train_loss -0.472 
+2025-05-05 20:27:28.411763: val_loss -0.4989 
+2025-05-05 20:27:28.442224: Pseudo dice [np.float32(0.8653), np.float32(0.8368), np.float32(0.8434), np.float32(0.9701), np.float32(0.8814), np.float32(0.9458), np.float32(0.9615), np.float32(0.9745), np.float32(0.9621), np.float32(0.9551), np.float32(0.9314), np.float32(0.962), np.float32(0.9593), np.float32(0.8977), np.float32(0.9523), np.float32(0.9433), np.float32(0.7872), np.float32(0.7559), np.float32(0.9176)] 
+2025-05-05 20:27:28.492319: Epoch time: 94.49 s 
+2025-05-05 20:27:30.031928:  
+2025-05-05 20:27:30.170829: Epoch 702 
+2025-05-05 20:27:30.200171: Current learning rate: 0.00678 
+2025-05-05 20:29:04.166790: train_loss -0.4736 
+2025-05-05 20:29:04.336056: val_loss -0.5035 
+2025-05-05 20:29:04.376150: Pseudo dice [np.float32(0.8318), np.float32(0.824), np.float32(0.8771), np.float32(0.974), np.float32(0.9051), np.float32(0.9431), np.float32(0.9635), np.float32(0.9722), np.float32(0.9563), np.float32(0.9665), np.float32(0.9472), np.float32(0.9632), np.float32(0.971), np.float32(0.8947), np.float32(0.9568), np.float32(0.9396), np.float32(0.8776), np.float32(0.8459), np.float32(0.9102)] 
+2025-05-05 20:29:04.405089: Epoch time: 94.14 s 
+2025-05-05 20:29:06.035452:  
+2025-05-05 20:29:06.169812: Epoch 703 
+2025-05-05 20:29:06.206146: Current learning rate: 0.00677 
+2025-05-05 20:30:42.095125: train_loss -0.4879 
+2025-05-05 20:30:42.120634: val_loss -0.503 
+2025-05-05 20:30:42.124812: Pseudo dice [np.float32(0.8465), np.float32(0.8022), np.float32(0.836), np.float32(0.9672), np.float32(0.8895), np.float32(0.9645), np.float32(0.9529), np.float32(0.9757), np.float32(0.9666), np.float32(0.966), np.float32(0.943), np.float32(0.9691), np.float32(0.965), np.float32(0.8981), np.float32(0.9633), np.float32(0.9551), np.float32(0.8734), np.float32(0.8891), np.float32(0.8922)] 
+2025-05-05 20:30:42.125290: Epoch time: 96.06 s 
+2025-05-05 20:30:43.608675:  
+2025-05-05 20:30:43.657793: Epoch 704 
+2025-05-05 20:30:43.666191: Current learning rate: 0.00677 
+2025-05-05 20:32:16.971065: train_loss -0.4865 
+2025-05-05 20:32:17.104556: val_loss -0.4734 
+2025-05-05 20:32:17.142992: Pseudo dice [np.float32(0.8087), np.float32(0.8473), np.float32(0.9184), np.float32(0.9766), np.float32(0.8788), np.float32(0.9593), np.float32(0.9665), np.float32(0.9782), np.float32(0.9553), np.float32(0.9736), np.float32(0.9432), np.float32(0.9634), np.float32(0.9666), np.float32(0.9086), np.float32(0.9582), np.float32(0.9412), np.float32(0.8875), np.float32(0.8863), np.float32(0.9223)] 
+2025-05-05 20:32:17.176679: Epoch time: 93.36 s 
+2025-05-05 20:32:18.711855:  
+2025-05-05 20:32:18.840596: Epoch 705 
+2025-05-05 20:32:18.879152: Current learning rate: 0.00676 
+2025-05-05 20:33:55.359001: train_loss -0.4927 
+2025-05-05 20:33:55.414362: val_loss -0.5119 
+2025-05-05 20:33:55.441314: Pseudo dice [np.float32(0.8396), np.float32(0.8319), np.float32(0.871), np.float32(0.9695), np.float32(0.8424), np.float32(0.9585), np.float32(0.9595), np.float32(0.9785), np.float32(0.9579), np.float32(0.9529), np.float32(0.9238), np.float32(0.9635), np.float32(0.9646), np.float32(0.888), np.float32(0.9469), np.float32(0.9553), np.float32(0.8397), np.float32(0.8312), np.float32(0.9093)] 
+2025-05-05 20:33:55.454981: Epoch time: 96.65 s 
+2025-05-05 20:33:57.006469:  
+2025-05-05 20:33:57.090496: Epoch 706 
+2025-05-05 20:33:57.127073: Current learning rate: 0.00676 
+2025-05-05 20:35:33.925390: train_loss -0.4841 
+2025-05-05 20:35:33.970152: val_loss -0.4981 
+2025-05-05 20:35:33.971918: Pseudo dice [np.float32(0.8554), np.float32(0.8423), np.float32(0.8411), np.float32(0.9783), np.float32(0.9117), np.float32(0.9592), np.float32(0.9636), np.float32(0.97), np.float32(0.9661), np.float32(0.9668), np.float32(0.9485), np.float32(0.9705), np.float32(0.9629), np.float32(0.8871), np.float32(0.9555), np.float32(0.9556), np.float32(0.8888), np.float32(0.9011), np.float32(0.9178)] 
+2025-05-05 20:35:34.005977: Epoch time: 96.92 s 
+2025-05-05 20:35:34.013819: Yayy! New best EMA pseudo Dice: 0.9211999773979187 
+2025-05-05 20:35:36.508584:  
+2025-05-05 20:35:36.783601: Epoch 707 
+2025-05-05 20:35:36.785430: Current learning rate: 0.00675 
+2025-05-05 20:37:16.091885: train_loss -0.4957 
+2025-05-05 20:37:16.172245: val_loss -0.5163 
+2025-05-05 20:37:16.200657: Pseudo dice [np.float32(0.8109), np.float32(0.8322), np.float32(0.9335), np.float32(0.9755), np.float32(0.8791), np.float32(0.9602), np.float32(0.9636), np.float32(0.9795), np.float32(0.9632), np.float32(0.9565), np.float32(0.9374), np.float32(0.9665), np.float32(0.964), np.float32(0.9029), np.float32(0.9568), np.float32(0.8929), np.float32(0.8972), np.float32(0.8912), np.float32(0.9127)] 
+2025-05-05 20:37:16.229792: Epoch time: 99.58 s 
+2025-05-05 20:37:16.258941: Yayy! New best EMA pseudo Dice: 0.921500027179718 
+2025-05-05 20:37:18.750751:  
+2025-05-05 20:37:18.789870: Epoch 708 
+2025-05-05 20:37:18.808735: Current learning rate: 0.00675 
+2025-05-05 20:38:58.733183: train_loss -0.4812 
+2025-05-05 20:38:58.851310: val_loss -0.4715 
+2025-05-05 20:38:58.875751: Pseudo dice [np.float32(0.8136), np.float32(0.8348), np.float32(0.8882), np.float32(0.9752), np.float32(0.9087), np.float32(0.9558), np.float32(0.9618), np.float32(0.9685), np.float32(0.9565), np.float32(0.9632), np.float32(0.9496), np.float32(0.9704), np.float32(0.97), np.float32(0.8747), np.float32(0.884), np.float32(0.9181), np.float32(0.8789), np.float32(0.8684), np.float32(0.9165)] 
+2025-05-05 20:38:58.914435: Epoch time: 99.98 s 
+2025-05-05 20:39:00.450060:  
+2025-05-05 20:39:00.565182: Epoch 709 
+2025-05-05 20:39:00.584217: Current learning rate: 0.00674 
+2025-05-05 20:40:36.088350: train_loss -0.5004 
+2025-05-05 20:40:36.211366: val_loss -0.48 
+2025-05-05 20:40:36.233563: Pseudo dice [np.float32(0.8335), np.float32(0.8343), np.float32(0.9206), np.float32(0.9773), np.float32(0.8716), np.float32(0.9453), np.float32(0.9519), np.float32(0.9698), np.float32(0.9564), np.float32(0.9485), np.float32(0.9122), np.float32(0.9552), np.float32(0.9566), np.float32(0.9053), np.float32(0.9632), np.float32(0.9567), np.float32(0.8647), np.float32(0.8428), np.float32(0.9116)] 
+2025-05-05 20:40:36.267830: Epoch time: 95.64 s 
+2025-05-05 20:40:41.367326:  
+2025-05-05 20:40:41.373370: Epoch 710 
+2025-05-05 20:40:41.377859: Current learning rate: 0.00674 
+2025-05-05 20:42:15.880063: train_loss -0.4604 
+2025-05-05 20:42:15.966794: val_loss -0.4895 
+2025-05-05 20:42:15.974349: Pseudo dice [np.float32(0.8425), np.float32(0.8527), np.float32(0.8222), np.float32(0.9518), np.float32(0.8604), np.float32(0.9559), np.float32(0.9336), np.float32(0.9666), np.float32(0.9526), np.float32(0.9648), np.float32(0.9431), np.float32(0.9436), np.float32(0.9672), np.float32(0.9053), np.float32(0.9605), np.float32(0.941), np.float32(0.8928), np.float32(0.9021), np.float32(0.9059)] 
+2025-05-05 20:42:15.974818: Epoch time: 94.51 s 
+2025-05-05 20:42:17.500987:  
+2025-05-05 20:42:17.622328: Epoch 711 
+2025-05-05 20:42:17.636961: Current learning rate: 0.00673 
+2025-05-05 20:43:51.304844: train_loss -0.4766 
+2025-05-05 20:43:51.403667: val_loss -0.4793 
+2025-05-05 20:43:51.430541: Pseudo dice [np.float32(0.843), np.float32(0.8353), np.float32(0.9046), np.float32(0.9741), np.float32(0.8897), np.float32(0.9575), np.float32(0.9606), np.float32(0.9783), np.float32(0.9539), np.float32(0.9695), np.float32(0.9392), np.float32(0.9683), np.float32(0.9679), np.float32(0.9104), np.float32(0.9591), np.float32(0.9445), np.float32(0.8785), np.float32(0.8636), np.float32(0.9067)] 
+2025-05-05 20:43:51.455966: Epoch time: 93.81 s 
+2025-05-05 20:43:52.972769:  
+2025-05-05 20:43:53.018133: Epoch 712 
+2025-05-05 20:43:53.022099: Current learning rate: 0.00673 
+2025-05-05 20:45:23.730822: train_loss -0.4798 
+2025-05-05 20:45:23.924121: val_loss -0.5023 
+2025-05-05 20:45:23.925535: Pseudo dice [np.float32(0.8204), np.float32(0.8299), np.float32(0.927), np.float32(0.9794), np.float32(0.8876), np.float32(0.9544), np.float32(0.9423), np.float32(0.9717), np.float32(0.9679), np.float32(0.9695), np.float32(0.934), np.float32(0.9579), np.float32(0.9587), np.float32(0.8922), np.float32(0.9602), np.float32(0.9526), np.float32(0.8687), np.float32(0.8814), np.float32(0.911)] 
+2025-05-05 20:45:23.929782: Epoch time: 90.76 s 
+2025-05-05 20:45:23.930354: Yayy! New best EMA pseudo Dice: 0.9218000173568726 
+2025-05-05 20:45:26.693769:  
+2025-05-05 20:45:26.787198: Epoch 713 
+2025-05-05 20:45:26.864323: Current learning rate: 0.00673 
+2025-05-05 20:47:09.233257: train_loss -0.492 
+2025-05-05 20:47:09.305400: val_loss -0.4937 
+2025-05-05 20:47:09.336604: Pseudo dice [np.float32(0.8215), np.float32(0.84), np.float32(0.8936), np.float32(0.9704), np.float32(0.7925), np.float32(0.9549), np.float32(0.9419), np.float32(0.9719), np.float32(0.9602), np.float32(0.9474), np.float32(0.9386), np.float32(0.9665), np.float32(0.9616), np.float32(0.889), np.float32(0.9596), np.float32(0.9576), np.float32(0.8868), np.float32(0.8841), np.float32(0.89)] 
+2025-05-05 20:47:09.356217: Epoch time: 102.54 s 
+2025-05-05 20:47:10.847390:  
+2025-05-05 20:47:10.890165: Epoch 714 
+2025-05-05 20:47:10.894821: Current learning rate: 0.00672 
+2025-05-05 20:48:52.549096: train_loss -0.5033 
+2025-05-05 20:48:52.615571: val_loss -0.4791 
+2025-05-05 20:48:52.623458: Pseudo dice [np.float32(0.8425), np.float32(0.8562), np.float32(0.9299), np.float32(0.9582), np.float32(0.6993), np.float32(0.9326), np.float32(0.963), np.float32(0.9778), np.float32(0.9529), np.float32(0.9498), np.float32(0.8938), np.float32(0.9637), np.float32(0.9565), np.float32(0.9038), np.float32(0.9617), np.float32(0.9455), np.float32(0.9058), np.float32(0.8721), np.float32(0.9147)] 
+2025-05-05 20:48:52.638740: Epoch time: 101.7 s 
+2025-05-05 20:48:54.170523:  
+2025-05-05 20:48:54.229556: Epoch 715 
+2025-05-05 20:48:54.245576: Current learning rate: 0.00672 
+2025-05-05 20:50:36.219407: train_loss -0.4664 
+2025-05-05 20:50:36.325540: val_loss -0.4705 
+2025-05-05 20:50:36.335570: Pseudo dice [np.float32(0.8297), np.float32(0.8236), np.float32(0.8955), np.float32(0.9644), np.float32(0.8208), np.float32(0.9363), np.float32(0.9621), np.float32(0.9768), np.float32(0.9603), np.float32(0.9703), np.float32(0.937), np.float32(0.9603), np.float32(0.9607), np.float32(0.8904), np.float32(0.9679), np.float32(0.9391), np.float32(0.8477), np.float32(0.8572), np.float32(0.9069)] 
+2025-05-05 20:50:36.336105: Epoch time: 102.05 s 
+2025-05-05 20:50:37.924896:  
+2025-05-05 20:50:37.990512: Epoch 716 
+2025-05-05 20:50:38.026872: Current learning rate: 0.00671 
+2025-05-05 20:52:10.709390: train_loss -0.4808 
+2025-05-05 20:52:10.775337: val_loss -0.4731 
+2025-05-05 20:52:10.783679: Pseudo dice [np.float32(0.8304), np.float32(0.852), np.float32(0.8126), np.float32(0.9735), np.float32(0.8436), np.float32(0.9552), np.float32(0.9431), np.float32(0.9763), np.float32(0.9383), np.float32(0.9555), np.float32(0.9435), np.float32(0.965), np.float32(0.9666), np.float32(0.9006), np.float32(0.9627), np.float32(0.9575), np.float32(0.879), np.float32(0.8714), np.float32(0.9121)] 
+2025-05-05 20:52:10.788553: Epoch time: 92.79 s 
+2025-05-05 20:52:12.464092:  
+2025-05-05 20:52:12.469223: Epoch 717 
+2025-05-05 20:52:12.469787: Current learning rate: 0.00671 
+2025-05-05 20:53:48.110851: train_loss -0.4734 
+2025-05-05 20:53:48.217849: val_loss -0.4931 
+2025-05-05 20:53:48.234884: Pseudo dice [np.float32(0.8661), np.float32(0.8398), np.float32(0.9298), np.float32(0.978), np.float32(0.8474), np.float32(0.9612), np.float32(0.953), np.float32(0.9787), np.float32(0.9546), np.float32(0.9465), np.float32(0.9266), np.float32(0.9709), np.float32(0.9567), np.float32(0.9047), np.float32(0.963), np.float32(0.9539), np.float32(0.8177), np.float32(0.8522), np.float32(0.9137)] 
+2025-05-05 20:53:48.247580: Epoch time: 95.65 s 
+2025-05-05 20:53:49.934726:  
+2025-05-05 20:53:50.022288: Epoch 718 
+2025-05-05 20:53:50.052604: Current learning rate: 0.0067 
+2025-05-05 20:55:28.001895: train_loss -0.4648 
+2025-05-05 20:55:28.111036: val_loss -0.4967 
+2025-05-05 20:55:28.154324: Pseudo dice [np.float32(0.8437), np.float32(0.8384), np.float32(0.8832), np.float32(0.9677), np.float32(0.8988), np.float32(0.9562), np.float32(0.9467), np.float32(0.9738), np.float32(0.9446), np.float32(0.9585), np.float32(0.9323), np.float32(0.9536), np.float32(0.9594), np.float32(0.8964), np.float32(0.9414), np.float32(0.9398), np.float32(0.8445), np.float32(0.83), np.float32(0.9266)] 
+2025-05-05 20:55:28.210872: Epoch time: 98.07 s 
+2025-05-05 20:55:29.839292:  
+2025-05-05 20:55:30.003850: Epoch 719 
+2025-05-05 20:55:30.004522: Current learning rate: 0.0067 
+2025-05-05 20:57:06.032300: train_loss -0.4686 
+2025-05-05 20:57:06.117701: val_loss -0.4751 
+2025-05-05 20:57:06.118756: Pseudo dice [np.float32(0.819), np.float32(0.7652), np.float32(0.9196), np.float32(0.9764), np.float32(0.8706), np.float32(0.9466), np.float32(0.9255), np.float32(0.9592), np.float32(0.9595), np.float32(0.9636), np.float32(0.9472), np.float32(0.9671), np.float32(0.9684), np.float32(0.8804), np.float32(0.9513), np.float32(0.9481), np.float32(0.8742), np.float32(0.8709), np.float32(0.9214)] 
+2025-05-05 20:57:06.119232: Epoch time: 96.19 s 
+2025-05-05 20:57:07.697627:  
+2025-05-05 20:57:07.810432: Epoch 720 
+2025-05-05 20:57:07.843440: Current learning rate: 0.00669 
+2025-05-05 20:58:45.008440: train_loss -0.4734 
+2025-05-05 20:58:45.158464: val_loss -0.4926 
+2025-05-05 20:58:45.195133: Pseudo dice [np.float32(0.8534), np.float32(0.8518), np.float32(0.8526), np.float32(0.9754), np.float32(0.8911), np.float32(0.9417), np.float32(0.9473), np.float32(0.9658), np.float32(0.9566), np.float32(0.97), np.float32(0.9084), np.float32(0.9682), np.float32(0.9675), np.float32(0.8857), np.float32(0.956), np.float32(0.9553), np.float32(0.8722), np.float32(0.859), np.float32(0.9125)] 
+2025-05-05 20:58:45.207989: Epoch time: 97.31 s 
+2025-05-05 20:58:46.930914:  
+2025-05-05 20:58:46.961265: Epoch 721 
+2025-05-05 20:58:46.969653: Current learning rate: 0.00669 
+2025-05-05 21:00:22.755508: train_loss -0.4741 
+2025-05-05 21:00:22.813003: val_loss -0.4732 
+2025-05-05 21:00:22.835722: Pseudo dice [np.float32(0.8268), np.float32(0.8264), np.float32(0.8903), np.float32(0.9481), np.float32(0.8792), np.float32(0.9492), np.float32(0.9428), np.float32(0.9681), np.float32(0.949), np.float32(0.9708), np.float32(0.9412), np.float32(0.9171), np.float32(0.961), np.float32(0.8785), np.float32(0.9353), np.float32(0.9199), np.float32(0.8777), np.float32(0.8664), np.float32(0.8903)] 
+2025-05-05 21:00:22.849226: Epoch time: 95.83 s 
+2025-05-05 21:00:24.406268:  
+2025-05-05 21:00:24.458419: Epoch 722 
+2025-05-05 21:00:24.475569: Current learning rate: 0.00668 
+2025-05-05 21:02:03.188933: train_loss -0.4668 
+2025-05-05 21:02:03.235110: val_loss -0.4956 
+2025-05-05 21:02:03.247576: Pseudo dice [np.float32(0.8328), np.float32(0.8282), np.float32(0.9189), np.float32(0.9756), np.float32(0.9065), np.float32(0.9596), np.float32(0.9486), np.float32(0.9748), np.float32(0.9638), np.float32(0.9594), np.float32(0.9496), np.float32(0.9624), np.float32(0.9687), np.float32(0.8862), np.float32(0.9558), np.float32(0.9513), np.float32(0.8783), np.float32(0.8883), np.float32(0.9026)] 
+2025-05-05 21:02:03.270427: Epoch time: 98.78 s 
+2025-05-05 21:02:04.779195:  
+2025-05-05 21:02:04.883172: Epoch 723 
+2025-05-05 21:02:04.912779: Current learning rate: 0.00668 
+2025-05-05 21:03:41.206396: train_loss -0.4772 
+2025-05-05 21:03:41.212044: val_loss -0.4779 
+2025-05-05 21:03:41.212891: Pseudo dice [np.float32(0.8074), np.float32(0.8126), np.float32(0.824), np.float32(0.9744), np.float32(0.8368), np.float32(0.9561), np.float32(0.9654), np.float32(0.9691), np.float32(0.9589), np.float32(0.9536), np.float32(0.9209), np.float32(0.962), np.float32(0.9585), np.float32(0.9008), np.float32(0.9666), np.float32(0.9502), np.float32(0.8764), np.float32(0.8716), np.float32(0.9007)] 
+2025-05-05 21:03:41.213449: Epoch time: 96.43 s 
+2025-05-05 21:03:42.720779:  
+2025-05-05 21:03:42.825557: Epoch 724 
+2025-05-05 21:03:42.829652: Current learning rate: 0.00667 
+2025-05-05 21:05:17.769611: train_loss -0.4644 
+2025-05-05 21:05:17.885673: val_loss -0.4616 
+2025-05-05 21:05:17.897286: Pseudo dice [np.float32(0.8474), np.float32(0.8594), np.float32(0.9326), np.float32(0.9758), np.float32(0.9208), np.float32(0.9513), np.float32(0.9643), np.float32(0.9709), np.float32(0.9491), np.float32(0.966), np.float32(0.95), np.float32(0.965), np.float32(0.9674), np.float32(0.8815), np.float32(0.9549), np.float32(0.9529), np.float32(0.8904), np.float32(0.9001), np.float32(0.9111)] 
+2025-05-05 21:05:17.912042: Epoch time: 95.05 s 
+2025-05-05 21:05:19.537441:  
+2025-05-05 21:05:19.585352: Epoch 725 
+2025-05-05 21:05:19.603815: Current learning rate: 0.00667 
+2025-05-05 21:06:53.290933: train_loss -0.4665 
+2025-05-05 21:06:53.385229: val_loss -0.5017 
+2025-05-05 21:06:53.413387: Pseudo dice [np.float32(0.8675), np.float32(0.8439), np.float32(0.9196), np.float32(0.9696), np.float32(0.8452), np.float32(0.9489), np.float32(0.9508), np.float32(0.9723), np.float32(0.9554), np.float32(0.9627), np.float32(0.9311), np.float32(0.9671), np.float32(0.9525), np.float32(0.896), np.float32(0.966), np.float32(0.945), np.float32(0.9048), np.float32(0.9021), np.float32(0.9072)] 
+2025-05-05 21:06:53.430724: Epoch time: 93.76 s 
+2025-05-05 21:06:55.022907:  
+2025-05-05 21:06:55.167190: Epoch 726 
+2025-05-05 21:06:55.197276: Current learning rate: 0.00666 
+2025-05-05 21:08:32.555832: train_loss -0.4856 
+2025-05-05 21:08:32.635336: val_loss -0.4779 
+2025-05-05 21:08:32.648313: Pseudo dice [np.float32(0.8089), np.float32(0.8331), np.float32(0.8943), np.float32(0.9726), np.float32(0.9072), np.float32(0.9319), np.float32(0.9518), np.float32(0.9717), np.float32(0.9201), np.float32(0.966), np.float32(0.9415), np.float32(0.9519), np.float32(0.9662), np.float32(0.8952), np.float32(0.9596), np.float32(0.9536), np.float32(0.8627), np.float32(0.887), np.float32(0.9152)] 
+2025-05-05 21:08:32.657089: Epoch time: 97.53 s 
+2025-05-05 21:08:37.756241:  
+2025-05-05 21:08:37.762465: Epoch 727 
+2025-05-05 21:08:37.763137: Current learning rate: 0.00666 
+2025-05-05 21:10:16.992926: train_loss -0.481 
+2025-05-05 21:10:17.078133: val_loss -0.4812 
+2025-05-05 21:10:17.128175: Pseudo dice [np.float32(0.8273), np.float32(0.8233), np.float32(0.9199), np.float32(0.9699), np.float32(0.8851), np.float32(0.9582), np.float32(0.9518), np.float32(0.9694), np.float32(0.9505), np.float32(0.9613), np.float32(0.9255), np.float32(0.9613), np.float32(0.9683), np.float32(0.8685), np.float32(0.9582), np.float32(0.9429), np.float32(0.8775), np.float32(0.8941), np.float32(0.9312)] 
+2025-05-05 21:10:17.138742: Epoch time: 99.24 s 
+2025-05-05 21:10:19.140475:  
+2025-05-05 21:10:19.195547: Epoch 728 
+2025-05-05 21:10:19.210173: Current learning rate: 0.00665 
+2025-05-05 21:11:51.503860: train_loss -0.4815 
+2025-05-05 21:11:51.582405: val_loss -0.4329 
+2025-05-05 21:11:51.631983: Pseudo dice [np.float32(0.8425), np.float32(0.8248), np.float32(0.9351), np.float32(0.9674), np.float32(0.9029), np.float32(0.9584), np.float32(0.9561), np.float32(0.9728), np.float32(0.9586), np.float32(0.9534), np.float32(0.9392), np.float32(0.9537), np.float32(0.9614), np.float32(0.9004), np.float32(0.9655), np.float32(0.9453), np.float32(0.8774), np.float32(0.8913), np.float32(0.9053)] 
+2025-05-05 21:11:51.672570: Epoch time: 92.36 s 
+2025-05-05 21:11:51.719910: Yayy! New best EMA pseudo Dice: 0.9218999743461609 
+2025-05-05 21:11:54.343701:  
+2025-05-05 21:11:54.388829: Epoch 729 
+2025-05-05 21:11:54.389296: Current learning rate: 0.00665 
+2025-05-05 21:13:38.560951: train_loss -0.4776 
+2025-05-05 21:13:38.666388: val_loss -0.4627 
+2025-05-05 21:13:38.672400: Pseudo dice [np.float32(0.8306), np.float32(0.8298), np.float32(0.9397), np.float32(0.9714), np.float32(0.9216), np.float32(0.9493), np.float32(0.9532), np.float32(0.9777), np.float32(0.9291), np.float32(0.9557), np.float32(0.9314), np.float32(0.9641), np.float32(0.9552), np.float32(0.8838), np.float32(0.9652), np.float32(0.9353), np.float32(0.863), np.float32(0.8613), np.float32(0.8972)] 
+2025-05-05 21:13:38.676917: Epoch time: 104.22 s 
+2025-05-05 21:13:40.346844:  
+2025-05-05 21:13:40.404393: Epoch 730 
+2025-05-05 21:13:40.413736: Current learning rate: 0.00665 
+2025-05-05 21:15:22.881863: train_loss -0.4897 
+2025-05-05 21:15:22.931623: val_loss -0.4876 
+2025-05-05 21:15:22.932498: Pseudo dice [np.float32(0.8505), np.float32(0.8684), np.float32(0.8858), np.float32(0.9736), np.float32(0.9281), np.float32(0.9576), np.float32(0.9673), np.float32(0.976), np.float32(0.9581), np.float32(0.9701), np.float32(0.9447), np.float32(0.9625), np.float32(0.9685), np.float32(0.9141), np.float32(0.968), np.float32(0.9518), np.float32(0.8794), np.float32(0.8942), np.float32(0.9048)] 
+2025-05-05 21:15:22.964973: Epoch time: 102.54 s 
+2025-05-05 21:15:23.005809: Yayy! New best EMA pseudo Dice: 0.9229999780654907 
+2025-05-05 21:15:25.503556:  
+2025-05-05 21:15:25.508491: Epoch 731 
+2025-05-05 21:15:25.508902: Current learning rate: 0.00664 
+2025-05-05 21:17:02.041965: train_loss -0.4887 
+2025-05-05 21:17:02.141414: val_loss -0.5052 
+2025-05-05 21:17:02.157912: Pseudo dice [np.float32(0.8378), np.float32(0.8465), np.float32(0.862), np.float32(0.9599), np.float32(0.8865), np.float32(0.9627), np.float32(0.9659), np.float32(0.9695), np.float32(0.9563), np.float32(0.9394), np.float32(0.9303), np.float32(0.9655), np.float32(0.9566), np.float32(0.8749), np.float32(0.9678), np.float32(0.9341), np.float32(0.8845), np.float32(0.8888), np.float32(0.8994)] 
+2025-05-05 21:17:02.175832: Epoch time: 96.54 s 
+2025-05-05 21:17:03.644952:  
+2025-05-05 21:17:03.681594: Epoch 732 
+2025-05-05 21:17:03.705319: Current learning rate: 0.00664 
+2025-05-05 21:18:40.599680: train_loss -0.4801 
+2025-05-05 21:18:40.629869: val_loss -0.519 
+2025-05-05 21:18:40.630658: Pseudo dice [np.float32(0.8401), np.float32(0.8275), np.float32(0.9011), np.float32(0.9621), np.float32(0.9011), np.float32(0.9606), np.float32(0.964), np.float32(0.9783), np.float32(0.968), np.float32(0.9642), np.float32(0.9338), np.float32(0.9721), np.float32(0.9578), np.float32(0.8951), np.float32(0.9688), np.float32(0.9539), np.float32(0.8834), np.float32(0.8228), np.float32(0.8904)] 
+2025-05-05 21:18:40.631088: Epoch time: 96.96 s 
+2025-05-05 21:18:42.097468:  
+2025-05-05 21:18:42.214405: Epoch 733 
+2025-05-05 21:18:42.233510: Current learning rate: 0.00663 
+2025-05-05 21:20:16.511916: train_loss -0.4916 
+2025-05-05 21:20:16.705499: val_loss -0.5175 
+2025-05-05 21:20:16.735389: Pseudo dice [np.float32(0.8517), np.float32(0.8415), np.float32(0.9402), np.float32(0.9756), np.float32(0.7987), np.float32(0.9342), np.float32(0.9612), np.float32(0.9795), np.float32(0.9598), np.float32(0.9588), np.float32(0.9449), np.float32(0.9667), np.float32(0.9624), np.float32(0.8937), np.float32(0.9682), np.float32(0.9516), np.float32(0.8549), np.float32(0.87), np.float32(0.9211)] 
+2025-05-05 21:20:16.777345: Epoch time: 94.42 s 
+2025-05-05 21:20:18.368961:  
+2025-05-05 21:20:18.512189: Epoch 734 
+2025-05-05 21:20:18.549497: Current learning rate: 0.00663 
+2025-05-05 21:21:50.780434: train_loss -0.4805 
+2025-05-05 21:21:50.919385: val_loss -0.4749 
+2025-05-05 21:21:50.948721: Pseudo dice [np.float32(0.8523), np.float32(0.8134), np.float32(0.9076), np.float32(0.9749), np.float32(0.8695), np.float32(0.9621), np.float32(0.9644), np.float32(0.9767), np.float32(0.9467), np.float32(0.9544), np.float32(0.9395), np.float32(0.9586), np.float32(0.9568), np.float32(0.9024), np.float32(0.9433), np.float32(0.9402), np.float32(0.8706), np.float32(0.9096), np.float32(0.9247)] 
+2025-05-05 21:21:50.957911: Epoch time: 92.41 s 
+2025-05-05 21:21:50.972795: Yayy! New best EMA pseudo Dice: 0.9229999780654907 
+2025-05-05 21:21:53.430886:  
+2025-05-05 21:21:53.473662: Epoch 735 
+2025-05-05 21:21:53.484728: Current learning rate: 0.00662 
+2025-05-05 21:23:32.956755: train_loss -0.4719 
+2025-05-05 21:23:33.085719: val_loss -0.483 
+2025-05-05 21:23:33.100728: Pseudo dice [np.float32(0.7955), np.float32(0.8213), np.float32(0.8262), np.float32(0.9737), np.float32(0.8774), np.float32(0.9605), np.float32(0.926), np.float32(0.966), np.float32(0.966), np.float32(0.9595), np.float32(0.9463), np.float32(0.9688), np.float32(0.9691), np.float32(0.8977), np.float32(0.9636), np.float32(0.9367), np.float32(0.8945), np.float32(0.8722), np.float32(0.9162)] 
+2025-05-05 21:23:33.108213: Epoch time: 99.53 s 
+2025-05-05 21:23:34.644468:  
+2025-05-05 21:23:34.749262: Epoch 736 
+2025-05-05 21:23:34.757954: Current learning rate: 0.00662 
+2025-05-05 21:25:11.221740: train_loss -0.481 
+2025-05-05 21:25:11.317189: val_loss -0.5167 
+2025-05-05 21:25:11.355871: Pseudo dice [np.float32(0.8349), np.float32(0.8494), np.float32(0.8665), np.float32(0.961), np.float32(0.8608), np.float32(0.9332), np.float32(0.9537), np.float32(0.9718), np.float32(0.96), np.float32(0.9636), np.float32(0.9443), np.float32(0.9627), np.float32(0.9706), np.float32(0.8911), np.float32(0.9631), np.float32(0.9464), np.float32(0.8903), np.float32(0.8223), np.float32(0.9184)] 
+2025-05-05 21:25:11.379845: Epoch time: 96.58 s 
+2025-05-05 21:25:12.865904:  
+2025-05-05 21:25:12.952010: Epoch 737 
+2025-05-05 21:25:12.960578: Current learning rate: 0.00661 
+2025-05-05 21:26:46.941342: train_loss -0.493 
+2025-05-05 21:26:46.989270: val_loss -0.4818 
+2025-05-05 21:26:46.990065: Pseudo dice [np.float32(0.8409), np.float32(0.8627), np.float32(0.8755), np.float32(0.9647), np.float32(0.7632), np.float32(0.9357), np.float32(0.9517), np.float32(0.9703), np.float32(0.9656), np.float32(0.9678), np.float32(0.9307), np.float32(0.9708), np.float32(0.9629), np.float32(0.8929), np.float32(0.9583), np.float32(0.9617), np.float32(0.8872), np.float32(0.8865), np.float32(0.9268)] 
+2025-05-05 21:26:46.994722: Epoch time: 94.08 s 
+2025-05-05 21:26:48.525372:  
+2025-05-05 21:26:48.583199: Epoch 738 
+2025-05-05 21:26:48.597856: Current learning rate: 0.00661 
+2025-05-05 21:28:24.525905: train_loss -0.4809 
+2025-05-05 21:28:24.578784: val_loss -0.4782 
+2025-05-05 21:28:24.579581: Pseudo dice [np.float32(0.8238), np.float32(0.8143), np.float32(0.9422), np.float32(0.9683), np.float32(0.8869), np.float32(0.9545), np.float32(0.965), np.float32(0.9678), np.float32(0.9591), np.float32(0.9622), np.float32(0.9517), np.float32(0.9591), np.float32(0.9456), np.float32(0.8904), np.float32(0.8717), np.float32(0.9511), np.float32(0.8581), np.float32(0.8612), np.float32(0.9023)] 
+2025-05-05 21:28:24.580175: Epoch time: 96.0 s 
+2025-05-05 21:28:26.197603:  
+2025-05-05 21:28:26.255790: Epoch 739 
+2025-05-05 21:28:26.256728: Current learning rate: 0.0066 
+2025-05-05 21:29:57.056017: train_loss -0.4806 
+2025-05-05 21:29:57.202584: val_loss -0.5214 
+2025-05-05 21:29:57.249268: Pseudo dice [np.float32(0.8365), np.float32(0.8383), np.float32(0.9273), np.float32(0.9706), np.float32(0.8864), np.float32(0.9615), np.float32(0.963), np.float32(0.9765), np.float32(0.9649), np.float32(0.9443), np.float32(0.9096), np.float32(0.9678), np.float32(0.965), np.float32(0.9034), np.float32(0.9669), np.float32(0.959), np.float32(0.8949), np.float32(0.8836), np.float32(0.9062)] 
+2025-05-05 21:29:57.288928: Epoch time: 90.86 s 
+2025-05-05 21:29:58.817643:  
+2025-05-05 21:29:58.934347: Epoch 740 
+2025-05-05 21:29:58.953009: Current learning rate: 0.0066 
+2025-05-05 21:31:34.814883: train_loss -0.4796 
+2025-05-05 21:31:34.940247: val_loss -0.5002 
+2025-05-05 21:31:34.990762: Pseudo dice [np.float32(0.8249), np.float32(0.8636), np.float32(0.9201), np.float32(0.9672), np.float32(0.8787), np.float32(0.9616), np.float32(0.9619), np.float32(0.9773), np.float32(0.9575), np.float32(0.9612), np.float32(0.9232), np.float32(0.9631), np.float32(0.9625), np.float32(0.9002), np.float32(0.9674), np.float32(0.9417), np.float32(0.8841), np.float32(0.8894), np.float32(0.9241)] 
+2025-05-05 21:31:35.026979: Epoch time: 96.0 s 
+2025-05-05 21:31:36.636371:  
+2025-05-05 21:31:36.758942: Epoch 741 
+2025-05-05 21:31:36.760325: Current learning rate: 0.00659 
+2025-05-05 21:33:16.251841: train_loss -0.504 
+2025-05-05 21:33:16.299541: val_loss -0.459 
+2025-05-05 21:33:16.308109: Pseudo dice [np.float32(0.8537), np.float32(0.8436), np.float32(0.9094), np.float32(0.9631), np.float32(0.8976), np.float32(0.9552), np.float32(0.9586), np.float32(0.9752), np.float32(0.9662), np.float32(0.9637), np.float32(0.9403), np.float32(0.9676), np.float32(0.9676), np.float32(0.8834), np.float32(0.961), np.float32(0.9518), np.float32(0.8051), np.float32(0.8277), np.float32(0.898)] 
+2025-05-05 21:33:16.308889: Epoch time: 99.62 s 
+2025-05-05 21:33:17.857784:  
+2025-05-05 21:33:17.971213: Epoch 742 
+2025-05-05 21:33:18.020352: Current learning rate: 0.00659 
+2025-05-05 21:34:53.598335: train_loss -0.482 
+2025-05-05 21:34:53.669336: val_loss -0.5117 
+2025-05-05 21:34:53.704468: Pseudo dice [np.float32(0.8331), np.float32(0.8244), np.float32(0.7057), np.float32(0.9768), np.float32(0.8822), np.float32(0.9521), np.float32(0.9659), np.float32(0.9794), np.float32(0.966), np.float32(0.9483), np.float32(0.9304), np.float32(0.9633), np.float32(0.9643), np.float32(0.8934), np.float32(0.9671), np.float32(0.9465), np.float32(0.8638), np.float32(0.8943), np.float32(0.9094)] 
+2025-05-05 21:34:53.739746: Epoch time: 95.74 s 
+2025-05-05 21:34:55.283509:  
+2025-05-05 21:34:55.383760: Epoch 743 
+2025-05-05 21:34:55.391693: Current learning rate: 0.00658 
+2025-05-05 21:36:29.971369: train_loss -0.485 
+2025-05-05 21:36:30.022645: val_loss -0.4983 
+2025-05-05 21:36:30.061805: Pseudo dice [np.float32(0.8308), np.float32(0.7956), np.float32(0.8366), np.float32(0.9736), np.float32(0.8807), np.float32(0.9486), np.float32(0.9599), np.float32(0.9754), np.float32(0.9663), np.float32(0.9674), np.float32(0.9476), np.float32(0.9622), np.float32(0.9707), np.float32(0.8905), np.float32(0.964), np.float32(0.9512), np.float32(0.8512), np.float32(0.8702), np.float32(0.9009)] 
+2025-05-05 21:36:30.083068: Epoch time: 94.69 s 
+2025-05-05 21:36:34.483424:  
+2025-05-05 21:36:34.489034: Epoch 744 
+2025-05-05 21:36:34.490644: Current learning rate: 0.00658 
+2025-05-05 21:38:06.607290: train_loss -0.4994 
+2025-05-05 21:38:06.661070: val_loss -0.4829 
+2025-05-05 21:38:06.672310: Pseudo dice [np.float32(0.8538), np.float32(0.8249), np.float32(0.9298), np.float32(0.9725), np.float32(0.8768), np.float32(0.9439), np.float32(0.9649), np.float32(0.9737), np.float32(0.963), np.float32(0.9542), np.float32(0.9143), np.float32(0.9651), np.float32(0.965), np.float32(0.8881), np.float32(0.9574), np.float32(0.9259), np.float32(0.8766), np.float32(0.8946), np.float32(0.8985)] 
+2025-05-05 21:38:06.688389: Epoch time: 92.12 s 
+2025-05-05 21:38:08.287890:  
+2025-05-05 21:38:08.422073: Epoch 745 
+2025-05-05 21:38:08.462388: Current learning rate: 0.00657 
+2025-05-05 21:39:44.614767: train_loss -0.4873 
+2025-05-05 21:39:44.748984: val_loss -0.4622 
+2025-05-05 21:39:44.789182: Pseudo dice [np.float32(0.7861), np.float32(0.8024), np.float32(0.9202), np.float32(0.9715), np.float32(0.9168), np.float32(0.9408), np.float32(0.9672), np.float32(0.972), np.float32(0.9551), np.float32(0.9602), np.float32(0.9457), np.float32(0.9644), np.float32(0.9578), np.float32(0.882), np.float32(0.9371), np.float32(0.9404), np.float32(0.9039), np.float32(0.9149), np.float32(0.9268)] 
+2025-05-05 21:39:44.821819: Epoch time: 96.33 s 
+2025-05-05 21:39:46.416453:  
+2025-05-05 21:39:46.512676: Epoch 746 
+2025-05-05 21:39:46.533562: Current learning rate: 0.00657 
+2025-05-05 21:41:20.965832: train_loss -0.4824 
+2025-05-05 21:41:21.062089: val_loss -0.4515 
+2025-05-05 21:41:21.080616: Pseudo dice [np.float32(0.8418), np.float32(0.8329), np.float32(0.8881), np.float32(0.968), np.float32(0.8729), np.float32(0.9545), np.float32(0.9636), np.float32(0.9667), np.float32(0.9656), np.float32(0.9652), np.float32(0.9375), np.float32(0.9696), np.float32(0.9702), np.float32(0.8819), np.float32(0.9651), np.float32(0.9403), np.float32(0.8831), np.float32(0.8594), np.float32(0.9167)] 
+2025-05-05 21:41:21.090913: Epoch time: 94.55 s 
+2025-05-05 21:41:22.625850:  
+2025-05-05 21:41:22.707744: Epoch 747 
+2025-05-05 21:41:22.769834: Current learning rate: 0.00656 
+2025-05-05 21:42:58.207204: train_loss -0.4869 
+2025-05-05 21:42:58.290220: val_loss -0.5242 
+2025-05-05 21:42:58.305116: Pseudo dice [np.float32(0.8302), np.float32(0.8341), np.float32(0.8842), np.float32(0.9686), np.float32(0.8843), np.float32(0.9583), np.float32(0.9612), np.float32(0.9635), np.float32(0.9665), np.float32(0.9576), np.float32(0.9427), np.float32(0.9716), np.float32(0.9643), np.float32(0.8844), np.float32(0.9344), np.float32(0.9297), np.float32(0.8976), np.float32(0.9001), np.float32(0.9206)] 
+2025-05-05 21:42:58.332161: Epoch time: 95.58 s 
+2025-05-05 21:42:59.787222:  
+2025-05-05 21:42:59.891425: Epoch 748 
+2025-05-05 21:42:59.931282: Current learning rate: 0.00656 
+2025-05-05 21:44:37.036683: train_loss -0.4982 
+2025-05-05 21:44:37.125551: val_loss -0.5019 
+2025-05-05 21:44:37.177383: Pseudo dice [np.float32(0.8382), np.float32(0.8504), np.float32(0.8269), np.float32(0.9644), np.float32(0.9114), np.float32(0.9404), np.float32(0.9529), np.float32(0.9763), np.float32(0.9642), np.float32(0.9508), np.float32(0.926), np.float32(0.9662), np.float32(0.9606), np.float32(0.8673), np.float32(0.9557), np.float32(0.9385), np.float32(0.8308), np.float32(0.8293), np.float32(0.9235)] 
+2025-05-05 21:44:37.235211: Epoch time: 97.25 s 
+2025-05-05 21:44:38.785665:  
+2025-05-05 21:44:38.888928: Epoch 749 
+2025-05-05 21:44:38.918914: Current learning rate: 0.00656 
+2025-05-05 21:46:12.745889: train_loss -0.4968 
+2025-05-05 21:46:12.887525: val_loss -0.4916 
+2025-05-05 21:46:12.906632: Pseudo dice [np.float32(0.8326), np.float32(0.8282), np.float32(0.6744), np.float32(0.9781), np.float32(0.8866), np.float32(0.9557), np.float32(0.9657), np.float32(0.9792), np.float32(0.9633), np.float32(0.9615), np.float32(0.9525), np.float32(0.9659), np.float32(0.9653), np.float32(0.889), np.float32(0.9637), np.float32(0.9468), np.float32(0.856), np.float32(0.8342), np.float32(0.9081)] 
+2025-05-05 21:46:12.913116: Epoch time: 93.96 s 
+2025-05-05 21:46:15.334954:  
+2025-05-05 21:46:15.444382: Epoch 750 
+2025-05-05 21:46:15.556054: Current learning rate: 0.00655 
+2025-05-05 21:47:51.752774: train_loss -0.4747 
+2025-05-05 21:47:51.800655: val_loss -0.5191 
+2025-05-05 21:47:51.801335: Pseudo dice [np.float32(0.8305), np.float32(0.8282), np.float32(0.8821), np.float32(0.9674), np.float32(0.9159), np.float32(0.9649), np.float32(0.9242), np.float32(0.957), np.float32(0.9614), np.float32(0.9669), np.float32(0.946), np.float32(0.9689), np.float32(0.9713), np.float32(0.9056), np.float32(0.9635), np.float32(0.9481), np.float32(0.8968), np.float32(0.874), np.float32(0.918)] 
+2025-05-05 21:47:51.801844: Epoch time: 96.42 s 
+2025-05-05 21:47:53.270899:  
+2025-05-05 21:47:53.375517: Epoch 751 
+2025-05-05 21:47:53.397890: Current learning rate: 0.00655 
+2025-05-05 21:49:27.493965: train_loss -0.5011 
+2025-05-05 21:49:27.583123: val_loss -0.4649 
+2025-05-05 21:49:27.605896: Pseudo dice [np.float32(0.8452), np.float32(0.8405), np.float32(0.9252), np.float32(0.9748), np.float32(0.8978), np.float32(0.9621), np.float32(0.9614), np.float32(0.9765), np.float32(0.9585), np.float32(0.9579), np.float32(0.927), np.float32(0.9607), np.float32(0.9571), np.float32(0.8902), np.float32(0.9668), np.float32(0.9536), np.float32(0.8095), np.float32(0.772), np.float32(0.904)] 
+2025-05-05 21:49:27.636928: Epoch time: 94.22 s 
+2025-05-05 21:49:29.381006:  
+2025-05-05 21:49:29.489899: Epoch 752 
+2025-05-05 21:49:29.565461: Current learning rate: 0.00654 
+2025-05-05 21:51:07.896941: train_loss -0.4902 
+2025-05-05 21:51:07.951423: val_loss -0.4921 
+2025-05-05 21:51:07.972265: Pseudo dice [np.float32(0.8494), np.float32(0.8578), np.float32(0.6865), np.float32(0.9642), np.float32(0.8865), np.float32(0.9628), np.float32(0.9632), np.float32(0.9629), np.float32(0.9672), np.float32(0.944), np.float32(0.8967), np.float32(0.9706), np.float32(0.9707), np.float32(0.8933), np.float32(0.9672), np.float32(0.9539), np.float32(0.9105), np.float32(0.8944), np.float32(0.9175)] 
+2025-05-05 21:51:07.989012: Epoch time: 98.52 s 
+2025-05-05 21:51:09.631243:  
+2025-05-05 21:51:09.753883: Epoch 753 
+2025-05-05 21:51:09.810053: Current learning rate: 0.00654 
+2025-05-05 21:52:45.695197: train_loss -0.4746 
+2025-05-05 21:52:45.811083: val_loss -0.4727 
+2025-05-05 21:52:45.812046: Pseudo dice [np.float32(0.8219), np.float32(0.8408), np.float32(0.9227), np.float32(0.9598), np.float32(0.8945), np.float32(0.9405), np.float32(0.9602), np.float32(0.9541), np.float32(0.9429), np.float32(0.9583), np.float32(0.94), np.float32(0.9642), np.float32(0.9684), np.float32(0.8817), np.float32(0.9615), np.float32(0.941), np.float32(0.8294), np.float32(0.8501), np.float32(0.909)] 
+2025-05-05 21:52:45.818158: Epoch time: 96.07 s 
+2025-05-05 21:52:47.407821:  
+2025-05-05 21:52:47.422558: Epoch 754 
+2025-05-05 21:52:47.423002: Current learning rate: 0.00653 
+2025-05-05 21:54:23.432959: train_loss -0.4768 
+2025-05-05 21:54:23.527383: val_loss -0.4773 
+2025-05-05 21:54:23.536638: Pseudo dice [np.float32(0.8394), np.float32(0.8443), np.float32(0.8474), np.float32(0.9736), np.float32(0.9124), np.float32(0.9572), np.float32(0.9695), np.float32(0.9753), np.float32(0.9589), np.float32(0.9703), np.float32(0.9355), np.float32(0.9596), np.float32(0.964), np.float32(0.8922), np.float32(0.9632), np.float32(0.9468), np.float32(0.8725), np.float32(0.8875), np.float32(0.9105)] 
+2025-05-05 21:54:23.554981: Epoch time: 96.03 s 
+2025-05-05 21:54:25.160597:  
+2025-05-05 21:54:25.254755: Epoch 755 
+2025-05-05 21:54:25.284139: Current learning rate: 0.00653 
+2025-05-05 21:56:01.668565: train_loss -0.4877 
+2025-05-05 21:56:01.773875: val_loss -0.5157 
+2025-05-05 21:56:01.804321: Pseudo dice [np.float32(0.7988), np.float32(0.8468), np.float32(0.9029), np.float32(0.9737), np.float32(0.8659), np.float32(0.9529), np.float32(0.9556), np.float32(0.9716), np.float32(0.9696), np.float32(0.9596), np.float32(0.9356), np.float32(0.9691), np.float32(0.9655), np.float32(0.8987), np.float32(0.97), np.float32(0.9541), np.float32(0.7618), np.float32(0.793), np.float32(0.9111)] 
+2025-05-05 21:56:01.862541: Epoch time: 96.51 s 
+2025-05-05 21:56:04.026018:  
+2025-05-05 21:56:04.050934: Epoch 756 
+2025-05-05 21:56:04.059124: Current learning rate: 0.00652 
+2025-05-05 21:57:37.168516: train_loss -0.4731 
+2025-05-05 21:57:37.230043: val_loss -0.4827 
+2025-05-05 21:57:37.242459: Pseudo dice [np.float32(0.8321), np.float32(0.8221), np.float32(0.836), np.float32(0.9739), np.float32(0.8821), np.float32(0.9514), np.float32(0.9505), np.float32(0.9651), np.float32(0.931), np.float32(0.9659), np.float32(0.945), np.float32(0.9529), np.float32(0.9584), np.float32(0.8915), np.float32(0.9525), np.float32(0.9424), np.float32(0.8771), np.float32(0.8781), np.float32(0.919)] 
+2025-05-05 21:57:37.262490: Epoch time: 93.14 s 
+2025-05-05 21:57:38.826334:  
+2025-05-05 21:57:38.925688: Epoch 757 
+2025-05-05 21:57:38.951115: Current learning rate: 0.00652 
+2025-05-05 21:59:15.257481: train_loss -0.4852 
+2025-05-05 21:59:15.341211: val_loss -0.5147 
+2025-05-05 21:59:15.359528: Pseudo dice [np.float32(0.8215), np.float32(0.8364), np.float32(0.8806), np.float32(0.9708), np.float32(0.902), np.float32(0.9609), np.float32(0.9538), np.float32(0.9781), np.float32(0.9626), np.float32(0.9566), np.float32(0.8986), np.float32(0.9678), np.float32(0.9574), np.float32(0.8985), np.float32(0.9649), np.float32(0.9471), np.float32(0.8499), np.float32(0.8682), np.float32(0.8897)] 
+2025-05-05 21:59:15.370792: Epoch time: 96.43 s 
+2025-05-05 21:59:16.918758:  
+2025-05-05 21:59:17.003711: Epoch 758 
+2025-05-05 21:59:17.044330: Current learning rate: 0.00651 
+2025-05-05 22:00:53.834617: train_loss -0.4843 
+2025-05-05 22:00:53.984226: val_loss -0.506 
+2025-05-05 22:00:54.037440: Pseudo dice [np.float32(0.824), np.float32(0.8262), np.float32(0.9279), np.float32(0.9457), np.float32(0.862), np.float32(0.9538), np.float32(0.9559), np.float32(0.9789), np.float32(0.955), np.float32(0.9628), np.float32(0.9289), np.float32(0.9535), np.float32(0.9571), np.float32(0.8977), np.float32(0.9668), np.float32(0.9512), np.float32(0.8462), np.float32(0.838), np.float32(0.9013)] 
+2025-05-05 22:00:54.077641: Epoch time: 96.92 s 
+2025-05-05 22:00:55.668294:  
+2025-05-05 22:00:55.797708: Epoch 759 
+2025-05-05 22:00:55.807356: Current learning rate: 0.00651 
+2025-05-05 22:02:27.604274: train_loss -0.4802 
+2025-05-05 22:02:27.628403: val_loss -0.4909 
+2025-05-05 22:02:27.632495: Pseudo dice [np.float32(0.8473), np.float32(0.8642), np.float32(0.9191), np.float32(0.9646), np.float32(0.822), np.float32(0.9611), np.float32(0.9577), np.float32(0.9757), np.float32(0.9705), np.float32(0.9675), np.float32(0.9461), np.float32(0.9732), np.float32(0.9687), np.float32(0.8945), np.float32(0.9634), np.float32(0.9468), np.float32(0.7897), np.float32(0.8169), np.float32(0.9039)] 
+2025-05-05 22:02:27.633025: Epoch time: 91.94 s 
+2025-05-05 22:02:29.122826:  
+2025-05-05 22:02:29.208762: Epoch 760 
+2025-05-05 22:02:29.224993: Current learning rate: 0.0065 
+2025-05-05 22:04:04.698352: train_loss -0.4582 
+2025-05-05 22:04:04.710681: val_loss -0.4708 
+2025-05-05 22:04:04.711387: Pseudo dice [np.float32(0.8275), np.float32(0.8158), np.float32(0.8926), np.float32(0.9741), np.float32(0.8887), np.float32(0.9524), np.float32(0.9609), np.float32(0.9662), np.float32(0.9517), np.float32(0.965), np.float32(0.9458), np.float32(0.9639), np.float32(0.9611), np.float32(0.8869), np.float32(0.8867), np.float32(0.9406), np.float32(0.8477), np.float32(0.8644), np.float32(0.922)] 
+2025-05-05 22:04:04.711896: Epoch time: 95.58 s 
+2025-05-05 22:04:09.919403:  
+2025-05-05 22:04:09.924119: Epoch 761 
+2025-05-05 22:04:09.924497: Current learning rate: 0.0065 
+2025-05-05 22:05:43.533522: train_loss -0.4804 
+2025-05-05 22:05:43.708567: val_loss -0.4786 
+2025-05-05 22:05:43.722915: Pseudo dice [np.float32(0.8246), np.float32(0.8421), np.float32(0.7956), np.float32(0.9732), np.float32(0.9184), np.float32(0.9609), np.float32(0.9613), np.float32(0.9786), np.float32(0.959), np.float32(0.9593), np.float32(0.9416), np.float32(0.9648), np.float32(0.9681), np.float32(0.8946), np.float32(0.961), np.float32(0.9408), np.float32(0.8316), np.float32(0.8914), np.float32(0.9007)] 
+2025-05-05 22:05:43.754059: Epoch time: 93.62 s 
+2025-05-05 22:05:45.299409:  
+2025-05-05 22:05:45.354822: Epoch 762 
+2025-05-05 22:05:45.384247: Current learning rate: 0.00649 
+2025-05-05 22:07:16.000013: train_loss -0.4621 
+2025-05-05 22:07:16.063862: val_loss -0.5091 
+2025-05-05 22:07:16.089820: Pseudo dice [np.float32(0.8276), np.float32(0.8071), np.float32(0.8391), np.float32(0.9726), np.float32(0.9045), np.float32(0.9542), np.float32(0.9622), np.float32(0.9756), np.float32(0.9434), np.float32(0.965), np.float32(0.9475), np.float32(0.9571), np.float32(0.9614), np.float32(0.9149), np.float32(0.9581), np.float32(0.9484), np.float32(0.8666), np.float32(0.8282), np.float32(0.9012)] 
+2025-05-05 22:07:16.101078: Epoch time: 90.7 s 
+2025-05-05 22:07:17.693771:  
+2025-05-05 22:07:17.801832: Epoch 763 
+2025-05-05 22:07:17.836167: Current learning rate: 0.00649 
+2025-05-05 22:08:57.456846: train_loss -0.4901 
+2025-05-05 22:08:57.583855: val_loss -0.5335 
+2025-05-05 22:08:57.620656: Pseudo dice [np.float32(0.8355), np.float32(0.8417), np.float32(0.9106), np.float32(0.9753), np.float32(0.891), np.float32(0.9516), np.float32(0.9584), np.float32(0.9701), np.float32(0.9674), np.float32(0.9517), np.float32(0.8841), np.float32(0.9698), np.float32(0.9639), np.float32(0.9142), np.float32(0.9671), np.float32(0.949), np.float32(0.8984), np.float32(0.9034), np.float32(0.9197)] 
+2025-05-05 22:08:57.652982: Epoch time: 99.76 s 
+2025-05-05 22:08:59.179405:  
+2025-05-05 22:08:59.259444: Epoch 764 
+2025-05-05 22:08:59.269305: Current learning rate: 0.00648 
+2025-05-05 22:10:34.159030: train_loss -0.469 
+2025-05-05 22:10:34.194599: val_loss -0.5026 
+2025-05-05 22:10:34.198640: Pseudo dice [np.float32(0.8239), np.float32(0.8292), np.float32(0.8868), np.float32(0.975), np.float32(0.8817), np.float32(0.9586), np.float32(0.9605), np.float32(0.9708), np.float32(0.9484), np.float32(0.9436), np.float32(0.9236), np.float32(0.9463), np.float32(0.9662), np.float32(0.9046), np.float32(0.9691), np.float32(0.9561), np.float32(0.8935), np.float32(0.8769), np.float32(0.9028)] 
+2025-05-05 22:10:34.199198: Epoch time: 94.98 s 
+2025-05-05 22:10:35.747466:  
+2025-05-05 22:10:35.913821: Epoch 765 
+2025-05-05 22:10:35.960243: Current learning rate: 0.00648 
+2025-05-05 22:12:07.297480: train_loss -0.4897 
+2025-05-05 22:12:07.375961: val_loss -0.4662 
+2025-05-05 22:12:07.434701: Pseudo dice [np.float32(0.8405), np.float32(0.8393), np.float32(0.929), np.float32(0.9701), np.float32(0.8984), np.float32(0.9607), np.float32(0.9648), np.float32(0.9721), np.float32(0.9613), np.float32(0.9687), np.float32(0.9361), np.float32(0.9652), np.float32(0.9683), np.float32(0.8905), np.float32(0.9642), np.float32(0.9534), np.float32(0.8775), np.float32(0.8864), np.float32(0.9155)] 
+2025-05-05 22:12:07.482035: Epoch time: 91.55 s 
+2025-05-05 22:12:08.977548:  
+2025-05-05 22:12:09.096240: Epoch 766 
+2025-05-05 22:12:09.122035: Current learning rate: 0.00648 
+2025-05-05 22:13:45.963534: train_loss -0.4896 
+2025-05-05 22:13:46.048800: val_loss -0.5195 
+2025-05-05 22:13:46.074819: Pseudo dice [np.float32(0.82), np.float32(0.8264), np.float32(0.9232), np.float32(0.9728), np.float32(0.8737), np.float32(0.963), np.float32(0.9603), np.float32(0.97), np.float32(0.9563), np.float32(0.9633), np.float32(0.9439), np.float32(0.9618), np.float32(0.9629), np.float32(0.9025), np.float32(0.9641), np.float32(0.9526), np.float32(0.8633), np.float32(0.8601), np.float32(0.9154)] 
+2025-05-05 22:13:46.084291: Epoch time: 96.99 s 
+2025-05-05 22:13:47.662608:  
+2025-05-05 22:13:47.844954: Epoch 767 
+2025-05-05 22:13:47.889089: Current learning rate: 0.00647 
+2025-05-05 22:15:24.253608: train_loss -0.4793 
+2025-05-05 22:15:24.337513: val_loss -0.4863 
+2025-05-05 22:15:24.375770: Pseudo dice [np.float32(0.7816), np.float32(0.8198), np.float32(0.8059), np.float32(0.9752), np.float32(0.8804), np.float32(0.9509), np.float32(0.9502), np.float32(0.9656), np.float32(0.9625), np.float32(0.9411), np.float32(0.9357), np.float32(0.9649), np.float32(0.9652), np.float32(0.8964), np.float32(0.9651), np.float32(0.9545), np.float32(0.873), np.float32(0.8726), np.float32(0.9156)] 
+2025-05-05 22:15:24.408119: Epoch time: 96.59 s 
+2025-05-05 22:15:25.956572:  
+2025-05-05 22:15:26.057198: Epoch 768 
+2025-05-05 22:15:26.083239: Current learning rate: 0.00647 
+2025-05-05 22:17:06.978033: train_loss -0.4769 
+2025-05-05 22:17:07.082935: val_loss -0.478 
+2025-05-05 22:17:07.096395: Pseudo dice [np.float32(0.8296), np.float32(0.8155), np.float32(0.9077), np.float32(0.9699), np.float32(0.895), np.float32(0.9452), np.float32(0.9605), np.float32(0.9783), np.float32(0.9552), np.float32(0.9652), np.float32(0.9533), np.float32(0.968), np.float32(0.9703), np.float32(0.8919), np.float32(0.9617), np.float32(0.9518), np.float32(0.8829), np.float32(0.864), np.float32(0.9186)] 
+2025-05-05 22:17:07.128156: Epoch time: 101.02 s 
+2025-05-05 22:17:08.681614:  
+2025-05-05 22:17:08.755693: Epoch 769 
+2025-05-05 22:17:08.801847: Current learning rate: 0.00646 
+2025-05-05 22:18:47.710377: train_loss -0.4867 
+2025-05-05 22:18:47.825864: val_loss -0.5228 
+2025-05-05 22:18:47.850055: Pseudo dice [np.float32(0.856), np.float32(0.8661), np.float32(0.9406), np.float32(0.9678), np.float32(0.8995), np.float32(0.9552), np.float32(0.967), np.float32(0.9753), np.float32(0.9624), np.float32(0.9575), np.float32(0.9379), np.float32(0.9688), np.float32(0.9665), np.float32(0.8996), np.float32(0.9671), np.float32(0.9531), np.float32(0.8545), np.float32(0.8733), np.float32(0.9026)] 
+2025-05-05 22:18:47.894521: Epoch time: 99.03 s 
+2025-05-05 22:18:49.463957:  
+2025-05-05 22:18:49.528262: Epoch 770 
+2025-05-05 22:18:49.551898: Current learning rate: 0.00646 
+2025-05-05 22:20:24.509821: train_loss -0.4886 
+2025-05-05 22:20:24.640665: val_loss -0.4912 
+2025-05-05 22:20:24.682730: Pseudo dice [np.float32(0.8557), np.float32(0.8511), np.float32(0.9323), np.float32(0.9701), np.float32(0.8671), np.float32(0.9617), np.float32(0.9647), np.float32(0.9765), np.float32(0.9686), np.float32(0.9568), np.float32(0.9418), np.float32(0.9675), np.float32(0.9516), np.float32(0.9008), np.float32(0.9689), np.float32(0.9526), np.float32(0.8728), np.float32(0.8654), np.float32(0.9155)] 
+2025-05-05 22:20:24.715090: Epoch time: 95.05 s 
+2025-05-05 22:20:26.316460:  
+2025-05-05 22:20:26.398469: Epoch 771 
+2025-05-05 22:20:26.431990: Current learning rate: 0.00645 
+2025-05-05 22:21:59.088130: train_loss -0.4927 
+2025-05-05 22:21:59.200818: val_loss -0.5196 
+2025-05-05 22:21:59.221570: Pseudo dice [np.float32(0.8429), np.float32(0.8079), np.float32(0.9098), np.float32(0.9684), np.float32(0.8999), np.float32(0.9594), np.float32(0.9565), np.float32(0.9743), np.float32(0.9626), np.float32(0.9638), np.float32(0.9414), np.float32(0.9692), np.float32(0.9634), np.float32(0.9046), np.float32(0.9629), np.float32(0.9543), np.float32(0.8776), np.float32(0.8627), np.float32(0.9215)] 
+2025-05-05 22:21:59.238298: Epoch time: 92.77 s 
+2025-05-05 22:21:59.262731: Yayy! New best EMA pseudo Dice: 0.9229999780654907 
+2025-05-05 22:22:02.067940:  
+2025-05-05 22:22:02.075561: Epoch 772 
+2025-05-05 22:22:02.076106: Current learning rate: 0.00645 
+2025-05-05 22:23:39.837514: train_loss -0.4914 
+2025-05-05 22:23:39.919722: val_loss -0.4869 
+2025-05-05 22:23:39.948927: Pseudo dice [np.float32(0.8407), np.float32(0.8327), np.float32(0.9156), np.float32(0.9714), np.float32(0.891), np.float32(0.9617), np.float32(0.965), np.float32(0.9716), np.float32(0.964), np.float32(0.9681), np.float32(0.9501), np.float32(0.9638), np.float32(0.9706), np.float32(0.8927), np.float32(0.9674), np.float32(0.9372), np.float32(0.8436), np.float32(0.8729), np.float32(0.894)] 
+2025-05-05 22:23:39.974180: Epoch time: 97.77 s 
+2025-05-05 22:23:40.011574: Yayy! New best EMA pseudo Dice: 0.9232000112533569 
+2025-05-05 22:23:42.400947:  
+2025-05-05 22:23:42.448290: Epoch 773 
+2025-05-05 22:23:42.459859: Current learning rate: 0.00644 
+2025-05-05 22:25:24.247298: train_loss -0.4929 
+2025-05-05 22:25:24.323710: val_loss -0.4687 
+2025-05-05 22:25:24.344610: Pseudo dice [np.float32(0.8075), np.float32(0.8362), np.float32(0.8208), np.float32(0.9764), np.float32(0.9093), np.float32(0.9568), np.float32(0.9619), np.float32(0.9795), np.float32(0.9569), np.float32(0.947), np.float32(0.925), np.float32(0.9602), np.float32(0.9617), np.float32(0.8973), np.float32(0.9646), np.float32(0.9438), np.float32(0.8933), np.float32(0.9015), np.float32(0.9241)] 
+2025-05-05 22:25:24.401156: Epoch time: 101.85 s 
+2025-05-05 22:25:25.990653:  
+2025-05-05 22:25:26.104938: Epoch 774 
+2025-05-05 22:25:26.150920: Current learning rate: 0.00644 
+2025-05-05 22:26:59.649074: train_loss -0.4823 
+2025-05-05 22:26:59.776152: val_loss -0.4626 
+2025-05-05 22:26:59.832506: Pseudo dice [np.float32(0.8399), np.float32(0.8368), np.float32(0.8912), np.float32(0.9723), np.float32(0.9223), np.float32(0.9643), np.float32(0.9579), np.float32(0.9781), np.float32(0.9554), np.float32(0.9674), np.float32(0.9442), np.float32(0.965), np.float32(0.9683), np.float32(0.9118), np.float32(0.9667), np.float32(0.9581), np.float32(0.8215), np.float32(0.832), np.float32(0.9116)] 
+2025-05-05 22:26:59.886696: Epoch time: 93.66 s 
+2025-05-05 22:26:59.920449: Yayy! New best EMA pseudo Dice: 0.9232000112533569 
+2025-05-05 22:27:02.730701:  
+2025-05-05 22:27:02.733487: Epoch 775 
+2025-05-05 22:27:02.733864: Current learning rate: 0.00643 
+2025-05-05 22:28:40.990898: train_loss -0.4627 
+2025-05-05 22:28:41.091225: val_loss -0.4461 
+2025-05-05 22:28:41.109923: Pseudo dice [np.float32(0.8421), np.float32(0.8603), np.float32(0.8552), np.float32(0.9776), np.float32(0.921), np.float32(0.9553), np.float32(0.9613), np.float32(0.979), np.float32(0.9596), np.float32(0.9596), np.float32(0.944), np.float32(0.9635), np.float32(0.9595), np.float32(0.8883), np.float32(0.953), np.float32(0.9475), np.float32(0.8824), np.float32(0.8584), np.float32(0.91)] 
+2025-05-05 22:28:41.131981: Epoch time: 98.26 s 
+2025-05-05 22:28:41.151909: Yayy! New best EMA pseudo Dice: 0.9233999848365784 
+2025-05-05 22:28:43.397628:  
+2025-05-05 22:28:43.403712: Epoch 776 
+2025-05-05 22:28:43.404342: Current learning rate: 0.00643 
+2025-05-05 22:30:20.286067: train_loss -0.46 
+2025-05-05 22:30:20.402627: val_loss -0.4893 
+2025-05-05 22:30:20.411080: Pseudo dice [np.float32(0.8312), np.float32(0.8283), np.float32(0.8984), np.float32(0.9729), np.float32(0.8875), np.float32(0.9611), np.float32(0.9541), np.float32(0.974), np.float32(0.9479), np.float32(0.9648), np.float32(0.9458), np.float32(0.9632), np.float32(0.9662), np.float32(0.8874), np.float32(0.9434), np.float32(0.9565), np.float32(0.8695), np.float32(0.8989), np.float32(0.9155)] 
+2025-05-05 22:30:20.432871: Epoch time: 96.89 s 
+2025-05-05 22:30:20.475687: Yayy! New best EMA pseudo Dice: 0.9235000014305115 
+2025-05-05 22:30:26.715307:  
+2025-05-05 22:30:26.720247: Epoch 777 
+2025-05-05 22:30:26.720671: Current learning rate: 0.00642 
+2025-05-05 22:32:06.898097: train_loss -0.4959 
+2025-05-05 22:32:07.080876: val_loss -0.5193 
+2025-05-05 22:32:07.158356: Pseudo dice [np.float32(0.838), np.float32(0.8491), np.float32(0.8976), np.float32(0.9697), np.float32(0.8632), np.float32(0.9558), np.float32(0.9445), np.float32(0.9756), np.float32(0.957), np.float32(0.9263), np.float32(0.9116), np.float32(0.967), np.float32(0.9555), np.float32(0.8935), np.float32(0.9659), np.float32(0.9625), np.float32(0.8613), np.float32(0.8839), np.float32(0.9219)] 
+2025-05-05 22:32:07.201742: Epoch time: 100.18 s 
+2025-05-05 22:32:08.972893:  
+2025-05-05 22:32:09.031542: Epoch 778 
+2025-05-05 22:32:09.094883: Current learning rate: 0.00642 
+2025-05-05 22:33:42.174852: train_loss -0.4737 
+2025-05-05 22:33:42.303233: val_loss -0.4966 
+2025-05-05 22:33:42.347049: Pseudo dice [np.float32(0.831), np.float32(0.8298), np.float32(0.935), np.float32(0.9748), np.float32(0.6554), np.float32(0.9273), np.float32(0.9646), np.float32(0.9789), np.float32(0.9629), np.float32(0.9663), np.float32(0.9346), np.float32(0.9595), np.float32(0.9594), np.float32(0.8918), np.float32(0.9667), np.float32(0.948), np.float32(0.897), np.float32(0.8951), np.float32(0.9278)] 
+2025-05-05 22:33:42.362029: Epoch time: 93.2 s 
+2025-05-05 22:33:43.918704:  
+2025-05-05 22:33:44.033947: Epoch 779 
+2025-05-05 22:33:44.089281: Current learning rate: 0.00641 
+2025-05-05 22:35:20.580961: train_loss -0.4894 
+2025-05-05 22:35:20.728011: val_loss -0.4863 
+2025-05-05 22:35:20.767294: Pseudo dice [np.float32(0.8494), np.float32(0.8224), np.float32(0.8293), np.float32(0.9806), np.float32(0.8952), np.float32(0.9602), np.float32(0.9545), np.float32(0.9762), np.float32(0.9633), np.float32(0.965), np.float32(0.937), np.float32(0.9619), np.float32(0.9655), np.float32(0.8892), np.float32(0.9671), np.float32(0.9511), np.float32(0.8358), np.float32(0.8538), np.float32(0.9316)] 
+2025-05-05 22:35:20.796955: Epoch time: 96.66 s 
+2025-05-05 22:35:22.445277:  
+2025-05-05 22:35:22.570604: Epoch 780 
+2025-05-05 22:35:22.604179: Current learning rate: 0.00641 
+2025-05-05 22:36:56.751294: train_loss -0.5103 
+2025-05-05 22:36:56.845816: val_loss -0.504 
+2025-05-05 22:36:56.869164: Pseudo dice [np.float32(0.8466), np.float32(0.8243), np.float32(0.9286), np.float32(0.9724), np.float32(0.8904), np.float32(0.9626), np.float32(0.9598), np.float32(0.9763), np.float32(0.9608), np.float32(0.9699), np.float32(0.9523), np.float32(0.9683), np.float32(0.9676), np.float32(0.8851), np.float32(0.9686), np.float32(0.9453), np.float32(0.8887), np.float32(0.8876), np.float32(0.9258)] 
+2025-05-05 22:36:56.880638: Epoch time: 94.31 s 
+2025-05-05 22:36:58.517956:  
+2025-05-05 22:36:58.560049: Epoch 781 
+2025-05-05 22:36:58.572940: Current learning rate: 0.0064 
+2025-05-05 22:38:33.524647: train_loss -0.4888 
+2025-05-05 22:38:33.557315: val_loss -0.4619 
+2025-05-05 22:38:33.573652: Pseudo dice [np.float32(0.8361), np.float32(0.8355), np.float32(0.8168), np.float32(0.965), np.float32(0.8491), np.float32(0.9572), np.float32(0.9607), np.float32(0.9718), np.float32(0.9194), np.float32(0.9647), np.float32(0.9385), np.float32(0.9473), np.float32(0.9609), np.float32(0.8911), np.float32(0.9579), np.float32(0.9409), np.float32(0.8487), np.float32(0.8464), np.float32(0.9126)] 
+2025-05-05 22:38:33.586057: Epoch time: 95.01 s 
+2025-05-05 22:38:35.137281:  
+2025-05-05 22:38:35.211583: Epoch 782 
+2025-05-05 22:38:35.225790: Current learning rate: 0.0064 
+2025-05-05 22:40:09.177473: train_loss -0.4956 
+2025-05-05 22:40:09.298726: val_loss -0.4627 
+2025-05-05 22:40:09.321137: Pseudo dice [np.float32(0.8275), np.float32(0.8369), np.float32(0.8962), np.float32(0.9709), np.float32(0.9035), np.float32(0.9467), np.float32(0.9526), np.float32(0.9761), np.float32(0.9693), np.float32(0.9263), np.float32(0.9234), np.float32(0.9719), np.float32(0.953), np.float32(0.8912), np.float32(0.9441), np.float32(0.9162), np.float32(0.8854), np.float32(0.8822), np.float32(0.9087)] 
+2025-05-05 22:40:09.350201: Epoch time: 94.04 s 
+2025-05-05 22:40:10.828377:  
+2025-05-05 22:40:10.916244: Epoch 783 
+2025-05-05 22:40:10.924183: Current learning rate: 0.00639 
+2025-05-05 22:41:48.764221: train_loss -0.4843 
+2025-05-05 22:41:48.854744: val_loss -0.5031 
+2025-05-05 22:41:48.867950: Pseudo dice [np.float32(0.8436), np.float32(0.8448), np.float32(0.9393), np.float32(0.9699), np.float32(0.9151), np.float32(0.9588), np.float32(0.9666), np.float32(0.9764), np.float32(0.9585), np.float32(0.9681), np.float32(0.9418), np.float32(0.9583), np.float32(0.9637), np.float32(0.8816), np.float32(0.9679), np.float32(0.948), np.float32(0.8812), np.float32(0.8761), np.float32(0.9269)] 
+2025-05-05 22:41:48.873667: Epoch time: 97.94 s 
+2025-05-05 22:41:50.348001:  
+2025-05-05 22:41:50.398657: Epoch 784 
+2025-05-05 22:41:50.413507: Current learning rate: 0.00639 
+2025-05-05 22:43:27.431457: train_loss -0.4797 
+2025-05-05 22:43:27.575380: val_loss -0.5023 
+2025-05-05 22:43:27.601168: Pseudo dice [np.float32(0.8127), np.float32(0.8222), np.float32(0.7968), np.float32(0.9594), np.float32(0.9119), np.float32(0.9516), np.float32(0.9593), np.float32(0.9724), np.float32(0.9631), np.float32(0.9607), np.float32(0.9353), np.float32(0.967), np.float32(0.9589), np.float32(0.8846), np.float32(0.9639), np.float32(0.9451), np.float32(0.8898), np.float32(0.8674), np.float32(0.9071)] 
+2025-05-05 22:43:27.630192: Epoch time: 97.08 s 
+2025-05-05 22:43:29.180774:  
+2025-05-05 22:43:29.248636: Epoch 785 
+2025-05-05 22:43:29.274392: Current learning rate: 0.00639 
+2025-05-05 22:45:04.511160: train_loss -0.4891 
+2025-05-05 22:45:04.631707: val_loss -0.4794 
+2025-05-05 22:45:04.659124: Pseudo dice [np.float32(0.8341), np.float32(0.8302), np.float32(0.8562), np.float32(0.9668), np.float32(0.8653), np.float32(0.9604), np.float32(0.9483), np.float32(0.9774), np.float32(0.9669), np.float32(0.9413), np.float32(0.9218), np.float32(0.9672), np.float32(0.9575), np.float32(0.8679), np.float32(0.965), np.float32(0.9427), np.float32(0.8694), np.float32(0.8721), np.float32(0.9164)] 
+2025-05-05 22:45:04.669767: Epoch time: 95.33 s 
+2025-05-05 22:45:06.258160:  
+2025-05-05 22:45:06.340999: Epoch 786 
+2025-05-05 22:45:06.366667: Current learning rate: 0.00638 
+2025-05-05 22:46:47.219795: train_loss -0.4951 
+2025-05-05 22:46:47.255316: val_loss -0.4944 
+2025-05-05 22:46:47.266781: Pseudo dice [np.float32(0.8093), np.float32(0.8343), np.float32(0.9063), np.float32(0.959), np.float32(0.882), np.float32(0.9526), np.float32(0.947), np.float32(0.9677), np.float32(0.9648), np.float32(0.9538), np.float32(0.9266), np.float32(0.9688), np.float32(0.9631), np.float32(0.8783), np.float32(0.9603), np.float32(0.9164), np.float32(0.8518), np.float32(0.8558), np.float32(0.9058)] 
+2025-05-05 22:46:47.288951: Epoch time: 100.96 s 
+2025-05-05 22:46:48.946083:  
+2025-05-05 22:46:49.066542: Epoch 787 
+2025-05-05 22:46:49.103476: Current learning rate: 0.00638 
+2025-05-05 22:48:26.945830: train_loss -0.4863 
+2025-05-05 22:48:27.067796: val_loss -0.4479 
+2025-05-05 22:48:27.091247: Pseudo dice [np.float32(0.8287), np.float32(0.8299), np.float32(0.8625), np.float32(0.9789), np.float32(0.8898), np.float32(0.9509), np.float32(0.9579), np.float32(0.9708), np.float32(0.9598), np.float32(0.9587), np.float32(0.9207), np.float32(0.9649), np.float32(0.9537), np.float32(0.8891), np.float32(0.9609), np.float32(0.9468), np.float32(0.8452), np.float32(0.8395), np.float32(0.8994)] 
+2025-05-05 22:48:27.099018: Epoch time: 98.0 s 
+2025-05-05 22:48:28.872195:  
+2025-05-05 22:48:28.900679: Epoch 788 
+2025-05-05 22:48:28.935920: Current learning rate: 0.00637 
+2025-05-05 22:50:02.683229: train_loss -0.5009 
+2025-05-05 22:50:02.760704: val_loss -0.4702 
+2025-05-05 22:50:02.784162: Pseudo dice [np.float32(0.8055), np.float32(0.8284), np.float32(0.8495), np.float32(0.967), np.float32(0.8761), np.float32(0.9372), np.float32(0.9539), np.float32(0.9539), np.float32(0.9643), np.float32(0.9624), np.float32(0.9098), np.float32(0.9664), np.float32(0.9616), np.float32(0.8968), np.float32(0.9237), np.float32(0.9238), np.float32(0.8919), np.float32(0.8963), np.float32(0.8894)] 
+2025-05-05 22:50:02.791085: Epoch time: 93.81 s 
+2025-05-05 22:50:04.320536:  
+2025-05-05 22:50:04.338729: Epoch 789 
+2025-05-05 22:50:04.339317: Current learning rate: 0.00637 
+2025-05-05 22:51:37.439835: train_loss -0.4899 
+2025-05-05 22:51:37.586621: val_loss -0.4575 
+2025-05-05 22:51:37.631046: Pseudo dice [np.float32(0.825), np.float32(0.8215), np.float32(0.8698), np.float32(0.9717), np.float32(0.9128), np.float32(0.9595), np.float32(0.9625), np.float32(0.9667), np.float32(0.96), np.float32(0.9572), np.float32(0.8859), np.float32(0.9544), np.float32(0.9685), np.float32(0.9092), np.float32(0.9627), np.float32(0.9492), np.float32(0.8124), np.float32(0.8529), np.float32(0.9077)] 
+2025-05-05 22:51:37.661951: Epoch time: 93.12 s 
+2025-05-05 22:51:39.491648:  
+2025-05-05 22:51:39.523859: Epoch 790 
+2025-05-05 22:51:39.561054: Current learning rate: 0.00636 
+2025-05-05 22:53:15.404540: train_loss -0.4695 
+2025-05-05 22:53:15.526777: val_loss -0.5154 
+2025-05-05 22:53:15.601600: Pseudo dice [np.float32(0.8162), np.float32(0.8269), np.float32(0.8035), np.float32(0.9662), np.float32(0.9086), np.float32(0.9458), np.float32(0.9619), np.float32(0.9742), np.float32(0.9649), np.float32(0.9617), np.float32(0.9409), np.float32(0.9694), np.float32(0.9693), np.float32(0.894), np.float32(0.9595), np.float32(0.9484), np.float32(0.8919), np.float32(0.9038), np.float32(0.9217)] 
+2025-05-05 22:53:15.638241: Epoch time: 95.91 s 
+2025-05-05 22:53:17.239702:  
+2025-05-05 22:53:17.291598: Epoch 791 
+2025-05-05 22:53:17.305796: Current learning rate: 0.00636 
+2025-05-05 22:54:53.523458: train_loss -0.4766 
+2025-05-05 22:54:53.630146: val_loss -0.5006 
+2025-05-05 22:54:53.665087: Pseudo dice [np.float32(0.8434), np.float32(0.824), np.float32(0.928), np.float32(0.9778), np.float32(0.9067), np.float32(0.958), np.float32(0.9642), np.float32(0.9756), np.float32(0.9596), np.float32(0.9592), np.float32(0.9486), np.float32(0.9611), np.float32(0.9661), np.float32(0.8917), np.float32(0.9617), np.float32(0.9398), np.float32(0.8465), np.float32(0.845), np.float32(0.9105)] 
+2025-05-05 22:54:53.686985: Epoch time: 96.29 s 
+2025-05-05 22:54:55.340402:  
+2025-05-05 22:54:55.407872: Epoch 792 
+2025-05-05 22:54:55.441221: Current learning rate: 0.00635 
+2025-05-05 22:56:34.804542: train_loss -0.4903 
+2025-05-05 22:56:34.844881: val_loss -0.4978 
+2025-05-05 22:56:34.845460: Pseudo dice [np.float32(0.8171), np.float32(0.8035), np.float32(0.8547), np.float32(0.9774), np.float32(0.907), np.float32(0.9514), np.float32(0.9514), np.float32(0.9712), np.float32(0.9551), np.float32(0.9639), np.float32(0.947), np.float32(0.9592), np.float32(0.9654), np.float32(0.8862), np.float32(0.9672), np.float32(0.9452), np.float32(0.8477), np.float32(0.8301), np.float32(0.9131)] 
+2025-05-05 22:56:34.845897: Epoch time: 99.47 s 
+2025-05-05 22:56:36.469705:  
+2025-05-05 22:56:36.620557: Epoch 793 
+2025-05-05 22:56:36.664722: Current learning rate: 0.00635 
+2025-05-05 22:58:11.147229: train_loss -0.4972 
+2025-05-05 22:58:11.221142: val_loss -0.5066 
+2025-05-05 22:58:11.260898: Pseudo dice [np.float32(0.822), np.float32(0.82), np.float32(0.8437), np.float32(0.9736), np.float32(0.895), np.float32(0.9531), np.float32(0.963), np.float32(0.9721), np.float32(0.941), np.float32(0.9668), np.float32(0.9471), np.float32(0.958), np.float32(0.9656), np.float32(0.8966), np.float32(0.9612), np.float32(0.9455), np.float32(0.8711), np.float32(0.8882), np.float32(0.9051)] 
+2025-05-05 22:58:11.290365: Epoch time: 94.68 s 
+2025-05-05 22:58:16.658546:  
+2025-05-05 22:58:16.664608: Epoch 794 
+2025-05-05 22:58:16.665084: Current learning rate: 0.00634 
+2025-05-05 22:59:50.822580: train_loss -0.4968 
+2025-05-05 22:59:50.931702: val_loss -0.5068 
+2025-05-05 22:59:50.992093: Pseudo dice [np.float32(0.831), np.float32(0.8308), np.float32(0.8942), np.float32(0.9718), np.float32(0.8861), np.float32(0.9611), np.float32(0.954), np.float32(0.9763), np.float32(0.9647), np.float32(0.9635), np.float32(0.9481), np.float32(0.961), np.float32(0.9717), np.float32(0.8944), np.float32(0.9635), np.float32(0.9537), np.float32(0.866), np.float32(0.8365), np.float32(0.9174)] 
+2025-05-05 22:59:51.041324: Epoch time: 94.17 s 
+2025-05-05 22:59:52.835268:  
+2025-05-05 22:59:52.850923: Epoch 795 
+2025-05-05 22:59:52.851386: Current learning rate: 0.00634 
+2025-05-05 23:01:27.216421: train_loss -0.4874 
+2025-05-05 23:01:27.256938: val_loss -0.4869 
+2025-05-05 23:01:27.261580: Pseudo dice [np.float32(0.8331), np.float32(0.8273), np.float32(0.9111), np.float32(0.9684), np.float32(0.8766), np.float32(0.9556), np.float32(0.963), np.float32(0.9782), np.float32(0.9634), np.float32(0.9643), np.float32(0.9473), np.float32(0.969), np.float32(0.9618), np.float32(0.9007), np.float32(0.97), np.float32(0.9539), np.float32(0.8588), np.float32(0.8706), np.float32(0.9128)] 
+2025-05-05 23:01:27.262332: Epoch time: 94.38 s 
+2025-05-05 23:01:28.797063:  
+2025-05-05 23:01:28.804349: Epoch 796 
+2025-05-05 23:01:28.809407: Current learning rate: 0.00633 
+2025-05-05 23:03:02.323411: train_loss -0.4951 
+2025-05-05 23:03:02.384498: val_loss -0.4732 
+2025-05-05 23:03:02.393232: Pseudo dice [np.float32(0.8366), np.float32(0.8031), np.float32(0.8675), np.float32(0.9744), np.float32(0.863), np.float32(0.9597), np.float32(0.957), np.float32(0.9763), np.float32(0.959), np.float32(0.9518), np.float32(0.9087), np.float32(0.968), np.float32(0.9617), np.float32(0.9084), np.float32(0.9713), np.float32(0.9605), np.float32(0.8814), np.float32(0.8507), np.float32(0.9109)] 
+2025-05-05 23:03:02.424353: Epoch time: 93.53 s 
+2025-05-05 23:03:04.064725:  
+2025-05-05 23:03:04.202445: Epoch 797 
+2025-05-05 23:03:04.245767: Current learning rate: 0.00633 
+2025-05-05 23:04:52.670986: train_loss -0.4991 
+2025-05-05 23:04:52.701041: val_loss -0.5222 
+2025-05-05 23:04:52.721118: Pseudo dice [np.float32(0.8477), np.float32(0.8456), np.float32(0.8602), np.float32(0.961), np.float32(0.8998), np.float32(0.963), np.float32(0.9552), np.float32(0.9771), np.float32(0.9577), np.float32(0.9564), np.float32(0.933), np.float32(0.9614), np.float32(0.9595), np.float32(0.9041), np.float32(0.9649), np.float32(0.9565), np.float32(0.8535), np.float32(0.8853), np.float32(0.9181)] 
+2025-05-05 23:04:52.723458: Epoch time: 108.61 s 
+2025-05-05 23:04:54.189150:  
+2025-05-05 23:04:54.197144: Epoch 798 
+2025-05-05 23:04:54.197603: Current learning rate: 0.00632 
+2025-05-05 23:06:33.554895: train_loss -0.4688 
+2025-05-05 23:06:33.569743: val_loss -0.5255 
+2025-05-05 23:06:33.574492: Pseudo dice [np.float32(0.8602), np.float32(0.8325), np.float32(0.8925), np.float32(0.9679), np.float32(0.9107), np.float32(0.9598), np.float32(0.9547), np.float32(0.9749), np.float32(0.9321), np.float32(0.9543), np.float32(0.933), np.float32(0.9574), np.float32(0.9633), np.float32(0.8859), np.float32(0.9588), np.float32(0.9506), np.float32(0.8196), np.float32(0.8522), np.float32(0.913)] 
+2025-05-05 23:06:33.574979: Epoch time: 99.37 s 
+2025-05-05 23:06:35.062548:  
+2025-05-05 23:06:35.123480: Epoch 799 
+2025-05-05 23:06:35.139708: Current learning rate: 0.00632 
+2025-05-05 23:08:14.775490: train_loss -0.4755 
+2025-05-05 23:08:14.897175: val_loss -0.4592 
+2025-05-05 23:08:14.919226: Pseudo dice [np.float32(0.856), np.float32(0.8473), np.float32(0.9143), np.float32(0.9518), np.float32(0.8902), np.float32(0.9445), np.float32(0.97), np.float32(0.9757), np.float32(0.9549), np.float32(0.9555), np.float32(0.944), np.float32(0.9646), np.float32(0.9601), np.float32(0.8862), np.float32(0.9557), np.float32(0.956), np.float32(0.8824), np.float32(0.7597), np.float32(0.9243)] 
+2025-05-05 23:08:14.937402: Epoch time: 99.71 s 
+2025-05-05 23:08:17.665935:  
+2025-05-05 23:08:17.711452: Epoch 800 
+2025-05-05 23:08:17.711922: Current learning rate: 0.00631 
+2025-05-05 23:10:00.169138: train_loss -0.4902 
+2025-05-05 23:10:00.172518: val_loss -0.4653 
+2025-05-05 23:10:00.172964: Pseudo dice [np.float32(0.841), np.float32(0.822), np.float32(0.8494), np.float32(0.9758), np.float32(0.9045), np.float32(0.9498), np.float32(0.9567), np.float32(0.9677), np.float32(0.951), np.float32(0.9542), np.float32(0.9476), np.float32(0.9638), np.float32(0.9644), np.float32(0.8786), np.float32(0.9422), np.float32(0.9424), np.float32(0.8585), np.float32(0.8819), np.float32(0.9082)] 
+2025-05-05 23:10:00.173289: Epoch time: 102.5 s 
+2025-05-05 23:10:01.726680:  
+2025-05-05 23:10:01.844198: Epoch 801 
+2025-05-05 23:10:01.888859: Current learning rate: 0.00631 
+2025-05-05 23:11:42.363893: train_loss -0.4644 
+2025-05-05 23:11:42.447616: val_loss -0.5037 
+2025-05-05 23:11:42.474480: Pseudo dice [np.float32(0.8111), np.float32(0.8546), np.float32(0.6159), np.float32(0.9761), np.float32(0.8781), np.float32(0.9425), np.float32(0.957), np.float32(0.9768), np.float32(0.9603), np.float32(0.9627), np.float32(0.9476), np.float32(0.966), np.float32(0.9627), np.float32(0.8916), np.float32(0.9614), np.float32(0.9306), np.float32(0.8871), np.float32(0.8682), np.float32(0.9059)] 
+2025-05-05 23:11:42.494227: Epoch time: 100.64 s 
+2025-05-05 23:11:44.048866:  
+2025-05-05 23:11:44.112193: Epoch 802 
+2025-05-05 23:11:44.116414: Current learning rate: 0.0063 
+2025-05-05 23:13:28.816638: train_loss -0.4805 
+2025-05-05 23:13:28.821350: val_loss -0.5026 
+2025-05-05 23:13:28.821905: Pseudo dice [np.float32(0.8399), np.float32(0.8237), np.float32(0.8906), np.float32(0.9619), np.float32(0.8973), np.float32(0.9544), np.float32(0.9614), np.float32(0.9755), np.float32(0.9456), np.float32(0.9528), np.float32(0.9344), np.float32(0.9612), np.float32(0.9573), np.float32(0.8829), np.float32(0.9512), np.float32(0.9526), np.float32(0.8719), np.float32(0.8661), np.float32(0.8814)] 
+2025-05-05 23:13:28.822429: Epoch time: 104.77 s 
+2025-05-05 23:13:30.409669:  
+2025-05-05 23:13:30.565154: Epoch 803 
+2025-05-05 23:13:30.566731: Current learning rate: 0.0063 
+2025-05-05 23:15:15.008904: train_loss -0.4833 
+2025-05-05 23:15:15.066741: val_loss -0.4909 
+2025-05-05 23:15:15.092644: Pseudo dice [np.float32(0.8222), np.float32(0.8573), np.float32(0.8918), np.float32(0.9746), np.float32(0.8652), np.float32(0.9499), np.float32(0.9565), np.float32(0.9746), np.float32(0.9685), np.float32(0.9548), np.float32(0.9329), np.float32(0.9721), np.float32(0.9661), np.float32(0.8886), np.float32(0.9594), np.float32(0.9329), np.float32(0.8796), np.float32(0.8983), np.float32(0.9069)] 
+2025-05-05 23:15:15.132936: Epoch time: 104.6 s 
+2025-05-05 23:15:16.784236:  
+2025-05-05 23:15:16.846426: Epoch 804 
+2025-05-05 23:15:16.861273: Current learning rate: 0.0063 
+2025-05-05 23:16:57.488409: train_loss -0.4688 
+2025-05-05 23:16:57.576725: val_loss -0.4822 
+2025-05-05 23:16:57.577528: Pseudo dice [np.float32(0.8196), np.float32(0.8227), np.float32(0.0), np.float32(0.9569), np.float32(0.8893), np.float32(0.9452), np.float32(0.9622), np.float32(0.9776), np.float32(0.955), np.float32(0.9623), np.float32(0.9362), np.float32(0.9646), np.float32(0.9645), np.float32(0.904), np.float32(0.9582), np.float32(0.9518), np.float32(0.8759), np.float32(0.9015), np.float32(0.9114)] 
+2025-05-05 23:16:57.589147: Epoch time: 100.71 s 
+2025-05-05 23:16:59.187670:  
+2025-05-05 23:16:59.244406: Epoch 805 
+2025-05-05 23:16:59.251850: Current learning rate: 0.00629 
+2025-05-05 23:18:35.524375: train_loss -0.4654 
+2025-05-05 23:18:35.562035: val_loss -0.4612 
+2025-05-05 23:18:35.562551: Pseudo dice [np.float32(0.8355), np.float32(0.8237), np.float32(0.9294), np.float32(0.9696), np.float32(0.8903), np.float32(0.9624), np.float32(0.956), np.float32(0.9809), np.float32(0.955), np.float32(0.97), np.float32(0.9426), np.float32(0.964), np.float32(0.9549), np.float32(0.8954), np.float32(0.9697), np.float32(0.9556), np.float32(0.8833), np.float32(0.9023), np.float32(0.8973)] 
+2025-05-05 23:18:35.562957: Epoch time: 96.34 s 
+2025-05-05 23:18:37.069866:  
+2025-05-05 23:18:37.127058: Epoch 806 
+2025-05-05 23:18:37.163958: Current learning rate: 0.00629 
+2025-05-05 23:20:16.273779: train_loss -0.4752 
+2025-05-05 23:20:16.313551: val_loss -0.4912 
+2025-05-05 23:20:16.326806: Pseudo dice [np.float32(0.8325), np.float32(0.8272), np.float32(0.7601), np.float32(0.9631), np.float32(0.8932), np.float32(0.9447), np.float32(0.9368), np.float32(0.9676), np.float32(0.9672), np.float32(0.951), np.float32(0.9428), np.float32(0.9724), np.float32(0.9666), np.float32(0.8846), np.float32(0.9679), np.float32(0.9287), np.float32(0.9046), np.float32(0.909), np.float32(0.9139)] 
+2025-05-05 23:20:16.354286: Epoch time: 99.21 s 
+2025-05-05 23:20:17.959997:  
+2025-05-05 23:20:18.073421: Epoch 807 
+2025-05-05 23:20:18.073862: Current learning rate: 0.00628 
+2025-05-05 23:21:55.710451: train_loss -0.4819 
+2025-05-05 23:21:55.786481: val_loss -0.4587 
+2025-05-05 23:21:55.811996: Pseudo dice [np.float32(0.7518), np.float32(0.8624), np.float32(0.4613), np.float32(0.9329), np.float32(0.8955), np.float32(0.9525), np.float32(0.9628), np.float32(0.9772), np.float32(0.9711), np.float32(0.9705), np.float32(0.9283), np.float32(0.9707), np.float32(0.9613), np.float32(0.8891), np.float32(0.9499), np.float32(0.9314), np.float32(0.8916), np.float32(0.8931), np.float32(0.9304)] 
+2025-05-05 23:21:55.839426: Epoch time: 97.75 s 
+2025-05-05 23:21:57.426242:  
+2025-05-05 23:21:57.441321: Epoch 808 
+2025-05-05 23:21:57.442429: Current learning rate: 0.00628 
+2025-05-05 23:23:33.778436: train_loss -0.4876 
+2025-05-05 23:23:33.843241: val_loss -0.5186 
+2025-05-05 23:23:33.858536: Pseudo dice [np.float32(0.8339), np.float32(0.8325), np.float32(0.8959), np.float32(0.9737), np.float32(0.9186), np.float32(0.9562), np.float32(0.9599), np.float32(0.9774), np.float32(0.9662), np.float32(0.9612), np.float32(0.9416), np.float32(0.9676), np.float32(0.9647), np.float32(0.8922), np.float32(0.9457), np.float32(0.9592), np.float32(0.8825), np.float32(0.8946), np.float32(0.9031)] 
+2025-05-05 23:23:33.883939: Epoch time: 96.35 s 
+2025-05-05 23:23:35.423917:  
+2025-05-05 23:23:35.508868: Epoch 809 
+2025-05-05 23:23:35.544776: Current learning rate: 0.00627 
+2025-05-05 23:25:18.299235: train_loss -0.4565 
+2025-05-05 23:25:18.351326: val_loss -0.4751 
+2025-05-05 23:25:18.370103: Pseudo dice [np.float32(0.8454), np.float32(0.8513), np.float32(0.8321), np.float32(0.9764), np.float32(0.89), np.float32(0.9517), np.float32(0.9585), np.float32(0.973), np.float32(0.9479), np.float32(0.9557), np.float32(0.9278), np.float32(0.9666), np.float32(0.9668), np.float32(0.8853), np.float32(0.9207), np.float32(0.9092), np.float32(0.8877), np.float32(0.8658), np.float32(0.9149)] 
+2025-05-05 23:25:18.385170: Epoch time: 102.88 s 
+2025-05-05 23:25:19.998548:  
+2025-05-05 23:25:20.081490: Epoch 810 
+2025-05-05 23:25:20.100149: Current learning rate: 0.00627 
+2025-05-05 23:27:09.040012: train_loss -0.4785 
+2025-05-05 23:27:09.227830: val_loss -0.4742 
+2025-05-05 23:27:09.257406: Pseudo dice [np.float32(0.8282), np.float32(0.855), np.float32(0.8996), np.float32(0.9676), np.float32(0.9127), np.float32(0.956), np.float32(0.9708), np.float32(0.979), np.float32(0.9639), np.float32(0.9572), np.float32(0.9293), np.float32(0.9676), np.float32(0.9598), np.float32(0.8986), np.float32(0.9653), np.float32(0.9363), np.float32(0.8165), np.float32(0.8193), np.float32(0.9054)] 
+2025-05-05 23:27:09.281656: Epoch time: 109.04 s 
+2025-05-05 23:27:14.742569:  
+2025-05-05 23:27:14.749056: Epoch 811 
+2025-05-05 23:27:14.749682: Current learning rate: 0.00626 
+2025-05-05 23:28:54.577947: train_loss -0.4772 
+2025-05-05 23:28:54.696778: val_loss -0.5009 
+2025-05-05 23:28:54.697639: Pseudo dice [np.float32(0.8397), np.float32(0.851), np.float32(0.8507), np.float32(0.9668), np.float32(0.8906), np.float32(0.9656), np.float32(0.9645), np.float32(0.9805), np.float32(0.9674), np.float32(0.9666), np.float32(0.9447), np.float32(0.9578), np.float32(0.9445), np.float32(0.9036), np.float32(0.9711), np.float32(0.9544), np.float32(0.8737), np.float32(0.8815), np.float32(0.9221)] 
+2025-05-05 23:28:54.698185: Epoch time: 99.84 s 
+2025-05-05 23:28:56.163732:  
+2025-05-05 23:28:56.259653: Epoch 812 
+2025-05-05 23:28:56.290845: Current learning rate: 0.00626 
+2025-05-05 23:30:32.369242: train_loss -0.4669 
+2025-05-05 23:30:32.441026: val_loss -0.4513 
+2025-05-05 23:30:32.470367: Pseudo dice [np.float32(0.8335), np.float32(0.8289), np.float32(0.9206), np.float32(0.9696), np.float32(0.8792), np.float32(0.9631), np.float32(0.947), np.float32(0.9707), np.float32(0.9245), np.float32(0.9477), np.float32(0.9085), np.float32(0.9238), np.float32(0.9559), np.float32(0.8799), np.float32(0.9663), np.float32(0.9437), np.float32(0.8316), np.float32(0.8412), np.float32(0.9205)] 
+2025-05-05 23:30:32.496519: Epoch time: 96.21 s 
+2025-05-05 23:30:34.146339:  
+2025-05-05 23:30:34.256387: Epoch 813 
+2025-05-05 23:30:34.291941: Current learning rate: 0.00625 
+2025-05-05 23:32:12.842748: train_loss -0.4747 
+2025-05-05 23:32:12.896404: val_loss -0.4736 
+2025-05-05 23:32:12.900173: Pseudo dice [np.float32(0.8283), np.float32(0.8497), np.float32(0.8452), np.float32(0.9533), np.float32(0.9055), np.float32(0.9642), np.float32(0.9655), np.float32(0.9733), np.float32(0.9671), np.float32(0.9673), np.float32(0.9171), np.float32(0.9732), np.float32(0.957), np.float32(0.9018), np.float32(0.9118), np.float32(0.9394), np.float32(0.8553), np.float32(0.8042), np.float32(0.9172)] 
+2025-05-05 23:32:12.900671: Epoch time: 98.7 s 
+2025-05-05 23:32:14.617732:  
+2025-05-05 23:32:14.626926: Epoch 814 
+2025-05-05 23:32:14.627704: Current learning rate: 0.00625 
+2025-05-05 23:33:49.370100: train_loss -0.4693 
+2025-05-05 23:33:49.514655: val_loss -0.4837 
+2025-05-05 23:33:49.516006: Pseudo dice [np.float32(0.848), np.float32(0.8492), np.float32(0.7926), np.float32(0.9663), np.float32(0.9075), np.float32(0.9537), np.float32(0.9615), np.float32(0.9752), np.float32(0.9614), np.float32(0.9643), np.float32(0.9385), np.float32(0.9523), np.float32(0.9655), np.float32(0.8984), np.float32(0.9585), np.float32(0.9339), np.float32(0.8296), np.float32(0.8276), np.float32(0.9106)] 
+2025-05-05 23:33:49.516500: Epoch time: 94.75 s 
+2025-05-05 23:33:51.107289:  
+2025-05-05 23:33:51.183049: Epoch 815 
+2025-05-05 23:33:51.183903: Current learning rate: 0.00624 
+2025-05-05 23:35:28.150808: train_loss -0.476 
+2025-05-05 23:35:28.176934: val_loss -0.4722 
+2025-05-05 23:35:28.189266: Pseudo dice [np.float32(0.8516), np.float32(0.8575), np.float32(0.7343), np.float32(0.9744), np.float32(0.9047), np.float32(0.9527), np.float32(0.9607), np.float32(0.9781), np.float32(0.9589), np.float32(0.9467), np.float32(0.9267), np.float32(0.966), np.float32(0.9484), np.float32(0.9029), np.float32(0.9653), np.float32(0.9491), np.float32(0.8723), np.float32(0.822), np.float32(0.9206)] 
+2025-05-05 23:35:28.228732: Epoch time: 97.04 s 
+2025-05-05 23:35:29.905416:  
+2025-05-05 23:35:29.961322: Epoch 816 
+2025-05-05 23:35:29.979866: Current learning rate: 0.00624 
+2025-05-05 23:37:06.276692: train_loss -0.4712 
+2025-05-05 23:37:06.354742: val_loss -0.4685 
+2025-05-05 23:37:06.362865: Pseudo dice [np.float32(0.8273), np.float32(0.8105), np.float32(0.8883), np.float32(0.952), np.float32(0.8862), np.float32(0.9453), np.float32(0.9526), np.float32(0.96), np.float32(0.9624), np.float32(0.9548), np.float32(0.9353), np.float32(0.964), np.float32(0.9626), np.float32(0.8811), np.float32(0.9572), np.float32(0.9391), np.float32(0.8563), np.float32(0.8321), np.float32(0.9182)] 
+2025-05-05 23:37:06.363717: Epoch time: 96.37 s 
+2025-05-05 23:37:07.890138:  
+2025-05-05 23:37:07.920809: Epoch 817 
+2025-05-05 23:37:07.925090: Current learning rate: 0.00623 
+2025-05-05 23:38:49.800632: train_loss -0.4616 
+2025-05-05 23:38:49.873540: val_loss -0.4559 
+2025-05-05 23:38:49.881457: Pseudo dice [np.float32(0.8393), np.float32(0.8117), np.float32(0.9416), np.float32(0.9636), np.float32(0.8409), np.float32(0.9258), np.float32(0.9429), np.float32(0.9576), np.float32(0.9417), np.float32(0.9645), np.float32(0.9443), np.float32(0.9669), np.float32(0.9684), np.float32(0.8812), np.float32(0.9561), np.float32(0.9469), np.float32(0.8778), np.float32(0.8951), np.float32(0.9149)] 
+2025-05-05 23:38:49.882202: Epoch time: 101.91 s 
+2025-05-05 23:38:51.543548:  
+2025-05-05 23:38:51.662345: Epoch 818 
+2025-05-05 23:38:51.712159: Current learning rate: 0.00623 
+2025-05-05 23:40:29.036109: train_loss -0.4618 
+2025-05-05 23:40:29.082376: val_loss -0.4318 
+2025-05-05 23:40:29.090477: Pseudo dice [np.float32(0.7485), np.float32(0.832), np.float32(0.8801), np.float32(0.9695), np.float32(0.8819), np.float32(0.9597), np.float32(0.9603), np.float32(0.9726), np.float32(0.9566), np.float32(0.9532), np.float32(0.939), np.float32(0.9625), np.float32(0.9524), np.float32(0.882), np.float32(0.9672), np.float32(0.9515), np.float32(0.765), np.float32(0.696), np.float32(0.897)] 
+2025-05-05 23:40:29.108701: Epoch time: 97.5 s 
+2025-05-05 23:40:30.735008:  
+2025-05-05 23:40:30.855742: Epoch 819 
+2025-05-05 23:40:30.889009: Current learning rate: 0.00622 
+2025-05-05 23:42:08.134357: train_loss -0.4783 
+2025-05-05 23:42:08.231849: val_loss -0.4488 
+2025-05-05 23:42:08.232636: Pseudo dice [np.float32(0.8283), np.float32(0.8303), np.float32(0.8903), np.float32(0.9721), np.float32(0.8865), np.float32(0.9442), np.float32(0.9336), np.float32(0.9716), np.float32(0.9495), np.float32(0.9648), np.float32(0.9382), np.float32(0.9552), np.float32(0.947), np.float32(0.8739), np.float32(0.9581), np.float32(0.9319), np.float32(0.8563), np.float32(0.8588), np.float32(0.9136)] 
+2025-05-05 23:42:08.233091: Epoch time: 97.4 s 
+2025-05-05 23:42:09.696182:  
+2025-05-05 23:42:09.848320: Epoch 820 
+2025-05-05 23:42:09.886498: Current learning rate: 0.00622 
+2025-05-05 23:43:50.131600: train_loss -0.4635 
+2025-05-05 23:43:50.278455: val_loss -0.5033 
+2025-05-05 23:43:50.295722: Pseudo dice [np.float32(0.8092), np.float32(0.8521), np.float32(0.8242), np.float32(0.9777), np.float32(0.8616), np.float32(0.948), np.float32(0.9663), np.float32(0.9759), np.float32(0.9671), np.float32(0.9691), np.float32(0.9448), np.float32(0.9607), np.float32(0.9662), np.float32(0.8976), np.float32(0.948), np.float32(0.9538), np.float32(0.9034), np.float32(0.8788), np.float32(0.9222)] 
+2025-05-05 23:43:50.315570: Epoch time: 100.44 s 
+2025-05-05 23:43:51.747697:  
+2025-05-05 23:43:51.849462: Epoch 821 
+2025-05-05 23:43:51.869986: Current learning rate: 0.00621 
+2025-05-05 23:45:23.098073: train_loss -0.4691 
+2025-05-05 23:45:23.178778: val_loss -0.4851 
+2025-05-05 23:45:23.189961: Pseudo dice [np.float32(0.8195), np.float32(0.8365), np.float32(0.8514), np.float32(0.9683), np.float32(0.8868), np.float32(0.9416), np.float32(0.9581), np.float32(0.9749), np.float32(0.9564), np.float32(0.9487), np.float32(0.9394), np.float32(0.9599), np.float32(0.9661), np.float32(0.8761), np.float32(0.954), np.float32(0.9233), np.float32(0.8466), np.float32(0.8529), np.float32(0.9162)] 
+2025-05-05 23:45:23.208116: Epoch time: 91.36 s 
+2025-05-05 23:45:24.677796:  
+2025-05-05 23:45:24.818424: Epoch 822 
+2025-05-05 23:45:24.897375: Current learning rate: 0.00621 
+2025-05-05 23:47:00.233878: train_loss -0.4742 
+2025-05-05 23:47:00.432509: val_loss -0.4755 
+2025-05-05 23:47:00.460082: Pseudo dice [np.float32(0.8468), np.float32(0.8255), np.float32(0.9331), np.float32(0.9707), np.float32(0.8624), np.float32(0.9369), np.float32(0.9324), np.float32(0.9779), np.float32(0.945), np.float32(0.9547), np.float32(0.9189), np.float32(0.9632), np.float32(0.9441), np.float32(0.8952), np.float32(0.9259), np.float32(0.9477), np.float32(0.8581), np.float32(0.8538), np.float32(0.9143)] 
+2025-05-05 23:47:00.485915: Epoch time: 95.56 s 
+2025-05-05 23:47:01.945960:  
+2025-05-05 23:47:02.034477: Epoch 823 
+2025-05-05 23:47:02.074614: Current learning rate: 0.00621 
+2025-05-05 23:48:38.007526: train_loss -0.4795 
+2025-05-05 23:48:38.020820: val_loss -0.4826 
+2025-05-05 23:48:38.032768: Pseudo dice [np.float32(0.8239), np.float32(0.8113), np.float32(0.7092), np.float32(0.9636), np.float32(0.9024), np.float32(0.9597), np.float32(0.9557), np.float32(0.9718), np.float32(0.9622), np.float32(0.9731), np.float32(0.9467), np.float32(0.966), np.float32(0.968), np.float32(0.8802), np.float32(0.9688), np.float32(0.9531), np.float32(0.8635), np.float32(0.8601), np.float32(0.9222)] 
+2025-05-05 23:48:38.043751: Epoch time: 96.06 s 
+2025-05-05 23:48:39.482338:  
+2025-05-05 23:48:39.590827: Epoch 824 
+2025-05-05 23:48:39.606005: Current learning rate: 0.0062 
+2025-05-05 23:50:16.191838: train_loss -0.4747 
+2025-05-05 23:50:16.230759: val_loss -0.4845 
+2025-05-05 23:50:16.232048: Pseudo dice [np.float32(0.8287), np.float32(0.8365), np.float32(0.9087), np.float32(0.9638), np.float32(0.8964), np.float32(0.9622), np.float32(0.9658), np.float32(0.9686), np.float32(0.9603), np.float32(0.9692), np.float32(0.9415), np.float32(0.9692), np.float32(0.9684), np.float32(0.8864), np.float32(0.9597), np.float32(0.943), np.float32(0.8852), np.float32(0.8956), np.float32(0.9072)] 
+2025-05-05 23:50:16.253793: Epoch time: 96.71 s 
+2025-05-05 23:50:17.692257:  
+2025-05-05 23:50:17.789228: Epoch 825 
+2025-05-05 23:50:17.790434: Current learning rate: 0.0062 
+2025-05-05 23:51:53.666236: train_loss -0.4837 
+2025-05-05 23:51:53.710334: val_loss -0.468 
+2025-05-05 23:51:53.729162: Pseudo dice [np.float32(0.843), np.float32(0.825), np.float32(0.9168), np.float32(0.9706), np.float32(0.9019), np.float32(0.9459), np.float32(0.9677), np.float32(0.9687), np.float32(0.9602), np.float32(0.9697), np.float32(0.9497), np.float32(0.9694), np.float32(0.9688), np.float32(0.9104), np.float32(0.9654), np.float32(0.9507), np.float32(0.8188), np.float32(0.8072), np.float32(0.92)] 
+2025-05-05 23:51:53.741235: Epoch time: 95.98 s 
+2025-05-05 23:51:55.295300:  
+2025-05-05 23:51:55.431490: Epoch 826 
+2025-05-05 23:51:55.457743: Current learning rate: 0.00619 
+2025-05-05 23:53:30.027466: train_loss -0.4913 
+2025-05-05 23:53:30.129789: val_loss -0.4646 
+2025-05-05 23:53:30.144331: Pseudo dice [np.float32(0.8107), np.float32(0.8179), np.float32(0.8854), np.float32(0.9297), np.float32(0.8814), np.float32(0.9499), np.float32(0.939), np.float32(0.9674), np.float32(0.9599), np.float32(0.9702), np.float32(0.9427), np.float32(0.9692), np.float32(0.9627), np.float32(0.9026), np.float32(0.9604), np.float32(0.9462), np.float32(0.8865), np.float32(0.8469), np.float32(0.9127)] 
+2025-05-05 23:53:30.170232: Epoch time: 94.73 s 
+2025-05-05 23:53:31.740063:  
+2025-05-05 23:53:31.839724: Epoch 827 
+2025-05-05 23:53:31.857384: Current learning rate: 0.00619 
+2025-05-05 23:55:08.080510: train_loss -0.4779 
+2025-05-05 23:55:08.271399: val_loss -0.4618 
+2025-05-05 23:55:08.305738: Pseudo dice [np.float32(0.843), np.float32(0.801), np.float32(0.8884), np.float32(0.9752), np.float32(0.9052), np.float32(0.9553), np.float32(0.9635), np.float32(0.969), np.float32(0.9421), np.float32(0.9601), np.float32(0.9401), np.float32(0.9686), np.float32(0.9703), np.float32(0.899), np.float32(0.9641), np.float32(0.9474), np.float32(0.8493), np.float32(0.8895), np.float32(0.9167)] 
+2025-05-05 23:55:08.326105: Epoch time: 96.34 s 
+2025-05-05 23:55:09.769322:  
+2025-05-05 23:55:09.901396: Epoch 828 
+2025-05-05 23:55:09.933034: Current learning rate: 0.00618 
+2025-05-05 23:56:46.967408: train_loss -0.4724 
+2025-05-05 23:56:47.106588: val_loss -0.5103 
+2025-05-05 23:56:47.145615: Pseudo dice [np.float32(0.85), np.float32(0.7961), np.float32(0.8791), np.float32(0.9668), np.float32(0.8812), np.float32(0.955), np.float32(0.9647), np.float32(0.976), np.float32(0.9515), np.float32(0.9612), np.float32(0.9268), np.float32(0.9712), np.float32(0.9607), np.float32(0.8981), np.float32(0.9675), np.float32(0.956), np.float32(0.8319), np.float32(0.8344), np.float32(0.9189)] 
+2025-05-05 23:56:47.192675: Epoch time: 97.2 s 
+2025-05-05 23:56:52.449652:  
+2025-05-05 23:56:52.455797: Epoch 829 
+2025-05-05 23:56:52.456282: Current learning rate: 0.00618 
+2025-05-05 23:58:27.159547: train_loss -0.4966 
+2025-05-05 23:58:27.272045: val_loss -0.4729 
+2025-05-05 23:58:27.302581: Pseudo dice [np.float32(0.8485), np.float32(0.8311), np.float32(0.9157), np.float32(0.953), np.float32(0.6961), np.float32(0.94), np.float32(0.9641), np.float32(0.9785), np.float32(0.9618), np.float32(0.9678), np.float32(0.9495), np.float32(0.9645), np.float32(0.9711), np.float32(0.9062), np.float32(0.9583), np.float32(0.9583), np.float32(0.8714), np.float32(0.8773), np.float32(0.9212)] 
+2025-05-05 23:58:27.326695: Epoch time: 94.71 s 
+2025-05-05 23:58:28.782749:  
+2025-05-05 23:58:28.888225: Epoch 830 
+2025-05-05 23:58:28.918876: Current learning rate: 0.00617 
+2025-05-06 00:00:05.139564: train_loss -0.4673 
+2025-05-06 00:00:05.224646: val_loss -0.5311 
+2025-05-06 00:00:05.225152: Pseudo dice [np.float32(0.8464), np.float32(0.8491), np.float32(0.8646), np.float32(0.9697), np.float32(0.8626), np.float32(0.9513), np.float32(0.9629), np.float32(0.9676), np.float32(0.9495), np.float32(0.9488), np.float32(0.9294), np.float32(0.9557), np.float32(0.9614), np.float32(0.8991), np.float32(0.9644), np.float32(0.9499), np.float32(0.9002), np.float32(0.8765), np.float32(0.917)] 
+2025-05-06 00:00:05.225843: Epoch time: 96.36 s 
+2025-05-06 00:00:06.692390:  
+2025-05-06 00:00:06.765432: Epoch 831 
+2025-05-06 00:00:06.795203: Current learning rate: 0.00617 
+2025-05-06 00:01:43.684332: train_loss -0.4857 
+2025-05-06 00:01:43.718998: val_loss -0.4742 
+2025-05-06 00:01:43.730686: Pseudo dice [np.float32(0.8237), np.float32(0.8428), np.float32(0.9147), np.float32(0.98), np.float32(0.9081), np.float32(0.9577), np.float32(0.9569), np.float32(0.9788), np.float32(0.9506), np.float32(0.949), np.float32(0.9251), np.float32(0.9546), np.float32(0.965), np.float32(0.899), np.float32(0.9663), np.float32(0.9311), np.float32(0.8581), np.float32(0.772), np.float32(0.9216)] 
+2025-05-06 00:01:43.748671: Epoch time: 96.99 s 
+2025-05-06 00:01:45.290066:  
+2025-05-06 00:01:45.390535: Epoch 832 
+2025-05-06 00:01:45.411126: Current learning rate: 0.00616 
+2025-05-06 00:03:22.443454: train_loss -0.464 
+2025-05-06 00:03:22.605748: val_loss -0.5024 
+2025-05-06 00:03:22.620868: Pseudo dice [np.float32(0.8355), np.float32(0.8407), np.float32(0.9426), np.float32(0.9708), np.float32(0.9029), np.float32(0.9563), np.float32(0.9606), np.float32(0.9689), np.float32(0.9591), np.float32(0.9587), np.float32(0.9475), np.float32(0.9607), np.float32(0.9644), np.float32(0.9043), np.float32(0.9613), np.float32(0.9316), np.float32(0.8622), np.float32(0.8394), np.float32(0.8923)] 
+2025-05-06 00:03:22.637711: Epoch time: 97.15 s 
+2025-05-06 00:03:24.131779:  
+2025-05-06 00:03:24.195261: Epoch 833 
+2025-05-06 00:03:24.220794: Current learning rate: 0.00616 
+2025-05-06 00:05:00.019431: train_loss -0.4823 
+2025-05-06 00:05:00.106065: val_loss -0.5127 
+2025-05-06 00:05:00.106857: Pseudo dice [np.float32(0.8411), np.float32(0.8392), np.float32(0.904), np.float32(0.9633), np.float32(0.8803), np.float32(0.954), np.float32(0.9663), np.float32(0.9729), np.float32(0.9539), np.float32(0.958), np.float32(0.9176), np.float32(0.965), np.float32(0.9584), np.float32(0.8938), np.float32(0.9633), np.float32(0.9397), np.float32(0.8759), np.float32(0.8802), np.float32(0.914)] 
+2025-05-06 00:05:00.107473: Epoch time: 95.89 s 
+2025-05-06 00:05:01.546527:  
+2025-05-06 00:05:01.594703: Epoch 834 
+2025-05-06 00:05:01.623583: Current learning rate: 0.00615 
+2025-05-06 00:06:39.130005: train_loss -0.4752 
+2025-05-06 00:06:39.179471: val_loss -0.4985 
+2025-05-06 00:06:39.202222: Pseudo dice [np.float32(0.8114), np.float32(0.8382), np.float32(0.8512), np.float32(0.9674), np.float32(0.8846), np.float32(0.9368), np.float32(0.96), np.float32(0.9797), np.float32(0.9534), np.float32(0.9582), np.float32(0.9341), np.float32(0.9643), np.float32(0.9612), np.float32(0.8942), np.float32(0.9677), np.float32(0.9413), np.float32(0.8838), np.float32(0.8802), np.float32(0.9143)] 
+2025-05-06 00:06:39.223344: Epoch time: 97.58 s 
+2025-05-06 00:06:40.679217:  
+2025-05-06 00:06:40.790079: Epoch 835 
+2025-05-06 00:06:40.818919: Current learning rate: 0.00615 
+2025-05-06 00:08:15.105735: train_loss -0.4659 
+2025-05-06 00:08:15.231729: val_loss -0.4737 
+2025-05-06 00:08:15.251115: Pseudo dice [np.float32(0.8399), np.float32(0.8402), np.float32(0.8924), np.float32(0.9729), np.float32(0.9094), np.float32(0.9555), np.float32(0.9633), np.float32(0.9733), np.float32(0.9576), np.float32(0.9581), np.float32(0.9485), np.float32(0.9588), np.float32(0.9655), np.float32(0.9034), np.float32(0.9452), np.float32(0.9424), np.float32(0.9001), np.float32(0.9151), np.float32(0.9217)] 
+2025-05-06 00:08:15.280505: Epoch time: 94.43 s 
+2025-05-06 00:08:16.942612:  
+2025-05-06 00:08:17.072606: Epoch 836 
+2025-05-06 00:08:17.137197: Current learning rate: 0.00614 
+2025-05-06 00:09:56.697512: train_loss -0.4834 
+2025-05-06 00:09:56.756519: val_loss -0.4801 
+2025-05-06 00:09:56.779072: Pseudo dice [np.float32(0.8362), np.float32(0.8236), np.float32(0.8959), np.float32(0.9755), np.float32(0.9116), np.float32(0.9579), np.float32(0.9609), np.float32(0.9778), np.float32(0.9676), np.float32(0.967), np.float32(0.9421), np.float32(0.9707), np.float32(0.9593), np.float32(0.8841), np.float32(0.9465), np.float32(0.9323), np.float32(0.8754), np.float32(0.8504), np.float32(0.9074)] 
+2025-05-06 00:09:56.814123: Epoch time: 99.76 s 
+2025-05-06 00:09:58.463443:  
+2025-05-06 00:09:58.503459: Epoch 837 
+2025-05-06 00:09:58.514674: Current learning rate: 0.00614 
+2025-05-06 00:11:36.453048: train_loss -0.4975 
+2025-05-06 00:11:36.585905: val_loss -0.5116 
+2025-05-06 00:11:36.597492: Pseudo dice [np.float32(0.8285), np.float32(0.846), np.float32(0.8508), np.float32(0.967), np.float32(0.897), np.float32(0.961), np.float32(0.9554), np.float32(0.9772), np.float32(0.9575), np.float32(0.9544), np.float32(0.9041), np.float32(0.9602), np.float32(0.9387), np.float32(0.9002), np.float32(0.965), np.float32(0.9425), np.float32(0.8804), np.float32(0.876), np.float32(0.8987)] 
+2025-05-06 00:11:36.614240: Epoch time: 97.99 s 
+2025-05-06 00:11:38.241208:  
+2025-05-06 00:11:38.284705: Epoch 838 
+2025-05-06 00:11:38.309889: Current learning rate: 0.00613 
+2025-05-06 00:13:15.774989: train_loss -0.4796 
+2025-05-06 00:13:15.870498: val_loss -0.4987 
+2025-05-06 00:13:15.913977: Pseudo dice [np.float32(0.8262), np.float32(0.8363), np.float32(0.8461), np.float32(0.9679), np.float32(0.8133), np.float32(0.9317), np.float32(0.9492), np.float32(0.956), np.float32(0.9655), np.float32(0.9538), np.float32(0.9429), np.float32(0.9651), np.float32(0.9618), np.float32(0.8961), np.float32(0.951), np.float32(0.9508), np.float32(0.8723), np.float32(0.8762), np.float32(0.8983)] 
+2025-05-06 00:13:15.939859: Epoch time: 97.53 s 
+2025-05-06 00:13:17.515268:  
+2025-05-06 00:13:17.578082: Epoch 839 
+2025-05-06 00:13:17.593586: Current learning rate: 0.00613 
+2025-05-06 00:14:53.742989: train_loss -0.4818 
+2025-05-06 00:14:53.864422: val_loss -0.4759 
+2025-05-06 00:14:53.898566: Pseudo dice [np.float32(0.8622), np.float32(0.8613), np.float32(0.865), np.float32(0.9732), np.float32(0.8704), np.float32(0.9573), np.float32(0.9557), np.float32(0.9763), np.float32(0.9586), np.float32(0.9697), np.float32(0.946), np.float32(0.9692), np.float32(0.9686), np.float32(0.8953), np.float32(0.9706), np.float32(0.9479), np.float32(0.8768), np.float32(0.8664), np.float32(0.9181)] 
+2025-05-06 00:14:53.930261: Epoch time: 96.23 s 
+2025-05-06 00:14:55.664586:  
+2025-05-06 00:14:55.727145: Epoch 840 
+2025-05-06 00:14:55.742004: Current learning rate: 0.00612 
+2025-05-06 00:16:29.411367: train_loss -0.4863 
+2025-05-06 00:16:29.635943: val_loss -0.4647 
+2025-05-06 00:16:29.637145: Pseudo dice [np.float32(0.8488), np.float32(0.8433), np.float32(0.9525), np.float32(0.9736), np.float32(0.8823), np.float32(0.9568), np.float32(0.9537), np.float32(0.9757), np.float32(0.9523), np.float32(0.956), np.float32(0.9193), np.float32(0.969), np.float32(0.9452), np.float32(0.8949), np.float32(0.9605), np.float32(0.9505), np.float32(0.8767), np.float32(0.8397), np.float32(0.8958)] 
+2025-05-06 00:16:29.637601: Epoch time: 93.75 s 
+2025-05-06 00:16:31.188658:  
+2025-05-06 00:16:31.249185: Epoch 841 
+2025-05-06 00:16:31.280144: Current learning rate: 0.00612 
+2025-05-06 00:18:06.860294: train_loss -0.4769 
+2025-05-06 00:18:06.943336: val_loss -0.4884 
+2025-05-06 00:18:06.977983: Pseudo dice [np.float32(0.8506), np.float32(0.8564), np.float32(0.8655), np.float32(0.9741), np.float32(0.8391), np.float32(0.9582), np.float32(0.9599), np.float32(0.9715), np.float32(0.9726), np.float32(0.9694), np.float32(0.9436), np.float32(0.9707), np.float32(0.9635), np.float32(0.8927), np.float32(0.9679), np.float32(0.9554), np.float32(0.8623), np.float32(0.8756), np.float32(0.9115)] 
+2025-05-06 00:18:07.001129: Epoch time: 95.67 s 
+2025-05-06 00:18:08.491733:  
+2025-05-06 00:18:08.494759: Epoch 842 
+2025-05-06 00:18:08.495086: Current learning rate: 0.00612 
+2025-05-06 00:19:46.441945: train_loss -0.4782 
+2025-05-06 00:19:46.518215: val_loss -0.5032 
+2025-05-06 00:19:46.557594: Pseudo dice [np.float32(0.85), np.float32(0.8463), np.float32(0.9184), np.float32(0.9664), np.float32(0.8688), np.float32(0.9579), np.float32(0.9598), np.float32(0.9623), np.float32(0.9585), np.float32(0.9603), np.float32(0.9402), np.float32(0.9589), np.float32(0.9668), np.float32(0.8894), np.float32(0.9619), np.float32(0.9486), np.float32(0.8776), np.float32(0.8633), np.float32(0.9016)] 
+2025-05-06 00:19:46.579762: Epoch time: 97.95 s 
+2025-05-06 00:19:48.045452:  
+2025-05-06 00:19:48.165878: Epoch 843 
+2025-05-06 00:19:48.230417: Current learning rate: 0.00611 
+2025-05-06 00:21:27.795942: train_loss -0.4746 
+2025-05-06 00:21:27.914138: val_loss -0.4946 
+2025-05-06 00:21:27.946872: Pseudo dice [np.float32(0.8126), np.float32(0.8395), np.float32(0.8055), np.float32(0.9734), np.float32(0.9087), np.float32(0.96), np.float32(0.9569), np.float32(0.9745), np.float32(0.963), np.float32(0.957), np.float32(0.9329), np.float32(0.9643), np.float32(0.9629), np.float32(0.9009), np.float32(0.9503), np.float32(0.953), np.float32(0.8636), np.float32(0.8614), np.float32(0.8851)] 
+2025-05-06 00:21:27.976231: Epoch time: 99.75 s 
+2025-05-06 00:21:29.590737:  
+2025-05-06 00:21:29.593878: Epoch 844 
+2025-05-06 00:21:29.599246: Current learning rate: 0.00611 
+2025-05-06 00:23:08.666187: train_loss -0.4741 
+2025-05-06 00:23:08.693680: val_loss -0.4539 
+2025-05-06 00:23:08.698210: Pseudo dice [np.float32(0.8056), np.float32(0.8246), np.float32(0.8994), np.float32(0.9773), np.float32(0.8808), np.float32(0.9625), np.float32(0.9538), np.float32(0.9756), np.float32(0.9477), np.float32(0.9488), np.float32(0.926), np.float32(0.9651), np.float32(0.9587), np.float32(0.8826), np.float32(0.9652), np.float32(0.9404), np.float32(0.8726), np.float32(0.8776), np.float32(0.9088)] 
+2025-05-06 00:23:08.700288: Epoch time: 99.08 s 
+2025-05-06 00:23:10.158450:  
+2025-05-06 00:23:10.317985: Epoch 845 
+2025-05-06 00:23:10.331271: Current learning rate: 0.0061 
+2025-05-06 00:24:49.039679: train_loss -0.4723 
+2025-05-06 00:24:49.096877: val_loss -0.4988 
+2025-05-06 00:24:49.124466: Pseudo dice [np.float32(0.8244), np.float32(0.8355), np.float32(0.87), np.float32(0.9709), np.float32(0.8477), np.float32(0.9625), np.float32(0.9526), np.float32(0.973), np.float32(0.9627), np.float32(0.9656), np.float32(0.9446), np.float32(0.9693), np.float32(0.9686), np.float32(0.9002), np.float32(0.9652), np.float32(0.9445), np.float32(0.8715), np.float32(0.8853), np.float32(0.9223)] 
+2025-05-06 00:24:49.149583: Epoch time: 98.88 s 
+2025-05-06 00:24:50.846082:  
+2025-05-06 00:24:50.951708: Epoch 846 
+2025-05-06 00:24:50.974105: Current learning rate: 0.0061 
+2025-05-06 00:26:25.484898: train_loss -0.483 
+2025-05-06 00:26:25.644907: val_loss -0.4707 
+2025-05-06 00:26:25.686709: Pseudo dice [np.float32(0.8453), np.float32(0.8376), np.float32(0.7673), np.float32(0.9538), np.float32(0.8892), np.float32(0.9623), np.float32(0.9387), np.float32(0.9802), np.float32(0.9607), np.float32(0.9726), np.float32(0.9223), np.float32(0.9658), np.float32(0.9565), np.float32(0.8936), np.float32(0.9668), np.float32(0.945), np.float32(0.7939), np.float32(0.8539), np.float32(0.9139)] 
+2025-05-06 00:26:25.724498: Epoch time: 94.64 s 
+2025-05-06 00:26:27.261103:  
+2025-05-06 00:26:27.292423: Epoch 847 
+2025-05-06 00:26:27.303777: Current learning rate: 0.00609 
+2025-05-06 00:28:03.970665: train_loss -0.4852 
+2025-05-06 00:28:04.089391: val_loss -0.5125 
+2025-05-06 00:28:04.116564: Pseudo dice [np.float32(0.8236), np.float32(0.8566), np.float32(0.8888), np.float32(0.9775), np.float32(0.8928), np.float32(0.9604), np.float32(0.9582), np.float32(0.9765), np.float32(0.965), np.float32(0.9662), np.float32(0.9431), np.float32(0.9721), np.float32(0.963), np.float32(0.9056), np.float32(0.9594), np.float32(0.9388), np.float32(0.8477), np.float32(0.8529), np.float32(0.9153)] 
+2025-05-06 00:28:04.129445: Epoch time: 96.71 s 
+2025-05-06 00:28:05.591043:  
+2025-05-06 00:28:05.770762: Epoch 848 
+2025-05-06 00:28:05.772010: Current learning rate: 0.00609 
+2025-05-06 00:29:40.298238: train_loss -0.4879 
+2025-05-06 00:29:40.328221: val_loss -0.5086 
+2025-05-06 00:29:40.343749: Pseudo dice [np.float32(0.8605), np.float32(0.835), np.float32(0.9259), np.float32(0.972), np.float32(0.8821), np.float32(0.959), np.float32(0.9559), np.float32(0.9659), np.float32(0.9604), np.float32(0.9503), np.float32(0.8946), np.float32(0.9685), np.float32(0.9523), np.float32(0.8786), np.float32(0.9649), np.float32(0.9386), np.float32(0.897), np.float32(0.8854), np.float32(0.9215)] 
+2025-05-06 00:29:40.353464: Epoch time: 94.71 s 
+2025-05-06 00:29:41.798327:  
+2025-05-06 00:29:41.919066: Epoch 849 
+2025-05-06 00:29:41.941135: Current learning rate: 0.00608 
+2025-05-06 00:31:17.865955: train_loss -0.4902 
+2025-05-06 00:31:17.923481: val_loss -0.514 
+2025-05-06 00:31:17.936693: Pseudo dice [np.float32(0.8254), np.float32(0.7929), np.float32(0.9021), np.float32(0.9675), np.float32(0.8925), np.float32(0.9467), np.float32(0.9605), np.float32(0.9677), np.float32(0.9672), np.float32(0.9577), np.float32(0.9481), np.float32(0.9714), np.float32(0.9632), np.float32(0.8891), np.float32(0.9651), np.float32(0.9428), np.float32(0.8419), np.float32(0.8252), np.float32(0.9128)] 
+2025-05-06 00:31:17.947874: Epoch time: 96.07 s 
+2025-05-06 00:31:20.155637:  
+2025-05-06 00:31:20.221681: Epoch 850 
+2025-05-06 00:31:20.241396: Current learning rate: 0.00608 
+2025-05-06 00:33:00.458797: train_loss -0.4787 
+2025-05-06 00:33:00.545751: val_loss -0.5037 
+2025-05-06 00:33:00.553826: Pseudo dice [np.float32(0.7994), np.float32(0.8193), np.float32(0.9082), np.float32(0.9692), np.float32(0.886), np.float32(0.9556), np.float32(0.9609), np.float32(0.9705), np.float32(0.9538), np.float32(0.9633), np.float32(0.936), np.float32(0.9593), np.float32(0.966), np.float32(0.9004), np.float32(0.9563), np.float32(0.9558), np.float32(0.7853), np.float32(0.803), np.float32(0.9047)] 
+2025-05-06 00:33:00.581874: Epoch time: 100.3 s 
+2025-05-06 00:33:01.980865:  
+2025-05-06 00:33:02.133745: Epoch 851 
+2025-05-06 00:33:02.134690: Current learning rate: 0.00607 
+2025-05-06 00:34:39.765221: train_loss -0.4949 
+2025-05-06 00:34:39.835746: val_loss -0.5219 
+2025-05-06 00:34:39.850547: Pseudo dice [np.float32(0.8135), np.float32(0.8465), np.float32(0.8961), np.float32(0.9783), np.float32(0.8714), np.float32(0.9567), np.float32(0.965), np.float32(0.9646), np.float32(0.9579), np.float32(0.966), np.float32(0.9458), np.float32(0.9713), np.float32(0.9721), np.float32(0.895), np.float32(0.9576), np.float32(0.9569), np.float32(0.8796), np.float32(0.9037), np.float32(0.9265)] 
+2025-05-06 00:34:39.862856: Epoch time: 97.79 s 
+2025-05-06 00:34:41.407244:  
+2025-05-06 00:34:41.478523: Epoch 852 
+2025-05-06 00:34:41.500708: Current learning rate: 0.00607 
+2025-05-06 00:36:18.046741: train_loss -0.4778 
+2025-05-06 00:36:18.064393: val_loss -0.4811 
+2025-05-06 00:36:18.065925: Pseudo dice [np.float32(0.8327), np.float32(0.844), np.float32(0.9158), np.float32(0.9707), np.float32(0.9148), np.float32(0.9435), np.float32(0.9584), np.float32(0.9761), np.float32(0.9587), np.float32(0.938), np.float32(0.8989), np.float32(0.9617), np.float32(0.9604), np.float32(0.8874), np.float32(0.8625), np.float32(0.9389), np.float32(0.8477), np.float32(0.8743), np.float32(0.9226)] 
+2025-05-06 00:36:18.066464: Epoch time: 96.64 s 
+2025-05-06 00:36:19.415933:  
+2025-05-06 00:36:19.487161: Epoch 853 
+2025-05-06 00:36:19.538803: Current learning rate: 0.00606 
+2025-05-06 00:37:58.379241: train_loss -0.4855 
+2025-05-06 00:37:58.438158: val_loss -0.5058 
+2025-05-06 00:37:58.449029: Pseudo dice [np.float32(0.8607), np.float32(0.8515), np.float32(0.9269), np.float32(0.968), np.float32(0.8974), np.float32(0.9608), np.float32(0.9684), np.float32(0.9805), np.float32(0.9601), np.float32(0.9657), np.float32(0.9403), np.float32(0.9643), np.float32(0.9564), np.float32(0.9027), np.float32(0.9613), np.float32(0.9497), np.float32(0.8901), np.float32(0.8373), np.float32(0.9147)] 
+2025-05-06 00:37:58.459865: Epoch time: 98.96 s 
+2025-05-06 00:37:59.877433:  
+2025-05-06 00:37:59.978415: Epoch 854 
+2025-05-06 00:37:59.999190: Current learning rate: 0.00606 
+2025-05-06 00:39:36.101880: train_loss -0.4838 
+2025-05-06 00:39:36.136684: val_loss -0.515 
+2025-05-06 00:39:36.137257: Pseudo dice [np.float32(0.8467), np.float32(0.8193), np.float32(0.8762), np.float32(0.9719), np.float32(0.9182), np.float32(0.9638), np.float32(0.9681), np.float32(0.9772), np.float32(0.9691), np.float32(0.9615), np.float32(0.9489), np.float32(0.972), np.float32(0.97), np.float32(0.889), np.float32(0.9708), np.float32(0.9606), np.float32(0.8122), np.float32(0.7535), np.float32(0.9237)] 
+2025-05-06 00:39:36.137926: Epoch time: 96.23 s 
+2025-05-06 00:39:37.794563:  
+2025-05-06 00:39:37.846005: Epoch 855 
+2025-05-06 00:39:37.849580: Current learning rate: 0.00605 
+2025-05-06 00:41:13.538381: train_loss -0.4905 
+2025-05-06 00:41:13.607286: val_loss -0.4851 
+2025-05-06 00:41:13.608282: Pseudo dice [np.float32(0.8325), np.float32(0.8358), np.float32(0.7777), np.float32(0.969), np.float32(0.9177), np.float32(0.953), np.float32(0.957), np.float32(0.9791), np.float32(0.926), np.float32(0.9596), np.float32(0.9238), np.float32(0.9613), np.float32(0.9553), np.float32(0.8943), np.float32(0.9646), np.float32(0.951), np.float32(0.7878), np.float32(0.7622), np.float32(0.9201)] 
+2025-05-06 00:41:13.613212: Epoch time: 95.75 s 
+2025-05-06 00:41:15.213138:  
+2025-05-06 00:41:15.319124: Epoch 856 
+2025-05-06 00:41:15.351045: Current learning rate: 0.00605 
+2025-05-06 00:42:50.687577: train_loss -0.488 
+2025-05-06 00:42:50.799417: val_loss -0.4866 
+2025-05-06 00:42:50.815964: Pseudo dice [np.float32(0.8445), np.float32(0.8304), np.float32(0.9343), np.float32(0.9719), np.float32(0.9107), np.float32(0.9587), np.float32(0.9643), np.float32(0.9782), np.float32(0.9642), np.float32(0.9621), np.float32(0.9387), np.float32(0.9697), np.float32(0.9657), np.float32(0.8867), np.float32(0.9486), np.float32(0.9565), np.float32(0.8331), np.float32(0.8585), np.float32(0.9129)] 
+2025-05-06 00:42:50.836486: Epoch time: 95.48 s 
+2025-05-06 00:42:52.377217:  
+2025-05-06 00:42:52.418956: Epoch 857 
+2025-05-06 00:42:52.435613: Current learning rate: 0.00604 
+2025-05-06 00:44:30.390889: train_loss -0.5027 
+2025-05-06 00:44:30.460218: val_loss -0.5188 
+2025-05-06 00:44:30.484989: Pseudo dice [np.float32(0.8658), np.float32(0.8492), np.float32(0.818), np.float32(0.978), np.float32(0.9228), np.float32(0.9559), np.float32(0.9692), np.float32(0.98), np.float32(0.9183), np.float32(0.9632), np.float32(0.9512), np.float32(0.9487), np.float32(0.9555), np.float32(0.9068), np.float32(0.9442), np.float32(0.9504), np.float32(0.8918), np.float32(0.8908), np.float32(0.915)] 
+2025-05-06 00:44:30.517618: Epoch time: 98.02 s 
+2025-05-06 00:44:32.045702:  
+2025-05-06 00:44:32.145879: Epoch 858 
+2025-05-06 00:44:32.173781: Current learning rate: 0.00604 
+2025-05-06 00:46:07.493416: train_loss -0.4813 
+2025-05-06 00:46:07.622701: val_loss -0.4727 
+2025-05-06 00:46:07.663097: Pseudo dice [np.float32(0.8633), np.float32(0.8513), np.float32(0.918), np.float32(0.9758), np.float32(0.9154), np.float32(0.9438), np.float32(0.9686), np.float32(0.9806), np.float32(0.9492), np.float32(0.9693), np.float32(0.95), np.float32(0.9629), np.float32(0.9694), np.float32(0.9091), np.float32(0.9692), np.float32(0.9573), np.float32(0.8677), np.float32(0.8395), np.float32(0.9225)] 
+2025-05-06 00:46:07.693775: Epoch time: 95.45 s 
+2025-05-06 00:46:09.211703:  
+2025-05-06 00:46:09.297374: Epoch 859 
+2025-05-06 00:46:09.321731: Current learning rate: 0.00603 
+2025-05-06 00:47:47.404506: train_loss -0.4934 
+2025-05-06 00:47:47.502434: val_loss -0.5009 
+2025-05-06 00:47:47.527260: Pseudo dice [np.float32(0.8562), np.float32(0.8335), np.float32(0.9107), np.float32(0.9756), np.float32(0.8471), np.float32(0.9537), np.float32(0.9616), np.float32(0.9639), np.float32(0.9524), np.float32(0.9695), np.float32(0.9341), np.float32(0.9702), np.float32(0.9657), np.float32(0.8857), np.float32(0.9571), np.float32(0.9123), np.float32(0.8735), np.float32(0.8946), np.float32(0.9145)] 
+2025-05-06 00:47:47.554815: Epoch time: 98.2 s 
+2025-05-06 00:47:49.020489:  
+2025-05-06 00:47:49.102151: Epoch 860 
+2025-05-06 00:47:49.127916: Current learning rate: 0.00603 
+2025-05-06 00:49:28.044098: train_loss -0.4689 
+2025-05-06 00:49:28.174003: val_loss -0.4887 
+2025-05-06 00:49:28.175529: Pseudo dice [np.float32(0.859), np.float32(0.8584), np.float32(0.8933), np.float32(0.9556), np.float32(0.87), np.float32(0.9591), np.float32(0.924), np.float32(0.9689), np.float32(0.9259), np.float32(0.9601), np.float32(0.9419), np.float32(0.9495), np.float32(0.9675), np.float32(0.8965), np.float32(0.9615), np.float32(0.9336), np.float32(0.8849), np.float32(0.8385), np.float32(0.89)] 
+2025-05-06 00:49:28.216102: Epoch time: 99.02 s 
+2025-05-06 00:49:29.745319:  
+2025-05-06 00:49:29.796556: Epoch 861 
+2025-05-06 00:49:29.829351: Current learning rate: 0.00602 
+2025-05-06 00:51:04.050612: train_loss -0.4876 
+2025-05-06 00:51:04.125568: val_loss -0.4819 
+2025-05-06 00:51:04.136448: Pseudo dice [np.float32(0.8328), np.float32(0.8481), np.float32(0.9408), np.float32(0.9763), np.float32(0.9012), np.float32(0.9628), np.float32(0.959), np.float32(0.9775), np.float32(0.9539), np.float32(0.9634), np.float32(0.9384), np.float32(0.9627), np.float32(0.9627), np.float32(0.8863), np.float32(0.9682), np.float32(0.9377), np.float32(0.8889), np.float32(0.8885), np.float32(0.9037)] 
+2025-05-06 00:51:04.167599: Epoch time: 94.31 s 
+2025-05-06 00:51:05.756018:  
+2025-05-06 00:51:05.864675: Epoch 862 
+2025-05-06 00:51:05.886681: Current learning rate: 0.00602 
+2025-05-06 00:52:44.237388: train_loss -0.4812 
+2025-05-06 00:52:44.317947: val_loss -0.4768 
+2025-05-06 00:52:44.326395: Pseudo dice [np.float32(0.8149), np.float32(0.8124), np.float32(0.9338), np.float32(0.9694), np.float32(0.8279), np.float32(0.9474), np.float32(0.9551), np.float32(0.9702), np.float32(0.9667), np.float32(0.9529), np.float32(0.9471), np.float32(0.9603), np.float32(0.9688), np.float32(0.8847), np.float32(0.9182), np.float32(0.944), np.float32(0.889), np.float32(0.8481), np.float32(0.9132)] 
+2025-05-06 00:52:44.344632: Epoch time: 98.48 s 
+2025-05-06 00:52:45.825538:  
+2025-05-06 00:52:45.849868: Epoch 863 
+2025-05-06 00:52:45.850330: Current learning rate: 0.00602 
+2025-05-06 00:54:23.318833: train_loss -0.4817 
+2025-05-06 00:54:23.354413: val_loss -0.4657 
+2025-05-06 00:54:23.355388: Pseudo dice [np.float32(0.8009), np.float32(0.8477), np.float32(0.9104), np.float32(0.9762), np.float32(0.8713), np.float32(0.9416), np.float32(0.9589), np.float32(0.9694), np.float32(0.9388), np.float32(0.9566), np.float32(0.9401), np.float32(0.9562), np.float32(0.9516), np.float32(0.8921), np.float32(0.9406), np.float32(0.948), np.float32(0.881), np.float32(0.8908), np.float32(0.9011)] 
+2025-05-06 00:54:23.359767: Epoch time: 97.49 s 
+2025-05-06 00:54:24.927213:  
+2025-05-06 00:54:24.995161: Epoch 864 
+2025-05-06 00:54:25.040821: Current learning rate: 0.00601 
+2025-05-06 00:56:02.630625: train_loss -0.4929 
+2025-05-06 00:56:02.683676: val_loss -0.5032 
+2025-05-06 00:56:02.698637: Pseudo dice [np.float32(0.8347), np.float32(0.865), np.float32(0.898), np.float32(0.9693), np.float32(0.9257), np.float32(0.9627), np.float32(0.9666), np.float32(0.9733), np.float32(0.9609), np.float32(0.9565), np.float32(0.9473), np.float32(0.9634), np.float32(0.961), np.float32(0.9085), np.float32(0.9599), np.float32(0.9437), np.float32(0.861), np.float32(0.859), np.float32(0.9158)] 
+2025-05-06 00:56:02.711720: Epoch time: 97.7 s 
+2025-05-06 00:56:07.781752:  
+2025-05-06 00:56:07.788352: Epoch 865 
+2025-05-06 00:56:07.789058: Current learning rate: 0.00601 
+2025-05-06 00:57:40.898113: train_loss -0.4791 
+2025-05-06 00:57:41.032642: val_loss -0.4952 
+2025-05-06 00:57:41.065963: Pseudo dice [np.float32(0.833), np.float32(0.8384), np.float32(0.9346), np.float32(0.9744), np.float32(0.8985), np.float32(0.9545), np.float32(0.9668), np.float32(0.9693), np.float32(0.9565), np.float32(0.9557), np.float32(0.9109), np.float32(0.9573), np.float32(0.9629), np.float32(0.8926), np.float32(0.9238), np.float32(0.9494), np.float32(0.8561), np.float32(0.8603), np.float32(0.9112)] 
+2025-05-06 00:57:41.100193: Epoch time: 93.12 s 
+2025-05-06 00:57:42.680964:  
+2025-05-06 00:57:42.705294: Epoch 866 
+2025-05-06 00:57:42.706070: Current learning rate: 0.006 
+2025-05-06 00:59:19.540977: train_loss -0.4859 
+2025-05-06 00:59:19.662297: val_loss -0.5176 
+2025-05-06 00:59:19.678465: Pseudo dice [np.float32(0.8288), np.float32(0.8115), np.float32(0.9071), np.float32(0.967), np.float32(0.9101), np.float32(0.9614), np.float32(0.9626), np.float32(0.9729), np.float32(0.9613), np.float32(0.9595), np.float32(0.949), np.float32(0.9636), np.float32(0.9622), np.float32(0.9055), np.float32(0.9466), np.float32(0.955), np.float32(0.8986), np.float32(0.8866), np.float32(0.911)] 
+2025-05-06 00:59:19.686679: Epoch time: 96.86 s 
+2025-05-06 00:59:21.095219:  
+2025-05-06 00:59:21.154262: Epoch 867 
+2025-05-06 00:59:21.167295: Current learning rate: 0.006 
+2025-05-06 01:00:58.525229: train_loss -0.4953 
+2025-05-06 01:00:58.643973: val_loss -0.4649 
+2025-05-06 01:00:58.689856: Pseudo dice [np.float32(0.8218), np.float32(0.8216), np.float32(0.8993), np.float32(0.9804), np.float32(0.9073), np.float32(0.9598), np.float32(0.9474), np.float32(0.966), np.float32(0.9681), np.float32(0.9532), np.float32(0.9406), np.float32(0.9664), np.float32(0.9582), np.float32(0.8878), np.float32(0.962), np.float32(0.9548), np.float32(0.8885), np.float32(0.8891), np.float32(0.91)] 
+2025-05-06 01:00:58.712019: Epoch time: 97.43 s 
+2025-05-06 01:01:00.267904:  
+2025-05-06 01:01:00.345539: Epoch 868 
+2025-05-06 01:01:00.369152: Current learning rate: 0.00599 
+2025-05-06 01:02:36.224458: train_loss -0.4751 
+2025-05-06 01:02:36.243683: val_loss -0.5017 
+2025-05-06 01:02:36.244448: Pseudo dice [np.float32(0.8181), np.float32(0.8388), np.float32(0.9016), np.float32(0.9756), np.float32(0.8804), np.float32(0.9563), np.float32(0.9577), np.float32(0.9792), np.float32(0.9525), np.float32(0.9624), np.float32(0.9163), np.float32(0.9692), np.float32(0.9584), np.float32(0.8923), np.float32(0.9683), np.float32(0.9455), np.float32(0.8821), np.float32(0.8889), np.float32(0.9184)] 
+2025-05-06 01:02:36.253376: Epoch time: 95.96 s 
+2025-05-06 01:02:37.689211:  
+2025-05-06 01:02:37.718597: Epoch 869 
+2025-05-06 01:02:37.719162: Current learning rate: 0.00599 
+2025-05-06 01:04:15.541562: train_loss -0.4846 
+2025-05-06 01:04:15.639201: val_loss -0.5158 
+2025-05-06 01:04:15.676044: Pseudo dice [np.float32(0.8406), np.float32(0.8408), np.float32(0.8151), np.float32(0.9723), np.float32(0.9107), np.float32(0.9585), np.float32(0.9636), np.float32(0.9787), np.float32(0.9578), np.float32(0.969), np.float32(0.9449), np.float32(0.9551), np.float32(0.9707), np.float32(0.9058), np.float32(0.9663), np.float32(0.9526), np.float32(0.8825), np.float32(0.8788), np.float32(0.9187)] 
+2025-05-06 01:04:15.716067: Epoch time: 97.85 s 
+2025-05-06 01:04:17.279832:  
+2025-05-06 01:04:17.396346: Epoch 870 
+2025-05-06 01:04:17.414827: Current learning rate: 0.00598 
+2025-05-06 01:05:51.334750: train_loss -0.4744 
+2025-05-06 01:05:51.484353: val_loss -0.5282 
+2025-05-06 01:05:51.523204: Pseudo dice [np.float32(0.8614), np.float32(0.8583), np.float32(0.8945), np.float32(0.9714), np.float32(0.9142), np.float32(0.9645), np.float32(0.9622), np.float32(0.9812), np.float32(0.959), np.float32(0.9593), np.float32(0.9532), np.float32(0.9645), np.float32(0.9684), np.float32(0.9074), np.float32(0.9626), np.float32(0.9489), np.float32(0.8665), np.float32(0.8877), np.float32(0.9259)] 
+2025-05-06 01:05:51.567453: Epoch time: 94.06 s 
+2025-05-06 01:05:51.600617: Yayy! New best EMA pseudo Dice: 0.9240999817848206 
+2025-05-06 01:05:54.005475:  
+2025-05-06 01:05:54.036421: Epoch 871 
+2025-05-06 01:05:54.044354: Current learning rate: 0.00598 
+2025-05-06 01:07:34.765407: train_loss -0.4708 
+2025-05-06 01:07:34.891446: val_loss -0.5046 
+2025-05-06 01:07:34.923500: Pseudo dice [np.float32(0.8138), np.float32(0.8353), np.float32(0.8986), np.float32(0.9741), np.float32(0.892), np.float32(0.9592), np.float32(0.9555), np.float32(0.9716), np.float32(0.9459), np.float32(0.9699), np.float32(0.941), np.float32(0.9585), np.float32(0.9701), np.float32(0.9005), np.float32(0.9474), np.float32(0.9509), np.float32(0.8618), np.float32(0.8839), np.float32(0.9189)] 
+2025-05-06 01:07:34.939460: Epoch time: 100.76 s 
+2025-05-06 01:07:36.402064:  
+2025-05-06 01:07:36.498320: Epoch 872 
+2025-05-06 01:07:36.520739: Current learning rate: 0.00597 
+2025-05-06 01:09:12.033335: train_loss -0.4895 
+2025-05-06 01:09:12.174510: val_loss -0.4363 
+2025-05-06 01:09:12.201071: Pseudo dice [np.float32(0.8449), np.float32(0.8358), np.float32(0.8637), np.float32(0.9645), np.float32(0.872), np.float32(0.9605), np.float32(0.9576), np.float32(0.976), np.float32(0.9543), np.float32(0.9599), np.float32(0.9448), np.float32(0.9524), np.float32(0.9618), np.float32(0.903), np.float32(0.9618), np.float32(0.9398), np.float32(0.8328), np.float32(0.8742), np.float32(0.8982)] 
+2025-05-06 01:09:12.229215: Epoch time: 95.63 s 
+2025-05-06 01:09:13.774086:  
+2025-05-06 01:09:13.828967: Epoch 873 
+2025-05-06 01:09:13.851987: Current learning rate: 0.00597 
+2025-05-06 01:10:54.438582: train_loss -0.4921 
+2025-05-06 01:10:54.551077: val_loss -0.4749 
+2025-05-06 01:10:54.583912: Pseudo dice [np.float32(0.8571), np.float32(0.8505), np.float32(0.8946), np.float32(0.9758), np.float32(0.8849), np.float32(0.9592), np.float32(0.9688), np.float32(0.9725), np.float32(0.9404), np.float32(0.9679), np.float32(0.9506), np.float32(0.9548), np.float32(0.9687), np.float32(0.8845), np.float32(0.9304), np.float32(0.9537), np.float32(0.8675), np.float32(0.8822), np.float32(0.9152)] 
+2025-05-06 01:10:54.609531: Epoch time: 100.67 s 
+2025-05-06 01:10:56.147305:  
+2025-05-06 01:10:56.199298: Epoch 874 
+2025-05-06 01:10:56.199779: Current learning rate: 0.00596 
+2025-05-06 01:12:30.849803: train_loss -0.4918 
+2025-05-06 01:12:30.979037: val_loss -0.4894 
+2025-05-06 01:12:31.021136: Pseudo dice [np.float32(0.8379), np.float32(0.8376), np.float32(0.872), np.float32(0.9767), np.float32(0.8975), np.float32(0.9613), np.float32(0.9673), np.float32(0.9791), np.float32(0.9623), np.float32(0.9489), np.float32(0.9371), np.float32(0.9641), np.float32(0.9682), np.float32(0.9028), np.float32(0.9617), np.float32(0.9527), np.float32(0.8466), np.float32(0.8977), np.float32(0.9251)] 
+2025-05-06 01:12:31.050353: Epoch time: 94.7 s 
+2025-05-06 01:12:32.559828:  
+2025-05-06 01:12:32.562875: Epoch 875 
+2025-05-06 01:12:32.563280: Current learning rate: 0.00596 
+2025-05-06 01:14:09.969090: train_loss -0.4827 
+2025-05-06 01:14:10.009157: val_loss -0.4775 
+2025-05-06 01:14:10.013907: Pseudo dice [np.float32(0.8542), np.float32(0.7595), np.float32(0.874), np.float32(0.9755), np.float32(0.9031), np.float32(0.9617), np.float32(0.9489), np.float32(0.9646), np.float32(0.9653), np.float32(0.9599), np.float32(0.9508), np.float32(0.9615), np.float32(0.967), np.float32(0.904), np.float32(0.9566), np.float32(0.9529), np.float32(0.8607), np.float32(0.879), np.float32(0.907)] 
+2025-05-06 01:14:10.014480: Epoch time: 97.41 s 
+2025-05-06 01:14:11.529334:  
+2025-05-06 01:14:11.720588: Epoch 876 
+2025-05-06 01:14:11.764142: Current learning rate: 0.00595 
+2025-05-06 01:15:48.777508: train_loss -0.4822 
+2025-05-06 01:15:48.876906: val_loss -0.4877 
+2025-05-06 01:15:48.935498: Pseudo dice [np.float32(0.8164), np.float32(0.8361), np.float32(0.8499), np.float32(0.97), np.float32(0.9052), np.float32(0.9577), np.float32(0.9503), np.float32(0.9799), np.float32(0.9535), np.float32(0.9651), np.float32(0.9348), np.float32(0.9551), np.float32(0.948), np.float32(0.8984), np.float32(0.9668), np.float32(0.9489), np.float32(0.8784), np.float32(0.8692), np.float32(0.9158)] 
+2025-05-06 01:15:48.977354: Epoch time: 97.25 s 
+2025-05-06 01:15:50.631226:  
+2025-05-06 01:15:50.728076: Epoch 877 
+2025-05-06 01:15:50.749205: Current learning rate: 0.00595 
+2025-05-06 01:17:30.392518: train_loss -0.4866 
+2025-05-06 01:17:30.514937: val_loss -0.4606 
+2025-05-06 01:17:30.544951: Pseudo dice [np.float32(0.814), np.float32(0.8511), np.float32(0.8991), np.float32(0.9712), np.float32(0.9081), np.float32(0.9637), np.float32(0.9561), np.float32(0.9765), np.float32(0.9515), np.float32(0.9632), np.float32(0.9458), np.float32(0.9658), np.float32(0.9637), np.float32(0.8896), np.float32(0.9695), np.float32(0.9531), np.float32(0.8861), np.float32(0.8919), np.float32(0.9191)] 
+2025-05-06 01:17:30.585552: Epoch time: 99.76 s 
+2025-05-06 01:17:32.212816:  
+2025-05-06 01:17:32.255824: Epoch 878 
+2025-05-06 01:17:32.280398: Current learning rate: 0.00594 
+2025-05-06 01:19:10.558467: train_loss -0.4903 
+2025-05-06 01:19:10.694980: val_loss -0.5122 
+2025-05-06 01:19:10.703464: Pseudo dice [np.float32(0.8584), np.float32(0.829), np.float32(0.9048), np.float32(0.9741), np.float32(0.8815), np.float32(0.9626), np.float32(0.9611), np.float32(0.9796), np.float32(0.9682), np.float32(0.9501), np.float32(0.94), np.float32(0.9658), np.float32(0.9671), np.float32(0.8999), np.float32(0.9659), np.float32(0.9617), np.float32(0.8764), np.float32(0.8736), np.float32(0.9115)] 
+2025-05-06 01:19:10.704768: Epoch time: 98.35 s 
+2025-05-06 01:19:10.705194: Yayy! New best EMA pseudo Dice: 0.9243000149726868 
+2025-05-06 01:19:13.084070:  
+2025-05-06 01:19:13.092435: Epoch 879 
+2025-05-06 01:19:13.093573: Current learning rate: 0.00594 
+2025-05-06 01:20:50.121401: train_loss -0.4886 
+2025-05-06 01:20:50.182382: val_loss -0.4985 
+2025-05-06 01:20:50.183393: Pseudo dice [np.float32(0.8439), np.float32(0.8371), np.float32(0.8829), np.float32(0.9716), np.float32(0.9024), np.float32(0.9487), np.float32(0.9604), np.float32(0.98), np.float32(0.9654), np.float32(0.9559), np.float32(0.9241), np.float32(0.9634), np.float32(0.9619), np.float32(0.8911), np.float32(0.9603), np.float32(0.943), np.float32(0.8375), np.float32(0.7838), np.float32(0.908)] 
+2025-05-06 01:20:50.184029: Epoch time: 97.04 s 
+2025-05-06 01:20:51.583033:  
+2025-05-06 01:20:51.630398: Epoch 880 
+2025-05-06 01:20:51.667244: Current learning rate: 0.00593 
+2025-05-06 01:22:29.636780: train_loss -0.4797 
+2025-05-06 01:22:29.707083: val_loss -0.4889 
+2025-05-06 01:22:29.715084: Pseudo dice [np.float32(0.8401), np.float32(0.827), np.float32(0.9225), np.float32(0.9688), np.float32(0.7473), np.float32(0.9066), np.float32(0.9591), np.float32(0.9756), np.float32(0.9637), np.float32(0.9563), np.float32(0.9393), np.float32(0.9601), np.float32(0.9577), np.float32(0.9032), np.float32(0.957), np.float32(0.9429), np.float32(0.8154), np.float32(0.8657), np.float32(0.9108)] 
+2025-05-06 01:22:29.715774: Epoch time: 98.05 s 
+2025-05-06 01:22:31.258758:  
+2025-05-06 01:22:31.264558: Epoch 881 
+2025-05-06 01:22:31.268697: Current learning rate: 0.00593 
+2025-05-06 01:24:07.483236: train_loss -0.4647 
+2025-05-06 01:24:07.580125: val_loss -0.5088 
+2025-05-06 01:24:07.594721: Pseudo dice [np.float32(0.8388), np.float32(0.8563), np.float32(0.8705), np.float32(0.9645), np.float32(0.8911), np.float32(0.9279), np.float32(0.9182), np.float32(0.9772), np.float32(0.9636), np.float32(0.9672), np.float32(0.9533), np.float32(0.9711), np.float32(0.9736), np.float32(0.895), np.float32(0.9629), np.float32(0.9549), np.float32(0.8402), np.float32(0.8603), np.float32(0.9126)] 
+2025-05-06 01:24:07.610531: Epoch time: 96.23 s 
+2025-05-06 01:24:09.097092:  
+2025-05-06 01:24:09.174610: Epoch 882 
+2025-05-06 01:24:09.183091: Current learning rate: 0.00592 
+2025-05-06 01:25:47.677610: train_loss -0.473 
+2025-05-06 01:25:47.770901: val_loss -0.4776 
+2025-05-06 01:25:47.818000: Pseudo dice [np.float32(0.8325), np.float32(0.8392), np.float32(0.9005), np.float32(0.976), np.float32(0.8986), np.float32(0.9628), np.float32(0.9566), np.float32(0.9754), np.float32(0.9638), np.float32(0.9657), np.float32(0.9466), np.float32(0.9649), np.float32(0.965), np.float32(0.8933), np.float32(0.9642), np.float32(0.944), np.float32(0.9007), np.float32(0.9003), np.float32(0.9049)] 
+2025-05-06 01:25:47.851556: Epoch time: 98.58 s 
+2025-05-06 01:25:53.198095:  
+2025-05-06 01:25:53.204100: Epoch 883 
+2025-05-06 01:25:53.205094: Current learning rate: 0.00592 
+2025-05-06 01:27:33.344092: train_loss -0.4841 
+2025-05-06 01:27:33.481126: val_loss -0.4589 
+2025-05-06 01:27:33.535868: Pseudo dice [np.float32(0.8534), np.float32(0.8313), np.float32(0.9122), np.float32(0.9729), np.float32(0.9131), np.float32(0.947), np.float32(0.9685), np.float32(0.979), np.float32(0.9647), np.float32(0.9699), np.float32(0.9474), np.float32(0.971), np.float32(0.9663), np.float32(0.9059), np.float32(0.9537), np.float32(0.9417), np.float32(0.8315), np.float32(0.8011), np.float32(0.9053)] 
+2025-05-06 01:27:33.594609: Epoch time: 100.15 s 
+2025-05-06 01:27:35.183657:  
+2025-05-06 01:27:35.266492: Epoch 884 
+2025-05-06 01:27:35.268481: Current learning rate: 0.00592 
+2025-05-06 01:29:12.222738: train_loss -0.4977 
+2025-05-06 01:29:12.297938: val_loss -0.4916 
+2025-05-06 01:29:12.335049: Pseudo dice [np.float32(0.849), np.float32(0.8136), np.float32(0.9178), np.float32(0.9764), np.float32(0.9112), np.float32(0.9633), np.float32(0.9531), np.float32(0.9736), np.float32(0.9549), np.float32(0.9508), np.float32(0.9444), np.float32(0.957), np.float32(0.9662), np.float32(0.9043), np.float32(0.9611), np.float32(0.9536), np.float32(0.8914), np.float32(0.8805), np.float32(0.9252)] 
+2025-05-06 01:29:12.349410: Epoch time: 97.04 s 
+2025-05-06 01:29:13.901167:  
+2025-05-06 01:29:13.923125: Epoch 885 
+2025-05-06 01:29:13.923856: Current learning rate: 0.00591 
+2025-05-06 01:30:47.439983: train_loss -0.4701 
+2025-05-06 01:30:47.620315: val_loss -0.4826 
+2025-05-06 01:30:47.663248: Pseudo dice [np.float32(0.8368), np.float32(0.844), np.float32(0.8711), np.float32(0.9539), np.float32(0.9049), np.float32(0.945), np.float32(0.9223), np.float32(0.9616), np.float32(0.9427), np.float32(0.9631), np.float32(0.941), np.float32(0.9654), np.float32(0.959), np.float32(0.9018), np.float32(0.9606), np.float32(0.9409), np.float32(0.8684), np.float32(0.8097), np.float32(0.9084)] 
+2025-05-06 01:30:47.704307: Epoch time: 93.54 s 
+2025-05-06 01:30:49.275042:  
+2025-05-06 01:30:49.365958: Epoch 886 
+2025-05-06 01:30:49.384156: Current learning rate: 0.00591 
+2025-05-06 01:32:26.567157: train_loss -0.5033 
+2025-05-06 01:32:26.702128: val_loss -0.5033 
+2025-05-06 01:32:26.733977: Pseudo dice [np.float32(0.8164), np.float32(0.8415), np.float32(0.9148), np.float32(0.9752), np.float32(0.9099), np.float32(0.9546), np.float32(0.9638), np.float32(0.9789), np.float32(0.9654), np.float32(0.9711), np.float32(0.951), np.float32(0.9745), np.float32(0.9704), np.float32(0.8833), np.float32(0.963), np.float32(0.9349), np.float32(0.9002), np.float32(0.9092), np.float32(0.9309)] 
+2025-05-06 01:32:26.756300: Epoch time: 97.29 s 
+2025-05-06 01:32:28.277846:  
+2025-05-06 01:32:28.348418: Epoch 887 
+2025-05-06 01:32:28.370899: Current learning rate: 0.0059 
+2025-05-06 01:34:03.141162: train_loss -0.4694 
+2025-05-06 01:34:03.194397: val_loss -0.5181 
+2025-05-06 01:34:03.202548: Pseudo dice [np.float32(0.8329), np.float32(0.8272), np.float32(0.8676), np.float32(0.9624), np.float32(0.9094), np.float32(0.9561), np.float32(0.9611), np.float32(0.9708), np.float32(0.9653), np.float32(0.9571), np.float32(0.9353), np.float32(0.97), np.float32(0.9609), np.float32(0.9026), np.float32(0.9674), np.float32(0.9562), np.float32(0.892), np.float32(0.89), np.float32(0.9178)] 
+2025-05-06 01:34:03.210043: Epoch time: 94.86 s 
+2025-05-06 01:34:04.638531:  
+2025-05-06 01:34:04.742312: Epoch 888 
+2025-05-06 01:34:04.780881: Current learning rate: 0.0059 
+2025-05-06 01:35:37.461236: train_loss -0.48 
+2025-05-06 01:35:37.538993: val_loss -0.5234 
+2025-05-06 01:35:37.565977: Pseudo dice [np.float32(0.839), np.float32(0.8516), np.float32(0.9317), np.float32(0.972), np.float32(0.8772), np.float32(0.9514), np.float32(0.9678), np.float32(0.9766), np.float32(0.9553), np.float32(0.9642), np.float32(0.9268), np.float32(0.9617), np.float32(0.963), np.float32(0.9008), np.float32(0.9673), np.float32(0.9573), np.float32(0.89), np.float32(0.8559), np.float32(0.9164)] 
+2025-05-06 01:35:37.594854: Epoch time: 92.82 s 
+2025-05-06 01:35:37.620487: Yayy! New best EMA pseudo Dice: 0.9243000149726868 
+2025-05-06 01:35:40.282305:  
+2025-05-06 01:35:40.341675: Epoch 889 
+2025-05-06 01:35:40.346063: Current learning rate: 0.00589 
+2025-05-06 01:37:14.345937: train_loss -0.4818 
+2025-05-06 01:37:14.397725: val_loss -0.5123 
+2025-05-06 01:37:14.412788: Pseudo dice [np.float32(0.8335), np.float32(0.8417), np.float32(0.8932), np.float32(0.9715), np.float32(0.9128), np.float32(0.958), np.float32(0.9662), np.float32(0.9775), np.float32(0.9416), np.float32(0.9653), np.float32(0.9477), np.float32(0.9713), np.float32(0.967), np.float32(0.8983), np.float32(0.9628), np.float32(0.9495), np.float32(0.8962), np.float32(0.8275), np.float32(0.9211)] 
+2025-05-06 01:37:14.431155: Epoch time: 94.06 s 
+2025-05-06 01:37:14.447903: Yayy! New best EMA pseudo Dice: 0.9244999885559082 
+2025-05-06 01:37:16.851123:  
+2025-05-06 01:37:16.875042: Epoch 890 
+2025-05-06 01:37:16.888137: Current learning rate: 0.00589 
+2025-05-06 01:38:53.259508: train_loss -0.4787 
+2025-05-06 01:38:53.331120: val_loss -0.4882 
+2025-05-06 01:38:53.339102: Pseudo dice [np.float32(0.833), np.float32(0.8314), np.float32(0.9087), np.float32(0.9706), np.float32(0.9066), np.float32(0.9498), np.float32(0.9591), np.float32(0.9698), np.float32(0.97), np.float32(0.9655), np.float32(0.9538), np.float32(0.9722), np.float32(0.9704), np.float32(0.8955), np.float32(0.9646), np.float32(0.9546), np.float32(0.8854), np.float32(0.8589), np.float32(0.9087)] 
+2025-05-06 01:38:53.343158: Epoch time: 96.41 s 
+2025-05-06 01:38:53.350849: Yayy! New best EMA pseudo Dice: 0.9248999953269958 
+2025-05-06 01:38:55.558842:  
+2025-05-06 01:38:55.684525: Epoch 891 
+2025-05-06 01:38:55.699640: Current learning rate: 0.00588 
+2025-05-06 01:40:33.561671: train_loss -0.4894 
+2025-05-06 01:40:33.596661: val_loss -0.5276 
+2025-05-06 01:40:33.605792: Pseudo dice [np.float32(0.8394), np.float32(0.837), np.float32(0.9232), np.float32(0.9768), np.float32(0.9209), np.float32(0.9513), np.float32(0.9596), np.float32(0.9767), np.float32(0.9646), np.float32(0.9656), np.float32(0.9379), np.float32(0.962), np.float32(0.966), np.float32(0.9023), np.float32(0.9302), np.float32(0.9414), np.float32(0.8171), np.float32(0.8544), np.float32(0.9102)] 
+2025-05-06 01:40:33.606347: Epoch time: 98.0 s 
+2025-05-06 01:40:35.024430:  
+2025-05-06 01:40:35.114336: Epoch 892 
+2025-05-06 01:40:35.149566: Current learning rate: 0.00588 
+2025-05-06 01:42:11.547206: train_loss -0.4765 
+2025-05-06 01:42:11.575864: val_loss -0.4731 
+2025-05-06 01:42:11.593067: Pseudo dice [np.float32(0.8428), np.float32(0.8343), np.float32(0.9397), np.float32(0.9703), np.float32(0.9124), np.float32(0.9469), np.float32(0.9678), np.float32(0.9734), np.float32(0.9601), np.float32(0.9632), np.float32(0.9457), np.float32(0.9614), np.float32(0.9696), np.float32(0.8855), np.float32(0.9438), np.float32(0.9305), np.float32(0.8803), np.float32(0.873), np.float32(0.9101)] 
+2025-05-06 01:42:11.610708: Epoch time: 96.52 s 
+2025-05-06 01:42:11.611875: Yayy! New best EMA pseudo Dice: 0.9248999953269958 
+2025-05-06 01:42:14.334902:  
+2025-05-06 01:42:14.336693: Epoch 893 
+2025-05-06 01:42:14.337038: Current learning rate: 0.00587 
+2025-05-06 01:43:49.903589: train_loss -0.4904 
+2025-05-06 01:43:49.989601: val_loss -0.4862 
+2025-05-06 01:43:50.005535: Pseudo dice [np.float32(0.8056), np.float32(0.8284), np.float32(0.9128), np.float32(0.966), np.float32(0.913), np.float32(0.949), np.float32(0.9591), np.float32(0.9769), np.float32(0.9671), np.float32(0.9571), np.float32(0.9367), np.float32(0.9677), np.float32(0.9634), np.float32(0.8886), np.float32(0.9645), np.float32(0.9559), np.float32(0.8389), np.float32(0.7863), np.float32(0.9188)] 
+2025-05-06 01:43:50.016310: Epoch time: 95.57 s 
+2025-05-06 01:43:51.430955:  
+2025-05-06 01:43:51.514166: Epoch 894 
+2025-05-06 01:43:51.546878: Current learning rate: 0.00587 
+2025-05-06 01:45:31.489845: train_loss -0.4691 
+2025-05-06 01:45:31.587916: val_loss -0.4801 
+2025-05-06 01:45:31.604808: Pseudo dice [np.float32(0.7713), np.float32(0.8281), np.float32(0.8218), np.float32(0.9481), np.float32(0.8712), np.float32(0.9416), np.float32(0.9486), np.float32(0.9691), np.float32(0.9453), np.float32(0.9619), np.float32(0.9295), np.float32(0.9667), np.float32(0.9594), np.float32(0.8888), np.float32(0.9509), np.float32(0.9441), np.float32(0.798), np.float32(0.8245), np.float32(0.9076)] 
+2025-05-06 01:45:31.616042: Epoch time: 100.06 s 
+2025-05-06 01:45:33.049142:  
+2025-05-06 01:45:33.051987: Epoch 895 
+2025-05-06 01:45:33.052475: Current learning rate: 0.00586 
+2025-05-06 01:47:07.247817: train_loss -0.4768 
+2025-05-06 01:47:07.256855: val_loss -0.4669 
+2025-05-06 01:47:07.259330: Pseudo dice [np.float32(0.8469), np.float32(0.8389), np.float32(0.8934), np.float32(0.975), np.float32(0.9097), np.float32(0.9574), np.float32(0.9592), np.float32(0.9781), np.float32(0.9687), np.float32(0.9621), np.float32(0.9134), np.float32(0.9604), np.float32(0.9664), np.float32(0.8817), np.float32(0.9436), np.float32(0.9409), np.float32(0.857), np.float32(0.8854), np.float32(0.8881)] 
+2025-05-06 01:47:07.259813: Epoch time: 94.2 s 
+2025-05-06 01:47:08.940416:  
+2025-05-06 01:47:09.045336: Epoch 896 
+2025-05-06 01:47:09.067823: Current learning rate: 0.00586 
+2025-05-06 01:48:46.380074: train_loss -0.4787 
+2025-05-06 01:48:46.459130: val_loss -0.5024 
+2025-05-06 01:48:46.461900: Pseudo dice [np.float32(0.8638), np.float32(0.8569), np.float32(0.9235), np.float32(0.9726), np.float32(0.919), np.float32(0.958), np.float32(0.9643), np.float32(0.9774), np.float32(0.9505), np.float32(0.9613), np.float32(0.9444), np.float32(0.9698), np.float32(0.9675), np.float32(0.9127), np.float32(0.953), np.float32(0.9489), np.float32(0.9027), np.float32(0.8954), np.float32(0.9342)] 
+2025-05-06 01:48:46.481953: Epoch time: 97.44 s 
+2025-05-06 01:48:47.959271:  
+2025-05-06 01:48:47.982624: Epoch 897 
+2025-05-06 01:48:47.983433: Current learning rate: 0.00585 
+2025-05-06 01:50:25.529079: train_loss -0.4761 
+2025-05-06 01:50:25.584679: val_loss -0.4989 
+2025-05-06 01:50:25.598826: Pseudo dice [np.float32(0.8519), np.float32(0.8374), np.float32(0.9128), np.float32(0.9722), np.float32(0.8492), np.float32(0.9511), np.float32(0.9614), np.float32(0.9773), np.float32(0.9605), np.float32(0.9723), np.float32(0.9408), np.float32(0.9644), np.float32(0.967), np.float32(0.9146), np.float32(0.9557), np.float32(0.9453), np.float32(0.8884), np.float32(0.8873), np.float32(0.8938)] 
+2025-05-06 01:50:25.624126: Epoch time: 97.57 s 
+2025-05-06 01:50:27.151850:  
+2025-05-06 01:50:27.200481: Epoch 898 
+2025-05-06 01:50:27.205683: Current learning rate: 0.00585 
+2025-05-06 01:52:07.379035: train_loss -0.472 
+2025-05-06 01:52:07.444567: val_loss -0.4871 
+2025-05-06 01:52:07.467011: Pseudo dice [np.float32(0.846), np.float32(0.8204), np.float32(0.8828), np.float32(0.9634), np.float32(0.8812), np.float32(0.9545), np.float32(0.957), np.float32(0.9735), np.float32(0.9516), np.float32(0.962), np.float32(0.9404), np.float32(0.959), np.float32(0.9643), np.float32(0.8699), np.float32(0.9472), np.float32(0.9522), np.float32(0.8849), np.float32(0.887), np.float32(0.9082)] 
+2025-05-06 01:52:07.496091: Epoch time: 100.23 s 
+2025-05-06 01:52:08.943937:  
+2025-05-06 01:52:08.963816: Epoch 899 
+2025-05-06 01:52:08.972007: Current learning rate: 0.00584 
+2025-05-06 01:53:44.525960: train_loss -0.469 
+2025-05-06 01:53:44.598424: val_loss -0.4918 
+2025-05-06 01:53:44.602424: Pseudo dice [np.float32(0.8106), np.float32(0.8326), np.float32(0.9332), np.float32(0.9464), np.float32(0.9033), np.float32(0.9498), np.float32(0.9433), np.float32(0.9689), np.float32(0.9543), np.float32(0.9549), np.float32(0.9394), np.float32(0.9598), np.float32(0.9289), np.float32(0.8828), np.float32(0.9497), np.float32(0.9403), np.float32(0.8844), np.float32(0.8782), np.float32(0.9174)] 
+2025-05-06 01:53:44.610156: Epoch time: 95.58 s 
+2025-05-06 01:53:47.983254:  
+2025-05-06 01:53:48.071990: Epoch 900 
+2025-05-06 01:53:48.123325: Current learning rate: 0.00584 
+2025-05-06 01:55:24.707146: train_loss -0.4814 
+2025-05-06 01:55:24.750983: val_loss -0.4938 
+2025-05-06 01:55:24.751730: Pseudo dice [np.float32(0.8571), np.float32(0.8241), np.float32(0.9105), np.float32(0.9711), np.float32(0.8657), np.float32(0.96), np.float32(0.965), np.float32(0.9608), np.float32(0.9591), np.float32(0.9611), np.float32(0.9496), np.float32(0.9534), np.float32(0.9667), np.float32(0.8963), np.float32(0.9589), np.float32(0.9412), np.float32(0.9056), np.float32(0.8906), np.float32(0.9144)] 
+2025-05-06 01:55:24.757392: Epoch time: 96.73 s 
+2025-05-06 01:55:29.343581:  
+2025-05-06 01:55:29.349408: Epoch 901 
+2025-05-06 01:55:29.349896: Current learning rate: 0.00583 
+2025-05-06 01:57:02.073162: train_loss -0.4565 
+2025-05-06 01:57:02.168405: val_loss -0.518 
+2025-05-06 01:57:02.202672: Pseudo dice [np.float32(0.8229), np.float32(0.8469), np.float32(0.8842), np.float32(0.9674), np.float32(0.883), np.float32(0.9549), np.float32(0.9456), np.float32(0.9729), np.float32(0.9499), np.float32(0.9537), np.float32(0.9153), np.float32(0.9727), np.float32(0.9498), np.float32(0.8816), np.float32(0.9578), np.float32(0.95), np.float32(0.8545), np.float32(0.8499), np.float32(0.902)] 
+2025-05-06 01:57:02.235962: Epoch time: 92.73 s 
+2025-05-06 01:57:03.777223:  
+2025-05-06 01:57:03.859737: Epoch 902 
+2025-05-06 01:57:03.911489: Current learning rate: 0.00583 
+2025-05-06 01:58:37.886972: train_loss -0.4747 
+2025-05-06 01:58:37.996466: val_loss -0.444 
+2025-05-06 01:58:38.026120: Pseudo dice [np.float32(0.8536), np.float32(0.8239), np.float32(0.8754), np.float32(0.9761), np.float32(0.8978), np.float32(0.9513), np.float32(0.9551), np.float32(0.964), np.float32(0.9551), np.float32(0.9676), np.float32(0.945), np.float32(0.9572), np.float32(0.9616), np.float32(0.9036), np.float32(0.9671), np.float32(0.9474), np.float32(0.8739), np.float32(0.8758), np.float32(0.9157)] 
+2025-05-06 01:58:38.039433: Epoch time: 94.11 s 
+2025-05-06 01:58:39.497491:  
+2025-05-06 01:58:39.594375: Epoch 903 
+2025-05-06 01:58:39.612922: Current learning rate: 0.00582 
+2025-05-06 02:00:19.134334: train_loss -0.473 
+2025-05-06 02:00:19.240047: val_loss -0.4902 
+2025-05-06 02:00:19.271708: Pseudo dice [np.float32(0.8396), np.float32(0.825), np.float32(0.9162), np.float32(0.897), np.float32(0.8699), np.float32(0.9475), np.float32(0.9596), np.float32(0.9681), np.float32(0.9391), np.float32(0.9536), np.float32(0.9287), np.float32(0.9572), np.float32(0.9667), np.float32(0.9039), np.float32(0.9658), np.float32(0.9491), np.float32(0.9058), np.float32(0.8744), np.float32(0.9248)] 
+2025-05-06 02:00:19.319073: Epoch time: 99.64 s 
+2025-05-06 02:00:20.890900:  
+2025-05-06 02:00:21.127559: Epoch 904 
+2025-05-06 02:00:21.128443: Current learning rate: 0.00582 
+2025-05-06 02:02:00.400283: train_loss -0.4919 
+2025-05-06 02:02:00.501297: val_loss -0.4982 
+2025-05-06 02:02:00.525070: Pseudo dice [np.float32(0.8236), np.float32(0.8183), np.float32(0.6851), np.float32(0.9716), np.float32(0.8879), np.float32(0.9532), np.float32(0.9534), np.float32(0.9728), np.float32(0.9684), np.float32(0.9627), np.float32(0.9408), np.float32(0.9664), np.float32(0.9618), np.float32(0.8991), np.float32(0.9518), np.float32(0.9404), np.float32(0.8638), np.float32(0.8585), np.float32(0.9106)] 
+2025-05-06 02:02:00.548994: Epoch time: 99.51 s 
+2025-05-06 02:02:02.116190:  
+2025-05-06 02:02:02.161584: Epoch 905 
+2025-05-06 02:02:02.172849: Current learning rate: 0.00581 
+2025-05-06 02:03:37.677424: train_loss -0.4724 
+2025-05-06 02:03:37.768457: val_loss -0.4849 
+2025-05-06 02:03:37.799860: Pseudo dice [np.float32(0.842), np.float32(0.817), np.float32(0.9033), np.float32(0.976), np.float32(0.9055), np.float32(0.961), np.float32(0.9649), np.float32(0.9767), np.float32(0.9524), np.float32(0.9501), np.float32(0.9226), np.float32(0.9717), np.float32(0.9633), np.float32(0.8886), np.float32(0.9643), np.float32(0.9529), np.float32(0.8971), np.float32(0.8999), np.float32(0.9193)] 
+2025-05-06 02:03:37.837525: Epoch time: 95.56 s 
+2025-05-06 02:03:39.234831:  
+2025-05-06 02:03:39.388754: Epoch 906 
+2025-05-06 02:03:39.433958: Current learning rate: 0.00581 
+2025-05-06 02:05:15.474540: train_loss -0.4801 
+2025-05-06 02:05:15.593972: val_loss -0.4995 
+2025-05-06 02:05:15.627827: Pseudo dice [np.float32(0.8391), np.float32(0.8344), np.float32(0.8794), np.float32(0.9717), np.float32(0.8693), np.float32(0.9545), np.float32(0.9276), np.float32(0.9741), np.float32(0.958), np.float32(0.9595), np.float32(0.9363), np.float32(0.9676), np.float32(0.9601), np.float32(0.9061), np.float32(0.9644), np.float32(0.9497), np.float32(0.8617), np.float32(0.9053), np.float32(0.899)] 
+2025-05-06 02:05:15.636628: Epoch time: 96.24 s 
+2025-05-06 02:05:17.381588:  
+2025-05-06 02:05:17.531936: Epoch 907 
+2025-05-06 02:05:17.575781: Current learning rate: 0.00581 
+2025-05-06 02:06:56.170002: train_loss -0.4808 
+2025-05-06 02:06:56.297388: val_loss -0.4974 
+2025-05-06 02:06:56.311976: Pseudo dice [np.float32(0.8517), np.float32(0.833), np.float32(0.8763), np.float32(0.976), np.float32(0.8804), np.float32(0.9541), np.float32(0.9288), np.float32(0.9708), np.float32(0.9617), np.float32(0.951), np.float32(0.9473), np.float32(0.9526), np.float32(0.9654), np.float32(0.8992), np.float32(0.9597), np.float32(0.9594), np.float32(0.8832), np.float32(0.876), np.float32(0.9117)] 
+2025-05-06 02:06:56.316658: Epoch time: 98.79 s 
+2025-05-06 02:06:57.880934:  
+2025-05-06 02:06:57.948499: Epoch 908 
+2025-05-06 02:06:57.974509: Current learning rate: 0.0058 
+2025-05-06 02:08:31.447385: train_loss -0.4934 
+2025-05-06 02:08:31.469649: val_loss -0.4648 
+2025-05-06 02:08:31.470491: Pseudo dice [np.float32(0.8091), np.float32(0.8078), np.float32(0.9415), np.float32(0.9776), np.float32(0.8927), np.float32(0.949), np.float32(0.9582), np.float32(0.9759), np.float32(0.9457), np.float32(0.9668), np.float32(0.9449), np.float32(0.9579), np.float32(0.9664), np.float32(0.8966), np.float32(0.8867), np.float32(0.9335), np.float32(0.8539), np.float32(0.9003), np.float32(0.9184)] 
+2025-05-06 02:08:31.471058: Epoch time: 93.57 s 
+2025-05-06 02:08:32.931277:  
+2025-05-06 02:08:32.975059: Epoch 909 
+2025-05-06 02:08:33.007038: Current learning rate: 0.0058 
+2025-05-06 02:10:11.261126: train_loss -0.4898 
+2025-05-06 02:10:11.345071: val_loss -0.4871 
+2025-05-06 02:10:11.350909: Pseudo dice [np.float32(0.8054), np.float32(0.8417), np.float32(0.9256), np.float32(0.9691), np.float32(0.9162), np.float32(0.9613), np.float32(0.9631), np.float32(0.9774), np.float32(0.9366), np.float32(0.9285), np.float32(0.9186), np.float32(0.95), np.float32(0.9467), np.float32(0.8949), np.float32(0.9642), np.float32(0.9509), np.float32(0.8644), np.float32(0.8498), np.float32(0.9233)] 
+2025-05-06 02:10:11.363513: Epoch time: 98.33 s 
+2025-05-06 02:10:12.846742:  
+2025-05-06 02:10:12.899206: Epoch 910 
+2025-05-06 02:10:12.928409: Current learning rate: 0.00579 
+2025-05-06 02:11:49.712640: train_loss -0.5044 
+2025-05-06 02:11:49.723921: val_loss -0.4954 
+2025-05-06 02:11:49.724906: Pseudo dice [np.float32(0.7836), np.float32(0.8387), np.float32(0.7731), np.float32(0.9745), np.float32(0.9104), np.float32(0.9547), np.float32(0.9552), np.float32(0.9705), np.float32(0.9654), np.float32(0.9607), np.float32(0.9415), np.float32(0.9711), np.float32(0.9666), np.float32(0.9061), np.float32(0.9652), np.float32(0.9134), np.float32(0.8699), np.float32(0.8395), np.float32(0.9122)] 
+2025-05-06 02:11:49.725529: Epoch time: 96.87 s 
+2025-05-06 02:11:51.313998:  
+2025-05-06 02:11:51.364748: Epoch 911 
+2025-05-06 02:11:51.368423: Current learning rate: 0.00579 
+2025-05-06 02:13:26.878356: train_loss -0.4908 
+2025-05-06 02:13:26.954670: val_loss -0.4979 
+2025-05-06 02:13:26.973064: Pseudo dice [np.float32(0.8481), np.float32(0.8492), np.float32(0.8859), np.float32(0.9668), np.float32(0.8904), np.float32(0.9597), np.float32(0.9628), np.float32(0.9806), np.float32(0.9561), np.float32(0.9664), np.float32(0.9451), np.float32(0.9705), np.float32(0.9683), np.float32(0.8979), np.float32(0.9668), np.float32(0.9473), np.float32(0.8861), np.float32(0.885), np.float32(0.9133)] 
+2025-05-06 02:13:26.991994: Epoch time: 95.57 s 
+2025-05-06 02:13:28.416121:  
+2025-05-06 02:13:28.540037: Epoch 912 
+2025-05-06 02:13:28.579202: Current learning rate: 0.00578 
+2025-05-06 02:15:05.461793: train_loss -0.4801 
+2025-05-06 02:15:05.570341: val_loss -0.5253 
+2025-05-06 02:15:05.600730: Pseudo dice [np.float32(0.8298), np.float32(0.8583), np.float32(0.8968), np.float32(0.9743), np.float32(0.8863), np.float32(0.9526), np.float32(0.9462), np.float32(0.9682), np.float32(0.9404), np.float32(0.9556), np.float32(0.9239), np.float32(0.9551), np.float32(0.97), np.float32(0.8994), np.float32(0.9594), np.float32(0.9567), np.float32(0.8772), np.float32(0.8843), np.float32(0.9038)] 
+2025-05-06 02:15:05.601967: Epoch time: 97.05 s 
+2025-05-06 02:15:07.092263:  
+2025-05-06 02:15:07.148674: Epoch 913 
+2025-05-06 02:15:07.154473: Current learning rate: 0.00578 
+2025-05-06 02:16:42.849173: train_loss -0.4882 
+2025-05-06 02:16:42.975876: val_loss -0.4735 
+2025-05-06 02:16:42.996675: Pseudo dice [np.float32(0.8124), np.float32(0.8308), np.float32(0.8451), np.float32(0.9699), np.float32(0.8994), np.float32(0.958), np.float32(0.9667), np.float32(0.9752), np.float32(0.9499), np.float32(0.9726), np.float32(0.9486), np.float32(0.9564), np.float32(0.9669), np.float32(0.8754), np.float32(0.9597), np.float32(0.9241), np.float32(0.8393), np.float32(0.871), np.float32(0.8999)] 
+2025-05-06 02:16:43.029165: Epoch time: 95.76 s 
+2025-05-06 02:16:44.527304:  
+2025-05-06 02:16:44.617920: Epoch 914 
+2025-05-06 02:16:44.636937: Current learning rate: 0.00577 
+2025-05-06 02:18:16.801409: train_loss -0.497 
+2025-05-06 02:18:16.960378: val_loss -0.5331 
+2025-05-06 02:18:16.971106: Pseudo dice [np.float32(0.8351), np.float32(0.871), np.float32(0.9209), np.float32(0.9784), np.float32(0.9042), np.float32(0.958), np.float32(0.9641), np.float32(0.9772), np.float32(0.9703), np.float32(0.9655), np.float32(0.9485), np.float32(0.9735), np.float32(0.9677), np.float32(0.9004), np.float32(0.949), np.float32(0.9481), np.float32(0.8889), np.float32(0.8879), np.float32(0.9126)] 
+2025-05-06 02:18:16.971986: Epoch time: 92.28 s 
+2025-05-06 02:18:18.484598:  
+2025-05-06 02:18:18.503100: Epoch 915 
+2025-05-06 02:18:18.503749: Current learning rate: 0.00577 
+2025-05-06 02:19:55.342982: train_loss -0.4705 
+2025-05-06 02:19:55.493402: val_loss -0.5204 
+2025-05-06 02:19:55.537768: Pseudo dice [np.float32(0.8376), np.float32(0.8208), np.float32(0.8077), np.float32(0.9673), np.float32(0.8785), np.float32(0.9583), np.float32(0.9592), np.float32(0.9738), np.float32(0.9691), np.float32(0.9639), np.float32(0.9421), np.float32(0.9714), np.float32(0.967), np.float32(0.8972), np.float32(0.9629), np.float32(0.9566), np.float32(0.8538), np.float32(0.858), np.float32(0.9078)] 
+2025-05-06 02:19:55.582171: Epoch time: 96.86 s 
+2025-05-06 02:19:57.085268:  
+2025-05-06 02:19:57.148139: Epoch 916 
+2025-05-06 02:19:57.149581: Current learning rate: 0.00576 
+2025-05-06 02:21:32.290386: train_loss -0.5049 
+2025-05-06 02:21:32.366877: val_loss -0.5042 
+2025-05-06 02:21:32.388986: Pseudo dice [np.float32(0.8586), np.float32(0.8627), np.float32(0.8953), np.float32(0.9718), np.float32(0.9136), np.float32(0.9545), np.float32(0.9512), np.float32(0.9774), np.float32(0.9547), np.float32(0.9658), np.float32(0.9407), np.float32(0.958), np.float32(0.965), np.float32(0.9091), np.float32(0.9622), np.float32(0.9558), np.float32(0.8576), np.float32(0.8795), np.float32(0.9136)] 
+2025-05-06 02:21:32.407916: Epoch time: 95.21 s 
+2025-05-06 02:21:33.915147:  
+2025-05-06 02:21:34.000858: Epoch 917 
+2025-05-06 02:21:34.008619: Current learning rate: 0.00576 
+2025-05-06 02:23:08.699070: train_loss -0.4793 
+2025-05-06 02:23:08.775642: val_loss -0.4562 
+2025-05-06 02:23:08.801873: Pseudo dice [np.float32(0.8301), np.float32(0.8401), np.float32(0.8571), np.float32(0.9805), np.float32(0.8761), np.float32(0.9572), np.float32(0.9586), np.float32(0.9724), np.float32(0.9666), np.float32(0.9659), np.float32(0.9368), np.float32(0.9637), np.float32(0.9625), np.float32(0.8899), np.float32(0.9519), np.float32(0.9495), np.float32(0.8793), np.float32(0.901), np.float32(0.9257)] 
+2025-05-06 02:23:08.836323: Epoch time: 94.79 s 
+2025-05-06 02:23:10.310841:  
+2025-05-06 02:23:10.430520: Epoch 918 
+2025-05-06 02:23:10.438180: Current learning rate: 0.00575 
+2025-05-06 02:24:43.843904: train_loss -0.4843 
+2025-05-06 02:24:43.884684: val_loss -0.4914 
+2025-05-06 02:24:43.957233: Pseudo dice [np.float32(0.8434), np.float32(0.8468), np.float32(0.9352), np.float32(0.9754), np.float32(0.9089), np.float32(0.9613), np.float32(0.9589), np.float32(0.9789), np.float32(0.9688), np.float32(0.9607), np.float32(0.8948), np.float32(0.9691), np.float32(0.9654), np.float32(0.8928), np.float32(0.9589), np.float32(0.9484), np.float32(0.8317), np.float32(0.8431), np.float32(0.9138)] 
+2025-05-06 02:24:43.980627: Epoch time: 93.53 s 
+2025-05-06 02:24:45.442714:  
+2025-05-06 02:24:45.538040: Epoch 919 
+2025-05-06 02:24:45.562016: Current learning rate: 0.00575 
+2025-05-06 02:26:18.786173: train_loss -0.4901 
+2025-05-06 02:26:18.920325: val_loss -0.4774 
+2025-05-06 02:26:18.972517: Pseudo dice [np.float32(0.8242), np.float32(0.8378), np.float32(0.9404), np.float32(0.9636), np.float32(0.8345), np.float32(0.9503), np.float32(0.9585), np.float32(0.971), np.float32(0.9596), np.float32(0.9554), np.float32(0.9476), np.float32(0.9692), np.float32(0.953), np.float32(0.9), np.float32(0.9679), np.float32(0.932), np.float32(0.904), np.float32(0.8982), np.float32(0.9204)] 
+2025-05-06 02:26:19.018286: Epoch time: 93.34 s 
+2025-05-06 02:26:24.416841:  
+2025-05-06 02:26:24.423392: Epoch 920 
+2025-05-06 02:26:24.423865: Current learning rate: 0.00574 
+2025-05-06 02:27:59.205317: train_loss -0.4915 
+2025-05-06 02:27:59.320118: val_loss -0.4947 
+2025-05-06 02:27:59.335101: Pseudo dice [np.float32(0.8293), np.float32(0.839), np.float32(0.9069), np.float32(0.9747), np.float32(0.889), np.float32(0.9513), np.float32(0.964), np.float32(0.9679), np.float32(0.9605), np.float32(0.9598), np.float32(0.9398), np.float32(0.968), np.float32(0.9646), np.float32(0.8928), np.float32(0.9575), np.float32(0.9362), np.float32(0.8768), np.float32(0.8973), np.float32(0.9084)] 
+2025-05-06 02:27:59.355384: Epoch time: 94.79 s 
+2025-05-06 02:28:00.832784:  
+2025-05-06 02:28:00.927888: Epoch 921 
+2025-05-06 02:28:00.954396: Current learning rate: 0.00574 
+2025-05-06 02:29:35.310452: train_loss -0.49 
+2025-05-06 02:29:35.409661: val_loss -0.5349 
+2025-05-06 02:29:35.457615: Pseudo dice [np.float32(0.8137), np.float32(0.8423), np.float32(0.8777), np.float32(0.9655), np.float32(0.8944), np.float32(0.9476), np.float32(0.9472), np.float32(0.9728), np.float32(0.9564), np.float32(0.9698), np.float32(0.9466), np.float32(0.9459), np.float32(0.9608), np.float32(0.8829), np.float32(0.9565), np.float32(0.9448), np.float32(0.8374), np.float32(0.8614), np.float32(0.903)] 
+2025-05-06 02:29:35.485145: Epoch time: 94.48 s 
+2025-05-06 02:29:37.012511:  
+2025-05-06 02:29:37.043400: Epoch 922 
+2025-05-06 02:29:37.047624: Current learning rate: 0.00573 
+2025-05-06 02:31:11.153716: train_loss -0.4968 
+2025-05-06 02:31:11.298890: val_loss -0.5216 
+2025-05-06 02:31:11.314425: Pseudo dice [np.float32(0.8411), np.float32(0.8493), np.float32(0.903), np.float32(0.9764), np.float32(0.9055), np.float32(0.9467), np.float32(0.9669), np.float32(0.9783), np.float32(0.9492), np.float32(0.9661), np.float32(0.9484), np.float32(0.9706), np.float32(0.9716), np.float32(0.9056), np.float32(0.9425), np.float32(0.9518), np.float32(0.8901), np.float32(0.8931), np.float32(0.9123)] 
+2025-05-06 02:31:11.332139: Epoch time: 94.14 s 
+2025-05-06 02:31:12.786565:  
+2025-05-06 02:31:12.813711: Epoch 923 
+2025-05-06 02:31:12.814166: Current learning rate: 0.00573 
+2025-05-06 02:32:48.509340: train_loss -0.4813 
+2025-05-06 02:32:48.656133: val_loss -0.4849 
+2025-05-06 02:32:48.686729: Pseudo dice [np.float32(0.8124), np.float32(0.8373), np.float32(0.928), np.float32(0.9707), np.float32(0.8936), np.float32(0.9595), np.float32(0.9591), np.float32(0.9792), np.float32(0.947), np.float32(0.9595), np.float32(0.9206), np.float32(0.9644), np.float32(0.9683), np.float32(0.8994), np.float32(0.9682), np.float32(0.9461), np.float32(0.8988), np.float32(0.8793), np.float32(0.9228)] 
+2025-05-06 02:32:48.699962: Epoch time: 95.72 s 
+2025-05-06 02:32:50.081982:  
+2025-05-06 02:32:50.278177: Epoch 924 
+2025-05-06 02:32:50.338660: Current learning rate: 0.00572 
+2025-05-06 02:34:24.615916: train_loss -0.4998 
+2025-05-06 02:34:24.719777: val_loss -0.4806 
+2025-05-06 02:34:24.749138: Pseudo dice [np.float32(0.8122), np.float32(0.8283), np.float32(0.8858), np.float32(0.9757), np.float32(0.8932), np.float32(0.9584), np.float32(0.9629), np.float32(0.9669), np.float32(0.9595), np.float32(0.9571), np.float32(0.9385), np.float32(0.9623), np.float32(0.9707), np.float32(0.8893), np.float32(0.9382), np.float32(0.9536), np.float32(0.8952), np.float32(0.8965), np.float32(0.9104)] 
+2025-05-06 02:34:24.759664: Epoch time: 94.54 s 
+2025-05-06 02:34:26.286076:  
+2025-05-06 02:34:26.328030: Epoch 925 
+2025-05-06 02:34:26.328547: Current learning rate: 0.00572 
+2025-05-06 02:36:05.086426: train_loss -0.4894 
+2025-05-06 02:36:05.104307: val_loss -0.4723 
+2025-05-06 02:36:05.108857: Pseudo dice [np.float32(0.8675), np.float32(0.8509), np.float32(0.7541), np.float32(0.9761), np.float32(0.9176), np.float32(0.9616), np.float32(0.9587), np.float32(0.9781), np.float32(0.9619), np.float32(0.9668), np.float32(0.9391), np.float32(0.9702), np.float32(0.9658), np.float32(0.8973), np.float32(0.9671), np.float32(0.9556), np.float32(0.8749), np.float32(0.868), np.float32(0.9269)] 
+2025-05-06 02:36:05.124997: Epoch time: 98.8 s 
+2025-05-06 02:36:06.558636:  
+2025-05-06 02:36:06.575454: Epoch 926 
+2025-05-06 02:36:06.576121: Current learning rate: 0.00571 
+2025-05-06 02:37:40.752652: train_loss -0.4883 
+2025-05-06 02:37:40.790969: val_loss -0.4957 
+2025-05-06 02:37:40.791830: Pseudo dice [np.float32(0.844), np.float32(0.8633), np.float32(0.9088), np.float32(0.9703), np.float32(0.8971), np.float32(0.9598), np.float32(0.9689), np.float32(0.9823), np.float32(0.964), np.float32(0.9674), np.float32(0.955), np.float32(0.9654), np.float32(0.9739), np.float32(0.9109), np.float32(0.9448), np.float32(0.9409), np.float32(0.8081), np.float32(0.8721), np.float32(0.9084)] 
+2025-05-06 02:37:40.792307: Epoch time: 94.2 s 
+2025-05-06 02:37:42.227986:  
+2025-05-06 02:37:42.363345: Epoch 927 
+2025-05-06 02:37:42.421154: Current learning rate: 0.00571 
+2025-05-06 02:39:19.526850: train_loss -0.4784 
+2025-05-06 02:39:19.556730: val_loss -0.4746 
+2025-05-06 02:39:19.565710: Pseudo dice [np.float32(0.8517), np.float32(0.8455), np.float32(0.7885), np.float32(0.9723), np.float32(0.902), np.float32(0.9412), np.float32(0.9603), np.float32(0.9763), np.float32(0.962), np.float32(0.9705), np.float32(0.9437), np.float32(0.9633), np.float32(0.9727), np.float32(0.8858), np.float32(0.9611), np.float32(0.9491), np.float32(0.851), np.float32(0.8457), np.float32(0.9198)] 
+2025-05-06 02:39:19.566660: Epoch time: 97.3 s 
+2025-05-06 02:39:21.001879:  
+2025-05-06 02:39:21.017908: Epoch 928 
+2025-05-06 02:39:21.041148: Current learning rate: 0.0057 
+2025-05-06 02:41:00.322320: train_loss -0.4954 
+2025-05-06 02:41:00.435462: val_loss -0.5 
+2025-05-06 02:41:00.466450: Pseudo dice [np.float32(0.8584), np.float32(0.8413), np.float32(0.9533), np.float32(0.9716), np.float32(0.9173), np.float32(0.9637), np.float32(0.969), np.float32(0.9712), np.float32(0.9649), np.float32(0.9733), np.float32(0.9497), np.float32(0.9706), np.float32(0.973), np.float32(0.9093), np.float32(0.9611), np.float32(0.9516), np.float32(0.8205), np.float32(0.8573), np.float32(0.9253)] 
+2025-05-06 02:41:00.467484: Epoch time: 99.32 s 
+2025-05-06 02:41:02.072831:  
+2025-05-06 02:41:02.146881: Epoch 929 
+2025-05-06 02:41:02.168419: Current learning rate: 0.0057 
+2025-05-06 02:42:37.039109: train_loss -0.4836 
+2025-05-06 02:42:37.128652: val_loss -0.4835 
+2025-05-06 02:42:37.141820: Pseudo dice [np.float32(0.8375), np.float32(0.8308), np.float32(0.9116), np.float32(0.973), np.float32(0.9048), np.float32(0.9578), np.float32(0.9592), np.float32(0.954), np.float32(0.9653), np.float32(0.9659), np.float32(0.9479), np.float32(0.9634), np.float32(0.9653), np.float32(0.9049), np.float32(0.967), np.float32(0.9426), np.float32(0.8648), np.float32(0.8779), np.float32(0.9129)] 
+2025-05-06 02:42:37.170215: Epoch time: 94.97 s 
+2025-05-06 02:42:38.695709:  
+2025-05-06 02:42:38.795913: Epoch 930 
+2025-05-06 02:42:38.831032: Current learning rate: 0.0057 
+2025-05-06 02:44:14.467103: train_loss -0.4706 
+2025-05-06 02:44:14.591385: val_loss -0.4518 
+2025-05-06 02:44:14.595384: Pseudo dice [np.float32(0.8492), np.float32(0.8411), np.float32(0.9242), np.float32(0.9632), np.float32(0.8906), np.float32(0.954), np.float32(0.9573), np.float32(0.9673), np.float32(0.9611), np.float32(0.9505), np.float32(0.9343), np.float32(0.965), np.float32(0.9685), np.float32(0.8962), np.float32(0.9351), np.float32(0.9485), np.float32(0.8439), np.float32(0.8531), np.float32(0.9225)] 
+2025-05-06 02:44:14.596220: Epoch time: 95.77 s 
+2025-05-06 02:44:16.103378:  
+2025-05-06 02:44:16.178594: Epoch 931 
+2025-05-06 02:44:16.200761: Current learning rate: 0.00569 
+2025-05-06 02:45:52.995594: train_loss -0.5002 
+2025-05-06 02:45:53.096465: val_loss -0.4817 
+2025-05-06 02:45:53.144526: Pseudo dice [np.float32(0.8523), np.float32(0.8499), np.float32(0.786), np.float32(0.9742), np.float32(0.9176), np.float32(0.9626), np.float32(0.9649), np.float32(0.98), np.float32(0.949), np.float32(0.9645), np.float32(0.9496), np.float32(0.9633), np.float32(0.9663), np.float32(0.9067), np.float32(0.9697), np.float32(0.9543), np.float32(0.8994), np.float32(0.8805), np.float32(0.9074)] 
+2025-05-06 02:45:53.191647: Epoch time: 96.89 s 
+2025-05-06 02:45:54.807009:  
+2025-05-06 02:45:54.854387: Epoch 932 
+2025-05-06 02:45:54.874506: Current learning rate: 0.00569 
+2025-05-06 02:47:33.237226: train_loss -0.4944 
+2025-05-06 02:47:33.292637: val_loss -0.4884 
+2025-05-06 02:47:33.332747: Pseudo dice [np.float32(0.8358), np.float32(0.8387), np.float32(0.9241), np.float32(0.9704), np.float32(0.8504), np.float32(0.9594), np.float32(0.9595), np.float32(0.9704), np.float32(0.9637), np.float32(0.9681), np.float32(0.9449), np.float32(0.9675), np.float32(0.9678), np.float32(0.8983), np.float32(0.9619), np.float32(0.9349), np.float32(0.873), np.float32(0.87), np.float32(0.9205)] 
+2025-05-06 02:47:33.345629: Epoch time: 98.43 s 
+2025-05-06 02:47:34.809803:  
+2025-05-06 02:47:34.818144: Epoch 933 
+2025-05-06 02:47:34.818662: Current learning rate: 0.00568 
+2025-05-06 02:49:11.404177: train_loss -0.4832 
+2025-05-06 02:49:11.487200: val_loss -0.5356 
+2025-05-06 02:49:11.509889: Pseudo dice [np.float32(0.8473), np.float32(0.85), np.float32(0.9162), np.float32(0.9693), np.float32(0.8755), np.float32(0.959), np.float32(0.9672), np.float32(0.9744), np.float32(0.967), np.float32(0.9514), np.float32(0.9432), np.float32(0.9673), np.float32(0.969), np.float32(0.914), np.float32(0.971), np.float32(0.9424), np.float32(0.9052), np.float32(0.9033), np.float32(0.9183)] 
+2025-05-06 02:49:11.544582: Epoch time: 96.6 s 
+2025-05-06 02:49:11.575761: Yayy! New best EMA pseudo Dice: 0.9254999756813049 
+2025-05-06 02:49:14.051536:  
+2025-05-06 02:49:14.056653: Epoch 934 
+2025-05-06 02:49:14.057098: Current learning rate: 0.00568 
+2025-05-06 02:50:51.626791: train_loss -0.4924 
+2025-05-06 02:50:51.687841: val_loss -0.5068 
+2025-05-06 02:50:51.699349: Pseudo dice [np.float32(0.8398), np.float32(0.8416), np.float32(0.9176), np.float32(0.9761), np.float32(0.8771), np.float32(0.9409), np.float32(0.9674), np.float32(0.9759), np.float32(0.9721), np.float32(0.9523), np.float32(0.9428), np.float32(0.9711), np.float32(0.968), np.float32(0.901), np.float32(0.9195), np.float32(0.9509), np.float32(0.9002), np.float32(0.9029), np.float32(0.9257)] 
+2025-05-06 02:50:51.710361: Epoch time: 97.58 s 
+2025-05-06 02:50:51.721825: Yayy! New best EMA pseudo Dice: 0.9258000254631042 
+2025-05-06 02:50:54.256806:  
+2025-05-06 02:50:54.359823: Epoch 935 
+2025-05-06 02:50:54.414573: Current learning rate: 0.00567 
+2025-05-06 02:52:30.348036: train_loss -0.4834 
+2025-05-06 02:52:30.433245: val_loss -0.5396 
+2025-05-06 02:52:30.471941: Pseudo dice [np.float32(0.843), np.float32(0.8639), np.float32(0.7993), np.float32(0.9626), np.float32(0.8946), np.float32(0.9552), np.float32(0.9726), np.float32(0.9767), np.float32(0.9624), np.float32(0.9711), np.float32(0.9415), np.float32(0.9714), np.float32(0.9589), np.float32(0.9045), np.float32(0.9607), np.float32(0.9192), np.float32(0.8115), np.float32(0.8557), np.float32(0.906)] 
+2025-05-06 02:52:30.517331: Epoch time: 96.09 s 
+2025-05-06 02:52:32.089127:  
+2025-05-06 02:52:32.166335: Epoch 936 
+2025-05-06 02:52:32.188935: Current learning rate: 0.00567 
+2025-05-06 02:54:08.976831: train_loss -0.4835 
+2025-05-06 02:54:09.065483: val_loss -0.5008 
+2025-05-06 02:54:09.080590: Pseudo dice [np.float32(0.8584), np.float32(0.8155), np.float32(0.8382), np.float32(0.976), np.float32(0.9008), np.float32(0.9557), np.float32(0.9538), np.float32(0.9577), np.float32(0.962), np.float32(0.9684), np.float32(0.9471), np.float32(0.9697), np.float32(0.9658), np.float32(0.8818), np.float32(0.9586), np.float32(0.9419), np.float32(0.8752), np.float32(0.9029), np.float32(0.9158)] 
+2025-05-06 02:54:09.088055: Epoch time: 96.89 s 
+2025-05-06 02:54:10.528599:  
+2025-05-06 02:54:10.623324: Epoch 937 
+2025-05-06 02:54:10.636537: Current learning rate: 0.00566 
+2025-05-06 02:55:43.059397: train_loss -0.4846 
+2025-05-06 02:55:43.158285: val_loss -0.5168 
+2025-05-06 02:55:43.194036: Pseudo dice [np.float32(0.8446), np.float32(0.854), np.float32(0.9053), np.float32(0.9782), np.float32(0.9005), np.float32(0.9569), np.float32(0.9658), np.float32(0.9733), np.float32(0.9601), np.float32(0.9639), np.float32(0.9576), np.float32(0.9663), np.float32(0.9731), np.float32(0.9102), np.float32(0.9704), np.float32(0.9634), np.float32(0.8641), np.float32(0.8112), np.float32(0.9219)] 
+2025-05-06 02:55:43.230692: Epoch time: 92.53 s 
+2025-05-06 02:55:44.925401:  
+2025-05-06 02:55:44.931208: Epoch 938 
+2025-05-06 02:55:44.931666: Current learning rate: 0.00566 
+2025-05-06 02:57:23.772571: train_loss -0.4723 
+2025-05-06 02:57:23.804962: val_loss -0.498 
+2025-05-06 02:57:23.836083: Pseudo dice [np.float32(0.8253), np.float32(0.835), np.float32(0.9056), np.float32(0.9693), np.float32(0.8992), np.float32(0.9598), np.float32(0.9598), np.float32(0.9715), np.float32(0.9611), np.float32(0.9621), np.float32(0.9356), np.float32(0.9681), np.float32(0.9684), np.float32(0.8949), np.float32(0.9372), np.float32(0.9348), np.float32(0.8433), np.float32(0.9017), np.float32(0.9121)] 
+2025-05-06 02:57:23.904890: Epoch time: 98.85 s 
+2025-05-06 02:57:29.391243:  
+2025-05-06 02:57:29.397300: Epoch 939 
+2025-05-06 02:57:29.397747: Current learning rate: 0.00565 
+2025-05-06 02:59:04.396514: train_loss -0.4851 
+2025-05-06 02:59:04.422569: val_loss -0.535 
+2025-05-06 02:59:04.423486: Pseudo dice [np.float32(0.8525), np.float32(0.838), np.float32(0.8951), np.float32(0.9726), np.float32(0.9096), np.float32(0.9604), np.float32(0.9635), np.float32(0.975), np.float32(0.9631), np.float32(0.9627), np.float32(0.9409), np.float32(0.9677), np.float32(0.9644), np.float32(0.9022), np.float32(0.9556), np.float32(0.9554), np.float32(0.9037), np.float32(0.8924), np.float32(0.9077)] 
+2025-05-06 02:59:04.431500: Epoch time: 95.01 s 
+2025-05-06 02:59:05.872348:  
+2025-05-06 02:59:05.883749: Epoch 940 
+2025-05-06 02:59:05.898313: Current learning rate: 0.00565 
+2025-05-06 03:00:43.776422: train_loss -0.5021 
+2025-05-06 03:00:43.982513: val_loss -0.5022 
+2025-05-06 03:00:44.014818: Pseudo dice [np.float32(0.845), np.float32(0.7891), np.float32(0.9011), np.float32(0.972), np.float32(0.9099), np.float32(0.955), np.float32(0.956), np.float32(0.9774), np.float32(0.9584), np.float32(0.9719), np.float32(0.9447), np.float32(0.9679), np.float32(0.9703), np.float32(0.8989), np.float32(0.962), np.float32(0.9519), np.float32(0.8443), np.float32(0.8595), np.float32(0.9185)] 
+2025-05-06 03:00:44.049702: Epoch time: 97.91 s 
+2025-05-06 03:00:45.585463:  
+2025-05-06 03:00:45.672384: Epoch 941 
+2025-05-06 03:00:45.719746: Current learning rate: 0.00564 
+2025-05-06 03:02:27.965933: train_loss -0.4925 
+2025-05-06 03:02:28.096868: val_loss -0.5038 
+2025-05-06 03:02:28.133176: Pseudo dice [np.float32(0.8477), np.float32(0.8405), np.float32(0.9102), np.float32(0.9788), np.float32(0.917), np.float32(0.9626), np.float32(0.9638), np.float32(0.9769), np.float32(0.9698), np.float32(0.9577), np.float32(0.9428), np.float32(0.9723), np.float32(0.9499), np.float32(0.8998), np.float32(0.9514), np.float32(0.9473), np.float32(0.8812), np.float32(0.8895), np.float32(0.9105)] 
+2025-05-06 03:02:28.195549: Epoch time: 102.38 s 
+2025-05-06 03:02:28.254163: Yayy! New best EMA pseudo Dice: 0.9258000254631042 
+2025-05-06 03:02:31.381449:  
+2025-05-06 03:02:31.386881: Epoch 942 
+2025-05-06 03:02:31.387342: Current learning rate: 0.00564 
+2025-05-06 03:04:11.502862: train_loss -0.455 
+2025-05-06 03:04:11.603320: val_loss -0.4958 
+2025-05-06 03:04:11.623457: Pseudo dice [np.float32(0.8494), np.float32(0.8456), np.float32(0.9129), np.float32(0.9744), np.float32(0.9079), np.float32(0.9543), np.float32(0.9654), np.float32(0.9775), np.float32(0.9611), np.float32(0.9655), np.float32(0.947), np.float32(0.9641), np.float32(0.9624), np.float32(0.8971), np.float32(0.9596), np.float32(0.9536), np.float32(0.893), np.float32(0.9004), np.float32(0.92)] 
+2025-05-06 03:04:11.644592: Epoch time: 100.12 s 
+2025-05-06 03:04:11.645727: Yayy! New best EMA pseudo Dice: 0.9265000224113464 
+2025-05-06 03:04:15.293347:  
+2025-05-06 03:04:15.319236: Epoch 943 
+2025-05-06 03:04:15.331212: Current learning rate: 0.00563 
+2025-05-06 03:05:54.667008: train_loss -0.465 
+2025-05-06 03:05:54.823894: val_loss -0.4591 
+2025-05-06 03:05:54.845089: Pseudo dice [np.float32(0.8567), np.float32(0.8378), np.float32(0.875), np.float32(0.9583), np.float32(0.9073), np.float32(0.9585), np.float32(0.9508), np.float32(0.9562), np.float32(0.9625), np.float32(0.9387), np.float32(0.9246), np.float32(0.965), np.float32(0.9366), np.float32(0.8861), np.float32(0.9646), np.float32(0.9527), np.float32(0.8558), np.float32(0.8904), np.float32(0.9061)] 
+2025-05-06 03:05:54.888906: Epoch time: 99.37 s 
+2025-05-06 03:05:56.419052:  
+2025-05-06 03:05:56.461878: Epoch 944 
+2025-05-06 03:05:56.462315: Current learning rate: 0.00563 
+2025-05-06 03:07:40.467997: train_loss -0.4909 
+2025-05-06 03:07:40.585894: val_loss -0.4951 
+2025-05-06 03:07:40.609434: Pseudo dice [np.float32(0.8493), np.float32(0.8386), np.float32(0.9185), np.float32(0.9711), np.float32(0.9013), np.float32(0.9498), np.float32(0.9605), np.float32(0.9736), np.float32(0.9636), np.float32(0.9521), np.float32(0.9268), np.float32(0.9651), np.float32(0.9478), np.float32(0.8926), np.float32(0.9482), np.float32(0.9456), np.float32(0.8771), np.float32(0.904), np.float32(0.8938)] 
+2025-05-06 03:07:40.623158: Epoch time: 104.05 s 
+2025-05-06 03:07:42.059272:  
+2025-05-06 03:07:42.140960: Epoch 945 
+2025-05-06 03:07:42.142022: Current learning rate: 0.00562 
+2025-05-06 03:09:21.077340: train_loss -0.4941 
+2025-05-06 03:09:21.233128: val_loss -0.5019 
+2025-05-06 03:09:21.234455: Pseudo dice [np.float32(0.8679), np.float32(0.8319), np.float32(0.9252), np.float32(0.9678), np.float32(0.8856), np.float32(0.9632), np.float32(0.9618), np.float32(0.9756), np.float32(0.9648), np.float32(0.9624), np.float32(0.9521), np.float32(0.9656), np.float32(0.9664), np.float32(0.9029), np.float32(0.9663), np.float32(0.959), np.float32(0.8819), np.float32(0.8744), np.float32(0.9008)] 
+2025-05-06 03:09:21.235095: Epoch time: 99.02 s 
+2025-05-06 03:09:22.727839:  
+2025-05-06 03:09:22.828224: Epoch 946 
+2025-05-06 03:09:22.839379: Current learning rate: 0.00562 
+2025-05-06 03:10:59.452098: train_loss -0.4736 
+2025-05-06 03:10:59.542513: val_loss -0.5022 
+2025-05-06 03:10:59.545022: Pseudo dice [np.float32(0.8642), np.float32(0.8318), np.float32(0.9379), np.float32(0.9761), np.float32(0.9293), np.float32(0.9448), np.float32(0.9572), np.float32(0.9785), np.float32(0.9641), np.float32(0.9628), np.float32(0.9369), np.float32(0.9626), np.float32(0.9662), np.float32(0.9012), np.float32(0.9329), np.float32(0.9599), np.float32(0.8729), np.float32(0.8843), np.float32(0.9137)] 
+2025-05-06 03:10:59.558150: Epoch time: 96.73 s 
+2025-05-06 03:10:59.559101: Yayy! New best EMA pseudo Dice: 0.9266999959945679 
+2025-05-06 03:11:02.650111:  
+2025-05-06 03:11:02.702904: Epoch 947 
+2025-05-06 03:11:02.714565: Current learning rate: 0.00561 
+2025-05-06 03:12:41.332546: train_loss -0.4712 
+2025-05-06 03:12:41.413978: val_loss -0.4772 
+2025-05-06 03:12:41.428695: Pseudo dice [np.float32(0.8128), np.float32(0.8499), np.float32(0.8855), np.float32(0.9704), np.float32(0.8939), np.float32(0.9636), np.float32(0.9647), np.float32(0.9701), np.float32(0.9656), np.float32(0.9696), np.float32(0.9465), np.float32(0.9693), np.float32(0.9652), np.float32(0.9051), np.float32(0.9622), np.float32(0.9338), np.float32(0.8734), np.float32(0.8714), np.float32(0.9233)] 
+2025-05-06 03:12:41.443178: Epoch time: 98.68 s 
+2025-05-06 03:12:42.946756:  
+2025-05-06 03:12:42.949584: Epoch 948 
+2025-05-06 03:12:42.950541: Current learning rate: 0.00561 
+2025-05-06 03:14:16.524544: train_loss -0.4911 
+2025-05-06 03:14:16.582718: val_loss -0.4941 
+2025-05-06 03:14:16.605533: Pseudo dice [np.float32(0.8568), np.float32(0.8598), np.float32(0.8195), np.float32(0.9646), np.float32(0.9157), np.float32(0.9472), np.float32(0.9643), np.float32(0.9716), np.float32(0.9611), np.float32(0.9723), np.float32(0.9541), np.float32(0.9686), np.float32(0.9715), np.float32(0.9016), np.float32(0.9496), np.float32(0.9543), np.float32(0.8598), np.float32(0.867), np.float32(0.9083)] 
+2025-05-06 03:14:16.612958: Epoch time: 93.58 s 
+2025-05-06 03:14:18.098470:  
+2025-05-06 03:14:18.133377: Epoch 949 
+2025-05-06 03:14:18.133832: Current learning rate: 0.0056 
+2025-05-06 03:15:57.752708: train_loss -0.493 
+2025-05-06 03:15:57.893931: val_loss -0.4549 
+2025-05-06 03:15:57.938824: Pseudo dice [np.float32(0.8281), np.float32(0.8087), np.float32(0.9151), np.float32(0.9736), np.float32(0.8968), np.float32(0.9473), np.float32(0.9584), np.float32(0.9724), np.float32(0.9625), np.float32(0.9667), np.float32(0.9407), np.float32(0.9461), np.float32(0.9717), np.float32(0.8975), np.float32(0.9386), np.float32(0.9443), np.float32(0.8995), np.float32(0.8692), np.float32(0.9243)] 
+2025-05-06 03:15:57.976559: Epoch time: 99.66 s 
+2025-05-06 03:16:00.525509:  
+2025-05-06 03:16:00.607797: Epoch 950 
+2025-05-06 03:16:00.629747: Current learning rate: 0.0056 
+2025-05-06 03:17:38.895780: train_loss -0.4851 
+2025-05-06 03:17:38.992645: val_loss -0.4993 
+2025-05-06 03:17:38.999614: Pseudo dice [np.float32(0.8195), np.float32(0.8287), np.float32(0.9024), np.float32(0.9754), np.float32(0.9008), np.float32(0.9568), np.float32(0.9543), np.float32(0.9698), np.float32(0.9597), np.float32(0.9544), np.float32(0.9501), np.float32(0.9612), np.float32(0.9677), np.float32(0.8933), np.float32(0.9551), np.float32(0.944), np.float32(0.8921), np.float32(0.8463), np.float32(0.9001)] 
+2025-05-06 03:17:39.000313: Epoch time: 98.37 s 
+2025-05-06 03:17:40.452247:  
+2025-05-06 03:17:40.522186: Epoch 951 
+2025-05-06 03:17:40.526531: Current learning rate: 0.00559 
+2025-05-06 03:19:17.247058: train_loss -0.482 
+2025-05-06 03:19:17.314812: val_loss -0.4924 
+2025-05-06 03:19:17.361110: Pseudo dice [np.float32(0.862), np.float32(0.8497), np.float32(0.8915), np.float32(0.9735), np.float32(0.8722), np.float32(0.9421), np.float32(0.9641), np.float32(0.9753), np.float32(0.9496), np.float32(0.9603), np.float32(0.9493), np.float32(0.9565), np.float32(0.9664), np.float32(0.8972), np.float32(0.9432), np.float32(0.9517), np.float32(0.8949), np.float32(0.897), np.float32(0.9193)] 
+2025-05-06 03:19:17.393763: Epoch time: 96.8 s 
+2025-05-06 03:19:18.918037:  
+2025-05-06 03:19:19.034124: Epoch 952 
+2025-05-06 03:19:19.035154: Current learning rate: 0.00559 
+2025-05-06 03:20:57.904952: train_loss -0.4885 
+2025-05-06 03:20:57.994907: val_loss -0.4818 
+2025-05-06 03:20:58.020972: Pseudo dice [np.float32(0.8269), np.float32(0.8669), np.float32(0.9077), np.float32(0.9649), np.float32(0.9085), np.float32(0.961), np.float32(0.9635), np.float32(0.9824), np.float32(0.9531), np.float32(0.9622), np.float32(0.9288), np.float32(0.9542), np.float32(0.9476), np.float32(0.8968), np.float32(0.9666), np.float32(0.9521), np.float32(0.8257), np.float32(0.8315), np.float32(0.9052)] 
+2025-05-06 03:20:58.050855: Epoch time: 98.99 s 
+2025-05-06 03:20:59.563597:  
+2025-05-06 03:20:59.615205: Epoch 953 
+2025-05-06 03:20:59.631062: Current learning rate: 0.00559 
+2025-05-06 03:22:37.461220: train_loss -0.5008 
+2025-05-06 03:22:37.553725: val_loss -0.4819 
+2025-05-06 03:22:37.581165: Pseudo dice [np.float32(0.8471), np.float32(0.8489), np.float32(0.861), np.float32(0.9794), np.float32(0.8879), np.float32(0.9551), np.float32(0.9602), np.float32(0.9759), np.float32(0.9647), np.float32(0.9615), np.float32(0.9392), np.float32(0.9662), np.float32(0.9594), np.float32(0.9004), np.float32(0.965), np.float32(0.9527), np.float32(0.8603), np.float32(0.8171), np.float32(0.9154)] 
+2025-05-06 03:22:37.609913: Epoch time: 97.9 s 
+2025-05-06 03:22:39.170979:  
+2025-05-06 03:22:39.296011: Epoch 954 
+2025-05-06 03:22:39.348194: Current learning rate: 0.00558 
+2025-05-06 03:24:16.496056: train_loss -0.4741 
+2025-05-06 03:24:16.601534: val_loss -0.4507 
+2025-05-06 03:24:16.613997: Pseudo dice [np.float32(0.8667), np.float32(0.813), np.float32(0.9141), np.float32(0.9636), np.float32(0.8534), np.float32(0.958), np.float32(0.9592), np.float32(0.977), np.float32(0.9642), np.float32(0.9577), np.float32(0.9462), np.float32(0.9718), np.float32(0.9651), np.float32(0.9004), np.float32(0.9646), np.float32(0.9555), np.float32(0.8884), np.float32(0.8934), np.float32(0.9198)] 
+2025-05-06 03:24:16.624738: Epoch time: 97.33 s 
+2025-05-06 03:24:18.066184:  
+2025-05-06 03:24:18.089439: Epoch 955 
+2025-05-06 03:24:18.122338: Current learning rate: 0.00558 
+2025-05-06 03:25:56.227000: train_loss -0.4729 
+2025-05-06 03:25:56.449957: val_loss -0.5225 
+2025-05-06 03:25:56.520410: Pseudo dice [np.float32(0.8656), np.float32(0.8159), np.float32(0.8953), np.float32(0.979), np.float32(0.902), np.float32(0.9576), np.float32(0.9647), np.float32(0.9687), np.float32(0.9566), np.float32(0.9664), np.float32(0.9504), np.float32(0.961), np.float32(0.9638), np.float32(0.9078), np.float32(0.9665), np.float32(0.9465), np.float32(0.8888), np.float32(0.8947), np.float32(0.9001)] 
+2025-05-06 03:25:56.552942: Epoch time: 98.16 s 
+2025-05-06 03:25:58.044301:  
+2025-05-06 03:25:58.130108: Epoch 956 
+2025-05-06 03:25:58.163354: Current learning rate: 0.00557 
+2025-05-06 03:27:39.323426: train_loss -0.5009 
+2025-05-06 03:27:39.512766: val_loss -0.4887 
+2025-05-06 03:27:39.539311: Pseudo dice [np.float32(0.8466), np.float32(0.7862), np.float32(0.8274), np.float32(0.966), np.float32(0.8871), np.float32(0.9538), np.float32(0.9633), np.float32(0.9731), np.float32(0.9543), np.float32(0.9593), np.float32(0.9362), np.float32(0.9712), np.float32(0.9583), np.float32(0.8753), np.float32(0.9582), np.float32(0.9582), np.float32(0.8894), np.float32(0.889), np.float32(0.9209)] 
+2025-05-06 03:27:39.557101: Epoch time: 101.28 s 
+2025-05-06 03:27:44.696715:  
+2025-05-06 03:27:44.700714: Epoch 957 
+2025-05-06 03:27:44.701246: Current learning rate: 0.00557 
+2025-05-06 03:29:22.761383: train_loss -0.4732 
+2025-05-06 03:29:22.898649: val_loss -0.5135 
+2025-05-06 03:29:22.934636: Pseudo dice [np.float32(0.8382), np.float32(0.8438), np.float32(0.8172), np.float32(0.9748), np.float32(0.9079), np.float32(0.9481), np.float32(0.9589), np.float32(0.9781), np.float32(0.9603), np.float32(0.9701), np.float32(0.9409), np.float32(0.9625), np.float32(0.9688), np.float32(0.8928), np.float32(0.9494), np.float32(0.9445), np.float32(0.9023), np.float32(0.8772), np.float32(0.9107)] 
+2025-05-06 03:29:22.952756: Epoch time: 98.07 s 
+2025-05-06 03:29:24.487655:  
+2025-05-06 03:29:24.530464: Epoch 958 
+2025-05-06 03:29:24.557884: Current learning rate: 0.00556 
+2025-05-06 03:31:00.305550: train_loss -0.4732 
+2025-05-06 03:31:00.448288: val_loss -0.5031 
+2025-05-06 03:31:00.450432: Pseudo dice [np.float32(0.8295), np.float32(0.8381), np.float32(0.9122), np.float32(0.9799), np.float32(0.9205), np.float32(0.9412), np.float32(0.9589), np.float32(0.9742), np.float32(0.967), np.float32(0.968), np.float32(0.955), np.float32(0.9663), np.float32(0.9716), np.float32(0.9072), np.float32(0.9654), np.float32(0.955), np.float32(0.8688), np.float32(0.8877), np.float32(0.9344)] 
+2025-05-06 03:31:00.450867: Epoch time: 95.82 s 
+2025-05-06 03:31:01.997166:  
+2025-05-06 03:31:02.073578: Epoch 959 
+2025-05-06 03:31:02.105216: Current learning rate: 0.00556 
+2025-05-06 03:32:40.472893: train_loss -0.4859 
+2025-05-06 03:32:40.674907: val_loss -0.52 
+2025-05-06 03:32:40.688143: Pseudo dice [np.float32(0.8643), np.float32(0.8505), np.float32(0.9039), np.float32(0.9715), np.float32(0.9214), np.float32(0.9496), np.float32(0.9652), np.float32(0.9697), np.float32(0.9632), np.float32(0.9662), np.float32(0.9513), np.float32(0.9678), np.float32(0.9582), np.float32(0.9125), np.float32(0.9533), np.float32(0.9544), np.float32(0.8901), np.float32(0.8967), np.float32(0.8949)] 
+2025-05-06 03:32:40.719641: Epoch time: 98.48 s 
+2025-05-06 03:32:42.312466:  
+2025-05-06 03:32:42.369211: Epoch 960 
+2025-05-06 03:32:42.382090: Current learning rate: 0.00555 
+2025-05-06 03:34:20.442434: train_loss -0.4862 
+2025-05-06 03:34:20.721973: val_loss -0.5072 
+2025-05-06 03:34:20.733147: Pseudo dice [np.float32(0.8369), np.float32(0.8359), np.float32(0.8474), np.float32(0.9646), np.float32(0.8953), np.float32(0.9619), np.float32(0.9586), np.float32(0.9741), np.float32(0.9585), np.float32(0.9687), np.float32(0.9369), np.float32(0.9678), np.float32(0.9524), np.float32(0.8988), np.float32(0.9598), np.float32(0.942), np.float32(0.8659), np.float32(0.8907), np.float32(0.8833)] 
+2025-05-06 03:34:20.734147: Epoch time: 98.13 s 
+2025-05-06 03:34:22.196602:  
+2025-05-06 03:34:22.263198: Epoch 961 
+2025-05-06 03:34:22.263778: Current learning rate: 0.00555 
+2025-05-06 03:36:01.829304: train_loss -0.4751 
+2025-05-06 03:36:01.977548: val_loss -0.4788 
+2025-05-06 03:36:01.986241: Pseudo dice [np.float32(0.8479), np.float32(0.8645), np.float32(0.889), np.float32(0.971), np.float32(0.8988), np.float32(0.956), np.float32(0.9624), np.float32(0.9755), np.float32(0.9512), np.float32(0.9467), np.float32(0.9348), np.float32(0.9504), np.float32(0.9578), np.float32(0.9014), np.float32(0.8911), np.float32(0.9465), np.float32(0.8224), np.float32(0.7908), np.float32(0.9199)] 
+2025-05-06 03:36:01.998525: Epoch time: 99.63 s 
+2025-05-06 03:36:03.535068:  
+2025-05-06 03:36:03.616114: Epoch 962 
+2025-05-06 03:36:03.641327: Current learning rate: 0.00554 
+2025-05-06 03:37:42.924177: train_loss -0.4895 
+2025-05-06 03:37:43.065992: val_loss -0.5038 
+2025-05-06 03:37:43.083098: Pseudo dice [np.float32(0.833), np.float32(0.8464), np.float32(0.8886), np.float32(0.9722), np.float32(0.8888), np.float32(0.9643), np.float32(0.9636), np.float32(0.9758), np.float32(0.9384), np.float32(0.9495), np.float32(0.9233), np.float32(0.967), np.float32(0.964), np.float32(0.9019), np.float32(0.9377), np.float32(0.9496), np.float32(0.8884), np.float32(0.9008), np.float32(0.9131)] 
+2025-05-06 03:37:43.086274: Epoch time: 99.39 s 
+2025-05-06 03:37:44.700450:  
+2025-05-06 03:37:44.842791: Epoch 963 
+2025-05-06 03:37:44.843580: Current learning rate: 0.00554 
+2025-05-06 03:39:23.579413: train_loss -0.4807 
+2025-05-06 03:39:23.775507: val_loss -0.4967 
+2025-05-06 03:39:23.799732: Pseudo dice [np.float32(0.8481), np.float32(0.8457), np.float32(0.9325), np.float32(0.9709), np.float32(0.914), np.float32(0.9644), np.float32(0.9682), np.float32(0.9754), np.float32(0.9564), np.float32(0.9613), np.float32(0.9512), np.float32(0.963), np.float32(0.9637), np.float32(0.9057), np.float32(0.9623), np.float32(0.956), np.float32(0.8624), np.float32(0.87), np.float32(0.9219)] 
+2025-05-06 03:39:23.821239: Epoch time: 98.88 s 
+2025-05-06 03:39:25.500629:  
+2025-05-06 03:39:25.552566: Epoch 964 
+2025-05-06 03:39:25.553396: Current learning rate: 0.00553 
+2025-05-06 03:41:05.264769: train_loss -0.4842 
+2025-05-06 03:41:05.488102: val_loss -0.5388 
+2025-05-06 03:41:05.533234: Pseudo dice [np.float32(0.8179), np.float32(0.853), np.float32(0.9272), np.float32(0.9638), np.float32(0.8909), np.float32(0.9633), np.float32(0.9653), np.float32(0.9711), np.float32(0.965), np.float32(0.9541), np.float32(0.9295), np.float32(0.9614), np.float32(0.9615), np.float32(0.9055), np.float32(0.9615), np.float32(0.9587), np.float32(0.8832), np.float32(0.8864), np.float32(0.9194)] 
+2025-05-06 03:41:05.578575: Epoch time: 99.77 s 
+2025-05-06 03:41:07.245561:  
+2025-05-06 03:41:07.268155: Epoch 965 
+2025-05-06 03:41:07.283416: Current learning rate: 0.00553 
+2025-05-06 03:42:44.102221: train_loss -0.5001 
+2025-05-06 03:42:44.306676: val_loss -0.4644 
+2025-05-06 03:42:44.350554: Pseudo dice [np.float32(0.813), np.float32(0.8079), np.float32(0.8467), np.float32(0.9727), np.float32(0.9129), np.float32(0.9603), np.float32(0.9651), np.float32(0.9771), np.float32(0.9568), np.float32(0.9524), np.float32(0.9239), np.float32(0.9689), np.float32(0.961), np.float32(0.8905), np.float32(0.9452), np.float32(0.9558), np.float32(0.8938), np.float32(0.8913), np.float32(0.9196)] 
+2025-05-06 03:42:44.375596: Epoch time: 96.86 s 
+2025-05-06 03:42:46.125660:  
+2025-05-06 03:42:46.153839: Epoch 966 
+2025-05-06 03:42:46.158609: Current learning rate: 0.00552 
+2025-05-06 03:44:24.709480: train_loss -0.4991 
+2025-05-06 03:44:24.864953: val_loss -0.4916 
+2025-05-06 03:44:24.880134: Pseudo dice [np.float32(0.8314), np.float32(0.8463), np.float32(0.8649), np.float32(0.9749), np.float32(0.9007), np.float32(0.9495), np.float32(0.9568), np.float32(0.9616), np.float32(0.9493), np.float32(0.9677), np.float32(0.9408), np.float32(0.9542), np.float32(0.9657), np.float32(0.8893), np.float32(0.9597), np.float32(0.9427), np.float32(0.8847), np.float32(0.874), np.float32(0.9252)] 
+2025-05-06 03:44:24.898441: Epoch time: 98.59 s 
+2025-05-06 03:44:26.498003:  
+2025-05-06 03:44:26.542810: Epoch 967 
+2025-05-06 03:44:26.567564: Current learning rate: 0.00552 
+2025-05-06 03:46:04.526718: train_loss -0.4993 
+2025-05-06 03:46:04.698276: val_loss -0.515 
+2025-05-06 03:46:04.723966: Pseudo dice [np.float32(0.8556), np.float32(0.8639), np.float32(0.9363), np.float32(0.9729), np.float32(0.8951), np.float32(0.956), np.float32(0.9715), np.float32(0.9788), np.float32(0.963), np.float32(0.9615), np.float32(0.9307), np.float32(0.967), np.float32(0.9695), np.float32(0.9102), np.float32(0.9557), np.float32(0.9529), np.float32(0.8228), np.float32(0.8074), np.float32(0.9095)] 
+2025-05-06 03:46:04.730455: Epoch time: 98.03 s 
+2025-05-06 03:46:06.240977:  
+2025-05-06 03:46:06.273746: Epoch 968 
+2025-05-06 03:46:06.278303: Current learning rate: 0.00551 
+2025-05-06 03:47:50.737806: train_loss -0.4951 
+2025-05-06 03:47:50.819167: val_loss -0.4703 
+2025-05-06 03:47:50.819901: Pseudo dice [np.float32(0.845), np.float32(0.866), np.float32(0.8973), np.float32(0.9704), np.float32(0.8878), np.float32(0.9566), np.float32(0.9627), np.float32(0.9738), np.float32(0.9575), np.float32(0.9682), np.float32(0.9417), np.float32(0.9652), np.float32(0.9668), np.float32(0.9009), np.float32(0.954), np.float32(0.9489), np.float32(0.8549), np.float32(0.858), np.float32(0.9147)] 
+2025-05-06 03:47:50.841581: Epoch time: 104.5 s 
+2025-05-06 03:47:52.470917:  
+2025-05-06 03:47:52.547246: Epoch 969 
+2025-05-06 03:47:52.558748: Current learning rate: 0.00551 
+2025-05-06 03:49:31.799237: train_loss -0.4697 
+2025-05-06 03:49:31.902503: val_loss -0.4976 
+2025-05-06 03:49:31.935549: Pseudo dice [np.float32(0.8361), np.float32(0.8374), np.float32(0.9175), np.float32(0.9754), np.float32(0.9018), np.float32(0.957), np.float32(0.9571), np.float32(0.977), np.float32(0.9685), np.float32(0.957), np.float32(0.9436), np.float32(0.9713), np.float32(0.9689), np.float32(0.8895), np.float32(0.9612), np.float32(0.9522), np.float32(0.8481), np.float32(0.8566), np.float32(0.9287)] 
+2025-05-06 03:49:31.957514: Epoch time: 99.33 s 
+2025-05-06 03:49:33.467500:  
+2025-05-06 03:49:33.490911: Epoch 970 
+2025-05-06 03:49:33.495179: Current learning rate: 0.0055 
+2025-05-06 03:51:16.525853: train_loss -0.4988 
+2025-05-06 03:51:16.624435: val_loss -0.4843 
+2025-05-06 03:51:16.645110: Pseudo dice [np.float32(0.8421), np.float32(0.8529), np.float32(0.8652), np.float32(0.9726), np.float32(0.8914), np.float32(0.9449), np.float32(0.9519), np.float32(0.9779), np.float32(0.9681), np.float32(0.9666), np.float32(0.9393), np.float32(0.9658), np.float32(0.9518), np.float32(0.8995), np.float32(0.9417), np.float32(0.9389), np.float32(0.8836), np.float32(0.8895), np.float32(0.9054)] 
+2025-05-06 03:51:16.646215: Epoch time: 103.06 s 
+2025-05-06 03:51:18.373624:  
+2025-05-06 03:51:18.469470: Epoch 971 
+2025-05-06 03:51:18.519112: Current learning rate: 0.0055 
+2025-05-06 03:52:57.291502: train_loss -0.4758 
+2025-05-06 03:52:57.563513: val_loss -0.5015 
+2025-05-06 03:52:57.567480: Pseudo dice [np.float32(0.8284), np.float32(0.8503), np.float32(0.9328), np.float32(0.9704), np.float32(0.9119), np.float32(0.9645), np.float32(0.9549), np.float32(0.9796), np.float32(0.9604), np.float32(0.9613), np.float32(0.9531), np.float32(0.9706), np.float32(0.9721), np.float32(0.8974), np.float32(0.9719), np.float32(0.9528), np.float32(0.8609), np.float32(0.8885), np.float32(0.9204)] 
+2025-05-06 03:52:57.610268: Epoch time: 98.92 s 
+2025-05-06 03:52:59.278415:  
+2025-05-06 03:52:59.353094: Epoch 972 
+2025-05-06 03:52:59.364595: Current learning rate: 0.00549 
+2025-05-06 03:54:42.955771: train_loss -0.485 
+2025-05-06 03:54:43.130154: val_loss -0.5004 
+2025-05-06 03:54:43.149195: Pseudo dice [np.float32(0.8323), np.float32(0.8417), np.float32(0.9487), np.float32(0.9715), np.float32(0.9144), np.float32(0.9603), np.float32(0.9653), np.float32(0.9791), np.float32(0.9539), np.float32(0.9587), np.float32(0.9445), np.float32(0.9667), np.float32(0.9645), np.float32(0.8983), np.float32(0.959), np.float32(0.9519), np.float32(0.887), np.float32(0.8576), np.float32(0.9208)] 
+2025-05-06 03:54:43.162626: Epoch time: 103.68 s 
+2025-05-06 03:54:44.703158:  
+2025-05-06 03:54:44.798661: Epoch 973 
+2025-05-06 03:54:44.827682: Current learning rate: 0.00549 
+2025-05-06 03:56:28.570775: train_loss -0.4898 
+2025-05-06 03:56:28.714866: val_loss -0.5094 
+2025-05-06 03:56:28.719685: Pseudo dice [np.float32(0.8282), np.float32(0.8549), np.float32(0.9164), np.float32(0.9712), np.float32(0.9253), np.float32(0.952), np.float32(0.9648), np.float32(0.9774), np.float32(0.9677), np.float32(0.9661), np.float32(0.9414), np.float32(0.9684), np.float32(0.9621), np.float32(0.9081), np.float32(0.9652), np.float32(0.9458), np.float32(0.9024), np.float32(0.8913), np.float32(0.9131)] 
+2025-05-06 03:56:28.730892: Epoch time: 103.87 s 
+2025-05-06 03:56:28.745213: Yayy! New best EMA pseudo Dice: 0.9269000291824341 
+2025-05-06 03:56:31.847567:  
+2025-05-06 03:56:31.940814: Epoch 974 
+2025-05-06 03:56:31.952531: Current learning rate: 0.00548 
+2025-05-06 03:58:19.297080: train_loss -0.4732 
+2025-05-06 03:58:19.520805: val_loss -0.4715 
+2025-05-06 03:58:19.521603: Pseudo dice [np.float32(0.8312), np.float32(0.8392), np.float32(0.9147), np.float32(0.9735), np.float32(0.6461), np.float32(0.9219), np.float32(0.9662), np.float32(0.9746), np.float32(0.9665), np.float32(0.9622), np.float32(0.9342), np.float32(0.9679), np.float32(0.9641), np.float32(0.8875), np.float32(0.9624), np.float32(0.9498), np.float32(0.852), np.float32(0.847), np.float32(0.9056)] 
+2025-05-06 03:58:19.523168: Epoch time: 107.45 s 
+2025-05-06 03:58:24.770240:  
+2025-05-06 03:58:24.783307: Epoch 975 
+2025-05-06 03:58:24.783838: Current learning rate: 0.00548 
+2025-05-06 04:00:04.511506: train_loss -0.4738 
+2025-05-06 04:00:04.690110: val_loss -0.5217 
+2025-05-06 04:00:04.714373: Pseudo dice [np.float32(0.8505), np.float32(0.8694), np.float32(0.8648), np.float32(0.9656), np.float32(0.8987), np.float32(0.9482), np.float32(0.9659), np.float32(0.9766), np.float32(0.9475), np.float32(0.9675), np.float32(0.9472), np.float32(0.9621), np.float32(0.9633), np.float32(0.8943), np.float32(0.9022), np.float32(0.9354), np.float32(0.8735), np.float32(0.8277), np.float32(0.8999)] 
+2025-05-06 04:00:04.748313: Epoch time: 99.74 s 
+2025-05-06 04:00:06.590768:  
+2025-05-06 04:00:06.661297: Epoch 976 
+2025-05-06 04:00:06.662054: Current learning rate: 0.00547 
+2025-05-06 04:01:47.879719: train_loss -0.4721 
+2025-05-06 04:01:48.080212: val_loss -0.4929 
+2025-05-06 04:01:48.101869: Pseudo dice [np.float32(0.8224), np.float32(0.8477), np.float32(0.9154), np.float32(0.9726), np.float32(0.8736), np.float32(0.9615), np.float32(0.961), np.float32(0.9787), np.float32(0.9587), np.float32(0.9674), np.float32(0.9435), np.float32(0.9629), np.float32(0.9667), np.float32(0.8976), np.float32(0.9651), np.float32(0.9468), np.float32(0.885), np.float32(0.8816), np.float32(0.9049)] 
+2025-05-06 04:01:48.131008: Epoch time: 101.29 s 
+2025-05-06 04:01:49.953891:  
+2025-05-06 04:01:50.020914: Epoch 977 
+2025-05-06 04:01:50.036008: Current learning rate: 0.00547 
+2025-05-06 04:03:30.647409: train_loss -0.4835 
+2025-05-06 04:03:30.961866: val_loss -0.5099 
+2025-05-06 04:03:30.994688: Pseudo dice [np.float32(0.8487), np.float32(0.8291), np.float32(0.8934), np.float32(0.9718), np.float32(0.8731), np.float32(0.9524), np.float32(0.9529), np.float32(0.9717), np.float32(0.9538), np.float32(0.9639), np.float32(0.9453), np.float32(0.9663), np.float32(0.9682), np.float32(0.8886), np.float32(0.9421), np.float32(0.9541), np.float32(0.7629), np.float32(0.865), np.float32(0.9007)] 
+2025-05-06 04:03:31.020406: Epoch time: 100.69 s 
+2025-05-06 04:03:32.558125:  
+2025-05-06 04:03:32.569066: Epoch 978 
+2025-05-06 04:03:32.569962: Current learning rate: 0.00546 
+2025-05-06 04:05:17.817783: train_loss -0.478 
+2025-05-06 04:05:18.011213: val_loss -0.5005 
+2025-05-06 04:05:18.033865: Pseudo dice [np.float32(0.8398), np.float32(0.8465), np.float32(0.9459), np.float32(0.9775), np.float32(0.8977), np.float32(0.9619), np.float32(0.9626), np.float32(0.9772), np.float32(0.9528), np.float32(0.9733), np.float32(0.9543), np.float32(0.9635), np.float32(0.9738), np.float32(0.9015), np.float32(0.9631), np.float32(0.9591), np.float32(0.8881), np.float32(0.8799), np.float32(0.9104)] 
+2025-05-06 04:05:18.068087: Epoch time: 105.26 s 
+2025-05-06 04:05:19.739935:  
+2025-05-06 04:05:19.849421: Epoch 979 
+2025-05-06 04:05:19.873474: Current learning rate: 0.00546 
+2025-05-06 04:07:00.926496: train_loss -0.4635 
+2025-05-06 04:07:01.123920: val_loss -0.4928 
+2025-05-06 04:07:01.149619: Pseudo dice [np.float32(0.8499), np.float32(0.8565), np.float32(0.93), np.float32(0.9745), np.float32(0.9125), np.float32(0.9569), np.float32(0.9593), np.float32(0.9739), np.float32(0.9693), np.float32(0.9638), np.float32(0.9332), np.float32(0.9686), np.float32(0.9573), np.float32(0.9036), np.float32(0.9341), np.float32(0.9426), np.float32(0.8485), np.float32(0.8713), np.float32(0.9295)] 
+2025-05-06 04:07:01.163781: Epoch time: 101.19 s 
+2025-05-06 04:07:02.761698:  
+2025-05-06 04:07:02.862166: Epoch 980 
+2025-05-06 04:07:02.869984: Current learning rate: 0.00546 
+2025-05-06 04:08:46.866153: train_loss -0.4753 
+2025-05-06 04:08:47.001454: val_loss -0.5155 
+2025-05-06 04:08:47.015567: Pseudo dice [np.float32(0.8504), np.float32(0.8635), np.float32(0.712), np.float32(0.9712), np.float32(0.8903), np.float32(0.9544), np.float32(0.9602), np.float32(0.9678), np.float32(0.9614), np.float32(0.9544), np.float32(0.9416), np.float32(0.9689), np.float32(0.9642), np.float32(0.893), np.float32(0.9465), np.float32(0.9538), np.float32(0.8414), np.float32(0.8503), np.float32(0.9151)] 
+2025-05-06 04:08:47.030139: Epoch time: 104.11 s 
+2025-05-06 04:08:48.565667:  
+2025-05-06 04:08:48.582351: Epoch 981 
+2025-05-06 04:08:48.590618: Current learning rate: 0.00545 
+2025-05-06 04:10:28.422655: train_loss -0.4887 
+2025-05-06 04:10:28.639041: val_loss -0.5161 
+2025-05-06 04:10:28.679495: Pseudo dice [np.float32(0.8359), np.float32(0.8323), np.float32(0.9026), np.float32(0.9615), np.float32(0.9032), np.float32(0.962), np.float32(0.9646), np.float32(0.9688), np.float32(0.9618), np.float32(0.9561), np.float32(0.9493), np.float32(0.9734), np.float32(0.9622), np.float32(0.9063), np.float32(0.9709), np.float32(0.9508), np.float32(0.8859), np.float32(0.8263), np.float32(0.9122)] 
+2025-05-06 04:10:28.742132: Epoch time: 99.86 s 
+2025-05-06 04:10:30.375473:  
+2025-05-06 04:10:30.468625: Epoch 982 
+2025-05-06 04:10:30.482760: Current learning rate: 0.00545 
+2025-05-06 04:12:16.831290: train_loss -0.489 
+2025-05-06 04:12:16.966434: val_loss -0.478 
+2025-05-06 04:12:16.974007: Pseudo dice [np.float32(0.8602), np.float32(0.8668), np.float32(0.9388), np.float32(0.9736), np.float32(0.9025), np.float32(0.9637), np.float32(0.9624), np.float32(0.9696), np.float32(0.9626), np.float32(0.9516), np.float32(0.914), np.float32(0.9667), np.float32(0.9653), np.float32(0.899), np.float32(0.9593), np.float32(0.9461), np.float32(0.8187), np.float32(0.879), np.float32(0.9098)] 
+2025-05-06 04:12:17.002427: Epoch time: 106.46 s 
+2025-05-06 04:12:18.589939:  
+2025-05-06 04:12:18.635335: Epoch 983 
+2025-05-06 04:12:18.647527: Current learning rate: 0.00544 
+2025-05-06 04:14:02.377178: train_loss -0.4835 
+2025-05-06 04:14:02.525615: val_loss -0.5137 
+2025-05-06 04:14:02.546082: Pseudo dice [np.float32(0.8408), np.float32(0.8682), np.float32(0.903), np.float32(0.9777), np.float32(0.9241), np.float32(0.955), np.float32(0.9577), np.float32(0.9746), np.float32(0.9581), np.float32(0.9614), np.float32(0.9285), np.float32(0.9635), np.float32(0.9618), np.float32(0.9021), np.float32(0.9681), np.float32(0.9378), np.float32(0.8651), np.float32(0.8839), np.float32(0.9144)] 
+2025-05-06 04:14:02.554674: Epoch time: 103.79 s 
+2025-05-06 04:14:04.037506:  
+2025-05-06 04:14:04.057201: Epoch 984 
+2025-05-06 04:14:04.058791: Current learning rate: 0.00544 
+2025-05-06 04:15:47.999815: train_loss -0.4552 
+2025-05-06 04:15:48.223075: val_loss -0.4619 
+2025-05-06 04:15:48.229469: Pseudo dice [np.float32(0.8477), np.float32(0.8252), np.float32(0.9302), np.float32(0.9725), np.float32(0.9193), np.float32(0.9629), np.float32(0.9566), np.float32(0.976), np.float32(0.9482), np.float32(0.9662), np.float32(0.9534), np.float32(0.952), np.float32(0.9743), np.float32(0.893), np.float32(0.9647), np.float32(0.9477), np.float32(0.8653), np.float32(0.8391), np.float32(0.9176)] 
+2025-05-06 04:15:48.244112: Epoch time: 103.96 s 
+2025-05-06 04:15:50.017169:  
+2025-05-06 04:15:50.068739: Epoch 985 
+2025-05-06 04:15:50.070546: Current learning rate: 0.00543 
+2025-05-06 04:17:33.034439: train_loss -0.4793 
+2025-05-06 04:17:33.172998: val_loss -0.5223 
+2025-05-06 04:17:33.191378: Pseudo dice [np.float32(0.8324), np.float32(0.857), np.float32(0.8695), np.float32(0.9745), np.float32(0.8454), np.float32(0.9594), np.float32(0.9639), np.float32(0.9764), np.float32(0.9682), np.float32(0.9702), np.float32(0.9357), np.float32(0.9691), np.float32(0.963), np.float32(0.9013), np.float32(0.9645), np.float32(0.956), np.float32(0.7797), np.float32(0.8224), np.float32(0.929)] 
+2025-05-06 04:17:33.211187: Epoch time: 103.02 s 
+2025-05-06 04:17:34.758985:  
+2025-05-06 04:17:34.862803: Epoch 986 
+2025-05-06 04:17:34.896390: Current learning rate: 0.00543 
+2025-05-06 04:19:14.112515: train_loss -0.4852 
+2025-05-06 04:19:14.279466: val_loss -0.4916 
+2025-05-06 04:19:14.289086: Pseudo dice [np.float32(0.8517), np.float32(0.8565), np.float32(0.8146), np.float32(0.9724), np.float32(0.9223), np.float32(0.9598), np.float32(0.9655), np.float32(0.9771), np.float32(0.9604), np.float32(0.9466), np.float32(0.9487), np.float32(0.9638), np.float32(0.9647), np.float32(0.9012), np.float32(0.9632), np.float32(0.9417), np.float32(0.8984), np.float32(0.8935), np.float32(0.9198)] 
+2025-05-06 04:19:14.300212: Epoch time: 99.35 s 
+2025-05-06 04:19:15.987710:  
+2025-05-06 04:19:16.040688: Epoch 987 
+2025-05-06 04:19:16.070787: Current learning rate: 0.00542 
+2025-05-06 04:20:57.003470: train_loss -0.4651 
+2025-05-06 04:20:57.145314: val_loss -0.5193 
+2025-05-06 04:20:57.172184: Pseudo dice [np.float32(0.8533), np.float32(0.8523), np.float32(0.8764), np.float32(0.966), np.float32(0.9267), np.float32(0.9625), np.float32(0.94), np.float32(0.9755), np.float32(0.9681), np.float32(0.9618), np.float32(0.9318), np.float32(0.9591), np.float32(0.9603), np.float32(0.8918), np.float32(0.9532), np.float32(0.9565), np.float32(0.8906), np.float32(0.8745), np.float32(0.9049)] 
+2025-05-06 04:20:57.185784: Epoch time: 101.02 s 
+2025-05-06 04:20:58.723046:  
+2025-05-06 04:20:58.824447: Epoch 988 
+2025-05-06 04:20:58.832140: Current learning rate: 0.00542 
+2025-05-06 04:22:36.484365: train_loss -0.4906 
+2025-05-06 04:22:36.605880: val_loss -0.4849 
+2025-05-06 04:22:36.633835: Pseudo dice [np.float32(0.8584), np.float32(0.8352), np.float32(0.8807), np.float32(0.9784), np.float32(0.8918), np.float32(0.9526), np.float32(0.9664), np.float32(0.9813), np.float32(0.9642), np.float32(0.956), np.float32(0.948), np.float32(0.9647), np.float32(0.9616), np.float32(0.9006), np.float32(0.9553), np.float32(0.953), np.float32(0.8559), np.float32(0.8833), np.float32(0.8992)] 
+2025-05-06 04:22:36.671438: Epoch time: 97.76 s 
+2025-05-06 04:22:38.262660:  
+2025-05-06 04:22:38.382554: Epoch 989 
+2025-05-06 04:22:38.401285: Current learning rate: 0.00541 
+2025-05-06 04:24:18.973683: train_loss -0.4952 
+2025-05-06 04:24:19.057822: val_loss -0.5029 
+2025-05-06 04:24:19.082054: Pseudo dice [np.float32(0.8403), np.float32(0.8296), np.float32(0.8496), np.float32(0.9737), np.float32(0.9096), np.float32(0.933), np.float32(0.958), np.float32(0.9736), np.float32(0.9628), np.float32(0.9677), np.float32(0.9503), np.float32(0.962), np.float32(0.9702), np.float32(0.889), np.float32(0.9395), np.float32(0.9501), np.float32(0.8741), np.float32(0.8959), np.float32(0.9047)] 
+2025-05-06 04:24:19.107541: Epoch time: 100.71 s 
+2025-05-06 04:24:20.636288:  
+2025-05-06 04:24:20.723924: Epoch 990 
+2025-05-06 04:24:20.743123: Current learning rate: 0.00541 
+2025-05-06 04:25:56.828691: train_loss -0.4873 
+2025-05-06 04:25:56.967190: val_loss -0.4894 
+2025-05-06 04:25:57.017052: Pseudo dice [np.float32(0.8329), np.float32(0.8089), np.float32(0.9272), np.float32(0.9795), np.float32(0.9206), np.float32(0.9562), np.float32(0.9704), np.float32(0.9778), np.float32(0.9555), np.float32(0.9609), np.float32(0.9418), np.float32(0.961), np.float32(0.9626), np.float32(0.8682), np.float32(0.9696), np.float32(0.9463), np.float32(0.8424), np.float32(0.8133), np.float32(0.9132)] 
+2025-05-06 04:25:57.025373: Epoch time: 96.19 s 
+2025-05-06 04:25:58.646319:  
+2025-05-06 04:25:58.731164: Epoch 991 
+2025-05-06 04:25:58.781432: Current learning rate: 0.0054 
+2025-05-06 04:27:34.730671: train_loss -0.4882 
+2025-05-06 04:27:34.819780: val_loss -0.4524 
+2025-05-06 04:27:34.820673: Pseudo dice [np.float32(0.8451), np.float32(0.8481), np.float32(0.9232), np.float32(0.9787), np.float32(0.8741), np.float32(0.9628), np.float32(0.9649), np.float32(0.9746), np.float32(0.968), np.float32(0.9681), np.float32(0.9464), np.float32(0.9638), np.float32(0.9676), np.float32(0.8965), np.float32(0.9568), np.float32(0.9542), np.float32(0.8346), np.float32(0.8704), np.float32(0.9125)] 
+2025-05-06 04:27:34.821157: Epoch time: 96.09 s 
+2025-05-06 04:27:36.359478:  
+2025-05-06 04:27:36.442088: Epoch 992 
+2025-05-06 04:27:36.480879: Current learning rate: 0.0054 
+2025-05-06 04:29:15.029760: train_loss -0.4727 
+2025-05-06 04:29:15.140758: val_loss -0.5044 
+2025-05-06 04:29:15.154193: Pseudo dice [np.float32(0.8596), np.float32(0.8576), np.float32(0.9367), np.float32(0.9626), np.float32(0.8904), np.float32(0.9609), np.float32(0.9624), np.float32(0.9766), np.float32(0.9592), np.float32(0.9524), np.float32(0.9482), np.float32(0.9675), np.float32(0.9631), np.float32(0.8954), np.float32(0.9661), np.float32(0.9525), np.float32(0.8991), np.float32(0.8386), np.float32(0.92)] 
+2025-05-06 04:29:15.155173: Epoch time: 98.67 s 
+2025-05-06 04:29:16.697848:  
+2025-05-06 04:29:16.717463: Epoch 993 
+2025-05-06 04:29:16.717958: Current learning rate: 0.00539 
+2025-05-06 04:30:57.666055: train_loss -0.4879 
+2025-05-06 04:30:57.805538: val_loss -0.4673 
+2025-05-06 04:30:57.825441: Pseudo dice [np.float32(0.8417), np.float32(0.8397), np.float32(0.8932), np.float32(0.9648), np.float32(0.9101), np.float32(0.9629), np.float32(0.9652), np.float32(0.9788), np.float32(0.9421), np.float32(0.9537), np.float32(0.9229), np.float32(0.9602), np.float32(0.9593), np.float32(0.9081), np.float32(0.9707), np.float32(0.9503), np.float32(0.7797), np.float32(0.7761), np.float32(0.9003)] 
+2025-05-06 04:30:57.849649: Epoch time: 100.97 s 
+2025-05-06 04:30:59.341795:  
+2025-05-06 04:30:59.455595: Epoch 994 
+2025-05-06 04:30:59.465867: Current learning rate: 0.00539 
+2025-05-06 04:32:40.048736: train_loss -0.4884 
+2025-05-06 04:32:40.176051: val_loss -0.4908 
+2025-05-06 04:32:40.207140: Pseudo dice [np.float32(0.8429), np.float32(0.8464), np.float32(0.896), np.float32(0.9633), np.float32(0.8581), np.float32(0.957), np.float32(0.9509), np.float32(0.9717), np.float32(0.9617), np.float32(0.9603), np.float32(0.9451), np.float32(0.9683), np.float32(0.9693), np.float32(0.906), np.float32(0.945), np.float32(0.946), np.float32(0.8781), np.float32(0.8804), np.float32(0.9138)] 
+2025-05-06 04:32:40.243829: Epoch time: 100.71 s 
+2025-05-06 04:32:41.793033:  
+2025-05-06 04:32:41.870155: Epoch 995 
+2025-05-06 04:32:41.879655: Current learning rate: 0.00538 
+2025-05-06 04:34:23.329684: train_loss -0.4816 
+2025-05-06 04:34:23.462020: val_loss -0.4805 
+2025-05-06 04:34:23.489728: Pseudo dice [np.float32(0.8619), np.float32(0.8104), np.float32(0.9518), np.float32(0.9677), np.float32(0.9053), np.float32(0.9572), np.float32(0.9576), np.float32(0.9673), np.float32(0.9605), np.float32(0.9584), np.float32(0.9441), np.float32(0.9456), np.float32(0.9624), np.float32(0.8861), np.float32(0.9655), np.float32(0.9267), np.float32(0.835), np.float32(0.8551), np.float32(0.9077)] 
+2025-05-06 04:34:23.524905: Epoch time: 101.54 s 
+2025-05-06 04:34:25.072602:  
+2025-05-06 04:34:25.103999: Epoch 996 
+2025-05-06 04:34:25.104844: Current learning rate: 0.00538 
+2025-05-06 04:36:00.412050: train_loss -0.4789 
+2025-05-06 04:36:00.539196: val_loss -0.5029 
+2025-05-06 04:36:00.557220: Pseudo dice [np.float32(0.8567), np.float32(0.8497), np.float32(0.8384), np.float32(0.9639), np.float32(0.8988), np.float32(0.9536), np.float32(0.9471), np.float32(0.9693), np.float32(0.9549), np.float32(0.9617), np.float32(0.9409), np.float32(0.9613), np.float32(0.9671), np.float32(0.8828), np.float32(0.9508), np.float32(0.9498), np.float32(0.8778), np.float32(0.8736), np.float32(0.9184)] 
+2025-05-06 04:36:00.587754: Epoch time: 95.34 s 
+2025-05-06 04:36:02.191584:  
+2025-05-06 04:36:02.379459: Epoch 997 
+2025-05-06 04:36:02.391972: Current learning rate: 0.00537 
+2025-05-06 04:37:39.953265: train_loss -0.4714 
+2025-05-06 04:37:40.009430: val_loss -0.4804 
+2025-05-06 04:37:40.014715: Pseudo dice [np.float32(0.8576), np.float32(0.8463), np.float32(0.9406), np.float32(0.9584), np.float32(0.9085), np.float32(0.9571), np.float32(0.9621), np.float32(0.9772), np.float32(0.9452), np.float32(0.9578), np.float32(0.9261), np.float32(0.9598), np.float32(0.9629), np.float32(0.8937), np.float32(0.9697), np.float32(0.95), np.float32(0.8128), np.float32(0.8685), np.float32(0.9119)] 
+2025-05-06 04:37:40.015329: Epoch time: 97.76 s 
+2025-05-06 04:37:41.502298:  
+2025-05-06 04:37:41.538156: Epoch 998 
+2025-05-06 04:37:41.554844: Current learning rate: 0.00537 
+2025-05-06 04:39:21.783848: train_loss -0.4786 
+2025-05-06 04:39:21.861322: val_loss -0.4931 
+2025-05-06 04:39:21.883905: Pseudo dice [np.float32(0.8071), np.float32(0.8438), np.float32(0.8829), np.float32(0.9621), np.float32(0.8709), np.float32(0.9438), np.float32(0.9621), np.float32(0.9771), np.float32(0.9372), np.float32(0.9564), np.float32(0.9351), np.float32(0.9569), np.float32(0.9675), np.float32(0.9017), np.float32(0.9258), np.float32(0.948), np.float32(0.8625), np.float32(0.8692), np.float32(0.9256)] 
+2025-05-06 04:39:21.895256: Epoch time: 100.28 s 
+2025-05-06 04:39:23.454609:  
+2025-05-06 04:39:23.537146: Epoch 999 
+2025-05-06 04:39:23.587717: Current learning rate: 0.00536 
+2025-05-06 04:40:58.424154: train_loss -0.4888 
+2025-05-06 04:40:58.501117: val_loss -0.5153 
+2025-05-06 04:40:58.541328: Pseudo dice [np.float32(0.8553), np.float32(0.7964), np.float32(0.7425), np.float32(0.9667), np.float32(0.8937), np.float32(0.9653), np.float32(0.9658), np.float32(0.9776), np.float32(0.9674), np.float32(0.9683), np.float32(0.956), np.float32(0.9681), np.float32(0.9716), np.float32(0.9023), np.float32(0.9641), np.float32(0.9562), np.float32(0.8628), np.float32(0.8733), np.float32(0.9174)] 
+2025-05-06 04:40:58.554664: Epoch time: 94.97 s 
+2025-05-06 04:41:00.908841:  
+2025-05-06 04:41:00.960597: Epoch 1000 
+2025-05-06 04:41:00.977484: Current learning rate: 0.00536 
+2025-05-06 04:42:41.600818: train_loss -0.4821 
+2025-05-06 04:42:41.740842: val_loss -0.5061 
+2025-05-06 04:42:41.743063: Pseudo dice [np.float32(0.8551), np.float32(0.866), np.float32(0.8989), np.float32(0.9681), np.float32(0.9212), np.float32(0.9652), np.float32(0.9603), np.float32(0.9778), np.float32(0.9613), np.float32(0.9594), np.float32(0.9504), np.float32(0.9688), np.float32(0.9597), np.float32(0.9041), np.float32(0.94), np.float32(0.9621), np.float32(0.8964), np.float32(0.8948), np.float32(0.9214)] 
+2025-05-06 04:42:41.760766: Epoch time: 100.69 s 
+2025-05-06 04:42:43.551893:  
+2025-05-06 04:42:43.645450: Epoch 1001 
+2025-05-06 04:42:43.677492: Current learning rate: 0.00535 
+2025-05-06 04:44:25.788900: train_loss -0.4879 
+2025-05-06 04:44:25.943791: val_loss -0.4504 
+2025-05-06 04:44:25.967579: Pseudo dice [np.float32(0.8352), np.float32(0.8494), np.float32(0.8818), np.float32(0.9714), np.float32(0.8903), np.float32(0.9554), np.float32(0.9642), np.float32(0.9582), np.float32(0.9639), np.float32(0.951), np.float32(0.93), np.float32(0.9565), np.float32(0.9622), np.float32(0.8925), np.float32(0.9582), np.float32(0.9577), np.float32(0.8704), np.float32(0.8873), np.float32(0.9092)] 
+2025-05-06 04:44:26.032899: Epoch time: 102.24 s 
+2025-05-06 04:44:27.672668:  
+2025-05-06 04:44:27.730859: Epoch 1002 
+2025-05-06 04:44:27.749822: Current learning rate: 0.00535 
+2025-05-06 04:46:09.114079: train_loss -0.4818 
+2025-05-06 04:46:09.345304: val_loss -0.4918 
+2025-05-06 04:46:09.399549: Pseudo dice [np.float32(0.8655), np.float32(0.855), np.float32(0.9318), np.float32(0.9803), np.float32(0.7577), np.float32(0.9209), np.float32(0.967), np.float32(0.9774), np.float32(0.9595), np.float32(0.9659), np.float32(0.9298), np.float32(0.9711), np.float32(0.9669), np.float32(0.9106), np.float32(0.9669), np.float32(0.9566), np.float32(0.8911), np.float32(0.9067), np.float32(0.9255)] 
+2025-05-06 04:46:09.442435: Epoch time: 101.44 s 
+2025-05-06 04:46:11.094552:  
+2025-05-06 04:46:11.176016: Epoch 1003 
+2025-05-06 04:46:11.182597: Current learning rate: 0.00534 
+2025-05-06 04:47:51.989286: train_loss -0.4801 
+2025-05-06 04:47:52.105125: val_loss -0.5033 
+2025-05-06 04:47:52.125745: Pseudo dice [np.float32(0.848), np.float32(0.8196), np.float32(0.7284), np.float32(0.9725), np.float32(0.9022), np.float32(0.9568), np.float32(0.9656), np.float32(0.9747), np.float32(0.9471), np.float32(0.9727), np.float32(0.9452), np.float32(0.9516), np.float32(0.9711), np.float32(0.8915), np.float32(0.9641), np.float32(0.8991), np.float32(0.8982), np.float32(0.8734), np.float32(0.9146)] 
+2025-05-06 04:47:52.141126: Epoch time: 100.9 s 
+2025-05-06 04:47:53.773161:  
+2025-05-06 04:47:53.796267: Epoch 1004 
+2025-05-06 04:47:53.818595: Current learning rate: 0.00534 
+2025-05-06 04:49:30.277158: train_loss -0.4954 
+2025-05-06 04:49:30.409044: val_loss -0.483 
+2025-05-06 04:49:30.431850: Pseudo dice [np.float32(0.8637), np.float32(0.8538), np.float32(0.9103), np.float32(0.9783), np.float32(0.9135), np.float32(0.9523), np.float32(0.9641), np.float32(0.9723), np.float32(0.9645), np.float32(0.9699), np.float32(0.9503), np.float32(0.9732), np.float32(0.9718), np.float32(0.9068), np.float32(0.9394), np.float32(0.9458), np.float32(0.889), np.float32(0.9068), np.float32(0.9124)] 
+2025-05-06 04:49:30.450138: Epoch time: 96.51 s 
+2025-05-06 04:49:32.012324:  
+2025-05-06 04:49:32.062170: Epoch 1005 
+2025-05-06 04:49:32.104221: Current learning rate: 0.00533 
+2025-05-06 04:51:11.478316: train_loss -0.493 
+2025-05-06 04:51:11.617705: val_loss -0.4801 
+2025-05-06 04:51:11.642283: Pseudo dice [np.float32(0.844), np.float32(0.8422), np.float32(0.8983), np.float32(0.9618), np.float32(0.901), np.float32(0.9593), np.float32(0.946), np.float32(0.9755), np.float32(0.9592), np.float32(0.961), np.float32(0.9315), np.float32(0.9694), np.float32(0.9669), np.float32(0.9005), np.float32(0.9482), np.float32(0.9569), np.float32(0.8611), np.float32(0.8942), np.float32(0.913)] 
+2025-05-06 04:51:11.662220: Epoch time: 99.47 s 
+2025-05-06 04:51:13.247921:  
+2025-05-06 04:51:13.312922: Epoch 1006 
+2025-05-06 04:51:13.338597: Current learning rate: 0.00533 
+2025-05-06 04:52:52.220050: train_loss -0.4738 
+2025-05-06 04:52:52.260621: val_loss -0.5197 
+2025-05-06 04:52:52.264693: Pseudo dice [np.float32(0.8223), np.float32(0.8202), np.float32(0.9036), np.float32(0.9697), np.float32(0.8716), np.float32(0.9597), np.float32(0.9649), np.float32(0.9733), np.float32(0.9657), np.float32(0.9686), np.float32(0.9507), np.float32(0.9692), np.float32(0.9612), np.float32(0.9064), np.float32(0.9624), np.float32(0.9455), np.float32(0.893), np.float32(0.8844), np.float32(0.9149)] 
+2025-05-06 04:52:52.277624: Epoch time: 98.97 s 
+2025-05-06 04:52:53.802321:  
+2025-05-06 04:52:53.848663: Epoch 1007 
+2025-05-06 04:52:53.867007: Current learning rate: 0.00533 
+2025-05-06 04:54:32.094653: train_loss -0.4979 
+2025-05-06 04:54:32.186814: val_loss -0.4978 
+2025-05-06 04:54:32.266027: Pseudo dice [np.float32(0.8581), np.float32(0.8513), np.float32(0.9252), np.float32(0.9713), np.float32(0.885), np.float32(0.9492), np.float32(0.9666), np.float32(0.9756), np.float32(0.9641), np.float32(0.9685), np.float32(0.9474), np.float32(0.9625), np.float32(0.9631), np.float32(0.9033), np.float32(0.95), np.float32(0.9465), np.float32(0.8599), np.float32(0.8485), np.float32(0.8943)] 
+2025-05-06 04:54:32.305899: Epoch time: 98.29 s 
+2025-05-06 04:54:33.983702:  
+2025-05-06 04:54:34.106730: Epoch 1008 
+2025-05-06 04:54:34.132658: Current learning rate: 0.00532 
+2025-05-06 04:56:08.869468: train_loss -0.4957 
+2025-05-06 04:56:08.931680: val_loss -0.5078 
+2025-05-06 04:56:08.949805: Pseudo dice [np.float32(0.8307), np.float32(0.8333), np.float32(0.8404), np.float32(0.9709), np.float32(0.9009), np.float32(0.9556), np.float32(0.9606), np.float32(0.978), np.float32(0.933), np.float32(0.9585), np.float32(0.9473), np.float32(0.9575), np.float32(0.968), np.float32(0.9085), np.float32(0.9578), np.float32(0.9365), np.float32(0.8467), np.float32(0.8789), np.float32(0.9087)] 
+2025-05-06 04:56:08.950543: Epoch time: 94.89 s 
+2025-05-06 04:56:10.490282:  
+2025-05-06 04:56:10.626145: Epoch 1009 
+2025-05-06 04:56:10.682333: Current learning rate: 0.00532 
+2025-05-06 04:57:50.961122: train_loss -0.4882 
+2025-05-06 04:57:51.035493: val_loss -0.5327 
+2025-05-06 04:57:51.076780: Pseudo dice [np.float32(0.825), np.float32(0.8341), np.float32(0.9255), np.float32(0.9803), np.float32(0.8962), np.float32(0.9603), np.float32(0.9565), np.float32(0.975), np.float32(0.9674), np.float32(0.9686), np.float32(0.9536), np.float32(0.9573), np.float32(0.9721), np.float32(0.893), np.float32(0.9482), np.float32(0.9544), np.float32(0.8567), np.float32(0.8513), np.float32(0.9058)] 
+2025-05-06 04:57:51.094459: Epoch time: 100.47 s 
+2025-05-06 04:57:52.646850:  
+2025-05-06 04:57:52.671695: Epoch 1010 
+2025-05-06 04:57:52.688851: Current learning rate: 0.00531 
+2025-05-06 04:59:27.844443: train_loss -0.4898 
+2025-05-06 04:59:27.925611: val_loss -0.5261 
+2025-05-06 04:59:27.943642: Pseudo dice [np.float32(0.8371), np.float32(0.8611), np.float32(0.92), np.float32(0.9705), np.float32(0.9392), np.float32(0.9571), np.float32(0.9672), np.float32(0.9807), np.float32(0.9526), np.float32(0.9675), np.float32(0.9403), np.float32(0.9691), np.float32(0.9621), np.float32(0.9002), np.float32(0.9551), np.float32(0.9502), np.float32(0.8796), np.float32(0.8688), np.float32(0.9307)] 
+2025-05-06 04:59:27.988682: Epoch time: 95.2 s 
+2025-05-06 04:59:29.566512:  
+2025-05-06 04:59:29.641185: Epoch 1011 
+2025-05-06 04:59:29.643285: Current learning rate: 0.00531 
+2025-05-06 05:01:08.434810: train_loss -0.4941 
+2025-05-06 05:01:08.511045: val_loss -0.4816 
+2025-05-06 05:01:08.511947: Pseudo dice [np.float32(0.8388), np.float32(0.8182), np.float32(0.8993), np.float32(0.9659), np.float32(0.8833), np.float32(0.9549), np.float32(0.9515), np.float32(0.978), np.float32(0.9726), np.float32(0.9623), np.float32(0.9207), np.float32(0.9713), np.float32(0.9618), np.float32(0.8916), np.float32(0.953), np.float32(0.9396), np.float32(0.888), np.float32(0.8937), np.float32(0.9094)] 
+2025-05-06 05:01:08.516423: Epoch time: 98.87 s 
+2025-05-06 05:01:13.565968:  
+2025-05-06 05:01:13.568997: Epoch 1012 
+2025-05-06 05:01:13.569430: Current learning rate: 0.0053 
+2025-05-06 05:02:50.034354: train_loss -0.4837 
+2025-05-06 05:02:50.212414: val_loss -0.5281 
+2025-05-06 05:02:50.255920: Pseudo dice [np.float32(0.8525), np.float32(0.8528), np.float32(0.8608), np.float32(0.9627), np.float32(0.9192), np.float32(0.9581), np.float32(0.9534), np.float32(0.9756), np.float32(0.9645), np.float32(0.9647), np.float32(0.9383), np.float32(0.9708), np.float32(0.9642), np.float32(0.8904), np.float32(0.9356), np.float32(0.9488), np.float32(0.8775), np.float32(0.8852), np.float32(0.9006)] 
+2025-05-06 05:02:50.309759: Epoch time: 96.47 s 
+2025-05-06 05:02:51.842332:  
+2025-05-06 05:02:51.898282: Epoch 1013 
+2025-05-06 05:02:51.921186: Current learning rate: 0.0053 
+2025-05-06 05:04:24.360311: train_loss -0.4772 
+2025-05-06 05:04:24.398916: val_loss -0.4769 
+2025-05-06 05:04:24.420480: Pseudo dice [np.float32(0.8489), np.float32(0.7906), np.float32(0.8611), np.float32(0.9776), np.float32(0.902), np.float32(0.9615), np.float32(0.9623), np.float32(0.964), np.float32(0.9635), np.float32(0.961), np.float32(0.9495), np.float32(0.9645), np.float32(0.9633), np.float32(0.8887), np.float32(0.9006), np.float32(0.9446), np.float32(0.8885), np.float32(0.899), np.float32(0.9101)] 
+2025-05-06 05:04:24.437118: Epoch time: 92.52 s 
+2025-05-06 05:04:26.007896:  
+2025-05-06 05:04:26.054986: Epoch 1014 
+2025-05-06 05:04:26.084282: Current learning rate: 0.00529 
+2025-05-06 05:06:01.955163: train_loss -0.4777 
+2025-05-06 05:06:02.022342: val_loss -0.4776 
+2025-05-06 05:06:02.044501: Pseudo dice [np.float32(0.8006), np.float32(0.837), np.float32(0.8331), np.float32(0.9745), np.float32(0.8966), np.float32(0.9583), np.float32(0.9592), np.float32(0.9791), np.float32(0.9671), np.float32(0.9608), np.float32(0.948), np.float32(0.9722), np.float32(0.9673), np.float32(0.8991), np.float32(0.9659), np.float32(0.9526), np.float32(0.8792), np.float32(0.8334), np.float32(0.9081)] 
+2025-05-06 05:06:02.066599: Epoch time: 95.95 s 
+2025-05-06 05:06:03.596375:  
+2025-05-06 05:06:03.707572: Epoch 1015 
+2025-05-06 05:06:03.733484: Current learning rate: 0.00529 
+2025-05-06 05:07:40.845956: train_loss -0.4864 
+2025-05-06 05:07:40.904558: val_loss -0.5163 
+2025-05-06 05:07:40.905663: Pseudo dice [np.float32(0.836), np.float32(0.8346), np.float32(0.8799), np.float32(0.9693), np.float32(0.906), np.float32(0.9624), np.float32(0.949), np.float32(0.9774), np.float32(0.9592), np.float32(0.9446), np.float32(0.9468), np.float32(0.9696), np.float32(0.9615), np.float32(0.9027), np.float32(0.9689), np.float32(0.9422), np.float32(0.8579), np.float32(0.8695), np.float32(0.902)] 
+2025-05-06 05:07:40.923134: Epoch time: 97.25 s 
+2025-05-06 05:07:42.407690:  
+2025-05-06 05:07:42.496610: Epoch 1016 
+2025-05-06 05:07:42.516388: Current learning rate: 0.00528 
+2025-05-06 05:09:20.331061: train_loss -0.4799 
+2025-05-06 05:09:20.450347: val_loss -0.4858 
+2025-05-06 05:09:20.498438: Pseudo dice [np.float32(0.8506), np.float32(0.8614), np.float32(0.8111), np.float32(0.9605), np.float32(0.8559), np.float32(0.9574), np.float32(0.9616), np.float32(0.9695), np.float32(0.9604), np.float32(0.9556), np.float32(0.9353), np.float32(0.9608), np.float32(0.9561), np.float32(0.9049), np.float32(0.9506), np.float32(0.9345), np.float32(0.883), np.float32(0.8918), np.float32(0.9065)] 
+2025-05-06 05:09:20.533477: Epoch time: 97.92 s 
+2025-05-06 05:09:22.048359:  
+2025-05-06 05:09:22.136405: Epoch 1017 
+2025-05-06 05:09:22.148183: Current learning rate: 0.00528 
+2025-05-06 05:10:58.265712: train_loss -0.491 
+2025-05-06 05:10:58.286873: val_loss -0.5193 
+2025-05-06 05:10:58.291355: Pseudo dice [np.float32(0.8406), np.float32(0.8356), np.float32(0.9378), np.float32(0.9562), np.float32(0.9084), np.float32(0.9635), np.float32(0.9609), np.float32(0.9792), np.float32(0.9464), np.float32(0.9663), np.float32(0.95), np.float32(0.9693), np.float32(0.9649), np.float32(0.8966), np.float32(0.9715), np.float32(0.9401), np.float32(0.8856), np.float32(0.8942), np.float32(0.9152)] 
+2025-05-06 05:10:58.291978: Epoch time: 96.22 s 
+2025-05-06 05:10:59.827915:  
+2025-05-06 05:10:59.927963: Epoch 1018 
+2025-05-06 05:10:59.971426: Current learning rate: 0.00527 
+2025-05-06 05:12:36.817454: train_loss -0.4734 
+2025-05-06 05:12:36.931501: val_loss -0.4958 
+2025-05-06 05:12:36.977360: Pseudo dice [np.float32(0.828), np.float32(0.8451), np.float32(0.8223), np.float32(0.9734), np.float32(0.909), np.float32(0.9599), np.float32(0.9606), np.float32(0.9723), np.float32(0.9658), np.float32(0.9613), np.float32(0.9432), np.float32(0.9692), np.float32(0.9667), np.float32(0.903), np.float32(0.9068), np.float32(0.9525), np.float32(0.9053), np.float32(0.9029), np.float32(0.9273)] 
+2025-05-06 05:12:37.000830: Epoch time: 96.99 s 
+2025-05-06 05:12:38.579040:  
+2025-05-06 05:12:38.627788: Epoch 1019 
+2025-05-06 05:12:38.642179: Current learning rate: 0.00527 
+2025-05-06 05:14:17.893581: train_loss -0.4701 
+2025-05-06 05:14:18.026049: val_loss -0.4863 
+2025-05-06 05:14:18.070612: Pseudo dice [np.float32(0.8511), np.float32(0.8615), np.float32(0.835), np.float32(0.9753), np.float32(0.9061), np.float32(0.9479), np.float32(0.9661), np.float32(0.974), np.float32(0.9634), np.float32(0.9683), np.float32(0.9453), np.float32(0.9671), np.float32(0.9607), np.float32(0.9047), np.float32(0.9628), np.float32(0.9597), np.float32(0.904), np.float32(0.9086), np.float32(0.9288)] 
+2025-05-06 05:14:18.112011: Epoch time: 99.32 s 
+2025-05-06 05:14:19.636459:  
+2025-05-06 05:14:19.740882: Epoch 1020 
+2025-05-06 05:14:19.763182: Current learning rate: 0.00526 
+2025-05-06 05:15:57.555302: train_loss -0.4866 
+2025-05-06 05:15:57.705808: val_loss -0.472 
+2025-05-06 05:15:57.747558: Pseudo dice [np.float32(0.8136), np.float32(0.8325), np.float32(0.9304), np.float32(0.9561), np.float32(0.8987), np.float32(0.9495), np.float32(0.9581), np.float32(0.9766), np.float32(0.9609), np.float32(0.9547), np.float32(0.9486), np.float32(0.9699), np.float32(0.9611), np.float32(0.9064), np.float32(0.9689), np.float32(0.9553), np.float32(0.8904), np.float32(0.8763), np.float32(0.9169)] 
+2025-05-06 05:15:57.772533: Epoch time: 97.92 s 
+2025-05-06 05:15:59.418879:  
+2025-05-06 05:15:59.472006: Epoch 1021 
+2025-05-06 05:15:59.472786: Current learning rate: 0.00526 
+2025-05-06 05:17:29.472806: train_loss -0.4724 
+2025-05-06 05:17:29.629323: val_loss -0.5246 
+2025-05-06 05:17:29.671160: Pseudo dice [np.float32(0.8267), np.float32(0.8456), np.float32(0.9171), np.float32(0.9635), np.float32(0.8836), np.float32(0.9591), np.float32(0.966), np.float32(0.9745), np.float32(0.9624), np.float32(0.9652), np.float32(0.9517), np.float32(0.97), np.float32(0.96), np.float32(0.8793), np.float32(0.9659), np.float32(0.9409), np.float32(0.8487), np.float32(0.8572), np.float32(0.92)] 
+2025-05-06 05:17:29.707279: Epoch time: 90.06 s 
+2025-05-06 05:17:31.211606:  
+2025-05-06 05:17:31.313949: Epoch 1022 
+2025-05-06 05:17:31.338062: Current learning rate: 0.00525 
+2025-05-06 05:19:06.547172: train_loss -0.465 
+2025-05-06 05:19:06.610528: val_loss -0.4578 
+2025-05-06 05:19:06.621261: Pseudo dice [np.float32(0.7852), np.float32(0.8219), np.float32(0.9163), np.float32(0.9753), np.float32(0.9246), np.float32(0.9325), np.float32(0.9419), np.float32(0.9758), np.float32(0.9728), np.float32(0.9601), np.float32(0.9243), np.float32(0.9709), np.float32(0.9593), np.float32(0.8786), np.float32(0.9562), np.float32(0.9426), np.float32(0.8413), np.float32(0.8311), np.float32(0.9134)] 
+2025-05-06 05:19:06.632773: Epoch time: 95.34 s 
+2025-05-06 05:19:08.283277:  
+2025-05-06 05:19:08.377290: Epoch 1023 
+2025-05-06 05:19:08.394768: Current learning rate: 0.00525 
+2025-05-06 05:20:45.611893: train_loss -0.482 
+2025-05-06 05:20:45.686235: val_loss -0.4789 
+2025-05-06 05:20:45.727161: Pseudo dice [np.float32(0.8621), np.float32(0.838), np.float32(0.8802), np.float32(0.9733), np.float32(0.9013), np.float32(0.9575), np.float32(0.9612), np.float32(0.975), np.float32(0.9645), np.float32(0.9442), np.float32(0.9494), np.float32(0.9654), np.float32(0.9591), np.float32(0.9051), np.float32(0.9597), np.float32(0.9539), np.float32(0.7575), np.float32(0.8468), np.float32(0.9151)] 
+2025-05-06 05:20:45.764485: Epoch time: 97.33 s 
+2025-05-06 05:20:47.260201:  
+2025-05-06 05:20:47.377604: Epoch 1024 
+2025-05-06 05:20:47.412596: Current learning rate: 0.00524 
+2025-05-06 05:22:24.429235: train_loss -0.493 
+2025-05-06 05:22:24.491535: val_loss -0.4794 
+2025-05-06 05:22:24.502902: Pseudo dice [np.float32(0.8469), np.float32(0.8437), np.float32(0.9539), np.float32(0.9723), np.float32(0.8873), np.float32(0.9622), np.float32(0.9606), np.float32(0.9732), np.float32(0.9439), np.float32(0.9522), np.float32(0.9494), np.float32(0.9606), np.float32(0.974), np.float32(0.9005), np.float32(0.9697), np.float32(0.9606), np.float32(0.8425), np.float32(0.8508), np.float32(0.9251)] 
+2025-05-06 05:22:24.513262: Epoch time: 97.17 s 
+2025-05-06 05:22:26.000851:  
+2025-05-06 05:22:26.038098: Epoch 1025 
+2025-05-06 05:22:26.050683: Current learning rate: 0.00524 
+2025-05-06 05:23:59.650906: train_loss -0.4905 
+2025-05-06 05:23:59.784145: val_loss -0.5194 
+2025-05-06 05:23:59.836162: Pseudo dice [np.float32(0.8387), np.float32(0.8378), np.float32(0.8941), np.float32(0.9753), np.float32(0.8362), np.float32(0.9546), np.float32(0.9601), np.float32(0.9773), np.float32(0.946), np.float32(0.9644), np.float32(0.9488), np.float32(0.97), np.float32(0.9692), np.float32(0.893), np.float32(0.9682), np.float32(0.9488), np.float32(0.8428), np.float32(0.8772), np.float32(0.9092)] 
+2025-05-06 05:23:59.840459: Epoch time: 93.65 s 
+2025-05-06 05:24:01.323136:  
+2025-05-06 05:24:01.356455: Epoch 1026 
+2025-05-06 05:24:01.360841: Current learning rate: 0.00523 
+2025-05-06 05:25:40.504462: train_loss -0.4843 
+2025-05-06 05:25:40.626791: val_loss -0.4825 
+2025-05-06 05:25:40.670062: Pseudo dice [np.float32(0.8269), np.float32(0.8526), np.float32(0.8993), np.float32(0.9656), np.float32(0.8582), np.float32(0.9523), np.float32(0.9618), np.float32(0.9754), np.float32(0.9557), np.float32(0.969), np.float32(0.9536), np.float32(0.9596), np.float32(0.9714), np.float32(0.903), np.float32(0.927), np.float32(0.9552), np.float32(0.8894), np.float32(0.883), np.float32(0.9167)] 
+2025-05-06 05:25:40.708031: Epoch time: 99.18 s 
+2025-05-06 05:25:42.232890:  
+2025-05-06 05:25:42.350128: Epoch 1027 
+2025-05-06 05:25:42.377960: Current learning rate: 0.00523 
+2025-05-06 05:27:21.074560: train_loss -0.4922 
+2025-05-06 05:27:21.082751: val_loss -0.4942 
+2025-05-06 05:27:21.083624: Pseudo dice [np.float32(0.8536), np.float32(0.8232), np.float32(0.9505), np.float32(0.9641), np.float32(0.8836), np.float32(0.9584), np.float32(0.9604), np.float32(0.9738), np.float32(0.9597), np.float32(0.943), np.float32(0.9347), np.float32(0.971), np.float32(0.965), np.float32(0.9002), np.float32(0.9603), np.float32(0.9577), np.float32(0.8894), np.float32(0.874), np.float32(0.9048)] 
+2025-05-06 05:27:21.084069: Epoch time: 98.84 s 
+2025-05-06 05:27:22.618172:  
+2025-05-06 05:27:22.692405: Epoch 1028 
+2025-05-06 05:27:22.721832: Current learning rate: 0.00522 
+2025-05-06 05:29:02.607153: train_loss -0.4894 
+2025-05-06 05:29:02.691648: val_loss -0.4735 
+2025-05-06 05:29:02.725501: Pseudo dice [np.float32(0.8095), np.float32(0.8473), np.float32(0.8368), np.float32(0.9741), np.float32(0.8652), np.float32(0.9642), np.float32(0.9619), np.float32(0.9666), np.float32(0.9659), np.float32(0.9222), np.float32(0.9154), np.float32(0.9689), np.float32(0.9684), np.float32(0.9042), np.float32(0.9616), np.float32(0.9464), np.float32(0.8685), np.float32(0.8942), np.float32(0.9223)] 
+2025-05-06 05:29:02.817837: Epoch time: 99.99 s 
+2025-05-06 05:29:04.384484:  
+2025-05-06 05:29:04.513415: Epoch 1029 
+2025-05-06 05:29:04.545105: Current learning rate: 0.00522 
+2025-05-06 05:30:40.171831: train_loss -0.473 
+2025-05-06 05:30:40.220814: val_loss -0.5108 
+2025-05-06 05:30:40.221539: Pseudo dice [np.float32(0.8401), np.float32(0.8424), np.float32(0.9165), np.float32(0.9636), np.float32(0.9034), np.float32(0.9527), np.float32(0.9539), np.float32(0.9741), np.float32(0.9617), np.float32(0.9497), np.float32(0.9311), np.float32(0.964), np.float32(0.9595), np.float32(0.8979), np.float32(0.958), np.float32(0.9466), np.float32(0.8702), np.float32(0.8697), np.float32(0.9112)] 
+2025-05-06 05:30:40.230289: Epoch time: 95.79 s 
+2025-05-06 05:30:45.812412:  
+2025-05-06 05:30:45.818310: Epoch 1030 
+2025-05-06 05:30:45.818770: Current learning rate: 0.00521 
+2025-05-06 05:32:21.046328: train_loss -0.488 
+2025-05-06 05:32:21.118456: val_loss -0.4921 
+2025-05-06 05:32:21.143013: Pseudo dice [np.float32(0.8358), np.float32(0.8412), np.float32(0.9176), np.float32(0.9718), np.float32(0.8781), np.float32(0.9461), np.float32(0.9601), np.float32(0.9723), np.float32(0.9677), np.float32(0.9609), np.float32(0.9439), np.float32(0.9677), np.float32(0.9555), np.float32(0.8973), np.float32(0.9571), np.float32(0.9494), np.float32(0.8772), np.float32(0.9088), np.float32(0.9062)] 
+2025-05-06 05:32:21.161589: Epoch time: 95.24 s 
+2025-05-06 05:32:22.676879:  
+2025-05-06 05:32:22.683091: Epoch 1031 
+2025-05-06 05:32:22.721950: Current learning rate: 0.00521 
+2025-05-06 05:34:03.874117: train_loss -0.4815 
+2025-05-06 05:34:03.955787: val_loss -0.4973 
+2025-05-06 05:34:03.975949: Pseudo dice [np.float32(0.8166), np.float32(0.7939), np.float32(0.7446), np.float32(0.9641), np.float32(0.8876), np.float32(0.9567), np.float32(0.9588), np.float32(0.9795), np.float32(0.9567), np.float32(0.9569), np.float32(0.925), np.float32(0.9655), np.float32(0.9659), np.float32(0.9041), np.float32(0.9577), np.float32(0.9577), np.float32(0.8965), np.float32(0.8888), np.float32(0.9152)] 
+2025-05-06 05:34:03.998560: Epoch time: 101.2 s 
+2025-05-06 05:34:05.438934:  
+2025-05-06 05:34:05.560770: Epoch 1032 
+2025-05-06 05:34:05.586776: Current learning rate: 0.0052 
+2025-05-06 05:35:40.481725: train_loss -0.4903 
+2025-05-06 05:35:40.640167: val_loss -0.4893 
+2025-05-06 05:35:40.679516: Pseudo dice [np.float32(0.8586), np.float32(0.863), np.float32(0.8424), np.float32(0.9757), np.float32(0.8845), np.float32(0.956), np.float32(0.9659), np.float32(0.9769), np.float32(0.9606), np.float32(0.9719), np.float32(0.9477), np.float32(0.9618), np.float32(0.9536), np.float32(0.9028), np.float32(0.9535), np.float32(0.9365), np.float32(0.8944), np.float32(0.9056), np.float32(0.9195)] 
+2025-05-06 05:35:40.698806: Epoch time: 95.04 s 
+2025-05-06 05:35:42.203819:  
+2025-05-06 05:35:42.281015: Epoch 1033 
+2025-05-06 05:35:42.314789: Current learning rate: 0.0052 
+2025-05-06 05:37:17.644167: train_loss -0.4887 
+2025-05-06 05:37:17.683331: val_loss -0.5163 
+2025-05-06 05:37:17.730783: Pseudo dice [np.float32(0.8479), np.float32(0.865), np.float32(0.8047), np.float32(0.9767), np.float32(0.8794), np.float32(0.9566), np.float32(0.9624), np.float32(0.9767), np.float32(0.9618), np.float32(0.9661), np.float32(0.9495), np.float32(0.9686), np.float32(0.968), np.float32(0.9077), np.float32(0.9641), np.float32(0.9476), np.float32(0.9014), np.float32(0.8963), np.float32(0.9206)] 
+2025-05-06 05:37:17.759461: Epoch time: 95.44 s 
+2025-05-06 05:37:19.420938:  
+2025-05-06 05:37:19.515374: Epoch 1034 
+2025-05-06 05:37:19.539078: Current learning rate: 0.00519 
+2025-05-06 05:38:58.103413: train_loss -0.4812 
+2025-05-06 05:38:58.166183: val_loss -0.535 
+2025-05-06 05:38:58.193541: Pseudo dice [np.float32(0.8582), np.float32(0.8392), np.float32(0.8608), np.float32(0.96), np.float32(0.8823), np.float32(0.9461), np.float32(0.9657), np.float32(0.9753), np.float32(0.9656), np.float32(0.9537), np.float32(0.9462), np.float32(0.9738), np.float32(0.9663), np.float32(0.9022), np.float32(0.9354), np.float32(0.9494), np.float32(0.8569), np.float32(0.8629), np.float32(0.9064)] 
+2025-05-06 05:38:58.197747: Epoch time: 98.68 s 
+2025-05-06 05:38:59.981876:  
+2025-05-06 05:39:00.059890: Epoch 1035 
+2025-05-06 05:39:00.085915: Current learning rate: 0.00519 
+2025-05-06 05:40:37.938520: train_loss -0.4873 
+2025-05-06 05:40:38.035713: val_loss -0.4974 
+2025-05-06 05:40:38.043675: Pseudo dice [np.float32(0.8461), np.float32(0.8256), np.float32(0.9416), np.float32(0.9686), np.float32(0.8859), np.float32(0.9615), np.float32(0.9639), np.float32(0.9769), np.float32(0.9563), np.float32(0.968), np.float32(0.9533), np.float32(0.9578), np.float32(0.9715), np.float32(0.901), np.float32(0.9624), np.float32(0.9585), np.float32(0.893), np.float32(0.8989), np.float32(0.9092)] 
+2025-05-06 05:40:38.065927: Epoch time: 97.96 s 
+2025-05-06 05:40:39.633042:  
+2025-05-06 05:40:39.639176: Epoch 1036 
+2025-05-06 05:40:39.639575: Current learning rate: 0.00518 
+2025-05-06 05:42:18.214690: train_loss -0.4855 
+2025-05-06 05:42:18.295159: val_loss -0.4851 
+2025-05-06 05:42:18.319375: Pseudo dice [np.float32(0.8413), np.float32(0.8383), np.float32(0.9348), np.float32(0.9733), np.float32(0.9004), np.float32(0.9607), np.float32(0.958), np.float32(0.9756), np.float32(0.9618), np.float32(0.9583), np.float32(0.9474), np.float32(0.9663), np.float32(0.9662), np.float32(0.9001), np.float32(0.9624), np.float32(0.9476), np.float32(0.8048), np.float32(0.8033), np.float32(0.9254)] 
+2025-05-06 05:42:18.382546: Epoch time: 98.58 s 
+2025-05-06 05:42:19.992063:  
+2025-05-06 05:42:20.071656: Epoch 1037 
+2025-05-06 05:42:20.080883: Current learning rate: 0.00518 
+2025-05-06 05:44:00.902563: train_loss -0.4787 
+2025-05-06 05:44:01.024922: val_loss -0.4916 
+2025-05-06 05:44:01.036391: Pseudo dice [np.float32(0.8353), np.float32(0.8373), np.float32(0.9328), np.float32(0.9751), np.float32(0.8906), np.float32(0.9561), np.float32(0.9575), np.float32(0.976), np.float32(0.9605), np.float32(0.9685), np.float32(0.9357), np.float32(0.9634), np.float32(0.9584), np.float32(0.8789), np.float32(0.9431), np.float32(0.9545), np.float32(0.8985), np.float32(0.8875), np.float32(0.9225)] 
+2025-05-06 05:44:01.044928: Epoch time: 100.91 s 
+2025-05-06 05:44:02.565581:  
+2025-05-06 05:44:02.623969: Epoch 1038 
+2025-05-06 05:44:02.624906: Current learning rate: 0.00518 
+2025-05-06 05:45:39.014258: train_loss -0.4597 
+2025-05-06 05:45:39.087280: val_loss -0.5172 
+2025-05-06 05:45:39.087873: Pseudo dice [np.float32(0.8394), np.float32(0.8676), np.float32(0.9284), np.float32(0.9737), np.float32(0.9044), np.float32(0.9585), np.float32(0.9491), np.float32(0.9738), np.float32(0.9665), np.float32(0.9624), np.float32(0.9465), np.float32(0.9649), np.float32(0.9652), np.float32(0.9129), np.float32(0.9636), np.float32(0.9423), np.float32(0.8622), np.float32(0.908), np.float32(0.9182)] 
+2025-05-06 05:45:39.102223: Epoch time: 96.45 s 
+2025-05-06 05:45:40.732668:  
+2025-05-06 05:45:40.865761: Epoch 1039 
+2025-05-06 05:45:40.920868: Current learning rate: 0.00517 
+2025-05-06 05:47:17.365549: train_loss -0.4912 
+2025-05-06 05:47:17.454113: val_loss -0.5163 
+2025-05-06 05:47:17.470690: Pseudo dice [np.float32(0.8152), np.float32(0.8571), np.float32(0.9237), np.float32(0.9723), np.float32(0.9236), np.float32(0.9645), np.float32(0.9687), np.float32(0.9809), np.float32(0.9576), np.float32(0.9748), np.float32(0.9555), np.float32(0.9686), np.float32(0.9708), np.float32(0.9112), np.float32(0.9683), np.float32(0.9624), np.float32(0.8056), np.float32(0.8629), np.float32(0.9157)] 
+2025-05-06 05:47:17.508542: Epoch time: 96.63 s 
+2025-05-06 05:47:19.032868:  
+2025-05-06 05:47:19.149692: Epoch 1040 
+2025-05-06 05:47:19.186950: Current learning rate: 0.00517 
+2025-05-06 05:48:53.745947: train_loss -0.5059 
+2025-05-06 05:48:53.797940: val_loss -0.5123 
+2025-05-06 05:48:53.805583: Pseudo dice [np.float32(0.8331), np.float32(0.861), np.float32(0.8755), np.float32(0.9706), np.float32(0.9045), np.float32(0.9631), np.float32(0.9638), np.float32(0.9777), np.float32(0.9617), np.float32(0.9557), np.float32(0.9379), np.float32(0.9698), np.float32(0.9629), np.float32(0.9102), np.float32(0.9676), np.float32(0.9531), np.float32(0.9079), np.float32(0.8749), np.float32(0.9195)] 
+2025-05-06 05:48:53.825441: Epoch time: 94.71 s 
+2025-05-06 05:48:55.484693:  
+2025-05-06 05:48:55.578264: Epoch 1041 
+2025-05-06 05:48:55.589508: Current learning rate: 0.00516 
+2025-05-06 05:50:28.024751: train_loss -0.4753 
+2025-05-06 05:50:28.141397: val_loss -0.4801 
+2025-05-06 05:50:28.145303: Pseudo dice [np.float32(0.8517), np.float32(0.8424), np.float32(0.9298), np.float32(0.9712), np.float32(0.914), np.float32(0.9574), np.float32(0.944), np.float32(0.9707), np.float32(0.9632), np.float32(0.95), np.float32(0.9379), np.float32(0.9666), np.float32(0.9688), np.float32(0.8873), np.float32(0.9613), np.float32(0.9586), np.float32(0.863), np.float32(0.8735), np.float32(0.8916)] 
+2025-05-06 05:50:28.161775: Epoch time: 92.54 s 
+2025-05-06 05:50:29.574701:  
+2025-05-06 05:50:29.651432: Epoch 1042 
+2025-05-06 05:50:29.665096: Current learning rate: 0.00516 
+2025-05-06 05:52:07.154900: train_loss -0.4896 
+2025-05-06 05:52:07.194747: val_loss -0.5115 
+2025-05-06 05:52:07.195632: Pseudo dice [np.float32(0.8475), np.float32(0.8415), np.float32(0.8755), np.float32(0.9771), np.float32(0.9113), np.float32(0.9622), np.float32(0.9661), np.float32(0.9728), np.float32(0.9653), np.float32(0.9366), np.float32(0.9418), np.float32(0.9678), np.float32(0.9651), np.float32(0.9104), np.float32(0.9612), np.float32(0.9581), np.float32(0.9056), np.float32(0.8888), np.float32(0.9226)] 
+2025-05-06 05:52:07.198335: Epoch time: 97.58 s 
+2025-05-06 05:52:08.941057:  
+2025-05-06 05:52:08.959603: Epoch 1043 
+2025-05-06 05:52:08.968683: Current learning rate: 0.00515 
+2025-05-06 05:53:47.894094: train_loss -0.4547 
+2025-05-06 05:53:47.998480: val_loss -0.4782 
+2025-05-06 05:53:47.999384: Pseudo dice [np.float32(0.8522), np.float32(0.8303), np.float32(0.9269), np.float32(0.9763), np.float32(0.8806), np.float32(0.9533), np.float32(0.958), np.float32(0.9776), np.float32(0.9558), np.float32(0.9668), np.float32(0.9495), np.float32(0.9674), np.float32(0.9694), np.float32(0.8994), np.float32(0.9639), np.float32(0.938), np.float32(0.8513), np.float32(0.872), np.float32(0.9271)] 
+2025-05-06 05:53:47.999941: Epoch time: 98.95 s 
+2025-05-06 05:53:49.614365:  
+2025-05-06 05:53:49.660810: Epoch 1044 
+2025-05-06 05:53:49.690270: Current learning rate: 0.00515 
+2025-05-06 05:55:31.645099: train_loss -0.4665 
+2025-05-06 05:55:31.735492: val_loss -0.4751 
+2025-05-06 05:55:31.763323: Pseudo dice [np.float32(0.8615), np.float32(0.8336), np.float32(0.9387), np.float32(0.9746), np.float32(0.8969), np.float32(0.9584), np.float32(0.9674), np.float32(0.9711), np.float32(0.9427), np.float32(0.9621), np.float32(0.9406), np.float32(0.9536), np.float32(0.9667), np.float32(0.9013), np.float32(0.966), np.float32(0.9514), np.float32(0.8355), np.float32(0.8684), np.float32(0.9155)] 
+2025-05-06 05:55:31.808876: Epoch time: 102.03 s 
+2025-05-06 05:55:33.420443:  
+2025-05-06 05:55:33.595681: Epoch 1045 
+2025-05-06 05:55:33.617059: Current learning rate: 0.00514 
+2025-05-06 05:57:09.422499: train_loss -0.4832 
+2025-05-06 05:57:09.505094: val_loss -0.5351 
+2025-05-06 05:57:09.527735: Pseudo dice [np.float32(0.8299), np.float32(0.8558), np.float32(0.7944), np.float32(0.9723), np.float32(0.8907), np.float32(0.9612), np.float32(0.9609), np.float32(0.9743), np.float32(0.9636), np.float32(0.9513), np.float32(0.9486), np.float32(0.9721), np.float32(0.9556), np.float32(0.9024), np.float32(0.961), np.float32(0.9516), np.float32(0.8918), np.float32(0.8798), np.float32(0.915)] 
+2025-05-06 05:57:09.586697: Epoch time: 96.0 s 
+2025-05-06 05:57:11.291441:  
+2025-05-06 05:57:11.382125: Epoch 1046 
+2025-05-06 05:57:11.415606: Current learning rate: 0.00514 
+2025-05-06 05:58:47.954901: train_loss -0.5025 
+2025-05-06 05:58:48.058049: val_loss -0.4717 
+2025-05-06 05:58:48.094298: Pseudo dice [np.float32(0.8281), np.float32(0.8263), np.float32(0.9115), np.float32(0.9691), np.float32(0.8996), np.float32(0.961), np.float32(0.9486), np.float32(0.9817), np.float32(0.9599), np.float32(0.9581), np.float32(0.9361), np.float32(0.9467), np.float32(0.9609), np.float32(0.9062), np.float32(0.9653), np.float32(0.9569), np.float32(0.9005), np.float32(0.8508), np.float32(0.9196)] 
+2025-05-06 05:58:48.132775: Epoch time: 96.66 s 
+2025-05-06 05:58:49.779587:  
+2025-05-06 05:58:49.814442: Epoch 1047 
+2025-05-06 05:58:49.815221: Current learning rate: 0.00513 
+2025-05-06 06:00:24.345342: train_loss -0.4658 
+2025-05-06 06:00:24.439047: val_loss -0.526 
+2025-05-06 06:00:24.465341: Pseudo dice [np.float32(0.832), np.float32(0.8344), np.float32(0.9095), np.float32(0.9589), np.float32(0.9008), np.float32(0.9601), np.float32(0.9633), np.float32(0.9729), np.float32(0.9566), np.float32(0.966), np.float32(0.9553), np.float32(0.961), np.float32(0.9709), np.float32(0.908), np.float32(0.9318), np.float32(0.8994), np.float32(0.8795), np.float32(0.8742), np.float32(0.9248)] 
+2025-05-06 06:00:24.506006: Epoch time: 94.57 s 
+2025-05-06 06:00:29.942285:  
+2025-05-06 06:00:29.948379: Epoch 1048 
+2025-05-06 06:00:29.948824: Current learning rate: 0.00513 
+2025-05-06 06:02:10.565082: train_loss -0.4839 
+2025-05-06 06:02:10.724073: val_loss -0.47 
+2025-05-06 06:02:10.754711: Pseudo dice [np.float32(0.8374), np.float32(0.8561), np.float32(0.8779), np.float32(0.9787), np.float32(0.8957), np.float32(0.9391), np.float32(0.9612), np.float32(0.9768), np.float32(0.9437), np.float32(0.9658), np.float32(0.947), np.float32(0.9717), np.float32(0.9626), np.float32(0.8832), np.float32(0.9617), np.float32(0.9574), np.float32(0.8921), np.float32(0.8877), np.float32(0.9167)] 
+2025-05-06 06:02:10.790278: Epoch time: 100.62 s 
+2025-05-06 06:02:12.304452:  
+2025-05-06 06:02:12.432858: Epoch 1049 
+2025-05-06 06:02:12.464533: Current learning rate: 0.00512 
+2025-05-06 06:03:50.488440: train_loss -0.4788 
+2025-05-06 06:03:50.558705: val_loss -0.5199 
+2025-05-06 06:03:50.581568: Pseudo dice [np.float32(0.8277), np.float32(0.8578), np.float32(0.9087), np.float32(0.9764), np.float32(0.9014), np.float32(0.9438), np.float32(0.9674), np.float32(0.9692), np.float32(0.9537), np.float32(0.9484), np.float32(0.9162), np.float32(0.9682), np.float32(0.9639), np.float32(0.9142), np.float32(0.9609), np.float32(0.9432), np.float32(0.8887), np.float32(0.8925), np.float32(0.9231)] 
+2025-05-06 06:03:50.611159: Epoch time: 98.19 s 
+2025-05-06 06:03:53.470051:  
+2025-05-06 06:03:53.489409: Epoch 1050 
+2025-05-06 06:03:53.490074: Current learning rate: 0.00512 
+2025-05-06 06:05:32.983123: train_loss -0.4862 
+2025-05-06 06:05:33.047674: val_loss -0.5118 
+2025-05-06 06:05:33.054894: Pseudo dice [np.float32(0.8161), np.float32(0.8329), np.float32(0.8035), np.float32(0.973), np.float32(0.8823), np.float32(0.9629), np.float32(0.9565), np.float32(0.9697), np.float32(0.9499), np.float32(0.9662), np.float32(0.945), np.float32(0.9671), np.float32(0.9643), np.float32(0.9017), np.float32(0.9496), np.float32(0.9302), np.float32(0.8657), np.float32(0.8937), np.float32(0.9148)] 
+2025-05-06 06:05:33.059142: Epoch time: 99.51 s 
+2025-05-06 06:05:34.511572:  
+2025-05-06 06:05:34.555685: Epoch 1051 
+2025-05-06 06:05:34.556806: Current learning rate: 0.00511 
+2025-05-06 06:07:10.519053: train_loss -0.4803 
+2025-05-06 06:07:10.642442: val_loss -0.4842 
+2025-05-06 06:07:10.683374: Pseudo dice [np.float32(0.8183), np.float32(0.8379), np.float32(0.8654), np.float32(0.9796), np.float32(0.9105), np.float32(0.949), np.float32(0.9604), np.float32(0.9768), np.float32(0.9665), np.float32(0.9659), np.float32(0.9358), np.float32(0.9683), np.float32(0.958), np.float32(0.892), np.float32(0.9596), np.float32(0.9559), np.float32(0.866), np.float32(0.8339), np.float32(0.9064)] 
+2025-05-06 06:07:10.730618: Epoch time: 96.01 s 
+2025-05-06 06:07:12.206566:  
+2025-05-06 06:07:12.311193: Epoch 1052 
+2025-05-06 06:07:12.344930: Current learning rate: 0.00511 
+2025-05-06 06:08:50.776429: train_loss -0.4773 
+2025-05-06 06:08:50.838373: val_loss -0.5182 
+2025-05-06 06:08:50.875386: Pseudo dice [np.float32(0.8422), np.float32(0.8225), np.float32(0.9008), np.float32(0.9785), np.float32(0.9141), np.float32(0.9631), np.float32(0.9614), np.float32(0.9782), np.float32(0.9611), np.float32(0.9693), np.float32(0.9284), np.float32(0.9711), np.float32(0.9602), np.float32(0.91), np.float32(0.9649), np.float32(0.952), np.float32(0.8474), np.float32(0.8705), np.float32(0.8971)] 
+2025-05-06 06:08:50.894319: Epoch time: 98.57 s 
+2025-05-06 06:08:52.390048:  
+2025-05-06 06:08:52.435319: Epoch 1053 
+2025-05-06 06:08:52.446713: Current learning rate: 0.0051 
+2025-05-06 06:10:29.500925: train_loss -0.4857 
+2025-05-06 06:10:29.592445: val_loss -0.5223 
+2025-05-06 06:10:29.632727: Pseudo dice [np.float32(0.8283), np.float32(0.8231), np.float32(0.897), np.float32(0.9668), np.float32(0.9364), np.float32(0.9568), np.float32(0.9456), np.float32(0.9764), np.float32(0.9614), np.float32(0.9605), np.float32(0.9468), np.float32(0.9684), np.float32(0.967), np.float32(0.9056), np.float32(0.966), np.float32(0.9556), np.float32(0.8572), np.float32(0.8744), np.float32(0.9125)] 
+2025-05-06 06:10:29.658762: Epoch time: 97.11 s 
+2025-05-06 06:10:31.268474:  
+2025-05-06 06:10:31.323385: Epoch 1054 
+2025-05-06 06:10:31.349476: Current learning rate: 0.0051 
+2025-05-06 06:12:06.886402: train_loss -0.4877 
+2025-05-06 06:12:06.984477: val_loss -0.5043 
+2025-05-06 06:12:06.986937: Pseudo dice [np.float32(0.8334), np.float32(0.8472), np.float32(0.9024), np.float32(0.9768), np.float32(0.8888), np.float32(0.9553), np.float32(0.9687), np.float32(0.9775), np.float32(0.9651), np.float32(0.9686), np.float32(0.9539), np.float32(0.9709), np.float32(0.9706), np.float32(0.9047), np.float32(0.9583), np.float32(0.9301), np.float32(0.9145), np.float32(0.8935), np.float32(0.9226)] 
+2025-05-06 06:12:06.987499: Epoch time: 95.62 s 
+2025-05-06 06:12:08.487920:  
+2025-05-06 06:12:08.578524: Epoch 1055 
+2025-05-06 06:12:08.582980: Current learning rate: 0.00509 
+2025-05-06 06:13:43.838705: train_loss -0.5063 
+2025-05-06 06:13:43.987152: val_loss -0.4955 
+2025-05-06 06:13:44.022898: Pseudo dice [np.float32(0.8273), np.float32(0.8193), np.float32(0.9065), np.float32(0.9723), np.float32(0.8697), np.float32(0.9642), np.float32(0.9654), np.float32(0.9673), np.float32(0.9614), np.float32(0.9639), np.float32(0.9536), np.float32(0.9656), np.float32(0.9659), np.float32(0.89), np.float32(0.9394), np.float32(0.9504), np.float32(0.8679), np.float32(0.8822), np.float32(0.9182)] 
+2025-05-06 06:13:44.060544: Epoch time: 95.35 s 
+2025-05-06 06:13:45.809324:  
+2025-05-06 06:13:45.839679: Epoch 1056 
+2025-05-06 06:13:45.840385: Current learning rate: 0.00509 
+2025-05-06 06:15:24.577165: train_loss -0.4806 
+2025-05-06 06:15:24.625012: val_loss -0.515 
+2025-05-06 06:15:24.632170: Pseudo dice [np.float32(0.8528), np.float32(0.83), np.float32(0.9426), np.float32(0.9763), np.float32(0.8602), np.float32(0.9647), np.float32(0.9599), np.float32(0.9716), np.float32(0.9532), np.float32(0.9511), np.float32(0.9372), np.float32(0.9622), np.float32(0.9543), np.float32(0.9064), np.float32(0.9631), np.float32(0.9487), np.float32(0.8618), np.float32(0.8627), np.float32(0.9216)] 
+2025-05-06 06:15:24.668951: Epoch time: 98.77 s 
+2025-05-06 06:15:26.268421:  
+2025-05-06 06:15:26.350245: Epoch 1057 
+2025-05-06 06:15:26.385555: Current learning rate: 0.00508 
+2025-05-06 06:17:01.256381: train_loss -0.4854 
+2025-05-06 06:17:01.346021: val_loss -0.4994 
+2025-05-06 06:17:01.368802: Pseudo dice [np.float32(0.8113), np.float32(0.8518), np.float32(0.9347), np.float32(0.971), np.float32(0.8889), np.float32(0.9548), np.float32(0.9639), np.float32(0.9741), np.float32(0.9646), np.float32(0.9673), np.float32(0.9411), np.float32(0.9682), np.float32(0.9624), np.float32(0.9151), np.float32(0.9666), np.float32(0.9535), np.float32(0.8794), np.float32(0.8901), np.float32(0.9158)] 
+2025-05-06 06:17:01.401893: Epoch time: 94.99 s 
+2025-05-06 06:17:02.950954:  
+2025-05-06 06:17:03.012401: Epoch 1058 
+2025-05-06 06:17:03.078803: Current learning rate: 0.00508 
+2025-05-06 06:18:43.974109: train_loss -0.5081 
+2025-05-06 06:18:44.113009: val_loss -0.543 
+2025-05-06 06:18:44.156917: Pseudo dice [np.float32(0.8687), np.float32(0.8441), np.float32(0.8945), np.float32(0.9662), np.float32(0.9159), np.float32(0.9625), np.float32(0.9626), np.float32(0.9793), np.float32(0.9688), np.float32(0.9698), np.float32(0.9536), np.float32(0.9717), np.float32(0.9707), np.float32(0.9081), np.float32(0.9624), np.float32(0.9288), np.float32(0.9033), np.float32(0.878), np.float32(0.9221)] 
+2025-05-06 06:18:44.196453: Epoch time: 101.02 s 
+2025-05-06 06:18:45.810210:  
+2025-05-06 06:18:45.874590: Epoch 1059 
+2025-05-06 06:18:45.902222: Current learning rate: 0.00507 
+2025-05-06 06:20:17.717333: train_loss -0.481 
+2025-05-06 06:20:17.801232: val_loss -0.4861 
+2025-05-06 06:20:17.824021: Pseudo dice [np.float32(0.849), np.float32(0.865), np.float32(0.7905), np.float32(0.9591), np.float32(0.9105), np.float32(0.9526), np.float32(0.9594), np.float32(0.9744), np.float32(0.9604), np.float32(0.9718), np.float32(0.9459), np.float32(0.969), np.float32(0.9714), np.float32(0.9144), np.float32(0.9597), np.float32(0.9538), np.float32(0.8739), np.float32(0.8508), np.float32(0.9152)] 
+2025-05-06 06:20:17.848160: Epoch time: 91.91 s 
+2025-05-06 06:20:19.410959:  
+2025-05-06 06:20:19.462584: Epoch 1060 
+2025-05-06 06:20:19.499794: Current learning rate: 0.00507 
+2025-05-06 06:21:56.581109: train_loss -0.4634 
+2025-05-06 06:21:56.636935: val_loss -0.5213 
+2025-05-06 06:21:56.674677: Pseudo dice [np.float32(0.8406), np.float32(0.8093), np.float32(0.7734), np.float32(0.9552), np.float32(0.8867), np.float32(0.9566), np.float32(0.9491), np.float32(0.9681), np.float32(0.9597), np.float32(0.9691), np.float32(0.9496), np.float32(0.9677), np.float32(0.971), np.float32(0.9108), np.float32(0.9657), np.float32(0.9466), np.float32(0.8748), np.float32(0.8935), np.float32(0.9319)] 
+2025-05-06 06:21:56.700675: Epoch time: 97.17 s 
+2025-05-06 06:21:58.173301:  
+2025-05-06 06:21:58.218443: Epoch 1061 
+2025-05-06 06:21:58.240695: Current learning rate: 0.00506 
+2025-05-06 06:23:36.557321: train_loss -0.474 
+2025-05-06 06:23:36.658795: val_loss -0.4816 
+2025-05-06 06:23:36.710171: Pseudo dice [np.float32(0.8513), np.float32(0.7735), np.float32(0.8925), np.float32(0.9791), np.float32(0.9084), np.float32(0.9578), np.float32(0.9666), np.float32(0.9725), np.float32(0.9637), np.float32(0.9565), np.float32(0.9459), np.float32(0.9695), np.float32(0.9605), np.float32(0.9092), np.float32(0.9532), np.float32(0.9572), np.float32(0.8911), np.float32(0.8832), np.float32(0.8916)] 
+2025-05-06 06:23:36.752738: Epoch time: 98.39 s 
+2025-05-06 06:23:38.384262:  
+2025-05-06 06:23:38.456903: Epoch 1062 
+2025-05-06 06:23:38.478739: Current learning rate: 0.00506 
+2025-05-06 06:25:15.155654: train_loss -0.48 
+2025-05-06 06:25:15.217500: val_loss -0.4888 
+2025-05-06 06:25:15.238136: Pseudo dice [np.float32(0.8338), np.float32(0.8117), np.float32(0.9203), np.float32(0.9695), np.float32(0.8793), np.float32(0.9582), np.float32(0.9563), np.float32(0.9723), np.float32(0.9538), np.float32(0.9585), np.float32(0.9539), np.float32(0.9539), np.float32(0.9672), np.float32(0.8927), np.float32(0.9701), np.float32(0.9567), np.float32(0.8507), np.float32(0.814), np.float32(0.9115)] 
+2025-05-06 06:25:15.258104: Epoch time: 96.77 s 
+2025-05-06 06:25:16.799722:  
+2025-05-06 06:25:16.890638: Epoch 1063 
+2025-05-06 06:25:16.931522: Current learning rate: 0.00505 
+2025-05-06 06:26:51.261477: train_loss -0.4751 
+2025-05-06 06:26:51.367944: val_loss -0.4976 
+2025-05-06 06:26:51.386151: Pseudo dice [np.float32(0.8343), np.float32(0.8379), np.float32(0.8981), np.float32(0.974), np.float32(0.8824), np.float32(0.96), np.float32(0.9658), np.float32(0.9556), np.float32(0.9545), np.float32(0.935), np.float32(0.9457), np.float32(0.9585), np.float32(0.9578), np.float32(0.8994), np.float32(0.9493), np.float32(0.9516), np.float32(0.8989), np.float32(0.9142), np.float32(0.9272)] 
+2025-05-06 06:26:51.406718: Epoch time: 94.46 s 
+2025-05-06 06:26:53.066299:  
+2025-05-06 06:26:53.083697: Epoch 1064 
+2025-05-06 06:26:53.084168: Current learning rate: 0.00505 
+2025-05-06 06:28:29.188221: train_loss -0.4602 
+2025-05-06 06:28:29.307276: val_loss -0.4578 
+2025-05-06 06:28:29.334636: Pseudo dice [np.float32(0.8467), np.float32(0.85), np.float32(0.944), np.float32(0.9734), np.float32(0.8924), np.float32(0.9526), np.float32(0.9635), np.float32(0.9701), np.float32(0.9525), np.float32(0.9506), np.float32(0.9316), np.float32(0.9586), np.float32(0.9656), np.float32(0.9127), np.float32(0.9678), np.float32(0.9533), np.float32(0.887), np.float32(0.8839), np.float32(0.9085)] 
+2025-05-06 06:28:29.371327: Epoch time: 96.12 s 
+2025-05-06 06:28:30.956427:  
+2025-05-06 06:28:31.008644: Epoch 1065 
+2025-05-06 06:28:31.030374: Current learning rate: 0.00504 
+2025-05-06 06:30:06.955243: train_loss -0.4835 
+2025-05-06 06:30:07.093467: val_loss -0.4756 
+2025-05-06 06:30:07.094448: Pseudo dice [np.float32(0.8484), np.float32(0.8502), np.float32(0.8974), np.float32(0.9796), np.float32(0.8789), np.float32(0.9514), np.float32(0.9574), np.float32(0.959), np.float32(0.9555), np.float32(0.9657), np.float32(0.9466), np.float32(0.9615), np.float32(0.9606), np.float32(0.8976), np.float32(0.9625), np.float32(0.9617), np.float32(0.8777), np.float32(0.8865), np.float32(0.9079)] 
+2025-05-06 06:30:07.095088: Epoch time: 96.0 s 
+2025-05-06 06:30:08.733394:  
+2025-05-06 06:30:08.804272: Epoch 1066 
+2025-05-06 06:30:08.833580: Current learning rate: 0.00504 
+2025-05-06 06:31:48.956459: train_loss -0.4792 
+2025-05-06 06:31:49.008048: val_loss -0.5178 
+2025-05-06 06:31:49.038645: Pseudo dice [np.float32(0.8354), np.float32(0.85), np.float32(0.9073), np.float32(0.966), np.float32(0.8993), np.float32(0.9577), np.float32(0.9648), np.float32(0.979), np.float32(0.9584), np.float32(0.9665), np.float32(0.9392), np.float32(0.9689), np.float32(0.9654), np.float32(0.8993), np.float32(0.9634), np.float32(0.9528), np.float32(0.8433), np.float32(0.8786), np.float32(0.9201)] 
+2025-05-06 06:31:49.042595: Epoch time: 100.22 s 
+2025-05-06 06:31:54.452371:  
+2025-05-06 06:31:54.458241: Epoch 1067 
+2025-05-06 06:31:54.458753: Current learning rate: 0.00503 
+2025-05-06 06:33:28.764224: train_loss -0.4752 
+2025-05-06 06:33:28.808809: val_loss -0.5342 
+2025-05-06 06:33:28.809535: Pseudo dice [np.float32(0.8458), np.float32(0.8252), np.float32(0.9353), np.float32(0.9793), np.float32(0.8708), np.float32(0.9493), np.float32(0.9621), np.float32(0.9738), np.float32(0.9586), np.float32(0.9635), np.float32(0.9472), np.float32(0.964), np.float32(0.9717), np.float32(0.8937), np.float32(0.9739), np.float32(0.9597), np.float32(0.8224), np.float32(0.8443), np.float32(0.9095)] 
+2025-05-06 06:33:28.823075: Epoch time: 94.31 s 
+2025-05-06 06:33:30.290825:  
+2025-05-06 06:33:30.458237: Epoch 1068 
+2025-05-06 06:33:30.519812: Current learning rate: 0.00503 
+2025-05-06 06:35:13.294267: train_loss -0.4789 
+2025-05-06 06:35:13.476042: val_loss -0.4677 
+2025-05-06 06:35:13.515938: Pseudo dice [np.float32(0.8476), np.float32(0.8323), np.float32(0.9428), np.float32(0.9681), np.float32(0.9011), np.float32(0.9633), np.float32(0.9609), np.float32(0.9742), np.float32(0.9692), np.float32(0.9641), np.float32(0.9526), np.float32(0.9729), np.float32(0.9639), np.float32(0.8883), np.float32(0.9631), np.float32(0.9417), np.float32(0.9033), np.float32(0.8736), np.float32(0.9194)] 
+2025-05-06 06:35:13.553105: Epoch time: 103.0 s 
+2025-05-06 06:35:15.030367:  
+2025-05-06 06:35:15.049602: Epoch 1069 
+2025-05-06 06:35:15.061173: Current learning rate: 0.00502 
+2025-05-06 06:36:55.542765: train_loss -0.4695 
+2025-05-06 06:36:55.712671: val_loss -0.5157 
+2025-05-06 06:36:55.770758: Pseudo dice [np.float32(0.8244), np.float32(0.8642), np.float32(0.8938), np.float32(0.9694), np.float32(0.9093), np.float32(0.9578), np.float32(0.962), np.float32(0.9764), np.float32(0.9611), np.float32(0.9714), np.float32(0.9491), np.float32(0.9678), np.float32(0.9705), np.float32(0.9078), np.float32(0.9128), np.float32(0.9431), np.float32(0.8877), np.float32(0.8925), np.float32(0.9202)] 
+2025-05-06 06:36:55.805983: Epoch time: 100.51 s 
+2025-05-06 06:36:57.766431:  
+2025-05-06 06:36:57.816141: Epoch 1070 
+2025-05-06 06:36:57.830918: Current learning rate: 0.00502 
+2025-05-06 06:38:33.151202: train_loss -0.4766 
+2025-05-06 06:38:33.294211: val_loss -0.4791 
+2025-05-06 06:38:33.339010: Pseudo dice [np.float32(0.8393), np.float32(0.8391), np.float32(0.9109), np.float32(0.9754), np.float32(0.9178), np.float32(0.9561), np.float32(0.9589), np.float32(0.9636), np.float32(0.9602), np.float32(0.9716), np.float32(0.9542), np.float32(0.9705), np.float32(0.9669), np.float32(0.8938), np.float32(0.9528), np.float32(0.9319), np.float32(0.9087), np.float32(0.8979), np.float32(0.9204)] 
+2025-05-06 06:38:33.393966: Epoch time: 95.39 s 
+2025-05-06 06:38:33.431209: Yayy! New best EMA pseudo Dice: 0.9269999861717224 
+2025-05-06 06:38:35.924211:  
+2025-05-06 06:38:35.976428: Epoch 1071 
+2025-05-06 06:38:35.987490: Current learning rate: 0.00502 
+2025-05-06 06:40:14.803031: train_loss -0.4834 
+2025-05-06 06:40:14.906197: val_loss -0.4445 
+2025-05-06 06:40:14.952377: Pseudo dice [np.float32(0.796), np.float32(0.7789), np.float32(0.9017), np.float32(0.9752), np.float32(0.9015), np.float32(0.9291), np.float32(0.9663), np.float32(0.9761), np.float32(0.9574), np.float32(0.9623), np.float32(0.951), np.float32(0.9541), np.float32(0.9625), np.float32(0.8758), np.float32(0.9297), np.float32(0.9347), np.float32(0.8535), np.float32(0.8822), np.float32(0.9)] 
+2025-05-06 06:40:14.975651: Epoch time: 98.88 s 
+2025-05-06 06:40:16.432363:  
+2025-05-06 06:40:16.544692: Epoch 1072 
+2025-05-06 06:40:16.578262: Current learning rate: 0.00501 
+2025-05-06 06:41:52.707733: train_loss -0.4924 
+2025-05-06 06:41:52.785970: val_loss -0.4952 
+2025-05-06 06:41:52.804755: Pseudo dice [np.float32(0.8275), np.float32(0.7883), np.float32(0.9071), np.float32(0.9365), np.float32(0.9198), np.float32(0.9598), np.float32(0.96), np.float32(0.9753), np.float32(0.9469), np.float32(0.9535), np.float32(0.9147), np.float32(0.9584), np.float32(0.9539), np.float32(0.9102), np.float32(0.9485), np.float32(0.9572), np.float32(0.8856), np.float32(0.892), np.float32(0.9131)] 
+2025-05-06 06:41:52.846547: Epoch time: 96.28 s 
+2025-05-06 06:41:54.431213:  
+2025-05-06 06:41:54.434177: Epoch 1073 
+2025-05-06 06:41:54.434591: Current learning rate: 0.00501 
+2025-05-06 06:43:32.544981: train_loss -0.5089 
+2025-05-06 06:43:32.661273: val_loss -0.4911 
+2025-05-06 06:43:32.662307: Pseudo dice [np.float32(0.8538), np.float32(0.8512), np.float32(0.9254), np.float32(0.9713), np.float32(0.8961), np.float32(0.9591), np.float32(0.942), np.float32(0.9744), np.float32(0.9594), np.float32(0.9643), np.float32(0.9467), np.float32(0.9591), np.float32(0.9674), np.float32(0.9024), np.float32(0.9535), np.float32(0.951), np.float32(0.8778), np.float32(0.8835), np.float32(0.8986)] 
+2025-05-06 06:43:32.680665: Epoch time: 98.12 s 
+2025-05-06 06:43:34.236156:  
+2025-05-06 06:43:34.291598: Epoch 1074 
+2025-05-06 06:43:34.292057: Current learning rate: 0.005 
+2025-05-06 06:45:09.925985: train_loss -0.5052 
+2025-05-06 06:45:09.989884: val_loss -0.5291 
+2025-05-06 06:45:10.003922: Pseudo dice [np.float32(0.8349), np.float32(0.8587), np.float32(0.8814), np.float32(0.9468), np.float32(0.9059), np.float32(0.948), np.float32(0.9609), np.float32(0.9737), np.float32(0.9514), np.float32(0.9601), np.float32(0.9478), np.float32(0.9583), np.float32(0.9598), np.float32(0.9023), np.float32(0.9113), np.float32(0.9415), np.float32(0.8766), np.float32(0.8726), np.float32(0.928)] 
+2025-05-06 06:45:10.016557: Epoch time: 95.69 s 
+2025-05-06 06:45:11.618462:  
+2025-05-06 06:45:11.696236: Epoch 1075 
+2025-05-06 06:45:11.698405: Current learning rate: 0.005 
+2025-05-06 06:46:47.980487: train_loss -0.457 
+2025-05-06 06:46:48.117684: val_loss -0.4781 
+2025-05-06 06:46:48.143878: Pseudo dice [np.float32(0.829), np.float32(0.8462), np.float32(0.9494), np.float32(0.9762), np.float32(0.8931), np.float32(0.956), np.float32(0.9587), np.float32(0.9754), np.float32(0.9474), np.float32(0.9645), np.float32(0.9432), np.float32(0.9483), np.float32(0.9697), np.float32(0.9001), np.float32(0.9588), np.float32(0.9456), np.float32(0.785), np.float32(0.7608), np.float32(0.92)] 
+2025-05-06 06:46:48.219415: Epoch time: 96.36 s 
+2025-05-06 06:46:49.878912:  
+2025-05-06 06:46:50.027273: Epoch 1076 
+2025-05-06 06:46:50.061741: Current learning rate: 0.00499 
+2025-05-06 06:48:23.357740: train_loss -0.4766 
+2025-05-06 06:48:23.577826: val_loss -0.5189 
+2025-05-06 06:48:23.579874: Pseudo dice [np.float32(0.8451), np.float32(0.8469), np.float32(0.8609), np.float32(0.969), np.float32(0.8956), np.float32(0.9611), np.float32(0.967), np.float32(0.9779), np.float32(0.9514), np.float32(0.9625), np.float32(0.9439), np.float32(0.9546), np.float32(0.9527), np.float32(0.9083), np.float32(0.9667), np.float32(0.9564), np.float32(0.8626), np.float32(0.8799), np.float32(0.9198)] 
+2025-05-06 06:48:23.580469: Epoch time: 93.48 s 
+2025-05-06 06:48:25.271830:  
+2025-05-06 06:48:25.367380: Epoch 1077 
+2025-05-06 06:48:25.436342: Current learning rate: 0.00499 
+2025-05-06 06:49:59.812703: train_loss -0.4936 
+2025-05-06 06:49:59.896532: val_loss -0.4773 
+2025-05-06 06:49:59.903864: Pseudo dice [np.float32(0.8221), np.float32(0.8301), np.float32(0.8835), np.float32(0.9769), np.float32(0.8616), np.float32(0.959), np.float32(0.9545), np.float32(0.9743), np.float32(0.9547), np.float32(0.967), np.float32(0.9345), np.float32(0.9621), np.float32(0.9622), np.float32(0.9043), np.float32(0.9587), np.float32(0.9472), np.float32(0.8818), np.float32(0.8756), np.float32(0.9186)] 
+2025-05-06 06:49:59.916304: Epoch time: 94.54 s 
+2025-05-06 06:50:01.457608:  
+2025-05-06 06:50:01.607958: Epoch 1078 
+2025-05-06 06:50:01.609168: Current learning rate: 0.00498 
+2025-05-06 06:51:38.765114: train_loss -0.4772 
+2025-05-06 06:51:38.867542: val_loss -0.4708 
+2025-05-06 06:51:38.900175: Pseudo dice [np.float32(0.8445), np.float32(0.8228), np.float32(0.5286), np.float32(0.9699), np.float32(0.9134), np.float32(0.9557), np.float32(0.9642), np.float32(0.9768), np.float32(0.9576), np.float32(0.9577), np.float32(0.9194), np.float32(0.9656), np.float32(0.9646), np.float32(0.8952), np.float32(0.9616), np.float32(0.9407), np.float32(0.8782), np.float32(0.8693), np.float32(0.9052)] 
+2025-05-06 06:51:38.923314: Epoch time: 97.31 s 
+2025-05-06 06:51:40.526291:  
+2025-05-06 06:51:40.560545: Epoch 1079 
+2025-05-06 06:51:40.573555: Current learning rate: 0.00498 
+2025-05-06 06:53:11.274128: train_loss -0.5027 
+2025-05-06 06:53:11.352823: val_loss -0.4885 
+2025-05-06 06:53:11.355396: Pseudo dice [np.float32(0.8228), np.float32(0.8386), np.float32(0.9283), np.float32(0.9771), np.float32(0.8863), np.float32(0.9413), np.float32(0.9621), np.float32(0.9739), np.float32(0.9573), np.float32(0.9642), np.float32(0.9418), np.float32(0.9715), np.float32(0.9652), np.float32(0.9028), np.float32(0.9648), np.float32(0.9569), np.float32(0.8987), np.float32(0.8707), np.float32(0.9196)] 
+2025-05-06 06:53:11.355965: Epoch time: 90.75 s 
+2025-05-06 06:53:13.220056:  
+2025-05-06 06:53:13.284082: Epoch 1080 
+2025-05-06 06:53:13.303038: Current learning rate: 0.00497 
+2025-05-06 06:54:51.034373: train_loss -0.4953 
+2025-05-06 06:54:51.085545: val_loss -0.4808 
+2025-05-06 06:54:51.089764: Pseudo dice [np.float32(0.8515), np.float32(0.8412), np.float32(0.9288), np.float32(0.9746), np.float32(0.8996), np.float32(0.9578), np.float32(0.9706), np.float32(0.9768), np.float32(0.9582), np.float32(0.9712), np.float32(0.9467), np.float32(0.9695), np.float32(0.9691), np.float32(0.9017), np.float32(0.933), np.float32(0.9574), np.float32(0.8802), np.float32(0.8772), np.float32(0.9194)] 
+2025-05-06 06:54:51.090535: Epoch time: 97.82 s 
+2025-05-06 06:54:52.596096:  
+2025-05-06 06:54:52.680150: Epoch 1081 
+2025-05-06 06:54:52.680918: Current learning rate: 0.00497 
+2025-05-06 06:56:28.271390: train_loss -0.4674 
+2025-05-06 06:56:28.426406: val_loss -0.475 
+2025-05-06 06:56:28.479578: Pseudo dice [np.float32(0.8608), np.float32(0.8476), np.float32(0.9107), np.float32(0.9643), np.float32(0.9116), np.float32(0.964), np.float32(0.9524), np.float32(0.9814), np.float32(0.967), np.float32(0.961), np.float32(0.9518), np.float32(0.9455), np.float32(0.9574), np.float32(0.896), np.float32(0.9535), np.float32(0.9557), np.float32(0.8648), np.float32(0.8829), np.float32(0.9261)] 
+2025-05-06 06:56:28.497143: Epoch time: 95.68 s 
+2025-05-06 06:56:30.023995:  
+2025-05-06 06:56:30.095686: Epoch 1082 
+2025-05-06 06:56:30.096729: Current learning rate: 0.00496 
+2025-05-06 06:58:07.407196: train_loss -0.4959 
+2025-05-06 06:58:07.414080: val_loss -0.4795 
+2025-05-06 06:58:07.414613: Pseudo dice [np.float32(0.8479), np.float32(0.856), np.float32(0.8626), np.float32(0.9585), np.float32(0.9163), np.float32(0.9578), np.float32(0.9629), np.float32(0.9611), np.float32(0.944), np.float32(0.9725), np.float32(0.955), np.float32(0.947), np.float32(0.969), np.float32(0.8977), np.float32(0.966), np.float32(0.9466), np.float32(0.8992), np.float32(0.8898), np.float32(0.9222)] 
+2025-05-06 06:58:07.415045: Epoch time: 97.39 s 
+2025-05-06 06:58:08.895623:  
+2025-05-06 06:58:09.024502: Epoch 1083 
+2025-05-06 06:58:09.060818: Current learning rate: 0.00496 
+2025-05-06 06:59:44.714669: train_loss -0.501 
+2025-05-06 06:59:44.777014: val_loss -0.5555 
+2025-05-06 06:59:44.814813: Pseudo dice [np.float32(0.8438), np.float32(0.832), np.float32(0.8953), np.float32(0.9694), np.float32(0.9089), np.float32(0.9478), np.float32(0.9667), np.float32(0.9727), np.float32(0.9641), np.float32(0.9613), np.float32(0.9478), np.float32(0.9714), np.float32(0.9691), np.float32(0.8918), np.float32(0.9407), np.float32(0.9491), np.float32(0.9005), np.float32(0.9146), np.float32(0.9084)] 
+2025-05-06 06:59:44.839994: Epoch time: 95.82 s 
+2025-05-06 06:59:46.380761:  
+2025-05-06 06:59:46.462717: Epoch 1084 
+2025-05-06 06:59:46.491841: Current learning rate: 0.00495 
+2025-05-06 07:01:20.774098: train_loss -0.4837 
+2025-05-06 07:01:20.933815: val_loss -0.4949 
+2025-05-06 07:01:20.991718: Pseudo dice [np.float32(0.8282), np.float32(0.8597), np.float32(0.8451), np.float32(0.9726), np.float32(0.8939), np.float32(0.9562), np.float32(0.9635), np.float32(0.9757), np.float32(0.9573), np.float32(0.967), np.float32(0.9478), np.float32(0.9698), np.float32(0.9645), np.float32(0.9054), np.float32(0.9672), np.float32(0.9521), np.float32(0.8734), np.float32(0.8758), np.float32(0.9197)] 
+2025-05-06 07:01:21.020962: Epoch time: 94.39 s 
+2025-05-06 07:01:26.204983:  
+2025-05-06 07:01:26.210736: Epoch 1085 
+2025-05-06 07:01:26.212075: Current learning rate: 0.00495 
+2025-05-06 07:03:00.724307: train_loss -0.4879 
+2025-05-06 07:03:00.846479: val_loss -0.4995 
+2025-05-06 07:03:00.861566: Pseudo dice [np.float32(0.8377), np.float32(0.8333), np.float32(0.9532), np.float32(0.9657), np.float32(0.9014), np.float32(0.9557), np.float32(0.9636), np.float32(0.9785), np.float32(0.9657), np.float32(0.961), np.float32(0.9381), np.float32(0.9622), np.float32(0.9615), np.float32(0.8963), np.float32(0.9625), np.float32(0.954), np.float32(0.8455), np.float32(0.8941), np.float32(0.9041)] 
+2025-05-06 07:03:00.865617: Epoch time: 94.52 s 
+2025-05-06 07:03:02.454356:  
+2025-05-06 07:03:02.498456: Epoch 1086 
+2025-05-06 07:03:02.544733: Current learning rate: 0.00494 
+2025-05-06 07:04:44.842379: train_loss -0.4765 
+2025-05-06 07:04:44.960909: val_loss -0.4869 
+2025-05-06 07:04:44.997828: Pseudo dice [np.float32(0.7987), np.float32(0.8322), np.float32(0.9363), np.float32(0.9757), np.float32(0.9121), np.float32(0.9605), np.float32(0.9643), np.float32(0.9793), np.float32(0.9664), np.float32(0.9662), np.float32(0.9395), np.float32(0.9696), np.float32(0.9681), np.float32(0.8891), np.float32(0.9643), np.float32(0.95), np.float32(0.8545), np.float32(0.8621), np.float32(0.9058)] 
+2025-05-06 07:04:45.031037: Epoch time: 102.39 s 
+2025-05-06 07:04:46.522041:  
+2025-05-06 07:04:46.539138: Epoch 1087 
+2025-05-06 07:04:46.554136: Current learning rate: 0.00494 
+2025-05-06 07:06:19.924814: train_loss -0.4826 
+2025-05-06 07:06:20.050329: val_loss -0.4631 
+2025-05-06 07:06:20.097939: Pseudo dice [np.float32(0.8243), np.float32(0.8411), np.float32(0.9422), np.float32(0.9693), np.float32(0.9), np.float32(0.9629), np.float32(0.9709), np.float32(0.9805), np.float32(0.9397), np.float32(0.9628), np.float32(0.9431), np.float32(0.9548), np.float32(0.9666), np.float32(0.9017), np.float32(0.9711), np.float32(0.9495), np.float32(0.9159), np.float32(0.9049), np.float32(0.9194)] 
+2025-05-06 07:06:20.134551: Epoch time: 93.4 s 
+2025-05-06 07:06:21.633591:  
+2025-05-06 07:06:21.668846: Epoch 1088 
+2025-05-06 07:06:21.669718: Current learning rate: 0.00493 
+2025-05-06 07:07:58.735045: train_loss -0.4806 
+2025-05-06 07:07:58.797395: val_loss -0.5058 
+2025-05-06 07:07:58.819000: Pseudo dice [np.float32(0.8185), np.float32(0.8375), np.float32(0.9396), np.float32(0.9784), np.float32(0.8714), np.float32(0.9588), np.float32(0.952), np.float32(0.9786), np.float32(0.9585), np.float32(0.9487), np.float32(0.8981), np.float32(0.9659), np.float32(0.9598), np.float32(0.9001), np.float32(0.9638), np.float32(0.9567), np.float32(0.8524), np.float32(0.825), np.float32(0.9118)] 
+2025-05-06 07:07:58.858207: Epoch time: 97.1 s 
+2025-05-06 07:08:00.338457:  
+2025-05-06 07:08:00.422611: Epoch 1089 
+2025-05-06 07:08:00.458516: Current learning rate: 0.00493 
+2025-05-06 07:09:39.250912: train_loss -0.4955 
+2025-05-06 07:09:39.308087: val_loss -0.5201 
+2025-05-06 07:09:39.325430: Pseudo dice [np.float32(0.8557), np.float32(0.8712), np.float32(0.9254), np.float32(0.9757), np.float32(0.9072), np.float32(0.9613), np.float32(0.9657), np.float32(0.9798), np.float32(0.9661), np.float32(0.9737), np.float32(0.9608), np.float32(0.9713), np.float32(0.9745), np.float32(0.9084), np.float32(0.9679), np.float32(0.9582), np.float32(0.8953), np.float32(0.9116), np.float32(0.9382)] 
+2025-05-06 07:09:39.354719: Epoch time: 98.91 s 
+2025-05-06 07:09:39.383855: Yayy! New best EMA pseudo Dice: 0.9271000027656555 
+2025-05-06 07:09:41.758403:  
+2025-05-06 07:09:41.827111: Epoch 1090 
+2025-05-06 07:09:41.835030: Current learning rate: 0.00492 
+2025-05-06 07:11:19.943318: train_loss -0.488 
+2025-05-06 07:11:20.036393: val_loss -0.5131 
+2025-05-06 07:11:20.064860: Pseudo dice [np.float32(0.8489), np.float32(0.8468), np.float32(0.908), np.float32(0.9644), np.float32(0.8905), np.float32(0.9633), np.float32(0.9611), np.float32(0.9734), np.float32(0.9674), np.float32(0.9704), np.float32(0.945), np.float32(0.9681), np.float32(0.9633), np.float32(0.91), np.float32(0.9688), np.float32(0.9567), np.float32(0.8837), np.float32(0.8857), np.float32(0.9217)] 
+2025-05-06 07:11:20.085338: Epoch time: 98.19 s 
+2025-05-06 07:11:20.107187: Yayy! New best EMA pseudo Dice: 0.9276000261306763 
+2025-05-06 07:11:22.689882:  
+2025-05-06 07:11:22.785509: Epoch 1091 
+2025-05-06 07:11:22.826770: Current learning rate: 0.00492 
+2025-05-06 07:13:00.285929: train_loss -0.4897 
+2025-05-06 07:13:00.350034: val_loss -0.5091 
+2025-05-06 07:13:00.358341: Pseudo dice [np.float32(0.8436), np.float32(0.7969), np.float32(0.957), np.float32(0.9783), np.float32(0.8616), np.float32(0.9583), np.float32(0.9677), np.float32(0.9802), np.float32(0.9659), np.float32(0.9687), np.float32(0.9362), np.float32(0.9655), np.float32(0.9578), np.float32(0.8957), np.float32(0.9674), np.float32(0.9563), np.float32(0.8523), np.float32(0.8655), np.float32(0.9084)] 
+2025-05-06 07:13:00.372974: Epoch time: 97.6 s 
+2025-05-06 07:13:01.882390:  
+2025-05-06 07:13:01.998065: Epoch 1092 
+2025-05-06 07:13:02.020819: Current learning rate: 0.00491 
+2025-05-06 07:14:40.570493: train_loss -0.4901 
+2025-05-06 07:14:40.718475: val_loss -0.5 
+2025-05-06 07:14:40.731466: Pseudo dice [np.float32(0.8593), np.float32(0.8429), np.float32(0.8936), np.float32(0.968), np.float32(0.8942), np.float32(0.9545), np.float32(0.9339), np.float32(0.9754), np.float32(0.9706), np.float32(0.9038), np.float32(0.9364), np.float32(0.9684), np.float32(0.9655), np.float32(0.9048), np.float32(0.9529), np.float32(0.9597), np.float32(0.858), np.float32(0.9083), np.float32(0.9339)] 
+2025-05-06 07:14:40.752544: Epoch time: 98.69 s 
+2025-05-06 07:14:42.276688:  
+2025-05-06 07:14:42.404134: Epoch 1093 
+2025-05-06 07:14:42.404896: Current learning rate: 0.00491 
+2025-05-06 07:16:22.100891: train_loss -0.4818 
+2025-05-06 07:16:22.158757: val_loss -0.4984 
+2025-05-06 07:16:22.180115: Pseudo dice [np.float32(0.7876), np.float32(0.8143), np.float32(0.9243), np.float32(0.9633), np.float32(0.8712), np.float32(0.9595), np.float32(0.9599), np.float32(0.9697), np.float32(0.9594), np.float32(0.954), np.float32(0.9397), np.float32(0.9698), np.float32(0.9599), np.float32(0.8909), np.float32(0.9686), np.float32(0.9502), np.float32(0.9024), np.float32(0.8683), np.float32(0.9181)] 
+2025-05-06 07:16:22.198280: Epoch time: 99.83 s 
+2025-05-06 07:16:23.919224:  
+2025-05-06 07:16:24.007054: Epoch 1094 
+2025-05-06 07:16:24.049720: Current learning rate: 0.0049 
+2025-05-06 07:17:59.449247: train_loss -0.4822 
+2025-05-06 07:17:59.569754: val_loss -0.4757 
+2025-05-06 07:17:59.617808: Pseudo dice [np.float32(0.8504), np.float32(0.8369), np.float32(0.9159), np.float32(0.9648), np.float32(0.9007), np.float32(0.9487), np.float32(0.9601), np.float32(0.9738), np.float32(0.9673), np.float32(0.9564), np.float32(0.9331), np.float32(0.9695), np.float32(0.9534), np.float32(0.9046), np.float32(0.9726), np.float32(0.9519), np.float32(0.8957), np.float32(0.8778), np.float32(0.9186)] 
+2025-05-06 07:17:59.673550: Epoch time: 95.53 s 
+2025-05-06 07:18:01.230728:  
+2025-05-06 07:18:01.233439: Epoch 1095 
+2025-05-06 07:18:01.233815: Current learning rate: 0.0049 
+2025-05-06 07:19:37.551481: train_loss -0.4679 
+2025-05-06 07:19:37.607327: val_loss -0.4641 
+2025-05-06 07:19:37.626388: Pseudo dice [np.float32(0.8344), np.float32(0.8055), np.float32(0.9361), np.float32(0.9483), np.float32(0.8836), np.float32(0.9442), np.float32(0.9632), np.float32(0.9732), np.float32(0.968), np.float32(0.9667), np.float32(0.9471), np.float32(0.9697), np.float32(0.9653), np.float32(0.8897), np.float32(0.956), np.float32(0.9381), np.float32(0.8619), np.float32(0.9038), np.float32(0.91)] 
+2025-05-06 07:19:37.650611: Epoch time: 96.32 s 
+2025-05-06 07:19:39.375509:  
+2025-05-06 07:19:39.530321: Epoch 1096 
+2025-05-06 07:19:39.559637: Current learning rate: 0.00489 
+2025-05-06 07:21:17.644174: train_loss -0.5088 
+2025-05-06 07:21:17.717716: val_loss -0.5091 
+2025-05-06 07:21:17.742718: Pseudo dice [np.float32(0.8568), np.float32(0.8403), np.float32(0.9318), np.float32(0.9787), np.float32(0.8942), np.float32(0.9621), np.float32(0.9556), np.float32(0.9758), np.float32(0.9691), np.float32(0.9718), np.float32(0.9474), np.float32(0.971), np.float32(0.9659), np.float32(0.9057), np.float32(0.9684), np.float32(0.9513), np.float32(0.8697), np.float32(0.8674), np.float32(0.9251)] 
+2025-05-06 07:21:17.774111: Epoch time: 98.27 s 
+2025-05-06 07:21:19.345497:  
+2025-05-06 07:21:19.400790: Epoch 1097 
+2025-05-06 07:21:19.408812: Current learning rate: 0.00489 
+2025-05-06 07:22:54.431186: train_loss -0.4896 
+2025-05-06 07:22:54.503694: val_loss -0.4696 
+2025-05-06 07:22:54.514289: Pseudo dice [np.float32(0.8128), np.float32(0.8264), np.float32(0.9254), np.float32(0.9767), np.float32(0.8871), np.float32(0.963), np.float32(0.9521), np.float32(0.9724), np.float32(0.9455), np.float32(0.9614), np.float32(0.952), np.float32(0.9594), np.float32(0.9731), np.float32(0.9102), np.float32(0.97), np.float32(0.9466), np.float32(0.8861), np.float32(0.8775), np.float32(0.9139)] 
+2025-05-06 07:22:54.536123: Epoch time: 95.09 s 
+2025-05-06 07:22:56.072815:  
+2025-05-06 07:22:56.103296: Epoch 1098 
+2025-05-06 07:22:56.103981: Current learning rate: 0.00488 
+2025-05-06 07:24:31.650944: train_loss -0.4784 
+2025-05-06 07:24:31.686424: val_loss -0.4621 
+2025-05-06 07:24:31.692917: Pseudo dice [np.float32(0.8394), np.float32(0.8367), np.float32(0.9204), np.float32(0.9767), np.float32(0.8895), np.float32(0.9587), np.float32(0.9627), np.float32(0.9738), np.float32(0.967), np.float32(0.9667), np.float32(0.932), np.float32(0.9645), np.float32(0.9687), np.float32(0.8901), np.float32(0.9691), np.float32(0.9336), np.float32(0.8606), np.float32(0.8604), np.float32(0.9042)] 
+2025-05-06 07:24:31.704100: Epoch time: 95.58 s 
+2025-05-06 07:24:33.248255:  
+2025-05-06 07:24:33.359148: Epoch 1099 
+2025-05-06 07:24:33.386058: Current learning rate: 0.00488 
+2025-05-06 07:26:10.313946: train_loss -0.4609 
+2025-05-06 07:26:10.401085: val_loss -0.5129 
+2025-05-06 07:26:10.422925: Pseudo dice [np.float32(0.8341), np.float32(0.8611), np.float32(0.9472), np.float32(0.9747), np.float32(0.9182), np.float32(0.9638), np.float32(0.9665), np.float32(0.9757), np.float32(0.9653), np.float32(0.9633), np.float32(0.9462), np.float32(0.9649), np.float32(0.9675), np.float32(0.9001), np.float32(0.9713), np.float32(0.9396), np.float32(0.8058), np.float32(0.8636), np.float32(0.9106)] 
+2025-05-06 07:26:10.452079: Epoch time: 97.07 s 
+2025-05-06 07:26:13.941339:  
+2025-05-06 07:26:13.988289: Epoch 1100 
+2025-05-06 07:26:14.052785: Current learning rate: 0.00487 
+2025-05-06 07:27:48.256091: train_loss -0.4951 
+2025-05-06 07:27:48.363460: val_loss -0.5055 
+2025-05-06 07:27:48.402631: Pseudo dice [np.float32(0.8464), np.float32(0.8514), np.float32(0.8571), np.float32(0.9544), np.float32(0.8966), np.float32(0.96), np.float32(0.9652), np.float32(0.9738), np.float32(0.9476), np.float32(0.9448), np.float32(0.92), np.float32(0.96), np.float32(0.9566), np.float32(0.9034), np.float32(0.963), np.float32(0.9537), np.float32(0.8674), np.float32(0.8726), np.float32(0.9052)] 
+2025-05-06 07:27:48.457064: Epoch time: 94.32 s 
+2025-05-06 07:27:49.958120:  
+2025-05-06 07:27:50.065498: Epoch 1101 
+2025-05-06 07:27:50.075920: Current learning rate: 0.00487 
+2025-05-06 07:29:25.997984: train_loss -0.4755 
+2025-05-06 07:29:26.054296: val_loss -0.5026 
+2025-05-06 07:29:26.054998: Pseudo dice [np.float32(0.836), np.float32(0.8387), np.float32(0.9252), np.float32(0.9721), np.float32(0.8127), np.float32(0.944), np.float32(0.9626), np.float32(0.9776), np.float32(0.9593), np.float32(0.962), np.float32(0.9456), np.float32(0.9646), np.float32(0.9677), np.float32(0.909), np.float32(0.9345), np.float32(0.966), np.float32(0.875), np.float32(0.8937), np.float32(0.911)] 
+2025-05-06 07:29:26.056009: Epoch time: 96.04 s 
+2025-05-06 07:29:31.726801:  
+2025-05-06 07:29:31.733469: Epoch 1102 
+2025-05-06 07:29:31.733974: Current learning rate: 0.00486 
+2025-05-06 07:31:10.812403: train_loss -0.4964 
+2025-05-06 07:31:10.930245: val_loss -0.4918 
+2025-05-06 07:31:10.950797: Pseudo dice [np.float32(0.8618), np.float32(0.8587), np.float32(0.9109), np.float32(0.9772), np.float32(0.9195), np.float32(0.9553), np.float32(0.9686), np.float32(0.9797), np.float32(0.9616), np.float32(0.9584), np.float32(0.9443), np.float32(0.9631), np.float32(0.9624), np.float32(0.9303), np.float32(0.9708), np.float32(0.9583), np.float32(0.8399), np.float32(0.8319), np.float32(0.919)] 
+2025-05-06 07:31:10.954983: Epoch time: 99.09 s 
+2025-05-06 07:31:12.547214:  
+2025-05-06 07:31:12.595184: Epoch 1103 
+2025-05-06 07:31:12.613598: Current learning rate: 0.00486 
+2025-05-06 07:32:49.090676: train_loss -0.4683 
+2025-05-06 07:32:49.128791: val_loss -0.4989 
+2025-05-06 07:32:49.140306: Pseudo dice [np.float32(0.8516), np.float32(0.8225), np.float32(0.9196), np.float32(0.9734), np.float32(0.8955), np.float32(0.957), np.float32(0.9621), np.float32(0.9769), np.float32(0.9666), np.float32(0.9613), np.float32(0.9406), np.float32(0.9682), np.float32(0.9535), np.float32(0.9094), np.float32(0.9553), np.float32(0.9526), np.float32(0.8199), np.float32(0.8323), np.float32(0.9189)] 
+2025-05-06 07:32:49.148396: Epoch time: 96.54 s 
+2025-05-06 07:32:50.786136:  
+2025-05-06 07:32:50.863692: Epoch 1104 
+2025-05-06 07:32:50.903592: Current learning rate: 0.00485 
+2025-05-06 07:34:26.098989: train_loss -0.4844 
+2025-05-06 07:34:26.145196: val_loss -0.4695 
+2025-05-06 07:34:26.146278: Pseudo dice [np.float32(0.8396), np.float32(0.8605), np.float32(0.8877), np.float32(0.9775), np.float32(0.9147), np.float32(0.9586), np.float32(0.9673), np.float32(0.9745), np.float32(0.9613), np.float32(0.9637), np.float32(0.9373), np.float32(0.9711), np.float32(0.9643), np.float32(0.9036), np.float32(0.9553), np.float32(0.9488), np.float32(0.8715), np.float32(0.8885), np.float32(0.9293)] 
+2025-05-06 07:34:26.146973: Epoch time: 95.31 s 
+2025-05-06 07:34:27.654258:  
+2025-05-06 07:34:27.758112: Epoch 1105 
+2025-05-06 07:34:27.783807: Current learning rate: 0.00485 
+2025-05-06 07:36:02.644382: train_loss -0.4902 
+2025-05-06 07:36:02.755640: val_loss -0.4943 
+2025-05-06 07:36:02.770889: Pseudo dice [np.float32(0.8437), np.float32(0.8297), np.float32(0.9505), np.float32(0.9766), np.float32(0.8546), np.float32(0.9571), np.float32(0.9686), np.float32(0.9646), np.float32(0.9598), np.float32(0.9615), np.float32(0.9515), np.float32(0.9632), np.float32(0.9633), np.float32(0.8988), np.float32(0.9655), np.float32(0.9533), np.float32(0.8868), np.float32(0.878), np.float32(0.9182)] 
+2025-05-06 07:36:02.789622: Epoch time: 94.99 s 
+2025-05-06 07:36:04.360420:  
+2025-05-06 07:36:04.517891: Epoch 1106 
+2025-05-06 07:36:04.533001: Current learning rate: 0.00484 
+2025-05-06 07:37:37.354478: train_loss -0.4945 
+2025-05-06 07:37:37.393433: val_loss -0.5226 
+2025-05-06 07:37:37.405164: Pseudo dice [np.float32(0.8549), np.float32(0.8471), np.float32(0.9294), np.float32(0.9669), np.float32(0.9182), np.float32(0.9585), np.float32(0.9558), np.float32(0.9768), np.float32(0.9542), np.float32(0.9624), np.float32(0.9427), np.float32(0.955), np.float32(0.9588), np.float32(0.8943), np.float32(0.9593), np.float32(0.9487), np.float32(0.8676), np.float32(0.8906), np.float32(0.9127)] 
+2025-05-06 07:37:37.423019: Epoch time: 93.0 s 
+2025-05-06 07:37:38.771148:  
+2025-05-06 07:37:38.778891: Epoch 1107 
+2025-05-06 07:37:38.779232: Current learning rate: 0.00484 
+2025-05-06 07:39:17.392716: train_loss -0.4956 
+2025-05-06 07:39:17.497803: val_loss -0.4848 
+2025-05-06 07:39:17.516307: Pseudo dice [np.float32(0.8611), np.float32(0.8673), np.float32(0.8841), np.float32(0.9752), np.float32(0.8795), np.float32(0.9567), np.float32(0.9635), np.float32(0.9696), np.float32(0.9625), np.float32(0.9544), np.float32(0.9236), np.float32(0.9675), np.float32(0.9552), np.float32(0.9102), np.float32(0.957), np.float32(0.9528), np.float32(0.9112), np.float32(0.9029), np.float32(0.93)] 
+2025-05-06 07:39:17.532908: Epoch time: 98.62 s 
+2025-05-06 07:39:19.081235:  
+2025-05-06 07:39:19.170146: Epoch 1108 
+2025-05-06 07:39:19.177834: Current learning rate: 0.00484 
+2025-05-06 07:40:56.113924: train_loss -0.4911 
+2025-05-06 07:40:56.265378: val_loss -0.5072 
+2025-05-06 07:40:56.288604: Pseudo dice [np.float32(0.8022), np.float32(0.8476), np.float32(0.9286), np.float32(0.967), np.float32(0.8561), np.float32(0.9622), np.float32(0.9577), np.float32(0.9769), np.float32(0.9677), np.float32(0.9664), np.float32(0.9446), np.float32(0.9689), np.float32(0.9648), np.float32(0.9015), np.float32(0.9625), np.float32(0.9574), np.float32(0.8933), np.float32(0.8804), np.float32(0.8981)] 
+2025-05-06 07:40:56.314881: Epoch time: 97.03 s 
+2025-05-06 07:40:57.774008:  
+2025-05-06 07:40:57.783043: Epoch 1109 
+2025-05-06 07:40:57.783860: Current learning rate: 0.00483 
+2025-05-06 07:42:33.221890: train_loss -0.4914 
+2025-05-06 07:42:33.271257: val_loss -0.5439 
+2025-05-06 07:42:33.289222: Pseudo dice [np.float32(0.8533), np.float32(0.8587), np.float32(0.8737), np.float32(0.9764), np.float32(0.9144), np.float32(0.9617), np.float32(0.9647), np.float32(0.9804), np.float32(0.9322), np.float32(0.963), np.float32(0.9556), np.float32(0.9632), np.float32(0.9712), np.float32(0.912), np.float32(0.9643), np.float32(0.9467), np.float32(0.9092), np.float32(0.9005), np.float32(0.9175)] 
+2025-05-06 07:42:33.308361: Epoch time: 95.45 s 
+2025-05-06 07:42:33.316326: Yayy! New best EMA pseudo Dice: 0.9279000163078308 
+2025-05-06 07:42:36.497302:  
+2025-05-06 07:42:36.540687: Epoch 1110 
+2025-05-06 07:42:36.541569: Current learning rate: 0.00483 
+2025-05-06 07:44:12.457871: train_loss -0.4878 
+2025-05-06 07:44:12.489178: val_loss -0.5131 
+2025-05-06 07:44:12.517704: Pseudo dice [np.float32(0.8416), np.float32(0.843), np.float32(0.8119), np.float32(0.9664), np.float32(0.9296), np.float32(0.9615), np.float32(0.9654), np.float32(0.975), np.float32(0.9348), np.float32(0.9581), np.float32(0.9017), np.float32(0.9689), np.float32(0.9648), np.float32(0.9003), np.float32(0.9704), np.float32(0.9586), np.float32(0.9069), np.float32(0.8965), np.float32(0.9244)] 
+2025-05-06 07:44:12.539469: Epoch time: 95.96 s 
+2025-05-06 07:44:13.980499:  
+2025-05-06 07:44:14.116704: Epoch 1111 
+2025-05-06 07:44:14.156836: Current learning rate: 0.00482 
+2025-05-06 07:45:49.644626: train_loss -0.4942 
+2025-05-06 07:45:49.728919: val_loss -0.4611 
+2025-05-06 07:45:49.754699: Pseudo dice [np.float32(0.8684), np.float32(0.8025), np.float32(0.9034), np.float32(0.9746), np.float32(0.9066), np.float32(0.9589), np.float32(0.9658), np.float32(0.9761), np.float32(0.9453), np.float32(0.955), np.float32(0.9257), np.float32(0.9506), np.float32(0.9586), np.float32(0.9037), np.float32(0.9689), np.float32(0.9411), np.float32(0.8665), np.float32(0.8173), np.float32(0.9091)] 
+2025-05-06 07:45:49.772922: Epoch time: 95.67 s 
+2025-05-06 07:45:51.289943:  
+2025-05-06 07:45:51.394213: Epoch 1112 
+2025-05-06 07:45:51.418601: Current learning rate: 0.00482 
+2025-05-06 07:47:27.802340: train_loss -0.4813 
+2025-05-06 07:47:27.875993: val_loss -0.4482 
+2025-05-06 07:47:27.895558: Pseudo dice [np.float32(0.8638), np.float32(0.8414), np.float32(0.8904), np.float32(0.9736), np.float32(0.9177), np.float32(0.9529), np.float32(0.9594), np.float32(0.9789), np.float32(0.9692), np.float32(0.9686), np.float32(0.9312), np.float32(0.9743), np.float32(0.9632), np.float32(0.9007), np.float32(0.9652), np.float32(0.9482), np.float32(0.8794), np.float32(0.8925), np.float32(0.9118)] 
+2025-05-06 07:47:27.896067: Epoch time: 96.51 s 
+2025-05-06 07:47:29.316188:  
+2025-05-06 07:47:29.354550: Epoch 1113 
+2025-05-06 07:47:29.365868: Current learning rate: 0.00481 
+2025-05-06 07:49:00.173418: train_loss -0.4736 
+2025-05-06 07:49:00.268027: val_loss -0.4795 
+2025-05-06 07:49:00.386106: Pseudo dice [np.float32(0.857), np.float32(0.8486), np.float32(0.9023), np.float32(0.9673), np.float32(0.9084), np.float32(0.9548), np.float32(0.961), np.float32(0.9747), np.float32(0.9519), np.float32(0.9659), np.float32(0.9551), np.float32(0.9615), np.float32(0.9684), np.float32(0.9099), np.float32(0.9594), np.float32(0.955), np.float32(0.8642), np.float32(0.8456), np.float32(0.9169)] 
+2025-05-06 07:49:00.408029: Epoch time: 90.86 s 
+2025-05-06 07:49:01.877897:  
+2025-05-06 07:49:01.897785: Epoch 1114 
+2025-05-06 07:49:01.910684: Current learning rate: 0.00481 
+2025-05-06 07:50:38.627292: train_loss -0.5065 
+2025-05-06 07:50:38.660792: val_loss -0.5316 
+2025-05-06 07:50:38.669966: Pseudo dice [np.float32(0.8603), np.float32(0.8323), np.float32(0.9261), np.float32(0.9628), np.float32(0.9084), np.float32(0.9553), np.float32(0.9647), np.float32(0.9804), np.float32(0.9609), np.float32(0.9639), np.float32(0.947), np.float32(0.9687), np.float32(0.9664), np.float32(0.8875), np.float32(0.9581), np.float32(0.9535), np.float32(0.892), np.float32(0.8915), np.float32(0.9109)] 
+2025-05-06 07:50:38.673766: Epoch time: 96.75 s 
+2025-05-06 07:50:40.205494:  
+2025-05-06 07:50:40.294390: Epoch 1115 
+2025-05-06 07:50:40.331422: Current learning rate: 0.0048 
+2025-05-06 07:52:18.894457: train_loss -0.4809 
+2025-05-06 07:52:18.940090: val_loss -0.4804 
+2025-05-06 07:52:18.985762: Pseudo dice [np.float32(0.8132), np.float32(0.8223), np.float32(0.8636), np.float32(0.9783), np.float32(0.9241), np.float32(0.9544), np.float32(0.9614), np.float32(0.9772), np.float32(0.9574), np.float32(0.9617), np.float32(0.9461), np.float32(0.9687), np.float32(0.9664), np.float32(0.8878), np.float32(0.9629), np.float32(0.9562), np.float32(0.873), np.float32(0.8859), np.float32(0.918)] 
+2025-05-06 07:52:19.011490: Epoch time: 98.69 s 
+2025-05-06 07:52:20.480385:  
+2025-05-06 07:52:20.524848: Epoch 1116 
+2025-05-06 07:52:20.544987: Current learning rate: 0.0048 
+2025-05-06 07:53:54.715339: train_loss -0.4942 
+2025-05-06 07:53:54.799459: val_loss -0.505 
+2025-05-06 07:53:54.823707: Pseudo dice [np.float32(0.8299), np.float32(0.8458), np.float32(0.9409), np.float32(0.9697), np.float32(0.9132), np.float32(0.953), np.float32(0.9626), np.float32(0.9739), np.float32(0.9612), np.float32(0.9642), np.float32(0.9479), np.float32(0.97), np.float32(0.9668), np.float32(0.8302), np.float32(0.9445), np.float32(0.9318), np.float32(0.861), np.float32(0.8644), np.float32(0.9009)] 
+2025-05-06 07:53:54.842313: Epoch time: 94.24 s 
+2025-05-06 07:53:56.269944:  
+2025-05-06 07:53:56.277887: Epoch 1117 
+2025-05-06 07:53:56.282552: Current learning rate: 0.00479 
+2025-05-06 07:55:32.784482: train_loss -0.483 
+2025-05-06 07:55:32.820109: val_loss -0.5023 
+2025-05-06 07:55:32.820690: Pseudo dice [np.float32(0.8424), np.float32(0.8508), np.float32(0.8664), np.float32(0.9751), np.float32(0.8939), np.float32(0.9528), np.float32(0.964), np.float32(0.9793), np.float32(0.9476), np.float32(0.9605), np.float32(0.9508), np.float32(0.9688), np.float32(0.9616), np.float32(0.8971), np.float32(0.9206), np.float32(0.9428), np.float32(0.897), np.float32(0.8755), np.float32(0.9103)] 
+2025-05-06 07:55:32.821153: Epoch time: 96.52 s 
+2025-05-06 07:55:34.315473:  
+2025-05-06 07:55:34.330611: Epoch 1118 
+2025-05-06 07:55:34.331154: Current learning rate: 0.00479 
+2025-05-06 07:57:08.291290: train_loss -0.4857 
+2025-05-06 07:57:08.462057: val_loss -0.4889 
+2025-05-06 07:57:08.507884: Pseudo dice [np.float32(0.8407), np.float32(0.8404), np.float32(0.9106), np.float32(0.9727), np.float32(0.9304), np.float32(0.9651), np.float32(0.9685), np.float32(0.9802), np.float32(0.9681), np.float32(0.9586), np.float32(0.9445), np.float32(0.9702), np.float32(0.9669), np.float32(0.906), np.float32(0.9721), np.float32(0.948), np.float32(0.8954), np.float32(0.9045), np.float32(0.9167)] 
+2025-05-06 07:57:08.522509: Epoch time: 93.98 s 
+2025-05-06 07:57:09.993871:  
+2025-05-06 07:57:10.033824: Epoch 1119 
+2025-05-06 07:57:10.086940: Current learning rate: 0.00478 
+2025-05-06 07:58:45.213742: train_loss -0.4951 
+2025-05-06 07:58:45.309033: val_loss -0.5311 
+2025-05-06 07:58:45.358551: Pseudo dice [np.float32(0.8294), np.float32(0.8449), np.float32(0.7407), np.float32(0.9739), np.float32(0.9051), np.float32(0.9593), np.float32(0.9626), np.float32(0.9781), np.float32(0.9587), np.float32(0.9625), np.float32(0.9325), np.float32(0.96), np.float32(0.9664), np.float32(0.9056), np.float32(0.9536), np.float32(0.9432), np.float32(0.8516), np.float32(0.888), np.float32(0.9228)] 
+2025-05-06 07:58:45.406224: Epoch time: 95.22 s 
+2025-05-06 07:58:46.959033:  
+2025-05-06 07:58:47.078121: Epoch 1120 
+2025-05-06 07:58:47.124106: Current learning rate: 0.00478 
+2025-05-06 08:00:26.889013: train_loss -0.4857 
+2025-05-06 08:00:26.922451: val_loss -0.5309 
+2025-05-06 08:00:26.929845: Pseudo dice [np.float32(0.8486), np.float32(0.8478), np.float32(0.8853), np.float32(0.9742), np.float32(0.8733), np.float32(0.964), np.float32(0.9581), np.float32(0.9663), np.float32(0.9651), np.float32(0.9619), np.float32(0.9346), np.float32(0.9646), np.float32(0.967), np.float32(0.8915), np.float32(0.968), np.float32(0.9531), np.float32(0.8812), np.float32(0.9031), np.float32(0.9239)] 
+2025-05-06 08:00:26.957309: Epoch time: 99.93 s 
+2025-05-06 08:00:31.517483:  
+2025-05-06 08:00:31.522191: Epoch 1121 
+2025-05-06 08:00:31.522680: Current learning rate: 0.00477 
+2025-05-06 08:02:07.692258: train_loss -0.4879 
+2025-05-06 08:02:07.735011: val_loss -0.5043 
+2025-05-06 08:02:07.772368: Pseudo dice [np.float32(0.8369), np.float32(0.8616), np.float32(0.7982), np.float32(0.9778), np.float32(0.8988), np.float32(0.9627), np.float32(0.9686), np.float32(0.9748), np.float32(0.942), np.float32(0.9365), np.float32(0.927), np.float32(0.9584), np.float32(0.9688), np.float32(0.9013), np.float32(0.9732), np.float32(0.9572), np.float32(0.8659), np.float32(0.873), np.float32(0.9308)] 
+2025-05-06 08:02:07.790459: Epoch time: 96.18 s 
+2025-05-06 08:02:09.212637:  
+2025-05-06 08:02:09.252067: Epoch 1122 
+2025-05-06 08:02:09.255982: Current learning rate: 0.00477 
+2025-05-06 08:03:42.799022: train_loss -0.4791 
+2025-05-06 08:03:42.903702: val_loss -0.5355 
+2025-05-06 08:03:42.947295: Pseudo dice [np.float32(0.8215), np.float32(0.8459), np.float32(0.9386), np.float32(0.9679), np.float32(0.9019), np.float32(0.9614), np.float32(0.9595), np.float32(0.9767), np.float32(0.9659), np.float32(0.9631), np.float32(0.9493), np.float32(0.9683), np.float32(0.9702), np.float32(0.8999), np.float32(0.9719), np.float32(0.9569), np.float32(0.7969), np.float32(0.7603), np.float32(0.9274)] 
+2025-05-06 08:03:42.983492: Epoch time: 93.59 s 
+2025-05-06 08:03:44.578427:  
+2025-05-06 08:03:44.653082: Epoch 1123 
+2025-05-06 08:03:44.657560: Current learning rate: 0.00476 
+2025-05-06 08:05:23.256152: train_loss -0.4852 
+2025-05-06 08:05:23.348767: val_loss -0.5038 
+2025-05-06 08:05:23.365733: Pseudo dice [np.float32(0.8321), np.float32(0.837), np.float32(0.927), np.float32(0.9682), np.float32(0.8114), np.float32(0.9631), np.float32(0.9472), np.float32(0.9698), np.float32(0.963), np.float32(0.9625), np.float32(0.95), np.float32(0.9676), np.float32(0.9713), np.float32(0.8872), np.float32(0.9603), np.float32(0.9396), np.float32(0.887), np.float32(0.8783), np.float32(0.9124)] 
+2025-05-06 08:05:23.383226: Epoch time: 98.68 s 
+2025-05-06 08:05:24.916514:  
+2025-05-06 08:05:24.987347: Epoch 1124 
+2025-05-06 08:05:24.987812: Current learning rate: 0.00476 
+2025-05-06 08:07:01.659466: train_loss -0.4917 
+2025-05-06 08:07:01.738742: val_loss -0.5034 
+2025-05-06 08:07:01.748514: Pseudo dice [np.float32(0.843), np.float32(0.84), np.float32(0.9276), np.float32(0.9754), np.float32(0.9202), np.float32(0.954), np.float32(0.964), np.float32(0.9806), np.float32(0.964), np.float32(0.9667), np.float32(0.9345), np.float32(0.9472), np.float32(0.9664), np.float32(0.898), np.float32(0.9633), np.float32(0.9431), np.float32(0.8541), np.float32(0.8974), np.float32(0.9207)] 
+2025-05-06 08:07:01.760849: Epoch time: 96.74 s 
+2025-05-06 08:07:03.207343:  
+2025-05-06 08:07:03.344631: Epoch 1125 
+2025-05-06 08:07:03.383307: Current learning rate: 0.00475 
+2025-05-06 08:08:42.547726: train_loss -0.511 
+2025-05-06 08:08:42.606467: val_loss -0.5029 
+2025-05-06 08:08:42.623542: Pseudo dice [np.float32(0.8233), np.float32(0.8452), np.float32(0.898), np.float32(0.9778), np.float32(0.9258), np.float32(0.9523), np.float32(0.9646), np.float32(0.976), np.float32(0.9372), np.float32(0.955), np.float32(0.9302), np.float32(0.9526), np.float32(0.9616), np.float32(0.8995), np.float32(0.9648), np.float32(0.9552), np.float32(0.8184), np.float32(0.7758), np.float32(0.9209)] 
+2025-05-06 08:08:42.631736: Epoch time: 99.34 s 
+2025-05-06 08:08:44.057733:  
+2025-05-06 08:08:44.118029: Epoch 1126 
+2025-05-06 08:08:44.146621: Current learning rate: 0.00475 
+2025-05-06 08:10:19.073435: train_loss -0.4746 
+2025-05-06 08:10:19.214792: val_loss -0.4888 
+2025-05-06 08:10:19.251426: Pseudo dice [np.float32(0.8282), np.float32(0.8582), np.float32(0.9056), np.float32(0.9737), np.float32(0.8713), np.float32(0.9593), np.float32(0.9619), np.float32(0.9774), np.float32(0.9527), np.float32(0.9604), np.float32(0.9445), np.float32(0.9503), np.float32(0.9642), np.float32(0.905), np.float32(0.9679), np.float32(0.9515), np.float32(0.9194), np.float32(0.9204), np.float32(0.9187)] 
+2025-05-06 08:10:19.272963: Epoch time: 95.02 s 
+2025-05-06 08:10:20.753653:  
+2025-05-06 08:10:20.844086: Epoch 1127 
+2025-05-06 08:10:20.853753: Current learning rate: 0.00474 
+2025-05-06 08:11:59.808091: train_loss -0.4974 
+2025-05-06 08:11:59.873456: val_loss -0.5177 
+2025-05-06 08:11:59.879502: Pseudo dice [np.float32(0.8469), np.float32(0.8443), np.float32(0.9465), np.float32(0.9727), np.float32(0.8841), np.float32(0.9652), np.float32(0.9592), np.float32(0.9753), np.float32(0.96), np.float32(0.9666), np.float32(0.9076), np.float32(0.9637), np.float32(0.9672), np.float32(0.904), np.float32(0.9062), np.float32(0.9406), np.float32(0.8942), np.float32(0.8672), np.float32(0.9183)] 
+2025-05-06 08:11:59.895062: Epoch time: 99.06 s 
+2025-05-06 08:12:01.378926:  
+2025-05-06 08:12:01.476391: Epoch 1128 
+2025-05-06 08:12:01.548223: Current learning rate: 0.00474 
+2025-05-06 08:13:39.263541: train_loss -0.4958 
+2025-05-06 08:13:39.352619: val_loss -0.5054 
+2025-05-06 08:13:39.362537: Pseudo dice [np.float32(0.8431), np.float32(0.8344), np.float32(0.7245), np.float32(0.9728), np.float32(0.881), np.float32(0.9489), np.float32(0.9481), np.float32(0.9753), np.float32(0.9621), np.float32(0.961), np.float32(0.9482), np.float32(0.9733), np.float32(0.9694), np.float32(0.8911), np.float32(0.9636), np.float32(0.9488), np.float32(0.8693), np.float32(0.8853), np.float32(0.9055)] 
+2025-05-06 08:13:39.371831: Epoch time: 97.89 s 
+2025-05-06 08:13:40.802838:  
+2025-05-06 08:13:40.896154: Epoch 1129 
+2025-05-06 08:13:40.937174: Current learning rate: 0.00473 
+2025-05-06 08:15:15.943660: train_loss -0.4972 
+2025-05-06 08:15:16.002218: val_loss -0.5007 
+2025-05-06 08:15:16.037844: Pseudo dice [np.float32(0.8503), np.float32(0.8329), np.float32(0.8743), np.float32(0.9659), np.float32(0.9033), np.float32(0.9646), np.float32(0.9595), np.float32(0.969), np.float32(0.9663), np.float32(0.9595), np.float32(0.933), np.float32(0.969), np.float32(0.9554), np.float32(0.8952), np.float32(0.9501), np.float32(0.9558), np.float32(0.8918), np.float32(0.8941), np.float32(0.9169)] 
+2025-05-06 08:15:16.113762: Epoch time: 95.14 s 
+2025-05-06 08:15:17.582558:  
+2025-05-06 08:15:17.666208: Epoch 1130 
+2025-05-06 08:15:17.683530: Current learning rate: 0.00473 
+2025-05-06 08:16:53.064232: train_loss -0.4871 
+2025-05-06 08:16:53.236171: val_loss -0.5051 
+2025-05-06 08:16:53.274492: Pseudo dice [np.float32(0.8487), np.float32(0.8601), np.float32(0.9138), np.float32(0.9715), np.float32(0.9018), np.float32(0.9514), np.float32(0.9583), np.float32(0.9746), np.float32(0.9591), np.float32(0.9384), np.float32(0.9368), np.float32(0.9668), np.float32(0.9668), np.float32(0.9075), np.float32(0.9562), np.float32(0.9539), np.float32(0.8808), np.float32(0.8843), np.float32(0.9208)] 
+2025-05-06 08:16:53.289186: Epoch time: 95.48 s 
+2025-05-06 08:16:54.721933:  
+2025-05-06 08:16:54.924829: Epoch 1131 
+2025-05-06 08:16:55.001745: Current learning rate: 0.00472 
+2025-05-06 08:18:35.004587: train_loss -0.5055 
+2025-05-06 08:18:35.046693: val_loss -0.5275 
+2025-05-06 08:18:35.076306: Pseudo dice [np.float32(0.804), np.float32(0.8348), np.float32(0.7358), np.float32(0.9704), np.float32(0.9163), np.float32(0.95), np.float32(0.9694), np.float32(0.9777), np.float32(0.9591), np.float32(0.967), np.float32(0.9471), np.float32(0.9704), np.float32(0.9665), np.float32(0.8938), np.float32(0.9675), np.float32(0.9578), np.float32(0.9047), np.float32(0.8793), np.float32(0.9166)] 
+2025-05-06 08:18:35.088536: Epoch time: 100.28 s 
+2025-05-06 08:18:36.789486:  
+2025-05-06 08:18:36.886396: Epoch 1132 
+2025-05-06 08:18:36.917802: Current learning rate: 0.00472 
+2025-05-06 08:20:12.206600: train_loss -0.4661 
+2025-05-06 08:20:12.302596: val_loss -0.4871 
+2025-05-06 08:20:12.307548: Pseudo dice [np.float32(0.8426), np.float32(0.8368), np.float32(0.9466), np.float32(0.9762), np.float32(0.9115), np.float32(0.9596), np.float32(0.9632), np.float32(0.9779), np.float32(0.9577), np.float32(0.9608), np.float32(0.9452), np.float32(0.965), np.float32(0.9646), np.float32(0.9049), np.float32(0.9534), np.float32(0.9492), np.float32(0.8885), np.float32(0.8898), np.float32(0.9237)] 
+2025-05-06 08:20:12.327825: Epoch time: 95.42 s 
+2025-05-06 08:20:13.937951:  
+2025-05-06 08:20:13.952636: Epoch 1133 
+2025-05-06 08:20:13.953152: Current learning rate: 0.00471 
+2025-05-06 08:21:50.221947: train_loss -0.4866 
+2025-05-06 08:21:50.293121: val_loss -0.473 
+2025-05-06 08:21:50.306153: Pseudo dice [np.float32(0.8189), np.float32(0.8283), np.float32(0.9207), np.float32(0.9632), np.float32(0.8792), np.float32(0.9539), np.float32(0.9619), np.float32(0.978), np.float32(0.9609), np.float32(0.9496), np.float32(0.9222), np.float32(0.969), np.float32(0.9353), np.float32(0.8903), np.float32(0.9628), np.float32(0.9385), np.float32(0.8925), np.float32(0.8967), np.float32(0.9103)] 
+2025-05-06 08:21:50.319385: Epoch time: 96.29 s 
+2025-05-06 08:21:51.835018:  
+2025-05-06 08:21:51.940520: Epoch 1134 
+2025-05-06 08:21:52.012838: Current learning rate: 0.00471 
+2025-05-06 08:23:27.598014: train_loss -0.5011 
+2025-05-06 08:23:27.687459: val_loss -0.5195 
+2025-05-06 08:23:27.716885: Pseudo dice [np.float32(0.8293), np.float32(0.848), np.float32(0.895), np.float32(0.9451), np.float32(0.8755), np.float32(0.9561), np.float32(0.9638), np.float32(0.9757), np.float32(0.9642), np.float32(0.9659), np.float32(0.9468), np.float32(0.9623), np.float32(0.9677), np.float32(0.9148), np.float32(0.9487), np.float32(0.9474), np.float32(0.8744), np.float32(0.8818), np.float32(0.9092)] 
+2025-05-06 08:23:27.733560: Epoch time: 95.76 s 
+2025-05-06 08:23:29.258838:  
+2025-05-06 08:23:29.415593: Epoch 1135 
+2025-05-06 08:23:29.453942: Current learning rate: 0.0047 
+2025-05-06 08:25:08.605919: train_loss -0.4907 
+2025-05-06 08:25:08.743760: val_loss -0.4926 
+2025-05-06 08:25:08.779129: Pseudo dice [np.float32(0.84), np.float32(0.8382), np.float32(0.864), np.float32(0.9802), np.float32(0.921), np.float32(0.9561), np.float32(0.9639), np.float32(0.9766), np.float32(0.9578), np.float32(0.9666), np.float32(0.9495), np.float32(0.9592), np.float32(0.9689), np.float32(0.8994), np.float32(0.9606), np.float32(0.9564), np.float32(0.7826), np.float32(0.834), np.float32(0.9206)] 
+2025-05-06 08:25:08.812274: Epoch time: 99.35 s 
+2025-05-06 08:25:10.327008:  
+2025-05-06 08:25:10.451315: Epoch 1136 
+2025-05-06 08:25:10.484470: Current learning rate: 0.0047 
+2025-05-06 08:26:41.376388: train_loss -0.4808 
+2025-05-06 08:26:41.446681: val_loss -0.4867 
+2025-05-06 08:26:41.458191: Pseudo dice [np.float32(0.8656), np.float32(0.8373), np.float32(0.944), np.float32(0.9772), np.float32(0.917), np.float32(0.9491), np.float32(0.9567), np.float32(0.9779), np.float32(0.9486), np.float32(0.9759), np.float32(0.9462), np.float32(0.961), np.float32(0.9657), np.float32(0.9142), np.float32(0.9644), np.float32(0.9556), np.float32(0.8516), np.float32(0.8796), np.float32(0.918)] 
+2025-05-06 08:26:41.469199: Epoch time: 91.05 s 
+2025-05-06 08:26:42.891654:  
+2025-05-06 08:26:42.903148: Epoch 1137 
+2025-05-06 08:26:42.903928: Current learning rate: 0.00469 
+2025-05-06 08:28:17.299261: train_loss -0.4922 
+2025-05-06 08:28:17.356467: val_loss -0.4862 
+2025-05-06 08:28:17.385810: Pseudo dice [np.float32(0.8572), np.float32(0.8403), np.float32(0.8936), np.float32(0.9602), np.float32(0.9166), np.float32(0.9599), np.float32(0.9697), np.float32(0.9758), np.float32(0.9614), np.float32(0.9647), np.float32(0.9459), np.float32(0.9681), np.float32(0.9673), np.float32(0.9055), np.float32(0.9427), np.float32(0.9386), np.float32(0.8722), np.float32(0.8558), np.float32(0.9218)] 
+2025-05-06 08:28:17.423495: Epoch time: 94.41 s 
+2025-05-06 08:28:19.180886:  
+2025-05-06 08:28:19.261097: Epoch 1138 
+2025-05-06 08:28:19.296374: Current learning rate: 0.00469 
+2025-05-06 08:29:53.662820: train_loss -0.4863 
+2025-05-06 08:29:53.762889: val_loss -0.5067 
+2025-05-06 08:29:53.777499: Pseudo dice [np.float32(0.8409), np.float32(0.8605), np.float32(0.9363), np.float32(0.9685), np.float32(0.8972), np.float32(0.9538), np.float32(0.9549), np.float32(0.9769), np.float32(0.9634), np.float32(0.9635), np.float32(0.9499), np.float32(0.9671), np.float32(0.9721), np.float32(0.89), np.float32(0.964), np.float32(0.9523), np.float32(0.8752), np.float32(0.8741), np.float32(0.9108)] 
+2025-05-06 08:29:53.809862: Epoch time: 94.48 s 
+2025-05-06 08:29:58.927728:  
+2025-05-06 08:29:58.933615: Epoch 1139 
+2025-05-06 08:29:58.934088: Current learning rate: 0.00468 
+2025-05-06 08:31:37.987015: train_loss -0.4891 
+2025-05-06 08:31:38.139615: val_loss -0.516 
+2025-05-06 08:31:38.168842: Pseudo dice [np.float32(0.8294), np.float32(0.8265), np.float32(0.7499), np.float32(0.9706), np.float32(0.9115), np.float32(0.9623), np.float32(0.9552), np.float32(0.9715), np.float32(0.9621), np.float32(0.9424), np.float32(0.9068), np.float32(0.9656), np.float32(0.9627), np.float32(0.9074), np.float32(0.9677), np.float32(0.9608), np.float32(0.8924), np.float32(0.9083), np.float32(0.9137)] 
+2025-05-06 08:31:38.186225: Epoch time: 99.06 s 
+2025-05-06 08:31:39.845195:  
+2025-05-06 08:31:39.956535: Epoch 1140 
+2025-05-06 08:31:39.984808: Current learning rate: 0.00468 
+2025-05-06 08:33:16.520193: train_loss -0.4963 
+2025-05-06 08:33:16.574610: val_loss -0.5064 
+2025-05-06 08:33:16.592581: Pseudo dice [np.float32(0.8431), np.float32(0.8207), np.float32(0.9155), np.float32(0.9721), np.float32(0.9174), np.float32(0.9611), np.float32(0.9553), np.float32(0.9736), np.float32(0.9704), np.float32(0.9663), np.float32(0.9521), np.float32(0.967), np.float32(0.968), np.float32(0.9018), np.float32(0.9369), np.float32(0.9447), np.float32(0.8149), np.float32(0.8621), np.float32(0.92)] 
+2025-05-06 08:33:16.593381: Epoch time: 96.68 s 
+2025-05-06 08:33:18.007034:  
+2025-05-06 08:33:18.056707: Epoch 1141 
+2025-05-06 08:33:18.062678: Current learning rate: 0.00467 
+2025-05-06 08:34:53.392586: train_loss -0.4889 
+2025-05-06 08:34:53.478365: val_loss -0.5134 
+2025-05-06 08:34:53.486427: Pseudo dice [np.float32(0.8387), np.float32(0.8587), np.float32(0.8646), np.float32(0.9554), np.float32(0.9142), np.float32(0.9585), np.float32(0.9695), np.float32(0.9774), np.float32(0.9692), np.float32(0.9592), np.float32(0.9433), np.float32(0.9712), np.float32(0.9712), np.float32(0.9121), np.float32(0.9683), np.float32(0.949), np.float32(0.9025), np.float32(0.906), np.float32(0.9269)] 
+2025-05-06 08:34:53.496899: Epoch time: 95.39 s 
+2025-05-06 08:34:54.973938:  
+2025-05-06 08:34:55.113616: Epoch 1142 
+2025-05-06 08:34:55.179802: Current learning rate: 0.00467 
+2025-05-06 08:36:30.712833: train_loss -0.487 
+2025-05-06 08:36:30.757509: val_loss -0.5175 
+2025-05-06 08:36:30.768703: Pseudo dice [np.float32(0.8507), np.float32(0.8166), np.float32(0.8774), np.float32(0.9767), np.float32(0.9096), np.float32(0.9669), np.float32(0.9612), np.float32(0.9712), np.float32(0.9602), np.float32(0.9539), np.float32(0.9452), np.float32(0.9658), np.float32(0.9734), np.float32(0.9072), np.float32(0.9713), np.float32(0.9517), np.float32(0.883), np.float32(0.8926), np.float32(0.9103)] 
+2025-05-06 08:36:30.773531: Epoch time: 95.74 s 
+2025-05-06 08:36:32.316370:  
+2025-05-06 08:36:32.394876: Epoch 1143 
+2025-05-06 08:36:32.413766: Current learning rate: 0.00466 
+2025-05-06 08:38:05.419635: train_loss -0.4751 
+2025-05-06 08:38:05.466894: val_loss -0.5058 
+2025-05-06 08:38:05.480006: Pseudo dice [np.float32(0.8433), np.float32(0.8388), np.float32(0.9285), np.float32(0.9723), np.float32(0.9134), np.float32(0.9491), np.float32(0.9547), np.float32(0.9715), np.float32(0.9596), np.float32(0.9723), np.float32(0.9521), np.float32(0.9638), np.float32(0.9709), np.float32(0.9085), np.float32(0.9617), np.float32(0.9575), np.float32(0.8684), np.float32(0.8567), np.float32(0.9066)] 
+2025-05-06 08:38:05.513471: Epoch time: 93.1 s 
+2025-05-06 08:38:07.034371:  
+2025-05-06 08:38:07.112898: Epoch 1144 
+2025-05-06 08:38:07.136408: Current learning rate: 0.00466 
+2025-05-06 08:39:41.859478: train_loss -0.4757 
+2025-05-06 08:39:41.945289: val_loss -0.5097 
+2025-05-06 08:39:41.986682: Pseudo dice [np.float32(0.8404), np.float32(0.8496), np.float32(0.9348), np.float32(0.9772), np.float32(0.8825), np.float32(0.957), np.float32(0.9337), np.float32(0.9644), np.float32(0.956), np.float32(0.9594), np.float32(0.9457), np.float32(0.9687), np.float32(0.965), np.float32(0.9033), np.float32(0.964), np.float32(0.954), np.float32(0.8839), np.float32(0.8912), np.float32(0.916)] 
+2025-05-06 08:39:42.025260: Epoch time: 94.83 s 
+2025-05-06 08:39:43.594642:  
+2025-05-06 08:39:43.708821: Epoch 1145 
+2025-05-06 08:39:43.737325: Current learning rate: 0.00465 
+2025-05-06 08:41:22.110859: train_loss -0.4651 
+2025-05-06 08:41:22.129918: val_loss -0.4711 
+2025-05-06 08:41:22.131076: Pseudo dice [np.float32(0.8326), np.float32(0.8342), np.float32(0.9053), np.float32(0.9749), np.float32(0.909), np.float32(0.9508), np.float32(0.9542), np.float32(0.9703), np.float32(0.9592), np.float32(0.9267), np.float32(0.9426), np.float32(0.9613), np.float32(0.9467), np.float32(0.9014), np.float32(0.9643), np.float32(0.9332), np.float32(0.9013), np.float32(0.8919), np.float32(0.8989)] 
+2025-05-06 08:41:22.131734: Epoch time: 98.52 s 
+2025-05-06 08:41:23.584350:  
+2025-05-06 08:41:23.626515: Epoch 1146 
+2025-05-06 08:41:23.643493: Current learning rate: 0.00465 
+2025-05-06 08:43:01.684340: train_loss -0.4768 
+2025-05-06 08:43:01.747224: val_loss -0.4816 
+2025-05-06 08:43:01.753004: Pseudo dice [np.float32(0.8568), np.float32(0.846), np.float32(0.935), np.float32(0.9712), np.float32(0.9199), np.float32(0.9598), np.float32(0.9649), np.float32(0.9671), np.float32(0.9619), np.float32(0.9677), np.float32(0.9467), np.float32(0.9707), np.float32(0.9683), np.float32(0.8988), np.float32(0.9631), np.float32(0.9523), np.float32(0.8928), np.float32(0.8948), np.float32(0.9197)] 
+2025-05-06 08:43:01.753677: Epoch time: 98.1 s 
+2025-05-06 08:43:03.268536:  
+2025-05-06 08:43:03.365001: Epoch 1147 
+2025-05-06 08:43:03.398130: Current learning rate: 0.00464 
+2025-05-06 08:44:41.727089: train_loss -0.4833 
+2025-05-06 08:44:41.851421: val_loss -0.4597 
+2025-05-06 08:44:41.869768: Pseudo dice [np.float32(0.8448), np.float32(0.8702), np.float32(0.9252), np.float32(0.9768), np.float32(0.9105), np.float32(0.9542), np.float32(0.9489), np.float32(0.9772), np.float32(0.9574), np.float32(0.9698), np.float32(0.9497), np.float32(0.9627), np.float32(0.9721), np.float32(0.9076), np.float32(0.9606), np.float32(0.947), np.float32(0.6909), np.float32(0.8511), np.float32(0.9234)] 
+2025-05-06 08:44:41.911243: Epoch time: 98.46 s 
+2025-05-06 08:44:43.603130:  
+2025-05-06 08:44:43.633456: Epoch 1148 
+2025-05-06 08:44:43.646421: Current learning rate: 0.00464 
+2025-05-06 08:46:21.827223: train_loss -0.4921 
+2025-05-06 08:46:21.880206: val_loss -0.5084 
+2025-05-06 08:46:21.881196: Pseudo dice [np.float32(0.8429), np.float32(0.8604), np.float32(0.9125), np.float32(0.9731), np.float32(0.8987), np.float32(0.9581), np.float32(0.9655), np.float32(0.9747), np.float32(0.9618), np.float32(0.9692), np.float32(0.9538), np.float32(0.9694), np.float32(0.9735), np.float32(0.8984), np.float32(0.966), np.float32(0.9526), np.float32(0.8761), np.float32(0.895), np.float32(0.9089)] 
+2025-05-06 08:46:21.882620: Epoch time: 98.23 s 
+2025-05-06 08:46:23.427850:  
+2025-05-06 08:46:23.479765: Epoch 1149 
+2025-05-06 08:46:23.494625: Current learning rate: 0.00463 
+2025-05-06 08:48:00.118150: train_loss -0.4954 
+2025-05-06 08:48:00.204047: val_loss -0.4402 
+2025-05-06 08:48:00.233695: Pseudo dice [np.float32(0.8579), np.float32(0.8526), np.float32(0.8321), np.float32(0.9792), np.float32(0.8736), np.float32(0.9609), np.float32(0.9594), np.float32(0.9776), np.float32(0.9599), np.float32(0.9465), np.float32(0.9085), np.float32(0.972), np.float32(0.9439), np.float32(0.898), np.float32(0.9692), np.float32(0.9538), np.float32(0.8395), np.float32(0.8738), np.float32(0.907)] 
+2025-05-06 08:48:00.264296: Epoch time: 96.69 s 
+2025-05-06 08:48:02.865131:  
+2025-05-06 08:48:02.903342: Epoch 1150 
+2025-05-06 08:48:02.911424: Current learning rate: 0.00463 
+2025-05-06 08:49:39.118974: train_loss -0.4867 
+2025-05-06 08:49:39.185336: val_loss -0.5139 
+2025-05-06 08:49:39.193948: Pseudo dice [np.float32(0.8564), np.float32(0.8397), np.float32(0.9097), np.float32(0.9427), np.float32(0.8993), np.float32(0.9608), np.float32(0.9651), np.float32(0.9763), np.float32(0.9446), np.float32(0.967), np.float32(0.9505), np.float32(0.9622), np.float32(0.9666), np.float32(0.9013), np.float32(0.9381), np.float32(0.9515), np.float32(0.8998), np.float32(0.8916), np.float32(0.9189)] 
+2025-05-06 08:49:39.195125: Epoch time: 96.26 s 
+2025-05-06 08:49:40.703841:  
+2025-05-06 08:49:40.798825: Epoch 1151 
+2025-05-06 08:49:40.848575: Current learning rate: 0.00462 
+2025-05-06 08:51:20.226241: train_loss -0.4854 
+2025-05-06 08:51:20.370916: val_loss -0.5016 
+2025-05-06 08:51:20.419859: Pseudo dice [np.float32(0.8249), np.float32(0.8429), np.float32(0.9281), np.float32(0.9744), np.float32(0.9276), np.float32(0.9536), np.float32(0.9536), np.float32(0.9695), np.float32(0.9718), np.float32(0.9643), np.float32(0.9408), np.float32(0.9723), np.float32(0.9702), np.float32(0.8927), np.float32(0.9617), np.float32(0.9412), np.float32(0.8983), np.float32(0.8975), np.float32(0.914)] 
+2025-05-06 08:51:20.456915: Epoch time: 99.52 s 
+2025-05-06 08:51:22.066828:  
+2025-05-06 08:51:22.069243: Epoch 1152 
+2025-05-06 08:51:22.069669: Current learning rate: 0.00462 
+2025-05-06 08:53:01.207072: train_loss -0.4956 
+2025-05-06 08:53:01.257308: val_loss -0.5083 
+2025-05-06 08:53:01.268651: Pseudo dice [np.float32(0.7941), np.float32(0.8115), np.float32(0.8779), np.float32(0.9772), np.float32(0.8946), np.float32(0.954), np.float32(0.9543), np.float32(0.9776), np.float32(0.9579), np.float32(0.9641), np.float32(0.9504), np.float32(0.9691), np.float32(0.9718), np.float32(0.9047), np.float32(0.9615), np.float32(0.9396), np.float32(0.8787), np.float32(0.8703), np.float32(0.9165)] 
+2025-05-06 08:53:01.286686: Epoch time: 99.14 s 
+2025-05-06 08:53:02.926397:  
+2025-05-06 08:53:03.068183: Epoch 1153 
+2025-05-06 08:53:03.095671: Current learning rate: 0.00461 
+2025-05-06 08:54:41.567348: train_loss -0.4705 
+2025-05-06 08:54:41.604796: val_loss -0.4989 
+2025-05-06 08:54:41.618334: Pseudo dice [np.float32(0.8317), np.float32(0.827), np.float32(0.9183), np.float32(0.9737), np.float32(0.8781), np.float32(0.9614), np.float32(0.9563), np.float32(0.9695), np.float32(0.9615), np.float32(0.9589), np.float32(0.9416), np.float32(0.9675), np.float32(0.9623), np.float32(0.8974), np.float32(0.9675), np.float32(0.9488), np.float32(0.8946), np.float32(0.9063), np.float32(0.9195)] 
+2025-05-06 08:54:41.619220: Epoch time: 98.64 s 
+2025-05-06 08:54:43.102456:  
+2025-05-06 08:54:43.219372: Epoch 1154 
+2025-05-06 08:54:43.253515: Current learning rate: 0.00461 
+2025-05-06 08:56:16.271874: train_loss -0.488 
+2025-05-06 08:56:16.381408: val_loss -0.5165 
+2025-05-06 08:56:16.427376: Pseudo dice [np.float32(0.8489), np.float32(0.8491), np.float32(0.7324), np.float32(0.9711), np.float32(0.9142), np.float32(0.9563), np.float32(0.961), np.float32(0.9799), np.float32(0.9484), np.float32(0.9679), np.float32(0.9299), np.float32(0.9606), np.float32(0.964), np.float32(0.9081), np.float32(0.9473), np.float32(0.9417), np.float32(0.8572), np.float32(0.869), np.float32(0.9194)] 
+2025-05-06 08:56:16.474464: Epoch time: 93.17 s 
+2025-05-06 08:56:18.052668:  
+2025-05-06 08:56:18.071188: Epoch 1155 
+2025-05-06 08:56:18.071659: Current learning rate: 0.00461 
+2025-05-06 08:57:59.908913: train_loss -0.4907 
+2025-05-06 08:58:00.005335: val_loss -0.4423 
+2025-05-06 08:58:00.021002: Pseudo dice [np.float32(0.8253), np.float32(0.8569), np.float32(0.8938), np.float32(0.9772), np.float32(0.9145), np.float32(0.9584), np.float32(0.9647), np.float32(0.9742), np.float32(0.9425), np.float32(0.9643), np.float32(0.9552), np.float32(0.9598), np.float32(0.9662), np.float32(0.8866), np.float32(0.9558), np.float32(0.9425), np.float32(0.8974), np.float32(0.901), np.float32(0.9125)] 
+2025-05-06 08:58:00.032382: Epoch time: 101.86 s 
+2025-05-06 08:58:05.365047:  
+2025-05-06 08:58:05.370197: Epoch 1156 
+2025-05-06 08:58:05.370703: Current learning rate: 0.0046 
+2025-05-06 08:59:36.687809: train_loss -0.4948 
+2025-05-06 08:59:36.772950: val_loss -0.4709 
+2025-05-06 08:59:36.801735: Pseudo dice [np.float32(0.8585), np.float32(0.843), np.float32(0.9343), np.float32(0.9746), np.float32(0.8997), np.float32(0.9602), np.float32(0.9656), np.float32(0.9693), np.float32(0.9711), np.float32(0.9552), np.float32(0.9131), np.float32(0.972), np.float32(0.9675), np.float32(0.9033), np.float32(0.9486), np.float32(0.9323), np.float32(0.8651), np.float32(0.9), np.float32(0.926)] 
+2025-05-06 08:59:36.829960: Epoch time: 91.32 s 
+2025-05-06 08:59:38.654667:  
+2025-05-06 08:59:38.687061: Epoch 1157 
+2025-05-06 08:59:38.704371: Current learning rate: 0.0046 
+2025-05-06 09:01:13.178475: train_loss -0.4672 
+2025-05-06 09:01:13.236064: val_loss -0.4675 
+2025-05-06 09:01:13.237395: Pseudo dice [np.float32(0.851), np.float32(0.7647), np.float32(0.9352), np.float32(0.9776), np.float32(0.8906), np.float32(0.9594), np.float32(0.96), np.float32(0.9768), np.float32(0.9565), np.float32(0.9645), np.float32(0.9479), np.float32(0.9619), np.float32(0.9712), np.float32(0.9054), np.float32(0.9177), np.float32(0.9396), np.float32(0.8925), np.float32(0.9068), np.float32(0.9207)] 
+2025-05-06 09:01:13.238039: Epoch time: 94.52 s 
+2025-05-06 09:01:14.719144:  
+2025-05-06 09:01:14.816162: Epoch 1158 
+2025-05-06 09:01:14.817116: Current learning rate: 0.00459 
+2025-05-06 09:02:48.692924: train_loss -0.4846 
+2025-05-06 09:02:48.712851: val_loss -0.5188 
+2025-05-06 09:02:48.713433: Pseudo dice [np.float32(0.8559), np.float32(0.8506), np.float32(0.927), np.float32(0.9701), np.float32(0.8842), np.float32(0.9585), np.float32(0.9614), np.float32(0.9594), np.float32(0.9686), np.float32(0.9742), np.float32(0.9535), np.float32(0.9688), np.float32(0.97), np.float32(0.9071), np.float32(0.968), np.float32(0.952), np.float32(0.8721), np.float32(0.8748), np.float32(0.9167)] 
+2025-05-06 09:02:48.724556: Epoch time: 93.98 s 
+2025-05-06 09:02:50.392122:  
+2025-05-06 09:02:50.445917: Epoch 1159 
+2025-05-06 09:02:50.447127: Current learning rate: 0.00459 
+2025-05-06 09:04:26.324368: train_loss -0.4722 
+2025-05-06 09:04:26.397133: val_loss -0.4929 
+2025-05-06 09:04:26.398288: Pseudo dice [np.float32(0.842), np.float32(0.8559), np.float32(0.8245), np.float32(0.9752), np.float32(0.9158), np.float32(0.9624), np.float32(0.9625), np.float32(0.9778), np.float32(0.9561), np.float32(0.9685), np.float32(0.9536), np.float32(0.9584), np.float32(0.9685), np.float32(0.902), np.float32(0.9484), np.float32(0.9514), np.float32(0.8915), np.float32(0.8981), np.float32(0.9165)] 
+2025-05-06 09:04:26.419231: Epoch time: 95.93 s 
+2025-05-06 09:04:27.879047:  
+2025-05-06 09:04:27.945816: Epoch 1160 
+2025-05-06 09:04:27.962004: Current learning rate: 0.00458 
+2025-05-06 09:06:04.711318: train_loss -0.4989 
+2025-05-06 09:06:04.803952: val_loss -0.4717 
+2025-05-06 09:06:04.842981: Pseudo dice [np.float32(0.8317), np.float32(0.8124), np.float32(0.9143), np.float32(0.9364), np.float32(0.9005), np.float32(0.9614), np.float32(0.9648), np.float32(0.9766), np.float32(0.9596), np.float32(0.9659), np.float32(0.9487), np.float32(0.9658), np.float32(0.9706), np.float32(0.8998), np.float32(0.9597), np.float32(0.9413), np.float32(0.8617), np.float32(0.8995), np.float32(0.8869)] 
+2025-05-06 09:06:04.883986: Epoch time: 96.83 s 
+2025-05-06 09:06:06.431017:  
+2025-05-06 09:06:06.635791: Epoch 1161 
+2025-05-06 09:06:06.666605: Current learning rate: 0.00458 
+2025-05-06 09:07:41.731498: train_loss -0.4821 
+2025-05-06 09:07:41.840519: val_loss -0.4997 
+2025-05-06 09:07:41.867507: Pseudo dice [np.float32(0.8345), np.float32(0.8352), np.float32(0.9159), np.float32(0.9663), np.float32(0.9259), np.float32(0.9612), np.float32(0.9601), np.float32(0.9748), np.float32(0.9517), np.float32(0.9576), np.float32(0.9439), np.float32(0.9628), np.float32(0.9641), np.float32(0.9124), np.float32(0.9592), np.float32(0.9582), np.float32(0.8508), np.float32(0.8729), np.float32(0.9089)] 
+2025-05-06 09:07:41.900578: Epoch time: 95.3 s 
+2025-05-06 09:07:43.427374:  
+2025-05-06 09:07:43.527641: Epoch 1162 
+2025-05-06 09:07:43.551502: Current learning rate: 0.00457 
+2025-05-06 09:09:18.345579: train_loss -0.5061 
+2025-05-06 09:09:18.375526: val_loss -0.5081 
+2025-05-06 09:09:18.416873: Pseudo dice [np.float32(0.8486), np.float32(0.8492), np.float32(0.9434), np.float32(0.9725), np.float32(0.9129), np.float32(0.9541), np.float32(0.9589), np.float32(0.9768), np.float32(0.9463), np.float32(0.961), np.float32(0.9486), np.float32(0.9527), np.float32(0.9659), np.float32(0.912), np.float32(0.925), np.float32(0.9595), np.float32(0.8963), np.float32(0.8879), np.float32(0.9204)] 
+2025-05-06 09:09:18.455572: Epoch time: 94.92 s 
+2025-05-06 09:09:19.981791:  
+2025-05-06 09:09:20.023393: Epoch 1163 
+2025-05-06 09:09:20.030033: Current learning rate: 0.00457 
+2025-05-06 09:10:55.109054: train_loss -0.4855 
+2025-05-06 09:10:55.207438: val_loss -0.4704 
+2025-05-06 09:10:55.224210: Pseudo dice [np.float32(0.8296), np.float32(0.8455), np.float32(0.9037), np.float32(0.9696), np.float32(0.9053), np.float32(0.9517), np.float32(0.9616), np.float32(0.962), np.float32(0.9639), np.float32(0.9486), np.float32(0.9175), np.float32(0.9702), np.float32(0.9564), np.float32(0.902), np.float32(0.9619), np.float32(0.9518), np.float32(0.9021), np.float32(0.883), np.float32(0.9097)] 
+2025-05-06 09:10:55.226028: Epoch time: 95.13 s 
+2025-05-06 09:10:56.639779:  
+2025-05-06 09:10:56.685310: Epoch 1164 
+2025-05-06 09:10:56.686177: Current learning rate: 0.00456 
+2025-05-06 09:12:29.045512: train_loss -0.4995 
+2025-05-06 09:12:29.132954: val_loss -0.4651 
+2025-05-06 09:12:29.153758: Pseudo dice [np.float32(0.8273), np.float32(0.8573), np.float32(0.9254), np.float32(0.9643), np.float32(0.9081), np.float32(0.9514), np.float32(0.9612), np.float32(0.9692), np.float32(0.9644), np.float32(0.9643), np.float32(0.9568), np.float32(0.9739), np.float32(0.9729), np.float32(0.8908), np.float32(0.9387), np.float32(0.9481), np.float32(0.8909), np.float32(0.8988), np.float32(0.8957)] 
+2025-05-06 09:12:29.157377: Epoch time: 92.41 s 
+2025-05-06 09:12:30.859241:  
+2025-05-06 09:12:30.873122: Epoch 1165 
+2025-05-06 09:12:30.907167: Current learning rate: 0.00456 
+2025-05-06 09:14:05.917486: train_loss -0.4961 
+2025-05-06 09:14:05.995598: val_loss -0.4766 
+2025-05-06 09:14:06.006583: Pseudo dice [np.float32(0.8397), np.float32(0.8471), np.float32(0.8956), np.float32(0.9574), np.float32(0.8752), np.float32(0.9237), np.float32(0.9416), np.float32(0.9746), np.float32(0.9525), np.float32(0.9563), np.float32(0.9021), np.float32(0.9449), np.float32(0.9535), np.float32(0.897), np.float32(0.9651), np.float32(0.9441), np.float32(0.8931), np.float32(0.8634), np.float32(0.907)] 
+2025-05-06 09:14:06.040624: Epoch time: 95.06 s 
+2025-05-06 09:14:07.540672:  
+2025-05-06 09:14:07.565102: Epoch 1166 
+2025-05-06 09:14:07.565748: Current learning rate: 0.00455 
+2025-05-06 09:15:42.780546: train_loss -0.4777 
+2025-05-06 09:15:42.819016: val_loss -0.4859 
+2025-05-06 09:15:42.826997: Pseudo dice [np.float32(0.8341), np.float32(0.799), np.float32(0.9354), np.float32(0.9603), np.float32(0.8701), np.float32(0.9607), np.float32(0.9546), np.float32(0.9709), np.float32(0.9618), np.float32(0.9673), np.float32(0.9436), np.float32(0.9663), np.float32(0.9672), np.float32(0.8955), np.float32(0.9648), np.float32(0.9505), np.float32(0.8863), np.float32(0.8949), np.float32(0.9134)] 
+2025-05-06 09:15:42.838141: Epoch time: 95.24 s 
+2025-05-06 09:15:44.485116:  
+2025-05-06 09:15:44.534786: Epoch 1167 
+2025-05-06 09:15:44.553180: Current learning rate: 0.00455 
+2025-05-06 09:17:20.828414: train_loss -0.4943 
+2025-05-06 09:17:20.931308: val_loss -0.4998 
+2025-05-06 09:17:20.971100: Pseudo dice [np.float32(0.8543), np.float32(0.8185), np.float32(0.865), np.float32(0.9725), np.float32(0.9215), np.float32(0.9499), np.float32(0.9349), np.float32(0.9765), np.float32(0.9612), np.float32(0.9731), np.float32(0.9514), np.float32(0.9687), np.float32(0.9692), np.float32(0.8942), np.float32(0.9575), np.float32(0.9409), np.float32(0.8949), np.float32(0.8921), np.float32(0.9164)] 
+2025-05-06 09:17:21.010426: Epoch time: 96.34 s 
+2025-05-06 09:17:22.551929:  
+2025-05-06 09:17:22.668875: Epoch 1168 
+2025-05-06 09:17:22.697991: Current learning rate: 0.00454 
+2025-05-06 09:18:59.543104: train_loss -0.4939 
+2025-05-06 09:18:59.622403: val_loss -0.4573 
+2025-05-06 09:18:59.651435: Pseudo dice [np.float32(0.838), np.float32(0.8153), np.float32(0.8943), np.float32(0.9761), np.float32(0.8962), np.float32(0.9488), np.float32(0.9324), np.float32(0.9509), np.float32(0.958), np.float32(0.9535), np.float32(0.9434), np.float32(0.9678), np.float32(0.9654), np.float32(0.8854), np.float32(0.9615), np.float32(0.9554), np.float32(0.8264), np.float32(0.8536), np.float32(0.9226)] 
+2025-05-06 09:18:59.669382: Epoch time: 96.99 s 
+2025-05-06 09:19:01.468946:  
+2025-05-06 09:19:01.509386: Epoch 1169 
+2025-05-06 09:19:01.531404: Current learning rate: 0.00454 
+2025-05-06 09:20:41.977469: train_loss -0.4838 
+2025-05-06 09:20:42.054241: val_loss -0.5345 
+2025-05-06 09:20:42.080660: Pseudo dice [np.float32(0.8462), np.float32(0.8341), np.float32(0.9397), np.float32(0.9628), np.float32(0.9128), np.float32(0.9585), np.float32(0.9658), np.float32(0.9766), np.float32(0.9494), np.float32(0.9653), np.float32(0.9437), np.float32(0.9564), np.float32(0.9656), np.float32(0.896), np.float32(0.9326), np.float32(0.9538), np.float32(0.8845), np.float32(0.8763), np.float32(0.9086)] 
+2025-05-06 09:20:42.105969: Epoch time: 100.51 s 
+2025-05-06 09:20:43.572676:  
+2025-05-06 09:20:43.639855: Epoch 1170 
+2025-05-06 09:20:43.694319: Current learning rate: 0.00453 
+2025-05-06 09:22:22.027116: train_loss -0.4732 
+2025-05-06 09:22:22.093711: val_loss -0.5249 
+2025-05-06 09:22:22.105685: Pseudo dice [np.float32(0.8406), np.float32(0.8386), np.float32(0.9186), np.float32(0.9673), np.float32(0.8946), np.float32(0.953), np.float32(0.9654), np.float32(0.9796), np.float32(0.9429), np.float32(0.9676), np.float32(0.9466), np.float32(0.9538), np.float32(0.9664), np.float32(0.9103), np.float32(0.9521), np.float32(0.9556), np.float32(0.8711), np.float32(0.8852), np.float32(0.9189)] 
+2025-05-06 09:22:22.113389: Epoch time: 98.46 s 
+2025-05-06 09:22:23.615985:  
+2025-05-06 09:22:23.641436: Epoch 1171 
+2025-05-06 09:22:23.642007: Current learning rate: 0.00453 
+2025-05-06 09:23:56.420770: train_loss -0.499 
+2025-05-06 09:23:56.516587: val_loss -0.4747 
+2025-05-06 09:23:56.542934: Pseudo dice [np.float32(0.8243), np.float32(0.8614), np.float32(0.9405), np.float32(0.9764), np.float32(0.9077), np.float32(0.9458), np.float32(0.9682), np.float32(0.9749), np.float32(0.9589), np.float32(0.9574), np.float32(0.9356), np.float32(0.9546), np.float32(0.9601), np.float32(0.9022), np.float32(0.9566), np.float32(0.9356), np.float32(0.8759), np.float32(0.8855), np.float32(0.9139)] 
+2025-05-06 09:23:56.569491: Epoch time: 92.81 s 
+2025-05-06 09:23:58.020204:  
+2025-05-06 09:23:58.031083: Epoch 1172 
+2025-05-06 09:23:58.031513: Current learning rate: 0.00452 
+2025-05-06 09:25:34.882459: train_loss -0.4825 
+2025-05-06 09:25:35.188767: val_loss -0.495 
+2025-05-06 09:25:35.190777: Pseudo dice [np.float32(0.8459), np.float32(0.8441), np.float32(0.5482), np.float32(0.97), np.float32(0.8454), np.float32(0.9592), np.float32(0.9614), np.float32(0.9741), np.float32(0.9627), np.float32(0.9642), np.float32(0.9338), np.float32(0.9627), np.float32(0.9598), np.float32(0.9002), np.float32(0.9617), np.float32(0.9402), np.float32(0.8834), np.float32(0.8591), np.float32(0.8961)] 
+2025-05-06 09:25:35.191521: Epoch time: 96.86 s 
+2025-05-06 09:25:37.013628:  
+2025-05-06 09:25:37.099117: Epoch 1173 
+2025-05-06 09:25:37.128163: Current learning rate: 0.00452 
+2025-05-06 09:27:14.720467: train_loss -0.4704 
+2025-05-06 09:27:14.746455: val_loss -0.4942 
+2025-05-06 09:27:14.747209: Pseudo dice [np.float32(0.843), np.float32(0.8396), np.float32(0.9108), np.float32(0.9715), np.float32(0.9003), np.float32(0.9566), np.float32(0.9651), np.float32(0.9763), np.float32(0.9614), np.float32(0.9608), np.float32(0.9369), np.float32(0.9653), np.float32(0.966), np.float32(0.8756), np.float32(0.9603), np.float32(0.9478), np.float32(0.8455), np.float32(0.8717), np.float32(0.9209)] 
+2025-05-06 09:27:14.747723: Epoch time: 97.71 s 
+2025-05-06 09:27:19.724963:  
+2025-05-06 09:27:19.730801: Epoch 1174 
+2025-05-06 09:27:19.731261: Current learning rate: 0.00451 
+2025-05-06 09:28:54.350106: train_loss -0.4941 
+2025-05-06 09:28:54.479054: val_loss -0.4737 
+2025-05-06 09:28:54.497339: Pseudo dice [np.float32(0.8539), np.float32(0.8531), np.float32(0.9197), np.float32(0.9612), np.float32(0.8944), np.float32(0.9636), np.float32(0.9642), np.float32(0.9755), np.float32(0.9502), np.float32(0.9551), np.float32(0.945), np.float32(0.9591), np.float32(0.9684), np.float32(0.9114), np.float32(0.9367), np.float32(0.9445), np.float32(0.8793), np.float32(0.8836), np.float32(0.907)] 
+2025-05-06 09:28:54.505065: Epoch time: 94.63 s 
+2025-05-06 09:28:56.100185:  
+2025-05-06 09:28:56.200912: Epoch 1175 
+2025-05-06 09:28:56.215864: Current learning rate: 0.00451 
+2025-05-06 09:30:36.922792: train_loss -0.4926 
+2025-05-06 09:30:36.976342: val_loss -0.4752 
+2025-05-06 09:30:36.984245: Pseudo dice [np.float32(0.8646), np.float32(0.8604), np.float32(0.899), np.float32(0.9682), np.float32(0.9193), np.float32(0.9598), np.float32(0.9544), np.float32(0.9763), np.float32(0.9589), np.float32(0.9682), np.float32(0.9503), np.float32(0.9567), np.float32(0.968), np.float32(0.896), np.float32(0.9705), np.float32(0.9563), np.float32(0.8194), np.float32(0.8696), np.float32(0.9269)] 
+2025-05-06 09:30:37.032535: Epoch time: 100.82 s 
+2025-05-06 09:30:38.614343:  
+2025-05-06 09:30:38.669993: Epoch 1176 
+2025-05-06 09:30:38.689742: Current learning rate: 0.0045 
+2025-05-06 09:32:16.871127: train_loss -0.5006 
+2025-05-06 09:32:16.952761: val_loss -0.4817 
+2025-05-06 09:32:16.960528: Pseudo dice [np.float32(0.8429), np.float32(0.8612), np.float32(0.9392), np.float32(0.9702), np.float32(0.893), np.float32(0.9612), np.float32(0.9681), np.float32(0.9791), np.float32(0.9656), np.float32(0.9464), np.float32(0.939), np.float32(0.9667), np.float32(0.9626), np.float32(0.9038), np.float32(0.9724), np.float32(0.9518), np.float32(0.8663), np.float32(0.8825), np.float32(0.9068)] 
+2025-05-06 09:32:16.963017: Epoch time: 98.26 s 
+2025-05-06 09:32:18.639336:  
+2025-05-06 09:32:18.739456: Epoch 1177 
+2025-05-06 09:32:18.767209: Current learning rate: 0.0045 
+2025-05-06 09:33:55.022912: train_loss -0.4974 
+2025-05-06 09:33:55.095731: val_loss -0.5112 
+2025-05-06 09:33:55.100684: Pseudo dice [np.float32(0.8482), np.float32(0.8574), np.float32(0.9163), np.float32(0.975), np.float32(0.8838), np.float32(0.9634), np.float32(0.9631), np.float32(0.9741), np.float32(0.9624), np.float32(0.9676), np.float32(0.9449), np.float32(0.9668), np.float32(0.9737), np.float32(0.9015), np.float32(0.9616), np.float32(0.9473), np.float32(0.8913), np.float32(0.8999), np.float32(0.9154)] 
+2025-05-06 09:33:55.104632: Epoch time: 96.39 s 
+2025-05-06 09:33:56.593946:  
+2025-05-06 09:33:56.640472: Epoch 1178 
+2025-05-06 09:33:56.640918: Current learning rate: 0.00449 
+2025-05-06 09:35:31.302981: train_loss -0.4735 
+2025-05-06 09:35:31.373204: val_loss -0.4631 
+2025-05-06 09:35:31.391766: Pseudo dice [np.float32(0.8479), np.float32(0.8358), np.float32(0.8618), np.float32(0.9484), np.float32(0.901), np.float32(0.9502), np.float32(0.9653), np.float32(0.9764), np.float32(0.9605), np.float32(0.9515), np.float32(0.9405), np.float32(0.9592), np.float32(0.9594), np.float32(0.9), np.float32(0.9625), np.float32(0.9509), np.float32(0.8941), np.float32(0.8605), np.float32(0.9044)] 
+2025-05-06 09:35:31.406021: Epoch time: 94.71 s 
+2025-05-06 09:35:32.953633:  
+2025-05-06 09:35:33.042577: Epoch 1179 
+2025-05-06 09:35:33.057176: Current learning rate: 0.00449 
+2025-05-06 09:37:07.126459: train_loss -0.4716 
+2025-05-06 09:37:07.166884: val_loss -0.4889 
+2025-05-06 09:37:07.183475: Pseudo dice [np.float32(0.8514), np.float32(0.838), np.float32(0.7542), np.float32(0.8524), np.float32(0.9066), np.float32(0.9612), np.float32(0.9463), np.float32(0.9777), np.float32(0.9727), np.float32(0.9534), np.float32(0.9376), np.float32(0.9748), np.float32(0.9586), np.float32(0.9098), np.float32(0.9682), np.float32(0.9507), np.float32(0.8974), np.float32(0.8694), np.float32(0.9095)] 
+2025-05-06 09:37:07.230348: Epoch time: 94.17 s 
+2025-05-06 09:37:08.791419:  
+2025-05-06 09:37:08.896608: Epoch 1180 
+2025-05-06 09:37:08.929897: Current learning rate: 0.00448 
+2025-05-06 09:38:43.044220: train_loss -0.4735 
+2025-05-06 09:38:43.195098: val_loss -0.5146 
+2025-05-06 09:38:43.231085: Pseudo dice [np.float32(0.8183), np.float32(0.8429), np.float32(0.92), np.float32(0.9731), np.float32(0.9238), np.float32(0.9562), np.float32(0.9613), np.float32(0.9788), np.float32(0.9503), np.float32(0.9552), np.float32(0.9423), np.float32(0.9638), np.float32(0.9694), np.float32(0.8946), np.float32(0.9634), np.float32(0.9496), np.float32(0.8522), np.float32(0.8691), np.float32(0.9179)] 
+2025-05-06 09:38:43.287145: Epoch time: 94.25 s 
+2025-05-06 09:38:44.935395:  
+2025-05-06 09:38:45.049450: Epoch 1181 
+2025-05-06 09:38:45.066726: Current learning rate: 0.00448 
+2025-05-06 09:40:19.246498: train_loss -0.4792 
+2025-05-06 09:40:19.351354: val_loss -0.4666 
+2025-05-06 09:40:19.372086: Pseudo dice [np.float32(0.8483), np.float32(0.8201), np.float32(0.798), np.float32(0.9751), np.float32(0.8835), np.float32(0.9557), np.float32(0.962), np.float32(0.9731), np.float32(0.9563), np.float32(0.9437), np.float32(0.9206), np.float32(0.9618), np.float32(0.9648), np.float32(0.9073), np.float32(0.9711), np.float32(0.9536), np.float32(0.9077), np.float32(0.9036), np.float32(0.9159)] 
+2025-05-06 09:40:19.390715: Epoch time: 94.32 s 
+2025-05-06 09:40:20.873987:  
+2025-05-06 09:40:20.885013: Epoch 1182 
+2025-05-06 09:40:20.885448: Current learning rate: 0.00447 
+2025-05-06 09:41:58.363837: train_loss -0.4694 
+2025-05-06 09:41:58.467751: val_loss -0.5348 
+2025-05-06 09:41:58.506915: Pseudo dice [np.float32(0.8463), np.float32(0.8423), np.float32(0.913), np.float32(0.9764), np.float32(0.9025), np.float32(0.9572), np.float32(0.9613), np.float32(0.9719), np.float32(0.9562), np.float32(0.9632), np.float32(0.9546), np.float32(0.9627), np.float32(0.9665), np.float32(0.8903), np.float32(0.9507), np.float32(0.9437), np.float32(0.8262), np.float32(0.8417), np.float32(0.9232)] 
+2025-05-06 09:41:58.536101: Epoch time: 97.49 s 
+2025-05-06 09:42:00.049411:  
+2025-05-06 09:42:00.125180: Epoch 1183 
+2025-05-06 09:42:00.147306: Current learning rate: 0.00447 
+2025-05-06 09:43:37.627581: train_loss -0.4956 
+2025-05-06 09:43:37.710968: val_loss -0.467 
+2025-05-06 09:43:37.728100: Pseudo dice [np.float32(0.8594), np.float32(0.8684), np.float32(0.661), np.float32(0.9755), np.float32(0.9018), np.float32(0.961), np.float32(0.9719), np.float32(0.9742), np.float32(0.9556), np.float32(0.9734), np.float32(0.9529), np.float32(0.9661), np.float32(0.97), np.float32(0.9023), np.float32(0.9666), np.float32(0.951), np.float32(0.8917), np.float32(0.9133), np.float32(0.9293)] 
+2025-05-06 09:43:37.744690: Epoch time: 97.58 s 
+2025-05-06 09:43:39.230265:  
+2025-05-06 09:43:39.292418: Epoch 1184 
+2025-05-06 09:43:39.302462: Current learning rate: 0.00446 
+2025-05-06 09:45:14.734152: train_loss -0.4916 
+2025-05-06 09:45:14.824496: val_loss -0.4917 
+2025-05-06 09:45:14.849099: Pseudo dice [np.float32(0.8191), np.float32(0.8495), np.float32(0.9396), np.float32(0.9638), np.float32(0.9183), np.float32(0.9595), np.float32(0.9645), np.float32(0.9755), np.float32(0.9628), np.float32(0.9594), np.float32(0.937), np.float32(0.9715), np.float32(0.9611), np.float32(0.8944), np.float32(0.9595), np.float32(0.9508), np.float32(0.8748), np.float32(0.8594), np.float32(0.9156)] 
+2025-05-06 09:45:14.874512: Epoch time: 95.51 s 
+2025-05-06 09:45:16.605695:  
+2025-05-06 09:45:16.609027: Epoch 1185 
+2025-05-06 09:45:16.613345: Current learning rate: 0.00446 
+2025-05-06 09:46:50.727735: train_loss -0.484 
+2025-05-06 09:46:50.808432: val_loss -0.4981 
+2025-05-06 09:46:50.839853: Pseudo dice [np.float32(0.8395), np.float32(0.8601), np.float32(0.9442), np.float32(0.9734), np.float32(0.9118), np.float32(0.9599), np.float32(0.9602), np.float32(0.9775), np.float32(0.9706), np.float32(0.9698), np.float32(0.9532), np.float32(0.9725), np.float32(0.9717), np.float32(0.9102), np.float32(0.9678), np.float32(0.9564), np.float32(0.8279), np.float32(0.8529), np.float32(0.9228)] 
+2025-05-06 09:46:50.864703: Epoch time: 94.12 s 
+2025-05-06 09:46:52.316773:  
+2025-05-06 09:46:52.406688: Epoch 1186 
+2025-05-06 09:46:52.421686: Current learning rate: 0.00445 
+2025-05-06 09:48:24.974157: train_loss -0.4848 
+2025-05-06 09:48:25.110484: val_loss -0.4584 
+2025-05-06 09:48:25.129400: Pseudo dice [np.float32(0.8099), np.float32(0.8097), np.float32(0.8234), np.float32(0.9717), np.float32(0.8933), np.float32(0.9526), np.float32(0.9598), np.float32(0.9725), np.float32(0.9621), np.float32(0.9712), np.float32(0.9468), np.float32(0.9693), np.float32(0.9709), np.float32(0.8791), np.float32(0.9682), np.float32(0.9448), np.float32(0.8784), np.float32(0.8962), np.float32(0.9143)] 
+2025-05-06 09:48:25.145503: Epoch time: 92.66 s 
+2025-05-06 09:48:26.630027:  
+2025-05-06 09:48:26.712041: Epoch 1187 
+2025-05-06 09:48:26.739435: Current learning rate: 0.00445 
+2025-05-06 09:50:05.908947: train_loss -0.4933 
+2025-05-06 09:50:05.989732: val_loss -0.5 
+2025-05-06 09:50:06.007185: Pseudo dice [np.float32(0.8521), np.float32(0.8296), np.float32(0.8688), np.float32(0.9695), np.float32(0.9078), np.float32(0.9611), np.float32(0.9685), np.float32(0.963), np.float32(0.9534), np.float32(0.9459), np.float32(0.9226), np.float32(0.9573), np.float32(0.962), np.float32(0.9082), np.float32(0.965), np.float32(0.9563), np.float32(0.8636), np.float32(0.8906), np.float32(0.9169)] 
+2025-05-06 09:50:06.033123: Epoch time: 99.28 s 
+2025-05-06 09:50:07.666007:  
+2025-05-06 09:50:07.807273: Epoch 1188 
+2025-05-06 09:50:07.820546: Current learning rate: 0.00444 
+2025-05-06 09:51:44.576818: train_loss -0.4715 
+2025-05-06 09:51:44.666862: val_loss -0.4823 
+2025-05-06 09:51:44.690706: Pseudo dice [np.float32(0.8479), np.float32(0.8499), np.float32(0.8355), np.float32(0.964), np.float32(0.8438), np.float32(0.9649), np.float32(0.9549), np.float32(0.9768), np.float32(0.961), np.float32(0.962), np.float32(0.9354), np.float32(0.9638), np.float32(0.9625), np.float32(0.9024), np.float32(0.969), np.float32(0.9654), np.float32(0.8701), np.float32(0.8847), np.float32(0.9004)] 
+2025-05-06 09:51:44.703949: Epoch time: 96.91 s 
+2025-05-06 09:51:46.235626:  
+2025-05-06 09:51:46.276233: Epoch 1189 
+2025-05-06 09:51:46.276918: Current learning rate: 0.00444 
+2025-05-06 09:53:26.660171: train_loss -0.495 
+2025-05-06 09:53:26.820952: val_loss -0.4972 
+2025-05-06 09:53:26.857628: Pseudo dice [np.float32(0.8343), np.float32(0.859), np.float32(0.8052), np.float32(0.9746), np.float32(0.9008), np.float32(0.962), np.float32(0.964), np.float32(0.9782), np.float32(0.9568), np.float32(0.9657), np.float32(0.9518), np.float32(0.9562), np.float32(0.9672), np.float32(0.9064), np.float32(0.9681), np.float32(0.9545), np.float32(0.8077), np.float32(0.8619), np.float32(0.9053)] 
+2025-05-06 09:53:26.897061: Epoch time: 100.43 s 
+2025-05-06 09:53:28.399732:  
+2025-05-06 09:53:28.487205: Epoch 1190 
+2025-05-06 09:53:28.523055: Current learning rate: 0.00443 
+2025-05-06 09:55:09.764817: train_loss -0.492 
+2025-05-06 09:55:09.847975: val_loss -0.5057 
+2025-05-06 09:55:09.866243: Pseudo dice [np.float32(0.8328), np.float32(0.863), np.float32(0.8738), np.float32(0.971), np.float32(0.8993), np.float32(0.9578), np.float32(0.9672), np.float32(0.9749), np.float32(0.9646), np.float32(0.9642), np.float32(0.9416), np.float32(0.9692), np.float32(0.9726), np.float32(0.9081), np.float32(0.9534), np.float32(0.9506), np.float32(0.9021), np.float32(0.9065), np.float32(0.9268)] 
+2025-05-06 09:55:09.888661: Epoch time: 101.37 s 
+2025-05-06 09:55:11.456834:  
+2025-05-06 09:55:11.530130: Epoch 1191 
+2025-05-06 09:55:11.551619: Current learning rate: 0.00443 
+2025-05-06 09:56:51.782446: train_loss -0.4704 
+2025-05-06 09:56:51.845722: val_loss -0.5055 
+2025-05-06 09:56:51.857676: Pseudo dice [np.float32(0.8377), np.float32(0.8393), np.float32(0.9207), np.float32(0.9777), np.float32(0.9016), np.float32(0.9565), np.float32(0.9553), np.float32(0.9683), np.float32(0.9546), np.float32(0.9643), np.float32(0.9509), np.float32(0.9659), np.float32(0.9677), np.float32(0.8965), np.float32(0.9147), np.float32(0.9432), np.float32(0.8795), np.float32(0.8581), np.float32(0.9189)] 
+2025-05-06 09:56:51.861825: Epoch time: 100.33 s 
+2025-05-06 09:56:56.932665:  
+2025-05-06 09:56:56.938445: Epoch 1192 
+2025-05-06 09:56:56.938914: Current learning rate: 0.00442 
+2025-05-06 09:58:35.509290: train_loss -0.4877 
+2025-05-06 09:58:35.535860: val_loss -0.4936 
+2025-05-06 09:58:35.543641: Pseudo dice [np.float32(0.8134), np.float32(0.8349), np.float32(0.9215), np.float32(0.9783), np.float32(0.9239), np.float32(0.9533), np.float32(0.9573), np.float32(0.9756), np.float32(0.9673), np.float32(0.9521), np.float32(0.9475), np.float32(0.973), np.float32(0.9612), np.float32(0.894), np.float32(0.9644), np.float32(0.9369), np.float32(0.8976), np.float32(0.8954), np.float32(0.903)] 
+2025-05-06 09:58:35.548388: Epoch time: 98.58 s 
+2025-05-06 09:58:37.033348:  
+2025-05-06 09:58:37.156271: Epoch 1193 
+2025-05-06 09:58:37.190449: Current learning rate: 0.00442 
+2025-05-06 10:00:15.157483: train_loss -0.4811 
+2025-05-06 10:00:15.305657: val_loss -0.473 
+2025-05-06 10:00:15.331219: Pseudo dice [np.float32(0.8475), np.float32(0.8589), np.float32(0.889), np.float32(0.9756), np.float32(0.9159), np.float32(0.9517), np.float32(0.9605), np.float32(0.963), np.float32(0.9573), np.float32(0.9607), np.float32(0.9337), np.float32(0.9673), np.float32(0.9598), np.float32(0.9009), np.float32(0.9593), np.float32(0.9406), np.float32(0.8725), np.float32(0.8628), np.float32(0.9004)] 
+2025-05-06 10:00:15.349824: Epoch time: 98.13 s 
+2025-05-06 10:00:16.953311:  
+2025-05-06 10:00:17.055938: Epoch 1194 
+2025-05-06 10:00:17.114981: Current learning rate: 0.00441 
+2025-05-06 10:01:53.771065: train_loss -0.4989 
+2025-05-06 10:01:53.886248: val_loss -0.5406 
+2025-05-06 10:01:53.909304: Pseudo dice [np.float32(0.8644), np.float32(0.8481), np.float32(0.9433), np.float32(0.9697), np.float32(0.7616), np.float32(0.9286), np.float32(0.9691), np.float32(0.9799), np.float32(0.9646), np.float32(0.9661), np.float32(0.9414), np.float32(0.9667), np.float32(0.9711), np.float32(0.9022), np.float32(0.9645), np.float32(0.9626), np.float32(0.8958), np.float32(0.8768), np.float32(0.9142)] 
+2025-05-06 10:01:53.954917: Epoch time: 96.82 s 
+2025-05-06 10:01:55.506405:  
+2025-05-06 10:01:55.590654: Epoch 1195 
+2025-05-06 10:01:55.601644: Current learning rate: 0.00441 
+2025-05-06 10:03:37.429334: train_loss -0.4799 
+2025-05-06 10:03:37.553416: val_loss -0.4975 
+2025-05-06 10:03:37.592125: Pseudo dice [np.float32(0.8424), np.float32(0.8558), np.float32(0.9187), np.float32(0.9726), np.float32(0.9181), np.float32(0.962), np.float32(0.9581), np.float32(0.9755), np.float32(0.9604), np.float32(0.949), np.float32(0.9423), np.float32(0.9679), np.float32(0.971), np.float32(0.8881), np.float32(0.9663), np.float32(0.9573), np.float32(0.871), np.float32(0.8819), np.float32(0.9054)] 
+2025-05-06 10:03:37.630801: Epoch time: 101.92 s 
+2025-05-06 10:03:39.187291:  
+2025-05-06 10:03:39.208165: Epoch 1196 
+2025-05-06 10:03:39.216028: Current learning rate: 0.0044 
+2025-05-06 10:05:13.739019: train_loss -0.4886 
+2025-05-06 10:05:13.789442: val_loss -0.4955 
+2025-05-06 10:05:13.813825: Pseudo dice [np.float32(0.8345), np.float32(0.8361), np.float32(0.9337), np.float32(0.9816), np.float32(0.8921), np.float32(0.9527), np.float32(0.9602), np.float32(0.9762), np.float32(0.9685), np.float32(0.9733), np.float32(0.946), np.float32(0.9625), np.float32(0.971), np.float32(0.8988), np.float32(0.9598), np.float32(0.9511), np.float32(0.8761), np.float32(0.8351), np.float32(0.8893)] 
+2025-05-06 10:05:13.842766: Epoch time: 94.55 s 
+2025-05-06 10:05:15.415753:  
+2025-05-06 10:05:15.538915: Epoch 1197 
+2025-05-06 10:05:15.583946: Current learning rate: 0.0044 
+2025-05-06 10:06:54.683832: train_loss -0.4957 
+2025-05-06 10:06:54.831843: val_loss -0.4881 
+2025-05-06 10:06:54.861446: Pseudo dice [np.float32(0.8509), np.float32(0.8459), np.float32(0.9377), np.float32(0.9727), np.float32(0.9028), np.float32(0.9658), np.float32(0.9639), np.float32(0.979), np.float32(0.9569), np.float32(0.9621), np.float32(0.9391), np.float32(0.9638), np.float32(0.9573), np.float32(0.9062), np.float32(0.9747), np.float32(0.9551), np.float32(0.8711), np.float32(0.8649), np.float32(0.9048)] 
+2025-05-06 10:06:54.881824: Epoch time: 99.27 s 
+2025-05-06 10:06:56.439013:  
+2025-05-06 10:06:56.507592: Epoch 1198 
+2025-05-06 10:06:56.516844: Current learning rate: 0.00439 
+2025-05-06 10:08:34.231404: train_loss -0.4893 
+2025-05-06 10:08:34.273440: val_loss -0.5008 
+2025-05-06 10:08:34.303527: Pseudo dice [np.float32(0.8655), np.float32(0.8376), np.float32(0.8045), np.float32(0.9773), np.float32(0.9151), np.float32(0.9629), np.float32(0.9628), np.float32(0.9644), np.float32(0.9651), np.float32(0.9635), np.float32(0.9194), np.float32(0.9693), np.float32(0.9556), np.float32(0.9072), np.float32(0.968), np.float32(0.9559), np.float32(0.9045), np.float32(0.9036), np.float32(0.9174)] 
+2025-05-06 10:08:34.367133: Epoch time: 97.79 s 
+2025-05-06 10:08:35.916159:  
+2025-05-06 10:08:36.036652: Epoch 1199 
+2025-05-06 10:08:36.054176: Current learning rate: 0.00439 
+2025-05-06 10:10:16.526662: train_loss -0.4881 
+2025-05-06 10:10:16.659031: val_loss -0.4486 
+2025-05-06 10:10:16.670332: Pseudo dice [np.float32(0.8665), np.float32(0.8599), np.float32(0.8903), np.float32(0.9697), np.float32(0.9142), np.float32(0.958), np.float32(0.9659), np.float32(0.9809), np.float32(0.9583), np.float32(0.9678), np.float32(0.9529), np.float32(0.9713), np.float32(0.9678), np.float32(0.8969), np.float32(0.9689), np.float32(0.9583), np.float32(0.8649), np.float32(0.8727), np.float32(0.9124)] 
+2025-05-06 10:10:16.688763: Epoch time: 100.61 s 
+2025-05-06 10:10:19.229017:  
+2025-05-06 10:10:19.292500: Epoch 1200 
+2025-05-06 10:10:19.317754: Current learning rate: 0.00438 
+2025-05-06 10:12:01.194998: train_loss -0.4953 
+2025-05-06 10:12:01.215471: val_loss -0.4762 
+2025-05-06 10:12:01.216145: Pseudo dice [np.float32(0.8198), np.float32(0.83), np.float32(0.8869), np.float32(0.9762), np.float32(0.8675), np.float32(0.9535), np.float32(0.96), np.float32(0.9784), np.float32(0.9508), np.float32(0.9613), np.float32(0.9281), np.float32(0.9619), np.float32(0.9597), np.float32(0.8953), np.float32(0.9614), np.float32(0.9499), np.float32(0.8393), np.float32(0.8229), np.float32(0.9188)] 
+2025-05-06 10:12:01.216744: Epoch time: 101.97 s 
+2025-05-06 10:12:02.652759:  
+2025-05-06 10:12:02.763934: Epoch 1201 
+2025-05-06 10:12:02.781860: Current learning rate: 0.00438 
+2025-05-06 10:13:35.698541: train_loss -0.4797 
+2025-05-06 10:13:35.773888: val_loss -0.4834 
+2025-05-06 10:13:35.778332: Pseudo dice [np.float32(0.8446), np.float32(0.8507), np.float32(0.9052), np.float32(0.9672), np.float32(0.9024), np.float32(0.9563), np.float32(0.9555), np.float32(0.9776), np.float32(0.9711), np.float32(0.9669), np.float32(0.9479), np.float32(0.9679), np.float32(0.9578), np.float32(0.9035), np.float32(0.9558), np.float32(0.9577), np.float32(0.9022), np.float32(0.8986), np.float32(0.9148)] 
+2025-05-06 10:13:35.814345: Epoch time: 93.05 s 
+2025-05-06 10:13:37.334843:  
+2025-05-06 10:13:37.370641: Epoch 1202 
+2025-05-06 10:13:37.383803: Current learning rate: 0.00437 
+2025-05-06 10:15:10.234824: train_loss -0.4985 
+2025-05-06 10:15:10.286363: val_loss -0.5091 
+2025-05-06 10:15:10.308038: Pseudo dice [np.float32(0.853), np.float32(0.8536), np.float32(0.8951), np.float32(0.9749), np.float32(0.9185), np.float32(0.9653), np.float32(0.966), np.float32(0.9702), np.float32(0.9645), np.float32(0.9471), np.float32(0.92), np.float32(0.968), np.float32(0.9593), np.float32(0.8987), np.float32(0.9467), np.float32(0.9589), np.float32(0.8853), np.float32(0.8965), np.float32(0.9183)] 
+2025-05-06 10:15:10.323901: Epoch time: 92.9 s 
+2025-05-06 10:15:11.835354:  
+2025-05-06 10:15:11.959773: Epoch 1203 
+2025-05-06 10:15:12.004699: Current learning rate: 0.00437 
+2025-05-06 10:16:45.510997: train_loss -0.4761 
+2025-05-06 10:16:45.562625: val_loss -0.4756 
+2025-05-06 10:16:45.563566: Pseudo dice [np.float32(0.8371), np.float32(0.8755), np.float32(0.9295), np.float32(0.976), np.float32(0.888), np.float32(0.9426), np.float32(0.9628), np.float32(0.9792), np.float32(0.9653), np.float32(0.9688), np.float32(0.9537), np.float32(0.9642), np.float32(0.9691), np.float32(0.9038), np.float32(0.9438), np.float32(0.9378), np.float32(0.8795), np.float32(0.8912), np.float32(0.9127)] 
+2025-05-06 10:16:45.564046: Epoch time: 93.68 s 
+2025-05-06 10:16:47.176636:  
+2025-05-06 10:16:47.211516: Epoch 1204 
+2025-05-06 10:16:47.241099: Current learning rate: 0.00436 
+2025-05-06 10:18:24.278785: train_loss -0.4819 
+2025-05-06 10:18:24.383833: val_loss -0.4826 
+2025-05-06 10:18:24.419919: Pseudo dice [np.float32(0.8429), np.float32(0.8411), np.float32(0.9458), np.float32(0.9761), np.float32(0.9093), np.float32(0.9508), np.float32(0.9606), np.float32(0.9774), np.float32(0.959), np.float32(0.9717), np.float32(0.95), np.float32(0.9682), np.float32(0.9692), np.float32(0.8882), np.float32(0.9706), np.float32(0.947), np.float32(0.8443), np.float32(0.8787), np.float32(0.9044)] 
+2025-05-06 10:18:24.441497: Epoch time: 97.1 s 
+2025-05-06 10:18:25.870939:  
+2025-05-06 10:18:26.016420: Epoch 1205 
+2025-05-06 10:18:26.053452: Current learning rate: 0.00436 
+2025-05-06 10:20:03.746116: train_loss -0.494 
+2025-05-06 10:20:03.858385: val_loss -0.4661 
+2025-05-06 10:20:03.859644: Pseudo dice [np.float32(0.8534), np.float32(0.8278), np.float32(0.6719), np.float32(0.9599), np.float32(0.9245), np.float32(0.9614), np.float32(0.9625), np.float32(0.981), np.float32(0.966), np.float32(0.9558), np.float32(0.9397), np.float32(0.9709), np.float32(0.9703), np.float32(0.9193), np.float32(0.9694), np.float32(0.9527), np.float32(0.8781), np.float32(0.7905), np.float32(0.9256)] 
+2025-05-06 10:20:03.860383: Epoch time: 97.88 s 
+2025-05-06 10:20:05.562921:  
+2025-05-06 10:20:05.611324: Epoch 1206 
+2025-05-06 10:20:05.611784: Current learning rate: 0.00435 
+2025-05-06 10:21:40.508166: train_loss -0.4834 
+2025-05-06 10:21:40.634873: val_loss -0.5028 
+2025-05-06 10:21:40.671313: Pseudo dice [np.float32(0.8118), np.float32(0.8429), np.float32(0.9319), np.float32(0.9738), np.float32(0.9053), np.float32(0.9596), np.float32(0.9592), np.float32(0.9791), np.float32(0.96), np.float32(0.9675), np.float32(0.9407), np.float32(0.9701), np.float32(0.9605), np.float32(0.9118), np.float32(0.9701), np.float32(0.9621), np.float32(0.8297), np.float32(0.845), np.float32(0.8997)] 
+2025-05-06 10:21:40.703500: Epoch time: 94.95 s 
+2025-05-06 10:21:42.177434:  
+2025-05-06 10:21:42.189415: Epoch 1207 
+2025-05-06 10:21:42.193810: Current learning rate: 0.00435 
+2025-05-06 10:23:17.988111: train_loss -0.4705 
+2025-05-06 10:23:18.110150: val_loss -0.5217 
+2025-05-06 10:23:18.144164: Pseudo dice [np.float32(0.8644), np.float32(0.8607), np.float32(0.7835), np.float32(0.9723), np.float32(0.7774), np.float32(0.9318), np.float32(0.9663), np.float32(0.9811), np.float32(0.9655), np.float32(0.9632), np.float32(0.9438), np.float32(0.9703), np.float32(0.9642), np.float32(0.8992), np.float32(0.9713), np.float32(0.9558), np.float32(0.8799), np.float32(0.9043), np.float32(0.9194)] 
+2025-05-06 10:23:18.182980: Epoch time: 95.81 s 
+2025-05-06 10:23:19.769127:  
+2025-05-06 10:23:19.888571: Epoch 1208 
+2025-05-06 10:23:19.923775: Current learning rate: 0.00434 
+2025-05-06 10:25:00.847101: train_loss -0.4712 
+2025-05-06 10:25:01.019198: val_loss -0.5036 
+2025-05-06 10:25:01.132065: Pseudo dice [np.float32(0.8569), np.float32(0.8485), np.float32(0.922), np.float32(0.9717), np.float32(0.8888), np.float32(0.9562), np.float32(0.9604), np.float32(0.9723), np.float32(0.9566), np.float32(0.9709), np.float32(0.956), np.float32(0.9594), np.float32(0.9698), np.float32(0.9124), np.float32(0.9601), np.float32(0.9503), np.float32(0.889), np.float32(0.8897), np.float32(0.9185)] 
+2025-05-06 10:25:01.174629: Epoch time: 101.08 s 
+2025-05-06 10:25:02.836033:  
+2025-05-06 10:25:02.886499: Epoch 1209 
+2025-05-06 10:25:02.912456: Current learning rate: 0.00434 
+2025-05-06 10:26:37.935036: train_loss -0.5033 
+2025-05-06 10:26:38.062932: val_loss -0.5312 
+2025-05-06 10:26:38.088566: Pseudo dice [np.float32(0.8475), np.float32(0.8414), np.float32(0.8078), np.float32(0.9699), np.float32(0.9159), np.float32(0.9603), np.float32(0.9548), np.float32(0.9777), np.float32(0.9615), np.float32(0.9564), np.float32(0.9476), np.float32(0.9692), np.float32(0.9685), np.float32(0.907), np.float32(0.9708), np.float32(0.9567), np.float32(0.8975), np.float32(0.8895), np.float32(0.9177)] 
+2025-05-06 10:26:38.123919: Epoch time: 95.1 s 
+2025-05-06 10:26:43.171755:  
+2025-05-06 10:26:43.177826: Epoch 1210 
+2025-05-06 10:26:43.178368: Current learning rate: 0.00433 
+2025-05-06 10:28:17.091577: train_loss -0.49 
+2025-05-06 10:28:17.151722: val_loss -0.4813 
+2025-05-06 10:28:17.168846: Pseudo dice [np.float32(0.8207), np.float32(0.8406), np.float32(0.918), np.float32(0.9755), np.float32(0.8795), np.float32(0.9535), np.float32(0.965), np.float32(0.9748), np.float32(0.9597), np.float32(0.9465), np.float32(0.9388), np.float32(0.9728), np.float32(0.9667), np.float32(0.9017), np.float32(0.9632), np.float32(0.9501), np.float32(0.8381), np.float32(0.8438), np.float32(0.9192)] 
+2025-05-06 10:28:17.195580: Epoch time: 93.92 s 
+2025-05-06 10:28:18.637275:  
+2025-05-06 10:28:18.736829: Epoch 1211 
+2025-05-06 10:28:18.807812: Current learning rate: 0.00433 
+2025-05-06 10:29:52.474347: train_loss -0.4744 
+2025-05-06 10:29:52.581013: val_loss -0.4732 
+2025-05-06 10:29:52.669941: Pseudo dice [np.float32(0.854), np.float32(0.8556), np.float32(0.9167), np.float32(0.973), np.float32(0.9013), np.float32(0.9414), np.float32(0.9571), np.float32(0.9774), np.float32(0.9664), np.float32(0.9604), np.float32(0.9486), np.float32(0.9677), np.float32(0.9722), np.float32(0.9205), np.float32(0.9649), np.float32(0.9587), np.float32(0.8867), np.float32(0.8957), np.float32(0.9265)] 
+2025-05-06 10:29:52.688168: Epoch time: 93.84 s 
+2025-05-06 10:29:54.150095:  
+2025-05-06 10:29:54.287152: Epoch 1212 
+2025-05-06 10:29:54.317858: Current learning rate: 0.00432 
+2025-05-06 10:31:33.259821: train_loss -0.4897 
+2025-05-06 10:31:33.353020: val_loss -0.5294 
+2025-05-06 10:31:33.371664: Pseudo dice [np.float32(0.8482), np.float32(0.8347), np.float32(0.909), np.float32(0.9727), np.float32(0.9135), np.float32(0.9569), np.float32(0.9638), np.float32(0.9809), np.float32(0.9607), np.float32(0.9587), np.float32(0.946), np.float32(0.9549), np.float32(0.9679), np.float32(0.8982), np.float32(0.9644), np.float32(0.9505), np.float32(0.884), np.float32(0.884), np.float32(0.9101)] 
+2025-05-06 10:31:33.391625: Epoch time: 99.11 s 
+2025-05-06 10:31:34.912855:  
+2025-05-06 10:31:34.956321: Epoch 1213 
+2025-05-06 10:31:34.957234: Current learning rate: 0.00432 
+2025-05-06 10:33:17.163476: train_loss -0.513 
+2025-05-06 10:33:17.207572: val_loss -0.521 
+2025-05-06 10:33:17.208724: Pseudo dice [np.float32(0.8659), np.float32(0.8502), np.float32(0.4528), np.float32(0.9288), np.float32(0.897), np.float32(0.9609), np.float32(0.9619), np.float32(0.9808), np.float32(0.9655), np.float32(0.9591), np.float32(0.9444), np.float32(0.9736), np.float32(0.9652), np.float32(0.9066), np.float32(0.9243), np.float32(0.9401), np.float32(0.8574), np.float32(0.8353), np.float32(0.9303)] 
+2025-05-06 10:33:17.213736: Epoch time: 102.25 s 
+2025-05-06 10:33:18.797244:  
+2025-05-06 10:33:18.903892: Epoch 1214 
+2025-05-06 10:33:18.943064: Current learning rate: 0.00431 
+2025-05-06 10:34:55.707548: train_loss -0.4888 
+2025-05-06 10:34:55.800597: val_loss -0.4999 
+2025-05-06 10:34:55.836656: Pseudo dice [np.float32(0.8067), np.float32(0.8396), np.float32(0.8556), np.float32(0.9763), np.float32(0.9082), np.float32(0.9602), np.float32(0.9698), np.float32(0.9755), np.float32(0.964), np.float32(0.9621), np.float32(0.951), np.float32(0.9641), np.float32(0.9661), np.float32(0.8919), np.float32(0.9506), np.float32(0.9518), np.float32(0.8823), np.float32(0.8923), np.float32(0.908)] 
+2025-05-06 10:34:55.861721: Epoch time: 96.91 s 
+2025-05-06 10:34:57.305688:  
+2025-05-06 10:34:57.428143: Epoch 1215 
+2025-05-06 10:34:57.464594: Current learning rate: 0.00431 
+2025-05-06 10:36:36.162243: train_loss -0.4988 
+2025-05-06 10:36:36.365243: val_loss -0.4784 
+2025-05-06 10:36:36.425610: Pseudo dice [np.float32(0.8481), np.float32(0.7891), np.float32(0.9104), np.float32(0.9677), np.float32(0.91), np.float32(0.9299), np.float32(0.9547), np.float32(0.9676), np.float32(0.9613), np.float32(0.9607), np.float32(0.9433), np.float32(0.943), np.float32(0.9432), np.float32(0.9064), np.float32(0.9643), np.float32(0.9534), np.float32(0.8879), np.float32(0.8909), np.float32(0.9159)] 
+2025-05-06 10:36:36.490201: Epoch time: 98.86 s 
+2025-05-06 10:36:37.976466:  
+2025-05-06 10:36:38.087029: Epoch 1216 
+2025-05-06 10:36:38.115151: Current learning rate: 0.0043 
+2025-05-06 10:38:15.424027: train_loss -0.4733 
+2025-05-06 10:38:15.543435: val_loss -0.5065 
+2025-05-06 10:38:15.557302: Pseudo dice [np.float32(0.8387), np.float32(0.838), np.float32(0.9355), np.float32(0.9781), np.float32(0.8854), np.float32(0.9678), np.float32(0.9593), np.float32(0.9724), np.float32(0.9592), np.float32(0.9638), np.float32(0.9492), np.float32(0.9651), np.float32(0.9662), np.float32(0.908), np.float32(0.973), np.float32(0.951), np.float32(0.8956), np.float32(0.8812), np.float32(0.9251)] 
+2025-05-06 10:38:15.591505: Epoch time: 97.45 s 
+2025-05-06 10:38:17.103774:  
+2025-05-06 10:38:17.199198: Epoch 1217 
+2025-05-06 10:38:17.200042: Current learning rate: 0.0043 
+2025-05-06 10:39:53.867681: train_loss -0.5037 
+2025-05-06 10:39:53.968160: val_loss -0.5104 
+2025-05-06 10:39:53.980674: Pseudo dice [np.float32(0.8459), np.float32(0.8388), np.float32(0.9017), np.float32(0.9672), np.float32(0.912), np.float32(0.9537), np.float32(0.9641), np.float32(0.9714), np.float32(0.9672), np.float32(0.953), np.float32(0.9066), np.float32(0.9534), np.float32(0.9317), np.float32(0.9094), np.float32(0.9629), np.float32(0.9555), np.float32(0.8981), np.float32(0.9198), np.float32(0.9089)] 
+2025-05-06 10:39:53.986342: Epoch time: 96.77 s 
+2025-05-06 10:39:55.476092:  
+2025-05-06 10:39:55.596516: Epoch 1218 
+2025-05-06 10:39:55.640392: Current learning rate: 0.00429 
+2025-05-06 10:41:28.289029: train_loss -0.5017 
+2025-05-06 10:41:28.413764: val_loss -0.5166 
+2025-05-06 10:41:28.414732: Pseudo dice [np.float32(0.851), np.float32(0.8341), np.float32(0.9172), np.float32(0.9732), np.float32(0.9101), np.float32(0.9596), np.float32(0.966), np.float32(0.9796), np.float32(0.966), np.float32(0.9567), np.float32(0.9383), np.float32(0.96), np.float32(0.9602), np.float32(0.9114), np.float32(0.9647), np.float32(0.9513), np.float32(0.8734), np.float32(0.8665), np.float32(0.9201)] 
+2025-05-06 10:41:28.436514: Epoch time: 92.81 s 
+2025-05-06 10:41:29.846304:  
+2025-05-06 10:41:29.981550: Epoch 1219 
+2025-05-06 10:41:29.996942: Current learning rate: 0.00429 
+2025-05-06 10:43:03.987486: train_loss -0.4874 
+2025-05-06 10:43:04.051087: val_loss -0.5277 
+2025-05-06 10:43:04.090598: Pseudo dice [np.float32(0.8393), np.float32(0.8565), np.float32(0.8917), np.float32(0.9774), np.float32(0.9103), np.float32(0.9577), np.float32(0.9676), np.float32(0.9637), np.float32(0.9656), np.float32(0.9711), np.float32(0.9388), np.float32(0.9672), np.float32(0.9679), np.float32(0.9085), np.float32(0.9568), np.float32(0.9517), np.float32(0.9015), np.float32(0.8882), np.float32(0.9244)] 
+2025-05-06 10:43:04.120497: Epoch time: 94.14 s 
+2025-05-06 10:43:05.767542:  
+2025-05-06 10:43:05.844713: Epoch 1220 
+2025-05-06 10:43:05.875193: Current learning rate: 0.00429 
+2025-05-06 10:44:50.265308: train_loss -0.472 
+2025-05-06 10:44:50.346541: val_loss -0.521 
+2025-05-06 10:44:50.368489: Pseudo dice [np.float32(0.8437), np.float32(0.8719), np.float32(0.869), np.float32(0.9752), np.float32(0.8819), np.float32(0.9598), np.float32(0.9516), np.float32(0.9612), np.float32(0.9611), np.float32(0.9618), np.float32(0.9425), np.float32(0.9588), np.float32(0.9553), np.float32(0.9055), np.float32(0.9636), np.float32(0.9458), np.float32(0.8947), np.float32(0.8912), np.float32(0.902)] 
+2025-05-06 10:44:50.409980: Epoch time: 104.5 s 
+2025-05-06 10:44:51.982353:  
+2025-05-06 10:44:52.077337: Epoch 1221 
+2025-05-06 10:44:52.094607: Current learning rate: 0.00428 
+2025-05-06 10:46:35.969449: train_loss -0.4769 
+2025-05-06 10:46:36.048989: val_loss -0.4916 
+2025-05-06 10:46:36.071644: Pseudo dice [np.float32(0.8152), np.float32(0.7947), np.float32(0.9401), np.float32(0.9698), np.float32(0.8608), np.float32(0.9538), np.float32(0.9573), np.float32(0.9786), np.float32(0.9571), np.float32(0.9665), np.float32(0.9459), np.float32(0.9657), np.float32(0.9641), np.float32(0.8936), np.float32(0.9572), np.float32(0.948), np.float32(0.8905), np.float32(0.8855), np.float32(0.9322)] 
+2025-05-06 10:46:36.114679: Epoch time: 103.99 s 
+2025-05-06 10:46:37.728220:  
+2025-05-06 10:46:37.813630: Epoch 1222 
+2025-05-06 10:46:37.818085: Current learning rate: 0.00428 
+2025-05-06 10:48:15.240806: train_loss -0.4744 
+2025-05-06 10:48:15.274503: val_loss -0.4956 
+2025-05-06 10:48:15.275152: Pseudo dice [np.float32(0.8431), np.float32(0.844), np.float32(0.8974), np.float32(0.9746), np.float32(0.8723), np.float32(0.964), np.float32(0.9644), np.float32(0.9714), np.float32(0.9654), np.float32(0.963), np.float32(0.927), np.float32(0.9701), np.float32(0.9456), np.float32(0.911), np.float32(0.9562), np.float32(0.954), np.float32(0.8443), np.float32(0.8715), np.float32(0.9072)] 
+2025-05-06 10:48:15.275604: Epoch time: 97.51 s 
+2025-05-06 10:48:16.871807:  
+2025-05-06 10:48:16.947927: Epoch 1223 
+2025-05-06 10:48:16.983782: Current learning rate: 0.00427 
+2025-05-06 10:49:52.047275: train_loss -0.4686 
+2025-05-06 10:49:52.141703: val_loss -0.4618 
+2025-05-06 10:49:52.174693: Pseudo dice [np.float32(0.8301), np.float32(0.8488), np.float32(0.9066), np.float32(0.9703), np.float32(0.9244), np.float32(0.9539), np.float32(0.964), np.float32(0.9797), np.float32(0.9695), np.float32(0.9569), np.float32(0.9514), np.float32(0.9713), np.float32(0.9672), np.float32(0.8783), np.float32(0.9611), np.float32(0.9511), np.float32(0.864), np.float32(0.785), np.float32(0.9188)] 
+2025-05-06 10:49:52.199442: Epoch time: 95.18 s 
+2025-05-06 10:49:53.673728:  
+2025-05-06 10:49:53.776026: Epoch 1224 
+2025-05-06 10:49:53.776807: Current learning rate: 0.00427 
+2025-05-06 10:51:35.702617: train_loss -0.4847 
+2025-05-06 10:51:35.776742: val_loss -0.5137 
+2025-05-06 10:51:35.778169: Pseudo dice [np.float32(0.8331), np.float32(0.8389), np.float32(0.9182), np.float32(0.9714), np.float32(0.9235), np.float32(0.9653), np.float32(0.9636), np.float32(0.9742), np.float32(0.9373), np.float32(0.9539), np.float32(0.9364), np.float32(0.966), np.float32(0.9673), np.float32(0.914), np.float32(0.9693), np.float32(0.9523), np.float32(0.8599), np.float32(0.841), np.float32(0.904)] 
+2025-05-06 10:51:35.778654: Epoch time: 102.03 s 
+2025-05-06 10:51:37.287650:  
+2025-05-06 10:51:37.437276: Epoch 1225 
+2025-05-06 10:51:37.463338: Current learning rate: 0.00426 
+2025-05-06 10:53:13.655070: train_loss -0.4765 
+2025-05-06 10:53:13.663117: val_loss -0.5016 
+2025-05-06 10:53:13.678350: Pseudo dice [np.float32(0.8055), np.float32(0.8345), np.float32(0.781), np.float32(0.9708), np.float32(0.9083), np.float32(0.959), np.float32(0.9515), np.float32(0.9759), np.float32(0.9608), np.float32(0.9642), np.float32(0.9326), np.float32(0.9581), np.float32(0.954), np.float32(0.9006), np.float32(0.9577), np.float32(0.9528), np.float32(0.8522), np.float32(0.8538), np.float32(0.9024)] 
+2025-05-06 10:53:13.714612: Epoch time: 96.37 s 
+2025-05-06 10:53:15.342209:  
+2025-05-06 10:53:15.361985: Epoch 1226 
+2025-05-06 10:53:15.362669: Current learning rate: 0.00426 
+2025-05-06 10:54:49.541839: train_loss -0.4855 
+2025-05-06 10:54:49.598042: val_loss -0.4871 
+2025-05-06 10:54:49.598995: Pseudo dice [np.float32(0.837), np.float32(0.8357), np.float32(0.9247), np.float32(0.9788), np.float32(0.9261), np.float32(0.9561), np.float32(0.9479), np.float32(0.9729), np.float32(0.9636), np.float32(0.9684), np.float32(0.9517), np.float32(0.9731), np.float32(0.9727), np.float32(0.9087), np.float32(0.9636), np.float32(0.9618), np.float32(0.894), np.float32(0.8875), np.float32(0.9254)] 
+2025-05-06 10:54:49.599443: Epoch time: 94.2 s 
+2025-05-06 10:54:51.264905:  
+2025-05-06 10:54:51.286220: Epoch 1227 
+2025-05-06 10:54:51.327111: Current learning rate: 0.00425 
+2025-05-06 10:56:26.279038: train_loss -0.5002 
+2025-05-06 10:56:26.346623: val_loss -0.4992 
+2025-05-06 10:56:26.357821: Pseudo dice [np.float32(0.8679), np.float32(0.8503), np.float32(0.9436), np.float32(0.9586), np.float32(0.9102), np.float32(0.9629), np.float32(0.9588), np.float32(0.9793), np.float32(0.9584), np.float32(0.958), np.float32(0.938), np.float32(0.9685), np.float32(0.9619), np.float32(0.9044), np.float32(0.9556), np.float32(0.9557), np.float32(0.9056), np.float32(0.888), np.float32(0.9176)] 
+2025-05-06 10:56:26.365447: Epoch time: 95.02 s 
+2025-05-06 10:56:31.401442:  
+2025-05-06 10:56:31.407320: Epoch 1228 
+2025-05-06 10:56:31.407915: Current learning rate: 0.00425 
+2025-05-06 10:58:12.043995: train_loss -0.4843 
+2025-05-06 10:58:12.097876: val_loss -0.5236 
+2025-05-06 10:58:12.102411: Pseudo dice [np.float32(0.8307), np.float32(0.8418), np.float32(0.9012), np.float32(0.9707), np.float32(0.9131), np.float32(0.9609), np.float32(0.9602), np.float32(0.9778), np.float32(0.9673), np.float32(0.9603), np.float32(0.9516), np.float32(0.9684), np.float32(0.9666), np.float32(0.9112), np.float32(0.9671), np.float32(0.952), np.float32(0.8626), np.float32(0.8682), np.float32(0.924)] 
+2025-05-06 10:58:12.129535: Epoch time: 100.64 s 
+2025-05-06 10:58:13.646063:  
+2025-05-06 10:58:13.693132: Epoch 1229 
+2025-05-06 10:58:13.697444: Current learning rate: 0.00424 
+2025-05-06 10:59:50.393660: train_loss -0.5074 
+2025-05-06 10:59:50.422630: val_loss -0.52 
+2025-05-06 10:59:50.427060: Pseudo dice [np.float32(0.8479), np.float32(0.8497), np.float32(0.9283), np.float32(0.9796), np.float32(0.9038), np.float32(0.9599), np.float32(0.9651), np.float32(0.9789), np.float32(0.9508), np.float32(0.9641), np.float32(0.9509), np.float32(0.9631), np.float32(0.9705), np.float32(0.9178), np.float32(0.969), np.float32(0.9581), np.float32(0.8662), np.float32(0.8753), np.float32(0.9166)] 
+2025-05-06 10:59:50.450434: Epoch time: 96.75 s 
+2025-05-06 10:59:51.990151:  
+2025-05-06 10:59:52.103863: Epoch 1230 
+2025-05-06 10:59:52.162376: Current learning rate: 0.00424 
+2025-05-06 11:01:25.347226: train_loss -0.5002 
+2025-05-06 11:01:25.419527: val_loss -0.4941 
+2025-05-06 11:01:25.431155: Pseudo dice [np.float32(0.8557), np.float32(0.8682), np.float32(0.9361), np.float32(0.9737), np.float32(0.9258), np.float32(0.9531), np.float32(0.9696), np.float32(0.9807), np.float32(0.9688), np.float32(0.9675), np.float32(0.9505), np.float32(0.972), np.float32(0.9689), np.float32(0.9078), np.float32(0.9461), np.float32(0.9458), np.float32(0.8428), np.float32(0.857), np.float32(0.9129)] 
+2025-05-06 11:01:25.443607: Epoch time: 93.36 s 
+2025-05-06 11:01:26.920036:  
+2025-05-06 11:01:26.963791: Epoch 1231 
+2025-05-06 11:01:26.984597: Current learning rate: 0.00423 
+2025-05-06 11:03:02.748024: train_loss -0.5024 
+2025-05-06 11:03:02.835338: val_loss -0.4562 
+2025-05-06 11:03:02.875997: Pseudo dice [np.float32(0.868), np.float32(0.8391), np.float32(0.8958), np.float32(0.9796), np.float32(0.9272), np.float32(0.9555), np.float32(0.9559), np.float32(0.9795), np.float32(0.959), np.float32(0.9634), np.float32(0.9102), np.float32(0.9501), np.float32(0.9609), np.float32(0.9212), np.float32(0.9669), np.float32(0.958), np.float32(0.8936), np.float32(0.8838), np.float32(0.9196)] 
+2025-05-06 11:03:02.881726: Epoch time: 95.83 s 
+2025-05-06 11:03:02.882525: Yayy! New best EMA pseudo Dice: 0.9279999732971191 
+2025-05-06 11:03:05.282325:  
+2025-05-06 11:03:05.464570: Epoch 1232 
+2025-05-06 11:03:05.521511: Current learning rate: 0.00423 
+2025-05-06 11:04:45.796930: train_loss -0.4991 
+2025-05-06 11:04:45.858030: val_loss -0.5005 
+2025-05-06 11:04:45.862735: Pseudo dice [np.float32(0.8309), np.float32(0.8678), np.float32(0.7686), np.float32(0.9619), np.float32(0.9071), np.float32(0.9597), np.float32(0.9602), np.float32(0.9774), np.float32(0.9681), np.float32(0.971), np.float32(0.9567), np.float32(0.9678), np.float32(0.9729), np.float32(0.9085), np.float32(0.9595), np.float32(0.955), np.float32(0.8615), np.float32(0.8731), np.float32(0.9167)] 
+2025-05-06 11:04:45.900436: Epoch time: 100.52 s 
+2025-05-06 11:04:47.429869:  
+2025-05-06 11:04:47.564154: Epoch 1233 
+2025-05-06 11:04:47.618986: Current learning rate: 0.00422 
+2025-05-06 11:06:22.487708: train_loss -0.4753 
+2025-05-06 11:06:22.653381: val_loss -0.4766 
+2025-05-06 11:06:22.731784: Pseudo dice [np.float32(0.8522), np.float32(0.8378), np.float32(0.8858), np.float32(0.9774), np.float32(0.8956), np.float32(0.9575), np.float32(0.9622), np.float32(0.9658), np.float32(0.9575), np.float32(0.9642), np.float32(0.9447), np.float32(0.9554), np.float32(0.9691), np.float32(0.9014), np.float32(0.9677), np.float32(0.9506), np.float32(0.8707), np.float32(0.8516), np.float32(0.9194)] 
+2025-05-06 11:06:22.771499: Epoch time: 95.06 s 
+2025-05-06 11:06:24.309630:  
+2025-05-06 11:06:24.457712: Epoch 1234 
+2025-05-06 11:06:24.498634: Current learning rate: 0.00422 
+2025-05-06 11:08:00.679289: train_loss -0.4889 
+2025-05-06 11:08:00.728579: val_loss -0.5039 
+2025-05-06 11:08:00.729552: Pseudo dice [np.float32(0.8275), np.float32(0.8179), np.float32(0.9051), np.float32(0.9731), np.float32(0.9173), np.float32(0.9611), np.float32(0.9622), np.float32(0.9787), np.float32(0.9587), np.float32(0.9608), np.float32(0.9404), np.float32(0.9651), np.float32(0.9646), np.float32(0.9049), np.float32(0.9652), np.float32(0.9545), np.float32(0.8825), np.float32(0.8864), np.float32(0.9284)] 
+2025-05-06 11:08:00.730320: Epoch time: 96.37 s 
+2025-05-06 11:08:02.288820:  
+2025-05-06 11:08:02.316751: Epoch 1235 
+2025-05-06 11:08:02.317430: Current learning rate: 0.00421 
+2025-05-06 11:09:40.071833: train_loss -0.4925 
+2025-05-06 11:09:40.102186: val_loss -0.5259 
+2025-05-06 11:09:40.110000: Pseudo dice [np.float32(0.8552), np.float32(0.867), np.float32(0.8553), np.float32(0.9736), np.float32(0.8937), np.float32(0.9661), np.float32(0.9648), np.float32(0.9779), np.float32(0.9691), np.float32(0.9741), np.float32(0.9569), np.float32(0.9712), np.float32(0.9735), np.float32(0.9158), np.float32(0.9693), np.float32(0.9497), np.float32(0.8813), np.float32(0.8786), np.float32(0.9239)] 
+2025-05-06 11:09:40.132572: Epoch time: 97.78 s 
+2025-05-06 11:09:40.171874: Yayy! New best EMA pseudo Dice: 0.9279999732971191 
+2025-05-06 11:09:43.078166:  
+2025-05-06 11:09:43.082237: Epoch 1236 
+2025-05-06 11:09:43.087687: Current learning rate: 0.00421 
+2025-05-06 11:11:19.263010: train_loss -0.4824 
+2025-05-06 11:11:19.269180: val_loss -0.4886 
+2025-05-06 11:11:19.269949: Pseudo dice [np.float32(0.8314), np.float32(0.8491), np.float32(0.9267), np.float32(0.9797), np.float32(0.8903), np.float32(0.9506), np.float32(0.9619), np.float32(0.9773), np.float32(0.9586), np.float32(0.9658), np.float32(0.9503), np.float32(0.961), np.float32(0.964), np.float32(0.8953), np.float32(0.9654), np.float32(0.9551), np.float32(0.8859), np.float32(0.7915), np.float32(0.9106)] 
+2025-05-06 11:11:19.270363: Epoch time: 96.19 s 
+2025-05-06 11:11:20.871154:  
+2025-05-06 11:11:21.012326: Epoch 1237 
+2025-05-06 11:11:21.056464: Current learning rate: 0.0042 
+2025-05-06 11:12:57.801826: train_loss -0.4733 
+2025-05-06 11:12:57.864791: val_loss -0.5188 
+2025-05-06 11:12:57.900601: Pseudo dice [np.float32(0.843), np.float32(0.8496), np.float32(0.8934), np.float32(0.9711), np.float32(0.9054), np.float32(0.9596), np.float32(0.9567), np.float32(0.9742), np.float32(0.9577), np.float32(0.9618), np.float32(0.9562), np.float32(0.9681), np.float32(0.9535), np.float32(0.8938), np.float32(0.9683), np.float32(0.9522), np.float32(0.9049), np.float32(0.8413), np.float32(0.925)] 
+2025-05-06 11:12:57.940893: Epoch time: 96.93 s 
+2025-05-06 11:12:59.399997:  
+2025-05-06 11:12:59.508479: Epoch 1238 
+2025-05-06 11:12:59.547275: Current learning rate: 0.0042 
+2025-05-06 11:14:35.450889: train_loss -0.4926 
+2025-05-06 11:14:35.573515: val_loss -0.5025 
+2025-05-06 11:14:35.599429: Pseudo dice [np.float32(0.8509), np.float32(0.8372), np.float32(0.9089), np.float32(0.9679), np.float32(0.8915), np.float32(0.9479), np.float32(0.9591), np.float32(0.9786), np.float32(0.9549), np.float32(0.9611), np.float32(0.9482), np.float32(0.9682), np.float32(0.9711), np.float32(0.9061), np.float32(0.958), np.float32(0.9538), np.float32(0.9011), np.float32(0.8858), np.float32(0.9032)] 
+2025-05-06 11:14:35.630160: Epoch time: 96.05 s 
+2025-05-06 11:14:37.183557:  
+2025-05-06 11:14:37.248364: Epoch 1239 
+2025-05-06 11:14:37.249582: Current learning rate: 0.00419 
+2025-05-06 11:16:09.201705: train_loss -0.4933 
+2025-05-06 11:16:09.247516: val_loss -0.4928 
+2025-05-06 11:16:09.266031: Pseudo dice [np.float32(0.8592), np.float32(0.861), np.float32(0.9219), np.float32(0.9727), np.float32(0.8629), np.float32(0.9604), np.float32(0.9662), np.float32(0.9764), np.float32(0.9619), np.float32(0.9611), np.float32(0.9439), np.float32(0.9628), np.float32(0.9682), np.float32(0.9101), np.float32(0.9706), np.float32(0.9549), np.float32(0.8873), np.float32(0.8919), np.float32(0.9098)] 
+2025-05-06 11:16:09.311762: Epoch time: 92.02 s 
+2025-05-06 11:16:09.358847: Yayy! New best EMA pseudo Dice: 0.9283000230789185 
+2025-05-06 11:16:12.107427:  
+2025-05-06 11:16:12.115363: Epoch 1240 
+2025-05-06 11:16:12.115832: Current learning rate: 0.00419 
+2025-05-06 11:17:49.741498: train_loss -0.496 
+2025-05-06 11:17:49.750335: val_loss -0.4964 
+2025-05-06 11:17:49.751134: Pseudo dice [np.float32(0.8441), np.float32(0.8335), np.float32(0.921), np.float32(0.974), np.float32(0.9036), np.float32(0.9632), np.float32(0.9662), np.float32(0.9763), np.float32(0.9566), np.float32(0.9643), np.float32(0.9403), np.float32(0.9547), np.float32(0.9702), np.float32(0.9118), np.float32(0.9682), np.float32(0.9653), np.float32(0.846), np.float32(0.8674), np.float32(0.9199)] 
+2025-05-06 11:17:49.783283: Epoch time: 97.64 s 
+2025-05-06 11:17:49.794246: Yayy! New best EMA pseudo Dice: 0.9283000230789185 
+2025-05-06 11:17:52.275441:  
+2025-05-06 11:17:52.289710: Epoch 1241 
+2025-05-06 11:17:52.293983: Current learning rate: 0.00418 
+2025-05-06 11:19:33.795783: train_loss -0.4746 
+2025-05-06 11:19:33.813403: val_loss -0.4985 
+2025-05-06 11:19:33.814054: Pseudo dice [np.float32(0.8618), np.float32(0.8483), np.float32(0.8967), np.float32(0.9571), np.float32(0.922), np.float32(0.9654), np.float32(0.9488), np.float32(0.9697), np.float32(0.9647), np.float32(0.9663), np.float32(0.9486), np.float32(0.9555), np.float32(0.9582), np.float32(0.9078), np.float32(0.9665), np.float32(0.9385), np.float32(0.871), np.float32(0.891), np.float32(0.9195)] 
+2025-05-06 11:19:33.814823: Epoch time: 101.52 s 
+2025-05-06 11:19:33.827978: Yayy! New best EMA pseudo Dice: 0.9283999800682068 
+2025-05-06 11:19:36.465267:  
+2025-05-06 11:19:36.526147: Epoch 1242 
+2025-05-06 11:19:36.527121: Current learning rate: 0.00418 
+2025-05-06 11:21:14.611523: train_loss -0.4835 
+2025-05-06 11:21:14.686478: val_loss -0.4944 
+2025-05-06 11:21:14.697843: Pseudo dice [np.float32(0.8196), np.float32(0.854), np.float32(0.791), np.float32(0.9726), np.float32(0.8621), np.float32(0.9373), np.float32(0.9439), np.float32(0.9775), np.float32(0.9626), np.float32(0.9684), np.float32(0.9523), np.float32(0.9701), np.float32(0.9703), np.float32(0.8994), np.float32(0.9436), np.float32(0.9414), np.float32(0.8697), np.float32(0.8476), np.float32(0.9031)] 
+2025-05-06 11:21:14.705607: Epoch time: 98.15 s 
+2025-05-06 11:21:16.290852:  
+2025-05-06 11:21:16.328499: Epoch 1243 
+2025-05-06 11:21:16.341596: Current learning rate: 0.00417 
+2025-05-06 11:22:54.410411: train_loss -0.4811 
+2025-05-06 11:22:54.545496: val_loss -0.4923 
+2025-05-06 11:22:54.590296: Pseudo dice [np.float32(0.8486), np.float32(0.8598), np.float32(0.8911), np.float32(0.9773), np.float32(0.9041), np.float32(0.9593), np.float32(0.9665), np.float32(0.9797), np.float32(0.9526), np.float32(0.9709), np.float32(0.9387), np.float32(0.9666), np.float32(0.9669), np.float32(0.8979), np.float32(0.9599), np.float32(0.9588), np.float32(0.8833), np.float32(0.8738), np.float32(0.9253)] 
+2025-05-06 11:22:54.642457: Epoch time: 98.12 s 
+2025-05-06 11:22:59.744628:  
+2025-05-06 11:22:59.750718: Epoch 1244 
+2025-05-06 11:22:59.751319: Current learning rate: 0.00417 
+2025-05-06 11:24:35.846521: train_loss -0.496 
+2025-05-06 11:24:35.998644: val_loss -0.5271 
+2025-05-06 11:24:36.030646: Pseudo dice [np.float32(0.8592), np.float32(0.856), np.float32(0.8988), np.float32(0.9728), np.float32(0.926), np.float32(0.9474), np.float32(0.957), np.float32(0.9773), np.float32(0.9652), np.float32(0.9508), np.float32(0.9528), np.float32(0.9652), np.float32(0.9691), np.float32(0.9087), np.float32(0.9547), np.float32(0.9503), np.float32(0.8782), np.float32(0.8855), np.float32(0.9147)] 
+2025-05-06 11:24:36.058346: Epoch time: 96.1 s 
+2025-05-06 11:24:37.650389:  
+2025-05-06 11:24:37.676139: Epoch 1245 
+2025-05-06 11:24:37.680709: Current learning rate: 0.00416 
+2025-05-06 11:26:11.212750: train_loss -0.4871 
+2025-05-06 11:26:11.245178: val_loss -0.5026 
+2025-05-06 11:26:11.265371: Pseudo dice [np.float32(0.8496), np.float32(0.8456), np.float32(0.8625), np.float32(0.9746), np.float32(0.8629), np.float32(0.9579), np.float32(0.9574), np.float32(0.9742), np.float32(0.9683), np.float32(0.9691), np.float32(0.9472), np.float32(0.9636), np.float32(0.9724), np.float32(0.8963), np.float32(0.9564), np.float32(0.9454), np.float32(0.8816), np.float32(0.9055), np.float32(0.9206)] 
+2025-05-06 11:26:11.282421: Epoch time: 93.56 s 
+2025-05-06 11:26:12.901731:  
+2025-05-06 11:26:12.964354: Epoch 1246 
+2025-05-06 11:26:12.986635: Current learning rate: 0.00416 
+2025-05-06 11:27:49.869258: train_loss -0.4853 
+2025-05-06 11:27:50.007096: val_loss -0.5225 
+2025-05-06 11:27:50.045652: Pseudo dice [np.float32(0.8211), np.float32(0.8523), np.float32(0.8788), np.float32(0.9633), np.float32(0.9212), np.float32(0.9491), np.float32(0.9507), np.float32(0.9794), np.float32(0.958), np.float32(0.9629), np.float32(0.9467), np.float32(0.9687), np.float32(0.9629), np.float32(0.8997), np.float32(0.959), np.float32(0.9585), np.float32(0.8861), np.float32(0.8966), np.float32(0.9252)] 
+2025-05-06 11:27:50.068458: Epoch time: 96.97 s 
+2025-05-06 11:27:51.615217:  
+2025-05-06 11:27:51.618569: Epoch 1247 
+2025-05-06 11:27:51.626570: Current learning rate: 0.00415 
+2025-05-06 11:29:23.367134: train_loss -0.5072 
+2025-05-06 11:29:23.467390: val_loss -0.5011 
+2025-05-06 11:29:23.500393: Pseudo dice [np.float32(0.8463), np.float32(0.8615), np.float32(0.9321), np.float32(0.9758), np.float32(0.9011), np.float32(0.9315), np.float32(0.9575), np.float32(0.974), np.float32(0.9536), np.float32(0.9593), np.float32(0.9465), np.float32(0.9625), np.float32(0.9673), np.float32(0.9012), np.float32(0.9378), np.float32(0.9351), np.float32(0.8047), np.float32(0.8693), np.float32(0.9331)] 
+2025-05-06 11:29:23.533525: Epoch time: 91.75 s 
+2025-05-06 11:29:25.129008:  
+2025-05-06 11:29:25.230640: Epoch 1248 
+2025-05-06 11:29:25.267554: Current learning rate: 0.00415 
+2025-05-06 11:31:01.693171: train_loss -0.4642 
+2025-05-06 11:31:01.778298: val_loss -0.5026 
+2025-05-06 11:31:01.779016: Pseudo dice [np.float32(0.852), np.float32(0.8553), np.float32(0.9138), np.float32(0.972), np.float32(0.8693), np.float32(0.9289), np.float32(0.9396), np.float32(0.9776), np.float32(0.9637), np.float32(0.9648), np.float32(0.9271), np.float32(0.9719), np.float32(0.9585), np.float32(0.9013), np.float32(0.9678), np.float32(0.9515), np.float32(0.8849), np.float32(0.8848), np.float32(0.9093)] 
+2025-05-06 11:31:01.779433: Epoch time: 96.57 s 
+2025-05-06 11:31:03.340987:  
+2025-05-06 11:31:03.466945: Epoch 1249 
+2025-05-06 11:31:03.504583: Current learning rate: 0.00414 
+2025-05-06 11:32:38.645223: train_loss -0.5057 
+2025-05-06 11:32:38.768378: val_loss -0.4894 
+2025-05-06 11:32:38.769545: Pseudo dice [np.float32(0.8466), np.float32(0.8516), np.float32(0.8684), np.float32(0.9771), np.float32(0.9056), np.float32(0.9607), np.float32(0.9585), np.float32(0.977), np.float32(0.9608), np.float32(0.9672), np.float32(0.9396), np.float32(0.9709), np.float32(0.9654), np.float32(0.8994), np.float32(0.9614), np.float32(0.9435), np.float32(0.848), np.float32(0.8528), np.float32(0.9215)] 
+2025-05-06 11:32:38.773945: Epoch time: 95.31 s 
+2025-05-06 11:32:41.230464:  
+2025-05-06 11:32:41.272884: Epoch 1250 
+2025-05-06 11:32:41.273394: Current learning rate: 0.00414 
+2025-05-06 11:34:23.528067: train_loss -0.4947 
+2025-05-06 11:34:23.617651: val_loss -0.4679 
+2025-05-06 11:34:23.675162: Pseudo dice [np.float32(0.8332), np.float32(0.8587), np.float32(0.8783), np.float32(0.9719), np.float32(0.9214), np.float32(0.9581), np.float32(0.9653), np.float32(0.9774), np.float32(0.9637), np.float32(0.9476), np.float32(0.9291), np.float32(0.9667), np.float32(0.9569), np.float32(0.9109), np.float32(0.9701), np.float32(0.9531), np.float32(0.9082), np.float32(0.9033), np.float32(0.8943)] 
+2025-05-06 11:34:23.728985: Epoch time: 102.3 s 
+2025-05-06 11:34:25.426318:  
+2025-05-06 11:34:25.440725: Epoch 1251 
+2025-05-06 11:34:25.441421: Current learning rate: 0.00413 
+2025-05-06 11:35:59.107482: train_loss -0.4781 
+2025-05-06 11:35:59.204381: val_loss -0.5136 
+2025-05-06 11:35:59.218987: Pseudo dice [np.float32(0.8735), np.float32(0.8715), np.float32(0.9037), np.float32(0.9707), np.float32(0.8825), np.float32(0.9607), np.float32(0.9648), np.float32(0.9796), np.float32(0.9731), np.float32(0.9622), np.float32(0.9363), np.float32(0.9757), np.float32(0.9702), np.float32(0.89), np.float32(0.929), np.float32(0.9502), np.float32(0.8219), np.float32(0.8727), np.float32(0.9291)] 
+2025-05-06 11:35:59.233719: Epoch time: 93.68 s 
+2025-05-06 11:36:00.658283:  
+2025-05-06 11:36:00.748285: Epoch 1252 
+2025-05-06 11:36:00.776556: Current learning rate: 0.00413 
+2025-05-06 11:37:38.179428: train_loss -0.4855 
+2025-05-06 11:37:38.332391: val_loss -0.5125 
+2025-05-06 11:37:38.369693: Pseudo dice [np.float32(0.8541), np.float32(0.8468), np.float32(0.9134), np.float32(0.9776), np.float32(0.8766), np.float32(0.9591), np.float32(0.9537), np.float32(0.9769), np.float32(0.9614), np.float32(0.9616), np.float32(0.9472), np.float32(0.9674), np.float32(0.9661), np.float32(0.9063), np.float32(0.9592), np.float32(0.9651), np.float32(0.887), np.float32(0.9026), np.float32(0.9213)] 
+2025-05-06 11:37:38.400352: Epoch time: 97.52 s 
+2025-05-06 11:37:40.202283:  
+2025-05-06 11:37:40.313402: Epoch 1253 
+2025-05-06 11:37:40.315828: Current learning rate: 0.00412 
+2025-05-06 11:39:12.492333: train_loss -0.5026 
+2025-05-06 11:39:12.508886: val_loss -0.5214 
+2025-05-06 11:39:12.514843: Pseudo dice [np.float32(0.8538), np.float32(0.8351), np.float32(0.8518), np.float32(0.9627), np.float32(0.9047), np.float32(0.9564), np.float32(0.9625), np.float32(0.9786), np.float32(0.9669), np.float32(0.9629), np.float32(0.9421), np.float32(0.9671), np.float32(0.971), np.float32(0.9156), np.float32(0.9677), np.float32(0.961), np.float32(0.8923), np.float32(0.8709), np.float32(0.9177)] 
+2025-05-06 11:39:12.515328: Epoch time: 92.29 s 
+2025-05-06 11:39:14.036618:  
+2025-05-06 11:39:14.085116: Epoch 1254 
+2025-05-06 11:39:14.110982: Current learning rate: 0.00412 
+2025-05-06 11:40:46.394826: train_loss -0.474 
+2025-05-06 11:40:46.507400: val_loss -0.4863 
+2025-05-06 11:40:46.566132: Pseudo dice [np.float32(0.8492), np.float32(0.8638), np.float32(0.7856), np.float32(0.968), np.float32(0.9215), np.float32(0.9469), np.float32(0.9693), np.float32(0.9798), np.float32(0.9646), np.float32(0.9695), np.float32(0.9503), np.float32(0.961), np.float32(0.9684), np.float32(0.902), np.float32(0.9571), np.float32(0.9595), np.float32(0.8533), np.float32(0.865), np.float32(0.9201)] 
+2025-05-06 11:40:46.625632: Epoch time: 92.36 s 
+2025-05-06 11:40:48.395951:  
+2025-05-06 11:40:48.454890: Epoch 1255 
+2025-05-06 11:40:48.479556: Current learning rate: 0.00411 
+2025-05-06 11:42:24.911297: train_loss -0.493 
+2025-05-06 11:42:24.972700: val_loss -0.498 
+2025-05-06 11:42:24.976977: Pseudo dice [np.float32(0.8675), np.float32(0.8545), np.float32(0.9016), np.float32(0.9792), np.float32(0.911), np.float32(0.9616), np.float32(0.9666), np.float32(0.9626), np.float32(0.9711), np.float32(0.9518), np.float32(0.9029), np.float32(0.9764), np.float32(0.9624), np.float32(0.9164), np.float32(0.9682), np.float32(0.9583), np.float32(0.8851), np.float32(0.8934), np.float32(0.9274)] 
+2025-05-06 11:42:24.993555: Epoch time: 96.52 s 
+2025-05-06 11:42:26.632148:  
+2025-05-06 11:42:26.698420: Epoch 1256 
+2025-05-06 11:42:26.718971: Current learning rate: 0.00411 
+2025-05-06 11:44:05.687709: train_loss -0.4856 
+2025-05-06 11:44:05.797003: val_loss -0.4981 
+2025-05-06 11:44:05.832132: Pseudo dice [np.float32(0.859), np.float32(0.8475), np.float32(0.9223), np.float32(0.9714), np.float32(0.904), np.float32(0.9587), np.float32(0.9649), np.float32(0.9743), np.float32(0.972), np.float32(0.9599), np.float32(0.9396), np.float32(0.9703), np.float32(0.9657), np.float32(0.909), np.float32(0.9681), np.float32(0.9528), np.float32(0.8973), np.float32(0.8806), np.float32(0.9261)] 
+2025-05-06 11:44:05.864282: Epoch time: 99.06 s 
+2025-05-06 11:44:05.907111: Yayy! New best EMA pseudo Dice: 0.9284999966621399 
+2025-05-06 11:44:08.355356:  
+2025-05-06 11:44:08.363727: Epoch 1257 
+2025-05-06 11:44:08.364343: Current learning rate: 0.0041 
+2025-05-06 11:45:45.972846: train_loss -0.4762 
+2025-05-06 11:45:46.075454: val_loss -0.5073 
+2025-05-06 11:45:46.076822: Pseudo dice [np.float32(0.8555), np.float32(0.8579), np.float32(0.9255), np.float32(0.9722), np.float32(0.8849), np.float32(0.9606), np.float32(0.962), np.float32(0.9693), np.float32(0.9649), np.float32(0.9606), np.float32(0.9481), np.float32(0.9706), np.float32(0.9722), np.float32(0.9056), np.float32(0.9614), np.float32(0.9376), np.float32(0.8735), np.float32(0.8438), np.float32(0.9183)] 
+2025-05-06 11:45:46.089827: Epoch time: 97.62 s 
+2025-05-06 11:45:46.090923: Yayy! New best EMA pseudo Dice: 0.9284999966621399 
+2025-05-06 11:45:48.576222:  
+2025-05-06 11:45:48.643693: Epoch 1258 
+2025-05-06 11:45:48.644947: Current learning rate: 0.0041 
+2025-05-06 11:47:25.330583: train_loss -0.4968 
+2025-05-06 11:47:25.427283: val_loss -0.4824 
+2025-05-06 11:47:25.476393: Pseudo dice [np.float32(0.8056), np.float32(0.8536), np.float32(0.8795), np.float32(0.9756), np.float32(0.8582), np.float32(0.956), np.float32(0.9587), np.float32(0.9725), np.float32(0.967), np.float32(0.9534), np.float32(0.937), np.float32(0.9688), np.float32(0.9581), np.float32(0.8986), np.float32(0.9325), np.float32(0.9463), np.float32(0.8905), np.float32(0.8531), np.float32(0.918)] 
+2025-05-06 11:47:25.502503: Epoch time: 96.76 s 
+2025-05-06 11:47:27.254420:  
+2025-05-06 11:47:27.315627: Epoch 1259 
+2025-05-06 11:47:27.357904: Current learning rate: 0.00409 
+2025-05-06 11:49:09.599408: train_loss -0.479 
+2025-05-06 11:49:09.678438: val_loss -0.4717 
+2025-05-06 11:49:09.690429: Pseudo dice [np.float32(0.8513), np.float32(0.8483), np.float32(0.8835), np.float32(0.973), np.float32(0.8831), np.float32(0.963), np.float32(0.9655), np.float32(0.977), np.float32(0.9641), np.float32(0.9609), np.float32(0.938), np.float32(0.9747), np.float32(0.9645), np.float32(0.9047), np.float32(0.9706), np.float32(0.9594), np.float32(0.8707), np.float32(0.8518), np.float32(0.9277)] 
+2025-05-06 11:49:09.691527: Epoch time: 102.35 s 
+2025-05-06 11:49:11.269027:  
+2025-05-06 11:49:11.307496: Epoch 1260 
+2025-05-06 11:49:11.323428: Current learning rate: 0.00409 
+2025-05-06 11:50:56.001282: train_loss -0.4877 
+2025-05-06 11:50:56.115174: val_loss -0.5554 
+2025-05-06 11:50:56.151528: Pseudo dice [np.float32(0.8408), np.float32(0.831), np.float32(0.9163), np.float32(0.9743), np.float32(0.8737), np.float32(0.9462), np.float32(0.9658), np.float32(0.9723), np.float32(0.9649), np.float32(0.9732), np.float32(0.9472), np.float32(0.9689), np.float32(0.9662), np.float32(0.9007), np.float32(0.9362), np.float32(0.9301), np.float32(0.8641), np.float32(0.8983), np.float32(0.9155)] 
+2025-05-06 11:50:56.207957: Epoch time: 104.73 s 
+2025-05-06 11:50:57.813824:  
+2025-05-06 11:50:57.868449: Epoch 1261 
+2025-05-06 11:50:57.906882: Current learning rate: 0.00408 
+2025-05-06 11:52:43.765273: train_loss -0.4891 
+2025-05-06 11:52:43.880753: val_loss -0.5104 
+2025-05-06 11:52:43.910302: Pseudo dice [np.float32(0.8657), np.float32(0.8624), np.float32(0.9042), np.float32(0.9724), np.float32(0.8864), np.float32(0.9547), np.float32(0.9686), np.float32(0.9802), np.float32(0.9724), np.float32(0.9678), np.float32(0.9541), np.float32(0.9691), np.float32(0.9753), np.float32(0.9139), np.float32(0.9507), np.float32(0.9465), np.float32(0.8896), np.float32(0.8458), np.float32(0.9231)] 
+2025-05-06 11:52:43.952232: Epoch time: 105.95 s 
+2025-05-06 11:52:49.194314:  
+2025-05-06 11:52:49.199875: Epoch 1262 
+2025-05-06 11:52:49.200301: Current learning rate: 0.00408 
+2025-05-06 11:54:27.473699: train_loss -0.4977 
+2025-05-06 11:54:27.576815: val_loss -0.5107 
+2025-05-06 11:54:27.628854: Pseudo dice [np.float32(0.8589), np.float32(0.8323), np.float32(0.8034), np.float32(0.969), np.float32(0.891), np.float32(0.9652), np.float32(0.964), np.float32(0.9698), np.float32(0.9563), np.float32(0.9678), np.float32(0.9422), np.float32(0.9726), np.float32(0.9639), np.float32(0.9029), np.float32(0.9673), np.float32(0.9586), np.float32(0.8702), np.float32(0.8975), np.float32(0.9218)] 
+2025-05-06 11:54:27.672968: Epoch time: 98.28 s 
+2025-05-06 11:54:29.257658:  
+2025-05-06 11:54:29.292479: Epoch 1263 
+2025-05-06 11:54:29.317308: Current learning rate: 0.00407 
+2025-05-06 11:56:09.367918: train_loss -0.5045 
+2025-05-06 11:56:09.443644: val_loss -0.5117 
+2025-05-06 11:56:09.466126: Pseudo dice [np.float32(0.8296), np.float32(0.8459), np.float32(0.8409), np.float32(0.9746), np.float32(0.9137), np.float32(0.9578), np.float32(0.9672), np.float32(0.9772), np.float32(0.9694), np.float32(0.972), np.float32(0.952), np.float32(0.971), np.float32(0.9695), np.float32(0.8985), np.float32(0.953), np.float32(0.9569), np.float32(0.8988), np.float32(0.8962), np.float32(0.9124)] 
+2025-05-06 11:56:09.500068: Epoch time: 100.11 s 
+2025-05-06 11:56:11.109858:  
+2025-05-06 11:56:11.157410: Epoch 1264 
+2025-05-06 11:56:11.174656: Current learning rate: 0.00407 
+2025-05-06 11:57:48.708555: train_loss -0.4906 
+2025-05-06 11:57:48.835097: val_loss -0.497 
+2025-05-06 11:57:48.841631: Pseudo dice [np.float32(0.8482), np.float32(0.817), np.float32(0.9121), np.float32(0.9686), np.float32(0.8391), np.float32(0.958), np.float32(0.9645), np.float32(0.9658), np.float32(0.9516), np.float32(0.9504), np.float32(0.9136), np.float32(0.9634), np.float32(0.9537), np.float32(0.9042), np.float32(0.9637), np.float32(0.9616), np.float32(0.8782), np.float32(0.8726), np.float32(0.9099)] 
+2025-05-06 11:57:48.842277: Epoch time: 97.6 s 
+2025-05-06 11:57:50.277011:  
+2025-05-06 11:57:50.320800: Epoch 1265 
+2025-05-06 11:57:50.354879: Current learning rate: 0.00406 
+2025-05-06 11:59:24.999393: train_loss -0.4892 
+2025-05-06 11:59:25.042515: val_loss -0.5108 
+2025-05-06 11:59:25.043239: Pseudo dice [np.float32(0.8276), np.float32(0.8532), np.float32(0.8933), np.float32(0.9738), np.float32(0.9029), np.float32(0.9606), np.float32(0.9595), np.float32(0.9709), np.float32(0.9538), np.float32(0.9607), np.float32(0.9319), np.float32(0.9606), np.float32(0.9537), np.float32(0.8855), np.float32(0.9623), np.float32(0.9538), np.float32(0.8561), np.float32(0.8846), np.float32(0.9148)] 
+2025-05-06 11:59:25.043885: Epoch time: 94.72 s 
+2025-05-06 11:59:26.625459:  
+2025-05-06 11:59:26.731432: Epoch 1266 
+2025-05-06 11:59:26.768273: Current learning rate: 0.00406 
+2025-05-06 12:01:00.297248: train_loss -0.4927 
+2025-05-06 12:01:00.417179: val_loss -0.5268 
+2025-05-06 12:01:00.461496: Pseudo dice [np.float32(0.853), np.float32(0.8286), np.float32(0.8807), np.float32(0.9681), np.float32(0.9096), np.float32(0.9552), np.float32(0.9585), np.float32(0.978), np.float32(0.962), np.float32(0.9592), np.float32(0.9483), np.float32(0.9621), np.float32(0.9712), np.float32(0.8813), np.float32(0.966), np.float32(0.943), np.float32(0.8225), np.float32(0.8663), np.float32(0.9141)] 
+2025-05-06 12:01:00.513809: Epoch time: 93.67 s 
+2025-05-06 12:01:02.024125:  
+2025-05-06 12:01:02.146952: Epoch 1267 
+2025-05-06 12:01:02.178216: Current learning rate: 0.00405 
+2025-05-06 12:02:36.520110: train_loss -0.4804 
+2025-05-06 12:02:36.645610: val_loss -0.5088 
+2025-05-06 12:02:36.702975: Pseudo dice [np.float32(0.8477), np.float32(0.8476), np.float32(0.4067), np.float32(0.9628), np.float32(0.9075), np.float32(0.9567), np.float32(0.9686), np.float32(0.9688), np.float32(0.9619), np.float32(0.9615), np.float32(0.9234), np.float32(0.9629), np.float32(0.9537), np.float32(0.9083), np.float32(0.9554), np.float32(0.946), np.float32(0.8977), np.float32(0.9022), np.float32(0.903)] 
+2025-05-06 12:02:36.743802: Epoch time: 94.5 s 
+2025-05-06 12:02:38.354194:  
+2025-05-06 12:02:38.428083: Epoch 1268 
+2025-05-06 12:02:38.450959: Current learning rate: 0.00405 
+2025-05-06 12:04:16.726309: train_loss -0.474 
+2025-05-06 12:04:16.823548: val_loss -0.4922 
+2025-05-06 12:04:16.851220: Pseudo dice [np.float32(0.858), np.float32(0.8558), np.float32(0.9182), np.float32(0.9689), np.float32(0.8688), np.float32(0.9574), np.float32(0.9596), np.float32(0.9775), np.float32(0.9525), np.float32(0.9483), np.float32(0.9271), np.float32(0.9682), np.float32(0.9669), np.float32(0.8929), np.float32(0.9643), np.float32(0.9554), np.float32(0.8591), np.float32(0.8304), np.float32(0.9126)] 
+2025-05-06 12:04:16.899760: Epoch time: 98.37 s 
+2025-05-06 12:04:18.504850:  
+2025-05-06 12:04:18.536922: Epoch 1269 
+2025-05-06 12:04:18.541096: Current learning rate: 0.00404 
+2025-05-06 12:05:57.952277: train_loss -0.4873 
+2025-05-06 12:05:58.050900: val_loss -0.4801 
+2025-05-06 12:05:58.095448: Pseudo dice [np.float32(0.844), np.float32(0.8339), np.float32(0.8182), np.float32(0.9776), np.float32(0.8926), np.float32(0.9588), np.float32(0.9504), np.float32(0.9689), np.float32(0.96), np.float32(0.973), np.float32(0.954), np.float32(0.9653), np.float32(0.9722), np.float32(0.8898), np.float32(0.9344), np.float32(0.9531), np.float32(0.8948), np.float32(0.859), np.float32(0.916)] 
+2025-05-06 12:05:58.106117: Epoch time: 99.45 s 
+2025-05-06 12:05:59.612466:  
+2025-05-06 12:05:59.785728: Epoch 1270 
+2025-05-06 12:05:59.832586: Current learning rate: 0.00404 
+2025-05-06 12:07:42.434711: train_loss -0.5045 
+2025-05-06 12:07:42.523766: val_loss -0.4841 
+2025-05-06 12:07:42.524333: Pseudo dice [np.float32(0.8619), np.float32(0.83), np.float32(0.8898), np.float32(0.9733), np.float32(0.9063), np.float32(0.9538), np.float32(0.9515), np.float32(0.9792), np.float32(0.9588), np.float32(0.9639), np.float32(0.9489), np.float32(0.9693), np.float32(0.9692), np.float32(0.9008), np.float32(0.9481), np.float32(0.9499), np.float32(0.8677), np.float32(0.8956), np.float32(0.9128)] 
+2025-05-06 12:07:42.524772: Epoch time: 102.82 s 
+2025-05-06 12:07:43.988519:  
+2025-05-06 12:07:44.011039: Epoch 1271 
+2025-05-06 12:07:44.022487: Current learning rate: 0.00403 
+2025-05-06 12:09:23.237477: train_loss -0.5028 
+2025-05-06 12:09:23.304958: val_loss -0.5151 
+2025-05-06 12:09:23.316395: Pseudo dice [np.float32(0.8107), np.float32(0.8405), np.float32(0.9243), np.float32(0.9739), np.float32(0.8815), np.float32(0.9595), np.float32(0.9592), np.float32(0.9806), np.float32(0.9579), np.float32(0.9685), np.float32(0.956), np.float32(0.9667), np.float32(0.9748), np.float32(0.8947), np.float32(0.9412), np.float32(0.9506), np.float32(0.9115), np.float32(0.9102), np.float32(0.9326)] 
+2025-05-06 12:09:23.320913: Epoch time: 99.25 s 
+2025-05-06 12:09:24.866760:  
+2025-05-06 12:09:24.948577: Epoch 1272 
+2025-05-06 12:09:24.973011: Current learning rate: 0.00403 
+2025-05-06 12:10:58.999199: train_loss -0.4717 
+2025-05-06 12:10:59.072008: val_loss -0.4904 
+2025-05-06 12:10:59.094975: Pseudo dice [np.float32(0.8462), np.float32(0.8437), np.float32(0.8855), np.float32(0.9724), np.float32(0.8677), np.float32(0.9572), np.float32(0.9563), np.float32(0.9708), np.float32(0.9632), np.float32(0.9567), np.float32(0.9508), np.float32(0.9709), np.float32(0.971), np.float32(0.906), np.float32(0.9475), np.float32(0.95), np.float32(0.8369), np.float32(0.8555), np.float32(0.9228)] 
+2025-05-06 12:10:59.105988: Epoch time: 94.14 s 
+2025-05-06 12:11:00.574794:  
+2025-05-06 12:11:00.698663: Epoch 1273 
+2025-05-06 12:11:00.717339: Current learning rate: 0.00402 
+2025-05-06 12:12:38.189042: train_loss -0.5037 
+2025-05-06 12:12:38.355679: val_loss -0.5144 
+2025-05-06 12:12:38.395148: Pseudo dice [np.float32(0.843), np.float32(0.8407), np.float32(0.9367), np.float32(0.9779), np.float32(0.9015), np.float32(0.9547), np.float32(0.9495), np.float32(0.9747), np.float32(0.9662), np.float32(0.9748), np.float32(0.9426), np.float32(0.966), np.float32(0.9718), np.float32(0.9011), np.float32(0.9584), np.float32(0.956), np.float32(0.8529), np.float32(0.8708), np.float32(0.9147)] 
+2025-05-06 12:12:38.428219: Epoch time: 97.62 s 
+2025-05-06 12:12:40.024192:  
+2025-05-06 12:12:40.027108: Epoch 1274 
+2025-05-06 12:12:40.027481: Current learning rate: 0.00402 
+2025-05-06 12:14:19.435905: train_loss -0.4892 
+2025-05-06 12:14:19.524228: val_loss -0.5036 
+2025-05-06 12:14:19.553796: Pseudo dice [np.float32(0.814), np.float32(0.8412), np.float32(0.8908), np.float32(0.9735), np.float32(0.9124), np.float32(0.9583), np.float32(0.9519), np.float32(0.9771), np.float32(0.959), np.float32(0.9628), np.float32(0.9397), np.float32(0.9658), np.float32(0.9603), np.float32(0.9014), np.float32(0.944), np.float32(0.948), np.float32(0.842), np.float32(0.8218), np.float32(0.9195)] 
+2025-05-06 12:14:19.593507: Epoch time: 99.41 s 
+2025-05-06 12:14:21.330075:  
+2025-05-06 12:14:21.449790: Epoch 1275 
+2025-05-06 12:14:21.479279: Current learning rate: 0.00401 
+2025-05-06 12:16:01.446615: train_loss -0.4981 
+2025-05-06 12:16:01.520295: val_loss -0.4932 
+2025-05-06 12:16:01.526186: Pseudo dice [np.float32(0.8463), np.float32(0.8185), np.float32(0.9073), np.float32(0.9691), np.float32(0.9105), np.float32(0.9593), np.float32(0.9609), np.float32(0.9788), np.float32(0.9644), np.float32(0.9689), np.float32(0.9513), np.float32(0.9699), np.float32(0.9731), np.float32(0.9053), np.float32(0.9697), np.float32(0.9537), np.float32(0.8495), np.float32(0.8701), np.float32(0.9149)] 
+2025-05-06 12:16:01.537190: Epoch time: 100.12 s 
+2025-05-06 12:16:03.047941:  
+2025-05-06 12:16:03.174436: Epoch 1276 
+2025-05-06 12:16:03.218495: Current learning rate: 0.00401 
+2025-05-06 12:17:36.999493: train_loss -0.4844 
+2025-05-06 12:17:37.065683: val_loss -0.4871 
+2025-05-06 12:17:37.083469: Pseudo dice [np.float32(0.8642), np.float32(0.8657), np.float32(0.9399), np.float32(0.9718), np.float32(0.8787), np.float32(0.964), np.float32(0.9608), np.float32(0.974), np.float32(0.9626), np.float32(0.9613), np.float32(0.9504), np.float32(0.9673), np.float32(0.9677), np.float32(0.9197), np.float32(0.9691), np.float32(0.9636), np.float32(0.9044), np.float32(0.8911), np.float32(0.9294)] 
+2025-05-06 12:17:37.118022: Epoch time: 93.95 s 
+2025-05-06 12:17:38.726832:  
+2025-05-06 12:17:38.844676: Epoch 1277 
+2025-05-06 12:17:38.879528: Current learning rate: 0.004 
+2025-05-06 12:19:19.338084: train_loss -0.4895 
+2025-05-06 12:19:19.455689: val_loss -0.5028 
+2025-05-06 12:19:19.501934: Pseudo dice [np.float32(0.8408), np.float32(0.859), np.float32(0.9031), np.float32(0.9814), np.float32(0.9099), np.float32(0.9649), np.float32(0.9651), np.float32(0.9778), np.float32(0.9465), np.float32(0.9647), np.float32(0.9382), np.float32(0.957), np.float32(0.9632), np.float32(0.9139), np.float32(0.9621), np.float32(0.953), np.float32(0.8461), np.float32(0.8803), np.float32(0.9269)] 
+2025-05-06 12:19:19.541367: Epoch time: 100.61 s 
+2025-05-06 12:19:21.207370:  
+2025-05-06 12:19:21.300663: Epoch 1278 
+2025-05-06 12:19:21.326536: Current learning rate: 0.004 
+2025-05-06 12:20:59.042563: train_loss -0.4812 
+2025-05-06 12:20:59.133044: val_loss -0.4685 
+2025-05-06 12:20:59.151745: Pseudo dice [np.float32(0.8424), np.float32(0.8389), np.float32(0.9144), np.float32(0.9724), np.float32(0.861), np.float32(0.9591), np.float32(0.9507), np.float32(0.9767), np.float32(0.9663), np.float32(0.9686), np.float32(0.9466), np.float32(0.9687), np.float32(0.9699), np.float32(0.8921), np.float32(0.9657), np.float32(0.9401), np.float32(0.8567), np.float32(0.8838), np.float32(0.9095)] 
+2025-05-06 12:20:59.152774: Epoch time: 97.84 s 
+2025-05-06 12:21:04.669140:  
+2025-05-06 12:21:04.672287: Epoch 1279 
+2025-05-06 12:21:04.672905: Current learning rate: 0.00399 
+2025-05-06 12:22:40.374711: train_loss -0.4927 
+2025-05-06 12:22:40.484896: val_loss -0.4899 
+2025-05-06 12:22:40.505440: Pseudo dice [np.float32(0.848), np.float32(0.8469), np.float32(0.7835), np.float32(0.9706), np.float32(0.92), np.float32(0.956), np.float32(0.9539), np.float32(0.9774), np.float32(0.9524), np.float32(0.9607), np.float32(0.9456), np.float32(0.9685), np.float32(0.9643), np.float32(0.9009), np.float32(0.9654), np.float32(0.9545), np.float32(0.9058), np.float32(0.9122), np.float32(0.9127)] 
+2025-05-06 12:22:40.546840: Epoch time: 95.71 s 
+2025-05-06 12:22:42.038450:  
+2025-05-06 12:22:42.117757: Epoch 1280 
+2025-05-06 12:22:42.151914: Current learning rate: 0.00399 
+2025-05-06 12:24:16.716194: train_loss -0.4883 
+2025-05-06 12:24:16.760825: val_loss -0.5018 
+2025-05-06 12:24:16.765713: Pseudo dice [np.float32(0.8432), np.float32(0.805), np.float32(0.9433), np.float32(0.9695), np.float32(0.8762), np.float32(0.9563), np.float32(0.9631), np.float32(0.9717), np.float32(0.9642), np.float32(0.9711), np.float32(0.9475), np.float32(0.9692), np.float32(0.9682), np.float32(0.8845), np.float32(0.9657), np.float32(0.9463), np.float32(0.8308), np.float32(0.7982), np.float32(0.9136)] 
+2025-05-06 12:24:16.789401: Epoch time: 94.68 s 
+2025-05-06 12:24:18.367859:  
+2025-05-06 12:24:18.471358: Epoch 1281 
+2025-05-06 12:24:18.475484: Current learning rate: 0.00398 
+2025-05-06 12:25:52.108196: train_loss -0.4788 
+2025-05-06 12:25:52.240704: val_loss -0.4835 
+2025-05-06 12:25:52.241516: Pseudo dice [np.float32(0.8415), np.float32(0.853), np.float32(0.9272), np.float32(0.9763), np.float32(0.9195), np.float32(0.9546), np.float32(0.9685), np.float32(0.9748), np.float32(0.9657), np.float32(0.9606), np.float32(0.9412), np.float32(0.9654), np.float32(0.9652), np.float32(0.9087), np.float32(0.9649), np.float32(0.9564), np.float32(0.8877), np.float32(0.891), np.float32(0.9083)] 
+2025-05-06 12:25:52.242061: Epoch time: 93.74 s 
+2025-05-06 12:25:53.845925:  
+2025-05-06 12:25:53.944548: Epoch 1282 
+2025-05-06 12:25:53.984720: Current learning rate: 0.00398 
+2025-05-06 12:27:25.399951: train_loss -0.4999 
+2025-05-06 12:27:25.483213: val_loss -0.5311 
+2025-05-06 12:27:25.492648: Pseudo dice [np.float32(0.8416), np.float32(0.8078), np.float32(0.9327), np.float32(0.9774), np.float32(0.9144), np.float32(0.9663), np.float32(0.9622), np.float32(0.9762), np.float32(0.9505), np.float32(0.9656), np.float32(0.9487), np.float32(0.9556), np.float32(0.9642), np.float32(0.9062), np.float32(0.9686), np.float32(0.951), np.float32(0.8917), np.float32(0.8911), np.float32(0.9131)] 
+2025-05-06 12:27:25.498694: Epoch time: 91.56 s 
+2025-05-06 12:27:27.153354:  
+2025-05-06 12:27:27.322554: Epoch 1283 
+2025-05-06 12:27:27.381888: Current learning rate: 0.00397 
+2025-05-06 12:29:01.365805: train_loss -0.4894 
+2025-05-06 12:29:01.420323: val_loss -0.4967 
+2025-05-06 12:29:01.452335: Pseudo dice [np.float32(0.8506), np.float32(0.8574), np.float32(0.9264), np.float32(0.9768), np.float32(0.895), np.float32(0.9547), np.float32(0.9615), np.float32(0.9746), np.float32(0.9638), np.float32(0.9665), np.float32(0.9444), np.float32(0.968), np.float32(0.9663), np.float32(0.9102), np.float32(0.9667), np.float32(0.9612), np.float32(0.8682), np.float32(0.8406), np.float32(0.9077)] 
+2025-05-06 12:29:01.476310: Epoch time: 94.21 s 
+2025-05-06 12:29:03.045227:  
+2025-05-06 12:29:03.063220: Epoch 1284 
+2025-05-06 12:29:03.067899: Current learning rate: 0.00397 
+2025-05-06 12:30:38.256279: train_loss -0.4794 
+2025-05-06 12:30:38.374939: val_loss -0.4908 
+2025-05-06 12:30:38.415668: Pseudo dice [np.float32(0.8506), np.float32(0.8444), np.float32(0.8787), np.float32(0.9794), np.float32(0.9241), np.float32(0.9609), np.float32(0.9619), np.float32(0.9767), np.float32(0.9544), np.float32(0.9689), np.float32(0.947), np.float32(0.9639), np.float32(0.9719), np.float32(0.8952), np.float32(0.9587), np.float32(0.9487), np.float32(0.8917), np.float32(0.9062), np.float32(0.9284)] 
+2025-05-06 12:30:38.485288: Epoch time: 95.21 s 
+2025-05-06 12:30:40.213637:  
+2025-05-06 12:30:40.276860: Epoch 1285 
+2025-05-06 12:30:40.320478: Current learning rate: 0.00396 
+2025-05-06 12:32:13.847334: train_loss -0.4957 
+2025-05-06 12:32:13.924052: val_loss -0.5339 
+2025-05-06 12:32:13.942107: Pseudo dice [np.float32(0.8734), np.float32(0.8602), np.float32(0.8724), np.float32(0.976), np.float32(0.9327), np.float32(0.9457), np.float32(0.9562), np.float32(0.982), np.float32(0.9659), np.float32(0.9694), np.float32(0.9521), np.float32(0.9712), np.float32(0.9724), np.float32(0.9102), np.float32(0.966), np.float32(0.9547), np.float32(0.8776), np.float32(0.8749), np.float32(0.918)] 
+2025-05-06 12:32:13.979428: Epoch time: 93.64 s 
+2025-05-06 12:32:15.551408:  
+2025-05-06 12:32:15.597415: Epoch 1286 
+2025-05-06 12:32:15.598176: Current learning rate: 0.00396 
+2025-05-06 12:33:49.207660: train_loss -0.5073 
+2025-05-06 12:33:49.281508: val_loss -0.5079 
+2025-05-06 12:33:49.293580: Pseudo dice [np.float32(0.8415), np.float32(0.8612), np.float32(0.8704), np.float32(0.9725), np.float32(0.8857), np.float32(0.961), np.float32(0.9655), np.float32(0.9757), np.float32(0.9594), np.float32(0.9741), np.float32(0.9468), np.float32(0.9687), np.float32(0.9716), np.float32(0.9082), np.float32(0.968), np.float32(0.9445), np.float32(0.8725), np.float32(0.9064), np.float32(0.9262)] 
+2025-05-06 12:33:49.294523: Epoch time: 93.66 s 
+2025-05-06 12:33:50.810679:  
+2025-05-06 12:33:50.832393: Epoch 1287 
+2025-05-06 12:33:50.879673: Current learning rate: 0.00395 
+2025-05-06 12:35:27.715109: train_loss -0.5005 
+2025-05-06 12:35:27.755858: val_loss -0.5328 
+2025-05-06 12:35:27.774858: Pseudo dice [np.float32(0.8384), np.float32(0.8311), np.float32(0.9254), np.float32(0.9741), np.float32(0.9112), np.float32(0.9565), np.float32(0.9547), np.float32(0.9735), np.float32(0.9682), np.float32(0.9628), np.float32(0.9471), np.float32(0.9637), np.float32(0.9681), np.float32(0.9058), np.float32(0.9643), np.float32(0.9588), np.float32(0.904), np.float32(0.8807), np.float32(0.9137)] 
+2025-05-06 12:35:27.801208: Epoch time: 96.91 s 
+2025-05-06 12:35:27.812088: Yayy! New best EMA pseudo Dice: 0.9287999868392944 
+2025-05-06 12:35:30.257726:  
+2025-05-06 12:35:30.266703: Epoch 1288 
+2025-05-06 12:35:30.270691: Current learning rate: 0.00395 
+2025-05-06 12:37:08.421770: train_loss -0.4893 
+2025-05-06 12:37:08.539837: val_loss -0.4801 
+2025-05-06 12:37:08.581842: Pseudo dice [np.float32(0.8181), np.float32(0.8595), np.float32(0.927), np.float32(0.9671), np.float32(0.8673), np.float32(0.9539), np.float32(0.9623), np.float32(0.9771), np.float32(0.9676), np.float32(0.9641), np.float32(0.9547), np.float32(0.97), np.float32(0.9727), np.float32(0.908), np.float32(0.9668), np.float32(0.9572), np.float32(0.8903), np.float32(0.8758), np.float32(0.9055)] 
+2025-05-06 12:37:08.601665: Epoch time: 98.17 s 
+2025-05-06 12:37:08.616032: Yayy! New best EMA pseudo Dice: 0.9289000034332275 
+2025-05-06 12:37:11.022483:  
+2025-05-06 12:37:11.063108: Epoch 1289 
+2025-05-06 12:37:11.063757: Current learning rate: 0.00394 
+2025-05-06 12:38:57.586910: train_loss -0.5049 
+2025-05-06 12:38:57.652354: val_loss -0.5337 
+2025-05-06 12:38:57.666007: Pseudo dice [np.float32(0.844), np.float32(0.865), np.float32(0.8998), np.float32(0.9715), np.float32(0.9198), np.float32(0.9573), np.float32(0.9724), np.float32(0.9798), np.float32(0.9627), np.float32(0.971), np.float32(0.9561), np.float32(0.9656), np.float32(0.9721), np.float32(0.9136), np.float32(0.9676), np.float32(0.9606), np.float32(0.8979), np.float32(0.8972), np.float32(0.9089)] 
+2025-05-06 12:38:57.675669: Epoch time: 106.57 s 
+2025-05-06 12:38:57.676363: Yayy! New best EMA pseudo Dice: 0.9296000003814697 
+2025-05-06 12:39:00.325791:  
+2025-05-06 12:39:00.511094: Epoch 1290 
+2025-05-06 12:39:00.520979: Current learning rate: 0.00394 
+2025-05-06 12:40:38.777855: train_loss -0.5144 
+2025-05-06 12:40:38.933766: val_loss -0.4823 
+2025-05-06 12:40:38.950423: Pseudo dice [np.float32(0.8143), np.float32(0.8229), np.float32(0.9273), np.float32(0.9747), np.float32(0.896), np.float32(0.9245), np.float32(0.9302), np.float32(0.9743), np.float32(0.9537), np.float32(0.968), np.float32(0.9553), np.float32(0.9585), np.float32(0.9735), np.float32(0.903), np.float32(0.9674), np.float32(0.9543), np.float32(0.8943), np.float32(0.8869), np.float32(0.9142)] 
+2025-05-06 12:40:38.986684: Epoch time: 98.45 s 
+2025-05-06 12:40:40.701848:  
+2025-05-06 12:40:40.805471: Epoch 1291 
+2025-05-06 12:40:40.817530: Current learning rate: 0.00393 
+2025-05-06 12:42:14.453674: train_loss -0.4929 
+2025-05-06 12:42:14.584217: val_loss -0.4829 
+2025-05-06 12:42:14.617627: Pseudo dice [np.float32(0.8514), np.float32(0.8077), np.float32(0.8865), np.float32(0.9781), np.float32(0.922), np.float32(0.9591), np.float32(0.9595), np.float32(0.9767), np.float32(0.9615), np.float32(0.959), np.float32(0.9378), np.float32(0.9653), np.float32(0.9671), np.float32(0.9106), np.float32(0.9634), np.float32(0.9582), np.float32(0.8675), np.float32(0.8677), np.float32(0.9167)] 
+2025-05-06 12:42:14.666483: Epoch time: 93.75 s 
+2025-05-06 12:42:16.206650:  
+2025-05-06 12:42:16.345623: Epoch 1292 
+2025-05-06 12:42:16.387084: Current learning rate: 0.00393 
+2025-05-06 12:43:50.857721: train_loss -0.4975 
+2025-05-06 12:43:50.939003: val_loss -0.5147 
+2025-05-06 12:43:50.940447: Pseudo dice [np.float32(0.8269), np.float32(0.8129), np.float32(0.9101), np.float32(0.9746), np.float32(0.9213), np.float32(0.9606), np.float32(0.9543), np.float32(0.9533), np.float32(0.9705), np.float32(0.9677), np.float32(0.956), np.float32(0.9647), np.float32(0.9726), np.float32(0.8977), np.float32(0.9696), np.float32(0.9361), np.float32(0.8802), np.float32(0.8944), np.float32(0.9113)] 
+2025-05-06 12:43:50.941044: Epoch time: 94.65 s 
+2025-05-06 12:43:52.398433:  
+2025-05-06 12:43:52.441015: Epoch 1293 
+2025-05-06 12:43:52.477304: Current learning rate: 0.00392 
+2025-05-06 12:45:28.156053: train_loss -0.4972 
+2025-05-06 12:45:28.234822: val_loss -0.5233 
+2025-05-06 12:45:28.262866: Pseudo dice [np.float32(0.8454), np.float32(0.865), np.float32(0.9138), np.float32(0.9705), np.float32(0.8946), np.float32(0.9543), np.float32(0.9701), np.float32(0.9761), np.float32(0.9694), np.float32(0.9742), np.float32(0.9543), np.float32(0.9682), np.float32(0.9733), np.float32(0.9071), np.float32(0.9683), np.float32(0.9525), np.float32(0.8741), np.float32(0.8747), np.float32(0.9281)] 
+2025-05-06 12:45:28.280809: Epoch time: 95.76 s 
+2025-05-06 12:45:29.884981:  
+2025-05-06 12:45:29.964347: Epoch 1294 
+2025-05-06 12:45:29.977723: Current learning rate: 0.00392 
+2025-05-06 12:47:05.554506: train_loss -0.5129 
+2025-05-06 12:47:05.647379: val_loss -0.4921 
+2025-05-06 12:47:05.691062: Pseudo dice [np.float32(0.8049), np.float32(0.8393), np.float32(0.9295), np.float32(0.9716), np.float32(0.8964), np.float32(0.9551), np.float32(0.9599), np.float32(0.9761), np.float32(0.9515), np.float32(0.9563), np.float32(0.93), np.float32(0.9538), np.float32(0.9538), np.float32(0.9106), np.float32(0.9647), np.float32(0.9509), np.float32(0.8948), np.float32(0.8781), np.float32(0.9103)] 
+2025-05-06 12:47:05.741220: Epoch time: 95.67 s 
+2025-05-06 12:47:07.305937:  
+2025-05-06 12:47:07.368985: Epoch 1295 
+2025-05-06 12:47:07.412446: Current learning rate: 0.00391 
+2025-05-06 12:48:43.533055: train_loss -0.459 
+2025-05-06 12:48:43.626698: val_loss -0.4806 
+2025-05-06 12:48:43.670352: Pseudo dice [np.float32(0.826), np.float32(0.8399), np.float32(0.8172), np.float32(0.9713), np.float32(0.9104), np.float32(0.9592), np.float32(0.9703), np.float32(0.978), np.float32(0.9689), np.float32(0.9603), np.float32(0.9496), np.float32(0.9684), np.float32(0.973), np.float32(0.9071), np.float32(0.9723), np.float32(0.9554), np.float32(0.9034), np.float32(0.8948), np.float32(0.926)] 
+2025-05-06 12:48:43.757184: Epoch time: 96.23 s 
+2025-05-06 12:48:45.456344:  
+2025-05-06 12:48:45.578505: Epoch 1296 
+2025-05-06 12:48:45.593834: Current learning rate: 0.00391 
+2025-05-06 12:50:20.666226: train_loss -0.4945 
+2025-05-06 12:50:20.753009: val_loss -0.5151 
+2025-05-06 12:50:20.769916: Pseudo dice [np.float32(0.8239), np.float32(0.8449), np.float32(0.8996), np.float32(0.9603), np.float32(0.9232), np.float32(0.9599), np.float32(0.9648), np.float32(0.9779), np.float32(0.9601), np.float32(0.9595), np.float32(0.9336), np.float32(0.9666), np.float32(0.9475), np.float32(0.9032), np.float32(0.9685), np.float32(0.9566), np.float32(0.8837), np.float32(0.8656), np.float32(0.9091)] 
+2025-05-06 12:50:20.784787: Epoch time: 95.21 s 
+2025-05-06 12:50:26.455191:  
+2025-05-06 12:50:26.461221: Epoch 1297 
+2025-05-06 12:50:26.461881: Current learning rate: 0.0039 
+2025-05-06 12:51:55.197883: train_loss -0.5024 
+2025-05-06 12:51:55.245917: val_loss -0.528 
+2025-05-06 12:51:55.262565: Pseudo dice [np.float32(0.8426), np.float32(0.8543), np.float32(0.8315), np.float32(0.979), np.float32(0.8963), np.float32(0.9628), np.float32(0.9578), np.float32(0.9793), np.float32(0.9611), np.float32(0.9632), np.float32(0.9419), np.float32(0.9725), np.float32(0.9677), np.float32(0.9073), np.float32(0.9694), np.float32(0.9569), np.float32(0.9069), np.float32(0.9037), np.float32(0.9282)] 
+2025-05-06 12:51:55.293772: Epoch time: 88.74 s 
+2025-05-06 12:51:57.030163:  
+2025-05-06 12:51:57.130350: Epoch 1298 
+2025-05-06 12:51:57.162804: Current learning rate: 0.0039 
+2025-05-06 12:53:29.329665: train_loss -0.4775 
+2025-05-06 12:53:29.410843: val_loss -0.483 
+2025-05-06 12:53:29.439354: Pseudo dice [np.float32(0.8192), np.float32(0.8776), np.float32(0.9296), np.float32(0.9695), np.float32(0.8822), np.float32(0.959), np.float32(0.9588), np.float32(0.9702), np.float32(0.9496), np.float32(0.9655), np.float32(0.9333), np.float32(0.9646), np.float32(0.9684), np.float32(0.8795), np.float32(0.9361), np.float32(0.9548), np.float32(0.8625), np.float32(0.8747), np.float32(0.9326)] 
+2025-05-06 12:53:29.464900: Epoch time: 92.3 s 
+2025-05-06 12:53:31.081609:  
+2025-05-06 12:53:31.147956: Epoch 1299 
+2025-05-06 12:53:31.162993: Current learning rate: 0.00389 
+2025-05-06 12:55:12.418307: train_loss -0.5012 
+2025-05-06 12:55:12.505213: val_loss -0.4893 
+2025-05-06 12:55:12.529825: Pseudo dice [np.float32(0.8357), np.float32(0.8189), np.float32(0.9051), np.float32(0.9718), np.float32(0.8715), np.float32(0.9343), np.float32(0.9676), np.float32(0.9711), np.float32(0.9504), np.float32(0.9565), np.float32(0.9265), np.float32(0.9678), np.float32(0.9643), np.float32(0.8972), np.float32(0.9585), np.float32(0.9409), np.float32(0.8666), np.float32(0.9182), np.float32(0.9198)] 
+2025-05-06 12:55:12.578349: Epoch time: 101.34 s 
+2025-05-06 12:55:15.098096:  
+2025-05-06 12:55:15.166866: Epoch 1300 
+2025-05-06 12:55:15.192515: Current learning rate: 0.00389 
+2025-05-06 12:56:50.689393: train_loss -0.4816 
+2025-05-06 12:56:50.764563: val_loss -0.5349 
+2025-05-06 12:56:50.765326: Pseudo dice [np.float32(0.8314), np.float32(0.8357), np.float32(0.9133), np.float32(0.9741), np.float32(0.9), np.float32(0.9609), np.float32(0.9584), np.float32(0.9776), np.float32(0.9663), np.float32(0.9607), np.float32(0.9403), np.float32(0.9679), np.float32(0.9674), np.float32(0.9111), np.float32(0.9672), np.float32(0.9485), np.float32(0.9081), np.float32(0.915), np.float32(0.9264)] 
+2025-05-06 12:56:50.805679: Epoch time: 95.59 s 
+2025-05-06 12:56:52.484183:  
+2025-05-06 12:56:52.566291: Epoch 1301 
+2025-05-06 12:56:52.579919: Current learning rate: 0.00388 
+2025-05-06 12:58:24.913172: train_loss -0.4884 
+2025-05-06 12:58:25.021683: val_loss -0.505 
+2025-05-06 12:58:25.046160: Pseudo dice [np.float32(0.8457), np.float32(0.8574), np.float32(0.9174), np.float32(0.9716), np.float32(0.9131), np.float32(0.9568), np.float32(0.9613), np.float32(0.9746), np.float32(0.9576), np.float32(0.9622), np.float32(0.9494), np.float32(0.9637), np.float32(0.9675), np.float32(0.9154), np.float32(0.9568), np.float32(0.9526), np.float32(0.8957), np.float32(0.8765), np.float32(0.9265)] 
+2025-05-06 12:58:25.095696: Epoch time: 92.43 s 
+2025-05-06 12:58:26.552179:  
+2025-05-06 12:58:26.557786: Epoch 1302 
+2025-05-06 12:58:26.558181: Current learning rate: 0.00388 
+2025-05-06 13:00:10.611619: train_loss -0.506 
+2025-05-06 13:00:10.742133: val_loss -0.4778 
+2025-05-06 13:00:10.875035: Pseudo dice [np.float32(0.8316), np.float32(0.8597), np.float32(0.9528), np.float32(0.9746), np.float32(0.8965), np.float32(0.9598), np.float32(0.9614), np.float32(0.9783), np.float32(0.9659), np.float32(0.9562), np.float32(0.9429), np.float32(0.9531), np.float32(0.966), np.float32(0.9005), np.float32(0.9664), np.float32(0.9557), np.float32(0.8194), np.float32(0.8603), np.float32(0.9079)] 
+2025-05-06 13:00:10.921921: Epoch time: 104.06 s 
+2025-05-06 13:00:12.548752:  
+2025-05-06 13:00:12.553813: Epoch 1303 
+2025-05-06 13:00:12.554239: Current learning rate: 0.00387 
+2025-05-06 13:01:52.423247: train_loss -0.4935 
+2025-05-06 13:01:52.507607: val_loss -0.5082 
+2025-05-06 13:01:52.552431: Pseudo dice [np.float32(0.8485), np.float32(0.8623), np.float32(0.9083), np.float32(0.9621), np.float32(0.8815), np.float32(0.9625), np.float32(0.9715), np.float32(0.975), np.float32(0.9649), np.float32(0.9678), np.float32(0.9394), np.float32(0.9685), np.float32(0.9657), np.float32(0.8956), np.float32(0.9537), np.float32(0.9502), np.float32(0.887), np.float32(0.8467), np.float32(0.9118)] 
+2025-05-06 13:01:52.583947: Epoch time: 99.88 s 
+2025-05-06 13:01:54.152215:  
+2025-05-06 13:01:54.226465: Epoch 1304 
+2025-05-06 13:01:54.261096: Current learning rate: 0.00387 
+2025-05-06 13:03:29.917356: train_loss -0.4852 
+2025-05-06 13:03:29.964929: val_loss -0.4958 
+2025-05-06 13:03:29.987582: Pseudo dice [np.float32(0.8434), np.float32(0.8617), np.float32(0.9572), np.float32(0.9764), np.float32(0.8963), np.float32(0.9676), np.float32(0.9628), np.float32(0.9791), np.float32(0.9705), np.float32(0.9646), np.float32(0.9433), np.float32(0.975), np.float32(0.9674), np.float32(0.8979), np.float32(0.9396), np.float32(0.9576), np.float32(0.8987), np.float32(0.8953), np.float32(0.914)] 
+2025-05-06 13:03:30.014020: Epoch time: 95.77 s 
+2025-05-06 13:03:31.602678:  
+2025-05-06 13:03:31.622235: Epoch 1305 
+2025-05-06 13:03:31.622932: Current learning rate: 0.00386 
+2025-05-06 13:05:09.323589: train_loss -0.4866 
+2025-05-06 13:05:09.344269: val_loss -0.4876 
+2025-05-06 13:05:09.344791: Pseudo dice [np.float32(0.8482), np.float32(0.8571), np.float32(0.9106), np.float32(0.9793), np.float32(0.8862), np.float32(0.9604), np.float32(0.9603), np.float32(0.9746), np.float32(0.9608), np.float32(0.9584), np.float32(0.9396), np.float32(0.9639), np.float32(0.9614), np.float32(0.9063), np.float32(0.9652), np.float32(0.9547), np.float32(0.8772), np.float32(0.8659), np.float32(0.9003)] 
+2025-05-06 13:05:09.345265: Epoch time: 97.72 s 
+2025-05-06 13:05:11.313630:  
+2025-05-06 13:05:11.432449: Epoch 1306 
+2025-05-06 13:05:11.462097: Current learning rate: 0.00386 
+2025-05-06 13:06:47.535923: train_loss -0.4953 
+2025-05-06 13:06:47.629590: val_loss -0.4968 
+2025-05-06 13:06:47.656956: Pseudo dice [np.float32(0.8682), np.float32(0.8319), np.float32(0.9294), np.float32(0.9742), np.float32(0.9233), np.float32(0.9479), np.float32(0.9632), np.float32(0.9773), np.float32(0.9596), np.float32(0.9422), np.float32(0.9394), np.float32(0.9607), np.float32(0.9581), np.float32(0.9168), np.float32(0.9388), np.float32(0.9524), np.float32(0.8658), np.float32(0.7736), np.float32(0.911)] 
+2025-05-06 13:06:47.674925: Epoch time: 96.22 s 
+2025-05-06 13:06:49.321980:  
+2025-05-06 13:06:49.343096: Epoch 1307 
+2025-05-06 13:06:49.354447: Current learning rate: 0.00385 
+2025-05-06 13:08:28.979693: train_loss -0.4973 
+2025-05-06 13:08:29.342673: val_loss -0.4712 
+2025-05-06 13:08:29.343621: Pseudo dice [np.float32(0.8566), np.float32(0.8628), np.float32(0.937), np.float32(0.9738), np.float32(0.8838), np.float32(0.9572), np.float32(0.9664), np.float32(0.9747), np.float32(0.9573), np.float32(0.9707), np.float32(0.9564), np.float32(0.9681), np.float32(0.9693), np.float32(0.9111), np.float32(0.9664), np.float32(0.9603), np.float32(0.8998), np.float32(0.8884), np.float32(0.9194)] 
+2025-05-06 13:08:29.361374: Epoch time: 99.66 s 
+2025-05-06 13:08:30.905564:  
+2025-05-06 13:08:30.912662: Epoch 1308 
+2025-05-06 13:08:30.913218: Current learning rate: 0.00385 
+2025-05-06 13:10:07.356646: train_loss -0.501 
+2025-05-06 13:10:07.435227: val_loss -0.5279 
+2025-05-06 13:10:07.459022: Pseudo dice [np.float32(0.8448), np.float32(0.8677), np.float32(0.8799), np.float32(0.9673), np.float32(0.9271), np.float32(0.9563), np.float32(0.9632), np.float32(0.9757), np.float32(0.9466), np.float32(0.9694), np.float32(0.9315), np.float32(0.9636), np.float32(0.9732), np.float32(0.9222), np.float32(0.917), np.float32(0.9436), np.float32(0.8449), np.float32(0.8862), np.float32(0.9268)] 
+2025-05-06 13:10:07.486132: Epoch time: 96.45 s 
+2025-05-06 13:10:09.035882:  
+2025-05-06 13:10:09.077851: Epoch 1309 
+2025-05-06 13:10:09.139373: Current learning rate: 0.00384 
+2025-05-06 13:11:43.390033: train_loss -0.5095 
+2025-05-06 13:11:43.422991: val_loss -0.4951 
+2025-05-06 13:11:43.424037: Pseudo dice [np.float32(0.8554), np.float32(0.8626), np.float32(0.8428), np.float32(0.9702), np.float32(0.9193), np.float32(0.961), np.float32(0.966), np.float32(0.9805), np.float32(0.9695), np.float32(0.966), np.float32(0.928), np.float32(0.9627), np.float32(0.9582), np.float32(0.9143), np.float32(0.9658), np.float32(0.9619), np.float32(0.8845), np.float32(0.9001), np.float32(0.9208)] 
+2025-05-06 13:11:43.424671: Epoch time: 94.36 s 
+2025-05-06 13:11:44.984675:  
+2025-05-06 13:11:45.101525: Epoch 1310 
+2025-05-06 13:11:45.128967: Current learning rate: 0.00384 
+2025-05-06 13:13:18.796692: train_loss -0.4937 
+2025-05-06 13:13:18.893786: val_loss -0.5072 
+2025-05-06 13:13:18.897975: Pseudo dice [np.float32(0.8337), np.float32(0.8252), np.float32(0.9368), np.float32(0.9827), np.float32(0.9109), np.float32(0.9622), np.float32(0.9689), np.float32(0.9724), np.float32(0.9621), np.float32(0.9699), np.float32(0.9484), np.float32(0.9691), np.float32(0.9595), np.float32(0.9068), np.float32(0.9689), np.float32(0.9607), np.float32(0.8987), np.float32(0.878), np.float32(0.9173)] 
+2025-05-06 13:13:18.898832: Epoch time: 93.81 s 
+2025-05-06 13:13:18.911481: Yayy! New best EMA pseudo Dice: 0.9296000003814697 
+2025-05-06 13:13:21.322814:  
+2025-05-06 13:13:21.338797: Epoch 1311 
+2025-05-06 13:13:21.353375: Current learning rate: 0.00383 
+2025-05-06 13:14:59.850837: train_loss -0.5102 
+2025-05-06 13:14:59.988932: val_loss -0.523 
+2025-05-06 13:15:00.008867: Pseudo dice [np.float32(0.8582), np.float32(0.8303), np.float32(0.9447), np.float32(0.9786), np.float32(0.9172), np.float32(0.9587), np.float32(0.9502), np.float32(0.976), np.float32(0.9678), np.float32(0.9527), np.float32(0.9444), np.float32(0.9655), np.float32(0.9643), np.float32(0.9103), np.float32(0.9527), np.float32(0.95), np.float32(0.8868), np.float32(0.8896), np.float32(0.9106)] 
+2025-05-06 13:15:00.013027: Epoch time: 98.53 s 
+2025-05-06 13:15:00.013473: Yayy! New best EMA pseudo Dice: 0.9298999905586243 
+2025-05-06 13:15:02.397921:  
+2025-05-06 13:15:02.403053: Epoch 1312 
+2025-05-06 13:15:02.403533: Current learning rate: 0.00383 
+2025-05-06 13:16:43.025158: train_loss -0.4882 
+2025-05-06 13:16:43.112687: val_loss -0.4941 
+2025-05-06 13:16:43.134554: Pseudo dice [np.float32(0.8343), np.float32(0.8319), np.float32(0.9269), np.float32(0.9771), np.float32(0.857), np.float32(0.9614), np.float32(0.9594), np.float32(0.9795), np.float32(0.9564), np.float32(0.9656), np.float32(0.9502), np.float32(0.9626), np.float32(0.9713), np.float32(0.9142), np.float32(0.9697), np.float32(0.9382), np.float32(0.8908), np.float32(0.9024), np.float32(0.9238)] 
+2025-05-06 13:16:43.161636: Epoch time: 100.63 s 
+2025-05-06 13:16:43.176592: Yayy! New best EMA pseudo Dice: 0.9298999905586243 
+2025-05-06 13:16:45.745073:  
+2025-05-06 13:16:45.750800: Epoch 1313 
+2025-05-06 13:16:45.751272: Current learning rate: 0.00382 
+2025-05-06 13:18:17.875231: train_loss -0.4925 
+2025-05-06 13:18:17.958081: val_loss -0.5219 
+2025-05-06 13:18:17.998716: Pseudo dice [np.float32(0.8379), np.float32(0.8592), np.float32(0.9261), np.float32(0.9724), np.float32(0.9061), np.float32(0.9584), np.float32(0.9661), np.float32(0.9776), np.float32(0.9635), np.float32(0.9647), np.float32(0.9434), np.float32(0.9654), np.float32(0.9716), np.float32(0.912), np.float32(0.9683), np.float32(0.9588), np.float32(0.865), np.float32(0.8691), np.float32(0.9134)] 
+2025-05-06 13:18:18.038740: Epoch time: 92.13 s 
+2025-05-06 13:18:18.078408: Yayy! New best EMA pseudo Dice: 0.9301000237464905 
+2025-05-06 13:18:24.972135:  
+2025-05-06 13:18:24.973891: Epoch 1314 
+2025-05-06 13:18:24.974287: Current learning rate: 0.00382 
+2025-05-06 13:20:05.765126: train_loss -0.4842 
+2025-05-06 13:20:05.789475: val_loss -0.4991 
+2025-05-06 13:20:05.796584: Pseudo dice [np.float32(0.8443), np.float32(0.8223), np.float32(0.8982), np.float32(0.9633), np.float32(0.9162), np.float32(0.9587), np.float32(0.9643), np.float32(0.9727), np.float32(0.9653), np.float32(0.9603), np.float32(0.8719), np.float32(0.9731), np.float32(0.9701), np.float32(0.9034), np.float32(0.9703), np.float32(0.9528), np.float32(0.8842), np.float32(0.8707), np.float32(0.911)] 
+2025-05-06 13:20:05.815493: Epoch time: 100.79 s 
+2025-05-06 13:20:07.330688:  
+2025-05-06 13:20:07.374503: Epoch 1315 
+2025-05-06 13:20:07.375449: Current learning rate: 0.00381 
+2025-05-06 13:21:49.783967: train_loss -0.5069 
+2025-05-06 13:21:49.787204: val_loss -0.4635 
+2025-05-06 13:21:49.787783: Pseudo dice [np.float32(0.8305), np.float32(0.8399), np.float32(0.9072), np.float32(0.9746), np.float32(0.8624), np.float32(0.9591), np.float32(0.9636), np.float32(0.9765), np.float32(0.9682), np.float32(0.9639), np.float32(0.9506), np.float32(0.968), np.float32(0.9551), np.float32(0.8984), np.float32(0.9429), np.float32(0.9549), np.float32(0.8743), np.float32(0.8497), np.float32(0.9079)] 
+2025-05-06 13:21:49.788192: Epoch time: 102.46 s 
+2025-05-06 13:21:51.316774:  
+2025-05-06 13:21:51.430797: Epoch 1316 
+2025-05-06 13:21:51.453081: Current learning rate: 0.00381 
+2025-05-06 13:23:31.068955: train_loss -0.4849 
+2025-05-06 13:23:31.078640: val_loss -0.4687 
+2025-05-06 13:23:31.079328: Pseudo dice [np.float32(0.8577), np.float32(0.8455), np.float32(0.8397), np.float32(0.9704), np.float32(0.918), np.float32(0.9572), np.float32(0.9599), np.float32(0.9797), np.float32(0.9672), np.float32(0.9704), np.float32(0.9591), np.float32(0.9661), np.float32(0.973), np.float32(0.9067), np.float32(0.9449), np.float32(0.9251), np.float32(0.8861), np.float32(0.9198), np.float32(0.9322)] 
+2025-05-06 13:23:31.083537: Epoch time: 99.75 s 
+2025-05-06 13:23:32.587831:  
+2025-05-06 13:23:32.701243: Epoch 1317 
+2025-05-06 13:23:32.716871: Current learning rate: 0.0038 
+2025-05-06 13:25:11.565692: train_loss -0.4995 
+2025-05-06 13:25:11.651206: val_loss -0.4912 
+2025-05-06 13:25:11.676561: Pseudo dice [np.float32(0.7707), np.float32(0.8339), np.float32(0.9216), np.float32(0.974), np.float32(0.8709), np.float32(0.964), np.float32(0.9668), np.float32(0.9754), np.float32(0.9664), np.float32(0.9613), np.float32(0.9332), np.float32(0.9682), np.float32(0.962), np.float32(0.912), np.float32(0.9717), np.float32(0.9608), np.float32(0.9149), np.float32(0.8893), np.float32(0.9145)] 
+2025-05-06 13:25:11.689399: Epoch time: 98.98 s 
+2025-05-06 13:25:13.201247:  
+2025-05-06 13:25:13.271521: Epoch 1318 
+2025-05-06 13:25:13.291885: Current learning rate: 0.0038 
+2025-05-06 13:26:48.066307: train_loss -0.5007 
+2025-05-06 13:26:48.131079: val_loss -0.5036 
+2025-05-06 13:26:48.131830: Pseudo dice [np.float32(0.8369), np.float32(0.8223), np.float32(0.9442), np.float32(0.9701), np.float32(0.9365), np.float32(0.9587), np.float32(0.9668), np.float32(0.9764), np.float32(0.9673), np.float32(0.9593), np.float32(0.9273), np.float32(0.9648), np.float32(0.9635), np.float32(0.9139), np.float32(0.9421), np.float32(0.9568), np.float32(0.8662), np.float32(0.8711), np.float32(0.9027)] 
+2025-05-06 13:26:48.148845: Epoch time: 94.87 s 
+2025-05-06 13:26:49.656824:  
+2025-05-06 13:26:49.704366: Epoch 1319 
+2025-05-06 13:26:49.720841: Current learning rate: 0.00379 
+2025-05-06 13:28:25.754059: train_loss -0.5014 
+2025-05-06 13:28:25.854809: val_loss -0.5326 
+2025-05-06 13:28:25.884358: Pseudo dice [np.float32(0.8613), np.float32(0.8472), np.float32(0.9344), np.float32(0.9697), np.float32(0.8561), np.float32(0.9609), np.float32(0.9513), np.float32(0.9783), np.float32(0.9632), np.float32(0.9742), np.float32(0.9539), np.float32(0.9687), np.float32(0.9707), np.float32(0.9075), np.float32(0.9714), np.float32(0.9492), np.float32(0.8637), np.float32(0.8682), np.float32(0.9126)] 
+2025-05-06 13:28:25.908299: Epoch time: 96.1 s 
+2025-05-06 13:28:27.488048:  
+2025-05-06 13:28:27.529453: Epoch 1320 
+2025-05-06 13:28:27.542763: Current learning rate: 0.00379 
+2025-05-06 13:30:03.107673: train_loss -0.49 
+2025-05-06 13:30:03.206230: val_loss -0.4663 
+2025-05-06 13:30:03.228877: Pseudo dice [np.float32(0.8358), np.float32(0.843), np.float32(0.9302), np.float32(0.9785), np.float32(0.8257), np.float32(0.955), np.float32(0.9661), np.float32(0.9756), np.float32(0.9652), np.float32(0.9501), np.float32(0.927), np.float32(0.9707), np.float32(0.9586), np.float32(0.9088), np.float32(0.9676), np.float32(0.9534), np.float32(0.8701), np.float32(0.8926), np.float32(0.9064)] 
+2025-05-06 13:30:03.263700: Epoch time: 95.62 s 
+2025-05-06 13:30:04.883056:  
+2025-05-06 13:30:05.019726: Epoch 1321 
+2025-05-06 13:30:05.044363: Current learning rate: 0.00378 
+2025-05-06 13:31:40.402299: train_loss -0.4943 
+2025-05-06 13:31:40.519791: val_loss -0.5015 
+2025-05-06 13:31:40.541897: Pseudo dice [np.float32(0.8541), np.float32(0.8473), np.float32(0.9357), np.float32(0.9777), np.float32(0.9033), np.float32(0.9599), np.float32(0.9648), np.float32(0.9794), np.float32(0.9573), np.float32(0.9622), np.float32(0.9357), np.float32(0.9603), np.float32(0.9699), np.float32(0.9053), np.float32(0.9663), np.float32(0.9518), np.float32(0.9011), np.float32(0.9124), np.float32(0.9175)] 
+2025-05-06 13:31:40.570427: Epoch time: 95.52 s 
+2025-05-06 13:31:42.122616:  
+2025-05-06 13:31:42.136602: Epoch 1322 
+2025-05-06 13:31:42.137378: Current learning rate: 0.00378 
+2025-05-06 13:33:21.409515: train_loss -0.4813 
+2025-05-06 13:33:21.514902: val_loss -0.4897 
+2025-05-06 13:33:21.559306: Pseudo dice [np.float32(0.8585), np.float32(0.843), np.float32(0.7195), np.float32(0.9625), np.float32(0.9162), np.float32(0.9607), np.float32(0.9532), np.float32(0.9801), np.float32(0.9722), np.float32(0.9525), np.float32(0.9453), np.float32(0.9735), np.float32(0.9712), np.float32(0.9068), np.float32(0.9519), np.float32(0.9546), np.float32(0.8489), np.float32(0.868), np.float32(0.9163)] 
+2025-05-06 13:33:21.605766: Epoch time: 99.29 s 
+2025-05-06 13:33:23.382340:  
+2025-05-06 13:33:23.418648: Epoch 1323 
+2025-05-06 13:33:23.444700: Current learning rate: 0.00377 
+2025-05-06 13:35:04.321000: train_loss -0.4839 
+2025-05-06 13:35:04.487125: val_loss -0.521 
+2025-05-06 13:35:04.511233: Pseudo dice [np.float32(0.8594), np.float32(0.8562), np.float32(0.8664), np.float32(0.9744), np.float32(0.9023), np.float32(0.9629), np.float32(0.9663), np.float32(0.9685), np.float32(0.9661), np.float32(0.9729), np.float32(0.9523), np.float32(0.9739), np.float32(0.9701), np.float32(0.9048), np.float32(0.9726), np.float32(0.9533), np.float32(0.8806), np.float32(0.904), np.float32(0.924)] 
+2025-05-06 13:35:04.553329: Epoch time: 100.94 s 
+2025-05-06 13:35:06.292091:  
+2025-05-06 13:35:06.374840: Epoch 1324 
+2025-05-06 13:35:06.432954: Current learning rate: 0.00377 
+2025-05-06 13:36:46.793003: train_loss -0.4795 
+2025-05-06 13:36:46.854496: val_loss -0.4725 
+2025-05-06 13:36:46.873393: Pseudo dice [np.float32(0.8473), np.float32(0.8628), np.float32(0.9301), np.float32(0.9777), np.float32(0.883), np.float32(0.962), np.float32(0.9612), np.float32(0.978), np.float32(0.9603), np.float32(0.9592), np.float32(0.9424), np.float32(0.9602), np.float32(0.9664), np.float32(0.9141), np.float32(0.965), np.float32(0.9546), np.float32(0.8861), np.float32(0.8943), np.float32(0.913)] 
+2025-05-06 13:36:46.874379: Epoch time: 100.5 s 
+2025-05-06 13:36:48.466107:  
+2025-05-06 13:36:48.525833: Epoch 1325 
+2025-05-06 13:36:48.544088: Current learning rate: 0.00376 
+2025-05-06 13:38:30.803187: train_loss -0.49 
+2025-05-06 13:38:30.911017: val_loss -0.5019 
+2025-05-06 13:38:30.948750: Pseudo dice [np.float32(0.8521), np.float32(0.8431), np.float32(0.9421), np.float32(0.9688), np.float32(0.918), np.float32(0.9405), np.float32(0.9675), np.float32(0.9832), np.float32(0.9435), np.float32(0.9603), np.float32(0.9412), np.float32(0.9633), np.float32(0.9703), np.float32(0.9092), np.float32(0.906), np.float32(0.9566), np.float32(0.8688), np.float32(0.8748), np.float32(0.929)] 
+2025-05-06 13:38:30.983153: Epoch time: 102.34 s 
+2025-05-06 13:38:32.737873:  
+2025-05-06 13:38:32.769709: Epoch 1326 
+2025-05-06 13:38:32.770894: Current learning rate: 0.00376 
+2025-05-06 13:40:07.487376: train_loss -0.4893 
+2025-05-06 13:40:07.613395: val_loss -0.4856 
+2025-05-06 13:40:07.663459: Pseudo dice [np.float32(0.8506), np.float32(0.8398), np.float32(0.8702), np.float32(0.9812), np.float32(0.8981), np.float32(0.9525), np.float32(0.9569), np.float32(0.9748), np.float32(0.9601), np.float32(0.9651), np.float32(0.9481), np.float32(0.9671), np.float32(0.9694), np.float32(0.904), np.float32(0.9288), np.float32(0.9538), np.float32(0.8917), np.float32(0.8876), np.float32(0.9149)] 
+2025-05-06 13:40:07.710535: Epoch time: 94.75 s 
+2025-05-06 13:40:09.339482:  
+2025-05-06 13:40:09.435053: Epoch 1327 
+2025-05-06 13:40:09.474402: Current learning rate: 0.00375 
+2025-05-06 13:41:43.981493: train_loss -0.4712 
+2025-05-06 13:41:44.035779: val_loss -0.4789 
+2025-05-06 13:41:44.053126: Pseudo dice [np.float32(0.8456), np.float32(0.8484), np.float32(0.8971), np.float32(0.9732), np.float32(0.9065), np.float32(0.9569), np.float32(0.9271), np.float32(0.9716), np.float32(0.9606), np.float32(0.9494), np.float32(0.9494), np.float32(0.9648), np.float32(0.9555), np.float32(0.9066), np.float32(0.9637), np.float32(0.9528), np.float32(0.8853), np.float32(0.8907), np.float32(0.9108)] 
+2025-05-06 13:41:44.070404: Epoch time: 94.64 s 
+2025-05-06 13:41:45.642883:  
+2025-05-06 13:41:45.849969: Epoch 1328 
+2025-05-06 13:41:45.883592: Current learning rate: 0.00375 
+2025-05-06 13:43:20.541073: train_loss -0.4803 
+2025-05-06 13:43:20.697866: val_loss -0.4779 
+2025-05-06 13:43:20.808658: Pseudo dice [np.float32(0.8495), np.float32(0.8485), np.float32(0.8587), np.float32(0.9738), np.float32(0.9128), np.float32(0.9622), np.float32(0.9656), np.float32(0.9803), np.float32(0.9614), np.float32(0.9679), np.float32(0.9575), np.float32(0.9614), np.float32(0.9721), np.float32(0.9055), np.float32(0.9666), np.float32(0.9518), np.float32(0.8846), np.float32(0.8884), np.float32(0.913)] 
+2025-05-06 13:43:20.902317: Epoch time: 94.9 s 
+2025-05-06 13:43:22.581439:  
+2025-05-06 13:43:22.616575: Epoch 1329 
+2025-05-06 13:43:22.620653: Current learning rate: 0.00374 
+2025-05-06 13:44:58.396097: train_loss -0.4801 
+2025-05-06 13:44:58.484745: val_loss -0.4539 
+2025-05-06 13:44:58.501155: Pseudo dice [np.float32(0.84), np.float32(0.8399), np.float32(0.8791), np.float32(0.9756), np.float32(0.8364), np.float32(0.9625), np.float32(0.9623), np.float32(0.9705), np.float32(0.9707), np.float32(0.9685), np.float32(0.9497), np.float32(0.971), np.float32(0.972), np.float32(0.8984), np.float32(0.9643), np.float32(0.9514), np.float32(0.881), np.float32(0.9038), np.float32(0.9188)] 
+2025-05-06 13:44:58.538938: Epoch time: 95.82 s 
+2025-05-06 13:45:00.023631:  
+2025-05-06 13:45:00.111892: Epoch 1330 
+2025-05-06 13:45:00.128725: Current learning rate: 0.00374 
+2025-05-06 13:46:41.683997: train_loss -0.4975 
+2025-05-06 13:46:41.695089: val_loss -0.4942 
+2025-05-06 13:46:41.699390: Pseudo dice [np.float32(0.8662), np.float32(0.8196), np.float32(0.843), np.float32(0.9636), np.float32(0.8879), np.float32(0.9642), np.float32(0.9373), np.float32(0.9736), np.float32(0.9648), np.float32(0.9622), np.float32(0.949), np.float32(0.9704), np.float32(0.9641), np.float32(0.9024), np.float32(0.9603), np.float32(0.9495), np.float32(0.9007), np.float32(0.8991), np.float32(0.9127)] 
+2025-05-06 13:46:41.712527: Epoch time: 101.66 s 
+2025-05-06 13:46:43.264710:  
+2025-05-06 13:46:43.306727: Epoch 1331 
+2025-05-06 13:46:43.318331: Current learning rate: 0.00373 
+2025-05-06 13:48:16.581798: train_loss -0.506 
+2025-05-06 13:48:16.743891: val_loss -0.5065 
+2025-05-06 13:48:16.777720: Pseudo dice [np.float32(0.7947), np.float32(0.8198), np.float32(0.8965), np.float32(0.9671), np.float32(0.9112), np.float32(0.9641), np.float32(0.9696), np.float32(0.9796), np.float32(0.9665), np.float32(0.961), np.float32(0.9534), np.float32(0.9687), np.float32(0.9689), np.float32(0.9119), np.float32(0.9676), np.float32(0.9439), np.float32(0.8964), np.float32(0.9037), np.float32(0.9205)] 
+2025-05-06 13:48:16.805861: Epoch time: 93.32 s 
+2025-05-06 13:48:22.140965:  
+2025-05-06 13:48:22.143700: Epoch 1332 
+2025-05-06 13:48:22.144155: Current learning rate: 0.00373 
+2025-05-06 13:50:00.623684: train_loss -0.4934 
+2025-05-06 13:50:00.745079: val_loss -0.5261 
+2025-05-06 13:50:00.805696: Pseudo dice [np.float32(0.8458), np.float32(0.8529), np.float32(0.8893), np.float32(0.9745), np.float32(0.8938), np.float32(0.956), np.float32(0.9621), np.float32(0.9797), np.float32(0.964), np.float32(0.9597), np.float32(0.9582), np.float32(0.9686), np.float32(0.9723), np.float32(0.9197), np.float32(0.9616), np.float32(0.9498), np.float32(0.8695), np.float32(0.7787), np.float32(0.9122)] 
+2025-05-06 13:50:00.836456: Epoch time: 98.48 s 
+2025-05-06 13:50:02.523763:  
+2025-05-06 13:50:02.568745: Epoch 1333 
+2025-05-06 13:50:02.580110: Current learning rate: 0.00372 
+2025-05-06 13:51:38.667059: train_loss -0.4944 
+2025-05-06 13:51:38.716257: val_loss -0.4904 
+2025-05-06 13:51:38.760239: Pseudo dice [np.float32(0.8292), np.float32(0.8437), np.float32(0.9115), np.float32(0.9724), np.float32(0.9039), np.float32(0.9367), np.float32(0.9625), np.float32(0.9545), np.float32(0.9655), np.float32(0.9502), np.float32(0.9063), np.float32(0.9719), np.float32(0.9658), np.float32(0.8875), np.float32(0.9647), np.float32(0.9517), np.float32(0.8938), np.float32(0.9029), np.float32(0.9205)] 
+2025-05-06 13:51:38.782750: Epoch time: 96.14 s 
+2025-05-06 13:51:40.338771:  
+2025-05-06 13:51:40.465792: Epoch 1334 
+2025-05-06 13:51:40.491543: Current learning rate: 0.00372 
+2025-05-06 13:53:15.738841: train_loss -0.4876 
+2025-05-06 13:53:15.748603: val_loss -0.4587 
+2025-05-06 13:53:15.749414: Pseudo dice [np.float32(0.8493), np.float32(0.8368), np.float32(0.8736), np.float32(0.9727), np.float32(0.9065), np.float32(0.9529), np.float32(0.9621), np.float32(0.9791), np.float32(0.9674), np.float32(0.9683), np.float32(0.9555), np.float32(0.9643), np.float32(0.9702), np.float32(0.9016), np.float32(0.9667), np.float32(0.9544), np.float32(0.8883), np.float32(0.8982), np.float32(0.9154)] 
+2025-05-06 13:53:15.749934: Epoch time: 95.4 s 
+2025-05-06 13:53:17.408084:  
+2025-05-06 13:53:17.519316: Epoch 1335 
+2025-05-06 13:53:17.568592: Current learning rate: 0.00371 
+2025-05-06 13:54:56.148015: train_loss -0.4834 
+2025-05-06 13:54:56.264374: val_loss -0.5164 
+2025-05-06 13:54:56.303470: Pseudo dice [np.float32(0.8334), np.float32(0.8458), np.float32(0.9405), np.float32(0.9724), np.float32(0.8997), np.float32(0.9537), np.float32(0.9629), np.float32(0.9745), np.float32(0.9661), np.float32(0.9451), np.float32(0.9483), np.float32(0.9735), np.float32(0.9586), np.float32(0.906), np.float32(0.9676), np.float32(0.9537), np.float32(0.8587), np.float32(0.8953), np.float32(0.9203)] 
+2025-05-06 13:54:56.340806: Epoch time: 98.74 s 
+2025-05-06 13:54:57.967142:  
+2025-05-06 13:54:57.974620: Epoch 1336 
+2025-05-06 13:54:57.975112: Current learning rate: 0.00371 
+2025-05-06 13:56:40.175488: train_loss -0.4923 
+2025-05-06 13:56:40.328526: val_loss -0.5007 
+2025-05-06 13:56:40.370540: Pseudo dice [np.float32(0.8496), np.float32(0.851), np.float32(0.8714), np.float32(0.9786), np.float32(0.9207), np.float32(0.9529), np.float32(0.9597), np.float32(0.9793), np.float32(0.9612), np.float32(0.9623), np.float32(0.954), np.float32(0.9684), np.float32(0.967), np.float32(0.9022), np.float32(0.9517), np.float32(0.9507), np.float32(0.8501), np.float32(0.8506), np.float32(0.9111)] 
+2025-05-06 13:56:40.416200: Epoch time: 102.21 s 
+2025-05-06 13:56:42.042332:  
+2025-05-06 13:56:42.130596: Epoch 1337 
+2025-05-06 13:56:42.175103: Current learning rate: 0.0037 
+2025-05-06 13:58:19.306407: train_loss -0.4944 
+2025-05-06 13:58:19.373016: val_loss -0.5263 
+2025-05-06 13:58:19.415093: Pseudo dice [np.float32(0.8631), np.float32(0.8302), np.float32(0.9172), np.float32(0.9779), np.float32(0.8941), np.float32(0.957), np.float32(0.9658), np.float32(0.9705), np.float32(0.9686), np.float32(0.9695), np.float32(0.9518), np.float32(0.9708), np.float32(0.9661), np.float32(0.9033), np.float32(0.9692), np.float32(0.9465), np.float32(0.8873), np.float32(0.9035), np.float32(0.9178)] 
+2025-05-06 13:58:19.451760: Epoch time: 97.27 s 
+2025-05-06 13:58:21.130814:  
+2025-05-06 13:58:21.173835: Epoch 1338 
+2025-05-06 13:58:21.201371: Current learning rate: 0.0037 
+2025-05-06 13:59:57.537688: train_loss -0.4939 
+2025-05-06 13:59:57.634002: val_loss -0.4846 
+2025-05-06 13:59:57.672720: Pseudo dice [np.float32(0.843), np.float32(0.8341), np.float32(0.9222), np.float32(0.9742), np.float32(0.9019), np.float32(0.9518), np.float32(0.9606), np.float32(0.9766), np.float32(0.9698), np.float32(0.9604), np.float32(0.9347), np.float32(0.9698), np.float32(0.9675), np.float32(0.9074), np.float32(0.9513), np.float32(0.9577), np.float32(0.8949), np.float32(0.8905), np.float32(0.9164)] 
+2025-05-06 13:59:57.732265: Epoch time: 96.41 s 
+2025-05-06 13:59:59.415345:  
+2025-05-06 13:59:59.480642: Epoch 1339 
+2025-05-06 13:59:59.510295: Current learning rate: 0.00369 
+2025-05-06 14:01:33.942114: train_loss -0.4895 
+2025-05-06 14:01:33.989048: val_loss -0.503 
+2025-05-06 14:01:33.989794: Pseudo dice [np.float32(0.8497), np.float32(0.849), np.float32(0.9159), np.float32(0.975), np.float32(0.9242), np.float32(0.9497), np.float32(0.9613), np.float32(0.9714), np.float32(0.9615), np.float32(0.9524), np.float32(0.9561), np.float32(0.9689), np.float32(0.9707), np.float32(0.8839), np.float32(0.9642), np.float32(0.949), np.float32(0.8604), np.float32(0.8839), np.float32(0.918)] 
+2025-05-06 14:01:33.997693: Epoch time: 94.53 s 
+2025-05-06 14:01:35.599859:  
+2025-05-06 14:01:35.653008: Epoch 1340 
+2025-05-06 14:01:35.671870: Current learning rate: 0.00369 
+2025-05-06 14:03:11.415223: train_loss -0.4927 
+2025-05-06 14:03:11.511749: val_loss -0.4995 
+2025-05-06 14:03:11.542928: Pseudo dice [np.float32(0.8535), np.float32(0.8323), np.float32(0.8897), np.float32(0.9739), np.float32(0.9039), np.float32(0.9411), np.float32(0.9594), np.float32(0.9774), np.float32(0.9557), np.float32(0.9699), np.float32(0.9482), np.float32(0.9594), np.float32(0.973), np.float32(0.9045), np.float32(0.9491), np.float32(0.9486), np.float32(0.9107), np.float32(0.9039), np.float32(0.9344)] 
+2025-05-06 14:03:11.555063: Epoch time: 95.82 s 
+2025-05-06 14:03:13.085782:  
+2025-05-06 14:03:13.178346: Epoch 1341 
+2025-05-06 14:03:13.200806: Current learning rate: 0.00368 
+2025-05-06 14:04:51.757718: train_loss -0.4955 
+2025-05-06 14:04:51.848653: val_loss -0.5044 
+2025-05-06 14:04:51.871540: Pseudo dice [np.float32(0.8335), np.float32(0.8305), np.float32(0.8398), np.float32(0.9602), np.float32(0.8794), np.float32(0.9619), np.float32(0.9665), np.float32(0.978), np.float32(0.9667), np.float32(0.9602), np.float32(0.9502), np.float32(0.9698), np.float32(0.9716), np.float32(0.8969), np.float32(0.9713), np.float32(0.9558), np.float32(0.8871), np.float32(0.8734), np.float32(0.9167)] 
+2025-05-06 14:04:51.908509: Epoch time: 98.67 s 
+2025-05-06 14:04:53.614012:  
+2025-05-06 14:04:53.739722: Epoch 1342 
+2025-05-06 14:04:53.740610: Current learning rate: 0.00368 
+2025-05-06 14:06:31.933210: train_loss -0.505 
+2025-05-06 14:06:32.037562: val_loss -0.5046 
+2025-05-06 14:06:32.062137: Pseudo dice [np.float32(0.8354), np.float32(0.8565), np.float32(0.959), np.float32(0.9754), np.float32(0.9121), np.float32(0.9377), np.float32(0.9619), np.float32(0.9801), np.float32(0.9635), np.float32(0.9691), np.float32(0.9436), np.float32(0.9683), np.float32(0.9655), np.float32(0.8965), np.float32(0.9264), np.float32(0.9565), np.float32(0.859), np.float32(0.8671), np.float32(0.9153)] 
+2025-05-06 14:06:32.086041: Epoch time: 98.32 s 
+2025-05-06 14:06:33.725379:  
+2025-05-06 14:06:33.850229: Epoch 1343 
+2025-05-06 14:06:33.851281: Current learning rate: 0.00367 
+2025-05-06 14:08:07.531973: train_loss -0.4836 
+2025-05-06 14:08:07.560424: val_loss -0.495 
+2025-05-06 14:08:07.561178: Pseudo dice [np.float32(0.8688), np.float32(0.8556), np.float32(0.8081), np.float32(0.9727), np.float32(0.9154), np.float32(0.9656), np.float32(0.9633), np.float32(0.9764), np.float32(0.9615), np.float32(0.9569), np.float32(0.9502), np.float32(0.9755), np.float32(0.9727), np.float32(0.9087), np.float32(0.9531), np.float32(0.9468), np.float32(0.8507), np.float32(0.8656), np.float32(0.9101)] 
+2025-05-06 14:08:07.566487: Epoch time: 93.81 s 
+2025-05-06 14:08:09.188000:  
+2025-05-06 14:08:09.282719: Epoch 1344 
+2025-05-06 14:08:09.305063: Current learning rate: 0.00367 
+2025-05-06 14:09:46.741994: train_loss -0.4918 
+2025-05-06 14:09:46.820816: val_loss -0.4938 
+2025-05-06 14:09:46.848122: Pseudo dice [np.float32(0.8342), np.float32(0.8593), np.float32(0.9329), np.float32(0.9765), np.float32(0.8939), np.float32(0.9645), np.float32(0.966), np.float32(0.9811), np.float32(0.9679), np.float32(0.9568), np.float32(0.9506), np.float32(0.9708), np.float32(0.9593), np.float32(0.9168), np.float32(0.9689), np.float32(0.961), np.float32(0.8415), np.float32(0.8347), np.float32(0.9184)] 
+2025-05-06 14:09:46.869530: Epoch time: 97.56 s 
+2025-05-06 14:09:48.450302:  
+2025-05-06 14:09:48.502127: Epoch 1345 
+2025-05-06 14:09:48.526116: Current learning rate: 0.00366 
+2025-05-06 14:11:28.198847: train_loss -0.4945 
+2025-05-06 14:11:28.319669: val_loss -0.4834 
+2025-05-06 14:11:28.366069: Pseudo dice [np.float32(0.8673), np.float32(0.8662), np.float32(0.9174), np.float32(0.977), np.float32(0.9022), np.float32(0.9534), np.float32(0.9639), np.float32(0.9804), np.float32(0.9606), np.float32(0.9632), np.float32(0.9475), np.float32(0.9682), np.float32(0.9679), np.float32(0.9098), np.float32(0.9674), np.float32(0.9477), np.float32(0.9008), np.float32(0.9061), np.float32(0.9232)] 
+2025-05-06 14:11:28.409865: Epoch time: 99.75 s 
+2025-05-06 14:11:30.031081:  
+2025-05-06 14:11:30.275956: Epoch 1346 
+2025-05-06 14:11:30.276877: Current learning rate: 0.00366 
+2025-05-06 14:13:11.200213: train_loss -0.5007 
+2025-05-06 14:13:11.253900: val_loss -0.5128 
+2025-05-06 14:13:11.269358: Pseudo dice [np.float32(0.8159), np.float32(0.8301), np.float32(0.8732), np.float32(0.9651), np.float32(0.8626), np.float32(0.9538), np.float32(0.962), np.float32(0.977), np.float32(0.9694), np.float32(0.9698), np.float32(0.9535), np.float32(0.9702), np.float32(0.9712), np.float32(0.9091), np.float32(0.9574), np.float32(0.9585), np.float32(0.8734), np.float32(0.8732), np.float32(0.9212)] 
+2025-05-06 14:13:11.282088: Epoch time: 101.17 s 
+2025-05-06 14:13:12.846663:  
+2025-05-06 14:13:13.000947: Epoch 1347 
+2025-05-06 14:13:13.030972: Current learning rate: 0.00365 
+2025-05-06 14:14:52.299419: train_loss -0.4893 
+2025-05-06 14:14:52.429480: val_loss -0.4869 
+2025-05-06 14:14:52.473899: Pseudo dice [np.float32(0.8502), np.float32(0.8353), np.float32(0.8726), np.float32(0.9792), np.float32(0.8994), np.float32(0.9623), np.float32(0.9549), np.float32(0.9752), np.float32(0.9532), np.float32(0.9663), np.float32(0.9298), np.float32(0.951), np.float32(0.959), np.float32(0.9007), np.float32(0.9664), np.float32(0.9509), np.float32(0.8893), np.float32(0.8979), np.float32(0.9133)] 
+2025-05-06 14:14:52.509324: Epoch time: 99.45 s 
+2025-05-06 14:14:54.116274:  
+2025-05-06 14:14:54.180365: Epoch 1348 
+2025-05-06 14:14:54.195185: Current learning rate: 0.00365 
+2025-05-06 14:16:31.248482: train_loss -0.4859 
+2025-05-06 14:16:31.392204: val_loss -0.4864 
+2025-05-06 14:16:31.427465: Pseudo dice [np.float32(0.8293), np.float32(0.8578), np.float32(0.9125), np.float32(0.9722), np.float32(0.9197), np.float32(0.958), np.float32(0.9637), np.float32(0.9809), np.float32(0.9634), np.float32(0.974), np.float32(0.9519), np.float32(0.9619), np.float32(0.9684), np.float32(0.8932), np.float32(0.9655), np.float32(0.9496), np.float32(0.8889), np.float32(0.8812), np.float32(0.9187)] 
+2025-05-06 14:16:31.472150: Epoch time: 97.13 s 
+2025-05-06 14:16:37.012279:  
+2025-05-06 14:16:37.023725: Epoch 1349 
+2025-05-06 14:16:37.024604: Current learning rate: 0.00364 
+2025-05-06 14:18:10.302223: train_loss -0.4981 
+2025-05-06 14:18:10.414798: val_loss -0.504 
+2025-05-06 14:18:10.419377: Pseudo dice [np.float32(0.8497), np.float32(0.8514), np.float32(0.9104), np.float32(0.9737), np.float32(0.9112), np.float32(0.9638), np.float32(0.9664), np.float32(0.9803), np.float32(0.9547), np.float32(0.9634), np.float32(0.9572), np.float32(0.969), np.float32(0.966), np.float32(0.9084), np.float32(0.9677), np.float32(0.951), np.float32(0.9153), np.float32(0.9035), np.float32(0.9246)] 
+2025-05-06 14:18:10.423667: Epoch time: 93.29 s 
+2025-05-06 14:18:13.040637:  
+2025-05-06 14:18:13.051792: Epoch 1350 
+2025-05-06 14:18:13.052814: Current learning rate: 0.00364 
+2025-05-06 14:19:51.439319: train_loss -0.4964 
+2025-05-06 14:19:51.562788: val_loss -0.5116 
+2025-05-06 14:19:51.620029: Pseudo dice [np.float32(0.8248), np.float32(0.8456), np.float32(0.8964), np.float32(0.9683), np.float32(0.8928), np.float32(0.9605), np.float32(0.9586), np.float32(0.9748), np.float32(0.9641), np.float32(0.9571), np.float32(0.9522), np.float32(0.9664), np.float32(0.9674), np.float32(0.9079), np.float32(0.9628), np.float32(0.9566), np.float32(0.8658), np.float32(0.8828), np.float32(0.9044)] 
+2025-05-06 14:19:51.699614: Epoch time: 98.4 s 
+2025-05-06 14:19:53.300423:  
+2025-05-06 14:19:53.338047: Epoch 1351 
+2025-05-06 14:19:53.338843: Current learning rate: 0.00363 
+2025-05-06 14:21:25.056167: train_loss -0.493 
+2025-05-06 14:21:25.108414: val_loss -0.5062 
+2025-05-06 14:21:25.132500: Pseudo dice [np.float32(0.8381), np.float32(0.8513), np.float32(0.8865), np.float32(0.9762), np.float32(0.8779), np.float32(0.9601), np.float32(0.9573), np.float32(0.977), np.float32(0.9564), np.float32(0.9682), np.float32(0.9545), np.float32(0.9607), np.float32(0.9715), np.float32(0.8966), np.float32(0.9581), np.float32(0.958), np.float32(0.8847), np.float32(0.891), np.float32(0.913)] 
+2025-05-06 14:21:25.170028: Epoch time: 91.76 s 
+2025-05-06 14:21:26.716567:  
+2025-05-06 14:21:26.799174: Epoch 1352 
+2025-05-06 14:21:26.799829: Current learning rate: 0.00363 
+2025-05-06 14:23:06.716742: train_loss -0.5054 
+2025-05-06 14:23:06.758885: val_loss -0.4808 
+2025-05-06 14:23:06.775299: Pseudo dice [np.float32(0.8397), np.float32(0.8387), np.float32(0.8811), np.float32(0.9757), np.float32(0.9066), np.float32(0.9523), np.float32(0.9573), np.float32(0.9786), np.float32(0.9525), np.float32(0.9497), np.float32(0.9393), np.float32(0.9592), np.float32(0.966), np.float32(0.89), np.float32(0.9544), np.float32(0.9455), np.float32(0.8911), np.float32(0.8896), np.float32(0.9142)] 
+2025-05-06 14:23:06.795777: Epoch time: 100.0 s 
+2025-05-06 14:23:08.284925:  
+2025-05-06 14:23:08.496002: Epoch 1353 
+2025-05-06 14:23:08.507852: Current learning rate: 0.00362 
+2025-05-06 14:24:47.397597: train_loss -0.5038 
+2025-05-06 14:24:47.516322: val_loss -0.5258 
+2025-05-06 14:24:47.531893: Pseudo dice [np.float32(0.8463), np.float32(0.8619), np.float32(0.8723), np.float32(0.941), np.float32(0.9034), np.float32(0.9577), np.float32(0.969), np.float32(0.9754), np.float32(0.9514), np.float32(0.96), np.float32(0.9154), np.float32(0.9618), np.float32(0.9647), np.float32(0.9168), np.float32(0.9552), np.float32(0.9422), np.float32(0.8933), np.float32(0.8907), np.float32(0.9146)] 
+2025-05-06 14:24:47.536148: Epoch time: 99.11 s 
+2025-05-06 14:24:49.043188:  
+2025-05-06 14:24:49.097770: Epoch 1354 
+2025-05-06 14:24:49.105371: Current learning rate: 0.00362 
+2025-05-06 14:26:28.343428: train_loss -0.4868 
+2025-05-06 14:26:28.391281: val_loss -0.4966 
+2025-05-06 14:26:28.396093: Pseudo dice [np.float32(0.8637), np.float32(0.8439), np.float32(0.8916), np.float32(0.9644), np.float32(0.9275), np.float32(0.96), np.float32(0.9626), np.float32(0.9791), np.float32(0.9648), np.float32(0.9661), np.float32(0.9371), np.float32(0.9668), np.float32(0.9553), np.float32(0.9134), np.float32(0.9564), np.float32(0.945), np.float32(0.8915), np.float32(0.908), np.float32(0.9215)] 
+2025-05-06 14:26:28.417966: Epoch time: 99.3 s 
+2025-05-06 14:26:29.990593:  
+2025-05-06 14:26:30.123327: Epoch 1355 
+2025-05-06 14:26:30.181944: Current learning rate: 0.00361 
+2025-05-06 14:28:14.467536: train_loss -0.4819 
+2025-05-06 14:28:14.558911: val_loss -0.5047 
+2025-05-06 14:28:14.586946: Pseudo dice [np.float32(0.8369), np.float32(0.8282), np.float32(0.9319), np.float32(0.9706), np.float32(0.8981), np.float32(0.9582), np.float32(0.9618), np.float32(0.9769), np.float32(0.9641), np.float32(0.9715), np.float32(0.9502), np.float32(0.9496), np.float32(0.9738), np.float32(0.9102), np.float32(0.9629), np.float32(0.9546), np.float32(0.8771), np.float32(0.8968), np.float32(0.9059)] 
+2025-05-06 14:28:14.605461: Epoch time: 104.48 s 
+2025-05-06 14:28:16.132030:  
+2025-05-06 14:28:16.134830: Epoch 1356 
+2025-05-06 14:28:16.135395: Current learning rate: 0.00361 
+2025-05-06 14:29:54.153729: train_loss -0.493 
+2025-05-06 14:29:54.182926: val_loss -0.4916 
+2025-05-06 14:29:54.187433: Pseudo dice [np.float32(0.8477), np.float32(0.8553), np.float32(0.7969), np.float32(0.9756), np.float32(0.8801), np.float32(0.9644), np.float32(0.9665), np.float32(0.9798), np.float32(0.9577), np.float32(0.9605), np.float32(0.9472), np.float32(0.9616), np.float32(0.9557), np.float32(0.8869), np.float32(0.9698), np.float32(0.9471), np.float32(0.8804), np.float32(0.8816), np.float32(0.9197)] 
+2025-05-06 14:29:54.188540: Epoch time: 98.02 s 
+2025-05-06 14:29:55.853368:  
+2025-05-06 14:29:55.944192: Epoch 1357 
+2025-05-06 14:29:55.961356: Current learning rate: 0.0036 
+2025-05-06 14:31:33.748302: train_loss -0.4926 
+2025-05-06 14:31:33.814703: val_loss -0.5108 
+2025-05-06 14:31:33.844762: Pseudo dice [np.float32(0.8259), np.float32(0.8169), np.float32(0.924), np.float32(0.9696), np.float32(0.9203), np.float32(0.951), np.float32(0.9596), np.float32(0.9669), np.float32(0.9609), np.float32(0.9426), np.float32(0.944), np.float32(0.9652), np.float32(0.9694), np.float32(0.8939), np.float32(0.9009), np.float32(0.9211), np.float32(0.8404), np.float32(0.8302), np.float32(0.9024)] 
+2025-05-06 14:31:33.869513: Epoch time: 97.9 s 
+2025-05-06 14:31:35.617915:  
+2025-05-06 14:31:35.707943: Epoch 1358 
+2025-05-06 14:31:35.729747: Current learning rate: 0.0036 
+2025-05-06 14:33:22.422650: train_loss -0.4911 
+2025-05-06 14:33:22.509349: val_loss -0.4712 
+2025-05-06 14:33:22.510395: Pseudo dice [np.float32(0.8438), np.float32(0.8636), np.float32(0.9351), np.float32(0.9738), np.float32(0.8662), np.float32(0.9632), np.float32(0.9633), np.float32(0.9733), np.float32(0.9604), np.float32(0.954), np.float32(0.9406), np.float32(0.9678), np.float32(0.967), np.float32(0.9107), np.float32(0.9742), np.float32(0.9483), np.float32(0.9009), np.float32(0.9007), np.float32(0.9133)] 
+2025-05-06 14:33:22.510982: Epoch time: 106.81 s 
+2025-05-06 14:33:24.116765:  
+2025-05-06 14:33:24.202928: Epoch 1359 
+2025-05-06 14:33:24.225746: Current learning rate: 0.00359 
+2025-05-06 14:35:06.367834: train_loss -0.4865 
+2025-05-06 14:35:06.456512: val_loss -0.4782 
+2025-05-06 14:35:06.465918: Pseudo dice [np.float32(0.8404), np.float32(0.8353), np.float32(0.9246), np.float32(0.9772), np.float32(0.904), np.float32(0.9411), np.float32(0.9524), np.float32(0.9761), np.float32(0.9642), np.float32(0.9383), np.float32(0.913), np.float32(0.9722), np.float32(0.9682), np.float32(0.9063), np.float32(0.9639), np.float32(0.9556), np.float32(0.8787), np.float32(0.8876), np.float32(0.9212)] 
+2025-05-06 14:35:06.482600: Epoch time: 102.25 s 
+2025-05-06 14:35:08.003556:  
+2025-05-06 14:35:08.074657: Epoch 1360 
+2025-05-06 14:35:08.077208: Current learning rate: 0.00359 
+2025-05-06 14:36:48.031126: train_loss -0.4976 
+2025-05-06 14:36:48.183796: val_loss -0.5102 
+2025-05-06 14:36:48.209830: Pseudo dice [np.float32(0.8625), np.float32(0.8344), np.float32(0.8102), np.float32(0.9727), np.float32(0.9117), np.float32(0.9636), np.float32(0.962), np.float32(0.9763), np.float32(0.9628), np.float32(0.9345), np.float32(0.9398), np.float32(0.9673), np.float32(0.9535), np.float32(0.9075), np.float32(0.9609), np.float32(0.9582), np.float32(0.8953), np.float32(0.8678), np.float32(0.9193)] 
+2025-05-06 14:36:48.218435: Epoch time: 100.03 s 
+2025-05-06 14:36:49.726036:  
+2025-05-06 14:36:49.840824: Epoch 1361 
+2025-05-06 14:36:49.857511: Current learning rate: 0.00358 
+2025-05-06 14:38:28.093640: train_loss -0.4918 
+2025-05-06 14:38:28.133444: val_loss -0.4991 
+2025-05-06 14:38:28.150722: Pseudo dice [np.float32(0.8374), np.float32(0.805), np.float32(0.911), np.float32(0.9742), np.float32(0.8948), np.float32(0.9511), np.float32(0.9658), np.float32(0.9789), np.float32(0.9636), np.float32(0.9522), np.float32(0.9475), np.float32(0.973), np.float32(0.9694), np.float32(0.905), np.float32(0.9709), np.float32(0.9554), np.float32(0.8646), np.float32(0.8732), np.float32(0.9032)] 
+2025-05-06 14:38:28.167149: Epoch time: 98.37 s 
+2025-05-06 14:38:29.729515:  
+2025-05-06 14:38:29.770679: Epoch 1362 
+2025-05-06 14:38:29.771534: Current learning rate: 0.00358 
+2025-05-06 14:40:10.919776: train_loss -0.4953 
+2025-05-06 14:40:11.009297: val_loss -0.4803 
+2025-05-06 14:40:11.030047: Pseudo dice [np.float32(0.8652), np.float32(0.8179), np.float32(0.9217), np.float32(0.9655), np.float32(0.9132), np.float32(0.948), np.float32(0.9613), np.float32(0.9735), np.float32(0.9464), np.float32(0.9725), np.float32(0.957), np.float32(0.9543), np.float32(0.9717), np.float32(0.9093), np.float32(0.9692), np.float32(0.9598), np.float32(0.9047), np.float32(0.9071), np.float32(0.9017)] 
+2025-05-06 14:40:11.049585: Epoch time: 101.19 s 
+2025-05-06 14:40:12.586030:  
+2025-05-06 14:40:12.630513: Epoch 1363 
+2025-05-06 14:40:12.668036: Current learning rate: 0.00357 
+2025-05-06 14:41:59.179319: train_loss -0.4948 
+2025-05-06 14:41:59.254546: val_loss -0.5305 
+2025-05-06 14:41:59.266520: Pseudo dice [np.float32(0.845), np.float32(0.8698), np.float32(0.9082), np.float32(0.9544), np.float32(0.9314), np.float32(0.9579), np.float32(0.9673), np.float32(0.9771), np.float32(0.9699), np.float32(0.9716), np.float32(0.9475), np.float32(0.9707), np.float32(0.9667), np.float32(0.9065), np.float32(0.9115), np.float32(0.9598), np.float32(0.9123), np.float32(0.9129), np.float32(0.9153)] 
+2025-05-06 14:41:59.267538: Epoch time: 106.59 s 
+2025-05-06 14:42:00.957730:  
+2025-05-06 14:42:01.017104: Epoch 1364 
+2025-05-06 14:42:01.018111: Current learning rate: 0.00357 
+2025-05-06 14:43:42.788906: train_loss -0.5024 
+2025-05-06 14:43:42.930734: val_loss -0.5089 
+2025-05-06 14:43:42.945171: Pseudo dice [np.float32(0.8398), np.float32(0.8536), np.float32(0.8983), np.float32(0.9755), np.float32(0.9104), np.float32(0.9599), np.float32(0.9581), np.float32(0.9797), np.float32(0.966), np.float32(0.962), np.float32(0.9513), np.float32(0.9741), np.float32(0.9697), np.float32(0.9119), np.float32(0.9676), np.float32(0.9583), np.float32(0.869), np.float32(0.881), np.float32(0.9216)] 
+2025-05-06 14:43:42.982484: Epoch time: 101.83 s 
+2025-05-06 14:43:44.666520:  
+2025-05-06 14:43:44.720171: Epoch 1365 
+2025-05-06 14:43:44.764369: Current learning rate: 0.00356 
+2025-05-06 14:45:22.327735: train_loss -0.4815 
+2025-05-06 14:45:22.417793: val_loss -0.508 
+2025-05-06 14:45:22.436902: Pseudo dice [np.float32(0.8491), np.float32(0.8567), np.float32(0.8952), np.float32(0.9705), np.float32(0.8724), np.float32(0.964), np.float32(0.9597), np.float32(0.9727), np.float32(0.9612), np.float32(0.9637), np.float32(0.9563), np.float32(0.9596), np.float32(0.9775), np.float32(0.8916), np.float32(0.964), np.float32(0.9537), np.float32(0.8583), np.float32(0.8571), np.float32(0.9237)] 
+2025-05-06 14:45:22.458757: Epoch time: 97.66 s 
+2025-05-06 14:45:23.948267:  
+2025-05-06 14:45:24.058725: Epoch 1366 
+2025-05-06 14:45:24.101280: Current learning rate: 0.00356 
+2025-05-06 14:46:58.751643: train_loss -0.4937 
+2025-05-06 14:46:58.817115: val_loss -0.4923 
+2025-05-06 14:46:58.830962: Pseudo dice [np.float32(0.8193), np.float32(0.866), np.float32(0.9135), np.float32(0.9673), np.float32(0.8616), np.float32(0.9458), np.float32(0.9671), np.float32(0.9733), np.float32(0.9519), np.float32(0.9669), np.float32(0.9487), np.float32(0.9613), np.float32(0.9655), np.float32(0.864), np.float32(0.9535), np.float32(0.9308), np.float32(0.878), np.float32(0.8718), np.float32(0.9082)] 
+2025-05-06 14:46:58.839597: Epoch time: 94.8 s 
+2025-05-06 14:47:04.348750:  
+2025-05-06 14:47:04.354472: Epoch 1367 
+2025-05-06 14:47:04.354866: Current learning rate: 0.00355 
+2025-05-06 14:48:36.928080: train_loss -0.5036 
+2025-05-06 14:48:36.967546: val_loss -0.5135 
+2025-05-06 14:48:36.971936: Pseudo dice [np.float32(0.8381), np.float32(0.8564), np.float32(0.9183), np.float32(0.977), np.float32(0.9192), np.float32(0.963), np.float32(0.9705), np.float32(0.9775), np.float32(0.9519), np.float32(0.9711), np.float32(0.9533), np.float32(0.9666), np.float32(0.9711), np.float32(0.9023), np.float32(0.9589), np.float32(0.9598), np.float32(0.8615), np.float32(0.8953), np.float32(0.9112)] 
+2025-05-06 14:48:36.984974: Epoch time: 92.58 s 
+2025-05-06 14:48:38.432699:  
+2025-05-06 14:48:38.495516: Epoch 1368 
+2025-05-06 14:48:38.514373: Current learning rate: 0.00355 
+2025-05-06 14:50:14.648112: train_loss -0.4982 
+2025-05-06 14:50:14.651093: val_loss -0.5105 
+2025-05-06 14:50:14.651578: Pseudo dice [np.float32(0.7927), np.float32(0.846), np.float32(0.8776), np.float32(0.9792), np.float32(0.9118), np.float32(0.958), np.float32(0.9691), np.float32(0.9801), np.float32(0.9689), np.float32(0.9604), np.float32(0.9511), np.float32(0.9702), np.float32(0.9674), np.float32(0.9125), np.float32(0.9625), np.float32(0.9556), np.float32(0.889), np.float32(0.9099), np.float32(0.9226)] 
+2025-05-06 14:50:14.651980: Epoch time: 96.22 s 
+2025-05-06 14:50:16.118577:  
+2025-05-06 14:50:16.199297: Epoch 1369 
+2025-05-06 14:50:16.211127: Current learning rate: 0.00354 
+2025-05-06 14:51:52.621617: train_loss -0.4903 
+2025-05-06 14:51:52.709958: val_loss -0.5448 
+2025-05-06 14:51:52.729463: Pseudo dice [np.float32(0.8484), np.float32(0.8421), np.float32(0.8709), np.float32(0.9679), np.float32(0.9167), np.float32(0.9526), np.float32(0.966), np.float32(0.9737), np.float32(0.962), np.float32(0.9725), np.float32(0.934), np.float32(0.968), np.float32(0.9729), np.float32(0.8894), np.float32(0.9629), np.float32(0.9552), np.float32(0.8901), np.float32(0.8984), np.float32(0.9097)] 
+2025-05-06 14:51:52.743503: Epoch time: 96.5 s 
+2025-05-06 14:51:54.380395:  
+2025-05-06 14:51:54.422030: Epoch 1370 
+2025-05-06 14:51:54.424084: Current learning rate: 0.00354 
+2025-05-06 14:53:30.522921: train_loss -0.4959 
+2025-05-06 14:53:30.627339: val_loss -0.523 
+2025-05-06 14:53:30.653307: Pseudo dice [np.float32(0.8356), np.float32(0.8019), np.float32(0.8928), np.float32(0.9767), np.float32(0.8866), np.float32(0.9571), np.float32(0.9616), np.float32(0.9689), np.float32(0.9585), np.float32(0.9633), np.float32(0.9407), np.float32(0.9712), np.float32(0.9629), np.float32(0.9013), np.float32(0.9675), np.float32(0.9623), np.float32(0.8901), np.float32(0.8791), np.float32(0.9061)] 
+2025-05-06 14:53:30.671328: Epoch time: 96.14 s 
+2025-05-06 14:53:32.204450:  
+2025-05-06 14:53:32.207381: Epoch 1371 
+2025-05-06 14:53:32.207978: Current learning rate: 0.00353 
+2025-05-06 14:55:08.576801: train_loss -0.4754 
+2025-05-06 14:55:08.705167: val_loss -0.4817 
+2025-05-06 14:55:08.727362: Pseudo dice [np.float32(0.8421), np.float32(0.8667), np.float32(0.8901), np.float32(0.9688), np.float32(0.9223), np.float32(0.9656), np.float32(0.958), np.float32(0.9747), np.float32(0.9641), np.float32(0.9681), np.float32(0.9482), np.float32(0.9651), np.float32(0.9675), np.float32(0.8963), np.float32(0.9691), np.float32(0.9585), np.float32(0.8914), np.float32(0.8808), np.float32(0.9263)] 
+2025-05-06 14:55:08.754011: Epoch time: 96.37 s 
+2025-05-06 14:55:10.371612:  
+2025-05-06 14:55:10.455523: Epoch 1372 
+2025-05-06 14:55:10.469856: Current learning rate: 0.00353 
+2025-05-06 14:56:50.772079: train_loss -0.479 
+2025-05-06 14:56:50.841772: val_loss -0.5138 
+2025-05-06 14:56:50.867996: Pseudo dice [np.float32(0.8386), np.float32(0.8513), np.float32(0.9364), np.float32(0.9777), np.float32(0.8826), np.float32(0.9619), np.float32(0.9647), np.float32(0.98), np.float32(0.9696), np.float32(0.968), np.float32(0.9501), np.float32(0.9517), np.float32(0.9698), np.float32(0.911), np.float32(0.9697), np.float32(0.9576), np.float32(0.8968), np.float32(0.9106), np.float32(0.9091)] 
+2025-05-06 14:56:50.906360: Epoch time: 100.4 s 
+2025-05-06 14:56:52.571819:  
+2025-05-06 14:56:52.659561: Epoch 1373 
+2025-05-06 14:56:52.681640: Current learning rate: 0.00352 
+2025-05-06 14:58:36.220128: train_loss -0.4939 
+2025-05-06 14:58:36.267499: val_loss -0.5374 
+2025-05-06 14:58:36.282193: Pseudo dice [np.float32(0.8545), np.float32(0.8577), np.float32(0.9027), np.float32(0.9615), np.float32(0.9198), np.float32(0.9646), np.float32(0.9675), np.float32(0.9696), np.float32(0.9644), np.float32(0.9709), np.float32(0.9569), np.float32(0.9687), np.float32(0.9716), np.float32(0.9056), np.float32(0.9632), np.float32(0.9512), np.float32(0.8795), np.float32(0.8757), np.float32(0.9141)] 
+2025-05-06 14:58:36.306119: Epoch time: 103.65 s 
+2025-05-06 14:58:38.244793:  
+2025-05-06 14:58:38.291720: Epoch 1374 
+2025-05-06 14:58:38.293300: Current learning rate: 0.00352 
+2025-05-06 15:00:10.507322: train_loss -0.4957 
+2025-05-06 15:00:10.635162: val_loss -0.5056 
+2025-05-06 15:00:10.661677: Pseudo dice [np.float32(0.8256), np.float32(0.8333), np.float32(0.8776), np.float32(0.9735), np.float32(0.9307), np.float32(0.9545), np.float32(0.9619), np.float32(0.975), np.float32(0.9565), np.float32(0.9677), np.float32(0.9525), np.float32(0.9667), np.float32(0.9731), np.float32(0.917), np.float32(0.9686), np.float32(0.952), np.float32(0.8735), np.float32(0.8803), np.float32(0.9146)] 
+2025-05-06 15:00:10.691979: Epoch time: 92.26 s 
+2025-05-06 15:00:12.274715:  
+2025-05-06 15:00:12.397300: Epoch 1375 
+2025-05-06 15:00:12.430100: Current learning rate: 0.00351 
+2025-05-06 15:01:44.000415: train_loss -0.51 
+2025-05-06 15:01:44.011255: val_loss -0.4979 
+2025-05-06 15:01:44.011846: Pseudo dice [np.float32(0.8645), np.float32(0.8649), np.float32(0.9024), np.float32(0.9794), np.float32(0.9154), np.float32(0.9589), np.float32(0.9604), np.float32(0.9794), np.float32(0.9513), np.float32(0.9725), np.float32(0.9552), np.float32(0.9623), np.float32(0.9676), np.float32(0.9153), np.float32(0.9694), np.float32(0.9585), np.float32(0.8854), np.float32(0.8776), np.float32(0.9111)] 
+2025-05-06 15:01:44.016020: Epoch time: 91.73 s 
+2025-05-06 15:01:44.016500: Yayy! New best EMA pseudo Dice: 0.9301000237464905 
+2025-05-06 15:01:46.969288:  
+2025-05-06 15:01:46.974455: Epoch 1376 
+2025-05-06 15:01:46.974935: Current learning rate: 0.00351 
+2025-05-06 15:03:16.537236: train_loss -0.5063 
+2025-05-06 15:03:16.599270: val_loss -0.4587 
+2025-05-06 15:03:16.610196: Pseudo dice [np.float32(0.8617), np.float32(0.8652), np.float32(0.9572), np.float32(0.9651), np.float32(0.9221), np.float32(0.9618), np.float32(0.9579), np.float32(0.9814), np.float32(0.9634), np.float32(0.9691), np.float32(0.9531), np.float32(0.9663), np.float32(0.968), np.float32(0.9042), np.float32(0.9707), np.float32(0.9598), np.float32(0.8905), np.float32(0.8959), np.float32(0.9198)] 
+2025-05-06 15:03:16.617814: Epoch time: 89.57 s 
+2025-05-06 15:03:16.618416: Yayy! New best EMA pseudo Dice: 0.9309999942779541 
+2025-05-06 15:03:19.524615:  
+2025-05-06 15:03:19.529906: Epoch 1377 
+2025-05-06 15:03:19.530366: Current learning rate: 0.0035 
+2025-05-06 15:04:53.335594: train_loss -0.4699 
+2025-05-06 15:04:53.406802: val_loss -0.4691 
+2025-05-06 15:04:53.442937: Pseudo dice [np.float32(0.8249), np.float32(0.8742), np.float32(0.8863), np.float32(0.9766), np.float32(0.9097), np.float32(0.9638), np.float32(0.9746), np.float32(0.9792), np.float32(0.9607), np.float32(0.97), np.float32(0.9474), np.float32(0.9529), np.float32(0.9724), np.float32(0.8969), np.float32(0.9633), np.float32(0.9467), np.float32(0.9034), np.float32(0.8687), np.float32(0.9206)] 
+2025-05-06 15:04:53.479163: Epoch time: 93.81 s 
+2025-05-06 15:04:53.530519: Yayy! New best EMA pseudo Dice: 0.9309999942779541 
+2025-05-06 15:04:56.079732:  
+2025-05-06 15:04:56.122768: Epoch 1378 
+2025-05-06 15:04:56.149298: Current learning rate: 0.0035 
+2025-05-06 15:06:27.310884: train_loss -0.504 
+2025-05-06 15:06:27.395571: val_loss -0.513 
+2025-05-06 15:06:27.420130: Pseudo dice [np.float32(0.8559), np.float32(0.8535), np.float32(0.6377), np.float32(0.9715), np.float32(0.9172), np.float32(0.9633), np.float32(0.9693), np.float32(0.9711), np.float32(0.9693), np.float32(0.9604), np.float32(0.9378), np.float32(0.9731), np.float32(0.9577), np.float32(0.9115), np.float32(0.9437), np.float32(0.9548), np.float32(0.8818), np.float32(0.8926), np.float32(0.9127)] 
+2025-05-06 15:06:27.458300: Epoch time: 91.23 s 
+2025-05-06 15:06:29.023245:  
+2025-05-06 15:06:29.098286: Epoch 1379 
+2025-05-06 15:06:29.140756: Current learning rate: 0.00349 
+2025-05-06 15:08:00.996448: train_loss -0.4941 
+2025-05-06 15:08:01.114872: val_loss -0.4926 
+2025-05-06 15:08:01.137929: Pseudo dice [np.float32(0.841), np.float32(0.8597), np.float32(0.901), np.float32(0.9666), np.float32(0.9158), np.float32(0.9523), np.float32(0.9548), np.float32(0.9746), np.float32(0.9532), np.float32(0.9603), np.float32(0.9548), np.float32(0.961), np.float32(0.9689), np.float32(0.899), np.float32(0.9616), np.float32(0.9458), np.float32(0.8777), np.float32(0.8759), np.float32(0.9227)] 
+2025-05-06 15:08:01.146014: Epoch time: 91.97 s 
+2025-05-06 15:08:02.790681:  
+2025-05-06 15:08:03.020224: Epoch 1380 
+2025-05-06 15:08:03.064452: Current learning rate: 0.00349 
+2025-05-06 15:09:33.920361: train_loss -0.4787 
+2025-05-06 15:09:33.939569: val_loss -0.4734 
+2025-05-06 15:09:33.941930: Pseudo dice [np.float32(0.7976), np.float32(0.8577), np.float32(0.8929), np.float32(0.9744), np.float32(0.879), np.float32(0.9555), np.float32(0.9603), np.float32(0.9748), np.float32(0.9651), np.float32(0.9463), np.float32(0.941), np.float32(0.9632), np.float32(0.9559), np.float32(0.8976), np.float32(0.9594), np.float32(0.9553), np.float32(0.8838), np.float32(0.8889), np.float32(0.9232)] 
+2025-05-06 15:09:33.946214: Epoch time: 91.13 s 
+2025-05-06 15:09:35.468414:  
+2025-05-06 15:09:35.603568: Epoch 1381 
+2025-05-06 15:09:35.629504: Current learning rate: 0.00348 
+2025-05-06 15:11:06.695971: train_loss -0.4896 
+2025-05-06 15:11:06.793736: val_loss -0.4686 
+2025-05-06 15:11:06.848615: Pseudo dice [np.float32(0.8395), np.float32(0.8421), np.float32(0.9107), np.float32(0.9758), np.float32(0.8823), np.float32(0.9641), np.float32(0.9581), np.float32(0.9716), np.float32(0.962), np.float32(0.9667), np.float32(0.9435), np.float32(0.9686), np.float32(0.9683), np.float32(0.9024), np.float32(0.9685), np.float32(0.9494), np.float32(0.8517), np.float32(0.8763), np.float32(0.9256)] 
+2025-05-06 15:11:06.877456: Epoch time: 91.23 s 
+2025-05-06 15:11:08.428264:  
+2025-05-06 15:11:08.554282: Epoch 1382 
+2025-05-06 15:11:08.618052: Current learning rate: 0.00348 
+2025-05-06 15:12:39.328268: train_loss -0.478 
+2025-05-06 15:12:39.423291: val_loss -0.5013 
+2025-05-06 15:12:39.447453: Pseudo dice [np.float32(0.8366), np.float32(0.8421), np.float32(0.8908), np.float32(0.9815), np.float32(0.9186), np.float32(0.948), np.float32(0.9635), np.float32(0.9805), np.float32(0.9703), np.float32(0.9675), np.float32(0.9455), np.float32(0.9719), np.float32(0.9726), np.float32(0.8865), np.float32(0.9668), np.float32(0.9458), np.float32(0.8515), np.float32(0.882), np.float32(0.9226)] 
+2025-05-06 15:12:39.486604: Epoch time: 90.9 s 
+2025-05-06 15:12:44.843843:  
+2025-05-06 15:12:44.854809: Epoch 1383 
+2025-05-06 15:12:44.855231: Current learning rate: 0.00347 
+2025-05-06 15:14:14.565209: train_loss -0.4685 
+2025-05-06 15:14:14.632498: val_loss -0.4683 
+2025-05-06 15:14:14.648282: Pseudo dice [np.float32(0.8651), np.float32(0.8646), np.float32(0.9265), np.float32(0.9773), np.float32(0.9081), np.float32(0.9623), np.float32(0.9662), np.float32(0.9795), np.float32(0.957), np.float32(0.9739), np.float32(0.9555), np.float32(0.9707), np.float32(0.9661), np.float32(0.9102), np.float32(0.9683), np.float32(0.9582), np.float32(0.8354), np.float32(0.8616), np.float32(0.9207)] 
+2025-05-06 15:14:14.670685: Epoch time: 89.72 s 
+2025-05-06 15:14:16.297941:  
+2025-05-06 15:14:16.370116: Epoch 1384 
+2025-05-06 15:14:16.378019: Current learning rate: 0.00346 
+2025-05-06 15:15:54.096686: train_loss -0.4875 
+2025-05-06 15:15:54.266204: val_loss -0.523 
+2025-05-06 15:15:54.304106: Pseudo dice [np.float32(0.8365), np.float32(0.8382), np.float32(0.9291), np.float32(0.9624), np.float32(0.9013), np.float32(0.9564), np.float32(0.9608), np.float32(0.9757), np.float32(0.9613), np.float32(0.9589), np.float32(0.9339), np.float32(0.9649), np.float32(0.9511), np.float32(0.9066), np.float32(0.9618), np.float32(0.9578), np.float32(0.9004), np.float32(0.9), np.float32(0.9019)] 
+2025-05-06 15:15:54.316966: Epoch time: 97.8 s 
+2025-05-06 15:15:55.976102:  
+2025-05-06 15:15:56.133358: Epoch 1385 
+2025-05-06 15:15:56.158070: Current learning rate: 0.00346 
+2025-05-06 15:17:29.314627: train_loss -0.4989 
+2025-05-06 15:17:29.341244: val_loss -0.4602 
+2025-05-06 15:17:29.345683: Pseudo dice [np.float32(0.8422), np.float32(0.8555), np.float32(0.9358), np.float32(0.9804), np.float32(0.9248), np.float32(0.9647), np.float32(0.9687), np.float32(0.9706), np.float32(0.9641), np.float32(0.9619), np.float32(0.9435), np.float32(0.959), np.float32(0.9647), np.float32(0.9089), np.float32(0.9165), np.float32(0.937), np.float32(0.8947), np.float32(0.8975), np.float32(0.9263)] 
+2025-05-06 15:17:29.357172: Epoch time: 93.34 s 
+2025-05-06 15:17:30.933173:  
+2025-05-06 15:17:31.033590: Epoch 1386 
+2025-05-06 15:17:31.056455: Current learning rate: 0.00345 
+2025-05-06 15:19:07.834376: train_loss -0.4944 
+2025-05-06 15:19:07.900357: val_loss -0.4613 
+2025-05-06 15:19:07.916841: Pseudo dice [np.float32(0.8357), np.float32(0.8485), np.float32(0.8982), np.float32(0.9797), np.float32(0.8842), np.float32(0.9549), np.float32(0.9622), np.float32(0.9777), np.float32(0.9549), np.float32(0.9567), np.float32(0.9475), np.float32(0.9646), np.float32(0.966), np.float32(0.9027), np.float32(0.9425), np.float32(0.9522), np.float32(0.8275), np.float32(0.8575), np.float32(0.9243)] 
+2025-05-06 15:19:07.941838: Epoch time: 96.9 s 
+2025-05-06 15:19:09.491289:  
+2025-05-06 15:19:09.574637: Epoch 1387 
+2025-05-06 15:19:09.609812: Current learning rate: 0.00345 
+2025-05-06 15:20:40.930714: train_loss -0.5112 
+2025-05-06 15:20:40.967155: val_loss -0.5129 
+2025-05-06 15:20:40.975991: Pseudo dice [np.float32(0.8529), np.float32(0.8432), np.float32(0.9593), np.float32(0.9754), np.float32(0.8664), np.float32(0.9627), np.float32(0.9675), np.float32(0.9791), np.float32(0.9646), np.float32(0.9603), np.float32(0.9488), np.float32(0.9735), np.float32(0.9716), np.float32(0.9047), np.float32(0.966), np.float32(0.9544), np.float32(0.8924), np.float32(0.8888), np.float32(0.9233)] 
+2025-05-06 15:20:40.979052: Epoch time: 91.44 s 
+2025-05-06 15:20:42.461987:  
+2025-05-06 15:20:42.482519: Epoch 1388 
+2025-05-06 15:20:42.488451: Current learning rate: 0.00344 
+2025-05-06 15:22:13.628453: train_loss -0.4906 
+2025-05-06 15:22:13.755507: val_loss -0.4697 
+2025-05-06 15:22:13.793803: Pseudo dice [np.float32(0.861), np.float32(0.8596), np.float32(0.8975), np.float32(0.972), np.float32(0.8727), np.float32(0.9486), np.float32(0.9686), np.float32(0.9757), np.float32(0.9593), np.float32(0.962), np.float32(0.9505), np.float32(0.9652), np.float32(0.9657), np.float32(0.91), np.float32(0.9594), np.float32(0.9518), np.float32(0.8898), np.float32(0.8915), np.float32(0.9163)] 
+2025-05-06 15:22:13.807922: Epoch time: 91.17 s 
+2025-05-06 15:22:15.412847:  
+2025-05-06 15:22:15.535145: Epoch 1389 
+2025-05-06 15:22:15.553319: Current learning rate: 0.00344 
+2025-05-06 15:23:47.617535: train_loss -0.5108 
+2025-05-06 15:23:47.663694: val_loss -0.5014 
+2025-05-06 15:23:47.664720: Pseudo dice [np.float32(0.8537), np.float32(0.8573), np.float32(0.9095), np.float32(0.979), np.float32(0.9051), np.float32(0.9583), np.float32(0.966), np.float32(0.9782), np.float32(0.9599), np.float32(0.972), np.float32(0.9528), np.float32(0.9644), np.float32(0.9711), np.float32(0.8989), np.float32(0.966), np.float32(0.9486), np.float32(0.8889), np.float32(0.8866), np.float32(0.9166)] 
+2025-05-06 15:23:47.665115: Epoch time: 92.21 s 
+2025-05-06 15:23:49.189581:  
+2025-05-06 15:23:49.258482: Epoch 1390 
+2025-05-06 15:23:49.273341: Current learning rate: 0.00343 
+2025-05-06 15:25:21.162719: train_loss -0.4897 
+2025-05-06 15:25:21.259141: val_loss -0.4817 
+2025-05-06 15:25:21.292069: Pseudo dice [np.float32(0.8099), np.float32(0.8504), np.float32(0.9095), np.float32(0.9684), np.float32(0.9026), np.float32(0.9568), np.float32(0.962), np.float32(0.9782), np.float32(0.9639), np.float32(0.9681), np.float32(0.9463), np.float32(0.9689), np.float32(0.9678), np.float32(0.9097), np.float32(0.9691), np.float32(0.9535), np.float32(0.8699), np.float32(0.865), np.float32(0.9184)] 
+2025-05-06 15:25:21.313737: Epoch time: 91.97 s 
+2025-05-06 15:25:22.989902:  
+2025-05-06 15:25:23.021471: Epoch 1391 
+2025-05-06 15:25:23.033167: Current learning rate: 0.00343 
+2025-05-06 15:27:00.969734: train_loss -0.4912 
+2025-05-06 15:27:01.064200: val_loss -0.5268 
+2025-05-06 15:27:01.088112: Pseudo dice [np.float32(0.845), np.float32(0.8538), np.float32(0.9285), np.float32(0.9623), np.float32(0.9109), np.float32(0.9609), np.float32(0.9609), np.float32(0.9785), np.float32(0.9628), np.float32(0.9667), np.float32(0.9463), np.float32(0.9635), np.float32(0.9694), np.float32(0.9013), np.float32(0.9665), np.float32(0.9456), np.float32(0.9007), np.float32(0.9022), np.float32(0.9225)] 
+2025-05-06 15:27:01.115601: Epoch time: 97.98 s 
+2025-05-06 15:27:02.756004:  
+2025-05-06 15:27:02.782409: Epoch 1392 
+2025-05-06 15:27:02.783508: Current learning rate: 0.00342 
+2025-05-06 15:28:32.206420: train_loss -0.4638 
+2025-05-06 15:28:32.290904: val_loss -0.4786 
+2025-05-06 15:28:32.317660: Pseudo dice [np.float32(0.8473), np.float32(0.8391), np.float32(0.8829), np.float32(0.9612), np.float32(0.8513), np.float32(0.9555), np.float32(0.9414), np.float32(0.9677), np.float32(0.9643), np.float32(0.937), np.float32(0.8952), np.float32(0.9675), np.float32(0.9647), np.float32(0.9005), np.float32(0.9674), np.float32(0.9592), np.float32(0.8419), np.float32(0.8907), np.float32(0.9179)] 
+2025-05-06 15:28:32.339459: Epoch time: 89.45 s 
+2025-05-06 15:28:33.933938:  
+2025-05-06 15:28:34.035933: Epoch 1393 
+2025-05-06 15:28:34.080549: Current learning rate: 0.00342 
+2025-05-06 15:30:10.910630: train_loss -0.4955 
+2025-05-06 15:30:11.015164: val_loss -0.5042 
+2025-05-06 15:30:11.048164: Pseudo dice [np.float32(0.8671), np.float32(0.8582), np.float32(0.9238), np.float32(0.9771), np.float32(0.908), np.float32(0.9551), np.float32(0.9663), np.float32(0.9799), np.float32(0.9686), np.float32(0.9597), np.float32(0.9513), np.float32(0.9666), np.float32(0.9716), np.float32(0.918), np.float32(0.9703), np.float32(0.9622), np.float32(0.8843), np.float32(0.8845), np.float32(0.92)] 
+2025-05-06 15:30:11.077991: Epoch time: 96.98 s 
+2025-05-06 15:30:12.866213:  
+2025-05-06 15:30:12.922482: Epoch 1394 
+2025-05-06 15:30:12.950349: Current learning rate: 0.00341 
+2025-05-06 15:31:44.523032: train_loss -0.4928 
+2025-05-06 15:31:44.599622: val_loss -0.5249 
+2025-05-06 15:31:44.613159: Pseudo dice [np.float32(0.8673), np.float32(0.8459), np.float32(0.8378), np.float32(0.967), np.float32(0.9025), np.float32(0.9636), np.float32(0.9647), np.float32(0.9777), np.float32(0.9689), np.float32(0.9678), np.float32(0.951), np.float32(0.972), np.float32(0.9678), np.float32(0.9154), np.float32(0.9622), np.float32(0.9602), np.float32(0.9007), np.float32(0.8982), np.float32(0.9091)] 
+2025-05-06 15:31:44.637213: Epoch time: 91.66 s 
+2025-05-06 15:31:46.215105:  
+2025-05-06 15:31:46.307164: Epoch 1395 
+2025-05-06 15:31:46.323019: Current learning rate: 0.00341 
+2025-05-06 15:33:17.587584: train_loss -0.5011 
+2025-05-06 15:33:17.714805: val_loss -0.4638 
+2025-05-06 15:33:17.747468: Pseudo dice [np.float32(0.836), np.float32(0.8318), np.float32(0.8854), np.float32(0.9754), np.float32(0.9069), np.float32(0.9592), np.float32(0.9645), np.float32(0.9761), np.float32(0.9652), np.float32(0.9755), np.float32(0.9545), np.float32(0.9711), np.float32(0.9693), np.float32(0.8956), np.float32(0.9405), np.float32(0.9316), np.float32(0.8916), np.float32(0.8683), np.float32(0.9272)] 
+2025-05-06 15:33:17.782487: Epoch time: 91.37 s 
+2025-05-06 15:33:19.371934:  
+2025-05-06 15:33:19.429410: Epoch 1396 
+2025-05-06 15:33:19.433945: Current learning rate: 0.0034 
+2025-05-06 15:34:51.638263: train_loss -0.4973 
+2025-05-06 15:34:51.688713: val_loss -0.4699 
+2025-05-06 15:34:51.699363: Pseudo dice [np.float32(0.8684), np.float32(0.8186), np.float32(0.9151), np.float32(0.9679), np.float32(0.9128), np.float32(0.9584), np.float32(0.9646), np.float32(0.9755), np.float32(0.9622), np.float32(0.9595), np.float32(0.9419), np.float32(0.9671), np.float32(0.9697), np.float32(0.9049), np.float32(0.9659), np.float32(0.9542), np.float32(0.8691), np.float32(0.8173), np.float32(0.9155)] 
+2025-05-06 15:34:51.724115: Epoch time: 92.27 s 
+2025-05-06 15:34:53.386168:  
+2025-05-06 15:34:53.437639: Epoch 1397 
+2025-05-06 15:34:53.441727: Current learning rate: 0.0034 
+2025-05-06 15:36:27.042833: train_loss -0.4977 
+2025-05-06 15:36:27.104882: val_loss -0.5228 
+2025-05-06 15:36:27.130965: Pseudo dice [np.float32(0.8569), np.float32(0.8582), np.float32(0.9029), np.float32(0.9776), np.float32(0.9114), np.float32(0.968), np.float32(0.9607), np.float32(0.9768), np.float32(0.9674), np.float32(0.969), np.float32(0.948), np.float32(0.9657), np.float32(0.9667), np.float32(0.9117), np.float32(0.964), np.float32(0.9505), np.float32(0.8921), np.float32(0.9031), np.float32(0.9117)] 
+2025-05-06 15:36:27.180742: Epoch time: 93.66 s 
+2025-05-06 15:36:28.843467:  
+2025-05-06 15:36:29.006356: Epoch 1398 
+2025-05-06 15:36:29.044287: Current learning rate: 0.00339 
+2025-05-06 15:38:03.509125: train_loss -0.5161 
+2025-05-06 15:38:03.596052: val_loss -0.526 
+2025-05-06 15:38:03.636215: Pseudo dice [np.float32(0.8492), np.float32(0.8375), np.float32(0.8641), np.float32(0.9769), np.float32(0.9026), np.float32(0.9617), np.float32(0.9433), np.float32(0.9592), np.float32(0.9692), np.float32(0.97), np.float32(0.9592), np.float32(0.9713), np.float32(0.9738), np.float32(0.9113), np.float32(0.97), np.float32(0.9586), np.float32(0.9022), np.float32(0.8907), np.float32(0.9107)] 
+2025-05-06 15:38:03.663938: Epoch time: 94.67 s 
+2025-05-06 15:38:05.358364:  
+2025-05-06 15:38:05.384768: Epoch 1399 
+2025-05-06 15:38:05.385274: Current learning rate: 0.00339 
+2025-05-06 15:39:42.485266: train_loss -0.493 
+2025-05-06 15:39:42.542066: val_loss -0.5076 
+2025-05-06 15:39:42.576946: Pseudo dice [np.float32(0.8103), np.float32(0.8516), np.float32(0.9157), np.float32(0.9793), np.float32(0.884), np.float32(0.9664), np.float32(0.9674), np.float32(0.9762), np.float32(0.9593), np.float32(0.9604), np.float32(0.9368), np.float32(0.9717), np.float32(0.9608), np.float32(0.908), np.float32(0.9587), np.float32(0.9448), np.float32(0.8734), np.float32(0.891), np.float32(0.9174)] 
+2025-05-06 15:39:42.601849: Epoch time: 97.13 s 
+2025-05-06 15:39:45.821924:  
+2025-05-06 15:39:45.827453: Epoch 1400 
+2025-05-06 15:39:45.827830: Current learning rate: 0.00338 
+2025-05-06 15:41:21.137755: train_loss -0.4897 
+2025-05-06 15:41:21.293543: val_loss -0.5094 
+2025-05-06 15:41:21.306387: Pseudo dice [np.float32(0.8471), np.float32(0.8425), np.float32(0.8825), np.float32(0.9585), np.float32(0.924), np.float32(0.9491), np.float32(0.966), np.float32(0.9781), np.float32(0.9702), np.float32(0.9539), np.float32(0.9205), np.float32(0.9718), np.float32(0.9643), np.float32(0.9102), np.float32(0.963), np.float32(0.9348), np.float32(0.8938), np.float32(0.8643), np.float32(0.9267)] 
+2025-05-06 15:41:21.322609: Epoch time: 95.32 s 
+2025-05-06 15:41:26.963627:  
+2025-05-06 15:41:26.970387: Epoch 1401 
+2025-05-06 15:41:26.970788: Current learning rate: 0.00338 
+2025-05-06 15:43:01.882626: train_loss -0.4776 
+2025-05-06 15:43:01.960794: val_loss -0.4675 
+2025-05-06 15:43:01.961749: Pseudo dice [np.float32(0.8658), np.float32(0.853), np.float32(0.9378), np.float32(0.9767), np.float32(0.8607), np.float32(0.9606), np.float32(0.968), np.float32(0.9818), np.float32(0.9556), np.float32(0.9577), np.float32(0.9221), np.float32(0.9624), np.float32(0.9583), np.float32(0.9035), np.float32(0.9676), np.float32(0.9618), np.float32(0.8791), np.float32(0.8378), np.float32(0.9264)] 
+2025-05-06 15:43:01.971418: Epoch time: 94.92 s 
+2025-05-06 15:43:03.725276:  
+2025-05-06 15:43:03.768334: Epoch 1402 
+2025-05-06 15:43:03.769085: Current learning rate: 0.00337 
+2025-05-06 15:44:35.514549: train_loss -0.4683 
+2025-05-06 15:44:35.630992: val_loss -0.5042 
+2025-05-06 15:44:35.675778: Pseudo dice [np.float32(0.8453), np.float32(0.8354), np.float32(0.8744), np.float32(0.9755), np.float32(0.9133), np.float32(0.9616), np.float32(0.9534), np.float32(0.9778), np.float32(0.9549), np.float32(0.9577), np.float32(0.9258), np.float32(0.9661), np.float32(0.9587), np.float32(0.902), np.float32(0.9585), np.float32(0.9503), np.float32(0.8855), np.float32(0.8986), np.float32(0.9102)] 
+2025-05-06 15:44:35.727459: Epoch time: 91.79 s 
+2025-05-06 15:44:37.266926:  
+2025-05-06 15:44:37.297867: Epoch 1403 
+2025-05-06 15:44:37.298750: Current learning rate: 0.00337 
+2025-05-06 15:46:13.238292: train_loss -0.4951 
+2025-05-06 15:46:13.323427: val_loss -0.4877 
+2025-05-06 15:46:13.349705: Pseudo dice [np.float32(0.8314), np.float32(0.842), np.float32(0.8582), np.float32(0.9768), np.float32(0.8885), np.float32(0.9536), np.float32(0.9578), np.float32(0.971), np.float32(0.9513), np.float32(0.9571), np.float32(0.947), np.float32(0.9672), np.float32(0.9592), np.float32(0.8993), np.float32(0.9554), np.float32(0.9539), np.float32(0.8927), np.float32(0.8556), np.float32(0.9015)] 
+2025-05-06 15:46:13.393607: Epoch time: 95.97 s 
+2025-05-06 15:46:14.967479:  
+2025-05-06 15:46:15.031926: Epoch 1404 
+2025-05-06 15:46:15.056053: Current learning rate: 0.00336 
+2025-05-06 15:47:49.941376: train_loss -0.4769 
+2025-05-06 15:47:50.008512: val_loss -0.4859 
+2025-05-06 15:47:50.027746: Pseudo dice [np.float32(0.8294), np.float32(0.8082), np.float32(0.8883), np.float32(0.9601), np.float32(0.9061), np.float32(0.9576), np.float32(0.9538), np.float32(0.971), np.float32(0.966), np.float32(0.9654), np.float32(0.9481), np.float32(0.9648), np.float32(0.9731), np.float32(0.9043), np.float32(0.9642), np.float32(0.9585), np.float32(0.9033), np.float32(0.9014), np.float32(0.9173)] 
+2025-05-06 15:47:50.031543: Epoch time: 94.98 s 
+2025-05-06 15:47:51.771139:  
+2025-05-06 15:47:51.935983: Epoch 1405 
+2025-05-06 15:47:51.966807: Current learning rate: 0.00336 
+2025-05-06 15:49:23.183768: train_loss -0.4991 
+2025-05-06 15:49:23.299417: val_loss -0.4721 
+2025-05-06 15:49:23.346782: Pseudo dice [np.float32(0.8433), np.float32(0.8263), np.float32(0.9388), np.float32(0.9436), np.float32(0.8763), np.float32(0.9602), np.float32(0.9659), np.float32(0.9756), np.float32(0.9614), np.float32(0.9663), np.float32(0.9546), np.float32(0.9614), np.float32(0.9626), np.float32(0.9039), np.float32(0.9669), np.float32(0.9413), np.float32(0.884), np.float32(0.8893), np.float32(0.9003)] 
+2025-05-06 15:49:23.386286: Epoch time: 91.41 s 
+2025-05-06 15:49:24.933672:  
+2025-05-06 15:49:25.003767: Epoch 1406 
+2025-05-06 15:49:25.036473: Current learning rate: 0.00335 
+2025-05-06 15:50:52.311463: train_loss -0.4897 
+2025-05-06 15:50:52.404916: val_loss -0.4998 
+2025-05-06 15:50:52.444842: Pseudo dice [np.float32(0.8553), np.float32(0.8599), np.float32(0.9058), np.float32(0.9596), np.float32(0.9236), np.float32(0.9571), np.float32(0.9568), np.float32(0.9751), np.float32(0.955), np.float32(0.9661), np.float32(0.9425), np.float32(0.9683), np.float32(0.9685), np.float32(0.9052), np.float32(0.9678), np.float32(0.9487), np.float32(0.8778), np.float32(0.8893), np.float32(0.913)] 
+2025-05-06 15:50:52.469762: Epoch time: 87.38 s 
+2025-05-06 15:50:54.098107:  
+2025-05-06 15:50:54.186714: Epoch 1407 
+2025-05-06 15:50:54.211943: Current learning rate: 0.00335 
+2025-05-06 15:52:23.388445: train_loss -0.4945 
+2025-05-06 15:52:23.439418: val_loss -0.4952 
+2025-05-06 15:52:23.443542: Pseudo dice [np.float32(0.8605), np.float32(0.8224), np.float32(0.9267), np.float32(0.9706), np.float32(0.9012), np.float32(0.9582), np.float32(0.9634), np.float32(0.9727), np.float32(0.9582), np.float32(0.9604), np.float32(0.9343), np.float32(0.9748), np.float32(0.963), np.float32(0.8971), np.float32(0.9475), np.float32(0.9543), np.float32(0.8722), np.float32(0.886), np.float32(0.9087)] 
+2025-05-06 15:52:23.444008: Epoch time: 89.29 s 
+2025-05-06 15:52:25.090504:  
+2025-05-06 15:52:25.189632: Epoch 1408 
+2025-05-06 15:52:25.226725: Current learning rate: 0.00334 
+2025-05-06 15:53:59.178668: train_loss -0.4778 
+2025-05-06 15:53:59.239909: val_loss -0.4753 
+2025-05-06 15:53:59.240726: Pseudo dice [np.float32(0.8255), np.float32(0.8753), np.float32(0.9257), np.float32(0.975), np.float32(0.9043), np.float32(0.9463), np.float32(0.9658), np.float32(0.9811), np.float32(0.9593), np.float32(0.9607), np.float32(0.9357), np.float32(0.969), np.float32(0.9675), np.float32(0.8994), np.float32(0.9651), np.float32(0.959), np.float32(0.8877), np.float32(0.8924), np.float32(0.9238)] 
+2025-05-06 15:53:59.260522: Epoch time: 94.09 s 
+2025-05-06 15:54:01.068178:  
+2025-05-06 15:54:01.199401: Epoch 1409 
+2025-05-06 15:54:01.242778: Current learning rate: 0.00334 
+2025-05-06 15:55:32.492050: train_loss -0.5007 
+2025-05-06 15:55:32.586750: val_loss -0.4883 
+2025-05-06 15:55:32.622351: Pseudo dice [np.float32(0.855), np.float32(0.8385), np.float32(0.9426), np.float32(0.9778), np.float32(0.8909), np.float32(0.9644), np.float32(0.9469), np.float32(0.9654), np.float32(0.9699), np.float32(0.9587), np.float32(0.9281), np.float32(0.9696), np.float32(0.9717), np.float32(0.9099), np.float32(0.9676), np.float32(0.9523), np.float32(0.8817), np.float32(0.8905), np.float32(0.9123)] 
+2025-05-06 15:55:32.663372: Epoch time: 91.43 s 
+2025-05-06 15:55:34.608598:  
+2025-05-06 15:55:34.659164: Epoch 1410 
+2025-05-06 15:55:34.671748: Current learning rate: 0.00333 
+2025-05-06 15:57:02.586521: train_loss -0.4992 
+2025-05-06 15:57:02.661878: val_loss -0.5256 
+2025-05-06 15:57:02.670193: Pseudo dice [np.float32(0.8342), np.float32(0.8366), np.float32(0.8836), np.float32(0.9722), np.float32(0.8897), np.float32(0.9616), np.float32(0.9654), np.float32(0.9743), np.float32(0.9474), np.float32(0.9672), np.float32(0.9473), np.float32(0.9625), np.float32(0.9706), np.float32(0.9041), np.float32(0.9201), np.float32(0.9437), np.float32(0.8955), np.float32(0.9008), np.float32(0.9126)] 
+2025-05-06 15:57:02.671069: Epoch time: 87.98 s 
+2025-05-06 15:57:04.399690:  
+2025-05-06 15:57:04.555376: Epoch 1411 
+2025-05-06 15:57:04.613127: Current learning rate: 0.00333 
+2025-05-06 15:58:39.740388: train_loss -0.5033 
+2025-05-06 15:58:39.849093: val_loss -0.4676 
+2025-05-06 15:58:39.868667: Pseudo dice [np.float32(0.8411), np.float32(0.851), np.float32(0.9459), np.float32(0.9737), np.float32(0.9141), np.float32(0.9444), np.float32(0.9577), np.float32(0.9763), np.float32(0.9686), np.float32(0.9496), np.float32(0.9483), np.float32(0.9661), np.float32(0.9615), np.float32(0.9054), np.float32(0.9484), np.float32(0.9568), np.float32(0.8759), np.float32(0.8882), np.float32(0.9004)] 
+2025-05-06 15:58:39.873197: Epoch time: 95.34 s 
+2025-05-06 15:58:41.568355:  
+2025-05-06 15:58:41.649001: Epoch 1412 
+2025-05-06 15:58:41.663934: Current learning rate: 0.00332 
+2025-05-06 16:00:13.299151: train_loss -0.4761 
+2025-05-06 16:00:13.366709: val_loss -0.4765 
+2025-05-06 16:00:13.382056: Pseudo dice [np.float32(0.8554), np.float32(0.8613), np.float32(0.8869), np.float32(0.9738), np.float32(0.903), np.float32(0.9597), np.float32(0.9464), np.float32(0.9826), np.float32(0.972), np.float32(0.971), np.float32(0.9542), np.float32(0.9597), np.float32(0.9756), np.float32(0.8993), np.float32(0.9707), np.float32(0.9596), np.float32(0.8719), np.float32(0.8812), np.float32(0.9187)] 
+2025-05-06 16:00:13.387751: Epoch time: 91.73 s 
+2025-05-06 16:00:15.078360:  
+2025-05-06 16:00:15.156956: Epoch 1413 
+2025-05-06 16:00:15.172628: Current learning rate: 0.00332 
+2025-05-06 16:01:45.357665: train_loss -0.4767 
+2025-05-06 16:01:45.420848: val_loss -0.5231 
+2025-05-06 16:01:45.421489: Pseudo dice [np.float32(0.8263), np.float32(0.8677), np.float32(0.9034), np.float32(0.9808), np.float32(0.9096), np.float32(0.9499), np.float32(0.9602), np.float32(0.9739), np.float32(0.9574), np.float32(0.9654), np.float32(0.9443), np.float32(0.9643), np.float32(0.9733), np.float32(0.9034), np.float32(0.952), np.float32(0.954), np.float32(0.8523), np.float32(0.8175), np.float32(0.9123)] 
+2025-05-06 16:01:45.421994: Epoch time: 90.28 s 
+2025-05-06 16:01:46.950501:  
+2025-05-06 16:01:47.049226: Epoch 1414 
+2025-05-06 16:01:47.067647: Current learning rate: 0.00331 
+2025-05-06 16:03:21.813133: train_loss -0.4805 
+2025-05-06 16:03:21.894937: val_loss -0.4922 
+2025-05-06 16:03:21.931875: Pseudo dice [np.float32(0.8548), np.float32(0.8169), np.float32(0.9338), np.float32(0.9694), np.float32(0.912), np.float32(0.9627), np.float32(0.9609), np.float32(0.9791), np.float32(0.9659), np.float32(0.9697), np.float32(0.9435), np.float32(0.972), np.float32(0.9711), np.float32(0.8984), np.float32(0.9664), np.float32(0.9635), np.float32(0.8986), np.float32(0.9153), np.float32(0.9224)] 
+2025-05-06 16:03:21.985850: Epoch time: 94.86 s 
+2025-05-06 16:03:23.622013:  
+2025-05-06 16:03:23.741407: Epoch 1415 
+2025-05-06 16:03:23.755607: Current learning rate: 0.00331 
+2025-05-06 16:04:56.034794: train_loss -0.4812 
+2025-05-06 16:04:56.140695: val_loss -0.4877 
+2025-05-06 16:04:56.159568: Pseudo dice [np.float32(0.87), np.float32(0.8278), np.float32(0.8995), np.float32(0.9722), np.float32(0.9224), np.float32(0.9631), np.float32(0.9699), np.float32(0.9706), np.float32(0.963), np.float32(0.971), np.float32(0.9455), np.float32(0.9694), np.float32(0.961), np.float32(0.9012), np.float32(0.9708), np.float32(0.9439), np.float32(0.8903), np.float32(0.8963), np.float32(0.913)] 
+2025-05-06 16:04:56.198727: Epoch time: 92.41 s 
+2025-05-06 16:04:57.886551:  
+2025-05-06 16:04:58.008614: Epoch 1416 
+2025-05-06 16:04:58.019840: Current learning rate: 0.0033 
+2025-05-06 16:06:30.006498: train_loss -0.5037 
+2025-05-06 16:06:30.102189: val_loss -0.5409 
+2025-05-06 16:06:30.126728: Pseudo dice [np.float32(0.829), np.float32(0.8612), np.float32(0.9041), np.float32(0.9794), np.float32(0.8988), np.float32(0.9642), np.float32(0.9523), np.float32(0.9799), np.float32(0.9582), np.float32(0.9711), np.float32(0.9465), np.float32(0.9688), np.float32(0.9721), np.float32(0.8992), np.float32(0.9658), np.float32(0.9664), np.float32(0.8643), np.float32(0.85), np.float32(0.9219)] 
+2025-05-06 16:06:30.155808: Epoch time: 92.12 s 
+2025-05-06 16:06:31.785415:  
+2025-05-06 16:06:31.887122: Epoch 1417 
+2025-05-06 16:06:31.923854: Current learning rate: 0.0033 
+2025-05-06 16:08:10.187008: train_loss -0.4953 
+2025-05-06 16:08:10.213559: val_loss -0.5202 
+2025-05-06 16:08:10.217607: Pseudo dice [np.float32(0.8586), np.float32(0.8562), np.float32(0.8543), np.float32(0.9754), np.float32(0.8966), np.float32(0.9616), np.float32(0.9655), np.float32(0.9796), np.float32(0.9676), np.float32(0.9708), np.float32(0.952), np.float32(0.9691), np.float32(0.9729), np.float32(0.9012), np.float32(0.9536), np.float32(0.9524), np.float32(0.908), np.float32(0.9167), np.float32(0.928)] 
+2025-05-06 16:08:10.218039: Epoch time: 98.4 s 
+2025-05-06 16:08:15.489442:  
+2025-05-06 16:08:15.494931: Epoch 1418 
+2025-05-06 16:08:15.495324: Current learning rate: 0.00329 
+2025-05-06 16:09:49.467755: train_loss -0.4866 
+2025-05-06 16:09:49.559907: val_loss -0.5278 
+2025-05-06 16:09:49.560430: Pseudo dice [np.float32(0.8568), np.float32(0.8448), np.float32(0.933), np.float32(0.9745), np.float32(0.8998), np.float32(0.9629), np.float32(0.9604), np.float32(0.9785), np.float32(0.966), np.float32(0.9705), np.float32(0.9587), np.float32(0.968), np.float32(0.9714), np.float32(0.9116), np.float32(0.9623), np.float32(0.953), np.float32(0.9011), np.float32(0.9032), np.float32(0.9249)] 
+2025-05-06 16:09:49.560847: Epoch time: 93.98 s 
+2025-05-06 16:09:51.100935:  
+2025-05-06 16:09:51.224458: Epoch 1419 
+2025-05-06 16:09:51.278580: Current learning rate: 0.00329 
+2025-05-06 16:11:24.671231: train_loss -0.5119 
+2025-05-06 16:11:24.773318: val_loss -0.5078 
+2025-05-06 16:11:24.781371: Pseudo dice [np.float32(0.8593), np.float32(0.8559), np.float32(0.9345), np.float32(0.9699), np.float32(0.9049), np.float32(0.9626), np.float32(0.9712), np.float32(0.9792), np.float32(0.9671), np.float32(0.9653), np.float32(0.9532), np.float32(0.9716), np.float32(0.9682), np.float32(0.9132), np.float32(0.9679), np.float32(0.9541), np.float32(0.906), np.float32(0.9097), np.float32(0.9236)] 
+2025-05-06 16:11:24.789028: Epoch time: 93.57 s 
+2025-05-06 16:11:24.805296: Yayy! New best EMA pseudo Dice: 0.9315999746322632 
+2025-05-06 16:11:27.751242:  
+2025-05-06 16:11:27.756912: Epoch 1420 
+2025-05-06 16:11:27.757545: Current learning rate: 0.00328 
+2025-05-06 16:13:03.927690: train_loss -0.4826 
+2025-05-06 16:13:04.058277: val_loss -0.5125 
+2025-05-06 16:13:04.072083: Pseudo dice [np.float32(0.8467), np.float32(0.8557), np.float32(0.9381), np.float32(0.9753), np.float32(0.9036), np.float32(0.9515), np.float32(0.9701), np.float32(0.9829), np.float32(0.9462), np.float32(0.9712), np.float32(0.938), np.float32(0.9633), np.float32(0.968), np.float32(0.9005), np.float32(0.9613), np.float32(0.9541), np.float32(0.8589), np.float32(0.8299), np.float32(0.9218)] 
+2025-05-06 16:13:04.083205: Epoch time: 96.18 s 
+2025-05-06 16:13:05.635387:  
+2025-05-06 16:13:05.675521: Epoch 1421 
+2025-05-06 16:13:05.697996: Current learning rate: 0.00328 
+2025-05-06 16:14:41.170995: train_loss -0.5019 
+2025-05-06 16:14:41.266974: val_loss -0.4947 
+2025-05-06 16:14:41.278327: Pseudo dice [np.float32(0.8368), np.float32(0.8498), np.float32(0.9105), np.float32(0.9797), np.float32(0.9331), np.float32(0.9528), np.float32(0.9574), np.float32(0.9801), np.float32(0.9556), np.float32(0.9585), np.float32(0.931), np.float32(0.9713), np.float32(0.9627), np.float32(0.9084), np.float32(0.9682), np.float32(0.961), np.float32(0.8809), np.float32(0.8991), np.float32(0.9176)] 
+2025-05-06 16:14:41.296217: Epoch time: 95.54 s 
+2025-05-06 16:14:42.805357:  
+2025-05-06 16:14:42.912398: Epoch 1422 
+2025-05-06 16:14:42.943635: Current learning rate: 0.00327 
+2025-05-06 16:16:19.230458: train_loss -0.4868 
+2025-05-06 16:16:19.324030: val_loss -0.5086 
+2025-05-06 16:16:19.357358: Pseudo dice [np.float32(0.8432), np.float32(0.8544), np.float32(0.9464), np.float32(0.9673), np.float32(0.9192), np.float32(0.9599), np.float32(0.9663), np.float32(0.9793), np.float32(0.9628), np.float32(0.9453), np.float32(0.9474), np.float32(0.9462), np.float32(0.9639), np.float32(0.9222), np.float32(0.9694), np.float32(0.9516), np.float32(0.8747), np.float32(0.8745), np.float32(0.9293)] 
+2025-05-06 16:16:19.382541: Epoch time: 96.43 s 
+2025-05-06 16:16:20.972345:  
+2025-05-06 16:16:21.083945: Epoch 1423 
+2025-05-06 16:16:21.113153: Current learning rate: 0.00327 
+2025-05-06 16:17:55.283860: train_loss -0.496 
+2025-05-06 16:17:55.333865: val_loss -0.4836 
+2025-05-06 16:17:55.334724: Pseudo dice [np.float32(0.8619), np.float32(0.8482), np.float32(0.9432), np.float32(0.9776), np.float32(0.9291), np.float32(0.9505), np.float32(0.9648), np.float32(0.98), np.float32(0.9654), np.float32(0.9739), np.float32(0.9511), np.float32(0.9708), np.float32(0.9699), np.float32(0.9112), np.float32(0.966), np.float32(0.9532), np.float32(0.8865), np.float32(0.9067), np.float32(0.922)] 
+2025-05-06 16:17:55.352077: Epoch time: 94.31 s 
+2025-05-06 16:17:55.362114: Yayy! New best EMA pseudo Dice: 0.932200014591217 
+2025-05-06 16:17:57.644034:  
+2025-05-06 16:17:57.646039: Epoch 1424 
+2025-05-06 16:17:57.646432: Current learning rate: 0.00326 
+2025-05-06 16:19:42.478902: train_loss -0.4874 
+2025-05-06 16:19:42.594937: val_loss -0.5299 
+2025-05-06 16:19:42.638891: Pseudo dice [np.float32(0.8461), np.float32(0.8594), np.float32(0.9148), np.float32(0.9742), np.float32(0.9083), np.float32(0.959), np.float32(0.9664), np.float32(0.9771), np.float32(0.9679), np.float32(0.9552), np.float32(0.9451), np.float32(0.9716), np.float32(0.9614), np.float32(0.9032), np.float32(0.9624), np.float32(0.9551), np.float32(0.8735), np.float32(0.8567), np.float32(0.9151)] 
+2025-05-06 16:19:42.675619: Epoch time: 104.84 s 
+2025-05-06 16:19:44.206070:  
+2025-05-06 16:19:44.326968: Epoch 1425 
+2025-05-06 16:19:44.366204: Current learning rate: 0.00326 
+2025-05-06 16:21:19.124758: train_loss -0.4885 
+2025-05-06 16:21:19.174674: val_loss -0.5407 
+2025-05-06 16:21:19.187044: Pseudo dice [np.float32(0.8465), np.float32(0.8198), np.float32(0.9104), np.float32(0.9701), np.float32(0.9246), np.float32(0.9614), np.float32(0.9681), np.float32(0.9736), np.float32(0.9602), np.float32(0.9657), np.float32(0.942), np.float32(0.968), np.float32(0.9714), np.float32(0.9046), np.float32(0.9612), np.float32(0.9615), np.float32(0.8616), np.float32(0.8815), np.float32(0.917)] 
+2025-05-06 16:21:19.187879: Epoch time: 94.92 s 
+2025-05-06 16:21:20.891343:  
+2025-05-06 16:21:21.005558: Epoch 1426 
+2025-05-06 16:21:21.032615: Current learning rate: 0.00325 
+2025-05-06 16:22:56.194716: train_loss -0.4974 
+2025-05-06 16:22:56.325563: val_loss -0.5086 
+2025-05-06 16:22:56.349466: Pseudo dice [np.float32(0.8323), np.float32(0.8328), np.float32(0.9245), np.float32(0.9762), np.float32(0.8922), np.float32(0.9629), np.float32(0.9575), np.float32(0.9787), np.float32(0.9681), np.float32(0.9606), np.float32(0.941), np.float32(0.968), np.float32(0.9643), np.float32(0.8924), np.float32(0.9694), np.float32(0.9518), np.float32(0.8571), np.float32(0.8306), np.float32(0.9133)] 
+2025-05-06 16:22:56.380814: Epoch time: 95.3 s 
+2025-05-06 16:22:58.052614:  
+2025-05-06 16:22:58.103702: Epoch 1427 
+2025-05-06 16:22:58.104802: Current learning rate: 0.00325 
+2025-05-06 16:24:34.345981: train_loss -0.5003 
+2025-05-06 16:24:34.442486: val_loss -0.5306 
+2025-05-06 16:24:34.494582: Pseudo dice [np.float32(0.8593), np.float32(0.8553), np.float32(0.9549), np.float32(0.9736), np.float32(0.9258), np.float32(0.9687), np.float32(0.9693), np.float32(0.9814), np.float32(0.9727), np.float32(0.9669), np.float32(0.9538), np.float32(0.9715), np.float32(0.9646), np.float32(0.9223), np.float32(0.9658), np.float32(0.9616), np.float32(0.9088), np.float32(0.8719), np.float32(0.9198)] 
+2025-05-06 16:24:34.537164: Epoch time: 96.29 s 
+2025-05-06 16:24:36.272172:  
+2025-05-06 16:24:36.351359: Epoch 1428 
+2025-05-06 16:24:36.394611: Current learning rate: 0.00324 
+2025-05-06 16:26:14.456438: train_loss -0.4865 
+2025-05-06 16:26:14.485105: val_loss -0.5176 
+2025-05-06 16:26:14.485624: Pseudo dice [np.float32(0.8426), np.float32(0.8649), np.float32(0.9329), np.float32(0.9738), np.float32(0.9333), np.float32(0.9618), np.float32(0.9659), np.float32(0.9794), np.float32(0.9662), np.float32(0.9599), np.float32(0.938), np.float32(0.9685), np.float32(0.9647), np.float32(0.9132), np.float32(0.9565), np.float32(0.9598), np.float32(0.8962), np.float32(0.9012), np.float32(0.9128)] 
+2025-05-06 16:26:14.489705: Epoch time: 98.19 s 
+2025-05-06 16:26:14.490422: Yayy! New best EMA pseudo Dice: 0.9325000047683716 
+2025-05-06 16:26:16.920603:  
+2025-05-06 16:26:17.000653: Epoch 1429 
+2025-05-06 16:26:17.024574: Current learning rate: 0.00324 
+2025-05-06 16:27:55.860687: train_loss -0.5095 
+2025-05-06 16:27:55.973620: val_loss -0.4897 
+2025-05-06 16:27:56.006768: Pseudo dice [np.float32(0.8431), np.float32(0.8603), np.float32(0.9294), np.float32(0.967), np.float32(0.9156), np.float32(0.9627), np.float32(0.9679), np.float32(0.9774), np.float32(0.9626), np.float32(0.9683), np.float32(0.9413), np.float32(0.967), np.float32(0.9649), np.float32(0.8945), np.float32(0.9626), np.float32(0.9487), np.float32(0.9135), np.float32(0.9002), np.float32(0.909)] 
+2025-05-06 16:27:56.038994: Epoch time: 98.94 s 
+2025-05-06 16:27:56.092858: Yayy! New best EMA pseudo Dice: 0.932699978351593 
+2025-05-06 16:27:58.654209:  
+2025-05-06 16:27:58.738452: Epoch 1430 
+2025-05-06 16:27:58.840495: Current learning rate: 0.00323 
+2025-05-06 16:29:36.371654: train_loss -0.4865 
+2025-05-06 16:29:36.499496: val_loss -0.4907 
+2025-05-06 16:29:36.525622: Pseudo dice [np.float32(0.8439), np.float32(0.8584), np.float32(0.9353), np.float32(0.97), np.float32(0.9296), np.float32(0.9618), np.float32(0.9649), np.float32(0.9815), np.float32(0.9651), np.float32(0.9646), np.float32(0.9525), np.float32(0.9745), np.float32(0.9621), np.float32(0.8877), np.float32(0.9723), np.float32(0.9584), np.float32(0.8664), np.float32(0.8908), np.float32(0.9184)] 
+2025-05-06 16:29:36.561996: Epoch time: 97.72 s 
+2025-05-06 16:29:36.595226: Yayy! New best EMA pseudo Dice: 0.9329000115394592 
+2025-05-06 16:29:39.040994:  
+2025-05-06 16:29:39.136024: Epoch 1431 
+2025-05-06 16:29:39.198893: Current learning rate: 0.00323 
+2025-05-06 16:31:16.116804: train_loss -0.4972 
+2025-05-06 16:31:16.239071: val_loss -0.4962 
+2025-05-06 16:31:16.292899: Pseudo dice [np.float32(0.8488), np.float32(0.85), np.float32(0.9221), np.float32(0.9751), np.float32(0.9305), np.float32(0.9592), np.float32(0.9611), np.float32(0.9728), np.float32(0.9621), np.float32(0.9646), np.float32(0.9553), np.float32(0.9605), np.float32(0.9685), np.float32(0.8984), np.float32(0.9483), np.float32(0.9394), np.float32(0.8888), np.float32(0.9054), np.float32(0.9129)] 
+2025-05-06 16:31:16.342219: Epoch time: 97.08 s 
+2025-05-06 16:31:17.929961:  
+2025-05-06 16:31:17.958309: Epoch 1432 
+2025-05-06 16:31:17.959280: Current learning rate: 0.00322 
+2025-05-06 16:32:50.523637: train_loss -0.4846 
+2025-05-06 16:32:50.612240: val_loss -0.4734 
+2025-05-06 16:32:50.636197: Pseudo dice [np.float32(0.8553), np.float32(0.8576), np.float32(0.9212), np.float32(0.9753), np.float32(0.7307), np.float32(0.9341), np.float32(0.9654), np.float32(0.9778), np.float32(0.9606), np.float32(0.964), np.float32(0.9536), np.float32(0.9649), np.float32(0.9671), np.float32(0.9042), np.float32(0.9687), np.float32(0.9468), np.float32(0.8621), np.float32(0.8823), np.float32(0.9197)] 
+2025-05-06 16:32:50.647331: Epoch time: 92.59 s 
+2025-05-06 16:32:52.245806:  
+2025-05-06 16:32:52.412294: Epoch 1433 
+2025-05-06 16:32:52.485387: Current learning rate: 0.00322 
+2025-05-06 16:34:25.496912: train_loss -0.486 
+2025-05-06 16:34:25.523817: val_loss -0.5175 
+2025-05-06 16:34:25.542607: Pseudo dice [np.float32(0.8406), np.float32(0.8426), np.float32(0.927), np.float32(0.9733), np.float32(0.9299), np.float32(0.9606), np.float32(0.9673), np.float32(0.978), np.float32(0.9638), np.float32(0.9672), np.float32(0.9464), np.float32(0.9699), np.float32(0.9671), np.float32(0.9134), np.float32(0.9619), np.float32(0.9423), np.float32(0.884), np.float32(0.8928), np.float32(0.9096)] 
+2025-05-06 16:34:25.559866: Epoch time: 93.25 s 
+2025-05-06 16:34:31.008411:  
+2025-05-06 16:34:31.014372: Epoch 1434 
+2025-05-06 16:34:31.014847: Current learning rate: 0.00321 
+2025-05-06 16:36:08.709275: train_loss -0.4805 
+2025-05-06 16:36:08.812392: val_loss -0.4558 
+2025-05-06 16:36:08.842362: Pseudo dice [np.float32(0.8503), np.float32(0.86), np.float32(0.9066), np.float32(0.9792), np.float32(0.9228), np.float32(0.9647), np.float32(0.9664), np.float32(0.9764), np.float32(0.9607), np.float32(0.9623), np.float32(0.9599), np.float32(0.9648), np.float32(0.9761), np.float32(0.9006), np.float32(0.9709), np.float32(0.965), np.float32(0.8841), np.float32(0.8476), np.float32(0.9171)] 
+2025-05-06 16:36:08.875675: Epoch time: 97.7 s 
+2025-05-06 16:36:10.472426:  
+2025-05-06 16:36:10.592635: Epoch 1435 
+2025-05-06 16:36:10.642022: Current learning rate: 0.00321 
+2025-05-06 16:37:50.007526: train_loss -0.4987 
+2025-05-06 16:37:50.194895: val_loss -0.4782 
+2025-05-06 16:37:50.236480: Pseudo dice [np.float32(0.8484), np.float32(0.8134), np.float32(0.9218), np.float32(0.9592), np.float32(0.884), np.float32(0.9603), np.float32(0.9518), np.float32(0.9781), np.float32(0.9597), np.float32(0.9657), np.float32(0.9491), np.float32(0.9686), np.float32(0.9638), np.float32(0.9053), np.float32(0.9567), np.float32(0.9565), np.float32(0.9035), np.float32(0.8748), np.float32(0.9145)] 
+2025-05-06 16:37:50.256647: Epoch time: 99.54 s 
+2025-05-06 16:37:51.809682:  
+2025-05-06 16:37:51.906135: Epoch 1436 
+2025-05-06 16:37:51.931737: Current learning rate: 0.0032 
+2025-05-06 16:39:26.060324: train_loss -0.5179 
+2025-05-06 16:39:26.186835: val_loss -0.4936 
+2025-05-06 16:39:26.216592: Pseudo dice [np.float32(0.8339), np.float32(0.8553), np.float32(0.8788), np.float32(0.9805), np.float32(0.9082), np.float32(0.9489), np.float32(0.9544), np.float32(0.9807), np.float32(0.9674), np.float32(0.9619), np.float32(0.9535), np.float32(0.9743), np.float32(0.9714), np.float32(0.9148), np.float32(0.9638), np.float32(0.9621), np.float32(0.8872), np.float32(0.8691), np.float32(0.9092)] 
+2025-05-06 16:39:26.271447: Epoch time: 94.25 s 
+2025-05-06 16:39:27.805994:  
+2025-05-06 16:39:27.899616: Epoch 1437 
+2025-05-06 16:39:27.936797: Current learning rate: 0.0032 
+2025-05-06 16:41:04.452824: train_loss -0.4957 
+2025-05-06 16:41:04.535451: val_loss -0.49 
+2025-05-06 16:41:04.568869: Pseudo dice [np.float32(0.8348), np.float32(0.8704), np.float32(0.9124), np.float32(0.9735), np.float32(0.898), np.float32(0.9521), np.float32(0.9637), np.float32(0.9784), np.float32(0.9646), np.float32(0.9634), np.float32(0.9475), np.float32(0.9669), np.float32(0.9683), np.float32(0.9129), np.float32(0.946), np.float32(0.9561), np.float32(0.8805), np.float32(0.8745), np.float32(0.9042)] 
+2025-05-06 16:41:04.606089: Epoch time: 96.65 s 
+2025-05-06 16:41:06.172006:  
+2025-05-06 16:41:06.297786: Epoch 1438 
+2025-05-06 16:41:06.310806: Current learning rate: 0.00319 
+2025-05-06 16:42:43.179661: train_loss -0.4911 
+2025-05-06 16:42:43.274870: val_loss -0.5087 
+2025-05-06 16:42:43.287533: Pseudo dice [np.float32(0.8438), np.float32(0.8388), np.float32(0.9317), np.float32(0.9736), np.float32(0.9208), np.float32(0.9641), np.float32(0.9677), np.float32(0.9728), np.float32(0.9535), np.float32(0.9678), np.float32(0.9567), np.float32(0.9584), np.float32(0.9728), np.float32(0.8977), np.float32(0.9565), np.float32(0.9578), np.float32(0.831), np.float32(0.8366), np.float32(0.9191)] 
+2025-05-06 16:42:43.299245: Epoch time: 97.01 s 
+2025-05-06 16:42:44.796194:  
+2025-05-06 16:42:44.915226: Epoch 1439 
+2025-05-06 16:42:44.959630: Current learning rate: 0.00319 
+2025-05-06 16:44:19.285088: train_loss -0.5012 
+2025-05-06 16:44:19.395666: val_loss -0.5301 
+2025-05-06 16:44:19.419122: Pseudo dice [np.float32(0.8396), np.float32(0.8423), np.float32(0.8944), np.float32(0.9656), np.float32(0.9019), np.float32(0.926), np.float32(0.9348), np.float32(0.9819), np.float32(0.9684), np.float32(0.9665), np.float32(0.9537), np.float32(0.9743), np.float32(0.9704), np.float32(0.9052), np.float32(0.9536), np.float32(0.9546), np.float32(0.9053), np.float32(0.924), np.float32(0.9257)] 
+2025-05-06 16:44:19.442660: Epoch time: 94.49 s 
+2025-05-06 16:44:20.926929:  
+2025-05-06 16:44:21.080179: Epoch 1440 
+2025-05-06 16:44:21.117501: Current learning rate: 0.00318 
+2025-05-06 16:45:57.915690: train_loss -0.5062 
+2025-05-06 16:45:58.057895: val_loss -0.5216 
+2025-05-06 16:45:58.060658: Pseudo dice [np.float32(0.8611), np.float32(0.8614), np.float32(0.9049), np.float32(0.9738), np.float32(0.9248), np.float32(0.9588), np.float32(0.9649), np.float32(0.9804), np.float32(0.9619), np.float32(0.9669), np.float32(0.9449), np.float32(0.9634), np.float32(0.9701), np.float32(0.9161), np.float32(0.969), np.float32(0.9548), np.float32(0.8684), np.float32(0.8528), np.float32(0.91)] 
+2025-05-06 16:45:58.068438: Epoch time: 96.99 s 
+2025-05-06 16:45:59.538995:  
+2025-05-06 16:45:59.651478: Epoch 1441 
+2025-05-06 16:45:59.712978: Current learning rate: 0.00317 
+2025-05-06 16:47:34.422451: train_loss -0.5171 
+2025-05-06 16:47:34.544687: val_loss -0.5184 
+2025-05-06 16:47:34.658090: Pseudo dice [np.float32(0.8648), np.float32(0.8568), np.float32(0.9288), np.float32(0.9668), np.float32(0.9117), np.float32(0.9639), np.float32(0.9677), np.float32(0.9759), np.float32(0.9526), np.float32(0.951), np.float32(0.949), np.float32(0.9664), np.float32(0.963), np.float32(0.9143), np.float32(0.9637), np.float32(0.9621), np.float32(0.8723), np.float32(0.8538), np.float32(0.9189)] 
+2025-05-06 16:47:34.692037: Epoch time: 94.88 s 
+2025-05-06 16:47:36.266461:  
+2025-05-06 16:47:36.344648: Epoch 1442 
+2025-05-06 16:47:36.375071: Current learning rate: 0.00317 
+2025-05-06 16:49:13.721136: train_loss -0.5059 
+2025-05-06 16:49:13.841503: val_loss -0.4959 
+2025-05-06 16:49:13.885154: Pseudo dice [np.float32(0.8686), np.float32(0.8735), np.float32(0.8928), np.float32(0.9767), np.float32(0.9134), np.float32(0.965), np.float32(0.967), np.float32(0.98), np.float32(0.966), np.float32(0.9704), np.float32(0.9449), np.float32(0.9712), np.float32(0.9648), np.float32(0.9169), np.float32(0.969), np.float32(0.9574), np.float32(0.8446), np.float32(0.8728), np.float32(0.9324)] 
+2025-05-06 16:49:13.903737: Epoch time: 97.46 s 
+2025-05-06 16:49:15.474962:  
+2025-05-06 16:49:15.592083: Epoch 1443 
+2025-05-06 16:49:15.640760: Current learning rate: 0.00316 
+2025-05-06 16:50:51.742593: train_loss -0.4958 
+2025-05-06 16:50:51.811756: val_loss -0.5089 
+2025-05-06 16:50:51.853708: Pseudo dice [np.float32(0.8625), np.float32(0.8812), np.float32(0.9369), np.float32(0.9799), np.float32(0.886), np.float32(0.9643), np.float32(0.9696), np.float32(0.9768), np.float32(0.9686), np.float32(0.9573), np.float32(0.9454), np.float32(0.9606), np.float32(0.9595), np.float32(0.9151), np.float32(0.9689), np.float32(0.9407), np.float32(0.8354), np.float32(0.8845), np.float32(0.9208)] 
+2025-05-06 16:50:51.880507: Epoch time: 96.27 s 
+2025-05-06 16:50:53.667062:  
+2025-05-06 16:50:53.710043: Epoch 1444 
+2025-05-06 16:50:53.749573: Current learning rate: 0.00316 
+2025-05-06 16:52:25.722813: train_loss -0.4845 
+2025-05-06 16:52:25.834833: val_loss -0.4701 
+2025-05-06 16:52:25.874124: Pseudo dice [np.float32(0.8453), np.float32(0.8575), np.float32(0.9083), np.float32(0.9782), np.float32(0.9092), np.float32(0.9634), np.float32(0.964), np.float32(0.9725), np.float32(0.9657), np.float32(0.945), np.float32(0.9428), np.float32(0.9704), np.float32(0.9718), np.float32(0.9074), np.float32(0.9701), np.float32(0.9506), np.float32(0.913), np.float32(0.9197), np.float32(0.9157)] 
+2025-05-06 16:52:25.924427: Epoch time: 92.06 s 
+2025-05-06 16:52:27.614289:  
+2025-05-06 16:52:27.673951: Epoch 1445 
+2025-05-06 16:52:27.703475: Current learning rate: 0.00315 
+2025-05-06 16:54:04.704230: train_loss -0.4978 
+2025-05-06 16:54:04.854009: val_loss -0.477 
+2025-05-06 16:54:04.886008: Pseudo dice [np.float32(0.8557), np.float32(0.8771), np.float32(0.9397), np.float32(0.9638), np.float32(0.9123), np.float32(0.9636), np.float32(0.9637), np.float32(0.9762), np.float32(0.9411), np.float32(0.9614), np.float32(0.9103), np.float32(0.9665), np.float32(0.9633), np.float32(0.9189), np.float32(0.9652), np.float32(0.9633), np.float32(0.9019), np.float32(0.8824), np.float32(0.9236)] 
+2025-05-06 16:54:04.911155: Epoch time: 97.09 s 
+2025-05-06 16:54:06.566622:  
+2025-05-06 16:54:06.665177: Epoch 1446 
+2025-05-06 16:54:06.710578: Current learning rate: 0.00315 
+2025-05-06 16:55:43.031625: train_loss -0.4864 
+2025-05-06 16:55:43.169276: val_loss -0.4694 
+2025-05-06 16:55:43.191654: Pseudo dice [np.float32(0.795), np.float32(0.8075), np.float32(0.9417), np.float32(0.9619), np.float32(0.8728), np.float32(0.9505), np.float32(0.9593), np.float32(0.9768), np.float32(0.9653), np.float32(0.9618), np.float32(0.9363), np.float32(0.9713), np.float32(0.9645), np.float32(0.8981), np.float32(0.9616), np.float32(0.9587), np.float32(0.9061), np.float32(0.8983), np.float32(0.9054)] 
+2025-05-06 16:55:43.204651: Epoch time: 96.47 s 
+2025-05-06 16:55:44.749227:  
+2025-05-06 16:55:44.752295: Epoch 1447 
+2025-05-06 16:55:44.752689: Current learning rate: 0.00314 
+2025-05-06 16:57:23.633783: train_loss -0.499 
+2025-05-06 16:57:23.663496: val_loss -0.5086 
+2025-05-06 16:57:23.667881: Pseudo dice [np.float32(0.8639), np.float32(0.8302), np.float32(0.9267), np.float32(0.9734), np.float32(0.9096), np.float32(0.9652), np.float32(0.9582), np.float32(0.9816), np.float32(0.9685), np.float32(0.9683), np.float32(0.9536), np.float32(0.9704), np.float32(0.9707), np.float32(0.913), np.float32(0.9685), np.float32(0.9652), np.float32(0.8753), np.float32(0.8939), np.float32(0.9116)] 
+2025-05-06 16:57:23.668301: Epoch time: 98.89 s 
+2025-05-06 16:57:25.231234:  
+2025-05-06 16:57:25.324171: Epoch 1448 
+2025-05-06 16:57:25.350381: Current learning rate: 0.00314 
+2025-05-06 16:58:57.427690: train_loss -0.5049 
+2025-05-06 16:58:57.545913: val_loss -0.4854 
+2025-05-06 16:58:57.572467: Pseudo dice [np.float32(0.8488), np.float32(0.8618), np.float32(0.9136), np.float32(0.9527), np.float32(0.8897), np.float32(0.9561), np.float32(0.9637), np.float32(0.9771), np.float32(0.9588), np.float32(0.9675), np.float32(0.9558), np.float32(0.968), np.float32(0.9685), np.float32(0.8998), np.float32(0.9655), np.float32(0.9572), np.float32(0.8457), np.float32(0.8774), np.float32(0.9001)] 
+2025-05-06 16:58:57.616640: Epoch time: 92.2 s 
+2025-05-06 16:58:59.303404:  
+2025-05-06 16:58:59.381438: Epoch 1449 
+2025-05-06 16:58:59.397974: Current learning rate: 0.00313 
+2025-05-06 17:00:33.195261: train_loss -0.4902 
+2025-05-06 17:00:33.226963: val_loss -0.5242 
+2025-05-06 17:00:33.233378: Pseudo dice [np.float32(0.8547), np.float32(0.8575), np.float32(0.9335), np.float32(0.9782), np.float32(0.9118), np.float32(0.9641), np.float32(0.9683), np.float32(0.9763), np.float32(0.9554), np.float32(0.9666), np.float32(0.9606), np.float32(0.9646), np.float32(0.9723), np.float32(0.8972), np.float32(0.9505), np.float32(0.952), np.float32(0.8636), np.float32(0.8801), np.float32(0.9304)] 
+2025-05-06 17:00:33.234065: Epoch time: 93.89 s 
+2025-05-06 17:00:36.160432:  
+2025-05-06 17:00:36.176899: Epoch 1450 
+2025-05-06 17:00:36.177801: Current learning rate: 0.00313 
+2025-05-06 17:02:17.995822: train_loss -0.4692 
+2025-05-06 17:02:18.026930: val_loss -0.4736 
+2025-05-06 17:02:18.032270: Pseudo dice [np.float32(0.8575), np.float32(0.8542), np.float32(0.8958), np.float32(0.971), np.float32(0.8821), np.float32(0.9588), np.float32(0.9676), np.float32(0.9807), np.float32(0.9628), np.float32(0.9654), np.float32(0.9393), np.float32(0.9706), np.float32(0.97), np.float32(0.9049), np.float32(0.9523), np.float32(0.9535), np.float32(0.8924), np.float32(0.8727), np.float32(0.9157)] 
+2025-05-06 17:02:18.052810: Epoch time: 101.84 s 
+2025-05-06 17:02:19.657887:  
+2025-05-06 17:02:19.704996: Epoch 1451 
+2025-05-06 17:02:19.723362: Current learning rate: 0.00312 
+2025-05-06 17:03:58.749644: train_loss -0.5148 
+2025-05-06 17:03:58.847458: val_loss -0.5493 
+2025-05-06 17:03:58.941633: Pseudo dice [np.float32(0.8604), np.float32(0.8468), np.float32(0.9137), np.float32(0.972), np.float32(0.9212), np.float32(0.9616), np.float32(0.9692), np.float32(0.9774), np.float32(0.9564), np.float32(0.9759), np.float32(0.9566), np.float32(0.9703), np.float32(0.9728), np.float32(0.9128), np.float32(0.9683), np.float32(0.9494), np.float32(0.904), np.float32(0.8839), np.float32(0.9337)] 
+2025-05-06 17:03:58.954468: Epoch time: 99.09 s 
+2025-05-06 17:04:04.224925:  
+2025-05-06 17:04:04.231232: Epoch 1452 
+2025-05-06 17:04:04.231727: Current learning rate: 0.00312 
+2025-05-06 17:05:35.735204: train_loss -0.487 
+2025-05-06 17:05:35.847078: val_loss -0.5401 
+2025-05-06 17:05:35.881831: Pseudo dice [np.float32(0.8531), np.float32(0.8628), np.float32(0.9461), np.float32(0.9795), np.float32(0.914), np.float32(0.9518), np.float32(0.9647), np.float32(0.9794), np.float32(0.9642), np.float32(0.9667), np.float32(0.9518), np.float32(0.9659), np.float32(0.9738), np.float32(0.9137), np.float32(0.9672), np.float32(0.9574), np.float32(0.905), np.float32(0.8758), np.float32(0.9324)] 
+2025-05-06 17:05:35.887604: Epoch time: 91.51 s 
+2025-05-06 17:05:37.453201:  
+2025-05-06 17:05:37.573451: Epoch 1453 
+2025-05-06 17:05:37.595592: Current learning rate: 0.00311 
+2025-05-06 17:07:20.177752: train_loss -0.5076 
+2025-05-06 17:07:20.291163: val_loss -0.5289 
+2025-05-06 17:07:20.333996: Pseudo dice [np.float32(0.861), np.float32(0.8809), np.float32(0.8237), np.float32(0.9761), np.float32(0.9177), np.float32(0.966), np.float32(0.97), np.float32(0.9823), np.float32(0.9688), np.float32(0.9633), np.float32(0.9467), np.float32(0.9722), np.float32(0.9657), np.float32(0.9056), np.float32(0.9549), np.float32(0.9586), np.float32(0.8771), np.float32(0.8812), np.float32(0.915)] 
+2025-05-06 17:07:20.377936: Epoch time: 102.73 s 
+2025-05-06 17:07:21.964998:  
+2025-05-06 17:07:22.028709: Epoch 1454 
+2025-05-06 17:07:22.082704: Current learning rate: 0.00311 
+2025-05-06 17:08:59.210741: train_loss -0.4761 
+2025-05-06 17:08:59.276260: val_loss -0.4553 
+2025-05-06 17:08:59.287940: Pseudo dice [np.float32(0.86), np.float32(0.8778), np.float32(0.9317), np.float32(0.98), np.float32(0.9228), np.float32(0.9604), np.float32(0.9666), np.float32(0.9757), np.float32(0.9657), np.float32(0.9602), np.float32(0.9467), np.float32(0.9728), np.float32(0.9681), np.float32(0.9112), np.float32(0.9585), np.float32(0.9631), np.float32(0.8934), np.float32(0.8954), np.float32(0.9309)] 
+2025-05-06 17:08:59.291566: Epoch time: 97.25 s 
+2025-05-06 17:08:59.291990: Yayy! New best EMA pseudo Dice: 0.9332000017166138 
+2025-05-06 17:09:01.724272:  
+2025-05-06 17:09:01.744162: Epoch 1455 
+2025-05-06 17:09:01.744940: Current learning rate: 0.0031 
+2025-05-06 17:10:40.997746: train_loss -0.4901 
+2025-05-06 17:10:41.103860: val_loss -0.5018 
+2025-05-06 17:10:41.138616: Pseudo dice [np.float32(0.8547), np.float32(0.8586), np.float32(0.8154), np.float32(0.9803), np.float32(0.9077), np.float32(0.9601), np.float32(0.9594), np.float32(0.98), np.float32(0.9655), np.float32(0.9676), np.float32(0.9539), np.float32(0.963), np.float32(0.9673), np.float32(0.9088), np.float32(0.9329), np.float32(0.9601), np.float32(0.8869), np.float32(0.8777), np.float32(0.924)] 
+2025-05-06 17:10:41.167501: Epoch time: 99.27 s 
+2025-05-06 17:10:42.767144:  
+2025-05-06 17:10:42.929913: Epoch 1456 
+2025-05-06 17:10:42.930860: Current learning rate: 0.0031 
+2025-05-06 17:12:22.951196: train_loss -0.5037 
+2025-05-06 17:12:23.012994: val_loss -0.5404 
+2025-05-06 17:12:23.027014: Pseudo dice [np.float32(0.858), np.float32(0.8354), np.float32(0.8093), np.float32(0.9736), np.float32(0.8976), np.float32(0.958), np.float32(0.966), np.float32(0.9765), np.float32(0.9608), np.float32(0.9676), np.float32(0.9567), np.float32(0.9643), np.float32(0.973), np.float32(0.9145), np.float32(0.9607), np.float32(0.9549), np.float32(0.8916), np.float32(0.9054), np.float32(0.9199)] 
+2025-05-06 17:12:23.030759: Epoch time: 100.19 s 
+2025-05-06 17:12:24.649891:  
+2025-05-06 17:12:24.692537: Epoch 1457 
+2025-05-06 17:12:24.693182: Current learning rate: 0.00309 
+2025-05-06 17:14:02.685554: train_loss -0.4911 
+2025-05-06 17:14:02.712300: val_loss -0.5034 
+2025-05-06 17:14:02.740191: Pseudo dice [np.float32(0.8569), np.float32(0.8548), np.float32(0.8659), np.float32(0.9765), np.float32(0.8854), np.float32(0.9615), np.float32(0.9582), np.float32(0.9775), np.float32(0.9661), np.float32(0.9541), np.float32(0.9338), np.float32(0.9699), np.float32(0.9655), np.float32(0.8947), np.float32(0.9655), np.float32(0.9572), np.float32(0.8788), np.float32(0.9023), np.float32(0.9084)] 
+2025-05-06 17:14:02.756407: Epoch time: 98.04 s 
+2025-05-06 17:14:04.312129:  
+2025-05-06 17:14:04.393332: Epoch 1458 
+2025-05-06 17:14:04.403143: Current learning rate: 0.00309 
+2025-05-06 17:15:41.140767: train_loss -0.5043 
+2025-05-06 17:15:41.319216: val_loss -0.517 
+2025-05-06 17:15:41.361193: Pseudo dice [np.float32(0.845), np.float32(0.8668), np.float32(0.9079), np.float32(0.9668), np.float32(0.9092), np.float32(0.9548), np.float32(0.9653), np.float32(0.9801), np.float32(0.9528), np.float32(0.9614), np.float32(0.9524), np.float32(0.9494), np.float32(0.9667), np.float32(0.9061), np.float32(0.9658), np.float32(0.9557), np.float32(0.89), np.float32(0.9099), np.float32(0.9106)] 
+2025-05-06 17:15:41.397770: Epoch time: 96.83 s 
+2025-05-06 17:15:42.902085:  
+2025-05-06 17:15:43.018476: Epoch 1459 
+2025-05-06 17:15:43.098263: Current learning rate: 0.00308 
+2025-05-06 17:17:23.533303: train_loss -0.4836 
+2025-05-06 17:17:23.635443: val_loss -0.5302 
+2025-05-06 17:17:23.660631: Pseudo dice [np.float32(0.8202), np.float32(0.8522), np.float32(0.9304), np.float32(0.9733), np.float32(0.8903), np.float32(0.9568), np.float32(0.9629), np.float32(0.9774), np.float32(0.9663), np.float32(0.97), np.float32(0.9477), np.float32(0.9698), np.float32(0.9621), np.float32(0.9183), np.float32(0.9666), np.float32(0.9619), np.float32(0.8904), np.float32(0.8869), np.float32(0.9305)] 
+2025-05-06 17:17:23.696862: Epoch time: 100.63 s 
+2025-05-06 17:17:25.371862:  
+2025-05-06 17:17:25.434059: Epoch 1460 
+2025-05-06 17:17:25.465909: Current learning rate: 0.00308 
+2025-05-06 17:19:02.387806: train_loss -0.5074 
+2025-05-06 17:19:02.544550: val_loss -0.4996 
+2025-05-06 17:19:02.595160: Pseudo dice [np.float32(0.8194), np.float32(0.8837), np.float32(0.8701), np.float32(0.9739), np.float32(0.9237), np.float32(0.9545), np.float32(0.9642), np.float32(0.9822), np.float32(0.9568), np.float32(0.9683), np.float32(0.9403), np.float32(0.9618), np.float32(0.9659), np.float32(0.9036), np.float32(0.9662), np.float32(0.949), np.float32(0.8797), np.float32(0.8932), np.float32(0.9114)] 
+2025-05-06 17:19:02.632224: Epoch time: 97.02 s 
+2025-05-06 17:19:04.417116:  
+2025-05-06 17:19:04.452891: Epoch 1461 
+2025-05-06 17:19:04.476571: Current learning rate: 0.00307 
+2025-05-06 17:20:46.737758: train_loss -0.5121 
+2025-05-06 17:20:46.853595: val_loss -0.4691 
+2025-05-06 17:20:46.896106: Pseudo dice [np.float32(0.8563), np.float32(0.8607), np.float32(0.9138), np.float32(0.977), np.float32(0.9329), np.float32(0.9632), np.float32(0.9643), np.float32(0.9715), np.float32(0.9635), np.float32(0.9647), np.float32(0.9523), np.float32(0.9718), np.float32(0.9692), np.float32(0.92), np.float32(0.9705), np.float32(0.9535), np.float32(0.8672), np.float32(0.8938), np.float32(0.9225)] 
+2025-05-06 17:20:46.931217: Epoch time: 102.32 s 
+2025-05-06 17:20:48.840767:  
+2025-05-06 17:20:48.899467: Epoch 1462 
+2025-05-06 17:20:48.923671: Current learning rate: 0.00307 
+2025-05-06 17:22:27.813608: train_loss -0.4986 
+2025-05-06 17:22:27.935292: val_loss -0.5131 
+2025-05-06 17:22:27.964516: Pseudo dice [np.float32(0.8693), np.float32(0.8423), np.float32(0.9164), np.float32(0.971), np.float32(0.8593), np.float32(0.9645), np.float32(0.9566), np.float32(0.9777), np.float32(0.9699), np.float32(0.959), np.float32(0.9241), np.float32(0.9695), np.float32(0.9489), np.float32(0.8908), np.float32(0.9673), np.float32(0.9581), np.float32(0.881), np.float32(0.9054), np.float32(0.9126)] 
+2025-05-06 17:22:27.999377: Epoch time: 98.97 s 
+2025-05-06 17:22:29.594069:  
+2025-05-06 17:22:29.772097: Epoch 1463 
+2025-05-06 17:22:29.772913: Current learning rate: 0.00306 
+2025-05-06 17:24:09.775583: train_loss -0.4973 
+2025-05-06 17:24:09.853565: val_loss -0.4867 
+2025-05-06 17:24:09.854514: Pseudo dice [np.float32(0.8616), np.float32(0.8517), np.float32(0.9214), np.float32(0.9785), np.float32(0.9073), np.float32(0.9488), np.float32(0.9645), np.float32(0.9766), np.float32(0.974), np.float32(0.9648), np.float32(0.9513), np.float32(0.9747), np.float32(0.9636), np.float32(0.8894), np.float32(0.9622), np.float32(0.9567), np.float32(0.8512), np.float32(0.876), np.float32(0.9009)] 
+2025-05-06 17:24:09.862107: Epoch time: 100.18 s 
+2025-05-06 17:24:11.405654:  
+2025-05-06 17:24:11.450439: Epoch 1464 
+2025-05-06 17:24:11.460591: Current learning rate: 0.00306 
+2025-05-06 17:25:50.664715: train_loss -0.5023 
+2025-05-06 17:25:50.787161: val_loss -0.4976 
+2025-05-06 17:25:50.788087: Pseudo dice [np.float32(0.8615), np.float32(0.8548), np.float32(0.8651), np.float32(0.9749), np.float32(0.9005), np.float32(0.9626), np.float32(0.9644), np.float32(0.9799), np.float32(0.9728), np.float32(0.9652), np.float32(0.9514), np.float32(0.9738), np.float32(0.9718), np.float32(0.9115), np.float32(0.9476), np.float32(0.9568), np.float32(0.8978), np.float32(0.8873), np.float32(0.9229)] 
+2025-05-06 17:25:50.788697: Epoch time: 99.26 s 
+2025-05-06 17:25:52.241812:  
+2025-05-06 17:25:52.295461: Epoch 1465 
+2025-05-06 17:25:52.303437: Current learning rate: 0.00305 
+2025-05-06 17:27:28.450391: train_loss -0.4879 
+2025-05-06 17:27:28.566446: val_loss -0.4749 
+2025-05-06 17:27:28.601149: Pseudo dice [np.float32(0.8429), np.float32(0.8719), np.float32(0.9325), np.float32(0.977), np.float32(0.9199), np.float32(0.9659), np.float32(0.9717), np.float32(0.9785), np.float32(0.9636), np.float32(0.9354), np.float32(0.8947), np.float32(0.9602), np.float32(0.9507), np.float32(0.8995), np.float32(0.9644), np.float32(0.9525), np.float32(0.8968), np.float32(0.9026), np.float32(0.9114)] 
+2025-05-06 17:27:28.633315: Epoch time: 96.21 s 
+2025-05-06 17:27:30.242873:  
+2025-05-06 17:27:30.302981: Epoch 1466 
+2025-05-06 17:27:30.358887: Current learning rate: 0.00305 
+2025-05-06 17:29:09.428120: train_loss -0.4894 
+2025-05-06 17:29:09.462768: val_loss -0.5147 
+2025-05-06 17:29:09.506531: Pseudo dice [np.float32(0.8418), np.float32(0.8637), np.float32(0.956), np.float32(0.9756), np.float32(0.9213), np.float32(0.9586), np.float32(0.97), np.float32(0.9755), np.float32(0.9638), np.float32(0.9686), np.float32(0.9524), np.float32(0.9667), np.float32(0.9674), np.float32(0.9104), np.float32(0.9669), np.float32(0.9412), np.float32(0.8778), np.float32(0.9), np.float32(0.926)] 
+2025-05-06 17:29:09.555207: Epoch time: 99.19 s 
+2025-05-06 17:29:11.221687:  
+2025-05-06 17:29:11.407530: Epoch 1467 
+2025-05-06 17:29:11.433433: Current learning rate: 0.00304 
+2025-05-06 17:30:50.777151: train_loss -0.4981 
+2025-05-06 17:30:50.796919: val_loss -0.5253 
+2025-05-06 17:30:50.798483: Pseudo dice [np.float32(0.8239), np.float32(0.8437), np.float32(0.9332), np.float32(0.9725), np.float32(0.9327), np.float32(0.9374), np.float32(0.9578), np.float32(0.979), np.float32(0.9559), np.float32(0.966), np.float32(0.9519), np.float32(0.9664), np.float32(0.9633), np.float32(0.8982), np.float32(0.9466), np.float32(0.9554), np.float32(0.8643), np.float32(0.8807), np.float32(0.9137)] 
+2025-05-06 17:30:50.799321: Epoch time: 99.56 s 
+2025-05-06 17:30:52.318593:  
+2025-05-06 17:30:52.385908: Epoch 1468 
+2025-05-06 17:30:52.436131: Current learning rate: 0.00304 
+2025-05-06 17:32:28.649975: train_loss -0.5047 
+2025-05-06 17:32:28.793647: val_loss -0.506 
+2025-05-06 17:32:28.820165: Pseudo dice [np.float32(0.8488), np.float32(0.8537), np.float32(0.8926), np.float32(0.9747), np.float32(0.911), np.float32(0.9613), np.float32(0.971), np.float32(0.9701), np.float32(0.9491), np.float32(0.9679), np.float32(0.9362), np.float32(0.9501), np.float32(0.9656), np.float32(0.9096), np.float32(0.9221), np.float32(0.9561), np.float32(0.8982), np.float32(0.8809), np.float32(0.9303)] 
+2025-05-06 17:32:28.848208: Epoch time: 96.33 s 
+2025-05-06 17:32:34.159175:  
+2025-05-06 17:32:34.165201: Epoch 1469 
+2025-05-06 17:32:34.165671: Current learning rate: 0.00303 
+2025-05-06 17:34:12.070335: train_loss -0.4938 
+2025-05-06 17:34:12.252429: val_loss -0.4772 
+2025-05-06 17:34:12.289610: Pseudo dice [np.float32(0.8288), np.float32(0.8559), np.float32(0.9207), np.float32(0.9773), np.float32(0.8952), np.float32(0.9544), np.float32(0.9708), np.float32(0.9737), np.float32(0.9568), np.float32(0.959), np.float32(0.9443), np.float32(0.9629), np.float32(0.9718), np.float32(0.913), np.float32(0.9546), np.float32(0.9489), np.float32(0.8598), np.float32(0.8675), np.float32(0.9264)] 
+2025-05-06 17:34:12.329241: Epoch time: 97.91 s 
+2025-05-06 17:34:14.152367:  
+2025-05-06 17:34:14.172474: Epoch 1470 
+2025-05-06 17:34:14.176729: Current learning rate: 0.00303 
+2025-05-06 17:35:54.093425: train_loss -0.4943 
+2025-05-06 17:35:54.204066: val_loss -0.5226 
+2025-05-06 17:35:54.230500: Pseudo dice [np.float32(0.8537), np.float32(0.8504), np.float32(0.9312), np.float32(0.9728), np.float32(0.9277), np.float32(0.961), np.float32(0.9597), np.float32(0.978), np.float32(0.9677), np.float32(0.9674), np.float32(0.9161), np.float32(0.974), np.float32(0.9529), np.float32(0.8939), np.float32(0.9696), np.float32(0.955), np.float32(0.8888), np.float32(0.906), np.float32(0.9199)] 
+2025-05-06 17:35:54.259895: Epoch time: 99.94 s 
+2025-05-06 17:35:56.088845:  
+2025-05-06 17:35:56.166761: Epoch 1471 
+2025-05-06 17:35:56.223238: Current learning rate: 0.00302 
+2025-05-06 17:37:30.103040: train_loss -0.4907 
+2025-05-06 17:37:30.210957: val_loss -0.5246 
+2025-05-06 17:37:30.244927: Pseudo dice [np.float32(0.8425), np.float32(0.8537), np.float32(0.9258), np.float32(0.9744), np.float32(0.9133), np.float32(0.9579), np.float32(0.9681), np.float32(0.9784), np.float32(0.965), np.float32(0.9661), np.float32(0.9466), np.float32(0.9706), np.float32(0.9664), np.float32(0.9025), np.float32(0.9675), np.float32(0.9528), np.float32(0.8857), np.float32(0.9063), np.float32(0.9267)] 
+2025-05-06 17:37:30.268973: Epoch time: 94.02 s 
+2025-05-06 17:37:31.852771:  
+2025-05-06 17:37:31.956886: Epoch 1472 
+2025-05-06 17:37:31.957584: Current learning rate: 0.00302 
+2025-05-06 17:39:06.782709: train_loss -0.4938 
+2025-05-06 17:39:06.908640: val_loss -0.5515 
+2025-05-06 17:39:06.952718: Pseudo dice [np.float32(0.8476), np.float32(0.8664), np.float32(0.9426), np.float32(0.9725), np.float32(0.9307), np.float32(0.9521), np.float32(0.9467), np.float32(0.9778), np.float32(0.9694), np.float32(0.9687), np.float32(0.9521), np.float32(0.9723), np.float32(0.9719), np.float32(0.9202), np.float32(0.9684), np.float32(0.9549), np.float32(0.8992), np.float32(0.9004), np.float32(0.9138)] 
+2025-05-06 17:39:06.991797: Epoch time: 94.93 s 
+2025-05-06 17:39:08.613925:  
+2025-05-06 17:39:08.728145: Epoch 1473 
+2025-05-06 17:39:08.778908: Current learning rate: 0.00301 
+2025-05-06 17:40:44.663088: train_loss -0.4994 
+2025-05-06 17:40:44.784639: val_loss -0.518 
+2025-05-06 17:40:44.813875: Pseudo dice [np.float32(0.8544), np.float32(0.8363), np.float32(0.9175), np.float32(0.9732), np.float32(0.926), np.float32(0.9621), np.float32(0.9619), np.float32(0.9687), np.float32(0.9653), np.float32(0.9629), np.float32(0.947), np.float32(0.9724), np.float32(0.9714), np.float32(0.9102), np.float32(0.9718), np.float32(0.9557), np.float32(0.8722), np.float32(0.8918), np.float32(0.9091)] 
+2025-05-06 17:40:44.912479: Epoch time: 96.05 s 
+2025-05-06 17:40:46.496151:  
+2025-05-06 17:40:46.610213: Epoch 1474 
+2025-05-06 17:40:46.648904: Current learning rate: 0.00301 
+2025-05-06 17:42:21.687055: train_loss -0.5023 
+2025-05-06 17:42:21.707441: val_loss -0.5053 
+2025-05-06 17:42:21.708355: Pseudo dice [np.float32(0.8502), np.float32(0.8288), np.float32(0.9505), np.float32(0.9732), np.float32(0.9172), np.float32(0.9511), np.float32(0.9659), np.float32(0.9711), np.float32(0.9608), np.float32(0.9654), np.float32(0.9533), np.float32(0.9698), np.float32(0.97), np.float32(0.9099), np.float32(0.9387), np.float32(0.9523), np.float32(0.9169), np.float32(0.9046), np.float32(0.9157)] 
+2025-05-06 17:42:21.708966: Epoch time: 95.19 s 
+2025-05-06 17:42:23.205344:  
+2025-05-06 17:42:23.346563: Epoch 1475 
+2025-05-06 17:42:23.391828: Current learning rate: 0.003 
+2025-05-06 17:43:57.569299: train_loss -0.4801 
+2025-05-06 17:43:57.617064: val_loss -0.4882 
+2025-05-06 17:43:57.619207: Pseudo dice [np.float32(0.8545), np.float32(0.8392), np.float32(0.9297), np.float32(0.9707), np.float32(0.8921), np.float32(0.9529), np.float32(0.951), np.float32(0.9742), np.float32(0.9672), np.float32(0.9398), np.float32(0.8907), np.float32(0.9633), np.float32(0.9649), np.float32(0.9112), np.float32(0.9629), np.float32(0.9571), np.float32(0.8517), np.float32(0.852), np.float32(0.9064)] 
+2025-05-06 17:43:57.639016: Epoch time: 94.37 s 
+2025-05-06 17:43:59.178772:  
+2025-05-06 17:43:59.269054: Epoch 1476 
+2025-05-06 17:43:59.297984: Current learning rate: 0.003 
+2025-05-06 17:45:35.940390: train_loss -0.5205 
+2025-05-06 17:45:35.986424: val_loss -0.4934 
+2025-05-06 17:45:35.995830: Pseudo dice [np.float32(0.8479), np.float32(0.8555), np.float32(0.8816), np.float32(0.9663), np.float32(0.9069), np.float32(0.9495), np.float32(0.9698), np.float32(0.9816), np.float32(0.968), np.float32(0.975), np.float32(0.954), np.float32(0.9679), np.float32(0.9756), np.float32(0.9168), np.float32(0.918), np.float32(0.9567), np.float32(0.882), np.float32(0.9067), np.float32(0.93)] 
+2025-05-06 17:45:36.008240: Epoch time: 96.76 s 
+2025-05-06 17:45:37.522586:  
+2025-05-06 17:45:37.585275: Epoch 1477 
+2025-05-06 17:45:37.592698: Current learning rate: 0.00299 
+2025-05-06 17:47:12.343548: train_loss -0.4874 
+2025-05-06 17:47:12.456912: val_loss -0.5143 
+2025-05-06 17:47:12.471999: Pseudo dice [np.float32(0.847), np.float32(0.8371), np.float32(0.9259), np.float32(0.9789), np.float32(0.8941), np.float32(0.9659), np.float32(0.9698), np.float32(0.9794), np.float32(0.9678), np.float32(0.9739), np.float32(0.9465), np.float32(0.9721), np.float32(0.9736), np.float32(0.9093), np.float32(0.9657), np.float32(0.9612), np.float32(0.8979), np.float32(0.9033), np.float32(0.9102)] 
+2025-05-06 17:47:12.484833: Epoch time: 94.82 s 
+2025-05-06 17:47:14.058137:  
+2025-05-06 17:47:14.099012: Epoch 1478 
+2025-05-06 17:47:14.114201: Current learning rate: 0.00299 
+2025-05-06 17:48:53.149485: train_loss -0.5069 
+2025-05-06 17:48:53.266839: val_loss -0.4568 
+2025-05-06 17:48:53.298048: Pseudo dice [np.float32(0.8703), np.float32(0.8256), np.float32(0.9386), np.float32(0.9795), np.float32(0.8905), np.float32(0.9668), np.float32(0.9661), np.float32(0.9721), np.float32(0.9696), np.float32(0.9614), np.float32(0.9395), np.float32(0.9709), np.float32(0.9664), np.float32(0.9122), np.float32(0.9673), np.float32(0.9567), np.float32(0.904), np.float32(0.9114), np.float32(0.9133)] 
+2025-05-06 17:48:53.302023: Epoch time: 99.09 s 
+2025-05-06 17:48:54.881029:  
+2025-05-06 17:48:55.027832: Epoch 1479 
+2025-05-06 17:48:55.076782: Current learning rate: 0.00298 
+2025-05-06 17:50:32.271312: train_loss -0.4893 
+2025-05-06 17:50:32.384285: val_loss -0.5371 
+2025-05-06 17:50:32.408646: Pseudo dice [np.float32(0.8464), np.float32(0.8605), np.float32(0.9354), np.float32(0.9783), np.float32(0.911), np.float32(0.9581), np.float32(0.9697), np.float32(0.9812), np.float32(0.9611), np.float32(0.9676), np.float32(0.9488), np.float32(0.9656), np.float32(0.9688), np.float32(0.903), np.float32(0.9631), np.float32(0.9549), np.float32(0.8843), np.float32(0.9047), np.float32(0.9248)] 
+2025-05-06 17:50:32.430413: Epoch time: 97.39 s 
+2025-05-06 17:50:34.065037:  
+2025-05-06 17:50:34.121280: Epoch 1480 
+2025-05-06 17:50:34.121870: Current learning rate: 0.00297 
+2025-05-06 17:52:09.682816: train_loss -0.4898 
+2025-05-06 17:52:09.709831: val_loss -0.4799 
+2025-05-06 17:52:09.717541: Pseudo dice [np.float32(0.8446), np.float32(0.822), np.float32(0.7844), np.float32(0.9745), np.float32(0.9043), np.float32(0.9578), np.float32(0.9512), np.float32(0.9606), np.float32(0.9582), np.float32(0.9612), np.float32(0.9362), np.float32(0.9685), np.float32(0.9658), np.float32(0.895), np.float32(0.9606), np.float32(0.941), np.float32(0.8793), np.float32(0.869), np.float32(0.9191)] 
+2025-05-06 17:52:09.721608: Epoch time: 95.62 s 
+2025-05-06 17:52:11.277941:  
+2025-05-06 17:52:11.298914: Epoch 1481 
+2025-05-06 17:52:11.299950: Current learning rate: 0.00297 
+2025-05-06 17:53:57.274055: train_loss -0.4965 
+2025-05-06 17:53:57.345077: val_loss -0.4904 
+2025-05-06 17:53:57.361153: Pseudo dice [np.float32(0.8558), np.float32(0.8669), np.float32(0.7745), np.float32(0.9627), np.float32(0.8855), np.float32(0.9594), np.float32(0.964), np.float32(0.9775), np.float32(0.9389), np.float32(0.9661), np.float32(0.9538), np.float32(0.969), np.float32(0.9703), np.float32(0.9099), np.float32(0.958), np.float32(0.9591), np.float32(0.8835), np.float32(0.8865), np.float32(0.9067)] 
+2025-05-06 17:53:57.368920: Epoch time: 106.0 s 
+2025-05-06 17:53:58.940140:  
+2025-05-06 17:53:58.983682: Epoch 1482 
+2025-05-06 17:53:58.984200: Current learning rate: 0.00296 
+2025-05-06 17:55:41.622151: train_loss -0.4995 
+2025-05-06 17:55:41.742473: val_loss -0.5307 
+2025-05-06 17:55:41.817823: Pseudo dice [np.float32(0.8429), np.float32(0.8672), np.float32(0.8901), np.float32(0.9606), np.float32(0.9164), np.float32(0.9531), np.float32(0.9666), np.float32(0.9806), np.float32(0.9465), np.float32(0.9663), np.float32(0.9442), np.float32(0.9589), np.float32(0.9668), np.float32(0.9049), np.float32(0.9698), np.float32(0.9542), np.float32(0.8914), np.float32(0.8986), np.float32(0.9094)] 
+2025-05-06 17:55:41.855541: Epoch time: 102.68 s 
+2025-05-06 17:55:43.560299:  
+2025-05-06 17:55:43.569278: Epoch 1483 
+2025-05-06 17:55:43.585773: Current learning rate: 0.00296 
+2025-05-06 17:57:18.266312: train_loss -0.5135 
+2025-05-06 17:57:18.329169: val_loss -0.4858 
+2025-05-06 17:57:18.351674: Pseudo dice [np.float32(0.8695), np.float32(0.87), np.float32(0.5763), np.float32(0.9789), np.float32(0.9034), np.float32(0.9667), np.float32(0.9649), np.float32(0.9823), np.float32(0.9715), np.float32(0.9655), np.float32(0.9578), np.float32(0.9721), np.float32(0.9736), np.float32(0.9163), np.float32(0.971), np.float32(0.9652), np.float32(0.8983), np.float32(0.9123), np.float32(0.9156)] 
+2025-05-06 17:57:18.380280: Epoch time: 94.71 s 
+2025-05-06 17:57:19.997830:  
+2025-05-06 17:57:20.149086: Epoch 1484 
+2025-05-06 17:57:20.183919: Current learning rate: 0.00295 
+2025-05-06 17:58:58.027756: train_loss -0.51 
+2025-05-06 17:58:58.099572: val_loss -0.5196 
+2025-05-06 17:58:58.112711: Pseudo dice [np.float32(0.8499), np.float32(0.861), np.float32(0.9183), np.float32(0.9729), np.float32(0.9257), np.float32(0.9601), np.float32(0.9542), np.float32(0.9782), np.float32(0.9644), np.float32(0.9556), np.float32(0.935), np.float32(0.9688), np.float32(0.9696), np.float32(0.9094), np.float32(0.9667), np.float32(0.9611), np.float32(0.8815), np.float32(0.8716), np.float32(0.9168)] 
+2025-05-06 17:58:58.124069: Epoch time: 98.03 s 
+2025-05-06 17:58:59.670124:  
+2025-05-06 17:58:59.748351: Epoch 1485 
+2025-05-06 17:58:59.756030: Current learning rate: 0.00295 
+2025-05-06 18:00:35.400228: train_loss -0.5009 
+2025-05-06 18:00:35.481311: val_loss -0.5335 
+2025-05-06 18:00:35.482148: Pseudo dice [np.float32(0.8363), np.float32(0.8566), np.float32(0.8651), np.float32(0.96), np.float32(0.889), np.float32(0.9531), np.float32(0.9671), np.float32(0.9758), np.float32(0.963), np.float32(0.9627), np.float32(0.9371), np.float32(0.9744), np.float32(0.9695), np.float32(0.9079), np.float32(0.9632), np.float32(0.955), np.float32(0.9122), np.float32(0.896), np.float32(0.9318)] 
+2025-05-06 18:00:35.487722: Epoch time: 95.73 s 
+2025-05-06 18:00:40.246977:  
+2025-05-06 18:00:40.253204: Epoch 1486 
+2025-05-06 18:00:40.253699: Current learning rate: 0.00294 
+2025-05-06 18:02:11.408359: train_loss -0.4935 
+2025-05-06 18:02:11.503194: val_loss -0.4974 
+2025-05-06 18:02:11.519895: Pseudo dice [np.float32(0.8317), np.float32(0.8277), np.float32(0.8974), np.float32(0.9776), np.float32(0.8897), np.float32(0.965), np.float32(0.9683), np.float32(0.9574), np.float32(0.9594), np.float32(0.9659), np.float32(0.9499), np.float32(0.9684), np.float32(0.9715), np.float32(0.901), np.float32(0.9718), np.float32(0.96), np.float32(0.8822), np.float32(0.8793), np.float32(0.907)] 
+2025-05-06 18:02:11.576977: Epoch time: 91.16 s 
+2025-05-06 18:02:13.174444:  
+2025-05-06 18:02:13.261955: Epoch 1487 
+2025-05-06 18:02:13.301912: Current learning rate: 0.00294 
+2025-05-06 18:03:43.290266: train_loss -0.4792 
+2025-05-06 18:03:43.417477: val_loss -0.5064 
+2025-05-06 18:03:43.443892: Pseudo dice [np.float32(0.8294), np.float32(0.8496), np.float32(0.877), np.float32(0.9735), np.float32(0.9111), np.float32(0.9543), np.float32(0.968), np.float32(0.9786), np.float32(0.971), np.float32(0.9677), np.float32(0.9583), np.float32(0.9695), np.float32(0.9698), np.float32(0.9063), np.float32(0.9528), np.float32(0.9534), np.float32(0.8957), np.float32(0.8909), np.float32(0.9267)] 
+2025-05-06 18:03:43.478981: Epoch time: 90.12 s 
+2025-05-06 18:03:45.029651:  
+2025-05-06 18:03:45.038776: Epoch 1488 
+2025-05-06 18:03:45.042788: Current learning rate: 0.00293 
+2025-05-06 18:05:20.447125: train_loss -0.4941 
+2025-05-06 18:05:20.516508: val_loss -0.494 
+2025-05-06 18:05:20.552588: Pseudo dice [np.float32(0.8481), np.float32(0.8504), np.float32(0.9378), np.float32(0.9699), np.float32(0.9173), np.float32(0.9571), np.float32(0.9517), np.float32(0.9744), np.float32(0.9661), np.float32(0.9523), np.float32(0.9446), np.float32(0.9731), np.float32(0.9717), np.float32(0.904), np.float32(0.9673), np.float32(0.9623), np.float32(0.903), np.float32(0.9041), np.float32(0.9198)] 
+2025-05-06 18:05:20.574367: Epoch time: 95.42 s 
+2025-05-06 18:05:22.071014:  
+2025-05-06 18:05:22.167873: Epoch 1489 
+2025-05-06 18:05:22.197316: Current learning rate: 0.00293 
+2025-05-06 18:06:57.461205: train_loss -0.5061 
+2025-05-06 18:06:57.511974: val_loss -0.489 
+2025-05-06 18:06:57.516092: Pseudo dice [np.float32(0.8548), np.float32(0.8534), np.float32(0.9213), np.float32(0.9754), np.float32(0.9197), np.float32(0.9652), np.float32(0.9647), np.float32(0.9722), np.float32(0.9707), np.float32(0.9658), np.float32(0.9542), np.float32(0.9696), np.float32(0.9681), np.float32(0.9092), np.float32(0.9584), np.float32(0.9526), np.float32(0.896), np.float32(0.9054), np.float32(0.9216)] 
+2025-05-06 18:06:57.519093: Epoch time: 95.39 s 
+2025-05-06 18:06:59.090111:  
+2025-05-06 18:06:59.139390: Epoch 1490 
+2025-05-06 18:06:59.172855: Current learning rate: 0.00292 
+2025-05-06 18:08:31.581246: train_loss -0.5025 
+2025-05-06 18:08:31.724054: val_loss -0.5254 
+2025-05-06 18:08:31.777375: Pseudo dice [np.float32(0.8518), np.float32(0.8139), np.float32(0.9098), np.float32(0.9786), np.float32(0.901), np.float32(0.9524), np.float32(0.9648), np.float32(0.9794), np.float32(0.9624), np.float32(0.9617), np.float32(0.9537), np.float32(0.9674), np.float32(0.9704), np.float32(0.8913), np.float32(0.9513), np.float32(0.9565), np.float32(0.8655), np.float32(0.9098), np.float32(0.9255)] 
+2025-05-06 18:08:31.812429: Epoch time: 92.49 s 
+2025-05-06 18:08:33.517394:  
+2025-05-06 18:08:33.526238: Epoch 1491 
+2025-05-06 18:08:33.526869: Current learning rate: 0.00292 
+2025-05-06 18:10:11.662887: train_loss -0.4867 
+2025-05-06 18:10:11.772408: val_loss -0.5187 
+2025-05-06 18:10:11.796630: Pseudo dice [np.float32(0.8533), np.float32(0.8492), np.float32(0.8445), np.float32(0.9796), np.float32(0.8946), np.float32(0.956), np.float32(0.9631), np.float32(0.9765), np.float32(0.9678), np.float32(0.9597), np.float32(0.9416), np.float32(0.9677), np.float32(0.9694), np.float32(0.9058), np.float32(0.9494), np.float32(0.9406), np.float32(0.896), np.float32(0.9017), np.float32(0.9047)] 
+2025-05-06 18:10:11.815108: Epoch time: 98.15 s 
+2025-05-06 18:10:13.298696:  
+2025-05-06 18:10:13.365312: Epoch 1492 
+2025-05-06 18:10:13.387810: Current learning rate: 0.00291 
+2025-05-06 18:11:52.887469: train_loss -0.4998 
+2025-05-06 18:11:52.971509: val_loss -0.4415 
+2025-05-06 18:11:53.002352: Pseudo dice [np.float32(0.8826), np.float32(0.8669), np.float32(0.8506), np.float32(0.9757), np.float32(0.8842), np.float32(0.9642), np.float32(0.9642), np.float32(0.9797), np.float32(0.9583), np.float32(0.9637), np.float32(0.9405), np.float32(0.9688), np.float32(0.9669), np.float32(0.9046), np.float32(0.9592), np.float32(0.953), np.float32(0.9091), np.float32(0.9041), np.float32(0.9082)] 
+2025-05-06 18:11:53.011471: Epoch time: 99.59 s 
+2025-05-06 18:11:54.605388:  
+2025-05-06 18:11:54.715102: Epoch 1493 
+2025-05-06 18:11:54.752345: Current learning rate: 0.00291 
+2025-05-06 18:13:40.475595: train_loss -0.4967 
+2025-05-06 18:13:40.650719: val_loss -0.451 
+2025-05-06 18:13:40.678123: Pseudo dice [np.float32(0.8589), np.float32(0.84), np.float32(0.9268), np.float32(0.9725), np.float32(0.9134), np.float32(0.9496), np.float32(0.9525), np.float32(0.9723), np.float32(0.9466), np.float32(0.9517), np.float32(0.9487), np.float32(0.9648), np.float32(0.9663), np.float32(0.8915), np.float32(0.9757), np.float32(0.9564), np.float32(0.8825), np.float32(0.8937), np.float32(0.8978)] 
+2025-05-06 18:13:40.693260: Epoch time: 105.87 s 
+2025-05-06 18:13:42.332609:  
+2025-05-06 18:13:42.430369: Epoch 1494 
+2025-05-06 18:13:42.463856: Current learning rate: 0.0029 
+2025-05-06 18:15:22.021197: train_loss -0.4978 
+2025-05-06 18:15:22.152988: val_loss -0.5111 
+2025-05-06 18:15:22.165113: Pseudo dice [np.float32(0.8539), np.float32(0.8509), np.float32(0.903), np.float32(0.959), np.float32(0.9267), np.float32(0.9569), np.float32(0.9651), np.float32(0.9789), np.float32(0.9555), np.float32(0.9635), np.float32(0.9506), np.float32(0.9557), np.float32(0.9677), np.float32(0.9097), np.float32(0.9625), np.float32(0.9506), np.float32(0.8621), np.float32(0.8873), np.float32(0.9189)] 
+2025-05-06 18:15:22.176472: Epoch time: 99.69 s 
+2025-05-06 18:15:23.968767:  
+2025-05-06 18:15:24.002766: Epoch 1495 
+2025-05-06 18:15:24.019014: Current learning rate: 0.0029 
+2025-05-06 18:17:02.805263: train_loss -0.5067 
+2025-05-06 18:17:02.882313: val_loss -0.4931 
+2025-05-06 18:17:02.927804: Pseudo dice [np.float32(0.8382), np.float32(0.8633), np.float32(0.9326), np.float32(0.9774), np.float32(0.907), np.float32(0.9624), np.float32(0.9671), np.float32(0.9811), np.float32(0.9734), np.float32(0.9678), np.float32(0.9517), np.float32(0.9735), np.float32(0.9653), np.float32(0.8937), np.float32(0.9592), np.float32(0.9374), np.float32(0.8941), np.float32(0.8816), np.float32(0.9171)] 
+2025-05-06 18:17:02.954808: Epoch time: 98.84 s 
+2025-05-06 18:17:04.664160:  
+2025-05-06 18:17:04.723856: Epoch 1496 
+2025-05-06 18:17:04.752740: Current learning rate: 0.00289 
+2025-05-06 18:18:41.757631: train_loss -0.4971 
+2025-05-06 18:18:41.824630: val_loss -0.4876 
+2025-05-06 18:18:41.858765: Pseudo dice [np.float32(0.8301), np.float32(0.8584), np.float32(0.925), np.float32(0.9695), np.float32(0.9274), np.float32(0.9655), np.float32(0.9662), np.float32(0.9786), np.float32(0.9617), np.float32(0.9649), np.float32(0.9585), np.float32(0.9722), np.float32(0.9718), np.float32(0.9135), np.float32(0.9693), np.float32(0.9548), np.float32(0.8359), np.float32(0.8911), np.float32(0.9141)] 
+2025-05-06 18:18:41.893474: Epoch time: 97.09 s 
+2025-05-06 18:18:43.486690:  
+2025-05-06 18:18:43.595829: Epoch 1497 
+2025-05-06 18:18:43.625674: Current learning rate: 0.00289 
+2025-05-06 18:20:17.696007: train_loss -0.4749 
+2025-05-06 18:20:17.776908: val_loss -0.4391 
+2025-05-06 18:20:17.823507: Pseudo dice [np.float32(0.8616), np.float32(0.8474), np.float32(0.9087), np.float32(0.9777), np.float32(0.8415), np.float32(0.9636), np.float32(0.9705), np.float32(0.9813), np.float32(0.9649), np.float32(0.9737), np.float32(0.9558), np.float32(0.9698), np.float32(0.9705), np.float32(0.8963), np.float32(0.9671), np.float32(0.9608), np.float32(0.8666), np.float32(0.9004), np.float32(0.8912)] 
+2025-05-06 18:20:17.841083: Epoch time: 94.21 s 
+2025-05-06 18:20:19.574681:  
+2025-05-06 18:20:19.618839: Epoch 1498 
+2025-05-06 18:20:19.649485: Current learning rate: 0.00288 
+2025-05-06 18:21:56.099575: train_loss -0.5033 
+2025-05-06 18:21:56.195622: val_loss -0.5277 
+2025-05-06 18:21:56.228045: Pseudo dice [np.float32(0.8573), np.float32(0.8638), np.float32(0.9398), np.float32(0.9744), np.float32(0.9319), np.float32(0.9653), np.float32(0.9691), np.float32(0.9797), np.float32(0.9667), np.float32(0.9689), np.float32(0.9499), np.float32(0.9741), np.float32(0.9638), np.float32(0.9144), np.float32(0.9666), np.float32(0.957), np.float32(0.8909), np.float32(0.9098), np.float32(0.9349)] 
+2025-05-06 18:21:56.260720: Epoch time: 96.53 s 
+2025-05-06 18:21:58.043919:  
+2025-05-06 18:21:58.093283: Epoch 1499 
+2025-05-06 18:21:58.130042: Current learning rate: 0.00288 
+2025-05-06 18:23:32.987095: train_loss -0.502 
+2025-05-06 18:23:33.043342: val_loss -0.4908 
+2025-05-06 18:23:33.044724: Pseudo dice [np.float32(0.8432), np.float32(0.8457), np.float32(0.8986), np.float32(0.9607), np.float32(0.9109), np.float32(0.961), np.float32(0.9665), np.float32(0.9779), np.float32(0.9668), np.float32(0.9701), np.float32(0.9497), np.float32(0.967), np.float32(0.9627), np.float32(0.8956), np.float32(0.9602), np.float32(0.9549), np.float32(0.8854), np.float32(0.896), np.float32(0.9011)] 
+2025-05-06 18:23:33.050488: Epoch time: 94.94 s 
+2025-05-06 18:23:35.881101:  
+2025-05-06 18:23:35.898128: Epoch 1500 
+2025-05-06 18:23:35.898536: Current learning rate: 0.00287 
+2025-05-06 18:25:14.458611: train_loss -0.4954 
+2025-05-06 18:25:14.546285: val_loss -0.4714 
+2025-05-06 18:25:14.562672: Pseudo dice [np.float32(0.8634), np.float32(0.8308), np.float32(0.8791), np.float32(0.9712), np.float32(0.8988), np.float32(0.9642), np.float32(0.969), np.float32(0.9821), np.float32(0.9566), np.float32(0.9697), np.float32(0.9396), np.float32(0.9671), np.float32(0.9693), np.float32(0.9071), np.float32(0.9705), np.float32(0.9494), np.float32(0.8559), np.float32(0.8891), np.float32(0.9269)] 
+2025-05-06 18:25:14.592684: Epoch time: 98.58 s 
+2025-05-06 18:25:16.237449:  
+2025-05-06 18:25:16.358783: Epoch 1501 
+2025-05-06 18:25:16.371989: Current learning rate: 0.00287 
+2025-05-06 18:26:53.910194: train_loss -0.5077 
+2025-05-06 18:26:53.999271: val_loss -0.5367 
+2025-05-06 18:26:54.028733: Pseudo dice [np.float32(0.8479), np.float32(0.8507), np.float32(0.9286), np.float32(0.9762), np.float32(0.8822), np.float32(0.9623), np.float32(0.9684), np.float32(0.9701), np.float32(0.9497), np.float32(0.9662), np.float32(0.95), np.float32(0.9667), np.float32(0.9681), np.float32(0.9119), np.float32(0.9656), np.float32(0.9546), np.float32(0.8757), np.float32(0.8705), np.float32(0.916)] 
+2025-05-06 18:26:54.040260: Epoch time: 97.67 s 
+2025-05-06 18:26:55.596855:  
+2025-05-06 18:26:55.719056: Epoch 1502 
+2025-05-06 18:26:55.738306: Current learning rate: 0.00286 
+2025-05-06 18:28:31.250495: train_loss -0.5139 
+2025-05-06 18:28:31.296272: val_loss -0.4619 
+2025-05-06 18:28:31.300293: Pseudo dice [np.float32(0.8371), np.float32(0.8381), np.float32(0.9195), np.float32(0.9669), np.float32(0.9108), np.float32(0.9607), np.float32(0.9582), np.float32(0.9786), np.float32(0.9525), np.float32(0.9503), np.float32(0.9472), np.float32(0.9649), np.float32(0.9659), np.float32(0.9159), np.float32(0.9656), np.float32(0.9651), np.float32(0.8771), np.float32(0.889), np.float32(0.9166)] 
+2025-05-06 18:28:31.324627: Epoch time: 95.65 s 
+2025-05-06 18:28:36.764021:  
+2025-05-06 18:28:36.846744: Epoch 1503 
+2025-05-06 18:28:36.847647: Current learning rate: 0.00286 
+2025-05-06 18:30:16.503167: train_loss -0.505 
+2025-05-06 18:30:16.710027: val_loss -0.4856 
+2025-05-06 18:30:16.757425: Pseudo dice [np.float32(0.8662), np.float32(0.8796), np.float32(0.9082), np.float32(0.9765), np.float32(0.9007), np.float32(0.9628), np.float32(0.96), np.float32(0.9791), np.float32(0.9594), np.float32(0.9687), np.float32(0.9552), np.float32(0.9683), np.float32(0.9676), np.float32(0.9183), np.float32(0.9674), np.float32(0.9627), np.float32(0.8628), np.float32(0.8921), np.float32(0.9252)] 
+2025-05-06 18:30:16.810917: Epoch time: 99.74 s 
+2025-05-06 18:30:18.542560:  
+2025-05-06 18:30:18.548303: Epoch 1504 
+2025-05-06 18:30:18.548880: Current learning rate: 0.00285 
+2025-05-06 18:32:02.779420: train_loss -0.4819 
+2025-05-06 18:32:02.893079: val_loss -0.4879 
+2025-05-06 18:32:02.937243: Pseudo dice [np.float32(0.8569), np.float32(0.8531), np.float32(0.7404), np.float32(0.9709), np.float32(0.9165), np.float32(0.9532), np.float32(0.9644), np.float32(0.9747), np.float32(0.9404), np.float32(0.9526), np.float32(0.9434), np.float32(0.9618), np.float32(0.9652), np.float32(0.9012), np.float32(0.9643), np.float32(0.951), np.float32(0.8784), np.float32(0.8862), np.float32(0.9217)] 
+2025-05-06 18:32:02.962923: Epoch time: 104.24 s 
+2025-05-06 18:32:04.652650:  
+2025-05-06 18:32:04.754878: Epoch 1505 
+2025-05-06 18:32:04.784606: Current learning rate: 0.00285 
+2025-05-06 18:33:38.601789: train_loss -0.5123 
+2025-05-06 18:33:38.670956: val_loss -0.4929 
+2025-05-06 18:33:38.675672: Pseudo dice [np.float32(0.8612), np.float32(0.8565), np.float32(0.8534), np.float32(0.9685), np.float32(0.9345), np.float32(0.9534), np.float32(0.9752), np.float32(0.9782), np.float32(0.9613), np.float32(0.9659), np.float32(0.9522), np.float32(0.9673), np.float32(0.9721), np.float32(0.9138), np.float32(0.8881), np.float32(0.9586), np.float32(0.877), np.float32(0.8924), np.float32(0.9343)] 
+2025-05-06 18:33:38.723331: Epoch time: 93.95 s 
+2025-05-06 18:33:40.294685:  
+2025-05-06 18:33:40.421945: Epoch 1506 
+2025-05-06 18:33:40.435732: Current learning rate: 0.00284 
+2025-05-06 18:35:18.224037: train_loss -0.4891 
+2025-05-06 18:35:18.309995: val_loss -0.5411 
+2025-05-06 18:35:18.328132: Pseudo dice [np.float32(0.8631), np.float32(0.8606), np.float32(0.9381), np.float32(0.9774), np.float32(0.8978), np.float32(0.9603), np.float32(0.9694), np.float32(0.9729), np.float32(0.9689), np.float32(0.9499), np.float32(0.9506), np.float32(0.9683), np.float32(0.9616), np.float32(0.9211), np.float32(0.9632), np.float32(0.9632), np.float32(0.9077), np.float32(0.8922), np.float32(0.9215)] 
+2025-05-06 18:35:18.353600: Epoch time: 97.93 s 
+2025-05-06 18:35:19.932438:  
+2025-05-06 18:35:20.000204: Epoch 1507 
+2025-05-06 18:35:20.043652: Current learning rate: 0.00284 
+2025-05-06 18:36:57.200976: train_loss -0.4965 
+2025-05-06 18:36:57.328153: val_loss -0.5086 
+2025-05-06 18:36:57.393364: Pseudo dice [np.float32(0.8441), np.float32(0.8578), np.float32(0.712), np.float32(0.976), np.float32(0.9115), np.float32(0.9588), np.float32(0.9549), np.float32(0.9807), np.float32(0.9658), np.float32(0.97), np.float32(0.9551), np.float32(0.9655), np.float32(0.969), np.float32(0.9139), np.float32(0.9673), np.float32(0.9652), np.float32(0.8307), np.float32(0.8307), np.float32(0.9318)] 
+2025-05-06 18:36:57.458763: Epoch time: 97.27 s 
+2025-05-06 18:36:59.126021:  
+2025-05-06 18:36:59.176991: Epoch 1508 
+2025-05-06 18:36:59.203417: Current learning rate: 0.00283 
+2025-05-06 18:38:34.824276: train_loss -0.4914 
+2025-05-06 18:38:34.930363: val_loss -0.4868 
+2025-05-06 18:38:34.968210: Pseudo dice [np.float32(0.8576), np.float32(0.8029), np.float32(0.9007), np.float32(0.9809), np.float32(0.9098), np.float32(0.966), np.float32(0.9452), np.float32(0.9616), np.float32(0.9688), np.float32(0.9708), np.float32(0.9499), np.float32(0.9691), np.float32(0.9681), np.float32(0.9132), np.float32(0.9718), np.float32(0.9547), np.float32(0.9286), np.float32(0.8968), np.float32(0.9251)] 
+2025-05-06 18:38:35.008864: Epoch time: 95.7 s 
+2025-05-06 18:38:36.700224:  
+2025-05-06 18:38:36.860440: Epoch 1509 
+2025-05-06 18:38:36.904073: Current learning rate: 0.00283 
+2025-05-06 18:40:14.750182: train_loss -0.5118 
+2025-05-06 18:40:14.818008: val_loss -0.4935 
+2025-05-06 18:40:14.836648: Pseudo dice [np.float32(0.8623), np.float32(0.8391), np.float32(0.9254), np.float32(0.9691), np.float32(0.9229), np.float32(0.9592), np.float32(0.9669), np.float32(0.9777), np.float32(0.9701), np.float32(0.9671), np.float32(0.9394), np.float32(0.9754), np.float32(0.9734), np.float32(0.8961), np.float32(0.9715), np.float32(0.9576), np.float32(0.8953), np.float32(0.8986), np.float32(0.9219)] 
+2025-05-06 18:40:14.858713: Epoch time: 98.05 s 
+2025-05-06 18:40:16.549289:  
+2025-05-06 18:40:16.729893: Epoch 1510 
+2025-05-06 18:40:16.754059: Current learning rate: 0.00282 
+2025-05-06 18:42:01.922834: train_loss -0.4967 
+2025-05-06 18:42:02.051790: val_loss -0.5239 
+2025-05-06 18:42:02.078266: Pseudo dice [np.float32(0.8581), np.float32(0.8587), np.float32(0.905), np.float32(0.9657), np.float32(0.8952), np.float32(0.9577), np.float32(0.9621), np.float32(0.9786), np.float32(0.9627), np.float32(0.9675), np.float32(0.9493), np.float32(0.9674), np.float32(0.9647), np.float32(0.9191), np.float32(0.9641), np.float32(0.9483), np.float32(0.8647), np.float32(0.8972), np.float32(0.9013)] 
+2025-05-06 18:42:02.092038: Epoch time: 105.37 s 
+2025-05-06 18:42:03.621292:  
+2025-05-06 18:42:03.679022: Epoch 1511 
+2025-05-06 18:42:03.692338: Current learning rate: 0.00281 
+2025-05-06 18:43:41.767109: train_loss -0.5156 
+2025-05-06 18:43:41.911602: val_loss -0.5254 
+2025-05-06 18:43:41.952694: Pseudo dice [np.float32(0.8593), np.float32(0.85), np.float32(0.9519), np.float32(0.9652), np.float32(0.9117), np.float32(0.9594), np.float32(0.962), np.float32(0.9799), np.float32(0.9655), np.float32(0.972), np.float32(0.9522), np.float32(0.9668), np.float32(0.9721), np.float32(0.9188), np.float32(0.9657), np.float32(0.9625), np.float32(0.8806), np.float32(0.8522), np.float32(0.8997)] 
+2025-05-06 18:43:41.991888: Epoch time: 98.15 s 
+2025-05-06 18:43:43.776063:  
+2025-05-06 18:43:43.844094: Epoch 1512 
+2025-05-06 18:43:43.907657: Current learning rate: 0.00281 
+2025-05-06 18:45:18.614542: train_loss -0.4927 
+2025-05-06 18:45:18.706749: val_loss -0.4982 
+2025-05-06 18:45:18.728006: Pseudo dice [np.float32(0.831), np.float32(0.8705), np.float32(0.8875), np.float32(0.9763), np.float32(0.9226), np.float32(0.9622), np.float32(0.9705), np.float32(0.9741), np.float32(0.9671), np.float32(0.9656), np.float32(0.9521), np.float32(0.973), np.float32(0.9718), np.float32(0.9083), np.float32(0.9634), np.float32(0.9559), np.float32(0.8908), np.float32(0.8948), np.float32(0.9204)] 
+2025-05-06 18:45:18.760237: Epoch time: 94.84 s 
+2025-05-06 18:45:20.367785:  
+2025-05-06 18:45:20.448017: Epoch 1513 
+2025-05-06 18:45:20.459077: Current learning rate: 0.0028 
+2025-05-06 18:46:57.683768: train_loss -0.5185 
+2025-05-06 18:46:57.715095: val_loss -0.5237 
+2025-05-06 18:46:57.715959: Pseudo dice [np.float32(0.8687), np.float32(0.8557), np.float32(0.8981), np.float32(0.9592), np.float32(0.9053), np.float32(0.9587), np.float32(0.9661), np.float32(0.9786), np.float32(0.9653), np.float32(0.9612), np.float32(0.951), np.float32(0.9721), np.float32(0.9715), np.float32(0.9148), np.float32(0.9607), np.float32(0.9449), np.float32(0.9038), np.float32(0.911), np.float32(0.9239)] 
+2025-05-06 18:46:57.716523: Epoch time: 97.32 s 
+2025-05-06 18:46:59.304948:  
+2025-05-06 18:46:59.389446: Epoch 1514 
+2025-05-06 18:46:59.408398: Current learning rate: 0.0028 
+2025-05-06 18:48:41.116889: train_loss -0.4848 
+2025-05-06 18:48:41.190448: val_loss -0.5235 
+2025-05-06 18:48:41.208837: Pseudo dice [np.float32(0.8317), np.float32(0.8598), np.float32(0.8767), np.float32(0.9696), np.float32(0.8878), np.float32(0.9637), np.float32(0.9595), np.float32(0.9775), np.float32(0.9702), np.float32(0.9661), np.float32(0.9346), np.float32(0.9685), np.float32(0.9694), np.float32(0.9015), np.float32(0.9702), np.float32(0.9594), np.float32(0.895), np.float32(0.9048), np.float32(0.9167)] 
+2025-05-06 18:48:41.234496: Epoch time: 101.81 s 
+2025-05-06 18:48:42.823222:  
+2025-05-06 18:48:42.943883: Epoch 1515 
+2025-05-06 18:48:42.981500: Current learning rate: 0.00279 
+2025-05-06 18:50:16.050879: train_loss -0.4982 
+2025-05-06 18:50:16.162640: val_loss -0.4865 
+2025-05-06 18:50:16.216858: Pseudo dice [np.float32(0.8223), np.float32(0.8667), np.float32(0.8987), np.float32(0.957), np.float32(0.8957), np.float32(0.9537), np.float32(0.9655), np.float32(0.9762), np.float32(0.9686), np.float32(0.9603), np.float32(0.9341), np.float32(0.9647), np.float32(0.968), np.float32(0.908), np.float32(0.9638), np.float32(0.9437), np.float32(0.8909), np.float32(0.8938), np.float32(0.9086)] 
+2025-05-06 18:50:16.260433: Epoch time: 93.23 s 
+2025-05-06 18:50:17.848210:  
+2025-05-06 18:50:17.856968: Epoch 1516 
+2025-05-06 18:50:17.857531: Current learning rate: 0.00279 
+2025-05-06 18:51:51.511476: train_loss -0.4997 
+2025-05-06 18:51:51.657546: val_loss -0.5128 
+2025-05-06 18:51:51.695874: Pseudo dice [np.float32(0.8063), np.float32(0.8236), np.float32(0.9335), np.float32(0.977), np.float32(0.9185), np.float32(0.9624), np.float32(0.967), np.float32(0.9715), np.float32(0.9613), np.float32(0.9591), np.float32(0.9348), np.float32(0.9712), np.float32(0.9662), np.float32(0.908), np.float32(0.9657), np.float32(0.9556), np.float32(0.8763), np.float32(0.8911), np.float32(0.9159)] 
+2025-05-06 18:51:51.737638: Epoch time: 93.66 s 
+2025-05-06 18:51:53.595245:  
+2025-05-06 18:51:53.716528: Epoch 1517 
+2025-05-06 18:51:53.750546: Current learning rate: 0.00278 
+2025-05-06 18:53:30.270946: train_loss -0.4975 
+2025-05-06 18:53:30.341534: val_loss -0.503 
+2025-05-06 18:53:30.367446: Pseudo dice [np.float32(0.8286), np.float32(0.8491), np.float32(0.926), np.float32(0.9784), np.float32(0.9223), np.float32(0.963), np.float32(0.9681), np.float32(0.9744), np.float32(0.9675), np.float32(0.9604), np.float32(0.9389), np.float32(0.966), np.float32(0.961), np.float32(0.9029), np.float32(0.9587), np.float32(0.9403), np.float32(0.7925), np.float32(0.835), np.float32(0.9189)] 
+2025-05-06 18:53:30.382349: Epoch time: 96.68 s 
+2025-05-06 18:53:31.930964:  
+2025-05-06 18:53:32.118118: Epoch 1518 
+2025-05-06 18:53:32.159039: Current learning rate: 0.00278 
+2025-05-06 18:55:14.426607: train_loss -0.4886 
+2025-05-06 18:55:14.475553: val_loss -0.5066 
+2025-05-06 18:55:14.519588: Pseudo dice [np.float32(0.8466), np.float32(0.8682), np.float32(0.8615), np.float32(0.9685), np.float32(0.9068), np.float32(0.9602), np.float32(0.9648), np.float32(0.9777), np.float32(0.9658), np.float32(0.9625), np.float32(0.9501), np.float32(0.968), np.float32(0.9634), np.float32(0.8961), np.float32(0.9683), np.float32(0.9526), np.float32(0.86), np.float32(0.8843), np.float32(0.9202)] 
+2025-05-06 18:55:14.525065: Epoch time: 102.5 s 
+2025-05-06 18:55:16.174371:  
+2025-05-06 18:55:16.275755: Epoch 1519 
+2025-05-06 18:55:16.299730: Current learning rate: 0.00277 
+2025-05-06 18:56:55.497861: train_loss -0.4947 
+2025-05-06 18:56:55.636406: val_loss -0.5266 
+2025-05-06 18:56:55.688755: Pseudo dice [np.float32(0.8648), np.float32(0.8502), np.float32(0.9335), np.float32(0.9638), np.float32(0.8923), np.float32(0.9613), np.float32(0.965), np.float32(0.9664), np.float32(0.9631), np.float32(0.9602), np.float32(0.9439), np.float32(0.9726), np.float32(0.9715), np.float32(0.9138), np.float32(0.9403), np.float32(0.9529), np.float32(0.8949), np.float32(0.9114), np.float32(0.9236)] 
+2025-05-06 18:56:55.767047: Epoch time: 99.32 s 
+2025-05-06 18:57:01.427864:  
+2025-05-06 18:57:01.433869: Epoch 1520 
+2025-05-06 18:57:01.434486: Current learning rate: 0.00277 
+2025-05-06 18:58:37.460628: train_loss -0.496 
+2025-05-06 18:58:37.496973: val_loss -0.5217 
+2025-05-06 18:58:37.504903: Pseudo dice [np.float32(0.8486), np.float32(0.8361), np.float32(0.8929), np.float32(0.9755), np.float32(0.9165), np.float32(0.9645), np.float32(0.9561), np.float32(0.9727), np.float32(0.9702), np.float32(0.9662), np.float32(0.8984), np.float32(0.9704), np.float32(0.9646), np.float32(0.9115), np.float32(0.9676), np.float32(0.9529), np.float32(0.8848), np.float32(0.8685), np.float32(0.9056)] 
+2025-05-06 18:58:37.505965: Epoch time: 96.03 s 
+2025-05-06 18:58:38.991394:  
+2025-05-06 18:58:39.091898: Epoch 1521 
+2025-05-06 18:58:39.131942: Current learning rate: 0.00276 
+2025-05-06 19:00:15.539505: train_loss -0.4868 
+2025-05-06 19:00:15.573366: val_loss -0.4696 
+2025-05-06 19:00:15.577881: Pseudo dice [np.float32(0.8486), np.float32(0.8418), np.float32(0.8697), np.float32(0.9707), np.float32(0.919), np.float32(0.9611), np.float32(0.9584), np.float32(0.974), np.float32(0.9632), np.float32(0.9665), np.float32(0.9535), np.float32(0.9644), np.float32(0.9657), np.float32(0.9124), np.float32(0.9627), np.float32(0.9602), np.float32(0.8886), np.float32(0.9013), np.float32(0.9221)] 
+2025-05-06 19:00:15.589361: Epoch time: 96.55 s 
+2025-05-06 19:00:17.091041:  
+2025-05-06 19:00:17.176613: Epoch 1522 
+2025-05-06 19:00:17.199983: Current learning rate: 0.00276 
+2025-05-06 19:01:54.734796: train_loss -0.509 
+2025-05-06 19:01:54.845910: val_loss -0.4923 
+2025-05-06 19:01:54.871665: Pseudo dice [np.float32(0.865), np.float32(0.8584), np.float32(0.7628), np.float32(0.9754), np.float32(0.8999), np.float32(0.9591), np.float32(0.9459), np.float32(0.9709), np.float32(0.9654), np.float32(0.9639), np.float32(0.9292), np.float32(0.9684), np.float32(0.9657), np.float32(0.9059), np.float32(0.9696), np.float32(0.9566), np.float32(0.89), np.float32(0.8991), np.float32(0.9177)] 
+2025-05-06 19:01:54.892608: Epoch time: 97.64 s 
+2025-05-06 19:01:56.473298:  
+2025-05-06 19:01:56.484010: Epoch 1523 
+2025-05-06 19:01:56.488290: Current learning rate: 0.00275 
+2025-05-06 19:03:31.552639: train_loss -0.5032 
+2025-05-06 19:03:31.598222: val_loss -0.5017 
+2025-05-06 19:03:31.599086: Pseudo dice [np.float32(0.8643), np.float32(0.8427), np.float32(0.9154), np.float32(0.9662), np.float32(0.928), np.float32(0.964), np.float32(0.9625), np.float32(0.9785), np.float32(0.9576), np.float32(0.9681), np.float32(0.9445), np.float32(0.9424), np.float32(0.9715), np.float32(0.9166), np.float32(0.9656), np.float32(0.9545), np.float32(0.8901), np.float32(0.9097), np.float32(0.9042)] 
+2025-05-06 19:03:31.599602: Epoch time: 95.08 s 
+2025-05-06 19:03:33.103712:  
+2025-05-06 19:03:33.106393: Epoch 1524 
+2025-05-06 19:03:33.110727: Current learning rate: 0.00275 
+2025-05-06 19:05:07.190020: train_loss -0.4759 
+2025-05-06 19:05:07.232120: val_loss -0.4869 
+2025-05-06 19:05:07.249409: Pseudo dice [np.float32(0.8352), np.float32(0.8588), np.float32(0.8878), np.float32(0.9641), np.float32(0.9088), np.float32(0.9565), np.float32(0.9589), np.float32(0.981), np.float32(0.9674), np.float32(0.9625), np.float32(0.9463), np.float32(0.9706), np.float32(0.963), np.float32(0.9095), np.float32(0.961), np.float32(0.9522), np.float32(0.8703), np.float32(0.8573), np.float32(0.905)] 
+2025-05-06 19:05:07.275244: Epoch time: 94.09 s 
+2025-05-06 19:05:09.071328:  
+2025-05-06 19:05:09.151203: Epoch 1525 
+2025-05-06 19:05:09.187298: Current learning rate: 0.00274 
+2025-05-06 19:06:44.709172: train_loss -0.4909 
+2025-05-06 19:06:44.832082: val_loss -0.4837 
+2025-05-06 19:06:44.859527: Pseudo dice [np.float32(0.8587), np.float32(0.8533), np.float32(0.9079), np.float32(0.9803), np.float32(0.9089), np.float32(0.9611), np.float32(0.9634), np.float32(0.9787), np.float32(0.9589), np.float32(0.9432), np.float32(0.9108), np.float32(0.9723), np.float32(0.9656), np.float32(0.9061), np.float32(0.9683), np.float32(0.9628), np.float32(0.8972), np.float32(0.9104), np.float32(0.9217)] 
+2025-05-06 19:06:44.874133: Epoch time: 95.64 s 
+2025-05-06 19:06:46.533747:  
+2025-05-06 19:06:46.667082: Epoch 1526 
+2025-05-06 19:06:46.700348: Current learning rate: 0.00274 
+2025-05-06 19:08:26.121136: train_loss -0.5006 
+2025-05-06 19:08:26.268518: val_loss -0.4918 
+2025-05-06 19:08:26.305725: Pseudo dice [np.float32(0.8572), np.float32(0.8535), np.float32(0.9136), np.float32(0.9769), np.float32(0.8807), np.float32(0.9628), np.float32(0.965), np.float32(0.9777), np.float32(0.9722), np.float32(0.9707), np.float32(0.9426), np.float32(0.9701), np.float32(0.9707), np.float32(0.9089), np.float32(0.9704), np.float32(0.9607), np.float32(0.7873), np.float32(0.8238), np.float32(0.9111)] 
+2025-05-06 19:08:26.349522: Epoch time: 99.59 s 
+2025-05-06 19:08:28.043024:  
+2025-05-06 19:08:28.079992: Epoch 1527 
+2025-05-06 19:08:28.084915: Current learning rate: 0.00273 
+2025-05-06 19:10:05.540679: train_loss -0.4987 
+2025-05-06 19:10:05.646673: val_loss -0.4854 
+2025-05-06 19:10:05.684377: Pseudo dice [np.float32(0.8522), np.float32(0.8481), np.float32(0.9259), np.float32(0.9802), np.float32(0.8677), np.float32(0.9609), np.float32(0.9669), np.float32(0.9791), np.float32(0.9604), np.float32(0.967), np.float32(0.9552), np.float32(0.9562), np.float32(0.9713), np.float32(0.9162), np.float32(0.9699), np.float32(0.961), np.float32(0.873), np.float32(0.904), np.float32(0.9135)] 
+2025-05-06 19:10:05.727124: Epoch time: 97.5 s 
+2025-05-06 19:10:07.491400:  
+2025-05-06 19:10:07.530109: Epoch 1528 
+2025-05-06 19:10:07.573888: Current learning rate: 0.00273 
+2025-05-06 19:11:43.602398: train_loss -0.5012 
+2025-05-06 19:11:43.621780: val_loss -0.5132 
+2025-05-06 19:11:43.647628: Pseudo dice [np.float32(0.8478), np.float32(0.8508), np.float32(0.9373), np.float32(0.9421), np.float32(0.9175), np.float32(0.9596), np.float32(0.9665), np.float32(0.9802), np.float32(0.9665), np.float32(0.968), np.float32(0.9476), np.float32(0.9713), np.float32(0.9681), np.float32(0.9137), np.float32(0.964), np.float32(0.9556), np.float32(0.8807), np.float32(0.8755), np.float32(0.9119)] 
+2025-05-06 19:11:43.672571: Epoch time: 96.11 s 
+2025-05-06 19:11:45.530525:  
+2025-05-06 19:11:45.556946: Epoch 1529 
+2025-05-06 19:11:45.564773: Current learning rate: 0.00272 
+2025-05-06 19:13:18.919078: train_loss -0.5048 
+2025-05-06 19:13:19.019736: val_loss -0.4659 
+2025-05-06 19:13:19.043944: Pseudo dice [np.float32(0.8106), np.float32(0.8121), np.float32(0.9261), np.float32(0.9793), np.float32(0.9053), np.float32(0.962), np.float32(0.9612), np.float32(0.9764), np.float32(0.9698), np.float32(0.959), np.float32(0.9269), np.float32(0.9694), np.float32(0.9517), np.float32(0.9108), np.float32(0.9678), np.float32(0.9573), np.float32(0.8923), np.float32(0.8888), np.float32(0.9223)] 
+2025-05-06 19:13:19.071390: Epoch time: 93.39 s 
+2025-05-06 19:13:20.663538:  
+2025-05-06 19:13:20.746391: Epoch 1530 
+2025-05-06 19:13:20.754755: Current learning rate: 0.00272 
+2025-05-06 19:14:58.795472: train_loss -0.4956 
+2025-05-06 19:14:58.890702: val_loss -0.5002 
+2025-05-06 19:14:58.917852: Pseudo dice [np.float32(0.8501), np.float32(0.8353), np.float32(0.9417), np.float32(0.973), np.float32(0.9136), np.float32(0.9653), np.float32(0.9685), np.float32(0.9787), np.float32(0.9641), np.float32(0.9638), np.float32(0.9321), np.float32(0.9692), np.float32(0.9683), np.float32(0.9065), np.float32(0.9706), np.float32(0.9595), np.float32(0.8998), np.float32(0.8958), np.float32(0.9231)] 
+2025-05-06 19:14:58.945360: Epoch time: 98.13 s 
+2025-05-06 19:15:00.591713:  
+2025-05-06 19:15:00.686525: Epoch 1531 
+2025-05-06 19:15:00.720639: Current learning rate: 0.00271 
+2025-05-06 19:16:37.516617: train_loss -0.4937 
+2025-05-06 19:16:37.685382: val_loss -0.4942 
+2025-05-06 19:16:37.694852: Pseudo dice [np.float32(0.8476), np.float32(0.8483), np.float32(0.9226), np.float32(0.9738), np.float32(0.9176), np.float32(0.9479), np.float32(0.9647), np.float32(0.9769), np.float32(0.9584), np.float32(0.9767), np.float32(0.9526), np.float32(0.9649), np.float32(0.9744), np.float32(0.9001), np.float32(0.9597), np.float32(0.9506), np.float32(0.9029), np.float32(0.9024), np.float32(0.9219)] 
+2025-05-06 19:16:37.719392: Epoch time: 96.93 s 
+2025-05-06 19:16:39.389494:  
+2025-05-06 19:16:39.395535: Epoch 1532 
+2025-05-06 19:16:39.398949: Current learning rate: 0.00271 
+2025-05-06 19:18:14.121228: train_loss -0.4863 
+2025-05-06 19:18:14.150840: val_loss -0.5176 
+2025-05-06 19:18:14.152307: Pseudo dice [np.float32(0.839), np.float32(0.8558), np.float32(0.9229), np.float32(0.9728), np.float32(0.9019), np.float32(0.9549), np.float32(0.9671), np.float32(0.9795), np.float32(0.9568), np.float32(0.9676), np.float32(0.9578), np.float32(0.9668), np.float32(0.9751), np.float32(0.9068), np.float32(0.9584), np.float32(0.9578), np.float32(0.8838), np.float32(0.899), np.float32(0.9283)] 
+2025-05-06 19:18:14.157836: Epoch time: 94.73 s 
+2025-05-06 19:18:15.743339:  
+2025-05-06 19:18:15.825913: Epoch 1533 
+2025-05-06 19:18:15.848073: Current learning rate: 0.0027 
+2025-05-06 19:19:55.357165: train_loss -0.5003 
+2025-05-06 19:19:55.466323: val_loss -0.5035 
+2025-05-06 19:19:55.481580: Pseudo dice [np.float32(0.8338), np.float32(0.8524), np.float32(0.7777), np.float32(0.9716), np.float32(0.8931), np.float32(0.9602), np.float32(0.9571), np.float32(0.9648), np.float32(0.9673), np.float32(0.9634), np.float32(0.9549), np.float32(0.9709), np.float32(0.9703), np.float32(0.9086), np.float32(0.9292), np.float32(0.9465), np.float32(0.9019), np.float32(0.8975), np.float32(0.9213)] 
+2025-05-06 19:19:55.514334: Epoch time: 99.62 s 
+2025-05-06 19:19:57.438128:  
+2025-05-06 19:19:57.519938: Epoch 1534 
+2025-05-06 19:19:57.605719: Current learning rate: 0.0027 
+2025-05-06 19:21:31.842206: train_loss -0.4963 
+2025-05-06 19:21:31.985831: val_loss -0.5133 
+2025-05-06 19:21:31.986876: Pseudo dice [np.float32(0.8385), np.float32(0.8524), np.float32(0.8752), np.float32(0.9801), np.float32(0.914), np.float32(0.9592), np.float32(0.9626), np.float32(0.9765), np.float32(0.9562), np.float32(0.9527), np.float32(0.9482), np.float32(0.9623), np.float32(0.9699), np.float32(0.9145), np.float32(0.9674), np.float32(0.9398), np.float32(0.8671), np.float32(0.8684), np.float32(0.9229)] 
+2025-05-06 19:21:31.987449: Epoch time: 94.41 s 
+2025-05-06 19:21:33.632792:  
+2025-05-06 19:21:33.760662: Epoch 1535 
+2025-05-06 19:21:33.800407: Current learning rate: 0.00269 
+2025-05-06 19:23:07.739454: train_loss -0.4997 
+2025-05-06 19:23:07.911791: val_loss -0.531 
+2025-05-06 19:23:07.957738: Pseudo dice [np.float32(0.8655), np.float32(0.8779), np.float32(0.899), np.float32(0.9729), np.float32(0.9123), np.float32(0.968), np.float32(0.9603), np.float32(0.9779), np.float32(0.9746), np.float32(0.9625), np.float32(0.9479), np.float32(0.9677), np.float32(0.9702), np.float32(0.9176), np.float32(0.9701), np.float32(0.9671), np.float32(0.8962), np.float32(0.9006), np.float32(0.9202)] 
+2025-05-06 19:23:08.015293: Epoch time: 94.11 s 
+2025-05-06 19:23:09.858564:  
+2025-05-06 19:23:09.945013: Epoch 1536 
+2025-05-06 19:23:09.963757: Current learning rate: 0.00268 
+2025-05-06 19:24:46.446184: train_loss -0.504 
+2025-05-06 19:24:46.514886: val_loss -0.5153 
+2025-05-06 19:24:46.532871: Pseudo dice [np.float32(0.8411), np.float32(0.8665), np.float32(0.896), np.float32(0.9805), np.float32(0.8882), np.float32(0.9656), np.float32(0.965), np.float32(0.9799), np.float32(0.9609), np.float32(0.9694), np.float32(0.9538), np.float32(0.9646), np.float32(0.9733), np.float32(0.915), np.float32(0.9705), np.float32(0.9551), np.float32(0.911), np.float32(0.9071), np.float32(0.92)] 
+2025-05-06 19:24:46.569427: Epoch time: 96.59 s 
+2025-05-06 19:24:52.069780:  
+2025-05-06 19:24:52.075708: Epoch 1537 
+2025-05-06 19:24:52.076139: Current learning rate: 0.00268 
+2025-05-06 19:26:32.132764: train_loss -0.5013 
+2025-05-06 19:26:32.212158: val_loss -0.5021 
+2025-05-06 19:26:32.231407: Pseudo dice [np.float32(0.8706), np.float32(0.848), np.float32(0.9135), np.float32(0.976), np.float32(0.8966), np.float32(0.9622), np.float32(0.9662), np.float32(0.9771), np.float32(0.9606), np.float32(0.9673), np.float32(0.9515), np.float32(0.9674), np.float32(0.9643), np.float32(0.9167), np.float32(0.9707), np.float32(0.945), np.float32(0.8866), np.float32(0.9099), np.float32(0.9145)] 
+2025-05-06 19:26:32.245689: Epoch time: 100.06 s 
+2025-05-06 19:26:33.851392:  
+2025-05-06 19:26:33.974428: Epoch 1538 
+2025-05-06 19:26:33.975370: Current learning rate: 0.00267 
+2025-05-06 19:28:12.301530: train_loss -0.4842 
+2025-05-06 19:28:12.465640: val_loss -0.5046 
+2025-05-06 19:28:12.501183: Pseudo dice [np.float32(0.8816), np.float32(0.8695), np.float32(0.9189), np.float32(0.9706), np.float32(0.9347), np.float32(0.9627), np.float32(0.96), np.float32(0.9804), np.float32(0.9644), np.float32(0.9707), np.float32(0.9543), np.float32(0.9631), np.float32(0.9697), np.float32(0.9157), np.float32(0.9623), np.float32(0.9564), np.float32(0.8845), np.float32(0.8724), np.float32(0.9232)] 
+2025-05-06 19:28:12.521815: Epoch time: 98.45 s 
+2025-05-06 19:28:14.345536:  
+2025-05-06 19:28:14.384191: Epoch 1539 
+2025-05-06 19:28:14.417849: Current learning rate: 0.00267 
+2025-05-06 19:29:56.389051: train_loss -0.4994 
+2025-05-06 19:29:56.507381: val_loss -0.4794 
+2025-05-06 19:29:56.549103: Pseudo dice [np.float32(0.843), np.float32(0.8486), np.float32(0.9473), np.float32(0.9728), np.float32(0.9189), np.float32(0.9632), np.float32(0.9651), np.float32(0.9729), np.float32(0.9601), np.float32(0.9654), np.float32(0.9493), np.float32(0.9665), np.float32(0.9648), np.float32(0.9094), np.float32(0.9345), np.float32(0.947), np.float32(0.865), np.float32(0.8699), np.float32(0.9095)] 
+2025-05-06 19:29:56.603325: Epoch time: 102.04 s 
+2025-05-06 19:29:58.263411:  
+2025-05-06 19:29:58.455290: Epoch 1540 
+2025-05-06 19:29:58.456626: Current learning rate: 0.00266 
+2025-05-06 19:31:38.323494: train_loss -0.5153 
+2025-05-06 19:31:38.396795: val_loss -0.5152 
+2025-05-06 19:31:38.399157: Pseudo dice [np.float32(0.8402), np.float32(0.84), np.float32(0.8826), np.float32(0.9748), np.float32(0.9265), np.float32(0.9555), np.float32(0.9654), np.float32(0.9799), np.float32(0.9669), np.float32(0.9669), np.float32(0.9469), np.float32(0.9697), np.float32(0.9726), np.float32(0.9096), np.float32(0.9689), np.float32(0.9605), np.float32(0.8978), np.float32(0.8994), np.float32(0.9136)] 
+2025-05-06 19:31:38.399961: Epoch time: 100.06 s 
+2025-05-06 19:31:40.090263:  
+2025-05-06 19:31:40.131131: Epoch 1541 
+2025-05-06 19:31:40.131617: Current learning rate: 0.00266 
+2025-05-06 19:33:21.535203: train_loss -0.4953 
+2025-05-06 19:33:21.606380: val_loss -0.5151 
+2025-05-06 19:33:21.607706: Pseudo dice [np.float32(0.8554), np.float32(0.8534), np.float32(0.8238), np.float32(0.977), np.float32(0.8994), np.float32(0.9629), np.float32(0.9573), np.float32(0.9767), np.float32(0.9606), np.float32(0.9694), np.float32(0.9514), np.float32(0.9667), np.float32(0.9653), np.float32(0.9171), np.float32(0.9642), np.float32(0.9608), np.float32(0.8992), np.float32(0.9072), np.float32(0.908)] 
+2025-05-06 19:33:21.608149: Epoch time: 101.45 s 
+2025-05-06 19:33:23.174451:  
+2025-05-06 19:33:23.257247: Epoch 1542 
+2025-05-06 19:33:23.269931: Current learning rate: 0.00265 
+2025-05-06 19:35:02.903133: train_loss -0.4912 
+2025-05-06 19:35:03.048627: val_loss -0.4609 
+2025-05-06 19:35:03.087631: Pseudo dice [np.float32(0.8605), np.float32(0.8598), np.float32(0.9214), np.float32(0.9653), np.float32(0.8993), np.float32(0.9599), np.float32(0.9685), np.float32(0.9754), np.float32(0.9682), np.float32(0.9601), np.float32(0.9533), np.float32(0.9712), np.float32(0.9706), np.float32(0.8987), np.float32(0.9605), np.float32(0.9464), np.float32(0.8695), np.float32(0.8631), np.float32(0.9197)] 
+2025-05-06 19:35:03.134779: Epoch time: 99.73 s 
+2025-05-06 19:35:04.928634:  
+2025-05-06 19:35:04.960243: Epoch 1543 
+2025-05-06 19:35:04.995039: Current learning rate: 0.00265 
+2025-05-06 19:36:42.577571: train_loss -0.5094 
+2025-05-06 19:36:42.756240: val_loss -0.4903 
+2025-05-06 19:36:42.769512: Pseudo dice [np.float32(0.8441), np.float32(0.8514), np.float32(0.9311), np.float32(0.971), np.float32(0.9122), np.float32(0.9683), np.float32(0.969), np.float32(0.9778), np.float32(0.9716), np.float32(0.965), np.float32(0.9472), np.float32(0.9709), np.float32(0.9624), np.float32(0.9141), np.float32(0.9737), np.float32(0.9284), np.float32(0.8906), np.float32(0.8935), np.float32(0.9368)] 
+2025-05-06 19:36:42.777947: Epoch time: 97.65 s 
+2025-05-06 19:36:44.407449:  
+2025-05-06 19:36:44.508212: Epoch 1544 
+2025-05-06 19:36:44.540826: Current learning rate: 0.00264 
+2025-05-06 19:38:23.750958: train_loss -0.4728 
+2025-05-06 19:38:23.766157: val_loss -0.5123 
+2025-05-06 19:38:23.770487: Pseudo dice [np.float32(0.8695), np.float32(0.8401), np.float32(0.9319), np.float32(0.9674), np.float32(0.8484), np.float32(0.9619), np.float32(0.9668), np.float32(0.9792), np.float32(0.9578), np.float32(0.9633), np.float32(0.9569), np.float32(0.9615), np.float32(0.976), np.float32(0.9299), np.float32(0.9711), np.float32(0.9664), np.float32(0.9106), np.float32(0.9267), np.float32(0.9307)] 
+2025-05-06 19:38:23.771234: Epoch time: 99.34 s 
+2025-05-06 19:38:25.522949:  
+2025-05-06 19:38:25.528731: Epoch 1545 
+2025-05-06 19:38:25.529169: Current learning rate: 0.00264 
+2025-05-06 19:40:01.380216: train_loss -0.4855 
+2025-05-06 19:40:01.479158: val_loss -0.5144 
+2025-05-06 19:40:01.486856: Pseudo dice [np.float32(0.8492), np.float32(0.8392), np.float32(0.9256), np.float32(0.9772), np.float32(0.8816), np.float32(0.9568), np.float32(0.9649), np.float32(0.9753), np.float32(0.9668), np.float32(0.9714), np.float32(0.9563), np.float32(0.9662), np.float32(0.9741), np.float32(0.9137), np.float32(0.9615), np.float32(0.9605), np.float32(0.8531), np.float32(0.8674), np.float32(0.9073)] 
+2025-05-06 19:40:01.487651: Epoch time: 95.86 s 
+2025-05-06 19:40:03.443677:  
+2025-05-06 19:40:03.472088: Epoch 1546 
+2025-05-06 19:40:03.508101: Current learning rate: 0.00263 
+2025-05-06 19:41:38.121204: train_loss -0.5027 
+2025-05-06 19:41:38.200630: val_loss -0.5032 
+2025-05-06 19:41:38.249258: Pseudo dice [np.float32(0.8582), np.float32(0.8359), np.float32(0.9283), np.float32(0.9763), np.float32(0.9006), np.float32(0.9113), np.float32(0.9374), np.float32(0.9829), np.float32(0.9616), np.float32(0.9654), np.float32(0.9516), np.float32(0.9704), np.float32(0.9667), np.float32(0.9114), np.float32(0.9509), np.float32(0.9467), np.float32(0.8779), np.float32(0.8825), np.float32(0.9293)] 
+2025-05-06 19:41:38.319460: Epoch time: 94.68 s 
+2025-05-06 19:41:39.898219:  
+2025-05-06 19:41:40.007995: Epoch 1547 
+2025-05-06 19:41:40.030319: Current learning rate: 0.00263 
+2025-05-06 19:43:14.244339: train_loss -0.4922 
+2025-05-06 19:43:14.348954: val_loss -0.5568 
+2025-05-06 19:43:14.357590: Pseudo dice [np.float32(0.8533), np.float32(0.8605), np.float32(0.9218), np.float32(0.9749), np.float32(0.9252), np.float32(0.9596), np.float32(0.9693), np.float32(0.9801), np.float32(0.9688), np.float32(0.9628), np.float32(0.941), np.float32(0.9693), np.float32(0.9638), np.float32(0.9156), np.float32(0.964), np.float32(0.96), np.float32(0.901), np.float32(0.9078), np.float32(0.9164)] 
+2025-05-06 19:43:14.392317: Epoch time: 94.35 s 
+2025-05-06 19:43:16.015485:  
+2025-05-06 19:43:16.096560: Epoch 1548 
+2025-05-06 19:43:16.120690: Current learning rate: 0.00262 
+2025-05-06 19:44:52.532499: train_loss -0.4879 
+2025-05-06 19:44:52.661413: val_loss -0.4628 
+2025-05-06 19:44:52.716083: Pseudo dice [np.float32(0.8538), np.float32(0.8778), np.float32(0.7085), np.float32(0.9671), np.float32(0.9074), np.float32(0.9527), np.float32(0.9511), np.float32(0.9767), np.float32(0.9703), np.float32(0.962), np.float32(0.9469), np.float32(0.9715), np.float32(0.9703), np.float32(0.9016), np.float32(0.9472), np.float32(0.9414), np.float32(0.8905), np.float32(0.8979), np.float32(0.9166)] 
+2025-05-06 19:44:52.750577: Epoch time: 96.52 s 
+2025-05-06 19:44:54.382140:  
+2025-05-06 19:44:54.425586: Epoch 1549 
+2025-05-06 19:44:54.433730: Current learning rate: 0.00262 
+2025-05-06 19:46:28.490534: train_loss -0.4929 
+2025-05-06 19:46:28.665059: val_loss -0.498 
+2025-05-06 19:46:28.701343: Pseudo dice [np.float32(0.8641), np.float32(0.8626), np.float32(0.9053), np.float32(0.9767), np.float32(0.9262), np.float32(0.963), np.float32(0.9701), np.float32(0.9781), np.float32(0.9523), np.float32(0.9719), np.float32(0.9597), np.float32(0.9654), np.float32(0.9752), np.float32(0.9132), np.float32(0.97), np.float32(0.9623), np.float32(0.8859), np.float32(0.8819), np.float32(0.9098)] 
+2025-05-06 19:46:28.732494: Epoch time: 94.11 s 
+2025-05-06 19:46:31.428739:  
+2025-05-06 19:46:31.437337: Epoch 1550 
+2025-05-06 19:46:31.437759: Current learning rate: 0.00261 
+2025-05-06 19:48:08.927571: train_loss -0.5175 
+2025-05-06 19:48:09.073946: val_loss -0.4778 
+2025-05-06 19:48:09.114448: Pseudo dice [np.float32(0.8476), np.float32(0.8128), np.float32(0.9132), np.float32(0.9758), np.float32(0.9131), np.float32(0.9603), np.float32(0.9355), np.float32(0.9472), np.float32(0.9593), np.float32(0.9689), np.float32(0.9478), np.float32(0.9555), np.float32(0.9683), np.float32(0.9038), np.float32(0.9679), np.float32(0.9483), np.float32(0.8451), np.float32(0.8455), np.float32(0.8947)] 
+2025-05-06 19:48:09.156489: Epoch time: 97.5 s 
+2025-05-06 19:48:10.940264:  
+2025-05-06 19:48:10.977063: Epoch 1551 
+2025-05-06 19:48:10.977903: Current learning rate: 0.00261 
+2025-05-06 19:49:46.030071: train_loss -0.505 
+2025-05-06 19:49:46.071869: val_loss -0.5078 
+2025-05-06 19:49:46.092420: Pseudo dice [np.float32(0.8708), np.float32(0.8559), np.float32(0.9153), np.float32(0.9737), np.float32(0.9081), np.float32(0.9574), np.float32(0.9652), np.float32(0.9758), np.float32(0.9469), np.float32(0.9755), np.float32(0.9555), np.float32(0.9573), np.float32(0.9723), np.float32(0.9119), np.float32(0.9667), np.float32(0.9584), np.float32(0.9221), np.float32(0.9179), np.float32(0.9216)] 
+2025-05-06 19:49:46.107529: Epoch time: 95.09 s 
+2025-05-06 19:49:47.675504:  
+2025-05-06 19:49:47.782226: Epoch 1552 
+2025-05-06 19:49:47.789903: Current learning rate: 0.0026 
+2025-05-06 19:51:24.614817: train_loss -0.4966 
+2025-05-06 19:51:24.752588: val_loss -0.5283 
+2025-05-06 19:51:24.777035: Pseudo dice [np.float32(0.848), np.float32(0.8766), np.float32(0.8479), np.float32(0.9739), np.float32(0.8941), np.float32(0.9596), np.float32(0.9691), np.float32(0.978), np.float32(0.96), np.float32(0.9767), np.float32(0.9477), np.float32(0.9665), np.float32(0.9703), np.float32(0.9137), np.float32(0.9665), np.float32(0.9581), np.float32(0.8923), np.float32(0.9029), np.float32(0.9275)] 
+2025-05-06 19:51:24.804837: Epoch time: 96.94 s 
+2025-05-06 19:51:26.480164:  
+2025-05-06 19:51:26.568474: Epoch 1553 
+2025-05-06 19:51:26.582959: Current learning rate: 0.0026 
+2025-05-06 19:53:01.567014: train_loss -0.5037 
+2025-05-06 19:53:01.671607: val_loss -0.5273 
+2025-05-06 19:53:01.704440: Pseudo dice [np.float32(0.8377), np.float32(0.8601), np.float32(0.7426), np.float32(0.967), np.float32(0.9214), np.float32(0.9595), np.float32(0.9628), np.float32(0.9804), np.float32(0.9522), np.float32(0.9686), np.float32(0.9579), np.float32(0.9624), np.float32(0.9739), np.float32(0.9019), np.float32(0.9647), np.float32(0.9612), np.float32(0.8881), np.float32(0.8713), np.float32(0.9089)] 
+2025-05-06 19:53:01.715466: Epoch time: 95.09 s 
+2025-05-06 19:53:06.994166:  
+2025-05-06 19:53:07.001739: Epoch 1554 
+2025-05-06 19:53:07.002141: Current learning rate: 0.00259 
+2025-05-06 19:54:43.937113: train_loss -0.4918 
+2025-05-06 19:54:44.090292: val_loss -0.5347 
+2025-05-06 19:54:44.130632: Pseudo dice [np.float32(0.8584), np.float32(0.8502), np.float32(0.9443), np.float32(0.9792), np.float32(0.9003), np.float32(0.9618), np.float32(0.9685), np.float32(0.9785), np.float32(0.9538), np.float32(0.9694), np.float32(0.9556), np.float32(0.973), np.float32(0.9711), np.float32(0.9144), np.float32(0.9672), np.float32(0.964), np.float32(0.8923), np.float32(0.91), np.float32(0.9208)] 
+2025-05-06 19:54:44.194043: Epoch time: 96.94 s 
+2025-05-06 19:54:45.717184:  
+2025-05-06 19:54:45.779465: Epoch 1555 
+2025-05-06 19:54:45.780196: Current learning rate: 0.00259 
+2025-05-06 19:56:21.854514: train_loss -0.5154 
+2025-05-06 19:56:21.940502: val_loss -0.5218 
+2025-05-06 19:56:21.959142: Pseudo dice [np.float32(0.8595), np.float32(0.852), np.float32(0.9282), np.float32(0.9811), np.float32(0.9111), np.float32(0.9673), np.float32(0.9651), np.float32(0.9806), np.float32(0.9746), np.float32(0.9647), np.float32(0.9485), np.float32(0.9697), np.float32(0.9702), np.float32(0.9206), np.float32(0.9672), np.float32(0.9643), np.float32(0.8792), np.float32(0.8827), np.float32(0.9166)] 
+2025-05-06 19:56:21.970075: Epoch time: 96.14 s 
+2025-05-06 19:56:23.584299:  
+2025-05-06 19:56:23.661963: Epoch 1556 
+2025-05-06 19:56:23.673332: Current learning rate: 0.00258 
+2025-05-06 19:58:02.689957: train_loss -0.495 
+2025-05-06 19:58:02.809500: val_loss -0.503 
+2025-05-06 19:58:02.857302: Pseudo dice [np.float32(0.8637), np.float32(0.8479), np.float32(0.9358), np.float32(0.969), np.float32(0.8696), np.float32(0.9617), np.float32(0.9586), np.float32(0.9751), np.float32(0.9647), np.float32(0.9731), np.float32(0.9601), np.float32(0.9706), np.float32(0.9727), np.float32(0.9116), np.float32(0.9646), np.float32(0.9579), np.float32(0.8597), np.float32(0.899), np.float32(0.926)] 
+2025-05-06 19:58:02.911621: Epoch time: 99.11 s 
+2025-05-06 19:58:04.438074:  
+2025-05-06 19:58:04.601133: Epoch 1557 
+2025-05-06 19:58:04.640893: Current learning rate: 0.00258 
+2025-05-06 19:59:42.804152: train_loss -0.4979 
+2025-05-06 19:59:42.927010: val_loss -0.5131 
+2025-05-06 19:59:42.949221: Pseudo dice [np.float32(0.8492), np.float32(0.8428), np.float32(0.9178), np.float32(0.9766), np.float32(0.9271), np.float32(0.9575), np.float32(0.9618), np.float32(0.9767), np.float32(0.9371), np.float32(0.9643), np.float32(0.9584), np.float32(0.9546), np.float32(0.973), np.float32(0.9082), np.float32(0.9664), np.float32(0.9611), np.float32(0.8989), np.float32(0.9119), np.float32(0.8991)] 
+2025-05-06 19:59:42.967299: Epoch time: 98.37 s 
+2025-05-06 19:59:44.685876:  
+2025-05-06 19:59:44.741118: Epoch 1558 
+2025-05-06 19:59:44.745687: Current learning rate: 0.00257 
+2025-05-06 20:01:24.683518: train_loss -0.4944 
+2025-05-06 20:01:24.743531: val_loss -0.5215 
+2025-05-06 20:01:24.744548: Pseudo dice [np.float32(0.8423), np.float32(0.8462), np.float32(0.8655), np.float32(0.9744), np.float32(0.9122), np.float32(0.9545), np.float32(0.9567), np.float32(0.9809), np.float32(0.9652), np.float32(0.9675), np.float32(0.9559), np.float32(0.9685), np.float32(0.972), np.float32(0.9021), np.float32(0.9656), np.float32(0.9468), np.float32(0.8374), np.float32(0.7949), np.float32(0.9053)] 
+2025-05-06 20:01:24.748758: Epoch time: 100.0 s 
+2025-05-06 20:01:26.296944:  
+2025-05-06 20:01:26.477650: Epoch 1559 
+2025-05-06 20:01:26.504661: Current learning rate: 0.00256 
+2025-05-06 20:03:02.563409: train_loss -0.4934 
+2025-05-06 20:03:02.698459: val_loss -0.5015 
+2025-05-06 20:03:02.768945: Pseudo dice [np.float32(0.8482), np.float32(0.8568), np.float32(0.9316), np.float32(0.9631), np.float32(0.9019), np.float32(0.9603), np.float32(0.9644), np.float32(0.9783), np.float32(0.9504), np.float32(0.9635), np.float32(0.9597), np.float32(0.9657), np.float32(0.9741), np.float32(0.9124), np.float32(0.9662), np.float32(0.9425), np.float32(0.8828), np.float32(0.8761), np.float32(0.9144)] 
+2025-05-06 20:03:02.812468: Epoch time: 96.27 s 
+2025-05-06 20:03:04.439124:  
+2025-05-06 20:03:04.486141: Epoch 1560 
+2025-05-06 20:03:04.518656: Current learning rate: 0.00256 
+2025-05-06 20:04:40.324500: train_loss -0.5005 
+2025-05-06 20:04:40.419830: val_loss -0.4768 
+2025-05-06 20:04:40.444408: Pseudo dice [np.float32(0.8593), np.float32(0.8498), np.float32(0.8241), np.float32(0.9721), np.float32(0.928), np.float32(0.9643), np.float32(0.9522), np.float32(0.9473), np.float32(0.9714), np.float32(0.9695), np.float32(0.954), np.float32(0.9709), np.float32(0.9671), np.float32(0.8934), np.float32(0.9682), np.float32(0.9551), np.float32(0.8999), np.float32(0.9133), np.float32(0.9216)] 
+2025-05-06 20:04:40.471014: Epoch time: 95.89 s 
+2025-05-06 20:04:41.976879:  
+2025-05-06 20:04:42.057164: Epoch 1561 
+2025-05-06 20:04:42.083598: Current learning rate: 0.00255 
+2025-05-06 20:06:20.136490: train_loss -0.513 
+2025-05-06 20:06:20.252496: val_loss -0.5355 
+2025-05-06 20:06:20.286853: Pseudo dice [np.float32(0.8578), np.float32(0.8224), np.float32(0.9127), np.float32(0.973), np.float32(0.9275), np.float32(0.9607), np.float32(0.9683), np.float32(0.9782), np.float32(0.9666), np.float32(0.9553), np.float32(0.9189), np.float32(0.9703), np.float32(0.974), np.float32(0.9052), np.float32(0.969), np.float32(0.964), np.float32(0.9079), np.float32(0.9105), np.float32(0.9299)] 
+2025-05-06 20:06:20.321670: Epoch time: 98.16 s 
+2025-05-06 20:06:21.921427:  
+2025-05-06 20:06:21.994467: Epoch 1562 
+2025-05-06 20:06:22.035327: Current learning rate: 0.00255 
+2025-05-06 20:07:54.813558: train_loss -0.4824 
+2025-05-06 20:07:54.903076: val_loss -0.451 
+2025-05-06 20:07:54.928790: Pseudo dice [np.float32(0.826), np.float32(0.8554), np.float32(0.9251), np.float32(0.9799), np.float32(0.9238), np.float32(0.9549), np.float32(0.9625), np.float32(0.9705), np.float32(0.9652), np.float32(0.9657), np.float32(0.9496), np.float32(0.97), np.float32(0.9689), np.float32(0.9101), np.float32(0.9603), np.float32(0.9594), np.float32(0.8853), np.float32(0.8899), np.float32(0.936)] 
+2025-05-06 20:07:54.939727: Epoch time: 92.89 s 
+2025-05-06 20:07:56.537611:  
+2025-05-06 20:07:56.618763: Epoch 1563 
+2025-05-06 20:07:56.651726: Current learning rate: 0.00254 
+2025-05-06 20:09:36.767457: train_loss -0.5065 
+2025-05-06 20:09:36.870260: val_loss -0.5215 
+2025-05-06 20:09:36.898019: Pseudo dice [np.float32(0.8348), np.float32(0.8684), np.float32(0.7725), np.float32(0.9813), np.float32(0.9164), np.float32(0.9605), np.float32(0.9711), np.float32(0.9822), np.float32(0.9751), np.float32(0.9744), np.float32(0.9582), np.float32(0.9736), np.float32(0.9713), np.float32(0.9122), np.float32(0.959), np.float32(0.9517), np.float32(0.8993), np.float32(0.9077), np.float32(0.93)] 
+2025-05-06 20:09:36.924277: Epoch time: 100.23 s 
+2025-05-06 20:09:38.537569:  
+2025-05-06 20:09:38.629700: Epoch 1564 
+2025-05-06 20:09:38.641376: Current learning rate: 0.00254 
+2025-05-06 20:11:20.873937: train_loss -0.5017 
+2025-05-06 20:11:20.943063: val_loss -0.5109 
+2025-05-06 20:11:20.949556: Pseudo dice [np.float32(0.8725), np.float32(0.8466), np.float32(0.7516), np.float32(0.9773), np.float32(0.9275), np.float32(0.9593), np.float32(0.9679), np.float32(0.9782), np.float32(0.9532), np.float32(0.9494), np.float32(0.9302), np.float32(0.9607), np.float32(0.9606), np.float32(0.9204), np.float32(0.9617), np.float32(0.9575), np.float32(0.8896), np.float32(0.8835), np.float32(0.9165)] 
+2025-05-06 20:11:20.969019: Epoch time: 102.34 s 
+2025-05-06 20:11:22.560009:  
+2025-05-06 20:11:22.608291: Epoch 1565 
+2025-05-06 20:11:22.630629: Current learning rate: 0.00253 
+2025-05-06 20:13:03.924518: train_loss -0.5009 
+2025-05-06 20:13:03.982482: val_loss -0.5417 
+2025-05-06 20:13:03.984689: Pseudo dice [np.float32(0.8462), np.float32(0.8629), np.float32(0.8815), np.float32(0.9749), np.float32(0.9051), np.float32(0.9588), np.float32(0.9561), np.float32(0.9792), np.float32(0.9625), np.float32(0.9625), np.float32(0.9569), np.float32(0.9697), np.float32(0.9732), np.float32(0.915), np.float32(0.9657), np.float32(0.9551), np.float32(0.8806), np.float32(0.8926), np.float32(0.9102)] 
+2025-05-06 20:13:03.985505: Epoch time: 101.37 s 
+2025-05-06 20:13:05.654173:  
+2025-05-06 20:13:05.736703: Epoch 1566 
+2025-05-06 20:13:05.766245: Current learning rate: 0.00253 
+2025-05-06 20:14:41.602374: train_loss -0.507 
+2025-05-06 20:14:41.691774: val_loss -0.5019 
+2025-05-06 20:14:41.721147: Pseudo dice [np.float32(0.8544), np.float32(0.8634), np.float32(0.9234), np.float32(0.9775), np.float32(0.9172), np.float32(0.9636), np.float32(0.9668), np.float32(0.9798), np.float32(0.9672), np.float32(0.9654), np.float32(0.9494), np.float32(0.9676), np.float32(0.9692), np.float32(0.9145), np.float32(0.9562), np.float32(0.9647), np.float32(0.8932), np.float32(0.8788), np.float32(0.9263)] 
+2025-05-06 20:14:41.722289: Epoch time: 95.95 s 
+2025-05-06 20:14:43.408771:  
+2025-05-06 20:14:43.443043: Epoch 1567 
+2025-05-06 20:14:43.452333: Current learning rate: 0.00252 
+2025-05-06 20:16:16.445374: train_loss -0.4821 
+2025-05-06 20:16:16.588328: val_loss -0.502 
+2025-05-06 20:16:16.662443: Pseudo dice [np.float32(0.8453), np.float32(0.8586), np.float32(0.9236), np.float32(0.9731), np.float32(0.9309), np.float32(0.9597), np.float32(0.9609), np.float32(0.9802), np.float32(0.9712), np.float32(0.966), np.float32(0.9467), np.float32(0.9734), np.float32(0.9637), np.float32(0.911), np.float32(0.9666), np.float32(0.9522), np.float32(0.891), np.float32(0.9009), np.float32(0.9268)] 
+2025-05-06 20:16:16.710316: Epoch time: 93.04 s 
+2025-05-06 20:16:18.378670:  
+2025-05-06 20:16:18.418884: Epoch 1568 
+2025-05-06 20:16:18.444958: Current learning rate: 0.00252 
+2025-05-06 20:17:55.529717: train_loss -0.4899 
+2025-05-06 20:17:55.620161: val_loss -0.5098 
+2025-05-06 20:17:55.680779: Pseudo dice [np.float32(0.8541), np.float32(0.8494), np.float32(0.938), np.float32(0.9777), np.float32(0.9207), np.float32(0.9582), np.float32(0.967), np.float32(0.9788), np.float32(0.9611), np.float32(0.9326), np.float32(0.9508), np.float32(0.9688), np.float32(0.9514), np.float32(0.9183), np.float32(0.9312), np.float32(0.9646), np.float32(0.8879), np.float32(0.9002), np.float32(0.9249)] 
+2025-05-06 20:17:55.708011: Epoch time: 97.15 s 
+2025-05-06 20:17:57.567051:  
+2025-05-06 20:17:57.569864: Epoch 1569 
+2025-05-06 20:17:57.570454: Current learning rate: 0.00251 
+2025-05-06 20:19:34.677289: train_loss -0.5106 
+2025-05-06 20:19:34.747837: val_loss -0.5041 
+2025-05-06 20:19:34.780210: Pseudo dice [np.float32(0.8626), np.float32(0.8563), np.float32(0.9288), np.float32(0.9781), np.float32(0.9222), np.float32(0.9555), np.float32(0.9673), np.float32(0.9795), np.float32(0.965), np.float32(0.9698), np.float32(0.9519), np.float32(0.9639), np.float32(0.9721), np.float32(0.8958), np.float32(0.9673), np.float32(0.9516), np.float32(0.8748), np.float32(0.8906), np.float32(0.9032)] 
+2025-05-06 20:19:34.800267: Epoch time: 97.11 s 
+2025-05-06 20:19:36.697270:  
+2025-05-06 20:19:36.742102: Epoch 1570 
+2025-05-06 20:19:36.766347: Current learning rate: 0.00251 
+2025-05-06 20:21:09.440406: train_loss -0.5038 
+2025-05-06 20:21:09.569848: val_loss -0.489 
+2025-05-06 20:21:09.592158: Pseudo dice [np.float32(0.8543), np.float32(0.8473), np.float32(0.9314), np.float32(0.9685), np.float32(0.9234), np.float32(0.9558), np.float32(0.9662), np.float32(0.973), np.float32(0.9481), np.float32(0.9584), np.float32(0.9479), np.float32(0.9601), np.float32(0.9732), np.float32(0.9107), np.float32(0.9421), np.float32(0.9497), np.float32(0.9074), np.float32(0.9025), np.float32(0.9294)] 
+2025-05-06 20:21:09.615920: Epoch time: 92.74 s 
+2025-05-06 20:21:14.497431:  
+2025-05-06 20:21:14.503185: Epoch 1571 
+2025-05-06 20:21:14.503576: Current learning rate: 0.0025 
+2025-05-06 20:22:49.120129: train_loss -0.5026 
+2025-05-06 20:22:49.243816: val_loss -0.519 
+2025-05-06 20:22:49.269875: Pseudo dice [np.float32(0.8491), np.float32(0.8664), np.float32(0.9371), np.float32(0.9768), np.float32(0.9263), np.float32(0.9646), np.float32(0.9654), np.float32(0.9804), np.float32(0.9648), np.float32(0.9626), np.float32(0.9426), np.float32(0.9728), np.float32(0.9659), np.float32(0.9225), np.float32(0.9712), np.float32(0.9585), np.float32(0.9048), np.float32(0.9123), np.float32(0.9217)] 
+2025-05-06 20:22:49.288296: Epoch time: 94.62 s 
+2025-05-06 20:22:49.306220: Yayy! New best EMA pseudo Dice: 0.9336000084877014 
+2025-05-06 20:22:52.068836:  
+2025-05-06 20:22:52.114386: Epoch 1572 
+2025-05-06 20:22:52.144069: Current learning rate: 0.0025 
+2025-05-06 20:24:33.932417: train_loss -0.4851 
+2025-05-06 20:24:34.005342: val_loss -0.4685 
+2025-05-06 20:24:34.028092: Pseudo dice [np.float32(0.8487), np.float32(0.8468), np.float32(0.9035), np.float32(0.9748), np.float32(0.9267), np.float32(0.9631), np.float32(0.9626), np.float32(0.9798), np.float32(0.9694), np.float32(0.9721), np.float32(0.9425), np.float32(0.9676), np.float32(0.9698), np.float32(0.8974), np.float32(0.9599), np.float32(0.9527), np.float32(0.9009), np.float32(0.8916), np.float32(0.9305)] 
+2025-05-06 20:24:34.029179: Epoch time: 101.86 s 
+2025-05-06 20:24:34.029905: Yayy! New best EMA pseudo Dice: 0.9337000250816345 
+2025-05-06 20:24:36.632736:  
+2025-05-06 20:24:36.670158: Epoch 1573 
+2025-05-06 20:24:36.682539: Current learning rate: 0.00249 
+2025-05-06 20:26:16.223569: train_loss -0.4992 
+2025-05-06 20:26:16.347910: val_loss -0.4759 
+2025-05-06 20:26:16.394663: Pseudo dice [np.float32(0.8041), np.float32(0.8354), np.float32(0.9149), np.float32(0.9733), np.float32(0.912), np.float32(0.9602), np.float32(0.9641), np.float32(0.9821), np.float32(0.9537), np.float32(0.9671), np.float32(0.9388), np.float32(0.9651), np.float32(0.9663), np.float32(0.8942), np.float32(0.9667), np.float32(0.959), np.float32(0.8731), np.float32(0.8729), np.float32(0.9173)] 
+2025-05-06 20:26:16.427495: Epoch time: 99.59 s 
+2025-05-06 20:26:18.014088:  
+2025-05-06 20:26:18.048611: Epoch 1574 
+2025-05-06 20:26:18.084498: Current learning rate: 0.00249 
+2025-05-06 20:27:55.194459: train_loss -0.4823 
+2025-05-06 20:27:55.488863: val_loss -0.4993 
+2025-05-06 20:27:55.490123: Pseudo dice [np.float32(0.8407), np.float32(0.8679), np.float32(0.8283), np.float32(0.9742), np.float32(0.9285), np.float32(0.9602), np.float32(0.969), np.float32(0.9794), np.float32(0.9657), np.float32(0.9674), np.float32(0.9462), np.float32(0.9623), np.float32(0.9729), np.float32(0.9085), np.float32(0.9691), np.float32(0.9572), np.float32(0.8969), np.float32(0.8815), np.float32(0.9298)] 
+2025-05-06 20:27:55.490711: Epoch time: 97.18 s 
+2025-05-06 20:27:57.104044:  
+2025-05-06 20:27:57.148622: Epoch 1575 
+2025-05-06 20:27:57.171533: Current learning rate: 0.00248 
+2025-05-06 20:29:30.911816: train_loss -0.5217 
+2025-05-06 20:29:30.965919: val_loss -0.4659 
+2025-05-06 20:29:30.997847: Pseudo dice [np.float32(0.8787), np.float32(0.8526), np.float32(0.9499), np.float32(0.9771), np.float32(0.8712), np.float32(0.9636), np.float32(0.953), np.float32(0.9436), np.float32(0.9498), np.float32(0.9504), np.float32(0.9292), np.float32(0.9642), np.float32(0.9597), np.float32(0.9155), np.float32(0.9317), np.float32(0.9579), np.float32(0.9203), np.float32(0.9034), np.float32(0.9241)] 
+2025-05-06 20:29:31.037700: Epoch time: 93.81 s 
+2025-05-06 20:29:32.879352:  
+2025-05-06 20:29:33.000448: Epoch 1576 
+2025-05-06 20:29:33.006581: Current learning rate: 0.00248 
+2025-05-06 20:31:11.391452: train_loss -0.5122 
+2025-05-06 20:31:11.537192: val_loss -0.5082 
+2025-05-06 20:31:11.554229: Pseudo dice [np.float32(0.8642), np.float32(0.8472), np.float32(0.8822), np.float32(0.9743), np.float32(0.9238), np.float32(0.9622), np.float32(0.9592), np.float32(0.9829), np.float32(0.9616), np.float32(0.9671), np.float32(0.9525), np.float32(0.9708), np.float32(0.97), np.float32(0.9041), np.float32(0.9637), np.float32(0.9582), np.float32(0.9043), np.float32(0.8942), np.float32(0.9061)] 
+2025-05-06 20:31:11.573328: Epoch time: 98.51 s 
+2025-05-06 20:31:13.140703:  
+2025-05-06 20:31:13.256299: Epoch 1577 
+2025-05-06 20:31:13.302053: Current learning rate: 0.00247 
+2025-05-06 20:32:48.178964: train_loss -0.501 
+2025-05-06 20:32:48.255142: val_loss -0.4871 
+2025-05-06 20:32:48.299036: Pseudo dice [np.float32(0.8541), np.float32(0.8679), np.float32(0.8924), np.float32(0.973), np.float32(0.9032), np.float32(0.9539), np.float32(0.9431), np.float32(0.978), np.float32(0.969), np.float32(0.9705), np.float32(0.948), np.float32(0.9681), np.float32(0.9707), np.float32(0.91), np.float32(0.9601), np.float32(0.9648), np.float32(0.9038), np.float32(0.8566), np.float32(0.9034)] 
+2025-05-06 20:32:48.346826: Epoch time: 95.04 s 
+2025-05-06 20:32:49.972796:  
+2025-05-06 20:32:49.998276: Epoch 1578 
+2025-05-06 20:32:49.999821: Current learning rate: 0.00247 
+2025-05-06 20:34:29.349542: train_loss -0.4937 
+2025-05-06 20:34:29.467218: val_loss -0.5606 
+2025-05-06 20:34:29.495146: Pseudo dice [np.float32(0.8481), np.float32(0.834), np.float32(0.9179), np.float32(0.9761), np.float32(0.9406), np.float32(0.9575), np.float32(0.9619), np.float32(0.981), np.float32(0.9707), np.float32(0.9637), np.float32(0.9536), np.float32(0.9713), np.float32(0.9723), np.float32(0.9108), np.float32(0.9674), np.float32(0.9546), np.float32(0.896), np.float32(0.8951), np.float32(0.9184)] 
+2025-05-06 20:34:29.520596: Epoch time: 99.38 s 
+2025-05-06 20:34:31.051500:  
+2025-05-06 20:34:31.160706: Epoch 1579 
+2025-05-06 20:34:31.199909: Current learning rate: 0.00246 
+2025-05-06 20:36:07.880898: train_loss -0.4874 
+2025-05-06 20:36:07.954210: val_loss -0.5022 
+2025-05-06 20:36:07.971792: Pseudo dice [np.float32(0.8629), np.float32(0.8502), np.float32(0.9334), np.float32(0.9735), np.float32(0.9133), np.float32(0.9628), np.float32(0.9684), np.float32(0.9778), np.float32(0.9714), np.float32(0.9612), np.float32(0.9467), np.float32(0.961), np.float32(0.9628), np.float32(0.9104), np.float32(0.962), np.float32(0.957), np.float32(0.8962), np.float32(0.8991), np.float32(0.9187)] 
+2025-05-06 20:36:07.972317: Epoch time: 96.83 s 
+2025-05-06 20:36:09.516431:  
+2025-05-06 20:36:09.535487: Epoch 1580 
+2025-05-06 20:36:09.536027: Current learning rate: 0.00245 
+2025-05-06 20:37:47.863972: train_loss -0.4901 
+2025-05-06 20:37:47.971512: val_loss -0.4683 
+2025-05-06 20:37:48.016142: Pseudo dice [np.float32(0.8573), np.float32(0.8593), np.float32(0.9375), np.float32(0.975), np.float32(0.9268), np.float32(0.9468), np.float32(0.9612), np.float32(0.9817), np.float32(0.9624), np.float32(0.9667), np.float32(0.9462), np.float32(0.9606), np.float32(0.9641), np.float32(0.909), np.float32(0.9529), np.float32(0.9605), np.float32(0.8958), np.float32(0.9098), np.float32(0.9213)] 
+2025-05-06 20:37:48.076954: Epoch time: 98.35 s 
+2025-05-06 20:37:48.129213: Yayy! New best EMA pseudo Dice: 0.9337999820709229 
+2025-05-06 20:37:51.328182:  
+2025-05-06 20:37:51.339497: Epoch 1581 
+2025-05-06 20:37:51.343657: Current learning rate: 0.00245 
+2025-05-06 20:39:26.152632: train_loss -0.5029 
+2025-05-06 20:39:26.286229: val_loss -0.536 
+2025-05-06 20:39:26.322475: Pseudo dice [np.float32(0.8738), np.float32(0.8524), np.float32(0.9018), np.float32(0.9765), np.float32(0.9031), np.float32(0.9441), np.float32(0.9534), np.float32(0.9796), np.float32(0.9666), np.float32(0.97), np.float32(0.9545), np.float32(0.9725), np.float32(0.9707), np.float32(0.9175), np.float32(0.9602), np.float32(0.958), np.float32(0.9065), np.float32(0.9149), np.float32(0.9195)] 
+2025-05-06 20:39:26.335089: Epoch time: 94.83 s 
+2025-05-06 20:39:26.342261: Yayy! New best EMA pseudo Dice: 0.9340000152587891 
+2025-05-06 20:39:28.861683:  
+2025-05-06 20:39:28.900581: Epoch 1582 
+2025-05-06 20:39:28.916238: Current learning rate: 0.00244 
+2025-05-06 20:41:12.672725: train_loss -0.5088 
+2025-05-06 20:41:12.886785: val_loss -0.4619 
+2025-05-06 20:41:12.923755: Pseudo dice [np.float32(0.8701), np.float32(0.8635), np.float32(0.93), np.float32(0.976), np.float32(0.9178), np.float32(0.9648), np.float32(0.9732), np.float32(0.9804), np.float32(0.9612), np.float32(0.9624), np.float32(0.9442), np.float32(0.9714), np.float32(0.9642), np.float32(0.9189), np.float32(0.9669), np.float32(0.9605), np.float32(0.8512), np.float32(0.8972), np.float32(0.9286)] 
+2025-05-06 20:41:12.945392: Epoch time: 103.81 s 
+2025-05-06 20:41:12.974302: Yayy! New best EMA pseudo Dice: 0.9343000054359436 
+2025-05-06 20:41:15.920192:  
+2025-05-06 20:41:15.940527: Epoch 1583 
+2025-05-06 20:41:15.942239: Current learning rate: 0.00244 
+2025-05-06 20:42:56.445795: train_loss -0.4947 
+2025-05-06 20:42:56.491784: val_loss -0.4842 
+2025-05-06 20:42:56.530346: Pseudo dice [np.float32(0.8604), np.float32(0.8561), np.float32(0.9289), np.float32(0.9795), np.float32(0.8899), np.float32(0.9598), np.float32(0.956), np.float32(0.971), np.float32(0.9368), np.float32(0.9585), np.float32(0.9503), np.float32(0.9536), np.float32(0.967), np.float32(0.8963), np.float32(0.9708), np.float32(0.9434), np.float32(0.8574), np.float32(0.8521), np.float32(0.915)] 
+2025-05-06 20:42:56.580756: Epoch time: 100.53 s 
+2025-05-06 20:42:58.193867:  
+2025-05-06 20:42:58.284235: Epoch 1584 
+2025-05-06 20:42:58.316710: Current learning rate: 0.00243 
+2025-05-06 20:44:31.139532: train_loss -0.4846 
+2025-05-06 20:44:31.272279: val_loss -0.4981 
+2025-05-06 20:44:31.306684: Pseudo dice [np.float32(0.8729), np.float32(0.8679), np.float32(0.7967), np.float32(0.955), np.float32(0.9079), np.float32(0.9662), np.float32(0.9691), np.float32(0.9669), np.float32(0.9629), np.float32(0.9719), np.float32(0.9454), np.float32(0.9726), np.float32(0.9717), np.float32(0.9148), np.float32(0.9726), np.float32(0.9555), np.float32(0.8805), np.float32(0.8778), np.float32(0.9201)] 
+2025-05-06 20:44:31.340497: Epoch time: 92.95 s 
+2025-05-06 20:44:33.054109:  
+2025-05-06 20:44:33.093698: Epoch 1585 
+2025-05-06 20:44:33.106911: Current learning rate: 0.00243 
+2025-05-06 20:46:11.433422: train_loss -0.4932 
+2025-05-06 20:46:11.506371: val_loss -0.4835 
+2025-05-06 20:46:11.542298: Pseudo dice [np.float32(0.8631), np.float32(0.8579), np.float32(0.9407), np.float32(0.9809), np.float32(0.9252), np.float32(0.9655), np.float32(0.9685), np.float32(0.9755), np.float32(0.9684), np.float32(0.9738), np.float32(0.9544), np.float32(0.9602), np.float32(0.9726), np.float32(0.9143), np.float32(0.9679), np.float32(0.949), np.float32(0.826), np.float32(0.8582), np.float32(0.9168)] 
+2025-05-06 20:46:11.552700: Epoch time: 98.38 s 
+2025-05-06 20:46:13.109709:  
+2025-05-06 20:46:13.136565: Epoch 1586 
+2025-05-06 20:46:13.171780: Current learning rate: 0.00242 
+2025-05-06 20:47:50.120248: train_loss -0.5096 
+2025-05-06 20:47:50.188511: val_loss -0.5131 
+2025-05-06 20:47:50.197016: Pseudo dice [np.float32(0.8395), np.float32(0.8443), np.float32(0.9214), np.float32(0.9806), np.float32(0.8964), np.float32(0.9558), np.float32(0.9617), np.float32(0.9807), np.float32(0.9643), np.float32(0.9588), np.float32(0.9362), np.float32(0.9632), np.float32(0.963), np.float32(0.9111), np.float32(0.9324), np.float32(0.9568), np.float32(0.8845), np.float32(0.8861), np.float32(0.9141)] 
+2025-05-06 20:47:50.208290: Epoch time: 97.01 s 
+2025-05-06 20:47:55.765814:  
+2025-05-06 20:47:55.770568: Epoch 1587 
+2025-05-06 20:47:55.771101: Current learning rate: 0.00242 
+2025-05-06 20:49:29.801422: train_loss -0.4825 
+2025-05-06 20:49:29.958373: val_loss -0.5208 
+2025-05-06 20:49:29.982896: Pseudo dice [np.float32(0.8111), np.float32(0.8499), np.float32(0.9221), np.float32(0.9694), np.float32(0.9265), np.float32(0.9615), np.float32(0.9633), np.float32(0.9746), np.float32(0.9615), np.float32(0.9656), np.float32(0.9486), np.float32(0.969), np.float32(0.973), np.float32(0.9162), np.float32(0.9257), np.float32(0.9633), np.float32(0.8825), np.float32(0.8817), np.float32(0.9068)] 
+2025-05-06 20:49:30.005138: Epoch time: 94.04 s 
+2025-05-06 20:49:31.566100:  
+2025-05-06 20:49:31.651937: Epoch 1588 
+2025-05-06 20:49:31.687297: Current learning rate: 0.00241 
+2025-05-06 20:51:06.882301: train_loss -0.4881 
+2025-05-06 20:51:06.984524: val_loss -0.5016 
+2025-05-06 20:51:06.997668: Pseudo dice [np.float32(0.8568), np.float32(0.8412), np.float32(0.9382), np.float32(0.9735), np.float32(0.91), np.float32(0.9628), np.float32(0.9705), np.float32(0.9526), np.float32(0.9701), np.float32(0.9509), np.float32(0.9545), np.float32(0.9739), np.float32(0.9693), np.float32(0.9), np.float32(0.9616), np.float32(0.9508), np.float32(0.8473), np.float32(0.886), np.float32(0.9174)] 
+2025-05-06 20:51:07.009515: Epoch time: 95.32 s 
+2025-05-06 20:51:08.691078:  
+2025-05-06 20:51:08.745652: Epoch 1589 
+2025-05-06 20:51:08.759032: Current learning rate: 0.00241 
+2025-05-06 20:52:43.744058: train_loss -0.5193 
+2025-05-06 20:52:43.876584: val_loss -0.5149 
+2025-05-06 20:52:43.909691: Pseudo dice [np.float32(0.8444), np.float32(0.8535), np.float32(0.8062), np.float32(0.9706), np.float32(0.9001), np.float32(0.956), np.float32(0.9644), np.float32(0.9749), np.float32(0.925), np.float32(0.9724), np.float32(0.9516), np.float32(0.9662), np.float32(0.9677), np.float32(0.9089), np.float32(0.954), np.float32(0.9508), np.float32(0.895), np.float32(0.8953), np.float32(0.9317)] 
+2025-05-06 20:52:43.942394: Epoch time: 95.05 s 
+2025-05-06 20:52:45.581353:  
+2025-05-06 20:52:45.684793: Epoch 1590 
+2025-05-06 20:52:45.717859: Current learning rate: 0.0024 
+2025-05-06 20:54:22.843279: train_loss -0.4988 
+2025-05-06 20:54:22.932387: val_loss -0.5078 
+2025-05-06 20:54:22.953212: Pseudo dice [np.float32(0.8378), np.float32(0.8484), np.float32(0.8059), np.float32(0.9714), np.float32(0.9219), np.float32(0.9617), np.float32(0.9602), np.float32(0.9682), np.float32(0.954), np.float32(0.9727), np.float32(0.9506), np.float32(0.9666), np.float32(0.9733), np.float32(0.9066), np.float32(0.964), np.float32(0.954), np.float32(0.8615), np.float32(0.8646), np.float32(0.9234)] 
+2025-05-06 20:54:22.974586: Epoch time: 97.26 s 
+2025-05-06 20:54:24.570734:  
+2025-05-06 20:54:24.683109: Epoch 1591 
+2025-05-06 20:54:24.739196: Current learning rate: 0.0024 
+2025-05-06 20:56:00.847247: train_loss -0.5134 
+2025-05-06 20:56:00.981376: val_loss -0.4744 
+2025-05-06 20:56:01.013471: Pseudo dice [np.float32(0.8312), np.float32(0.8556), np.float32(0.9044), np.float32(0.9696), np.float32(0.9239), np.float32(0.9527), np.float32(0.9612), np.float32(0.9624), np.float32(0.9475), np.float32(0.9728), np.float32(0.9524), np.float32(0.9448), np.float32(0.9719), np.float32(0.9135), np.float32(0.9679), np.float32(0.9512), np.float32(0.8994), np.float32(0.8857), np.float32(0.9313)] 
+2025-05-06 20:56:01.045738: Epoch time: 96.28 s 
+2025-05-06 20:56:02.668293:  
+2025-05-06 20:56:02.790866: Epoch 1592 
+2025-05-06 20:56:02.816756: Current learning rate: 0.00239 
+2025-05-06 20:57:37.214803: train_loss -0.4911 
+2025-05-06 20:57:37.303877: val_loss -0.4823 
+2025-05-06 20:57:37.334820: Pseudo dice [np.float32(0.8297), np.float32(0.8418), np.float32(0.9435), np.float32(0.9788), np.float32(0.9234), np.float32(0.9607), np.float32(0.9549), np.float32(0.981), np.float32(0.9591), np.float32(0.9582), np.float32(0.9355), np.float32(0.9665), np.float32(0.9668), np.float32(0.9145), np.float32(0.9686), np.float32(0.9522), np.float32(0.8769), np.float32(0.8831), np.float32(0.9182)] 
+2025-05-06 20:57:37.359320: Epoch time: 94.55 s 
+2025-05-06 20:57:39.082699:  
+2025-05-06 20:57:39.135456: Epoch 1593 
+2025-05-06 20:57:39.168705: Current learning rate: 0.00239 
+2025-05-06 20:59:20.851123: train_loss -0.5003 
+2025-05-06 20:59:20.969503: val_loss -0.4906 
+2025-05-06 20:59:20.970487: Pseudo dice [np.float32(0.8508), np.float32(0.8592), np.float32(0.9101), np.float32(0.9535), np.float32(0.9191), np.float32(0.9609), np.float32(0.9718), np.float32(0.9772), np.float32(0.9692), np.float32(0.9745), np.float32(0.9584), np.float32(0.9698), np.float32(0.9689), np.float32(0.9111), np.float32(0.9656), np.float32(0.9569), np.float32(0.8834), np.float32(0.8982), np.float32(0.9131)] 
+2025-05-06 20:59:20.978838: Epoch time: 101.77 s 
+2025-05-06 20:59:22.801139:  
+2025-05-06 20:59:22.886913: Epoch 1594 
+2025-05-06 20:59:22.906345: Current learning rate: 0.00238 
+2025-05-06 21:01:00.335691: train_loss -0.5182 
+2025-05-06 21:01:00.425740: val_loss -0.5356 
+2025-05-06 21:01:00.468344: Pseudo dice [np.float32(0.8584), np.float32(0.8528), np.float32(0.9347), np.float32(0.9705), np.float32(0.9184), np.float32(0.9604), np.float32(0.9585), np.float32(0.9742), np.float32(0.9639), np.float32(0.9483), np.float32(0.9415), np.float32(0.9723), np.float32(0.9668), np.float32(0.9124), np.float32(0.9665), np.float32(0.9535), np.float32(0.841), np.float32(0.8991), np.float32(0.9298)] 
+2025-05-06 21:01:00.504354: Epoch time: 97.54 s 
+2025-05-06 21:01:02.303877:  
+2025-05-06 21:01:02.325728: Epoch 1595 
+2025-05-06 21:01:02.347790: Current learning rate: 0.00238 
+2025-05-06 21:02:36.780597: train_loss -0.5041 
+2025-05-06 21:02:36.881380: val_loss -0.4777 
+2025-05-06 21:02:36.914899: Pseudo dice [np.float32(0.8457), np.float32(0.8383), np.float32(0.9158), np.float32(0.9728), np.float32(0.9348), np.float32(0.9649), np.float32(0.9521), np.float32(0.9573), np.float32(0.9478), np.float32(0.9553), np.float32(0.9442), np.float32(0.9646), np.float32(0.972), np.float32(0.8934), np.float32(0.9712), np.float32(0.9507), np.float32(0.8233), np.float32(0.8617), np.float32(0.924)] 
+2025-05-06 21:02:36.948802: Epoch time: 94.48 s 
+2025-05-06 21:02:38.668826:  
+2025-05-06 21:02:38.804602: Epoch 1596 
+2025-05-06 21:02:38.832448: Current learning rate: 0.00237 
+2025-05-06 21:04:21.164720: train_loss -0.5051 
+2025-05-06 21:04:21.253829: val_loss -0.4944 
+2025-05-06 21:04:21.271073: Pseudo dice [np.float32(0.8507), np.float32(0.8539), np.float32(0.8896), np.float32(0.9689), np.float32(0.9095), np.float32(0.9611), np.float32(0.957), np.float32(0.9752), np.float32(0.9592), np.float32(0.972), np.float32(0.9552), np.float32(0.9712), np.float32(0.972), np.float32(0.9029), np.float32(0.947), np.float32(0.956), np.float32(0.8132), np.float32(0.8399), np.float32(0.9093)] 
+2025-05-06 21:04:21.296721: Epoch time: 102.5 s 
+2025-05-06 21:04:23.070291:  
+2025-05-06 21:04:23.073006: Epoch 1597 
+2025-05-06 21:04:23.073613: Current learning rate: 0.00237 
+2025-05-06 21:06:00.972211: train_loss -0.4999 
+2025-05-06 21:06:01.041304: val_loss -0.4715 
+2025-05-06 21:06:01.064468: Pseudo dice [np.float32(0.8591), np.float32(0.8366), np.float32(0.922), np.float32(0.973), np.float32(0.9263), np.float32(0.9634), np.float32(0.9667), np.float32(0.9765), np.float32(0.9651), np.float32(0.9637), np.float32(0.959), np.float32(0.9702), np.float32(0.971), np.float32(0.8903), np.float32(0.9644), np.float32(0.9464), np.float32(0.8815), np.float32(0.8958), np.float32(0.9232)] 
+2025-05-06 21:06:01.106756: Epoch time: 97.9 s 
+2025-05-06 21:06:02.896983:  
+2025-05-06 21:06:02.927834: Epoch 1598 
+2025-05-06 21:06:02.942164: Current learning rate: 0.00236 
+2025-05-06 21:07:43.310535: train_loss -0.4872 
+2025-05-06 21:07:43.444775: val_loss -0.5177 
+2025-05-06 21:07:43.478271: Pseudo dice [np.float32(0.862), np.float32(0.8718), np.float32(0.937), np.float32(0.9754), np.float32(0.9262), np.float32(0.9492), np.float32(0.9651), np.float32(0.9752), np.float32(0.9649), np.float32(0.9574), np.float32(0.9441), np.float32(0.9631), np.float32(0.9664), np.float32(0.9124), np.float32(0.954), np.float32(0.9547), np.float32(0.8838), np.float32(0.9061), np.float32(0.9287)] 
+2025-05-06 21:07:43.503906: Epoch time: 100.41 s 
+2025-05-06 21:07:45.214013:  
+2025-05-06 21:07:45.241010: Epoch 1599 
+2025-05-06 21:07:45.248673: Current learning rate: 0.00235 
+2025-05-06 21:09:27.363888: train_loss -0.4927 
+2025-05-06 21:09:27.416210: val_loss -0.4904 
+2025-05-06 21:09:27.440289: Pseudo dice [np.float32(0.8478), np.float32(0.8524), np.float32(0.9338), np.float32(0.9649), np.float32(0.9113), np.float32(0.9637), np.float32(0.9563), np.float32(0.9476), np.float32(0.9592), np.float32(0.9635), np.float32(0.9454), np.float32(0.9686), np.float32(0.9725), np.float32(0.9162), np.float32(0.9618), np.float32(0.9455), np.float32(0.8793), np.float32(0.8371), np.float32(0.9095)] 
+2025-05-06 21:09:27.465223: Epoch time: 102.15 s 
+2025-05-06 21:09:30.084348:  
+2025-05-06 21:09:30.098287: Epoch 1600 
+2025-05-06 21:09:30.098752: Current learning rate: 0.00235 
+2025-05-06 21:11:07.756551: train_loss -0.5015 
+2025-05-06 21:11:07.848927: val_loss -0.4824 
+2025-05-06 21:11:07.882042: Pseudo dice [np.float32(0.8165), np.float32(0.8578), np.float32(0.9187), np.float32(0.9736), np.float32(0.878), np.float32(0.9501), np.float32(0.9632), np.float32(0.9805), np.float32(0.9576), np.float32(0.9686), np.float32(0.9533), np.float32(0.9387), np.float32(0.9691), np.float32(0.9141), np.float32(0.9725), np.float32(0.9587), np.float32(0.8894), np.float32(0.8994), np.float32(0.9238)] 
+2025-05-06 21:11:07.915799: Epoch time: 97.67 s 
+2025-05-06 21:11:09.536844:  
+2025-05-06 21:11:09.646213: Epoch 1601 
+2025-05-06 21:11:09.675411: Current learning rate: 0.00234 
+2025-05-06 21:12:47.253810: train_loss -0.4909 
+2025-05-06 21:12:47.294851: val_loss -0.4551 
+2025-05-06 21:12:47.299203: Pseudo dice [np.float32(0.8496), np.float32(0.8752), np.float32(0.7358), np.float32(0.9464), np.float32(0.9105), np.float32(0.9537), np.float32(0.944), np.float32(0.9612), np.float32(0.9393), np.float32(0.9642), np.float32(0.9427), np.float32(0.9574), np.float32(0.9679), np.float32(0.9146), np.float32(0.9509), np.float32(0.9583), np.float32(0.8975), np.float32(0.8635), np.float32(0.9101)] 
+2025-05-06 21:12:47.300013: Epoch time: 97.72 s 
+2025-05-06 21:12:49.023794:  
+2025-05-06 21:12:49.136745: Epoch 1602 
+2025-05-06 21:12:49.176021: Current learning rate: 0.00234 
+2025-05-06 21:14:32.092577: train_loss -0.4935 
+2025-05-06 21:14:32.201710: val_loss -0.5215 
+2025-05-06 21:14:32.240728: Pseudo dice [np.float32(0.8603), np.float32(0.8718), np.float32(0.9115), np.float32(0.9609), np.float32(0.9104), np.float32(0.9573), np.float32(0.9606), np.float32(0.9774), np.float32(0.9615), np.float32(0.9652), np.float32(0.9389), np.float32(0.969), np.float32(0.9678), np.float32(0.9083), np.float32(0.9595), np.float32(0.9578), np.float32(0.8626), np.float32(0.8709), np.float32(0.9238)] 
+2025-05-06 21:14:32.274979: Epoch time: 103.07 s 
+2025-05-06 21:14:33.961591:  
+2025-05-06 21:14:34.044353: Epoch 1603 
+2025-05-06 21:14:34.061765: Current learning rate: 0.00233 
+2025-05-06 21:16:12.630003: train_loss -0.5049 
+2025-05-06 21:16:12.740751: val_loss -0.466 
+2025-05-06 21:16:12.768712: Pseudo dice [np.float32(0.8448), np.float32(0.8711), np.float32(0.9334), np.float32(0.9768), np.float32(0.8898), np.float32(0.9656), np.float32(0.9681), np.float32(0.9768), np.float32(0.9691), np.float32(0.959), np.float32(0.9493), np.float32(0.9698), np.float32(0.971), np.float32(0.9067), np.float32(0.9636), np.float32(0.9587), np.float32(0.9031), np.float32(0.858), np.float32(0.9042)] 
+2025-05-06 21:16:12.769489: Epoch time: 98.67 s 
+2025-05-06 21:16:17.389231:  
+2025-05-06 21:16:17.395277: Epoch 1604 
+2025-05-06 21:16:17.395638: Current learning rate: 0.00233 
+2025-05-06 21:17:56.560400: train_loss -0.4898 
+2025-05-06 21:17:56.646573: val_loss -0.5043 
+2025-05-06 21:17:56.674972: Pseudo dice [np.float32(0.8573), np.float32(0.8319), np.float32(0.8809), np.float32(0.9752), np.float32(0.9063), np.float32(0.9605), np.float32(0.9654), np.float32(0.9785), np.float32(0.9456), np.float32(0.9541), np.float32(0.9483), np.float32(0.9692), np.float32(0.9653), np.float32(0.9062), np.float32(0.9554), np.float32(0.9559), np.float32(0.8995), np.float32(0.91), np.float32(0.9181)] 
+2025-05-06 21:17:56.689533: Epoch time: 99.17 s 
+2025-05-06 21:17:58.320778:  
+2025-05-06 21:17:58.445287: Epoch 1605 
+2025-05-06 21:17:58.477354: Current learning rate: 0.00232 
+2025-05-06 21:19:32.693110: train_loss -0.5011 
+2025-05-06 21:19:32.814101: val_loss -0.5019 
+2025-05-06 21:19:32.850482: Pseudo dice [np.float32(0.8626), np.float32(0.8694), np.float32(0.9293), np.float32(0.9742), np.float32(0.9074), np.float32(0.9578), np.float32(0.9689), np.float32(0.9805), np.float32(0.9665), np.float32(0.9592), np.float32(0.9391), np.float32(0.9713), np.float32(0.9489), np.float32(0.9021), np.float32(0.9616), np.float32(0.957), np.float32(0.9105), np.float32(0.9171), np.float32(0.9219)] 
+2025-05-06 21:19:32.861585: Epoch time: 94.37 s 
+2025-05-06 21:19:34.385154:  
+2025-05-06 21:19:34.419671: Epoch 1606 
+2025-05-06 21:19:34.427413: Current learning rate: 0.00232 
+2025-05-06 21:21:12.627018: train_loss -0.5028 
+2025-05-06 21:21:12.700288: val_loss -0.5388 
+2025-05-06 21:21:12.718663: Pseudo dice [np.float32(0.8474), np.float32(0.8803), np.float32(0.9417), np.float32(0.9727), np.float32(0.9369), np.float32(0.9617), np.float32(0.97), np.float32(0.9812), np.float32(0.9543), np.float32(0.9689), np.float32(0.9629), np.float32(0.9622), np.float32(0.9724), np.float32(0.9172), np.float32(0.9653), np.float32(0.9515), np.float32(0.9052), np.float32(0.9083), np.float32(0.9246)] 
+2025-05-06 21:21:12.726525: Epoch time: 98.24 s 
+2025-05-06 21:21:14.372258:  
+2025-05-06 21:21:14.464432: Epoch 1607 
+2025-05-06 21:21:14.464947: Current learning rate: 0.00231 
+2025-05-06 21:22:51.312369: train_loss -0.4997 
+2025-05-06 21:22:51.367275: val_loss -0.5208 
+2025-05-06 21:22:51.384904: Pseudo dice [np.float32(0.84), np.float32(0.8245), np.float32(0.9241), np.float32(0.972), np.float32(0.9195), np.float32(0.9593), np.float32(0.9609), np.float32(0.9796), np.float32(0.9656), np.float32(0.9719), np.float32(0.9512), np.float32(0.9654), np.float32(0.9678), np.float32(0.8982), np.float32(0.9607), np.float32(0.9509), np.float32(0.8628), np.float32(0.8816), np.float32(0.9196)] 
+2025-05-06 21:22:51.400313: Epoch time: 96.94 s 
+2025-05-06 21:22:53.083155:  
+2025-05-06 21:22:53.171509: Epoch 1608 
+2025-05-06 21:22:53.203406: Current learning rate: 0.00231 
+2025-05-06 21:24:32.918531: train_loss -0.4963 
+2025-05-06 21:24:32.992785: val_loss -0.5018 
+2025-05-06 21:24:33.011699: Pseudo dice [np.float32(0.8552), np.float32(0.8574), np.float32(0.9122), np.float32(0.9745), np.float32(0.9309), np.float32(0.9632), np.float32(0.9692), np.float32(0.9731), np.float32(0.9565), np.float32(0.9707), np.float32(0.9508), np.float32(0.9644), np.float32(0.969), np.float32(0.9143), np.float32(0.9702), np.float32(0.9597), np.float32(0.8909), np.float32(0.8938), np.float32(0.8981)] 
+2025-05-06 21:24:33.044739: Epoch time: 99.84 s 
+2025-05-06 21:24:34.779786:  
+2025-05-06 21:24:34.877388: Epoch 1609 
+2025-05-06 21:24:34.893996: Current learning rate: 0.0023 
+2025-05-06 21:26:18.484311: train_loss -0.501 
+2025-05-06 21:26:18.681578: val_loss -0.4531 
+2025-05-06 21:26:18.721450: Pseudo dice [np.float32(0.8101), np.float32(0.8704), np.float32(0.8925), np.float32(0.9768), np.float32(0.8841), np.float32(0.9534), np.float32(0.9615), np.float32(0.9801), np.float32(0.9604), np.float32(0.9689), np.float32(0.9521), np.float32(0.9713), np.float32(0.973), np.float32(0.9002), np.float32(0.9688), np.float32(0.9452), np.float32(0.859), np.float32(0.8538), np.float32(0.9176)] 
+2025-05-06 21:26:18.753761: Epoch time: 103.71 s 
+2025-05-06 21:26:20.439383:  
+2025-05-06 21:26:20.463032: Epoch 1610 
+2025-05-06 21:26:20.471627: Current learning rate: 0.0023 
+2025-05-06 21:27:58.118053: train_loss -0.508 
+2025-05-06 21:27:58.186845: val_loss -0.5183 
+2025-05-06 21:27:58.205173: Pseudo dice [np.float32(0.8731), np.float32(0.8637), np.float32(0.9118), np.float32(0.9664), np.float32(0.9152), np.float32(0.9606), np.float32(0.9688), np.float32(0.9766), np.float32(0.9678), np.float32(0.9576), np.float32(0.9388), np.float32(0.9717), np.float32(0.9688), np.float32(0.92), np.float32(0.9643), np.float32(0.959), np.float32(0.8778), np.float32(0.8664), np.float32(0.9251)] 
+2025-05-06 21:27:58.246464: Epoch time: 97.68 s 
+2025-05-06 21:28:00.005857:  
+2025-05-06 21:28:00.207682: Epoch 1611 
+2025-05-06 21:28:00.208714: Current learning rate: 0.00229 
+2025-05-06 21:29:34.119389: train_loss -0.4959 
+2025-05-06 21:29:34.225995: val_loss -0.5341 
+2025-05-06 21:29:34.244197: Pseudo dice [np.float32(0.8388), np.float32(0.8617), np.float32(0.8838), np.float32(0.9739), np.float32(0.9132), np.float32(0.955), np.float32(0.9665), np.float32(0.9752), np.float32(0.952), np.float32(0.9648), np.float32(0.9493), np.float32(0.9669), np.float32(0.9687), np.float32(0.9231), np.float32(0.9561), np.float32(0.9604), np.float32(0.9087), np.float32(0.8854), np.float32(0.9278)] 
+2025-05-06 21:29:34.269512: Epoch time: 94.11 s 
+2025-05-06 21:29:36.010704:  
+2025-05-06 21:29:36.019841: Epoch 1612 
+2025-05-06 21:29:36.020482: Current learning rate: 0.00229 
+2025-05-06 21:31:13.872164: train_loss -0.5109 
+2025-05-06 21:31:13.976019: val_loss -0.5086 
+2025-05-06 21:31:14.004816: Pseudo dice [np.float32(0.8446), np.float32(0.8373), np.float32(0.819), np.float32(0.9147), np.float32(0.9103), np.float32(0.9563), np.float32(0.9639), np.float32(0.9789), np.float32(0.9478), np.float32(0.9642), np.float32(0.9552), np.float32(0.9657), np.float32(0.9716), np.float32(0.9016), np.float32(0.9616), np.float32(0.9603), np.float32(0.9048), np.float32(0.9017), np.float32(0.9097)] 
+2025-05-06 21:31:14.044221: Epoch time: 97.86 s 
+2025-05-06 21:31:15.729224:  
+2025-05-06 21:31:15.860053: Epoch 1613 
+2025-05-06 21:31:15.883977: Current learning rate: 0.00228 
+2025-05-06 21:32:49.972670: train_loss -0.5102 
+2025-05-06 21:32:50.105475: val_loss -0.4972 
+2025-05-06 21:32:50.141742: Pseudo dice [np.float32(0.8515), np.float32(0.8423), np.float32(0.9132), np.float32(0.9714), np.float32(0.8954), np.float32(0.9643), np.float32(0.9689), np.float32(0.9776), np.float32(0.9674), np.float32(0.9484), np.float32(0.9425), np.float32(0.9696), np.float32(0.9695), np.float32(0.9214), np.float32(0.9685), np.float32(0.965), np.float32(0.8846), np.float32(0.884), np.float32(0.9023)] 
+2025-05-06 21:32:50.169898: Epoch time: 94.24 s 
+2025-05-06 21:32:51.908603:  
+2025-05-06 21:32:51.909837: Epoch 1614 
+2025-05-06 21:32:51.910318: Current learning rate: 0.00228 
+2025-05-06 21:34:25.904920: train_loss -0.5124 
+2025-05-06 21:34:26.041673: val_loss -0.5027 
+2025-05-06 21:34:26.063109: Pseudo dice [np.float32(0.8291), np.float32(0.8672), np.float32(0.8626), np.float32(0.9505), np.float32(0.9245), np.float32(0.9476), np.float32(0.9639), np.float32(0.97), np.float32(0.9508), np.float32(0.9722), np.float32(0.9386), np.float32(0.9722), np.float32(0.9684), np.float32(0.9137), np.float32(0.9028), np.float32(0.9583), np.float32(0.9113), np.float32(0.8708), np.float32(0.9276)] 
+2025-05-06 21:34:26.091822: Epoch time: 94.0 s 
+2025-05-06 21:34:27.747223:  
+2025-05-06 21:34:27.876393: Epoch 1615 
+2025-05-06 21:34:27.898863: Current learning rate: 0.00227 
+2025-05-06 21:36:07.745327: train_loss -0.5001 
+2025-05-06 21:36:07.826884: val_loss -0.4958 
+2025-05-06 21:36:07.851271: Pseudo dice [np.float32(0.8758), np.float32(0.8684), np.float32(0.9387), np.float32(0.9705), np.float32(0.9189), np.float32(0.9612), np.float32(0.9631), np.float32(0.965), np.float32(0.9626), np.float32(0.9536), np.float32(0.9531), np.float32(0.9636), np.float32(0.9729), np.float32(0.9104), np.float32(0.9602), np.float32(0.9576), np.float32(0.908), np.float32(0.8905), np.float32(0.9223)] 
+2025-05-06 21:36:07.892817: Epoch time: 100.0 s 
+2025-05-06 21:36:09.533118:  
+2025-05-06 21:36:09.665274: Epoch 1616 
+2025-05-06 21:36:09.682197: Current learning rate: 0.00226 
+2025-05-06 21:37:46.494866: train_loss -0.5109 
+2025-05-06 21:37:46.574414: val_loss -0.471 
+2025-05-06 21:37:46.594891: Pseudo dice [np.float32(0.8594), np.float32(0.8536), np.float32(0.9111), np.float32(0.971), np.float32(0.9076), np.float32(0.9571), np.float32(0.9665), np.float32(0.9776), np.float32(0.9501), np.float32(0.9599), np.float32(0.9321), np.float32(0.9584), np.float32(0.9482), np.float32(0.9154), np.float32(0.9636), np.float32(0.9491), np.float32(0.8534), np.float32(0.854), np.float32(0.9183)] 
+2025-05-06 21:37:46.621371: Epoch time: 96.96 s 
+2025-05-06 21:37:48.322788:  
+2025-05-06 21:37:48.416756: Epoch 1617 
+2025-05-06 21:37:48.427750: Current learning rate: 0.00226 
+2025-05-06 21:39:24.364792: train_loss -0.4973 
+2025-05-06 21:39:24.507429: val_loss -0.5157 
+2025-05-06 21:39:24.531432: Pseudo dice [np.float32(0.8559), np.float32(0.87), np.float32(0.8526), np.float32(0.9753), np.float32(0.9382), np.float32(0.9535), np.float32(0.9681), np.float32(0.9818), np.float32(0.961), np.float32(0.9724), np.float32(0.9285), np.float32(0.9703), np.float32(0.9699), np.float32(0.9067), np.float32(0.9588), np.float32(0.9628), np.float32(0.8964), np.float32(0.891), np.float32(0.9278)] 
+2025-05-06 21:39:24.539651: Epoch time: 96.04 s 
+2025-05-06 21:39:26.195074:  
+2025-05-06 21:39:26.362954: Epoch 1618 
+2025-05-06 21:39:26.392299: Current learning rate: 0.00225 
+2025-05-06 21:40:59.373156: train_loss -0.507 
+2025-05-06 21:40:59.470498: val_loss -0.4912 
+2025-05-06 21:40:59.499315: Pseudo dice [np.float32(0.8496), np.float32(0.879), np.float32(0.9076), np.float32(0.9735), np.float32(0.9044), np.float32(0.9692), np.float32(0.9679), np.float32(0.9709), np.float32(0.9676), np.float32(0.9704), np.float32(0.9472), np.float32(0.9633), np.float32(0.9737), np.float32(0.9152), np.float32(0.9725), np.float32(0.9607), np.float32(0.8918), np.float32(0.8784), np.float32(0.9269)] 
+2025-05-06 21:40:59.515743: Epoch time: 93.18 s 
+2025-05-06 21:41:01.203383:  
+2025-05-06 21:41:01.280563: Epoch 1619 
+2025-05-06 21:41:01.304977: Current learning rate: 0.00225 
+2025-05-06 21:42:42.702904: train_loss -0.4982 
+2025-05-06 21:42:42.799160: val_loss -0.5013 
+2025-05-06 21:42:42.805160: Pseudo dice [np.float32(0.8341), np.float32(0.8525), np.float32(0.8584), np.float32(0.9704), np.float32(0.9248), np.float32(0.9582), np.float32(0.9587), np.float32(0.9757), np.float32(0.9617), np.float32(0.9557), np.float32(0.9529), np.float32(0.9683), np.float32(0.9625), np.float32(0.9128), np.float32(0.9495), np.float32(0.9474), np.float32(0.8659), np.float32(0.8906), np.float32(0.9211)] 
+2025-05-06 21:42:42.844198: Epoch time: 101.5 s 
+2025-05-06 21:42:44.684296:  
+2025-05-06 21:42:44.753767: Epoch 1620 
+2025-05-06 21:42:44.792999: Current learning rate: 0.00224 
+2025-05-06 21:44:28.399812: train_loss -0.5244 
+2025-05-06 21:44:28.403823: val_loss -0.4833 
+2025-05-06 21:44:28.414371: Pseudo dice [np.float32(0.8589), np.float32(0.8618), np.float32(0.8757), np.float32(0.9809), np.float32(0.9156), np.float32(0.9611), np.float32(0.9635), np.float32(0.9813), np.float32(0.9676), np.float32(0.9705), np.float32(0.9473), np.float32(0.975), np.float32(0.9627), np.float32(0.9041), np.float32(0.9687), np.float32(0.9598), np.float32(0.8818), np.float32(0.9018), np.float32(0.9244)] 
+2025-05-06 21:44:28.420978: Epoch time: 103.72 s 
+2025-05-06 21:44:34.142426:  
+2025-05-06 21:44:34.147575: Epoch 1621 
+2025-05-06 21:44:34.148068: Current learning rate: 0.00224 
+2025-05-06 21:46:09.402099: train_loss -0.4892 
+2025-05-06 21:46:09.556991: val_loss -0.5083 
+2025-05-06 21:46:09.615139: Pseudo dice [np.float32(0.8523), np.float32(0.8538), np.float32(0.8966), np.float32(0.9624), np.float32(0.9093), np.float32(0.9603), np.float32(0.9624), np.float32(0.9757), np.float32(0.9617), np.float32(0.9583), np.float32(0.932), np.float32(0.9723), np.float32(0.9595), np.float32(0.9066), np.float32(0.9692), np.float32(0.9541), np.float32(0.9194), np.float32(0.9051), np.float32(0.9274)] 
+2025-05-06 21:46:09.681400: Epoch time: 95.26 s 
+2025-05-06 21:46:11.374895:  
+2025-05-06 21:46:11.479795: Epoch 1622 
+2025-05-06 21:46:11.586832: Current learning rate: 0.00223 
+2025-05-06 21:47:47.387357: train_loss -0.5034 
+2025-05-06 21:47:47.475880: val_loss -0.5027 
+2025-05-06 21:47:47.546779: Pseudo dice [np.float32(0.8614), np.float32(0.8686), np.float32(0.8994), np.float32(0.9736), np.float32(0.9302), np.float32(0.9644), np.float32(0.9673), np.float32(0.9803), np.float32(0.969), np.float32(0.9582), np.float32(0.9496), np.float32(0.974), np.float32(0.9686), np.float32(0.9153), np.float32(0.9662), np.float32(0.9574), np.float32(0.8836), np.float32(0.9012), np.float32(0.9285)] 
+2025-05-06 21:47:47.578193: Epoch time: 96.01 s 
+2025-05-06 21:47:49.184484:  
+2025-05-06 21:47:49.221826: Epoch 1623 
+2025-05-06 21:47:49.233035: Current learning rate: 0.00223 
+2025-05-06 21:49:28.341512: train_loss -0.4883 
+2025-05-06 21:49:28.417329: val_loss -0.4708 
+2025-05-06 21:49:28.439616: Pseudo dice [np.float32(0.8684), np.float32(0.8671), np.float32(0.9274), np.float32(0.9772), np.float32(0.9094), np.float32(0.9698), np.float32(0.9578), np.float32(0.9807), np.float32(0.9672), np.float32(0.9722), np.float32(0.9578), np.float32(0.9745), np.float32(0.9727), np.float32(0.9143), np.float32(0.9686), np.float32(0.9562), np.float32(0.854), np.float32(0.8832), np.float32(0.9162)] 
+2025-05-06 21:49:28.470321: Epoch time: 99.16 s 
+2025-05-06 21:49:30.060260:  
+2025-05-06 21:49:30.094752: Epoch 1624 
+2025-05-06 21:49:30.097065: Current learning rate: 0.00222 
+2025-05-06 21:51:09.355854: train_loss -0.4884 
+2025-05-06 21:51:09.469743: val_loss -0.4995 
+2025-05-06 21:51:09.527515: Pseudo dice [np.float32(0.8439), np.float32(0.8377), np.float32(0.928), np.float32(0.9732), np.float32(0.9038), np.float32(0.9642), np.float32(0.9541), np.float32(0.9683), np.float32(0.9559), np.float32(0.965), np.float32(0.9499), np.float32(0.9576), np.float32(0.9697), np.float32(0.9023), np.float32(0.9643), np.float32(0.9569), np.float32(0.8798), np.float32(0.8893), np.float32(0.9075)] 
+2025-05-06 21:51:09.549016: Epoch time: 99.3 s 
+2025-05-06 21:51:11.110495:  
+2025-05-06 21:51:11.221135: Epoch 1625 
+2025-05-06 21:51:11.255342: Current learning rate: 0.00222 
+2025-05-06 21:52:49.303384: train_loss -0.4936 
+2025-05-06 21:52:49.368309: val_loss -0.4933 
+2025-05-06 21:52:49.418632: Pseudo dice [np.float32(0.8764), np.float32(0.8817), np.float32(0.9189), np.float32(0.9749), np.float32(0.9426), np.float32(0.9632), np.float32(0.9665), np.float32(0.982), np.float32(0.9632), np.float32(0.9705), np.float32(0.9588), np.float32(0.9713), np.float32(0.9752), np.float32(0.9223), np.float32(0.9674), np.float32(0.9632), np.float32(0.8271), np.float32(0.8772), np.float32(0.9366)] 
+2025-05-06 21:52:49.456405: Epoch time: 98.19 s 
+2025-05-06 21:52:50.948794:  
+2025-05-06 21:52:50.980216: Epoch 1626 
+2025-05-06 21:52:50.982954: Current learning rate: 0.00221 
+2025-05-06 21:54:25.948394: train_loss -0.5084 
+2025-05-06 21:54:26.083969: val_loss -0.5084 
+2025-05-06 21:54:26.112905: Pseudo dice [np.float32(0.8374), np.float32(0.8637), np.float32(0.8959), np.float32(0.9769), np.float32(0.9298), np.float32(0.963), np.float32(0.9621), np.float32(0.9699), np.float32(0.9724), np.float32(0.9507), np.float32(0.9394), np.float32(0.9719), np.float32(0.9701), np.float32(0.9057), np.float32(0.9728), np.float32(0.9609), np.float32(0.8978), np.float32(0.8997), np.float32(0.9103)] 
+2025-05-06 21:54:26.121200: Epoch time: 95.0 s 
+2025-05-06 21:54:27.688496:  
+2025-05-06 21:54:27.747986: Epoch 1627 
+2025-05-06 21:54:27.759496: Current learning rate: 0.00221 
+2025-05-06 21:56:01.301630: train_loss -0.516 
+2025-05-06 21:56:01.370102: val_loss -0.5296 
+2025-05-06 21:56:01.406926: Pseudo dice [np.float32(0.8445), np.float32(0.8501), np.float32(0.9219), np.float32(0.9749), np.float32(0.9202), np.float32(0.9593), np.float32(0.9634), np.float32(0.9784), np.float32(0.9525), np.float32(0.9657), np.float32(0.9574), np.float32(0.9659), np.float32(0.974), np.float32(0.9047), np.float32(0.9647), np.float32(0.954), np.float32(0.9063), np.float32(0.8984), np.float32(0.9162)] 
+2025-05-06 21:56:01.452319: Epoch time: 93.61 s 
+2025-05-06 21:56:03.194564:  
+2025-05-06 21:56:03.221483: Epoch 1628 
+2025-05-06 21:56:03.222236: Current learning rate: 0.0022 
+2025-05-06 21:57:37.559988: train_loss -0.4954 
+2025-05-06 21:57:37.682207: val_loss -0.5214 
+2025-05-06 21:57:37.712900: Pseudo dice [np.float32(0.8633), np.float32(0.8771), np.float32(0.9481), np.float32(0.9737), np.float32(0.9379), np.float32(0.9592), np.float32(0.9657), np.float32(0.9775), np.float32(0.9551), np.float32(0.9647), np.float32(0.9457), np.float32(0.9676), np.float32(0.9737), np.float32(0.9171), np.float32(0.9582), np.float32(0.9537), np.float32(0.8566), np.float32(0.8541), np.float32(0.9163)] 
+2025-05-06 21:57:37.740385: Epoch time: 94.37 s 
+2025-05-06 21:57:39.350003:  
+2025-05-06 21:57:39.484566: Epoch 1629 
+2025-05-06 21:57:39.500867: Current learning rate: 0.0022 
+2025-05-06 21:59:18.512063: train_loss -0.4754 
+2025-05-06 21:59:18.643773: val_loss -0.517 
+2025-05-06 21:59:18.660274: Pseudo dice [np.float32(0.8422), np.float32(0.8675), np.float32(0.9344), np.float32(0.9762), np.float32(0.9185), np.float32(0.9629), np.float32(0.965), np.float32(0.9812), np.float32(0.9676), np.float32(0.9613), np.float32(0.9422), np.float32(0.9689), np.float32(0.9691), np.float32(0.9142), np.float32(0.9688), np.float32(0.9616), np.float32(0.8768), np.float32(0.8903), np.float32(0.9298)] 
+2025-05-06 21:59:18.660865: Epoch time: 99.16 s 
+2025-05-06 21:59:20.286197:  
+2025-05-06 21:59:20.337396: Epoch 1630 
+2025-05-06 21:59:20.395566: Current learning rate: 0.00219 
+2025-05-06 22:00:56.579151: train_loss -0.5059 
+2025-05-06 22:00:56.727222: val_loss -0.495 
+2025-05-06 22:00:56.727870: Pseudo dice [np.float32(0.8618), np.float32(0.8793), np.float32(0.939), np.float32(0.9765), np.float32(0.9212), np.float32(0.9511), np.float32(0.9545), np.float32(0.9834), np.float32(0.9681), np.float32(0.9693), np.float32(0.9541), np.float32(0.9755), np.float32(0.9696), np.float32(0.9184), np.float32(0.959), np.float32(0.9597), np.float32(0.9239), np.float32(0.9192), np.float32(0.9406)] 
+2025-05-06 22:00:56.728259: Epoch time: 96.29 s 
+2025-05-06 22:00:56.728581: Yayy! New best EMA pseudo Dice: 0.9348999857902527 
+2025-05-06 22:00:59.725828:  
+2025-05-06 22:00:59.865549: Epoch 1631 
+2025-05-06 22:00:59.903103: Current learning rate: 0.00218 
+2025-05-06 22:02:38.756239: train_loss -0.49 
+2025-05-06 22:02:38.837139: val_loss -0.4955 
+2025-05-06 22:02:38.848746: Pseudo dice [np.float32(0.8626), np.float32(0.8677), np.float32(0.909), np.float32(0.9232), np.float32(0.9117), np.float32(0.9642), np.float32(0.9631), np.float32(0.9765), np.float32(0.9569), np.float32(0.961), np.float32(0.9487), np.float32(0.9657), np.float32(0.9654), np.float32(0.9123), np.float32(0.9728), np.float32(0.9598), np.float32(0.8408), np.float32(0.8397), np.float32(0.9159)] 
+2025-05-06 22:02:38.861694: Epoch time: 99.03 s 
+2025-05-06 22:02:40.534102:  
+2025-05-06 22:02:40.630165: Epoch 1632 
+2025-05-06 22:02:40.680517: Current learning rate: 0.00218 
+2025-05-06 22:04:14.917444: train_loss -0.4859 
+2025-05-06 22:04:15.010786: val_loss -0.5322 
+2025-05-06 22:04:15.018120: Pseudo dice [np.float32(0.8613), np.float32(0.8701), np.float32(0.9439), np.float32(0.968), np.float32(0.9167), np.float32(0.965), np.float32(0.9613), np.float32(0.98), np.float32(0.9676), np.float32(0.9722), np.float32(0.9463), np.float32(0.9708), np.float32(0.9564), np.float32(0.9192), np.float32(0.9622), np.float32(0.9627), np.float32(0.8754), np.float32(0.8935), np.float32(0.9096)] 
+2025-05-06 22:04:15.041537: Epoch time: 94.38 s 
+2025-05-06 22:04:16.700828:  
+2025-05-06 22:04:16.791692: Epoch 1633 
+2025-05-06 22:04:16.850960: Current learning rate: 0.00217 
+2025-05-06 22:05:59.193236: train_loss -0.4952 
+2025-05-06 22:05:59.301724: val_loss -0.5767 
+2025-05-06 22:05:59.331290: Pseudo dice [np.float32(0.8479), np.float32(0.8619), np.float32(0.9394), np.float32(0.9729), np.float32(0.9171), np.float32(0.9572), np.float32(0.9565), np.float32(0.9776), np.float32(0.9609), np.float32(0.9715), np.float32(0.9505), np.float32(0.97), np.float32(0.9698), np.float32(0.9119), np.float32(0.968), np.float32(0.9638), np.float32(0.896), np.float32(0.8937), np.float32(0.9205)] 
+2025-05-06 22:05:59.392108: Epoch time: 102.49 s 
+2025-05-06 22:06:00.999029:  
+2025-05-06 22:06:01.093427: Epoch 1634 
+2025-05-06 22:06:01.112196: Current learning rate: 0.00217 
+2025-05-06 22:07:41.132389: train_loss -0.5203 
+2025-05-06 22:07:41.251310: val_loss -0.5043 
+2025-05-06 22:07:41.284838: Pseudo dice [np.float32(0.8543), np.float32(0.8452), np.float32(0.8536), np.float32(0.9752), np.float32(0.9243), np.float32(0.9572), np.float32(0.9562), np.float32(0.9804), np.float32(0.9642), np.float32(0.9737), np.float32(0.9516), np.float32(0.9611), np.float32(0.9733), np.float32(0.9187), np.float32(0.97), np.float32(0.9417), np.float32(0.8919), np.float32(0.9028), np.float32(0.9096)] 
+2025-05-06 22:07:41.317990: Epoch time: 100.13 s 
+2025-05-06 22:07:42.939706:  
+2025-05-06 22:07:43.044394: Epoch 1635 
+2025-05-06 22:07:43.073503: Current learning rate: 0.00216 
+2025-05-06 22:09:21.382593: train_loss -0.5021 
+2025-05-06 22:09:21.452195: val_loss -0.4986 
+2025-05-06 22:09:21.478357: Pseudo dice [np.float32(0.8597), np.float32(0.8609), np.float32(0.9322), np.float32(0.9787), np.float32(0.895), np.float32(0.9672), np.float32(0.9646), np.float32(0.9728), np.float32(0.9682), np.float32(0.9672), np.float32(0.9552), np.float32(0.9603), np.float32(0.9688), np.float32(0.9137), np.float32(0.9526), np.float32(0.9543), np.float32(0.9089), np.float32(0.9055), np.float32(0.9192)] 
+2025-05-06 22:09:21.505008: Epoch time: 98.44 s 
+2025-05-06 22:09:23.132226:  
+2025-05-06 22:09:23.206017: Epoch 1636 
+2025-05-06 22:09:23.217156: Current learning rate: 0.00216 
+2025-05-06 22:10:57.382218: train_loss -0.5052 
+2025-05-06 22:10:57.489787: val_loss -0.4986 
+2025-05-06 22:10:57.516410: Pseudo dice [np.float32(0.8451), np.float32(0.8496), np.float32(0.9372), np.float32(0.9767), np.float32(0.9104), np.float32(0.9629), np.float32(0.9642), np.float32(0.9735), np.float32(0.9649), np.float32(0.9638), np.float32(0.9579), np.float32(0.977), np.float32(0.9672), np.float32(0.9146), np.float32(0.9686), np.float32(0.9586), np.float32(0.8931), np.float32(0.8983), np.float32(0.9257)] 
+2025-05-06 22:10:57.527661: Epoch time: 94.25 s 
+2025-05-06 22:10:57.543141: Yayy! New best EMA pseudo Dice: 0.9350000023841858 
+2025-05-06 22:11:03.869414:  
+2025-05-06 22:11:03.875236: Epoch 1637 
+2025-05-06 22:11:03.875721: Current learning rate: 0.00215 
+2025-05-06 22:12:44.548021: train_loss -0.4977 
+2025-05-06 22:12:44.616600: val_loss -0.4933 
+2025-05-06 22:12:44.639295: Pseudo dice [np.float32(0.8445), np.float32(0.8259), np.float32(0.9004), np.float32(0.9731), np.float32(0.9312), np.float32(0.964), np.float32(0.9636), np.float32(0.9806), np.float32(0.9538), np.float32(0.9653), np.float32(0.9527), np.float32(0.9595), np.float32(0.9666), np.float32(0.8958), np.float32(0.9641), np.float32(0.9628), np.float32(0.9049), np.float32(0.8838), np.float32(0.9101)] 
+2025-05-06 22:12:44.661793: Epoch time: 100.68 s 
+2025-05-06 22:12:46.216218:  
+2025-05-06 22:12:46.278202: Epoch 1638 
+2025-05-06 22:12:46.293081: Current learning rate: 0.00215 
+2025-05-06 22:14:30.537653: train_loss -0.4945 
+2025-05-06 22:14:30.654201: val_loss -0.5125 
+2025-05-06 22:14:30.680312: Pseudo dice [np.float32(0.84), np.float32(0.8572), np.float32(0.9311), np.float32(0.9603), np.float32(0.9225), np.float32(0.9639), np.float32(0.9678), np.float32(0.9775), np.float32(0.9644), np.float32(0.9672), np.float32(0.952), np.float32(0.9674), np.float32(0.9721), np.float32(0.9073), np.float32(0.9633), np.float32(0.9574), np.float32(0.901), np.float32(0.8783), np.float32(0.9152)] 
+2025-05-06 22:14:30.715774: Epoch time: 104.32 s 
+2025-05-06 22:14:32.216032:  
+2025-05-06 22:14:32.393769: Epoch 1639 
+2025-05-06 22:14:32.467675: Current learning rate: 0.00214 
+2025-05-06 22:16:08.799224: train_loss -0.5137 
+2025-05-06 22:16:08.870362: val_loss -0.5135 
+2025-05-06 22:16:08.904932: Pseudo dice [np.float32(0.8596), np.float32(0.8673), np.float32(0.9486), np.float32(0.9739), np.float32(0.914), np.float32(0.968), np.float32(0.9685), np.float32(0.9794), np.float32(0.967), np.float32(0.9739), np.float32(0.9549), np.float32(0.9742), np.float32(0.9709), np.float32(0.9237), np.float32(0.9727), np.float32(0.9553), np.float32(0.8424), np.float32(0.8922), np.float32(0.9051)] 
+2025-05-06 22:16:08.907024: Epoch time: 96.58 s 
+2025-05-06 22:16:10.437318:  
+2025-05-06 22:16:10.522899: Epoch 1640 
+2025-05-06 22:16:10.538776: Current learning rate: 0.00214 
+2025-05-06 22:17:44.674956: train_loss -0.5027 
+2025-05-06 22:17:44.765066: val_loss -0.5057 
+2025-05-06 22:17:44.785347: Pseudo dice [np.float32(0.8668), np.float32(0.8522), np.float32(0.941), np.float32(0.9699), np.float32(0.9157), np.float32(0.9554), np.float32(0.9606), np.float32(0.978), np.float32(0.9694), np.float32(0.962), np.float32(0.9509), np.float32(0.9675), np.float32(0.9686), np.float32(0.9108), np.float32(0.9576), np.float32(0.9482), np.float32(0.8987), np.float32(0.8721), np.float32(0.9052)] 
+2025-05-06 22:17:44.798424: Epoch time: 94.24 s 
+2025-05-06 22:17:46.278213:  
+2025-05-06 22:17:46.281302: Epoch 1641 
+2025-05-06 22:17:46.281725: Current learning rate: 0.00213 
+2025-05-06 22:19:23.250555: train_loss -0.4967 
+2025-05-06 22:19:23.313556: val_loss -0.5211 
+2025-05-06 22:19:23.326114: Pseudo dice [np.float32(0.8694), np.float32(0.8518), np.float32(0.9413), np.float32(0.978), np.float32(0.9172), np.float32(0.9656), np.float32(0.9659), np.float32(0.9789), np.float32(0.9773), np.float32(0.968), np.float32(0.9528), np.float32(0.9761), np.float32(0.9742), np.float32(0.909), np.float32(0.9712), np.float32(0.9639), np.float32(0.8944), np.float32(0.8936), np.float32(0.9232)] 
+2025-05-06 22:19:23.326732: Epoch time: 96.97 s 
+2025-05-06 22:19:23.331675: Yayy! New best EMA pseudo Dice: 0.9355000257492065 
+2025-05-06 22:19:26.267802:  
+2025-05-06 22:19:26.272651: Epoch 1642 
+2025-05-06 22:19:26.273012: Current learning rate: 0.00213 
+2025-05-06 22:21:05.231195: train_loss -0.4897 
+2025-05-06 22:21:05.317746: val_loss -0.4945 
+2025-05-06 22:21:05.325610: Pseudo dice [np.float32(0.8526), np.float32(0.8682), np.float32(0.8768), np.float32(0.9726), np.float32(0.9356), np.float32(0.9575), np.float32(0.9697), np.float32(0.9802), np.float32(0.94), np.float32(0.9627), np.float32(0.95), np.float32(0.9686), np.float32(0.9661), np.float32(0.9012), np.float32(0.9629), np.float32(0.9537), np.float32(0.8938), np.float32(0.8928), np.float32(0.9172)] 
+2025-05-06 22:21:05.332959: Epoch time: 98.96 s 
+2025-05-06 22:21:07.249724:  
+2025-05-06 22:21:07.263384: Epoch 1643 
+2025-05-06 22:21:07.264230: Current learning rate: 0.00212 
+2025-05-06 22:22:44.586334: train_loss -0.5056 
+2025-05-06 22:22:44.650154: val_loss -0.5376 
+2025-05-06 22:22:44.673135: Pseudo dice [np.float32(0.8622), np.float32(0.8598), np.float32(0.8141), np.float32(0.9682), np.float32(0.9114), np.float32(0.9645), np.float32(0.9652), np.float32(0.968), np.float32(0.9666), np.float32(0.9723), np.float32(0.9519), np.float32(0.9657), np.float32(0.9746), np.float32(0.915), np.float32(0.9499), np.float32(0.9553), np.float32(0.882), np.float32(0.8808), np.float32(0.9252)] 
+2025-05-06 22:22:44.673960: Epoch time: 97.34 s 
+2025-05-06 22:22:46.271933:  
+2025-05-06 22:22:46.369513: Epoch 1644 
+2025-05-06 22:22:46.384941: Current learning rate: 0.00212 
+2025-05-06 22:24:22.291702: train_loss -0.5007 
+2025-05-06 22:24:22.419566: val_loss -0.5216 
+2025-05-06 22:24:22.434493: Pseudo dice [np.float32(0.8123), np.float32(0.8642), np.float32(0.929), np.float32(0.9778), np.float32(0.9306), np.float32(0.9634), np.float32(0.9706), np.float32(0.9701), np.float32(0.9702), np.float32(0.9681), np.float32(0.957), np.float32(0.9738), np.float32(0.9749), np.float32(0.913), np.float32(0.9634), np.float32(0.9568), np.float32(0.8484), np.float32(0.8575), np.float32(0.9167)] 
+2025-05-06 22:24:22.447237: Epoch time: 96.02 s 
+2025-05-06 22:24:24.008659:  
+2025-05-06 22:24:24.083264: Epoch 1645 
+2025-05-06 22:24:24.090513: Current learning rate: 0.00211 
+2025-05-06 22:26:04.940623: train_loss -0.5038 
+2025-05-06 22:26:04.979817: val_loss -0.5436 
+2025-05-06 22:26:04.980614: Pseudo dice [np.float32(0.859), np.float32(0.8485), np.float32(0.9189), np.float32(0.9749), np.float32(0.9072), np.float32(0.9577), np.float32(0.9623), np.float32(0.9749), np.float32(0.962), np.float32(0.9566), np.float32(0.941), np.float32(0.966), np.float32(0.9643), np.float32(0.9065), np.float32(0.969), np.float32(0.9507), np.float32(0.8421), np.float32(0.9037), np.float32(0.9178)] 
+2025-05-06 22:26:04.981166: Epoch time: 100.93 s 
+2025-05-06 22:26:06.581724:  
+2025-05-06 22:26:06.677675: Epoch 1646 
+2025-05-06 22:26:06.775917: Current learning rate: 0.0021 
+2025-05-06 22:27:43.312550: train_loss -0.5034 
+2025-05-06 22:27:43.444590: val_loss -0.5567 
+2025-05-06 22:27:43.456657: Pseudo dice [np.float32(0.8637), np.float32(0.8423), np.float32(0.93), np.float32(0.9447), np.float32(0.9323), np.float32(0.9697), np.float32(0.9681), np.float32(0.9768), np.float32(0.9705), np.float32(0.9614), np.float32(0.9508), np.float32(0.9745), np.float32(0.9475), np.float32(0.9283), np.float32(0.9483), np.float32(0.9619), np.float32(0.8923), np.float32(0.88), np.float32(0.9235)] 
+2025-05-06 22:27:43.469450: Epoch time: 96.73 s 
+2025-05-06 22:27:45.023325:  
+2025-05-06 22:27:45.065780: Epoch 1647 
+2025-05-06 22:27:45.073822: Current learning rate: 0.0021 
+2025-05-06 22:29:24.559923: train_loss -0.4996 
+2025-05-06 22:29:24.623803: val_loss -0.496 
+2025-05-06 22:29:24.658350: Pseudo dice [np.float32(0.8512), np.float32(0.8542), np.float32(0.7722), np.float32(0.9755), np.float32(0.8094), np.float32(0.9605), np.float32(0.9602), np.float32(0.9743), np.float32(0.9547), np.float32(0.9707), np.float32(0.9559), np.float32(0.9715), np.float32(0.9702), np.float32(0.9036), np.float32(0.9691), np.float32(0.9555), np.float32(0.8897), np.float32(0.8946), np.float32(0.912)] 
+2025-05-06 22:29:24.676848: Epoch time: 99.54 s 
+2025-05-06 22:29:26.154987:  
+2025-05-06 22:29:26.349276: Epoch 1648 
+2025-05-06 22:29:26.392735: Current learning rate: 0.00209 
+2025-05-06 22:31:03.361596: train_loss -0.5092 
+2025-05-06 22:31:03.416437: val_loss -0.5057 
+2025-05-06 22:31:03.442459: Pseudo dice [np.float32(0.8557), np.float32(0.8588), np.float32(0.8171), np.float32(0.9728), np.float32(0.9106), np.float32(0.9638), np.float32(0.968), np.float32(0.9804), np.float32(0.9514), np.float32(0.9669), np.float32(0.9456), np.float32(0.9683), np.float32(0.9707), np.float32(0.9139), np.float32(0.9704), np.float32(0.9638), np.float32(0.8981), np.float32(0.8993), np.float32(0.9323)] 
+2025-05-06 22:31:03.463968: Epoch time: 97.21 s 
+2025-05-06 22:31:05.037372:  
+2025-05-06 22:31:05.057317: Epoch 1649 
+2025-05-06 22:31:05.061606: Current learning rate: 0.00209 
+2025-05-06 22:32:49.063786: train_loss -0.5112 
+2025-05-06 22:32:49.119331: val_loss -0.5025 
+2025-05-06 22:32:49.120505: Pseudo dice [np.float32(0.8506), np.float32(0.8697), np.float32(0.9184), np.float32(0.973), np.float32(0.8976), np.float32(0.9632), np.float32(0.9604), np.float32(0.9699), np.float32(0.952), np.float32(0.9654), np.float32(0.9538), np.float32(0.9641), np.float32(0.973), np.float32(0.9072), np.float32(0.9619), np.float32(0.9537), np.float32(0.9074), np.float32(0.8909), np.float32(0.9211)] 
+2025-05-06 22:32:49.121194: Epoch time: 104.03 s 
+2025-05-06 22:32:52.333050:  
+2025-05-06 22:32:52.376496: Epoch 1650 
+2025-05-06 22:32:52.380587: Current learning rate: 0.00208 
+2025-05-06 22:34:27.276906: train_loss -0.5042 
+2025-05-06 22:34:27.375587: val_loss -0.5071 
+2025-05-06 22:34:27.405927: Pseudo dice [np.float32(0.8615), np.float32(0.8741), np.float32(0.911), np.float32(0.9775), np.float32(0.9019), np.float32(0.9637), np.float32(0.9394), np.float32(0.9771), np.float32(0.9619), np.float32(0.9736), np.float32(0.9485), np.float32(0.9694), np.float32(0.9695), np.float32(0.9112), np.float32(0.9667), np.float32(0.9607), np.float32(0.9026), np.float32(0.917), np.float32(0.9166)] 
+2025-05-06 22:34:27.436536: Epoch time: 94.95 s 
+2025-05-06 22:34:29.089491:  
+2025-05-06 22:34:29.215792: Epoch 1651 
+2025-05-06 22:34:29.294456: Current learning rate: 0.00208 
+2025-05-06 22:36:07.876653: train_loss -0.4944 
+2025-05-06 22:36:07.954689: val_loss -0.517 
+2025-05-06 22:36:07.984417: Pseudo dice [np.float32(0.8541), np.float32(0.8549), np.float32(0.8646), np.float32(0.977), np.float32(0.9196), np.float32(0.9497), np.float32(0.9676), np.float32(0.9809), np.float32(0.9585), np.float32(0.9681), np.float32(0.9542), np.float32(0.9619), np.float32(0.9704), np.float32(0.9058), np.float32(0.9674), np.float32(0.9582), np.float32(0.8671), np.float32(0.8794), np.float32(0.9136)] 
+2025-05-06 22:36:08.012728: Epoch time: 98.79 s 
+2025-05-06 22:36:09.682719:  
+2025-05-06 22:36:09.775903: Epoch 1652 
+2025-05-06 22:36:09.803315: Current learning rate: 0.00207 
+2025-05-06 22:37:49.489739: train_loss -0.4932 
+2025-05-06 22:37:49.587002: val_loss -0.5209 
+2025-05-06 22:37:49.620290: Pseudo dice [np.float32(0.8552), np.float32(0.8721), np.float32(0.8755), np.float32(0.9801), np.float32(0.9125), np.float32(0.9654), np.float32(0.9666), np.float32(0.9784), np.float32(0.9686), np.float32(0.9737), np.float32(0.9538), np.float32(0.972), np.float32(0.9762), np.float32(0.9244), np.float32(0.97), np.float32(0.9611), np.float32(0.9093), np.float32(0.9066), np.float32(0.9106)] 
+2025-05-06 22:37:49.666662: Epoch time: 99.81 s 
+2025-05-06 22:37:51.336066:  
+2025-05-06 22:37:51.408970: Epoch 1653 
+2025-05-06 22:37:51.449772: Current learning rate: 0.00207 
+2025-05-06 22:39:26.255925: train_loss -0.5043 
+2025-05-06 22:39:26.367343: val_loss -0.5022 
+2025-05-06 22:39:26.368124: Pseudo dice [np.float32(0.8511), np.float32(0.8628), np.float32(0.94), np.float32(0.9805), np.float32(0.9056), np.float32(0.9636), np.float32(0.9679), np.float32(0.9749), np.float32(0.9549), np.float32(0.963), np.float32(0.9276), np.float32(0.9693), np.float32(0.9663), np.float32(0.9108), np.float32(0.9719), np.float32(0.9608), np.float32(0.8381), np.float32(0.8276), np.float32(0.9193)] 
+2025-05-06 22:39:26.384295: Epoch time: 94.92 s 
+2025-05-06 22:39:27.933861:  
+2025-05-06 22:39:27.996474: Epoch 1654 
+2025-05-06 22:39:28.014581: Current learning rate: 0.00206 
+2025-05-06 22:41:03.169625: train_loss -0.5001 
+2025-05-06 22:41:03.336598: val_loss -0.5101 
+2025-05-06 22:41:03.382026: Pseudo dice [np.float32(0.8526), np.float32(0.8341), np.float32(0.934), np.float32(0.9742), np.float32(0.9008), np.float32(0.9549), np.float32(0.9675), np.float32(0.9753), np.float32(0.9611), np.float32(0.9646), np.float32(0.9467), np.float32(0.9682), np.float32(0.9683), np.float32(0.908), np.float32(0.9698), np.float32(0.9584), np.float32(0.8564), np.float32(0.9043), np.float32(0.9193)] 
+2025-05-06 22:41:03.423656: Epoch time: 95.24 s 
+2025-05-06 22:41:08.093013:  
+2025-05-06 22:41:08.098850: Epoch 1655 
+2025-05-06 22:41:08.099294: Current learning rate: 0.00206 
+2025-05-06 22:42:50.326017: train_loss -0.5055 
+2025-05-06 22:42:50.330283: val_loss -0.514 
+2025-05-06 22:42:50.330802: Pseudo dice [np.float32(0.839), np.float32(0.8708), np.float32(0.838), np.float32(0.9778), np.float32(0.9087), np.float32(0.9535), np.float32(0.9672), np.float32(0.9795), np.float32(0.9685), np.float32(0.9693), np.float32(0.9611), np.float32(0.9683), np.float32(0.9721), np.float32(0.9032), np.float32(0.9614), np.float32(0.9552), np.float32(0.8957), np.float32(0.9183), np.float32(0.9321)] 
+2025-05-06 22:42:50.331198: Epoch time: 102.23 s 
+2025-05-06 22:42:51.857541:  
+2025-05-06 22:42:51.986706: Epoch 1656 
+2025-05-06 22:42:52.013114: Current learning rate: 0.00205 
+2025-05-06 22:44:35.523847: train_loss -0.5088 
+2025-05-06 22:44:35.638120: val_loss -0.5106 
+2025-05-06 22:44:35.649577: Pseudo dice [np.float32(0.8496), np.float32(0.8505), np.float32(0.9495), np.float32(0.9757), np.float32(0.9108), np.float32(0.9602), np.float32(0.9685), np.float32(0.9783), np.float32(0.968), np.float32(0.9689), np.float32(0.9354), np.float32(0.9668), np.float32(0.9693), np.float32(0.907), np.float32(0.9578), np.float32(0.9576), np.float32(0.8704), np.float32(0.8834), np.float32(0.9229)] 
+2025-05-06 22:44:35.666102: Epoch time: 103.67 s 
+2025-05-06 22:44:37.174485:  
+2025-05-06 22:44:37.334440: Epoch 1657 
+2025-05-06 22:44:37.362105: Current learning rate: 0.00205 
+2025-05-06 22:46:13.462269: train_loss -0.4876 
+2025-05-06 22:46:13.527797: val_loss -0.4579 
+2025-05-06 22:46:13.538481: Pseudo dice [np.float32(0.8252), np.float32(0.8611), np.float32(0.9057), np.float32(0.9744), np.float32(0.9097), np.float32(0.9513), np.float32(0.9554), np.float32(0.9783), np.float32(0.953), np.float32(0.9676), np.float32(0.9462), np.float32(0.968), np.float32(0.9703), np.float32(0.9156), np.float32(0.9139), np.float32(0.9629), np.float32(0.903), np.float32(0.8985), np.float32(0.923)] 
+2025-05-06 22:46:13.576584: Epoch time: 96.29 s 
+2025-05-06 22:46:15.178265:  
+2025-05-06 22:46:15.262910: Epoch 1658 
+2025-05-06 22:46:15.263687: Current learning rate: 0.00204 
+2025-05-06 22:47:51.622097: train_loss -0.5157 
+2025-05-06 22:47:51.748141: val_loss -0.5063 
+2025-05-06 22:47:51.783304: Pseudo dice [np.float32(0.8464), np.float32(0.8535), np.float32(0.9288), np.float32(0.9691), np.float32(0.9072), np.float32(0.9544), np.float32(0.9645), np.float32(0.9777), np.float32(0.9667), np.float32(0.9753), np.float32(0.9536), np.float32(0.9695), np.float32(0.9726), np.float32(0.9157), np.float32(0.9579), np.float32(0.9568), np.float32(0.9144), np.float32(0.9168), np.float32(0.9096)] 
+2025-05-06 22:47:51.829484: Epoch time: 96.44 s 
+2025-05-06 22:47:53.455400:  
+2025-05-06 22:47:53.562032: Epoch 1659 
+2025-05-06 22:47:53.581906: Current learning rate: 0.00203 
+2025-05-06 22:49:31.574041: train_loss -0.5042 
+2025-05-06 22:49:31.632267: val_loss -0.5062 
+2025-05-06 22:49:31.668979: Pseudo dice [np.float32(0.8823), np.float32(0.8591), np.float32(0.8796), np.float32(0.9763), np.float32(0.9306), np.float32(0.963), np.float32(0.9646), np.float32(0.9768), np.float32(0.9464), np.float32(0.9673), np.float32(0.9497), np.float32(0.9576), np.float32(0.9732), np.float32(0.9234), np.float32(0.9703), np.float32(0.9632), np.float32(0.9032), np.float32(0.8853), np.float32(0.9315)] 
+2025-05-06 22:49:31.722604: Epoch time: 98.12 s 
+2025-05-06 22:49:33.273882:  
+2025-05-06 22:49:33.312047: Epoch 1660 
+2025-05-06 22:49:33.331640: Current learning rate: 0.00203 
+2025-05-06 22:51:06.595880: train_loss -0.4904 
+2025-05-06 22:51:06.702917: val_loss -0.4833 
+2025-05-06 22:51:06.731642: Pseudo dice [np.float32(0.8465), np.float32(0.8571), np.float32(0.9197), np.float32(0.9722), np.float32(0.8951), np.float32(0.9645), np.float32(0.9681), np.float32(0.9794), np.float32(0.9626), np.float32(0.9721), np.float32(0.9565), np.float32(0.9712), np.float32(0.9704), np.float32(0.9079), np.float32(0.9686), np.float32(0.9568), np.float32(0.8579), np.float32(0.8902), np.float32(0.9279)] 
+2025-05-06 22:51:06.764028: Epoch time: 93.32 s 
+2025-05-06 22:51:08.466950:  
+2025-05-06 22:51:08.477112: Epoch 1661 
+2025-05-06 22:51:08.507903: Current learning rate: 0.00202 
+2025-05-06 22:52:40.940876: train_loss -0.5225 
+2025-05-06 22:52:41.036936: val_loss -0.5049 
+2025-05-06 22:52:41.048453: Pseudo dice [np.float32(0.8415), np.float32(0.8628), np.float32(0.84), np.float32(0.9785), np.float32(0.8746), np.float32(0.9485), np.float32(0.9698), np.float32(0.9738), np.float32(0.9685), np.float32(0.9587), np.float32(0.9454), np.float32(0.9731), np.float32(0.9681), np.float32(0.9094), np.float32(0.9646), np.float32(0.9558), np.float32(0.8918), np.float32(0.8855), np.float32(0.9195)] 
+2025-05-06 22:52:41.055818: Epoch time: 92.48 s 
+2025-05-06 22:52:42.663463:  
+2025-05-06 22:52:42.769134: Epoch 1662 
+2025-05-06 22:52:42.780591: Current learning rate: 0.00202 
+2025-05-06 22:54:17.320585: train_loss -0.4976 
+2025-05-06 22:54:17.396791: val_loss -0.5341 
+2025-05-06 22:54:17.451535: Pseudo dice [np.float32(0.8708), np.float32(0.8675), np.float32(0.92), np.float32(0.9708), np.float32(0.9192), np.float32(0.9593), np.float32(0.9665), np.float32(0.981), np.float32(0.9621), np.float32(0.9745), np.float32(0.9436), np.float32(0.9593), np.float32(0.9677), np.float32(0.9209), np.float32(0.9682), np.float32(0.9624), np.float32(0.8652), np.float32(0.8988), np.float32(0.9238)] 
+2025-05-06 22:54:17.502859: Epoch time: 94.66 s 
+2025-05-06 22:54:19.394017:  
+2025-05-06 22:54:19.455104: Epoch 1663 
+2025-05-06 22:54:19.466424: Current learning rate: 0.00201 
+2025-05-06 22:55:55.385925: train_loss -0.4941 
+2025-05-06 22:55:55.497465: val_loss -0.5287 
+2025-05-06 22:55:55.499072: Pseudo dice [np.float32(0.8491), np.float32(0.848), np.float32(0.9223), np.float32(0.9702), np.float32(0.9228), np.float32(0.9614), np.float32(0.9691), np.float32(0.9781), np.float32(0.961), np.float32(0.9681), np.float32(0.9489), np.float32(0.9742), np.float32(0.9696), np.float32(0.9163), np.float32(0.9643), np.float32(0.9603), np.float32(0.872), np.float32(0.8856), np.float32(0.9169)] 
+2025-05-06 22:55:55.526640: Epoch time: 95.99 s 
+2025-05-06 22:55:57.235545:  
+2025-05-06 22:55:57.274998: Epoch 1664 
+2025-05-06 22:55:57.293626: Current learning rate: 0.00201 
+2025-05-06 22:57:33.749961: train_loss -0.4994 
+2025-05-06 22:57:33.828158: val_loss -0.5088 
+2025-05-06 22:57:33.842355: Pseudo dice [np.float32(0.8751), np.float32(0.8678), np.float32(0.8875), np.float32(0.9763), np.float32(0.9026), np.float32(0.9636), np.float32(0.9641), np.float32(0.98), np.float32(0.9756), np.float32(0.9736), np.float32(0.9499), np.float32(0.9772), np.float32(0.9702), np.float32(0.9137), np.float32(0.971), np.float32(0.9635), np.float32(0.8946), np.float32(0.9049), np.float32(0.9166)] 
+2025-05-06 22:57:33.878178: Epoch time: 96.52 s 
+2025-05-06 22:57:35.507097:  
+2025-05-06 22:57:35.598393: Epoch 1665 
+2025-05-06 22:57:35.617385: Current learning rate: 0.002 
+2025-05-06 22:59:08.565376: train_loss -0.5071 
+2025-05-06 22:59:08.608737: val_loss -0.5091 
+2025-05-06 22:59:08.614510: Pseudo dice [np.float32(0.8722), np.float32(0.8726), np.float32(0.9073), np.float32(0.9627), np.float32(0.9235), np.float32(0.9636), np.float32(0.9601), np.float32(0.9811), np.float32(0.9697), np.float32(0.971), np.float32(0.956), np.float32(0.9731), np.float32(0.9726), np.float32(0.914), np.float32(0.9648), np.float32(0.9621), np.float32(0.8795), np.float32(0.9068), np.float32(0.9208)] 
+2025-05-06 22:59:08.614995: Epoch time: 93.06 s 
+2025-05-06 22:59:10.189439:  
+2025-05-06 22:59:10.250648: Epoch 1666 
+2025-05-06 22:59:10.291483: Current learning rate: 0.002 
+2025-05-06 23:00:47.225982: train_loss -0.4944 
+2025-05-06 23:00:47.303858: val_loss -0.5414 
+2025-05-06 23:00:47.320119: Pseudo dice [np.float32(0.8428), np.float32(0.8315), np.float32(0.9442), np.float32(0.978), np.float32(0.9105), np.float32(0.9618), np.float32(0.9665), np.float32(0.9577), np.float32(0.9551), np.float32(0.9708), np.float32(0.951), np.float32(0.9611), np.float32(0.9687), np.float32(0.8933), np.float32(0.9326), np.float32(0.9581), np.float32(0.9038), np.float32(0.9246), np.float32(0.9253)] 
+2025-05-06 23:00:47.341846: Epoch time: 97.04 s 
+2025-05-06 23:00:49.029033:  
+2025-05-06 23:00:49.075955: Epoch 1667 
+2025-05-06 23:00:49.112996: Current learning rate: 0.00199 
+2025-05-06 23:02:32.656488: train_loss -0.5041 
+2025-05-06 23:02:32.739312: val_loss -0.4911 
+2025-05-06 23:02:32.758072: Pseudo dice [np.float32(0.844), np.float32(0.863), np.float32(0.9019), np.float32(0.9741), np.float32(0.9229), np.float32(0.9605), np.float32(0.9635), np.float32(0.9816), np.float32(0.9713), np.float32(0.9662), np.float32(0.9568), np.float32(0.9714), np.float32(0.9701), np.float32(0.9168), np.float32(0.963), np.float32(0.961), np.float32(0.8407), np.float32(0.9057), np.float32(0.92)] 
+2025-05-06 23:02:32.769397: Epoch time: 103.63 s 
+2025-05-06 23:02:34.432579:  
+2025-05-06 23:02:34.500024: Epoch 1668 
+2025-05-06 23:02:34.535023: Current learning rate: 0.00199 
+2025-05-06 23:04:11.811879: train_loss -0.5159 
+2025-05-06 23:04:11.953732: val_loss -0.5004 
+2025-05-06 23:04:11.973343: Pseudo dice [np.float32(0.856), np.float32(0.8511), np.float32(0.9398), np.float32(0.9776), np.float32(0.9185), np.float32(0.9611), np.float32(0.9585), np.float32(0.9792), np.float32(0.968), np.float32(0.9638), np.float32(0.9508), np.float32(0.9702), np.float32(0.9699), np.float32(0.9138), np.float32(0.9652), np.float32(0.9638), np.float32(0.8735), np.float32(0.8688), np.float32(0.9097)] 
+2025-05-06 23:04:11.987881: Epoch time: 97.38 s 
+2025-05-06 23:04:13.650367:  
+2025-05-06 23:04:13.752910: Epoch 1669 
+2025-05-06 23:04:13.763924: Current learning rate: 0.00198 
+2025-05-06 23:05:52.381724: train_loss -0.4887 
+2025-05-06 23:05:52.481419: val_loss -0.48 
+2025-05-06 23:05:52.505582: Pseudo dice [np.float32(0.8673), np.float32(0.8646), np.float32(0.9033), np.float32(0.9756), np.float32(0.8889), np.float32(0.9497), np.float32(0.9638), np.float32(0.9658), np.float32(0.9705), np.float32(0.9563), np.float32(0.9434), np.float32(0.9683), np.float32(0.9662), np.float32(0.9092), np.float32(0.9714), np.float32(0.9601), np.float32(0.8588), np.float32(0.8741), np.float32(0.9279)] 
+2025-05-06 23:05:52.530745: Epoch time: 98.73 s 
+2025-05-06 23:05:54.236230:  
+2025-05-06 23:05:54.313544: Epoch 1670 
+2025-05-06 23:05:54.321453: Current learning rate: 0.00198 
+2025-05-06 23:07:27.657870: train_loss -0.4896 
+2025-05-06 23:07:27.755820: val_loss -0.5221 
+2025-05-06 23:07:27.785085: Pseudo dice [np.float32(0.8458), np.float32(0.8602), np.float32(0.8667), np.float32(0.974), np.float32(0.9174), np.float32(0.964), np.float32(0.9657), np.float32(0.962), np.float32(0.9641), np.float32(0.9682), np.float32(0.9593), np.float32(0.9655), np.float32(0.9684), np.float32(0.9054), np.float32(0.9703), np.float32(0.9571), np.float32(0.8754), np.float32(0.9151), np.float32(0.9226)] 
+2025-05-06 23:07:27.831159: Epoch time: 93.42 s 
+2025-05-06 23:07:29.567004:  
+2025-05-06 23:07:29.635193: Epoch 1671 
+2025-05-06 23:07:29.663024: Current learning rate: 0.00197 
+2025-05-06 23:09:10.544121: train_loss -0.4846 
+2025-05-06 23:09:10.779427: val_loss -0.4535 
+2025-05-06 23:09:10.827421: Pseudo dice [np.float32(0.8192), np.float32(0.8586), np.float32(0.9323), np.float32(0.976), np.float32(0.9257), np.float32(0.9602), np.float32(0.9649), np.float32(0.9781), np.float32(0.9676), np.float32(0.9655), np.float32(0.9211), np.float32(0.9717), np.float32(0.9573), np.float32(0.9019), np.float32(0.9402), np.float32(0.9517), np.float32(0.8513), np.float32(0.8738), np.float32(0.9121)] 
+2025-05-06 23:09:10.834475: Epoch time: 100.98 s 
+2025-05-06 23:09:16.001269:  
+2025-05-06 23:09:16.006783: Epoch 1672 
+2025-05-06 23:09:16.007133: Current learning rate: 0.00196 
+2025-05-06 23:10:52.829808: train_loss -0.4939 
+2025-05-06 23:10:52.890038: val_loss -0.4991 
+2025-05-06 23:10:52.890790: Pseudo dice [np.float32(0.8402), np.float32(0.8423), np.float32(0.8502), np.float32(0.976), np.float32(0.8959), np.float32(0.9568), np.float32(0.9658), np.float32(0.9794), np.float32(0.9482), np.float32(0.9624), np.float32(0.9412), np.float32(0.9653), np.float32(0.9699), np.float32(0.9111), np.float32(0.9678), np.float32(0.9612), np.float32(0.8851), np.float32(0.9016), np.float32(0.9145)] 
+2025-05-06 23:10:52.899993: Epoch time: 96.83 s 
+2025-05-06 23:10:54.364717:  
+2025-05-06 23:10:54.400334: Epoch 1673 
+2025-05-06 23:10:54.401069: Current learning rate: 0.00196 
+2025-05-06 23:12:30.847302: train_loss -0.4999 
+2025-05-06 23:12:30.910752: val_loss -0.4755 
+2025-05-06 23:12:30.928176: Pseudo dice [np.float32(0.8449), np.float32(0.8688), np.float32(0.9079), np.float32(0.9649), np.float32(0.9298), np.float32(0.9625), np.float32(0.9532), np.float32(0.9584), np.float32(0.9645), np.float32(0.967), np.float32(0.9536), np.float32(0.9669), np.float32(0.9738), np.float32(0.9132), np.float32(0.9622), np.float32(0.9596), np.float32(0.8981), np.float32(0.9146), np.float32(0.9114)] 
+2025-05-06 23:12:30.943189: Epoch time: 96.48 s 
+2025-05-06 23:12:32.619517:  
+2025-05-06 23:12:32.729527: Epoch 1674 
+2025-05-06 23:12:32.742531: Current learning rate: 0.00195 
+2025-05-06 23:14:06.565666: train_loss -0.481 
+2025-05-06 23:14:06.626957: val_loss -0.5283 
+2025-05-06 23:14:06.637609: Pseudo dice [np.float32(0.8644), np.float32(0.8626), np.float32(0.9162), np.float32(0.9689), np.float32(0.9356), np.float32(0.9566), np.float32(0.9703), np.float32(0.9778), np.float32(0.9543), np.float32(0.9709), np.float32(0.9595), np.float32(0.9701), np.float32(0.9671), np.float32(0.9132), np.float32(0.9654), np.float32(0.9621), np.float32(0.9017), np.float32(0.905), np.float32(0.9191)] 
+2025-05-06 23:14:06.638521: Epoch time: 93.95 s 
+2025-05-06 23:14:08.201593:  
+2025-05-06 23:14:08.226401: Epoch 1675 
+2025-05-06 23:14:08.237693: Current learning rate: 0.00195 
+2025-05-06 23:15:42.116083: train_loss -0.4982 
+2025-05-06 23:15:42.214508: val_loss -0.4737 
+2025-05-06 23:15:42.231530: Pseudo dice [np.float32(0.8455), np.float32(0.8755), np.float32(0.897), np.float32(0.9717), np.float32(0.9348), np.float32(0.9624), np.float32(0.9669), np.float32(0.9792), np.float32(0.9648), np.float32(0.9428), np.float32(0.9074), np.float32(0.9706), np.float32(0.969), np.float32(0.9109), np.float32(0.9672), np.float32(0.947), np.float32(0.847), np.float32(0.8652), np.float32(0.9148)] 
+2025-05-06 23:15:42.249411: Epoch time: 93.92 s 
+2025-05-06 23:15:43.754670:  
+2025-05-06 23:15:43.861564: Epoch 1676 
+2025-05-06 23:15:43.886625: Current learning rate: 0.00194 
+2025-05-06 23:17:16.817656: train_loss -0.5071 
+2025-05-06 23:17:16.904674: val_loss -0.5354 
+2025-05-06 23:17:16.921654: Pseudo dice [np.float32(0.8631), np.float32(0.8375), np.float32(0.9465), np.float32(0.9773), np.float32(0.9219), np.float32(0.9667), np.float32(0.9702), np.float32(0.9818), np.float32(0.9675), np.float32(0.9649), np.float32(0.9518), np.float32(0.9695), np.float32(0.9687), np.float32(0.922), np.float32(0.9734), np.float32(0.9675), np.float32(0.8572), np.float32(0.8122), np.float32(0.9209)] 
+2025-05-06 23:17:16.932417: Epoch time: 93.06 s 
+2025-05-06 23:17:18.518589:  
+2025-05-06 23:17:18.589129: Epoch 1677 
+2025-05-06 23:17:18.611918: Current learning rate: 0.00194 
+2025-05-06 23:18:53.601808: train_loss -0.4898 
+2025-05-06 23:18:53.613883: val_loss -0.4977 
+2025-05-06 23:18:53.618361: Pseudo dice [np.float32(0.86), np.float32(0.8548), np.float32(0.9126), np.float32(0.9782), np.float32(0.8875), np.float32(0.9468), np.float32(0.9647), np.float32(0.9752), np.float32(0.9274), np.float32(0.9585), np.float32(0.956), np.float32(0.967), np.float32(0.9696), np.float32(0.9165), np.float32(0.947), np.float32(0.9578), np.float32(0.8992), np.float32(0.9092), np.float32(0.9111)] 
+2025-05-06 23:18:53.627580: Epoch time: 95.08 s 
+2025-05-06 23:18:55.525261:  
+2025-05-06 23:18:55.555607: Epoch 1678 
+2025-05-06 23:18:55.566896: Current learning rate: 0.00193 
+2025-05-06 23:20:33.247926: train_loss -0.5013 
+2025-05-06 23:20:33.273407: val_loss -0.5289 
+2025-05-06 23:20:33.274412: Pseudo dice [np.float32(0.8407), np.float32(0.8626), np.float32(0.918), np.float32(0.9719), np.float32(0.9142), np.float32(0.9634), np.float32(0.9671), np.float32(0.9772), np.float32(0.9715), np.float32(0.9701), np.float32(0.9521), np.float32(0.9728), np.float32(0.9698), np.float32(0.9233), np.float32(0.9585), np.float32(0.9546), np.float32(0.9031), np.float32(0.8889), np.float32(0.9181)] 
+2025-05-06 23:20:33.281453: Epoch time: 97.72 s 
+2025-05-06 23:20:34.972374:  
+2025-05-06 23:20:35.039477: Epoch 1679 
+2025-05-06 23:20:35.063524: Current learning rate: 0.00193 
+2025-05-06 23:22:08.921304: train_loss -0.4908 
+2025-05-06 23:22:09.005792: val_loss -0.5075 
+2025-05-06 23:22:09.035822: Pseudo dice [np.float32(0.8383), np.float32(0.8387), np.float32(0.9219), np.float32(0.977), np.float32(0.9084), np.float32(0.9624), np.float32(0.9607), np.float32(0.9803), np.float32(0.9569), np.float32(0.9557), np.float32(0.9461), np.float32(0.9619), np.float32(0.9704), np.float32(0.9095), np.float32(0.9514), np.float32(0.9499), np.float32(0.9105), np.float32(0.9058), np.float32(0.9158)] 
+2025-05-06 23:22:09.061069: Epoch time: 93.95 s 
+2025-05-06 23:22:10.892310:  
+2025-05-06 23:22:10.968496: Epoch 1680 
+2025-05-06 23:22:10.983493: Current learning rate: 0.00192 
+2025-05-06 23:23:44.157383: train_loss -0.4957 
+2025-05-06 23:23:44.253024: val_loss -0.4998 
+2025-05-06 23:23:44.278110: Pseudo dice [np.float32(0.8706), np.float32(0.8587), np.float32(0.926), np.float32(0.9721), np.float32(0.9009), np.float32(0.966), np.float32(0.9665), np.float32(0.976), np.float32(0.9598), np.float32(0.9672), np.float32(0.9397), np.float32(0.9721), np.float32(0.9578), np.float32(0.924), np.float32(0.967), np.float32(0.96), np.float32(0.8873), np.float32(0.893), np.float32(0.9125)] 
+2025-05-06 23:23:44.298541: Epoch time: 93.27 s 
+2025-05-06 23:23:45.967328:  
+2025-05-06 23:23:46.094711: Epoch 1681 
+2025-05-06 23:23:46.124481: Current learning rate: 0.00192 
+2025-05-06 23:25:25.172882: train_loss -0.4952 
+2025-05-06 23:25:25.292798: val_loss -0.5428 
+2025-05-06 23:25:25.298806: Pseudo dice [np.float32(0.8494), np.float32(0.8638), np.float32(0.9029), np.float32(0.9734), np.float32(0.8891), np.float32(0.9685), np.float32(0.9686), np.float32(0.9773), np.float32(0.9672), np.float32(0.968), np.float32(0.9533), np.float32(0.9746), np.float32(0.9743), np.float32(0.914), np.float32(0.9733), np.float32(0.9612), np.float32(0.8967), np.float32(0.864), np.float32(0.9216)] 
+2025-05-06 23:25:25.299881: Epoch time: 99.21 s 
+2025-05-06 23:25:26.854100:  
+2025-05-06 23:25:26.928619: Epoch 1682 
+2025-05-06 23:25:26.946248: Current learning rate: 0.00191 
+2025-05-06 23:27:03.273854: train_loss -0.4893 
+2025-05-06 23:27:03.321838: val_loss -0.4888 
+2025-05-06 23:27:03.322750: Pseudo dice [np.float32(0.8419), np.float32(0.8522), np.float32(0.8804), np.float32(0.9817), np.float32(0.918), np.float32(0.9601), np.float32(0.9593), np.float32(0.9676), np.float32(0.9519), np.float32(0.9661), np.float32(0.9468), np.float32(0.9593), np.float32(0.9696), np.float32(0.9046), np.float32(0.9654), np.float32(0.9517), np.float32(0.892), np.float32(0.8933), np.float32(0.9168)] 
+2025-05-06 23:27:03.332340: Epoch time: 96.42 s 
+2025-05-06 23:27:04.946502:  
+2025-05-06 23:27:05.000640: Epoch 1683 
+2025-05-06 23:27:05.021098: Current learning rate: 0.00191 
+2025-05-06 23:28:43.055489: train_loss -0.5175 
+2025-05-06 23:28:43.121159: val_loss -0.4951 
+2025-05-06 23:28:43.133021: Pseudo dice [np.float32(0.8486), np.float32(0.8729), np.float32(0.8908), np.float32(0.9773), np.float32(0.9203), np.float32(0.9514), np.float32(0.9526), np.float32(0.9811), np.float32(0.966), np.float32(0.9604), np.float32(0.9506), np.float32(0.9745), np.float32(0.9735), np.float32(0.92), np.float32(0.9646), np.float32(0.9553), np.float32(0.9107), np.float32(0.9088), np.float32(0.9211)] 
+2025-05-06 23:28:43.147321: Epoch time: 98.11 s 
+2025-05-06 23:28:45.062901:  
+2025-05-06 23:28:45.069075: Epoch 1684 
+2025-05-06 23:28:45.069583: Current learning rate: 0.0019 
+2025-05-06 23:30:20.120359: train_loss -0.5134 
+2025-05-06 23:30:20.182807: val_loss -0.5328 
+2025-05-06 23:30:20.186689: Pseudo dice [np.float32(0.8515), np.float32(0.8619), np.float32(0.7828), np.float32(0.9733), np.float32(0.9108), np.float32(0.9615), np.float32(0.9692), np.float32(0.9806), np.float32(0.9562), np.float32(0.9666), np.float32(0.9421), np.float32(0.9687), np.float32(0.9675), np.float32(0.9107), np.float32(0.9681), np.float32(0.9545), np.float32(0.8882), np.float32(0.8876), np.float32(0.9197)] 
+2025-05-06 23:30:20.187656: Epoch time: 95.06 s 
+2025-05-06 23:30:21.742687:  
+2025-05-06 23:30:21.779464: Epoch 1685 
+2025-05-06 23:30:21.787622: Current learning rate: 0.00189 
+2025-05-06 23:32:01.970681: train_loss -0.4878 
+2025-05-06 23:32:02.024758: val_loss -0.4991 
+2025-05-06 23:32:02.037535: Pseudo dice [np.float32(0.8425), np.float32(0.8654), np.float32(0.942), np.float32(0.9748), np.float32(0.9313), np.float32(0.9657), np.float32(0.9669), np.float32(0.9823), np.float32(0.963), np.float32(0.968), np.float32(0.9465), np.float32(0.9661), np.float32(0.9679), np.float32(0.9282), np.float32(0.9711), np.float32(0.9621), np.float32(0.907), np.float32(0.9084), np.float32(0.9071)] 
+2025-05-06 23:32:02.066046: Epoch time: 100.23 s 
+2025-05-06 23:32:03.768030:  
+2025-05-06 23:32:03.852965: Epoch 1686 
+2025-05-06 23:32:03.882788: Current learning rate: 0.00189 
+2025-05-06 23:33:36.057265: train_loss -0.4999 
+2025-05-06 23:33:36.150533: val_loss -0.5315 
+2025-05-06 23:33:36.151704: Pseudo dice [np.float32(0.8791), np.float32(0.8646), np.float32(0.9115), np.float32(0.9803), np.float32(0.8909), np.float32(0.967), np.float32(0.9666), np.float32(0.9794), np.float32(0.9687), np.float32(0.9691), np.float32(0.9471), np.float32(0.9742), np.float32(0.9719), np.float32(0.9148), np.float32(0.9615), np.float32(0.9644), np.float32(0.9089), np.float32(0.9159), np.float32(0.9106)] 
+2025-05-06 23:33:36.152367: Epoch time: 92.29 s 
+2025-05-06 23:33:37.783763:  
+2025-05-06 23:33:37.855587: Epoch 1687 
+2025-05-06 23:33:37.885908: Current learning rate: 0.00188 
+2025-05-06 23:35:16.310057: train_loss -0.5005 
+2025-05-06 23:35:16.387791: val_loss -0.4978 
+2025-05-06 23:35:16.426218: Pseudo dice [np.float32(0.8697), np.float32(0.8561), np.float32(0.8505), np.float32(0.9715), np.float32(0.9167), np.float32(0.9644), np.float32(0.9684), np.float32(0.98), np.float32(0.9725), np.float32(0.9741), np.float32(0.9565), np.float32(0.9749), np.float32(0.9726), np.float32(0.9173), np.float32(0.959), np.float32(0.9537), np.float32(0.9009), np.float32(0.9084), np.float32(0.9169)] 
+2025-05-06 23:35:16.464691: Epoch time: 98.53 s 
+2025-05-06 23:35:18.105522:  
+2025-05-06 23:35:18.205814: Epoch 1688 
+2025-05-06 23:35:18.237570: Current learning rate: 0.00188 
+2025-05-06 23:36:52.796858: train_loss -0.5077 
+2025-05-06 23:36:52.890476: val_loss -0.4825 
+2025-05-06 23:36:52.910827: Pseudo dice [np.float32(0.8424), np.float32(0.8561), np.float32(0.9208), np.float32(0.9788), np.float32(0.9013), np.float32(0.9606), np.float32(0.9586), np.float32(0.9772), np.float32(0.9606), np.float32(0.9543), np.float32(0.9307), np.float32(0.9643), np.float32(0.9713), np.float32(0.8967), np.float32(0.9688), np.float32(0.9611), np.float32(0.9111), np.float32(0.9092), np.float32(0.9293)] 
+2025-05-06 23:36:52.918023: Epoch time: 94.69 s 
+2025-05-06 23:36:58.199221:  
+2025-05-06 23:36:58.204893: Epoch 1689 
+2025-05-06 23:36:58.205361: Current learning rate: 0.00187 
+2025-05-06 23:38:35.741755: train_loss -0.4962 
+2025-05-06 23:38:35.790203: val_loss -0.5088 
+2025-05-06 23:38:35.805298: Pseudo dice [np.float32(0.8611), np.float32(0.8766), np.float32(0.9314), np.float32(0.976), np.float32(0.914), np.float32(0.9607), np.float32(0.9683), np.float32(0.9776), np.float32(0.9598), np.float32(0.9598), np.float32(0.9536), np.float32(0.9701), np.float32(0.9697), np.float32(0.9291), np.float32(0.9686), np.float32(0.9568), np.float32(0.921), np.float32(0.9072), np.float32(0.9343)] 
+2025-05-06 23:38:35.813572: Epoch time: 97.54 s 
+2025-05-06 23:38:37.404438:  
+2025-05-06 23:38:37.507729: Epoch 1690 
+2025-05-06 23:38:37.562246: Current learning rate: 0.00187 
+2025-05-06 23:40:14.282372: train_loss -0.4915 
+2025-05-06 23:40:14.426749: val_loss -0.5027 
+2025-05-06 23:40:14.471495: Pseudo dice [np.float32(0.8503), np.float32(0.8574), np.float32(0.925), np.float32(0.9734), np.float32(0.8926), np.float32(0.9607), np.float32(0.9658), np.float32(0.9803), np.float32(0.9668), np.float32(0.9663), np.float32(0.9551), np.float32(0.9729), np.float32(0.9676), np.float32(0.9133), np.float32(0.9678), np.float32(0.9607), np.float32(0.8726), np.float32(0.8691), np.float32(0.9071)] 
+2025-05-06 23:40:14.522883: Epoch time: 96.88 s 
+2025-05-06 23:40:16.187900:  
+2025-05-06 23:40:16.236227: Epoch 1691 
+2025-05-06 23:40:16.237056: Current learning rate: 0.00186 
+2025-05-06 23:41:52.691115: train_loss -0.4998 
+2025-05-06 23:41:52.758659: val_loss -0.5161 
+2025-05-06 23:41:52.759771: Pseudo dice [np.float32(0.8607), np.float32(0.8622), np.float32(0.9299), np.float32(0.9803), np.float32(0.91), np.float32(0.9634), np.float32(0.9639), np.float32(0.9791), np.float32(0.9582), np.float32(0.9715), np.float32(0.9599), np.float32(0.9663), np.float32(0.9718), np.float32(0.9163), np.float32(0.9665), np.float32(0.9594), np.float32(0.9122), np.float32(0.9161), np.float32(0.9163)] 
+2025-05-06 23:41:52.760527: Epoch time: 96.5 s 
+2025-05-06 23:41:52.766400: Yayy! New best EMA pseudo Dice: 0.9355000257492065 
+2025-05-06 23:41:55.633088:  
+2025-05-06 23:41:55.637602: Epoch 1692 
+2025-05-06 23:41:55.638172: Current learning rate: 0.00186 
+2025-05-06 23:43:35.969541: train_loss -0.4923 
+2025-05-06 23:43:36.052204: val_loss -0.5426 
+2025-05-06 23:43:36.106667: Pseudo dice [np.float32(0.8598), np.float32(0.8138), np.float32(0.9299), np.float32(0.9644), np.float32(0.931), np.float32(0.964), np.float32(0.956), np.float32(0.9797), np.float32(0.9653), np.float32(0.9641), np.float32(0.9527), np.float32(0.974), np.float32(0.9645), np.float32(0.9028), np.float32(0.9709), np.float32(0.9635), np.float32(0.9099), np.float32(0.909), np.float32(0.9335)] 
+2025-05-06 23:43:36.144735: Epoch time: 100.34 s 
+2025-05-06 23:43:36.167053: Yayy! New best EMA pseudo Dice: 0.935699999332428 
+2025-05-06 23:43:38.878800:  
+2025-05-06 23:43:39.105560: Epoch 1693 
+2025-05-06 23:43:39.114518: Current learning rate: 0.00185 
+2025-05-06 23:45:16.792022: train_loss -0.5013 
+2025-05-06 23:45:16.951560: val_loss -0.5275 
+2025-05-06 23:45:16.973948: Pseudo dice [np.float32(0.8701), np.float32(0.8778), np.float32(0.8142), np.float32(0.9777), np.float32(0.9128), np.float32(0.9603), np.float32(0.9727), np.float32(0.9829), np.float32(0.9667), np.float32(0.9706), np.float32(0.9374), np.float32(0.9737), np.float32(0.9586), np.float32(0.9252), np.float32(0.9631), np.float32(0.9572), np.float32(0.8881), np.float32(0.8985), np.float32(0.9291)] 
+2025-05-06 23:45:16.984662: Epoch time: 97.91 s 
+2025-05-06 23:45:18.524313:  
+2025-05-06 23:45:18.599919: Epoch 1694 
+2025-05-06 23:45:18.629300: Current learning rate: 0.00185 
+2025-05-06 23:46:51.082684: train_loss -0.5043 
+2025-05-06 23:46:51.193717: val_loss -0.5008 
+2025-05-06 23:46:51.246062: Pseudo dice [np.float32(0.8573), np.float32(0.8601), np.float32(0.9156), np.float32(0.9738), np.float32(0.8898), np.float32(0.9527), np.float32(0.9585), np.float32(0.9819), np.float32(0.9709), np.float32(0.9664), np.float32(0.9536), np.float32(0.9721), np.float32(0.9711), np.float32(0.9103), np.float32(0.9606), np.float32(0.9533), np.float32(0.8926), np.float32(0.8928), np.float32(0.9177)] 
+2025-05-06 23:46:51.303061: Epoch time: 92.56 s 
+2025-05-06 23:46:53.159530:  
+2025-05-06 23:46:53.305985: Epoch 1695 
+2025-05-06 23:46:53.329592: Current learning rate: 0.00184 
+2025-05-06 23:48:28.187199: train_loss -0.5002 
+2025-05-06 23:48:28.281498: val_loss -0.5164 
+2025-05-06 23:48:28.332367: Pseudo dice [np.float32(0.8585), np.float32(0.8608), np.float32(0.9219), np.float32(0.9723), np.float32(0.9204), np.float32(0.9645), np.float32(0.9638), np.float32(0.9809), np.float32(0.9726), np.float32(0.9618), np.float32(0.9529), np.float32(0.9706), np.float32(0.9711), np.float32(0.9204), np.float32(0.9673), np.float32(0.9599), np.float32(0.8952), np.float32(0.9229), np.float32(0.9321)] 
+2025-05-06 23:48:28.386042: Epoch time: 95.03 s 
+2025-05-06 23:48:28.445418: Yayy! New best EMA pseudo Dice: 0.9358999729156494 
+2025-05-06 23:48:31.288203:  
+2025-05-06 23:48:31.339065: Epoch 1696 
+2025-05-06 23:48:31.356763: Current learning rate: 0.00184 
+2025-05-06 23:50:10.269884: train_loss -0.5075 
+2025-05-06 23:50:10.347256: val_loss -0.518 
+2025-05-06 23:50:10.353066: Pseudo dice [np.float32(0.8698), np.float32(0.8798), np.float32(0.943), np.float32(0.9709), np.float32(0.9286), np.float32(0.964), np.float32(0.9672), np.float32(0.9756), np.float32(0.97), np.float32(0.9685), np.float32(0.9473), np.float32(0.9677), np.float32(0.9709), np.float32(0.9181), np.float32(0.9716), np.float32(0.9642), np.float32(0.893), np.float32(0.8791), np.float32(0.9219)] 
+2025-05-06 23:50:10.357187: Epoch time: 98.98 s 
+2025-05-06 23:50:10.373659: Yayy! New best EMA pseudo Dice: 0.9363999962806702 
+2025-05-06 23:50:13.631706:  
+2025-05-06 23:50:13.651365: Epoch 1697 
+2025-05-06 23:50:13.662498: Current learning rate: 0.00183 
+2025-05-06 23:51:50.154773: train_loss -0.5116 
+2025-05-06 23:51:50.310654: val_loss -0.4843 
+2025-05-06 23:51:50.333626: Pseudo dice [np.float32(0.8664), np.float32(0.8516), np.float32(0.9364), np.float32(0.9745), np.float32(0.8961), np.float32(0.9649), np.float32(0.9607), np.float32(0.9813), np.float32(0.9665), np.float32(0.9675), np.float32(0.96), np.float32(0.9656), np.float32(0.9735), np.float32(0.9153), np.float32(0.9651), np.float32(0.9576), np.float32(0.9086), np.float32(0.9216), np.float32(0.9334)] 
+2025-05-06 23:51:50.363812: Epoch time: 96.52 s 
+2025-05-06 23:51:50.385974: Yayy! New best EMA pseudo Dice: 0.9368000030517578 
+2025-05-06 23:51:52.898728:  
+2025-05-06 23:51:52.900486: Epoch 1698 
+2025-05-06 23:51:52.900948: Current learning rate: 0.00182 
+2025-05-06 23:53:31.627768: train_loss -0.5047 
+2025-05-06 23:53:31.725507: val_loss -0.496 
+2025-05-06 23:53:31.772086: Pseudo dice [np.float32(0.8569), np.float32(0.8651), np.float32(0.9553), np.float32(0.9754), np.float32(0.917), np.float32(0.9574), np.float32(0.9666), np.float32(0.9756), np.float32(0.967), np.float32(0.9728), np.float32(0.9503), np.float32(0.9688), np.float32(0.9688), np.float32(0.9131), np.float32(0.9647), np.float32(0.9424), np.float32(0.8601), np.float32(0.8971), np.float32(0.9275)] 
+2025-05-06 23:53:31.794124: Epoch time: 98.73 s 
+2025-05-06 23:53:31.809318: Yayy! New best EMA pseudo Dice: 0.9368000030517578 
+2025-05-06 23:53:34.481427:  
+2025-05-06 23:53:34.622943: Epoch 1699 
+2025-05-06 23:53:34.623530: Current learning rate: 0.00182 
+2025-05-06 23:55:12.036562: train_loss -0.5065 
+2025-05-06 23:55:12.104665: val_loss -0.4901 
+2025-05-06 23:55:12.129854: Pseudo dice [np.float32(0.848), np.float32(0.8754), np.float32(0.9205), np.float32(0.9784), np.float32(0.9115), np.float32(0.965), np.float32(0.9662), np.float32(0.9796), np.float32(0.9699), np.float32(0.9701), np.float32(0.9499), np.float32(0.9672), np.float32(0.9757), np.float32(0.9162), np.float32(0.9645), np.float32(0.9585), np.float32(0.9115), np.float32(0.9116), np.float32(0.9244)] 
+2025-05-06 23:55:12.205693: Epoch time: 97.56 s 
+2025-05-06 23:55:13.278471: Yayy! New best EMA pseudo Dice: 0.9370999932289124 
+2025-05-06 23:55:16.478484:  
+2025-05-06 23:55:16.483983: Epoch 1700 
+2025-05-06 23:55:16.484371: Current learning rate: 0.00181 
+2025-05-06 23:56:54.496771: train_loss -0.5047 
+2025-05-06 23:56:54.592348: val_loss -0.5157 
+2025-05-06 23:56:54.625582: Pseudo dice [np.float32(0.8623), np.float32(0.8567), np.float32(0.9431), np.float32(0.9705), np.float32(0.9231), np.float32(0.9634), np.float32(0.9623), np.float32(0.9767), np.float32(0.9609), np.float32(0.9637), np.float32(0.9533), np.float32(0.9715), np.float32(0.972), np.float32(0.9114), np.float32(0.9709), np.float32(0.9636), np.float32(0.8719), np.float32(0.8934), np.float32(0.9114)] 
+2025-05-06 23:56:54.666691: Epoch time: 98.02 s 
+2025-05-06 23:56:56.264567:  
+2025-05-06 23:56:56.338735: Epoch 1701 
+2025-05-06 23:56:56.360895: Current learning rate: 0.00181 
+2025-05-06 23:58:32.583791: train_loss -0.4944 
+2025-05-06 23:58:32.663800: val_loss -0.4999 
+2025-05-06 23:58:32.693474: Pseudo dice [np.float32(0.8643), np.float32(0.8701), np.float32(0.9495), np.float32(0.9747), np.float32(0.9347), np.float32(0.9577), np.float32(0.9697), np.float32(0.9787), np.float32(0.9639), np.float32(0.9717), np.float32(0.9391), np.float32(0.9628), np.float32(0.9704), np.float32(0.9181), np.float32(0.9519), np.float32(0.9602), np.float32(0.8761), np.float32(0.9109), np.float32(0.9114)] 
+2025-05-06 23:58:32.751696: Epoch time: 96.32 s 
+2025-05-06 23:58:32.763371: Yayy! New best EMA pseudo Dice: 0.9373000264167786 
+2025-05-06 23:58:36.141333:  
+2025-05-06 23:58:36.148832: Epoch 1702 
+2025-05-06 23:58:36.192722: Current learning rate: 0.0018 
+2025-05-07 00:00:15.831277: train_loss -0.5051 
+2025-05-07 00:00:15.976909: val_loss -0.4812 
+2025-05-07 00:00:15.982685: Pseudo dice [np.float32(0.8713), np.float32(0.8706), np.float32(0.9356), np.float32(0.9779), np.float32(0.911), np.float32(0.9655), np.float32(0.9673), np.float32(0.9758), np.float32(0.9701), np.float32(0.964), np.float32(0.9545), np.float32(0.9704), np.float32(0.9676), np.float32(0.9095), np.float32(0.9646), np.float32(0.9605), np.float32(0.8409), np.float32(0.8904), np.float32(0.9141)] 
+2025-05-07 00:00:16.005024: Epoch time: 99.69 s 
+2025-05-07 00:00:17.528565:  
+2025-05-07 00:00:17.623824: Epoch 1703 
+2025-05-07 00:00:17.667680: Current learning rate: 0.0018 
+2025-05-07 00:01:53.960023: train_loss -0.4903 
+2025-05-07 00:01:54.025054: val_loss -0.5049 
+2025-05-07 00:01:54.040344: Pseudo dice [np.float32(0.8461), np.float32(0.8605), np.float32(0.9136), np.float32(0.9648), np.float32(0.9071), np.float32(0.9632), np.float32(0.9681), np.float32(0.9805), np.float32(0.9576), np.float32(0.9642), np.float32(0.9543), np.float32(0.9702), np.float32(0.9702), np.float32(0.9209), np.float32(0.9698), np.float32(0.9582), np.float32(0.896), np.float32(0.8759), np.float32(0.9243)] 
+2025-05-07 00:01:54.067935: Epoch time: 96.43 s 
+2025-05-07 00:01:55.878884:  
+2025-05-07 00:01:56.006095: Epoch 1704 
+2025-05-07 00:01:56.017555: Current learning rate: 0.00179 
+2025-05-07 00:03:32.833846: train_loss -0.5117 
+2025-05-07 00:03:32.882548: val_loss -0.4565 
+2025-05-07 00:03:32.898277: Pseudo dice [np.float32(0.8648), np.float32(0.8631), np.float32(0.7231), np.float32(0.974), np.float32(0.9127), np.float32(0.9672), np.float32(0.9613), np.float32(0.9679), np.float32(0.9735), np.float32(0.9642), np.float32(0.9604), np.float32(0.9706), np.float32(0.9751), np.float32(0.9197), np.float32(0.9656), np.float32(0.9678), np.float32(0.8829), np.float32(0.8727), np.float32(0.9325)] 
+2025-05-07 00:03:32.916115: Epoch time: 96.96 s 
+2025-05-07 00:03:38.022231:  
+2025-05-07 00:03:38.027754: Epoch 1705 
+2025-05-07 00:03:38.028185: Current learning rate: 0.00179 
+2025-05-07 00:05:13.155174: train_loss -0.5327 
+2025-05-07 00:05:13.276267: val_loss -0.5418 
+2025-05-07 00:05:13.277022: Pseudo dice [np.float32(0.8508), np.float32(0.833), np.float32(0.9121), np.float32(0.954), np.float32(0.9022), np.float32(0.9644), np.float32(0.9693), np.float32(0.9732), np.float32(0.9704), np.float32(0.9694), np.float32(0.9441), np.float32(0.9722), np.float32(0.9654), np.float32(0.9182), np.float32(0.9715), np.float32(0.9569), np.float32(0.9108), np.float32(0.9177), np.float32(0.9126)] 
+2025-05-07 00:05:13.277507: Epoch time: 95.13 s 
+2025-05-07 00:05:14.793455:  
+2025-05-07 00:05:14.843684: Epoch 1706 
+2025-05-07 00:05:14.844585: Current learning rate: 0.00178 
+2025-05-07 00:06:53.190561: train_loss -0.4992 
+2025-05-07 00:06:53.274658: val_loss -0.4412 
+2025-05-07 00:06:53.300411: Pseudo dice [np.float32(0.859), np.float32(0.8275), np.float32(0.9228), np.float32(0.9821), np.float32(0.9216), np.float32(0.9576), np.float32(0.9493), np.float32(0.9662), np.float32(0.9704), np.float32(0.9578), np.float32(0.9527), np.float32(0.9719), np.float32(0.9688), np.float32(0.9062), np.float32(0.9541), np.float32(0.9598), np.float32(0.9036), np.float32(0.903), np.float32(0.9308)] 
+2025-05-07 00:06:53.345853: Epoch time: 98.4 s 
+2025-05-07 00:06:55.052437:  
+2025-05-07 00:06:55.103992: Epoch 1707 
+2025-05-07 00:06:55.119166: Current learning rate: 0.00178 
+2025-05-07 00:08:32.359547: train_loss -0.5212 
+2025-05-07 00:08:32.433947: val_loss -0.5206 
+2025-05-07 00:08:32.447448: Pseudo dice [np.float32(0.8602), np.float32(0.8747), np.float32(0.9628), np.float32(0.9774), np.float32(0.935), np.float32(0.9651), np.float32(0.9681), np.float32(0.9779), np.float32(0.9645), np.float32(0.9658), np.float32(0.9579), np.float32(0.9705), np.float32(0.9726), np.float32(0.9267), np.float32(0.9661), np.float32(0.9653), np.float32(0.9102), np.float32(0.9098), np.float32(0.9159)] 
+2025-05-07 00:08:32.466193: Epoch time: 97.31 s 
+2025-05-07 00:08:34.138806:  
+2025-05-07 00:08:34.165240: Epoch 1708 
+2025-05-07 00:08:34.165963: Current learning rate: 0.00177 
+2025-05-07 00:10:15.359917: train_loss -0.4847 
+2025-05-07 00:10:15.403403: val_loss -0.4741 
+2025-05-07 00:10:15.411465: Pseudo dice [np.float32(0.8521), np.float32(0.8637), np.float32(0.9545), np.float32(0.9769), np.float32(0.9215), np.float32(0.9626), np.float32(0.9605), np.float32(0.9737), np.float32(0.9659), np.float32(0.9663), np.float32(0.9477), np.float32(0.9704), np.float32(0.9657), np.float32(0.9149), np.float32(0.9679), np.float32(0.9624), np.float32(0.8971), np.float32(0.905), np.float32(0.9089)] 
+2025-05-07 00:10:15.412061: Epoch time: 101.22 s 
+2025-05-07 00:10:16.941033:  
+2025-05-07 00:10:17.021039: Epoch 1709 
+2025-05-07 00:10:17.030923: Current learning rate: 0.00176 
+2025-05-07 00:11:55.645576: train_loss -0.4974 
+2025-05-07 00:11:55.725884: val_loss -0.5358 
+2025-05-07 00:11:55.738693: Pseudo dice [np.float32(0.8657), np.float32(0.8406), np.float32(0.8898), np.float32(0.9771), np.float32(0.9375), np.float32(0.9611), np.float32(0.9661), np.float32(0.9739), np.float32(0.9677), np.float32(0.9675), np.float32(0.9562), np.float32(0.9708), np.float32(0.9684), np.float32(0.9177), np.float32(0.9678), np.float32(0.9598), np.float32(0.8893), np.float32(0.9136), np.float32(0.91)] 
+2025-05-07 00:11:55.739423: Epoch time: 98.71 s 
+2025-05-07 00:11:57.319507:  
+2025-05-07 00:11:57.469221: Epoch 1710 
+2025-05-07 00:11:57.516005: Current learning rate: 0.00176 
+2025-05-07 00:13:42.611646: train_loss -0.4998 
+2025-05-07 00:13:42.706983: val_loss -0.5281 
+2025-05-07 00:13:42.781711: Pseudo dice [np.float32(0.8591), np.float32(0.8915), np.float32(0.94), np.float32(0.9725), np.float32(0.9336), np.float32(0.965), np.float32(0.9627), np.float32(0.9823), np.float32(0.9624), np.float32(0.9664), np.float32(0.9542), np.float32(0.9616), np.float32(0.9712), np.float32(0.9231), np.float32(0.9731), np.float32(0.967), np.float32(0.8659), np.float32(0.8857), np.float32(0.9149)] 
+2025-05-07 00:13:42.822975: Epoch time: 105.29 s 
+2025-05-07 00:13:44.525479:  
+2025-05-07 00:13:44.674645: Epoch 1711 
+2025-05-07 00:13:44.714088: Current learning rate: 0.00175 
+2025-05-07 00:15:27.339674: train_loss -0.5079 
+2025-05-07 00:15:27.443993: val_loss -0.4984 
+2025-05-07 00:15:27.475663: Pseudo dice [np.float32(0.8456), np.float32(0.859), np.float32(0.9329), np.float32(0.9765), np.float32(0.9159), np.float32(0.9636), np.float32(0.9636), np.float32(0.974), np.float32(0.9648), np.float32(0.9703), np.float32(0.9588), np.float32(0.9672), np.float32(0.9759), np.float32(0.9038), np.float32(0.9733), np.float32(0.9616), np.float32(0.9081), np.float32(0.8917), np.float32(0.9124)] 
+2025-05-07 00:15:27.510911: Epoch time: 102.82 s 
+2025-05-07 00:15:29.103596:  
+2025-05-07 00:15:29.176122: Epoch 1712 
+2025-05-07 00:15:29.224165: Current learning rate: 0.00175 
+2025-05-07 00:17:03.846428: train_loss -0.5114 
+2025-05-07 00:17:04.014443: val_loss -0.5057 
+2025-05-07 00:17:04.044332: Pseudo dice [np.float32(0.8628), np.float32(0.8655), np.float32(0.86), np.float32(0.9694), np.float32(0.9282), np.float32(0.9592), np.float32(0.9723), np.float32(0.9833), np.float32(0.9688), np.float32(0.975), np.float32(0.9486), np.float32(0.9677), np.float32(0.9741), np.float32(0.9254), np.float32(0.9741), np.float32(0.9622), np.float32(0.8705), np.float32(0.8891), np.float32(0.9192)] 
+2025-05-07 00:17:04.064368: Epoch time: 94.74 s 
+2025-05-07 00:17:05.870448:  
+2025-05-07 00:17:05.956566: Epoch 1713 
+2025-05-07 00:17:05.978274: Current learning rate: 0.00174 
+2025-05-07 00:18:44.841734: train_loss -0.4819 
+2025-05-07 00:18:44.979507: val_loss -0.5015 
+2025-05-07 00:18:45.009501: Pseudo dice [np.float32(0.8538), np.float32(0.8284), np.float32(0.8842), np.float32(0.9795), np.float32(0.8875), np.float32(0.9648), np.float32(0.9662), np.float32(0.9706), np.float32(0.9538), np.float32(0.9666), np.float32(0.95), np.float32(0.9479), np.float32(0.971), np.float32(0.9121), np.float32(0.9695), np.float32(0.9604), np.float32(0.866), np.float32(0.9116), np.float32(0.9161)] 
+2025-05-07 00:18:45.024330: Epoch time: 98.97 s 
+2025-05-07 00:18:46.686343:  
+2025-05-07 00:18:46.754804: Epoch 1714 
+2025-05-07 00:18:46.767804: Current learning rate: 0.00174 
+2025-05-07 00:20:21.870769: train_loss -0.4891 
+2025-05-07 00:20:21.927844: val_loss -0.507 
+2025-05-07 00:20:21.961034: Pseudo dice [np.float32(0.867), np.float32(0.8628), np.float32(0.9303), np.float32(0.9812), np.float32(0.9281), np.float32(0.964), np.float32(0.961), np.float32(0.9821), np.float32(0.9698), np.float32(0.9723), np.float32(0.9557), np.float32(0.9696), np.float32(0.9768), np.float32(0.9112), np.float32(0.9704), np.float32(0.9567), np.float32(0.8774), np.float32(0.8197), np.float32(0.9189)] 
+2025-05-07 00:20:21.978373: Epoch time: 95.19 s 
+2025-05-07 00:20:23.562687:  
+2025-05-07 00:20:23.641030: Epoch 1715 
+2025-05-07 00:20:23.647834: Current learning rate: 0.00173 
+2025-05-07 00:22:03.970842: train_loss -0.5053 
+2025-05-07 00:22:03.994632: val_loss -0.5253 
+2025-05-07 00:22:04.021428: Pseudo dice [np.float32(0.8686), np.float32(0.8872), np.float32(0.921), np.float32(0.9706), np.float32(0.9091), np.float32(0.9616), np.float32(0.9703), np.float32(0.9796), np.float32(0.9703), np.float32(0.9655), np.float32(0.9531), np.float32(0.9658), np.float32(0.9695), np.float32(0.9154), np.float32(0.967), np.float32(0.9636), np.float32(0.9119), np.float32(0.919), np.float32(0.9166)] 
+2025-05-07 00:22:04.025056: Epoch time: 100.41 s 
+2025-05-07 00:22:05.575408:  
+2025-05-07 00:22:05.659848: Epoch 1716 
+2025-05-07 00:22:05.711153: Current learning rate: 0.00173 
+2025-05-07 00:23:46.182592: train_loss -0.5054 
+2025-05-07 00:23:46.288012: val_loss -0.481 
+2025-05-07 00:23:46.343100: Pseudo dice [np.float32(0.854), np.float32(0.8628), np.float32(0.8964), np.float32(0.9623), np.float32(0.9077), np.float32(0.9362), np.float32(0.9674), np.float32(0.9771), np.float32(0.9585), np.float32(0.9724), np.float32(0.9506), np.float32(0.9717), np.float32(0.9656), np.float32(0.8997), np.float32(0.9313), np.float32(0.9448), np.float32(0.8818), np.float32(0.8915), np.float32(0.9368)] 
+2025-05-07 00:23:46.401007: Epoch time: 100.61 s 
+2025-05-07 00:23:48.019466:  
+2025-05-07 00:23:48.095851: Epoch 1717 
+2025-05-07 00:23:48.100564: Current learning rate: 0.00172 
+2025-05-07 00:25:23.679051: train_loss -0.5058 
+2025-05-07 00:25:23.796488: val_loss -0.4914 
+2025-05-07 00:25:23.813157: Pseudo dice [np.float32(0.8188), np.float32(0.8541), np.float32(0.9275), np.float32(0.9783), np.float32(0.921), np.float32(0.9513), np.float32(0.9634), np.float32(0.9816), np.float32(0.9666), np.float32(0.9597), np.float32(0.9286), np.float32(0.9699), np.float32(0.9654), np.float32(0.9032), np.float32(0.9261), np.float32(0.9574), np.float32(0.9015), np.float32(0.9162), np.float32(0.9315)] 
+2025-05-07 00:25:23.820700: Epoch time: 95.66 s 
+2025-05-07 00:25:25.328529:  
+2025-05-07 00:25:25.422870: Epoch 1718 
+2025-05-07 00:25:25.465322: Current learning rate: 0.00172 
+2025-05-07 00:27:01.596630: train_loss -0.5143 
+2025-05-07 00:27:01.692557: val_loss -0.484 
+2025-05-07 00:27:01.710915: Pseudo dice [np.float32(0.8569), np.float32(0.8573), np.float32(0.7829), np.float32(0.9775), np.float32(0.9238), np.float32(0.9643), np.float32(0.9598), np.float32(0.979), np.float32(0.972), np.float32(0.9688), np.float32(0.9555), np.float32(0.9733), np.float32(0.9678), np.float32(0.9222), np.float32(0.9731), np.float32(0.9657), np.float32(0.9228), np.float32(0.9188), np.float32(0.9307)] 
+2025-05-07 00:27:01.716766: Epoch time: 96.27 s 
+2025-05-07 00:27:03.352666:  
+2025-05-07 00:27:03.424574: Epoch 1719 
+2025-05-07 00:27:03.452444: Current learning rate: 0.00171 
+2025-05-07 00:28:46.346194: train_loss -0.5074 
+2025-05-07 00:28:46.508578: val_loss -0.5232 
+2025-05-07 00:28:46.545436: Pseudo dice [np.float32(0.8543), np.float32(0.8599), np.float32(0.9393), np.float32(0.9708), np.float32(0.9237), np.float32(0.9646), np.float32(0.9712), np.float32(0.9745), np.float32(0.9644), np.float32(0.9535), np.float32(0.9559), np.float32(0.9697), np.float32(0.9721), np.float32(0.9143), np.float32(0.9658), np.float32(0.9638), np.float32(0.8927), np.float32(0.914), np.float32(0.919)] 
+2025-05-07 00:28:46.575763: Epoch time: 102.99 s 
+2025-05-07 00:28:48.406834:  
+2025-05-07 00:28:48.444122: Epoch 1720 
+2025-05-07 00:28:48.458943: Current learning rate: 0.0017 
+2025-05-07 00:30:27.088976: train_loss -0.5019 
+2025-05-07 00:30:27.235494: val_loss -0.524 
+2025-05-07 00:30:27.265314: Pseudo dice [np.float32(0.8615), np.float32(0.8755), np.float32(0.9129), np.float32(0.9741), np.float32(0.9173), np.float32(0.9606), np.float32(0.9661), np.float32(0.9784), np.float32(0.9591), np.float32(0.9683), np.float32(0.9507), np.float32(0.9651), np.float32(0.9735), np.float32(0.9177), np.float32(0.964), np.float32(0.9602), np.float32(0.8959), np.float32(0.9172), np.float32(0.9216)] 
+2025-05-07 00:30:27.295672: Epoch time: 98.68 s 
+2025-05-07 00:30:28.799420:  
+2025-05-07 00:30:28.896968: Epoch 1721 
+2025-05-07 00:30:28.930656: Current learning rate: 0.0017 
+2025-05-07 00:32:03.292381: train_loss -0.4853 
+2025-05-07 00:32:03.419163: val_loss -0.5316 
+2025-05-07 00:32:03.459347: Pseudo dice [np.float32(0.8743), np.float32(0.8397), np.float32(0.9317), np.float32(0.9818), np.float32(0.8795), np.float32(0.9665), np.float32(0.9578), np.float32(0.9756), np.float32(0.9628), np.float32(0.9693), np.float32(0.9572), np.float32(0.972), np.float32(0.9736), np.float32(0.9174), np.float32(0.9728), np.float32(0.9638), np.float32(0.8519), np.float32(0.8577), np.float32(0.9214)] 
+2025-05-07 00:32:03.492754: Epoch time: 94.49 s 
+2025-05-07 00:32:08.658258:  
+2025-05-07 00:32:08.664190: Epoch 1722 
+2025-05-07 00:32:08.664671: Current learning rate: 0.00169 
+2025-05-07 00:33:42.597179: train_loss -0.5032 
+2025-05-07 00:33:42.703624: val_loss -0.5187 
+2025-05-07 00:33:42.737015: Pseudo dice [np.float32(0.8856), np.float32(0.8744), np.float32(0.9084), np.float32(0.9717), np.float32(0.9281), np.float32(0.9676), np.float32(0.9683), np.float32(0.9808), np.float32(0.9683), np.float32(0.9768), np.float32(0.9575), np.float32(0.972), np.float32(0.9709), np.float32(0.916), np.float32(0.9702), np.float32(0.9591), np.float32(0.8927), np.float32(0.9124), np.float32(0.9237)] 
+2025-05-07 00:33:42.766706: Epoch time: 93.94 s 
+2025-05-07 00:33:44.471548:  
+2025-05-07 00:33:44.552807: Epoch 1723 
+2025-05-07 00:33:44.584093: Current learning rate: 0.00169 
+2025-05-07 00:35:23.350245: train_loss -0.5108 
+2025-05-07 00:35:23.456331: val_loss -0.4641 
+2025-05-07 00:35:23.496677: Pseudo dice [np.float32(0.8633), np.float32(0.86), np.float32(0.8783), np.float32(0.9774), np.float32(0.9355), np.float32(0.947), np.float32(0.9697), np.float32(0.976), np.float32(0.9629), np.float32(0.9719), np.float32(0.9484), np.float32(0.9644), np.float32(0.9708), np.float32(0.9157), np.float32(0.9606), np.float32(0.9584), np.float32(0.8958), np.float32(0.8771), np.float32(0.9165)] 
+2025-05-07 00:35:23.523685: Epoch time: 98.88 s 
+2025-05-07 00:35:25.065379:  
+2025-05-07 00:35:25.107540: Epoch 1724 
+2025-05-07 00:35:25.129590: Current learning rate: 0.00168 
+2025-05-07 00:37:01.512223: train_loss -0.498 
+2025-05-07 00:37:01.515392: val_loss -0.5288 
+2025-05-07 00:37:01.515900: Pseudo dice [np.float32(0.8541), np.float32(0.866), np.float32(0.8429), np.float32(0.9673), np.float32(0.9426), np.float32(0.9515), np.float32(0.9688), np.float32(0.9803), np.float32(0.9621), np.float32(0.9611), np.float32(0.9413), np.float32(0.9694), np.float32(0.9655), np.float32(0.9231), np.float32(0.9601), np.float32(0.9647), np.float32(0.8906), np.float32(0.9171), np.float32(0.9252)] 
+2025-05-07 00:37:01.516326: Epoch time: 96.45 s 
+2025-05-07 00:37:03.098612:  
+2025-05-07 00:37:03.209047: Epoch 1725 
+2025-05-07 00:37:03.221884: Current learning rate: 0.00168 
+2025-05-07 00:38:43.467504: train_loss -0.5181 
+2025-05-07 00:38:43.536156: val_loss -0.4757 
+2025-05-07 00:38:43.542332: Pseudo dice [np.float32(0.8641), np.float32(0.8713), np.float32(0.8803), np.float32(0.978), np.float32(0.9262), np.float32(0.9632), np.float32(0.964), np.float32(0.9796), np.float32(0.9751), np.float32(0.9622), np.float32(0.957), np.float32(0.9584), np.float32(0.9744), np.float32(0.8973), np.float32(0.9615), np.float32(0.9607), np.float32(0.8942), np.float32(0.8957), np.float32(0.9183)] 
+2025-05-07 00:38:43.571903: Epoch time: 100.37 s 
+2025-05-07 00:38:45.211509:  
+2025-05-07 00:38:45.324366: Epoch 1726 
+2025-05-07 00:38:45.360626: Current learning rate: 0.00167 
+2025-05-07 00:40:24.226722: train_loss -0.5054 
+2025-05-07 00:40:24.399673: val_loss -0.5419 
+2025-05-07 00:40:24.418189: Pseudo dice [np.float32(0.8371), np.float32(0.8483), np.float32(0.9159), np.float32(0.9726), np.float32(0.9147), np.float32(0.941), np.float32(0.9664), np.float32(0.9807), np.float32(0.967), np.float32(0.9655), np.float32(0.9456), np.float32(0.9706), np.float32(0.9706), np.float32(0.9068), np.float32(0.9306), np.float32(0.9557), np.float32(0.8977), np.float32(0.9034), np.float32(0.9046)] 
+2025-05-07 00:40:24.454317: Epoch time: 99.02 s 
+2025-05-07 00:40:26.150335:  
+2025-05-07 00:40:26.225650: Epoch 1727 
+2025-05-07 00:40:26.236607: Current learning rate: 0.00167 
+2025-05-07 00:41:56.193415: train_loss -0.5192 
+2025-05-07 00:41:56.293085: val_loss -0.5337 
+2025-05-07 00:41:56.317139: Pseudo dice [np.float32(0.8638), np.float32(0.8637), np.float32(0.9132), np.float32(0.9801), np.float32(0.9177), np.float32(0.9628), np.float32(0.9646), np.float32(0.977), np.float32(0.9711), np.float32(0.966), np.float32(0.9609), np.float32(0.9747), np.float32(0.9747), np.float32(0.9033), np.float32(0.9605), np.float32(0.964), np.float32(0.8943), np.float32(0.8943), np.float32(0.9248)] 
+2025-05-07 00:41:56.343481: Epoch time: 90.04 s 
+2025-05-07 00:41:57.958042:  
+2025-05-07 00:41:58.040465: Epoch 1728 
+2025-05-07 00:41:58.061565: Current learning rate: 0.00166 
+2025-05-07 00:43:33.451313: train_loss -0.4953 
+2025-05-07 00:43:33.593417: val_loss -0.5114 
+2025-05-07 00:43:33.639424: Pseudo dice [np.float32(0.8591), np.float32(0.8558), np.float32(0.9282), np.float32(0.9736), np.float32(0.9035), np.float32(0.9642), np.float32(0.9574), np.float32(0.9677), np.float32(0.9606), np.float32(0.967), np.float32(0.9561), np.float32(0.9648), np.float32(0.9745), np.float32(0.9155), np.float32(0.9712), np.float32(0.956), np.float32(0.8677), np.float32(0.8677), np.float32(0.9231)] 
+2025-05-07 00:43:33.674320: Epoch time: 95.49 s 
+2025-05-07 00:43:35.484116:  
+2025-05-07 00:43:35.527130: Epoch 1729 
+2025-05-07 00:43:35.622116: Current learning rate: 0.00165 
+2025-05-07 00:45:11.240199: train_loss -0.5069 
+2025-05-07 00:45:11.333468: val_loss -0.5055 
+2025-05-07 00:45:11.351775: Pseudo dice [np.float32(0.8548), np.float32(0.8487), np.float32(0.9422), np.float32(0.9676), np.float32(0.9246), np.float32(0.9631), np.float32(0.9686), np.float32(0.978), np.float32(0.9662), np.float32(0.9639), np.float32(0.9424), np.float32(0.9709), np.float32(0.9721), np.float32(0.9197), np.float32(0.9743), np.float32(0.9629), np.float32(0.8984), np.float32(0.8902), np.float32(0.9224)] 
+2025-05-07 00:45:11.368301: Epoch time: 95.76 s 
+2025-05-07 00:45:12.898010:  
+2025-05-07 00:45:12.966039: Epoch 1730 
+2025-05-07 00:45:12.974060: Current learning rate: 0.00165 
+2025-05-07 00:46:51.252002: train_loss -0.5153 
+2025-05-07 00:46:51.336033: val_loss -0.4721 
+2025-05-07 00:46:51.347328: Pseudo dice [np.float32(0.8707), np.float32(0.8552), np.float32(0.9037), np.float32(0.9771), np.float32(0.9284), np.float32(0.9646), np.float32(0.9718), np.float32(0.9809), np.float32(0.9757), np.float32(0.9704), np.float32(0.9555), np.float32(0.9731), np.float32(0.9714), np.float32(0.9079), np.float32(0.9671), np.float32(0.9497), np.float32(0.9042), np.float32(0.9198), np.float32(0.926)] 
+2025-05-07 00:46:51.356486: Epoch time: 98.36 s 
+2025-05-07 00:46:52.855579:  
+2025-05-07 00:46:52.863208: Epoch 1731 
+2025-05-07 00:46:52.863593: Current learning rate: 0.00164 
+2025-05-07 00:48:31.987329: train_loss -0.5123 
+2025-05-07 00:48:32.068377: val_loss -0.5474 
+2025-05-07 00:48:32.101592: Pseudo dice [np.float32(0.8507), np.float32(0.8429), np.float32(0.9597), np.float32(0.9763), np.float32(0.9158), np.float32(0.9608), np.float32(0.9547), np.float32(0.9761), np.float32(0.9572), np.float32(0.9625), np.float32(0.9572), np.float32(0.9701), np.float32(0.9648), np.float32(0.9159), np.float32(0.9614), np.float32(0.9579), np.float32(0.897), np.float32(0.8937), np.float32(0.9105)] 
+2025-05-07 00:48:32.128699: Epoch time: 99.13 s 
+2025-05-07 00:48:33.849948:  
+2025-05-07 00:48:33.930409: Epoch 1732 
+2025-05-07 00:48:33.956469: Current learning rate: 0.00164 
+2025-05-07 00:50:06.441807: train_loss -0.4959 
+2025-05-07 00:50:06.528532: val_loss -0.5383 
+2025-05-07 00:50:06.547631: Pseudo dice [np.float32(0.8612), np.float32(0.8606), np.float32(0.9418), np.float32(0.9756), np.float32(0.9259), np.float32(0.9657), np.float32(0.9678), np.float32(0.976), np.float32(0.9555), np.float32(0.966), np.float32(0.955), np.float32(0.9662), np.float32(0.9716), np.float32(0.9192), np.float32(0.9692), np.float32(0.9595), np.float32(0.8971), np.float32(0.9108), np.float32(0.9234)] 
+2025-05-07 00:50:06.565898: Epoch time: 92.59 s 
+2025-05-07 00:50:08.447318:  
+2025-05-07 00:50:08.495754: Epoch 1733 
+2025-05-07 00:50:08.527049: Current learning rate: 0.00163 
+2025-05-07 00:51:43.001646: train_loss -0.5226 
+2025-05-07 00:51:43.171797: val_loss -0.5013 
+2025-05-07 00:51:43.210027: Pseudo dice [np.float32(0.8815), np.float32(0.8499), np.float32(0.9279), np.float32(0.9733), np.float32(0.9343), np.float32(0.9672), np.float32(0.9685), np.float32(0.9815), np.float32(0.9576), np.float32(0.9709), np.float32(0.9523), np.float32(0.968), np.float32(0.9594), np.float32(0.9229), np.float32(0.9637), np.float32(0.9607), np.float32(0.878), np.float32(0.8444), np.float32(0.9082)] 
+2025-05-07 00:51:43.251312: Epoch time: 94.56 s 
+2025-05-07 00:51:44.984714:  
+2025-05-07 00:51:45.014896: Epoch 1734 
+2025-05-07 00:51:45.026358: Current learning rate: 0.00163 
+2025-05-07 00:53:22.104971: train_loss -0.5056 
+2025-05-07 00:53:22.186068: val_loss -0.5156 
+2025-05-07 00:53:22.192047: Pseudo dice [np.float32(0.8793), np.float32(0.8657), np.float32(0.9229), np.float32(0.9784), np.float32(0.9179), np.float32(0.9654), np.float32(0.9647), np.float32(0.983), np.float32(0.9702), np.float32(0.9715), np.float32(0.9622), np.float32(0.9711), np.float32(0.9664), np.float32(0.9238), np.float32(0.9714), np.float32(0.9607), np.float32(0.8862), np.float32(0.9055), np.float32(0.9167)] 
+2025-05-07 00:53:22.199898: Epoch time: 97.12 s 
+2025-05-07 00:53:23.758248:  
+2025-05-07 00:53:23.780283: Epoch 1735 
+2025-05-07 00:53:23.794996: Current learning rate: 0.00162 
+2025-05-07 00:54:56.925962: train_loss -0.5048 
+2025-05-07 00:54:56.996077: val_loss -0.5072 
+2025-05-07 00:54:56.997100: Pseudo dice [np.float32(0.8409), np.float32(0.8502), np.float32(0.9239), np.float32(0.9787), np.float32(0.9126), np.float32(0.9683), np.float32(0.9652), np.float32(0.978), np.float32(0.965), np.float32(0.9536), np.float32(0.923), np.float32(0.9754), np.float32(0.9717), np.float32(0.9119), np.float32(0.9689), np.float32(0.9497), np.float32(0.8895), np.float32(0.9212), np.float32(0.9276)] 
+2025-05-07 00:54:56.997537: Epoch time: 93.17 s 
+2025-05-07 00:54:58.708302:  
+2025-05-07 00:54:58.855591: Epoch 1736 
+2025-05-07 00:54:58.863453: Current learning rate: 0.00162 
+2025-05-07 00:56:35.987941: train_loss -0.5126 
+2025-05-07 00:56:36.050085: val_loss -0.5204 
+2025-05-07 00:56:36.096161: Pseudo dice [np.float32(0.846), np.float32(0.8627), np.float32(0.8279), np.float32(0.9658), np.float32(0.9099), np.float32(0.9546), np.float32(0.9689), np.float32(0.9825), np.float32(0.9667), np.float32(0.9682), np.float32(0.9579), np.float32(0.9702), np.float32(0.973), np.float32(0.9047), np.float32(0.9528), np.float32(0.956), np.float32(0.8933), np.float32(0.9096), np.float32(0.9273)] 
+2025-05-07 00:56:36.132615: Epoch time: 97.28 s 
+2025-05-07 00:56:37.806174:  
+2025-05-07 00:56:37.841329: Epoch 1737 
+2025-05-07 00:56:37.841959: Current learning rate: 0.00161 
+2025-05-07 00:58:16.890080: train_loss -0.503 
+2025-05-07 00:58:17.015806: val_loss -0.4709 
+2025-05-07 00:58:17.050835: Pseudo dice [np.float32(0.8595), np.float32(0.8823), np.float32(0.932), np.float32(0.9707), np.float32(0.7145), np.float32(0.9228), np.float32(0.9679), np.float32(0.9826), np.float32(0.9561), np.float32(0.9738), np.float32(0.9473), np.float32(0.9505), np.float32(0.9725), np.float32(0.9258), np.float32(0.9665), np.float32(0.9652), np.float32(0.9019), np.float32(0.901), np.float32(0.9313)] 
+2025-05-07 00:58:17.088377: Epoch time: 99.09 s 
+2025-05-07 00:58:18.936332:  
+2025-05-07 00:58:18.984209: Epoch 1738 
+2025-05-07 00:58:19.001372: Current learning rate: 0.00161 
+2025-05-07 00:59:54.319944: train_loss -0.5073 
+2025-05-07 00:59:54.418105: val_loss -0.5048 
+2025-05-07 00:59:54.449648: Pseudo dice [np.float32(0.8645), np.float32(0.8773), np.float32(0.9407), np.float32(0.9786), np.float32(0.9362), np.float32(0.9516), np.float32(0.968), np.float32(0.982), np.float32(0.9603), np.float32(0.9738), np.float32(0.9371), np.float32(0.9691), np.float32(0.9757), np.float32(0.9093), np.float32(0.954), np.float32(0.9566), np.float32(0.9174), np.float32(0.9143), np.float32(0.9251)] 
+2025-05-07 00:59:54.494527: Epoch time: 95.38 s 
+2025-05-07 00:59:56.095018:  
+2025-05-07 00:59:56.203527: Epoch 1739 
+2025-05-07 00:59:56.227498: Current learning rate: 0.0016 
+2025-05-07 01:01:28.537032: train_loss -0.5058 
+2025-05-07 01:01:28.615642: val_loss -0.5215 
+2025-05-07 01:01:28.653079: Pseudo dice [np.float32(0.842), np.float32(0.8691), np.float32(0.9491), np.float32(0.9757), np.float32(0.9223), np.float32(0.9654), np.float32(0.9684), np.float32(0.9787), np.float32(0.9721), np.float32(0.9578), np.float32(0.9404), np.float32(0.9724), np.float32(0.974), np.float32(0.9205), np.float32(0.9692), np.float32(0.9629), np.float32(0.9175), np.float32(0.8956), np.float32(0.937)] 
+2025-05-07 01:01:28.674708: Epoch time: 92.44 s 
+2025-05-07 01:01:33.739673:  
+2025-05-07 01:01:33.745332: Epoch 1740 
+2025-05-07 01:01:33.745703: Current learning rate: 0.00159 
+2025-05-07 01:03:08.506474: train_loss -0.5189 
+2025-05-07 01:03:08.602271: val_loss -0.5128 
+2025-05-07 01:03:08.621493: Pseudo dice [np.float32(0.8541), np.float32(0.8592), np.float32(0.9183), np.float32(0.9679), np.float32(0.8978), np.float32(0.9253), np.float32(0.9435), np.float32(0.981), np.float32(0.9576), np.float32(0.964), np.float32(0.9475), np.float32(0.9691), np.float32(0.9669), np.float32(0.914), np.float32(0.9671), np.float32(0.961), np.float32(0.84), np.float32(0.8665), np.float32(0.9164)] 
+2025-05-07 01:03:08.640697: Epoch time: 94.77 s 
+2025-05-07 01:03:10.261752:  
+2025-05-07 01:03:10.393342: Epoch 1741 
+2025-05-07 01:03:10.453696: Current learning rate: 0.00159 
+2025-05-07 01:04:51.601347: train_loss -0.4917 
+2025-05-07 01:04:51.687846: val_loss -0.5095 
+2025-05-07 01:04:51.703724: Pseudo dice [np.float32(0.8627), np.float32(0.8669), np.float32(0.9456), np.float32(0.9649), np.float32(0.904), np.float32(0.9644), np.float32(0.948), np.float32(0.9708), np.float32(0.9626), np.float32(0.9562), np.float32(0.9348), np.float32(0.9666), np.float32(0.9667), np.float32(0.917), np.float32(0.969), np.float32(0.9618), np.float32(0.8612), np.float32(0.8995), np.float32(0.9222)] 
+2025-05-07 01:04:51.727643: Epoch time: 101.34 s 
+2025-05-07 01:04:53.220246:  
+2025-05-07 01:04:53.308648: Epoch 1742 
+2025-05-07 01:04:53.341984: Current learning rate: 0.00158 
+2025-05-07 01:06:26.547397: train_loss -0.5089 
+2025-05-07 01:06:26.663875: val_loss -0.5313 
+2025-05-07 01:06:26.686685: Pseudo dice [np.float32(0.8578), np.float32(0.8627), np.float32(0.68), np.float32(0.9675), np.float32(0.9235), np.float32(0.9656), np.float32(0.9676), np.float32(0.9802), np.float32(0.9646), np.float32(0.9683), np.float32(0.9467), np.float32(0.9672), np.float32(0.9691), np.float32(0.9233), np.float32(0.9689), np.float32(0.9603), np.float32(0.897), np.float32(0.9094), np.float32(0.9226)] 
+2025-05-07 01:06:26.717759: Epoch time: 93.33 s 
+2025-05-07 01:06:28.271448:  
+2025-05-07 01:06:28.391857: Epoch 1743 
+2025-05-07 01:06:28.426667: Current learning rate: 0.00158 
+2025-05-07 01:08:05.608165: train_loss -0.5078 
+2025-05-07 01:08:05.650544: val_loss -0.4861 
+2025-05-07 01:08:05.654980: Pseudo dice [np.float32(0.8467), np.float32(0.8416), np.float32(0.9094), np.float32(0.9633), np.float32(0.9333), np.float32(0.9644), np.float32(0.9652), np.float32(0.9788), np.float32(0.9702), np.float32(0.9732), np.float32(0.9651), np.float32(0.9728), np.float32(0.9772), np.float32(0.9184), np.float32(0.9684), np.float32(0.9638), np.float32(0.8804), np.float32(0.9066), np.float32(0.9241)] 
+2025-05-07 01:08:05.655535: Epoch time: 97.34 s 
+2025-05-07 01:08:07.251297:  
+2025-05-07 01:08:07.398126: Epoch 1744 
+2025-05-07 01:08:07.456735: Current learning rate: 0.00157 
+2025-05-07 01:09:45.056401: train_loss -0.5016 
+2025-05-07 01:09:45.229004: val_loss -0.5071 
+2025-05-07 01:09:45.265696: Pseudo dice [np.float32(0.8571), np.float32(0.8672), np.float32(0.945), np.float32(0.9689), np.float32(0.9252), np.float32(0.9528), np.float32(0.9674), np.float32(0.9813), np.float32(0.9731), np.float32(0.9662), np.float32(0.9568), np.float32(0.9744), np.float32(0.9731), np.float32(0.9184), np.float32(0.9034), np.float32(0.9593), np.float32(0.8746), np.float32(0.8413), np.float32(0.9098)] 
+2025-05-07 01:09:45.297287: Epoch time: 97.81 s 
+2025-05-07 01:09:46.973514:  
+2025-05-07 01:09:46.982480: Epoch 1745 
+2025-05-07 01:09:46.982926: Current learning rate: 0.00157 
+2025-05-07 01:11:24.697552: train_loss -0.5103 
+2025-05-07 01:11:24.894209: val_loss -0.5144 
+2025-05-07 01:11:24.944188: Pseudo dice [np.float32(0.8382), np.float32(0.8558), np.float32(0.904), np.float32(0.9769), np.float32(0.914), np.float32(0.9605), np.float32(0.9538), np.float32(0.9778), np.float32(0.958), np.float32(0.9641), np.float32(0.9507), np.float32(0.968), np.float32(0.9679), np.float32(0.9056), np.float32(0.9707), np.float32(0.9564), np.float32(0.8941), np.float32(0.9051), np.float32(0.9138)] 
+2025-05-07 01:11:24.978225: Epoch time: 97.73 s 
+2025-05-07 01:11:26.698090:  
+2025-05-07 01:11:26.739166: Epoch 1746 
+2025-05-07 01:11:26.740152: Current learning rate: 0.00156 
+2025-05-07 01:13:03.571026: train_loss -0.5063 
+2025-05-07 01:13:03.677564: val_loss -0.4916 
+2025-05-07 01:13:03.703517: Pseudo dice [np.float32(0.8411), np.float32(0.8504), np.float32(0.917), np.float32(0.9782), np.float32(0.9137), np.float32(0.9614), np.float32(0.9498), np.float32(0.9743), np.float32(0.9677), np.float32(0.969), np.float32(0.9552), np.float32(0.9746), np.float32(0.9733), np.float32(0.9162), np.float32(0.9711), np.float32(0.9585), np.float32(0.8669), np.float32(0.8915), np.float32(0.9018)] 
+2025-05-07 01:13:03.730723: Epoch time: 96.87 s 
+2025-05-07 01:13:05.443701:  
+2025-05-07 01:13:05.553454: Epoch 1747 
+2025-05-07 01:13:05.575650: Current learning rate: 0.00156 
+2025-05-07 01:14:44.076214: train_loss -0.5055 
+2025-05-07 01:14:44.183708: val_loss -0.5012 
+2025-05-07 01:14:44.217679: Pseudo dice [np.float32(0.8353), np.float32(0.8131), np.float32(0.7369), np.float32(0.9788), np.float32(0.9174), np.float32(0.9448), np.float32(0.9402), np.float32(0.973), np.float32(0.9698), np.float32(0.9606), np.float32(0.9425), np.float32(0.9741), np.float32(0.9672), np.float32(0.9175), np.float32(0.968), np.float32(0.9604), np.float32(0.867), np.float32(0.8828), np.float32(0.9143)] 
+2025-05-07 01:14:44.233664: Epoch time: 98.63 s 
+2025-05-07 01:14:45.947228:  
+2025-05-07 01:14:46.035201: Epoch 1748 
+2025-05-07 01:14:46.061495: Current learning rate: 0.00155 
+2025-05-07 01:16:22.830791: train_loss -0.4987 
+2025-05-07 01:16:22.857374: val_loss -0.4471 
+2025-05-07 01:16:22.862244: Pseudo dice [np.float32(0.857), np.float32(0.8209), np.float32(0.9253), np.float32(0.9769), np.float32(0.9086), np.float32(0.9657), np.float32(0.9695), np.float32(0.9746), np.float32(0.9728), np.float32(0.968), np.float32(0.9603), np.float32(0.9739), np.float32(0.9636), np.float32(0.8966), np.float32(0.9581), np.float32(0.9501), np.float32(0.9147), np.float32(0.9017), np.float32(0.9058)] 
+2025-05-07 01:16:22.862674: Epoch time: 96.88 s 
+2025-05-07 01:16:24.517483:  
+2025-05-07 01:16:24.619652: Epoch 1749 
+2025-05-07 01:16:24.663920: Current learning rate: 0.00154 
+2025-05-07 01:18:06.526124: train_loss -0.5165 
+2025-05-07 01:18:06.651941: val_loss -0.5374 
+2025-05-07 01:18:06.708458: Pseudo dice [np.float32(0.8664), np.float32(0.8433), np.float32(0.9275), np.float32(0.9654), np.float32(0.9073), np.float32(0.9635), np.float32(0.9679), np.float32(0.9806), np.float32(0.9632), np.float32(0.9707), np.float32(0.9454), np.float32(0.9689), np.float32(0.9693), np.float32(0.9198), np.float32(0.9662), np.float32(0.9578), np.float32(0.8656), np.float32(0.8521), np.float32(0.9189)] 
+2025-05-07 01:18:06.755781: Epoch time: 102.01 s 
+2025-05-07 01:18:09.500232:  
+2025-05-07 01:18:09.540827: Epoch 1750 
+2025-05-07 01:18:09.555722: Current learning rate: 0.00154 
+2025-05-07 01:19:46.478354: train_loss -0.5188 
+2025-05-07 01:19:46.616767: val_loss -0.5463 
+2025-05-07 01:19:46.676792: Pseudo dice [np.float32(0.8679), np.float32(0.8681), np.float32(0.8974), np.float32(0.9715), np.float32(0.9211), np.float32(0.9636), np.float32(0.9672), np.float32(0.9757), np.float32(0.9484), np.float32(0.9591), np.float32(0.9535), np.float32(0.9725), np.float32(0.9724), np.float32(0.9299), np.float32(0.9696), np.float32(0.9621), np.float32(0.9076), np.float32(0.9018), np.float32(0.9219)] 
+2025-05-07 01:19:46.726323: Epoch time: 96.98 s 
+2025-05-07 01:19:48.341576:  
+2025-05-07 01:19:48.366278: Epoch 1751 
+2025-05-07 01:19:48.402550: Current learning rate: 0.00153 
+2025-05-07 01:21:29.525853: train_loss -0.5183 
+2025-05-07 01:21:29.638024: val_loss -0.5059 
+2025-05-07 01:21:29.657209: Pseudo dice [np.float32(0.8484), np.float32(0.8348), np.float32(0.9185), np.float32(0.9721), np.float32(0.9017), np.float32(0.9653), np.float32(0.9689), np.float32(0.9755), np.float32(0.96), np.float32(0.9732), np.float32(0.9382), np.float32(0.964), np.float32(0.9703), np.float32(0.9236), np.float32(0.9728), np.float32(0.9604), np.float32(0.8962), np.float32(0.9007), np.float32(0.9167)] 
+2025-05-07 01:21:29.680427: Epoch time: 101.19 s 
+2025-05-07 01:21:31.320843:  
+2025-05-07 01:21:31.397783: Epoch 1752 
+2025-05-07 01:21:31.405871: Current learning rate: 0.00153 
+2025-05-07 01:23:12.180895: train_loss -0.513 
+2025-05-07 01:23:12.274361: val_loss -0.5355 
+2025-05-07 01:23:12.313438: Pseudo dice [np.float32(0.8689), np.float32(0.8515), np.float32(0.9056), np.float32(0.9684), np.float32(0.8972), np.float32(0.9551), np.float32(0.9519), np.float32(0.9814), np.float32(0.9738), np.float32(0.9619), np.float32(0.9573), np.float32(0.9753), np.float32(0.9731), np.float32(0.914), np.float32(0.968), np.float32(0.9509), np.float32(0.9021), np.float32(0.9129), np.float32(0.9327)] 
+2025-05-07 01:23:12.346613: Epoch time: 100.86 s 
+2025-05-07 01:23:13.962641:  
+2025-05-07 01:23:14.046334: Epoch 1753 
+2025-05-07 01:23:14.097679: Current learning rate: 0.00152 
+2025-05-07 01:24:53.134881: train_loss -0.5147 
+2025-05-07 01:24:53.195548: val_loss -0.5388 
+2025-05-07 01:24:53.217839: Pseudo dice [np.float32(0.8607), np.float32(0.8737), np.float32(0.895), np.float32(0.9714), np.float32(0.9344), np.float32(0.9556), np.float32(0.9683), np.float32(0.9825), np.float32(0.9651), np.float32(0.976), np.float32(0.9593), np.float32(0.9698), np.float32(0.9741), np.float32(0.9245), np.float32(0.9631), np.float32(0.963), np.float32(0.8397), np.float32(0.899), np.float32(0.9337)] 
+2025-05-07 01:24:53.243271: Epoch time: 99.17 s 
+2025-05-07 01:24:54.865554:  
+2025-05-07 01:24:54.949716: Epoch 1754 
+2025-05-07 01:24:54.980985: Current learning rate: 0.00152 
+2025-05-07 01:26:32.334726: train_loss -0.5134 
+2025-05-07 01:26:32.431393: val_loss -0.5096 
+2025-05-07 01:26:32.453961: Pseudo dice [np.float32(0.8541), np.float32(0.8638), np.float32(0.9332), np.float32(0.974), np.float32(0.9259), np.float32(0.9634), np.float32(0.9637), np.float32(0.9797), np.float32(0.9632), np.float32(0.9607), np.float32(0.9478), np.float32(0.9725), np.float32(0.969), np.float32(0.9164), np.float32(0.9694), np.float32(0.9516), np.float32(0.895), np.float32(0.8845), np.float32(0.9136)] 
+2025-05-07 01:26:32.464855: Epoch time: 97.47 s 
+2025-05-07 01:26:34.029982:  
+2025-05-07 01:26:34.082492: Epoch 1755 
+2025-05-07 01:26:34.083511: Current learning rate: 0.00151 
+2025-05-07 01:28:10.484266: train_loss -0.5249 
+2025-05-07 01:28:10.597975: val_loss -0.4667 
+2025-05-07 01:28:10.636940: Pseudo dice [np.float32(0.8395), np.float32(0.883), np.float32(0.9353), np.float32(0.9789), np.float32(0.9328), np.float32(0.959), np.float32(0.968), np.float32(0.9811), np.float32(0.9555), np.float32(0.9694), np.float32(0.9554), np.float32(0.9612), np.float32(0.9673), np.float32(0.902), np.float32(0.9678), np.float32(0.9595), np.float32(0.9052), np.float32(0.913), np.float32(0.9194)] 
+2025-05-07 01:28:10.682146: Epoch time: 96.46 s 
+2025-05-07 01:28:12.421746:  
+2025-05-07 01:28:12.468343: Epoch 1756 
+2025-05-07 01:28:12.492112: Current learning rate: 0.00151 
+2025-05-07 01:29:45.995629: train_loss -0.4947 
+2025-05-07 01:29:46.047382: val_loss -0.5143 
+2025-05-07 01:29:46.048782: Pseudo dice [np.float32(0.8394), np.float32(0.8771), np.float32(0.8535), np.float32(0.9782), np.float32(0.9194), np.float32(0.9649), np.float32(0.9706), np.float32(0.9757), np.float32(0.9692), np.float32(0.9656), np.float32(0.9567), np.float32(0.9686), np.float32(0.9704), np.float32(0.8983), np.float32(0.9711), np.float32(0.9578), np.float32(0.8856), np.float32(0.9006), np.float32(0.9158)] 
+2025-05-07 01:29:46.064177: Epoch time: 93.58 s 
+2025-05-07 01:29:47.614181:  
+2025-05-07 01:29:47.716482: Epoch 1757 
+2025-05-07 01:29:47.753963: Current learning rate: 0.0015 
+2025-05-07 01:31:26.766550: train_loss -0.4973 
+2025-05-07 01:31:26.873646: val_loss -0.5422 
+2025-05-07 01:31:26.902644: Pseudo dice [np.float32(0.8693), np.float32(0.8382), np.float32(0.9368), np.float32(0.9762), np.float32(0.9113), np.float32(0.9605), np.float32(0.9663), np.float32(0.9764), np.float32(0.9695), np.float32(0.9691), np.float32(0.9409), np.float32(0.9734), np.float32(0.9771), np.float32(0.9093), np.float32(0.9682), np.float32(0.9591), np.float32(0.9017), np.float32(0.9019), np.float32(0.9323)] 
+2025-05-07 01:31:26.938692: Epoch time: 99.15 s 
+2025-05-07 01:31:32.488664:  
+2025-05-07 01:31:32.494267: Epoch 1758 
+2025-05-07 01:31:32.494631: Current learning rate: 0.00149 
+2025-05-07 01:33:09.176450: train_loss -0.5099 
+2025-05-07 01:33:09.291514: val_loss -0.5045 
+2025-05-07 01:33:09.293893: Pseudo dice [np.float32(0.8603), np.float32(0.8724), np.float32(0.9261), np.float32(0.9827), np.float32(0.88), np.float32(0.9444), np.float32(0.9679), np.float32(0.9788), np.float32(0.971), np.float32(0.9753), np.float32(0.9626), np.float32(0.9695), np.float32(0.9695), np.float32(0.9103), np.float32(0.967), np.float32(0.9558), np.float32(0.8824), np.float32(0.9054), np.float32(0.9276)] 
+2025-05-07 01:33:09.301042: Epoch time: 96.69 s 
+2025-05-07 01:33:11.035218:  
+2025-05-07 01:33:11.115925: Epoch 1759 
+2025-05-07 01:33:11.132763: Current learning rate: 0.00149 
+2025-05-07 01:34:51.860219: train_loss -0.5086 
+2025-05-07 01:34:51.973061: val_loss -0.5301 
+2025-05-07 01:34:52.015901: Pseudo dice [np.float32(0.8343), np.float32(0.8668), np.float32(0.8905), np.float32(0.9774), np.float32(0.9064), np.float32(0.9601), np.float32(0.9711), np.float32(0.9792), np.float32(0.9675), np.float32(0.965), np.float32(0.9514), np.float32(0.9708), np.float32(0.9736), np.float32(0.9195), np.float32(0.9684), np.float32(0.9576), np.float32(0.8974), np.float32(0.8847), np.float32(0.9333)] 
+2025-05-07 01:34:52.044592: Epoch time: 100.83 s 
+2025-05-07 01:34:53.837953:  
+2025-05-07 01:34:53.905149: Epoch 1760 
+2025-05-07 01:34:53.967237: Current learning rate: 0.00148 
+2025-05-07 01:36:29.792750: train_loss -0.5009 
+2025-05-07 01:36:29.931727: val_loss -0.5113 
+2025-05-07 01:36:29.939733: Pseudo dice [np.float32(0.8586), np.float32(0.863), np.float32(0.8473), np.float32(0.9711), np.float32(0.9178), np.float32(0.9668), np.float32(0.9692), np.float32(0.9819), np.float32(0.9709), np.float32(0.9713), np.float32(0.9539), np.float32(0.9734), np.float32(0.9708), np.float32(0.9154), np.float32(0.9665), np.float32(0.9571), np.float32(0.9021), np.float32(0.88), np.float32(0.9217)] 
+2025-05-07 01:36:29.951449: Epoch time: 95.96 s 
+2025-05-07 01:36:31.477194:  
+2025-05-07 01:36:31.553208: Epoch 1761 
+2025-05-07 01:36:31.553854: Current learning rate: 0.00148 
+2025-05-07 01:38:11.546643: train_loss -0.5072 
+2025-05-07 01:38:11.802203: val_loss -0.4945 
+2025-05-07 01:38:11.815996: Pseudo dice [np.float32(0.8743), np.float32(0.8587), np.float32(0.9472), np.float32(0.9821), np.float32(0.8839), np.float32(0.967), np.float32(0.9707), np.float32(0.9823), np.float32(0.9728), np.float32(0.9593), np.float32(0.9474), np.float32(0.9716), np.float32(0.9704), np.float32(0.9161), np.float32(0.9695), np.float32(0.9532), np.float32(0.8979), np.float32(0.9058), np.float32(0.9212)] 
+2025-05-07 01:38:11.825697: Epoch time: 100.07 s 
+2025-05-07 01:38:13.296432:  
+2025-05-07 01:38:13.484526: Epoch 1762 
+2025-05-07 01:38:13.517526: Current learning rate: 0.00147 
+2025-05-07 01:39:56.013351: train_loss -0.514 
+2025-05-07 01:39:56.057575: val_loss -0.5088 
+2025-05-07 01:39:56.058642: Pseudo dice [np.float32(0.8616), np.float32(0.8546), np.float32(0.9245), np.float32(0.9789), np.float32(0.9238), np.float32(0.9585), np.float32(0.9639), np.float32(0.981), np.float32(0.9662), np.float32(0.9738), np.float32(0.948), np.float32(0.975), np.float32(0.9639), np.float32(0.918), np.float32(0.9667), np.float32(0.958), np.float32(0.878), np.float32(0.9106), np.float32(0.9208)] 
+2025-05-07 01:39:56.059275: Epoch time: 102.72 s 
+2025-05-07 01:39:57.538820:  
+2025-05-07 01:39:57.586064: Epoch 1763 
+2025-05-07 01:39:57.586717: Current learning rate: 0.00147 
+2025-05-07 01:41:30.631528: train_loss -0.5086 
+2025-05-07 01:41:30.723561: val_loss -0.5339 
+2025-05-07 01:41:30.727878: Pseudo dice [np.float32(0.8625), np.float32(0.871), np.float32(0.9553), np.float32(0.9761), np.float32(0.9301), np.float32(0.96), np.float32(0.9724), np.float32(0.9823), np.float32(0.963), np.float32(0.9774), np.float32(0.9625), np.float32(0.9716), np.float32(0.9748), np.float32(0.9234), np.float32(0.9654), np.float32(0.9632), np.float32(0.8863), np.float32(0.8271), np.float32(0.927)] 
+2025-05-07 01:41:30.745974: Epoch time: 93.09 s 
+2025-05-07 01:41:32.570657:  
+2025-05-07 01:41:32.659832: Epoch 1764 
+2025-05-07 01:41:32.685965: Current learning rate: 0.00146 
+2025-05-07 01:43:10.971884: train_loss -0.5048 
+2025-05-07 01:43:11.110940: val_loss -0.5233 
+2025-05-07 01:43:11.139192: Pseudo dice [np.float32(0.8389), np.float32(0.8579), np.float32(0.9059), np.float32(0.9702), np.float32(0.9183), np.float32(0.9545), np.float32(0.9637), np.float32(0.9815), np.float32(0.9737), np.float32(0.9767), np.float32(0.9597), np.float32(0.9676), np.float32(0.9718), np.float32(0.8993), np.float32(0.9623), np.float32(0.9501), np.float32(0.8792), np.float32(0.8779), np.float32(0.9208)] 
+2025-05-07 01:43:11.167799: Epoch time: 98.4 s 
+2025-05-07 01:43:12.681942:  
+2025-05-07 01:43:12.784979: Epoch 1765 
+2025-05-07 01:43:12.794332: Current learning rate: 0.00146 
+2025-05-07 01:44:49.428717: train_loss -0.5092 
+2025-05-07 01:44:49.531511: val_loss -0.5428 
+2025-05-07 01:44:49.549647: Pseudo dice [np.float32(0.8687), np.float32(0.8728), np.float32(0.9531), np.float32(0.9737), np.float32(0.9226), np.float32(0.9679), np.float32(0.9678), np.float32(0.9801), np.float32(0.9643), np.float32(0.9732), np.float32(0.9515), np.float32(0.9681), np.float32(0.9721), np.float32(0.9261), np.float32(0.9742), np.float32(0.9631), np.float32(0.8975), np.float32(0.9183), np.float32(0.9371)] 
+2025-05-07 01:44:49.583573: Epoch time: 96.75 s 
+2025-05-07 01:44:51.164753:  
+2025-05-07 01:44:51.193645: Epoch 1766 
+2025-05-07 01:44:51.211776: Current learning rate: 0.00145 
+2025-05-07 01:46:27.965738: train_loss -0.4947 
+2025-05-07 01:46:28.012299: val_loss -0.5203 
+2025-05-07 01:46:28.013638: Pseudo dice [np.float32(0.8673), np.float32(0.857), np.float32(0.9084), np.float32(0.9779), np.float32(0.9173), np.float32(0.9629), np.float32(0.9679), np.float32(0.9588), np.float32(0.9668), np.float32(0.9742), np.float32(0.9554), np.float32(0.9699), np.float32(0.9725), np.float32(0.9185), np.float32(0.9698), np.float32(0.9519), np.float32(0.9243), np.float32(0.9254), np.float32(0.9261)] 
+2025-05-07 01:46:28.033616: Epoch time: 96.8 s 
+2025-05-07 01:46:28.041034: Yayy! New best EMA pseudo Dice: 0.9373999834060669 
+2025-05-07 01:46:30.931586:  
+2025-05-07 01:46:30.993022: Epoch 1767 
+2025-05-07 01:46:31.017702: Current learning rate: 0.00144 
+2025-05-07 01:48:08.673182: train_loss -0.5125 
+2025-05-07 01:48:08.752484: val_loss -0.5029 
+2025-05-07 01:48:08.782314: Pseudo dice [np.float32(0.8637), np.float32(0.8545), np.float32(0.9201), np.float32(0.9778), np.float32(0.8862), np.float32(0.9628), np.float32(0.9632), np.float32(0.9782), np.float32(0.9721), np.float32(0.9678), np.float32(0.9585), np.float32(0.9742), np.float32(0.9729), np.float32(0.9229), np.float32(0.9687), np.float32(0.961), np.float32(0.8956), np.float32(0.9), np.float32(0.9164)] 
+2025-05-07 01:48:08.810477: Epoch time: 97.74 s 
+2025-05-07 01:48:08.820532: Yayy! New best EMA pseudo Dice: 0.9373999834060669 
+2025-05-07 01:48:11.704767:  
+2025-05-07 01:48:11.710475: Epoch 1768 
+2025-05-07 01:48:11.710958: Current learning rate: 0.00144 
+2025-05-07 01:49:49.176757: train_loss -0.4993 
+2025-05-07 01:49:49.268081: val_loss -0.5307 
+2025-05-07 01:49:49.300283: Pseudo dice [np.float32(0.8698), np.float32(0.8786), np.float32(0.9283), np.float32(0.9693), np.float32(0.9298), np.float32(0.9641), np.float32(0.9715), np.float32(0.9794), np.float32(0.9623), np.float32(0.9715), np.float32(0.9373), np.float32(0.965), np.float32(0.9742), np.float32(0.9146), np.float32(0.9695), np.float32(0.9563), np.float32(0.8917), np.float32(0.8784), np.float32(0.9194)] 
+2025-05-07 01:49:49.320703: Epoch time: 97.47 s 
+2025-05-07 01:49:49.339088: Yayy! New best EMA pseudo Dice: 0.9375 
+2025-05-07 01:49:52.177860:  
+2025-05-07 01:49:52.180286: Epoch 1769 
+2025-05-07 01:49:52.180851: Current learning rate: 0.00143 
+2025-05-07 01:51:28.419170: train_loss -0.5088 
+2025-05-07 01:51:28.439831: val_loss -0.5184 
+2025-05-07 01:51:28.440531: Pseudo dice [np.float32(0.8484), np.float32(0.8675), np.float32(0.9146), np.float32(0.9788), np.float32(0.9416), np.float32(0.956), np.float32(0.9688), np.float32(0.9785), np.float32(0.9651), np.float32(0.9678), np.float32(0.9519), np.float32(0.9676), np.float32(0.9704), np.float32(0.9138), np.float32(0.9653), np.float32(0.9548), np.float32(0.903), np.float32(0.9099), np.float32(0.9094)] 
+2025-05-07 01:51:28.441052: Epoch time: 96.24 s 
+2025-05-07 01:51:28.445702: Yayy! New best EMA pseudo Dice: 0.9376000165939331 
+2025-05-07 01:51:31.208413:  
+2025-05-07 01:51:31.311703: Epoch 1770 
+2025-05-07 01:51:31.343200: Current learning rate: 0.00143 
+2025-05-07 01:53:07.945964: train_loss -0.5101 
+2025-05-07 01:53:08.000111: val_loss -0.5175 
+2025-05-07 01:53:08.018473: Pseudo dice [np.float32(0.8593), np.float32(0.8695), np.float32(0.9222), np.float32(0.9804), np.float32(0.9265), np.float32(0.9611), np.float32(0.9672), np.float32(0.9754), np.float32(0.9659), np.float32(0.9697), np.float32(0.9592), np.float32(0.9722), np.float32(0.9637), np.float32(0.9183), np.float32(0.9573), np.float32(0.9624), np.float32(0.895), np.float32(0.8851), np.float32(0.9233)] 
+2025-05-07 01:53:08.026906: Epoch time: 96.74 s 
+2025-05-07 01:53:08.027912: Yayy! New best EMA pseudo Dice: 0.9376999735832214 
+2025-05-07 01:53:10.817510:  
+2025-05-07 01:53:10.822561: Epoch 1771 
+2025-05-07 01:53:10.822941: Current learning rate: 0.00142 
+2025-05-07 01:54:46.463810: train_loss -0.5182 
+2025-05-07 01:54:46.616021: val_loss -0.5238 
+2025-05-07 01:54:46.617300: Pseudo dice [np.float32(0.8463), np.float32(0.8475), np.float32(0.8567), np.float32(0.9757), np.float32(0.9056), np.float32(0.9589), np.float32(0.9673), np.float32(0.979), np.float32(0.9642), np.float32(0.9707), np.float32(0.9519), np.float32(0.9719), np.float32(0.9732), np.float32(0.9021), np.float32(0.9636), np.float32(0.9542), np.float32(0.9021), np.float32(0.873), np.float32(0.9158)] 
+2025-05-07 01:54:46.618005: Epoch time: 95.65 s 
+2025-05-07 01:54:48.168221:  
+2025-05-07 01:54:48.190068: Epoch 1772 
+2025-05-07 01:54:48.194535: Current learning rate: 0.00142 
+2025-05-07 01:56:22.468392: train_loss -0.5071 
+2025-05-07 01:56:22.591418: val_loss -0.5191 
+2025-05-07 01:56:22.619435: Pseudo dice [np.float32(0.8587), np.float32(0.8744), np.float32(0.897), np.float32(0.9756), np.float32(0.9204), np.float32(0.967), np.float32(0.97), np.float32(0.9795), np.float32(0.973), np.float32(0.966), np.float32(0.9519), np.float32(0.9726), np.float32(0.969), np.float32(0.9179), np.float32(0.9719), np.float32(0.9563), np.float32(0.8743), np.float32(0.8989), np.float32(0.9204)] 
+2025-05-07 01:56:22.623482: Epoch time: 94.3 s 
+2025-05-07 01:56:24.253634:  
+2025-05-07 01:56:24.337569: Epoch 1773 
+2025-05-07 01:56:24.350413: Current learning rate: 0.00141 
+2025-05-07 01:57:55.750571: train_loss -0.4933 
+2025-05-07 01:57:55.803471: val_loss -0.5357 
+2025-05-07 01:57:55.813208: Pseudo dice [np.float32(0.8744), np.float32(0.8709), np.float32(0.9383), np.float32(0.9809), np.float32(0.943), np.float32(0.9611), np.float32(0.9687), np.float32(0.9817), np.float32(0.9658), np.float32(0.9703), np.float32(0.9491), np.float32(0.9717), np.float32(0.9636), np.float32(0.9241), np.float32(0.9693), np.float32(0.957), np.float32(0.9113), np.float32(0.9247), np.float32(0.9324)] 
+2025-05-07 01:57:55.817048: Epoch time: 91.5 s 
+2025-05-07 01:57:55.828422: Yayy! New best EMA pseudo Dice: 0.9379000067710876 
+2025-05-07 01:58:02.089213:  
+2025-05-07 01:58:02.092455: Epoch 1774 
+2025-05-07 01:58:02.092827: Current learning rate: 0.00141 
+2025-05-07 01:59:43.348714: train_loss -0.5282 
+2025-05-07 01:59:43.415679: val_loss -0.5446 
+2025-05-07 01:59:43.447340: Pseudo dice [np.float32(0.8528), np.float32(0.8724), np.float32(0.945), np.float32(0.9656), np.float32(0.9232), np.float32(0.9576), np.float32(0.9703), np.float32(0.9815), np.float32(0.9698), np.float32(0.9644), np.float32(0.9624), np.float32(0.9719), np.float32(0.9664), np.float32(0.9236), np.float32(0.9382), np.float32(0.9505), np.float32(0.9203), np.float32(0.922), np.float32(0.9095)] 
+2025-05-07 01:59:43.472848: Epoch time: 101.26 s 
+2025-05-07 01:59:43.498066: Yayy! New best EMA pseudo Dice: 0.9380999803543091 
+2025-05-07 01:59:46.916433:  
+2025-05-07 01:59:46.959892: Epoch 1775 
+2025-05-07 01:59:46.960785: Current learning rate: 0.0014 
+2025-05-07 02:01:27.537859: train_loss -0.5287 
+2025-05-07 02:01:27.639415: val_loss -0.5143 
+2025-05-07 02:01:27.661843: Pseudo dice [np.float32(0.862), np.float32(0.8642), np.float32(0.9428), np.float32(0.9699), np.float32(0.9232), np.float32(0.9596), np.float32(0.9696), np.float32(0.9819), np.float32(0.971), np.float32(0.9693), np.float32(0.9525), np.float32(0.9727), np.float32(0.9719), np.float32(0.9194), np.float32(0.9596), np.float32(0.9628), np.float32(0.8979), np.float32(0.8971), np.float32(0.9221)] 
+2025-05-07 02:01:27.677268: Epoch time: 100.62 s 
+2025-05-07 02:01:27.701421: Yayy! New best EMA pseudo Dice: 0.9383999705314636 
+2025-05-07 02:01:32.361344:  
+2025-05-07 02:01:32.500499: Epoch 1776 
+2025-05-07 02:01:32.516198: Current learning rate: 0.00139 
+2025-05-07 02:03:09.301312: train_loss -0.5062 
+2025-05-07 02:03:09.339390: val_loss -0.472 
+2025-05-07 02:03:09.361480: Pseudo dice [np.float32(0.8474), np.float32(0.8492), np.float32(0.9199), np.float32(0.972), np.float32(0.9224), np.float32(0.9592), np.float32(0.9642), np.float32(0.9813), np.float32(0.9594), np.float32(0.9632), np.float32(0.9523), np.float32(0.961), np.float32(0.973), np.float32(0.9218), np.float32(0.9315), np.float32(0.9613), np.float32(0.9085), np.float32(0.8787), np.float32(0.9285)] 
+2025-05-07 02:03:09.362785: Epoch time: 96.94 s 
+2025-05-07 02:03:10.869741:  
+2025-05-07 02:03:10.993773: Epoch 1777 
+2025-05-07 02:03:11.055760: Current learning rate: 0.00139 
+2025-05-07 02:04:48.502770: train_loss -0.5156 
+2025-05-07 02:04:48.530704: val_loss -0.5329 
+2025-05-07 02:04:48.542602: Pseudo dice [np.float32(0.876), np.float32(0.8529), np.float32(0.9046), np.float32(0.9748), np.float32(0.9127), np.float32(0.9689), np.float32(0.9625), np.float32(0.9791), np.float32(0.9665), np.float32(0.9635), np.float32(0.9492), np.float32(0.9657), np.float32(0.9679), np.float32(0.9231), np.float32(0.9633), np.float32(0.9546), np.float32(0.8981), np.float32(0.917), np.float32(0.9206)] 
+2025-05-07 02:04:48.559613: Epoch time: 97.63 s 
+2025-05-07 02:04:50.235092:  
+2025-05-07 02:04:50.318540: Epoch 1778 
+2025-05-07 02:04:50.362098: Current learning rate: 0.00138 
+2025-05-07 02:06:27.398914: train_loss -0.505 
+2025-05-07 02:06:27.490803: val_loss -0.5528 
+2025-05-07 02:06:27.518096: Pseudo dice [np.float32(0.8323), np.float32(0.8386), np.float32(0.8895), np.float32(0.9764), np.float32(0.9276), np.float32(0.9563), np.float32(0.9689), np.float32(0.9746), np.float32(0.9638), np.float32(0.9636), np.float32(0.9542), np.float32(0.9645), np.float32(0.9683), np.float32(0.9165), np.float32(0.971), np.float32(0.965), np.float32(0.9003), np.float32(0.9078), np.float32(0.9129)] 
+2025-05-07 02:06:27.525354: Epoch time: 97.17 s 
+2025-05-07 02:06:29.074408:  
+2025-05-07 02:06:29.197831: Epoch 1779 
+2025-05-07 02:06:29.220210: Current learning rate: 0.00138 
+2025-05-07 02:08:11.117383: train_loss -0.4939 
+2025-05-07 02:08:11.255659: val_loss -0.5268 
+2025-05-07 02:08:11.262387: Pseudo dice [np.float32(0.8643), np.float32(0.8589), np.float32(0.94), np.float32(0.9779), np.float32(0.9211), np.float32(0.9567), np.float32(0.9645), np.float32(0.9798), np.float32(0.963), np.float32(0.9712), np.float32(0.9539), np.float32(0.9639), np.float32(0.9745), np.float32(0.9235), np.float32(0.9713), np.float32(0.9607), np.float32(0.8685), np.float32(0.8816), np.float32(0.921)] 
+2025-05-07 02:08:11.299585: Epoch time: 102.04 s 
+2025-05-07 02:08:12.844429:  
+2025-05-07 02:08:12.963967: Epoch 1780 
+2025-05-07 02:08:13.017100: Current learning rate: 0.00137 
+2025-05-07 02:09:51.369725: train_loss -0.5178 
+2025-05-07 02:09:51.454909: val_loss -0.5065 
+2025-05-07 02:09:51.487784: Pseudo dice [np.float32(0.8287), np.float32(0.8621), np.float32(0.9076), np.float32(0.9784), np.float32(0.8677), np.float32(0.951), np.float32(0.9587), np.float32(0.9743), np.float32(0.9676), np.float32(0.9758), np.float32(0.9534), np.float32(0.9683), np.float32(0.9689), np.float32(0.9144), np.float32(0.9689), np.float32(0.958), np.float32(0.8751), np.float32(0.8845), np.float32(0.9059)] 
+2025-05-07 02:09:51.518723: Epoch time: 98.53 s 
+2025-05-07 02:09:53.227492:  
+2025-05-07 02:09:53.326618: Epoch 1781 
+2025-05-07 02:09:53.360673: Current learning rate: 0.00137 
+2025-05-07 02:11:29.494018: train_loss -0.4929 
+2025-05-07 02:11:29.522508: val_loss -0.524 
+2025-05-07 02:11:29.537408: Pseudo dice [np.float32(0.8592), np.float32(0.8632), np.float32(0.9098), np.float32(0.9717), np.float32(0.9288), np.float32(0.9571), np.float32(0.9602), np.float32(0.9812), np.float32(0.9645), np.float32(0.9555), np.float32(0.9567), np.float32(0.9686), np.float32(0.9736), np.float32(0.9223), np.float32(0.9681), np.float32(0.9626), np.float32(0.8921), np.float32(0.9113), np.float32(0.9138)] 
+2025-05-07 02:11:29.545179: Epoch time: 96.27 s 
+2025-05-07 02:11:31.222644:  
+2025-05-07 02:11:31.262273: Epoch 1782 
+2025-05-07 02:11:31.266505: Current learning rate: 0.00136 
+2025-05-07 02:13:12.667760: train_loss -0.5116 
+2025-05-07 02:13:12.838555: val_loss -0.4843 
+2025-05-07 02:13:12.839500: Pseudo dice [np.float32(0.8535), np.float32(0.875), np.float32(0.9418), np.float32(0.9759), np.float32(0.9158), np.float32(0.9608), np.float32(0.973), np.float32(0.9778), np.float32(0.9632), np.float32(0.9637), np.float32(0.9457), np.float32(0.9722), np.float32(0.9704), np.float32(0.9152), np.float32(0.9689), np.float32(0.9533), np.float32(0.9036), np.float32(0.9155), np.float32(0.9277)] 
+2025-05-07 02:13:12.839991: Epoch time: 101.45 s 
+2025-05-07 02:13:14.373537:  
+2025-05-07 02:13:14.434696: Epoch 1783 
+2025-05-07 02:13:14.435456: Current learning rate: 0.00135 
+2025-05-07 02:14:51.071261: train_loss -0.515 
+2025-05-07 02:14:51.209622: val_loss -0.531 
+2025-05-07 02:14:51.234166: Pseudo dice [np.float32(0.8667), np.float32(0.8731), np.float32(0.9484), np.float32(0.9648), np.float32(0.9387), np.float32(0.9609), np.float32(0.964), np.float32(0.9836), np.float32(0.9674), np.float32(0.9649), np.float32(0.9406), np.float32(0.9692), np.float32(0.9644), np.float32(0.9103), np.float32(0.9719), np.float32(0.9507), np.float32(0.916), np.float32(0.9129), np.float32(0.9291)] 
+2025-05-07 02:14:51.260442: Epoch time: 96.7 s 
+2025-05-07 02:14:52.877360:  
+2025-05-07 02:14:52.974511: Epoch 1784 
+2025-05-07 02:14:53.014286: Current learning rate: 0.00135 
+2025-05-07 02:16:28.858335: train_loss -0.5038 
+2025-05-07 02:16:28.982553: val_loss -0.5231 
+2025-05-07 02:16:29.020175: Pseudo dice [np.float32(0.8789), np.float32(0.873), np.float32(0.903), np.float32(0.9709), np.float32(0.9364), np.float32(0.9634), np.float32(0.9677), np.float32(0.9818), np.float32(0.9675), np.float32(0.9715), np.float32(0.9633), np.float32(0.9714), np.float32(0.971), np.float32(0.9234), np.float32(0.969), np.float32(0.9612), np.float32(0.9075), np.float32(0.9167), np.float32(0.9222)] 
+2025-05-07 02:16:29.053587: Epoch time: 95.98 s 
+2025-05-07 02:16:30.775656:  
+2025-05-07 02:16:30.823238: Epoch 1785 
+2025-05-07 02:16:30.845215: Current learning rate: 0.00134 
+2025-05-07 02:18:08.322131: train_loss -0.5137 
+2025-05-07 02:18:08.433328: val_loss -0.5296 
+2025-05-07 02:18:08.437339: Pseudo dice [np.float32(0.8567), np.float32(0.8695), np.float32(0.9134), np.float32(0.9775), np.float32(0.9167), np.float32(0.9666), np.float32(0.9648), np.float32(0.9795), np.float32(0.9676), np.float32(0.9722), np.float32(0.9596), np.float32(0.972), np.float32(0.9636), np.float32(0.923), np.float32(0.9696), np.float32(0.957), np.float32(0.8854), np.float32(0.8823), np.float32(0.9126)] 
+2025-05-07 02:18:08.437884: Epoch time: 97.55 s 
+2025-05-07 02:18:10.130687:  
+2025-05-07 02:18:10.227752: Epoch 1786 
+2025-05-07 02:18:10.269524: Current learning rate: 0.00134 
+2025-05-07 02:19:44.613724: train_loss -0.5181 
+2025-05-07 02:19:44.730361: val_loss -0.5229 
+2025-05-07 02:19:44.761566: Pseudo dice [np.float32(0.8725), np.float32(0.8814), np.float32(0.92), np.float32(0.9785), np.float32(0.9288), np.float32(0.9609), np.float32(0.9655), np.float32(0.9806), np.float32(0.9574), np.float32(0.9649), np.float32(0.9532), np.float32(0.9626), np.float32(0.975), np.float32(0.9159), np.float32(0.9676), np.float32(0.9668), np.float32(0.8908), np.float32(0.8423), np.float32(0.9265)] 
+2025-05-07 02:19:44.793001: Epoch time: 94.48 s 
+2025-05-07 02:19:46.434026:  
+2025-05-07 02:19:46.553814: Epoch 1787 
+2025-05-07 02:19:46.590625: Current learning rate: 0.00133 
+2025-05-07 02:21:25.058552: train_loss -0.4975 
+2025-05-07 02:21:25.099113: val_loss -0.5164 
+2025-05-07 02:21:25.100087: Pseudo dice [np.float32(0.8553), np.float32(0.8781), np.float32(0.9224), np.float32(0.971), np.float32(0.9205), np.float32(0.9644), np.float32(0.9712), np.float32(0.9752), np.float32(0.9633), np.float32(0.9683), np.float32(0.9423), np.float32(0.9577), np.float32(0.9608), np.float32(0.9107), np.float32(0.9675), np.float32(0.9638), np.float32(0.8808), np.float32(0.8497), np.float32(0.9092)] 
+2025-05-07 02:21:25.114274: Epoch time: 98.63 s 
+2025-05-07 02:21:26.791806:  
+2025-05-07 02:21:26.833494: Epoch 1788 
+2025-05-07 02:21:26.834105: Current learning rate: 0.00133 
+2025-05-07 02:23:06.193459: train_loss -0.5159 
+2025-05-07 02:23:06.267385: val_loss -0.4868 
+2025-05-07 02:23:06.295607: Pseudo dice [np.float32(0.8629), np.float32(0.859), np.float32(0.9207), np.float32(0.9809), np.float32(0.9225), np.float32(0.9619), np.float32(0.9702), np.float32(0.9811), np.float32(0.9692), np.float32(0.9682), np.float32(0.9512), np.float32(0.9602), np.float32(0.9622), np.float32(0.9041), np.float32(0.9662), np.float32(0.9561), np.float32(0.87), np.float32(0.8975), np.float32(0.924)] 
+2025-05-07 02:23:06.314934: Epoch time: 99.4 s 
+2025-05-07 02:23:07.919929:  
+2025-05-07 02:23:08.048880: Epoch 1789 
+2025-05-07 02:23:08.084940: Current learning rate: 0.00132 
+2025-05-07 02:24:42.292380: train_loss -0.4895 
+2025-05-07 02:24:42.351329: val_loss -0.5248 
+2025-05-07 02:24:42.374141: Pseudo dice [np.float32(0.841), np.float32(0.8767), np.float32(0.9474), np.float32(0.9802), np.float32(0.9268), np.float32(0.9649), np.float32(0.9746), np.float32(0.9785), np.float32(0.9591), np.float32(0.9744), np.float32(0.9594), np.float32(0.97), np.float32(0.9739), np.float32(0.9186), np.float32(0.9687), np.float32(0.9608), np.float32(0.8971), np.float32(0.8993), np.float32(0.9266)] 
+2025-05-07 02:24:42.414375: Epoch time: 94.37 s 
+2025-05-07 02:24:43.936517:  
+2025-05-07 02:24:43.971382: Epoch 1790 
+2025-05-07 02:24:43.986370: Current learning rate: 0.00132 
+2025-05-07 02:26:20.586802: train_loss -0.4997 
+2025-05-07 02:26:20.698483: val_loss -0.5443 
+2025-05-07 02:26:20.717357: Pseudo dice [np.float32(0.8738), np.float32(0.8653), np.float32(0.9316), np.float32(0.9723), np.float32(0.9116), np.float32(0.963), np.float32(0.9636), np.float32(0.9776), np.float32(0.9734), np.float32(0.9706), np.float32(0.9583), np.float32(0.9674), np.float32(0.9701), np.float32(0.9184), np.float32(0.9711), np.float32(0.9661), np.float32(0.9106), np.float32(0.9225), np.float32(0.9181)] 
+2025-05-07 02:26:20.745422: Epoch time: 96.65 s 
+2025-05-07 02:26:20.779169: Yayy! New best EMA pseudo Dice: 0.9383999705314636 
+2025-05-07 02:26:27.096235:  
+2025-05-07 02:26:27.102318: Epoch 1791 
+2025-05-07 02:26:27.102811: Current learning rate: 0.00131 
+2025-05-07 02:28:05.057607: train_loss -0.517 
+2025-05-07 02:28:05.177002: val_loss -0.4707 
+2025-05-07 02:28:05.195721: Pseudo dice [np.float32(0.8394), np.float32(0.8787), np.float32(0.9095), np.float32(0.9768), np.float32(0.9077), np.float32(0.9593), np.float32(0.9704), np.float32(0.9818), np.float32(0.96), np.float32(0.9693), np.float32(0.9412), np.float32(0.9698), np.float32(0.9551), np.float32(0.9232), np.float32(0.9685), np.float32(0.9638), np.float32(0.8735), np.float32(0.8767), np.float32(0.93)] 
+2025-05-07 02:28:05.200017: Epoch time: 97.96 s 
+2025-05-07 02:28:06.867476:  
+2025-05-07 02:28:06.977174: Epoch 1792 
+2025-05-07 02:28:07.013467: Current learning rate: 0.0013 
+2025-05-07 02:29:43.656749: train_loss -0.5074 
+2025-05-07 02:29:43.810807: val_loss -0.5283 
+2025-05-07 02:29:43.836986: Pseudo dice [np.float32(0.8495), np.float32(0.869), np.float32(0.8716), np.float32(0.9708), np.float32(0.9132), np.float32(0.958), np.float32(0.9691), np.float32(0.9821), np.float32(0.9697), np.float32(0.9681), np.float32(0.9521), np.float32(0.9724), np.float32(0.9595), np.float32(0.9241), np.float32(0.9669), np.float32(0.9683), np.float32(0.8826), np.float32(0.8996), np.float32(0.9307)] 
+2025-05-07 02:29:43.860873: Epoch time: 96.79 s 
+2025-05-07 02:29:45.525954:  
+2025-05-07 02:29:45.609764: Epoch 1793 
+2025-05-07 02:29:45.639276: Current learning rate: 0.0013 
+2025-05-07 02:31:23.335555: train_loss -0.5267 
+2025-05-07 02:31:23.477286: val_loss -0.5026 
+2025-05-07 02:31:23.520036: Pseudo dice [np.float32(0.8551), np.float32(0.8579), np.float32(0.3612), np.float32(0.9767), np.float32(0.9163), np.float32(0.9602), np.float32(0.9675), np.float32(0.9802), np.float32(0.9745), np.float32(0.9742), np.float32(0.9603), np.float32(0.9738), np.float32(0.9733), np.float32(0.9141), np.float32(0.9672), np.float32(0.9617), np.float32(0.8913), np.float32(0.8956), np.float32(0.9254)] 
+2025-05-07 02:31:23.561844: Epoch time: 97.81 s 
+2025-05-07 02:31:25.157853:  
+2025-05-07 02:31:25.271807: Epoch 1794 
+2025-05-07 02:31:25.301103: Current learning rate: 0.00129 
+2025-05-07 02:33:05.681916: train_loss -0.5182 
+2025-05-07 02:33:05.767744: val_loss -0.4917 
+2025-05-07 02:33:05.769754: Pseudo dice [np.float32(0.8566), np.float32(0.8647), np.float32(0.9108), np.float32(0.9733), np.float32(0.925), np.float32(0.9588), np.float32(0.9658), np.float32(0.9825), np.float32(0.9689), np.float32(0.964), np.float32(0.953), np.float32(0.9726), np.float32(0.9549), np.float32(0.9164), np.float32(0.9726), np.float32(0.9619), np.float32(0.909), np.float32(0.9007), np.float32(0.9243)] 
+2025-05-07 02:33:05.770449: Epoch time: 100.53 s 
+2025-05-07 02:33:07.344773:  
+2025-05-07 02:33:07.395994: Epoch 1795 
+2025-05-07 02:33:07.407120: Current learning rate: 0.00129 
+2025-05-07 02:34:45.264610: train_loss -0.4958 
+2025-05-07 02:34:45.394320: val_loss -0.5194 
+2025-05-07 02:34:45.431947: Pseudo dice [np.float32(0.864), np.float32(0.8596), np.float32(0.8261), np.float32(0.9729), np.float32(0.9215), np.float32(0.9632), np.float32(0.9668), np.float32(0.9724), np.float32(0.9687), np.float32(0.9693), np.float32(0.9539), np.float32(0.9721), np.float32(0.9692), np.float32(0.9192), np.float32(0.9695), np.float32(0.9581), np.float32(0.8926), np.float32(0.9128), np.float32(0.9204)] 
+2025-05-07 02:34:45.464400: Epoch time: 97.92 s 
+2025-05-07 02:34:47.082694:  
+2025-05-07 02:34:47.140732: Epoch 1796 
+2025-05-07 02:34:47.155631: Current learning rate: 0.00128 
+2025-05-07 02:36:26.303435: train_loss -0.5067 
+2025-05-07 02:36:26.369672: val_loss -0.4971 
+2025-05-07 02:36:26.397487: Pseudo dice [np.float32(0.8686), np.float32(0.8883), np.float32(0.9247), np.float32(0.9785), np.float32(0.9231), np.float32(0.9628), np.float32(0.9711), np.float32(0.9793), np.float32(0.9672), np.float32(0.9747), np.float32(0.9532), np.float32(0.9661), np.float32(0.9711), np.float32(0.9143), np.float32(0.9701), np.float32(0.9642), np.float32(0.8717), np.float32(0.9071), np.float32(0.9116)] 
+2025-05-07 02:36:26.415704: Epoch time: 99.22 s 
+2025-05-07 02:36:27.967447:  
+2025-05-07 02:36:28.070013: Epoch 1797 
+2025-05-07 02:36:28.084964: Current learning rate: 0.00128 
+2025-05-07 02:38:04.043107: train_loss -0.4993 
+2025-05-07 02:38:04.135690: val_loss -0.4933 
+2025-05-07 02:38:04.180383: Pseudo dice [np.float32(0.8524), np.float32(0.8604), np.float32(0.9483), np.float32(0.9781), np.float32(0.9346), np.float32(0.9674), np.float32(0.9635), np.float32(0.9761), np.float32(0.9752), np.float32(0.968), np.float32(0.9184), np.float32(0.9736), np.float32(0.9577), np.float32(0.9288), np.float32(0.9609), np.float32(0.9665), np.float32(0.8941), np.float32(0.8928), np.float32(0.9176)] 
+2025-05-07 02:38:04.218569: Epoch time: 96.08 s 
+2025-05-07 02:38:05.832421:  
+2025-05-07 02:38:05.881649: Epoch 1798 
+2025-05-07 02:38:05.886303: Current learning rate: 0.00127 
+2025-05-07 02:39:37.553149: train_loss -0.5112 
+2025-05-07 02:39:37.674001: val_loss -0.5313 
+2025-05-07 02:39:37.689296: Pseudo dice [np.float32(0.8741), np.float32(0.8556), np.float32(0.9211), np.float32(0.9772), np.float32(0.9267), np.float32(0.9642), np.float32(0.9636), np.float32(0.9806), np.float32(0.9631), np.float32(0.9649), np.float32(0.9431), np.float32(0.9693), np.float32(0.9645), np.float32(0.92), np.float32(0.9634), np.float32(0.9559), np.float32(0.9044), np.float32(0.9172), np.float32(0.9216)] 
+2025-05-07 02:39:37.709660: Epoch time: 91.73 s 
+2025-05-07 02:39:39.317881:  
+2025-05-07 02:39:39.404775: Epoch 1799 
+2025-05-07 02:39:39.423435: Current learning rate: 0.00126 
+2025-05-07 02:41:13.213144: train_loss -0.5167 
+2025-05-07 02:41:13.251285: val_loss -0.4972 
+2025-05-07 02:41:13.252655: Pseudo dice [np.float32(0.8614), np.float32(0.8574), np.float32(0.9154), np.float32(0.9719), np.float32(0.9054), np.float32(0.9516), np.float32(0.9593), np.float32(0.9675), np.float32(0.9694), np.float32(0.9766), np.float32(0.9581), np.float32(0.972), np.float32(0.9772), np.float32(0.908), np.float32(0.967), np.float32(0.9577), np.float32(0.8928), np.float32(0.9223), np.float32(0.929)] 
+2025-05-07 02:41:13.253063: Epoch time: 93.9 s 
+2025-05-07 02:41:16.370360:  
+2025-05-07 02:41:16.398158: Epoch 1800 
+2025-05-07 02:41:16.410738: Current learning rate: 0.00126 
+2025-05-07 02:42:54.608068: train_loss -0.5215 
+2025-05-07 02:42:54.660442: val_loss -0.528 
+2025-05-07 02:42:54.678419: Pseudo dice [np.float32(0.8172), np.float32(0.8447), np.float32(0.9103), np.float32(0.9805), np.float32(0.9173), np.float32(0.9644), np.float32(0.9704), np.float32(0.9795), np.float32(0.9746), np.float32(0.9717), np.float32(0.9591), np.float32(0.9716), np.float32(0.9722), np.float32(0.9238), np.float32(0.9627), np.float32(0.9659), np.float32(0.9089), np.float32(0.927), np.float32(0.9304)] 
+2025-05-07 02:42:54.700162: Epoch time: 98.24 s 
+2025-05-07 02:42:56.262974:  
+2025-05-07 02:42:56.351593: Epoch 1801 
+2025-05-07 02:42:56.381513: Current learning rate: 0.00125 
+2025-05-07 02:44:34.475380: train_loss -0.5093 
+2025-05-07 02:44:34.590118: val_loss -0.5337 
+2025-05-07 02:44:34.608428: Pseudo dice [np.float32(0.874), np.float32(0.8608), np.float32(0.9259), np.float32(0.9818), np.float32(0.9075), np.float32(0.9716), np.float32(0.9645), np.float32(0.9811), np.float32(0.9716), np.float32(0.9681), np.float32(0.9532), np.float32(0.9727), np.float32(0.9696), np.float32(0.9221), np.float32(0.9757), np.float32(0.967), np.float32(0.8879), np.float32(0.8915), np.float32(0.931)] 
+2025-05-07 02:44:34.618287: Epoch time: 98.21 s 
+2025-05-07 02:44:36.141012:  
+2025-05-07 02:44:36.213378: Epoch 1802 
+2025-05-07 02:44:36.226193: Current learning rate: 0.00125 
+2025-05-07 02:46:17.177466: train_loss -0.5151 
+2025-05-07 02:46:17.287307: val_loss -0.5396 
+2025-05-07 02:46:17.327243: Pseudo dice [np.float32(0.843), np.float32(0.8587), np.float32(0.9438), np.float32(0.9738), np.float32(0.8842), np.float32(0.9563), np.float32(0.9645), np.float32(0.9764), np.float32(0.954), np.float32(0.9762), np.float32(0.96), np.float32(0.9649), np.float32(0.977), np.float32(0.9157), np.float32(0.9649), np.float32(0.9528), np.float32(0.9082), np.float32(0.9245), np.float32(0.9254)] 
+2025-05-07 02:46:17.372897: Epoch time: 101.04 s 
+2025-05-07 02:46:19.052986:  
+2025-05-07 02:46:19.089593: Epoch 1803 
+2025-05-07 02:46:19.113514: Current learning rate: 0.00124 
+2025-05-07 02:47:54.968143: train_loss -0.5079 
+2025-05-07 02:47:55.100922: val_loss -0.5219 
+2025-05-07 02:47:55.112585: Pseudo dice [np.float32(0.8532), np.float32(0.8614), np.float32(0.6416), np.float32(0.9754), np.float32(0.9349), np.float32(0.9625), np.float32(0.9637), np.float32(0.9747), np.float32(0.9614), np.float32(0.97), np.float32(0.9489), np.float32(0.9697), np.float32(0.9714), np.float32(0.9188), np.float32(0.9609), np.float32(0.9622), np.float32(0.9126), np.float32(0.9248), np.float32(0.9143)] 
+2025-05-07 02:47:55.138678: Epoch time: 95.92 s 
+2025-05-07 02:47:56.629100:  
+2025-05-07 02:47:56.704068: Epoch 1804 
+2025-05-07 02:47:56.722574: Current learning rate: 0.00124 
+2025-05-07 02:49:34.134548: train_loss -0.5009 
+2025-05-07 02:49:34.215780: val_loss -0.5664 
+2025-05-07 02:49:34.245452: Pseudo dice [np.float32(0.8817), np.float32(0.8631), np.float32(0.9314), np.float32(0.9643), np.float32(0.9394), np.float32(0.9594), np.float32(0.9716), np.float32(0.9797), np.float32(0.9701), np.float32(0.9718), np.float32(0.9494), np.float32(0.969), np.float32(0.9743), np.float32(0.9211), np.float32(0.9691), np.float32(0.9636), np.float32(0.9014), np.float32(0.907), np.float32(0.9111)] 
+2025-05-07 02:49:34.283669: Epoch time: 97.51 s 
+2025-05-07 02:49:36.138177:  
+2025-05-07 02:49:36.175761: Epoch 1805 
+2025-05-07 02:49:36.192601: Current learning rate: 0.00123 
+2025-05-07 02:51:11.946116: train_loss -0.5108 
+2025-05-07 02:51:12.027992: val_loss -0.5039 
+2025-05-07 02:51:12.039486: Pseudo dice [np.float32(0.8719), np.float32(0.8605), np.float32(0.9382), np.float32(0.9823), np.float32(0.9197), np.float32(0.9677), np.float32(0.9708), np.float32(0.9788), np.float32(0.9678), np.float32(0.9666), np.float32(0.9485), np.float32(0.9706), np.float32(0.961), np.float32(0.921), np.float32(0.9726), np.float32(0.9595), np.float32(0.9016), np.float32(0.9117), np.float32(0.9187)] 
+2025-05-07 02:51:12.054563: Epoch time: 95.81 s 
+2025-05-07 02:51:13.723810:  
+2025-05-07 02:51:13.815023: Epoch 1806 
+2025-05-07 02:51:13.818907: Current learning rate: 0.00122 
+2025-05-07 02:52:51.477487: train_loss -0.5061 
+2025-05-07 02:52:51.545635: val_loss -0.5125 
+2025-05-07 02:52:51.584172: Pseudo dice [np.float32(0.8649), np.float32(0.849), np.float32(0.9125), np.float32(0.9739), np.float32(0.9253), np.float32(0.9654), np.float32(0.9669), np.float32(0.9776), np.float32(0.9434), np.float32(0.9663), np.float32(0.9427), np.float32(0.9454), np.float32(0.9561), np.float32(0.9166), np.float32(0.9704), np.float32(0.9622), np.float32(0.8931), np.float32(0.9133), np.float32(0.9214)] 
+2025-05-07 02:52:51.591530: Epoch time: 97.76 s 
+2025-05-07 02:52:53.092124:  
+2025-05-07 02:52:53.212171: Epoch 1807 
+2025-05-07 02:52:53.242104: Current learning rate: 0.00122 
+2025-05-07 02:54:26.671125: train_loss -0.4964 
+2025-05-07 02:54:26.823020: val_loss -0.5694 
+2025-05-07 02:54:26.871034: Pseudo dice [np.float32(0.8689), np.float32(0.869), np.float32(0.9083), np.float32(0.9799), np.float32(0.9278), np.float32(0.9657), np.float32(0.9679), np.float32(0.9804), np.float32(0.9665), np.float32(0.9687), np.float32(0.9434), np.float32(0.9691), np.float32(0.9723), np.float32(0.9168), np.float32(0.9674), np.float32(0.9593), np.float32(0.9194), np.float32(0.8939), np.float32(0.9174)] 
+2025-05-07 02:54:26.911684: Epoch time: 93.58 s 
+2025-05-07 02:54:28.997002:  
+2025-05-07 02:54:29.044514: Epoch 1808 
+2025-05-07 02:54:29.051667: Current learning rate: 0.00121 
+2025-05-07 02:56:07.320549: train_loss -0.5273 
+2025-05-07 02:56:07.459484: val_loss -0.5288 
+2025-05-07 02:56:07.471062: Pseudo dice [np.float32(0.8502), np.float32(0.8352), np.float32(0.9008), np.float32(0.9796), np.float32(0.9147), np.float32(0.964), np.float32(0.9665), np.float32(0.9734), np.float32(0.9612), np.float32(0.9642), np.float32(0.9416), np.float32(0.9698), np.float32(0.9683), np.float32(0.9145), np.float32(0.9652), np.float32(0.9535), np.float32(0.8783), np.float32(0.8855), np.float32(0.9001)] 
+2025-05-07 02:56:07.471817: Epoch time: 98.32 s 
+2025-05-07 02:56:12.599107:  
+2025-05-07 02:56:12.605096: Epoch 1809 
+2025-05-07 02:56:12.605710: Current learning rate: 0.00121 
+2025-05-07 02:57:47.888468: train_loss -0.5005 
+2025-05-07 02:57:47.980921: val_loss -0.5287 
+2025-05-07 02:57:47.999583: Pseudo dice [np.float32(0.861), np.float32(0.8509), np.float32(0.9273), np.float32(0.9658), np.float32(0.9367), np.float32(0.9496), np.float32(0.9656), np.float32(0.9826), np.float32(0.965), np.float32(0.9744), np.float32(0.9585), np.float32(0.9717), np.float32(0.9764), np.float32(0.9213), np.float32(0.9569), np.float32(0.9575), np.float32(0.8949), np.float32(0.9039), np.float32(0.914)] 
+2025-05-07 02:57:48.028402: Epoch time: 95.29 s 
+2025-05-07 02:57:49.742453:  
+2025-05-07 02:57:49.766350: Epoch 1810 
+2025-05-07 02:57:49.773148: Current learning rate: 0.0012 
+2025-05-07 02:59:27.568095: train_loss -0.5063 
+2025-05-07 02:59:27.679220: val_loss -0.5149 
+2025-05-07 02:59:27.701890: Pseudo dice [np.float32(0.8423), np.float32(0.8488), np.float32(0.9205), np.float32(0.9764), np.float32(0.9066), np.float32(0.9639), np.float32(0.9641), np.float32(0.9805), np.float32(0.9585), np.float32(0.9682), np.float32(0.9579), np.float32(0.9634), np.float32(0.9712), np.float32(0.9086), np.float32(0.9687), np.float32(0.955), np.float32(0.8989), np.float32(0.9106), np.float32(0.9314)] 
+2025-05-07 02:59:27.716678: Epoch time: 97.83 s 
+2025-05-07 02:59:29.199439:  
+2025-05-07 02:59:29.285053: Epoch 1811 
+2025-05-07 02:59:29.296545: Current learning rate: 0.0012 
+2025-05-07 03:01:10.141085: train_loss -0.4855 
+2025-05-07 03:01:10.346302: val_loss -0.4974 
+2025-05-07 03:01:10.385248: Pseudo dice [np.float32(0.8575), np.float32(0.8487), np.float32(0.9111), np.float32(0.9719), np.float32(0.8995), np.float32(0.9647), np.float32(0.9549), np.float32(0.976), np.float32(0.9681), np.float32(0.961), np.float32(0.9311), np.float32(0.9721), np.float32(0.9674), np.float32(0.9119), np.float32(0.9735), np.float32(0.9623), np.float32(0.8993), np.float32(0.9126), np.float32(0.9237)] 
+2025-05-07 03:01:10.415924: Epoch time: 100.94 s 
+2025-05-07 03:01:12.333485:  
+2025-05-07 03:01:12.389331: Epoch 1812 
+2025-05-07 03:01:12.390793: Current learning rate: 0.00119 
+2025-05-07 03:02:51.448035: train_loss -0.5049 
+2025-05-07 03:02:51.537662: val_loss -0.5119 
+2025-05-07 03:02:51.543589: Pseudo dice [np.float32(0.873), np.float32(0.8756), np.float32(0.8953), np.float32(0.9738), np.float32(0.927), np.float32(0.9663), np.float32(0.9713), np.float32(0.9811), np.float32(0.9629), np.float32(0.9674), np.float32(0.9454), np.float32(0.9642), np.float32(0.9647), np.float32(0.9162), np.float32(0.9595), np.float32(0.9595), np.float32(0.9078), np.float32(0.8829), np.float32(0.9264)] 
+2025-05-07 03:02:51.544520: Epoch time: 99.12 s 
+2025-05-07 03:02:53.047246:  
+2025-05-07 03:02:53.129555: Epoch 1813 
+2025-05-07 03:02:53.130396: Current learning rate: 0.00119 
+2025-05-07 03:04:34.640661: train_loss -0.5125 
+2025-05-07 03:04:34.792490: val_loss -0.4574 
+2025-05-07 03:04:34.827373: Pseudo dice [np.float32(0.8252), np.float32(0.8601), np.float32(0.8628), np.float32(0.9785), np.float32(0.8931), np.float32(0.96), np.float32(0.9696), np.float32(0.9829), np.float32(0.9576), np.float32(0.9732), np.float32(0.9548), np.float32(0.976), np.float32(0.9758), np.float32(0.918), np.float32(0.9565), np.float32(0.9629), np.float32(0.8857), np.float32(0.9019), np.float32(0.9084)] 
+2025-05-07 03:04:34.836713: Epoch time: 101.59 s 
+2025-05-07 03:04:36.399596:  
+2025-05-07 03:04:36.475639: Epoch 1814 
+2025-05-07 03:04:36.512670: Current learning rate: 0.00118 
+2025-05-07 03:06:14.880242: train_loss -0.4926 
+2025-05-07 03:06:14.998461: val_loss -0.5362 
+2025-05-07 03:06:15.047429: Pseudo dice [np.float32(0.8698), np.float32(0.8624), np.float32(0.9578), np.float32(0.9735), np.float32(0.8908), np.float32(0.9651), np.float32(0.9693), np.float32(0.9817), np.float32(0.9664), np.float32(0.9579), np.float32(0.9416), np.float32(0.9684), np.float32(0.9706), np.float32(0.9234), np.float32(0.9685), np.float32(0.9571), np.float32(0.9025), np.float32(0.9075), np.float32(0.9256)] 
+2025-05-07 03:06:15.076362: Epoch time: 98.48 s 
+2025-05-07 03:06:16.656849:  
+2025-05-07 03:06:16.775602: Epoch 1815 
+2025-05-07 03:06:16.805439: Current learning rate: 0.00117 
+2025-05-07 03:07:58.380150: train_loss -0.5073 
+2025-05-07 03:07:58.549606: val_loss -0.5523 
+2025-05-07 03:07:58.590513: Pseudo dice [np.float32(0.8651), np.float32(0.8681), np.float32(0.9573), np.float32(0.9782), np.float32(0.9096), np.float32(0.9634), np.float32(0.9694), np.float32(0.9798), np.float32(0.9708), np.float32(0.972), np.float32(0.9535), np.float32(0.9768), np.float32(0.9739), np.float32(0.9177), np.float32(0.9643), np.float32(0.9596), np.float32(0.9028), np.float32(0.9137), np.float32(0.9221)] 
+2025-05-07 03:07:58.640690: Epoch time: 101.72 s 
+2025-05-07 03:08:00.301499:  
+2025-05-07 03:08:00.363495: Epoch 1816 
+2025-05-07 03:08:00.397776: Current learning rate: 0.00117 
+2025-05-07 03:09:37.972444: train_loss -0.5029 
+2025-05-07 03:09:38.136526: val_loss -0.5346 
+2025-05-07 03:09:38.151504: Pseudo dice [np.float32(0.8553), np.float32(0.8634), np.float32(0.9052), np.float32(0.9783), np.float32(0.9236), np.float32(0.9567), np.float32(0.9624), np.float32(0.9761), np.float32(0.976), np.float32(0.9691), np.float32(0.9595), np.float32(0.9776), np.float32(0.9703), np.float32(0.9198), np.float32(0.9628), np.float32(0.9582), np.float32(0.8789), np.float32(0.8327), np.float32(0.914)] 
+2025-05-07 03:09:38.174780: Epoch time: 97.67 s 
+2025-05-07 03:09:39.795752:  
+2025-05-07 03:09:39.938148: Epoch 1817 
+2025-05-07 03:09:39.986511: Current learning rate: 0.00116 
+2025-05-07 03:11:19.485825: train_loss -0.514 
+2025-05-07 03:11:19.631888: val_loss -0.4939 
+2025-05-07 03:11:19.656760: Pseudo dice [np.float32(0.8712), np.float32(0.8491), np.float32(0.8971), np.float32(0.9745), np.float32(0.9236), np.float32(0.9659), np.float32(0.9672), np.float32(0.9773), np.float32(0.9666), np.float32(0.9669), np.float32(0.9579), np.float32(0.9702), np.float32(0.9725), np.float32(0.9207), np.float32(0.9704), np.float32(0.9581), np.float32(0.9104), np.float32(0.9088), np.float32(0.9222)] 
+2025-05-07 03:11:19.679059: Epoch time: 99.69 s 
+2025-05-07 03:11:21.402122:  
+2025-05-07 03:11:21.461105: Epoch 1818 
+2025-05-07 03:11:21.472464: Current learning rate: 0.00116 
+2025-05-07 03:13:04.182958: train_loss -0.5006 
+2025-05-07 03:13:04.193905: val_loss -0.4859 
+2025-05-07 03:13:04.202948: Pseudo dice [np.float32(0.8627), np.float32(0.871), np.float32(0.8127), np.float32(0.975), np.float32(0.929), np.float32(0.9639), np.float32(0.971), np.float32(0.9808), np.float32(0.9684), np.float32(0.9769), np.float32(0.9575), np.float32(0.9713), np.float32(0.9737), np.float32(0.9203), np.float32(0.9572), np.float32(0.9525), np.float32(0.8607), np.float32(0.8686), np.float32(0.933)] 
+2025-05-07 03:13:04.212381: Epoch time: 102.78 s 
+2025-05-07 03:13:05.785829:  
+2025-05-07 03:13:05.875970: Epoch 1819 
+2025-05-07 03:13:05.888245: Current learning rate: 0.00115 
+2025-05-07 03:14:44.885743: train_loss -0.5091 
+2025-05-07 03:14:44.960228: val_loss -0.5173 
+2025-05-07 03:14:44.961383: Pseudo dice [np.float32(0.8558), np.float32(0.8735), np.float32(0.8242), np.float32(0.981), np.float32(0.9256), np.float32(0.9648), np.float32(0.9686), np.float32(0.9822), np.float32(0.9622), np.float32(0.9702), np.float32(0.9509), np.float32(0.9671), np.float32(0.9725), np.float32(0.9211), np.float32(0.9692), np.float32(0.9576), np.float32(0.8985), np.float32(0.8979), np.float32(0.9189)] 
+2025-05-07 03:14:44.963710: Epoch time: 99.1 s 
+2025-05-07 03:14:46.542360:  
+2025-05-07 03:14:46.602714: Epoch 1820 
+2025-05-07 03:14:46.615801: Current learning rate: 0.00115 
+2025-05-07 03:16:28.136828: train_loss -0.5021 
+2025-05-07 03:16:28.221631: val_loss -0.5226 
+2025-05-07 03:16:28.229526: Pseudo dice [np.float32(0.8492), np.float32(0.8793), np.float32(0.8936), np.float32(0.9744), np.float32(0.9285), np.float32(0.9685), np.float32(0.9677), np.float32(0.98), np.float32(0.9636), np.float32(0.9713), np.float32(0.9509), np.float32(0.9631), np.float32(0.975), np.float32(0.925), np.float32(0.9698), np.float32(0.962), np.float32(0.9116), np.float32(0.9229), np.float32(0.927)] 
+2025-05-07 03:16:28.262872: Epoch time: 101.6 s 
+2025-05-07 03:16:29.924484:  
+2025-05-07 03:16:29.960978: Epoch 1821 
+2025-05-07 03:16:29.969773: Current learning rate: 0.00114 
+2025-05-07 03:18:07.294168: train_loss -0.5144 
+2025-05-07 03:18:07.337468: val_loss -0.5222 
+2025-05-07 03:18:07.338335: Pseudo dice [np.float32(0.8378), np.float32(0.8753), np.float32(0.9374), np.float32(0.9755), np.float32(0.922), np.float32(0.9635), np.float32(0.9682), np.float32(0.9772), np.float32(0.9734), np.float32(0.9733), np.float32(0.9586), np.float32(0.9685), np.float32(0.9758), np.float32(0.9186), np.float32(0.9684), np.float32(0.9657), np.float32(0.8808), np.float32(0.8797), np.float32(0.919)] 
+2025-05-07 03:18:07.338849: Epoch time: 97.37 s 
+2025-05-07 03:18:08.871630:  
+2025-05-07 03:18:08.963899: Epoch 1822 
+2025-05-07 03:18:08.989157: Current learning rate: 0.00113 
+2025-05-07 03:19:43.719024: train_loss -0.5078 
+2025-05-07 03:19:43.914734: val_loss -0.5137 
+2025-05-07 03:19:43.919481: Pseudo dice [np.float32(0.8568), np.float32(0.8587), np.float32(0.9456), np.float32(0.9663), np.float32(0.9255), np.float32(0.951), np.float32(0.9663), np.float32(0.9798), np.float32(0.966), np.float32(0.9696), np.float32(0.9569), np.float32(0.9727), np.float32(0.97), np.float32(0.9179), np.float32(0.9514), np.float32(0.9601), np.float32(0.889), np.float32(0.888), np.float32(0.9207)] 
+2025-05-07 03:19:43.920463: Epoch time: 94.85 s 
+2025-05-07 03:19:45.460901:  
+2025-05-07 03:19:45.534571: Epoch 1823 
+2025-05-07 03:19:45.556967: Current learning rate: 0.00113 
+2025-05-07 03:21:23.565977: train_loss -0.5284 
+2025-05-07 03:21:23.687610: val_loss -0.5145 
+2025-05-07 03:21:23.726268: Pseudo dice [np.float32(0.8608), np.float32(0.8518), np.float32(0.931), np.float32(0.974), np.float32(0.9172), np.float32(0.9659), np.float32(0.9674), np.float32(0.9788), np.float32(0.9611), np.float32(0.9729), np.float32(0.9461), np.float32(0.968), np.float32(0.9696), np.float32(0.9228), np.float32(0.9701), np.float32(0.9653), np.float32(0.9028), np.float32(0.9015), np.float32(0.9101)] 
+2025-05-07 03:21:23.730333: Epoch time: 98.11 s 
+2025-05-07 03:21:25.224997:  
+2025-05-07 03:21:25.272347: Epoch 1824 
+2025-05-07 03:21:25.305073: Current learning rate: 0.00112 
+2025-05-07 03:23:08.777650: train_loss -0.5064 
+2025-05-07 03:23:08.878060: val_loss -0.5067 
+2025-05-07 03:23:08.914020: Pseudo dice [np.float32(0.8626), np.float32(0.8575), np.float32(0.9493), np.float32(0.9782), np.float32(0.9054), np.float32(0.9637), np.float32(0.9696), np.float32(0.9817), np.float32(0.9613), np.float32(0.9745), np.float32(0.9605), np.float32(0.966), np.float32(0.9621), np.float32(0.9249), np.float32(0.9684), np.float32(0.9601), np.float32(0.8788), np.float32(0.908), np.float32(0.9274)] 
+2025-05-07 03:23:08.933950: Epoch time: 103.55 s 
+2025-05-07 03:23:10.487692:  
+2025-05-07 03:23:10.516911: Epoch 1825 
+2025-05-07 03:23:10.525454: Current learning rate: 0.00112 
+2025-05-07 03:24:50.027843: train_loss -0.5099 
+2025-05-07 03:24:50.099602: val_loss -0.4889 
+2025-05-07 03:24:50.106532: Pseudo dice [np.float32(0.864), np.float32(0.8707), np.float32(0.9349), np.float32(0.9734), np.float32(0.9337), np.float32(0.9627), np.float32(0.9739), np.float32(0.9779), np.float32(0.9614), np.float32(0.9634), np.float32(0.9587), np.float32(0.963), np.float32(0.9645), np.float32(0.9119), np.float32(0.9621), np.float32(0.9577), np.float32(0.9006), np.float32(0.9075), np.float32(0.9115)] 
+2025-05-07 03:24:50.123647: Epoch time: 99.54 s 
+2025-05-07 03:24:54.637311:  
+2025-05-07 03:24:54.641469: Epoch 1826 
+2025-05-07 03:24:54.641861: Current learning rate: 0.00111 
+2025-05-07 03:26:32.285486: train_loss -0.4929 
+2025-05-07 03:26:32.474890: val_loss -0.5546 
+2025-05-07 03:26:32.509053: Pseudo dice [np.float32(0.8808), np.float32(0.8687), np.float32(0.9326), np.float32(0.9792), np.float32(0.9364), np.float32(0.9582), np.float32(0.9673), np.float32(0.9804), np.float32(0.9717), np.float32(0.9712), np.float32(0.9555), np.float32(0.9764), np.float32(0.9703), np.float32(0.9082), np.float32(0.9457), np.float32(0.9592), np.float32(0.8832), np.float32(0.9137), np.float32(0.9287)] 
+2025-05-07 03:26:32.542727: Epoch time: 97.65 s 
+2025-05-07 03:26:34.293917:  
+2025-05-07 03:26:34.369925: Epoch 1827 
+2025-05-07 03:26:34.415220: Current learning rate: 0.0011 
+2025-05-07 03:28:13.892939: train_loss -0.4906 
+2025-05-07 03:28:14.062869: val_loss -0.516 
+2025-05-07 03:28:14.094677: Pseudo dice [np.float32(0.8447), np.float32(0.8578), np.float32(0.9382), np.float32(0.9754), np.float32(0.9356), np.float32(0.9633), np.float32(0.9701), np.float32(0.9818), np.float32(0.9669), np.float32(0.967), np.float32(0.9556), np.float32(0.9687), np.float32(0.9696), np.float32(0.9177), np.float32(0.9719), np.float32(0.9659), np.float32(0.8658), np.float32(0.8657), np.float32(0.895)] 
+2025-05-07 03:28:14.095232: Epoch time: 99.6 s 
+2025-05-07 03:28:15.762516:  
+2025-05-07 03:28:15.870281: Epoch 1828 
+2025-05-07 03:28:15.870946: Current learning rate: 0.0011 
+2025-05-07 03:30:01.369331: train_loss -0.5001 
+2025-05-07 03:30:01.476882: val_loss -0.5237 
+2025-05-07 03:30:01.512601: Pseudo dice [np.float32(0.8647), np.float32(0.8654), np.float32(0.9337), np.float32(0.978), np.float32(0.9284), np.float32(0.965), np.float32(0.9708), np.float32(0.9776), np.float32(0.9637), np.float32(0.9743), np.float32(0.9602), np.float32(0.9681), np.float32(0.9731), np.float32(0.9134), np.float32(0.9605), np.float32(0.9636), np.float32(0.8872), np.float32(0.8869), np.float32(0.9215)] 
+2025-05-07 03:30:01.517004: Epoch time: 105.61 s 
+2025-05-07 03:30:03.227129:  
+2025-05-07 03:30:03.274390: Epoch 1829 
+2025-05-07 03:30:03.274805: Current learning rate: 0.00109 
+2025-05-07 03:31:44.165327: train_loss -0.5125 
+2025-05-07 03:31:44.273901: val_loss -0.5001 
+2025-05-07 03:31:44.275733: Pseudo dice [np.float32(0.8466), np.float32(0.8492), np.float32(0.92), np.float32(0.9781), np.float32(0.9184), np.float32(0.9661), np.float32(0.9639), np.float32(0.9823), np.float32(0.967), np.float32(0.9663), np.float32(0.9528), np.float32(0.9619), np.float32(0.9672), np.float32(0.9154), np.float32(0.9726), np.float32(0.9602), np.float32(0.8984), np.float32(0.9205), np.float32(0.9077)] 
+2025-05-07 03:31:44.280075: Epoch time: 100.94 s 
+2025-05-07 03:31:45.980904:  
+2025-05-07 03:31:46.017231: Epoch 1830 
+2025-05-07 03:31:46.028487: Current learning rate: 0.00109 
+2025-05-07 03:33:27.189979: train_loss -0.5107 
+2025-05-07 03:33:27.372322: val_loss -0.5299 
+2025-05-07 03:33:27.374345: Pseudo dice [np.float32(0.8583), np.float32(0.851), np.float32(0.9252), np.float32(0.979), np.float32(0.9135), np.float32(0.9635), np.float32(0.9587), np.float32(0.9769), np.float32(0.9702), np.float32(0.9739), np.float32(0.9627), np.float32(0.9729), np.float32(0.9748), np.float32(0.9107), np.float32(0.9664), np.float32(0.9649), np.float32(0.8846), np.float32(0.9001), np.float32(0.9212)] 
+2025-05-07 03:33:27.378286: Epoch time: 101.21 s 
+2025-05-07 03:33:28.960919:  
+2025-05-07 03:33:29.073285: Epoch 1831 
+2025-05-07 03:33:29.104216: Current learning rate: 0.00108 
+2025-05-07 03:35:12.310092: train_loss -0.5141 
+2025-05-07 03:35:12.466465: val_loss -0.5674 
+2025-05-07 03:35:12.497266: Pseudo dice [np.float32(0.88), np.float32(0.8668), np.float32(0.9258), np.float32(0.9742), np.float32(0.9485), np.float32(0.9659), np.float32(0.9708), np.float32(0.9836), np.float32(0.9721), np.float32(0.9748), np.float32(0.9546), np.float32(0.9708), np.float32(0.9712), np.float32(0.9196), np.float32(0.969), np.float32(0.9587), np.float32(0.9002), np.float32(0.9205), np.float32(0.9288)] 
+2025-05-07 03:35:12.543283: Epoch time: 103.35 s 
+2025-05-07 03:35:12.588359: Yayy! New best EMA pseudo Dice: 0.9387999773025513 
+2025-05-07 03:35:15.367698:  
+2025-05-07 03:35:15.372559: Epoch 1832 
+2025-05-07 03:35:15.372995: Current learning rate: 0.00108 
+2025-05-07 03:36:55.809701: train_loss -0.5179 
+2025-05-07 03:36:55.953233: val_loss -0.5272 
+2025-05-07 03:36:55.972558: Pseudo dice [np.float32(0.8671), np.float32(0.8628), np.float32(0.9343), np.float32(0.9779), np.float32(0.9242), np.float32(0.9623), np.float32(0.9671), np.float32(0.9783), np.float32(0.9637), np.float32(0.963), np.float32(0.9552), np.float32(0.9675), np.float32(0.9706), np.float32(0.9199), np.float32(0.9696), np.float32(0.9627), np.float32(0.9143), np.float32(0.8682), np.float32(0.921)] 
+2025-05-07 03:36:55.993122: Epoch time: 100.44 s 
+2025-05-07 03:36:56.000749: Yayy! New best EMA pseudo Dice: 0.9387999773025513 
+2025-05-07 03:36:58.827126:  
+2025-05-07 03:36:58.874326: Epoch 1833 
+2025-05-07 03:36:58.895449: Current learning rate: 0.00107 
+2025-05-07 03:38:42.047827: train_loss -0.5117 
+2025-05-07 03:38:42.249675: val_loss -0.5258 
+2025-05-07 03:38:42.285988: Pseudo dice [np.float32(0.8515), np.float32(0.8725), np.float32(0.8202), np.float32(0.9769), np.float32(0.9221), np.float32(0.9561), np.float32(0.9703), np.float32(0.9794), np.float32(0.9613), np.float32(0.9716), np.float32(0.96), np.float32(0.9712), np.float32(0.9748), np.float32(0.9186), np.float32(0.9668), np.float32(0.9605), np.float32(0.8947), np.float32(0.8938), np.float32(0.922)] 
+2025-05-07 03:38:42.324192: Epoch time: 103.22 s 
+2025-05-07 03:38:43.953575:  
+2025-05-07 03:38:44.014203: Epoch 1834 
+2025-05-07 03:38:44.030667: Current learning rate: 0.00106 
+2025-05-07 03:40:27.181282: train_loss -0.5122 
+2025-05-07 03:40:27.423100: val_loss -0.508 
+2025-05-07 03:40:27.424418: Pseudo dice [np.float32(0.8863), np.float32(0.8796), np.float32(0.9316), np.float32(0.9679), np.float32(0.767), np.float32(0.9409), np.float32(0.9676), np.float32(0.9781), np.float32(0.9673), np.float32(0.9735), np.float32(0.9435), np.float32(0.97), np.float32(0.9664), np.float32(0.9221), np.float32(0.9729), np.float32(0.958), np.float32(0.882), np.float32(0.9017), np.float32(0.9081)] 
+2025-05-07 03:40:27.443852: Epoch time: 103.23 s 
+2025-05-07 03:40:29.006040:  
+2025-05-07 03:40:29.073966: Epoch 1835 
+2025-05-07 03:40:29.096755: Current learning rate: 0.00106 
+2025-05-07 03:42:13.913333: train_loss -0.513 
+2025-05-07 03:42:14.069129: val_loss -0.5441 
+2025-05-07 03:42:14.103008: Pseudo dice [np.float32(0.8512), np.float32(0.8589), np.float32(0.9496), np.float32(0.978), np.float32(0.905), np.float32(0.957), np.float32(0.9704), np.float32(0.9795), np.float32(0.9702), np.float32(0.9792), np.float32(0.9615), np.float32(0.9741), np.float32(0.9786), np.float32(0.9239), np.float32(0.9655), np.float32(0.9563), np.float32(0.9152), np.float32(0.9251), np.float32(0.9311)] 
+2025-05-07 03:42:14.118998: Epoch time: 104.91 s 
+2025-05-07 03:42:16.004837:  
+2025-05-07 03:42:16.121381: Epoch 1836 
+2025-05-07 03:42:16.122560: Current learning rate: 0.00105 
+2025-05-07 03:43:57.025496: train_loss -0.5132 
+2025-05-07 03:43:57.165439: val_loss -0.4734 
+2025-05-07 03:43:57.191793: Pseudo dice [np.float32(0.8626), np.float32(0.8702), np.float32(0.93), np.float32(0.9732), np.float32(0.9285), np.float32(0.9473), np.float32(0.9667), np.float32(0.9716), np.float32(0.9733), np.float32(0.9643), np.float32(0.9538), np.float32(0.9709), np.float32(0.9708), np.float32(0.9152), np.float32(0.9692), np.float32(0.9607), np.float32(0.8885), np.float32(0.8636), np.float32(0.9224)] 
+2025-05-07 03:43:57.215478: Epoch time: 101.02 s 
+2025-05-07 03:43:58.829097:  
+2025-05-07 03:43:58.861685: Epoch 1837 
+2025-05-07 03:43:58.862183: Current learning rate: 0.00105 
+2025-05-07 03:45:39.037743: train_loss -0.4978 
+2025-05-07 03:45:39.191483: val_loss -0.5106 
+2025-05-07 03:45:39.192706: Pseudo dice [np.float32(0.8764), np.float32(0.8494), np.float32(0.9216), np.float32(0.978), np.float32(0.9104), np.float32(0.9659), np.float32(0.9624), np.float32(0.9799), np.float32(0.9547), np.float32(0.9699), np.float32(0.9598), np.float32(0.9728), np.float32(0.9693), np.float32(0.9125), np.float32(0.9696), np.float32(0.9598), np.float32(0.9073), np.float32(0.918), np.float32(0.9226)] 
+2025-05-07 03:45:39.193358: Epoch time: 100.21 s 
+2025-05-07 03:45:40.725104:  
+2025-05-07 03:45:40.878016: Epoch 1838 
+2025-05-07 03:45:40.896963: Current learning rate: 0.00104 
+2025-05-07 03:47:23.362607: train_loss -0.4979 
+2025-05-07 03:47:23.544130: val_loss -0.4961 
+2025-05-07 03:47:23.573858: Pseudo dice [np.float32(0.8694), np.float32(0.8672), np.float32(0.803), np.float32(0.9825), np.float32(0.9404), np.float32(0.9695), np.float32(0.9636), np.float32(0.982), np.float32(0.9737), np.float32(0.9697), np.float32(0.96), np.float32(0.9763), np.float32(0.9653), np.float32(0.9272), np.float32(0.9731), np.float32(0.96), np.float32(0.9129), np.float32(0.9196), np.float32(0.9063)] 
+2025-05-07 03:47:23.599236: Epoch time: 102.64 s 
+2025-05-07 03:47:25.265652:  
+2025-05-07 03:47:25.382143: Epoch 1839 
+2025-05-07 03:47:25.420559: Current learning rate: 0.00104 
+2025-05-07 03:49:09.041811: train_loss -0.5097 
+2025-05-07 03:49:09.172511: val_loss -0.503 
+2025-05-07 03:49:09.198976: Pseudo dice [np.float32(0.8698), np.float32(0.8624), np.float32(0.9346), np.float32(0.9749), np.float32(0.9046), np.float32(0.9625), np.float32(0.9641), np.float32(0.9779), np.float32(0.9511), np.float32(0.9654), np.float32(0.9503), np.float32(0.9724), np.float32(0.9735), np.float32(0.9144), np.float32(0.9712), np.float32(0.9622), np.float32(0.9085), np.float32(0.9061), np.float32(0.9205)] 
+2025-05-07 03:49:09.203110: Epoch time: 103.78 s 
+2025-05-07 03:49:10.819080:  
+2025-05-07 03:49:10.885202: Epoch 1840 
+2025-05-07 03:49:10.906979: Current learning rate: 0.00103 
+2025-05-07 03:50:50.591034: train_loss -0.5393 
+2025-05-07 03:50:50.735576: val_loss -0.4806 
+2025-05-07 03:50:50.771791: Pseudo dice [np.float32(0.855), np.float32(0.8632), np.float32(0.9217), np.float32(0.9784), np.float32(0.9122), np.float32(0.9669), np.float32(0.965), np.float32(0.9793), np.float32(0.9679), np.float32(0.9679), np.float32(0.9496), np.float32(0.9728), np.float32(0.977), np.float32(0.9164), np.float32(0.973), np.float32(0.9618), np.float32(0.8903), np.float32(0.8764), np.float32(0.915)] 
+2025-05-07 03:50:50.823420: Epoch time: 99.77 s 
+2025-05-07 03:50:52.512610:  
+2025-05-07 03:50:52.686771: Epoch 1841 
+2025-05-07 03:50:52.688362: Current learning rate: 0.00102 
+2025-05-07 03:52:34.928577: train_loss -0.5229 
+2025-05-07 03:52:35.223874: val_loss -0.4886 
+2025-05-07 03:52:35.237556: Pseudo dice [np.float32(0.874), np.float32(0.8787), np.float32(0.8571), np.float32(0.9739), np.float32(0.9163), np.float32(0.9635), np.float32(0.9744), np.float32(0.9833), np.float32(0.9674), np.float32(0.9704), np.float32(0.9351), np.float32(0.9716), np.float32(0.9709), np.float32(0.9176), np.float32(0.9726), np.float32(0.9677), np.float32(0.852), np.float32(0.8607), np.float32(0.9317)] 
+2025-05-07 03:52:35.242025: Epoch time: 102.42 s 
+2025-05-07 03:52:36.793692:  
+2025-05-07 03:52:36.843836: Epoch 1842 
+2025-05-07 03:52:36.877164: Current learning rate: 0.00102 
+2025-05-07 03:54:16.998045: train_loss -0.5188 
+2025-05-07 03:54:17.133744: val_loss -0.4916 
+2025-05-07 03:54:17.145388: Pseudo dice [np.float32(0.8455), np.float32(0.8762), np.float32(0.9514), np.float32(0.9755), np.float32(0.921), np.float32(0.9587), np.float32(0.9706), np.float32(0.9785), np.float32(0.9629), np.float32(0.9744), np.float32(0.958), np.float32(0.9739), np.float32(0.9761), np.float32(0.9133), np.float32(0.9347), np.float32(0.9574), np.float32(0.8929), np.float32(0.8961), np.float32(0.9203)] 
+2025-05-07 03:54:17.146295: Epoch time: 100.21 s 
+2025-05-07 03:54:18.967525:  
+2025-05-07 03:54:18.976479: Epoch 1843 
+2025-05-07 03:54:19.007635: Current learning rate: 0.00101 
+2025-05-07 03:56:00.116609: train_loss -0.5114 
+2025-05-07 03:56:00.346632: val_loss -0.5063 
+2025-05-07 03:56:00.380218: Pseudo dice [np.float32(0.8851), np.float32(0.8766), np.float32(0.9452), np.float32(0.9758), np.float32(0.931), np.float32(0.9678), np.float32(0.9711), np.float32(0.9814), np.float32(0.9681), np.float32(0.9687), np.float32(0.9547), np.float32(0.9717), np.float32(0.967), np.float32(0.9216), np.float32(0.9725), np.float32(0.9659), np.float32(0.9084), np.float32(0.9111), np.float32(0.9204)] 
+2025-05-07 03:56:00.427269: Epoch time: 101.15 s 
+2025-05-07 03:56:05.896042:  
+2025-05-07 03:56:05.899475: Epoch 1844 
+2025-05-07 03:56:05.899938: Current learning rate: 0.00101 
+2025-05-07 03:57:51.462352: train_loss -0.5189 
+2025-05-07 03:57:51.650808: val_loss -0.5025 
+2025-05-07 03:57:51.674641: Pseudo dice [np.float32(0.8723), np.float32(0.8585), np.float32(0.942), np.float32(0.9697), np.float32(0.9362), np.float32(0.963), np.float32(0.9694), np.float32(0.9825), np.float32(0.953), np.float32(0.9571), np.float32(0.957), np.float32(0.9594), np.float32(0.969), np.float32(0.9258), np.float32(0.9723), np.float32(0.9624), np.float32(0.9178), np.float32(0.9234), np.float32(0.9313)] 
+2025-05-07 03:57:51.700494: Epoch time: 105.57 s 
+2025-05-07 03:57:51.710314: Yayy! New best EMA pseudo Dice: 0.9391000270843506 
+2025-05-07 03:57:55.217636:  
+2025-05-07 03:57:55.331861: Epoch 1845 
+2025-05-07 03:57:55.343683: Current learning rate: 0.001 
+2025-05-07 03:59:45.105030: train_loss -0.5058 
+2025-05-07 03:59:45.260073: val_loss -0.5063 
+2025-05-07 03:59:45.304072: Pseudo dice [np.float32(0.881), np.float32(0.8706), np.float32(0.9301), np.float32(0.9786), np.float32(0.9316), np.float32(0.9586), np.float32(0.9654), np.float32(0.9792), np.float32(0.9649), np.float32(0.9579), np.float32(0.9381), np.float32(0.9718), np.float32(0.9548), np.float32(0.9185), np.float32(0.8933), np.float32(0.9537), np.float32(0.8817), np.float32(0.901), np.float32(0.9219)] 
+2025-05-07 03:59:45.350291: Epoch time: 109.89 s 
+2025-05-07 03:59:47.060888:  
+2025-05-07 03:59:47.097082: Epoch 1846 
+2025-05-07 03:59:47.112465: Current learning rate: 0.001 
+2025-05-07 04:01:27.284655: train_loss -0.518 
+2025-05-07 04:01:27.411602: val_loss -0.504 
+2025-05-07 04:01:27.433812: Pseudo dice [np.float32(0.8555), np.float32(0.8693), np.float32(0.9589), np.float32(0.9694), np.float32(0.9195), np.float32(0.9655), np.float32(0.9686), np.float32(0.98), np.float32(0.9623), np.float32(0.9741), np.float32(0.9533), np.float32(0.9671), np.float32(0.9763), np.float32(0.9225), np.float32(0.97), np.float32(0.9628), np.float32(0.9219), np.float32(0.9231), np.float32(0.9261)] 
+2025-05-07 04:01:27.446700: Epoch time: 100.23 s 
+2025-05-07 04:01:27.459992: Yayy! New best EMA pseudo Dice: 0.9391999840736389 
+2025-05-07 04:01:30.450081:  
+2025-05-07 04:01:30.489984: Epoch 1847 
+2025-05-07 04:01:30.492244: Current learning rate: 0.00099 
+2025-05-07 04:03:16.965241: train_loss -0.5021 
+2025-05-07 04:03:17.071810: val_loss -0.4931 
+2025-05-07 04:03:17.088334: Pseudo dice [np.float32(0.8425), np.float32(0.8585), np.float32(0.9144), np.float32(0.9767), np.float32(0.9351), np.float32(0.9581), np.float32(0.9684), np.float32(0.9733), np.float32(0.9653), np.float32(0.9612), np.float32(0.9503), np.float32(0.9714), np.float32(0.9736), np.float32(0.9131), np.float32(0.9653), np.float32(0.9611), np.float32(0.9192), np.float32(0.9107), np.float32(0.9198)] 
+2025-05-07 04:03:17.103646: Epoch time: 106.52 s 
+2025-05-07 04:03:18.719530:  
+2025-05-07 04:03:18.739804: Epoch 1848 
+2025-05-07 04:03:18.744349: Current learning rate: 0.00098 
+2025-05-07 04:05:01.812566: train_loss -0.4959 
+2025-05-07 04:05:01.958193: val_loss -0.5598 
+2025-05-07 04:05:01.976606: Pseudo dice [np.float32(0.8612), np.float32(0.8667), np.float32(0.9538), np.float32(0.9752), np.float32(0.9283), np.float32(0.9636), np.float32(0.9659), np.float32(0.9779), np.float32(0.9533), np.float32(0.97), np.float32(0.9495), np.float32(0.9715), np.float32(0.9728), np.float32(0.9187), np.float32(0.9704), np.float32(0.9638), np.float32(0.9189), np.float32(0.9071), np.float32(0.9306)] 
+2025-05-07 04:05:01.997080: Epoch time: 103.09 s 
+2025-05-07 04:05:02.030557: Yayy! New best EMA pseudo Dice: 0.9395999908447266 
+2025-05-07 04:05:05.461499:  
+2025-05-07 04:05:05.496288: Epoch 1849 
+2025-05-07 04:05:05.507517: Current learning rate: 0.00098 
+2025-05-07 04:06:52.444695: train_loss -0.4963 
+2025-05-07 04:06:52.673332: val_loss -0.5024 
+2025-05-07 04:06:52.681683: Pseudo dice [np.float32(0.8705), np.float32(0.854), np.float32(0.902), np.float32(0.9715), np.float32(0.9137), np.float32(0.9476), np.float32(0.967), np.float32(0.9796), np.float32(0.9665), np.float32(0.9708), np.float32(0.9575), np.float32(0.9733), np.float32(0.973), np.float32(0.9213), np.float32(0.9198), np.float32(0.9519), np.float32(0.91), np.float32(0.9089), np.float32(0.911)] 
+2025-05-07 04:06:52.685383: Epoch time: 106.98 s 
+2025-05-07 04:06:55.864786:  
+2025-05-07 04:06:55.919552: Epoch 1850 
+2025-05-07 04:06:55.920757: Current learning rate: 0.00097 
+2025-05-07 04:08:43.434871: train_loss -0.5059 
+2025-05-07 04:08:43.558746: val_loss -0.5072 
+2025-05-07 04:08:43.596800: Pseudo dice [np.float32(0.8629), np.float32(0.8753), np.float32(0.8671), np.float32(0.983), np.float32(0.9368), np.float32(0.9617), np.float32(0.9718), np.float32(0.9799), np.float32(0.9681), np.float32(0.97), np.float32(0.9556), np.float32(0.9619), np.float32(0.9708), np.float32(0.9232), np.float32(0.9404), np.float32(0.9643), np.float32(0.904), np.float32(0.9114), np.float32(0.9174)] 
+2025-05-07 04:08:43.611837: Epoch time: 107.57 s 
+2025-05-07 04:08:45.319985:  
+2025-05-07 04:08:45.364055: Epoch 1851 
+2025-05-07 04:08:45.388072: Current learning rate: 0.00097 
+2025-05-07 04:10:29.542364: train_loss -0.5261 
+2025-05-07 04:10:29.773580: val_loss -0.4774 
+2025-05-07 04:10:29.784075: Pseudo dice [np.float32(0.8574), np.float32(0.8473), np.float32(0.9035), np.float32(0.966), np.float32(0.8744), np.float32(0.9631), np.float32(0.9646), np.float32(0.973), np.float32(0.9671), np.float32(0.9717), np.float32(0.9169), np.float32(0.968), np.float32(0.9698), np.float32(0.9135), np.float32(0.962), np.float32(0.9631), np.float32(0.8696), np.float32(0.8748), np.float32(0.9285)] 
+2025-05-07 04:10:29.799168: Epoch time: 104.22 s 
+2025-05-07 04:10:31.459738:  
+2025-05-07 04:10:31.538357: Epoch 1852 
+2025-05-07 04:10:31.564158: Current learning rate: 0.00096 
+2025-05-07 04:12:07.867767: train_loss -0.5199 
+2025-05-07 04:12:07.986166: val_loss -0.5186 
+2025-05-07 04:12:08.008392: Pseudo dice [np.float32(0.8502), np.float32(0.8703), np.float32(0.9274), np.float32(0.9796), np.float32(0.8939), np.float32(0.9618), np.float32(0.9684), np.float32(0.9846), np.float32(0.9623), np.float32(0.9702), np.float32(0.9616), np.float32(0.9728), np.float32(0.9752), np.float32(0.9145), np.float32(0.9703), np.float32(0.9524), np.float32(0.8789), np.float32(0.891), np.float32(0.9212)] 
+2025-05-07 04:12:08.012317: Epoch time: 96.41 s 
+2025-05-07 04:12:09.690213:  
+2025-05-07 04:12:09.762546: Epoch 1853 
+2025-05-07 04:12:09.767647: Current learning rate: 0.00095 
+2025-05-07 04:13:52.682012: train_loss -0.5033 
+2025-05-07 04:13:52.917202: val_loss -0.5067 
+2025-05-07 04:13:52.963524: Pseudo dice [np.float32(0.8509), np.float32(0.8628), np.float32(0.9186), np.float32(0.973), np.float32(0.9268), np.float32(0.9634), np.float32(0.9712), np.float32(0.9824), np.float32(0.9654), np.float32(0.9652), np.float32(0.9575), np.float32(0.9702), np.float32(0.9726), np.float32(0.9095), np.float32(0.9675), np.float32(0.9582), np.float32(0.893), np.float32(0.88), np.float32(0.9092)] 
+2025-05-07 04:13:52.986712: Epoch time: 102.99 s 
+2025-05-07 04:13:54.529304:  
+2025-05-07 04:13:54.641386: Epoch 1854 
+2025-05-07 04:13:54.665859: Current learning rate: 0.00095 
+2025-05-07 04:15:38.839482: train_loss -0.5232 
+2025-05-07 04:15:39.037490: val_loss -0.4705 
+2025-05-07 04:15:39.075030: Pseudo dice [np.float32(0.8712), np.float32(0.8385), np.float32(0.9057), np.float32(0.972), np.float32(0.9131), np.float32(0.9659), np.float32(0.9711), np.float32(0.9771), np.float32(0.9631), np.float32(0.9703), np.float32(0.9578), np.float32(0.9667), np.float32(0.9718), np.float32(0.9215), np.float32(0.9698), np.float32(0.9655), np.float32(0.8732), np.float32(0.9008), np.float32(0.9202)] 
+2025-05-07 04:15:39.143279: Epoch time: 104.31 s 
+2025-05-07 04:15:40.982894:  
+2025-05-07 04:15:41.055331: Epoch 1855 
+2025-05-07 04:15:41.075408: Current learning rate: 0.00094 
+2025-05-07 04:17:23.900643: train_loss -0.5304 
+2025-05-07 04:17:24.053142: val_loss -0.5069 
+2025-05-07 04:17:24.079623: Pseudo dice [np.float32(0.8566), np.float32(0.8665), np.float32(0.8857), np.float32(0.9646), np.float32(0.7168), np.float32(0.9348), np.float32(0.9728), np.float32(0.9837), np.float32(0.9568), np.float32(0.9698), np.float32(0.9595), np.float32(0.9576), np.float32(0.9722), np.float32(0.9208), np.float32(0.9718), np.float32(0.9678), np.float32(0.8532), np.float32(0.912), np.float32(0.9182)] 
+2025-05-07 04:17:24.105864: Epoch time: 102.92 s 
+2025-05-07 04:17:25.885779:  
+2025-05-07 04:17:25.944361: Epoch 1856 
+2025-05-07 04:17:25.972153: Current learning rate: 0.00094 
+2025-05-07 04:19:14.212652: train_loss -0.5147 
+2025-05-07 04:19:14.373584: val_loss -0.5099 
+2025-05-07 04:19:14.405488: Pseudo dice [np.float32(0.8433), np.float32(0.8747), np.float32(0.8985), np.float32(0.9816), np.float32(0.9133), np.float32(0.9646), np.float32(0.9727), np.float32(0.9836), np.float32(0.9703), np.float32(0.9745), np.float32(0.9577), np.float32(0.9711), np.float32(0.9749), np.float32(0.9271), np.float32(0.9733), np.float32(0.9602), np.float32(0.9048), np.float32(0.9268), np.float32(0.9302)] 
+2025-05-07 04:19:14.452784: Epoch time: 108.33 s 
+2025-05-07 04:19:16.192317:  
+2025-05-07 04:19:16.198755: Epoch 1857 
+2025-05-07 04:19:16.199468: Current learning rate: 0.00093 
+2025-05-07 04:20:56.912428: train_loss -0.5068 
+2025-05-07 04:20:57.070112: val_loss -0.5378 
+2025-05-07 04:20:57.093114: Pseudo dice [np.float32(0.8525), np.float32(0.8471), np.float32(0.8766), np.float32(0.9723), np.float32(0.928), np.float32(0.9629), np.float32(0.9646), np.float32(0.9747), np.float32(0.9682), np.float32(0.9659), np.float32(0.9499), np.float32(0.973), np.float32(0.967), np.float32(0.914), np.float32(0.9714), np.float32(0.9608), np.float32(0.8831), np.float32(0.9112), np.float32(0.9166)] 
+2025-05-07 04:20:57.126494: Epoch time: 100.72 s 
+2025-05-07 04:20:58.865500:  
+2025-05-07 04:20:58.900837: Epoch 1858 
+2025-05-07 04:20:58.926799: Current learning rate: 0.00092 
+2025-05-07 04:22:44.509607: train_loss -0.5107 
+2025-05-07 04:22:44.713689: val_loss -0.5229 
+2025-05-07 04:22:44.731440: Pseudo dice [np.float32(0.831), np.float32(0.8764), np.float32(0.951), np.float32(0.973), np.float32(0.9293), np.float32(0.9603), np.float32(0.9697), np.float32(0.9818), np.float32(0.9643), np.float32(0.9643), np.float32(0.9617), np.float32(0.9708), np.float32(0.9741), np.float32(0.9236), np.float32(0.9646), np.float32(0.9637), np.float32(0.883), np.float32(0.8963), np.float32(0.9166)] 
+2025-05-07 04:22:44.741657: Epoch time: 105.65 s 
+2025-05-07 04:22:46.400714:  
+2025-05-07 04:22:46.455623: Epoch 1859 
+2025-05-07 04:22:46.481478: Current learning rate: 0.00092 
+2025-05-07 04:24:34.738286: train_loss -0.5108 
+2025-05-07 04:24:34.959824: val_loss -0.4886 
+2025-05-07 04:24:35.001709: Pseudo dice [np.float32(0.8624), np.float32(0.8575), np.float32(0.9308), np.float32(0.9798), np.float32(0.9333), np.float32(0.9621), np.float32(0.9425), np.float32(0.9789), np.float32(0.9715), np.float32(0.9737), np.float32(0.9568), np.float32(0.9766), np.float32(0.976), np.float32(0.9198), np.float32(0.9675), np.float32(0.9506), np.float32(0.8932), np.float32(0.8882), np.float32(0.9294)] 
+2025-05-07 04:24:35.002615: Epoch time: 108.34 s 
+2025-05-07 04:24:36.540261:  
+2025-05-07 04:24:36.559816: Epoch 1860 
+2025-05-07 04:24:36.576185: Current learning rate: 0.00091 
+2025-05-07 04:26:24.059989: train_loss -0.5052 
+2025-05-07 04:26:24.278659: val_loss -0.5155 
+2025-05-07 04:26:24.326262: Pseudo dice [np.float32(0.8698), np.float32(0.869), np.float32(0.8519), np.float32(0.9781), np.float32(0.9139), np.float32(0.9671), np.float32(0.9621), np.float32(0.9805), np.float32(0.9721), np.float32(0.9722), np.float32(0.9591), np.float32(0.9735), np.float32(0.9749), np.float32(0.9101), np.float32(0.9628), np.float32(0.9568), np.float32(0.9077), np.float32(0.9152), np.float32(0.9184)] 
+2025-05-07 04:26:24.377831: Epoch time: 107.52 s 
+2025-05-07 04:26:29.821204:  
+2025-05-07 04:26:29.824790: Epoch 1861 
+2025-05-07 04:26:29.825394: Current learning rate: 0.00091 
+2025-05-07 04:28:15.920518: train_loss -0.5089 
+2025-05-07 04:28:16.036070: val_loss -0.4985 
+2025-05-07 04:28:16.046222: Pseudo dice [np.float32(0.8552), np.float32(0.8574), np.float32(0.9306), np.float32(0.9699), np.float32(0.8976), np.float32(0.9643), np.float32(0.9668), np.float32(0.9801), np.float32(0.9677), np.float32(0.9683), np.float32(0.9539), np.float32(0.9742), np.float32(0.9673), np.float32(0.9073), np.float32(0.9688), np.float32(0.9616), np.float32(0.8871), np.float32(0.9014), np.float32(0.9219)] 
+2025-05-07 04:28:16.058087: Epoch time: 106.1 s 
+2025-05-07 04:28:17.566164:  
+2025-05-07 04:28:17.642985: Epoch 1862 
+2025-05-07 04:28:17.645952: Current learning rate: 0.0009 
+2025-05-07 04:30:00.063497: train_loss -0.5184 
+2025-05-07 04:30:00.190868: val_loss -0.5281 
+2025-05-07 04:30:00.216503: Pseudo dice [np.float32(0.868), np.float32(0.8757), np.float32(0.9143), np.float32(0.9766), np.float32(0.9352), np.float32(0.9632), np.float32(0.9705), np.float32(0.9829), np.float32(0.9661), np.float32(0.9734), np.float32(0.9466), np.float32(0.9727), np.float32(0.9711), np.float32(0.9222), np.float32(0.9639), np.float32(0.9332), np.float32(0.8837), np.float32(0.8797), np.float32(0.9217)] 
+2025-05-07 04:30:00.257452: Epoch time: 102.5 s 
+2025-05-07 04:30:01.931175:  
+2025-05-07 04:30:02.051690: Epoch 1863 
+2025-05-07 04:30:02.091769: Current learning rate: 0.0009 
+2025-05-07 04:31:51.359658: train_loss -0.5259 
+2025-05-07 04:31:51.457883: val_loss -0.5184 
+2025-05-07 04:31:51.498941: Pseudo dice [np.float32(0.8649), np.float32(0.8707), np.float32(0.9208), np.float32(0.9777), np.float32(0.9272), np.float32(0.9659), np.float32(0.9633), np.float32(0.9828), np.float32(0.9607), np.float32(0.9631), np.float32(0.9569), np.float32(0.9724), np.float32(0.973), np.float32(0.9181), np.float32(0.9713), np.float32(0.9682), np.float32(0.8854), np.float32(0.8833), np.float32(0.927)] 
+2025-05-07 04:31:51.528031: Epoch time: 109.43 s 
+2025-05-07 04:31:53.138649:  
+2025-05-07 04:31:53.146498: Epoch 1864 
+2025-05-07 04:31:53.168824: Current learning rate: 0.00089 
+2025-05-07 04:33:34.848969: train_loss -0.5116 
+2025-05-07 04:33:35.024877: val_loss -0.5024 
+2025-05-07 04:33:35.074238: Pseudo dice [np.float32(0.8118), np.float32(0.8548), np.float32(0.9556), np.float32(0.9761), np.float32(0.9155), np.float32(0.9639), np.float32(0.9378), np.float32(0.9753), np.float32(0.9747), np.float32(0.9769), np.float32(0.9616), np.float32(0.974), np.float32(0.976), np.float32(0.9142), np.float32(0.9713), np.float32(0.9662), np.float32(0.8648), np.float32(0.8618), np.float32(0.9196)] 
+2025-05-07 04:33:35.123622: Epoch time: 101.71 s 
+2025-05-07 04:33:36.769069:  
+2025-05-07 04:33:36.790747: Epoch 1865 
+2025-05-07 04:33:36.791595: Current learning rate: 0.00088 
+2025-05-07 04:35:21.871556: train_loss -0.5048 
+2025-05-07 04:35:21.934897: val_loss -0.4965 
+2025-05-07 04:35:21.945982: Pseudo dice [np.float32(0.8537), np.float32(0.8713), np.float32(0.9505), np.float32(0.971), np.float32(0.9284), np.float32(0.969), np.float32(0.9689), np.float32(0.9818), np.float32(0.9656), np.float32(0.9711), np.float32(0.9543), np.float32(0.9701), np.float32(0.9704), np.float32(0.9086), np.float32(0.969), np.float32(0.9538), np.float32(0.9243), np.float32(0.9261), np.float32(0.9199)] 
+2025-05-07 04:35:21.953207: Epoch time: 105.1 s 
+2025-05-07 04:35:23.616951:  
+2025-05-07 04:35:23.660668: Epoch 1866 
+2025-05-07 04:35:23.676161: Current learning rate: 0.00088 
+2025-05-07 04:37:08.183246: train_loss -0.4848 
+2025-05-07 04:37:08.338715: val_loss -0.5055 
+2025-05-07 04:37:08.391873: Pseudo dice [np.float32(0.8578), np.float32(0.8617), np.float32(0.9383), np.float32(0.9771), np.float32(0.9186), np.float32(0.9653), np.float32(0.9677), np.float32(0.9826), np.float32(0.9668), np.float32(0.9651), np.float32(0.9585), np.float32(0.9727), np.float32(0.9729), np.float32(0.9243), np.float32(0.9613), np.float32(0.9597), np.float32(0.8864), np.float32(0.9129), np.float32(0.9289)] 
+2025-05-07 04:37:08.474721: Epoch time: 104.57 s 
+2025-05-07 04:37:10.294481:  
+2025-05-07 04:37:10.336291: Epoch 1867 
+2025-05-07 04:37:10.360876: Current learning rate: 0.00087 
+2025-05-07 04:38:55.963879: train_loss -0.5087 
+2025-05-07 04:38:56.080571: val_loss -0.4917 
+2025-05-07 04:38:56.095608: Pseudo dice [np.float32(0.8669), np.float32(0.8748), np.float32(0.9492), np.float32(0.9753), np.float32(0.9058), np.float32(0.9478), np.float32(0.9549), np.float32(0.9797), np.float32(0.9649), np.float32(0.9606), np.float32(0.9237), np.float32(0.9735), np.float32(0.9683), np.float32(0.9172), np.float32(0.9591), np.float32(0.9601), np.float32(0.874), np.float32(0.8815), np.float32(0.9102)] 
+2025-05-07 04:38:56.121009: Epoch time: 105.67 s 
+2025-05-07 04:38:57.721035:  
+2025-05-07 04:38:57.800063: Epoch 1868 
+2025-05-07 04:38:57.839936: Current learning rate: 0.00087 
+2025-05-07 04:40:47.837612: train_loss -0.4898 
+2025-05-07 04:40:47.957966: val_loss -0.5187 
+2025-05-07 04:40:47.973645: Pseudo dice [np.float32(0.8531), np.float32(0.8666), np.float32(0.8737), np.float32(0.979), np.float32(0.9294), np.float32(0.9623), np.float32(0.9648), np.float32(0.9822), np.float32(0.9703), np.float32(0.9624), np.float32(0.956), np.float32(0.9729), np.float32(0.9728), np.float32(0.9132), np.float32(0.9703), np.float32(0.9528), np.float32(0.9151), np.float32(0.9122), np.float32(0.9175)] 
+2025-05-07 04:40:47.984666: Epoch time: 110.12 s 
+2025-05-07 04:40:49.567383:  
+2025-05-07 04:40:49.592630: Epoch 1869 
+2025-05-07 04:40:49.597989: Current learning rate: 0.00086 
+2025-05-07 04:42:32.328709: train_loss -0.5064 
+2025-05-07 04:42:32.460149: val_loss -0.4915 
+2025-05-07 04:42:32.483968: Pseudo dice [np.float32(0.8698), np.float32(0.8238), np.float32(0.958), np.float32(0.9683), np.float32(0.9137), np.float32(0.9717), np.float32(0.9672), np.float32(0.9816), np.float32(0.9713), np.float32(0.9676), np.float32(0.9507), np.float32(0.9712), np.float32(0.9756), np.float32(0.9265), np.float32(0.9756), np.float32(0.9683), np.float32(0.8663), np.float32(0.8811), np.float32(0.9109)] 
+2025-05-07 04:42:32.498154: Epoch time: 102.76 s 
+2025-05-07 04:42:34.191875:  
+2025-05-07 04:42:34.258119: Epoch 1870 
+2025-05-07 04:42:34.258971: Current learning rate: 0.00085 
+2025-05-07 04:44:17.873061: train_loss -0.5179 
+2025-05-07 04:44:18.017237: val_loss -0.5317 
+2025-05-07 04:44:18.056207: Pseudo dice [np.float32(0.8628), np.float32(0.8646), np.float32(0.9207), np.float32(0.9773), np.float32(0.9269), np.float32(0.9567), np.float32(0.9702), np.float32(0.9754), np.float32(0.9689), np.float32(0.968), np.float32(0.9463), np.float32(0.9703), np.float32(0.9774), np.float32(0.9271), np.float32(0.972), np.float32(0.9651), np.float32(0.9011), np.float32(0.9105), np.float32(0.9356)] 
+2025-05-07 04:44:18.070637: Epoch time: 103.68 s 
+2025-05-07 04:44:19.892983:  
+2025-05-07 04:44:19.897525: Epoch 1871 
+2025-05-07 04:44:19.898993: Current learning rate: 0.00085 
+2025-05-07 04:46:03.380401: train_loss -0.5153 
+2025-05-07 04:46:03.519837: val_loss -0.54 
+2025-05-07 04:46:03.567100: Pseudo dice [np.float32(0.8736), np.float32(0.8586), np.float32(0.9174), np.float32(0.9769), np.float32(0.9287), np.float32(0.9633), np.float32(0.9647), np.float32(0.9814), np.float32(0.9724), np.float32(0.9731), np.float32(0.9482), np.float32(0.9744), np.float32(0.9751), np.float32(0.9263), np.float32(0.9661), np.float32(0.9643), np.float32(0.908), np.float32(0.9117), np.float32(0.9169)] 
+2025-05-07 04:46:03.610471: Epoch time: 103.49 s 
+2025-05-07 04:46:05.408410:  
+2025-05-07 04:46:05.455608: Epoch 1872 
+2025-05-07 04:46:05.480002: Current learning rate: 0.00084 
+2025-05-07 04:47:52.250192: train_loss -0.5118 
+2025-05-07 04:47:52.436606: val_loss -0.5562 
+2025-05-07 04:47:52.441291: Pseudo dice [np.float32(0.8627), np.float32(0.8719), np.float32(0.8873), np.float32(0.9682), np.float32(0.9202), np.float32(0.9528), np.float32(0.9686), np.float32(0.9827), np.float32(0.9609), np.float32(0.9712), np.float32(0.9586), np.float32(0.9686), np.float32(0.974), np.float32(0.9164), np.float32(0.9022), np.float32(0.9537), np.float32(0.9065), np.float32(0.9017), np.float32(0.922)] 
+2025-05-07 04:47:52.451159: Epoch time: 106.84 s 
+2025-05-07 04:47:54.134048:  
+2025-05-07 04:47:54.255121: Epoch 1873 
+2025-05-07 04:47:54.303010: Current learning rate: 0.00084 
+2025-05-07 04:49:36.556299: train_loss -0.5005 
+2025-05-07 04:49:36.676578: val_loss -0.5263 
+2025-05-07 04:49:36.722827: Pseudo dice [np.float32(0.8768), np.float32(0.8699), np.float32(0.9503), np.float32(0.9804), np.float32(0.9147), np.float32(0.9625), np.float32(0.9676), np.float32(0.9815), np.float32(0.9655), np.float32(0.9724), np.float32(0.9603), np.float32(0.9614), np.float32(0.9705), np.float32(0.9241), np.float32(0.972), np.float32(0.9643), np.float32(0.8723), np.float32(0.8591), np.float32(0.9238)] 
+2025-05-07 04:49:36.760237: Epoch time: 102.42 s 
+2025-05-07 04:49:38.684463:  
+2025-05-07 04:49:38.720470: Epoch 1874 
+2025-05-07 04:49:38.757845: Current learning rate: 0.00083 
+2025-05-07 04:51:17.894296: train_loss -0.52 
+2025-05-07 04:51:18.011869: val_loss -0.5484 
+2025-05-07 04:51:18.025106: Pseudo dice [np.float32(0.864), np.float32(0.8751), np.float32(0.9538), np.float32(0.9766), np.float32(0.9277), np.float32(0.964), np.float32(0.9712), np.float32(0.9807), np.float32(0.9634), np.float32(0.9646), np.float32(0.9478), np.float32(0.966), np.float32(0.9757), np.float32(0.9272), np.float32(0.9563), np.float32(0.9601), np.float32(0.911), np.float32(0.913), np.float32(0.935)] 
+2025-05-07 04:51:18.049377: Epoch time: 99.21 s 
+2025-05-07 04:51:19.787856:  
+2025-05-07 04:51:19.796959: Epoch 1875 
+2025-05-07 04:51:19.801464: Current learning rate: 0.00082 
+2025-05-07 04:53:04.644419: train_loss -0.5352 
+2025-05-07 04:53:04.796505: val_loss -0.4948 
+2025-05-07 04:53:04.821542: Pseudo dice [np.float32(0.8717), np.float32(0.8703), np.float32(0.8723), np.float32(0.9814), np.float32(0.9181), np.float32(0.9655), np.float32(0.968), np.float32(0.9841), np.float32(0.9697), np.float32(0.9719), np.float32(0.9562), np.float32(0.9764), np.float32(0.9703), np.float32(0.9284), np.float32(0.9718), np.float32(0.9687), np.float32(0.8912), np.float32(0.9004), np.float32(0.9348)] 
+2025-05-07 04:53:04.850499: Epoch time: 104.86 s 
+2025-05-07 04:53:06.659540:  
+2025-05-07 04:53:06.727898: Epoch 1876 
+2025-05-07 04:53:06.749852: Current learning rate: 0.00082 
+2025-05-07 04:54:50.533334: train_loss -0.5079 
+2025-05-07 04:54:50.694874: val_loss -0.5146 
+2025-05-07 04:54:50.731612: Pseudo dice [np.float32(0.8688), np.float32(0.8376), np.float32(0.9315), np.float32(0.9511), np.float32(0.9328), np.float32(0.9653), np.float32(0.9683), np.float32(0.9745), np.float32(0.9676), np.float32(0.9661), np.float32(0.9461), np.float32(0.9674), np.float32(0.9619), np.float32(0.9203), np.float32(0.9643), np.float32(0.9629), np.float32(0.8284), np.float32(0.8221), np.float32(0.9121)] 
+2025-05-07 04:54:50.737550: Epoch time: 103.88 s 
+2025-05-07 04:54:52.244616:  
+2025-05-07 04:54:52.355975: Epoch 1877 
+2025-05-07 04:54:52.381990: Current learning rate: 0.00081 
+2025-05-07 04:56:32.688366: train_loss -0.5093 
+2025-05-07 04:56:32.790460: val_loss -0.5307 
+2025-05-07 04:56:32.808907: Pseudo dice [np.float32(0.8635), np.float32(0.8593), np.float32(0.9382), np.float32(0.9713), np.float32(0.9249), np.float32(0.9643), np.float32(0.9667), np.float32(0.9797), np.float32(0.9711), np.float32(0.9622), np.float32(0.9612), np.float32(0.9753), np.float32(0.9726), np.float32(0.9185), np.float32(0.9695), np.float32(0.9661), np.float32(0.9124), np.float32(0.9218), np.float32(0.9236)] 
+2025-05-07 04:56:32.810095: Epoch time: 100.45 s 
+2025-05-07 04:56:38.320999:  
+2025-05-07 04:56:38.325709: Epoch 1878 
+2025-05-07 04:56:38.326232: Current learning rate: 0.00081 
+2025-05-07 04:58:18.557443: train_loss -0.5096 
+2025-05-07 04:58:18.676227: val_loss -0.5146 
+2025-05-07 04:58:18.715776: Pseudo dice [np.float32(0.8542), np.float32(0.8751), np.float32(0.9222), np.float32(0.9769), np.float32(0.9291), np.float32(0.9616), np.float32(0.9655), np.float32(0.9797), np.float32(0.9693), np.float32(0.9704), np.float32(0.9514), np.float32(0.9737), np.float32(0.9711), np.float32(0.9144), np.float32(0.9714), np.float32(0.9567), np.float32(0.867), np.float32(0.9018), np.float32(0.9068)] 
+2025-05-07 04:58:18.746210: Epoch time: 100.24 s 
+2025-05-07 04:58:20.275756:  
+2025-05-07 04:58:20.311146: Epoch 1879 
+2025-05-07 04:58:20.311870: Current learning rate: 0.0008 
+2025-05-07 04:59:54.808935: train_loss -0.5174 
+2025-05-07 04:59:54.960579: val_loss -0.5002 
+2025-05-07 04:59:55.031029: Pseudo dice [np.float32(0.8806), np.float32(0.8697), np.float32(0.868), np.float32(0.9811), np.float32(0.9385), np.float32(0.9625), np.float32(0.9698), np.float32(0.9831), np.float32(0.9527), np.float32(0.9626), np.float32(0.9545), np.float32(0.9572), np.float32(0.9716), np.float32(0.9298), np.float32(0.9721), np.float32(0.9644), np.float32(0.868), np.float32(0.9207), np.float32(0.9128)] 
+2025-05-07 04:59:55.088696: Epoch time: 94.53 s 
+2025-05-07 04:59:56.726581:  
+2025-05-07 04:59:56.799356: Epoch 1880 
+2025-05-07 04:59:56.836175: Current learning rate: 0.00079 
+2025-05-07 05:01:37.970773: train_loss -0.5158 
+2025-05-07 05:01:38.028758: val_loss -0.5271 
+2025-05-07 05:01:38.029591: Pseudo dice [np.float32(0.8655), np.float32(0.8674), np.float32(0.9464), np.float32(0.9723), np.float32(0.9318), np.float32(0.9602), np.float32(0.971), np.float32(0.9809), np.float32(0.9709), np.float32(0.9708), np.float32(0.9564), np.float32(0.9756), np.float32(0.9634), np.float32(0.9142), np.float32(0.9522), np.float32(0.9546), np.float32(0.8926), np.float32(0.9194), np.float32(0.9247)] 
+2025-05-07 05:01:38.034767: Epoch time: 101.25 s 
+2025-05-07 05:01:39.675545:  
+2025-05-07 05:01:39.826437: Epoch 1881 
+2025-05-07 05:01:39.862821: Current learning rate: 0.00079 
+2025-05-07 05:03:17.132935: train_loss -0.524 
+2025-05-07 05:03:17.166304: val_loss -0.5149 
+2025-05-07 05:03:17.179873: Pseudo dice [np.float32(0.8662), np.float32(0.8867), np.float32(0.9388), np.float32(0.9818), np.float32(0.9197), np.float32(0.9641), np.float32(0.9684), np.float32(0.9791), np.float32(0.9663), np.float32(0.9641), np.float32(0.9594), np.float32(0.9739), np.float32(0.9742), np.float32(0.9221), np.float32(0.9694), np.float32(0.9595), np.float32(0.9158), np.float32(0.9081), np.float32(0.9179)] 
+2025-05-07 05:03:17.203627: Epoch time: 97.46 s 
+2025-05-07 05:03:18.841953:  
+2025-05-07 05:03:18.940059: Epoch 1882 
+2025-05-07 05:03:18.972173: Current learning rate: 0.00078 
+2025-05-07 05:04:56.688450: train_loss -0.5154 
+2025-05-07 05:04:56.790872: val_loss -0.4969 
+2025-05-07 05:04:56.818458: Pseudo dice [np.float32(0.8327), np.float32(0.8592), np.float32(0.9028), np.float32(0.978), np.float32(0.9226), np.float32(0.9615), np.float32(0.9714), np.float32(0.9797), np.float32(0.9608), np.float32(0.9658), np.float32(0.9587), np.float32(0.9684), np.float32(0.9727), np.float32(0.9145), np.float32(0.9683), np.float32(0.9621), np.float32(0.8848), np.float32(0.889), np.float32(0.9287)] 
+2025-05-07 05:04:56.846533: Epoch time: 97.85 s 
+2025-05-07 05:04:58.418102:  
+2025-05-07 05:04:58.463789: Epoch 1883 
+2025-05-07 05:04:58.493541: Current learning rate: 0.00078 
+2025-05-07 05:06:35.323676: train_loss -0.5062 
+2025-05-07 05:06:35.400448: val_loss -0.5418 
+2025-05-07 05:06:35.413096: Pseudo dice [np.float32(0.8712), np.float32(0.8542), np.float32(0.9358), np.float32(0.9793), np.float32(0.9202), np.float32(0.9686), np.float32(0.9694), np.float32(0.9826), np.float32(0.9694), np.float32(0.9695), np.float32(0.9594), np.float32(0.972), np.float32(0.9736), np.float32(0.9266), np.float32(0.972), np.float32(0.9635), np.float32(0.8948), np.float32(0.9025), np.float32(0.9234)] 
+2025-05-07 05:06:35.425680: Epoch time: 96.91 s 
+2025-05-07 05:06:36.922539:  
+2025-05-07 05:06:36.943472: Epoch 1884 
+2025-05-07 05:06:36.956766: Current learning rate: 0.00077 
+2025-05-07 05:08:17.381930: train_loss -0.5126 
+2025-05-07 05:08:17.456267: val_loss -0.4802 
+2025-05-07 05:08:17.499290: Pseudo dice [np.float32(0.8649), np.float32(0.8646), np.float32(0.8403), np.float32(0.9828), np.float32(0.8995), np.float32(0.9635), np.float32(0.9632), np.float32(0.9806), np.float32(0.9677), np.float32(0.9733), np.float32(0.9584), np.float32(0.9652), np.float32(0.9751), np.float32(0.9098), np.float32(0.966), np.float32(0.9609), np.float32(0.845), np.float32(0.8385), np.float32(0.9247)] 
+2025-05-07 05:08:17.514299: Epoch time: 100.46 s 
+2025-05-07 05:08:19.023832:  
+2025-05-07 05:08:19.148674: Epoch 1885 
+2025-05-07 05:08:19.165571: Current learning rate: 0.00077 
+2025-05-07 05:10:03.359226: train_loss -0.4988 
+2025-05-07 05:10:03.481073: val_loss -0.5373 
+2025-05-07 05:10:03.499657: Pseudo dice [np.float32(0.8684), np.float32(0.8747), np.float32(0.9002), np.float32(0.9727), np.float32(0.9191), np.float32(0.9598), np.float32(0.9686), np.float32(0.9801), np.float32(0.9696), np.float32(0.967), np.float32(0.9567), np.float32(0.9703), np.float32(0.9729), np.float32(0.9096), np.float32(0.9705), np.float32(0.9591), np.float32(0.8894), np.float32(0.8995), np.float32(0.9267)] 
+2025-05-07 05:10:03.539257: Epoch time: 104.34 s 
+2025-05-07 05:10:05.075230:  
+2025-05-07 05:10:05.214347: Epoch 1886 
+2025-05-07 05:10:05.236444: Current learning rate: 0.00076 
+2025-05-07 05:11:49.229841: train_loss -0.5074 
+2025-05-07 05:11:49.300263: val_loss -0.5332 
+2025-05-07 05:11:49.316953: Pseudo dice [np.float32(0.8662), np.float32(0.856), np.float32(0.9305), np.float32(0.9748), np.float32(0.9319), np.float32(0.964), np.float32(0.9703), np.float32(0.9826), np.float32(0.9727), np.float32(0.97), np.float32(0.956), np.float32(0.9729), np.float32(0.9734), np.float32(0.9152), np.float32(0.9678), np.float32(0.963), np.float32(0.9067), np.float32(0.9115), np.float32(0.9185)] 
+2025-05-07 05:11:49.342744: Epoch time: 104.16 s 
+2025-05-07 05:11:51.012257:  
+2025-05-07 05:11:51.127138: Epoch 1887 
+2025-05-07 05:11:51.145579: Current learning rate: 0.00075 
+2025-05-07 05:13:30.887459: train_loss -0.5213 
+2025-05-07 05:13:30.974192: val_loss -0.5012 
+2025-05-07 05:13:30.982968: Pseudo dice [np.float32(0.8552), np.float32(0.8836), np.float32(0.9539), np.float32(0.9739), np.float32(0.9246), np.float32(0.9644), np.float32(0.9708), np.float32(0.9819), np.float32(0.9672), np.float32(0.9725), np.float32(0.9595), np.float32(0.9741), np.float32(0.9738), np.float32(0.9138), np.float32(0.9668), np.float32(0.9619), np.float32(0.8975), np.float32(0.9133), np.float32(0.9322)] 
+2025-05-07 05:13:30.983656: Epoch time: 99.88 s 
+2025-05-07 05:13:32.530821:  
+2025-05-07 05:13:32.674809: Epoch 1888 
+2025-05-07 05:13:32.715701: Current learning rate: 0.00075 
+2025-05-07 05:15:09.663723: train_loss -0.495 
+2025-05-07 05:15:09.761992: val_loss -0.5391 
+2025-05-07 05:15:09.779021: Pseudo dice [np.float32(0.8684), np.float32(0.8703), np.float32(0.9182), np.float32(0.9678), np.float32(0.9239), np.float32(0.9563), np.float32(0.9712), np.float32(0.9804), np.float32(0.9573), np.float32(0.9641), np.float32(0.9617), np.float32(0.9655), np.float32(0.9743), np.float32(0.922), np.float32(0.9689), np.float32(0.9552), np.float32(0.8915), np.float32(0.9023), np.float32(0.9035)] 
+2025-05-07 05:15:09.802971: Epoch time: 97.13 s 
+2025-05-07 05:15:11.470608:  
+2025-05-07 05:15:11.609671: Epoch 1889 
+2025-05-07 05:15:11.636678: Current learning rate: 0.00074 
+2025-05-07 05:16:53.036542: train_loss -0.4993 
+2025-05-07 05:16:53.127232: val_loss -0.5202 
+2025-05-07 05:16:53.181958: Pseudo dice [np.float32(0.8666), np.float32(0.8533), np.float32(0.9461), np.float32(0.9738), np.float32(0.936), np.float32(0.9671), np.float32(0.9692), np.float32(0.9837), np.float32(0.9725), np.float32(0.9714), np.float32(0.9573), np.float32(0.9734), np.float32(0.9735), np.float32(0.9212), np.float32(0.9708), np.float32(0.9665), np.float32(0.905), np.float32(0.9006), np.float32(0.9256)] 
+2025-05-07 05:16:53.207308: Epoch time: 101.57 s 
+2025-05-07 05:16:54.764621:  
+2025-05-07 05:16:54.891660: Epoch 1890 
+2025-05-07 05:16:54.946755: Current learning rate: 0.00074 
+2025-05-07 05:18:37.364245: train_loss -0.5122 
+2025-05-07 05:18:37.416162: val_loss -0.5376 
+2025-05-07 05:18:37.430572: Pseudo dice [np.float32(0.8668), np.float32(0.8752), np.float32(0.84), np.float32(0.9766), np.float32(0.9226), np.float32(0.9657), np.float32(0.9705), np.float32(0.9759), np.float32(0.9601), np.float32(0.9707), np.float32(0.938), np.float32(0.9659), np.float32(0.9775), np.float32(0.923), np.float32(0.9673), np.float32(0.9579), np.float32(0.9105), np.float32(0.9216), np.float32(0.9376)] 
+2025-05-07 05:18:37.435228: Epoch time: 102.6 s 
+2025-05-07 05:18:39.068285:  
+2025-05-07 05:18:39.144361: Epoch 1891 
+2025-05-07 05:18:39.187158: Current learning rate: 0.00073 
+2025-05-07 05:20:17.868886: train_loss -0.5057 
+2025-05-07 05:20:17.937751: val_loss -0.4956 
+2025-05-07 05:20:17.950192: Pseudo dice [np.float32(0.8625), np.float32(0.8618), np.float32(0.9459), np.float32(0.9801), np.float32(0.9238), np.float32(0.9602), np.float32(0.9529), np.float32(0.9721), np.float32(0.958), np.float32(0.9577), np.float32(0.9573), np.float32(0.9736), np.float32(0.9723), np.float32(0.9196), np.float32(0.9542), np.float32(0.9605), np.float32(0.9097), np.float32(0.9211), np.float32(0.9314)] 
+2025-05-07 05:20:17.999722: Epoch time: 98.8 s 
+2025-05-07 05:20:19.686416:  
+2025-05-07 05:20:19.847078: Epoch 1892 
+2025-05-07 05:20:19.875116: Current learning rate: 0.00072 
+2025-05-07 05:21:56.671538: train_loss -0.5145 
+2025-05-07 05:21:56.744453: val_loss -0.5685 
+2025-05-07 05:21:56.800076: Pseudo dice [np.float32(0.8701), np.float32(0.8624), np.float32(0.9138), np.float32(0.9566), np.float32(0.9156), np.float32(0.9616), np.float32(0.9658), np.float32(0.9815), np.float32(0.973), np.float32(0.9705), np.float32(0.9637), np.float32(0.9726), np.float32(0.9745), np.float32(0.9179), np.float32(0.9694), np.float32(0.9649), np.float32(0.9103), np.float32(0.9118), np.float32(0.9202)] 
+2025-05-07 05:21:56.850120: Epoch time: 96.99 s 
+2025-05-07 05:21:56.888467: Yayy! New best EMA pseudo Dice: 0.9397000074386597 
+2025-05-07 05:22:00.040004:  
+2025-05-07 05:22:00.044865: Epoch 1893 
+2025-05-07 05:22:00.045206: Current learning rate: 0.00072 
+2025-05-07 05:23:45.710280: train_loss -0.508 
+2025-05-07 05:23:45.794395: val_loss -0.4919 
+2025-05-07 05:23:45.826623: Pseudo dice [np.float32(0.8728), np.float32(0.8882), np.float32(0.9153), np.float32(0.9829), np.float32(0.9442), np.float32(0.9629), np.float32(0.9722), np.float32(0.9826), np.float32(0.9628), np.float32(0.9674), np.float32(0.951), np.float32(0.9663), np.float32(0.9712), np.float32(0.9189), np.float32(0.9707), np.float32(0.9557), np.float32(0.9192), np.float32(0.9215), np.float32(0.9369)] 
+2025-05-07 05:23:45.844220: Epoch time: 105.67 s 
+2025-05-07 05:23:45.866111: Yayy! New best EMA pseudo Dice: 0.9402999877929688 
+2025-05-07 05:23:48.544298:  
+2025-05-07 05:23:48.591851: Epoch 1894 
+2025-05-07 05:23:48.593410: Current learning rate: 0.00071 
+2025-05-07 05:25:28.064650: train_loss -0.5289 
+2025-05-07 05:25:28.104930: val_loss -0.5041 
+2025-05-07 05:25:28.105953: Pseudo dice [np.float32(0.8634), np.float32(0.8577), np.float32(0.943), np.float32(0.9706), np.float32(0.9346), np.float32(0.9627), np.float32(0.9568), np.float32(0.973), np.float32(0.9625), np.float32(0.9692), np.float32(0.9525), np.float32(0.9692), np.float32(0.9711), np.float32(0.9224), np.float32(0.9681), np.float32(0.9292), np.float32(0.8796), np.float32(0.8949), np.float32(0.913)] 
+2025-05-07 05:25:28.149972: Epoch time: 99.52 s 
+2025-05-07 05:25:29.647245:  
+2025-05-07 05:25:29.739870: Epoch 1895 
+2025-05-07 05:25:29.768368: Current learning rate: 0.0007 
+2025-05-07 05:27:08.040660: train_loss -0.5182 
+2025-05-07 05:27:08.151629: val_loss -0.511 
+2025-05-07 05:27:08.187638: Pseudo dice [np.float32(0.8694), np.float32(0.8652), np.float32(0.9346), np.float32(0.9782), np.float32(0.9209), np.float32(0.9626), np.float32(0.9693), np.float32(0.9802), np.float32(0.9721), np.float32(0.9762), np.float32(0.9585), np.float32(0.9691), np.float32(0.9781), np.float32(0.9171), np.float32(0.9711), np.float32(0.9555), np.float32(0.9121), np.float32(0.9127), np.float32(0.9182)] 
+2025-05-07 05:27:08.210222: Epoch time: 98.39 s 
+2025-05-07 05:27:13.556498:  
+2025-05-07 05:27:13.562563: Epoch 1896 
+2025-05-07 05:27:13.563159: Current learning rate: 0.0007 
+2025-05-07 05:28:50.287213: train_loss -0.5259 
+2025-05-07 05:28:50.318042: val_loss -0.5266 
+2025-05-07 05:28:50.327528: Pseudo dice [np.float32(0.8483), np.float32(0.8601), np.float32(0.9345), np.float32(0.9824), np.float32(0.9359), np.float32(0.9636), np.float32(0.9716), np.float32(0.9742), np.float32(0.9667), np.float32(0.9771), np.float32(0.9634), np.float32(0.9732), np.float32(0.9765), np.float32(0.925), np.float32(0.968), np.float32(0.9548), np.float32(0.915), np.float32(0.9178), np.float32(0.9292)] 
+2025-05-07 05:28:50.351983: Epoch time: 96.73 s 
+2025-05-07 05:28:50.365393: Yayy! New best EMA pseudo Dice: 0.9405999779701233 
+2025-05-07 05:28:53.206188:  
+2025-05-07 05:28:53.229958: Epoch 1897 
+2025-05-07 05:28:53.230672: Current learning rate: 0.00069 
+2025-05-07 05:30:31.227978: train_loss -0.5328 
+2025-05-07 05:30:31.299921: val_loss -0.5202 
+2025-05-07 05:30:31.321826: Pseudo dice [np.float32(0.8446), np.float32(0.8713), np.float32(0.9341), np.float32(0.9816), np.float32(0.9214), np.float32(0.9637), np.float32(0.9658), np.float32(0.9827), np.float32(0.9705), np.float32(0.9636), np.float32(0.9545), np.float32(0.9701), np.float32(0.9617), np.float32(0.9237), np.float32(0.9723), np.float32(0.9612), np.float32(0.9082), np.float32(0.9119), np.float32(0.9199)] 
+2025-05-07 05:30:31.341927: Epoch time: 98.02 s 
+2025-05-07 05:30:31.352998: Yayy! New best EMA pseudo Dice: 0.9406999945640564 
+2025-05-07 05:30:34.018103:  
+2025-05-07 05:30:34.052675: Epoch 1898 
+2025-05-07 05:30:34.064273: Current learning rate: 0.00069 
+2025-05-07 05:32:17.491993: train_loss -0.517 
+2025-05-07 05:32:17.587669: val_loss -0.5408 
+2025-05-07 05:32:17.624773: Pseudo dice [np.float32(0.8646), np.float32(0.8803), np.float32(0.9362), np.float32(0.9746), np.float32(0.9345), np.float32(0.9642), np.float32(0.9702), np.float32(0.9806), np.float32(0.9679), np.float32(0.9737), np.float32(0.9477), np.float32(0.9742), np.float32(0.9753), np.float32(0.9158), np.float32(0.946), np.float32(0.9615), np.float32(0.8994), np.float32(0.9053), np.float32(0.9269)] 
+2025-05-07 05:32:17.659677: Epoch time: 103.48 s 
+2025-05-07 05:32:17.707233: Yayy! New best EMA pseudo Dice: 0.9408000111579895 
+2025-05-07 05:32:20.469152:  
+2025-05-07 05:32:20.479906: Epoch 1899 
+2025-05-07 05:32:20.484228: Current learning rate: 0.00068 
+2025-05-07 05:33:57.848789: train_loss -0.5153 
+2025-05-07 05:33:57.942268: val_loss -0.4685 
+2025-05-07 05:33:57.955407: Pseudo dice [np.float32(0.8449), np.float32(0.841), np.float32(0.9438), np.float32(0.9801), np.float32(0.9313), np.float32(0.964), np.float32(0.9647), np.float32(0.9831), np.float32(0.966), np.float32(0.974), np.float32(0.9632), np.float32(0.9719), np.float32(0.9762), np.float32(0.92), np.float32(0.9613), np.float32(0.9588), np.float32(0.9029), np.float32(0.9076), np.float32(0.9268)] 
+2025-05-07 05:33:57.956041: Epoch time: 97.38 s 
+2025-05-07 05:33:59.225060: Yayy! New best EMA pseudo Dice: 0.9408000111579895 
+2025-05-07 05:34:02.332894:  
+2025-05-07 05:34:02.339990: Epoch 1900 
+2025-05-07 05:34:02.340580: Current learning rate: 0.00067 
+2025-05-07 05:35:42.973785: train_loss -0.5181 
+2025-05-07 05:35:43.107555: val_loss -0.5539 
+2025-05-07 05:35:43.152793: Pseudo dice [np.float32(0.8753), np.float32(0.8586), np.float32(0.9431), np.float32(0.975), np.float32(0.9191), np.float32(0.9602), np.float32(0.9607), np.float32(0.9811), np.float32(0.9688), np.float32(0.9731), np.float32(0.9595), np.float32(0.9738), np.float32(0.9761), np.float32(0.9176), np.float32(0.9706), np.float32(0.9587), np.float32(0.8906), np.float32(0.8954), np.float32(0.9296)] 
+2025-05-07 05:35:43.199118: Epoch time: 100.64 s 
+2025-05-07 05:35:43.236398: Yayy! New best EMA pseudo Dice: 0.9409000277519226 
+2025-05-07 05:35:45.962750:  
+2025-05-07 05:35:46.020011: Epoch 1901 
+2025-05-07 05:35:46.021033: Current learning rate: 0.00067 
+2025-05-07 05:37:25.372661: train_loss -0.536 
+2025-05-07 05:37:25.448348: val_loss -0.5252 
+2025-05-07 05:37:25.459772: Pseudo dice [np.float32(0.8733), np.float32(0.8553), np.float32(0.8512), np.float32(0.976), np.float32(0.9214), np.float32(0.9666), np.float32(0.9687), np.float32(0.983), np.float32(0.9699), np.float32(0.9712), np.float32(0.9578), np.float32(0.971), np.float32(0.9741), np.float32(0.9234), np.float32(0.9676), np.float32(0.9545), np.float32(0.8715), np.float32(0.9009), np.float32(0.9162)] 
+2025-05-07 05:37:25.473042: Epoch time: 99.41 s 
+2025-05-07 05:37:27.056274:  
+2025-05-07 05:37:27.141036: Epoch 1902 
+2025-05-07 05:37:27.186686: Current learning rate: 0.00066 
+2025-05-07 05:39:03.143708: train_loss -0.5199 
+2025-05-07 05:39:03.225313: val_loss -0.505 
+2025-05-07 05:39:03.229487: Pseudo dice [np.float32(0.8639), np.float32(0.8613), np.float32(0.8463), np.float32(0.9801), np.float32(0.9133), np.float32(0.9627), np.float32(0.9609), np.float32(0.9764), np.float32(0.9506), np.float32(0.9736), np.float32(0.9583), np.float32(0.9678), np.float32(0.9747), np.float32(0.9139), np.float32(0.9398), np.float32(0.9608), np.float32(0.8993), np.float32(0.9135), np.float32(0.938)] 
+2025-05-07 05:39:03.229987: Epoch time: 96.09 s 
+2025-05-07 05:39:04.700248:  
+2025-05-07 05:39:04.808125: Epoch 1903 
+2025-05-07 05:39:04.826553: Current learning rate: 0.00066 
+2025-05-07 05:40:42.651221: train_loss -0.5112 
+2025-05-07 05:40:42.693095: val_loss -0.4956 
+2025-05-07 05:40:42.703014: Pseudo dice [np.float32(0.8659), np.float32(0.8666), np.float32(0.9519), np.float32(0.98), np.float32(0.934), np.float32(0.9641), np.float32(0.9696), np.float32(0.9772), np.float32(0.9687), np.float32(0.9574), np.float32(0.9346), np.float32(0.9718), np.float32(0.9725), np.float32(0.921), np.float32(0.9634), np.float32(0.9664), np.float32(0.8975), np.float32(0.9046), np.float32(0.9066)] 
+2025-05-07 05:40:42.703644: Epoch time: 97.95 s 
+2025-05-07 05:40:44.161344:  
+2025-05-07 05:40:44.284845: Epoch 1904 
+2025-05-07 05:40:44.309016: Current learning rate: 0.00065 
+2025-05-07 05:42:23.730591: train_loss -0.5262 
+2025-05-07 05:42:23.883075: val_loss -0.5024 
+2025-05-07 05:42:23.929152: Pseudo dice [np.float32(0.861), np.float32(0.8708), np.float32(0.6237), np.float32(0.9783), np.float32(0.9252), np.float32(0.957), np.float32(0.9699), np.float32(0.9841), np.float32(0.9726), np.float32(0.9728), np.float32(0.9585), np.float32(0.9706), np.float32(0.9764), np.float32(0.9196), np.float32(0.9259), np.float32(0.962), np.float32(0.8934), np.float32(0.8988), np.float32(0.9282)] 
+2025-05-07 05:42:23.980623: Epoch time: 99.57 s 
+2025-05-07 05:42:25.496592:  
+2025-05-07 05:42:25.600944: Epoch 1905 
+2025-05-07 05:42:25.601759: Current learning rate: 0.00064 
+2025-05-07 05:44:05.890095: train_loss -0.5166 
+2025-05-07 05:44:06.000928: val_loss -0.5091 
+2025-05-07 05:44:06.021677: Pseudo dice [np.float32(0.8558), np.float32(0.8618), np.float32(0.9514), np.float32(0.9774), np.float32(0.9262), np.float32(0.9636), np.float32(0.9622), np.float32(0.9828), np.float32(0.9666), np.float32(0.9739), np.float32(0.9532), np.float32(0.9692), np.float32(0.9706), np.float32(0.9264), np.float32(0.9693), np.float32(0.9677), np.float32(0.8811), np.float32(0.8975), np.float32(0.934)] 
+2025-05-07 05:44:06.072243: Epoch time: 100.39 s 
+2025-05-07 05:44:07.678533:  
+2025-05-07 05:44:07.757060: Epoch 1906 
+2025-05-07 05:44:07.786447: Current learning rate: 0.00064 
+2025-05-07 05:45:43.925851: train_loss -0.5058 
+2025-05-07 05:45:44.079904: val_loss -0.4683 
+2025-05-07 05:45:44.082120: Pseudo dice [np.float32(0.8804), np.float32(0.8728), np.float32(0.893), np.float32(0.975), np.float32(0.9109), np.float32(0.965), np.float32(0.9711), np.float32(0.977), np.float32(0.9691), np.float32(0.9624), np.float32(0.9395), np.float32(0.9672), np.float32(0.9695), np.float32(0.9199), np.float32(0.9651), np.float32(0.9657), np.float32(0.904), np.float32(0.9126), np.float32(0.9244)] 
+2025-05-07 05:45:44.082775: Epoch time: 96.25 s 
+2025-05-07 05:45:45.713014:  
+2025-05-07 05:45:45.764642: Epoch 1907 
+2025-05-07 05:45:45.781367: Current learning rate: 0.00063 
+2025-05-07 05:47:34.506202: train_loss -0.5182 
+2025-05-07 05:47:34.635846: val_loss -0.5199 
+2025-05-07 05:47:34.647835: Pseudo dice [np.float32(0.8294), np.float32(0.8624), np.float32(0.9124), np.float32(0.9753), np.float32(0.9149), np.float32(0.962), np.float32(0.9722), np.float32(0.9801), np.float32(0.9594), np.float32(0.964), np.float32(0.9518), np.float32(0.9674), np.float32(0.9716), np.float32(0.9208), np.float32(0.9702), np.float32(0.9637), np.float32(0.9235), np.float32(0.9136), np.float32(0.9322)] 
+2025-05-07 05:47:34.648589: Epoch time: 108.79 s 
+2025-05-07 05:47:36.305312:  
+2025-05-07 05:47:36.369352: Epoch 1908 
+2025-05-07 05:47:36.382640: Current learning rate: 0.00063 
+2025-05-07 05:49:17.528998: train_loss -0.5221 
+2025-05-07 05:49:17.585701: val_loss -0.5465 
+2025-05-07 05:49:17.586455: Pseudo dice [np.float32(0.8619), np.float32(0.8716), np.float32(0.908), np.float32(0.9812), np.float32(0.9374), np.float32(0.9612), np.float32(0.9718), np.float32(0.9656), np.float32(0.9701), np.float32(0.9671), np.float32(0.9542), np.float32(0.9698), np.float32(0.9729), np.float32(0.9298), np.float32(0.9677), np.float32(0.9613), np.float32(0.925), np.float32(0.9169), np.float32(0.9377)] 
+2025-05-07 05:49:17.586993: Epoch time: 101.22 s 
+2025-05-07 05:49:19.305264:  
+2025-05-07 05:49:19.311286: Epoch 1909 
+2025-05-07 05:49:19.311943: Current learning rate: 0.00062 
+2025-05-07 05:50:54.551852: train_loss -0.5219 
+2025-05-07 05:50:54.724929: val_loss -0.5571 
+2025-05-07 05:50:54.785181: Pseudo dice [np.float32(0.8759), np.float32(0.8727), np.float32(0.9322), np.float32(0.9679), np.float32(0.8958), np.float32(0.9648), np.float32(0.9534), np.float32(0.9805), np.float32(0.9525), np.float32(0.9686), np.float32(0.9547), np.float32(0.966), np.float32(0.9724), np.float32(0.9193), np.float32(0.9705), np.float32(0.9575), np.float32(0.9116), np.float32(0.9258), np.float32(0.9267)] 
+2025-05-07 05:50:54.791323: Epoch time: 95.25 s 
+2025-05-07 05:50:56.452142:  
+2025-05-07 05:50:56.507390: Epoch 1910 
+2025-05-07 05:50:56.532861: Current learning rate: 0.00061 
+2025-05-07 05:52:34.537553: train_loss -0.4946 
+2025-05-07 05:52:34.572733: val_loss -0.5307 
+2025-05-07 05:52:34.573505: Pseudo dice [np.float32(0.8744), np.float32(0.868), np.float32(0.9012), np.float32(0.9799), np.float32(0.9149), np.float32(0.965), np.float32(0.9685), np.float32(0.9834), np.float32(0.9661), np.float32(0.9774), np.float32(0.9619), np.float32(0.9694), np.float32(0.9752), np.float32(0.9229), np.float32(0.9695), np.float32(0.965), np.float32(0.8961), np.float32(0.9193), np.float32(0.9298)] 
+2025-05-07 05:52:34.595818: Epoch time: 98.09 s 
+2025-05-07 05:52:36.171837:  
+2025-05-07 05:52:36.271528: Epoch 1911 
+2025-05-07 05:52:36.311421: Current learning rate: 0.00061 
+2025-05-07 05:54:17.604406: train_loss -0.5201 
+2025-05-07 05:54:17.666537: val_loss -0.5367 
+2025-05-07 05:54:17.679691: Pseudo dice [np.float32(0.8743), np.float32(0.8686), np.float32(0.9128), np.float32(0.9742), np.float32(0.9214), np.float32(0.9613), np.float32(0.9672), np.float32(0.9793), np.float32(0.9547), np.float32(0.9598), np.float32(0.9588), np.float32(0.9582), np.float32(0.9736), np.float32(0.9223), np.float32(0.9585), np.float32(0.9666), np.float32(0.9091), np.float32(0.8881), np.float32(0.9226)] 
+2025-05-07 05:54:17.692604: Epoch time: 101.43 s 
+2025-05-07 05:54:22.612844:  
+2025-05-07 05:54:22.618676: Epoch 1912 
+2025-05-07 05:54:22.619096: Current learning rate: 0.0006 
+2025-05-07 05:56:02.318558: train_loss -0.5075 
+2025-05-07 05:56:02.388757: val_loss -0.5215 
+2025-05-07 05:56:02.442735: Pseudo dice [np.float32(0.8717), np.float32(0.857), np.float32(0.9458), np.float32(0.9805), np.float32(0.9247), np.float32(0.9493), np.float32(0.9697), np.float32(0.9789), np.float32(0.9704), np.float32(0.9766), np.float32(0.9649), np.float32(0.9713), np.float32(0.9779), np.float32(0.9174), np.float32(0.9678), np.float32(0.9599), np.float32(0.8806), np.float32(0.8914), np.float32(0.9218)] 
+2025-05-07 05:56:02.467655: Epoch time: 99.71 s 
+2025-05-07 05:56:03.959527:  
+2025-05-07 05:56:04.032277: Epoch 1913 
+2025-05-07 05:56:04.044978: Current learning rate: 0.0006 
+2025-05-07 05:57:42.272048: train_loss -0.5023 
+2025-05-07 05:57:42.377299: val_loss -0.5076 
+2025-05-07 05:57:42.398989: Pseudo dice [np.float32(0.8507), np.float32(0.8495), np.float32(0.9503), np.float32(0.9762), np.float32(0.9318), np.float32(0.963), np.float32(0.9688), np.float32(0.9765), np.float32(0.9628), np.float32(0.9682), np.float32(0.9527), np.float32(0.9713), np.float32(0.9651), np.float32(0.9129), np.float32(0.9669), np.float32(0.9623), np.float32(0.8758), np.float32(0.9188), np.float32(0.9307)] 
+2025-05-07 05:57:42.416645: Epoch time: 98.31 s 
+2025-05-07 05:57:44.007821:  
+2025-05-07 05:57:44.177559: Epoch 1914 
+2025-05-07 05:57:44.281457: Current learning rate: 0.00059 
+2025-05-07 05:59:21.068093: train_loss -0.5259 
+2025-05-07 05:59:21.194192: val_loss -0.5004 
+2025-05-07 05:59:21.203930: Pseudo dice [np.float32(0.8422), np.float32(0.8385), np.float32(0.9398), np.float32(0.9755), np.float32(0.9141), np.float32(0.968), np.float32(0.9629), np.float32(0.9797), np.float32(0.9739), np.float32(0.957), np.float32(0.9559), np.float32(0.9708), np.float32(0.9751), np.float32(0.9235), np.float32(0.9705), np.float32(0.9586), np.float32(0.8772), np.float32(0.8969), np.float32(0.9149)] 
+2025-05-07 05:59:21.222409: Epoch time: 97.06 s 
+2025-05-07 05:59:23.003870:  
+2025-05-07 05:59:23.060061: Epoch 1915 
+2025-05-07 05:59:23.089201: Current learning rate: 0.00058 
+2025-05-07 06:01:00.422236: train_loss -0.5253 
+2025-05-07 06:01:00.513508: val_loss -0.5225 
+2025-05-07 06:01:00.533762: Pseudo dice [np.float32(0.8763), np.float32(0.886), np.float32(0.8755), np.float32(0.9766), np.float32(0.9337), np.float32(0.9554), np.float32(0.9665), np.float32(0.9804), np.float32(0.9723), np.float32(0.9765), np.float32(0.9591), np.float32(0.9783), np.float32(0.9757), np.float32(0.9189), np.float32(0.9478), np.float32(0.9644), np.float32(0.9245), np.float32(0.9318), np.float32(0.9406)] 
+2025-05-07 06:01:00.548154: Epoch time: 97.42 s 
+2025-05-07 06:01:02.292064:  
+2025-05-07 06:01:02.317034: Epoch 1916 
+2025-05-07 06:01:02.341017: Current learning rate: 0.00058 
+2025-05-07 06:02:43.379712: train_loss -0.5207 
+2025-05-07 06:02:43.460353: val_loss -0.5388 
+2025-05-07 06:02:43.494051: Pseudo dice [np.float32(0.8788), np.float32(0.8724), np.float32(0.9111), np.float32(0.9803), np.float32(0.9165), np.float32(0.9693), np.float32(0.9669), np.float32(0.9807), np.float32(0.9616), np.float32(0.9699), np.float32(0.9577), np.float32(0.9728), np.float32(0.9769), np.float32(0.9242), np.float32(0.9763), np.float32(0.9675), np.float32(0.8992), np.float32(0.9075), np.float32(0.9287)] 
+2025-05-07 06:02:43.519875: Epoch time: 101.09 s 
+2025-05-07 06:02:45.161000:  
+2025-05-07 06:02:45.193927: Epoch 1917 
+2025-05-07 06:02:45.194863: Current learning rate: 0.00057 
+2025-05-07 06:04:24.928281: train_loss -0.4818 
+2025-05-07 06:04:24.966517: val_loss -0.4857 
+2025-05-07 06:04:24.980140: Pseudo dice [np.float32(0.8756), np.float32(0.8539), np.float32(0.9387), np.float32(0.9768), np.float32(0.9354), np.float32(0.9661), np.float32(0.9643), np.float32(0.9802), np.float32(0.9752), np.float32(0.9626), np.float32(0.9536), np.float32(0.9769), np.float32(0.9667), np.float32(0.9231), np.float32(0.9727), np.float32(0.9629), np.float32(0.9071), np.float32(0.9155), np.float32(0.9149)] 
+2025-05-07 06:04:25.002695: Epoch time: 99.77 s 
+2025-05-07 06:04:26.577508:  
+2025-05-07 06:04:26.720272: Epoch 1918 
+2025-05-07 06:04:26.768852: Current learning rate: 0.00056 
+2025-05-07 06:06:06.968248: train_loss -0.5082 
+2025-05-07 06:06:07.088151: val_loss -0.537 
+2025-05-07 06:06:07.094368: Pseudo dice [np.float32(0.8613), np.float32(0.884), np.float32(0.9331), np.float32(0.9803), np.float32(0.9372), np.float32(0.963), np.float32(0.9522), np.float32(0.9797), np.float32(0.9729), np.float32(0.9739), np.float32(0.9535), np.float32(0.9721), np.float32(0.9736), np.float32(0.9254), np.float32(0.9742), np.float32(0.966), np.float32(0.9011), np.float32(0.9166), np.float32(0.9247)] 
+2025-05-07 06:06:07.095052: Epoch time: 100.39 s 
+2025-05-07 06:06:08.661561:  
+2025-05-07 06:06:08.767021: Epoch 1919 
+2025-05-07 06:06:08.804539: Current learning rate: 0.00056 
+2025-05-07 06:07:44.658108: train_loss -0.5022 
+2025-05-07 06:07:44.693888: val_loss -0.525 
+2025-05-07 06:07:44.694787: Pseudo dice [np.float32(0.8703), np.float32(0.8647), np.float32(0.9283), np.float32(0.9792), np.float32(0.918), np.float32(0.9684), np.float32(0.9703), np.float32(0.9793), np.float32(0.9712), np.float32(0.9666), np.float32(0.9539), np.float32(0.974), np.float32(0.9751), np.float32(0.9242), np.float32(0.9741), np.float32(0.9654), np.float32(0.9194), np.float32(0.903), np.float32(0.9226)] 
+2025-05-07 06:07:44.695313: Epoch time: 96.0 s 
+2025-05-07 06:07:44.695687: Yayy! New best EMA pseudo Dice: 0.9412000179290771 
+2025-05-07 06:07:48.001398:  
+2025-05-07 06:07:48.006501: Epoch 1920 
+2025-05-07 06:07:48.006968: Current learning rate: 0.00055 
+2025-05-07 06:09:25.239524: train_loss -0.5111 
+2025-05-07 06:09:25.319978: val_loss -0.5061 
+2025-05-07 06:09:25.341028: Pseudo dice [np.float32(0.8536), np.float32(0.8695), np.float32(0.8995), np.float32(0.9754), np.float32(0.946), np.float32(0.9567), np.float32(0.9686), np.float32(0.9817), np.float32(0.966), np.float32(0.967), np.float32(0.9547), np.float32(0.9711), np.float32(0.9712), np.float32(0.92), np.float32(0.9457), np.float32(0.9539), np.float32(0.8827), np.float32(0.8929), np.float32(0.9346)] 
+2025-05-07 06:09:25.375587: Epoch time: 97.24 s 
+2025-05-07 06:09:27.204223:  
+2025-05-07 06:09:27.222170: Epoch 1921 
+2025-05-07 06:09:27.223114: Current learning rate: 0.00055 
+2025-05-07 06:11:07.127442: train_loss -0.5228 
+2025-05-07 06:11:07.226040: val_loss -0.5037 
+2025-05-07 06:11:07.264670: Pseudo dice [np.float32(0.8721), np.float32(0.8656), np.float32(0.9576), np.float32(0.9804), np.float32(0.9142), np.float32(0.9591), np.float32(0.9702), np.float32(0.9807), np.float32(0.9701), np.float32(0.9675), np.float32(0.9532), np.float32(0.9676), np.float32(0.9719), np.float32(0.9206), np.float32(0.9274), np.float32(0.9685), np.float32(0.9017), np.float32(0.9084), np.float32(0.9379)] 
+2025-05-07 06:11:07.319672: Epoch time: 99.92 s 
+2025-05-07 06:11:08.967179:  
+2025-05-07 06:11:09.046424: Epoch 1922 
+2025-05-07 06:11:09.072093: Current learning rate: 0.00054 
+2025-05-07 06:12:45.368488: train_loss -0.5258 
+2025-05-07 06:12:45.451016: val_loss -0.5331 
+2025-05-07 06:12:45.471188: Pseudo dice [np.float32(0.8689), np.float32(0.8698), np.float32(0.9343), np.float32(0.9789), np.float32(0.9375), np.float32(0.9598), np.float32(0.9682), np.float32(0.9808), np.float32(0.9732), np.float32(0.9692), np.float32(0.9542), np.float32(0.976), np.float32(0.9746), np.float32(0.9292), np.float32(0.9693), np.float32(0.9639), np.float32(0.9072), np.float32(0.9125), np.float32(0.9284)] 
+2025-05-07 06:12:45.501938: Epoch time: 96.4 s 
+2025-05-07 06:12:45.534406: Yayy! New best EMA pseudo Dice: 0.9412999749183655 
+2025-05-07 06:12:48.507442:  
+2025-05-07 06:12:48.538633: Epoch 1923 
+2025-05-07 06:12:48.564463: Current learning rate: 0.00053 
+2025-05-07 06:14:30.729236: train_loss -0.5209 
+2025-05-07 06:14:30.767991: val_loss -0.5214 
+2025-05-07 06:14:30.778982: Pseudo dice [np.float32(0.8749), np.float32(0.8585), np.float32(0.9375), np.float32(0.981), np.float32(0.926), np.float32(0.9642), np.float32(0.9632), np.float32(0.9775), np.float32(0.9649), np.float32(0.969), np.float32(0.9613), np.float32(0.9728), np.float32(0.9754), np.float32(0.9222), np.float32(0.9727), np.float32(0.9674), np.float32(0.8753), np.float32(0.9005), np.float32(0.9159)] 
+2025-05-07 06:14:30.807169: Epoch time: 102.22 s 
+2025-05-07 06:14:32.408344:  
+2025-05-07 06:14:32.487780: Epoch 1924 
+2025-05-07 06:14:32.525625: Current learning rate: 0.00053 
+2025-05-07 06:16:14.512566: train_loss -0.5156 
+2025-05-07 06:16:14.617697: val_loss -0.5307 
+2025-05-07 06:16:14.656926: Pseudo dice [np.float32(0.8554), np.float32(0.877), np.float32(0.8224), np.float32(0.9707), np.float32(0.9178), np.float32(0.9638), np.float32(0.9747), np.float32(0.9781), np.float32(0.9702), np.float32(0.9667), np.float32(0.9605), np.float32(0.9714), np.float32(0.9766), np.float32(0.9235), np.float32(0.9643), np.float32(0.9581), np.float32(0.9078), np.float32(0.9094), np.float32(0.9224)] 
+2025-05-07 06:16:14.680028: Epoch time: 102.11 s 
+2025-05-07 06:16:16.329588:  
+2025-05-07 06:16:16.384292: Epoch 1925 
+2025-05-07 06:16:16.413728: Current learning rate: 0.00052 
+2025-05-07 06:17:56.586911: train_loss -0.5137 
+2025-05-07 06:17:56.631299: val_loss -0.4835 
+2025-05-07 06:17:56.632307: Pseudo dice [np.float32(0.8555), np.float32(0.8521), np.float32(0.95), np.float32(0.9774), np.float32(0.9146), np.float32(0.9659), np.float32(0.962), np.float32(0.9801), np.float32(0.9662), np.float32(0.9679), np.float32(0.932), np.float32(0.9732), np.float32(0.9729), np.float32(0.9218), np.float32(0.9701), np.float32(0.9679), np.float32(0.8904), np.float32(0.8921), np.float32(0.9153)] 
+2025-05-07 06:17:56.652103: Epoch time: 100.26 s 
+2025-05-07 06:17:58.357493:  
+2025-05-07 06:17:58.417746: Epoch 1926 
+2025-05-07 06:17:58.429175: Current learning rate: 0.00051 
+2025-05-07 06:19:39.961411: train_loss -0.4928 
+2025-05-07 06:19:40.088122: val_loss -0.4966 
+2025-05-07 06:19:40.121744: Pseudo dice [np.float32(0.872), np.float32(0.8718), np.float32(0.9375), np.float32(0.9753), np.float32(0.8913), np.float32(0.9654), np.float32(0.97), np.float32(0.9813), np.float32(0.9617), np.float32(0.9694), np.float32(0.9611), np.float32(0.9745), np.float32(0.9655), np.float32(0.9149), np.float32(0.9577), np.float32(0.9569), np.float32(0.9144), np.float32(0.9075), np.float32(0.9192)] 
+2025-05-07 06:19:40.145923: Epoch time: 101.61 s 
+2025-05-07 06:19:41.794218:  
+2025-05-07 06:19:41.891885: Epoch 1927 
+2025-05-07 06:19:41.908381: Current learning rate: 0.00051 
+2025-05-07 06:21:24.207349: train_loss -0.5071 
+2025-05-07 06:21:24.318888: val_loss -0.5011 
+2025-05-07 06:21:24.330756: Pseudo dice [np.float32(0.8576), np.float32(0.8763), np.float32(0.9257), np.float32(0.9724), np.float32(0.9303), np.float32(0.9604), np.float32(0.9638), np.float32(0.9798), np.float32(0.9684), np.float32(0.9536), np.float32(0.9015), np.float32(0.9687), np.float32(0.9662), np.float32(0.9181), np.float32(0.9672), np.float32(0.964), np.float32(0.9134), np.float32(0.9176), np.float32(0.9218)] 
+2025-05-07 06:21:24.343825: Epoch time: 102.41 s 
+2025-05-07 06:21:25.939843:  
+2025-05-07 06:21:26.024398: Epoch 1928 
+2025-05-07 06:21:26.061651: Current learning rate: 0.0005 
+2025-05-07 06:23:06.763109: train_loss -0.5047 
+2025-05-07 06:23:06.817783: val_loss -0.5415 
+2025-05-07 06:23:06.832751: Pseudo dice [np.float32(0.8702), np.float32(0.8458), np.float32(0.9358), np.float32(0.9682), np.float32(0.9474), np.float32(0.9636), np.float32(0.9685), np.float32(0.9813), np.float32(0.9745), np.float32(0.9721), np.float32(0.9539), np.float32(0.9782), np.float32(0.9682), np.float32(0.9269), np.float32(0.9725), np.float32(0.9653), np.float32(0.9117), np.float32(0.9123), np.float32(0.9168)] 
+2025-05-07 06:23:06.840424: Epoch time: 100.83 s 
+2025-05-07 06:23:12.144761:  
+2025-05-07 06:23:12.152161: Epoch 1929 
+2025-05-07 06:23:12.152759: Current learning rate: 0.0005 
+2025-05-07 06:24:51.584424: train_loss -0.5189 
+2025-05-07 06:24:51.647495: val_loss -0.5225 
+2025-05-07 06:24:51.664077: Pseudo dice [np.float32(0.8532), np.float32(0.8599), np.float32(0.9311), np.float32(0.9791), np.float32(0.9265), np.float32(0.9617), np.float32(0.9686), np.float32(0.9812), np.float32(0.9678), np.float32(0.9698), np.float32(0.965), np.float32(0.9662), np.float32(0.9751), np.float32(0.9158), np.float32(0.9626), np.float32(0.955), np.float32(0.8857), np.float32(0.9048), np.float32(0.9228)] 
+2025-05-07 06:24:51.679675: Epoch time: 99.44 s 
+2025-05-07 06:24:53.268897:  
+2025-05-07 06:24:53.341054: Epoch 1930 
+2025-05-07 06:24:53.400585: Current learning rate: 0.00049 
+2025-05-07 06:26:32.243927: train_loss -0.5118 
+2025-05-07 06:26:32.382879: val_loss -0.5255 
+2025-05-07 06:26:32.428915: Pseudo dice [np.float32(0.856), np.float32(0.8682), np.float32(0.9569), np.float32(0.9643), np.float32(0.9128), np.float32(0.9629), np.float32(0.9661), np.float32(0.9815), np.float32(0.9675), np.float32(0.9621), np.float32(0.9587), np.float32(0.9703), np.float32(0.9724), np.float32(0.9091), np.float32(0.9612), np.float32(0.9603), np.float32(0.908), np.float32(0.9217), np.float32(0.9234)] 
+2025-05-07 06:26:32.463400: Epoch time: 98.98 s 
+2025-05-07 06:26:34.176275:  
+2025-05-07 06:26:34.231214: Epoch 1931 
+2025-05-07 06:26:34.260018: Current learning rate: 0.00048 
+2025-05-07 06:28:14.261153: train_loss -0.5023 
+2025-05-07 06:28:14.313356: val_loss -0.5076 
+2025-05-07 06:28:14.327354: Pseudo dice [np.float32(0.8498), np.float32(0.8777), np.float32(0.9287), np.float32(0.9787), np.float32(0.9122), np.float32(0.9587), np.float32(0.968), np.float32(0.9803), np.float32(0.969), np.float32(0.9721), np.float32(0.9585), np.float32(0.9753), np.float32(0.9758), np.float32(0.9179), np.float32(0.9453), np.float32(0.9551), np.float32(0.9093), np.float32(0.9206), np.float32(0.9338)] 
+2025-05-07 06:28:14.348684: Epoch time: 100.09 s 
+2025-05-07 06:28:15.890427:  
+2025-05-07 06:28:16.009332: Epoch 1932 
+2025-05-07 06:28:16.049060: Current learning rate: 0.00048 
+2025-05-07 06:29:53.775770: train_loss -0.5034 
+2025-05-07 06:29:53.862954: val_loss -0.4916 
+2025-05-07 06:29:53.879468: Pseudo dice [np.float32(0.8703), np.float32(0.8656), np.float32(0.9403), np.float32(0.966), np.float32(0.8968), np.float32(0.9687), np.float32(0.9689), np.float32(0.981), np.float32(0.9695), np.float32(0.9659), np.float32(0.9598), np.float32(0.9687), np.float32(0.9727), np.float32(0.9274), np.float32(0.9725), np.float32(0.9652), np.float32(0.9232), np.float32(0.9249), np.float32(0.9266)] 
+2025-05-07 06:29:53.900151: Epoch time: 97.89 s 
+2025-05-07 06:29:55.477517:  
+2025-05-07 06:29:55.508876: Epoch 1933 
+2025-05-07 06:29:55.519837: Current learning rate: 0.00047 
+2025-05-07 06:31:32.260003: train_loss -0.4945 
+2025-05-07 06:31:32.346634: val_loss -0.5597 
+2025-05-07 06:31:32.377730: Pseudo dice [np.float32(0.8559), np.float32(0.873), np.float32(0.9637), np.float32(0.9736), np.float32(0.9355), np.float32(0.9551), np.float32(0.9665), np.float32(0.9811), np.float32(0.9595), np.float32(0.9713), np.float32(0.9647), np.float32(0.9734), np.float32(0.9709), np.float32(0.9182), np.float32(0.9597), np.float32(0.9577), np.float32(0.913), np.float32(0.9222), np.float32(0.9187)] 
+2025-05-07 06:31:32.415798: Epoch time: 96.78 s 
+2025-05-07 06:31:34.183950:  
+2025-05-07 06:31:34.205233: Epoch 1934 
+2025-05-07 06:31:34.211358: Current learning rate: 0.00046 
+2025-05-07 06:33:09.729177: train_loss -0.4923 
+2025-05-07 06:33:09.745801: val_loss -0.566 
+2025-05-07 06:33:09.754233: Pseudo dice [np.float32(0.8726), np.float32(0.8375), np.float32(0.9256), np.float32(0.9705), np.float32(0.9335), np.float32(0.9661), np.float32(0.9671), np.float32(0.968), np.float32(0.9723), np.float32(0.9697), np.float32(0.9572), np.float32(0.9725), np.float32(0.9774), np.float32(0.923), np.float32(0.9716), np.float32(0.9674), np.float32(0.911), np.float32(0.9107), np.float32(0.9183)] 
+2025-05-07 06:33:09.755347: Epoch time: 95.55 s 
+2025-05-07 06:33:09.755933: Yayy! New best EMA pseudo Dice: 0.9412999749183655 
+2025-05-07 06:33:12.787787:  
+2025-05-07 06:33:12.792920: Epoch 1935 
+2025-05-07 06:33:12.793365: Current learning rate: 0.00046 
+2025-05-07 06:34:45.406275: train_loss -0.495 
+2025-05-07 06:34:45.452066: val_loss -0.5336 
+2025-05-07 06:34:45.456518: Pseudo dice [np.float32(0.8786), np.float32(0.8775), np.float32(0.9359), np.float32(0.9834), np.float32(0.9009), np.float32(0.9631), np.float32(0.9674), np.float32(0.9814), np.float32(0.9723), np.float32(0.9709), np.float32(0.9587), np.float32(0.9768), np.float32(0.9773), np.float32(0.9304), np.float32(0.9637), np.float32(0.9683), np.float32(0.925), np.float32(0.9228), np.float32(0.9307)] 
+2025-05-07 06:34:45.457432: Epoch time: 92.62 s 
+2025-05-07 06:34:45.457967: Yayy! New best EMA pseudo Dice: 0.9419000148773193 
+2025-05-07 06:34:48.105867:  
+2025-05-07 06:34:48.113703: Epoch 1936 
+2025-05-07 06:34:48.114145: Current learning rate: 0.00045 
+2025-05-07 06:36:27.991769: train_loss -0.5249 
+2025-05-07 06:36:28.043838: val_loss -0.5244 
+2025-05-07 06:36:28.089419: Pseudo dice [np.float32(0.8561), np.float32(0.8802), np.float32(0.9313), np.float32(0.9783), np.float32(0.9213), np.float32(0.9645), np.float32(0.968), np.float32(0.9812), np.float32(0.9626), np.float32(0.978), np.float32(0.9582), np.float32(0.9691), np.float32(0.9754), np.float32(0.9251), np.float32(0.9599), np.float32(0.9678), np.float32(0.897), np.float32(0.9138), np.float32(0.933)] 
+2025-05-07 06:36:28.114566: Epoch time: 99.89 s 
+2025-05-07 06:36:28.159047: Yayy! New best EMA pseudo Dice: 0.9419999718666077 
+2025-05-07 06:36:31.051175:  
+2025-05-07 06:36:31.056996: Epoch 1937 
+2025-05-07 06:36:31.057460: Current learning rate: 0.00045 
+2025-05-07 06:38:18.755001: train_loss -0.5142 
+2025-05-07 06:38:18.864059: val_loss -0.5653 
+2025-05-07 06:38:18.878054: Pseudo dice [np.float32(0.8605), np.float32(0.8759), np.float32(0.939), np.float32(0.9709), np.float32(0.9022), np.float32(0.9644), np.float32(0.964), np.float32(0.9813), np.float32(0.9738), np.float32(0.9776), np.float32(0.9659), np.float32(0.9737), np.float32(0.9719), np.float32(0.9233), np.float32(0.957), np.float32(0.9654), np.float32(0.9036), np.float32(0.9146), np.float32(0.9271)] 
+2025-05-07 06:38:18.885535: Epoch time: 107.7 s 
+2025-05-07 06:38:18.897341: Yayy! New best EMA pseudo Dice: 0.9420999884605408 
+2025-05-07 06:38:21.793756:  
+2025-05-07 06:38:21.835334: Epoch 1938 
+2025-05-07 06:38:21.836155: Current learning rate: 0.00044 
+2025-05-07 06:40:02.284530: train_loss -0.5054 
+2025-05-07 06:40:02.306677: val_loss -0.499 
+2025-05-07 06:40:02.307242: Pseudo dice [np.float32(0.8732), np.float32(0.8633), np.float32(0.8799), np.float32(0.9849), np.float32(0.9114), np.float32(0.9554), np.float32(0.9555), np.float32(0.9727), np.float32(0.9521), np.float32(0.9725), np.float32(0.9595), np.float32(0.9532), np.float32(0.9701), np.float32(0.9171), np.float32(0.9675), np.float32(0.9573), np.float32(0.8673), np.float32(0.8851), np.float32(0.9322)] 
+2025-05-07 06:40:02.318562: Epoch time: 100.49 s 
+2025-05-07 06:40:03.898664:  
+2025-05-07 06:40:03.980506: Epoch 1939 
+2025-05-07 06:40:04.003215: Current learning rate: 0.00043 
+2025-05-07 06:41:46.645646: train_loss -0.5311 
+2025-05-07 06:41:46.743870: val_loss -0.5244 
+2025-05-07 06:41:46.770736: Pseudo dice [np.float32(0.8638), np.float32(0.8651), np.float32(0.9363), np.float32(0.9736), np.float32(0.9352), np.float32(0.9662), np.float32(0.9639), np.float32(0.9798), np.float32(0.9675), np.float32(0.9717), np.float32(0.9636), np.float32(0.9702), np.float32(0.9727), np.float32(0.9241), np.float32(0.9288), np.float32(0.9578), np.float32(0.9109), np.float32(0.9135), np.float32(0.9183)] 
+2025-05-07 06:41:46.804509: Epoch time: 102.75 s 
+2025-05-07 06:41:48.510008:  
+2025-05-07 06:41:48.667727: Epoch 1940 
+2025-05-07 06:41:48.668678: Current learning rate: 0.00043 
+2025-05-07 06:43:30.010144: train_loss -0.5339 
+2025-05-07 06:43:30.132711: val_loss -0.4962 
+2025-05-07 06:43:30.159231: Pseudo dice [np.float32(0.8614), np.float32(0.8769), np.float32(0.9266), np.float32(0.9725), np.float32(0.917), np.float32(0.9571), np.float32(0.9719), np.float32(0.9793), np.float32(0.9717), np.float32(0.9735), np.float32(0.9546), np.float32(0.9695), np.float32(0.9646), np.float32(0.9143), np.float32(0.9571), np.float32(0.9583), np.float32(0.8934), np.float32(0.906), np.float32(0.9201)] 
+2025-05-07 06:43:30.181438: Epoch time: 101.5 s 
+2025-05-07 06:43:31.788739:  
+2025-05-07 06:43:31.821182: Epoch 1941 
+2025-05-07 06:43:31.828795: Current learning rate: 0.00042 
+2025-05-07 06:45:06.042092: train_loss -0.5201 
+2025-05-07 06:45:06.233609: val_loss -0.5024 
+2025-05-07 06:45:06.307545: Pseudo dice [np.float32(0.8711), np.float32(0.8812), np.float32(0.8665), np.float32(0.9776), np.float32(0.936), np.float32(0.9643), np.float32(0.9733), np.float32(0.9827), np.float32(0.9645), np.float32(0.9668), np.float32(0.9606), np.float32(0.9718), np.float32(0.9782), np.float32(0.9168), np.float32(0.9686), np.float32(0.9607), np.float32(0.8941), np.float32(0.9088), np.float32(0.9151)] 
+2025-05-07 06:45:06.335027: Epoch time: 94.25 s 
+2025-05-07 06:45:07.963550:  
+2025-05-07 06:45:08.058247: Epoch 1942 
+2025-05-07 06:45:08.059165: Current learning rate: 0.00041 
+2025-05-07 06:46:46.747118: train_loss -0.5156 
+2025-05-07 06:46:46.919025: val_loss -0.5276 
+2025-05-07 06:46:46.997873: Pseudo dice [np.float32(0.8727), np.float32(0.8684), np.float32(0.9641), np.float32(0.968), np.float32(0.9164), np.float32(0.9636), np.float32(0.9661), np.float32(0.9805), np.float32(0.969), np.float32(0.974), np.float32(0.9627), np.float32(0.9717), np.float32(0.979), np.float32(0.9225), np.float32(0.9727), np.float32(0.9678), np.float32(0.9128), np.float32(0.9162), np.float32(0.9364)] 
+2025-05-07 06:46:47.016027: Epoch time: 98.79 s 
+2025-05-07 06:46:48.525175:  
+2025-05-07 06:46:48.741537: Epoch 1943 
+2025-05-07 06:46:48.768786: Current learning rate: 0.00041 
+2025-05-07 06:48:28.537539: train_loss -0.5072 
+2025-05-07 06:48:28.648278: val_loss -0.5143 
+2025-05-07 06:48:28.676031: Pseudo dice [np.float32(0.8562), np.float32(0.8736), np.float32(0.9194), np.float32(0.9793), np.float32(0.9335), np.float32(0.9654), np.float32(0.9674), np.float32(0.9814), np.float32(0.9685), np.float32(0.9706), np.float32(0.9572), np.float32(0.974), np.float32(0.9694), np.float32(0.9197), np.float32(0.9654), np.float32(0.964), np.float32(0.9025), np.float32(0.918), np.float32(0.9207)] 
+2025-05-07 06:48:28.692234: Epoch time: 100.01 s 
+2025-05-07 06:48:30.260113:  
+2025-05-07 06:48:30.342348: Epoch 1944 
+2025-05-07 06:48:30.361379: Current learning rate: 0.0004 
+2025-05-07 06:50:06.298486: train_loss -0.4931 
+2025-05-07 06:50:06.337264: val_loss -0.5573 
+2025-05-07 06:50:06.338807: Pseudo dice [np.float32(0.8596), np.float32(0.8725), np.float32(0.9326), np.float32(0.9662), np.float32(0.9385), np.float32(0.9647), np.float32(0.9664), np.float32(0.9819), np.float32(0.976), np.float32(0.9764), np.float32(0.961), np.float32(0.9752), np.float32(0.9771), np.float32(0.9245), np.float32(0.9632), np.float32(0.959), np.float32(0.8912), np.float32(0.8974), np.float32(0.925)] 
+2025-05-07 06:50:06.351406: Epoch time: 96.04 s 
+2025-05-07 06:50:07.976662:  
+2025-05-07 06:50:08.069486: Epoch 1945 
+2025-05-07 06:50:08.100857: Current learning rate: 0.00039 
+2025-05-07 06:51:43.296353: train_loss -0.524 
+2025-05-07 06:51:43.431706: val_loss -0.5149 
+2025-05-07 06:51:43.477497: Pseudo dice [np.float32(0.8544), np.float32(0.8621), np.float32(0.94), np.float32(0.9725), np.float32(0.9287), np.float32(0.9644), np.float32(0.9438), np.float32(0.9824), np.float32(0.9715), np.float32(0.9697), np.float32(0.9586), np.float32(0.9755), np.float32(0.9699), np.float32(0.9182), np.float32(0.9704), np.float32(0.9555), np.float32(0.8904), np.float32(0.9119), np.float32(0.9033)] 
+2025-05-07 06:51:43.510371: Epoch time: 95.32 s 
+2025-05-07 06:51:48.932994:  
+2025-05-07 06:51:48.938613: Epoch 1946 
+2025-05-07 06:51:48.939087: Current learning rate: 0.00039 
+2025-05-07 06:53:27.646482: train_loss -0.531 
+2025-05-07 06:53:27.770460: val_loss -0.4519 
+2025-05-07 06:53:27.812670: Pseudo dice [np.float32(0.8512), np.float32(0.8611), np.float32(0.939), np.float32(0.9755), np.float32(0.9275), np.float32(0.9685), np.float32(0.9673), np.float32(0.981), np.float32(0.9643), np.float32(0.9642), np.float32(0.9375), np.float32(0.9705), np.float32(0.9758), np.float32(0.9219), np.float32(0.9734), np.float32(0.9612), np.float32(0.9077), np.float32(0.902), np.float32(0.9255)] 
+2025-05-07 06:53:27.857184: Epoch time: 98.71 s 
+2025-05-07 06:53:29.441866:  
+2025-05-07 06:53:29.515913: Epoch 1947 
+2025-05-07 06:53:29.538457: Current learning rate: 0.00038 
+2025-05-07 06:55:11.608896: train_loss -0.5254 
+2025-05-07 06:55:11.801097: val_loss -0.5411 
+2025-05-07 06:55:11.835466: Pseudo dice [np.float32(0.8753), np.float32(0.8809), np.float32(0.9269), np.float32(0.9781), np.float32(0.9293), np.float32(0.9697), np.float32(0.9697), np.float32(0.9834), np.float32(0.974), np.float32(0.9686), np.float32(0.9434), np.float32(0.9751), np.float32(0.9704), np.float32(0.9332), np.float32(0.9702), np.float32(0.9625), np.float32(0.9031), np.float32(0.9059), np.float32(0.9271)] 
+2025-05-07 06:55:11.877779: Epoch time: 102.17 s 
+2025-05-07 06:55:13.404901:  
+2025-05-07 06:55:13.475601: Epoch 1948 
+2025-05-07 06:55:13.499886: Current learning rate: 0.00037 
+2025-05-07 06:56:51.206860: train_loss -0.5255 
+2025-05-07 06:56:51.245990: val_loss -0.4844 
+2025-05-07 06:56:51.246974: Pseudo dice [np.float32(0.8548), np.float32(0.8529), np.float32(0.9363), np.float32(0.975), np.float32(0.9291), np.float32(0.9643), np.float32(0.9711), np.float32(0.9776), np.float32(0.9711), np.float32(0.9459), np.float32(0.9533), np.float32(0.9732), np.float32(0.9643), np.float32(0.9041), np.float32(0.9697), np.float32(0.9559), np.float32(0.8933), np.float32(0.9134), np.float32(0.9188)] 
+2025-05-07 06:56:51.247416: Epoch time: 97.8 s 
+2025-05-07 06:56:52.841904:  
+2025-05-07 06:56:52.915268: Epoch 1949 
+2025-05-07 06:56:52.931984: Current learning rate: 0.00037 
+2025-05-07 06:58:36.271647: train_loss -0.5245 
+2025-05-07 06:58:36.390274: val_loss -0.5417 
+2025-05-07 06:58:36.424282: Pseudo dice [np.float32(0.8717), np.float32(0.8635), np.float32(0.9142), np.float32(0.9793), np.float32(0.9344), np.float32(0.9644), np.float32(0.9703), np.float32(0.9803), np.float32(0.9699), np.float32(0.9727), np.float32(0.9551), np.float32(0.9745), np.float32(0.9729), np.float32(0.9195), np.float32(0.9698), np.float32(0.9655), np.float32(0.9124), np.float32(0.9125), np.float32(0.9133)] 
+2025-05-07 06:58:36.440041: Epoch time: 103.43 s 
+2025-05-07 06:58:39.277786:  
+2025-05-07 06:58:39.317962: Epoch 1950 
+2025-05-07 06:58:39.325784: Current learning rate: 0.00036 
+2025-05-07 07:00:21.893282: train_loss -0.503 
+2025-05-07 07:00:22.019155: val_loss -0.5348 
+2025-05-07 07:00:22.048957: Pseudo dice [np.float32(0.8551), np.float32(0.8649), np.float32(0.8933), np.float32(0.9765), np.float32(0.9223), np.float32(0.9626), np.float32(0.9682), np.float32(0.9799), np.float32(0.9724), np.float32(0.9659), np.float32(0.946), np.float32(0.9735), np.float32(0.9738), np.float32(0.9275), np.float32(0.9657), np.float32(0.9516), np.float32(0.9061), np.float32(0.9224), np.float32(0.9323)] 
+2025-05-07 07:00:22.059866: Epoch time: 102.62 s 
+2025-05-07 07:00:23.728649:  
+2025-05-07 07:00:23.777766: Epoch 1951 
+2025-05-07 07:00:23.778593: Current learning rate: 0.00036 
+2025-05-07 07:01:58.594940: train_loss -0.5224 
+2025-05-07 07:01:58.704236: val_loss -0.5039 
+2025-05-07 07:01:58.715999: Pseudo dice [np.float32(0.8567), np.float32(0.8602), np.float32(0.9238), np.float32(0.9801), np.float32(0.9246), np.float32(0.965), np.float32(0.9685), np.float32(0.983), np.float32(0.9692), np.float32(0.9687), np.float32(0.9519), np.float32(0.973), np.float32(0.9786), np.float32(0.9239), np.float32(0.9715), np.float32(0.9667), np.float32(0.9014), np.float32(0.9008), np.float32(0.9352)] 
+2025-05-07 07:01:58.729225: Epoch time: 94.87 s 
+2025-05-07 07:02:00.324458:  
+2025-05-07 07:02:00.377052: Epoch 1952 
+2025-05-07 07:02:00.377769: Current learning rate: 0.00035 
+2025-05-07 07:03:36.029090: train_loss -0.517 
+2025-05-07 07:03:36.056738: val_loss -0.4793 
+2025-05-07 07:03:36.057578: Pseudo dice [np.float32(0.8782), np.float32(0.8826), np.float32(0.9434), np.float32(0.9799), np.float32(0.9329), np.float32(0.9662), np.float32(0.9753), np.float32(0.98), np.float32(0.9606), np.float32(0.9782), np.float32(0.9649), np.float32(0.9676), np.float32(0.978), np.float32(0.9272), np.float32(0.9534), np.float32(0.9571), np.float32(0.9212), np.float32(0.9295), np.float32(0.9387)] 
+2025-05-07 07:03:36.058084: Epoch time: 95.71 s 
+2025-05-07 07:03:36.058649: Yayy! New best EMA pseudo Dice: 0.9420999884605408 
+2025-05-07 07:03:38.906528:  
+2025-05-07 07:03:38.940227: Epoch 1953 
+2025-05-07 07:03:38.951339: Current learning rate: 0.00034 
+2025-05-07 07:05:21.356025: train_loss -0.4925 
+2025-05-07 07:05:21.486796: val_loss -0.5176 
+2025-05-07 07:05:21.538382: Pseudo dice [np.float32(0.8724), np.float32(0.8918), np.float32(0.9148), np.float32(0.976), np.float32(0.9144), np.float32(0.9569), np.float32(0.9688), np.float32(0.9849), np.float32(0.9685), np.float32(0.972), np.float32(0.9608), np.float32(0.9752), np.float32(0.9752), np.float32(0.9234), np.float32(0.9513), np.float32(0.9605), np.float32(0.9081), np.float32(0.9215), np.float32(0.9288)] 
+2025-05-07 07:05:21.578350: Epoch time: 102.45 s 
+2025-05-07 07:05:21.618598: Yayy! New best EMA pseudo Dice: 0.9422000050544739 
+2025-05-07 07:05:24.452076:  
+2025-05-07 07:05:24.457721: Epoch 1954 
+2025-05-07 07:05:24.458170: Current learning rate: 0.00034 
+2025-05-07 07:07:04.290761: train_loss -0.5206 
+2025-05-07 07:07:04.419404: val_loss -0.4969 
+2025-05-07 07:07:04.447893: Pseudo dice [np.float32(0.8747), np.float32(0.876), np.float32(0.9376), np.float32(0.9793), np.float32(0.9269), np.float32(0.9661), np.float32(0.9576), np.float32(0.9818), np.float32(0.9706), np.float32(0.9737), np.float32(0.9601), np.float32(0.971), np.float32(0.9759), np.float32(0.9242), np.float32(0.9738), np.float32(0.9683), np.float32(0.913), np.float32(0.9105), np.float32(0.9204)] 
+2025-05-07 07:07:04.462211: Epoch time: 99.84 s 
+2025-05-07 07:07:04.472405: Yayy! New best EMA pseudo Dice: 0.9424999952316284 
+2025-05-07 07:07:07.716380:  
+2025-05-07 07:07:07.797029: Epoch 1955 
+2025-05-07 07:07:07.797832: Current learning rate: 0.00033 
+2025-05-07 07:08:40.553089: train_loss -0.5151 
+2025-05-07 07:08:40.697301: val_loss -0.5394 
+2025-05-07 07:08:40.733899: Pseudo dice [np.float32(0.8647), np.float32(0.8756), np.float32(0.9436), np.float32(0.9731), np.float32(0.9348), np.float32(0.9583), np.float32(0.9705), np.float32(0.9815), np.float32(0.9751), np.float32(0.972), np.float32(0.958), np.float32(0.9753), np.float32(0.9769), np.float32(0.9224), np.float32(0.9532), np.float32(0.9633), np.float32(0.9145), np.float32(0.9283), np.float32(0.9359)] 
+2025-05-07 07:08:40.780289: Epoch time: 92.84 s 
+2025-05-07 07:08:40.816495: Yayy! New best EMA pseudo Dice: 0.9429000020027161 
+2025-05-07 07:08:43.643285:  
+2025-05-07 07:08:43.648359: Epoch 1956 
+2025-05-07 07:08:43.648843: Current learning rate: 0.00032 
+2025-05-07 07:10:23.563329: train_loss -0.5062 
+2025-05-07 07:10:23.771366: val_loss -0.5032 
+2025-05-07 07:10:23.814327: Pseudo dice [np.float32(0.8572), np.float32(0.8804), np.float32(0.9397), np.float32(0.9784), np.float32(0.9147), np.float32(0.96), np.float32(0.968), np.float32(0.9794), np.float32(0.9729), np.float32(0.9696), np.float32(0.9629), np.float32(0.9777), np.float32(0.9776), np.float32(0.9248), np.float32(0.9429), np.float32(0.9632), np.float32(0.8599), np.float32(0.8706), np.float32(0.9249)] 
+2025-05-07 07:10:23.826128: Epoch time: 99.92 s 
+2025-05-07 07:10:25.463453:  
+2025-05-07 07:10:25.551680: Epoch 1957 
+2025-05-07 07:10:25.586113: Current learning rate: 0.00032 
+2025-05-07 07:12:03.088627: train_loss -0.5113 
+2025-05-07 07:12:03.174198: val_loss -0.5214 
+2025-05-07 07:12:03.209041: Pseudo dice [np.float32(0.8513), np.float32(0.8619), np.float32(0.9236), np.float32(0.9761), np.float32(0.9172), np.float32(0.9657), np.float32(0.971), np.float32(0.981), np.float32(0.9645), np.float32(0.9636), np.float32(0.9589), np.float32(0.9688), np.float32(0.9739), np.float32(0.9256), np.float32(0.9731), np.float32(0.9619), np.float32(0.9006), np.float32(0.9146), np.float32(0.9142)] 
+2025-05-07 07:12:03.281193: Epoch time: 97.63 s 
+2025-05-07 07:12:05.050809:  
+2025-05-07 07:12:05.084424: Epoch 1958 
+2025-05-07 07:12:05.106802: Current learning rate: 0.00031 
+2025-05-07 07:13:44.317364: train_loss -0.5188 
+2025-05-07 07:13:44.328746: val_loss -0.5315 
+2025-05-07 07:13:44.329305: Pseudo dice [np.float32(0.8729), np.float32(0.8705), np.float32(0.9446), np.float32(0.9758), np.float32(0.9416), np.float32(0.9619), np.float32(0.9593), np.float32(0.9782), np.float32(0.9671), np.float32(0.9621), np.float32(0.9547), np.float32(0.9641), np.float32(0.9605), np.float32(0.926), np.float32(0.9712), np.float32(0.9665), np.float32(0.8612), np.float32(0.8369), np.float32(0.9213)] 
+2025-05-07 07:13:44.329801: Epoch time: 99.27 s 
+2025-05-07 07:13:45.914828:  
+2025-05-07 07:13:45.981013: Epoch 1959 
+2025-05-07 07:13:46.026844: Current learning rate: 0.0003 
+2025-05-07 07:15:20.349755: train_loss -0.527 
+2025-05-07 07:15:20.433352: val_loss -0.5166 
+2025-05-07 07:15:20.446342: Pseudo dice [np.float32(0.8588), np.float32(0.8716), np.float32(0.8893), np.float32(0.9772), np.float32(0.9315), np.float32(0.9589), np.float32(0.971), np.float32(0.9839), np.float32(0.9721), np.float32(0.9711), np.float32(0.9585), np.float32(0.9749), np.float32(0.9789), np.float32(0.9201), np.float32(0.9695), np.float32(0.9668), np.float32(0.8792), np.float32(0.9026), np.float32(0.9331)] 
+2025-05-07 07:15:20.471503: Epoch time: 94.44 s 
+2025-05-07 07:15:22.046401:  
+2025-05-07 07:15:22.341472: Epoch 1960 
+2025-05-07 07:15:22.342248: Current learning rate: 0.0003 
+2025-05-07 07:17:03.297948: train_loss -0.516 
+2025-05-07 07:17:03.337431: val_loss -0.5587 
+2025-05-07 07:17:03.338295: Pseudo dice [np.float32(0.8428), np.float32(0.8355), np.float32(0.9346), np.float32(0.9632), np.float32(0.9221), np.float32(0.9597), np.float32(0.9649), np.float32(0.979), np.float32(0.97), np.float32(0.9667), np.float32(0.9597), np.float32(0.9701), np.float32(0.9718), np.float32(0.9004), np.float32(0.9691), np.float32(0.96), np.float32(0.9156), np.float32(0.9268), np.float32(0.9279)] 
+2025-05-07 07:17:03.338882: Epoch time: 101.25 s 
+2025-05-07 07:17:04.974620:  
+2025-05-07 07:17:05.103707: Epoch 1961 
+2025-05-07 07:17:05.141324: Current learning rate: 0.00029 
+2025-05-07 07:18:40.569244: train_loss -0.5142 
+2025-05-07 07:18:40.647112: val_loss -0.5276 
+2025-05-07 07:18:40.678804: Pseudo dice [np.float32(0.8633), np.float32(0.879), np.float32(0.9386), np.float32(0.9759), np.float32(0.9112), np.float32(0.9646), np.float32(0.9697), np.float32(0.9832), np.float32(0.9636), np.float32(0.9707), np.float32(0.9618), np.float32(0.9697), np.float32(0.9753), np.float32(0.9254), np.float32(0.9698), np.float32(0.9685), np.float32(0.9155), np.float32(0.9237), np.float32(0.9337)] 
+2025-05-07 07:18:40.719429: Epoch time: 95.6 s 
+2025-05-07 07:18:42.302210:  
+2025-05-07 07:18:42.392199: Epoch 1962 
+2025-05-07 07:18:42.422129: Current learning rate: 0.00028 
+2025-05-07 07:20:22.574472: train_loss -0.5145 
+2025-05-07 07:20:22.664151: val_loss -0.5518 
+2025-05-07 07:20:22.675245: Pseudo dice [np.float32(0.8551), np.float32(0.8593), np.float32(0.8925), np.float32(0.9784), np.float32(0.9315), np.float32(0.9591), np.float32(0.9651), np.float32(0.98), np.float32(0.9616), np.float32(0.9754), np.float32(0.9633), np.float32(0.9676), np.float32(0.9762), np.float32(0.9209), np.float32(0.9608), np.float32(0.9593), np.float32(0.9128), np.float32(0.92), np.float32(0.9222)] 
+2025-05-07 07:20:22.690464: Epoch time: 100.27 s 
+2025-05-07 07:20:28.186996:  
+2025-05-07 07:20:28.192457: Epoch 1963 
+2025-05-07 07:20:28.192917: Current learning rate: 0.00028 
+2025-05-07 07:22:04.774273: train_loss -0.5204 
+2025-05-07 07:22:04.894823: val_loss -0.5079 
+2025-05-07 07:22:04.953826: Pseudo dice [np.float32(0.8501), np.float32(0.8565), np.float32(0.9568), np.float32(0.9793), np.float32(0.9286), np.float32(0.9677), np.float32(0.9668), np.float32(0.9818), np.float32(0.9746), np.float32(0.9715), np.float32(0.9633), np.float32(0.9697), np.float32(0.9739), np.float32(0.918), np.float32(0.9363), np.float32(0.9658), np.float32(0.8886), np.float32(0.9048), np.float32(0.9204)] 
+2025-05-07 07:22:05.001663: Epoch time: 96.59 s 
+2025-05-07 07:22:06.569271:  
+2025-05-07 07:22:06.580064: Epoch 1964 
+2025-05-07 07:22:06.580566: Current learning rate: 0.00027 
+2025-05-07 07:23:42.605694: train_loss -0.5334 
+2025-05-07 07:23:42.692917: val_loss -0.4896 
+2025-05-07 07:23:42.721997: Pseudo dice [np.float32(0.8824), np.float32(0.8641), np.float32(0.943), np.float32(0.9772), np.float32(0.9336), np.float32(0.964), np.float32(0.9694), np.float32(0.9809), np.float32(0.9701), np.float32(0.9749), np.float32(0.9601), np.float32(0.9701), np.float32(0.9773), np.float32(0.9244), np.float32(0.9723), np.float32(0.9649), np.float32(0.9074), np.float32(0.9274), np.float32(0.9288)] 
+2025-05-07 07:23:42.745799: Epoch time: 96.04 s 
+2025-05-07 07:23:44.398860:  
+2025-05-07 07:23:44.465475: Epoch 1965 
+2025-05-07 07:23:44.474766: Current learning rate: 0.00026 
+2025-05-07 07:25:24.760730: train_loss -0.5192 
+2025-05-07 07:25:24.799278: val_loss -0.5125 
+2025-05-07 07:25:24.807017: Pseudo dice [np.float32(0.8855), np.float32(0.8715), np.float32(0.8948), np.float32(0.9777), np.float32(0.9371), np.float32(0.9681), np.float32(0.9611), np.float32(0.9817), np.float32(0.972), np.float32(0.9734), np.float32(0.9595), np.float32(0.9679), np.float32(0.9743), np.float32(0.9258), np.float32(0.9728), np.float32(0.9666), np.float32(0.8991), np.float32(0.9167), np.float32(0.9229)] 
+2025-05-07 07:25:24.821563: Epoch time: 100.36 s 
+2025-05-07 07:25:26.380400:  
+2025-05-07 07:25:26.494963: Epoch 1966 
+2025-05-07 07:25:26.540930: Current learning rate: 0.00026 
+2025-05-07 07:27:05.817607: train_loss -0.5252 
+2025-05-07 07:27:05.937458: val_loss -0.5391 
+2025-05-07 07:27:05.983753: Pseudo dice [np.float32(0.8676), np.float32(0.8745), np.float32(0.9372), np.float32(0.9734), np.float32(0.9182), np.float32(0.9612), np.float32(0.9673), np.float32(0.9795), np.float32(0.9727), np.float32(0.9743), np.float32(0.9656), np.float32(0.974), np.float32(0.979), np.float32(0.9197), np.float32(0.967), np.float32(0.96), np.float32(0.922), np.float32(0.9185), np.float32(0.924)] 
+2025-05-07 07:27:06.016981: Epoch time: 99.44 s 
+2025-05-07 07:27:07.615436:  
+2025-05-07 07:27:07.680808: Epoch 1967 
+2025-05-07 07:27:07.700187: Current learning rate: 0.00025 
+2025-05-07 07:28:44.490304: train_loss -0.5187 
+2025-05-07 07:28:44.617936: val_loss -0.4977 
+2025-05-07 07:28:44.645748: Pseudo dice [np.float32(0.8624), np.float32(0.8831), np.float32(0.9368), np.float32(0.9736), np.float32(0.9065), np.float32(0.9634), np.float32(0.9723), np.float32(0.9811), np.float32(0.9579), np.float32(0.9637), np.float32(0.9529), np.float32(0.9748), np.float32(0.9776), np.float32(0.9207), np.float32(0.9723), np.float32(0.9656), np.float32(0.8969), np.float32(0.9185), np.float32(0.9162)] 
+2025-05-07 07:28:44.678347: Epoch time: 96.88 s 
+2025-05-07 07:28:46.221628:  
+2025-05-07 07:28:46.314141: Epoch 1968 
+2025-05-07 07:28:46.350442: Current learning rate: 0.00024 
+2025-05-07 07:30:29.627850: train_loss -0.4942 
+2025-05-07 07:30:29.736302: val_loss -0.5231 
+2025-05-07 07:30:29.750893: Pseudo dice [np.float32(0.8331), np.float32(0.8565), np.float32(0.9283), np.float32(0.9815), np.float32(0.9136), np.float32(0.9598), np.float32(0.9674), np.float32(0.9804), np.float32(0.9611), np.float32(0.9693), np.float32(0.9593), np.float32(0.9686), np.float32(0.9749), np.float32(0.9246), np.float32(0.9743), np.float32(0.9658), np.float32(0.8883), np.float32(0.8857), np.float32(0.9165)] 
+2025-05-07 07:30:29.780036: Epoch time: 103.41 s 
+2025-05-07 07:30:31.476920:  
+2025-05-07 07:30:31.571295: Epoch 1969 
+2025-05-07 07:30:31.583056: Current learning rate: 0.00024 
+2025-05-07 07:32:14.893276: train_loss -0.5113 
+2025-05-07 07:32:15.022055: val_loss -0.5145 
+2025-05-07 07:32:15.052008: Pseudo dice [np.float32(0.8575), np.float32(0.8736), np.float32(0.9476), np.float32(0.9713), np.float32(0.9285), np.float32(0.9639), np.float32(0.9712), np.float32(0.9827), np.float32(0.9677), np.float32(0.9749), np.float32(0.9576), np.float32(0.9611), np.float32(0.9702), np.float32(0.927), np.float32(0.9617), np.float32(0.9626), np.float32(0.9138), np.float32(0.9168), np.float32(0.9216)] 
+2025-05-07 07:32:15.072456: Epoch time: 103.42 s 
+2025-05-07 07:32:16.730147:  
+2025-05-07 07:32:16.850117: Epoch 1970 
+2025-05-07 07:32:16.879066: Current learning rate: 0.00023 
+2025-05-07 07:33:55.707579: train_loss -0.5357 
+2025-05-07 07:33:55.870092: val_loss -0.556 
+2025-05-07 07:33:55.926023: Pseudo dice [np.float32(0.8691), np.float32(0.8537), np.float32(0.9002), np.float32(0.971), np.float32(0.9222), np.float32(0.9557), np.float32(0.9684), np.float32(0.9721), np.float32(0.9633), np.float32(0.9727), np.float32(0.9559), np.float32(0.9674), np.float32(0.9727), np.float32(0.9147), np.float32(0.954), np.float32(0.9556), np.float32(0.9088), np.float32(0.9151), np.float32(0.922)] 
+2025-05-07 07:33:55.970331: Epoch time: 98.98 s 
+2025-05-07 07:33:57.653692:  
+2025-05-07 07:33:57.764396: Epoch 1971 
+2025-05-07 07:33:57.851189: Current learning rate: 0.00022 
+2025-05-07 07:35:39.861594: train_loss -0.504 
+2025-05-07 07:35:39.978812: val_loss -0.5318 
+2025-05-07 07:35:39.980341: Pseudo dice [np.float32(0.8828), np.float32(0.8715), np.float32(0.9129), np.float32(0.9703), np.float32(0.9298), np.float32(0.9641), np.float32(0.9682), np.float32(0.9742), np.float32(0.9689), np.float32(0.9733), np.float32(0.9612), np.float32(0.9673), np.float32(0.9732), np.float32(0.926), np.float32(0.9745), np.float32(0.961), np.float32(0.8934), np.float32(0.8767), np.float32(0.9244)] 
+2025-05-07 07:35:39.994313: Epoch time: 102.21 s 
+2025-05-07 07:35:41.535918:  
+2025-05-07 07:35:41.642955: Epoch 1972 
+2025-05-07 07:35:41.683683: Current learning rate: 0.00021 
+2025-05-07 07:37:21.156243: train_loss -0.5133 
+2025-05-07 07:37:21.234304: val_loss -0.4915 
+2025-05-07 07:37:21.261226: Pseudo dice [np.float32(0.8519), np.float32(0.8857), np.float32(0.9509), np.float32(0.9668), np.float32(0.9324), np.float32(0.9671), np.float32(0.9718), np.float32(0.983), np.float32(0.9636), np.float32(0.9706), np.float32(0.9577), np.float32(0.9741), np.float32(0.9746), np.float32(0.9276), np.float32(0.9722), np.float32(0.9684), np.float32(0.9025), np.float32(0.9138), np.float32(0.9332)] 
+2025-05-07 07:37:21.286107: Epoch time: 99.62 s 
+2025-05-07 07:37:22.954127:  
+2025-05-07 07:37:23.007152: Epoch 1973 
+2025-05-07 07:37:23.013646: Current learning rate: 0.00021 
+2025-05-07 07:39:08.183246: train_loss -0.5139 
+2025-05-07 07:39:08.243082: val_loss -0.5254 
+2025-05-07 07:39:08.263041: Pseudo dice [np.float32(0.8708), np.float32(0.8695), np.float32(0.931), np.float32(0.9809), np.float32(0.9016), np.float32(0.9623), np.float32(0.9718), np.float32(0.9809), np.float32(0.9747), np.float32(0.9733), np.float32(0.9587), np.float32(0.9701), np.float32(0.9754), np.float32(0.922), np.float32(0.9694), np.float32(0.9635), np.float32(0.8665), np.float32(0.8738), np.float32(0.9391)] 
+2025-05-07 07:39:08.274264: Epoch time: 105.23 s 
+2025-05-07 07:39:09.993006:  
+2025-05-07 07:39:10.025951: Epoch 1974 
+2025-05-07 07:39:10.039629: Current learning rate: 0.0002 
+2025-05-07 07:40:40.790957: train_loss -0.5039 
+2025-05-07 07:40:40.817543: val_loss -0.5466 
+2025-05-07 07:40:40.834144: Pseudo dice [np.float32(0.8618), np.float32(0.8657), np.float32(0.9099), np.float32(0.9786), np.float32(0.9104), np.float32(0.9607), np.float32(0.9695), np.float32(0.9794), np.float32(0.9702), np.float32(0.9617), np.float32(0.9553), np.float32(0.9719), np.float32(0.9658), np.float32(0.9142), np.float32(0.971), np.float32(0.9637), np.float32(0.9003), np.float32(0.9029), np.float32(0.9287)] 
+2025-05-07 07:40:40.866900: Epoch time: 90.8 s 
+2025-05-07 07:40:42.599289:  
+2025-05-07 07:40:42.753679: Epoch 1975 
+2025-05-07 07:40:42.784873: Current learning rate: 0.00019 
+2025-05-07 07:42:14.824160: train_loss -0.5114 
+2025-05-07 07:42:14.932212: val_loss -0.5268 
+2025-05-07 07:42:14.972817: Pseudo dice [np.float32(0.8779), np.float32(0.8833), np.float32(0.9404), np.float32(0.9738), np.float32(0.9195), np.float32(0.9657), np.float32(0.9691), np.float32(0.9825), np.float32(0.968), np.float32(0.9723), np.float32(0.9624), np.float32(0.9717), np.float32(0.9773), np.float32(0.9286), np.float32(0.9711), np.float32(0.9673), np.float32(0.9044), np.float32(0.9149), np.float32(0.9263)] 
+2025-05-07 07:42:15.001758: Epoch time: 92.23 s 
+2025-05-07 07:42:16.818068:  
+2025-05-07 07:42:16.869237: Epoch 1976 
+2025-05-07 07:42:16.905621: Current learning rate: 0.00019 
+2025-05-07 07:43:49.264420: train_loss -0.5168 
+2025-05-07 07:43:49.332304: val_loss -0.5146 
+2025-05-07 07:43:49.345392: Pseudo dice [np.float32(0.8774), np.float32(0.8848), np.float32(0.949), np.float32(0.981), np.float32(0.9206), np.float32(0.9657), np.float32(0.9723), np.float32(0.9727), np.float32(0.9579), np.float32(0.9722), np.float32(0.9519), np.float32(0.9692), np.float32(0.9686), np.float32(0.9237), np.float32(0.9707), np.float32(0.9612), np.float32(0.917), np.float32(0.8974), np.float32(0.9177)] 
+2025-05-07 07:43:49.370861: Epoch time: 92.45 s 
+2025-05-07 07:43:51.049693:  
+2025-05-07 07:43:51.143202: Epoch 1977 
+2025-05-07 07:43:51.154458: Current learning rate: 0.00018 
+2025-05-07 07:45:22.153631: train_loss -0.5095 
+2025-05-07 07:45:22.219207: val_loss -0.4931 
+2025-05-07 07:45:22.227058: Pseudo dice [np.float32(0.8784), np.float32(0.824), np.float32(0.9529), np.float32(0.9731), np.float32(0.9288), np.float32(0.9636), np.float32(0.9744), np.float32(0.9796), np.float32(0.9707), np.float32(0.9623), np.float32(0.9415), np.float32(0.9691), np.float32(0.9739), np.float32(0.9193), np.float32(0.9743), np.float32(0.9652), np.float32(0.915), np.float32(0.9026), np.float32(0.9354)] 
+2025-05-07 07:45:22.227799: Epoch time: 91.11 s 
+2025-05-07 07:45:23.791691:  
+2025-05-07 07:45:23.872234: Epoch 1978 
+2025-05-07 07:45:23.900559: Current learning rate: 0.00017 
+2025-05-07 07:46:57.220593: train_loss -0.5327 
+2025-05-07 07:46:57.277312: val_loss -0.5051 
+2025-05-07 07:46:57.322197: Pseudo dice [np.float32(0.8579), np.float32(0.8658), np.float32(0.921), np.float32(0.9767), np.float32(0.9377), np.float32(0.9613), np.float32(0.9696), np.float32(0.9819), np.float32(0.9689), np.float32(0.9617), np.float32(0.952), np.float32(0.9654), np.float32(0.9689), np.float32(0.9215), np.float32(0.97), np.float32(0.9653), np.float32(0.8798), np.float32(0.8761), np.float32(0.912)] 
+2025-05-07 07:46:57.362974: Epoch time: 93.43 s 
+2025-05-07 07:46:59.049546:  
+2025-05-07 07:46:59.096356: Epoch 1979 
+2025-05-07 07:46:59.115471: Current learning rate: 0.00017 
+2025-05-07 07:48:31.595728: train_loss -0.5242 
+2025-05-07 07:48:31.707237: val_loss -0.4715 
+2025-05-07 07:48:31.732980: Pseudo dice [np.float32(0.8649), np.float32(0.8786), np.float32(0.8643), np.float32(0.9751), np.float32(0.9282), np.float32(0.9681), np.float32(0.968), np.float32(0.9834), np.float32(0.9709), np.float32(0.9728), np.float32(0.9625), np.float32(0.9691), np.float32(0.9746), np.float32(0.9265), np.float32(0.9667), np.float32(0.9683), np.float32(0.9022), np.float32(0.9035), np.float32(0.9126)] 
+2025-05-07 07:48:31.764543: Epoch time: 92.55 s 
+2025-05-07 07:48:36.776162:  
+2025-05-07 07:48:36.781656: Epoch 1980 
+2025-05-07 07:48:36.782002: Current learning rate: 0.00016 
+2025-05-07 07:50:09.593793: train_loss -0.5259 
+2025-05-07 07:50:09.621781: val_loss -0.5071 
+2025-05-07 07:50:09.626487: Pseudo dice [np.float32(0.8648), np.float32(0.8766), np.float32(0.9429), np.float32(0.9756), np.float32(0.9328), np.float32(0.9632), np.float32(0.9641), np.float32(0.9839), np.float32(0.9697), np.float32(0.9622), np.float32(0.9582), np.float32(0.9759), np.float32(0.9749), np.float32(0.9147), np.float32(0.9696), np.float32(0.9662), np.float32(0.9101), np.float32(0.9072), np.float32(0.9266)] 
+2025-05-07 07:50:09.651778: Epoch time: 92.82 s 
+2025-05-07 07:50:11.297830:  
+2025-05-07 07:50:11.378368: Epoch 1981 
+2025-05-07 07:50:11.405390: Current learning rate: 0.00015 
+2025-05-07 07:51:42.980756: train_loss -0.5147 
+2025-05-07 07:51:43.025990: val_loss -0.5047 
+2025-05-07 07:51:43.026632: Pseudo dice [np.float32(0.8676), np.float32(0.8859), np.float32(0.9468), np.float32(0.9733), np.float32(0.9371), np.float32(0.9647), np.float32(0.9651), np.float32(0.9813), np.float32(0.972), np.float32(0.9645), np.float32(0.9494), np.float32(0.973), np.float32(0.9764), np.float32(0.9198), np.float32(0.9716), np.float32(0.9585), np.float32(0.8952), np.float32(0.9099), np.float32(0.9313)] 
+2025-05-07 07:51:43.027126: Epoch time: 91.68 s 
+2025-05-07 07:51:44.780349:  
+2025-05-07 07:51:44.822489: Epoch 1982 
+2025-05-07 07:51:44.823105: Current learning rate: 0.00014 
+2025-05-07 07:53:17.011467: train_loss -0.5404 
+2025-05-07 07:53:17.120784: val_loss -0.5098 
+2025-05-07 07:53:17.143011: Pseudo dice [np.float32(0.8436), np.float32(0.8588), np.float32(0.918), np.float32(0.9806), np.float32(0.8843), np.float32(0.9598), np.float32(0.9594), np.float32(0.9745), np.float32(0.962), np.float32(0.9741), np.float32(0.9612), np.float32(0.9724), np.float32(0.9734), np.float32(0.9109), np.float32(0.9716), np.float32(0.9621), np.float32(0.9083), np.float32(0.9092), np.float32(0.9262)] 
+2025-05-07 07:53:17.188804: Epoch time: 92.23 s 
+2025-05-07 07:53:18.883022:  
+2025-05-07 07:53:18.987727: Epoch 1983 
+2025-05-07 07:53:19.006494: Current learning rate: 0.00014 
+2025-05-07 07:54:50.982770: train_loss -0.5112 
+2025-05-07 07:54:51.095766: val_loss -0.5404 
+2025-05-07 07:54:51.147038: Pseudo dice [np.float32(0.8565), np.float32(0.8675), np.float32(0.878), np.float32(0.9566), np.float32(0.9229), np.float32(0.9657), np.float32(0.9698), np.float32(0.9838), np.float32(0.9659), np.float32(0.9746), np.float32(0.9625), np.float32(0.9722), np.float32(0.9795), np.float32(0.929), np.float32(0.9707), np.float32(0.9699), np.float32(0.9035), np.float32(0.9036), np.float32(0.9183)] 
+2025-05-07 07:54:51.184655: Epoch time: 92.1 s 
+2025-05-07 07:54:52.903417:  
+2025-05-07 07:54:52.965487: Epoch 1984 
+2025-05-07 07:54:53.025578: Current learning rate: 0.00013 
+2025-05-07 07:56:26.440430: train_loss -0.5427 
+2025-05-07 07:56:26.585450: val_loss -0.5178 
+2025-05-07 07:56:26.608345: Pseudo dice [np.float32(0.8702), np.float32(0.8858), np.float32(0.9478), np.float32(0.9704), np.float32(0.9351), np.float32(0.9654), np.float32(0.9735), np.float32(0.9805), np.float32(0.9498), np.float32(0.9697), np.float32(0.9653), np.float32(0.9739), np.float32(0.9771), np.float32(0.9281), np.float32(0.9719), np.float32(0.9639), np.float32(0.8896), np.float32(0.9211), np.float32(0.9273)] 
+2025-05-07 07:56:26.637908: Epoch time: 93.54 s 
+2025-05-07 07:56:28.360750:  
+2025-05-07 07:56:28.401966: Epoch 1985 
+2025-05-07 07:56:28.424436: Current learning rate: 0.00012 
+2025-05-07 07:57:57.672083: train_loss -0.5172 
+2025-05-07 07:57:57.757018: val_loss -0.5523 
+2025-05-07 07:57:57.773532: Pseudo dice [np.float32(0.8688), np.float32(0.8701), np.float32(0.9139), np.float32(0.9741), np.float32(0.922), np.float32(0.965), np.float32(0.9681), np.float32(0.9743), np.float32(0.9617), np.float32(0.9636), np.float32(0.9563), np.float32(0.9765), np.float32(0.9729), np.float32(0.9196), np.float32(0.9575), np.float32(0.9613), np.float32(0.9061), np.float32(0.8937), np.float32(0.9294)] 
+2025-05-07 07:57:57.777711: Epoch time: 89.31 s 
+2025-05-07 07:57:59.368330:  
+2025-05-07 07:57:59.529494: Epoch 1986 
+2025-05-07 07:57:59.575171: Current learning rate: 0.00011 
+2025-05-07 07:59:30.497143: train_loss -0.5128 
+2025-05-07 07:59:30.574989: val_loss -0.5251 
+2025-05-07 07:59:30.587326: Pseudo dice [np.float32(0.8787), np.float32(0.8743), np.float32(0.9543), np.float32(0.9763), np.float32(0.9143), np.float32(0.9651), np.float32(0.9655), np.float32(0.9818), np.float32(0.972), np.float32(0.9751), np.float32(0.9662), np.float32(0.9743), np.float32(0.9775), np.float32(0.9277), np.float32(0.973), np.float32(0.965), np.float32(0.8579), np.float32(0.8766), np.float32(0.9203)] 
+2025-05-07 07:59:30.601935: Epoch time: 91.13 s 
+2025-05-07 07:59:32.353271:  
+2025-05-07 07:59:32.437356: Epoch 1987 
+2025-05-07 07:59:32.468106: Current learning rate: 0.00011 
+2025-05-07 08:01:11.982667: train_loss -0.529 
+2025-05-07 08:01:12.057918: val_loss -0.5111 
+2025-05-07 08:01:12.071056: Pseudo dice [np.float32(0.8721), np.float32(0.8628), np.float32(0.9216), np.float32(0.9781), np.float32(0.9314), np.float32(0.9666), np.float32(0.9684), np.float32(0.9822), np.float32(0.9726), np.float32(0.9737), np.float32(0.9591), np.float32(0.9764), np.float32(0.9731), np.float32(0.9187), np.float32(0.969), np.float32(0.9623), np.float32(0.9191), np.float32(0.9135), np.float32(0.9205)] 
+2025-05-07 08:01:12.083672: Epoch time: 99.63 s 
+2025-05-07 08:01:13.758121:  
+2025-05-07 08:01:13.878433: Epoch 1988 
+2025-05-07 08:01:13.894166: Current learning rate: 0.0001 
+2025-05-07 08:02:45.869222: train_loss -0.5135 
+2025-05-07 08:02:45.950036: val_loss -0.5399 
+2025-05-07 08:02:45.951704: Pseudo dice [np.float32(0.8414), np.float32(0.8831), np.float32(0.923), np.float32(0.9766), np.float32(0.9315), np.float32(0.9583), np.float32(0.9691), np.float32(0.982), np.float32(0.9718), np.float32(0.9703), np.float32(0.9634), np.float32(0.9699), np.float32(0.976), np.float32(0.9206), np.float32(0.9703), np.float32(0.9572), np.float32(0.9116), np.float32(0.9105), np.float32(0.9272)] 
+2025-05-07 08:02:45.952321: Epoch time: 92.11 s 
+2025-05-07 08:02:47.659905:  
+2025-05-07 08:02:47.735699: Epoch 1989 
+2025-05-07 08:02:47.748585: Current learning rate: 9e-05 
+2025-05-07 08:04:19.805389: train_loss -0.5048 
+2025-05-07 08:04:19.925947: val_loss -0.5016 
+2025-05-07 08:04:19.968771: Pseudo dice [np.float32(0.8738), np.float32(0.8545), np.float32(0.9512), np.float32(0.9802), np.float32(0.9316), np.float32(0.9636), np.float32(0.9496), np.float32(0.9748), np.float32(0.9569), np.float32(0.9719), np.float32(0.9503), np.float32(0.9691), np.float32(0.9736), np.float32(0.9219), np.float32(0.9429), np.float32(0.9642), np.float32(0.9023), np.float32(0.8922), np.float32(0.923)] 
+2025-05-07 08:04:19.990832: Epoch time: 92.15 s 
+2025-05-07 08:04:21.551270:  
+2025-05-07 08:04:21.710357: Epoch 1990 
+2025-05-07 08:04:21.766295: Current learning rate: 8e-05 
+2025-05-07 08:05:55.033361: train_loss -0.5267 
+2025-05-07 08:05:55.163927: val_loss -0.5079 
+2025-05-07 08:05:55.184298: Pseudo dice [np.float32(0.8552), np.float32(0.8697), np.float32(0.9374), np.float32(0.974), np.float32(0.9272), np.float32(0.9588), np.float32(0.976), np.float32(0.9726), np.float32(0.9716), np.float32(0.9708), np.float32(0.9614), np.float32(0.9662), np.float32(0.9666), np.float32(0.9116), np.float32(0.9502), np.float32(0.9629), np.float32(0.9111), np.float32(0.9241), np.float32(0.9267)] 
+2025-05-07 08:05:55.214027: Epoch time: 93.48 s 
+2025-05-07 08:05:56.818852:  
+2025-05-07 08:05:56.864738: Epoch 1991 
+2025-05-07 08:05:56.876113: Current learning rate: 8e-05 
+2025-05-07 08:07:31.137846: train_loss -0.506 
+2025-05-07 08:07:31.214016: val_loss -0.5157 
+2025-05-07 08:07:31.232438: Pseudo dice [np.float32(0.8544), np.float32(0.877), np.float32(0.9404), np.float32(0.9732), np.float32(0.9344), np.float32(0.9685), np.float32(0.9684), np.float32(0.9819), np.float32(0.9654), np.float32(0.966), np.float32(0.952), np.float32(0.9673), np.float32(0.9753), np.float32(0.9224), np.float32(0.9718), np.float32(0.967), np.float32(0.9145), np.float32(0.9248), np.float32(0.9168)] 
+2025-05-07 08:07:31.248611: Epoch time: 94.32 s 
+2025-05-07 08:07:33.304689:  
+2025-05-07 08:07:33.321551: Epoch 1992 
+2025-05-07 08:07:33.322070: Current learning rate: 7e-05 
+2025-05-07 08:09:05.501086: train_loss -0.5094 
+2025-05-07 08:09:05.653683: val_loss -0.5248 
+2025-05-07 08:09:05.710702: Pseudo dice [np.float32(0.8835), np.float32(0.8671), np.float32(0.9297), np.float32(0.9814), np.float32(0.9252), np.float32(0.97), np.float32(0.9672), np.float32(0.9832), np.float32(0.9674), np.float32(0.9571), np.float32(0.9567), np.float32(0.9653), np.float32(0.9726), np.float32(0.9309), np.float32(0.9727), np.float32(0.9596), np.float32(0.9206), np.float32(0.9153), np.float32(0.9198)] 
+2025-05-07 08:09:05.731872: Epoch time: 92.2 s 
+2025-05-07 08:09:07.310147:  
+2025-05-07 08:09:07.364535: Epoch 1993 
+2025-05-07 08:09:07.375782: Current learning rate: 6e-05 
+2025-05-07 08:10:39.596604: train_loss -0.5196 
+2025-05-07 08:10:39.697134: val_loss -0.5128 
+2025-05-07 08:10:39.708871: Pseudo dice [np.float32(0.8826), np.float32(0.8634), np.float32(0.9347), np.float32(0.9654), np.float32(0.932), np.float32(0.9692), np.float32(0.9684), np.float32(0.9795), np.float32(0.9644), np.float32(0.9686), np.float32(0.9592), np.float32(0.9713), np.float32(0.975), np.float32(0.9282), np.float32(0.9631), np.float32(0.9619), np.float32(0.8815), np.float32(0.8912), np.float32(0.9242)] 
+2025-05-07 08:10:39.709412: Epoch time: 92.29 s 
+2025-05-07 08:10:41.307962:  
+2025-05-07 08:10:41.460797: Epoch 1994 
+2025-05-07 08:10:41.501320: Current learning rate: 5e-05 
+2025-05-07 08:12:11.696445: train_loss -0.5213 
+2025-05-07 08:12:11.790856: val_loss -0.4945 
+2025-05-07 08:12:11.798517: Pseudo dice [np.float32(0.8674), np.float32(0.8165), np.float32(0.9505), np.float32(0.96), np.float32(0.9367), np.float32(0.9583), np.float32(0.9573), np.float32(0.9806), np.float32(0.9719), np.float32(0.9685), np.float32(0.9567), np.float32(0.9766), np.float32(0.972), np.float32(0.9209), np.float32(0.9708), np.float32(0.966), np.float32(0.8925), np.float32(0.9105), np.float32(0.9087)] 
+2025-05-07 08:12:11.803918: Epoch time: 90.39 s 
+2025-05-07 08:12:13.335355:  
+2025-05-07 08:12:13.437907: Epoch 1995 
+2025-05-07 08:12:13.474546: Current learning rate: 5e-05 
+2025-05-07 08:13:46.037639: train_loss -0.5202 
+2025-05-07 08:13:46.077177: val_loss -0.5338 
+2025-05-07 08:13:46.115911: Pseudo dice [np.float32(0.8738), np.float32(0.8812), np.float32(0.8981), np.float32(0.9753), np.float32(0.928), np.float32(0.9684), np.float32(0.9711), np.float32(0.9831), np.float32(0.9714), np.float32(0.9757), np.float32(0.9626), np.float32(0.9754), np.float32(0.9685), np.float32(0.9274), np.float32(0.9629), np.float32(0.9669), np.float32(0.9104), np.float32(0.9209), np.float32(0.9172)] 
+2025-05-07 08:13:46.141020: Epoch time: 92.7 s 
+2025-05-07 08:13:47.730231:  
+2025-05-07 08:13:47.765680: Epoch 1996 
+2025-05-07 08:13:47.782158: Current learning rate: 4e-05 
+2025-05-07 08:15:22.462638: train_loss -0.5385 
+2025-05-07 08:15:22.531017: val_loss -0.5411 
+2025-05-07 08:15:22.547214: Pseudo dice [np.float32(0.8852), np.float32(0.8622), np.float32(0.9177), np.float32(0.9769), np.float32(0.9263), np.float32(0.9651), np.float32(0.9701), np.float32(0.9825), np.float32(0.969), np.float32(0.9735), np.float32(0.9625), np.float32(0.966), np.float32(0.9745), np.float32(0.9266), np.float32(0.9688), np.float32(0.9671), np.float32(0.8983), np.float32(0.9206), np.float32(0.9149)] 
+2025-05-07 08:15:22.548205: Epoch time: 94.73 s 
+2025-05-07 08:15:27.834341:  
+2025-05-07 08:15:27.839159: Epoch 1997 
+2025-05-07 08:15:27.839548: Current learning rate: 3e-05 
+2025-05-07 08:17:01.155118: train_loss -0.521 
+2025-05-07 08:17:01.225545: val_loss -0.5107 
+2025-05-07 08:17:01.237366: Pseudo dice [np.float32(0.8208), np.float32(0.8886), np.float32(0.9359), np.float32(0.979), np.float32(0.9377), np.float32(0.9648), np.float32(0.9719), np.float32(0.9831), np.float32(0.9623), np.float32(0.9647), np.float32(0.9575), np.float32(0.9673), np.float32(0.972), np.float32(0.926), np.float32(0.9674), np.float32(0.9654), np.float32(0.8767), np.float32(0.8821), np.float32(0.9083)] 
+2025-05-07 08:17:01.258286: Epoch time: 93.32 s 
+2025-05-07 08:17:02.832358:  
+2025-05-07 08:17:02.947647: Epoch 1998 
+2025-05-07 08:17:02.973311: Current learning rate: 2e-05 
+2025-05-07 08:18:35.447773: train_loss -0.5075 
+2025-05-07 08:18:35.627841: val_loss -0.5237 
+2025-05-07 08:18:35.653740: Pseudo dice [np.float32(0.882), np.float32(0.882), np.float32(0.9555), np.float32(0.9798), np.float32(0.9104), np.float32(0.9685), np.float32(0.9732), np.float32(0.9818), np.float32(0.9729), np.float32(0.9682), np.float32(0.9534), np.float32(0.9722), np.float32(0.9754), np.float32(0.9299), np.float32(0.9726), np.float32(0.965), np.float32(0.8645), np.float32(0.8738), np.float32(0.9274)] 
+2025-05-07 08:18:35.678311: Epoch time: 92.62 s 
+2025-05-07 08:18:37.415513:  
+2025-05-07 08:18:37.533565: Epoch 1999 
+2025-05-07 08:18:37.556325: Current learning rate: 1e-05 
+2025-05-07 08:20:07.028395: train_loss -0.5234 
+2025-05-07 08:20:07.175659: val_loss -0.546 
+2025-05-07 08:20:07.211872: Pseudo dice [np.float32(0.8809), np.float32(0.8769), np.float32(0.9355), np.float32(0.9802), np.float32(0.933), np.float32(0.9556), np.float32(0.9695), np.float32(0.9806), np.float32(0.9661), np.float32(0.9599), np.float32(0.9598), np.float32(0.973), np.float32(0.9725), np.float32(0.9243), np.float32(0.9608), np.float32(0.9592), np.float32(0.9097), np.float32(0.9156), np.float32(0.9167)] 
+2025-05-07 08:20:07.263200: Epoch time: 89.61 s 
+2025-05-07 08:20:10.160927: Training done. 
+2025-05-07 08:20:11.038714: predicting 0001 
+2025-05-07 08:20:11.144175: 0001, shape torch.Size([1, 608, 295, 295]), rank 0 
+2025-05-07 08:20:25.814236: predicting 0002 
+2025-05-07 08:20:25.883141: 0002, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 08:20:29.551697: predicting 0003 
+2025-05-07 08:20:29.621670: 0003, shape torch.Size([1, 651, 296, 296]), rank 0 
+2025-05-07 08:20:35.231817: predicting 0004 
+2025-05-07 08:20:35.294118: 0004, shape torch.Size([1, 652, 293, 293]), rank 0 
+2025-05-07 08:20:41.248044: predicting 0005 
+2025-05-07 08:20:41.315873: 0005, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 08:20:47.592503: predicting 0006 
+2025-05-07 08:20:47.655619: 0006, shape torch.Size([1, 650, 264, 264]), rank 0 
+2025-05-07 08:20:52.470771: predicting 0007 
+2025-05-07 08:20:52.530014: 0007, shape torch.Size([1, 616, 301, 301]), rank 0 
+2025-05-07 08:20:58.677896: predicting 0008 
+2025-05-07 08:20:58.743121: 0008, shape torch.Size([1, 651, 317, 317]), rank 0 
+2025-05-07 08:21:04.098117: predicting 0009 
+2025-05-07 08:21:04.154974: 0009, shape torch.Size([1, 566, 287, 287]), rank 0 
+2025-05-07 08:21:08.095723: predicting 0010 
+2025-05-07 08:21:08.154099: 0010, shape torch.Size([1, 652, 300, 300]), rank 0 
+2025-05-07 08:21:14.134511: predicting 0011 
+2025-05-07 08:21:14.203484: 0011, shape torch.Size([1, 567, 291, 291]), rank 0 
+2025-05-07 08:21:20.646934: predicting 0012 
+2025-05-07 08:21:20.710977: 0012, shape torch.Size([1, 818, 307, 307]), rank 0 
+2025-05-07 08:21:29.442358: predicting 0013 
+2025-05-07 08:21:29.496032: 0013, shape torch.Size([1, 566, 277, 277]), rank 0 
+2025-05-07 08:21:34.618336: predicting 0014 
+2025-05-07 08:21:34.683376: 0014, shape torch.Size([1, 575, 313, 313]), rank 0 
+2025-05-07 08:21:41.322052: predicting 0015 
+2025-05-07 08:21:41.391091: 0015, shape torch.Size([1, 651, 289, 289]), rank 0 
+2025-05-07 08:21:47.915302: predicting 0016 
+2025-05-07 08:21:47.981205: 0016, shape torch.Size([1, 565, 281, 281]), rank 0 
+2025-05-07 08:21:51.385811: predicting 0017 
+2025-05-07 08:21:51.457958: 0017, shape torch.Size([1, 651, 302, 302]), rank 0 
+2025-05-07 08:21:58.530318: predicting 0018 
+2025-05-07 08:21:58.590975: 0018, shape torch.Size([1, 630, 319, 319]), rank 0 
+2025-05-07 08:22:03.429074: predicting 0019 
+2025-05-07 08:22:03.519495: 0019, shape torch.Size([1, 652, 298, 298]), rank 0 
+2025-05-07 08:22:09.505639: predicting 0020 
+2025-05-07 08:22:09.569352: 0020, shape torch.Size([1, 736, 325, 325]), rank 0 
+2025-05-07 08:22:17.856340: predicting 0021 
+2025-05-07 08:22:17.934672: 0021, shape torch.Size([1, 651, 319, 319]), rank 0 
+2025-05-07 08:22:22.772657: predicting 0022 
+2025-05-07 08:22:22.827832: 0022, shape torch.Size([1, 566, 251, 251]), rank 0 
+2025-05-07 08:22:26.615019: predicting 0023 
+2025-05-07 08:22:26.677463: 0023, shape torch.Size([1, 652, 263, 263]), rank 0 
+2025-05-07 08:22:31.350348: predicting 0024 
+2025-05-07 08:22:31.408027: 0024, shape torch.Size([1, 736, 297, 297]), rank 0 
+2025-05-07 08:22:36.883105: predicting 0025 
+2025-05-07 08:22:36.938146: 0025, shape torch.Size([1, 652, 267, 267]), rank 0 
+2025-05-07 08:22:40.492709: predicting 0026 
+2025-05-07 08:22:40.552628: 0026, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 08:22:46.048301: predicting 0027 
+2025-05-07 08:22:46.111836: 0027, shape torch.Size([1, 660, 333, 333]), rank 0 
+2025-05-07 08:22:52.533154: predicting 0028 
+2025-05-07 08:22:52.583980: 0028, shape torch.Size([1, 1153, 329, 329]), rank 0 
+2025-05-07 08:23:05.470766: predicting 0029 
+2025-05-07 08:23:05.541433: 0029, shape torch.Size([1, 595, 296, 296]), rank 0 
+2025-05-07 08:23:12.950135: predicting 0030 
+2025-05-07 08:23:13.020314: 0030, shape torch.Size([1, 651, 305, 305]), rank 0 
+2025-05-07 08:23:17.592299: predicting 0031 
+2025-05-07 08:23:17.659026: 0031, shape torch.Size([1, 650, 286, 286]), rank 0 
+2025-05-07 08:23:23.036670: predicting 0032 
+2025-05-07 08:23:23.108336: 0032, shape torch.Size([1, 1318, 333, 333]), rank 0 
+2025-05-07 08:23:35.616753: predicting 0033 
+2025-05-07 08:23:35.753576: 0033, shape torch.Size([1, 651, 284, 284]), rank 0 
+2025-05-07 08:23:39.249094: predicting 0034 
+2025-05-07 08:23:39.305180: 0034, shape torch.Size([1, 650, 287, 287]), rank 0 
+2025-05-07 08:23:44.160651: predicting 0035 
+2025-05-07 08:23:44.217946: 0035, shape torch.Size([1, 735, 319, 319]), rank 0 
+2025-05-07 08:23:51.924031: predicting 0036 
+2025-05-07 08:23:51.976500: 0036, shape torch.Size([1, 650, 314, 314]), rank 0 
+2025-05-07 08:23:59.030292: predicting 0037 
+2025-05-07 08:23:59.102775: 0037, shape torch.Size([1, 1070, 303, 303]), rank 0 
+2025-05-07 08:24:10.218679: predicting 0038 
+2025-05-07 08:24:10.299630: 0038, shape torch.Size([1, 568, 287, 287]), rank 0 
+2025-05-07 08:24:14.525431: predicting 0039 
+2025-05-07 08:24:14.580649: 0039, shape torch.Size([1, 566, 282, 282]), rank 0 
+2025-05-07 08:24:19.270642: predicting 0040 
+2025-05-07 08:24:19.347052: 0040, shape torch.Size([1, 400, 295, 295]), rank 0 
+2025-05-07 08:24:24.149426: predicting 0041 
+2025-05-07 08:24:24.226342: 0041, shape torch.Size([1, 653, 298, 298]), rank 0 
+2025-05-07 08:24:29.058826: predicting 0042 
+2025-05-07 08:24:29.146864: 0042, shape torch.Size([1, 618, 297, 297]), rank 0 
+2025-05-07 08:24:34.874746: predicting 0043 
+2025-05-07 08:24:34.939958: 0043, shape torch.Size([1, 638, 283, 283]), rank 0 
+2025-05-07 08:24:38.636718: predicting 0044 
+2025-05-07 08:24:38.703670: 0044, shape torch.Size([1, 652, 325, 325]), rank 0 
+2025-05-07 08:24:46.502771: predicting 0045 
+2025-05-07 08:24:46.578628: 0045, shape torch.Size([1, 548, 295, 295]), rank 0 
+2025-05-07 08:24:50.385723: predicting 0046 
+2025-05-07 08:24:50.461651: 0046, shape torch.Size([1, 635, 251, 251]), rank 0 
+2025-05-07 08:24:55.384075: predicting 0047 
+2025-05-07 08:24:55.454504: 0047, shape torch.Size([1, 1175, 333, 333]), rank 0 
+2025-05-07 08:25:07.314062: predicting 0048 
+2025-05-07 08:25:07.412877: 0048, shape torch.Size([1, 568, 251, 251]), rank 0 
+2025-05-07 08:25:10.411280: predicting 0049 
+2025-05-07 08:25:10.477014: 0049, shape torch.Size([1, 648, 271, 271]), rank 0 
+2025-05-07 08:25:15.283282: predicting 0050 
+2025-05-07 08:25:15.352395: 0050, shape torch.Size([1, 566, 292, 292]), rank 0 
+2025-05-07 08:25:22.003751: predicting 0051 
+2025-05-07 08:25:22.077804: 0051, shape torch.Size([1, 735, 333, 333]), rank 0 
+2025-05-07 08:25:29.756900: predicting 0052 
+2025-05-07 08:25:29.837915: 0052, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 08:25:35.800096: predicting 0053 
+2025-05-07 08:25:35.863464: 0053, shape torch.Size([1, 566, 258, 258]), rank 0 
+2025-05-07 08:25:39.191504: predicting 0054 
+2025-05-07 08:25:39.251995: 0054, shape torch.Size([1, 566, 280, 280]), rank 0 
+2025-05-07 08:25:42.614141: predicting 0055 
+2025-05-07 08:25:42.680280: 0055, shape torch.Size([1, 1238, 318, 318]), rank 0 
+2025-05-07 08:25:54.775785: predicting 0056 
+2025-05-07 08:25:54.860582: 0056, shape torch.Size([1, 583, 274, 274]), rank 0 
+2025-05-07 08:26:00.700222: predicting 0057 
+2025-05-07 08:26:00.796767: 0057, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 08:26:05.311711: predicting 0058 
+2025-05-07 08:26:05.375537: 0058, shape torch.Size([1, 618, 305, 305]), rank 0 
+2025-05-07 08:26:11.047450: predicting 0059 
+2025-05-07 08:26:11.104371: 0059, shape torch.Size([1, 735, 289, 289]), rank 0 
+2025-05-07 08:26:16.828944: predicting 0060 
+2025-05-07 08:26:16.937322: 0060, shape torch.Size([1, 566, 283, 283]), rank 0 
+2025-05-07 08:26:20.667590: predicting 0061 
+2025-05-07 08:26:20.734198: 0061, shape torch.Size([1, 568, 271, 271]), rank 0 
+2025-05-07 08:26:24.623933: predicting 0062 
+2025-05-07 08:26:24.732444: 0062, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 08:26:31.917745: predicting 0063 
+2025-05-07 08:26:31.980913: 0063, shape torch.Size([1, 652, 282, 282]), rank 0 
+2025-05-07 08:26:36.939821: predicting 0064 
+2025-05-07 08:26:37.002745: 0064, shape torch.Size([1, 616, 302, 302]), rank 0 
+2025-05-07 08:26:44.497748: predicting 0065 
+2025-05-07 08:26:44.582047: 0065, shape torch.Size([1, 605, 315, 315]), rank 0 
+2025-05-07 08:26:49.734707: predicting 0066 
+2025-05-07 08:26:49.794628: 0066, shape torch.Size([1, 986, 317, 317]), rank 0 
+2025-05-07 08:27:00.059935: predicting 0067 
+2025-05-07 08:27:00.125164: 0067, shape torch.Size([1, 652, 291, 291]), rank 0 
+2025-05-07 08:27:06.232757: predicting 0068 
+2025-05-07 08:27:06.293075: 0068, shape torch.Size([1, 1183, 319, 319]), rank 0 
+2025-05-07 08:27:17.943866: predicting 0069 
+2025-05-07 08:27:18.017853: 0069, shape torch.Size([1, 566, 279, 279]), rank 0 
+2025-05-07 08:27:23.826439: predicting 0070 
+2025-05-07 08:27:23.884513: 0070, shape torch.Size([1, 566, 301, 301]), rank 0 
+2025-05-07 08:27:29.638533: predicting 0071 
+2025-05-07 08:27:29.698104: 0071, shape torch.Size([1, 651, 294, 294]), rank 0 
+2025-05-07 08:27:36.114907: predicting 0072 
+2025-05-07 08:27:36.169259: 0072, shape torch.Size([1, 652, 267, 267]), rank 0 
+2025-05-07 08:27:40.740047: predicting 0073 
+2025-05-07 08:27:40.807974: 0073, shape torch.Size([1, 651, 307, 307]), rank 0 
+2025-05-07 08:27:47.033599: predicting 0074 
+2025-05-07 08:27:47.095065: 0074, shape torch.Size([1, 566, 299, 299]), rank 0 
+2025-05-07 08:27:53.675016: predicting 0075 
+2025-05-07 08:27:53.740796: 0075, shape torch.Size([1, 568, 247, 247]), rank 0 
+2025-05-07 08:27:57.082870: predicting 0076 
+2025-05-07 08:27:57.147431: 0076, shape torch.Size([1, 651, 290, 290]), rank 0 
+2025-05-07 08:28:02.142349: predicting 0077 
+2025-05-07 08:28:02.220989: 0077, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 08:28:07.302929: predicting 0078 
+2025-05-07 08:28:07.372452: 0078, shape torch.Size([1, 570, 283, 283]), rank 0 
+2025-05-07 08:28:11.251943: predicting 0079 
+2025-05-07 08:28:11.412868: 0079, shape torch.Size([1, 567, 304, 304]), rank 0 
+2025-05-07 08:28:16.874882: predicting 0080 
+2025-05-07 08:28:16.931434: 0080, shape torch.Size([1, 451, 263, 263]), rank 0 
+2025-05-07 08:28:21.057275: predicting 0081 
+2025-05-07 08:28:21.113989: 0081, shape torch.Size([1, 568, 240, 240]), rank 0 
+2025-05-07 08:28:23.529554: predicting 0082 
+2025-05-07 08:28:23.607480: 0082, shape torch.Size([1, 1073, 279, 279]), rank 0 
+2025-05-07 08:28:31.760118: predicting 0083 
+2025-05-07 08:28:31.920033: 0083, shape torch.Size([1, 566, 274, 274]), rank 0 
+2025-05-07 08:28:36.012008: predicting 0084 
+2025-05-07 08:28:36.099650: 0084, shape torch.Size([1, 566, 259, 259]), rank 0 
+2025-05-07 08:28:40.523314: predicting 0085 
+2025-05-07 08:28:40.590676: 0085, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 08:28:45.422876: predicting 0086 
+2025-05-07 08:28:45.474196: 0086, shape torch.Size([1, 561, 319, 319]), rank 0 
+2025-05-07 08:28:51.182124: predicting 0087 
+2025-05-07 08:28:51.261725: 0087, shape torch.Size([1, 568, 251, 251]), rank 0 
+2025-05-07 08:28:56.488636: predicting 0088 
+2025-05-07 08:28:56.547977: 0088, shape torch.Size([1, 652, 261, 261]), rank 0 
+2025-05-07 08:29:00.225434: predicting 0089 
+2025-05-07 08:29:00.283277: 0089, shape torch.Size([1, 652, 322, 322]), rank 0 
+2025-05-07 08:29:05.109678: predicting 0090 
+2025-05-07 08:29:05.165111: 0090, shape torch.Size([1, 1239, 273, 273]), rank 0 
+2025-05-07 08:29:15.822638: predicting 0091 
+2025-05-07 08:29:15.878899: 0091, shape torch.Size([1, 1070, 287, 287]), rank 0 
+2025-05-07 08:29:23.263284: predicting 0092 
+2025-05-07 08:29:23.346111: 0092, shape torch.Size([1, 601, 291, 291]), rank 0 
+2025-05-07 08:29:29.308516: predicting 0093 
+2025-05-07 08:29:29.370671: 0093, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 08:29:35.417041: predicting 0094 
+2025-05-07 08:29:35.471762: 0094, shape torch.Size([1, 568, 249, 249]), rank 0 
+2025-05-07 08:29:40.400357: predicting 0095 
+2025-05-07 08:29:40.458736: 0095, shape torch.Size([1, 1238, 322, 322]), rank 0 
+2025-05-07 08:29:53.210207: predicting 0096 
+2025-05-07 08:29:53.288373: 0096, shape torch.Size([1, 651, 286, 286]), rank 0 
+2025-05-07 08:29:59.212895: predicting 0097 
+2025-05-07 08:29:59.267927: 0097, shape torch.Size([1, 483, 255, 255]), rank 0 
+2025-05-07 08:30:01.963447: predicting 0098 
+2025-05-07 08:30:02.016727: 0098, shape torch.Size([1, 1118, 306, 306]), rank 0 
+2025-05-07 08:30:12.722643: predicting 0099 
+2025-05-07 08:30:12.795074: 0099, shape torch.Size([1, 652, 293, 293]), rank 0 
+2025-05-07 08:30:19.055112: predicting 0100 
+2025-05-07 08:30:19.119957: 0100, shape torch.Size([1, 530, 275, 275]), rank 0 
+2025-05-07 08:30:23.627383: predicting 0101 
+2025-05-07 08:30:23.677427: 0101, shape torch.Size([1, 651, 269, 269]), rank 0 
+2025-05-07 08:30:27.350598: predicting 0102 
+2025-05-07 08:30:27.396298: 0102, shape torch.Size([1, 566, 271, 271]), rank 0 
+2025-05-07 08:30:31.923374: predicting 0103 
+2025-05-07 08:30:31.994831: 0103, shape torch.Size([1, 651, 253, 253]), rank 0 
+2025-05-07 08:30:37.571173: predicting 0104 
+2025-05-07 08:30:37.638083: 0104, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 08:30:45.154978: predicting 0105 
+2025-05-07 08:30:45.215126: 0105, shape torch.Size([1, 595, 281, 281]), rank 0 
+2025-05-07 08:30:49.776553: predicting 0106 
+2025-05-07 08:30:49.828120: 0106, shape torch.Size([1, 735, 312, 312]), rank 0 
+2025-05-07 08:30:57.579264: predicting 0107 
+2025-05-07 08:30:57.645793: 0107, shape torch.Size([1, 630, 274, 274]), rank 0 
+2025-05-07 08:31:03.116826: predicting 0108 
+2025-05-07 08:31:03.188903: 0108, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 08:31:09.346096: predicting 0109 
+2025-05-07 08:31:09.408384: 0109, shape torch.Size([1, 651, 279, 279]), rank 0 
+2025-05-07 08:31:13.296896: predicting 0110 
+2025-05-07 08:31:13.371515: 0110, shape torch.Size([1, 566, 276, 276]), rank 0 
+2025-05-07 08:31:18.510496: predicting 0111 
+2025-05-07 08:31:18.582088: 0111, shape torch.Size([1, 568, 263, 263]), rank 0 
+2025-05-07 08:31:22.695199: predicting 0112 
+2025-05-07 08:31:22.748187: 0112, shape torch.Size([1, 566, 284, 284]), rank 0 
+2025-05-07 08:31:26.884157: predicting 0113 
+2025-05-07 08:31:26.951679: 0113, shape torch.Size([1, 651, 307, 307]), rank 0 
+2025-05-07 08:31:34.722503: predicting 0114 
+2025-05-07 08:31:34.802235: 0114, shape torch.Size([1, 651, 290, 290]), rank 0 
+2025-05-07 08:31:40.851216: predicting 0115 
+2025-05-07 08:31:40.913600: 0115, shape torch.Size([1, 568, 249, 249]), rank 0 
+2025-05-07 08:31:44.195875: predicting 0116 
+2025-05-07 08:31:44.267471: 0116, shape torch.Size([1, 591, 279, 279]), rank 0 
+2025-05-07 08:31:49.822651: predicting 0117 
+2025-05-07 08:31:49.892112: 0117, shape torch.Size([1, 652, 283, 283]), rank 0 
+2025-05-07 08:31:53.780630: predicting 0118 
+2025-05-07 08:31:53.846641: 0118, shape torch.Size([1, 583, 327, 327]), rank 0 
+2025-05-07 08:31:58.862936: predicting 0119 
+2025-05-07 08:31:58.924005: 0119, shape torch.Size([1, 1153, 315, 315]), rank 0 
+2025-05-07 08:32:11.160857: predicting 0120 
+2025-05-07 08:32:11.231710: 0120, shape torch.Size([1, 595, 282, 282]), rank 0 
+2025-05-07 08:32:15.188504: predicting 0121 
+2025-05-07 08:32:15.258385: 0121, shape torch.Size([1, 568, 271, 271]), rank 0 
+2025-05-07 08:32:20.013049: predicting 0122 
+2025-05-07 08:32:20.073839: 0122, shape torch.Size([1, 651, 292, 292]), rank 0 
+2025-05-07 08:32:24.634712: predicting 0123 
+2025-05-07 08:32:24.704408: 0123, shape torch.Size([1, 568, 269, 269]), rank 0 
+2025-05-07 08:32:29.353070: predicting 0124 
+2025-05-07 08:32:29.418281: 0124, shape torch.Size([1, 566, 295, 295]), rank 0 
+2025-05-07 08:32:34.740092: predicting 0125 
+2025-05-07 08:32:34.792951: 0125, shape torch.Size([1, 580, 312, 312]), rank 0 
+2025-05-07 08:32:41.313814: predicting 0126 
+2025-05-07 08:32:41.401300: 0126, shape torch.Size([1, 548, 295, 295]), rank 0 
+2025-05-07 08:32:45.921234: predicting 0127 
+2025-05-07 08:32:45.995716: 0127, shape torch.Size([1, 651, 317, 317]), rank 0 
+2025-05-07 08:32:51.293701: predicting 0128 
+2025-05-07 08:32:51.364390: 0128, shape torch.Size([1, 568, 263, 263]), rank 0 
+2025-05-07 08:32:55.501337: predicting 0129 
+2025-05-07 08:32:55.577420: 0129, shape torch.Size([1, 651, 275, 275]), rank 0 
+2025-05-07 08:33:00.841994: predicting 0130 
+2025-05-07 08:33:00.899347: 0130, shape torch.Size([1, 566, 284, 284]), rank 0 
+2025-05-07 08:33:06.292313: predicting 0131 
+2025-05-07 08:33:06.350239: 0131, shape torch.Size([1, 566, 314, 314]), rank 0 
+2025-05-07 08:33:12.844005: predicting 0132 
+2025-05-07 08:33:12.899643: 0132, shape torch.Size([1, 566, 321, 321]), rank 0 
+2025-05-07 08:33:19.192539: predicting 0133 
+2025-05-07 08:33:19.245097: 0133, shape torch.Size([1, 651, 314, 314]), rank 0 
+2025-05-07 08:33:26.659856: predicting 0134 
+2025-05-07 08:33:26.736144: 0134, shape torch.Size([1, 566, 294, 294]), rank 0 
+2025-05-07 08:33:32.986942: predicting 0135 
+2025-05-07 08:33:33.042235: 0135, shape torch.Size([1, 508, 303, 303]), rank 0 
+2025-05-07 08:33:38.099849: predicting 0136 
+2025-05-07 08:33:38.157624: 0136, shape torch.Size([1, 568, 267, 267]), rank 0 
+2025-05-07 08:33:41.777995: predicting 0137 
+2025-05-07 08:33:41.828212: 0137, shape torch.Size([1, 483, 277, 277]), rank 0 
+2025-05-07 08:33:46.231271: predicting 0138 
+2025-05-07 08:33:46.303678: 0138, shape torch.Size([1, 648, 308, 308]), rank 0 
+2025-05-07 08:33:50.677080: predicting 0139 
+2025-05-07 08:33:50.725153: 0139, shape torch.Size([1, 645, 333, 333]), rank 0 
+2025-05-07 08:33:57.036111: predicting 0140 
+2025-05-07 08:33:57.114978: 0140, shape torch.Size([1, 566, 283, 283]), rank 0 
+2025-05-07 08:34:01.503848: predicting 0141 
+2025-05-07 08:34:01.552198: 0141, shape torch.Size([1, 1196, 300, 300]), rank 0 
+2025-05-07 08:34:12.267302: predicting 0142 
+2025-05-07 08:34:12.341177: 0142, shape torch.Size([1, 651, 279, 279]), rank 0 
+2025-05-07 08:34:18.002742: predicting 0143 
+2025-05-07 08:34:18.058062: 0143, shape torch.Size([1, 483, 306, 306]), rank 0 
+2025-05-07 08:34:23.693418: predicting 0144 
+2025-05-07 08:34:23.744333: 0144, shape torch.Size([1, 675, 316, 316]), rank 0 
+2025-05-07 08:34:29.555061: predicting 0145 
+2025-05-07 08:34:29.613455: 0145, shape torch.Size([1, 686, 313, 313]), rank 0 
+2025-05-07 08:34:38.563931: predicting 0146 
+2025-05-07 08:34:38.618509: 0146, shape torch.Size([1, 566, 333, 333]), rank 0 
+2025-05-07 08:34:45.362777: predicting 0147 
+2025-05-07 08:34:45.411248: 0147, shape torch.Size([1, 651, 306, 306]), rank 0 
+2025-05-07 08:34:50.675930: predicting 0148 
+2025-05-07 08:34:50.843117: 0148, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 08:34:57.419268: predicting 0149 
+2025-05-07 08:34:57.477801: 0149, shape torch.Size([1, 566, 326, 326]), rank 0 
+2025-05-07 08:35:02.622557: predicting 0150 
+2025-05-07 08:35:02.676354: 0150, shape torch.Size([1, 651, 317, 317]), rank 0 
+2025-05-07 08:35:10.158097: predicting 0151 
+2025-05-07 08:35:10.208428: 0151, shape torch.Size([1, 1178, 312, 312]), rank 0 
+2025-05-07 08:35:21.401994: predicting 0152 
+2025-05-07 08:35:21.558789: 0152, shape torch.Size([1, 1238, 297, 297]), rank 0 
+2025-05-07 08:35:34.051133: predicting 0153 
+2025-05-07 08:35:34.111770: 0153, shape torch.Size([1, 1238, 309, 309]), rank 0 
+2025-05-07 08:35:45.822764: predicting 0154 
+2025-05-07 08:35:45.892298: 0154, shape torch.Size([1, 652, 279, 279]), rank 0 
+2025-05-07 08:35:51.155641: predicting 0155 
+2025-05-07 08:35:51.213406: 0155, shape torch.Size([1, 578, 280, 280]), rank 0 
+2025-05-07 08:35:55.945555: predicting 0156 
+2025-05-07 08:35:55.994193: 0156, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 08:35:59.895663: predicting 0157 
+2025-05-07 08:35:59.949947: 0157, shape torch.Size([1, 568, 269, 269]), rank 0 
+2025-05-07 08:36:05.473505: predicting 0158 
+2025-05-07 08:36:05.525524: 0158, shape torch.Size([1, 613, 285, 285]), rank 0 
+2025-05-07 08:36:09.305234: predicting 0159 
+2025-05-07 08:36:09.372493: 0159, shape torch.Size([1, 543, 251, 251]), rank 0 
+2025-05-07 08:36:13.635547: predicting 0160 
+2025-05-07 08:36:13.728439: 0160, shape torch.Size([1, 566, 272, 272]), rank 0 
+2025-05-07 08:36:17.116934: predicting 0161 
+2025-05-07 08:36:17.163031: 0161, shape torch.Size([1, 651, 280, 280]), rank 0 
+2025-05-07 08:36:21.846504: predicting 0162 
+2025-05-07 08:36:21.899397: 0162, shape torch.Size([1, 1153, 291, 291]), rank 0 
+2025-05-07 08:36:33.926533: predicting 0163 
+2025-05-07 08:36:34.001194: 0163, shape torch.Size([1, 1238, 295, 295]), rank 0 
+2025-05-07 08:36:47.216800: predicting 0164 
+2025-05-07 08:36:47.286430: 0164, shape torch.Size([1, 566, 295, 295]), rank 0 
+2025-05-07 08:36:53.302520: predicting 0165 
+2025-05-07 08:36:53.370683: 0165, shape torch.Size([1, 1238, 300, 300]), rank 0 
+2025-05-07 08:37:08.549396: predicting 0166 
+2025-05-07 08:37:08.618843: 0166, shape torch.Size([1, 651, 275, 275]), rank 0 
+2025-05-07 08:37:12.119044: predicting 0167 
+2025-05-07 08:37:12.301409: 0167, shape torch.Size([1, 1116, 312, 312]), rank 0 
+2025-05-07 08:37:22.186000: predicting 0168 
+2025-05-07 08:37:22.248371: 0168, shape torch.Size([1, 1221, 297, 297]), rank 0 
+2025-05-07 08:37:33.872898: predicting 0169 
+2025-05-07 08:37:33.932755: 0169, shape torch.Size([1, 1151, 333, 333]), rank 0 
+2025-05-07 08:37:45.592410: predicting 0170 
+2025-05-07 08:37:45.666549: 0170, shape torch.Size([1, 652, 333, 333]), rank 0 
+2025-05-07 08:37:50.949885: predicting 0171 
+2025-05-07 08:37:51.019619: 0171, shape torch.Size([1, 651, 311, 311]), rank 0 
+2025-05-07 08:37:58.870802: predicting 0172 
+2025-05-07 08:37:58.920976: 0172, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 08:38:02.562204: predicting 0173 
+2025-05-07 08:38:02.648047: 0173, shape torch.Size([1, 578, 311, 311]), rank 0 
+2025-05-07 08:38:09.366822: predicting 0174 
+2025-05-07 08:38:09.422633: 0174, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 08:38:16.816930: predicting 0175 
+2025-05-07 08:38:16.867132: 0175, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 08:38:22.785048: predicting 0176 
+2025-05-07 08:38:22.838382: 0176, shape torch.Size([1, 638, 285, 285]), rank 0 
+2025-05-07 08:38:26.428102: predicting 0177 
+2025-05-07 08:38:26.479393: 0177, shape torch.Size([1, 651, 324, 324]), rank 0 
+2025-05-07 08:38:33.058417: predicting 0178 
+2025-05-07 08:38:33.110594: 0178, shape torch.Size([1, 1238, 321, 321]), rank 0 
+2025-05-07 08:38:45.660079: predicting 0179 
+2025-05-07 08:38:45.725684: 0179, shape torch.Size([1, 566, 261, 261]), rank 0 
+2025-05-07 08:38:48.780051: predicting 0180 
+2025-05-07 08:38:48.835860: 0180, shape torch.Size([1, 566, 301, 301]), rank 0 
+2025-05-07 08:38:55.843215: predicting 0181 
+2025-05-07 08:38:55.897508: 0181, shape torch.Size([1, 1070, 325, 325]), rank 0 
+2025-05-07 08:39:06.543968: predicting 0182 
+2025-05-07 08:39:06.597566: 0182, shape torch.Size([1, 1175, 333, 333]), rank 0 
+2025-05-07 08:39:19.389795: predicting 0183 
+2025-05-07 08:39:19.465366: 0183, shape torch.Size([1, 735, 291, 291]), rank 0 
+2025-05-07 08:39:26.846365: predicting 0184 
+2025-05-07 08:39:26.900188: 0184, shape torch.Size([1, 568, 241, 241]), rank 0 
+2025-05-07 08:39:31.739073: predicting 0185 
+2025-05-07 08:39:31.793508: 0185, shape torch.Size([1, 508, 294, 294]), rank 0 
+2025-05-07 08:39:36.536434: predicting 0186 
+2025-05-07 08:39:36.585726: 0186, shape torch.Size([1, 1160, 273, 273]), rank 0 
+2025-05-07 08:39:43.567479: predicting 0187 
+2025-05-07 08:39:43.618973: 0187, shape torch.Size([1, 1228, 333, 333]), rank 0 
+2025-05-07 08:39:56.797018: predicting 0188 
+2025-05-07 08:39:56.884698: 0188, shape torch.Size([1, 560, 297, 297]), rank 0 
+2025-05-07 08:40:02.947210: predicting 0189 
+2025-05-07 08:40:03.022443: 0189, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 08:40:08.277719: predicting 0190 
+2025-05-07 08:40:08.801424: 0190, shape torch.Size([1, 483, 305, 305]), rank 0 
+2025-05-07 08:40:14.042742: predicting 0191 
+2025-05-07 08:40:14.111398: 0191, shape torch.Size([1, 566, 291, 291]), rank 0 
+2025-05-07 08:40:18.574697: predicting 0192 
+2025-05-07 08:40:18.845041: 0192, shape torch.Size([1, 616, 326, 326]), rank 0 
+2025-05-07 08:40:24.038972: predicting 0193 
+2025-05-07 08:40:24.092489: 0193, shape torch.Size([1, 623, 317, 317]), rank 0 
+2025-05-07 08:40:29.629994: predicting 0194 
+2025-05-07 08:40:29.688415: 0194, shape torch.Size([1, 651, 321, 321]), rank 0 
+2025-05-07 08:40:37.438423: predicting 0195 
+2025-05-07 08:40:37.486662: 0195, shape torch.Size([1, 650, 333, 333]), rank 0 
+2025-05-07 08:40:44.676849: predicting 0196 
+2025-05-07 08:40:44.749316: 0196, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 08:40:50.100786: predicting 0197 
+2025-05-07 08:40:50.148362: 0197, shape torch.Size([1, 652, 287, 287]), rank 0 
+2025-05-07 08:40:55.674356: predicting 0198 
+2025-05-07 08:40:55.749642: 0198, shape torch.Size([1, 651, 289, 289]), rank 0 
+2025-05-07 08:41:00.343269: predicting 0199 
+2025-05-07 08:41:00.397904: 0199, shape torch.Size([1, 652, 329, 329]), rank 0 
+2025-05-07 08:41:07.450337: predicting 0200 
+2025-05-07 08:41:07.507628: 0200, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 08:41:12.822312: predicting 0201 
+2025-05-07 08:41:12.889190: 0201, shape torch.Size([1, 652, 278, 278]), rank 0 
+2025-05-07 08:41:17.413975: predicting 0202 
+2025-05-07 08:41:17.472976: 0202, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 08:41:23.648933: predicting 0203 
+2025-05-07 08:41:23.720707: 0203, shape torch.Size([1, 651, 319, 319]), rank 0 
+2025-05-07 08:41:29.598215: predicting 0204 
+2025-05-07 08:41:29.660085: 0204, shape torch.Size([1, 473, 333, 333]), rank 0 
+2025-05-07 08:41:33.726912: predicting 0205 
+2025-05-07 08:41:33.783378: 0205, shape torch.Size([1, 601, 312, 312]), rank 0 
+2025-05-07 08:41:39.627889: predicting 0206 
+2025-05-07 08:41:39.696488: 0206, shape torch.Size([1, 568, 257, 257]), rank 0 
+2025-05-07 08:41:44.850325: predicting 0207 
+2025-05-07 08:41:44.907804: 0207, shape torch.Size([1, 651, 331, 331]), rank 0 
+2025-05-07 08:41:50.672508: predicting 0208 
+2025-05-07 08:41:50.788990: 0208, shape torch.Size([1, 645, 333, 333]), rank 0 
+2025-05-07 08:41:58.541897: predicting 0209 
+2025-05-07 08:41:58.597787: 0209, shape torch.Size([1, 661, 326, 326]), rank 0 
+2025-05-07 08:42:05.226095: predicting 0210 
+2025-05-07 08:42:05.274734: 0210, shape torch.Size([1, 1321, 320, 320]), rank 0 
+2025-05-07 08:42:18.432876: predicting 0211 
+2025-05-07 08:42:18.518926: 0211, shape torch.Size([1, 566, 254, 254]), rank 0 
+2025-05-07 08:42:21.393014: predicting 0212 
+2025-05-07 08:42:21.441286: 0212, shape torch.Size([1, 670, 285, 285]), rank 0 
+2025-05-07 08:42:27.478238: predicting 0213 
+2025-05-07 08:42:27.547232: 0213, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 08:42:32.891185: predicting 0214 
+2025-05-07 08:42:32.970186: 0214, shape torch.Size([1, 651, 276, 276]), rank 0 
+2025-05-07 08:42:37.376615: predicting 0215 
+2025-05-07 08:42:37.424401: 0215, shape torch.Size([1, 651, 314, 314]), rank 0 
+2025-05-07 08:42:42.725696: predicting 0216 
+2025-05-07 08:42:42.790796: 0216, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 08:42:48.623795: predicting 0217 
+2025-05-07 08:42:48.687792: 0217, shape torch.Size([1, 651, 307, 307]), rank 0 
+2025-05-07 08:42:55.342982: predicting 0218 
+2025-05-07 08:42:55.398449: 0218, shape torch.Size([1, 651, 309, 309]), rank 0 
+2025-05-07 08:43:00.285594: predicting 0219 
+2025-05-07 08:43:00.338608: 0219, shape torch.Size([1, 566, 293, 293]), rank 0 
+2025-05-07 08:43:05.619165: predicting 0220 
+2025-05-07 08:43:05.683142: 0220, shape torch.Size([1, 568, 239, 239]), rank 0 
+2025-05-07 08:43:09.318073: predicting 0221 
+2025-05-07 08:43:09.370119: 0221, shape torch.Size([1, 595, 260, 260]), rank 0 
+2025-05-07 08:43:12.693792: predicting 0222 
+2025-05-07 08:43:12.953960: 0222, shape torch.Size([1, 652, 263, 263]), rank 0 
+2025-05-07 08:43:16.953206: predicting 0223 
+2025-05-07 08:43:17.036536: 0223, shape torch.Size([1, 683, 304, 304]), rank 0 
+2025-05-07 08:43:25.460001: predicting 0224 
+2025-05-07 08:43:25.516660: 0224, shape torch.Size([1, 1238, 305, 305]), rank 0 
+2025-05-07 08:43:38.628813: predicting 0225 
+2025-05-07 08:43:38.687972: 0225, shape torch.Size([1, 566, 299, 299]), rank 0 
+2025-05-07 08:43:43.433921: predicting 0226 
+2025-05-07 08:43:43.487770: 0226, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 08:43:48.581185: predicting 0227 
+2025-05-07 08:43:48.637593: 0227, shape torch.Size([1, 651, 268, 268]), rank 0 
+2025-05-07 08:43:53.157348: predicting 0228 
+2025-05-07 08:43:53.203612: 0228, shape torch.Size([1, 566, 259, 259]), rank 0 
+2025-05-07 08:43:58.485294: predicting 0229 
+2025-05-07 08:43:58.669164: 0229, shape torch.Size([1, 652, 301, 301]), rank 0 
+2025-05-07 08:44:03.645641: predicting 0230 
+2025-05-07 08:44:03.851893: 0230, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 08:44:09.070049: predicting 0231 
+2025-05-07 08:44:09.117685: 0231, shape torch.Size([1, 1186, 251, 251]), rank 0 
+2025-05-07 08:44:16.078553: predicting 0232 
+2025-05-07 08:44:16.146146: 0232, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 08:44:21.388099: predicting 0233 
+2025-05-07 08:44:21.442554: 0233, shape torch.Size([1, 652, 286, 286]), rank 0 
+2025-05-07 08:44:25.882258: predicting 0234 
+2025-05-07 08:44:25.944285: 0234, shape torch.Size([1, 601, 251, 251]), rank 0 
+2025-05-07 08:44:30.853131: predicting 0235 
+2025-05-07 08:44:30.898818: 0235, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 08:44:37.397957: predicting 0236 
+2025-05-07 08:44:37.451174: 0236, shape torch.Size([1, 651, 278, 278]), rank 0 
+2025-05-07 08:44:41.691444: predicting 0237 
+2025-05-07 08:44:41.742394: 0237, shape torch.Size([1, 621, 281, 281]), rank 0 
+2025-05-07 08:44:45.172284: predicting 0238 
+2025-05-07 08:44:45.219015: 0238, shape torch.Size([1, 566, 279, 279]), rank 0 
+2025-05-07 08:44:48.950566: predicting 0239 
+2025-05-07 08:44:48.993603: 0239, shape torch.Size([1, 566, 277, 277]), rank 0 
+2025-05-07 08:44:54.592676: predicting 0240 
+2025-05-07 08:44:54.640526: 0240, shape torch.Size([1, 651, 272, 272]), rank 0 
+2025-05-07 08:44:58.858145: predicting 0241 
+2025-05-07 08:44:58.920654: 0241, shape torch.Size([1, 652, 277, 277]), rank 0 
+2025-05-07 08:45:02.210644: predicting 0242 
+2025-05-07 08:45:02.262884: 0242, shape torch.Size([1, 566, 305, 305]), rank 0 
+2025-05-07 08:45:08.031438: predicting 0243 
+2025-05-07 08:45:08.078295: 0243, shape torch.Size([1, 1071, 303, 303]), rank 0 
+2025-05-07 08:45:17.919271: predicting 0244 
+2025-05-07 08:45:18.001295: 0244, shape torch.Size([1, 651, 315, 315]), rank 0 
+2025-05-07 08:45:25.542668: predicting 0245 
+2025-05-07 08:45:25.741005: 0245, shape torch.Size([1, 1175, 288, 288]), rank 0 
+2025-05-07 08:45:35.411740: predicting 0246 
+2025-05-07 08:45:35.467716: 0246, shape torch.Size([1, 653, 295, 295]), rank 0 
+2025-05-07 08:45:40.178941: predicting 0247 
+2025-05-07 08:45:40.236538: 0247, shape torch.Size([1, 568, 281, 281]), rank 0 
+2025-05-07 08:45:43.978267: predicting 0248 
+2025-05-07 08:45:44.050390: 0248, shape torch.Size([1, 566, 333, 333]), rank 0 
+2025-05-07 08:45:50.442995: predicting 0249 
+2025-05-07 08:45:50.497930: 0249, shape torch.Size([1, 567, 302, 302]), rank 0 
+2025-05-07 08:45:57.181329: predicting 0250 
+2025-05-07 08:45:57.240946: 0250, shape torch.Size([1, 570, 236, 236]), rank 0 
+2025-05-07 08:46:00.240618: predicting 0251 
+2025-05-07 08:46:00.286029: 0251, shape torch.Size([1, 483, 271, 271]), rank 0 
+2025-05-07 08:46:04.120846: predicting 0252 
+2025-05-07 08:46:04.170033: 0252, shape torch.Size([1, 652, 269, 269]), rank 0 
+2025-05-07 08:46:07.889902: predicting 0253 
+2025-05-07 08:46:07.938690: 0253, shape torch.Size([1, 653, 307, 307]), rank 0 
+2025-05-07 08:46:15.254873: predicting 0254 
+2025-05-07 08:46:15.322275: 0254, shape torch.Size([1, 651, 268, 268]), rank 0 
+2025-05-07 08:46:18.885388: predicting 0255 
+2025-05-07 08:46:18.941388: 0255, shape torch.Size([1, 1166, 302, 302]), rank 0 
+2025-05-07 08:46:30.000479: predicting 0256 
+2025-05-07 08:46:30.067032: 0256, shape torch.Size([1, 566, 296, 296]), rank 0 
+2025-05-07 08:46:35.546671: predicting 0257 
+2025-05-07 08:46:35.603397: 0257, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 08:46:40.829340: predicting 0258 
+2025-05-07 08:46:40.895823: 0258, shape torch.Size([1, 566, 279, 279]), rank 0 
+2025-05-07 08:46:44.502735: predicting 0259 
+2025-05-07 08:46:44.550050: 0259, shape torch.Size([1, 1153, 316, 316]), rank 0 
+2025-05-07 08:46:58.941236: predicting 0260 
+2025-05-07 08:46:59.018790: 0260, shape torch.Size([1, 901, 322, 322]), rank 0 
+2025-05-07 08:47:08.331630: predicting 0261 
+2025-05-07 08:47:08.393148: 0261, shape torch.Size([1, 566, 289, 289]), rank 0 
+2025-05-07 08:47:14.555243: predicting 0262 
+2025-05-07 08:47:14.615499: 0262, shape torch.Size([1, 652, 277, 277]), rank 0 
+2025-05-07 08:47:18.907258: predicting 0263 
+2025-05-07 08:47:18.977594: 0263, shape torch.Size([1, 651, 248, 248]), rank 0 
+2025-05-07 08:47:22.487903: predicting 0264 
+2025-05-07 08:47:22.544237: 0264, shape torch.Size([1, 651, 274, 274]), rank 0 
+2025-05-07 08:47:28.886608: predicting 0265 
+2025-05-07 08:47:28.951403: 0265, shape torch.Size([1, 1188, 309, 309]), rank 0 
+2025-05-07 08:47:38.297544: predicting 0266 
+2025-05-07 08:47:38.372245: 0266, shape torch.Size([1, 568, 253, 253]), rank 0 
+2025-05-07 08:47:42.169481: predicting 0267 
+2025-05-07 08:47:42.221195: 0267, shape torch.Size([1, 1238, 307, 307]), rank 0 
+2025-05-07 08:47:54.031947: predicting 0268 
+2025-05-07 08:47:54.118152: 0268, shape torch.Size([1, 1153, 318, 318]), rank 0 
+2025-05-07 08:48:06.673110: predicting 0269 
+2025-05-07 08:48:06.750292: 0269, shape torch.Size([1, 1238, 315, 315]), rank 0 
+2025-05-07 08:48:18.880705: predicting 0270 
+2025-05-07 08:48:18.978768: 0270, shape torch.Size([1, 652, 265, 265]), rank 0 
+2025-05-07 08:48:24.304509: predicting 0271 
+2025-05-07 08:48:24.352353: 0271, shape torch.Size([1, 1248, 291, 291]), rank 0 
+2025-05-07 08:48:35.554915: predicting 0272 
+2025-05-07 08:48:35.657840: 0272, shape torch.Size([1, 635, 309, 309]), rank 0 
+2025-05-07 08:48:40.322598: predicting 0273 
+2025-05-07 08:48:40.392630: 0273, shape torch.Size([1, 691, 333, 333]), rank 0 
+2025-05-07 08:48:49.176716: predicting 0274 
+2025-05-07 08:48:49.236526: 0274, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 08:48:56.257816: predicting 0275 
+2025-05-07 08:48:56.316427: 0275, shape torch.Size([1, 1153, 268, 268]), rank 0 
+2025-05-07 08:49:03.567214: predicting 0276 
+2025-05-07 08:49:03.623119: 0276, shape torch.Size([1, 1130, 275, 275]), rank 0 
+2025-05-07 08:49:11.985403: predicting 0277 
+2025-05-07 08:49:12.041941: 0277, shape torch.Size([1, 660, 259, 259]), rank 0 
+2025-05-07 08:49:16.889707: predicting 0278 
+2025-05-07 08:49:16.943800: 0278, shape torch.Size([1, 651, 315, 315]), rank 0 
+2025-05-07 08:49:24.131078: predicting 0279 
+2025-05-07 08:49:24.184261: 0279, shape torch.Size([1, 1261, 311, 311]), rank 0 
+2025-05-07 08:49:37.145681: predicting 0280 
+2025-05-07 08:49:37.232643: 0280, shape torch.Size([1, 660, 311, 311]), rank 0 
+2025-05-07 08:49:43.109115: predicting 0281 
+2025-05-07 08:49:43.167907: 0281, shape torch.Size([1, 818, 277, 277]), rank 0 
+2025-05-07 08:49:50.728698: predicting 0282 
+2025-05-07 08:49:50.786300: 0282, shape torch.Size([1, 651, 327, 327]), rank 0 
+2025-05-07 08:49:56.232946: predicting 0283 
+2025-05-07 08:49:56.289587: 0283, shape torch.Size([1, 568, 301, 301]), rank 0 
+2025-05-07 08:50:03.524592: predicting 0284 
+2025-05-07 08:50:03.575601: 0284, shape torch.Size([1, 1173, 299, 299]), rank 0 
+2025-05-07 08:50:12.779287: predicting 0285 
+2025-05-07 08:50:12.840740: 0285, shape torch.Size([1, 651, 269, 269]), rank 0 
+2025-05-07 08:50:17.181593: predicting 0286 
+2025-05-07 08:50:17.234410: 0286, shape torch.Size([1, 566, 292, 292]), rank 0 
+2025-05-07 08:50:21.607034: predicting 0287 
+2025-05-07 08:50:21.658789: 0287, shape torch.Size([1, 651, 272, 272]), rank 0 
+2025-05-07 08:50:25.988819: predicting 0288 
+2025-05-07 08:50:26.049358: 0288, shape torch.Size([1, 580, 291, 291]), rank 0 
+2025-05-07 08:50:32.863978: predicting 0289 
+2025-05-07 08:50:33.385340: 0289, shape torch.Size([1, 568, 253, 253]), rank 0 
+2025-05-07 08:50:36.830513: predicting 0290 
+2025-05-07 08:50:36.887442: 0290, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 08:50:41.977743: predicting 0291 
+2025-05-07 08:50:42.043123: 0291, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 08:50:47.250273: predicting 0292 
+2025-05-07 08:50:47.308105: 0292, shape torch.Size([1, 1238, 315, 315]), rank 0 
+2025-05-07 08:50:59.045771: predicting 0293 
+2025-05-07 08:50:59.105737: 0293, shape torch.Size([1, 1170, 274, 274]), rank 0 
+2025-05-07 08:51:06.712011: predicting 0294 
+2025-05-07 08:51:06.776277: 0294, shape torch.Size([1, 483, 282, 282]), rank 0 
+2025-05-07 08:51:10.938632: predicting 0295 
+2025-05-07 08:51:10.983445: 0295, shape torch.Size([1, 608, 303, 303]), rank 0 
+2025-05-07 08:51:15.584981: predicting 0296 
+2025-05-07 08:51:15.662428: 0296, shape torch.Size([1, 652, 315, 315]), rank 0 
+2025-05-07 08:51:21.630117: predicting 0297 
+2025-05-07 08:51:21.684888: 0297, shape torch.Size([1, 630, 273, 273]), rank 0 
+2025-05-07 08:51:27.538838: predicting 0298 
+2025-05-07 08:51:27.588913: 0298, shape torch.Size([1, 651, 311, 311]), rank 0 
+2025-05-07 08:51:34.367179: predicting 0299 
+2025-05-07 08:51:34.496038: 0299, shape torch.Size([1, 730, 289, 289]), rank 0 
+2025-05-07 08:51:39.727921: predicting 0300 
+2025-05-07 08:51:39.779391: 0300, shape torch.Size([1, 561, 293, 293]), rank 0 
+2025-05-07 08:51:44.850572: predicting 0301 
+2025-05-07 08:51:44.899816: 0301, shape torch.Size([1, 1153, 324, 324]), rank 0 
+2025-05-07 08:51:56.697533: predicting 0302 
+2025-05-07 08:51:56.752105: 0302, shape torch.Size([1, 613, 333, 333]), rank 0 
+2025-05-07 08:52:03.387109: predicting 0303 
+2025-05-07 08:52:03.444078: 0303, shape torch.Size([1, 638, 333, 333]), rank 0 
+2025-05-07 08:52:09.917221: predicting 0304 
+2025-05-07 08:52:09.972589: 0304, shape torch.Size([1, 650, 292, 292]), rank 0 
+2025-05-07 08:52:15.225441: predicting 0305 
+2025-05-07 08:52:15.275664: 0305, shape torch.Size([1, 518, 320, 320]), rank 0 
+2025-05-07 08:52:20.432715: predicting 0306 
+2025-05-07 08:52:20.556611: 0306, shape torch.Size([1, 652, 257, 257]), rank 0 
+2025-05-07 08:52:24.269680: predicting 0307 
+2025-05-07 08:52:24.374813: 0307, shape torch.Size([1, 605, 305, 305]), rank 0 
+2025-05-07 08:52:32.256456: predicting 0308 
+2025-05-07 08:52:32.332204: 0308, shape torch.Size([1, 626, 280, 280]), rank 0 
+2025-05-07 08:52:35.814195: predicting 0309 
+2025-05-07 08:52:35.865830: 0309, shape torch.Size([1, 566, 313, 313]), rank 0 
+2025-05-07 08:52:41.767500: predicting 0310 
+2025-05-07 08:52:41.817446: 0310, shape torch.Size([1, 651, 277, 277]), rank 0 
+2025-05-07 08:52:45.547790: predicting 0311 
+2025-05-07 08:52:45.594646: 0311, shape torch.Size([1, 651, 289, 289]), rank 0 
+2025-05-07 08:52:52.706835: predicting 0312 
+2025-05-07 08:52:52.756569: 0312, shape torch.Size([1, 1161, 311, 311]), rank 0 
+2025-05-07 08:53:03.764977: predicting 0313 
+2025-05-07 08:53:03.827057: 0313, shape torch.Size([1, 643, 293, 293]), rank 0 
+2025-05-07 08:53:10.017531: predicting 0314 
+2025-05-07 08:53:10.063239: 0314, shape torch.Size([1, 700, 333, 333]), rank 0 
+2025-05-07 08:53:17.542159: predicting 0315 
+2025-05-07 08:53:17.595995: 0315, shape torch.Size([1, 526, 269, 269]), rank 0 
+2025-05-07 08:53:20.569938: predicting 0316 
+2025-05-07 08:53:20.623262: 0316, shape torch.Size([1, 640, 298, 298]), rank 0 
+2025-05-07 08:53:25.541257: predicting 0317 
+2025-05-07 08:53:25.599153: 0317, shape torch.Size([1, 566, 278, 278]), rank 0 
+2025-05-07 08:53:29.748886: predicting 0318 
+2025-05-07 08:53:29.798239: 0318, shape torch.Size([1, 726, 333, 333]), rank 0 
+2025-05-07 08:53:38.173697: predicting 0319 
+2025-05-07 08:53:38.235770: 0319, shape torch.Size([1, 748, 333, 333]), rank 0 
+2025-05-07 08:53:45.174852: predicting 0320 
+2025-05-07 08:53:45.226611: 0320, shape torch.Size([1, 740, 333, 333]), rank 0 
+2025-05-07 08:53:52.512114: predicting 0321 
+2025-05-07 08:53:52.731387: 0321, shape torch.Size([1, 700, 333, 333]), rank 0 
+2025-05-07 08:54:01.181352: predicting 0322 
+2025-05-07 08:54:01.241390: 0322, shape torch.Size([1, 618, 299, 299]), rank 0 
+2025-05-07 08:54:08.525201: predicting 0323 
+2025-05-07 08:54:08.574418: 0323, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 08:54:14.885294: predicting 0324 
+2025-05-07 08:54:14.936510: 0324, shape torch.Size([1, 651, 280, 280]), rank 0 
+2025-05-07 08:54:19.079157: predicting 0325 
+2025-05-07 08:54:19.123381: 0325, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 08:54:25.704982: predicting 0326 
+2025-05-07 08:54:25.785337: 0326, shape torch.Size([1, 652, 267, 267]), rank 0 
+2025-05-07 08:54:30.543808: predicting 0327 
+2025-05-07 08:54:30.599483: 0327, shape torch.Size([1, 623, 282, 282]), rank 0 
+2025-05-07 08:54:34.444703: predicting 0328 
+2025-05-07 08:54:34.504144: 0328, shape torch.Size([1, 596, 314, 314]), rank 0 
+2025-05-07 08:54:41.496714: predicting 0329 
+2025-05-07 08:54:41.560569: 0329, shape torch.Size([1, 523, 302, 302]), rank 0 
+2025-05-07 08:54:47.353310: predicting 0330 
+2025-05-07 08:54:47.422238: 0330, shape torch.Size([1, 568, 252, 252]), rank 0 
+2025-05-07 08:54:51.842905: predicting 0331 
+2025-05-07 08:54:51.893344: 0331, shape torch.Size([1, 613, 275, 275]), rank 0 
+2025-05-07 08:54:55.658631: predicting 0332 
+2025-05-07 08:54:55.734417: 0332, shape torch.Size([1, 568, 282, 282]), rank 0 
+2025-05-07 08:55:00.723523: predicting 0333 
+2025-05-07 08:55:00.786742: 0333, shape torch.Size([1, 652, 245, 245]), rank 0 
+2025-05-07 08:55:03.841334: predicting 0334 
+2025-05-07 08:55:03.884976: 0334, shape torch.Size([1, 566, 301, 301]), rank 0 
+2025-05-07 08:55:10.399614: predicting 0335 
+2025-05-07 08:55:10.457010: 0335, shape torch.Size([1, 566, 259, 259]), rank 0 
+2025-05-07 08:55:13.774815: predicting 0336 
+2025-05-07 08:55:13.819439: 0336, shape torch.Size([1, 483, 293, 293]), rank 0 
+2025-05-07 08:55:19.499564: predicting 0337 
+2025-05-07 08:55:19.680349: 0337, shape torch.Size([1, 553, 292, 292]), rank 0 
+2025-05-07 08:55:24.739651: predicting 0338 
+2025-05-07 08:55:25.156620: 0338, shape torch.Size([1, 651, 278, 278]), rank 0 
+2025-05-07 08:55:28.826011: predicting 0339 
+2025-05-07 08:55:28.883765: 0339, shape torch.Size([1, 1100, 331, 331]), rank 0 
+2025-05-07 08:55:41.372403: predicting 0340 
+2025-05-07 08:55:41.449321: 0340, shape torch.Size([1, 566, 297, 297]), rank 0 
+2025-05-07 08:55:47.964598: predicting 0341 
+2025-05-07 08:55:48.029982: 0341, shape torch.Size([1, 478, 273, 273]), rank 0 
+2025-05-07 08:55:51.854580: predicting 0342 
+2025-05-07 08:55:51.904665: 0342, shape torch.Size([1, 568, 290, 290]), rank 0 
+2025-05-07 08:55:56.831884: predicting 0343 
+2025-05-07 08:55:56.886856: 0343, shape torch.Size([1, 602, 299, 299]), rank 0 
+2025-05-07 08:56:04.255334: predicting 0344 
+2025-05-07 08:56:04.320238: 0344, shape torch.Size([1, 564, 249, 249]), rank 0 
+2025-05-07 08:56:08.040875: predicting 0345 
+2025-05-07 08:56:08.093887: 0345, shape torch.Size([1, 483, 303, 303]), rank 0 
+2025-05-07 08:56:11.873454: predicting 0346 
+2025-05-07 08:56:11.930968: 0346, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 08:56:17.135517: predicting 0347 
+2025-05-07 08:56:17.195783: 0347, shape torch.Size([1, 651, 289, 289]), rank 0 
+2025-05-07 08:56:24.462415: predicting 0348 
+2025-05-07 08:56:24.514757: 0348, shape torch.Size([1, 638, 292, 292]), rank 0 
+2025-05-07 08:56:28.928195: predicting 0349 
+2025-05-07 08:56:28.999610: 0349, shape torch.Size([1, 566, 270, 270]), rank 0 
+2025-05-07 08:56:32.162163: predicting 0350 
+2025-05-07 08:56:32.219306: 0350, shape torch.Size([1, 651, 315, 315]), rank 0 
+2025-05-07 08:56:39.473283: predicting 0351 
+2025-05-07 08:56:39.531857: 0351, shape torch.Size([1, 483, 305, 305]), rank 0 
+2025-05-07 08:56:43.772640: predicting 0352 
+2025-05-07 08:56:43.828348: 0352, shape torch.Size([1, 651, 282, 282]), rank 0 
+2025-05-07 08:56:47.495244: predicting 0353 
+2025-05-07 08:56:47.545908: 0353, shape torch.Size([1, 651, 272, 272]), rank 0 
+2025-05-07 08:56:52.619965: predicting 0354 
+2025-05-07 08:56:52.673001: 0354, shape torch.Size([1, 631, 308, 308]), rank 0 
+2025-05-07 08:56:59.353931: predicting 0355 
+2025-05-07 08:56:59.398296: 0355, shape torch.Size([1, 1161, 319, 319]), rank 0 
+2025-05-07 08:57:10.757170: predicting 0356 
+2025-05-07 08:57:10.819094: 0356, shape torch.Size([1, 1161, 333, 333]), rank 0 
+2025-05-07 08:57:23.593132: predicting 0357 
+2025-05-07 08:57:23.657626: 0357, shape torch.Size([1, 623, 310, 310]), rank 0 
+2025-05-07 08:57:28.241856: predicting 0358 
+2025-05-07 08:57:28.285971: 0358, shape torch.Size([1, 588, 295, 295]), rank 0 
+2025-05-07 08:57:33.571021: predicting 0359 
+2025-05-07 08:57:33.623382: 0359, shape torch.Size([1, 652, 285, 285]), rank 0 
+2025-05-07 08:57:38.850995: predicting 0360 
+2025-05-07 08:57:38.902039: 0360, shape torch.Size([1, 586, 312, 312]), rank 0 
+2025-05-07 08:57:44.862909: predicting 0361 
+2025-05-07 08:57:44.915586: 0361, shape torch.Size([1, 568, 269, 269]), rank 0 
+2025-05-07 08:57:48.805193: predicting 0362 
+2025-05-07 08:57:48.860648: 0362, shape torch.Size([1, 1088, 279, 279]), rank 0 
+2025-05-07 08:57:55.647351: predicting 0363 
+2025-05-07 08:57:55.706262: 0363, shape torch.Size([1, 1070, 300, 300]), rank 0 
+2025-05-07 08:58:06.629249: predicting 0364 
+2025-05-07 08:58:06.698035: 0364, shape torch.Size([1, 652, 303, 303]), rank 0 
+2025-05-07 08:58:12.705048: predicting 0365 
+2025-05-07 08:58:12.754344: 0365, shape torch.Size([1, 652, 252, 252]), rank 0 
+2025-05-07 08:58:17.950958: predicting 0366 
+2025-05-07 08:58:18.012223: 0366, shape torch.Size([1, 566, 312, 312]), rank 0 
+2025-05-07 08:58:23.294775: predicting 0367 
+2025-05-07 08:58:23.351335: 0367, shape torch.Size([1, 652, 327, 327]), rank 0 
+2025-05-07 08:58:31.087233: predicting 0368 
+2025-05-07 08:58:31.142538: 0368, shape torch.Size([1, 600, 288, 288]), rank 0 
+2025-05-07 08:58:34.837821: predicting 0369 
+2025-05-07 08:58:34.891448: 0369, shape torch.Size([1, 623, 333, 333]), rank 0 
+2025-05-07 08:58:41.396293: predicting 0370 
+2025-05-07 08:58:41.459566: 0370, shape torch.Size([1, 566, 307, 307]), rank 0 
+2025-05-07 08:58:47.307460: predicting 0371 
+2025-05-07 08:58:47.349011: 0371, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 08:58:53.366294: predicting 0372 
+2025-05-07 08:58:53.415240: 0372, shape torch.Size([1, 1283, 317, 317]), rank 0 
+2025-05-07 08:59:07.458331: predicting 0373 
+2025-05-07 08:59:07.531087: 0373, shape torch.Size([1, 635, 321, 321]), rank 0 
+2025-05-07 08:59:13.033226: predicting 0374 
+2025-05-07 08:59:13.095061: 0374, shape torch.Size([1, 1070, 297, 297]), rank 0 
+2025-05-07 08:59:23.451956: predicting 0375 
+2025-05-07 08:59:23.496435: 0375, shape torch.Size([1, 575, 281, 281]), rank 0 
+2025-05-07 08:59:28.861723: predicting 0376 
+2025-05-07 08:59:28.906243: 0376, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 08:59:32.636116: predicting 0377 
+2025-05-07 08:59:32.693747: 0377, shape torch.Size([1, 566, 315, 315]), rank 0 
+2025-05-07 08:59:40.111633: predicting 0378 
+2025-05-07 08:59:40.162365: 0378, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 08:59:44.642198: predicting 0379 
+2025-05-07 08:59:44.688941: 0379, shape torch.Size([1, 568, 255, 255]), rank 0 
+2025-05-07 08:59:48.130720: predicting 0380 
+2025-05-07 08:59:48.174955: 0380, shape torch.Size([1, 566, 284, 284]), rank 0 
+2025-05-07 08:59:52.723271: predicting 0381 
+2025-05-07 08:59:52.774137: 0381, shape torch.Size([1, 1153, 293, 293]), rank 0 
+2025-05-07 09:00:02.576353: predicting 0382 
+2025-05-07 09:00:02.635195: 0382, shape torch.Size([1, 567, 287, 287]), rank 0 
+2025-05-07 09:00:06.681213: predicting 0383 
+2025-05-07 09:00:06.752888: 0383, shape torch.Size([1, 568, 259, 259]), rank 0 
+2025-05-07 09:00:12.050569: predicting 0384 
+2025-05-07 09:00:12.098180: 0384, shape torch.Size([1, 1321, 318, 318]), rank 0 
+2025-05-07 09:00:23.404497: predicting 0385 
+2025-05-07 09:00:23.461550: 0385, shape torch.Size([1, 575, 306, 306]), rank 0 
+2025-05-07 09:00:30.997957: predicting 0386 
+2025-05-07 09:00:31.047299: 0386, shape torch.Size([1, 652, 255, 255]), rank 0 
+2025-05-07 09:00:35.109812: predicting 0387 
+2025-05-07 09:00:35.158741: 0387, shape torch.Size([1, 652, 249, 249]), rank 0 
+2025-05-07 09:00:38.508446: predicting 0388 
+2025-05-07 09:00:38.566477: 0388, shape torch.Size([1, 651, 313, 313]), rank 0 
+2025-05-07 09:00:46.489585: predicting 0389 
+2025-05-07 09:00:46.540305: 0389, shape torch.Size([1, 1153, 301, 301]), rank 0 
+2025-05-07 09:00:56.224613: predicting 0390 
+2025-05-07 09:00:56.285077: 0390, shape torch.Size([1, 548, 253, 253]), rank 0 
+2025-05-07 09:00:59.373887: predicting 0391 
+2025-05-07 09:00:59.560134: 0391, shape torch.Size([1, 618, 293, 293]), rank 0 
+2025-05-07 09:01:06.320366: predicting 0392 
+2025-05-07 09:01:06.382520: 0392, shape torch.Size([1, 651, 270, 270]), rank 0 
+2025-05-07 09:01:10.168042: predicting 0393 
+2025-05-07 09:01:10.223032: 0393, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 09:01:18.901710: predicting 0394 
+2025-05-07 09:01:18.973264: 0394, shape torch.Size([1, 483, 287, 287]), rank 0 
+2025-05-07 09:01:21.725323: predicting 0395 
+2025-05-07 09:01:21.776502: 0395, shape torch.Size([1, 566, 303, 303]), rank 0 
+2025-05-07 09:01:28.018099: predicting 0396 
+2025-05-07 09:01:28.073426: 0396, shape torch.Size([1, 648, 254, 254]), rank 0 
+2025-05-07 09:01:32.758286: predicting 0397 
+2025-05-07 09:01:32.826511: 0397, shape torch.Size([1, 566, 264, 264]), rank 0 
+2025-05-07 09:01:35.991493: predicting 0398 
+2025-05-07 09:01:36.040376: 0398, shape torch.Size([1, 651, 279, 279]), rank 0 
+2025-05-07 09:01:40.256737: predicting 0399 
+2025-05-07 09:01:40.325427: 0399, shape torch.Size([1, 566, 255, 255]), rank 0 
+2025-05-07 09:01:45.492258: predicting 0400 
+2025-05-07 09:01:45.548628: 0400, shape torch.Size([1, 631, 296, 296]), rank 0 
+2025-05-07 09:01:50.225159: predicting 0401 
+2025-05-07 09:01:50.283851: 0401, shape torch.Size([1, 596, 275, 275]), rank 0 
+2025-05-07 09:01:54.718625: predicting 0402 
+2025-05-07 09:01:54.768376: 0402, shape torch.Size([1, 652, 259, 259]), rank 0 
+2025-05-07 09:01:59.760891: predicting 0403 
+2025-05-07 09:01:59.835772: 0403, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 09:02:03.172887: predicting 0404 
+2025-05-07 09:02:03.225682: 0404, shape torch.Size([1, 568, 290, 290]), rank 0 
+2025-05-07 09:02:09.842144: predicting 0405 
+2025-05-07 09:02:09.921310: 0405, shape torch.Size([1, 652, 303, 303]), rank 0 
+2025-05-07 09:02:16.613199: predicting 0406 
+2025-05-07 09:02:16.669011: 0406, shape torch.Size([1, 568, 263, 263]), rank 0 
+2025-05-07 09:02:21.677153: predicting 0407 
+2025-05-07 09:02:21.722468: 0407, shape torch.Size([1, 623, 255, 255]), rank 0 
+2025-05-07 09:02:25.119137: predicting 0408 
+2025-05-07 09:02:25.168775: 0408, shape torch.Size([1, 570, 285, 285]), rank 0 
+2025-05-07 09:02:28.890293: predicting 0409 
+2025-05-07 09:02:28.961136: 0409, shape torch.Size([1, 652, 270, 270]), rank 0 
+2025-05-07 09:02:34.637755: predicting 0410 
+2025-05-07 09:02:34.695682: 0410, shape torch.Size([1, 1216, 325, 325]), rank 0 
+2025-05-07 09:02:43.801710: predicting 0411 
+2025-05-07 09:02:43.873704: 0411, shape torch.Size([1, 1261, 304, 304]), rank 0 
+2025-05-07 09:02:56.747696: predicting 0412 
+2025-05-07 09:02:56.817377: 0412, shape torch.Size([1, 735, 315, 315]), rank 0 
+2025-05-07 09:03:04.614345: predicting 0413 
+2025-05-07 09:03:04.664882: 0413, shape torch.Size([1, 566, 317, 317]), rank 0 
+2025-05-07 09:03:10.970840: predicting 0414 
+2025-05-07 09:03:11.032453: 0414, shape torch.Size([1, 565, 281, 281]), rank 0 
+2025-05-07 09:03:14.524808: predicting 0415 
+2025-05-07 09:03:14.573063: 0415, shape torch.Size([1, 670, 297, 297]), rank 0 
+2025-05-07 09:03:21.770539: predicting 0416 
+2025-05-07 09:03:21.820355: 0416, shape torch.Size([1, 1153, 297, 297]), rank 0 
+2025-05-07 09:03:33.335344: predicting 0417 
+2025-05-07 09:03:33.395523: 0417, shape torch.Size([1, 566, 311, 311]), rank 0 
+2025-05-07 09:03:38.689200: predicting 0418 
+2025-05-07 09:03:38.787023: 0418, shape torch.Size([1, 652, 267, 267]), rank 0 
+2025-05-07 09:03:43.205031: predicting 0419 
+2025-05-07 09:03:43.257874: 0419, shape torch.Size([1, 650, 312, 312]), rank 0 
+2025-05-07 09:03:50.548305: predicting 0420 
+2025-05-07 09:03:50.598181: 0420, shape torch.Size([1, 656, 273, 273]), rank 0 
+2025-05-07 09:03:54.943296: predicting 0421 
+2025-05-07 09:03:54.991744: 0421, shape torch.Size([1, 566, 272, 272]), rank 0 
+2025-05-07 09:03:58.589297: predicting 0422 
+2025-05-07 09:03:58.641067: 0422, shape torch.Size([1, 566, 300, 300]), rank 0 
+2025-05-07 09:04:05.727328: predicting 0423 
+2025-05-07 09:04:05.791817: 0423, shape torch.Size([1, 545, 247, 247]), rank 0 
+2025-05-07 09:04:08.551421: predicting 0424 
+2025-05-07 09:04:08.608846: 0424, shape torch.Size([1, 567, 265, 265]), rank 0 
+2025-05-07 09:04:13.258525: predicting 0425 
+2025-05-07 09:04:13.305897: 0425, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:04:19.485188: predicting 0426 
+2025-05-07 09:04:19.536055: 0426, shape torch.Size([1, 734, 297, 297]), rank 0 
+2025-05-07 09:04:25.767974: predicting 0427 
+2025-05-07 09:04:25.818069: 0427, shape torch.Size([1, 626, 279, 279]), rank 0 
+2025-05-07 09:04:29.580074: predicting 0428 
+2025-05-07 09:04:29.626002: 0428, shape torch.Size([1, 651, 309, 309]), rank 0 
+2025-05-07 09:04:34.915108: predicting 0429 
+2025-05-07 09:04:34.966339: 0429, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 09:04:40.791915: predicting 0430 
+2025-05-07 09:04:40.842811: 0430, shape torch.Size([1, 635, 296, 296]), rank 0 
+2025-05-07 09:04:47.911539: predicting 0431 
+2025-05-07 09:04:48.056859: 0431, shape torch.Size([1, 608, 300, 300]), rank 0 
+2025-05-07 09:04:54.341882: predicting 0432 
+2025-05-07 09:04:54.386879: 0432, shape torch.Size([1, 1270, 333, 333]), rank 0 
+2025-05-07 09:05:07.701321: predicting 0433 
+2025-05-07 09:05:07.794666: 0433, shape torch.Size([1, 1153, 295, 295]), rank 0 
+2025-05-07 09:05:17.995458: predicting 0434 
+2025-05-07 09:05:18.056264: 0434, shape torch.Size([1, 1160, 268, 268]), rank 0 
+2025-05-07 09:05:25.943269: predicting 0435 
+2025-05-07 09:05:26.000403: 0435, shape torch.Size([1, 650, 271, 271]), rank 0 
+2025-05-07 09:05:31.187815: predicting 0436 
+2025-05-07 09:05:31.260710: 0436, shape torch.Size([1, 568, 300, 300]), rank 0 
+2025-05-07 09:05:35.965745: predicting 0437 
+2025-05-07 09:05:36.042403: 0437, shape torch.Size([1, 651, 259, 259]), rank 0 
+2025-05-07 09:05:40.550641: predicting 0438 
+2025-05-07 09:05:40.620434: 0438, shape torch.Size([1, 652, 239, 239]), rank 0 
+2025-05-07 09:05:43.869791: predicting 0439 
+2025-05-07 09:05:43.910393: 0439, shape torch.Size([1, 483, 249, 249]), rank 0 
+2025-05-07 09:05:47.689341: predicting 0440 
+2025-05-07 09:05:47.736598: 0440, shape torch.Size([1, 652, 273, 273]), rank 0 
+2025-05-07 09:05:51.184916: predicting 0441 
+2025-05-07 09:05:51.235184: 0441, shape torch.Size([1, 568, 267, 267]), rank 0 
+2025-05-07 09:05:55.017509: predicting 0442 
+2025-05-07 09:05:55.066374: 0442, shape torch.Size([1, 596, 333, 333]), rank 0 
+2025-05-07 09:06:02.214467: predicting 0443 
+2025-05-07 09:06:02.266898: 0443, shape torch.Size([1, 566, 278, 278]), rank 0 
+2025-05-07 09:06:05.764801: predicting 0444 
+2025-05-07 09:06:05.812570: 0444, shape torch.Size([1, 566, 281, 281]), rank 0 
+2025-05-07 09:06:10.228104: predicting 0445 
+2025-05-07 09:06:10.271244: 0445, shape torch.Size([1, 903, 314, 314]), rank 0 
+2025-05-07 09:06:18.321997: predicting 0446 
+2025-05-07 09:06:18.386820: 0446, shape torch.Size([1, 400, 260, 260]), rank 0 
+2025-05-07 09:06:20.291960: predicting 0447 
+2025-05-07 09:06:20.645644: 0447, shape torch.Size([1, 566, 273, 273]), rank 0 
+2025-05-07 09:06:25.119124: predicting 0448 
+2025-05-07 09:06:25.187543: 0448, shape torch.Size([1, 631, 318, 318]), rank 0 
+2025-05-07 09:06:30.474713: predicting 0449 
+2025-05-07 09:06:30.630562: 0449, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 09:06:35.258623: predicting 0450 
+2025-05-07 09:06:35.361177: 0450, shape torch.Size([1, 591, 281, 281]), rank 0 
+2025-05-07 09:06:40.779076: predicting 0451 
+2025-05-07 09:06:40.832389: 0451, shape torch.Size([1, 1126, 284, 284]), rank 0 
+2025-05-07 09:06:49.395746: predicting 0452 
+2025-05-07 09:06:49.452034: 0452, shape torch.Size([1, 735, 293, 293]), rank 0 
+2025-05-07 09:06:55.987065: predicting 0453 
+2025-05-07 09:06:56.093670: 0453, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:07:02.416803: predicting 0454 
+2025-05-07 09:07:02.472735: 0454, shape torch.Size([1, 568, 279, 279]), rank 0 
+2025-05-07 09:07:06.595412: predicting 0455 
+2025-05-07 09:07:06.660501: 0455, shape torch.Size([1, 566, 309, 309]), rank 0 
+2025-05-07 09:07:11.569260: predicting 0456 
+2025-05-07 09:07:11.615949: 0456, shape torch.Size([1, 1101, 289, 289]), rank 0 
+2025-05-07 09:07:21.846715: predicting 0457 
+2025-05-07 09:07:21.920608: 0457, shape torch.Size([1, 652, 264, 264]), rank 0 
+2025-05-07 09:07:26.729115: predicting 0458 
+2025-05-07 09:07:26.811268: 0458, shape torch.Size([1, 650, 280, 280]), rank 0 
+2025-05-07 09:07:32.045105: predicting 0459 
+2025-05-07 09:07:32.136695: 0459, shape torch.Size([1, 1178, 295, 295]), rank 0 
+2025-05-07 09:07:44.213526: predicting 0460 
+2025-05-07 09:07:44.282526: 0460, shape torch.Size([1, 591, 266, 266]), rank 0 
+2025-05-07 09:07:47.513146: predicting 0461 
+2025-05-07 09:07:47.643616: 0461, shape torch.Size([1, 601, 333, 333]), rank 0 
+2025-05-07 09:07:53.565067: predicting 0462 
+2025-05-07 09:07:53.629156: 0462, shape torch.Size([1, 610, 312, 312]), rank 0 
+2025-05-07 09:07:59.908800: predicting 0463 
+2025-05-07 09:07:59.969436: 0463, shape torch.Size([1, 600, 323, 323]), rank 0 
+2025-05-07 09:08:07.055442: predicting 0464 
+2025-05-07 09:08:07.116587: 0464, shape torch.Size([1, 630, 313, 313]), rank 0 
+2025-05-07 09:08:13.858992: predicting 0465 
+2025-05-07 09:08:13.911958: 0465, shape torch.Size([1, 652, 271, 271]), rank 0 
+2025-05-07 09:08:17.501746: predicting 0466 
+2025-05-07 09:08:17.553543: 0466, shape torch.Size([1, 566, 322, 322]), rank 0 
+2025-05-07 09:08:24.778877: predicting 0467 
+2025-05-07 09:08:24.832132: 0467, shape torch.Size([1, 543, 251, 251]), rank 0 
+2025-05-07 09:08:28.046242: predicting 0468 
+2025-05-07 09:08:28.093717: 0468, shape torch.Size([1, 683, 311, 311]), rank 0 
+2025-05-07 09:08:35.054015: predicting 0469 
+2025-05-07 09:08:35.115466: 0469, shape torch.Size([1, 566, 295, 295]), rank 0 
+2025-05-07 09:08:39.847545: predicting 0470 
+2025-05-07 09:08:39.900410: 0470, shape torch.Size([1, 1243, 322, 322]), rank 0 
+2025-05-07 09:08:51.590786: predicting 0471 
+2025-05-07 09:08:51.662854: 0471, shape torch.Size([1, 735, 307, 307]), rank 0 
+2025-05-07 09:08:58.987299: predicting 0472 
+2025-05-07 09:08:59.050290: 0472, shape torch.Size([1, 735, 324, 324]), rank 0 
+2025-05-07 09:09:06.531226: predicting 0473 
+2025-05-07 09:09:06.590649: 0473, shape torch.Size([1, 651, 326, 326]), rank 0 
+2025-05-07 09:09:12.774721: predicting 0474 
+2025-05-07 09:09:12.836628: 0474, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 09:09:18.801672: predicting 0475 
+2025-05-07 09:09:18.859697: 0475, shape torch.Size([1, 578, 291, 291]), rank 0 
+2025-05-07 09:09:23.392390: predicting 0476 
+2025-05-07 09:09:23.436198: 0476, shape torch.Size([1, 1226, 305, 305]), rank 0 
+2025-05-07 09:09:35.250382: predicting 0477 
+2025-05-07 09:09:35.320060: 0477, shape torch.Size([1, 652, 273, 273]), rank 0 
+2025-05-07 09:09:41.103365: predicting 0478 
+2025-05-07 09:09:41.150147: 0478, shape torch.Size([1, 1239, 314, 314]), rank 0 
+2025-05-07 09:09:53.702921: predicting 0479 
+2025-05-07 09:09:53.772684: 0479, shape torch.Size([1, 650, 293, 293]), rank 0 
+2025-05-07 09:09:59.055772: predicting 0480 
+2025-05-07 09:09:59.115415: 0480, shape torch.Size([1, 650, 285, 285]), rank 0 
+2025-05-07 09:10:03.390681: predicting 0481 
+2025-05-07 09:10:03.452621: 0481, shape torch.Size([1, 995, 315, 315]), rank 0 
+2025-05-07 09:10:12.026620: predicting 0482 
+2025-05-07 09:10:12.088345: 0482, shape torch.Size([1, 652, 279, 279]), rank 0 
+2025-05-07 09:10:15.405404: predicting 0483 
+2025-05-07 09:10:15.475966: 0483, shape torch.Size([1, 575, 277, 277]), rank 0 
+2025-05-07 09:10:20.106765: predicting 0484 
+2025-05-07 09:10:20.151459: 0484, shape torch.Size([1, 1296, 333, 333]), rank 0 
+2025-05-07 09:10:33.202943: predicting 0485 
+2025-05-07 09:10:33.275028: 0485, shape torch.Size([1, 651, 269, 269]), rank 0 
+2025-05-07 09:10:37.484128: predicting 0486 
+2025-05-07 09:10:37.628046: 0486, shape torch.Size([1, 568, 277, 277]), rank 0 
+2025-05-07 09:10:41.636088: predicting 0487 
+2025-05-07 09:10:41.729333: 0487, shape torch.Size([1, 648, 293, 293]), rank 0 
+2025-05-07 09:10:46.603782: predicting 0488 
+2025-05-07 09:10:46.665211: 0488, shape torch.Size([1, 819, 269, 269]), rank 0 
+2025-05-07 09:10:53.306737: predicting 0489 
+2025-05-07 09:10:53.368373: 0489, shape torch.Size([1, 610, 302, 302]), rank 0 
+2025-05-07 09:11:00.417784: predicting 0490 
+2025-05-07 09:11:00.471831: 0490, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:11:06.818982: predicting 0491 
+2025-05-07 09:11:07.045521: 0491, shape torch.Size([1, 652, 271, 271]), rank 0 
+2025-05-07 09:11:12.210877: predicting 0492 
+2025-05-07 09:11:12.262185: 0492, shape torch.Size([1, 651, 296, 296]), rank 0 
+2025-05-07 09:11:18.184356: predicting 0493 
+2025-05-07 09:11:18.255040: 0493, shape torch.Size([1, 652, 275, 275]), rank 0 
+2025-05-07 09:11:21.721390: predicting 0494 
+2025-05-07 09:11:21.772085: 0494, shape torch.Size([1, 566, 333, 333]), rank 0 
+2025-05-07 09:11:27.915864: predicting 0495 
+2025-05-07 09:11:27.975193: 0495, shape torch.Size([1, 565, 307, 307]), rank 0 
+2025-05-07 09:11:34.356684: predicting 0496 
+2025-05-07 09:11:34.406086: 0496, shape torch.Size([1, 566, 283, 283]), rank 0 
+2025-05-07 09:11:39.413026: predicting 0497 
+2025-05-07 09:11:39.459033: 0497, shape torch.Size([1, 1216, 331, 331]), rank 0 
+2025-05-07 09:11:51.018055: predicting 0498 
+2025-05-07 09:11:51.091775: 0498, shape torch.Size([1, 568, 253, 253]), rank 0 
+2025-05-07 09:11:54.901206: predicting 0499 
+2025-05-07 09:11:54.945340: 0499, shape torch.Size([1, 651, 328, 328]), rank 0 
+2025-05-07 09:12:01.331112: predicting 0500 
+2025-05-07 09:12:01.390082: 0500, shape torch.Size([1, 566, 281, 281]), rank 0 
+2025-05-07 09:12:05.269269: predicting 0501 
+2025-05-07 09:12:05.316401: 0501, shape torch.Size([1, 618, 282, 282]), rank 0 
+2025-05-07 09:12:11.028824: predicting 0502 
+2025-05-07 09:12:11.095199: 0502, shape torch.Size([1, 650, 298, 298]), rank 0 
+2025-05-07 09:12:16.245226: predicting 0503 
+2025-05-07 09:12:16.302014: 0503, shape torch.Size([1, 651, 287, 287]), rank 0 
+2025-05-07 09:12:21.223930: predicting 0504 
+2025-05-07 09:12:21.282445: 0504, shape torch.Size([1, 595, 290, 290]), rank 0 
+2025-05-07 09:12:27.354300: predicting 0505 
+2025-05-07 09:12:27.423363: 0505, shape torch.Size([1, 545, 257, 257]), rank 0 
+2025-05-07 09:12:31.130765: predicting 0506 
+2025-05-07 09:12:31.179787: 0506, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 09:12:37.768311: predicting 0507 
+2025-05-07 09:12:37.819902: 0507, shape torch.Size([1, 623, 276, 276]), rank 0 
+2025-05-07 09:12:41.600506: predicting 0508 
+2025-05-07 09:12:41.644574: 0508, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:12:47.568123: predicting 0509 
+2025-05-07 09:12:47.630009: 0509, shape torch.Size([1, 651, 318, 318]), rank 0 
+2025-05-07 09:12:55.450568: predicting 0510 
+2025-05-07 09:12:55.500357: 0510, shape torch.Size([1, 688, 333, 333]), rank 0 
+2025-05-07 09:13:01.199039: predicting 0511 
+2025-05-07 09:13:01.258979: 0511, shape torch.Size([1, 651, 276, 276]), rank 0 
+2025-05-07 09:13:06.752712: predicting 0512 
+2025-05-07 09:13:06.810147: 0512, shape torch.Size([1, 651, 311, 311]), rank 0 
+2025-05-07 09:13:13.431607: predicting 0513 
+2025-05-07 09:13:13.479361: 0513, shape torch.Size([1, 1238, 309, 309]), rank 0 
+2025-05-07 09:13:25.869342: predicting 0514 
+2025-05-07 09:13:25.945461: 0514, shape torch.Size([1, 623, 293, 293]), rank 0 
+2025-05-07 09:13:31.496844: predicting 0515 
+2025-05-07 09:13:31.548061: 0515, shape torch.Size([1, 566, 298, 298]), rank 0 
+2025-05-07 09:13:37.730937: predicting 0516 
+2025-05-07 09:13:37.783088: 0516, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 09:13:42.739517: predicting 0517 
+2025-05-07 09:13:42.789371: 0517, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:13:48.772331: predicting 0518 
+2025-05-07 09:13:48.819396: 0518, shape torch.Size([1, 705, 333, 333]), rank 0 
+2025-05-07 09:13:55.930792: predicting 0519 
+2025-05-07 09:13:56.294504: 0519, shape torch.Size([1, 652, 274, 274]), rank 0 
+2025-05-07 09:14:00.810133: predicting 0520 
+2025-05-07 09:14:00.875401: 0520, shape torch.Size([1, 566, 288, 288]), rank 0 
+2025-05-07 09:14:06.457056: predicting 0521 
+2025-05-07 09:14:06.571765: 0521, shape torch.Size([1, 568, 287, 287]), rank 0 
+2025-05-07 09:14:15.265941: predicting 0522 
+2025-05-07 09:14:15.384229: 0522, shape torch.Size([1, 635, 317, 317]), rank 0 
+2025-05-07 09:14:21.589263: predicting 0523 
+2025-05-07 09:14:21.652333: 0523, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 09:14:27.588888: predicting 0524 
+2025-05-07 09:14:27.655520: 0524, shape torch.Size([1, 566, 279, 279]), rank 0 
+2025-05-07 09:14:31.963035: predicting 0525 
+2025-05-07 09:14:32.010377: 0525, shape torch.Size([1, 635, 302, 302]), rank 0 
+2025-05-07 09:14:37.583659: predicting 0526 
+2025-05-07 09:14:37.651304: 0526, shape torch.Size([1, 553, 293, 293]), rank 0 
+2025-05-07 09:14:41.918483: predicting 0527 
+2025-05-07 09:14:41.967086: 0527, shape torch.Size([1, 650, 265, 265]), rank 0 
+2025-05-07 09:14:45.325201: predicting 0528 
+2025-05-07 09:14:45.378714: 0528, shape torch.Size([1, 652, 288, 288]), rank 0 
+2025-05-07 09:14:50.370313: predicting 0529 
+2025-05-07 09:14:50.424760: 0529, shape torch.Size([1, 567, 259, 259]), rank 0 
+2025-05-07 09:14:54.691878: predicting 0530 
+2025-05-07 09:14:54.744644: 0530, shape torch.Size([1, 573, 247, 247]), rank 0 
+2025-05-07 09:14:57.699585: predicting 0531 
+2025-05-07 09:14:57.746354: 0531, shape torch.Size([1, 1153, 315, 315]), rank 0 
+2025-05-07 09:15:09.105324: predicting 0532 
+2025-05-07 09:15:09.173141: 0532, shape torch.Size([1, 631, 284, 284]), rank 0 
+2025-05-07 09:15:12.893191: predicting 0533 
+2025-05-07 09:15:12.957775: 0533, shape torch.Size([1, 635, 298, 298]), rank 0 
+2025-05-07 09:15:19.215361: predicting 0534 
+2025-05-07 09:15:19.258859: 0534, shape torch.Size([1, 1053, 299, 299]), rank 0 
+2025-05-07 09:15:29.296437: predicting 0535 
+2025-05-07 09:15:29.374866: 0535, shape torch.Size([1, 1056, 277, 277]), rank 0 
+2025-05-07 09:15:36.258600: predicting 0536 
+2025-05-07 09:15:36.356720: 0536, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:15:42.628350: predicting 0537 
+2025-05-07 09:15:42.681451: 0537, shape torch.Size([1, 661, 291, 291]), rank 0 
+2025-05-07 09:15:47.817991: predicting 0538 
+2025-05-07 09:15:47.872239: 0538, shape torch.Size([1, 518, 318, 318]), rank 0 
+2025-05-07 09:15:53.730787: predicting 0539 
+2025-05-07 09:15:53.944910: 0539, shape torch.Size([1, 670, 333, 333]), rank 0 
+2025-05-07 09:16:02.153152: predicting 0540 
+2025-05-07 09:16:02.212621: 0540, shape torch.Size([1, 818, 329, 329]), rank 0 
+2025-05-07 09:16:10.594594: predicting 0541 
+2025-05-07 09:16:10.649782: 0541, shape torch.Size([1, 735, 331, 331]), rank 0 
+2025-05-07 09:16:18.731084: predicting 0542 
+2025-05-07 09:16:18.786780: 0542, shape torch.Size([1, 665, 333, 333]), rank 0 
+2025-05-07 09:16:25.883039: predicting 0543 
+2025-05-07 09:16:25.953325: 0543, shape torch.Size([1, 661, 333, 333]), rank 0 
+2025-05-07 09:16:31.943992: predicting 0544 
+2025-05-07 09:16:32.001939: 0544, shape torch.Size([1, 1153, 305, 305]), rank 0 
+2025-05-07 09:16:41.595319: predicting 0545 
+2025-05-07 09:16:41.657465: 0545, shape torch.Size([1, 621, 333, 333]), rank 0 
+2025-05-07 09:16:48.738812: predicting 0546 
+2025-05-07 09:16:48.798347: 0546, shape torch.Size([1, 566, 281, 281]), rank 0 
+2025-05-07 09:16:54.339150: predicting 0547 
+2025-05-07 09:16:54.389164: 0547, shape torch.Size([1, 535, 283, 283]), rank 0 
+2025-05-07 09:16:57.488170: predicting 0548 
+2025-05-07 09:16:57.535161: 0548, shape torch.Size([1, 1237, 297, 297]), rank 0 
+2025-05-07 09:17:09.564902: predicting 0549 
+2025-05-07 09:17:09.620959: 0549, shape torch.Size([1, 465, 263, 263]), rank 0 
+2025-05-07 09:17:13.157308: predicting 0550 
+2025-05-07 09:17:13.209274: 0550, shape torch.Size([1, 681, 307, 307]), rank 0 
+2025-05-07 09:17:19.150195: predicting 0551 
+2025-05-07 09:17:19.201073: 0551, shape torch.Size([1, 653, 303, 303]), rank 0 
+2025-05-07 09:17:25.191664: predicting 0552 
+2025-05-07 09:17:25.245525: 0552, shape torch.Size([1, 610, 333, 333]), rank 0 
+2025-05-07 09:17:30.232734: predicting 0553 
+2025-05-07 09:17:30.284530: 0553, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:17:36.233029: predicting 0554 
+2025-05-07 09:17:36.293513: 0554, shape torch.Size([1, 651, 311, 311]), rank 0 
+2025-05-07 09:17:42.589244: predicting 0555 
+2025-05-07 09:17:42.645700: 0555, shape torch.Size([1, 701, 333, 333]), rank 0 
+2025-05-07 09:17:51.377299: predicting 0556 
+2025-05-07 09:17:51.432338: 0556, shape torch.Size([1, 895, 333, 333]), rank 0 
+2025-05-07 09:18:00.601134: predicting 0557 
+2025-05-07 09:18:00.667787: 0557, shape torch.Size([1, 651, 305, 305]), rank 0 
+2025-05-07 09:18:06.088727: predicting 0558 
+2025-05-07 09:18:06.135553: 0558, shape torch.Size([1, 1096, 333, 333]), rank 0 
+2025-05-07 09:18:16.657214: predicting 0559 
+2025-05-07 09:18:16.733149: 0559, shape torch.Size([1, 566, 286, 286]), rank 0 
+2025-05-07 09:18:22.517824: predicting 0560 
+2025-05-07 09:18:22.587528: 0560, shape torch.Size([1, 656, 310, 310]), rank 0 
+2025-05-07 09:18:28.816854: predicting 0561 
+2025-05-07 09:18:28.883703: 0561, shape torch.Size([1, 666, 299, 299]), rank 0 
+2025-05-07 09:18:34.840128: predicting 0562 
+2025-05-07 09:18:34.889016: 0562, shape torch.Size([1, 631, 333, 333]), rank 0 
+2025-05-07 09:18:41.501867: predicting 0563 
+2025-05-07 09:18:41.557846: 0563, shape torch.Size([1, 610, 314, 314]), rank 0 
+2025-05-07 09:18:46.480562: predicting 0564 
+2025-05-07 09:18:46.527699: 0564, shape torch.Size([1, 986, 321, 321]), rank 0 
+2025-05-07 09:18:57.780597: predicting 0565 
+2025-05-07 09:18:57.847865: 0565, shape torch.Size([1, 901, 327, 327]), rank 0 
+2025-05-07 09:19:06.797724: predicting 0566 
+2025-05-07 09:19:06.857971: 0566, shape torch.Size([1, 986, 329, 329]), rank 0 
+2025-05-07 09:19:18.694484: predicting 0567 
+2025-05-07 09:19:18.764658: 0567, shape torch.Size([1, 601, 286, 286]), rank 0 
+2025-05-07 09:19:22.102188: predicting 0568 
+2025-05-07 09:19:22.150946: 0568, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:19:28.117074: predicting 0569 
+2025-05-07 09:19:28.174477: 0569, shape torch.Size([1, 566, 281, 281]), rank 0 
+2025-05-07 09:19:33.390212: predicting 0570 
+2025-05-07 09:19:33.444949: 0570, shape torch.Size([1, 567, 297, 297]), rank 0 
+2025-05-07 09:19:39.384463: predicting 0571 
+2025-05-07 09:19:39.434838: 0571, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 09:19:44.263528: predicting 0572 
+2025-05-07 09:19:44.314042: 0572, shape torch.Size([1, 1305, 323, 323]), rank 0 
+2025-05-07 09:19:58.045856: predicting 0573 
+2025-05-07 09:19:58.116478: 0573, shape torch.Size([1, 651, 306, 306]), rank 0 
+2025-05-07 09:20:04.755625: predicting 0574 
+2025-05-07 09:20:04.808706: 0574, shape torch.Size([1, 613, 295, 295]), rank 0 
+2025-05-07 09:20:10.281748: predicting 0575 
+2025-05-07 09:20:10.329845: 0575, shape torch.Size([1, 1238, 319, 319]), rank 0 
+2025-05-07 09:20:23.308627: predicting 0576 
+2025-05-07 09:20:23.375059: 0576, shape torch.Size([1, 565, 302, 302]), rank 0 
+2025-05-07 09:20:29.115173: predicting 0577 
+2025-05-07 09:20:29.162279: 0577, shape torch.Size([1, 595, 282, 282]), rank 0 
+2025-05-07 09:20:34.235033: predicting 0578 
+2025-05-07 09:20:34.285522: 0578, shape torch.Size([1, 651, 262, 262]), rank 0 
+2025-05-07 09:20:37.813860: predicting 0579 
+2025-05-07 09:20:37.871042: 0579, shape torch.Size([1, 566, 273, 273]), rank 0 
+2025-05-07 09:20:42.885334: predicting 0580 
+2025-05-07 09:20:42.934146: 0580, shape torch.Size([1, 566, 295, 295]), rank 0 
+2025-05-07 09:20:47.598567: predicting 0581 
+2025-05-07 09:20:47.644903: 0581, shape torch.Size([1, 651, 292, 292]), rank 0 
+2025-05-07 09:20:52.913677: predicting 0582 
+2025-05-07 09:20:52.965909: 0582, shape torch.Size([1, 575, 281, 281]), rank 0 
+2025-05-07 09:20:58.104563: predicting 0583 
+2025-05-07 09:20:58.152128: 0583, shape torch.Size([1, 568, 258, 258]), rank 0 
+2025-05-07 09:21:03.435568: predicting 0584 
+2025-05-07 09:21:03.478544: 0584, shape torch.Size([1, 1261, 305, 305]), rank 0 
+2025-05-07 09:21:13.148411: predicting 0585 
+2025-05-07 09:21:13.211592: 0585, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:21:19.221657: predicting 0586 
+2025-05-07 09:21:19.282001: 0586, shape torch.Size([1, 566, 305, 305]), rank 0 
+2025-05-07 09:21:23.948702: predicting 0587 
+2025-05-07 09:21:24.005714: 0587, shape torch.Size([1, 652, 288, 288]), rank 0 
+2025-05-07 09:21:29.814013: predicting 0588 
+2025-05-07 09:21:29.872980: 0588, shape torch.Size([1, 566, 293, 293]), rank 0 
+2025-05-07 09:21:34.216526: predicting 0589 
+2025-05-07 09:21:34.259502: 0589, shape torch.Size([1, 1135, 297, 297]), rank 0 
+2025-05-07 09:21:48.394684: predicting 0590 
+2025-05-07 09:21:48.460305: 0590, shape torch.Size([1, 664, 281, 281]), rank 0 
+2025-05-07 09:21:52.028706: predicting 0591 
+2025-05-07 09:21:52.092922: 0591, shape torch.Size([1, 566, 299, 299]), rank 0 
+2025-05-07 09:21:58.863817: predicting 0592 
+2025-05-07 09:21:58.921175: 0592, shape torch.Size([1, 651, 267, 267]), rank 0 
+2025-05-07 09:22:02.618018: predicting 0593 
+2025-05-07 09:22:02.672409: 0593, shape torch.Size([1, 651, 304, 304]), rank 0 
+2025-05-07 09:22:07.768923: predicting 0594 
+2025-05-07 09:22:07.845234: 0594, shape torch.Size([1, 486, 279, 279]), rank 0 
+2025-05-07 09:22:12.406070: predicting 0595 
+2025-05-07 09:22:12.474797: 0595, shape torch.Size([1, 575, 251, 251]), rank 0 
+2025-05-07 09:22:15.901303: predicting 0596 
+2025-05-07 09:22:15.947339: 0596, shape torch.Size([1, 652, 313, 313]), rank 0 
+2025-05-07 09:22:22.072052: predicting 0597 
+2025-05-07 09:22:22.134695: 0597, shape torch.Size([1, 651, 307, 307]), rank 0 
+2025-05-07 09:22:29.683820: predicting 0598 
+2025-05-07 09:22:29.739329: 0598, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:22:34.049351: predicting 0599 
+2025-05-07 09:22:34.120473: 0599, shape torch.Size([1, 567, 267, 267]), rank 0 
+2025-05-07 09:22:39.493672: predicting 0600 
+2025-05-07 09:22:39.549081: 0600, shape torch.Size([1, 568, 283, 283]), rank 0 
+2025-05-07 09:22:43.004189: predicting 0601 
+2025-05-07 09:22:43.049136: 0601, shape torch.Size([1, 566, 308, 308]), rank 0 
+2025-05-07 09:22:48.584443: predicting 0602 
+2025-05-07 09:22:48.639424: 0602, shape torch.Size([1, 566, 305, 305]), rank 0 
+2025-05-07 09:22:53.991293: predicting 0603 
+2025-05-07 09:22:54.043945: 0603, shape torch.Size([1, 568, 251, 251]), rank 0 
+2025-05-07 09:22:58.111607: predicting 0604 
+2025-05-07 09:22:58.155674: 0604, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 09:23:03.191150: predicting 0605 
+2025-05-07 09:23:03.239907: 0605, shape torch.Size([1, 735, 319, 319]), rank 0 
+2025-05-07 09:23:09.568203: predicting 0606 
+2025-05-07 09:23:09.619235: 0606, shape torch.Size([1, 678, 333, 333]), rank 0 
+2025-05-07 09:23:17.824287: predicting 0607 
+2025-05-07 09:23:17.873854: 0607, shape torch.Size([1, 1273, 333, 333]), rank 0 
+2025-05-07 09:23:32.399444: predicting 0608 
+2025-05-07 09:23:32.474467: 0608, shape torch.Size([1, 566, 271, 271]), rank 0 
+2025-05-07 09:23:37.704411: predicting 0609 
+2025-05-07 09:23:37.756694: 0609, shape torch.Size([1, 648, 269, 269]), rank 0 
+2025-05-07 09:23:41.230814: predicting 0610 
+2025-05-07 09:23:41.277334: 0610, shape torch.Size([1, 651, 283, 283]), rank 0 
+2025-05-07 09:23:46.784163: predicting 0611 
+2025-05-07 09:23:46.835559: 0611, shape torch.Size([1, 566, 290, 290]), rank 0 
+2025-05-07 09:23:51.465573: predicting 0612 
+2025-05-07 09:23:51.521205: 0612, shape torch.Size([1, 648, 297, 297]), rank 0 
+2025-05-07 09:23:58.566409: predicting 0613 
+2025-05-07 09:23:58.617966: 0613, shape torch.Size([1, 568, 256, 256]), rank 0 
+2025-05-07 09:24:01.747663: predicting 0614 
+2025-05-07 09:24:01.794986: 0614, shape torch.Size([1, 651, 283, 283]), rank 0 
+2025-05-07 09:24:05.813048: predicting 0615 
+2025-05-07 09:24:05.856581: 0615, shape torch.Size([1, 1321, 327, 327]), rank 0 
+2025-05-07 09:24:19.192440: predicting 0616 
+2025-05-07 09:24:19.250398: 0616, shape torch.Size([1, 1305, 327, 327]), rank 0 
+2025-05-07 09:24:33.100890: predicting 0617 
+2025-05-07 09:24:33.207081: 0617, shape torch.Size([1, 1070, 323, 323]), rank 0 
+2025-05-07 09:24:43.376890: predicting 0618 
+2025-05-07 09:24:43.432660: 0618, shape torch.Size([1, 651, 323, 323]), rank 0 
+2025-05-07 09:24:50.508512: predicting 0619 
+2025-05-07 09:24:50.565575: 0619, shape torch.Size([1, 651, 325, 325]), rank 0 
+2025-05-07 09:24:56.972941: predicting 0620 
+2025-05-07 09:24:57.056642: 0620, shape torch.Size([1, 651, 289, 289]), rank 0 
+2025-05-07 09:25:04.631513: predicting 0621 
+2025-05-07 09:25:04.698501: 0621, shape torch.Size([1, 651, 318, 318]), rank 0 
+2025-05-07 09:25:09.622829: predicting 0622 
+2025-05-07 09:25:09.701120: 0622, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:25:15.558892: predicting 0623 
+2025-05-07 09:25:15.614305: 0623, shape torch.Size([1, 613, 285, 285]), rank 0 
+2025-05-07 09:25:20.930363: predicting 0624 
+2025-05-07 09:25:20.994588: 0624, shape torch.Size([1, 613, 301, 301]), rank 0 
+2025-05-07 09:25:25.417900: predicting 0625 
+2025-05-07 09:25:25.481737: 0625, shape torch.Size([1, 566, 250, 250]), rank 0 
+2025-05-07 09:25:30.227574: predicting 0626 
+2025-05-07 09:25:30.277511: 0626, shape torch.Size([1, 635, 311, 311]), rank 0 
+2025-05-07 09:25:34.986747: predicting 0627 
+2025-05-07 09:25:35.041159: 0627, shape torch.Size([1, 665, 290, 290]), rank 0 
+2025-05-07 09:25:41.657536: predicting 0628 
+2025-05-07 09:25:41.703778: 0628, shape torch.Size([1, 736, 287, 287]), rank 0 
+2025-05-07 09:25:48.041095: predicting 0629 
+2025-05-07 09:25:48.095039: 0629, shape torch.Size([1, 1072, 319, 319]), rank 0 
+2025-05-07 09:25:57.881119: predicting 0630 
+2025-05-07 09:25:57.957659: 0630, shape torch.Size([1, 651, 304, 304]), rank 0 
+2025-05-07 09:26:05.323986: predicting 0631 
+2025-05-07 09:26:05.369951: 0631, shape torch.Size([1, 651, 279, 279]), rank 0 
+2025-05-07 09:26:09.247983: predicting 0632 
+2025-05-07 09:26:09.300828: 0632, shape torch.Size([1, 583, 269, 269]), rank 0 
+2025-05-07 09:26:13.344571: predicting 0633 
+2025-05-07 09:26:13.397841: 0633, shape torch.Size([1, 568, 268, 268]), rank 0 
+2025-05-07 09:26:17.525469: predicting 0634 
+2025-05-07 09:26:17.570589: 0634, shape torch.Size([1, 588, 299, 299]), rank 0 
+2025-05-07 09:26:22.649228: predicting 0635 
+2025-05-07 09:26:22.717950: 0635, shape torch.Size([1, 652, 249, 249]), rank 0 
+2025-05-07 09:26:27.747027: predicting 0636 
+2025-05-07 09:26:27.790610: 0636, shape torch.Size([1, 1145, 306, 306]), rank 0 
+2025-05-07 09:26:38.349514: predicting 0637 
+2025-05-07 09:26:38.410127: 0637, shape torch.Size([1, 605, 330, 330]), rank 0 
+2025-05-07 09:26:45.126723: predicting 0638 
+2025-05-07 09:26:45.180426: 0638, shape torch.Size([1, 651, 302, 302]), rank 0 
+2025-05-07 09:26:52.698125: predicting 0639 
+2025-05-07 09:26:52.746270: 0639, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 09:26:55.969748: predicting 0640 
+2025-05-07 09:26:56.024027: 0640, shape torch.Size([1, 568, 261, 261]), rank 0 
+2025-05-07 09:27:00.991052: predicting 0641 
+2025-05-07 09:27:01.033777: 0641, shape torch.Size([1, 691, 333, 333]), rank 0 
+2025-05-07 09:27:06.426912: predicting 0642 
+2025-05-07 09:27:06.487479: 0642, shape torch.Size([1, 651, 271, 271]), rank 0 
+2025-05-07 09:27:10.725619: predicting 0643 
+2025-05-07 09:27:10.769549: 0643, shape torch.Size([1, 1153, 305, 305]), rank 0 
+2025-05-07 09:27:22.530344: predicting 0644 
+2025-05-07 09:27:22.590857: 0644, shape torch.Size([1, 670, 281, 281]), rank 0 
+2025-05-07 09:27:26.278072: predicting 0645 
+2025-05-07 09:27:26.324029: 0645, shape torch.Size([1, 1238, 306, 306]), rank 0 
+2025-05-07 09:27:38.185788: predicting 0646 
+2025-05-07 09:27:38.245902: 0646, shape torch.Size([1, 1238, 310, 310]), rank 0 
+2025-05-07 09:27:50.941148: predicting 0647 
+2025-05-07 09:27:51.007411: 0647, shape torch.Size([1, 566, 286, 286]), rank 0 
+2025-05-07 09:27:54.552389: predicting 0648 
+2025-05-07 09:27:54.740381: 0648, shape torch.Size([1, 651, 317, 317]), rank 0 
+2025-05-07 09:28:02.130696: predicting 0649 
+2025-05-07 09:28:02.183592: 0649, shape torch.Size([1, 1153, 317, 317]), rank 0 
+2025-05-07 09:28:13.208967: predicting 0650 
+2025-05-07 09:28:13.275229: 0650, shape torch.Size([1, 901, 297, 297]), rank 0 
+2025-05-07 09:28:21.389348: predicting 0651 
+2025-05-07 09:28:21.465453: 0651, shape torch.Size([1, 638, 283, 283]), rank 0 
+2025-05-07 09:28:27.306603: predicting 0652 
+2025-05-07 09:28:27.361522: 0652, shape torch.Size([1, 901, 285, 285]), rank 0 
+2025-05-07 09:28:32.789369: predicting 0653 
+2025-05-07 09:28:32.864476: 0653, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:28:38.534133: predicting 0654 
+2025-05-07 09:28:38.578921: 0654, shape torch.Size([1, 651, 308, 308]), rank 0 
+2025-05-07 09:28:44.223899: predicting 0655 
+2025-05-07 09:28:44.278058: 0655, shape torch.Size([1, 651, 293, 293]), rank 0 
+2025-05-07 09:28:50.071614: predicting 0656 
+2025-05-07 09:28:50.119408: 0656, shape torch.Size([1, 566, 273, 273]), rank 0 
+2025-05-07 09:28:53.611890: predicting 0657 
+2025-05-07 09:28:53.663543: 0657, shape torch.Size([1, 566, 297, 297]), rank 0 
+2025-05-07 09:28:59.749513: predicting 0658 
+2025-05-07 09:28:59.825040: 0658, shape torch.Size([1, 568, 239, 239]), rank 0 
+2025-05-07 09:29:03.118575: predicting 0659 
+2025-05-07 09:29:03.160113: 0659, shape torch.Size([1, 1321, 314, 314]), rank 0 
+2025-05-07 09:29:15.279521: predicting 0660 
+2025-05-07 09:29:15.354444: 0660, shape torch.Size([1, 566, 266, 266]), rank 0 
+2025-05-07 09:29:20.777291: predicting 0661 
+2025-05-07 09:29:20.840121: 0661, shape torch.Size([1, 566, 297, 297]), rank 0 
+2025-05-07 09:29:25.393206: predicting 0662 
+2025-05-07 09:29:25.456428: 0662, shape torch.Size([1, 566, 301, 301]), rank 0 
+2025-05-07 09:29:31.079666: predicting 0663 
+2025-05-07 09:29:31.138479: 0663, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:29:38.101444: predicting 0664 
+2025-05-07 09:29:38.184008: 0664, shape torch.Size([1, 651, 313, 313]), rank 0 
+2025-05-07 09:29:43.332726: predicting 0665 
+2025-05-07 09:29:43.384881: 0665, shape torch.Size([1, 660, 300, 300]), rank 0 
+2025-05-07 09:29:50.095264: predicting 0666 
+2025-05-07 09:29:50.152545: 0666, shape torch.Size([1, 651, 309, 309]), rank 0 
+2025-05-07 09:29:56.048167: predicting 0667 
+2025-05-07 09:29:56.101815: 0667, shape torch.Size([1, 483, 299, 299]), rank 0 
+2025-05-07 09:30:02.079960: predicting 0668 
+2025-05-07 09:30:02.126462: 0668, shape torch.Size([1, 651, 311, 311]), rank 0 
+2025-05-07 09:30:06.803043: predicting 0669 
+2025-05-07 09:30:06.872618: 0669, shape torch.Size([1, 618, 306, 306]), rank 0 
+2025-05-07 09:30:12.961360: predicting 0670 
+2025-05-07 09:30:13.017463: 0670, shape torch.Size([1, 601, 299, 299]), rank 0 
+2025-05-07 09:30:20.000357: predicting 0671 
+2025-05-07 09:30:20.051775: 0671, shape torch.Size([1, 651, 309, 309]), rank 0 
+2025-05-07 09:30:24.679636: predicting 0672 
+2025-05-07 09:30:24.729824: 0672, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:30:30.945342: predicting 0673 
+2025-05-07 09:30:30.997351: 0673, shape torch.Size([1, 638, 321, 321]), rank 0 
+2025-05-07 09:30:37.826678: predicting 0674 
+2025-05-07 09:30:37.874178: 0674, shape torch.Size([1, 651, 293, 293]), rank 0 
+2025-05-07 09:30:43.481966: predicting 0675 
+2025-05-07 09:30:43.535430: 0675, shape torch.Size([1, 651, 265, 265]), rank 0 
+2025-05-07 09:30:47.243629: predicting 0676 
+2025-05-07 09:30:47.294800: 0676, shape torch.Size([1, 651, 313, 313]), rank 0 
+2025-05-07 09:30:55.008438: predicting 0677 
+2025-05-07 09:30:55.060278: 0677, shape torch.Size([1, 567, 302, 302]), rank 0 
+2025-05-07 09:30:59.441415: predicting 0678 
+2025-05-07 09:30:59.515237: 0678, shape torch.Size([1, 568, 274, 274]), rank 0 
+2025-05-07 09:31:04.456482: predicting 0679 
+2025-05-07 09:31:04.510192: 0679, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:31:10.368092: predicting 0680 
+2025-05-07 09:31:10.411026: 0680, shape torch.Size([1, 1238, 321, 321]), rank 0 
+2025-05-07 09:31:21.808831: predicting 0681 
+2025-05-07 09:31:21.884668: 0681, shape torch.Size([1, 566, 286, 286]), rank 0 
+2025-05-07 09:31:26.828169: predicting 0682 
+2025-05-07 09:31:26.881400: 0682, shape torch.Size([1, 1238, 289, 289]), rank 0 
+2025-05-07 09:31:38.447835: predicting 0683 
+2025-05-07 09:31:38.513552: 0683, shape torch.Size([1, 1238, 314, 314]), rank 0 
+2025-05-07 09:31:51.055742: predicting 0684 
+2025-05-07 09:31:51.141917: 0684, shape torch.Size([1, 1238, 297, 297]), rank 0 
+2025-05-07 09:32:04.002853: predicting 0685 
+2025-05-07 09:32:04.056077: 0685, shape torch.Size([1, 618, 333, 333]), rank 0 
+2025-05-07 09:32:09.438860: predicting 0686 
+2025-05-07 09:32:09.485604: 0686, shape torch.Size([1, 735, 324, 324]), rank 0 
+2025-05-07 09:32:16.668072: predicting 0687 
+2025-05-07 09:32:16.858500: 0687, shape torch.Size([1, 660, 313, 313]), rank 0 
+2025-05-07 09:32:22.849143: predicting 0688 
+2025-05-07 09:32:22.913774: 0688, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:32:27.660812: predicting 0689 
+2025-05-07 09:32:27.716758: 0689, shape torch.Size([1, 626, 293, 293]), rank 0 
+2025-05-07 09:32:34.116764: predicting 0690 
+2025-05-07 09:32:34.175989: 0690, shape torch.Size([1, 566, 276, 276]), rank 0 
+2025-05-07 09:32:39.333397: predicting 0691 
+2025-05-07 09:32:39.389364: 0691, shape torch.Size([1, 566, 283, 283]), rank 0 
+2025-05-07 09:32:42.775347: predicting 0692 
+2025-05-07 09:32:42.843619: 0692, shape torch.Size([1, 566, 254, 254]), rank 0 
+2025-05-07 09:32:47.834133: predicting 0693 
+2025-05-07 09:32:47.888500: 0693, shape torch.Size([1, 652, 271, 271]), rank 0 
+2025-05-07 09:32:51.533264: predicting 0694 
+2025-05-07 09:32:51.580538: 0694, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:32:58.600620: predicting 0695 
+2025-05-07 09:32:58.657633: 0695, shape torch.Size([1, 651, 327, 327]), rank 0 
+2025-05-07 09:33:05.879327: predicting 0696 
+2025-05-07 09:33:05.936337: 0696, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:33:11.848741: predicting 0697 
+2025-05-07 09:33:11.925706: 0697, shape torch.Size([1, 651, 268, 268]), rank 0 
+2025-05-07 09:33:16.266439: predicting 0698 
+2025-05-07 09:33:16.320075: 0698, shape torch.Size([1, 618, 305, 305]), rank 0 
+2025-05-07 09:33:21.568502: predicting 0699 
+2025-05-07 09:33:21.629117: 0699, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 09:33:25.801597: predicting 0700 
+2025-05-07 09:33:25.883057: 0700, shape torch.Size([1, 568, 267, 267]), rank 0 
+2025-05-07 09:33:30.138968: predicting 0701 
+2025-05-07 09:33:30.193954: 0701, shape torch.Size([1, 650, 305, 305]), rank 0 
+2025-05-07 09:33:37.091306: predicting 0702 
+2025-05-07 09:33:37.143778: 0702, shape torch.Size([1, 735, 305, 305]), rank 0 
+2025-05-07 09:33:44.501775: predicting 0703 
+2025-05-07 09:33:44.557320: 0703, shape torch.Size([1, 740, 294, 294]), rank 0 
+2025-05-07 09:33:51.135822: predicting 0704 
+2025-05-07 09:33:51.185438: 0704, shape torch.Size([1, 735, 304, 304]), rank 0 
+2025-05-07 09:33:57.602130: predicting 0705 
+2025-05-07 09:33:57.659395: 0705, shape torch.Size([1, 651, 263, 263]), rank 0 
+2025-05-07 09:34:02.090397: predicting 0706 
+2025-05-07 09:34:02.144984: 0706, shape torch.Size([1, 651, 312, 312]), rank 0 
+2025-05-07 09:34:09.619404: predicting 0707 
+2025-05-07 09:34:09.678104: 0707, shape torch.Size([1, 695, 333, 333]), rank 0 
+2025-05-07 09:34:18.348467: predicting 0708 
+2025-05-07 09:34:18.400006: 0708, shape torch.Size([1, 651, 284, 284]), rank 0 
+2025-05-07 09:34:23.588521: predicting 0709 
+2025-05-07 09:34:23.645688: 0709, shape torch.Size([1, 553, 285, 285]), rank 0 
+2025-05-07 09:34:28.456451: predicting 0710 
+2025-05-07 09:34:28.519868: 0710, shape torch.Size([1, 651, 274, 274]), rank 0 
+2025-05-07 09:34:32.041067: predicting 0711 
+2025-05-07 09:34:32.099224: 0711, shape torch.Size([1, 651, 292, 292]), rank 0 
+2025-05-07 09:34:38.474195: predicting 0712 
+2025-05-07 09:34:38.517799: 0712, shape torch.Size([1, 1178, 313, 313]), rank 0 
+2025-05-07 09:34:48.858727: predicting 0713 
+2025-05-07 09:34:48.929647: 0713, shape torch.Size([1, 566, 285, 285]), rank 0 
+2025-05-07 09:34:55.227640: predicting 0714 
+2025-05-07 09:34:55.293638: 0714, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:35:01.271555: predicting 0715 
+2025-05-07 09:35:01.340451: 0715, shape torch.Size([1, 565, 295, 295]), rank 0 
+2025-05-07 09:35:05.898964: predicting 0716 
+2025-05-07 09:35:05.955464: 0716, shape torch.Size([1, 565, 283, 283]), rank 0 
+2025-05-07 09:35:09.803967: predicting 0717 
+2025-05-07 09:35:09.849712: 0717, shape torch.Size([1, 586, 309, 309]), rank 0 
+2025-05-07 09:35:15.369584: predicting 0718 
+2025-05-07 09:35:15.424626: 0718, shape torch.Size([1, 568, 263, 263]), rank 0 
+2025-05-07 09:35:20.578962: predicting 0719 
+2025-05-07 09:35:20.626302: 0719, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:35:27.003397: predicting 0720 
+2025-05-07 09:35:27.058554: 0720, shape torch.Size([1, 566, 315, 315]), rank 0 
+2025-05-07 09:35:31.670957: predicting 0721 
+2025-05-07 09:35:31.718099: 0721, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:35:37.672734: predicting 0722 
+2025-05-07 09:35:37.722655: 0722, shape torch.Size([1, 660, 323, 323]), rank 0 
+2025-05-07 09:35:44.437343: predicting 0723 
+2025-05-07 09:35:44.488479: 0723, shape torch.Size([1, 661, 333, 333]), rank 0 
+2025-05-07 09:35:51.595686: predicting 0724 
+2025-05-07 09:35:51.648648: 0724, shape torch.Size([1, 651, 319, 319]), rank 0 
+2025-05-07 09:35:57.715906: predicting 0725 
+2025-05-07 09:35:57.764839: 0725, shape torch.Size([1, 651, 281, 281]), rank 0 
+2025-05-07 09:36:02.362794: predicting 0726 
+2025-05-07 09:36:02.414347: 0726, shape torch.Size([1, 483, 314, 314]), rank 0 
+2025-05-07 09:36:08.240803: predicting 0727 
+2025-05-07 09:36:08.300871: 0727, shape torch.Size([1, 483, 274, 274]), rank 0 
+2025-05-07 09:36:11.114813: predicting 0728 
+2025-05-07 09:36:11.161113: 0728, shape torch.Size([1, 643, 315, 315]), rank 0 
+2025-05-07 09:36:18.501954: predicting 0729 
+2025-05-07 09:36:18.560659: 0729, shape torch.Size([1, 651, 293, 293]), rank 0 
+2025-05-07 09:36:24.714238: predicting 0730 
+2025-05-07 09:36:24.767393: 0730, shape torch.Size([1, 566, 294, 294]), rank 0 
+2025-05-07 09:36:30.474458: predicting 0731 
+2025-05-07 09:36:30.526517: 0731, shape torch.Size([1, 652, 293, 293]), rank 0 
+2025-05-07 09:36:35.255650: predicting 0732 
+2025-05-07 09:36:35.303244: 0732, shape torch.Size([1, 650, 313, 313]), rank 0 
+2025-05-07 09:36:42.513703: predicting 0733 
+2025-05-07 09:36:42.565217: 0733, shape torch.Size([1, 600, 284, 284]), rank 0 
+2025-05-07 09:36:46.152857: predicting 0734 
+2025-05-07 09:36:46.204973: 0734, shape torch.Size([1, 651, 305, 305]), rank 0 
+2025-05-07 09:36:52.310682: predicting 0735 
+2025-05-07 09:36:52.361160: 0735, shape torch.Size([1, 652, 289, 289]), rank 0 
+2025-05-07 09:36:58.291785: predicting 0736 
+2025-05-07 09:36:58.360067: 0736, shape torch.Size([1, 1155, 299, 299]), rank 0 
+2025-05-07 09:37:09.520332: predicting 0737 
+2025-05-07 09:37:09.588955: 0737, shape torch.Size([1, 568, 245, 245]), rank 0 
+2025-05-07 09:37:12.814246: predicting 0738 
+2025-05-07 09:37:12.990064: 0738, shape torch.Size([1, 566, 259, 259]), rank 0 
+2025-05-07 09:37:17.925186: predicting 0739 
+2025-05-07 09:37:17.974797: 0739, shape torch.Size([1, 568, 238, 238]), rank 0 
+2025-05-07 09:37:20.576000: predicting 0740 
+2025-05-07 09:37:20.633615: 0740, shape torch.Size([1, 566, 300, 300]), rank 0 
+2025-05-07 09:37:26.789818: predicting 0741 
+2025-05-07 09:37:26.841057: 0741, shape torch.Size([1, 651, 307, 307]), rank 0 
+2025-05-07 09:37:32.150237: predicting 0742 
+2025-05-07 09:37:32.200647: 0742, shape torch.Size([1, 1153, 333, 333]), rank 0 
+2025-05-07 09:37:43.627321: predicting 0743 
+2025-05-07 09:37:43.692347: 0743, shape torch.Size([1, 630, 279, 279]), rank 0 
+2025-05-07 09:37:47.057830: predicting 0744 
+2025-05-07 09:37:47.250568: 0744, shape torch.Size([1, 568, 301, 301]), rank 0 
+2025-05-07 09:37:52.926995: predicting 0745 
+2025-05-07 09:37:52.978220: 0745, shape torch.Size([1, 645, 321, 321]), rank 0 
+2025-05-07 09:38:00.276371: predicting 0746 
+2025-05-07 09:38:00.328831: 0746, shape torch.Size([1, 566, 262, 262]), rank 0 
+2025-05-07 09:38:04.935807: predicting 0747 
+2025-05-07 09:38:04.973954: 0747, shape torch.Size([1, 566, 262, 262]), rank 0 
+2025-05-07 09:38:08.201746: predicting 0748 
+2025-05-07 09:38:08.246891: 0748, shape torch.Size([1, 578, 278, 278]), rank 0 
+2025-05-07 09:38:12.372547: predicting 0749 
+2025-05-07 09:38:12.431097: 0749, shape torch.Size([1, 591, 279, 279]), rank 0 
+2025-05-07 09:38:17.269418: predicting 0750 
+2025-05-07 09:38:17.327955: 0750, shape torch.Size([1, 638, 285, 285]), rank 0 
+2025-05-07 09:38:23.112349: predicting 0751 
+2025-05-07 09:38:23.160766: 0751, shape torch.Size([1, 651, 291, 291]), rank 0 
+2025-05-07 09:38:27.690024: predicting 0752 
+2025-05-07 09:38:27.745494: 0752, shape torch.Size([1, 901, 295, 295]), rank 0 
+2025-05-07 09:38:36.269374: predicting 0753 
+2025-05-07 09:38:36.339169: 0753, shape torch.Size([1, 483, 296, 296]), rank 0 
+2025-05-07 09:38:40.757345: predicting 0754 
+2025-05-07 09:38:40.807046: 0754, shape torch.Size([1, 483, 279, 279]), rank 0 
+2025-05-07 09:38:45.109663: predicting 0755 
+2025-05-07 09:38:45.165592: 0755, shape torch.Size([1, 640, 277, 277]), rank 0 
+2025-05-07 09:38:49.432164: predicting 0756 
+2025-05-07 09:38:49.479494: 0756, shape torch.Size([1, 652, 241, 241]), rank 0 
+2025-05-07 09:38:53.017839: predicting 0757 
+2025-05-07 09:38:53.081468: 0757, shape torch.Size([1, 545, 278, 278]), rank 0 
+2025-05-07 09:38:57.591279: predicting 0758 
+2025-05-07 09:38:57.638626: 0758, shape torch.Size([1, 561, 287, 287]), rank 0 
+2025-05-07 09:39:01.462828: predicting 0759 
+2025-05-07 09:39:01.523491: 0759, shape torch.Size([1, 566, 274, 274]), rank 0 
+2025-05-07 09:39:05.526998: predicting 0760 
+2025-05-07 09:39:05.571379: 0760, shape torch.Size([1, 651, 305, 305]), rank 0 
+2025-05-07 09:39:10.459003: predicting 0761 
+2025-05-07 09:39:10.523422: 0761, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:39:16.159516: predicting 0762 
+2025-05-07 09:39:16.221691: 0762, shape torch.Size([1, 566, 289, 289]), rank 0 
+2025-05-07 09:39:20.685485: predicting 0763 
+2025-05-07 09:39:20.736554: 0763, shape torch.Size([1, 901, 310, 310]), rank 0 
+2025-05-07 09:39:28.887578: predicting 0764 
+2025-05-07 09:39:28.942126: 0764, shape torch.Size([1, 601, 271, 271]), rank 0 
+2025-05-07 09:39:32.161520: predicting 0765 
+2025-05-07 09:39:32.207458: 0765, shape torch.Size([1, 651, 274, 274]), rank 0 
+2025-05-07 09:39:36.416047: predicting 0766 
+2025-05-07 09:39:36.462361: 0766, shape torch.Size([1, 818, 306, 306]), rank 0 
+2025-05-07 09:39:45.702004: predicting 0767 
+2025-05-07 09:39:45.763148: 0767, shape torch.Size([1, 521, 282, 282]), rank 0 
+2025-05-07 09:39:50.304156: predicting 0768 
+2025-05-07 09:39:50.346731: 0768, shape torch.Size([1, 1073, 272, 272]), rank 0 
+2025-05-07 09:39:56.341898: predicting 0769 
+2025-05-07 09:39:56.410841: 0769, shape torch.Size([1, 652, 261, 261]), rank 0 
+2025-05-07 09:40:00.155375: predicting 0770 
+2025-05-07 09:40:00.206353: 0770, shape torch.Size([1, 630, 295, 295]), rank 0 
+2025-05-07 09:40:05.303522: predicting 0771 
+2025-05-07 09:40:05.355893: 0771, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:40:10.249893: predicting 0772 
+2025-05-07 09:40:10.299069: 0772, shape torch.Size([1, 1153, 285, 285]), rank 0 
+2025-05-07 09:40:20.418180: predicting 0773 
+2025-05-07 09:40:20.490590: 0773, shape torch.Size([1, 651, 308, 308]), rank 0 
+2025-05-07 09:40:27.337762: predicting 0774 
+2025-05-07 09:40:27.390941: 0774, shape torch.Size([1, 818, 320, 320]), rank 0 
+2025-05-07 09:40:35.508227: predicting 0775 
+2025-05-07 09:40:35.566421: 0775, shape torch.Size([1, 566, 278, 278]), rank 0 
+2025-05-07 09:40:39.146070: predicting 0776 
+2025-05-07 09:40:39.194371: 0776, shape torch.Size([1, 651, 261, 261]), rank 0 
+2025-05-07 09:40:43.790641: predicting 0777 
+2025-05-07 09:40:43.840350: 0777, shape torch.Size([1, 650, 333, 333]), rank 0 
+2025-05-07 09:40:50.628055: predicting 0778 
+2025-05-07 09:40:50.691641: 0778, shape torch.Size([1, 660, 301, 301]), rank 0 
+2025-05-07 09:40:57.108954: predicting 0779 
+2025-05-07 09:40:57.167197: 0779, shape torch.Size([1, 566, 265, 265]), rank 0 
+2025-05-07 09:41:01.906958: predicting 0780 
+2025-05-07 09:41:01.956313: 0780, shape torch.Size([1, 535, 304, 304]), rank 0 
+2025-05-07 09:41:06.020895: predicting 0781 
+2025-05-07 09:41:06.073376: 0781, shape torch.Size([1, 566, 267, 267]), rank 0 
+2025-05-07 09:41:10.036339: predicting 0782 
+2025-05-07 09:41:10.090102: 0782, shape torch.Size([1, 735, 308, 308]), rank 0 
+2025-05-07 09:41:17.423066: predicting 0783 
+2025-05-07 09:41:17.488806: 0783, shape torch.Size([1, 566, 255, 255]), rank 0 
+2025-05-07 09:41:22.690791: predicting 0784 
+2025-05-07 09:41:22.744189: 0784, shape torch.Size([1, 650, 284, 284]), rank 0 
+2025-05-07 09:41:26.327634: predicting 0785 
+2025-05-07 09:41:26.390466: 0785, shape torch.Size([1, 483, 280, 280]), rank 0 
+2025-05-07 09:41:29.836200: predicting 0786 
+2025-05-07 09:41:29.884464: 0786, shape torch.Size([1, 651, 292, 292]), rank 0 
+2025-05-07 09:41:35.058501: predicting 0787 
+2025-05-07 09:41:35.148677: 0787, shape torch.Size([1, 566, 322, 322]), rank 0 
+2025-05-07 09:41:40.602328: predicting 0788 
+2025-05-07 09:41:40.654991: 0788, shape torch.Size([1, 566, 285, 285]), rank 0 
+2025-05-07 09:41:46.212429: predicting 0789 
+2025-05-07 09:41:46.262530: 0789, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:41:52.020916: predicting 0790 
+2025-05-07 09:41:52.081725: 0790, shape torch.Size([1, 651, 278, 278]), rank 0 
+2025-05-07 09:41:55.946885: predicting 0791 
+2025-05-07 09:41:55.999979: 0791, shape torch.Size([1, 600, 315, 315]), rank 0 
+2025-05-07 09:42:01.605762: predicting 0792 
+2025-05-07 09:42:01.684721: 0792, shape torch.Size([1, 566, 287, 287]), rank 0 
+2025-05-07 09:42:07.187828: predicting 0793 
+2025-05-07 09:42:07.236496: 0793, shape torch.Size([1, 1070, 329, 329]), rank 0 
+2025-05-07 09:42:16.601566: predicting 0794 
+2025-05-07 09:42:16.660856: 0794, shape torch.Size([1, 1266, 320, 320]), rank 0 
+2025-05-07 09:42:29.284248: predicting 0795 
+2025-05-07 09:42:29.354140: 0795, shape torch.Size([1, 568, 238, 238]), rank 0 
+2025-05-07 09:42:31.651413: predicting 0796 
+2025-05-07 09:42:31.695159: 0796, shape torch.Size([1, 1138, 333, 333]), rank 0 
+2025-05-07 09:42:44.494534: predicting 0797 
+2025-05-07 09:42:44.556527: 0797, shape torch.Size([1, 570, 290, 290]), rank 0 
+2025-05-07 09:42:49.128018: predicting 0798 
+2025-05-07 09:42:49.392825: 0798, shape torch.Size([1, 601, 315, 315]), rank 0 
+2025-05-07 09:42:55.111754: predicting 0799 
+2025-05-07 09:42:55.172369: 0799, shape torch.Size([1, 588, 301, 301]), rank 0 
+2025-05-07 09:43:02.427299: predicting 0800 
+2025-05-07 09:43:02.482351: 0800, shape torch.Size([1, 586, 293, 293]), rank 0 
+2025-05-07 09:43:07.322302: predicting 0801 
+2025-05-07 09:43:07.378635: 0801, shape torch.Size([1, 578, 315, 315]), rank 0 
+2025-05-07 09:43:13.983183: predicting 0802 
+2025-05-07 09:43:14.034900: 0802, shape torch.Size([1, 652, 291, 291]), rank 0 
+2025-05-07 09:43:19.635048: predicting 0803 
+2025-05-07 09:43:19.691461: 0803, shape torch.Size([1, 566, 263, 263]), rank 0 
+2025-05-07 09:43:23.247908: predicting 0804 
+2025-05-07 09:43:23.309844: 0804, shape torch.Size([1, 566, 292, 292]), rank 0 
+2025-05-07 09:43:29.537179: predicting 0805 
+2025-05-07 09:43:29.587884: 0805, shape torch.Size([1, 551, 311, 311]), rank 0 
+2025-05-07 09:43:33.549669: predicting 0806 
+2025-05-07 09:43:33.608270: 0806, shape torch.Size([1, 635, 268, 268]), rank 0 
+2025-05-07 09:43:38.542763: predicting 0807 
+2025-05-07 09:43:38.591717: 0807, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:43:44.735225: predicting 0808 
+2025-05-07 09:43:44.787525: 0808, shape torch.Size([1, 680, 320, 320]), rank 0 
+2025-05-07 09:43:52.787261: predicting 0809 
+2025-05-07 09:43:52.837235: 0809, shape torch.Size([1, 651, 300, 300]), rank 0 
+2025-05-07 09:43:57.454869: predicting 0810 
+2025-05-07 09:43:57.506235: 0810, shape torch.Size([1, 1153, 333, 333]), rank 0 
+2025-05-07 09:44:08.685414: predicting 0811 
+2025-05-07 09:44:09.069180: 0811, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:44:15.375608: predicting 0812 
+2025-05-07 09:44:15.438816: 0812, shape torch.Size([1, 651, 312, 312]), rank 0 
+2025-05-07 09:44:22.058033: predicting 0813 
+2025-05-07 09:44:22.104630: 0813, shape torch.Size([1, 901, 315, 315]), rank 0 
+2025-05-07 09:44:30.252064: predicting 0814 
+2025-05-07 09:44:30.301338: 0814, shape torch.Size([1, 1121, 326, 326]), rank 0 
+2025-05-07 09:44:42.132780: predicting 0815 
+2025-05-07 09:44:42.201222: 0815, shape torch.Size([1, 735, 291, 291]), rank 0 
+2025-05-07 09:44:47.824043: predicting 0816 
+2025-05-07 09:44:47.873664: 0816, shape torch.Size([1, 651, 288, 288]), rank 0 
+2025-05-07 09:44:52.223836: predicting 0817 
+2025-05-07 09:44:52.274963: 0817, shape torch.Size([1, 623, 309, 309]), rank 0 
+2025-05-07 09:44:59.754010: predicting 0818 
+2025-05-07 09:44:59.830344: 0818, shape torch.Size([1, 605, 301, 301]), rank 0 
+2025-05-07 09:45:05.415844: predicting 0819 
+2025-05-07 09:45:05.472551: 0819, shape torch.Size([1, 652, 308, 308]), rank 0 
+2025-05-07 09:45:10.435291: predicting 0820 
+2025-05-07 09:45:10.482215: 0820, shape torch.Size([1, 902, 294, 294]), rank 0 
+2025-05-07 09:45:18.128531: predicting 0821 
+2025-05-07 09:45:18.186461: 0821, shape torch.Size([1, 566, 282, 282]), rank 0 
+2025-05-07 09:45:22.521913: predicting 0822 
+2025-05-07 09:45:22.568218: 0822, shape torch.Size([1, 626, 277, 277]), rank 0 
+2025-05-07 09:45:26.317519: predicting 0823 
+2025-05-07 09:45:26.368184: 0823, shape torch.Size([1, 568, 270, 270]), rank 0 
+2025-05-07 09:45:31.989006: predicting 0824 
+2025-05-07 09:45:32.074659: 0824, shape torch.Size([1, 621, 295, 295]), rank 0 
+2025-05-07 09:45:36.679520: predicting 0825 
+2025-05-07 09:45:36.722791: 0825, shape torch.Size([1, 1148, 294, 294]), rank 0 
+2025-05-07 09:45:48.170378: predicting 0826 
+2025-05-07 09:45:48.240754: 0826, shape torch.Size([1, 901, 304, 304]), rank 0 
+2025-05-07 09:45:58.172789: predicting 0827 
+2025-05-07 09:45:58.234413: 0827, shape torch.Size([1, 735, 288, 288]), rank 0 
+2025-05-07 09:46:03.148384: predicting 0828 
+2025-05-07 09:46:03.199663: 0828, shape torch.Size([1, 651, 324, 324]), rank 0 
+2025-05-07 09:46:08.393544: predicting 0829 
+2025-05-07 09:46:08.466307: 0829, shape torch.Size([1, 1238, 299, 299]), rank 0 
+2025-05-07 09:46:21.250435: predicting 0830 
+2025-05-07 09:46:21.314130: 0830, shape torch.Size([1, 566, 267, 267]), rank 0 
+2025-05-07 09:46:24.544422: predicting 0831 
+2025-05-07 09:46:24.723701: 0831, shape torch.Size([1, 675, 331, 331]), rank 0 
+2025-05-07 09:46:31.304213: predicting 0832 
+2025-05-07 09:46:31.352981: 0832, shape torch.Size([1, 1070, 295, 295]), rank 0 
+2025-05-07 09:46:41.098214: predicting 0833 
+2025-05-07 09:46:41.160277: 0833, shape torch.Size([1, 596, 303, 303]), rank 0 
+2025-05-07 09:46:47.959532: predicting 0834 
+2025-05-07 09:46:48.007099: 0834, shape torch.Size([1, 1153, 293, 293]), rank 0 
+2025-05-07 09:46:59.675777: predicting 0835 
+2025-05-07 09:46:59.731864: 0835, shape torch.Size([1, 1256, 313, 313]), rank 0 
+2025-05-07 09:47:11.312747: predicting 0836 
+2025-05-07 09:47:11.370460: 0836, shape torch.Size([1, 1153, 302, 302]), rank 0 
+2025-05-07 09:47:22.340813: predicting 0837 
+2025-05-07 09:47:22.412669: 0837, shape torch.Size([1, 1175, 285, 285]), rank 0 
+2025-05-07 09:47:32.184100: predicting 0838 
+2025-05-07 09:47:32.247454: 0838, shape torch.Size([1, 651, 271, 271]), rank 0 
+2025-05-07 09:47:37.718936: predicting 0839 
+2025-05-07 09:47:37.773563: 0839, shape torch.Size([1, 1238, 331, 331]), rank 0 
+2025-05-07 09:47:50.207023: predicting 0840 
+2025-05-07 09:47:50.283748: 0840, shape torch.Size([1, 651, 302, 302]), rank 0 
+2025-05-07 09:47:56.888906: predicting 0841 
+2025-05-07 09:47:56.942248: 0841, shape torch.Size([1, 651, 317, 317]), rank 0 
+2025-05-07 09:48:01.684960: predicting 0842 
+2025-05-07 09:48:01.759465: 0842, shape torch.Size([1, 651, 312, 312]), rank 0 
+2025-05-07 09:48:08.393089: predicting 0843 
+2025-05-07 09:48:08.444191: 0843, shape torch.Size([1, 650, 333, 333]), rank 0 
+2025-05-07 09:48:16.312910: predicting 0844 
+2025-05-07 09:48:16.362861: 0844, shape torch.Size([1, 652, 283, 283]), rank 0 
+2025-05-07 09:48:20.356100: predicting 0845 
+2025-05-07 09:48:20.422601: 0845, shape torch.Size([1, 568, 300, 300]), rank 0 
+2025-05-07 09:48:26.352882: predicting 0846 
+2025-05-07 09:48:26.399104: 0846, shape torch.Size([1, 913, 304, 304]), rank 0 
+2025-05-07 09:48:36.220614: predicting 0847 
+2025-05-07 09:48:36.276216: 0847, shape torch.Size([1, 566, 320, 320]), rank 0 
+2025-05-07 09:48:42.376434: predicting 0848 
+2025-05-07 09:48:42.427541: 0848, shape torch.Size([1, 656, 319, 319]), rank 0 
+2025-05-07 09:48:47.119809: predicting 0849 
+2025-05-07 09:48:47.167362: 0849, shape torch.Size([1, 651, 329, 329]), rank 0 
+2025-05-07 09:48:54.147711: predicting 0850 
+2025-05-07 09:48:54.197106: 0850, shape torch.Size([1, 568, 271, 271]), rank 0 
+2025-05-07 09:48:58.611900: predicting 0851 
+2025-05-07 09:48:58.671989: 0851, shape torch.Size([1, 651, 295, 295]), rank 0 
+2025-05-07 09:49:03.699071: predicting 0852 
+2025-05-07 09:49:03.756343: 0852, shape torch.Size([1, 583, 298, 298]), rank 0 
+2025-05-07 09:49:10.899965: predicting 0853 
+2025-05-07 09:49:10.959989: 0853, shape torch.Size([1, 566, 299, 299]), rank 0 
+2025-05-07 09:49:16.260860: predicting 0854 
+2025-05-07 09:49:16.308994: 0854, shape torch.Size([1, 1070, 279, 279]), rank 0 
+2025-05-07 09:49:22.694659: predicting 0855 
+2025-05-07 09:49:22.755314: 0855, shape torch.Size([1, 568, 300, 300]), rank 0 
+2025-05-07 09:49:28.443590: predicting 0856 
+2025-05-07 09:49:28.513440: 0856, shape torch.Size([1, 566, 272, 272]), rank 0 
+2025-05-07 09:49:33.021511: predicting 0857 
+2025-05-07 09:49:33.074984: 0857, shape torch.Size([1, 566, 291, 291]), rank 0 
+2025-05-07 09:49:37.754567: predicting 0858 
+2025-05-07 09:49:37.807567: 0858, shape torch.Size([1, 568, 240, 240]), rank 0 
+2025-05-07 09:49:41.578283: predicting 0859 
+2025-05-07 09:49:41.637074: 0859, shape torch.Size([1, 940, 333, 333]), rank 0 
+2025-05-07 09:49:50.527878: predicting 0860 
+2025-05-07 09:49:50.589675: 0860, shape torch.Size([1, 566, 299, 299]), rank 0 
+2025-05-07 09:49:55.325752: predicting 0861 
+2025-05-07 09:49:55.394180: 0861, shape torch.Size([1, 483, 277, 277]), rank 0 
+2025-05-07 09:49:58.713854: predicting 0862 
+2025-05-07 09:49:58.773428: 0862, shape torch.Size([1, 566, 277, 277]), rank 0 
+2025-05-07 09:50:02.173046: predicting 0863 
+2025-05-07 09:50:02.217109: 0863, shape torch.Size([1, 645, 296, 296]), rank 0 
+2025-05-07 09:50:08.058688: predicting 0864 
+2025-05-07 09:50:08.111233: 0864, shape torch.Size([1, 656, 291, 291]), rank 0 
+2025-05-07 09:50:14.516007: predicting 0865 
+2025-05-07 09:50:14.568965: 0865, shape torch.Size([1, 635, 317, 317]), rank 0 
+2025-05-07 09:50:19.702615: predicting 0866 
+2025-05-07 09:50:19.753713: 0866, shape torch.Size([1, 566, 281, 281]), rank 0 
+2025-05-07 09:50:23.964977: predicting 0867 
+2025-05-07 09:50:24.016732: 0867, shape torch.Size([1, 735, 333, 333]), rank 0 
+2025-05-07 09:50:32.147078: predicting 0868 
+2025-05-07 09:50:32.220327: 0868, shape torch.Size([1, 691, 295, 295]), rank 0 
+2025-05-07 09:50:38.908560: predicting 0869 
+2025-05-07 09:50:38.964327: 0869, shape torch.Size([1, 735, 333, 333]), rank 0 
+2025-05-07 09:50:45.258447: predicting 0870 
+2025-05-07 09:50:45.316708: 0870, shape torch.Size([1, 735, 333, 333]), rank 0 
+2025-05-07 09:50:53.018796: predicting 0871 
+2025-05-07 09:50:53.077206: 0871, shape torch.Size([1, 1321, 315, 315]), rank 0 
+2025-05-07 09:51:05.472105: predicting 0872 
+2025-05-07 09:51:05.536881: 0872, shape torch.Size([1, 1153, 313, 313]), rank 0 
+2025-05-07 09:51:17.495331: predicting 0873 
+2025-05-07 09:51:17.563622: 0873, shape torch.Size([1, 651, 297, 297]), rank 0 
+2025-05-07 09:51:24.505985: predicting 0874 
+2025-05-07 09:51:24.562178: 0874, shape torch.Size([1, 566, 333, 333]), rank 0 
+2025-05-07 09:51:30.834909: predicting 0875 
+2025-05-07 09:51:30.895943: 0875, shape torch.Size([1, 652, 267, 267]), rank 0 
+2025-05-07 09:51:34.467978: predicting 0876 
+2025-05-07 09:51:34.515861: 0876, shape torch.Size([1, 1238, 311, 311]), rank 0 
+2025-05-07 09:51:46.788151: predicting 0877 
+2025-05-07 09:51:46.860013: 0877, shape torch.Size([1, 651, 283, 283]), rank 0 
+2025-05-07 09:51:50.302172: predicting 0878 
+2025-05-07 09:51:50.359784: 0878, shape torch.Size([1, 638, 289, 289]), rank 0 
+2025-05-07 09:51:57.095338: predicting 0879 
+2025-05-07 09:51:57.153287: 0879, shape torch.Size([1, 652, 236, 236]), rank 0 
+2025-05-07 09:51:59.509803: predicting 0880 
+2025-05-07 09:51:59.585205: 0880, shape torch.Size([1, 600, 287, 287]), rank 0 
+2025-05-07 09:52:04.669680: predicting 0881 
+2025-05-07 09:52:04.709996: 0881, shape torch.Size([1, 651, 280, 280]), rank 0 
+2025-05-07 09:52:09.080417: predicting 0882 
+2025-05-07 09:52:09.123431: 0882, shape torch.Size([1, 566, 275, 275]), rank 0 
+2025-05-07 09:52:12.487783: predicting 0883 
+2025-05-07 09:52:12.533350: 0883, shape torch.Size([1, 566, 301, 301]), rank 0 
+2025-05-07 09:52:18.450768: predicting 0884 
+2025-05-07 09:52:18.503893: 0884, shape torch.Size([1, 651, 305, 305]), rank 0 
+2025-05-07 09:52:24.199631: predicting 0885 
+2025-05-07 09:52:24.252522: 0885, shape torch.Size([1, 567, 297, 297]), rank 0 
+2025-05-07 09:52:28.484504: predicting 0886 
+2025-05-07 09:52:28.532367: 0886, shape torch.Size([1, 601, 279, 279]), rank 0 
+2025-05-07 09:52:33.720437: predicting 0887 
+2025-05-07 09:52:33.772284: 0887, shape torch.Size([1, 600, 275, 275]), rank 0 
+2025-05-07 09:52:37.138388: predicting 0888 
+2025-05-07 09:52:37.201990: 0888, shape torch.Size([1, 568, 273, 273]), rank 0 
+2025-05-07 09:52:42.142659: predicting 0889 
+2025-05-07 09:52:42.192144: 0889, shape torch.Size([1, 651, 300, 300]), rank 0 
+2025-05-07 09:52:46.774753: predicting 0890 
+2025-05-07 09:52:46.823403: 0890, shape torch.Size([1, 566, 333, 333]), rank 0 
+2025-05-07 09:52:52.257520: predicting 0891 
+2025-05-07 09:52:52.313587: 0891, shape torch.Size([1, 568, 296, 296]), rank 0 
+2025-05-07 09:52:59.348074: predicting 0892 
+2025-05-07 09:52:59.397965: 0892, shape torch.Size([1, 651, 309, 309]), rank 0 
+2025-05-07 09:53:04.398090: predicting 0893 
+2025-05-07 09:53:04.448840: 0893, shape torch.Size([1, 591, 307, 307]), rank 0 
+2025-05-07 09:53:09.439917: predicting 0894 
+2025-05-07 09:53:09.496636: 0894, shape torch.Size([1, 651, 331, 331]), rank 0 
+2025-05-07 09:53:17.178818: predicting 0895 
+2025-05-07 09:53:17.235910: 0895, shape torch.Size([1, 591, 323, 323]), rank 0 
+2025-05-07 09:53:23.360034: predicting 0896 
+2025-05-07 09:53:23.417769: 0896, shape torch.Size([1, 566, 280, 280]), rank 0 
+2025-05-07 09:53:28.999580: predicting 0897 
+2025-05-07 09:53:29.069293: 0897, shape torch.Size([1, 651, 267, 267]), rank 0 
+2025-05-07 09:53:32.480481: predicting 0898 
+2025-05-07 09:53:32.527705: 0898, shape torch.Size([1, 566, 294, 294]), rank 0 
+2025-05-07 09:53:38.443990: predicting 0899 
+2025-05-07 09:53:38.492036: 0899, shape torch.Size([1, 566, 271, 271]), rank 0 
+2025-05-07 09:53:41.979610: predicting 0900 
+2025-05-07 09:53:42.229943: 0900, shape torch.Size([1, 645, 283, 283]), rank 0 
+2025-05-07 09:53:47.688317: predicting 0901 
+2025-05-07 09:53:47.734752: 0901, shape torch.Size([1, 651, 277, 277]), rank 0 
+2025-05-07 09:53:51.286770: predicting 0902 
+2025-05-07 09:53:51.333121: 0902, shape torch.Size([1, 485, 290, 290]), rank 0 
+2025-05-07 09:53:57.331916: predicting 0903 
+2025-05-07 09:53:57.476217: 0903, shape torch.Size([1, 651, 316, 316]), rank 0 
+2025-05-07 09:54:03.206134: predicting 0904 
+2025-05-07 09:54:03.265285: 0904, shape torch.Size([1, 651, 273, 273]), rank 0 
+2025-05-07 09:54:06.892510: predicting 0905 
+2025-05-07 09:54:06.947433: 0905, shape torch.Size([1, 570, 297, 297]), rank 0 
+2025-05-07 09:54:13.784598: predicting 0906 
+2025-05-07 09:54:13.838063: 0906, shape torch.Size([1, 566, 306, 306]), rank 0 
+2025-05-07 09:54:18.949044: predicting 0907 
+2025-05-07 09:54:18.996221: 0907, shape torch.Size([1, 1153, 309, 309]), rank 0 
+2025-05-07 09:54:28.506990: predicting 0908 
+2025-05-07 09:54:28.566490: 0908, shape torch.Size([1, 675, 319, 319]), rank 0 
+2025-05-07 09:54:34.795896: predicting 0909 
+2025-05-07 09:54:34.856623: 0909, shape torch.Size([1, 566, 284, 284]), rank 0 
+2025-05-07 09:54:38.513847: predicting 0910 
+2025-05-07 09:54:38.560929: 0910, shape torch.Size([1, 601, 295, 295]), rank 0 
+2025-05-07 09:54:44.862633: predicting 0911 
+2025-05-07 09:54:44.939777: 0911, shape torch.Size([1, 605, 300, 300]), rank 0 
+2025-05-07 09:54:49.983352: predicting 0912 
+2025-05-07 09:54:50.045064: 0912, shape torch.Size([1, 556, 273, 273]), rank 0 
+2025-05-07 09:54:54.917239: predicting 0913 
+2025-05-07 09:54:54.977108: 0913, shape torch.Size([1, 591, 271, 271]), rank 0 
+2025-05-07 09:54:58.886125: predicting 0914 
+2025-05-07 09:54:59.009290: 0914, shape torch.Size([1, 1321, 308, 308]), rank 0 
+2025-05-07 09:55:10.879373: predicting 0915 
+2025-05-07 09:55:10.946330: 0915, shape torch.Size([1, 568, 247, 247]), rank 0 
+2025-05-07 09:55:14.566591: predicting 0916 
+2025-05-07 09:55:14.854732: 0916, shape torch.Size([1, 530, 260, 260]), rank 0 
+2025-05-07 09:55:18.017107: predicting 0917 
+2025-05-07 09:55:18.059109: 0917, shape torch.Size([1, 553, 245, 245]), rank 0 
+2025-05-07 09:55:21.306593: predicting 0918 
+2025-05-07 09:55:21.355922: 0918, shape torch.Size([1, 566, 303, 303]), rank 0 
+2025-05-07 09:55:26.899412: predicting 0919 
+2025-05-07 09:55:26.949617: 0919, shape torch.Size([1, 591, 249, 249]), rank 0 
+2025-05-07 09:55:30.825499: predicting 0920 
+2025-05-07 09:55:30.873657: 0920, shape torch.Size([1, 651, 280, 280]), rank 0 
+2025-05-07 09:55:35.282678: predicting 0921 
+2025-05-07 09:55:35.346278: 0921, shape torch.Size([1, 640, 316, 316]), rank 0 
+2025-05-07 09:55:41.167386: predicting 0922 
+2025-05-07 09:55:41.224090: 0922, shape torch.Size([1, 651, 303, 303]), rank 0 
+2025-05-07 09:55:45.894888: predicting 0923 
+2025-05-07 09:55:45.947759: 0923, shape torch.Size([1, 1070, 313, 313]), rank 0 
+2025-05-07 09:55:56.485094: predicting 0924 
+2025-05-07 09:55:56.540842: 0924, shape torch.Size([1, 1153, 333, 333]), rank 0 
+2025-05-07 09:56:07.496408: predicting 0925 
+2025-05-07 09:56:07.567728: 0925, shape torch.Size([1, 483, 333, 333]), rank 0 
+2025-05-07 09:56:11.343002: predicting 0926 
+2025-05-07 09:56:11.405645: 0926, shape torch.Size([1, 651, 285, 285]), rank 0 
+2025-05-07 09:56:16.396492: predicting 0927 
+2025-05-07 09:56:16.456765: 0927, shape torch.Size([1, 591, 271, 271]), rank 0 
+2025-05-07 09:56:19.962883: predicting 0928 
+2025-05-07 09:56:20.023397: 0928, shape torch.Size([1, 651, 327, 327]), rank 0 
+2025-05-07 09:56:26.080629: predicting 0929 
+2025-05-07 09:56:26.137969: 0929, shape torch.Size([1, 652, 311, 311]), rank 0 
+2025-05-07 09:56:33.585104: predicting 0930 
+2025-05-07 09:56:33.645559: 0930, shape torch.Size([1, 610, 301, 301]), rank 0 
+2025-05-07 09:56:40.037283: predicting 0931 
+2025-05-07 09:56:40.093807: 0931, shape torch.Size([1, 623, 305, 305]), rank 0 
+2025-05-07 09:56:45.276895: predicting 0932 
+2025-05-07 09:56:45.335993: 0932, shape torch.Size([1, 566, 278, 278]), rank 0 
+2025-05-07 09:56:49.348615: predicting 0933 
+2025-05-07 09:56:49.398098: 0933, shape torch.Size([1, 651, 287, 287]), rank 0 
+2025-05-07 09:56:54.619502: predicting 0934 
+2025-05-07 09:56:54.675768: 0934, shape torch.Size([1, 580, 285, 285]), rank 0 
+2025-05-07 09:56:58.288388: predicting 0935 
+2025-05-07 09:56:58.343102: 0935, shape torch.Size([1, 635, 293, 293]), rank 0 
+2025-05-07 09:57:04.541540: predicting 0936 
+2025-05-07 09:57:04.597964: 0936, shape torch.Size([1, 623, 283, 283]), rank 0 
+2025-05-07 09:57:08.586116: predicting 0937 
+2025-05-07 09:57:08.633402: 0937, shape torch.Size([1, 648, 275, 275]), rank 0 
+2025-05-07 09:57:12.057764: predicting 0938 
+2025-05-07 09:57:12.105653: 0938, shape torch.Size([1, 1121, 298, 298]), rank 0 
+2025-05-07 09:57:26.202265: predicting 0939 
+2025-05-07 09:57:26.266650: 0939, shape torch.Size([1, 651, 301, 301]), rank 0 
+2025-05-07 09:57:30.852782: predicting 0940 
+2025-05-07 09:57:30.906088: 0940, shape torch.Size([1, 642, 288, 288]), rank 0 
+2025-05-07 09:57:35.692993: predicting 0941 
+2025-05-07 09:57:35.754907: 0941, shape torch.Size([1, 588, 263, 263]), rank 0 
+2025-05-07 09:57:39.872492: predicting 0942 
+2025-05-07 09:57:39.921151: 0942, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 09:57:44.586531: predicting 0943 
+2025-05-07 09:57:44.633067: 0943, shape torch.Size([1, 735, 330, 330]), rank 0 
+2025-05-07 09:57:51.867476: predicting 0944 
+2025-05-07 09:57:51.917214: 0944, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:57:58.873209: predicting 0945 
+2025-05-07 09:57:58.923282: 0945, shape torch.Size([1, 651, 333, 333]), rank 0 
+2025-05-07 09:58:06.370321: predicting 0946 
+2025-05-07 09:58:06.424203: 0946, shape torch.Size([1, 651, 299, 299]), rank 0 
+2025-05-07 09:58:11.829515: predicting 0947 
+2025-05-07 09:58:11.875717: 0947, shape torch.Size([1, 566, 279, 279]), rank 0 
+2025-05-07 09:58:15.601207: predicting 0948 
+2025-05-07 09:58:15.651644: 0948, shape torch.Size([1, 735, 271, 271]), rank 0 
diff --git a/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/plans.json b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/plans.json
new file mode 100755
index 0000000000000000000000000000000000000000..44dfee1464645a7459c8d70f021524e51ce0e541
--- /dev/null
+++ b/MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/plans.json
@@ -0,0 +1,521 @@
+{
+    "dataset_name": "Dataset123_Organs",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.5,
+        1.5,
+        1.5
+    ],
+    "original_median_shape_after_transp": [
+        649,
+        318,
+        318
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 30,
+            "patch_size": [
+                320,
+                320
+            ],
+            "median_image_size_in_voxels": [
+                318.0,
+                318.0
+            ],
+            "spacing": [
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        512,
+                        512,
+                        512
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv2d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_conv_per_stage": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                224,
+                96,
+                96
+            ],
+            "median_image_size_in_voxels": [
+                319,
+                156,
+                156
+            ],
+            "spacing": [
+                3.049191159690605,
+                3.049191159690605,
+                3.049191159690605
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            1,
+                            1
+                        ]
+                    ],
+                    "n_conv_per_stage": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                224,
+                96,
+                96
+            ],
+            "median_image_size_in_voxels": [
+                649.0,
+                318.0,
+                318.0
+            ],
+            "spacing": [
+                1.5,
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            1,
+                            1
+                        ]
+                    ],
+                    "n_conv_per_stage": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3033.969970703125,
+            "mean": -300.0020751953125,
+            "median": -1.0779765844345093,
+            "min": -1331.90234375,
+            "percentile_00_5": -959.2193603515625,
+            "percentile_99_5": 195.26037063598665,
+            "std": 406.9311828613281
+        }
+    }
+}
\ No newline at end of file
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset_fingerprint.json b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset_fingerprint.json
deleted file mode 100644
index 9804943af2edce625741c06ad114fb5c61f6d688..0000000000000000000000000000000000000000
--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/dataset_fingerprint.json
+++ /dev/null
@@ -1,16848 +0,0 @@
-{
-    "foreground_intensity_properties_per_channel": {
-        "0": {
-            "max": 2981.83154296875,
-            "mean": -306.5704650878906,
-            "median": -7.578986644744873,
-            "min": -1138.905029296875,
-            "percentile_00_5": -952.3096923828125,
-            "percentile_99_5": 193.60693359375,
-            "std": 407.40484619140625
-        }
-    },
-    "median_relative_size_after_cropping": 1.0,
-    "shapes_after_crop": [
-        [
-            163,
-            68,
-            68
-        ],
-        [
-            152,
-            78,
-            78
-        ],
-        [
-            142,
-            65,
-            65
-        ],
-        [
-            144,
-            76,
-            76
-        ],
-        [
-            163,
-            68,
-            68
-        ],
-        [
-            163,
-            78,
-            78
-        ],
-        [
-            309,
-            74,
-            74
-        ],
-        [
-            116,
-            66,
-            66
-        ],
-        [
-            163,
-            81,
-            81
-        ],
-        [
-            142,
-            77,
-            77
-        ],
-        [
-            142,
-            76,
-            76
-        ],
-        [
-            142,
-            68,
-            68
-        ],
-        [
-            284,
-            74,
-            74
-        ],
-        [
-            142,
-            60,
-            60
-        ],
-        [
-            142,
-            65,
-            65
-        ],
-        [
-            162,
-            73,
-            73
-        ],
-        [
-            205,
-            67,
-            67
-        ],
-        [
-            163,
-            74,
-            74
-        ],
-        [
-            163,
-            72,
-            72
-        ],
-        [
-            288,
-            79,
-            79
-        ],
-        [
-            127,
-            76,
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-        [
-            121,
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-        [
-            142,
-            82,
-            82
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-        [
-            122,
-            70,
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-        ],
-        [
-            142,
-            71,
-            71
-        ],
-        [
-            142,
-            68,
-            68
-        ],
-        [
-            294,
-            83,
-            83
-        ],
-        [
-            130,
-            80,
-            80
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-        [
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-            70,
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-        [
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-            74,
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-        [
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-        [
-            163,
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diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/checkpoint_best.pth b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/checkpoint_best.pth
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-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': False}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 8902,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c63580>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62da0>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [176, 80, 80], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c636a0>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62bc0>",
-    "dataloader_val.num_processes": "6",
-    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
-    "dataset_json": "{'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "fold": "all",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA A100 80GB PCIe",
-    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7ebfa1d77730>",
-    "hostname": "metazoo",
-    "inference_allowed_mirroring_axes": "(0, 1, 2)",
-    "initial_lr": "0.01",
-    "is_cascaded": "False",
-    "is_ddp": "False",
-    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7ebfa1d77a30>",
-    "local_rank": "0",
-    "log_file": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7ebfa1d778b0>",
-    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7ebfa1da26e0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "PlainConvUNet",
-    "num_epochs": "2000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all",
-    "output_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "plans_manager": "{'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}",
-    "preprocessed_dataset_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs",
-    "save_every": "50",
-    "torch_version": "2.3.1+cu121",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
\ No newline at end of file
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png
deleted file mode 100644
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--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2ca2f7d347ad5a95c026fc945a68204ec3b459cb161939db17c6c037baa26458
-size 611209
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_35_49.txt b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_35_49.txt
deleted file mode 100644
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--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_35_49.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-
-#######################################################################
-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': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': False} 
- 
-These are the global plan.json settings:
- {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], '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': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}} 
- 
-2024-08-27 12:35:52.178022: unpacking dataset... 
-2024-08-27 12:36:03.383381: unpacking done... 
-2024-08-27 12:36:03.384249: do_dummy_2d_data_aug: False 
-2024-08-27 12:36:03.404946: Unable to plot network architecture: 
-2024-08-27 12:36:03.405035: No module named 'hiddenlayer' 
-2024-08-27 12:36:03.410572:  
-2024-08-27 12:36:03.410657: Epoch 0 
-2024-08-27 12:36:03.410768: Current learning rate: 0.01 
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt
deleted file mode 100644
index 17de73f47e8a9428011afc5cddee25612a44ef16..0000000000000000000000000000000000000000
--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt
+++ /dev/null
@@ -1,16014 +0,0 @@
-
-#######################################################################
-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': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], '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], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[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': False} 
- 
-These are the global plan.json settings:
- {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], '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': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}} 
- 
-2024-08-27 12:36:39.198818: unpacking dataset... 
-2024-08-27 12:36:42.119171: unpacking done... 
-2024-08-27 12:36:42.119888: do_dummy_2d_data_aug: False 
-2024-08-27 12:36:42.140977: Unable to plot network architecture: 
-2024-08-27 12:36:42.141062: No module named 'hiddenlayer' 
-2024-08-27 12:36:42.146270:  
-2024-08-27 12:36:42.146348: Epoch 0 
-2024-08-27 12:36:42.146447: Current learning rate: 0.01 
-2024-08-27 12:39:01.034269: train_loss 0.3123 
-2024-08-27 12:39:01.034829: val_loss 0.1158 
-2024-08-27 12:39:01.035354: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:39:01.035613: Epoch time: 138.89 s 
-2024-08-27 12:39:01.035757: Yayy! New best EMA pseudo Dice: 0.0 
-2024-08-27 12:39:04.175160:  
-2024-08-27 12:39:04.175865: Epoch 1 
-2024-08-27 12:39:04.176150: Current learning rate: 0.01 
-2024-08-27 12:40:45.057936: train_loss 0.0973 
-2024-08-27 12:40:45.058289: val_loss 0.063 
-2024-08-27 12:40:45.058621: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:40:45.058763: Epoch time: 100.89 s 
-2024-08-27 12:40:46.452353:  
-2024-08-27 12:40:46.452567: Epoch 2 
-2024-08-27 12:40:46.452675: Current learning rate: 0.00999 
-2024-08-27 12:42:37.513522: train_loss 0.0465 
-2024-08-27 12:42:37.513834: val_loss 0.0149 
-2024-08-27 12:42:37.514026: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3153, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:42:37.514123: Epoch time: 111.06 s 
-2024-08-27 12:42:37.514178: Yayy! New best EMA pseudo Dice: 0.0018 
-2024-08-27 12:42:39.355332:  
-2024-08-27 12:42:39.355759: Epoch 3 
-2024-08-27 12:42:39.355954: Current learning rate: 0.00999 
-2024-08-27 12:44:31.299467: train_loss 0.0011 
-2024-08-27 12:44:31.299804: val_loss -0.027 
-2024-08-27 12:44:31.300002: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:44:31.300094: Epoch time: 111.95 s 
-2024-08-27 12:44:31.300151: Yayy! New best EMA pseudo Dice: 0.004 
-2024-08-27 12:44:33.010657:  
-2024-08-27 12:44:33.010961: Epoch 4 
-2024-08-27 12:44:33.011096: Current learning rate: 0.00998 
-2024-08-27 12:46:21.649923: train_loss -0.0465 
-2024-08-27 12:46:21.650524: val_loss -0.0817 
-2024-08-27 12:46:21.651215: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4045, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:46:21.651497: Epoch time: 108.64 s 
-2024-08-27 12:46:21.651697: Yayy! New best EMA pseudo Dice: 0.0058 
-2024-08-27 12:46:23.956226:  
-2024-08-27 12:46:23.956498: Epoch 5 
-2024-08-27 12:46:23.956650: Current learning rate: 0.00998 
-2024-08-27 12:48:15.702865: train_loss -0.0963 
-2024-08-27 12:48:15.703841: val_loss -0.131 
-2024-08-27 12:48:15.704074: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3389, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:48:15.704220: Epoch time: 111.75 s 
-2024-08-27 12:48:15.704280: Yayy! New best EMA pseudo Dice: 0.0071 
-2024-08-27 12:48:17.351923:  
-2024-08-27 12:48:17.352124: Epoch 6 
-2024-08-27 12:48:17.352226: Current learning rate: 0.00997 
-2024-08-27 12:49:54.906653: train_loss -0.1417 
-2024-08-27 12:49:54.906956: val_loss -0.1835 
-2024-08-27 12:49:54.907210: Pseudo dice [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.72, 0.0, 0.0, 0.0, 0.008, 0.3236, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:49:54.907319: Epoch time: 97.56 s 
-2024-08-27 12:49:54.907711: Yayy! New best EMA pseudo Dice: 0.0122 
-2024-08-27 12:49:56.733202:  
-2024-08-27 12:49:56.733463: Epoch 7 
-2024-08-27 12:49:56.733662: Current learning rate: 0.00997 
-2024-08-27 12:51:25.141818: train_loss -0.1991 
-2024-08-27 12:51:25.142156: val_loss -0.2307 
-2024-08-27 12:51:25.142358: Pseudo dice [0.0, 0.0, 0.0, 0.6473, 0.0, 0.0, 0.0, 0.8077, 0.0, 0.0, 0.0, 0.3668, 0.0001, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:51:25.142453: Epoch time: 88.41 s 
-2024-08-27 12:51:25.142510: Yayy! New best EMA pseudo Dice: 0.0211 
-2024-08-27 12:51:26.846318:  
-2024-08-27 12:51:26.846552: Epoch 8 
-2024-08-27 12:51:26.846720: Current learning rate: 0.00996 
-2024-08-27 12:53:04.115993: train_loss -0.2329 
-2024-08-27 12:53:04.116255: val_loss -0.2605 
-2024-08-27 12:53:04.116456: Pseudo dice [0.0, 0.0, 0.0, 0.8389, 0.0, 0.0, 0.0, 0.8774, 0.0, 0.0, 0.0, 0.3247, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:53:04.116550: Epoch time: 97.27 s 
-2024-08-27 12:53:04.116606: Yayy! New best EMA pseudo Dice: 0.0304 
-2024-08-27 12:53:05.713075:  
-2024-08-27 12:53:05.713259: Epoch 9 
-2024-08-27 12:53:05.713427: Current learning rate: 0.00996 
-2024-08-27 12:54:38.862801: train_loss -0.2574 
-2024-08-27 12:54:38.863050: val_loss -0.2903 
-2024-08-27 12:54:38.863230: Pseudo dice [0.0, 0.0, 0.0, 0.875, 0.0, 0.0, 0.0, 0.8734, 0.3448, 0.5538, 0.0, 0.7438, 0.5143, 0.0, 0.0, 0.0, 0.0, 0.0, nan] 
-2024-08-27 12:54:38.863379: Epoch time: 93.15 s 
-2024-08-27 12:54:38.863441: Yayy! New best EMA pseudo Dice: 0.049 
-2024-08-27 12:54:40.485617:  
-2024-08-27 12:54:40.485759: Epoch 10 
-2024-08-27 12:54:40.485847: Current learning rate: 0.00995 
-2024-08-27 12:56:09.178217: train_loss -0.2994 
-2024-08-27 12:56:09.178445: val_loss -0.3406 
-2024-08-27 12:56:09.178613: Pseudo dice [0.0, 0.0, 0.0, 0.8984, 0.0, 0.0, 0.0, 0.8693, 0.7722, 0.725, 0.0, 0.8605, 0.8418, 0.0, 0.6099, 0.563, 0.0, 0.0, nan] 
-2024-08-27 12:56:09.178695: Epoch time: 88.69 s 
-2024-08-27 12:56:09.178747: Yayy! New best EMA pseudo Dice: 0.0782 
-2024-08-27 12:56:10.879682:  
-2024-08-27 12:56:10.879901: Epoch 11 
-2024-08-27 12:56:10.880004: Current learning rate: 0.00995 
-2024-08-27 12:57:46.119167: train_loss -0.3545 
-2024-08-27 12:57:46.119427: val_loss -0.3931 
-2024-08-27 12:57:46.119597: Pseudo dice [0.0, 0.0, 0.0, 0.9301, 0.0, 0.3925, 0.01, 0.8874, 0.8406, 0.7998, 0.0, 0.8917, 0.798, 0.0, 0.7079, 0.6302, 0.0, 0.0, nan] 
-2024-08-27 12:57:46.119697: Epoch time: 95.24 s 
-2024-08-27 12:57:46.119749: Yayy! New best EMA pseudo Dice: 0.1087 
-2024-08-27 12:57:47.651372:  
-2024-08-27 12:57:47.651729: Epoch 12 
-2024-08-27 12:57:47.651871: Current learning rate: 0.00995 
-2024-08-27 12:59:24.491835: train_loss -0.3996 
-2024-08-27 12:59:24.492237: val_loss -0.4486 
-2024-08-27 12:59:24.492424: Pseudo dice [0.0, 0.0, 0.0, 0.9281, 0.0, 0.8021, 0.7626, 0.8831, 0.8584, 0.7925, 0.5668, 0.8918, 0.8595, 0.0, 0.8148, 0.6884, 0.0, 0.0, nan] 
-2024-08-27 12:59:24.492520: Epoch time: 96.84 s 
-2024-08-27 12:59:24.492571: Yayy! New best EMA pseudo Dice: 0.147 
-2024-08-27 12:59:25.992390:  
-2024-08-27 12:59:25.992773: Epoch 13 
-2024-08-27 12:59:25.992963: Current learning rate: 0.00994 
-2024-08-27 13:00:58.647667: train_loss -0.4525 
-2024-08-27 13:00:58.647886: val_loss -0.4679 
-2024-08-27 13:00:58.648051: Pseudo dice [0.0, 0.0, 0.0, 0.9292, 0.0, 0.8041, 0.7961, 0.8783, 0.8433, 0.8492, 0.7749, 0.8892, 0.8728, 0.4494, 0.833, 0.7039, 0.0, 0.0, nan] 
-2024-08-27 13:00:58.648131: Epoch time: 92.66 s 
-2024-08-27 13:00:58.648181: Yayy! New best EMA pseudo Dice: 0.1857 
-2024-08-27 13:01:00.198127:  
-2024-08-27 13:01:00.198400: Epoch 14 
-2024-08-27 13:01:00.198507: Current learning rate: 0.00994 
-2024-08-27 13:02:37.430439: train_loss -0.4692 
-2024-08-27 13:02:37.430662: val_loss -0.5083 
-2024-08-27 13:02:37.430825: Pseudo dice [0.0, 0.0, 0.0, 0.9365, 0.0, 0.8402, 0.8034, 0.8886, 0.8519, 0.8776, 0.8034, 0.8913, 0.8876, 0.4915, 0.8381, 0.7199, 0.0, 0.0, nan] 
-2024-08-27 13:02:37.430903: Epoch time: 97.23 s 
-2024-08-27 13:02:37.430953: Yayy! New best EMA pseudo Dice: 0.2218 
-2024-08-27 13:02:38.956873:  
-2024-08-27 13:02:38.957263: Epoch 15 
-2024-08-27 13:02:38.957354: Current learning rate: 0.00993 
-2024-08-27 13:04:13.247680: train_loss -0.5014 
-2024-08-27 13:04:13.248280: val_loss -0.5256 
-2024-08-27 13:04:13.248554: Pseudo dice [0.0, 0.0, 0.0, 0.9338, 0.0, 0.8373, 0.8196, 0.8975, 0.875, 0.8675, 0.7952, 0.9032, 0.8863, 0.5376, 0.8428, 0.7491, 0.0, 0.0, nan] 
-2024-08-27 13:04:13.248755: Epoch time: 94.29 s 
-2024-08-27 13:04:13.248933: Yayy! New best EMA pseudo Dice: 0.2548 
-2024-08-27 13:04:14.767730:  
-2024-08-27 13:04:14.767888: Epoch 16 
-2024-08-27 13:04:14.767982: Current learning rate: 0.00993 
-2024-08-27 13:05:42.592860: train_loss -0.5189 
-2024-08-27 13:05:42.593112: val_loss -0.5364 
-2024-08-27 13:05:42.593300: Pseudo dice [0.0, 0.0, 0.6673, 0.9347, 0.0, 0.8423, 0.8185, 0.8982, 0.8747, 0.8745, 0.8028, 0.8992, 0.8822, 0.5289, 0.8335, 0.7474, 0.0, 0.0, nan] 
-2024-08-27 13:05:42.593390: Epoch time: 87.83 s 
-2024-08-27 13:05:42.593445: Yayy! New best EMA pseudo Dice: 0.2883 
-2024-08-27 13:05:44.615568:  
-2024-08-27 13:05:44.615826: Epoch 17 
-2024-08-27 13:05:44.615921: Current learning rate: 0.00992 
-2024-08-27 13:07:16.293258: train_loss -0.5234 
-2024-08-27 13:07:16.293511: val_loss -0.5517 
-2024-08-27 13:07:16.293700: Pseudo dice [0.0, 0.0, 0.6729, 0.9442, 0.0, 0.8402, 0.8593, 0.9074, 0.8803, 0.8724, 0.8012, 0.9071, 0.8893, 0.565, 0.8389, 0.7648, 0.0, 0.0, nan] 
-2024-08-27 13:07:16.294156: Epoch time: 91.68 s 
-2024-08-27 13:07:16.294237: Yayy! New best EMA pseudo Dice: 0.3191 
-2024-08-27 13:07:17.904294:  
-2024-08-27 13:07:17.904722: Epoch 18 
-2024-08-27 13:07:17.904828: Current learning rate: 0.00992 
-2024-08-27 13:08:56.353316: train_loss -0.5306 
-2024-08-27 13:08:56.353546: val_loss -0.5569 
-2024-08-27 13:08:56.353725: Pseudo dice [0.0, 0.0, 0.7073, 0.9505, 0.0, 0.866, 0.8663, 0.9079, 0.8789, 0.8876, 0.8274, 0.9113, 0.9012, 0.551, 0.8469, 0.7397, 0.0, 0.0, nan] 
-2024-08-27 13:08:56.353808: Epoch time: 98.45 s 
-2024-08-27 13:08:56.353861: Yayy! New best EMA pseudo Dice: 0.3474 
-2024-08-27 13:08:57.924800:  
-2024-08-27 13:08:57.925098: Epoch 19 
-2024-08-27 13:08:57.925193: Current learning rate: 0.00991 
-2024-08-27 13:10:30.228016: train_loss -0.5424 
-2024-08-27 13:10:30.228290: val_loss -0.5663 
-2024-08-27 13:10:30.228506: Pseudo dice [0.0, 0.0, 0.7272, 0.9518, 0.0, 0.8558, 0.855, 0.9051, 0.8902, 0.8878, 0.801, 0.9094, 0.886, 0.5774, 0.8686, 0.7804, 0.0, 0.0, nan] 
-2024-08-27 13:10:30.228627: Epoch time: 92.3 s 
-2024-08-27 13:10:30.228686: Yayy! New best EMA pseudo Dice: 0.3732 
-2024-08-27 13:10:31.819061:  
-2024-08-27 13:10:31.819220: Epoch 20 
-2024-08-27 13:10:31.819313: Current learning rate: 0.00991 
-2024-08-27 13:12:04.419539: train_loss -0.5498 
-2024-08-27 13:12:04.419770: val_loss -0.5698 
-2024-08-27 13:12:04.419931: Pseudo dice [0.0, 0.0, 0.6969, 0.9369, 0.0, 0.8727, 0.8669, 0.91, 0.8957, 0.8879, 0.8224, 0.907, 0.8997, 0.57, 0.8587, 0.7773, 0.0, 0.0, nan] 
-2024-08-27 13:12:04.420014: Epoch time: 92.6 s 
-2024-08-27 13:12:04.420066: Yayy! New best EMA pseudo Dice: 0.3965 
-2024-08-27 13:12:05.952104:  
-2024-08-27 13:12:05.952270: Epoch 21 
-2024-08-27 13:12:05.952373: Current learning rate: 0.00991 
-2024-08-27 13:13:40.376276: train_loss -0.5595 
-2024-08-27 13:13:40.376556: val_loss -0.5804 
-2024-08-27 13:13:40.376739: Pseudo dice [0.0, 0.0, 0.7445, 0.9536, 0.0, 0.8838, 0.8783, 0.9186, 0.8748, 0.8799, 0.8219, 0.9057, 0.9035, 0.5854, 0.8854, 0.7904, 0.0, 0.0, nan] 
-2024-08-27 13:13:40.376828: Epoch time: 94.43 s 
-2024-08-27 13:13:40.376880: Yayy! New best EMA pseudo Dice: 0.4181 
-2024-08-27 13:13:42.051955:  
-2024-08-27 13:13:42.052331: Epoch 22 
-2024-08-27 13:13:42.052424: Current learning rate: 0.0099 
-2024-08-27 13:15:11.763515: train_loss -0.567 
-2024-08-27 13:15:11.763733: val_loss -0.5859 
-2024-08-27 13:15:11.763881: Pseudo dice [0.0, 0.0, 0.7288, 0.9554, 0.0, 0.8922, 0.8795, 0.9199, 0.8934, 0.8916, 0.8219, 0.922, 0.9102, 0.6244, 0.8893, 0.8007, 0.0, 0.0, nan] 
-2024-08-27 13:15:11.764083: Epoch time: 89.71 s 
-2024-08-27 13:15:11.764132: Yayy! New best EMA pseudo Dice: 0.4381 
-2024-08-27 13:15:13.155064:  
-2024-08-27 13:15:13.155246: Epoch 23 
-2024-08-27 13:15:13.155343: Current learning rate: 0.0099 
-2024-08-27 13:16:43.353631: train_loss -0.5708 
-2024-08-27 13:16:43.354059: val_loss -0.5837 
-2024-08-27 13:16:43.354542: Pseudo dice [0.0, 0.0, 0.7707, 0.9573, 0.0, 0.8915, 0.8711, 0.9222, 0.9023, 0.8878, 0.8314, 0.9217, 0.9009, 0.6142, 0.8926, 0.8103, 0.0, 0.0, nan] 
-2024-08-27 13:16:43.354738: Epoch time: 90.2 s 
-2024-08-27 13:16:43.354878: Yayy! New best EMA pseudo Dice: 0.4564 
-2024-08-27 13:16:45.159327:  
-2024-08-27 13:16:45.159656: Epoch 24 
-2024-08-27 13:16:45.159765: Current learning rate: 0.00989 
-2024-08-27 13:18:19.920193: train_loss -0.5726 
-2024-08-27 13:18:19.920456: val_loss -0.5931 
-2024-08-27 13:18:19.920643: Pseudo dice [0.0, 0.0, 0.7461, 0.9586, 0.0001, 0.9033, 0.8932, 0.9264, 0.897, 0.8865, 0.8204, 0.9223, 0.9035, 0.6324, 0.8808, 0.8177, 0.0, 0.0, nan] 
-2024-08-27 13:18:19.920738: Epoch time: 94.76 s 
-2024-08-27 13:18:19.920791: Yayy! New best EMA pseudo Dice: 0.4729 
-2024-08-27 13:18:21.459527:  
-2024-08-27 13:18:21.459803: Epoch 25 
-2024-08-27 13:18:21.459904: Current learning rate: 0.00989 
-2024-08-27 13:19:52.997810: train_loss -0.5737 
-2024-08-27 13:19:52.998073: val_loss -0.5944 
-2024-08-27 13:19:52.998252: Pseudo dice [0.0, 0.0, 0.7765, 0.9597, 0.2603, 0.903, 0.8873, 0.923, 0.8922, 0.9012, 0.817, 0.9165, 0.898, 0.6294, 0.89, 0.8151, 0.0, 0.0, nan] 
-2024-08-27 13:19:52.998345: Epoch time: 91.54 s 
-2024-08-27 13:19:52.998397: Yayy! New best EMA pseudo Dice: 0.4893 
-2024-08-27 13:19:54.499361:  
-2024-08-27 13:19:54.499867: Epoch 26 
-2024-08-27 13:19:54.500023: Current learning rate: 0.00988 
-2024-08-27 13:21:31.824259: train_loss -0.5779 
-2024-08-27 13:21:31.824506: val_loss -0.5881 
-2024-08-27 13:21:31.824675: Pseudo dice [0.0, 0.0, 0.7714, 0.9606, 0.4641, 0.8945, 0.8704, 0.9187, 0.8886, 0.8728, 0.8056, 0.916, 0.9092, 0.6229, 0.8799, 0.8149, 0.0, 0.0, nan] 
-2024-08-27 13:21:31.824759: Epoch time: 97.33 s 
-2024-08-27 13:21:31.824808: Yayy! New best EMA pseudo Dice: 0.5048 
-2024-08-27 13:21:33.314494:  
-2024-08-27 13:21:33.314639: Epoch 27 
-2024-08-27 13:21:33.314725: Current learning rate: 0.00988 
-2024-08-27 13:23:10.587730: train_loss -0.5815 
-2024-08-27 13:23:10.588002: val_loss -0.5928 
-2024-08-27 13:23:10.588248: Pseudo dice [0.0, 0.0, 0.7537, 0.9602, 0.5205, 0.8821, 0.8833, 0.9152, 0.8923, 0.8856, 0.8289, 0.9098, 0.9088, 0.6351, 0.8927, 0.8059, 0.0, 0.0, nan] 
-2024-08-27 13:23:10.588353: Epoch time: 97.27 s 
-2024-08-27 13:23:10.588408: Yayy! New best EMA pseudo Dice: 0.5192 
-2024-08-27 13:23:12.461941:  
-2024-08-27 13:23:12.462418: Epoch 28 
-2024-08-27 13:23:12.462651: Current learning rate: 0.00987 
-2024-08-27 13:24:45.265014: train_loss -0.5879 
-2024-08-27 13:24:45.265590: val_loss -0.6034 
-2024-08-27 13:24:45.265819: Pseudo dice [0.0, 0.0, 0.7984, 0.9604, 0.6005, 0.9092, 0.9026, 0.9266, 0.8914, 0.8842, 0.809, 0.9113, 0.9012, 0.65, 0.9036, 0.8158, 0.0, 0.0, nan] 
-2024-08-27 13:24:45.265960: Epoch time: 92.8 s 
-2024-08-27 13:24:45.266023: Yayy! New best EMA pseudo Dice: 0.5332 
-2024-08-27 13:24:46.844128:  
-2024-08-27 13:24:46.844567: Epoch 29 
-2024-08-27 13:24:46.844678: Current learning rate: 0.00987 
-2024-08-27 13:26:19.082286: train_loss -0.5922 
-2024-08-27 13:26:19.082603: val_loss -0.6072 
-2024-08-27 13:26:19.082804: Pseudo dice [0.0, 0.0, 0.791, 0.9645, 0.5788, 0.8907, 0.8856, 0.9268, 0.8925, 0.8975, 0.8372, 0.9116, 0.9139, 0.6449, 0.9048, 0.8373, 0.0, 0.0, nan] 
-2024-08-27 13:26:19.082929: Epoch time: 92.24 s 
-2024-08-27 13:26:19.082989: Yayy! New best EMA pseudo Dice: 0.5458 
-2024-08-27 13:26:20.708586:  
-2024-08-27 13:26:20.708869: Epoch 30 
-2024-08-27 13:26:20.708972: Current learning rate: 0.00986 
-2024-08-27 13:27:55.927892: train_loss -0.5948 
-2024-08-27 13:27:55.928170: val_loss -0.6199 
-2024-08-27 13:27:55.928354: Pseudo dice [0.0, 0.0, 0.8037, 0.9655, 0.5974, 0.9036, 0.9053, 0.9337, 0.9043, 0.9003, 0.8417, 0.9269, 0.9179, 0.6681, 0.892, 0.8466, 0.0, 0.0, nan] 
-2024-08-27 13:27:55.928492: Epoch time: 95.22 s 
-2024-08-27 13:27:55.928557: Yayy! New best EMA pseudo Dice: 0.5579 
-2024-08-27 13:27:57.538235:  
-2024-08-27 13:27:57.538482: Epoch 31 
-2024-08-27 13:27:57.538584: Current learning rate: 0.00986 
-2024-08-27 13:29:35.111532: train_loss -0.5972 
-2024-08-27 13:29:35.111797: val_loss -0.6151 
-2024-08-27 13:29:35.111973: Pseudo dice [0.0, 0.0, 0.8117, 0.9626, 0.6268, 0.9052, 0.8887, 0.9324, 0.8972, 0.8873, 0.8371, 0.9159, 0.9118, 0.6767, 0.8998, 0.8403, 0.0, 0.0, nan] 
-2024-08-27 13:29:35.112065: Epoch time: 97.57 s 
-2024-08-27 13:29:35.112119: Yayy! New best EMA pseudo Dice: 0.5688 
-2024-08-27 13:29:36.923756:  
-2024-08-27 13:29:36.923923: Epoch 32 
-2024-08-27 13:29:36.924024: Current learning rate: 0.00986 
-2024-08-27 13:31:09.130979: train_loss -0.5974 
-2024-08-27 13:31:09.131514: val_loss -0.6118 
-2024-08-27 13:31:09.131733: Pseudo dice [0.0, 0.0, 0.7985, 0.9658, 0.6433, 0.9105, 0.8816, 0.9301, 0.8973, 0.8886, 0.8218, 0.9126, 0.9089, 0.6866, 0.9076, 0.8403, 0.0, 0.0, nan] 
-2024-08-27 13:31:09.131870: Epoch time: 92.21 s 
-2024-08-27 13:31:09.131926: Yayy! New best EMA pseudo Dice: 0.5785 
-2024-08-27 13:31:10.777661:  
-2024-08-27 13:31:10.777910: Epoch 33 
-2024-08-27 13:31:10.778009: Current learning rate: 0.00985 
-2024-08-27 13:32:39.695116: train_loss -0.6004 
-2024-08-27 13:32:39.695445: val_loss -0.6117 
-2024-08-27 13:32:39.695637: Pseudo dice [0.0, 0.0, 0.8098, 0.9671, 0.6446, 0.907, 0.8911, 0.9369, 0.8867, 0.8933, 0.7996, 0.9215, 0.8884, 0.6906, 0.915, 0.8529, 0.0, 0.0, nan] 
-2024-08-27 13:32:39.695752: Epoch time: 88.92 s 
-2024-08-27 13:32:39.695806: Yayy! New best EMA pseudo Dice: 0.5874 
-2024-08-27 13:32:41.599841:  
-2024-08-27 13:32:41.600004: Epoch 34 
-2024-08-27 13:32:41.600095: Current learning rate: 0.00985 
-2024-08-27 13:34:16.097311: train_loss -0.6038 
-2024-08-27 13:34:16.097566: val_loss -0.6143 
-2024-08-27 13:34:16.097755: Pseudo dice [0.0, 0.0, 0.8158, 0.9699, 0.6351, 0.9131, 0.9084, 0.9327, 0.8946, 0.883, 0.8432, 0.9175, 0.9144, 0.659, 0.9077, 0.8332, 0.0, 0.0, nan] 
-2024-08-27 13:34:16.097847: Epoch time: 94.5 s 
-2024-08-27 13:34:16.097900: Yayy! New best EMA pseudo Dice: 0.5955 
-2024-08-27 13:34:17.978984:  
-2024-08-27 13:34:17.979160: Epoch 35 
-2024-08-27 13:34:17.979264: Current learning rate: 0.00984 
-2024-08-27 13:35:51.914384: train_loss -0.6021 
-2024-08-27 13:35:51.914640: val_loss -0.6155 
-2024-08-27 13:35:51.914818: Pseudo dice [0.0, 0.0, 0.7875, 0.957, 0.5857, 0.9026, 0.8902, 0.9325, 0.8896, 0.8963, 0.8532, 0.9136, 0.9142, 0.6764, 0.9002, 0.8471, 0.0, 0.0, nan] 
-2024-08-27 13:35:51.914924: Epoch time: 93.94 s 
-2024-08-27 13:35:51.914983: Yayy! New best EMA pseudo Dice: 0.6023 
-2024-08-27 13:35:53.493824:  
-2024-08-27 13:35:53.493981: Epoch 36 
-2024-08-27 13:35:53.494074: Current learning rate: 0.00984 
-2024-08-27 13:37:26.063793: train_loss -0.6046 
-2024-08-27 13:37:26.064041: val_loss -0.62 
-2024-08-27 13:37:26.064218: Pseudo dice [0.0, 0.0, 0.8114, 0.9666, 0.6709, 0.9099, 0.9061, 0.9272, 0.9033, 0.8948, 0.8465, 0.9157, 0.9137, 0.6706, 0.9029, 0.8569, 0.0, 0.0, nan] 
-2024-08-27 13:37:26.064307: Epoch time: 92.57 s 
-2024-08-27 13:37:26.064358: Yayy! New best EMA pseudo Dice: 0.6093 
-2024-08-27 13:37:27.669364:  
-2024-08-27 13:37:27.670092: Epoch 37 
-2024-08-27 13:37:27.670202: Current learning rate: 0.00983 
-2024-08-27 13:38:56.566438: train_loss -0.6059 
-2024-08-27 13:38:56.566686: val_loss -0.629 
-2024-08-27 13:38:56.566857: Pseudo dice [0.0, 0.0, 0.8265, 0.9698, 0.6859, 0.9174, 0.9094, 0.9367, 0.9113, 0.895, 0.8428, 0.9231, 0.9236, 0.6995, 0.9064, 0.8669, 0.0, 0.0, nan] 
-2024-08-27 13:38:56.566943: Epoch time: 88.9 s 
-2024-08-27 13:38:56.566999: Yayy! New best EMA pseudo Dice: 0.6162 
-2024-08-27 13:38:58.134399:  
-2024-08-27 13:38:58.134543: Epoch 38 
-2024-08-27 13:38:58.134632: Current learning rate: 0.00983 
-2024-08-27 13:40:39.344571: train_loss -0.6298 
-2024-08-27 13:40:39.344848: val_loss -0.6715 
-2024-08-27 13:40:39.345507: Pseudo dice [0.0, 0.0, 0.8116, 0.9526, 0.6601, 0.9126, 0.9075, 0.9382, 0.8954, 0.8873, 0.8355, 0.9139, 0.903, 0.677, 0.915, 0.8453, 0.0555, 0.3587, nan] 
-2024-08-27 13:40:39.345721: Epoch time: 101.21 s 
-2024-08-27 13:40:39.345793: Yayy! New best EMA pseudo Dice: 0.6238 
-2024-08-27 13:40:41.419126:  
-2024-08-27 13:40:41.419484: Epoch 39 
-2024-08-27 13:40:41.419589: Current learning rate: 0.00982 
-2024-08-27 13:42:17.355604: train_loss -0.6556 
-2024-08-27 13:42:17.355850: val_loss -0.6779 
-2024-08-27 13:42:17.356024: Pseudo dice [0.0, 0.0, 0.7758, 0.9445, 0.6263, 0.8927, 0.8796, 0.9238, 0.913, 0.8983, 0.8281, 0.9282, 0.9136, 0.604, 0.8824, 0.8091, 0.5128, 0.5577, nan] 
-2024-08-27 13:42:17.356110: Epoch time: 95.94 s 
-2024-08-27 13:42:17.356162: Yayy! New best EMA pseudo Dice: 0.6331 
-2024-08-27 13:42:18.983091:  
-2024-08-27 13:42:18.983263: Epoch 40 
-2024-08-27 13:42:18.983363: Current learning rate: 0.00982 
-2024-08-27 13:43:55.695146: train_loss -0.6512 
-2024-08-27 13:43:55.695386: val_loss -0.6908 
-2024-08-27 13:43:55.695565: Pseudo dice [0.0, 0.0, 0.8116, 0.9581, 0.6587, 0.905, 0.9066, 0.9277, 0.9027, 0.899, 0.8429, 0.9262, 0.9165, 0.6355, 0.9072, 0.8388, 0.5322, 0.5399, nan] 
-2024-08-27 13:43:55.695657: Epoch time: 96.71 s 
-2024-08-27 13:43:55.695711: Yayy! New best EMA pseudo Dice: 0.6426 
-2024-08-27 13:43:57.363511:  
-2024-08-27 13:43:57.363698: Epoch 41 
-2024-08-27 13:43:57.363794: Current learning rate: 0.00982 
-2024-08-27 13:45:36.660611: train_loss -0.6694 
-2024-08-27 13:45:36.660823: val_loss -0.6855 
-2024-08-27 13:45:36.660977: Pseudo dice [0.0, 0.0, 0.8159, 0.9578, 0.6742, 0.8928, 0.8947, 0.9228, 0.8878, 0.901, 0.8371, 0.9176, 0.9148, 0.6681, 0.8929, 0.8397, 0.6166, 0.6199, nan] 
-2024-08-27 13:45:36.661055: Epoch time: 99.3 s 
-2024-08-27 13:45:36.661100: Yayy! New best EMA pseudo Dice: 0.652 
-2024-08-27 13:45:38.109732:  
-2024-08-27 13:45:38.110015: Epoch 42 
-2024-08-27 13:45:38.110112: Current learning rate: 0.00981 
-2024-08-27 13:47:15.722921: train_loss -0.6652 
-2024-08-27 13:47:15.723210: val_loss -0.6922 
-2024-08-27 13:47:15.723421: Pseudo dice [0.0, 0.0, 0.8101, 0.9666, 0.6409, 0.8961, 0.8992, 0.9346, 0.8879, 0.8838, 0.8344, 0.9133, 0.9125, 0.6784, 0.9158, 0.8408, 0.6377, 0.6247, nan] 
-2024-08-27 13:47:15.723521: Epoch time: 97.61 s 
-2024-08-27 13:47:15.723582: Yayy! New best EMA pseudo Dice: 0.6605 
-2024-08-27 13:47:17.427366:  
-2024-08-27 13:47:17.427853: Epoch 43 
-2024-08-27 13:47:17.427951: Current learning rate: 0.00981 
-2024-08-27 13:48:50.130496: train_loss -0.6659 
-2024-08-27 13:48:50.131178: val_loss -0.6869 
-2024-08-27 13:48:50.131398: Pseudo dice [0.0, 0.0, 0.7966, 0.964, 0.6472, 0.9016, 0.9052, 0.9376, 0.8992, 0.8851, 0.8347, 0.9159, 0.913, 0.6921, 0.9005, 0.8273, 0.6295, 0.6093, nan] 
-2024-08-27 13:48:50.131549: Epoch time: 92.7 s 
-2024-08-27 13:48:50.131603: Yayy! New best EMA pseudo Dice: 0.6681 
-2024-08-27 13:48:51.644814:  
-2024-08-27 13:48:51.645123: Epoch 44 
-2024-08-27 13:48:51.645222: Current learning rate: 0.0098 
-2024-08-27 13:50:23.430683: train_loss -0.6767 
-2024-08-27 13:50:23.431047: val_loss -0.7016 
-2024-08-27 13:50:23.431246: Pseudo dice [0.0, 0.0, 0.8175, 0.9619, 0.6291, 0.9068, 0.9057, 0.9374, 0.9061, 0.8997, 0.842, 0.9253, 0.9152, 0.6807, 0.914, 0.8516, 0.6457, 0.5876, nan] 
-2024-08-27 13:50:23.431344: Epoch time: 91.79 s 
-2024-08-27 13:50:23.431399: Yayy! New best EMA pseudo Dice: 0.6753 
-2024-08-27 13:50:25.005052:  
-2024-08-27 13:50:25.005401: Epoch 45 
-2024-08-27 13:50:25.005509: Current learning rate: 0.0098 
-2024-08-27 13:51:58.611631: train_loss -0.6799 
-2024-08-27 13:51:58.611859: val_loss -0.698 
-2024-08-27 13:51:58.612033: Pseudo dice [0.0, 0.0, 0.8201, 0.9682, 0.6458, 0.9163, 0.9104, 0.9341, 0.9059, 0.8946, 0.8454, 0.9266, 0.9183, 0.6789, 0.9071, 0.8395, 0.6495, 0.6534, nan] 
-2024-08-27 13:51:58.612117: Epoch time: 93.61 s 
-2024-08-27 13:51:58.612167: Yayy! New best EMA pseudo Dice: 0.6823 
-2024-08-27 13:52:00.074914:  
-2024-08-27 13:52:00.075334: Epoch 46 
-2024-08-27 13:52:00.075418: Current learning rate: 0.00979 
-2024-08-27 13:53:35.623490: train_loss -0.6758 
-2024-08-27 13:53:35.623780: val_loss -0.698 
-2024-08-27 13:53:35.623953: Pseudo dice [0.0, 0.0, 0.8222, 0.9635, 0.687, 0.9165, 0.9132, 0.9413, 0.8888, 0.9015, 0.8307, 0.9188, 0.9016, 0.6685, 0.9075, 0.8614, 0.6674, 0.6268, nan] 
-2024-08-27 13:53:35.624045: Epoch time: 95.55 s 
-2024-08-27 13:53:35.624095: Yayy! New best EMA pseudo Dice: 0.6886 
-2024-08-27 13:53:37.115552:  
-2024-08-27 13:53:37.115835: Epoch 47 
-2024-08-27 13:53:37.115929: Current learning rate: 0.00979 
-2024-08-27 13:55:11.194127: train_loss -0.6786 
-2024-08-27 13:55:11.194434: val_loss -0.7115 
-2024-08-27 13:55:11.194741: Pseudo dice [0.0, 0.0, 0.8349, 0.9672, 0.7029, 0.922, 0.9251, 0.9415, 0.9105, 0.8993, 0.8206, 0.9281, 0.9117, 0.7148, 0.9183, 0.8688, 0.6584, 0.6286, nan] 
-2024-08-27 13:55:11.194908: Epoch time: 94.08 s 
-2024-08-27 13:55:11.195052: Yayy! New best EMA pseudo Dice: 0.6951 
-2024-08-27 13:55:12.745611:  
-2024-08-27 13:55:12.745768: Epoch 48 
-2024-08-27 13:55:12.745868: Current learning rate: 0.00978 
-2024-08-27 13:56:52.315583: train_loss -0.6812 
-2024-08-27 13:56:52.315876: val_loss -0.7022 
-2024-08-27 13:56:52.316072: Pseudo dice [0.0, 0.0, 0.8333, 0.9636, 0.6758, 0.918, 0.9076, 0.937, 0.9, 0.8764, 0.8259, 0.9143, 0.9092, 0.7092, 0.9167, 0.8582, 0.7029, 0.6853, nan] 
-2024-08-27 13:56:52.316180: Epoch time: 99.57 s 
-2024-08-27 13:56:52.316242: Yayy! New best EMA pseudo Dice: 0.7007 
-2024-08-27 13:56:53.885776:  
-2024-08-27 13:56:53.886084: Epoch 49 
-2024-08-27 13:56:53.886199: Current learning rate: 0.00978 
-2024-08-27 13:58:23.688462: train_loss -0.6744 
-2024-08-27 13:58:23.688747: val_loss -0.694 
-2024-08-27 13:58:23.688949: Pseudo dice [0.0, 0.0, 0.8143, 0.9688, 0.6386, 0.8959, 0.8969, 0.9368, 0.8872, 0.8631, 0.819, 0.9096, 0.9056, 0.6926, 0.8967, 0.85, 0.6976, 0.6572, nan] 
-2024-08-27 13:58:23.689054: Epoch time: 89.8 s 
-2024-08-27 13:58:23.893579: Yayy! New best EMA pseudo Dice: 0.7047 
-2024-08-27 13:58:25.695009:  
-2024-08-27 13:58:25.695331: Epoch 50 
-2024-08-27 13:58:25.695430: Current learning rate: 0.00977 
-2024-08-27 14:00:03.569262: train_loss -0.6858 
-2024-08-27 14:00:03.569545: val_loss -0.7105 
-2024-08-27 14:00:03.569718: Pseudo dice [0.0, 0.0, 0.8055, 0.9631, 0.7078, 0.9137, 0.92, 0.9376, 0.8965, 0.8846, 0.8378, 0.922, 0.9134, 0.718, 0.9233, 0.8665, 0.6924, 0.6921, nan] 
-2024-08-27 14:00:03.569811: Epoch time: 97.88 s 
-2024-08-27 14:00:03.569861: Yayy! New best EMA pseudo Dice: 0.7098 
-2024-08-27 14:00:05.172196:  
-2024-08-27 14:00:05.173052: Epoch 51 
-2024-08-27 14:00:05.173338: Current learning rate: 0.00977 
-2024-08-27 14:01:41.816214: train_loss -0.6919 
-2024-08-27 14:01:41.816483: val_loss -0.7142 
-2024-08-27 14:01:41.816683: Pseudo dice [0.0, 0.0, 0.8079, 0.968, 0.7419, 0.91, 0.9092, 0.9409, 0.9012, 0.8915, 0.8487, 0.9216, 0.9105, 0.717, 0.9274, 0.8704, 0.6774, 0.7026, nan] 
-2024-08-27 14:01:41.816775: Epoch time: 96.65 s 
-2024-08-27 14:01:41.816830: Yayy! New best EMA pseudo Dice: 0.7146 
-2024-08-27 14:01:43.326078:  
-2024-08-27 14:01:43.326276: Epoch 52 
-2024-08-27 14:01:43.326375: Current learning rate: 0.00977 
-2024-08-27 14:03:22.046366: train_loss -0.689 
-2024-08-27 14:03:22.046639: val_loss -0.716 
-2024-08-27 14:03:22.046826: Pseudo dice [0.0, 0.0, 0.8323, 0.9656, 0.735, 0.9162, 0.9097, 0.9438, 0.9091, 0.9107, 0.8439, 0.9277, 0.9116, 0.7157, 0.9254, 0.8665, 0.6995, 0.6825, nan] 
-2024-08-27 14:03:22.046928: Epoch time: 98.72 s 
-2024-08-27 14:03:22.046983: Yayy! New best EMA pseudo Dice: 0.7192 
-2024-08-27 14:03:23.622375:  
-2024-08-27 14:03:23.622579: Epoch 53 
-2024-08-27 14:03:23.622683: Current learning rate: 0.00976 
-2024-08-27 14:04:51.492701: train_loss -0.692 
-2024-08-27 14:04:51.492967: val_loss -0.7225 
-2024-08-27 14:04:51.493151: Pseudo dice [0.0, 0.0, 0.8299, 0.9698, 0.7179, 0.9268, 0.9231, 0.941, 0.9137, 0.9122, 0.8623, 0.9312, 0.9237, 0.7252, 0.925, 0.8581, 0.7007, 0.6935, nan] 
-2024-08-27 14:04:51.493241: Epoch time: 87.87 s 
-2024-08-27 14:04:51.493292: Yayy! New best EMA pseudo Dice: 0.7237 
-2024-08-27 14:04:53.090007:  
-2024-08-27 14:04:53.090333: Epoch 54 
-2024-08-27 14:04:53.090431: Current learning rate: 0.00976 
-2024-08-27 14:06:22.218018: train_loss -0.6935 
-2024-08-27 14:06:22.218313: val_loss -0.7261 
-2024-08-27 14:06:22.218498: Pseudo dice [0.0, 0.0, 0.8345, 0.9702, 0.7278, 0.9255, 0.916, 0.9491, 0.9116, 0.9075, 0.8511, 0.9297, 0.9213, 0.7303, 0.9257, 0.8718, 0.6828, 0.688, nan] 
-2024-08-27 14:06:22.218593: Epoch time: 89.13 s 
-2024-08-27 14:06:22.218647: Yayy! New best EMA pseudo Dice: 0.7277 
-2024-08-27 14:06:23.766497:  
-2024-08-27 14:06:23.766670: Epoch 55 
-2024-08-27 14:06:23.766764: Current learning rate: 0.00975 
-2024-08-27 14:07:56.887209: train_loss -0.6986 
-2024-08-27 14:07:56.887463: val_loss -0.7237 
-2024-08-27 14:07:56.887702: Pseudo dice [0.0, 0.0, 0.8381, 0.9715, 0.7232, 0.926, 0.9295, 0.9444, 0.9095, 0.9071, 0.8324, 0.9317, 0.9216, 0.7346, 0.9344, 0.8804, 0.7052, 0.6979, nan] 
-2024-08-27 14:07:56.887804: Epoch time: 93.12 s 
-2024-08-27 14:07:56.887855: Yayy! New best EMA pseudo Dice: 0.7315 
-2024-08-27 14:07:58.774368:  
-2024-08-27 14:07:58.774544: Epoch 56 
-2024-08-27 14:07:58.774642: Current learning rate: 0.00975 
-2024-08-27 14:09:31.715662: train_loss -0.6985 
-2024-08-27 14:09:31.715937: val_loss -0.7113 
-2024-08-27 14:09:31.716113: Pseudo dice [0.0, 0.0, 0.8369, 0.9703, 0.709, 0.9102, 0.9091, 0.9455, 0.8987, 0.9006, 0.831, 0.9218, 0.9196, 0.7048, 0.9246, 0.8633, 0.6907, 0.7012, nan] 
-2024-08-27 14:09:31.716209: Epoch time: 92.94 s 
-2024-08-27 14:09:31.716262: Yayy! New best EMA pseudo Dice: 0.7341 
-2024-08-27 14:09:33.319128:  
-2024-08-27 14:09:33.319461: Epoch 57 
-2024-08-27 14:09:33.319567: Current learning rate: 0.00974 
-2024-08-27 14:11:00.464016: train_loss -0.6973 
-2024-08-27 14:11:00.464618: val_loss -0.7174 
-2024-08-27 14:11:00.464844: Pseudo dice [0.0, 0.0, 0.8331, 0.9692, 0.7259, 0.9248, 0.9233, 0.9467, 0.911, 0.8932, 0.8355, 0.9305, 0.9193, 0.7247, 0.9351, 0.8909, 0.6983, 0.6803, nan] 
-2024-08-27 14:11:00.465329: Epoch time: 87.15 s 
-2024-08-27 14:11:00.465423: Yayy! New best EMA pseudo Dice: 0.7371 
-2024-08-27 14:11:01.980682:  
-2024-08-27 14:11:01.981121: Epoch 58 
-2024-08-27 14:11:01.981277: Current learning rate: 0.00974 
-2024-08-27 14:12:39.913112: train_loss -0.6974 
-2024-08-27 14:12:39.913615: val_loss -0.7198 
-2024-08-27 14:12:39.913810: Pseudo dice [0.0, 0.0, 0.8565, 0.9714, 0.7359, 0.9181, 0.9217, 0.9486, 0.9081, 0.9064, 0.8546, 0.9248, 0.9249, 0.7391, 0.92, 0.876, 0.7265, 0.7088, nan] 
-2024-08-27 14:12:39.913927: Epoch time: 97.93 s 
-2024-08-27 14:12:39.913980: Yayy! New best EMA pseudo Dice: 0.7403 
-2024-08-27 14:12:41.457053:  
-2024-08-27 14:12:41.457712: Epoch 59 
-2024-08-27 14:12:41.457827: Current learning rate: 0.00973 
-2024-08-27 14:14:13.849288: train_loss -0.7037 
-2024-08-27 14:14:13.849878: val_loss -0.7228 
-2024-08-27 14:14:13.850112: Pseudo dice [0.0, 0.0, 0.8302, 0.9683, 0.7125, 0.9168, 0.9174, 0.9472, 0.8968, 0.8911, 0.8447, 0.9158, 0.9108, 0.7435, 0.9258, 0.8841, 0.7033, 0.7046, nan] 
-2024-08-27 14:14:13.850263: Epoch time: 92.39 s 
-2024-08-27 14:14:13.850320: Yayy! New best EMA pseudo Dice: 0.7424 
-2024-08-27 14:14:15.622661:  
-2024-08-27 14:14:15.622988: Epoch 60 
-2024-08-27 14:14:15.623271: Current learning rate: 0.00973 
-2024-08-27 14:15:48.993206: train_loss -0.7027 
-2024-08-27 14:15:48.993871: val_loss -0.7176 
-2024-08-27 14:15:48.994158: Pseudo dice [0.0, 0.0, 0.8397, 0.9703, 0.7427, 0.9246, 0.9147, 0.9393, 0.8975, 0.8929, 0.8622, 0.9224, 0.9263, 0.7282, 0.9224, 0.8838, 0.7163, 0.7091, nan] 
-2024-08-27 14:15:48.994356: Epoch time: 93.37 s 
-2024-08-27 14:15:48.994485: Yayy! New best EMA pseudo Dice: 0.7448 
-2024-08-27 14:15:51.140912:  
-2024-08-27 14:15:51.141165: Epoch 61 
-2024-08-27 14:15:51.141277: Current learning rate: 0.00973 
-2024-08-27 14:17:21.778799: train_loss -0.6994 
-2024-08-27 14:17:21.779063: val_loss -0.7161 
-2024-08-27 14:17:21.779257: Pseudo dice [0.0, 0.0, 0.8486, 0.9675, 0.6861, 0.9194, 0.9131, 0.9467, 0.8986, 0.907, 0.8605, 0.92, 0.9199, 0.7004, 0.9161, 0.869, 0.7132, 0.7016, nan] 
-2024-08-27 14:17:21.779381: Epoch time: 90.64 s 
-2024-08-27 14:17:21.779439: Yayy! New best EMA pseudo Dice: 0.7464 
-2024-08-27 14:17:23.742090:  
-2024-08-27 14:17:23.742409: Epoch 62 
-2024-08-27 14:17:23.742576: Current learning rate: 0.00972 
-2024-08-27 14:18:55.615484: train_loss -0.7026 
-2024-08-27 14:18:55.615753: val_loss -0.7232 
-2024-08-27 14:18:55.615957: Pseudo dice [0.0, 0.0, 0.8399, 0.9611, 0.7442, 0.9203, 0.9119, 0.9443, 0.898, 0.8919, 0.8649, 0.9243, 0.9229, 0.7522, 0.9327, 0.8869, 0.7127, 0.7094, nan] 
-2024-08-27 14:18:55.616059: Epoch time: 91.87 s 
-2024-08-27 14:18:55.616120: Yayy! New best EMA pseudo Dice: 0.7485 
-2024-08-27 14:18:57.304727:  
-2024-08-27 14:18:57.305030: Epoch 63 
-2024-08-27 14:18:57.305150: Current learning rate: 0.00972 
-2024-08-27 14:20:31.679154: train_loss -0.703 
-2024-08-27 14:20:31.679446: val_loss -0.7258 
-2024-08-27 14:20:31.679644: Pseudo dice [0.0, 0.0, 0.8399, 0.9725, 0.7259, 0.9185, 0.9216, 0.9474, 0.9075, 0.9083, 0.8583, 0.923, 0.9259, 0.7512, 0.9271, 0.8843, 0.7285, 0.6819, nan] 
-2024-08-27 14:20:31.679742: Epoch time: 94.38 s 
-2024-08-27 14:20:31.679796: Yayy! New best EMA pseudo Dice: 0.7504 
-2024-08-27 14:20:33.329708:  
-2024-08-27 14:20:33.330040: Epoch 64 
-2024-08-27 14:20:33.330143: Current learning rate: 0.00971 
-2024-08-27 14:22:07.516232: train_loss -0.7045 
-2024-08-27 14:22:07.516577: val_loss -0.7169 
-2024-08-27 14:22:07.516838: Pseudo dice [0.0, 0.0, 0.835, 0.9699, 0.7195, 0.9109, 0.9096, 0.9447, 0.9126, 0.8992, 0.8617, 0.93, 0.928, 0.7411, 0.9251, 0.8828, 0.7164, 0.7013, nan] 
-2024-08-27 14:22:07.517066: Epoch time: 94.19 s 
-2024-08-27 14:22:07.517230: Yayy! New best EMA pseudo Dice: 0.752 
-2024-08-27 14:22:09.046777:  
-2024-08-27 14:22:09.047136: Epoch 65 
-2024-08-27 14:22:09.047377: Current learning rate: 0.00971 
-2024-08-27 14:23:44.039741: train_loss -0.7075 
-2024-08-27 14:23:44.040027: val_loss -0.7246 
-2024-08-27 14:23:44.040253: Pseudo dice [0.0, 0.0, 0.8547, 0.97, 0.7698, 0.9246, 0.9259, 0.9406, 0.9109, 0.9014, 0.8497, 0.9243, 0.9183, 0.7275, 0.9165, 0.8939, 0.7188, 0.6927, nan] 
-2024-08-27 14:23:44.040365: Epoch time: 94.99 s 
-2024-08-27 14:23:44.040426: Yayy! New best EMA pseudo Dice: 0.7537 
-2024-08-27 14:23:45.817487:  
-2024-08-27 14:23:45.817794: Epoch 66 
-2024-08-27 14:23:45.817998: Current learning rate: 0.0097 
-2024-08-27 14:25:25.786829: train_loss -0.703 
-2024-08-27 14:25:25.787460: val_loss -0.7261 
-2024-08-27 14:25:25.787688: Pseudo dice [0.0, 0.0, 0.8265, 0.9664, 0.7275, 0.9169, 0.9234, 0.9499, 0.9081, 0.9095, 0.8716, 0.9306, 0.9251, 0.7268, 0.9251, 0.889, 0.7262, 0.7399, nan] 
-2024-08-27 14:25:25.787797: Epoch time: 99.97 s 
-2024-08-27 14:25:25.787858: Yayy! New best EMA pseudo Dice: 0.7553 
-2024-08-27 14:25:27.695606:  
-2024-08-27 14:25:27.695888: Epoch 67 
-2024-08-27 14:25:27.695992: Current learning rate: 0.0097 
-2024-08-27 14:27:05.889760: train_loss -0.7109 
-2024-08-27 14:27:05.890017: val_loss -0.7215 
-2024-08-27 14:27:05.890209: Pseudo dice [0.0, 0.0, 0.8572, 0.9714, 0.7124, 0.9261, 0.925, 0.9336, 0.9106, 0.901, 0.8576, 0.9288, 0.9258, 0.7203, 0.9272, 0.8508, 0.7271, 0.7168, nan] 
-2024-08-27 14:27:05.890309: Epoch time: 98.19 s 
-2024-08-27 14:27:05.890362: Yayy! New best EMA pseudo Dice: 0.7564 
-2024-08-27 14:27:07.537467:  
-2024-08-27 14:27:07.537731: Epoch 68 
-2024-08-27 14:27:07.537830: Current learning rate: 0.00969 
-2024-08-27 14:28:48.467347: train_loss -0.7089 
-2024-08-27 14:28:48.467659: val_loss -0.732 
-2024-08-27 14:28:48.467891: Pseudo dice [0.0, 0.0, 0.8416, 0.9674, 0.7476, 0.9333, 0.9316, 0.9491, 0.9178, 0.9148, 0.8668, 0.9379, 0.931, 0.756, 0.94, 0.8969, 0.7434, 0.7317, nan] 
-2024-08-27 14:28:48.468012: Epoch time: 100.93 s 
-2024-08-27 14:28:48.468089: Yayy! New best EMA pseudo Dice: 0.7586 
-2024-08-27 14:28:50.115156:  
-2024-08-27 14:28:50.115552: Epoch 69 
-2024-08-27 14:28:50.115692: Current learning rate: 0.00969 
-2024-08-27 14:30:27.973487: train_loss -0.7092 
-2024-08-27 14:30:27.973735: val_loss -0.7258 
-2024-08-27 14:30:27.973917: Pseudo dice [0.0, 0.0, 0.832, 0.9694, 0.7595, 0.9152, 0.9219, 0.9459, 0.9128, 0.921, 0.879, 0.9331, 0.9335, 0.7325, 0.9242, 0.8869, 0.7525, 0.7406, nan] 
-2024-08-27 14:30:27.974013: Epoch time: 97.86 s 
-2024-08-27 14:30:27.974070: Yayy! New best EMA pseudo Dice: 0.7603 
-2024-08-27 14:30:29.652294:  
-2024-08-27 14:30:29.652499: Epoch 70 
-2024-08-27 14:30:29.652604: Current learning rate: 0.00968 
-2024-08-27 14:32:03.838772: train_loss -0.7108 
-2024-08-27 14:32:03.839051: val_loss -0.7265 
-2024-08-27 14:32:03.839229: Pseudo dice [0.0, 0.0, 0.8626, 0.9707, 0.7369, 0.9275, 0.9255, 0.9426, 0.9028, 0.9049, 0.865, 0.9212, 0.9278, 0.7648, 0.9345, 0.8909, 0.7379, 0.7245, nan] 
-2024-08-27 14:32:03.839321: Epoch time: 94.19 s 
-2024-08-27 14:32:03.839377: Yayy! New best EMA pseudo Dice: 0.7617 
-2024-08-27 14:32:05.474781:  
-2024-08-27 14:32:05.474970: Epoch 71 
-2024-08-27 14:32:05.475067: Current learning rate: 0.00968 
-2024-08-27 14:33:37.910001: train_loss -0.7119 
-2024-08-27 14:33:37.910260: val_loss -0.7322 
-2024-08-27 14:33:37.910444: Pseudo dice [0.0, 0.0, 0.8543, 0.9731, 0.7283, 0.9301, 0.928, 0.9495, 0.9123, 0.9169, 0.8715, 0.9292, 0.9257, 0.752, 0.9365, 0.8885, 0.7578, 0.7318, nan] 
-2024-08-27 14:33:37.910540: Epoch time: 92.44 s 
-2024-08-27 14:33:37.910594: Yayy! New best EMA pseudo Dice: 0.7632 
-2024-08-27 14:33:39.670008:  
-2024-08-27 14:33:39.670218: Epoch 72 
-2024-08-27 14:33:39.670318: Current learning rate: 0.00968 
-2024-08-27 14:35:20.802204: train_loss -0.7107 
-2024-08-27 14:35:20.802478: val_loss -0.7342 
-2024-08-27 14:35:20.802659: Pseudo dice [0.0, 0.0, 0.8474, 0.9731, 0.7282, 0.925, 0.9252, 0.9491, 0.9238, 0.9139, 0.8805, 0.9369, 0.9259, 0.7656, 0.933, 0.8961, 0.7257, 0.7393, nan] 
-2024-08-27 14:35:20.802752: Epoch time: 101.13 s 
-2024-08-27 14:35:20.802806: Yayy! New best EMA pseudo Dice: 0.7646 
-2024-08-27 14:35:22.794251:  
-2024-08-27 14:35:22.794461: Epoch 73 
-2024-08-27 14:35:22.794567: Current learning rate: 0.00967 
-2024-08-27 14:37:03.769417: train_loss -0.716 
-2024-08-27 14:37:03.769664: val_loss -0.7299 
-2024-08-27 14:37:03.769847: Pseudo dice [0.0, 0.0, 0.8591, 0.9713, 0.7632, 0.9326, 0.9349, 0.9507, 0.919, 0.8971, 0.8693, 0.9335, 0.926, 0.7468, 0.9346, 0.8924, 0.7375, 0.7283, nan] 
-2024-08-27 14:37:03.769940: Epoch time: 100.98 s 
-2024-08-27 14:37:03.769998: Yayy! New best EMA pseudo Dice: 0.7659 
-2024-08-27 14:37:05.372741:  
-2024-08-27 14:37:05.372927: Epoch 74 
-2024-08-27 14:37:05.373019: Current learning rate: 0.00967 
-2024-08-27 14:38:39.016787: train_loss -0.7086 
-2024-08-27 14:38:39.017341: val_loss -0.7316 
-2024-08-27 14:38:39.017548: Pseudo dice [0.0, 0.0, 0.8473, 0.9729, 0.7868, 0.9311, 0.9266, 0.9469, 0.9262, 0.9161, 0.8875, 0.9414, 0.9343, 0.7428, 0.9406, 0.8878, 0.6958, 0.7193, nan] 
-2024-08-27 14:38:39.017637: Epoch time: 93.64 s 
-2024-08-27 14:38:39.017688: Yayy! New best EMA pseudo Dice: 0.7671 
-2024-08-27 14:38:40.641389:  
-2024-08-27 14:38:40.641570: Epoch 75 
-2024-08-27 14:38:40.641668: Current learning rate: 0.00966 
-2024-08-27 14:40:26.130702: train_loss -0.7116 
-2024-08-27 14:40:26.130969: val_loss -0.729 
-2024-08-27 14:40:26.131143: Pseudo dice [0.0, 0.0, 0.8413, 0.966, 0.7236, 0.9305, 0.9282, 0.9493, 0.9167, 0.925, 0.8713, 0.9335, 0.9261, 0.7397, 0.933, 0.8828, 0.7354, 0.7334, nan] 
-2024-08-27 14:40:26.131230: Epoch time: 105.49 s 
-2024-08-27 14:40:26.131281: Yayy! New best EMA pseudo Dice: 0.7678 
-2024-08-27 14:40:27.785816:  
-2024-08-27 14:40:27.786050: Epoch 76 
-2024-08-27 14:40:27.786152: Current learning rate: 0.00966 
-2024-08-27 14:42:07.877214: train_loss -0.7005 
-2024-08-27 14:42:07.877495: val_loss -0.7278 
-2024-08-27 14:42:07.877667: Pseudo dice [0.0, 0.0, 0.8486, 0.9722, 0.7556, 0.9304, 0.9313, 0.9512, 0.9195, 0.9104, 0.8893, 0.9363, 0.9308, 0.7519, 0.9396, 0.8931, 0.7194, 0.6641, nan] 
-2024-08-27 14:42:07.877754: Epoch time: 100.09 s 
-2024-08-27 14:42:07.877802: Yayy! New best EMA pseudo Dice: 0.7685 
-2024-08-27 14:42:09.717146:  
-2024-08-27 14:42:09.717314: Epoch 77 
-2024-08-27 14:42:09.717415: Current learning rate: 0.00965 
-2024-08-27 14:43:37.367247: train_loss -0.7164 
-2024-08-27 14:43:37.367752: val_loss -0.7398 
-2024-08-27 14:43:37.367973: Pseudo dice [0.0, 0.0, 0.8526, 0.9725, 0.7543, 0.9317, 0.924, 0.9505, 0.9219, 0.9141, 0.8913, 0.942, 0.9345, 0.7668, 0.9308, 0.8929, 0.7271, 0.6994, nan] 
-2024-08-27 14:43:37.368086: Epoch time: 87.65 s 
-2024-08-27 14:43:37.368462: Yayy! New best EMA pseudo Dice: 0.7695 
-2024-08-27 14:43:39.306158:  
-2024-08-27 14:43:39.306328: Epoch 78 
-2024-08-27 14:43:39.306425: Current learning rate: 0.00965 
-2024-08-27 14:45:18.050223: train_loss -0.7161 
-2024-08-27 14:45:18.050475: val_loss -0.7307 
-2024-08-27 14:45:18.050647: Pseudo dice [0.0, 0.0, 0.8465, 0.9722, 0.7789, 0.9294, 0.9286, 0.9507, 0.9249, 0.9202, 0.8806, 0.9403, 0.9379, 0.764, 0.9252, 0.8948, 0.7305, 0.7314, nan] 
-2024-08-27 14:45:18.050731: Epoch time: 98.74 s 
-2024-08-27 14:45:18.050781: Yayy! New best EMA pseudo Dice: 0.7706 
-2024-08-27 14:45:19.664683:  
-2024-08-27 14:45:19.664843: Epoch 79 
-2024-08-27 14:45:19.664943: Current learning rate: 0.00964 
-2024-08-27 14:46:56.981584: train_loss -0.7144 
-2024-08-27 14:46:56.981854: val_loss -0.7284 
-2024-08-27 14:46:56.982034: Pseudo dice [0.0, 0.0, 0.8154, 0.9584, 0.7407, 0.9252, 0.9179, 0.9444, 0.9169, 0.9128, 0.8841, 0.9352, 0.9355, 0.7509, 0.9256, 0.8832, 0.7394, 0.7251, nan] 
-2024-08-27 14:46:56.982128: Epoch time: 97.32 s 
-2024-08-27 14:46:56.982184: Yayy! New best EMA pseudo Dice: 0.7708 
-2024-08-27 14:46:58.625915:  
-2024-08-27 14:46:58.626132: Epoch 80 
-2024-08-27 14:46:58.626235: Current learning rate: 0.00964 
-2024-08-27 14:48:37.567256: train_loss -0.7082 
-2024-08-27 14:48:37.567492: val_loss -0.7268 
-2024-08-27 14:48:37.567669: Pseudo dice [0.0, 0.0, 0.8413, 0.9693, 0.7369, 0.9251, 0.9243, 0.9477, 0.9134, 0.9006, 0.8811, 0.9367, 0.9337, 0.7628, 0.9121, 0.8771, 0.7311, 0.7181, nan] 
-2024-08-27 14:48:37.567759: Epoch time: 98.94 s 
-2024-08-27 14:48:37.567812: Yayy! New best EMA pseudo Dice: 0.771 
-2024-08-27 14:48:39.292944:  
-2024-08-27 14:48:39.293253: Epoch 81 
-2024-08-27 14:48:39.293361: Current learning rate: 0.00963 
-2024-08-27 14:50:22.174077: train_loss -0.7069 
-2024-08-27 14:50:22.174312: val_loss -0.7332 
-2024-08-27 14:50:22.174491: Pseudo dice [0.0, 0.0, 0.8644, 0.9734, 0.742, 0.921, 0.921, 0.9521, 0.9265, 0.9276, 0.8862, 0.9388, 0.9373, 0.7398, 0.9349, 0.9002, 0.7571, 0.7384, nan] 
-2024-08-27 14:50:22.174575: Epoch time: 102.88 s 
-2024-08-27 14:50:22.174627: Yayy! New best EMA pseudo Dice: 0.772 
-2024-08-27 14:50:23.750312:  
-2024-08-27 14:50:23.750798: Epoch 82 
-2024-08-27 14:50:23.750903: Current learning rate: 0.00963 
-2024-08-27 14:52:04.152633: train_loss -0.715 
-2024-08-27 14:52:04.152906: val_loss -0.7352 
-2024-08-27 14:52:04.153090: Pseudo dice [0.0, 0.0, 0.8558, 0.9732, 0.7739, 0.9346, 0.9315, 0.9458, 0.9241, 0.908, 0.8764, 0.938, 0.9352, 0.7505, 0.9299, 0.896, 0.7434, 0.7341, nan] 
-2024-08-27 14:52:04.153225: Epoch time: 100.4 s 
-2024-08-27 14:52:04.153280: Yayy! New best EMA pseudo Dice: 0.7729 
-2024-08-27 14:52:05.988031:  
-2024-08-27 14:52:05.988227: Epoch 83 
-2024-08-27 14:52:05.988333: Current learning rate: 0.00963 
-2024-08-27 14:53:42.023292: train_loss -0.7183 
-2024-08-27 14:53:42.023565: val_loss -0.7326 
-2024-08-27 14:53:42.023752: Pseudo dice [0.0, 0.0, 0.8411, 0.9724, 0.7914, 0.9337, 0.9338, 0.9493, 0.9264, 0.9201, 0.8852, 0.9425, 0.9381, 0.7677, 0.9337, 0.8904, 0.734, 0.7104, nan] 
-2024-08-27 14:53:42.023849: Epoch time: 96.04 s 
-2024-08-27 14:53:42.023905: Yayy! New best EMA pseudo Dice: 0.7738 
-2024-08-27 14:53:43.599210:  
-2024-08-27 14:53:43.599641: Epoch 84 
-2024-08-27 14:53:43.599745: Current learning rate: 0.00962 
-2024-08-27 14:55:22.972752: train_loss -0.7197 
-2024-08-27 14:55:22.973018: val_loss -0.7324 
-2024-08-27 14:55:22.973212: Pseudo dice [0.0, 0.0, 0.8413, 0.9719, 0.7611, 0.9271, 0.929, 0.9485, 0.917, 0.9243, 0.8909, 0.9335, 0.9293, 0.7667, 0.9315, 0.8946, 0.7466, 0.7477, nan] 
-2024-08-27 14:55:22.973315: Epoch time: 99.37 s 
-2024-08-27 14:55:22.973372: Yayy! New best EMA pseudo Dice: 0.7745 
-2024-08-27 14:55:24.471798:  
-2024-08-27 14:55:24.472066: Epoch 85 
-2024-08-27 14:55:24.472163: Current learning rate: 0.00962 
-2024-08-27 14:56:54.265232: train_loss -0.722 
-2024-08-27 14:56:54.265782: val_loss -0.7383 
-2024-08-27 14:56:54.266034: Pseudo dice [0.0, 0.0, 0.8431, 0.971, 0.7652, 0.9328, 0.9365, 0.9543, 0.9318, 0.9265, 0.8964, 0.9447, 0.9432, 0.7772, 0.9272, 0.902, 0.7254, 0.725, nan] 
-2024-08-27 14:56:54.266200: Epoch time: 89.79 s 
-2024-08-27 14:56:54.266255: Yayy! New best EMA pseudo Dice: 0.7754 
-2024-08-27 14:56:55.819664:  
-2024-08-27 14:56:55.820028: Epoch 86 
-2024-08-27 14:56:55.820147: Current learning rate: 0.00961 
-2024-08-27 14:58:23.907773: train_loss -0.7216 
-2024-08-27 14:58:23.908302: val_loss -0.737 
-2024-08-27 14:58:23.908539: Pseudo dice [0.0, 0.0, 0.8584, 0.9737, 0.7618, 0.9246, 0.926, 0.949, 0.9248, 0.9233, 0.8877, 0.9374, 0.9358, 0.7725, 0.9278, 0.8954, 0.7702, 0.7671, nan] 
-2024-08-27 14:58:23.908687: Epoch time: 88.09 s 
-2024-08-27 14:58:23.908753: Yayy! New best EMA pseudo Dice: 0.7764 
-2024-08-27 14:58:25.641964:  
-2024-08-27 14:58:25.642186: Epoch 87 
-2024-08-27 14:58:25.642286: Current learning rate: 0.00961 
-2024-08-27 15:00:03.966151: train_loss -0.7158 
-2024-08-27 15:00:03.966464: val_loss -0.7368 
-2024-08-27 15:00:03.966659: Pseudo dice [0.0, 0.0, 0.8364, 0.9713, 0.7792, 0.9212, 0.9177, 0.9519, 0.9202, 0.9164, 0.8865, 0.9333, 0.9366, 0.7722, 0.9297, 0.8986, 0.7577, 0.7474, nan] 
-2024-08-27 15:00:03.966787: Epoch time: 98.33 s 
-2024-08-27 15:00:03.966841: Yayy! New best EMA pseudo Dice: 0.777 
-2024-08-27 15:00:05.705240:  
-2024-08-27 15:00:05.705418: Epoch 88 
-2024-08-27 15:00:05.705517: Current learning rate: 0.0096 
-2024-08-27 15:01:37.824275: train_loss -0.7196 
-2024-08-27 15:01:37.824570: val_loss -0.736 
-2024-08-27 15:01:37.824761: Pseudo dice [0.0, 0.0, 0.8449, 0.9735, 0.7791, 0.9302, 0.9335, 0.9486, 0.9293, 0.9185, 0.8815, 0.9394, 0.9358, 0.7715, 0.9206, 0.8878, 0.7566, 0.7402, nan] 
-2024-08-27 15:01:37.824860: Epoch time: 92.12 s 
-2024-08-27 15:01:37.824915: Yayy! New best EMA pseudo Dice: 0.7775 
-2024-08-27 15:01:39.697987:  
-2024-08-27 15:01:39.698174: Epoch 89 
-2024-08-27 15:01:39.698271: Current learning rate: 0.0096 
-2024-08-27 15:03:14.723724: train_loss -0.7207 
-2024-08-27 15:03:14.724167: val_loss -0.744 
-2024-08-27 15:03:14.724644: Pseudo dice [0.0, 0.0, 0.8585, 0.9746, 0.7598, 0.9275, 0.9285, 0.9565, 0.9286, 0.9321, 0.8929, 0.9415, 0.9378, 0.7842, 0.9358, 0.8991, 0.7712, 0.7762, nan] 
-2024-08-27 15:03:14.724985: Epoch time: 95.03 s 
-2024-08-27 15:03:14.725077: Yayy! New best EMA pseudo Dice: 0.7787 
-2024-08-27 15:03:16.237890:  
-2024-08-27 15:03:16.238075: Epoch 90 
-2024-08-27 15:03:16.238174: Current learning rate: 0.00959 
-2024-08-27 15:04:44.280939: train_loss -0.721 
-2024-08-27 15:04:44.281183: val_loss -0.7359 
-2024-08-27 15:04:44.281363: Pseudo dice [0.0, 0.0, 0.8587, 0.9718, 0.764, 0.9293, 0.9233, 0.9538, 0.9277, 0.9178, 0.8639, 0.9367, 0.9197, 0.7791, 0.9361, 0.9079, 0.7216, 0.7306, nan] 
-2024-08-27 15:04:44.281472: Epoch time: 88.04 s 
-2024-08-27 15:04:44.281529: Yayy! New best EMA pseudo Dice: 0.7789 
-2024-08-27 15:04:45.853394:  
-2024-08-27 15:04:45.853613: Epoch 91 
-2024-08-27 15:04:45.853711: Current learning rate: 0.00959 
-2024-08-27 15:06:31.139084: train_loss -0.7211 
-2024-08-27 15:06:31.139359: val_loss -0.7412 
-2024-08-27 15:06:31.139542: Pseudo dice [0.0, 0.0, 0.8629, 0.9735, 0.7839, 0.93, 0.9228, 0.9539, 0.9206, 0.9193, 0.895, 0.9304, 0.9332, 0.7691, 0.9419, 0.9039, 0.7385, 0.7236, nan] 
-2024-08-27 15:06:31.139638: Epoch time: 105.29 s 
-2024-08-27 15:06:31.139692: Yayy! New best EMA pseudo Dice: 0.7793 
-2024-08-27 15:06:32.689665:  
-2024-08-27 15:06:32.689860: Epoch 92 
-2024-08-27 15:06:32.689976: Current learning rate: 0.00959 
-2024-08-27 15:08:04.694679: train_loss -0.7213 
-2024-08-27 15:08:04.694988: val_loss -0.7462 
-2024-08-27 15:08:04.695190: Pseudo dice [0.0, 0.0, 0.8662, 0.9692, 0.773, 0.935, 0.937, 0.9553, 0.9241, 0.9278, 0.8901, 0.9417, 0.9424, 0.7725, 0.9384, 0.8994, 0.7576, 0.7465, nan] 
-2024-08-27 15:08:04.695319: Epoch time: 92.01 s 
-2024-08-27 15:08:04.695374: Yayy! New best EMA pseudo Dice: 0.7801 
-2024-08-27 15:08:06.273779:  
-2024-08-27 15:08:06.274172: Epoch 93 
-2024-08-27 15:08:06.274281: Current learning rate: 0.00958 
-2024-08-27 15:09:46.389024: train_loss -0.7234 
-2024-08-27 15:09:46.389275: val_loss -0.7442 
-2024-08-27 15:09:46.389448: Pseudo dice [0.0, 0.0, 0.8555, 0.9737, 0.7897, 0.929, 0.9322, 0.9533, 0.9301, 0.9295, 0.8971, 0.9397, 0.9409, 0.7587, 0.9399, 0.909, 0.7496, 0.7238, nan] 
-2024-08-27 15:09:46.389537: Epoch time: 100.12 s 
-2024-08-27 15:09:46.389587: Yayy! New best EMA pseudo Dice: 0.7807 
-2024-08-27 15:09:47.850181:  
-2024-08-27 15:09:47.850381: Epoch 94 
-2024-08-27 15:09:47.850519: Current learning rate: 0.00958 
-2024-08-27 15:11:21.723033: train_loss -0.721 
-2024-08-27 15:11:21.723380: val_loss -0.7385 
-2024-08-27 15:11:21.723682: Pseudo dice [0.0, 0.0, 0.8745, 0.9711, 0.7607, 0.9291, 0.9127, 0.9437, 0.9196, 0.9288, 0.8806, 0.9459, 0.9376, 0.7633, 0.928, 0.8947, 0.7665, 0.7478, nan] 
-2024-08-27 15:11:21.723937: Epoch time: 93.87 s 
-2024-08-27 15:11:21.724084: Yayy! New best EMA pseudo Dice: 0.781 
-2024-08-27 15:11:23.580038:  
-2024-08-27 15:11:23.580208: Epoch 95 
-2024-08-27 15:11:23.580302: Current learning rate: 0.00957 
-2024-08-27 15:12:54.122663: train_loss -0.714 
-2024-08-27 15:12:54.122940: val_loss -0.7372 
-2024-08-27 15:12:54.123122: Pseudo dice [0.0, 0.0, 0.8613, 0.9723, 0.7437, 0.9332, 0.9335, 0.9458, 0.9295, 0.9214, 0.8822, 0.9441, 0.9297, 0.77, 0.939, 0.9066, 0.7516, 0.73, nan] 
-2024-08-27 15:12:54.123226: Epoch time: 90.54 s 
-2024-08-27 15:12:54.123285: Yayy! New best EMA pseudo Dice: 0.7812 
-2024-08-27 15:12:55.810526:  
-2024-08-27 15:12:55.810894: Epoch 96 
-2024-08-27 15:12:55.810996: Current learning rate: 0.00957 
-2024-08-27 15:14:28.373906: train_loss -0.7214 
-2024-08-27 15:14:28.374221: val_loss -0.7298 
-2024-08-27 15:14:28.374458: Pseudo dice [0.0, 0.0, 0.8403, 0.9735, 0.7749, 0.9255, 0.9223, 0.9506, 0.9045, 0.9065, 0.8826, 0.9193, 0.9223, 0.7701, 0.9378, 0.9047, 0.7693, 0.7431, nan] 
-2024-08-27 15:14:28.374575: Epoch time: 92.56 s 
-2024-08-27 15:14:29.764044:  
-2024-08-27 15:14:29.764296: Epoch 97 
-2024-08-27 15:14:29.764413: Current learning rate: 0.00956 
-2024-08-27 15:16:00.388861: train_loss -0.7119 
-2024-08-27 15:16:00.389329: val_loss -0.74 
-2024-08-27 15:16:00.389520: Pseudo dice [0.0, 0.0, 0.853, 0.9627, 0.7731, 0.9372, 0.9313, 0.9535, 0.9301, 0.9271, 0.8875, 0.9451, 0.9399, 0.7865, 0.9399, 0.9097, 0.7682, 0.7647, nan] 
-2024-08-27 15:16:00.389608: Epoch time: 90.63 s 
-2024-08-27 15:16:00.389657: Yayy! New best EMA pseudo Dice: 0.782 
-2024-08-27 15:16:01.877295:  
-2024-08-27 15:16:01.877924: Epoch 98 
-2024-08-27 15:16:01.878021: Current learning rate: 0.00956 
-2024-08-27 15:17:38.265477: train_loss -0.7219 
-2024-08-27 15:17:38.265724: val_loss -0.7337 
-2024-08-27 15:17:38.265908: Pseudo dice [0.0, 0.0, 0.8342, 0.9731, 0.7592, 0.9166, 0.9259, 0.9478, 0.9317, 0.9244, 0.8859, 0.9449, 0.935, 0.7691, 0.9158, 0.8897, 0.7596, 0.7584, nan] 
-2024-08-27 15:17:38.266018: Epoch time: 96.39 s 
-2024-08-27 15:17:39.587402:  
-2024-08-27 15:17:39.587578: Epoch 99 
-2024-08-27 15:17:39.587667: Current learning rate: 0.00955 
-2024-08-27 15:19:17.262032: train_loss -0.716 
-2024-08-27 15:19:17.262337: val_loss -0.7359 
-2024-08-27 15:19:17.262519: Pseudo dice [0.0, 0.0, 0.8375, 0.9671, 0.7691, 0.9325, 0.9252, 0.9454, 0.9305, 0.9259, 0.8867, 0.9477, 0.9394, 0.7579, 0.9279, 0.8962, 0.718, 0.7301, nan] 
-2024-08-27 15:19:17.262607: Epoch time: 97.68 s 
-2024-08-27 15:19:18.951796:  
-2024-08-27 15:19:18.951962: Epoch 100 
-2024-08-27 15:19:18.952055: Current learning rate: 0.00955 
-2024-08-27 15:20:53.417296: train_loss -0.7171 
-2024-08-27 15:20:53.417535: val_loss -0.7411 
-2024-08-27 15:20:53.417695: Pseudo dice [0.0, 0.0, 0.8599, 0.9728, 0.7582, 0.9284, 0.929, 0.9531, 0.9286, 0.9275, 0.8883, 0.9452, 0.9397, 0.7682, 0.9377, 0.9001, 0.7671, 0.7487, nan] 
-2024-08-27 15:20:53.417778: Epoch time: 94.47 s 
-2024-08-27 15:20:53.417831: Yayy! New best EMA pseudo Dice: 0.7822 
-2024-08-27 15:20:55.097885:  
-2024-08-27 15:20:55.098441: Epoch 101 
-2024-08-27 15:20:55.098552: Current learning rate: 0.00954 
-2024-08-27 15:22:31.406332: train_loss -0.7221 
-2024-08-27 15:22:31.406608: val_loss -0.7443 
-2024-08-27 15:22:31.406811: Pseudo dice [0.0, 0.0, 0.8676, 0.973, 0.7131, 0.9374, 0.9388, 0.9562, 0.9284, 0.9261, 0.8734, 0.941, 0.9327, 0.7832, 0.9446, 0.9127, 0.7675, 0.7563, nan] 
-2024-08-27 15:22:31.406913: Epoch time: 96.31 s 
-2024-08-27 15:22:31.406974: Yayy! New best EMA pseudo Dice: 0.7826 
-2024-08-27 15:22:33.127182:  
-2024-08-27 15:22:33.127722: Epoch 102 
-2024-08-27 15:22:33.127834: Current learning rate: 0.00954 
-2024-08-27 15:24:03.364361: train_loss -0.7231 
-2024-08-27 15:24:03.364675: val_loss -0.7369 
-2024-08-27 15:24:03.364893: Pseudo dice [0.0, 0.0, 0.8682, 0.9724, 0.7661, 0.9264, 0.9268, 0.9452, 0.9284, 0.9232, 0.8925, 0.941, 0.9344, 0.7485, 0.9268, 0.8846, 0.7576, 0.7315, nan] 
-2024-08-27 15:24:03.364998: Epoch time: 90.24 s 
-2024-08-27 15:24:04.840729:  
-2024-08-27 15:24:04.840918: Epoch 103 
-2024-08-27 15:24:04.841017: Current learning rate: 0.00954 
-2024-08-27 15:25:45.060101: train_loss -0.7166 
-2024-08-27 15:25:45.060378: val_loss -0.7448 
-2024-08-27 15:25:45.060564: Pseudo dice [0.0, 0.0, 0.8635, 0.9698, 0.7538, 0.936, 0.936, 0.9509, 0.9215, 0.9277, 0.8962, 0.9402, 0.9428, 0.769, 0.9301, 0.9057, 0.7857, 0.7773, nan] 
-2024-08-27 15:25:45.060658: Epoch time: 100.22 s 
-2024-08-27 15:25:45.060710: Yayy! New best EMA pseudo Dice: 0.7832 
-2024-08-27 15:25:46.596054:  
-2024-08-27 15:25:46.596375: Epoch 104 
-2024-08-27 15:25:46.596488: Current learning rate: 0.00953 
-2024-08-27 15:27:21.288238: train_loss -0.7235 
-2024-08-27 15:27:21.288630: val_loss -0.7452 
-2024-08-27 15:27:21.288856: Pseudo dice [0.0, 0.0, 0.8265, 0.9731, 0.7777, 0.9385, 0.928, 0.9552, 0.9286, 0.9271, 0.8978, 0.9415, 0.9392, 0.782, 0.9328, 0.9104, 0.7631, 0.7572, nan] 
-2024-08-27 15:27:21.288960: Epoch time: 94.69 s 
-2024-08-27 15:27:21.289014: Yayy! New best EMA pseudo Dice: 0.7836 
-2024-08-27 15:27:22.886569:  
-2024-08-27 15:27:22.886781: Epoch 105 
-2024-08-27 15:27:22.886889: Current learning rate: 0.00953 
-2024-08-27 15:28:53.038900: train_loss -0.7239 
-2024-08-27 15:28:53.039171: val_loss -0.7517 
-2024-08-27 15:28:53.039351: Pseudo dice [0.0, 0.0, 0.8761, 0.9733, 0.7892, 0.9373, 0.939, 0.9556, 0.9357, 0.9354, 0.9069, 0.9484, 0.9491, 0.7749, 0.9381, 0.9071, 0.796, 0.7757, nan] 
-2024-08-27 15:28:53.039445: Epoch time: 90.15 s 
-2024-08-27 15:28:53.039501: Yayy! New best EMA pseudo Dice: 0.7849 
-2024-08-27 15:28:54.815127:  
-2024-08-27 15:28:54.815542: Epoch 106 
-2024-08-27 15:28:54.815640: Current learning rate: 0.00952 
-2024-08-27 15:30:27.141102: train_loss -0.7169 
-2024-08-27 15:30:27.141342: val_loss -0.7478 
-2024-08-27 15:30:27.141518: Pseudo dice [0.0, 0.0, 0.8563, 0.973, 0.7461, 0.9289, 0.9336, 0.9486, 0.9381, 0.9315, 0.8968, 0.9493, 0.9407, 0.7649, 0.937, 0.9077, 0.773, 0.7811, nan] 
-2024-08-27 15:30:27.141607: Epoch time: 92.33 s 
-2024-08-27 15:30:27.141672: Yayy! New best EMA pseudo Dice: 0.7854 
-2024-08-27 15:30:28.631580:  
-2024-08-27 15:30:28.631754: Epoch 107 
-2024-08-27 15:30:28.631844: Current learning rate: 0.00952 
-2024-08-27 15:31:58.081082: train_loss -0.7196 
-2024-08-27 15:31:58.081323: val_loss -0.7384 
-2024-08-27 15:31:58.081494: Pseudo dice [0.0, 0.0, 0.8613, 0.9716, 0.782, 0.9372, 0.9362, 0.9502, 0.9288, 0.9257, 0.8897, 0.9377, 0.9397, 0.7544, 0.9302, 0.8999, 0.7687, 0.7515, nan] 
-2024-08-27 15:31:58.081581: Epoch time: 89.45 s 
-2024-08-27 15:31:58.081634: Yayy! New best EMA pseudo Dice: 0.7855 
-2024-08-27 15:31:59.564729:  
-2024-08-27 15:31:59.564947: Epoch 108 
-2024-08-27 15:31:59.565050: Current learning rate: 0.00951 
-2024-08-27 15:33:31.276170: train_loss -0.7239 
-2024-08-27 15:33:31.276415: val_loss -0.7424 
-2024-08-27 15:33:31.276603: Pseudo dice [0.0, 0.0, 0.8534, 0.973, 0.7685, 0.9332, 0.9324, 0.9502, 0.9375, 0.9321, 0.8994, 0.9474, 0.9446, 0.77, 0.944, 0.9092, 0.7774, 0.7651, nan] 
-2024-08-27 15:33:31.276691: Epoch time: 91.71 s 
-2024-08-27 15:33:31.276743: Yayy! New best EMA pseudo Dice: 0.7861 
-2024-08-27 15:33:32.790713:  
-2024-08-27 15:33:32.791097: Epoch 109 
-2024-08-27 15:33:32.791191: Current learning rate: 0.00951 
-2024-08-27 15:35:08.717205: train_loss -0.7214 
-2024-08-27 15:35:08.717469: val_loss -0.7432 
-2024-08-27 15:35:08.717640: Pseudo dice [0.0, 0.0, 0.8686, 0.9747, 0.7827, 0.9354, 0.9376, 0.9487, 0.9346, 0.928, 0.898, 0.9439, 0.942, 0.7768, 0.9421, 0.9069, 0.7344, 0.7027, nan] 
-2024-08-27 15:35:08.717729: Epoch time: 95.93 s 
-2024-08-27 15:35:08.717781: Yayy! New best EMA pseudo Dice: 0.7861 
-2024-08-27 15:35:10.297956:  
-2024-08-27 15:35:10.298190: Epoch 110 
-2024-08-27 15:35:10.298286: Current learning rate: 0.0095 
-2024-08-27 15:36:40.636956: train_loss -0.7297 
-2024-08-27 15:36:40.637220: val_loss -0.7446 
-2024-08-27 15:36:40.637402: Pseudo dice [0.0, 0.0, 0.8679, 0.9695, 0.7646, 0.9373, 0.9377, 0.9535, 0.9278, 0.9253, 0.8888, 0.945, 0.9405, 0.7994, 0.9364, 0.9066, 0.7737, 0.7738, nan] 
-2024-08-27 15:36:40.637500: Epoch time: 90.34 s 
-2024-08-27 15:36:40.637554: Yayy! New best EMA pseudo Dice: 0.7867 
-2024-08-27 15:36:42.262655:  
-2024-08-27 15:36:42.262853: Epoch 111 
-2024-08-27 15:36:42.262952: Current learning rate: 0.0095 
-2024-08-27 15:38:15.416506: train_loss -0.7264 
-2024-08-27 15:38:15.416739: val_loss -0.7435 
-2024-08-27 15:38:15.416912: Pseudo dice [0.0, 0.0, 0.851, 0.9718, 0.7714, 0.9359, 0.9389, 0.9549, 0.9358, 0.9339, 0.901, 0.9462, 0.9416, 0.7774, 0.9381, 0.9046, 0.732, 0.7249, nan] 
-2024-08-27 15:38:15.417001: Epoch time: 93.15 s 
-2024-08-27 15:38:16.936457:  
-2024-08-27 15:38:16.936765: Epoch 112 
-2024-08-27 15:38:16.936962: Current learning rate: 0.00949 
-2024-08-27 15:39:50.842108: train_loss -0.7302 
-2024-08-27 15:39:50.842376: val_loss -0.7453 
-2024-08-27 15:39:50.842553: Pseudo dice [0.0, 0.0, 0.8673, 0.9723, 0.7965, 0.924, 0.9314, 0.9532, 0.9256, 0.9157, 0.8971, 0.9387, 0.9339, 0.7796, 0.9212, 0.9108, 0.7645, 0.7425, nan] 
-2024-08-27 15:39:50.842646: Epoch time: 93.91 s 
-2024-08-27 15:39:50.842700: Yayy! New best EMA pseudo Dice: 0.7867 
-2024-08-27 15:39:52.405707:  
-2024-08-27 15:39:52.406159: Epoch 113 
-2024-08-27 15:39:52.406261: Current learning rate: 0.00949 
-2024-08-27 15:41:26.525246: train_loss -0.726 
-2024-08-27 15:41:26.525518: val_loss -0.7433 
-2024-08-27 15:41:26.525702: Pseudo dice [0.0, 0.0, 0.8852, 0.9695, 0.7943, 0.9352, 0.9406, 0.9482, 0.9252, 0.9158, 0.9014, 0.9444, 0.9453, 0.7594, 0.9348, 0.9038, 0.7879, 0.7785, nan] 
-2024-08-27 15:41:26.525801: Epoch time: 94.12 s 
-2024-08-27 15:41:26.525857: Yayy! New best EMA pseudo Dice: 0.7873 
-2024-08-27 15:41:28.101697:  
-2024-08-27 15:41:28.101852: Epoch 114 
-2024-08-27 15:41:28.101945: Current learning rate: 0.00949 
-2024-08-27 15:43:01.515974: train_loss -0.7266 
-2024-08-27 15:43:01.516210: val_loss -0.7409 
-2024-08-27 15:43:01.516387: Pseudo dice [0.0, 0.0, 0.8662, 0.9747, 0.7163, 0.9267, 0.9249, 0.9456, 0.9315, 0.9295, 0.9016, 0.9424, 0.9456, 0.7531, 0.9336, 0.8937, 0.7615, 0.7479, nan] 
-2024-08-27 15:43:01.516501: Epoch time: 93.42 s 
-2024-08-27 15:43:02.782369:  
-2024-08-27 15:43:02.782623: Epoch 115 
-2024-08-27 15:43:02.782724: Current learning rate: 0.00948 
-2024-08-27 15:44:35.359336: train_loss -0.7297 
-2024-08-27 15:44:35.359681: val_loss -0.748 
-2024-08-27 15:44:35.360060: Pseudo dice [0.0, 0.0, 0.8781, 0.975, 0.7893, 0.9333, 0.9361, 0.9441, 0.9323, 0.9309, 0.8914, 0.9444, 0.9416, 0.7654, 0.937, 0.8979, 0.7379, 0.7262, nan] 
-2024-08-27 15:44:35.360204: Epoch time: 92.58 s 
-2024-08-27 15:44:36.733169:  
-2024-08-27 15:44:36.733365: Epoch 116 
-2024-08-27 15:44:36.733472: Current learning rate: 0.00948 
-2024-08-27 15:46:06.046971: train_loss -0.7277 
-2024-08-27 15:46:06.047232: val_loss -0.7527 
-2024-08-27 15:46:06.047439: Pseudo dice [0.0, 0.0, 0.8701, 0.9683, 0.7788, 0.9376, 0.9353, 0.9551, 0.937, 0.9343, 0.9055, 0.9526, 0.9448, 0.7754, 0.9375, 0.9055, 0.7741, 0.7807, nan] 
-2024-08-27 15:46:06.047536: Epoch time: 89.31 s 
-2024-08-27 15:46:06.047591: Yayy! New best EMA pseudo Dice: 0.7876 
-2024-08-27 15:46:07.710372:  
-2024-08-27 15:46:07.710551: Epoch 117 
-2024-08-27 15:46:07.710646: Current learning rate: 0.00947 
-2024-08-27 15:47:45.656241: train_loss -0.714 
-2024-08-27 15:47:45.656535: val_loss -0.7351 
-2024-08-27 15:47:45.656745: Pseudo dice [0.0, 0.0, 0.857, 0.9724, 0.781, 0.9335, 0.9219, 0.9463, 0.9326, 0.9263, 0.8962, 0.9463, 0.9389, 0.7898, 0.9424, 0.8938, 0.7664, 0.7582, nan] 
-2024-08-27 15:47:45.656852: Epoch time: 97.95 s 
-2024-08-27 15:47:45.656914: Yayy! New best EMA pseudo Dice: 0.7877 
-2024-08-27 15:47:47.591527:  
-2024-08-27 15:47:47.591720: Epoch 118 
-2024-08-27 15:47:47.591825: Current learning rate: 0.00947 
-2024-08-27 15:49:16.475748: train_loss -0.7205 
-2024-08-27 15:49:16.476035: val_loss -0.7352 
-2024-08-27 15:49:16.476229: Pseudo dice [0.0, 0.0, 0.8143, 0.9709, 0.7702, 0.9233, 0.9192, 0.9396, 0.9275, 0.9273, 0.9033, 0.9459, 0.9431, 0.7589, 0.9299, 0.9039, 0.7753, 0.7805, nan] 
-2024-08-27 15:49:16.476327: Epoch time: 88.89 s 
-2024-08-27 15:49:17.861449:  
-2024-08-27 15:49:17.861652: Epoch 119 
-2024-08-27 15:49:17.861759: Current learning rate: 0.00946 
-2024-08-27 15:50:56.570751: train_loss -0.7203 
-2024-08-27 15:50:56.571018: val_loss -0.7451 
-2024-08-27 15:50:56.571213: Pseudo dice [0.0, 0.0, 0.8281, 0.9742, 0.7768, 0.942, 0.9368, 0.9549, 0.9267, 0.9247, 0.9015, 0.9455, 0.9418, 0.7864, 0.9422, 0.9158, 0.7516, 0.7509, nan] 
-2024-08-27 15:50:56.571314: Epoch time: 98.71 s 
-2024-08-27 15:50:57.921004:  
-2024-08-27 15:50:57.921315: Epoch 120 
-2024-08-27 15:50:57.921412: Current learning rate: 0.00946 
-2024-08-27 15:52:36.029723: train_loss -0.7259 
-2024-08-27 15:52:36.030029: val_loss -0.7479 
-2024-08-27 15:52:36.030220: Pseudo dice [0.0, 0.0, 0.8568, 0.9726, 0.7633, 0.9381, 0.9401, 0.9507, 0.9369, 0.93, 0.9062, 0.9491, 0.9447, 0.7887, 0.9325, 0.9048, 0.778, 0.7776, nan] 
-2024-08-27 15:52:36.030318: Epoch time: 98.11 s 
-2024-08-27 15:52:36.030373: Yayy! New best EMA pseudo Dice: 0.7881 
-2024-08-27 15:52:37.656801:  
-2024-08-27 15:52:37.657006: Epoch 121 
-2024-08-27 15:52:37.657105: Current learning rate: 0.00945 
-2024-08-27 15:54:12.945256: train_loss -0.7275 
-2024-08-27 15:54:12.945529: val_loss -0.7462 
-2024-08-27 15:54:12.945702: Pseudo dice [0.0, 0.0, 0.8556, 0.9713, 0.7967, 0.9396, 0.9417, 0.9549, 0.9354, 0.9302, 0.8968, 0.9501, 0.9446, 0.7991, 0.948, 0.9092, 0.7601, 0.7443, nan] 
-2024-08-27 15:54:12.946265: Epoch time: 95.29 s 
-2024-08-27 15:54:12.946346: Yayy! New best EMA pseudo Dice: 0.7887 
-2024-08-27 15:54:14.515556:  
-2024-08-27 15:54:14.515734: Epoch 122 
-2024-08-27 15:54:14.515835: Current learning rate: 0.00945 
-2024-08-27 15:55:44.238987: train_loss -0.7307 
-2024-08-27 15:55:44.239225: val_loss -0.7453 
-2024-08-27 15:55:44.239388: Pseudo dice [0.0, 0.0, 0.8473, 0.9758, 0.771, 0.9361, 0.9399, 0.947, 0.9318, 0.9329, 0.9073, 0.9475, 0.9431, 0.7857, 0.9368, 0.902, 0.7829, 0.7712, nan] 
-2024-08-27 15:55:44.239470: Epoch time: 89.72 s 
-2024-08-27 15:55:44.239520: Yayy! New best EMA pseudo Dice: 0.789 
-2024-08-27 15:55:45.730859:  
-2024-08-27 15:55:45.731136: Epoch 123 
-2024-08-27 15:55:45.731243: Current learning rate: 0.00944 
-2024-08-27 15:57:23.659940: train_loss -0.7261 
-2024-08-27 15:57:23.660230: val_loss -0.739 
-2024-08-27 15:57:23.660468: Pseudo dice [0.0, 0.0, 0.8424, 0.9734, 0.7909, 0.9389, 0.9356, 0.9486, 0.9329, 0.9112, 0.8937, 0.9465, 0.9381, 0.781, 0.9395, 0.9088, 0.7572, 0.7544, nan] 
-2024-08-27 15:57:23.660639: Epoch time: 97.93 s 
-2024-08-27 15:57:25.291252:  
-2024-08-27 15:57:25.291463: Epoch 124 
-2024-08-27 15:57:25.291559: Current learning rate: 0.00944 
-2024-08-27 15:59:09.452107: train_loss -0.729 
-2024-08-27 15:59:09.452360: val_loss -0.7549 
-2024-08-27 15:59:09.452539: Pseudo dice [0.0, 0.0, 0.8649, 0.9735, 0.7897, 0.9412, 0.9374, 0.9533, 0.9392, 0.9316, 0.9048, 0.9513, 0.9452, 0.7857, 0.9444, 0.9072, 0.7713, 0.7681, nan] 
-2024-08-27 15:59:09.452634: Epoch time: 104.16 s 
-2024-08-27 15:59:09.452686: Yayy! New best EMA pseudo Dice: 0.7895 
-2024-08-27 15:59:10.928490:  
-2024-08-27 15:59:10.928793: Epoch 125 
-2024-08-27 15:59:10.928892: Current learning rate: 0.00944 
-2024-08-27 16:00:42.926109: train_loss -0.7281 
-2024-08-27 16:00:42.926366: val_loss -0.7516 
-2024-08-27 16:00:42.926534: Pseudo dice [0.0, 0.0, 0.8641, 0.9745, 0.8014, 0.9355, 0.9351, 0.9552, 0.9362, 0.9349, 0.9054, 0.95, 0.9432, 0.7977, 0.9268, 0.91, 0.7595, 0.7354, nan] 
-2024-08-27 16:00:42.926623: Epoch time: 92.0 s 
-2024-08-27 16:00:42.926676: Yayy! New best EMA pseudo Dice: 0.7898 
-2024-08-27 16:00:44.548313:  
-2024-08-27 16:00:44.548574: Epoch 126 
-2024-08-27 16:00:44.548689: Current learning rate: 0.00943 
-2024-08-27 16:02:18.735369: train_loss -0.7319 
-2024-08-27 16:02:18.735669: val_loss -0.7484 
-2024-08-27 16:02:18.735851: Pseudo dice [0.0, 0.0, 0.8691, 0.9753, 0.7617, 0.9356, 0.9385, 0.9558, 0.94, 0.9285, 0.8994, 0.9529, 0.944, 0.7869, 0.9411, 0.9118, 0.7718, 0.7612, nan] 
-2024-08-27 16:02:18.735946: Epoch time: 94.19 s 
-2024-08-27 16:02:18.736001: Yayy! New best EMA pseudo Dice: 0.7902 
-2024-08-27 16:02:20.358433:  
-2024-08-27 16:02:20.358623: Epoch 127 
-2024-08-27 16:02:20.358722: Current learning rate: 0.00943 
-2024-08-27 16:03:53.091637: train_loss -0.7337 
-2024-08-27 16:03:53.091858: val_loss -0.7521 
-2024-08-27 16:03:53.092021: Pseudo dice [0.0, 0.0, 0.873, 0.9754, 0.788, 0.9262, 0.9272, 0.9552, 0.9362, 0.9366, 0.8965, 0.9482, 0.9439, 0.789, 0.9421, 0.9083, 0.7806, 0.7663, nan] 
-2024-08-27 16:03:53.092105: Epoch time: 92.73 s 
-2024-08-27 16:03:53.092155: Yayy! New best EMA pseudo Dice: 0.7905 
-2024-08-27 16:03:54.719337:  
-2024-08-27 16:03:54.719607: Epoch 128 
-2024-08-27 16:03:54.719712: Current learning rate: 0.00942 
-2024-08-27 16:05:27.770314: train_loss -0.7327 
-2024-08-27 16:05:27.770587: val_loss -0.7516 
-2024-08-27 16:05:27.770773: Pseudo dice [0.0, 0.0, 0.8827, 0.9735, 0.7644, 0.9301, 0.9303, 0.9532, 0.9238, 0.9242, 0.9004, 0.9384, 0.9413, 0.7888, 0.9443, 0.902, 0.7429, 0.7346, nan] 
-2024-08-27 16:05:27.770876: Epoch time: 93.05 s 
-2024-08-27 16:05:29.328295:  
-2024-08-27 16:05:29.328628: Epoch 129 
-2024-08-27 16:05:29.328857: Current learning rate: 0.00942 
-2024-08-27 16:07:05.505478: train_loss -0.7274 
-2024-08-27 16:07:05.505709: val_loss -0.754 
-2024-08-27 16:07:05.505881: Pseudo dice [0.0, 0.0, 0.871, 0.9735, 0.7604, 0.9249, 0.9278, 0.9536, 0.935, 0.9345, 0.9024, 0.9517, 0.9421, 0.7865, 0.9452, 0.91, 0.7875, 0.7804, nan] 
-2024-08-27 16:07:05.505968: Epoch time: 96.18 s 
-2024-08-27 16:07:05.506020: Yayy! New best EMA pseudo Dice: 0.7906 
-2024-08-27 16:07:07.821124:  
-2024-08-27 16:07:07.821325: Epoch 130 
-2024-08-27 16:07:07.821443: Current learning rate: 0.00941 
-2024-08-27 16:08:45.797838: train_loss -0.7282 
-2024-08-27 16:08:45.798079: val_loss -0.755 
-2024-08-27 16:08:45.798256: Pseudo dice [0.0, 0.0, 0.8722, 0.9742, 0.7903, 0.9263, 0.9392, 0.9567, 0.9379, 0.9379, 0.904, 0.9518, 0.951, 0.7756, 0.9418, 0.9092, 0.7587, 0.7415, nan] 
-2024-08-27 16:08:45.798343: Epoch time: 97.98 s 
-2024-08-27 16:08:45.798395: Yayy! New best EMA pseudo Dice: 0.7908 
-2024-08-27 16:08:47.397426:  
-2024-08-27 16:08:47.397696: Epoch 131 
-2024-08-27 16:08:47.397802: Current learning rate: 0.00941 
-2024-08-27 16:10:20.181937: train_loss -0.7264 
-2024-08-27 16:10:20.182193: val_loss -0.7508 
-2024-08-27 16:10:20.182382: Pseudo dice [0.0, 0.0, 0.8546, 0.9743, 0.7803, 0.9422, 0.9453, 0.9586, 0.9358, 0.929, 0.9066, 0.9503, 0.948, 0.7944, 0.9429, 0.9027, 0.7319, 0.7037, nan] 
-2024-08-27 16:10:20.182484: Epoch time: 92.79 s 
-2024-08-27 16:10:21.515899:  
-2024-08-27 16:10:21.516395: Epoch 132 
-2024-08-27 16:10:21.516510: Current learning rate: 0.0094 
-2024-08-27 16:11:50.612709: train_loss -0.7255 
-2024-08-27 16:11:50.613006: val_loss -0.7419 
-2024-08-27 16:11:50.613230: Pseudo dice [0.0, 0.0, 0.8365, 0.9676, 0.7939, 0.939, 0.9372, 0.9536, 0.9355, 0.9174, 0.9021, 0.9441, 0.9446, 0.787, 0.942, 0.9069, 0.7528, 0.7533, nan] 
-2024-08-27 16:11:50.613337: Epoch time: 89.1 s 
-2024-08-27 16:11:52.104749:  
-2024-08-27 16:11:52.105052: Epoch 133 
-2024-08-27 16:11:52.105164: Current learning rate: 0.0094 
-2024-08-27 16:13:28.132056: train_loss -0.7271 
-2024-08-27 16:13:28.132358: val_loss -0.7356 
-2024-08-27 16:13:28.132614: Pseudo dice [0.0, 0.0, 0.8677, 0.9746, 0.7672, 0.9303, 0.938, 0.9508, 0.9285, 0.9307, 0.9048, 0.9435, 0.9433, 0.7815, 0.9369, 0.9091, 0.7599, 0.7442, nan] 
-2024-08-27 16:13:28.132733: Epoch time: 96.03 s 
-2024-08-27 16:13:29.593404:  
-2024-08-27 16:13:29.593724: Epoch 134 
-2024-08-27 16:13:29.593832: Current learning rate: 0.00939 
-2024-08-27 16:15:04.465944: train_loss -0.7286 
-2024-08-27 16:15:04.466555: val_loss -0.7468 
-2024-08-27 16:15:04.466784: Pseudo dice [0.0, 0.0, 0.8602, 0.974, 0.8211, 0.94, 0.9405, 0.9526, 0.9267, 0.9305, 0.9043, 0.9443, 0.9406, 0.7947, 0.9396, 0.9101, 0.7599, 0.7576, nan] 
-2024-08-27 16:15:04.466955: Epoch time: 94.87 s 
-2024-08-27 16:15:04.467017: Yayy! New best EMA pseudo Dice: 0.7908 
-2024-08-27 16:15:06.573113:  
-2024-08-27 16:15:06.573296: Epoch 135 
-2024-08-27 16:15:06.573386: Current learning rate: 0.00939 
-2024-08-27 16:16:39.810436: train_loss -0.724 
-2024-08-27 16:16:39.810970: val_loss -0.7397 
-2024-08-27 16:16:39.811164: Pseudo dice [0.0, 0.0, 0.8594, 0.962, 0.7743, 0.9282, 0.9217, 0.9475, 0.938, 0.9269, 0.8944, 0.9514, 0.9435, 0.7759, 0.9399, 0.8901, 0.7703, 0.7633, nan] 
-2024-08-27 16:16:39.811300: Epoch time: 93.24 s 
-2024-08-27 16:16:41.129593:  
-2024-08-27 16:16:41.129750: Epoch 136 
-2024-08-27 16:16:41.129843: Current learning rate: 0.00939 
-2024-08-27 16:18:11.920474: train_loss -0.7241 
-2024-08-27 16:18:11.920771: val_loss -0.7399 
-2024-08-27 16:18:11.920962: Pseudo dice [0.0, 0.0, 0.8636, 0.9739, 0.7645, 0.9216, 0.9195, 0.9528, 0.9177, 0.9118, 0.8899, 0.9319, 0.9301, 0.7837, 0.9392, 0.9109, 0.7536, 0.7582, nan] 
-2024-08-27 16:18:11.921053: Epoch time: 90.79 s 
-2024-08-27 16:18:13.307851:  
-2024-08-27 16:18:13.308193: Epoch 137 
-2024-08-27 16:18:13.308315: Current learning rate: 0.00938 
-2024-08-27 16:19:48.601894: train_loss -0.7289 
-2024-08-27 16:19:48.602131: val_loss -0.7434 
-2024-08-27 16:19:48.602306: Pseudo dice [0.0, 0.0, 0.8683, 0.9723, 0.7285, 0.9415, 0.9314, 0.954, 0.9318, 0.9396, 0.9044, 0.9494, 0.9492, 0.7835, 0.9438, 0.9112, 0.7884, 0.7871, nan] 
-2024-08-27 16:19:48.602393: Epoch time: 95.29 s 
-2024-08-27 16:19:49.944128:  
-2024-08-27 16:19:49.944300: Epoch 138 
-2024-08-27 16:19:49.944396: Current learning rate: 0.00938 
-2024-08-27 16:21:27.375742: train_loss -0.7307 
-2024-08-27 16:21:27.376073: val_loss -0.7509 
-2024-08-27 16:21:27.376410: Pseudo dice [0.0, 0.0, 0.8708, 0.9735, 0.7931, 0.9316, 0.9371, 0.9492, 0.9363, 0.9382, 0.9074, 0.9496, 0.9472, 0.7782, 0.9333, 0.9113, 0.7508, 0.7478, nan] 
-2024-08-27 16:21:27.376521: Epoch time: 97.43 s 
-2024-08-27 16:21:28.757640:  
-2024-08-27 16:21:28.757814: Epoch 139 
-2024-08-27 16:21:28.757907: Current learning rate: 0.00937 
-2024-08-27 16:23:03.155770: train_loss -0.7301 
-2024-08-27 16:23:03.156320: val_loss -0.7571 
-2024-08-27 16:23:03.156631: Pseudo dice [0.0, 0.0, 0.8584, 0.9733, 0.8029, 0.9374, 0.9422, 0.9551, 0.9348, 0.9338, 0.9108, 0.9495, 0.9489, 0.7962, 0.9419, 0.9107, 0.7766, 0.7775, nan] 
-2024-08-27 16:23:03.156827: Epoch time: 94.4 s 
-2024-08-27 16:23:03.156956: Yayy! New best EMA pseudo Dice: 0.7911 
-2024-08-27 16:23:05.069115:  
-2024-08-27 16:23:05.069297: Epoch 140 
-2024-08-27 16:23:05.069391: Current learning rate: 0.00937 
-2024-08-27 16:24:38.687364: train_loss -0.7349 
-2024-08-27 16:24:38.687671: val_loss -0.7472 
-2024-08-27 16:24:38.687904: Pseudo dice [0.0, 0.0, 0.8544, 0.9754, 0.7632, 0.928, 0.9281, 0.9557, 0.9381, 0.9256, 0.8973, 0.9528, 0.946, 0.783, 0.9365, 0.9085, 0.7969, 0.773, nan] 
-2024-08-27 16:24:38.688033: Epoch time: 93.62 s 
-2024-08-27 16:24:38.688111: Yayy! New best EMA pseudo Dice: 0.7913 
-2024-08-27 16:24:40.429612:  
-2024-08-27 16:24:40.429964: Epoch 141 
-2024-08-27 16:24:40.430064: Current learning rate: 0.00936 
-2024-08-27 16:26:06.615649: train_loss -0.7309 
-2024-08-27 16:26:06.615935: val_loss -0.7539 
-2024-08-27 16:26:06.616123: Pseudo dice [0.0, 0.0, 0.8802, 0.9726, 0.7608, 0.9373, 0.9443, 0.9578, 0.9367, 0.9298, 0.9126, 0.9443, 0.9514, 0.792, 0.9446, 0.9212, 0.7886, 0.7653, nan] 
-2024-08-27 16:26:06.616218: Epoch time: 86.19 s 
-2024-08-27 16:26:06.616268: Yayy! New best EMA pseudo Dice: 0.7918 
-2024-08-27 16:26:08.304000:  
-2024-08-27 16:26:08.304169: Epoch 142 
-2024-08-27 16:26:08.304261: Current learning rate: 0.00936 
-2024-08-27 16:27:45.806823: train_loss -0.7284 
-2024-08-27 16:27:45.807097: val_loss -0.7544 
-2024-08-27 16:27:45.807289: Pseudo dice [0.0, 0.0, 0.8709, 0.9746, 0.801, 0.9383, 0.9395, 0.9583, 0.9383, 0.9374, 0.9069, 0.9465, 0.95, 0.7931, 0.9374, 0.9173, 0.7932, 0.7858, nan] 
-2024-08-27 16:27:45.807389: Epoch time: 97.5 s 
-2024-08-27 16:27:45.807448: Yayy! New best EMA pseudo Dice: 0.7926 
-2024-08-27 16:27:47.408931:  
-2024-08-27 16:27:47.409231: Epoch 143 
-2024-08-27 16:27:47.409333: Current learning rate: 0.00935 
-2024-08-27 16:29:21.144688: train_loss -0.7314 
-2024-08-27 16:29:21.144973: val_loss -0.749 
-2024-08-27 16:29:21.145160: Pseudo dice [0.0, 0.0, 0.8628, 0.9736, 0.8051, 0.9383, 0.9416, 0.9573, 0.935, 0.9329, 0.9079, 0.948, 0.9456, 0.7906, 0.9401, 0.911, 0.7899, 0.7817, nan] 
-2024-08-27 16:29:21.145256: Epoch time: 93.74 s 
-2024-08-27 16:29:21.145309: Yayy! New best EMA pseudo Dice: 0.7931 
-2024-08-27 16:29:22.733870:  
-2024-08-27 16:29:22.734036: Epoch 144 
-2024-08-27 16:29:22.734134: Current learning rate: 0.00935 
-2024-08-27 16:31:00.485930: train_loss -0.7317 
-2024-08-27 16:31:00.486192: val_loss -0.749 
-2024-08-27 16:31:00.486404: Pseudo dice [0.0, 0.0, 0.8768, 0.9735, 0.7636, 0.9364, 0.9321, 0.9582, 0.9321, 0.9287, 0.895, 0.9431, 0.9372, 0.7969, 0.9459, 0.9129, 0.7775, 0.7575, nan] 
-2024-08-27 16:31:00.486509: Epoch time: 97.75 s 
-2024-08-27 16:31:01.900297:  
-2024-08-27 16:31:01.900438: Epoch 145 
-2024-08-27 16:31:01.900534: Current learning rate: 0.00935 
-2024-08-27 16:32:39.176801: train_loss -0.7326 
-2024-08-27 16:32:39.177065: val_loss -0.7514 
-2024-08-27 16:32:39.177246: Pseudo dice [0.0, 0.0, 0.8412, 0.9715, 0.7787, 0.9282, 0.9339, 0.9499, 0.9333, 0.9449, 0.9153, 0.9492, 0.954, 0.7936, 0.9457, 0.9124, 0.7817, 0.7687, nan] 
-2024-08-27 16:32:39.177342: Epoch time: 97.28 s 
-2024-08-27 16:32:39.177396: Yayy! New best EMA pseudo Dice: 0.7932 
-2024-08-27 16:32:41.053864:  
-2024-08-27 16:32:41.054152: Epoch 146 
-2024-08-27 16:32:41.054261: Current learning rate: 0.00934 
-2024-08-27 16:34:12.747653: train_loss -0.7322 
-2024-08-27 16:34:12.748053: val_loss -0.7612 
-2024-08-27 16:34:12.748256: Pseudo dice [0.0, 0.0, 0.8949, 0.9754, 0.783, 0.9286, 0.9399, 0.9597, 0.9373, 0.9411, 0.9132, 0.9516, 0.9482, 0.7987, 0.9307, 0.9165, 0.7643, 0.7702, nan] 
-2024-08-27 16:34:12.748357: Epoch time: 91.69 s 
-2024-08-27 16:34:12.748412: Yayy! New best EMA pseudo Dice: 0.7936 
-2024-08-27 16:34:14.492157:  
-2024-08-27 16:34:14.492351: Epoch 147 
-2024-08-27 16:34:14.492465: Current learning rate: 0.00934 
-2024-08-27 16:35:48.508317: train_loss -0.7355 
-2024-08-27 16:35:48.508578: val_loss -0.7513 
-2024-08-27 16:35:48.508741: Pseudo dice [0.0, 0.0, 0.8659, 0.9755, 0.7677, 0.9287, 0.9341, 0.9539, 0.9371, 0.9274, 0.9042, 0.953, 0.9465, 0.8061, 0.9417, 0.9116, 0.7931, 0.7854, nan] 
-2024-08-27 16:35:48.508832: Epoch time: 94.02 s 
-2024-08-27 16:35:48.508886: Yayy! New best EMA pseudo Dice: 0.7939 
-2024-08-27 16:35:50.039520:  
-2024-08-27 16:35:50.039838: Epoch 148 
-2024-08-27 16:35:50.039939: Current learning rate: 0.00933 
-2024-08-27 16:37:22.521575: train_loss -0.7348 
-2024-08-27 16:37:22.521809: val_loss -0.7558 
-2024-08-27 16:37:22.521982: Pseudo dice [0.0, 0.0, 0.8774, 0.9737, 0.8005, 0.9358, 0.9411, 0.9553, 0.939, 0.9437, 0.9078, 0.9514, 0.9475, 0.7978, 0.9423, 0.9182, 0.7926, 0.7925, nan] 
-2024-08-27 16:37:22.522073: Epoch time: 92.48 s 
-2024-08-27 16:37:22.522126: Yayy! New best EMA pseudo Dice: 0.7946 
-2024-08-27 16:37:24.138994:  
-2024-08-27 16:37:24.139422: Epoch 149 
-2024-08-27 16:37:24.139552: Current learning rate: 0.00933 
-2024-08-27 16:39:04.264923: train_loss -0.7274 
-2024-08-27 16:39:04.265204: val_loss -0.7501 
-2024-08-27 16:39:04.265398: Pseudo dice [0.0, 0.0, 0.8678, 0.973, 0.7993, 0.9288, 0.9301, 0.9435, 0.9387, 0.9299, 0.9035, 0.952, 0.9469, 0.792, 0.9177, 0.9057, 0.7601, 0.7517, nan] 
-2024-08-27 16:39:04.265506: Epoch time: 100.13 s 
-2024-08-27 16:39:05.940186:  
-2024-08-27 16:39:05.940494: Epoch 150 
-2024-08-27 16:39:05.940598: Current learning rate: 0.00932 
-2024-08-27 16:40:38.519670: train_loss -0.7284 
-2024-08-27 16:40:38.519930: val_loss -0.7508 
-2024-08-27 16:40:38.520110: Pseudo dice [0.0, 0.0, 0.8633, 0.9742, 0.8036, 0.9323, 0.9365, 0.9556, 0.9412, 0.9372, 0.9061, 0.9461, 0.9461, 0.7932, 0.9474, 0.9104, 0.7586, 0.7567, nan] 
-2024-08-27 16:40:38.520208: Epoch time: 92.58 s 
-2024-08-27 16:40:40.177697:  
-2024-08-27 16:40:40.178025: Epoch 151 
-2024-08-27 16:40:40.178137: Current learning rate: 0.00932 
-2024-08-27 16:42:21.005482: train_loss -0.731 
-2024-08-27 16:42:21.005741: val_loss -0.7536 
-2024-08-27 16:42:21.005911: Pseudo dice [0.0, 0.0, 0.8706, 0.9732, 0.789, 0.9303, 0.9336, 0.9548, 0.9411, 0.9362, 0.9029, 0.951, 0.9512, 0.7941, 0.9409, 0.9088, 0.7858, 0.789, nan] 
-2024-08-27 16:42:21.005999: Epoch time: 100.83 s 
-2024-08-27 16:42:21.006053: Yayy! New best EMA pseudo Dice: 0.7946 
-2024-08-27 16:42:22.664578:  
-2024-08-27 16:42:22.664773: Epoch 152 
-2024-08-27 16:42:22.664869: Current learning rate: 0.00931 
-2024-08-27 16:44:00.559931: train_loss -0.7347 
-2024-08-27 16:44:00.560186: val_loss -0.7555 
-2024-08-27 16:44:00.560361: Pseudo dice [0.0, 0.0, 0.8884, 0.976, 0.8202, 0.9388, 0.9392, 0.9602, 0.9381, 0.9351, 0.9151, 0.9487, 0.9496, 0.8083, 0.9501, 0.9175, 0.7674, 0.7693, nan] 
-2024-08-27 16:44:00.560459: Epoch time: 97.9 s 
-2024-08-27 16:44:00.560513: Yayy! New best EMA pseudo Dice: 0.7953 
-2024-08-27 16:44:02.204089:  
-2024-08-27 16:44:02.204370: Epoch 153 
-2024-08-27 16:44:02.204474: Current learning rate: 0.00931 
-2024-08-27 16:45:36.195280: train_loss -0.7351 
-2024-08-27 16:45:36.195548: val_loss -0.7444 
-2024-08-27 16:45:36.195716: Pseudo dice [0.0, 0.0, 0.8583, 0.9739, 0.7674, 0.9298, 0.933, 0.9499, 0.9345, 0.9298, 0.9158, 0.9471, 0.9467, 0.7744, 0.9248, 0.903, 0.7835, 0.7917, nan] 
-2024-08-27 16:45:36.195809: Epoch time: 93.99 s 
-2024-08-27 16:45:37.635581:  
-2024-08-27 16:45:37.635774: Epoch 154 
-2024-08-27 16:45:37.635873: Current learning rate: 0.0093 
-2024-08-27 16:47:10.996813: train_loss -0.73 
-2024-08-27 16:47:10.997096: val_loss -0.7508 
-2024-08-27 16:47:10.997264: Pseudo dice [0.0, 0.0, 0.8769, 0.9712, 0.8056, 0.9431, 0.9391, 0.9542, 0.9392, 0.9418, 0.909, 0.9508, 0.9434, 0.7817, 0.9357, 0.9136, 0.7904, 0.7717, nan] 
-2024-08-27 16:47:10.997355: Epoch time: 93.36 s 
-2024-08-27 16:47:10.997408: Yayy! New best EMA pseudo Dice: 0.7953 
-2024-08-27 16:47:12.672314:  
-2024-08-27 16:47:12.672503: Epoch 155 
-2024-08-27 16:47:12.672607: Current learning rate: 0.0093 
-2024-08-27 16:48:52.258242: train_loss -0.7235 
-2024-08-27 16:48:52.258505: val_loss -0.7356 
-2024-08-27 16:48:52.258681: Pseudo dice [0.0, 0.0, 0.8564, 0.9744, 0.7415, 0.9148, 0.9253, 0.9532, 0.9146, 0.9009, 0.8761, 0.9253, 0.9182, 0.7795, 0.9324, 0.9148, 0.7429, 0.727, nan] 
-2024-08-27 16:48:52.258779: Epoch time: 99.59 s 
-2024-08-27 16:48:53.659059:  
-2024-08-27 16:48:53.659361: Epoch 156 
-2024-08-27 16:48:53.659466: Current learning rate: 0.0093 
-2024-08-27 16:50:28.923193: train_loss -0.7236 
-2024-08-27 16:50:28.923441: val_loss -0.7503 
-2024-08-27 16:50:28.923609: Pseudo dice [0.0, 0.0, 0.867, 0.9688, 0.7666, 0.9396, 0.9386, 0.9553, 0.9359, 0.936, 0.9029, 0.9504, 0.948, 0.792, 0.9256, 0.9079, 0.77, 0.7624, nan] 
-2024-08-27 16:50:28.923701: Epoch time: 95.26 s 
-2024-08-27 16:50:30.450191:  
-2024-08-27 16:50:30.450457: Epoch 157 
-2024-08-27 16:50:30.450552: Current learning rate: 0.00929 
-2024-08-27 16:52:01.318201: train_loss -0.7319 
-2024-08-27 16:52:01.318454: val_loss -0.7555 
-2024-08-27 16:52:01.318630: Pseudo dice [0.0, 0.0, 0.883, 0.9729, 0.7765, 0.9326, 0.939, 0.9534, 0.9359, 0.9249, 0.9098, 0.946, 0.9479, 0.796, 0.9344, 0.9021, 0.7652, 0.7446, nan] 
-2024-08-27 16:52:01.318723: Epoch time: 90.87 s 
-2024-08-27 16:52:02.633839:  
-2024-08-27 16:52:02.634010: Epoch 158 
-2024-08-27 16:52:02.634098: Current learning rate: 0.00929 
-2024-08-27 16:53:32.137933: train_loss -0.7328 
-2024-08-27 16:53:32.138203: val_loss -0.7544 
-2024-08-27 16:53:32.138392: Pseudo dice [0.0, 0.0, 0.8477, 0.975, 0.7945, 0.9432, 0.9498, 0.9576, 0.9361, 0.9396, 0.9144, 0.946, 0.9506, 0.8071, 0.9313, 0.9134, 0.7849, 0.7786, nan] 
-2024-08-27 16:53:32.138492: Epoch time: 89.5 s 
-2024-08-27 16:53:33.580053:  
-2024-08-27 16:53:33.580384: Epoch 159 
-2024-08-27 16:53:33.580500: Current learning rate: 0.00928 
-2024-08-27 16:55:11.596551: train_loss -0.7309 
-2024-08-27 16:55:11.596806: val_loss -0.7567 
-2024-08-27 16:55:11.596979: Pseudo dice [0.0, 0.0, 0.8603, 0.9739, 0.7855, 0.9376, 0.9368, 0.9561, 0.9431, 0.9477, 0.9172, 0.9527, 0.9502, 0.7896, 0.918, 0.9071, 0.7879, 0.7818, nan] 
-2024-08-27 16:55:11.597071: Epoch time: 98.02 s 
-2024-08-27 16:55:13.016165:  
-2024-08-27 16:55:13.016354: Epoch 160 
-2024-08-27 16:55:13.016476: Current learning rate: 0.00928 
-2024-08-27 16:56:48.379710: train_loss -0.7364 
-2024-08-27 16:56:48.379982: val_loss -0.7623 
-2024-08-27 16:56:48.380157: Pseudo dice [0.0, 0.0, 0.8715, 0.9748, 0.8143, 0.9428, 0.9463, 0.9599, 0.9491, 0.9438, 0.92, 0.9573, 0.9526, 0.8012, 0.9406, 0.9127, 0.7944, 0.7857, nan] 
-2024-08-27 16:56:48.380249: Epoch time: 95.36 s 
-2024-08-27 16:56:49.806199:  
-2024-08-27 16:56:49.806712: Epoch 161 
-2024-08-27 16:56:49.807428: Current learning rate: 0.00927 
-2024-08-27 16:58:22.077929: train_loss -0.7371 
-2024-08-27 16:58:22.078187: val_loss -0.7501 
-2024-08-27 16:58:22.078373: Pseudo dice [0.0, 0.0, 0.8781, 0.9755, 0.7808, 0.9374, 0.9365, 0.9553, 0.9375, 0.9389, 0.9085, 0.9485, 0.9453, 0.7952, 0.9417, 0.9151, 0.7682, 0.7701, nan] 
-2024-08-27 16:58:22.078468: Epoch time: 92.27 s 
-2024-08-27 16:58:23.462824:  
-2024-08-27 16:58:23.463378: Epoch 162 
-2024-08-27 16:58:23.463490: Current learning rate: 0.00927 
-2024-08-27 16:59:51.879554: train_loss -0.7324 
-2024-08-27 16:59:51.879794: val_loss -0.7517 
-2024-08-27 16:59:51.879972: Pseudo dice [0.0, 0.0, 0.886, 0.9728, 0.7481, 0.9383, 0.939, 0.9488, 0.9357, 0.9332, 0.9044, 0.9477, 0.9472, 0.787, 0.9439, 0.9114, 0.7697, 0.7664, nan] 
-2024-08-27 16:59:51.880061: Epoch time: 88.42 s 
-2024-08-27 16:59:53.423465:  
-2024-08-27 16:59:53.423927: Epoch 163 
-2024-08-27 16:59:53.424039: Current learning rate: 0.00926 
-2024-08-27 17:01:30.077847: train_loss -0.7329 
-2024-08-27 17:01:30.078121: val_loss -0.7629 
-2024-08-27 17:01:30.078306: Pseudo dice [0.0, 0.0, 0.8544, 0.9714, 0.7859, 0.9417, 0.9444, 0.9563, 0.9379, 0.9392, 0.9116, 0.9528, 0.9487, 0.8016, 0.9374, 0.9138, 0.7889, 0.775, nan] 
-2024-08-27 17:01:30.078402: Epoch time: 96.66 s 
-2024-08-27 17:01:30.078459: Yayy! New best EMA pseudo Dice: 0.7953 
-2024-08-27 17:01:31.798465:  
-2024-08-27 17:01:31.798854: Epoch 164 
-2024-08-27 17:01:31.799063: Current learning rate: 0.00926 
-2024-08-27 17:03:04.237748: train_loss -0.7298 
-2024-08-27 17:03:04.238007: val_loss -0.7461 
-2024-08-27 17:03:04.238182: Pseudo dice [0.0, 0.0, 0.852, 0.9703, 0.7988, 0.9286, 0.9303, 0.9581, 0.9284, 0.9266, 0.906, 0.9413, 0.9397, 0.7834, 0.9462, 0.9096, 0.7862, 0.7875, nan] 
-2024-08-27 17:03:04.238277: Epoch time: 92.44 s 
-2024-08-27 17:03:05.660106:  
-2024-08-27 17:03:05.660503: Epoch 165 
-2024-08-27 17:03:05.660676: Current learning rate: 0.00925 
-2024-08-27 17:04:35.507478: train_loss -0.726 
-2024-08-27 17:04:35.507737: val_loss -0.7438 
-2024-08-27 17:04:35.507920: Pseudo dice [0.0, 0.0, 0.8552, 0.9731, 0.7701, 0.9301, 0.9311, 0.9484, 0.9382, 0.9299, 0.9004, 0.9487, 0.9444, 0.7739, 0.9364, 0.9018, 0.7771, 0.7666, nan] 
-2024-08-27 17:04:35.508018: Epoch time: 89.85 s 
-2024-08-27 17:04:36.903197:  
-2024-08-27 17:04:36.903566: Epoch 166 
-2024-08-27 17:04:36.903740: Current learning rate: 0.00925 
-2024-08-27 17:06:14.166697: train_loss -0.7223 
-2024-08-27 17:06:14.166971: val_loss -0.7511 
-2024-08-27 17:06:14.167165: Pseudo dice [0.0, 0.0, 0.8737, 0.9728, 0.7847, 0.9338, 0.9344, 0.9562, 0.9409, 0.9328, 0.9092, 0.9519, 0.9471, 0.8018, 0.9393, 0.9091, 0.7804, 0.7613, nan] 
-2024-08-27 17:06:14.167271: Epoch time: 97.26 s 
-2024-08-27 17:06:15.549751:  
-2024-08-27 17:06:15.550015: Epoch 167 
-2024-08-27 17:06:15.550159: Current learning rate: 0.00925 
-2024-08-27 17:07:45.291307: train_loss -0.7331 
-2024-08-27 17:07:45.291556: val_loss -0.7509 
-2024-08-27 17:07:45.291758: Pseudo dice [0.0, 0.0, 0.8723, 0.9762, 0.7874, 0.9262, 0.9396, 0.9571, 0.9326, 0.933, 0.9001, 0.9417, 0.944, 0.7947, 0.9427, 0.9124, 0.7734, 0.7719, nan] 
-2024-08-27 17:07:45.291875: Epoch time: 89.74 s 
-2024-08-27 17:07:46.921036:  
-2024-08-27 17:07:46.921559: Epoch 168 
-2024-08-27 17:07:46.921693: Current learning rate: 0.00924 
-2024-08-27 17:09:26.296810: train_loss -0.7287 
-2024-08-27 17:09:26.297057: val_loss -0.7509 
-2024-08-27 17:09:26.297223: Pseudo dice [0.0, 0.0, 0.8655, 0.9747, 0.7908, 0.9378, 0.9386, 0.9549, 0.9326, 0.9265, 0.9058, 0.9429, 0.9442, 0.8073, 0.9347, 0.9182, 0.777, 0.7645, nan] 
-2024-08-27 17:09:26.297304: Epoch time: 99.38 s 
-2024-08-27 17:09:27.602627:  
-2024-08-27 17:09:27.602801: Epoch 169 
-2024-08-27 17:09:27.602900: Current learning rate: 0.00924 
-2024-08-27 17:10:57.519290: train_loss -0.7307 
-2024-08-27 17:10:57.519550: val_loss -0.7474 
-2024-08-27 17:10:57.519729: Pseudo dice [0.0, 0.0, 0.8691, 0.9758, 0.7881, 0.9319, 0.934, 0.9459, 0.9438, 0.9327, 0.9083, 0.9528, 0.9468, 0.8027, 0.933, 0.9139, 0.7418, 0.7455, nan] 
-2024-08-27 17:10:57.519823: Epoch time: 89.92 s 
-2024-08-27 17:10:58.851759:  
-2024-08-27 17:10:58.851945: Epoch 170 
-2024-08-27 17:10:58.852046: Current learning rate: 0.00923 
-2024-08-27 17:12:26.544029: train_loss -0.7319 
-2024-08-27 17:12:26.544340: val_loss -0.7474 
-2024-08-27 17:12:26.544609: Pseudo dice [0.0, 0.0, 0.8573, 0.9759, 0.789, 0.9401, 0.9381, 0.9548, 0.9369, 0.9398, 0.9085, 0.9527, 0.9469, 0.7923, 0.945, 0.9106, 0.7553, 0.7494, nan] 
-2024-08-27 17:12:26.544754: Epoch time: 87.69 s 
-2024-08-27 17:12:27.995377:  
-2024-08-27 17:12:27.995537: Epoch 171 
-2024-08-27 17:12:27.995647: Current learning rate: 0.00923 
-2024-08-27 17:13:59.485901: train_loss -0.7294 
-2024-08-27 17:13:59.486165: val_loss -0.7429 
-2024-08-27 17:13:59.486341: Pseudo dice [0.0, 0.0, 0.8662, 0.9746, 0.7763, 0.9292, 0.9274, 0.9562, 0.9336, 0.925, 0.9087, 0.9477, 0.9483, 0.7977, 0.9355, 0.8993, 0.731, 0.7561, nan] 
-2024-08-27 17:13:59.486435: Epoch time: 91.49 s 
-2024-08-27 17:14:00.811808:  
-2024-08-27 17:14:00.811977: Epoch 172 
-2024-08-27 17:14:00.812073: Current learning rate: 0.00922 
-2024-08-27 17:15:36.941795: train_loss -0.7339 
-2024-08-27 17:15:36.942064: val_loss -0.7571 
-2024-08-27 17:15:36.942246: Pseudo dice [0.0, 0.0, 0.8797, 0.9753, 0.7681, 0.93, 0.934, 0.9567, 0.9325, 0.9386, 0.9193, 0.9455, 0.95, 0.7986, 0.9425, 0.9139, 0.7834, 0.7799, nan] 
-2024-08-27 17:15:36.942342: Epoch time: 96.13 s 
-2024-08-27 17:15:38.288699:  
-2024-08-27 17:15:38.288850: Epoch 173 
-2024-08-27 17:15:38.288939: Current learning rate: 0.00922 
-2024-08-27 17:17:09.636258: train_loss -0.7347 
-2024-08-27 17:17:09.636519: val_loss -0.7545 
-2024-08-27 17:17:09.636704: Pseudo dice [0.0, 0.0, 0.8667, 0.9757, 0.7908, 0.9331, 0.9317, 0.9599, 0.9392, 0.9387, 0.9104, 0.9533, 0.9497, 0.8045, 0.9417, 0.9142, 0.7814, 0.7654, nan] 
-2024-08-27 17:17:09.636795: Epoch time: 91.35 s 
-2024-08-27 17:17:11.311306:  
-2024-08-27 17:17:11.311498: Epoch 174 
-2024-08-27 17:17:11.311599: Current learning rate: 0.00921 
-2024-08-27 17:18:46.593443: train_loss -0.7309 
-2024-08-27 17:18:46.593687: val_loss -0.7576 
-2024-08-27 17:18:46.593862: Pseudo dice [0.0, 0.0, 0.875, 0.966, 0.7998, 0.944, 0.9472, 0.9507, 0.9427, 0.94, 0.9121, 0.9498, 0.9515, 0.8071, 0.9425, 0.9091, 0.7941, 0.7891, nan] 
-2024-08-27 17:18:46.593954: Epoch time: 95.28 s 
-2024-08-27 17:18:46.594007: Yayy! New best EMA pseudo Dice: 0.7954 
-2024-08-27 17:18:48.194777:  
-2024-08-27 17:18:48.194971: Epoch 175 
-2024-08-27 17:18:48.195072: Current learning rate: 0.00921 
-2024-08-27 17:20:18.379128: train_loss -0.7393 
-2024-08-27 17:20:18.379388: val_loss -0.7558 
-2024-08-27 17:20:18.379554: Pseudo dice [0.0, 0.0, 0.8625, 0.9753, 0.7841, 0.9407, 0.9422, 0.9596, 0.94, 0.9436, 0.9208, 0.9563, 0.9551, 0.802, 0.945, 0.9175, 0.7758, 0.7584, nan] 
-2024-08-27 17:20:18.379646: Epoch time: 90.19 s 
-2024-08-27 17:20:18.379698: Yayy! New best EMA pseudo Dice: 0.7957 
-2024-08-27 17:20:19.965061:  
-2024-08-27 17:20:19.965376: Epoch 176 
-2024-08-27 17:20:19.965475: Current learning rate: 0.0092 
-2024-08-27 17:21:51.547349: train_loss -0.74 
-2024-08-27 17:21:51.547601: val_loss -0.7616 
-2024-08-27 17:21:51.547773: Pseudo dice [0.0, 0.0, 0.8942, 0.9745, 0.8165, 0.9353, 0.9391, 0.9564, 0.9405, 0.9422, 0.9182, 0.9524, 0.9504, 0.8093, 0.9401, 0.915, 0.7952, 0.7802, nan] 
-2024-08-27 17:21:51.547866: Epoch time: 91.58 s 
-2024-08-27 17:21:51.547917: Yayy! New best EMA pseudo Dice: 0.7965 
-2024-08-27 17:21:53.379973:  
-2024-08-27 17:21:53.380125: Epoch 177 
-2024-08-27 17:21:53.380222: Current learning rate: 0.0092 
-2024-08-27 17:23:26.343645: train_loss -0.7348 
-2024-08-27 17:23:26.343912: val_loss -0.7563 
-2024-08-27 17:23:26.344089: Pseudo dice [0.0, 0.0, 0.8909, 0.9755, 0.7651, 0.9426, 0.9431, 0.9586, 0.9401, 0.9397, 0.9079, 0.9507, 0.9462, 0.8097, 0.9489, 0.9183, 0.7826, 0.7781, nan] 
-2024-08-27 17:23:26.344177: Epoch time: 92.96 s 
-2024-08-27 17:23:26.344228: Yayy! New best EMA pseudo Dice: 0.7968 
-2024-08-27 17:23:27.942791:  
-2024-08-27 17:23:27.942979: Epoch 178 
-2024-08-27 17:23:27.943072: Current learning rate: 0.0092 
-2024-08-27 17:25:03.083893: train_loss -0.7382 
-2024-08-27 17:25:03.084168: val_loss -0.7636 
-2024-08-27 17:25:03.084373: Pseudo dice [0.0, 0.0, 0.8899, 0.9735, 0.7907, 0.9432, 0.9435, 0.9624, 0.9437, 0.9432, 0.9167, 0.9525, 0.9515, 0.8158, 0.942, 0.9192, 0.7877, 0.7928, nan] 
-2024-08-27 17:25:03.084487: Epoch time: 95.14 s 
-2024-08-27 17:25:03.084551: Yayy! New best EMA pseudo Dice: 0.7975 
-2024-08-27 17:25:05.120362:  
-2024-08-27 17:25:05.120752: Epoch 179 
-2024-08-27 17:25:05.120860: Current learning rate: 0.00919 
-2024-08-27 17:26:34.548859: train_loss -0.7371 
-2024-08-27 17:26:34.549124: val_loss -0.7546 
-2024-08-27 17:26:34.549310: Pseudo dice [0.0, 0.0, 0.8874, 0.9766, 0.8048, 0.9393, 0.9454, 0.9561, 0.936, 0.9386, 0.9086, 0.9502, 0.9498, 0.8077, 0.9439, 0.9159, 0.7908, 0.781, nan] 
-2024-08-27 17:26:34.549407: Epoch time: 89.43 s 
-2024-08-27 17:26:34.549463: Yayy! New best EMA pseudo Dice: 0.7979 
-2024-08-27 17:26:36.215778:  
-2024-08-27 17:26:36.216169: Epoch 180 
-2024-08-27 17:26:36.216260: Current learning rate: 0.00919 
-2024-08-27 17:28:11.116875: train_loss -0.735 
-2024-08-27 17:28:11.117115: val_loss -0.7519 
-2024-08-27 17:28:11.117280: Pseudo dice [0.0, 0.0, 0.8823, 0.974, 0.768, 0.9377, 0.9379, 0.9588, 0.9349, 0.9403, 0.9143, 0.9501, 0.9511, 0.7975, 0.9428, 0.9093, 0.768, 0.7405, nan] 
-2024-08-27 17:28:11.117409: Epoch time: 94.9 s 
-2024-08-27 17:28:12.482207:  
-2024-08-27 17:28:12.482366: Epoch 181 
-2024-08-27 17:28:12.482459: Current learning rate: 0.00918 
-2024-08-27 17:29:43.665096: train_loss -0.7347 
-2024-08-27 17:29:43.665349: val_loss -0.7524 
-2024-08-27 17:29:43.665523: Pseudo dice [0.0, 0.0, 0.8586, 0.9647, 0.8039, 0.9395, 0.9356, 0.9525, 0.9332, 0.9312, 0.9069, 0.9509, 0.9476, 0.7917, 0.9331, 0.9117, 0.7774, 0.7823, nan] 
-2024-08-27 17:29:43.665615: Epoch time: 91.18 s 
-2024-08-27 17:29:45.012115:  
-2024-08-27 17:29:45.012312: Epoch 182 
-2024-08-27 17:29:45.012408: Current learning rate: 0.00918 
-2024-08-27 17:31:19.527012: train_loss -0.7331 
-2024-08-27 17:31:19.527282: val_loss -0.7519 
-2024-08-27 17:31:19.527457: Pseudo dice [0.0, 0.0, 0.8657, 0.9748, 0.8031, 0.9433, 0.9466, 0.9596, 0.9403, 0.9328, 0.9012, 0.9487, 0.9455, 0.8063, 0.9349, 0.9156, 0.7762, 0.7684, nan] 
-2024-08-27 17:31:19.527551: Epoch time: 94.52 s 
-2024-08-27 17:31:20.828874:  
-2024-08-27 17:31:20.829028: Epoch 183 
-2024-08-27 17:31:20.829112: Current learning rate: 0.00917 
-2024-08-27 17:32:52.111377: train_loss -0.7379 
-2024-08-27 17:32:52.111626: val_loss -0.7604 
-2024-08-27 17:32:52.111810: Pseudo dice [0.0, 0.0, 0.8704, 0.9672, 0.818, 0.9378, 0.9352, 0.9573, 0.9376, 0.9397, 0.9173, 0.9559, 0.9491, 0.7972, 0.9424, 0.911, 0.7991, 0.7859, nan] 
-2024-08-27 17:32:52.111905: Epoch time: 91.28 s 
-2024-08-27 17:32:53.774354:  
-2024-08-27 17:32:53.774552: Epoch 184 
-2024-08-27 17:32:53.774649: Current learning rate: 0.00917 
-2024-08-27 17:34:24.395037: train_loss -0.7383 
-2024-08-27 17:34:24.395307: val_loss -0.7585 
-2024-08-27 17:34:24.395479: Pseudo dice [0.0, 0.0, 0.8812, 0.9738, 0.7981, 0.9472, 0.9492, 0.9598, 0.9418, 0.9368, 0.9032, 0.9526, 0.9443, 0.8073, 0.948, 0.9193, 0.785, 0.7647, nan] 
-2024-08-27 17:34:24.395570: Epoch time: 90.62 s 
-2024-08-27 17:34:24.395622: Yayy! New best EMA pseudo Dice: 0.7981 
-2024-08-27 17:34:26.036855:  
-2024-08-27 17:34:26.037051: Epoch 185 
-2024-08-27 17:34:26.037152: Current learning rate: 0.00916 
-2024-08-27 17:36:01.279916: train_loss -0.7418 
-2024-08-27 17:36:01.280200: val_loss -0.754 
-2024-08-27 17:36:01.280387: Pseudo dice [0.0, 0.0, 0.8895, 0.9742, 0.8156, 0.9316, 0.9376, 0.957, 0.9415, 0.9266, 0.9114, 0.9494, 0.9463, 0.8124, 0.9356, 0.921, 0.8081, 0.7983, nan] 
-2024-08-27 17:36:01.280586: Epoch time: 95.24 s 
-2024-08-27 17:36:01.280649: Yayy! New best EMA pseudo Dice: 0.7986 
-2024-08-27 17:36:02.977446:  
-2024-08-27 17:36:02.977657: Epoch 186 
-2024-08-27 17:36:02.977772: Current learning rate: 0.00916 
-2024-08-27 17:37:37.024148: train_loss -0.7303 
-2024-08-27 17:37:37.024379: val_loss -0.7476 
-2024-08-27 17:37:37.024569: Pseudo dice [0.0, 0.0, 0.869, 0.9726, 0.7771, 0.9177, 0.9149, 0.9459, 0.9354, 0.9361, 0.9146, 0.9439, 0.9444, 0.8112, 0.9306, 0.8986, 0.7817, 0.7739, nan] 
-2024-08-27 17:37:37.024662: Epoch time: 94.05 s 
-2024-08-27 17:37:38.365562:  
-2024-08-27 17:37:38.365845: Epoch 187 
-2024-08-27 17:37:38.365938: Current learning rate: 0.00915 
-2024-08-27 17:39:16.034459: train_loss -0.732 
-2024-08-27 17:39:16.034682: val_loss -0.76 
-2024-08-27 17:39:16.034868: Pseudo dice [0.0, 0.0, 0.8702, 0.9752, 0.8171, 0.9334, 0.9347, 0.957, 0.9426, 0.9338, 0.9103, 0.9511, 0.9523, 0.8051, 0.9429, 0.9213, 0.7947, 0.7902, nan] 
-2024-08-27 17:39:16.034961: Epoch time: 97.67 s 
-2024-08-27 17:39:17.368061:  
-2024-08-27 17:39:17.368237: Epoch 188 
-2024-08-27 17:39:17.368331: Current learning rate: 0.00915 
-2024-08-27 17:40:53.720997: train_loss -0.7373 
-2024-08-27 17:40:53.721274: val_loss -0.7616 
-2024-08-27 17:40:53.721449: Pseudo dice [0.0, 0.0, 0.8807, 0.9759, 0.7817, 0.9417, 0.9425, 0.9611, 0.9414, 0.9277, 0.9157, 0.9544, 0.9446, 0.8036, 0.9424, 0.918, 0.7843, 0.7773, nan] 
-2024-08-27 17:40:53.721541: Epoch time: 96.35 s 
-2024-08-27 17:40:55.127043:  
-2024-08-27 17:40:55.127249: Epoch 189 
-2024-08-27 17:40:55.127356: Current learning rate: 0.00915 
-2024-08-27 17:42:32.451967: train_loss -0.7377 
-2024-08-27 17:42:32.452675: val_loss -0.7539 
-2024-08-27 17:42:32.452891: Pseudo dice [0.0, 0.0, 0.8368, 0.9731, 0.7982, 0.9407, 0.9381, 0.9539, 0.9422, 0.9344, 0.9031, 0.9525, 0.9528, 0.8029, 0.9411, 0.9138, 0.7912, 0.7706, nan] 
-2024-08-27 17:42:32.453038: Epoch time: 97.33 s 
-2024-08-27 17:42:34.199584:  
-2024-08-27 17:42:34.199922: Epoch 190 
-2024-08-27 17:42:34.200016: Current learning rate: 0.00914 
-2024-08-27 17:44:00.469765: train_loss -0.7392 
-2024-08-27 17:44:00.470111: val_loss -0.7529 
-2024-08-27 17:44:00.470309: Pseudo dice [0.0, 0.0, 0.8767, 0.9729, 0.7979, 0.9295, 0.9361, 0.9545, 0.937, 0.9305, 0.9062, 0.9485, 0.947, 0.7989, 0.932, 0.9158, 0.7879, 0.7911, nan] 
-2024-08-27 17:44:00.470406: Epoch time: 86.27 s 
-2024-08-27 17:44:01.840999:  
-2024-08-27 17:44:01.841231: Epoch 191 
-2024-08-27 17:44:01.841339: Current learning rate: 0.00914 
-2024-08-27 17:45:35.949727: train_loss -0.7331 
-2024-08-27 17:45:35.950003: val_loss -0.7471 
-2024-08-27 17:45:35.950168: Pseudo dice [0.0, 0.0, 0.8473, 0.9751, 0.7929, 0.9313, 0.9329, 0.9581, 0.9252, 0.9196, 0.8925, 0.9368, 0.9347, 0.8006, 0.9439, 0.9074, 0.7692, 0.7552, nan] 
-2024-08-27 17:45:35.950252: Epoch time: 94.11 s 
-2024-08-27 17:45:37.249767:  
-2024-08-27 17:45:37.249987: Epoch 192 
-2024-08-27 17:45:37.250193: Current learning rate: 0.00913 
-2024-08-27 17:47:06.226812: train_loss -0.7366 
-2024-08-27 17:47:06.227051: val_loss -0.7521 
-2024-08-27 17:47:06.227233: Pseudo dice [0.0, 0.0, 0.865, 0.9745, 0.8133, 0.9376, 0.9379, 0.9561, 0.9409, 0.9334, 0.904, 0.9527, 0.9516, 0.7924, 0.9392, 0.9028, 0.7889, 0.7475, nan] 
-2024-08-27 17:47:06.227325: Epoch time: 88.98 s 
-2024-08-27 17:47:07.609084:  
-2024-08-27 17:47:07.609559: Epoch 193 
-2024-08-27 17:47:07.609710: Current learning rate: 0.00913 
-2024-08-27 17:48:41.350831: train_loss -0.7307 
-2024-08-27 17:48:41.351108: val_loss -0.7519 
-2024-08-27 17:48:41.351915: Pseudo dice [0.0, 0.0, 0.8834, 0.9747, 0.7523, 0.9241, 0.9382, 0.9557, 0.9381, 0.9427, 0.9135, 0.9468, 0.9437, 0.7926, 0.9365, 0.9063, 0.7676, 0.7632, nan] 
-2024-08-27 17:48:41.352080: Epoch time: 93.74 s 
-2024-08-27 17:48:42.743103:  
-2024-08-27 17:48:42.743261: Epoch 194 
-2024-08-27 17:48:42.743357: Current learning rate: 0.00912 
-2024-08-27 17:50:14.463578: train_loss -0.7275 
-2024-08-27 17:50:14.463812: val_loss -0.7432 
-2024-08-27 17:50:14.463992: Pseudo dice [0.0, 0.0, 0.8829, 0.9751, 0.7677, 0.9254, 0.9299, 0.9501, 0.9285, 0.9218, 0.9063, 0.9377, 0.9441, 0.7998, 0.93, 0.9125, 0.7778, 0.7897, nan] 
-2024-08-27 17:50:14.464123: Epoch time: 91.72 s 
-2024-08-27 17:50:16.176592:  
-2024-08-27 17:50:16.176745: Epoch 195 
-2024-08-27 17:50:16.176835: Current learning rate: 0.00912 
-2024-08-27 17:51:47.380656: train_loss -0.7323 
-2024-08-27 17:51:47.380921: val_loss -0.7462 
-2024-08-27 17:51:47.381111: Pseudo dice [0.0, 0.0, 0.8363, 0.9731, 0.7728, 0.9295, 0.9374, 0.9579, 0.936, 0.9367, 0.8983, 0.9459, 0.9446, 0.7965, 0.9408, 0.912, 0.7601, 0.7802, nan] 
-2024-08-27 17:51:47.381203: Epoch time: 91.2 s 
-2024-08-27 17:51:48.823477:  
-2024-08-27 17:51:48.823658: Epoch 196 
-2024-08-27 17:51:48.823749: Current learning rate: 0.00911 
-2024-08-27 17:53:21.220002: train_loss -0.7317 
-2024-08-27 17:53:21.220227: val_loss -0.7662 
-2024-08-27 17:53:21.220396: Pseudo dice [0.0, 0.0, 0.8744, 0.9755, 0.8016, 0.9441, 0.9454, 0.9585, 0.9446, 0.9397, 0.9194, 0.9575, 0.9536, 0.7972, 0.9446, 0.9136, 0.8037, 0.7704, nan] 
-2024-08-27 17:53:21.220493: Epoch time: 92.4 s 
-2024-08-27 17:53:22.634439:  
-2024-08-27 17:53:22.634583: Epoch 197 
-2024-08-27 17:53:22.634670: Current learning rate: 0.00911 
-2024-08-27 17:54:56.218149: train_loss -0.7388 
-2024-08-27 17:54:56.218428: val_loss -0.7535 
-2024-08-27 17:54:56.218634: Pseudo dice [0.0, 0.0, 0.8764, 0.9742, 0.8017, 0.9397, 0.9341, 0.9537, 0.9363, 0.9366, 0.9103, 0.951, 0.9507, 0.7901, 0.9475, 0.9165, 0.7531, 0.7746, nan] 
-2024-08-27 17:54:56.218738: Epoch time: 93.58 s 
-2024-08-27 17:54:57.671453:  
-2024-08-27 17:54:57.671737: Epoch 198 
-2024-08-27 17:54:57.671973: Current learning rate: 0.0091 
-2024-08-27 17:56:34.228284: train_loss -0.7356 
-2024-08-27 17:56:34.228518: val_loss -0.7577 
-2024-08-27 17:56:34.228685: Pseudo dice [0.0, 0.0, 0.8734, 0.9725, 0.7953, 0.9435, 0.9422, 0.9593, 0.94, 0.934, 0.9158, 0.9508, 0.9448, 0.8114, 0.9456, 0.9127, 0.7806, 0.7803, nan] 
-2024-08-27 17:56:34.228821: Epoch time: 96.56 s 
-2024-08-27 17:56:35.890152:  
-2024-08-27 17:56:35.890390: Epoch 199 
-2024-08-27 17:56:35.890515: Current learning rate: 0.0091 
-2024-08-27 17:58:08.905227: train_loss -0.7351 
-2024-08-27 17:58:08.905460: val_loss -0.7582 
-2024-08-27 17:58:08.905630: Pseudo dice [0.0, 0.0, 0.8877, 0.9727, 0.7807, 0.9445, 0.9426, 0.9583, 0.9462, 0.9458, 0.9113, 0.955, 0.9507, 0.8082, 0.941, 0.9165, 0.8051, 0.8043, nan] 
-2024-08-27 17:58:08.905719: Epoch time: 93.02 s 
-2024-08-27 17:58:10.512329:  
-2024-08-27 17:58:10.512500: Epoch 200 
-2024-08-27 17:58:10.512594: Current learning rate: 0.0091 
-2024-08-27 17:59:47.358349: train_loss -0.7378 
-2024-08-27 17:59:47.358604: val_loss -0.7554 
-2024-08-27 17:59:47.358793: Pseudo dice [0.0, 0.0, 0.8651, 0.9769, 0.7991, 0.9255, 0.928, 0.96, 0.9313, 0.9357, 0.9141, 0.9432, 0.9438, 0.8061, 0.9406, 0.9111, 0.7912, 0.7902, nan] 
-2024-08-27 17:59:47.358887: Epoch time: 96.85 s 
-2024-08-27 17:59:49.101022:  
-2024-08-27 17:59:49.101221: Epoch 201 
-2024-08-27 17:59:49.101317: Current learning rate: 0.00909 
-2024-08-27 18:01:19.821332: train_loss -0.7388 
-2024-08-27 18:01:19.821595: val_loss -0.7555 
-2024-08-27 18:01:19.821776: Pseudo dice [0.0, 0.0, 0.8742, 0.9756, 0.77, 0.9343, 0.9369, 0.9586, 0.9456, 0.937, 0.912, 0.9518, 0.9463, 0.8139, 0.9391, 0.9189, 0.7867, 0.7686, nan] 
-2024-08-27 18:01:19.821871: Epoch time: 90.72 s 
-2024-08-27 18:01:21.144026:  
-2024-08-27 18:01:21.144319: Epoch 202 
-2024-08-27 18:01:21.144415: Current learning rate: 0.00909 
-2024-08-27 18:02:55.253314: train_loss -0.7379 
-2024-08-27 18:02:55.253570: val_loss -0.749 
-2024-08-27 18:02:55.253784: Pseudo dice [0.0, 0.0, 0.8808, 0.9749, 0.7963, 0.9263, 0.9379, 0.9536, 0.9329, 0.9285, 0.8885, 0.94, 0.9384, 0.792, 0.9349, 0.9201, 0.7834, 0.7715, nan] 
-2024-08-27 18:02:55.253903: Epoch time: 94.11 s 
-2024-08-27 18:02:56.639519:  
-2024-08-27 18:02:56.639803: Epoch 203 
-2024-08-27 18:02:56.639919: Current learning rate: 0.00908 
-2024-08-27 18:04:25.352770: train_loss -0.7367 
-2024-08-27 18:04:25.353016: val_loss -0.7573 
-2024-08-27 18:04:25.353196: Pseudo dice [0.0, 0.0, 0.8705, 0.975, 0.7792, 0.9318, 0.9371, 0.957, 0.9453, 0.9406, 0.9193, 0.9562, 0.9523, 0.8117, 0.9376, 0.9204, 0.7927, 0.8034, nan] 
-2024-08-27 18:04:25.353283: Epoch time: 88.71 s 
-2024-08-27 18:04:26.667629:  
-2024-08-27 18:04:26.667802: Epoch 204 
-2024-08-27 18:04:26.667897: Current learning rate: 0.00908 
-2024-08-27 18:05:58.041378: train_loss -0.7379 
-2024-08-27 18:05:58.041647: val_loss -0.7513 
-2024-08-27 18:05:58.041836: Pseudo dice [0.0, 0.0, 0.8826, 0.9744, 0.8141, 0.9204, 0.9371, 0.9547, 0.9334, 0.9357, 0.9105, 0.9449, 0.944, 0.7911, 0.9336, 0.9166, 0.7991, 0.8019, nan] 
-2024-08-27 18:05:58.041929: Epoch time: 91.37 s 
-2024-08-27 18:05:59.388590:  
-2024-08-27 18:05:59.388756: Epoch 205 
-2024-08-27 18:05:59.388854: Current learning rate: 0.00907 
-2024-08-27 18:07:32.630507: train_loss -0.7355 
-2024-08-27 18:07:32.630759: val_loss -0.7603 
-2024-08-27 18:07:32.630973: Pseudo dice [0.0, 0.0, 0.8734, 0.9755, 0.7877, 0.9346, 0.9405, 0.9518, 0.9415, 0.9402, 0.9086, 0.9504, 0.9505, 0.8107, 0.9066, 0.9106, 0.7641, 0.7563, nan] 
-2024-08-27 18:07:32.631064: Epoch time: 93.24 s 
-2024-08-27 18:07:33.841980:  
-2024-08-27 18:07:33.842198: Epoch 206 
-2024-08-27 18:07:33.842295: Current learning rate: 0.00907 
-2024-08-27 18:09:05.312321: train_loss -0.7338 
-2024-08-27 18:09:05.312760: val_loss -0.7558 
-2024-08-27 18:09:05.312967: Pseudo dice [0.0, 0.0, 0.8626, 0.9716, 0.7465, 0.9384, 0.9318, 0.954, 0.9387, 0.9368, 0.8967, 0.9489, 0.947, 0.7907, 0.9415, 0.9056, 0.7722, 0.757, nan] 
-2024-08-27 18:09:05.313061: Epoch time: 91.47 s 
-2024-08-27 18:09:07.031540:  
-2024-08-27 18:09:07.031714: Epoch 207 
-2024-08-27 18:09:07.031813: Current learning rate: 0.00906 
-2024-08-27 18:10:36.194783: train_loss -0.7353 
-2024-08-27 18:10:36.195051: val_loss -0.7512 
-2024-08-27 18:10:36.195264: Pseudo dice [0.0, 0.0, 0.8663, 0.9747, 0.8075, 0.9412, 0.9377, 0.9528, 0.9416, 0.9264, 0.9119, 0.9468, 0.9516, 0.7887, 0.9319, 0.91, 0.8044, 0.7968, nan] 
-2024-08-27 18:10:36.195363: Epoch time: 89.16 s 
-2024-08-27 18:10:37.495628:  
-2024-08-27 18:10:37.495930: Epoch 208 
-2024-08-27 18:10:37.496027: Current learning rate: 0.00906 
-2024-08-27 18:12:06.694613: train_loss -0.7325 
-2024-08-27 18:12:06.694987: val_loss -0.7474 
-2024-08-27 18:12:06.695277: Pseudo dice [0.0, 0.0, 0.8713, 0.9712, 0.7744, 0.9366, 0.9426, 0.9523, 0.9394, 0.9203, 0.8971, 0.9491, 0.9383, 0.7776, 0.9405, 0.9103, 0.7874, 0.796, nan] 
-2024-08-27 18:12:06.695483: Epoch time: 89.2 s 
-2024-08-27 18:12:08.016128:  
-2024-08-27 18:12:08.016636: Epoch 209 
-2024-08-27 18:12:08.016732: Current learning rate: 0.00905 
-2024-08-27 18:13:40.068226: train_loss -0.7319 
-2024-08-27 18:13:40.068480: val_loss -0.7529 
-2024-08-27 18:13:40.068650: Pseudo dice [0.0, 0.0, 0.8481, 0.9733, 0.7973, 0.9401, 0.9429, 0.9548, 0.9446, 0.9376, 0.9106, 0.9512, 0.9472, 0.821, 0.9466, 0.9218, 0.7723, 0.7376, nan] 
-2024-08-27 18:13:40.068733: Epoch time: 92.05 s 
-2024-08-27 18:13:41.330045:  
-2024-08-27 18:13:41.330231: Epoch 210 
-2024-08-27 18:13:41.330328: Current learning rate: 0.00905 
-2024-08-27 18:15:21.619931: train_loss -0.7353 
-2024-08-27 18:15:21.620232: val_loss -0.7632 
-2024-08-27 18:15:21.620474: Pseudo dice [0.0, 0.0, 0.8786, 0.9765, 0.8072, 0.9237, 0.9394, 0.9568, 0.9415, 0.9383, 0.925, 0.9527, 0.9486, 0.7994, 0.9401, 0.9171, 0.7796, 0.7975, nan] 
-2024-08-27 18:15:21.620589: Epoch time: 100.29 s 
-2024-08-27 18:15:22.973322:  
-2024-08-27 18:15:22.973527: Epoch 211 
-2024-08-27 18:15:22.973641: Current learning rate: 0.00905 
-2024-08-27 18:16:56.484901: train_loss -0.7376 
-2024-08-27 18:16:56.485165: val_loss -0.7535 
-2024-08-27 18:16:56.485358: Pseudo dice [0.0, 0.0, 0.8649, 0.9713, 0.8061, 0.9393, 0.9445, 0.9533, 0.9447, 0.9361, 0.9118, 0.9553, 0.9472, 0.7913, 0.9315, 0.9169, 0.7776, 0.7727, nan] 
-2024-08-27 18:16:56.485460: Epoch time: 93.51 s 
-2024-08-27 18:16:57.832440:  
-2024-08-27 18:16:57.832754: Epoch 212 
-2024-08-27 18:16:57.832864: Current learning rate: 0.00904 
-2024-08-27 18:18:30.312202: train_loss -0.7376 
-2024-08-27 18:18:30.312442: val_loss -0.7584 
-2024-08-27 18:18:30.312623: Pseudo dice [0.0, 0.0, 0.8788, 0.9739, 0.8054, 0.9407, 0.944, 0.9524, 0.9422, 0.9401, 0.9055, 0.9538, 0.9532, 0.8001, 0.947, 0.9091, 0.7874, 0.7764, nan] 
-2024-08-27 18:18:30.312723: Epoch time: 92.48 s 
-2024-08-27 18:18:31.961378:  
-2024-08-27 18:18:31.961857: Epoch 213 
-2024-08-27 18:18:31.961964: Current learning rate: 0.00904 
-2024-08-27 18:20:02.587626: train_loss -0.7394 
-2024-08-27 18:20:02.587853: val_loss -0.7599 
-2024-08-27 18:20:02.588031: Pseudo dice [0.0, 0.0, 0.8747, 0.976, 0.7767, 0.9364, 0.939, 0.9537, 0.9428, 0.9382, 0.9123, 0.9562, 0.9514, 0.8167, 0.9484, 0.921, 0.7966, 0.7981, nan] 
-2024-08-27 18:20:02.588116: Epoch time: 90.63 s 
-2024-08-27 18:20:03.823738:  
-2024-08-27 18:20:03.823899: Epoch 214 
-2024-08-27 18:20:03.823983: Current learning rate: 0.00903 
-2024-08-27 18:21:36.311529: train_loss -0.7384 
-2024-08-27 18:21:36.311775: val_loss -0.7594 
-2024-08-27 18:21:36.311959: Pseudo dice [0.0, 0.0, 0.8869, 0.9742, 0.7903, 0.9397, 0.9448, 0.9576, 0.9425, 0.9415, 0.9136, 0.9545, 0.9517, 0.7989, 0.9397, 0.9244, 0.7883, 0.7667, nan] 
-2024-08-27 18:21:36.312074: Epoch time: 92.49 s 
-2024-08-27 18:21:37.657638:  
-2024-08-27 18:21:37.657964: Epoch 215 
-2024-08-27 18:21:37.658072: Current learning rate: 0.00903 
-2024-08-27 18:23:08.576546: train_loss -0.7362 
-2024-08-27 18:23:08.576818: val_loss -0.7534 
-2024-08-27 18:23:08.577020: Pseudo dice [0.0, 0.0, 0.8543, 0.9742, 0.7862, 0.9362, 0.9394, 0.9554, 0.9398, 0.9302, 0.9141, 0.956, 0.9472, 0.801, 0.937, 0.9181, 0.7959, 0.7849, nan] 
-2024-08-27 18:23:08.577120: Epoch time: 90.92 s 
-2024-08-27 18:23:09.903608:  
-2024-08-27 18:23:09.903936: Epoch 216 
-2024-08-27 18:23:09.904037: Current learning rate: 0.00902 
-2024-08-27 18:24:39.349549: train_loss -0.7402 
-2024-08-27 18:24:39.350243: val_loss -0.7603 
-2024-08-27 18:24:39.350469: Pseudo dice [0.0, 0.0, 0.8897, 0.975, 0.8119, 0.9405, 0.9438, 0.9598, 0.9459, 0.9472, 0.9145, 0.9575, 0.9495, 0.8166, 0.943, 0.9143, 0.8195, 0.7954, nan] 
-2024-08-27 18:24:39.350626: Epoch time: 89.45 s 
-2024-08-27 18:24:39.350682: Yayy! New best EMA pseudo Dice: 0.7993 
-2024-08-27 18:24:40.952096:  
-2024-08-27 18:24:40.952273: Epoch 217 
-2024-08-27 18:24:40.952368: Current learning rate: 0.00902 
-2024-08-27 18:26:15.812518: train_loss -0.7371 
-2024-08-27 18:26:15.812891: val_loss -0.7553 
-2024-08-27 18:26:15.813069: Pseudo dice [0.0, 0.0, 0.8618, 0.9698, 0.8041, 0.9331, 0.9415, 0.9588, 0.9385, 0.9474, 0.9202, 0.9488, 0.9512, 0.7948, 0.9391, 0.9078, 0.799, 0.7851, nan] 
-2024-08-27 18:26:15.813183: Epoch time: 94.86 s 
-2024-08-27 18:26:15.813238: Yayy! New best EMA pseudo Dice: 0.7994 
-2024-08-27 18:26:17.409882:  
-2024-08-27 18:26:17.410084: Epoch 218 
-2024-08-27 18:26:17.410193: Current learning rate: 0.00901 
-2024-08-27 18:27:52.803745: train_loss -0.7374 
-2024-08-27 18:27:52.804058: val_loss -0.7561 
-2024-08-27 18:27:52.804351: Pseudo dice [0.0, 0.0, 0.8857, 0.9748, 0.825, 0.9331, 0.9392, 0.9602, 0.9356, 0.9378, 0.9108, 0.9427, 0.9451, 0.8027, 0.953, 0.9214, 0.7697, 0.7598, nan] 
-2024-08-27 18:27:52.804485: Epoch time: 95.39 s 
-2024-08-27 18:27:52.804564: Yayy! New best EMA pseudo Dice: 0.7995 
-2024-08-27 18:27:54.920292:  
-2024-08-27 18:27:54.920794: Epoch 219 
-2024-08-27 18:27:54.920900: Current learning rate: 0.00901 
-2024-08-27 18:29:24.894295: train_loss -0.7394 
-2024-08-27 18:29:24.894548: val_loss -0.764 
-2024-08-27 18:29:24.894726: Pseudo dice [0.0, 0.0, 0.8897, 0.9714, 0.8114, 0.9401, 0.9385, 0.9549, 0.9448, 0.9329, 0.9165, 0.9527, 0.9546, 0.8193, 0.9478, 0.9243, 0.7993, 0.7973, nan] 
-2024-08-27 18:29:24.894819: Epoch time: 89.97 s 
-2024-08-27 18:29:24.894874: Yayy! New best EMA pseudo Dice: 0.8 
-2024-08-27 18:29:26.573434:  
-2024-08-27 18:29:26.573648: Epoch 220 
-2024-08-27 18:29:26.573756: Current learning rate: 0.009 
-2024-08-27 18:31:02.435398: train_loss -0.7358 
-2024-08-27 18:31:02.435642: val_loss -0.7566 
-2024-08-27 18:31:02.435817: Pseudo dice [0.0, 0.0, 0.8679, 0.9749, 0.7872, 0.9358, 0.9338, 0.9532, 0.9364, 0.9358, 0.9066, 0.9419, 0.9411, 0.8047, 0.9437, 0.9044, 0.8006, 0.7818, nan] 
-2024-08-27 18:31:02.435902: Epoch time: 95.86 s 
-2024-08-27 18:31:03.660070:  
-2024-08-27 18:31:03.660396: Epoch 221 
-2024-08-27 18:31:03.660490: Current learning rate: 0.009 
-2024-08-27 18:32:33.644077: train_loss -0.7375 
-2024-08-27 18:32:33.644356: val_loss -0.7567 
-2024-08-27 18:32:33.644559: Pseudo dice [0.0, 0.0, 0.8861, 0.9761, 0.7782, 0.9361, 0.942, 0.9575, 0.9346, 0.9297, 0.9159, 0.9488, 0.9451, 0.8045, 0.9453, 0.9152, 0.7979, 0.795, nan] 
-2024-08-27 18:32:33.644662: Epoch time: 89.98 s 
-2024-08-27 18:32:34.955065:  
-2024-08-27 18:32:34.955244: Epoch 222 
-2024-08-27 18:32:34.955341: Current learning rate: 0.009 
-2024-08-27 18:34:05.275698: train_loss -0.7392 
-2024-08-27 18:34:05.275951: val_loss -0.757 
-2024-08-27 18:34:05.276126: Pseudo dice [0.0, 0.0, 0.8761, 0.9758, 0.751, 0.9419, 0.9451, 0.9587, 0.9421, 0.9375, 0.9007, 0.9504, 0.9456, 0.7949, 0.9393, 0.9068, 0.8079, 0.8002, nan] 
-2024-08-27 18:34:05.276222: Epoch time: 90.32 s 
-2024-08-27 18:34:06.601073:  
-2024-08-27 18:34:06.601256: Epoch 223 
-2024-08-27 18:34:06.601353: Current learning rate: 0.00899 
-2024-08-27 18:35:38.248617: train_loss -0.738 
-2024-08-27 18:35:38.248854: val_loss -0.7476 
-2024-08-27 18:35:38.249030: Pseudo dice [0.0, 0.0, 0.8672, 0.9748, 0.7881, 0.9318, 0.9386, 0.9501, 0.9329, 0.9249, 0.9063, 0.9436, 0.9433, 0.7842, 0.9469, 0.9096, 0.7807, 0.7698, nan] 
-2024-08-27 18:35:38.249119: Epoch time: 91.65 s 
-2024-08-27 18:35:39.547584:  
-2024-08-27 18:35:39.547836: Epoch 224 
-2024-08-27 18:35:39.547941: Current learning rate: 0.00899 
-2024-08-27 18:37:16.130746: train_loss -0.7399 
-2024-08-27 18:37:16.131002: val_loss -0.7655 
-2024-08-27 18:37:16.131171: Pseudo dice [0.0, 0.0, 0.8732, 0.9767, 0.82, 0.9383, 0.9417, 0.9601, 0.9401, 0.9397, 0.9147, 0.9548, 0.9491, 0.8105, 0.9437, 0.9273, 0.7975, 0.7832, nan] 
-2024-08-27 18:37:16.131306: Epoch time: 96.58 s 
-2024-08-27 18:37:17.453730:  
-2024-08-27 18:37:17.453971: Epoch 225 
-2024-08-27 18:37:17.454068: Current learning rate: 0.00898 
-2024-08-27 18:38:48.312931: train_loss -0.7407 
-2024-08-27 18:38:48.313251: val_loss -0.757 
-2024-08-27 18:38:48.313499: Pseudo dice [0.0, 0.0, 0.8634, 0.9696, 0.8052, 0.94, 0.9424, 0.9577, 0.9404, 0.9286, 0.9107, 0.9516, 0.9498, 0.8102, 0.9416, 0.9161, 0.7733, 0.7838, nan] 
-2024-08-27 18:38:48.313679: Epoch time: 90.86 s 
-2024-08-27 18:38:49.551382:  
-2024-08-27 18:38:49.551572: Epoch 226 
-2024-08-27 18:38:49.551664: Current learning rate: 0.00898 
-2024-08-27 18:40:27.687840: train_loss -0.7381 
-2024-08-27 18:40:27.688103: val_loss -0.7571 
-2024-08-27 18:40:27.688293: Pseudo dice [0.0, 0.0, 0.8718, 0.9751, 0.7905, 0.9409, 0.9437, 0.9571, 0.9436, 0.9402, 0.92, 0.9528, 0.954, 0.8013, 0.9456, 0.9138, 0.8004, 0.797, nan] 
-2024-08-27 18:40:27.688387: Epoch time: 98.14 s 
-2024-08-27 18:40:29.012071:  
-2024-08-27 18:40:29.012233: Epoch 227 
-2024-08-27 18:40:29.012323: Current learning rate: 0.00897 
-2024-08-27 18:42:08.306623: train_loss -0.7371 
-2024-08-27 18:42:08.306866: val_loss -0.7576 
-2024-08-27 18:42:08.307064: Pseudo dice [0.0, 0.0, 0.8349, 0.9753, 0.8023, 0.9433, 0.9394, 0.9556, 0.936, 0.9336, 0.9188, 0.9512, 0.9536, 0.8128, 0.9471, 0.9222, 0.7853, 0.7734, nan] 
-2024-08-27 18:42:08.307155: Epoch time: 99.3 s 
-2024-08-27 18:42:09.565559:  
-2024-08-27 18:42:09.565834: Epoch 228 
-2024-08-27 18:42:09.565929: Current learning rate: 0.00897 
-2024-08-27 18:43:45.272747: train_loss -0.7334 
-2024-08-27 18:43:45.273008: val_loss -0.7575 
-2024-08-27 18:43:45.273179: Pseudo dice [0.0, 0.0, 0.8681, 0.9727, 0.818, 0.9429, 0.9439, 0.945, 0.9429, 0.9379, 0.9128, 0.9517, 0.9482, 0.8169, 0.9424, 0.9099, 0.7802, 0.7449, nan] 
-2024-08-27 18:43:45.273268: Epoch time: 95.71 s 
-2024-08-27 18:43:46.539772:  
-2024-08-27 18:43:46.539941: Epoch 229 
-2024-08-27 18:43:46.540043: Current learning rate: 0.00896 
-2024-08-27 18:45:23.875416: train_loss -0.7336 
-2024-08-27 18:45:23.875721: val_loss -0.7485 
-2024-08-27 18:45:23.875959: Pseudo dice [0.0, 0.0, 0.8833, 0.9701, 0.7907, 0.9154, 0.928, 0.9556, 0.9264, 0.9196, 0.8886, 0.9323, 0.9363, 0.7887, 0.9321, 0.9122, 0.7749, 0.7828, nan] 
-2024-08-27 18:45:23.876077: Epoch time: 97.34 s 
-2024-08-27 18:45:25.559784:  
-2024-08-27 18:45:25.560009: Epoch 230 
-2024-08-27 18:45:25.560114: Current learning rate: 0.00896 
-2024-08-27 18:47:02.158753: train_loss -0.735 
-2024-08-27 18:47:02.159012: val_loss -0.7462 
-2024-08-27 18:47:02.159181: Pseudo dice [0.0, 0.0, 0.884, 0.9741, 0.7289, 0.9424, 0.9392, 0.9464, 0.9399, 0.9352, 0.9055, 0.9483, 0.9448, 0.7576, 0.9421, 0.9067, 0.765, 0.7576, nan] 
-2024-08-27 18:47:02.159272: Epoch time: 96.6 s 
-2024-08-27 18:47:03.451029:  
-2024-08-27 18:47:03.451397: Epoch 231 
-2024-08-27 18:47:03.451498: Current learning rate: 0.00895 
-2024-08-27 18:48:39.360753: train_loss -0.7387 
-2024-08-27 18:48:39.361004: val_loss -0.754 
-2024-08-27 18:48:39.361214: Pseudo dice [0.0, 0.0, 0.877, 0.9746, 0.7911, 0.9371, 0.9381, 0.9543, 0.9408, 0.9397, 0.9138, 0.9519, 0.9474, 0.8121, 0.9298, 0.9152, 0.7987, 0.7919, nan] 
-2024-08-27 18:48:39.361319: Epoch time: 95.91 s 
-2024-08-27 18:48:40.799969:  
-2024-08-27 18:48:40.800289: Epoch 232 
-2024-08-27 18:48:40.800392: Current learning rate: 0.00895 
-2024-08-27 18:50:15.491890: train_loss -0.7322 
-2024-08-27 18:50:15.492162: val_loss -0.7463 
-2024-08-27 18:50:15.492335: Pseudo dice [0.0, 0.0, 0.8708, 0.9756, 0.8098, 0.9402, 0.941, 0.9546, 0.9347, 0.9301, 0.9099, 0.9439, 0.9425, 0.8065, 0.9329, 0.9145, 0.7642, 0.7436, nan] 
-2024-08-27 18:50:15.492435: Epoch time: 94.69 s 
-2024-08-27 18:50:16.790700:  
-2024-08-27 18:50:16.790957: Epoch 233 
-2024-08-27 18:50:16.791053: Current learning rate: 0.00895 
-2024-08-27 18:51:53.604161: train_loss -0.7336 
-2024-08-27 18:51:53.604693: val_loss -0.7589 
-2024-08-27 18:51:53.604928: Pseudo dice [0.0, 0.0, 0.8866, 0.9656, 0.769, 0.9329, 0.9302, 0.9591, 0.9393, 0.9374, 0.9117, 0.9452, 0.9489, 0.8033, 0.9455, 0.9135, 0.7893, 0.7875, nan] 
-2024-08-27 18:51:53.605151: Epoch time: 96.81 s 
-2024-08-27 18:51:54.955789:  
-2024-08-27 18:51:54.955971: Epoch 234 
-2024-08-27 18:51:54.956068: Current learning rate: 0.00894 
-2024-08-27 18:53:25.402413: train_loss -0.7305 
-2024-08-27 18:53:25.402661: val_loss -0.7531 
-2024-08-27 18:53:25.402889: Pseudo dice [0.0, 0.0, 0.8716, 0.9752, 0.7946, 0.9405, 0.9361, 0.9546, 0.9402, 0.9327, 0.9077, 0.9536, 0.9491, 0.8003, 0.945, 0.9151, 0.7823, 0.7797, nan] 
-2024-08-27 18:53:25.402984: Epoch time: 90.45 s 
-2024-08-27 18:53:26.655417:  
-2024-08-27 18:53:26.655930: Epoch 235 
-2024-08-27 18:53:26.656041: Current learning rate: 0.00894 
-2024-08-27 18:54:59.623772: train_loss -0.7313 
-2024-08-27 18:54:59.624202: val_loss -0.7545 
-2024-08-27 18:54:59.624453: Pseudo dice [0.0, 0.0, 0.8784, 0.9758, 0.8023, 0.9389, 0.9362, 0.951, 0.9302, 0.9346, 0.9068, 0.9453, 0.9454, 0.7977, 0.9418, 0.9166, 0.8, 0.7691, nan] 
-2024-08-27 18:54:59.624566: Epoch time: 92.97 s 
-2024-08-27 18:55:00.950710:  
-2024-08-27 18:55:00.950978: Epoch 236 
-2024-08-27 18:55:00.951068: Current learning rate: 0.00893 
-2024-08-27 18:56:31.348513: train_loss -0.7404 
-2024-08-27 18:56:31.348797: val_loss -0.7566 
-2024-08-27 18:56:31.348990: Pseudo dice [0.0, 0.0, 0.8798, 0.9753, 0.8042, 0.938, 0.9378, 0.9583, 0.9398, 0.9319, 0.9088, 0.9481, 0.9498, 0.8184, 0.9436, 0.9216, 0.7839, 0.7514, nan] 
-2024-08-27 18:56:31.349093: Epoch time: 90.4 s 
-2024-08-27 18:56:32.939796:  
-2024-08-27 18:56:32.940007: Epoch 237 
-2024-08-27 18:56:32.940109: Current learning rate: 0.00893 
-2024-08-27 18:58:04.778894: train_loss -0.7365 
-2024-08-27 18:58:04.779149: val_loss -0.7517 
-2024-08-27 18:58:04.779322: Pseudo dice [0.0, 0.0, 0.8656, 0.9645, 0.7846, 0.9319, 0.9361, 0.9531, 0.932, 0.9408, 0.9151, 0.9473, 0.9466, 0.7962, 0.9232, 0.9083, 0.7883, 0.7971, nan] 
-2024-08-27 18:58:04.779413: Epoch time: 91.84 s 
-2024-08-27 18:58:06.068865:  
-2024-08-27 18:58:06.069029: Epoch 238 
-2024-08-27 18:58:06.069123: Current learning rate: 0.00892 
-2024-08-27 18:59:43.446096: train_loss -0.7352 
-2024-08-27 18:59:43.446334: val_loss -0.7602 
-2024-08-27 18:59:43.446511: Pseudo dice [0.0, 0.0, 0.8512, 0.9739, 0.8239, 0.9343, 0.933, 0.9558, 0.9452, 0.9438, 0.9184, 0.9545, 0.949, 0.7928, 0.9455, 0.9119, 0.7857, 0.7873, nan] 
-2024-08-27 18:59:43.446604: Epoch time: 97.38 s 
-2024-08-27 18:59:44.685690:  
-2024-08-27 18:59:44.685870: Epoch 239 
-2024-08-27 18:59:44.685960: Current learning rate: 0.00892 
-2024-08-27 19:01:18.191436: train_loss -0.7422 
-2024-08-27 19:01:18.191661: val_loss -0.7647 
-2024-08-27 19:01:18.191833: Pseudo dice [0.0, 0.0, 0.8832, 0.9747, 0.7778, 0.939, 0.9359, 0.9577, 0.941, 0.944, 0.9208, 0.9541, 0.9521, 0.819, 0.9474, 0.9121, 0.7923, 0.7944, nan] 
-2024-08-27 19:01:18.191920: Epoch time: 93.51 s 
-2024-08-27 19:01:19.439896:  
-2024-08-27 19:01:19.440058: Epoch 240 
-2024-08-27 19:01:19.440153: Current learning rate: 0.00891 
-2024-08-27 19:02:51.697803: train_loss -0.7421 
-2024-08-27 19:02:51.698066: val_loss -0.7615 
-2024-08-27 19:02:51.698241: Pseudo dice [0.0, 0.0, 0.8696, 0.9758, 0.8044, 0.9303, 0.9383, 0.9611, 0.9429, 0.9399, 0.9167, 0.9553, 0.9527, 0.814, 0.9482, 0.9229, 0.8086, 0.8034, nan] 
-2024-08-27 19:02:51.698335: Epoch time: 92.26 s 
-2024-08-27 19:02:52.988116:  
-2024-08-27 19:02:52.988312: Epoch 241 
-2024-08-27 19:02:52.988415: Current learning rate: 0.00891 
-2024-08-27 19:04:22.661630: train_loss -0.7423 
-2024-08-27 19:04:22.661912: val_loss -0.76 
-2024-08-27 19:04:22.662117: Pseudo dice [0.0, 0.0, 0.8902, 0.9756, 0.8033, 0.9364, 0.9377, 0.9575, 0.9393, 0.9398, 0.9146, 0.9506, 0.9493, 0.8006, 0.9464, 0.923, 0.8045, 0.8128, nan] 
-2024-08-27 19:04:22.662366: Epoch time: 89.67 s 
-2024-08-27 19:04:23.981816:  
-2024-08-27 19:04:23.982127: Epoch 242 
-2024-08-27 19:04:23.982230: Current learning rate: 0.0089 
-2024-08-27 19:06:00.995463: train_loss -0.7382 
-2024-08-27 19:06:00.995716: val_loss -0.7684 
-2024-08-27 19:06:00.995890: Pseudo dice [0.0, 0.0, 0.895, 0.9766, 0.8025, 0.9357, 0.9423, 0.9618, 0.9466, 0.944, 0.9161, 0.9553, 0.9503, 0.8339, 0.9454, 0.9281, 0.7941, 0.7765, nan] 
-2024-08-27 19:06:00.995981: Epoch time: 97.01 s 
-2024-08-27 19:06:00.996031: Yayy! New best EMA pseudo Dice: 0.8004 
-2024-08-27 19:06:02.809510:  
-2024-08-27 19:06:02.809714: Epoch 243 
-2024-08-27 19:06:02.809811: Current learning rate: 0.0089 
-2024-08-27 19:07:35.248874: train_loss -0.7444 
-2024-08-27 19:07:35.249373: val_loss -0.7657 
-2024-08-27 19:07:35.249558: Pseudo dice [0.0, 0.0, 0.89, 0.9754, 0.822, 0.9405, 0.9407, 0.956, 0.9446, 0.9399, 0.9166, 0.9543, 0.954, 0.8159, 0.9483, 0.926, 0.8026, 0.7952, nan] 
-2024-08-27 19:07:35.249640: Epoch time: 92.44 s 
-2024-08-27 19:07:35.249688: Yayy! New best EMA pseudo Dice: 0.801 
-2024-08-27 19:07:36.717694:  
-2024-08-27 19:07:36.717972: Epoch 244 
-2024-08-27 19:07:36.718077: Current learning rate: 0.00889 
-2024-08-27 19:09:17.411088: train_loss -0.7417 
-2024-08-27 19:09:17.411339: val_loss -0.76 
-2024-08-27 19:09:17.411523: Pseudo dice [0.0, 0.0, 0.8768, 0.9761, 0.8138, 0.9446, 0.9483, 0.9606, 0.9391, 0.9315, 0.9196, 0.9513, 0.9512, 0.8283, 0.9471, 0.9244, 0.7905, 0.7787, nan] 
-2024-08-27 19:09:17.411622: Epoch time: 100.69 s 
-2024-08-27 19:09:17.411680: Yayy! New best EMA pseudo Dice: 0.8014 
-2024-08-27 19:09:19.526134:  
-2024-08-27 19:09:19.526464: Epoch 245 
-2024-08-27 19:09:19.526567: Current learning rate: 0.00889 
-2024-08-27 19:10:52.528072: train_loss -0.7416 
-2024-08-27 19:10:52.528309: val_loss -0.7643 
-2024-08-27 19:10:52.528526: Pseudo dice [0.0, 0.0, 0.8868, 0.9749, 0.7853, 0.941, 0.9365, 0.958, 0.9475, 0.9363, 0.9131, 0.9563, 0.9526, 0.8174, 0.943, 0.9148, 0.7992, 0.7846, nan] 
-2024-08-27 19:10:52.529174: Epoch time: 93.0 s 
-2024-08-27 19:10:52.529363: Yayy! New best EMA pseudo Dice: 0.8015 
-2024-08-27 19:10:54.225310:  
-2024-08-27 19:10:54.225483: Epoch 246 
-2024-08-27 19:10:54.225574: Current learning rate: 0.00889 
-2024-08-27 19:12:26.324051: train_loss -0.7412 
-2024-08-27 19:12:26.324301: val_loss -0.7711 
-2024-08-27 19:12:26.324473: Pseudo dice [0.0, 0.0, 0.9016, 0.9766, 0.7785, 0.9418, 0.9476, 0.9526, 0.9468, 0.937, 0.9114, 0.9568, 0.9517, 0.8232, 0.9497, 0.9236, 0.815, 0.8057, nan] 
-2024-08-27 19:12:26.324564: Epoch time: 92.1 s 
-2024-08-27 19:12:26.324617: Yayy! New best EMA pseudo Dice: 0.802 
-2024-08-27 19:12:27.848485:  
-2024-08-27 19:12:27.848634: Epoch 247 
-2024-08-27 19:12:27.848720: Current learning rate: 0.00888 
-2024-08-27 19:13:57.752200: train_loss -0.7395 
-2024-08-27 19:13:57.752869: val_loss -0.7591 
-2024-08-27 19:13:57.753342: Pseudo dice [0.0, 0.0, 0.8871, 0.9761, 0.805, 0.9404, 0.9468, 0.9581, 0.9428, 0.9346, 0.9151, 0.9549, 0.9542, 0.829, 0.9412, 0.9232, 0.7784, 0.8093, nan] 
-2024-08-27 19:13:57.753504: Epoch time: 89.9 s 
-2024-08-27 19:13:57.753727: Yayy! New best EMA pseudo Dice: 0.8024 
-2024-08-27 19:13:59.644647:  
-2024-08-27 19:13:59.645056: Epoch 248 
-2024-08-27 19:13:59.645200: Current learning rate: 0.00888 
-2024-08-27 19:15:30.810202: train_loss -0.7479 
-2024-08-27 19:15:30.810465: val_loss -0.769 
-2024-08-27 19:15:30.810641: Pseudo dice [0.0, 0.0, 0.8823, 0.9751, 0.8319, 0.9432, 0.9416, 0.9615, 0.9435, 0.9448, 0.9213, 0.9544, 0.9557, 0.8241, 0.9415, 0.9241, 0.8046, 0.7882, nan] 
-2024-08-27 19:15:30.810736: Epoch time: 91.17 s 
-2024-08-27 19:15:30.810790: Yayy! New best EMA pseudo Dice: 0.8029 
-2024-08-27 19:15:32.405059:  
-2024-08-27 19:15:32.405238: Epoch 249 
-2024-08-27 19:15:32.405332: Current learning rate: 0.00887 
-2024-08-27 19:17:02.301138: train_loss -0.7418 
-2024-08-27 19:17:02.301394: val_loss -0.7705 
-2024-08-27 19:17:02.301571: Pseudo dice [0.0, 0.0, 0.8521, 0.9777, 0.8087, 0.937, 0.9435, 0.9592, 0.9488, 0.9464, 0.9301, 0.9559, 0.958, 0.8205, 0.941, 0.9151, 0.7896, 0.7823, nan] 
-2024-08-27 19:17:02.301667: Epoch time: 89.9 s 
-2024-08-27 19:17:02.602122: Yayy! New best EMA pseudo Dice: 0.803 
-2024-08-27 19:17:04.130900:  
-2024-08-27 19:17:04.131246: Epoch 250 
-2024-08-27 19:17:04.131340: Current learning rate: 0.00887 
-2024-08-27 19:18:39.154711: train_loss -0.7375 
-2024-08-27 19:18:39.154993: val_loss -0.7634 
-2024-08-27 19:18:39.155175: Pseudo dice [0.0, 0.0, 0.8736, 0.9755, 0.806, 0.9439, 0.9409, 0.9594, 0.9399, 0.9412, 0.9186, 0.9513, 0.9537, 0.8184, 0.9482, 0.9237, 0.7899, 0.8024, nan] 
-2024-08-27 19:18:39.155273: Epoch time: 95.02 s 
-2024-08-27 19:18:39.155326: Yayy! New best EMA pseudo Dice: 0.8031 
-2024-08-27 19:18:40.799286:  
-2024-08-27 19:18:40.799462: Epoch 251 
-2024-08-27 19:18:40.799567: Current learning rate: 0.00886 
-2024-08-27 19:20:12.522129: train_loss -0.7442 
-2024-08-27 19:20:12.522428: val_loss -0.7546 
-2024-08-27 19:20:12.522633: Pseudo dice [0.0, 0.0, 0.8686, 0.9767, 0.8093, 0.9379, 0.9415, 0.9504, 0.9357, 0.9366, 0.9045, 0.9443, 0.942, 0.7891, 0.9446, 0.9131, 0.7799, 0.7666, nan] 
-2024-08-27 19:20:12.522742: Epoch time: 91.72 s 
-2024-08-27 19:20:13.875089:  
-2024-08-27 19:20:13.875277: Epoch 252 
-2024-08-27 19:20:13.875392: Current learning rate: 0.00886 
-2024-08-27 19:21:45.322551: train_loss -0.741 
-2024-08-27 19:21:45.322801: val_loss -0.7594 
-2024-08-27 19:21:45.322985: Pseudo dice [0.0, 0.0, 0.8937, 0.9728, 0.815, 0.9389, 0.9386, 0.9594, 0.9448, 0.944, 0.9124, 0.9565, 0.9523, 0.8143, 0.9368, 0.9164, 0.804, 0.8013, nan] 
-2024-08-27 19:21:45.323078: Epoch time: 91.45 s 
-2024-08-27 19:21:46.939268:  
-2024-08-27 19:21:46.939612: Epoch 253 
-2024-08-27 19:21:46.939775: Current learning rate: 0.00885 
-2024-08-27 19:23:17.991721: train_loss -0.7373 
-2024-08-27 19:23:17.992141: val_loss -0.7579 
-2024-08-27 19:23:17.992328: Pseudo dice [0.0, 0.0, 0.8791, 0.9734, 0.8162, 0.9402, 0.9415, 0.9584, 0.9372, 0.9444, 0.9156, 0.951, 0.9514, 0.8129, 0.9415, 0.913, 0.7917, 0.7893, nan] 
-2024-08-27 19:23:17.992418: Epoch time: 91.05 s 
-2024-08-27 19:23:19.279457:  
-2024-08-27 19:23:19.279815: Epoch 254 
-2024-08-27 19:23:19.279913: Current learning rate: 0.00885 
-2024-08-27 19:24:55.052705: train_loss -0.74 
-2024-08-27 19:24:55.052964: val_loss -0.7622 
-2024-08-27 19:24:55.053156: Pseudo dice [0.0, 0.0, 0.8522, 0.9741, 0.7717, 0.9378, 0.9451, 0.96, 0.9396, 0.9445, 0.9193, 0.9469, 0.9518, 0.8149, 0.9483, 0.9222, 0.8005, 0.7906, nan] 
-2024-08-27 19:24:55.053256: Epoch time: 95.77 s 
-2024-08-27 19:24:56.407202:  
-2024-08-27 19:24:56.407440: Epoch 255 
-2024-08-27 19:24:56.407546: Current learning rate: 0.00884 
-2024-08-27 19:26:28.086089: train_loss -0.7351 
-2024-08-27 19:26:28.086317: val_loss -0.7588 
-2024-08-27 19:26:28.086485: Pseudo dice [0.0, 0.0, 0.8716, 0.9752, 0.7965, 0.9406, 0.9402, 0.9576, 0.9393, 0.925, 0.9142, 0.9516, 0.9519, 0.797, 0.95, 0.9118, 0.786, 0.7682, nan] 
-2024-08-27 19:26:28.086571: Epoch time: 91.68 s 
-2024-08-27 19:26:29.349445:  
-2024-08-27 19:26:29.349747: Epoch 256 
-2024-08-27 19:26:29.349845: Current learning rate: 0.00884 
-2024-08-27 19:28:10.637864: train_loss -0.7397 
-2024-08-27 19:28:10.638103: val_loss -0.7598 
-2024-08-27 19:28:10.638272: Pseudo dice [0.0, 0.0, 0.8875, 0.9744, 0.8228, 0.9325, 0.9282, 0.9589, 0.9282, 0.9312, 0.9121, 0.9394, 0.9436, 0.7867, 0.9335, 0.9195, 0.813, 0.7966, nan] 
-2024-08-27 19:28:10.638361: Epoch time: 101.29 s 
-2024-08-27 19:28:11.899883:  
-2024-08-27 19:28:11.900189: Epoch 257 
-2024-08-27 19:28:11.900295: Current learning rate: 0.00884 
-2024-08-27 19:29:52.519400: train_loss -0.737 
-2024-08-27 19:29:52.519646: val_loss -0.761 
-2024-08-27 19:29:52.519810: Pseudo dice [0.0, 0.0, 0.8847, 0.9724, 0.8219, 0.9421, 0.9454, 0.9622, 0.9449, 0.9278, 0.9147, 0.9536, 0.9518, 0.8184, 0.9503, 0.9208, 0.7823, 0.7927, nan] 
-2024-08-27 19:29:52.519901: Epoch time: 100.62 s 
-2024-08-27 19:29:53.759889:  
-2024-08-27 19:29:53.760147: Epoch 258 
-2024-08-27 19:29:53.760243: Current learning rate: 0.00883 
-2024-08-27 19:31:23.885253: train_loss -0.7446 
-2024-08-27 19:31:23.885486: val_loss -0.7585 
-2024-08-27 19:31:23.885668: Pseudo dice [0.0, 0.0, 0.8652, 0.9752, 0.7964, 0.9382, 0.9391, 0.9619, 0.9383, 0.9318, 0.9201, 0.9529, 0.9553, 0.811, 0.9486, 0.9116, 0.7723, 0.7952, nan] 
-2024-08-27 19:31:23.885768: Epoch time: 90.13 s 
-2024-08-27 19:31:25.153885:  
-2024-08-27 19:31:25.154333: Epoch 259 
-2024-08-27 19:31:25.154430: Current learning rate: 0.00883 
-2024-08-27 19:32:53.770092: train_loss -0.746 
-2024-08-27 19:32:53.770351: val_loss -0.7581 
-2024-08-27 19:32:53.770522: Pseudo dice [0.0, 0.0, 0.866, 0.9755, 0.7918, 0.9386, 0.9448, 0.9574, 0.931, 0.9379, 0.9208, 0.9521, 0.954, 0.8184, 0.9232, 0.9072, 0.8153, 0.797, nan] 
-2024-08-27 19:32:53.770612: Epoch time: 88.62 s 
-2024-08-27 19:32:55.402062:  
-2024-08-27 19:32:55.402374: Epoch 260 
-2024-08-27 19:32:55.402474: Current learning rate: 0.00882 
-2024-08-27 19:34:27.937067: train_loss -0.7425 
-2024-08-27 19:34:27.937354: val_loss -0.7647 
-2024-08-27 19:34:27.937565: Pseudo dice [0.0, 0.0, 0.8726, 0.9742, 0.8284, 0.9357, 0.9424, 0.9624, 0.9437, 0.9363, 0.9131, 0.955, 0.9516, 0.8175, 0.9467, 0.9222, 0.7676, 0.7903, nan] 
-2024-08-27 19:34:27.937675: Epoch time: 92.54 s 
-2024-08-27 19:34:29.485277:  
-2024-08-27 19:34:29.485766: Epoch 261 
-2024-08-27 19:34:29.485954: Current learning rate: 0.00882 
-2024-08-27 19:36:03.135779: train_loss -0.7412 
-2024-08-27 19:36:03.136046: val_loss -0.7612 
-2024-08-27 19:36:03.136215: Pseudo dice [0.0, 0.0, 0.8627, 0.9733, 0.7815, 0.9369, 0.9426, 0.9578, 0.944, 0.9479, 0.9231, 0.9577, 0.9541, 0.8111, 0.9454, 0.9178, 0.8161, 0.8044, nan] 
-2024-08-27 19:36:03.136302: Epoch time: 93.65 s 
-2024-08-27 19:36:04.458144:  
-2024-08-27 19:36:04.458456: Epoch 262 
-2024-08-27 19:36:04.458563: Current learning rate: 0.00881 
-2024-08-27 19:37:37.292051: train_loss -0.7319 
-2024-08-27 19:37:37.292308: val_loss -0.7511 
-2024-08-27 19:37:37.292487: Pseudo dice [0.0, 0.0, 0.8513, 0.975, 0.7976, 0.9249, 0.9304, 0.9553, 0.9399, 0.9323, 0.9087, 0.9461, 0.9495, 0.8081, 0.9311, 0.9147, 0.7911, 0.8015, nan] 
-2024-08-27 19:37:37.292583: Epoch time: 92.83 s 
-2024-08-27 19:37:38.900661:  
-2024-08-27 19:37:38.901002: Epoch 263 
-2024-08-27 19:37:38.901101: Current learning rate: 0.00881 
-2024-08-27 19:39:06.599116: train_loss -0.7336 
-2024-08-27 19:39:06.599437: val_loss -0.7532 
-2024-08-27 19:39:06.599810: Pseudo dice [0.0, 0.0, 0.869, 0.9675, 0.7953, 0.9346, 0.9315, 0.9544, 0.9416, 0.9454, 0.9112, 0.9563, 0.9466, 0.8103, 0.9398, 0.9143, 0.7862, 0.7789, nan] 
-2024-08-27 19:39:06.600008: Epoch time: 87.7 s 
-2024-08-27 19:39:07.909670:  
-2024-08-27 19:39:07.909852: Epoch 264 
-2024-08-27 19:39:07.909953: Current learning rate: 0.0088 
-2024-08-27 19:40:44.635664: train_loss -0.7372 
-2024-08-27 19:40:44.635894: val_loss -0.7511 
-2024-08-27 19:40:44.636065: Pseudo dice [0.0, 0.0, 0.8716, 0.9753, 0.7698, 0.9301, 0.9251, 0.9577, 0.9359, 0.9381, 0.9112, 0.9548, 0.9527, 0.8111, 0.9211, 0.9084, 0.8006, 0.8086, nan] 
-2024-08-27 19:40:44.636158: Epoch time: 96.73 s 
-2024-08-27 19:40:46.167389:  
-2024-08-27 19:40:46.167587: Epoch 265 
-2024-08-27 19:40:46.167677: Current learning rate: 0.0088 
-2024-08-27 19:42:20.382878: train_loss -0.7339 
-2024-08-27 19:42:20.383143: val_loss -0.7533 
-2024-08-27 19:42:20.383321: Pseudo dice [0.0, 0.0, 0.8614, 0.9737, 0.7948, 0.933, 0.9308, 0.9496, 0.9325, 0.9353, 0.914, 0.9481, 0.9486, 0.7975, 0.9202, 0.9103, 0.7993, 0.7993, nan] 
-2024-08-27 19:42:20.383413: Epoch time: 94.22 s 
-2024-08-27 19:42:21.808892:  
-2024-08-27 19:42:21.809592: Epoch 266 
-2024-08-27 19:42:21.809710: Current learning rate: 0.00879 
-2024-08-27 19:43:59.198480: train_loss -0.7327 
-2024-08-27 19:43:59.198720: val_loss -0.7544 
-2024-08-27 19:43:59.198895: Pseudo dice [0.0, 0.0, 0.8732, 0.9729, 0.8116, 0.9401, 0.945, 0.9565, 0.9387, 0.9318, 0.9036, 0.9473, 0.947, 0.805, 0.9401, 0.9201, 0.7988, 0.7705, nan] 
-2024-08-27 19:43:59.198985: Epoch time: 97.39 s 
-2024-08-27 19:44:00.512030:  
-2024-08-27 19:44:00.512212: Epoch 267 
-2024-08-27 19:44:00.512310: Current learning rate: 0.00879 
-2024-08-27 19:45:28.510632: train_loss -0.7393 
-2024-08-27 19:45:28.510906: val_loss -0.7591 
-2024-08-27 19:45:28.511080: Pseudo dice [0.0, 0.0, 0.878, 0.9749, 0.8172, 0.944, 0.9435, 0.9598, 0.9386, 0.939, 0.9226, 0.9527, 0.9529, 0.8282, 0.9472, 0.9263, 0.7955, 0.792, nan] 
-2024-08-27 19:45:28.511173: Epoch time: 88.0 s 
-2024-08-27 19:45:29.859153:  
-2024-08-27 19:45:29.859535: Epoch 268 
-2024-08-27 19:45:29.859638: Current learning rate: 0.00879 
-2024-08-27 19:47:00.889926: train_loss -0.7432 
-2024-08-27 19:47:00.890180: val_loss -0.7606 
-2024-08-27 19:47:00.890361: Pseudo dice [0.0, 0.0, 0.8825, 0.9735, 0.812, 0.9419, 0.945, 0.9606, 0.9479, 0.9453, 0.9195, 0.9569, 0.9531, 0.8049, 0.9289, 0.9168, 0.7993, 0.7968, nan] 
-2024-08-27 19:47:00.890927: Epoch time: 91.03 s 
-2024-08-27 19:47:02.232745:  
-2024-08-27 19:47:02.233059: Epoch 269 
-2024-08-27 19:47:02.233167: Current learning rate: 0.00878 
-2024-08-27 19:48:35.471489: train_loss -0.7427 
-2024-08-27 19:48:35.471746: val_loss -0.7569 
-2024-08-27 19:48:35.471923: Pseudo dice [0.0, 0.0, 0.8712, 0.9753, 0.8283, 0.9372, 0.9435, 0.9575, 0.9378, 0.9422, 0.9004, 0.9486, 0.9473, 0.8128, 0.9433, 0.9197, 0.8031, 0.7745, nan] 
-2024-08-27 19:48:35.472019: Epoch time: 93.24 s 
-2024-08-27 19:48:36.769171:  
-2024-08-27 19:48:36.769327: Epoch 270 
-2024-08-27 19:48:36.769433: Current learning rate: 0.00878 
-2024-08-27 19:50:11.692276: train_loss -0.7446 
-2024-08-27 19:50:11.692536: val_loss -0.7587 
-2024-08-27 19:50:11.692707: Pseudo dice [0.0, 0.0, 0.8854, 0.9759, 0.8249, 0.9347, 0.9392, 0.9582, 0.9354, 0.9328, 0.9141, 0.9484, 0.9461, 0.8041, 0.9453, 0.9225, 0.8048, 0.802, nan] 
-2024-08-27 19:50:11.692792: Epoch time: 94.92 s 
-2024-08-27 19:50:12.965699:  
-2024-08-27 19:50:12.965860: Epoch 271 
-2024-08-27 19:50:12.965952: Current learning rate: 0.00877 
-2024-08-27 19:51:48.201872: train_loss -0.7387 
-2024-08-27 19:51:48.202116: val_loss -0.7617 
-2024-08-27 19:51:48.202295: Pseudo dice [0.0, 0.0, 0.8762, 0.9726, 0.8392, 0.9411, 0.9392, 0.961, 0.9419, 0.9504, 0.9258, 0.9512, 0.9542, 0.8208, 0.9358, 0.9174, 0.8047, 0.7799, nan] 
-2024-08-27 19:51:48.202427: Epoch time: 95.24 s 
-2024-08-27 19:51:49.715760:  
-2024-08-27 19:51:49.715935: Epoch 272 
-2024-08-27 19:51:49.716029: Current learning rate: 0.00877 
-2024-08-27 19:53:20.515332: train_loss -0.7376 
-2024-08-27 19:53:20.515590: val_loss -0.767 
-2024-08-27 19:53:20.515776: Pseudo dice [0.0, 0.0, 0.8853, 0.9753, 0.8297, 0.9439, 0.9466, 0.9611, 0.9414, 0.9436, 0.9179, 0.9538, 0.952, 0.8129, 0.9537, 0.9246, 0.8049, 0.7968, nan] 
-2024-08-27 19:53:20.515874: Epoch time: 90.8 s 
-2024-08-27 19:53:22.141314:  
-2024-08-27 19:53:22.141497: Epoch 273 
-2024-08-27 19:53:22.141593: Current learning rate: 0.00876 
-2024-08-27 19:54:59.647178: train_loss -0.7346 
-2024-08-27 19:54:59.647413: val_loss -0.7495 
-2024-08-27 19:54:59.647582: Pseudo dice [0.0, 0.0, 0.8638, 0.9743, 0.7863, 0.9372, 0.9349, 0.9542, 0.9431, 0.9387, 0.8983, 0.9508, 0.9413, 0.8123, 0.934, 0.907, 0.7737, 0.7293, nan] 
-2024-08-27 19:54:59.647668: Epoch time: 97.51 s 
-2024-08-27 19:55:00.926005:  
-2024-08-27 19:55:00.926201: Epoch 274 
-2024-08-27 19:55:00.926302: Current learning rate: 0.00876 
-2024-08-27 19:56:34.540990: train_loss -0.7319 
-2024-08-27 19:56:34.541287: val_loss -0.7602 
-2024-08-27 19:56:34.541580: Pseudo dice [0.0, 0.0, 0.8816, 0.9753, 0.7905, 0.94, 0.9391, 0.9545, 0.9443, 0.9388, 0.9167, 0.9517, 0.9522, 0.7912, 0.9477, 0.916, 0.8025, 0.7771, nan] 
-2024-08-27 19:56:34.541678: Epoch time: 93.62 s 
-2024-08-27 19:56:35.885321:  
-2024-08-27 19:56:35.885546: Epoch 275 
-2024-08-27 19:56:35.885654: Current learning rate: 0.00875 
-2024-08-27 19:58:10.673749: train_loss -0.7381 
-2024-08-27 19:58:10.674002: val_loss -0.757 
-2024-08-27 19:58:10.674178: Pseudo dice [0.0, 0.0, 0.859, 0.975, 0.7899, 0.9404, 0.9446, 0.9611, 0.9447, 0.9247, 0.9084, 0.9537, 0.9482, 0.8281, 0.9479, 0.9232, 0.7661, 0.7474, nan] 
-2024-08-27 19:58:10.674267: Epoch time: 94.79 s 
-2024-08-27 19:58:11.982856:  
-2024-08-27 19:58:11.983079: Epoch 276 
-2024-08-27 19:58:11.983240: Current learning rate: 0.00875 
-2024-08-27 19:59:46.679855: train_loss -0.7401 
-2024-08-27 19:59:46.680107: val_loss -0.7539 
-2024-08-27 19:59:46.680288: Pseudo dice [0.0, 0.0, 0.8946, 0.9746, 0.8254, 0.9395, 0.9432, 0.9557, 0.9346, 0.9326, 0.908, 0.9437, 0.9509, 0.7952, 0.9512, 0.9218, 0.8098, 0.7969, nan] 
-2024-08-27 19:59:46.680382: Epoch time: 94.7 s 
-2024-08-27 19:59:48.234740:  
-2024-08-27 19:59:48.234983: Epoch 277 
-2024-08-27 19:59:48.235072: Current learning rate: 0.00874 
-2024-08-27 20:01:26.075114: train_loss -0.7382 
-2024-08-27 20:01:26.075393: val_loss -0.7652 
-2024-08-27 20:01:26.075613: Pseudo dice [0.0, 0.0, 0.8814, 0.9753, 0.8088, 0.9455, 0.9415, 0.9593, 0.9437, 0.9416, 0.9198, 0.9559, 0.9549, 0.8147, 0.9498, 0.9165, 0.7957, 0.7855, nan] 
-2024-08-27 20:01:26.075711: Epoch time: 97.84 s 
-2024-08-27 20:01:27.353677:  
-2024-08-27 20:01:27.354146: Epoch 278 
-2024-08-27 20:01:27.354245: Current learning rate: 0.00874 
-2024-08-27 20:03:01.224283: train_loss -0.7345 
-2024-08-27 20:03:01.224519: val_loss -0.7548 
-2024-08-27 20:03:01.224680: Pseudo dice [0.0, 0.0, 0.8523, 0.9622, 0.7843, 0.9349, 0.9384, 0.9595, 0.9397, 0.9364, 0.9112, 0.9495, 0.948, 0.8079, 0.9419, 0.9014, 0.8113, 0.7962, nan] 
-2024-08-27 20:03:01.224763: Epoch time: 93.87 s 
-2024-08-27 20:03:02.489922:  
-2024-08-27 20:03:02.490321: Epoch 279 
-2024-08-27 20:03:02.490516: Current learning rate: 0.00874 
-2024-08-27 20:04:33.288011: train_loss -0.7326 
-2024-08-27 20:04:33.288265: val_loss -0.7583 
-2024-08-27 20:04:33.288460: Pseudo dice [0.0, 0.0, 0.8649, 0.9742, 0.8226, 0.9465, 0.9481, 0.9601, 0.94, 0.9332, 0.9132, 0.9517, 0.9514, 0.8168, 0.9387, 0.923, 0.7914, 0.7937, nan] 
-2024-08-27 20:04:33.288561: Epoch time: 90.8 s 
-2024-08-27 20:04:34.695026:  
-2024-08-27 20:04:34.695218: Epoch 280 
-2024-08-27 20:04:34.695348: Current learning rate: 0.00873 
-2024-08-27 20:06:13.416494: train_loss -0.7322 
-2024-08-27 20:06:13.416759: val_loss -0.7381 
-2024-08-27 20:06:13.416922: Pseudo dice [0.0, 0.0, 0.8478, 0.9751, 0.7478, 0.9259, 0.9318, 0.9455, 0.9384, 0.9357, 0.8787, 0.948, 0.9392, 0.7715, 0.9394, 0.9045, 0.7622, 0.7582, nan] 
-2024-08-27 20:06:13.417011: Epoch time: 98.72 s 
-2024-08-27 20:06:14.702319:  
-2024-08-27 20:06:14.702486: Epoch 281 
-2024-08-27 20:06:14.702579: Current learning rate: 0.00873 
-2024-08-27 20:07:48.251433: train_loss -0.72 
-2024-08-27 20:07:48.251712: val_loss -0.7444 
-2024-08-27 20:07:48.251884: Pseudo dice [0.0, 0.0, 0.8727, 0.9752, 0.7889, 0.9234, 0.9216, 0.9556, 0.9216, 0.9138, 0.8944, 0.9365, 0.9362, 0.7947, 0.9358, 0.9132, 0.7975, 0.77, nan] 
-2024-08-27 20:07:48.251980: Epoch time: 93.55 s 
-2024-08-27 20:07:49.632649:  
-2024-08-27 20:07:49.632846: Epoch 282 
-2024-08-27 20:07:49.632957: Current learning rate: 0.00872 
-2024-08-27 20:09:20.390899: train_loss -0.7251 
-2024-08-27 20:09:20.391145: val_loss -0.7454 
-2024-08-27 20:09:20.391332: Pseudo dice [0.0, 0.0, 0.8599, 0.9742, 0.7591, 0.9341, 0.9384, 0.9523, 0.9319, 0.9223, 0.9062, 0.9503, 0.9386, 0.7958, 0.9328, 0.9124, 0.7936, 0.7816, nan] 
-2024-08-27 20:09:20.391433: Epoch time: 90.76 s 
-2024-08-27 20:09:22.151111:  
-2024-08-27 20:09:22.151484: Epoch 283 
-2024-08-27 20:09:22.151607: Current learning rate: 0.00872 
-2024-08-27 20:10:53.257643: train_loss -0.7328 
-2024-08-27 20:10:53.257873: val_loss -0.7561 
-2024-08-27 20:10:53.258038: Pseudo dice [0.0, 0.0, 0.8773, 0.9709, 0.8085, 0.9342, 0.93, 0.9584, 0.938, 0.9178, 0.9004, 0.9522, 0.9447, 0.8084, 0.9413, 0.9135, 0.8003, 0.7892, nan] 
-2024-08-27 20:10:53.258122: Epoch time: 91.11 s 
-2024-08-27 20:10:54.556872:  
-2024-08-27 20:10:54.557073: Epoch 284 
-2024-08-27 20:10:54.557174: Current learning rate: 0.00871 
-2024-08-27 20:12:25.194083: train_loss -0.7385 
-2024-08-27 20:12:25.194340: val_loss -0.7546 
-2024-08-27 20:12:25.194514: Pseudo dice [0.0, 0.0, 0.889, 0.9744, 0.8088, 0.9338, 0.9312, 0.9586, 0.9368, 0.9308, 0.9151, 0.9481, 0.9518, 0.7979, 0.94, 0.9154, 0.7775, 0.7614, nan] 
-2024-08-27 20:12:25.194607: Epoch time: 90.64 s 
-2024-08-27 20:12:26.541053:  
-2024-08-27 20:12:26.541239: Epoch 285 
-2024-08-27 20:12:26.541340: Current learning rate: 0.00871 
-2024-08-27 20:14:02.427838: train_loss -0.7375 
-2024-08-27 20:14:02.428142: val_loss -0.7621 
-2024-08-27 20:14:02.428622: Pseudo dice [0.0, 0.0, 0.8673, 0.9753, 0.8137, 0.9366, 0.9408, 0.9589, 0.9475, 0.9378, 0.9131, 0.9546, 0.9495, 0.8164, 0.9367, 0.9168, 0.7901, 0.8023, nan] 
-2024-08-27 20:14:02.428795: Epoch time: 95.89 s 
-2024-08-27 20:14:03.655850:  
-2024-08-27 20:14:03.656081: Epoch 286 
-2024-08-27 20:14:03.656179: Current learning rate: 0.0087 
-2024-08-27 20:15:34.115229: train_loss -0.731 
-2024-08-27 20:15:34.115482: val_loss -0.7547 
-2024-08-27 20:15:34.115655: Pseudo dice [0.0, 0.0, 0.8841, 0.9757, 0.8078, 0.9366, 0.9415, 0.9559, 0.938, 0.9331, 0.914, 0.9506, 0.9502, 0.807, 0.941, 0.9194, 0.8147, 0.8018, nan] 
-2024-08-27 20:15:34.115743: Epoch time: 90.46 s 
-2024-08-27 20:15:35.452513:  
-2024-08-27 20:15:35.452725: Epoch 287 
-2024-08-27 20:15:35.452829: Current learning rate: 0.0087 
-2024-08-27 20:17:10.911998: train_loss -0.7364 
-2024-08-27 20:17:10.912274: val_loss -0.7609 
-2024-08-27 20:17:10.912470: Pseudo dice [0.0, 0.0, 0.8847, 0.9761, 0.8247, 0.9473, 0.9469, 0.9583, 0.9382, 0.9434, 0.9214, 0.9551, 0.9558, 0.8266, 0.9486, 0.9159, 0.7817, 0.7855, nan] 
-2024-08-27 20:17:10.912572: Epoch time: 95.46 s 
-2024-08-27 20:17:12.208452:  
-2024-08-27 20:17:12.208615: Epoch 288 
-2024-08-27 20:17:12.208715: Current learning rate: 0.00869 
-2024-08-27 20:18:46.527580: train_loss -0.7358 
-2024-08-27 20:18:46.527816: val_loss -0.7554 
-2024-08-27 20:18:46.527984: Pseudo dice [0.0, 0.0, 0.8847, 0.9685, 0.8299, 0.9291, 0.9306, 0.9543, 0.9476, 0.9298, 0.9049, 0.9573, 0.9485, 0.822, 0.9477, 0.9132, 0.7959, 0.7762, nan] 
-2024-08-27 20:18:46.528068: Epoch time: 94.32 s 
-2024-08-27 20:18:48.081068:  
-2024-08-27 20:18:48.081455: Epoch 289 
-2024-08-27 20:18:48.081560: Current learning rate: 0.00869 
-2024-08-27 20:20:24.069021: train_loss -0.74 
-2024-08-27 20:20:24.069324: val_loss -0.7615 
-2024-08-27 20:20:24.069560: Pseudo dice [0.0, 0.0, 0.8782, 0.9749, 0.8037, 0.9367, 0.9377, 0.9636, 0.9375, 0.9372, 0.9198, 0.9472, 0.9448, 0.8235, 0.9419, 0.928, 0.7992, 0.8061, nan] 
-2024-08-27 20:20:24.069674: Epoch time: 95.99 s 
-2024-08-27 20:20:25.511129:  
-2024-08-27 20:20:25.511301: Epoch 290 
-2024-08-27 20:20:25.511395: Current learning rate: 0.00868 
-2024-08-27 20:22:02.078483: train_loss -0.7421 
-2024-08-27 20:22:02.078748: val_loss -0.7615 
-2024-08-27 20:22:02.078923: Pseudo dice [0.0, 0.0, 0.8876, 0.9758, 0.8176, 0.9243, 0.9289, 0.9574, 0.9355, 0.9347, 0.9037, 0.943, 0.9451, 0.8176, 0.941, 0.9164, 0.8113, 0.7966, nan] 
-2024-08-27 20:22:02.079021: Epoch time: 96.57 s 
-2024-08-27 20:22:03.416230:  
-2024-08-27 20:22:03.416402: Epoch 291 
-2024-08-27 20:22:03.416509: Current learning rate: 0.00868 
-2024-08-27 20:23:39.877825: train_loss -0.7431 
-2024-08-27 20:23:39.878069: val_loss -0.7672 
-2024-08-27 20:23:39.878244: Pseudo dice [0.0, 0.0, 0.8925, 0.9751, 0.807, 0.9322, 0.9382, 0.9551, 0.9463, 0.9412, 0.913, 0.9586, 0.9526, 0.8066, 0.943, 0.9219, 0.7697, 0.7512, nan] 
-2024-08-27 20:23:39.878339: Epoch time: 96.46 s 
-2024-08-27 20:23:41.222288:  
-2024-08-27 20:23:41.222774: Epoch 292 
-2024-08-27 20:23:41.222873: Current learning rate: 0.00868 
-2024-08-27 20:25:11.659767: train_loss -0.739 
-2024-08-27 20:25:11.660000: val_loss -0.7584 
-2024-08-27 20:25:11.660164: Pseudo dice [0.0, 0.0, 0.8782, 0.9748, 0.8077, 0.9356, 0.9351, 0.9487, 0.9456, 0.9393, 0.9147, 0.9558, 0.9508, 0.8045, 0.9209, 0.9135, 0.7935, 0.7779, nan] 
-2024-08-27 20:25:11.660248: Epoch time: 90.44 s 
-2024-08-27 20:25:12.900802:  
-2024-08-27 20:25:12.901098: Epoch 293 
-2024-08-27 20:25:12.901199: Current learning rate: 0.00867 
-2024-08-27 20:26:45.111893: train_loss -0.7383 
-2024-08-27 20:26:45.112136: val_loss -0.7671 
-2024-08-27 20:26:45.112316: Pseudo dice [0.0, 0.0, 0.8876, 0.9745, 0.794, 0.9456, 0.938, 0.9597, 0.9443, 0.9486, 0.9199, 0.9536, 0.9548, 0.8038, 0.9482, 0.9191, 0.7857, 0.766, nan] 
-2024-08-27 20:26:45.112405: Epoch time: 92.21 s 
-2024-08-27 20:26:46.436840:  
-2024-08-27 20:26:46.437099: Epoch 294 
-2024-08-27 20:26:46.437279: Current learning rate: 0.00867 
-2024-08-27 20:28:21.379508: train_loss -0.7416 
-2024-08-27 20:28:21.379752: val_loss -0.7562 
-2024-08-27 20:28:21.379935: Pseudo dice [0.0, 0.0, 0.8711, 0.9751, 0.7909, 0.9442, 0.9479, 0.9594, 0.9417, 0.9352, 0.9097, 0.9517, 0.9495, 0.8132, 0.934, 0.9227, 0.8022, 0.7948, nan] 
-2024-08-27 20:28:21.380032: Epoch time: 94.94 s 
-2024-08-27 20:28:22.714094:  
-2024-08-27 20:28:22.714387: Epoch 295 
-2024-08-27 20:28:22.714490: Current learning rate: 0.00866 
-2024-08-27 20:29:57.623467: train_loss -0.7403 
-2024-08-27 20:29:57.623723: val_loss -0.7633 
-2024-08-27 20:29:57.623896: Pseudo dice [0.0, 0.0, 0.8398, 0.9753, 0.8163, 0.9432, 0.9459, 0.9593, 0.9433, 0.9483, 0.9203, 0.9534, 0.9561, 0.8204, 0.9309, 0.9206, 0.7858, 0.7908, nan] 
-2024-08-27 20:29:57.623985: Epoch time: 94.91 s 
-2024-08-27 20:29:58.962473:  
-2024-08-27 20:29:58.962845: Epoch 296 
-2024-08-27 20:29:58.962945: Current learning rate: 0.00866 
-2024-08-27 20:31:33.687466: train_loss -0.7411 
-2024-08-27 20:31:33.687719: val_loss -0.757 
-2024-08-27 20:31:33.687887: Pseudo dice [0.0, 0.0, 0.857, 0.9758, 0.7958, 0.9196, 0.9281, 0.9577, 0.93, 0.9145, 0.9114, 0.943, 0.9458, 0.7981, 0.9399, 0.9159, 0.7932, 0.7905, nan] 
-2024-08-27 20:31:33.687980: Epoch time: 94.73 s 
-2024-08-27 20:31:35.117447:  
-2024-08-27 20:31:35.117747: Epoch 297 
-2024-08-27 20:31:35.117858: Current learning rate: 0.00865 
-2024-08-27 20:33:12.264451: train_loss -0.7357 
-2024-08-27 20:33:12.264687: val_loss -0.7513 
-2024-08-27 20:33:12.264853: Pseudo dice [0.0, 0.0, 0.8624, 0.9755, 0.8138, 0.9322, 0.9387, 0.9529, 0.9262, 0.9181, 0.9046, 0.9378, 0.9395, 0.8055, 0.941, 0.9154, 0.795, 0.7854, nan] 
-2024-08-27 20:33:12.264939: Epoch time: 97.15 s 
-2024-08-27 20:33:13.531697:  
-2024-08-27 20:33:13.531863: Epoch 298 
-2024-08-27 20:33:13.531954: Current learning rate: 0.00865 
-2024-08-27 20:34:49.060125: train_loss -0.7429 
-2024-08-27 20:34:49.060425: val_loss -0.7673 
-2024-08-27 20:34:49.060624: Pseudo dice [0.0, 0.0, 0.8703, 0.9751, 0.8327, 0.9431, 0.9468, 0.9589, 0.9474, 0.9464, 0.9091, 0.954, 0.9531, 0.821, 0.9338, 0.9257, 0.8031, 0.7887, nan] 
-2024-08-27 20:34:49.060726: Epoch time: 95.53 s 
-2024-08-27 20:34:50.360787:  
-2024-08-27 20:34:50.360961: Epoch 299 
-2024-08-27 20:34:50.361069: Current learning rate: 0.00864 
-2024-08-27 20:36:21.000922: train_loss -0.7424 
-2024-08-27 20:36:21.001153: val_loss -0.7569 
-2024-08-27 20:36:21.001323: Pseudo dice [0.0, 0.0, 0.8665, 0.9762, 0.8011, 0.9306, 0.9375, 0.9532, 0.9284, 0.9317, 0.9063, 0.9427, 0.9395, 0.8117, 0.9423, 0.922, 0.7996, 0.7714, nan] 
-2024-08-27 20:36:21.001411: Epoch time: 90.64 s 
-2024-08-27 20:36:22.585448:  
-2024-08-27 20:36:22.585606: Epoch 300 
-2024-08-27 20:36:22.585700: Current learning rate: 0.00864 
-2024-08-27 20:37:53.643342: train_loss -0.7436 
-2024-08-27 20:37:53.643587: val_loss -0.7586 
-2024-08-27 20:37:53.643764: Pseudo dice [0.0, 0.0, 0.871, 0.9756, 0.8462, 0.9388, 0.9454, 0.9597, 0.9424, 0.9313, 0.9162, 0.9502, 0.955, 0.8215, 0.948, 0.915, 0.7849, 0.7707, nan] 
-2024-08-27 20:37:53.643853: Epoch time: 91.06 s 
-2024-08-27 20:37:55.300291:  
-2024-08-27 20:37:55.300499: Epoch 301 
-2024-08-27 20:37:55.300601: Current learning rate: 0.00863 
-2024-08-27 20:39:26.577198: train_loss -0.7459 
-2024-08-27 20:39:26.577454: val_loss -0.7602 
-2024-08-27 20:39:26.577689: Pseudo dice [0.0, 0.0, 0.89, 0.9758, 0.8114, 0.9386, 0.9373, 0.9561, 0.942, 0.9451, 0.9179, 0.9522, 0.9497, 0.8008, 0.948, 0.9168, 0.8126, 0.8174, nan] 
-2024-08-27 20:39:26.577797: Epoch time: 91.28 s 
-2024-08-27 20:39:27.889220:  
-2024-08-27 20:39:27.889412: Epoch 302 
-2024-08-27 20:39:27.889610: Current learning rate: 0.00863 
-2024-08-27 20:41:00.268659: train_loss -0.7395 
-2024-08-27 20:41:00.268897: val_loss -0.7594 
-2024-08-27 20:41:00.269110: Pseudo dice [0.0, 0.0, 0.8792, 0.9758, 0.8052, 0.9385, 0.941, 0.952, 0.9408, 0.9218, 0.9142, 0.9536, 0.942, 0.8178, 0.9392, 0.903, 0.768, 0.7681, nan] 
-2024-08-27 20:41:00.269201: Epoch time: 92.38 s 
-2024-08-27 20:41:01.590101:  
-2024-08-27 20:41:01.590297: Epoch 303 
-2024-08-27 20:41:01.590397: Current learning rate: 0.00863 
-2024-08-27 20:42:38.237368: train_loss -0.7424 
-2024-08-27 20:42:38.237619: val_loss -0.7614 
-2024-08-27 20:42:38.237793: Pseudo dice [0.0, 0.0, 0.9016, 0.9752, 0.8294, 0.9252, 0.9318, 0.961, 0.9409, 0.9326, 0.9175, 0.9516, 0.9543, 0.8146, 0.9487, 0.919, 0.8028, 0.8103, nan] 
-2024-08-27 20:42:38.237883: Epoch time: 96.65 s 
-2024-08-27 20:42:39.535951:  
-2024-08-27 20:42:39.536180: Epoch 304 
-2024-08-27 20:42:39.536289: Current learning rate: 0.00862 
-2024-08-27 20:44:11.114607: train_loss -0.7418 
-2024-08-27 20:44:11.114869: val_loss -0.7636 
-2024-08-27 20:44:11.115055: Pseudo dice [0.0, 0.0, 0.8932, 0.9763, 0.8182, 0.9406, 0.9478, 0.9635, 0.9452, 0.9404, 0.9111, 0.9543, 0.9511, 0.8197, 0.9474, 0.9177, 0.7999, 0.7835, nan] 
-2024-08-27 20:44:11.115152: Epoch time: 91.58 s 
-2024-08-27 20:44:12.456173:  
-2024-08-27 20:44:12.456363: Epoch 305 
-2024-08-27 20:44:12.456469: Current learning rate: 0.00862 
-2024-08-27 20:45:48.366159: train_loss -0.7452 
-2024-08-27 20:45:48.366449: val_loss -0.7632 
-2024-08-27 20:45:48.366676: Pseudo dice [0.0, 0.0, 0.8964, 0.9746, 0.7951, 0.9328, 0.9373, 0.9621, 0.9423, 0.9403, 0.9231, 0.9541, 0.9544, 0.815, 0.9466, 0.9225, 0.7926, 0.7986, nan] 
-2024-08-27 20:45:48.366789: Epoch time: 95.91 s 
-2024-08-27 20:45:49.732268:  
-2024-08-27 20:45:49.732441: Epoch 306 
-2024-08-27 20:45:49.732555: Current learning rate: 0.00861 
-2024-08-27 20:47:23.149110: train_loss -0.7437 
-2024-08-27 20:47:23.149329: val_loss -0.7644 
-2024-08-27 20:47:23.149507: Pseudo dice [0.0, 0.0, 0.8814, 0.9753, 0.8342, 0.9361, 0.942, 0.9618, 0.945, 0.9393, 0.9122, 0.9558, 0.9501, 0.8222, 0.9514, 0.9263, 0.825, 0.7999, nan] 
-2024-08-27 20:47:23.149598: Epoch time: 93.42 s 
-2024-08-27 20:47:24.649355:  
-2024-08-27 20:47:24.649583: Epoch 307 
-2024-08-27 20:47:24.649681: Current learning rate: 0.00861 
-2024-08-27 20:48:55.028500: train_loss -0.7473 
-2024-08-27 20:48:55.028707: val_loss -0.764 
-2024-08-27 20:48:55.028870: Pseudo dice [0.0, 0.0, 0.8997, 0.9759, 0.8184, 0.9435, 0.9457, 0.9628, 0.9461, 0.9316, 0.9028, 0.9528, 0.9497, 0.8321, 0.9433, 0.9269, 0.7956, 0.7745, nan] 
-2024-08-27 20:48:55.028954: Epoch time: 90.38 s 
-2024-08-27 20:48:55.029006: Yayy! New best EMA pseudo Dice: 0.8033 
-2024-08-27 20:48:56.583706:  
-2024-08-27 20:48:56.583917: Epoch 308 
-2024-08-27 20:48:56.584011: Current learning rate: 0.0086 
-2024-08-27 20:50:30.392908: train_loss -0.745 
-2024-08-27 20:50:30.393176: val_loss -0.7662 
-2024-08-27 20:50:30.393354: Pseudo dice [0.0, 0.0, 0.8685, 0.976, 0.8319, 0.946, 0.9471, 0.9625, 0.9409, 0.9355, 0.9145, 0.9514, 0.9546, 0.8353, 0.9494, 0.9272, 0.8047, 0.8016, nan] 
-2024-08-27 20:50:30.393737: Epoch time: 93.81 s 
-2024-08-27 20:50:30.393819: Yayy! New best EMA pseudo Dice: 0.8038 
-2024-08-27 20:50:31.966594:  
-2024-08-27 20:50:31.966755: Epoch 309 
-2024-08-27 20:50:31.966854: Current learning rate: 0.0086 
-2024-08-27 20:51:58.133018: train_loss -0.7472 
-2024-08-27 20:51:58.133275: val_loss -0.7623 
-2024-08-27 20:51:58.133457: Pseudo dice [0.0, 0.0, 0.8624, 0.976, 0.8142, 0.9421, 0.9426, 0.9624, 0.9412, 0.946, 0.9238, 0.9533, 0.9567, 0.8168, 0.9328, 0.9197, 0.8059, 0.8096, nan] 
-2024-08-27 20:51:58.133559: Epoch time: 86.17 s 
-2024-08-27 20:51:58.133620: Yayy! New best EMA pseudo Dice: 0.804 
-2024-08-27 20:51:59.760425:  
-2024-08-27 20:51:59.760631: Epoch 310 
-2024-08-27 20:51:59.760730: Current learning rate: 0.00859 
-2024-08-27 20:53:33.746934: train_loss -0.7424 
-2024-08-27 20:53:33.747176: val_loss -0.7659 
-2024-08-27 20:53:33.747353: Pseudo dice [0.0, 0.0, 0.8784, 0.9734, 0.816, 0.9343, 0.9412, 0.9601, 0.943, 0.9377, 0.9164, 0.9569, 0.9589, 0.8238, 0.9477, 0.9277, 0.7816, 0.7648, nan] 
-2024-08-27 20:53:33.747448: Epoch time: 93.99 s 
-2024-08-27 20:53:35.097795:  
-2024-08-27 20:53:35.098189: Epoch 311 
-2024-08-27 20:53:35.098287: Current learning rate: 0.00859 
-2024-08-27 20:55:14.037840: train_loss -0.7458 
-2024-08-27 20:55:14.038123: val_loss -0.7667 
-2024-08-27 20:55:14.038407: Pseudo dice [0.0, 0.0, 0.8802, 0.9769, 0.8224, 0.9411, 0.9458, 0.9613, 0.9432, 0.9457, 0.9291, 0.9536, 0.9576, 0.8307, 0.9331, 0.9286, 0.7982, 0.7913, nan] 
-2024-08-27 20:55:14.038519: Epoch time: 98.94 s 
-2024-08-27 20:55:14.038584: Yayy! New best EMA pseudo Dice: 0.8043 
-2024-08-27 20:55:15.907959:  
-2024-08-27 20:55:15.908384: Epoch 312 
-2024-08-27 20:55:15.908553: Current learning rate: 0.00858 
-2024-08-27 20:56:47.019066: train_loss -0.7456 
-2024-08-27 20:56:47.019313: val_loss -0.7648 
-2024-08-27 20:56:47.019490: Pseudo dice [0.0, 0.0, 0.8692, 0.9763, 0.8284, 0.9445, 0.9463, 0.9595, 0.9441, 0.9451, 0.9209, 0.9539, 0.9542, 0.8285, 0.9495, 0.9239, 0.8033, 0.7993, nan] 
-2024-08-27 20:56:47.019582: Epoch time: 91.11 s 
-2024-08-27 20:56:47.019635: Yayy! New best EMA pseudo Dice: 0.8047 
-2024-08-27 20:56:48.859536:  
-2024-08-27 20:56:48.859962: Epoch 313 
-2024-08-27 20:56:48.860059: Current learning rate: 0.00858 
-2024-08-27 20:58:18.794181: train_loss -0.7509 
-2024-08-27 20:58:18.794424: val_loss -0.7711 
-2024-08-27 20:58:18.794590: Pseudo dice [0.0, 0.0, 0.8898, 0.9775, 0.8541, 0.9491, 0.9529, 0.961, 0.9459, 0.947, 0.9219, 0.9538, 0.9551, 0.8273, 0.9515, 0.9279, 0.8215, 0.8132, nan] 
-2024-08-27 20:58:18.794719: Epoch time: 89.94 s 
-2024-08-27 20:58:18.794778: Yayy! New best EMA pseudo Dice: 0.8056 
-2024-08-27 20:58:20.435187:  
-2024-08-27 20:58:20.435584: Epoch 314 
-2024-08-27 20:58:20.435683: Current learning rate: 0.00858 
-2024-08-27 20:59:54.236359: train_loss -0.7463 
-2024-08-27 20:59:54.236609: val_loss -0.765 
-2024-08-27 20:59:54.236781: Pseudo dice [0.0, 0.0, 0.8943, 0.9765, 0.8168, 0.9431, 0.9461, 0.9602, 0.9435, 0.9454, 0.9234, 0.9527, 0.9527, 0.8254, 0.9466, 0.9273, 0.8085, 0.8114, nan] 
-2024-08-27 20:59:54.236866: Epoch time: 93.8 s 
-2024-08-27 20:59:54.236919: Yayy! New best EMA pseudo Dice: 0.806 
-2024-08-27 20:59:55.814373:  
-2024-08-27 20:59:55.814803: Epoch 315 
-2024-08-27 20:59:55.814904: Current learning rate: 0.00857 
-2024-08-27 21:01:25.628777: train_loss -0.7411 
-2024-08-27 21:01:25.629025: val_loss -0.7619 
-2024-08-27 21:01:25.629192: Pseudo dice [0.0, 0.0, 0.8901, 0.9765, 0.8146, 0.9458, 0.9454, 0.9547, 0.9438, 0.9411, 0.922, 0.9539, 0.95, 0.811, 0.941, 0.9222, 0.8077, 0.797, nan] 
-2024-08-27 21:01:25.629282: Epoch time: 89.82 s 
-2024-08-27 21:01:25.629337: Yayy! New best EMA pseudo Dice: 0.8061 
-2024-08-27 21:01:27.133735:  
-2024-08-27 21:01:27.133899: Epoch 316 
-2024-08-27 21:01:27.133989: Current learning rate: 0.00857 
-2024-08-27 21:03:00.167457: train_loss -0.7408 
-2024-08-27 21:03:00.167681: val_loss -0.7637 
-2024-08-27 21:03:00.167857: Pseudo dice [0.0, 0.0, 0.847, 0.9752, 0.818, 0.9415, 0.9436, 0.9588, 0.9315, 0.9421, 0.9222, 0.9461, 0.956, 0.8294, 0.9489, 0.922, 0.8043, 0.8078, nan] 
-2024-08-27 21:03:00.167940: Epoch time: 93.03 s 
-2024-08-27 21:03:01.534466:  
-2024-08-27 21:03:01.534834: Epoch 317 
-2024-08-27 21:03:01.534940: Current learning rate: 0.00856 
-2024-08-27 21:04:30.069989: train_loss -0.7472 
-2024-08-27 21:04:30.070256: val_loss -0.7685 
-2024-08-27 21:04:30.070435: Pseudo dice [0.0, 0.0, 0.8902, 0.9756, 0.8243, 0.9451, 0.949, 0.959, 0.9469, 0.9498, 0.9203, 0.9564, 0.955, 0.8339, 0.9478, 0.9218, 0.8233, 0.8074, nan] 
-2024-08-27 21:04:30.070534: Epoch time: 88.54 s 
-2024-08-27 21:04:30.070590: Yayy! New best EMA pseudo Dice: 0.8065 
-2024-08-27 21:04:32.045852:  
-2024-08-27 21:04:32.046046: Epoch 318 
-2024-08-27 21:04:32.046151: Current learning rate: 0.00856 
-2024-08-27 21:06:10.724611: train_loss -0.7407 
-2024-08-27 21:06:10.724899: val_loss -0.766 
-2024-08-27 21:06:10.725103: Pseudo dice [0.0, 0.0, 0.9002, 0.9743, 0.8215, 0.9375, 0.9439, 0.96, 0.9478, 0.9506, 0.9333, 0.9572, 0.9572, 0.8227, 0.9449, 0.9179, 0.7954, 0.7742, nan] 
-2024-08-27 21:06:10.725205: Epoch time: 98.68 s 
-2024-08-27 21:06:10.725261: Yayy! New best EMA pseudo Dice: 0.8066 
-2024-08-27 21:06:12.363511:  
-2024-08-27 21:06:12.363693: Epoch 319 
-2024-08-27 21:06:12.363787: Current learning rate: 0.00855 
-2024-08-27 21:07:51.224586: train_loss -0.7414 
-2024-08-27 21:07:51.224832: val_loss -0.7697 
-2024-08-27 21:07:51.225010: Pseudo dice [0.0, 0.0, 0.8664, 0.9757, 0.8086, 0.9416, 0.9461, 0.9554, 0.944, 0.9434, 0.9236, 0.9566, 0.9538, 0.8256, 0.9495, 0.9259, 0.7994, 0.7994, nan] 
-2024-08-27 21:07:51.225101: Epoch time: 98.86 s 
-2024-08-27 21:07:52.629111:  
-2024-08-27 21:07:52.629383: Epoch 320 
-2024-08-27 21:07:52.629479: Current learning rate: 0.00855 
-2024-08-27 21:09:30.574262: train_loss -0.7392 
-2024-08-27 21:09:30.574541: val_loss -0.7569 
-2024-08-27 21:09:30.574753: Pseudo dice [0.0, 0.0, 0.8858, 0.9771, 0.8263, 0.9418, 0.9446, 0.9576, 0.9436, 0.9412, 0.9104, 0.951, 0.9505, 0.8119, 0.944, 0.9196, 0.8112, 0.8041, nan] 
-2024-08-27 21:09:30.574856: Epoch time: 97.95 s 
-2024-08-27 21:09:32.005308:  
-2024-08-27 21:09:32.005461: Epoch 321 
-2024-08-27 21:09:32.005551: Current learning rate: 0.00854 
-2024-08-27 21:11:02.979902: train_loss -0.7476 
-2024-08-27 21:11:02.980160: val_loss -0.764 
-2024-08-27 21:11:02.980332: Pseudo dice [0.0, 0.0, 0.8732, 0.9737, 0.7954, 0.9387, 0.943, 0.9621, 0.9455, 0.9433, 0.9197, 0.9549, 0.9523, 0.8308, 0.9496, 0.924, 0.7909, 0.7886, nan] 
-2024-08-27 21:11:02.980419: Epoch time: 90.98 s 
-2024-08-27 21:11:04.372236:  
-2024-08-27 21:11:04.372395: Epoch 322 
-2024-08-27 21:11:04.372501: Current learning rate: 0.00854 
-2024-08-27 21:12:36.921410: train_loss -0.747 
-2024-08-27 21:12:36.921670: val_loss -0.7674 
-2024-08-27 21:12:36.921841: Pseudo dice [0.0, 0.0, 0.8779, 0.9735, 0.8522, 0.9478, 0.9455, 0.9581, 0.9407, 0.9458, 0.9195, 0.9541, 0.9536, 0.8209, 0.9422, 0.9205, 0.819, 0.8148, nan] 
-2024-08-27 21:12:36.921952: Epoch time: 92.55 s 
-2024-08-27 21:12:36.922006: Yayy! New best EMA pseudo Dice: 0.8068 
-2024-08-27 21:12:38.754700:  
-2024-08-27 21:12:38.755087: Epoch 323 
-2024-08-27 21:12:38.755186: Current learning rate: 0.00853 
-2024-08-27 21:14:09.559914: train_loss -0.7441 
-2024-08-27 21:14:09.560152: val_loss -0.7631 
-2024-08-27 21:14:09.560313: Pseudo dice [0.0, 0.0, 0.8867, 0.9736, 0.7998, 0.9346, 0.93, 0.9596, 0.9469, 0.9403, 0.9196, 0.9579, 0.9527, 0.8295, 0.9452, 0.9198, 0.805, 0.7957, nan] 
-2024-08-27 21:14:09.560399: Epoch time: 90.81 s 
-2024-08-27 21:14:10.966087:  
-2024-08-27 21:14:10.966264: Epoch 324 
-2024-08-27 21:14:10.966360: Current learning rate: 0.00853 
-2024-08-27 21:15:48.180935: train_loss -0.7383 
-2024-08-27 21:15:48.181197: val_loss -0.7585 
-2024-08-27 21:15:48.181374: Pseudo dice [0.0, 0.0, 0.869, 0.9751, 0.8154, 0.9378, 0.9407, 0.955, 0.9319, 0.9347, 0.9141, 0.9479, 0.9486, 0.8145, 0.9457, 0.9134, 0.7994, 0.7854, nan] 
-2024-08-27 21:15:48.181468: Epoch time: 97.22 s 
-2024-08-27 21:15:49.479774:  
-2024-08-27 21:15:49.480128: Epoch 325 
-2024-08-27 21:15:49.480232: Current learning rate: 0.00852 
-2024-08-27 21:17:28.404225: train_loss -0.7455 
-2024-08-27 21:17:28.404516: val_loss -0.7588 
-2024-08-27 21:17:28.404715: Pseudo dice [0.0, 0.0, 0.867, 0.9758, 0.8185, 0.9353, 0.9355, 0.9599, 0.9389, 0.9267, 0.9209, 0.9547, 0.9554, 0.8064, 0.9428, 0.9205, 0.7572, 0.7867, nan] 
-2024-08-27 21:17:28.404819: Epoch time: 98.93 s 
-2024-08-27 21:17:29.811638:  
-2024-08-27 21:17:29.811838: Epoch 326 
-2024-08-27 21:17:29.811944: Current learning rate: 0.00852 
-2024-08-27 21:19:01.657835: train_loss -0.7418 
-2024-08-27 21:19:01.658194: val_loss -0.7643 
-2024-08-27 21:19:01.658541: Pseudo dice [0.0, 0.0, 0.8923, 0.9748, 0.813, 0.9414, 0.9438, 0.9625, 0.9413, 0.9361, 0.9167, 0.9509, 0.9496, 0.8328, 0.9402, 0.9292, 0.8037, 0.8029, nan] 
-2024-08-27 21:19:01.658691: Epoch time: 91.85 s 
-2024-08-27 21:19:03.062544:  
-2024-08-27 21:19:03.062746: Epoch 327 
-2024-08-27 21:19:03.062850: Current learning rate: 0.00852 
-2024-08-27 21:20:39.749685: train_loss -0.7466 
-2024-08-27 21:20:39.749938: val_loss -0.7609 
-2024-08-27 21:20:39.750102: Pseudo dice [0.0, 0.0, 0.8929, 0.9757, 0.8062, 0.9399, 0.9426, 0.9597, 0.9342, 0.9379, 0.9127, 0.9453, 0.9499, 0.8235, 0.9439, 0.9239, 0.7957, 0.7961, nan] 
-2024-08-27 21:20:39.750189: Epoch time: 96.69 s 
-2024-08-27 21:20:41.036469:  
-2024-08-27 21:20:41.036857: Epoch 328 
-2024-08-27 21:20:41.036989: Current learning rate: 0.00851 
-2024-08-27 21:22:17.647975: train_loss -0.7463 
-2024-08-27 21:22:17.648223: val_loss -0.7652 
-2024-08-27 21:22:17.648438: Pseudo dice [0.0, 0.0, 0.8827, 0.9754, 0.8313, 0.9412, 0.9424, 0.9593, 0.9384, 0.9438, 0.9143, 0.9507, 0.9511, 0.8284, 0.9465, 0.9176, 0.8056, 0.7868, nan] 
-2024-08-27 21:22:17.648551: Epoch time: 96.61 s 
-2024-08-27 21:22:19.240154:  
-2024-08-27 21:22:19.240348: Epoch 329 
-2024-08-27 21:22:19.240494: Current learning rate: 0.00851 
-2024-08-27 21:23:53.789320: train_loss -0.7483 
-2024-08-27 21:23:53.789583: val_loss -0.7667 
-2024-08-27 21:23:53.789756: Pseudo dice [0.0, 0.0, 0.8975, 0.977, 0.8445, 0.9419, 0.9454, 0.9611, 0.944, 0.9412, 0.9247, 0.9579, 0.9566, 0.8286, 0.9449, 0.922, 0.8008, 0.799, nan] 
-2024-08-27 21:23:53.789848: Epoch time: 94.55 s 
-2024-08-27 21:23:55.146706:  
-2024-08-27 21:23:55.146959: Epoch 330 
-2024-08-27 21:23:55.147054: Current learning rate: 0.0085 
-2024-08-27 21:25:24.078656: train_loss -0.7443 
-2024-08-27 21:25:24.078918: val_loss -0.7651 
-2024-08-27 21:25:24.079118: Pseudo dice [0.0, 0.0, 0.8645, 0.9775, 0.8292, 0.9432, 0.944, 0.9613, 0.9377, 0.9411, 0.916, 0.9525, 0.9525, 0.8232, 0.9382, 0.9285, 0.8015, 0.8059, nan] 
-2024-08-27 21:25:24.079216: Epoch time: 88.93 s 
-2024-08-27 21:25:25.435254:  
-2024-08-27 21:25:25.435562: Epoch 331 
-2024-08-27 21:25:25.435666: Current learning rate: 0.0085 
-2024-08-27 21:27:03.256324: train_loss -0.7457 
-2024-08-27 21:27:03.256603: val_loss -0.7633 
-2024-08-27 21:27:03.256811: Pseudo dice [0.0, 0.0, 0.8912, 0.9758, 0.8151, 0.9473, 0.9436, 0.9618, 0.9459, 0.9496, 0.9228, 0.9554, 0.9541, 0.8158, 0.9404, 0.9292, 0.7861, 0.778, nan] 
-2024-08-27 21:27:03.256933: Epoch time: 97.82 s 
-2024-08-27 21:27:04.726885:  
-2024-08-27 21:27:04.727190: Epoch 332 
-2024-08-27 21:27:04.727290: Current learning rate: 0.00849 
-2024-08-27 21:28:41.856318: train_loss -0.7437 
-2024-08-27 21:28:41.856544: val_loss -0.7635 
-2024-08-27 21:28:41.856707: Pseudo dice [0.0, 0.0, 0.8929, 0.9768, 0.818, 0.9394, 0.9409, 0.9599, 0.9323, 0.9351, 0.9197, 0.9419, 0.9417, 0.819, 0.9493, 0.927, 0.8108, 0.8082, nan] 
-2024-08-27 21:28:41.856787: Epoch time: 97.13 s 
-2024-08-27 21:28:43.148394:  
-2024-08-27 21:28:43.148576: Epoch 333 
-2024-08-27 21:28:43.148669: Current learning rate: 0.00849 
-2024-08-27 21:30:11.586589: train_loss -0.7483 
-2024-08-27 21:30:11.586838: val_loss -0.7698 
-2024-08-27 21:30:11.587011: Pseudo dice [0.0, 0.0, 0.8709, 0.9706, 0.8155, 0.9369, 0.9383, 0.965, 0.9507, 0.9484, 0.921, 0.9569, 0.9542, 0.8313, 0.955, 0.9267, 0.8243, 0.8035, nan] 
-2024-08-27 21:30:11.587106: Epoch time: 88.44 s 
-2024-08-27 21:30:12.890407:  
-2024-08-27 21:30:12.890558: Epoch 334 
-2024-08-27 21:30:12.890657: Current learning rate: 0.00848 
-2024-08-27 21:31:50.154711: train_loss -0.7462 
-2024-08-27 21:31:50.154955: val_loss -0.7572 
-2024-08-27 21:31:50.155149: Pseudo dice [0.0, 0.0, 0.8773, 0.9717, 0.8162, 0.9397, 0.941, 0.9592, 0.9396, 0.9462, 0.9101, 0.9509, 0.9528, 0.8179, 0.9412, 0.9229, 0.815, 0.8153, nan] 
-2024-08-27 21:31:50.155255: Epoch time: 97.27 s 
-2024-08-27 21:31:51.733241:  
-2024-08-27 21:31:51.733625: Epoch 335 
-2024-08-27 21:31:51.733748: Current learning rate: 0.00848 
-2024-08-27 21:33:28.737359: train_loss -0.7473 
-2024-08-27 21:33:28.737660: val_loss -0.7697 
-2024-08-27 21:33:28.737899: Pseudo dice [0.0, 0.0, 0.8974, 0.9758, 0.8422, 0.9449, 0.9454, 0.9624, 0.9418, 0.9316, 0.9218, 0.9537, 0.9511, 0.8358, 0.9495, 0.926, 0.8204, 0.8207, nan] 
-2024-08-27 21:33:28.738005: Epoch time: 97.01 s 
-2024-08-27 21:33:28.738070: Yayy! New best EMA pseudo Dice: 0.8071 
-2024-08-27 21:33:30.415621:  
-2024-08-27 21:33:30.415846: Epoch 336 
-2024-08-27 21:33:30.415949: Current learning rate: 0.00847 
-2024-08-27 21:35:04.834680: train_loss -0.7495 
-2024-08-27 21:35:04.834936: val_loss -0.765 
-2024-08-27 21:35:04.835113: Pseudo dice [0.0, 0.0, 0.9023, 0.9768, 0.8071, 0.9345, 0.944, 0.9644, 0.9431, 0.949, 0.9288, 0.9548, 0.9568, 0.8387, 0.9511, 0.9258, 0.8066, 0.7939, nan] 
-2024-08-27 21:35:04.835213: Epoch time: 94.42 s 
-2024-08-27 21:35:04.835268: Yayy! New best EMA pseudo Dice: 0.8074 
-2024-08-27 21:35:06.460998:  
-2024-08-27 21:35:06.461327: Epoch 337 
-2024-08-27 21:35:06.461495: Current learning rate: 0.00847 
-2024-08-27 21:36:37.362020: train_loss -0.7497 
-2024-08-27 21:36:37.362444: val_loss -0.7719 
-2024-08-27 21:36:37.362759: Pseudo dice [0.0, 0.0, 0.8612, 0.9764, 0.8215, 0.9338, 0.9389, 0.9629, 0.9473, 0.9382, 0.9221, 0.9587, 0.957, 0.8365, 0.943, 0.9332, 0.8228, 0.7976, nan] 
-2024-08-27 21:36:37.362894: Epoch time: 90.9 s 
-2024-08-27 21:36:37.362995: Yayy! New best EMA pseudo Dice: 0.8075 
-2024-08-27 21:36:39.487982:  
-2024-08-27 21:36:39.488190: Epoch 338 
-2024-08-27 21:36:39.488306: Current learning rate: 0.00847 
-2024-08-27 21:38:08.479578: train_loss -0.7475 
-2024-08-27 21:38:08.480058: val_loss -0.7451 
-2024-08-27 21:38:08.480463: Pseudo dice [0.0, 0.0, 0.8694, 0.9751, 0.8047, 0.9245, 0.9335, 0.9553, 0.9227, 0.9285, 0.8963, 0.9249, 0.9201, 0.8168, 0.9342, 0.9131, 0.7689, 0.7751, nan] 
-2024-08-27 21:38:08.480557: Epoch time: 88.99 s 
-2024-08-27 21:38:09.797855:  
-2024-08-27 21:38:09.798124: Epoch 339 
-2024-08-27 21:38:09.798221: Current learning rate: 0.00846 
-2024-08-27 21:39:45.581630: train_loss -0.7412 
-2024-08-27 21:39:45.581870: val_loss -0.7639 
-2024-08-27 21:39:45.582045: Pseudo dice [0.0, 0.0, 0.89, 0.9743, 0.8401, 0.9428, 0.9453, 0.9606, 0.9466, 0.9406, 0.9075, 0.9539, 0.9506, 0.8198, 0.9435, 0.9234, 0.8141, 0.8056, nan] 
-2024-08-27 21:39:45.582130: Epoch time: 95.78 s 
-2024-08-27 21:39:46.901627:  
-2024-08-27 21:39:46.901784: Epoch 340 
-2024-08-27 21:39:46.901897: Current learning rate: 0.00846 
-2024-08-27 21:41:20.137090: train_loss -0.7458 
-2024-08-27 21:41:20.137326: val_loss -0.7632 
-2024-08-27 21:41:20.137494: Pseudo dice [0.0, 0.0, 0.8776, 0.9749, 0.8057, 0.9366, 0.9388, 0.957, 0.9425, 0.9417, 0.9172, 0.9564, 0.954, 0.8289, 0.9496, 0.9246, 0.8032, 0.7946, nan] 
-2024-08-27 21:41:20.137579: Epoch time: 93.24 s 
-2024-08-27 21:41:21.671419:  
-2024-08-27 21:41:21.671585: Epoch 341 
-2024-08-27 21:41:21.671680: Current learning rate: 0.00845 
-2024-08-27 21:42:58.130307: train_loss -0.7366 
-2024-08-27 21:42:58.130599: val_loss -0.7587 
-2024-08-27 21:42:58.130784: Pseudo dice [0.0, 0.0, 0.8917, 0.9755, 0.7847, 0.9304, 0.9349, 0.9614, 0.9432, 0.938, 0.9155, 0.9546, 0.9524, 0.8186, 0.9498, 0.9165, 0.8123, 0.7698, nan] 
-2024-08-27 21:42:58.130877: Epoch time: 96.46 s 
-2024-08-27 21:42:59.458982:  
-2024-08-27 21:42:59.459171: Epoch 342 
-2024-08-27 21:42:59.459261: Current learning rate: 0.00845 
-2024-08-27 21:44:29.405744: train_loss -0.7375 
-2024-08-27 21:44:29.406003: val_loss -0.7619 
-2024-08-27 21:44:29.406181: Pseudo dice [0.0, 0.0, 0.8908, 0.9737, 0.7975, 0.9371, 0.9378, 0.9601, 0.9357, 0.938, 0.905, 0.942, 0.9411, 0.8237, 0.9486, 0.9275, 0.7995, 0.784, nan] 
-2024-08-27 21:44:29.406272: Epoch time: 89.95 s 
-2024-08-27 21:44:30.756202:  
-2024-08-27 21:44:30.756537: Epoch 343 
-2024-08-27 21:44:30.756634: Current learning rate: 0.00844 
-2024-08-27 21:46:04.157586: train_loss -0.7452 
-2024-08-27 21:46:04.157825: val_loss -0.7618 
-2024-08-27 21:46:04.158001: Pseudo dice [0.0, 0.0, 0.8633, 0.9741, 0.8214, 0.9447, 0.9481, 0.9605, 0.9448, 0.9388, 0.918, 0.9534, 0.9486, 0.8335, 0.9337, 0.9223, 0.7901, 0.8079, nan] 
-2024-08-27 21:46:04.158090: Epoch time: 93.4 s 
-2024-08-27 21:46:05.472197:  
-2024-08-27 21:46:05.472361: Epoch 344 
-2024-08-27 21:46:05.472460: Current learning rate: 0.00844 
-2024-08-27 21:47:40.040104: train_loss -0.748 
-2024-08-27 21:47:40.040471: val_loss -0.77 
-2024-08-27 21:47:40.040681: Pseudo dice [0.0, 0.0, 0.8859, 0.9751, 0.834, 0.9484, 0.9515, 0.9605, 0.9439, 0.946, 0.9204, 0.954, 0.9559, 0.8328, 0.946, 0.9323, 0.8035, 0.809, nan] 
-2024-08-27 21:47:40.040780: Epoch time: 94.57 s 
-2024-08-27 21:47:41.379743:  
-2024-08-27 21:47:41.379962: Epoch 345 
-2024-08-27 21:47:41.380072: Current learning rate: 0.00843 
-2024-08-27 21:49:12.898153: train_loss -0.7481 
-2024-08-27 21:49:12.898446: val_loss -0.7619 
-2024-08-27 21:49:12.898652: Pseudo dice [0.0, 0.0, 0.8574, 0.9769, 0.8235, 0.9408, 0.9442, 0.9571, 0.9423, 0.9379, 0.9204, 0.9513, 0.9501, 0.823, 0.9417, 0.93, 0.7859, 0.7842, nan] 
-2024-08-27 21:49:12.899206: Epoch time: 91.52 s 
-2024-08-27 21:49:14.387327:  
-2024-08-27 21:49:14.387628: Epoch 346 
-2024-08-27 21:49:14.387720: Current learning rate: 0.00843 
-2024-08-27 21:50:52.937098: train_loss -0.7393 
-2024-08-27 21:50:52.937338: val_loss -0.7651 
-2024-08-27 21:50:52.937504: Pseudo dice [0.0, 0.0, 0.8909, 0.9753, 0.8388, 0.9451, 0.9442, 0.9622, 0.9446, 0.939, 0.9154, 0.9555, 0.9494, 0.8241, 0.9503, 0.9242, 0.807, 0.7939, nan] 
-2024-08-27 21:50:52.937591: Epoch time: 98.55 s 
-2024-08-27 21:50:54.554805:  
-2024-08-27 21:50:54.554999: Epoch 347 
-2024-08-27 21:50:54.555092: Current learning rate: 0.00842 
-2024-08-27 21:52:31.981721: train_loss -0.747 
-2024-08-27 21:52:31.981943: val_loss -0.7579 
-2024-08-27 21:52:31.982119: Pseudo dice [0.0, 0.0, 0.8813, 0.9757, 0.7909, 0.9349, 0.9348, 0.9611, 0.9188, 0.929, 0.9104, 0.9299, 0.9365, 0.829, 0.9455, 0.9163, 0.7919, 0.8155, nan] 
-2024-08-27 21:52:31.982211: Epoch time: 97.43 s 
-2024-08-27 21:52:33.301100:  
-2024-08-27 21:52:33.301282: Epoch 348 
-2024-08-27 21:52:33.301396: Current learning rate: 0.00842 
-2024-08-27 21:54:11.504257: train_loss -0.7439 
-2024-08-27 21:54:11.504520: val_loss -0.7627 
-2024-08-27 21:54:11.504691: Pseudo dice [0.0, 0.0, 0.8912, 0.9751, 0.8212, 0.9426, 0.9441, 0.9603, 0.9464, 0.9462, 0.9276, 0.9584, 0.9555, 0.8125, 0.9484, 0.9203, 0.8159, 0.8017, nan] 
-2024-08-27 21:54:11.504780: Epoch time: 98.2 s 
-2024-08-27 21:54:12.848163:  
-2024-08-27 21:54:12.848335: Epoch 349 
-2024-08-27 21:54:12.848449: Current learning rate: 0.00841 
-2024-08-27 21:55:45.714091: train_loss -0.7498 
-2024-08-27 21:55:45.714313: val_loss -0.7761 
-2024-08-27 21:55:45.714483: Pseudo dice [0.0, 0.0, 0.8992, 0.9759, 0.8234, 0.9459, 0.9475, 0.9636, 0.9457, 0.9459, 0.9295, 0.958, 0.9585, 0.8369, 0.9489, 0.9269, 0.8037, 0.8184, nan] 
-2024-08-27 21:55:45.714567: Epoch time: 92.87 s 
-2024-08-27 21:55:47.279755:  
-2024-08-27 21:55:47.280050: Epoch 350 
-2024-08-27 21:55:47.280147: Current learning rate: 0.00841 
-2024-08-27 21:57:12.165035: train_loss -0.7451 
-2024-08-27 21:57:12.165272: val_loss -0.7586 
-2024-08-27 21:57:12.165420: Pseudo dice [0.0, 0.0, 0.8811, 0.9743, 0.8263, 0.9373, 0.9425, 0.9575, 0.9463, 0.9467, 0.9207, 0.9588, 0.9554, 0.7486, 0.9411, 0.9109, 0.805, 0.7891, nan] 
-2024-08-27 21:57:12.165545: Epoch time: 84.89 s 
-2024-08-27 21:57:13.443042:  
-2024-08-27 21:57:13.443203: Epoch 351 
-2024-08-27 21:57:13.443290: Current learning rate: 0.00841 
-2024-08-27 21:58:41.699040: train_loss -0.7393 
-2024-08-27 21:58:41.699264: val_loss -0.7625 
-2024-08-27 21:58:41.699429: Pseudo dice [0.0, 0.0, 0.895, 0.9763, 0.8176, 0.9299, 0.9383, 0.955, 0.9424, 0.9379, 0.9212, 0.9505, 0.9482, 0.8182, 0.9498, 0.9185, 0.7971, 0.8061, nan] 
-2024-08-27 21:58:41.699515: Epoch time: 88.26 s 
-2024-08-27 21:58:43.198359:  
-2024-08-27 21:58:43.198536: Epoch 352 
-2024-08-27 21:58:43.198630: Current learning rate: 0.0084 
-2024-08-27 22:00:13.382492: train_loss -0.7392 
-2024-08-27 22:00:13.382743: val_loss -0.7703 
-2024-08-27 22:00:13.382916: Pseudo dice [0.0, 0.0, 0.8827, 0.9761, 0.8275, 0.9429, 0.95, 0.9623, 0.9484, 0.944, 0.925, 0.959, 0.9571, 0.8313, 0.9411, 0.9293, 0.7997, 0.7875, nan] 
-2024-08-27 22:00:13.383009: Epoch time: 90.18 s 
-2024-08-27 22:00:14.721291:  
-2024-08-27 22:00:14.721930: Epoch 353 
-2024-08-27 22:00:14.722049: Current learning rate: 0.0084 
-2024-08-27 22:01:46.988493: train_loss -0.7444 
-2024-08-27 22:01:46.988755: val_loss -0.7565 
-2024-08-27 22:01:46.988910: Pseudo dice [0.0, 0.0, 0.8802, 0.9754, 0.8113, 0.935, 0.9334, 0.9554, 0.9361, 0.93, 0.9243, 0.9512, 0.9542, 0.7919, 0.9426, 0.9128, 0.7884, 0.7956, nan] 
-2024-08-27 22:01:46.988996: Epoch time: 92.27 s 
-2024-08-27 22:01:48.245846:  
-2024-08-27 22:01:48.246182: Epoch 354 
-2024-08-27 22:01:48.246272: Current learning rate: 0.00839 
-2024-08-27 22:03:21.090527: train_loss -0.7394 
-2024-08-27 22:03:21.090763: val_loss -0.7607 
-2024-08-27 22:03:21.090925: Pseudo dice [0.0, 0.0, 0.8695, 0.9771, 0.8055, 0.9375, 0.9401, 0.9557, 0.9449, 0.9421, 0.9145, 0.9563, 0.9485, 0.8076, 0.9277, 0.9176, 0.8169, 0.8035, nan] 
-2024-08-27 22:03:21.091011: Epoch time: 92.85 s 
-2024-08-27 22:03:22.368982:  
-2024-08-27 22:03:22.369328: Epoch 355 
-2024-08-27 22:03:22.369424: Current learning rate: 0.00839 
-2024-08-27 22:04:48.577579: train_loss -0.7445 
-2024-08-27 22:04:48.578071: val_loss -0.7623 
-2024-08-27 22:04:48.578338: Pseudo dice [0.0, 0.0, 0.8814, 0.9767, 0.8276, 0.937, 0.9395, 0.9607, 0.9451, 0.9478, 0.9239, 0.9538, 0.9546, 0.8095, 0.947, 0.9164, 0.808, 0.7929, nan] 
-2024-08-27 22:04:48.578468: Epoch time: 86.21 s 
-2024-08-27 22:04:49.851884:  
-2024-08-27 22:04:49.852083: Epoch 356 
-2024-08-27 22:04:49.852188: Current learning rate: 0.00838 
-2024-08-27 22:06:18.436183: train_loss -0.7392 
-2024-08-27 22:06:18.436488: val_loss -0.7578 
-2024-08-27 22:06:18.436680: Pseudo dice [0.0, 0.0, 0.8682, 0.9742, 0.7987, 0.9403, 0.9426, 0.9565, 0.9427, 0.9399, 0.917, 0.9493, 0.9495, 0.8013, 0.9454, 0.9019, 0.803, 0.8003, nan] 
-2024-08-27 22:06:18.436796: Epoch time: 88.59 s 
-2024-08-27 22:06:19.826985:  
-2024-08-27 22:06:19.827167: Epoch 357 
-2024-08-27 22:06:19.827275: Current learning rate: 0.00838 
-2024-08-27 22:07:51.866414: train_loss -0.742 
-2024-08-27 22:07:51.866911: val_loss -0.7574 
-2024-08-27 22:07:51.867093: Pseudo dice [0.0, 0.0, 0.8849, 0.9758, 0.8203, 0.9323, 0.9347, 0.9603, 0.9403, 0.9398, 0.9102, 0.9525, 0.9471, 0.8194, 0.9487, 0.9272, 0.7936, 0.7789, nan] 
-2024-08-27 22:07:51.867226: Epoch time: 92.04 s 
-2024-08-27 22:07:53.435673:  
-2024-08-27 22:07:53.435946: Epoch 358 
-2024-08-27 22:07:53.436061: Current learning rate: 0.00837 
-2024-08-27 22:09:27.993520: train_loss -0.7442 
-2024-08-27 22:09:27.993811: val_loss -0.7635 
-2024-08-27 22:09:27.994038: Pseudo dice [0.0, 0.0, 0.8881, 0.9753, 0.8434, 0.9444, 0.9472, 0.9585, 0.9401, 0.946, 0.9197, 0.9519, 0.9502, 0.8278, 0.9361, 0.9211, 0.7491, 0.778, nan] 
-2024-08-27 22:09:27.994173: Epoch time: 94.56 s 
-2024-08-27 22:09:29.319533:  
-2024-08-27 22:09:29.319721: Epoch 359 
-2024-08-27 22:09:29.319816: Current learning rate: 0.00837 
-2024-08-27 22:10:59.924591: train_loss -0.7439 
-2024-08-27 22:10:59.924834: val_loss -0.7594 
-2024-08-27 22:10:59.924994: Pseudo dice [0.0, 0.0, 0.8433, 0.9757, 0.8298, 0.9378, 0.945, 0.9612, 0.9388, 0.9373, 0.9172, 0.9454, 0.951, 0.824, 0.948, 0.9174, 0.7936, 0.7885, nan] 
-2024-08-27 22:10:59.925080: Epoch time: 90.61 s 
-2024-08-27 22:11:01.172836:  
-2024-08-27 22:11:01.173012: Epoch 360 
-2024-08-27 22:11:01.173118: Current learning rate: 0.00836 
-2024-08-27 22:12:35.481496: train_loss -0.7441 
-2024-08-27 22:12:35.481703: val_loss -0.7788 
-2024-08-27 22:12:35.481858: Pseudo dice [0.0, 0.0, 0.8929, 0.9754, 0.814, 0.9462, 0.9452, 0.9607, 0.9456, 0.9442, 0.9111, 0.9587, 0.9536, 0.8274, 0.9468, 0.925, 0.8067, 0.796, nan] 
-2024-08-27 22:12:35.481938: Epoch time: 94.31 s 
-2024-08-27 22:12:36.762256:  
-2024-08-27 22:12:36.762820: Epoch 361 
-2024-08-27 22:12:36.762915: Current learning rate: 0.00836 
-2024-08-27 22:14:07.220189: train_loss -0.7442 
-2024-08-27 22:14:07.220441: val_loss -0.7607 
-2024-08-27 22:14:07.220601: Pseudo dice [0.0, 0.0, 0.8615, 0.9764, 0.8268, 0.945, 0.9471, 0.9626, 0.9431, 0.9344, 0.9123, 0.9498, 0.9529, 0.8232, 0.9471, 0.9211, 0.8042, 0.7724, nan] 
-2024-08-27 22:14:07.220679: Epoch time: 90.46 s 
-2024-08-27 22:14:08.511281:  
-2024-08-27 22:14:08.511444: Epoch 362 
-2024-08-27 22:14:08.511534: Current learning rate: 0.00836 
-2024-08-27 22:15:42.223001: train_loss -0.7482 
-2024-08-27 22:15:42.223262: val_loss -0.77 
-2024-08-27 22:15:42.223424: Pseudo dice [0.0, 0.0, 0.8994, 0.9759, 0.8146, 0.9422, 0.9443, 0.961, 0.9482, 0.9452, 0.9268, 0.9591, 0.9551, 0.8315, 0.9399, 0.9177, 0.8021, 0.8029, nan] 
-2024-08-27 22:15:42.223515: Epoch time: 93.71 s 
-2024-08-27 22:15:43.737620:  
-2024-08-27 22:15:43.737780: Epoch 363 
-2024-08-27 22:15:43.737872: Current learning rate: 0.00835 
-2024-08-27 22:17:11.997351: train_loss -0.7511 
-2024-08-27 22:17:11.997605: val_loss -0.7653 
-2024-08-27 22:17:11.997782: Pseudo dice [0.0, 0.0, 0.8806, 0.9757, 0.8431, 0.9461, 0.9465, 0.9628, 0.9402, 0.9397, 0.9231, 0.9548, 0.9604, 0.8339, 0.9511, 0.9317, 0.8301, 0.8132, nan] 
-2024-08-27 22:17:11.997868: Epoch time: 88.26 s 
-2024-08-27 22:17:13.314291:  
-2024-08-27 22:17:13.314466: Epoch 364 
-2024-08-27 22:17:13.314579: Current learning rate: 0.00835 
-2024-08-27 22:18:43.919267: train_loss -0.7449 
-2024-08-27 22:18:43.919497: val_loss -0.7621 
-2024-08-27 22:18:43.919663: Pseudo dice [0.0, 0.0, 0.8672, 0.9759, 0.801, 0.9439, 0.9455, 0.9584, 0.9459, 0.944, 0.912, 0.957, 0.9548, 0.8224, 0.9472, 0.9214, 0.7954, 0.8, nan] 
-2024-08-27 22:18:43.919869: Epoch time: 90.61 s 
-2024-08-27 22:18:45.147506:  
-2024-08-27 22:18:45.147676: Epoch 365 
-2024-08-27 22:18:45.147776: Current learning rate: 0.00834 
-2024-08-27 22:20:16.942260: train_loss -0.7436 
-2024-08-27 22:20:16.942494: val_loss -0.7663 
-2024-08-27 22:20:16.942654: Pseudo dice [0.0, 0.0, 0.9005, 0.9745, 0.7996, 0.9384, 0.9363, 0.9571, 0.9435, 0.936, 0.9165, 0.9555, 0.9531, 0.8309, 0.9409, 0.9153, 0.803, 0.8131, nan] 
-2024-08-27 22:20:16.942736: Epoch time: 91.8 s 
-2024-08-27 22:20:18.183613:  
-2024-08-27 22:20:18.184112: Epoch 366 
-2024-08-27 22:20:18.184225: Current learning rate: 0.00834 
-2024-08-27 22:21:46.272799: train_loss -0.7458 
-2024-08-27 22:21:46.273151: val_loss -0.7632 
-2024-08-27 22:21:46.273323: Pseudo dice [0.0, 0.0, 0.8958, 0.9738, 0.8041, 0.9364, 0.9387, 0.9571, 0.9485, 0.9396, 0.9147, 0.9569, 0.9542, 0.8168, 0.9408, 0.9253, 0.7992, 0.8116, nan] 
-2024-08-27 22:21:46.273411: Epoch time: 88.09 s 
-2024-08-27 22:21:47.486449:  
-2024-08-27 22:21:47.486625: Epoch 367 
-2024-08-27 22:21:47.486728: Current learning rate: 0.00833 
-2024-08-27 22:23:18.003229: train_loss -0.7462 
-2024-08-27 22:23:18.003488: val_loss -0.7585 
-2024-08-27 22:23:18.003689: Pseudo dice [0.0, 0.0, 0.8938, 0.9767, 0.817, 0.9429, 0.9304, 0.9596, 0.9349, 0.9347, 0.9024, 0.9454, 0.9506, 0.8194, 0.937, 0.9137, 0.809, 0.7942, nan] 
-2024-08-27 22:23:18.003797: Epoch time: 90.52 s 
-2024-08-27 22:23:19.335079:  
-2024-08-27 22:23:19.335228: Epoch 368 
-2024-08-27 22:23:19.335308: Current learning rate: 0.00833 
-2024-08-27 22:24:45.905180: train_loss -0.7435 
-2024-08-27 22:24:45.905618: val_loss -0.7604 
-2024-08-27 22:24:45.905809: Pseudo dice [0.0, 0.0, 0.8828, 0.9735, 0.7907, 0.93, 0.9415, 0.9569, 0.943, 0.9492, 0.9263, 0.9571, 0.9552, 0.8269, 0.945, 0.9243, 0.7864, 0.7907, nan] 
-2024-08-27 22:24:45.905903: Epoch time: 86.57 s 
-2024-08-27 22:24:47.446705:  
-2024-08-27 22:24:47.447232: Epoch 369 
-2024-08-27 22:24:47.447339: Current learning rate: 0.00832 
-2024-08-27 22:26:11.950833: train_loss -0.7451 
-2024-08-27 22:26:11.951055: val_loss -0.7643 
-2024-08-27 22:26:11.951221: Pseudo dice [0.0, 0.0, 0.8657, 0.9747, 0.853, 0.946, 0.9459, 0.9625, 0.9447, 0.9442, 0.9181, 0.9543, 0.9512, 0.8386, 0.9446, 0.9297, 0.7832, 0.8034, nan] 
-2024-08-27 22:26:11.951307: Epoch time: 84.5 s 
-2024-08-27 22:26:13.104949:  
-2024-08-27 22:26:13.105115: Epoch 370 
-2024-08-27 22:26:13.105200: Current learning rate: 0.00832 
-2024-08-27 22:27:40.633934: train_loss -0.743 
-2024-08-27 22:27:40.634181: val_loss -0.7659 
-2024-08-27 22:27:40.634357: Pseudo dice [0.0, 0.0, 0.8907, 0.9747, 0.8211, 0.9376, 0.945, 0.9618, 0.9488, 0.9363, 0.9173, 0.9536, 0.9558, 0.8177, 0.9493, 0.9182, 0.8002, 0.8193, nan] 
-2024-08-27 22:27:40.634447: Epoch time: 87.53 s 
-2024-08-27 22:27:41.895310:  
-2024-08-27 22:27:41.895465: Epoch 371 
-2024-08-27 22:27:41.895555: Current learning rate: 0.00831 
-2024-08-27 22:29:08.489616: train_loss -0.7482 
-2024-08-27 22:29:08.489866: val_loss -0.768 
-2024-08-27 22:29:08.490047: Pseudo dice [0.0, 0.0, 0.8897, 0.9764, 0.8268, 0.9402, 0.9372, 0.9609, 0.9431, 0.9448, 0.927, 0.953, 0.9569, 0.8182, 0.9381, 0.9225, 0.7991, 0.8059, nan] 
-2024-08-27 22:29:08.490140: Epoch time: 86.6 s 
-2024-08-27 22:29:09.745279:  
-2024-08-27 22:29:09.745716: Epoch 372 
-2024-08-27 22:29:09.745913: Current learning rate: 0.00831 
-2024-08-27 22:30:31.966130: train_loss -0.7417 
-2024-08-27 22:30:31.966384: val_loss -0.7598 
-2024-08-27 22:30:31.966550: Pseudo dice [0.0, 0.0, 0.8716, 0.9705, 0.8215, 0.9429, 0.9431, 0.9528, 0.9445, 0.9444, 0.9024, 0.9574, 0.9497, 0.8216, 0.9478, 0.922, 0.8066, 0.7903, nan] 
-2024-08-27 22:30:31.966636: Epoch time: 82.22 s 
-2024-08-27 22:30:33.177435:  
-2024-08-27 22:30:33.177636: Epoch 373 
-2024-08-27 22:30:33.177792: Current learning rate: 0.0083 
-2024-08-27 22:32:01.563183: train_loss -0.7393 
-2024-08-27 22:32:01.563431: val_loss -0.7687 
-2024-08-27 22:32:01.563601: Pseudo dice [0.0, 0.0, 0.8926, 0.9753, 0.8047, 0.939, 0.9428, 0.9578, 0.9421, 0.9372, 0.9222, 0.9514, 0.9543, 0.8078, 0.9417, 0.9172, 0.7982, 0.8199, nan] 
-2024-08-27 22:32:01.563688: Epoch time: 88.39 s 
-2024-08-27 22:32:02.825352:  
-2024-08-27 22:32:02.825664: Epoch 374 
-2024-08-27 22:32:02.825765: Current learning rate: 0.0083 
-2024-08-27 22:33:33.066935: train_loss -0.743 
-2024-08-27 22:33:33.067190: val_loss -0.7585 
-2024-08-27 22:33:33.067406: Pseudo dice [0.0, 0.0, 0.8751, 0.9747, 0.7832, 0.9345, 0.9361, 0.9552, 0.9436, 0.9393, 0.9151, 0.9529, 0.9527, 0.8149, 0.9493, 0.9258, 0.7871, 0.8, nan] 
-2024-08-27 22:33:33.067515: Epoch time: 90.24 s 
-2024-08-27 22:33:34.626565:  
-2024-08-27 22:33:34.626959: Epoch 375 
-2024-08-27 22:33:34.627061: Current learning rate: 0.0083 
-2024-08-27 22:35:03.692612: train_loss -0.7435 
-2024-08-27 22:35:03.692866: val_loss -0.7732 
-2024-08-27 22:35:03.693033: Pseudo dice [0.0, 0.0, 0.8756, 0.9756, 0.8272, 0.9462, 0.9479, 0.962, 0.942, 0.9496, 0.9285, 0.9541, 0.9581, 0.829, 0.9478, 0.9219, 0.8206, 0.8211, nan] 
-2024-08-27 22:35:03.693120: Epoch time: 89.07 s 
-2024-08-27 22:35:04.971564:  
-2024-08-27 22:35:04.971736: Epoch 376 
-2024-08-27 22:35:04.971842: Current learning rate: 0.00829 
-2024-08-27 22:36:33.816913: train_loss -0.7461 
-2024-08-27 22:36:33.817146: val_loss -0.7647 
-2024-08-27 22:36:33.817321: Pseudo dice [0.0, 0.0, 0.8995, 0.9758, 0.824, 0.94, 0.9447, 0.9622, 0.9428, 0.9469, 0.9302, 0.9507, 0.9594, 0.8191, 0.9476, 0.9222, 0.816, 0.8013, nan] 
-2024-08-27 22:36:33.817414: Epoch time: 88.85 s 
-2024-08-27 22:36:35.066695:  
-2024-08-27 22:36:35.067033: Epoch 377 
-2024-08-27 22:36:35.067129: Current learning rate: 0.00829 
-2024-08-27 22:38:06.124074: train_loss -0.7395 
-2024-08-27 22:38:06.124325: val_loss -0.7641 
-2024-08-27 22:38:06.124501: Pseudo dice [0.0, 0.0, 0.8856, 0.9763, 0.8323, 0.9418, 0.9462, 0.9613, 0.9398, 0.9482, 0.9174, 0.952, 0.9533, 0.8194, 0.9465, 0.9253, 0.7852, 0.7856, nan] 
-2024-08-27 22:38:06.124594: Epoch time: 91.06 s 
-2024-08-27 22:38:07.387696:  
-2024-08-27 22:38:07.388091: Epoch 378 
-2024-08-27 22:38:07.388184: Current learning rate: 0.00828 
-2024-08-27 22:39:32.960826: train_loss -0.7459 
-2024-08-27 22:39:32.961263: val_loss -0.7598 
-2024-08-27 22:39:32.961503: Pseudo dice [0.0, 0.0, 0.8789, 0.9745, 0.8155, 0.9398, 0.943, 0.9589, 0.9417, 0.9469, 0.9219, 0.9555, 0.955, 0.814, 0.9427, 0.917, 0.8122, 0.8177, nan] 
-2024-08-27 22:39:32.961611: Epoch time: 85.57 s 
-2024-08-27 22:39:34.312062:  
-2024-08-27 22:39:34.312224: Epoch 379 
-2024-08-27 22:39:34.312320: Current learning rate: 0.00828 
-2024-08-27 22:41:00.134784: train_loss -0.7449 
-2024-08-27 22:41:00.135048: val_loss -0.7572 
-2024-08-27 22:41:00.135217: Pseudo dice [0.0, 0.0, 0.8948, 0.9751, 0.7839, 0.9299, 0.9383, 0.959, 0.9296, 0.9295, 0.9177, 0.9424, 0.9413, 0.8253, 0.9394, 0.9191, 0.79, 0.7937, nan] 
-2024-08-27 22:41:00.135307: Epoch time: 85.82 s 
-2024-08-27 22:41:01.361984:  
-2024-08-27 22:41:01.362163: Epoch 380 
-2024-08-27 22:41:01.362257: Current learning rate: 0.00827 
-2024-08-27 22:42:30.586438: train_loss -0.7399 
-2024-08-27 22:42:30.586686: val_loss -0.7574 
-2024-08-27 22:42:30.586871: Pseudo dice [0.0, 0.0, 0.8888, 0.9749, 0.7784, 0.9267, 0.9281, 0.9569, 0.9349, 0.929, 0.9108, 0.9425, 0.9433, 0.8205, 0.9375, 0.924, 0.8054, 0.8248, nan] 
-2024-08-27 22:42:30.586959: Epoch time: 89.23 s 
-2024-08-27 22:42:32.310360:  
-2024-08-27 22:42:32.310522: Epoch 381 
-2024-08-27 22:42:32.310618: Current learning rate: 0.00827 
-2024-08-27 22:43:57.156578: train_loss -0.7354 
-2024-08-27 22:43:57.156828: val_loss -0.7562 
-2024-08-27 22:43:57.156986: Pseudo dice [0.0, 0.0, 0.8828, 0.9748, 0.7928, 0.9357, 0.9383, 0.95, 0.9242, 0.9328, 0.9129, 0.9383, 0.9432, 0.8191, 0.9367, 0.9156, 0.7828, 0.783, nan] 
-2024-08-27 22:43:57.157068: Epoch time: 84.85 s 
-2024-08-27 22:43:58.459038:  
-2024-08-27 22:43:58.459350: Epoch 382 
-2024-08-27 22:43:58.459446: Current learning rate: 0.00826 
-2024-08-27 22:45:25.113868: train_loss -0.735 
-2024-08-27 22:45:25.114099: val_loss -0.753 
-2024-08-27 22:45:25.114308: Pseudo dice [0.0, 0.0, 0.8754, 0.973, 0.767, 0.9315, 0.9352, 0.9571, 0.9403, 0.9281, 0.9102, 0.9527, 0.9444, 0.8042, 0.923, 0.9182, 0.7616, 0.7432, nan] 
-2024-08-27 22:45:25.114493: Epoch time: 86.66 s 
-2024-08-27 22:45:26.485407:  
-2024-08-27 22:45:26.485742: Epoch 383 
-2024-08-27 22:45:26.485926: Current learning rate: 0.00826 
-2024-08-27 22:46:48.261145: train_loss -0.7411 
-2024-08-27 22:46:48.261579: val_loss -0.7578 
-2024-08-27 22:46:48.261768: Pseudo dice [0.0, 0.0, 0.8723, 0.9725, 0.8244, 0.9318, 0.9411, 0.9562, 0.9387, 0.9457, 0.9189, 0.9452, 0.9526, 0.827, 0.9326, 0.9183, 0.7921, 0.7885, nan] 
-2024-08-27 22:46:48.261900: Epoch time: 81.78 s 
-2024-08-27 22:46:49.548987:  
-2024-08-27 22:46:49.549167: Epoch 384 
-2024-08-27 22:46:49.549261: Current learning rate: 0.00825 
-2024-08-27 22:48:20.233346: train_loss -0.7339 
-2024-08-27 22:48:20.233603: val_loss -0.7357 
-2024-08-27 22:48:20.233760: Pseudo dice [0.0, 0.0, 0.8713, 0.9709, 0.7619, 0.9095, 0.909, 0.9419, 0.9094, 0.914, 0.8953, 0.9201, 0.9172, 0.7927, 0.8875, 0.9065, 0.7401, 0.763, nan] 
-2024-08-27 22:48:20.233841: Epoch time: 90.69 s 
-2024-08-27 22:48:21.474267:  
-2024-08-27 22:48:21.474814: Epoch 385 
-2024-08-27 22:48:21.474918: Current learning rate: 0.00825 
-2024-08-27 22:49:48.607759: train_loss -0.7236 
-2024-08-27 22:49:48.608020: val_loss -0.7501 
-2024-08-27 22:49:48.608185: Pseudo dice [0.0, 0.0, 0.8775, 0.9709, 0.784, 0.9412, 0.9429, 0.9563, 0.9376, 0.9334, 0.9142, 0.949, 0.9503, 0.8178, 0.938, 0.9144, 0.8011, 0.7895, nan] 
-2024-08-27 22:49:48.608273: Epoch time: 87.13 s 
-2024-08-27 22:49:50.202361:  
-2024-08-27 22:49:50.202846: Epoch 386 
-2024-08-27 22:49:50.202954: Current learning rate: 0.00824 
-2024-08-27 22:51:23.174329: train_loss -0.7343 
-2024-08-27 22:51:23.174574: val_loss -0.7569 
-2024-08-27 22:51:23.174725: Pseudo dice [0.0, 0.0, 0.8672, 0.9763, 0.7719, 0.9316, 0.9388, 0.9575, 0.9422, 0.9367, 0.9101, 0.9529, 0.9419, 0.8032, 0.9437, 0.9078, 0.7971, 0.7974, nan] 
-2024-08-27 22:51:23.174803: Epoch time: 92.97 s 
-2024-08-27 22:51:24.413824:  
-2024-08-27 22:51:24.414121: Epoch 387 
-2024-08-27 22:51:24.414218: Current learning rate: 0.00824 
-2024-08-27 22:52:49.308170: train_loss -0.733 
-2024-08-27 22:52:49.308401: val_loss -0.7574 
-2024-08-27 22:52:49.308567: Pseudo dice [0.0, 0.0, 0.8529, 0.9734, 0.7935, 0.9431, 0.9453, 0.956, 0.945, 0.9476, 0.9215, 0.9549, 0.9554, 0.8181, 0.9434, 0.9163, 0.7918, 0.7714, nan] 
-2024-08-27 22:52:49.308651: Epoch time: 84.9 s 
-2024-08-27 22:52:50.528962:  
-2024-08-27 22:52:50.529479: Epoch 388 
-2024-08-27 22:52:50.529573: Current learning rate: 0.00824 
-2024-08-27 22:54:15.863020: train_loss -0.7405 
-2024-08-27 22:54:15.863330: val_loss -0.762 
-2024-08-27 22:54:15.863619: Pseudo dice [0.0, 0.0, 0.8801, 0.974, 0.7883, 0.9339, 0.9359, 0.9606, 0.9248, 0.9229, 0.8989, 0.9379, 0.9344, 0.8094, 0.9417, 0.9248, 0.767, 0.7669, nan] 
-2024-08-27 22:54:15.863765: Epoch time: 85.33 s 
-2024-08-27 22:54:17.159343:  
-2024-08-27 22:54:17.159519: Epoch 389 
-2024-08-27 22:54:17.159606: Current learning rate: 0.00823 
-2024-08-27 22:55:47.229967: train_loss -0.737 
-2024-08-27 22:55:47.230319: val_loss -0.7655 
-2024-08-27 22:55:47.230559: Pseudo dice [0.0, 0.0, 0.8787, 0.9754, 0.8214, 0.9365, 0.9329, 0.9563, 0.9425, 0.9428, 0.9224, 0.9548, 0.9499, 0.8245, 0.9371, 0.9241, 0.8088, 0.8059, nan] 
-2024-08-27 22:55:47.230650: Epoch time: 90.07 s 
-2024-08-27 22:55:48.453323:  
-2024-08-27 22:55:48.453465: Epoch 390 
-2024-08-27 22:55:48.453558: Current learning rate: 0.00823 
-2024-08-27 22:57:18.742459: train_loss -0.7393 
-2024-08-27 22:57:18.742697: val_loss -0.7573 
-2024-08-27 22:57:18.742856: Pseudo dice [0.0, 0.0, 0.892, 0.9751, 0.7825, 0.9442, 0.9434, 0.9574, 0.9424, 0.9302, 0.9084, 0.9511, 0.9481, 0.8053, 0.9398, 0.9146, 0.8, 0.781, nan] 
-2024-08-27 22:57:18.742941: Epoch time: 90.29 s 
-2024-08-27 22:57:20.026939:  
-2024-08-27 22:57:20.027127: Epoch 391 
-2024-08-27 22:57:20.027234: Current learning rate: 0.00822 
-2024-08-27 22:58:55.466299: train_loss -0.7396 
-2024-08-27 22:58:55.466746: val_loss -0.7635 
-2024-08-27 22:58:55.466934: Pseudo dice [0.0, 0.0, 0.8834, 0.9737, 0.8204, 0.9435, 0.9457, 0.9596, 0.9471, 0.9435, 0.9181, 0.9576, 0.9521, 0.8279, 0.9454, 0.9222, 0.7987, 0.7892, nan] 
-2024-08-27 22:58:55.467055: Epoch time: 95.44 s 
-2024-08-27 22:58:57.121346:  
-2024-08-27 22:58:57.121524: Epoch 392 
-2024-08-27 22:58:57.121618: Current learning rate: 0.00822 
-2024-08-27 23:00:22.377847: train_loss -0.7398 
-2024-08-27 23:00:22.378190: val_loss -0.7583 
-2024-08-27 23:00:22.378377: Pseudo dice [0.0, 0.0, 0.885, 0.9731, 0.8005, 0.9439, 0.9474, 0.958, 0.9443, 0.9393, 0.9153, 0.9526, 0.9488, 0.8161, 0.9472, 0.9194, 0.7984, 0.7909, nan] 
-2024-08-27 23:00:22.378502: Epoch time: 85.26 s 
-2024-08-27 23:00:23.663576:  
-2024-08-27 23:00:23.664146: Epoch 393 
-2024-08-27 23:00:23.664244: Current learning rate: 0.00821 
-2024-08-27 23:01:47.376955: train_loss -0.7403 
-2024-08-27 23:01:47.377185: val_loss -0.7682 
-2024-08-27 23:01:47.377351: Pseudo dice [0.0, 0.0, 0.8816, 0.9746, 0.8147, 0.9429, 0.9484, 0.9585, 0.9431, 0.9483, 0.9274, 0.9579, 0.9582, 0.8125, 0.9461, 0.924, 0.8045, 0.7823, nan] 
-2024-08-27 23:01:47.377438: Epoch time: 83.71 s 
-2024-08-27 23:01:48.847046:  
-2024-08-27 23:01:48.847347: Epoch 394 
-2024-08-27 23:01:48.847440: Current learning rate: 0.00821 
-2024-08-27 23:03:12.826654: train_loss -0.7415 
-2024-08-27 23:03:12.826929: val_loss -0.753 
-2024-08-27 23:03:12.827159: Pseudo dice [0.0, 0.0, 0.8711, 0.9752, 0.7897, 0.9367, 0.9382, 0.9534, 0.9367, 0.9319, 0.8981, 0.9491, 0.947, 0.7996, 0.9403, 0.9186, 0.7941, 0.7884, nan] 
-2024-08-27 23:03:12.827275: Epoch time: 83.98 s 
-2024-08-27 23:03:14.141768:  
-2024-08-27 23:03:14.142047: Epoch 395 
-2024-08-27 23:03:14.142143: Current learning rate: 0.0082 
-2024-08-27 23:04:40.125688: train_loss -0.738 
-2024-08-27 23:04:40.126308: val_loss -0.7561 
-2024-08-27 23:04:40.126502: Pseudo dice [0.0, 0.0, 0.8765, 0.9761, 0.7938, 0.9311, 0.9353, 0.9555, 0.931, 0.9236, 0.9125, 0.9424, 0.9437, 0.8178, 0.948, 0.9153, 0.7887, 0.7709, nan] 
-2024-08-27 23:04:40.126649: Epoch time: 85.98 s 
-2024-08-27 23:04:41.391357:  
-2024-08-27 23:04:41.391510: Epoch 396 
-2024-08-27 23:04:41.391604: Current learning rate: 0.0082 
-2024-08-27 23:06:14.542385: train_loss -0.7382 
-2024-08-27 23:06:14.542852: val_loss -0.7642 
-2024-08-27 23:06:14.543040: Pseudo dice [0.0, 0.0, 0.871, 0.9757, 0.8288, 0.9303, 0.937, 0.9577, 0.9413, 0.9383, 0.9167, 0.9529, 0.9517, 0.8154, 0.9371, 0.9205, 0.8032, 0.798, nan] 
-2024-08-27 23:06:14.543168: Epoch time: 93.15 s 
-2024-08-27 23:06:15.812627:  
-2024-08-27 23:06:15.812814: Epoch 397 
-2024-08-27 23:06:15.812911: Current learning rate: 0.00819 
-2024-08-27 23:07:39.026218: train_loss -0.7393 
-2024-08-27 23:07:39.026850: val_loss -0.7639 
-2024-08-27 23:07:39.027009: Pseudo dice [0.0, 0.0, 0.8808, 0.9755, 0.7859, 0.9357, 0.941, 0.9584, 0.9412, 0.9436, 0.9231, 0.9522, 0.9527, 0.8051, 0.9403, 0.9209, 0.7867, 0.7863, nan] 
-2024-08-27 23:07:39.027113: Epoch time: 83.21 s 
-2024-08-27 23:07:40.804268:  
-2024-08-27 23:07:40.804466: Epoch 398 
-2024-08-27 23:07:40.804563: Current learning rate: 0.00819 
-2024-08-27 23:09:02.375754: train_loss -0.7468 
-2024-08-27 23:09:02.376003: val_loss -0.769 
-2024-08-27 23:09:02.376173: Pseudo dice [0.0, 0.0, 0.884, 0.9756, 0.8408, 0.9429, 0.9484, 0.961, 0.941, 0.9442, 0.925, 0.9541, 0.956, 0.8284, 0.9519, 0.9265, 0.811, 0.8046, nan] 
-2024-08-27 23:09:02.376258: Epoch time: 81.57 s 
-2024-08-27 23:09:03.657593:  
-2024-08-27 23:09:03.658015: Epoch 399 
-2024-08-27 23:09:03.658210: Current learning rate: 0.00819 
-2024-08-27 23:10:30.226325: train_loss -0.74 
-2024-08-27 23:10:30.226547: val_loss -0.7673 
-2024-08-27 23:10:30.226706: Pseudo dice [0.0, 0.0, 0.8902, 0.9767, 0.8144, 0.9469, 0.9468, 0.96, 0.9436, 0.9402, 0.9226, 0.9543, 0.952, 0.8278, 0.9511, 0.9241, 0.7947, 0.7903, nan] 
-2024-08-27 23:10:30.226788: Epoch time: 86.57 s 
-2024-08-27 23:10:31.723806:  
-2024-08-27 23:10:31.724012: Epoch 400 
-2024-08-27 23:10:31.724170: Current learning rate: 0.00818 
-2024-08-27 23:11:58.258381: train_loss -0.7443 
-2024-08-27 23:11:58.259106: val_loss -0.7643 
-2024-08-27 23:11:58.259278: Pseudo dice [0.0, 0.0, 0.8832, 0.9762, 0.8296, 0.9361, 0.9387, 0.9612, 0.9435, 0.9401, 0.9143, 0.9572, 0.9538, 0.8306, 0.9487, 0.9283, 0.7954, 0.7991, nan] 
-2024-08-27 23:11:58.259356: Epoch time: 86.54 s 
-2024-08-27 23:11:59.492683:  
-2024-08-27 23:11:59.493042: Epoch 401 
-2024-08-27 23:11:59.493139: Current learning rate: 0.00818 
-2024-08-27 23:13:30.154859: train_loss -0.7458 
-2024-08-27 23:13:30.155106: val_loss -0.765 
-2024-08-27 23:13:30.155272: Pseudo dice [0.0, 0.0, 0.8975, 0.9724, 0.8284, 0.9379, 0.9373, 0.9595, 0.9447, 0.944, 0.9216, 0.9576, 0.9504, 0.8234, 0.9473, 0.9174, 0.8132, 0.796, nan] 
-2024-08-27 23:13:30.155357: Epoch time: 90.66 s 
-2024-08-27 23:13:31.434669:  
-2024-08-27 23:13:31.435046: Epoch 402 
-2024-08-27 23:13:31.435141: Current learning rate: 0.00817 
-2024-08-27 23:14:55.389618: train_loss -0.7455 
-2024-08-27 23:14:55.389861: val_loss -0.7667 
-2024-08-27 23:14:55.390033: Pseudo dice [0.0, 0.0, 0.8939, 0.9756, 0.7947, 0.9394, 0.9401, 0.961, 0.9355, 0.9365, 0.9138, 0.9464, 0.9468, 0.8151, 0.949, 0.924, 0.8162, 0.812, nan] 
-2024-08-27 23:14:55.390126: Epoch time: 83.96 s 
-2024-08-27 23:14:56.899779:  
-2024-08-27 23:14:56.899959: Epoch 403 
-2024-08-27 23:14:56.900046: Current learning rate: 0.00817 
-2024-08-27 23:16:23.558030: train_loss -0.7468 
-2024-08-27 23:16:23.558266: val_loss -0.7685 
-2024-08-27 23:16:23.558433: Pseudo dice [0.0, 0.0, 0.8994, 0.9732, 0.828, 0.9405, 0.9416, 0.962, 0.9426, 0.9434, 0.9224, 0.954, 0.9564, 0.8183, 0.9508, 0.9203, 0.8284, 0.8203, nan] 
-2024-08-27 23:16:23.558516: Epoch time: 86.66 s 
-2024-08-27 23:16:24.821474:  
-2024-08-27 23:16:24.821808: Epoch 404 
-2024-08-27 23:16:24.821894: Current learning rate: 0.00816 
-2024-08-27 23:17:54.324286: train_loss -0.743 
-2024-08-27 23:17:54.324606: val_loss -0.757 
-2024-08-27 23:17:54.324872: Pseudo dice [0.0, 0.0, 0.8683, 0.9763, 0.7743, 0.9389, 0.9421, 0.9551, 0.9407, 0.9452, 0.9191, 0.9559, 0.9548, 0.8058, 0.9358, 0.9167, 0.7791, 0.7954, nan] 
-2024-08-27 23:17:54.324988: Epoch time: 89.5 s 
-2024-08-27 23:17:55.594417:  
-2024-08-27 23:17:55.594635: Epoch 405 
-2024-08-27 23:17:55.594732: Current learning rate: 0.00816 
-2024-08-27 23:19:27.860301: train_loss -0.7415 
-2024-08-27 23:19:27.860570: val_loss -0.7628 
-2024-08-27 23:19:27.860785: Pseudo dice [0.0, 0.0, 0.8877, 0.9764, 0.7947, 0.9386, 0.9379, 0.9596, 0.9461, 0.9355, 0.9118, 0.9526, 0.9512, 0.8239, 0.9511, 0.9202, 0.8131, 0.808, nan] 
-2024-08-27 23:19:27.860898: Epoch time: 92.27 s 
-2024-08-27 23:19:29.202779:  
-2024-08-27 23:19:29.202934: Epoch 406 
-2024-08-27 23:19:29.203017: Current learning rate: 0.00815 
-2024-08-27 23:20:52.310090: train_loss -0.7466 
-2024-08-27 23:20:52.310317: val_loss -0.7667 
-2024-08-27 23:20:52.310472: Pseudo dice [0.0, 0.0, 0.8775, 0.9729, 0.8271, 0.9413, 0.9425, 0.9609, 0.9464, 0.9379, 0.9108, 0.9553, 0.9513, 0.8266, 0.948, 0.9246, 0.7792, 0.7762, nan] 
-2024-08-27 23:20:52.310552: Epoch time: 83.11 s 
-2024-08-27 23:20:53.580189:  
-2024-08-27 23:20:53.580351: Epoch 407 
-2024-08-27 23:20:53.580438: Current learning rate: 0.00815 
-2024-08-27 23:22:17.101920: train_loss -0.7479 
-2024-08-27 23:22:17.102141: val_loss -0.7625 
-2024-08-27 23:22:17.102308: Pseudo dice [0.0, 0.0, 0.8729, 0.9734, 0.7973, 0.9342, 0.9441, 0.9603, 0.9477, 0.9392, 0.9203, 0.9541, 0.9498, 0.8222, 0.9375, 0.9225, 0.8184, 0.8057, nan] 
-2024-08-27 23:22:17.102394: Epoch time: 83.52 s 
-2024-08-27 23:22:18.350827:  
-2024-08-27 23:22:18.350982: Epoch 408 
-2024-08-27 23:22:18.351064: Current learning rate: 0.00814 
-2024-08-27 23:23:43.848804: train_loss -0.7474 
-2024-08-27 23:23:43.849012: val_loss -0.7637 
-2024-08-27 23:23:43.849166: Pseudo dice [0.0, 0.0, 0.8807, 0.9744, 0.8089, 0.9468, 0.9488, 0.9645, 0.9421, 0.9362, 0.9224, 0.9523, 0.9513, 0.8228, 0.9498, 0.9181, 0.8006, 0.8106, nan] 
-2024-08-27 23:23:43.849248: Epoch time: 85.5 s 
-2024-08-27 23:23:45.274130:  
-2024-08-27 23:23:45.274286: Epoch 409 
-2024-08-27 23:23:45.274378: Current learning rate: 0.00814 
-2024-08-27 23:25:11.210232: train_loss -0.7473 
-2024-08-27 23:25:11.210469: val_loss -0.764 
-2024-08-27 23:25:11.210636: Pseudo dice [0.0, 0.0, 0.8581, 0.975, 0.8219, 0.9354, 0.934, 0.9609, 0.9358, 0.9241, 0.916, 0.9467, 0.9479, 0.8208, 0.944, 0.9258, 0.8135, 0.7996, nan] 
-2024-08-27 23:25:11.210720: Epoch time: 85.94 s 
-2024-08-27 23:25:12.484158:  
-2024-08-27 23:25:12.484325: Epoch 410 
-2024-08-27 23:25:12.484407: Current learning rate: 0.00813 
-2024-08-27 23:26:43.824036: train_loss -0.7478 
-2024-08-27 23:26:43.824264: val_loss -0.7711 
-2024-08-27 23:26:43.824447: Pseudo dice [0.0, 0.0, 0.882, 0.975, 0.8356, 0.9431, 0.9424, 0.9603, 0.9438, 0.9504, 0.9263, 0.951, 0.9571, 0.8362, 0.9484, 0.9301, 0.8046, 0.8061, nan] 
-2024-08-27 23:26:43.824541: Epoch time: 91.34 s 
-2024-08-27 23:26:45.049109:  
-2024-08-27 23:26:45.049269: Epoch 411 
-2024-08-27 23:26:45.049360: Current learning rate: 0.00813 
-2024-08-27 23:28:13.391235: train_loss -0.7475 
-2024-08-27 23:28:13.391471: val_loss -0.769 
-2024-08-27 23:28:13.391622: Pseudo dice [0.0, 0.0, 0.8547, 0.975, 0.8372, 0.9449, 0.9427, 0.9591, 0.9434, 0.9505, 0.9202, 0.9555, 0.9545, 0.8264, 0.9516, 0.924, 0.8139, 0.8036, nan] 
-2024-08-27 23:28:13.391701: Epoch time: 88.34 s 
-2024-08-27 23:28:14.617599:  
-2024-08-27 23:28:14.617794: Epoch 412 
-2024-08-27 23:28:14.617890: Current learning rate: 0.00813 
-2024-08-27 23:29:42.426886: train_loss -0.7427 
-2024-08-27 23:29:42.427129: val_loss -0.7659 
-2024-08-27 23:29:42.427285: Pseudo dice [0.0, 0.0, 0.8836, 0.9756, 0.8163, 0.9357, 0.9436, 0.9583, 0.9478, 0.9433, 0.9181, 0.96, 0.9543, 0.8165, 0.9533, 0.9242, 0.8157, 0.8027, nan] 
-2024-08-27 23:29:42.427368: Epoch time: 87.81 s 
-2024-08-27 23:29:43.630058:  
-2024-08-27 23:29:43.630403: Epoch 413 
-2024-08-27 23:29:43.630502: Current learning rate: 0.00812 
-2024-08-27 23:31:13.996455: train_loss -0.7468 
-2024-08-27 23:31:13.996686: val_loss -0.757 
-2024-08-27 23:31:13.996843: Pseudo dice [0.0, 0.0, 0.8647, 0.976, 0.821, 0.9322, 0.9335, 0.9599, 0.9152, 0.917, 0.9108, 0.9298, 0.935, 0.8305, 0.9466, 0.9283, 0.7967, 0.8036, nan] 
-2024-08-27 23:31:13.996926: Epoch time: 90.37 s 
-2024-08-27 23:31:15.352626:  
-2024-08-27 23:31:15.352841: Epoch 414 
-2024-08-27 23:31:15.352931: Current learning rate: 0.00812 
-2024-08-27 23:32:43.338392: train_loss -0.7494 
-2024-08-27 23:32:43.338622: val_loss -0.7667 
-2024-08-27 23:32:43.338830: Pseudo dice [0.0, 0.0, 0.8798, 0.9769, 0.8186, 0.9379, 0.9448, 0.963, 0.9392, 0.9448, 0.9215, 0.9475, 0.9524, 0.8245, 0.9479, 0.9271, 0.7921, 0.7831, nan] 
-2024-08-27 23:32:43.338927: Epoch time: 87.99 s 
-2024-08-27 23:32:44.534285:  
-2024-08-27 23:32:44.534591: Epoch 415 
-2024-08-27 23:32:44.534692: Current learning rate: 0.00811 
-2024-08-27 23:34:14.964829: train_loss -0.7492 
-2024-08-27 23:34:14.965112: val_loss -0.7623 
-2024-08-27 23:34:14.965410: Pseudo dice [0.0, 0.0, 0.8809, 0.9764, 0.772, 0.9443, 0.943, 0.9632, 0.9437, 0.932, 0.9109, 0.9542, 0.9474, 0.8373, 0.9498, 0.9299, 0.8162, 0.815, nan] 
-2024-08-27 23:34:14.965515: Epoch time: 90.43 s 
-2024-08-27 23:34:16.254462:  
-2024-08-27 23:34:16.254626: Epoch 416 
-2024-08-27 23:34:16.254710: Current learning rate: 0.00811 
-2024-08-27 23:35:46.229237: train_loss -0.7457 
-2024-08-27 23:35:46.229474: val_loss -0.7635 
-2024-08-27 23:35:46.229678: Pseudo dice [0.0, 0.0, 0.8795, 0.9779, 0.8192, 0.9327, 0.9354, 0.9614, 0.9485, 0.9446, 0.9185, 0.9512, 0.9524, 0.8156, 0.9439, 0.9218, 0.8052, 0.8169, nan] 
-2024-08-27 23:35:46.229777: Epoch time: 89.98 s 
-2024-08-27 23:35:47.416511:  
-2024-08-27 23:35:47.416990: Epoch 417 
-2024-08-27 23:35:47.417108: Current learning rate: 0.0081 
-2024-08-27 23:37:17.231962: train_loss -0.7451 
-2024-08-27 23:37:17.232196: val_loss -0.7717 
-2024-08-27 23:37:17.232360: Pseudo dice [0.0, 0.0, 0.8929, 0.9765, 0.8252, 0.9447, 0.9486, 0.9607, 0.9356, 0.9372, 0.9152, 0.9541, 0.9556, 0.8279, 0.9463, 0.9284, 0.8212, 0.8027, nan] 
-2024-08-27 23:37:17.232521: Epoch time: 89.82 s 
-2024-08-27 23:37:18.363129:  
-2024-08-27 23:37:18.363296: Epoch 418 
-2024-08-27 23:37:18.363386: Current learning rate: 0.0081 
-2024-08-27 23:38:44.793985: train_loss -0.7442 
-2024-08-27 23:38:44.794211: val_loss -0.7668 
-2024-08-27 23:38:44.794353: Pseudo dice [0.0, 0.0, 0.8897, 0.9726, 0.8178, 0.9347, 0.9413, 0.9594, 0.9372, 0.9313, 0.9151, 0.9449, 0.946, 0.826, 0.942, 0.9246, 0.8131, 0.7946, nan] 
-2024-08-27 23:38:44.794431: Epoch time: 86.43 s 
-2024-08-27 23:38:45.994262:  
-2024-08-27 23:38:45.994772: Epoch 419 
-2024-08-27 23:38:45.994870: Current learning rate: 0.00809 
-2024-08-27 23:40:14.683579: train_loss -0.7436 
-2024-08-27 23:40:14.683809: val_loss -0.764 
-2024-08-27 23:40:14.683984: Pseudo dice [0.0, 0.0, 0.8825, 0.9767, 0.8182, 0.9325, 0.9359, 0.9552, 0.9397, 0.9237, 0.9191, 0.9553, 0.9528, 0.8115, 0.9339, 0.9178, 0.815, 0.7881, nan] 
-2024-08-27 23:40:14.684071: Epoch time: 88.69 s 
-2024-08-27 23:40:15.885889:  
-2024-08-27 23:40:15.886163: Epoch 420 
-2024-08-27 23:40:15.886254: Current learning rate: 0.00809 
-2024-08-27 23:41:43.339661: train_loss -0.7423 
-2024-08-27 23:41:43.339889: val_loss -0.766 
-2024-08-27 23:41:43.340048: Pseudo dice [0.0, 0.0, 0.888, 0.9746, 0.825, 0.9416, 0.9487, 0.9615, 0.9506, 0.9407, 0.9332, 0.9574, 0.9594, 0.8092, 0.9387, 0.9254, 0.795, 0.8132, nan] 
-2024-08-27 23:41:43.340131: Epoch time: 87.45 s 
-2024-08-27 23:41:44.519719:  
-2024-08-27 23:41:44.520008: Epoch 421 
-2024-08-27 23:41:44.520110: Current learning rate: 0.00808 
-2024-08-27 23:43:09.939887: train_loss -0.7433 
-2024-08-27 23:43:09.940130: val_loss -0.7658 
-2024-08-27 23:43:09.940307: Pseudo dice [0.0, 0.0, 0.8818, 0.9746, 0.8315, 0.9453, 0.9473, 0.9616, 0.946, 0.9463, 0.9199, 0.9551, 0.9527, 0.8323, 0.9388, 0.9205, 0.8102, 0.796, nan] 
-2024-08-27 23:43:09.940420: Epoch time: 85.42 s 
-2024-08-27 23:43:11.083337:  
-2024-08-27 23:43:11.083600: Epoch 422 
-2024-08-27 23:43:11.083704: Current learning rate: 0.00808 
-2024-08-27 23:44:36.826530: train_loss -0.745 
-2024-08-27 23:44:36.827050: val_loss -0.7671 
-2024-08-27 23:44:36.827214: Pseudo dice [0.0, 0.0, 0.8841, 0.9765, 0.7983, 0.9214, 0.932, 0.956, 0.9414, 0.9329, 0.9211, 0.949, 0.9477, 0.8251, 0.9415, 0.9198, 0.8342, 0.8183, nan] 
-2024-08-27 23:44:36.827293: Epoch time: 85.74 s 
-2024-08-27 23:44:38.344965:  
-2024-08-27 23:44:38.345189: Epoch 423 
-2024-08-27 23:44:38.345318: Current learning rate: 0.00807 
-2024-08-27 23:46:11.372825: train_loss -0.7513 
-2024-08-27 23:46:11.373219: val_loss -0.7687 
-2024-08-27 23:46:11.373385: Pseudo dice [0.0, 0.0, 0.881, 0.9762, 0.8168, 0.9431, 0.9409, 0.9596, 0.9432, 0.9443, 0.9088, 0.9557, 0.9515, 0.8212, 0.9444, 0.9209, 0.8087, 0.8171, nan] 
-2024-08-27 23:46:11.373471: Epoch time: 93.03 s 
-2024-08-27 23:46:12.570469:  
-2024-08-27 23:46:12.570952: Epoch 424 
-2024-08-27 23:46:12.571056: Current learning rate: 0.00807 
-2024-08-27 23:47:38.965110: train_loss -0.7532 
-2024-08-27 23:47:38.965341: val_loss -0.7719 
-2024-08-27 23:47:38.965496: Pseudo dice [0.0, 0.0, 0.8901, 0.9764, 0.8578, 0.9474, 0.9477, 0.9629, 0.948, 0.9492, 0.9208, 0.956, 0.9581, 0.8391, 0.9475, 0.9305, 0.8032, 0.8168, nan] 
-2024-08-27 23:47:38.965575: Epoch time: 86.4 s 
-2024-08-27 23:47:40.210818:  
-2024-08-27 23:47:40.211050: Epoch 425 
-2024-08-27 23:47:40.211140: Current learning rate: 0.00807 
-2024-08-27 23:49:07.174956: train_loss -0.7477 
-2024-08-27 23:49:07.175276: val_loss -0.76 
-2024-08-27 23:49:07.175478: Pseudo dice [0.0, 0.0, 0.8843, 0.9757, 0.8395, 0.9285, 0.938, 0.9636, 0.9423, 0.9319, 0.9274, 0.9508, 0.9557, 0.8238, 0.9403, 0.9207, 0.802, 0.7677, nan] 
-2024-08-27 23:49:07.175634: Epoch time: 86.96 s 
-2024-08-27 23:49:08.604058:  
-2024-08-27 23:49:08.604229: Epoch 426 
-2024-08-27 23:49:08.604321: Current learning rate: 0.00806 
-2024-08-27 23:50:34.216075: train_loss -0.7514 
-2024-08-27 23:50:34.216315: val_loss -0.7716 
-2024-08-27 23:50:34.216492: Pseudo dice [0.0, 0.0, 0.8838, 0.9761, 0.8096, 0.9375, 0.9377, 0.9633, 0.938, 0.9429, 0.9144, 0.9452, 0.9488, 0.8319, 0.9493, 0.9232, 0.8141, 0.8106, nan] 
-2024-08-27 23:50:34.216584: Epoch time: 85.61 s 
-2024-08-27 23:50:35.434379:  
-2024-08-27 23:50:35.434567: Epoch 427 
-2024-08-27 23:50:35.434659: Current learning rate: 0.00806 
-2024-08-27 23:52:02.333670: train_loss -0.7498 
-2024-08-27 23:52:02.333926: val_loss -0.7692 
-2024-08-27 23:52:02.334098: Pseudo dice [0.0, 0.0, 0.8685, 0.9772, 0.8269, 0.9392, 0.9424, 0.9604, 0.9394, 0.9385, 0.9159, 0.9488, 0.9441, 0.8237, 0.9451, 0.9162, 0.8207, 0.8126, nan] 
-2024-08-27 23:52:02.334186: Epoch time: 86.9 s 
-2024-08-27 23:52:03.518262:  
-2024-08-27 23:52:03.518429: Epoch 428 
-2024-08-27 23:52:03.518523: Current learning rate: 0.00805 
-2024-08-27 23:53:32.955758: train_loss -0.7531 
-2024-08-27 23:53:32.956018: val_loss -0.7667 
-2024-08-27 23:53:32.956187: Pseudo dice [0.0, 0.0, 0.8805, 0.9764, 0.8225, 0.9468, 0.9484, 0.9623, 0.9464, 0.9538, 0.9243, 0.9552, 0.956, 0.8232, 0.9459, 0.9207, 0.8085, 0.8113, nan] 
-2024-08-27 23:53:32.956276: Epoch time: 89.44 s 
-2024-08-27 23:53:34.172125:  
-2024-08-27 23:53:34.172309: Epoch 429 
-2024-08-27 23:53:34.172404: Current learning rate: 0.00805 
-2024-08-27 23:54:56.704176: train_loss -0.7505 
-2024-08-27 23:54:56.704464: val_loss -0.7626 
-2024-08-27 23:54:56.704678: Pseudo dice [0.0, 0.0, 0.8865, 0.9775, 0.8296, 0.9387, 0.933, 0.9634, 0.9375, 0.9391, 0.9151, 0.944, 0.9465, 0.835, 0.9477, 0.9219, 0.8211, 0.8133, nan] 
-2024-08-27 23:54:56.704785: Epoch time: 82.53 s 
-2024-08-27 23:54:57.988729:  
-2024-08-27 23:54:57.988971: Epoch 430 
-2024-08-27 23:54:57.989067: Current learning rate: 0.00804 
-2024-08-27 23:56:24.909889: train_loss -0.75 
-2024-08-27 23:56:24.910125: val_loss -0.7663 
-2024-08-27 23:56:24.910277: Pseudo dice [0.0, 0.0, 0.8964, 0.9697, 0.828, 0.9455, 0.9494, 0.962, 0.9409, 0.9419, 0.9218, 0.9524, 0.9515, 0.8272, 0.9336, 0.9148, 0.7809, 0.7996, nan] 
-2024-08-27 23:56:24.910357: Epoch time: 86.92 s 
-2024-08-27 23:56:26.096565:  
-2024-08-27 23:56:26.097085: Epoch 431 
-2024-08-27 23:56:26.097193: Current learning rate: 0.00804 
-2024-08-27 23:57:57.091393: train_loss -0.7475 
-2024-08-27 23:57:57.091758: val_loss -0.7621 
-2024-08-27 23:57:57.091922: Pseudo dice [0.0, 0.0, 0.8813, 0.976, 0.8242, 0.9374, 0.9418, 0.9615, 0.9355, 0.9349, 0.9126, 0.9458, 0.9437, 0.8203, 0.9449, 0.9268, 0.8141, 0.8168, nan] 
-2024-08-27 23:57:57.092001: Epoch time: 91.0 s 
-2024-08-27 23:57:58.549555:  
-2024-08-27 23:57:58.549748: Epoch 432 
-2024-08-27 23:57:58.549848: Current learning rate: 0.00803 
-2024-08-27 23:59:24.055493: train_loss -0.7475 
-2024-08-27 23:59:24.055881: val_loss -0.7621 
-2024-08-27 23:59:24.056184: Pseudo dice [0.0, 0.0, 0.8975, 0.9763, 0.8266, 0.9394, 0.9407, 0.9628, 0.9384, 0.9315, 0.9142, 0.9479, 0.9426, 0.833, 0.9516, 0.9243, 0.8144, 0.8087, nan] 
-2024-08-27 23:59:24.056388: Epoch time: 85.51 s 
-2024-08-27 23:59:25.455702:  
-2024-08-27 23:59:25.455985: Epoch 433 
-2024-08-27 23:59:25.456071: Current learning rate: 0.00803 
-2024-08-28 00:00:53.706485: train_loss -0.7427 
-2024-08-28 00:00:53.706739: val_loss -0.7576 
-2024-08-28 00:00:53.706906: Pseudo dice [0.0, 0.0, 0.8582, 0.9766, 0.7928, 0.9385, 0.9429, 0.9583, 0.9434, 0.9243, 0.9073, 0.9537, 0.9489, 0.8041, 0.9372, 0.92, 0.8081, 0.8001, nan] 
-2024-08-28 00:00:53.706995: Epoch time: 88.25 s 
-2024-08-28 00:00:54.939412:  
-2024-08-28 00:00:54.939708: Epoch 434 
-2024-08-28 00:00:54.939807: Current learning rate: 0.00802 
-2024-08-28 00:02:27.530825: train_loss -0.7432 
-2024-08-28 00:02:27.531074: val_loss -0.7611 
-2024-08-28 00:02:27.531243: Pseudo dice [0.0, 0.0, 0.8824, 0.9756, 0.823, 0.9429, 0.9491, 0.9621, 0.9391, 0.942, 0.917, 0.9525, 0.9536, 0.8238, 0.9399, 0.9247, 0.7995, 0.7853, nan] 
-2024-08-28 00:02:27.531331: Epoch time: 92.59 s 
-2024-08-28 00:02:28.756562:  
-2024-08-28 00:02:28.756738: Epoch 435 
-2024-08-28 00:02:28.756832: Current learning rate: 0.00802 
-2024-08-28 00:03:51.824524: train_loss -0.7435 
-2024-08-28 00:03:51.824773: val_loss -0.7602 
-2024-08-28 00:03:51.824931: Pseudo dice [0.0, 0.0, 0.8838, 0.9745, 0.7949, 0.9412, 0.9427, 0.9586, 0.9412, 0.9323, 0.9082, 0.9501, 0.9501, 0.8255, 0.9468, 0.9227, 0.8043, 0.7925, nan] 
-2024-08-28 00:03:51.825015: Epoch time: 83.07 s 
-2024-08-28 00:03:53.024771:  
-2024-08-28 00:03:53.025121: Epoch 436 
-2024-08-28 00:03:53.025222: Current learning rate: 0.00801 
-2024-08-28 00:05:22.309675: train_loss -0.739 
-2024-08-28 00:05:22.309920: val_loss -0.7601 
-2024-08-28 00:05:22.310090: Pseudo dice [0.0, 0.0, 0.8935, 0.9731, 0.8162, 0.9367, 0.9356, 0.9561, 0.9439, 0.9362, 0.9172, 0.9559, 0.9517, 0.8224, 0.9445, 0.9203, 0.7941, 0.7883, nan] 
-2024-08-28 00:05:22.310178: Epoch time: 89.29 s 
-2024-08-28 00:05:23.530973:  
-2024-08-28 00:05:23.531183: Epoch 437 
-2024-08-28 00:05:23.531334: Current learning rate: 0.00801 
-2024-08-28 00:06:49.770996: train_loss -0.7479 
-2024-08-28 00:06:49.771421: val_loss -0.7678 
-2024-08-28 00:06:49.771761: Pseudo dice [0.0, 0.0, 0.8878, 0.9775, 0.8299, 0.9442, 0.9449, 0.9629, 0.9506, 0.95, 0.9277, 0.9583, 0.9545, 0.8301, 0.9453, 0.9228, 0.7852, 0.8043, nan] 
-2024-08-28 00:06:49.771991: Epoch time: 86.24 s 
-2024-08-28 00:06:50.981328:  
-2024-08-28 00:06:50.981576: Epoch 438 
-2024-08-28 00:06:50.981679: Current learning rate: 0.00801 
-2024-08-28 00:08:23.796042: train_loss -0.7447 
-2024-08-28 00:08:23.796256: val_loss -0.768 
-2024-08-28 00:08:23.796420: Pseudo dice [0.0, 0.0, 0.896, 0.9752, 0.8201, 0.9447, 0.9467, 0.9613, 0.948, 0.9427, 0.9225, 0.9596, 0.9552, 0.8238, 0.9461, 0.9255, 0.8031, 0.7887, nan] 
-2024-08-28 00:08:23.796511: Epoch time: 92.82 s 
-2024-08-28 00:08:25.250033:  
-2024-08-28 00:08:25.250364: Epoch 439 
-2024-08-28 00:08:25.250468: Current learning rate: 0.008 
-2024-08-28 00:09:49.074186: train_loss -0.7479 
-2024-08-28 00:09:49.074507: val_loss -0.7661 
-2024-08-28 00:09:49.074678: Pseudo dice [0.0, 0.0, 0.8841, 0.9753, 0.8375, 0.9422, 0.9466, 0.9645, 0.946, 0.9402, 0.9116, 0.9527, 0.9527, 0.8278, 0.9421, 0.9286, 0.7923, 0.7926, nan] 
-2024-08-28 00:09:49.074767: Epoch time: 83.82 s 
-2024-08-28 00:09:50.226811:  
-2024-08-28 00:09:50.227108: Epoch 440 
-2024-08-28 00:09:50.227201: Current learning rate: 0.008 
-2024-08-28 00:11:18.343106: train_loss -0.7467 
-2024-08-28 00:11:18.343405: val_loss -0.7641 
-2024-08-28 00:11:18.343622: Pseudo dice [0.0, 0.0, 0.8849, 0.9752, 0.82, 0.9445, 0.9386, 0.9597, 0.9393, 0.9384, 0.9116, 0.9568, 0.9486, 0.8175, 0.9454, 0.9229, 0.7909, 0.8042, nan] 
-2024-08-28 00:11:18.343730: Epoch time: 88.12 s 
-2024-08-28 00:11:19.554039:  
-2024-08-28 00:11:19.554381: Epoch 441 
-2024-08-28 00:11:19.554476: Current learning rate: 0.00799 
-2024-08-28 00:12:45.300643: train_loss -0.7443 
-2024-08-28 00:12:45.300889: val_loss -0.7654 
-2024-08-28 00:12:45.301044: Pseudo dice [0.0, 0.0, 0.8886, 0.9749, 0.8105, 0.9409, 0.9456, 0.9617, 0.9443, 0.943, 0.9128, 0.9561, 0.9526, 0.8351, 0.9505, 0.9258, 0.7972, 0.8051, nan] 
-2024-08-28 00:12:45.301129: Epoch time: 85.75 s 
-2024-08-28 00:12:46.530688:  
-2024-08-28 00:12:46.530973: Epoch 442 
-2024-08-28 00:12:46.531071: Current learning rate: 0.00799 
-2024-08-28 00:14:13.044701: train_loss -0.7486 
-2024-08-28 00:14:13.044909: val_loss -0.7613 
-2024-08-28 00:14:13.045075: Pseudo dice [0.0, 0.0, 0.8844, 0.9753, 0.7946, 0.9431, 0.9422, 0.9574, 0.9389, 0.9324, 0.9257, 0.9496, 0.9493, 0.8229, 0.946, 0.9268, 0.8113, 0.8072, nan] 
-2024-08-28 00:14:13.045161: Epoch time: 86.51 s 
-2024-08-28 00:14:14.233188:  
-2024-08-28 00:14:14.233473: Epoch 443 
-2024-08-28 00:14:14.233569: Current learning rate: 0.00798 
-2024-08-28 00:15:40.926726: train_loss -0.7474 
-2024-08-28 00:15:40.926965: val_loss -0.7668 
-2024-08-28 00:15:40.927125: Pseudo dice [0.0, 0.0, 0.8869, 0.9751, 0.8394, 0.9467, 0.9504, 0.9616, 0.9463, 0.9456, 0.9082, 0.9535, 0.9514, 0.8204, 0.9426, 0.9269, 0.8194, 0.812, nan] 
-2024-08-28 00:15:40.927210: Epoch time: 86.69 s 
-2024-08-28 00:15:42.559259:  
-2024-08-28 00:15:42.559549: Epoch 444 
-2024-08-28 00:15:42.559672: Current learning rate: 0.00798 
-2024-08-28 00:17:13.487911: train_loss -0.7503 
-2024-08-28 00:17:13.488182: val_loss -0.7699 
-2024-08-28 00:17:13.488348: Pseudo dice [0.0, 0.0, 0.881, 0.9768, 0.8279, 0.9492, 0.9503, 0.963, 0.9509, 0.9417, 0.9198, 0.9577, 0.9558, 0.828, 0.9441, 0.9306, 0.829, 0.8156, nan] 
-2024-08-28 00:17:13.488450: Epoch time: 90.93 s 
-2024-08-28 00:17:13.488504: Yayy! New best EMA pseudo Dice: 0.8076 
-2024-08-28 00:17:14.993565:  
-2024-08-28 00:17:14.993839: Epoch 445 
-2024-08-28 00:17:14.993932: Current learning rate: 0.00797 
-2024-08-28 00:18:40.939409: train_loss -0.751 
-2024-08-28 00:18:40.939651: val_loss -0.7707 
-2024-08-28 00:18:40.939821: Pseudo dice [0.0, 0.0, 0.8927, 0.9754, 0.8041, 0.9459, 0.9459, 0.9643, 0.9448, 0.946, 0.9222, 0.9574, 0.9538, 0.8305, 0.9489, 0.9264, 0.7959, 0.8249, nan] 
-2024-08-28 00:18:40.939912: Epoch time: 85.95 s 
-2024-08-28 00:18:40.939962: Yayy! New best EMA pseudo Dice: 0.8079 
-2024-08-28 00:18:42.450454:  
-2024-08-28 00:18:42.450650: Epoch 446 
-2024-08-28 00:18:42.450741: Current learning rate: 0.00797 
-2024-08-28 00:20:09.851115: train_loss -0.7512 
-2024-08-28 00:20:09.851345: val_loss -0.777 
-2024-08-28 00:20:09.851500: Pseudo dice [0.0, 0.0, 0.8906, 0.9768, 0.832, 0.9469, 0.9466, 0.9635, 0.9444, 0.9455, 0.9321, 0.9545, 0.959, 0.8323, 0.9436, 0.9278, 0.8167, 0.8183, nan] 
-2024-08-28 00:20:09.851581: Epoch time: 87.4 s 
-2024-08-28 00:20:09.851628: Yayy! New best EMA pseudo Dice: 0.8084 
-2024-08-28 00:20:11.362320:  
-2024-08-28 00:20:11.362633: Epoch 447 
-2024-08-28 00:20:11.362731: Current learning rate: 0.00796 
-2024-08-28 00:21:41.916903: train_loss -0.7495 
-2024-08-28 00:21:41.917160: val_loss -0.7711 
-2024-08-28 00:21:41.917342: Pseudo dice [0.0, 0.0, 0.8803, 0.9765, 0.8088, 0.9485, 0.952, 0.964, 0.9484, 0.9449, 0.916, 0.9588, 0.9525, 0.835, 0.9513, 0.9266, 0.8145, 0.8065, nan] 
-2024-08-28 00:21:41.917452: Epoch time: 90.56 s 
-2024-08-28 00:21:41.917506: Yayy! New best EMA pseudo Dice: 0.8085 
-2024-08-28 00:21:43.398474:  
-2024-08-28 00:21:43.398642: Epoch 448 
-2024-08-28 00:21:43.398742: Current learning rate: 0.00796 
-2024-08-28 00:23:13.462833: train_loss -0.7463 
-2024-08-28 00:23:13.463482: val_loss -0.7656 
-2024-08-28 00:23:13.463719: Pseudo dice [0.0, 0.0, 0.8761, 0.9768, 0.8259, 0.9417, 0.9438, 0.9566, 0.9415, 0.9307, 0.9177, 0.9519, 0.9511, 0.8391, 0.9451, 0.9238, 0.8011, 0.824, nan] 
-2024-08-28 00:23:13.463912: Epoch time: 90.06 s 
-2024-08-28 00:23:14.747330:  
-2024-08-28 00:23:14.747852: Epoch 449 
-2024-08-28 00:23:14.748053: Current learning rate: 0.00795 
-2024-08-28 00:24:42.723752: train_loss -0.7493 
-2024-08-28 00:24:42.724075: val_loss -0.7694 
-2024-08-28 00:24:42.724250: Pseudo dice [0.0, 0.0, 0.8928, 0.9757, 0.8518, 0.9433, 0.9464, 0.961, 0.944, 0.94, 0.9241, 0.9522, 0.9571, 0.8252, 0.9305, 0.9317, 0.8275, 0.8143, nan] 
-2024-08-28 00:24:42.724339: Epoch time: 87.98 s 
-2024-08-28 00:24:43.037228: Yayy! New best EMA pseudo Dice: 0.8089 
-2024-08-28 00:24:44.805351:  
-2024-08-28 00:24:44.805537: Epoch 450 
-2024-08-28 00:24:44.805642: Current learning rate: 0.00795 
-2024-08-28 00:26:10.558181: train_loss -0.7543 
-2024-08-28 00:26:10.558417: val_loss -0.7691 
-2024-08-28 00:26:10.558583: Pseudo dice [0.0, 0.0, 0.8638, 0.9739, 0.8274, 0.9443, 0.9462, 0.9622, 0.9486, 0.9439, 0.9239, 0.9572, 0.9551, 0.835, 0.9387, 0.9279, 0.8105, 0.8042, nan] 
-2024-08-28 00:26:10.558667: Epoch time: 85.75 s 
-2024-08-28 00:26:10.558717: Yayy! New best EMA pseudo Dice: 0.8089 
-2024-08-28 00:26:12.028015:  
-2024-08-28 00:26:12.028252: Epoch 451 
-2024-08-28 00:26:12.028360: Current learning rate: 0.00795 
-2024-08-28 00:27:36.729891: train_loss -0.7504 
-2024-08-28 00:27:36.730108: val_loss -0.7676 
-2024-08-28 00:27:36.730270: Pseudo dice [0.0, 0.0, 0.8762, 0.9765, 0.8287, 0.9417, 0.9443, 0.9565, 0.9489, 0.9399, 0.9254, 0.9582, 0.956, 0.8342, 0.9403, 0.917, 0.8146, 0.8157, nan] 
-2024-08-28 00:27:36.730352: Epoch time: 84.7 s 
-2024-08-28 00:27:36.730401: Yayy! New best EMA pseudo Dice: 0.809 
-2024-08-28 00:27:38.148423:  
-2024-08-28 00:27:38.148584: Epoch 452 
-2024-08-28 00:27:38.148669: Current learning rate: 0.00794 
-2024-08-28 00:29:07.072031: train_loss -0.7461 
-2024-08-28 00:29:07.072264: val_loss -0.7636 
-2024-08-28 00:29:07.072438: Pseudo dice [0.0, 0.0, 0.8917, 0.9741, 0.8355, 0.9422, 0.9419, 0.9608, 0.9488, 0.9366, 0.9106, 0.9583, 0.9508, 0.8375, 0.9412, 0.9276, 0.7998, 0.8076, nan] 
-2024-08-28 00:29:07.072528: Epoch time: 88.92 s 
-2024-08-28 00:29:07.072579: Yayy! New best EMA pseudo Dice: 0.809 
-2024-08-28 00:29:08.577607:  
-2024-08-28 00:29:08.577830: Epoch 453 
-2024-08-28 00:29:08.578016: Current learning rate: 0.00794 
-2024-08-28 00:30:35.301080: train_loss -0.7434 
-2024-08-28 00:30:35.301378: val_loss -0.7697 
-2024-08-28 00:30:35.301555: Pseudo dice [0.0, 0.0, 0.8822, 0.9725, 0.8138, 0.9434, 0.9405, 0.9622, 0.945, 0.9483, 0.9189, 0.9563, 0.953, 0.8265, 0.945, 0.9149, 0.8, 0.8027, nan] 
-2024-08-28 00:30:35.301648: Epoch time: 86.72 s 
-2024-08-28 00:30:36.491351:  
-2024-08-28 00:30:36.491500: Epoch 454 
-2024-08-28 00:30:36.491592: Current learning rate: 0.00793 
-2024-08-28 00:32:00.646502: train_loss -0.7444 
-2024-08-28 00:32:00.646790: val_loss -0.7674 
-2024-08-28 00:32:00.647041: Pseudo dice [0.0, 0.0, 0.8899, 0.9753, 0.8325, 0.9465, 0.9465, 0.9628, 0.9467, 0.949, 0.9276, 0.9557, 0.9575, 0.817, 0.9497, 0.9254, 0.8291, 0.8293, nan] 
-2024-08-28 00:32:00.647157: Epoch time: 84.16 s 
-2024-08-28 00:32:00.647222: Yayy! New best EMA pseudo Dice: 0.8092 
-2024-08-28 00:32:02.458623:  
-2024-08-28 00:32:02.458913: Epoch 455 
-2024-08-28 00:32:02.459005: Current learning rate: 0.00793 
-2024-08-28 00:33:32.177264: train_loss -0.7461 
-2024-08-28 00:33:32.177488: val_loss -0.7598 
-2024-08-28 00:33:32.177650: Pseudo dice [0.0, 0.0, 0.8866, 0.9768, 0.8241, 0.9348, 0.9372, 0.9565, 0.9334, 0.9312, 0.9143, 0.9464, 0.9454, 0.8143, 0.9409, 0.9226, 0.819, 0.8181, nan] 
-2024-08-28 00:33:32.177730: Epoch time: 89.72 s 
-2024-08-28 00:33:33.367480:  
-2024-08-28 00:33:33.367883: Epoch 456 
-2024-08-28 00:33:33.367991: Current learning rate: 0.00792 
-2024-08-28 00:35:01.087344: train_loss -0.7458 
-2024-08-28 00:35:01.087634: val_loss -0.7679 
-2024-08-28 00:35:01.087833: Pseudo dice [0.0, 0.0, 0.8997, 0.9765, 0.8371, 0.9449, 0.9423, 0.9641, 0.9445, 0.938, 0.9197, 0.9554, 0.9549, 0.8353, 0.9462, 0.9253, 0.8104, 0.8172, nan] 
-2024-08-28 00:35:01.088017: Epoch time: 87.72 s 
-2024-08-28 00:35:02.372598:  
-2024-08-28 00:35:02.372973: Epoch 457 
-2024-08-28 00:35:02.373087: Current learning rate: 0.00792 
-2024-08-28 00:36:27.013794: train_loss -0.7491 
-2024-08-28 00:36:27.014024: val_loss -0.7683 
-2024-08-28 00:36:27.014191: Pseudo dice [0.0, 0.0, 0.8883, 0.9763, 0.8326, 0.9367, 0.9413, 0.9632, 0.9357, 0.9334, 0.919, 0.9449, 0.9443, 0.8318, 0.9439, 0.9307, 0.8096, 0.8057, nan] 
-2024-08-28 00:36:27.014278: Epoch time: 84.64 s 
-2024-08-28 00:36:28.165814:  
-2024-08-28 00:36:28.166022: Epoch 458 
-2024-08-28 00:36:28.166121: Current learning rate: 0.00791 
-2024-08-28 00:37:51.137872: train_loss -0.7463 
-2024-08-28 00:37:51.138154: val_loss -0.7673 
-2024-08-28 00:37:51.138402: Pseudo dice [0.0, 0.0, 0.8651, 0.9773, 0.8168, 0.9411, 0.9442, 0.962, 0.9368, 0.9443, 0.9203, 0.9523, 0.9526, 0.8132, 0.9463, 0.9194, 0.7787, 0.7684, nan] 
-2024-08-28 00:37:51.138509: Epoch time: 82.97 s 
-2024-08-28 00:37:52.458596:  
-2024-08-28 00:37:52.458745: Epoch 459 
-2024-08-28 00:37:52.458839: Current learning rate: 0.00791 
-2024-08-28 00:39:16.930502: train_loss -0.7436 
-2024-08-28 00:39:16.930971: val_loss -0.7669 
-2024-08-28 00:39:16.931179: Pseudo dice [0.0, 0.0, 0.8796, 0.975, 0.8322, 0.9468, 0.9465, 0.9569, 0.9473, 0.9476, 0.9258, 0.9589, 0.9564, 0.8335, 0.9509, 0.923, 0.8052, 0.7953, nan] 
-2024-08-28 00:39:16.931313: Epoch time: 84.47 s 
-2024-08-28 00:39:18.235626:  
-2024-08-28 00:39:18.235795: Epoch 460 
-2024-08-28 00:39:18.235886: Current learning rate: 0.0079 
-2024-08-28 00:40:47.318921: train_loss -0.7479 
-2024-08-28 00:40:47.319215: val_loss -0.7703 
-2024-08-28 00:40:47.319405: Pseudo dice [0.0, 0.0, 0.9023, 0.9763, 0.7963, 0.9389, 0.9385, 0.9595, 0.9469, 0.9437, 0.9192, 0.9569, 0.9546, 0.8388, 0.9419, 0.931, 0.8096, 0.8134, nan] 
-2024-08-28 00:40:47.319513: Epoch time: 89.08 s 
-2024-08-28 00:40:48.793565:  
-2024-08-28 00:40:48.793726: Epoch 461 
-2024-08-28 00:40:48.793828: Current learning rate: 0.0079 
-2024-08-28 00:42:10.845250: train_loss -0.7497 
-2024-08-28 00:42:10.845503: val_loss -0.7773 
-2024-08-28 00:42:10.845678: Pseudo dice [0.0, 0.0, 0.8811, 0.9762, 0.8395, 0.9394, 0.9477, 0.9597, 0.9485, 0.9479, 0.9156, 0.9597, 0.9558, 0.8369, 0.9412, 0.9276, 0.8198, 0.8149, nan] 
-2024-08-28 00:42:10.845770: Epoch time: 82.05 s 
-2024-08-28 00:42:12.059785:  
-2024-08-28 00:42:12.060153: Epoch 462 
-2024-08-28 00:42:12.060317: Current learning rate: 0.00789 
-2024-08-28 00:43:38.298950: train_loss -0.7492 
-2024-08-28 00:43:38.299245: val_loss -0.7695 
-2024-08-28 00:43:38.299562: Pseudo dice [0.0, 0.0, 0.9005, 0.9759, 0.8465, 0.9426, 0.9485, 0.96, 0.9408, 0.9398, 0.9182, 0.9572, 0.9538, 0.8364, 0.9495, 0.9328, 0.8158, 0.8187, nan] 
-2024-08-28 00:43:38.299736: Epoch time: 86.24 s 
-2024-08-28 00:43:38.299877: Yayy! New best EMA pseudo Dice: 0.8093 
-2024-08-28 00:43:39.741732:  
-2024-08-28 00:43:39.741891: Epoch 463 
-2024-08-28 00:43:39.741978: Current learning rate: 0.00789 
-2024-08-28 00:45:08.160398: train_loss -0.7531 
-2024-08-28 00:45:08.160640: val_loss -0.7654 
-2024-08-28 00:45:08.160800: Pseudo dice [0.0, 0.0, 0.8903, 0.9749, 0.8272, 0.9421, 0.9475, 0.9549, 0.9457, 0.9444, 0.9268, 0.9574, 0.955, 0.8211, 0.9527, 0.9291, 0.8232, 0.8092, nan] 
-2024-08-28 00:45:08.160887: Epoch time: 88.42 s 
-2024-08-28 00:45:08.160936: Yayy! New best EMA pseudo Dice: 0.8095 
-2024-08-28 00:45:09.649197:  
-2024-08-28 00:45:09.649377: Epoch 464 
-2024-08-28 00:45:09.649466: Current learning rate: 0.00789 
-2024-08-28 00:46:37.723087: train_loss -0.7538 
-2024-08-28 00:46:37.723353: val_loss -0.7695 
-2024-08-28 00:46:37.723527: Pseudo dice [0.0, 0.0, 0.8828, 0.9772, 0.8254, 0.9469, 0.9489, 0.9631, 0.9495, 0.9508, 0.9201, 0.9573, 0.955, 0.8331, 0.9483, 0.9279, 0.811, 0.8028, nan] 
-2024-08-28 00:46:37.723643: Epoch time: 88.07 s 
-2024-08-28 00:46:37.723703: Yayy! New best EMA pseudo Dice: 0.8097 
-2024-08-28 00:46:39.229337:  
-2024-08-28 00:46:39.229581: Epoch 465 
-2024-08-28 00:46:39.229682: Current learning rate: 0.00788 
-2024-08-28 00:47:59.771363: train_loss -0.7495 
-2024-08-28 00:47:59.771600: val_loss -0.7769 
-2024-08-28 00:47:59.771756: Pseudo dice [0.0, 0.0, 0.8998, 0.9769, 0.8381, 0.9409, 0.9438, 0.9622, 0.9407, 0.9438, 0.9221, 0.9574, 0.9545, 0.8251, 0.9368, 0.9198, 0.8162, 0.8164, nan] 
-2024-08-28 00:47:59.771840: Epoch time: 80.54 s 
-2024-08-28 00:47:59.771889: Yayy! New best EMA pseudo Dice: 0.8098 
-2024-08-28 00:48:01.253537:  
-2024-08-28 00:48:01.253733: Epoch 466 
-2024-08-28 00:48:01.253839: Current learning rate: 0.00788 
-2024-08-28 00:49:29.984205: train_loss -0.7482 
-2024-08-28 00:49:29.984424: val_loss -0.7682 
-2024-08-28 00:49:29.984602: Pseudo dice [0.0, 0.0, 0.8806, 0.9765, 0.8406, 0.9451, 0.9484, 0.9653, 0.9465, 0.9458, 0.9238, 0.9567, 0.9567, 0.8257, 0.9491, 0.9235, 0.8252, 0.8141, nan] 
-2024-08-28 00:49:29.984688: Epoch time: 88.73 s 
-2024-08-28 00:49:29.984737: Yayy! New best EMA pseudo Dice: 0.81 
-2024-08-28 00:49:31.736732:  
-2024-08-28 00:49:31.736911: Epoch 467 
-2024-08-28 00:49:31.736993: Current learning rate: 0.00787 
-2024-08-28 00:50:56.865800: train_loss -0.7474 
-2024-08-28 00:50:56.866924: val_loss -0.7695 
-2024-08-28 00:50:56.867118: Pseudo dice [0.0, 0.0, 0.899, 0.9765, 0.8352, 0.9443, 0.9467, 0.9623, 0.9441, 0.9446, 0.9228, 0.9533, 0.9527, 0.8304, 0.9506, 0.9264, 0.8187, 0.8182, nan] 
-2024-08-28 00:50:56.867273: Epoch time: 85.13 s 
-2024-08-28 00:50:56.867323: Yayy! New best EMA pseudo Dice: 0.8103 
-2024-08-28 00:50:58.362046:  
-2024-08-28 00:50:58.362402: Epoch 468 
-2024-08-28 00:50:58.362508: Current learning rate: 0.00787 
-2024-08-28 00:52:25.898361: train_loss -0.7514 
-2024-08-28 00:52:25.898673: val_loss -0.7662 
-2024-08-28 00:52:25.898831: Pseudo dice [0.0, 0.0, 0.8758, 0.9763, 0.8312, 0.9381, 0.945, 0.958, 0.9463, 0.942, 0.9165, 0.9557, 0.955, 0.8256, 0.9471, 0.9242, 0.8088, 0.7963, nan] 
-2024-08-28 00:52:25.898912: Epoch time: 87.54 s 
-2024-08-28 00:52:27.116932:  
-2024-08-28 00:52:27.117186: Epoch 469 
-2024-08-28 00:52:27.117412: Current learning rate: 0.00786 
-2024-08-28 00:53:56.553121: train_loss -0.7362 
-2024-08-28 00:53:56.553368: val_loss -0.7594 
-2024-08-28 00:53:56.553547: Pseudo dice [0.0, 0.0, 0.8605, 0.9762, 0.8013, 0.9439, 0.9352, 0.9605, 0.9465, 0.9383, 0.9163, 0.9576, 0.9536, 0.8203, 0.9494, 0.9183, 0.7978, 0.7965, nan] 
-2024-08-28 00:53:56.553638: Epoch time: 89.44 s 
-2024-08-28 00:53:57.762490:  
-2024-08-28 00:53:57.762660: Epoch 470 
-2024-08-28 00:53:57.762748: Current learning rate: 0.00786 
-2024-08-28 00:55:21.756546: train_loss -0.7442 
-2024-08-28 00:55:21.757045: val_loss -0.7711 
-2024-08-28 00:55:21.757246: Pseudo dice [0.0, 0.0, 0.8984, 0.9761, 0.8431, 0.9358, 0.948, 0.9628, 0.9469, 0.9463, 0.9239, 0.9605, 0.9552, 0.8255, 0.9469, 0.9328, 0.8197, 0.801, nan] 
-2024-08-28 00:55:21.757396: Epoch time: 83.99 s 
-2024-08-28 00:55:22.972079:  
-2024-08-28 00:55:22.972270: Epoch 471 
-2024-08-28 00:55:22.972374: Current learning rate: 0.00785 
-2024-08-28 00:56:49.409600: train_loss -0.7489 
-2024-08-28 00:56:49.409915: val_loss -0.7714 
-2024-08-28 00:56:49.410097: Pseudo dice [0.0, 0.0, 0.8884, 0.9767, 0.8128, 0.9436, 0.9484, 0.9619, 0.9462, 0.9465, 0.9218, 0.9556, 0.9575, 0.8232, 0.9474, 0.9216, 0.8079, 0.7849, nan] 
-2024-08-28 00:56:49.410210: Epoch time: 86.44 s 
-2024-08-28 00:56:50.588500:  
-2024-08-28 00:56:50.589119: Epoch 472 
-2024-08-28 00:56:50.589213: Current learning rate: 0.00785 
-2024-08-28 00:58:20.140681: train_loss -0.7513 
-2024-08-28 00:58:20.140922: val_loss -0.7721 
-2024-08-28 00:58:20.141088: Pseudo dice [0.0, 0.0, 0.8856, 0.9762, 0.8371, 0.9393, 0.9426, 0.9621, 0.9454, 0.9401, 0.9249, 0.958, 0.9593, 0.8371, 0.9508, 0.93, 0.8115, 0.8125, nan] 
-2024-08-28 00:58:20.141175: Epoch time: 89.55 s 
-2024-08-28 00:58:21.575923:  
-2024-08-28 00:58:21.576169: Epoch 473 
-2024-08-28 00:58:21.576261: Current learning rate: 0.00784 
-2024-08-28 00:59:47.911820: train_loss -0.748 
-2024-08-28 00:59:47.912455: val_loss -0.761 
-2024-08-28 00:59:47.912673: Pseudo dice [0.0, 0.0, 0.8998, 0.9706, 0.7908, 0.9412, 0.9437, 0.9586, 0.9427, 0.9406, 0.9214, 0.9541, 0.9523, 0.8266, 0.9434, 0.9241, 0.8108, 0.8061, nan] 
-2024-08-28 00:59:47.912784: Epoch time: 86.34 s 
-2024-08-28 00:59:49.073997:  
-2024-08-28 00:59:49.074400: Epoch 474 
-2024-08-28 00:59:49.074505: Current learning rate: 0.00784 
-2024-08-28 01:01:15.753990: train_loss -0.7448 
-2024-08-28 01:01:15.754230: val_loss -0.7683 
-2024-08-28 01:01:15.754400: Pseudo dice [0.0, 0.0, 0.8933, 0.9775, 0.829, 0.9397, 0.9504, 0.9583, 0.9471, 0.9386, 0.9063, 0.9573, 0.9517, 0.8323, 0.9468, 0.9303, 0.8123, 0.8009, nan] 
-2024-08-28 01:01:15.754487: Epoch time: 86.68 s 
-2024-08-28 01:01:16.975808:  
-2024-08-28 01:01:16.975994: Epoch 475 
-2024-08-28 01:01:16.976090: Current learning rate: 0.00783 
-2024-08-28 01:02:44.412248: train_loss -0.7474 
-2024-08-28 01:02:44.412689: val_loss -0.7696 
-2024-08-28 01:02:44.412902: Pseudo dice [0.0, 0.0, 0.9025, 0.9762, 0.8373, 0.9452, 0.9491, 0.9626, 0.9449, 0.9368, 0.9267, 0.9554, 0.955, 0.8216, 0.9408, 0.9278, 0.8093, 0.7972, nan] 
-2024-08-28 01:02:44.412989: Epoch time: 87.44 s 
-2024-08-28 01:02:45.590825:  
-2024-08-28 01:02:45.591130: Epoch 476 
-2024-08-28 01:02:45.591231: Current learning rate: 0.00783 
-2024-08-28 01:04:09.733457: train_loss -0.744 
-2024-08-28 01:04:09.733710: val_loss -0.7565 
-2024-08-28 01:04:09.733875: Pseudo dice [0.0, 0.0, 0.8853, 0.9764, 0.7793, 0.9427, 0.9421, 0.9589, 0.931, 0.9321, 0.9112, 0.9473, 0.9433, 0.8302, 0.9479, 0.9202, 0.809, 0.783, nan] 
-2024-08-28 01:04:09.733967: Epoch time: 84.14 s 
-2024-08-28 01:04:10.965330:  
-2024-08-28 01:04:10.965488: Epoch 477 
-2024-08-28 01:04:10.965572: Current learning rate: 0.00783 
-2024-08-28 01:05:39.363421: train_loss -0.7376 
-2024-08-28 01:05:39.363706: val_loss -0.7577 
-2024-08-28 01:05:39.363923: Pseudo dice [0.0, 0.0, 0.8774, 0.9726, 0.8011, 0.9337, 0.9393, 0.9591, 0.9382, 0.9286, 0.9218, 0.9538, 0.9566, 0.8268, 0.943, 0.9188, 0.8083, 0.7972, nan] 
-2024-08-28 01:05:39.364032: Epoch time: 88.4 s 
-2024-08-28 01:05:40.652410:  
-2024-08-28 01:05:40.652593: Epoch 478 
-2024-08-28 01:05:40.652682: Current learning rate: 0.00782 
-2024-08-28 01:07:07.248553: train_loss -0.7401 
-2024-08-28 01:07:07.248798: val_loss -0.7628 
-2024-08-28 01:07:07.248962: Pseudo dice [0.0, 0.0, 0.8524, 0.9734, 0.801, 0.9429, 0.9452, 0.9596, 0.9471, 0.9376, 0.9183, 0.9557, 0.9516, 0.8267, 0.9463, 0.9175, 0.8031, 0.8073, nan] 
-2024-08-28 01:07:07.249048: Epoch time: 86.6 s 
-2024-08-28 01:07:08.744272:  
-2024-08-28 01:07:08.744591: Epoch 479 
-2024-08-28 01:07:08.744682: Current learning rate: 0.00782 
-2024-08-28 01:08:37.739874: train_loss -0.7433 
-2024-08-28 01:08:37.740110: val_loss -0.7687 
-2024-08-28 01:08:37.740273: Pseudo dice [0.0, 0.0, 0.8899, 0.9764, 0.8196, 0.9431, 0.9462, 0.963, 0.9446, 0.9459, 0.9217, 0.9547, 0.9551, 0.8355, 0.9529, 0.9279, 0.8014, 0.7931, nan] 
-2024-08-28 01:08:37.740351: Epoch time: 89.0 s 
-2024-08-28 01:08:38.938440:  
-2024-08-28 01:08:38.938627: Epoch 480 
-2024-08-28 01:08:38.938707: Current learning rate: 0.00781 
-2024-08-28 01:10:12.560073: train_loss -0.7408 
-2024-08-28 01:10:12.560310: val_loss -0.7571 
-2024-08-28 01:10:12.560472: Pseudo dice [0.0, 0.0, 0.8935, 0.9732, 0.822, 0.9333, 0.9356, 0.9572, 0.9357, 0.9352, 0.9102, 0.9437, 0.9452, 0.8252, 0.9372, 0.922, 0.8145, 0.8062, nan] 
-2024-08-28 01:10:12.560597: Epoch time: 93.62 s 
-2024-08-28 01:10:13.768354:  
-2024-08-28 01:10:13.768544: Epoch 481 
-2024-08-28 01:10:13.768638: Current learning rate: 0.00781 
-2024-08-28 01:11:45.457403: train_loss -0.7458 
-2024-08-28 01:11:45.457637: val_loss -0.7701 
-2024-08-28 01:11:45.457803: Pseudo dice [0.0, 0.0, 0.8894, 0.969, 0.8136, 0.9401, 0.9444, 0.9624, 0.9489, 0.9427, 0.9219, 0.954, 0.9533, 0.8337, 0.9462, 0.9258, 0.8094, 0.8172, nan] 
-2024-08-28 01:11:45.457889: Epoch time: 91.69 s 
-2024-08-28 01:11:46.660045:  
-2024-08-28 01:11:46.660338: Epoch 482 
-2024-08-28 01:11:46.660448: Current learning rate: 0.0078 
-2024-08-28 01:13:06.378887: train_loss -0.7487 
-2024-08-28 01:13:06.379130: val_loss -0.771 
-2024-08-28 01:13:06.379296: Pseudo dice [0.0, 0.0, 0.8774, 0.9775, 0.8229, 0.9392, 0.9429, 0.9596, 0.9384, 0.9432, 0.9171, 0.9477, 0.9507, 0.834, 0.9489, 0.9297, 0.8225, 0.8203, nan] 
-2024-08-28 01:13:06.379382: Epoch time: 79.72 s 
-2024-08-28 01:13:07.583321:  
-2024-08-28 01:13:07.583466: Epoch 483 
-2024-08-28 01:13:07.583554: Current learning rate: 0.0078 
-2024-08-28 01:14:33.475740: train_loss -0.7485 
-2024-08-28 01:14:33.475973: val_loss -0.7685 
-2024-08-28 01:14:33.476136: Pseudo dice [0.0, 0.0, 0.8643, 0.9719, 0.8336, 0.9467, 0.9499, 0.9626, 0.9483, 0.9326, 0.9179, 0.9536, 0.9535, 0.8292, 0.9487, 0.9216, 0.817, 0.8053, nan] 
-2024-08-28 01:14:33.476220: Epoch time: 85.89 s 
-2024-08-28 01:14:34.698840:  
-2024-08-28 01:14:34.699016: Epoch 484 
-2024-08-28 01:14:34.699112: Current learning rate: 0.00779 
-2024-08-28 01:16:06.297347: train_loss -0.747 
-2024-08-28 01:16:06.297600: val_loss -0.7715 
-2024-08-28 01:16:06.297776: Pseudo dice [0.0, 0.0, 0.8828, 0.9736, 0.8483, 0.941, 0.9467, 0.9642, 0.9483, 0.9389, 0.9154, 0.9557, 0.9545, 0.8362, 0.9508, 0.9294, 0.8202, 0.8183, nan] 
-2024-08-28 01:16:06.297872: Epoch time: 91.6 s 
-2024-08-28 01:16:07.752536:  
-2024-08-28 01:16:07.752710: Epoch 485 
-2024-08-28 01:16:07.752804: Current learning rate: 0.00779 
-2024-08-28 01:17:34.762962: train_loss -0.7479 
-2024-08-28 01:17:34.763185: val_loss -0.7792 
-2024-08-28 01:17:34.763357: Pseudo dice [0.0, 0.0, 0.8966, 0.9761, 0.8408, 0.9484, 0.9485, 0.9629, 0.9446, 0.9457, 0.9283, 0.9546, 0.9555, 0.8377, 0.954, 0.9288, 0.808, 0.8086, nan] 
-2024-08-28 01:17:34.763443: Epoch time: 87.01 s 
-2024-08-28 01:17:35.956705:  
-2024-08-28 01:17:35.957028: Epoch 486 
-2024-08-28 01:17:35.957129: Current learning rate: 0.00778 
-2024-08-28 01:19:03.344888: train_loss -0.749 
-2024-08-28 01:19:03.345253: val_loss -0.766 
-2024-08-28 01:19:03.345509: Pseudo dice [0.0, 0.0, 0.8834, 0.9773, 0.8149, 0.9408, 0.9303, 0.9612, 0.9436, 0.9341, 0.8932, 0.957, 0.9437, 0.8317, 0.9495, 0.9182, 0.801, 0.7938, nan] 
-2024-08-28 01:19:03.345707: Epoch time: 87.39 s 
-2024-08-28 01:19:04.518701:  
-2024-08-28 01:19:04.518874: Epoch 487 
-2024-08-28 01:19:04.518960: Current learning rate: 0.00778 
-2024-08-28 01:20:32.395281: train_loss -0.7501 
-2024-08-28 01:20:32.395532: val_loss -0.7653 
-2024-08-28 01:20:32.395697: Pseudo dice [0.0, 0.0, 0.8516, 0.9751, 0.8369, 0.9395, 0.945, 0.9621, 0.9306, 0.9373, 0.9124, 0.944, 0.9425, 0.8369, 0.95, 0.9255, 0.8134, 0.7951, nan] 
-2024-08-28 01:20:32.395783: Epoch time: 87.88 s 
-2024-08-28 01:20:33.610511:  
-2024-08-28 01:20:33.610666: Epoch 488 
-2024-08-28 01:20:33.610760: Current learning rate: 0.00777 
-2024-08-28 01:22:01.877959: train_loss -0.7485 
-2024-08-28 01:22:01.878216: val_loss -0.7624 
-2024-08-28 01:22:01.878397: Pseudo dice [0.0, 0.0, 0.8838, 0.9776, 0.8143, 0.946, 0.9489, 0.9594, 0.941, 0.9406, 0.905, 0.955, 0.9537, 0.8282, 0.9518, 0.9316, 0.7964, 0.8029, nan] 
-2024-08-28 01:22:01.878489: Epoch time: 88.27 s 
-2024-08-28 01:22:03.098209:  
-2024-08-28 01:22:03.098394: Epoch 489 
-2024-08-28 01:22:03.098497: Current learning rate: 0.00777 
-2024-08-28 01:23:37.692099: train_loss -0.744 
-2024-08-28 01:23:37.692350: val_loss -0.7688 
-2024-08-28 01:23:37.692538: Pseudo dice [0.0, 0.0, 0.8871, 0.9763, 0.8411, 0.9461, 0.9457, 0.9616, 0.9438, 0.9494, 0.9211, 0.9536, 0.9529, 0.8385, 0.9468, 0.9265, 0.8083, 0.8079, nan] 
-2024-08-28 01:23:37.692637: Epoch time: 94.59 s 
-2024-08-28 01:23:39.192503:  
-2024-08-28 01:23:39.193027: Epoch 490 
-2024-08-28 01:23:39.193140: Current learning rate: 0.00777 
-2024-08-28 01:25:10.615724: train_loss -0.7488 
-2024-08-28 01:25:10.616016: val_loss -0.77 
-2024-08-28 01:25:10.616234: Pseudo dice [0.0, 0.0, 0.9004, 0.9738, 0.8459, 0.9444, 0.9455, 0.9595, 0.9471, 0.9483, 0.9242, 0.9555, 0.9553, 0.8218, 0.9405, 0.9285, 0.8103, 0.8164, nan] 
-2024-08-28 01:25:10.616343: Epoch time: 91.42 s 
-2024-08-28 01:25:12.189306:  
-2024-08-28 01:25:12.189512: Epoch 491 
-2024-08-28 01:25:12.189610: Current learning rate: 0.00776 
-2024-08-28 01:26:38.776706: train_loss -0.75 
-2024-08-28 01:26:38.776997: val_loss -0.7697 
-2024-08-28 01:26:38.777243: Pseudo dice [0.0, 0.0, 0.8758, 0.9764, 0.8446, 0.9512, 0.953, 0.9663, 0.9431, 0.951, 0.9296, 0.9589, 0.9595, 0.8358, 0.9354, 0.9318, 0.8125, 0.8161, nan] 
-2024-08-28 01:26:38.777351: Epoch time: 86.59 s 
-2024-08-28 01:26:40.431900:  
-2024-08-28 01:26:40.432331: Epoch 492 
-2024-08-28 01:26:40.432464: Current learning rate: 0.00776 
-2024-08-28 01:28:11.445954: train_loss -0.7487 
-2024-08-28 01:28:11.446212: val_loss -0.7721 
-2024-08-28 01:28:11.446374: Pseudo dice [0.0, 0.0, 0.8657, 0.9764, 0.8227, 0.9443, 0.9428, 0.9633, 0.9464, 0.9292, 0.9207, 0.9598, 0.9568, 0.8217, 0.9459, 0.9292, 0.8145, 0.8171, nan] 
-2024-08-28 01:28:11.446458: Epoch time: 91.02 s 
-2024-08-28 01:28:12.655671:  
-2024-08-28 01:28:12.655849: Epoch 493 
-2024-08-28 01:28:12.655937: Current learning rate: 0.00775 
-2024-08-28 01:29:41.652210: train_loss -0.7506 
-2024-08-28 01:29:41.652479: val_loss -0.7663 
-2024-08-28 01:29:41.652646: Pseudo dice [0.0, 0.0, 0.8935, 0.974, 0.8201, 0.9441, 0.9461, 0.9614, 0.9463, 0.9416, 0.9235, 0.9554, 0.9532, 0.8297, 0.953, 0.9291, 0.8183, 0.824, nan] 
-2024-08-28 01:29:41.652729: Epoch time: 89.0 s 
-2024-08-28 01:29:42.887593:  
-2024-08-28 01:29:42.888066: Epoch 494 
-2024-08-28 01:29:42.888256: Current learning rate: 0.00775 
-2024-08-28 01:31:08.651800: train_loss -0.7483 
-2024-08-28 01:31:08.652022: val_loss -0.7676 
-2024-08-28 01:31:08.652182: Pseudo dice [0.0, 0.0, 0.8798, 0.9774, 0.8233, 0.9425, 0.946, 0.9553, 0.949, 0.9489, 0.9199, 0.9576, 0.9545, 0.8105, 0.9499, 0.9291, 0.8187, 0.7955, nan] 
-2024-08-28 01:31:08.652267: Epoch time: 85.76 s 
-2024-08-28 01:31:09.877991:  
-2024-08-28 01:31:09.878318: Epoch 495 
-2024-08-28 01:31:09.878417: Current learning rate: 0.00774 
-2024-08-28 01:32:33.612070: train_loss -0.7496 
-2024-08-28 01:32:33.612315: val_loss -0.7656 
-2024-08-28 01:32:33.612490: Pseudo dice [0.0, 0.0, 0.8831, 0.9768, 0.7965, 0.9356, 0.9371, 0.9618, 0.9468, 0.9408, 0.9152, 0.9557, 0.9565, 0.8319, 0.9518, 0.9303, 0.8128, 0.8163, nan] 
-2024-08-28 01:32:33.612579: Epoch time: 83.73 s 
-2024-08-28 01:32:34.835369:  
-2024-08-28 01:32:34.835832: Epoch 496 
-2024-08-28 01:32:34.835923: Current learning rate: 0.00774 
-2024-08-28 01:34:02.726426: train_loss -0.7488 
-2024-08-28 01:34:02.726731: val_loss -0.7691 
-2024-08-28 01:34:02.726964: Pseudo dice [0.0, 0.0, 0.8908, 0.9763, 0.8287, 0.9487, 0.9489, 0.9614, 0.9465, 0.935, 0.9252, 0.9589, 0.9552, 0.8291, 0.944, 0.9251, 0.8057, 0.8073, nan] 
-2024-08-28 01:34:02.727078: Epoch time: 87.89 s 
-2024-08-28 01:34:04.089603:  
-2024-08-28 01:34:04.089999: Epoch 497 
-2024-08-28 01:34:04.090201: Current learning rate: 0.00773 
-2024-08-28 01:35:29.836400: train_loss -0.7501 
-2024-08-28 01:35:29.836648: val_loss -0.7643 
-2024-08-28 01:35:29.836820: Pseudo dice [0.0, 0.0, 0.8673, 0.9777, 0.8268, 0.9366, 0.9414, 0.9597, 0.945, 0.9464, 0.9241, 0.9569, 0.9585, 0.826, 0.9416, 0.9258, 0.7933, 0.7845, nan] 
-2024-08-28 01:35:29.836917: Epoch time: 85.75 s 
-2024-08-28 01:35:31.316344:  
-2024-08-28 01:35:31.316613: Epoch 498 
-2024-08-28 01:35:31.316713: Current learning rate: 0.00773 
-2024-08-28 01:36:59.609471: train_loss -0.743 
-2024-08-28 01:36:59.609712: val_loss -0.7649 
-2024-08-28 01:36:59.609873: Pseudo dice [0.0, 0.0, 0.9026, 0.9746, 0.8152, 0.9439, 0.9464, 0.9589, 0.9432, 0.9431, 0.9238, 0.948, 0.9562, 0.8232, 0.9487, 0.9284, 0.7938, 0.787, nan] 
-2024-08-28 01:36:59.609957: Epoch time: 88.29 s 
-2024-08-28 01:37:00.841121:  
-2024-08-28 01:37:00.841538: Epoch 499 
-2024-08-28 01:37:00.841724: Current learning rate: 0.00772 
-2024-08-28 01:38:28.476665: train_loss -0.7465 
-2024-08-28 01:38:28.476911: val_loss -0.7604 
-2024-08-28 01:38:28.477068: Pseudo dice [0.0, 0.0, 0.8658, 0.9752, 0.7833, 0.9377, 0.9378, 0.9554, 0.9461, 0.9392, 0.923, 0.9535, 0.9561, 0.8127, 0.9421, 0.9202, 0.8, 0.7904, nan] 
-2024-08-28 01:38:28.477515: Epoch time: 87.64 s 
-2024-08-28 01:38:30.042100:  
-2024-08-28 01:38:30.042253: Epoch 500 
-2024-08-28 01:38:30.042339: Current learning rate: 0.00772 
-2024-08-28 01:39:55.256366: train_loss -0.7381 
-2024-08-28 01:39:55.256615: val_loss -0.7681 
-2024-08-28 01:39:55.256771: Pseudo dice [0.0, 0.0, 0.8841, 0.9746, 0.8207, 0.938, 0.9414, 0.9575, 0.9492, 0.9455, 0.9241, 0.9562, 0.9532, 0.8331, 0.9406, 0.929, 0.8119, 0.7944, nan] 
-2024-08-28 01:39:55.256851: Epoch time: 85.21 s 
-2024-08-28 01:39:56.482539:  
-2024-08-28 01:39:56.482963: Epoch 501 
-2024-08-28 01:39:56.483057: Current learning rate: 0.00771 
-2024-08-28 01:41:28.549224: train_loss -0.7463 
-2024-08-28 01:41:28.549465: val_loss -0.7654 
-2024-08-28 01:41:28.549633: Pseudo dice [0.0, 0.0, 0.9042, 0.9766, 0.8158, 0.9354, 0.9407, 0.9614, 0.9363, 0.9362, 0.9028, 0.946, 0.9377, 0.8337, 0.942, 0.9199, 0.811, 0.8072, nan] 
-2024-08-28 01:41:28.549718: Epoch time: 92.07 s 
-2024-08-28 01:41:29.777306:  
-2024-08-28 01:41:29.777470: Epoch 502 
-2024-08-28 01:41:29.777558: Current learning rate: 0.00771 
-2024-08-28 01:42:58.109841: train_loss -0.7506 
-2024-08-28 01:42:58.110094: val_loss -0.7695 
-2024-08-28 01:42:58.110262: Pseudo dice [0.0, 0.0, 0.8752, 0.977, 0.833, 0.9442, 0.9419, 0.966, 0.9422, 0.9416, 0.9232, 0.9531, 0.9514, 0.8385, 0.9474, 0.9319, 0.8092, 0.7705, nan] 
-2024-08-28 01:42:58.110346: Epoch time: 88.33 s 
-2024-08-28 01:42:59.335541:  
-2024-08-28 01:42:59.335913: Epoch 503 
-2024-08-28 01:42:59.336020: Current learning rate: 0.0077 
-2024-08-28 01:44:23.489017: train_loss -0.7485 
-2024-08-28 01:44:23.489225: val_loss -0.7629 
-2024-08-28 01:44:23.489380: Pseudo dice [0.0, 0.0, 0.8796, 0.9743, 0.8299, 0.9433, 0.948, 0.9555, 0.9487, 0.9278, 0.9194, 0.9548, 0.95, 0.8094, 0.948, 0.9254, 0.8094, 0.8016, nan] 
-2024-08-28 01:44:23.489460: Epoch time: 84.15 s 
-2024-08-28 01:44:24.959920:  
-2024-08-28 01:44:24.960314: Epoch 504 
-2024-08-28 01:44:24.960424: Current learning rate: 0.0077 
-2024-08-28 01:45:52.100368: train_loss -0.7447 
-2024-08-28 01:45:52.100613: val_loss -0.7663 
-2024-08-28 01:45:52.100766: Pseudo dice [0.0, 0.0, 0.8925, 0.9772, 0.8255, 0.9444, 0.9466, 0.9566, 0.9483, 0.9513, 0.9212, 0.9562, 0.9515, 0.8343, 0.9271, 0.9277, 0.8166, 0.8053, nan] 
-2024-08-28 01:45:52.100846: Epoch time: 87.14 s 
-2024-08-28 01:45:53.318277:  
-2024-08-28 01:45:53.318471: Epoch 505 
-2024-08-28 01:45:53.318578: Current learning rate: 0.0077 
-2024-08-28 01:47:20.302006: train_loss -0.745 
-2024-08-28 01:47:20.302244: val_loss -0.7649 
-2024-08-28 01:47:20.302401: Pseudo dice [0.0, 0.0, 0.8892, 0.9754, 0.806, 0.9388, 0.9474, 0.9584, 0.9383, 0.9429, 0.9142, 0.9497, 0.9493, 0.8197, 0.9425, 0.922, 0.8128, 0.7925, nan] 
-2024-08-28 01:47:20.302484: Epoch time: 86.98 s 
-2024-08-28 01:47:21.537321:  
-2024-08-28 01:47:21.537523: Epoch 506 
-2024-08-28 01:47:21.537670: Current learning rate: 0.00769 
-2024-08-28 01:48:47.111836: train_loss -0.7426 
-2024-08-28 01:48:47.112103: val_loss -0.7688 
-2024-08-28 01:48:47.112271: Pseudo dice [0.0, 0.0, 0.8933, 0.9764, 0.8338, 0.9479, 0.9464, 0.9608, 0.9366, 0.9384, 0.9202, 0.9551, 0.9476, 0.8446, 0.9405, 0.9294, 0.8011, 0.7845, nan] 
-2024-08-28 01:48:47.112360: Epoch time: 85.58 s 
-2024-08-28 01:48:48.330173:  
-2024-08-28 01:48:48.330550: Epoch 507 
-2024-08-28 01:48:48.330647: Current learning rate: 0.00769 
-2024-08-28 01:50:17.052063: train_loss -0.7447 
-2024-08-28 01:50:17.052313: val_loss -0.7605 
-2024-08-28 01:50:17.052499: Pseudo dice [0.0, 0.0, 0.8814, 0.9765, 0.8249, 0.9397, 0.9368, 0.9538, 0.9408, 0.9496, 0.9278, 0.951, 0.9573, 0.8234, 0.9445, 0.9219, 0.8118, 0.7959, nan] 
-2024-08-28 01:50:17.052595: Epoch time: 88.72 s 
-2024-08-28 01:50:18.291979:  
-2024-08-28 01:50:18.292253: Epoch 508 
-2024-08-28 01:50:18.292352: Current learning rate: 0.00768 
-2024-08-28 01:51:46.911325: train_loss -0.7416 
-2024-08-28 01:51:46.911555: val_loss -0.7599 
-2024-08-28 01:51:46.911729: Pseudo dice [0.0, 0.0, 0.8972, 0.976, 0.7957, 0.9362, 0.9351, 0.9606, 0.9368, 0.9354, 0.9112, 0.9432, 0.948, 0.8135, 0.9469, 0.915, 0.7781, 0.7963, nan] 
-2024-08-28 01:51:46.911814: Epoch time: 88.62 s 
-2024-08-28 01:51:48.379348:  
-2024-08-28 01:51:48.379556: Epoch 509 
-2024-08-28 01:51:48.379662: Current learning rate: 0.00768 
-2024-08-28 01:53:14.574934: train_loss -0.747 
-2024-08-28 01:53:14.575390: val_loss -0.7683 
-2024-08-28 01:53:14.575706: Pseudo dice [0.0, 0.0, 0.8773, 0.9759, 0.8287, 0.9463, 0.9482, 0.9606, 0.9474, 0.9411, 0.9219, 0.9586, 0.9551, 0.84, 0.9481, 0.9282, 0.8108, 0.8176, nan] 
-2024-08-28 01:53:14.575922: Epoch time: 86.2 s 
-2024-08-28 01:53:15.941602:  
-2024-08-28 01:53:15.941833: Epoch 510 
-2024-08-28 01:53:15.941958: Current learning rate: 0.00767 
-2024-08-28 01:54:41.584731: train_loss -0.7415 
-2024-08-28 01:54:41.584942: val_loss -0.7613 
-2024-08-28 01:54:41.585095: Pseudo dice [0.0, 0.0, 0.8831, 0.9735, 0.7899, 0.9356, 0.9265, 0.9495, 0.9371, 0.9457, 0.9226, 0.9533, 0.9542, 0.8204, 0.9394, 0.9229, 0.7974, 0.8053, nan] 
-2024-08-28 01:54:41.585173: Epoch time: 85.64 s 
-2024-08-28 01:54:42.817255:  
-2024-08-28 01:54:42.817423: Epoch 511 
-2024-08-28 01:54:42.817516: Current learning rate: 0.00767 
-2024-08-28 01:56:07.742263: train_loss -0.7376 
-2024-08-28 01:56:07.742533: val_loss -0.7611 
-2024-08-28 01:56:07.742770: Pseudo dice [0.0, 0.0, 0.8928, 0.9747, 0.8224, 0.9452, 0.9447, 0.9599, 0.941, 0.9426, 0.9266, 0.9542, 0.9553, 0.826, 0.9475, 0.9189, 0.7877, 0.7369, nan] 
-2024-08-28 01:56:07.742879: Epoch time: 84.93 s 
-2024-08-28 01:56:09.249270:  
-2024-08-28 01:56:09.249534: Epoch 512 
-2024-08-28 01:56:09.249689: Current learning rate: 0.00766 
-2024-08-28 01:57:38.403945: train_loss -0.7404 
-2024-08-28 01:57:38.404183: val_loss -0.7664 
-2024-08-28 01:57:38.404337: Pseudo dice [0.0, 0.0, 0.8802, 0.9768, 0.791, 0.9365, 0.9411, 0.958, 0.9348, 0.9286, 0.9078, 0.9433, 0.9465, 0.8316, 0.9464, 0.9231, 0.8014, 0.7959, nan] 
-2024-08-28 01:57:38.404416: Epoch time: 89.16 s 
-2024-08-28 01:57:39.556028:  
-2024-08-28 01:57:39.556178: Epoch 513 
-2024-08-28 01:57:39.556265: Current learning rate: 0.00766 
-2024-08-28 01:59:04.721560: train_loss -0.7431 
-2024-08-28 01:59:04.721781: val_loss -0.7699 
-2024-08-28 01:59:04.721949: Pseudo dice [0.0, 0.0, 0.8929, 0.9753, 0.8112, 0.9495, 0.9503, 0.9614, 0.9427, 0.9441, 0.9051, 0.9564, 0.948, 0.826, 0.9345, 0.9299, 0.7979, 0.8204, nan] 
-2024-08-28 01:59:04.722033: Epoch time: 85.17 s 
-2024-08-28 01:59:05.895479:  
-2024-08-28 01:59:05.895785: Epoch 514 
-2024-08-28 01:59:05.895887: Current learning rate: 0.00765 
-2024-08-28 02:00:32.742134: train_loss -0.7475 
-2024-08-28 02:00:32.742600: val_loss -0.768 
-2024-08-28 02:00:32.742808: Pseudo dice [0.0, 0.0, 0.8895, 0.9762, 0.8309, 0.946, 0.9468, 0.9633, 0.9475, 0.9463, 0.9326, 0.959, 0.9581, 0.8349, 0.939, 0.9176, 0.8202, 0.8147, nan] 
-2024-08-28 02:00:32.742947: Epoch time: 86.85 s 
-2024-08-28 02:00:33.968225:  
-2024-08-28 02:00:33.968385: Epoch 515 
-2024-08-28 02:00:33.968485: Current learning rate: 0.00765 
-2024-08-28 02:02:01.179756: train_loss -0.7481 
-2024-08-28 02:02:01.180349: val_loss -0.7657 
-2024-08-28 02:02:01.180610: Pseudo dice [0.0, 0.0, 0.9015, 0.9724, 0.8156, 0.9453, 0.9509, 0.9608, 0.944, 0.9476, 0.9273, 0.9582, 0.9589, 0.8258, 0.9358, 0.9201, 0.8156, 0.8074, nan] 
-2024-08-28 02:02:01.180871: Epoch time: 87.21 s 
-2024-08-28 02:02:02.660728:  
-2024-08-28 02:02:02.660925: Epoch 516 
-2024-08-28 02:02:02.661017: Current learning rate: 0.00764 
-2024-08-28 02:03:25.344970: train_loss -0.7493 
-2024-08-28 02:03:25.345228: val_loss -0.7637 
-2024-08-28 02:03:25.345399: Pseudo dice [0.0, 0.0, 0.8654, 0.9758, 0.8369, 0.9356, 0.9446, 0.9583, 0.9284, 0.933, 0.8999, 0.9416, 0.9347, 0.8279, 0.9372, 0.9152, 0.7941, 0.8093, nan] 
-2024-08-28 02:03:25.345485: Epoch time: 82.69 s 
-2024-08-28 02:03:26.590869:  
-2024-08-28 02:03:26.591369: Epoch 517 
-2024-08-28 02:03:26.591468: Current learning rate: 0.00764 
-2024-08-28 02:04:58.888070: train_loss -0.7478 
-2024-08-28 02:04:58.888308: val_loss -0.7705 
-2024-08-28 02:04:58.888468: Pseudo dice [0.0, 0.0, 0.8899, 0.9753, 0.8335, 0.9469, 0.9537, 0.9612, 0.9478, 0.9414, 0.925, 0.9556, 0.9569, 0.8284, 0.9558, 0.9291, 0.7951, 0.8002, nan] 
-2024-08-28 02:04:58.888551: Epoch time: 92.3 s 
-2024-08-28 02:05:00.075514:  
-2024-08-28 02:05:00.075702: Epoch 518 
-2024-08-28 02:05:00.075788: Current learning rate: 0.00764 
-2024-08-28 02:06:24.648907: train_loss -0.7466 
-2024-08-28 02:06:24.649157: val_loss -0.7608 
-2024-08-28 02:06:24.649316: Pseudo dice [0.0, 0.0, 0.8893, 0.9758, 0.8015, 0.9356, 0.9355, 0.9598, 0.924, 0.9273, 0.9083, 0.9366, 0.9388, 0.8199, 0.9481, 0.9223, 0.8042, 0.8122, nan] 
-2024-08-28 02:06:24.649402: Epoch time: 84.57 s 
-2024-08-28 02:06:25.879868:  
-2024-08-28 02:06:25.880045: Epoch 519 
-2024-08-28 02:06:25.880137: Current learning rate: 0.00763 
-2024-08-28 02:07:54.672839: train_loss -0.7489 
-2024-08-28 02:07:54.673054: val_loss -0.7696 
-2024-08-28 02:07:54.673218: Pseudo dice [0.0, 0.0, 0.899, 0.9774, 0.8166, 0.9387, 0.9448, 0.9615, 0.9365, 0.9327, 0.9098, 0.9458, 0.9493, 0.84, 0.9482, 0.9277, 0.8011, 0.8081, nan] 
-2024-08-28 02:07:54.673302: Epoch time: 88.79 s 
-2024-08-28 02:07:55.843222:  
-2024-08-28 02:07:55.843383: Epoch 520 
-2024-08-28 02:07:55.843467: Current learning rate: 0.00763 
-2024-08-28 02:09:22.214163: train_loss -0.7478 
-2024-08-28 02:09:22.214381: val_loss -0.7761 
-2024-08-28 02:09:22.214542: Pseudo dice [0.0, 0.0, 0.8898, 0.9734, 0.8355, 0.9445, 0.9494, 0.9663, 0.9459, 0.9447, 0.9258, 0.9566, 0.9513, 0.8414, 0.9488, 0.9368, 0.8069, 0.8133, nan] 
-2024-08-28 02:09:22.214626: Epoch time: 86.37 s 
-2024-08-28 02:09:23.418183:  
-2024-08-28 02:09:23.418334: Epoch 521 
-2024-08-28 02:09:23.418423: Current learning rate: 0.00762 
-2024-08-28 02:10:48.155870: train_loss -0.748 
-2024-08-28 02:10:48.156114: val_loss -0.7684 
-2024-08-28 02:10:48.156283: Pseudo dice [0.0, 0.0, 0.8941, 0.9761, 0.8276, 0.945, 0.9456, 0.9581, 0.9425, 0.9371, 0.927, 0.9531, 0.9578, 0.8285, 0.9465, 0.9232, 0.8093, 0.8056, nan] 
-2024-08-28 02:10:48.156369: Epoch time: 84.74 s 
-2024-08-28 02:10:49.647522:  
-2024-08-28 02:10:49.647692: Epoch 522 
-2024-08-28 02:10:49.647792: Current learning rate: 0.00762 
-2024-08-28 02:12:15.121629: train_loss -0.7493 
-2024-08-28 02:12:15.121898: val_loss -0.7688 
-2024-08-28 02:12:15.122104: Pseudo dice [0.0, 0.0, 0.8784, 0.9757, 0.8413, 0.9446, 0.9487, 0.965, 0.946, 0.9471, 0.9268, 0.9527, 0.9582, 0.821, 0.9455, 0.9249, 0.8123, 0.8229, nan] 
-2024-08-28 02:12:15.122210: Epoch time: 85.47 s 
-2024-08-28 02:12:16.304899:  
-2024-08-28 02:12:16.305080: Epoch 523 
-2024-08-28 02:12:16.305167: Current learning rate: 0.00761 
-2024-08-28 02:13:41.541402: train_loss -0.7517 
-2024-08-28 02:13:41.541653: val_loss -0.7681 
-2024-08-28 02:13:41.541823: Pseudo dice [0.0, 0.0, 0.8958, 0.9763, 0.8467, 0.9339, 0.9487, 0.9629, 0.9466, 0.9455, 0.92, 0.9524, 0.9556, 0.8321, 0.9521, 0.9213, 0.8146, 0.804, nan] 
-2024-08-28 02:13:41.541914: Epoch time: 85.24 s 
-2024-08-28 02:13:42.751002:  
-2024-08-28 02:13:42.751199: Epoch 524 
-2024-08-28 02:13:42.751290: Current learning rate: 0.00761 
-2024-08-28 02:15:08.730091: train_loss -0.7487 
-2024-08-28 02:15:08.730307: val_loss -0.7666 
-2024-08-28 02:15:08.730470: Pseudo dice [0.0, 0.0, 0.8774, 0.9765, 0.809, 0.9413, 0.9464, 0.9579, 0.9467, 0.9475, 0.9224, 0.9589, 0.9529, 0.8425, 0.9457, 0.9269, 0.8137, 0.8091, nan] 
-2024-08-28 02:15:08.730552: Epoch time: 85.98 s 
-2024-08-28 02:15:09.925978:  
-2024-08-28 02:15:09.926159: Epoch 525 
-2024-08-28 02:15:09.926251: Current learning rate: 0.0076 
-2024-08-28 02:16:35.174818: train_loss -0.746 
-2024-08-28 02:16:35.175072: val_loss -0.7756 
-2024-08-28 02:16:35.175236: Pseudo dice [0.0, 0.0, 0.8919, 0.9726, 0.8306, 0.9349, 0.9378, 0.9633, 0.9401, 0.9462, 0.9172, 0.9481, 0.9489, 0.8451, 0.9532, 0.933, 0.8183, 0.8067, nan] 
-2024-08-28 02:16:35.175322: Epoch time: 85.25 s 
-2024-08-28 02:16:36.399136:  
-2024-08-28 02:16:36.399430: Epoch 526 
-2024-08-28 02:16:36.399518: Current learning rate: 0.0076 
-2024-08-28 02:18:02.661503: train_loss -0.7523 
-2024-08-28 02:18:02.661765: val_loss -0.7662 
-2024-08-28 02:18:02.661924: Pseudo dice [0.0, 0.0, 0.8541, 0.9763, 0.8386, 0.9428, 0.9415, 0.9543, 0.943, 0.935, 0.9229, 0.9489, 0.9551, 0.8252, 0.9319, 0.9208, 0.7771, 0.7962, nan] 
-2024-08-28 02:18:02.662008: Epoch time: 86.26 s 
-2024-08-28 02:18:04.115235:  
-2024-08-28 02:18:04.115447: Epoch 527 
-2024-08-28 02:18:04.115549: Current learning rate: 0.00759 
-2024-08-28 02:19:33.571800: train_loss -0.7502 
-2024-08-28 02:19:33.572035: val_loss -0.7638 
-2024-08-28 02:19:33.572193: Pseudo dice [0.0, 0.0, 0.8809, 0.9741, 0.8289, 0.9389, 0.9419, 0.9648, 0.9303, 0.9336, 0.9194, 0.9442, 0.9434, 0.8199, 0.9502, 0.9249, 0.8097, 0.8082, nan] 
-2024-08-28 02:19:33.572272: Epoch time: 89.46 s 
-2024-08-28 02:19:34.732661:  
-2024-08-28 02:19:34.732820: Epoch 528 
-2024-08-28 02:19:34.732908: Current learning rate: 0.00759 
-2024-08-28 02:20:57.056646: train_loss -0.7416 
-2024-08-28 02:20:57.056901: val_loss -0.7664 
-2024-08-28 02:20:57.057057: Pseudo dice [0.0, 0.0, 0.8746, 0.9736, 0.8099, 0.9387, 0.9404, 0.9537, 0.9449, 0.9503, 0.9185, 0.9551, 0.9509, 0.8154, 0.9434, 0.9186, 0.8243, 0.8189, nan] 
-2024-08-28 02:20:57.057137: Epoch time: 82.32 s 
-2024-08-28 02:20:58.257615:  
-2024-08-28 02:20:58.257964: Epoch 529 
-2024-08-28 02:20:58.258063: Current learning rate: 0.00758 
-2024-08-28 02:22:27.110091: train_loss -0.7481 
-2024-08-28 02:22:27.110349: val_loss -0.77 
-2024-08-28 02:22:27.110515: Pseudo dice [0.0, 0.0, 0.8845, 0.9771, 0.815, 0.9439, 0.9462, 0.9653, 0.9402, 0.9414, 0.9266, 0.9514, 0.9547, 0.8317, 0.9477, 0.9229, 0.8011, 0.7915, nan] 
-2024-08-28 02:22:27.110606: Epoch time: 88.85 s 
-2024-08-28 02:22:28.295555:  
-2024-08-28 02:22:28.295704: Epoch 530 
-2024-08-28 02:22:28.295782: Current learning rate: 0.00758 
-2024-08-28 02:23:57.162274: train_loss -0.7474 
-2024-08-28 02:23:57.162524: val_loss -0.7759 
-2024-08-28 02:23:57.162734: Pseudo dice [0.0, 0.0, 0.9025, 0.9769, 0.8318, 0.9449, 0.9468, 0.9626, 0.9486, 0.9478, 0.9228, 0.9568, 0.9566, 0.8413, 0.9543, 0.9331, 0.8373, 0.8151, nan] 
-2024-08-28 02:23:57.162839: Epoch time: 88.87 s 
-2024-08-28 02:23:58.427666:  
-2024-08-28 02:23:58.428021: Epoch 531 
-2024-08-28 02:23:58.428120: Current learning rate: 0.00758 
-2024-08-28 02:25:26.948646: train_loss -0.748 
-2024-08-28 02:25:26.948871: val_loss -0.7615 
-2024-08-28 02:25:26.949048: Pseudo dice [0.0, 0.0, 0.8935, 0.9749, 0.8164, 0.939, 0.9403, 0.9588, 0.9441, 0.9309, 0.9238, 0.9502, 0.9447, 0.8226, 0.931, 0.9257, 0.8117, 0.8108, nan] 
-2024-08-28 02:25:26.949141: Epoch time: 88.52 s 
-2024-08-28 02:25:28.186165:  
-2024-08-28 02:25:28.186355: Epoch 532 
-2024-08-28 02:25:28.186520: Current learning rate: 0.00757 
-2024-08-28 02:26:56.062055: train_loss -0.7429 
-2024-08-28 02:26:56.062291: val_loss -0.765 
-2024-08-28 02:26:56.062446: Pseudo dice [0.0, 0.0, 0.8917, 0.9757, 0.8217, 0.9334, 0.9377, 0.959, 0.946, 0.9493, 0.925, 0.9568, 0.9541, 0.8284, 0.9476, 0.9287, 0.8256, 0.7945, nan] 
-2024-08-28 02:26:56.062526: Epoch time: 87.88 s 
-2024-08-28 02:26:57.299134:  
-2024-08-28 02:26:57.299312: Epoch 533 
-2024-08-28 02:26:57.299404: Current learning rate: 0.00757 
-2024-08-28 02:28:22.188409: train_loss -0.7441 
-2024-08-28 02:28:22.188662: val_loss -0.7599 
-2024-08-28 02:28:22.188817: Pseudo dice [0.0, 0.0, 0.8845, 0.9765, 0.8216, 0.9372, 0.9429, 0.9576, 0.9453, 0.9465, 0.9225, 0.9538, 0.9579, 0.8244, 0.945, 0.9186, 0.8039, 0.8006, nan] 
-2024-08-28 02:28:22.188898: Epoch time: 84.89 s 
-2024-08-28 02:28:23.424699:  
-2024-08-28 02:28:23.424933: Epoch 534 
-2024-08-28 02:28:23.425030: Current learning rate: 0.00756 
-2024-08-28 02:29:53.911988: train_loss -0.7478 
-2024-08-28 02:29:53.912215: val_loss -0.7654 
-2024-08-28 02:29:53.912383: Pseudo dice [0.0, 0.0, 0.8778, 0.9772, 0.8321, 0.9417, 0.9386, 0.9591, 0.9418, 0.9319, 0.9236, 0.9518, 0.9576, 0.8292, 0.9479, 0.931, 0.8308, 0.8089, nan] 
-2024-08-28 02:29:53.912478: Epoch time: 90.49 s 
-2024-08-28 02:29:55.144690:  
-2024-08-28 02:29:55.145023: Epoch 535 
-2024-08-28 02:29:55.145116: Current learning rate: 0.00756 
-2024-08-28 02:31:20.251968: train_loss -0.7483 
-2024-08-28 02:31:20.252228: val_loss -0.7624 
-2024-08-28 02:31:20.252384: Pseudo dice [0.0, 0.0, 0.8525, 0.9702, 0.8399, 0.9413, 0.9416, 0.9608, 0.9305, 0.9261, 0.9193, 0.9493, 0.9491, 0.8184, 0.9457, 0.9224, 0.8119, 0.8207, nan] 
-2024-08-28 02:31:20.252482: Epoch time: 85.11 s 
-2024-08-28 02:31:21.405957:  
-2024-08-28 02:31:21.406135: Epoch 536 
-2024-08-28 02:31:21.406227: Current learning rate: 0.00755 
-2024-08-28 02:32:48.254888: train_loss -0.7469 
-2024-08-28 02:32:48.255141: val_loss -0.7713 
-2024-08-28 02:32:48.255319: Pseudo dice [0.0, 0.0, 0.8804, 0.9763, 0.8258, 0.9429, 0.9438, 0.9635, 0.9442, 0.9436, 0.9288, 0.9589, 0.9582, 0.8274, 0.9481, 0.9169, 0.7985, 0.8228, nan] 
-2024-08-28 02:32:48.255412: Epoch time: 86.85 s 
-2024-08-28 02:32:49.464326:  
-2024-08-28 02:32:49.464586: Epoch 537 
-2024-08-28 02:32:49.464675: Current learning rate: 0.00755 
-2024-08-28 02:34:22.058768: train_loss -0.7464 
-2024-08-28 02:34:22.059010: val_loss -0.7604 
-2024-08-28 02:34:22.059163: Pseudo dice [0.0, 0.0, 0.8921, 0.9633, 0.8306, 0.9319, 0.9326, 0.9563, 0.9436, 0.9394, 0.9119, 0.9508, 0.9515, 0.8145, 0.941, 0.9223, 0.8111, 0.8156, nan] 
-2024-08-28 02:34:22.059249: Epoch time: 92.6 s 
-2024-08-28 02:34:23.261274:  
-2024-08-28 02:34:23.261444: Epoch 538 
-2024-08-28 02:34:23.261536: Current learning rate: 0.00754 
-2024-08-28 02:35:51.946051: train_loss -0.75 
-2024-08-28 02:35:51.946320: val_loss -0.7669 
-2024-08-28 02:35:51.946499: Pseudo dice [0.0, 0.0, 0.9058, 0.9745, 0.8365, 0.9473, 0.9477, 0.9638, 0.9495, 0.945, 0.918, 0.9573, 0.9557, 0.8417, 0.9508, 0.9253, 0.8223, 0.8013, nan] 
-2024-08-28 02:35:51.946596: Epoch time: 88.69 s 
-2024-08-28 02:35:53.411442:  
-2024-08-28 02:35:53.411695: Epoch 539 
-2024-08-28 02:35:53.411833: Current learning rate: 0.00754 
-2024-08-28 02:37:17.452028: train_loss -0.7505 
-2024-08-28 02:37:17.452248: val_loss -0.7696 
-2024-08-28 02:37:17.452410: Pseudo dice [0.0, 0.0, 0.8981, 0.9759, 0.8272, 0.9369, 0.9395, 0.9664, 0.9429, 0.9381, 0.9209, 0.9501, 0.9512, 0.8338, 0.9539, 0.9251, 0.8236, 0.8175, nan] 
-2024-08-28 02:37:17.452502: Epoch time: 84.04 s 
-2024-08-28 02:37:18.634479:  
-2024-08-28 02:37:18.635038: Epoch 540 
-2024-08-28 02:37:18.635130: Current learning rate: 0.00753 
-2024-08-28 02:38:45.523417: train_loss -0.7502 
-2024-08-28 02:38:45.523657: val_loss -0.7698 
-2024-08-28 02:38:45.523823: Pseudo dice [0.0, 0.0, 0.8848, 0.9759, 0.8136, 0.9468, 0.9461, 0.9612, 0.9437, 0.9407, 0.9277, 0.9558, 0.9523, 0.828, 0.9451, 0.9293, 0.8174, 0.8053, nan] 
-2024-08-28 02:38:45.523907: Epoch time: 86.89 s 
-2024-08-28 02:38:46.746456:  
-2024-08-28 02:38:46.746682: Epoch 541 
-2024-08-28 02:38:46.746848: Current learning rate: 0.00753 
-2024-08-28 02:40:15.927868: train_loss -0.7538 
-2024-08-28 02:40:15.928499: val_loss -0.769 
-2024-08-28 02:40:15.928717: Pseudo dice [0.0, 0.0, 0.8939, 0.9755, 0.834, 0.9422, 0.9457, 0.9595, 0.953, 0.9457, 0.921, 0.9614, 0.9575, 0.8374, 0.9474, 0.9265, 0.8032, 0.8011, nan] 
-2024-08-28 02:40:15.928890: Epoch time: 89.18 s 
-2024-08-28 02:40:17.221605:  
-2024-08-28 02:40:17.221821: Epoch 542 
-2024-08-28 02:40:17.221973: Current learning rate: 0.00752 
-2024-08-28 02:41:44.216213: train_loss -0.7524 
-2024-08-28 02:41:44.216445: val_loss -0.7681 
-2024-08-28 02:41:44.216614: Pseudo dice [0.0, 0.0, 0.8725, 0.9753, 0.8229, 0.9439, 0.9479, 0.9631, 0.9454, 0.9405, 0.9105, 0.9506, 0.9523, 0.8369, 0.9493, 0.9258, 0.826, 0.814, nan] 
-2024-08-28 02:41:44.216696: Epoch time: 87.0 s 
-2024-08-28 02:41:45.424647:  
-2024-08-28 02:41:45.424811: Epoch 543 
-2024-08-28 02:41:45.424898: Current learning rate: 0.00752 
-2024-08-28 02:43:11.512705: train_loss -0.7487 
-2024-08-28 02:43:11.512927: val_loss -0.7652 
-2024-08-28 02:43:11.513165: Pseudo dice [0.0, 0.0, 0.8786, 0.975, 0.7931, 0.9356, 0.9395, 0.9562, 0.947, 0.9417, 0.9279, 0.9555, 0.9576, 0.8065, 0.9408, 0.9178, 0.8081, 0.8069, nan] 
-2024-08-28 02:43:11.513278: Epoch time: 86.09 s 
-2024-08-28 02:43:12.682001:  
-2024-08-28 02:43:12.682145: Epoch 544 
-2024-08-28 02:43:12.682238: Current learning rate: 0.00751 
-2024-08-28 02:44:42.110912: train_loss -0.7482 
-2024-08-28 02:44:42.111142: val_loss -0.769 
-2024-08-28 02:44:42.111308: Pseudo dice [0.0, 0.0, 0.9054, 0.9754, 0.7931, 0.9304, 0.9375, 0.9595, 0.9403, 0.9402, 0.9183, 0.952, 0.9543, 0.8312, 0.9478, 0.9199, 0.8106, 0.8016, nan] 
-2024-08-28 02:44:42.111390: Epoch time: 89.43 s 
-2024-08-28 02:44:43.690827:  
-2024-08-28 02:44:43.691160: Epoch 545 
-2024-08-28 02:44:43.691268: Current learning rate: 0.00751 
-2024-08-28 02:46:10.733168: train_loss -0.7454 
-2024-08-28 02:46:10.733380: val_loss -0.7679 
-2024-08-28 02:46:10.733574: Pseudo dice [0.0, 0.0, 0.8864, 0.9762, 0.839, 0.9427, 0.9434, 0.9646, 0.9443, 0.9449, 0.9267, 0.9528, 0.9562, 0.832, 0.9468, 0.9241, 0.8176, 0.8166, nan] 
-2024-08-28 02:46:10.733662: Epoch time: 87.04 s 
-2024-08-28 02:46:11.957358:  
-2024-08-28 02:46:11.957551: Epoch 546 
-2024-08-28 02:46:11.957644: Current learning rate: 0.00751 
-2024-08-28 02:47:40.382391: train_loss -0.7486 
-2024-08-28 02:47:40.382614: val_loss -0.7649 
-2024-08-28 02:47:40.382775: Pseudo dice [0.0, 0.0, 0.8884, 0.9762, 0.8297, 0.9402, 0.9424, 0.9621, 0.9484, 0.9311, 0.9108, 0.9533, 0.955, 0.8382, 0.9325, 0.9278, 0.8036, 0.8038, nan] 
-2024-08-28 02:47:40.382857: Epoch time: 88.43 s 
-2024-08-28 02:47:41.605750:  
-2024-08-28 02:47:41.606512: Epoch 547 
-2024-08-28 02:47:41.606643: Current learning rate: 0.0075 
-2024-08-28 02:49:10.498690: train_loss -0.7448 
-2024-08-28 02:49:10.499032: val_loss -0.7683 
-2024-08-28 02:49:10.499206: Pseudo dice [0.0, 0.0, 0.9054, 0.976, 0.8328, 0.9439, 0.9475, 0.9592, 0.9344, 0.9317, 0.9294, 0.9502, 0.9568, 0.8433, 0.9453, 0.9322, 0.8132, 0.8102, nan] 
-2024-08-28 02:49:10.499322: Epoch time: 88.89 s 
-2024-08-28 02:49:11.733385:  
-2024-08-28 02:49:11.733549: Epoch 548 
-2024-08-28 02:49:11.733636: Current learning rate: 0.0075 
-2024-08-28 02:50:40.910801: train_loss -0.7484 
-2024-08-28 02:50:40.911302: val_loss -0.7725 
-2024-08-28 02:50:40.911502: Pseudo dice [0.0, 0.0, 0.899, 0.9763, 0.8534, 0.943, 0.9483, 0.9637, 0.9431, 0.9432, 0.9252, 0.9564, 0.9564, 0.8417, 0.9403, 0.9351, 0.82, 0.8107, nan] 
-2024-08-28 02:50:40.911597: Epoch time: 89.18 s 
-2024-08-28 02:50:42.154750:  
-2024-08-28 02:50:42.155113: Epoch 549 
-2024-08-28 02:50:42.155217: Current learning rate: 0.00749 
-2024-08-28 02:52:13.288453: train_loss -0.753 
-2024-08-28 02:52:13.288660: val_loss -0.7679 
-2024-08-28 02:52:13.288825: Pseudo dice [0.0, 0.0, 0.8939, 0.9764, 0.809, 0.9403, 0.9424, 0.9613, 0.9471, 0.9414, 0.9225, 0.9581, 0.9586, 0.8337, 0.944, 0.9301, 0.8164, 0.8219, nan] 
-2024-08-28 02:52:13.288906: Epoch time: 91.13 s 
-2024-08-28 02:52:14.820481:  
-2024-08-28 02:52:14.820695: Epoch 550 
-2024-08-28 02:52:14.820886: Current learning rate: 0.00749 
-2024-08-28 02:53:43.489355: train_loss -0.752 
-2024-08-28 02:53:43.489570: val_loss -0.7734 
-2024-08-28 02:53:43.489734: Pseudo dice [0.0, 0.0, 0.8889, 0.9774, 0.8318, 0.9478, 0.9522, 0.9637, 0.9469, 0.9492, 0.9293, 0.9532, 0.9607, 0.8314, 0.9512, 0.9278, 0.8281, 0.8151, nan] 
-2024-08-28 02:53:43.489850: Epoch time: 88.67 s 
-2024-08-28 02:53:45.000547:  
-2024-08-28 02:53:45.000738: Epoch 551 
-2024-08-28 02:53:45.000840: Current learning rate: 0.00748 
-2024-08-28 02:55:12.895612: train_loss -0.7506 
-2024-08-28 02:55:12.895846: val_loss -0.7699 
-2024-08-28 02:55:12.896024: Pseudo dice [0.0, 0.0, 0.8922, 0.977, 0.8493, 0.9444, 0.9452, 0.9623, 0.9516, 0.9356, 0.9125, 0.9604, 0.9544, 0.836, 0.9567, 0.9268, 0.8165, 0.8156, nan] 
-2024-08-28 02:55:12.896109: Epoch time: 87.9 s 
-2024-08-28 02:55:12.896161: Yayy! New best EMA pseudo Dice: 0.8105 
-2024-08-28 02:55:14.374941:  
-2024-08-28 02:55:14.375104: Epoch 552 
-2024-08-28 02:55:14.375193: Current learning rate: 0.00748 
-2024-08-28 02:56:37.140064: train_loss -0.7516 
-2024-08-28 02:56:37.140294: val_loss -0.7739 
-2024-08-28 02:56:37.140468: Pseudo dice [0.0, 0.0, 0.8893, 0.9763, 0.8436, 0.9435, 0.9451, 0.9658, 0.9466, 0.9455, 0.9341, 0.9604, 0.9622, 0.8345, 0.9479, 0.9275, 0.8166, 0.8209, nan] 
-2024-08-28 02:56:37.140549: Epoch time: 82.77 s 
-2024-08-28 02:56:37.140596: Yayy! New best EMA pseudo Dice: 0.8109 
-2024-08-28 02:56:38.679518:  
-2024-08-28 02:56:38.679925: Epoch 553 
-2024-08-28 02:56:38.680028: Current learning rate: 0.00747 
-2024-08-28 02:58:04.604907: train_loss -0.7541 
-2024-08-28 02:58:04.605133: val_loss -0.7704 
-2024-08-28 02:58:04.605294: Pseudo dice [0.0, 0.0, 0.8991, 0.976, 0.8281, 0.9393, 0.9411, 0.9618, 0.9509, 0.9432, 0.9273, 0.9563, 0.9566, 0.832, 0.95, 0.9265, 0.8174, 0.7975, nan] 
-2024-08-28 02:58:04.605391: Epoch time: 85.93 s 
-2024-08-28 02:58:04.605440: Yayy! New best EMA pseudo Dice: 0.811 
-2024-08-28 02:58:06.105643:  
-2024-08-28 02:58:06.105783: Epoch 554 
-2024-08-28 02:58:06.105865: Current learning rate: 0.00747 
-2024-08-28 02:59:35.896719: train_loss -0.7498 
-2024-08-28 02:59:35.896947: val_loss -0.7606 
-2024-08-28 02:59:35.897126: Pseudo dice [0.0, 0.0, 0.8777, 0.9731, 0.8027, 0.9437, 0.949, 0.955, 0.9466, 0.9488, 0.9286, 0.9592, 0.957, 0.8232, 0.9383, 0.9069, 0.7883, 0.7973, nan] 
-2024-08-28 02:59:35.897210: Epoch time: 89.79 s 
-2024-08-28 02:59:37.097287:  
-2024-08-28 02:59:37.097443: Epoch 555 
-2024-08-28 02:59:37.097527: Current learning rate: 0.00746 
-2024-08-28 03:01:10.859651: train_loss -0.7415 
-2024-08-28 03:01:10.859873: val_loss -0.7692 
-2024-08-28 03:01:10.860041: Pseudo dice [0.0, 0.0, 0.8908, 0.9732, 0.8412, 0.9413, 0.9359, 0.959, 0.9434, 0.9431, 0.9188, 0.9608, 0.9561, 0.8357, 0.9481, 0.9262, 0.8153, 0.793, nan] 
-2024-08-28 03:01:10.860127: Epoch time: 93.76 s 
-2024-08-28 03:01:12.468729:  
-2024-08-28 03:01:12.468922: Epoch 556 
-2024-08-28 03:01:12.469065: Current learning rate: 0.00746 
-2024-08-28 03:02:40.226521: train_loss -0.7485 
-2024-08-28 03:02:40.226745: val_loss -0.7705 
-2024-08-28 03:02:40.226913: Pseudo dice [0.0, 0.0, 0.8896, 0.9745, 0.815, 0.9436, 0.9444, 0.9653, 0.9496, 0.9451, 0.9248, 0.9606, 0.9587, 0.8277, 0.9429, 0.9174, 0.8197, 0.813, nan] 
-2024-08-28 03:02:40.226995: Epoch time: 87.76 s 
-2024-08-28 03:02:41.745387:  
-2024-08-28 03:02:41.745572: Epoch 557 
-2024-08-28 03:02:41.745660: Current learning rate: 0.00745 
-2024-08-28 03:04:08.892984: train_loss -0.7483 
-2024-08-28 03:04:08.893233: val_loss -0.7672 
-2024-08-28 03:04:08.893413: Pseudo dice [0.0, 0.0, 0.8931, 0.9757, 0.8209, 0.9476, 0.9487, 0.9594, 0.9474, 0.936, 0.9151, 0.9575, 0.9528, 0.8247, 0.9506, 0.9264, 0.805, 0.8008, nan] 
-2024-08-28 03:04:08.893503: Epoch time: 87.15 s 
-2024-08-28 03:04:10.163359:  
-2024-08-28 03:04:10.163615: Epoch 558 
-2024-08-28 03:04:10.163757: Current learning rate: 0.00745 
-2024-08-28 03:05:39.655522: train_loss -0.7488 
-2024-08-28 03:05:39.655909: val_loss -0.7691 
-2024-08-28 03:05:39.656167: Pseudo dice [0.0, 0.0, 0.8908, 0.9764, 0.8203, 0.9462, 0.9517, 0.9613, 0.9469, 0.9448, 0.916, 0.9583, 0.9558, 0.8262, 0.9503, 0.9279, 0.8023, 0.7925, nan] 
-2024-08-28 03:05:39.656294: Epoch time: 89.49 s 
-2024-08-28 03:05:40.961461:  
-2024-08-28 03:05:40.961615: Epoch 559 
-2024-08-28 03:05:40.961703: Current learning rate: 0.00745 
-2024-08-28 03:07:06.541578: train_loss -0.7514 
-2024-08-28 03:07:06.541912: val_loss -0.7645 
-2024-08-28 03:07:06.542139: Pseudo dice [0.0, 0.0, 0.8942, 0.9727, 0.817, 0.9388, 0.9355, 0.9603, 0.9388, 0.926, 0.9032, 0.9417, 0.9416, 0.8272, 0.9389, 0.9227, 0.8123, 0.8051, nan] 
-2024-08-28 03:07:06.542244: Epoch time: 85.58 s 
-2024-08-28 03:07:07.795696:  
-2024-08-28 03:07:07.795982: Epoch 560 
-2024-08-28 03:07:07.796081: Current learning rate: 0.00744 
-2024-08-28 03:08:33.347095: train_loss -0.747 
-2024-08-28 03:08:33.347304: val_loss -0.7678 
-2024-08-28 03:08:33.347471: Pseudo dice [0.0, 0.0, 0.8985, 0.9754, 0.8141, 0.9412, 0.9482, 0.9585, 0.9452, 0.9501, 0.9191, 0.9524, 0.957, 0.8366, 0.9419, 0.9276, 0.8297, 0.8207, nan] 
-2024-08-28 03:08:33.347690: Epoch time: 85.55 s 
-2024-08-28 03:08:34.550720:  
-2024-08-28 03:08:34.551185: Epoch 561 
-2024-08-28 03:08:34.551285: Current learning rate: 0.00744 
-2024-08-28 03:10:07.405311: train_loss -0.7489 
-2024-08-28 03:10:07.405551: val_loss -0.7683 
-2024-08-28 03:10:07.405745: Pseudo dice [0.0, 0.0, 0.8924, 0.9755, 0.8297, 0.9342, 0.9452, 0.9599, 0.9419, 0.9441, 0.9204, 0.9542, 0.9578, 0.8151, 0.9508, 0.92, 0.8246, 0.8156, nan] 
-2024-08-28 03:10:07.405831: Epoch time: 92.86 s 
-2024-08-28 03:10:08.670268:  
-2024-08-28 03:10:08.670428: Epoch 562 
-2024-08-28 03:10:08.670519: Current learning rate: 0.00743 
-2024-08-28 03:11:36.676183: train_loss -0.7434 
-2024-08-28 03:11:36.676634: val_loss -0.7632 
-2024-08-28 03:11:36.676872: Pseudo dice [0.0, 0.0, 0.8719, 0.9757, 0.8425, 0.939, 0.9369, 0.9654, 0.9262, 0.9288, 0.9075, 0.9355, 0.9378, 0.8324, 0.9473, 0.9306, 0.8188, 0.8081, nan] 
-2024-08-28 03:11:36.676998: Epoch time: 88.01 s 
-2024-08-28 03:11:38.205405:  
-2024-08-28 03:11:38.206450: Epoch 563 
-2024-08-28 03:11:38.207357: Current learning rate: 0.00743 
-2024-08-28 03:13:05.852105: train_loss -0.7481 
-2024-08-28 03:13:05.852560: val_loss -0.7719 
-2024-08-28 03:13:05.852764: Pseudo dice [0.0, 0.0, 0.9005, 0.9765, 0.845, 0.9452, 0.9517, 0.9582, 0.9489, 0.9486, 0.9226, 0.9593, 0.9586, 0.8393, 0.9513, 0.9332, 0.8259, 0.8046, nan] 
-2024-08-28 03:13:05.852867: Epoch time: 87.65 s 
-2024-08-28 03:13:07.072695:  
-2024-08-28 03:13:07.072868: Epoch 564 
-2024-08-28 03:13:07.072956: Current learning rate: 0.00742 
-2024-08-28 03:14:32.777477: train_loss -0.751 
-2024-08-28 03:14:32.777846: val_loss -0.7609 
-2024-08-28 03:14:32.778028: Pseudo dice [0.0, 0.0, 0.896, 0.9766, 0.7791, 0.9323, 0.9334, 0.9572, 0.9378, 0.9353, 0.9205, 0.9472, 0.9506, 0.8313, 0.9531, 0.9193, 0.8198, 0.8181, nan] 
-2024-08-28 03:14:32.778117: Epoch time: 85.71 s 
-2024-08-28 03:14:34.314903:  
-2024-08-28 03:14:34.315393: Epoch 565 
-2024-08-28 03:14:34.315528: Current learning rate: 0.00742 
-2024-08-28 03:15:54.632566: train_loss -0.7503 
-2024-08-28 03:15:54.632846: val_loss -0.7719 
-2024-08-28 03:15:54.633030: Pseudo dice [0.0, 0.0, 0.8829, 0.9759, 0.8446, 0.9387, 0.9445, 0.9631, 0.944, 0.9468, 0.9288, 0.9574, 0.957, 0.8257, 0.9447, 0.929, 0.8166, 0.8122, nan] 
-2024-08-28 03:15:54.633132: Epoch time: 80.32 s 
-2024-08-28 03:15:55.825106:  
-2024-08-28 03:15:55.825497: Epoch 566 
-2024-08-28 03:15:55.825606: Current learning rate: 0.00741 
-2024-08-28 03:17:26.997443: train_loss -0.7518 
-2024-08-28 03:17:26.997749: val_loss -0.7698 
-2024-08-28 03:17:26.997943: Pseudo dice [0.0, 0.0, 0.8956, 0.975, 0.8256, 0.9479, 0.9453, 0.9636, 0.9477, 0.9462, 0.922, 0.9589, 0.9565, 0.8378, 0.952, 0.9254, 0.81, 0.8041, nan] 
-2024-08-28 03:17:26.998036: Epoch time: 91.17 s 
-2024-08-28 03:17:28.218849:  
-2024-08-28 03:17:28.219032: Epoch 567 
-2024-08-28 03:17:28.219125: Current learning rate: 0.00741 
-2024-08-28 03:18:49.812327: train_loss -0.7502 
-2024-08-28 03:18:49.812774: val_loss -0.7747 
-2024-08-28 03:18:49.812950: Pseudo dice [0.0, 0.0, 0.8848, 0.9764, 0.8432, 0.9485, 0.9441, 0.96, 0.9519, 0.947, 0.9245, 0.9603, 0.9547, 0.8337, 0.9443, 0.9237, 0.8127, 0.8052, nan] 
-2024-08-28 03:18:49.813081: Epoch time: 81.59 s 
-2024-08-28 03:18:51.032166:  
-2024-08-28 03:18:51.032464: Epoch 568 
-2024-08-28 03:18:51.032565: Current learning rate: 0.0074 
-2024-08-28 03:20:16.436614: train_loss -0.7459 
-2024-08-28 03:20:16.436844: val_loss -0.7694 
-2024-08-28 03:20:16.436999: Pseudo dice [0.0, 0.0, 0.8895, 0.9729, 0.8342, 0.9379, 0.9435, 0.9614, 0.9487, 0.9462, 0.9287, 0.9572, 0.9574, 0.8286, 0.942, 0.9295, 0.8197, 0.813, nan] 
-2024-08-28 03:20:16.437080: Epoch time: 85.41 s 
-2024-08-28 03:20:17.902182:  
-2024-08-28 03:20:17.902372: Epoch 569 
-2024-08-28 03:20:17.902483: Current learning rate: 0.0074 
-2024-08-28 03:21:45.250489: train_loss -0.7445 
-2024-08-28 03:21:45.250719: val_loss -0.767 
-2024-08-28 03:21:45.250888: Pseudo dice [0.0, 0.0, 0.8939, 0.9757, 0.8213, 0.9401, 0.9412, 0.9614, 0.9436, 0.9462, 0.9211, 0.9568, 0.9544, 0.8337, 0.9432, 0.93, 0.7987, 0.8192, nan] 
-2024-08-28 03:21:45.251017: Epoch time: 87.35 s 
-2024-08-28 03:21:46.471715:  
-2024-08-28 03:21:46.471891: Epoch 570 
-2024-08-28 03:21:46.471978: Current learning rate: 0.00739 
-2024-08-28 03:23:12.801000: train_loss -0.7475 
-2024-08-28 03:23:12.801560: val_loss -0.7691 
-2024-08-28 03:23:12.801748: Pseudo dice [0.0, 0.0, 0.8739, 0.9752, 0.8288, 0.9435, 0.947, 0.9619, 0.9489, 0.9401, 0.9258, 0.9599, 0.9581, 0.8134, 0.9515, 0.9268, 0.7956, 0.7927, nan] 
-2024-08-28 03:23:12.801834: Epoch time: 86.33 s 
-2024-08-28 03:23:13.987262:  
-2024-08-28 03:23:13.987444: Epoch 571 
-2024-08-28 03:23:13.987534: Current learning rate: 0.00739 
-2024-08-28 03:24:44.011597: train_loss -0.7492 
-2024-08-28 03:24:44.011827: val_loss -0.7641 
-2024-08-28 03:24:44.012067: Pseudo dice [0.0, 0.0, 0.8948, 0.9774, 0.8368, 0.936, 0.9401, 0.9634, 0.9491, 0.9399, 0.9199, 0.9584, 0.9473, 0.8362, 0.9356, 0.9322, 0.8215, 0.8125, nan] 
-2024-08-28 03:24:44.012464: Epoch time: 90.03 s 
-2024-08-28 03:24:45.229594:  
-2024-08-28 03:24:45.230016: Epoch 572 
-2024-08-28 03:24:45.230218: Current learning rate: 0.00738 
-2024-08-28 03:26:10.378286: train_loss -0.7518 
-2024-08-28 03:26:10.378508: val_loss -0.7729 
-2024-08-28 03:26:10.378664: Pseudo dice [0.0, 0.0, 0.893, 0.976, 0.8338, 0.9451, 0.9454, 0.9634, 0.9495, 0.948, 0.9274, 0.9599, 0.9571, 0.8393, 0.9511, 0.9192, 0.8265, 0.8159, nan] 
-2024-08-28 03:26:10.378748: Epoch time: 85.15 s 
-2024-08-28 03:26:11.589692:  
-2024-08-28 03:26:11.589858: Epoch 573 
-2024-08-28 03:26:11.589944: Current learning rate: 0.00738 
-2024-08-28 03:27:41.144842: train_loss -0.7517 
-2024-08-28 03:27:41.145086: val_loss -0.7716 
-2024-08-28 03:27:41.145255: Pseudo dice [0.0, 0.0, 0.8878, 0.9749, 0.8076, 0.9411, 0.9454, 0.9605, 0.9524, 0.9473, 0.929, 0.962, 0.9605, 0.8384, 0.9558, 0.9258, 0.7917, 0.7865, nan] 
-2024-08-28 03:27:41.145336: Epoch time: 89.56 s 
-2024-08-28 03:27:42.387277:  
-2024-08-28 03:27:42.387452: Epoch 574 
-2024-08-28 03:27:42.387639: Current learning rate: 0.00738 
-2024-08-28 03:29:05.675955: train_loss -0.7483 
-2024-08-28 03:29:05.676194: val_loss -0.764 
-2024-08-28 03:29:05.676359: Pseudo dice [0.0, 0.0, 0.8836, 0.9734, 0.8322, 0.9397, 0.9483, 0.9594, 0.9461, 0.9366, 0.9144, 0.9532, 0.9536, 0.8268, 0.9475, 0.9263, 0.8073, 0.8093, nan] 
-2024-08-28 03:29:05.676458: Epoch time: 83.29 s 
-2024-08-28 03:29:06.929742:  
-2024-08-28 03:29:06.929910: Epoch 575 
-2024-08-28 03:29:06.930009: Current learning rate: 0.00737 
-2024-08-28 03:30:31.798808: train_loss -0.7432 
-2024-08-28 03:30:31.799050: val_loss -0.7632 
-2024-08-28 03:30:31.799251: Pseudo dice [0.0, 0.0, 0.8684, 0.9769, 0.8071, 0.9385, 0.9416, 0.9605, 0.9441, 0.9423, 0.9122, 0.9526, 0.9508, 0.8262, 0.947, 0.9285, 0.8043, 0.8102, nan] 
-2024-08-28 03:30:31.799374: Epoch time: 84.87 s 
-2024-08-28 03:30:33.039872:  
-2024-08-28 03:30:33.040250: Epoch 576 
-2024-08-28 03:30:33.040364: Current learning rate: 0.00737 
-2024-08-28 03:31:59.323061: train_loss -0.7452 
-2024-08-28 03:31:59.323310: val_loss -0.7683 
-2024-08-28 03:31:59.323479: Pseudo dice [0.0, 0.0, 0.8897, 0.9745, 0.8199, 0.9419, 0.9459, 0.962, 0.945, 0.9423, 0.923, 0.9556, 0.9535, 0.821, 0.9441, 0.9263, 0.8142, 0.8128, nan] 
-2024-08-28 03:31:59.323574: Epoch time: 86.28 s 
-2024-08-28 03:32:00.577204:  
-2024-08-28 03:32:00.577446: Epoch 577 
-2024-08-28 03:32:00.577556: Current learning rate: 0.00736 
-2024-08-28 03:33:24.428452: train_loss -0.7484 
-2024-08-28 03:33:24.428668: val_loss -0.7719 
-2024-08-28 03:33:24.428831: Pseudo dice [0.0, 0.0, 0.8955, 0.9744, 0.8361, 0.9383, 0.9436, 0.9599, 0.9521, 0.9528, 0.9232, 0.9567, 0.9567, 0.8123, 0.9429, 0.922, 0.8225, 0.812, nan] 
-2024-08-28 03:33:24.428914: Epoch time: 83.85 s 
-2024-08-28 03:33:25.667980:  
-2024-08-28 03:33:25.668387: Epoch 578 
-2024-08-28 03:33:25.668503: Current learning rate: 0.00736 
-2024-08-28 03:34:51.476385: train_loss -0.7496 
-2024-08-28 03:34:51.476626: val_loss -0.7719 
-2024-08-28 03:34:51.476783: Pseudo dice [0.0, 0.0, 0.8825, 0.9758, 0.8288, 0.9429, 0.9435, 0.9582, 0.9439, 0.9374, 0.9164, 0.9553, 0.9545, 0.8308, 0.9432, 0.9295, 0.8072, 0.8011, nan] 
-2024-08-28 03:34:51.476868: Epoch time: 85.81 s 
-2024-08-28 03:34:52.701385:  
-2024-08-28 03:34:52.701527: Epoch 579 
-2024-08-28 03:34:52.701616: Current learning rate: 0.00735 
-2024-08-28 03:36:15.587749: train_loss -0.7499 
-2024-08-28 03:36:15.587973: val_loss -0.7659 
-2024-08-28 03:36:15.588139: Pseudo dice [0.0, 0.0, 0.8815, 0.9768, 0.8247, 0.9438, 0.9437, 0.9595, 0.9418, 0.9405, 0.9193, 0.956, 0.957, 0.835, 0.9515, 0.9307, 0.8083, 0.7928, nan] 
-2024-08-28 03:36:15.588222: Epoch time: 82.89 s 
-2024-08-28 03:36:16.834885:  
-2024-08-28 03:36:16.835091: Epoch 580 
-2024-08-28 03:36:16.835192: Current learning rate: 0.00735 
-2024-08-28 03:37:48.271459: train_loss -0.7486 
-2024-08-28 03:37:48.271673: val_loss -0.7711 
-2024-08-28 03:37:48.271832: Pseudo dice [0.0, 0.0, 0.8732, 0.9755, 0.8137, 0.9386, 0.9466, 0.9639, 0.9476, 0.9349, 0.9204, 0.9545, 0.9545, 0.8409, 0.9501, 0.9317, 0.7859, 0.8052, nan] 
-2024-08-28 03:37:48.271932: Epoch time: 91.44 s 
-2024-08-28 03:37:49.787773:  
-2024-08-28 03:37:49.788108: Epoch 581 
-2024-08-28 03:37:49.788199: Current learning rate: 0.00734 
-2024-08-28 03:39:15.861551: train_loss -0.7503 
-2024-08-28 03:39:15.862067: val_loss -0.7721 
-2024-08-28 03:39:15.862272: Pseudo dice [0.0, 0.0, 0.8882, 0.9766, 0.8311, 0.9452, 0.9446, 0.9631, 0.9484, 0.9446, 0.9107, 0.9566, 0.9567, 0.8364, 0.9512, 0.9346, 0.815, 0.8211, nan] 
-2024-08-28 03:39:15.862393: Epoch time: 86.07 s 
-2024-08-28 03:39:17.135140:  
-2024-08-28 03:39:17.135750: Epoch 582 
-2024-08-28 03:39:17.136138: Current learning rate: 0.00734 
-2024-08-28 03:40:45.745840: train_loss -0.7491 
-2024-08-28 03:40:45.746087: val_loss -0.7718 
-2024-08-28 03:40:45.746239: Pseudo dice [0.0, 0.0, 0.8914, 0.9758, 0.8393, 0.9409, 0.9441, 0.9619, 0.9438, 0.9419, 0.9201, 0.959, 0.9521, 0.8268, 0.9423, 0.9273, 0.8194, 0.8285, nan] 
-2024-08-28 03:40:45.746324: Epoch time: 88.61 s 
-2024-08-28 03:40:46.972623:  
-2024-08-28 03:40:46.972921: Epoch 583 
-2024-08-28 03:40:46.973090: Current learning rate: 0.00733 
-2024-08-28 03:42:12.863567: train_loss -0.7502 
-2024-08-28 03:42:12.863795: val_loss -0.7716 
-2024-08-28 03:42:12.863966: Pseudo dice [0.0, 0.0, 0.8968, 0.9757, 0.846, 0.9416, 0.9454, 0.9607, 0.9465, 0.9431, 0.9216, 0.9607, 0.9556, 0.8393, 0.9525, 0.9232, 0.8204, 0.8104, nan] 
-2024-08-28 03:42:12.864053: Epoch time: 85.89 s 
-2024-08-28 03:42:14.117773:  
-2024-08-28 03:42:14.117970: Epoch 584 
-2024-08-28 03:42:14.118076: Current learning rate: 0.00733 
-2024-08-28 03:43:37.393384: train_loss -0.7498 
-2024-08-28 03:43:37.393630: val_loss -0.7577 
-2024-08-28 03:43:37.393781: Pseudo dice [0.0, 0.0, 0.8879, 0.9737, 0.8101, 0.9428, 0.9444, 0.9557, 0.9423, 0.9388, 0.9146, 0.9563, 0.9536, 0.8191, 0.9438, 0.9221, 0.8126, 0.8125, nan] 
-2024-08-28 03:43:37.393868: Epoch time: 83.28 s 
-2024-08-28 03:43:38.581692:  
-2024-08-28 03:43:38.581853: Epoch 585 
-2024-08-28 03:43:38.581951: Current learning rate: 0.00732 
-2024-08-28 03:45:04.876732: train_loss -0.7442 
-2024-08-28 03:45:04.876954: val_loss -0.7652 
-2024-08-28 03:45:04.877114: Pseudo dice [0.0, 0.0, 0.8953, 0.9765, 0.8108, 0.934, 0.9342, 0.955, 0.9417, 0.9365, 0.9156, 0.9504, 0.9464, 0.8396, 0.9429, 0.9294, 0.8136, 0.8113, nan] 
-2024-08-28 03:45:04.877199: Epoch time: 86.3 s 
-2024-08-28 03:45:06.087484:  
-2024-08-28 03:45:06.087653: Epoch 586 
-2024-08-28 03:45:06.087743: Current learning rate: 0.00732 
-2024-08-28 03:46:33.186711: train_loss -0.7466 
-2024-08-28 03:46:33.186912: val_loss -0.7646 
-2024-08-28 03:46:33.187062: Pseudo dice [0.0, 0.0, 0.8839, 0.9759, 0.8248, 0.9411, 0.946, 0.9553, 0.9449, 0.9364, 0.9167, 0.9547, 0.9567, 0.8225, 0.9383, 0.9214, 0.814, 0.8006, nan] 
-2024-08-28 03:46:33.187142: Epoch time: 87.1 s 
-2024-08-28 03:46:34.745227:  
-2024-08-28 03:46:34.745496: Epoch 587 
-2024-08-28 03:46:34.745590: Current learning rate: 0.00731 
-2024-08-28 03:48:05.681318: train_loss -0.747 
-2024-08-28 03:48:05.681577: val_loss -0.7628 
-2024-08-28 03:48:05.681734: Pseudo dice [0.0, 0.0, 0.9008, 0.9747, 0.8116, 0.9401, 0.9386, 0.9627, 0.9363, 0.934, 0.9195, 0.9442, 0.9491, 0.8305, 0.9507, 0.9245, 0.8074, 0.7994, nan] 
-2024-08-28 03:48:05.681819: Epoch time: 90.94 s 
-2024-08-28 03:48:06.919561:  
-2024-08-28 03:48:06.919761: Epoch 588 
-2024-08-28 03:48:06.919864: Current learning rate: 0.00731 
-2024-08-28 03:49:34.745079: train_loss -0.747 
-2024-08-28 03:49:34.745323: val_loss -0.7664 
-2024-08-28 03:49:34.745530: Pseudo dice [0.0, 0.0, 0.8822, 0.9761, 0.8377, 0.9417, 0.947, 0.9635, 0.9477, 0.9414, 0.9201, 0.958, 0.9529, 0.8252, 0.948, 0.9233, 0.8152, 0.7904, nan] 
-2024-08-28 03:49:34.745621: Epoch time: 87.83 s 
-2024-08-28 03:49:35.968997:  
-2024-08-28 03:49:35.969275: Epoch 589 
-2024-08-28 03:49:35.969378: Current learning rate: 0.00731 
-2024-08-28 03:51:04.853280: train_loss -0.7439 
-2024-08-28 03:51:04.853527: val_loss -0.7674 
-2024-08-28 03:51:04.854212: Pseudo dice [0.0, 0.0, 0.8998, 0.9736, 0.8106, 0.9457, 0.9415, 0.9611, 0.9459, 0.9397, 0.923, 0.9568, 0.9527, 0.8426, 0.9499, 0.9276, 0.8039, 0.7993, nan] 
-2024-08-28 03:51:04.854370: Epoch time: 88.89 s 
-2024-08-28 03:51:06.062588:  
-2024-08-28 03:51:06.062762: Epoch 590 
-2024-08-28 03:51:06.062854: Current learning rate: 0.0073 
-2024-08-28 03:52:33.883890: train_loss -0.7436 
-2024-08-28 03:52:33.884152: val_loss -0.7582 
-2024-08-28 03:52:33.884314: Pseudo dice [0.0, 0.0, 0.8913, 0.9754, 0.7944, 0.9322, 0.9367, 0.9577, 0.941, 0.9386, 0.917, 0.9516, 0.9555, 0.8209, 0.9299, 0.917, 0.8001, 0.8077, nan] 
-2024-08-28 03:52:33.884439: Epoch time: 87.82 s 
-2024-08-28 03:52:35.122823:  
-2024-08-28 03:52:35.123192: Epoch 591 
-2024-08-28 03:52:35.123288: Current learning rate: 0.0073 
-2024-08-28 03:54:02.535744: train_loss -0.743 
-2024-08-28 03:54:02.535973: val_loss -0.7678 
-2024-08-28 03:54:02.536139: Pseudo dice [0.0, 0.0, 0.8615, 0.9748, 0.8051, 0.9373, 0.9471, 0.962, 0.9436, 0.9447, 0.922, 0.9551, 0.9536, 0.8289, 0.9462, 0.9225, 0.7942, 0.7975, nan] 
-2024-08-28 03:54:02.536223: Epoch time: 87.41 s 
-2024-08-28 03:54:03.708072:  
-2024-08-28 03:54:03.708229: Epoch 592 
-2024-08-28 03:54:03.708321: Current learning rate: 0.00729 
-2024-08-28 03:55:30.924732: train_loss -0.7489 
-2024-08-28 03:55:30.924987: val_loss -0.767 
-2024-08-28 03:55:30.925168: Pseudo dice [0.0, 0.0, 0.8758, 0.9761, 0.8004, 0.9411, 0.9443, 0.9615, 0.9425, 0.9414, 0.9207, 0.9533, 0.9566, 0.834, 0.9303, 0.9264, 0.8085, 0.7945, nan] 
-2024-08-28 03:55:30.925263: Epoch time: 87.22 s 
-2024-08-28 03:55:32.413670:  
-2024-08-28 03:55:32.414168: Epoch 593 
-2024-08-28 03:55:32.414260: Current learning rate: 0.00729 
-2024-08-28 03:57:01.695792: train_loss -0.7447 
-2024-08-28 03:57:01.696037: val_loss -0.7628 
-2024-08-28 03:57:01.696213: Pseudo dice [0.0, 0.0, 0.8572, 0.9709, 0.7892, 0.9313, 0.9389, 0.9556, 0.9442, 0.9461, 0.9186, 0.9544, 0.9556, 0.827, 0.9452, 0.9181, 0.8029, 0.8111, nan] 
-2024-08-28 03:57:01.696298: Epoch time: 89.28 s 
-2024-08-28 03:57:02.938491:  
-2024-08-28 03:57:02.938677: Epoch 594 
-2024-08-28 03:57:02.938776: Current learning rate: 0.00728 
-2024-08-28 03:58:25.842804: train_loss -0.7412 
-2024-08-28 03:58:25.843150: val_loss -0.7693 
-2024-08-28 03:58:25.843356: Pseudo dice [0.0, 0.0, 0.8788, 0.9757, 0.8077, 0.9451, 0.9486, 0.9606, 0.9427, 0.9455, 0.9135, 0.9491, 0.9561, 0.8267, 0.9463, 0.9236, 0.8209, 0.8119, nan] 
-2024-08-28 03:58:25.843463: Epoch time: 82.91 s 
-2024-08-28 03:58:27.076425:  
-2024-08-28 03:58:27.076820: Epoch 595 
-2024-08-28 03:58:27.076924: Current learning rate: 0.00728 
-2024-08-28 03:59:58.315849: train_loss -0.7479 
-2024-08-28 03:59:58.316095: val_loss -0.7707 
-2024-08-28 03:59:58.316267: Pseudo dice [0.0, 0.0, 0.9, 0.9768, 0.8322, 0.9403, 0.9451, 0.9662, 0.9467, 0.9509, 0.9282, 0.9585, 0.9586, 0.8285, 0.9489, 0.9291, 0.8, 0.8204, nan] 
-2024-08-28 03:59:58.316354: Epoch time: 91.24 s 
-2024-08-28 03:59:59.570231:  
-2024-08-28 03:59:59.570432: Epoch 596 
-2024-08-28 03:59:59.570535: Current learning rate: 0.00727 
-2024-08-28 04:01:20.866428: train_loss -0.7507 
-2024-08-28 04:01:20.866649: val_loss -0.7679 
-2024-08-28 04:01:20.866824: Pseudo dice [0.0, 0.0, 0.8907, 0.9752, 0.8357, 0.9403, 0.9471, 0.9613, 0.9461, 0.9478, 0.9208, 0.9548, 0.9557, 0.8075, 0.9412, 0.9198, 0.8281, 0.8092, nan] 
-2024-08-28 04:01:20.866914: Epoch time: 81.3 s 
-2024-08-28 04:01:22.125754:  
-2024-08-28 04:01:22.125930: Epoch 597 
-2024-08-28 04:01:22.126021: Current learning rate: 0.00727 
-2024-08-28 04:02:52.845448: train_loss -0.7501 
-2024-08-28 04:02:52.845699: val_loss -0.7668 
-2024-08-28 04:02:52.845876: Pseudo dice [0.0, 0.0, 0.8742, 0.9753, 0.8398, 0.9402, 0.9368, 0.9564, 0.9434, 0.935, 0.9061, 0.9548, 0.9533, 0.8221, 0.94, 0.927, 0.8109, 0.8082, nan] 
-2024-08-28 04:02:52.845969: Epoch time: 90.72 s 
-2024-08-28 04:02:54.078717:  
-2024-08-28 04:02:54.078975: Epoch 598 
-2024-08-28 04:02:54.079079: Current learning rate: 0.00726 
-2024-08-28 04:04:25.346581: train_loss -0.7541 
-2024-08-28 04:04:25.346809: val_loss -0.7705 
-2024-08-28 04:04:25.346975: Pseudo dice [0.0, 0.0, 0.905, 0.9742, 0.8246, 0.9382, 0.9428, 0.9636, 0.9401, 0.94, 0.9078, 0.947, 0.9412, 0.8389, 0.949, 0.9261, 0.8219, 0.815, nan] 
-2024-08-28 04:04:25.347077: Epoch time: 91.27 s 
-2024-08-28 04:04:26.550563:  
-2024-08-28 04:04:26.550725: Epoch 599 
-2024-08-28 04:04:26.550815: Current learning rate: 0.00726 
-2024-08-28 04:05:54.402740: train_loss -0.7515 
-2024-08-28 04:05:54.402970: val_loss -0.766 
-2024-08-28 04:05:54.403140: Pseudo dice [0.0, 0.0, 0.8767, 0.9768, 0.8518, 0.9495, 0.9488, 0.9596, 0.9413, 0.9437, 0.9142, 0.955, 0.9522, 0.8274, 0.9314, 0.929, 0.8262, 0.8213, nan] 
-2024-08-28 04:05:54.403230: Epoch time: 87.85 s 
-2024-08-28 04:05:56.086319:  
-2024-08-28 04:05:56.086493: Epoch 600 
-2024-08-28 04:05:56.086594: Current learning rate: 0.00725 
-2024-08-28 04:07:26.193386: train_loss -0.7434 
-2024-08-28 04:07:26.193649: val_loss -0.7651 
-2024-08-28 04:07:26.193816: Pseudo dice [0.0, 0.0, 0.8879, 0.9774, 0.8318, 0.9388, 0.9406, 0.9602, 0.9387, 0.9384, 0.9146, 0.9496, 0.947, 0.8374, 0.9484, 0.9236, 0.8182, 0.8046, nan] 
-2024-08-28 04:07:26.193903: Epoch time: 90.11 s 
-2024-08-28 04:07:27.415854:  
-2024-08-28 04:07:27.416082: Epoch 601 
-2024-08-28 04:07:27.416176: Current learning rate: 0.00725 
-2024-08-28 04:08:56.791559: train_loss -0.747 
-2024-08-28 04:08:56.791778: val_loss -0.7709 
-2024-08-28 04:08:56.791938: Pseudo dice [0.0, 0.0, 0.8993, 0.9756, 0.8323, 0.9459, 0.9428, 0.9593, 0.9506, 0.9503, 0.9244, 0.9604, 0.9601, 0.8332, 0.9454, 0.9294, 0.8264, 0.8123, nan] 
-2024-08-28 04:08:56.792023: Epoch time: 89.38 s 
-2024-08-28 04:08:58.013257:  
-2024-08-28 04:08:58.013601: Epoch 602 
-2024-08-28 04:08:58.013693: Current learning rate: 0.00724 
-2024-08-28 04:10:29.137001: train_loss -0.7369 
-2024-08-28 04:10:29.137260: val_loss -0.7629 
-2024-08-28 04:10:29.137427: Pseudo dice [0.0, 0.0, 0.8685, 0.9762, 0.8172, 0.9403, 0.9446, 0.959, 0.9428, 0.9304, 0.917, 0.9562, 0.9522, 0.8303, 0.9463, 0.9265, 0.8056, 0.802, nan] 
-2024-08-28 04:10:29.137513: Epoch time: 91.12 s 
-2024-08-28 04:10:30.335687:  
-2024-08-28 04:10:30.335868: Epoch 603 
-2024-08-28 04:10:30.335971: Current learning rate: 0.00724 
-2024-08-28 04:12:00.636906: train_loss -0.7475 
-2024-08-28 04:12:00.637140: val_loss -0.7771 
-2024-08-28 04:12:00.637307: Pseudo dice [0.0, 0.0, 0.8922, 0.9763, 0.8402, 0.9511, 0.9545, 0.9626, 0.9445, 0.952, 0.9316, 0.9557, 0.9586, 0.8381, 0.9561, 0.9346, 0.8058, 0.8078, nan] 
-2024-08-28 04:12:00.637390: Epoch time: 90.3 s 
-2024-08-28 04:12:01.873404:  
-2024-08-28 04:12:01.873579: Epoch 604 
-2024-08-28 04:12:01.873670: Current learning rate: 0.00724 
-2024-08-28 04:13:31.617116: train_loss -0.7525 
-2024-08-28 04:13:31.617350: val_loss -0.7734 
-2024-08-28 04:13:31.617513: Pseudo dice [0.0, 0.0, 0.8887, 0.9765, 0.8253, 0.9456, 0.9453, 0.9614, 0.9487, 0.9433, 0.9309, 0.9589, 0.9571, 0.8359, 0.9498, 0.9223, 0.8164, 0.8202, nan] 
-2024-08-28 04:13:31.617598: Epoch time: 89.74 s 
-2024-08-28 04:13:32.996903:  
-2024-08-28 04:13:32.997089: Epoch 605 
-2024-08-28 04:13:32.997180: Current learning rate: 0.00723 
-2024-08-28 04:14:58.263235: train_loss -0.75 
-2024-08-28 04:14:58.263459: val_loss -0.7727 
-2024-08-28 04:14:58.263622: Pseudo dice [0.0, 0.0, 0.9022, 0.9742, 0.8138, 0.9482, 0.944, 0.9628, 0.9458, 0.9382, 0.9196, 0.953, 0.9545, 0.8353, 0.9501, 0.9241, 0.8187, 0.8177, nan] 
-2024-08-28 04:14:58.263704: Epoch time: 85.27 s 
-2024-08-28 04:14:59.429299:  
-2024-08-28 04:14:59.429583: Epoch 606 
-2024-08-28 04:14:59.429675: Current learning rate: 0.00723 
-2024-08-28 04:16:23.829376: train_loss -0.7504 
-2024-08-28 04:16:23.829846: val_loss -0.7664 
-2024-08-28 04:16:23.830278: Pseudo dice [0.0, 0.0, 0.8988, 0.9755, 0.8462, 0.9474, 0.95, 0.9634, 0.9398, 0.9388, 0.9222, 0.9545, 0.9505, 0.8434, 0.9476, 0.9294, 0.8146, 0.8208, nan] 
-2024-08-28 04:16:23.830429: Epoch time: 84.4 s 
-2024-08-28 04:16:25.512117:  
-2024-08-28 04:16:25.512353: Epoch 607 
-2024-08-28 04:16:25.512455: Current learning rate: 0.00722 
-2024-08-28 04:17:48.079396: train_loss -0.7491 
-2024-08-28 04:17:48.079644: val_loss -0.7709 
-2024-08-28 04:17:48.079809: Pseudo dice [0.0, 0.0, 0.8949, 0.9766, 0.834, 0.9422, 0.9444, 0.9622, 0.9504, 0.9484, 0.9268, 0.9583, 0.9588, 0.8353, 0.9463, 0.9259, 0.8184, 0.8157, nan] 
-2024-08-28 04:17:48.079951: Epoch time: 82.57 s 
-2024-08-28 04:17:49.309567:  
-2024-08-28 04:17:49.309730: Epoch 608 
-2024-08-28 04:17:49.309815: Current learning rate: 0.00722 
-2024-08-28 04:19:12.246051: train_loss -0.7507 
-2024-08-28 04:19:12.246301: val_loss -0.7705 
-2024-08-28 04:19:12.246478: Pseudo dice [0.0, 0.0, 0.9018, 0.976, 0.8119, 0.9463, 0.9523, 0.9633, 0.9477, 0.9457, 0.9259, 0.9547, 0.9522, 0.8336, 0.9477, 0.9308, 0.8125, 0.8, nan] 
-2024-08-28 04:19:12.246573: Epoch time: 82.94 s 
-2024-08-28 04:19:13.481778:  
-2024-08-28 04:19:13.481947: Epoch 609 
-2024-08-28 04:19:13.482043: Current learning rate: 0.00721 
-2024-08-28 04:20:40.614690: train_loss -0.754 
-2024-08-28 04:20:40.614947: val_loss -0.7723 
-2024-08-28 04:20:40.615125: Pseudo dice [0.0, 0.0, 0.8921, 0.9769, 0.828, 0.9459, 0.9472, 0.9596, 0.9452, 0.9486, 0.9231, 0.9569, 0.9578, 0.8354, 0.9513, 0.927, 0.8127, 0.8096, nan] 
-2024-08-28 04:20:40.615225: Epoch time: 87.13 s 
-2024-08-28 04:20:41.865554:  
-2024-08-28 04:20:41.865724: Epoch 610 
-2024-08-28 04:20:41.865807: Current learning rate: 0.00721 
-2024-08-28 04:22:07.613618: train_loss -0.7518 
-2024-08-28 04:22:07.613852: val_loss -0.7722 
-2024-08-28 04:22:07.614014: Pseudo dice [0.0, 0.0, 0.8871, 0.9719, 0.8301, 0.9419, 0.9412, 0.9603, 0.9453, 0.9465, 0.9205, 0.9555, 0.9537, 0.8185, 0.9432, 0.9304, 0.8171, 0.8052, nan] 
-2024-08-28 04:22:07.614100: Epoch time: 85.75 s 
-2024-08-28 04:22:09.021861:  
-2024-08-28 04:22:09.022034: Epoch 611 
-2024-08-28 04:22:09.022121: Current learning rate: 0.0072 
-2024-08-28 04:23:32.794955: train_loss -0.7482 
-2024-08-28 04:23:32.795236: val_loss -0.77 
-2024-08-28 04:23:32.795477: Pseudo dice [0.0, 0.0, 0.8777, 0.9765, 0.8307, 0.9459, 0.9492, 0.9632, 0.9522, 0.9394, 0.915, 0.9595, 0.9515, 0.836, 0.9479, 0.9272, 0.8037, 0.8038, nan] 
-2024-08-28 04:23:32.795574: Epoch time: 83.77 s 
-2024-08-28 04:23:33.998899:  
-2024-08-28 04:23:33.999223: Epoch 612 
-2024-08-28 04:23:33.999310: Current learning rate: 0.0072 
-2024-08-28 04:24:54.992273: train_loss -0.7471 
-2024-08-28 04:24:54.992519: val_loss -0.7651 
-2024-08-28 04:24:54.992672: Pseudo dice [0.0, 0.0, 0.8646, 0.9756, 0.839, 0.9389, 0.9399, 0.9583, 0.9431, 0.9421, 0.9192, 0.9545, 0.9514, 0.8211, 0.9442, 0.9295, 0.7966, 0.8024, nan] 
-2024-08-28 04:24:54.992752: Epoch time: 80.99 s 
-2024-08-28 04:24:56.220523:  
-2024-08-28 04:24:56.220691: Epoch 613 
-2024-08-28 04:24:56.220776: Current learning rate: 0.00719 
-2024-08-28 04:26:23.144235: train_loss -0.747 
-2024-08-28 04:26:23.144497: val_loss -0.7748 
-2024-08-28 04:26:23.144667: Pseudo dice [0.0, 0.0, 0.8824, 0.9765, 0.8271, 0.9473, 0.9478, 0.9631, 0.9456, 0.9452, 0.9274, 0.9584, 0.9593, 0.8324, 0.949, 0.93, 0.826, 0.8254, nan] 
-2024-08-28 04:26:23.144753: Epoch time: 86.92 s 
-2024-08-28 04:26:24.367113:  
-2024-08-28 04:26:24.367441: Epoch 614 
-2024-08-28 04:26:24.367541: Current learning rate: 0.00719 
-2024-08-28 04:27:53.184700: train_loss -0.7501 
-2024-08-28 04:27:53.184940: val_loss -0.772 
-2024-08-28 04:27:53.185093: Pseudo dice [0.0, 0.0, 0.8763, 0.9773, 0.8124, 0.9431, 0.9472, 0.9618, 0.9448, 0.9366, 0.9165, 0.9523, 0.9562, 0.8346, 0.9307, 0.9301, 0.8147, 0.802, nan] 
-2024-08-28 04:27:53.185177: Epoch time: 88.82 s 
-2024-08-28 04:27:54.391642:  
-2024-08-28 04:27:54.391814: Epoch 615 
-2024-08-28 04:27:54.391909: Current learning rate: 0.00718 
-2024-08-28 04:29:15.525551: train_loss -0.7439 
-2024-08-28 04:29:15.525824: val_loss -0.7572 
-2024-08-28 04:29:15.525981: Pseudo dice [0.0, 0.0, 0.8735, 0.9729, 0.8032, 0.9248, 0.9328, 0.9558, 0.934, 0.9338, 0.9126, 0.9439, 0.9441, 0.7699, 0.9287, 0.8988, 0.7871, 0.787, nan] 
-2024-08-28 04:29:15.526065: Epoch time: 81.13 s 
-2024-08-28 04:29:16.759399:  
-2024-08-28 04:29:16.759584: Epoch 616 
-2024-08-28 04:29:16.759679: Current learning rate: 0.00718 
-2024-08-28 04:30:44.113945: train_loss -0.7423 
-2024-08-28 04:30:44.114431: val_loss -0.7666 
-2024-08-28 04:30:44.114634: Pseudo dice [0.0, 0.0, 0.8922, 0.9743, 0.8281, 0.9408, 0.9481, 0.9629, 0.9498, 0.9497, 0.9283, 0.9569, 0.9578, 0.8354, 0.9527, 0.933, 0.7979, 0.8051, nan] 
-2024-08-28 04:30:44.114776: Epoch time: 87.36 s 
-2024-08-28 04:30:45.737437:  
-2024-08-28 04:30:45.737590: Epoch 617 
-2024-08-28 04:30:45.737682: Current learning rate: 0.00717 
-2024-08-28 04:32:14.279675: train_loss -0.7503 
-2024-08-28 04:32:14.280020: val_loss -0.7732 
-2024-08-28 04:32:14.280207: Pseudo dice [0.0, 0.0, 0.8423, 0.9626, 0.8174, 0.9476, 0.9472, 0.9628, 0.9506, 0.9512, 0.9225, 0.9588, 0.9578, 0.829, 0.9528, 0.9317, 0.791, 0.7966, nan] 
-2024-08-28 04:32:14.280358: Epoch time: 88.54 s 
-2024-08-28 04:32:15.506947:  
-2024-08-28 04:32:15.507299: Epoch 618 
-2024-08-28 04:32:15.507404: Current learning rate: 0.00717 
-2024-08-28 04:33:40.232553: train_loss -0.7498 
-2024-08-28 04:33:40.233029: val_loss -0.7678 
-2024-08-28 04:33:40.233203: Pseudo dice [0.0, 0.0, 0.874, 0.9735, 0.8324, 0.9433, 0.9438, 0.9637, 0.941, 0.9473, 0.9287, 0.9536, 0.9574, 0.8185, 0.9449, 0.9184, 0.8044, 0.8227, nan] 
-2024-08-28 04:33:40.233326: Epoch time: 84.73 s 
-2024-08-28 04:33:41.495042:  
-2024-08-28 04:33:41.495208: Epoch 619 
-2024-08-28 04:33:41.495299: Current learning rate: 0.00717 
-2024-08-28 04:35:14.682063: train_loss -0.7465 
-2024-08-28 04:35:14.682390: val_loss -0.7699 
-2024-08-28 04:35:14.682590: Pseudo dice [0.0, 0.0, 0.8898, 0.9736, 0.8248, 0.9471, 0.95, 0.9653, 0.9481, 0.945, 0.9237, 0.9585, 0.9523, 0.838, 0.9514, 0.9246, 0.8155, 0.8153, nan] 
-2024-08-28 04:35:14.682708: Epoch time: 93.19 s 
-2024-08-28 04:35:15.881812:  
-2024-08-28 04:35:15.881963: Epoch 620 
-2024-08-28 04:35:15.882053: Current learning rate: 0.00716 
-2024-08-28 04:36:39.254677: train_loss -0.7461 
-2024-08-28 04:36:39.254959: val_loss -0.7639 
-2024-08-28 04:36:39.255180: Pseudo dice [0.0, 0.0, 0.8911, 0.9757, 0.8281, 0.9372, 0.9376, 0.9637, 0.9258, 0.9325, 0.9091, 0.9355, 0.9379, 0.8438, 0.9477, 0.9314, 0.8192, 0.8194, nan] 
-2024-08-28 04:36:39.255292: Epoch time: 83.37 s 
-2024-08-28 04:36:40.561178:  
-2024-08-28 04:36:40.561357: Epoch 621 
-2024-08-28 04:36:40.561456: Current learning rate: 0.00716 
-2024-08-28 04:38:03.588758: train_loss -0.7531 
-2024-08-28 04:38:03.589073: val_loss -0.7695 
-2024-08-28 04:38:03.589223: Pseudo dice [0.0, 0.0, 0.8925, 0.9764, 0.8397, 0.9382, 0.94, 0.9583, 0.9472, 0.9383, 0.9273, 0.9568, 0.9572, 0.8282, 0.9506, 0.9282, 0.8076, 0.819, nan] 
-2024-08-28 04:38:03.589302: Epoch time: 83.03 s 
-2024-08-28 04:38:04.766294:  
-2024-08-28 04:38:04.766562: Epoch 622 
-2024-08-28 04:38:04.766661: Current learning rate: 0.00715 
-2024-08-28 04:39:36.559095: train_loss -0.7504 
-2024-08-28 04:39:36.559330: val_loss -0.7777 
-2024-08-28 04:39:36.559493: Pseudo dice [0.0, 0.0, 0.8999, 0.9748, 0.8465, 0.9425, 0.9457, 0.9657, 0.9454, 0.9449, 0.9209, 0.9559, 0.9513, 0.8462, 0.9531, 0.9287, 0.8115, 0.8251, nan] 
-2024-08-28 04:39:36.559577: Epoch time: 91.79 s 
-2024-08-28 04:39:38.109756:  
-2024-08-28 04:39:38.109955: Epoch 623 
-2024-08-28 04:39:38.110057: Current learning rate: 0.00715 
-2024-08-28 04:41:03.578330: train_loss -0.7528 
-2024-08-28 04:41:03.578561: val_loss -0.7739 
-2024-08-28 04:41:03.578716: Pseudo dice [0.0, 0.0, 0.8859, 0.9753, 0.8408, 0.9391, 0.9461, 0.9637, 0.9487, 0.9404, 0.9186, 0.9598, 0.9576, 0.839, 0.9526, 0.9313, 0.8037, 0.7957, nan] 
-2024-08-28 04:41:03.578796: Epoch time: 85.47 s 
-2024-08-28 04:41:04.834391:  
-2024-08-28 04:41:04.834574: Epoch 624 
-2024-08-28 04:41:04.834660: Current learning rate: 0.00714 
-2024-08-28 04:42:34.097435: train_loss -0.7497 
-2024-08-28 04:42:34.097676: val_loss -0.7699 
-2024-08-28 04:42:34.097843: Pseudo dice [0.0, 0.0, 0.8767, 0.9753, 0.7993, 0.9451, 0.9489, 0.9628, 0.945, 0.9408, 0.9244, 0.9569, 0.9568, 0.8325, 0.9402, 0.9318, 0.8073, 0.7309, nan] 
-2024-08-28 04:42:34.097934: Epoch time: 89.26 s 
-2024-08-28 04:42:35.335440:  
-2024-08-28 04:42:35.335836: Epoch 625 
-2024-08-28 04:42:35.336028: Current learning rate: 0.00714 
-2024-08-28 04:44:00.737751: train_loss -0.7485 
-2024-08-28 04:44:00.737994: val_loss -0.7675 
-2024-08-28 04:44:00.738162: Pseudo dice [0.0, 0.0, 0.8842, 0.9763, 0.8367, 0.9437, 0.9464, 0.9615, 0.9489, 0.9431, 0.9178, 0.9603, 0.9583, 0.8236, 0.9466, 0.9252, 0.8226, 0.8339, nan] 
-2024-08-28 04:44:00.738265: Epoch time: 85.4 s 
-2024-08-28 04:44:01.964628:  
-2024-08-28 04:44:01.965091: Epoch 626 
-2024-08-28 04:44:01.965184: Current learning rate: 0.00713 
-2024-08-28 04:45:26.579715: train_loss -0.7561 
-2024-08-28 04:45:26.579948: val_loss -0.7709 
-2024-08-28 04:45:26.580111: Pseudo dice [0.0, 0.0, 0.9037, 0.9761, 0.8411, 0.9478, 0.9541, 0.9638, 0.9486, 0.9485, 0.9202, 0.9576, 0.952, 0.8453, 0.9487, 0.9305, 0.8234, 0.8179, nan] 
-2024-08-28 04:45:26.580194: Epoch time: 84.62 s 
-2024-08-28 04:45:27.853108:  
-2024-08-28 04:45:27.853410: Epoch 627 
-2024-08-28 04:45:27.853508: Current learning rate: 0.00713 
-2024-08-28 04:46:54.990904: train_loss -0.7564 
-2024-08-28 04:46:54.991142: val_loss -0.7691 
-2024-08-28 04:46:54.991306: Pseudo dice [0.0, 0.0, 0.8941, 0.9748, 0.8101, 0.9343, 0.9416, 0.96, 0.9443, 0.9396, 0.9097, 0.9486, 0.951, 0.8382, 0.9547, 0.9337, 0.8206, 0.8126, nan] 
-2024-08-28 04:46:54.991391: Epoch time: 87.14 s 
-2024-08-28 04:46:56.222364:  
-2024-08-28 04:46:56.222523: Epoch 628 
-2024-08-28 04:46:56.222608: Current learning rate: 0.00712 
-2024-08-28 04:48:20.888553: train_loss -0.7535 
-2024-08-28 04:48:20.888770: val_loss -0.7742 
-2024-08-28 04:48:20.888935: Pseudo dice [0.0, 0.0, 0.9061, 0.9759, 0.8212, 0.9471, 0.9536, 0.965, 0.9497, 0.9468, 0.919, 0.9563, 0.9551, 0.8367, 0.9497, 0.9355, 0.8303, 0.796, nan] 
-2024-08-28 04:48:20.889021: Epoch time: 84.67 s 
-2024-08-28 04:48:22.153705:  
-2024-08-28 04:48:22.153972: Epoch 629 
-2024-08-28 04:48:22.154074: Current learning rate: 0.00712 
-2024-08-28 04:49:53.219061: train_loss -0.749 
-2024-08-28 04:49:53.219295: val_loss -0.7687 
-2024-08-28 04:49:53.219461: Pseudo dice [0.0, 0.0, 0.8632, 0.9765, 0.8217, 0.9459, 0.9429, 0.9648, 0.9433, 0.9364, 0.9246, 0.9549, 0.9552, 0.8413, 0.9543, 0.9295, 0.8084, 0.8111, nan] 
-2024-08-28 04:49:53.219546: Epoch time: 91.07 s 
-2024-08-28 04:49:54.461537:  
-2024-08-28 04:49:54.461730: Epoch 630 
-2024-08-28 04:49:54.461815: Current learning rate: 0.00711 
-2024-08-28 04:51:32.646987: train_loss -0.7501 
-2024-08-28 04:51:32.647229: val_loss -0.7725 
-2024-08-28 04:51:32.647389: Pseudo dice [0.0, 0.0, 0.8948, 0.9751, 0.8062, 0.9372, 0.9426, 0.96, 0.9457, 0.9509, 0.9251, 0.9579, 0.9578, 0.838, 0.9502, 0.9276, 0.824, 0.8266, nan] 
-2024-08-28 04:51:32.647478: Epoch time: 98.19 s 
-2024-08-28 04:51:33.881820:  
-2024-08-28 04:51:33.882281: Epoch 631 
-2024-08-28 04:51:33.882384: Current learning rate: 0.00711 
-2024-08-28 04:52:58.133506: train_loss -0.7495 
-2024-08-28 04:52:58.133743: val_loss -0.7659 
-2024-08-28 04:52:58.133895: Pseudo dice [0.0, 0.0, 0.8674, 0.977, 0.8434, 0.9436, 0.9477, 0.9651, 0.9476, 0.9397, 0.9192, 0.9568, 0.9545, 0.8422, 0.9538, 0.9279, 0.8176, 0.8064, nan] 
-2024-08-28 04:52:58.133972: Epoch time: 84.25 s 
-2024-08-28 04:52:59.378681:  
-2024-08-28 04:52:59.378851: Epoch 632 
-2024-08-28 04:52:59.378953: Current learning rate: 0.0071 
-2024-08-28 04:54:28.145832: train_loss -0.7514 
-2024-08-28 04:54:28.146058: val_loss -0.7743 
-2024-08-28 04:54:28.146222: Pseudo dice [0.0, 0.0, 0.8927, 0.9773, 0.8393, 0.9469, 0.9505, 0.9623, 0.946, 0.9498, 0.9226, 0.9561, 0.9572, 0.8352, 0.9421, 0.9242, 0.8246, 0.8222, nan] 
-2024-08-28 04:54:28.146307: Epoch time: 88.77 s 
-2024-08-28 04:54:28.146359: Yayy! New best EMA pseudo Dice: 0.811 
-2024-08-28 04:54:29.655507:  
-2024-08-28 04:54:29.655707: Epoch 633 
-2024-08-28 04:54:29.655810: Current learning rate: 0.0071 
-2024-08-28 04:55:54.419501: train_loss -0.7484 
-2024-08-28 04:55:54.419754: val_loss -0.7623 
-2024-08-28 04:55:54.419911: Pseudo dice [0.0, 0.0, 0.8767, 0.9646, 0.797, 0.9316, 0.9377, 0.9566, 0.9436, 0.9435, 0.9296, 0.9529, 0.9525, 0.8304, 0.9352, 0.9234, 0.8082, 0.793, nan] 
-2024-08-28 04:55:54.419991: Epoch time: 84.76 s 
-2024-08-28 04:55:55.746624:  
-2024-08-28 04:55:55.746850: Epoch 634 
-2024-08-28 04:55:55.746949: Current learning rate: 0.0071 
-2024-08-28 04:57:19.477962: train_loss -0.748 
-2024-08-28 04:57:19.478204: val_loss -0.7672 
-2024-08-28 04:57:19.478385: Pseudo dice [0.0, 0.0, 0.8856, 0.9744, 0.7998, 0.9401, 0.9457, 0.9617, 0.9433, 0.9439, 0.918, 0.9573, 0.9471, 0.8279, 0.9376, 0.93, 0.7962, 0.8064, nan] 
-2024-08-28 04:57:19.478498: Epoch time: 83.73 s 
-2024-08-28 04:57:20.900629:  
-2024-08-28 04:57:20.900810: Epoch 635 
-2024-08-28 04:57:20.900905: Current learning rate: 0.00709 
-2024-08-28 04:58:40.241546: train_loss -0.7494 
-2024-08-28 04:58:40.241800: val_loss -0.7746 
-2024-08-28 04:58:40.241960: Pseudo dice [0.0, 0.0, 0.8983, 0.976, 0.8237, 0.9337, 0.9416, 0.9625, 0.947, 0.9441, 0.9289, 0.956, 0.9536, 0.8334, 0.9442, 0.9282, 0.8217, 0.8269, nan] 
-2024-08-28 04:58:40.242045: Epoch time: 79.34 s 
-2024-08-28 04:58:41.496598:  
-2024-08-28 04:58:41.496788: Epoch 636 
-2024-08-28 04:58:41.496881: Current learning rate: 0.00709 
-2024-08-28 05:00:10.251695: train_loss -0.7504 
-2024-08-28 05:00:10.251930: val_loss -0.7745 
-2024-08-28 05:00:10.252118: Pseudo dice [0.0, 0.0, 0.8989, 0.9781, 0.8495, 0.9464, 0.9503, 0.966, 0.9498, 0.9437, 0.9277, 0.9576, 0.956, 0.8379, 0.9531, 0.932, 0.8236, 0.808, nan] 
-2024-08-28 05:00:10.252220: Epoch time: 88.76 s 
-2024-08-28 05:00:11.483201:  
-2024-08-28 05:00:11.483381: Epoch 637 
-2024-08-28 05:00:11.483496: Current learning rate: 0.00708 
-2024-08-28 05:01:38.296236: train_loss -0.7518 
-2024-08-28 05:01:38.296498: val_loss -0.7703 
-2024-08-28 05:01:38.296656: Pseudo dice [0.0, 0.0, 0.8709, 0.9767, 0.8381, 0.9439, 0.9437, 0.9631, 0.9522, 0.9442, 0.9259, 0.9572, 0.9588, 0.8327, 0.9536, 0.9204, 0.8239, 0.8184, nan] 
-2024-08-28 05:01:38.296770: Epoch time: 86.81 s 
-2024-08-28 05:01:39.529185:  
-2024-08-28 05:01:39.529372: Epoch 638 
-2024-08-28 05:01:39.529476: Current learning rate: 0.00708 
-2024-08-28 05:03:04.951977: train_loss -0.7526 
-2024-08-28 05:03:04.952222: val_loss -0.7678 
-2024-08-28 05:03:04.952382: Pseudo dice [0.0, 0.0, 0.8818, 0.9772, 0.7896, 0.9311, 0.94, 0.9587, 0.9464, 0.9364, 0.929, 0.9595, 0.9595, 0.839, 0.9489, 0.9291, 0.7899, 0.8127, nan] 
-2024-08-28 05:03:04.952475: Epoch time: 85.42 s 
-2024-08-28 05:03:06.204245:  
-2024-08-28 05:03:06.204596: Epoch 639 
-2024-08-28 05:03:06.204690: Current learning rate: 0.00707 
-2024-08-28 05:04:29.827049: train_loss -0.7512 
-2024-08-28 05:04:29.827376: val_loss -0.7741 
-2024-08-28 05:04:29.827538: Pseudo dice [0.0, 0.0, 0.8951, 0.9768, 0.7956, 0.9377, 0.9419, 0.9607, 0.95, 0.9467, 0.9353, 0.9585, 0.9593, 0.8384, 0.9451, 0.9339, 0.8177, 0.8087, nan] 
-2024-08-28 05:04:29.827622: Epoch time: 83.62 s 
-2024-08-28 05:04:31.005196:  
-2024-08-28 05:04:31.005356: Epoch 640 
-2024-08-28 05:04:31.005450: Current learning rate: 0.00707 
-2024-08-28 05:05:55.097183: train_loss -0.7566 
-2024-08-28 05:05:55.097416: val_loss -0.7803 
-2024-08-28 05:05:55.097575: Pseudo dice [0.0, 0.0, 0.8898, 0.9775, 0.8462, 0.9452, 0.9478, 0.9609, 0.9444, 0.9484, 0.9353, 0.9609, 0.9605, 0.8407, 0.9546, 0.9344, 0.8196, 0.8194, nan] 
-2024-08-28 05:05:55.097662: Epoch time: 84.09 s 
-2024-08-28 05:05:55.097747: Yayy! New best EMA pseudo Dice: 0.8111 
-2024-08-28 05:05:56.855222:  
-2024-08-28 05:05:56.855400: Epoch 641 
-2024-08-28 05:05:56.855484: Current learning rate: 0.00706 
-2024-08-28 05:07:24.836944: train_loss -0.7564 
-2024-08-28 05:07:24.837174: val_loss -0.7673 
-2024-08-28 05:07:24.837341: Pseudo dice [0.0, 0.0, 0.8949, 0.9776, 0.8256, 0.9337, 0.937, 0.9587, 0.9454, 0.9458, 0.9196, 0.9582, 0.9564, 0.8332, 0.9491, 0.9313, 0.8179, 0.798, nan] 
-2024-08-28 05:07:24.837429: Epoch time: 87.98 s 
-2024-08-28 05:07:26.041024:  
-2024-08-28 05:07:26.041239: Epoch 642 
-2024-08-28 05:07:26.041333: Current learning rate: 0.00706 
-2024-08-28 05:08:57.647973: train_loss -0.7521 
-2024-08-28 05:08:57.648230: val_loss -0.7748 
-2024-08-28 05:08:57.648451: Pseudo dice [0.0, 0.0, 0.9032, 0.9769, 0.8127, 0.9469, 0.9492, 0.9638, 0.9485, 0.9391, 0.926, 0.9544, 0.9585, 0.8473, 0.9497, 0.9307, 0.8214, 0.8211, nan] 
-2024-08-28 05:08:57.648560: Epoch time: 91.61 s 
-2024-08-28 05:08:57.648624: Yayy! New best EMA pseudo Dice: 0.8113 
-2024-08-28 05:08:59.441407:  
-2024-08-28 05:08:59.441735: Epoch 643 
-2024-08-28 05:08:59.441833: Current learning rate: 0.00705 
-2024-08-28 05:10:25.958226: train_loss -0.749 
-2024-08-28 05:10:25.958741: val_loss -0.7692 
-2024-08-28 05:10:25.958943: Pseudo dice [0.0, 0.0, 0.8886, 0.9761, 0.8449, 0.9383, 0.9415, 0.964, 0.9355, 0.9443, 0.9176, 0.9478, 0.944, 0.8387, 0.9509, 0.9197, 0.8041, 0.8152, nan] 
-2024-08-28 05:10:25.959078: Epoch time: 86.52 s 
-2024-08-28 05:10:27.315593:  
-2024-08-28 05:10:27.315970: Epoch 644 
-2024-08-28 05:10:27.316226: Current learning rate: 0.00705 
-2024-08-28 05:11:56.798295: train_loss -0.7511 
-2024-08-28 05:11:56.798518: val_loss -0.7698 
-2024-08-28 05:11:56.798692: Pseudo dice [0.0, 0.0, 0.887, 0.9772, 0.8141, 0.9402, 0.946, 0.9642, 0.946, 0.949, 0.9235, 0.9569, 0.9534, 0.829, 0.952, 0.9311, 0.8239, 0.8129, nan] 
-2024-08-28 05:11:56.798812: Epoch time: 89.48 s 
-2024-08-28 05:11:58.113054:  
-2024-08-28 05:11:58.113541: Epoch 645 
-2024-08-28 05:11:58.114007: Current learning rate: 0.00704 
-2024-08-28 05:13:25.486752: train_loss -0.7504 
-2024-08-28 05:13:25.487185: val_loss -0.775 
-2024-08-28 05:13:25.487358: Pseudo dice [0.0, 0.0, 0.8878, 0.9771, 0.7977, 0.9403, 0.941, 0.961, 0.9497, 0.9554, 0.934, 0.9565, 0.9588, 0.8404, 0.9454, 0.9295, 0.8317, 0.8216, nan] 
-2024-08-28 05:13:25.487454: Epoch time: 87.38 s 
-2024-08-28 05:13:25.487522: Yayy! New best EMA pseudo Dice: 0.8113 
-2024-08-28 05:13:27.411337:  
-2024-08-28 05:13:27.411515: Epoch 646 
-2024-08-28 05:13:27.411611: Current learning rate: 0.00704 
-2024-08-28 05:14:55.491202: train_loss -0.7486 
-2024-08-28 05:14:55.491437: val_loss -0.7616 
-2024-08-28 05:14:55.491605: Pseudo dice [0.0, 0.0, 0.8833, 0.9752, 0.8176, 0.9327, 0.9318, 0.957, 0.9316, 0.9273, 0.8999, 0.9395, 0.9402, 0.828, 0.9462, 0.9204, 0.8176, 0.8059, nan] 
-2024-08-28 05:14:55.491699: Epoch time: 88.08 s 
-2024-08-28 05:14:56.689551:  
-2024-08-28 05:14:56.689695: Epoch 647 
-2024-08-28 05:14:56.689783: Current learning rate: 0.00703 
-2024-08-28 05:16:21.541339: train_loss -0.7486 
-2024-08-28 05:16:21.541885: val_loss -0.769 
-2024-08-28 05:16:21.542055: Pseudo dice [0.0, 0.0, 0.8756, 0.9758, 0.8267, 0.9456, 0.9468, 0.964, 0.9526, 0.9528, 0.9297, 0.9611, 0.9564, 0.8316, 0.9514, 0.9331, 0.8189, 0.8123, nan] 
-2024-08-28 05:16:21.542181: Epoch time: 84.85 s 
-2024-08-28 05:16:22.812904:  
-2024-08-28 05:16:22.813236: Epoch 648 
-2024-08-28 05:16:22.813328: Current learning rate: 0.00703 
-2024-08-28 05:17:49.424858: train_loss -0.7531 
-2024-08-28 05:17:49.425091: val_loss -0.7722 
-2024-08-28 05:17:49.425246: Pseudo dice [0.0, 0.0, 0.8977, 0.9758, 0.819, 0.9458, 0.947, 0.9621, 0.9465, 0.9312, 0.92, 0.9581, 0.9577, 0.8312, 0.9507, 0.9238, 0.8123, 0.8053, nan] 
-2024-08-28 05:17:49.425352: Epoch time: 86.61 s 
-2024-08-28 05:17:50.629378:  
-2024-08-28 05:17:50.629597: Epoch 649 
-2024-08-28 05:17:50.629685: Current learning rate: 0.00703 
-2024-08-28 05:19:20.296685: train_loss -0.755 
-2024-08-28 05:19:20.296939: val_loss -0.777 
-2024-08-28 05:19:20.297106: Pseudo dice [0.0, 0.0, 0.8673, 0.9745, 0.8535, 0.9489, 0.951, 0.9648, 0.9536, 0.9476, 0.9277, 0.9619, 0.9602, 0.8494, 0.9579, 0.9328, 0.8229, 0.8204, nan] 
-2024-08-28 05:19:20.297193: Epoch time: 89.67 s 
-2024-08-28 05:19:21.831677:  
-2024-08-28 05:19:21.831887: Epoch 650 
-2024-08-28 05:19:21.831986: Current learning rate: 0.00702 
-2024-08-28 05:20:49.322383: train_loss -0.7574 
-2024-08-28 05:20:49.322626: val_loss -0.7729 
-2024-08-28 05:20:49.322787: Pseudo dice [0.0, 0.0, 0.8899, 0.9764, 0.8524, 0.9463, 0.9478, 0.9641, 0.9539, 0.9483, 0.9232, 0.9605, 0.9586, 0.8404, 0.953, 0.9229, 0.8273, 0.8261, nan] 
-2024-08-28 05:20:49.322873: Epoch time: 87.49 s 
-2024-08-28 05:20:49.322923: Yayy! New best EMA pseudo Dice: 0.8117 
-2024-08-28 05:20:51.195561:  
-2024-08-28 05:20:51.195892: Epoch 651 
-2024-08-28 05:20:51.196000: Current learning rate: 0.00702 
-2024-08-28 05:22:24.706510: train_loss -0.7527 
-2024-08-28 05:22:24.706846: val_loss -0.7722 
-2024-08-28 05:22:24.707100: Pseudo dice [0.0, 0.0, 0.9014, 0.9753, 0.8367, 0.9402, 0.9402, 0.9638, 0.9421, 0.9322, 0.9132, 0.9471, 0.9476, 0.821, 0.9508, 0.9314, 0.8341, 0.8334, nan] 
-2024-08-28 05:22:24.707315: Epoch time: 93.51 s 
-2024-08-28 05:22:25.977013:  
-2024-08-28 05:22:25.977226: Epoch 652 
-2024-08-28 05:22:25.977346: Current learning rate: 0.00701 
-2024-08-28 05:23:58.427886: train_loss -0.7543 
-2024-08-28 05:23:58.428135: val_loss -0.7754 
-2024-08-28 05:23:58.428359: Pseudo dice [0.0, 0.0, 0.8892, 0.9765, 0.853, 0.9468, 0.9521, 0.9644, 0.9517, 0.9514, 0.925, 0.9579, 0.961, 0.8397, 0.9274, 0.9344, 0.8166, 0.8164, nan] 
-2024-08-28 05:23:58.428479: Epoch time: 92.45 s 
-2024-08-28 05:23:58.428548: Yayy! New best EMA pseudo Dice: 0.812 
-2024-08-28 05:24:00.072457:  
-2024-08-28 05:24:00.072628: Epoch 653 
-2024-08-28 05:24:00.072711: Current learning rate: 0.00701 
-2024-08-28 05:25:21.095629: train_loss -0.7511 
-2024-08-28 05:25:21.095881: val_loss -0.7722 
-2024-08-28 05:25:21.096048: Pseudo dice [0.0, 0.0, 0.9061, 0.9767, 0.8122, 0.94, 0.9455, 0.9642, 0.9499, 0.9405, 0.932, 0.9592, 0.9535, 0.8353, 0.9395, 0.9327, 0.8261, 0.8353, nan] 
-2024-08-28 05:25:21.096133: Epoch time: 81.02 s 
-2024-08-28 05:25:21.096180: Yayy! New best EMA pseudo Dice: 0.8122 
-2024-08-28 05:25:22.661744:  
-2024-08-28 05:25:22.661946: Epoch 654 
-2024-08-28 05:25:22.662049: Current learning rate: 0.007 
-2024-08-28 05:26:50.080534: train_loss -0.7564 
-2024-08-28 05:26:50.080772: val_loss -0.7714 
-2024-08-28 05:26:50.080941: Pseudo dice [0.0, 0.0, 0.8941, 0.9748, 0.8444, 0.9466, 0.9492, 0.9594, 0.9546, 0.9402, 0.9288, 0.9603, 0.9595, 0.8384, 0.956, 0.9323, 0.8198, 0.8074, nan] 
-2024-08-28 05:26:50.081026: Epoch time: 87.42 s 
-2024-08-28 05:26:50.081079: Yayy! New best EMA pseudo Dice: 0.8125 
-2024-08-28 05:26:51.654451:  
-2024-08-28 05:26:51.654789: Epoch 655 
-2024-08-28 05:26:51.654882: Current learning rate: 0.007 
-2024-08-28 05:28:21.378625: train_loss -0.7486 
-2024-08-28 05:28:21.378872: val_loss -0.7646 
-2024-08-28 05:28:21.379039: Pseudo dice [0.0, 0.0, 0.8822, 0.9738, 0.8296, 0.9339, 0.9415, 0.9572, 0.9446, 0.9432, 0.9273, 0.9578, 0.9552, 0.8265, 0.9457, 0.9275, 0.8087, 0.8082, nan] 
-2024-08-28 05:28:21.379126: Epoch time: 89.72 s 
-2024-08-28 05:28:22.613903:  
-2024-08-28 05:28:22.614064: Epoch 656 
-2024-08-28 05:28:22.614163: Current learning rate: 0.00699 
-2024-08-28 05:29:51.690023: train_loss -0.7437 
-2024-08-28 05:29:51.690267: val_loss -0.7676 
-2024-08-28 05:29:51.690434: Pseudo dice [0.0, 0.0, 0.8907, 0.9681, 0.8314, 0.9424, 0.9451, 0.9612, 0.9462, 0.9432, 0.9223, 0.9557, 0.9567, 0.8313, 0.945, 0.931, 0.808, 0.8018, nan] 
-2024-08-28 05:29:51.690521: Epoch time: 89.08 s 
-2024-08-28 05:29:53.231631:  
-2024-08-28 05:29:53.231848: Epoch 657 
-2024-08-28 05:29:53.231946: Current learning rate: 0.00699 
-2024-08-28 05:31:26.980932: train_loss -0.7487 
-2024-08-28 05:31:26.981168: val_loss -0.7711 
-2024-08-28 05:31:26.981334: Pseudo dice [0.0, 0.0, 0.8941, 0.9767, 0.8355, 0.9404, 0.9463, 0.9663, 0.9447, 0.9325, 0.922, 0.9527, 0.9536, 0.835, 0.9516, 0.9309, 0.8288, 0.8171, nan] 
-2024-08-28 05:31:26.981414: Epoch time: 93.75 s 
-2024-08-28 05:31:28.179343:  
-2024-08-28 05:31:28.179529: Epoch 658 
-2024-08-28 05:31:28.179620: Current learning rate: 0.00698 
-2024-08-28 05:32:53.130720: train_loss -0.7535 
-2024-08-28 05:32:53.130969: val_loss -0.7756 
-2024-08-28 05:32:53.131133: Pseudo dice [0.0, 0.0, 0.895, 0.9762, 0.8214, 0.941, 0.9432, 0.9563, 0.9468, 0.9495, 0.923, 0.9542, 0.9546, 0.8363, 0.9503, 0.9319, 0.8212, 0.8298, nan] 
-2024-08-28 05:32:53.131219: Epoch time: 84.95 s 
-2024-08-28 05:32:54.331176:  
-2024-08-28 05:32:54.331337: Epoch 659 
-2024-08-28 05:32:54.331428: Current learning rate: 0.00698 
-2024-08-28 05:34:25.652269: train_loss -0.7521 
-2024-08-28 05:34:25.652518: val_loss -0.7801 
-2024-08-28 05:34:25.652676: Pseudo dice [0.0, 0.0, 0.8856, 0.9766, 0.844, 0.9421, 0.9455, 0.9617, 0.9503, 0.9474, 0.9295, 0.96, 0.9581, 0.8428, 0.9519, 0.9321, 0.81, 0.8107, nan] 
-2024-08-28 05:34:25.652757: Epoch time: 91.32 s 
-2024-08-28 05:34:26.896524:  
-2024-08-28 05:34:26.896685: Epoch 660 
-2024-08-28 05:34:26.896775: Current learning rate: 0.00697 
-2024-08-28 05:35:54.004215: train_loss -0.7517 
-2024-08-28 05:35:54.004516: val_loss -0.7723 
-2024-08-28 05:35:54.004704: Pseudo dice [0.0, 0.0, 0.9052, 0.9772, 0.843, 0.9464, 0.9467, 0.9656, 0.9434, 0.948, 0.928, 0.9535, 0.9585, 0.8491, 0.9546, 0.9321, 0.8262, 0.8158, nan] 
-2024-08-28 05:35:54.004823: Epoch time: 87.11 s 
-2024-08-28 05:35:54.004889: Yayy! New best EMA pseudo Dice: 0.8126 
-2024-08-28 05:35:55.550504:  
-2024-08-28 05:35:55.550781: Epoch 661 
-2024-08-28 05:35:55.550879: Current learning rate: 0.00697 
-2024-08-28 05:37:24.491377: train_loss -0.7484 
-2024-08-28 05:37:24.491635: val_loss -0.7639 
-2024-08-28 05:37:24.491792: Pseudo dice [0.0, 0.0, 0.8914, 0.974, 0.7901, 0.9414, 0.9434, 0.951, 0.9399, 0.9451, 0.9182, 0.9482, 0.9457, 0.8298, 0.9439, 0.9159, 0.8023, 0.8314, nan] 
-2024-08-28 05:37:24.491873: Epoch time: 88.94 s 
-2024-08-28 05:37:25.733631:  
-2024-08-28 05:37:25.733959: Epoch 662 
-2024-08-28 05:37:25.734056: Current learning rate: 0.00696 
-2024-08-28 05:38:56.551252: train_loss -0.7436 
-2024-08-28 05:38:56.551490: val_loss -0.7745 
-2024-08-28 05:38:56.551657: Pseudo dice [0.0, 0.0, 0.8902, 0.9764, 0.8286, 0.9448, 0.9418, 0.962, 0.9482, 0.9516, 0.9338, 0.9591, 0.9625, 0.8336, 0.9482, 0.9253, 0.8123, 0.8246, nan] 
-2024-08-28 05:38:56.551744: Epoch time: 90.82 s 
-2024-08-28 05:38:58.036397:  
-2024-08-28 05:38:58.036593: Epoch 663 
-2024-08-28 05:38:58.036689: Current learning rate: 0.00696 
-2024-08-28 05:40:26.205840: train_loss -0.7462 
-2024-08-28 05:40:26.206073: val_loss -0.7625 
-2024-08-28 05:40:26.206237: Pseudo dice [0.0, 0.0, 0.8748, 0.9757, 0.8391, 0.9459, 0.9455, 0.9634, 0.9382, 0.9423, 0.9194, 0.9533, 0.9501, 0.8351, 0.9367, 0.9304, 0.8264, 0.8231, nan] 
-2024-08-28 05:40:26.206324: Epoch time: 88.17 s 
-2024-08-28 05:40:27.451309:  
-2024-08-28 05:40:27.451481: Epoch 664 
-2024-08-28 05:40:27.451566: Current learning rate: 0.00696 
-2024-08-28 05:41:51.705303: train_loss -0.7531 
-2024-08-28 05:41:51.705655: val_loss -0.7715 
-2024-08-28 05:41:51.705943: Pseudo dice [0.0, 0.0, 0.8861, 0.9754, 0.8324, 0.945, 0.9468, 0.964, 0.9375, 0.9375, 0.9302, 0.9512, 0.9589, 0.8333, 0.951, 0.9251, 0.8202, 0.8125, nan] 
-2024-08-28 05:41:51.706090: Epoch time: 84.25 s 
-2024-08-28 05:41:53.013363:  
-2024-08-28 05:41:53.013521: Epoch 665 
-2024-08-28 05:41:53.013614: Current learning rate: 0.00695 
-2024-08-28 05:43:22.519063: train_loss -0.7531 
-2024-08-28 05:43:22.519300: val_loss -0.7713 
-2024-08-28 05:43:22.519448: Pseudo dice [0.0, 0.0, 0.9016, 0.9764, 0.8083, 0.9376, 0.9414, 0.9627, 0.9425, 0.9412, 0.9192, 0.9539, 0.9546, 0.8416, 0.9497, 0.9323, 0.8334, 0.8108, nan] 
-2024-08-28 05:43:22.519526: Epoch time: 89.51 s 
-2024-08-28 05:43:23.780735:  
-2024-08-28 05:43:23.781053: Epoch 666 
-2024-08-28 05:43:23.781150: Current learning rate: 0.00695 
-2024-08-28 05:44:58.605039: train_loss -0.7463 
-2024-08-28 05:44:58.605531: val_loss -0.7617 
-2024-08-28 05:44:58.605720: Pseudo dice [0.0, 0.0, 0.8766, 0.9763, 0.8158, 0.9355, 0.9358, 0.9627, 0.9332, 0.931, 0.9082, 0.9436, 0.9398, 0.823, 0.9452, 0.9195, 0.8083, 0.8099, nan] 
-2024-08-28 05:44:58.605814: Epoch time: 94.83 s 
-2024-08-28 05:44:59.883779:  
-2024-08-28 05:44:59.883941: Epoch 667 
-2024-08-28 05:44:59.884035: Current learning rate: 0.00694 
-2024-08-28 05:46:31.766782: train_loss -0.7451 
-2024-08-28 05:46:31.767061: val_loss -0.7692 
-2024-08-28 05:46:31.767289: Pseudo dice [0.0, 0.0, 0.8899, 0.9687, 0.8409, 0.9456, 0.948, 0.9623, 0.9471, 0.9536, 0.9303, 0.9597, 0.9585, 0.8325, 0.941, 0.9072, 0.817, 0.8091, nan] 
-2024-08-28 05:46:31.767405: Epoch time: 91.88 s 
-2024-08-28 05:46:33.045375:  
-2024-08-28 05:46:33.045652: Epoch 668 
-2024-08-28 05:46:33.045753: Current learning rate: 0.00694 
-2024-08-28 05:48:01.077939: train_loss -0.7466 
-2024-08-28 05:48:01.078165: val_loss -0.7605 
-2024-08-28 05:48:01.078332: Pseudo dice [0.0, 0.0, 0.8934, 0.9747, 0.8146, 0.9386, 0.9392, 0.9569, 0.9476, 0.9286, 0.912, 0.9588, 0.9538, 0.8292, 0.9385, 0.9284, 0.7906, 0.8156, nan] 
-2024-08-28 05:48:01.078413: Epoch time: 88.03 s 
-2024-08-28 05:48:02.551062:  
-2024-08-28 05:48:02.551403: Epoch 669 
-2024-08-28 05:48:02.551497: Current learning rate: 0.00693 
-2024-08-28 05:49:31.562447: train_loss -0.7475 
-2024-08-28 05:49:31.562713: val_loss -0.7661 
-2024-08-28 05:49:31.562866: Pseudo dice [0.0, 0.0, 0.8346, 0.9746, 0.8131, 0.9431, 0.9462, 0.9611, 0.9469, 0.9467, 0.9253, 0.9569, 0.9569, 0.827, 0.9438, 0.9264, 0.8003, 0.8036, nan] 
-2024-08-28 05:49:31.562949: Epoch time: 89.01 s 
-2024-08-28 05:49:32.825129:  
-2024-08-28 05:49:32.825627: Epoch 670 
-2024-08-28 05:49:32.825725: Current learning rate: 0.00693 
-2024-08-28 05:50:59.313972: train_loss -0.743 
-2024-08-28 05:50:59.314198: val_loss -0.7694 
-2024-08-28 05:50:59.314350: Pseudo dice [0.0, 0.0, 0.8927, 0.9756, 0.8041, 0.9277, 0.9356, 0.9617, 0.9488, 0.9295, 0.9184, 0.9575, 0.9541, 0.8181, 0.9457, 0.9207, 0.8061, 0.8074, nan] 
-2024-08-28 05:50:59.314426: Epoch time: 86.49 s 
-2024-08-28 05:51:00.548217:  
-2024-08-28 05:51:00.548582: Epoch 671 
-2024-08-28 05:51:00.548675: Current learning rate: 0.00692 
-2024-08-28 05:52:25.414660: train_loss -0.7504 
-2024-08-28 05:52:25.414919: val_loss -0.7669 
-2024-08-28 05:52:25.415074: Pseudo dice [0.0, 0.0, 0.8805, 0.9747, 0.8069, 0.9316, 0.9346, 0.9616, 0.9429, 0.9407, 0.9165, 0.9492, 0.9529, 0.8263, 0.9458, 0.9276, 0.8119, 0.8201, nan] 
-2024-08-28 05:52:25.415158: Epoch time: 84.87 s 
-2024-08-28 05:52:26.702126:  
-2024-08-28 05:52:26.702403: Epoch 672 
-2024-08-28 05:52:26.702503: Current learning rate: 0.00692 
-2024-08-28 05:53:53.293502: train_loss -0.7577 
-2024-08-28 05:53:53.293769: val_loss -0.771 
-2024-08-28 05:53:53.293982: Pseudo dice [0.0, 0.0, 0.8977, 0.9774, 0.8475, 0.9427, 0.9435, 0.9651, 0.9472, 0.9396, 0.9233, 0.9574, 0.9583, 0.8407, 0.9392, 0.9271, 0.8212, 0.8131, nan] 
-2024-08-28 05:53:53.294093: Epoch time: 86.59 s 
-2024-08-28 05:53:54.676070:  
-2024-08-28 05:53:54.676281: Epoch 673 
-2024-08-28 05:53:54.676431: Current learning rate: 0.00691 
-2024-08-28 05:55:19.178985: train_loss -0.7509 
-2024-08-28 05:55:19.179238: val_loss -0.7664 
-2024-08-28 05:55:19.179393: Pseudo dice [0.0, 0.0, 0.8669, 0.9766, 0.8325, 0.9307, 0.9329, 0.9614, 0.9378, 0.9405, 0.9207, 0.9496, 0.9464, 0.8335, 0.9499, 0.9246, 0.818, 0.7934, nan] 
-2024-08-28 05:55:19.179474: Epoch time: 84.5 s 
-2024-08-28 05:55:20.639980:  
-2024-08-28 05:55:20.640154: Epoch 674 
-2024-08-28 05:55:20.640243: Current learning rate: 0.00691 
-2024-08-28 05:56:46.077052: train_loss -0.7489 
-2024-08-28 05:56:46.077285: val_loss -0.7708 
-2024-08-28 05:56:46.077445: Pseudo dice [0.0, 0.0, 0.8932, 0.9756, 0.8342, 0.9461, 0.9493, 0.9613, 0.9457, 0.9492, 0.9267, 0.9534, 0.9565, 0.8299, 0.9504, 0.9269, 0.8318, 0.8169, nan] 
-2024-08-28 05:56:46.077528: Epoch time: 85.44 s 
-2024-08-28 05:56:47.316977:  
-2024-08-28 05:56:47.317161: Epoch 675 
-2024-08-28 05:56:47.317252: Current learning rate: 0.0069 
-2024-08-28 05:58:19.569719: train_loss -0.7449 
-2024-08-28 05:58:19.569978: val_loss -0.7644 
-2024-08-28 05:58:19.570162: Pseudo dice [0.0, 0.0, 0.8508, 0.9725, 0.8137, 0.9366, 0.9463, 0.9625, 0.9489, 0.9376, 0.9257, 0.9608, 0.9593, 0.8329, 0.9486, 0.9217, 0.7991, 0.7955, nan] 
-2024-08-28 05:58:19.570264: Epoch time: 92.25 s 
-2024-08-28 05:58:20.789501:  
-2024-08-28 05:58:20.789748: Epoch 676 
-2024-08-28 05:58:20.789843: Current learning rate: 0.0069 
-2024-08-28 05:59:48.131437: train_loss -0.7457 
-2024-08-28 05:59:48.131685: val_loss -0.7723 
-2024-08-28 05:59:48.131854: Pseudo dice [0.0, 0.0, 0.8838, 0.976, 0.8483, 0.9468, 0.9519, 0.9631, 0.9483, 0.9515, 0.9232, 0.9587, 0.9569, 0.8439, 0.9522, 0.9336, 0.8021, 0.8001, nan] 
-2024-08-28 05:59:48.131948: Epoch time: 87.34 s 
-2024-08-28 05:59:49.405972:  
-2024-08-28 05:59:49.406162: Epoch 677 
-2024-08-28 05:59:49.406260: Current learning rate: 0.00689 
-2024-08-28 06:01:16.488507: train_loss -0.7548 
-2024-08-28 06:01:16.488762: val_loss -0.7723 
-2024-08-28 06:01:16.488928: Pseudo dice [0.0, 0.0, 0.8853, 0.9743, 0.8358, 0.9467, 0.9439, 0.9637, 0.9516, 0.9452, 0.9232, 0.9597, 0.958, 0.8355, 0.9405, 0.9287, 0.8223, 0.809, nan] 
-2024-08-28 06:01:16.489013: Epoch time: 87.08 s 
-2024-08-28 06:01:17.778977:  
-2024-08-28 06:01:17.779172: Epoch 678 
-2024-08-28 06:01:17.779266: Current learning rate: 0.00689 
-2024-08-28 06:02:46.078477: train_loss -0.7527 
-2024-08-28 06:02:46.079057: val_loss -0.7694 
-2024-08-28 06:02:46.079231: Pseudo dice [0.0, 0.0, 0.8938, 0.9727, 0.8429, 0.9389, 0.9404, 0.9623, 0.9484, 0.9483, 0.9277, 0.952, 0.9589, 0.8399, 0.9446, 0.9278, 0.8039, 0.7971, nan] 
-2024-08-28 06:02:46.079314: Epoch time: 88.3 s 
-2024-08-28 06:02:47.343050:  
-2024-08-28 06:02:47.343223: Epoch 679 
-2024-08-28 06:02:47.343323: Current learning rate: 0.00688 
-2024-08-28 06:04:15.286888: train_loss -0.7454 
-2024-08-28 06:04:15.287122: val_loss -0.7616 
-2024-08-28 06:04:15.287282: Pseudo dice [0.0, 0.0, 0.8829, 0.9742, 0.8222, 0.9394, 0.9421, 0.9618, 0.9459, 0.9512, 0.9284, 0.9568, 0.957, 0.8364, 0.9444, 0.9331, 0.8193, 0.8088, nan] 
-2024-08-28 06:04:15.287369: Epoch time: 87.94 s 
-2024-08-28 06:04:16.776119:  
-2024-08-28 06:04:16.776724: Epoch 680 
-2024-08-28 06:04:16.776823: Current learning rate: 0.00688 
-2024-08-28 06:05:36.831885: train_loss -0.7467 
-2024-08-28 06:05:36.832141: val_loss -0.7616 
-2024-08-28 06:05:36.832306: Pseudo dice [0.0, 0.0, 0.8775, 0.9723, 0.8137, 0.9438, 0.9445, 0.9599, 0.9462, 0.9273, 0.9088, 0.951, 0.9526, 0.8257, 0.9467, 0.9176, 0.8116, 0.7796, nan] 
-2024-08-28 06:05:36.832392: Epoch time: 80.06 s 
-2024-08-28 06:05:38.081715:  
-2024-08-28 06:05:38.081938: Epoch 681 
-2024-08-28 06:05:38.082041: Current learning rate: 0.00688 
-2024-08-28 06:07:12.309621: train_loss -0.7458 
-2024-08-28 06:07:12.309859: val_loss -0.7693 
-2024-08-28 06:07:12.310030: Pseudo dice [0.0, 0.0, 0.8852, 0.9745, 0.8399, 0.9455, 0.9476, 0.9623, 0.9496, 0.9458, 0.9157, 0.9574, 0.9552, 0.8394, 0.9362, 0.926, 0.8157, 0.8064, nan] 
-2024-08-28 06:07:12.310120: Epoch time: 94.23 s 
-2024-08-28 06:07:13.549723:  
-2024-08-28 06:07:13.549898: Epoch 682 
-2024-08-28 06:07:13.549993: Current learning rate: 0.00687 
-2024-08-28 06:08:43.310005: train_loss -0.7479 
-2024-08-28 06:08:43.310298: val_loss -0.7725 
-2024-08-28 06:08:43.310550: Pseudo dice [0.0, 0.0, 0.903, 0.9745, 0.815, 0.9428, 0.9489, 0.9602, 0.9458, 0.938, 0.9232, 0.9566, 0.9533, 0.8358, 0.9556, 0.9294, 0.8232, 0.8229, nan] 
-2024-08-28 06:08:43.310682: Epoch time: 89.76 s 
-2024-08-28 06:08:44.569991:  
-2024-08-28 06:08:44.570170: Epoch 683 
-2024-08-28 06:08:44.570262: Current learning rate: 0.00687 
-2024-08-28 06:10:12.519847: train_loss -0.7482 
-2024-08-28 06:10:12.520086: val_loss -0.7774 
-2024-08-28 06:10:12.520235: Pseudo dice [0.0, 0.0, 0.8905, 0.9735, 0.8331, 0.9444, 0.9474, 0.9637, 0.9493, 0.9455, 0.9315, 0.959, 0.9553, 0.8328, 0.9497, 0.9306, 0.8077, 0.7932, nan] 
-2024-08-28 06:10:12.520316: Epoch time: 87.95 s 
-2024-08-28 06:10:13.785796:  
-2024-08-28 06:10:13.785967: Epoch 684 
-2024-08-28 06:10:13.786058: Current learning rate: 0.00686 
-2024-08-28 06:11:38.342503: train_loss -0.7446 
-2024-08-28 06:11:38.342748: val_loss -0.7614 
-2024-08-28 06:11:38.342914: Pseudo dice [0.0, 0.0, 0.8908, 0.9759, 0.8149, 0.9352, 0.9413, 0.9541, 0.9449, 0.946, 0.922, 0.9557, 0.9544, 0.815, 0.9424, 0.9183, 0.8246, 0.8089, nan] 
-2024-08-28 06:11:38.342999: Epoch time: 84.56 s 
-2024-08-28 06:11:39.577593:  
-2024-08-28 06:11:39.577767: Epoch 685 
-2024-08-28 06:11:39.577844: Current learning rate: 0.00686 
-2024-08-28 06:13:03.547616: train_loss -0.7456 
-2024-08-28 06:13:03.547831: val_loss -0.768 
-2024-08-28 06:13:03.547978: Pseudo dice [0.0, 0.0, 0.8843, 0.9761, 0.8403, 0.934, 0.9376, 0.9605, 0.9382, 0.9352, 0.9195, 0.9457, 0.946, 0.8388, 0.9393, 0.9273, 0.8004, 0.8162, nan] 
-2024-08-28 06:13:03.548056: Epoch time: 83.97 s 
-2024-08-28 06:13:04.957789:  
-2024-08-28 06:13:04.957957: Epoch 686 
-2024-08-28 06:13:04.958049: Current learning rate: 0.00685 
-2024-08-28 06:14:30.755961: train_loss -0.7557 
-2024-08-28 06:14:30.756224: val_loss -0.7752 
-2024-08-28 06:14:30.756388: Pseudo dice [0.0, 0.0, 0.8722, 0.9765, 0.825, 0.9442, 0.9481, 0.9628, 0.9506, 0.9518, 0.9201, 0.9591, 0.9591, 0.8395, 0.9258, 0.9303, 0.8208, 0.8039, nan] 
-2024-08-28 06:14:30.756490: Epoch time: 85.8 s 
-2024-08-28 06:14:32.026142:  
-2024-08-28 06:14:32.026322: Epoch 687 
-2024-08-28 06:14:32.026407: Current learning rate: 0.00685 
-2024-08-28 06:15:57.041169: train_loss -0.7528 
-2024-08-28 06:15:57.041420: val_loss -0.7733 
-2024-08-28 06:15:57.041584: Pseudo dice [0.0, 0.0, 0.8954, 0.9728, 0.8193, 0.9453, 0.9473, 0.9644, 0.9472, 0.9435, 0.9256, 0.9578, 0.9575, 0.8407, 0.9564, 0.9306, 0.8362, 0.8293, nan] 
-2024-08-28 06:15:57.041668: Epoch time: 85.02 s 
-2024-08-28 06:15:58.315436:  
-2024-08-28 06:15:58.315597: Epoch 688 
-2024-08-28 06:15:58.315683: Current learning rate: 0.00684 
-2024-08-28 06:17:27.182814: train_loss -0.7516 
-2024-08-28 06:17:27.183043: val_loss -0.7722 
-2024-08-28 06:17:27.183214: Pseudo dice [0.0, 0.0, 0.8662, 0.9757, 0.8186, 0.9406, 0.9431, 0.9614, 0.9489, 0.9449, 0.9298, 0.9585, 0.9593, 0.8367, 0.9533, 0.9275, 0.8111, 0.8009, nan] 
-2024-08-28 06:17:27.183298: Epoch time: 88.87 s 
-2024-08-28 06:17:28.392921:  
-2024-08-28 06:17:28.393100: Epoch 689 
-2024-08-28 06:17:28.393186: Current learning rate: 0.00684 
-2024-08-28 06:18:51.985731: train_loss -0.7508 
-2024-08-28 06:18:51.986153: val_loss -0.7734 
-2024-08-28 06:18:51.986350: Pseudo dice [0.0, 0.0, 0.8591, 0.9747, 0.8301, 0.9433, 0.9388, 0.9641, 0.9486, 0.9388, 0.9189, 0.9586, 0.9573, 0.8339, 0.9519, 0.9289, 0.8259, 0.8204, nan] 
-2024-08-28 06:18:51.986448: Epoch time: 83.59 s 
-2024-08-28 06:18:53.256511:  
-2024-08-28 06:18:53.256676: Epoch 690 
-2024-08-28 06:18:53.256764: Current learning rate: 0.00683 
-2024-08-28 06:20:19.731818: train_loss -0.7532 
-2024-08-28 06:20:19.732068: val_loss -0.7726 
-2024-08-28 06:20:19.732231: Pseudo dice [0.0, 0.0, 0.8866, 0.9768, 0.8484, 0.9368, 0.943, 0.9655, 0.9434, 0.937, 0.9121, 0.9511, 0.9445, 0.8484, 0.9416, 0.9265, 0.8113, 0.813, nan] 
-2024-08-28 06:20:19.732320: Epoch time: 86.48 s 
-2024-08-28 06:20:20.971833:  
-2024-08-28 06:20:20.972010: Epoch 691 
-2024-08-28 06:20:20.972097: Current learning rate: 0.00683 
-2024-08-28 06:21:45.306834: train_loss -0.7553 
-2024-08-28 06:21:45.307072: val_loss -0.7728 
-2024-08-28 06:21:45.307241: Pseudo dice [0.0, 0.0, 0.8891, 0.9759, 0.8419, 0.9398, 0.9466, 0.9621, 0.9487, 0.9493, 0.9331, 0.9592, 0.9599, 0.844, 0.9419, 0.9219, 0.8312, 0.8229, nan] 
-2024-08-28 06:21:45.307329: Epoch time: 84.34 s 
-2024-08-28 06:21:46.743915:  
-2024-08-28 06:21:46.744091: Epoch 692 
-2024-08-28 06:21:46.744188: Current learning rate: 0.00682 
-2024-08-28 06:23:10.470691: train_loss -0.7529 
-2024-08-28 06:23:10.470922: val_loss -0.7565 
-2024-08-28 06:23:10.471075: Pseudo dice [0.0, 0.0, 0.8903, 0.9737, 0.8388, 0.9339, 0.9382, 0.9538, 0.9283, 0.9295, 0.9055, 0.9388, 0.9384, 0.813, 0.935, 0.9306, 0.8009, 0.826, nan] 
-2024-08-28 06:23:10.471155: Epoch time: 83.73 s 
-2024-08-28 06:23:11.726108:  
-2024-08-28 06:23:11.726423: Epoch 693 
-2024-08-28 06:23:11.726519: Current learning rate: 0.00682 
-2024-08-28 06:24:41.235802: train_loss -0.7537 
-2024-08-28 06:24:41.236033: val_loss -0.7732 
-2024-08-28 06:24:41.236181: Pseudo dice [0.0, 0.0, 0.901, 0.9771, 0.8522, 0.9467, 0.9512, 0.9642, 0.9479, 0.9434, 0.9195, 0.9597, 0.9592, 0.8428, 0.9482, 0.9311, 0.8283, 0.8204, nan] 
-2024-08-28 06:24:41.236259: Epoch time: 89.51 s 
-2024-08-28 06:24:42.515545:  
-2024-08-28 06:24:42.516124: Epoch 694 
-2024-08-28 06:24:42.516240: Current learning rate: 0.00681 
-2024-08-28 06:26:08.218608: train_loss -0.7571 
-2024-08-28 06:26:08.218842: val_loss -0.782 
-2024-08-28 06:26:08.219000: Pseudo dice [0.0, 0.0, 0.9046, 0.9754, 0.8483, 0.9382, 0.9418, 0.9659, 0.943, 0.946, 0.9141, 0.9512, 0.9528, 0.8365, 0.9522, 0.9354, 0.8304, 0.8088, nan] 
-2024-08-28 06:26:08.219087: Epoch time: 85.7 s 
-2024-08-28 06:26:09.490312:  
-2024-08-28 06:26:09.490657: Epoch 695 
-2024-08-28 06:26:09.490758: Current learning rate: 0.00681 
-2024-08-28 06:27:33.793003: train_loss -0.7536 
-2024-08-28 06:27:33.793251: val_loss -0.7741 
-2024-08-28 06:27:33.793536: Pseudo dice [0.0, 0.0, 0.8983, 0.9745, 0.8033, 0.9408, 0.9444, 0.9625, 0.9433, 0.943, 0.9253, 0.9535, 0.9595, 0.8398, 0.953, 0.9311, 0.8166, 0.8192, nan] 
-2024-08-28 06:27:33.793645: Epoch time: 84.3 s 
-2024-08-28 06:27:35.029506:  
-2024-08-28 06:27:35.029666: Epoch 696 
-2024-08-28 06:27:35.029760: Current learning rate: 0.0068 
-2024-08-28 06:29:02.405116: train_loss -0.7534 
-2024-08-28 06:29:02.405372: val_loss -0.7746 
-2024-08-28 06:29:02.405525: Pseudo dice [0.0, 0.0, 0.8969, 0.9759, 0.8347, 0.9459, 0.9485, 0.9639, 0.9513, 0.9499, 0.9211, 0.9575, 0.9547, 0.8387, 0.9544, 0.9272, 0.8116, 0.8135, nan] 
-2024-08-28 06:29:02.405605: Epoch time: 87.38 s 
-2024-08-28 06:29:03.674701:  
-2024-08-28 06:29:03.675080: Epoch 697 
-2024-08-28 06:29:03.675182: Current learning rate: 0.0068 
-2024-08-28 06:30:36.730886: train_loss -0.7539 
-2024-08-28 06:30:36.731136: val_loss -0.7711 
-2024-08-28 06:30:36.731747: Pseudo dice [0.0, 0.0, 0.8922, 0.9774, 0.848, 0.9441, 0.9471, 0.9649, 0.9468, 0.9442, 0.9237, 0.9581, 0.957, 0.8454, 0.9533, 0.928, 0.8241, 0.823, nan] 
-2024-08-28 06:30:36.731849: Epoch time: 93.06 s 
-2024-08-28 06:30:38.278809:  
-2024-08-28 06:30:38.278989: Epoch 698 
-2024-08-28 06:30:38.279078: Current learning rate: 0.0068 
-2024-08-28 06:32:11.626204: train_loss -0.7531 
-2024-08-28 06:32:11.626441: val_loss -0.7754 
-2024-08-28 06:32:11.626603: Pseudo dice [0.0, 0.0, 0.9033, 0.9769, 0.8243, 0.9428, 0.9407, 0.9637, 0.9489, 0.9354, 0.9148, 0.9588, 0.9533, 0.8375, 0.947, 0.9226, 0.8212, 0.8132, nan] 
-2024-08-28 06:32:11.626684: Epoch time: 93.35 s 
-2024-08-28 06:32:12.872393:  
-2024-08-28 06:32:12.872555: Epoch 699 
-2024-08-28 06:32:12.872636: Current learning rate: 0.00679 
-2024-08-28 06:33:42.404963: train_loss -0.7498 
-2024-08-28 06:33:42.405184: val_loss -0.7712 
-2024-08-28 06:33:42.405347: Pseudo dice [0.0, 0.0, 0.8893, 0.9732, 0.8415, 0.9406, 0.9478, 0.9636, 0.9492, 0.949, 0.919, 0.9591, 0.9583, 0.8317, 0.9513, 0.9296, 0.8106, 0.8048, nan] 
-2024-08-28 06:33:42.405434: Epoch time: 89.53 s 
-2024-08-28 06:33:43.958775:  
-2024-08-28 06:33:43.959068: Epoch 700 
-2024-08-28 06:33:43.959159: Current learning rate: 0.00679 
-2024-08-28 06:35:10.180740: train_loss -0.7509 
-2024-08-28 06:35:10.180976: val_loss -0.7729 
-2024-08-28 06:35:10.181145: Pseudo dice [0.0, 0.0, 0.8962, 0.9768, 0.8243, 0.9463, 0.9477, 0.9655, 0.9506, 0.9563, 0.9306, 0.9569, 0.9592, 0.8432, 0.9506, 0.9204, 0.8236, 0.8203, nan] 
-2024-08-28 06:35:10.181230: Epoch time: 86.22 s 
-2024-08-28 06:35:11.455028:  
-2024-08-28 06:35:11.455211: Epoch 701 
-2024-08-28 06:35:11.455304: Current learning rate: 0.00678 
-2024-08-28 06:36:41.595630: train_loss -0.7541 
-2024-08-28 06:36:41.595860: val_loss -0.7685 
-2024-08-28 06:36:41.596016: Pseudo dice [0.0, 0.0, 0.8801, 0.9759, 0.8349, 0.944, 0.9468, 0.9605, 0.948, 0.9506, 0.9263, 0.9607, 0.9569, 0.8362, 0.9531, 0.9338, 0.8101, 0.8135, nan] 
-2024-08-28 06:36:41.596097: Epoch time: 90.14 s 
-2024-08-28 06:36:42.774702:  
-2024-08-28 06:36:42.774866: Epoch 702 
-2024-08-28 06:36:42.774962: Current learning rate: 0.00678 
-2024-08-28 06:38:06.089723: train_loss -0.7568 
-2024-08-28 06:38:06.089968: val_loss -0.773 
-2024-08-28 06:38:06.090133: Pseudo dice [0.0, 0.0, 0.8759, 0.9767, 0.8188, 0.9436, 0.9523, 0.9577, 0.9462, 0.948, 0.9268, 0.9553, 0.9571, 0.8422, 0.9417, 0.9336, 0.8392, 0.8202, nan] 
-2024-08-28 06:38:06.090219: Epoch time: 83.32 s 
-2024-08-28 06:38:07.334537:  
-2024-08-28 06:38:07.334855: Epoch 703 
-2024-08-28 06:38:07.334952: Current learning rate: 0.00677 
-2024-08-28 06:39:36.943444: train_loss -0.7531 
-2024-08-28 06:39:36.943751: val_loss -0.7737 
-2024-08-28 06:39:36.943970: Pseudo dice [0.0, 0.0, 0.8921, 0.9768, 0.8275, 0.9424, 0.9466, 0.9579, 0.954, 0.9498, 0.927, 0.9613, 0.9617, 0.8315, 0.947, 0.9307, 0.8068, 0.8002, nan] 
-2024-08-28 06:39:36.944077: Epoch time: 89.61 s 
-2024-08-28 06:39:38.241015:  
-2024-08-28 06:39:38.241433: Epoch 704 
-2024-08-28 06:39:38.241532: Current learning rate: 0.00677 
-2024-08-28 06:41:06.400598: train_loss -0.7563 
-2024-08-28 06:41:06.400800: val_loss -0.7747 
-2024-08-28 06:41:06.400948: Pseudo dice [0.0, 0.0, 0.8774, 0.977, 0.8234, 0.9415, 0.9497, 0.9668, 0.9511, 0.9478, 0.9313, 0.9611, 0.9614, 0.8545, 0.9514, 0.9312, 0.8248, 0.8241, nan] 
-2024-08-28 06:41:06.401026: Epoch time: 88.16 s 
-2024-08-28 06:41:07.589740:  
-2024-08-28 06:41:07.590022: Epoch 705 
-2024-08-28 06:41:07.590114: Current learning rate: 0.00676 
-2024-08-28 06:42:35.185309: train_loss -0.7554 
-2024-08-28 06:42:35.185582: val_loss -0.7783 
-2024-08-28 06:42:35.185740: Pseudo dice [0.0, 0.0, 0.8954, 0.9752, 0.8293, 0.9494, 0.9491, 0.9662, 0.9464, 0.9499, 0.9255, 0.9557, 0.9602, 0.8475, 0.9559, 0.9337, 0.8143, 0.8267, nan] 
-2024-08-28 06:42:35.185827: Epoch time: 87.6 s 
-2024-08-28 06:42:35.185878: Yayy! New best EMA pseudo Dice: 0.8128 
-2024-08-28 06:42:36.668329:  
-2024-08-28 06:42:36.669425: Epoch 706 
-2024-08-28 06:42:36.669542: Current learning rate: 0.00676 
-2024-08-28 06:44:06.244751: train_loss -0.7556 
-2024-08-28 06:44:06.244982: val_loss -0.7746 
-2024-08-28 06:44:06.245136: Pseudo dice [0.0, 0.0, 0.8784, 0.9751, 0.808, 0.9358, 0.938, 0.9594, 0.9364, 0.939, 0.9188, 0.9452, 0.95, 0.8462, 0.9487, 0.9291, 0.822, 0.82, nan] 
-2024-08-28 06:44:06.245218: Epoch time: 89.58 s 
-2024-08-28 06:44:07.452567:  
-2024-08-28 06:44:07.452720: Epoch 707 
-2024-08-28 06:44:07.452811: Current learning rate: 0.00675 
-2024-08-28 06:45:31.389793: train_loss -0.7567 
-2024-08-28 06:45:31.390049: val_loss -0.7718 
-2024-08-28 06:45:31.390223: Pseudo dice [0.0, 0.0, 0.8826, 0.9775, 0.8418, 0.9459, 0.9505, 0.9633, 0.9494, 0.9336, 0.923, 0.9577, 0.9579, 0.8488, 0.9517, 0.9291, 0.8295, 0.8238, nan] 
-2024-08-28 06:45:31.390312: Epoch time: 83.94 s 
-2024-08-28 06:45:32.650500:  
-2024-08-28 06:45:32.650667: Epoch 708 
-2024-08-28 06:45:32.650758: Current learning rate: 0.00675 
-2024-08-28 06:46:56.286376: train_loss -0.7563 
-2024-08-28 06:46:56.286692: val_loss -0.7828 
-2024-08-28 06:46:56.286950: Pseudo dice [0.0, 0.0, 0.8996, 0.9759, 0.8552, 0.9509, 0.9529, 0.9662, 0.9538, 0.9563, 0.9297, 0.9586, 0.9618, 0.8515, 0.9566, 0.9334, 0.8227, 0.8201, nan] 
-2024-08-28 06:46:56.287074: Epoch time: 83.64 s 
-2024-08-28 06:46:56.287148: Yayy! New best EMA pseudo Dice: 0.8133 
-2024-08-28 06:46:58.459414:  
-2024-08-28 06:46:58.459619: Epoch 709 
-2024-08-28 06:46:58.459726: Current learning rate: 0.00674 
-2024-08-28 06:48:27.537199: train_loss -0.7579 
-2024-08-28 06:48:27.537719: val_loss -0.7777 
-2024-08-28 06:48:27.537898: Pseudo dice [0.0, 0.0, 0.8974, 0.9749, 0.8413, 0.9453, 0.9502, 0.9591, 0.9522, 0.9492, 0.9292, 0.9598, 0.9596, 0.8402, 0.9487, 0.9297, 0.8294, 0.8203, nan] 
-2024-08-28 06:48:27.537986: Epoch time: 89.08 s 
-2024-08-28 06:48:27.538034: Yayy! New best EMA pseudo Dice: 0.8135 
-2024-08-28 06:48:29.066448:  
-2024-08-28 06:48:29.066773: Epoch 710 
-2024-08-28 06:48:29.066875: Current learning rate: 0.00674 
-2024-08-28 06:49:57.539816: train_loss -0.7547 
-2024-08-28 06:49:57.540098: val_loss -0.7785 
-2024-08-28 06:49:57.540269: Pseudo dice [0.0, 0.0, 0.8901, 0.9766, 0.8474, 0.9478, 0.9507, 0.9643, 0.9518, 0.9514, 0.9306, 0.9592, 0.9585, 0.8507, 0.9465, 0.9378, 0.8315, 0.8291, nan] 
-2024-08-28 06:49:57.540361: Epoch time: 88.47 s 
-2024-08-28 06:49:57.540412: Yayy! New best EMA pseudo Dice: 0.814 
-2024-08-28 06:49:59.187298:  
-2024-08-28 06:49:59.187878: Epoch 711 
-2024-08-28 06:49:59.188065: Current learning rate: 0.00673 
-2024-08-28 06:51:22.689161: train_loss -0.7547 
-2024-08-28 06:51:22.689405: val_loss -0.7761 
-2024-08-28 06:51:22.689602: Pseudo dice [0.0, 0.0, 0.8871, 0.9766, 0.8211, 0.9477, 0.9505, 0.962, 0.9553, 0.9522, 0.9302, 0.9614, 0.962, 0.8438, 0.9452, 0.9341, 0.8153, 0.8084, nan] 
-2024-08-28 06:51:22.689697: Epoch time: 83.5 s 
-2024-08-28 06:51:22.689755: Yayy! New best EMA pseudo Dice: 0.814 
-2024-08-28 06:51:24.261407:  
-2024-08-28 06:51:24.261659: Epoch 712 
-2024-08-28 06:51:24.261768: Current learning rate: 0.00673 
-2024-08-28 06:52:52.692480: train_loss -0.7564 
-2024-08-28 06:52:52.692729: val_loss -0.7753 
-2024-08-28 06:52:52.692890: Pseudo dice [0.0, 0.0, 0.8799, 0.9762, 0.8423, 0.9438, 0.9466, 0.9655, 0.9394, 0.9478, 0.9265, 0.9458, 0.9511, 0.8463, 0.9551, 0.9334, 0.8276, 0.8225, nan] 
-2024-08-28 06:52:52.692979: Epoch time: 88.43 s 
-2024-08-28 06:52:53.916964:  
-2024-08-28 06:52:53.917241: Epoch 713 
-2024-08-28 06:52:53.917340: Current learning rate: 0.00673 
-2024-08-28 06:54:21.368913: train_loss -0.7569 
-2024-08-28 06:54:21.369164: val_loss -0.7683 
-2024-08-28 06:54:21.369347: Pseudo dice [0.0, 0.0, 0.8961, 0.9753, 0.8332, 0.934, 0.9393, 0.9608, 0.9449, 0.9468, 0.926, 0.9539, 0.9563, 0.8302, 0.9516, 0.9304, 0.8344, 0.8327, nan] 
-2024-08-28 06:54:21.369452: Epoch time: 87.45 s 
-2024-08-28 06:54:22.635228:  
-2024-08-28 06:54:22.635400: Epoch 714 
-2024-08-28 06:54:22.635495: Current learning rate: 0.00672 
-2024-08-28 06:55:47.453457: train_loss -0.7527 
-2024-08-28 06:55:47.453809: val_loss -0.7754 
-2024-08-28 06:55:47.454069: Pseudo dice [0.0, 0.0, 0.8871, 0.9759, 0.834, 0.9455, 0.9474, 0.9645, 0.9491, 0.9462, 0.9291, 0.9602, 0.9605, 0.8343, 0.9421, 0.9264, 0.7997, 0.8036, nan] 
-2024-08-28 06:55:47.454305: Epoch time: 84.82 s 
-2024-08-28 06:55:48.945934:  
-2024-08-28 06:55:48.946224: Epoch 715 
-2024-08-28 06:55:48.946321: Current learning rate: 0.00672 
-2024-08-28 06:57:12.522773: train_loss -0.7556 
-2024-08-28 06:57:12.522990: val_loss -0.772 
-2024-08-28 06:57:12.523146: Pseudo dice [0.0, 0.0, 0.8892, 0.9771, 0.8402, 0.9419, 0.9472, 0.9636, 0.9457, 0.9423, 0.9303, 0.9555, 0.9586, 0.8399, 0.9504, 0.9256, 0.8199, 0.8158, nan] 
-2024-08-28 06:57:12.523224: Epoch time: 83.58 s 
-2024-08-28 06:57:13.729714:  
-2024-08-28 06:57:13.730161: Epoch 716 
-2024-08-28 06:57:13.730263: Current learning rate: 0.00671 
-2024-08-28 06:58:42.018507: train_loss -0.7552 
-2024-08-28 06:58:42.018746: val_loss -0.7762 
-2024-08-28 06:58:42.018900: Pseudo dice [0.0, 0.0, 0.8993, 0.9765, 0.8403, 0.9431, 0.9444, 0.9661, 0.9393, 0.9455, 0.9277, 0.9547, 0.957, 0.8365, 0.9576, 0.9314, 0.8345, 0.8263, nan] 
-2024-08-28 06:58:42.018981: Epoch time: 88.29 s 
-2024-08-28 06:58:43.216682:  
-2024-08-28 06:58:43.217006: Epoch 717 
-2024-08-28 06:58:43.217107: Current learning rate: 0.00671 
-2024-08-28 07:00:08.348908: train_loss -0.7563 
-2024-08-28 07:00:08.349156: val_loss -0.7732 
-2024-08-28 07:00:08.349325: Pseudo dice [0.0, 0.0, 0.8887, 0.9754, 0.8305, 0.9392, 0.9454, 0.9641, 0.9503, 0.9486, 0.9283, 0.9572, 0.9609, 0.8351, 0.949, 0.9305, 0.8076, 0.8126, nan] 
-2024-08-28 07:00:08.349473: Epoch time: 85.13 s 
-2024-08-28 07:00:09.591941:  
-2024-08-28 07:00:09.592404: Epoch 718 
-2024-08-28 07:00:09.592501: Current learning rate: 0.0067 
-2024-08-28 07:01:43.597700: train_loss -0.7568 
-2024-08-28 07:01:43.597979: val_loss -0.7762 
-2024-08-28 07:01:43.598196: Pseudo dice [0.0, 0.0, 0.8956, 0.9758, 0.8571, 0.943, 0.9499, 0.9661, 0.9525, 0.9487, 0.9232, 0.9577, 0.9575, 0.8376, 0.9525, 0.934, 0.802, 0.8207, nan] 
-2024-08-28 07:01:43.598303: Epoch time: 94.01 s 
-2024-08-28 07:01:44.863895:  
-2024-08-28 07:01:44.864037: Epoch 719 
-2024-08-28 07:01:44.864120: Current learning rate: 0.0067 
-2024-08-28 07:03:10.129642: train_loss -0.7539 
-2024-08-28 07:03:10.129891: val_loss -0.7668 
-2024-08-28 07:03:10.130065: Pseudo dice [0.0, 0.0, 0.8839, 0.976, 0.8293, 0.9469, 0.9483, 0.9593, 0.9419, 0.9439, 0.9277, 0.9567, 0.96, 0.8341, 0.9475, 0.9293, 0.8245, 0.8003, nan] 
-2024-08-28 07:03:10.130152: Epoch time: 85.27 s 
-2024-08-28 07:03:11.627416:  
-2024-08-28 07:03:11.627612: Epoch 720 
-2024-08-28 07:03:11.627704: Current learning rate: 0.00669 
-2024-08-28 07:04:37.252683: train_loss -0.7518 
-2024-08-28 07:04:37.252924: val_loss -0.777 
-2024-08-28 07:04:37.253095: Pseudo dice [0.0, 0.0, 0.8957, 0.9756, 0.8517, 0.9452, 0.9508, 0.9638, 0.9531, 0.9391, 0.9327, 0.9599, 0.9568, 0.8428, 0.9565, 0.9279, 0.832, 0.8157, nan] 
-2024-08-28 07:04:37.253183: Epoch time: 85.63 s 
-2024-08-28 07:04:38.517692:  
-2024-08-28 07:04:38.517875: Epoch 721 
-2024-08-28 07:04:38.517967: Current learning rate: 0.00669 
-2024-08-28 07:06:02.527046: train_loss -0.7537 
-2024-08-28 07:06:02.527543: val_loss -0.7732 
-2024-08-28 07:06:02.527787: Pseudo dice [0.0, 0.0, 0.901, 0.9772, 0.8367, 0.9481, 0.949, 0.964, 0.9511, 0.9338, 0.925, 0.9553, 0.956, 0.8483, 0.9483, 0.9316, 0.8284, 0.8154, nan] 
-2024-08-28 07:06:02.527902: Epoch time: 84.01 s 
-2024-08-28 07:06:02.527956: Yayy! New best EMA pseudo Dice: 0.814 
-2024-08-28 07:06:04.867469:  
-2024-08-28 07:06:04.867873: Epoch 722 
-2024-08-28 07:06:04.868030: Current learning rate: 0.00668 
-2024-08-28 07:07:31.430101: train_loss -0.7529 
-2024-08-28 07:07:31.430348: val_loss -0.7749 
-2024-08-28 07:07:31.430516: Pseudo dice [0.0, 0.0, 0.888, 0.9766, 0.8406, 0.9418, 0.9459, 0.9613, 0.9511, 0.9437, 0.9272, 0.9614, 0.9589, 0.8412, 0.9495, 0.9364, 0.8259, 0.8244, nan] 
-2024-08-28 07:07:31.430605: Epoch time: 86.56 s 
-2024-08-28 07:07:31.430655: Yayy! New best EMA pseudo Dice: 0.8142 
-2024-08-28 07:07:33.033284:  
-2024-08-28 07:07:33.033462: Epoch 723 
-2024-08-28 07:07:33.033546: Current learning rate: 0.00668 
-2024-08-28 07:08:58.273721: train_loss -0.7531 
-2024-08-28 07:08:58.273961: val_loss -0.7741 
-2024-08-28 07:08:58.274114: Pseudo dice [0.0, 0.0, 0.887, 0.9755, 0.8354, 0.943, 0.9453, 0.963, 0.9512, 0.9521, 0.9318, 0.9641, 0.9628, 0.8393, 0.9526, 0.9275, 0.81, 0.8225, nan] 
-2024-08-28 07:08:58.274194: Epoch time: 85.24 s 
-2024-08-28 07:08:58.274242: Yayy! New best EMA pseudo Dice: 0.8142 
-2024-08-28 07:08:59.864002:  
-2024-08-28 07:08:59.864184: Epoch 724 
-2024-08-28 07:08:59.864287: Current learning rate: 0.00667 
-2024-08-28 07:10:31.861462: train_loss -0.7551 
-2024-08-28 07:10:31.862066: val_loss -0.7721 
-2024-08-28 07:10:31.862239: Pseudo dice [0.0, 0.0, 0.8612, 0.976, 0.8331, 0.9483, 0.9485, 0.9639, 0.9546, 0.9447, 0.9244, 0.9625, 0.9584, 0.8373, 0.941, 0.929, 0.8196, 0.8216, nan] 
-2024-08-28 07:10:31.862324: Epoch time: 92.0 s 
-2024-08-28 07:10:33.130662:  
-2024-08-28 07:10:33.130863: Epoch 725 
-2024-08-28 07:10:33.130963: Current learning rate: 0.00667 
-2024-08-28 07:12:00.384916: train_loss -0.7582 
-2024-08-28 07:12:00.385170: val_loss -0.7729 
-2024-08-28 07:12:00.385330: Pseudo dice [0.0, 0.0, 0.9016, 0.9782, 0.8505, 0.946, 0.945, 0.9623, 0.9487, 0.9502, 0.9337, 0.959, 0.9575, 0.8224, 0.9523, 0.9278, 0.8393, 0.8315, nan] 
-2024-08-28 07:12:00.385411: Epoch time: 87.26 s 
-2024-08-28 07:12:00.385459: Yayy! New best EMA pseudo Dice: 0.8143 
-2024-08-28 07:12:02.191288:  
-2024-08-28 07:12:02.191554: Epoch 726 
-2024-08-28 07:12:02.191651: Current learning rate: 0.00666 
-2024-08-28 07:13:29.111418: train_loss -0.7568 
-2024-08-28 07:13:29.111658: val_loss -0.7763 
-2024-08-28 07:13:29.111822: Pseudo dice [0.0, 0.0, 0.8907, 0.9763, 0.8462, 0.9473, 0.9468, 0.9647, 0.9473, 0.9407, 0.9314, 0.9569, 0.9577, 0.8396, 0.9535, 0.9357, 0.829, 0.8263, nan] 
-2024-08-28 07:13:29.111909: Epoch time: 86.92 s 
-2024-08-28 07:13:29.111960: Yayy! New best EMA pseudo Dice: 0.8145 
-2024-08-28 07:13:30.699564:  
-2024-08-28 07:13:30.699760: Epoch 727 
-2024-08-28 07:13:30.699855: Current learning rate: 0.00666 
-2024-08-28 07:14:56.614291: train_loss -0.7567 
-2024-08-28 07:14:56.614546: val_loss -0.7755 
-2024-08-28 07:14:56.614710: Pseudo dice [0.0, 0.0, 0.8892, 0.9771, 0.8381, 0.9357, 0.9431, 0.9654, 0.9416, 0.9447, 0.9197, 0.9429, 0.9491, 0.8362, 0.9437, 0.9346, 0.8107, 0.794, nan] 
-2024-08-28 07:14:56.614795: Epoch time: 85.92 s 
-2024-08-28 07:14:57.925906:  
-2024-08-28 07:14:57.926191: Epoch 728 
-2024-08-28 07:14:57.926296: Current learning rate: 0.00665 
-2024-08-28 07:16:24.679703: train_loss -0.756 
-2024-08-28 07:16:24.679932: val_loss -0.771 
-2024-08-28 07:16:24.680093: Pseudo dice [0.0, 0.0, 0.8701, 0.9714, 0.8268, 0.947, 0.9505, 0.959, 0.9526, 0.9506, 0.9317, 0.9611, 0.9618, 0.8414, 0.9493, 0.926, 0.8106, 0.7978, nan] 
-2024-08-28 07:16:24.680175: Epoch time: 86.75 s 
-2024-08-28 07:16:26.061962:  
-2024-08-28 07:16:26.062142: Epoch 729 
-2024-08-28 07:16:26.062252: Current learning rate: 0.00665 
-2024-08-28 07:17:45.080589: train_loss -0.7552 
-2024-08-28 07:17:45.080832: val_loss -0.7709 
-2024-08-28 07:17:45.081001: Pseudo dice [0.0, 0.0, 0.8952, 0.9778, 0.8416, 0.9434, 0.9443, 0.965, 0.9393, 0.9378, 0.9193, 0.9481, 0.9508, 0.8478, 0.9454, 0.9343, 0.8029, 0.8018, nan] 
-2024-08-28 07:17:45.081091: Epoch time: 79.02 s 
-2024-08-28 07:17:46.329710:  
-2024-08-28 07:17:46.329947: Epoch 730 
-2024-08-28 07:17:46.330040: Current learning rate: 0.00665 
-2024-08-28 07:19:12.403524: train_loss -0.7543 
-2024-08-28 07:19:12.403855: val_loss -0.7685 
-2024-08-28 07:19:12.404089: Pseudo dice [0.0, 0.0, 0.9006, 0.9754, 0.8313, 0.9475, 0.9499, 0.9633, 0.9442, 0.9363, 0.92, 0.9554, 0.9525, 0.8391, 0.9517, 0.9333, 0.8259, 0.8176, nan] 
-2024-08-28 07:19:12.404294: Epoch time: 86.07 s 
-2024-08-28 07:19:13.915169:  
-2024-08-28 07:19:13.915373: Epoch 731 
-2024-08-28 07:19:13.915466: Current learning rate: 0.00664 
-2024-08-28 07:20:41.221092: train_loss -0.7517 
-2024-08-28 07:20:41.221344: val_loss -0.7725 
-2024-08-28 07:20:41.221542: Pseudo dice [0.0, 0.0, 0.89, 0.9778, 0.8179, 0.9379, 0.9441, 0.958, 0.9419, 0.9334, 0.9243, 0.9496, 0.95, 0.8298, 0.9515, 0.9269, 0.8118, 0.8137, nan] 
-2024-08-28 07:20:41.221645: Epoch time: 87.31 s 
-2024-08-28 07:20:42.466689:  
-2024-08-28 07:20:42.466864: Epoch 732 
-2024-08-28 07:20:42.466962: Current learning rate: 0.00664 
-2024-08-28 07:22:05.201955: train_loss -0.7557 
-2024-08-28 07:22:05.202211: val_loss -0.7776 
-2024-08-28 07:22:05.202378: Pseudo dice [0.0, 0.0, 0.8849, 0.9753, 0.8416, 0.9451, 0.95, 0.9649, 0.9485, 0.9451, 0.9307, 0.9572, 0.9633, 0.8397, 0.9474, 0.9348, 0.8039, 0.8035, nan] 
-2024-08-28 07:22:05.202465: Epoch time: 82.74 s 
-2024-08-28 07:22:06.452956:  
-2024-08-28 07:22:06.453144: Epoch 733 
-2024-08-28 07:22:06.453247: Current learning rate: 0.00663 
-2024-08-28 07:23:29.648079: train_loss -0.755 
-2024-08-28 07:23:29.648375: val_loss -0.7727 
-2024-08-28 07:23:29.648610: Pseudo dice [0.0, 0.0, 0.894, 0.9767, 0.8506, 0.9427, 0.9467, 0.9648, 0.9462, 0.9443, 0.9194, 0.9566, 0.9575, 0.8354, 0.9541, 0.9229, 0.821, 0.8294, nan] 
-2024-08-28 07:23:29.648723: Epoch time: 83.2 s 
-2024-08-28 07:23:30.958043:  
-2024-08-28 07:23:30.958211: Epoch 734 
-2024-08-28 07:23:30.958319: Current learning rate: 0.00663 
-2024-08-28 07:25:00.412537: train_loss -0.7558 
-2024-08-28 07:25:00.412786: val_loss -0.7732 
-2024-08-28 07:25:00.412970: Pseudo dice [0.0, 0.0, 0.8912, 0.9769, 0.8345, 0.9407, 0.9467, 0.9637, 0.9374, 0.9383, 0.9229, 0.9445, 0.9487, 0.8446, 0.9398, 0.9296, 0.8141, 0.8089, nan] 
-2024-08-28 07:25:00.413059: Epoch time: 89.46 s 
-2024-08-28 07:25:01.692208:  
-2024-08-28 07:25:01.692395: Epoch 735 
-2024-08-28 07:25:01.692500: Current learning rate: 0.00662 
-2024-08-28 07:26:27.048167: train_loss -0.7563 
-2024-08-28 07:26:27.048404: val_loss -0.7779 
-2024-08-28 07:26:27.048582: Pseudo dice [0.0, 0.0, 0.902, 0.9775, 0.8449, 0.9421, 0.9422, 0.9625, 0.9338, 0.9408, 0.9233, 0.9455, 0.9478, 0.8446, 0.9561, 0.9227, 0.8256, 0.8096, nan] 
-2024-08-28 07:26:27.048676: Epoch time: 85.36 s 
-2024-08-28 07:26:28.284527:  
-2024-08-28 07:26:28.284710: Epoch 736 
-2024-08-28 07:26:28.284798: Current learning rate: 0.00662 
-2024-08-28 07:27:54.480489: train_loss -0.7575 
-2024-08-28 07:27:54.480721: val_loss -0.7759 
-2024-08-28 07:27:54.480893: Pseudo dice [0.0, 0.0, 0.8951, 0.9764, 0.8341, 0.9444, 0.9424, 0.9647, 0.9473, 0.9486, 0.9261, 0.9602, 0.9612, 0.853, 0.9549, 0.9287, 0.8261, 0.8291, nan] 
-2024-08-28 07:27:54.480976: Epoch time: 86.2 s 
-2024-08-28 07:27:56.000295:  
-2024-08-28 07:27:56.000622: Epoch 737 
-2024-08-28 07:27:56.000740: Current learning rate: 0.00661 
-2024-08-28 07:29:24.711582: train_loss -0.7578 
-2024-08-28 07:29:24.711833: val_loss -0.7738 
-2024-08-28 07:29:24.711988: Pseudo dice [0.0, 0.0, 0.8975, 0.9762, 0.8304, 0.9462, 0.9475, 0.9641, 0.9482, 0.9535, 0.9262, 0.9533, 0.955, 0.8394, 0.9398, 0.9279, 0.8331, 0.821, nan] 
-2024-08-28 07:29:24.712070: Epoch time: 88.71 s 
-2024-08-28 07:29:25.987936:  
-2024-08-28 07:29:25.988301: Epoch 738 
-2024-08-28 07:29:25.988404: Current learning rate: 0.00661 
-2024-08-28 07:30:53.746180: train_loss -0.7542 
-2024-08-28 07:30:53.746439: val_loss -0.7811 
-2024-08-28 07:30:53.746613: Pseudo dice [0.0, 0.0, 0.8714, 0.9754, 0.8416, 0.9458, 0.9528, 0.9637, 0.9528, 0.9579, 0.9291, 0.9632, 0.9627, 0.8393, 0.9473, 0.9334, 0.8409, 0.8297, nan] 
-2024-08-28 07:30:53.746706: Epoch time: 87.76 s 
-2024-08-28 07:30:55.015028:  
-2024-08-28 07:30:55.015207: Epoch 739 
-2024-08-28 07:30:55.015310: Current learning rate: 0.0066 
-2024-08-28 07:32:16.914637: train_loss -0.7556 
-2024-08-28 07:32:16.914909: val_loss -0.7763 
-2024-08-28 07:32:16.915114: Pseudo dice [0.0, 0.0, 0.904, 0.976, 0.8364, 0.9481, 0.9485, 0.9676, 0.9493, 0.9464, 0.9304, 0.9544, 0.9565, 0.8479, 0.9436, 0.935, 0.8203, 0.823, nan] 
-2024-08-28 07:32:16.915241: Epoch time: 81.9 s 
-2024-08-28 07:32:18.218757:  
-2024-08-28 07:32:18.219261: Epoch 740 
-2024-08-28 07:32:18.219458: Current learning rate: 0.0066 
-2024-08-28 07:33:48.631221: train_loss -0.7538 
-2024-08-28 07:33:48.631432: val_loss -0.7715 
-2024-08-28 07:33:48.631580: Pseudo dice [0.0, 0.0, 0.9088, 0.9766, 0.8432, 0.9432, 0.9458, 0.9651, 0.9371, 0.9367, 0.9259, 0.9483, 0.9485, 0.843, 0.9469, 0.9322, 0.8251, 0.8312, nan] 
-2024-08-28 07:33:48.631655: Epoch time: 90.41 s 
-2024-08-28 07:33:50.226680:  
-2024-08-28 07:33:50.226985: Epoch 741 
-2024-08-28 07:33:50.227102: Current learning rate: 0.00659 
-2024-08-28 07:35:15.107764: train_loss -0.7535 
-2024-08-28 07:35:15.108015: val_loss -0.7746 
-2024-08-28 07:35:15.108173: Pseudo dice [0.0, 0.0, 0.9053, 0.9762, 0.8454, 0.9444, 0.9473, 0.9634, 0.9524, 0.9514, 0.9316, 0.9595, 0.954, 0.8427, 0.9533, 0.9339, 0.83, 0.8252, nan] 
-2024-08-28 07:35:15.108255: Epoch time: 84.88 s 
-2024-08-28 07:35:16.389402:  
-2024-08-28 07:35:16.389915: Epoch 742 
-2024-08-28 07:35:16.390013: Current learning rate: 0.00659 
-2024-08-28 07:36:42.983346: train_loss -0.756 
-2024-08-28 07:36:42.983565: val_loss -0.7781 
-2024-08-28 07:36:42.983716: Pseudo dice [0.0, 0.0, 0.8953, 0.977, 0.8436, 0.9485, 0.9514, 0.9646, 0.952, 0.9483, 0.9234, 0.9617, 0.9563, 0.8464, 0.9519, 0.9306, 0.8311, 0.8272, nan] 
-2024-08-28 07:36:42.983801: Epoch time: 86.59 s 
-2024-08-28 07:36:42.983848: Yayy! New best EMA pseudo Dice: 0.8146 
-2024-08-28 07:36:44.718977:  
-2024-08-28 07:36:44.719128: Epoch 743 
-2024-08-28 07:36:44.719223: Current learning rate: 0.00658 
-2024-08-28 07:38:09.121331: train_loss -0.7554 
-2024-08-28 07:38:09.121594: val_loss -0.7714 
-2024-08-28 07:38:09.121761: Pseudo dice [0.0, 0.0, 0.8954, 0.9773, 0.8439, 0.9432, 0.9483, 0.9616, 0.9479, 0.9453, 0.9265, 0.9567, 0.9564, 0.8292, 0.9384, 0.9297, 0.8285, 0.824, nan] 
-2024-08-28 07:38:09.121849: Epoch time: 84.4 s 
-2024-08-28 07:38:10.347713:  
-2024-08-28 07:38:10.348135: Epoch 744 
-2024-08-28 07:38:10.348321: Current learning rate: 0.00658 
-2024-08-28 07:39:40.969142: train_loss -0.7484 
-2024-08-28 07:39:40.969381: val_loss -0.749 
-2024-08-28 07:39:40.969546: Pseudo dice [0.0, 0.0, 0.864, 0.9744, 0.7471, 0.9306, 0.9224, 0.9493, 0.9393, 0.9383, 0.9186, 0.9478, 0.9479, 0.7838, 0.9256, 0.9053, 0.7837, 0.799, nan] 
-2024-08-28 07:39:40.969631: Epoch time: 90.62 s 
-2024-08-28 07:39:42.224339:  
-2024-08-28 07:39:42.224518: Epoch 745 
-2024-08-28 07:39:42.224609: Current learning rate: 0.00657 
-2024-08-28 07:41:08.294760: train_loss -0.7385 
-2024-08-28 07:41:08.294994: val_loss -0.7619 
-2024-08-28 07:41:08.295168: Pseudo dice [0.0, 0.0, 0.8851, 0.9765, 0.8083, 0.9476, 0.9447, 0.9556, 0.9463, 0.9213, 0.9144, 0.961, 0.9507, 0.824, 0.9428, 0.9269, 0.8063, 0.8011, nan] 
-2024-08-28 07:41:08.295258: Epoch time: 86.07 s 
-2024-08-28 07:41:09.646827:  
-2024-08-28 07:41:09.647036: Epoch 746 
-2024-08-28 07:41:09.647146: Current learning rate: 0.00657 
-2024-08-28 07:42:33.977341: train_loss -0.7458 
-2024-08-28 07:42:33.977626: val_loss -0.7687 
-2024-08-28 07:42:33.977852: Pseudo dice [0.0, 0.0, 0.9015, 0.9743, 0.8093, 0.9381, 0.9392, 0.9618, 0.9448, 0.9446, 0.9255, 0.9582, 0.9584, 0.8308, 0.9513, 0.9255, 0.817, 0.7852, nan] 
-2024-08-28 07:42:33.977960: Epoch time: 84.33 s 
-2024-08-28 07:42:35.285964:  
-2024-08-28 07:42:35.286130: Epoch 747 
-2024-08-28 07:42:35.286221: Current learning rate: 0.00656 
-2024-08-28 07:44:05.295615: train_loss -0.7464 
-2024-08-28 07:44:05.295860: val_loss -0.7776 
-2024-08-28 07:44:05.296024: Pseudo dice [0.0, 0.0, 0.8736, 0.9755, 0.8287, 0.9454, 0.9463, 0.9635, 0.9475, 0.9449, 0.9269, 0.9615, 0.9571, 0.8393, 0.9434, 0.9333, 0.82, 0.8239, nan] 
-2024-08-28 07:44:05.296114: Epoch time: 90.01 s 
-2024-08-28 07:44:06.518261:  
-2024-08-28 07:44:06.518413: Epoch 748 
-2024-08-28 07:44:06.518510: Current learning rate: 0.00656 
-2024-08-28 07:45:34.585197: train_loss -0.7507 
-2024-08-28 07:45:34.585449: val_loss -0.7727 
-2024-08-28 07:45:34.585613: Pseudo dice [0.0, 0.0, 0.8861, 0.9771, 0.8326, 0.9464, 0.9499, 0.9643, 0.9465, 0.9499, 0.931, 0.9589, 0.9575, 0.8363, 0.9499, 0.9285, 0.8053, 0.8162, nan] 
-2024-08-28 07:45:34.585721: Epoch time: 88.07 s 
-2024-08-28 07:45:36.093765:  
-2024-08-28 07:45:36.093942: Epoch 749 
-2024-08-28 07:45:36.094042: Current learning rate: 0.00656 
-2024-08-28 07:47:05.559210: train_loss -0.748 
-2024-08-28 07:47:05.559456: val_loss -0.7623 
-2024-08-28 07:47:05.559619: Pseudo dice [0.0, 0.0, 0.8825, 0.975, 0.7847, 0.9438, 0.9398, 0.9571, 0.9219, 0.9418, 0.9197, 0.9417, 0.9518, 0.8275, 0.9412, 0.9223, 0.798, 0.807, nan] 
-2024-08-28 07:47:05.559710: Epoch time: 89.47 s 
-2024-08-28 07:47:07.372633:  
-2024-08-28 07:47:07.373081: Epoch 750 
-2024-08-28 07:47:07.373213: Current learning rate: 0.00655 
-2024-08-28 07:48:35.153952: train_loss -0.7508 
-2024-08-28 07:48:35.154476: val_loss -0.7644 
-2024-08-28 07:48:35.154656: Pseudo dice [0.0, 0.0, 0.9034, 0.9757, 0.8493, 0.9445, 0.9455, 0.9619, 0.9444, 0.9336, 0.9198, 0.9526, 0.9529, 0.8395, 0.9465, 0.9289, 0.8396, 0.8338, nan] 
-2024-08-28 07:48:35.154791: Epoch time: 87.78 s 
-2024-08-28 07:48:36.401557:  
-2024-08-28 07:48:36.401710: Epoch 751 
-2024-08-28 07:48:36.401798: Current learning rate: 0.00655 
-2024-08-28 07:49:59.856199: train_loss -0.7489 
-2024-08-28 07:49:59.856464: val_loss -0.769 
-2024-08-28 07:49:59.856646: Pseudo dice [0.0, 0.0, 0.8811, 0.9764, 0.811, 0.9371, 0.9443, 0.9644, 0.9478, 0.9403, 0.9272, 0.9529, 0.9534, 0.8291, 0.9524, 0.926, 0.8222, 0.8126, nan] 
-2024-08-28 07:49:59.856778: Epoch time: 83.46 s 
-2024-08-28 07:50:01.094408:  
-2024-08-28 07:50:01.094778: Epoch 752 
-2024-08-28 07:50:01.094871: Current learning rate: 0.00654 
-2024-08-28 07:51:23.031734: train_loss -0.746 
-2024-08-28 07:51:23.031956: val_loss -0.7752 
-2024-08-28 07:51:23.032118: Pseudo dice [0.0, 0.0, 0.8894, 0.9748, 0.8492, 0.9476, 0.9525, 0.9609, 0.9509, 0.9536, 0.9305, 0.9569, 0.9533, 0.8406, 0.9495, 0.9297, 0.8239, 0.8148, nan] 
-2024-08-28 07:51:23.032201: Epoch time: 81.94 s 
-2024-08-28 07:51:24.288505:  
-2024-08-28 07:51:24.288673: Epoch 753 
-2024-08-28 07:51:24.288766: Current learning rate: 0.00654 
-2024-08-28 07:52:50.024333: train_loss -0.751 
-2024-08-28 07:52:50.024832: val_loss -0.7737 
-2024-08-28 07:52:50.025006: Pseudo dice [0.0, 0.0, 0.8886, 0.9763, 0.8475, 0.9416, 0.9451, 0.9614, 0.9453, 0.9427, 0.9284, 0.9565, 0.9589, 0.8445, 0.9401, 0.9271, 0.8173, 0.8137, nan] 
-2024-08-28 07:52:50.025147: Epoch time: 85.74 s 
-2024-08-28 07:52:51.310347:  
-2024-08-28 07:52:51.310511: Epoch 754 
-2024-08-28 07:52:51.310605: Current learning rate: 0.00653 
-2024-08-28 07:54:17.805430: train_loss -0.7499 
-2024-08-28 07:54:17.805768: val_loss -0.7728 
-2024-08-28 07:54:17.805954: Pseudo dice [0.0, 0.0, 0.8902, 0.9753, 0.8372, 0.9391, 0.9438, 0.9626, 0.9468, 0.941, 0.9315, 0.96, 0.9587, 0.8288, 0.9408, 0.9298, 0.8207, 0.8216, nan] 
-2024-08-28 07:54:17.806076: Epoch time: 86.5 s 
-2024-08-28 07:54:19.431443:  
-2024-08-28 07:54:19.431828: Epoch 755 
-2024-08-28 07:54:19.431923: Current learning rate: 0.00653 
-2024-08-28 07:55:47.771829: train_loss -0.747 
-2024-08-28 07:55:47.772054: val_loss -0.7646 
-2024-08-28 07:55:47.772220: Pseudo dice [0.0, 0.0, 0.8868, 0.9756, 0.8243, 0.9413, 0.944, 0.9597, 0.9434, 0.9363, 0.9074, 0.9523, 0.9468, 0.8191, 0.9394, 0.9216, 0.8136, 0.8136, nan] 
-2024-08-28 07:55:47.772300: Epoch time: 88.34 s 
-2024-08-28 07:55:49.006493:  
-2024-08-28 07:55:49.006851: Epoch 756 
-2024-08-28 07:55:49.006955: Current learning rate: 0.00652 
-2024-08-28 07:57:16.674984: train_loss -0.7515 
-2024-08-28 07:57:16.675228: val_loss -0.7702 
-2024-08-28 07:57:16.675398: Pseudo dice [0.0, 0.0, 0.8806, 0.9764, 0.8314, 0.9405, 0.9404, 0.9631, 0.9345, 0.9378, 0.9184, 0.9437, 0.9472, 0.8376, 0.9514, 0.927, 0.8277, 0.816, nan] 
-2024-08-28 07:57:16.675499: Epoch time: 87.67 s 
-2024-08-28 07:57:17.933174:  
-2024-08-28 07:57:17.933555: Epoch 757 
-2024-08-28 07:57:17.933652: Current learning rate: 0.00652 
-2024-08-28 07:58:45.125623: train_loss -0.7519 
-2024-08-28 07:58:45.126028: val_loss -0.774 
-2024-08-28 07:58:45.126195: Pseudo dice [0.0, 0.0, 0.8926, 0.9756, 0.8291, 0.9447, 0.9465, 0.9641, 0.9432, 0.9471, 0.9284, 0.9584, 0.9569, 0.8374, 0.9534, 0.9327, 0.8272, 0.8118, nan] 
-2024-08-28 07:58:45.126294: Epoch time: 87.19 s 
-2024-08-28 07:58:46.375777:  
-2024-08-28 07:58:46.376261: Epoch 758 
-2024-08-28 07:58:46.376360: Current learning rate: 0.00651 
-2024-08-28 08:00:09.850102: train_loss -0.753 
-2024-08-28 08:00:09.850614: val_loss -0.7703 
-2024-08-28 08:00:09.850797: Pseudo dice [0.0, 0.0, 0.8965, 0.977, 0.8208, 0.9412, 0.9464, 0.9601, 0.9488, 0.9483, 0.9272, 0.9589, 0.9567, 0.837, 0.9517, 0.9285, 0.827, 0.8111, nan] 
-2024-08-28 08:00:09.850937: Epoch time: 83.47 s 
-2024-08-28 08:00:11.085519:  
-2024-08-28 08:00:11.085810: Epoch 759 
-2024-08-28 08:00:11.085914: Current learning rate: 0.00651 
-2024-08-28 08:01:39.378211: train_loss -0.7484 
-2024-08-28 08:01:39.378458: val_loss -0.7674 
-2024-08-28 08:01:39.378637: Pseudo dice [0.0, 0.0, 0.9002, 0.9724, 0.8249, 0.9505, 0.95, 0.9647, 0.947, 0.9268, 0.9117, 0.9561, 0.9537, 0.8459, 0.9498, 0.9321, 0.826, 0.8038, nan] 
-2024-08-28 08:01:39.378726: Epoch time: 88.29 s 
-2024-08-28 08:01:40.602243:  
-2024-08-28 08:01:40.602408: Epoch 760 
-2024-08-28 08:01:40.602492: Current learning rate: 0.0065 
-2024-08-28 08:03:04.682554: train_loss -0.7542 
-2024-08-28 08:03:04.682783: val_loss -0.772 
-2024-08-28 08:03:04.682939: Pseudo dice [0.0, 0.0, 0.8924, 0.9751, 0.8286, 0.9378, 0.9406, 0.9649, 0.945, 0.9398, 0.9216, 0.9523, 0.9467, 0.8311, 0.946, 0.9302, 0.8087, 0.8229, nan] 
-2024-08-28 08:03:04.683021: Epoch time: 84.08 s 
-2024-08-28 08:03:06.290065:  
-2024-08-28 08:03:06.290242: Epoch 761 
-2024-08-28 08:03:06.290333: Current learning rate: 0.0065 
-2024-08-28 08:04:30.594838: train_loss -0.7489 
-2024-08-28 08:04:30.595422: val_loss -0.7757 
-2024-08-28 08:04:30.595674: Pseudo dice [0.0, 0.0, 0.8839, 0.9689, 0.8182, 0.9429, 0.9367, 0.963, 0.9495, 0.9339, 0.9214, 0.9596, 0.9549, 0.8275, 0.9507, 0.9284, 0.8287, 0.8173, nan] 
-2024-08-28 08:04:30.595848: Epoch time: 84.31 s 
-2024-08-28 08:04:31.897383:  
-2024-08-28 08:04:31.897565: Epoch 762 
-2024-08-28 08:04:31.897678: Current learning rate: 0.00649 
-2024-08-28 08:05:57.639977: train_loss -0.7441 
-2024-08-28 08:05:57.640566: val_loss -0.7689 
-2024-08-28 08:05:57.640726: Pseudo dice [0.0, 0.0, 0.8923, 0.9753, 0.7952, 0.9444, 0.9455, 0.9522, 0.9524, 0.9468, 0.9207, 0.9595, 0.9583, 0.8236, 0.9319, 0.9163, 0.819, 0.8218, nan] 
-2024-08-28 08:05:57.640845: Epoch time: 85.74 s 
-2024-08-28 08:05:58.890122:  
-2024-08-28 08:05:58.890306: Epoch 763 
-2024-08-28 08:05:58.890401: Current learning rate: 0.00649 
-2024-08-28 08:07:26.485104: train_loss -0.7474 
-2024-08-28 08:07:26.485534: val_loss -0.758 
-2024-08-28 08:07:26.485747: Pseudo dice [0.0, 0.0, 0.8705, 0.9749, 0.8083, 0.9373, 0.9351, 0.9583, 0.9457, 0.9455, 0.923, 0.9577, 0.9531, 0.82, 0.9412, 0.9138, 0.8129, 0.7998, nan] 
-2024-08-28 08:07:26.485840: Epoch time: 87.6 s 
-2024-08-28 08:07:27.730158:  
-2024-08-28 08:07:27.730322: Epoch 764 
-2024-08-28 08:07:27.730412: Current learning rate: 0.00648 
-2024-08-28 08:08:55.021853: train_loss -0.7429 
-2024-08-28 08:08:55.022090: val_loss -0.7632 
-2024-08-28 08:08:55.022245: Pseudo dice [0.0, 0.0, 0.8754, 0.969, 0.8207, 0.9432, 0.9384, 0.9609, 0.9472, 0.9416, 0.9219, 0.9582, 0.9537, 0.8117, 0.9467, 0.9147, 0.7961, 0.7753, nan] 
-2024-08-28 08:08:55.022331: Epoch time: 87.29 s 
-2024-08-28 08:08:56.247778:  
-2024-08-28 08:08:56.247941: Epoch 765 
-2024-08-28 08:08:56.248034: Current learning rate: 0.00648 
-2024-08-28 08:10:25.838048: train_loss -0.7418 
-2024-08-28 08:10:25.838467: val_loss -0.7717 
-2024-08-28 08:10:25.838660: Pseudo dice [0.0, 0.0, 0.8801, 0.9762, 0.83, 0.9409, 0.9437, 0.9625, 0.9425, 0.9486, 0.9275, 0.9547, 0.9567, 0.8373, 0.9389, 0.9269, 0.8192, 0.7966, nan] 
-2024-08-28 08:10:25.838748: Epoch time: 89.59 s 
-2024-08-28 08:10:27.117971:  
-2024-08-28 08:10:27.118349: Epoch 766 
-2024-08-28 08:10:27.118445: Current learning rate: 0.00648 
-2024-08-28 08:11:53.462641: train_loss -0.7509 
-2024-08-28 08:11:53.462880: val_loss -0.7714 
-2024-08-28 08:11:53.463044: Pseudo dice [0.0, 0.0, 0.8659, 0.9766, 0.8264, 0.9414, 0.9446, 0.9646, 0.9505, 0.943, 0.9235, 0.9594, 0.9583, 0.8386, 0.9498, 0.9313, 0.8002, 0.7982, nan] 
-2024-08-28 08:11:53.463144: Epoch time: 86.35 s 
-2024-08-28 08:11:55.022121:  
-2024-08-28 08:11:55.022415: Epoch 767 
-2024-08-28 08:11:55.022512: Current learning rate: 0.00647 
-2024-08-28 08:13:29.374832: train_loss -0.7442 
-2024-08-28 08:13:29.375112: val_loss -0.7703 
-2024-08-28 08:13:29.375331: Pseudo dice [0.0, 0.0, 0.8531, 0.9767, 0.8226, 0.9458, 0.9469, 0.9623, 0.9495, 0.9383, 0.9267, 0.9594, 0.9561, 0.8271, 0.9517, 0.9278, 0.8054, 0.8162, nan] 
-2024-08-28 08:13:29.375446: Epoch time: 94.35 s 
-2024-08-28 08:13:30.719409:  
-2024-08-28 08:13:30.719789: Epoch 768 
-2024-08-28 08:13:30.719892: Current learning rate: 0.00647 
-2024-08-28 08:15:00.784846: train_loss -0.7499 
-2024-08-28 08:15:00.785105: val_loss -0.7655 
-2024-08-28 08:15:00.785270: Pseudo dice [0.0, 0.0, 0.8777, 0.9763, 0.8322, 0.9473, 0.9472, 0.9623, 0.9401, 0.9401, 0.9074, 0.9497, 0.9505, 0.8362, 0.9501, 0.9308, 0.7941, 0.7995, nan] 
-2024-08-28 08:15:00.785360: Epoch time: 90.07 s 
-2024-08-28 08:15:02.027276:  
-2024-08-28 08:15:02.027430: Epoch 769 
-2024-08-28 08:15:02.027529: Current learning rate: 0.00646 
-2024-08-28 08:16:27.943996: train_loss -0.7483 
-2024-08-28 08:16:27.944227: val_loss -0.7692 
-2024-08-28 08:16:27.944378: Pseudo dice [0.0, 0.0, 0.8949, 0.9765, 0.8374, 0.9448, 0.9464, 0.9585, 0.949, 0.9465, 0.9246, 0.9585, 0.9579, 0.8352, 0.952, 0.9322, 0.8211, 0.8215, nan] 
-2024-08-28 08:16:27.944473: Epoch time: 85.92 s 
-2024-08-28 08:16:29.183748:  
-2024-08-28 08:16:29.183920: Epoch 770 
-2024-08-28 08:16:29.184011: Current learning rate: 0.00646 
-2024-08-28 08:17:53.800740: train_loss -0.7537 
-2024-08-28 08:17:53.800971: val_loss -0.7671 
-2024-08-28 08:17:53.801139: Pseudo dice [0.0, 0.0, 0.8925, 0.9748, 0.8407, 0.944, 0.9495, 0.9601, 0.9449, 0.9408, 0.9247, 0.9539, 0.9577, 0.8407, 0.9422, 0.9292, 0.8299, 0.824, nan] 
-2024-08-28 08:17:53.801226: Epoch time: 84.62 s 
-2024-08-28 08:17:55.095623:  
-2024-08-28 08:17:55.095799: Epoch 771 
-2024-08-28 08:17:55.095888: Current learning rate: 0.00645 
-2024-08-28 08:19:22.180158: train_loss -0.7551 
-2024-08-28 08:19:22.180579: val_loss -0.7734 
-2024-08-28 08:19:22.180768: Pseudo dice [0.0, 0.0, 0.9017, 0.9765, 0.8198, 0.947, 0.9486, 0.962, 0.9527, 0.9531, 0.9329, 0.961, 0.9593, 0.8408, 0.9528, 0.9335, 0.8194, 0.8216, nan] 
-2024-08-28 08:19:22.180861: Epoch time: 87.09 s 
-2024-08-28 08:19:23.413539:  
-2024-08-28 08:19:23.413683: Epoch 772 
-2024-08-28 08:19:23.413783: Current learning rate: 0.00645 
-2024-08-28 08:20:54.106867: train_loss -0.7543 
-2024-08-28 08:20:54.107103: val_loss -0.7742 
-2024-08-28 08:20:54.107269: Pseudo dice [0.0, 0.0, 0.8981, 0.9756, 0.8592, 0.9461, 0.9501, 0.968, 0.9482, 0.9461, 0.9229, 0.9556, 0.9519, 0.8459, 0.9446, 0.9272, 0.8157, 0.8175, nan] 
-2024-08-28 08:20:54.107357: Epoch time: 90.69 s 
-2024-08-28 08:20:55.666227:  
-2024-08-28 08:20:55.666599: Epoch 773 
-2024-08-28 08:20:55.666695: Current learning rate: 0.00644 
-2024-08-28 08:22:23.166187: train_loss -0.7503 
-2024-08-28 08:22:23.166444: val_loss -0.7755 
-2024-08-28 08:22:23.166615: Pseudo dice [0.0, 0.0, 0.9029, 0.9765, 0.8378, 0.9431, 0.9458, 0.9627, 0.9355, 0.9414, 0.9196, 0.9431, 0.9477, 0.8431, 0.9536, 0.9313, 0.8174, 0.8175, nan] 
-2024-08-28 08:22:23.166706: Epoch time: 87.5 s 
-2024-08-28 08:22:24.468347:  
-2024-08-28 08:22:24.468597: Epoch 774 
-2024-08-28 08:22:24.468698: Current learning rate: 0.00644 
-2024-08-28 08:23:49.445174: train_loss -0.75 
-2024-08-28 08:23:49.445422: val_loss -0.7747 
-2024-08-28 08:23:49.445576: Pseudo dice [0.0, 0.0, 0.8927, 0.9758, 0.834, 0.9479, 0.954, 0.9609, 0.951, 0.9535, 0.9319, 0.9584, 0.9604, 0.8374, 0.951, 0.9271, 0.8284, 0.8158, nan] 
-2024-08-28 08:23:49.445658: Epoch time: 84.98 s 
-2024-08-28 08:23:50.752223:  
-2024-08-28 08:23:50.752660: Epoch 775 
-2024-08-28 08:23:50.752928: Current learning rate: 0.00643 
-2024-08-28 08:25:17.723919: train_loss -0.7525 
-2024-08-28 08:25:17.724187: val_loss -0.7712 
-2024-08-28 08:25:17.724416: Pseudo dice [0.0, 0.0, 0.8824, 0.9761, 0.8273, 0.9409, 0.9455, 0.9609, 0.9437, 0.9481, 0.9224, 0.9541, 0.9548, 0.8308, 0.9522, 0.9322, 0.8105, 0.8012, nan] 
-2024-08-28 08:25:17.724604: Epoch time: 86.97 s 
-2024-08-28 08:25:19.097401:  
-2024-08-28 08:25:19.097728: Epoch 776 
-2024-08-28 08:25:19.097826: Current learning rate: 0.00643 
-2024-08-28 08:26:45.277629: train_loss -0.7509 
-2024-08-28 08:26:45.277868: val_loss -0.7768 
-2024-08-28 08:26:45.278031: Pseudo dice [0.0, 0.0, 0.8881, 0.9768, 0.8312, 0.9449, 0.9481, 0.9619, 0.9481, 0.9437, 0.9282, 0.9585, 0.9585, 0.8437, 0.9496, 0.9282, 0.8231, 0.8206, nan] 
-2024-08-28 08:26:45.278117: Epoch time: 86.18 s 
-2024-08-28 08:26:46.495274:  
-2024-08-28 08:26:46.495572: Epoch 777 
-2024-08-28 08:26:46.495666: Current learning rate: 0.00642 
-2024-08-28 08:28:08.763193: train_loss -0.752 
-2024-08-28 08:28:08.763448: val_loss -0.7645 
-2024-08-28 08:28:08.763663: Pseudo dice [0.0, 0.0, 0.8788, 0.9664, 0.8327, 0.9468, 0.9487, 0.9615, 0.9431, 0.9463, 0.9196, 0.9592, 0.9548, 0.8218, 0.9468, 0.919, 0.821, 0.8151, nan] 
-2024-08-28 08:28:08.763775: Epoch time: 82.27 s 
-2024-08-28 08:28:10.271890:  
-2024-08-28 08:28:10.272202: Epoch 778 
-2024-08-28 08:28:10.272295: Current learning rate: 0.00642 
-2024-08-28 08:29:37.594958: train_loss -0.7518 
-2024-08-28 08:29:37.595195: val_loss -0.7683 
-2024-08-28 08:29:37.595354: Pseudo dice [0.0, 0.0, 0.8861, 0.9764, 0.8422, 0.9416, 0.9412, 0.9604, 0.9418, 0.937, 0.912, 0.9494, 0.9495, 0.8412, 0.9459, 0.9312, 0.8077, 0.8097, nan] 
-2024-08-28 08:29:37.595434: Epoch time: 87.32 s 
-2024-08-28 08:29:39.089420:  
-2024-08-28 08:29:39.089769: Epoch 779 
-2024-08-28 08:29:39.089864: Current learning rate: 0.00641 
-2024-08-28 08:31:08.875270: train_loss -0.7535 
-2024-08-28 08:31:08.875508: val_loss -0.7737 
-2024-08-28 08:31:08.875677: Pseudo dice [0.0, 0.0, 0.855, 0.9765, 0.8313, 0.9468, 0.9512, 0.963, 0.9498, 0.9463, 0.928, 0.9565, 0.9592, 0.8477, 0.9507, 0.9325, 0.8247, 0.8243, nan] 
-2024-08-28 08:31:08.875773: Epoch time: 89.79 s 
-2024-08-28 08:31:10.169549:  
-2024-08-28 08:31:10.169741: Epoch 780 
-2024-08-28 08:31:10.169840: Current learning rate: 0.00641 
-2024-08-28 08:32:36.687572: train_loss -0.7506 
-2024-08-28 08:32:36.687809: val_loss -0.7699 
-2024-08-28 08:32:36.687968: Pseudo dice [0.0, 0.0, 0.8554, 0.9742, 0.8117, 0.9373, 0.9443, 0.961, 0.9488, 0.951, 0.9266, 0.9531, 0.96, 0.8419, 0.9504, 0.9219, 0.7977, 0.8154, nan] 
-2024-08-28 08:32:36.688051: Epoch time: 86.52 s 
-2024-08-28 08:32:37.936125:  
-2024-08-28 08:32:37.936438: Epoch 781 
-2024-08-28 08:32:37.936541: Current learning rate: 0.0064 
-2024-08-28 08:34:12.396544: train_loss -0.755 
-2024-08-28 08:34:12.396804: val_loss -0.7683 
-2024-08-28 08:34:12.396980: Pseudo dice [0.0, 0.0, 0.9027, 0.976, 0.8473, 0.9367, 0.9382, 0.9671, 0.938, 0.9367, 0.9231, 0.9473, 0.9463, 0.8429, 0.9434, 0.9357, 0.8138, 0.8122, nan] 
-2024-08-28 08:34:12.397074: Epoch time: 94.46 s 
-2024-08-28 08:34:13.623169:  
-2024-08-28 08:34:13.623344: Epoch 782 
-2024-08-28 08:34:13.623425: Current learning rate: 0.0064 
-2024-08-28 08:35:38.527883: train_loss -0.7551 
-2024-08-28 08:35:38.528130: val_loss -0.7747 
-2024-08-28 08:35:38.528297: Pseudo dice [0.0, 0.0, 0.8945, 0.9774, 0.8526, 0.9422, 0.9447, 0.9682, 0.9509, 0.9455, 0.9272, 0.9613, 0.9582, 0.8361, 0.9525, 0.9324, 0.8328, 0.8126, nan] 
-2024-08-28 08:35:38.528383: Epoch time: 84.91 s 
-2024-08-28 08:35:39.795461:  
-2024-08-28 08:35:39.795629: Epoch 783 
-2024-08-28 08:35:39.795716: Current learning rate: 0.00639 
-2024-08-28 08:37:05.869045: train_loss -0.7559 
-2024-08-28 08:37:05.869385: val_loss -0.7705 
-2024-08-28 08:37:05.869627: Pseudo dice [0.0, 0.0, 0.8933, 0.9781, 0.8222, 0.9476, 0.9508, 0.9611, 0.9518, 0.9382, 0.9238, 0.9616, 0.9569, 0.8343, 0.9335, 0.9337, 0.8331, 0.8233, nan] 
-2024-08-28 08:37:05.869814: Epoch time: 86.07 s 
-2024-08-28 08:37:07.166793:  
-2024-08-28 08:37:07.167062: Epoch 784 
-2024-08-28 08:37:07.167150: Current learning rate: 0.00639 
-2024-08-28 08:38:35.837469: train_loss -0.754 
-2024-08-28 08:38:35.837707: val_loss -0.7724 
-2024-08-28 08:38:35.837868: Pseudo dice [0.0, 0.0, 0.8997, 0.9765, 0.8459, 0.9413, 0.9491, 0.9652, 0.9533, 0.9496, 0.9169, 0.9622, 0.9584, 0.8362, 0.9487, 0.9285, 0.8121, 0.8163, nan] 
-2024-08-28 08:38:35.837969: Epoch time: 88.67 s 
-2024-08-28 08:38:37.371566:  
-2024-08-28 08:38:37.371973: Epoch 785 
-2024-08-28 08:38:37.372082: Current learning rate: 0.00639 
-2024-08-28 08:40:03.271175: train_loss -0.7511 
-2024-08-28 08:40:03.271405: val_loss -0.7762 
-2024-08-28 08:40:03.271558: Pseudo dice [0.0, 0.0, 0.8723, 0.9772, 0.8513, 0.9442, 0.9489, 0.9616, 0.9535, 0.9506, 0.9196, 0.9579, 0.9554, 0.8405, 0.9517, 0.923, 0.7834, 0.7699, nan] 
-2024-08-28 08:40:03.271638: Epoch time: 85.9 s 
-2024-08-28 08:40:04.517282:  
-2024-08-28 08:40:04.517442: Epoch 786 
-2024-08-28 08:40:04.517525: Current learning rate: 0.00638 
-2024-08-28 08:41:27.979654: train_loss -0.7511 
-2024-08-28 08:41:27.979892: val_loss -0.7768 
-2024-08-28 08:41:27.980053: Pseudo dice [0.0, 0.0, 0.8747, 0.9769, 0.828, 0.9463, 0.9532, 0.9653, 0.9506, 0.9513, 0.9249, 0.9598, 0.9582, 0.8404, 0.9565, 0.9305, 0.8103, 0.8126, nan] 
-2024-08-28 08:41:27.980140: Epoch time: 83.46 s 
-2024-08-28 08:41:29.262545:  
-2024-08-28 08:41:29.262864: Epoch 787 
-2024-08-28 08:41:29.262955: Current learning rate: 0.00638 
-2024-08-28 08:43:01.252502: train_loss -0.7506 
-2024-08-28 08:43:01.252764: val_loss -0.7742 
-2024-08-28 08:43:01.252977: Pseudo dice [0.0, 0.0, 0.9056, 0.9765, 0.8491, 0.9444, 0.9482, 0.9657, 0.9508, 0.9508, 0.9257, 0.9608, 0.9579, 0.8408, 0.9527, 0.9293, 0.8328, 0.837, nan] 
-2024-08-28 08:43:01.253085: Epoch time: 91.99 s 
-2024-08-28 08:43:02.532175:  
-2024-08-28 08:43:02.532335: Epoch 788 
-2024-08-28 08:43:02.532433: Current learning rate: 0.00637 
-2024-08-28 08:44:30.513835: train_loss -0.7554 
-2024-08-28 08:44:30.514073: val_loss -0.7751 
-2024-08-28 08:44:30.514238: Pseudo dice [0.0, 0.0, 0.8922, 0.977, 0.8499, 0.9509, 0.9517, 0.9664, 0.9453, 0.9433, 0.9347, 0.9585, 0.9584, 0.8497, 0.9561, 0.9303, 0.8216, 0.8198, nan] 
-2024-08-28 08:44:30.514321: Epoch time: 87.98 s 
-2024-08-28 08:44:31.779871:  
-2024-08-28 08:44:31.780031: Epoch 789 
-2024-08-28 08:44:31.780111: Current learning rate: 0.00637 
-2024-08-28 08:46:04.034088: train_loss -0.7544 
-2024-08-28 08:46:04.034329: val_loss -0.7738 
-2024-08-28 08:46:04.034502: Pseudo dice [0.0, 0.0, 0.8893, 0.9761, 0.8381, 0.9475, 0.9476, 0.9618, 0.9501, 0.9543, 0.9188, 0.9582, 0.958, 0.8399, 0.9484, 0.9283, 0.821, 0.8087, nan] 
-2024-08-28 08:46:04.034587: Epoch time: 92.25 s 
-2024-08-28 08:46:05.313196:  
-2024-08-28 08:46:05.313585: Epoch 790 
-2024-08-28 08:46:05.313784: Current learning rate: 0.00636 
-2024-08-28 08:47:29.494096: train_loss -0.7593 
-2024-08-28 08:47:29.494345: val_loss -0.7716 
-2024-08-28 08:47:29.494518: Pseudo dice [0.0, 0.0, 0.8974, 0.9754, 0.8432, 0.945, 0.9474, 0.9596, 0.9487, 0.9471, 0.914, 0.9567, 0.9558, 0.8215, 0.9507, 0.9282, 0.8272, 0.838, nan] 
-2024-08-28 08:47:29.494607: Epoch time: 84.18 s 
-2024-08-28 08:47:30.961015:  
-2024-08-28 08:47:30.961336: Epoch 791 
-2024-08-28 08:47:30.961487: Current learning rate: 0.00636 
-2024-08-28 08:49:04.556653: train_loss -0.753 
-2024-08-28 08:49:04.556889: val_loss -0.7724 
-2024-08-28 08:49:04.557061: Pseudo dice [0.0, 0.0, 0.8769, 0.9733, 0.8433, 0.9427, 0.9503, 0.9635, 0.952, 0.9506, 0.9289, 0.9592, 0.9604, 0.8348, 0.9459, 0.9291, 0.7993, 0.7928, nan] 
-2024-08-28 08:49:04.557155: Epoch time: 93.6 s 
-2024-08-28 08:49:06.005721:  
-2024-08-28 08:49:06.005902: Epoch 792 
-2024-08-28 08:49:06.005993: Current learning rate: 0.00635 
-2024-08-28 08:50:29.664161: train_loss -0.7516 
-2024-08-28 08:50:29.664423: val_loss -0.7686 
-2024-08-28 08:50:29.664604: Pseudo dice [0.0, 0.0, 0.8966, 0.9776, 0.8199, 0.9411, 0.9457, 0.9627, 0.945, 0.9501, 0.9291, 0.9546, 0.9588, 0.8351, 0.9483, 0.9216, 0.8219, 0.8025, nan] 
-2024-08-28 08:50:29.664693: Epoch time: 83.66 s 
-2024-08-28 08:50:30.927488:  
-2024-08-28 08:50:30.927890: Epoch 793 
-2024-08-28 08:50:30.927993: Current learning rate: 0.00635 
-2024-08-28 08:51:53.926708: train_loss -0.7547 
-2024-08-28 08:51:53.926971: val_loss -0.7707 
-2024-08-28 08:51:53.927132: Pseudo dice [0.0, 0.0, 0.8735, 0.9763, 0.8488, 0.9455, 0.9446, 0.9618, 0.9519, 0.9512, 0.9231, 0.9608, 0.9591, 0.8449, 0.9434, 0.9234, 0.7998, 0.7993, nan] 
-2024-08-28 08:51:53.927450: Epoch time: 83.0 s 
-2024-08-28 08:51:55.184158:  
-2024-08-28 08:51:55.184401: Epoch 794 
-2024-08-28 08:51:55.184494: Current learning rate: 0.00634 
-2024-08-28 08:53:22.166769: train_loss -0.7545 
-2024-08-28 08:53:22.167006: val_loss -0.7713 
-2024-08-28 08:53:22.167177: Pseudo dice [0.0, 0.0, 0.8996, 0.9765, 0.843, 0.9403, 0.9429, 0.961, 0.9447, 0.9486, 0.9248, 0.9486, 0.9505, 0.8431, 0.95, 0.9235, 0.8209, 0.8077, nan] 
-2024-08-28 08:53:22.167262: Epoch time: 86.98 s 
-2024-08-28 08:53:23.548327:  
-2024-08-28 08:53:23.548782: Epoch 795 
-2024-08-28 08:53:23.548879: Current learning rate: 0.00634 
-2024-08-28 08:54:54.581108: train_loss -0.7545 
-2024-08-28 08:54:54.581361: val_loss -0.7697 
-2024-08-28 08:54:54.581522: Pseudo dice [0.0, 0.0, 0.8997, 0.9765, 0.8343, 0.9484, 0.9511, 0.9606, 0.9493, 0.9313, 0.9221, 0.9589, 0.9587, 0.8351, 0.9435, 0.9285, 0.8241, 0.8089, nan] 
-2024-08-28 08:54:54.581607: Epoch time: 91.03 s 
-2024-08-28 08:54:55.844784:  
-2024-08-28 08:54:55.845128: Epoch 796 
-2024-08-28 08:54:55.845227: Current learning rate: 0.00633 
-2024-08-28 08:56:23.454770: train_loss -0.7532 
-2024-08-28 08:56:23.455033: val_loss -0.7776 
-2024-08-28 08:56:23.455241: Pseudo dice [0.0, 0.0, 0.9052, 0.976, 0.842, 0.9463, 0.9502, 0.9605, 0.9528, 0.9507, 0.9313, 0.9606, 0.9614, 0.8477, 0.9498, 0.9332, 0.8278, 0.8253, nan] 
-2024-08-28 08:56:23.455345: Epoch time: 87.61 s 
-2024-08-28 08:56:25.348092:  
-2024-08-28 08:56:25.348342: Epoch 797 
-2024-08-28 08:56:25.348517: Current learning rate: 0.00633 
-2024-08-28 08:57:58.548893: train_loss -0.7551 
-2024-08-28 08:57:58.549120: val_loss -0.7756 
-2024-08-28 08:57:58.549296: Pseudo dice [0.0, 0.0, 0.9064, 0.9751, 0.8488, 0.944, 0.9465, 0.9635, 0.9483, 0.944, 0.9313, 0.9532, 0.9592, 0.8438, 0.9473, 0.9289, 0.8299, 0.8275, nan] 
-2024-08-28 08:57:58.549431: Epoch time: 93.2 s 
-2024-08-28 08:57:59.781163:  
-2024-08-28 08:57:59.781328: Epoch 798 
-2024-08-28 08:57:59.781425: Current learning rate: 0.00632 
-2024-08-28 08:59:21.763494: train_loss -0.757 
-2024-08-28 08:59:21.764708: val_loss -0.7699 
-2024-08-28 08:59:21.764928: Pseudo dice [0.0, 0.0, 0.8682, 0.9746, 0.8461, 0.946, 0.948, 0.9617, 0.9451, 0.9478, 0.9083, 0.9543, 0.9564, 0.829, 0.9546, 0.9278, 0.8171, 0.8184, nan] 
-2024-08-28 08:59:21.765066: Epoch time: 81.98 s 
-2024-08-28 08:59:23.053653:  
-2024-08-28 08:59:23.053844: Epoch 799 
-2024-08-28 08:59:23.053935: Current learning rate: 0.00632 
-2024-08-28 09:00:51.275967: train_loss -0.7502 
-2024-08-28 09:00:51.276388: val_loss -0.7668 
-2024-08-28 09:00:51.276939: Pseudo dice [0.0, 0.0, 0.8495, 0.9738, 0.8233, 0.9415, 0.9483, 0.9588, 0.9358, 0.9447, 0.925, 0.9496, 0.9519, 0.8267, 0.941, 0.9184, 0.7821, 0.785, nan] 
-2024-08-28 09:00:51.277102: Epoch time: 88.22 s 
-2024-08-28 09:00:53.221120:  
-2024-08-28 09:00:53.221303: Epoch 800 
-2024-08-28 09:00:53.221405: Current learning rate: 0.00631 
-2024-08-28 09:02:19.628913: train_loss -0.7464 
-2024-08-28 09:02:19.629130: val_loss -0.7675 
-2024-08-28 09:02:19.629295: Pseudo dice [0.0, 0.0, 0.8878, 0.9758, 0.8218, 0.9422, 0.9467, 0.96, 0.9406, 0.9289, 0.9113, 0.9543, 0.9523, 0.8399, 0.9482, 0.9203, 0.7903, 0.8082, nan] 
-2024-08-28 09:02:19.629387: Epoch time: 86.41 s 
-2024-08-28 09:02:20.927715:  
-2024-08-28 09:02:20.927962: Epoch 801 
-2024-08-28 09:02:20.928117: Current learning rate: 0.00631 
-2024-08-28 09:03:46.932551: train_loss -0.7518 
-2024-08-28 09:03:46.932811: val_loss -0.7645 
-2024-08-28 09:03:46.933020: Pseudo dice [0.0, 0.0, 0.882, 0.9745, 0.8137, 0.9384, 0.9435, 0.9603, 0.9347, 0.9387, 0.9123, 0.9446, 0.9444, 0.8338, 0.9456, 0.9306, 0.8213, 0.8101, nan] 
-2024-08-28 09:03:46.933128: Epoch time: 86.01 s 
-2024-08-28 09:03:48.157571:  
-2024-08-28 09:03:48.157732: Epoch 802 
-2024-08-28 09:03:48.157822: Current learning rate: 0.0063 
-2024-08-28 09:05:17.226499: train_loss -0.7512 
-2024-08-28 09:05:17.226737: val_loss -0.7633 
-2024-08-28 09:05:17.226897: Pseudo dice [0.0, 0.0, 0.8947, 0.9759, 0.8391, 0.9416, 0.9421, 0.9545, 0.9349, 0.9244, 0.9053, 0.9485, 0.9463, 0.8369, 0.9497, 0.9291, 0.8034, 0.7961, nan] 
-2024-08-28 09:05:17.226995: Epoch time: 89.07 s 
-2024-08-28 09:05:18.894036:  
-2024-08-28 09:05:18.894220: Epoch 803 
-2024-08-28 09:05:18.894315: Current learning rate: 0.0063 
-2024-08-28 09:06:43.963191: train_loss -0.7558 
-2024-08-28 09:06:43.963402: val_loss -0.7778 
-2024-08-28 09:06:43.963565: Pseudo dice [0.0, 0.0, 0.8904, 0.9767, 0.847, 0.9507, 0.9507, 0.9664, 0.9482, 0.949, 0.9306, 0.9526, 0.9606, 0.8462, 0.9557, 0.9395, 0.8267, 0.8292, nan] 
-2024-08-28 09:06:43.963648: Epoch time: 85.07 s 
-2024-08-28 09:06:45.221514:  
-2024-08-28 09:06:45.221807: Epoch 804 
-2024-08-28 09:06:45.222072: Current learning rate: 0.0063 
-2024-08-28 09:08:13.431836: train_loss -0.7541 
-2024-08-28 09:08:13.432045: val_loss -0.7761 
-2024-08-28 09:08:13.432243: Pseudo dice [0.0, 0.0, 0.9013, 0.9742, 0.8462, 0.9466, 0.9434, 0.9623, 0.9464, 0.9476, 0.9225, 0.9622, 0.953, 0.8402, 0.9486, 0.931, 0.8224, 0.8088, nan] 
-2024-08-28 09:08:13.432355: Epoch time: 88.21 s 
-2024-08-28 09:08:14.774845:  
-2024-08-28 09:08:14.775050: Epoch 805 
-2024-08-28 09:08:14.775151: Current learning rate: 0.00629 
-2024-08-28 09:09:36.974096: train_loss -0.7547 
-2024-08-28 09:09:36.974342: val_loss -0.7701 
-2024-08-28 09:09:36.974506: Pseudo dice [0.0, 0.0, 0.9038, 0.9765, 0.8379, 0.9464, 0.9491, 0.963, 0.9461, 0.9458, 0.9263, 0.958, 0.9587, 0.8383, 0.9497, 0.9206, 0.8159, 0.8209, nan] 
-2024-08-28 09:09:36.974594: Epoch time: 82.2 s 
-2024-08-28 09:09:38.276976:  
-2024-08-28 09:09:38.277407: Epoch 806 
-2024-08-28 09:09:38.277515: Current learning rate: 0.00629 
-2024-08-28 09:11:04.348674: train_loss -0.7521 
-2024-08-28 09:11:04.348933: val_loss -0.7784 
-2024-08-28 09:11:04.349145: Pseudo dice [0.0, 0.0, 0.9029, 0.9756, 0.8174, 0.9403, 0.9449, 0.9596, 0.954, 0.9446, 0.9314, 0.961, 0.9569, 0.8322, 0.9562, 0.9379, 0.8311, 0.8329, nan] 
-2024-08-28 09:11:04.349254: Epoch time: 86.07 s 
-2024-08-28 09:11:05.904533:  
-2024-08-28 09:11:05.905079: Epoch 807 
-2024-08-28 09:11:05.905203: Current learning rate: 0.00628 
-2024-08-28 09:12:31.410727: train_loss -0.7494 
-2024-08-28 09:12:31.411179: val_loss -0.7726 
-2024-08-28 09:12:31.411810: Pseudo dice [0.0, 0.0, 0.8952, 0.9772, 0.7996, 0.945, 0.945, 0.9618, 0.9499, 0.9468, 0.9267, 0.9572, 0.9571, 0.8265, 0.9482, 0.9284, 0.827, 0.8233, nan] 
-2024-08-28 09:12:31.412113: Epoch time: 85.51 s 
-2024-08-28 09:12:32.751659:  
-2024-08-28 09:12:32.751814: Epoch 808 
-2024-08-28 09:12:32.751909: Current learning rate: 0.00628 
-2024-08-28 09:13:58.078116: train_loss -0.7527 
-2024-08-28 09:13:58.078332: val_loss -0.7753 
-2024-08-28 09:13:58.078483: Pseudo dice [0.0, 0.0, 0.8796, 0.9765, 0.8368, 0.9413, 0.9436, 0.9635, 0.9464, 0.9482, 0.9272, 0.9558, 0.9582, 0.8404, 0.9476, 0.9351, 0.8075, 0.7973, nan] 
-2024-08-28 09:13:58.078565: Epoch time: 85.33 s 
-2024-08-28 09:13:59.624515:  
-2024-08-28 09:13:59.624804: Epoch 809 
-2024-08-28 09:13:59.624899: Current learning rate: 0.00627 
-2024-08-28 09:15:18.209258: train_loss -0.7612 
-2024-08-28 09:15:18.209495: val_loss -0.7729 
-2024-08-28 09:15:18.209661: Pseudo dice [0.0, 0.0, 0.8968, 0.9766, 0.8374, 0.9428, 0.9436, 0.9644, 0.9511, 0.9487, 0.9243, 0.9577, 0.9603, 0.8479, 0.9484, 0.9344, 0.8205, 0.8294, nan] 
-2024-08-28 09:15:18.209803: Epoch time: 78.59 s 
-2024-08-28 09:15:19.493845:  
-2024-08-28 09:15:19.494024: Epoch 810 
-2024-08-28 09:15:19.494116: Current learning rate: 0.00627 
-2024-08-28 09:16:46.052185: train_loss -0.7567 
-2024-08-28 09:16:46.052419: val_loss -0.7654 
-2024-08-28 09:16:46.052638: Pseudo dice [0.0, 0.0, 0.8845, 0.9769, 0.8426, 0.9343, 0.9386, 0.9583, 0.9379, 0.9338, 0.9179, 0.9481, 0.948, 0.8192, 0.9383, 0.9257, 0.8271, 0.8226, nan] 
-2024-08-28 09:16:46.052732: Epoch time: 86.56 s 
-2024-08-28 09:16:47.384021:  
-2024-08-28 09:16:47.384476: Epoch 811 
-2024-08-28 09:16:47.384657: Current learning rate: 0.00626 
-2024-08-28 09:18:15.704124: train_loss -0.7525 
-2024-08-28 09:18:15.704401: val_loss -0.7759 
-2024-08-28 09:18:15.704572: Pseudo dice [0.0, 0.0, 0.8836, 0.9757, 0.8245, 0.9407, 0.9431, 0.9652, 0.9508, 0.9425, 0.9273, 0.9581, 0.9597, 0.8379, 0.9569, 0.9331, 0.8138, 0.8104, nan] 
-2024-08-28 09:18:15.704671: Epoch time: 88.32 s 
-2024-08-28 09:18:16.932351:  
-2024-08-28 09:18:16.932540: Epoch 812 
-2024-08-28 09:18:16.932716: Current learning rate: 0.00626 
-2024-08-28 09:19:44.955734: train_loss -0.7543 
-2024-08-28 09:19:44.956150: val_loss -0.7726 
-2024-08-28 09:19:44.956386: Pseudo dice [0.0, 0.0, 0.8781, 0.9757, 0.8503, 0.9464, 0.951, 0.9647, 0.9439, 0.9465, 0.9256, 0.9601, 0.9568, 0.841, 0.9509, 0.9321, 0.8203, 0.8168, nan] 
-2024-08-28 09:19:44.956489: Epoch time: 88.02 s 
-2024-08-28 09:19:46.211051:  
-2024-08-28 09:19:46.211362: Epoch 813 
-2024-08-28 09:19:46.211457: Current learning rate: 0.00625 
-2024-08-28 09:21:13.642231: train_loss -0.7555 
-2024-08-28 09:21:13.642759: val_loss -0.7717 
-2024-08-28 09:21:13.643256: Pseudo dice [0.0, 0.0, 0.89, 0.9762, 0.8464, 0.9464, 0.9483, 0.9603, 0.9413, 0.9344, 0.922, 0.9534, 0.9557, 0.8485, 0.9525, 0.932, 0.8265, 0.8233, nan] 
-2024-08-28 09:21:13.643414: Epoch time: 87.43 s 
-2024-08-28 09:21:15.146681:  
-2024-08-28 09:21:15.146834: Epoch 814 
-2024-08-28 09:21:15.146923: Current learning rate: 0.00625 
-2024-08-28 09:22:40.444477: train_loss -0.7564 
-2024-08-28 09:22:40.444862: val_loss -0.7662 
-2024-08-28 09:22:40.445165: Pseudo dice [0.0, 0.0, 0.8418, 0.9736, 0.8167, 0.9436, 0.9478, 0.9625, 0.9447, 0.9526, 0.9159, 0.957, 0.9509, 0.8368, 0.9445, 0.9289, 0.8188, 0.8213, nan] 
-2024-08-28 09:22:40.445359: Epoch time: 85.3 s 
-2024-08-28 09:22:41.709012:  
-2024-08-28 09:22:41.709304: Epoch 815 
-2024-08-28 09:22:41.709399: Current learning rate: 0.00624 
-2024-08-28 09:24:06.186475: train_loss -0.7555 
-2024-08-28 09:24:06.186696: val_loss -0.7784 
-2024-08-28 09:24:06.186859: Pseudo dice [0.0, 0.0, 0.8951, 0.9765, 0.8338, 0.9484, 0.9515, 0.9578, 0.9493, 0.9489, 0.9241, 0.9609, 0.9546, 0.8407, 0.9489, 0.9302, 0.8335, 0.8172, nan] 
-2024-08-28 09:24:06.186944: Epoch time: 84.48 s 
-2024-08-28 09:24:07.430128:  
-2024-08-28 09:24:07.430318: Epoch 816 
-2024-08-28 09:24:07.430457: Current learning rate: 0.00624 
-2024-08-28 09:25:41.225995: train_loss -0.7499 
-2024-08-28 09:25:41.226237: val_loss -0.7758 
-2024-08-28 09:25:41.226442: Pseudo dice [0.0, 0.0, 0.888, 0.9751, 0.8541, 0.9453, 0.9539, 0.9631, 0.9503, 0.9542, 0.9239, 0.9608, 0.9595, 0.837, 0.9548, 0.9283, 0.8295, 0.8137, nan] 
-2024-08-28 09:25:41.226544: Epoch time: 93.8 s 
-2024-08-28 09:25:42.635286:  
-2024-08-28 09:25:42.635716: Epoch 817 
-2024-08-28 09:25:42.635808: Current learning rate: 0.00623 
-2024-08-28 09:27:09.946926: train_loss -0.7573 
-2024-08-28 09:27:09.947175: val_loss -0.7763 
-2024-08-28 09:27:09.947350: Pseudo dice [0.0, 0.0, 0.8816, 0.9747, 0.8282, 0.9395, 0.9445, 0.9636, 0.9432, 0.9475, 0.9252, 0.9586, 0.9574, 0.8457, 0.9529, 0.9292, 0.8065, 0.8159, nan] 
-2024-08-28 09:27:09.947437: Epoch time: 87.31 s 
-2024-08-28 09:27:11.192708:  
-2024-08-28 09:27:11.192856: Epoch 818 
-2024-08-28 09:27:11.192936: Current learning rate: 0.00623 
-2024-08-28 09:28:33.020996: train_loss -0.7516 
-2024-08-28 09:28:33.021239: val_loss -0.7724 
-2024-08-28 09:28:33.021415: Pseudo dice [0.0, 0.0, 0.8778, 0.9761, 0.8395, 0.9416, 0.9475, 0.9637, 0.9441, 0.9358, 0.9224, 0.9528, 0.9528, 0.8335, 0.9413, 0.9267, 0.8127, 0.8012, nan] 
-2024-08-28 09:28:33.021507: Epoch time: 81.83 s 
-2024-08-28 09:28:34.284073:  
-2024-08-28 09:28:34.284230: Epoch 819 
-2024-08-28 09:28:34.284321: Current learning rate: 0.00622 
-2024-08-28 09:30:00.265601: train_loss -0.7487 
-2024-08-28 09:30:00.265832: val_loss -0.7757 
-2024-08-28 09:30:00.266002: Pseudo dice [0.0, 0.0, 0.8705, 0.975, 0.8163, 0.938, 0.9448, 0.964, 0.954, 0.9489, 0.9279, 0.9594, 0.9597, 0.8418, 0.9516, 0.9353, 0.826, 0.8219, nan] 
-2024-08-28 09:30:00.266086: Epoch time: 85.98 s 
-2024-08-28 09:30:01.725578:  
-2024-08-28 09:30:01.725724: Epoch 820 
-2024-08-28 09:30:01.725821: Current learning rate: 0.00622 
-2024-08-28 09:31:29.680996: train_loss -0.7555 
-2024-08-28 09:31:29.681230: val_loss -0.7729 
-2024-08-28 09:31:29.681391: Pseudo dice [0.0, 0.0, 0.8813, 0.9758, 0.8304, 0.9428, 0.9473, 0.9619, 0.9507, 0.948, 0.9262, 0.9558, 0.9587, 0.8414, 0.9506, 0.9294, 0.8247, 0.8236, nan] 
-2024-08-28 09:31:29.681488: Epoch time: 87.96 s 
-2024-08-28 09:31:30.857595:  
-2024-08-28 09:31:30.857848: Epoch 821 
-2024-08-28 09:31:30.857943: Current learning rate: 0.00621 
-2024-08-28 09:33:00.376034: train_loss -0.757 
-2024-08-28 09:33:00.376275: val_loss -0.7772 
-2024-08-28 09:33:00.376457: Pseudo dice [0.0, 0.0, 0.8945, 0.9772, 0.8566, 0.9519, 0.9526, 0.9674, 0.95, 0.9537, 0.9266, 0.9616, 0.9581, 0.8581, 0.9559, 0.9388, 0.8297, 0.7969, nan] 
-2024-08-28 09:33:00.376552: Epoch time: 89.52 s 
-2024-08-28 09:33:01.564897:  
-2024-08-28 09:33:01.565036: Epoch 822 
-2024-08-28 09:33:01.565125: Current learning rate: 0.00621 
-2024-08-28 09:34:29.544403: train_loss -0.7569 
-2024-08-28 09:34:29.544642: val_loss -0.7713 
-2024-08-28 09:34:29.544819: Pseudo dice [0.0, 0.0, 0.902, 0.9765, 0.8371, 0.9397, 0.9446, 0.9604, 0.9526, 0.952, 0.9268, 0.9574, 0.9591, 0.8374, 0.9474, 0.9313, 0.826, 0.8125, nan] 
-2024-08-28 09:34:29.544908: Epoch time: 87.98 s 
-2024-08-28 09:34:30.687770:  
-2024-08-28 09:34:30.688046: Epoch 823 
-2024-08-28 09:34:30.688138: Current learning rate: 0.00621 
-2024-08-28 09:35:56.771355: train_loss -0.7528 
-2024-08-28 09:35:56.771618: val_loss -0.7719 
-2024-08-28 09:35:56.771804: Pseudo dice [0.0, 0.0, 0.8339, 0.9744, 0.8191, 0.9442, 0.9458, 0.9631, 0.9467, 0.9403, 0.918, 0.9575, 0.9503, 0.8497, 0.9323, 0.9319, 0.8174, 0.8235, nan] 
-2024-08-28 09:35:56.771947: Epoch time: 86.08 s 
-2024-08-28 09:35:57.940791:  
-2024-08-28 09:35:57.941323: Epoch 824 
-2024-08-28 09:35:57.941422: Current learning rate: 0.0062 
-2024-08-28 09:37:23.363379: train_loss -0.7505 
-2024-08-28 09:37:23.363918: val_loss -0.7799 
-2024-08-28 09:37:23.364084: Pseudo dice [0.0, 0.0, 0.892, 0.9762, 0.8478, 0.9471, 0.9458, 0.963, 0.9508, 0.9578, 0.9336, 0.9576, 0.9612, 0.8394, 0.9477, 0.927, 0.8367, 0.8336, nan] 
-2024-08-28 09:37:23.364238: Epoch time: 85.42 s 
-2024-08-28 09:37:24.600918:  
-2024-08-28 09:37:24.779340: Epoch 825 
-2024-08-28 09:37:24.779812: Current learning rate: 0.0062 
-2024-08-28 09:38:54.119728: train_loss -0.7584 
-2024-08-28 09:38:54.119956: val_loss -0.7811 
-2024-08-28 09:38:54.120112: Pseudo dice [0.0, 0.0, 0.8913, 0.9754, 0.8236, 0.9418, 0.9421, 0.9639, 0.9519, 0.9445, 0.9282, 0.9619, 0.9561, 0.8354, 0.9524, 0.927, 0.8135, 0.8201, nan] 
-2024-08-28 09:38:54.120192: Epoch time: 89.52 s 
-2024-08-28 09:38:55.285631:  
-2024-08-28 09:38:55.285796: Epoch 826 
-2024-08-28 09:38:55.285880: Current learning rate: 0.00619 
-2024-08-28 09:40:23.976083: train_loss -0.7659 
-2024-08-28 09:40:23.976325: val_loss -0.7748 
-2024-08-28 09:40:23.976506: Pseudo dice [0.0, 0.0, 0.886, 0.9769, 0.7941, 0.9469, 0.9478, 0.9601, 0.9442, 0.9404, 0.9286, 0.9511, 0.9536, 0.8414, 0.9504, 0.9284, 0.8099, 0.8132, nan] 
-2024-08-28 09:40:23.976601: Epoch time: 88.69 s 
-2024-08-28 09:40:25.639491:  
-2024-08-28 09:40:25.640156: Epoch 827 
-2024-08-28 09:40:25.640701: Current learning rate: 0.00619 
-2024-08-28 09:41:52.731630: train_loss -0.7635 
-2024-08-28 09:41:52.731984: val_loss -0.7758 
-2024-08-28 09:41:52.732162: Pseudo dice [0.0, 0.0, 0.885, 0.9765, 0.8125, 0.9377, 0.942, 0.964, 0.9454, 0.9306, 0.9254, 0.9554, 0.9559, 0.843, 0.9459, 0.9299, 0.8204, 0.8156, nan] 
-2024-08-28 09:41:52.732284: Epoch time: 87.09 s 
-2024-08-28 09:41:53.952554:  
-2024-08-28 09:41:53.952725: Epoch 828 
-2024-08-28 09:41:53.952811: Current learning rate: 0.00618 
-2024-08-28 09:43:27.813785: train_loss -0.7633 
-2024-08-28 09:43:27.814020: val_loss -0.7873 
-2024-08-28 09:43:27.814179: Pseudo dice [0.0, 0.0, 0.9072, 0.9758, 0.8341, 0.9476, 0.95, 0.9651, 0.9481, 0.9428, 0.9276, 0.9615, 0.96, 0.8406, 0.954, 0.9357, 0.8266, 0.8161, nan] 
-2024-08-28 09:43:27.814268: Epoch time: 93.86 s 
-2024-08-28 09:43:29.051039:  
-2024-08-28 09:43:29.051475: Epoch 829 
-2024-08-28 09:43:29.051575: Current learning rate: 0.00618 
-2024-08-28 09:45:00.773745: train_loss -0.7564 
-2024-08-28 09:45:00.773961: val_loss -0.7842 
-2024-08-28 09:45:00.774114: Pseudo dice [0.0, 0.0, 0.8974, 0.9774, 0.8355, 0.9456, 0.9492, 0.9612, 0.9508, 0.9527, 0.9284, 0.9578, 0.96, 0.8413, 0.9463, 0.9339, 0.8242, 0.8167, nan] 
-2024-08-28 09:45:00.774209: Epoch time: 91.72 s 
-2024-08-28 09:45:01.910641:  
-2024-08-28 09:45:01.910907: Epoch 830 
-2024-08-28 09:45:01.910999: Current learning rate: 0.00617 
-2024-08-28 09:46:27.277076: train_loss -0.757 
-2024-08-28 09:46:27.277319: val_loss -0.7808 
-2024-08-28 09:46:27.277487: Pseudo dice [0.0, 0.0, 0.8908, 0.9765, 0.8466, 0.948, 0.9509, 0.9618, 0.9505, 0.9441, 0.9233, 0.9608, 0.9603, 0.8336, 0.9383, 0.9245, 0.8304, 0.81, nan] 
-2024-08-28 09:46:27.277573: Epoch time: 85.37 s 
-2024-08-28 09:46:28.456268:  
-2024-08-28 09:46:28.456414: Epoch 831 
-2024-08-28 09:46:28.456530: Current learning rate: 0.00617 
-2024-08-28 09:47:51.584713: train_loss -0.7635 
-2024-08-28 09:47:51.584934: val_loss -0.7855 
-2024-08-28 09:47:51.585098: Pseudo dice [0.0, 0.0, 0.8573, 0.9771, 0.8251, 0.9434, 0.9485, 0.9625, 0.9502, 0.9529, 0.9285, 0.9563, 0.959, 0.838, 0.9469, 0.931, 0.825, 0.8192, nan] 
-2024-08-28 09:47:51.585180: Epoch time: 83.13 s 
-2024-08-28 09:47:52.787981:  
-2024-08-28 09:47:52.788143: Epoch 832 
-2024-08-28 09:47:52.788227: Current learning rate: 0.00616 
-2024-08-28 09:49:19.012331: train_loss -0.7628 
-2024-08-28 09:49:19.012700: val_loss -0.7834 
-2024-08-28 09:49:19.012902: Pseudo dice [0.0, 0.0, 0.8885, 0.9726, 0.844, 0.9437, 0.9425, 0.9659, 0.9495, 0.9399, 0.9302, 0.9612, 0.9576, 0.8531, 0.9491, 0.9354, 0.8224, 0.8223, nan] 
-2024-08-28 09:49:19.012999: Epoch time: 86.23 s 
-2024-08-28 09:49:20.503309:  
-2024-08-28 09:49:20.503638: Epoch 833 
-2024-08-28 09:49:20.503737: Current learning rate: 0.00616 
-2024-08-28 09:50:43.420189: train_loss -0.7653 
-2024-08-28 09:50:43.420419: val_loss -0.7874 
-2024-08-28 09:50:43.420611: Pseudo dice [0.0, 0.0, 0.8894, 0.9773, 0.826, 0.9443, 0.9526, 0.964, 0.9424, 0.9498, 0.9245, 0.9531, 0.9576, 0.8498, 0.9562, 0.9312, 0.8334, 0.8311, nan] 
-2024-08-28 09:50:43.420697: Epoch time: 82.92 s 
-2024-08-28 09:50:44.689252:  
-2024-08-28 09:50:44.689770: Epoch 834 
-2024-08-28 09:50:44.689953: Current learning rate: 0.00615 
-2024-08-28 09:52:10.456266: train_loss -0.7599 
-2024-08-28 09:52:10.456493: val_loss -0.7778 
-2024-08-28 09:52:10.456667: Pseudo dice [0.0, 0.0, 0.8873, 0.9761, 0.8475, 0.9411, 0.9424, 0.9622, 0.9488, 0.9407, 0.9216, 0.9593, 0.9559, 0.8363, 0.9457, 0.9293, 0.8125, 0.8244, nan] 
-2024-08-28 09:52:10.456798: Epoch time: 85.77 s 
-2024-08-28 09:52:11.648729:  
-2024-08-28 09:52:11.648885: Epoch 835 
-2024-08-28 09:52:11.648974: Current learning rate: 0.00615 
-2024-08-28 09:53:38.098911: train_loss -0.7646 
-2024-08-28 09:53:38.099182: val_loss -0.778 
-2024-08-28 09:53:38.099355: Pseudo dice [0.0, 0.0, 0.8784, 0.9779, 0.8269, 0.9298, 0.9431, 0.9613, 0.9479, 0.9494, 0.9302, 0.9577, 0.9555, 0.8301, 0.9455, 0.9285, 0.8096, 0.7968, nan] 
-2024-08-28 09:53:38.099439: Epoch time: 86.45 s 
-2024-08-28 09:53:39.278780:  
-2024-08-28 09:53:39.278983: Epoch 836 
-2024-08-28 09:53:39.279122: Current learning rate: 0.00614 
-2024-08-28 09:55:03.779582: train_loss -0.7605 
-2024-08-28 09:55:03.779795: val_loss -0.7835 
-2024-08-28 09:55:03.780000: Pseudo dice [0.0, 0.0, 0.868, 0.976, 0.8378, 0.9438, 0.9516, 0.9638, 0.9513, 0.9385, 0.9227, 0.9586, 0.9551, 0.8361, 0.9503, 0.9341, 0.8286, 0.81, nan] 
-2024-08-28 09:55:03.780083: Epoch time: 84.5 s 
-2024-08-28 09:55:04.971173:  
-2024-08-28 09:55:04.971724: Epoch 837 
-2024-08-28 09:55:04.971822: Current learning rate: 0.00614 
-2024-08-28 09:56:31.988101: train_loss -0.7586 
-2024-08-28 09:56:31.988341: val_loss -0.7805 
-2024-08-28 09:56:31.988516: Pseudo dice [0.0, 0.0, 0.8839, 0.9737, 0.831, 0.9445, 0.9484, 0.9619, 0.9496, 0.9476, 0.9271, 0.9601, 0.955, 0.8434, 0.9481, 0.9279, 0.8029, 0.7942, nan] 
-2024-08-28 09:56:31.988604: Epoch time: 87.02 s 
-2024-08-28 09:56:33.176245:  
-2024-08-28 09:56:33.176641: Epoch 838 
-2024-08-28 09:56:33.176749: Current learning rate: 0.00613 
-2024-08-28 09:58:01.127261: train_loss -0.76 
-2024-08-28 09:58:01.127517: val_loss -0.7808 
-2024-08-28 09:58:01.127736: Pseudo dice [0.0, 0.0, 0.8855, 0.9776, 0.8423, 0.9468, 0.9512, 0.9653, 0.9481, 0.9411, 0.9295, 0.9545, 0.9528, 0.8467, 0.9489, 0.9258, 0.8226, 0.813, nan] 
-2024-08-28 09:58:01.127857: Epoch time: 87.95 s 
-2024-08-28 09:58:02.739475:  
-2024-08-28 09:58:02.739640: Epoch 839 
-2024-08-28 09:58:02.739726: Current learning rate: 0.00613 
-2024-08-28 09:59:28.328986: train_loss -0.7562 
-2024-08-28 09:59:28.329448: val_loss -0.7806 
-2024-08-28 09:59:28.329638: Pseudo dice [0.0, 0.0, 0.8873, 0.974, 0.8253, 0.9476, 0.9489, 0.9632, 0.9525, 0.9518, 0.919, 0.9603, 0.9549, 0.829, 0.953, 0.9315, 0.7974, 0.7873, nan] 
-2024-08-28 09:59:28.329794: Epoch time: 85.59 s 
-2024-08-28 09:59:29.505854:  
-2024-08-28 09:59:29.506013: Epoch 840 
-2024-08-28 09:59:29.506102: Current learning rate: 0.00612 
-2024-08-28 10:00:56.872325: train_loss -0.7603 
-2024-08-28 10:00:56.872562: val_loss -0.7833 
-2024-08-28 10:00:56.872730: Pseudo dice [0.0, 0.0, 0.8757, 0.9769, 0.8464, 0.9424, 0.9455, 0.9633, 0.9517, 0.9458, 0.9257, 0.9591, 0.9593, 0.843, 0.9474, 0.9364, 0.8305, 0.8358, nan] 
-2024-08-28 10:00:56.872813: Epoch time: 87.37 s 
-2024-08-28 10:00:58.041396:  
-2024-08-28 10:00:58.041552: Epoch 841 
-2024-08-28 10:00:58.041639: Current learning rate: 0.00612 
-2024-08-28 10:02:23.181336: train_loss -0.7622 
-2024-08-28 10:02:23.181570: val_loss -0.7818 
-2024-08-28 10:02:23.181736: Pseudo dice [0.0, 0.0, 0.9015, 0.9756, 0.8374, 0.9429, 0.94, 0.9627, 0.9459, 0.9375, 0.9226, 0.9575, 0.958, 0.8414, 0.9453, 0.9265, 0.8167, 0.8125, nan] 
-2024-08-28 10:02:23.181828: Epoch time: 85.14 s 
-2024-08-28 10:02:24.360609:  
-2024-08-28 10:02:24.360892: Epoch 842 
-2024-08-28 10:02:24.360978: Current learning rate: 0.00612 
-2024-08-28 10:03:53.759624: train_loss -0.7596 
-2024-08-28 10:03:53.759847: val_loss -0.7863 
-2024-08-28 10:03:53.760024: Pseudo dice [0.0, 0.0, 0.8904, 0.9772, 0.8412, 0.9492, 0.9475, 0.9655, 0.9582, 0.952, 0.9331, 0.9638, 0.9603, 0.8374, 0.9564, 0.9227, 0.825, 0.8217, nan] 
-2024-08-28 10:03:53.760112: Epoch time: 89.4 s 
-2024-08-28 10:03:54.952446:  
-2024-08-28 10:03:54.952609: Epoch 843 
-2024-08-28 10:03:54.952698: Current learning rate: 0.00611 
-2024-08-28 10:05:19.047869: train_loss -0.7651 
-2024-08-28 10:05:19.048088: val_loss -0.7851 
-2024-08-28 10:05:19.048252: Pseudo dice [0.0, 0.0, 0.9065, 0.9772, 0.8473, 0.9439, 0.9478, 0.962, 0.9419, 0.9449, 0.9259, 0.9483, 0.9554, 0.8453, 0.9501, 0.9349, 0.8178, 0.8215, nan] 
-2024-08-28 10:05:19.048339: Epoch time: 84.1 s 
-2024-08-28 10:05:20.262815:  
-2024-08-28 10:05:20.263000: Epoch 844 
-2024-08-28 10:05:20.263093: Current learning rate: 0.00611 
-2024-08-28 10:06:47.295994: train_loss -0.7657 
-2024-08-28 10:06:47.296233: val_loss -0.77 
-2024-08-28 10:06:47.296403: Pseudo dice [0.0, 0.0, 0.8839, 0.9754, 0.845, 0.9363, 0.9379, 0.9618, 0.9388, 0.9322, 0.9114, 0.9506, 0.9438, 0.8267, 0.9543, 0.9284, 0.8241, 0.8275, nan] 
-2024-08-28 10:06:47.296501: Epoch time: 87.03 s 
-2024-08-28 10:06:48.470452:  
-2024-08-28 10:06:48.470590: Epoch 845 
-2024-08-28 10:06:48.470679: Current learning rate: 0.0061 
-2024-08-28 10:08:18.037060: train_loss -0.7589 
-2024-08-28 10:08:18.037309: val_loss -0.7787 
-2024-08-28 10:08:18.037492: Pseudo dice [0.0, 0.0, 0.8986, 0.9749, 0.8365, 0.9395, 0.9376, 0.9615, 0.9475, 0.9433, 0.9234, 0.9581, 0.9548, 0.8464, 0.9452, 0.9316, 0.8213, 0.8217, nan] 
-2024-08-28 10:08:18.037579: Epoch time: 89.57 s 
-2024-08-28 10:08:19.498135:  
-2024-08-28 10:08:19.498694: Epoch 846 
-2024-08-28 10:08:19.498795: Current learning rate: 0.0061 
-2024-08-28 10:09:44.235191: train_loss -0.7647 
-2024-08-28 10:09:44.235433: val_loss -0.7783 
-2024-08-28 10:09:44.235601: Pseudo dice [0.0, 0.0, 0.8996, 0.9751, 0.841, 0.9389, 0.9465, 0.9571, 0.9476, 0.9459, 0.9201, 0.956, 0.9564, 0.8212, 0.9449, 0.9277, 0.8289, 0.8322, nan] 
-2024-08-28 10:09:44.235688: Epoch time: 84.74 s 
-2024-08-28 10:09:45.439981:  
-2024-08-28 10:09:45.440220: Epoch 847 
-2024-08-28 10:09:45.440464: Current learning rate: 0.00609 
-2024-08-28 10:11:11.780851: train_loss -0.7632 
-2024-08-28 10:11:11.781121: val_loss -0.7846 
-2024-08-28 10:11:11.781282: Pseudo dice [0.0, 0.0, 0.8914, 0.9762, 0.8382, 0.9466, 0.9503, 0.967, 0.9538, 0.9529, 0.9274, 0.9595, 0.9596, 0.841, 0.9579, 0.9357, 0.833, 0.8239, nan] 
-2024-08-28 10:11:11.781393: Epoch time: 86.34 s 
-2024-08-28 10:11:12.956072:  
-2024-08-28 10:11:12.956202: Epoch 848 
-2024-08-28 10:11:12.956289: Current learning rate: 0.00609 
-2024-08-28 10:12:43.307241: train_loss -0.7605 
-2024-08-28 10:12:43.307513: val_loss -0.78 
-2024-08-28 10:12:43.307738: Pseudo dice [0.0, 0.0, 0.8908, 0.9754, 0.8373, 0.9446, 0.9437, 0.9613, 0.952, 0.9512, 0.9334, 0.9584, 0.9638, 0.838, 0.9378, 0.9254, 0.82, 0.8147, nan] 
-2024-08-28 10:12:43.307850: Epoch time: 90.35 s 
-2024-08-28 10:12:44.511708:  
-2024-08-28 10:12:44.511866: Epoch 849 
-2024-08-28 10:12:44.511950: Current learning rate: 0.00608 
-2024-08-28 10:14:08.282186: train_loss -0.7659 
-2024-08-28 10:14:08.282401: val_loss -0.7808 
-2024-08-28 10:14:08.282562: Pseudo dice [0.0, 0.0, 0.8885, 0.9763, 0.8307, 0.9387, 0.9392, 0.9648, 0.9431, 0.9377, 0.9179, 0.9483, 0.9486, 0.8327, 0.9491, 0.924, 0.8182, 0.8174, nan] 
-2024-08-28 10:14:08.282642: Epoch time: 83.77 s 
-2024-08-28 10:14:09.845012:  
-2024-08-28 10:14:09.845525: Epoch 850 
-2024-08-28 10:14:09.845690: Current learning rate: 0.00608 
-2024-08-28 10:15:34.045560: train_loss -0.7687 
-2024-08-28 10:15:34.046072: val_loss -0.7858 
-2024-08-28 10:15:34.046249: Pseudo dice [0.0, 0.0, 0.8856, 0.9757, 0.8249, 0.9385, 0.9416, 0.9667, 0.9557, 0.9547, 0.925, 0.9634, 0.9604, 0.851, 0.9543, 0.9316, 0.8267, 0.835, nan] 
-2024-08-28 10:15:34.046377: Epoch time: 84.2 s 
-2024-08-28 10:15:35.207546:  
-2024-08-28 10:15:35.207703: Epoch 851 
-2024-08-28 10:15:35.207794: Current learning rate: 0.00607 
-2024-08-28 10:17:03.277691: train_loss -0.7637 
-2024-08-28 10:17:03.277948: val_loss -0.7754 
-2024-08-28 10:17:03.278126: Pseudo dice [0.0, 0.0, 0.8619, 0.977, 0.7759, 0.9506, 0.9532, 0.9591, 0.9466, 0.9501, 0.924, 0.9563, 0.9588, 0.8323, 0.9535, 0.9282, 0.8167, 0.8105, nan] 
-2024-08-28 10:17:03.278217: Epoch time: 88.07 s 
-2024-08-28 10:17:04.722285:  
-2024-08-28 10:17:04.722455: Epoch 852 
-2024-08-28 10:17:04.722539: Current learning rate: 0.00607 
-2024-08-28 10:18:30.157130: train_loss -0.7645 
-2024-08-28 10:18:30.157369: val_loss -0.7844 
-2024-08-28 10:18:30.157537: Pseudo dice [0.0, 0.0, 0.8913, 0.9764, 0.8217, 0.9471, 0.9478, 0.9596, 0.9538, 0.9521, 0.9299, 0.9602, 0.9574, 0.8452, 0.9433, 0.9259, 0.8281, 0.8173, nan] 
-2024-08-28 10:18:30.157622: Epoch time: 85.44 s 
-2024-08-28 10:18:31.353803:  
-2024-08-28 10:18:31.354318: Epoch 853 
-2024-08-28 10:18:31.354436: Current learning rate: 0.00606 
-2024-08-28 10:20:03.562871: train_loss -0.7583 
-2024-08-28 10:20:03.563090: val_loss -0.7814 
-2024-08-28 10:20:03.563254: Pseudo dice [0.0, 0.0, 0.905, 0.9768, 0.8301, 0.9449, 0.9492, 0.9648, 0.9492, 0.9422, 0.93, 0.9578, 0.9628, 0.8437, 0.9529, 0.9297, 0.8173, 0.8217, nan] 
-2024-08-28 10:20:03.563334: Epoch time: 92.21 s 
-2024-08-28 10:20:04.752083:  
-2024-08-28 10:20:04.752263: Epoch 854 
-2024-08-28 10:20:04.752347: Current learning rate: 0.00606 
-2024-08-28 10:21:32.311021: train_loss -0.7602 
-2024-08-28 10:21:32.311268: val_loss -0.7766 
-2024-08-28 10:21:32.311431: Pseudo dice [0.0, 0.0, 0.8858, 0.9761, 0.7816, 0.9394, 0.9381, 0.9627, 0.9435, 0.9322, 0.9232, 0.9514, 0.9488, 0.8359, 0.9469, 0.9145, 0.8064, 0.8224, nan] 
-2024-08-28 10:21:32.311517: Epoch time: 87.56 s 
-2024-08-28 10:21:33.494091:  
-2024-08-28 10:21:33.494268: Epoch 855 
-2024-08-28 10:21:33.494362: Current learning rate: 0.00605 
-2024-08-28 10:23:03.324804: train_loss -0.7551 
-2024-08-28 10:23:03.325240: val_loss -0.7754 
-2024-08-28 10:23:03.325544: Pseudo dice [0.0, 0.0, 0.8813, 0.976, 0.8236, 0.9464, 0.9464, 0.9601, 0.9471, 0.9486, 0.9144, 0.9575, 0.9557, 0.8201, 0.9427, 0.9274, 0.8126, 0.8034, nan] 
-2024-08-28 10:23:03.325681: Epoch time: 89.83 s 
-2024-08-28 10:23:04.501266:  
-2024-08-28 10:23:04.501446: Epoch 856 
-2024-08-28 10:23:04.501549: Current learning rate: 0.00605 
-2024-08-28 10:24:28.312144: train_loss -0.7529 
-2024-08-28 10:24:28.312358: val_loss -0.7781 
-2024-08-28 10:24:28.312544: Pseudo dice [0.0, 0.0, 0.8801, 0.9723, 0.7682, 0.9383, 0.9408, 0.9632, 0.9495, 0.9462, 0.9283, 0.9587, 0.9576, 0.8366, 0.9513, 0.9247, 0.8163, 0.8076, nan] 
-2024-08-28 10:24:28.312646: Epoch time: 83.81 s 
-2024-08-28 10:24:29.427627:  
-2024-08-28 10:24:29.427787: Epoch 857 
-2024-08-28 10:24:29.427880: Current learning rate: 0.00604 
-2024-08-28 10:25:57.093565: train_loss -0.7598 
-2024-08-28 10:25:57.093802: val_loss -0.7682 
-2024-08-28 10:25:57.094045: Pseudo dice [0.0, 0.0, 0.8949, 0.9753, 0.8018, 0.9376, 0.9398, 0.9622, 0.9293, 0.9271, 0.9195, 0.9457, 0.9511, 0.8254, 0.9382, 0.9136, 0.7996, 0.8043, nan] 
-2024-08-28 10:25:57.094194: Epoch time: 87.67 s 
-2024-08-28 10:25:58.549785:  
-2024-08-28 10:25:58.549967: Epoch 858 
-2024-08-28 10:25:58.550060: Current learning rate: 0.00604 
-2024-08-28 10:27:29.218834: train_loss -0.7566 
-2024-08-28 10:27:29.219068: val_loss -0.7895 
-2024-08-28 10:27:29.219226: Pseudo dice [0.0, 0.0, 0.8887, 0.9736, 0.8378, 0.9457, 0.945, 0.9576, 0.9532, 0.9516, 0.9282, 0.9584, 0.9629, 0.8344, 0.9496, 0.9289, 0.8038, 0.8075, nan] 
-2024-08-28 10:27:29.219303: Epoch time: 90.67 s 
-2024-08-28 10:27:30.331428:  
-2024-08-28 10:27:30.331746: Epoch 859 
-2024-08-28 10:27:30.331838: Current learning rate: 0.00603 
-2024-08-28 10:28:53.032935: train_loss -0.7629 
-2024-08-28 10:28:53.033174: val_loss -0.7801 
-2024-08-28 10:28:53.033333: Pseudo dice [0.0, 0.0, 0.8738, 0.9753, 0.8163, 0.9388, 0.9429, 0.9597, 0.9489, 0.9432, 0.9231, 0.9582, 0.9583, 0.8331, 0.955, 0.9217, 0.8055, 0.8261, nan] 
-2024-08-28 10:28:53.033421: Epoch time: 82.7 s 
-2024-08-28 10:28:54.213904:  
-2024-08-28 10:28:54.214087: Epoch 860 
-2024-08-28 10:28:54.214197: Current learning rate: 0.00603 
-2024-08-28 10:30:22.980107: train_loss -0.7526 
-2024-08-28 10:30:22.980350: val_loss -0.7751 
-2024-08-28 10:30:22.980527: Pseudo dice [0.0, 0.0, 0.8925, 0.9749, 0.8035, 0.927, 0.9285, 0.9629, 0.9439, 0.9409, 0.9271, 0.9493, 0.9485, 0.8337, 0.9513, 0.9272, 0.8144, 0.8068, nan] 
-2024-08-28 10:30:22.980619: Epoch time: 88.77 s 
-2024-08-28 10:30:24.327502:  
-2024-08-28 10:30:24.327673: Epoch 861 
-2024-08-28 10:30:24.327759: Current learning rate: 0.00602 
-2024-08-28 10:31:49.991520: train_loss -0.7582 
-2024-08-28 10:31:49.991762: val_loss -0.7829 
-2024-08-28 10:31:49.991937: Pseudo dice [0.0, 0.0, 0.8861, 0.9774, 0.817, 0.949, 0.9516, 0.9612, 0.9443, 0.9484, 0.9321, 0.9582, 0.9596, 0.8367, 0.953, 0.9303, 0.8134, 0.8105, nan] 
-2024-08-28 10:31:49.992027: Epoch time: 85.66 s 
-2024-08-28 10:31:51.524827:  
-2024-08-28 10:31:51.525130: Epoch 862 
-2024-08-28 10:31:51.525228: Current learning rate: 0.00602 
-2024-08-28 10:33:15.199424: train_loss -0.7568 
-2024-08-28 10:33:15.199658: val_loss -0.7738 
-2024-08-28 10:33:15.199828: Pseudo dice [0.0, 0.0, 0.8924, 0.9704, 0.8143, 0.9406, 0.9421, 0.9584, 0.946, 0.9399, 0.9247, 0.9562, 0.9457, 0.8259, 0.949, 0.926, 0.8001, 0.7969, nan] 
-2024-08-28 10:33:15.199916: Epoch time: 83.68 s 
-2024-08-28 10:33:16.437289:  
-2024-08-28 10:33:16.437613: Epoch 863 
-2024-08-28 10:33:16.437705: Current learning rate: 0.00602 
-2024-08-28 10:34:48.262115: train_loss -0.7527 
-2024-08-28 10:34:48.262342: val_loss -0.7799 
-2024-08-28 10:34:48.262495: Pseudo dice [0.0, 0.0, 0.8833, 0.9757, 0.8465, 0.9427, 0.9443, 0.9612, 0.9442, 0.9434, 0.9278, 0.9563, 0.9593, 0.8324, 0.9546, 0.9291, 0.8209, 0.8051, nan] 
-2024-08-28 10:34:48.262572: Epoch time: 91.83 s 
-2024-08-28 10:34:49.702158:  
-2024-08-28 10:34:49.702365: Epoch 864 
-2024-08-28 10:34:49.702461: Current learning rate: 0.00601 
-2024-08-28 10:36:17.628208: train_loss -0.7562 
-2024-08-28 10:36:17.628450: val_loss -0.7601 
-2024-08-28 10:36:17.628605: Pseudo dice [0.0, 0.0, 0.8911, 0.97, 0.8009, 0.9269, 0.9273, 0.9508, 0.9251, 0.9288, 0.9128, 0.9278, 0.9379, 0.8204, 0.9338, 0.916, 0.7977, 0.7911, nan] 
-2024-08-28 10:36:17.628684: Epoch time: 87.93 s 
-2024-08-28 10:36:18.814264:  
-2024-08-28 10:36:18.814676: Epoch 865 
-2024-08-28 10:36:18.814774: Current learning rate: 0.00601 
-2024-08-28 10:37:48.730458: train_loss -0.7482 
-2024-08-28 10:37:48.730694: val_loss -0.7764 
-2024-08-28 10:37:48.730869: Pseudo dice [0.0, 0.0, 0.8661, 0.9734, 0.7993, 0.9337, 0.9389, 0.96, 0.9493, 0.9436, 0.9261, 0.9562, 0.9523, 0.8257, 0.9491, 0.9198, 0.8192, 0.8221, nan] 
-2024-08-28 10:37:48.730977: Epoch time: 89.92 s 
-2024-08-28 10:37:49.936407:  
-2024-08-28 10:37:49.936579: Epoch 866 
-2024-08-28 10:37:49.936663: Current learning rate: 0.006 
-2024-08-28 10:39:20.081199: train_loss -0.7518 
-2024-08-28 10:39:20.081435: val_loss -0.7726 
-2024-08-28 10:39:20.081595: Pseudo dice [0.0, 0.0, 0.8873, 0.9738, 0.7925, 0.9384, 0.9433, 0.9539, 0.9477, 0.943, 0.9198, 0.9525, 0.9566, 0.8257, 0.9384, 0.9197, 0.8176, 0.8001, nan] 
-2024-08-28 10:39:20.081680: Epoch time: 90.15 s 
-2024-08-28 10:39:21.288729:  
-2024-08-28 10:39:21.288959: Epoch 867 
-2024-08-28 10:39:21.289065: Current learning rate: 0.006 
-2024-08-28 10:40:48.595497: train_loss -0.7512 
-2024-08-28 10:40:48.595771: val_loss -0.7672 
-2024-08-28 10:40:48.595985: Pseudo dice [0.0, 0.0, 0.8632, 0.9764, 0.7296, 0.9435, 0.9411, 0.9615, 0.9476, 0.9374, 0.9167, 0.9566, 0.954, 0.8184, 0.9428, 0.9017, 0.8096, 0.7922, nan] 
-2024-08-28 10:40:48.596094: Epoch time: 87.31 s 
-2024-08-28 10:40:49.830079:  
-2024-08-28 10:40:49.830425: Epoch 868 
-2024-08-28 10:40:49.830525: Current learning rate: 0.00599 
-2024-08-28 10:42:19.017275: train_loss -0.7527 
-2024-08-28 10:42:19.017556: val_loss -0.7764 
-2024-08-28 10:42:19.017733: Pseudo dice [0.0, 0.0, 0.889, 0.9776, 0.7918, 0.9458, 0.9388, 0.958, 0.9436, 0.9508, 0.917, 0.9558, 0.9541, 0.8215, 0.9505, 0.9253, 0.814, 0.8009, nan] 
-2024-08-28 10:42:19.017872: Epoch time: 89.19 s 
-2024-08-28 10:42:20.227955:  
-2024-08-28 10:42:20.228279: Epoch 869 
-2024-08-28 10:42:20.228384: Current learning rate: 0.00599 
-2024-08-28 10:43:47.161836: train_loss -0.7537 
-2024-08-28 10:43:47.162152: val_loss -0.7742 
-2024-08-28 10:43:47.162409: Pseudo dice [0.0, 0.0, 0.8931, 0.9766, 0.7754, 0.9386, 0.9479, 0.9586, 0.9388, 0.949, 0.9174, 0.95, 0.9498, 0.8277, 0.9454, 0.9228, 0.8257, 0.8076, nan] 
-2024-08-28 10:43:47.162528: Epoch time: 86.93 s 
-2024-08-28 10:43:49.118051:  
-2024-08-28 10:43:49.118648: Epoch 870 
-2024-08-28 10:43:49.118941: Current learning rate: 0.00598 
-2024-08-28 10:45:19.350025: train_loss -0.7559 
-2024-08-28 10:45:19.350265: val_loss -0.7807 
-2024-08-28 10:45:19.350437: Pseudo dice [0.0, 0.0, 0.8849, 0.9757, 0.8322, 0.9404, 0.9391, 0.9613, 0.9449, 0.9492, 0.9326, 0.9583, 0.9596, 0.8287, 0.947, 0.9235, 0.8043, 0.7915, nan] 
-2024-08-28 10:45:19.350526: Epoch time: 90.23 s 
-2024-08-28 10:45:20.564009:  
-2024-08-28 10:45:20.564174: Epoch 871 
-2024-08-28 10:45:20.564274: Current learning rate: 0.00598 
-2024-08-28 10:46:47.719764: train_loss -0.7601 
-2024-08-28 10:46:47.719989: val_loss -0.7745 
-2024-08-28 10:46:47.720148: Pseudo dice [0.0, 0.0, 0.8718, 0.9762, 0.7758, 0.9247, 0.9301, 0.9601, 0.9275, 0.9259, 0.9094, 0.9387, 0.9397, 0.8295, 0.9502, 0.923, 0.8109, 0.7924, nan] 
-2024-08-28 10:46:47.720233: Epoch time: 87.16 s 
-2024-08-28 10:46:48.910922:  
-2024-08-28 10:46:48.911077: Epoch 872 
-2024-08-28 10:46:48.911166: Current learning rate: 0.00597 
-2024-08-28 10:48:14.705128: train_loss -0.757 
-2024-08-28 10:48:14.705344: val_loss -0.7768 
-2024-08-28 10:48:14.705505: Pseudo dice [0.0, 0.0, 0.8709, 0.976, 0.7547, 0.9386, 0.9412, 0.9624, 0.9419, 0.943, 0.9288, 0.9456, 0.9504, 0.8303, 0.9456, 0.9289, 0.8195, 0.8106, nan] 
-2024-08-28 10:48:14.705589: Epoch time: 85.79 s 
-2024-08-28 10:48:15.885424:  
-2024-08-28 10:48:15.885597: Epoch 873 
-2024-08-28 10:48:15.885695: Current learning rate: 0.00597 
-2024-08-28 10:49:48.655393: train_loss -0.7563 
-2024-08-28 10:49:48.655621: val_loss -0.7837 
-2024-08-28 10:49:48.655784: Pseudo dice [0.0, 0.0, 0.8854, 0.9748, 0.8288, 0.9476, 0.9452, 0.9589, 0.9496, 0.9513, 0.9254, 0.9591, 0.9583, 0.8365, 0.9536, 0.9245, 0.8097, 0.8165, nan] 
-2024-08-28 10:49:48.655869: Epoch time: 92.77 s 
-2024-08-28 10:49:49.855295:  
-2024-08-28 10:49:49.855459: Epoch 874 
-2024-08-28 10:49:49.855554: Current learning rate: 0.00596 
-2024-08-28 10:51:17.816385: train_loss -0.7611 
-2024-08-28 10:51:17.816663: val_loss -0.7776 
-2024-08-28 10:51:17.816826: Pseudo dice [0.0, 0.0, 0.8727, 0.9768, 0.8189, 0.9396, 0.9434, 0.9609, 0.9391, 0.9351, 0.922, 0.9474, 0.95, 0.8329, 0.9396, 0.9311, 0.8259, 0.8238, nan] 
-2024-08-28 10:51:17.816931: Epoch time: 87.96 s 
-2024-08-28 10:51:19.028633:  
-2024-08-28 10:51:19.028814: Epoch 875 
-2024-08-28 10:51:19.028918: Current learning rate: 0.00596 
-2024-08-28 10:52:48.655130: train_loss -0.7633 
-2024-08-28 10:52:48.655386: val_loss -0.7809 
-2024-08-28 10:52:48.655555: Pseudo dice [0.0, 0.0, 0.889, 0.975, 0.8211, 0.9471, 0.9473, 0.9607, 0.9424, 0.9513, 0.9287, 0.9556, 0.9558, 0.8287, 0.9496, 0.931, 0.83, 0.8256, nan] 
-2024-08-28 10:52:48.655648: Epoch time: 89.63 s 
-2024-08-28 10:52:50.097913:  
-2024-08-28 10:52:50.098195: Epoch 876 
-2024-08-28 10:52:50.098293: Current learning rate: 0.00595 
-2024-08-28 10:54:16.451641: train_loss -0.7607 
-2024-08-28 10:54:16.451893: val_loss -0.7776 
-2024-08-28 10:54:16.452054: Pseudo dice [0.0, 0.0, 0.8897, 0.9743, 0.7689, 0.9474, 0.9523, 0.9616, 0.9559, 0.9557, 0.934, 0.9614, 0.959, 0.8296, 0.9444, 0.9193, 0.8015, 0.7919, nan] 
-2024-08-28 10:54:16.452140: Epoch time: 86.35 s 
-2024-08-28 10:54:17.865401:  
-2024-08-28 10:54:17.865580: Epoch 877 
-2024-08-28 10:54:17.865681: Current learning rate: 0.00595 
-2024-08-28 10:55:42.717193: train_loss -0.7601 
-2024-08-28 10:55:42.717861: val_loss -0.7732 
-2024-08-28 10:55:42.718090: Pseudo dice [0.0, 0.0, 0.8908, 0.976, 0.7966, 0.936, 0.9393, 0.9626, 0.9409, 0.9404, 0.9207, 0.9479, 0.9477, 0.8205, 0.9438, 0.9253, 0.8104, 0.8133, nan] 
-2024-08-28 10:55:42.718199: Epoch time: 84.85 s 
-2024-08-28 10:55:43.990734:  
-2024-08-28 10:55:43.991223: Epoch 878 
-2024-08-28 10:55:43.991323: Current learning rate: 0.00594 
-2024-08-28 10:57:12.288068: train_loss -0.7546 
-2024-08-28 10:57:12.288531: val_loss -0.7821 
-2024-08-28 10:57:12.288718: Pseudo dice [0.0, 0.0, 0.8916, 0.9749, 0.7778, 0.9432, 0.9451, 0.9626, 0.9486, 0.94, 0.9275, 0.9574, 0.9565, 0.8327, 0.9479, 0.9304, 0.8144, 0.8083, nan] 
-2024-08-28 10:57:12.288810: Epoch time: 88.3 s 
-2024-08-28 10:57:13.483194:  
-2024-08-28 10:57:13.483378: Epoch 879 
-2024-08-28 10:57:13.483476: Current learning rate: 0.00594 
-2024-08-28 10:58:44.933666: train_loss -0.7594 
-2024-08-28 10:58:44.933913: val_loss -0.7783 
-2024-08-28 10:58:44.934070: Pseudo dice [0.0, 0.0, 0.8899, 0.9754, 0.8077, 0.9396, 0.9388, 0.9632, 0.9323, 0.9361, 0.9208, 0.9433, 0.9476, 0.8382, 0.9531, 0.9232, 0.8205, 0.8093, nan] 
-2024-08-28 10:58:44.934163: Epoch time: 91.45 s 
-2024-08-28 10:58:46.159070:  
-2024-08-28 10:58:46.159405: Epoch 880 
-2024-08-28 10:58:46.159588: Current learning rate: 0.00593 
-2024-08-28 11:00:09.279002: train_loss -0.7528 
-2024-08-28 11:00:09.279268: val_loss -0.77 
-2024-08-28 11:00:09.279478: Pseudo dice [0.0, 0.0, 0.8824, 0.9753, 0.7649, 0.944, 0.942, 0.961, 0.9406, 0.9378, 0.9226, 0.9533, 0.9575, 0.8295, 0.9407, 0.9247, 0.8047, 0.8002, nan] 
-2024-08-28 11:00:09.279629: Epoch time: 83.12 s 
-2024-08-28 11:00:10.570374:  
-2024-08-28 11:00:10.570558: Epoch 881 
-2024-08-28 11:00:10.570667: Current learning rate: 0.00593 
-2024-08-28 11:01:39.048276: train_loss -0.7585 
-2024-08-28 11:01:39.048523: val_loss -0.7769 
-2024-08-28 11:01:39.048698: Pseudo dice [0.0, 0.0, 0.8878, 0.9709, 0.8076, 0.9341, 0.9378, 0.9619, 0.9422, 0.94, 0.916, 0.9474, 0.9491, 0.832, 0.943, 0.9307, 0.8318, 0.8356, nan] 
-2024-08-28 11:01:39.048789: Epoch time: 88.48 s 
-2024-08-28 11:01:40.389327:  
-2024-08-28 11:01:40.389631: Epoch 882 
-2024-08-28 11:01:40.389752: Current learning rate: 0.00592 
-2024-08-28 11:03:10.698843: train_loss -0.7564 
-2024-08-28 11:03:10.699367: val_loss -0.7791 
-2024-08-28 11:03:10.699564: Pseudo dice [0.0, 0.0, 0.8649, 0.9766, 0.8196, 0.9445, 0.9477, 0.9633, 0.9473, 0.9402, 0.9112, 0.9563, 0.955, 0.8407, 0.9549, 0.9268, 0.821, 0.8193, nan] 
-2024-08-28 11:03:10.699700: Epoch time: 90.31 s 
-2024-08-28 11:03:12.297861:  
-2024-08-28 11:03:12.298162: Epoch 883 
-2024-08-28 11:03:12.298252: Current learning rate: 0.00592 
-2024-08-28 11:04:40.173698: train_loss -0.7607 
-2024-08-28 11:04:40.173944: val_loss -0.7647 
-2024-08-28 11:04:40.174111: Pseudo dice [0.0, 0.0, 0.8805, 0.9771, 0.711, 0.9324, 0.9389, 0.9555, 0.9473, 0.9464, 0.9196, 0.955, 0.9558, 0.8005, 0.9454, 0.9177, 0.8126, 0.8104, nan] 
-2024-08-28 11:04:40.174210: Epoch time: 87.88 s 
-2024-08-28 11:04:41.365496:  
-2024-08-28 11:04:41.365663: Epoch 884 
-2024-08-28 11:04:41.365748: Current learning rate: 0.00592 
-2024-08-28 11:06:05.995636: train_loss -0.7605 
-2024-08-28 11:06:05.995920: val_loss -0.784 
-2024-08-28 11:06:05.996130: Pseudo dice [0.0, 0.0, 0.8884, 0.9737, 0.8194, 0.9479, 0.9481, 0.963, 0.9477, 0.9445, 0.9303, 0.9591, 0.9574, 0.8436, 0.9556, 0.9266, 0.8121, 0.8073, nan] 
-2024-08-28 11:06:05.996238: Epoch time: 84.63 s 
-2024-08-28 11:06:07.284040:  
-2024-08-28 11:06:07.284369: Epoch 885 
-2024-08-28 11:06:07.284508: Current learning rate: 0.00591 
-2024-08-28 11:07:36.181817: train_loss -0.7622 
-2024-08-28 11:07:36.182062: val_loss -0.7812 
-2024-08-28 11:07:36.182243: Pseudo dice [0.0, 0.0, 0.881, 0.976, 0.8188, 0.9462, 0.9456, 0.9636, 0.9476, 0.9501, 0.9199, 0.9576, 0.958, 0.8438, 0.9535, 0.9281, 0.812, 0.8112, nan] 
-2024-08-28 11:07:36.182327: Epoch time: 88.9 s 
-2024-08-28 11:07:37.397082:  
-2024-08-28 11:07:37.397237: Epoch 886 
-2024-08-28 11:07:37.397319: Current learning rate: 0.00591 
-2024-08-28 11:09:03.683644: train_loss -0.7619 
-2024-08-28 11:09:03.683876: val_loss -0.7815 
-2024-08-28 11:09:03.684045: Pseudo dice [0.0, 0.0, 0.8889, 0.9754, 0.849, 0.9466, 0.9432, 0.9631, 0.9456, 0.9469, 0.9299, 0.9553, 0.959, 0.8432, 0.942, 0.93, 0.8181, 0.8131, nan] 
-2024-08-28 11:09:03.684135: Epoch time: 86.29 s 
-2024-08-28 11:09:04.876498:  
-2024-08-28 11:09:04.876657: Epoch 887 
-2024-08-28 11:09:04.876757: Current learning rate: 0.0059 
-2024-08-28 11:10:34.254535: train_loss -0.7595 
-2024-08-28 11:10:34.255247: val_loss -0.787 
-2024-08-28 11:10:34.255759: Pseudo dice [0.0, 0.0, 0.884, 0.9757, 0.8049, 0.9397, 0.944, 0.9645, 0.9473, 0.9449, 0.9316, 0.9568, 0.9597, 0.8297, 0.9547, 0.9283, 0.8322, 0.8292, nan] 
-2024-08-28 11:10:34.255931: Epoch time: 89.38 s 
-2024-08-28 11:10:35.611284:  
-2024-08-28 11:10:35.611760: Epoch 888 
-2024-08-28 11:10:35.612053: Current learning rate: 0.0059 
-2024-08-28 11:12:05.878771: train_loss -0.7605 
-2024-08-28 11:12:05.879017: val_loss -0.7754 
-2024-08-28 11:12:05.879223: Pseudo dice [0.0, 0.0, 0.887, 0.9758, 0.8163, 0.9438, 0.9468, 0.9605, 0.9467, 0.9464, 0.9276, 0.9559, 0.9592, 0.8379, 0.9485, 0.9243, 0.8144, 0.8157, nan] 
-2024-08-28 11:12:05.879313: Epoch time: 90.27 s 
-2024-08-28 11:12:07.417496:  
-2024-08-28 11:12:07.417694: Epoch 889 
-2024-08-28 11:12:07.417792: Current learning rate: 0.00589 
-2024-08-28 11:13:31.375918: train_loss -0.7597 
-2024-08-28 11:13:31.376177: val_loss -0.7807 
-2024-08-28 11:13:31.376335: Pseudo dice [0.0, 0.0, 0.8837, 0.9772, 0.813, 0.9415, 0.9469, 0.9607, 0.9484, 0.9366, 0.9238, 0.9593, 0.96, 0.8335, 0.9418, 0.9265, 0.8109, 0.8252, nan] 
-2024-08-28 11:13:31.376422: Epoch time: 83.96 s 
-2024-08-28 11:13:32.584015:  
-2024-08-28 11:13:32.584355: Epoch 890 
-2024-08-28 11:13:32.584457: Current learning rate: 0.00589 
-2024-08-28 11:14:54.039172: train_loss -0.7584 
-2024-08-28 11:14:54.039426: val_loss -0.787 
-2024-08-28 11:14:54.039590: Pseudo dice [0.0, 0.0, 0.8611, 0.9756, 0.8282, 0.9494, 0.9514, 0.9639, 0.9526, 0.9511, 0.9301, 0.9622, 0.9616, 0.8399, 0.9557, 0.9267, 0.825, 0.8125, nan] 
-2024-08-28 11:14:54.039681: Epoch time: 81.46 s 
-2024-08-28 11:14:55.230731:  
-2024-08-28 11:14:55.230975: Epoch 891 
-2024-08-28 11:14:55.231074: Current learning rate: 0.00588 
-2024-08-28 11:16:26.416924: train_loss -0.7556 
-2024-08-28 11:16:26.417186: val_loss -0.7821 
-2024-08-28 11:16:26.417352: Pseudo dice [0.0, 0.0, 0.8839, 0.9761, 0.8036, 0.9488, 0.9509, 0.9593, 0.9484, 0.9499, 0.9267, 0.9586, 0.9597, 0.8344, 0.9382, 0.9318, 0.825, 0.809, nan] 
-2024-08-28 11:16:26.417441: Epoch time: 91.19 s 
-2024-08-28 11:16:27.612583:  
-2024-08-28 11:16:27.612740: Epoch 892 
-2024-08-28 11:16:27.612830: Current learning rate: 0.00588 
-2024-08-28 11:17:58.816150: train_loss -0.7572 
-2024-08-28 11:17:58.816418: val_loss -0.7786 
-2024-08-28 11:17:58.816595: Pseudo dice [0.0, 0.0, 0.8858, 0.9771, 0.8326, 0.943, 0.9435, 0.9555, 0.9486, 0.9422, 0.9296, 0.9578, 0.961, 0.84, 0.9404, 0.9294, 0.8205, 0.8093, nan] 
-2024-08-28 11:17:58.816703: Epoch time: 91.2 s 
-2024-08-28 11:17:59.989239:  
-2024-08-28 11:17:59.989386: Epoch 893 
-2024-08-28 11:17:59.989477: Current learning rate: 0.00587 
-2024-08-28 11:19:30.048061: train_loss -0.7592 
-2024-08-28 11:19:30.048313: val_loss -0.7806 
-2024-08-28 11:19:30.048486: Pseudo dice [0.0, 0.0, 0.8878, 0.9744, 0.801, 0.9418, 0.9449, 0.962, 0.9503, 0.9423, 0.9196, 0.9608, 0.958, 0.8322, 0.9533, 0.9152, 0.8177, 0.828, nan] 
-2024-08-28 11:19:30.048574: Epoch time: 90.06 s 
-2024-08-28 11:19:31.242973:  
-2024-08-28 11:19:31.243278: Epoch 894 
-2024-08-28 11:19:31.243374: Current learning rate: 0.00587 
-2024-08-28 11:20:55.934539: train_loss -0.7546 
-2024-08-28 11:20:55.935060: val_loss -0.7742 
-2024-08-28 11:20:55.935265: Pseudo dice [0.0, 0.0, 0.8951, 0.976, 0.8092, 0.9419, 0.9463, 0.9586, 0.9478, 0.9524, 0.9225, 0.9561, 0.9544, 0.8288, 0.9359, 0.9237, 0.8245, 0.8281, nan] 
-2024-08-28 11:20:55.935423: Epoch time: 84.69 s 
-2024-08-28 11:20:57.488478:  
-2024-08-28 11:20:57.488627: Epoch 895 
-2024-08-28 11:20:57.488719: Current learning rate: 0.00586 
-2024-08-28 11:22:26.244339: train_loss -0.7562 
-2024-08-28 11:22:26.244694: val_loss -0.7848 
-2024-08-28 11:22:26.244869: Pseudo dice [0.0, 0.0, 0.8789, 0.975, 0.7979, 0.9326, 0.9388, 0.9615, 0.9521, 0.9518, 0.9327, 0.9585, 0.9599, 0.8364, 0.9488, 0.9244, 0.8415, 0.8295, nan] 
-2024-08-28 11:22:26.244986: Epoch time: 88.76 s 
-2024-08-28 11:22:27.826676:  
-2024-08-28 11:22:27.827347: Epoch 896 
-2024-08-28 11:22:27.827458: Current learning rate: 0.00586 
-2024-08-28 11:23:52.907674: train_loss -0.7576 
-2024-08-28 11:23:52.907964: val_loss -0.779 
-2024-08-28 11:23:52.908125: Pseudo dice [0.0, 0.0, 0.87, 0.9751, 0.8201, 0.9469, 0.9446, 0.9628, 0.944, 0.9455, 0.927, 0.9558, 0.9552, 0.8348, 0.9481, 0.9178, 0.8132, 0.8162, nan] 
-2024-08-28 11:23:52.908211: Epoch time: 85.08 s 
-2024-08-28 11:23:54.079508:  
-2024-08-28 11:23:54.079841: Epoch 897 
-2024-08-28 11:23:54.079937: Current learning rate: 0.00585 
-2024-08-28 11:25:20.885560: train_loss -0.7609 
-2024-08-28 11:25:20.885810: val_loss -0.7859 
-2024-08-28 11:25:20.885987: Pseudo dice [0.0, 0.0, 0.8791, 0.9769, 0.8393, 0.9486, 0.9466, 0.9646, 0.9496, 0.9436, 0.9278, 0.9608, 0.9594, 0.8413, 0.9536, 0.9324, 0.8192, 0.8231, nan] 
-2024-08-28 11:25:20.886072: Epoch time: 86.81 s 
-2024-08-28 11:25:22.080832:  
-2024-08-28 11:25:22.081202: Epoch 898 
-2024-08-28 11:25:22.081305: Current learning rate: 0.00585 
-2024-08-28 11:26:47.828535: train_loss -0.7646 
-2024-08-28 11:26:47.828798: val_loss -0.7814 
-2024-08-28 11:26:47.828982: Pseudo dice [0.0, 0.0, 0.8939, 0.9769, 0.8467, 0.9482, 0.949, 0.9665, 0.9449, 0.9506, 0.9325, 0.9593, 0.9591, 0.843, 0.9412, 0.938, 0.8188, 0.8102, nan] 
-2024-08-28 11:26:47.829073: Epoch time: 85.75 s 
-2024-08-28 11:26:49.033503:  
-2024-08-28 11:26:49.033675: Epoch 899 
-2024-08-28 11:26:49.033775: Current learning rate: 0.00584 
-2024-08-28 11:28:14.910015: train_loss -0.7651 
-2024-08-28 11:28:14.910592: val_loss -0.7804 
-2024-08-28 11:28:14.910794: Pseudo dice [0.0, 0.0, 0.9008, 0.9739, 0.822, 0.9395, 0.946, 0.9497, 0.9522, 0.9471, 0.9247, 0.96, 0.9562, 0.8194, 0.9475, 0.9199, 0.8293, 0.8275, nan] 
-2024-08-28 11:28:14.910938: Epoch time: 85.88 s 
-2024-08-28 11:28:16.496000:  
-2024-08-28 11:28:16.496404: Epoch 900 
-2024-08-28 11:28:16.496588: Current learning rate: 0.00584 
-2024-08-28 11:29:45.027636: train_loss -0.7599 
-2024-08-28 11:29:45.027904: val_loss -0.785 
-2024-08-28 11:29:45.028070: Pseudo dice [0.0, 0.0, 0.8906, 0.9755, 0.8233, 0.9448, 0.9462, 0.9601, 0.9541, 0.9431, 0.9309, 0.9619, 0.9611, 0.8361, 0.9514, 0.9236, 0.8414, 0.8146, nan] 
-2024-08-28 11:29:45.028158: Epoch time: 88.53 s 
-2024-08-28 11:29:46.552849:  
-2024-08-28 11:29:46.553158: Epoch 901 
-2024-08-28 11:29:46.553250: Current learning rate: 0.00583 
-2024-08-28 11:31:11.851964: train_loss -0.7593 
-2024-08-28 11:31:11.852188: val_loss -0.7789 
-2024-08-28 11:31:11.852340: Pseudo dice [0.0, 0.0, 0.8851, 0.9761, 0.8322, 0.9448, 0.9472, 0.9656, 0.9451, 0.9395, 0.9197, 0.9551, 0.9536, 0.8298, 0.9505, 0.9252, 0.8233, 0.7939, nan] 
-2024-08-28 11:31:11.852444: Epoch time: 85.3 s 
-2024-08-28 11:31:13.213943:  
-2024-08-28 11:31:13.214283: Epoch 902 
-2024-08-28 11:31:13.214390: Current learning rate: 0.00583 
-2024-08-28 11:32:39.271234: train_loss -0.7603 
-2024-08-28 11:32:39.271603: val_loss -0.7828 
-2024-08-28 11:32:39.271884: Pseudo dice [0.0, 0.0, 0.8996, 0.9764, 0.6731, 0.9472, 0.9469, 0.9659, 0.9536, 0.9514, 0.9325, 0.9621, 0.9605, 0.8441, 0.9528, 0.9288, 0.8211, 0.8174, nan] 
-2024-08-28 11:32:39.271976: Epoch time: 86.06 s 
-2024-08-28 11:32:40.435164:  
-2024-08-28 11:32:40.435344: Epoch 903 
-2024-08-28 11:32:40.435429: Current learning rate: 0.00582 
-2024-08-28 11:34:10.034869: train_loss -0.7645 
-2024-08-28 11:34:10.035088: val_loss -0.784 
-2024-08-28 11:34:10.035250: Pseudo dice [0.0, 0.0, 0.8627, 0.9752, 0.798, 0.9446, 0.9436, 0.9616, 0.9487, 0.9428, 0.9233, 0.9592, 0.9538, 0.8394, 0.9487, 0.9283, 0.8209, 0.8235, nan] 
-2024-08-28 11:34:10.035333: Epoch time: 89.6 s 
-2024-08-28 11:34:11.180063:  
-2024-08-28 11:34:11.180283: Epoch 904 
-2024-08-28 11:34:11.180386: Current learning rate: 0.00582 
-2024-08-28 11:35:36.240608: train_loss -0.7598 
-2024-08-28 11:35:36.240852: val_loss -0.7762 
-2024-08-28 11:35:36.241002: Pseudo dice [0.0, 0.0, 0.8808, 0.9754, 0.8216, 0.9413, 0.9449, 0.9591, 0.9508, 0.9522, 0.9295, 0.9587, 0.9568, 0.8296, 0.9504, 0.9248, 0.7922, 0.8058, nan] 
-2024-08-28 11:35:36.241080: Epoch time: 85.06 s 
-2024-08-28 11:35:37.464354:  
-2024-08-28 11:35:37.464646: Epoch 905 
-2024-08-28 11:35:37.464740: Current learning rate: 0.00581 
-2024-08-28 11:37:00.552493: train_loss -0.7567 
-2024-08-28 11:37:00.552722: val_loss -0.778 
-2024-08-28 11:37:00.552890: Pseudo dice [0.0, 0.0, 0.8873, 0.9755, 0.8166, 0.9423, 0.945, 0.9626, 0.9475, 0.934, 0.93, 0.9569, 0.957, 0.8301, 0.953, 0.9283, 0.8278, 0.8289, nan] 
-2024-08-28 11:37:00.552977: Epoch time: 83.09 s 
-2024-08-28 11:37:01.689079:  
-2024-08-28 11:37:01.689254: Epoch 906 
-2024-08-28 11:37:01.689357: Current learning rate: 0.00581 
-2024-08-28 11:38:29.813473: train_loss -0.7554 
-2024-08-28 11:38:29.813815: val_loss -0.7782 
-2024-08-28 11:38:29.814073: Pseudo dice [0.0, 0.0, 0.8603, 0.9768, 0.8274, 0.9448, 0.9469, 0.9649, 0.9499, 0.9465, 0.9213, 0.9597, 0.9532, 0.8336, 0.9408, 0.9251, 0.8067, 0.8116, nan] 
-2024-08-28 11:38:29.814288: Epoch time: 88.13 s 
-2024-08-28 11:38:31.226712:  
-2024-08-28 11:38:31.227065: Epoch 907 
-2024-08-28 11:38:31.227159: Current learning rate: 0.00581 
-2024-08-28 11:40:00.050760: train_loss -0.7596 
-2024-08-28 11:40:00.051000: val_loss -0.7828 
-2024-08-28 11:40:00.051161: Pseudo dice [0.0, 0.0, 0.8825, 0.9757, 0.8188, 0.9464, 0.9471, 0.964, 0.9523, 0.9567, 0.9373, 0.9603, 0.9627, 0.8315, 0.9496, 0.9306, 0.8201, 0.8214, nan] 
-2024-08-28 11:40:00.051248: Epoch time: 88.82 s 
-2024-08-28 11:40:01.245886:  
-2024-08-28 11:40:01.246054: Epoch 908 
-2024-08-28 11:40:01.246143: Current learning rate: 0.0058 
-2024-08-28 11:41:22.213158: train_loss -0.7603 
-2024-08-28 11:41:22.213394: val_loss -0.7824 
-2024-08-28 11:41:22.213554: Pseudo dice [0.0, 0.0, 0.8943, 0.9757, 0.8362, 0.9466, 0.9506, 0.957, 0.9521, 0.9514, 0.9262, 0.9583, 0.9581, 0.8376, 0.9442, 0.9272, 0.8214, 0.8067, nan] 
-2024-08-28 11:41:22.213639: Epoch time: 80.97 s 
-2024-08-28 11:41:23.775522:  
-2024-08-28 11:41:23.775720: Epoch 909 
-2024-08-28 11:41:23.775833: Current learning rate: 0.0058 
-2024-08-28 11:42:48.793561: train_loss -0.7591 
-2024-08-28 11:42:48.793797: val_loss -0.7683 
-2024-08-28 11:42:48.793967: Pseudo dice [0.0, 0.0, 0.89, 0.969, 0.6974, 0.9391, 0.9403, 0.9481, 0.9511, 0.9465, 0.9236, 0.9583, 0.9584, 0.8151, 0.9475, 0.9197, 0.8151, 0.813, nan] 
-2024-08-28 11:42:48.794092: Epoch time: 85.02 s 
-2024-08-28 11:42:49.988502:  
-2024-08-28 11:42:49.988680: Epoch 910 
-2024-08-28 11:42:49.988780: Current learning rate: 0.00579 
-2024-08-28 11:44:18.466516: train_loss -0.7565 
-2024-08-28 11:44:18.466732: val_loss -0.7839 
-2024-08-28 11:44:18.466903: Pseudo dice [0.0, 0.0, 0.8964, 0.9762, 0.8154, 0.9491, 0.9497, 0.9603, 0.9494, 0.948, 0.9313, 0.9593, 0.9589, 0.8427, 0.9546, 0.9287, 0.8145, 0.8249, nan] 
-2024-08-28 11:44:18.467004: Epoch time: 88.48 s 
-2024-08-28 11:44:19.692446:  
-2024-08-28 11:44:19.692630: Epoch 911 
-2024-08-28 11:44:19.692741: Current learning rate: 0.00579 
-2024-08-28 11:45:42.829695: train_loss -0.7633 
-2024-08-28 11:45:42.829969: val_loss -0.7797 
-2024-08-28 11:45:42.830148: Pseudo dice [0.0, 0.0, 0.8914, 0.9768, 0.8207, 0.9473, 0.9487, 0.9639, 0.947, 0.947, 0.9271, 0.9534, 0.96, 0.8381, 0.951, 0.9309, 0.8261, 0.8157, nan] 
-2024-08-28 11:45:42.830245: Epoch time: 83.14 s 
-2024-08-28 11:45:44.064248:  
-2024-08-28 11:45:44.064755: Epoch 912 
-2024-08-28 11:45:44.064857: Current learning rate: 0.00578 
-2024-08-28 11:47:12.976182: train_loss -0.7628 
-2024-08-28 11:47:12.976460: val_loss -0.7829 
-2024-08-28 11:47:12.976627: Pseudo dice [0.0, 0.0, 0.8766, 0.9758, 0.806, 0.9426, 0.9509, 0.9632, 0.954, 0.9557, 0.9229, 0.9591, 0.9575, 0.837, 0.9528, 0.9284, 0.8139, 0.8286, nan] 
-2024-08-28 11:47:12.976725: Epoch time: 88.91 s 
-2024-08-28 11:47:14.170487:  
-2024-08-28 11:47:14.170746: Epoch 913 
-2024-08-28 11:47:14.170843: Current learning rate: 0.00578 
-2024-08-28 11:48:42.244312: train_loss -0.7598 
-2024-08-28 11:48:42.244758: val_loss -0.7839 
-2024-08-28 11:48:42.244954: Pseudo dice [0.0, 0.0, 0.8934, 0.9766, 0.7881, 0.9454, 0.9477, 0.9612, 0.9518, 0.9436, 0.9281, 0.9584, 0.9589, 0.8368, 0.9363, 0.9256, 0.8196, 0.7996, nan] 
-2024-08-28 11:48:42.245050: Epoch time: 88.07 s 
-2024-08-28 11:48:43.707425:  
-2024-08-28 11:48:43.707598: Epoch 914 
-2024-08-28 11:48:43.707683: Current learning rate: 0.00577 
-2024-08-28 11:50:11.934217: train_loss -0.7532 
-2024-08-28 11:50:11.934476: val_loss -0.7718 
-2024-08-28 11:50:11.934645: Pseudo dice [0.0, 0.0, 0.8801, 0.9754, 0.816, 0.935, 0.9444, 0.9581, 0.9451, 0.9434, 0.9206, 0.9543, 0.9558, 0.8251, 0.9462, 0.925, 0.7973, 0.8023, nan] 
-2024-08-28 11:50:11.934736: Epoch time: 88.23 s 
-2024-08-28 11:50:13.121706:  
-2024-08-28 11:50:13.122012: Epoch 915 
-2024-08-28 11:50:13.122112: Current learning rate: 0.00577 
-2024-08-28 11:51:40.590701: train_loss -0.7554 
-2024-08-28 11:51:40.590947: val_loss -0.7798 
-2024-08-28 11:51:40.591109: Pseudo dice [0.0, 0.0, 0.8727, 0.9763, 0.7932, 0.9477, 0.9493, 0.9631, 0.9501, 0.9541, 0.929, 0.9578, 0.961, 0.8367, 0.9518, 0.9286, 0.8132, 0.822, nan] 
-2024-08-28 11:51:40.591194: Epoch time: 87.47 s 
-2024-08-28 11:51:41.755121:  
-2024-08-28 11:51:41.755297: Epoch 916 
-2024-08-28 11:51:41.755388: Current learning rate: 0.00576 
-2024-08-28 11:53:08.901690: train_loss -0.7585 
-2024-08-28 11:53:08.901962: val_loss -0.7793 
-2024-08-28 11:53:08.902130: Pseudo dice [0.0, 0.0, 0.898, 0.9739, 0.8164, 0.935, 0.9388, 0.9627, 0.9469, 0.9286, 0.9233, 0.9504, 0.9452, 0.8261, 0.9381, 0.9283, 0.817, 0.8207, nan] 
-2024-08-28 11:53:08.902220: Epoch time: 87.15 s 
-2024-08-28 11:53:10.120640:  
-2024-08-28 11:53:10.120825: Epoch 917 
-2024-08-28 11:53:10.120924: Current learning rate: 0.00576 
-2024-08-28 11:54:36.419946: train_loss -0.76 
-2024-08-28 11:54:36.420153: val_loss -0.779 
-2024-08-28 11:54:36.420306: Pseudo dice [0.0, 0.0, 0.8593, 0.9764, 0.8275, 0.9456, 0.9474, 0.9598, 0.9505, 0.9425, 0.9225, 0.9599, 0.9562, 0.834, 0.9443, 0.9205, 0.8236, 0.8194, nan] 
-2024-08-28 11:54:36.420384: Epoch time: 86.3 s 
-2024-08-28 11:54:37.546040:  
-2024-08-28 11:54:37.546194: Epoch 918 
-2024-08-28 11:54:37.546282: Current learning rate: 0.00575 
-2024-08-28 11:56:08.737448: train_loss -0.7624 
-2024-08-28 11:56:08.737675: val_loss -0.7816 
-2024-08-28 11:56:08.737835: Pseudo dice [0.0, 0.0, 0.8817, 0.9714, 0.8134, 0.944, 0.9428, 0.9635, 0.9551, 0.9507, 0.9298, 0.961, 0.9576, 0.8326, 0.9518, 0.9199, 0.8191, 0.8189, nan] 
-2024-08-28 11:56:08.737923: Epoch time: 91.19 s 
-2024-08-28 11:56:09.929629:  
-2024-08-28 11:56:09.929780: Epoch 919 
-2024-08-28 11:56:09.929870: Current learning rate: 0.00575 
-2024-08-28 11:57:35.311964: train_loss -0.7621 
-2024-08-28 11:57:35.312480: val_loss -0.7852 
-2024-08-28 11:57:35.312656: Pseudo dice [0.0, 0.0, 0.9143, 0.9765, 0.8272, 0.946, 0.9479, 0.9564, 0.9525, 0.9546, 0.9343, 0.9594, 0.9618, 0.8454, 0.9468, 0.9325, 0.8307, 0.819, nan] 
-2024-08-28 11:57:35.312760: Epoch time: 85.38 s 
-2024-08-28 11:57:36.777372:  
-2024-08-28 11:57:36.777698: Epoch 920 
-2024-08-28 11:57:36.777791: Current learning rate: 0.00574 
-2024-08-28 11:59:02.230875: train_loss -0.7646 
-2024-08-28 11:59:02.231102: val_loss -0.7851 
-2024-08-28 11:59:02.231278: Pseudo dice [0.0, 0.0, 0.9087, 0.9771, 0.8431, 0.9443, 0.9478, 0.9636, 0.9504, 0.9556, 0.9336, 0.955, 0.9585, 0.8375, 0.9547, 0.9292, 0.8406, 0.8424, nan] 
-2024-08-28 11:59:02.231366: Epoch time: 85.45 s 
-2024-08-28 11:59:03.425706:  
-2024-08-28 11:59:03.425977: Epoch 921 
-2024-08-28 11:59:03.426073: Current learning rate: 0.00574 
-2024-08-28 12:00:29.725520: train_loss -0.7637 
-2024-08-28 12:00:29.725759: val_loss -0.7847 
-2024-08-28 12:00:29.725922: Pseudo dice [0.0, 0.0, 0.8909, 0.9766, 0.8269, 0.9383, 0.9427, 0.9585, 0.9437, 0.9379, 0.9138, 0.9532, 0.9498, 0.8336, 0.9455, 0.9303, 0.8219, 0.8264, nan] 
-2024-08-28 12:00:29.726006: Epoch time: 86.3 s 
-2024-08-28 12:00:30.880283:  
-2024-08-28 12:00:30.880468: Epoch 922 
-2024-08-28 12:00:30.880559: Current learning rate: 0.00573 
-2024-08-28 12:02:00.959367: train_loss -0.7653 
-2024-08-28 12:02:00.959615: val_loss -0.777 
-2024-08-28 12:02:00.959774: Pseudo dice [0.0, 0.0, 0.884, 0.9772, 0.7545, 0.9454, 0.9479, 0.9524, 0.9507, 0.9483, 0.9207, 0.9573, 0.9574, 0.8451, 0.9359, 0.931, 0.8103, 0.8097, nan] 
-2024-08-28 12:02:00.959862: Epoch time: 90.08 s 
-2024-08-28 12:02:02.166529:  
-2024-08-28 12:02:02.166693: Epoch 923 
-2024-08-28 12:02:02.166780: Current learning rate: 0.00573 
-2024-08-28 12:03:30.136399: train_loss -0.7629 
-2024-08-28 12:03:30.136662: val_loss -0.7822 
-2024-08-28 12:03:30.136835: Pseudo dice [0.0, 0.0, 0.8985, 0.9744, 0.8371, 0.9423, 0.9472, 0.9633, 0.9486, 0.9428, 0.9307, 0.9586, 0.959, 0.8369, 0.9458, 0.9288, 0.819, 0.8304, nan] 
-2024-08-28 12:03:30.136928: Epoch time: 87.97 s 
-2024-08-28 12:03:31.323372:  
-2024-08-28 12:03:31.323535: Epoch 924 
-2024-08-28 12:03:31.323626: Current learning rate: 0.00572 
-2024-08-28 12:04:52.491711: train_loss -0.7624 
-2024-08-28 12:04:52.491947: val_loss -0.7788 
-2024-08-28 12:04:52.492096: Pseudo dice [0.0, 0.0, 0.8831, 0.9762, 0.766, 0.9422, 0.9455, 0.9608, 0.9492, 0.9399, 0.9246, 0.9575, 0.9552, 0.832, 0.9512, 0.9259, 0.8287, 0.8127, nan] 
-2024-08-28 12:04:52.492175: Epoch time: 81.17 s 
-2024-08-28 12:04:53.680698:  
-2024-08-28 12:04:53.680873: Epoch 925 
-2024-08-28 12:04:53.680967: Current learning rate: 0.00572 
-2024-08-28 12:06:20.519909: train_loss -0.7597 
-2024-08-28 12:06:20.520407: val_loss -0.7799 
-2024-08-28 12:06:20.520635: Pseudo dice [0.0, 0.0, 0.8794, 0.9752, 0.8122, 0.9416, 0.9464, 0.9605, 0.9476, 0.9415, 0.929, 0.9591, 0.9589, 0.838, 0.933, 0.9265, 0.7901, 0.7847, nan] 
-2024-08-28 12:06:20.520770: Epoch time: 86.84 s 
-2024-08-28 12:06:22.068618:  
-2024-08-28 12:06:22.068920: Epoch 926 
-2024-08-28 12:06:22.069018: Current learning rate: 0.00571 
-2024-08-28 12:07:48.598538: train_loss -0.7586 
-2024-08-28 12:07:48.598894: val_loss -0.7811 
-2024-08-28 12:07:48.599080: Pseudo dice [0.0, 0.0, 0.8913, 0.9766, 0.8145, 0.9462, 0.9486, 0.9635, 0.9504, 0.9504, 0.93, 0.9611, 0.9601, 0.8418, 0.939, 0.9267, 0.8162, 0.7968, nan] 
-2024-08-28 12:07:48.599198: Epoch time: 86.53 s 
-2024-08-28 12:07:49.785275:  
-2024-08-28 12:07:49.785430: Epoch 927 
-2024-08-28 12:07:49.785519: Current learning rate: 0.00571 
-2024-08-28 12:09:18.661229: train_loss -0.7662 
-2024-08-28 12:09:18.661529: val_loss -0.7808 
-2024-08-28 12:09:18.661781: Pseudo dice [0.0, 0.0, 0.9052, 0.9763, 0.8222, 0.9449, 0.9468, 0.9623, 0.9514, 0.9473, 0.9229, 0.9603, 0.959, 0.8226, 0.951, 0.9239, 0.8194, 0.8313, nan] 
-2024-08-28 12:09:18.662148: Epoch time: 88.88 s 
-2024-08-28 12:09:19.824362:  
-2024-08-28 12:09:19.824672: Epoch 928 
-2024-08-28 12:09:19.824765: Current learning rate: 0.0057 
-2024-08-28 12:10:45.159304: train_loss -0.7582 
-2024-08-28 12:10:45.159574: val_loss -0.7746 
-2024-08-28 12:10:45.159790: Pseudo dice [0.0, 0.0, 0.8888, 0.9751, 0.8211, 0.9426, 0.9473, 0.9618, 0.946, 0.9448, 0.926, 0.9621, 0.9566, 0.8278, 0.9456, 0.9264, 0.8137, 0.8021, nan] 
-2024-08-28 12:10:45.159912: Epoch time: 85.34 s 
-2024-08-28 12:10:46.383984:  
-2024-08-28 12:10:46.384132: Epoch 929 
-2024-08-28 12:10:46.384215: Current learning rate: 0.0057 
-2024-08-28 12:12:14.877116: train_loss -0.7612 
-2024-08-28 12:12:14.877592: val_loss -0.7862 
-2024-08-28 12:12:14.877819: Pseudo dice [0.0, 0.0, 0.9091, 0.9759, 0.8257, 0.9427, 0.9438, 0.9631, 0.9475, 0.9554, 0.931, 0.9626, 0.9588, 0.8447, 0.9533, 0.9282, 0.8253, 0.8212, nan] 
-2024-08-28 12:12:14.877958: Epoch time: 88.49 s 
-2024-08-28 12:12:16.067359:  
-2024-08-28 12:12:16.067572: Epoch 930 
-2024-08-28 12:12:16.067670: Current learning rate: 0.0057 
-2024-08-28 12:13:37.657601: train_loss -0.7638 
-2024-08-28 12:13:37.657932: val_loss -0.7852 
-2024-08-28 12:13:37.658176: Pseudo dice [0.0, 0.0, 0.9011, 0.976, 0.832, 0.9398, 0.9437, 0.9599, 0.9478, 0.9413, 0.9312, 0.9599, 0.9573, 0.834, 0.9391, 0.9257, 0.8016, 0.8078, nan] 
-2024-08-28 12:13:37.658315: Epoch time: 81.59 s 
-2024-08-28 12:13:38.924084:  
-2024-08-28 12:13:38.924365: Epoch 931 
-2024-08-28 12:13:38.924465: Current learning rate: 0.00569 
-2024-08-28 12:15:02.747795: train_loss -0.7611 
-2024-08-28 12:15:02.748039: val_loss -0.7818 
-2024-08-28 12:15:02.748208: Pseudo dice [0.0, 0.0, 0.8825, 0.9762, 0.818, 0.9361, 0.9456, 0.9636, 0.9524, 0.9519, 0.9252, 0.9565, 0.9564, 0.8355, 0.9485, 0.9269, 0.8095, 0.8276, nan] 
-2024-08-28 12:15:02.748298: Epoch time: 83.82 s 
-2024-08-28 12:15:04.290730:  
-2024-08-28 12:15:04.290934: Epoch 932 
-2024-08-28 12:15:04.291032: Current learning rate: 0.00569 
-2024-08-28 12:16:27.983335: train_loss -0.7628 
-2024-08-28 12:16:27.983587: val_loss -0.7831 
-2024-08-28 12:16:27.983754: Pseudo dice [0.0, 0.0, 0.8755, 0.9753, 0.8111, 0.9419, 0.9485, 0.9635, 0.9481, 0.9494, 0.9217, 0.9545, 0.9547, 0.8412, 0.9509, 0.9247, 0.81, 0.7999, nan] 
-2024-08-28 12:16:27.983837: Epoch time: 83.69 s 
-2024-08-28 12:16:29.213558:  
-2024-08-28 12:16:29.213843: Epoch 933 
-2024-08-28 12:16:29.213949: Current learning rate: 0.00568 
-2024-08-28 12:18:00.413456: train_loss -0.7541 
-2024-08-28 12:18:00.413714: val_loss -0.7794 
-2024-08-28 12:18:00.413883: Pseudo dice [0.0, 0.0, 0.8911, 0.9743, 0.7873, 0.9422, 0.9362, 0.9609, 0.9508, 0.9394, 0.9134, 0.9607, 0.9509, 0.8316, 0.9517, 0.9185, 0.7961, 0.8213, nan] 
-2024-08-28 12:18:00.413971: Epoch time: 91.2 s 
-2024-08-28 12:18:01.665563:  
-2024-08-28 12:18:01.665753: Epoch 934 
-2024-08-28 12:18:01.665848: Current learning rate: 0.00568 
-2024-08-28 12:19:34.305142: train_loss -0.7487 
-2024-08-28 12:19:34.305422: val_loss -0.7843 
-2024-08-28 12:19:34.305636: Pseudo dice [0.0, 0.0, 0.8927, 0.9766, 0.8382, 0.9443, 0.9497, 0.9597, 0.9494, 0.9491, 0.9235, 0.9567, 0.9597, 0.8336, 0.9457, 0.9266, 0.8103, 0.8359, nan] 
-2024-08-28 12:19:34.305747: Epoch time: 92.64 s 
-2024-08-28 12:19:35.587783:  
-2024-08-28 12:19:35.588264: Epoch 935 
-2024-08-28 12:19:35.588360: Current learning rate: 0.00567 
-2024-08-28 12:21:01.774017: train_loss -0.7514 
-2024-08-28 12:21:01.774262: val_loss -0.7754 
-2024-08-28 12:21:01.774441: Pseudo dice [0.0, 0.0, 0.8995, 0.9758, 0.7633, 0.9431, 0.947, 0.9593, 0.954, 0.9481, 0.929, 0.9612, 0.9572, 0.8312, 0.9431, 0.9244, 0.8172, 0.8045, nan] 
-2024-08-28 12:21:01.774535: Epoch time: 86.19 s 
-2024-08-28 12:21:03.145202:  
-2024-08-28 12:21:03.145465: Epoch 936 
-2024-08-28 12:21:03.145574: Current learning rate: 0.00567 
-2024-08-28 12:22:33.741531: train_loss -0.7492 
-2024-08-28 12:22:33.741768: val_loss -0.7732 
-2024-08-28 12:22:33.741933: Pseudo dice [0.0, 0.0, 0.8871, 0.9748, 0.7912, 0.9405, 0.9422, 0.9602, 0.9523, 0.9469, 0.92, 0.961, 0.9553, 0.8295, 0.9496, 0.9214, 0.8285, 0.8194, nan] 
-2024-08-28 12:22:33.742019: Epoch time: 90.6 s 
-2024-08-28 12:22:34.961229:  
-2024-08-28 12:22:34.961390: Epoch 937 
-2024-08-28 12:22:34.961478: Current learning rate: 0.00566 
-2024-08-28 12:24:05.108604: train_loss -0.7586 
-2024-08-28 12:24:05.108845: val_loss -0.7819 
-2024-08-28 12:24:05.109407: Pseudo dice [0.0, 0.0, 0.8969, 0.9746, 0.8185, 0.9481, 0.9486, 0.9615, 0.9508, 0.9479, 0.9109, 0.9583, 0.9543, 0.8393, 0.9399, 0.9313, 0.8322, 0.8228, nan] 
-2024-08-28 12:24:05.109534: Epoch time: 90.15 s 
-2024-08-28 12:24:06.326522:  
-2024-08-28 12:24:06.326695: Epoch 938 
-2024-08-28 12:24:06.326790: Current learning rate: 0.00566 
-2024-08-28 12:25:37.228837: train_loss -0.7598 
-2024-08-28 12:25:37.229077: val_loss -0.7796 
-2024-08-28 12:25:37.229233: Pseudo dice [0.0, 0.0, 0.8978, 0.9723, 0.8126, 0.9417, 0.9413, 0.9602, 0.9484, 0.9428, 0.9296, 0.9574, 0.9559, 0.8286, 0.9311, 0.9264, 0.8179, 0.7851, nan] 
-2024-08-28 12:25:37.229315: Epoch time: 90.9 s 
-2024-08-28 12:25:38.642286:  
-2024-08-28 12:25:38.642461: Epoch 939 
-2024-08-28 12:25:38.642558: Current learning rate: 0.00565 
-2024-08-28 12:27:02.323286: train_loss -0.7574 
-2024-08-28 12:27:02.323531: val_loss -0.778 
-2024-08-28 12:27:02.323685: Pseudo dice [0.0, 0.0, 0.9013, 0.9749, 0.8212, 0.9391, 0.9438, 0.9633, 0.9366, 0.9373, 0.92, 0.9447, 0.9509, 0.8341, 0.9373, 0.9275, 0.8167, 0.8044, nan] 
-2024-08-28 12:27:02.323766: Epoch time: 83.68 s 
-2024-08-28 12:27:03.465993:  
-2024-08-28 12:27:03.466183: Epoch 940 
-2024-08-28 12:27:03.466282: Current learning rate: 0.00565 
-2024-08-28 12:28:33.205900: train_loss -0.7573 
-2024-08-28 12:28:33.206213: val_loss -0.7829 
-2024-08-28 12:28:33.206478: Pseudo dice [0.0, 0.0, 0.9084, 0.9736, 0.8359, 0.9417, 0.9455, 0.9615, 0.9505, 0.9487, 0.9317, 0.9608, 0.9568, 0.8314, 0.9419, 0.923, 0.8204, 0.8206, nan] 
-2024-08-28 12:28:33.206605: Epoch time: 89.74 s 
-2024-08-28 12:28:34.510720:  
-2024-08-28 12:28:34.511114: Epoch 941 
-2024-08-28 12:28:34.511224: Current learning rate: 0.00564 
-2024-08-28 12:29:56.344010: train_loss -0.7606 
-2024-08-28 12:29:56.344273: val_loss -0.7818 
-2024-08-28 12:29:56.344465: Pseudo dice [0.0, 0.0, 0.8859, 0.9751, 0.7926, 0.9471, 0.9478, 0.9621, 0.9512, 0.9514, 0.9255, 0.9583, 0.957, 0.8368, 0.9534, 0.9236, 0.8153, 0.8164, nan] 
-2024-08-28 12:29:56.344559: Epoch time: 81.83 s 
-2024-08-28 12:29:57.532802:  
-2024-08-28 12:29:57.533087: Epoch 942 
-2024-08-28 12:29:57.533177: Current learning rate: 0.00564 
-2024-08-28 12:31:18.986296: train_loss -0.7631 
-2024-08-28 12:31:18.986554: val_loss -0.7767 
-2024-08-28 12:31:18.986728: Pseudo dice [0.0, 0.0, 0.8833, 0.976, 0.8375, 0.948, 0.9528, 0.9603, 0.9477, 0.9437, 0.9226, 0.9588, 0.9577, 0.8449, 0.9269, 0.931, 0.8357, 0.8374, nan] 
-2024-08-28 12:31:18.986829: Epoch time: 81.45 s 
-2024-08-28 12:31:20.176031:  
-2024-08-28 12:31:20.176227: Epoch 943 
-2024-08-28 12:31:20.176320: Current learning rate: 0.00563 
-2024-08-28 12:32:48.428235: train_loss -0.7637 
-2024-08-28 12:32:48.428500: val_loss -0.785 
-2024-08-28 12:32:48.428670: Pseudo dice [0.0, 0.0, 0.8864, 0.9738, 0.8274, 0.9454, 0.9439, 0.9648, 0.9494, 0.9489, 0.9335, 0.9583, 0.9572, 0.8318, 0.9466, 0.9299, 0.814, 0.8091, nan] 
-2024-08-28 12:32:48.428755: Epoch time: 88.25 s 
-2024-08-28 12:32:49.628116:  
-2024-08-28 12:32:49.628293: Epoch 944 
-2024-08-28 12:32:49.628385: Current learning rate: 0.00563 
-2024-08-28 12:34:18.255084: train_loss -0.7594 
-2024-08-28 12:34:18.255496: val_loss -0.7813 
-2024-08-28 12:34:18.255663: Pseudo dice [0.0, 0.0, 0.8945, 0.9768, 0.7899, 0.9378, 0.9358, 0.9582, 0.9473, 0.9528, 0.9265, 0.9555, 0.9583, 0.8386, 0.9457, 0.9243, 0.8315, 0.8159, nan] 
-2024-08-28 12:34:18.255748: Epoch time: 88.63 s 
-2024-08-28 12:34:19.950457:  
-2024-08-28 12:34:19.950655: Epoch 945 
-2024-08-28 12:34:19.950767: Current learning rate: 0.00562 
-2024-08-28 12:35:47.081785: train_loss -0.7618 
-2024-08-28 12:35:47.082345: val_loss -0.7743 
-2024-08-28 12:35:47.082562: Pseudo dice [0.0, 0.0, 0.8816, 0.9765, 0.8232, 0.9462, 0.9477, 0.9573, 0.953, 0.9477, 0.9303, 0.9603, 0.9586, 0.8293, 0.9311, 0.9216, 0.8113, 0.8125, nan] 
-2024-08-28 12:35:47.082668: Epoch time: 87.13 s 
-2024-08-28 12:35:48.552195:  
-2024-08-28 12:35:48.552377: Epoch 946 
-2024-08-28 12:35:48.552483: Current learning rate: 0.00562 
-2024-08-28 12:37:17.153644: train_loss -0.7522 
-2024-08-28 12:37:17.153877: val_loss -0.7779 
-2024-08-28 12:37:17.154056: Pseudo dice [0.0, 0.0, 0.8866, 0.973, 0.8015, 0.9419, 0.9471, 0.958, 0.9475, 0.948, 0.9191, 0.9574, 0.9538, 0.8361, 0.943, 0.9181, 0.8181, 0.7987, nan] 
-2024-08-28 12:37:17.154162: Epoch time: 88.6 s 
-2024-08-28 12:37:18.405480:  
-2024-08-28 12:37:18.405746: Epoch 947 
-2024-08-28 12:37:18.405846: Current learning rate: 0.00561 
-2024-08-28 12:38:48.119681: train_loss -0.7555 
-2024-08-28 12:38:48.119961: val_loss -0.779 
-2024-08-28 12:38:48.120122: Pseudo dice [0.0, 0.0, 0.884, 0.9765, 0.8305, 0.9433, 0.9442, 0.9639, 0.9427, 0.9516, 0.9289, 0.9578, 0.9551, 0.8346, 0.9529, 0.9301, 0.8222, 0.8255, nan] 
-2024-08-28 12:38:48.120209: Epoch time: 89.72 s 
-2024-08-28 12:38:49.335122:  
-2024-08-28 12:38:49.335287: Epoch 948 
-2024-08-28 12:38:49.335382: Current learning rate: 0.00561 
-2024-08-28 12:40:15.943428: train_loss -0.7589 
-2024-08-28 12:40:15.943686: val_loss -0.7828 
-2024-08-28 12:40:15.943854: Pseudo dice [0.0, 0.0, 0.8804, 0.9758, 0.8249, 0.9459, 0.9472, 0.9633, 0.9424, 0.9504, 0.9309, 0.9591, 0.9604, 0.8319, 0.9543, 0.9218, 0.802, 0.8001, nan] 
-2024-08-28 12:40:15.943942: Epoch time: 86.61 s 
-2024-08-28 12:40:17.142961:  
-2024-08-28 12:40:17.143301: Epoch 949 
-2024-08-28 12:40:17.143396: Current learning rate: 0.0056 
-2024-08-28 12:41:44.816851: train_loss -0.7526 
-2024-08-28 12:41:44.817096: val_loss -0.7838 
-2024-08-28 12:41:44.817259: Pseudo dice [0.0, 0.0, 0.8987, 0.9768, 0.8157, 0.9476, 0.951, 0.9588, 0.9503, 0.9549, 0.9339, 0.9598, 0.9607, 0.8403, 0.9544, 0.9256, 0.8157, 0.8202, nan] 
-2024-08-28 12:41:44.817345: Epoch time: 87.67 s 
-2024-08-28 12:41:46.387796:  
-2024-08-28 12:41:46.387954: Epoch 950 
-2024-08-28 12:41:46.388054: Current learning rate: 0.0056 
-2024-08-28 12:43:15.485796: train_loss -0.7613 
-2024-08-28 12:43:15.486024: val_loss -0.7633 
-2024-08-28 12:43:15.486187: Pseudo dice [0.0, 0.0, 0.8698, 0.976, 0.7754, 0.937, 0.9367, 0.9604, 0.9323, 0.9331, 0.9211, 0.9425, 0.9469, 0.8088, 0.936, 0.9191, 0.8067, 0.8101, nan] 
-2024-08-28 12:43:15.486273: Epoch time: 89.1 s 
-2024-08-28 12:43:16.903103:  
-2024-08-28 12:43:16.903432: Epoch 951 
-2024-08-28 12:43:16.903515: Current learning rate: 0.00559 
-2024-08-28 12:44:43.885489: train_loss -0.7599 
-2024-08-28 12:44:43.885732: val_loss -0.78 
-2024-08-28 12:44:43.885902: Pseudo dice [0.0, 0.0, 0.8921, 0.9749, 0.807, 0.9489, 0.945, 0.9639, 0.9465, 0.9421, 0.9294, 0.959, 0.9585, 0.845, 0.956, 0.9298, 0.8135, 0.7852, nan] 
-2024-08-28 12:44:43.885986: Epoch time: 86.98 s 
-2024-08-28 12:44:45.070199:  
-2024-08-28 12:44:45.070480: Epoch 952 
-2024-08-28 12:44:45.070578: Current learning rate: 0.00559 
-2024-08-28 12:46:12.789899: train_loss -0.7607 
-2024-08-28 12:46:12.790126: val_loss -0.7781 
-2024-08-28 12:46:12.790278: Pseudo dice [0.0, 0.0, 0.8827, 0.9754, 0.8294, 0.9433, 0.9492, 0.9595, 0.9478, 0.9465, 0.9236, 0.9518, 0.9575, 0.8451, 0.9506, 0.9315, 0.8166, 0.8185, nan] 
-2024-08-28 12:46:12.790358: Epoch time: 87.72 s 
-2024-08-28 12:46:13.962950:  
-2024-08-28 12:46:13.963113: Epoch 953 
-2024-08-28 12:46:13.963216: Current learning rate: 0.00559 
-2024-08-28 12:47:44.653023: train_loss -0.7607 
-2024-08-28 12:47:44.653267: val_loss -0.7865 
-2024-08-28 12:47:44.653428: Pseudo dice [0.0, 0.0, 0.887, 0.9756, 0.8287, 0.9399, 0.9418, 0.9647, 0.9505, 0.9453, 0.9297, 0.9571, 0.9564, 0.8373, 0.945, 0.9333, 0.8151, 0.8142, nan] 
-2024-08-28 12:47:44.653513: Epoch time: 90.69 s 
-2024-08-28 12:47:45.847392:  
-2024-08-28 12:47:45.847625: Epoch 954 
-2024-08-28 12:47:45.847720: Current learning rate: 0.00558 
-2024-08-28 12:49:07.968312: train_loss -0.77 
-2024-08-28 12:49:07.968585: val_loss -0.7837 
-2024-08-28 12:49:07.968769: Pseudo dice [0.0, 0.0, 0.875, 0.9779, 0.8396, 0.9476, 0.9515, 0.9631, 0.9526, 0.9461, 0.9319, 0.9596, 0.9588, 0.8471, 0.9438, 0.9339, 0.8203, 0.8182, nan] 
-2024-08-28 12:49:07.968872: Epoch time: 82.12 s 
-2024-08-28 12:49:09.187533:  
-2024-08-28 12:49:09.187874: Epoch 955 
-2024-08-28 12:49:09.187968: Current learning rate: 0.00558 
-2024-08-28 12:50:35.482040: train_loss -0.7628 
-2024-08-28 12:50:35.482355: val_loss -0.7798 
-2024-08-28 12:50:35.482610: Pseudo dice [0.0, 0.0, 0.8744, 0.9768, 0.8247, 0.9458, 0.9478, 0.9632, 0.9504, 0.9408, 0.9285, 0.9564, 0.9575, 0.8335, 0.9545, 0.933, 0.8131, 0.8073, nan] 
-2024-08-28 12:50:35.482759: Epoch time: 86.3 s 
-2024-08-28 12:50:36.784966:  
-2024-08-28 12:50:36.785326: Epoch 956 
-2024-08-28 12:50:36.785533: Current learning rate: 0.00557 
-2024-08-28 12:52:08.445458: train_loss -0.7571 
-2024-08-28 12:52:08.445714: val_loss -0.7712 
-2024-08-28 12:52:08.445916: Pseudo dice [0.0, 0.0, 0.8805, 0.9732, 0.7532, 0.9344, 0.9345, 0.9577, 0.9453, 0.9467, 0.9221, 0.9537, 0.9552, 0.8239, 0.9492, 0.9064, 0.8064, 0.8042, nan] 
-2024-08-28 12:52:08.446019: Epoch time: 91.66 s 
-2024-08-28 12:52:09.792515:  
-2024-08-28 12:52:09.792693: Epoch 957 
-2024-08-28 12:52:09.792782: Current learning rate: 0.00557 
-2024-08-28 12:53:37.570140: train_loss -0.7531 
-2024-08-28 12:53:37.570385: val_loss -0.7617 
-2024-08-28 12:53:37.570549: Pseudo dice [0.0, 0.0, 0.866, 0.9749, 0.682, 0.9342, 0.9366, 0.9569, 0.9445, 0.9473, 0.92, 0.958, 0.9568, 0.7938, 0.9499, 0.9152, 0.8132, 0.8052, nan] 
-2024-08-28 12:53:37.570635: Epoch time: 87.78 s 
-2024-08-28 12:53:39.075520:  
-2024-08-28 12:53:39.076003: Epoch 958 
-2024-08-28 12:53:39.076107: Current learning rate: 0.00556 
-2024-08-28 12:55:03.499145: train_loss -0.7598 
-2024-08-28 12:55:03.499368: val_loss -0.7746 
-2024-08-28 12:55:03.499539: Pseudo dice [0.0, 0.0, 0.865, 0.9746, 0.8194, 0.9442, 0.9486, 0.9656, 0.9448, 0.9439, 0.9274, 0.9557, 0.9618, 0.8272, 0.9468, 0.9285, 0.8147, 0.8172, nan] 
-2024-08-28 12:55:03.499621: Epoch time: 84.42 s 
-2024-08-28 12:55:04.698042:  
-2024-08-28 12:55:04.698222: Epoch 959 
-2024-08-28 12:55:04.698314: Current learning rate: 0.00556 
-2024-08-28 12:56:28.893075: train_loss -0.7573 
-2024-08-28 12:56:28.893330: val_loss -0.7722 
-2024-08-28 12:56:28.893498: Pseudo dice [0.0, 0.0, 0.8608, 0.972, 0.7904, 0.9466, 0.9439, 0.9576, 0.947, 0.9467, 0.923, 0.9547, 0.9552, 0.8138, 0.9505, 0.9191, 0.8093, 0.8069, nan] 
-2024-08-28 12:56:28.893585: Epoch time: 84.2 s 
-2024-08-28 12:56:30.104270:  
-2024-08-28 12:56:30.104450: Epoch 960 
-2024-08-28 12:56:30.104551: Current learning rate: 0.00555 
-2024-08-28 12:57:55.997750: train_loss -0.7524 
-2024-08-28 12:57:55.998014: val_loss -0.7703 
-2024-08-28 12:57:55.998187: Pseudo dice [0.0, 0.0, 0.8524, 0.9752, 0.7831, 0.9356, 0.9429, 0.9561, 0.9457, 0.9403, 0.9258, 0.9585, 0.9521, 0.8228, 0.9431, 0.9268, 0.8176, 0.8173, nan] 
-2024-08-28 12:57:55.998312: Epoch time: 85.89 s 
-2024-08-28 12:57:57.211509:  
-2024-08-28 12:57:57.211683: Epoch 961 
-2024-08-28 12:57:57.211776: Current learning rate: 0.00555 
-2024-08-28 12:59:23.818475: train_loss -0.7583 
-2024-08-28 12:59:23.818713: val_loss -0.7823 
-2024-08-28 12:59:23.818878: Pseudo dice [0.0, 0.0, 0.8974, 0.9766, 0.7773, 0.9424, 0.9449, 0.9597, 0.941, 0.9453, 0.9249, 0.9485, 0.9495, 0.8322, 0.9495, 0.9192, 0.8326, 0.8297, nan] 
-2024-08-28 12:59:23.818964: Epoch time: 86.61 s 
-2024-08-28 12:59:25.046373:  
-2024-08-28 12:59:25.046873: Epoch 962 
-2024-08-28 12:59:25.046953: Current learning rate: 0.00554 
-2024-08-28 13:00:54.647823: train_loss -0.7551 
-2024-08-28 13:00:54.648065: val_loss -0.7726 
-2024-08-28 13:00:54.648232: Pseudo dice [0.0, 0.0, 0.8716, 0.9754, 0.7399, 0.9414, 0.9413, 0.96, 0.949, 0.94, 0.9272, 0.9553, 0.9551, 0.8286, 0.9474, 0.9234, 0.7994, 0.7943, nan] 
-2024-08-28 13:00:54.648318: Epoch time: 89.6 s 
-2024-08-28 13:00:55.849096:  
-2024-08-28 13:00:55.849252: Epoch 963 
-2024-08-28 13:00:55.849341: Current learning rate: 0.00554 
-2024-08-28 13:02:21.269129: train_loss -0.7597 
-2024-08-28 13:02:21.269387: val_loss -0.7822 
-2024-08-28 13:02:21.269686: Pseudo dice [0.0, 0.0, 0.8587, 0.9748, 0.8117, 0.9443, 0.9469, 0.9655, 0.9539, 0.947, 0.9254, 0.962, 0.9607, 0.8366, 0.9514, 0.9283, 0.8109, 0.8074, nan] 
-2024-08-28 13:02:21.269893: Epoch time: 85.42 s 
-2024-08-28 13:02:22.737098:  
-2024-08-28 13:02:22.737437: Epoch 964 
-2024-08-28 13:02:22.737529: Current learning rate: 0.00553 
-2024-08-28 13:03:47.226372: train_loss -0.7627 
-2024-08-28 13:03:47.226608: val_loss -0.7823 
-2024-08-28 13:03:47.226898: Pseudo dice [0.0, 0.0, 0.8753, 0.9758, 0.8081, 0.946, 0.9456, 0.9644, 0.9534, 0.9506, 0.9197, 0.9603, 0.9575, 0.8449, 0.9553, 0.9369, 0.8274, 0.8106, nan] 
-2024-08-28 13:03:47.226999: Epoch time: 84.49 s 
-2024-08-28 13:03:48.389629:  
-2024-08-28 13:03:48.389796: Epoch 965 
-2024-08-28 13:03:48.389883: Current learning rate: 0.00553 
-2024-08-28 13:05:11.449721: train_loss -0.7615 
-2024-08-28 13:05:11.450373: val_loss -0.7753 
-2024-08-28 13:05:11.450598: Pseudo dice [0.0, 0.0, 0.8789, 0.975, 0.7613, 0.9396, 0.9421, 0.9607, 0.9504, 0.9543, 0.9237, 0.9567, 0.953, 0.7944, 0.9471, 0.9259, 0.8268, 0.8207, nan] 
-2024-08-28 13:05:11.450780: Epoch time: 83.06 s 
-2024-08-28 13:05:12.798898:  
-2024-08-28 13:05:12.799071: Epoch 966 
-2024-08-28 13:05:12.799157: Current learning rate: 0.00552 
-2024-08-28 13:06:42.227662: train_loss -0.7594 
-2024-08-28 13:06:42.227978: val_loss -0.7812 
-2024-08-28 13:06:42.228327: Pseudo dice [0.0, 0.0, 0.901, 0.9761, 0.807, 0.9496, 0.9528, 0.9617, 0.9503, 0.9522, 0.9217, 0.9606, 0.9621, 0.8381, 0.952, 0.9292, 0.8161, 0.8316, nan] 
-2024-08-28 13:06:42.228492: Epoch time: 89.43 s 
-2024-08-28 13:06:43.504097:  
-2024-08-28 13:06:43.504435: Epoch 967 
-2024-08-28 13:06:43.504534: Current learning rate: 0.00552 
-2024-08-28 13:08:03.985590: train_loss -0.762 
-2024-08-28 13:08:03.986050: val_loss -0.7896 
-2024-08-28 13:08:03.986246: Pseudo dice [0.0, 0.0, 0.8705, 0.9773, 0.826, 0.9398, 0.9462, 0.9656, 0.9509, 0.9453, 0.9338, 0.9621, 0.9607, 0.846, 0.9345, 0.9272, 0.8214, 0.8173, nan] 
-2024-08-28 13:08:03.986381: Epoch time: 80.48 s 
-2024-08-28 13:08:05.176236:  
-2024-08-28 13:08:05.176571: Epoch 968 
-2024-08-28 13:08:05.176668: Current learning rate: 0.00551 
-2024-08-28 13:09:33.536210: train_loss -0.7623 
-2024-08-28 13:09:33.536505: val_loss -0.7792 
-2024-08-28 13:09:33.536684: Pseudo dice [0.0, 0.0, 0.8661, 0.9743, 0.827, 0.9384, 0.9429, 0.9647, 0.9422, 0.9516, 0.9222, 0.96, 0.9581, 0.8426, 0.9502, 0.9291, 0.8104, 0.8125, nan] 
-2024-08-28 13:09:33.536801: Epoch time: 88.36 s 
-2024-08-28 13:09:34.765850:  
-2024-08-28 13:09:34.766022: Epoch 969 
-2024-08-28 13:09:34.766113: Current learning rate: 0.00551 
-2024-08-28 13:11:05.835232: train_loss -0.7624 
-2024-08-28 13:11:05.835476: val_loss -0.7768 
-2024-08-28 13:11:05.835633: Pseudo dice [0.0, 0.0, 0.8983, 0.9757, 0.8401, 0.945, 0.9505, 0.962, 0.9497, 0.9504, 0.921, 0.958, 0.9557, 0.8289, 0.9458, 0.9349, 0.8268, 0.8234, nan] 
-2024-08-28 13:11:05.835713: Epoch time: 91.07 s 
-2024-08-28 13:11:07.279750:  
-2024-08-28 13:11:07.280253: Epoch 970 
-2024-08-28 13:11:07.280343: Current learning rate: 0.0055 
-2024-08-28 13:12:36.617825: train_loss -0.7624 
-2024-08-28 13:12:36.618080: val_loss -0.7787 
-2024-08-28 13:12:36.618246: Pseudo dice [0.0, 0.0, 0.8755, 0.9749, 0.8106, 0.9462, 0.9407, 0.9598, 0.943, 0.9449, 0.9223, 0.9558, 0.9573, 0.8261, 0.9502, 0.9272, 0.7981, 0.7994, nan] 
-2024-08-28 13:12:36.618334: Epoch time: 89.34 s 
-2024-08-28 13:12:37.814434:  
-2024-08-28 13:12:37.814728: Epoch 971 
-2024-08-28 13:12:37.814827: Current learning rate: 0.0055 
-2024-08-28 13:14:01.642385: train_loss -0.7609 
-2024-08-28 13:14:01.642936: val_loss -0.7778 
-2024-08-28 13:14:01.643138: Pseudo dice [0.0, 0.0, 0.8924, 0.9751, 0.8056, 0.946, 0.9463, 0.9605, 0.952, 0.9487, 0.9282, 0.9578, 0.9578, 0.8355, 0.9461, 0.9245, 0.7944, 0.8, nan] 
-2024-08-28 13:14:01.643295: Epoch time: 83.83 s 
-2024-08-28 13:14:02.832068:  
-2024-08-28 13:14:02.832241: Epoch 972 
-2024-08-28 13:14:02.832327: Current learning rate: 0.00549 
-2024-08-28 13:15:25.966621: train_loss -0.7636 
-2024-08-28 13:15:25.966949: val_loss -0.7837 
-2024-08-28 13:15:25.967133: Pseudo dice [0.0, 0.0, 0.8862, 0.9766, 0.7557, 0.9462, 0.9466, 0.961, 0.9508, 0.9537, 0.9289, 0.9605, 0.9632, 0.8382, 0.9464, 0.9337, 0.8164, 0.8139, nan] 
-2024-08-28 13:15:25.967248: Epoch time: 83.14 s 
-2024-08-28 13:15:27.219286:  
-2024-08-28 13:15:27.219484: Epoch 973 
-2024-08-28 13:15:27.219680: Current learning rate: 0.00549 
-2024-08-28 13:16:52.009685: train_loss -0.7628 
-2024-08-28 13:16:52.009942: val_loss -0.7846 
-2024-08-28 13:16:52.010146: Pseudo dice [0.0, 0.0, 0.8895, 0.976, 0.7824, 0.9404, 0.9425, 0.9632, 0.9384, 0.931, 0.9138, 0.9472, 0.9436, 0.8442, 0.9512, 0.9274, 0.8234, 0.8086, nan] 
-2024-08-28 13:16:52.010247: Epoch time: 84.79 s 
-2024-08-28 13:16:53.217464:  
-2024-08-28 13:16:53.217683: Epoch 974 
-2024-08-28 13:16:53.217774: Current learning rate: 0.00548 
-2024-08-28 13:18:22.984812: train_loss -0.7628 
-2024-08-28 13:18:22.985042: val_loss -0.7827 
-2024-08-28 13:18:22.985204: Pseudo dice [0.0, 0.0, 0.8904, 0.9764, 0.8243, 0.9407, 0.9478, 0.9617, 0.9498, 0.9411, 0.9259, 0.9598, 0.9582, 0.8417, 0.9475, 0.9291, 0.8189, 0.8105, nan] 
-2024-08-28 13:18:22.985289: Epoch time: 89.77 s 
-2024-08-28 13:18:24.234468:  
-2024-08-28 13:18:24.234662: Epoch 975 
-2024-08-28 13:18:24.234810: Current learning rate: 0.00548 
-2024-08-28 13:19:47.371886: train_loss -0.7633 
-2024-08-28 13:19:47.372118: val_loss -0.788 
-2024-08-28 13:19:47.372285: Pseudo dice [0.0, 0.0, 0.8965, 0.9753, 0.8183, 0.9476, 0.9496, 0.9612, 0.9449, 0.9541, 0.9275, 0.957, 0.9565, 0.8466, 0.9514, 0.9305, 0.81, 0.8128, nan] 
-2024-08-28 13:19:47.372373: Epoch time: 83.14 s 
-2024-08-28 13:19:48.903219:  
-2024-08-28 13:19:48.903434: Epoch 976 
-2024-08-28 13:19:48.903534: Current learning rate: 0.00547 
-2024-08-28 13:21:15.289219: train_loss -0.7618 
-2024-08-28 13:21:15.289474: val_loss -0.7834 
-2024-08-28 13:21:15.289653: Pseudo dice [0.0, 0.0, 0.9004, 0.9746, 0.8227, 0.9444, 0.9434, 0.9646, 0.9535, 0.952, 0.9264, 0.9563, 0.9541, 0.8439, 0.9376, 0.9274, 0.8278, 0.8202, nan] 
-2024-08-28 13:21:15.289743: Epoch time: 86.39 s 
-2024-08-28 13:21:16.437311:  
-2024-08-28 13:21:16.437678: Epoch 977 
-2024-08-28 13:21:16.437769: Current learning rate: 0.00547 
-2024-08-28 13:22:41.515215: train_loss -0.7633 
-2024-08-28 13:22:41.515546: val_loss -0.7774 
-2024-08-28 13:22:41.515729: Pseudo dice [0.0, 0.0, 0.8802, 0.9766, 0.8234, 0.9399, 0.9427, 0.9561, 0.9477, 0.9438, 0.9305, 0.9523, 0.957, 0.8371, 0.9417, 0.9261, 0.8248, 0.8256, nan] 
-2024-08-28 13:22:41.515818: Epoch time: 85.08 s 
-2024-08-28 13:22:42.755349:  
-2024-08-28 13:22:42.755527: Epoch 978 
-2024-08-28 13:22:42.755622: Current learning rate: 0.00546 
-2024-08-28 13:24:09.094671: train_loss -0.7612 
-2024-08-28 13:24:09.094983: val_loss -0.7849 
-2024-08-28 13:24:09.095196: Pseudo dice [0.0, 0.0, 0.8814, 0.976, 0.8184, 0.9447, 0.9506, 0.9634, 0.9453, 0.9486, 0.9272, 0.9557, 0.9591, 0.8359, 0.9483, 0.9281, 0.8211, 0.8076, nan] 
-2024-08-28 13:24:09.095402: Epoch time: 86.34 s 
-2024-08-28 13:24:10.315654:  
-2024-08-28 13:24:10.315804: Epoch 979 
-2024-08-28 13:24:10.315893: Current learning rate: 0.00546 
-2024-08-28 13:25:39.282767: train_loss -0.763 
-2024-08-28 13:25:39.283257: val_loss -0.7833 
-2024-08-28 13:25:39.283446: Pseudo dice [0.0, 0.0, 0.8799, 0.9767, 0.8425, 0.9408, 0.9473, 0.961, 0.9498, 0.9431, 0.9354, 0.9568, 0.9619, 0.8377, 0.9487, 0.9324, 0.8218, 0.8039, nan] 
-2024-08-28 13:25:39.283586: Epoch time: 88.97 s 
-2024-08-28 13:25:40.541470:  
-2024-08-28 13:25:40.541884: Epoch 980 
-2024-08-28 13:25:40.541996: Current learning rate: 0.00546 
-2024-08-28 13:27:07.492278: train_loss -0.763 
-2024-08-28 13:27:07.492605: val_loss -0.7841 
-2024-08-28 13:27:07.492843: Pseudo dice [0.0, 0.0, 0.8901, 0.9774, 0.8393, 0.9488, 0.9515, 0.9644, 0.9534, 0.9421, 0.9286, 0.9621, 0.9585, 0.8321, 0.9527, 0.923, 0.8235, 0.8065, nan] 
-2024-08-28 13:27:07.492980: Epoch time: 86.95 s 
-2024-08-28 13:27:08.772565:  
-2024-08-28 13:27:08.772750: Epoch 981 
-2024-08-28 13:27:08.772855: Current learning rate: 0.00545 
-2024-08-28 13:28:28.318255: train_loss -0.7612 
-2024-08-28 13:28:28.318491: val_loss -0.7771 
-2024-08-28 13:28:28.318655: Pseudo dice [0.0, 0.0, 0.8817, 0.9755, 0.8089, 0.9332, 0.9341, 0.9609, 0.9425, 0.9483, 0.9253, 0.9545, 0.9567, 0.838, 0.9492, 0.9263, 0.8135, 0.8199, nan] 
-2024-08-28 13:28:28.318738: Epoch time: 79.55 s 
-2024-08-28 13:28:29.898888:  
-2024-08-28 13:28:29.899060: Epoch 982 
-2024-08-28 13:28:29.899159: Current learning rate: 0.00545 
-2024-08-28 13:29:52.124168: train_loss -0.7666 
-2024-08-28 13:29:52.124549: val_loss -0.7811 
-2024-08-28 13:29:52.124892: Pseudo dice [0.0, 0.0, 0.8905, 0.9756, 0.8396, 0.944, 0.9485, 0.9612, 0.9559, 0.9418, 0.9275, 0.9626, 0.9624, 0.8334, 0.9389, 0.9277, 0.826, 0.8146, nan] 
-2024-08-28 13:29:52.125012: Epoch time: 82.23 s 
-2024-08-28 13:29:53.418227:  
-2024-08-28 13:29:53.418516: Epoch 983 
-2024-08-28 13:29:53.418612: Current learning rate: 0.00544 
-2024-08-28 13:31:18.953599: train_loss -0.7603 
-2024-08-28 13:31:18.953838: val_loss -0.7858 
-2024-08-28 13:31:18.953993: Pseudo dice [0.0, 0.0, 0.8972, 0.9764, 0.8463, 0.9456, 0.9502, 0.965, 0.9485, 0.9403, 0.9258, 0.9593, 0.9598, 0.8381, 0.9466, 0.9278, 0.8196, 0.8252, nan] 
-2024-08-28 13:31:18.954075: Epoch time: 85.54 s 
-2024-08-28 13:31:20.180724:  
-2024-08-28 13:31:20.181383: Epoch 984 
-2024-08-28 13:31:20.181623: Current learning rate: 0.00544 
-2024-08-28 13:32:50.365727: train_loss -0.7655 
-2024-08-28 13:32:50.365982: val_loss -0.7899 
-2024-08-28 13:32:50.366192: Pseudo dice [0.0, 0.0, 0.8796, 0.9766, 0.8496, 0.9478, 0.9501, 0.9666, 0.9468, 0.9571, 0.9334, 0.9539, 0.9616, 0.8464, 0.9546, 0.9297, 0.8183, 0.8254, nan] 
-2024-08-28 13:32:50.366300: Epoch time: 90.19 s 
-2024-08-28 13:32:51.594575:  
-2024-08-28 13:32:51.595129: Epoch 985 
-2024-08-28 13:32:51.595239: Current learning rate: 0.00543 
-2024-08-28 13:34:19.995299: train_loss -0.765 
-2024-08-28 13:34:19.995542: val_loss -0.7837 
-2024-08-28 13:34:19.995711: Pseudo dice [0.0, 0.0, 0.904, 0.9755, 0.8133, 0.9465, 0.9466, 0.9616, 0.9565, 0.9464, 0.9225, 0.9647, 0.9573, 0.8481, 0.9498, 0.9327, 0.8233, 0.8155, nan] 
-2024-08-28 13:34:19.995798: Epoch time: 88.4 s 
-2024-08-28 13:34:21.205535:  
-2024-08-28 13:34:21.205822: Epoch 986 
-2024-08-28 13:34:21.205915: Current learning rate: 0.00543 
-2024-08-28 13:35:47.392875: train_loss -0.7635 
-2024-08-28 13:35:47.393433: val_loss -0.7842 
-2024-08-28 13:35:47.393710: Pseudo dice [0.0, 0.0, 0.8925, 0.975, 0.7939, 0.9447, 0.9458, 0.9605, 0.9524, 0.9497, 0.9236, 0.9591, 0.9575, 0.8335, 0.9309, 0.922, 0.8332, 0.8191, nan] 
-2024-08-28 13:35:47.393869: Epoch time: 86.19 s 
-2024-08-28 13:35:48.597376:  
-2024-08-28 13:35:48.597518: Epoch 987 
-2024-08-28 13:35:48.597607: Current learning rate: 0.00542 
-2024-08-28 13:37:13.782059: train_loss -0.7632 
-2024-08-28 13:37:13.782387: val_loss -0.784 
-2024-08-28 13:37:13.782570: Pseudo dice [0.0, 0.0, 0.9054, 0.9769, 0.811, 0.9403, 0.9469, 0.9615, 0.9473, 0.9395, 0.9135, 0.9548, 0.9574, 0.8426, 0.9499, 0.936, 0.8139, 0.8184, nan] 
-2024-08-28 13:37:13.782684: Epoch time: 85.19 s 
-2024-08-28 13:37:15.438493:  
-2024-08-28 13:37:15.438693: Epoch 988 
-2024-08-28 13:37:15.438813: Current learning rate: 0.00542 
-2024-08-28 13:38:42.021948: train_loss -0.768 
-2024-08-28 13:38:42.022177: val_loss -0.779 
-2024-08-28 13:38:42.022353: Pseudo dice [0.0, 0.0, 0.904, 0.976, 0.8437, 0.947, 0.9518, 0.964, 0.949, 0.9499, 0.9302, 0.956, 0.9583, 0.8262, 0.9478, 0.929, 0.819, 0.8189, nan] 
-2024-08-28 13:38:42.022441: Epoch time: 86.58 s 
-2024-08-28 13:38:43.240122:  
-2024-08-28 13:38:43.240412: Epoch 989 
-2024-08-28 13:38:43.240517: Current learning rate: 0.00541 
-2024-08-28 13:40:11.900481: train_loss -0.763 
-2024-08-28 13:40:11.900709: val_loss -0.7769 
-2024-08-28 13:40:11.900862: Pseudo dice [0.0, 0.0, 0.8773, 0.9765, 0.7665, 0.9437, 0.9463, 0.9648, 0.9489, 0.9494, 0.9357, 0.957, 0.9601, 0.8431, 0.9463, 0.9212, 0.818, 0.8083, nan] 
-2024-08-28 13:40:11.900944: Epoch time: 88.66 s 
-2024-08-28 13:40:13.079886:  
-2024-08-28 13:40:13.080415: Epoch 990 
-2024-08-28 13:40:13.080576: Current learning rate: 0.00541 
-2024-08-28 13:41:44.107314: train_loss -0.7607 
-2024-08-28 13:41:44.107529: val_loss -0.7909 
-2024-08-28 13:41:44.107694: Pseudo dice [0.0, 0.0, 0.8763, 0.976, 0.8382, 0.9472, 0.9521, 0.9612, 0.9416, 0.9416, 0.9273, 0.9529, 0.957, 0.8341, 0.9482, 0.9329, 0.821, 0.8298, nan] 
-2024-08-28 13:41:44.107778: Epoch time: 91.03 s 
-2024-08-28 13:41:45.242002:  
-2024-08-28 13:41:45.242169: Epoch 991 
-2024-08-28 13:41:45.242256: Current learning rate: 0.0054 
-2024-08-28 13:43:10.635255: train_loss -0.7634 
-2024-08-28 13:43:10.635485: val_loss -0.7887 
-2024-08-28 13:43:10.635647: Pseudo dice [0.0, 0.0, 0.8667, 0.9766, 0.8338, 0.9493, 0.9521, 0.963, 0.9513, 0.9416, 0.9196, 0.9617, 0.9567, 0.8481, 0.954, 0.9327, 0.8303, 0.8153, nan] 
-2024-08-28 13:43:10.635730: Epoch time: 85.39 s 
-2024-08-28 13:43:11.829748:  
-2024-08-28 13:43:11.830060: Epoch 992 
-2024-08-28 13:43:11.830207: Current learning rate: 0.0054 
-2024-08-28 13:44:38.156032: train_loss -0.7595 
-2024-08-28 13:44:38.156248: val_loss -0.7817 
-2024-08-28 13:44:38.156394: Pseudo dice [0.0, 0.0, 0.867, 0.9771, 0.8398, 0.9379, 0.9402, 0.963, 0.9475, 0.9536, 0.9186, 0.9578, 0.9565, 0.8461, 0.9447, 0.9368, 0.8078, 0.8171, nan] 
-2024-08-28 13:44:38.156493: Epoch time: 86.33 s 
-2024-08-28 13:44:39.347059:  
-2024-08-28 13:44:39.347324: Epoch 993 
-2024-08-28 13:44:39.347413: Current learning rate: 0.00539 
-2024-08-28 13:46:02.177799: train_loss -0.7632 
-2024-08-28 13:46:02.178026: val_loss -0.784 
-2024-08-28 13:46:02.178186: Pseudo dice [0.0, 0.0, 0.8928, 0.9743, 0.856, 0.9492, 0.9499, 0.9643, 0.9474, 0.9456, 0.9276, 0.959, 0.9569, 0.8389, 0.9567, 0.9378, 0.828, 0.8328, nan] 
-2024-08-28 13:46:02.178267: Epoch time: 82.83 s 
-2024-08-28 13:46:03.402945:  
-2024-08-28 13:46:03.403102: Epoch 994 
-2024-08-28 13:46:03.403194: Current learning rate: 0.00539 
-2024-08-28 13:47:29.130973: train_loss -0.7614 
-2024-08-28 13:47:29.131478: val_loss -0.7868 
-2024-08-28 13:47:29.131670: Pseudo dice [0.0, 0.0, 0.8865, 0.9765, 0.849, 0.9445, 0.9527, 0.9661, 0.9513, 0.9482, 0.9288, 0.9582, 0.96, 0.8384, 0.9504, 0.9267, 0.8119, 0.8193, nan] 
-2024-08-28 13:47:29.131817: Epoch time: 85.73 s 
-2024-08-28 13:47:30.369834:  
-2024-08-28 13:47:30.370297: Epoch 995 
-2024-08-28 13:47:30.370518: Current learning rate: 0.00538 
-2024-08-28 13:48:55.038041: train_loss -0.7611 
-2024-08-28 13:48:55.038357: val_loss -0.7847 
-2024-08-28 13:48:55.038526: Pseudo dice [0.0, 0.0, 0.8941, 0.9737, 0.8483, 0.945, 0.9468, 0.9674, 0.9487, 0.9456, 0.9161, 0.9595, 0.9573, 0.837, 0.9546, 0.9322, 0.8169, 0.8113, nan] 
-2024-08-28 13:48:55.038611: Epoch time: 84.67 s 
-2024-08-28 13:48:56.236655:  
-2024-08-28 13:48:56.236823: Epoch 996 
-2024-08-28 13:48:56.236918: Current learning rate: 0.00538 
-2024-08-28 13:50:28.632712: train_loss -0.7592 
-2024-08-28 13:50:28.632945: val_loss -0.7716 
-2024-08-28 13:50:28.633113: Pseudo dice [0.0, 0.0, 0.866, 0.9746, 0.816, 0.9358, 0.9441, 0.9576, 0.9452, 0.93, 0.9117, 0.9567, 0.9538, 0.8305, 0.9453, 0.9218, 0.81, 0.7957, nan] 
-2024-08-28 13:50:28.633198: Epoch time: 92.4 s 
-2024-08-28 13:50:29.834821:  
-2024-08-28 13:50:29.835090: Epoch 997 
-2024-08-28 13:50:29.835223: Current learning rate: 0.00537 
-2024-08-28 13:51:58.287987: train_loss -0.7567 
-2024-08-28 13:51:58.288251: val_loss -0.7857 
-2024-08-28 13:51:58.288417: Pseudo dice [0.0, 0.0, 0.8887, 0.9752, 0.8153, 0.9471, 0.9483, 0.9636, 0.9533, 0.9553, 0.9335, 0.9601, 0.9606, 0.8293, 0.9533, 0.925, 0.8112, 0.8137, nan] 
-2024-08-28 13:51:58.288522: Epoch time: 88.45 s 
-2024-08-28 13:51:59.485750:  
-2024-08-28 13:51:59.486274: Epoch 998 
-2024-08-28 13:51:59.486382: Current learning rate: 0.00537 
-2024-08-28 13:53:25.528569: train_loss -0.7591 
-2024-08-28 13:53:25.529053: val_loss -0.7854 
-2024-08-28 13:53:25.529216: Pseudo dice [0.0, 0.0, 0.8917, 0.9756, 0.8293, 0.9406, 0.9446, 0.9632, 0.9514, 0.9493, 0.9336, 0.9611, 0.9606, 0.8378, 0.9398, 0.9298, 0.8188, 0.8193, nan] 
-2024-08-28 13:53:25.529299: Epoch time: 86.04 s 
-2024-08-28 13:53:26.765489:  
-2024-08-28 13:53:26.765933: Epoch 999 
-2024-08-28 13:53:26.766023: Current learning rate: 0.00536 
-2024-08-28 13:54:50.975223: train_loss -0.7601 
-2024-08-28 13:54:50.975470: val_loss -0.781 
-2024-08-28 13:54:50.975626: Pseudo dice [0.0, 0.0, 0.9009, 0.9768, 0.8029, 0.9476, 0.9483, 0.9612, 0.9431, 0.9392, 0.9178, 0.9546, 0.9588, 0.8414, 0.921, 0.929, 0.8321, 0.8294, nan] 
-2024-08-28 13:54:50.975709: Epoch time: 84.21 s 
-2024-08-28 13:54:52.841860:  
-2024-08-28 13:54:52.842136: Epoch 1000 
-2024-08-28 13:54:52.842243: Current learning rate: 0.00536 
-2024-08-28 13:56:16.779847: train_loss -0.7623 
-2024-08-28 13:56:16.780102: val_loss -0.7845 
-2024-08-28 13:56:16.780266: Pseudo dice [0.0, 0.0, 0.9015, 0.977, 0.7983, 0.9519, 0.9534, 0.9659, 0.9499, 0.9528, 0.9237, 0.9604, 0.9579, 0.8422, 0.9456, 0.9316, 0.8017, 0.8023, nan] 
-2024-08-28 13:56:16.780357: Epoch time: 83.94 s 
-2024-08-28 13:56:17.958351:  
-2024-08-28 13:56:17.958581: Epoch 1001 
-2024-08-28 13:56:17.958688: Current learning rate: 0.00535 
-2024-08-28 13:57:44.770850: train_loss -0.7613 
-2024-08-28 13:57:44.771405: val_loss -0.7822 
-2024-08-28 13:57:44.771578: Pseudo dice [0.0, 0.0, 0.9012, 0.9755, 0.8218, 0.9463, 0.9445, 0.9594, 0.9486, 0.9541, 0.9268, 0.9564, 0.9543, 0.8288, 0.947, 0.929, 0.8152, 0.7953, nan] 
-2024-08-28 13:57:44.771655: Epoch time: 86.81 s 
-2024-08-28 13:57:45.994053:  
-2024-08-28 13:57:45.994240: Epoch 1002 
-2024-08-28 13:57:45.994332: Current learning rate: 0.00535 
-2024-08-28 13:59:11.150587: train_loss -0.7604 
-2024-08-28 13:59:11.150972: val_loss -0.7796 
-2024-08-28 13:59:11.151176: Pseudo dice [0.0, 0.0, 0.9026, 0.9754, 0.8087, 0.946, 0.9479, 0.9643, 0.9393, 0.9445, 0.9235, 0.9484, 0.9549, 0.8254, 0.9519, 0.9286, 0.827, 0.8133, nan] 
-2024-08-28 13:59:11.151267: Epoch time: 85.16 s 
-2024-08-28 13:59:12.375820:  
-2024-08-28 13:59:12.376199: Epoch 1003 
-2024-08-28 13:59:12.376296: Current learning rate: 0.00534 
-2024-08-28 14:00:41.414708: train_loss -0.7637 
-2024-08-28 14:00:41.414973: val_loss -0.7892 
-2024-08-28 14:00:41.415154: Pseudo dice [0.0, 0.0, 0.9019, 0.9776, 0.8368, 0.9465, 0.9488, 0.9605, 0.9479, 0.9522, 0.9332, 0.9613, 0.9622, 0.843, 0.9546, 0.9342, 0.8224, 0.8023, nan] 
-2024-08-28 14:00:41.415297: Epoch time: 89.04 s 
-2024-08-28 14:00:42.641121:  
-2024-08-28 14:00:42.641449: Epoch 1004 
-2024-08-28 14:00:42.641551: Current learning rate: 0.00534 
-2024-08-28 14:02:11.413533: train_loss -0.7661 
-2024-08-28 14:02:11.413982: val_loss -0.7886 
-2024-08-28 14:02:11.414178: Pseudo dice [0.0, 0.0, 0.8943, 0.976, 0.8358, 0.9468, 0.9487, 0.9638, 0.9504, 0.9531, 0.9261, 0.9628, 0.9594, 0.8388, 0.9547, 0.9236, 0.8305, 0.8393, nan] 
-2024-08-28 14:02:11.414266: Epoch time: 88.77 s 
-2024-08-28 14:02:12.638651:  
-2024-08-28 14:02:12.639092: Epoch 1005 
-2024-08-28 14:02:12.639194: Current learning rate: 0.00533 
-2024-08-28 14:03:36.881986: train_loss -0.7634 
-2024-08-28 14:03:36.882208: val_loss -0.7785 
-2024-08-28 14:03:36.882405: Pseudo dice [0.0, 0.0, 0.8963, 0.976, 0.8289, 0.9421, 0.9474, 0.9593, 0.947, 0.9482, 0.9305, 0.9554, 0.9571, 0.8287, 0.9458, 0.9232, 0.8221, 0.8137, nan] 
-2024-08-28 14:03:36.882512: Epoch time: 84.24 s 
-2024-08-28 14:03:38.095534:  
-2024-08-28 14:03:38.095685: Epoch 1006 
-2024-08-28 14:03:38.095794: Current learning rate: 0.00533 
-2024-08-28 14:05:02.295481: train_loss -0.76 
-2024-08-28 14:05:02.295733: val_loss -0.7705 
-2024-08-28 14:05:02.295897: Pseudo dice [0.0, 0.0, 0.8904, 0.9739, 0.7529, 0.945, 0.9359, 0.9486, 0.9414, 0.9503, 0.9227, 0.9509, 0.9577, 0.8238, 0.9322, 0.9199, 0.7939, 0.7918, nan] 
-2024-08-28 14:05:02.295984: Epoch time: 84.2 s 
-2024-08-28 14:05:03.751494:  
-2024-08-28 14:05:03.751683: Epoch 1007 
-2024-08-28 14:05:03.751772: Current learning rate: 0.00533 
-2024-08-28 14:06:28.740283: train_loss -0.7512 
-2024-08-28 14:06:28.740618: val_loss -0.7838 
-2024-08-28 14:06:28.740789: Pseudo dice [0.0, 0.0, 0.8833, 0.9755, 0.8151, 0.9415, 0.9419, 0.9617, 0.9501, 0.9512, 0.9295, 0.96, 0.9552, 0.8351, 0.9522, 0.9289, 0.8015, 0.809, nan] 
-2024-08-28 14:06:28.740877: Epoch time: 84.99 s 
-2024-08-28 14:06:29.979929:  
-2024-08-28 14:06:29.980117: Epoch 1008 
-2024-08-28 14:06:29.980209: Current learning rate: 0.00532 
-2024-08-28 14:07:58.394829: train_loss -0.7614 
-2024-08-28 14:07:58.395072: val_loss -0.7826 
-2024-08-28 14:07:58.395224: Pseudo dice [0.0, 0.0, 0.8972, 0.9761, 0.818, 0.9433, 0.947, 0.9606, 0.9493, 0.9411, 0.9252, 0.9591, 0.9529, 0.84, 0.9412, 0.9251, 0.8183, 0.8293, nan] 
-2024-08-28 14:07:58.395302: Epoch time: 88.42 s 
-2024-08-28 14:07:59.578156:  
-2024-08-28 14:07:59.578325: Epoch 1009 
-2024-08-28 14:07:59.578424: Current learning rate: 0.00532 
-2024-08-28 14:09:26.107356: train_loss -0.7527 
-2024-08-28 14:09:26.107601: val_loss -0.7737 
-2024-08-28 14:09:26.107768: Pseudo dice [0.0, 0.0, 0.8731, 0.9753, 0.7463, 0.942, 0.9419, 0.9571, 0.943, 0.9472, 0.9252, 0.9552, 0.9586, 0.8197, 0.9292, 0.9192, 0.8275, 0.8138, nan] 
-2024-08-28 14:09:26.107889: Epoch time: 86.53 s 
-2024-08-28 14:09:27.331758:  
-2024-08-28 14:09:27.331930: Epoch 1010 
-2024-08-28 14:09:27.332024: Current learning rate: 0.00531 
-2024-08-28 14:10:54.397014: train_loss -0.7547 
-2024-08-28 14:10:54.397497: val_loss -0.7818 
-2024-08-28 14:10:54.397712: Pseudo dice [0.0, 0.0, 0.902, 0.9759, 0.8073, 0.9425, 0.9481, 0.9617, 0.9486, 0.9504, 0.9331, 0.9568, 0.9563, 0.8328, 0.9464, 0.9277, 0.8191, 0.8187, nan] 
-2024-08-28 14:10:54.397847: Epoch time: 87.07 s 
-2024-08-28 14:10:55.630827:  
-2024-08-28 14:10:55.631054: Epoch 1011 
-2024-08-28 14:10:55.631156: Current learning rate: 0.00531 
-2024-08-28 14:12:19.563620: train_loss -0.7571 
-2024-08-28 14:12:19.564391: val_loss -0.7857 
-2024-08-28 14:12:19.564585: Pseudo dice [0.0, 0.0, 0.8963, 0.9754, 0.827, 0.9489, 0.9452, 0.9598, 0.9514, 0.9437, 0.927, 0.957, 0.9601, 0.8408, 0.9549, 0.93, 0.8189, 0.8274, nan] 
-2024-08-28 14:12:19.564734: Epoch time: 83.93 s 
-2024-08-28 14:12:21.048404:  
-2024-08-28 14:12:21.048632: Epoch 1012 
-2024-08-28 14:12:21.048749: Current learning rate: 0.0053 
-2024-08-28 14:13:51.620965: train_loss -0.7608 
-2024-08-28 14:13:51.621741: val_loss -0.7873 
-2024-08-28 14:13:51.621931: Pseudo dice [0.0, 0.0, 0.8831, 0.977, 0.8478, 0.9468, 0.9441, 0.9621, 0.9531, 0.9513, 0.9242, 0.9619, 0.9613, 0.8446, 0.9549, 0.9295, 0.8117, 0.8126, nan] 
-2024-08-28 14:13:51.622068: Epoch time: 90.57 s 
-2024-08-28 14:13:53.158749:  
-2024-08-28 14:13:53.158897: Epoch 1013 
-2024-08-28 14:13:53.158979: Current learning rate: 0.0053 
-2024-08-28 14:15:23.649238: train_loss -0.7616 
-2024-08-28 14:15:23.649499: val_loss -0.7788 
-2024-08-28 14:15:23.649668: Pseudo dice [0.0, 0.0, 0.8751, 0.9751, 0.8065, 0.9367, 0.9401, 0.9621, 0.9498, 0.95, 0.9263, 0.9584, 0.9586, 0.8409, 0.935, 0.9286, 0.824, 0.8243, nan] 
-2024-08-28 14:15:23.649760: Epoch time: 90.49 s 
-2024-08-28 14:15:24.907538:  
-2024-08-28 14:15:24.907984: Epoch 1014 
-2024-08-28 14:15:24.908087: Current learning rate: 0.00529 
-2024-08-28 14:16:51.833166: train_loss -0.7601 
-2024-08-28 14:16:51.833489: val_loss -0.7765 
-2024-08-28 14:16:51.833682: Pseudo dice [0.0, 0.0, 0.871, 0.971, 0.826, 0.9414, 0.9461, 0.9628, 0.9502, 0.9367, 0.9189, 0.9612, 0.9598, 0.8348, 0.9513, 0.9261, 0.8072, 0.8091, nan] 
-2024-08-28 14:16:51.833777: Epoch time: 86.93 s 
-2024-08-28 14:16:53.030806:  
-2024-08-28 14:16:53.031285: Epoch 1015 
-2024-08-28 14:16:53.031389: Current learning rate: 0.00529 
-2024-08-28 14:18:16.835359: train_loss -0.7538 
-2024-08-28 14:18:16.835662: val_loss -0.7772 
-2024-08-28 14:18:16.835852: Pseudo dice [0.0, 0.0, 0.8992, 0.9756, 0.8008, 0.9418, 0.946, 0.9585, 0.9476, 0.9273, 0.9313, 0.9578, 0.9628, 0.836, 0.9476, 0.9229, 0.811, 0.8242, nan] 
-2024-08-28 14:18:16.835947: Epoch time: 83.81 s 
-2024-08-28 14:18:18.060096:  
-2024-08-28 14:18:18.060254: Epoch 1016 
-2024-08-28 14:18:18.060348: Current learning rate: 0.00528 
-2024-08-28 14:19:41.728706: train_loss -0.7624 
-2024-08-28 14:19:41.728935: val_loss -0.7807 
-2024-08-28 14:19:41.729093: Pseudo dice [0.0, 0.0, 0.8867, 0.9765, 0.8303, 0.9463, 0.9454, 0.9648, 0.9498, 0.944, 0.9296, 0.9601, 0.9613, 0.8339, 0.9476, 0.9267, 0.8128, 0.8254, nan] 
-2024-08-28 14:19:41.729174: Epoch time: 83.67 s 
-2024-08-28 14:19:42.936883:  
-2024-08-28 14:19:42.937041: Epoch 1017 
-2024-08-28 14:19:42.937125: Current learning rate: 0.00528 
-2024-08-28 14:21:10.735342: train_loss -0.7618 
-2024-08-28 14:21:10.735568: val_loss -0.786 
-2024-08-28 14:21:10.735723: Pseudo dice [0.0, 0.0, 0.8946, 0.9775, 0.8243, 0.9439, 0.9459, 0.9636, 0.9425, 0.9509, 0.9244, 0.9562, 0.9576, 0.8311, 0.9484, 0.9291, 0.8085, 0.8276, nan] 
-2024-08-28 14:21:10.735804: Epoch time: 87.8 s 
-2024-08-28 14:21:11.880589:  
-2024-08-28 14:21:11.880919: Epoch 1018 
-2024-08-28 14:21:11.881006: Current learning rate: 0.00527 
-2024-08-28 14:22:37.673438: train_loss -0.7622 
-2024-08-28 14:22:37.673698: val_loss -0.7786 
-2024-08-28 14:22:37.673912: Pseudo dice [0.0, 0.0, 0.8846, 0.976, 0.823, 0.9468, 0.9454, 0.9619, 0.9464, 0.9488, 0.9258, 0.9606, 0.9537, 0.8367, 0.9526, 0.918, 0.8165, 0.8028, nan] 
-2024-08-28 14:22:37.674021: Epoch time: 85.79 s 
-2024-08-28 14:22:39.089128:  
-2024-08-28 14:22:39.089558: Epoch 1019 
-2024-08-28 14:22:39.089652: Current learning rate: 0.00527 
-2024-08-28 14:24:03.581255: train_loss -0.7584 
-2024-08-28 14:24:03.581467: val_loss -0.7816 
-2024-08-28 14:24:03.581633: Pseudo dice [0.0, 0.0, 0.8922, 0.9761, 0.7992, 0.9461, 0.9472, 0.9632, 0.9526, 0.9569, 0.9325, 0.9598, 0.961, 0.8371, 0.9412, 0.9261, 0.8044, 0.8171, nan] 
-2024-08-28 14:24:03.581713: Epoch time: 84.49 s 
-2024-08-28 14:24:04.797445:  
-2024-08-28 14:24:04.797614: Epoch 1020 
-2024-08-28 14:24:04.797707: Current learning rate: 0.00526 
-2024-08-28 14:25:32.907156: train_loss -0.7606 
-2024-08-28 14:25:32.907373: val_loss -0.7908 
-2024-08-28 14:25:32.907541: Pseudo dice [0.0, 0.0, 0.9019, 0.9767, 0.8328, 0.942, 0.9473, 0.966, 0.95, 0.9484, 0.9296, 0.9614, 0.9607, 0.8409, 0.9553, 0.9332, 0.8211, 0.8232, nan] 
-2024-08-28 14:25:32.907625: Epoch time: 88.11 s 
-2024-08-28 14:25:34.107095:  
-2024-08-28 14:25:34.107229: Epoch 1021 
-2024-08-28 14:25:34.107311: Current learning rate: 0.00526 
-2024-08-28 14:26:57.980227: train_loss -0.7634 
-2024-08-28 14:26:57.980516: val_loss -0.7836 
-2024-08-28 14:26:57.980690: Pseudo dice [0.0, 0.0, 0.8828, 0.9763, 0.8325, 0.9374, 0.942, 0.9659, 0.9486, 0.9502, 0.9296, 0.9578, 0.9599, 0.8354, 0.9575, 0.9344, 0.8182, 0.8046, nan] 
-2024-08-28 14:26:57.980776: Epoch time: 83.87 s 
-2024-08-28 14:26:59.163862:  
-2024-08-28 14:26:59.164017: Epoch 1022 
-2024-08-28 14:26:59.164115: Current learning rate: 0.00525 
-2024-08-28 14:28:28.160903: train_loss -0.7633 
-2024-08-28 14:28:28.161120: val_loss -0.7785 
-2024-08-28 14:28:28.161282: Pseudo dice [0.0, 0.0, 0.8922, 0.9773, 0.7127, 0.9474, 0.9464, 0.96, 0.9487, 0.9488, 0.9311, 0.9624, 0.9582, 0.8348, 0.9487, 0.9268, 0.8347, 0.827, nan] 
-2024-08-28 14:28:28.161362: Epoch time: 89.0 s 
-2024-08-28 14:28:29.359950:  
-2024-08-28 14:28:29.360315: Epoch 1023 
-2024-08-28 14:28:29.360405: Current learning rate: 0.00525 
-2024-08-28 14:29:50.426112: train_loss -0.7622 
-2024-08-28 14:29:50.426337: val_loss -0.771 
-2024-08-28 14:29:50.426501: Pseudo dice [0.0, 0.0, 0.8656, 0.9752, 0.7789, 0.9382, 0.9374, 0.9565, 0.9496, 0.941, 0.9263, 0.9606, 0.9582, 0.8157, 0.9384, 0.9167, 0.8011, 0.802, nan] 
-2024-08-28 14:29:50.426585: Epoch time: 81.07 s 
-2024-08-28 14:29:51.621953:  
-2024-08-28 14:29:51.622097: Epoch 1024 
-2024-08-28 14:29:51.622186: Current learning rate: 0.00524 
-2024-08-28 14:31:18.686871: train_loss -0.76 
-2024-08-28 14:31:18.687084: val_loss -0.7789 
-2024-08-28 14:31:18.687243: Pseudo dice [0.0, 0.0, 0.9006, 0.9757, 0.7984, 0.9479, 0.9488, 0.958, 0.9466, 0.947, 0.9259, 0.9575, 0.9574, 0.848, 0.9521, 0.9296, 0.8198, 0.8217, nan] 
-2024-08-28 14:31:18.687324: Epoch time: 87.07 s 
-2024-08-28 14:31:20.125086:  
-2024-08-28 14:31:20.125312: Epoch 1025 
-2024-08-28 14:31:20.125396: Current learning rate: 0.00524 
-2024-08-28 14:32:44.667418: train_loss -0.7591 
-2024-08-28 14:32:44.667652: val_loss -0.7756 
-2024-08-28 14:32:44.667812: Pseudo dice [0.0, 0.0, 0.8806, 0.9759, 0.8235, 0.9458, 0.9486, 0.9617, 0.9466, 0.9322, 0.927, 0.9556, 0.9564, 0.8326, 0.9426, 0.9218, 0.8104, 0.8063, nan] 
-2024-08-28 14:32:44.667892: Epoch time: 84.54 s 
-2024-08-28 14:32:45.967360:  
-2024-08-28 14:32:45.967545: Epoch 1026 
-2024-08-28 14:32:45.967630: Current learning rate: 0.00523 
-2024-08-28 14:34:14.892739: train_loss -0.7581 
-2024-08-28 14:34:14.892961: val_loss -0.7827 
-2024-08-28 14:34:14.893125: Pseudo dice [0.0, 0.0, 0.8831, 0.9746, 0.8262, 0.948, 0.9415, 0.9633, 0.9468, 0.9406, 0.927, 0.9552, 0.955, 0.8314, 0.9495, 0.9263, 0.8232, 0.817, nan] 
-2024-08-28 14:34:14.893285: Epoch time: 88.93 s 
-2024-08-28 14:34:16.051802:  
-2024-08-28 14:34:16.052096: Epoch 1027 
-2024-08-28 14:34:16.052184: Current learning rate: 0.00523 
-2024-08-28 14:35:39.622314: train_loss -0.7561 
-2024-08-28 14:35:39.622529: val_loss -0.779 
-2024-08-28 14:35:39.622680: Pseudo dice [0.0, 0.0, 0.8604, 0.9741, 0.8008, 0.942, 0.9443, 0.9612, 0.9487, 0.9481, 0.9277, 0.9586, 0.9556, 0.8368, 0.9537, 0.9257, 0.8046, 0.81, nan] 
-2024-08-28 14:35:39.622760: Epoch time: 83.57 s 
-2024-08-28 14:35:40.778414:  
-2024-08-28 14:35:40.778605: Epoch 1028 
-2024-08-28 14:35:40.778699: Current learning rate: 0.00522 
-2024-08-28 14:37:09.244600: train_loss -0.7549 
-2024-08-28 14:37:09.244825: val_loss -0.7821 
-2024-08-28 14:37:09.244986: Pseudo dice [0.0, 0.0, 0.8673, 0.9764, 0.8106, 0.9418, 0.9407, 0.9616, 0.9449, 0.9503, 0.9318, 0.9575, 0.9607, 0.832, 0.9506, 0.9255, 0.8244, 0.8235, nan] 
-2024-08-28 14:37:09.245070: Epoch time: 88.47 s 
-2024-08-28 14:37:10.450624:  
-2024-08-28 14:37:10.450787: Epoch 1029 
-2024-08-28 14:37:10.450874: Current learning rate: 0.00522 
-2024-08-28 14:38:38.104575: train_loss -0.7645 
-2024-08-28 14:38:38.105198: val_loss -0.781 
-2024-08-28 14:38:38.105456: Pseudo dice [0.0, 0.0, 0.8666, 0.9764, 0.8501, 0.9444, 0.9456, 0.9661, 0.9475, 0.9541, 0.9308, 0.9582, 0.9572, 0.8388, 0.9494, 0.9325, 0.8111, 0.8208, nan] 
-2024-08-28 14:38:38.105698: Epoch time: 87.65 s 
-2024-08-28 14:38:39.314609:  
-2024-08-28 14:38:39.315073: Epoch 1030 
-2024-08-28 14:38:39.315164: Current learning rate: 0.00521 
-2024-08-28 14:40:09.002717: train_loss -0.7584 
-2024-08-28 14:40:09.002931: val_loss -0.7805 
-2024-08-28 14:40:09.003093: Pseudo dice [0.0, 0.0, 0.9038, 0.9756, 0.8275, 0.9412, 0.944, 0.9613, 0.9489, 0.9489, 0.9297, 0.9572, 0.9573, 0.8408, 0.945, 0.9283, 0.8263, 0.8059, nan] 
-2024-08-28 14:40:09.003170: Epoch time: 89.69 s 
-2024-08-28 14:40:10.491770:  
-2024-08-28 14:40:10.492148: Epoch 1031 
-2024-08-28 14:40:10.492247: Current learning rate: 0.00521 
-2024-08-28 14:41:37.603853: train_loss -0.7598 
-2024-08-28 14:41:37.604148: val_loss -0.7874 
-2024-08-28 14:41:37.604439: Pseudo dice [0.0, 0.0, 0.9037, 0.9765, 0.817, 0.9502, 0.9504, 0.9646, 0.9459, 0.9438, 0.9262, 0.9599, 0.9581, 0.8435, 0.9432, 0.9297, 0.8279, 0.8272, nan] 
-2024-08-28 14:41:37.604530: Epoch time: 87.11 s 
-2024-08-28 14:41:38.849102:  
-2024-08-28 14:41:38.849368: Epoch 1032 
-2024-08-28 14:41:38.849557: Current learning rate: 0.0052 
-2024-08-28 14:43:08.880933: train_loss -0.7623 
-2024-08-28 14:43:08.881164: val_loss -0.7835 
-2024-08-28 14:43:08.881334: Pseudo dice [0.0, 0.0, 0.9011, 0.9753, 0.8396, 0.944, 0.9466, 0.9658, 0.9465, 0.9473, 0.9267, 0.9558, 0.9569, 0.8403, 0.9508, 0.9285, 0.799, 0.8062, nan] 
-2024-08-28 14:43:08.881423: Epoch time: 90.03 s 
-2024-08-28 14:43:10.105616:  
-2024-08-28 14:43:10.105950: Epoch 1033 
-2024-08-28 14:43:10.106040: Current learning rate: 0.0052 
-2024-08-28 14:44:38.624349: train_loss -0.7648 
-2024-08-28 14:44:38.624590: val_loss -0.7862 
-2024-08-28 14:44:38.624762: Pseudo dice [0.0, 0.0, 0.9075, 0.9752, 0.8318, 0.9449, 0.9482, 0.9652, 0.9486, 0.9412, 0.9341, 0.9586, 0.9588, 0.8389, 0.9542, 0.934, 0.826, 0.8188, nan] 
-2024-08-28 14:44:38.624848: Epoch time: 88.52 s 
-2024-08-28 14:44:39.852926:  
-2024-08-28 14:44:39.853201: Epoch 1034 
-2024-08-28 14:44:39.853295: Current learning rate: 0.00519 
-2024-08-28 14:46:00.312942: train_loss -0.7632 
-2024-08-28 14:46:00.313182: val_loss -0.7793 
-2024-08-28 14:46:00.313347: Pseudo dice [0.0, 0.0, 0.9034, 0.9773, 0.8289, 0.9373, 0.9371, 0.9564, 0.9487, 0.9496, 0.9295, 0.96, 0.9567, 0.8262, 0.9404, 0.9298, 0.8081, 0.811, nan] 
-2024-08-28 14:46:00.313433: Epoch time: 80.46 s 
-2024-08-28 14:46:01.517004:  
-2024-08-28 14:46:01.517475: Epoch 1035 
-2024-08-28 14:46:01.517576: Current learning rate: 0.00519 
-2024-08-28 14:47:28.053642: train_loss -0.7646 
-2024-08-28 14:47:28.053874: val_loss -0.7794 
-2024-08-28 14:47:28.054039: Pseudo dice [0.0, 0.0, 0.8848, 0.9752, 0.8057, 0.9382, 0.9372, 0.959, 0.9498, 0.9446, 0.9217, 0.9584, 0.9567, 0.8343, 0.9427, 0.9259, 0.8126, 0.8126, nan] 
-2024-08-28 14:47:28.054123: Epoch time: 86.54 s 
-2024-08-28 14:47:29.233376:  
-2024-08-28 14:47:29.233533: Epoch 1036 
-2024-08-28 14:47:29.233622: Current learning rate: 0.00518 
-2024-08-28 14:48:54.196352: train_loss -0.7603 
-2024-08-28 14:48:54.196591: val_loss -0.7889 
-2024-08-28 14:48:54.196759: Pseudo dice [0.0, 0.0, 0.8978, 0.9771, 0.8424, 0.9477, 0.9512, 0.9656, 0.9463, 0.9468, 0.9313, 0.9591, 0.9603, 0.8473, 0.9498, 0.9214, 0.8233, 0.8187, nan] 
-2024-08-28 14:48:54.196841: Epoch time: 84.96 s 
-2024-08-28 14:48:55.685930:  
-2024-08-28 14:48:55.686347: Epoch 1037 
-2024-08-28 14:48:55.686440: Current learning rate: 0.00518 
-2024-08-28 14:50:23.636451: train_loss -0.7632 
-2024-08-28 14:50:23.636712: val_loss -0.7843 
-2024-08-28 14:50:23.636877: Pseudo dice [0.0, 0.0, 0.892, 0.9754, 0.8207, 0.9445, 0.9457, 0.9655, 0.9509, 0.9505, 0.9266, 0.9583, 0.9599, 0.8407, 0.9515, 0.932, 0.8182, 0.8159, nan] 
-2024-08-28 14:50:23.637010: Epoch time: 87.95 s 
-2024-08-28 14:50:24.831005:  
-2024-08-28 14:50:24.831611: Epoch 1038 
-2024-08-28 14:50:24.832006: Current learning rate: 0.00518 
-2024-08-28 14:51:46.315402: train_loss -0.7653 
-2024-08-28 14:51:46.315628: val_loss -0.7835 
-2024-08-28 14:51:46.315796: Pseudo dice [0.0, 0.0, 0.8967, 0.9783, 0.8191, 0.9463, 0.9463, 0.9633, 0.956, 0.9534, 0.9274, 0.9632, 0.959, 0.8328, 0.953, 0.926, 0.8208, 0.8123, nan] 
-2024-08-28 14:51:46.315883: Epoch time: 81.49 s 
-2024-08-28 14:51:47.527564:  
-2024-08-28 14:51:47.527778: Epoch 1039 
-2024-08-28 14:51:47.527865: Current learning rate: 0.00517 
-2024-08-28 14:53:17.591860: train_loss -0.7577 
-2024-08-28 14:53:17.592094: val_loss -0.7798 
-2024-08-28 14:53:17.592251: Pseudo dice [0.0, 0.0, 0.8739, 0.975, 0.757, 0.9412, 0.9431, 0.9601, 0.9552, 0.9507, 0.9347, 0.9604, 0.9594, 0.8407, 0.942, 0.9289, 0.8261, 0.823, nan] 
-2024-08-28 14:53:17.592333: Epoch time: 90.07 s 
-2024-08-28 14:53:19.326772:  
-2024-08-28 14:53:19.326992: Epoch 1040 
-2024-08-28 14:53:19.327119: Current learning rate: 0.00517 
-2024-08-28 14:54:50.460859: train_loss -0.7594 
-2024-08-28 14:54:50.461099: val_loss -0.7882 
-2024-08-28 14:54:50.461260: Pseudo dice [0.0, 0.0, 0.8936, 0.9763, 0.8312, 0.9444, 0.9492, 0.9629, 0.9529, 0.9503, 0.93, 0.9621, 0.9608, 0.8341, 0.9485, 0.9351, 0.8366, 0.8332, nan] 
-2024-08-28 14:54:50.461349: Epoch time: 91.14 s 
-2024-08-28 14:54:51.633696:  
-2024-08-28 14:54:51.633879: Epoch 1041 
-2024-08-28 14:54:51.633973: Current learning rate: 0.00516 
-2024-08-28 14:56:17.359820: train_loss -0.7626 
-2024-08-28 14:56:17.360047: val_loss -0.7764 
-2024-08-28 14:56:17.360211: Pseudo dice [0.0, 0.0, 0.8785, 0.9765, 0.8015, 0.9449, 0.9435, 0.9661, 0.9367, 0.9327, 0.9182, 0.946, 0.9455, 0.8391, 0.9532, 0.9239, 0.8161, 0.8044, nan] 
-2024-08-28 14:56:17.360293: Epoch time: 85.73 s 
-2024-08-28 14:56:18.548577:  
-2024-08-28 14:56:18.548965: Epoch 1042 
-2024-08-28 14:56:18.549155: Current learning rate: 0.00516 
-2024-08-28 14:57:47.778177: train_loss -0.7642 
-2024-08-28 14:57:47.778401: val_loss -0.7834 
-2024-08-28 14:57:47.778553: Pseudo dice [0.0, 0.0, 0.888, 0.9756, 0.806, 0.9385, 0.9472, 0.9572, 0.9562, 0.9537, 0.9283, 0.9626, 0.9587, 0.8419, 0.9498, 0.9245, 0.8364, 0.8123, nan] 
-2024-08-28 14:57:47.778632: Epoch time: 89.23 s 
-2024-08-28 14:57:49.241589:  
-2024-08-28 14:57:49.241775: Epoch 1043 
-2024-08-28 14:57:49.241858: Current learning rate: 0.00515 
-2024-08-28 14:59:16.800681: train_loss -0.765 
-2024-08-28 14:59:16.800898: val_loss -0.7844 
-2024-08-28 14:59:16.801052: Pseudo dice [0.0, 0.0, 0.8734, 0.9775, 0.7945, 0.9432, 0.9446, 0.956, 0.9506, 0.9536, 0.9357, 0.9624, 0.9603, 0.8409, 0.9583, 0.9339, 0.8332, 0.8191, nan] 
-2024-08-28 14:59:16.801130: Epoch time: 87.56 s 
-2024-08-28 14:59:17.948323:  
-2024-08-28 14:59:17.948463: Epoch 1044 
-2024-08-28 14:59:17.948546: Current learning rate: 0.00515 
-2024-08-28 15:00:47.606513: train_loss -0.768 
-2024-08-28 15:00:47.606732: val_loss -0.7863 
-2024-08-28 15:00:47.606908: Pseudo dice [0.0, 0.0, 0.8981, 0.9768, 0.8113, 0.9444, 0.9454, 0.9605, 0.9482, 0.9433, 0.9185, 0.9569, 0.9555, 0.8503, 0.9472, 0.9287, 0.8213, 0.8074, nan] 
-2024-08-28 15:00:47.606990: Epoch time: 89.66 s 
-2024-08-28 15:00:48.809093:  
-2024-08-28 15:00:48.809455: Epoch 1045 
-2024-08-28 15:00:48.809541: Current learning rate: 0.00514 
-2024-08-28 15:02:19.149416: train_loss -0.7653 
-2024-08-28 15:02:19.149675: val_loss -0.7914 
-2024-08-28 15:02:19.149846: Pseudo dice [0.0, 0.0, 0.8858, 0.9768, 0.8497, 0.9502, 0.9495, 0.9634, 0.951, 0.9514, 0.9338, 0.9567, 0.9629, 0.8516, 0.9452, 0.9328, 0.8391, 0.8254, nan] 
-2024-08-28 15:02:19.149951: Epoch time: 90.34 s 
-2024-08-28 15:02:20.328073:  
-2024-08-28 15:02:20.328225: Epoch 1046 
-2024-08-28 15:02:20.328309: Current learning rate: 0.00514 
-2024-08-28 15:03:47.598700: train_loss -0.7709 
-2024-08-28 15:03:47.599213: val_loss -0.7817 
-2024-08-28 15:03:47.599378: Pseudo dice [0.0, 0.0, 0.9092, 0.9768, 0.8378, 0.9438, 0.9496, 0.9636, 0.9436, 0.9484, 0.924, 0.959, 0.9574, 0.8508, 0.9497, 0.9313, 0.834, 0.8295, nan] 
-2024-08-28 15:03:47.599458: Epoch time: 87.27 s 
-2024-08-28 15:03:48.814836:  
-2024-08-28 15:03:48.814982: Epoch 1047 
-2024-08-28 15:03:48.815072: Current learning rate: 0.00513 
-2024-08-28 15:05:14.233086: train_loss -0.7686 
-2024-08-28 15:05:14.233307: val_loss -0.7889 
-2024-08-28 15:05:14.233464: Pseudo dice [0.0, 0.0, 0.8714, 0.9777, 0.8498, 0.9498, 0.9508, 0.9633, 0.9521, 0.9485, 0.9335, 0.9614, 0.9586, 0.8472, 0.9497, 0.936, 0.8192, 0.8101, nan] 
-2024-08-28 15:05:14.233544: Epoch time: 85.42 s 
-2024-08-28 15:05:15.399568:  
-2024-08-28 15:05:15.399856: Epoch 1048 
-2024-08-28 15:05:15.399945: Current learning rate: 0.00513 
-2024-08-28 15:06:41.056835: train_loss -0.7654 
-2024-08-28 15:06:41.057087: val_loss -0.7839 
-2024-08-28 15:06:41.057312: Pseudo dice [0.0, 0.0, 0.899, 0.9764, 0.8209, 0.9452, 0.9468, 0.959, 0.9506, 0.9477, 0.9296, 0.9578, 0.9579, 0.8301, 0.9453, 0.9244, 0.8295, 0.8437, nan] 
-2024-08-28 15:06:41.057544: Epoch time: 85.66 s 
-2024-08-28 15:06:42.528136:  
-2024-08-28 15:06:42.528421: Epoch 1049 
-2024-08-28 15:06:42.528523: Current learning rate: 0.00512 
-2024-08-28 15:08:13.279781: train_loss -0.7559 
-2024-08-28 15:08:13.280220: val_loss -0.7817 
-2024-08-28 15:08:13.280495: Pseudo dice [0.0, 0.0, 0.8795, 0.9766, 0.7929, 0.93, 0.9381, 0.9595, 0.9527, 0.9435, 0.9296, 0.9603, 0.9565, 0.831, 0.952, 0.9258, 0.8098, 0.8088, nan] 
-2024-08-28 15:08:13.280648: Epoch time: 90.75 s 
-2024-08-28 15:08:14.836788:  
-2024-08-28 15:08:14.836956: Epoch 1050 
-2024-08-28 15:08:14.837041: Current learning rate: 0.00512 
-2024-08-28 15:09:41.487488: train_loss -0.7596 
-2024-08-28 15:09:41.487722: val_loss -0.7881 
-2024-08-28 15:09:41.487877: Pseudo dice [0.0, 0.0, 0.8671, 0.9761, 0.8322, 0.947, 0.9498, 0.9602, 0.953, 0.9486, 0.9354, 0.9597, 0.9621, 0.8416, 0.9479, 0.9296, 0.8191, 0.8245, nan] 
-2024-08-28 15:09:41.487955: Epoch time: 86.65 s 
-2024-08-28 15:09:42.698326:  
-2024-08-28 15:09:42.698498: Epoch 1051 
-2024-08-28 15:09:42.698593: Current learning rate: 0.00511 
-2024-08-28 15:11:02.410226: train_loss -0.7618 
-2024-08-28 15:11:02.410466: val_loss -0.7831 
-2024-08-28 15:11:02.410694: Pseudo dice [0.0, 0.0, 0.8845, 0.9741, 0.8384, 0.9485, 0.9496, 0.964, 0.9506, 0.9498, 0.9257, 0.9593, 0.963, 0.8454, 0.9511, 0.9327, 0.8171, 0.8117, nan] 
-2024-08-28 15:11:02.410804: Epoch time: 79.71 s 
-2024-08-28 15:11:03.633316:  
-2024-08-28 15:11:03.633445: Epoch 1052 
-2024-08-28 15:11:03.633526: Current learning rate: 0.00511 
-2024-08-28 15:12:22.212102: train_loss -0.7649 
-2024-08-28 15:12:22.212288: val_loss -0.7845 
-2024-08-28 15:12:22.212439: Pseudo dice [0.0, 0.0, 0.8869, 0.9777, 0.8466, 0.9446, 0.9445, 0.965, 0.9461, 0.9453, 0.9238, 0.9585, 0.958, 0.8383, 0.9508, 0.9298, 0.8003, 0.8038, nan] 
-2024-08-28 15:12:22.212511: Epoch time: 78.58 s 
-2024-08-28 15:12:23.460011:  
-2024-08-28 15:12:23.460137: Epoch 1053 
-2024-08-28 15:12:23.460217: Current learning rate: 0.0051 
-2024-08-28 15:13:40.008744: train_loss -0.7635 
-2024-08-28 15:13:40.008965: val_loss -0.7795 
-2024-08-28 15:13:40.009162: Pseudo dice [0.0, 0.0, 0.895, 0.9766, 0.723, 0.9475, 0.9508, 0.9639, 0.9512, 0.948, 0.927, 0.9588, 0.9585, 0.8381, 0.9398, 0.9263, 0.8318, 0.8254, nan] 
-2024-08-28 15:13:40.009253: Epoch time: 76.55 s 
-2024-08-28 15:13:41.173571:  
-2024-08-28 15:13:41.173722: Epoch 1054 
-2024-08-28 15:13:41.173810: Current learning rate: 0.0051 
-2024-08-28 15:15:01.982012: train_loss -0.763 
-2024-08-28 15:15:01.982226: val_loss -0.7873 
-2024-08-28 15:15:01.982380: Pseudo dice [0.0, 0.0, 0.8984, 0.9762, 0.8272, 0.9478, 0.9498, 0.9632, 0.9546, 0.9504, 0.9355, 0.9592, 0.9641, 0.8479, 0.9498, 0.9288, 0.8227, 0.8297, nan] 
-2024-08-28 15:15:01.982477: Epoch time: 80.81 s 
-2024-08-28 15:15:03.338530:  
-2024-08-28 15:15:03.338807: Epoch 1055 
-2024-08-28 15:15:03.338889: Current learning rate: 0.00509 
-2024-08-28 15:16:23.893769: train_loss -0.7625 
-2024-08-28 15:16:23.893987: val_loss -0.7837 
-2024-08-28 15:16:23.894139: Pseudo dice [0.0, 0.0, 0.8651, 0.9751, 0.8314, 0.945, 0.9445, 0.9645, 0.9508, 0.9418, 0.9336, 0.958, 0.959, 0.8435, 0.9506, 0.9275, 0.8143, 0.8055, nan] 
-2024-08-28 15:16:23.894218: Epoch time: 80.56 s 
-2024-08-28 15:16:24.941859:  
-2024-08-28 15:16:24.942000: Epoch 1056 
-2024-08-28 15:16:24.942083: Current learning rate: 0.00509 
-2024-08-28 15:17:44.568049: train_loss -0.7668 
-2024-08-28 15:17:44.568256: val_loss -0.7767 
-2024-08-28 15:17:44.568407: Pseudo dice [0.0, 0.0, 0.8947, 0.9757, 0.6657, 0.9469, 0.9468, 0.9615, 0.9513, 0.9458, 0.9288, 0.9601, 0.9597, 0.8353, 0.948, 0.9286, 0.8249, 0.8178, nan] 
-2024-08-28 15:17:44.568490: Epoch time: 79.63 s 
-2024-08-28 15:17:45.616105:  
-2024-08-28 15:17:45.616275: Epoch 1057 
-2024-08-28 15:17:45.616357: Current learning rate: 0.00508 
-2024-08-28 15:19:01.209527: train_loss -0.7645 
-2024-08-28 15:19:01.209739: val_loss -0.778 
-2024-08-28 15:19:01.209888: Pseudo dice [0.0, 0.0, 0.8822, 0.9745, 0.6995, 0.9408, 0.9394, 0.96, 0.9492, 0.95, 0.9271, 0.9594, 0.9579, 0.8182, 0.9562, 0.9233, 0.809, 0.8111, nan] 
-2024-08-28 15:19:01.209963: Epoch time: 75.59 s 
-2024-08-28 15:19:02.328545:  
-2024-08-28 15:19:02.328759: Epoch 1058 
-2024-08-28 15:19:02.328838: Current learning rate: 0.00508 
-2024-08-28 15:20:20.858848: train_loss -0.7635 
-2024-08-28 15:20:20.859057: val_loss -0.7835 
-2024-08-28 15:20:20.859216: Pseudo dice [0.0, 0.0, 0.8942, 0.9745, 0.833, 0.9465, 0.9489, 0.9654, 0.9485, 0.9514, 0.934, 0.9586, 0.9571, 0.8425, 0.9492, 0.9248, 0.8176, 0.8259, nan] 
-2024-08-28 15:20:20.859298: Epoch time: 78.53 s 
-2024-08-28 15:20:21.942392:  
-2024-08-28 15:20:21.942547: Epoch 1059 
-2024-08-28 15:20:21.942634: Current learning rate: 0.00507 
-2024-08-28 15:21:43.536876: train_loss -0.7647 
-2024-08-28 15:21:43.537082: val_loss -0.7855 
-2024-08-28 15:21:43.537233: Pseudo dice [0.0, 0.0, 0.8689, 0.9763, 0.8393, 0.9452, 0.9504, 0.9663, 0.9508, 0.9575, 0.9363, 0.9545, 0.9628, 0.8413, 0.9451, 0.9289, 0.8306, 0.83, nan] 
-2024-08-28 15:21:43.537309: Epoch time: 81.6 s 
-2024-08-28 15:21:44.684297:  
-2024-08-28 15:21:44.684436: Epoch 1060 
-2024-08-28 15:21:44.684511: Current learning rate: 0.00507 
-2024-08-28 15:23:04.963858: train_loss -0.7616 
-2024-08-28 15:23:04.964076: val_loss -0.7814 
-2024-08-28 15:23:04.964323: Pseudo dice [0.0, 0.0, 0.8823, 0.9748, 0.8227, 0.9445, 0.9493, 0.963, 0.9463, 0.9339, 0.9187, 0.9565, 0.9536, 0.8395, 0.9514, 0.9261, 0.8149, 0.8212, nan] 
-2024-08-28 15:23:04.964477: Epoch time: 80.28 s 
-2024-08-28 15:23:06.218016:  
-2024-08-28 15:23:06.218158: Epoch 1061 
-2024-08-28 15:23:06.218230: Current learning rate: 0.00506 
-2024-08-28 15:24:26.362998: train_loss -0.7622 
-2024-08-28 15:24:26.363198: val_loss -0.7836 
-2024-08-28 15:24:26.363348: Pseudo dice [0.0, 0.0, 0.893, 0.9673, 0.8096, 0.9452, 0.952, 0.9659, 0.9473, 0.9521, 0.9206, 0.9583, 0.9567, 0.8327, 0.9518, 0.9303, 0.8265, 0.8334, nan] 
-2024-08-28 15:24:26.363422: Epoch time: 80.15 s 
-2024-08-28 15:24:27.401212:  
-2024-08-28 15:24:27.401364: Epoch 1062 
-2024-08-28 15:24:27.401446: Current learning rate: 0.00506 
-2024-08-28 15:25:50.767589: train_loss -0.7596 
-2024-08-28 15:25:50.767808: val_loss -0.7809 
-2024-08-28 15:25:50.767984: Pseudo dice [0.0, 0.0, 0.8899, 0.9763, 0.8215, 0.9424, 0.9472, 0.96, 0.9477, 0.9425, 0.9246, 0.9552, 0.9541, 0.831, 0.9517, 0.9292, 0.8119, 0.8005, nan] 
-2024-08-28 15:25:50.768063: Epoch time: 83.37 s 
-2024-08-28 15:25:51.851715:  
-2024-08-28 15:25:51.851869: Epoch 1063 
-2024-08-28 15:25:51.851953: Current learning rate: 0.00505 
-2024-08-28 15:27:13.990644: train_loss -0.7644 
-2024-08-28 15:27:13.991023: val_loss -0.7877 
-2024-08-28 15:27:13.991181: Pseudo dice [0.0, 0.0, 0.9078, 0.9767, 0.8364, 0.9486, 0.9512, 0.9656, 0.9504, 0.9445, 0.9282, 0.96, 0.9558, 0.8497, 0.9429, 0.9309, 0.8466, 0.846, nan] 
-2024-08-28 15:27:13.991273: Epoch time: 82.14 s 
-2024-08-28 15:27:15.072368:  
-2024-08-28 15:27:15.072822: Epoch 1064 
-2024-08-28 15:27:15.072964: Current learning rate: 0.00505 
-2024-08-28 15:28:36.875174: train_loss -0.7654 
-2024-08-28 15:28:36.875365: val_loss -0.7795 
-2024-08-28 15:28:36.875517: Pseudo dice [0.0, 0.0, 0.8935, 0.977, 0.8373, 0.9394, 0.9405, 0.9501, 0.9507, 0.9574, 0.9352, 0.9598, 0.9612, 0.8357, 0.9424, 0.924, 0.8172, 0.8007, nan] 
-2024-08-28 15:28:36.875589: Epoch time: 81.8 s 
-2024-08-28 15:28:37.955297:  
-2024-08-28 15:28:37.955492: Epoch 1065 
-2024-08-28 15:28:37.955575: Current learning rate: 0.00504 
-2024-08-28 15:29:56.083454: train_loss -0.7622 
-2024-08-28 15:29:56.083660: val_loss -0.7869 
-2024-08-28 15:29:56.083814: Pseudo dice [0.0, 0.0, 0.8921, 0.9753, 0.8284, 0.951, 0.9489, 0.9632, 0.9499, 0.9556, 0.9246, 0.9614, 0.9599, 0.8308, 0.9581, 0.9326, 0.8211, 0.8267, nan] 
-2024-08-28 15:29:56.083891: Epoch time: 78.13 s 
-2024-08-28 15:29:57.537185:  
-2024-08-28 15:29:57.537349: Epoch 1066 
-2024-08-28 15:29:57.537452: Current learning rate: 0.00504 
-2024-08-28 15:31:17.199124: train_loss -0.7642 
-2024-08-28 15:31:17.199324: val_loss -0.7852 
-2024-08-28 15:31:17.199466: Pseudo dice [0.0, 0.0, 0.8996, 0.9764, 0.8088, 0.9463, 0.951, 0.9587, 0.9499, 0.952, 0.9253, 0.9578, 0.9613, 0.8456, 0.946, 0.9321, 0.8275, 0.8272, nan] 
-2024-08-28 15:31:17.199538: Epoch time: 79.66 s 
-2024-08-28 15:31:18.507800:  
-2024-08-28 15:31:18.508096: Epoch 1067 
-2024-08-28 15:31:18.508178: Current learning rate: 0.00503 
-2024-08-28 15:32:38.042264: train_loss -0.7649 
-2024-08-28 15:32:38.042495: val_loss -0.7816 
-2024-08-28 15:32:38.042658: Pseudo dice [0.0, 0.0, 0.872, 0.9754, 0.8212, 0.9422, 0.9476, 0.9592, 0.945, 0.9338, 0.9229, 0.9585, 0.9545, 0.8378, 0.9444, 0.9272, 0.8191, 0.788, nan] 
-2024-08-28 15:32:38.042793: Epoch time: 79.54 s 
-2024-08-28 15:32:39.216435:  
-2024-08-28 15:32:39.216789: Epoch 1068 
-2024-08-28 15:32:39.216885: Current learning rate: 0.00503 
-2024-08-28 15:34:08.916848: train_loss -0.763 
-2024-08-28 15:34:08.917081: val_loss -0.7831 
-2024-08-28 15:34:08.917245: Pseudo dice [0.0, 0.0, 0.8914, 0.975, 0.8296, 0.9463, 0.948, 0.9581, 0.9521, 0.9524, 0.9285, 0.9625, 0.9588, 0.8346, 0.9414, 0.9227, 0.8277, 0.8175, nan] 
-2024-08-28 15:34:08.917325: Epoch time: 89.7 s 
-2024-08-28 15:34:10.136465:  
-2024-08-28 15:34:10.136642: Epoch 1069 
-2024-08-28 15:34:10.136892: Current learning rate: 0.00502 
-2024-08-28 15:35:37.617186: train_loss -0.7611 
-2024-08-28 15:35:37.617421: val_loss -0.7881 
-2024-08-28 15:35:37.617576: Pseudo dice [0.0, 0.0, 0.8618, 0.9742, 0.8468, 0.9453, 0.9437, 0.965, 0.949, 0.9539, 0.932, 0.9604, 0.9602, 0.8341, 0.9392, 0.932, 0.8225, 0.8139, nan] 
-2024-08-28 15:35:37.617656: Epoch time: 87.48 s 
-2024-08-28 15:35:38.795783:  
-2024-08-28 15:35:38.795969: Epoch 1070 
-2024-08-28 15:35:38.796060: Current learning rate: 0.00502 
-2024-08-28 15:37:03.434749: train_loss -0.7626 
-2024-08-28 15:37:03.435031: val_loss -0.7855 
-2024-08-28 15:37:03.435194: Pseudo dice [0.0, 0.0, 0.8993, 0.9754, 0.8129, 0.9437, 0.946, 0.9594, 0.9473, 0.9538, 0.9317, 0.9578, 0.9604, 0.8311, 0.9534, 0.9292, 0.8293, 0.8319, nan] 
-2024-08-28 15:37:03.435417: Epoch time: 84.64 s 
-2024-08-28 15:37:04.612567:  
-2024-08-28 15:37:04.612709: Epoch 1071 
-2024-08-28 15:37:04.612794: Current learning rate: 0.00502 
-2024-08-28 15:38:32.933741: train_loss -0.7601 
-2024-08-28 15:38:32.933978: val_loss -0.7812 
-2024-08-28 15:38:32.934146: Pseudo dice [0.0, 0.0, 0.8944, 0.9742, 0.8392, 0.9366, 0.9422, 0.9615, 0.9543, 0.9512, 0.9273, 0.9546, 0.9583, 0.8399, 0.9343, 0.9262, 0.8233, 0.8183, nan] 
-2024-08-28 15:38:32.934231: Epoch time: 88.32 s 
-2024-08-28 15:38:34.130981:  
-2024-08-28 15:38:34.131323: Epoch 1072 
-2024-08-28 15:38:34.131414: Current learning rate: 0.00501 
-2024-08-28 15:39:57.248205: train_loss -0.7594 
-2024-08-28 15:39:57.248491: val_loss -0.7791 
-2024-08-28 15:39:57.248671: Pseudo dice [0.0, 0.0, 0.8979, 0.9755, 0.8177, 0.9435, 0.944, 0.9601, 0.9436, 0.9437, 0.9318, 0.9598, 0.9571, 0.8381, 0.9496, 0.9232, 0.8326, 0.8326, nan] 
-2024-08-28 15:39:57.248803: Epoch time: 83.12 s 
-2024-08-28 15:39:58.448397:  
-2024-08-28 15:39:58.448536: Epoch 1073 
-2024-08-28 15:39:58.448614: Current learning rate: 0.00501 
-2024-08-28 15:41:22.428587: train_loss -0.7639 
-2024-08-28 15:41:22.428797: val_loss -0.7814 
-2024-08-28 15:41:22.428964: Pseudo dice [0.0, 0.0, 0.9005, 0.9769, 0.8393, 0.9432, 0.9467, 0.9656, 0.9471, 0.9378, 0.9265, 0.9604, 0.9587, 0.8371, 0.9488, 0.9338, 0.8256, 0.835, nan] 
-2024-08-28 15:41:22.429043: Epoch time: 83.98 s 
-2024-08-28 15:41:23.549500:  
-2024-08-28 15:41:23.549681: Epoch 1074 
-2024-08-28 15:41:23.549770: Current learning rate: 0.005 
-2024-08-28 15:42:48.017558: train_loss -0.7641 
-2024-08-28 15:42:48.018039: val_loss -0.7836 
-2024-08-28 15:42:48.018270: Pseudo dice [0.0, 0.0, 0.8955, 0.9767, 0.8432, 0.9462, 0.9519, 0.965, 0.9501, 0.9576, 0.9304, 0.9587, 0.9591, 0.8418, 0.9516, 0.9373, 0.8107, 0.8286, nan] 
-2024-08-28 15:42:48.018424: Epoch time: 84.47 s 
-2024-08-28 15:42:49.267500:  
-2024-08-28 15:42:49.267672: Epoch 1075 
-2024-08-28 15:42:49.267766: Current learning rate: 0.005 
-2024-08-28 15:44:17.573237: train_loss -0.7681 
-2024-08-28 15:44:17.573946: val_loss -0.7873 
-2024-08-28 15:44:17.574133: Pseudo dice [0.0, 0.0, 0.876, 0.9777, 0.842, 0.9484, 0.9476, 0.965, 0.9496, 0.9413, 0.9316, 0.9577, 0.96, 0.8331, 0.9525, 0.925, 0.8403, 0.8211, nan] 
-2024-08-28 15:44:17.574230: Epoch time: 88.31 s 
-2024-08-28 15:44:18.804819:  
-2024-08-28 15:44:18.805282: Epoch 1076 
-2024-08-28 15:44:18.805382: Current learning rate: 0.00499 
-2024-08-28 15:45:53.285377: train_loss -0.7654 
-2024-08-28 15:45:53.285898: val_loss -0.782 
-2024-08-28 15:45:53.286077: Pseudo dice [0.0, 0.0, 0.8833, 0.9771, 0.836, 0.9362, 0.9409, 0.9653, 0.9417, 0.9454, 0.9206, 0.9517, 0.9495, 0.8505, 0.936, 0.9312, 0.8136, 0.808, nan] 
-2024-08-28 15:45:53.286214: Epoch time: 94.48 s 
-2024-08-28 15:45:54.464325:  
-2024-08-28 15:45:54.464482: Epoch 1077 
-2024-08-28 15:45:54.464573: Current learning rate: 0.00499 
-2024-08-28 15:47:18.761873: train_loss -0.7647 
-2024-08-28 15:47:18.762216: val_loss -0.7852 
-2024-08-28 15:47:18.762419: Pseudo dice [0.0, 0.0, 0.9088, 0.9624, 0.8437, 0.947, 0.9542, 0.9648, 0.9537, 0.9516, 0.9298, 0.9591, 0.9611, 0.8447, 0.9557, 0.9294, 0.8215, 0.8092, nan] 
-2024-08-28 15:47:18.762532: Epoch time: 84.3 s 
-2024-08-28 15:47:19.973865:  
-2024-08-28 15:47:19.974204: Epoch 1078 
-2024-08-28 15:47:19.974413: Current learning rate: 0.00498 
-2024-08-28 15:48:45.682751: train_loss -0.7631 
-2024-08-28 15:48:45.682962: val_loss -0.7869 
-2024-08-28 15:48:45.683116: Pseudo dice [0.0, 0.0, 0.9063, 0.9755, 0.8292, 0.951, 0.9532, 0.961, 0.9559, 0.9496, 0.9254, 0.9628, 0.9581, 0.8446, 0.9536, 0.9318, 0.8305, 0.8216, nan] 
-2024-08-28 15:48:45.683209: Epoch time: 85.71 s 
-2024-08-28 15:48:47.189211:  
-2024-08-28 15:48:47.189384: Epoch 1079 
-2024-08-28 15:48:47.189474: Current learning rate: 0.00498 
-2024-08-28 15:50:17.371661: train_loss -0.7623 
-2024-08-28 15:50:17.371897: val_loss -0.7781 
-2024-08-28 15:50:17.372065: Pseudo dice [0.0, 0.0, 0.8594, 0.9755, 0.8287, 0.9404, 0.9414, 0.9592, 0.9437, 0.9361, 0.9226, 0.9538, 0.9494, 0.8415, 0.95, 0.9309, 0.8238, 0.8196, nan] 
-2024-08-28 15:50:17.372149: Epoch time: 90.18 s 
-2024-08-28 15:50:18.640471:  
-2024-08-28 15:50:18.640651: Epoch 1080 
-2024-08-28 15:50:18.640744: Current learning rate: 0.00497 
-2024-08-28 15:51:41.955338: train_loss -0.7646 
-2024-08-28 15:51:41.955603: val_loss -0.7835 
-2024-08-28 15:51:41.955757: Pseudo dice [0.0, 0.0, 0.8998, 0.9769, 0.8362, 0.9391, 0.9422, 0.9614, 0.9382, 0.9467, 0.9229, 0.9509, 0.9504, 0.8314, 0.9439, 0.9332, 0.8079, 0.8076, nan] 
-2024-08-28 15:51:41.955840: Epoch time: 83.32 s 
-2024-08-28 15:51:43.120353:  
-2024-08-28 15:51:43.120503: Epoch 1081 
-2024-08-28 15:51:43.120592: Current learning rate: 0.00497 
-2024-08-28 15:53:05.356738: train_loss -0.7594 
-2024-08-28 15:53:05.357241: val_loss -0.7821 
-2024-08-28 15:53:05.357552: Pseudo dice [0.0, 0.0, 0.8966, 0.9751, 0.8416, 0.9483, 0.947, 0.9617, 0.9458, 0.9484, 0.9198, 0.9559, 0.9514, 0.8316, 0.946, 0.9325, 0.8191, 0.8163, nan] 
-2024-08-28 15:53:05.357758: Epoch time: 82.24 s 
-2024-08-28 15:53:06.553137:  
-2024-08-28 15:53:06.553470: Epoch 1082 
-2024-08-28 15:53:06.553569: Current learning rate: 0.00496 
-2024-08-28 15:54:31.771938: train_loss -0.7619 
-2024-08-28 15:54:31.772463: val_loss -0.7849 
-2024-08-28 15:54:31.772641: Pseudo dice [0.0, 0.0, 0.8892, 0.9763, 0.816, 0.9435, 0.9481, 0.9652, 0.9567, 0.9556, 0.9304, 0.9638, 0.9601, 0.8425, 0.9449, 0.9309, 0.8292, 0.8202, nan] 
-2024-08-28 15:54:31.772748: Epoch time: 85.22 s 
-2024-08-28 15:54:33.031763:  
-2024-08-28 15:54:33.031924: Epoch 1083 
-2024-08-28 15:54:33.032017: Current learning rate: 0.00496 
-2024-08-28 15:56:03.254273: train_loss -0.7594 
-2024-08-28 15:56:03.254530: val_loss -0.7821 
-2024-08-28 15:56:03.254694: Pseudo dice [0.0, 0.0, 0.8957, 0.9739, 0.8138, 0.9471, 0.9476, 0.9618, 0.9549, 0.9439, 0.9154, 0.9616, 0.9575, 0.8395, 0.9437, 0.9289, 0.8179, 0.8064, nan] 
-2024-08-28 15:56:03.254776: Epoch time: 90.22 s 
-2024-08-28 15:56:04.467193:  
-2024-08-28 15:56:04.467582: Epoch 1084 
-2024-08-28 15:56:04.467676: Current learning rate: 0.00495 
-2024-08-28 15:57:32.646979: train_loss -0.7643 
-2024-08-28 15:57:32.647241: val_loss -0.7861 
-2024-08-28 15:57:32.647451: Pseudo dice [0.0, 0.0, 0.8766, 0.9752, 0.8242, 0.9457, 0.95, 0.9616, 0.9555, 0.9428, 0.9278, 0.9618, 0.9555, 0.8349, 0.9472, 0.9197, 0.8277, 0.8121, nan] 
-2024-08-28 15:57:32.647555: Epoch time: 88.18 s 
-2024-08-28 15:57:34.019925:  
-2024-08-28 15:57:34.020338: Epoch 1085 
-2024-08-28 15:57:34.020531: Current learning rate: 0.00495 
-2024-08-28 15:59:04.578790: train_loss -0.7622 
-2024-08-28 15:59:04.579039: val_loss -0.7853 
-2024-08-28 15:59:04.579201: Pseudo dice [0.0, 0.0, 0.855, 0.9757, 0.8195, 0.9474, 0.9515, 0.9632, 0.9475, 0.9388, 0.9271, 0.9581, 0.9607, 0.8386, 0.9518, 0.9266, 0.8186, 0.8235, nan] 
-2024-08-28 15:59:04.579287: Epoch time: 90.56 s 
-2024-08-28 15:59:06.108391:  
-2024-08-28 15:59:06.108890: Epoch 1086 
-2024-08-28 15:59:06.108997: Current learning rate: 0.00494 
-2024-08-28 16:00:29.990679: train_loss -0.7657 
-2024-08-28 16:00:29.990926: val_loss -0.7865 
-2024-08-28 16:00:29.991092: Pseudo dice [0.0, 0.0, 0.9002, 0.9753, 0.8376, 0.9429, 0.9428, 0.9625, 0.9531, 0.9396, 0.9384, 0.9613, 0.9625, 0.8347, 0.9361, 0.9295, 0.8318, 0.8276, nan] 
-2024-08-28 16:00:29.991178: Epoch time: 83.88 s 
-2024-08-28 16:00:31.373300:  
-2024-08-28 16:00:31.373531: Epoch 1087 
-2024-08-28 16:00:31.373652: Current learning rate: 0.00494 
-2024-08-28 16:01:55.454403: train_loss -0.7663 
-2024-08-28 16:01:55.455051: val_loss -0.7881 
-2024-08-28 16:01:55.455278: Pseudo dice [0.0, 0.0, 0.898, 0.9772, 0.8439, 0.9477, 0.9455, 0.9634, 0.9514, 0.9491, 0.9364, 0.9594, 0.9598, 0.8363, 0.9485, 0.9379, 0.8168, 0.8217, nan] 
-2024-08-28 16:01:55.455457: Epoch time: 84.08 s 
-2024-08-28 16:01:56.727410:  
-2024-08-28 16:01:56.727714: Epoch 1088 
-2024-08-28 16:01:56.727813: Current learning rate: 0.00493 
-2024-08-28 16:03:21.138516: train_loss -0.7681 
-2024-08-28 16:03:21.138798: val_loss -0.7871 
-2024-08-28 16:03:21.139029: Pseudo dice [0.0, 0.0, 0.8862, 0.9759, 0.7844, 0.9406, 0.9494, 0.9635, 0.9488, 0.9484, 0.9355, 0.9585, 0.9621, 0.8384, 0.9501, 0.935, 0.8222, 0.8214, nan] 
-2024-08-28 16:03:21.139137: Epoch time: 84.41 s 
-2024-08-28 16:03:22.475815:  
-2024-08-28 16:03:22.476303: Epoch 1089 
-2024-08-28 16:03:22.476408: Current learning rate: 0.00493 
-2024-08-28 16:04:47.949656: train_loss -0.7627 
-2024-08-28 16:04:47.949996: val_loss -0.7713 
-2024-08-28 16:04:47.950190: Pseudo dice [0.0, 0.0, 0.8798, 0.9757, 0.8094, 0.9384, 0.937, 0.9559, 0.9385, 0.9412, 0.9203, 0.9475, 0.9495, 0.8287, 0.947, 0.9223, 0.8058, 0.8083, nan] 
-2024-08-28 16:04:47.950273: Epoch time: 85.47 s 
-2024-08-28 16:04:49.169469:  
-2024-08-28 16:04:49.169622: Epoch 1090 
-2024-08-28 16:04:49.169698: Current learning rate: 0.00492 
-2024-08-28 16:06:16.268128: train_loss -0.7619 
-2024-08-28 16:06:16.268348: val_loss -0.7823 
-2024-08-28 16:06:16.268515: Pseudo dice [0.0, 0.0, 0.8895, 0.9758, 0.8309, 0.9419, 0.9478, 0.9617, 0.946, 0.953, 0.9247, 0.9582, 0.9554, 0.8348, 0.9372, 0.9293, 0.8165, 0.8131, nan] 
-2024-08-28 16:06:16.268613: Epoch time: 87.1 s 
-2024-08-28 16:06:17.570791:  
-2024-08-28 16:06:17.570951: Epoch 1091 
-2024-08-28 16:06:17.571043: Current learning rate: 0.00492 
-2024-08-28 16:07:44.246761: train_loss -0.7631 
-2024-08-28 16:07:44.246998: val_loss -0.7862 
-2024-08-28 16:07:44.247157: Pseudo dice [0.0, 0.0, 0.8761, 0.976, 0.8198, 0.9375, 0.9427, 0.9626, 0.947, 0.9489, 0.9294, 0.9531, 0.9525, 0.8139, 0.9419, 0.9244, 0.8191, 0.8134, nan] 
-2024-08-28 16:07:44.247242: Epoch time: 86.68 s 
-2024-08-28 16:07:45.808160:  
-2024-08-28 16:07:45.808330: Epoch 1092 
-2024-08-28 16:07:45.808435: Current learning rate: 0.00491 
-2024-08-28 16:09:04.274364: train_loss -0.7637 
-2024-08-28 16:09:04.274601: val_loss -0.7824 
-2024-08-28 16:09:04.274765: Pseudo dice [0.0, 0.0, 0.8905, 0.9736, 0.825, 0.9451, 0.9456, 0.965, 0.9517, 0.9473, 0.9311, 0.9585, 0.9596, 0.8335, 0.9512, 0.926, 0.8085, 0.8021, nan] 
-2024-08-28 16:09:04.274851: Epoch time: 78.47 s 
-2024-08-28 16:09:05.516000:  
-2024-08-28 16:09:05.516194: Epoch 1093 
-2024-08-28 16:09:05.516388: Current learning rate: 0.00491 
-2024-08-28 16:10:30.905377: train_loss -0.7631 
-2024-08-28 16:10:30.905596: val_loss -0.7854 
-2024-08-28 16:10:30.905763: Pseudo dice [0.0, 0.0, 0.8861, 0.9767, 0.8186, 0.9456, 0.9497, 0.9643, 0.957, 0.956, 0.9346, 0.9635, 0.9607, 0.8396, 0.9491, 0.9315, 0.8169, 0.8169, nan] 
-2024-08-28 16:10:30.905846: Epoch time: 85.39 s 
-2024-08-28 16:10:32.114963:  
-2024-08-28 16:10:32.115141: Epoch 1094 
-2024-08-28 16:10:32.115231: Current learning rate: 0.0049 
-2024-08-28 16:11:59.671656: train_loss -0.7612 
-2024-08-28 16:11:59.672234: val_loss -0.7838 
-2024-08-28 16:11:59.672411: Pseudo dice [0.0, 0.0, 0.892, 0.9765, 0.8287, 0.9439, 0.9454, 0.9658, 0.947, 0.9424, 0.9344, 0.9607, 0.9605, 0.8438, 0.9428, 0.9324, 0.7983, 0.8045, nan] 
-2024-08-28 16:11:59.672565: Epoch time: 87.56 s 
-2024-08-28 16:12:00.881191:  
-2024-08-28 16:12:00.881516: Epoch 1095 
-2024-08-28 16:12:00.881617: Current learning rate: 0.0049 
-2024-08-28 16:13:28.814172: train_loss -0.7637 
-2024-08-28 16:13:28.814694: val_loss -0.7823 
-2024-08-28 16:13:28.814962: Pseudo dice [0.0, 0.0, 0.8837, 0.9765, 0.84, 0.9434, 0.9454, 0.9642, 0.9541, 0.9521, 0.9235, 0.9607, 0.9561, 0.8417, 0.9531, 0.9329, 0.8166, 0.8176, nan] 
-2024-08-28 16:13:28.815075: Epoch time: 87.93 s 
-2024-08-28 16:13:30.051321:  
-2024-08-28 16:13:30.051704: Epoch 1096 
-2024-08-28 16:13:30.051811: Current learning rate: 0.00489 
-2024-08-28 16:14:58.678116: train_loss -0.7609 
-2024-08-28 16:14:58.678628: val_loss -0.7788 
-2024-08-28 16:14:58.678812: Pseudo dice [0.0, 0.0, 0.8889, 0.9747, 0.8277, 0.9387, 0.9402, 0.9632, 0.9429, 0.9461, 0.9245, 0.9573, 0.9607, 0.8233, 0.9526, 0.9296, 0.8072, 0.8094, nan] 
-2024-08-28 16:14:58.678954: Epoch time: 88.63 s 
-2024-08-28 16:14:59.908707:  
-2024-08-28 16:14:59.908874: Epoch 1097 
-2024-08-28 16:14:59.908967: Current learning rate: 0.00489 
-2024-08-28 16:16:26.131304: train_loss -0.7637 
-2024-08-28 16:16:26.131625: val_loss -0.7843 
-2024-08-28 16:16:26.131804: Pseudo dice [0.0, 0.0, 0.9093, 0.9757, 0.8195, 0.9411, 0.9452, 0.9586, 0.9504, 0.9457, 0.9301, 0.9588, 0.9588, 0.8319, 0.9553, 0.9276, 0.8071, 0.8288, nan] 
-2024-08-28 16:16:26.131934: Epoch time: 86.22 s 
-2024-08-28 16:16:27.618989:  
-2024-08-28 16:16:27.619217: Epoch 1098 
-2024-08-28 16:16:27.619305: Current learning rate: 0.00488 
-2024-08-28 16:17:55.824229: train_loss -0.7637 
-2024-08-28 16:17:55.824447: val_loss -0.778 
-2024-08-28 16:17:55.824614: Pseudo dice [0.0, 0.0, 0.8743, 0.9774, 0.7776, 0.9422, 0.9448, 0.9607, 0.9492, 0.9482, 0.9309, 0.9613, 0.9615, 0.8286, 0.9401, 0.9246, 0.8141, 0.8025, nan] 
-2024-08-28 16:17:55.824692: Epoch time: 88.21 s 
-2024-08-28 16:17:57.034515:  
-2024-08-28 16:17:57.034668: Epoch 1099 
-2024-08-28 16:17:57.034750: Current learning rate: 0.00488 
-2024-08-28 16:19:19.507087: train_loss -0.7619 
-2024-08-28 16:19:19.507329: val_loss -0.7856 
-2024-08-28 16:19:19.507500: Pseudo dice [0.0, 0.0, 0.9018, 0.9755, 0.8271, 0.9461, 0.9497, 0.9623, 0.953, 0.9456, 0.9348, 0.962, 0.9623, 0.8387, 0.9552, 0.9363, 0.8345, 0.8251, nan] 
-2024-08-28 16:19:19.507715: Epoch time: 82.47 s 
-2024-08-28 16:19:21.393636:  
-2024-08-28 16:19:21.393792: Epoch 1100 
-2024-08-28 16:19:21.393874: Current learning rate: 0.00487 
-2024-08-28 16:20:49.958631: train_loss -0.7634 
-2024-08-28 16:20:49.958848: val_loss -0.7823 
-2024-08-28 16:20:49.959005: Pseudo dice [0.0, 0.0, 0.9058, 0.974, 0.839, 0.9404, 0.9445, 0.9634, 0.9493, 0.9452, 0.9193, 0.9559, 0.9546, 0.8398, 0.9543, 0.9289, 0.8236, 0.8312, nan] 
-2024-08-28 16:20:49.959084: Epoch time: 88.57 s 
-2024-08-28 16:20:51.076492:  
-2024-08-28 16:20:51.076812: Epoch 1101 
-2024-08-28 16:20:51.076908: Current learning rate: 0.00487 
-2024-08-28 16:22:18.063517: train_loss -0.7637 
-2024-08-28 16:22:18.063876: val_loss -0.7783 
-2024-08-28 16:22:18.064059: Pseudo dice [0.0, 0.0, 0.8672, 0.9748, 0.7927, 0.94, 0.9456, 0.965, 0.9513, 0.9431, 0.9262, 0.9536, 0.9583, 0.8406, 0.954, 0.9298, 0.8085, 0.809, nan] 
-2024-08-28 16:22:18.064147: Epoch time: 86.99 s 
-2024-08-28 16:22:19.284620:  
-2024-08-28 16:22:19.284795: Epoch 1102 
-2024-08-28 16:22:19.284885: Current learning rate: 0.00486 
-2024-08-28 16:23:45.365278: train_loss -0.7629 
-2024-08-28 16:23:45.365525: val_loss -0.7827 
-2024-08-28 16:23:45.365738: Pseudo dice [0.0, 0.0, 0.8743, 0.9773, 0.8311, 0.9386, 0.9383, 0.9637, 0.9507, 0.9464, 0.9309, 0.9613, 0.9602, 0.8339, 0.9337, 0.9215, 0.8188, 0.8133, nan] 
-2024-08-28 16:23:45.365846: Epoch time: 86.08 s 
-2024-08-28 16:23:46.624603:  
-2024-08-28 16:23:46.624788: Epoch 1103 
-2024-08-28 16:23:46.624872: Current learning rate: 0.00486 
-2024-08-28 16:25:13.593125: train_loss -0.7657 
-2024-08-28 16:25:13.593556: val_loss -0.7881 
-2024-08-28 16:25:13.593741: Pseudo dice [0.0, 0.0, 0.8927, 0.9771, 0.8343, 0.9425, 0.9436, 0.9655, 0.9479, 0.9528, 0.9293, 0.9595, 0.9588, 0.8373, 0.9471, 0.9327, 0.8128, 0.812, nan] 
-2024-08-28 16:25:13.593826: Epoch time: 86.97 s 
-2024-08-28 16:25:14.762577:  
-2024-08-28 16:25:14.762849: Epoch 1104 
-2024-08-28 16:25:14.762933: Current learning rate: 0.00485 
-2024-08-28 16:26:40.778557: train_loss -0.7652 
-2024-08-28 16:26:40.778794: val_loss -0.7856 
-2024-08-28 16:26:40.778949: Pseudo dice [0.0, 0.0, 0.8975, 0.9776, 0.8221, 0.9466, 0.9474, 0.9655, 0.9517, 0.9537, 0.9345, 0.9608, 0.9611, 0.8432, 0.9522, 0.9319, 0.8357, 0.8085, nan] 
-2024-08-28 16:26:40.779096: Epoch time: 86.02 s 
-2024-08-28 16:26:42.267273:  
-2024-08-28 16:26:42.267433: Epoch 1105 
-2024-08-28 16:26:42.267525: Current learning rate: 0.00485 
-2024-08-28 16:28:08.943837: train_loss -0.7636 
-2024-08-28 16:28:08.944055: val_loss -0.7752 
-2024-08-28 16:28:08.944259: Pseudo dice [0.0, 0.0, 0.8599, 0.975, 0.7965, 0.944, 0.9468, 0.9582, 0.9483, 0.9462, 0.9178, 0.9586, 0.9537, 0.8197, 0.946, 0.9293, 0.7933, 0.7416, nan] 
-2024-08-28 16:28:08.944353: Epoch time: 86.68 s 
-2024-08-28 16:28:10.164151:  
-2024-08-28 16:28:10.164313: Epoch 1106 
-2024-08-28 16:28:10.164426: Current learning rate: 0.00484 
-2024-08-28 16:29:34.905497: train_loss -0.762 
-2024-08-28 16:29:34.905764: val_loss -0.784 
-2024-08-28 16:29:34.905966: Pseudo dice [0.0, 0.0, 0.8468, 0.974, 0.8411, 0.9427, 0.9454, 0.9606, 0.9484, 0.9506, 0.9191, 0.9624, 0.9587, 0.844, 0.9427, 0.9305, 0.8209, 0.8192, nan] 
-2024-08-28 16:29:34.906084: Epoch time: 84.74 s 
-2024-08-28 16:29:36.091515:  
-2024-08-28 16:29:36.091809: Epoch 1107 
-2024-08-28 16:29:36.091906: Current learning rate: 0.00484 
-2024-08-28 16:30:59.707198: train_loss -0.7669 
-2024-08-28 16:30:59.707513: val_loss -0.7841 
-2024-08-28 16:30:59.707737: Pseudo dice [0.0, 0.0, 0.8825, 0.9776, 0.8336, 0.9429, 0.9451, 0.9612, 0.9455, 0.9455, 0.9273, 0.9595, 0.9554, 0.838, 0.9405, 0.9272, 0.8272, 0.8158, nan] 
-2024-08-28 16:30:59.707871: Epoch time: 83.62 s 
-2024-08-28 16:31:01.019439:  
-2024-08-28 16:31:01.019701: Epoch 1108 
-2024-08-28 16:31:01.019799: Current learning rate: 0.00484 
-2024-08-28 16:32:29.144636: train_loss -0.7595 
-2024-08-28 16:32:29.144878: val_loss -0.7797 
-2024-08-28 16:32:29.145047: Pseudo dice [0.0, 0.0, 0.8573, 0.9759, 0.8181, 0.9433, 0.9445, 0.961, 0.9521, 0.95, 0.931, 0.9617, 0.9612, 0.8361, 0.9344, 0.9258, 0.8011, 0.8209, nan] 
-2024-08-28 16:32:29.145153: Epoch time: 88.13 s 
-2024-08-28 16:32:30.362663:  
-2024-08-28 16:32:30.363009: Epoch 1109 
-2024-08-28 16:32:30.363105: Current learning rate: 0.00483 
-2024-08-28 16:33:56.751686: train_loss -0.7572 
-2024-08-28 16:33:56.751891: val_loss -0.7846 
-2024-08-28 16:33:56.752039: Pseudo dice [0.0, 0.0, 0.902, 0.9773, 0.8311, 0.9468, 0.9468, 0.9589, 0.9485, 0.9521, 0.9269, 0.96, 0.9595, 0.8347, 0.9488, 0.9254, 0.8075, 0.8225, nan] 
-2024-08-28 16:33:56.752112: Epoch time: 86.39 s 
-2024-08-28 16:33:57.840847:  
-2024-08-28 16:33:57.840982: Epoch 1110 
-2024-08-28 16:33:57.841067: Current learning rate: 0.00483 
-2024-08-28 16:35:27.545600: train_loss -0.7623 
-2024-08-28 16:35:27.545819: val_loss -0.7797 
-2024-08-28 16:35:27.545983: Pseudo dice [0.0, 0.0, 0.8806, 0.977, 0.8016, 0.948, 0.9472, 0.9602, 0.9537, 0.9533, 0.9324, 0.9611, 0.96, 0.8385, 0.9504, 0.9266, 0.8192, 0.8096, nan] 
-2024-08-28 16:35:27.546114: Epoch time: 89.71 s 
-2024-08-28 16:35:29.049558:  
-2024-08-28 16:35:29.049905: Epoch 1111 
-2024-08-28 16:35:29.049989: Current learning rate: 0.00482 
-2024-08-28 16:36:51.746749: train_loss -0.7567 
-2024-08-28 16:36:51.747270: val_loss -0.7813 
-2024-08-28 16:36:51.747455: Pseudo dice [0.0, 0.0, 0.8962, 0.9769, 0.8145, 0.938, 0.9417, 0.9641, 0.9433, 0.9498, 0.9244, 0.9559, 0.9599, 0.8364, 0.9535, 0.9288, 0.816, 0.8196, nan] 
-2024-08-28 16:36:51.747588: Epoch time: 82.7 s 
-2024-08-28 16:36:52.982091:  
-2024-08-28 16:36:52.982282: Epoch 1112 
-2024-08-28 16:36:52.982371: Current learning rate: 0.00482 
-2024-08-28 16:38:21.150441: train_loss -0.7594 
-2024-08-28 16:38:21.150748: val_loss -0.7786 
-2024-08-28 16:38:21.150999: Pseudo dice [0.0, 0.0, 0.8786, 0.9717, 0.8237, 0.9443, 0.9477, 0.9634, 0.9466, 0.9496, 0.9252, 0.9541, 0.9593, 0.8308, 0.947, 0.93, 0.8214, 0.8117, nan] 
-2024-08-28 16:38:21.151117: Epoch time: 88.17 s 
-2024-08-28 16:38:22.768772:  
-2024-08-28 16:38:22.769343: Epoch 1113 
-2024-08-28 16:38:22.769438: Current learning rate: 0.00481 
-2024-08-28 16:39:47.091505: train_loss -0.7615 
-2024-08-28 16:39:47.092003: val_loss -0.779 
-2024-08-28 16:39:47.092217: Pseudo dice [0.0, 0.0, 0.8875, 0.9743, 0.8274, 0.9409, 0.9447, 0.9618, 0.9519, 0.9302, 0.9233, 0.9572, 0.9591, 0.8292, 0.9456, 0.9244, 0.8247, 0.8193, nan] 
-2024-08-28 16:39:47.092359: Epoch time: 84.32 s 
-2024-08-28 16:39:48.355384:  
-2024-08-28 16:39:48.355533: Epoch 1114 
-2024-08-28 16:39:48.355623: Current learning rate: 0.00481 
-2024-08-28 16:41:13.518626: train_loss -0.7594 
-2024-08-28 16:41:13.519018: val_loss -0.7838 
-2024-08-28 16:41:13.519295: Pseudo dice [0.0, 0.0, 0.8809, 0.9755, 0.8154, 0.9448, 0.9484, 0.9628, 0.9482, 0.9454, 0.9301, 0.9578, 0.9589, 0.8425, 0.9444, 0.9332, 0.8242, 0.8227, nan] 
-2024-08-28 16:41:13.519549: Epoch time: 85.16 s 
-2024-08-28 16:41:14.723407:  
-2024-08-28 16:41:14.723591: Epoch 1115 
-2024-08-28 16:41:14.723685: Current learning rate: 0.0048 
-2024-08-28 16:42:38.096399: train_loss -0.7641 
-2024-08-28 16:42:38.096617: val_loss -0.7806 
-2024-08-28 16:42:38.096781: Pseudo dice [0.0, 0.0, 0.8889, 0.9761, 0.8024, 0.9361, 0.9318, 0.9607, 0.947, 0.9392, 0.922, 0.9565, 0.9578, 0.8296, 0.9497, 0.9298, 0.8143, 0.8062, nan] 
-2024-08-28 16:42:38.096909: Epoch time: 83.37 s 
-2024-08-28 16:42:39.260481:  
-2024-08-28 16:42:39.260752: Epoch 1116 
-2024-08-28 16:42:39.260838: Current learning rate: 0.0048 
-2024-08-28 16:44:05.187454: train_loss -0.7621 
-2024-08-28 16:44:05.187987: val_loss -0.7909 
-2024-08-28 16:44:05.188270: Pseudo dice [0.0, 0.0, 0.8949, 0.9766, 0.8315, 0.9471, 0.9515, 0.9647, 0.9541, 0.9432, 0.9169, 0.9636, 0.9575, 0.8443, 0.9527, 0.929, 0.8119, 0.813, nan] 
-2024-08-28 16:44:05.188504: Epoch time: 85.93 s 
-2024-08-28 16:44:06.647345:  
-2024-08-28 16:44:06.647491: Epoch 1117 
-2024-08-28 16:44:06.647583: Current learning rate: 0.00479 
-2024-08-28 16:45:35.738402: train_loss -0.7583 
-2024-08-28 16:45:35.738626: val_loss -0.7768 
-2024-08-28 16:45:35.738789: Pseudo dice [0.0, 0.0, 0.906, 0.9753, 0.8427, 0.9444, 0.9473, 0.9583, 0.9437, 0.9387, 0.906, 0.9531, 0.9512, 0.8388, 0.9542, 0.9227, 0.8184, 0.8076, nan] 
-2024-08-28 16:45:35.738910: Epoch time: 89.09 s 
-2024-08-28 16:45:36.912147:  
-2024-08-28 16:45:36.912289: Epoch 1118 
-2024-08-28 16:45:36.912375: Current learning rate: 0.00479 
-2024-08-28 16:46:59.500827: train_loss -0.7608 
-2024-08-28 16:46:59.501112: val_loss -0.7757 
-2024-08-28 16:46:59.501312: Pseudo dice [0.0, 0.0, 0.8854, 0.9768, 0.8081, 0.9408, 0.9414, 0.9597, 0.946, 0.9444, 0.9281, 0.9564, 0.9575, 0.818, 0.9341, 0.926, 0.8092, 0.7995, nan] 
-2024-08-28 16:46:59.501406: Epoch time: 82.59 s 
-2024-08-28 16:47:00.705849:  
-2024-08-28 16:47:00.706012: Epoch 1119 
-2024-08-28 16:47:00.706126: Current learning rate: 0.00478 
-2024-08-28 16:48:28.786916: train_loss -0.7617 
-2024-08-28 16:48:28.787164: val_loss -0.7739 
-2024-08-28 16:48:28.787338: Pseudo dice [0.0, 0.0, 0.886, 0.9758, 0.8231, 0.9374, 0.9352, 0.9625, 0.9317, 0.9327, 0.9192, 0.9449, 0.9514, 0.8352, 0.9509, 0.9231, 0.8094, 0.8025, nan] 
-2024-08-28 16:48:28.787428: Epoch time: 88.08 s 
-2024-08-28 16:48:30.021913:  
-2024-08-28 16:48:30.022070: Epoch 1120 
-2024-08-28 16:48:30.022164: Current learning rate: 0.00478 
-2024-08-28 16:49:58.534321: train_loss -0.7621 
-2024-08-28 16:49:58.534521: val_loss -0.7836 
-2024-08-28 16:49:58.534708: Pseudo dice [0.0, 0.0, 0.9046, 0.9776, 0.8383, 0.9394, 0.9431, 0.9649, 0.9418, 0.9454, 0.9243, 0.9498, 0.9523, 0.8433, 0.9494, 0.9333, 0.8267, 0.8332, nan] 
-2024-08-28 16:49:58.534810: Epoch time: 88.51 s 
-2024-08-28 16:49:59.672511:  
-2024-08-28 16:49:59.673033: Epoch 1121 
-2024-08-28 16:49:59.673221: Current learning rate: 0.00477 
-2024-08-28 16:51:23.949529: train_loss -0.7683 
-2024-08-28 16:51:23.949776: val_loss -0.7832 
-2024-08-28 16:51:23.949969: Pseudo dice [0.0, 0.0, 0.8777, 0.9758, 0.8297, 0.9395, 0.9463, 0.9642, 0.953, 0.9481, 0.928, 0.9603, 0.9599, 0.8354, 0.9489, 0.9261, 0.8248, 0.8112, nan] 
-2024-08-28 16:51:23.950065: Epoch time: 84.28 s 
-2024-08-28 16:51:25.212126:  
-2024-08-28 16:51:25.212400: Epoch 1122 
-2024-08-28 16:51:25.212520: Current learning rate: 0.00477 
-2024-08-28 16:52:51.738490: train_loss -0.7623 
-2024-08-28 16:52:51.738740: val_loss -0.7797 
-2024-08-28 16:52:51.738915: Pseudo dice [0.0, 0.0, 0.8727, 0.9765, 0.783, 0.9434, 0.9481, 0.9644, 0.9552, 0.9428, 0.9356, 0.9621, 0.9604, 0.8291, 0.9538, 0.9313, 0.8172, 0.8155, nan] 
-2024-08-28 16:52:51.739004: Epoch time: 86.53 s 
-2024-08-28 16:52:52.916603:  
-2024-08-28 16:52:52.916749: Epoch 1123 
-2024-08-28 16:52:52.916845: Current learning rate: 0.00476 
-2024-08-28 16:54:16.104121: train_loss -0.7617 
-2024-08-28 16:54:16.104917: val_loss -0.7776 
-2024-08-28 16:54:16.105165: Pseudo dice [0.0, 0.0, 0.9025, 0.9766, 0.8351, 0.9433, 0.9459, 0.9577, 0.9501, 0.9436, 0.9217, 0.9578, 0.9582, 0.8306, 0.9525, 0.9288, 0.8159, 0.813, nan] 
-2024-08-28 16:54:16.105331: Epoch time: 83.19 s 
-2024-08-28 16:54:17.705775:  
-2024-08-28 16:54:17.705943: Epoch 1124 
-2024-08-28 16:54:17.706045: Current learning rate: 0.00476 
-2024-08-28 16:55:37.734050: train_loss -0.7645 
-2024-08-28 16:55:37.734585: val_loss -0.7808 
-2024-08-28 16:55:37.735056: Pseudo dice [0.0, 0.0, 0.8911, 0.9752, 0.8078, 0.9471, 0.9449, 0.9571, 0.9468, 0.936, 0.9315, 0.9598, 0.9598, 0.8313, 0.9453, 0.9237, 0.8222, 0.8179, nan] 
-2024-08-28 16:55:37.735223: Epoch time: 80.03 s 
-2024-08-28 16:55:38.940452:  
-2024-08-28 16:55:38.940811: Epoch 1125 
-2024-08-28 16:55:38.940899: Current learning rate: 0.00475 
-2024-08-28 16:57:02.245122: train_loss -0.7586 
-2024-08-28 16:57:02.245582: val_loss -0.7689 
-2024-08-28 16:57:02.245815: Pseudo dice [0.0, 0.0, 0.8763, 0.9751, 0.83, 0.9292, 0.9338, 0.9582, 0.9478, 0.9335, 0.9163, 0.958, 0.9528, 0.8146, 0.9242, 0.9185, 0.8157, 0.8246, nan] 
-2024-08-28 16:57:02.245920: Epoch time: 83.31 s 
-2024-08-28 16:57:03.502403:  
-2024-08-28 16:57:03.502867: Epoch 1126 
-2024-08-28 16:57:03.502955: Current learning rate: 0.00475 
-2024-08-28 16:58:28.942534: train_loss -0.753 
-2024-08-28 16:58:28.942765: val_loss -0.7782 
-2024-08-28 16:58:28.942932: Pseudo dice [0.0, 0.0, 0.8934, 0.9755, 0.8019, 0.9375, 0.9365, 0.9626, 0.9531, 0.9471, 0.9311, 0.9619, 0.9582, 0.8144, 0.9505, 0.9202, 0.7968, 0.7542, nan] 
-2024-08-28 16:58:28.943028: Epoch time: 85.44 s 
-2024-08-28 16:58:30.132106:  
-2024-08-28 16:58:30.132272: Epoch 1127 
-2024-08-28 16:58:30.132358: Current learning rate: 0.00474 
-2024-08-28 16:59:54.993151: train_loss -0.7557 
-2024-08-28 16:59:54.993382: val_loss -0.779 
-2024-08-28 16:59:54.993549: Pseudo dice [0.0, 0.0, 0.8886, 0.9744, 0.8368, 0.9493, 0.9488, 0.9638, 0.9477, 0.9453, 0.9186, 0.9588, 0.9532, 0.8303, 0.9387, 0.9235, 0.8118, 0.7989, nan] 
-2024-08-28 16:59:54.993633: Epoch time: 84.86 s 
-2024-08-28 16:59:56.192384:  
-2024-08-28 16:59:56.192564: Epoch 1128 
-2024-08-28 16:59:56.192646: Current learning rate: 0.00474 
-2024-08-28 17:01:19.608639: train_loss -0.7576 
-2024-08-28 17:01:19.608871: val_loss -0.7804 
-2024-08-28 17:01:19.609034: Pseudo dice [0.0, 0.0, 0.8968, 0.9761, 0.8291, 0.9416, 0.9445, 0.9648, 0.9342, 0.9312, 0.9189, 0.9439, 0.9503, 0.8338, 0.9439, 0.9304, 0.8175, 0.8131, nan] 
-2024-08-28 17:01:19.609116: Epoch time: 83.42 s 
-2024-08-28 17:01:20.821175:  
-2024-08-28 17:01:20.821434: Epoch 1129 
-2024-08-28 17:01:20.821520: Current learning rate: 0.00473 
-2024-08-28 17:02:45.383339: train_loss -0.764 
-2024-08-28 17:02:45.383619: val_loss -0.789 
-2024-08-28 17:02:45.383831: Pseudo dice [0.0, 0.0, 0.8726, 0.9737, 0.8461, 0.947, 0.9508, 0.9659, 0.9459, 0.9327, 0.9229, 0.9564, 0.959, 0.849, 0.9493, 0.9337, 0.8104, 0.8144, nan] 
-2024-08-28 17:02:45.383939: Epoch time: 84.56 s 
-2024-08-28 17:02:47.003944:  
-2024-08-28 17:02:47.004200: Epoch 1130 
-2024-08-28 17:02:47.004309: Current learning rate: 0.00473 
-2024-08-28 17:04:10.666325: train_loss -0.7643 
-2024-08-28 17:04:10.666866: val_loss -0.791 
-2024-08-28 17:04:10.667041: Pseudo dice [0.0, 0.0, 0.9002, 0.9766, 0.8503, 0.9431, 0.9484, 0.9669, 0.9554, 0.9499, 0.9236, 0.9576, 0.9586, 0.8467, 0.9547, 0.9339, 0.8227, 0.8374, nan] 
-2024-08-28 17:04:10.667167: Epoch time: 83.66 s 
-2024-08-28 17:04:11.885432:  
-2024-08-28 17:04:11.885830: Epoch 1131 
-2024-08-28 17:04:11.886008: Current learning rate: 0.00472 
-2024-08-28 17:05:36.039850: train_loss -0.7668 
-2024-08-28 17:05:36.040094: val_loss -0.7935 
-2024-08-28 17:05:36.040259: Pseudo dice [0.0, 0.0, 0.9022, 0.9766, 0.8442, 0.9507, 0.9526, 0.9657, 0.9505, 0.9514, 0.938, 0.9611, 0.9616, 0.8506, 0.9452, 0.9359, 0.8263, 0.8274, nan] 
-2024-08-28 17:05:36.040343: Epoch time: 84.16 s 
-2024-08-28 17:05:37.272279:  
-2024-08-28 17:05:37.272749: Epoch 1132 
-2024-08-28 17:05:37.272861: Current learning rate: 0.00472 
-2024-08-28 17:07:06.441061: train_loss -0.7643 
-2024-08-28 17:07:06.441738: val_loss -0.7847 
-2024-08-28 17:07:06.442021: Pseudo dice [0.0, 0.0, 0.9107, 0.9761, 0.8473, 0.9483, 0.9497, 0.9659, 0.9484, 0.9482, 0.911, 0.9578, 0.9497, 0.8438, 0.955, 0.9278, 0.8281, 0.8367, nan] 
-2024-08-28 17:07:06.442111: Epoch time: 89.17 s 
-2024-08-28 17:07:07.661210:  
-2024-08-28 17:07:07.661562: Epoch 1133 
-2024-08-28 17:07:07.661658: Current learning rate: 0.00471 
-2024-08-28 17:08:33.431559: train_loss -0.7663 
-2024-08-28 17:08:33.431791: val_loss -0.7897 
-2024-08-28 17:08:33.431942: Pseudo dice [0.0, 0.0, 0.8871, 0.9765, 0.8536, 0.949, 0.9483, 0.965, 0.9524, 0.9556, 0.9341, 0.9621, 0.962, 0.8491, 0.9543, 0.9315, 0.831, 0.8339, nan] 
-2024-08-28 17:08:33.432022: Epoch time: 85.77 s 
-2024-08-28 17:08:34.606866:  
-2024-08-28 17:08:34.607043: Epoch 1134 
-2024-08-28 17:08:34.607129: Current learning rate: 0.00471 
-2024-08-28 17:10:01.032994: train_loss -0.7651 
-2024-08-28 17:10:01.033316: val_loss -0.7816 
-2024-08-28 17:10:01.033484: Pseudo dice [0.0, 0.0, 0.8657, 0.9758, 0.8228, 0.9477, 0.9499, 0.9644, 0.9518, 0.9516, 0.9275, 0.9587, 0.9597, 0.8437, 0.9554, 0.9387, 0.8358, 0.8136, nan] 
-2024-08-28 17:10:01.033571: Epoch time: 86.43 s 
-2024-08-28 17:10:02.260198:  
-2024-08-28 17:10:02.260377: Epoch 1135 
-2024-08-28 17:10:02.260475: Current learning rate: 0.0047 
-2024-08-28 17:11:27.881428: train_loss -0.7654 
-2024-08-28 17:11:27.881676: val_loss -0.7872 
-2024-08-28 17:11:27.881894: Pseudo dice [0.0, 0.0, 0.9019, 0.9766, 0.8231, 0.9435, 0.9481, 0.9648, 0.9507, 0.944, 0.9268, 0.9554, 0.9548, 0.8432, 0.9458, 0.9342, 0.8167, 0.8186, nan] 
-2024-08-28 17:11:27.882398: Epoch time: 85.62 s 
-2024-08-28 17:11:29.474596:  
-2024-08-28 17:11:29.474746: Epoch 1136 
-2024-08-28 17:11:29.474825: Current learning rate: 0.0047 
-2024-08-28 17:12:52.480644: train_loss -0.7659 
-2024-08-28 17:12:52.480874: val_loss -0.7906 
-2024-08-28 17:12:52.481024: Pseudo dice [0.0, 0.0, 0.9036, 0.9731, 0.8491, 0.9468, 0.9474, 0.9677, 0.9523, 0.9511, 0.9358, 0.9607, 0.9603, 0.8416, 0.9542, 0.9325, 0.7863, 0.7739, nan] 
-2024-08-28 17:12:52.481105: Epoch time: 83.01 s 
-2024-08-28 17:12:53.942094:  
-2024-08-28 17:12:53.942497: Epoch 1137 
-2024-08-28 17:12:53.942607: Current learning rate: 0.00469 
-2024-08-28 17:14:18.867963: train_loss -0.7632 
-2024-08-28 17:14:18.868205: val_loss -0.7889 
-2024-08-28 17:14:18.868363: Pseudo dice [0.0, 0.0, 0.9004, 0.9773, 0.8206, 0.9408, 0.9456, 0.964, 0.9516, 0.9485, 0.9381, 0.9614, 0.9634, 0.8474, 0.9492, 0.9336, 0.8294, 0.8233, nan] 
-2024-08-28 17:14:18.868452: Epoch time: 84.93 s 
-2024-08-28 17:14:20.084629:  
-2024-08-28 17:14:20.085077: Epoch 1138 
-2024-08-28 17:14:20.085173: Current learning rate: 0.00469 
-2024-08-28 17:15:47.837064: train_loss -0.7655 
-2024-08-28 17:15:47.837294: val_loss -0.7902 
-2024-08-28 17:15:47.837456: Pseudo dice [0.0, 0.0, 0.8968, 0.9754, 0.842, 0.9477, 0.9501, 0.9633, 0.9517, 0.951, 0.9323, 0.9593, 0.9599, 0.851, 0.9519, 0.9343, 0.8346, 0.8291, nan] 
-2024-08-28 17:15:47.837545: Epoch time: 87.75 s 
-2024-08-28 17:15:48.998948:  
-2024-08-28 17:15:48.999143: Epoch 1139 
-2024-08-28 17:15:48.999237: Current learning rate: 0.00468 
-2024-08-28 17:17:17.422386: train_loss -0.7613 
-2024-08-28 17:17:17.422650: val_loss -0.7806 
-2024-08-28 17:17:17.422812: Pseudo dice [0.0, 0.0, 0.8992, 0.9761, 0.8155, 0.9474, 0.948, 0.9638, 0.9502, 0.9505, 0.9273, 0.9612, 0.9596, 0.8412, 0.9501, 0.92, 0.8247, 0.8199, nan] 
-2024-08-28 17:17:17.422896: Epoch time: 88.42 s 
-2024-08-28 17:17:18.679723:  
-2024-08-28 17:17:18.679919: Epoch 1140 
-2024-08-28 17:17:18.680025: Current learning rate: 0.00468 
-2024-08-28 17:18:48.761021: train_loss -0.7675 
-2024-08-28 17:18:48.761258: val_loss -0.7809 
-2024-08-28 17:18:48.761413: Pseudo dice [0.0, 0.0, 0.8901, 0.9772, 0.8052, 0.9387, 0.945, 0.9598, 0.9512, 0.9507, 0.9323, 0.9608, 0.9596, 0.8433, 0.9505, 0.9341, 0.833, 0.8311, nan] 
-2024-08-28 17:18:48.761497: Epoch time: 90.08 s 
-2024-08-28 17:18:49.935911:  
-2024-08-28 17:18:49.936070: Epoch 1141 
-2024-08-28 17:18:49.936157: Current learning rate: 0.00467 
-2024-08-28 17:20:16.690741: train_loss -0.766 
-2024-08-28 17:20:16.690995: val_loss -0.7898 
-2024-08-28 17:20:16.691523: Pseudo dice [0.0, 0.0, 0.8984, 0.9768, 0.8282, 0.944, 0.9492, 0.966, 0.9531, 0.9521, 0.9339, 0.9606, 0.9604, 0.8512, 0.9528, 0.9334, 0.8234, 0.8239, nan] 
-2024-08-28 17:20:16.691823: Epoch time: 86.76 s 
-2024-08-28 17:20:17.962278:  
-2024-08-28 17:20:17.962440: Epoch 1142 
-2024-08-28 17:20:17.962538: Current learning rate: 0.00467 
-2024-08-28 17:21:42.927682: train_loss -0.7647 
-2024-08-28 17:21:42.927906: val_loss -0.7868 
-2024-08-28 17:21:42.928069: Pseudo dice [0.0, 0.0, 0.897, 0.9757, 0.8301, 0.9392, 0.9453, 0.9656, 0.9507, 0.9439, 0.9279, 0.9619, 0.9581, 0.8518, 0.9511, 0.9304, 0.8356, 0.8227, nan] 
-2024-08-28 17:21:42.928152: Epoch time: 84.97 s 
-2024-08-28 17:21:42.928204: Yayy! New best EMA pseudo Dice: 0.8146 
-2024-08-28 17:21:44.792564:  
-2024-08-28 17:21:44.792722: Epoch 1143 
-2024-08-28 17:21:44.792812: Current learning rate: 0.00466 
-2024-08-28 17:23:11.864116: train_loss -0.7675 
-2024-08-28 17:23:11.864422: val_loss -0.7873 
-2024-08-28 17:23:11.864654: Pseudo dice [0.0, 0.0, 0.8859, 0.9749, 0.84, 0.9459, 0.9507, 0.9621, 0.9519, 0.9518, 0.9323, 0.9612, 0.9595, 0.8502, 0.9518, 0.9311, 0.8212, 0.7981, nan] 
-2024-08-28 17:23:11.864761: Epoch time: 87.07 s 
-2024-08-28 17:23:11.864822: Yayy! New best EMA pseudo Dice: 0.8146 
-2024-08-28 17:23:13.600158:  
-2024-08-28 17:23:13.600357: Epoch 1144 
-2024-08-28 17:23:13.600448: Current learning rate: 0.00466 
-2024-08-28 17:24:38.957785: train_loss -0.7683 
-2024-08-28 17:24:38.958031: val_loss -0.7869 
-2024-08-28 17:24:38.958190: Pseudo dice [0.0, 0.0, 0.8825, 0.9774, 0.8385, 0.9465, 0.9533, 0.9661, 0.9505, 0.9522, 0.9322, 0.9592, 0.9602, 0.8417, 0.9465, 0.9348, 0.8223, 0.8153, nan] 
-2024-08-28 17:24:38.958278: Epoch time: 85.36 s 
-2024-08-28 17:24:38.958327: Yayy! New best EMA pseudo Dice: 0.8147 
-2024-08-28 17:24:40.793229:  
-2024-08-28 17:24:40.793396: Epoch 1145 
-2024-08-28 17:24:40.793482: Current learning rate: 0.00465 
-2024-08-28 17:26:05.623144: train_loss -0.7687 
-2024-08-28 17:26:05.623388: val_loss -0.7873 
-2024-08-28 17:26:05.623544: Pseudo dice [0.0, 0.0, 0.9027, 0.977, 0.8228, 0.9437, 0.9489, 0.9658, 0.9479, 0.9416, 0.9301, 0.9619, 0.9585, 0.8463, 0.953, 0.9273, 0.8136, 0.8247, nan] 
-2024-08-28 17:26:05.623627: Epoch time: 84.83 s 
-2024-08-28 17:26:05.623676: Yayy! New best EMA pseudo Dice: 0.8147 
-2024-08-28 17:26:07.236897:  
-2024-08-28 17:26:07.237072: Epoch 1146 
-2024-08-28 17:26:07.237165: Current learning rate: 0.00465 
-2024-08-28 17:27:31.233534: train_loss -0.7638 
-2024-08-28 17:27:31.233757: val_loss -0.7901 
-2024-08-28 17:27:31.233918: Pseudo dice [0.0, 0.0, 0.8916, 0.9771, 0.8368, 0.9441, 0.95, 0.9625, 0.9555, 0.9529, 0.929, 0.9623, 0.9612, 0.8398, 0.9427, 0.9261, 0.8341, 0.8308, nan] 
-2024-08-28 17:27:31.233994: Epoch time: 84.0 s 
-2024-08-28 17:27:31.234041: Yayy! New best EMA pseudo Dice: 0.8149 
-2024-08-28 17:27:32.924326:  
-2024-08-28 17:27:32.924494: Epoch 1147 
-2024-08-28 17:27:32.924586: Current learning rate: 0.00464 
-2024-08-28 17:29:00.144594: train_loss -0.768 
-2024-08-28 17:29:00.144845: val_loss -0.7877 
-2024-08-28 17:29:00.145014: Pseudo dice [0.0, 0.0, 0.8937, 0.9747, 0.8277, 0.9459, 0.9472, 0.9623, 0.9522, 0.957, 0.9357, 0.9624, 0.9627, 0.8437, 0.9543, 0.9254, 0.8235, 0.8197, nan] 
-2024-08-28 17:29:00.145105: Epoch time: 87.22 s 
-2024-08-28 17:29:00.145158: Yayy! New best EMA pseudo Dice: 0.815 
-2024-08-28 17:29:01.977715:  
-2024-08-28 17:29:01.978040: Epoch 1148 
-2024-08-28 17:29:01.978132: Current learning rate: 0.00464 
-2024-08-28 17:30:23.787220: train_loss -0.7686 
-2024-08-28 17:30:23.787598: val_loss -0.7927 
-2024-08-28 17:30:23.787813: Pseudo dice [0.0, 0.0, 0.8971, 0.9776, 0.8437, 0.9531, 0.9569, 0.966, 0.9559, 0.9592, 0.9365, 0.9621, 0.9632, 0.8568, 0.9419, 0.9388, 0.8223, 0.8343, nan] 
-2024-08-28 17:30:23.787897: Epoch time: 81.81 s 
-2024-08-28 17:30:23.787947: Yayy! New best EMA pseudo Dice: 0.8155 
-2024-08-28 17:30:25.369014:  
-2024-08-28 17:30:25.369282: Epoch 1149 
-2024-08-28 17:30:25.369380: Current learning rate: 0.00463 
-2024-08-28 17:31:45.973785: train_loss -0.7712 
-2024-08-28 17:31:45.974030: val_loss -0.7836 
-2024-08-28 17:31:45.974201: Pseudo dice [0.0, 0.0, 0.8933, 0.9775, 0.8563, 0.9465, 0.9502, 0.9635, 0.9492, 0.9383, 0.9146, 0.9608, 0.9569, 0.8472, 0.9406, 0.9372, 0.8177, 0.8117, nan] 
-2024-08-28 17:31:45.974292: Epoch time: 80.61 s 
-2024-08-28 17:31:47.616062:  
-2024-08-28 17:31:47.616241: Epoch 1150 
-2024-08-28 17:31:47.616336: Current learning rate: 0.00463 
-2024-08-28 17:33:08.253077: train_loss -0.7647 
-2024-08-28 17:33:08.253487: val_loss -0.7836 
-2024-08-28 17:33:08.253670: Pseudo dice [0.0, 0.0, 0.8731, 0.9748, 0.8112, 0.9466, 0.9512, 0.9622, 0.953, 0.9463, 0.9329, 0.9629, 0.9623, 0.8377, 0.953, 0.9235, 0.8163, 0.798, nan] 
-2024-08-28 17:33:08.253753: Epoch time: 80.64 s 
-2024-08-28 17:33:09.485581:  
-2024-08-28 17:33:09.485882: Epoch 1151 
-2024-08-28 17:33:09.485971: Current learning rate: 0.00462 
-2024-08-28 17:34:33.307946: train_loss -0.7642 
-2024-08-28 17:34:33.308212: val_loss -0.7788 
-2024-08-28 17:34:33.308382: Pseudo dice [0.0, 0.0, 0.8788, 0.9771, 0.8182, 0.9443, 0.9442, 0.9606, 0.9485, 0.942, 0.9281, 0.9559, 0.9591, 0.8361, 0.9521, 0.9301, 0.8133, 0.8206, nan] 
-2024-08-28 17:34:33.308481: Epoch time: 83.82 s 
-2024-08-28 17:34:34.564078:  
-2024-08-28 17:34:34.564237: Epoch 1152 
-2024-08-28 17:34:34.564324: Current learning rate: 0.00462 
-2024-08-28 17:36:02.645820: train_loss -0.7637 
-2024-08-28 17:36:02.646039: val_loss -0.7814 
-2024-08-28 17:36:02.646184: Pseudo dice [0.0, 0.0, 0.9047, 0.9757, 0.8074, 0.9295, 0.9288, 0.9609, 0.9547, 0.951, 0.9312, 0.9616, 0.9577, 0.8399, 0.9474, 0.9288, 0.8276, 0.8221, nan] 
-2024-08-28 17:36:02.646276: Epoch time: 88.08 s 
-2024-08-28 17:36:03.849624:  
-2024-08-28 17:36:03.849873: Epoch 1153 
-2024-08-28 17:36:03.849964: Current learning rate: 0.00461 
-2024-08-28 17:37:26.065945: train_loss -0.7609 
-2024-08-28 17:37:26.066193: val_loss -0.7797 
-2024-08-28 17:37:26.066370: Pseudo dice [0.0, 0.0, 0.9021, 0.9771, 0.816, 0.9419, 0.9411, 0.9596, 0.9455, 0.9419, 0.9124, 0.9559, 0.9529, 0.8354, 0.9463, 0.9304, 0.8379, 0.8221, nan] 
-2024-08-28 17:37:26.066511: Epoch time: 82.22 s 
-2024-08-28 17:37:27.598117:  
-2024-08-28 17:37:27.598263: Epoch 1154 
-2024-08-28 17:37:27.598358: Current learning rate: 0.00461 
-2024-08-28 17:38:52.718239: train_loss -0.7645 
-2024-08-28 17:38:52.718471: val_loss -0.7826 
-2024-08-28 17:38:52.718635: Pseudo dice [0.0, 0.0, 0.9026, 0.9763, 0.8322, 0.9443, 0.9414, 0.9605, 0.9502, 0.9452, 0.9308, 0.9591, 0.9603, 0.8315, 0.9503, 0.9293, 0.8135, 0.8136, nan] 
-2024-08-28 17:38:52.718720: Epoch time: 85.12 s 
-2024-08-28 17:38:53.973499:  
-2024-08-28 17:38:53.973917: Epoch 1155 
-2024-08-28 17:38:53.974013: Current learning rate: 0.00461 
-2024-08-28 17:40:15.675147: train_loss -0.766 
-2024-08-28 17:40:15.675403: val_loss -0.7821 
-2024-08-28 17:40:15.675603: Pseudo dice [0.0, 0.0, 0.8878, 0.9774, 0.8298, 0.9471, 0.9461, 0.9645, 0.9505, 0.948, 0.9249, 0.963, 0.9597, 0.8404, 0.952, 0.9339, 0.8422, 0.81, nan] 
-2024-08-28 17:40:15.675701: Epoch time: 81.7 s 
-2024-08-28 17:40:16.903252:  
-2024-08-28 17:40:16.903440: Epoch 1156 
-2024-08-28 17:40:16.903530: Current learning rate: 0.0046 
-2024-08-28 17:41:39.534043: train_loss -0.7667 
-2024-08-28 17:41:39.534272: val_loss -0.7833 
-2024-08-28 17:41:39.534432: Pseudo dice [0.0, 0.0, 0.8808, 0.9761, 0.8315, 0.945, 0.9483, 0.9635, 0.9479, 0.9508, 0.9293, 0.9578, 0.9608, 0.8458, 0.956, 0.9315, 0.8249, 0.8274, nan] 
-2024-08-28 17:41:39.534513: Epoch time: 82.63 s 
-2024-08-28 17:41:40.734066:  
-2024-08-28 17:41:40.734480: Epoch 1157 
-2024-08-28 17:41:40.734579: Current learning rate: 0.0046 
-2024-08-28 17:43:08.200847: train_loss -0.7634 
-2024-08-28 17:43:08.201096: val_loss -0.7906 
-2024-08-28 17:43:08.201270: Pseudo dice [0.0, 0.0, 0.9034, 0.9753, 0.8287, 0.9476, 0.9506, 0.966, 0.9507, 0.9512, 0.931, 0.9553, 0.957, 0.8478, 0.9524, 0.9342, 0.8345, 0.8363, nan] 
-2024-08-28 17:43:08.201364: Epoch time: 87.47 s 
-2024-08-28 17:43:09.448161:  
-2024-08-28 17:43:09.448341: Epoch 1158 
-2024-08-28 17:43:09.448440: Current learning rate: 0.00459 
-2024-08-28 17:44:32.520352: train_loss -0.7666 
-2024-08-28 17:44:32.520601: val_loss -0.7919 
-2024-08-28 17:44:32.520774: Pseudo dice [0.0, 0.0, 0.9117, 0.9759, 0.8397, 0.9472, 0.9489, 0.9656, 0.9524, 0.9524, 0.9221, 0.9612, 0.9579, 0.8385, 0.9576, 0.9328, 0.8349, 0.8306, nan] 
-2024-08-28 17:44:32.520864: Epoch time: 83.07 s 
-2024-08-28 17:44:33.972299:  
-2024-08-28 17:44:33.972672: Epoch 1159 
-2024-08-28 17:44:33.972771: Current learning rate: 0.00459 
-2024-08-28 17:46:02.323453: train_loss -0.7651 
-2024-08-28 17:46:02.323681: val_loss -0.7871 
-2024-08-28 17:46:02.323837: Pseudo dice [0.0, 0.0, 0.9032, 0.976, 0.8225, 0.9456, 0.9463, 0.9626, 0.9477, 0.9498, 0.9314, 0.9585, 0.9604, 0.8499, 0.9556, 0.9281, 0.8265, 0.8193, nan] 
-2024-08-28 17:46:02.323917: Epoch time: 88.35 s 
-2024-08-28 17:46:03.693470:  
-2024-08-28 17:46:03.693623: Epoch 1160 
-2024-08-28 17:46:03.693712: Current learning rate: 0.00458 
-2024-08-28 17:47:33.739001: train_loss -0.7666 
-2024-08-28 17:47:33.739260: val_loss -0.7838 
-2024-08-28 17:47:33.739458: Pseudo dice [0.0, 0.0, 0.8972, 0.9761, 0.8294, 0.9479, 0.9499, 0.9661, 0.9486, 0.945, 0.9252, 0.9569, 0.9579, 0.8417, 0.9515, 0.932, 0.8317, 0.8028, nan] 
-2024-08-28 17:47:33.739552: Epoch time: 90.05 s 
-2024-08-28 17:47:34.998440:  
-2024-08-28 17:47:34.998966: Epoch 1161 
-2024-08-28 17:47:34.999063: Current learning rate: 0.00458 
-2024-08-28 17:49:03.090541: train_loss -0.7699 
-2024-08-28 17:49:03.090812: val_loss -0.784 
-2024-08-28 17:49:03.090998: Pseudo dice [0.0, 0.0, 0.8662, 0.977, 0.8331, 0.9466, 0.9469, 0.9637, 0.9456, 0.9463, 0.9241, 0.9581, 0.958, 0.8448, 0.9481, 0.9327, 0.8136, 0.8214, nan] 
-2024-08-28 17:49:03.091109: Epoch time: 88.09 s 
-2024-08-28 17:49:04.329401:  
-2024-08-28 17:49:04.329705: Epoch 1162 
-2024-08-28 17:49:04.329800: Current learning rate: 0.00457 
-2024-08-28 17:50:25.366055: train_loss -0.7693 
-2024-08-28 17:50:25.366289: val_loss -0.7864 
-2024-08-28 17:50:25.366456: Pseudo dice [0.0, 0.0, 0.9012, 0.9752, 0.8244, 0.9467, 0.9477, 0.9658, 0.946, 0.9483, 0.9225, 0.9608, 0.9582, 0.844, 0.9521, 0.933, 0.8266, 0.8222, nan] 
-2024-08-28 17:50:25.366546: Epoch time: 81.04 s 
-2024-08-28 17:50:26.615193:  
-2024-08-28 17:50:26.615359: Epoch 1163 
-2024-08-28 17:50:26.615448: Current learning rate: 0.00457 
-2024-08-28 17:51:49.280558: train_loss -0.768 
-2024-08-28 17:51:49.280790: val_loss -0.7888 
-2024-08-28 17:51:49.280938: Pseudo dice [0.0, 0.0, 0.8956, 0.9752, 0.8349, 0.9489, 0.9466, 0.9647, 0.9556, 0.9566, 0.9256, 0.964, 0.9617, 0.8427, 0.9492, 0.9334, 0.8371, 0.8348, nan] 
-2024-08-28 17:51:49.281016: Epoch time: 82.67 s 
-2024-08-28 17:51:50.458885:  
-2024-08-28 17:51:50.459148: Epoch 1164 
-2024-08-28 17:51:50.459246: Current learning rate: 0.00456 
-2024-08-28 17:53:11.830702: train_loss -0.7663 
-2024-08-28 17:53:11.830938: val_loss -0.7838 
-2024-08-28 17:53:11.831102: Pseudo dice [0.0, 0.0, 0.8918, 0.9742, 0.8316, 0.9455, 0.9455, 0.9634, 0.9524, 0.9493, 0.9307, 0.9637, 0.9605, 0.8358, 0.9443, 0.9275, 0.8403, 0.8132, nan] 
-2024-08-28 17:53:11.831179: Epoch time: 81.37 s 
-2024-08-28 17:53:13.041302:  
-2024-08-28 17:53:13.041486: Epoch 1165 
-2024-08-28 17:53:13.041585: Current learning rate: 0.00456 
-2024-08-28 17:54:38.280278: train_loss -0.7684 
-2024-08-28 17:54:38.280553: val_loss -0.7891 
-2024-08-28 17:54:38.280712: Pseudo dice [0.0, 0.0, 0.9049, 0.9777, 0.8464, 0.9432, 0.9525, 0.9622, 0.9529, 0.9515, 0.9331, 0.9612, 0.9591, 0.8464, 0.9522, 0.9341, 0.8399, 0.8356, nan] 
-2024-08-28 17:54:38.280801: Epoch time: 85.24 s 
-2024-08-28 17:54:38.280857: Yayy! New best EMA pseudo Dice: 0.8156 
-2024-08-28 17:54:40.142679:  
-2024-08-28 17:54:40.142841: Epoch 1166 
-2024-08-28 17:54:40.142940: Current learning rate: 0.00455 
-2024-08-28 17:56:08.583146: train_loss -0.7651 
-2024-08-28 17:56:08.583388: val_loss -0.7845 
-2024-08-28 17:56:08.583556: Pseudo dice [0.0, 0.0, 0.8951, 0.9772, 0.8279, 0.9412, 0.9455, 0.9615, 0.946, 0.9426, 0.9317, 0.9572, 0.9603, 0.8282, 0.9506, 0.9274, 0.8236, 0.8239, nan] 
-2024-08-28 17:56:08.583639: Epoch time: 88.44 s 
-2024-08-28 17:56:09.828474:  
-2024-08-28 17:56:09.828653: Epoch 1167 
-2024-08-28 17:56:09.828750: Current learning rate: 0.00455 
-2024-08-28 17:57:35.557013: train_loss -0.7632 
-2024-08-28 17:57:35.557271: val_loss -0.7937 
-2024-08-28 17:57:35.557451: Pseudo dice [0.0, 0.0, 0.8881, 0.9763, 0.8425, 0.9422, 0.9487, 0.9611, 0.9495, 0.9495, 0.9278, 0.9579, 0.9599, 0.8442, 0.9483, 0.9332, 0.8188, 0.8236, nan] 
-2024-08-28 17:57:35.557546: Epoch time: 85.73 s 
-2024-08-28 17:57:36.819753:  
-2024-08-28 17:57:36.820016: Epoch 1168 
-2024-08-28 17:57:36.820113: Current learning rate: 0.00454 
-2024-08-28 17:59:05.202130: train_loss -0.7527 
-2024-08-28 17:59:05.202348: val_loss -0.7685 
-2024-08-28 17:59:05.202504: Pseudo dice [0.0, 0.0, 0.8812, 0.9745, 0.7874, 0.9363, 0.9317, 0.958, 0.9431, 0.9428, 0.9237, 0.9511, 0.957, 0.8156, 0.9346, 0.9254, 0.7939, 0.7866, nan] 
-2024-08-28 17:59:05.202591: Epoch time: 88.38 s 
-2024-08-28 17:59:06.511004:  
-2024-08-28 17:59:06.511169: Epoch 1169 
-2024-08-28 17:59:06.511258: Current learning rate: 0.00454 
-2024-08-28 18:00:32.189254: train_loss -0.7506 
-2024-08-28 18:00:32.189491: val_loss -0.7823 
-2024-08-28 18:00:32.189646: Pseudo dice [0.0, 0.0, 0.8948, 0.9745, 0.8451, 0.9417, 0.9456, 0.9632, 0.9367, 0.9449, 0.9214, 0.9475, 0.9474, 0.8299, 0.9491, 0.9266, 0.8185, 0.8269, nan] 
-2024-08-28 18:00:32.189726: Epoch time: 85.68 s 
-2024-08-28 18:00:33.416919:  
-2024-08-28 18:00:33.417088: Epoch 1170 
-2024-08-28 18:00:33.417183: Current learning rate: 0.00453 
-2024-08-28 18:02:00.239846: train_loss -0.7526 
-2024-08-28 18:02:00.240092: val_loss -0.7638 
-2024-08-28 18:02:00.240271: Pseudo dice [0.0, 0.0, 0.8851, 0.9715, 0.6566, 0.9453, 0.9428, 0.9596, 0.9451, 0.933, 0.9069, 0.9566, 0.9499, 0.8226, 0.9404, 0.9069, 0.8253, 0.8155, nan] 
-2024-08-28 18:02:00.240361: Epoch time: 86.82 s 
-2024-08-28 18:02:01.458594:  
-2024-08-28 18:02:01.458767: Epoch 1171 
-2024-08-28 18:02:01.458855: Current learning rate: 0.00453 
-2024-08-28 18:03:27.496799: train_loss -0.7575 
-2024-08-28 18:03:27.497044: val_loss -0.779 
-2024-08-28 18:03:27.497200: Pseudo dice [0.0, 0.0, 0.8946, 0.9754, 0.8265, 0.946, 0.9482, 0.9636, 0.9485, 0.9543, 0.9258, 0.9594, 0.9566, 0.8385, 0.9563, 0.9256, 0.8269, 0.8272, nan] 
-2024-08-28 18:03:27.497282: Epoch time: 86.04 s 
-2024-08-28 18:03:28.756046:  
-2024-08-28 18:03:28.756228: Epoch 1172 
-2024-08-28 18:03:28.756325: Current learning rate: 0.00452 
-2024-08-28 18:04:56.269343: train_loss -0.7575 
-2024-08-28 18:04:56.269591: val_loss -0.7762 
-2024-08-28 18:04:56.269745: Pseudo dice [0.0, 0.0, 0.8683, 0.9768, 0.8214, 0.9407, 0.9349, 0.9588, 0.9461, 0.9366, 0.9301, 0.958, 0.9591, 0.8317, 0.9486, 0.9226, 0.811, 0.8107, nan] 
-2024-08-28 18:04:56.269832: Epoch time: 87.51 s 
-2024-08-28 18:04:57.799648:  
-2024-08-28 18:04:57.799894: Epoch 1173 
-2024-08-28 18:04:57.799989: Current learning rate: 0.00452 
-2024-08-28 18:06:27.305256: train_loss -0.7543 
-2024-08-28 18:06:27.305506: val_loss -0.7787 
-2024-08-28 18:06:27.305682: Pseudo dice [0.0, 0.0, 0.8966, 0.9761, 0.8383, 0.9409, 0.9443, 0.9606, 0.947, 0.9488, 0.9276, 0.9566, 0.9556, 0.8401, 0.9367, 0.925, 0.8338, 0.8332, nan] 
-2024-08-28 18:06:27.305773: Epoch time: 89.51 s 
-2024-08-28 18:06:28.548663:  
-2024-08-28 18:06:28.548866: Epoch 1174 
-2024-08-28 18:06:28.548952: Current learning rate: 0.00451 
-2024-08-28 18:07:58.606725: train_loss -0.7619 
-2024-08-28 18:07:58.607053: val_loss -0.7809 
-2024-08-28 18:07:58.607437: Pseudo dice [0.0, 0.0, 0.891, 0.9773, 0.8179, 0.9345, 0.9345, 0.9554, 0.9514, 0.9465, 0.9266, 0.96, 0.9613, 0.8399, 0.9536, 0.9266, 0.8286, 0.8344, nan] 
-2024-08-28 18:07:58.607621: Epoch time: 90.06 s 
-2024-08-28 18:08:00.071393:  
-2024-08-28 18:08:00.071576: Epoch 1175 
-2024-08-28 18:08:00.071667: Current learning rate: 0.00451 
-2024-08-28 18:09:26.971142: train_loss -0.7606 
-2024-08-28 18:09:26.971390: val_loss -0.78 
-2024-08-28 18:09:26.971542: Pseudo dice [0.0, 0.0, 0.9001, 0.9757, 0.8348, 0.9398, 0.9429, 0.9649, 0.9369, 0.9334, 0.926, 0.947, 0.9462, 0.837, 0.9518, 0.9213, 0.8299, 0.8306, nan] 
-2024-08-28 18:09:26.971621: Epoch time: 86.9 s 
-2024-08-28 18:09:28.189125:  
-2024-08-28 18:09:28.189287: Epoch 1176 
-2024-08-28 18:09:28.189379: Current learning rate: 0.0045 
-2024-08-28 18:10:55.355078: train_loss -0.7612 
-2024-08-28 18:10:55.355458: val_loss -0.7829 
-2024-08-28 18:10:55.355676: Pseudo dice [0.0, 0.0, 0.8843, 0.9768, 0.8207, 0.9455, 0.9466, 0.9615, 0.9545, 0.9552, 0.9282, 0.9625, 0.9614, 0.8357, 0.9539, 0.9267, 0.7962, 0.8177, nan] 
-2024-08-28 18:10:55.355854: Epoch time: 87.17 s 
-2024-08-28 18:10:56.531781:  
-2024-08-28 18:10:56.531957: Epoch 1177 
-2024-08-28 18:10:56.532044: Current learning rate: 0.0045 
-2024-08-28 18:12:24.025637: train_loss -0.7598 
-2024-08-28 18:12:24.025910: val_loss -0.7898 
-2024-08-28 18:12:24.026122: Pseudo dice [0.0, 0.0, 0.8922, 0.9769, 0.8106, 0.9377, 0.9435, 0.9639, 0.9519, 0.9501, 0.9309, 0.9608, 0.9615, 0.8405, 0.9506, 0.931, 0.8147, 0.8177, nan] 
-2024-08-28 18:12:24.026226: Epoch time: 87.49 s 
-2024-08-28 18:12:25.348119:  
-2024-08-28 18:12:25.348281: Epoch 1178 
-2024-08-28 18:12:25.348366: Current learning rate: 0.00449 
-2024-08-28 18:13:49.875925: train_loss -0.7651 
-2024-08-28 18:13:49.876141: val_loss -0.7772 
-2024-08-28 18:13:49.876292: Pseudo dice [0.0, 0.0, 0.8785, 0.9767, 0.8082, 0.9379, 0.9394, 0.9615, 0.9363, 0.9386, 0.9205, 0.9478, 0.9472, 0.8168, 0.9301, 0.9103, 0.8193, 0.8188, nan] 
-2024-08-28 18:13:49.876367: Epoch time: 84.53 s 
-2024-08-28 18:13:51.200524:  
-2024-08-28 18:13:51.200973: Epoch 1179 
-2024-08-28 18:13:51.201069: Current learning rate: 0.00449 
-2024-08-28 18:15:16.375924: train_loss -0.761 
-2024-08-28 18:15:16.376176: val_loss -0.7806 
-2024-08-28 18:15:16.376354: Pseudo dice [0.0, 0.0, 0.8794, 0.9764, 0.8534, 0.9382, 0.9383, 0.9624, 0.9505, 0.9339, 0.9291, 0.9589, 0.957, 0.8434, 0.9517, 0.9309, 0.8226, 0.8246, nan] 
-2024-08-28 18:15:16.376452: Epoch time: 85.18 s 
-2024-08-28 18:15:17.583400:  
-2024-08-28 18:15:17.583710: Epoch 1180 
-2024-08-28 18:15:17.583805: Current learning rate: 0.00448 
-2024-08-28 18:16:42.894759: train_loss -0.7663 
-2024-08-28 18:16:42.895005: val_loss -0.7871 
-2024-08-28 18:16:42.895168: Pseudo dice [0.0, 0.0, 0.8835, 0.9762, 0.8357, 0.9432, 0.9459, 0.9614, 0.9521, 0.9434, 0.9263, 0.9611, 0.9609, 0.8425, 0.9505, 0.9344, 0.8143, 0.8347, nan] 
-2024-08-28 18:16:42.895255: Epoch time: 85.31 s 
-2024-08-28 18:16:44.135496:  
-2024-08-28 18:16:44.135779: Epoch 1181 
-2024-08-28 18:16:44.135885: Current learning rate: 0.00448 
-2024-08-28 18:18:11.549699: train_loss -0.7602 
-2024-08-28 18:18:11.549917: val_loss -0.7821 
-2024-08-28 18:18:11.550080: Pseudo dice [0.0, 0.0, 0.89, 0.9773, 0.8289, 0.9482, 0.9503, 0.9638, 0.9454, 0.9388, 0.9271, 0.9599, 0.9582, 0.8305, 0.9476, 0.9215, 0.8198, 0.8244, nan] 
-2024-08-28 18:18:11.550165: Epoch time: 87.41 s 
-2024-08-28 18:18:12.777560:  
-2024-08-28 18:18:12.777934: Epoch 1182 
-2024-08-28 18:18:12.778029: Current learning rate: 0.00447 
-2024-08-28 18:19:40.791304: train_loss -0.7584 
-2024-08-28 18:19:40.791540: val_loss -0.7782 
-2024-08-28 18:19:40.791693: Pseudo dice [0.0, 0.0, 0.8976, 0.9767, 0.7827, 0.9417, 0.948, 0.9616, 0.9493, 0.9445, 0.9317, 0.9599, 0.959, 0.8354, 0.9477, 0.9236, 0.8068, 0.7805, nan] 
-2024-08-28 18:19:40.791777: Epoch time: 88.01 s 
-2024-08-28 18:19:41.971154:  
-2024-08-28 18:19:41.971322: Epoch 1183 
-2024-08-28 18:19:41.971408: Current learning rate: 0.00447 
-2024-08-28 18:21:09.404661: train_loss -0.7627 
-2024-08-28 18:21:09.404904: val_loss -0.7899 
-2024-08-28 18:21:09.405069: Pseudo dice [0.0, 0.0, 0.8996, 0.9738, 0.8363, 0.9464, 0.9503, 0.9617, 0.9546, 0.955, 0.9254, 0.9612, 0.9571, 0.837, 0.9559, 0.9298, 0.8342, 0.8239, nan] 
-2024-08-28 18:21:09.405152: Epoch time: 87.43 s 
-2024-08-28 18:21:10.862196:  
-2024-08-28 18:21:10.862370: Epoch 1184 
-2024-08-28 18:21:10.862463: Current learning rate: 0.00446 
-2024-08-28 18:22:39.165101: train_loss -0.7634 
-2024-08-28 18:22:39.165352: val_loss -0.7749 
-2024-08-28 18:22:39.165617: Pseudo dice [0.0, 0.0, 0.864, 0.9749, 0.8365, 0.9444, 0.9453, 0.9615, 0.9421, 0.9358, 0.9138, 0.9513, 0.9455, 0.8389, 0.9327, 0.9302, 0.8244, 0.8151, nan] 
-2024-08-28 18:22:39.165781: Epoch time: 88.3 s 
-2024-08-28 18:22:40.384369:  
-2024-08-28 18:22:40.384695: Epoch 1185 
-2024-08-28 18:22:40.384795: Current learning rate: 0.00446 
-2024-08-28 18:23:59.106543: train_loss -0.7606 
-2024-08-28 18:23:59.107095: val_loss -0.7893 
-2024-08-28 18:23:59.107286: Pseudo dice [0.0, 0.0, 0.886, 0.9758, 0.8357, 0.943, 0.9451, 0.9604, 0.9541, 0.9506, 0.9255, 0.9617, 0.9587, 0.8337, 0.9549, 0.9312, 0.8249, 0.8179, nan] 
-2024-08-28 18:23:59.107426: Epoch time: 78.72 s 
-2024-08-28 18:24:00.358057:  
-2024-08-28 18:24:00.358527: Epoch 1186 
-2024-08-28 18:24:00.358632: Current learning rate: 0.00445 
-2024-08-28 18:25:23.850956: train_loss -0.7647 
-2024-08-28 18:25:23.851526: val_loss -0.7893 
-2024-08-28 18:25:23.851815: Pseudo dice [0.0, 0.0, 0.9015, 0.9759, 0.8477, 0.9419, 0.9506, 0.9646, 0.9488, 0.9476, 0.9344, 0.9591, 0.9586, 0.8398, 0.944, 0.9292, 0.841, 0.8404, nan] 
-2024-08-28 18:25:23.852078: Epoch time: 83.49 s 
-2024-08-28 18:25:25.132466:  
-2024-08-28 18:25:25.132629: Epoch 1187 
-2024-08-28 18:25:25.132725: Current learning rate: 0.00445 
-2024-08-28 18:26:48.256364: train_loss -0.7698 
-2024-08-28 18:26:48.256695: val_loss -0.7836 
-2024-08-28 18:26:48.256877: Pseudo dice [0.0, 0.0, 0.8628, 0.974, 0.8336, 0.9449, 0.9465, 0.9623, 0.9471, 0.9524, 0.9286, 0.9581, 0.96, 0.8432, 0.9484, 0.9315, 0.8344, 0.8186, nan] 
-2024-08-28 18:26:48.256993: Epoch time: 83.12 s 
-2024-08-28 18:26:49.492423:  
-2024-08-28 18:26:49.492957: Epoch 1188 
-2024-08-28 18:26:49.493239: Current learning rate: 0.00444 
-2024-08-28 18:28:13.721056: train_loss -0.7639 
-2024-08-28 18:28:13.721266: val_loss -0.7833 
-2024-08-28 18:28:13.721431: Pseudo dice [0.0, 0.0, 0.8893, 0.975, 0.8505, 0.9451, 0.9482, 0.9653, 0.9507, 0.951, 0.9237, 0.9613, 0.9609, 0.8407, 0.9554, 0.9352, 0.8183, 0.8079, nan] 
-2024-08-28 18:28:13.721516: Epoch time: 84.23 s 
-2024-08-28 18:28:14.942031:  
-2024-08-28 18:28:14.942251: Epoch 1189 
-2024-08-28 18:28:14.942402: Current learning rate: 0.00444 
-2024-08-28 18:29:43.454345: train_loss -0.7633 
-2024-08-28 18:29:43.454564: val_loss -0.7899 
-2024-08-28 18:29:43.454762: Pseudo dice [0.0, 0.0, 0.8967, 0.9764, 0.8449, 0.9477, 0.9511, 0.9643, 0.9527, 0.9528, 0.9356, 0.96, 0.9638, 0.8392, 0.9552, 0.9257, 0.8266, 0.8199, nan] 
-2024-08-28 18:29:43.454856: Epoch time: 88.51 s 
-2024-08-28 18:29:44.695168:  
-2024-08-28 18:29:44.695538: Epoch 1190 
-2024-08-28 18:29:44.695634: Current learning rate: 0.00443 
-2024-08-28 18:31:11.867571: train_loss -0.7592 
-2024-08-28 18:31:11.868050: val_loss -0.7776 
-2024-08-28 18:31:11.868443: Pseudo dice [0.0, 0.0, 0.8773, 0.975, 0.8093, 0.9422, 0.9378, 0.9571, 0.9503, 0.942, 0.9228, 0.9552, 0.9547, 0.838, 0.9459, 0.9205, 0.8229, 0.823, nan] 
-2024-08-28 18:31:11.868552: Epoch time: 87.17 s 
-2024-08-28 18:31:13.405928:  
-2024-08-28 18:31:13.406088: Epoch 1191 
-2024-08-28 18:31:13.406176: Current learning rate: 0.00443 
-2024-08-28 18:32:41.189020: train_loss -0.7569 
-2024-08-28 18:32:41.189263: val_loss -0.7813 
-2024-08-28 18:32:41.189418: Pseudo dice [0.0, 0.0, 0.8725, 0.9758, 0.8345, 0.944, 0.9427, 0.9622, 0.951, 0.9447, 0.9232, 0.9608, 0.9602, 0.8361, 0.9468, 0.9267, 0.8232, 0.8099, nan] 
-2024-08-28 18:32:41.189502: Epoch time: 87.78 s 
-2024-08-28 18:32:42.345892:  
-2024-08-28 18:32:42.346153: Epoch 1192 
-2024-08-28 18:32:42.346254: Current learning rate: 0.00442 
-2024-08-28 18:34:07.874587: train_loss -0.759 
-2024-08-28 18:34:07.874856: val_loss -0.7831 
-2024-08-28 18:34:07.875033: Pseudo dice [0.0, 0.0, 0.8994, 0.9748, 0.8375, 0.9472, 0.9436, 0.9624, 0.9522, 0.9499, 0.9323, 0.9607, 0.9622, 0.8398, 0.9495, 0.9306, 0.8222, 0.8258, nan] 
-2024-08-28 18:34:07.875131: Epoch time: 85.53 s 
-2024-08-28 18:34:09.110930:  
-2024-08-28 18:34:09.111101: Epoch 1193 
-2024-08-28 18:34:09.111191: Current learning rate: 0.00442 
-2024-08-28 18:35:32.719093: train_loss -0.7665 
-2024-08-28 18:35:32.719365: val_loss -0.7914 
-2024-08-28 18:35:32.719621: Pseudo dice [0.0, 0.0, 0.9069, 0.9767, 0.8419, 0.9496, 0.9522, 0.9673, 0.9497, 0.9541, 0.9278, 0.9614, 0.9611, 0.8482, 0.9565, 0.9351, 0.8354, 0.82, nan] 
-2024-08-28 18:35:32.719753: Epoch time: 83.61 s 
-2024-08-28 18:35:34.079313:  
-2024-08-28 18:35:34.079600: Epoch 1194 
-2024-08-28 18:35:34.079698: Current learning rate: 0.00441 
-2024-08-28 18:36:57.713235: train_loss -0.7673 
-2024-08-28 18:36:57.713473: val_loss -0.79 
-2024-08-28 18:36:57.713631: Pseudo dice [0.0, 0.0, 0.895, 0.9755, 0.8346, 0.9472, 0.9494, 0.9603, 0.9512, 0.9599, 0.938, 0.9611, 0.963, 0.8442, 0.9367, 0.9341, 0.8205, 0.8231, nan] 
-2024-08-28 18:36:57.713713: Epoch time: 83.63 s 
-2024-08-28 18:36:58.948271:  
-2024-08-28 18:36:58.948449: Epoch 1195 
-2024-08-28 18:36:58.948537: Current learning rate: 0.00441 
-2024-08-28 18:38:25.082580: train_loss -0.7682 
-2024-08-28 18:38:25.082781: val_loss -0.792 
-2024-08-28 18:38:25.082930: Pseudo dice [0.0, 0.0, 0.9044, 0.9774, 0.7777, 0.9486, 0.9485, 0.9607, 0.954, 0.9503, 0.9338, 0.9595, 0.9608, 0.8477, 0.9543, 0.9321, 0.8355, 0.8296, nan] 
-2024-08-28 18:38:25.083009: Epoch time: 86.14 s 
-2024-08-28 18:38:26.324572:  
-2024-08-28 18:38:26.324711: Epoch 1196 
-2024-08-28 18:38:26.324797: Current learning rate: 0.0044 
-2024-08-28 18:39:55.018289: train_loss -0.7623 
-2024-08-28 18:39:55.018520: val_loss -0.7908 
-2024-08-28 18:39:55.018690: Pseudo dice [0.0, 0.0, 0.8874, 0.9762, 0.8434, 0.9509, 0.9488, 0.9667, 0.9554, 0.9506, 0.9376, 0.9628, 0.9634, 0.8568, 0.9477, 0.9366, 0.8361, 0.8339, nan] 
-2024-08-28 18:39:55.018779: Epoch time: 88.69 s 
-2024-08-28 18:39:56.492611:  
-2024-08-28 18:39:56.493013: Epoch 1197 
-2024-08-28 18:39:56.493116: Current learning rate: 0.0044 
-2024-08-28 18:41:19.403944: train_loss -0.7658 
-2024-08-28 18:41:19.404295: val_loss -0.7787 
-2024-08-28 18:41:19.404551: Pseudo dice [0.0, 0.0, 0.8911, 0.9763, 0.8177, 0.9392, 0.9452, 0.9624, 0.9407, 0.9481, 0.9228, 0.9527, 0.9545, 0.84, 0.9441, 0.9277, 0.8042, 0.8037, nan] 
-2024-08-28 18:41:19.404747: Epoch time: 82.91 s 
-2024-08-28 18:41:20.603768:  
-2024-08-28 18:41:20.603925: Epoch 1198 
-2024-08-28 18:41:20.604025: Current learning rate: 0.00439 
-2024-08-28 18:42:45.860376: train_loss -0.7601 
-2024-08-28 18:42:45.860634: val_loss -0.7854 
-2024-08-28 18:42:45.860798: Pseudo dice [0.0, 0.0, 0.8947, 0.9736, 0.8332, 0.947, 0.9533, 0.9655, 0.9475, 0.9529, 0.9243, 0.9598, 0.958, 0.8342, 0.9504, 0.9205, 0.8204, 0.7899, nan] 
-2024-08-28 18:42:45.860884: Epoch time: 85.26 s 
-2024-08-28 18:42:47.065143:  
-2024-08-28 18:42:47.065449: Epoch 1199 
-2024-08-28 18:42:47.065553: Current learning rate: 0.00439 
-2024-08-28 18:44:15.686247: train_loss -0.7622 
-2024-08-28 18:44:15.686495: val_loss -0.7836 
-2024-08-28 18:44:15.686652: Pseudo dice [0.0, 0.0, 0.8899, 0.9754, 0.8276, 0.9437, 0.948, 0.9656, 0.9493, 0.9507, 0.9336, 0.9631, 0.9637, 0.8337, 0.9464, 0.9265, 0.8349, 0.8328, nan] 
-2024-08-28 18:44:15.686737: Epoch time: 88.62 s 
-2024-08-28 18:44:17.268196:  
-2024-08-28 18:44:17.268342: Epoch 1200 
-2024-08-28 18:44:17.268439: Current learning rate: 0.00438 
-2024-08-28 18:45:43.111741: train_loss -0.7642 
-2024-08-28 18:45:43.111975: val_loss -0.78 
-2024-08-28 18:45:43.112141: Pseudo dice [0.0, 0.0, 0.8965, 0.9756, 0.8101, 0.9482, 0.9459, 0.9609, 0.9467, 0.9472, 0.925, 0.9586, 0.9581, 0.8473, 0.9502, 0.9283, 0.8345, 0.8072, nan] 
-2024-08-28 18:45:43.112274: Epoch time: 85.84 s 
-2024-08-28 18:45:44.408120:  
-2024-08-28 18:45:44.408292: Epoch 1201 
-2024-08-28 18:45:44.408382: Current learning rate: 0.00438 
-2024-08-28 18:47:11.273112: train_loss -0.7639 
-2024-08-28 18:47:11.273370: val_loss -0.7784 
-2024-08-28 18:47:11.273539: Pseudo dice [0.0, 0.0, 0.8661, 0.9754, 0.8235, 0.9378, 0.9419, 0.9554, 0.9477, 0.9425, 0.9262, 0.9601, 0.9575, 0.8203, 0.9303, 0.9244, 0.8171, 0.8148, nan] 
-2024-08-28 18:47:11.273625: Epoch time: 86.87 s 
-2024-08-28 18:47:12.515100:  
-2024-08-28 18:47:12.515360: Epoch 1202 
-2024-08-28 18:47:12.515463: Current learning rate: 0.00437 
-2024-08-28 18:48:34.815798: train_loss -0.76 
-2024-08-28 18:48:34.816049: val_loss -0.7848 
-2024-08-28 18:48:34.816228: Pseudo dice [0.0, 0.0, 0.8905, 0.9763, 0.8047, 0.9467, 0.9469, 0.9633, 0.9528, 0.9562, 0.9331, 0.9618, 0.9623, 0.84, 0.9586, 0.9333, 0.8275, 0.8365, nan] 
-2024-08-28 18:48:34.816315: Epoch time: 82.3 s 
-2024-08-28 18:48:36.303169:  
-2024-08-28 18:48:36.303320: Epoch 1203 
-2024-08-28 18:48:36.303419: Current learning rate: 0.00437 
-2024-08-28 18:50:01.088485: train_loss -0.7588 
-2024-08-28 18:50:01.088725: val_loss -0.7808 
-2024-08-28 18:50:01.088889: Pseudo dice [0.0, 0.0, 0.8816, 0.9759, 0.8052, 0.9351, 0.9448, 0.9657, 0.9502, 0.9375, 0.9257, 0.9607, 0.957, 0.8248, 0.9525, 0.9312, 0.8233, 0.8205, nan] 
-2024-08-28 18:50:01.088976: Epoch time: 84.79 s 
-2024-08-28 18:50:02.357284:  
-2024-08-28 18:50:02.357727: Epoch 1204 
-2024-08-28 18:50:02.357824: Current learning rate: 0.00436 
-2024-08-28 18:51:30.512697: train_loss -0.7649 
-2024-08-28 18:51:30.512941: val_loss -0.7834 
-2024-08-28 18:51:30.513135: Pseudo dice [0.0, 0.0, 0.8696, 0.9756, 0.844, 0.9462, 0.9413, 0.959, 0.9458, 0.9536, 0.9298, 0.9582, 0.9609, 0.8365, 0.9515, 0.9305, 0.8331, 0.8284, nan] 
-2024-08-28 18:51:30.513227: Epoch time: 88.16 s 
-2024-08-28 18:51:31.841480:  
-2024-08-28 18:51:31.841759: Epoch 1205 
-2024-08-28 18:51:31.841860: Current learning rate: 0.00436 
-2024-08-28 18:52:56.451891: train_loss -0.7696 
-2024-08-28 18:52:56.452120: val_loss -0.7901 
-2024-08-28 18:52:56.452283: Pseudo dice [0.0, 0.0, 0.8837, 0.9738, 0.8534, 0.9492, 0.9493, 0.9645, 0.9535, 0.951, 0.9356, 0.9604, 0.962, 0.8496, 0.9407, 0.9356, 0.831, 0.8169, nan] 
-2024-08-28 18:52:56.452367: Epoch time: 84.61 s 
-2024-08-28 18:52:57.697953:  
-2024-08-28 18:52:57.698143: Epoch 1206 
-2024-08-28 18:52:57.698235: Current learning rate: 0.00435 
-2024-08-28 18:54:20.330499: train_loss -0.7606 
-2024-08-28 18:54:20.330719: val_loss -0.778 
-2024-08-28 18:54:20.330903: Pseudo dice [0.0, 0.0, 0.8912, 0.9738, 0.8513, 0.9477, 0.9524, 0.9643, 0.9411, 0.933, 0.9279, 0.956, 0.9544, 0.8333, 0.9419, 0.9294, 0.8199, 0.825, nan] 
-2024-08-28 18:54:20.330995: Epoch time: 82.63 s 
-2024-08-28 18:54:21.599803:  
-2024-08-28 18:54:21.599973: Epoch 1207 
-2024-08-28 18:54:21.600065: Current learning rate: 0.00435 
-2024-08-28 18:55:51.415401: train_loss -0.7676 
-2024-08-28 18:55:51.415669: val_loss -0.7851 
-2024-08-28 18:55:51.415844: Pseudo dice [0.0, 0.0, 0.878, 0.9755, 0.8591, 0.9446, 0.9488, 0.9615, 0.9508, 0.9401, 0.93, 0.9591, 0.9598, 0.8436, 0.9459, 0.9347, 0.8214, 0.8198, nan] 
-2024-08-28 18:55:51.415935: Epoch time: 89.82 s 
-2024-08-28 18:55:52.855699:  
-2024-08-28 18:55:52.855941: Epoch 1208 
-2024-08-28 18:55:52.856091: Current learning rate: 0.00434 
-2024-08-28 18:57:18.331099: train_loss -0.7605 
-2024-08-28 18:57:18.331348: val_loss -0.7824 
-2024-08-28 18:57:18.331549: Pseudo dice [0.0, 0.0, 0.8883, 0.9744, 0.838, 0.9512, 0.9518, 0.9634, 0.9527, 0.9522, 0.9246, 0.9594, 0.9581, 0.8389, 0.9529, 0.9357, 0.8225, 0.8197, nan] 
-2024-08-28 18:57:18.331651: Epoch time: 85.48 s 
-2024-08-28 18:57:19.890401:  
-2024-08-28 18:57:19.890598: Epoch 1209 
-2024-08-28 18:57:19.890696: Current learning rate: 0.00434 
-2024-08-28 18:58:47.499941: train_loss -0.7634 
-2024-08-28 18:58:47.500184: val_loss -0.7821 
-2024-08-28 18:58:47.500343: Pseudo dice [0.0, 0.0, 0.9042, 0.976, 0.8461, 0.9477, 0.9484, 0.9665, 0.9311, 0.9425, 0.913, 0.9461, 0.9442, 0.8455, 0.9486, 0.933, 0.8297, 0.8247, nan] 
-2024-08-28 18:58:47.500436: Epoch time: 87.61 s 
-2024-08-28 18:58:48.725139:  
-2024-08-28 18:58:48.725346: Epoch 1210 
-2024-08-28 18:58:48.725439: Current learning rate: 0.00433 
-2024-08-28 19:00:13.948600: train_loss -0.7645 
-2024-08-28 19:00:13.948833: val_loss -0.7866 
-2024-08-28 19:00:13.948982: Pseudo dice [0.0, 0.0, 0.9115, 0.976, 0.8263, 0.9449, 0.9493, 0.9604, 0.9519, 0.951, 0.9319, 0.9628, 0.9621, 0.8435, 0.9537, 0.9309, 0.8223, 0.8214, nan] 
-2024-08-28 19:00:13.949061: Epoch time: 85.22 s 
-2024-08-28 19:00:15.122397:  
-2024-08-28 19:00:15.122744: Epoch 1211 
-2024-08-28 19:00:15.122836: Current learning rate: 0.00433 
-2024-08-28 19:01:36.656946: train_loss -0.7646 
-2024-08-28 19:01:36.657176: val_loss -0.7857 
-2024-08-28 19:01:36.657329: Pseudo dice [0.0, 0.0, 0.8675, 0.9777, 0.8198, 0.9414, 0.9497, 0.9653, 0.9534, 0.9539, 0.9326, 0.962, 0.9559, 0.8442, 0.9416, 0.9322, 0.8199, 0.7932, nan] 
-2024-08-28 19:01:36.657411: Epoch time: 81.54 s 
-2024-08-28 19:01:37.783583:  
-2024-08-28 19:01:37.783758: Epoch 1212 
-2024-08-28 19:01:37.783856: Current learning rate: 0.00432 
-2024-08-28 19:03:01.980322: train_loss -0.7642 
-2024-08-28 19:03:01.980572: val_loss -0.7879 
-2024-08-28 19:03:01.980717: Pseudo dice [0.0, 0.0, 0.903, 0.9764, 0.8588, 0.9463, 0.9474, 0.9601, 0.9507, 0.9434, 0.927, 0.9607, 0.9614, 0.8399, 0.9505, 0.9284, 0.8131, 0.8225, nan] 
-2024-08-28 19:03:01.980794: Epoch time: 84.2 s 
-2024-08-28 19:03:03.161646:  
-2024-08-28 19:03:03.161814: Epoch 1213 
-2024-08-28 19:03:03.161906: Current learning rate: 0.00432 
-2024-08-28 19:04:27.689034: train_loss -0.7644 
-2024-08-28 19:04:27.689250: val_loss -0.7793 
-2024-08-28 19:04:27.689409: Pseudo dice [0.0, 0.0, 0.913, 0.9744, 0.8411, 0.9373, 0.9446, 0.963, 0.9517, 0.9458, 0.925, 0.9589, 0.9544, 0.8245, 0.9497, 0.9311, 0.8298, 0.8138, nan] 
-2024-08-28 19:04:27.689495: Epoch time: 84.53 s 
-2024-08-28 19:04:28.912766:  
-2024-08-28 19:04:28.912920: Epoch 1214 
-2024-08-28 19:04:28.913009: Current learning rate: 0.00431 
-2024-08-28 19:05:48.117568: train_loss -0.7635 
-2024-08-28 19:05:48.117854: val_loss -0.7862 
-2024-08-28 19:05:48.118008: Pseudo dice [0.0, 0.0, 0.908, 0.9752, 0.8152, 0.9408, 0.9406, 0.9648, 0.9508, 0.9429, 0.9272, 0.9619, 0.9589, 0.8404, 0.9518, 0.936, 0.812, 0.83, nan] 
-2024-08-28 19:05:48.118088: Epoch time: 79.21 s 
-2024-08-28 19:05:49.556956:  
-2024-08-28 19:05:49.557131: Epoch 1215 
-2024-08-28 19:05:49.557218: Current learning rate: 0.00431 
-2024-08-28 19:07:16.278036: train_loss -0.7565 
-2024-08-28 19:07:16.278734: val_loss -0.7816 
-2024-08-28 19:07:16.278973: Pseudo dice [0.0, 0.0, 0.8938, 0.9759, 0.8231, 0.9348, 0.9435, 0.961, 0.9531, 0.9455, 0.9191, 0.9596, 0.9561, 0.8225, 0.9442, 0.9282, 0.8154, 0.8107, nan] 
-2024-08-28 19:07:16.279149: Epoch time: 86.72 s 
-2024-08-28 19:07:17.847737:  
-2024-08-28 19:07:17.848264: Epoch 1216 
-2024-08-28 19:07:17.848622: Current learning rate: 0.0043 
-2024-08-28 19:08:45.603213: train_loss -0.7552 
-2024-08-28 19:08:45.603635: val_loss -0.782 
-2024-08-28 19:08:45.603915: Pseudo dice [0.0, 0.0, 0.8565, 0.9743, 0.7834, 0.9408, 0.9418, 0.9631, 0.9512, 0.9426, 0.9262, 0.9608, 0.9568, 0.8338, 0.9499, 0.9287, 0.812, 0.798, nan] 
-2024-08-28 19:08:45.604072: Epoch time: 87.76 s 
-2024-08-28 19:08:47.165274:  
-2024-08-28 19:08:47.165750: Epoch 1217 
-2024-08-28 19:08:47.165857: Current learning rate: 0.0043 
-2024-08-28 19:10:13.739187: train_loss -0.7567 
-2024-08-28 19:10:13.739673: val_loss -0.7827 
-2024-08-28 19:10:13.739843: Pseudo dice [0.0, 0.0, 0.871, 0.9753, 0.8099, 0.9466, 0.9482, 0.9623, 0.9506, 0.9472, 0.9363, 0.9608, 0.9607, 0.8393, 0.9517, 0.9288, 0.8193, 0.8083, nan] 
-2024-08-28 19:10:13.739928: Epoch time: 86.57 s 
-2024-08-28 19:10:14.964921:  
-2024-08-28 19:10:14.965176: Epoch 1218 
-2024-08-28 19:10:14.965261: Current learning rate: 0.00429 
-2024-08-28 19:11:44.420453: train_loss -0.7585 
-2024-08-28 19:11:44.420692: val_loss -0.7771 
-2024-08-28 19:11:44.420838: Pseudo dice [0.0, 0.0, 0.8758, 0.976, 0.8081, 0.9405, 0.9444, 0.9607, 0.9473, 0.9408, 0.9255, 0.9571, 0.9567, 0.8375, 0.9465, 0.9275, 0.8273, 0.8041, nan] 
-2024-08-28 19:11:44.420918: Epoch time: 89.46 s 
-2024-08-28 19:11:45.581505:  
-2024-08-28 19:11:45.581906: Epoch 1219 
-2024-08-28 19:11:45.582003: Current learning rate: 0.00429 
-2024-08-28 19:13:15.095346: train_loss -0.7574 
-2024-08-28 19:13:15.095572: val_loss -0.7846 
-2024-08-28 19:13:15.095727: Pseudo dice [0.0, 0.0, 0.8993, 0.9752, 0.8362, 0.9447, 0.9497, 0.9607, 0.9504, 0.9517, 0.9349, 0.9606, 0.9594, 0.8361, 0.9511, 0.9286, 0.8315, 0.8236, nan] 
-2024-08-28 19:13:15.095808: Epoch time: 89.51 s 
-2024-08-28 19:13:16.258802:  
-2024-08-28 19:13:16.259094: Epoch 1220 
-2024-08-28 19:13:16.259185: Current learning rate: 0.00429 
-2024-08-28 19:14:40.569975: train_loss -0.7651 
-2024-08-28 19:14:40.570217: val_loss -0.7826 
-2024-08-28 19:14:40.570381: Pseudo dice [0.0, 0.0, 0.882, 0.9777, 0.8459, 0.9457, 0.9531, 0.9646, 0.9489, 0.9496, 0.9179, 0.957, 0.9569, 0.8285, 0.9558, 0.928, 0.8181, 0.8173, nan] 
-2024-08-28 19:14:40.570515: Epoch time: 84.31 s 
-2024-08-28 19:14:41.757538:  
-2024-08-28 19:14:41.757711: Epoch 1221 
-2024-08-28 19:14:41.757793: Current learning rate: 0.00428 
-2024-08-28 19:16:06.867696: train_loss -0.7648 
-2024-08-28 19:16:06.867934: val_loss -0.7895 
-2024-08-28 19:16:06.868090: Pseudo dice [0.0, 0.0, 0.9046, 0.9755, 0.8424, 0.9495, 0.9518, 0.9664, 0.9542, 0.9516, 0.9347, 0.9576, 0.9608, 0.844, 0.9519, 0.9345, 0.8273, 0.8212, nan] 
-2024-08-28 19:16:06.868170: Epoch time: 85.11 s 
-2024-08-28 19:16:08.732171:  
-2024-08-28 19:16:08.732377: Epoch 1222 
-2024-08-28 19:16:08.732496: Current learning rate: 0.00428 
-2024-08-28 19:17:34.787883: train_loss -0.7697 
-2024-08-28 19:17:34.788324: val_loss -0.7799 
-2024-08-28 19:17:34.788509: Pseudo dice [0.0, 0.0, 0.9011, 0.9757, 0.8097, 0.9444, 0.9487, 0.9581, 0.9445, 0.9465, 0.9266, 0.9529, 0.9572, 0.825, 0.9432, 0.9283, 0.8349, 0.8364, nan] 
-2024-08-28 19:17:34.788602: Epoch time: 86.06 s 
-2024-08-28 19:17:35.995050:  
-2024-08-28 19:17:35.995331: Epoch 1223 
-2024-08-28 19:17:35.995423: Current learning rate: 0.00427 
-2024-08-28 19:19:04.153144: train_loss -0.7616 
-2024-08-28 19:19:04.153375: val_loss -0.7816 
-2024-08-28 19:19:04.153529: Pseudo dice [0.0, 0.0, 0.8987, 0.9761, 0.8149, 0.9505, 0.955, 0.9657, 0.9484, 0.9489, 0.9264, 0.9614, 0.9585, 0.849, 0.9562, 0.9307, 0.8312, 0.8138, nan] 
-2024-08-28 19:19:04.153609: Epoch time: 88.16 s 
-2024-08-28 19:19:05.485349:  
-2024-08-28 19:19:05.485534: Epoch 1224 
-2024-08-28 19:19:05.485627: Current learning rate: 0.00427 
-2024-08-28 19:20:30.949014: train_loss -0.7671 
-2024-08-28 19:20:30.949273: val_loss -0.7818 
-2024-08-28 19:20:30.949420: Pseudo dice [0.0, 0.0, 0.8822, 0.9764, 0.8481, 0.9447, 0.9525, 0.9617, 0.9527, 0.9383, 0.9325, 0.9596, 0.9587, 0.8401, 0.945, 0.9247, 0.8218, 0.807, nan] 
-2024-08-28 19:20:30.949500: Epoch time: 85.46 s 
-2024-08-28 19:20:32.143762:  
-2024-08-28 19:20:32.143935: Epoch 1225 
-2024-08-28 19:20:32.144029: Current learning rate: 0.00426 
-2024-08-28 19:21:57.735310: train_loss -0.7625 
-2024-08-28 19:21:57.735527: val_loss -0.7919 
-2024-08-28 19:21:57.735684: Pseudo dice [0.0, 0.0, 0.8907, 0.9769, 0.8559, 0.9479, 0.9512, 0.9652, 0.9434, 0.9523, 0.931, 0.9587, 0.9573, 0.8574, 0.9411, 0.9366, 0.8343, 0.8203, nan] 
-2024-08-28 19:21:57.735808: Epoch time: 85.59 s 
-2024-08-28 19:21:59.088433:  
-2024-08-28 19:21:59.088696: Epoch 1226 
-2024-08-28 19:21:59.088802: Current learning rate: 0.00426 
-2024-08-28 19:23:29.461602: train_loss -0.7644 
-2024-08-28 19:23:29.461817: val_loss -0.7863 
-2024-08-28 19:23:29.461971: Pseudo dice [0.0, 0.0, 0.8703, 0.9762, 0.8433, 0.9405, 0.9444, 0.9563, 0.954, 0.9514, 0.9308, 0.9601, 0.9618, 0.8449, 0.9483, 0.9304, 0.8298, 0.8163, nan] 
-2024-08-28 19:23:29.462047: Epoch time: 90.37 s 
-2024-08-28 19:23:30.977903:  
-2024-08-28 19:23:30.978231: Epoch 1227 
-2024-08-28 19:23:30.978335: Current learning rate: 0.00425 
-2024-08-28 19:24:56.022108: train_loss -0.767 
-2024-08-28 19:24:56.022365: val_loss -0.7732 
-2024-08-28 19:24:56.022524: Pseudo dice [0.0, 0.0, 0.8781, 0.9762, 0.7809, 0.9376, 0.931, 0.9609, 0.9446, 0.9486, 0.931, 0.9623, 0.957, 0.8333, 0.9393, 0.925, 0.821, 0.8218, nan] 
-2024-08-28 19:24:56.022606: Epoch time: 85.04 s 
-2024-08-28 19:24:57.198314:  
-2024-08-28 19:24:57.198481: Epoch 1228 
-2024-08-28 19:24:57.198573: Current learning rate: 0.00425 
-2024-08-28 19:26:21.959285: train_loss -0.7616 
-2024-08-28 19:26:21.959528: val_loss -0.7884 
-2024-08-28 19:26:21.959692: Pseudo dice [0.0, 0.0, 0.8605, 0.9756, 0.7925, 0.95, 0.9476, 0.9638, 0.9526, 0.9524, 0.9281, 0.959, 0.9592, 0.846, 0.9492, 0.9304, 0.8289, 0.8262, nan] 
-2024-08-28 19:26:21.959773: Epoch time: 84.76 s 
-2024-08-28 19:26:23.193790:  
-2024-08-28 19:26:23.193974: Epoch 1229 
-2024-08-28 19:26:23.194067: Current learning rate: 0.00424 
-2024-08-28 19:27:53.852088: train_loss -0.7623 
-2024-08-28 19:27:53.852344: val_loss -0.7857 
-2024-08-28 19:27:53.852520: Pseudo dice [0.0, 0.0, 0.8954, 0.9775, 0.7623, 0.9448, 0.9471, 0.9656, 0.9484, 0.9431, 0.9311, 0.9597, 0.9586, 0.844, 0.9546, 0.9346, 0.8471, 0.8348, nan] 
-2024-08-28 19:27:53.852611: Epoch time: 90.66 s 
-2024-08-28 19:27:55.078757:  
-2024-08-28 19:27:55.078978: Epoch 1230 
-2024-08-28 19:27:55.079068: Current learning rate: 0.00424 
-2024-08-28 19:29:22.433989: train_loss -0.7648 
-2024-08-28 19:29:22.434211: val_loss -0.7852 
-2024-08-28 19:29:22.434366: Pseudo dice [0.0, 0.0, 0.8938, 0.976, 0.8253, 0.9436, 0.9484, 0.9609, 0.9525, 0.944, 0.9163, 0.9596, 0.9578, 0.84, 0.9569, 0.9324, 0.8266, 0.8159, nan] 
-2024-08-28 19:29:22.434450: Epoch time: 87.36 s 
-2024-08-28 19:29:23.603007:  
-2024-08-28 19:29:23.603181: Epoch 1231 
-2024-08-28 19:29:23.603272: Current learning rate: 0.00423 
-2024-08-28 19:30:48.521461: train_loss -0.7674 
-2024-08-28 19:30:48.521720: val_loss -0.7849 
-2024-08-28 19:30:48.521910: Pseudo dice [0.0, 0.0, 0.8819, 0.977, 0.8411, 0.946, 0.9488, 0.9619, 0.9584, 0.9412, 0.9278, 0.9648, 0.96, 0.8341, 0.9506, 0.9324, 0.8167, 0.831, nan] 
-2024-08-28 19:30:48.522198: Epoch time: 84.92 s 
-2024-08-28 19:30:49.786881:  
-2024-08-28 19:30:49.787204: Epoch 1232 
-2024-08-28 19:30:49.787296: Current learning rate: 0.00423 
-2024-08-28 19:32:12.239986: train_loss -0.7678 
-2024-08-28 19:32:12.240225: val_loss -0.789 
-2024-08-28 19:32:12.240393: Pseudo dice [0.0, 0.0, 0.904, 0.9765, 0.8576, 0.9478, 0.9509, 0.9663, 0.9532, 0.9514, 0.9311, 0.9626, 0.9611, 0.841, 0.9472, 0.9334, 0.8275, 0.8233, nan] 
-2024-08-28 19:32:12.240520: Epoch time: 82.45 s 
-2024-08-28 19:32:13.730449:  
-2024-08-28 19:32:13.730929: Epoch 1233 
-2024-08-28 19:32:13.731044: Current learning rate: 0.00422 
-2024-08-28 19:33:44.626693: train_loss -0.7665 
-2024-08-28 19:33:44.626933: val_loss -0.7848 
-2024-08-28 19:33:44.627131: Pseudo dice [0.0, 0.0, 0.9069, 0.9765, 0.8338, 0.9452, 0.9487, 0.9631, 0.954, 0.9478, 0.9312, 0.9609, 0.9597, 0.8355, 0.9562, 0.9297, 0.8142, 0.8325, nan] 
-2024-08-28 19:33:44.627249: Epoch time: 90.9 s 
-2024-08-28 19:33:45.856485:  
-2024-08-28 19:33:45.856673: Epoch 1234 
-2024-08-28 19:33:45.856776: Current learning rate: 0.00422 
-2024-08-28 19:35:15.682847: train_loss -0.7598 
-2024-08-28 19:35:15.683083: val_loss -0.7861 
-2024-08-28 19:35:15.683236: Pseudo dice [0.0, 0.0, 0.8983, 0.976, 0.8142, 0.9442, 0.951, 0.9652, 0.9517, 0.9512, 0.9345, 0.9586, 0.9613, 0.8438, 0.9539, 0.9279, 0.8354, 0.8111, nan] 
-2024-08-28 19:35:15.683314: Epoch time: 89.83 s 
-2024-08-28 19:35:16.887526:  
-2024-08-28 19:35:16.887698: Epoch 1235 
-2024-08-28 19:35:16.887793: Current learning rate: 0.00421 
-2024-08-28 19:36:40.355590: train_loss -0.7657 
-2024-08-28 19:36:40.355839: val_loss -0.7858 
-2024-08-28 19:36:40.356007: Pseudo dice [0.0, 0.0, 0.898, 0.9736, 0.8394, 0.9483, 0.9507, 0.967, 0.952, 0.954, 0.932, 0.9599, 0.9584, 0.8397, 0.9539, 0.9277, 0.8267, 0.8224, nan] 
-2024-08-28 19:36:40.356097: Epoch time: 83.47 s 
-2024-08-28 19:36:41.622794:  
-2024-08-28 19:36:41.622963: Epoch 1236 
-2024-08-28 19:36:41.623062: Current learning rate: 0.00421 
-2024-08-28 19:38:12.526207: train_loss -0.7657 
-2024-08-28 19:38:12.526491: val_loss -0.7838 
-2024-08-28 19:38:12.526658: Pseudo dice [0.0, 0.0, 0.8625, 0.9749, 0.8345, 0.9464, 0.9484, 0.9617, 0.948, 0.9553, 0.9262, 0.9621, 0.961, 0.8375, 0.9408, 0.9308, 0.8056, 0.8156, nan] 
-2024-08-28 19:38:12.526742: Epoch time: 90.9 s 
-2024-08-28 19:38:13.814670:  
-2024-08-28 19:38:13.815187: Epoch 1237 
-2024-08-28 19:38:13.815284: Current learning rate: 0.0042 
-2024-08-28 19:39:36.516506: train_loss -0.7686 
-2024-08-28 19:39:36.516750: val_loss -0.7833 
-2024-08-28 19:39:36.516915: Pseudo dice [0.0, 0.0, 0.9124, 0.9775, 0.8369, 0.9362, 0.9434, 0.9587, 0.9406, 0.9334, 0.9203, 0.956, 0.9534, 0.8347, 0.9494, 0.9326, 0.8238, 0.8267, nan] 
-2024-08-28 19:39:36.517000: Epoch time: 82.7 s 
-2024-08-28 19:39:37.746409:  
-2024-08-28 19:39:37.746684: Epoch 1238 
-2024-08-28 19:39:37.746776: Current learning rate: 0.0042 
-2024-08-28 19:40:59.398044: train_loss -0.7701 
-2024-08-28 19:40:59.398300: val_loss -0.7911 
-2024-08-28 19:40:59.398469: Pseudo dice [0.0, 0.0, 0.8995, 0.9772, 0.8413, 0.949, 0.9504, 0.9654, 0.9563, 0.9488, 0.9357, 0.962, 0.9626, 0.8466, 0.9571, 0.9323, 0.821, 0.8198, nan] 
-2024-08-28 19:40:59.398557: Epoch time: 81.65 s 
-2024-08-28 19:41:00.638514:  
-2024-08-28 19:41:00.638776: Epoch 1239 
-2024-08-28 19:41:00.638881: Current learning rate: 0.00419 
-2024-08-28 19:42:28.608793: train_loss -0.7656 
-2024-08-28 19:42:28.609021: val_loss -0.7895 
-2024-08-28 19:42:28.609185: Pseudo dice [0.0, 0.0, 0.9044, 0.9765, 0.8135, 0.9478, 0.9497, 0.9606, 0.9507, 0.9541, 0.9315, 0.9604, 0.9594, 0.838, 0.9531, 0.927, 0.831, 0.847, nan] 
-2024-08-28 19:42:28.609269: Epoch time: 87.97 s 
-2024-08-28 19:42:30.063241:  
-2024-08-28 19:42:30.063555: Epoch 1240 
-2024-08-28 19:42:30.063652: Current learning rate: 0.00419 
-2024-08-28 19:43:51.119789: train_loss -0.7699 
-2024-08-28 19:43:51.120075: val_loss -0.7845 
-2024-08-28 19:43:51.120368: Pseudo dice [0.0, 0.0, 0.9058, 0.9761, 0.8333, 0.941, 0.9444, 0.9587, 0.9558, 0.9475, 0.9279, 0.962, 0.96, 0.8505, 0.9483, 0.9327, 0.8387, 0.8399, nan] 
-2024-08-28 19:43:51.120476: Epoch time: 81.06 s 
-2024-08-28 19:43:52.369915:  
-2024-08-28 19:43:52.370185: Epoch 1241 
-2024-08-28 19:43:52.370286: Current learning rate: 0.00418 
-2024-08-28 19:45:19.766171: train_loss -0.7604 
-2024-08-28 19:45:19.766426: val_loss -0.7801 
-2024-08-28 19:45:19.766598: Pseudo dice [0.0, 0.0, 0.893, 0.9736, 0.8383, 0.9398, 0.9416, 0.9599, 0.9437, 0.9428, 0.9205, 0.9561, 0.9529, 0.8291, 0.9442, 0.9256, 0.8265, 0.8224, nan] 
-2024-08-28 19:45:19.766684: Epoch time: 87.4 s 
-2024-08-28 19:45:21.002091:  
-2024-08-28 19:45:21.002275: Epoch 1242 
-2024-08-28 19:45:21.002370: Current learning rate: 0.00418 
-2024-08-28 19:46:46.866924: train_loss -0.7618 
-2024-08-28 19:46:46.867162: val_loss -0.7876 
-2024-08-28 19:46:46.867336: Pseudo dice [0.0, 0.0, 0.8938, 0.9771, 0.8338, 0.9441, 0.948, 0.9626, 0.9507, 0.9504, 0.9351, 0.9618, 0.962, 0.8463, 0.9482, 0.9352, 0.8176, 0.8106, nan] 
-2024-08-28 19:46:46.867427: Epoch time: 85.87 s 
-2024-08-28 19:46:48.086847:  
-2024-08-28 19:46:48.087147: Epoch 1243 
-2024-08-28 19:46:48.087241: Current learning rate: 0.00417 
-2024-08-28 19:48:15.815889: train_loss -0.7648 
-2024-08-28 19:48:15.816139: val_loss -0.7896 
-2024-08-28 19:48:15.816304: Pseudo dice [0.0, 0.0, 0.9022, 0.9779, 0.8385, 0.9481, 0.9507, 0.9635, 0.9496, 0.9529, 0.9294, 0.958, 0.9593, 0.8419, 0.946, 0.933, 0.8234, 0.8154, nan] 
-2024-08-28 19:48:15.816394: Epoch time: 87.73 s 
-2024-08-28 19:48:17.063715:  
-2024-08-28 19:48:17.063871: Epoch 1244 
-2024-08-28 19:48:17.063965: Current learning rate: 0.00417 
-2024-08-28 19:49:45.580496: train_loss -0.7643 
-2024-08-28 19:49:45.580721: val_loss -0.7856 
-2024-08-28 19:49:45.580886: Pseudo dice [0.0, 0.0, 0.8844, 0.975, 0.82, 0.9447, 0.9471, 0.9624, 0.9548, 0.9453, 0.9336, 0.962, 0.9625, 0.8491, 0.9537, 0.9375, 0.8198, 0.8254, nan] 
-2024-08-28 19:49:45.580969: Epoch time: 88.52 s 
-2024-08-28 19:49:46.854237:  
-2024-08-28 19:49:46.854520: Epoch 1245 
-2024-08-28 19:49:46.854612: Current learning rate: 0.00416 
-2024-08-28 19:51:13.873239: train_loss -0.7658 
-2024-08-28 19:51:13.873461: val_loss -0.778 
-2024-08-28 19:51:13.873616: Pseudo dice [0.0, 0.0, 0.9025, 0.9762, 0.7906, 0.9493, 0.9539, 0.9621, 0.9472, 0.9437, 0.9206, 0.9548, 0.9552, 0.8468, 0.938, 0.9344, 0.8266, 0.8221, nan] 
-2024-08-28 19:51:13.873700: Epoch time: 87.02 s 
-2024-08-28 19:51:15.378327:  
-2024-08-28 19:51:15.378500: Epoch 1246 
-2024-08-28 19:51:15.378594: Current learning rate: 0.00416 
-2024-08-28 19:52:47.634992: train_loss -0.7642 
-2024-08-28 19:52:47.635238: val_loss -0.7925 
-2024-08-28 19:52:47.635399: Pseudo dice [0.0, 0.0, 0.8948, 0.9756, 0.8453, 0.9489, 0.9493, 0.9667, 0.955, 0.9563, 0.936, 0.9624, 0.961, 0.8455, 0.9529, 0.932, 0.8275, 0.8265, nan] 
-2024-08-28 19:52:47.635530: Epoch time: 92.26 s 
-2024-08-28 19:52:48.853431:  
-2024-08-28 19:52:48.853589: Epoch 1247 
-2024-08-28 19:52:48.853673: Current learning rate: 0.00415 
-2024-08-28 19:54:10.478565: train_loss -0.7653 
-2024-08-28 19:54:10.478801: val_loss -0.7835 
-2024-08-28 19:54:10.478956: Pseudo dice [0.0, 0.0, 0.9119, 0.9755, 0.8197, 0.948, 0.9475, 0.9646, 0.9521, 0.9465, 0.9306, 0.9627, 0.9626, 0.8519, 0.9564, 0.9339, 0.8161, 0.8047, nan] 
-2024-08-28 19:54:10.479035: Epoch time: 81.63 s 
-2024-08-28 19:54:11.702962:  
-2024-08-28 19:54:11.703468: Epoch 1248 
-2024-08-28 19:54:11.703570: Current learning rate: 0.00415 
-2024-08-28 19:55:38.771203: train_loss -0.7659 
-2024-08-28 19:55:38.771423: val_loss -0.7865 
-2024-08-28 19:55:38.771611: Pseudo dice [0.0, 0.0, 0.8982, 0.9776, 0.8417, 0.9451, 0.9459, 0.9639, 0.9512, 0.9365, 0.9317, 0.9585, 0.9606, 0.8467, 0.9517, 0.9282, 0.8189, 0.8271, nan] 
-2024-08-28 19:55:38.771700: Epoch time: 87.07 s 
-2024-08-28 19:55:39.935726:  
-2024-08-28 19:55:39.936213: Epoch 1249 
-2024-08-28 19:55:39.936309: Current learning rate: 0.00414 
-2024-08-28 19:57:05.698206: train_loss -0.7648 
-2024-08-28 19:57:05.698459: val_loss -0.7884 
-2024-08-28 19:57:05.698630: Pseudo dice [0.0, 0.0, 0.9052, 0.9772, 0.834, 0.9486, 0.951, 0.9663, 0.9534, 0.951, 0.9383, 0.9598, 0.9612, 0.8489, 0.9445, 0.9353, 0.8402, 0.847, nan] 
-2024-08-28 19:57:05.698724: Epoch time: 85.76 s 
-2024-08-28 19:57:06.145234: Yayy! New best EMA pseudo Dice: 0.8158 
-2024-08-28 19:57:07.724688:  
-2024-08-28 19:57:07.724851: Epoch 1250 
-2024-08-28 19:57:07.724941: Current learning rate: 0.00414 
-2024-08-28 19:58:28.423014: train_loss -0.7693 
-2024-08-28 19:58:28.423238: val_loss -0.792 
-2024-08-28 19:58:28.423399: Pseudo dice [0.0, 0.0, 0.8822, 0.9773, 0.8473, 0.9506, 0.9537, 0.968, 0.9537, 0.9499, 0.9226, 0.9626, 0.9609, 0.8557, 0.9509, 0.9369, 0.8357, 0.8239, nan] 
-2024-08-28 19:58:28.423481: Epoch time: 80.7 s 
-2024-08-28 19:58:28.423528: Yayy! New best EMA pseudo Dice: 0.8161 
-2024-08-28 19:58:30.238643:  
-2024-08-28 19:58:30.238813: Epoch 1251 
-2024-08-28 19:58:30.238919: Current learning rate: 0.00413 
-2024-08-28 19:59:58.104995: train_loss -0.7671 
-2024-08-28 19:59:58.105323: val_loss -0.7876 
-2024-08-28 19:59:58.105499: Pseudo dice [0.0, 0.0, 0.9019, 0.9769, 0.8225, 0.9498, 0.9505, 0.9636, 0.9515, 0.9515, 0.9312, 0.9571, 0.9618, 0.8474, 0.9502, 0.9204, 0.8312, 0.8224, nan] 
-2024-08-28 19:59:58.105587: Epoch time: 87.87 s 
-2024-08-28 19:59:58.105633: Yayy! New best EMA pseudo Dice: 0.8161 
-2024-08-28 19:59:59.728270:  
-2024-08-28 19:59:59.728446: Epoch 1252 
-2024-08-28 19:59:59.728544: Current learning rate: 0.00413 
-2024-08-28 20:01:20.175185: train_loss -0.7645 
-2024-08-28 20:01:20.175430: val_loss -0.7915 
-2024-08-28 20:01:20.175627: Pseudo dice [0.0, 0.0, 0.8929, 0.9768, 0.8289, 0.9424, 0.9458, 0.9629, 0.9513, 0.9554, 0.9338, 0.9575, 0.9613, 0.8447, 0.9527, 0.9329, 0.8374, 0.8189, nan] 
-2024-08-28 20:01:20.175721: Epoch time: 80.45 s 
-2024-08-28 20:01:20.175773: Yayy! New best EMA pseudo Dice: 0.8161 
-2024-08-28 20:01:21.929751:  
-2024-08-28 20:01:21.930015: Epoch 1253 
-2024-08-28 20:01:21.930117: Current learning rate: 0.00412 
-2024-08-28 20:02:48.719973: train_loss -0.7699 
-2024-08-28 20:02:48.720220: val_loss -0.779 
-2024-08-28 20:02:48.720408: Pseudo dice [0.0, 0.0, 0.9095, 0.9773, 0.8044, 0.9328, 0.9374, 0.9679, 0.94, 0.934, 0.9316, 0.9495, 0.9488, 0.8537, 0.9459, 0.9351, 0.8194, 0.8196, nan] 
-2024-08-28 20:02:48.720510: Epoch time: 86.79 s 
-2024-08-28 20:02:49.909003:  
-2024-08-28 20:02:49.909164: Epoch 1254 
-2024-08-28 20:02:49.909253: Current learning rate: 0.00412 
-2024-08-28 20:04:12.440883: train_loss -0.7687 
-2024-08-28 20:04:12.441141: val_loss -0.7915 
-2024-08-28 20:04:12.441305: Pseudo dice [0.0, 0.0, 0.909, 0.9774, 0.8627, 0.9435, 0.9487, 0.9659, 0.9542, 0.9543, 0.9346, 0.9623, 0.9609, 0.8559, 0.9391, 0.9342, 0.829, 0.8317, nan] 
-2024-08-28 20:04:12.441390: Epoch time: 82.53 s 
-2024-08-28 20:04:13.669727:  
-2024-08-28 20:04:13.669945: Epoch 1255 
-2024-08-28 20:04:13.670032: Current learning rate: 0.00411 
-2024-08-28 20:05:42.067463: train_loss -0.7669 
-2024-08-28 20:05:42.067693: val_loss -0.7859 
-2024-08-28 20:05:42.067842: Pseudo dice [0.0, 0.0, 0.8758, 0.9768, 0.8363, 0.9439, 0.9422, 0.9646, 0.9516, 0.9506, 0.9261, 0.9619, 0.9594, 0.8352, 0.9496, 0.9296, 0.8035, 0.8159, nan] 
-2024-08-28 20:05:42.067921: Epoch time: 88.4 s 
-2024-08-28 20:05:43.162176:  
-2024-08-28 20:05:43.162345: Epoch 1256 
-2024-08-28 20:05:43.162442: Current learning rate: 0.00411 
-2024-08-28 20:07:07.589144: train_loss -0.7674 
-2024-08-28 20:07:07.589401: val_loss -0.7899 
-2024-08-28 20:07:07.589566: Pseudo dice [0.0, 0.0, 0.867, 0.9752, 0.8549, 0.9494, 0.9511, 0.9658, 0.9527, 0.9493, 0.925, 0.958, 0.9566, 0.852, 0.956, 0.9361, 0.8342, 0.8328, nan] 
-2024-08-28 20:07:07.589650: Epoch time: 84.43 s 
-2024-08-28 20:07:09.093409:  
-2024-08-28 20:07:09.093603: Epoch 1257 
-2024-08-28 20:07:09.093685: Current learning rate: 0.0041 
-2024-08-28 20:08:29.653633: train_loss -0.771 
-2024-08-28 20:08:29.654005: val_loss -0.7875 
-2024-08-28 20:08:29.654194: Pseudo dice [0.0, 0.0, 0.9066, 0.9768, 0.8611, 0.9481, 0.9505, 0.9658, 0.9442, 0.9525, 0.9295, 0.9598, 0.9583, 0.8511, 0.953, 0.9374, 0.8356, 0.8373, nan] 
-2024-08-28 20:08:29.654285: Epoch time: 80.56 s 
-2024-08-28 20:08:29.654648: Yayy! New best EMA pseudo Dice: 0.8164 
-2024-08-28 20:08:31.300755:  
-2024-08-28 20:08:31.301140: Epoch 1258 
-2024-08-28 20:08:31.301240: Current learning rate: 0.0041 
-2024-08-28 20:09:57.600618: train_loss -0.7683 
-2024-08-28 20:09:57.600873: val_loss -0.7905 
-2024-08-28 20:09:57.601024: Pseudo dice [0.0, 0.0, 0.9076, 0.9769, 0.8509, 0.9449, 0.9437, 0.9607, 0.9508, 0.9517, 0.935, 0.959, 0.9614, 0.8466, 0.94, 0.9359, 0.8437, 0.836, nan] 
-2024-08-28 20:09:57.601106: Epoch time: 86.3 s 
-2024-08-28 20:09:57.601154: Yayy! New best EMA pseudo Dice: 0.8166 
-2024-08-28 20:09:59.198570:  
-2024-08-28 20:09:59.198789: Epoch 1259 
-2024-08-28 20:09:59.198876: Current learning rate: 0.00409 
-2024-08-28 20:11:21.549497: train_loss -0.7711 
-2024-08-28 20:11:21.549727: val_loss -0.7896 
-2024-08-28 20:11:21.549904: Pseudo dice [0.0, 0.0, 0.9039, 0.9776, 0.8397, 0.9458, 0.9486, 0.9683, 0.9496, 0.9483, 0.9278, 0.9596, 0.9573, 0.8497, 0.9537, 0.9349, 0.8274, 0.8344, nan] 
-2024-08-28 20:11:21.549997: Epoch time: 82.35 s 
-2024-08-28 20:11:21.550202: Yayy! New best EMA pseudo Dice: 0.8168 
-2024-08-28 20:11:23.132615:  
-2024-08-28 20:11:23.132897: Epoch 1260 
-2024-08-28 20:11:23.132992: Current learning rate: 0.00409 
-2024-08-28 20:12:49.491435: train_loss -0.7677 
-2024-08-28 20:12:49.491661: val_loss -0.7878 
-2024-08-28 20:12:49.491814: Pseudo dice [0.0, 0.0, 0.8837, 0.975, 0.8322, 0.9487, 0.9458, 0.9636, 0.9538, 0.9527, 0.9375, 0.9626, 0.9618, 0.8371, 0.9484, 0.9318, 0.8271, 0.8202, nan] 
-2024-08-28 20:12:49.491894: Epoch time: 86.36 s 
-2024-08-28 20:12:50.665456:  
-2024-08-28 20:12:50.665830: Epoch 1261 
-2024-08-28 20:12:50.665960: Current learning rate: 0.00408 
-2024-08-28 20:14:15.491909: train_loss -0.7614 
-2024-08-28 20:14:15.492146: val_loss -0.7894 
-2024-08-28 20:14:15.492295: Pseudo dice [0.0, 0.0, 0.8964, 0.9759, 0.8138, 0.943, 0.9444, 0.9644, 0.9507, 0.9538, 0.9301, 0.9573, 0.9598, 0.8429, 0.9538, 0.9332, 0.8339, 0.8401, nan] 
-2024-08-28 20:14:15.492374: Epoch time: 84.83 s 
-2024-08-28 20:14:16.694901:  
-2024-08-28 20:14:16.695070: Epoch 1262 
-2024-08-28 20:14:16.695154: Current learning rate: 0.00408 
-2024-08-28 20:15:44.815808: train_loss -0.7649 
-2024-08-28 20:15:44.816035: val_loss -0.7867 
-2024-08-28 20:15:44.816248: Pseudo dice [0.0, 0.0, 0.8851, 0.9777, 0.8379, 0.9466, 0.9456, 0.966, 0.9529, 0.9423, 0.9316, 0.9621, 0.9608, 0.837, 0.9483, 0.93, 0.8356, 0.8206, nan] 
-2024-08-28 20:15:44.816337: Epoch time: 88.12 s 
-2024-08-28 20:15:46.297031:  
-2024-08-28 20:15:46.297403: Epoch 1263 
-2024-08-28 20:15:46.297585: Current learning rate: 0.00407 
-2024-08-28 20:17:09.837361: train_loss -0.7676 
-2024-08-28 20:17:09.838066: val_loss -0.7826 
-2024-08-28 20:17:09.838479: Pseudo dice [0.0, 0.0, 0.8943, 0.9756, 0.8093, 0.9487, 0.9481, 0.9597, 0.9489, 0.9449, 0.9288, 0.9606, 0.9597, 0.8409, 0.9528, 0.9299, 0.816, 0.8225, nan] 
-2024-08-28 20:17:09.838617: Epoch time: 83.54 s 
-2024-08-28 20:17:11.027099:  
-2024-08-28 20:17:11.027251: Epoch 1264 
-2024-08-28 20:17:11.027343: Current learning rate: 0.00407 
-2024-08-28 20:18:33.183727: train_loss -0.7681 
-2024-08-28 20:18:33.184005: val_loss -0.786 
-2024-08-28 20:18:33.184239: Pseudo dice [0.0, 0.0, 0.8892, 0.9762, 0.8156, 0.9437, 0.9481, 0.9632, 0.9513, 0.9551, 0.9301, 0.9605, 0.9625, 0.8363, 0.9495, 0.9317, 0.8191, 0.8129, nan] 
-2024-08-28 20:18:33.184420: Epoch time: 82.16 s 
-2024-08-28 20:18:34.438668:  
-2024-08-28 20:18:34.439117: Epoch 1265 
-2024-08-28 20:18:34.439266: Current learning rate: 0.00406 
-2024-08-28 20:19:57.711322: train_loss -0.7637 
-2024-08-28 20:19:57.711563: val_loss -0.7887 
-2024-08-28 20:19:57.711728: Pseudo dice [0.0, 0.0, 0.8947, 0.9752, 0.795, 0.9485, 0.951, 0.9656, 0.9488, 0.9517, 0.9333, 0.9621, 0.9612, 0.842, 0.9553, 0.9291, 0.8285, 0.819, nan] 
-2024-08-28 20:19:57.711811: Epoch time: 83.27 s 
-2024-08-28 20:19:58.919177:  
-2024-08-28 20:19:58.919456: Epoch 1266 
-2024-08-28 20:19:58.919547: Current learning rate: 0.00406 
-2024-08-28 20:21:22.267246: train_loss -0.7652 
-2024-08-28 20:21:22.267546: val_loss -0.7844 
-2024-08-28 20:21:22.267804: Pseudo dice [0.0, 0.0, 0.8868, 0.9752, 0.8324, 0.9427, 0.9413, 0.9611, 0.9442, 0.9495, 0.933, 0.9578, 0.9615, 0.843, 0.9471, 0.9309, 0.8186, 0.8102, nan] 
-2024-08-28 20:21:22.267914: Epoch time: 83.35 s 
-2024-08-28 20:21:23.640526:  
-2024-08-28 20:21:23.641057: Epoch 1267 
-2024-08-28 20:21:23.641151: Current learning rate: 0.00405 
-2024-08-28 20:22:54.643264: train_loss -0.7672 
-2024-08-28 20:22:54.643829: val_loss -0.7801 
-2024-08-28 20:22:54.644038: Pseudo dice [0.0, 0.0, 0.8747, 0.9759, 0.7568, 0.9365, 0.9461, 0.9622, 0.9427, 0.9457, 0.9306, 0.9551, 0.9602, 0.8376, 0.9471, 0.9236, 0.8237, 0.8306, nan] 
-2024-08-28 20:22:54.644177: Epoch time: 91.0 s 
-2024-08-28 20:22:55.895421:  
-2024-08-28 20:22:55.895845: Epoch 1268 
-2024-08-28 20:22:55.895928: Current learning rate: 0.00405 
-2024-08-28 20:24:22.646414: train_loss -0.7668 
-2024-08-28 20:24:22.646637: val_loss -0.7884 
-2024-08-28 20:24:22.646809: Pseudo dice [0.0, 0.0, 0.8927, 0.9757, 0.8444, 0.945, 0.9482, 0.9669, 0.9479, 0.9467, 0.9308, 0.9547, 0.9609, 0.8449, 0.9384, 0.9252, 0.8246, 0.8126, nan] 
-2024-08-28 20:24:22.646894: Epoch time: 86.75 s 
-2024-08-28 20:24:24.148746:  
-2024-08-28 20:24:24.148916: Epoch 1269 
-2024-08-28 20:24:24.149004: Current learning rate: 0.00404 
-2024-08-28 20:25:52.718174: train_loss -0.7664 
-2024-08-28 20:25:52.718422: val_loss -0.7912 
-2024-08-28 20:25:52.718593: Pseudo dice [0.0, 0.0, 0.9001, 0.9763, 0.8402, 0.9462, 0.9457, 0.9667, 0.9521, 0.9557, 0.9291, 0.9629, 0.9588, 0.8475, 0.9561, 0.9302, 0.8204, 0.8141, nan] 
-2024-08-28 20:25:52.718682: Epoch time: 88.57 s 
-2024-08-28 20:25:53.962501:  
-2024-08-28 20:25:53.962644: Epoch 1270 
-2024-08-28 20:25:53.962724: Current learning rate: 0.00404 
-2024-08-28 20:27:20.854244: train_loss -0.7657 
-2024-08-28 20:27:20.854473: val_loss -0.7889 
-2024-08-28 20:27:20.854641: Pseudo dice [0.0, 0.0, 0.8968, 0.9773, 0.8351, 0.944, 0.9487, 0.9623, 0.9494, 0.9453, 0.9341, 0.961, 0.9628, 0.8419, 0.9472, 0.9277, 0.8298, 0.8271, nan] 
-2024-08-28 20:27:20.854725: Epoch time: 86.89 s 
-2024-08-28 20:27:22.091348:  
-2024-08-28 20:27:22.091764: Epoch 1271 
-2024-08-28 20:27:22.091907: Current learning rate: 0.00403 
-2024-08-28 20:28:46.733399: train_loss -0.766 
-2024-08-28 20:28:46.733618: val_loss -0.7864 
-2024-08-28 20:28:46.733827: Pseudo dice [0.0, 0.0, 0.9045, 0.9762, 0.847, 0.9464, 0.9458, 0.9642, 0.9507, 0.9556, 0.934, 0.9535, 0.9592, 0.8438, 0.9575, 0.9294, 0.83, 0.8337, nan] 
-2024-08-28 20:28:46.733912: Epoch time: 84.64 s 
-2024-08-28 20:28:48.006467:  
-2024-08-28 20:28:48.006636: Epoch 1272 
-2024-08-28 20:28:48.006724: Current learning rate: 0.00403 
-2024-08-28 20:30:12.516792: train_loss -0.772 
-2024-08-28 20:30:12.517072: val_loss -0.7959 
-2024-08-28 20:30:12.517242: Pseudo dice [0.0, 0.0, 0.9133, 0.9761, 0.8632, 0.9527, 0.9541, 0.9669, 0.9512, 0.9502, 0.9347, 0.9598, 0.9609, 0.8513, 0.9571, 0.9376, 0.8399, 0.8436, nan] 
-2024-08-28 20:30:12.517326: Epoch time: 84.51 s 
-2024-08-28 20:30:13.743948:  
-2024-08-28 20:30:13.744151: Epoch 1273 
-2024-08-28 20:30:13.744245: Current learning rate: 0.00402 
-2024-08-28 20:31:37.138750: train_loss -0.7704 
-2024-08-28 20:31:37.139316: val_loss -0.7896 
-2024-08-28 20:31:37.139481: Pseudo dice [0.0, 0.0, 0.8905, 0.9734, 0.8324, 0.9428, 0.9434, 0.9668, 0.9565, 0.9513, 0.9357, 0.9631, 0.9619, 0.8337, 0.9495, 0.9329, 0.8083, 0.8207, nan] 
-2024-08-28 20:31:37.139612: Epoch time: 83.4 s 
-2024-08-28 20:31:38.362098:  
-2024-08-28 20:31:38.362281: Epoch 1274 
-2024-08-28 20:31:38.362373: Current learning rate: 0.00402 
-2024-08-28 20:33:06.050374: train_loss -0.7681 
-2024-08-28 20:33:06.050605: val_loss -0.7895 
-2024-08-28 20:33:06.050753: Pseudo dice [0.0, 0.0, 0.8967, 0.9775, 0.8484, 0.9454, 0.947, 0.9676, 0.9549, 0.9307, 0.9318, 0.9613, 0.9609, 0.8443, 0.9462, 0.9347, 0.8208, 0.8254, nan] 
-2024-08-28 20:33:06.050833: Epoch time: 87.69 s 
-2024-08-28 20:33:07.235243:  
-2024-08-28 20:33:07.235386: Epoch 1275 
-2024-08-28 20:33:07.235482: Current learning rate: 0.00401 
-2024-08-28 20:34:32.869658: train_loss -0.7668 
-2024-08-28 20:34:32.869887: val_loss -0.7811 
-2024-08-28 20:34:32.870038: Pseudo dice [0.0, 0.0, 0.8805, 0.9758, 0.8369, 0.9451, 0.9501, 0.9615, 0.9508, 0.9504, 0.9345, 0.9602, 0.9633, 0.8362, 0.9494, 0.928, 0.8208, 0.8096, nan] 
-2024-08-28 20:34:32.870116: Epoch time: 85.64 s 
-2024-08-28 20:34:34.423674:  
-2024-08-28 20:34:34.423958: Epoch 1276 
-2024-08-28 20:34:34.424048: Current learning rate: 0.00401 
-2024-08-28 20:36:02.907269: train_loss -0.7624 
-2024-08-28 20:36:02.907517: val_loss -0.78 
-2024-08-28 20:36:02.907699: Pseudo dice [0.0, 0.0, 0.8854, 0.9769, 0.8494, 0.9429, 0.9469, 0.9605, 0.9425, 0.9442, 0.9223, 0.9553, 0.9575, 0.8312, 0.9457, 0.9275, 0.8283, 0.8303, nan] 
-2024-08-28 20:36:02.907789: Epoch time: 88.48 s 
-2024-08-28 20:36:04.099718:  
-2024-08-28 20:36:04.099888: Epoch 1277 
-2024-08-28 20:36:04.099986: Current learning rate: 0.004 
-2024-08-28 20:37:30.649669: train_loss -0.7643 
-2024-08-28 20:37:30.650072: val_loss -0.7916 
-2024-08-28 20:37:30.650220: Pseudo dice [0.0, 0.0, 0.8976, 0.9749, 0.8263, 0.9475, 0.9502, 0.9656, 0.9534, 0.95, 0.9297, 0.9586, 0.9591, 0.8438, 0.9446, 0.9325, 0.8339, 0.8241, nan] 
-2024-08-28 20:37:30.650298: Epoch time: 86.55 s 
-2024-08-28 20:37:31.953896:  
-2024-08-28 20:37:31.954398: Epoch 1278 
-2024-08-28 20:37:31.954488: Current learning rate: 0.004 
-2024-08-28 20:38:54.603628: train_loss -0.7656 
-2024-08-28 20:38:54.603868: val_loss -0.7849 
-2024-08-28 20:38:54.604033: Pseudo dice [0.0, 0.0, 0.8799, 0.9771, 0.858, 0.9482, 0.9509, 0.9624, 0.9454, 0.9356, 0.9283, 0.9529, 0.9563, 0.8473, 0.946, 0.9258, 0.8252, 0.8227, nan] 
-2024-08-28 20:38:54.604122: Epoch time: 82.65 s 
-2024-08-28 20:38:55.854236:  
-2024-08-28 20:38:55.854696: Epoch 1279 
-2024-08-28 20:38:55.854790: Current learning rate: 0.00399 
-2024-08-28 20:40:24.358200: train_loss -0.7633 
-2024-08-28 20:40:24.358662: val_loss -0.786 
-2024-08-28 20:40:24.358860: Pseudo dice [0.0, 0.0, 0.8936, 0.9766, 0.8262, 0.9457, 0.9488, 0.9669, 0.9513, 0.9529, 0.9336, 0.9633, 0.9599, 0.8468, 0.9498, 0.9345, 0.8336, 0.8244, nan] 
-2024-08-28 20:40:24.358989: Epoch time: 88.5 s 
-2024-08-28 20:40:25.550281:  
-2024-08-28 20:40:25.550449: Epoch 1280 
-2024-08-28 20:40:25.550536: Current learning rate: 0.00399 
-2024-08-28 20:41:51.159527: train_loss -0.7687 
-2024-08-28 20:41:51.159869: val_loss -0.7899 
-2024-08-28 20:41:51.160071: Pseudo dice [0.0, 0.0, 0.8943, 0.9776, 0.8522, 0.9518, 0.9543, 0.9639, 0.9488, 0.9454, 0.9276, 0.962, 0.9584, 0.8569, 0.9551, 0.936, 0.8344, 0.8317, nan] 
-2024-08-28 20:41:51.160200: Epoch time: 85.61 s 
-2024-08-28 20:41:52.665974:  
-2024-08-28 20:41:52.666171: Epoch 1281 
-2024-08-28 20:41:52.666265: Current learning rate: 0.00398 
-2024-08-28 20:43:23.443866: train_loss -0.7683 
-2024-08-28 20:43:23.444137: val_loss -0.79 
-2024-08-28 20:43:23.444309: Pseudo dice [0.0, 0.0, 0.9034, 0.976, 0.8475, 0.9497, 0.9505, 0.9667, 0.948, 0.9421, 0.9372, 0.9568, 0.9574, 0.8499, 0.9386, 0.937, 0.8259, 0.8431, nan] 
-2024-08-28 20:43:23.444397: Epoch time: 90.78 s 
-2024-08-28 20:43:24.703423:  
-2024-08-28 20:43:24.703618: Epoch 1282 
-2024-08-28 20:43:24.703716: Current learning rate: 0.00398 
-2024-08-28 20:44:50.278412: train_loss -0.7691 
-2024-08-28 20:44:50.278618: val_loss -0.787 
-2024-08-28 20:44:50.278768: Pseudo dice [0.0, 0.0, 0.8763, 0.9765, 0.8511, 0.9438, 0.9462, 0.966, 0.9522, 0.9453, 0.9351, 0.9587, 0.9617, 0.8492, 0.9512, 0.9306, 0.8286, 0.8322, nan] 
-2024-08-28 20:44:50.278844: Epoch time: 85.58 s 
-2024-08-28 20:44:51.523802:  
-2024-08-28 20:44:51.524016: Epoch 1283 
-2024-08-28 20:44:51.524114: Current learning rate: 0.00397 
-2024-08-28 20:46:22.643717: train_loss -0.7677 
-2024-08-28 20:46:22.643956: val_loss -0.7907 
-2024-08-28 20:46:22.644121: Pseudo dice [0.0, 0.0, 0.9072, 0.9764, 0.8414, 0.9521, 0.9515, 0.9664, 0.9562, 0.9516, 0.9357, 0.9614, 0.9635, 0.8541, 0.9486, 0.9333, 0.845, 0.8305, nan] 
-2024-08-28 20:46:22.644200: Epoch time: 91.12 s 
-2024-08-28 20:46:22.644247: Yayy! New best EMA pseudo Dice: 0.8168 
-2024-08-28 20:46:24.304437:  
-2024-08-28 20:46:24.304579: Epoch 1284 
-2024-08-28 20:46:24.304671: Current learning rate: 0.00397 
-2024-08-28 20:47:50.582788: train_loss -0.77 
-2024-08-28 20:47:50.583042: val_loss -0.789 
-2024-08-28 20:47:50.583202: Pseudo dice [0.0, 0.0, 0.8927, 0.9772, 0.8139, 0.9435, 0.9431, 0.9678, 0.9524, 0.9426, 0.936, 0.959, 0.9599, 0.8458, 0.9579, 0.9296, 0.8093, 0.8054, nan] 
-2024-08-28 20:47:50.583284: Epoch time: 86.28 s 
-2024-08-28 20:47:51.860480:  
-2024-08-28 20:47:51.860697: Epoch 1285 
-2024-08-28 20:47:51.860793: Current learning rate: 0.00396 
-2024-08-28 20:49:20.296825: train_loss -0.7622 
-2024-08-28 20:49:20.297272: val_loss -0.7833 
-2024-08-28 20:49:20.297521: Pseudo dice [0.0, 0.0, 0.8895, 0.9767, 0.8403, 0.9455, 0.9459, 0.9633, 0.9548, 0.9541, 0.9332, 0.9649, 0.9614, 0.8347, 0.9418, 0.9234, 0.8166, 0.7941, nan] 
-2024-08-28 20:49:20.297630: Epoch time: 88.44 s 
-2024-08-28 20:49:21.543688:  
-2024-08-28 20:49:21.543843: Epoch 1286 
-2024-08-28 20:49:21.543945: Current learning rate: 0.00396 
-2024-08-28 20:50:49.421780: train_loss -0.7662 
-2024-08-28 20:50:49.422025: val_loss -0.7856 
-2024-08-28 20:50:49.422196: Pseudo dice [0.0, 0.0, 0.8982, 0.976, 0.8262, 0.9443, 0.9452, 0.9571, 0.9539, 0.9446, 0.9355, 0.9578, 0.9598, 0.8418, 0.925, 0.9315, 0.8237, 0.8277, nan] 
-2024-08-28 20:50:49.422284: Epoch time: 87.88 s 
-2024-08-28 20:50:51.031324:  
-2024-08-28 20:50:51.031484: Epoch 1287 
-2024-08-28 20:50:51.031585: Current learning rate: 0.00395 
-2024-08-28 20:52:16.544832: train_loss -0.7659 
-2024-08-28 20:52:16.545048: val_loss -0.7902 
-2024-08-28 20:52:16.545213: Pseudo dice [0.0, 0.0, 0.8713, 0.9776, 0.8322, 0.9441, 0.947, 0.9649, 0.9502, 0.9527, 0.9328, 0.9585, 0.963, 0.8359, 0.9509, 0.9326, 0.8168, 0.8054, nan] 
-2024-08-28 20:52:16.545296: Epoch time: 85.51 s 
-2024-08-28 20:52:17.780631:  
-2024-08-28 20:52:17.780792: Epoch 1288 
-2024-08-28 20:52:17.780871: Current learning rate: 0.00395 
-2024-08-28 20:53:46.353545: train_loss -0.7661 
-2024-08-28 20:53:46.353784: val_loss -0.7907 
-2024-08-28 20:53:46.353949: Pseudo dice [0.0, 0.0, 0.9027, 0.977, 0.8496, 0.9472, 0.9457, 0.9649, 0.9479, 0.9496, 0.9301, 0.9569, 0.9599, 0.8472, 0.9284, 0.9331, 0.8296, 0.8378, nan] 
-2024-08-28 20:53:46.354036: Epoch time: 88.57 s 
-2024-08-28 20:53:47.597131:  
-2024-08-28 20:53:47.597712: Epoch 1289 
-2024-08-28 20:53:47.597816: Current learning rate: 0.00394 
-2024-08-28 20:55:17.222144: train_loss -0.7696 
-2024-08-28 20:55:17.222378: val_loss -0.79 
-2024-08-28 20:55:17.222543: Pseudo dice [0.0, 0.0, 0.8914, 0.9765, 0.849, 0.9466, 0.9534, 0.9638, 0.9496, 0.9528, 0.9301, 0.9584, 0.9623, 0.8508, 0.9497, 0.9291, 0.8432, 0.8319, nan] 
-2024-08-28 20:55:17.222634: Epoch time: 89.63 s 
-2024-08-28 20:55:18.453156:  
-2024-08-28 20:55:18.453325: Epoch 1290 
-2024-08-28 20:55:18.453412: Current learning rate: 0.00394 
-2024-08-28 20:56:47.064751: train_loss -0.7688 
-2024-08-28 20:56:47.065135: val_loss -0.786 
-2024-08-28 20:56:47.065377: Pseudo dice [0.0, 0.0, 0.9044, 0.9765, 0.8484, 0.9512, 0.9508, 0.9679, 0.9466, 0.935, 0.9296, 0.9562, 0.9591, 0.8469, 0.9555, 0.9363, 0.8336, 0.8269, nan] 
-2024-08-28 20:56:47.065470: Epoch time: 88.61 s 
-2024-08-28 20:56:48.289849:  
-2024-08-28 20:56:48.290015: Epoch 1291 
-2024-08-28 20:56:48.290107: Current learning rate: 0.00393 
-2024-08-28 20:58:12.891351: train_loss -0.7686 
-2024-08-28 20:58:12.891579: val_loss -0.7899 
-2024-08-28 20:58:12.891754: Pseudo dice [0.0, 0.0, 0.9094, 0.9769, 0.8576, 0.9474, 0.95, 0.9634, 0.9489, 0.9497, 0.93, 0.9586, 0.9555, 0.8469, 0.9517, 0.9329, 0.8278, 0.819, nan] 
-2024-08-28 20:58:12.891842: Epoch time: 84.6 s 
-2024-08-28 20:58:14.134582:  
-2024-08-28 20:58:14.134939: Epoch 1292 
-2024-08-28 20:58:14.135083: Current learning rate: 0.00393 
-2024-08-28 20:59:46.260221: train_loss -0.7627 
-2024-08-28 20:59:46.260455: val_loss -0.7844 
-2024-08-28 20:59:46.260630: Pseudo dice [0.0, 0.0, 0.8966, 0.9771, 0.8429, 0.9456, 0.9484, 0.9643, 0.9479, 0.9498, 0.9277, 0.9556, 0.9568, 0.8463, 0.9469, 0.9294, 0.8079, 0.7763, nan] 
-2024-08-28 20:59:46.260718: Epoch time: 92.13 s 
-2024-08-28 20:59:47.771428:  
-2024-08-28 20:59:47.771577: Epoch 1293 
-2024-08-28 20:59:47.771669: Current learning rate: 0.00392 
-2024-08-28 21:01:12.544330: train_loss -0.7628 
-2024-08-28 21:01:12.544646: val_loss -0.7836 
-2024-08-28 21:01:12.544948: Pseudo dice [0.0, 0.0, 0.8998, 0.9771, 0.7973, 0.9324, 0.9409, 0.9645, 0.9502, 0.9552, 0.9267, 0.9598, 0.9589, 0.8451, 0.951, 0.9299, 0.8049, 0.8141, nan] 
-2024-08-28 21:01:12.545097: Epoch time: 84.77 s 
-2024-08-28 21:01:13.821197:  
-2024-08-28 21:01:13.821515: Epoch 1294 
-2024-08-28 21:01:13.821617: Current learning rate: 0.00392 
-2024-08-28 21:02:42.374102: train_loss -0.764 
-2024-08-28 21:02:42.374360: val_loss -0.785 
-2024-08-28 21:02:42.374525: Pseudo dice [0.0, 0.0, 0.8859, 0.9755, 0.8324, 0.9421, 0.9446, 0.9613, 0.9539, 0.9468, 0.9246, 0.9585, 0.9596, 0.8415, 0.9537, 0.9255, 0.8368, 0.8309, nan] 
-2024-08-28 21:02:42.374604: Epoch time: 88.55 s 
-2024-08-28 21:02:43.604388:  
-2024-08-28 21:02:43.604851: Epoch 1295 
-2024-08-28 21:02:43.604962: Current learning rate: 0.00391 
-2024-08-28 21:04:09.364701: train_loss -0.7659 
-2024-08-28 21:04:09.364932: val_loss -0.7899 
-2024-08-28 21:04:09.365090: Pseudo dice [0.0, 0.0, 0.9036, 0.9772, 0.81, 0.9465, 0.9488, 0.9623, 0.9498, 0.9523, 0.9357, 0.954, 0.9546, 0.8509, 0.9578, 0.9298, 0.8401, 0.8342, nan] 
-2024-08-28 21:04:09.365171: Epoch time: 85.76 s 
-2024-08-28 21:04:10.535094:  
-2024-08-28 21:04:10.535734: Epoch 1296 
-2024-08-28 21:04:10.535829: Current learning rate: 0.00391 
-2024-08-28 21:05:40.874035: train_loss -0.7686 
-2024-08-28 21:05:40.874263: val_loss -0.7887 
-2024-08-28 21:05:40.874427: Pseudo dice [0.0, 0.0, 0.8836, 0.9761, 0.8449, 0.9385, 0.9396, 0.9651, 0.9523, 0.95, 0.922, 0.9592, 0.9603, 0.8437, 0.9499, 0.9298, 0.8157, 0.8184, nan] 
-2024-08-28 21:05:40.874510: Epoch time: 90.34 s 
-2024-08-28 21:05:42.114391:  
-2024-08-28 21:05:42.114760: Epoch 1297 
-2024-08-28 21:05:42.115076: Current learning rate: 0.0039 
-2024-08-28 21:07:09.222289: train_loss -0.766 
-2024-08-28 21:07:09.222813: val_loss -0.7878 
-2024-08-28 21:07:09.223058: Pseudo dice [0.0, 0.0, 0.9092, 0.9776, 0.8478, 0.9466, 0.9524, 0.9671, 0.9488, 0.9464, 0.9264, 0.9617, 0.9604, 0.8484, 0.9558, 0.9391, 0.8283, 0.8252, nan] 
-2024-08-28 21:07:09.223157: Epoch time: 87.11 s 
-2024-08-28 21:07:10.469761:  
-2024-08-28 21:07:10.469922: Epoch 1298 
-2024-08-28 21:07:10.470007: Current learning rate: 0.0039 
-2024-08-28 21:08:36.397137: train_loss -0.765 
-2024-08-28 21:08:36.397680: val_loss -0.7847 
-2024-08-28 21:08:36.397856: Pseudo dice [0.0, 0.0, 0.8955, 0.9754, 0.8299, 0.9498, 0.9504, 0.9654, 0.958, 0.9475, 0.927, 0.9622, 0.9571, 0.8348, 0.9512, 0.9325, 0.8333, 0.8254, nan] 
-2024-08-28 21:08:36.397982: Epoch time: 85.93 s 
-2024-08-28 21:08:37.581016:  
-2024-08-28 21:08:37.581390: Epoch 1299 
-2024-08-28 21:08:37.581491: Current learning rate: 0.00389 
-2024-08-28 21:10:10.129262: train_loss -0.767 
-2024-08-28 21:10:10.129499: val_loss -0.782 
-2024-08-28 21:10:10.129649: Pseudo dice [0.0, 0.0, 0.886, 0.9773, 0.8504, 0.9494, 0.9506, 0.9662, 0.9481, 0.9458, 0.93, 0.9589, 0.9588, 0.8486, 0.9456, 0.9366, 0.821, 0.8053, nan] 
-2024-08-28 21:10:10.129721: Epoch time: 92.55 s 
-2024-08-28 21:10:11.762934:  
-2024-08-28 21:10:11.763203: Epoch 1300 
-2024-08-28 21:10:11.763291: Current learning rate: 0.00389 
-2024-08-28 21:11:35.115724: train_loss -0.7703 
-2024-08-28 21:11:35.115966: val_loss -0.7856 
-2024-08-28 21:11:35.116131: Pseudo dice [0.0, 0.0, 0.9008, 0.9749, 0.8445, 0.9454, 0.95, 0.9656, 0.9528, 0.9328, 0.9359, 0.9619, 0.9631, 0.8362, 0.9531, 0.932, 0.8267, 0.8285, nan] 
-2024-08-28 21:11:35.116217: Epoch time: 83.35 s 
-2024-08-28 21:11:36.340609:  
-2024-08-28 21:11:36.340777: Epoch 1301 
-2024-08-28 21:11:36.340866: Current learning rate: 0.00388 
-2024-08-28 21:13:03.468028: train_loss -0.7694 
-2024-08-28 21:13:03.468271: val_loss -0.7894 
-2024-08-28 21:13:03.468440: Pseudo dice [0.0, 0.0, 0.9003, 0.9719, 0.8581, 0.9512, 0.9493, 0.968, 0.9542, 0.9502, 0.9242, 0.9599, 0.9593, 0.8471, 0.958, 0.9375, 0.8221, 0.8207, nan] 
-2024-08-28 21:13:03.468528: Epoch time: 87.13 s 
-2024-08-28 21:13:04.653090:  
-2024-08-28 21:13:04.653267: Epoch 1302 
-2024-08-28 21:13:04.653366: Current learning rate: 0.00388 
-2024-08-28 21:14:31.147990: train_loss -0.7696 
-2024-08-28 21:14:31.148238: val_loss -0.7891 
-2024-08-28 21:14:31.148393: Pseudo dice [0.0, 0.0, 0.9008, 0.9766, 0.8286, 0.9401, 0.9473, 0.9646, 0.954, 0.9534, 0.9293, 0.9634, 0.9641, 0.8497, 0.9516, 0.9296, 0.817, 0.827, nan] 
-2024-08-28 21:14:31.148529: Epoch time: 86.5 s 
-2024-08-28 21:14:32.388659:  
-2024-08-28 21:14:32.388860: Epoch 1303 
-2024-08-28 21:14:32.388993: Current learning rate: 0.00387 
-2024-08-28 21:16:00.330482: train_loss -0.7696 
-2024-08-28 21:16:00.330720: val_loss -0.7915 
-2024-08-28 21:16:00.330896: Pseudo dice [0.0, 0.0, 0.8938, 0.9775, 0.8351, 0.9405, 0.9417, 0.9625, 0.9435, 0.9506, 0.9289, 0.9546, 0.9612, 0.8471, 0.9493, 0.9335, 0.8198, 0.8109, nan] 
-2024-08-28 21:16:00.330988: Epoch time: 87.94 s 
-2024-08-28 21:16:01.792938:  
-2024-08-28 21:16:01.793239: Epoch 1304 
-2024-08-28 21:16:01.793347: Current learning rate: 0.00387 
-2024-08-28 21:17:30.097863: train_loss -0.7633 
-2024-08-28 21:17:30.098118: val_loss -0.7819 
-2024-08-28 21:17:30.098283: Pseudo dice [0.0, 0.0, 0.8812, 0.9768, 0.8206, 0.9459, 0.9479, 0.9633, 0.9479, 0.9453, 0.9191, 0.9603, 0.9499, 0.8536, 0.9492, 0.9331, 0.8203, 0.8215, nan] 
-2024-08-28 21:17:30.098371: Epoch time: 88.31 s 
-2024-08-28 21:17:31.564761:  
-2024-08-28 21:17:31.564926: Epoch 1305 
-2024-08-28 21:17:31.565019: Current learning rate: 0.00386 
-2024-08-28 21:18:57.489449: train_loss -0.7655 
-2024-08-28 21:18:57.489703: val_loss -0.785 
-2024-08-28 21:18:57.489863: Pseudo dice [0.0, 0.0, 0.8989, 0.9761, 0.841, 0.9456, 0.946, 0.963, 0.9546, 0.9497, 0.9306, 0.9597, 0.9598, 0.8371, 0.9541, 0.9287, 0.8426, 0.8134, nan] 
-2024-08-28 21:18:57.489948: Epoch time: 85.93 s 
-2024-08-28 21:18:58.737788:  
-2024-08-28 21:18:58.738276: Epoch 1306 
-2024-08-28 21:18:58.738384: Current learning rate: 0.00386 
-2024-08-28 21:20:27.355020: train_loss -0.7605 
-2024-08-28 21:20:27.355275: val_loss -0.7873 
-2024-08-28 21:20:27.355439: Pseudo dice [0.0, 0.0, 0.8882, 0.9772, 0.8413, 0.9445, 0.9459, 0.9638, 0.9522, 0.9569, 0.9345, 0.9568, 0.9612, 0.8314, 0.9468, 0.9273, 0.8315, 0.8269, nan] 
-2024-08-28 21:20:27.355527: Epoch time: 88.62 s 
-2024-08-28 21:20:28.651266:  
-2024-08-28 21:20:28.651641: Epoch 1307 
-2024-08-28 21:20:28.651776: Current learning rate: 0.00385 
-2024-08-28 21:21:50.371395: train_loss -0.7617 
-2024-08-28 21:21:50.371617: val_loss -0.7872 
-2024-08-28 21:21:50.371777: Pseudo dice [0.0, 0.0, 0.8926, 0.9775, 0.8385, 0.9445, 0.9478, 0.9591, 0.953, 0.9449, 0.9249, 0.9607, 0.9591, 0.8351, 0.9522, 0.9339, 0.8227, 0.8165, nan] 
-2024-08-28 21:21:50.371863: Epoch time: 81.72 s 
-2024-08-28 21:21:51.571949:  
-2024-08-28 21:21:51.572336: Epoch 1308 
-2024-08-28 21:21:51.572443: Current learning rate: 0.00385 
-2024-08-28 21:23:20.777576: train_loss -0.7647 
-2024-08-28 21:23:20.777815: val_loss -0.7855 
-2024-08-28 21:23:20.777968: Pseudo dice [0.0, 0.0, 0.9014, 0.9753, 0.8303, 0.9473, 0.9458, 0.9656, 0.951, 0.9497, 0.9325, 0.9612, 0.9593, 0.8418, 0.9554, 0.9298, 0.8388, 0.8338, nan] 
-2024-08-28 21:23:20.778045: Epoch time: 89.21 s 
-2024-08-28 21:23:21.976888:  
-2024-08-28 21:23:21.977046: Epoch 1309 
-2024-08-28 21:23:21.977135: Current learning rate: 0.00384 
-2024-08-28 21:24:46.481908: train_loss -0.7676 
-2024-08-28 21:24:46.482164: val_loss -0.7922 
-2024-08-28 21:24:46.482330: Pseudo dice [0.0, 0.0, 0.8739, 0.977, 0.8361, 0.944, 0.944, 0.9654, 0.9491, 0.9511, 0.9311, 0.9562, 0.9626, 0.8485, 0.9551, 0.9329, 0.8194, 0.8274, nan] 
-2024-08-28 21:24:46.482424: Epoch time: 84.51 s 
-2024-08-28 21:24:47.703496:  
-2024-08-28 21:24:47.703723: Epoch 1310 
-2024-08-28 21:24:47.703904: Current learning rate: 0.00384 
-2024-08-28 21:26:15.507976: train_loss -0.7678 
-2024-08-28 21:26:15.508199: val_loss -0.7846 
-2024-08-28 21:26:15.508355: Pseudo dice [0.0, 0.0, 0.8678, 0.9762, 0.8539, 0.9423, 0.9419, 0.9609, 0.9478, 0.9495, 0.9265, 0.9582, 0.9603, 0.8321, 0.9446, 0.932, 0.7991, 0.809, nan] 
-2024-08-28 21:26:15.508443: Epoch time: 87.81 s 
-2024-08-28 21:26:16.898228:  
-2024-08-28 21:26:16.898495: Epoch 1311 
-2024-08-28 21:26:16.898590: Current learning rate: 0.00383 
-2024-08-28 21:27:40.502308: train_loss -0.7676 
-2024-08-28 21:27:40.502583: val_loss -0.7863 
-2024-08-28 21:27:40.502788: Pseudo dice [0.0, 0.0, 0.8941, 0.9767, 0.8364, 0.9477, 0.9526, 0.9629, 0.9535, 0.9455, 0.9391, 0.9614, 0.961, 0.8469, 0.9553, 0.9341, 0.8145, 0.8124, nan] 
-2024-08-28 21:27:40.502892: Epoch time: 83.6 s 
-2024-08-28 21:27:41.752584:  
-2024-08-28 21:27:41.752997: Epoch 1312 
-2024-08-28 21:27:41.753089: Current learning rate: 0.00383 
-2024-08-28 21:29:04.742364: train_loss -0.7638 
-2024-08-28 21:29:04.742615: val_loss -0.7872 
-2024-08-28 21:29:04.742778: Pseudo dice [0.0, 0.0, 0.8998, 0.9774, 0.8132, 0.9438, 0.9442, 0.9632, 0.9494, 0.955, 0.9317, 0.9604, 0.9601, 0.8394, 0.9502, 0.9317, 0.8329, 0.8312, nan] 
-2024-08-28 21:29:04.742860: Epoch time: 82.99 s 
-2024-08-28 21:29:05.947932:  
-2024-08-28 21:29:05.948102: Epoch 1313 
-2024-08-28 21:29:05.948206: Current learning rate: 0.00382 
-2024-08-28 21:30:30.807356: train_loss -0.7644 
-2024-08-28 21:30:30.808023: val_loss -0.7929 
-2024-08-28 21:30:30.808242: Pseudo dice [0.0, 0.0, 0.8849, 0.9761, 0.8477, 0.9495, 0.9515, 0.9653, 0.951, 0.9517, 0.9354, 0.9627, 0.9616, 0.8528, 0.9575, 0.9339, 0.8201, 0.8311, nan] 
-2024-08-28 21:30:30.808325: Epoch time: 84.86 s 
-2024-08-28 21:30:32.047000:  
-2024-08-28 21:30:32.047168: Epoch 1314 
-2024-08-28 21:30:32.047258: Current learning rate: 0.00382 
-2024-08-28 21:31:55.304242: train_loss -0.7717 
-2024-08-28 21:31:55.304495: val_loss -0.7845 
-2024-08-28 21:31:55.304658: Pseudo dice [0.0, 0.0, 0.8929, 0.9697, 0.8412, 0.944, 0.9485, 0.9626, 0.9529, 0.9486, 0.9212, 0.9638, 0.9602, 0.8416, 0.9559, 0.9307, 0.8223, 0.8274, nan] 
-2024-08-28 21:31:55.304740: Epoch time: 83.26 s 
-2024-08-28 21:31:56.501408:  
-2024-08-28 21:31:56.501591: Epoch 1315 
-2024-08-28 21:31:56.501685: Current learning rate: 0.00381 
-2024-08-28 21:33:24.960138: train_loss -0.7644 
-2024-08-28 21:33:24.960372: val_loss -0.7833 
-2024-08-28 21:33:24.960545: Pseudo dice [0.0, 0.0, 0.8994, 0.977, 0.7772, 0.9397, 0.9466, 0.9619, 0.9487, 0.9499, 0.928, 0.9575, 0.9602, 0.8296, 0.9526, 0.9314, 0.8277, 0.8294, nan] 
-2024-08-28 21:33:24.960632: Epoch time: 88.46 s 
-2024-08-28 21:33:26.188501:  
-2024-08-28 21:33:26.188674: Epoch 1316 
-2024-08-28 21:33:26.188766: Current learning rate: 0.00381 
-2024-08-28 21:34:54.511652: train_loss -0.7664 
-2024-08-28 21:34:54.511878: val_loss -0.7941 
-2024-08-28 21:34:54.512031: Pseudo dice [0.0, 0.0, 0.8961, 0.976, 0.8436, 0.9515, 0.9536, 0.9635, 0.9575, 0.9521, 0.9367, 0.9653, 0.9626, 0.8462, 0.9545, 0.9272, 0.8348, 0.8413, nan] 
-2024-08-28 21:34:54.512110: Epoch time: 88.32 s 
-2024-08-28 21:34:55.705480:  
-2024-08-28 21:34:55.705659: Epoch 1317 
-2024-08-28 21:34:55.705749: Current learning rate: 0.0038 
-2024-08-28 21:36:19.550546: train_loss -0.763 
-2024-08-28 21:36:19.550777: val_loss -0.7811 
-2024-08-28 21:36:19.550938: Pseudo dice [0.0, 0.0, 0.8948, 0.9762, 0.819, 0.9438, 0.9445, 0.9647, 0.9494, 0.9502, 0.9321, 0.9578, 0.959, 0.8399, 0.9489, 0.9284, 0.8182, 0.8212, nan] 
-2024-08-28 21:36:19.551023: Epoch time: 83.85 s 
-2024-08-28 21:36:20.954145:  
-2024-08-28 21:36:20.954317: Epoch 1318 
-2024-08-28 21:36:20.954404: Current learning rate: 0.0038 
-2024-08-28 21:37:40.724898: train_loss -0.7665 
-2024-08-28 21:37:40.725641: val_loss -0.7881 
-2024-08-28 21:37:40.725838: Pseudo dice [0.0, 0.0, 0.8791, 0.9756, 0.8382, 0.9494, 0.9513, 0.9643, 0.9458, 0.9536, 0.9325, 0.955, 0.9595, 0.8529, 0.9553, 0.9354, 0.8039, 0.8148, nan] 
-2024-08-28 21:37:40.725941: Epoch time: 79.77 s 
-2024-08-28 21:37:41.948742:  
-2024-08-28 21:37:41.949080: Epoch 1319 
-2024-08-28 21:37:41.949200: Current learning rate: 0.00379 
-2024-08-28 21:39:02.990232: train_loss -0.7689 
-2024-08-28 21:39:02.990566: val_loss -0.783 
-2024-08-28 21:39:02.990764: Pseudo dice [0.0, 0.0, 0.8938, 0.9769, 0.8242, 0.9442, 0.944, 0.9633, 0.9473, 0.9267, 0.9303, 0.959, 0.9627, 0.8469, 0.95, 0.9332, 0.8295, 0.8309, nan] 
-2024-08-28 21:39:02.990865: Epoch time: 81.04 s 
-2024-08-28 21:39:04.237809:  
-2024-08-28 21:39:04.238090: Epoch 1320 
-2024-08-28 21:39:04.238189: Current learning rate: 0.00379 
-2024-08-28 21:40:28.396344: train_loss -0.7692 
-2024-08-28 21:40:28.396645: val_loss -0.7858 
-2024-08-28 21:40:28.396822: Pseudo dice [0.0, 0.0, 0.8905, 0.976, 0.7927, 0.9499, 0.9513, 0.9661, 0.9551, 0.9485, 0.9295, 0.9605, 0.9553, 0.848, 0.9567, 0.9341, 0.8338, 0.8236, nan] 
-2024-08-28 21:40:28.396911: Epoch time: 84.16 s 
-2024-08-28 21:40:29.591350:  
-2024-08-28 21:40:29.591689: Epoch 1321 
-2024-08-28 21:40:29.591790: Current learning rate: 0.00378 
-2024-08-28 21:41:55.413505: train_loss -0.7638 
-2024-08-28 21:41:55.413758: val_loss -0.7868 
-2024-08-28 21:41:55.413936: Pseudo dice [0.0, 0.0, 0.896, 0.9766, 0.8369, 0.947, 0.9507, 0.9591, 0.9533, 0.9454, 0.9233, 0.9605, 0.9569, 0.8396, 0.941, 0.9291, 0.8225, 0.8094, nan] 
-2024-08-28 21:41:55.414047: Epoch time: 85.82 s 
-2024-08-28 21:41:56.669501:  
-2024-08-28 21:41:56.669820: Epoch 1322 
-2024-08-28 21:41:56.669932: Current learning rate: 0.00378 
-2024-08-28 21:43:22.662573: train_loss -0.7649 
-2024-08-28 21:43:22.662812: val_loss -0.7785 
-2024-08-28 21:43:22.662982: Pseudo dice [0.0, 0.0, 0.8868, 0.9764, 0.7827, 0.9443, 0.9505, 0.9625, 0.949, 0.9454, 0.9286, 0.9609, 0.9593, 0.8281, 0.9475, 0.9218, 0.8229, 0.8144, nan] 
-2024-08-28 21:43:22.663069: Epoch time: 85.99 s 
-2024-08-28 21:43:24.158697:  
-2024-08-28 21:43:24.158870: Epoch 1323 
-2024-08-28 21:43:24.158963: Current learning rate: 0.00377 
-2024-08-28 21:44:49.256985: train_loss -0.7655 
-2024-08-28 21:44:49.257235: val_loss -0.782 
-2024-08-28 21:44:49.257400: Pseudo dice [0.0, 0.0, 0.8967, 0.9764, 0.8424, 0.9485, 0.9508, 0.9646, 0.9453, 0.9522, 0.9301, 0.9554, 0.9582, 0.8436, 0.9348, 0.9351, 0.8113, 0.7858, nan] 
-2024-08-28 21:44:49.257491: Epoch time: 85.1 s 
-2024-08-28 21:44:50.488925:  
-2024-08-28 21:44:50.489114: Epoch 1324 
-2024-08-28 21:44:50.489202: Current learning rate: 0.00377 
-2024-08-28 21:46:18.316288: train_loss -0.7656 
-2024-08-28 21:46:18.316585: val_loss -0.783 
-2024-08-28 21:46:18.316814: Pseudo dice [0.0, 0.0, 0.8929, 0.977, 0.8466, 0.9382, 0.9455, 0.9596, 0.9517, 0.9524, 0.9221, 0.9607, 0.9564, 0.846, 0.9479, 0.9297, 0.8421, 0.8244, nan] 
-2024-08-28 21:46:18.317013: Epoch time: 87.83 s 
-2024-08-28 21:46:19.564214:  
-2024-08-28 21:46:19.564411: Epoch 1325 
-2024-08-28 21:46:19.564511: Current learning rate: 0.00376 
-2024-08-28 21:47:50.127357: train_loss -0.7668 
-2024-08-28 21:47:50.127585: val_loss -0.792 
-2024-08-28 21:47:50.127729: Pseudo dice [0.0, 0.0, 0.9044, 0.9762, 0.8213, 0.9453, 0.943, 0.9632, 0.9555, 0.9563, 0.926, 0.9626, 0.9597, 0.843, 0.9366, 0.9319, 0.83, 0.8215, nan] 
-2024-08-28 21:47:50.127809: Epoch time: 90.56 s 
-2024-08-28 21:47:51.638301:  
-2024-08-28 21:47:51.638480: Epoch 1326 
-2024-08-28 21:47:51.638578: Current learning rate: 0.00376 
-2024-08-28 21:49:16.977792: train_loss -0.7603 
-2024-08-28 21:49:16.978011: val_loss -0.7917 
-2024-08-28 21:49:16.978178: Pseudo dice [0.0, 0.0, 0.9072, 0.9745, 0.8439, 0.946, 0.9478, 0.9612, 0.9532, 0.9566, 0.9381, 0.9626, 0.9623, 0.8445, 0.9484, 0.9307, 0.8236, 0.8245, nan] 
-2024-08-28 21:49:16.978261: Epoch time: 85.34 s 
-2024-08-28 21:49:18.236678:  
-2024-08-28 21:49:18.236842: Epoch 1327 
-2024-08-28 21:49:18.236930: Current learning rate: 0.00375 
-2024-08-28 21:50:42.238486: train_loss -0.7649 
-2024-08-28 21:50:42.238721: val_loss -0.7812 
-2024-08-28 21:50:42.238881: Pseudo dice [0.0, 0.0, 0.892, 0.9762, 0.835, 0.9438, 0.9521, 0.9658, 0.9491, 0.9494, 0.9256, 0.9569, 0.9578, 0.8431, 0.9458, 0.9242, 0.8429, 0.8489, nan] 
-2024-08-28 21:50:42.238963: Epoch time: 84.0 s 
-2024-08-28 21:50:43.500966:  
-2024-08-28 21:50:43.501384: Epoch 1328 
-2024-08-28 21:50:43.501480: Current learning rate: 0.00375 
-2024-08-28 21:52:12.201638: train_loss -0.7609 
-2024-08-28 21:52:12.202316: val_loss -0.7749 
-2024-08-28 21:52:12.202592: Pseudo dice [0.0, 0.0, 0.8783, 0.9754, 0.848, 0.9444, 0.9444, 0.9601, 0.938, 0.9377, 0.9181, 0.9515, 0.9535, 0.8387, 0.952, 0.9245, 0.8269, 0.8126, nan] 
-2024-08-28 21:52:12.202796: Epoch time: 88.7 s 
-2024-08-28 21:52:13.498407:  
-2024-08-28 21:52:13.498667: Epoch 1329 
-2024-08-28 21:52:13.498755: Current learning rate: 0.00374 
-2024-08-28 21:53:37.456405: train_loss -0.7665 
-2024-08-28 21:53:37.456662: val_loss -0.7842 
-2024-08-28 21:53:37.456837: Pseudo dice [0.0, 0.0, 0.8941, 0.976, 0.8338, 0.9425, 0.9448, 0.9596, 0.9528, 0.9454, 0.9295, 0.9602, 0.9598, 0.8458, 0.9516, 0.9292, 0.8373, 0.8316, nan] 
-2024-08-28 21:53:37.456930: Epoch time: 83.96 s 
-2024-08-28 21:53:38.935485:  
-2024-08-28 21:53:38.935918: Epoch 1330 
-2024-08-28 21:53:38.936034: Current learning rate: 0.00374 
-2024-08-28 21:55:04.631607: train_loss -0.7678 
-2024-08-28 21:55:04.631861: val_loss -0.7922 
-2024-08-28 21:55:04.632019: Pseudo dice [0.0, 0.0, 0.8955, 0.9762, 0.8494, 0.9474, 0.952, 0.9663, 0.952, 0.9556, 0.9257, 0.9625, 0.9582, 0.8473, 0.9416, 0.9289, 0.8256, 0.8266, nan] 
-2024-08-28 21:55:04.632097: Epoch time: 85.7 s 
-2024-08-28 21:55:05.872261:  
-2024-08-28 21:55:05.872532: Epoch 1331 
-2024-08-28 21:55:05.872628: Current learning rate: 0.00373 
-2024-08-28 21:56:34.048372: train_loss -0.7624 
-2024-08-28 21:56:34.048640: val_loss -0.7848 
-2024-08-28 21:56:34.048791: Pseudo dice [0.0, 0.0, 0.8864, 0.9772, 0.8211, 0.9453, 0.9408, 0.9598, 0.9514, 0.9548, 0.9298, 0.9588, 0.9586, 0.8243, 0.9477, 0.924, 0.8251, 0.8308, nan] 
-2024-08-28 21:56:34.048873: Epoch time: 88.18 s 
-2024-08-28 21:56:35.280504:  
-2024-08-28 21:56:35.280685: Epoch 1332 
-2024-08-28 21:56:35.280782: Current learning rate: 0.00373 
-2024-08-28 21:58:03.971010: train_loss -0.7654 
-2024-08-28 21:58:03.971234: val_loss -0.7836 
-2024-08-28 21:58:03.971392: Pseudo dice [0.0, 0.0, 0.8949, 0.9752, 0.8173, 0.9458, 0.9477, 0.9604, 0.9528, 0.9543, 0.9304, 0.9596, 0.9629, 0.8398, 0.9408, 0.9307, 0.8333, 0.8397, nan] 
-2024-08-28 21:58:03.971481: Epoch time: 88.69 s 
-2024-08-28 21:58:05.228986:  
-2024-08-28 21:58:05.229222: Epoch 1333 
-2024-08-28 21:58:05.229320: Current learning rate: 0.00372 
-2024-08-28 21:59:32.608335: train_loss -0.7674 
-2024-08-28 21:59:32.608586: val_loss -0.7971 
-2024-08-28 21:59:32.608758: Pseudo dice [0.0, 0.0, 0.9052, 0.9772, 0.8558, 0.9462, 0.9523, 0.9658, 0.9537, 0.9492, 0.9344, 0.9609, 0.9629, 0.8456, 0.9564, 0.9305, 0.8436, 0.8345, nan] 
-2024-08-28 21:59:32.608847: Epoch time: 87.38 s 
-2024-08-28 21:59:33.817889:  
-2024-08-28 21:59:33.818389: Epoch 1334 
-2024-08-28 21:59:33.818489: Current learning rate: 0.00372 
-2024-08-28 22:00:57.260761: train_loss -0.7681 
-2024-08-28 22:00:57.261017: val_loss -0.7896 
-2024-08-28 22:00:57.261193: Pseudo dice [0.0, 0.0, 0.8954, 0.9751, 0.8254, 0.9457, 0.9511, 0.9593, 0.9545, 0.9524, 0.939, 0.9626, 0.9645, 0.8482, 0.9535, 0.9372, 0.8457, 0.8362, nan] 
-2024-08-28 22:00:57.261286: Epoch time: 83.44 s 
-2024-08-28 22:00:58.763240:  
-2024-08-28 22:00:58.763401: Epoch 1335 
-2024-08-28 22:00:58.763482: Current learning rate: 0.00371 
-2024-08-28 22:02:27.670870: train_loss -0.7694 
-2024-08-28 22:02:27.671153: val_loss -0.7921 
-2024-08-28 22:02:27.671315: Pseudo dice [0.0, 0.0, 0.9055, 0.9771, 0.8524, 0.9471, 0.9486, 0.9677, 0.9521, 0.9558, 0.9345, 0.9617, 0.9613, 0.8541, 0.9517, 0.9345, 0.8396, 0.8445, nan] 
-2024-08-28 22:02:27.671398: Epoch time: 88.91 s 
-2024-08-28 22:02:28.907917:  
-2024-08-28 22:02:28.908442: Epoch 1336 
-2024-08-28 22:02:28.908545: Current learning rate: 0.00371 
-2024-08-28 22:03:52.452252: train_loss -0.7701 
-2024-08-28 22:03:52.452483: val_loss -0.7864 
-2024-08-28 22:03:52.452648: Pseudo dice [0.0, 0.0, 0.8945, 0.9772, 0.8382, 0.9447, 0.949, 0.9636, 0.9462, 0.9537, 0.9373, 0.9615, 0.9622, 0.8403, 0.9501, 0.9336, 0.8436, 0.8372, nan] 
-2024-08-28 22:03:52.452732: Epoch time: 83.55 s 
-2024-08-28 22:03:53.725076:  
-2024-08-28 22:03:53.725217: Epoch 1337 
-2024-08-28 22:03:53.725310: Current learning rate: 0.0037 
-2024-08-28 22:05:20.924969: train_loss -0.7714 
-2024-08-28 22:05:20.925219: val_loss -0.7856 
-2024-08-28 22:05:20.925384: Pseudo dice [0.0, 0.0, 0.8882, 0.9764, 0.847, 0.9488, 0.9511, 0.9678, 0.9514, 0.9435, 0.929, 0.9581, 0.9608, 0.8459, 0.9495, 0.9344, 0.8422, 0.8282, nan] 
-2024-08-28 22:05:20.925476: Epoch time: 87.2 s 
-2024-08-28 22:05:20.925531: Yayy! New best EMA pseudo Dice: 0.8169 
-2024-08-28 22:05:22.558897:  
-2024-08-28 22:05:22.559395: Epoch 1338 
-2024-08-28 22:05:22.559478: Current learning rate: 0.0037 
-2024-08-28 22:06:46.879420: train_loss -0.7681 
-2024-08-28 22:06:46.879649: val_loss -0.7879 
-2024-08-28 22:06:46.879832: Pseudo dice [0.0, 0.0, 0.9027, 0.9773, 0.8426, 0.9501, 0.952, 0.9672, 0.9509, 0.9557, 0.9373, 0.9619, 0.9612, 0.8435, 0.9451, 0.9357, 0.8427, 0.8326, nan] 
-2024-08-28 22:06:46.879919: Epoch time: 84.32 s 
-2024-08-28 22:06:46.879972: Yayy! New best EMA pseudo Dice: 0.8172 
-2024-08-28 22:06:48.541241:  
-2024-08-28 22:06:48.541722: Epoch 1339 
-2024-08-28 22:06:48.541817: Current learning rate: 0.00369 
-2024-08-28 22:08:18.737174: train_loss -0.7677 
-2024-08-28 22:08:18.737426: val_loss -0.7891 
-2024-08-28 22:08:18.737597: Pseudo dice [0.0, 0.0, 0.8947, 0.9773, 0.8578, 0.9501, 0.9505, 0.9656, 0.9543, 0.9542, 0.9388, 0.9639, 0.9647, 0.8466, 0.9563, 0.9359, 0.8342, 0.8254, nan] 
-2024-08-28 22:08:18.737702: Epoch time: 90.2 s 
-2024-08-28 22:08:18.737754: Yayy! New best EMA pseudo Dice: 0.8175 
-2024-08-28 22:08:20.411063:  
-2024-08-28 22:08:20.411215: Epoch 1340 
-2024-08-28 22:08:20.411309: Current learning rate: 0.00369 
-2024-08-28 22:09:41.419462: train_loss -0.7698 
-2024-08-28 22:09:41.419690: val_loss -0.7881 
-2024-08-28 22:09:41.419861: Pseudo dice [0.0, 0.0, 0.8925, 0.9761, 0.8325, 0.9449, 0.9495, 0.9659, 0.9518, 0.9564, 0.927, 0.9578, 0.9613, 0.8512, 0.9543, 0.9304, 0.8276, 0.8343, nan] 
-2024-08-28 22:09:41.419945: Epoch time: 81.01 s 
-2024-08-28 22:09:42.969658:  
-2024-08-28 22:09:42.970012: Epoch 1341 
-2024-08-28 22:09:42.970104: Current learning rate: 0.00368 
-2024-08-28 22:11:10.172188: train_loss -0.7643 
-2024-08-28 22:11:10.172452: val_loss -0.7834 
-2024-08-28 22:11:10.172610: Pseudo dice [0.0, 0.0, 0.8795, 0.9747, 0.8457, 0.943, 0.9482, 0.9655, 0.9522, 0.9382, 0.9295, 0.9604, 0.9581, 0.8497, 0.9521, 0.9346, 0.8181, 0.8211, nan] 
-2024-08-28 22:11:10.172694: Epoch time: 87.2 s 
-2024-08-28 22:11:11.405912:  
-2024-08-28 22:11:11.406295: Epoch 1342 
-2024-08-28 22:11:11.406389: Current learning rate: 0.00368 
-2024-08-28 22:12:35.617124: train_loss -0.7696 
-2024-08-28 22:12:35.617378: val_loss -0.7824 
-2024-08-28 22:12:35.617537: Pseudo dice [0.0, 0.0, 0.8638, 0.9768, 0.7833, 0.9337, 0.9353, 0.9655, 0.9507, 0.952, 0.9307, 0.9586, 0.9605, 0.8407, 0.9507, 0.9324, 0.8386, 0.8405, nan] 
-2024-08-28 22:12:35.617623: Epoch time: 84.21 s 
-2024-08-28 22:12:36.867348:  
-2024-08-28 22:12:36.867690: Epoch 1343 
-2024-08-28 22:12:36.867789: Current learning rate: 0.00367 
-2024-08-28 22:14:03.400735: train_loss -0.7696 
-2024-08-28 22:14:03.400975: val_loss -0.7886 
-2024-08-28 22:14:03.401138: Pseudo dice [0.0, 0.0, 0.9003, 0.9769, 0.8452, 0.9449, 0.949, 0.9642, 0.9491, 0.9525, 0.9216, 0.9595, 0.9601, 0.8479, 0.9519, 0.9356, 0.8267, 0.8313, nan] 
-2024-08-28 22:14:03.401221: Epoch time: 86.53 s 
-2024-08-28 22:14:04.667000:  
-2024-08-28 22:14:04.667192: Epoch 1344 
-2024-08-28 22:14:04.667290: Current learning rate: 0.00367 
-2024-08-28 22:15:32.547592: train_loss -0.7677 
-2024-08-28 22:15:32.547845: val_loss -0.7849 
-2024-08-28 22:15:32.548022: Pseudo dice [0.0, 0.0, 0.8963, 0.9755, 0.8595, 0.9473, 0.9515, 0.966, 0.9528, 0.9469, 0.9269, 0.9581, 0.9571, 0.8514, 0.953, 0.9373, 0.8301, 0.8249, nan] 
-2024-08-28 22:15:32.548110: Epoch time: 87.88 s 
-2024-08-28 22:15:33.791725:  
-2024-08-28 22:15:33.791903: Epoch 1345 
-2024-08-28 22:15:33.791993: Current learning rate: 0.00366 
-2024-08-28 22:16:56.869293: train_loss -0.7698 
-2024-08-28 22:16:56.869530: val_loss -0.7875 
-2024-08-28 22:16:56.869704: Pseudo dice [0.0, 0.0, 0.8804, 0.9757, 0.8455, 0.951, 0.9521, 0.9667, 0.9501, 0.9477, 0.933, 0.9569, 0.9593, 0.8444, 0.954, 0.9313, 0.8212, 0.8223, nan] 
-2024-08-28 22:16:56.869792: Epoch time: 83.08 s 
-2024-08-28 22:16:58.106480:  
-2024-08-28 22:16:58.106652: Epoch 1346 
-2024-08-28 22:16:58.106737: Current learning rate: 0.00366 
-2024-08-28 22:18:19.888206: train_loss -0.7687 
-2024-08-28 22:18:19.888438: val_loss -0.7912 
-2024-08-28 22:18:19.888602: Pseudo dice [0.0, 0.0, 0.9074, 0.9764, 0.8522, 0.9441, 0.9502, 0.9618, 0.9496, 0.9511, 0.9306, 0.9587, 0.9601, 0.8472, 0.9363, 0.9234, 0.8325, 0.8329, nan] 
-2024-08-28 22:18:19.888686: Epoch time: 81.78 s 
-2024-08-28 22:18:21.312289:  
-2024-08-28 22:18:21.312472: Epoch 1347 
-2024-08-28 22:18:21.312572: Current learning rate: 0.00365 
-2024-08-28 22:19:57.142570: train_loss -0.7619 
-2024-08-28 22:19:57.142782: val_loss -0.7827 
-2024-08-28 22:19:57.142948: Pseudo dice [0.0, 0.0, 0.9002, 0.9781, 0.8499, 0.9454, 0.9481, 0.9662, 0.9471, 0.9411, 0.9325, 0.9585, 0.9591, 0.8363, 0.9494, 0.9374, 0.8231, 0.8085, nan] 
-2024-08-28 22:19:57.143032: Epoch time: 95.83 s 
-2024-08-28 22:19:58.326375:  
-2024-08-28 22:19:58.326528: Epoch 1348 
-2024-08-28 22:19:58.326613: Current learning rate: 0.00365 
-2024-08-28 22:21:23.472613: train_loss -0.7642 
-2024-08-28 22:21:23.472911: val_loss -0.784 
-2024-08-28 22:21:23.473115: Pseudo dice [0.0, 0.0, 0.8901, 0.9769, 0.8356, 0.9452, 0.9476, 0.9613, 0.9492, 0.9456, 0.92, 0.9605, 0.9548, 0.8496, 0.9529, 0.9298, 0.8169, 0.8172, nan] 
-2024-08-28 22:21:23.473244: Epoch time: 85.15 s 
-2024-08-28 22:21:24.912163:  
-2024-08-28 22:21:24.912322: Epoch 1349 
-2024-08-28 22:21:24.912409: Current learning rate: 0.00364 
-2024-08-28 22:22:47.748109: train_loss -0.7645 
-2024-08-28 22:22:47.748353: val_loss -0.7703 
-2024-08-28 22:22:47.748526: Pseudo dice [0.0, 0.0, 0.8808, 0.9757, 0.7771, 0.9482, 0.9433, 0.9507, 0.9464, 0.9485, 0.9217, 0.9593, 0.9609, 0.8384, 0.9484, 0.9239, 0.8111, 0.8092, nan] 
-2024-08-28 22:22:47.748616: Epoch time: 82.84 s 
-2024-08-28 22:22:49.446546:  
-2024-08-28 22:22:49.446723: Epoch 1350 
-2024-08-28 22:22:49.446808: Current learning rate: 0.00364 
-2024-08-28 22:24:16.646747: train_loss -0.7623 
-2024-08-28 22:24:16.646988: val_loss -0.7904 
-2024-08-28 22:24:16.647154: Pseudo dice [0.0, 0.0, 0.895, 0.9778, 0.8489, 0.9426, 0.9465, 0.9659, 0.9512, 0.9539, 0.9316, 0.9625, 0.9589, 0.8445, 0.9499, 0.9348, 0.8193, 0.8236, nan] 
-2024-08-28 22:24:16.647243: Epoch time: 87.2 s 
-2024-08-28 22:24:17.946919:  
-2024-08-28 22:24:17.947104: Epoch 1351 
-2024-08-28 22:24:17.947198: Current learning rate: 0.00363 
-2024-08-28 22:25:40.397165: train_loss -0.765 
-2024-08-28 22:25:40.397815: val_loss -0.7865 
-2024-08-28 22:25:40.398050: Pseudo dice [0.0, 0.0, 0.8717, 0.9747, 0.8301, 0.9454, 0.948, 0.9619, 0.9551, 0.9568, 0.932, 0.963, 0.962, 0.8416, 0.9316, 0.9282, 0.8273, 0.8204, nan] 
-2024-08-28 22:25:40.398154: Epoch time: 82.45 s 
-2024-08-28 22:25:41.591429:  
-2024-08-28 22:25:41.591882: Epoch 1352 
-2024-08-28 22:25:41.592071: Current learning rate: 0.00363 
-2024-08-28 22:27:02.281035: train_loss -0.7659 
-2024-08-28 22:27:02.281283: val_loss -0.7857 
-2024-08-28 22:27:02.281447: Pseudo dice [0.0, 0.0, 0.8942, 0.9774, 0.8051, 0.936, 0.9407, 0.9642, 0.9531, 0.9393, 0.9369, 0.9591, 0.9615, 0.8508, 0.9396, 0.9292, 0.8269, 0.8225, nan] 
-2024-08-28 22:27:02.281537: Epoch time: 80.69 s 
-2024-08-28 22:27:03.730745:  
-2024-08-28 22:27:03.731256: Epoch 1353 
-2024-08-28 22:27:03.731354: Current learning rate: 0.00362 
-2024-08-28 22:28:29.984843: train_loss -0.7573 
-2024-08-28 22:28:29.985081: val_loss -0.7797 
-2024-08-28 22:28:29.985237: Pseudo dice [0.0, 0.0, 0.8865, 0.9709, 0.8217, 0.9453, 0.9517, 0.9609, 0.9486, 0.941, 0.9274, 0.9588, 0.9581, 0.8417, 0.9469, 0.9174, 0.8244, 0.8221, nan] 
-2024-08-28 22:28:29.985320: Epoch time: 86.25 s 
-2024-08-28 22:28:31.249062:  
-2024-08-28 22:28:31.249223: Epoch 1354 
-2024-08-28 22:28:31.249314: Current learning rate: 0.00362 
-2024-08-28 22:29:56.744262: train_loss -0.7613 
-2024-08-28 22:29:56.744518: val_loss -0.7808 
-2024-08-28 22:29:56.744667: Pseudo dice [0.0, 0.0, 0.86, 0.9766, 0.8421, 0.947, 0.9505, 0.9632, 0.9497, 0.9516, 0.9353, 0.9591, 0.9612, 0.8403, 0.9417, 0.9254, 0.8232, 0.818, nan] 
-2024-08-28 22:29:56.744746: Epoch time: 85.5 s 
-2024-08-28 22:29:57.974909:  
-2024-08-28 22:29:57.975078: Epoch 1355 
-2024-08-28 22:29:57.975165: Current learning rate: 0.00361 
-2024-08-28 22:31:24.598920: train_loss -0.7686 
-2024-08-28 22:31:24.599146: val_loss -0.7864 
-2024-08-28 22:31:24.599311: Pseudo dice [0.0, 0.0, 0.8957, 0.9753, 0.8425, 0.9478, 0.9457, 0.9657, 0.9489, 0.9537, 0.9323, 0.9598, 0.9603, 0.8427, 0.9546, 0.9369, 0.8187, 0.8211, nan] 
-2024-08-28 22:31:24.599661: Epoch time: 86.62 s 
-2024-08-28 22:31:25.842091:  
-2024-08-28 22:31:25.842258: Epoch 1356 
-2024-08-28 22:31:25.842342: Current learning rate: 0.00361 
-2024-08-28 22:32:51.496764: train_loss -0.7652 
-2024-08-28 22:32:51.497007: val_loss -0.7848 
-2024-08-28 22:32:51.497180: Pseudo dice [0.0, 0.0, 0.8907, 0.9768, 0.7878, 0.9477, 0.9485, 0.9642, 0.9517, 0.9541, 0.9297, 0.9604, 0.9598, 0.8395, 0.954, 0.925, 0.8168, 0.8294, nan] 
-2024-08-28 22:32:51.497267: Epoch time: 85.66 s 
-2024-08-28 22:32:52.733218:  
-2024-08-28 22:32:52.733413: Epoch 1357 
-2024-08-28 22:32:52.733511: Current learning rate: 0.0036 
-2024-08-28 22:34:19.438001: train_loss -0.7674 
-2024-08-28 22:34:19.438297: val_loss -0.7817 
-2024-08-28 22:34:19.438532: Pseudo dice [0.0, 0.0, 0.8856, 0.9769, 0.8443, 0.9438, 0.949, 0.9603, 0.9467, 0.9427, 0.9257, 0.9555, 0.9561, 0.839, 0.9534, 0.9314, 0.8278, 0.8273, nan] 
-2024-08-28 22:34:19.438663: Epoch time: 86.71 s 
-2024-08-28 22:34:20.791913:  
-2024-08-28 22:34:20.792097: Epoch 1358 
-2024-08-28 22:34:20.792188: Current learning rate: 0.0036 
-2024-08-28 22:35:42.228558: train_loss -0.7669 
-2024-08-28 22:35:42.229135: val_loss -0.784 
-2024-08-28 22:35:42.229384: Pseudo dice [0.0, 0.0, 0.884, 0.9768, 0.8341, 0.9409, 0.9471, 0.9653, 0.9504, 0.9472, 0.9331, 0.9606, 0.9634, 0.8378, 0.9475, 0.9292, 0.8177, 0.8261, nan] 
-2024-08-28 22:35:42.229557: Epoch time: 81.44 s 
-2024-08-28 22:35:43.838515:  
-2024-08-28 22:35:43.838681: Epoch 1359 
-2024-08-28 22:35:43.838775: Current learning rate: 0.00359 
-2024-08-28 22:37:09.831064: train_loss -0.7672 
-2024-08-28 22:37:09.831349: val_loss -0.7871 
-2024-08-28 22:37:09.831517: Pseudo dice [0.0, 0.0, 0.8999, 0.9767, 0.851, 0.9453, 0.9477, 0.9646, 0.9549, 0.9529, 0.9266, 0.9616, 0.9616, 0.8474, 0.9529, 0.937, 0.8388, 0.8248, nan] 
-2024-08-28 22:37:09.831604: Epoch time: 85.99 s 
-2024-08-28 22:37:11.071394:  
-2024-08-28 22:37:11.071565: Epoch 1360 
-2024-08-28 22:37:11.071655: Current learning rate: 0.00359 
-2024-08-28 22:38:34.417493: train_loss -0.7688 
-2024-08-28 22:38:34.417730: val_loss -0.7872 
-2024-08-28 22:38:34.417901: Pseudo dice [0.0, 0.0, 0.9058, 0.9777, 0.8349, 0.9442, 0.9478, 0.963, 0.9503, 0.9449, 0.9211, 0.9568, 0.9553, 0.8401, 0.9534, 0.9315, 0.837, 0.8178, nan] 
-2024-08-28 22:38:34.418044: Epoch time: 83.35 s 
-2024-08-28 22:38:35.685260:  
-2024-08-28 22:38:35.685778: Epoch 1361 
-2024-08-28 22:38:35.685877: Current learning rate: 0.00358 
-2024-08-28 22:40:00.798802: train_loss -0.7664 
-2024-08-28 22:40:00.799031: val_loss -0.7881 
-2024-08-28 22:40:00.799186: Pseudo dice [0.0, 0.0, 0.8891, 0.9771, 0.8253, 0.9445, 0.9489, 0.9602, 0.9533, 0.9482, 0.9318, 0.9637, 0.959, 0.844, 0.9481, 0.9321, 0.8401, 0.8246, nan] 
-2024-08-28 22:40:00.799263: Epoch time: 85.11 s 
-2024-08-28 22:40:02.042884:  
-2024-08-28 22:40:02.043365: Epoch 1362 
-2024-08-28 22:40:02.043463: Current learning rate: 0.00358 
-2024-08-28 22:41:28.301892: train_loss -0.7608 
-2024-08-28 22:41:28.302269: val_loss -0.7841 
-2024-08-28 22:41:28.302459: Pseudo dice [0.0, 0.0, 0.8942, 0.9774, 0.8517, 0.9383, 0.9405, 0.9616, 0.9507, 0.9403, 0.9358, 0.9612, 0.9622, 0.8402, 0.9443, 0.9234, 0.8225, 0.8205, nan] 
-2024-08-28 22:41:28.302546: Epoch time: 86.26 s 
-2024-08-28 22:41:29.525646:  
-2024-08-28 22:41:29.526014: Epoch 1363 
-2024-08-28 22:41:29.526103: Current learning rate: 0.00357 
-2024-08-28 22:43:02.131244: train_loss -0.7655 
-2024-08-28 22:43:02.131483: val_loss -0.79 
-2024-08-28 22:43:02.131644: Pseudo dice [0.0, 0.0, 0.9015, 0.977, 0.832, 0.9436, 0.9502, 0.9681, 0.957, 0.9587, 0.939, 0.9642, 0.9627, 0.8531, 0.9533, 0.9323, 0.822, 0.8159, nan] 
-2024-08-28 22:43:02.131724: Epoch time: 92.61 s 
-2024-08-28 22:43:03.377905:  
-2024-08-28 22:43:03.378047: Epoch 1364 
-2024-08-28 22:43:03.378132: Current learning rate: 0.00357 
-2024-08-28 22:44:27.806088: train_loss -0.7664 
-2024-08-28 22:44:27.806367: val_loss -0.7851 
-2024-08-28 22:44:27.806574: Pseudo dice [0.0, 0.0, 0.8977, 0.9768, 0.8491, 0.947, 0.9507, 0.9606, 0.9531, 0.9457, 0.9341, 0.96, 0.9607, 0.847, 0.952, 0.9353, 0.8279, 0.824, nan] 
-2024-08-28 22:44:27.806679: Epoch time: 84.43 s 
-2024-08-28 22:44:29.389488:  
-2024-08-28 22:44:29.389863: Epoch 1365 
-2024-08-28 22:44:29.389979: Current learning rate: 0.00356 
-2024-08-28 22:45:56.673004: train_loss -0.7702 
-2024-08-28 22:45:56.673242: val_loss -0.787 
-2024-08-28 22:45:56.673400: Pseudo dice [0.0, 0.0, 0.8969, 0.9772, 0.8245, 0.9497, 0.952, 0.9673, 0.953, 0.9499, 0.9334, 0.963, 0.9632, 0.8393, 0.9451, 0.9327, 0.8437, 0.8265, nan] 
-2024-08-28 22:45:56.673484: Epoch time: 87.28 s 
-2024-08-28 22:45:57.946555:  
-2024-08-28 22:45:57.947045: Epoch 1366 
-2024-08-28 22:45:57.947142: Current learning rate: 0.00356 
-2024-08-28 22:47:19.874886: train_loss -0.7693 
-2024-08-28 22:47:19.875136: val_loss -0.781 
-2024-08-28 22:47:19.875376: Pseudo dice [0.0, 0.0, 0.8972, 0.9773, 0.8461, 0.941, 0.9449, 0.9672, 0.9468, 0.9312, 0.9313, 0.958, 0.9615, 0.8271, 0.9547, 0.9312, 0.8173, 0.8186, nan] 
-2024-08-28 22:47:19.875505: Epoch time: 81.93 s 
-2024-08-28 22:47:21.302878:  
-2024-08-28 22:47:21.303013: Epoch 1367 
-2024-08-28 22:47:21.303093: Current learning rate: 0.00355 
-2024-08-28 22:48:47.857667: train_loss -0.7669 
-2024-08-28 22:48:47.857913: val_loss -0.7866 
-2024-08-28 22:48:47.858080: Pseudo dice [0.0, 0.0, 0.907, 0.9769, 0.8427, 0.9467, 0.9486, 0.9656, 0.9498, 0.9459, 0.9252, 0.9583, 0.9544, 0.8478, 0.9564, 0.9357, 0.8268, 0.8291, nan] 
-2024-08-28 22:48:47.858170: Epoch time: 86.56 s 
-2024-08-28 22:48:49.003497:  
-2024-08-28 22:48:49.003782: Epoch 1368 
-2024-08-28 22:48:49.003861: Current learning rate: 0.00355 
-2024-08-28 22:50:13.812792: train_loss -0.7679 
-2024-08-28 22:50:13.813047: val_loss -0.7805 
-2024-08-28 22:50:13.813265: Pseudo dice [0.0, 0.0, 0.8944, 0.9766, 0.8392, 0.9445, 0.9461, 0.9595, 0.9415, 0.9459, 0.9258, 0.9541, 0.9594, 0.8288, 0.9476, 0.9281, 0.8364, 0.8296, nan] 
-2024-08-28 22:50:13.813353: Epoch time: 84.81 s 
-2024-08-28 22:50:15.047016:  
-2024-08-28 22:50:15.047441: Epoch 1369 
-2024-08-28 22:50:15.047533: Current learning rate: 0.00354 
-2024-08-28 22:51:44.646022: train_loss -0.7674 
-2024-08-28 22:51:44.646282: val_loss -0.7911 
-2024-08-28 22:51:44.646439: Pseudo dice [0.0, 0.0, 0.9101, 0.9759, 0.8292, 0.9494, 0.9512, 0.9656, 0.9512, 0.9491, 0.9264, 0.9608, 0.9593, 0.8477, 0.9535, 0.9304, 0.8181, 0.8355, nan] 
-2024-08-28 22:51:44.646524: Epoch time: 89.6 s 
-2024-08-28 22:51:45.901574:  
-2024-08-28 22:51:45.901986: Epoch 1370 
-2024-08-28 22:51:45.902085: Current learning rate: 0.00354 
-2024-08-28 22:53:06.512066: train_loss -0.7708 
-2024-08-28 22:53:06.512572: val_loss -0.7903 
-2024-08-28 22:53:06.512761: Pseudo dice [0.0, 0.0, 0.9045, 0.9759, 0.8327, 0.9475, 0.9487, 0.9636, 0.9549, 0.9578, 0.9391, 0.9635, 0.9631, 0.8429, 0.9541, 0.937, 0.8296, 0.8329, nan] 
-2024-08-28 22:53:06.512904: Epoch time: 80.61 s 
-2024-08-28 22:53:07.770646:  
-2024-08-28 22:53:07.770812: Epoch 1371 
-2024-08-28 22:53:07.770902: Current learning rate: 0.00353 
-2024-08-28 22:54:35.855058: train_loss -0.7692 
-2024-08-28 22:54:35.855371: val_loss -0.789 
-2024-08-28 22:54:35.855532: Pseudo dice [0.0, 0.0, 0.9112, 0.9773, 0.8365, 0.9434, 0.9443, 0.9648, 0.9515, 0.9538, 0.9217, 0.9542, 0.9591, 0.8313, 0.9517, 0.934, 0.836, 0.837, nan] 
-2024-08-28 22:54:35.855617: Epoch time: 88.09 s 
-2024-08-28 22:54:37.062206:  
-2024-08-28 22:54:37.062355: Epoch 1372 
-2024-08-28 22:54:37.062445: Current learning rate: 0.00353 
-2024-08-28 22:56:04.770983: train_loss -0.7694 
-2024-08-28 22:56:04.771221: val_loss -0.7891 
-2024-08-28 22:56:04.771387: Pseudo dice [0.0, 0.0, 0.9053, 0.9768, 0.8253, 0.9482, 0.9522, 0.9631, 0.9526, 0.9494, 0.9272, 0.9615, 0.9593, 0.8443, 0.9524, 0.9352, 0.8389, 0.826, nan] 
-2024-08-28 22:56:04.771471: Epoch time: 87.71 s 
-2024-08-28 22:56:06.031066:  
-2024-08-28 22:56:06.031489: Epoch 1373 
-2024-08-28 22:56:06.031589: Current learning rate: 0.00352 
-2024-08-28 22:57:31.342940: train_loss -0.7647 
-2024-08-28 22:57:31.343168: val_loss -0.7859 
-2024-08-28 22:57:31.343305: Pseudo dice [0.0, 0.0, 0.8962, 0.9756, 0.81, 0.949, 0.9492, 0.9653, 0.9415, 0.946, 0.9297, 0.9569, 0.9596, 0.8473, 0.9536, 0.9305, 0.8202, 0.8271, nan] 
-2024-08-28 22:57:31.343381: Epoch time: 85.31 s 
-2024-08-28 22:57:32.580646:  
-2024-08-28 22:57:32.580881: Epoch 1374 
-2024-08-28 22:57:32.580976: Current learning rate: 0.00352 
-2024-08-28 22:58:55.428601: train_loss -0.7682 
-2024-08-28 22:58:55.428823: val_loss -0.7866 
-2024-08-28 22:58:55.428972: Pseudo dice [0.0, 0.0, 0.8965, 0.9771, 0.8487, 0.9436, 0.9489, 0.9666, 0.9559, 0.9463, 0.9313, 0.9627, 0.9596, 0.8413, 0.9536, 0.9332, 0.8283, 0.823, nan] 
-2024-08-28 22:58:55.429052: Epoch time: 82.85 s 
-2024-08-28 22:58:56.693523:  
-2024-08-28 22:58:56.693706: Epoch 1375 
-2024-08-28 22:58:56.693787: Current learning rate: 0.00351 
-2024-08-28 23:00:23.410835: train_loss -0.7679 
-2024-08-28 23:00:23.411087: val_loss -0.7817 
-2024-08-28 23:00:23.411243: Pseudo dice [0.0, 0.0, 0.8991, 0.978, 0.8332, 0.9467, 0.9498, 0.962, 0.9503, 0.9398, 0.9321, 0.954, 0.9614, 0.8311, 0.9458, 0.9251, 0.8219, 0.8157, nan] 
-2024-08-28 23:00:23.411339: Epoch time: 86.72 s 
-2024-08-28 23:00:24.647936:  
-2024-08-28 23:00:24.648096: Epoch 1376 
-2024-08-28 23:00:24.648181: Current learning rate: 0.00351 
-2024-08-28 23:01:50.739639: train_loss -0.7654 
-2024-08-28 23:01:50.740194: val_loss -0.7852 
-2024-08-28 23:01:50.740364: Pseudo dice [0.0, 0.0, 0.8987, 0.9777, 0.8555, 0.9411, 0.9516, 0.9633, 0.9515, 0.9551, 0.9314, 0.961, 0.9599, 0.8475, 0.95, 0.9317, 0.8266, 0.8118, nan] 
-2024-08-28 23:01:50.740519: Epoch time: 86.09 s 
-2024-08-28 23:01:52.319204:  
-2024-08-28 23:01:52.319409: Epoch 1377 
-2024-08-28 23:01:52.319500: Current learning rate: 0.0035 
-2024-08-28 23:03:17.991805: train_loss -0.7691 
-2024-08-28 23:03:17.992224: val_loss -0.7891 
-2024-08-28 23:03:17.992612: Pseudo dice [0.0, 0.0, 0.8961, 0.9773, 0.8406, 0.9486, 0.952, 0.9666, 0.9524, 0.954, 0.9339, 0.9601, 0.9606, 0.8522, 0.9399, 0.9329, 0.843, 0.8218, nan] 
-2024-08-28 23:03:17.992884: Epoch time: 85.67 s 
-2024-08-28 23:03:19.291637:  
-2024-08-28 23:03:19.292109: Epoch 1378 
-2024-08-28 23:03:19.292195: Current learning rate: 0.0035 
-2024-08-28 23:04:47.539731: train_loss -0.7685 
-2024-08-28 23:04:47.539945: val_loss -0.7904 
-2024-08-28 23:04:47.540107: Pseudo dice [0.0, 0.0, 0.8971, 0.9758, 0.8373, 0.9471, 0.9455, 0.9616, 0.9536, 0.9506, 0.9323, 0.9596, 0.9601, 0.8387, 0.955, 0.9281, 0.8406, 0.8322, nan] 
-2024-08-28 23:04:47.540188: Epoch time: 88.25 s 
-2024-08-28 23:04:48.761046:  
-2024-08-28 23:04:48.761401: Epoch 1379 
-2024-08-28 23:04:48.761498: Current learning rate: 0.00349 
-2024-08-28 23:06:15.659278: train_loss -0.7665 
-2024-08-28 23:06:15.659483: val_loss -0.7882 
-2024-08-28 23:06:15.659637: Pseudo dice [0.0, 0.0, 0.9009, 0.9769, 0.8599, 0.9446, 0.9498, 0.9661, 0.9505, 0.9469, 0.9298, 0.9609, 0.9604, 0.8546, 0.9509, 0.9353, 0.8215, 0.8061, nan] 
-2024-08-28 23:06:15.659715: Epoch time: 86.9 s 
-2024-08-28 23:06:16.884893:  
-2024-08-28 23:06:16.885054: Epoch 1380 
-2024-08-28 23:06:16.885148: Current learning rate: 0.00349 
-2024-08-28 23:07:42.975858: train_loss -0.7697 
-2024-08-28 23:07:42.976206: val_loss -0.7868 
-2024-08-28 23:07:42.976383: Pseudo dice [0.0, 0.0, 0.9016, 0.9759, 0.8496, 0.9471, 0.9501, 0.9613, 0.9523, 0.9467, 0.9325, 0.9579, 0.9605, 0.8557, 0.9375, 0.9263, 0.8215, 0.8249, nan] 
-2024-08-28 23:07:42.976482: Epoch time: 86.09 s 
-2024-08-28 23:07:44.222720:  
-2024-08-28 23:07:44.222928: Epoch 1381 
-2024-08-28 23:07:44.223027: Current learning rate: 0.00348 
-2024-08-28 23:09:12.985731: train_loss -0.7694 
-2024-08-28 23:09:12.986207: val_loss -0.7835 
-2024-08-28 23:09:12.986384: Pseudo dice [0.0, 0.0, 0.8991, 0.9764, 0.7974, 0.9457, 0.9478, 0.9614, 0.9481, 0.9501, 0.9324, 0.9572, 0.9589, 0.8384, 0.9529, 0.922, 0.829, 0.8267, nan] 
-2024-08-28 23:09:12.986529: Epoch time: 88.76 s 
-2024-08-28 23:09:14.278307:  
-2024-08-28 23:09:14.278494: Epoch 1382 
-2024-08-28 23:09:14.278586: Current learning rate: 0.00348 
-2024-08-28 23:10:42.613388: train_loss -0.7661 
-2024-08-28 23:10:42.613709: val_loss -0.7835 
-2024-08-28 23:10:42.613884: Pseudo dice [0.0, 0.0, 0.887, 0.976, 0.7997, 0.9489, 0.9498, 0.9601, 0.9493, 0.9509, 0.934, 0.9573, 0.9573, 0.8473, 0.9504, 0.9324, 0.8358, 0.8298, nan] 
-2024-08-28 23:10:42.613993: Epoch time: 88.34 s 
-2024-08-28 23:10:44.208445:  
-2024-08-28 23:10:44.208783: Epoch 1383 
-2024-08-28 23:10:44.208889: Current learning rate: 0.00347 
-2024-08-28 23:12:10.157808: train_loss -0.7652 
-2024-08-28 23:12:10.158061: val_loss -0.7881 
-2024-08-28 23:12:10.158221: Pseudo dice [0.0, 0.0, 0.9066, 0.9782, 0.8402, 0.939, 0.9512, 0.9608, 0.9502, 0.9481, 0.9353, 0.9597, 0.9618, 0.8512, 0.9487, 0.9292, 0.8278, 0.8224, nan] 
-2024-08-28 23:12:10.158304: Epoch time: 85.95 s 
-2024-08-28 23:12:11.416676:  
-2024-08-28 23:12:11.416838: Epoch 1384 
-2024-08-28 23:12:11.416929: Current learning rate: 0.00346 
-2024-08-28 23:13:38.805912: train_loss -0.7663 
-2024-08-28 23:13:38.806140: val_loss -0.7895 
-2024-08-28 23:13:38.806291: Pseudo dice [0.0, 0.0, 0.8921, 0.9766, 0.8302, 0.942, 0.9483, 0.9659, 0.9542, 0.9516, 0.9359, 0.9642, 0.9623, 0.8416, 0.9552, 0.9363, 0.8316, 0.8305, nan] 
-2024-08-28 23:13:38.806386: Epoch time: 87.39 s 
-2024-08-28 23:13:40.038510:  
-2024-08-28 23:13:40.038693: Epoch 1385 
-2024-08-28 23:13:40.038793: Current learning rate: 0.00346 
-2024-08-28 23:15:05.024642: train_loss -0.7648 
-2024-08-28 23:15:05.024873: val_loss -0.7805 
-2024-08-28 23:15:05.025036: Pseudo dice [0.0, 0.0, 0.8947, 0.975, 0.8233, 0.9479, 0.9487, 0.9612, 0.949, 0.9535, 0.9312, 0.9547, 0.9623, 0.8514, 0.9473, 0.9366, 0.8265, 0.8307, nan] 
-2024-08-28 23:15:05.025120: Epoch time: 84.99 s 
-2024-08-28 23:15:06.293043:  
-2024-08-28 23:15:06.293293: Epoch 1386 
-2024-08-28 23:15:06.293388: Current learning rate: 0.00345 
-2024-08-28 23:16:31.147337: train_loss -0.7701 
-2024-08-28 23:16:31.147794: val_loss -0.7927 
-2024-08-28 23:16:31.147984: Pseudo dice [0.0, 0.0, 0.8973, 0.9762, 0.8629, 0.9502, 0.951, 0.9644, 0.953, 0.9496, 0.9339, 0.9616, 0.9647, 0.8541, 0.9558, 0.9366, 0.834, 0.8295, nan] 
-2024-08-28 23:16:31.148076: Epoch time: 84.86 s 
-2024-08-28 23:16:32.409803:  
-2024-08-28 23:16:32.410047: Epoch 1387 
-2024-08-28 23:16:32.410148: Current learning rate: 0.00345 
-2024-08-28 23:17:56.830450: train_loss -0.7706 
-2024-08-28 23:17:56.830741: val_loss -0.7805 
-2024-08-28 23:17:56.831103: Pseudo dice [0.0, 0.0, 0.9003, 0.9766, 0.7976, 0.9436, 0.9415, 0.9546, 0.9485, 0.9464, 0.9295, 0.9555, 0.9577, 0.8267, 0.9496, 0.9258, 0.8349, 0.814, nan] 
-2024-08-28 23:17:56.831275: Epoch time: 84.42 s 
-2024-08-28 23:17:58.177058:  
-2024-08-28 23:17:58.177575: Epoch 1388 
-2024-08-28 23:17:58.177673: Current learning rate: 0.00344 
-2024-08-28 23:19:26.910121: train_loss -0.7632 
-2024-08-28 23:19:26.910531: val_loss -0.7747 
-2024-08-28 23:19:26.910706: Pseudo dice [0.0, 0.0, 0.8975, 0.977, 0.8087, 0.9316, 0.9412, 0.9624, 0.9403, 0.9467, 0.9269, 0.9498, 0.948, 0.8405, 0.9387, 0.9297, 0.8203, 0.8187, nan] 
-2024-08-28 23:19:26.910794: Epoch time: 88.73 s 
-2024-08-28 23:19:28.155603:  
-2024-08-28 23:19:28.155785: Epoch 1389 
-2024-08-28 23:19:28.155884: Current learning rate: 0.00344 
-2024-08-28 23:20:55.189240: train_loss -0.7643 
-2024-08-28 23:20:55.189450: val_loss -0.7914 
-2024-08-28 23:20:55.189630: Pseudo dice [0.0, 0.0, 0.8936, 0.9762, 0.8248, 0.949, 0.947, 0.9593, 0.9537, 0.9496, 0.9341, 0.9606, 0.9626, 0.8441, 0.9484, 0.9361, 0.8301, 0.8311, nan] 
-2024-08-28 23:20:55.189715: Epoch time: 87.03 s 
-2024-08-28 23:20:56.464051:  
-2024-08-28 23:20:56.464243: Epoch 1390 
-2024-08-28 23:20:56.464335: Current learning rate: 0.00343 
-2024-08-28 23:22:15.360754: train_loss -0.7647 
-2024-08-28 23:22:15.361010: val_loss -0.7918 
-2024-08-28 23:22:15.361190: Pseudo dice [0.0, 0.0, 0.8975, 0.9767, 0.8346, 0.9482, 0.9453, 0.9663, 0.9539, 0.9451, 0.9322, 0.9636, 0.9611, 0.8478, 0.9515, 0.9314, 0.8313, 0.8296, nan] 
-2024-08-28 23:22:15.361283: Epoch time: 78.9 s 
-2024-08-28 23:22:16.644265:  
-2024-08-28 23:22:16.644771: Epoch 1391 
-2024-08-28 23:22:16.644870: Current learning rate: 0.00343 
-2024-08-28 23:23:42.793083: train_loss -0.7663 
-2024-08-28 23:23:42.793339: val_loss -0.7864 
-2024-08-28 23:23:42.793487: Pseudo dice [0.0, 0.0, 0.8975, 0.9758, 0.8341, 0.9526, 0.9523, 0.9665, 0.9506, 0.9543, 0.9196, 0.9611, 0.959, 0.8489, 0.9565, 0.9383, 0.8226, 0.8245, nan] 
-2024-08-28 23:23:42.793569: Epoch time: 86.15 s 
-2024-08-28 23:23:44.080840:  
-2024-08-28 23:23:44.081190: Epoch 1392 
-2024-08-28 23:23:44.081312: Current learning rate: 0.00342 
-2024-08-28 23:25:11.491350: train_loss -0.7651 
-2024-08-28 23:25:11.491655: val_loss -0.7941 
-2024-08-28 23:25:11.491845: Pseudo dice [0.0, 0.0, 0.8943, 0.9769, 0.8527, 0.9463, 0.9468, 0.9664, 0.9498, 0.9518, 0.9313, 0.9589, 0.9601, 0.849, 0.9407, 0.9314, 0.8269, 0.8328, nan] 
-2024-08-28 23:25:11.491937: Epoch time: 87.41 s 
-2024-08-28 23:25:12.766602:  
-2024-08-28 23:25:12.766913: Epoch 1393 
-2024-08-28 23:25:12.767016: Current learning rate: 0.00342 
-2024-08-28 23:26:38.325548: train_loss -0.7668 
-2024-08-28 23:26:38.325786: val_loss -0.7858 
-2024-08-28 23:26:38.325974: Pseudo dice [0.0, 0.0, 0.8968, 0.9765, 0.8433, 0.9461, 0.9512, 0.9658, 0.9517, 0.9508, 0.9322, 0.9601, 0.9613, 0.8472, 0.9451, 0.9341, 0.8195, 0.7871, nan] 
-2024-08-28 23:26:38.326067: Epoch time: 85.56 s 
-2024-08-28 23:26:39.600148:  
-2024-08-28 23:26:39.600322: Epoch 1394 
-2024-08-28 23:26:39.600419: Current learning rate: 0.00341 
-2024-08-28 23:28:02.720183: train_loss -0.7658 
-2024-08-28 23:28:02.720481: val_loss -0.7832 
-2024-08-28 23:28:02.720702: Pseudo dice [0.0, 0.0, 0.8909, 0.977, 0.8125, 0.9428, 0.9389, 0.9639, 0.9506, 0.9534, 0.9179, 0.9615, 0.9566, 0.8343, 0.945, 0.9326, 0.8261, 0.8334, nan] 
-2024-08-28 23:28:02.720815: Epoch time: 83.12 s 
-2024-08-28 23:28:04.251749:  
-2024-08-28 23:28:04.252132: Epoch 1395 
-2024-08-28 23:28:04.252242: Current learning rate: 0.00341 
-2024-08-28 23:29:33.416261: train_loss -0.7616 
-2024-08-28 23:29:33.416521: val_loss -0.7784 
-2024-08-28 23:29:33.416683: Pseudo dice [0.0, 0.0, 0.898, 0.9726, 0.7907, 0.9451, 0.948, 0.9602, 0.9504, 0.9458, 0.9236, 0.9565, 0.955, 0.824, 0.9411, 0.925, 0.8366, 0.8387, nan] 
-2024-08-28 23:29:33.416767: Epoch time: 89.17 s 
-2024-08-28 23:29:34.712239:  
-2024-08-28 23:29:34.712395: Epoch 1396 
-2024-08-28 23:29:34.712495: Current learning rate: 0.0034 
-2024-08-28 23:30:59.886148: train_loss -0.76 
-2024-08-28 23:30:59.886558: val_loss -0.7862 
-2024-08-28 23:30:59.886866: Pseudo dice [0.0, 0.0, 0.8739, 0.9763, 0.8416, 0.948, 0.9511, 0.9605, 0.9487, 0.9502, 0.933, 0.9584, 0.9611, 0.8416, 0.9478, 0.9305, 0.8155, 0.8278, nan] 
-2024-08-28 23:30:59.887034: Epoch time: 85.17 s 
-2024-08-28 23:31:01.132208:  
-2024-08-28 23:31:01.132361: Epoch 1397 
-2024-08-28 23:31:01.132452: Current learning rate: 0.0034 
-2024-08-28 23:32:21.946597: train_loss -0.7611 
-2024-08-28 23:32:21.946861: val_loss -0.7899 
-2024-08-28 23:32:21.947027: Pseudo dice [0.0, 0.0, 0.887, 0.975, 0.8167, 0.9474, 0.9508, 0.9617, 0.9496, 0.9494, 0.9322, 0.9627, 0.9606, 0.8408, 0.9514, 0.9306, 0.8251, 0.8192, nan] 
-2024-08-28 23:32:21.947115: Epoch time: 80.82 s 
-2024-08-28 23:32:23.239126:  
-2024-08-28 23:32:23.239549: Epoch 1398 
-2024-08-28 23:32:23.239735: Current learning rate: 0.00339 
-2024-08-28 23:33:49.741341: train_loss -0.7602 
-2024-08-28 23:33:49.741926: val_loss -0.7789 
-2024-08-28 23:33:49.742153: Pseudo dice [0.0, 0.0, 0.9009, 0.9769, 0.8431, 0.9396, 0.9468, 0.9635, 0.9459, 0.9432, 0.9279, 0.9573, 0.9601, 0.8444, 0.954, 0.9317, 0.8324, 0.8175, nan] 
-2024-08-28 23:33:49.742288: Epoch time: 86.5 s 
-2024-08-28 23:33:51.001681:  
-2024-08-28 23:33:51.001853: Epoch 1399 
-2024-08-28 23:33:51.001948: Current learning rate: 0.00339 
-2024-08-28 23:35:12.971780: train_loss -0.7692 
-2024-08-28 23:35:12.972251: val_loss -0.7855 
-2024-08-28 23:35:12.972422: Pseudo dice [0.0, 0.0, 0.9064, 0.9761, 0.845, 0.9456, 0.9479, 0.9635, 0.9509, 0.9464, 0.9244, 0.9576, 0.9593, 0.849, 0.9484, 0.9293, 0.8378, 0.8368, nan] 
-2024-08-28 23:35:12.972548: Epoch time: 81.97 s 
-2024-08-28 23:35:14.695693:  
-2024-08-28 23:35:14.695971: Epoch 1400 
-2024-08-28 23:35:14.696066: Current learning rate: 0.00338 
-2024-08-28 23:36:38.208185: train_loss -0.7652 
-2024-08-28 23:36:38.208411: val_loss -0.7903 
-2024-08-28 23:36:38.208587: Pseudo dice [0.0, 0.0, 0.9003, 0.9776, 0.8364, 0.9389, 0.95, 0.9643, 0.951, 0.9489, 0.9308, 0.9594, 0.9611, 0.8518, 0.9487, 0.9335, 0.8295, 0.8359, nan] 
-2024-08-28 23:36:38.208674: Epoch time: 83.51 s 
-2024-08-28 23:36:39.823786:  
-2024-08-28 23:36:39.824230: Epoch 1401 
-2024-08-28 23:36:39.824333: Current learning rate: 0.00338 
-2024-08-28 23:38:05.690657: train_loss -0.7684 
-2024-08-28 23:38:05.690889: val_loss -0.7789 
-2024-08-28 23:38:05.691077: Pseudo dice [0.0, 0.0, 0.8914, 0.9771, 0.8465, 0.9431, 0.9451, 0.9632, 0.9424, 0.9439, 0.9225, 0.9492, 0.9501, 0.8322, 0.9533, 0.9352, 0.8219, 0.8289, nan] 
-2024-08-28 23:38:05.691162: Epoch time: 85.87 s 
-2024-08-28 23:38:06.904841:  
-2024-08-28 23:38:06.904995: Epoch 1402 
-2024-08-28 23:38:06.905079: Current learning rate: 0.00337 
-2024-08-28 23:39:35.611678: train_loss -0.767 
-2024-08-28 23:39:35.611873: val_loss -0.7932 
-2024-08-28 23:39:35.612047: Pseudo dice [0.0, 0.0, 0.9111, 0.9766, 0.8465, 0.946, 0.9494, 0.9638, 0.9531, 0.9496, 0.9327, 0.9625, 0.9563, 0.8456, 0.9501, 0.932, 0.8375, 0.8481, nan] 
-2024-08-28 23:39:35.612129: Epoch time: 88.71 s 
-2024-08-28 23:39:36.813693:  
-2024-08-28 23:39:36.814063: Epoch 1403 
-2024-08-28 23:39:36.814157: Current learning rate: 0.00337 
-2024-08-28 23:40:56.971561: train_loss -0.7563 
-2024-08-28 23:40:56.971818: val_loss -0.7862 
-2024-08-28 23:40:56.971977: Pseudo dice [0.0, 0.0, 0.9044, 0.9775, 0.8313, 0.9462, 0.9451, 0.9622, 0.948, 0.9434, 0.9253, 0.9566, 0.9591, 0.8396, 0.9552, 0.9356, 0.83, 0.8388, nan] 
-2024-08-28 23:40:56.972064: Epoch time: 80.16 s 
-2024-08-28 23:40:58.242545:  
-2024-08-28 23:40:58.242955: Epoch 1404 
-2024-08-28 23:40:58.243125: Current learning rate: 0.00336 
-2024-08-28 23:42:24.456967: train_loss -0.7653 
-2024-08-28 23:42:24.457182: val_loss -0.7918 
-2024-08-28 23:42:24.457342: Pseudo dice [0.0, 0.0, 0.9064, 0.977, 0.8545, 0.9472, 0.9491, 0.9646, 0.9534, 0.9452, 0.9317, 0.9617, 0.96, 0.8355, 0.9525, 0.9349, 0.8365, 0.8031, nan] 
-2024-08-28 23:42:24.457425: Epoch time: 86.22 s 
-2024-08-28 23:42:25.707967:  
-2024-08-28 23:42:25.708359: Epoch 1405 
-2024-08-28 23:42:25.708491: Current learning rate: 0.00336 
-2024-08-28 23:43:51.614136: train_loss -0.7649 
-2024-08-28 23:43:51.614349: val_loss -0.7881 
-2024-08-28 23:43:51.614501: Pseudo dice [0.0, 0.0, 0.8634, 0.9769, 0.8548, 0.9388, 0.9393, 0.9682, 0.9508, 0.9483, 0.9305, 0.9585, 0.9604, 0.8506, 0.9565, 0.9347, 0.8088, 0.8143, nan] 
-2024-08-28 23:43:51.614578: Epoch time: 85.91 s 
-2024-08-28 23:43:52.814682:  
-2024-08-28 23:43:52.814816: Epoch 1406 
-2024-08-28 23:43:52.814901: Current learning rate: 0.00335 
-2024-08-28 23:45:19.466245: train_loss -0.7657 
-2024-08-28 23:45:19.466516: val_loss -0.7956 
-2024-08-28 23:45:19.466736: Pseudo dice [0.0, 0.0, 0.9094, 0.9773, 0.8428, 0.9486, 0.9524, 0.9638, 0.9474, 0.9556, 0.9303, 0.9587, 0.9598, 0.8534, 0.9493, 0.9378, 0.8451, 0.8363, nan] 
-2024-08-28 23:45:19.466845: Epoch time: 86.65 s 
-2024-08-28 23:45:21.112202:  
-2024-08-28 23:45:21.113087: Epoch 1407 
-2024-08-28 23:45:21.113232: Current learning rate: 0.00335 
-2024-08-28 23:46:51.354355: train_loss -0.7713 
-2024-08-28 23:46:51.354882: val_loss -0.7953 
-2024-08-28 23:46:51.355093: Pseudo dice [0.0, 0.0, 0.9133, 0.9748, 0.8449, 0.9434, 0.9469, 0.9641, 0.9497, 0.9506, 0.9369, 0.9603, 0.9619, 0.8562, 0.9514, 0.9363, 0.835, 0.8341, nan] 
-2024-08-28 23:46:51.355197: Epoch time: 90.24 s 
-2024-08-28 23:46:52.594651:  
-2024-08-28 23:46:52.594920: Epoch 1408 
-2024-08-28 23:46:52.595003: Current learning rate: 0.00334 
-2024-08-28 23:48:22.262755: train_loss -0.7644 
-2024-08-28 23:48:22.262988: val_loss -0.7871 
-2024-08-28 23:48:22.263144: Pseudo dice [0.0, 0.0, 0.8869, 0.9766, 0.8594, 0.9449, 0.9527, 0.9651, 0.9537, 0.9468, 0.9324, 0.9603, 0.9624, 0.8481, 0.9528, 0.9392, 0.8375, 0.8344, nan] 
-2024-08-28 23:48:22.263222: Epoch time: 89.67 s 
-2024-08-28 23:48:23.532411:  
-2024-08-28 23:48:23.532937: Epoch 1409 
-2024-08-28 23:48:23.533045: Current learning rate: 0.00334 
-2024-08-28 23:49:49.504815: train_loss -0.7675 
-2024-08-28 23:49:49.505045: val_loss -0.7918 
-2024-08-28 23:49:49.505201: Pseudo dice [0.0, 0.0, 0.8783, 0.9778, 0.8615, 0.9488, 0.952, 0.9672, 0.9559, 0.9545, 0.9339, 0.9622, 0.9618, 0.8598, 0.9483, 0.9322, 0.8194, 0.818, nan] 
-2024-08-28 23:49:49.505326: Epoch time: 85.97 s 
-2024-08-28 23:49:50.760771:  
-2024-08-28 23:49:50.760936: Epoch 1410 
-2024-08-28 23:49:50.761020: Current learning rate: 0.00333 
-2024-08-28 23:51:17.987030: train_loss -0.7694 
-2024-08-28 23:51:17.987620: val_loss -0.7841 
-2024-08-28 23:51:17.987842: Pseudo dice [0.0, 0.0, 0.8856, 0.9769, 0.8284, 0.939, 0.9452, 0.9651, 0.9527, 0.9432, 0.9352, 0.9613, 0.96, 0.846, 0.9401, 0.931, 0.825, 0.8246, nan] 
-2024-08-28 23:51:17.987993: Epoch time: 87.23 s 
-2024-08-28 23:51:19.415078:  
-2024-08-28 23:51:19.415255: Epoch 1411 
-2024-08-28 23:51:19.415349: Current learning rate: 0.00333 
-2024-08-28 23:52:46.276599: train_loss -0.7645 
-2024-08-28 23:52:46.276876: val_loss -0.7824 
-2024-08-28 23:52:46.277119: Pseudo dice [0.0, 0.0, 0.9023, 0.9751, 0.8086, 0.9408, 0.9436, 0.9569, 0.9424, 0.9487, 0.9238, 0.9609, 0.9572, 0.8459, 0.9412, 0.9302, 0.8112, 0.8262, nan] 
-2024-08-28 23:52:46.277246: Epoch time: 86.86 s 
-2024-08-28 23:52:47.610131:  
-2024-08-28 23:52:47.610323: Epoch 1412 
-2024-08-28 23:52:47.610421: Current learning rate: 0.00332 
-2024-08-28 23:54:21.137101: train_loss -0.7597 
-2024-08-28 23:54:21.137352: val_loss -0.784 
-2024-08-28 23:54:21.137516: Pseudo dice [0.0, 0.0, 0.9056, 0.9772, 0.8372, 0.9482, 0.9463, 0.9617, 0.9498, 0.9361, 0.9293, 0.9531, 0.9572, 0.8501, 0.9541, 0.9319, 0.8406, 0.8257, nan] 
-2024-08-28 23:54:21.137600: Epoch time: 93.53 s 
-2024-08-28 23:54:22.732015:  
-2024-08-28 23:54:22.732190: Epoch 1413 
-2024-08-28 23:54:22.732287: Current learning rate: 0.00332 
-2024-08-28 23:55:47.748565: train_loss -0.7686 
-2024-08-28 23:55:47.748837: val_loss -0.7888 
-2024-08-28 23:55:47.749148: Pseudo dice [0.0, 0.0, 0.8996, 0.9771, 0.8484, 0.9497, 0.948, 0.9653, 0.9556, 0.9504, 0.9317, 0.9635, 0.961, 0.8503, 0.9499, 0.9405, 0.8403, 0.8389, nan] 
-2024-08-28 23:55:47.749313: Epoch time: 85.02 s 
-2024-08-28 23:55:49.123197:  
-2024-08-28 23:55:49.123567: Epoch 1414 
-2024-08-28 23:55:49.123668: Current learning rate: 0.00331 
-2024-08-28 23:57:13.507708: train_loss -0.7681 
-2024-08-28 23:57:13.507972: val_loss -0.7866 
-2024-08-28 23:57:13.508138: Pseudo dice [0.0, 0.0, 0.8975, 0.9754, 0.834, 0.9459, 0.9449, 0.9615, 0.9514, 0.9455, 0.9325, 0.9624, 0.9592, 0.8477, 0.9509, 0.925, 0.828, 0.832, nan] 
-2024-08-28 23:57:13.508228: Epoch time: 84.39 s 
-2024-08-28 23:57:14.785321:  
-2024-08-28 23:57:14.785856: Epoch 1415 
-2024-08-28 23:57:14.785958: Current learning rate: 0.00331 
-2024-08-28 23:58:41.996001: train_loss -0.7725 
-2024-08-28 23:58:41.996338: val_loss -0.7847 
-2024-08-28 23:58:41.996608: Pseudo dice [0.0, 0.0, 0.8916, 0.9765, 0.841, 0.9479, 0.9496, 0.9647, 0.9469, 0.9394, 0.9305, 0.9564, 0.9602, 0.8503, 0.9405, 0.9375, 0.8189, 0.8222, nan] 
-2024-08-28 23:58:41.996727: Epoch time: 87.21 s 
-2024-08-28 23:58:43.529575:  
-2024-08-28 23:58:43.530190: Epoch 1416 
-2024-08-28 23:58:43.530422: Current learning rate: 0.0033 
-2024-08-29 00:00:08.318991: train_loss -0.7707 
-2024-08-29 00:00:08.319228: val_loss -0.7941 
-2024-08-29 00:00:08.319399: Pseudo dice [0.0, 0.0, 0.8929, 0.9773, 0.8246, 0.9487, 0.9515, 0.9642, 0.9549, 0.958, 0.9372, 0.9639, 0.9647, 0.8531, 0.9557, 0.938, 0.826, 0.8314, nan] 
-2024-08-29 00:00:08.319486: Epoch time: 84.79 s 
-2024-08-29 00:00:09.599545:  
-2024-08-29 00:00:09.599934: Epoch 1417 
-2024-08-29 00:00:09.600044: Current learning rate: 0.0033 
-2024-08-29 00:01:35.574101: train_loss -0.7713 
-2024-08-29 00:01:35.574372: val_loss -0.7875 
-2024-08-29 00:01:35.574546: Pseudo dice [0.0, 0.0, 0.9033, 0.9765, 0.8461, 0.9492, 0.9541, 0.9643, 0.9496, 0.9489, 0.9334, 0.9586, 0.9609, 0.8545, 0.9481, 0.938, 0.8334, 0.8327, nan] 
-2024-08-29 00:01:35.574639: Epoch time: 85.98 s 
-2024-08-29 00:01:36.866506:  
-2024-08-29 00:01:36.866676: Epoch 1418 
-2024-08-29 00:01:36.866765: Current learning rate: 0.00329 
-2024-08-29 00:02:57.880873: train_loss -0.7634 
-2024-08-29 00:02:57.881126: val_loss -0.7905 
-2024-08-29 00:02:57.881296: Pseudo dice [0.0, 0.0, 0.8891, 0.9741, 0.8563, 0.9516, 0.9513, 0.9643, 0.9525, 0.9474, 0.9305, 0.9617, 0.9591, 0.8543, 0.9581, 0.9307, 0.8199, 0.8176, nan] 
-2024-08-29 00:02:57.881381: Epoch time: 81.02 s 
-2024-08-29 00:02:59.404024:  
-2024-08-29 00:02:59.404189: Epoch 1419 
-2024-08-29 00:02:59.404284: Current learning rate: 0.00329 
-2024-08-29 00:04:25.992987: train_loss -0.7667 
-2024-08-29 00:04:25.993246: val_loss -0.7864 
-2024-08-29 00:04:25.993407: Pseudo dice [0.0, 0.0, 0.8866, 0.9772, 0.8395, 0.9442, 0.9466, 0.9638, 0.9507, 0.9545, 0.93, 0.958, 0.9584, 0.8518, 0.9511, 0.9306, 0.8341, 0.8283, nan] 
-2024-08-29 00:04:25.993633: Epoch time: 86.59 s 
-2024-08-29 00:04:27.284137:  
-2024-08-29 00:04:27.284319: Epoch 1420 
-2024-08-29 00:04:27.284406: Current learning rate: 0.00328 
-2024-08-29 00:05:51.478036: train_loss -0.7703 
-2024-08-29 00:05:51.478268: val_loss -0.7923 
-2024-08-29 00:05:51.478440: Pseudo dice [0.0, 0.0, 0.882, 0.9749, 0.8467, 0.9479, 0.9503, 0.9666, 0.9556, 0.9538, 0.9323, 0.964, 0.9613, 0.8493, 0.9458, 0.9381, 0.8403, 0.8288, nan] 
-2024-08-29 00:05:51.478523: Epoch time: 84.19 s 
-2024-08-29 00:05:52.743683:  
-2024-08-29 00:05:52.743870: Epoch 1421 
-2024-08-29 00:05:52.743965: Current learning rate: 0.00328 
-2024-08-29 00:07:20.234448: train_loss -0.7707 
-2024-08-29 00:07:20.234688: val_loss -0.7842 
-2024-08-29 00:07:20.234851: Pseudo dice [0.0, 0.0, 0.8761, 0.977, 0.8479, 0.9491, 0.9542, 0.9658, 0.9463, 0.9477, 0.9356, 0.9568, 0.964, 0.8484, 0.9524, 0.9383, 0.8361, 0.8388, nan] 
-2024-08-29 00:07:20.234932: Epoch time: 87.49 s 
-2024-08-29 00:07:21.464998:  
-2024-08-29 00:07:21.465163: Epoch 1422 
-2024-08-29 00:07:21.465256: Current learning rate: 0.00327 
-2024-08-29 00:08:46.326571: train_loss -0.7705 
-2024-08-29 00:08:46.326827: val_loss -0.7857 
-2024-08-29 00:08:46.326986: Pseudo dice [0.0, 0.0, 0.903, 0.9759, 0.8496, 0.9474, 0.9478, 0.967, 0.9402, 0.9423, 0.9298, 0.9547, 0.9604, 0.8553, 0.9494, 0.9371, 0.8433, 0.8427, nan] 
-2024-08-29 00:08:46.327228: Epoch time: 84.86 s 
-2024-08-29 00:08:46.327287: Yayy! New best EMA pseudo Dice: 0.8177 
-2024-08-29 00:08:48.027842:  
-2024-08-29 00:08:48.028002: Epoch 1423 
-2024-08-29 00:08:48.028084: Current learning rate: 0.00327 
-2024-08-29 00:10:09.289980: train_loss -0.7672 
-2024-08-29 00:10:09.290408: val_loss -0.7848 
-2024-08-29 00:10:09.290663: Pseudo dice [0.0, 0.0, 0.8949, 0.9778, 0.853, 0.9447, 0.9454, 0.9646, 0.9484, 0.9403, 0.9358, 0.96, 0.9611, 0.8463, 0.951, 0.9355, 0.8091, 0.811, nan] 
-2024-08-29 00:10:09.290777: Epoch time: 81.26 s 
-2024-08-29 00:10:10.683428:  
-2024-08-29 00:10:10.683743: Epoch 1424 
-2024-08-29 00:10:10.683849: Current learning rate: 0.00326 
-2024-08-29 00:11:34.806767: train_loss -0.7708 
-2024-08-29 00:11:34.807030: val_loss -0.7856 
-2024-08-29 00:11:34.807204: Pseudo dice [0.0, 0.0, 0.8935, 0.9765, 0.838, 0.9473, 0.9514, 0.9642, 0.9514, 0.9552, 0.9351, 0.9614, 0.9628, 0.8359, 0.9494, 0.9352, 0.8246, 0.8309, nan] 
-2024-08-29 00:11:34.807289: Epoch time: 84.12 s 
-2024-08-29 00:11:36.575138:  
-2024-08-29 00:11:36.575336: Epoch 1425 
-2024-08-29 00:11:36.575441: Current learning rate: 0.00326 
-2024-08-29 00:13:03.587780: train_loss -0.7666 
-2024-08-29 00:13:03.588003: val_loss -0.7827 
-2024-08-29 00:13:03.588168: Pseudo dice [0.0, 0.0, 0.8819, 0.9765, 0.855, 0.9485, 0.9484, 0.9628, 0.9499, 0.9485, 0.9314, 0.9614, 0.9581, 0.8438, 0.9433, 0.9316, 0.8131, 0.8099, nan] 
-2024-08-29 00:13:03.588248: Epoch time: 87.01 s 
-2024-08-29 00:13:04.875067:  
-2024-08-29 00:13:04.875252: Epoch 1426 
-2024-08-29 00:13:04.875344: Current learning rate: 0.00325 
-2024-08-29 00:14:31.550206: train_loss -0.7686 
-2024-08-29 00:14:31.550448: val_loss -0.7968 
-2024-08-29 00:14:31.550609: Pseudo dice [0.0, 0.0, 0.904, 0.9766, 0.8535, 0.9483, 0.9492, 0.9642, 0.952, 0.9498, 0.9338, 0.9605, 0.9633, 0.8537, 0.949, 0.9374, 0.8425, 0.8434, nan] 
-2024-08-29 00:14:31.550692: Epoch time: 86.68 s 
-2024-08-29 00:14:32.834989:  
-2024-08-29 00:14:32.835169: Epoch 1427 
-2024-08-29 00:14:32.835258: Current learning rate: 0.00325 
-2024-08-29 00:16:00.508237: train_loss -0.7704 
-2024-08-29 00:16:00.508492: val_loss -0.7929 
-2024-08-29 00:16:00.508658: Pseudo dice [0.0, 0.0, 0.9045, 0.977, 0.8439, 0.9449, 0.9466, 0.9648, 0.9564, 0.9403, 0.9369, 0.9606, 0.9628, 0.8418, 0.9412, 0.9308, 0.8319, 0.8252, nan] 
-2024-08-29 00:16:00.508779: Epoch time: 87.67 s 
-2024-08-29 00:16:01.780936:  
-2024-08-29 00:16:01.781384: Epoch 1428 
-2024-08-29 00:16:01.781553: Current learning rate: 0.00324 
-2024-08-29 00:17:22.653106: train_loss -0.7731 
-2024-08-29 00:17:22.653345: val_loss -0.7901 
-2024-08-29 00:17:22.653507: Pseudo dice [0.0, 0.0, 0.8906, 0.9774, 0.8393, 0.9497, 0.9513, 0.968, 0.9576, 0.9514, 0.9337, 0.963, 0.958, 0.848, 0.9524, 0.9351, 0.8293, 0.8276, nan] 
-2024-08-29 00:17:22.653591: Epoch time: 80.87 s 
-2024-08-29 00:17:23.917824:  
-2024-08-29 00:17:23.918176: Epoch 1429 
-2024-08-29 00:17:23.918272: Current learning rate: 0.00324 
-2024-08-29 00:18:50.849254: train_loss -0.7671 
-2024-08-29 00:18:50.849486: val_loss -0.7858 
-2024-08-29 00:18:50.849645: Pseudo dice [0.0, 0.0, 0.8928, 0.9776, 0.8464, 0.9484, 0.9529, 0.9653, 0.9499, 0.9447, 0.932, 0.9582, 0.9628, 0.8522, 0.9584, 0.9324, 0.8014, 0.8296, nan] 
-2024-08-29 00:18:50.849730: Epoch time: 86.93 s 
-2024-08-29 00:18:52.108643:  
-2024-08-29 00:18:52.108811: Epoch 1430 
-2024-08-29 00:18:52.108907: Current learning rate: 0.00323 
-2024-08-29 00:20:11.546474: train_loss -0.7691 
-2024-08-29 00:20:11.547216: val_loss -0.7902 
-2024-08-29 00:20:11.547523: Pseudo dice [0.0, 0.0, 0.9054, 0.9777, 0.8565, 0.9469, 0.9497, 0.9678, 0.9503, 0.9539, 0.9357, 0.9578, 0.9589, 0.8538, 0.9471, 0.9385, 0.8387, 0.8352, nan] 
-2024-08-29 00:20:11.547753: Epoch time: 79.44 s 
-2024-08-29 00:20:11.547851: Yayy! New best EMA pseudo Dice: 0.8179 
-2024-08-29 00:20:13.658298:  
-2024-08-29 00:20:13.658647: Epoch 1431 
-2024-08-29 00:20:13.658741: Current learning rate: 0.00323 
-2024-08-29 00:21:37.726985: train_loss -0.7725 
-2024-08-29 00:21:37.727491: val_loss -0.785 
-2024-08-29 00:21:37.727681: Pseudo dice [0.0, 0.0, 0.8988, 0.9765, 0.8291, 0.939, 0.9442, 0.9652, 0.9486, 0.9363, 0.9348, 0.9595, 0.9566, 0.8462, 0.9515, 0.9337, 0.832, 0.8168, nan] 
-2024-08-29 00:21:37.727812: Epoch time: 84.07 s 
-2024-08-29 00:21:39.042281:  
-2024-08-29 00:21:39.042695: Epoch 1432 
-2024-08-29 00:21:39.042800: Current learning rate: 0.00322 
-2024-08-29 00:23:03.500140: train_loss -0.7705 
-2024-08-29 00:23:03.500449: val_loss -0.792 
-2024-08-29 00:23:03.500616: Pseudo dice [0.0, 0.0, 0.9059, 0.9763, 0.8624, 0.9425, 0.9477, 0.966, 0.952, 0.9566, 0.9364, 0.9616, 0.9624, 0.8345, 0.954, 0.9374, 0.8317, 0.8373, nan] 
-2024-08-29 00:23:03.500698: Epoch time: 84.46 s 
-2024-08-29 00:23:04.757560:  
-2024-08-29 00:23:04.757987: Epoch 1433 
-2024-08-29 00:23:04.758094: Current learning rate: 0.00322 
-2024-08-29 00:24:29.097769: train_loss -0.7707 
-2024-08-29 00:24:29.098011: val_loss -0.7897 
-2024-08-29 00:24:29.098172: Pseudo dice [0.0, 0.0, 0.8963, 0.9769, 0.824, 0.9447, 0.9445, 0.9656, 0.95, 0.9534, 0.9254, 0.9638, 0.9605, 0.8449, 0.9535, 0.9365, 0.8201, 0.8214, nan] 
-2024-08-29 00:24:29.098253: Epoch time: 84.34 s 
-2024-08-29 00:24:30.377048:  
-2024-08-29 00:24:30.377604: Epoch 1434 
-2024-08-29 00:24:30.377713: Current learning rate: 0.00321 
-2024-08-29 00:25:52.389956: train_loss -0.7672 
-2024-08-29 00:25:52.390207: val_loss -0.7873 
-2024-08-29 00:25:52.390370: Pseudo dice [0.0, 0.0, 0.8856, 0.9776, 0.811, 0.9364, 0.9394, 0.9607, 0.9537, 0.9564, 0.9381, 0.9626, 0.9635, 0.8442, 0.9537, 0.9366, 0.8315, 0.8303, nan] 
-2024-08-29 00:25:52.390459: Epoch time: 82.01 s 
-2024-08-29 00:25:53.667764:  
-2024-08-29 00:25:53.667927: Epoch 1435 
-2024-08-29 00:25:53.668024: Current learning rate: 0.00321 
-2024-08-29 00:27:21.123091: train_loss -0.7643 
-2024-08-29 00:27:21.123333: val_loss -0.7881 
-2024-08-29 00:27:21.123505: Pseudo dice [0.0, 0.0, 0.8887, 0.9771, 0.8356, 0.9479, 0.9468, 0.9674, 0.9531, 0.9498, 0.9381, 0.9618, 0.9629, 0.8422, 0.9555, 0.9303, 0.8337, 0.8276, nan] 
-2024-08-29 00:27:21.123597: Epoch time: 87.46 s 
-2024-08-29 00:27:22.407926:  
-2024-08-29 00:27:22.408111: Epoch 1436 
-2024-08-29 00:27:22.408211: Current learning rate: 0.0032 
-2024-08-29 00:28:48.091091: train_loss -0.7704 
-2024-08-29 00:28:48.091315: val_loss -0.7944 
-2024-08-29 00:28:48.091476: Pseudo dice [0.0, 0.0, 0.9054, 0.9772, 0.8522, 0.9473, 0.9543, 0.9672, 0.9553, 0.9536, 0.9396, 0.9608, 0.9637, 0.8514, 0.9522, 0.9381, 0.8368, 0.8305, nan] 
-2024-08-29 00:28:48.091557: Epoch time: 85.68 s 
-2024-08-29 00:28:49.591477:  
-2024-08-29 00:28:49.591652: Epoch 1437 
-2024-08-29 00:28:49.591743: Current learning rate: 0.0032 
-2024-08-29 00:30:19.686841: train_loss -0.7682 
-2024-08-29 00:30:19.687086: val_loss -0.7909 
-2024-08-29 00:30:19.687258: Pseudo dice [0.0, 0.0, 0.9039, 0.9772, 0.8454, 0.9474, 0.9541, 0.9683, 0.9568, 0.9574, 0.9324, 0.9615, 0.9606, 0.849, 0.9557, 0.9349, 0.8397, 0.834, nan] 
-2024-08-29 00:30:19.687349: Epoch time: 90.1 s 
-2024-08-29 00:30:19.687403: Yayy! New best EMA pseudo Dice: 0.8182 
-2024-08-29 00:30:21.399488:  
-2024-08-29 00:30:21.399676: Epoch 1438 
-2024-08-29 00:30:21.399770: Current learning rate: 0.00319 
-2024-08-29 00:31:52.085229: train_loss -0.7686 
-2024-08-29 00:31:52.085476: val_loss -0.7899 
-2024-08-29 00:31:52.085644: Pseudo dice [0.0, 0.0, 0.9016, 0.9774, 0.8429, 0.9465, 0.9456, 0.9676, 0.9467, 0.9478, 0.9214, 0.9579, 0.9579, 0.8467, 0.9565, 0.9347, 0.8188, 0.8219, nan] 
-2024-08-29 00:31:52.085731: Epoch time: 90.69 s 
-2024-08-29 00:31:53.368616:  
-2024-08-29 00:31:53.368802: Epoch 1439 
-2024-08-29 00:31:53.368892: Current learning rate: 0.00319 
-2024-08-29 00:33:16.657165: train_loss -0.7739 
-2024-08-29 00:33:16.657411: val_loss -0.7947 
-2024-08-29 00:33:16.657568: Pseudo dice [0.0, 0.0, 0.9125, 0.9776, 0.8705, 0.9497, 0.9556, 0.9684, 0.9505, 0.9506, 0.9329, 0.9596, 0.9623, 0.8511, 0.949, 0.9392, 0.8396, 0.8457, nan] 
-2024-08-29 00:33:16.657650: Epoch time: 83.29 s 
-2024-08-29 00:33:16.657696: Yayy! New best EMA pseudo Dice: 0.8185 
-2024-08-29 00:33:18.298526:  
-2024-08-29 00:33:18.298687: Epoch 1440 
-2024-08-29 00:33:18.298782: Current learning rate: 0.00318 
-2024-08-29 00:34:40.625319: train_loss -0.7736 
-2024-08-29 00:34:40.625550: val_loss -0.7911 
-2024-08-29 00:34:40.625711: Pseudo dice [0.0, 0.0, 0.9001, 0.9751, 0.862, 0.9491, 0.9533, 0.9681, 0.9531, 0.9511, 0.9384, 0.96, 0.9628, 0.8561, 0.9561, 0.9387, 0.8435, 0.8355, nan] 
-2024-08-29 00:34:40.625797: Epoch time: 82.33 s 
-2024-08-29 00:34:40.625849: Yayy! New best EMA pseudo Dice: 0.8189 
-2024-08-29 00:34:42.332827:  
-2024-08-29 00:34:42.333069: Epoch 1441 
-2024-08-29 00:34:42.333171: Current learning rate: 0.00317 
-2024-08-29 00:35:58.879889: train_loss -0.7722 
-2024-08-29 00:35:58.880163: val_loss -0.7933 
-2024-08-29 00:35:58.880347: Pseudo dice [0.0, 0.0, 0.91, 0.9777, 0.8363, 0.9487, 0.9523, 0.9659, 0.9565, 0.9523, 0.9351, 0.9639, 0.962, 0.8537, 0.9531, 0.9379, 0.8432, 0.8408, nan] 
-2024-08-29 00:35:58.880451: Epoch time: 76.55 s 
-2024-08-29 00:35:58.880511: Yayy! New best EMA pseudo Dice: 0.8191 
-2024-08-29 00:36:00.837627:  
-2024-08-29 00:36:00.837938: Epoch 1442 
-2024-08-29 00:36:00.838029: Current learning rate: 0.00317 
-2024-08-29 00:37:31.621298: train_loss -0.7663 
-2024-08-29 00:37:31.621548: val_loss -0.7887 
-2024-08-29 00:37:31.621707: Pseudo dice [0.0, 0.0, 0.8865, 0.9775, 0.8255, 0.9457, 0.9501, 0.9615, 0.9528, 0.9535, 0.9356, 0.9587, 0.9617, 0.8427, 0.9433, 0.9331, 0.8216, 0.8265, nan] 
-2024-08-29 00:37:31.621794: Epoch time: 90.78 s 
-2024-08-29 00:37:32.901859:  
-2024-08-29 00:37:32.902032: Epoch 1443 
-2024-08-29 00:37:32.902119: Current learning rate: 0.00316 
-2024-08-29 00:38:57.611343: train_loss -0.7693 
-2024-08-29 00:38:57.611623: val_loss -0.788 
-2024-08-29 00:38:57.611840: Pseudo dice [0.0, 0.0, 0.8963, 0.9768, 0.8333, 0.9506, 0.9499, 0.9662, 0.9471, 0.9493, 0.9349, 0.9581, 0.9601, 0.8516, 0.9522, 0.9346, 0.8277, 0.8408, nan] 
-2024-08-29 00:38:57.611947: Epoch time: 84.71 s 
-2024-08-29 00:38:58.904118:  
-2024-08-29 00:38:58.904310: Epoch 1444 
-2024-08-29 00:38:58.904409: Current learning rate: 0.00316 
-2024-08-29 00:40:21.236844: train_loss -0.7688 
-2024-08-29 00:40:21.237101: val_loss -0.7873 
-2024-08-29 00:40:21.237263: Pseudo dice [0.0, 0.0, 0.8992, 0.9778, 0.842, 0.9434, 0.9447, 0.9656, 0.9541, 0.9544, 0.9388, 0.9626, 0.9637, 0.8489, 0.9537, 0.9347, 0.8331, 0.8271, nan] 
-2024-08-29 00:40:21.237351: Epoch time: 82.33 s 
-2024-08-29 00:40:22.505698:  
-2024-08-29 00:40:22.505995: Epoch 1445 
-2024-08-29 00:40:22.506089: Current learning rate: 0.00315 
-2024-08-29 00:41:44.032059: train_loss -0.7713 
-2024-08-29 00:41:44.032305: val_loss -0.7877 
-2024-08-29 00:41:44.032489: Pseudo dice [0.0, 0.0, 0.8754, 0.9781, 0.8485, 0.9479, 0.9514, 0.961, 0.9533, 0.9533, 0.9378, 0.9602, 0.9612, 0.8493, 0.9444, 0.9308, 0.8287, 0.8299, nan] 
-2024-08-29 00:41:44.032582: Epoch time: 81.53 s 
-2024-08-29 00:41:45.316016:  
-2024-08-29 00:41:45.316203: Epoch 1446 
-2024-08-29 00:41:45.316296: Current learning rate: 0.00315 
-2024-08-29 00:43:07.139384: train_loss -0.7708 
-2024-08-29 00:43:07.139611: val_loss -0.7918 
-2024-08-29 00:43:07.139782: Pseudo dice [0.0, 0.0, 0.8866, 0.9752, 0.8593, 0.9487, 0.9507, 0.9658, 0.9514, 0.9551, 0.9358, 0.9618, 0.9623, 0.8529, 0.9559, 0.9344, 0.8256, 0.8326, nan] 
-2024-08-29 00:43:07.139868: Epoch time: 81.82 s 
-2024-08-29 00:43:08.411500:  
-2024-08-29 00:43:08.411860: Epoch 1447 
-2024-08-29 00:43:08.411959: Current learning rate: 0.00314 
-2024-08-29 00:44:33.912936: train_loss -0.7678 
-2024-08-29 00:44:33.913198: val_loss -0.7938 
-2024-08-29 00:44:33.913365: Pseudo dice [0.0, 0.0, 0.8949, 0.9755, 0.8513, 0.9503, 0.952, 0.9656, 0.9473, 0.9552, 0.9353, 0.9565, 0.9622, 0.8507, 0.9488, 0.9364, 0.8234, 0.8269, nan] 
-2024-08-29 00:44:33.913457: Epoch time: 85.5 s 
-2024-08-29 00:44:35.665672:  
-2024-08-29 00:44:35.665862: Epoch 1448 
-2024-08-29 00:44:35.665949: Current learning rate: 0.00314 
-2024-08-29 00:46:03.060597: train_loss -0.772 
-2024-08-29 00:46:03.060804: val_loss -0.7911 
-2024-08-29 00:46:03.060971: Pseudo dice [0.0, 0.0, 0.9122, 0.977, 0.8482, 0.9512, 0.9497, 0.9626, 0.9523, 0.9518, 0.9371, 0.9613, 0.963, 0.854, 0.9458, 0.9374, 0.8364, 0.835, nan] 
-2024-08-29 00:46:03.061050: Epoch time: 87.4 s 
-2024-08-29 00:46:04.299048:  
-2024-08-29 00:46:04.299366: Epoch 1449 
-2024-08-29 00:46:04.299464: Current learning rate: 0.00313 
-2024-08-29 00:47:28.655604: train_loss -0.7685 
-2024-08-29 00:47:28.655853: val_loss -0.7859 
-2024-08-29 00:47:28.656003: Pseudo dice [0.0, 0.0, 0.9079, 0.9769, 0.8428, 0.9376, 0.9406, 0.96, 0.9512, 0.9506, 0.9299, 0.9619, 0.9587, 0.8263, 0.9435, 0.9289, 0.8296, 0.8138, nan] 
-2024-08-29 00:47:28.656084: Epoch time: 84.36 s 
-2024-08-29 00:47:30.330566:  
-2024-08-29 00:47:30.330735: Epoch 1450 
-2024-08-29 00:47:30.330815: Current learning rate: 0.00313 
-2024-08-29 00:48:58.680506: train_loss -0.7684 
-2024-08-29 00:48:58.680749: val_loss -0.7958 
-2024-08-29 00:48:58.680916: Pseudo dice [0.0, 0.0, 0.9073, 0.9783, 0.8425, 0.9512, 0.948, 0.9628, 0.953, 0.9566, 0.9398, 0.9626, 0.9638, 0.8503, 0.9564, 0.9358, 0.8452, 0.8421, nan] 
-2024-08-29 00:48:58.681003: Epoch time: 88.35 s 
-2024-08-29 00:48:59.961260:  
-2024-08-29 00:48:59.961442: Epoch 1451 
-2024-08-29 00:48:59.961549: Current learning rate: 0.00312 
-2024-08-29 00:50:26.860844: train_loss -0.7695 
-2024-08-29 00:50:26.861148: val_loss -0.7831 
-2024-08-29 00:50:26.861328: Pseudo dice [0.0, 0.0, 0.8983, 0.9773, 0.8372, 0.9495, 0.951, 0.962, 0.9528, 0.942, 0.93, 0.9555, 0.9603, 0.8391, 0.9485, 0.9331, 0.8411, 0.8255, nan] 
-2024-08-29 00:50:26.861513: Epoch time: 86.9 s 
-2024-08-29 00:50:28.134977:  
-2024-08-29 00:50:28.135453: Epoch 1452 
-2024-08-29 00:50:28.135637: Current learning rate: 0.00312 
-2024-08-29 00:51:52.174912: train_loss -0.7692 
-2024-08-29 00:51:52.175154: val_loss -0.7938 
-2024-08-29 00:51:52.175321: Pseudo dice [0.0, 0.0, 0.9041, 0.9755, 0.855, 0.9454, 0.9487, 0.9678, 0.9517, 0.9479, 0.9307, 0.9574, 0.9578, 0.8436, 0.9372, 0.9382, 0.8419, 0.8367, nan] 
-2024-08-29 00:51:52.175409: Epoch time: 84.04 s 
-2024-08-29 00:51:53.444018:  
-2024-08-29 00:51:53.444238: Epoch 1453 
-2024-08-29 00:51:53.444332: Current learning rate: 0.00311 
-2024-08-29 00:53:20.478499: train_loss -0.7691 
-2024-08-29 00:53:20.478730: val_loss -0.794 
-2024-08-29 00:53:20.478876: Pseudo dice [0.0, 0.0, 0.9033, 0.9775, 0.8352, 0.9494, 0.953, 0.9664, 0.9524, 0.9572, 0.9339, 0.9627, 0.9624, 0.8465, 0.9545, 0.9369, 0.8526, 0.8365, nan] 
-2024-08-29 00:53:20.478954: Epoch time: 87.04 s 
-2024-08-29 00:53:21.946003:  
-2024-08-29 00:53:21.946197: Epoch 1454 
-2024-08-29 00:53:21.946290: Current learning rate: 0.00311 
-2024-08-29 00:54:47.159492: train_loss -0.7706 
-2024-08-29 00:54:47.159734: val_loss -0.7872 
-2024-08-29 00:54:47.159904: Pseudo dice [0.0, 0.0, 0.881, 0.9763, 0.842, 0.9393, 0.9436, 0.9666, 0.9544, 0.9515, 0.9346, 0.9634, 0.9631, 0.8277, 0.9531, 0.9343, 0.8234, 0.8264, nan] 
-2024-08-29 00:54:47.159990: Epoch time: 85.21 s 
-2024-08-29 00:54:48.400827:  
-2024-08-29 00:54:48.400983: Epoch 1455 
-2024-08-29 00:54:48.401078: Current learning rate: 0.0031 
-2024-08-29 00:56:14.571660: train_loss -0.7665 
-2024-08-29 00:56:14.571910: val_loss -0.7853 
-2024-08-29 00:56:14.572091: Pseudo dice [0.0, 0.0, 0.8979, 0.9766, 0.8374, 0.9451, 0.9477, 0.9629, 0.9561, 0.9476, 0.9337, 0.9619, 0.9624, 0.8302, 0.9471, 0.9319, 0.8319, 0.8332, nan] 
-2024-08-29 00:56:14.572180: Epoch time: 86.17 s 
-2024-08-29 00:56:15.896873:  
-2024-08-29 00:56:15.897253: Epoch 1456 
-2024-08-29 00:56:15.897357: Current learning rate: 0.0031 
-2024-08-29 00:57:44.208204: train_loss -0.767 
-2024-08-29 00:57:44.208441: val_loss -0.7846 
-2024-08-29 00:57:44.208612: Pseudo dice [0.0, 0.0, 0.9035, 0.9763, 0.858, 0.949, 0.9515, 0.9641, 0.9474, 0.9452, 0.9279, 0.9554, 0.9562, 0.836, 0.9556, 0.9332, 0.8327, 0.8346, nan] 
-2024-08-29 00:57:44.208701: Epoch time: 88.31 s 
-2024-08-29 00:57:45.492342:  
-2024-08-29 00:57:45.492901: Epoch 1457 
-2024-08-29 00:57:45.493005: Current learning rate: 0.00309 
-2024-08-29 00:59:14.784281: train_loss -0.7687 
-2024-08-29 00:59:14.784608: val_loss -0.7864 
-2024-08-29 00:59:14.784878: Pseudo dice [0.0, 0.0, 0.8789, 0.9761, 0.8446, 0.9414, 0.9419, 0.9647, 0.9472, 0.9466, 0.9304, 0.9573, 0.9598, 0.8501, 0.9554, 0.9356, 0.8276, 0.825, nan] 
-2024-08-29 00:59:14.785015: Epoch time: 89.29 s 
-2024-08-29 00:59:16.095834:  
-2024-08-29 00:59:16.095999: Epoch 1458 
-2024-08-29 00:59:16.096087: Current learning rate: 0.00309 
-2024-08-29 01:00:46.048051: train_loss -0.7669 
-2024-08-29 01:00:46.048384: val_loss -0.7866 
-2024-08-29 01:00:46.048674: Pseudo dice [0.0, 0.0, 0.9041, 0.9773, 0.8131, 0.9492, 0.9515, 0.9623, 0.9467, 0.951, 0.9275, 0.9581, 0.9564, 0.8579, 0.9505, 0.9343, 0.8404, 0.8341, nan] 
-2024-08-29 01:00:46.048828: Epoch time: 89.95 s 
-2024-08-29 01:00:47.297823:  
-2024-08-29 01:00:47.298200: Epoch 1459 
-2024-08-29 01:00:47.298297: Current learning rate: 0.00308 
-2024-08-29 01:02:13.072965: train_loss -0.7697 
-2024-08-29 01:02:13.073226: val_loss -0.7846 
-2024-08-29 01:02:13.073395: Pseudo dice [0.0, 0.0, 0.861, 0.9768, 0.8357, 0.9449, 0.9494, 0.9637, 0.9511, 0.9471, 0.9258, 0.9559, 0.9602, 0.8328, 0.9515, 0.932, 0.8161, 0.8286, nan] 
-2024-08-29 01:02:13.073484: Epoch time: 85.78 s 
-2024-08-29 01:02:14.632926:  
-2024-08-29 01:02:14.633211: Epoch 1460 
-2024-08-29 01:02:14.633312: Current learning rate: 0.00308 
-2024-08-29 01:03:45.516271: train_loss -0.763 
-2024-08-29 01:03:45.516531: val_loss -0.7855 
-2024-08-29 01:03:45.516699: Pseudo dice [0.0, 0.0, 0.9024, 0.9778, 0.8415, 0.9435, 0.9489, 0.9618, 0.9517, 0.9463, 0.9181, 0.9624, 0.9594, 0.8426, 0.9437, 0.9327, 0.8182, 0.8259, nan] 
-2024-08-29 01:03:45.516787: Epoch time: 90.88 s 
-2024-08-29 01:03:46.800582:  
-2024-08-29 01:03:46.800961: Epoch 1461 
-2024-08-29 01:03:46.801052: Current learning rate: 0.00307 
-2024-08-29 01:05:10.013386: train_loss -0.766 
-2024-08-29 01:05:10.013626: val_loss -0.7856 
-2024-08-29 01:05:10.013797: Pseudo dice [0.0, 0.0, 0.8954, 0.9774, 0.8268, 0.9483, 0.9504, 0.96, 0.9531, 0.9467, 0.9333, 0.9609, 0.9585, 0.8484, 0.9483, 0.9251, 0.8369, 0.8194, nan] 
-2024-08-29 01:05:10.013886: Epoch time: 83.21 s 
-2024-08-29 01:05:11.288200:  
-2024-08-29 01:05:11.288624: Epoch 1462 
-2024-08-29 01:05:11.288807: Current learning rate: 0.00307 
-2024-08-29 01:06:39.813712: train_loss -0.7656 
-2024-08-29 01:06:39.814317: val_loss -0.7867 
-2024-08-29 01:06:39.814671: Pseudo dice [0.0, 0.0, 0.8828, 0.9778, 0.8, 0.9486, 0.9505, 0.9669, 0.9539, 0.9479, 0.9173, 0.9626, 0.9535, 0.8455, 0.9522, 0.9278, 0.8229, 0.8204, nan] 
-2024-08-29 01:06:39.814920: Epoch time: 88.53 s 
-2024-08-29 01:06:41.230278:  
-2024-08-29 01:06:41.230628: Epoch 1463 
-2024-08-29 01:06:41.230747: Current learning rate: 0.00306 
-2024-08-29 01:08:05.449923: train_loss -0.7681 
-2024-08-29 01:08:05.450419: val_loss -0.7872 
-2024-08-29 01:08:05.450596: Pseudo dice [0.0, 0.0, 0.9095, 0.9771, 0.8034, 0.9319, 0.9432, 0.9663, 0.9524, 0.9497, 0.9351, 0.9597, 0.9607, 0.8393, 0.9534, 0.9372, 0.8271, 0.8356, nan] 
-2024-08-29 01:08:05.450852: Epoch time: 84.22 s 
-2024-08-29 01:08:07.055350:  
-2024-08-29 01:08:07.055587: Epoch 1464 
-2024-08-29 01:08:07.055720: Current learning rate: 0.00306 
-2024-08-29 01:09:38.398450: train_loss -0.77 
-2024-08-29 01:09:38.398745: val_loss -0.7899 
-2024-08-29 01:09:38.398927: Pseudo dice [0.0, 0.0, 0.8975, 0.9769, 0.851, 0.949, 0.9548, 0.968, 0.9523, 0.946, 0.9328, 0.9595, 0.9586, 0.8577, 0.9518, 0.9374, 0.8305, 0.8323, nan] 
-2024-08-29 01:09:38.399037: Epoch time: 91.34 s 
-2024-08-29 01:09:39.676772:  
-2024-08-29 01:09:39.676936: Epoch 1465 
-2024-08-29 01:09:39.677027: Current learning rate: 0.00305 
-2024-08-29 01:11:06.232225: train_loss -0.7689 
-2024-08-29 01:11:06.232475: val_loss -0.7913 
-2024-08-29 01:11:06.232645: Pseudo dice [0.0, 0.0, 0.907, 0.9775, 0.8501, 0.9511, 0.9517, 0.9678, 0.9557, 0.9616, 0.9382, 0.9629, 0.9661, 0.843, 0.9446, 0.9353, 0.8445, 0.8421, nan] 
-2024-08-29 01:11:06.232735: Epoch time: 86.56 s 
-2024-08-29 01:11:07.765397:  
-2024-08-29 01:11:07.765573: Epoch 1466 
-2024-08-29 01:11:07.765670: Current learning rate: 0.00305 
-2024-08-29 01:12:40.798208: train_loss -0.7692 
-2024-08-29 01:12:40.798438: val_loss -0.7886 
-2024-08-29 01:12:40.798606: Pseudo dice [0.0, 0.0, 0.9146, 0.9771, 0.8545, 0.9439, 0.9507, 0.9623, 0.9544, 0.9606, 0.9399, 0.9594, 0.9624, 0.845, 0.9525, 0.9308, 0.8332, 0.8275, nan] 
-2024-08-29 01:12:40.798694: Epoch time: 93.03 s 
-2024-08-29 01:12:42.075332:  
-2024-08-29 01:12:42.075525: Epoch 1467 
-2024-08-29 01:12:42.075621: Current learning rate: 0.00304 
-2024-08-29 01:14:06.808754: train_loss -0.7744 
-2024-08-29 01:14:06.808980: val_loss -0.7853 
-2024-08-29 01:14:06.809141: Pseudo dice [0.0, 0.0, 0.8975, 0.9777, 0.8286, 0.9389, 0.9442, 0.964, 0.9537, 0.9392, 0.9293, 0.9614, 0.9608, 0.8428, 0.9448, 0.9319, 0.8183, 0.8315, nan] 
-2024-08-29 01:14:06.809273: Epoch time: 84.73 s 
-2024-08-29 01:14:08.112899:  
-2024-08-29 01:14:08.113067: Epoch 1468 
-2024-08-29 01:14:08.113166: Current learning rate: 0.00304 
-2024-08-29 01:15:39.546565: train_loss -0.7747 
-2024-08-29 01:15:39.546801: val_loss -0.7928 
-2024-08-29 01:15:39.546952: Pseudo dice [0.0, 0.0, 0.9147, 0.9785, 0.8514, 0.9444, 0.9497, 0.9657, 0.9468, 0.9518, 0.9278, 0.9535, 0.9519, 0.86, 0.9472, 0.9344, 0.8452, 0.8414, nan] 
-2024-08-29 01:15:39.547032: Epoch time: 91.43 s 
-2024-08-29 01:15:40.790391:  
-2024-08-29 01:15:40.790582: Epoch 1469 
-2024-08-29 01:15:40.790674: Current learning rate: 0.00303 
-2024-08-29 01:17:09.992129: train_loss -0.7705 
-2024-08-29 01:17:09.992341: val_loss -0.781 
-2024-08-29 01:17:09.992505: Pseudo dice [0.0, 0.0, 0.893, 0.9769, 0.8501, 0.9442, 0.9475, 0.9547, 0.9515, 0.9379, 0.9318, 0.9581, 0.9542, 0.8341, 0.9497, 0.9288, 0.8409, 0.8254, nan] 
-2024-08-29 01:17:09.992589: Epoch time: 89.2 s 
-2024-08-29 01:17:11.260146:  
-2024-08-29 01:17:11.260315: Epoch 1470 
-2024-08-29 01:17:11.260413: Current learning rate: 0.00303 
-2024-08-29 01:18:39.501035: train_loss -0.7739 
-2024-08-29 01:18:39.501517: val_loss -0.7909 
-2024-08-29 01:18:39.501704: Pseudo dice [0.0, 0.0, 0.9025, 0.9758, 0.8337, 0.9512, 0.9527, 0.9649, 0.949, 0.9443, 0.929, 0.962, 0.9605, 0.8545, 0.9552, 0.9354, 0.8345, 0.8436, nan] 
-2024-08-29 01:18:39.501892: Epoch time: 88.24 s 
-2024-08-29 01:18:40.781443:  
-2024-08-29 01:18:40.781603: Epoch 1471 
-2024-08-29 01:18:40.781700: Current learning rate: 0.00302 
-2024-08-29 01:20:09.108444: train_loss -0.7725 
-2024-08-29 01:20:09.108684: val_loss -0.7899 
-2024-08-29 01:20:09.108850: Pseudo dice [0.0, 0.0, 0.9026, 0.9773, 0.8459, 0.9482, 0.9478, 0.9677, 0.955, 0.9515, 0.9326, 0.962, 0.9634, 0.8501, 0.9552, 0.9339, 0.8344, 0.8265, nan] 
-2024-08-29 01:20:09.108984: Epoch time: 88.33 s 
-2024-08-29 01:20:10.569824:  
-2024-08-29 01:20:10.570230: Epoch 1472 
-2024-08-29 01:20:10.570439: Current learning rate: 0.00302 
-2024-08-29 01:21:33.888525: train_loss -0.7741 
-2024-08-29 01:21:33.888751: val_loss -0.793 
-2024-08-29 01:21:33.888927: Pseudo dice [0.0, 0.0, 0.8987, 0.9777, 0.8529, 0.9496, 0.9509, 0.9662, 0.9558, 0.9567, 0.9393, 0.964, 0.9622, 0.8482, 0.9559, 0.9369, 0.8301, 0.8269, nan] 
-2024-08-29 01:21:33.889017: Epoch time: 83.32 s 
-2024-08-29 01:21:35.176070:  
-2024-08-29 01:21:35.176609: Epoch 1473 
-2024-08-29 01:21:35.176767: Current learning rate: 0.00301 
-2024-08-29 01:23:02.555681: train_loss -0.7778 
-2024-08-29 01:23:02.555994: val_loss -0.7947 
-2024-08-29 01:23:02.556171: Pseudo dice [0.0, 0.0, 0.8953, 0.9758, 0.8513, 0.9505, 0.953, 0.967, 0.9516, 0.9486, 0.9247, 0.9595, 0.958, 0.8562, 0.9532, 0.9363, 0.8503, 0.8404, nan] 
-2024-08-29 01:23:02.556261: Epoch time: 87.38 s 
-2024-08-29 01:23:03.785320:  
-2024-08-29 01:23:03.785850: Epoch 1474 
-2024-08-29 01:23:03.785952: Current learning rate: 0.00301 
-2024-08-29 01:24:27.927750: train_loss -0.7721 
-2024-08-29 01:24:27.928000: val_loss -0.7939 
-2024-08-29 01:24:27.928157: Pseudo dice [0.0, 0.0, 0.8871, 0.9728, 0.8548, 0.9471, 0.95, 0.964, 0.9519, 0.9543, 0.939, 0.9621, 0.9632, 0.8562, 0.956, 0.9372, 0.8269, 0.8283, nan] 
-2024-08-29 01:24:27.928247: Epoch time: 84.14 s 
-2024-08-29 01:24:29.249823:  
-2024-08-29 01:24:29.250013: Epoch 1475 
-2024-08-29 01:24:29.250098: Current learning rate: 0.003 
-2024-08-29 01:25:59.617851: train_loss -0.766 
-2024-08-29 01:25:59.618171: val_loss -0.7879 
-2024-08-29 01:25:59.618392: Pseudo dice [0.0, 0.0, 0.903, 0.9775, 0.8558, 0.9458, 0.9481, 0.965, 0.9515, 0.9478, 0.9321, 0.9582, 0.9612, 0.8412, 0.9466, 0.9352, 0.8505, 0.824, nan] 
-2024-08-29 01:25:59.618500: Epoch time: 90.37 s 
-2024-08-29 01:26:01.232463:  
-2024-08-29 01:26:01.232630: Epoch 1476 
-2024-08-29 01:26:01.232723: Current learning rate: 0.003 
-2024-08-29 01:27:26.538760: train_loss -0.7716 
-2024-08-29 01:27:26.538991: val_loss -0.7931 
-2024-08-29 01:27:26.539154: Pseudo dice [0.0, 0.0, 0.9109, 0.972, 0.8598, 0.9525, 0.955, 0.963, 0.9563, 0.9543, 0.9391, 0.9636, 0.9628, 0.8603, 0.946, 0.928, 0.8471, 0.8335, nan] 
-2024-08-29 01:27:26.539286: Epoch time: 85.31 s 
-2024-08-29 01:27:27.804373:  
-2024-08-29 01:27:27.804656: Epoch 1477 
-2024-08-29 01:27:27.804758: Current learning rate: 0.00299 
-2024-08-29 01:28:47.034107: train_loss -0.7692 
-2024-08-29 01:28:47.034369: val_loss -0.791 
-2024-08-29 01:28:47.034574: Pseudo dice [0.0, 0.0, 0.8991, 0.9759, 0.8539, 0.9494, 0.9509, 0.9679, 0.9524, 0.9526, 0.9341, 0.9635, 0.9634, 0.8526, 0.9581, 0.9411, 0.8295, 0.8206, nan] 
-2024-08-29 01:28:47.034677: Epoch time: 79.23 s 
-2024-08-29 01:28:48.540369:  
-2024-08-29 01:28:48.540677: Epoch 1478 
-2024-08-29 01:28:48.540774: Current learning rate: 0.00299 
-2024-08-29 01:30:12.487155: train_loss -0.7724 
-2024-08-29 01:30:12.487438: val_loss -0.785 
-2024-08-29 01:30:12.487649: Pseudo dice [0.0, 0.0, 0.9052, 0.9758, 0.8425, 0.9418, 0.9486, 0.9638, 0.9532, 0.9469, 0.9375, 0.9612, 0.9624, 0.8434, 0.9315, 0.9273, 0.8324, 0.8308, nan] 
-2024-08-29 01:30:12.487757: Epoch time: 83.95 s 
-2024-08-29 01:30:13.829452:  
-2024-08-29 01:30:13.829624: Epoch 1479 
-2024-08-29 01:30:13.829711: Current learning rate: 0.00298 
-2024-08-29 01:31:41.953356: train_loss -0.7711 
-2024-08-29 01:31:41.953610: val_loss -0.7911 
-2024-08-29 01:31:41.953780: Pseudo dice [0.0, 0.0, 0.8978, 0.9745, 0.8488, 0.9457, 0.9478, 0.9653, 0.9543, 0.9537, 0.9365, 0.9618, 0.9619, 0.8403, 0.942, 0.9269, 0.8262, 0.8143, nan] 
-2024-08-29 01:31:41.953870: Epoch time: 88.12 s 
-2024-08-29 01:31:43.223980:  
-2024-08-29 01:31:43.224143: Epoch 1480 
-2024-08-29 01:31:43.224235: Current learning rate: 0.00297 
-2024-08-29 01:33:05.425736: train_loss -0.7721 
-2024-08-29 01:33:05.426052: val_loss -0.7928 
-2024-08-29 01:33:05.426238: Pseudo dice [0.0, 0.0, 0.8963, 0.9754, 0.8544, 0.9507, 0.9506, 0.9667, 0.9493, 0.9468, 0.9334, 0.9561, 0.9556, 0.8517, 0.956, 0.9416, 0.8315, 0.8253, nan] 
-2024-08-29 01:33:05.426337: Epoch time: 82.2 s 
-2024-08-29 01:33:06.689610:  
-2024-08-29 01:33:06.689947: Epoch 1481 
-2024-08-29 01:33:06.690056: Current learning rate: 0.00297 
-2024-08-29 01:34:31.583759: train_loss -0.7688 
-2024-08-29 01:34:31.583980: val_loss -0.7918 
-2024-08-29 01:34:31.584151: Pseudo dice [0.0, 0.0, 0.8944, 0.9771, 0.8558, 0.9488, 0.9471, 0.9671, 0.9534, 0.9577, 0.9339, 0.9631, 0.9602, 0.8558, 0.9544, 0.9424, 0.8163, 0.8109, nan] 
-2024-08-29 01:34:31.584239: Epoch time: 84.89 s 
-2024-08-29 01:34:32.854603:  
-2024-08-29 01:34:32.854915: Epoch 1482 
-2024-08-29 01:34:32.855009: Current learning rate: 0.00296 
-2024-08-29 01:35:52.270788: train_loss -0.7732 
-2024-08-29 01:35:52.271009: val_loss -0.7916 
-2024-08-29 01:35:52.271196: Pseudo dice [0.0, 0.0, 0.8837, 0.9771, 0.8565, 0.948, 0.9512, 0.9645, 0.9525, 0.95, 0.9368, 0.9614, 0.9629, 0.8489, 0.9541, 0.9359, 0.8375, 0.8335, nan] 
-2024-08-29 01:35:52.271285: Epoch time: 79.42 s 
-2024-08-29 01:35:53.541124:  
-2024-08-29 01:35:53.541284: Epoch 1483 
-2024-08-29 01:35:53.541377: Current learning rate: 0.00296 
-2024-08-29 01:37:21.401662: train_loss -0.7701 
-2024-08-29 01:37:21.401902: val_loss -0.7844 
-2024-08-29 01:37:21.402071: Pseudo dice [0.0, 0.0, 0.874, 0.9765, 0.8169, 0.9449, 0.9424, 0.9619, 0.9545, 0.9513, 0.9298, 0.9627, 0.9611, 0.8487, 0.9373, 0.9267, 0.8358, 0.8195, nan] 
-2024-08-29 01:37:21.402163: Epoch time: 87.86 s 
-2024-08-29 01:37:22.905092:  
-2024-08-29 01:37:22.905247: Epoch 1484 
-2024-08-29 01:37:22.905340: Current learning rate: 0.00295 
-2024-08-29 01:38:47.056674: train_loss -0.7691 
-2024-08-29 01:38:47.056912: val_loss -0.7923 
-2024-08-29 01:38:47.057069: Pseudo dice [0.0, 0.0, 0.9126, 0.9758, 0.8598, 0.9512, 0.9535, 0.96, 0.9543, 0.954, 0.9381, 0.9612, 0.9603, 0.8502, 0.9528, 0.9299, 0.8314, 0.8421, nan] 
-2024-08-29 01:38:47.057152: Epoch time: 84.15 s 
-2024-08-29 01:38:48.299988:  
-2024-08-29 01:38:48.300172: Epoch 1485 
-2024-08-29 01:38:48.300264: Current learning rate: 0.00295 
-2024-08-29 01:40:16.702923: train_loss -0.7676 
-2024-08-29 01:40:16.703142: val_loss -0.7886 
-2024-08-29 01:40:16.703305: Pseudo dice [0.0, 0.0, 0.9049, 0.9768, 0.8346, 0.9479, 0.9458, 0.962, 0.9538, 0.9552, 0.9351, 0.9623, 0.9617, 0.8393, 0.9545, 0.9369, 0.8441, 0.8407, nan] 
-2024-08-29 01:40:16.703388: Epoch time: 88.4 s 
-2024-08-29 01:40:17.952050:  
-2024-08-29 01:40:17.952339: Epoch 1486 
-2024-08-29 01:40:17.952439: Current learning rate: 0.00294 
-2024-08-29 01:41:44.485500: train_loss -0.7709 
-2024-08-29 01:41:44.485796: val_loss -0.7915 
-2024-08-29 01:41:44.486026: Pseudo dice [0.0, 0.0, 0.8935, 0.9767, 0.8435, 0.9492, 0.9493, 0.9625, 0.9508, 0.9527, 0.9242, 0.9617, 0.961, 0.8404, 0.9516, 0.9316, 0.8153, 0.8073, nan] 
-2024-08-29 01:41:44.486142: Epoch time: 86.53 s 
-2024-08-29 01:41:45.854288:  
-2024-08-29 01:41:45.854798: Epoch 1487 
-2024-08-29 01:41:45.854900: Current learning rate: 0.00294 
-2024-08-29 01:43:12.999704: train_loss -0.7665 
-2024-08-29 01:43:12.999938: val_loss -0.7845 
-2024-08-29 01:43:13.000104: Pseudo dice [0.0, 0.0, 0.8936, 0.9715, 0.8354, 0.9412, 0.9487, 0.9609, 0.9502, 0.9534, 0.9348, 0.9572, 0.962, 0.8361, 0.9437, 0.9289, 0.8342, 0.8331, nan] 
-2024-08-29 01:43:13.000188: Epoch time: 87.15 s 
-2024-08-29 01:43:14.291512:  
-2024-08-29 01:43:14.291870: Epoch 1488 
-2024-08-29 01:43:14.291964: Current learning rate: 0.00293 
-2024-08-29 01:44:38.559878: train_loss -0.7694 
-2024-08-29 01:44:38.560116: val_loss -0.7838 
-2024-08-29 01:44:38.560287: Pseudo dice [0.0, 0.0, 0.8984, 0.9769, 0.679, 0.9473, 0.9537, 0.9633, 0.9532, 0.9518, 0.9317, 0.9627, 0.963, 0.8452, 0.9549, 0.9309, 0.8328, 0.8297, nan] 
-2024-08-29 01:44:38.560371: Epoch time: 84.27 s 
-2024-08-29 01:44:39.853319:  
-2024-08-29 01:44:39.853462: Epoch 1489 
-2024-08-29 01:44:39.853558: Current learning rate: 0.00293 
-2024-08-29 01:46:07.624066: train_loss -0.7694 
-2024-08-29 01:46:07.624288: val_loss -0.7857 
-2024-08-29 01:46:07.624462: Pseudo dice [0.0, 0.0, 0.9012, 0.978, 0.8224, 0.9446, 0.9467, 0.9664, 0.9378, 0.9376, 0.9285, 0.9473, 0.9478, 0.8483, 0.9532, 0.936, 0.8201, 0.8243, nan] 
-2024-08-29 01:46:07.624544: Epoch time: 87.77 s 
-2024-08-29 01:46:09.027569:  
-2024-08-29 01:46:09.027888: Epoch 1490 
-2024-08-29 01:46:09.027985: Current learning rate: 0.00292 
-2024-08-29 01:47:37.554302: train_loss -0.7711 
-2024-08-29 01:47:37.554525: val_loss -0.7832 
-2024-08-29 01:47:37.554672: Pseudo dice [0.0, 0.0, 0.8906, 0.9763, 0.831, 0.9425, 0.9531, 0.962, 0.9542, 0.9448, 0.9372, 0.9629, 0.9628, 0.8365, 0.9469, 0.9353, 0.819, 0.8243, nan] 
-2024-08-29 01:47:37.554748: Epoch time: 88.53 s 
-2024-08-29 01:47:38.758248:  
-2024-08-29 01:47:38.758408: Epoch 1491 
-2024-08-29 01:47:38.758489: Current learning rate: 0.00292 
-2024-08-29 01:49:01.678802: train_loss -0.7657 
-2024-08-29 01:49:01.679039: val_loss -0.7881 
-2024-08-29 01:49:01.679207: Pseudo dice [0.0, 0.0, 0.8963, 0.9763, 0.8286, 0.9466, 0.9458, 0.9627, 0.9553, 0.945, 0.9301, 0.9611, 0.9587, 0.8405, 0.9541, 0.9346, 0.8228, 0.8268, nan] 
-2024-08-29 01:49:01.679291: Epoch time: 82.92 s 
-2024-08-29 01:49:02.925252:  
-2024-08-29 01:49:02.925535: Epoch 1492 
-2024-08-29 01:49:02.925636: Current learning rate: 0.00291 
-2024-08-29 01:50:28.327257: train_loss -0.7709 
-2024-08-29 01:50:28.327517: val_loss -0.7911 
-2024-08-29 01:50:28.327695: Pseudo dice [0.0, 0.0, 0.9068, 0.9761, 0.851, 0.9438, 0.9474, 0.9654, 0.9526, 0.9521, 0.9385, 0.9626, 0.9634, 0.8453, 0.951, 0.9337, 0.8363, 0.8357, nan] 
-2024-08-29 01:50:28.327785: Epoch time: 85.4 s 
-2024-08-29 01:50:29.599567:  
-2024-08-29 01:50:29.599745: Epoch 1493 
-2024-08-29 01:50:29.599832: Current learning rate: 0.00291 
-2024-08-29 01:51:58.771461: train_loss -0.7693 
-2024-08-29 01:51:58.771689: val_loss -0.7865 
-2024-08-29 01:51:58.771886: Pseudo dice [0.0, 0.0, 0.9131, 0.9765, 0.8165, 0.941, 0.9424, 0.9633, 0.9509, 0.9468, 0.9267, 0.9559, 0.959, 0.8335, 0.9534, 0.93, 0.8452, 0.8404, nan] 
-2024-08-29 01:51:58.771981: Epoch time: 89.17 s 
-2024-08-29 01:52:00.058792:  
-2024-08-29 01:52:00.059066: Epoch 1494 
-2024-08-29 01:52:00.059155: Current learning rate: 0.0029 
-2024-08-29 01:53:31.522276: train_loss -0.7681 
-2024-08-29 01:53:31.522496: val_loss -0.7894 
-2024-08-29 01:53:31.522667: Pseudo dice [0.0, 0.0, 0.9019, 0.9772, 0.8407, 0.9443, 0.9485, 0.9645, 0.9509, 0.9411, 0.9364, 0.962, 0.9616, 0.8555, 0.9568, 0.9409, 0.8393, 0.8433, nan] 
-2024-08-29 01:53:31.522753: Epoch time: 91.46 s 
-2024-08-29 01:53:32.936504:  
-2024-08-29 01:53:32.936675: Epoch 1495 
-2024-08-29 01:53:32.936788: Current learning rate: 0.0029 
-2024-08-29 01:55:02.367401: train_loss -0.7678 
-2024-08-29 01:55:02.367653: val_loss -0.791 
-2024-08-29 01:55:02.367828: Pseudo dice [0.0, 0.0, 0.9065, 0.9767, 0.8454, 0.9468, 0.9431, 0.9604, 0.9563, 0.9507, 0.9318, 0.9634, 0.9622, 0.8505, 0.9355, 0.9309, 0.8365, 0.8359, nan] 
-2024-08-29 01:55:02.367920: Epoch time: 89.43 s 
-2024-08-29 01:55:03.954082:  
-2024-08-29 01:55:03.954296: Epoch 1496 
-2024-08-29 01:55:03.954393: Current learning rate: 0.00289 
-2024-08-29 01:56:33.247351: train_loss -0.7664 
-2024-08-29 01:56:33.247600: val_loss -0.7826 
-2024-08-29 01:56:33.247766: Pseudo dice [0.0, 0.0, 0.8816, 0.9764, 0.8057, 0.9434, 0.9397, 0.9615, 0.9525, 0.9474, 0.9289, 0.9613, 0.9576, 0.8391, 0.947, 0.9306, 0.8207, 0.8123, nan] 
-2024-08-29 01:56:33.247852: Epoch time: 89.29 s 
-2024-08-29 01:56:34.488791:  
-2024-08-29 01:56:34.488952: Epoch 1497 
-2024-08-29 01:56:34.489044: Current learning rate: 0.00289 
-2024-08-29 01:58:01.533640: train_loss -0.7696 
-2024-08-29 01:58:01.533900: val_loss -0.7845 
-2024-08-29 01:58:01.534079: Pseudo dice [0.0, 0.0, 0.9082, 0.9769, 0.7899, 0.9475, 0.9473, 0.9658, 0.9519, 0.9447, 0.9282, 0.9582, 0.959, 0.8466, 0.9513, 0.9329, 0.8356, 0.8346, nan] 
-2024-08-29 01:58:01.534171: Epoch time: 87.05 s 
-2024-08-29 01:58:02.877432:  
-2024-08-29 01:58:02.877683: Epoch 1498 
-2024-08-29 01:58:02.877793: Current learning rate: 0.00288 
-2024-08-29 01:59:28.894312: train_loss -0.7682 
-2024-08-29 01:59:28.894547: val_loss -0.7852 
-2024-08-29 01:59:28.894711: Pseudo dice [0.0, 0.0, 0.8938, 0.9758, 0.8224, 0.9448, 0.945, 0.9671, 0.9516, 0.9383, 0.9351, 0.9619, 0.9632, 0.8549, 0.9473, 0.9324, 0.823, 0.8156, nan] 
-2024-08-29 01:59:28.894795: Epoch time: 86.02 s 
-2024-08-29 01:59:30.164974:  
-2024-08-29 01:59:30.165417: Epoch 1499 
-2024-08-29 01:59:30.165513: Current learning rate: 0.00288 
-2024-08-29 02:00:54.104609: train_loss -0.7691 
-2024-08-29 02:00:54.104883: val_loss -0.7901 
-2024-08-29 02:00:54.105094: Pseudo dice [0.0, 0.0, 0.9005, 0.9765, 0.796, 0.944, 0.9469, 0.9636, 0.9543, 0.9501, 0.9359, 0.9614, 0.9632, 0.8352, 0.9511, 0.9306, 0.8146, 0.8245, nan] 
-2024-08-29 02:00:54.105200: Epoch time: 83.94 s 
-2024-08-29 02:00:55.882943:  
-2024-08-29 02:00:55.883518: Epoch 1500 
-2024-08-29 02:00:55.883629: Current learning rate: 0.00287 
-2024-08-29 02:02:19.263318: train_loss -0.7672 
-2024-08-29 02:02:19.263556: val_loss -0.7928 
-2024-08-29 02:02:19.263731: Pseudo dice [0.0, 0.0, 0.905, 0.9775, 0.8236, 0.9477, 0.9477, 0.9627, 0.9525, 0.9482, 0.9332, 0.9616, 0.9625, 0.8394, 0.9561, 0.936, 0.8152, 0.826, nan] 
-2024-08-29 02:02:19.263821: Epoch time: 83.38 s 
-2024-08-29 02:02:20.629305:  
-2024-08-29 02:02:20.629553: Epoch 1501 
-2024-08-29 02:02:20.629649: Current learning rate: 0.00287 
-2024-08-29 02:03:50.433629: train_loss -0.7664 
-2024-08-29 02:03:50.433867: val_loss -0.7896 
-2024-08-29 02:03:50.434030: Pseudo dice [0.0, 0.0, 0.9087, 0.9762, 0.8342, 0.9495, 0.9522, 0.9644, 0.9511, 0.9584, 0.9368, 0.959, 0.9603, 0.852, 0.9552, 0.9375, 0.8399, 0.8329, nan] 
-2024-08-29 02:03:50.434110: Epoch time: 89.81 s 
-2024-08-29 02:03:51.868995:  
-2024-08-29 02:03:51.869191: Epoch 1502 
-2024-08-29 02:03:51.869286: Current learning rate: 0.00286 
-2024-08-29 02:05:13.193414: train_loss -0.7731 
-2024-08-29 02:05:13.193642: val_loss -0.7868 
-2024-08-29 02:05:13.193797: Pseudo dice [0.0, 0.0, 0.8974, 0.9774, 0.7996, 0.9493, 0.951, 0.9674, 0.9535, 0.9546, 0.9324, 0.9628, 0.9607, 0.8445, 0.9515, 0.9352, 0.8299, 0.828, nan] 
-2024-08-29 02:05:13.193879: Epoch time: 81.33 s 
-2024-08-29 02:05:14.409295:  
-2024-08-29 02:05:14.409618: Epoch 1503 
-2024-08-29 02:05:14.409710: Current learning rate: 0.00286 
-2024-08-29 02:06:36.922201: train_loss -0.7701 
-2024-08-29 02:06:36.922442: val_loss -0.7913 
-2024-08-29 02:06:36.922614: Pseudo dice [0.0, 0.0, 0.9021, 0.9753, 0.8478, 0.9485, 0.9533, 0.9676, 0.9526, 0.9474, 0.9331, 0.96, 0.9598, 0.8395, 0.9499, 0.9381, 0.8458, 0.8264, nan] 
-2024-08-29 02:06:36.922703: Epoch time: 82.51 s 
-2024-08-29 02:06:38.230589:  
-2024-08-29 02:06:38.230760: Epoch 1504 
-2024-08-29 02:06:38.230859: Current learning rate: 0.00285 
-2024-08-29 02:08:06.100682: train_loss -0.7731 
-2024-08-29 02:08:06.100935: val_loss -0.7914 
-2024-08-29 02:08:06.101108: Pseudo dice [0.0, 0.0, 0.9098, 0.9776, 0.8551, 0.9492, 0.9528, 0.9666, 0.9525, 0.9519, 0.9414, 0.9614, 0.9657, 0.8587, 0.9538, 0.9391, 0.8326, 0.8328, nan] 
-2024-08-29 02:08:06.101201: Epoch time: 87.87 s 
-2024-08-29 02:08:07.478323:  
-2024-08-29 02:08:07.478584: Epoch 1505 
-2024-08-29 02:08:07.478704: Current learning rate: 0.00285 
-2024-08-29 02:09:36.870780: train_loss -0.7732 
-2024-08-29 02:09:36.871066: val_loss -0.7932 
-2024-08-29 02:09:36.871225: Pseudo dice [0.0, 0.0, 0.9017, 0.9782, 0.8496, 0.9457, 0.948, 0.9674, 0.9512, 0.9523, 0.9359, 0.9596, 0.962, 0.8556, 0.9463, 0.94, 0.8394, 0.8294, nan] 
-2024-08-29 02:09:36.871326: Epoch time: 89.39 s 
-2024-08-29 02:09:38.141032:  
-2024-08-29 02:09:38.141538: Epoch 1506 
-2024-08-29 02:09:38.141653: Current learning rate: 0.00284 
-2024-08-29 02:11:03.659209: train_loss -0.7716 
-2024-08-29 02:11:03.659470: val_loss -0.7873 
-2024-08-29 02:11:03.659653: Pseudo dice [0.0, 0.0, 0.8957, 0.9781, 0.8421, 0.946, 0.9486, 0.9658, 0.9545, 0.9466, 0.9345, 0.9609, 0.9624, 0.8514, 0.9464, 0.9326, 0.8481, 0.8357, nan] 
-2024-08-29 02:11:03.659752: Epoch time: 85.52 s 
-2024-08-29 02:11:04.943207:  
-2024-08-29 02:11:04.943770: Epoch 1507 
-2024-08-29 02:11:04.943864: Current learning rate: 0.00284 
-2024-08-29 02:12:36.420601: train_loss -0.7705 
-2024-08-29 02:12:36.420890: val_loss -0.7938 
-2024-08-29 02:12:36.421112: Pseudo dice [0.0, 0.0, 0.9115, 0.9765, 0.8184, 0.9496, 0.9514, 0.9674, 0.9525, 0.9579, 0.9294, 0.9604, 0.9619, 0.856, 0.9509, 0.9402, 0.8405, 0.8471, nan] 
-2024-08-29 02:12:36.421222: Epoch time: 91.48 s 
-2024-08-29 02:12:37.978303:  
-2024-08-29 02:12:37.978585: Epoch 1508 
-2024-08-29 02:12:37.978688: Current learning rate: 0.00283 
-2024-08-29 02:14:04.230501: train_loss -0.7743 
-2024-08-29 02:14:04.230911: val_loss -0.7931 
-2024-08-29 02:14:04.231159: Pseudo dice [0.0, 0.0, 0.8922, 0.9758, 0.8251, 0.9517, 0.9534, 0.9633, 0.9544, 0.938, 0.9318, 0.9613, 0.9628, 0.85, 0.9551, 0.9342, 0.8346, 0.8468, nan] 
-2024-08-29 02:14:04.231407: Epoch time: 86.25 s 
-2024-08-29 02:14:05.487746:  
-2024-08-29 02:14:05.487947: Epoch 1509 
-2024-08-29 02:14:05.488038: Current learning rate: 0.00283 
-2024-08-29 02:15:32.530256: train_loss -0.7711 
-2024-08-29 02:15:32.530509: val_loss -0.7921 
-2024-08-29 02:15:32.530665: Pseudo dice [0.0, 0.0, 0.8936, 0.9762, 0.822, 0.9503, 0.9528, 0.9663, 0.9527, 0.9572, 0.9319, 0.963, 0.9654, 0.8504, 0.9572, 0.9341, 0.8396, 0.8324, nan] 
-2024-08-29 02:15:32.530744: Epoch time: 87.04 s 
-2024-08-29 02:15:33.794759:  
-2024-08-29 02:15:33.794930: Epoch 1510 
-2024-08-29 02:15:33.795024: Current learning rate: 0.00282 
-2024-08-29 02:17:01.435253: train_loss -0.7722 
-2024-08-29 02:17:01.435681: val_loss -0.7956 
-2024-08-29 02:17:01.435916: Pseudo dice [0.0, 0.0, 0.902, 0.9774, 0.8611, 0.9511, 0.9521, 0.968, 0.9537, 0.9552, 0.9383, 0.9609, 0.9631, 0.8542, 0.9552, 0.9347, 0.8508, 0.8381, nan] 
-2024-08-29 02:17:01.436025: Epoch time: 87.64 s 
-2024-08-29 02:17:02.884681:  
-2024-08-29 02:17:02.884870: Epoch 1511 
-2024-08-29 02:17:02.884970: Current learning rate: 0.00281 
-2024-08-29 02:18:26.739807: train_loss -0.7726 
-2024-08-29 02:18:26.740049: val_loss -0.7922 
-2024-08-29 02:18:26.740214: Pseudo dice [0.0, 0.0, 0.9102, 0.9786, 0.8617, 0.947, 0.9509, 0.9659, 0.9532, 0.95, 0.9289, 0.9566, 0.9584, 0.8427, 0.9459, 0.9412, 0.8331, 0.8359, nan] 
-2024-08-29 02:18:26.740301: Epoch time: 83.86 s 
-2024-08-29 02:18:28.188125:  
-2024-08-29 02:18:28.188530: Epoch 1512 
-2024-08-29 02:18:28.188663: Current learning rate: 0.00281 
-2024-08-29 02:19:52.772094: train_loss -0.7716 
-2024-08-29 02:19:52.772335: val_loss -0.7916 
-2024-08-29 02:19:52.772504: Pseudo dice [0.0, 0.0, 0.9034, 0.9754, 0.8568, 0.945, 0.9476, 0.9659, 0.9524, 0.9536, 0.9292, 0.962, 0.9624, 0.8494, 0.9536, 0.9383, 0.841, 0.842, nan] 
-2024-08-29 02:19:52.772592: Epoch time: 84.58 s 
-2024-08-29 02:19:54.033745:  
-2024-08-29 02:19:54.033912: Epoch 1513 
-2024-08-29 02:19:54.034001: Current learning rate: 0.0028 
-2024-08-29 02:21:24.285589: train_loss -0.7738 
-2024-08-29 02:21:24.285839: val_loss -0.7968 
-2024-08-29 02:21:24.286006: Pseudo dice [0.0, 0.0, 0.8956, 0.9776, 0.8506, 0.947, 0.95, 0.9652, 0.955, 0.9532, 0.9356, 0.9629, 0.9631, 0.8594, 0.9476, 0.938, 0.8462, 0.8422, nan] 
-2024-08-29 02:21:24.286092: Epoch time: 90.25 s 
-2024-08-29 02:21:24.286141: Yayy! New best EMA pseudo Dice: 0.8193 
-2024-08-29 02:21:26.239057:  
-2024-08-29 02:21:26.239253: Epoch 1514 
-2024-08-29 02:21:26.239339: Current learning rate: 0.0028 
-2024-08-29 02:22:54.491723: train_loss -0.7702 
-2024-08-29 02:22:54.491975: val_loss -0.7905 
-2024-08-29 02:22:54.492141: Pseudo dice [0.0, 0.0, 0.8979, 0.9767, 0.8289, 0.9432, 0.9463, 0.9659, 0.9513, 0.9535, 0.9385, 0.9602, 0.9635, 0.8493, 0.942, 0.9343, 0.8254, 0.824, nan] 
-2024-08-29 02:22:54.492226: Epoch time: 88.25 s 
-2024-08-29 02:22:55.697003:  
-2024-08-29 02:22:55.697438: Epoch 1515 
-2024-08-29 02:22:55.697539: Current learning rate: 0.00279 
-2024-08-29 02:24:21.408165: train_loss -0.769 
-2024-08-29 02:24:21.408387: val_loss -0.7853 
-2024-08-29 02:24:21.408566: Pseudo dice [0.0, 0.0, 0.8695, 0.977, 0.7976, 0.9404, 0.948, 0.961, 0.9554, 0.9523, 0.9335, 0.9633, 0.9645, 0.851, 0.9515, 0.9327, 0.823, 0.8099, nan] 
-2024-08-29 02:24:21.408657: Epoch time: 85.71 s 
-2024-08-29 02:24:22.659034:  
-2024-08-29 02:24:22.659587: Epoch 1516 
-2024-08-29 02:24:22.659694: Current learning rate: 0.00279 
-2024-08-29 02:25:46.611513: train_loss -0.7712 
-2024-08-29 02:25:46.611768: val_loss -0.7874 
-2024-08-29 02:25:46.611933: Pseudo dice [0.0, 0.0, 0.8948, 0.977, 0.845, 0.9438, 0.9488, 0.9659, 0.9504, 0.9542, 0.9311, 0.9621, 0.9613, 0.846, 0.9482, 0.9344, 0.828, 0.813, nan] 
-2024-08-29 02:25:46.612022: Epoch time: 83.95 s 
-2024-08-29 02:25:47.858658:  
-2024-08-29 02:25:47.858844: Epoch 1517 
-2024-08-29 02:25:47.858933: Current learning rate: 0.00278 
-2024-08-29 02:27:14.838786: train_loss -0.7725 
-2024-08-29 02:27:14.839255: val_loss -0.7877 
-2024-08-29 02:27:14.839573: Pseudo dice [0.0, 0.0, 0.8945, 0.9775, 0.8512, 0.9442, 0.9478, 0.965, 0.9501, 0.9488, 0.9319, 0.9565, 0.956, 0.839, 0.9533, 0.9341, 0.8341, 0.825, nan] 
-2024-08-29 02:27:14.839716: Epoch time: 86.98 s 
-2024-08-29 02:27:16.090045:  
-2024-08-29 02:27:16.090399: Epoch 1518 
-2024-08-29 02:27:16.090498: Current learning rate: 0.00278 
-2024-08-29 02:28:45.422056: train_loss -0.7711 
-2024-08-29 02:28:45.422305: val_loss -0.7946 
-2024-08-29 02:28:45.422468: Pseudo dice [0.0, 0.0, 0.9174, 0.9773, 0.859, 0.9518, 0.9531, 0.9646, 0.9507, 0.955, 0.9357, 0.9591, 0.9592, 0.8596, 0.9567, 0.9414, 0.8482, 0.8415, nan] 
-2024-08-29 02:28:45.422556: Epoch time: 89.33 s 
-2024-08-29 02:28:46.678321:  
-2024-08-29 02:28:46.678488: Epoch 1519 
-2024-08-29 02:28:46.678571: Current learning rate: 0.00277 
-2024-08-29 02:30:20.422839: train_loss -0.7755 
-2024-08-29 02:30:20.423157: val_loss -0.786 
-2024-08-29 02:30:20.423471: Pseudo dice [0.0, 0.0, 0.8793, 0.9775, 0.8548, 0.9497, 0.9536, 0.9625, 0.953, 0.9447, 0.9305, 0.9612, 0.9615, 0.8594, 0.956, 0.9403, 0.8172, 0.8269, nan] 
-2024-08-29 02:30:20.423623: Epoch time: 93.75 s 
-2024-08-29 02:30:22.057499:  
-2024-08-29 02:30:22.057711: Epoch 1520 
-2024-08-29 02:30:22.057802: Current learning rate: 0.00277 
-2024-08-29 02:31:47.343548: train_loss -0.7725 
-2024-08-29 02:31:47.343772: val_loss -0.7891 
-2024-08-29 02:31:47.343938: Pseudo dice [0.0, 0.0, 0.905, 0.9767, 0.839, 0.9438, 0.9467, 0.9681, 0.9529, 0.9313, 0.937, 0.9612, 0.9617, 0.8525, 0.9556, 0.9323, 0.8336, 0.8248, nan] 
-2024-08-29 02:31:47.344023: Epoch time: 85.29 s 
-2024-08-29 02:31:48.612631:  
-2024-08-29 02:31:48.613019: Epoch 1521 
-2024-08-29 02:31:48.613120: Current learning rate: 0.00276 
-2024-08-29 02:33:14.040661: train_loss -0.7766 
-2024-08-29 02:33:14.040920: val_loss -0.787 
-2024-08-29 02:33:14.041099: Pseudo dice [0.0, 0.0, 0.8964, 0.9777, 0.8319, 0.9435, 0.9467, 0.9639, 0.9524, 0.9498, 0.9398, 0.9627, 0.9615, 0.8497, 0.9454, 0.9341, 0.8315, 0.8158, nan] 
-2024-08-29 02:33:14.041238: Epoch time: 85.43 s 
-2024-08-29 02:33:15.314575:  
-2024-08-29 02:33:15.314755: Epoch 1522 
-2024-08-29 02:33:15.314849: Current learning rate: 0.00276 
-2024-08-29 02:34:40.172239: train_loss -0.7725 
-2024-08-29 02:34:40.172676: val_loss -0.7902 
-2024-08-29 02:34:40.172937: Pseudo dice [0.0, 0.0, 0.9051, 0.9763, 0.8596, 0.9494, 0.9524, 0.9634, 0.9551, 0.9536, 0.9433, 0.9578, 0.9625, 0.8506, 0.9513, 0.9295, 0.8317, 0.8334, nan] 
-2024-08-29 02:34:40.173107: Epoch time: 84.86 s 
-2024-08-29 02:34:41.416807:  
-2024-08-29 02:34:41.417155: Epoch 1523 
-2024-08-29 02:34:41.417255: Current learning rate: 0.00275 
-2024-08-29 02:36:08.257670: train_loss -0.7694 
-2024-08-29 02:36:08.258094: val_loss -0.7909 
-2024-08-29 02:36:08.258280: Pseudo dice [0.0, 0.0, 0.9141, 0.9775, 0.8402, 0.949, 0.9483, 0.9607, 0.951, 0.9506, 0.9278, 0.9555, 0.9611, 0.8493, 0.9463, 0.935, 0.8361, 0.8335, nan] 
-2024-08-29 02:36:08.258375: Epoch time: 86.84 s 
-2024-08-29 02:36:09.543205:  
-2024-08-29 02:36:09.543489: Epoch 1524 
-2024-08-29 02:36:09.543593: Current learning rate: 0.00275 
-2024-08-29 02:37:38.527007: train_loss -0.7713 
-2024-08-29 02:37:38.527268: val_loss -0.7918 
-2024-08-29 02:37:38.527446: Pseudo dice [0.0, 0.0, 0.8833, 0.9765, 0.8463, 0.9494, 0.9552, 0.9678, 0.9535, 0.9555, 0.937, 0.9632, 0.964, 0.8534, 0.9602, 0.9389, 0.8315, 0.8211, nan] 
-2024-08-29 02:37:38.527536: Epoch time: 88.98 s 
-2024-08-29 02:37:39.805393:  
-2024-08-29 02:37:39.805571: Epoch 1525 
-2024-08-29 02:37:39.805666: Current learning rate: 0.00274 
-2024-08-29 02:39:00.077079: train_loss -0.7741 
-2024-08-29 02:39:00.077316: val_loss -0.7925 
-2024-08-29 02:39:00.077476: Pseudo dice [0.0, 0.0, 0.9079, 0.977, 0.8704, 0.952, 0.9565, 0.9685, 0.9527, 0.9552, 0.9405, 0.9594, 0.9617, 0.8599, 0.9452, 0.9371, 0.8396, 0.8274, nan] 
-2024-08-29 02:39:00.077564: Epoch time: 80.27 s 
-2024-08-29 02:39:01.557939:  
-2024-08-29 02:39:01.558420: Epoch 1526 
-2024-08-29 02:39:01.558514: Current learning rate: 0.00274 
-2024-08-29 02:40:27.284564: train_loss -0.7744 
-2024-08-29 02:40:27.284810: val_loss -0.7894 
-2024-08-29 02:40:27.284991: Pseudo dice [0.0, 0.0, 0.8908, 0.9759, 0.8413, 0.9464, 0.9518, 0.9639, 0.9541, 0.9552, 0.9383, 0.964, 0.9613, 0.852, 0.9454, 0.9292, 0.8328, 0.8338, nan] 
-2024-08-29 02:40:27.285076: Epoch time: 85.73 s 
-2024-08-29 02:40:28.584581:  
-2024-08-29 02:40:28.584776: Epoch 1527 
-2024-08-29 02:40:28.584877: Current learning rate: 0.00273 
-2024-08-29 02:41:55.943417: train_loss -0.7691 
-2024-08-29 02:41:55.943733: val_loss -0.7917 
-2024-08-29 02:41:55.943905: Pseudo dice [0.0, 0.0, 0.8875, 0.9765, 0.8488, 0.9483, 0.9509, 0.9674, 0.9527, 0.9574, 0.9355, 0.9647, 0.9627, 0.851, 0.9578, 0.9395, 0.836, 0.8074, nan] 
-2024-08-29 02:41:55.944000: Epoch time: 87.36 s 
-2024-08-29 02:41:57.246954:  
-2024-08-29 02:41:57.247331: Epoch 1528 
-2024-08-29 02:41:57.247426: Current learning rate: 0.00273 
-2024-08-29 02:43:29.050527: train_loss -0.774 
-2024-08-29 02:43:29.050790: val_loss -0.7917 
-2024-08-29 02:43:29.050955: Pseudo dice [0.0, 0.0, 0.8979, 0.9758, 0.8272, 0.9477, 0.9512, 0.9628, 0.9524, 0.9425, 0.9353, 0.9632, 0.9603, 0.8499, 0.9441, 0.9302, 0.835, 0.8406, nan] 
-2024-08-29 02:43:29.051043: Epoch time: 91.8 s 
-2024-08-29 02:43:30.307120:  
-2024-08-29 02:43:30.307289: Epoch 1529 
-2024-08-29 02:43:30.307379: Current learning rate: 0.00272 
-2024-08-29 02:44:59.037185: train_loss -0.7708 
-2024-08-29 02:44:59.037536: val_loss -0.7828 
-2024-08-29 02:44:59.037719: Pseudo dice [0.0, 0.0, 0.9053, 0.9777, 0.8181, 0.9427, 0.9486, 0.9648, 0.9477, 0.9396, 0.9326, 0.9605, 0.9586, 0.8437, 0.9486, 0.9358, 0.8331, 0.828, nan] 
-2024-08-29 02:44:59.037844: Epoch time: 88.73 s 
-2024-08-29 02:45:00.309928:  
-2024-08-29 02:45:00.310310: Epoch 1530 
-2024-08-29 02:45:00.310408: Current learning rate: 0.00272 
-2024-08-29 02:46:27.412090: train_loss -0.7697 
-2024-08-29 02:46:27.412749: val_loss -0.7922 
-2024-08-29 02:46:27.412965: Pseudo dice [0.0, 0.0, 0.9095, 0.9777, 0.8384, 0.9483, 0.9509, 0.9638, 0.9536, 0.958, 0.937, 0.9621, 0.9633, 0.8551, 0.9479, 0.9379, 0.8427, 0.8298, nan] 
-2024-08-29 02:46:27.413093: Epoch time: 87.1 s 
-2024-08-29 02:46:28.680080:  
-2024-08-29 02:46:28.680506: Epoch 1531 
-2024-08-29 02:46:28.680621: Current learning rate: 0.00271 
-2024-08-29 02:47:53.269982: train_loss -0.7652 
-2024-08-29 02:47:53.270276: val_loss -0.7923 
-2024-08-29 02:47:53.270452: Pseudo dice [0.0, 0.0, 0.9032, 0.9775, 0.8325, 0.9507, 0.952, 0.9647, 0.9516, 0.9515, 0.9298, 0.9636, 0.9639, 0.8464, 0.9521, 0.9356, 0.8352, 0.8328, nan] 
-2024-08-29 02:47:53.270542: Epoch time: 84.59 s 
-2024-08-29 02:47:54.905793:  
-2024-08-29 02:47:54.905967: Epoch 1532 
-2024-08-29 02:47:54.906048: Current learning rate: 0.00271 
-2024-08-29 02:49:22.140485: train_loss -0.7724 
-2024-08-29 02:49:22.140734: val_loss -0.7873 
-2024-08-29 02:49:22.140934: Pseudo dice [0.0, 0.0, 0.9067, 0.978, 0.8502, 0.9454, 0.9475, 0.9612, 0.9538, 0.9513, 0.9271, 0.9584, 0.9548, 0.8266, 0.9574, 0.9367, 0.8392, 0.8457, nan] 
-2024-08-29 02:49:22.141029: Epoch time: 87.24 s 
-2024-08-29 02:49:23.437408:  
-2024-08-29 02:49:23.437580: Epoch 1533 
-2024-08-29 02:49:23.437667: Current learning rate: 0.0027 
-2024-08-29 02:50:52.588063: train_loss -0.7706 
-2024-08-29 02:50:52.588274: val_loss -0.7857 
-2024-08-29 02:50:52.588442: Pseudo dice [0.0, 0.0, 0.9097, 0.9772, 0.8404, 0.9499, 0.9516, 0.9646, 0.9434, 0.9521, 0.9343, 0.9556, 0.9587, 0.8466, 0.9533, 0.9338, 0.8282, 0.8423, nan] 
-2024-08-29 02:50:52.588526: Epoch time: 89.15 s 
-2024-08-29 02:50:53.848324:  
-2024-08-29 02:50:53.848505: Epoch 1534 
-2024-08-29 02:50:53.848593: Current learning rate: 0.0027 
-2024-08-29 02:52:21.216369: train_loss -0.7698 
-2024-08-29 02:52:21.216855: val_loss -0.7842 
-2024-08-29 02:52:21.217077: Pseudo dice [0.0, 0.0, 0.9036, 0.9777, 0.8525, 0.9452, 0.9451, 0.9627, 0.9486, 0.9484, 0.9366, 0.958, 0.9569, 0.8477, 0.9519, 0.935, 0.8108, 0.8126, nan] 
-2024-08-29 02:52:21.217181: Epoch time: 87.37 s 
-2024-08-29 02:52:22.672380:  
-2024-08-29 02:52:22.672561: Epoch 1535 
-2024-08-29 02:52:22.672651: Current learning rate: 0.00269 
-2024-08-29 02:53:49.786512: train_loss -0.7658 
-2024-08-29 02:53:49.786728: val_loss -0.7785 
-2024-08-29 02:53:49.786892: Pseudo dice [0.0, 0.0, 0.8892, 0.9764, 0.8242, 0.9432, 0.948, 0.9606, 0.947, 0.948, 0.9163, 0.9575, 0.955, 0.8323, 0.9486, 0.9327, 0.8339, 0.8314, nan] 
-2024-08-29 02:53:49.786973: Epoch time: 87.11 s 
-2024-08-29 02:53:51.131845:  
-2024-08-29 02:53:51.132000: Epoch 1536 
-2024-08-29 02:53:51.132081: Current learning rate: 0.00268 
-2024-08-29 02:55:14.006888: train_loss -0.7703 
-2024-08-29 02:55:14.007138: val_loss -0.7898 
-2024-08-29 02:55:14.007302: Pseudo dice [0.0, 0.0, 0.8939, 0.9772, 0.8545, 0.9465, 0.9506, 0.9635, 0.9512, 0.9586, 0.9385, 0.9605, 0.9628, 0.8408, 0.9529, 0.9346, 0.8359, 0.8228, nan] 
-2024-08-29 02:55:14.007392: Epoch time: 82.88 s 
-2024-08-29 02:55:15.264492:  
-2024-08-29 02:55:15.264939: Epoch 1537 
-2024-08-29 02:55:15.265040: Current learning rate: 0.00268 
-2024-08-29 02:56:39.237907: train_loss -0.7687 
-2024-08-29 02:56:39.238135: val_loss -0.788 
-2024-08-29 02:56:39.238295: Pseudo dice [0.0, 0.0, 0.8968, 0.9746, 0.838, 0.9449, 0.9513, 0.9651, 0.9524, 0.9547, 0.9393, 0.9628, 0.9616, 0.8453, 0.9538, 0.9361, 0.8401, 0.8342, nan] 
-2024-08-29 02:56:39.238380: Epoch time: 83.97 s 
-2024-08-29 02:56:40.793066:  
-2024-08-29 02:56:40.793245: Epoch 1538 
-2024-08-29 02:56:40.793339: Current learning rate: 0.00267 
-2024-08-29 02:58:13.287388: train_loss -0.7702 
-2024-08-29 02:58:13.287600: val_loss -0.7801 
-2024-08-29 02:58:13.287766: Pseudo dice [0.0, 0.0, 0.9061, 0.9748, 0.7835, 0.9453, 0.9451, 0.9577, 0.957, 0.9493, 0.9383, 0.9618, 0.9609, 0.836, 0.9443, 0.9262, 0.8255, 0.8131, nan] 
-2024-08-29 02:58:13.287844: Epoch time: 92.5 s 
-2024-08-29 02:58:14.571886:  
-2024-08-29 02:58:14.572041: Epoch 1539 
-2024-08-29 02:58:14.572133: Current learning rate: 0.00267 
-2024-08-29 02:59:44.568759: train_loss -0.7699 
-2024-08-29 02:59:44.568988: val_loss -0.793 
-2024-08-29 02:59:44.569158: Pseudo dice [0.0, 0.0, 0.9066, 0.9773, 0.8392, 0.949, 0.9514, 0.9653, 0.9512, 0.9528, 0.939, 0.96, 0.9624, 0.8567, 0.9558, 0.9355, 0.8496, 0.8428, nan] 
-2024-08-29 02:59:44.569239: Epoch time: 90.0 s 
-2024-08-29 02:59:45.835997:  
-2024-08-29 02:59:45.836172: Epoch 1540 
-2024-08-29 02:59:45.836262: Current learning rate: 0.00266 
-2024-08-29 03:01:18.472670: train_loss -0.7719 
-2024-08-29 03:01:18.472903: val_loss -0.7899 
-2024-08-29 03:01:18.473070: Pseudo dice [0.0, 0.0, 0.904, 0.9773, 0.8377, 0.9481, 0.9536, 0.9669, 0.9563, 0.949, 0.9375, 0.9602, 0.9642, 0.8529, 0.9535, 0.9354, 0.8484, 0.8373, nan] 
-2024-08-29 03:01:18.473153: Epoch time: 92.64 s 
-2024-08-29 03:01:19.712975:  
-2024-08-29 03:01:19.713148: Epoch 1541 
-2024-08-29 03:01:19.713233: Current learning rate: 0.00266 
-2024-08-29 03:02:46.593820: train_loss -0.7682 
-2024-08-29 03:02:46.594045: val_loss -0.7869 
-2024-08-29 03:02:46.594204: Pseudo dice [0.0, 0.0, 0.9083, 0.9769, 0.8534, 0.9465, 0.9442, 0.9644, 0.9471, 0.9499, 0.9208, 0.9604, 0.9564, 0.8522, 0.9555, 0.9364, 0.8283, 0.8344, nan] 
-2024-08-29 03:02:46.594285: Epoch time: 86.88 s 
-2024-08-29 03:02:47.834374:  
-2024-08-29 03:02:47.834892: Epoch 1542 
-2024-08-29 03:02:47.834988: Current learning rate: 0.00265 
-2024-08-29 03:04:15.337776: train_loss -0.7685 
-2024-08-29 03:04:15.338003: val_loss -0.7861 
-2024-08-29 03:04:15.338169: Pseudo dice [0.0, 0.0, 0.8989, 0.9766, 0.8289, 0.9472, 0.9487, 0.9655, 0.9531, 0.9374, 0.9357, 0.959, 0.9626, 0.8517, 0.9514, 0.9317, 0.8279, 0.8332, nan] 
-2024-08-29 03:04:15.338253: Epoch time: 87.5 s 
-2024-08-29 03:04:16.588133:  
-2024-08-29 03:04:16.588275: Epoch 1543 
-2024-08-29 03:04:16.588356: Current learning rate: 0.00265 
-2024-08-29 03:05:43.584991: train_loss -0.7677 
-2024-08-29 03:05:43.585223: val_loss -0.79 
-2024-08-29 03:05:43.585376: Pseudo dice [0.0, 0.0, 0.9052, 0.9773, 0.8335, 0.948, 0.9488, 0.9664, 0.9544, 0.9527, 0.9307, 0.9619, 0.9599, 0.8556, 0.9567, 0.9362, 0.8364, 0.8351, nan] 
-2024-08-29 03:05:43.585460: Epoch time: 87.0 s 
-2024-08-29 03:05:45.144442:  
-2024-08-29 03:05:45.144623: Epoch 1544 
-2024-08-29 03:05:45.144713: Current learning rate: 0.00264 
-2024-08-29 03:07:01.898729: train_loss -0.7715 
-2024-08-29 03:07:01.898977: val_loss -0.7887 
-2024-08-29 03:07:01.899166: Pseudo dice [0.0, 0.0, 0.9091, 0.9765, 0.8587, 0.9515, 0.9527, 0.9678, 0.9507, 0.9485, 0.9333, 0.955, 0.9569, 0.8549, 0.9508, 0.9346, 0.8266, 0.8416, nan] 
-2024-08-29 03:07:01.899256: Epoch time: 76.76 s 
-2024-08-29 03:07:03.201155:  
-2024-08-29 03:07:03.201377: Epoch 1545 
-2024-08-29 03:07:03.201469: Current learning rate: 0.00264 
-2024-08-29 03:08:30.374002: train_loss -0.7693 
-2024-08-29 03:08:30.374232: val_loss -0.795 
-2024-08-29 03:08:30.374395: Pseudo dice [0.0, 0.0, 0.9109, 0.9768, 0.8447, 0.9473, 0.949, 0.9622, 0.9516, 0.9552, 0.9299, 0.9608, 0.9605, 0.8562, 0.9521, 0.9362, 0.8372, 0.8302, nan] 
-2024-08-29 03:08:30.374475: Epoch time: 87.17 s 
-2024-08-29 03:08:31.650618:  
-2024-08-29 03:08:31.650777: Epoch 1546 
-2024-08-29 03:08:31.650869: Current learning rate: 0.00263 
-2024-08-29 03:09:59.112119: train_loss -0.7729 
-2024-08-29 03:09:59.112367: val_loss -0.7877 
-2024-08-29 03:09:59.112566: Pseudo dice [0.0, 0.0, 0.8992, 0.9777, 0.8319, 0.9479, 0.9516, 0.9655, 0.9533, 0.9463, 0.9386, 0.9621, 0.9626, 0.8549, 0.9547, 0.9324, 0.8254, 0.8328, nan] 
-2024-08-29 03:09:59.112663: Epoch time: 87.46 s 
-2024-08-29 03:10:00.362420:  
-2024-08-29 03:10:00.362784: Epoch 1547 
-2024-08-29 03:10:00.362874: Current learning rate: 0.00263 
-2024-08-29 03:11:26.318541: train_loss -0.7663 
-2024-08-29 03:11:26.318776: val_loss -0.7915 
-2024-08-29 03:11:26.318944: Pseudo dice [0.0, 0.0, 0.9062, 0.9769, 0.8538, 0.9419, 0.9457, 0.9673, 0.9512, 0.9467, 0.9316, 0.9571, 0.9579, 0.84, 0.9501, 0.9361, 0.8271, 0.8237, nan] 
-2024-08-29 03:11:26.319030: Epoch time: 85.96 s 
-2024-08-29 03:11:27.570484:  
-2024-08-29 03:11:27.570667: Epoch 1548 
-2024-08-29 03:11:27.570757: Current learning rate: 0.00262 
-2024-08-29 03:12:54.760752: train_loss -0.769 
-2024-08-29 03:12:54.761017: val_loss -0.7887 
-2024-08-29 03:12:54.761233: Pseudo dice [0.0, 0.0, 0.8753, 0.9776, 0.8414, 0.9491, 0.9494, 0.9654, 0.9548, 0.9573, 0.9306, 0.9588, 0.9585, 0.8368, 0.9429, 0.9328, 0.8442, 0.8358, nan] 
-2024-08-29 03:12:54.761347: Epoch time: 87.19 s 
-2024-08-29 03:12:56.110435:  
-2024-08-29 03:12:56.110644: Epoch 1549 
-2024-08-29 03:12:56.110732: Current learning rate: 0.00262 
-2024-08-29 03:14:20.246103: train_loss -0.7718 
-2024-08-29 03:14:20.246339: val_loss -0.7947 
-2024-08-29 03:14:20.246499: Pseudo dice [0.0, 0.0, 0.9056, 0.9762, 0.8621, 0.9501, 0.9526, 0.968, 0.9516, 0.9508, 0.9317, 0.9546, 0.9596, 0.8493, 0.9555, 0.936, 0.8505, 0.8326, nan] 
-2024-08-29 03:14:20.246580: Epoch time: 84.14 s 
-2024-08-29 03:14:22.257436:  
-2024-08-29 03:14:22.257750: Epoch 1550 
-2024-08-29 03:14:22.257839: Current learning rate: 0.00261 
-2024-08-29 03:15:44.879539: train_loss -0.773 
-2024-08-29 03:15:44.879767: val_loss -0.7911 
-2024-08-29 03:15:44.879934: Pseudo dice [0.0, 0.0, 0.8998, 0.9781, 0.8464, 0.9511, 0.9526, 0.9643, 0.9549, 0.9485, 0.9359, 0.9635, 0.9614, 0.8588, 0.9555, 0.9393, 0.8376, 0.8426, nan] 
-2024-08-29 03:15:44.880020: Epoch time: 82.62 s 
-2024-08-29 03:15:46.144521:  
-2024-08-29 03:15:46.144717: Epoch 1551 
-2024-08-29 03:15:46.144806: Current learning rate: 0.00261 
-2024-08-29 03:17:06.874781: train_loss -0.7717 
-2024-08-29 03:17:06.875029: val_loss -0.7895 
-2024-08-29 03:17:06.875201: Pseudo dice [0.0, 0.0, 0.9105, 0.9766, 0.8602, 0.95, 0.9518, 0.9671, 0.9504, 0.9529, 0.935, 0.9581, 0.9605, 0.8518, 0.9588, 0.9363, 0.8398, 0.8383, nan] 
-2024-08-29 03:17:06.875288: Epoch time: 80.73 s 
-2024-08-29 03:17:06.875340: Yayy! New best EMA pseudo Dice: 0.8194 
-2024-08-29 03:17:08.613608:  
-2024-08-29 03:17:08.613922: Epoch 1552 
-2024-08-29 03:17:08.614013: Current learning rate: 0.0026 
-2024-08-29 03:18:33.392348: train_loss -0.7714 
-2024-08-29 03:18:33.392617: val_loss -0.7883 
-2024-08-29 03:18:33.392791: Pseudo dice [0.0, 0.0, 0.9034, 0.978, 0.8354, 0.9423, 0.9473, 0.9652, 0.9529, 0.9539, 0.9276, 0.9596, 0.9603, 0.8462, 0.9497, 0.9388, 0.8379, 0.8357, nan] 
-2024-08-29 03:18:33.392879: Epoch time: 84.78 s 
-2024-08-29 03:18:34.671345:  
-2024-08-29 03:18:34.671592: Epoch 1553 
-2024-08-29 03:18:34.671697: Current learning rate: 0.0026 
-2024-08-29 03:20:04.770598: train_loss -0.7706 
-2024-08-29 03:20:04.770896: val_loss -0.7884 
-2024-08-29 03:20:04.771087: Pseudo dice [0.0, 0.0, 0.9068, 0.9741, 0.8357, 0.9462, 0.9515, 0.965, 0.9521, 0.9524, 0.9355, 0.9625, 0.9586, 0.8556, 0.9489, 0.9371, 0.8216, 0.8309, nan] 
-2024-08-29 03:20:04.771176: Epoch time: 90.1 s 
-2024-08-29 03:20:06.034019:  
-2024-08-29 03:20:06.034176: Epoch 1554 
-2024-08-29 03:20:06.034265: Current learning rate: 0.00259 
-2024-08-29 03:21:31.500321: train_loss -0.7652 
-2024-08-29 03:21:31.501215: val_loss -0.7917 
-2024-08-29 03:21:31.501465: Pseudo dice [0.0, 0.0, 0.9032, 0.9765, 0.8566, 0.9534, 0.9557, 0.9666, 0.9548, 0.9491, 0.9255, 0.9638, 0.9622, 0.8555, 0.947, 0.9422, 0.8444, 0.838, nan] 
-2024-08-29 03:21:31.501652: Epoch time: 85.47 s 
-2024-08-29 03:21:31.501718: Yayy! New best EMA pseudo Dice: 0.8195 
-2024-08-29 03:21:33.711722:  
-2024-08-29 03:21:33.711907: Epoch 1555 
-2024-08-29 03:21:33.712011: Current learning rate: 0.00259 
-2024-08-29 03:22:57.769705: train_loss -0.7667 
-2024-08-29 03:22:57.769961: val_loss -0.7837 
-2024-08-29 03:22:57.770123: Pseudo dice [0.0, 0.0, 0.8976, 0.9766, 0.845, 0.9479, 0.9521, 0.9629, 0.9523, 0.9489, 0.9235, 0.9618, 0.9586, 0.8463, 0.9402, 0.9349, 0.8448, 0.8387, nan] 
-2024-08-29 03:22:57.770207: Epoch time: 84.06 s 
-2024-08-29 03:22:59.047822:  
-2024-08-29 03:22:59.048228: Epoch 1556 
-2024-08-29 03:22:59.048322: Current learning rate: 0.00258 
-2024-08-29 03:24:24.281467: train_loss -0.7686 
-2024-08-29 03:24:24.281705: val_loss -0.7878 
-2024-08-29 03:24:24.281880: Pseudo dice [0.0, 0.0, 0.8778, 0.976, 0.8386, 0.9476, 0.9542, 0.9661, 0.9545, 0.9513, 0.9334, 0.9619, 0.9601, 0.8446, 0.9569, 0.9327, 0.8388, 0.8267, nan] 
-2024-08-29 03:24:24.281969: Epoch time: 85.23 s 
-2024-08-29 03:24:25.579477:  
-2024-08-29 03:24:25.579652: Epoch 1557 
-2024-08-29 03:24:25.579739: Current learning rate: 0.00258 
-2024-08-29 03:25:58.317199: train_loss -0.7693 
-2024-08-29 03:25:58.317441: val_loss -0.7887 
-2024-08-29 03:25:58.317611: Pseudo dice [0.0, 0.0, 0.9016, 0.9778, 0.8491, 0.9425, 0.9435, 0.9625, 0.9473, 0.9525, 0.9356, 0.96, 0.9625, 0.8469, 0.9538, 0.9382, 0.8407, 0.835, nan] 
-2024-08-29 03:25:58.317697: Epoch time: 92.74 s 
-2024-08-29 03:25:59.578983:  
-2024-08-29 03:25:59.579161: Epoch 1558 
-2024-08-29 03:25:59.579249: Current learning rate: 0.00257 
-2024-08-29 03:27:28.710881: train_loss -0.7687 
-2024-08-29 03:27:28.711120: val_loss -0.791 
-2024-08-29 03:27:28.711294: Pseudo dice [0.0, 0.0, 0.882, 0.9762, 0.8429, 0.9447, 0.9497, 0.9643, 0.9551, 0.9416, 0.9267, 0.9615, 0.9608, 0.8475, 0.9483, 0.9334, 0.8358, 0.8417, nan] 
-2024-08-29 03:27:28.711384: Epoch time: 89.13 s 
-2024-08-29 03:27:29.988903:  
-2024-08-29 03:27:29.989189: Epoch 1559 
-2024-08-29 03:27:29.989288: Current learning rate: 0.00256 
-2024-08-29 03:28:57.833859: train_loss -0.7709 
-2024-08-29 03:28:57.834106: val_loss -0.7894 
-2024-08-29 03:28:57.834273: Pseudo dice [0.0, 0.0, 0.9083, 0.9746, 0.8527, 0.9501, 0.9559, 0.963, 0.9539, 0.9529, 0.9274, 0.9635, 0.9606, 0.8503, 0.9509, 0.937, 0.8474, 0.8349, nan] 
-2024-08-29 03:28:57.834362: Epoch time: 87.85 s 
-2024-08-29 03:28:59.125213:  
-2024-08-29 03:28:59.125385: Epoch 1560 
-2024-08-29 03:28:59.125473: Current learning rate: 0.00256 
-2024-08-29 03:30:27.407152: train_loss -0.7715 
-2024-08-29 03:30:27.407390: val_loss -0.7909 
-2024-08-29 03:30:27.407541: Pseudo dice [0.0, 0.0, 0.9028, 0.9774, 0.8334, 0.9442, 0.9496, 0.9674, 0.9523, 0.9492, 0.9363, 0.9594, 0.9623, 0.8544, 0.9531, 0.9396, 0.844, 0.8279, nan] 
-2024-08-29 03:30:27.407618: Epoch time: 88.28 s 
-2024-08-29 03:30:28.960389:  
-2024-08-29 03:30:28.960723: Epoch 1561 
-2024-08-29 03:30:28.960818: Current learning rate: 0.00255 
-2024-08-29 03:31:55.851829: train_loss -0.7724 
-2024-08-29 03:31:55.852071: val_loss -0.7897 
-2024-08-29 03:31:55.852234: Pseudo dice [0.0, 0.0, 0.8894, 0.978, 0.8601, 0.9471, 0.9529, 0.9611, 0.9559, 0.9478, 0.927, 0.9614, 0.9592, 0.8579, 0.9483, 0.9377, 0.8409, 0.8427, nan] 
-2024-08-29 03:31:55.852321: Epoch time: 86.89 s 
-2024-08-29 03:31:57.179384:  
-2024-08-29 03:31:57.179560: Epoch 1562 
-2024-08-29 03:31:57.179647: Current learning rate: 0.00255 
-2024-08-29 03:33:26.708123: train_loss -0.7751 
-2024-08-29 03:33:26.708777: val_loss -0.7939 
-2024-08-29 03:33:26.709107: Pseudo dice [0.0, 0.0, 0.8856, 0.9762, 0.8533, 0.9491, 0.9533, 0.9671, 0.9546, 0.9547, 0.9381, 0.9644, 0.9649, 0.8534, 0.9543, 0.9384, 0.8403, 0.8472, nan] 
-2024-08-29 03:33:26.709353: Epoch time: 89.53 s 
-2024-08-29 03:33:26.709472: Yayy! New best EMA pseudo Dice: 0.8197 
-2024-08-29 03:33:28.568015:  
-2024-08-29 03:33:28.568438: Epoch 1563 
-2024-08-29 03:33:28.568531: Current learning rate: 0.00254 
-2024-08-29 03:34:57.971548: train_loss -0.7715 
-2024-08-29 03:34:57.971768: val_loss -0.7962 
-2024-08-29 03:34:57.971929: Pseudo dice [0.0, 0.0, 0.9212, 0.9776, 0.8552, 0.9457, 0.9468, 0.9664, 0.9591, 0.962, 0.9418, 0.9668, 0.9641, 0.8592, 0.9555, 0.9393, 0.8353, 0.8398, nan] 
-2024-08-29 03:34:57.972009: Epoch time: 89.4 s 
-2024-08-29 03:34:57.972056: Yayy! New best EMA pseudo Dice: 0.8202 
-2024-08-29 03:34:59.578411:  
-2024-08-29 03:34:59.578569: Epoch 1564 
-2024-08-29 03:34:59.578662: Current learning rate: 0.00254 
-2024-08-29 03:36:27.999330: train_loss -0.7744 
-2024-08-29 03:36:27.999546: val_loss -0.7957 
-2024-08-29 03:36:27.999697: Pseudo dice [0.0, 0.0, 0.8963, 0.9776, 0.8331, 0.9484, 0.9524, 0.9692, 0.9534, 0.9582, 0.9389, 0.9608, 0.9638, 0.8546, 0.9528, 0.9406, 0.8288, 0.8277, nan] 
-2024-08-29 03:36:27.999775: Epoch time: 88.42 s 
-2024-08-29 03:36:29.224156:  
-2024-08-29 03:36:29.224328: Epoch 1565 
-2024-08-29 03:36:29.224420: Current learning rate: 0.00253 
-2024-08-29 03:38:00.801136: train_loss -0.7728 
-2024-08-29 03:38:00.801373: val_loss -0.7864 
-2024-08-29 03:38:00.801544: Pseudo dice [0.0, 0.0, 0.9049, 0.9769, 0.8438, 0.9451, 0.9491, 0.9587, 0.9518, 0.9521, 0.9385, 0.9592, 0.9616, 0.8532, 0.9448, 0.937, 0.8319, 0.8198, nan] 
-2024-08-29 03:38:00.801633: Epoch time: 91.58 s 
-2024-08-29 03:38:02.073415:  
-2024-08-29 03:38:02.073568: Epoch 1566 
-2024-08-29 03:38:02.073660: Current learning rate: 0.00253 
-2024-08-29 03:39:36.633941: train_loss -0.7737 
-2024-08-29 03:39:36.634164: val_loss -0.791 
-2024-08-29 03:39:36.634322: Pseudo dice [0.0, 0.0, 0.8888, 0.9783, 0.852, 0.9472, 0.9508, 0.9663, 0.9516, 0.9546, 0.9337, 0.9625, 0.9575, 0.8568, 0.9517, 0.9396, 0.8333, 0.8284, nan] 
-2024-08-29 03:39:36.634418: Epoch time: 94.56 s 
-2024-08-29 03:39:37.876034:  
-2024-08-29 03:39:37.876215: Epoch 1567 
-2024-08-29 03:39:37.876305: Current learning rate: 0.00252 
-2024-08-29 03:41:08.704902: train_loss -0.7697 
-2024-08-29 03:41:08.705174: val_loss -0.7904 
-2024-08-29 03:41:08.705344: Pseudo dice [0.0, 0.0, 0.9114, 0.9774, 0.8238, 0.9368, 0.9428, 0.9676, 0.9539, 0.9437, 0.9341, 0.9606, 0.9621, 0.8568, 0.9403, 0.932, 0.838, 0.8386, nan] 
-2024-08-29 03:41:08.705439: Epoch time: 90.83 s 
-2024-08-29 03:41:10.027725:  
-2024-08-29 03:41:10.027937: Epoch 1568 
-2024-08-29 03:41:10.028028: Current learning rate: 0.00252 
-2024-08-29 03:42:31.078672: train_loss -0.7736 
-2024-08-29 03:42:31.078907: val_loss -0.7921 
-2024-08-29 03:42:31.079046: Pseudo dice [0.0, 0.0, 0.9158, 0.9769, 0.8256, 0.9475, 0.9534, 0.9646, 0.9523, 0.9557, 0.9363, 0.9642, 0.9636, 0.8594, 0.9551, 0.9333, 0.8405, 0.8269, nan] 
-2024-08-29 03:42:31.079166: Epoch time: 81.05 s 
-2024-08-29 03:42:32.191339:  
-2024-08-29 03:42:32.191487: Epoch 1569 
-2024-08-29 03:42:32.191571: Current learning rate: 0.00251 
-2024-08-29 03:43:50.245390: train_loss -0.7716 
-2024-08-29 03:43:50.246108: val_loss -0.7862 
-2024-08-29 03:43:50.246264: Pseudo dice [0.0, 0.0, 0.8888, 0.9772, 0.8003, 0.9489, 0.9513, 0.9602, 0.955, 0.9515, 0.9373, 0.9607, 0.9613, 0.8407, 0.951, 0.9287, 0.8228, 0.8213, nan] 
-2024-08-29 03:43:50.246386: Epoch time: 78.05 s 
-2024-08-29 03:43:51.364272:  
-2024-08-29 03:43:51.364408: Epoch 1570 
-2024-08-29 03:43:51.364496: Current learning rate: 0.00251 
-2024-08-29 03:45:07.017767: train_loss -0.7661 
-2024-08-29 03:45:07.017965: val_loss -0.7901 
-2024-08-29 03:45:07.018107: Pseudo dice [0.0, 0.0, 0.901, 0.9766, 0.8313, 0.9503, 0.9522, 0.9642, 0.943, 0.9474, 0.9391, 0.9619, 0.9624, 0.8435, 0.9528, 0.9339, 0.8226, 0.8197, nan] 
-2024-08-29 03:45:07.018179: Epoch time: 75.65 s 
-2024-08-29 03:45:08.107189:  
-2024-08-29 03:45:08.107320: Epoch 1571 
-2024-08-29 03:45:08.107405: Current learning rate: 0.0025 
-2024-08-29 03:46:27.863081: train_loss -0.7691 
-2024-08-29 03:46:27.863282: val_loss -0.7892 
-2024-08-29 03:46:27.863425: Pseudo dice [0.0, 0.0, 0.9038, 0.9756, 0.8329, 0.9467, 0.9491, 0.9629, 0.9496, 0.9514, 0.9304, 0.9597, 0.9549, 0.8503, 0.9496, 0.929, 0.8355, 0.8262, nan] 
-2024-08-29 03:46:27.863508: Epoch time: 79.76 s 
-2024-08-29 03:46:29.239304:  
-2024-08-29 03:46:29.239447: Epoch 1572 
-2024-08-29 03:46:29.239528: Current learning rate: 0.0025 
-2024-08-29 03:47:48.711350: train_loss -0.7746 
-2024-08-29 03:47:48.711948: val_loss -0.7932 
-2024-08-29 03:47:48.712123: Pseudo dice [0.0, 0.0, 0.8827, 0.9755, 0.8689, 0.951, 0.953, 0.9682, 0.953, 0.95, 0.9335, 0.9595, 0.9604, 0.854, 0.9488, 0.941, 0.8056, 0.8054, nan] 
-2024-08-29 03:47:48.712201: Epoch time: 79.47 s 
-2024-08-29 03:47:49.811359:  
-2024-08-29 03:47:49.811503: Epoch 1573 
-2024-08-29 03:47:49.811583: Current learning rate: 0.00249 
-2024-08-29 03:49:07.219805: train_loss -0.7687 
-2024-08-29 03:49:07.220004: val_loss -0.7862 
-2024-08-29 03:49:07.220144: Pseudo dice [0.0, 0.0, 0.8952, 0.9774, 0.8506, 0.9465, 0.9458, 0.9628, 0.9532, 0.9405, 0.9285, 0.9595, 0.9553, 0.8392, 0.9519, 0.9317, 0.8327, 0.8235, nan] 
-2024-08-29 03:49:07.220213: Epoch time: 77.41 s 
-2024-08-29 03:49:08.330285:  
-2024-08-29 03:49:08.330604: Epoch 1574 
-2024-08-29 03:49:08.330685: Current learning rate: 0.00249 
-2024-08-29 03:50:27.449458: train_loss -0.769 
-2024-08-29 03:50:27.449660: val_loss -0.7869 
-2024-08-29 03:50:27.449803: Pseudo dice [0.0, 0.0, 0.8981, 0.9753, 0.8461, 0.946, 0.9517, 0.9618, 0.9547, 0.9444, 0.9324, 0.9632, 0.9586, 0.8475, 0.9506, 0.9344, 0.8311, 0.8276, nan] 
-2024-08-29 03:50:27.449874: Epoch time: 79.12 s 
-2024-08-29 03:50:28.540190:  
-2024-08-29 03:50:28.540321: Epoch 1575 
-2024-08-29 03:50:28.540401: Current learning rate: 0.00248 
-2024-08-29 03:51:47.164753: train_loss -0.7754 
-2024-08-29 03:51:47.164967: val_loss -0.7933 
-2024-08-29 03:51:47.165109: Pseudo dice [0.0, 0.0, 0.9085, 0.977, 0.8703, 0.9477, 0.9505, 0.9656, 0.9531, 0.954, 0.9407, 0.9621, 0.9635, 0.8461, 0.9471, 0.9379, 0.8354, 0.8425, nan] 
-2024-08-29 03:51:47.165181: Epoch time: 78.63 s 
-2024-08-29 03:51:48.257350:  
-2024-08-29 03:51:48.257705: Epoch 1576 
-2024-08-29 03:51:48.257974: Current learning rate: 0.00248 
-2024-08-29 03:53:07.147542: train_loss -0.7669 
-2024-08-29 03:53:07.147743: val_loss -0.7867 
-2024-08-29 03:53:07.147890: Pseudo dice [0.0, 0.0, 0.8985, 0.9772, 0.8205, 0.9432, 0.9476, 0.9661, 0.9461, 0.9478, 0.928, 0.9565, 0.9612, 0.8436, 0.9458, 0.9287, 0.8255, 0.8303, nan] 
-2024-08-29 03:53:07.147964: Epoch time: 78.89 s 
-2024-08-29 03:53:08.253220:  
-2024-08-29 03:53:08.253434: Epoch 1577 
-2024-08-29 03:53:08.253522: Current learning rate: 0.00247 
-2024-08-29 03:54:23.979097: train_loss -0.771 
-2024-08-29 03:54:23.979299: val_loss -0.7899 
-2024-08-29 03:54:23.979450: Pseudo dice [0.0, 0.0, 0.9073, 0.976, 0.813, 0.9464, 0.9458, 0.9638, 0.9502, 0.9517, 0.9388, 0.9606, 0.9634, 0.8499, 0.9507, 0.933, 0.8375, 0.8304, nan] 
-2024-08-29 03:54:23.979524: Epoch time: 75.73 s 
-2024-08-29 03:54:25.319442:  
-2024-08-29 03:54:25.319781: Epoch 1578 
-2024-08-29 03:54:25.319861: Current learning rate: 0.00247 
-2024-08-29 03:55:45.332943: train_loss -0.7706 
-2024-08-29 03:55:45.333140: val_loss -0.7947 
-2024-08-29 03:55:45.333282: Pseudo dice [0.0, 0.0, 0.9024, 0.977, 0.8344, 0.9479, 0.9532, 0.9676, 0.9544, 0.9518, 0.9403, 0.9631, 0.9645, 0.8592, 0.9543, 0.9389, 0.8406, 0.8478, nan] 
-2024-08-29 03:55:45.333600: Epoch time: 80.01 s 
-2024-08-29 03:55:46.419418:  
-2024-08-29 03:55:46.419555: Epoch 1579 
-2024-08-29 03:55:46.419635: Current learning rate: 0.00246 
-2024-08-29 03:57:02.613355: train_loss -0.7728 
-2024-08-29 03:57:02.613559: val_loss -0.7934 
-2024-08-29 03:57:02.613696: Pseudo dice [0.0, 0.0, 0.8948, 0.9781, 0.858, 0.9487, 0.9503, 0.9657, 0.9561, 0.9514, 0.9371, 0.9597, 0.9633, 0.8543, 0.9556, 0.9369, 0.8408, 0.8456, nan] 
-2024-08-29 03:57:02.613763: Epoch time: 76.19 s 
-2024-08-29 03:57:03.709761:  
-2024-08-29 03:57:03.710069: Epoch 1580 
-2024-08-29 03:57:03.710144: Current learning rate: 0.00245 
-2024-08-29 03:58:23.742630: train_loss -0.773 
-2024-08-29 03:58:23.742992: val_loss -0.7885 
-2024-08-29 03:58:23.743176: Pseudo dice [0.0, 0.0, 0.9023, 0.9775, 0.855, 0.9437, 0.946, 0.9673, 0.9515, 0.9395, 0.9357, 0.9581, 0.959, 0.855, 0.9569, 0.9371, 0.8318, 0.8353, nan] 
-2024-08-29 03:58:23.743318: Epoch time: 80.03 s 
-2024-08-29 03:58:24.831153:  
-2024-08-29 03:58:24.831461: Epoch 1581 
-2024-08-29 03:58:24.831541: Current learning rate: 0.00245 
-2024-08-29 03:59:39.945412: train_loss -0.7733 
-2024-08-29 03:59:39.945619: val_loss -0.7913 
-2024-08-29 03:59:39.945758: Pseudo dice [0.0, 0.0, 0.8942, 0.9765, 0.8548, 0.9523, 0.9557, 0.9674, 0.9577, 0.9601, 0.9408, 0.9649, 0.9637, 0.8594, 0.9565, 0.9408, 0.8522, 0.8128, nan] 
-2024-08-29 03:59:39.945827: Epoch time: 75.12 s 
-2024-08-29 03:59:41.048199:  
-2024-08-29 03:59:41.048339: Epoch 1582 
-2024-08-29 03:59:41.048415: Current learning rate: 0.00244 
-2024-08-29 04:01:04.060333: train_loss -0.7736 
-2024-08-29 04:01:04.060543: val_loss -0.7929 
-2024-08-29 04:01:04.060690: Pseudo dice [0.0, 0.0, 0.9024, 0.9768, 0.834, 0.9484, 0.9536, 0.966, 0.9524, 0.954, 0.9349, 0.9609, 0.963, 0.8571, 0.9522, 0.939, 0.8289, 0.8336, nan] 
-2024-08-29 04:01:04.060763: Epoch time: 83.01 s 
-2024-08-29 04:01:05.152478:  
-2024-08-29 04:01:05.152611: Epoch 1583 
-2024-08-29 04:01:05.152687: Current learning rate: 0.00244 
-2024-08-29 04:02:21.117276: train_loss -0.7743 
-2024-08-29 04:02:21.117629: val_loss -0.7871 
-2024-08-29 04:02:21.117821: Pseudo dice [0.0, 0.0, 0.9023, 0.9776, 0.8175, 0.9485, 0.9513, 0.9634, 0.9524, 0.9492, 0.929, 0.9615, 0.9628, 0.8531, 0.9475, 0.9389, 0.8358, 0.8321, nan] 
-2024-08-29 04:02:21.117913: Epoch time: 75.97 s 
-2024-08-29 04:02:22.421797:  
-2024-08-29 04:02:22.421919: Epoch 1584 
-2024-08-29 04:02:22.422004: Current learning rate: 0.00243 
-2024-08-29 04:03:41.259643: train_loss -0.7734 
-2024-08-29 04:03:41.259839: val_loss -0.7914 
-2024-08-29 04:03:41.259982: Pseudo dice [0.0, 0.0, 0.9032, 0.9772, 0.8526, 0.9469, 0.9515, 0.9666, 0.9505, 0.9451, 0.9378, 0.9631, 0.9629, 0.8543, 0.9523, 0.9384, 0.8411, 0.8343, nan] 
-2024-08-29 04:03:41.260055: Epoch time: 78.84 s 
-2024-08-29 04:03:42.366053:  
-2024-08-29 04:03:42.366209: Epoch 1585 
-2024-08-29 04:03:42.366286: Current learning rate: 0.00243 
-2024-08-29 04:04:59.694958: train_loss -0.7751 
-2024-08-29 04:04:59.695169: val_loss -0.7974 
-2024-08-29 04:04:59.695318: Pseudo dice [0.0, 0.0, 0.8958, 0.977, 0.8527, 0.9501, 0.9518, 0.9668, 0.9523, 0.9576, 0.9376, 0.9605, 0.9663, 0.856, 0.9491, 0.9353, 0.8461, 0.8288, nan] 
-2024-08-29 04:04:59.695395: Epoch time: 77.33 s 
-2024-08-29 04:05:00.814700:  
-2024-08-29 04:05:00.815025: Epoch 1586 
-2024-08-29 04:05:00.815105: Current learning rate: 0.00242 
-2024-08-29 04:06:18.141100: train_loss -0.7752 
-2024-08-29 04:06:18.141308: val_loss -0.7964 
-2024-08-29 04:06:18.141454: Pseudo dice [0.0, 0.0, 0.8957, 0.9771, 0.859, 0.9491, 0.9525, 0.9681, 0.9534, 0.9552, 0.932, 0.9646, 0.9607, 0.8523, 0.9483, 0.9396, 0.8216, 0.8234, nan] 
-2024-08-29 04:06:18.141526: Epoch time: 77.33 s 
-2024-08-29 04:06:19.238669:  
-2024-08-29 04:06:19.238966: Epoch 1587 
-2024-08-29 04:06:19.239040: Current learning rate: 0.00242 
-2024-08-29 04:07:32.529063: train_loss -0.7734 
-2024-08-29 04:07:32.529261: val_loss -0.7907 
-2024-08-29 04:07:32.529398: Pseudo dice [0.0, 0.0, 0.9063, 0.9773, 0.795, 0.9424, 0.9483, 0.9641, 0.9553, 0.9527, 0.9391, 0.9621, 0.9627, 0.8552, 0.9548, 0.9377, 0.8279, 0.8225, nan] 
-2024-08-29 04:07:32.529468: Epoch time: 73.29 s 
-2024-08-29 04:07:33.622930:  
-2024-08-29 04:07:33.623064: Epoch 1588 
-2024-08-29 04:07:33.623140: Current learning rate: 0.00241 
-2024-08-29 04:08:47.487726: train_loss -0.7708 
-2024-08-29 04:08:47.487916: val_loss -0.7947 
-2024-08-29 04:08:47.488055: Pseudo dice [0.0, 0.0, 0.908, 0.9783, 0.8285, 0.9458, 0.9519, 0.9654, 0.9536, 0.9448, 0.9425, 0.9617, 0.9638, 0.8472, 0.9549, 0.9406, 0.8319, 0.8277, nan] 
-2024-08-29 04:08:47.488127: Epoch time: 73.87 s 
-2024-08-29 04:08:48.660220:  
-2024-08-29 04:08:48.660349: Epoch 1589 
-2024-08-29 04:08:48.660421: Current learning rate: 0.00241 
-2024-08-29 04:10:03.937520: train_loss -0.7727 
-2024-08-29 04:10:03.937706: val_loss -0.7952 
-2024-08-29 04:10:03.937846: Pseudo dice [0.0, 0.0, 0.9087, 0.9786, 0.8413, 0.946, 0.9525, 0.9699, 0.9488, 0.9549, 0.9383, 0.9571, 0.9636, 0.8563, 0.9588, 0.9429, 0.8457, 0.8318, nan] 
-2024-08-29 04:10:03.937930: Epoch time: 75.28 s 
-2024-08-29 04:10:05.248493:  
-2024-08-29 04:10:05.248637: Epoch 1590 
-2024-08-29 04:10:05.248709: Current learning rate: 0.0024 
-2024-08-29 04:11:22.790316: train_loss -0.7748 
-2024-08-29 04:11:22.790536: val_loss -0.7851 
-2024-08-29 04:11:22.790680: Pseudo dice [0.0, 0.0, 0.9072, 0.9775, 0.8461, 0.9478, 0.9483, 0.9639, 0.9513, 0.9462, 0.9328, 0.9611, 0.9577, 0.8413, 0.9505, 0.9375, 0.843, 0.8436, nan] 
-2024-08-29 04:11:22.790756: Epoch time: 77.54 s 
-2024-08-29 04:11:23.910665:  
-2024-08-29 04:11:23.910820: Epoch 1591 
-2024-08-29 04:11:23.910893: Current learning rate: 0.0024 
-2024-08-29 04:12:42.212758: train_loss -0.772 
-2024-08-29 04:12:42.213196: val_loss -0.7961 
-2024-08-29 04:12:42.213348: Pseudo dice [0.0, 0.0, 0.908, 0.9765, 0.859, 0.9471, 0.9509, 0.9681, 0.9549, 0.9535, 0.9384, 0.9629, 0.9627, 0.8504, 0.9488, 0.9362, 0.8394, 0.8376, nan] 
-2024-08-29 04:12:42.213423: Epoch time: 78.3 s 
-2024-08-29 04:12:43.334560:  
-2024-08-29 04:12:43.335058: Epoch 1592 
-2024-08-29 04:12:43.335222: Current learning rate: 0.00239 
-2024-08-29 04:13:57.905966: train_loss -0.7731 
-2024-08-29 04:13:57.906438: val_loss -0.7943 
-2024-08-29 04:13:57.906579: Pseudo dice [0.0, 0.0, 0.9037, 0.9767, 0.8525, 0.9477, 0.9505, 0.9658, 0.953, 0.9448, 0.9393, 0.9624, 0.9614, 0.8552, 0.946, 0.9397, 0.8148, 0.8334, nan] 
-2024-08-29 04:13:57.906643: Epoch time: 74.57 s 
-2024-08-29 04:13:59.014528:  
-2024-08-29 04:13:59.014671: Epoch 1593 
-2024-08-29 04:13:59.014750: Current learning rate: 0.00239 
-2024-08-29 04:15:14.160146: train_loss -0.7751 
-2024-08-29 04:15:14.160347: val_loss -0.788 
-2024-08-29 04:15:14.160498: Pseudo dice [0.0, 0.0, 0.9168, 0.9774, 0.8533, 0.9484, 0.9515, 0.967, 0.9546, 0.9499, 0.9388, 0.9647, 0.9631, 0.8545, 0.9471, 0.9368, 0.8434, 0.8367, nan] 
-2024-08-29 04:15:14.160574: Epoch time: 75.15 s 
-2024-08-29 04:15:15.260552:  
-2024-08-29 04:15:15.260862: Epoch 1594 
-2024-08-29 04:15:15.260943: Current learning rate: 0.00238 
-2024-08-29 04:16:29.337566: train_loss -0.7762 
-2024-08-29 04:16:29.337767: val_loss -0.7995 
-2024-08-29 04:16:29.337910: Pseudo dice [0.0, 0.0, 0.9153, 0.9773, 0.8492, 0.9498, 0.953, 0.966, 0.9542, 0.9566, 0.9349, 0.9618, 0.964, 0.8567, 0.9573, 0.943, 0.845, 0.8484, nan] 
-2024-08-29 04:16:29.337980: Epoch time: 74.08 s 
-2024-08-29 04:16:29.338022: Yayy! New best EMA pseudo Dice: 0.8205 
-2024-08-29 04:16:31.048459:  
-2024-08-29 04:16:31.048776: Epoch 1595 
-2024-08-29 04:16:31.048859: Current learning rate: 0.00238 
-2024-08-29 04:17:44.138635: train_loss -0.7726 
-2024-08-29 04:17:44.138841: val_loss -0.7943 
-2024-08-29 04:17:44.138988: Pseudo dice [0.0, 0.0, 0.8964, 0.9755, 0.7971, 0.9433, 0.9443, 0.9633, 0.9574, 0.9568, 0.9415, 0.965, 0.9635, 0.8486, 0.9568, 0.9338, 0.8427, 0.8409, nan] 
-2024-08-29 04:17:44.139108: Epoch time: 73.09 s 
-2024-08-29 04:17:45.236871:  
-2024-08-29 04:17:45.237013: Epoch 1596 
-2024-08-29 04:17:45.237093: Current learning rate: 0.00237 
-2024-08-29 04:18:58.210117: train_loss -0.7713 
-2024-08-29 04:18:58.210318: val_loss -0.7965 
-2024-08-29 04:18:58.210460: Pseudo dice [0.0, 0.0, 0.9102, 0.9777, 0.8468, 0.9452, 0.9529, 0.9684, 0.9525, 0.9497, 0.936, 0.9582, 0.9598, 0.8523, 0.9557, 0.9401, 0.8283, 0.8312, nan] 
-2024-08-29 04:18:58.210531: Epoch time: 72.97 s 
-2024-08-29 04:18:59.316379:  
-2024-08-29 04:18:59.316521: Epoch 1597 
-2024-08-29 04:18:59.316603: Current learning rate: 0.00237 
-2024-08-29 04:20:17.143160: train_loss -0.7731 
-2024-08-29 04:20:17.143367: val_loss -0.7921 
-2024-08-29 04:20:17.143510: Pseudo dice [0.0, 0.0, 0.9049, 0.9773, 0.8482, 0.9493, 0.9495, 0.9655, 0.9565, 0.9604, 0.9411, 0.965, 0.9666, 0.854, 0.9413, 0.9364, 0.8488, 0.8373, nan] 
-2024-08-29 04:20:17.143584: Epoch time: 77.83 s 
-2024-08-29 04:20:18.249347:  
-2024-08-29 04:20:18.249506: Epoch 1598 
-2024-08-29 04:20:18.249588: Current learning rate: 0.00236 
-2024-08-29 04:21:34.078893: train_loss -0.7712 
-2024-08-29 04:21:34.079089: val_loss -0.7936 
-2024-08-29 04:21:34.079226: Pseudo dice [0.0, 0.0, 0.908, 0.9769, 0.8511, 0.9472, 0.9516, 0.9675, 0.9552, 0.9545, 0.9367, 0.9635, 0.9652, 0.8456, 0.9578, 0.9371, 0.8442, 0.8455, nan] 
-2024-08-29 04:21:34.079294: Epoch time: 75.83 s 
-2024-08-29 04:21:34.079335: Yayy! New best EMA pseudo Dice: 0.8207 
-2024-08-29 04:21:35.608879:  
-2024-08-29 04:21:35.609014: Epoch 1599 
-2024-08-29 04:21:35.609095: Current learning rate: 0.00235 
-2024-08-29 04:22:55.972180: train_loss -0.7722 
-2024-08-29 04:22:55.972374: val_loss -0.7892 
-2024-08-29 04:22:55.972519: Pseudo dice [0.0, 0.0, 0.892, 0.9783, 0.8492, 0.9456, 0.9469, 0.9624, 0.9531, 0.9482, 0.9352, 0.96, 0.9612, 0.8509, 0.9481, 0.9358, 0.8304, 0.8167, nan] 
-2024-08-29 04:22:55.972592: Epoch time: 80.36 s 
-2024-08-29 04:22:57.463540:  
-2024-08-29 04:22:57.464076: Epoch 1600 
-2024-08-29 04:22:57.464169: Current learning rate: 0.00235 
-2024-08-29 04:24:13.774890: train_loss -0.7718 
-2024-08-29 04:24:13.775099: val_loss -0.7913 
-2024-08-29 04:24:13.775246: Pseudo dice [0.0, 0.0, 0.8953, 0.9772, 0.8491, 0.9499, 0.9551, 0.9636, 0.9553, 0.9502, 0.9325, 0.9595, 0.9602, 0.8553, 0.9527, 0.9358, 0.8353, 0.8511, nan] 
-2024-08-29 04:24:13.775322: Epoch time: 76.31 s 
-2024-08-29 04:24:15.101362:  
-2024-08-29 04:24:15.101507: Epoch 1601 
-2024-08-29 04:24:15.101663: Current learning rate: 0.00234 
-2024-08-29 04:25:32.795303: train_loss -0.7733 
-2024-08-29 04:25:32.795519: val_loss -0.7921 
-2024-08-29 04:25:32.795671: Pseudo dice [0.0, 0.0, 0.8764, 0.9774, 0.7819, 0.9516, 0.9532, 0.964, 0.9578, 0.9549, 0.9352, 0.9647, 0.9582, 0.8438, 0.9562, 0.9324, 0.841, 0.8376, nan] 
-2024-08-29 04:25:32.795749: Epoch time: 77.69 s 
-2024-08-29 04:25:33.906905:  
-2024-08-29 04:25:33.907051: Epoch 1602 
-2024-08-29 04:25:33.907142: Current learning rate: 0.00234 
-2024-08-29 04:26:49.621422: train_loss -0.7693 
-2024-08-29 04:26:49.621628: val_loss -0.7896 
-2024-08-29 04:26:49.621772: Pseudo dice [0.0, 0.0, 0.9056, 0.9773, 0.8485, 0.9501, 0.9512, 0.9664, 0.9528, 0.9545, 0.9294, 0.9613, 0.9632, 0.8597, 0.9419, 0.9363, 0.8, 0.8387, nan] 
-2024-08-29 04:26:49.621842: Epoch time: 75.72 s 
-2024-08-29 04:26:50.725733:  
-2024-08-29 04:26:50.725886: Epoch 1603 
-2024-08-29 04:26:50.725962: Current learning rate: 0.00233 
-2024-08-29 04:28:09.060743: train_loss -0.7695 
-2024-08-29 04:28:09.060956: val_loss -0.7929 
-2024-08-29 04:28:09.061103: Pseudo dice [0.0, 0.0, 0.9047, 0.9768, 0.8283, 0.9425, 0.9468, 0.9626, 0.9517, 0.9578, 0.9369, 0.9596, 0.9619, 0.8423, 0.9523, 0.9326, 0.8369, 0.8331, nan] 
-2024-08-29 04:28:09.061176: Epoch time: 78.34 s 
-2024-08-29 04:28:10.156463:  
-2024-08-29 04:28:10.156595: Epoch 1604 
-2024-08-29 04:28:10.156666: Current learning rate: 0.00233 
-2024-08-29 04:29:22.150784: train_loss -0.7722 
-2024-08-29 04:29:22.150959: val_loss -0.7927 
-2024-08-29 04:29:22.151100: Pseudo dice [0.0, 0.0, 0.9144, 0.9744, 0.8408, 0.9478, 0.9486, 0.9676, 0.9549, 0.9537, 0.9313, 0.963, 0.962, 0.8447, 0.9493, 0.933, 0.8335, 0.8354, nan] 
-2024-08-29 04:29:22.151168: Epoch time: 72.0 s 
-2024-08-29 04:29:23.242159:  
-2024-08-29 04:29:23.242290: Epoch 1605 
-2024-08-29 04:29:23.242372: Current learning rate: 0.00232 
-2024-08-29 04:30:42.492018: train_loss -0.7694 
-2024-08-29 04:30:42.492215: val_loss -0.7922 
-2024-08-29 04:30:42.492363: Pseudo dice [0.0, 0.0, 0.9124, 0.9777, 0.8425, 0.951, 0.9552, 0.9632, 0.9583, 0.9499, 0.9373, 0.9633, 0.963, 0.8535, 0.954, 0.9303, 0.8426, 0.8406, nan] 
-2024-08-29 04:30:42.492447: Epoch time: 79.25 s 
-2024-08-29 04:30:43.603437:  
-2024-08-29 04:30:43.603706: Epoch 1606 
-2024-08-29 04:30:43.603792: Current learning rate: 0.00232 
-2024-08-29 04:32:05.542287: train_loss -0.7713 
-2024-08-29 04:32:05.542493: val_loss -0.795 
-2024-08-29 04:32:05.542639: Pseudo dice [0.0, 0.0, 0.9061, 0.9746, 0.8577, 0.9477, 0.9514, 0.9644, 0.9536, 0.9518, 0.937, 0.9633, 0.9627, 0.854, 0.9401, 0.935, 0.8295, 0.8292, nan] 
-2024-08-29 04:32:05.542711: Epoch time: 81.94 s 
-2024-08-29 04:32:06.900656:  
-2024-08-29 04:32:06.900920: Epoch 1607 
-2024-08-29 04:32:06.900999: Current learning rate: 0.00231 
-2024-08-29 04:33:27.262995: train_loss -0.7718 
-2024-08-29 04:33:27.263195: val_loss -0.7914 
-2024-08-29 04:33:27.263334: Pseudo dice [0.0, 0.0, 0.9002, 0.9781, 0.8323, 0.9459, 0.9433, 0.9659, 0.9561, 0.9546, 0.9358, 0.9607, 0.9632, 0.8485, 0.9521, 0.932, 0.8326, 0.8307, nan] 
-2024-08-29 04:33:27.263404: Epoch time: 80.36 s 
-2024-08-29 04:33:28.389055:  
-2024-08-29 04:33:28.389393: Epoch 1608 
-2024-08-29 04:33:28.389473: Current learning rate: 0.00231 
-2024-08-29 04:34:44.819885: train_loss -0.7696 
-2024-08-29 04:34:44.820094: val_loss -0.7886 
-2024-08-29 04:34:44.820244: Pseudo dice [0.0, 0.0, 0.8965, 0.9767, 0.857, 0.9493, 0.9489, 0.9617, 0.9536, 0.949, 0.9343, 0.9593, 0.9622, 0.8515, 0.9447, 0.934, 0.8273, 0.8233, nan] 
-2024-08-29 04:34:44.820318: Epoch time: 76.43 s 
-2024-08-29 04:34:45.956484:  
-2024-08-29 04:34:45.956913: Epoch 1609 
-2024-08-29 04:34:45.956999: Current learning rate: 0.0023 
-2024-08-29 04:36:04.299000: train_loss -0.7698 
-2024-08-29 04:36:04.299211: val_loss -0.7952 
-2024-08-29 04:36:04.299363: Pseudo dice [0.0, 0.0, 0.9093, 0.9771, 0.8615, 0.9518, 0.9539, 0.9655, 0.9561, 0.9553, 0.9319, 0.9614, 0.9616, 0.8589, 0.946, 0.9362, 0.8505, 0.8373, nan] 
-2024-08-29 04:36:04.299436: Epoch time: 78.34 s 
-2024-08-29 04:36:05.403185:  
-2024-08-29 04:36:05.403702: Epoch 1610 
-2024-08-29 04:36:05.403787: Current learning rate: 0.0023 
-2024-08-29 04:37:22.665800: train_loss -0.7749 
-2024-08-29 04:37:22.665982: val_loss -0.7925 
-2024-08-29 04:37:22.666125: Pseudo dice [0.0, 0.0, 0.8954, 0.9768, 0.8592, 0.952, 0.9562, 0.9654, 0.9566, 0.9481, 0.9272, 0.9643, 0.9602, 0.8572, 0.9392, 0.9388, 0.8546, 0.8475, nan] 
-2024-08-29 04:37:22.666195: Epoch time: 77.26 s 
-2024-08-29 04:37:23.787967:  
-2024-08-29 04:37:23.788093: Epoch 1611 
-2024-08-29 04:37:23.788167: Current learning rate: 0.00229 
-2024-08-29 04:38:39.817458: train_loss -0.7734 
-2024-08-29 04:38:39.817672: val_loss -0.7965 
-2024-08-29 04:38:39.817810: Pseudo dice [0.0, 0.0, 0.9042, 0.9767, 0.8624, 0.9498, 0.9541, 0.9671, 0.956, 0.9539, 0.9381, 0.9626, 0.9625, 0.852, 0.9586, 0.9399, 0.8373, 0.8421, nan] 
-2024-08-29 04:38:39.817882: Epoch time: 76.03 s 
-2024-08-29 04:38:40.932991:  
-2024-08-29 04:38:40.933522: Epoch 1612 
-2024-08-29 04:38:40.933603: Current learning rate: 0.00229 
-2024-08-29 04:39:57.063217: train_loss -0.7741 
-2024-08-29 04:39:57.063427: val_loss -0.7868 
-2024-08-29 04:39:57.063576: Pseudo dice [0.0, 0.0, 0.8941, 0.9774, 0.8382, 0.9439, 0.9479, 0.961, 0.9512, 0.9466, 0.9298, 0.9572, 0.9623, 0.8406, 0.9357, 0.9332, 0.8445, 0.8377, nan] 
-2024-08-29 04:39:57.063649: Epoch time: 76.13 s 
-2024-08-29 04:39:58.380899:  
-2024-08-29 04:39:58.381021: Epoch 1613 
-2024-08-29 04:39:58.381101: Current learning rate: 0.00228 
-2024-08-29 04:41:12.428691: train_loss -0.772 
-2024-08-29 04:41:12.428967: val_loss -0.7892 
-2024-08-29 04:41:12.429220: Pseudo dice [0.0, 0.0, 0.9026, 0.9762, 0.8212, 0.948, 0.9488, 0.9601, 0.9504, 0.95, 0.935, 0.9598, 0.9643, 0.8368, 0.953, 0.9338, 0.8372, 0.8362, nan] 
-2024-08-29 04:41:12.429373: Epoch time: 74.05 s 
-2024-08-29 04:41:13.537216:  
-2024-08-29 04:41:13.537360: Epoch 1614 
-2024-08-29 04:41:13.537442: Current learning rate: 0.00228 
-2024-08-29 04:42:29.782275: train_loss -0.7721 
-2024-08-29 04:42:29.782469: val_loss -0.7886 
-2024-08-29 04:42:29.782607: Pseudo dice [0.0, 0.0, 0.8771, 0.9775, 0.8357, 0.9469, 0.9516, 0.9652, 0.9549, 0.9461, 0.9302, 0.963, 0.9622, 0.8543, 0.9551, 0.9293, 0.8399, 0.8387, nan] 
-2024-08-29 04:42:29.782674: Epoch time: 76.25 s 
-2024-08-29 04:42:30.877721:  
-2024-08-29 04:42:30.877867: Epoch 1615 
-2024-08-29 04:42:30.877953: Current learning rate: 0.00227 
-2024-08-29 04:43:45.154146: train_loss -0.7731 
-2024-08-29 04:43:45.154339: val_loss -0.7881 
-2024-08-29 04:43:45.154477: Pseudo dice [0.0, 0.0, 0.8806, 0.9784, 0.8346, 0.9434, 0.9468, 0.9674, 0.9557, 0.9496, 0.9348, 0.9614, 0.9591, 0.859, 0.9473, 0.9378, 0.8164, 0.8179, nan] 
-2024-08-29 04:43:45.154547: Epoch time: 74.28 s 
-2024-08-29 04:43:46.280638:  
-2024-08-29 04:43:46.280773: Epoch 1616 
-2024-08-29 04:43:46.280855: Current learning rate: 0.00226 
-2024-08-29 04:45:01.883183: train_loss -0.7772 
-2024-08-29 04:45:01.883381: val_loss -0.7884 
-2024-08-29 04:45:01.883527: Pseudo dice [0.0, 0.0, 0.9137, 0.9775, 0.8432, 0.9451, 0.9479, 0.9673, 0.9535, 0.953, 0.9362, 0.9618, 0.9607, 0.854, 0.9537, 0.9378, 0.8428, 0.8306, nan] 
-2024-08-29 04:45:01.883602: Epoch time: 75.6 s 
-2024-08-29 04:45:02.976179:  
-2024-08-29 04:45:02.976316: Epoch 1617 
-2024-08-29 04:45:02.976396: Current learning rate: 0.00226 
-2024-08-29 04:46:18.161932: train_loss -0.7732 
-2024-08-29 04:46:18.162137: val_loss -0.7942 
-2024-08-29 04:46:18.162291: Pseudo dice [0.0, 0.0, 0.8904, 0.9751, 0.8462, 0.9475, 0.9494, 0.9659, 0.9533, 0.9511, 0.938, 0.9634, 0.9647, 0.8512, 0.9456, 0.9291, 0.8278, 0.8353, nan] 
-2024-08-29 04:46:18.162367: Epoch time: 75.19 s 
-2024-08-29 04:46:19.262762:  
-2024-08-29 04:46:19.263036: Epoch 1618 
-2024-08-29 04:46:19.263263: Current learning rate: 0.00225 
-2024-08-29 04:47:35.818198: train_loss -0.7722 
-2024-08-29 04:47:35.818609: val_loss -0.7893 
-2024-08-29 04:47:35.818765: Pseudo dice [0.0, 0.0, 0.9051, 0.9765, 0.8514, 0.9474, 0.9499, 0.966, 0.9525, 0.9453, 0.9278, 0.9624, 0.9609, 0.854, 0.9532, 0.9362, 0.8452, 0.8419, nan] 
-2024-08-29 04:47:35.818840: Epoch time: 76.56 s 
-2024-08-29 04:47:36.938461:  
-2024-08-29 04:47:36.938623: Epoch 1619 
-2024-08-29 04:47:36.938700: Current learning rate: 0.00225 
-2024-08-29 04:48:52.521240: train_loss -0.7737 
-2024-08-29 04:48:52.521446: val_loss -0.797 
-2024-08-29 04:48:52.521587: Pseudo dice [0.0, 0.0, 0.9104, 0.9768, 0.8634, 0.952, 0.9521, 0.968, 0.9552, 0.9439, 0.9359, 0.9622, 0.964, 0.8594, 0.9532, 0.9392, 0.8416, 0.8466, nan] 
-2024-08-29 04:48:52.521656: Epoch time: 75.58 s 
-2024-08-29 04:48:53.643979:  
-2024-08-29 04:48:53.644151: Epoch 1620 
-2024-08-29 04:48:53.644236: Current learning rate: 0.00224 
-2024-08-29 04:50:08.655771: train_loss -0.7799 
-2024-08-29 04:50:08.655965: val_loss -0.7956 
-2024-08-29 04:50:08.656115: Pseudo dice [0.0, 0.0, 0.9081, 0.9759, 0.8537, 0.9493, 0.9534, 0.9698, 0.9518, 0.9538, 0.9373, 0.9649, 0.9635, 0.8594, 0.9544, 0.929, 0.8324, 0.8409, nan] 
-2024-08-29 04:50:08.656188: Epoch time: 75.01 s 
-2024-08-29 04:50:09.751451:  
-2024-08-29 04:50:09.751595: Epoch 1621 
-2024-08-29 04:50:09.751674: Current learning rate: 0.00224 
-2024-08-29 04:51:25.802464: train_loss -0.7747 
-2024-08-29 04:51:25.802656: val_loss -0.7883 
-2024-08-29 04:51:25.802805: Pseudo dice [0.0, 0.0, 0.9008, 0.9765, 0.8415, 0.9486, 0.9482, 0.9618, 0.9545, 0.942, 0.9325, 0.9598, 0.9625, 0.8556, 0.9531, 0.9366, 0.8324, 0.8307, nan] 
-2024-08-29 04:51:25.802881: Epoch time: 76.05 s 
-2024-08-29 04:51:26.919307:  
-2024-08-29 04:51:26.919622: Epoch 1622 
-2024-08-29 04:51:26.919706: Current learning rate: 0.00223 
-2024-08-29 04:52:49.211830: train_loss -0.7744 
-2024-08-29 04:52:49.212029: val_loss -0.7928 
-2024-08-29 04:52:49.212173: Pseudo dice [0.0, 0.0, 0.9073, 0.9756, 0.8587, 0.9496, 0.9512, 0.9655, 0.9495, 0.9386, 0.9271, 0.9599, 0.9595, 0.8602, 0.9491, 0.9377, 0.8405, 0.8403, nan] 
-2024-08-29 04:52:49.212250: Epoch time: 82.29 s 
-2024-08-29 04:52:50.319984:  
-2024-08-29 04:52:50.320273: Epoch 1623 
-2024-08-29 04:52:50.320354: Current learning rate: 0.00223 
-2024-08-29 04:54:03.586586: train_loss -0.7742 
-2024-08-29 04:54:03.586778: val_loss -0.7951 
-2024-08-29 04:54:03.586918: Pseudo dice [0.0, 0.0, 0.8932, 0.9775, 0.8631, 0.9477, 0.9523, 0.9683, 0.9535, 0.9476, 0.9345, 0.9592, 0.9618, 0.8552, 0.9547, 0.938, 0.8293, 0.8406, nan] 
-2024-08-29 04:54:03.586990: Epoch time: 73.27 s 
-2024-08-29 04:54:04.906003:  
-2024-08-29 04:54:04.906141: Epoch 1624 
-2024-08-29 04:54:04.906222: Current learning rate: 0.00222 
-2024-08-29 04:55:19.021470: train_loss -0.7762 
-2024-08-29 04:55:19.021683: val_loss -0.7975 
-2024-08-29 04:55:19.021830: Pseudo dice [0.0, 0.0, 0.9115, 0.9774, 0.8464, 0.9527, 0.9536, 0.9668, 0.9548, 0.9469, 0.9395, 0.9642, 0.9655, 0.8528, 0.9496, 0.9422, 0.8325, 0.8382, nan] 
-2024-08-29 04:55:19.021901: Epoch time: 74.12 s 
-2024-08-29 04:55:20.112924:  
-2024-08-29 04:55:20.113217: Epoch 1625 
-2024-08-29 04:55:20.113300: Current learning rate: 0.00222 
-2024-08-29 04:56:41.526307: train_loss -0.7762 
-2024-08-29 04:56:41.526509: val_loss -0.7859 
-2024-08-29 04:56:41.526651: Pseudo dice [0.0, 0.0, 0.9129, 0.9779, 0.8367, 0.9482, 0.9516, 0.9623, 0.9529, 0.9376, 0.9231, 0.9622, 0.9601, 0.85, 0.9503, 0.9398, 0.8156, 0.8265, nan] 
-2024-08-29 04:56:41.526723: Epoch time: 81.41 s 
-2024-08-29 04:56:42.631645:  
-2024-08-29 04:56:42.631780: Epoch 1626 
-2024-08-29 04:56:42.631853: Current learning rate: 0.00221 
-2024-08-29 04:58:03.527362: train_loss -0.7708 
-2024-08-29 04:58:03.527561: val_loss -0.7949 
-2024-08-29 04:58:03.527706: Pseudo dice [0.0, 0.0, 0.9107, 0.9776, 0.8364, 0.9503, 0.9512, 0.9669, 0.9585, 0.9539, 0.9405, 0.9638, 0.9639, 0.8572, 0.9557, 0.9351, 0.8385, 0.8343, nan] 
-2024-08-29 04:58:03.527783: Epoch time: 80.9 s 
-2024-08-29 04:58:04.632387:  
-2024-08-29 04:58:04.632874: Epoch 1627 
-2024-08-29 04:58:04.632962: Current learning rate: 0.00221 
-2024-08-29 04:59:23.197872: train_loss -0.7735 
-2024-08-29 04:59:23.198070: val_loss -0.7967 
-2024-08-29 04:59:23.198215: Pseudo dice [0.0, 0.0, 0.9049, 0.9763, 0.858, 0.9472, 0.9467, 0.9696, 0.9557, 0.9552, 0.9394, 0.9634, 0.9657, 0.855, 0.949, 0.9395, 0.8357, 0.8345, nan] 
-2024-08-29 04:59:23.198289: Epoch time: 78.57 s 
-2024-08-29 04:59:24.304932:  
-2024-08-29 04:59:24.305213: Epoch 1628 
-2024-08-29 04:59:24.305296: Current learning rate: 0.0022 
-2024-08-29 05:00:45.957783: train_loss -0.7747 
-2024-08-29 05:00:45.957978: val_loss -0.7874 
-2024-08-29 05:00:45.958122: Pseudo dice [0.0, 0.0, 0.8964, 0.9761, 0.7826, 0.9468, 0.9538, 0.96, 0.948, 0.939, 0.9373, 0.957, 0.9614, 0.8423, 0.9545, 0.9323, 0.8284, 0.8243, nan] 
-2024-08-29 05:00:45.958199: Epoch time: 81.65 s 
-2024-08-29 05:00:47.066197:  
-2024-08-29 05:00:47.066511: Epoch 1629 
-2024-08-29 05:00:47.066597: Current learning rate: 0.0022 
-2024-08-29 05:02:04.779712: train_loss -0.7745 
-2024-08-29 05:02:04.779925: val_loss -0.7919 
-2024-08-29 05:02:04.780075: Pseudo dice [0.0, 0.0, 0.9005, 0.9751, 0.8101, 0.9437, 0.9521, 0.9675, 0.9529, 0.9575, 0.939, 0.9603, 0.9637, 0.8515, 0.956, 0.9377, 0.8346, 0.82, nan] 
-2024-08-29 05:02:04.780173: Epoch time: 77.71 s 
-2024-08-29 05:02:06.103580:  
-2024-08-29 05:02:06.104028: Epoch 1630 
-2024-08-29 05:02:06.104245: Current learning rate: 0.00219 
-2024-08-29 05:03:21.611445: train_loss -0.7756 
-2024-08-29 05:03:21.611647: val_loss -0.7972 
-2024-08-29 05:03:21.611785: Pseudo dice [0.0, 0.0, 0.9159, 0.9747, 0.814, 0.9451, 0.9525, 0.969, 0.9525, 0.9602, 0.9393, 0.9631, 0.9618, 0.8574, 0.9564, 0.9405, 0.8422, 0.8313, nan] 
-2024-08-29 05:03:21.611853: Epoch time: 75.51 s 
-2024-08-29 05:03:22.729971:  
-2024-08-29 05:03:22.730125: Epoch 1631 
-2024-08-29 05:03:22.730204: Current learning rate: 0.00218 
-2024-08-29 05:04:41.459239: train_loss -0.7741 
-2024-08-29 05:04:41.459444: val_loss -0.7974 
-2024-08-29 05:04:41.459580: Pseudo dice [0.0, 0.0, 0.8954, 0.9767, 0.8583, 0.9488, 0.9508, 0.9684, 0.9576, 0.9517, 0.9379, 0.9633, 0.9636, 0.8505, 0.9491, 0.9412, 0.8306, 0.8432, nan] 
-2024-08-29 05:04:41.459649: Epoch time: 78.73 s 
-2024-08-29 05:04:42.579198:  
-2024-08-29 05:04:42.579530: Epoch 1632 
-2024-08-29 05:04:42.579612: Current learning rate: 0.00218 
-2024-08-29 05:05:57.464495: train_loss -0.7736 
-2024-08-29 05:05:57.464706: val_loss -0.7972 
-2024-08-29 05:05:57.464878: Pseudo dice [0.0, 0.0, 0.9155, 0.978, 0.8453, 0.953, 0.9557, 0.9615, 0.9549, 0.9568, 0.9426, 0.9608, 0.9642, 0.859, 0.9573, 0.94, 0.8522, 0.8445, nan] 
-2024-08-29 05:05:57.464980: Epoch time: 74.89 s 
-2024-08-29 05:05:58.568270:  
-2024-08-29 05:05:58.568418: Epoch 1633 
-2024-08-29 05:05:58.568498: Current learning rate: 0.00217 
-2024-08-29 05:07:12.590485: train_loss -0.7752 
-2024-08-29 05:07:12.590698: val_loss -0.7877 
-2024-08-29 05:07:12.590847: Pseudo dice [0.0, 0.0, 0.8964, 0.9766, 0.8587, 0.947, 0.9507, 0.9649, 0.949, 0.9506, 0.9383, 0.9608, 0.9628, 0.8416, 0.9468, 0.9369, 0.8364, 0.8406, nan] 
-2024-08-29 05:07:12.590922: Epoch time: 74.02 s 
-2024-08-29 05:07:13.693199:  
-2024-08-29 05:07:13.693399: Epoch 1634 
-2024-08-29 05:07:13.693485: Current learning rate: 0.00217 
-2024-08-29 05:08:28.203958: train_loss -0.7755 
-2024-08-29 05:08:28.204154: val_loss -0.7898 
-2024-08-29 05:08:28.204349: Pseudo dice [0.0, 0.0, 0.8878, 0.978, 0.8593, 0.9468, 0.9488, 0.9671, 0.9537, 0.9497, 0.9376, 0.9624, 0.962, 0.8537, 0.9531, 0.9383, 0.833, 0.8346, nan] 
-2024-08-29 05:08:28.204452: Epoch time: 74.51 s 
-2024-08-29 05:08:29.315104:  
-2024-08-29 05:08:29.315395: Epoch 1635 
-2024-08-29 05:08:29.315478: Current learning rate: 0.00216 
-2024-08-29 05:09:46.357850: train_loss -0.7772 
-2024-08-29 05:09:46.358176: val_loss -0.7952 
-2024-08-29 05:09:46.358456: Pseudo dice [0.0, 0.0, 0.9049, 0.9764, 0.8513, 0.9502, 0.9537, 0.963, 0.9564, 0.9579, 0.9382, 0.9606, 0.9632, 0.8566, 0.9522, 0.94, 0.8395, 0.8286, nan] 
-2024-08-29 05:09:46.358571: Epoch time: 77.04 s 
-2024-08-29 05:09:47.679278:  
-2024-08-29 05:09:47.679429: Epoch 1636 
-2024-08-29 05:09:47.679505: Current learning rate: 0.00216 
-2024-08-29 05:10:57.904408: train_loss -0.7737 
-2024-08-29 05:10:57.904625: val_loss -0.7895 
-2024-08-29 05:10:57.904778: Pseudo dice [0.0, 0.0, 0.8963, 0.9778, 0.8442, 0.9462, 0.951, 0.9669, 0.9531, 0.9421, 0.9351, 0.9626, 0.962, 0.851, 0.9491, 0.9376, 0.8363, 0.827, nan] 
-2024-08-29 05:10:57.904851: Epoch time: 70.23 s 
-2024-08-29 05:10:59.015552:  
-2024-08-29 05:10:59.015694: Epoch 1637 
-2024-08-29 05:10:59.015769: Current learning rate: 0.00215 
-2024-08-29 05:12:14.775534: train_loss -0.7727 
-2024-08-29 05:12:14.775752: val_loss -0.7908 
-2024-08-29 05:12:14.775898: Pseudo dice [0.0, 0.0, 0.904, 0.9763, 0.8496, 0.9464, 0.952, 0.9662, 0.9521, 0.9545, 0.9345, 0.958, 0.9606, 0.838, 0.9513, 0.9393, 0.836, 0.8414, nan] 
-2024-08-29 05:12:14.775975: Epoch time: 75.76 s 
-2024-08-29 05:12:15.861142:  
-2024-08-29 05:12:15.861293: Epoch 1638 
-2024-08-29 05:12:15.861371: Current learning rate: 0.00215 
-2024-08-29 05:13:31.082887: train_loss -0.7719 
-2024-08-29 05:13:31.083079: val_loss -0.7863 
-2024-08-29 05:13:31.083233: Pseudo dice [0.0, 0.0, 0.8751, 0.9732, 0.8238, 0.9433, 0.9453, 0.9627, 0.958, 0.95, 0.9368, 0.9614, 0.9635, 0.8402, 0.9474, 0.9325, 0.8142, 0.8166, nan] 
-2024-08-29 05:13:31.083306: Epoch time: 75.22 s 
-2024-08-29 05:13:32.166275:  
-2024-08-29 05:13:32.166419: Epoch 1639 
-2024-08-29 05:13:32.166497: Current learning rate: 0.00214 
-2024-08-29 05:14:47.529272: train_loss -0.7686 
-2024-08-29 05:14:47.529476: val_loss -0.79 
-2024-08-29 05:14:47.529622: Pseudo dice [0.0, 0.0, 0.9138, 0.977, 0.8278, 0.9486, 0.9506, 0.9647, 0.9509, 0.9494, 0.9314, 0.9597, 0.9606, 0.8501, 0.952, 0.9372, 0.843, 0.8363, nan] 
-2024-08-29 05:14:47.529701: Epoch time: 75.36 s 
-2024-08-29 05:14:48.594992:  
-2024-08-29 05:14:48.595404: Epoch 1640 
-2024-08-29 05:14:48.595489: Current learning rate: 0.00214 
-2024-08-29 05:16:03.288486: train_loss -0.7686 
-2024-08-29 05:16:03.288685: val_loss -0.7925 
-2024-08-29 05:16:03.288826: Pseudo dice [0.0, 0.0, 0.9022, 0.9758, 0.8373, 0.9493, 0.9523, 0.9653, 0.9565, 0.9443, 0.9379, 0.9633, 0.964, 0.8436, 0.9481, 0.9348, 0.8343, 0.8371, nan] 
-2024-08-29 05:16:03.288900: Epoch time: 74.69 s 
-2024-08-29 05:16:04.591974:  
-2024-08-29 05:16:04.592124: Epoch 1641 
-2024-08-29 05:16:04.592221: Current learning rate: 0.00213 
-2024-08-29 05:17:21.026729: train_loss -0.774 
-2024-08-29 05:17:21.026928: val_loss -0.7941 
-2024-08-29 05:17:21.027067: Pseudo dice [0.0, 0.0, 0.9119, 0.9764, 0.8298, 0.9444, 0.9512, 0.9666, 0.9568, 0.944, 0.9363, 0.963, 0.9616, 0.8557, 0.9586, 0.9277, 0.8399, 0.8407, nan] 
-2024-08-29 05:17:21.027138: Epoch time: 76.44 s 
-2024-08-29 05:17:22.095741:  
-2024-08-29 05:17:22.095893: Epoch 1642 
-2024-08-29 05:17:22.095968: Current learning rate: 0.00213 
-2024-08-29 05:18:37.098125: train_loss -0.772 
-2024-08-29 05:18:37.098324: val_loss -0.7951 
-2024-08-29 05:18:37.098467: Pseudo dice [0.0, 0.0, 0.9038, 0.9777, 0.86, 0.9504, 0.9515, 0.9672, 0.9531, 0.9557, 0.937, 0.9588, 0.9649, 0.8528, 0.9523, 0.9389, 0.8373, 0.8375, nan] 
-2024-08-29 05:18:37.098535: Epoch time: 75.0 s 
-2024-08-29 05:18:38.169419:  
-2024-08-29 05:18:38.169555: Epoch 1643 
-2024-08-29 05:18:38.169627: Current learning rate: 0.00212 
-2024-08-29 05:19:56.771220: train_loss -0.7678 
-2024-08-29 05:19:56.771472: val_loss -0.7881 
-2024-08-29 05:19:56.771675: Pseudo dice [0.0, 0.0, 0.894, 0.9769, 0.8455, 0.9422, 0.9466, 0.9656, 0.9547, 0.9546, 0.9365, 0.9588, 0.9612, 0.8504, 0.9385, 0.9354, 0.8334, 0.8334, nan] 
-2024-08-29 05:19:56.771778: Epoch time: 78.6 s 
-2024-08-29 05:19:57.965030:  
-2024-08-29 05:19:57.965368: Epoch 1644 
-2024-08-29 05:19:57.965451: Current learning rate: 0.00212 
-2024-08-29 05:21:15.668289: train_loss -0.7662 
-2024-08-29 05:21:15.668490: val_loss -0.7779 
-2024-08-29 05:21:15.668629: Pseudo dice [0.0, 0.0, 0.8938, 0.9761, 0.8128, 0.942, 0.9451, 0.9571, 0.9554, 0.9494, 0.9281, 0.9592, 0.9576, 0.8271, 0.9479, 0.9255, 0.8026, 0.8192, nan] 
-2024-08-29 05:21:15.668697: Epoch time: 77.7 s 
-2024-08-29 05:21:16.739026:  
-2024-08-29 05:21:16.739327: Epoch 1645 
-2024-08-29 05:21:16.739408: Current learning rate: 0.00211 
-2024-08-29 05:22:33.404909: train_loss -0.767 
-2024-08-29 05:22:33.405098: val_loss -0.7957 
-2024-08-29 05:22:33.405243: Pseudo dice [0.0, 0.0, 0.9095, 0.9786, 0.8524, 0.9474, 0.9477, 0.9661, 0.9493, 0.9522, 0.9308, 0.9621, 0.9622, 0.8536, 0.9481, 0.9362, 0.8291, 0.8422, nan] 
-2024-08-29 05:22:33.405314: Epoch time: 76.67 s 
-2024-08-29 05:22:34.472144:  
-2024-08-29 05:22:34.472520: Epoch 1646 
-2024-08-29 05:22:34.472601: Current learning rate: 0.0021 
-2024-08-29 05:23:50.979785: train_loss -0.7735 
-2024-08-29 05:23:50.979985: val_loss -0.7918 
-2024-08-29 05:23:50.980131: Pseudo dice [0.0, 0.0, 0.8826, 0.9779, 0.8347, 0.9486, 0.9527, 0.9645, 0.9541, 0.9585, 0.9345, 0.962, 0.9629, 0.8444, 0.9554, 0.9404, 0.8359, 0.8387, nan] 
-2024-08-29 05:23:50.980201: Epoch time: 76.51 s 
-2024-08-29 05:23:52.294822:  
-2024-08-29 05:23:52.294958: Epoch 1647 
-2024-08-29 05:23:52.295046: Current learning rate: 0.0021 
-2024-08-29 05:25:06.936953: train_loss -0.7755 
-2024-08-29 05:25:06.937291: val_loss -0.7922 
-2024-08-29 05:25:06.937449: Pseudo dice [0.0, 0.0, 0.9021, 0.9753, 0.8617, 0.9499, 0.9486, 0.9654, 0.9565, 0.948, 0.9387, 0.9646, 0.9652, 0.8557, 0.9481, 0.938, 0.8376, 0.8435, nan] 
-2024-08-29 05:25:06.937526: Epoch time: 74.64 s 
-2024-08-29 05:25:07.996439:  
-2024-08-29 05:25:07.996592: Epoch 1648 
-2024-08-29 05:25:07.996667: Current learning rate: 0.00209 
-2024-08-29 05:26:27.820071: train_loss -0.7711 
-2024-08-29 05:26:27.820281: val_loss -0.7943 
-2024-08-29 05:26:27.820424: Pseudo dice [0.0, 0.0, 0.9018, 0.9781, 0.8598, 0.9483, 0.9512, 0.9658, 0.9533, 0.954, 0.9306, 0.9635, 0.9583, 0.8552, 0.9505, 0.9364, 0.8217, 0.8291, nan] 
-2024-08-29 05:26:27.820562: Epoch time: 79.82 s 
-2024-08-29 05:26:28.904147:  
-2024-08-29 05:26:28.904281: Epoch 1649 
-2024-08-29 05:26:28.904357: Current learning rate: 0.00209 
-2024-08-29 05:27:43.781559: train_loss -0.7752 
-2024-08-29 05:27:43.781737: val_loss -0.7926 
-2024-08-29 05:27:43.781885: Pseudo dice [0.0, 0.0, 0.8989, 0.9759, 0.8498, 0.9476, 0.9543, 0.9686, 0.9559, 0.9567, 0.9356, 0.9635, 0.963, 0.854, 0.9552, 0.9403, 0.8486, 0.8377, nan] 
-2024-08-29 05:27:43.781958: Epoch time: 74.88 s 
-2024-08-29 05:27:45.261055:  
-2024-08-29 05:27:45.261307: Epoch 1650 
-2024-08-29 05:27:45.261388: Current learning rate: 0.00208 
-2024-08-29 05:29:03.583720: train_loss -0.7717 
-2024-08-29 05:29:03.584040: val_loss -0.792 
-2024-08-29 05:29:03.584181: Pseudo dice [0.0, 0.0, 0.9014, 0.978, 0.8464, 0.9446, 0.9499, 0.9672, 0.954, 0.9551, 0.9384, 0.9609, 0.9615, 0.8523, 0.947, 0.9345, 0.852, 0.842, nan] 
-2024-08-29 05:29:03.584252: Epoch time: 78.32 s 
-2024-08-29 05:29:04.667844:  
-2024-08-29 05:29:04.667974: Epoch 1651 
-2024-08-29 05:29:04.668052: Current learning rate: 0.00208 
-2024-08-29 05:30:20.827599: train_loss -0.7708 
-2024-08-29 05:30:20.827798: val_loss -0.7925 
-2024-08-29 05:30:20.827940: Pseudo dice [0.0, 0.0, 0.8917, 0.9772, 0.8562, 0.9508, 0.9542, 0.9657, 0.9565, 0.9576, 0.9365, 0.9641, 0.9598, 0.8611, 0.9574, 0.942, 0.8384, 0.8312, nan] 
-2024-08-29 05:30:20.828011: Epoch time: 76.16 s 
-2024-08-29 05:30:21.900235:  
-2024-08-29 05:30:21.900393: Epoch 1652 
-2024-08-29 05:30:21.900487: Current learning rate: 0.00207 
-2024-08-29 05:31:40.403449: train_loss -0.7755 
-2024-08-29 05:31:40.403646: val_loss -0.7943 
-2024-08-29 05:31:40.403789: Pseudo dice [0.0, 0.0, 0.9018, 0.9759, 0.8667, 0.9497, 0.9522, 0.9655, 0.952, 0.9522, 0.9367, 0.9637, 0.9628, 0.8438, 0.9505, 0.9371, 0.8444, 0.8339, nan] 
-2024-08-29 05:31:40.403858: Epoch time: 78.5 s 
-2024-08-29 05:31:41.675142:  
-2024-08-29 05:31:41.675259: Epoch 1653 
-2024-08-29 05:31:41.675346: Current learning rate: 0.00207 
-2024-08-29 05:32:59.672421: train_loss -0.7723 
-2024-08-29 05:32:59.672709: val_loss -0.7933 
-2024-08-29 05:32:59.672865: Pseudo dice [0.0, 0.0, 0.9154, 0.9767, 0.8532, 0.9498, 0.951, 0.9643, 0.9572, 0.9411, 0.9357, 0.9645, 0.9629, 0.8548, 0.9482, 0.9387, 0.8499, 0.8491, nan] 
-2024-08-29 05:32:59.672942: Epoch time: 78.0 s 
-2024-08-29 05:33:00.742585:  
-2024-08-29 05:33:00.742729: Epoch 1654 
-2024-08-29 05:33:00.742821: Current learning rate: 0.00206 
-2024-08-29 05:34:22.703472: train_loss -0.7744 
-2024-08-29 05:34:22.703694: val_loss -0.7955 
-2024-08-29 05:34:22.703851: Pseudo dice [0.0, 0.0, 0.9079, 0.9771, 0.8525, 0.9479, 0.951, 0.9669, 0.9565, 0.9561, 0.942, 0.9644, 0.963, 0.8592, 0.9549, 0.9373, 0.837, 0.8245, nan] 
-2024-08-29 05:34:22.703939: Epoch time: 81.96 s 
-2024-08-29 05:34:22.703990: Yayy! New best EMA pseudo Dice: 0.8207 
-2024-08-29 05:34:24.201361:  
-2024-08-29 05:34:24.201658: Epoch 1655 
-2024-08-29 05:34:24.201744: Current learning rate: 0.00206 
-2024-08-29 05:35:38.306852: train_loss -0.7728 
-2024-08-29 05:35:38.307053: val_loss -0.7884 
-2024-08-29 05:35:38.307192: Pseudo dice [0.0, 0.0, 0.9113, 0.9763, 0.8196, 0.9456, 0.9468, 0.9673, 0.9416, 0.9488, 0.9327, 0.9562, 0.9582, 0.8521, 0.9484, 0.937, 0.8226, 0.8286, nan] 
-2024-08-29 05:35:38.307259: Epoch time: 74.11 s 
-2024-08-29 05:35:39.386776:  
-2024-08-29 05:35:39.386906: Epoch 1656 
-2024-08-29 05:35:39.386983: Current learning rate: 0.00205 
-2024-08-29 05:36:58.793078: train_loss -0.775 
-2024-08-29 05:36:58.793272: val_loss -0.7949 
-2024-08-29 05:36:58.793411: Pseudo dice [0.0, 0.0, 0.9005, 0.9769, 0.8472, 0.9514, 0.9531, 0.9641, 0.9555, 0.9466, 0.9369, 0.9631, 0.9629, 0.8524, 0.9532, 0.9375, 0.8369, 0.8319, nan] 
-2024-08-29 05:36:58.793481: Epoch time: 79.41 s 
-2024-08-29 05:36:59.860752:  
-2024-08-29 05:36:59.860881: Epoch 1657 
-2024-08-29 05:36:59.860959: Current learning rate: 0.00205 
-2024-08-29 05:38:18.917084: train_loss -0.7772 
-2024-08-29 05:38:18.917290: val_loss -0.7954 
-2024-08-29 05:38:18.917426: Pseudo dice [0.0, 0.0, 0.9092, 0.9769, 0.8453, 0.9527, 0.9553, 0.9662, 0.9525, 0.9533, 0.9321, 0.9598, 0.96, 0.8535, 0.9604, 0.9428, 0.8457, 0.8367, nan] 
-2024-08-29 05:38:18.917620: Epoch time: 79.06 s 
-2024-08-29 05:38:20.000041:  
-2024-08-29 05:38:20.000594: Epoch 1658 
-2024-08-29 05:38:20.000681: Current learning rate: 0.00204 
-2024-08-29 05:39:34.708098: train_loss -0.7789 
-2024-08-29 05:39:34.708454: val_loss -0.7957 
-2024-08-29 05:39:34.708830: Pseudo dice [0.0, 0.0, 0.9136, 0.977, 0.8451, 0.9533, 0.9551, 0.9687, 0.9538, 0.9558, 0.9378, 0.9578, 0.9622, 0.8616, 0.9613, 0.942, 0.8508, 0.8481, nan] 
-2024-08-29 05:39:34.708993: Epoch time: 74.71 s 
-2024-08-29 05:39:34.709116: Yayy! New best EMA pseudo Dice: 0.8209 
-2024-08-29 05:39:36.575359:  
-2024-08-29 05:39:36.575494: Epoch 1659 
-2024-08-29 05:39:36.575584: Current learning rate: 0.00203 
-2024-08-29 05:40:55.742486: train_loss -0.7737 
-2024-08-29 05:40:55.742921: val_loss -0.7881 
-2024-08-29 05:40:55.743071: Pseudo dice [0.0, 0.0, 0.9056, 0.9775, 0.8585, 0.9494, 0.9511, 0.9648, 0.953, 0.9527, 0.9397, 0.9634, 0.9658, 0.8508, 0.9461, 0.9357, 0.8315, 0.8343, nan] 
-2024-08-29 05:40:55.743139: Epoch time: 79.17 s 
-2024-08-29 05:40:55.743178: Yayy! New best EMA pseudo Dice: 0.8209 
-2024-08-29 05:40:57.249935:  
-2024-08-29 05:40:57.250136: Epoch 1660 
-2024-08-29 05:40:57.250353: Current learning rate: 0.00203 
-2024-08-29 05:42:16.838222: train_loss -0.7709 
-2024-08-29 05:42:16.838421: val_loss -0.7838 
-2024-08-29 05:42:16.838568: Pseudo dice [0.0, 0.0, 0.8805, 0.9768, 0.8459, 0.9518, 0.9504, 0.9652, 0.9522, 0.9412, 0.9352, 0.9599, 0.9603, 0.8555, 0.936, 0.9387, 0.8356, 0.8295, nan] 
-2024-08-29 05:42:16.838656: Epoch time: 79.59 s 
-2024-08-29 05:42:17.902970:  
-2024-08-29 05:42:17.903103: Epoch 1661 
-2024-08-29 05:42:17.903178: Current learning rate: 0.00202 
-2024-08-29 05:43:36.930981: train_loss -0.7683 
-2024-08-29 05:43:36.931389: val_loss -0.7891 
-2024-08-29 05:43:36.931539: Pseudo dice [0.0, 0.0, 0.8979, 0.9762, 0.8399, 0.9517, 0.9494, 0.9641, 0.9536, 0.9378, 0.935, 0.9619, 0.9623, 0.8126, 0.9512, 0.9291, 0.8303, 0.834, nan] 
-2024-08-29 05:43:36.931612: Epoch time: 79.03 s 
-2024-08-29 05:43:37.998331:  
-2024-08-29 05:43:37.998794: Epoch 1662 
-2024-08-29 05:43:37.998883: Current learning rate: 0.00202 
-2024-08-29 05:44:53.409388: train_loss -0.7705 
-2024-08-29 05:44:53.409620: val_loss -0.7907 
-2024-08-29 05:44:53.409765: Pseudo dice [0.0, 0.0, 0.9079, 0.9766, 0.842, 0.9521, 0.9544, 0.9663, 0.957, 0.956, 0.9399, 0.9645, 0.9636, 0.8542, 0.9558, 0.9336, 0.8477, 0.8438, nan] 
-2024-08-29 05:44:53.409841: Epoch time: 75.41 s 
-2024-08-29 05:44:54.495723:  
-2024-08-29 05:44:54.495978: Epoch 1663 
-2024-08-29 05:44:54.496059: Current learning rate: 0.00201 
-2024-08-29 05:46:12.711102: train_loss -0.7727 
-2024-08-29 05:46:12.711499: val_loss -0.7934 
-2024-08-29 05:46:12.711779: Pseudo dice [0.0, 0.0, 0.9033, 0.9774, 0.8116, 0.9404, 0.9458, 0.966, 0.9576, 0.9571, 0.9377, 0.9645, 0.9658, 0.8625, 0.9563, 0.9415, 0.8337, 0.8164, nan] 
-2024-08-29 05:46:12.711952: Epoch time: 78.22 s 
-2024-08-29 05:46:13.792997:  
-2024-08-29 05:46:13.793128: Epoch 1664 
-2024-08-29 05:46:13.793201: Current learning rate: 0.00201 
-2024-08-29 05:47:29.283155: train_loss -0.7774 
-2024-08-29 05:47:29.283659: val_loss -0.7947 
-2024-08-29 05:47:29.283813: Pseudo dice [0.0, 0.0, 0.9096, 0.9784, 0.8552, 0.949, 0.9537, 0.9668, 0.954, 0.948, 0.9312, 0.9617, 0.9625, 0.8554, 0.9574, 0.938, 0.8317, 0.8386, nan] 
-2024-08-29 05:47:29.283884: Epoch time: 75.49 s 
-2024-08-29 05:47:30.618083:  
-2024-08-29 05:47:30.618436: Epoch 1665 
-2024-08-29 05:47:30.618526: Current learning rate: 0.002 
-2024-08-29 05:48:41.914067: train_loss -0.7771 
-2024-08-29 05:48:41.914267: val_loss -0.7968 
-2024-08-29 05:48:41.914417: Pseudo dice [0.0, 0.0, 0.9072, 0.9781, 0.8532, 0.9528, 0.9549, 0.9665, 0.9578, 0.9516, 0.9248, 0.9637, 0.962, 0.8644, 0.9594, 0.9403, 0.8379, 0.826, nan] 
-2024-08-29 05:48:41.914489: Epoch time: 71.3 s 
-2024-08-29 05:48:42.974873:  
-2024-08-29 05:48:42.975192: Epoch 1666 
-2024-08-29 05:48:42.975271: Current learning rate: 0.002 
-2024-08-29 05:49:59.252154: train_loss -0.7735 
-2024-08-29 05:49:59.252349: val_loss -0.7935 
-2024-08-29 05:49:59.252506: Pseudo dice [0.0, 0.0, 0.904, 0.9768, 0.8114, 0.9446, 0.9503, 0.9641, 0.9533, 0.9412, 0.9269, 0.9622, 0.962, 0.851, 0.9367, 0.9374, 0.8331, 0.835, nan] 
-2024-08-29 05:49:59.252578: Epoch time: 76.28 s 
-2024-08-29 05:50:00.324690:  
-2024-08-29 05:50:00.324835: Epoch 1667 
-2024-08-29 05:50:00.324911: Current learning rate: 0.00199 
-2024-08-29 05:51:20.844901: train_loss -0.7754 
-2024-08-29 05:51:20.845116: val_loss -0.7978 
-2024-08-29 05:51:20.845262: Pseudo dice [0.0, 0.0, 0.9048, 0.9786, 0.8573, 0.9492, 0.9495, 0.9683, 0.9558, 0.9568, 0.9408, 0.9623, 0.9644, 0.8512, 0.946, 0.9374, 0.836, 0.8386, nan] 
-2024-08-29 05:51:20.845339: Epoch time: 80.52 s 
-2024-08-29 05:51:21.931461:  
-2024-08-29 05:51:21.931813: Epoch 1668 
-2024-08-29 05:51:21.931897: Current learning rate: 0.00199 
-2024-08-29 05:52:38.122393: train_loss -0.7797 
-2024-08-29 05:52:38.122598: val_loss -0.8005 
-2024-08-29 05:52:38.122739: Pseudo dice [0.0, 0.0, 0.9122, 0.978, 0.8746, 0.9477, 0.9479, 0.969, 0.9543, 0.9451, 0.9425, 0.9609, 0.9676, 0.8572, 0.9571, 0.9391, 0.8467, 0.8513, nan] 
-2024-08-29 05:52:38.122811: Epoch time: 76.19 s 
-2024-08-29 05:52:39.210135:  
-2024-08-29 05:52:39.210271: Epoch 1669 
-2024-08-29 05:52:39.210350: Current learning rate: 0.00198 
-2024-08-29 05:53:54.564855: train_loss -0.7784 
-2024-08-29 05:53:54.565046: val_loss -0.7903 
-2024-08-29 05:53:54.565189: Pseudo dice [0.0, 0.0, 0.8965, 0.9753, 0.8413, 0.9459, 0.9464, 0.9673, 0.959, 0.9517, 0.9411, 0.9643, 0.964, 0.8563, 0.9513, 0.9413, 0.8297, 0.8308, nan] 
-2024-08-29 05:53:54.565270: Epoch time: 75.36 s 
-2024-08-29 05:53:55.649710:  
-2024-08-29 05:53:55.650016: Epoch 1670 
-2024-08-29 05:53:55.650094: Current learning rate: 0.00198 
-2024-08-29 05:55:15.524349: train_loss -0.7734 
-2024-08-29 05:55:15.524543: val_loss -0.7955 
-2024-08-29 05:55:15.524689: Pseudo dice [0.0, 0.0, 0.9101, 0.9762, 0.8302, 0.9448, 0.9462, 0.9617, 0.9541, 0.9492, 0.9333, 0.9623, 0.9607, 0.8471, 0.9388, 0.9303, 0.8316, 0.8318, nan] 
-2024-08-29 05:55:15.524760: Epoch time: 79.88 s 
-2024-08-29 05:55:16.927840:  
-2024-08-29 05:55:16.927987: Epoch 1671 
-2024-08-29 05:55:16.928066: Current learning rate: 0.00197 
-2024-08-29 05:56:29.284318: train_loss -0.7742 
-2024-08-29 05:56:29.284526: val_loss -0.7977 
-2024-08-29 05:56:29.284669: Pseudo dice [0.0, 0.0, 0.8926, 0.9767, 0.8626, 0.9504, 0.9563, 0.9695, 0.9507, 0.9549, 0.9367, 0.96, 0.9643, 0.8576, 0.9582, 0.942, 0.8326, 0.8281, nan] 
-2024-08-29 05:56:29.284738: Epoch time: 72.36 s 
-2024-08-29 05:56:30.384075:  
-2024-08-29 05:56:30.384219: Epoch 1672 
-2024-08-29 05:56:30.384300: Current learning rate: 0.00196 
-2024-08-29 05:57:49.245916: train_loss -0.7705 
-2024-08-29 05:57:49.246330: val_loss -0.7916 
-2024-08-29 05:57:49.246470: Pseudo dice [0.0, 0.0, 0.8983, 0.9747, 0.8524, 0.95, 0.9509, 0.9649, 0.9532, 0.9548, 0.9341, 0.9603, 0.9597, 0.8476, 0.9508, 0.9333, 0.8347, 0.8396, nan] 
-2024-08-29 05:57:49.246534: Epoch time: 78.86 s 
-2024-08-29 05:57:50.325377:  
-2024-08-29 05:57:50.325656: Epoch 1673 
-2024-08-29 05:57:50.325737: Current learning rate: 0.00196 
-2024-08-29 05:59:07.103994: train_loss -0.771 
-2024-08-29 05:59:07.104185: val_loss -0.7837 
-2024-08-29 05:59:07.104335: Pseudo dice [0.0, 0.0, 0.8875, 0.9765, 0.8467, 0.9404, 0.9462, 0.9661, 0.9511, 0.9465, 0.9276, 0.961, 0.9611, 0.8452, 0.9432, 0.9312, 0.8221, 0.8255, nan] 
-2024-08-29 05:59:07.104406: Epoch time: 76.78 s 
-2024-08-29 05:59:08.190627:  
-2024-08-29 05:59:08.190768: Epoch 1674 
-2024-08-29 05:59:08.190846: Current learning rate: 0.00195 
-2024-08-29 06:00:23.561370: train_loss -0.7707 
-2024-08-29 06:00:23.561589: val_loss -0.7933 
-2024-08-29 06:00:23.561792: Pseudo dice [0.0, 0.0, 0.8936, 0.9756, 0.856, 0.9516, 0.9538, 0.9668, 0.9524, 0.9558, 0.9324, 0.959, 0.9642, 0.8567, 0.9582, 0.9352, 0.8333, 0.8322, nan] 
-2024-08-29 06:00:23.561892: Epoch time: 75.37 s 
-2024-08-29 06:00:24.677932:  
-2024-08-29 06:00:24.678224: Epoch 1675 
-2024-08-29 06:00:24.678304: Current learning rate: 0.00195 
-2024-08-29 06:01:42.095034: train_loss -0.7726 
-2024-08-29 06:01:42.095229: val_loss -0.7963 
-2024-08-29 06:01:42.095370: Pseudo dice [0.0, 0.0, 0.9171, 0.9769, 0.8438, 0.9452, 0.9484, 0.9684, 0.953, 0.9466, 0.9328, 0.9586, 0.9594, 0.8575, 0.9556, 0.9409, 0.8322, 0.8334, nan] 
-2024-08-29 06:01:42.095439: Epoch time: 77.42 s 
-2024-08-29 06:01:43.182204:  
-2024-08-29 06:01:43.182344: Epoch 1676 
-2024-08-29 06:01:43.182420: Current learning rate: 0.00194 
-2024-08-29 06:03:03.308981: train_loss -0.7707 
-2024-08-29 06:03:03.309179: val_loss -0.7888 
-2024-08-29 06:03:03.309318: Pseudo dice [0.0, 0.0, 0.9031, 0.977, 0.8535, 0.9514, 0.9534, 0.9687, 0.9524, 0.9416, 0.9332, 0.9623, 0.9608, 0.8622, 0.9524, 0.9396, 0.8349, 0.8422, nan] 
-2024-08-29 06:03:03.309386: Epoch time: 80.13 s 
-2024-08-29 06:03:04.679814:  
-2024-08-29 06:03:04.680133: Epoch 1677 
-2024-08-29 06:03:04.680220: Current learning rate: 0.00194 
-2024-08-29 06:04:22.920793: train_loss -0.7711 
-2024-08-29 06:04:22.921009: val_loss -0.7942 
-2024-08-29 06:04:22.921154: Pseudo dice [0.0, 0.0, 0.9032, 0.9787, 0.8611, 0.9487, 0.9495, 0.9683, 0.9549, 0.9539, 0.9372, 0.9631, 0.9634, 0.8512, 0.9555, 0.9388, 0.8312, 0.8166, nan] 
-2024-08-29 06:04:22.921228: Epoch time: 78.24 s 
-2024-08-29 06:04:24.011745:  
-2024-08-29 06:04:24.011886: Epoch 1678 
-2024-08-29 06:04:24.011965: Current learning rate: 0.00193 
-2024-08-29 06:05:41.516595: train_loss -0.7713 
-2024-08-29 06:05:41.516837: val_loss -0.793 
-2024-08-29 06:05:41.517042: Pseudo dice [0.0, 0.0, 0.884, 0.976, 0.8439, 0.9499, 0.955, 0.9655, 0.9537, 0.9534, 0.9347, 0.9621, 0.964, 0.8599, 0.9487, 0.9414, 0.8364, 0.8355, nan] 
-2024-08-29 06:05:41.517155: Epoch time: 77.51 s 
-2024-08-29 06:05:42.827594:  
-2024-08-29 06:05:42.827734: Epoch 1679 
-2024-08-29 06:05:42.827812: Current learning rate: 0.00193 
-2024-08-29 06:07:01.569480: train_loss -0.7697 
-2024-08-29 06:07:01.569678: val_loss -0.7939 
-2024-08-29 06:07:01.569818: Pseudo dice [0.0, 0.0, 0.9021, 0.9769, 0.8212, 0.9527, 0.954, 0.9688, 0.9567, 0.9547, 0.9382, 0.9638, 0.9645, 0.8621, 0.958, 0.9401, 0.8447, 0.8395, nan] 
-2024-08-29 06:07:01.569892: Epoch time: 78.74 s 
-2024-08-29 06:07:02.664372:  
-2024-08-29 06:07:02.664534: Epoch 1680 
-2024-08-29 06:07:02.664612: Current learning rate: 0.00192 
-2024-08-29 06:08:23.373842: train_loss -0.7689 
-2024-08-29 06:08:23.374034: val_loss -0.7938 
-2024-08-29 06:08:23.374177: Pseudo dice [0.0, 0.0, 0.9053, 0.9777, 0.8552, 0.942, 0.9455, 0.9656, 0.955, 0.9493, 0.9329, 0.9617, 0.9627, 0.8569, 0.9489, 0.9363, 0.8342, 0.8348, nan] 
-2024-08-29 06:08:23.374253: Epoch time: 80.71 s 
-2024-08-29 06:08:24.470369:  
-2024-08-29 06:08:24.470529: Epoch 1681 
-2024-08-29 06:08:24.470602: Current learning rate: 0.00192 
-2024-08-29 06:09:36.628236: train_loss -0.7751 
-2024-08-29 06:09:36.628448: val_loss -0.7867 
-2024-08-29 06:09:36.628600: Pseudo dice [0.0, 0.0, 0.8887, 0.9768, 0.8222, 0.9438, 0.9483, 0.9676, 0.9459, 0.9468, 0.932, 0.9567, 0.9591, 0.8507, 0.9398, 0.9348, 0.8261, 0.8272, nan] 
-2024-08-29 06:09:36.628673: Epoch time: 72.16 s 
-2024-08-29 06:09:37.726177:  
-2024-08-29 06:09:37.726315: Epoch 1682 
-2024-08-29 06:09:37.726386: Current learning rate: 0.00191 
-2024-08-29 06:10:51.386455: train_loss -0.7694 
-2024-08-29 06:10:51.386753: val_loss -0.7959 
-2024-08-29 06:10:51.387014: Pseudo dice [0.0, 0.0, 0.9078, 0.9762, 0.8476, 0.953, 0.9527, 0.9661, 0.954, 0.9497, 0.9403, 0.9616, 0.9616, 0.8571, 0.9599, 0.9366, 0.8403, 0.8471, nan] 
-2024-08-29 06:10:51.387145: Epoch time: 73.66 s 
-2024-08-29 06:10:52.715256:  
-2024-08-29 06:10:52.715591: Epoch 1683 
-2024-08-29 06:10:52.715681: Current learning rate: 0.00191 
-2024-08-29 06:12:11.239732: train_loss -0.7765 
-2024-08-29 06:12:11.239949: val_loss -0.7875 
-2024-08-29 06:12:11.240087: Pseudo dice [0.0, 0.0, 0.9009, 0.9774, 0.8537, 0.9467, 0.952, 0.9622, 0.953, 0.9497, 0.9369, 0.962, 0.9651, 0.8456, 0.9555, 0.9385, 0.8445, 0.8387, nan] 
-2024-08-29 06:12:11.240156: Epoch time: 78.53 s 
-2024-08-29 06:12:12.329824:  
-2024-08-29 06:12:12.329981: Epoch 1684 
-2024-08-29 06:12:12.330056: Current learning rate: 0.0019 
-2024-08-29 06:13:30.853065: train_loss -0.7722 
-2024-08-29 06:13:30.853254: val_loss -0.7905 
-2024-08-29 06:13:30.853406: Pseudo dice [0.0, 0.0, 0.9055, 0.977, 0.8437, 0.9477, 0.9482, 0.9645, 0.9496, 0.9486, 0.9323, 0.9621, 0.9635, 0.8511, 0.9558, 0.9349, 0.8257, 0.8244, nan] 
-2024-08-29 06:13:30.853481: Epoch time: 78.52 s 
-2024-08-29 06:13:31.947565:  
-2024-08-29 06:13:31.947704: Epoch 1685 
-2024-08-29 06:13:31.947775: Current learning rate: 0.00189 
-2024-08-29 06:14:44.280106: train_loss -0.7736 
-2024-08-29 06:14:44.280303: val_loss -0.795 
-2024-08-29 06:14:44.280458: Pseudo dice [0.0, 0.0, 0.8987, 0.9769, 0.8553, 0.9506, 0.952, 0.9682, 0.9524, 0.9524, 0.9402, 0.962, 0.9627, 0.8635, 0.9533, 0.9388, 0.8337, 0.8401, nan] 
-2024-08-29 06:14:44.280533: Epoch time: 72.33 s 
-2024-08-29 06:14:45.363040:  
-2024-08-29 06:14:45.363193: Epoch 1686 
-2024-08-29 06:14:45.363271: Current learning rate: 0.00189 
-2024-08-29 06:16:02.010267: train_loss -0.7762 
-2024-08-29 06:16:02.010498: val_loss -0.7968 
-2024-08-29 06:16:02.010658: Pseudo dice [0.0, 0.0, 0.9056, 0.9773, 0.854, 0.9498, 0.9524, 0.9637, 0.9501, 0.951, 0.926, 0.9603, 0.9597, 0.8587, 0.9552, 0.9381, 0.8431, 0.8429, nan] 
-2024-08-29 06:16:02.010738: Epoch time: 76.65 s 
-2024-08-29 06:16:03.181502:  
-2024-08-29 06:16:03.181630: Epoch 1687 
-2024-08-29 06:16:03.181701: Current learning rate: 0.00188 
-2024-08-29 06:17:18.939132: train_loss -0.7734 
-2024-08-29 06:17:18.939586: val_loss -0.7933 
-2024-08-29 06:17:18.939740: Pseudo dice [0.0, 0.0, 0.9011, 0.9773, 0.8438, 0.9521, 0.9555, 0.9688, 0.9507, 0.9509, 0.9369, 0.9581, 0.9609, 0.8578, 0.9595, 0.9391, 0.8385, 0.8437, nan] 
-2024-08-29 06:17:18.939825: Epoch time: 75.76 s 
-2024-08-29 06:17:20.037621:  
-2024-08-29 06:17:20.037748: Epoch 1688 
-2024-08-29 06:17:20.037824: Current learning rate: 0.00188 
-2024-08-29 06:18:35.557109: train_loss -0.7755 
-2024-08-29 06:18:35.557311: val_loss -0.7914 
-2024-08-29 06:18:35.557454: Pseudo dice [0.0, 0.0, 0.9102, 0.9768, 0.8235, 0.949, 0.9509, 0.964, 0.9538, 0.9489, 0.9372, 0.9609, 0.9632, 0.8534, 0.9507, 0.9414, 0.8341, 0.8426, nan] 
-2024-08-29 06:18:35.557531: Epoch time: 75.52 s 
-2024-08-29 06:18:36.882017:  
-2024-08-29 06:18:36.882163: Epoch 1689 
-2024-08-29 06:18:36.882253: Current learning rate: 0.00187 
-2024-08-29 06:19:57.532149: train_loss -0.7701 
-2024-08-29 06:19:57.532590: val_loss -0.7946 
-2024-08-29 06:19:57.532760: Pseudo dice [0.0, 0.0, 0.9057, 0.9779, 0.8454, 0.9487, 0.9506, 0.9673, 0.9511, 0.9484, 0.9396, 0.9615, 0.9632, 0.8596, 0.9527, 0.9391, 0.8424, 0.8371, nan] 
-2024-08-29 06:19:57.532889: Epoch time: 80.65 s 
-2024-08-29 06:19:58.613842:  
-2024-08-29 06:19:58.614061: Epoch 1690 
-2024-08-29 06:19:58.614143: Current learning rate: 0.00187 
-2024-08-29 06:21:18.507000: train_loss -0.7762 
-2024-08-29 06:21:18.507201: val_loss -0.7947 
-2024-08-29 06:21:18.507340: Pseudo dice [0.0, 0.0, 0.9089, 0.9766, 0.854, 0.9442, 0.9507, 0.9616, 0.9508, 0.955, 0.9331, 0.9585, 0.962, 0.852, 0.9475, 0.9339, 0.8401, 0.8372, nan] 
-2024-08-29 06:21:18.507413: Epoch time: 79.89 s 
-2024-08-29 06:21:19.607481:  
-2024-08-29 06:21:19.607633: Epoch 1691 
-2024-08-29 06:21:19.607715: Current learning rate: 0.00186 
-2024-08-29 06:22:41.064899: train_loss -0.7729 
-2024-08-29 06:22:41.065087: val_loss -0.7875 
-2024-08-29 06:22:41.065224: Pseudo dice [0.0, 0.0, 0.8955, 0.9773, 0.8226, 0.9467, 0.9482, 0.9616, 0.9559, 0.9442, 0.9334, 0.9605, 0.9595, 0.838, 0.9534, 0.9277, 0.8403, 0.838, nan] 
-2024-08-29 06:22:41.065293: Epoch time: 81.46 s 
-2024-08-29 06:22:42.143844:  
-2024-08-29 06:22:42.144166: Epoch 1692 
-2024-08-29 06:22:42.144244: Current learning rate: 0.00186 
-2024-08-29 06:23:56.618644: train_loss -0.7716 
-2024-08-29 06:23:56.618843: val_loss -0.7885 
-2024-08-29 06:23:56.618989: Pseudo dice [0.0, 0.0, 0.8731, 0.9775, 0.8405, 0.9498, 0.9546, 0.9665, 0.9575, 0.9594, 0.9316, 0.9642, 0.9619, 0.8509, 0.956, 0.9334, 0.845, 0.8457, nan] 
-2024-08-29 06:23:56.619060: Epoch time: 74.48 s 
-2024-08-29 06:23:57.711227:  
-2024-08-29 06:23:57.711365: Epoch 1693 
-2024-08-29 06:23:57.711438: Current learning rate: 0.00185 
-2024-08-29 06:25:14.369894: train_loss -0.7717 
-2024-08-29 06:25:14.370098: val_loss -0.793 
-2024-08-29 06:25:14.370236: Pseudo dice [0.0, 0.0, 0.8842, 0.9776, 0.8281, 0.9435, 0.9506, 0.9602, 0.952, 0.9557, 0.9278, 0.9582, 0.9576, 0.8501, 0.9567, 0.939, 0.8443, 0.8256, nan] 
-2024-08-29 06:25:14.370312: Epoch time: 76.66 s 
-2024-08-29 06:25:15.464994:  
-2024-08-29 06:25:15.465126: Epoch 1694 
-2024-08-29 06:25:15.465205: Current learning rate: 0.00185 
-2024-08-29 06:26:31.125699: train_loss -0.7735 
-2024-08-29 06:26:31.125898: val_loss -0.7979 
-2024-08-29 06:26:31.126042: Pseudo dice [0.0, 0.0, 0.8696, 0.9768, 0.8578, 0.9489, 0.9535, 0.9667, 0.9572, 0.9578, 0.9352, 0.9623, 0.9621, 0.8589, 0.9497, 0.9352, 0.8358, 0.8345, nan] 
-2024-08-29 06:26:31.126113: Epoch time: 75.66 s 
-2024-08-29 06:26:32.465289:  
-2024-08-29 06:26:32.465606: Epoch 1695 
-2024-08-29 06:26:32.465687: Current learning rate: 0.00184 
-2024-08-29 06:27:54.005216: train_loss -0.7731 
-2024-08-29 06:27:54.005422: val_loss -0.7953 
-2024-08-29 06:27:54.005560: Pseudo dice [0.0, 0.0, 0.898, 0.9774, 0.8422, 0.9477, 0.9522, 0.9675, 0.9565, 0.9605, 0.9403, 0.9629, 0.9655, 0.8535, 0.9523, 0.9377, 0.8308, 0.834, nan] 
-2024-08-29 06:27:54.005630: Epoch time: 81.54 s 
-2024-08-29 06:27:55.099987:  
-2024-08-29 06:27:55.100122: Epoch 1696 
-2024-08-29 06:27:55.100197: Current learning rate: 0.00184 
-2024-08-29 06:29:09.236579: train_loss -0.7748 
-2024-08-29 06:29:09.236897: val_loss -0.7943 
-2024-08-29 06:29:09.237241: Pseudo dice [0.0, 0.0, 0.9054, 0.9766, 0.8487, 0.9468, 0.9491, 0.9643, 0.9586, 0.9501, 0.9368, 0.9618, 0.9629, 0.8553, 0.9558, 0.9378, 0.8315, 0.8373, nan] 
-2024-08-29 06:29:09.237328: Epoch time: 74.14 s 
-2024-08-29 06:29:10.332165:  
-2024-08-29 06:29:10.332324: Epoch 1697 
-2024-08-29 06:29:10.332406: Current learning rate: 0.00183 
-2024-08-29 06:30:29.067695: train_loss -0.7763 
-2024-08-29 06:30:29.067903: val_loss -0.7943 
-2024-08-29 06:30:29.068042: Pseudo dice [0.0, 0.0, 0.9094, 0.9778, 0.8459, 0.9531, 0.9526, 0.9669, 0.9542, 0.959, 0.9412, 0.9611, 0.9622, 0.855, 0.9537, 0.9346, 0.855, 0.8314, nan] 
-2024-08-29 06:30:29.068114: Epoch time: 78.74 s 
-2024-08-29 06:30:30.149013:  
-2024-08-29 06:30:30.149146: Epoch 1698 
-2024-08-29 06:30:30.149221: Current learning rate: 0.00182 
-2024-08-29 06:31:43.595841: train_loss -0.7778 
-2024-08-29 06:31:43.596041: val_loss -0.8002 
-2024-08-29 06:31:43.596179: Pseudo dice [0.0, 0.0, 0.9105, 0.9781, 0.8474, 0.9499, 0.9535, 0.9668, 0.9561, 0.958, 0.9377, 0.9635, 0.9643, 0.8599, 0.9501, 0.9358, 0.8569, 0.8426, nan] 
-2024-08-29 06:31:43.596252: Epoch time: 73.45 s 
-2024-08-29 06:31:44.702648:  
-2024-08-29 06:31:44.702788: Epoch 1699 
-2024-08-29 06:31:44.702871: Current learning rate: 0.00182 
-2024-08-29 06:33:02.720563: train_loss -0.7724 
-2024-08-29 06:33:02.720769: val_loss -0.7946 
-2024-08-29 06:33:02.720910: Pseudo dice [0.0, 0.0, 0.9056, 0.9768, 0.8432, 0.9509, 0.9547, 0.9661, 0.9534, 0.9527, 0.9295, 0.9602, 0.959, 0.8635, 0.9438, 0.9353, 0.8462, 0.8354, nan] 
-2024-08-29 06:33:02.720982: Epoch time: 78.02 s 
-2024-08-29 06:33:04.251854:  
-2024-08-29 06:33:04.252116: Epoch 1700 
-2024-08-29 06:33:04.252199: Current learning rate: 0.00181 
-2024-08-29 06:34:21.047043: train_loss -0.7704 
-2024-08-29 06:34:21.047239: val_loss -0.7945 
-2024-08-29 06:34:21.047380: Pseudo dice [0.0, 0.0, 0.9098, 0.9768, 0.8343, 0.95, 0.9532, 0.9668, 0.9507, 0.9425, 0.9291, 0.9608, 0.9608, 0.8594, 0.9573, 0.9401, 0.8415, 0.824, nan] 
-2024-08-29 06:34:21.047450: Epoch time: 76.8 s 
-2024-08-29 06:34:22.364977:  
-2024-08-29 06:34:22.365278: Epoch 1701 
-2024-08-29 06:34:22.365368: Current learning rate: 0.00181 
-2024-08-29 06:35:42.440606: train_loss -0.7659 
-2024-08-29 06:35:42.440795: val_loss -0.7891 
-2024-08-29 06:35:42.440937: Pseudo dice [0.0, 0.0, 0.9017, 0.9764, 0.8016, 0.9481, 0.9475, 0.9604, 0.9528, 0.9487, 0.9386, 0.9617, 0.9642, 0.8507, 0.9459, 0.9339, 0.834, 0.8245, nan] 
-2024-08-29 06:35:42.441007: Epoch time: 80.08 s 
-2024-08-29 06:35:43.529699:  
-2024-08-29 06:35:43.530039: Epoch 1702 
-2024-08-29 06:35:43.530115: Current learning rate: 0.0018 
-2024-08-29 06:37:02.347043: train_loss -0.7722 
-2024-08-29 06:37:02.347249: val_loss -0.7945 
-2024-08-29 06:37:02.347392: Pseudo dice [0.0, 0.0, 0.899, 0.9774, 0.8618, 0.9467, 0.9488, 0.9668, 0.9514, 0.9553, 0.9373, 0.9566, 0.9628, 0.8605, 0.9508, 0.9399, 0.8468, 0.8442, nan] 
-2024-08-29 06:37:02.347512: Epoch time: 78.82 s 
-2024-08-29 06:37:03.432957:  
-2024-08-29 06:37:03.433108: Epoch 1703 
-2024-08-29 06:37:03.433188: Current learning rate: 0.0018 
-2024-08-29 06:38:18.169158: train_loss -0.777 
-2024-08-29 06:38:18.169387: val_loss -0.7918 
-2024-08-29 06:38:18.169536: Pseudo dice [0.0, 0.0, 0.9058, 0.977, 0.8291, 0.9427, 0.9474, 0.9639, 0.9554, 0.9504, 0.938, 0.9637, 0.9625, 0.8516, 0.9531, 0.9333, 0.827, 0.8364, nan] 
-2024-08-29 06:38:18.169612: Epoch time: 74.74 s 
-2024-08-29 06:38:19.257904:  
-2024-08-29 06:38:19.258057: Epoch 1704 
-2024-08-29 06:38:19.258134: Current learning rate: 0.00179 
-2024-08-29 06:39:35.883022: train_loss -0.7749 
-2024-08-29 06:39:35.883237: val_loss -0.7966 
-2024-08-29 06:39:35.883380: Pseudo dice [0.0, 0.0, 0.9008, 0.9768, 0.8613, 0.9452, 0.9486, 0.9651, 0.9573, 0.9534, 0.9359, 0.9616, 0.9623, 0.8624, 0.9537, 0.9411, 0.8471, 0.8493, nan] 
-2024-08-29 06:39:35.883452: Epoch time: 76.63 s 
-2024-08-29 06:39:36.961321:  
-2024-08-29 06:39:36.961468: Epoch 1705 
-2024-08-29 06:39:36.961552: Current learning rate: 0.00179 
-2024-08-29 06:40:54.633423: train_loss -0.7746 
-2024-08-29 06:40:54.633624: val_loss -0.7946 
-2024-08-29 06:40:54.633762: Pseudo dice [0.0, 0.0, 0.892, 0.9759, 0.8481, 0.9478, 0.9488, 0.9664, 0.952, 0.9581, 0.9359, 0.9571, 0.9637, 0.8548, 0.9551, 0.9402, 0.8391, 0.8384, nan] 
-2024-08-29 06:40:54.633844: Epoch time: 77.67 s 
-2024-08-29 06:40:55.727396:  
-2024-08-29 06:40:55.727552: Epoch 1706 
-2024-08-29 06:40:55.727632: Current learning rate: 0.00178 
-2024-08-29 06:42:07.110190: train_loss -0.7748 
-2024-08-29 06:42:07.110381: val_loss -0.7919 
-2024-08-29 06:42:07.110518: Pseudo dice [0.0, 0.0, 0.8999, 0.9763, 0.8451, 0.9504, 0.954, 0.9649, 0.954, 0.9522, 0.9372, 0.963, 0.9617, 0.8548, 0.957, 0.9404, 0.847, 0.8351, nan] 
-2024-08-29 06:42:07.110586: Epoch time: 71.38 s 
-2024-08-29 06:42:08.447859:  
-2024-08-29 06:42:08.448187: Epoch 1707 
-2024-08-29 06:42:08.448283: Current learning rate: 0.00178 
-2024-08-29 06:43:27.914748: train_loss -0.7753 
-2024-08-29 06:43:27.914953: val_loss -0.7937 
-2024-08-29 06:43:27.915091: Pseudo dice [0.0, 0.0, 0.9102, 0.9773, 0.8401, 0.9494, 0.9518, 0.9645, 0.9541, 0.9521, 0.9314, 0.9622, 0.9619, 0.857, 0.9502, 0.9376, 0.8435, 0.8476, nan] 
-2024-08-29 06:43:27.915162: Epoch time: 79.47 s 
-2024-08-29 06:43:28.998701:  
-2024-08-29 06:43:28.999149: Epoch 1708 
-2024-08-29 06:43:28.999231: Current learning rate: 0.00177 
-2024-08-29 06:44:46.828683: train_loss -0.7729 
-2024-08-29 06:44:46.828875: val_loss -0.7952 
-2024-08-29 06:44:46.829012: Pseudo dice [0.0, 0.0, 0.8797, 0.9781, 0.846, 0.9516, 0.9542, 0.9686, 0.9607, 0.9533, 0.942, 0.9662, 0.9657, 0.8559, 0.9531, 0.938, 0.8477, 0.8428, nan] 
-2024-08-29 06:44:46.829081: Epoch time: 77.83 s 
-2024-08-29 06:44:46.829123: Yayy! New best EMA pseudo Dice: 0.821 
-2024-08-29 06:44:48.319359:  
-2024-08-29 06:44:48.319495: Epoch 1709 
-2024-08-29 06:44:48.319574: Current learning rate: 0.00176 
-2024-08-29 06:46:04.751489: train_loss -0.7764 
-2024-08-29 06:46:04.751699: val_loss -0.7941 
-2024-08-29 06:46:04.751842: Pseudo dice [0.0, 0.0, 0.9144, 0.9781, 0.8597, 0.9505, 0.9516, 0.9667, 0.955, 0.9543, 0.9327, 0.9642, 0.9632, 0.8632, 0.9578, 0.9435, 0.8484, 0.8479, nan] 
-2024-08-29 06:46:04.751914: Epoch time: 76.43 s 
-2024-08-29 06:46:04.751956: Yayy! New best EMA pseudo Dice: 0.8214 
-2024-08-29 06:46:06.251975:  
-2024-08-29 06:46:06.252124: Epoch 1710 
-2024-08-29 06:46:06.252201: Current learning rate: 0.00176 
-2024-08-29 06:47:20.198372: train_loss -0.7765 
-2024-08-29 06:47:20.198577: val_loss -0.7904 
-2024-08-29 06:47:20.198724: Pseudo dice [0.0, 0.0, 0.9139, 0.9765, 0.8571, 0.9536, 0.9567, 0.9663, 0.9539, 0.9506, 0.9389, 0.9576, 0.9623, 0.8574, 0.9572, 0.9334, 0.8403, 0.8372, nan] 
-2024-08-29 06:47:20.198793: Epoch time: 73.95 s 
-2024-08-29 06:47:20.198838: Yayy! New best EMA pseudo Dice: 0.8216 
-2024-08-29 06:47:21.699499:  
-2024-08-29 06:47:21.699655: Epoch 1711 
-2024-08-29 06:47:21.699733: Current learning rate: 0.00175 
-2024-08-29 06:48:36.822841: train_loss -0.7773 
-2024-08-29 06:48:36.823047: val_loss -0.7954 
-2024-08-29 06:48:36.823189: Pseudo dice [0.0, 0.0, 0.9107, 0.9766, 0.8763, 0.9512, 0.956, 0.9688, 0.9527, 0.9476, 0.9389, 0.9608, 0.962, 0.8622, 0.9576, 0.9399, 0.8512, 0.8522, nan] 
-2024-08-29 06:48:36.823264: Epoch time: 75.12 s 
-2024-08-29 06:48:36.823307: Yayy! New best EMA pseudo Dice: 0.822 
-2024-08-29 06:48:38.335530:  
-2024-08-29 06:48:38.335803: Epoch 1712 
-2024-08-29 06:48:38.335885: Current learning rate: 0.00175 
-2024-08-29 06:49:57.835319: train_loss -0.7751 
-2024-08-29 06:49:57.835536: val_loss -0.7996 
-2024-08-29 06:49:57.835680: Pseudo dice [0.0, 0.0, 0.9105, 0.978, 0.8717, 0.9488, 0.9517, 0.9679, 0.9561, 0.9568, 0.9405, 0.9636, 0.9656, 0.8582, 0.9501, 0.9374, 0.8342, 0.8378, nan] 
-2024-08-29 06:49:57.835753: Epoch time: 79.5 s 
-2024-08-29 06:49:57.835795: Yayy! New best EMA pseudo Dice: 0.8222 
-2024-08-29 06:49:59.775649:  
-2024-08-29 06:49:59.775806: Epoch 1713 
-2024-08-29 06:49:59.775883: Current learning rate: 0.00174 
-2024-08-29 06:51:14.009385: train_loss -0.7767 
-2024-08-29 06:51:14.009641: val_loss -0.7959 
-2024-08-29 06:51:14.009782: Pseudo dice [0.0, 0.0, 0.9103, 0.9785, 0.8491, 0.9483, 0.9529, 0.9662, 0.9544, 0.9465, 0.9412, 0.9612, 0.9653, 0.8563, 0.9548, 0.9375, 0.8411, 0.839, nan] 
-2024-08-29 06:51:14.009866: Epoch time: 74.23 s 
-2024-08-29 06:51:14.009909: Yayy! New best EMA pseudo Dice: 0.8222 
-2024-08-29 06:51:15.507880:  
-2024-08-29 06:51:15.508200: Epoch 1714 
-2024-08-29 06:51:15.508280: Current learning rate: 0.00174 
-2024-08-29 06:52:34.437141: train_loss -0.7759 
-2024-08-29 06:52:34.437350: val_loss -0.7918 
-2024-08-29 06:52:34.437494: Pseudo dice [0.0, 0.0, 0.9096, 0.9768, 0.8646, 0.9468, 0.9532, 0.9638, 0.9552, 0.9551, 0.9366, 0.9634, 0.9633, 0.8429, 0.951, 0.9402, 0.8438, 0.8447, nan] 
-2024-08-29 06:52:34.437568: Epoch time: 78.93 s 
-2024-08-29 06:52:34.437611: Yayy! New best EMA pseudo Dice: 0.8223 
-2024-08-29 06:52:35.931116:  
-2024-08-29 06:52:35.931246: Epoch 1715 
-2024-08-29 06:52:35.931318: Current learning rate: 0.00173 
-2024-08-29 06:53:57.721277: train_loss -0.7742 
-2024-08-29 06:53:57.721453: val_loss -0.7957 
-2024-08-29 06:53:57.721591: Pseudo dice [0.0, 0.0, 0.905, 0.979, 0.8491, 0.9516, 0.9542, 0.9666, 0.9571, 0.9594, 0.9398, 0.964, 0.9648, 0.8536, 0.9583, 0.9396, 0.8409, 0.8433, nan] 
-2024-08-29 06:53:57.721843: Epoch time: 81.79 s 
-2024-08-29 06:53:57.721916: Yayy! New best EMA pseudo Dice: 0.8224 
-2024-08-29 06:53:59.204080:  
-2024-08-29 06:53:59.204232: Epoch 1716 
-2024-08-29 06:53:59.204308: Current learning rate: 0.00173 
-2024-08-29 06:55:14.181432: train_loss -0.7738 
-2024-08-29 06:55:14.181624: val_loss -0.7936 
-2024-08-29 06:55:14.181768: Pseudo dice [0.0, 0.0, 0.9014, 0.977, 0.84, 0.9479, 0.9502, 0.9672, 0.9545, 0.9509, 0.9393, 0.9627, 0.9654, 0.8525, 0.9589, 0.9406, 0.838, 0.8407, nan] 
-2024-08-29 06:55:14.181840: Epoch time: 74.98 s 
-2024-08-29 06:55:15.260492:  
-2024-08-29 06:55:15.260631: Epoch 1717 
-2024-08-29 06:55:15.260710: Current learning rate: 0.00172 
-2024-08-29 06:56:35.334715: train_loss -0.7713 
-2024-08-29 06:56:35.334920: val_loss -0.7949 
-2024-08-29 06:56:35.335064: Pseudo dice [0.0, 0.0, 0.8866, 0.9773, 0.8361, 0.9483, 0.9529, 0.9664, 0.9549, 0.9479, 0.937, 0.9616, 0.964, 0.8522, 0.9503, 0.9342, 0.8523, 0.8439, nan] 
-2024-08-29 06:56:35.335139: Epoch time: 80.07 s 
-2024-08-29 06:56:36.601842:  
-2024-08-29 06:56:36.602092: Epoch 1718 
-2024-08-29 06:56:36.602171: Current learning rate: 0.00172 
-2024-08-29 06:57:54.540075: train_loss -0.7748 
-2024-08-29 06:57:54.540404: val_loss -0.7975 
-2024-08-29 06:57:54.540633: Pseudo dice [0.0, 0.0, 0.9113, 0.9773, 0.8402, 0.9493, 0.9521, 0.9653, 0.9558, 0.947, 0.9333, 0.9645, 0.9613, 0.8631, 0.9398, 0.9434, 0.8294, 0.8394, nan] 
-2024-08-29 06:57:54.540815: Epoch time: 77.94 s 
-2024-08-29 06:57:55.625008:  
-2024-08-29 06:57:55.625330: Epoch 1719 
-2024-08-29 06:57:55.625412: Current learning rate: 0.00171 
-2024-08-29 06:59:06.715918: train_loss -0.7814 
-2024-08-29 06:59:06.716128: val_loss -0.7974 
-2024-08-29 06:59:06.716269: Pseudo dice [0.0, 0.0, 0.9037, 0.9778, 0.8473, 0.9516, 0.9534, 0.969, 0.956, 0.9494, 0.9401, 0.9607, 0.9638, 0.8623, 0.9561, 0.9397, 0.8337, 0.8399, nan] 
-2024-08-29 06:59:06.716340: Epoch time: 71.09 s 
-2024-08-29 06:59:07.815096:  
-2024-08-29 06:59:07.815244: Epoch 1720 
-2024-08-29 06:59:07.815329: Current learning rate: 0.0017 
-2024-08-29 07:00:28.011970: train_loss -0.7767 
-2024-08-29 07:00:28.012178: val_loss -0.7936 
-2024-08-29 07:00:28.012321: Pseudo dice [0.0, 0.0, 0.9012, 0.9786, 0.8626, 0.9502, 0.951, 0.968, 0.9566, 0.9476, 0.9337, 0.9628, 0.9616, 0.866, 0.9487, 0.9429, 0.8431, 0.8293, nan] 
-2024-08-29 07:00:28.012396: Epoch time: 80.2 s 
-2024-08-29 07:00:29.093371:  
-2024-08-29 07:00:29.093631: Epoch 1721 
-2024-08-29 07:00:29.093713: Current learning rate: 0.0017 
-2024-08-29 07:01:40.744720: train_loss -0.7806 
-2024-08-29 07:01:40.744914: val_loss -0.7955 
-2024-08-29 07:01:40.745053: Pseudo dice [0.0, 0.0, 0.8953, 0.978, 0.8395, 0.9506, 0.9541, 0.9679, 0.9571, 0.9567, 0.9366, 0.964, 0.9618, 0.8578, 0.9586, 0.9399, 0.8486, 0.8415, nan] 
-2024-08-29 07:01:40.745173: Epoch time: 71.65 s 
-2024-08-29 07:01:41.829468:  
-2024-08-29 07:01:41.829818: Epoch 1722 
-2024-08-29 07:01:41.829989: Current learning rate: 0.00169 
-2024-08-29 07:03:00.267099: train_loss -0.7762 
-2024-08-29 07:03:00.267308: val_loss -0.7942 
-2024-08-29 07:03:00.267454: Pseudo dice [0.0, 0.0, 0.8989, 0.9785, 0.8523, 0.9448, 0.9508, 0.9685, 0.9535, 0.9489, 0.9417, 0.9649, 0.9645, 0.8482, 0.9588, 0.9395, 0.845, 0.8414, nan] 
-2024-08-29 07:03:00.267526: Epoch time: 78.44 s 
-2024-08-29 07:03:01.350130:  
-2024-08-29 07:03:01.350259: Epoch 1723 
-2024-08-29 07:03:01.350333: Current learning rate: 0.00169 
-2024-08-29 07:04:14.043548: train_loss -0.7784 
-2024-08-29 07:04:14.043747: val_loss -0.7956 
-2024-08-29 07:04:14.043892: Pseudo dice [0.0, 0.0, 0.9039, 0.9789, 0.867, 0.9502, 0.9536, 0.969, 0.9574, 0.9505, 0.938, 0.9633, 0.9651, 0.8605, 0.9607, 0.9428, 0.8393, 0.8451, nan] 
-2024-08-29 07:04:14.043967: Epoch time: 72.69 s 
-2024-08-29 07:04:15.344143:  
-2024-08-29 07:04:15.344485: Epoch 1724 
-2024-08-29 07:04:15.344572: Current learning rate: 0.00168 
-2024-08-29 07:05:32.906909: train_loss -0.7764 
-2024-08-29 07:05:32.907433: val_loss -0.7958 
-2024-08-29 07:05:32.907670: Pseudo dice [0.0, 0.0, 0.9165, 0.9768, 0.8446, 0.9445, 0.9482, 0.9655, 0.9575, 0.9602, 0.9371, 0.964, 0.9644, 0.8492, 0.9531, 0.9372, 0.8359, 0.8437, nan] 
-2024-08-29 07:05:32.907818: Epoch time: 77.56 s 
-2024-08-29 07:05:33.989042:  
-2024-08-29 07:05:33.989183: Epoch 1725 
-2024-08-29 07:05:33.989264: Current learning rate: 0.00168 
-2024-08-29 07:06:46.367314: train_loss -0.7804 
-2024-08-29 07:06:46.367521: val_loss -0.7937 
-2024-08-29 07:06:46.367666: Pseudo dice [0.0, 0.0, 0.9165, 0.9775, 0.8485, 0.9531, 0.9536, 0.9683, 0.9527, 0.9408, 0.9297, 0.9603, 0.9628, 0.8664, 0.9583, 0.9465, 0.8385, 0.8416, nan] 
-2024-08-29 07:06:46.367739: Epoch time: 72.38 s 
-2024-08-29 07:06:46.367782: Yayy! New best EMA pseudo Dice: 0.8224 
-2024-08-29 07:06:47.873081:  
-2024-08-29 07:06:47.873226: Epoch 1726 
-2024-08-29 07:06:47.873304: Current learning rate: 0.00167 
-2024-08-29 07:08:03.420822: train_loss -0.7753 
-2024-08-29 07:08:03.421031: val_loss -0.7978 
-2024-08-29 07:08:03.421172: Pseudo dice [0.0, 0.0, 0.9165, 0.977, 0.8404, 0.9452, 0.9517, 0.9668, 0.9571, 0.9607, 0.9422, 0.9643, 0.9658, 0.8595, 0.9626, 0.9433, 0.8479, 0.8527, nan] 
-2024-08-29 07:08:03.421243: Epoch time: 75.55 s 
-2024-08-29 07:08:03.421284: Yayy! New best EMA pseudo Dice: 0.8227 
-2024-08-29 07:08:04.935560:  
-2024-08-29 07:08:04.935830: Epoch 1727 
-2024-08-29 07:08:04.935907: Current learning rate: 0.00167 
-2024-08-29 07:09:16.859213: train_loss -0.7762 
-2024-08-29 07:09:16.859392: val_loss -0.7899 
-2024-08-29 07:09:16.859532: Pseudo dice [0.0, 0.0, 0.892, 0.9776, 0.8253, 0.9449, 0.9469, 0.9663, 0.952, 0.9565, 0.9366, 0.9601, 0.9626, 0.8456, 0.9512, 0.9391, 0.833, 0.8414, nan] 
-2024-08-29 07:09:16.859603: Epoch time: 71.92 s 
-2024-08-29 07:09:17.948187:  
-2024-08-29 07:09:17.948536: Epoch 1728 
-2024-08-29 07:09:17.948617: Current learning rate: 0.00166 
-2024-08-29 07:10:29.616381: train_loss -0.7784 
-2024-08-29 07:10:29.616579: val_loss -0.7958 
-2024-08-29 07:10:29.616727: Pseudo dice [0.0, 0.0, 0.9046, 0.9775, 0.8657, 0.9448, 0.9461, 0.97, 0.9599, 0.9575, 0.9433, 0.9652, 0.9637, 0.8579, 0.9531, 0.9364, 0.8463, 0.8469, nan] 
-2024-08-29 07:10:29.616801: Epoch time: 71.67 s 
-2024-08-29 07:10:30.698351:  
-2024-08-29 07:10:30.698486: Epoch 1729 
-2024-08-29 07:10:30.698561: Current learning rate: 0.00165 
-2024-08-29 07:11:49.072787: train_loss -0.777 
-2024-08-29 07:11:49.072989: val_loss -0.7948 
-2024-08-29 07:11:49.073131: Pseudo dice [0.0, 0.0, 0.9139, 0.9775, 0.8272, 0.9395, 0.9423, 0.9644, 0.9523, 0.9472, 0.9382, 0.9602, 0.9628, 0.8404, 0.954, 0.9407, 0.8427, 0.846, nan] 
-2024-08-29 07:11:49.073202: Epoch time: 78.38 s 
-2024-08-29 07:11:50.400391:  
-2024-08-29 07:11:50.400657: Epoch 1730 
-2024-08-29 07:11:50.400739: Current learning rate: 0.00165 
-2024-08-29 07:13:07.472826: train_loss -0.7743 
-2024-08-29 07:13:07.473018: val_loss -0.7981 
-2024-08-29 07:13:07.473158: Pseudo dice [0.0, 0.0, 0.9044, 0.9774, 0.8582, 0.9442, 0.9467, 0.9677, 0.9547, 0.956, 0.9322, 0.9636, 0.9597, 0.8547, 0.9555, 0.938, 0.8448, 0.8406, nan] 
-2024-08-29 07:13:07.473272: Epoch time: 77.07 s 
-2024-08-29 07:13:08.556369:  
-2024-08-29 07:13:08.556692: Epoch 1731 
-2024-08-29 07:13:08.556777: Current learning rate: 0.00164 
-2024-08-29 07:14:26.084826: train_loss -0.7745 
-2024-08-29 07:14:26.085040: val_loss -0.7939 
-2024-08-29 07:14:26.085192: Pseudo dice [0.0, 0.0, 0.9129, 0.9773, 0.857, 0.9458, 0.9471, 0.9647, 0.9565, 0.9534, 0.94, 0.964, 0.9648, 0.8566, 0.9573, 0.933, 0.8391, 0.8466, nan] 
-2024-08-29 07:14:26.085262: Epoch time: 77.53 s 
-2024-08-29 07:14:27.171775:  
-2024-08-29 07:14:27.171910: Epoch 1732 
-2024-08-29 07:14:27.171992: Current learning rate: 0.00164 
-2024-08-29 07:15:43.225188: train_loss -0.774 
-2024-08-29 07:15:43.225383: val_loss -0.7961 
-2024-08-29 07:15:43.225524: Pseudo dice [0.0, 0.0, 0.921, 0.9754, 0.8631, 0.952, 0.9551, 0.9685, 0.9568, 0.9547, 0.9384, 0.9653, 0.9633, 0.8495, 0.9444, 0.9379, 0.8334, 0.8499, nan] 
-2024-08-29 07:15:43.225594: Epoch time: 76.05 s 
-2024-08-29 07:15:44.526469:  
-2024-08-29 07:15:44.526766: Epoch 1733 
-2024-08-29 07:15:44.526842: Current learning rate: 0.00163 
-2024-08-29 07:17:00.602121: train_loss -0.7768 
-2024-08-29 07:17:00.602323: val_loss -0.7986 
-2024-08-29 07:17:00.602465: Pseudo dice [0.0, 0.0, 0.9098, 0.9766, 0.8621, 0.9494, 0.9551, 0.9699, 0.954, 0.948, 0.9358, 0.9625, 0.9611, 0.8699, 0.957, 0.9386, 0.8479, 0.8414, nan] 
-2024-08-29 07:17:00.602544: Epoch time: 76.08 s 
-2024-08-29 07:17:01.661285:  
-2024-08-29 07:17:01.661543: Epoch 1734 
-2024-08-29 07:17:01.661624: Current learning rate: 0.00163 
-2024-08-29 07:18:19.637492: train_loss -0.7774 
-2024-08-29 07:18:19.637682: val_loss -0.7942 
-2024-08-29 07:18:19.637828: Pseudo dice [0.0, 0.0, 0.9099, 0.9782, 0.8623, 0.9471, 0.9504, 0.9673, 0.949, 0.9499, 0.9381, 0.9606, 0.9632, 0.8626, 0.9554, 0.9411, 0.843, 0.8434, nan] 
-2024-08-29 07:18:19.637904: Epoch time: 77.98 s 
-2024-08-29 07:18:20.729172:  
-2024-08-29 07:18:20.729304: Epoch 1735 
-2024-08-29 07:18:20.729378: Current learning rate: 0.00162 
-2024-08-29 07:19:34.966607: train_loss -0.7773 
-2024-08-29 07:19:34.966808: val_loss -0.7951 
-2024-08-29 07:19:34.966949: Pseudo dice [0.0, 0.0, 0.9006, 0.9773, 0.8672, 0.9494, 0.9517, 0.9635, 0.9573, 0.9568, 0.9402, 0.9648, 0.9651, 0.8603, 0.9518, 0.9395, 0.8277, 0.839, nan] 
-2024-08-29 07:19:34.967023: Epoch time: 74.24 s 
-2024-08-29 07:19:34.967066: Yayy! New best EMA pseudo Dice: 0.8227 
-2024-08-29 07:19:36.650718:  
-2024-08-29 07:19:36.650856: Epoch 1736 
-2024-08-29 07:19:36.650934: Current learning rate: 0.00162 
-2024-08-29 07:20:52.212939: train_loss -0.7778 
-2024-08-29 07:20:52.213263: val_loss -0.7965 
-2024-08-29 07:20:52.213408: Pseudo dice [0.0, 0.0, 0.888, 0.9774, 0.8308, 0.9486, 0.9553, 0.9652, 0.9544, 0.9518, 0.939, 0.964, 0.9629, 0.855, 0.9521, 0.9363, 0.8232, 0.8272, nan] 
-2024-08-29 07:20:52.213481: Epoch time: 75.56 s 
-2024-08-29 07:20:53.299025:  
-2024-08-29 07:20:53.299170: Epoch 1737 
-2024-08-29 07:20:53.299240: Current learning rate: 0.00161 
-2024-08-29 07:22:10.497655: train_loss -0.7752 
-2024-08-29 07:22:10.497853: val_loss -0.7949 
-2024-08-29 07:22:10.497995: Pseudo dice [0.0, 0.0, 0.9089, 0.9765, 0.8666, 0.9519, 0.9545, 0.9682, 0.9554, 0.9489, 0.9363, 0.9651, 0.963, 0.8683, 0.9565, 0.9405, 0.8375, 0.8358, nan] 
-2024-08-29 07:22:10.498066: Epoch time: 77.2 s 
-2024-08-29 07:22:11.590104:  
-2024-08-29 07:22:11.590239: Epoch 1738 
-2024-08-29 07:22:11.590316: Current learning rate: 0.00161 
-2024-08-29 07:23:30.393171: train_loss -0.7777 
-2024-08-29 07:23:30.393380: val_loss -0.7969 
-2024-08-29 07:23:30.393522: Pseudo dice [0.0, 0.0, 0.9098, 0.977, 0.8648, 0.9499, 0.9537, 0.968, 0.9551, 0.9507, 0.9253, 0.9582, 0.9625, 0.8607, 0.9556, 0.9376, 0.8445, 0.8449, nan] 
-2024-08-29 07:23:30.393592: Epoch time: 78.8 s 
-2024-08-29 07:23:31.488073:  
-2024-08-29 07:23:31.488204: Epoch 1739 
-2024-08-29 07:23:31.488284: Current learning rate: 0.0016 
-2024-08-29 07:24:43.535905: train_loss -0.7799 
-2024-08-29 07:24:43.536109: val_loss -0.7974 
-2024-08-29 07:24:43.536262: Pseudo dice [0.0, 0.0, 0.9102, 0.9772, 0.8675, 0.9525, 0.9521, 0.9708, 0.9562, 0.9557, 0.9397, 0.961, 0.9654, 0.8662, 0.9543, 0.9424, 0.8528, 0.8438, nan] 
-2024-08-29 07:24:43.536335: Epoch time: 72.05 s 
-2024-08-29 07:24:43.536393: Yayy! New best EMA pseudo Dice: 0.8229 
-2024-08-29 07:24:45.047019:  
-2024-08-29 07:24:45.047154: Epoch 1740 
-2024-08-29 07:24:45.047232: Current learning rate: 0.00159 
-2024-08-29 07:26:03.358263: train_loss -0.7785 
-2024-08-29 07:26:03.358477: val_loss -0.7935 
-2024-08-29 07:26:03.358621: Pseudo dice [0.0, 0.0, 0.8983, 0.9778, 0.8549, 0.9489, 0.9493, 0.9673, 0.9544, 0.9502, 0.9401, 0.9596, 0.9638, 0.8667, 0.9553, 0.9441, 0.8502, 0.8543, nan] 
-2024-08-29 07:26:03.358695: Epoch time: 78.31 s 
-2024-08-29 07:26:03.358735: Yayy! New best EMA pseudo Dice: 0.823 
-2024-08-29 07:26:05.043539:  
-2024-08-29 07:26:05.043673: Epoch 1741 
-2024-08-29 07:26:05.043758: Current learning rate: 0.00159 
-2024-08-29 07:27:23.742067: train_loss -0.7778 
-2024-08-29 07:27:23.742278: val_loss -0.7947 
-2024-08-29 07:27:23.742529: Pseudo dice [0.0, 0.0, 0.9182, 0.9777, 0.8761, 0.9494, 0.9514, 0.9678, 0.955, 0.9563, 0.9359, 0.9634, 0.9634, 0.8505, 0.9535, 0.9414, 0.844, 0.8343, nan] 
-2024-08-29 07:27:23.742705: Epoch time: 78.7 s 
-2024-08-29 07:27:23.742752: Yayy! New best EMA pseudo Dice: 0.8232 
-2024-08-29 07:27:25.239249:  
-2024-08-29 07:27:25.239401: Epoch 1742 
-2024-08-29 07:27:25.239475: Current learning rate: 0.00158 
-2024-08-29 07:28:39.385032: train_loss -0.7815 
-2024-08-29 07:28:39.385236: val_loss -0.8028 
-2024-08-29 07:28:39.385375: Pseudo dice [0.0, 0.0, 0.9034, 0.9768, 0.8524, 0.9525, 0.9552, 0.967, 0.9548, 0.9537, 0.9368, 0.9641, 0.9636, 0.857, 0.9541, 0.9377, 0.8416, 0.8428, nan] 
-2024-08-29 07:28:39.385447: Epoch time: 74.15 s 
-2024-08-29 07:28:40.461720:  
-2024-08-29 07:28:40.461855: Epoch 1743 
-2024-08-29 07:28:40.461933: Current learning rate: 0.00158 
-2024-08-29 07:29:58.642436: train_loss -0.7777 
-2024-08-29 07:29:58.642678: val_loss -0.7984 
-2024-08-29 07:29:58.642825: Pseudo dice [0.0, 0.0, 0.9207, 0.9778, 0.799, 0.9484, 0.9533, 0.9661, 0.9526, 0.9516, 0.9403, 0.9608, 0.9602, 0.8571, 0.9583, 0.9358, 0.8437, 0.8475, nan] 
-2024-08-29 07:29:58.642900: Epoch time: 78.18 s 
-2024-08-29 07:29:59.723773:  
-2024-08-29 07:29:59.723934: Epoch 1744 
-2024-08-29 07:29:59.724016: Current learning rate: 0.00157 
-2024-08-29 07:31:13.433599: train_loss -0.777 
-2024-08-29 07:31:13.433791: val_loss -0.7929 
-2024-08-29 07:31:13.433927: Pseudo dice [0.0, 0.0, 0.9007, 0.9772, 0.8427, 0.9412, 0.9441, 0.9669, 0.9475, 0.9475, 0.9337, 0.9508, 0.9533, 0.8574, 0.9554, 0.9355, 0.829, 0.8344, nan] 
-2024-08-29 07:31:13.433996: Epoch time: 73.71 s 
-2024-08-29 07:31:14.498043:  
-2024-08-29 07:31:14.498353: Epoch 1745 
-2024-08-29 07:31:14.498441: Current learning rate: 0.00157 
-2024-08-29 07:32:31.375924: train_loss -0.7771 
-2024-08-29 07:32:31.376132: val_loss -0.795 
-2024-08-29 07:32:31.376315: Pseudo dice [0.0, 0.0, 0.8953, 0.9768, 0.8538, 0.9383, 0.9455, 0.9668, 0.9556, 0.9546, 0.936, 0.963, 0.9642, 0.8554, 0.9389, 0.9385, 0.8366, 0.8365, nan] 
-2024-08-29 07:32:31.376414: Epoch time: 76.88 s 
-2024-08-29 07:32:32.614572:  
-2024-08-29 07:32:32.614877: Epoch 1746 
-2024-08-29 07:32:32.614977: Current learning rate: 0.00156 
-2024-08-29 07:33:50.224717: train_loss -0.7761 
-2024-08-29 07:33:50.224929: val_loss -0.7965 
-2024-08-29 07:33:50.225075: Pseudo dice [0.0, 0.0, 0.9102, 0.9763, 0.868, 0.9518, 0.9527, 0.9675, 0.9544, 0.9542, 0.9359, 0.9599, 0.9578, 0.8637, 0.9554, 0.9404, 0.8478, 0.8544, nan] 
-2024-08-29 07:33:50.225148: Epoch time: 77.61 s 
-2024-08-29 07:33:51.529556:  
-2024-08-29 07:33:51.529724: Epoch 1747 
-2024-08-29 07:33:51.529804: Current learning rate: 0.00156 
-2024-08-29 07:35:08.947174: train_loss -0.776 
-2024-08-29 07:35:08.947374: val_loss -0.7953 
-2024-08-29 07:35:08.947523: Pseudo dice [0.0, 0.0, 0.899, 0.9778, 0.8507, 0.9479, 0.9496, 0.969, 0.9561, 0.9573, 0.9351, 0.9595, 0.9619, 0.858, 0.9502, 0.9366, 0.8401, 0.8361, nan] 
-2024-08-29 07:35:08.947598: Epoch time: 77.42 s 
-2024-08-29 07:35:10.021284:  
-2024-08-29 07:35:10.021611: Epoch 1748 
-2024-08-29 07:35:10.021695: Current learning rate: 0.00155 
-2024-08-29 07:36:21.744123: train_loss -0.779 
-2024-08-29 07:36:21.744526: val_loss -0.7992 
-2024-08-29 07:36:21.744762: Pseudo dice [0.0, 0.0, 0.898, 0.9768, 0.8573, 0.9507, 0.9547, 0.9672, 0.949, 0.9549, 0.9407, 0.9586, 0.9637, 0.8604, 0.9544, 0.9415, 0.8433, 0.8406, nan] 
-2024-08-29 07:36:21.744843: Epoch time: 71.72 s 
-2024-08-29 07:36:22.823342:  
-2024-08-29 07:36:22.823629: Epoch 1749 
-2024-08-29 07:36:22.823709: Current learning rate: 0.00154 
-2024-08-29 07:37:32.694320: train_loss -0.7811 
-2024-08-29 07:37:32.694534: val_loss -0.7931 
-2024-08-29 07:37:32.694685: Pseudo dice [0.0, 0.0, 0.9023, 0.9783, 0.8582, 0.9487, 0.9519, 0.9673, 0.9501, 0.9481, 0.9368, 0.9566, 0.9608, 0.8533, 0.9555, 0.9387, 0.8406, 0.8401, nan] 
-2024-08-29 07:37:32.694756: Epoch time: 69.87 s 
-2024-08-29 07:37:34.856776:  
-2024-08-29 07:37:34.856902: Epoch 1750 
-2024-08-29 07:37:34.856980: Current learning rate: 0.00154 
-2024-08-29 07:38:49.013271: train_loss -0.7803 
-2024-08-29 07:38:49.013469: val_loss -0.7984 
-2024-08-29 07:38:49.013611: Pseudo dice [0.0, 0.0, 0.9047, 0.978, 0.8558, 0.9503, 0.9543, 0.9652, 0.955, 0.9568, 0.9376, 0.9639, 0.964, 0.861, 0.9555, 0.9342, 0.8374, 0.8374, nan] 
-2024-08-29 07:38:49.013681: Epoch time: 74.16 s 
-2024-08-29 07:38:50.098632:  
-2024-08-29 07:38:50.098762: Epoch 1751 
-2024-08-29 07:38:50.098835: Current learning rate: 0.00153 
-2024-08-29 07:40:10.626498: train_loss -0.7768 
-2024-08-29 07:40:10.626701: val_loss -0.797 
-2024-08-29 07:40:10.626841: Pseudo dice [0.0, 0.0, 0.9062, 0.9783, 0.8548, 0.9455, 0.9555, 0.9644, 0.9566, 0.9502, 0.9344, 0.9617, 0.9617, 0.8682, 0.9569, 0.9399, 0.8433, 0.8313, nan] 
-2024-08-29 07:40:10.626911: Epoch time: 80.53 s 
-2024-08-29 07:40:11.714890:  
-2024-08-29 07:40:11.715033: Epoch 1752 
-2024-08-29 07:40:11.715117: Current learning rate: 0.00153 
-2024-08-29 07:41:29.400240: train_loss -0.776 
-2024-08-29 07:41:29.400432: val_loss -0.794 
-2024-08-29 07:41:29.400575: Pseudo dice [0.0, 0.0, 0.9061, 0.9774, 0.8337, 0.9494, 0.9513, 0.9651, 0.957, 0.9586, 0.9441, 0.9641, 0.9672, 0.8542, 0.9534, 0.9382, 0.8381, 0.839, nan] 
-2024-08-29 07:41:29.400650: Epoch time: 77.69 s 
-2024-08-29 07:41:30.727465:  
-2024-08-29 07:41:30.727625: Epoch 1753 
-2024-08-29 07:41:30.727706: Current learning rate: 0.00152 
-2024-08-29 07:42:50.958372: train_loss -0.7755 
-2024-08-29 07:42:50.959237: val_loss -0.7931 
-2024-08-29 07:42:50.959494: Pseudo dice [0.0, 0.0, 0.8803, 0.9774, 0.839, 0.9496, 0.9521, 0.965, 0.9535, 0.9501, 0.9378, 0.9572, 0.961, 0.8571, 0.9537, 0.9413, 0.8388, 0.8349, nan] 
-2024-08-29 07:42:50.959645: Epoch time: 80.23 s 
-2024-08-29 07:42:52.043478:  
-2024-08-29 07:42:52.043621: Epoch 1754 
-2024-08-29 07:42:52.043695: Current learning rate: 0.00152 
-2024-08-29 07:44:06.501680: train_loss -0.7767 
-2024-08-29 07:44:06.501879: val_loss -0.7979 
-2024-08-29 07:44:06.502030: Pseudo dice [0.0, 0.0, 0.9014, 0.9768, 0.8639, 0.9494, 0.951, 0.9673, 0.9584, 0.9531, 0.9421, 0.9647, 0.9655, 0.8532, 0.9581, 0.9408, 0.8455, 0.8419, nan] 
-2024-08-29 07:44:06.502104: Epoch time: 74.46 s 
-2024-08-29 07:44:07.593433:  
-2024-08-29 07:44:07.593805: Epoch 1755 
-2024-08-29 07:44:07.593890: Current learning rate: 0.00151 
-2024-08-29 07:45:30.991966: train_loss -0.7768 
-2024-08-29 07:45:30.992167: val_loss -0.7911 
-2024-08-29 07:45:30.992318: Pseudo dice [0.0, 0.0, 0.9094, 0.9769, 0.8504, 0.948, 0.9486, 0.9656, 0.9491, 0.9517, 0.937, 0.9597, 0.9621, 0.8477, 0.9505, 0.933, 0.844, 0.8462, nan] 
-2024-08-29 07:45:30.992401: Epoch time: 83.4 s 
-2024-08-29 07:45:32.075253:  
-2024-08-29 07:45:32.075389: Epoch 1756 
-2024-08-29 07:45:32.075469: Current learning rate: 0.00151 
-2024-08-29 07:46:47.526173: train_loss -0.7754 
-2024-08-29 07:46:47.526383: val_loss -0.7997 
-2024-08-29 07:46:47.526532: Pseudo dice [0.0, 0.0, 0.91, 0.9758, 0.8565, 0.9497, 0.9524, 0.9648, 0.9562, 0.9558, 0.9421, 0.9631, 0.9643, 0.859, 0.9545, 0.9389, 0.8442, 0.8475, nan] 
-2024-08-29 07:46:47.526609: Epoch time: 75.45 s 
-2024-08-29 07:46:48.631824:  
-2024-08-29 07:46:48.631964: Epoch 1757 
-2024-08-29 07:46:48.632045: Current learning rate: 0.0015 
-2024-08-29 07:48:06.048737: train_loss -0.7731 
-2024-08-29 07:48:06.048938: val_loss -0.7942 
-2024-08-29 07:48:06.049098: Pseudo dice [0.0, 0.0, 0.9109, 0.977, 0.8571, 0.952, 0.953, 0.9652, 0.9552, 0.9493, 0.9403, 0.9632, 0.9634, 0.8622, 0.9565, 0.9353, 0.8496, 0.848, nan] 
-2024-08-29 07:48:06.049177: Epoch time: 77.42 s 
-2024-08-29 07:48:07.133910:  
-2024-08-29 07:48:07.134037: Epoch 1758 
-2024-08-29 07:48:07.134117: Current learning rate: 0.00149 
-2024-08-29 07:49:25.830039: train_loss -0.7806 
-2024-08-29 07:49:25.830238: val_loss -0.7945 
-2024-08-29 07:49:25.830385: Pseudo dice [0.0, 0.0, 0.8871, 0.9769, 0.8576, 0.9477, 0.953, 0.9622, 0.9523, 0.9504, 0.9391, 0.9602, 0.9629, 0.8608, 0.9517, 0.9385, 0.8242, 0.8397, nan] 
-2024-08-29 07:49:25.830460: Epoch time: 78.7 s 
-2024-08-29 07:49:27.166520:  
-2024-08-29 07:49:27.166654: Epoch 1759 
-2024-08-29 07:49:27.166733: Current learning rate: 0.00149 
-2024-08-29 07:50:41.125353: train_loss -0.7787 
-2024-08-29 07:50:41.125558: val_loss -0.8007 
-2024-08-29 07:50:41.125702: Pseudo dice [0.0, 0.0, 0.9055, 0.9765, 0.8583, 0.9494, 0.9514, 0.9677, 0.9569, 0.9492, 0.9403, 0.9637, 0.9626, 0.857, 0.9548, 0.9398, 0.8338, 0.8349, nan] 
-2024-08-29 07:50:41.125773: Epoch time: 73.96 s 
-2024-08-29 07:50:42.195449:  
-2024-08-29 07:50:42.195582: Epoch 1760 
-2024-08-29 07:50:42.195657: Current learning rate: 0.00148 
-2024-08-29 07:52:03.066018: train_loss -0.7787 
-2024-08-29 07:52:03.066222: val_loss -0.7998 
-2024-08-29 07:52:03.066374: Pseudo dice [0.0, 0.0, 0.9077, 0.9766, 0.8733, 0.9485, 0.9496, 0.9652, 0.9511, 0.9478, 0.9336, 0.9608, 0.9622, 0.8555, 0.9602, 0.9381, 0.8362, 0.8452, nan] 
-2024-08-29 07:52:03.066449: Epoch time: 80.87 s 
-2024-08-29 07:52:04.149101:  
-2024-08-29 07:52:04.149230: Epoch 1761 
-2024-08-29 07:52:04.149302: Current learning rate: 0.00148 
-2024-08-29 07:53:21.986377: train_loss -0.777 
-2024-08-29 07:53:21.986591: val_loss -0.7978 
-2024-08-29 07:53:21.986735: Pseudo dice [0.0, 0.0, 0.9006, 0.9781, 0.871, 0.9518, 0.9544, 0.968, 0.9563, 0.9558, 0.9337, 0.9617, 0.9626, 0.8591, 0.9577, 0.9305, 0.8434, 0.8471, nan] 
-2024-08-29 07:53:21.986808: Epoch time: 77.84 s 
-2024-08-29 07:53:23.086876:  
-2024-08-29 07:53:23.087008: Epoch 1762 
-2024-08-29 07:53:23.087080: Current learning rate: 0.00147 
-2024-08-29 07:54:42.463278: train_loss -0.779 
-2024-08-29 07:54:42.463471: val_loss -0.7966 
-2024-08-29 07:54:42.463619: Pseudo dice [0.0, 0.0, 0.9021, 0.9775, 0.8616, 0.9461, 0.9492, 0.9651, 0.9553, 0.9433, 0.9338, 0.9632, 0.9644, 0.8499, 0.9544, 0.937, 0.842, 0.8431, nan] 
-2024-08-29 07:54:42.463695: Epoch time: 79.38 s 
-2024-08-29 07:54:43.551717:  
-2024-08-29 07:54:43.551842: Epoch 1763 
-2024-08-29 07:54:43.551922: Current learning rate: 0.00147 
-2024-08-29 07:56:02.254974: train_loss -0.7744 
-2024-08-29 07:56:02.255169: val_loss -0.7949 
-2024-08-29 07:56:02.255310: Pseudo dice [0.0, 0.0, 0.9148, 0.9774, 0.8576, 0.9499, 0.9521, 0.9651, 0.9565, 0.9422, 0.9365, 0.9633, 0.9641, 0.8641, 0.9576, 0.9411, 0.8359, 0.8408, nan] 
-2024-08-29 07:56:02.255480: Epoch time: 78.7 s 
-2024-08-29 07:56:03.339263:  
-2024-08-29 07:56:03.339391: Epoch 1764 
-2024-08-29 07:56:03.339471: Current learning rate: 0.00146 
-2024-08-29 07:57:17.827837: train_loss -0.7759 
-2024-08-29 07:57:17.828039: val_loss -0.7988 
-2024-08-29 07:57:17.828179: Pseudo dice [0.0, 0.0, 0.8989, 0.9775, 0.86, 0.9494, 0.9489, 0.9693, 0.957, 0.9582, 0.9418, 0.965, 0.9655, 0.8617, 0.9581, 0.9395, 0.8355, 0.8273, nan] 
-2024-08-29 07:57:17.828296: Epoch time: 74.49 s 
-2024-08-29 07:57:19.128578:  
-2024-08-29 07:57:19.128719: Epoch 1765 
-2024-08-29 07:57:19.128798: Current learning rate: 0.00146 
-2024-08-29 07:58:34.978604: train_loss -0.773 
-2024-08-29 07:58:34.978885: val_loss -0.7966 
-2024-08-29 07:58:34.979110: Pseudo dice [0.0, 0.0, 0.9047, 0.9777, 0.8634, 0.9492, 0.9543, 0.9677, 0.9576, 0.9523, 0.9425, 0.9642, 0.9628, 0.8606, 0.9494, 0.9383, 0.8502, 0.851, nan] 
-2024-08-29 07:58:34.979333: Epoch time: 75.85 s 
-2024-08-29 07:58:36.062679:  
-2024-08-29 07:58:36.062939: Epoch 1766 
-2024-08-29 07:58:36.063024: Current learning rate: 0.00145 
-2024-08-29 07:59:52.442678: train_loss -0.7724 
-2024-08-29 07:59:52.442885: val_loss -0.7978 
-2024-08-29 07:59:52.443034: Pseudo dice [0.0, 0.0, 0.9085, 0.9773, 0.8449, 0.9519, 0.957, 0.9681, 0.9537, 0.953, 0.9342, 0.9632, 0.9629, 0.8641, 0.959, 0.94, 0.856, 0.8467, nan] 
-2024-08-29 07:59:52.443105: Epoch time: 76.38 s 
-2024-08-29 07:59:53.513660:  
-2024-08-29 07:59:53.513907: Epoch 1767 
-2024-08-29 07:59:53.513991: Current learning rate: 0.00144 
-2024-08-29 08:01:12.114330: train_loss -0.7739 
-2024-08-29 08:01:12.114540: val_loss -0.7985 
-2024-08-29 08:01:12.114687: Pseudo dice [0.0, 0.0, 0.9063, 0.9767, 0.8453, 0.9458, 0.9503, 0.9625, 0.9534, 0.9498, 0.9335, 0.9585, 0.9595, 0.8465, 0.9497, 0.9369, 0.8491, 0.8354, nan] 
-2024-08-29 08:01:12.114759: Epoch time: 78.6 s 
-2024-08-29 08:01:13.204390:  
-2024-08-29 08:01:13.204726: Epoch 1768 
-2024-08-29 08:01:13.204807: Current learning rate: 0.00144 
-2024-08-29 08:02:31.680915: train_loss -0.7747 
-2024-08-29 08:02:31.681109: val_loss -0.8021 
-2024-08-29 08:02:31.681254: Pseudo dice [0.0, 0.0, 0.9025, 0.9783, 0.8615, 0.9514, 0.9534, 0.9626, 0.9593, 0.9601, 0.9405, 0.9641, 0.9661, 0.856, 0.9532, 0.9406, 0.8482, 0.8358, nan] 
-2024-08-29 08:02:31.681328: Epoch time: 78.48 s 
-2024-08-29 08:02:32.772489:  
-2024-08-29 08:02:32.772763: Epoch 1769 
-2024-08-29 08:02:32.772857: Current learning rate: 0.00143 
-2024-08-29 08:03:50.877592: train_loss -0.7792 
-2024-08-29 08:03:50.877797: val_loss -0.7959 
-2024-08-29 08:03:50.877943: Pseudo dice [0.0, 0.0, 0.8964, 0.9781, 0.8521, 0.955, 0.956, 0.968, 0.9582, 0.954, 0.9372, 0.9655, 0.9639, 0.8659, 0.9591, 0.9439, 0.8425, 0.8434, nan] 
-2024-08-29 08:03:50.878071: Epoch time: 78.11 s 
-2024-08-29 08:03:51.947950:  
-2024-08-29 08:03:51.948083: Epoch 1770 
-2024-08-29 08:03:51.948161: Current learning rate: 0.00143 
-2024-08-29 08:05:08.644904: train_loss -0.7751 
-2024-08-29 08:05:08.645101: val_loss -0.8 
-2024-08-29 08:05:08.645236: Pseudo dice [0.0, 0.0, 0.9137, 0.9775, 0.865, 0.9531, 0.9529, 0.9668, 0.9536, 0.9562, 0.9403, 0.9624, 0.9618, 0.8588, 0.9629, 0.9414, 0.8501, 0.8349, nan] 
-2024-08-29 08:05:08.645306: Epoch time: 76.7 s 
-2024-08-29 08:05:08.645349: Yayy! New best EMA pseudo Dice: 0.8232 
-2024-08-29 08:05:10.380719:  
-2024-08-29 08:05:10.380849: Epoch 1771 
-2024-08-29 08:05:10.380928: Current learning rate: 0.00142 
-2024-08-29 08:06:26.789537: train_loss -0.776 
-2024-08-29 08:06:26.789734: val_loss -0.7953 
-2024-08-29 08:06:26.789876: Pseudo dice [0.0, 0.0, 0.8972, 0.978, 0.8532, 0.9497, 0.9526, 0.9661, 0.9574, 0.9476, 0.9429, 0.9637, 0.9649, 0.8602, 0.9518, 0.9372, 0.8526, 0.8399, nan] 
-2024-08-29 08:06:26.789946: Epoch time: 76.41 s 
-2024-08-29 08:06:27.865938:  
-2024-08-29 08:06:27.866060: Epoch 1772 
-2024-08-29 08:06:27.866133: Current learning rate: 0.00142 
-2024-08-29 08:07:44.938991: train_loss -0.7778 
-2024-08-29 08:07:44.939196: val_loss -0.7906 
-2024-08-29 08:07:44.939345: Pseudo dice [0.0, 0.0, 0.9055, 0.9784, 0.8726, 0.9422, 0.9497, 0.9666, 0.9504, 0.9547, 0.9354, 0.9598, 0.9628, 0.8485, 0.9543, 0.9418, 0.8494, 0.8496, nan] 
-2024-08-29 08:07:44.939416: Epoch time: 77.07 s 
-2024-08-29 08:07:44.939457: Yayy! New best EMA pseudo Dice: 0.8232 
-2024-08-29 08:07:46.438264:  
-2024-08-29 08:07:46.438408: Epoch 1773 
-2024-08-29 08:07:46.438484: Current learning rate: 0.00141 
-2024-08-29 08:09:09.989159: train_loss -0.7749 
-2024-08-29 08:09:09.989338: val_loss -0.7928 
-2024-08-29 08:09:09.989478: Pseudo dice [0.0, 0.0, 0.9104, 0.9769, 0.8453, 0.9464, 0.9476, 0.9659, 0.9553, 0.9558, 0.9318, 0.9628, 0.9619, 0.8524, 0.9519, 0.9371, 0.84, 0.8347, nan] 
-2024-08-29 08:09:09.989545: Epoch time: 83.55 s 
-2024-08-29 08:09:11.066453:  
-2024-08-29 08:09:11.066587: Epoch 1774 
-2024-08-29 08:09:11.066669: Current learning rate: 0.00141 
-2024-08-29 08:10:29.163265: train_loss -0.7757 
-2024-08-29 08:10:29.163548: val_loss -0.8013 
-2024-08-29 08:10:29.163792: Pseudo dice [0.0, 0.0, 0.9105, 0.9774, 0.8596, 0.9498, 0.9539, 0.9686, 0.9532, 0.9572, 0.9404, 0.9649, 0.9633, 0.8571, 0.9541, 0.9416, 0.8491, 0.8387, nan] 
-2024-08-29 08:10:29.163911: Epoch time: 78.1 s 
-2024-08-29 08:10:30.287984:  
-2024-08-29 08:10:30.288119: Epoch 1775 
-2024-08-29 08:10:30.288206: Current learning rate: 0.0014 
-2024-08-29 08:11:43.074959: train_loss -0.7769 
-2024-08-29 08:11:43.075417: val_loss -0.7944 
-2024-08-29 08:11:43.075697: Pseudo dice [0.0, 0.0, 0.9026, 0.9775, 0.8571, 0.9502, 0.95, 0.9679, 0.9551, 0.9527, 0.9392, 0.9602, 0.9613, 0.8519, 0.9575, 0.9404, 0.844, 0.8401, nan] 
-2024-08-29 08:11:43.075891: Epoch time: 72.79 s 
-2024-08-29 08:11:44.148722:  
-2024-08-29 08:11:44.148867: Epoch 1776 
-2024-08-29 08:11:44.148942: Current learning rate: 0.00139 
-2024-08-29 08:12:59.973499: train_loss -0.7798 
-2024-08-29 08:12:59.973704: val_loss -0.7922 
-2024-08-29 08:12:59.973850: Pseudo dice [0.0, 0.0, 0.9007, 0.9782, 0.8736, 0.9498, 0.953, 0.9608, 0.9574, 0.9525, 0.9383, 0.9633, 0.9646, 0.855, 0.9525, 0.9369, 0.8357, 0.8337, nan] 
-2024-08-29 08:12:59.973922: Epoch time: 75.83 s 
-2024-08-29 08:13:01.305348:  
-2024-08-29 08:13:01.305499: Epoch 1777 
-2024-08-29 08:13:01.305579: Current learning rate: 0.00139 
-2024-08-29 08:14:15.563490: train_loss -0.7793 
-2024-08-29 08:14:15.563701: val_loss -0.8013 
-2024-08-29 08:14:15.563846: Pseudo dice [0.0, 0.0, 0.912, 0.9787, 0.8721, 0.9499, 0.9568, 0.9693, 0.9552, 0.9551, 0.9335, 0.9627, 0.9616, 0.8633, 0.9586, 0.9425, 0.846, 0.8469, nan] 
-2024-08-29 08:14:15.563918: Epoch time: 74.26 s 
-2024-08-29 08:14:15.563959: Yayy! New best EMA pseudo Dice: 0.8233 
-2024-08-29 08:14:17.107209:  
-2024-08-29 08:14:17.107343: Epoch 1778 
-2024-08-29 08:14:17.107424: Current learning rate: 0.00138 
-2024-08-29 08:15:30.483550: train_loss -0.7824 
-2024-08-29 08:15:30.483751: val_loss -0.796 
-2024-08-29 08:15:30.483891: Pseudo dice [0.0, 0.0, 0.9064, 0.9778, 0.868, 0.9518, 0.9536, 0.967, 0.9542, 0.959, 0.9424, 0.963, 0.9631, 0.856, 0.9562, 0.941, 0.8256, 0.8315, nan] 
-2024-08-29 08:15:30.483961: Epoch time: 73.38 s 
-2024-08-29 08:15:31.571886:  
-2024-08-29 08:15:31.572323: Epoch 1779 
-2024-08-29 08:15:31.572454: Current learning rate: 0.00138 
-2024-08-29 08:16:47.010728: train_loss -0.7813 
-2024-08-29 08:16:47.010915: val_loss -0.7957 
-2024-08-29 08:16:47.011055: Pseudo dice [0.0, 0.0, 0.9, 0.9773, 0.8695, 0.9429, 0.9472, 0.9658, 0.9567, 0.9499, 0.9338, 0.9626, 0.9635, 0.8578, 0.9592, 0.9411, 0.8475, 0.8466, nan] 
-2024-08-29 08:16:47.011125: Epoch time: 75.44 s 
-2024-08-29 08:16:47.011168: Yayy! New best EMA pseudo Dice: 0.8233 
-2024-08-29 08:16:48.516511:  
-2024-08-29 08:16:48.516643: Epoch 1780 
-2024-08-29 08:16:48.516721: Current learning rate: 0.00137 
-2024-08-29 08:18:10.216329: train_loss -0.7721 
-2024-08-29 08:18:10.216714: val_loss -0.8015 
-2024-08-29 08:18:10.216859: Pseudo dice [0.0, 0.0, 0.9105, 0.9768, 0.872, 0.9488, 0.9483, 0.9689, 0.9549, 0.955, 0.9426, 0.9641, 0.9647, 0.8543, 0.9572, 0.9434, 0.8531, 0.8509, nan] 
-2024-08-29 08:18:10.216930: Epoch time: 81.7 s 
-2024-08-29 08:18:10.216971: Yayy! New best EMA pseudo Dice: 0.8235 
-2024-08-29 08:18:11.738564:  
-2024-08-29 08:18:11.738824: Epoch 1781 
-2024-08-29 08:18:11.738902: Current learning rate: 0.00137 
-2024-08-29 08:19:25.976630: train_loss -0.7784 
-2024-08-29 08:19:25.976826: val_loss -0.7967 
-2024-08-29 08:19:25.976975: Pseudo dice [0.0, 0.0, 0.9097, 0.9768, 0.8577, 0.9476, 0.9533, 0.9665, 0.9581, 0.952, 0.9387, 0.9635, 0.9639, 0.8624, 0.9575, 0.9381, 0.826, 0.8435, nan] 
-2024-08-29 08:19:25.977051: Epoch time: 74.24 s 
-2024-08-29 08:19:27.248732:  
-2024-08-29 08:19:27.249154: Epoch 1782 
-2024-08-29 08:19:27.249302: Current learning rate: 0.00136 
-2024-08-29 08:20:39.889134: train_loss -0.7812 
-2024-08-29 08:20:39.889340: val_loss -0.7977 
-2024-08-29 08:20:39.889482: Pseudo dice [0.0, 0.0, 0.9129, 0.9773, 0.8623, 0.9524, 0.9549, 0.9689, 0.9603, 0.9508, 0.9393, 0.9663, 0.9649, 0.8572, 0.9596, 0.9413, 0.8311, 0.8324, nan] 
-2024-08-29 08:20:39.889553: Epoch time: 72.64 s 
-2024-08-29 08:20:39.889595: Yayy! New best EMA pseudo Dice: 0.8235 
-2024-08-29 08:20:41.401503:  
-2024-08-29 08:20:41.401812: Epoch 1783 
-2024-08-29 08:20:41.401901: Current learning rate: 0.00135 
-2024-08-29 08:21:58.848362: train_loss -0.7754 
-2024-08-29 08:21:58.848563: val_loss -0.7938 
-2024-08-29 08:21:58.848714: Pseudo dice [0.0, 0.0, 0.8978, 0.9773, 0.8507, 0.9449, 0.9442, 0.9636, 0.9528, 0.9562, 0.9406, 0.9619, 0.9668, 0.8582, 0.9571, 0.9425, 0.8392, 0.828, nan] 
-2024-08-29 08:21:58.848803: Epoch time: 77.45 s 
-2024-08-29 08:21:59.925382:  
-2024-08-29 08:21:59.925671: Epoch 1784 
-2024-08-29 08:21:59.925756: Current learning rate: 0.00135 
-2024-08-29 08:23:20.330727: train_loss -0.7783 
-2024-08-29 08:23:20.330929: val_loss -0.7936 
-2024-08-29 08:23:20.331067: Pseudo dice [0.0, 0.0, 0.8995, 0.9779, 0.8577, 0.9485, 0.9529, 0.9655, 0.9537, 0.9554, 0.9415, 0.9625, 0.9643, 0.857, 0.9465, 0.9392, 0.844, 0.8407, nan] 
-2024-08-29 08:23:20.331138: Epoch time: 80.41 s 
-2024-08-29 08:23:21.412253:  
-2024-08-29 08:23:21.412721: Epoch 1785 
-2024-08-29 08:23:21.412807: Current learning rate: 0.00134 
-2024-08-29 08:24:37.834968: train_loss -0.7775 
-2024-08-29 08:24:37.835185: val_loss -0.796 
-2024-08-29 08:24:37.835342: Pseudo dice [0.0, 0.0, 0.8952, 0.9775, 0.8567, 0.9516, 0.9557, 0.9694, 0.9565, 0.9587, 0.9392, 0.9655, 0.9648, 0.861, 0.9579, 0.9411, 0.8424, 0.8454, nan] 
-2024-08-29 08:24:37.835418: Epoch time: 76.42 s 
-2024-08-29 08:24:38.906554:  
-2024-08-29 08:24:38.906710: Epoch 1786 
-2024-08-29 08:24:38.906790: Current learning rate: 0.00134 
-2024-08-29 08:25:56.677410: train_loss -0.7772 
-2024-08-29 08:25:56.677605: val_loss -0.7962 
-2024-08-29 08:25:56.677743: Pseudo dice [0.0, 0.0, 0.8842, 0.9773, 0.842, 0.95, 0.951, 0.9648, 0.9539, 0.9592, 0.9403, 0.9618, 0.9645, 0.8414, 0.9549, 0.9381, 0.8293, 0.8287, nan] 
-2024-08-29 08:25:56.677814: Epoch time: 77.77 s 
-2024-08-29 08:25:57.762589:  
-2024-08-29 08:25:57.762723: Epoch 1787 
-2024-08-29 08:25:57.762798: Current learning rate: 0.00133 
-2024-08-29 08:27:10.874216: train_loss -0.78 
-2024-08-29 08:27:10.874409: val_loss -0.7907 
-2024-08-29 08:27:10.874552: Pseudo dice [0.0, 0.0, 0.909, 0.977, 0.8676, 0.9484, 0.9505, 0.9666, 0.9547, 0.952, 0.9325, 0.9627, 0.9624, 0.8434, 0.9566, 0.9381, 0.8272, 0.8233, nan] 
-2024-08-29 08:27:10.874623: Epoch time: 73.11 s 
-2024-08-29 08:27:12.191649:  
-2024-08-29 08:27:12.191937: Epoch 1788 
-2024-08-29 08:27:12.192014: Current learning rate: 0.00133 
-2024-08-29 08:28:29.566023: train_loss -0.7774 
-2024-08-29 08:28:29.566229: val_loss -0.7959 
-2024-08-29 08:28:29.566378: Pseudo dice [0.0, 0.0, 0.9096, 0.978, 0.8597, 0.951, 0.9556, 0.9695, 0.9587, 0.9509, 0.9394, 0.9646, 0.9632, 0.8547, 0.9584, 0.9402, 0.8395, 0.8317, nan] 
-2024-08-29 08:28:29.566453: Epoch time: 77.38 s 
-2024-08-29 08:28:30.638829:  
-2024-08-29 08:28:30.638960: Epoch 1789 
-2024-08-29 08:28:30.639039: Current learning rate: 0.00132 
-2024-08-29 08:29:51.298517: train_loss -0.7765 
-2024-08-29 08:29:51.298723: val_loss -0.7979 
-2024-08-29 08:29:51.298859: Pseudo dice [0.0, 0.0, 0.9019, 0.9779, 0.8391, 0.9419, 0.9495, 0.9689, 0.9502, 0.9543, 0.9411, 0.9596, 0.9648, 0.8608, 0.9582, 0.9368, 0.8409, 0.8406, nan] 
-2024-08-29 08:29:51.298929: Epoch time: 80.66 s 
-2024-08-29 08:29:52.384848:  
-2024-08-29 08:29:52.385010: Epoch 1790 
-2024-08-29 08:29:52.385100: Current learning rate: 0.00132 
-2024-08-29 08:31:10.490324: train_loss -0.7783 
-2024-08-29 08:31:10.490852: val_loss -0.8018 
-2024-08-29 08:31:10.491077: Pseudo dice [0.0, 0.0, 0.9144, 0.9778, 0.859, 0.948, 0.9534, 0.9684, 0.9589, 0.9559, 0.9432, 0.9645, 0.9649, 0.8615, 0.9602, 0.9434, 0.8513, 0.8531, nan] 
-2024-08-29 08:31:10.491180: Epoch time: 78.11 s 
-2024-08-29 08:31:11.714466:  
-2024-08-29 08:31:11.715032: Epoch 1791 
-2024-08-29 08:31:11.715312: Current learning rate: 0.00131 
-2024-08-29 08:32:29.560510: train_loss -0.7804 
-2024-08-29 08:32:29.560874: val_loss -0.7977 
-2024-08-29 08:32:29.561065: Pseudo dice [0.0, 0.0, 0.913, 0.9781, 0.8837, 0.9533, 0.955, 0.9692, 0.9538, 0.9483, 0.9382, 0.9626, 0.9643, 0.8644, 0.95, 0.9441, 0.84, 0.8441, nan] 
-2024-08-29 08:32:29.561158: Epoch time: 77.85 s 
-2024-08-29 08:32:30.652184:  
-2024-08-29 08:32:30.652322: Epoch 1792 
-2024-08-29 08:32:30.652403: Current learning rate: 0.0013 
-2024-08-29 08:33:44.261455: train_loss -0.781 
-2024-08-29 08:33:44.261920: val_loss -0.7969 
-2024-08-29 08:33:44.262108: Pseudo dice [0.0, 0.0, 0.9119, 0.9757, 0.8547, 0.9467, 0.9485, 0.9633, 0.9589, 0.9452, 0.9294, 0.9643, 0.9625, 0.8553, 0.9507, 0.9367, 0.8609, 0.8514, nan] 
-2024-08-29 08:33:44.262240: Epoch time: 73.61 s 
-2024-08-29 08:33:45.409272:  
-2024-08-29 08:33:45.409421: Epoch 1793 
-2024-08-29 08:33:45.409508: Current learning rate: 0.0013 
-2024-08-29 08:35:04.911034: train_loss -0.7799 
-2024-08-29 08:35:04.911252: val_loss -0.7999 
-2024-08-29 08:35:04.911402: Pseudo dice [0.0, 0.0, 0.9053, 0.9786, 0.8586, 0.9529, 0.9536, 0.9693, 0.9556, 0.9553, 0.9375, 0.9619, 0.9624, 0.861, 0.9593, 0.9405, 0.8469, 0.8451, nan] 
-2024-08-29 08:35:04.911477: Epoch time: 79.5 s 
-2024-08-29 08:35:06.304672:  
-2024-08-29 08:35:06.305002: Epoch 1794 
-2024-08-29 08:35:06.305092: Current learning rate: 0.00129 
-2024-08-29 08:36:21.024999: train_loss -0.7741 
-2024-08-29 08:36:21.025281: val_loss -0.7898 
-2024-08-29 08:36:21.025435: Pseudo dice [0.0, 0.0, 0.8956, 0.9783, 0.8639, 0.9497, 0.9533, 0.9674, 0.9489, 0.9513, 0.9362, 0.9602, 0.959, 0.8523, 0.9562, 0.9358, 0.8427, 0.8321, nan] 
-2024-08-29 08:36:21.025523: Epoch time: 74.72 s 
-2024-08-29 08:36:22.122636:  
-2024-08-29 08:36:22.122785: Epoch 1795 
-2024-08-29 08:36:22.122873: Current learning rate: 0.00129 
-2024-08-29 08:37:38.009140: train_loss -0.7803 
-2024-08-29 08:37:38.009396: val_loss -0.8018 
-2024-08-29 08:37:38.009571: Pseudo dice [0.0, 0.0, 0.9183, 0.9776, 0.866, 0.9509, 0.9534, 0.9654, 0.9534, 0.954, 0.944, 0.9618, 0.9658, 0.8661, 0.9491, 0.9386, 0.848, 0.8409, nan] 
-2024-08-29 08:37:38.009666: Epoch time: 75.89 s 
-2024-08-29 08:37:39.142619:  
-2024-08-29 08:37:39.142883: Epoch 1796 
-2024-08-29 08:37:39.142974: Current learning rate: 0.00128 
-2024-08-29 08:38:57.274167: train_loss -0.7798 
-2024-08-29 08:38:57.274456: val_loss -0.7982 
-2024-08-29 08:38:57.274624: Pseudo dice [0.0, 0.0, 0.904, 0.9771, 0.8838, 0.9495, 0.9513, 0.9663, 0.9547, 0.9539, 0.9373, 0.9617, 0.966, 0.8613, 0.9591, 0.9406, 0.8467, 0.8504, nan] 
-2024-08-29 08:38:57.274707: Epoch time: 78.13 s 
-2024-08-29 08:38:57.274754: Yayy! New best EMA pseudo Dice: 0.8236 
-2024-08-29 08:38:58.802686:  
-2024-08-29 08:38:58.802841: Epoch 1797 
-2024-08-29 08:38:58.802924: Current learning rate: 0.00128 
-2024-08-29 08:40:12.545446: train_loss -0.78 
-2024-08-29 08:40:12.545863: val_loss -0.7977 
-2024-08-29 08:40:12.546011: Pseudo dice [0.0, 0.0, 0.904, 0.9776, 0.8781, 0.9502, 0.9526, 0.9698, 0.9526, 0.9502, 0.9374, 0.9625, 0.961, 0.861, 0.9484, 0.9406, 0.8397, 0.8482, nan] 
-2024-08-29 08:40:12.546228: Epoch time: 73.74 s 
-2024-08-29 08:40:12.546273: Yayy! New best EMA pseudo Dice: 0.8237 
-2024-08-29 08:40:14.094820:  
-2024-08-29 08:40:14.094972: Epoch 1798 
-2024-08-29 08:40:14.095050: Current learning rate: 0.00127 
-2024-08-29 08:41:31.208586: train_loss -0.7768 
-2024-08-29 08:41:31.208792: val_loss -0.7981 
-2024-08-29 08:41:31.208934: Pseudo dice [0.0, 0.0, 0.9076, 0.9777, 0.842, 0.9429, 0.9494, 0.9656, 0.9541, 0.9476, 0.9379, 0.9608, 0.9614, 0.8672, 0.9415, 0.9393, 0.8395, 0.8439, nan] 
-2024-08-29 08:41:31.209009: Epoch time: 77.11 s 
-2024-08-29 08:41:32.321539:  
-2024-08-29 08:41:32.321684: Epoch 1799 
-2024-08-29 08:41:32.321765: Current learning rate: 0.00126 
-2024-08-29 08:42:50.962696: train_loss -0.7798 
-2024-08-29 08:42:50.962910: val_loss -0.797 
-2024-08-29 08:42:50.963059: Pseudo dice [0.0, 0.0, 0.912, 0.9777, 0.8606, 0.953, 0.954, 0.9634, 0.9559, 0.9564, 0.9326, 0.9652, 0.9626, 0.8635, 0.9566, 0.9412, 0.8527, 0.8472, nan] 
-2024-08-29 08:42:50.963140: Epoch time: 78.64 s 
-2024-08-29 08:42:52.751854:  
-2024-08-29 08:42:52.751997: Epoch 1800 
-2024-08-29 08:42:52.752091: Current learning rate: 0.00126 
-2024-08-29 08:44:08.300595: train_loss -0.779 
-2024-08-29 08:44:08.300836: val_loss -0.7951 
-2024-08-29 08:44:08.300982: Pseudo dice [0.0, 0.0, 0.9026, 0.9773, 0.8665, 0.9491, 0.9517, 0.9685, 0.9574, 0.9404, 0.9365, 0.9607, 0.96, 0.856, 0.9497, 0.9374, 0.8318, 0.8325, nan] 
-2024-08-29 08:44:08.301058: Epoch time: 75.55 s 
-2024-08-29 08:44:09.403776:  
-2024-08-29 08:44:09.404218: Epoch 1801 
-2024-08-29 08:44:09.404311: Current learning rate: 0.00125 
-2024-08-29 08:45:32.517003: train_loss -0.7773 
-2024-08-29 08:45:32.517199: val_loss -0.8011 
-2024-08-29 08:45:32.517342: Pseudo dice [0.0, 0.0, 0.9024, 0.978, 0.8605, 0.9518, 0.9523, 0.9692, 0.9564, 0.9593, 0.9426, 0.962, 0.964, 0.8667, 0.9566, 0.9404, 0.8476, 0.8353, nan] 
-2024-08-29 08:45:32.517417: Epoch time: 83.11 s 
-2024-08-29 08:45:33.613754:  
-2024-08-29 08:45:33.614011: Epoch 1802 
-2024-08-29 08:45:33.614105: Current learning rate: 0.00125 
-2024-08-29 08:46:49.581051: train_loss -0.7819 
-2024-08-29 08:46:49.581268: val_loss -0.797 
-2024-08-29 08:46:49.581409: Pseudo dice [0.0, 0.0, 0.9209, 0.9783, 0.8665, 0.951, 0.954, 0.9661, 0.9545, 0.9572, 0.9406, 0.9617, 0.9654, 0.8636, 0.9564, 0.9422, 0.8456, 0.8473, nan] 
-2024-08-29 08:46:49.581485: Epoch time: 75.97 s 
-2024-08-29 08:46:49.581529: Yayy! New best EMA pseudo Dice: 0.8238 
-2024-08-29 08:46:51.162515:  
-2024-08-29 08:46:51.162768: Epoch 1803 
-2024-08-29 08:46:51.162854: Current learning rate: 0.00124 
-2024-08-29 08:48:05.515831: train_loss -0.7797 
-2024-08-29 08:48:05.516042: val_loss -0.8023 
-2024-08-29 08:48:05.516199: Pseudo dice [0.0, 0.0, 0.9016, 0.9776, 0.8669, 0.9492, 0.9523, 0.9637, 0.9581, 0.9562, 0.9353, 0.9639, 0.9603, 0.8604, 0.9586, 0.937, 0.8485, 0.8393, nan] 
-2024-08-29 08:48:05.516276: Epoch time: 74.35 s 
-2024-08-29 08:48:05.516319: Yayy! New best EMA pseudo Dice: 0.8238 
-2024-08-29 08:48:07.071139:  
-2024-08-29 08:48:07.071336: Epoch 1804 
-2024-08-29 08:48:07.071425: Current learning rate: 0.00124 
-2024-08-29 08:49:29.195551: train_loss -0.7817 
-2024-08-29 08:49:29.195768: val_loss -0.7996 
-2024-08-29 08:49:29.195905: Pseudo dice [0.0, 0.0, 0.9048, 0.9783, 0.8757, 0.9527, 0.9551, 0.9691, 0.9573, 0.9519, 0.9414, 0.9627, 0.9661, 0.864, 0.9567, 0.9431, 0.8416, 0.8433, nan] 
-2024-08-29 08:49:29.195978: Epoch time: 82.13 s 
-2024-08-29 08:49:29.196020: Yayy! New best EMA pseudo Dice: 0.824 
-2024-08-29 08:49:30.793005:  
-2024-08-29 08:49:30.793141: Epoch 1805 
-2024-08-29 08:49:30.793232: Current learning rate: 0.00123 
-2024-08-29 08:50:45.896871: train_loss -0.7776 
-2024-08-29 08:50:45.897106: val_loss -0.7938 
-2024-08-29 08:50:45.897248: Pseudo dice [0.0, 0.0, 0.8856, 0.9772, 0.8557, 0.9517, 0.954, 0.9641, 0.9541, 0.952, 0.9388, 0.9637, 0.9645, 0.8555, 0.9505, 0.939, 0.8307, 0.8335, nan] 
-2024-08-29 08:50:45.897322: Epoch time: 75.1 s 
-2024-08-29 08:50:47.228797:  
-2024-08-29 08:50:47.228944: Epoch 1806 
-2024-08-29 08:50:47.229023: Current learning rate: 0.00122 
-2024-08-29 08:52:01.192380: train_loss -0.7769 
-2024-08-29 08:52:01.192612: val_loss -0.8013 
-2024-08-29 08:52:01.192765: Pseudo dice [0.0, 0.0, 0.9032, 0.976, 0.872, 0.9498, 0.9514, 0.9606, 0.9546, 0.9524, 0.9392, 0.962, 0.9653, 0.856, 0.9517, 0.9379, 0.8401, 0.847, nan] 
-2024-08-29 08:52:01.192841: Epoch time: 73.96 s 
-2024-08-29 08:52:02.303432:  
-2024-08-29 08:52:02.303751: Epoch 1807 
-2024-08-29 08:52:02.303905: Current learning rate: 0.00122 
-2024-08-29 08:53:19.531062: train_loss -0.7785 
-2024-08-29 08:53:19.531482: val_loss -0.8014 
-2024-08-29 08:53:19.531629: Pseudo dice [0.0, 0.0, 0.9128, 0.9774, 0.8704, 0.9518, 0.9505, 0.9671, 0.9523, 0.9527, 0.9377, 0.9606, 0.9644, 0.8644, 0.9545, 0.9414, 0.8525, 0.8498, nan] 
-2024-08-29 08:53:19.531701: Epoch time: 77.23 s 
-2024-08-29 08:53:20.620013:  
-2024-08-29 08:53:20.620152: Epoch 1808 
-2024-08-29 08:53:20.620229: Current learning rate: 0.00121 
-2024-08-29 08:54:34.864162: train_loss -0.7818 
-2024-08-29 08:54:34.864397: val_loss -0.8006 
-2024-08-29 08:54:34.864563: Pseudo dice [0.0, 0.0, 0.8991, 0.9765, 0.8784, 0.9502, 0.956, 0.9676, 0.9568, 0.955, 0.9376, 0.9637, 0.9622, 0.8648, 0.9551, 0.9411, 0.8437, 0.8273, nan] 
-2024-08-29 08:54:34.864647: Epoch time: 74.24 s 
-2024-08-29 08:54:35.990353:  
-2024-08-29 08:54:35.990814: Epoch 1809 
-2024-08-29 08:54:35.990912: Current learning rate: 0.00121 
-2024-08-29 08:55:54.443425: train_loss -0.7794 
-2024-08-29 08:55:54.443651: val_loss -0.7979 
-2024-08-29 08:55:54.443796: Pseudo dice [0.0, 0.0, 0.9135, 0.977, 0.8616, 0.9479, 0.9506, 0.9614, 0.9529, 0.9444, 0.9388, 0.9606, 0.9628, 0.8668, 0.9469, 0.9375, 0.8452, 0.8448, nan] 
-2024-08-29 08:55:54.443883: Epoch time: 78.45 s 
-2024-08-29 08:55:55.583430:  
-2024-08-29 08:55:55.583680: Epoch 1810 
-2024-08-29 08:55:55.583769: Current learning rate: 0.0012 
-2024-08-29 08:57:11.544333: train_loss -0.7811 
-2024-08-29 08:57:11.544575: val_loss -0.8001 
-2024-08-29 08:57:11.544726: Pseudo dice [0.0, 0.0, 0.915, 0.9779, 0.8305, 0.9433, 0.9492, 0.9699, 0.9513, 0.9521, 0.9433, 0.9631, 0.964, 0.8638, 0.9568, 0.939, 0.8509, 0.8518, nan] 
-2024-08-29 08:57:11.544817: Epoch time: 75.96 s 
-2024-08-29 08:57:12.651970:  
-2024-08-29 08:57:12.652234: Epoch 1811 
-2024-08-29 08:57:12.652325: Current learning rate: 0.0012 
-2024-08-29 08:58:30.988719: train_loss -0.7783 
-2024-08-29 08:58:30.988928: val_loss -0.8003 
-2024-08-29 08:58:30.989068: Pseudo dice [0.0, 0.0, 0.8988, 0.9742, 0.8505, 0.9429, 0.9488, 0.9698, 0.9536, 0.9571, 0.9387, 0.9589, 0.9629, 0.8569, 0.9574, 0.9406, 0.8365, 0.8403, nan] 
-2024-08-29 08:58:30.989142: Epoch time: 78.34 s 
-2024-08-29 08:58:32.295618:  
-2024-08-29 08:58:32.295975: Epoch 1812 
-2024-08-29 08:58:32.296067: Current learning rate: 0.00119 
-2024-08-29 08:59:49.383591: train_loss -0.7753 
-2024-08-29 08:59:49.383812: val_loss -0.796 
-2024-08-29 08:59:49.383961: Pseudo dice [0.0, 0.0, 0.9037, 0.9774, 0.8513, 0.9455, 0.949, 0.9659, 0.9538, 0.9533, 0.9395, 0.9645, 0.9635, 0.8537, 0.9586, 0.9365, 0.847, 0.8409, nan] 
-2024-08-29 08:59:49.384037: Epoch time: 77.09 s 
-2024-08-29 08:59:50.489362:  
-2024-08-29 08:59:50.489515: Epoch 1813 
-2024-08-29 08:59:50.489595: Current learning rate: 0.00119 
-2024-08-29 09:01:07.761025: train_loss -0.7797 
-2024-08-29 09:01:07.761245: val_loss -0.7941 
-2024-08-29 09:01:07.761384: Pseudo dice [0.0, 0.0, 0.9073, 0.9774, 0.8234, 0.9381, 0.9426, 0.9617, 0.9562, 0.9555, 0.9381, 0.9623, 0.9642, 0.8546, 0.9509, 0.935, 0.8454, 0.8363, nan] 
-2024-08-29 09:01:07.761455: Epoch time: 77.27 s 
-2024-08-29 09:01:08.863734:  
-2024-08-29 09:01:08.863865: Epoch 1814 
-2024-08-29 09:01:08.863944: Current learning rate: 0.00118 
-2024-08-29 09:02:30.790772: train_loss -0.7786 
-2024-08-29 09:02:30.790998: val_loss -0.7934 
-2024-08-29 09:02:30.791143: Pseudo dice [0.0, 0.0, 0.9049, 0.9769, 0.8692, 0.9486, 0.9526, 0.9661, 0.952, 0.9484, 0.9412, 0.9625, 0.9644, 0.8562, 0.9573, 0.9398, 0.8561, 0.847, nan] 
-2024-08-29 09:02:30.791220: Epoch time: 81.93 s 
-2024-08-29 09:02:31.895902:  
-2024-08-29 09:02:31.896062: Epoch 1815 
-2024-08-29 09:02:31.896145: Current learning rate: 0.00117 
-2024-08-29 09:03:46.924756: train_loss -0.7744 
-2024-08-29 09:03:46.924994: val_loss -0.7927 
-2024-08-29 09:03:46.925140: Pseudo dice [0.0, 0.0, 0.9175, 0.979, 0.8531, 0.9453, 0.9504, 0.9643, 0.9542, 0.9476, 0.9406, 0.9644, 0.9644, 0.8642, 0.9498, 0.9392, 0.8556, 0.8391, nan] 
-2024-08-29 09:03:46.925218: Epoch time: 75.03 s 
-2024-08-29 09:03:48.060067:  
-2024-08-29 09:03:48.060454: Epoch 1816 
-2024-08-29 09:03:48.060565: Current learning rate: 0.00117 
-2024-08-29 09:05:04.527492: train_loss -0.7784 
-2024-08-29 09:05:04.527723: val_loss -0.7998 
-2024-08-29 09:05:04.527872: Pseudo dice [0.0, 0.0, 0.8973, 0.9776, 0.8587, 0.9497, 0.9525, 0.9665, 0.9574, 0.9618, 0.9414, 0.9639, 0.9636, 0.8633, 0.9598, 0.9417, 0.8454, 0.8509, nan] 
-2024-08-29 09:05:04.527949: Epoch time: 76.47 s 
-2024-08-29 09:05:05.817004:  
-2024-08-29 09:05:05.817399: Epoch 1817 
-2024-08-29 09:05:05.817486: Current learning rate: 0.00116 
-2024-08-29 09:06:26.946319: train_loss -0.78 
-2024-08-29 09:06:26.946552: val_loss -0.7961 
-2024-08-29 09:06:26.946712: Pseudo dice [0.0, 0.0, 0.9118, 0.9773, 0.8735, 0.9493, 0.9562, 0.9697, 0.9548, 0.9562, 0.9464, 0.9623, 0.9652, 0.8646, 0.954, 0.935, 0.8432, 0.8479, nan] 
-2024-08-29 09:06:26.946792: Epoch time: 81.13 s 
-2024-08-29 09:06:28.260900:  
-2024-08-29 09:06:28.261064: Epoch 1818 
-2024-08-29 09:06:28.261145: Current learning rate: 0.00116 
-2024-08-29 09:07:41.561815: train_loss -0.7802 
-2024-08-29 09:07:41.562036: val_loss -0.7975 
-2024-08-29 09:07:41.562179: Pseudo dice [0.0, 0.0, 0.9177, 0.9776, 0.857, 0.9463, 0.9523, 0.9687, 0.9564, 0.9543, 0.9402, 0.9655, 0.9649, 0.8525, 0.9508, 0.9387, 0.8583, 0.8508, nan] 
-2024-08-29 09:07:41.562269: Epoch time: 73.3 s 
-2024-08-29 09:07:42.671249:  
-2024-08-29 09:07:42.671386: Epoch 1819 
-2024-08-29 09:07:42.671464: Current learning rate: 0.00115 
-2024-08-29 09:08:57.077110: train_loss -0.7796 
-2024-08-29 09:08:57.077327: val_loss -0.7986 
-2024-08-29 09:08:57.077479: Pseudo dice [0.0, 0.0, 0.9077, 0.9767, 0.856, 0.9442, 0.9457, 0.9639, 0.9568, 0.9488, 0.9415, 0.9653, 0.9658, 0.8567, 0.9555, 0.9408, 0.8456, 0.853, nan] 
-2024-08-29 09:08:57.077565: Epoch time: 74.41 s 
-2024-08-29 09:08:58.173760:  
-2024-08-29 09:08:58.174027: Epoch 1820 
-2024-08-29 09:08:58.174113: Current learning rate: 0.00115 
-2024-08-29 09:10:12.863827: train_loss -0.7788 
-2024-08-29 09:10:12.864043: val_loss -0.7991 
-2024-08-29 09:10:12.864185: Pseudo dice [0.0, 0.0, 0.9146, 0.9766, 0.8598, 0.9486, 0.9512, 0.9676, 0.9531, 0.9492, 0.9344, 0.9617, 0.9635, 0.8588, 0.9504, 0.9429, 0.8535, 0.8456, nan] 
-2024-08-29 09:10:12.864258: Epoch time: 74.69 s 
-2024-08-29 09:10:13.980178:  
-2024-08-29 09:10:13.980330: Epoch 1821 
-2024-08-29 09:10:13.980415: Current learning rate: 0.00114 
-2024-08-29 09:11:34.949809: train_loss -0.7798 
-2024-08-29 09:11:34.950034: val_loss -0.7945 
-2024-08-29 09:11:34.950187: Pseudo dice [0.0, 0.0, 0.9127, 0.9776, 0.8601, 0.9472, 0.9486, 0.9654, 0.9575, 0.9491, 0.9385, 0.962, 0.9633, 0.8485, 0.9568, 0.9397, 0.8515, 0.8397, nan] 
-2024-08-29 09:11:34.950266: Epoch time: 80.97 s 
-2024-08-29 09:11:36.066622:  
-2024-08-29 09:11:36.066760: Epoch 1822 
-2024-08-29 09:11:36.066835: Current learning rate: 0.00113 
-2024-08-29 09:12:53.796707: train_loss -0.7803 
-2024-08-29 09:12:53.796910: val_loss -0.7998 
-2024-08-29 09:12:53.797048: Pseudo dice [0.0, 0.0, 0.9088, 0.9769, 0.8527, 0.9496, 0.9534, 0.9691, 0.9544, 0.9549, 0.9421, 0.9626, 0.9672, 0.869, 0.9491, 0.9413, 0.8355, 0.8361, nan] 
-2024-08-29 09:12:53.797121: Epoch time: 77.73 s 
-2024-08-29 09:12:54.888478:  
-2024-08-29 09:12:54.888622: Epoch 1823 
-2024-08-29 09:12:54.888709: Current learning rate: 0.00113 
-2024-08-29 09:14:11.588873: train_loss -0.7789 
-2024-08-29 09:14:11.589082: val_loss -0.7964 
-2024-08-29 09:14:11.589219: Pseudo dice [0.0, 0.0, 0.9082, 0.9767, 0.8702, 0.9516, 0.9517, 0.97, 0.955, 0.9575, 0.9395, 0.9632, 0.9631, 0.8601, 0.9498, 0.9362, 0.8365, 0.8484, nan] 
-2024-08-29 09:14:11.589293: Epoch time: 76.7 s 
-2024-08-29 09:14:12.696531:  
-2024-08-29 09:14:12.696680: Epoch 1824 
-2024-08-29 09:14:12.696764: Current learning rate: 0.00112 
-2024-08-29 09:15:30.699424: train_loss -0.7744 
-2024-08-29 09:15:30.699649: val_loss -0.7996 
-2024-08-29 09:15:30.699799: Pseudo dice [0.0, 0.0, 0.89, 0.9763, 0.869, 0.9534, 0.9569, 0.9678, 0.9579, 0.9582, 0.9398, 0.9667, 0.9667, 0.8623, 0.9531, 0.9414, 0.8334, 0.833, nan] 
-2024-08-29 09:15:30.699873: Epoch time: 78.0 s 
-2024-08-29 09:15:31.993439:  
-2024-08-29 09:15:31.993582: Epoch 1825 
-2024-08-29 09:15:31.993669: Current learning rate: 0.00112 
-2024-08-29 09:16:47.904532: train_loss -0.7792 
-2024-08-29 09:16:47.904758: val_loss -0.7906 
-2024-08-29 09:16:47.904910: Pseudo dice [0.0, 0.0, 0.9098, 0.9771, 0.8679, 0.9489, 0.9522, 0.9682, 0.9541, 0.9563, 0.94, 0.9646, 0.9631, 0.8618, 0.951, 0.9403, 0.8448, 0.8419, nan] 
-2024-08-29 09:16:47.904987: Epoch time: 75.91 s 
-2024-08-29 09:16:48.980828:  
-2024-08-29 09:16:48.981079: Epoch 1826 
-2024-08-29 09:16:48.981165: Current learning rate: 0.00111 
-2024-08-29 09:18:04.457053: train_loss -0.7825 
-2024-08-29 09:18:04.457273: val_loss -0.7964 
-2024-08-29 09:18:04.457409: Pseudo dice [0.0, 0.0, 0.9171, 0.9763, 0.8601, 0.9527, 0.959, 0.97, 0.9557, 0.9523, 0.942, 0.9619, 0.9635, 0.863, 0.9503, 0.9446, 0.8525, 0.8494, nan] 
-2024-08-29 09:18:04.457483: Epoch time: 75.48 s 
-2024-08-29 09:18:04.457526: Yayy! New best EMA pseudo Dice: 0.8241 
-2024-08-29 09:18:05.982472:  
-2024-08-29 09:18:05.982719: Epoch 1827 
-2024-08-29 09:18:05.982811: Current learning rate: 0.0011 
-2024-08-29 09:19:18.646586: train_loss -0.7795 
-2024-08-29 09:19:18.646883: val_loss -0.8011 
-2024-08-29 09:19:18.647037: Pseudo dice [0.0, 0.0, 0.9033, 0.9768, 0.8551, 0.9515, 0.955, 0.9688, 0.9484, 0.9616, 0.9376, 0.9645, 0.9638, 0.8685, 0.9619, 0.9406, 0.8466, 0.8453, nan] 
-2024-08-29 09:19:18.647130: Epoch time: 72.66 s 
-2024-08-29 09:19:18.647176: Yayy! New best EMA pseudo Dice: 0.8242 
-2024-08-29 09:19:20.188292:  
-2024-08-29 09:19:20.188425: Epoch 1828 
-2024-08-29 09:19:20.188554: Current learning rate: 0.0011 
-2024-08-29 09:20:36.437714: train_loss -0.7808 
-2024-08-29 09:20:36.437948: val_loss -0.7987 
-2024-08-29 09:20:36.438092: Pseudo dice [0.0, 0.0, 0.9062, 0.9784, 0.8699, 0.9467, 0.9523, 0.9657, 0.954, 0.9513, 0.9394, 0.9647, 0.961, 0.859, 0.9551, 0.9402, 0.8481, 0.8483, nan] 
-2024-08-29 09:20:36.438169: Epoch time: 76.25 s 
-2024-08-29 09:20:36.438210: Yayy! New best EMA pseudo Dice: 0.8242 
-2024-08-29 09:20:37.976448:  
-2024-08-29 09:20:37.976726: Epoch 1829 
-2024-08-29 09:20:37.976818: Current learning rate: 0.00109 
-2024-08-29 09:21:59.253576: train_loss -0.7779 
-2024-08-29 09:21:59.253786: val_loss -0.7974 
-2024-08-29 09:21:59.253924: Pseudo dice [0.0, 0.0, 0.9107, 0.9776, 0.868, 0.9481, 0.9513, 0.9691, 0.9557, 0.9478, 0.9424, 0.9637, 0.9661, 0.86, 0.9518, 0.9421, 0.849, 0.853, nan] 
-2024-08-29 09:21:59.253998: Epoch time: 81.28 s 
-2024-08-29 09:21:59.254038: Yayy! New best EMA pseudo Dice: 0.8243 
-2024-08-29 09:22:00.997643:  
-2024-08-29 09:22:00.997812: Epoch 1830 
-2024-08-29 09:22:00.997895: Current learning rate: 0.00109 
-2024-08-29 09:23:20.514402: train_loss -0.7773 
-2024-08-29 09:23:20.514634: val_loss -0.7971 
-2024-08-29 09:23:20.514776: Pseudo dice [0.0, 0.0, 0.9046, 0.9774, 0.842, 0.9501, 0.95, 0.966, 0.9526, 0.9522, 0.9412, 0.9629, 0.9643, 0.8565, 0.9497, 0.9376, 0.8482, 0.8414, nan] 
-2024-08-29 09:23:20.514851: Epoch time: 79.52 s 
-2024-08-29 09:23:21.616518:  
-2024-08-29 09:23:21.616789: Epoch 1831 
-2024-08-29 09:23:21.616882: Current learning rate: 0.00108 
-2024-08-29 09:24:38.427530: train_loss -0.7783 
-2024-08-29 09:24:38.427752: val_loss -0.7988 
-2024-08-29 09:24:38.427889: Pseudo dice [0.0, 0.0, 0.9079, 0.9782, 0.8635, 0.9518, 0.9555, 0.9684, 0.9566, 0.9478, 0.9379, 0.9621, 0.9658, 0.859, 0.949, 0.9435, 0.8427, 0.8501, nan] 
-2024-08-29 09:24:38.427964: Epoch time: 76.81 s 
-2024-08-29 09:24:39.537150:  
-2024-08-29 09:24:39.537298: Epoch 1832 
-2024-08-29 09:24:39.537390: Current learning rate: 0.00108 
-2024-08-29 09:25:54.127111: train_loss -0.7791 
-2024-08-29 09:25:54.127338: val_loss -0.7989 
-2024-08-29 09:25:54.127479: Pseudo dice [0.0, 0.0, 0.901, 0.9782, 0.8718, 0.9465, 0.9506, 0.967, 0.9575, 0.9418, 0.9415, 0.9629, 0.9639, 0.8677, 0.9553, 0.9429, 0.8475, 0.8578, nan] 
-2024-08-29 09:25:54.127552: Epoch time: 74.59 s 
-2024-08-29 09:25:55.267661:  
-2024-08-29 09:25:55.267805: Epoch 1833 
-2024-08-29 09:25:55.267889: Current learning rate: 0.00107 
-2024-08-29 09:27:12.552813: train_loss -0.7812 
-2024-08-29 09:27:12.553051: val_loss -0.7983 
-2024-08-29 09:27:12.553249: Pseudo dice [0.0, 0.0, 0.9025, 0.9775, 0.8696, 0.95, 0.9534, 0.9659, 0.9569, 0.9575, 0.9429, 0.9618, 0.9668, 0.8633, 0.9532, 0.941, 0.8326, 0.844, nan] 
-2024-08-29 09:27:12.553332: Epoch time: 77.29 s 
-2024-08-29 09:27:13.643662:  
-2024-08-29 09:27:13.644022: Epoch 1834 
-2024-08-29 09:27:13.644111: Current learning rate: 0.00106 
-2024-08-29 09:28:35.316672: train_loss -0.7781 
-2024-08-29 09:28:35.316896: val_loss -0.8012 
-2024-08-29 09:28:35.317047: Pseudo dice [0.0, 0.0, 0.912, 0.9771, 0.8676, 0.9511, 0.9522, 0.9693, 0.9543, 0.9595, 0.9411, 0.9646, 0.9627, 0.8673, 0.9604, 0.9432, 0.8579, 0.8529, nan] 
-2024-08-29 09:28:35.317124: Epoch time: 81.67 s 
-2024-08-29 09:28:35.317169: Yayy! New best EMA pseudo Dice: 0.8246 
-2024-08-29 09:28:37.076828:  
-2024-08-29 09:28:37.077063: Epoch 1835 
-2024-08-29 09:28:37.077156: Current learning rate: 0.00106 
-2024-08-29 09:29:51.530546: train_loss -0.7817 
-2024-08-29 09:29:51.530775: val_loss -0.7962 
-2024-08-29 09:29:51.531132: Pseudo dice [0.0, 0.0, 0.914, 0.9788, 0.8671, 0.9459, 0.9547, 0.9681, 0.9499, 0.9554, 0.9404, 0.9643, 0.9656, 0.8509, 0.9545, 0.9425, 0.8533, 0.8519, nan] 
-2024-08-29 09:29:51.531217: Epoch time: 74.45 s 
-2024-08-29 09:29:51.531276: Yayy! New best EMA pseudo Dice: 0.8246 
-2024-08-29 09:29:53.085807:  
-2024-08-29 09:29:53.086079: Epoch 1836 
-2024-08-29 09:29:53.086174: Current learning rate: 0.00105 
-2024-08-29 09:31:08.381620: train_loss -0.781 
-2024-08-29 09:31:08.381841: val_loss -0.7988 
-2024-08-29 09:31:08.381984: Pseudo dice [0.0, 0.0, 0.9128, 0.9782, 0.8533, 0.9503, 0.9547, 0.9698, 0.9548, 0.9479, 0.9402, 0.9641, 0.9652, 0.8666, 0.9617, 0.9438, 0.8464, 0.8503, nan] 
-2024-08-29 09:31:08.382061: Epoch time: 75.3 s 
-2024-08-29 09:31:08.382107: Yayy! New best EMA pseudo Dice: 0.8247 
-2024-08-29 09:31:09.935864:  
-2024-08-29 09:31:09.936012: Epoch 1837 
-2024-08-29 09:31:09.936103: Current learning rate: 0.00105 
-2024-08-29 09:32:26.959711: train_loss -0.7815 
-2024-08-29 09:32:26.959923: val_loss -0.8052 
-2024-08-29 09:32:26.960073: Pseudo dice [0.0, 0.0, 0.9196, 0.9777, 0.8739, 0.9502, 0.9543, 0.969, 0.9569, 0.9585, 0.9405, 0.965, 0.9658, 0.8641, 0.9603, 0.9438, 0.8605, 0.8531, nan] 
-2024-08-29 09:32:26.960151: Epoch time: 77.02 s 
-2024-08-29 09:32:26.960196: Yayy! New best EMA pseudo Dice: 0.8251 
-2024-08-29 09:32:28.488773:  
-2024-08-29 09:32:28.488921: Epoch 1838 
-2024-08-29 09:32:28.489006: Current learning rate: 0.00104 
-2024-08-29 09:33:45.921201: train_loss -0.7798 
-2024-08-29 09:33:45.921594: val_loss -0.8028 
-2024-08-29 09:33:45.921752: Pseudo dice [0.0, 0.0, 0.9103, 0.9775, 0.8693, 0.9527, 0.9567, 0.9706, 0.9555, 0.9549, 0.9365, 0.9641, 0.9647, 0.868, 0.9518, 0.9386, 0.8591, 0.8538, nan] 
-2024-08-29 09:33:45.921834: Epoch time: 77.43 s 
-2024-08-29 09:33:45.921880: Yayy! New best EMA pseudo Dice: 0.8253 
-2024-08-29 09:33:47.509471:  
-2024-08-29 09:33:47.509637: Epoch 1839 
-2024-08-29 09:33:47.509721: Current learning rate: 0.00104 
-2024-08-29 09:35:03.769104: train_loss -0.7788 
-2024-08-29 09:35:03.769322: val_loss -0.7963 
-2024-08-29 09:35:03.769469: Pseudo dice [0.0, 0.0, 0.9199, 0.9773, 0.8671, 0.9526, 0.9542, 0.9703, 0.9552, 0.9472, 0.939, 0.9635, 0.9612, 0.8636, 0.9615, 0.9457, 0.8491, 0.8565, nan] 
-2024-08-29 09:35:03.769547: Epoch time: 76.26 s 
-2024-08-29 09:35:03.769591: Yayy! New best EMA pseudo Dice: 0.8254 
-2024-08-29 09:35:05.313213:  
-2024-08-29 09:35:05.313345: Epoch 1840 
-2024-08-29 09:35:05.313433: Current learning rate: 0.00103 
-2024-08-29 09:36:23.877525: train_loss -0.7792 
-2024-08-29 09:36:23.877845: val_loss -0.7966 
-2024-08-29 09:36:23.878003: Pseudo dice [0.0, 0.0, 0.9048, 0.9776, 0.86, 0.9533, 0.9566, 0.9697, 0.9564, 0.9524, 0.9403, 0.9628, 0.9661, 0.867, 0.9571, 0.9403, 0.8512, 0.8516, nan] 
-2024-08-29 09:36:23.878085: Epoch time: 78.56 s 
-2024-08-29 09:36:23.878132: Yayy! New best EMA pseudo Dice: 0.8255 
-2024-08-29 09:36:25.652802:  
-2024-08-29 09:36:25.652967: Epoch 1841 
-2024-08-29 09:36:25.653052: Current learning rate: 0.00102 
-2024-08-29 09:37:41.022500: train_loss -0.7844 
-2024-08-29 09:37:41.022711: val_loss -0.8015 
-2024-08-29 09:37:41.022856: Pseudo dice [0.0, 0.0, 0.9133, 0.978, 0.8672, 0.9542, 0.9563, 0.9693, 0.9592, 0.9488, 0.9371, 0.9667, 0.9626, 0.8644, 0.9596, 0.9412, 0.8472, 0.853, nan] 
-2024-08-29 09:37:41.022933: Epoch time: 75.37 s 
-2024-08-29 09:37:41.022977: Yayy! New best EMA pseudo Dice: 0.8256 
-2024-08-29 09:37:42.574721:  
-2024-08-29 09:37:42.574879: Epoch 1842 
-2024-08-29 09:37:42.574974: Current learning rate: 0.00102 
-2024-08-29 09:38:59.897340: train_loss -0.7815 
-2024-08-29 09:38:59.897558: val_loss -0.8025 
-2024-08-29 09:38:59.897695: Pseudo dice [0.0, 0.0, 0.9077, 0.9786, 0.8601, 0.9515, 0.9539, 0.9687, 0.9547, 0.9519, 0.9382, 0.9638, 0.9647, 0.8686, 0.9584, 0.9412, 0.8481, 0.8499, nan] 
-2024-08-29 09:38:59.897771: Epoch time: 77.32 s 
-2024-08-29 09:39:00.999393:  
-2024-08-29 09:39:00.999591: Epoch 1843 
-2024-08-29 09:39:00.999685: Current learning rate: 0.00101 
-2024-08-29 09:40:17.780731: train_loss -0.7814 
-2024-08-29 09:40:17.780954: val_loss -0.7993 
-2024-08-29 09:40:17.781104: Pseudo dice [0.0, 0.0, 0.8994, 0.9769, 0.8708, 0.9499, 0.9535, 0.9691, 0.9545, 0.9557, 0.9426, 0.9638, 0.9651, 0.8698, 0.9617, 0.945, 0.8587, 0.8529, nan] 
-2024-08-29 09:40:17.781181: Epoch time: 76.78 s 
-2024-08-29 09:40:17.781238: Yayy! New best EMA pseudo Dice: 0.8258 
-2024-08-29 09:40:19.354907:  
-2024-08-29 09:40:19.355061: Epoch 1844 
-2024-08-29 09:40:19.355146: Current learning rate: 0.00101 
-2024-08-29 09:41:32.886302: train_loss -0.7805 
-2024-08-29 09:41:32.886532: val_loss -0.7967 
-2024-08-29 09:41:32.886679: Pseudo dice [0.0, 0.0, 0.9133, 0.9782, 0.8756, 0.9459, 0.9538, 0.9621, 0.9572, 0.9618, 0.9371, 0.9653, 0.9657, 0.8547, 0.9366, 0.9408, 0.8455, 0.8487, nan] 
-2024-08-29 09:41:32.886755: Epoch time: 73.53 s 
-2024-08-29 09:41:34.012236:  
-2024-08-29 09:41:34.012375: Epoch 1845 
-2024-08-29 09:41:34.012463: Current learning rate: 0.001 
-2024-08-29 09:42:49.219650: train_loss -0.7818 
-2024-08-29 09:42:49.219861: val_loss -0.7969 
-2024-08-29 09:42:49.220004: Pseudo dice [0.0, 0.0, 0.901, 0.9782, 0.8811, 0.9513, 0.9529, 0.9695, 0.9546, 0.9571, 0.9425, 0.9628, 0.9637, 0.8676, 0.9523, 0.9404, 0.8461, 0.8337, nan] 
-2024-08-29 09:42:49.220081: Epoch time: 75.21 s 
-2024-08-29 09:42:50.524851:  
-2024-08-29 09:42:50.525120: Epoch 1846 
-2024-08-29 09:42:50.525213: Current learning rate: 0.001 
-2024-08-29 09:44:05.737411: train_loss -0.7796 
-2024-08-29 09:44:05.737630: val_loss -0.8031 
-2024-08-29 09:44:05.737768: Pseudo dice [0.0, 0.0, 0.9059, 0.9784, 0.8605, 0.9489, 0.9532, 0.9685, 0.9599, 0.9642, 0.9436, 0.9628, 0.9657, 0.8655, 0.9447, 0.9434, 0.8472, 0.8527, nan] 
-2024-08-29 09:44:05.737870: Epoch time: 75.21 s 
-2024-08-29 09:44:06.847892:  
-2024-08-29 09:44:06.848054: Epoch 1847 
-2024-08-29 09:44:06.848134: Current learning rate: 0.00099 
-2024-08-29 09:45:27.517833: train_loss -0.7812 
-2024-08-29 09:45:27.518034: val_loss -0.8008 
-2024-08-29 09:45:27.518189: Pseudo dice [0.0, 0.0, 0.9134, 0.9774, 0.8713, 0.953, 0.9559, 0.9679, 0.9554, 0.9555, 0.9383, 0.96, 0.9641, 0.8656, 0.9575, 0.9403, 0.8526, 0.8528, nan] 
-2024-08-29 09:45:27.518268: Epoch time: 80.67 s 
-2024-08-29 09:45:28.606540:  
-2024-08-29 09:45:28.606683: Epoch 1848 
-2024-08-29 09:45:28.606771: Current learning rate: 0.00098 
-2024-08-29 09:46:49.603703: train_loss -0.7812 
-2024-08-29 09:46:49.603914: val_loss -0.7985 
-2024-08-29 09:46:49.604062: Pseudo dice [0.0, 0.0, 0.9089, 0.9784, 0.8687, 0.9499, 0.9545, 0.9679, 0.9552, 0.9543, 0.9366, 0.9631, 0.9626, 0.8439, 0.9568, 0.9388, 0.8493, 0.8402, nan] 
-2024-08-29 09:46:49.604138: Epoch time: 81.0 s 
-2024-08-29 09:46:50.706104:  
-2024-08-29 09:46:50.706255: Epoch 1849 
-2024-08-29 09:46:50.706335: Current learning rate: 0.00098 
-2024-08-29 09:48:09.614769: train_loss -0.7809 
-2024-08-29 09:48:09.614990: val_loss -0.7985 
-2024-08-29 09:48:09.615137: Pseudo dice [0.0, 0.0, 0.9155, 0.9782, 0.8791, 0.9547, 0.956, 0.9625, 0.9549, 0.9596, 0.943, 0.9625, 0.9664, 0.8744, 0.9625, 0.9476, 0.8587, 0.8525, nan] 
-2024-08-29 09:48:09.615214: Epoch time: 78.91 s 
-2024-08-29 09:48:10.092971: Yayy! New best EMA pseudo Dice: 0.8259 
-2024-08-29 09:48:11.577567:  
-2024-08-29 09:48:11.577839: Epoch 1850 
-2024-08-29 09:48:11.577923: Current learning rate: 0.00097 
-2024-08-29 09:49:23.724421: train_loss -0.7815 
-2024-08-29 09:49:23.724706: val_loss -0.7998 
-2024-08-29 09:49:23.724858: Pseudo dice [0.0, 0.0, 0.9162, 0.9788, 0.8687, 0.9486, 0.9503, 0.967, 0.9536, 0.959, 0.9372, 0.9633, 0.9621, 0.8716, 0.9536, 0.9453, 0.8476, 0.8437, nan] 
-2024-08-29 09:49:23.724938: Epoch time: 72.15 s 
-2024-08-29 09:49:24.811711:  
-2024-08-29 09:49:24.811857: Epoch 1851 
-2024-08-29 09:49:24.811942: Current learning rate: 0.00097 
-2024-08-29 09:50:44.583262: train_loss -0.7824 
-2024-08-29 09:50:44.583679: val_loss -0.8022 
-2024-08-29 09:50:44.583831: Pseudo dice [0.0, 0.0, 0.9174, 0.9781, 0.866, 0.9514, 0.9556, 0.968, 0.9615, 0.9479, 0.939, 0.9645, 0.9647, 0.8648, 0.9514, 0.9403, 0.8563, 0.8557, nan] 
-2024-08-29 09:50:44.583905: Epoch time: 79.77 s 
-2024-08-29 09:50:44.583947: Yayy! New best EMA pseudo Dice: 0.826 
-2024-08-29 09:50:46.321479:  
-2024-08-29 09:50:46.321733: Epoch 1852 
-2024-08-29 09:50:46.321827: Current learning rate: 0.00096 
-2024-08-29 09:52:02.249443: train_loss -0.7836 
-2024-08-29 09:52:02.249676: val_loss -0.7999 
-2024-08-29 09:52:02.249840: Pseudo dice [0.0, 0.0, 0.9158, 0.978, 0.8448, 0.944, 0.9507, 0.9687, 0.956, 0.9561, 0.9377, 0.9625, 0.9657, 0.8636, 0.9556, 0.9394, 0.8469, 0.8388, nan] 
-2024-08-29 09:52:02.249919: Epoch time: 75.93 s 
-2024-08-29 09:52:03.361818:  
-2024-08-29 09:52:03.362130: Epoch 1853 
-2024-08-29 09:52:03.362216: Current learning rate: 0.00095 
-2024-08-29 09:53:21.587961: train_loss -0.7835 
-2024-08-29 09:53:21.588196: val_loss -0.8012 
-2024-08-29 09:53:21.588348: Pseudo dice [0.0, 0.0, 0.9187, 0.978, 0.8791, 0.9479, 0.9533, 0.9674, 0.9552, 0.9549, 0.9369, 0.9653, 0.9642, 0.8622, 0.9392, 0.9435, 0.8507, 0.8559, nan] 
-2024-08-29 09:53:21.588435: Epoch time: 78.23 s 
-2024-08-29 09:53:22.705809:  
-2024-08-29 09:53:22.705955: Epoch 1854 
-2024-08-29 09:53:22.706038: Current learning rate: 0.00095 
-2024-08-29 09:54:42.097022: train_loss -0.7821 
-2024-08-29 09:54:42.097237: val_loss -0.7985 
-2024-08-29 09:54:42.097377: Pseudo dice [0.0, 0.0, 0.8856, 0.9781, 0.8666, 0.9462, 0.9494, 0.9663, 0.9553, 0.9364, 0.9394, 0.9618, 0.9672, 0.8587, 0.9478, 0.9383, 0.8494, 0.8468, nan] 
-2024-08-29 09:54:42.097454: Epoch time: 79.39 s 
-2024-08-29 09:54:43.181651:  
-2024-08-29 09:54:43.181785: Epoch 1855 
-2024-08-29 09:54:43.181875: Current learning rate: 0.00094 
-2024-08-29 09:56:01.283736: train_loss -0.7823 
-2024-08-29 09:56:01.283944: val_loss -0.7983 
-2024-08-29 09:56:01.284128: Pseudo dice [0.0, 0.0, 0.9178, 0.9778, 0.861, 0.9526, 0.9557, 0.9681, 0.9584, 0.9621, 0.9418, 0.9649, 0.9681, 0.8652, 0.9544, 0.9419, 0.8569, 0.8444, nan] 
-2024-08-29 09:56:01.284203: Epoch time: 78.1 s 
-2024-08-29 09:56:02.396966:  
-2024-08-29 09:56:02.397109: Epoch 1856 
-2024-08-29 09:56:02.397200: Current learning rate: 0.00094 
-2024-08-29 09:57:21.434451: train_loss -0.7815 
-2024-08-29 09:57:21.434671: val_loss -0.8023 
-2024-08-29 09:57:21.434824: Pseudo dice [0.0, 0.0, 0.9174, 0.9777, 0.8451, 0.9465, 0.9511, 0.9684, 0.9561, 0.9542, 0.9385, 0.9627, 0.9628, 0.8696, 0.9478, 0.9437, 0.8431, 0.8465, nan] 
-2024-08-29 09:57:21.434901: Epoch time: 79.04 s 
-2024-08-29 09:57:22.550260:  
-2024-08-29 09:57:22.550416: Epoch 1857 
-2024-08-29 09:57:22.550505: Current learning rate: 0.00093 
-2024-08-29 09:58:41.571835: train_loss -0.7829 
-2024-08-29 09:58:41.572047: val_loss -0.7974 
-2024-08-29 09:58:41.572189: Pseudo dice [0.0, 0.0, 0.9072, 0.9784, 0.8611, 0.9486, 0.9478, 0.9684, 0.9463, 0.9502, 0.9315, 0.9498, 0.9523, 0.8616, 0.9557, 0.9426, 0.8561, 0.8571, nan] 
-2024-08-29 09:58:41.572263: Epoch time: 79.02 s 
-2024-08-29 09:58:42.903982:  
-2024-08-29 09:58:42.904126: Epoch 1858 
-2024-08-29 09:58:42.904215: Current learning rate: 0.00092 
-2024-08-29 09:59:58.678909: train_loss -0.7844 
-2024-08-29 09:59:58.679138: val_loss -0.7966 
-2024-08-29 09:59:58.679301: Pseudo dice [0.0, 0.0, 0.9067, 0.9788, 0.8711, 0.9481, 0.9524, 0.9707, 0.9563, 0.9605, 0.9427, 0.9644, 0.9662, 0.8622, 0.9603, 0.9398, 0.8385, 0.8428, nan] 
-2024-08-29 09:59:58.679384: Epoch time: 75.78 s 
-2024-08-29 09:59:59.831479:  
-2024-08-29 09:59:59.831654: Epoch 1859 
-2024-08-29 09:59:59.831738: Current learning rate: 0.00092 
-2024-08-29 10:01:17.552803: train_loss -0.78 
-2024-08-29 10:01:17.553054: val_loss -0.8025 
-2024-08-29 10:01:17.553251: Pseudo dice [0.0, 0.0, 0.9116, 0.978, 0.8567, 0.9491, 0.9522, 0.9676, 0.9541, 0.9533, 0.9425, 0.9643, 0.9666, 0.8582, 0.9601, 0.9382, 0.8455, 0.8447, nan] 
-2024-08-29 10:01:17.553354: Epoch time: 77.72 s 
-2024-08-29 10:01:18.719886:  
-2024-08-29 10:01:18.720167: Epoch 1860 
-2024-08-29 10:01:18.720251: Current learning rate: 0.00091 
-2024-08-29 10:02:36.475241: train_loss -0.7813 
-2024-08-29 10:02:36.475477: val_loss -0.8004 
-2024-08-29 10:02:36.475624: Pseudo dice [0.0, 0.0, 0.9225, 0.9779, 0.8733, 0.9505, 0.9522, 0.9683, 0.956, 0.963, 0.9454, 0.963, 0.9653, 0.8607, 0.9539, 0.9424, 0.8601, 0.8534, nan] 
-2024-08-29 10:02:36.475701: Epoch time: 77.76 s 
-2024-08-29 10:02:37.570841:  
-2024-08-29 10:02:37.570983: Epoch 1861 
-2024-08-29 10:02:37.571070: Current learning rate: 0.00091 
-2024-08-29 10:03:57.989385: train_loss -0.7826 
-2024-08-29 10:03:57.989590: val_loss -0.8079 
-2024-08-29 10:03:57.989748: Pseudo dice [0.0, 0.0, 0.915, 0.9777, 0.86, 0.9458, 0.9512, 0.9706, 0.9553, 0.9627, 0.9449, 0.9642, 0.9667, 0.8681, 0.9599, 0.9422, 0.8597, 0.8568, nan] 
-2024-08-29 10:03:57.989823: Epoch time: 80.42 s 
-2024-08-29 10:03:59.102398:  
-2024-08-29 10:03:59.102540: Epoch 1862 
-2024-08-29 10:03:59.102624: Current learning rate: 0.0009 
-2024-08-29 10:05:23.174829: train_loss -0.7815 
-2024-08-29 10:05:23.175051: val_loss -0.8019 
-2024-08-29 10:05:23.175204: Pseudo dice [0.0, 0.0, 0.9089, 0.9785, 0.8705, 0.9507, 0.9548, 0.9668, 0.9543, 0.9513, 0.9397, 0.9601, 0.965, 0.851, 0.9574, 0.9391, 0.8426, 0.8515, nan] 
-2024-08-29 10:05:23.175329: Epoch time: 84.07 s 
-2024-08-29 10:05:24.276685:  
-2024-08-29 10:05:24.276828: Epoch 1863 
-2024-08-29 10:05:24.276906: Current learning rate: 0.0009 
-2024-08-29 10:06:41.977624: train_loss -0.7821 
-2024-08-29 10:06:41.977824: val_loss -0.8025 
-2024-08-29 10:06:41.977984: Pseudo dice [0.0, 0.0, 0.9252, 0.9791, 0.8669, 0.9503, 0.9555, 0.9701, 0.9603, 0.9602, 0.9458, 0.9672, 0.9673, 0.8664, 0.9603, 0.9473, 0.8545, 0.8523, nan] 
-2024-08-29 10:06:41.978067: Epoch time: 77.7 s 
-2024-08-29 10:06:43.286061:  
-2024-08-29 10:06:43.286392: Epoch 1864 
-2024-08-29 10:06:43.286489: Current learning rate: 0.00089 
-2024-08-29 10:07:56.077013: train_loss -0.7793 
-2024-08-29 10:07:56.077335: val_loss -0.8037 
-2024-08-29 10:07:56.077534: Pseudo dice [0.0, 0.0, 0.9172, 0.9783, 0.8672, 0.9491, 0.9554, 0.9642, 0.9532, 0.9537, 0.9403, 0.9619, 0.9645, 0.8586, 0.9598, 0.9407, 0.8563, 0.8586, nan] 
-2024-08-29 10:07:56.077613: Epoch time: 72.79 s 
-2024-08-29 10:07:56.077656: Yayy! New best EMA pseudo Dice: 0.826 
-2024-08-29 10:07:57.619029:  
-2024-08-29 10:07:57.619189: Epoch 1865 
-2024-08-29 10:07:57.619272: Current learning rate: 0.00088 
-2024-08-29 10:09:15.723282: train_loss -0.7786 
-2024-08-29 10:09:15.723508: val_loss -0.8013 
-2024-08-29 10:09:15.723660: Pseudo dice [0.0, 0.0, 0.9221, 0.9785, 0.8751, 0.9529, 0.9553, 0.9678, 0.9567, 0.9562, 0.9417, 0.9635, 0.9647, 0.8624, 0.9565, 0.9408, 0.8564, 0.8483, nan] 
-2024-08-29 10:09:15.723736: Epoch time: 78.1 s 
-2024-08-29 10:09:15.723780: Yayy! New best EMA pseudo Dice: 0.8262 
-2024-08-29 10:09:17.252835:  
-2024-08-29 10:09:17.253116: Epoch 1866 
-2024-08-29 10:09:17.253200: Current learning rate: 0.00088 
-2024-08-29 10:10:33.468492: train_loss -0.7796 
-2024-08-29 10:10:33.468728: val_loss -0.7967 
-2024-08-29 10:10:33.468874: Pseudo dice [0.0, 0.0, 0.8984, 0.9787, 0.8603, 0.9506, 0.9528, 0.9689, 0.9539, 0.9535, 0.9371, 0.9611, 0.9638, 0.865, 0.9522, 0.9407, 0.8625, 0.8541, nan] 
-2024-08-29 10:10:33.468953: Epoch time: 76.22 s 
-2024-08-29 10:10:34.553858:  
-2024-08-29 10:10:34.554013: Epoch 1867 
-2024-08-29 10:10:34.554097: Current learning rate: 0.00087 
-2024-08-29 10:11:49.252643: train_loss -0.7819 
-2024-08-29 10:11:49.252861: val_loss -0.7951 
-2024-08-29 10:11:49.253000: Pseudo dice [0.0, 0.0, 0.901, 0.9782, 0.8761, 0.9506, 0.9543, 0.9693, 0.9531, 0.9473, 0.9377, 0.9591, 0.9625, 0.865, 0.9515, 0.9354, 0.8477, 0.8532, nan] 
-2024-08-29 10:11:49.253074: Epoch time: 74.7 s 
-2024-08-29 10:11:50.338411:  
-2024-08-29 10:11:50.338706: Epoch 1868 
-2024-08-29 10:11:50.338783: Current learning rate: 0.00087 
-2024-08-29 10:13:04.876557: train_loss -0.782 
-2024-08-29 10:13:04.876755: val_loss -0.7998 
-2024-08-29 10:13:04.876898: Pseudo dice [0.0, 0.0, 0.9054, 0.9769, 0.8688, 0.9523, 0.9552, 0.9694, 0.9567, 0.9544, 0.9415, 0.9616, 0.968, 0.8678, 0.9616, 0.9409, 0.8455, 0.8529, nan] 
-2024-08-29 10:13:04.876973: Epoch time: 74.54 s 
-2024-08-29 10:13:05.959955:  
-2024-08-29 10:13:05.960089: Epoch 1869 
-2024-08-29 10:13:05.960166: Current learning rate: 0.00086 
-2024-08-29 10:14:19.981186: train_loss -0.7808 
-2024-08-29 10:14:19.981454: val_loss -0.8035 
-2024-08-29 10:14:19.981660: Pseudo dice [0.0, 0.0, 0.9221, 0.9781, 0.8691, 0.9511, 0.9547, 0.97, 0.9552, 0.9619, 0.9439, 0.9633, 0.9659, 0.8706, 0.9557, 0.9437, 0.8469, 0.8353, nan] 
-2024-08-29 10:14:19.981767: Epoch time: 74.02 s 
-2024-08-29 10:14:21.308026:  
-2024-08-29 10:14:21.308280: Epoch 1870 
-2024-08-29 10:14:21.308382: Current learning rate: 0.00085 
-2024-08-29 10:15:34.970013: train_loss -0.7798 
-2024-08-29 10:15:34.970220: val_loss -0.7994 
-2024-08-29 10:15:34.970363: Pseudo dice [0.0, 0.0, 0.9134, 0.9784, 0.8709, 0.9527, 0.9538, 0.9696, 0.956, 0.9527, 0.9391, 0.9608, 0.9629, 0.8629, 0.9588, 0.9446, 0.8444, 0.8497, nan] 
-2024-08-29 10:15:34.970442: Epoch time: 73.66 s 
-2024-08-29 10:15:36.073326:  
-2024-08-29 10:15:36.073483: Epoch 1871 
-2024-08-29 10:15:36.073571: Current learning rate: 0.00085 
-2024-08-29 10:16:57.121614: train_loss -0.7802 
-2024-08-29 10:16:57.121855: val_loss -0.7977 
-2024-08-29 10:16:57.122008: Pseudo dice [0.0, 0.0, 0.9067, 0.9771, 0.8712, 0.9493, 0.9517, 0.9667, 0.9533, 0.9474, 0.9403, 0.9608, 0.9641, 0.8616, 0.9568, 0.9448, 0.8431, 0.845, nan] 
-2024-08-29 10:16:57.122091: Epoch time: 81.05 s 
-2024-08-29 10:16:58.251901:  
-2024-08-29 10:16:58.252222: Epoch 1872 
-2024-08-29 10:16:58.252314: Current learning rate: 0.00084 
-2024-08-29 10:18:14.836191: train_loss -0.7823 
-2024-08-29 10:18:14.836422: val_loss -0.7975 
-2024-08-29 10:18:14.836577: Pseudo dice [0.0, 0.0, 0.8967, 0.9773, 0.8297, 0.937, 0.947, 0.9706, 0.9546, 0.9522, 0.9375, 0.9631, 0.9625, 0.8621, 0.9542, 0.9437, 0.843, 0.8443, nan] 
-2024-08-29 10:18:14.836656: Epoch time: 76.59 s 
-2024-08-29 10:18:16.731102:  
-2024-08-29 10:18:16.731524: Epoch 1873 
-2024-08-29 10:18:16.731619: Current learning rate: 0.00084 
-2024-08-29 10:19:33.783062: train_loss -0.785 
-2024-08-29 10:19:33.783281: val_loss -0.7942 
-2024-08-29 10:19:33.783417: Pseudo dice [0.0, 0.0, 0.9051, 0.9785, 0.8374, 0.9475, 0.9535, 0.9669, 0.9551, 0.955, 0.9385, 0.9627, 0.9662, 0.8668, 0.9521, 0.9373, 0.8428, 0.8359, nan] 
-2024-08-29 10:19:33.783490: Epoch time: 77.05 s 
-2024-08-29 10:19:34.898683:  
-2024-08-29 10:19:34.898999: Epoch 1874 
-2024-08-29 10:19:34.899089: Current learning rate: 0.00083 
-2024-08-29 10:20:53.118931: train_loss -0.7818 
-2024-08-29 10:20:53.119152: val_loss -0.7993 
-2024-08-29 10:20:53.119299: Pseudo dice [0.0, 0.0, 0.9124, 0.9781, 0.8829, 0.9509, 0.9537, 0.9705, 0.9546, 0.9468, 0.9419, 0.9585, 0.9634, 0.8687, 0.9562, 0.9408, 0.8409, 0.8388, nan] 
-2024-08-29 10:20:53.119373: Epoch time: 78.22 s 
-2024-08-29 10:20:54.216327:  
-2024-08-29 10:20:54.216483: Epoch 1875 
-2024-08-29 10:20:54.216561: Current learning rate: 0.00082 
-2024-08-29 10:22:05.493605: train_loss -0.7813 
-2024-08-29 10:22:05.493811: val_loss -0.8004 
-2024-08-29 10:22:05.493956: Pseudo dice [0.0, 0.0, 0.9077, 0.9769, 0.8567, 0.9523, 0.9557, 0.9705, 0.9563, 0.954, 0.9416, 0.9609, 0.961, 0.8675, 0.9612, 0.9432, 0.8403, 0.8404, nan] 
-2024-08-29 10:22:05.494030: Epoch time: 71.28 s 
-2024-08-29 10:22:06.807996:  
-2024-08-29 10:22:06.808162: Epoch 1876 
-2024-08-29 10:22:06.808246: Current learning rate: 0.00082 
-2024-08-29 10:23:22.342680: train_loss -0.7824 
-2024-08-29 10:23:22.342896: val_loss -0.8023 
-2024-08-29 10:23:22.343039: Pseudo dice [0.0, 0.0, 0.9136, 0.9785, 0.8704, 0.9525, 0.9552, 0.9696, 0.9545, 0.9564, 0.9409, 0.9651, 0.9647, 0.863, 0.9559, 0.9436, 0.846, 0.846, nan] 
-2024-08-29 10:23:22.343112: Epoch time: 75.54 s 
-2024-08-29 10:23:23.474982:  
-2024-08-29 10:23:23.475140: Epoch 1877 
-2024-08-29 10:23:23.475221: Current learning rate: 0.00081 
-2024-08-29 10:24:44.208926: train_loss -0.7816 
-2024-08-29 10:24:44.209141: val_loss -0.7991 
-2024-08-29 10:24:44.209279: Pseudo dice [0.0, 0.0, 0.9109, 0.9784, 0.8412, 0.9439, 0.9499, 0.966, 0.9559, 0.9547, 0.943, 0.9639, 0.9669, 0.8662, 0.955, 0.938, 0.8397, 0.8387, nan] 
-2024-08-29 10:24:44.209358: Epoch time: 80.73 s 
-2024-08-29 10:24:45.323713:  
-2024-08-29 10:24:45.324010: Epoch 1878 
-2024-08-29 10:24:45.324094: Current learning rate: 0.00081 
-2024-08-29 10:25:59.654732: train_loss -0.785 
-2024-08-29 10:25:59.654950: val_loss -0.8053 
-2024-08-29 10:25:59.655127: Pseudo dice [0.0, 0.0, 0.8957, 0.9759, 0.8637, 0.954, 0.9565, 0.9699, 0.9569, 0.9552, 0.9406, 0.9639, 0.9656, 0.8702, 0.9543, 0.9401, 0.8429, 0.853, nan] 
-2024-08-29 10:25:59.655253: Epoch time: 74.33 s 
-2024-08-29 10:26:00.771244:  
-2024-08-29 10:26:00.771376: Epoch 1879 
-2024-08-29 10:26:00.771461: Current learning rate: 0.0008 
-2024-08-29 10:27:18.836984: train_loss -0.7823 
-2024-08-29 10:27:18.837196: val_loss -0.7956 
-2024-08-29 10:27:18.837374: Pseudo dice [0.0, 0.0, 0.906, 0.9778, 0.8661, 0.9486, 0.9533, 0.9673, 0.952, 0.9553, 0.9386, 0.9621, 0.9627, 0.8479, 0.9474, 0.942, 0.8533, 0.8495, nan] 
-2024-08-29 10:27:18.837460: Epoch time: 78.07 s 
-2024-08-29 10:27:19.943637:  
-2024-08-29 10:27:19.943791: Epoch 1880 
-2024-08-29 10:27:19.943880: Current learning rate: 0.00079 
-2024-08-29 10:28:37.142299: train_loss -0.7824 
-2024-08-29 10:28:37.142766: val_loss -0.8069 
-2024-08-29 10:28:37.142916: Pseudo dice [0.0, 0.0, 0.9063, 0.9773, 0.8707, 0.9536, 0.9562, 0.9683, 0.9573, 0.9568, 0.9351, 0.9624, 0.964, 0.8681, 0.9597, 0.944, 0.8627, 0.8512, nan] 
-2024-08-29 10:28:37.143005: Epoch time: 77.2 s 
-2024-08-29 10:28:38.256658:  
-2024-08-29 10:28:38.256820: Epoch 1881 
-2024-08-29 10:28:38.256903: Current learning rate: 0.00079 
-2024-08-29 10:29:50.582656: train_loss -0.7839 
-2024-08-29 10:29:50.582876: val_loss -0.7992 
-2024-08-29 10:29:50.583031: Pseudo dice [0.0, 0.0, 0.9215, 0.9784, 0.8806, 0.9498, 0.953, 0.9684, 0.955, 0.9477, 0.9413, 0.9619, 0.9635, 0.8647, 0.9555, 0.9445, 0.8576, 0.8577, nan] 
-2024-08-29 10:29:50.583113: Epoch time: 72.33 s 
-2024-08-29 10:29:51.912987:  
-2024-08-29 10:29:51.913154: Epoch 1882 
-2024-08-29 10:29:51.913236: Current learning rate: 0.00078 
-2024-08-29 10:31:11.633878: train_loss -0.7853 
-2024-08-29 10:31:11.634093: val_loss -0.8014 
-2024-08-29 10:31:11.634239: Pseudo dice [0.0, 0.0, 0.918, 0.9783, 0.8608, 0.9517, 0.9549, 0.9703, 0.9564, 0.9518, 0.9445, 0.9662, 0.9668, 0.8652, 0.962, 0.9418, 0.8535, 0.8593, nan] 
-2024-08-29 10:31:11.634315: Epoch time: 79.72 s 
-2024-08-29 10:31:12.728279:  
-2024-08-29 10:31:12.728464: Epoch 1883 
-2024-08-29 10:31:12.728557: Current learning rate: 0.00078 
-2024-08-29 10:32:25.882155: train_loss -0.781 
-2024-08-29 10:32:25.882381: val_loss -0.8024 
-2024-08-29 10:32:25.882542: Pseudo dice [0.0, 0.0, 0.9091, 0.9784, 0.8694, 0.9496, 0.954, 0.9665, 0.9589, 0.9553, 0.9406, 0.9647, 0.9644, 0.8585, 0.9567, 0.9406, 0.8349, 0.8458, nan] 
-2024-08-29 10:32:25.882626: Epoch time: 73.15 s 
-2024-08-29 10:32:27.270435:  
-2024-08-29 10:32:27.270599: Epoch 1884 
-2024-08-29 10:32:27.270681: Current learning rate: 0.00077 
-2024-08-29 10:33:46.110326: train_loss -0.782 
-2024-08-29 10:33:46.110526: val_loss -0.8015 
-2024-08-29 10:33:46.110662: Pseudo dice [0.0, 0.0, 0.9115, 0.9781, 0.8682, 0.9538, 0.9551, 0.9682, 0.9572, 0.9583, 0.9424, 0.9629, 0.9649, 0.8724, 0.954, 0.9466, 0.8441, 0.845, nan] 
-2024-08-29 10:33:46.110737: Epoch time: 78.84 s 
-2024-08-29 10:33:47.207181:  
-2024-08-29 10:33:47.207325: Epoch 1885 
-2024-08-29 10:33:47.207405: Current learning rate: 0.00077 
-2024-08-29 10:35:01.034197: train_loss -0.7835 
-2024-08-29 10:35:01.034413: val_loss -0.7978 
-2024-08-29 10:35:01.034563: Pseudo dice [0.0, 0.0, 0.9089, 0.9781, 0.8837, 0.9518, 0.9531, 0.9686, 0.9552, 0.9487, 0.9396, 0.9634, 0.9627, 0.8712, 0.9566, 0.945, 0.8576, 0.8566, nan] 
-2024-08-29 10:35:01.034644: Epoch time: 73.83 s 
-2024-08-29 10:35:02.137816:  
-2024-08-29 10:35:02.138205: Epoch 1886 
-2024-08-29 10:35:02.138414: Current learning rate: 0.00076 
-2024-08-29 10:36:17.141795: train_loss -0.7863 
-2024-08-29 10:36:17.141987: val_loss -0.7978 
-2024-08-29 10:36:17.142132: Pseudo dice [0.0, 0.0, 0.9068, 0.9776, 0.871, 0.9528, 0.9554, 0.9649, 0.955, 0.9479, 0.9392, 0.9579, 0.9622, 0.8533, 0.9596, 0.9456, 0.8515, 0.8468, nan] 
-2024-08-29 10:36:17.142205: Epoch time: 75.0 s 
-2024-08-29 10:36:18.290892:  
-2024-08-29 10:36:18.291185: Epoch 1887 
-2024-08-29 10:36:18.291270: Current learning rate: 0.00075 
-2024-08-29 10:37:39.872492: train_loss -0.7839 
-2024-08-29 10:37:39.872701: val_loss -0.8034 
-2024-08-29 10:37:39.872849: Pseudo dice [0.0, 0.0, 0.9163, 0.9782, 0.8509, 0.9444, 0.9501, 0.9686, 0.9593, 0.9606, 0.9413, 0.9659, 0.9658, 0.8583, 0.9594, 0.9433, 0.8553, 0.8571, nan] 
-2024-08-29 10:37:39.872927: Epoch time: 81.58 s 
-2024-08-29 10:37:40.986461:  
-2024-08-29 10:37:40.986609: Epoch 1888 
-2024-08-29 10:37:40.986693: Current learning rate: 0.00075 
-2024-08-29 10:38:59.874109: train_loss -0.7848 
-2024-08-29 10:38:59.874310: val_loss -0.8011 
-2024-08-29 10:38:59.874452: Pseudo dice [0.0, 0.0, 0.9071, 0.9782, 0.8629, 0.9469, 0.9545, 0.9685, 0.9605, 0.955, 0.9459, 0.9605, 0.9647, 0.8698, 0.9624, 0.9433, 0.8521, 0.8519, nan] 
-2024-08-29 10:38:59.874522: Epoch time: 78.89 s 
-2024-08-29 10:39:00.981762:  
-2024-08-29 10:39:00.982154: Epoch 1889 
-2024-08-29 10:39:00.982327: Current learning rate: 0.00074 
-2024-08-29 10:40:16.824825: train_loss -0.7837 
-2024-08-29 10:40:16.825037: val_loss -0.8055 
-2024-08-29 10:40:16.825200: Pseudo dice [0.0, 0.0, 0.9078, 0.9768, 0.8697, 0.9501, 0.9535, 0.9691, 0.9567, 0.9505, 0.9433, 0.9637, 0.9654, 0.8695, 0.954, 0.9461, 0.8392, 0.8409, nan] 
-2024-08-29 10:40:16.825287: Epoch time: 75.84 s 
-2024-08-29 10:40:17.942937:  
-2024-08-29 10:40:17.943078: Epoch 1890 
-2024-08-29 10:40:17.943159: Current learning rate: 0.00074 
-2024-08-29 10:41:34.947552: train_loss -0.7838 
-2024-08-29 10:41:34.947783: val_loss -0.8042 
-2024-08-29 10:41:34.947934: Pseudo dice [0.0, 0.0, 0.9125, 0.9777, 0.8824, 0.9508, 0.9545, 0.9684, 0.958, 0.9563, 0.9404, 0.9651, 0.966, 0.8664, 0.9527, 0.9447, 0.8554, 0.8537, nan] 
-2024-08-29 10:41:34.948015: Epoch time: 77.01 s 
-2024-08-29 10:41:36.048659:  
-2024-08-29 10:41:36.048828: Epoch 1891 
-2024-08-29 10:41:36.048913: Current learning rate: 0.00073 
-2024-08-29 10:42:48.036882: train_loss -0.7845 
-2024-08-29 10:42:48.037099: val_loss -0.8049 
-2024-08-29 10:42:48.037253: Pseudo dice [0.0, 0.0, 0.9152, 0.9791, 0.8658, 0.9498, 0.9557, 0.9703, 0.9601, 0.947, 0.9447, 0.964, 0.9674, 0.8681, 0.9542, 0.9445, 0.8561, 0.8524, nan] 
-2024-08-29 10:42:48.037330: Epoch time: 71.99 s 
-2024-08-29 10:42:48.037375: Yayy! New best EMA pseudo Dice: 0.8263 
-2024-08-29 10:42:49.775950:  
-2024-08-29 10:42:49.776398: Epoch 1892 
-2024-08-29 10:42:49.776496: Current learning rate: 0.00072 
-2024-08-29 10:44:03.606935: train_loss -0.7848 
-2024-08-29 10:44:03.607159: val_loss -0.7992 
-2024-08-29 10:44:03.607300: Pseudo dice [0.0, 0.0, 0.905, 0.9777, 0.8723, 0.9529, 0.9563, 0.9701, 0.9594, 0.9568, 0.9405, 0.9663, 0.9617, 0.8651, 0.9557, 0.9461, 0.8522, 0.8539, nan] 
-2024-08-29 10:44:03.607372: Epoch time: 73.83 s 
-2024-08-29 10:44:03.607413: Yayy! New best EMA pseudo Dice: 0.8264 
-2024-08-29 10:44:05.167828:  
-2024-08-29 10:44:05.167982: Epoch 1893 
-2024-08-29 10:44:05.168066: Current learning rate: 0.00072 
-2024-08-29 10:45:21.126072: train_loss -0.7838 
-2024-08-29 10:45:21.126299: val_loss -0.7986 
-2024-08-29 10:45:21.126443: Pseudo dice [0.0, 0.0, 0.9042, 0.9781, 0.8749, 0.953, 0.9534, 0.9702, 0.9562, 0.9566, 0.9415, 0.9662, 0.9628, 0.8676, 0.9612, 0.9453, 0.848, 0.8496, nan] 
-2024-08-29 10:45:21.126515: Epoch time: 75.96 s 
-2024-08-29 10:45:21.126555: Yayy! New best EMA pseudo Dice: 0.8265 
-2024-08-29 10:45:22.871428:  
-2024-08-29 10:45:22.871678: Epoch 1894 
-2024-08-29 10:45:22.871772: Current learning rate: 0.00071 
-2024-08-29 10:46:35.042966: train_loss -0.7817 
-2024-08-29 10:46:35.043178: val_loss -0.7994 
-2024-08-29 10:46:35.043325: Pseudo dice [0.0, 0.0, 0.9171, 0.9785, 0.8782, 0.9498, 0.9518, 0.9701, 0.9535, 0.9532, 0.9443, 0.962, 0.9645, 0.8683, 0.9537, 0.9449, 0.8599, 0.8543, nan] 
-2024-08-29 10:46:35.043398: Epoch time: 72.17 s 
-2024-08-29 10:46:35.043441: Yayy! New best EMA pseudo Dice: 0.8266 
-2024-08-29 10:46:36.607688:  
-2024-08-29 10:46:36.607838: Epoch 1895 
-2024-08-29 10:46:36.607929: Current learning rate: 0.0007 
-2024-08-29 10:47:53.294548: train_loss -0.7847 
-2024-08-29 10:47:53.294781: val_loss -0.7986 
-2024-08-29 10:47:53.294930: Pseudo dice [0.0, 0.0, 0.9116, 0.9784, 0.8694, 0.9513, 0.9532, 0.9679, 0.9567, 0.9592, 0.9437, 0.9632, 0.9671, 0.8483, 0.9549, 0.9394, 0.851, 0.8476, nan] 
-2024-08-29 10:47:53.295009: Epoch time: 76.69 s 
-2024-08-29 10:47:54.401347:  
-2024-08-29 10:47:54.401611: Epoch 1896 
-2024-08-29 10:47:54.401696: Current learning rate: 0.0007 
-2024-08-29 10:49:12.836835: train_loss -0.7843 
-2024-08-29 10:49:12.837107: val_loss -0.8019 
-2024-08-29 10:49:12.837352: Pseudo dice [0.0, 0.0, 0.9114, 0.9786, 0.8595, 0.9537, 0.9549, 0.9701, 0.952, 0.9598, 0.9409, 0.9639, 0.9657, 0.8684, 0.9564, 0.9445, 0.8543, 0.8509, nan] 
-2024-08-29 10:49:12.837436: Epoch time: 78.44 s 
-2024-08-29 10:49:13.953161:  
-2024-08-29 10:49:13.953296: Epoch 1897 
-2024-08-29 10:49:13.953380: Current learning rate: 0.00069 
-2024-08-29 10:50:29.483151: train_loss -0.7852 
-2024-08-29 10:50:29.483355: val_loss -0.7997 
-2024-08-29 10:50:29.483507: Pseudo dice [0.0, 0.0, 0.9127, 0.9781, 0.8653, 0.9514, 0.9567, 0.9697, 0.9583, 0.9517, 0.9402, 0.9655, 0.9642, 0.8635, 0.9609, 0.9373, 0.8408, 0.8463, nan] 
-2024-08-29 10:50:29.483584: Epoch time: 75.53 s 
-2024-08-29 10:50:30.597528:  
-2024-08-29 10:50:30.597742: Epoch 1898 
-2024-08-29 10:50:30.597823: Current learning rate: 0.00069 
-2024-08-29 10:51:50.706637: train_loss -0.7847 
-2024-08-29 10:51:50.706855: val_loss -0.8002 
-2024-08-29 10:51:50.707000: Pseudo dice [0.0, 0.0, 0.9142, 0.9781, 0.862, 0.9449, 0.9528, 0.9691, 0.9535, 0.9534, 0.9433, 0.9619, 0.9652, 0.8695, 0.9573, 0.9434, 0.8627, 0.8602, nan] 
-2024-08-29 10:51:50.707074: Epoch time: 80.11 s 
-2024-08-29 10:51:51.817844:  
-2024-08-29 10:51:51.818186: Epoch 1899 
-2024-08-29 10:51:51.818269: Current learning rate: 0.00068 
-2024-08-29 10:53:08.054320: train_loss -0.7803 
-2024-08-29 10:53:08.054521: val_loss -0.7973 
-2024-08-29 10:53:08.054667: Pseudo dice [0.0, 0.0, 0.9126, 0.9777, 0.8742, 0.9484, 0.9519, 0.9683, 0.9511, 0.9517, 0.9404, 0.9601, 0.964, 0.8524, 0.9598, 0.9403, 0.8476, 0.844, nan] 
-2024-08-29 10:53:08.054741: Epoch time: 76.24 s 
-2024-08-29 10:53:09.816036:  
-2024-08-29 10:53:09.816214: Epoch 1900 
-2024-08-29 10:53:09.816312: Current learning rate: 0.00067 
-2024-08-29 10:54:24.595283: train_loss -0.7827 
-2024-08-29 10:54:24.595514: val_loss -0.7995 
-2024-08-29 10:54:24.595662: Pseudo dice [0.0, 0.0, 0.9046, 0.9778, 0.8689, 0.9513, 0.9534, 0.9651, 0.9552, 0.951, 0.9376, 0.9611, 0.9635, 0.8639, 0.9592, 0.9425, 0.8522, 0.8403, nan] 
-2024-08-29 10:54:24.595742: Epoch time: 74.78 s 
-2024-08-29 10:54:25.705566:  
-2024-08-29 10:54:25.705715: Epoch 1901 
-2024-08-29 10:54:25.705791: Current learning rate: 0.00067 
-2024-08-29 10:55:43.641010: train_loss -0.7826 
-2024-08-29 10:55:43.641307: val_loss -0.8026 
-2024-08-29 10:55:43.641536: Pseudo dice [0.0, 0.0, 0.9171, 0.9779, 0.8724, 0.9524, 0.957, 0.9683, 0.9591, 0.9633, 0.943, 0.9637, 0.9637, 0.868, 0.9555, 0.9441, 0.8464, 0.8473, nan] 
-2024-08-29 10:55:43.641673: Epoch time: 77.94 s 
-2024-08-29 10:55:44.781409:  
-2024-08-29 10:55:44.781856: Epoch 1902 
-2024-08-29 10:55:44.781950: Current learning rate: 0.00066 
-2024-08-29 10:57:02.009728: train_loss -0.782 
-2024-08-29 10:57:02.009947: val_loss -0.8065 
-2024-08-29 10:57:02.010103: Pseudo dice [0.0, 0.0, 0.9199, 0.9778, 0.88, 0.9545, 0.9578, 0.9715, 0.9586, 0.9627, 0.9455, 0.9662, 0.967, 0.8652, 0.9615, 0.9467, 0.8589, 0.8629, nan] 
-2024-08-29 10:57:02.010186: Epoch time: 77.23 s 
-2024-08-29 10:57:02.010236: Yayy! New best EMA pseudo Dice: 0.8268 
-2024-08-29 10:57:03.569049:  
-2024-08-29 10:57:03.569231: Epoch 1903 
-2024-08-29 10:57:03.569322: Current learning rate: 0.00066 
-2024-08-29 10:58:24.248585: train_loss -0.7822 
-2024-08-29 10:58:24.249047: val_loss -0.8013 
-2024-08-29 10:58:24.249192: Pseudo dice [0.0, 0.0, 0.9179, 0.9768, 0.8822, 0.949, 0.9513, 0.9678, 0.9569, 0.9527, 0.9405, 0.9618, 0.9657, 0.8671, 0.9543, 0.9411, 0.85, 0.8502, nan] 
-2024-08-29 10:58:24.249261: Epoch time: 80.68 s 
-2024-08-29 10:58:24.249300: Yayy! New best EMA pseudo Dice: 0.8268 
-2024-08-29 10:58:25.801999:  
-2024-08-29 10:58:25.802136: Epoch 1904 
-2024-08-29 10:58:25.802212: Current learning rate: 0.00065 
-2024-08-29 10:59:45.123097: train_loss -0.7794 
-2024-08-29 10:59:45.123298: val_loss -0.8022 
-2024-08-29 10:59:45.123432: Pseudo dice [0.0, 0.0, 0.9042, 0.9779, 0.8692, 0.9495, 0.9528, 0.9696, 0.9577, 0.9613, 0.9421, 0.9603, 0.9624, 0.8638, 0.9535, 0.9425, 0.8453, 0.8507, nan] 
-2024-08-29 10:59:45.123502: Epoch time: 79.32 s 
-2024-08-29 10:59:46.209296:  
-2024-08-29 10:59:46.209531: Epoch 1905 
-2024-08-29 10:59:46.209627: Current learning rate: 0.00064 
-2024-08-29 11:01:00.490053: train_loss -0.7854 
-2024-08-29 11:01:00.490669: val_loss -0.8017 
-2024-08-29 11:01:00.490879: Pseudo dice [0.0, 0.0, 0.9061, 0.9783, 0.8633, 0.9499, 0.9521, 0.9655, 0.9588, 0.9543, 0.9443, 0.9646, 0.966, 0.8679, 0.9491, 0.938, 0.839, 0.8524, nan] 
-2024-08-29 11:01:00.490967: Epoch time: 74.28 s 
-2024-08-29 11:01:01.838187:  
-2024-08-29 11:01:01.838427: Epoch 1906 
-2024-08-29 11:01:01.838519: Current learning rate: 0.00064 
-2024-08-29 11:02:18.807506: train_loss -0.7857 
-2024-08-29 11:02:18.807832: val_loss -0.8047 
-2024-08-29 11:02:18.807982: Pseudo dice [0.0, 0.0, 0.9137, 0.9773, 0.8652, 0.9515, 0.9538, 0.9716, 0.957, 0.9534, 0.9459, 0.9637, 0.9673, 0.8685, 0.9592, 0.9459, 0.8581, 0.8565, nan] 
-2024-08-29 11:02:18.808091: Epoch time: 76.97 s 
-2024-08-29 11:02:19.946455:  
-2024-08-29 11:02:19.946621: Epoch 1907 
-2024-08-29 11:02:19.946700: Current learning rate: 0.00063 
-2024-08-29 11:03:37.637592: train_loss -0.7812 
-2024-08-29 11:03:37.637814: val_loss -0.799 
-2024-08-29 11:03:37.637962: Pseudo dice [0.0, 0.0, 0.9141, 0.9777, 0.8609, 0.9476, 0.9496, 0.9644, 0.9557, 0.9608, 0.9372, 0.9628, 0.9646, 0.8467, 0.9511, 0.942, 0.8495, 0.853, nan] 
-2024-08-29 11:03:37.638038: Epoch time: 77.69 s 
-2024-08-29 11:03:38.753752:  
-2024-08-29 11:03:38.754042: Epoch 1908 
-2024-08-29 11:03:38.754127: Current learning rate: 0.00063 
-2024-08-29 11:04:51.658504: train_loss -0.785 
-2024-08-29 11:04:51.658726: val_loss -0.8024 
-2024-08-29 11:04:51.658871: Pseudo dice [0.0, 0.0, 0.9116, 0.9781, 0.8648, 0.9512, 0.9533, 0.9697, 0.9566, 0.9609, 0.9443, 0.962, 0.9654, 0.8698, 0.9543, 0.9449, 0.8542, 0.8448, nan] 
-2024-08-29 11:04:51.658945: Epoch time: 72.91 s 
-2024-08-29 11:04:52.786273:  
-2024-08-29 11:04:52.786421: Epoch 1909 
-2024-08-29 11:04:52.786508: Current learning rate: 0.00062 
-2024-08-29 11:06:14.779737: train_loss -0.784 
-2024-08-29 11:06:14.779934: val_loss -0.8003 
-2024-08-29 11:06:14.780087: Pseudo dice [0.0, 0.0, 0.9102, 0.9784, 0.8587, 0.9503, 0.9536, 0.9658, 0.9593, 0.9569, 0.9427, 0.9623, 0.9661, 0.8678, 0.9554, 0.9454, 0.8351, 0.8452, nan] 
-2024-08-29 11:06:14.780165: Epoch time: 81.99 s 
-2024-08-29 11:06:15.919548:  
-2024-08-29 11:06:15.919703: Epoch 1910 
-2024-08-29 11:06:15.919784: Current learning rate: 0.00061 
-2024-08-29 11:07:32.844886: train_loss -0.7806 
-2024-08-29 11:07:32.845113: val_loss -0.8042 
-2024-08-29 11:07:32.845259: Pseudo dice [0.0, 0.0, 0.9094, 0.9788, 0.8725, 0.9545, 0.9571, 0.972, 0.9589, 0.9527, 0.941, 0.9633, 0.9659, 0.8679, 0.9614, 0.9431, 0.8474, 0.8587, nan] 
-2024-08-29 11:07:32.845336: Epoch time: 76.93 s 
-2024-08-29 11:07:34.176671:  
-2024-08-29 11:07:34.176842: Epoch 1911 
-2024-08-29 11:07:34.176923: Current learning rate: 0.00061 
-2024-08-29 11:08:48.057014: train_loss -0.7826 
-2024-08-29 11:08:48.057221: val_loss -0.8085 
-2024-08-29 11:08:48.057367: Pseudo dice [0.0, 0.0, 0.9174, 0.9786, 0.8564, 0.9481, 0.954, 0.9698, 0.9605, 0.9642, 0.945, 0.966, 0.9687, 0.8743, 0.9577, 0.9466, 0.8542, 0.8605, nan] 
-2024-08-29 11:08:48.057439: Epoch time: 73.88 s 
-2024-08-29 11:08:49.196229:  
-2024-08-29 11:08:49.196369: Epoch 1912 
-2024-08-29 11:08:49.196452: Current learning rate: 0.0006 
-2024-08-29 11:10:04.276390: train_loss -0.7834 
-2024-08-29 11:10:04.276601: val_loss -0.8063 
-2024-08-29 11:10:04.276753: Pseudo dice [0.0, 0.0, 0.9157, 0.9789, 0.8861, 0.9525, 0.9552, 0.9699, 0.9533, 0.9598, 0.9429, 0.9644, 0.9654, 0.8752, 0.9582, 0.9447, 0.854, 0.8509, nan] 
-2024-08-29 11:10:04.276829: Epoch time: 75.08 s 
-2024-08-29 11:10:04.276872: Yayy! New best EMA pseudo Dice: 0.8271 
-2024-08-29 11:10:05.876607:  
-2024-08-29 11:10:05.876768: Epoch 1913 
-2024-08-29 11:10:05.876857: Current learning rate: 0.0006 
-2024-08-29 11:11:21.990022: train_loss -0.782 
-2024-08-29 11:11:21.990236: val_loss -0.801 
-2024-08-29 11:11:21.990381: Pseudo dice [0.0, 0.0, 0.9152, 0.9784, 0.8707, 0.9516, 0.9545, 0.9704, 0.9523, 0.9548, 0.9412, 0.962, 0.9634, 0.8669, 0.9499, 0.9374, 0.8376, 0.8427, nan] 
-2024-08-29 11:11:21.990458: Epoch time: 76.11 s 
-2024-08-29 11:11:23.141079:  
-2024-08-29 11:11:23.141340: Epoch 1914 
-2024-08-29 11:11:23.141419: Current learning rate: 0.00059 
-2024-08-29 11:12:37.413684: train_loss -0.7858 
-2024-08-29 11:12:37.414295: val_loss -0.8023 
-2024-08-29 11:12:37.414449: Pseudo dice [0.0, 0.0, 0.9023, 0.9782, 0.88, 0.9534, 0.9532, 0.9675, 0.9552, 0.9399, 0.9439, 0.9623, 0.9652, 0.8716, 0.9595, 0.9436, 0.8424, 0.8392, nan] 
-2024-08-29 11:12:37.414528: Epoch time: 74.27 s 
-2024-08-29 11:12:38.531960:  
-2024-08-29 11:12:38.532094: Epoch 1915 
-2024-08-29 11:12:38.532173: Current learning rate: 0.00058 
-2024-08-29 11:13:55.297390: train_loss -0.7842 
-2024-08-29 11:13:55.297597: val_loss -0.7988 
-2024-08-29 11:13:55.297742: Pseudo dice [0.0, 0.0, 0.9204, 0.9788, 0.8742, 0.9491, 0.9525, 0.9634, 0.9585, 0.955, 0.935, 0.9634, 0.963, 0.8503, 0.9575, 0.9428, 0.8489, 0.8512, nan] 
-2024-08-29 11:13:55.297814: Epoch time: 76.77 s 
-2024-08-29 11:13:56.410837:  
-2024-08-29 11:13:56.410991: Epoch 1916 
-2024-08-29 11:13:56.411070: Current learning rate: 0.00058 
-2024-08-29 11:15:10.881891: train_loss -0.7852 
-2024-08-29 11:15:10.882090: val_loss -0.7981 
-2024-08-29 11:15:10.882226: Pseudo dice [0.0, 0.0, 0.8897, 0.9773, 0.8752, 0.951, 0.9504, 0.9684, 0.9582, 0.9592, 0.94, 0.9648, 0.963, 0.8656, 0.9542, 0.9441, 0.8312, 0.8435, nan] 
-2024-08-29 11:15:10.882299: Epoch time: 74.47 s 
-2024-08-29 11:15:12.226834:  
-2024-08-29 11:15:12.227151: Epoch 1917 
-2024-08-29 11:15:12.227242: Current learning rate: 0.00057 
-2024-08-29 11:16:27.723041: train_loss -0.7847 
-2024-08-29 11:16:27.723273: val_loss -0.8074 
-2024-08-29 11:16:27.723419: Pseudo dice [0.0, 0.0, 0.9171, 0.9772, 0.8661, 0.9532, 0.9571, 0.9673, 0.9582, 0.965, 0.9464, 0.9646, 0.9663, 0.8686, 0.9598, 0.9456, 0.861, 0.8568, nan] 
-2024-08-29 11:16:27.723501: Epoch time: 75.5 s 
-2024-08-29 11:16:28.863707:  
-2024-08-29 11:16:28.863861: Epoch 1918 
-2024-08-29 11:16:28.863939: Current learning rate: 0.00056 
-2024-08-29 11:17:51.990781: train_loss -0.7853 
-2024-08-29 11:17:51.991002: val_loss -0.8012 
-2024-08-29 11:17:51.991155: Pseudo dice [0.0, 0.0, 0.9169, 0.9772, 0.8693, 0.9525, 0.9541, 0.9695, 0.9548, 0.9557, 0.9429, 0.9628, 0.9662, 0.8688, 0.9561, 0.9417, 0.8646, 0.8568, nan] 
-2024-08-29 11:17:51.991234: Epoch time: 83.13 s 
-2024-08-29 11:17:53.122369:  
-2024-08-29 11:17:53.122523: Epoch 1919 
-2024-08-29 11:17:53.122612: Current learning rate: 0.00056 
-2024-08-29 11:19:09.112759: train_loss -0.7839 
-2024-08-29 11:19:09.112983: val_loss -0.8035 
-2024-08-29 11:19:09.113125: Pseudo dice [0.0, 0.0, 0.9144, 0.9775, 0.871, 0.9479, 0.9539, 0.9685, 0.9562, 0.9556, 0.9435, 0.9625, 0.9662, 0.8641, 0.9541, 0.9441, 0.8667, 0.8595, nan] 
-2024-08-29 11:19:09.113199: Epoch time: 75.99 s 
-2024-08-29 11:19:10.251703:  
-2024-08-29 11:19:10.251848: Epoch 1920 
-2024-08-29 11:19:10.251928: Current learning rate: 0.00055 
-2024-08-29 11:20:21.067936: train_loss -0.784 
-2024-08-29 11:20:21.068130: val_loss -0.8023 
-2024-08-29 11:20:21.068271: Pseudo dice [0.0, 0.0, 0.9261, 0.9789, 0.8796, 0.95, 0.9545, 0.9653, 0.9562, 0.9571, 0.9417, 0.9624, 0.9628, 0.8672, 0.9583, 0.9441, 0.8593, 0.8587, nan] 
-2024-08-29 11:20:21.068344: Epoch time: 70.82 s 
-2024-08-29 11:20:21.068386: Yayy! New best EMA pseudo Dice: 0.8272 
-2024-08-29 11:20:22.631473:  
-2024-08-29 11:20:22.631615: Epoch 1921 
-2024-08-29 11:20:22.631692: Current learning rate: 0.00055 
-2024-08-29 11:21:40.165366: train_loss -0.7835 
-2024-08-29 11:21:40.165581: val_loss -0.8012 
-2024-08-29 11:21:40.165720: Pseudo dice [0.0, 0.0, 0.916, 0.9775, 0.8155, 0.9374, 0.9445, 0.9685, 0.9599, 0.9561, 0.9399, 0.9639, 0.9657, 0.8734, 0.9612, 0.9471, 0.8609, 0.8543, nan] 
-2024-08-29 11:21:40.165791: Epoch time: 77.53 s 
-2024-08-29 11:21:41.327394:  
-2024-08-29 11:21:41.327658: Epoch 1922 
-2024-08-29 11:21:41.327745: Current learning rate: 0.00054 
-2024-08-29 11:22:55.478669: train_loss -0.7848 
-2024-08-29 11:22:55.478888: val_loss -0.8016 
-2024-08-29 11:22:55.479024: Pseudo dice [0.0, 0.0, 0.9057, 0.9781, 0.8677, 0.9504, 0.9544, 0.9675, 0.9565, 0.96, 0.9417, 0.9626, 0.9661, 0.8683, 0.9499, 0.9443, 0.8536, 0.8548, nan] 
-2024-08-29 11:22:55.479114: Epoch time: 74.15 s 
-2024-08-29 11:22:56.813896:  
-2024-08-29 11:22:56.814052: Epoch 1923 
-2024-08-29 11:22:56.814133: Current learning rate: 0.00053 
-2024-08-29 11:24:20.339484: train_loss -0.7849 
-2024-08-29 11:24:20.339689: val_loss -0.8037 
-2024-08-29 11:24:20.339842: Pseudo dice [0.0, 0.0, 0.9195, 0.9775, 0.8823, 0.9545, 0.9573, 0.9701, 0.9547, 0.9579, 0.9441, 0.9662, 0.9657, 0.8732, 0.9634, 0.9446, 0.8595, 0.865, nan] 
-2024-08-29 11:24:20.339922: Epoch time: 83.53 s 
-2024-08-29 11:24:20.339969: Yayy! New best EMA pseudo Dice: 0.8273 
-2024-08-29 11:24:21.938160:  
-2024-08-29 11:24:21.938336: Epoch 1924 
-2024-08-29 11:24:21.938427: Current learning rate: 0.00053 
-2024-08-29 11:25:37.205774: train_loss -0.7833 
-2024-08-29 11:25:37.206005: val_loss -0.8052 
-2024-08-29 11:25:37.206154: Pseudo dice [0.0, 0.0, 0.922, 0.98, 0.8734, 0.9528, 0.957, 0.9715, 0.958, 0.9598, 0.9427, 0.9663, 0.9658, 0.8687, 0.9612, 0.9431, 0.8499, 0.8564, nan] 
-2024-08-29 11:25:37.206233: Epoch time: 75.27 s 
-2024-08-29 11:25:37.206279: Yayy! New best EMA pseudo Dice: 0.8275 
-2024-08-29 11:25:38.796542:  
-2024-08-29 11:25:38.796693: Epoch 1925 
-2024-08-29 11:25:38.796777: Current learning rate: 0.00052 
-2024-08-29 11:26:52.349652: train_loss -0.7853 
-2024-08-29 11:26:52.349866: val_loss -0.805 
-2024-08-29 11:26:52.350019: Pseudo dice [0.0, 0.0, 0.9135, 0.9789, 0.8857, 0.9541, 0.9557, 0.9704, 0.958, 0.9606, 0.9409, 0.9663, 0.9656, 0.8735, 0.9616, 0.9459, 0.8524, 0.8594, nan] 
-2024-08-29 11:26:52.350095: Epoch time: 73.55 s 
-2024-08-29 11:26:52.350140: Yayy! New best EMA pseudo Dice: 0.8278 
-2024-08-29 11:26:53.921067:  
-2024-08-29 11:26:53.921399: Epoch 1926 
-2024-08-29 11:26:53.921484: Current learning rate: 0.00051 
-2024-08-29 11:28:13.834678: train_loss -0.7828 
-2024-08-29 11:28:13.834897: val_loss -0.7996 
-2024-08-29 11:28:13.835051: Pseudo dice [0.0, 0.0, 0.9139, 0.9769, 0.8713, 0.951, 0.9558, 0.9694, 0.9545, 0.9551, 0.9417, 0.9626, 0.9667, 0.8581, 0.9586, 0.9404, 0.8643, 0.8543, nan] 
-2024-08-29 11:28:13.835130: Epoch time: 79.91 s 
-2024-08-29 11:28:14.949949:  
-2024-08-29 11:28:14.950244: Epoch 1927 
-2024-08-29 11:28:14.950323: Current learning rate: 0.00051 
-2024-08-29 11:29:34.448154: train_loss -0.7816 
-2024-08-29 11:29:34.448354: val_loss -0.7988 
-2024-08-29 11:29:34.448510: Pseudo dice [0.0, 0.0, 0.9047, 0.9773, 0.8789, 0.9513, 0.9553, 0.9678, 0.9571, 0.9546, 0.9405, 0.9651, 0.9664, 0.8678, 0.9526, 0.9468, 0.848, 0.8444, nan] 
-2024-08-29 11:29:34.448588: Epoch time: 79.5 s 
-2024-08-29 11:29:35.755958:  
-2024-08-29 11:29:35.756271: Epoch 1928 
-2024-08-29 11:29:35.756365: Current learning rate: 0.0005 
-2024-08-29 11:30:55.546543: train_loss -0.7802 
-2024-08-29 11:30:55.547408: val_loss -0.8003 
-2024-08-29 11:30:55.547791: Pseudo dice [0.0, 0.0, 0.9161, 0.9788, 0.8761, 0.9506, 0.954, 0.9691, 0.9538, 0.9407, 0.9391, 0.9632, 0.9644, 0.8592, 0.9581, 0.941, 0.8363, 0.8398, nan] 
-2024-08-29 11:30:55.547980: Epoch time: 79.79 s 
-2024-08-29 11:30:56.682850:  
-2024-08-29 11:30:56.683026: Epoch 1929 
-2024-08-29 11:30:56.683104: Current learning rate: 0.0005 
-2024-08-29 11:32:13.305115: train_loss -0.7821 
-2024-08-29 11:32:13.305317: val_loss -0.8015 
-2024-08-29 11:32:13.305466: Pseudo dice [0.0, 0.0, 0.9189, 0.9783, 0.8778, 0.9504, 0.9559, 0.9691, 0.9558, 0.9437, 0.943, 0.964, 0.9678, 0.8667, 0.9565, 0.943, 0.8592, 0.8684, nan] 
-2024-08-29 11:32:13.305595: Epoch time: 76.62 s 
-2024-08-29 11:32:14.434416:  
-2024-08-29 11:32:14.434765: Epoch 1930 
-2024-08-29 11:32:14.434851: Current learning rate: 0.00049 
-2024-08-29 11:33:36.763582: train_loss -0.7831 
-2024-08-29 11:33:36.763795: val_loss -0.8015 
-2024-08-29 11:33:36.763946: Pseudo dice [0.0, 0.0, 0.918, 0.9777, 0.8705, 0.9538, 0.9573, 0.9707, 0.9577, 0.9546, 0.9445, 0.9649, 0.9674, 0.8695, 0.9583, 0.9428, 0.8569, 0.8548, nan] 
-2024-08-29 11:33:36.764022: Epoch time: 82.33 s 
-2024-08-29 11:33:37.884914:  
-2024-08-29 11:33:37.885071: Epoch 1931 
-2024-08-29 11:33:37.885153: Current learning rate: 0.00048 
-2024-08-29 11:34:57.426570: train_loss -0.7852 
-2024-08-29 11:34:57.426774: val_loss -0.8019 
-2024-08-29 11:34:57.426921: Pseudo dice [0.0, 0.0, 0.9241, 0.9787, 0.8789, 0.9534, 0.9575, 0.9708, 0.9558, 0.9556, 0.9386, 0.9637, 0.9645, 0.8613, 0.9547, 0.945, 0.8584, 0.853, nan] 
-2024-08-29 11:34:57.426998: Epoch time: 79.54 s 
-2024-08-29 11:34:58.550225:  
-2024-08-29 11:34:58.550359: Epoch 1932 
-2024-08-29 11:34:58.550436: Current learning rate: 0.00048 
-2024-08-29 11:36:12.798774: train_loss -0.7844 
-2024-08-29 11:36:12.799014: val_loss -0.8032 
-2024-08-29 11:36:12.799165: Pseudo dice [0.0, 0.0, 0.9118, 0.9786, 0.8831, 0.9516, 0.9565, 0.9692, 0.9596, 0.9592, 0.9444, 0.9666, 0.966, 0.8714, 0.958, 0.9456, 0.8621, 0.8663, nan] 
-2024-08-29 11:36:12.799246: Epoch time: 74.25 s 
-2024-08-29 11:36:12.799294: Yayy! New best EMA pseudo Dice: 0.828 
-2024-08-29 11:36:14.360362:  
-2024-08-29 11:36:14.360510: Epoch 1933 
-2024-08-29 11:36:14.360595: Current learning rate: 0.00047 
-2024-08-29 11:37:31.657735: train_loss -0.7855 
-2024-08-29 11:37:31.657936: val_loss -0.8025 
-2024-08-29 11:37:31.658091: Pseudo dice [0.0, 0.0, 0.912, 0.9782, 0.8737, 0.9523, 0.9544, 0.9669, 0.9603, 0.9579, 0.9441, 0.9664, 0.9683, 0.868, 0.9589, 0.9421, 0.8558, 0.858, nan] 
-2024-08-29 11:37:31.658169: Epoch time: 77.3 s 
-2024-08-29 11:37:31.658217: Yayy! New best EMA pseudo Dice: 0.8281 
-2024-08-29 11:37:33.415828:  
-2024-08-29 11:37:33.416311: Epoch 1934 
-2024-08-29 11:37:33.416396: Current learning rate: 0.00046 
-2024-08-29 11:38:44.575191: train_loss -0.7874 
-2024-08-29 11:38:44.575517: val_loss -0.8046 
-2024-08-29 11:38:44.575667: Pseudo dice [0.0, 0.0, 0.9066, 0.9782, 0.8796, 0.9513, 0.9549, 0.971, 0.9564, 0.9546, 0.9393, 0.9623, 0.9677, 0.8711, 0.9609, 0.9475, 0.8527, 0.8548, nan] 
-2024-08-29 11:38:44.575748: Epoch time: 71.16 s 
-2024-08-29 11:38:44.575794: Yayy! New best EMA pseudo Dice: 0.8281 
-2024-08-29 11:38:46.162483:  
-2024-08-29 11:38:46.162643: Epoch 1935 
-2024-08-29 11:38:46.162724: Current learning rate: 0.00046 
-2024-08-29 11:40:03.623241: train_loss -0.7833 
-2024-08-29 11:40:03.623455: val_loss -0.8053 
-2024-08-29 11:40:03.623599: Pseudo dice [0.0, 0.0, 0.9024, 0.978, 0.8633, 0.9515, 0.9554, 0.97, 0.9599, 0.9576, 0.9403, 0.9661, 0.9677, 0.8657, 0.9556, 0.9454, 0.8541, 0.8561, nan] 
-2024-08-29 11:40:03.623676: Epoch time: 77.46 s 
-2024-08-29 11:40:04.772569:  
-2024-08-29 11:40:04.772816: Epoch 1936 
-2024-08-29 11:40:04.772908: Current learning rate: 0.00045 
-2024-08-29 11:41:17.286020: train_loss -0.7845 
-2024-08-29 11:41:17.286217: val_loss -0.7986 
-2024-08-29 11:41:17.286364: Pseudo dice [0.0, 0.0, 0.8999, 0.979, 0.8801, 0.9448, 0.9507, 0.9666, 0.9553, 0.9604, 0.9419, 0.9643, 0.9632, 0.8525, 0.9511, 0.9423, 0.8307, 0.831, nan] 
-2024-08-29 11:41:17.286441: Epoch time: 72.51 s 
-2024-08-29 11:41:18.419279:  
-2024-08-29 11:41:18.419529: Epoch 1937 
-2024-08-29 11:41:18.419614: Current learning rate: 0.00045 
-2024-08-29 11:42:35.675001: train_loss -0.7842 
-2024-08-29 11:42:35.675218: val_loss -0.8041 
-2024-08-29 11:42:35.675355: Pseudo dice [0.0, 0.0, 0.9134, 0.9773, 0.8841, 0.9545, 0.958, 0.9722, 0.9526, 0.9499, 0.9392, 0.9566, 0.9639, 0.8741, 0.9522, 0.9479, 0.8568, 0.8624, nan] 
-2024-08-29 11:42:35.675427: Epoch time: 77.26 s 
-2024-08-29 11:42:36.815221:  
-2024-08-29 11:42:36.815369: Epoch 1938 
-2024-08-29 11:42:36.815460: Current learning rate: 0.00044 
-2024-08-29 11:43:53.528435: train_loss -0.7821 
-2024-08-29 11:43:53.528646: val_loss -0.8007 
-2024-08-29 11:43:53.528790: Pseudo dice [0.0, 0.0, 0.9111, 0.9772, 0.8718, 0.9509, 0.9525, 0.9654, 0.9577, 0.9503, 0.9432, 0.9643, 0.968, 0.8613, 0.9521, 0.9372, 0.8554, 0.8565, nan] 
-2024-08-29 11:43:53.528864: Epoch time: 76.71 s 
-2024-08-29 11:43:54.682357:  
-2024-08-29 11:43:54.682498: Epoch 1939 
-2024-08-29 11:43:54.682587: Current learning rate: 0.00043 
-2024-08-29 11:45:11.094498: train_loss -0.7837 
-2024-08-29 11:45:11.094720: val_loss -0.7991 
-2024-08-29 11:45:11.094863: Pseudo dice [0.0, 0.0, 0.9045, 0.9784, 0.8565, 0.9482, 0.9509, 0.9696, 0.9586, 0.9486, 0.9394, 0.9635, 0.9647, 0.8668, 0.9595, 0.9447, 0.8372, 0.8343, nan] 
-2024-08-29 11:45:11.094981: Epoch time: 76.41 s 
-2024-08-29 11:45:12.410554:  
-2024-08-29 11:45:12.410718: Epoch 1940 
-2024-08-29 11:45:12.410809: Current learning rate: 0.00043 
-2024-08-29 11:46:28.418672: train_loss -0.7841 
-2024-08-29 11:46:28.418904: val_loss -0.7998 
-2024-08-29 11:46:28.419059: Pseudo dice [0.0, 0.0, 0.9082, 0.9783, 0.8753, 0.954, 0.9574, 0.97, 0.9561, 0.9592, 0.9451, 0.9655, 0.9671, 0.867, 0.9592, 0.9461, 0.8532, 0.8485, nan] 
-2024-08-29 11:46:28.419138: Epoch time: 76.01 s 
-2024-08-29 11:46:29.584782:  
-2024-08-29 11:46:29.584918: Epoch 1941 
-2024-08-29 11:46:29.585010: Current learning rate: 0.00042 
-2024-08-29 11:47:45.714460: train_loss -0.7821 
-2024-08-29 11:47:45.714693: val_loss -0.8032 
-2024-08-29 11:47:45.714842: Pseudo dice [0.0, 0.0, 0.922, 0.9785, 0.8617, 0.951, 0.9545, 0.9696, 0.9599, 0.9613, 0.9453, 0.9664, 0.9678, 0.8739, 0.9596, 0.9451, 0.8618, 0.8527, nan] 
-2024-08-29 11:47:45.714918: Epoch time: 76.13 s 
-2024-08-29 11:47:46.835058:  
-2024-08-29 11:47:46.835207: Epoch 1942 
-2024-08-29 11:47:46.835290: Current learning rate: 0.00041 
-2024-08-29 11:49:04.794726: train_loss -0.7853 
-2024-08-29 11:49:04.794942: val_loss -0.8047 
-2024-08-29 11:49:04.795090: Pseudo dice [0.0, 0.0, 0.9101, 0.9778, 0.8768, 0.9536, 0.9565, 0.9702, 0.9603, 0.9559, 0.9408, 0.9634, 0.9657, 0.8744, 0.9583, 0.9492, 0.8557, 0.8534, nan] 
-2024-08-29 11:49:04.795166: Epoch time: 77.96 s 
-2024-08-29 11:49:05.930554:  
-2024-08-29 11:49:05.930720: Epoch 1943 
-2024-08-29 11:49:05.930801: Current learning rate: 0.00041 
-2024-08-29 11:50:16.853897: train_loss -0.7852 
-2024-08-29 11:50:16.854129: val_loss -0.8057 
-2024-08-29 11:50:16.854290: Pseudo dice [0.0, 0.0, 0.896, 0.978, 0.8773, 0.9493, 0.9544, 0.9671, 0.9603, 0.9595, 0.9423, 0.9661, 0.9674, 0.8674, 0.9509, 0.9425, 0.8457, 0.839, nan] 
-2024-08-29 11:50:16.854416: Epoch time: 70.92 s 
-2024-08-29 11:50:17.994342:  
-2024-08-29 11:50:17.994506: Epoch 1944 
-2024-08-29 11:50:17.994589: Current learning rate: 0.0004 
-2024-08-29 11:51:41.031505: train_loss -0.7838 
-2024-08-29 11:51:41.031701: val_loss -0.8013 
-2024-08-29 11:51:41.031845: Pseudo dice [0.0, 0.0, 0.9002, 0.977, 0.8715, 0.9523, 0.9568, 0.9692, 0.9587, 0.9559, 0.9441, 0.9638, 0.9652, 0.8541, 0.9543, 0.9434, 0.8631, 0.8557, nan] 
-2024-08-29 11:51:41.031922: Epoch time: 83.04 s 
-2024-08-29 11:51:42.148114:  
-2024-08-29 11:51:42.148378: Epoch 1945 
-2024-08-29 11:51:42.148470: Current learning rate: 0.00039 
-2024-08-29 11:53:00.199645: train_loss -0.783 
-2024-08-29 11:53:00.199893: val_loss -0.803 
-2024-08-29 11:53:00.200060: Pseudo dice [0.0, 0.0, 0.8983, 0.9782, 0.8513, 0.944, 0.9496, 0.9679, 0.9598, 0.9581, 0.9435, 0.9655, 0.9674, 0.8689, 0.9568, 0.9429, 0.8544, 0.8546, nan] 
-2024-08-29 11:53:00.200149: Epoch time: 78.05 s 
-2024-08-29 11:53:01.549353:  
-2024-08-29 11:53:01.549675: Epoch 1946 
-2024-08-29 11:53:01.549751: Current learning rate: 0.00039 
-2024-08-29 11:54:19.700119: train_loss -0.7852 
-2024-08-29 11:54:19.700324: val_loss -0.8041 
-2024-08-29 11:54:19.700469: Pseudo dice [0.0, 0.0, 0.9151, 0.9781, 0.8742, 0.9526, 0.955, 0.9724, 0.9584, 0.9593, 0.9433, 0.9645, 0.967, 0.8666, 0.9619, 0.943, 0.8567, 0.8552, nan] 
-2024-08-29 11:54:19.700545: Epoch time: 78.15 s 
-2024-08-29 11:54:20.802708:  
-2024-08-29 11:54:20.803059: Epoch 1947 
-2024-08-29 11:54:20.803138: Current learning rate: 0.00038 
-2024-08-29 11:55:32.923073: train_loss -0.7855 
-2024-08-29 11:55:32.923295: val_loss -0.8048 
-2024-08-29 11:55:32.923449: Pseudo dice [0.0, 0.0, 0.905, 0.979, 0.8771, 0.9509, 0.9552, 0.9682, 0.9566, 0.9528, 0.9421, 0.966, 0.9654, 0.8721, 0.9603, 0.9459, 0.8655, 0.8608, nan] 
-2024-08-29 11:55:32.923533: Epoch time: 72.12 s 
-2024-08-29 11:55:34.053676:  
-2024-08-29 11:55:34.053805: Epoch 1948 
-2024-08-29 11:55:34.053878: Current learning rate: 0.00037 
-2024-08-29 11:56:50.188719: train_loss -0.7844 
-2024-08-29 11:56:50.188936: val_loss -0.801 
-2024-08-29 11:56:50.189082: Pseudo dice [0.0, 0.0, 0.8999, 0.9789, 0.8755, 0.9543, 0.9555, 0.9652, 0.9591, 0.9573, 0.9477, 0.9649, 0.9682, 0.873, 0.9572, 0.9455, 0.8492, 0.8496, nan] 
-2024-08-29 11:56:50.189216: Epoch time: 76.14 s 
-2024-08-29 11:56:51.318286:  
-2024-08-29 11:56:51.318441: Epoch 1949 
-2024-08-29 11:56:51.318520: Current learning rate: 0.00037 
-2024-08-29 11:58:07.192975: train_loss -0.7863 
-2024-08-29 11:58:07.193191: val_loss -0.8063 
-2024-08-29 11:58:07.193331: Pseudo dice [0.0, 0.0, 0.9238, 0.9785, 0.8763, 0.9532, 0.9554, 0.9692, 0.9577, 0.9534, 0.9403, 0.9647, 0.9654, 0.8736, 0.9575, 0.9432, 0.8522, 0.8555, nan] 
-2024-08-29 11:58:07.193404: Epoch time: 75.88 s 
-2024-08-29 11:58:08.810557:  
-2024-08-29 11:58:08.810718: Epoch 1950 
-2024-08-29 11:58:08.810806: Current learning rate: 0.00036 
-2024-08-29 11:59:24.981550: train_loss -0.7854 
-2024-08-29 11:59:24.981775: val_loss -0.8067 
-2024-08-29 11:59:24.981948: Pseudo dice [0.0, 0.0, 0.9198, 0.978, 0.8878, 0.9533, 0.9596, 0.9701, 0.9584, 0.9573, 0.9465, 0.9646, 0.9683, 0.8742, 0.9576, 0.9426, 0.8549, 0.861, nan] 
-2024-08-29 11:59:24.982034: Epoch time: 76.17 s 
-2024-08-29 11:59:26.137182:  
-2024-08-29 11:59:26.137334: Epoch 1951 
-2024-08-29 11:59:26.137412: Current learning rate: 0.00036 
-2024-08-29 12:00:39.442161: train_loss -0.7864 
-2024-08-29 12:00:39.442368: val_loss -0.8034 
-2024-08-29 12:00:39.442513: Pseudo dice [0.0, 0.0, 0.9151, 0.9783, 0.8658, 0.9523, 0.9542, 0.9687, 0.96, 0.964, 0.9453, 0.9649, 0.9696, 0.866, 0.9594, 0.9425, 0.8595, 0.8507, nan] 
-2024-08-29 12:00:39.442590: Epoch time: 73.31 s 
-2024-08-29 12:00:39.442632: Yayy! New best EMA pseudo Dice: 0.8281 
-2024-08-29 12:00:41.807116:  
-2024-08-29 12:00:41.807262: Epoch 1952 
-2024-08-29 12:00:41.807345: Current learning rate: 0.00035 
-2024-08-29 12:01:55.617092: train_loss -0.784 
-2024-08-29 12:01:55.617324: val_loss -0.8052 
-2024-08-29 12:01:55.617464: Pseudo dice [0.0, 0.0, 0.9244, 0.9781, 0.8694, 0.9539, 0.9565, 0.9704, 0.9583, 0.9568, 0.9413, 0.9638, 0.9643, 0.8744, 0.955, 0.9457, 0.8573, 0.8537, nan] 
-2024-08-29 12:01:55.617538: Epoch time: 73.81 s 
-2024-08-29 12:01:55.617580: Yayy! New best EMA pseudo Dice: 0.8282 
-2024-08-29 12:01:57.345820:  
-2024-08-29 12:01:57.346144: Epoch 1953 
-2024-08-29 12:01:57.346234: Current learning rate: 0.00034 
-2024-08-29 12:03:12.805898: train_loss -0.7846 
-2024-08-29 12:03:12.806117: val_loss -0.8015 
-2024-08-29 12:03:12.806253: Pseudo dice [0.0, 0.0, 0.9067, 0.9785, 0.8815, 0.9522, 0.957, 0.9705, 0.959, 0.9555, 0.9431, 0.9645, 0.9682, 0.8732, 0.9532, 0.9434, 0.8524, 0.8526, nan] 
-2024-08-29 12:03:12.806330: Epoch time: 75.46 s 
-2024-08-29 12:03:12.806373: Yayy! New best EMA pseudo Dice: 0.8282 
-2024-08-29 12:03:14.387388:  
-2024-08-29 12:03:14.387538: Epoch 1954 
-2024-08-29 12:03:14.387617: Current learning rate: 0.00034 
-2024-08-29 12:04:30.277093: train_loss -0.7848 
-2024-08-29 12:04:30.277298: val_loss -0.8043 
-2024-08-29 12:04:30.277450: Pseudo dice [0.0, 0.0, 0.9048, 0.9784, 0.8745, 0.9541, 0.9566, 0.971, 0.9561, 0.9554, 0.9432, 0.9645, 0.9664, 0.875, 0.958, 0.9472, 0.8493, 0.8476, nan] 
-2024-08-29 12:04:30.277526: Epoch time: 75.89 s 
-2024-08-29 12:04:31.428929:  
-2024-08-29 12:04:31.429080: Epoch 1955 
-2024-08-29 12:04:31.429172: Current learning rate: 0.00033 
-2024-08-29 12:05:48.315365: train_loss -0.7842 
-2024-08-29 12:05:48.315568: val_loss -0.8113 
-2024-08-29 12:05:48.315721: Pseudo dice [0.0, 0.0, 0.9173, 0.9776, 0.8765, 0.9496, 0.955, 0.9693, 0.9567, 0.9592, 0.9412, 0.9654, 0.9659, 0.8702, 0.9563, 0.9409, 0.8454, 0.8474, nan] 
-2024-08-29 12:05:48.315796: Epoch time: 76.89 s 
-2024-08-29 12:05:49.455107:  
-2024-08-29 12:05:49.455382: Epoch 1956 
-2024-08-29 12:05:49.455467: Current learning rate: 0.00032 
-2024-08-29 12:07:04.567829: train_loss -0.7854 
-2024-08-29 12:07:04.568026: val_loss -0.8038 
-2024-08-29 12:07:04.568164: Pseudo dice [0.0, 0.0, 0.9003, 0.9787, 0.8807, 0.953, 0.9549, 0.9708, 0.9516, 0.9604, 0.9446, 0.9624, 0.9652, 0.8706, 0.9593, 0.9487, 0.8401, 0.8495, nan] 
-2024-08-29 12:07:04.568236: Epoch time: 75.11 s 
-2024-08-29 12:07:05.879059:  
-2024-08-29 12:07:05.879207: Epoch 1957 
-2024-08-29 12:07:05.879290: Current learning rate: 0.00032 
-2024-08-29 12:08:22.604596: train_loss -0.7833 
-2024-08-29 12:08:22.604800: val_loss -0.8043 
-2024-08-29 12:08:22.604961: Pseudo dice [0.0, 0.0, 0.9113, 0.9782, 0.8691, 0.9513, 0.9528, 0.9674, 0.9587, 0.959, 0.9421, 0.9658, 0.9651, 0.8666, 0.9567, 0.9438, 0.8461, 0.8419, nan] 
-2024-08-29 12:08:22.605037: Epoch time: 76.73 s 
-2024-08-29 12:08:23.740424:  
-2024-08-29 12:08:23.740572: Epoch 1958 
-2024-08-29 12:08:23.740660: Current learning rate: 0.00031 
-2024-08-29 12:09:45.868777: train_loss -0.7851 
-2024-08-29 12:09:45.869019: val_loss -0.8045 
-2024-08-29 12:09:45.869167: Pseudo dice [0.0, 0.0, 0.9226, 0.9784, 0.8837, 0.9538, 0.9579, 0.9716, 0.9579, 0.9598, 0.9426, 0.9632, 0.9663, 0.8695, 0.9587, 0.9465, 0.8568, 0.8556, nan] 
-2024-08-29 12:09:45.869246: Epoch time: 82.13 s 
-2024-08-29 12:09:47.011757:  
-2024-08-29 12:09:47.011893: Epoch 1959 
-2024-08-29 12:09:47.011982: Current learning rate: 0.0003 
-2024-08-29 12:11:01.366246: train_loss -0.7882 
-2024-08-29 12:11:01.366468: val_loss -0.8022 
-2024-08-29 12:11:01.366630: Pseudo dice [0.0, 0.0, 0.9194, 0.9786, 0.873, 0.952, 0.9528, 0.9636, 0.9545, 0.9588, 0.9434, 0.9641, 0.9658, 0.8708, 0.9493, 0.939, 0.8579, 0.8508, nan] 
-2024-08-29 12:11:01.366712: Epoch time: 74.36 s 
-2024-08-29 12:11:02.520306:  
-2024-08-29 12:11:02.520811: Epoch 1960 
-2024-08-29 12:11:02.520895: Current learning rate: 0.0003 
-2024-08-29 12:12:18.339849: train_loss -0.7862 
-2024-08-29 12:12:18.340065: val_loss -0.8053 
-2024-08-29 12:12:18.340215: Pseudo dice [0.0, 0.0, 0.9106, 0.9786, 0.8809, 0.9498, 0.9539, 0.9704, 0.9538, 0.9563, 0.9437, 0.9623, 0.9656, 0.8705, 0.9571, 0.9462, 0.8658, 0.8591, nan] 
-2024-08-29 12:12:18.340293: Epoch time: 75.82 s 
-2024-08-29 12:12:19.490701:  
-2024-08-29 12:12:19.491001: Epoch 1961 
-2024-08-29 12:12:19.491083: Current learning rate: 0.00029 
-2024-08-29 12:13:40.740512: train_loss -0.7839 
-2024-08-29 12:13:40.740732: val_loss -0.8052 
-2024-08-29 12:13:40.740877: Pseudo dice [0.0, 0.0, 0.9171, 0.9776, 0.873, 0.9521, 0.9577, 0.9704, 0.9571, 0.9495, 0.9358, 0.9644, 0.9651, 0.8641, 0.9574, 0.9428, 0.8564, 0.8631, nan] 
-2024-08-29 12:13:40.740952: Epoch time: 81.25 s 
-2024-08-29 12:13:41.877279:  
-2024-08-29 12:13:41.877701: Epoch 1962 
-2024-08-29 12:13:41.877787: Current learning rate: 0.00028 
-2024-08-29 12:14:58.102803: train_loss -0.7841 
-2024-08-29 12:14:58.103082: val_loss -0.8025 
-2024-08-29 12:14:58.103337: Pseudo dice [0.0, 0.0, 0.9164, 0.9785, 0.8745, 0.9525, 0.9567, 0.9688, 0.959, 0.9596, 0.9496, 0.9668, 0.9662, 0.8717, 0.9599, 0.9422, 0.8585, 0.8545, nan] 
-2024-08-29 12:14:58.103614: Epoch time: 76.23 s 
-2024-08-29 12:14:58.103764: Yayy! New best EMA pseudo Dice: 0.8283 
-2024-08-29 12:14:59.864259:  
-2024-08-29 12:14:59.864494: Epoch 1963 
-2024-08-29 12:14:59.864660: Current learning rate: 0.00028 
-2024-08-29 12:16:15.876756: train_loss -0.7844 
-2024-08-29 12:16:15.876981: val_loss -0.808 
-2024-08-29 12:16:15.877134: Pseudo dice [0.0, 0.0, 0.9186, 0.9784, 0.8831, 0.9547, 0.9585, 0.9703, 0.9617, 0.9514, 0.9473, 0.9675, 0.9663, 0.8743, 0.9561, 0.9458, 0.8655, 0.8608, nan] 
-2024-08-29 12:16:15.877259: Epoch time: 76.01 s 
-2024-08-29 12:16:15.877311: Yayy! New best EMA pseudo Dice: 0.8286 
-2024-08-29 12:16:17.458962:  
-2024-08-29 12:16:17.459274: Epoch 1964 
-2024-08-29 12:16:17.459363: Current learning rate: 0.00027 
-2024-08-29 12:17:31.102926: train_loss -0.7864 
-2024-08-29 12:17:31.103158: val_loss -0.8039 
-2024-08-29 12:17:31.103297: Pseudo dice [0.0, 0.0, 0.9215, 0.9788, 0.8771, 0.9512, 0.9532, 0.9702, 0.9557, 0.9472, 0.9433, 0.9632, 0.9658, 0.8682, 0.9615, 0.9449, 0.8577, 0.8553, nan] 
-2024-08-29 12:17:31.103370: Epoch time: 73.64 s 
-2024-08-29 12:17:31.103411: Yayy! New best EMA pseudo Dice: 0.8286 
-2024-08-29 12:17:32.680465:  
-2024-08-29 12:17:32.680626: Epoch 1965 
-2024-08-29 12:17:32.680710: Current learning rate: 0.00026 
-2024-08-29 12:18:51.873094: train_loss -0.7865 
-2024-08-29 12:18:51.873305: val_loss -0.7999 
-2024-08-29 12:18:51.873458: Pseudo dice [0.0, 0.0, 0.8917, 0.9785, 0.8714, 0.95, 0.9523, 0.9673, 0.9586, 0.9572, 0.9449, 0.9665, 0.9682, 0.8671, 0.9553, 0.9434, 0.849, 0.8393, nan] 
-2024-08-29 12:18:51.873535: Epoch time: 79.19 s 
-2024-08-29 12:18:52.990606:  
-2024-08-29 12:18:52.990807: Epoch 1966 
-2024-08-29 12:18:52.990899: Current learning rate: 0.00026 
-2024-08-29 12:20:10.426479: train_loss -0.7854 
-2024-08-29 12:20:10.426706: val_loss -0.802 
-2024-08-29 12:20:10.426850: Pseudo dice [0.0, 0.0, 0.9027, 0.978, 0.8863, 0.9506, 0.9527, 0.9677, 0.9593, 0.9494, 0.9449, 0.9626, 0.9678, 0.8671, 0.9576, 0.9438, 0.8507, 0.8456, nan] 
-2024-08-29 12:20:10.426924: Epoch time: 77.44 s 
-2024-08-29 12:20:11.519469:  
-2024-08-29 12:20:11.519598: Epoch 1967 
-2024-08-29 12:20:11.519687: Current learning rate: 0.00025 
-2024-08-29 12:21:24.904803: train_loss -0.7836 
-2024-08-29 12:21:24.905010: val_loss -0.8048 
-2024-08-29 12:21:24.905150: Pseudo dice [0.0, 0.0, 0.9115, 0.9783, 0.877, 0.9523, 0.9556, 0.9696, 0.963, 0.965, 0.9462, 0.967, 0.9682, 0.8631, 0.954, 0.9427, 0.8457, 0.8466, nan] 
-2024-08-29 12:21:24.905223: Epoch time: 73.39 s 
-2024-08-29 12:21:26.218420:  
-2024-08-29 12:21:26.218737: Epoch 1968 
-2024-08-29 12:21:26.218816: Current learning rate: 0.00024 
-2024-08-29 12:22:44.202083: train_loss -0.7864 
-2024-08-29 12:22:44.202283: val_loss -0.8032 
-2024-08-29 12:22:44.202431: Pseudo dice [0.0, 0.0, 0.9232, 0.9788, 0.8748, 0.9539, 0.9566, 0.9707, 0.9542, 0.958, 0.943, 0.9639, 0.9678, 0.8685, 0.9604, 0.9486, 0.849, 0.8533, nan] 
-2024-08-29 12:22:44.202506: Epoch time: 77.98 s 
-2024-08-29 12:22:45.343985:  
-2024-08-29 12:22:45.344133: Epoch 1969 
-2024-08-29 12:22:45.344223: Current learning rate: 0.00024 
-2024-08-29 12:24:02.392325: train_loss -0.7858 
-2024-08-29 12:24:02.392562: val_loss -0.7993 
-2024-08-29 12:24:02.392714: Pseudo dice [0.0, 0.0, 0.9032, 0.9793, 0.8692, 0.9509, 0.9533, 0.9703, 0.9559, 0.9472, 0.9464, 0.9629, 0.9671, 0.8714, 0.9519, 0.9441, 0.8495, 0.8529, nan] 
-2024-08-29 12:24:02.392790: Epoch time: 77.05 s 
-2024-08-29 12:24:03.523588:  
-2024-08-29 12:24:03.523732: Epoch 1970 
-2024-08-29 12:24:03.523815: Current learning rate: 0.00023 
-2024-08-29 12:25:17.056622: train_loss -0.7868 
-2024-08-29 12:25:17.056846: val_loss -0.799 
-2024-08-29 12:25:17.056989: Pseudo dice [0.0, 0.0, 0.8934, 0.9786, 0.8797, 0.9479, 0.9531, 0.9689, 0.9578, 0.9562, 0.9451, 0.9647, 0.9659, 0.8607, 0.9517, 0.9432, 0.8602, 0.8579, nan] 
-2024-08-29 12:25:17.057064: Epoch time: 73.53 s 
-2024-08-29 12:25:18.166008:  
-2024-08-29 12:25:18.166151: Epoch 1971 
-2024-08-29 12:25:18.166227: Current learning rate: 0.00022 
-2024-08-29 12:26:39.741084: train_loss -0.787 
-2024-08-29 12:26:39.741285: val_loss -0.8087 
-2024-08-29 12:26:39.741429: Pseudo dice [0.0, 0.0, 0.9156, 0.9794, 0.8691, 0.9532, 0.9575, 0.9716, 0.9602, 0.9516, 0.9447, 0.964, 0.9671, 0.8706, 0.9574, 0.9459, 0.855, 0.8534, nan] 
-2024-08-29 12:26:39.741501: Epoch time: 81.58 s 
-2024-08-29 12:26:40.863868:  
-2024-08-29 12:26:40.864146: Epoch 1972 
-2024-08-29 12:26:40.864231: Current learning rate: 0.00021 
-2024-08-29 12:27:53.545515: train_loss -0.7882 
-2024-08-29 12:27:53.545739: val_loss -0.8062 
-2024-08-29 12:27:53.545887: Pseudo dice [0.0, 0.0, 0.9107, 0.9785, 0.8768, 0.9513, 0.955, 0.9695, 0.9584, 0.9527, 0.9434, 0.9634, 0.9619, 0.8714, 0.9548, 0.9429, 0.8421, 0.8433, nan] 
-2024-08-29 12:27:53.545964: Epoch time: 72.68 s 
-2024-08-29 12:27:54.674618:  
-2024-08-29 12:27:54.674762: Epoch 1973 
-2024-08-29 12:27:54.674843: Current learning rate: 0.00021 
-2024-08-29 12:29:09.894371: train_loss -0.7866 
-2024-08-29 12:29:09.894604: val_loss -0.8037 
-2024-08-29 12:29:09.894758: Pseudo dice [0.0, 0.0, 0.9215, 0.9784, 0.8526, 0.9464, 0.9527, 0.9699, 0.9585, 0.9637, 0.943, 0.9653, 0.9678, 0.8709, 0.9511, 0.945, 0.8635, 0.8628, nan] 
-2024-08-29 12:29:09.894837: Epoch time: 75.22 s 
-2024-08-29 12:29:11.247313:  
-2024-08-29 12:29:11.247573: Epoch 1974 
-2024-08-29 12:29:11.247668: Current learning rate: 0.0002 
-2024-08-29 12:30:28.953906: train_loss -0.7824 
-2024-08-29 12:30:28.954094: val_loss -0.8043 
-2024-08-29 12:30:28.954232: Pseudo dice [0.0, 0.0, 0.9198, 0.9786, 0.8736, 0.949, 0.9527, 0.9653, 0.9616, 0.9612, 0.9395, 0.9667, 0.9663, 0.8669, 0.9572, 0.9449, 0.8678, 0.8643, nan] 
-2024-08-29 12:30:28.954304: Epoch time: 77.71 s 
-2024-08-29 12:30:30.102959:  
-2024-08-29 12:30:30.103297: Epoch 1975 
-2024-08-29 12:30:30.103393: Current learning rate: 0.00019 
-2024-08-29 12:31:45.438568: train_loss -0.7879 
-2024-08-29 12:31:45.438843: val_loss -0.8035 
-2024-08-29 12:31:45.439094: Pseudo dice [0.0, 0.0, 0.9203, 0.9782, 0.8717, 0.9537, 0.9551, 0.9695, 0.9565, 0.9569, 0.9426, 0.9652, 0.9656, 0.8731, 0.9546, 0.9478, 0.8563, 0.8459, nan] 
-2024-08-29 12:31:45.439271: Epoch time: 75.34 s 
-2024-08-29 12:31:46.577366:  
-2024-08-29 12:31:46.577651: Epoch 1976 
-2024-08-29 12:31:46.577747: Current learning rate: 0.00019 
-2024-08-29 12:33:01.928403: train_loss -0.7848 
-2024-08-29 12:33:01.928646: val_loss -0.8055 
-2024-08-29 12:33:01.928786: Pseudo dice [0.0, 0.0, 0.9153, 0.9782, 0.8766, 0.9537, 0.9588, 0.972, 0.959, 0.959, 0.9438, 0.9649, 0.9653, 0.8754, 0.9606, 0.9442, 0.8542, 0.8547, nan] 
-2024-08-29 12:33:01.928859: Epoch time: 75.35 s 
-2024-08-29 12:33:03.091315:  
-2024-08-29 12:33:03.091782: Epoch 1977 
-2024-08-29 12:33:03.091874: Current learning rate: 0.00018 
-2024-08-29 12:34:20.656480: train_loss -0.7823 
-2024-08-29 12:34:20.656706: val_loss -0.799 
-2024-08-29 12:34:20.656842: Pseudo dice [0.0, 0.0, 0.9083, 0.9789, 0.8778, 0.9522, 0.9555, 0.9637, 0.9571, 0.9617, 0.9402, 0.962, 0.9628, 0.8568, 0.9529, 0.9441, 0.8458, 0.8487, nan] 
-2024-08-29 12:34:20.656962: Epoch time: 77.57 s 
-2024-08-29 12:34:21.783690:  
-2024-08-29 12:34:21.783954: Epoch 1978 
-2024-08-29 12:34:21.784038: Current learning rate: 0.00017 
-2024-08-29 12:35:40.858059: train_loss -0.7888 
-2024-08-29 12:35:40.858302: val_loss -0.8049 
-2024-08-29 12:35:40.858458: Pseudo dice [0.0, 0.0, 0.9199, 0.9783, 0.8742, 0.9528, 0.9567, 0.9705, 0.9593, 0.9573, 0.9453, 0.9635, 0.9649, 0.8692, 0.9624, 0.9452, 0.857, 0.8563, nan] 
-2024-08-29 12:35:40.858540: Epoch time: 79.08 s 
-2024-08-29 12:35:41.966986:  
-2024-08-29 12:35:41.967132: Epoch 1979 
-2024-08-29 12:35:41.967211: Current learning rate: 0.00017 
-2024-08-29 12:37:00.575883: train_loss -0.7825 
-2024-08-29 12:37:00.576223: val_loss -0.8065 
-2024-08-29 12:37:00.576461: Pseudo dice [0.0, 0.0, 0.9257, 0.9785, 0.8889, 0.9508, 0.9529, 0.9688, 0.959, 0.952, 0.9437, 0.9634, 0.9666, 0.8673, 0.9521, 0.9475, 0.873, 0.8716, nan] 
-2024-08-29 12:37:00.576641: Epoch time: 78.61 s 
-2024-08-29 12:37:01.897688:  
-2024-08-29 12:37:01.897825: Epoch 1980 
-2024-08-29 12:37:01.897916: Current learning rate: 0.00016 
-2024-08-29 12:38:17.708344: train_loss -0.7878 
-2024-08-29 12:38:17.708574: val_loss -0.8039 
-2024-08-29 12:38:17.708724: Pseudo dice [0.0, 0.0, 0.9236, 0.979, 0.8714, 0.9539, 0.9582, 0.9713, 0.9571, 0.9573, 0.9446, 0.9621, 0.9667, 0.8696, 0.9566, 0.945, 0.8442, 0.8518, nan] 
-2024-08-29 12:38:17.708805: Epoch time: 75.81 s 
-2024-08-29 12:38:18.850255:  
-2024-08-29 12:38:18.850623: Epoch 1981 
-2024-08-29 12:38:18.850842: Current learning rate: 0.00015 
-2024-08-29 12:39:35.896680: train_loss -0.7858 
-2024-08-29 12:39:35.896914: val_loss -0.8063 
-2024-08-29 12:39:35.897061: Pseudo dice [0.0, 0.0, 0.902, 0.9788, 0.8913, 0.9538, 0.9568, 0.9675, 0.9571, 0.9556, 0.9444, 0.9621, 0.9645, 0.8711, 0.9578, 0.9468, 0.8509, 0.8596, nan] 
-2024-08-29 12:39:35.897138: Epoch time: 77.05 s 
-2024-08-29 12:39:37.053212:  
-2024-08-29 12:39:37.053726: Epoch 1982 
-2024-08-29 12:39:37.053816: Current learning rate: 0.00014 
-2024-08-29 12:40:57.858930: train_loss -0.7844 
-2024-08-29 12:40:57.859155: val_loss -0.808 
-2024-08-29 12:40:57.859294: Pseudo dice [0.0, 0.0, 0.9176, 0.9786, 0.862, 0.9507, 0.9552, 0.9708, 0.9582, 0.9592, 0.9432, 0.9652, 0.9663, 0.8705, 0.9613, 0.9457, 0.8494, 0.8437, nan] 
-2024-08-29 12:40:57.859370: Epoch time: 80.81 s 
-2024-08-29 12:40:58.997910:  
-2024-08-29 12:40:58.998073: Epoch 1983 
-2024-08-29 12:40:58.998149: Current learning rate: 0.00014 
-2024-08-29 12:42:09.161620: train_loss -0.7866 
-2024-08-29 12:42:09.161831: val_loss -0.808 
-2024-08-29 12:42:09.161970: Pseudo dice [0.0, 0.0, 0.9158, 0.978, 0.8698, 0.9532, 0.9578, 0.9712, 0.9601, 0.9631, 0.945, 0.9648, 0.9666, 0.8738, 0.9613, 0.9459, 0.8555, 0.8595, nan] 
-2024-08-29 12:42:09.162046: Epoch time: 70.16 s 
-2024-08-29 12:42:09.162104: Yayy! New best EMA pseudo Dice: 0.8286 
-2024-08-29 12:42:10.760586:  
-2024-08-29 12:42:10.760731: Epoch 1984 
-2024-08-29 12:42:10.760818: Current learning rate: 0.00013 
-2024-08-29 12:43:28.727970: train_loss -0.7878 
-2024-08-29 12:43:28.728187: val_loss -0.8059 
-2024-08-29 12:43:28.728328: Pseudo dice [0.0, 0.0, 0.9102, 0.9779, 0.8898, 0.9529, 0.9566, 0.97, 0.9578, 0.9625, 0.9444, 0.9655, 0.9655, 0.8718, 0.9511, 0.9484, 0.8573, 0.8557, nan] 
-2024-08-29 12:43:28.728400: Epoch time: 77.97 s 
-2024-08-29 12:43:28.728446: Yayy! New best EMA pseudo Dice: 0.8288 
-2024-08-29 12:43:30.317615:  
-2024-08-29 12:43:30.317985: Epoch 1985 
-2024-08-29 12:43:30.318077: Current learning rate: 0.00012 
-2024-08-29 12:44:48.188854: train_loss -0.7882 
-2024-08-29 12:44:48.189084: val_loss -0.8075 
-2024-08-29 12:44:48.189235: Pseudo dice [0.0, 0.0, 0.9209, 0.979, 0.8874, 0.9514, 0.9521, 0.9696, 0.9603, 0.957, 0.9447, 0.9653, 0.9674, 0.871, 0.955, 0.946, 0.8548, 0.8613, nan] 
-2024-08-29 12:44:48.189309: Epoch time: 77.87 s 
-2024-08-29 12:44:48.189352: Yayy! New best EMA pseudo Dice: 0.8289 
-2024-08-29 12:44:50.178792:  
-2024-08-29 12:44:50.178942: Epoch 1986 
-2024-08-29 12:44:50.179195: Current learning rate: 0.00011 
-2024-08-29 12:46:07.989663: train_loss -0.7882 
-2024-08-29 12:46:07.989917: val_loss -0.8086 
-2024-08-29 12:46:07.990116: Pseudo dice [0.0, 0.0, 0.912, 0.9781, 0.8715, 0.9522, 0.9545, 0.9706, 0.956, 0.961, 0.9462, 0.963, 0.9681, 0.8675, 0.9576, 0.9466, 0.8484, 0.8584, nan] 
-2024-08-29 12:46:07.990218: Epoch time: 77.81 s 
-2024-08-29 12:46:09.209362:  
-2024-08-29 12:46:09.209652: Epoch 1987 
-2024-08-29 12:46:09.209736: Current learning rate: 0.00011 
-2024-08-29 12:47:27.326880: train_loss -0.7862 
-2024-08-29 12:47:27.327097: val_loss -0.8076 
-2024-08-29 12:47:27.327253: Pseudo dice [0.0, 0.0, 0.9203, 0.9794, 0.8838, 0.9577, 0.9594, 0.9698, 0.9574, 0.9617, 0.947, 0.9667, 0.9672, 0.874, 0.9584, 0.9471, 0.8579, 0.8593, nan] 
-2024-08-29 12:47:27.327330: Epoch time: 78.12 s 
-2024-08-29 12:47:27.327374: Yayy! New best EMA pseudo Dice: 0.8291 
-2024-08-29 12:47:28.939774:  
-2024-08-29 12:47:28.939974: Epoch 1988 
-2024-08-29 12:47:28.940056: Current learning rate: 0.0001 
-2024-08-29 12:48:46.603554: train_loss -0.7859 
-2024-08-29 12:48:46.603768: val_loss -0.8015 
-2024-08-29 12:48:46.603917: Pseudo dice [0.0, 0.0, 0.9083, 0.9773, 0.8889, 0.9541, 0.9553, 0.9673, 0.9593, 0.9566, 0.945, 0.9643, 0.9662, 0.8704, 0.9548, 0.9447, 0.8589, 0.8539, nan] 
-2024-08-29 12:48:46.603993: Epoch time: 77.66 s 
-2024-08-29 12:48:46.604037: Yayy! New best EMA pseudo Dice: 0.8291 
-2024-08-29 12:48:48.203310:  
-2024-08-29 12:48:48.203703: Epoch 1989 
-2024-08-29 12:48:48.203873: Current learning rate: 9e-05 
-2024-08-29 12:50:03.503013: train_loss -0.7875 
-2024-08-29 12:50:03.503242: val_loss -0.8011 
-2024-08-29 12:50:03.503393: Pseudo dice [0.0, 0.0, 0.9184, 0.9784, 0.8938, 0.9526, 0.9561, 0.9715, 0.9583, 0.9554, 0.9416, 0.9631, 0.9651, 0.8733, 0.9578, 0.9481, 0.8638, 0.865, nan] 
-2024-08-29 12:50:03.503474: Epoch time: 75.3 s 
-2024-08-29 12:50:03.503520: Yayy! New best EMA pseudo Dice: 0.8293 
-2024-08-29 12:50:05.084100:  
-2024-08-29 12:50:05.084249: Epoch 1990 
-2024-08-29 12:50:05.084333: Current learning rate: 8e-05 
-2024-08-29 12:51:20.201645: train_loss -0.7889 
-2024-08-29 12:51:20.201864: val_loss -0.8088 
-2024-08-29 12:51:20.202003: Pseudo dice [0.0, 0.0, 0.9165, 0.978, 0.8855, 0.952, 0.9561, 0.969, 0.9593, 0.9628, 0.9464, 0.966, 0.9682, 0.8712, 0.9525, 0.9466, 0.8594, 0.8552, nan] 
-2024-08-29 12:51:20.202076: Epoch time: 75.12 s 
-2024-08-29 12:51:20.202119: Yayy! New best EMA pseudo Dice: 0.8294 
-2024-08-29 12:51:22.161043:  
-2024-08-29 12:51:22.161230: Epoch 1991 
-2024-08-29 12:51:22.161324: Current learning rate: 8e-05 
-2024-08-29 12:52:38.112116: train_loss -0.7886 
-2024-08-29 12:52:38.112309: val_loss -0.8021 
-2024-08-29 12:52:38.112465: Pseudo dice [0.0, 0.0, 0.9201, 0.9791, 0.871, 0.9511, 0.9517, 0.9665, 0.9562, 0.9559, 0.9421, 0.9669, 0.9665, 0.8701, 0.9584, 0.9446, 0.8609, 0.8564, nan] 
-2024-08-29 12:52:38.112554: Epoch time: 75.95 s 
-2024-08-29 12:52:39.254432:  
-2024-08-29 12:52:39.254611: Epoch 1992 
-2024-08-29 12:52:39.254693: Current learning rate: 7e-05 
-2024-08-29 12:53:55.447275: train_loss -0.7861 
-2024-08-29 12:53:55.447490: val_loss -0.805 
-2024-08-29 12:53:55.447638: Pseudo dice [0.0, 0.0, 0.9233, 0.9784, 0.8645, 0.9503, 0.9535, 0.968, 0.9555, 0.9577, 0.9442, 0.9635, 0.966, 0.8679, 0.9597, 0.9406, 0.8597, 0.8544, nan] 
-2024-08-29 12:53:55.447714: Epoch time: 76.19 s 
-2024-08-29 12:53:56.567178:  
-2024-08-29 12:53:56.567338: Epoch 1993 
-2024-08-29 12:53:56.567419: Current learning rate: 6e-05 
-2024-08-29 12:55:09.280638: train_loss -0.7907 
-2024-08-29 12:55:09.280874: val_loss -0.8071 
-2024-08-29 12:55:09.281025: Pseudo dice [0.0, 0.0, 0.9065, 0.9782, 0.8874, 0.955, 0.9577, 0.9723, 0.9598, 0.9588, 0.9458, 0.9654, 0.9677, 0.8733, 0.9625, 0.9418, 0.8529, 0.8535, nan] 
-2024-08-29 12:55:09.281102: Epoch time: 72.71 s 
-2024-08-29 12:55:10.421117:  
-2024-08-29 12:55:10.421260: Epoch 1994 
-2024-08-29 12:55:10.421348: Current learning rate: 5e-05 
-2024-08-29 12:56:27.384055: train_loss -0.7846 
-2024-08-29 12:56:27.384275: val_loss -0.8019 
-2024-08-29 12:56:27.384412: Pseudo dice [0.0, 0.0, 0.9094, 0.9783, 0.8527, 0.9456, 0.9499, 0.9693, 0.9554, 0.9609, 0.9451, 0.9651, 0.9679, 0.8687, 0.9592, 0.9478, 0.8468, 0.8562, nan] 
-2024-08-29 12:56:27.384496: Epoch time: 76.96 s 
-2024-08-29 12:56:28.509989:  
-2024-08-29 12:56:28.510156: Epoch 1995 
-2024-08-29 12:56:28.510241: Current learning rate: 5e-05 
-2024-08-29 12:57:44.622321: train_loss -0.7867 
-2024-08-29 12:57:44.622533: val_loss -0.8069 
-2024-08-29 12:57:44.622671: Pseudo dice [0.0, 0.0, 0.9212, 0.9782, 0.8608, 0.9466, 0.9546, 0.97, 0.9614, 0.9568, 0.9456, 0.9665, 0.9686, 0.8725, 0.9583, 0.9483, 0.8607, 0.8599, nan] 
-2024-08-29 12:57:44.622744: Epoch time: 76.11 s 
-2024-08-29 12:57:45.752556:  
-2024-08-29 12:57:45.752840: Epoch 1996 
-2024-08-29 12:57:45.752921: Current learning rate: 4e-05 
-2024-08-29 12:59:05.148158: train_loss -0.787 
-2024-08-29 12:59:05.148386: val_loss -0.8082 
-2024-08-29 12:59:05.148542: Pseudo dice [0.0, 0.0, 0.9134, 0.9784, 0.8862, 0.9528, 0.9556, 0.972, 0.9594, 0.9581, 0.9395, 0.9621, 0.9647, 0.8743, 0.9618, 0.9485, 0.864, 0.8546, nan] 
-2024-08-29 12:59:05.148618: Epoch time: 79.4 s 
-2024-08-29 12:59:06.519643:  
-2024-08-29 12:59:06.519774: Epoch 1997 
-2024-08-29 12:59:06.519860: Current learning rate: 3e-05 
-2024-08-29 13:00:22.596016: train_loss -0.7893 
-2024-08-29 13:00:22.596244: val_loss -0.8015 
-2024-08-29 13:00:22.596387: Pseudo dice [0.0, 0.0, 0.9111, 0.9783, 0.8765, 0.9539, 0.9575, 0.9709, 0.9565, 0.9564, 0.9431, 0.9665, 0.9682, 0.8734, 0.9604, 0.9457, 0.8488, 0.849, nan] 
-2024-08-29 13:00:22.596471: Epoch time: 76.08 s 
-2024-08-29 13:00:23.728219:  
-2024-08-29 13:00:23.728527: Epoch 1998 
-2024-08-29 13:00:23.728610: Current learning rate: 2e-05 
-2024-08-29 13:01:45.219904: train_loss -0.787 
-2024-08-29 13:01:45.220109: val_loss -0.8046 
-2024-08-29 13:01:45.220250: Pseudo dice [0.0, 0.0, 0.9211, 0.9778, 0.879, 0.9531, 0.9564, 0.9678, 0.9591, 0.9607, 0.9493, 0.9644, 0.9671, 0.8723, 0.9531, 0.9457, 0.8611, 0.8596, nan] 
-2024-08-29 13:01:45.220325: Epoch time: 81.49 s 
-2024-08-29 13:01:46.363413:  
-2024-08-29 13:01:46.363567: Epoch 1999 
-2024-08-29 13:01:46.363667: Current learning rate: 1e-05 
-2024-08-29 13:03:00.794757: train_loss -0.7866 
-2024-08-29 13:03:00.794983: val_loss -0.8025 
-2024-08-29 13:03:00.795136: Pseudo dice [0.0, 0.0, 0.9094, 0.9778, 0.8781, 0.9511, 0.9521, 0.9682, 0.9587, 0.9587, 0.9455, 0.963, 0.9675, 0.8724, 0.9569, 0.9454, 0.8509, 0.8505, nan] 
-2024-08-29 13:03:00.795217: Epoch time: 74.43 s 
-2024-08-29 13:03:02.581196: predicting 0001 
-2024-08-29 13:03:02.986372: predicting 0002 
-2024-08-29 13:03:03.176544: predicting 0003 
-2024-08-29 13:03:03.344173: predicting 0004 
-2024-08-29 13:03:03.498039: predicting 0005 
-2024-08-29 13:03:03.631483: predicting 0006 
-2024-08-29 13:03:03.773746: predicting 0007 
-2024-08-29 13:03:04.184524: predicting 0008 
-2024-08-29 13:03:04.314015: predicting 0009 
-2024-08-29 13:03:04.799901: predicting 0010 
-2024-08-29 13:03:04.960421: predicting 0011 
-2024-08-29 13:03:05.122522: predicting 0012 
-2024-08-29 13:03:05.278437: predicting 0013 
-2024-08-29 13:03:05.781296: predicting 0014 
-2024-08-29 13:03:05.955290: predicting 0015 
-2024-08-29 13:03:06.147218: predicting 0016 
-2024-08-29 13:03:06.285785: predicting 0017 
-2024-08-29 13:03:06.567986: predicting 0018 
-2024-08-29 13:03:06.733940: predicting 0019 
-2024-08-29 13:03:06.909310: predicting 0020 
-2024-08-29 13:03:07.341944: predicting 0021 
-2024-08-29 13:03:07.574992: predicting 0022 
-2024-08-29 13:03:07.703853: predicting 0023 
-2024-08-29 13:03:08.200505: predicting 0024 
-2024-08-29 13:03:08.369871: predicting 0025 
-2024-08-29 13:03:08.497936: predicting 0026 
-2024-08-29 13:03:08.626876: predicting 0027 
-2024-08-29 13:03:10.036353: predicting 0028 
-2024-08-29 13:03:10.254694: predicting 0029 
-2024-08-29 13:03:10.417342: predicting 0030 
-2024-08-29 13:03:10.597408: predicting 0031 
-2024-08-29 13:03:11.103756: predicting 0032 
-2024-08-29 13:03:11.279953: predicting 0033 
-2024-08-29 13:03:11.463267: predicting 0034 
-2024-08-29 13:03:11.840551: predicting 0035 
-2024-08-29 13:03:12.052752: predicting 0036 
-2024-08-29 13:03:12.224363: predicting 0037 
-2024-08-29 13:03:12.388857: predicting 0038 
-2024-08-29 13:03:12.576895: predicting 0039 
-2024-08-29 13:03:13.103558: predicting 0040 
-2024-08-29 13:03:13.278574: predicting 0041 
-2024-08-29 13:03:13.419924: predicting 0042 
-2024-08-29 13:03:13.594782: predicting 0043 
-2024-08-29 13:03:13.742957: predicting 0044 
-2024-08-29 13:03:13.932970: predicting 0045 
-2024-08-29 13:03:14.237486: predicting 0046 
-2024-08-29 13:03:14.434638: predicting 0047 
-2024-08-29 13:03:14.598127: predicting 0048 
-2024-08-29 13:03:14.745649: predicting 0049 
-2024-08-29 13:03:15.280571: predicting 0050 
-2024-08-29 13:03:15.467110: predicting 0051 
-2024-08-29 13:03:15.612098: predicting 0052 
-2024-08-29 13:03:15.756292: predicting 0053 
-2024-08-29 13:03:15.903003: predicting 0054 
-2024-08-29 13:03:16.407416: predicting 0055 
-2024-08-29 13:03:17.879755: predicting 0056 
-2024-08-29 13:03:18.472799: predicting 0057 
-2024-08-29 13:03:18.785581: predicting 0058 
-2024-08-29 13:03:18.982977: predicting 0059 
-2024-08-29 13:03:19.159080: predicting 0060 
-2024-08-29 13:03:19.612570: predicting 0061 
-2024-08-29 13:03:20.098419: predicting 0062 
-2024-08-29 13:03:20.352090: predicting 0063 
-2024-08-29 13:03:20.528518: predicting 0064 
-2024-08-29 13:03:20.674904: predicting 0065 
-2024-08-29 13:03:20.889508: predicting 0066 
-2024-08-29 13:03:21.330455: predicting 0067 
-2024-08-29 13:03:21.544968: predicting 0068 
-2024-08-29 13:03:21.689716: predicting 0069 
-2024-08-29 13:03:21.886174: predicting 0070 
-2024-08-29 13:03:22.067195: predicting 0071 
-2024-08-29 13:03:22.568314: predicting 0072 
-2024-08-29 13:03:22.752252: predicting 0073 
-2024-08-29 13:03:22.946122: predicting 0074 
-2024-08-29 13:03:23.329370: predicting 0075 
-2024-08-29 13:03:23.549289: predicting 0076 
-2024-08-29 13:03:23.677752: predicting 0077 
-2024-08-29 13:03:23.869332: predicting 0078 
-2024-08-29 13:03:24.046069: predicting 0079 
-2024-08-29 13:03:24.173727: predicting 0080 
-2024-08-29 13:03:24.322025: predicting 0081 
-2024-08-29 13:03:24.518739: predicting 0082 
-2024-08-29 13:03:24.706923: predicting 0083 
-2024-08-29 13:03:24.887997: predicting 0084 
-2024-08-29 13:03:25.331819: predicting 0085 
-2024-08-29 13:03:25.576389: predicting 0086 
-2024-08-29 13:03:25.719528: predicting 0087 
-2024-08-29 13:03:25.913660: predicting 0088 
-2024-08-29 13:03:26.082702: predicting 0089 
-2024-08-29 13:03:26.277532: predicting 0090 
-2024-08-29 13:03:26.442380: predicting 0091 
-2024-08-29 13:03:26.626190: predicting 0092 
-2024-08-29 13:03:26.831324: predicting 0093 
-2024-08-29 13:03:27.010947: predicting 0094 
-2024-08-29 13:03:27.184796: predicting 0095 
-2024-08-29 13:03:28.138928: predicting 0096 
-2024-08-29 13:03:28.334836: predicting 0097 
-2024-08-29 13:03:28.520615: predicting 0098 
-2024-08-29 13:03:28.762237: predicting 0099 
-2024-08-29 13:03:28.930346: predicting 0100 
-2024-08-29 13:03:29.073139: predicting 0101 
-2024-08-29 13:03:29.216585: predicting 0102 
-2024-08-29 13:03:29.754707: predicting 0103 
-2024-08-29 13:03:31.205357: predicting 0104 
-2024-08-29 13:03:31.416811: predicting 0105 
-2024-08-29 13:03:31.616332: predicting 0106 
-2024-08-29 13:03:33.066875: predicting 0107 
-2024-08-29 13:03:33.294390: predicting 0108 
-2024-08-29 13:03:33.457031: predicting 0109 
-2024-08-29 13:03:33.638128: predicting 0110 
-2024-08-29 13:03:33.932260: predicting 0111 
-2024-08-29 13:03:34.112021: predicting 0112 
-2024-08-29 13:03:34.643870: predicting 0113 
-2024-08-29 13:03:35.081513: predicting 0114 
-2024-08-29 13:03:35.282506: predicting 0115 
-2024-08-29 13:03:35.477766: predicting 0116 
-2024-08-29 13:03:35.606500: predicting 0117 
-2024-08-29 13:03:35.775386: predicting 0118 
-2024-08-29 13:03:35.950894: predicting 0119 
-2024-08-29 13:03:36.090491: predicting 0120 
-2024-08-29 13:03:36.268235: predicting 0121 
-2024-08-29 13:03:36.444471: predicting 0122 
-2024-08-29 13:03:36.585139: predicting 0123 
-2024-08-29 13:03:36.730069: predicting 0124 
-2024-08-29 13:03:37.132683: predicting 0125 
-2024-08-29 13:03:37.624664: predicting 0126 
-2024-08-29 13:03:37.837819: predicting 0127 
-2024-08-29 13:03:37.979442: predicting 0128 
-2024-08-29 13:03:38.160778: predicting 0129 
-2024-08-29 13:03:38.289360: predicting 0130 
-2024-08-29 13:03:38.588359: predicting 0131 
-2024-08-29 13:03:38.779015: predicting 0132 
-2024-08-29 13:03:38.952101: predicting 0133 
-2024-08-29 13:03:39.225320: predicting 0134 
-2024-08-29 13:03:40.206938: predicting 0135 
-2024-08-29 13:03:40.742713: predicting 0136 
-2024-08-29 13:03:40.916359: predicting 0137 
-2024-08-29 13:03:41.130546: predicting 0138 
-2024-08-29 13:03:41.314808: predicting 0139 
-2024-08-29 13:03:41.456163: predicting 0140 
-2024-08-29 13:03:41.596588: predicting 0141 
-2024-08-29 13:03:41.770648: predicting 0142 
-2024-08-29 13:03:41.912833: predicting 0143 
-2024-08-29 13:03:42.088984: predicting 0144 
-2024-08-29 13:03:42.228623: predicting 0145 
-2024-08-29 13:03:42.627873: predicting 0146 
-2024-08-29 13:03:42.834067: predicting 0147 
-2024-08-29 13:03:43.250631: predicting 0148 
-2024-08-29 13:03:43.452846: predicting 0149 
-2024-08-29 13:03:43.849584: predicting 0150 
-2024-08-29 13:03:44.045039: predicting 0151 
-2024-08-29 13:03:44.186892: predicting 0152 
-2024-08-29 13:03:44.315747: predicting 0153 
-2024-08-29 13:03:44.507356: predicting 0154 
-2024-08-29 13:03:44.674922: predicting 0155 
-2024-08-29 13:03:44.836160: predicting 0156 
-2024-08-29 13:03:46.270601: predicting 0157 
-2024-08-29 13:03:46.486343: predicting 0158 
-2024-08-29 13:03:46.625583: predicting 0159 
-2024-08-29 13:03:46.753238: predicting 0160 
-2024-08-29 13:03:47.721143: predicting 0161 
-2024-08-29 13:03:47.902839: predicting 0162 
-2024-08-29 13:03:48.062115: predicting 0163 
-2024-08-29 13:03:48.501039: predicting 0164 
-2024-08-29 13:03:48.715499: predicting 0165 
-2024-08-29 13:03:48.854441: predicting 0166 
-2024-08-29 13:03:48.981514: predicting 0167 
-2024-08-29 13:03:49.136667: predicting 0168 
-2024-08-29 13:03:49.299440: predicting 0169 
-2024-08-29 13:03:49.724116: predicting 0170 
-2024-08-29 13:03:49.932575: predicting 0171 
-2024-08-29 13:03:50.093073: predicting 0172 
-2024-08-29 13:03:50.482059: predicting 0173 
-2024-08-29 13:03:50.948682: predicting 0174 
-2024-08-29 13:03:51.417247: predicting 0175 
-2024-08-29 13:03:51.628488: predicting 0176 
-2024-08-29 13:03:51.770491: predicting 0177 
-2024-08-29 13:03:51.913448: predicting 0178 
-2024-08-29 13:03:52.088196: predicting 0179 
-2024-08-29 13:03:52.512961: predicting 0180 
-2024-08-29 13:03:52.712305: predicting 0181 
-2024-08-29 13:03:52.877373: predicting 0182 
-2024-08-29 13:03:53.017229: predicting 0183 
-2024-08-29 13:03:53.515737: predicting 0184 
-2024-08-29 13:03:54.052860: predicting 0185 
-2024-08-29 13:03:54.233499: predicting 0186 
-2024-08-29 13:03:54.396598: predicting 0187 
-2024-08-29 13:03:54.790505: predicting 0188 
-2024-08-29 13:03:55.223162: predicting 0189 
-2024-08-29 13:03:55.406548: predicting 0190 
-2024-08-29 13:03:55.548095: predicting 0191 
-2024-08-29 13:03:55.727849: predicting 0192 
-2024-08-29 13:03:56.158838: predicting 0193 
-2024-08-29 13:03:56.631753: predicting 0194 
-2024-08-29 13:03:57.180396: predicting 0195 
-2024-08-29 13:03:57.349258: predicting 0196 
-2024-08-29 13:03:57.531526: predicting 0197 
-2024-08-29 13:03:57.692487: predicting 0198 
-2024-08-29 13:03:57.852815: predicting 0199 
-2024-08-29 13:03:58.375016: predicting 0200 
-2024-08-29 13:03:58.913246: predicting 0201 
-2024-08-29 13:03:59.089753: predicting 0202 
-2024-08-29 13:03:59.620420: predicting 0203 
-2024-08-29 13:04:01.074554: predicting 0204 
-2024-08-29 13:04:01.549908: predicting 0205 
-2024-08-29 13:04:01.757271: predicting 0206 
-2024-08-29 13:04:02.741215: predicting 0207 
-2024-08-29 13:04:02.935778: predicting 0208 
-2024-08-29 13:04:03.230620: predicting 0209 
-2024-08-29 13:04:03.399959: predicting 0210 
-2024-08-29 13:04:03.578076: predicting 0211 
-2024-08-29 13:04:03.743451: predicting 0212 
-2024-08-29 13:04:04.035513: predicting 0213 
-2024-08-29 13:04:04.203890: predicting 0214 
-2024-08-29 13:04:04.380650: predicting 0215 
-2024-08-29 13:04:04.522659: predicting 0216 
-2024-08-29 13:04:04.678543: predicting 0217 
-2024-08-29 13:04:04.843242: predicting 0218 
-2024-08-29 13:04:05.008157: predicting 0219 
-2024-08-29 13:04:05.292129: predicting 0220 
-2024-08-29 13:04:05.490776: predicting 0221 
-2024-08-29 13:04:05.913111: predicting 0222 
-2024-08-29 13:04:06.124785: predicting 0223 
-2024-08-29 13:04:06.318204: predicting 0224 
-2024-08-29 13:04:06.485766: predicting 0225 
-2024-08-29 13:04:06.645112: predicting 0226 
-2024-08-29 13:04:06.820129: predicting 0227 
-2024-08-29 13:04:06.996659: predicting 0228 
-2024-08-29 13:04:07.124848: predicting 0229 
-2024-08-29 13:04:07.301933: predicting 0230 
-2024-08-29 13:04:07.466814: predicting 0231 
-2024-08-29 13:04:07.608918: predicting 0232 
-2024-08-29 13:04:07.786034: predicting 0233 
-2024-08-29 13:04:08.080550: predicting 0234 
-2024-08-29 13:04:08.258160: predicting 0235 
-2024-08-29 13:04:08.422477: predicting 0236 
-2024-08-29 13:04:08.563513: predicting 0237 
-2024-08-29 13:04:08.883723: predicting 0238 
-2024-08-29 13:04:09.435777: predicting 0239 
-2024-08-29 13:04:09.609589: predicting 0240 
-2024-08-29 13:04:09.807844: predicting 0241 
-2024-08-29 13:04:09.975737: predicting 0242 
-2024-08-29 13:04:10.139184: predicting 0243 
-2024-08-29 13:04:10.562069: predicting 0244 
-2024-08-29 13:04:10.760387: predicting 0245 
-2024-08-29 13:04:10.902828: predicting 0246 
-2024-08-29 13:04:11.319012: predicting 0247 
-2024-08-29 13:04:11.514114: predicting 0248 
-2024-08-29 13:04:11.692967: predicting 0249 
-2024-08-29 13:04:11.852673: predicting 0250 
-2024-08-29 13:04:12.012520: predicting 0251 
-2024-08-29 13:04:12.182124: predicting 0252 
-2024-08-29 13:04:12.348619: predicting 0253 
-2024-08-29 13:04:13.803261: predicting 0254 
-2024-08-29 13:04:14.026415: predicting 0255 
-2024-08-29 13:04:14.169860: predicting 0256 
-2024-08-29 13:04:15.585186: predicting 0257 
-2024-08-29 13:04:15.789804: predicting 0258 
-2024-08-29 13:04:16.274663: predicting 0259 
-2024-08-29 13:04:16.449688: predicting 0260 
-2024-08-29 13:04:16.879633: predicting 0261 
-2024-08-29 13:04:17.341198: predicting 0262 
-2024-08-29 13:04:17.523790: predicting 0263 
-2024-08-29 13:04:17.652951: predicting 0264 
-2024-08-29 13:04:17.820745: predicting 0265 
-2024-08-29 13:04:17.982301: predicting 0266 
-2024-08-29 13:04:18.140135: predicting 0267 
-2024-08-29 13:04:18.303455: predicting 0268 
-2024-08-29 13:04:18.444207: predicting 0269 
-2024-08-29 13:04:19.864666: predicting 0270 
-2024-08-29 13:04:20.089477: predicting 0271 
-2024-08-29 13:04:20.254477: predicting 0272 
-2024-08-29 13:04:20.426219: predicting 0273 
-2024-08-29 13:04:20.568728: predicting 0274 
-2024-08-29 13:04:20.716076: predicting 0275 
-2024-08-29 13:04:21.012195: predicting 0276 
-2024-08-29 13:04:21.178644: predicting 0277 
-2024-08-29 13:04:22.611945: predicting 0278 
-2024-08-29 13:04:23.162919: predicting 0279 
-2024-08-29 13:04:23.689865: predicting 0280 
-2024-08-29 13:04:23.864920: predicting 0281 
-2024-08-29 13:04:24.050154: predicting 0282 
-2024-08-29 13:04:24.222915: predicting 0283 
-2024-08-29 13:04:24.350332: predicting 0284 
-2024-08-29 13:04:25.429070: predicting 0285 
-2024-08-29 13:04:25.615953: predicting 0286 
-2024-08-29 13:04:25.793113: predicting 0287 
-2024-08-29 13:04:25.957987: predicting 0288 
-2024-08-29 13:04:26.338281: predicting 0289 
-2024-08-29 13:04:26.529664: predicting 0290 
-2024-08-29 13:04:26.701571: predicting 0291 
-2024-08-29 13:04:27.094138: predicting 0292 
-2024-08-29 13:04:27.283906: predicting 0293 
-2024-08-29 13:04:27.476894: predicting 0294 
-2024-08-29 13:04:27.643297: predicting 0295 
-2024-08-29 13:04:28.032833: predicting 0296 
-2024-08-29 13:04:28.246990: predicting 0297 
-2024-08-29 13:04:28.406277: predicting 0298 
-2024-08-29 13:04:28.789620: predicting 0299 
-2024-08-29 13:04:28.990691: predicting 0300 
-2024-08-29 13:04:29.151628: predicting 0301 
-2024-08-29 13:04:29.296184: predicting 0302 
-2024-08-29 13:04:29.487118: predicting 0303 
-2024-08-29 13:04:29.651535: predicting 0304 
-2024-08-29 13:04:29.832189: predicting 0305 
-2024-08-29 13:04:30.009667: predicting 0306 
-2024-08-29 13:04:30.138585: predicting 0307 
-2024-08-29 13:04:30.279329: predicting 0308 
-2024-08-29 13:04:30.419095: predicting 0309 
-2024-08-29 13:04:30.598336: predicting 0310 
-2024-08-29 13:04:30.740296: predicting 0311 
-2024-08-29 13:04:30.903691: predicting 0312 
-2024-08-29 13:04:31.081147: predicting 0313 
-2024-08-29 13:04:31.241460: predicting 0314 
-2024-08-29 13:04:31.427959: predicting 0315 
-2024-08-29 13:04:31.955446: predicting 0316 
-2024-08-29 13:04:32.145239: predicting 0317 
-2024-08-29 13:04:32.332767: predicting 0318 
-2024-08-29 13:04:32.498680: predicting 0319 
-2024-08-29 13:04:32.654810: predicting 0320 
-2024-08-29 13:04:32.950398: predicting 0321 
-2024-08-29 13:04:33.139956: predicting 0322 
-2024-08-29 13:04:33.308984: predicting 0323 
-2024-08-29 13:04:33.501497: predicting 0324 
-2024-08-29 13:04:33.921820: predicting 0325 
-2024-08-29 13:04:34.125042: predicting 0326 
-2024-08-29 13:04:34.428376: predicting 0327 
-2024-08-29 13:04:35.438017: predicting 0328 
-2024-08-29 13:04:36.447986: predicting 0329 
-2024-08-29 13:04:36.926256: predicting 0330 
-2024-08-29 13:04:37.459563: predicting 0331 
-2024-08-29 13:04:37.655081: predicting 0332 
-2024-08-29 13:04:37.828857: predicting 0333 
-2024-08-29 13:04:37.972357: predicting 0334 
-2024-08-29 13:04:38.138553: predicting 0335 
-2024-08-29 13:04:38.296580: predicting 0336 
-2024-08-29 13:04:38.460326: predicting 0337 
-2024-08-29 13:04:38.650372: predicting 0338 
-2024-08-29 13:04:38.816948: predicting 0339 
-2024-08-29 13:04:38.958931: predicting 0340 
-2024-08-29 13:04:39.458143: predicting 0341 
-2024-08-29 13:04:39.633089: predicting 0342 
-2024-08-29 13:04:39.777691: predicting 0343 
-2024-08-29 13:04:40.052564: predicting 0344 
-2024-08-29 13:04:40.229473: predicting 0345 
-2024-08-29 13:04:40.372751: predicting 0346 
-2024-08-29 13:04:40.552101: predicting 0347 
-2024-08-29 13:04:40.694132: predicting 0348 
-2024-08-29 13:04:40.833686: predicting 0349 
-2024-08-29 13:04:40.991461: predicting 0350 
-2024-08-29 13:04:41.160415: predicting 0351 
-2024-08-29 13:04:41.303774: predicting 0352 
-2024-08-29 13:04:41.483161: predicting 0353 
-2024-08-29 13:04:41.967266: predicting 0354 
-2024-08-29 13:04:42.139818: predicting 0355 
-2024-08-29 13:04:42.286941: predicting 0356 
-2024-08-29 13:04:42.443017: predicting 0357 
-2024-08-29 13:04:42.607299: predicting 0358 
-2024-08-29 13:04:42.798291: predicting 0359 
-2024-08-29 13:04:42.962062: predicting 0360 
-2024-08-29 13:04:43.140793: predicting 0361 
-2024-08-29 13:04:43.311873: predicting 0362 
-2024-08-29 13:04:43.452101: predicting 0363 
-2024-08-29 13:04:43.592314: predicting 0364 
-2024-08-29 13:04:43.767737: predicting 0365 
-2024-08-29 13:04:43.925095: predicting 0366 
-2024-08-29 13:04:44.105474: predicting 0367 
-2024-08-29 13:04:44.290463: predicting 0368 
-2024-08-29 13:04:44.478099: predicting 0369 
-2024-08-29 13:04:44.659263: predicting 0370 
-2024-08-29 13:04:44.844512: predicting 0371 
-2024-08-29 13:04:45.025221: predicting 0372 
-2024-08-29 13:04:45.190072: predicting 0373 
-2024-08-29 13:04:45.332625: predicting 0374 
-2024-08-29 13:04:45.524539: predicting 0375 
-2024-08-29 13:04:45.701379: predicting 0376 
-2024-08-29 13:04:46.640227: predicting 0377 
-2024-08-29 13:04:47.636740: predicting 0378 
-2024-08-29 13:04:48.631519: predicting 0379 
-2024-08-29 13:04:49.173936: predicting 0380 
-2024-08-29 13:04:49.353251: predicting 0381 
-2024-08-29 13:04:49.511714: predicting 0382 
-2024-08-29 13:04:49.703449: predicting 0383 
-2024-08-29 13:04:49.897121: predicting 0384 
-2024-08-29 13:04:50.093640: predicting 0385 
-2024-08-29 13:04:50.646911: predicting 0386 
-2024-08-29 13:04:52.121025: predicting 0387 
-2024-08-29 13:04:52.380102: predicting 0388 
-2024-08-29 13:04:52.527275: predicting 0389 
-2024-08-29 13:04:52.674616: predicting 0390 
-2024-08-29 13:04:52.803283: predicting 0391 
-2024-08-29 13:04:53.208381: predicting 0392 
-2024-08-29 13:04:53.678449: predicting 0393 
-2024-08-29 13:04:53.879577: predicting 0394 
-2024-08-29 13:04:54.068412: predicting 0395 
-2024-08-29 13:04:54.251933: predicting 0396 
-2024-08-29 13:04:54.462385: predicting 0397 
-2024-08-29 13:04:54.644219: predicting 0398 
-2024-08-29 13:04:55.065880: predicting 0399 
-2024-08-29 13:04:55.272409: predicting 0400 
-2024-08-29 13:04:55.441617: predicting 0401 
-2024-08-29 13:04:55.570557: predicting 0402 
-2024-08-29 13:04:55.735594: predicting 0403 
-2024-08-29 13:04:55.913870: predicting 0404 
-2024-08-29 13:04:56.108149: predicting 0405 
-2024-08-29 13:04:56.257335: predicting 0406 
-2024-08-29 13:04:56.386003: predicting 0407 
-2024-08-29 13:04:56.533142: predicting 0408 
-2024-08-29 13:04:56.678879: predicting 0409 
-2024-08-29 13:04:56.870926: predicting 0410 
-2024-08-29 13:04:57.014692: predicting 0411 
-2024-08-29 13:04:57.207047: predicting 0412 
-2024-08-29 13:04:57.355139: predicting 0413 
-2024-08-29 13:04:57.503846: predicting 0414 
-2024-08-29 13:04:57.699315: predicting 0415 
-2024-08-29 13:04:57.891350: predicting 0416 
-2024-08-29 13:04:58.053864: predicting 0417 
-2024-08-29 13:04:58.235651: predicting 0418 
-2024-08-29 13:04:58.723326: predicting 0419 
-2024-08-29 13:04:58.898583: predicting 0420 
-2024-08-29 13:04:59.300260: predicting 0421 
-2024-08-29 13:04:59.518963: predicting 0422 
-2024-08-29 13:04:59.711073: predicting 0423 
-2024-08-29 13:04:59.904791: predicting 0424 
-2024-08-29 13:05:00.458643: predicting 0425 
-2024-08-29 13:05:00.661646: predicting 0426 
-2024-08-29 13:05:00.862478: predicting 0427 
-2024-08-29 13:05:01.060997: predicting 0428 
-2024-08-29 13:05:01.507477: predicting 0429 
-2024-08-29 13:05:02.002552: predicting 0430 
-2024-08-29 13:05:02.487423: predicting 0431 
-2024-08-29 13:05:02.723847: predicting 0432 
-2024-08-29 13:05:02.918184: predicting 0433 
-2024-08-29 13:05:03.112070: predicting 0434 
-2024-08-29 13:05:04.535507: predicting 0435 
-2024-08-29 13:05:04.774615: predicting 0436 
-2024-08-29 13:05:05.320831: predicting 0437 
-2024-08-29 13:05:05.519138: predicting 0438 
-2024-08-29 13:05:05.698594: predicting 0439 
-2024-08-29 13:05:05.827182: predicting 0440 
-2024-08-29 13:05:05.975858: predicting 0441 
-2024-08-29 13:05:06.822836: predicting 0442 
-2024-08-29 13:05:07.180077: predicting 0443 
-2024-08-29 13:05:07.394906: predicting 0444 
-2024-08-29 13:05:07.576357: predicting 0445 
-2024-08-29 13:05:07.750489: predicting 0446 
-2024-08-29 13:05:07.878751: predicting 0447 
-2024-08-29 13:05:08.049590: predicting 0448 
-2024-08-29 13:05:08.179172: predicting 0449 
-2024-08-29 13:05:08.376551: predicting 0450 
-2024-08-29 13:05:08.522720: predicting 0451 
-2024-08-29 13:05:08.705289: predicting 0452 
-2024-08-29 13:05:08.852056: predicting 0453 
-2024-08-29 13:05:09.026169: predicting 0454 
-2024-08-29 13:05:09.172294: predicting 0455 
-2024-08-29 13:05:09.702566: predicting 0456 
-2024-08-29 13:05:09.890879: predicting 0457 
-2024-08-29 13:05:10.037543: predicting 0458 
-2024-08-29 13:05:11.463736: predicting 0459 
-2024-08-29 13:05:11.720024: predicting 0460 
-2024-08-29 13:05:11.880970: predicting 0461 
-2024-08-29 13:05:12.077275: predicting 0462 
-2024-08-29 13:05:12.265167: predicting 0463 
-2024-08-29 13:05:12.551054: predicting 0464 
-2024-08-29 13:05:12.756873: predicting 0465 
-2024-08-29 13:05:12.883411: predicting 0466 
-2024-08-29 13:05:13.371931: predicting 0467 
-2024-08-29 13:05:13.563512: predicting 0468 
-2024-08-29 13:05:13.709761: predicting 0469 
-2024-08-29 13:05:13.903244: predicting 0470 
-2024-08-29 13:05:14.050784: predicting 0471 
-2024-08-29 13:05:14.197489: predicting 0472 
-2024-08-29 13:05:14.393830: predicting 0473 
-2024-08-29 13:05:14.522666: predicting 0474 
-2024-08-29 13:05:14.971815: predicting 0475 
-2024-08-29 13:05:15.451350: predicting 0476 
-2024-08-29 13:05:15.676482: predicting 0477 
-2024-08-29 13:05:16.128815: predicting 0478 
-2024-08-29 13:05:16.358540: predicting 0479 
-2024-08-29 13:05:16.807773: predicting 0480 
-2024-08-29 13:05:17.269035: predicting 0481 
-2024-08-29 13:05:18.745397: predicting 0482 
-2024-08-29 13:05:19.318054: predicting 0483 
-2024-08-29 13:05:19.514311: predicting 0484 
-2024-08-29 13:05:19.686369: predicting 0485 
-2024-08-29 13:05:19.864130: predicting 0486 
-2024-08-29 13:05:20.047283: predicting 0487 
-2024-08-29 13:05:20.214956: predicting 0488 
-2024-08-29 13:05:20.378012: predicting 0489 
-2024-08-29 13:05:20.914728: predicting 0490 
-2024-08-29 13:05:21.350482: predicting 0491 
-2024-08-29 13:05:21.563539: predicting 0492 
-2024-08-29 13:05:21.708651: predicting 0493 
-2024-08-29 13:05:23.319847: predicting 0494 
-2024-08-29 13:05:23.554187: predicting 0495 
-2024-08-29 13:05:23.699543: predicting 0496 
-2024-08-29 13:05:23.846344: predicting 0497 
-2024-08-29 13:05:24.041975: predicting 0498 
-2024-08-29 13:05:24.231301: predicting 0499 
-2024-08-29 13:05:24.673572: predicting 0500 
-2024-08-29 13:05:26.186715: predicting 0501 
-2024-08-29 13:05:26.431049: predicting 0502 
-2024-08-29 13:05:26.576285: predicting 0503 
-2024-08-29 13:05:26.771822: predicting 0504 
-2024-08-29 13:05:26.960772: predicting 0505 
-2024-08-29 13:05:27.134773: predicting 0506 
-2024-08-29 13:05:27.296933: predicting 0507 
-2024-08-29 13:05:27.472120: predicting 0508 
-2024-08-29 13:05:27.647965: predicting 0509 
-2024-08-29 13:05:27.824711: predicting 0510 
-2024-08-29 13:05:28.001312: predicting 0511 
-2024-08-29 13:05:28.159449: predicting 0512 
-2024-08-29 13:05:28.685324: predicting 0513 
-2024-08-29 13:05:29.676216: predicting 0514 
-2024-08-29 13:05:30.674318: predicting 0515 
-2024-08-29 13:05:31.216333: predicting 0516 
-2024-08-29 13:05:31.752279: predicting 0517 
-2024-08-29 13:05:31.927520: predicting 0518 
-2024-08-29 13:05:32.071727: predicting 0519 
-2024-08-29 13:05:32.497073: predicting 0520 
-2024-08-29 13:05:32.939825: predicting 0521 
-2024-08-29 13:05:33.134034: predicting 0522 
-2024-08-29 13:05:33.279567: predicting 0523 
-2024-08-29 13:05:33.704033: predicting 0524 
-2024-08-29 13:05:33.905125: predicting 0525 
-2024-08-29 13:05:34.176560: predicting 0526 
-2024-08-29 13:05:34.348372: predicting 0527 
-2024-08-29 13:05:34.475843: predicting 0528 
-2024-08-29 13:05:34.868746: predicting 0529 
-2024-08-29 13:05:36.823619: predicting 0530 
-2024-08-29 13:05:37.043471: predicting 0531 
-2024-08-29 13:05:37.218172: predicting 0532 
-2024-08-29 13:05:37.362046: predicting 0533 
-2024-08-29 13:05:37.539300: predicting 0534 
-2024-08-29 13:05:38.057432: predicting 0535 
-2024-08-29 13:05:38.246733: predicting 0536 
-2024-08-29 13:05:38.436447: predicting 0537 
-2024-08-29 13:05:38.609780: predicting 0538 
-2024-08-29 13:05:38.775507: predicting 0539 
-2024-08-29 13:05:38.916579: predicting 0540 
-2024-08-29 13:05:39.436224: predicting 0541 
-2024-08-29 13:05:39.612253: predicting 0542 
-2024-08-29 13:05:39.774070: predicting 0543 
-2024-08-29 13:05:39.967736: predicting 0544 
-2024-08-29 13:05:40.152359: predicting 0545 
-2024-08-29 13:05:40.663179: predicting 0546 
-2024-08-29 13:05:40.866273: predicting 0547 
-2024-08-29 13:05:41.037159: predicting 0548 
-2024-08-29 13:05:41.570795: predicting 0549 
-2024-08-29 13:05:42.009779: predicting 0550 
-2024-08-29 13:05:42.200628: predicting 0551 
-2024-08-29 13:05:42.740895: predicting 0552 
-2024-08-29 13:05:42.915398: predicting 0553 
-2024-08-29 13:05:43.095479: predicting 0554 
-2024-08-29 13:05:43.264710: predicting 0555 
-2024-08-29 13:05:43.784315: predicting 0556 
-2024-08-29 13:05:43.957207: predicting 0557 
-2024-08-29 13:05:44.403948: predicting 0558 
-2024-08-29 13:05:44.639479: predicting 0559 
-2024-08-29 13:05:45.072764: predicting 0560 
-2024-08-29 13:05:45.278484: predicting 0561 
-2024-08-29 13:05:46.241999: predicting 0562 
-2024-08-29 13:05:46.566824: predicting 0563 
-2024-08-29 13:05:47.096479: predicting 0564 
-2024-08-29 13:05:47.272109: predicting 0565 
-2024-08-29 13:05:47.419088: predicting 0566 
-2024-08-29 13:05:47.560620: predicting 0567 
-2024-08-29 13:05:47.701852: predicting 0568 
-2024-08-29 13:05:47.842609: predicting 0569 
-2024-08-29 13:05:47.971560: predicting 0570 
-2024-08-29 13:05:49.156735: predicting 0571 
-2024-08-29 13:05:49.365962: predicting 0572 
-2024-08-29 13:05:49.538348: predicting 0573 
-2024-08-29 13:05:49.700290: predicting 0574 
-2024-08-29 13:05:49.866571: predicting 0575 
-2024-08-29 13:05:50.059807: predicting 0576 
-2024-08-29 13:05:50.225286: predicting 0577 
-2024-08-29 13:05:50.402372: predicting 0578 
-2024-08-29 13:05:50.801610: predicting 0579 
-2024-08-29 13:05:51.000406: predicting 0580 
-2024-08-29 13:05:51.140869: predicting 0581 
-2024-08-29 13:05:51.523909: predicting 0582 
-2024-08-29 13:05:51.735543: predicting 0583 
-2024-08-29 13:05:51.908826: predicting 0584 
-2024-08-29 13:05:52.036650: predicting 0585 
-2024-08-29 13:05:52.166115: predicting 0586 
-2024-08-29 13:05:52.686653: predicting 0587 
-2024-08-29 13:05:53.224250: predicting 0588 
-2024-08-29 13:05:53.757821: predicting 0589 
-2024-08-29 13:05:54.934368: predicting 0590 
-2024-08-29 13:05:55.169279: predicting 0591 
-2024-08-29 13:05:55.335932: predicting 0592 
-2024-08-29 13:05:55.531080: predicting 0593 
-2024-08-29 13:05:55.700649: predicting 0594 
-2024-08-29 13:05:55.898600: predicting 0595 
-2024-08-29 13:05:56.083658: predicting 0596 
-2024-08-29 13:05:56.253910: predicting 0597 
-2024-08-29 13:05:56.392579: predicting 0598 
-2024-08-29 13:05:56.520064: predicting 0599 
-2024-08-29 13:05:56.696543: predicting 0600 
-2024-08-29 13:05:57.126828: predicting 0601 
-2024-08-29 13:05:57.332246: predicting 0602 
-2024-08-29 13:05:57.509904: predicting 0603 
-2024-08-29 13:05:57.653039: predicting 0604 
-2024-08-29 13:05:57.808145: predicting 0605 
-2024-08-29 13:05:57.976286: predicting 0606 
-2024-08-29 13:05:58.134558: predicting 0607 
-2024-08-29 13:05:58.317471: predicting 0608 
-2024-08-29 13:05:58.499851: predicting 0609 
-2024-08-29 13:05:58.667398: predicting 0610 
-2024-08-29 13:05:58.845553: predicting 0611 
-2024-08-29 13:05:59.000854: predicting 0612 
-2024-08-29 13:05:59.182487: predicting 0613 
-2024-08-29 13:05:59.346295: predicting 0614 
-2024-08-29 13:05:59.505999: predicting 0615 
-2024-08-29 13:05:59.696616: predicting 0616 
-2024-08-29 13:05:59.888430: predicting 0617 
-2024-08-29 13:06:00.066152: predicting 0618 
-2024-08-29 13:06:01.487081: predicting 0619 
-2024-08-29 13:06:02.992674: predicting 0620 
-2024-08-29 13:06:05.033191: predicting 0621 
-2024-08-29 13:06:05.580563: predicting 0622 
-2024-08-29 13:06:05.883742: predicting 0623 
-2024-08-29 13:06:06.316964: predicting 0624 
-2024-08-29 13:06:06.518711: predicting 0625 
-2024-08-29 13:06:06.819499: predicting 0626 
-2024-08-29 13:06:07.340155: predicting 0627 
-2024-08-29 13:06:07.865688: predicting 0628 
-2024-08-29 13:06:08.039937: predicting 0629 
-2024-08-29 13:06:08.207902: predicting 0630 
-2024-08-29 13:06:08.347700: predicting 0631 
-2024-08-29 13:06:08.488360: predicting 0632 
-2024-08-29 13:06:08.651087: predicting 0633 
-2024-08-29 13:06:08.822211: predicting 0634 
-2024-08-29 13:06:09.343911: predicting 0635 
-2024-08-29 13:06:09.536299: predicting 0636 
-2024-08-29 13:06:09.709160: predicting 0637 
-2024-08-29 13:06:09.888709: predicting 0638 
-2024-08-29 13:06:10.050241: predicting 0639 
-2024-08-29 13:06:10.179734: predicting 0640 
-2024-08-29 13:06:10.347719: predicting 0641 
-2024-08-29 13:06:10.625247: predicting 0642 
-2024-08-29 13:06:11.047850: predicting 0643 
-2024-08-29 13:06:11.253573: predicting 0644 
-2024-08-29 13:06:11.670583: predicting 0645 
-2024-08-29 13:06:11.870617: predicting 0646 
-2024-08-29 13:06:12.061857: predicting 0647 
-2024-08-29 13:06:12.231570: predicting 0648 
-2024-08-29 13:06:12.674943: predicting 0649 
-2024-08-29 13:06:13.142058: predicting 0650 
-2024-08-29 13:06:13.351257: predicting 0651 
-2024-08-29 13:06:13.499463: predicting 0652 
-2024-08-29 13:06:13.681236: predicting 0653 
-2024-08-29 13:06:13.822065: predicting 0654 
-2024-08-29 13:06:14.244092: predicting 0655 
-2024-08-29 13:06:14.450881: predicting 0656 
-2024-08-29 13:06:14.591422: predicting 0657 
-2024-08-29 13:06:14.732517: predicting 0658 
-2024-08-29 13:06:14.940970: predicting 0659 
-2024-08-29 13:06:15.932534: predicting 0660 
-2024-08-29 13:06:16.178270: predicting 0661 
-2024-08-29 13:06:16.323464: predicting 0662 
-2024-08-29 13:06:16.519667: predicting 0663 
-2024-08-29 13:06:16.713700: predicting 0664 
-2024-08-29 13:06:16.909120: predicting 0665 
-2024-08-29 13:06:17.095061: predicting 0666 
-2024-08-29 13:06:17.310889: predicting 0667 
-2024-08-29 13:06:17.495553: predicting 0668 
-2024-08-29 13:06:17.693973: predicting 0669 
-2024-08-29 13:06:17.876375: predicting 0670 
-2024-08-29 13:06:18.023981: predicting 0671 
-2024-08-29 13:06:18.226111: predicting 0672 
-2024-08-29 13:06:18.405792: predicting 0673 
-2024-08-29 13:06:18.580848: predicting 0674 
-2024-08-29 13:06:18.773265: predicting 0675 
-2024-08-29 13:06:18.938923: predicting 0676 
-2024-08-29 13:06:19.112765: predicting 0677 
-2024-08-29 13:06:19.277731: predicting 0678 
-2024-08-29 13:06:19.420884: predicting 0679 
-2024-08-29 13:06:19.562006: predicting 0680 
-2024-08-29 13:06:19.955036: predicting 0681 
-2024-08-29 13:06:20.412003: predicting 0682 
-2024-08-29 13:06:20.880045: predicting 0683 
-2024-08-29 13:06:21.084156: predicting 0684 
-2024-08-29 13:06:21.488703: predicting 0685 
-2024-08-29 13:06:21.703739: predicting 0686 
-2024-08-29 13:06:22.207093: predicting 0687 
-2024-08-29 13:06:23.189279: predicting 0688 
-2024-08-29 13:06:23.371344: predicting 0689 
-2024-08-29 13:06:23.543974: predicting 0690 
-2024-08-29 13:06:23.687824: predicting 0691 
-2024-08-29 13:06:23.851955: predicting 0692 
-2024-08-29 13:06:24.032380: predicting 0693 
-2024-08-29 13:06:24.174486: predicting 0694 
-2024-08-29 13:06:24.338675: predicting 0695 
-2024-08-29 13:06:24.498889: predicting 0696 
-2024-08-29 13:06:24.641344: predicting 0697 
-2024-08-29 13:06:24.769699: predicting 0698 
-2024-08-29 13:06:24.909642: predicting 0699 
-2024-08-29 13:06:25.410264: predicting 0700 
-2024-08-29 13:06:25.941859: predicting 0701 
-2024-08-29 13:06:26.231423: predicting 0702 
-2024-08-29 13:06:26.531188: predicting 0703 
-2024-08-29 13:06:26.817617: predicting 0704 
-2024-08-29 13:06:26.987448: predicting 0705 
-2024-08-29 13:06:27.166346: predicting 0706 
-2024-08-29 13:06:27.307996: predicting 0707 
-2024-08-29 13:06:27.452518: predicting 0708 
-2024-08-29 13:06:27.597656: predicting 0709 
-2024-08-29 13:06:27.777193: predicting 0710 
-2024-08-29 13:06:27.944545: predicting 0711 
-2024-08-29 13:06:28.126344: predicting 0712 
-2024-08-29 13:06:28.298608: predicting 0713 
-2024-08-29 13:06:28.830554: predicting 0714 
-2024-08-29 13:06:29.351688: predicting 0715 
-2024-08-29 13:06:29.896221: predicting 0716 
-2024-08-29 13:06:30.089682: predicting 0717 
-2024-08-29 13:06:30.238444: predicting 0718 
-2024-08-29 13:06:30.704718: predicting 0719 
-2024-08-29 13:06:30.937927: predicting 0720 
-2024-08-29 13:06:31.132528: predicting 0721 
-2024-08-29 13:06:31.310283: predicting 0722 
-2024-08-29 13:06:31.484159: predicting 0723 
-2024-08-29 13:06:31.630555: predicting 0724 
-2024-08-29 13:06:31.805753: predicting 0725 
-2024-08-29 13:06:31.984666: predicting 0726 
-2024-08-29 13:06:32.457273: predicting 0727 
-2024-08-29 13:06:32.646750: predicting 0728 
-2024-08-29 13:06:32.796941: predicting 0729 
-2024-08-29 13:06:32.973068: predicting 0730 
-2024-08-29 13:06:33.155194: predicting 0731 
-2024-08-29 13:06:33.334734: predicting 0732 
-2024-08-29 13:06:33.500234: predicting 0733 
-2024-08-29 13:06:33.668143: predicting 0734 
-2024-08-29 13:06:33.840225: predicting 0735 
-2024-08-29 13:06:34.007879: predicting 0736 
-2024-08-29 13:06:34.400441: predicting 0737 
-2024-08-29 13:06:34.593889: predicting 0738 
-2024-08-29 13:06:34.722231: predicting 0739 
-2024-08-29 13:06:34.851609: predicting 0740 
-2024-08-29 13:06:35.018692: predicting 0741 
-2024-08-29 13:06:35.197370: predicting 0742 
-2024-08-29 13:06:36.613084: predicting 0743 
-2024-08-29 13:06:36.828560: predicting 0744 
-2024-08-29 13:06:37.005559: predicting 0745 
-2024-08-29 13:06:37.185455: predicting 0746 
-2024-08-29 13:06:37.353868: predicting 0747 
-2024-08-29 13:06:37.496996: predicting 0748 
-2024-08-29 13:06:37.641403: predicting 0749 
-2024-08-29 13:06:37.789261: predicting 0750 
-2024-08-29 13:06:37.931344: predicting 0751 
-2024-08-29 13:06:38.079922: predicting 0752 
-2024-08-29 13:06:38.261554: predicting 0753 
-2024-08-29 13:06:38.438397: predicting 0754 
-2024-08-29 13:06:38.633936: predicting 0755 
-2024-08-29 13:06:38.831597: predicting 0756 
-2024-08-29 13:06:39.096005: predicting 0757 
-2024-08-29 13:06:39.300650: predicting 0758 
-2024-08-29 13:06:39.498167: predicting 0759 
-2024-08-29 13:06:39.876241: predicting 0760 
-2024-08-29 13:06:40.085915: predicting 0761 
-2024-08-29 13:06:40.260876: predicting 0762 
-2024-08-29 13:06:40.554625: predicting 0763 
-2024-08-29 13:06:40.749950: predicting 0764 
-2024-08-29 13:06:40.895025: predicting 0765 
-2024-08-29 13:06:41.150125: predicting 0766 
-2024-08-29 13:06:41.324993: predicting 0767 
-2024-08-29 13:06:41.579607: predicting 0768 
-2024-08-29 13:06:41.750560: predicting 0769 
-2024-08-29 13:06:41.929871: predicting 0770 
-2024-08-29 13:06:42.070362: predicting 0771 
-2024-08-29 13:06:42.236443: predicting 0772 
-2024-08-29 13:06:42.849781: predicting 0773 
-2024-08-29 13:06:43.166944: predicting 0774 
-2024-08-29 13:06:43.352013: predicting 0775 
-2024-08-29 13:06:43.493336: predicting 0776 
-2024-08-29 13:06:43.669873: predicting 0777 
-2024-08-29 13:06:43.811766: predicting 0778 
-2024-08-29 13:06:43.940662: predicting 0779 
-2024-08-29 13:06:44.082594: predicting 0780 
-2024-08-29 13:06:44.224869: predicting 0781 
-2024-08-29 13:06:44.365956: predicting 0782 
-2024-08-29 13:06:44.847250: predicting 0783 
-2024-08-29 13:06:45.036571: predicting 0784 
-2024-08-29 13:06:45.212606: predicting 0785 
-2024-08-29 13:06:45.377695: predicting 0786 
-2024-08-29 13:06:45.520492: predicting 0787 
-2024-08-29 13:06:45.662953: predicting 0788 
-2024-08-29 13:06:45.840547: predicting 0789 
-2024-08-29 13:06:45.982679: predicting 0790 
-2024-08-29 13:06:46.147129: predicting 0791 
-2024-08-29 13:06:46.325776: predicting 0792 
-2024-08-29 13:06:46.492052: predicting 0793 
-2024-08-29 13:06:47.928969: predicting 0794 
-2024-08-29 13:06:48.399988: predicting 0795 
-2024-08-29 13:06:48.614040: predicting 0796 
-2024-08-29 13:06:50.019120: predicting 0797 
-2024-08-29 13:06:50.233884: predicting 0798 
-2024-08-29 13:06:50.377056: predicting 0799 
-2024-08-29 13:06:50.547890: predicting 0800 
-2024-08-29 13:06:50.731468: predicting 0801 
-2024-08-29 13:06:50.907413: predicting 0802 
-2024-08-29 13:06:51.073543: predicting 0803 
-2024-08-29 13:06:51.236406: predicting 0804 
-2024-08-29 13:06:51.400109: predicting 0805 
-2024-08-29 13:06:51.541100: predicting 0806 
-2024-08-29 13:06:51.702311: predicting 0807 
-2024-08-29 13:06:52.184979: predicting 0808 
-2024-08-29 13:06:52.375367: predicting 0809 
-2024-08-29 13:06:52.546098: predicting 0810 
-2024-08-29 13:06:53.993735: predicting 0811 
-2024-08-29 13:06:54.208539: predicting 0812 
-2024-08-29 13:06:54.393126: predicting 0813 
-2024-08-29 13:06:54.681856: predicting 0814 
-2024-08-29 13:06:56.141275: predicting 0815 
-2024-08-29 13:06:56.350613: predicting 0816 
-2024-08-29 13:06:56.478781: predicting 0817 
-2024-08-29 13:06:56.734730: predicting 0818 
-2024-08-29 13:06:56.916737: predicting 0819 
-2024-08-29 13:06:57.084265: predicting 0820 
-2024-08-29 13:06:57.246767: predicting 0821 
-2024-08-29 13:06:57.412070: predicting 0822 
-2024-08-29 13:06:57.848335: predicting 0823 
-2024-08-29 13:06:58.052783: predicting 0824 
-2024-08-29 13:06:58.236595: predicting 0825 
-2024-08-29 13:06:58.409251: predicting 0826 
-2024-08-29 13:06:58.570320: predicting 0827 
-2024-08-29 13:06:58.748847: predicting 0828 
-2024-08-29 13:06:58.914576: predicting 0829 
-2024-08-29 13:06:59.101551: predicting 0830 
-2024-08-29 13:06:59.281501: predicting 0831 
-2024-08-29 13:06:59.459467: predicting 0832 
-2024-08-29 13:06:59.622428: predicting 0833 
-2024-08-29 13:06:59.924325: predicting 0834 
-2024-08-29 13:07:00.105916: predicting 0835 
-2024-08-29 13:07:00.587996: predicting 0836 
-2024-08-29 13:07:00.759642: predicting 0837 
-2024-08-29 13:07:00.887858: predicting 0838 
-2024-08-29 13:07:01.066943: predicting 0839 
-2024-08-29 13:07:01.208064: predicting 0840 
-2024-08-29 13:07:01.367810: predicting 0841 
-2024-08-29 13:07:01.509571: predicting 0842 
-2024-08-29 13:07:01.899994: predicting 0843 
-2024-08-29 13:07:02.909651: predicting 0844 
-2024-08-29 13:07:03.504832: predicting 0845 
-2024-08-29 13:07:03.680241: predicting 0846 
-2024-08-29 13:07:04.086171: predicting 0847 
-2024-08-29 13:07:04.407977: predicting 0848 
-2024-08-29 13:07:05.381417: predicting 0849 
-2024-08-29 13:07:05.575823: predicting 0850 
-2024-08-29 13:07:06.534657: predicting 0851 
-2024-08-29 13:07:07.542389: predicting 0852 
-2024-08-29 13:07:08.007909: predicting 0853 
-2024-08-29 13:07:08.370745: predicting 0854 
-2024-08-29 13:07:08.585396: predicting 0855 
-2024-08-29 13:07:08.767177: predicting 0856 
-2024-08-29 13:07:08.955039: predicting 0857 
-2024-08-29 13:07:09.489542: predicting 0858 
-2024-08-29 13:07:09.677347: predicting 0859 
-2024-08-29 13:07:09.932878: predicting 0860 
-2024-08-29 13:07:10.456982: predicting 0861 
-2024-08-29 13:07:10.889629: predicting 0862 
-2024-08-29 13:07:11.094543: predicting 0863 
-2024-08-29 13:07:11.576384: predicting 0864 
-2024-08-29 13:07:12.004441: predicting 0865 
-2024-08-29 13:07:12.558995: predicting 0866 
-2024-08-29 13:07:12.950440: predicting 0867 
-2024-08-29 13:07:13.403572: predicting 0868 
-2024-08-29 13:07:13.873202: predicting 0869 
-2024-08-29 13:07:14.306016: predicting 0870 
-2024-08-29 13:07:14.494019: predicting 0871 
-2024-08-29 13:07:14.656173: predicting 0872 
-2024-08-29 13:07:16.078344: predicting 0873 
-2024-08-29 13:07:16.301669: predicting 0874 
-2024-08-29 13:07:16.822017: predicting 0875 
-2024-08-29 13:07:16.991775: predicting 0876 
-2024-08-29 13:07:17.400970: predicting 0877 
-2024-08-29 13:07:17.612333: predicting 0878 
-2024-08-29 13:07:17.792539: predicting 0879 
-2024-08-29 13:07:17.973967: predicting 0880 
-2024-08-29 13:07:18.102550: predicting 0881 
-2024-08-29 13:07:18.277731: predicting 0882 
-2024-08-29 13:07:18.453864: predicting 0883 
-2024-08-29 13:07:18.595961: predicting 0884 
-2024-08-29 13:07:18.773309: predicting 0885 
-2024-08-29 13:07:18.919521: predicting 0886 
-2024-08-29 13:07:19.094349: predicting 0887 
-2024-08-29 13:07:19.238876: predicting 0888 
-2024-08-29 13:07:19.404738: predicting 0889 
-2024-08-29 13:07:19.552408: predicting 0890 
-2024-08-29 13:07:20.086655: predicting 0891 
-2024-08-29 13:07:20.274046: predicting 0892 
-2024-08-29 13:07:20.452370: predicting 0893 
-2024-08-29 13:07:20.640558: predicting 0894 
-2024-08-29 13:07:21.147642: predicting 0895 
-2024-08-29 13:07:21.319691: predicting 0896 
-2024-08-29 13:07:21.466359: predicting 0897 
-2024-08-29 13:07:21.608404: predicting 0898 
-2024-08-29 13:07:21.777337: predicting 0899 
-2024-08-29 13:07:22.263234: predicting 0900 
-2024-08-29 13:07:22.430321: predicting 0901 
-2024-08-29 13:07:22.576584: predicting 0902 
-2024-08-29 13:07:22.752196: predicting 0903 
-2024-08-29 13:07:22.929887: predicting 0904 
-2024-08-29 13:07:23.409757: predicting 0905 
-2024-08-29 13:07:23.589414: predicting 0906 
-2024-08-29 13:07:23.740320: predicting 0907 
-2024-08-29 13:07:24.274679: predicting 0908 
-2024-08-29 13:07:24.462228: predicting 0909 
-2024-08-29 13:07:24.656255: predicting 0910 
-2024-08-29 13:07:24.831227: predicting 0911 
-2024-08-29 13:07:25.026270: predicting 0912 
-2024-08-29 13:07:25.221285: predicting 0913 
-2024-08-29 13:07:25.375518: predicting 0914 
-2024-08-29 13:07:25.557903: predicting 0915 
-2024-08-29 13:07:25.735292: predicting 0916 
-2024-08-29 13:07:25.908372: predicting 0917 
-2024-08-29 13:07:26.102273: predicting 0918 
-2024-08-29 13:07:26.231925: predicting 0919 
-2024-08-29 13:07:26.686811: predicting 0920 
-2024-08-29 13:07:26.919458: predicting 0921 
-2024-08-29 13:07:27.126431: predicting 0922 
-2024-08-29 13:07:27.276712: predicting 0923 
-2024-08-29 13:07:27.453297: predicting 0924 
-2024-08-29 13:07:27.629983: predicting 0925 
-2024-08-29 13:07:27.800632: predicting 0926 
-2024-08-29 13:07:27.963006: predicting 0927 
-2024-08-29 13:07:28.141599: predicting 0928 
-2024-08-29 13:07:28.302641: predicting 0929 
-2024-08-29 13:07:28.442971: predicting 0930 
-2024-08-29 13:07:28.586811: predicting 0931 
-2024-08-29 13:07:29.006957: predicting 0932 
-2024-08-29 13:07:29.204120: predicting 0933 
-2024-08-29 13:07:29.624577: predicting 0934 
-2024-08-29 13:07:31.125577: predicting 0935 
-2024-08-29 13:07:31.603471: predicting 0936 
-2024-08-29 13:07:31.814090: predicting 0937 
-2024-08-29 13:07:31.941260: predicting 0938 
-2024-08-29 13:07:32.069525: predicting 0939 
-2024-08-29 13:07:32.199005: predicting 0940 
-2024-08-29 13:07:32.393059: predicting 0941 
-2024-08-29 13:07:32.578815: predicting 0942 
-2024-08-29 13:07:32.727732: predicting 0943 
-2024-08-29 13:07:33.714457: predicting 0944 
-2024-08-29 13:07:34.268502: predicting 0945 
-2024-08-29 13:07:34.802229: predicting 0946 
-2024-08-29 13:07:35.007964: predicting 0947 
-2024-08-29 13:07:35.288366: predicting 0948 
-2024-08-29 13:07:35.451166: predicting 0949 
-2024-08-29 13:07:35.621235: predicting 0950 
-2024-08-29 13:07:35.769356: predicting 0951 
-2024-08-29 13:07:35.933234: predicting 0952 
-2024-08-29 13:07:36.109393: predicting 0953 
-2024-08-29 13:07:36.284074: predicting 0954 
-2024-08-29 13:07:36.764869: predicting 0955 
-2024-08-29 13:07:36.940424: predicting 0956 
-2024-08-29 13:07:37.069748: predicting 0957 
-2024-08-29 13:07:37.252060: predicting 0958 
-2024-08-29 13:07:37.380694: predicting 0959 
-2024-08-29 13:07:37.542414: predicting 0960 
-2024-08-29 13:07:37.722744: predicting 0961 
-2024-08-29 13:07:37.864888: predicting 0962 
-2024-08-29 13:07:38.286964: predicting 0963 
-2024-08-29 13:07:38.840857: predicting 0964 
-2024-08-29 13:07:39.020982: predicting 0965 
-2024-08-29 13:07:39.203764: predicting 0966 
-2024-08-29 13:07:39.716331: predicting 0967 
-2024-08-29 13:07:39.903125: predicting 0968 
-2024-08-29 13:07:40.074850: predicting 0969 
-2024-08-29 13:07:41.006119: predicting 0970 
-2024-08-29 13:07:41.195774: predicting 0971 
-2024-08-29 13:07:41.625073: predicting 0972 
-2024-08-29 13:07:41.834346: predicting 0973 
-2024-08-29 13:07:43.954394: predicting 0974 
-2024-08-29 13:07:44.180525: predicting 0975 
-2024-08-29 13:07:44.606542: predicting 0976 
-2024-08-29 13:07:44.822326: predicting 0977 
-2024-08-29 13:07:44.952121: predicting 0978 
-2024-08-29 13:07:45.116819: predicting 0979 
-2024-08-29 13:07:45.508069: predicting 0980 
-2024-08-29 13:07:45.724392: predicting 0981 
-2024-08-29 13:07:46.126217: predicting 0982 
-2024-08-29 13:07:47.123565: predicting 0983 
-2024-08-29 13:07:47.308873: predicting 0984 
-2024-08-29 13:07:47.477234: predicting 0985 
-2024-08-29 13:07:47.642778: predicting 0986 
-2024-08-29 13:07:49.060461: predicting 0987 
-2024-08-29 13:07:49.273626: predicting 0988 
-2024-08-29 13:07:49.442879: predicting 0989 
-2024-08-29 13:07:49.929402: predicting 0990 
-2024-08-29 13:07:50.103642: predicting 0991 
-2024-08-29 13:07:50.288486: predicting 0992 
-2024-08-29 13:07:50.449109: predicting 0993 
-2024-08-29 13:07:50.613438: predicting 0994 
-2024-08-29 13:07:51.134705: predicting 0995 
-2024-08-29 13:07:51.645257: predicting 0996 
-2024-08-29 13:07:51.828150: predicting 0997 
-2024-08-29 13:07:52.000436: predicting 0998 
-2024-08-29 13:07:52.182564: predicting 0999 
-2024-08-29 13:07:53.143023: predicting 1000 
-2024-08-29 13:07:53.685577: predicting 10001 
-2024-08-29 13:07:54.202361: predicting 10002 
-2024-08-29 13:07:54.730145: predicting 10003 
-2024-08-29 13:07:55.241377: predicting 10004 
-2024-08-29 13:07:56.223945: predicting 10005 
-2024-08-29 13:07:56.747341: predicting 10006 
-2024-08-29 13:07:57.278405: predicting 10007 
-2024-08-29 13:07:57.796431: predicting 10008 
-2024-08-29 13:07:58.779882: predicting 10009 
-2024-08-29 13:07:59.752344: predicting 1001 
-2024-08-29 13:07:59.945889: predicting 10010 
-2024-08-29 13:08:00.907593: predicting 10011 
-2024-08-29 13:08:01.897180: predicting 10012 
-2024-08-29 13:08:02.421274: predicting 10013 
-2024-08-29 13:08:03.403538: predicting 10014 
-2024-08-29 13:08:03.926218: predicting 10015 
-2024-08-29 13:08:04.456618: predicting 10017 
-2024-08-29 13:08:04.973296: predicting 10018 
-2024-08-29 13:08:05.499056: predicting 10019 
-2024-08-29 13:08:06.015375: predicting 1002 
-2024-08-29 13:08:06.198942: predicting 10020 
-2024-08-29 13:08:06.707047: predicting 10021 
-2024-08-29 13:08:07.232272: predicting 10022 
-2024-08-29 13:08:07.749432: predicting 10023 
-2024-08-29 13:08:08.280503: predicting 10024 
-2024-08-29 13:08:08.795791: predicting 10025 
-2024-08-29 13:08:09.297382: predicting 10027 
-2024-08-29 13:08:09.828913: predicting 10028 
-2024-08-29 13:08:10.345287: predicting 1003 
-2024-08-29 13:08:10.512627: predicting 10030 
-2024-08-29 13:08:10.995192: predicting 10031 
-2024-08-29 13:08:11.508080: predicting 10032 
-2024-08-29 13:08:12.008193: predicting 10033 
-2024-08-29 13:08:12.526917: predicting 10034 
-2024-08-29 13:08:13.037313: predicting 10035 
-2024-08-29 13:08:13.563357: predicting 10036 
-2024-08-29 13:08:14.081724: predicting 10037 
-2024-08-29 13:08:14.616970: predicting 10038 
-2024-08-29 13:08:15.127772: predicting 10039 
-2024-08-29 13:08:15.627848: predicting 1004 
-2024-08-29 13:08:15.794965: predicting 10041 
-2024-08-29 13:08:16.277941: predicting 10042 
-2024-08-29 13:08:16.804822: predicting 10043 
-2024-08-29 13:08:17.321213: predicting 10045 
-2024-08-29 13:08:17.849330: predicting 10046 
-2024-08-29 13:08:18.360341: predicting 10047 
-2024-08-29 13:08:19.337652: predicting 10048 
-2024-08-29 13:08:19.862297: predicting 10049 
-2024-08-29 13:08:20.390148: predicting 1005 
-2024-08-29 13:08:20.817694: predicting 10050 
-2024-08-29 13:08:21.359942: predicting 10051 
-2024-08-29 13:08:21.882323: predicting 10052 
-2024-08-29 13:08:22.392733: predicting 10053 
-2024-08-29 13:08:22.915728: predicting 10054 
-2024-08-29 13:08:23.426747: predicting 10055 
-2024-08-29 13:08:23.951818: predicting 10056 
-2024-08-29 13:08:24.467240: predicting 10057 
-2024-08-29 13:08:24.992810: predicting 10058 
-2024-08-29 13:08:25.508198: predicting 1006 
-2024-08-29 13:08:25.930498: predicting 10060 
-2024-08-29 13:08:26.484995: predicting 10061 
-2024-08-29 13:08:26.991584: predicting 10062 
-2024-08-29 13:08:27.493456: predicting 10063 
-2024-08-29 13:08:28.023889: predicting 10064 
-2024-08-29 13:08:28.541932: predicting 10065 
-2024-08-29 13:08:29.072069: predicting 10066 
-2024-08-29 13:08:29.588488: predicting 10067 
-2024-08-29 13:08:30.119360: predicting 10068 
-2024-08-29 13:08:30.634948: predicting 10069 
-2024-08-29 13:08:31.164102: predicting 1007 
-2024-08-29 13:08:31.334732: predicting 10070 
-2024-08-29 13:08:31.841402: predicting 10071 
-2024-08-29 13:08:32.364715: predicting 10072 
-2024-08-29 13:08:32.875047: predicting 10073 
-2024-08-29 13:08:33.399776: predicting 10075 
-2024-08-29 13:08:33.914412: predicting 1008 
-2024-08-29 13:08:35.349839: predicting 10080 
-2024-08-29 13:08:35.901366: predicting 10081 
-2024-08-29 13:08:36.413558: predicting 10082 
-2024-08-29 13:08:36.914017: predicting 10083 
-2024-08-29 13:08:37.436940: predicting 10084 
-2024-08-29 13:08:37.947698: predicting 10085 
-2024-08-29 13:08:38.478167: predicting 10086 
-2024-08-29 13:08:38.996100: predicting 10087 
-2024-08-29 13:08:39.977670: predicting 10088 
-2024-08-29 13:08:40.500668: predicting 10089 
-2024-08-29 13:08:41.030723: predicting 1009 
-2024-08-29 13:08:41.437004: predicting 10090 
-2024-08-29 13:08:41.963434: predicting 10092 
-2024-08-29 13:08:42.465167: predicting 10093 
-2024-08-29 13:08:42.996626: predicting 10094 
-2024-08-29 13:08:43.512197: predicting 10095 
-2024-08-29 13:08:44.040348: predicting 10096 
-2024-08-29 13:08:44.553766: predicting 10097 
-2024-08-29 13:08:45.065713: predicting 10099 
-2024-08-29 13:08:45.566898: predicting 1010 
-2024-08-29 13:08:45.993912: predicting 10100 
-2024-08-29 13:08:46.528321: predicting 10101 
-2024-08-29 13:08:47.058130: predicting 10102 
-2024-08-29 13:08:47.573499: predicting 10103 
-2024-08-29 13:08:48.098745: predicting 10104 
-2024-08-29 13:08:48.615696: predicting 10105 
-2024-08-29 13:08:49.567156: predicting 10106 
-2024-08-29 13:08:50.556370: predicting 10107 
-2024-08-29 13:08:51.079302: predicting 10108 
-2024-08-29 13:08:51.622099: predicting 10109 
-2024-08-29 13:08:52.133051: predicting 1011 
-2024-08-29 13:08:52.308306: predicting 10110 
-2024-08-29 13:08:52.814918: predicting 10111 
-2024-08-29 13:08:53.349306: predicting 10112 
-2024-08-29 13:08:53.860193: predicting 10113 
-2024-08-29 13:08:54.390759: predicting 10114 
-2024-08-29 13:08:54.908388: predicting 10115 
-2024-08-29 13:08:55.886877: predicting 10116 
-2024-08-29 13:08:56.410022: predicting 10117 
-2024-08-29 13:08:57.391960: predicting 10118 
-2024-08-29 13:08:57.913466: predicting 10119 
-2024-08-29 13:08:58.444249: predicting 1012 
-2024-08-29 13:08:58.620881: predicting 10120 
-2024-08-29 13:08:59.089879: predicting 10121 
-2024-08-29 13:08:59.620661: predicting 10122 
-2024-08-29 13:09:00.136452: predicting 10123 
-2024-08-29 13:09:01.118956: predicting 10124 
-2024-08-29 13:09:01.641319: predicting 10125 
-2024-08-29 13:09:02.171429: predicting 10126 
-2024-08-29 13:09:02.687476: predicting 10127 
-2024-08-29 13:09:03.234861: predicting 10128 
-2024-08-29 13:09:03.767305: predicting 10129 
-2024-08-29 13:09:04.281214: predicting 1013 
-2024-08-29 13:09:04.485902: predicting 10130 
-2024-08-29 13:09:05.457020: predicting 10131 
-2024-08-29 13:09:06.012823: predicting 10132 
-2024-08-29 13:09:06.546040: predicting 10133 
-2024-08-29 13:09:07.093879: predicting 10134 
-2024-08-29 13:09:07.625148: predicting 10135 
-2024-08-29 13:09:08.173634: predicting 10136 
-2024-08-29 13:09:08.708535: predicting 10137 
-2024-08-29 13:09:09.256171: predicting 10138 
-2024-08-29 13:09:09.782299: predicting 10139 
-2024-08-29 13:09:10.317032: predicting 1014 
-2024-08-29 13:09:11.776588: predicting 10140 
-2024-08-29 13:09:12.353604: predicting 10141 
-2024-08-29 13:09:12.898611: predicting 10142 
-2024-08-29 13:09:13.422954: predicting 10143 
-2024-08-29 13:09:13.970498: predicting 10144 
-2024-08-29 13:09:14.952590: predicting 10145 
-2024-08-29 13:09:15.505754: predicting 10146 
-2024-08-29 13:09:16.030537: predicting 10147 
-2024-08-29 13:09:16.574320: predicting 10148 
-2024-08-29 13:09:17.100217: predicting 10149 
-2024-08-29 13:09:17.647187: predicting 10150 
-2024-08-29 13:09:18.172558: predicting 10151 
-2024-08-29 13:09:18.703003: predicting 10152 
-2024-08-29 13:09:19.217659: predicting 10154 
-2024-08-29 13:09:19.765462: predicting 10155 
-2024-08-29 13:09:20.298995: predicting 10156 
-2024-08-29 13:09:20.839838: predicting 10157 
-2024-08-29 13:09:21.374056: predicting 10158 
-2024-08-29 13:09:21.923581: predicting 10159 
-2024-08-29 13:09:22.455476: predicting 10160 
-2024-08-29 13:09:22.996141: predicting 10161 
-2024-08-29 13:09:23.530612: predicting 10162 
-2024-08-29 13:09:24.078406: predicting 10163 
-2024-08-29 13:09:24.611030: predicting 10164 
-2024-08-29 13:09:25.149109: predicting 10165 
-2024-08-29 13:09:26.131131: predicting 10166 
-2024-08-29 13:09:26.685826: predicting 10167 
-2024-08-29 13:09:27.219020: predicting 10168 
-2024-08-29 13:09:27.767822: predicting 10169 
-2024-08-29 13:09:28.299587: predicting 10171 
-2024-08-29 13:09:28.846633: predicting 10172 
-2024-08-29 13:09:29.379202: predicting 10173 
-2024-08-29 13:09:29.917500: predicting 10174 
-2024-08-29 13:09:30.444577: predicting 10175 
-2024-08-29 13:09:30.992547: predicting 10176 
-2024-08-29 13:09:31.509094: predicting 10178 
-2024-08-29 13:09:32.037627: predicting 10180 
-2024-08-29 13:09:32.547588: predicting 10181 
-2024-08-29 13:09:33.048542: predicting 10182 
-2024-08-29 13:09:33.574391: predicting 10184 
-2024-08-29 13:09:34.540335: predicting 10185 
-2024-08-29 13:09:35.074677: predicting 10186 
-2024-08-29 13:09:35.584893: predicting 10187 
-2024-08-29 13:09:36.112049: predicting 10188 
-2024-08-29 13:09:36.622225: predicting 10189 
-2024-08-29 13:09:37.166320: predicting 10190 
-2024-08-29 13:09:37.692996: predicting 10191 
-2024-08-29 13:09:38.242608: predicting 10192 
-2024-08-29 13:09:38.776409: predicting 10194 
-2024-08-29 13:09:39.324863: predicting 10195 
-2024-08-29 13:09:39.857007: predicting 10196 
-2024-08-29 13:09:40.370208: predicting 10198 
-2024-08-29 13:09:40.907502: predicting 10199 
-2024-08-29 13:09:41.433810: predicting 10200 
-2024-08-29 13:09:42.434175: predicting 10201 
-2024-08-29 13:09:42.977299: predicting 10202 
-2024-08-29 13:09:43.513795: predicting 10203 
-2024-08-29 13:09:44.046348: predicting 10204 
-2024-08-29 13:09:44.577970: predicting 10205 
-2024-08-29 13:09:45.089071: predicting 10206 
-2024-08-29 13:09:45.617042: predicting 10207 
-2024-08-29 13:09:46.126729: predicting 10208 
-2024-08-29 13:09:46.657835: predicting 10210 
-2024-08-29 13:09:47.176050: predicting 1466294_ExPETRA_001 
-2024-08-29 13:09:48.624904: predicting 1466294_ExPETRA_003 
-2024-08-29 13:09:50.132957: predicting 1466294_ExPETRA_004 
-2024-08-29 13:09:51.640989: predicting 1466294_ExPETRA_005 
-2024-08-29 13:09:53.148163: predicting 1466294_ExPETRA_006 
-2024-08-29 13:09:54.657649: predicting 1466294_ExPETRA_007 
-2024-08-29 13:09:56.164904: predicting 1466294_ExPETRA_008 
-2024-08-29 13:09:57.671525: predicting 1466294_ExPETRA_009 
-2024-08-29 13:09:59.177351: predicting 1466294_ExPETRA_010 
-2024-08-29 13:10:00.683455: predicting 1466294_ExPETRA_011 
-2024-08-29 13:10:02.189995: predicting 1466294_ExPETRA_012 
-2024-08-29 13:10:03.697705: predicting 1466294_ExPETRA_013 
-2024-08-29 13:10:05.204054: predicting 1466294_ExPETRA_014 
-2024-08-29 13:10:06.711637: predicting 1466294_ExPETRA_015 
-2024-08-29 13:10:08.218038: predicting 1466294_ExPETRA_017 
-2024-08-29 13:10:09.725602: predicting 1479228_ExPsA_001 
-2024-08-29 13:10:11.231027: predicting 1479228_ExPsA_002 
-2024-08-29 13:10:12.736487: predicting 1479228_ExPsA_003 
-2024-08-29 13:10:14.242385: predicting 1479228_ExPsA_004 
-2024-08-29 13:10:15.749827: predicting 1479228_ExPsA_005 
-2024-08-29 13:10:17.256888: predicting 1479228_ExPsA_006 
-2024-08-29 13:10:18.763722: predicting 1479228_ExPsA_007 
-2024-08-29 13:10:20.270555: predicting 1479228_ExPsA_008 
-2024-08-29 13:10:21.777185: predicting 1479228_ExPsA_009 
-2024-08-29 13:10:23.284052: predicting 1479228_ExPsA_010 
-2024-08-29 13:10:24.792584: predicting 1479228_ExPsA_011 
-2024-08-29 13:10:26.299954: predicting 1479228_ExPsA_012 
-2024-08-29 13:10:27.806735: predicting 1479228_ExPsA_013 
-2024-08-29 13:10:29.312471: predicting 1479228_ExPsA_014 
-2024-08-29 13:10:30.818687: predicting 1479228_ExPsA_015 
-2024-08-29 13:10:32.326105: predicting 1479228_ExPsA_016 
-2024-08-29 13:10:33.832115: predicting 1479228_ExPsA_017 
-2024-08-29 13:10:35.338550: predicting 1479228_ExPsA_018 
-2024-08-29 13:10:36.844493: predicting 1479228_ExPsA_019 
-2024-08-29 13:10:38.351291: predicting 1479228_ExPsA_020 
-2024-08-29 13:10:39.861828: predicting 1479228_ExPsA_021 
-2024-08-29 13:10:41.369474: predicting 1479228_ExPsA_022 
-2024-08-29 13:10:42.879330: predicting 1479228_ExPsA_023 
-2024-08-29 13:10:44.387138: predicting 1479228_ExPsA_024 
-2024-08-29 13:10:45.893870: predicting 1479228_ExPsA_025 
-2024-08-29 13:10:47.400784: predicting 1479228_ExPsA_026 
-2024-08-29 13:10:48.907617: predicting 1479228_ExPsA_027 
-2024-08-29 13:10:50.414730: predicting 1479228_ExPsA_028 
-2024-08-29 13:10:51.923299: predicting 20001 
-2024-08-29 13:10:52.489105: predicting 20002 
-2024-08-29 13:10:53.466909: predicting 20004 
-2024-08-29 13:10:53.990776: predicting 20005 
-2024-08-29 13:10:54.975242: predicting 20006 
-2024-08-29 13:10:55.499648: predicting 20007 
-2024-08-29 13:10:56.025574: predicting 20008 
-2024-08-29 13:10:56.543441: predicting 20009 
-2024-08-29 13:10:57.074298: predicting 20010 
-2024-08-29 13:10:57.590625: predicting 20011 
-2024-08-29 13:10:58.568760: predicting 20012 
-2024-08-29 13:10:59.091563: predicting 20013 
-2024-08-29 13:10:59.616302: predicting 20014 
-2024-08-29 13:11:00.132768: predicting 20015 
-2024-08-29 13:11:00.638991: predicting 20018 
-2024-08-29 13:11:01.141931: predicting 20019 
-2024-08-29 13:11:01.673044: predicting 20020 
-2024-08-29 13:11:02.188222: predicting 20022 
-2024-08-29 13:11:02.715023: predicting 20023 
-2024-08-29 13:11:03.232322: predicting 20024 
-2024-08-29 13:11:03.755080: predicting 20025 
-2024-08-29 13:11:04.266183: predicting 20026 
-2024-08-29 13:11:04.796441: predicting 20027 
-2024-08-29 13:11:05.312505: predicting 20028 
-2024-08-29 13:11:05.840514: predicting 20029 
-2024-08-29 13:11:06.350738: predicting 20030 
-2024-08-29 13:11:06.862142: predicting 20031 
-2024-08-29 13:11:07.364130: predicting 20032 
-2024-08-29 13:11:07.893313: predicting 20033 
-2024-08-29 13:11:08.419081: predicting 20034 
-2024-08-29 13:11:08.965539: predicting 20035 
-2024-08-29 13:11:09.499438: predicting 20036 
-2024-08-29 13:11:10.028727: predicting 20037 
-2024-08-29 13:11:10.541181: predicting 20038 
-2024-08-29 13:11:11.079247: predicting 20039 
-2024-08-29 13:11:11.605807: predicting 20043 
-2024-08-29 13:11:12.603785: predicting 20044 
-2024-08-29 13:11:13.145799: predicting 20045 
-2024-08-29 13:11:13.691596: predicting 20046 
-2024-08-29 13:11:14.223669: predicting 20047 
-2024-08-29 13:11:14.763890: predicting 20048 
-2024-08-29 13:11:15.295806: predicting 20049 
-2024-08-29 13:11:15.824837: predicting 20050 
-2024-08-29 13:11:16.338550: predicting 20051 
-2024-08-29 13:11:17.329538: predicting 20052 
-2024-08-29 13:11:17.872100: predicting 20053 
-2024-08-29 13:11:18.417526: predicting 20054 
-2024-08-29 13:11:18.951006: predicting 20055 
-2024-08-29 13:11:19.465131: predicting 20056 
-2024-08-29 13:11:20.463872: predicting 20057 
-2024-08-29 13:11:21.006234: predicting 20058 
-2024-08-29 13:11:22.006079: predicting 20059 
-2024-08-29 13:11:22.547501: predicting 20061 
-2024-08-29 13:11:23.094716: predicting 20062 
-2024-08-29 13:11:23.628095: predicting 20063 
-2024-08-29 13:11:24.175905: predicting 20064 
-2024-08-29 13:11:24.709579: predicting 20065 
-2024-08-29 13:11:25.257484: predicting 20066 
-2024-08-29 13:11:25.791100: predicting 20067 
-2024-08-29 13:11:26.337902: predicting 20068 
-2024-08-29 13:11:26.870945: predicting 20070 
-2024-08-29 13:11:27.418867: predicting 20071 
-2024-08-29 13:11:27.949874: predicting 20072 
-2024-08-29 13:11:28.478546: predicting 20074 
-2024-08-29 13:11:28.991620: predicting 20075 
-2024-08-29 13:11:29.529949: predicting 20077 
-2024-08-29 13:11:30.064610: predicting 20079 
-2024-08-29 13:11:30.610376: predicting 20081 
-2024-08-29 13:11:31.133613: predicting 20082 
-2024-08-29 13:11:31.672295: predicting 20083 
-2024-08-29 13:11:32.648171: predicting 20084 
-2024-08-29 13:11:33.219033: predicting 20085 
-2024-08-29 13:11:33.752296: predicting 20087 
-2024-08-29 13:11:34.297400: predicting 20088 
-2024-08-29 13:11:34.823558: predicting 20089 
-2024-08-29 13:11:35.369702: predicting 20090 
-2024-08-29 13:11:35.899261: predicting 20091 
-2024-08-29 13:11:36.413764: predicting 20092 
-2024-08-29 13:11:36.955620: predicting 20093 
-2024-08-29 13:11:37.487862: predicting 20094 
-2024-08-29 13:11:38.034579: predicting 20095 
-2024-08-29 13:11:38.568341: predicting 20096 
-2024-08-29 13:11:39.566452: predicting 20097 
-2024-08-29 13:11:40.108355: predicting 20098 
-2024-08-29 13:11:40.645866: predicting 20099 
-2024-08-29 13:11:41.170837: predicting 20100 
-2024-08-29 13:11:41.718084: predicting 20101 
-2024-08-29 13:11:42.699022: predicting 20102 
-2024-08-29 13:11:43.251947: predicting 20103 
-2024-08-29 13:11:43.777685: predicting 20104 
-2024-08-29 13:11:44.325011: predicting 20106 
-2024-08-29 13:11:44.857726: predicting 20108 
-2024-08-29 13:11:45.857199: predicting 20109 
-2024-08-29 13:11:46.397684: predicting 20110 
-2024-08-29 13:11:47.361360: predicting 20111 
-2024-08-29 13:11:47.933492: predicting 20112 
-2024-08-29 13:11:48.466132: predicting 20113 
-2024-08-29 13:11:49.006562: predicting 20114 
-2024-08-29 13:11:49.541479: predicting 20115 
-2024-08-29 13:11:50.088167: predicting 20116 
-2024-08-29 13:11:50.622601: predicting 20117 
-2024-08-29 13:11:51.167490: predicting 20118 
-2024-08-29 13:11:51.692142: predicting 20119 
-2024-08-29 13:11:52.230457: predicting 20121 
-2024-08-29 13:11:52.756611: predicting 20122 
-2024-08-29 13:11:53.293674: predicting 20123 
-2024-08-29 13:11:53.819572: predicting 20124 
-2024-08-29 13:11:54.359517: predicting 20125 
-2024-08-29 13:11:54.894954: predicting 20126 
-2024-08-29 13:11:55.443196: predicting 20127 
-2024-08-29 13:11:55.977885: predicting 20128 
-2024-08-29 13:11:56.525354: predicting 20129 
-2024-08-29 13:11:57.509755: predicting 20131 
-2024-08-29 13:11:58.065472: predicting 20132 
-2024-08-29 13:11:58.598535: predicting 20133 
-2024-08-29 13:11:59.136364: predicting 20134 
-2024-08-29 13:11:59.662945: predicting 20135 
-2024-08-29 13:12:00.207840: predicting 20136 
-2024-08-29 13:12:00.733694: predicting 20137 
-2024-08-29 13:12:01.279968: predicting 20138 
-2024-08-29 13:12:02.255442: predicting 20139 
-2024-08-29 13:12:02.810414: predicting 20140 
-2024-08-29 13:12:03.336395: predicting 20141 
-2024-08-29 13:12:03.874042: predicting 20143 
-2024-08-29 13:12:04.401040: predicting 20144 
-2024-08-29 13:12:04.938373: predicting 20145 
-2024-08-29 13:12:05.464084: predicting 20146 
-2024-08-29 13:12:06.003484: predicting 20147 
-2024-08-29 13:12:06.530188: predicting 20148 
-2024-08-29 13:12:07.077985: predicting 20149 
-2024-08-29 13:12:07.610777: predicting 20150 
-2024-08-29 13:12:08.158038: predicting 20151 
-2024-08-29 13:12:08.691308: predicting 20152 
-2024-08-29 13:12:09.682258: predicting 20153 
-2024-08-29 13:12:10.225791: predicting 20154 
-2024-08-29 13:12:10.773689: predicting 20155 
-2024-08-29 13:12:11.306154: predicting 20156 
-2024-08-29 13:12:11.844749: predicting 20157 
-2024-08-29 13:12:12.378103: predicting 20158 
-2024-08-29 13:12:12.925321: predicting 20159 
-2024-08-29 13:12:13.459388: predicting 20160 
-2024-08-29 13:12:14.005745: predicting 20161 
-2024-08-29 13:12:14.531236: predicting 20162 
-2024-08-29 13:12:15.079121: predicting 20163 
-2024-08-29 13:12:15.614378: predicting 20164 
-2024-08-29 13:12:16.162101: predicting 20165 
-2024-08-29 13:12:16.694410: predicting 20166 
-2024-08-29 13:12:17.239083: predicting 20167 
-2024-08-29 13:12:17.766479: predicting 20168 
-2024-08-29 13:12:18.313916: predicting 20169 
-2024-08-29 13:12:18.847772: predicting 20170 
-2024-08-29 13:12:19.393129: predicting 20171 
-2024-08-29 13:12:19.916450: predicting 20172 
-2024-08-29 13:12:20.430534: predicting 20173 
-2024-08-29 13:12:20.968788: predicting 20174 
-2024-08-29 13:12:21.495573: predicting 20175 
-2024-08-29 13:12:22.044386: predicting 20176 
-2024-08-29 13:12:22.577601: predicting 20177 
-2024-08-29 13:12:23.577776: predicting 20178 
-2024-08-29 13:12:24.119142: predicting 20179 
-2024-08-29 13:12:24.672900: predicting 20180 
-2024-08-29 13:12:25.207604: predicting 20181 
-2024-08-29 13:12:25.751256: predicting 20182 
-2024-08-29 13:12:26.276257: predicting 20183 
-2024-08-29 13:12:26.822586: predicting 20184 
-2024-08-29 13:12:27.346256: predicting 20185 
-2024-08-29 13:12:27.886683: predicting 20186 
-2024-08-29 13:12:28.421937: predicting 20187 
-2024-08-29 13:12:28.969017: predicting 20188 
-2024-08-29 13:12:29.502357: predicting 20189 
-2024-08-29 13:12:30.047622: predicting 20190 
-2024-08-29 13:12:30.572479: predicting 20191 
-2024-08-29 13:12:31.119155: predicting 20192 
-2024-08-29 13:12:31.652546: predicting 20195 
-2024-08-29 13:12:32.165768: predicting 20196 
-2024-08-29 13:12:32.703181: predicting 20197 
-2024-08-29 13:12:33.229777: predicting 20198 
-2024-08-29 13:12:33.746854: predicting 20199 
-2024-08-29 13:12:34.296473: predicting 20200 
-2024-08-29 13:12:34.831245: predicting 20201 
-2024-08-29 13:12:35.379169: predicting 20202 
-2024-08-29 13:12:35.913435: predicting 20203 
-2024-08-29 13:12:36.460007: predicting 20204 
-2024-08-29 13:12:36.986886: predicting 20205 
-2024-08-29 13:12:37.536169: predicting 20206 
-2024-08-29 13:12:38.070181: predicting 20207 
-2024-08-29 13:12:38.614751: predicting 20208 
-2024-08-29 13:12:39.589722: predicting 20209 
-2024-08-29 13:12:40.147017: predicting 20210 
-2024-08-29 13:12:40.680677: predicting 20211 
-2024-08-29 13:12:41.220677: predicting 20212 
-2024-08-29 13:12:41.755474: predicting 20213 
-2024-08-29 13:12:42.304208: predicting 20214 
-2024-08-29 13:12:42.839277: predicting 20215 
-2024-08-29 13:12:43.840784: predicting 20216 
-2024-08-29 13:12:44.832394: predicting FI001DA 
-2024-08-29 13:12:45.397978: predicting FI002DGA 
-2024-08-29 13:12:46.442322: predicting FI003DT 
-2024-08-29 13:12:47.654951: predicting FI004DE 
-2024-08-29 13:12:48.700712: predicting FI005CO 
-2024-08-29 13:12:49.312699: predicting FI006CM 
-2024-08-29 13:12:50.550411: predicting FI007BL 
-2024-08-29 13:12:51.104901: predicting FI008BD 
-2024-08-29 13:12:51.676252: predicting FI009TO 
-2024-08-29 13:12:52.203416: predicting FI010BT 
-2024-08-29 13:12:53.486530: predicting FI011BM 
-2024-08-29 13:12:54.020444: predicting FI012CS 
-2024-08-29 13:12:54.572437: predicting FI013DGP 
-2024-08-29 13:12:55.106026: predicting FI014GB 
-2024-08-29 13:12:56.389354: predicting FI015GE 
-2024-08-29 13:12:56.923874: predicting FI016RG 
-2024-08-29 13:12:57.471846: predicting FI017RA 
-2024-08-29 13:12:58.007783: predicting FI018TS 
-2024-08-29 13:12:59.335329: predicting FI019RA 
-2024-08-29 13:12:59.870070: predicting FI020BG 
-2024-08-29 13:13:00.424321: predicting FI021AA 
-2024-08-29 13:13:00.978322: predicting FI022AL 
-2024-08-29 13:13:02.247222: predicting FI023BV 
-2024-08-29 13:13:02.774706: predicting FI024BL 
-2024-08-29 13:13:03.326917: predicting FI025DPF 
-2024-08-29 13:13:03.895261: predicting FI026CF 
-2024-08-29 13:13:04.976919: predicting FI027CV 
-2024-08-29 13:13:05.529242: predicting FI028CL 
-2024-08-29 13:13:06.062736: predicting FI029CG 
-2024-08-29 13:13:06.608648: predicting FI030CD 
-2024-08-29 13:13:07.141633: predicting FI031CI 
-2024-08-29 13:13:08.311453: predicting FI032CE 
-2024-08-29 13:13:08.856394: predicting FI033EB 
-2024-08-29 13:13:09.422452: predicting FI034FF 
-2024-08-29 13:13:09.978369: predicting FI035FC 
-2024-08-29 13:13:11.306985: predicting FI036FG 
-2024-08-29 13:13:11.863547: predicting FI037FR 
-2024-08-29 13:13:12.437423: predicting FI038FGi 
-2024-08-29 13:13:12.992322: predicting FI039GR 
-2024-08-29 13:13:14.411600: predicting FI040GV 
-2024-08-29 13:13:14.973473: predicting FI041PA 
-2024-08-29 13:13:15.550245: predicting FI042SE 
-2024-08-29 13:13:16.105575: predicting FI043GA 
-2024-08-29 13:13:17.193165: predicting FI044IS 
-2024-08-29 13:13:17.727892: predicting FI045LG 
-2024-08-29 13:13:18.280785: predicting FI046ML 
-2024-08-29 13:13:18.815861: predicting FI047MR 
-2024-08-29 13:13:20.137398: predicting FI048MI 
-2024-08-29 13:13:20.678506: predicting FI049MS 
-2024-08-29 13:13:21.707481: predicting FI050MG 
-2024-08-29 13:13:23.021990: predicting FI051NF 
-2024-08-29 13:13:23.596073: predicting FI052NVV 
-2024-08-29 13:13:24.151803: predicting FI053NM 
-2024-08-29 13:13:24.738642: predicting FI054GN 
-2024-08-29 13:13:25.980881: predicting FI055PN 
-2024-08-29 13:13:26.566993: predicting FI056OD 
-2024-08-29 13:13:27.121332: predicting FI057PR 
-2024-08-29 13:13:27.704376: predicting FI058MLu 
-2024-08-29 13:13:28.935011: predicting FI059QF 
-2024-08-29 13:13:29.499784: predicting FI060PS 
-2024-08-29 13:13:30.057057: predicting FI061SRF 
-2024-08-29 13:13:30.635291: predicting FI062SG 
-2024-08-29 13:13:31.987147: predicting FI063TI 
-2024-08-29 13:13:32.561013: predicting FI064TR 
-2024-08-29 13:13:33.115613: predicting FI065VE 
-2024-08-29 13:13:33.680333: predicting FI066VG 
-2024-08-29 13:13:34.975007: predicting FI067WW 
-2024-08-29 13:13:35.549073: predicting FI068WS 
-2024-08-29 13:13:36.105288: predicting FI069ZM 
-2024-08-29 13:13:36.650162: predicting FI070PRe 
-2024-08-29 13:13:37.850017: predicting FI071CN 
-2024-08-29 13:13:38.404326: predicting FI072SAC 
-2024-08-29 13:13:38.939974: predicting FI073MM 
-2024-08-29 13:13:39.513149: predicting FI074MA 
-2024-08-29 13:13:40.795425: predicting FI075RG 
-2024-08-29 13:13:41.360897: predicting FI076RN 
-2024-08-29 13:13:41.915320: predicting FI077BS 
-2024-08-29 13:13:42.479354: predicting FI078SG 
-2024-08-29 13:13:43.681935: predicting FI079CM 
-2024-08-29 13:13:44.247557: predicting FI080FP 
-2024-08-29 13:13:44.782112: predicting FI081EA 
-2024-08-29 13:13:45.342224: predicting FI082MA 
-2024-08-29 13:13:46.688128: predicting FI083CS 
-2024-08-29 13:13:47.242440: predicting FI084GM 
-2024-08-29 13:13:47.777530: predicting FI085GN 
-2024-08-29 13:13:48.333632: predicting FI086VP 
-2024-08-29 13:13:49.541138: predicting FI087GF 
-2024-08-29 13:13:50.091567: predicting FI088BV 
-2024-08-29 13:13:50.620378: predicting FI089AD 
-2024-08-29 13:13:51.181382: predicting FI090BR 
-2024-08-29 13:13:52.457693: predicting FI091PA 
-2024-08-29 13:13:53.011429: predicting FI092BM 
-2024-08-29 13:13:53.547251: predicting FI093TO 
-2024-08-29 13:13:54.103429: predicting FI094PP 
-2024-08-29 13:13:55.403567: predicting FI095VG 
-2024-08-29 13:13:55.959343: predicting FI096NB 
-2024-08-29 13:13:56.502998: predicting FI097SL 
-2024-08-29 13:13:57.050148: predicting FI098BP 
-2024-08-29 13:13:58.333660: predicting FI099ZE 
-2024-08-29 13:13:58.904191: predicting FI100MF 
-2024-08-29 13:13:59.462527: predicting FI101SA 
-2024-08-29 13:14:00.016803: predicting FI102CA 
-2024-08-29 13:14:01.260685: predicting FI103PM 
-2024-08-29 13:14:01.814332: predicting FI104BA 
-2024-08-29 13:14:02.369392: predicting FI105FD 
-2024-08-29 13:14:02.923573: predicting FI106MA 
-2024-08-29 13:14:04.270202: predicting FI107CM 
-2024-08-29 13:14:04.830922: predicting FI108AG 
-2024-08-29 13:14:05.365677: predicting FI109BP 
-2024-08-29 13:14:05.919789: predicting FI110BV 
-2024-08-29 13:14:07.683614: predicting FI111CA 
-2024-08-29 13:14:08.287431: predicting FI112CM 
-2024-08-29 13:14:08.843556: predicting FI113CL 
-2024-08-29 13:14:10.122046: predicting FI114OE 
-2024-08-29 13:14:10.681904: predicting FI115FG 
-2024-08-29 13:14:11.216895: predicting FI116GI 
-2024-08-29 13:14:11.761055: predicting FI117LL 
-2024-08-29 13:14:13.075740: predicting FI118MV 
-2024-08-29 13:14:13.629154: predicting FI119MM 
-2024-08-29 13:14:14.164495: predicting FI120MA 
-2024-08-29 13:14:14.724672: predicting FI121PM 
-2024-08-29 13:14:15.993514: predicting FI122PG 
-2024-08-29 13:14:16.570519: predicting FI123RR 
-2024-08-29 13:14:17.120678: predicting FI124RM 
-2024-08-29 13:14:17.684410: predicting FI125TN 
-2024-08-29 13:14:19.033745: predicting FI126BC 
-2024-08-29 13:14:19.599843: predicting FI127BG 
-2024-08-29 13:14:20.153170: predicting FI128CP 
-2024-08-29 13:14:20.689020: predicting FI129CD 
-2024-08-29 13:14:22.028296: predicting FI130FR 
-2024-08-29 13:14:22.563354: predicting FI131FD 
-2024-08-29 13:14:23.117710: predicting FI132MR 
-2024-08-29 13:14:23.683791: predicting FI133PM 
-2024-08-29 13:14:24.980981: predicting FI134RR 
-2024-08-29 13:14:25.540416: predicting FI135RC 
-2024-08-29 13:14:26.074020: predicting FI136BL 
-2024-08-29 13:14:26.627795: predicting FI137BA 
-2024-08-29 13:14:27.935454: predicting FI138BS 
-2024-08-29 13:14:28.494818: predicting FI139CP 
-2024-08-29 13:14:29.067683: predicting FI140CS 
-2024-08-29 13:14:29.672164: predicting FI141CR 
-2024-08-29 13:14:30.953577: predicting FI142CM 
-2024-08-29 13:14:31.507136: predicting FI143CC 
-2024-08-29 13:14:32.041546: predicting FI144CM 
-2024-08-29 13:14:32.602402: predicting FI145CR 
-2024-08-29 13:14:33.929691: predicting FI146RR 
-2024-08-29 13:14:34.485922: predicting FI147PP 
-2024-08-29 13:14:35.026443: predicting FI148CM 
-2024-08-29 13:14:35.576821: predicting FI149PSA 
-2024-08-29 13:14:36.872883: predicting FI150CS 
-2024-08-29 13:14:37.441534: predicting FI151DPI 
-2024-08-29 13:14:37.988344: predicting FI152FA 
-2024-08-29 13:14:38.545822: predicting FI153PP 
-2024-08-29 13:14:39.806142: predicting FI154OA 
-2024-08-29 13:14:40.369858: predicting FI155NR 
-2024-08-29 13:14:40.912568: predicting FI156FL 
-2024-08-29 13:14:41.465816: predicting FI157NF 
-2024-08-29 13:14:42.781053: predicting FI158BM 
-2024-08-29 13:14:43.326835: predicting FI159DG 
-2024-08-29 13:14:43.843907: predicting FI160EBP 
-2024-08-29 13:14:44.399747: predicting FI161FF 
-2024-08-29 13:14:45.743521: predicting FI162MA 
-2024-08-29 13:14:46.288686: predicting FI163PA 
-2024-08-29 13:14:46.817196: predicting FI164PS 
-2024-08-29 13:14:47.337388: predicting FI165PA 
-2024-08-29 13:14:48.425440: predicting FI166TR 
-2024-08-29 13:14:48.961149: predicting FI167CF 
-2024-08-29 13:14:49.514098: predicting FI168BM 
-2024-08-29 13:14:50.048415: predicting FI169GG 
-2024-08-29 13:14:50.602119: predicting FI170SSA 
-2024-08-29 13:14:51.571604: predicting FI171CG 
-2024-08-29 13:14:52.106737: predicting FI172MR 
-2024-08-29 13:14:52.634500: predicting FI173PC 
-2024-08-29 13:14:53.182703: predicting FI174LP 
-2024-08-29 13:14:53.718282: predicting FI175MGV 
-2024-08-29 13:14:54.794810: predicting FI176VG 
-2024-08-29 13:14:55.330780: predicting FI177CI 
-2024-08-29 13:14:55.872894: predicting FI178TG 
-2024-08-29 13:14:56.381643: predicting FI179DCF 
-2024-08-29 13:14:57.693773: predicting FI180RL 
-2024-08-29 13:14:58.228270: predicting FI181BG 
-2024-08-29 13:14:58.774276: predicting FI182RMG 
-2024-08-29 13:14:59.290521: predicting FI183GM 
-2024-08-29 13:14:59.820894: predicting FI184BC 
-2024-08-29 13:15:00.337405: predicting FI185RR 
-2024-08-29 13:15:00.867630: predicting FI186MA 
-2024-08-29 13:15:01.385566: predicting FI187MM 
-2024-08-29 13:15:01.937310: predicting FI188NR 
-2024-08-29 13:15:02.453905: predicting FI189VA 
-2024-08-29 13:15:03.698974: predicting FI190ST 
-2024-08-29 13:15:04.241029: predicting FI191RP 
-2024-08-29 13:15:04.787532: predicting FI192PG 
-2024-08-29 13:15:05.305454: predicting FI193MD 
-2024-08-29 13:15:05.854934: predicting FI194KA 
-2024-08-29 13:15:06.372671: predicting FI195GP 
-2024-08-29 13:15:06.903573: predicting FI196FL 
-2024-08-29 13:15:07.421380: predicting FI197DG 
-2024-08-29 13:15:07.952433: predicting FI198CG 
-2024-08-29 13:15:08.471041: predicting FI199BL 
-2024-08-29 13:15:09.000868: predicting GUC_Sub01 
-2024-08-29 13:15:10.712804: predicting GUC_Sub02 
-2024-08-29 13:15:12.419457: predicting GUC_Sub03 
-2024-08-29 13:15:14.073899: predicting GUC_Sub04 
-2024-08-29 13:15:15.728062: predicting GUC_Sub06 
-2024-08-29 13:15:17.387314: predicting GUC_Sub07 
-2024-08-29 13:15:19.752742: predicting GUC_Sub08 
-2024-08-29 13:15:21.453813: predicting GUC_Sub09 
-2024-08-29 13:15:23.103567: predicting GUC_Sub11 
-2024-08-29 13:15:24.754195: predicting HN_Sub001 
-2024-08-29 13:15:26.358000: predicting HN_Sub002 
-2024-08-29 13:15:27.864173: predicting HN_Sub003 
-2024-08-29 13:15:29.370602: predicting HN_Sub004 
-2024-08-29 13:15:30.876361: predicting HN_Sub005 
-2024-08-29 13:15:32.381803: predicting sub001 
-2024-08-29 13:15:33.886939: predicting sub002 
-2024-08-29 13:15:35.391953: predicting sub003 
-2024-08-29 13:15:36.897348: predicting sub004 
-2024-08-29 13:15:38.406159: predicting sub005 
-2024-08-29 13:15:39.913256: predicting sub006 
-2024-08-29 13:15:41.422299: predicting sub007 
-2024-08-29 13:15:42.931398: predicting sub008 
-2024-08-29 13:15:44.439111: predicting sub009 
-2024-08-29 13:15:45.963612: predicting sub010 
-2024-08-29 13:15:47.472620: predicting sub011 
-2024-08-29 13:15:48.980559: predicting sub012 
-2024-08-29 13:15:50.490023: predicting sub013 
-2024-08-29 13:15:51.997939: predicting sub015 
-2024-08-29 13:15:53.504694: predicting sub017 
-2024-08-29 13:15:55.012194: predicting sub018 
-2024-08-29 13:15:56.520315: predicting sub020 
-2024-08-29 13:15:58.027919: predicting sub021 
-2024-08-29 13:15:59.536033: predicting sub022 
-2024-08-29 13:16:01.043762: predicting sub023 
-2024-08-29 13:16:02.550647: predicting sub024 
-2024-08-29 13:16:04.057812: predicting sub025 
-2024-08-29 13:16:05.565098: predicting sub026 
-2024-08-29 13:16:07.072398: predicting sub027 
-2024-08-29 13:16:08.580122: predicting sub030 
-2024-08-29 13:16:10.087677: predicting sub031 
-2024-08-29 13:16:11.595647: predicting sub032 
-2024-08-29 13:16:13.104789: predicting sub033 
-2024-08-29 13:16:14.611441: predicting sub034 
-2024-08-29 13:16:46.075586: Validation complete 
-2024-08-29 13:16:46.075698: Mean Validation Dice:  nan 
diff --git a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/plans.json b/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/plans.json
deleted file mode 100644
index 33adb4bea7cfb482fcf679bc78fb9c7cc00bc918..0000000000000000000000000000000000000000
--- a/MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/plans.json
+++ /dev/null
@@ -1,272 +0,0 @@
-{
-    "dataset_name": "Dataset145_Fast_organs",
-    "plans_name": "nnUNetPlans",
-    "original_median_spacing_after_transp": [
-        6.0,
-        6.0,
-        6.0
-    ],
-    "original_median_shape_after_transp": [
-        162,
-        80,
-        80
-    ],
-    "image_reader_writer": "SimpleITKIO",
-    "transpose_forward": [
-        0,
-        1,
-        2
-    ],
-    "transpose_backward": [
-        0,
-        1,
-        2
-    ],
-    "configurations": {
-        "2d": {
-            "data_identifier": "nnUNetPlans_2d",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 492,
-            "patch_size": [
-                80,
-                80
-            ],
-            "median_image_size_in_voxels": [
-                80.0,
-                80.0
-            ],
-            "spacing": [
-                6.0,
-                6.0
-            ],
-            "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
-            ],
-            "n_conv_per_stage_decoder": [
-                2,
-                2,
-                2,
-                2
-            ],
-            "num_pool_per_axis": [
-                4,
-                4
-            ],
-            "pool_op_kernel_sizes": [
-                [
-                    1,
-                    1
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ]
-            ],
-            "conv_kernel_sizes": [
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ]
-            ],
-            "unet_max_num_features": 512,
-            "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": null
-            },
-            "resampling_fn_seg_kwargs": {
-                "is_seg": true,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
-            "resampling_fn_probabilities_kwargs": {
-                "is_seg": false,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "batch_dice": true
-        },
-        "3d_fullres": {
-            "data_identifier": "nnUNetPlans_3d_fullres",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 4,
-            "patch_size": [
-                176,
-                80,
-                80
-            ],
-            "median_image_size_in_voxels": [
-                162.0,
-                80.0,
-                80.0
-            ],
-            "spacing": [
-                6.0,
-                6.0,
-                6.0
-            ],
-            "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
-            ],
-            "n_conv_per_stage_decoder": [
-                2,
-                2,
-                2,
-                2
-            ],
-            "num_pool_per_axis": [
-                4,
-                4,
-                4
-            ],
-            "pool_op_kernel_sizes": [
-                [
-                    1,
-                    1,
-                    1
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ]
-            ],
-            "conv_kernel_sizes": [
-                [
-                    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": null
-            },
-            "resampling_fn_seg_kwargs": {
-                "is_seg": true,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
-            "resampling_fn_probabilities_kwargs": {
-                "is_seg": false,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "batch_dice": false
-        }
-    },
-    "experiment_planner_used": "ExperimentPlanner",
-    "label_manager": "LabelManager",
-    "foreground_intensity_properties_per_channel": {
-        "0": {
-            "max": 2981.83154296875,
-            "mean": -306.5704650878906,
-            "median": -7.578986644744873,
-            "min": -1138.905029296875,
-            "percentile_00_5": -952.3096923828125,
-            "percentile_99_5": 193.60693359375,
-            "std": 407.40484619140625
-        }
-    }
-}
\ No newline at end of file