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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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+{
+    "name": "Prostate158",
+    "description": "Prostate cancer segmentation dataset",
+    "channel_names": {
+        "0": "T2",
+        "1": "ADC",
+        "2": "DFI"
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+        "prostate_outer": 2,
+        "tumor": 3
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+    "numTraining": 139,
+    "numTest": 19,
+    "file_ending": ".nii.gz"
+}

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json

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+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fe17c7ca020>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fe17c7c8c40>",
+    "dataloader_train.num_processes": "4",
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+    "dataloader_val.num_processes": "2",
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+    "dataset_json": "{'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
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+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A10G",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fe17d646560>",
+    "hostname": "s-osbm-jupyter-f0b83-8689bbb555-5t6kn",
+    "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 0x7fe17d6464a0>",
+    "local_rank": "0",
+    "log_file": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2023_7_24_00_01_52.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fe17d646410>",
+    "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 0x7fe17d646470>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[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]], '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': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
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+    "num_input_channels": "3",
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+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: 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)",
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+    "output_folder_base": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[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]], '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': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}",
+    "preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset001_Prostate158/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset001_Prostate158",
+    "save_every": "50",
+    "torch_version": "2.0.1+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2023_7_24_00_01_52.txt

@@ -0,0 +1,1194 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}} 
+ 
+2023-07-24 00:01:53.902022: unpacking dataset... 
+2023-07-24 00:02:07.139120: unpacking done... 
+2023-07-24 00:02:07.196625: do_dummy_2d_data_aug: True 
+2023-07-24 00:02:07.197410: Creating new 5-fold cross-validation split... 
+2023-07-24 00:02:07.198499: Desired fold for training: 0 
+2023-07-24 00:02:07.198552: This split has 111 training and 28 validation cases. 
+2023-07-24 00:02:14.074938: Unable to plot network architecture: 
+2023-07-24 00:02:14.075182: module 'torch.onnx' has no attribute '_optimize_trace' 
+2023-07-24 00:02:14.123964:  
+2023-07-24 00:02:14.124048: Epoch 0 
+2023-07-24 00:02:14.124163: Current learning rate: 0.01 
+2023-07-24 00:06:16.568555: train_loss -0.0413 
+2023-07-24 00:06:16.568763: val_loss -0.1561 
+2023-07-24 00:06:16.568854: Pseudo dice [0.6633, 0.3715, 0.0] 
+2023-07-24 00:06:16.568946: Epoch time: 242.45 s 
+2023-07-24 00:06:16.569015: Yayy! New best EMA pseudo Dice: 0.3449 
+2023-07-24 00:06:19.487634:  
+2023-07-24 00:06:19.487758: Epoch 1 
+2023-07-24 00:06:19.487858: Current learning rate: 0.00999 
+2023-07-24 00:09:51.638274: train_loss -0.2546 
+2023-07-24 00:09:51.638539: val_loss -0.3228 
+2023-07-24 00:09:51.638626: Pseudo dice [0.7605, 0.5865, 0.0] 
+2023-07-24 00:09:51.638798: Epoch time: 212.15 s 
+2023-07-24 00:09:51.638938: Yayy! New best EMA pseudo Dice: 0.3553 
+2023-07-24 00:09:54.319529:  
+2023-07-24 00:09:54.319663: Epoch 2 
+2023-07-24 00:09:54.319769: Current learning rate: 0.00998 
+2023-07-24 00:13:25.194643: train_loss -0.324 
+2023-07-24 00:13:25.194925: val_loss -0.3233 
+2023-07-24 00:13:25.195090: Pseudo dice [0.7874, 0.5763, 0.0] 
+2023-07-24 00:13:25.195179: Epoch time: 210.88 s 
+2023-07-24 00:13:25.195318: Yayy! New best EMA pseudo Dice: 0.3652 
+2023-07-24 00:13:27.399833:  
+2023-07-24 00:13:27.399949: Epoch 3 
+2023-07-24 00:13:27.400062: Current learning rate: 0.00997 
+2023-07-24 00:17:09.770445: train_loss -0.3629 
+2023-07-24 00:17:09.770754: val_loss -0.3386 
+2023-07-24 00:17:09.771257: Pseudo dice [0.7865, 0.6213, 0.0] 
+2023-07-24 00:17:09.771582: Epoch time: 222.37 s 
+2023-07-24 00:17:09.771953: Yayy! New best EMA pseudo Dice: 0.3756 
+2023-07-24 00:17:13.443594:  
+2023-07-24 00:17:13.443887: Epoch 4 
+2023-07-24 00:17:13.444007: Current learning rate: 0.00996 
+2023-07-24 00:20:25.372916: train_loss -0.4038 
+2023-07-24 00:20:25.373092: val_loss -0.4029 
+2023-07-24 00:20:25.373185: Pseudo dice [0.821, 0.6488, 0.2694] 
+2023-07-24 00:20:25.373266: Epoch time: 191.93 s 
+2023-07-24 00:20:25.373332: Yayy! New best EMA pseudo Dice: 0.3961 
+2023-07-24 00:20:27.527769:  
+2023-07-24 00:20:27.527886: Epoch 5 
+2023-07-24 00:20:27.527997: Current learning rate: 0.00995 
+2023-07-24 00:23:59.957678: train_loss -0.4395 
+2023-07-24 00:23:59.957844: val_loss -0.4287 
+2023-07-24 00:23:59.957929: Pseudo dice [0.8103, 0.634, 0.4383] 
+2023-07-24 00:23:59.958011: Epoch time: 212.43 s 
+2023-07-24 00:23:59.958077: Yayy! New best EMA pseudo Dice: 0.4192 
+2023-07-24 00:24:01.987802:  
+2023-07-24 00:24:01.987912: Epoch 6 
+2023-07-24 00:24:01.988024: Current learning rate: 0.00995 
+2023-07-24 00:27:44.002411: train_loss -0.4486 
+2023-07-24 00:27:44.002615: val_loss -0.4226 
+2023-07-24 00:27:44.002702: Pseudo dice [0.8368, 0.6463, 0.3067] 
+2023-07-24 00:27:44.002805: Epoch time: 222.02 s 
+2023-07-24 00:27:44.002879: Yayy! New best EMA pseudo Dice: 0.4369 
+2023-07-24 00:27:46.502084:  
+2023-07-24 00:27:46.502206: Epoch 7 
+2023-07-24 00:27:46.502326: Current learning rate: 0.00994 
+2023-07-24 00:31:13.044165: train_loss -0.4879 
+2023-07-24 00:31:13.044357: val_loss -0.4522 
+2023-07-24 00:31:13.044453: Pseudo dice [0.8328, 0.6665, 0.389] 
+2023-07-24 00:31:13.044543: Epoch time: 206.54 s 
+2023-07-24 00:31:13.050315: Yayy! New best EMA pseudo Dice: 0.4562 
+2023-07-24 00:31:15.979445:  
+2023-07-24 00:31:15.979643: Epoch 8 
+2023-07-24 00:31:15.979780: Current learning rate: 0.00993 
+2023-07-24 00:34:55.354722: train_loss -0.5049 
+2023-07-24 00:34:55.355004: val_loss -0.4647 
+2023-07-24 00:34:55.355092: Pseudo dice [0.8501, 0.6851, 0.4456] 
+2023-07-24 00:34:55.355233: Epoch time: 219.38 s 
+2023-07-24 00:34:55.355299: Yayy! New best EMA pseudo Dice: 0.4766 
+2023-07-24 00:34:57.734633:  
+2023-07-24 00:34:57.734877: Epoch 9 
+2023-07-24 00:34:57.734988: Current learning rate: 0.00992 
+2023-07-24 00:38:36.038373: train_loss -0.5101 
+2023-07-24 00:38:36.038563: val_loss -0.4518 
+2023-07-24 00:38:36.038657: Pseudo dice [0.8332, 0.6664, 0.4652] 
+2023-07-24 00:38:36.038745: Epoch time: 218.3 s 
+2023-07-24 00:38:36.038840: Yayy! New best EMA pseudo Dice: 0.4944 
+2023-07-24 00:38:39.940725:  
+2023-07-24 00:38:39.941038: Epoch 10 
+2023-07-24 00:38:39.941158: Current learning rate: 0.00991 
+2023-07-24 00:42:04.394748: train_loss -0.5153 
+2023-07-24 00:42:04.398857: val_loss -0.4822 
+2023-07-24 00:42:04.399046: Pseudo dice [0.8432, 0.6794, 0.4922] 
+2023-07-24 00:42:04.399148: Epoch time: 204.46 s 
+2023-07-24 00:42:04.399216: Yayy! New best EMA pseudo Dice: 0.5121 
+2023-07-24 00:42:08.116886:  
+2023-07-24 00:42:08.117016: Epoch 11 
+2023-07-24 00:42:08.117131: Current learning rate: 0.0099 
+2023-07-24 00:45:37.933030: train_loss -0.5128 
+2023-07-24 00:45:37.933209: val_loss -0.4819 
+2023-07-24 00:45:37.933295: Pseudo dice [0.8511, 0.6729, 0.4875] 
+2023-07-24 00:45:37.933378: Epoch time: 209.82 s 
+2023-07-24 00:45:37.933442: Yayy! New best EMA pseudo Dice: 0.528 
+2023-07-24 00:45:41.223381:  
+2023-07-24 00:45:41.223522: Epoch 12 
+2023-07-24 00:45:41.223654: Current learning rate: 0.00989 
+2023-07-24 00:49:18.457412: train_loss -0.5139 
+2023-07-24 00:49:18.457591: val_loss -0.4663 
+2023-07-24 00:49:18.457679: Pseudo dice [0.8304, 0.6664, 0.4751] 
+2023-07-24 00:49:18.457762: Epoch time: 217.24 s 
+2023-07-24 00:49:18.457826: Yayy! New best EMA pseudo Dice: 0.5409 
+2023-07-24 00:49:21.452374:  
+2023-07-24 00:49:21.452548: Epoch 13 
+2023-07-24 00:49:21.452664: Current learning rate: 0.00988 
+2023-07-24 00:52:53.617019: train_loss -0.5306 
+2023-07-24 00:52:53.617260: val_loss -0.4947 
+2023-07-24 00:52:53.617345: Pseudo dice [0.8434, 0.6944, 0.4883] 
+2023-07-24 00:52:53.617487: Epoch time: 212.17 s 
+2023-07-24 00:52:53.617552: Yayy! New best EMA pseudo Dice: 0.5544 
+2023-07-24 00:52:55.726403:  
+2023-07-24 00:52:55.726518: Epoch 14 
+2023-07-24 00:52:55.726615: Current learning rate: 0.00987 
+2023-07-24 00:56:22.506680: train_loss -0.5446 
+2023-07-24 00:56:22.506907: val_loss -0.4714 
+2023-07-24 00:56:22.507013: Pseudo dice [0.8411, 0.671, 0.4914] 
+2023-07-24 00:56:22.507121: Epoch time: 206.78 s 
+2023-07-24 00:56:22.507219: Yayy! New best EMA pseudo Dice: 0.5657 
+2023-07-24 00:56:24.667025:  
+2023-07-24 00:56:24.667140: Epoch 15 
+2023-07-24 00:56:24.667252: Current learning rate: 0.00986 
+2023-07-24 00:59:50.747144: train_loss -0.5394 
+2023-07-24 00:59:50.747421: val_loss -0.4625 
+2023-07-24 00:59:50.747515: Pseudo dice [0.8447, 0.654, 0.5712] 
+2023-07-24 00:59:50.747690: Epoch time: 206.08 s 
+2023-07-24 00:59:50.747818: Yayy! New best EMA pseudo Dice: 0.5781 
+2023-07-24 00:59:53.732963:  
+2023-07-24 00:59:53.733104: Epoch 16 
+2023-07-24 00:59:53.733224: Current learning rate: 0.00986 
+2023-07-24 01:03:24.895722: train_loss -0.5551 
+2023-07-24 01:03:24.895922: val_loss -0.496 
+2023-07-24 01:03:24.896017: Pseudo dice [0.8705, 0.6946, 0.4975] 
+2023-07-24 01:03:24.896102: Epoch time: 211.16 s 
+2023-07-24 01:03:24.896200: Yayy! New best EMA pseudo Dice: 0.5891 
+2023-07-24 01:03:27.655639:  
+2023-07-24 01:03:27.655761: Epoch 17 
+2023-07-24 01:03:27.655877: Current learning rate: 0.00985 
+2023-07-24 01:07:09.366258: train_loss -0.5496 
+2023-07-24 01:07:09.371549: val_loss -0.4745 
+2023-07-24 01:07:09.371795: Pseudo dice [0.8679, 0.6897, 0.514] 
+2023-07-24 01:07:09.371953: Epoch time: 221.71 s 
+2023-07-24 01:07:09.372037: Yayy! New best EMA pseudo Dice: 0.5992 
+2023-07-24 01:07:12.715889:  
+2023-07-24 01:07:12.716015: Epoch 18 
+2023-07-24 01:07:12.716132: Current learning rate: 0.00984 
+2023-07-24 01:10:59.095254: train_loss -0.5406 
+2023-07-24 01:10:59.095483: val_loss -0.483 
+2023-07-24 01:10:59.095583: Pseudo dice [0.8634, 0.6795, 0.5385] 
+2023-07-24 01:10:59.095723: Epoch time: 226.38 s 
+2023-07-24 01:10:59.095788: Yayy! New best EMA pseudo Dice: 0.6087 
+2023-07-24 01:11:01.356395:  
+2023-07-24 01:11:01.356525: Epoch 19 
+2023-07-24 01:11:01.356640: Current learning rate: 0.00983 
+2023-07-24 01:14:19.901330: train_loss -0.5567 
+2023-07-24 01:14:19.901525: val_loss -0.4783 
+2023-07-24 01:14:19.901613: Pseudo dice [0.8492, 0.6553, 0.535] 
+2023-07-24 01:14:19.901693: Epoch time: 198.55 s 
+2023-07-24 01:14:19.901747: Yayy! New best EMA pseudo Dice: 0.6158 
+2023-07-24 01:14:22.511663:  
+2023-07-24 01:14:22.511957: Epoch 20 
+2023-07-24 01:14:22.512064: Current learning rate: 0.00982 
+2023-07-24 01:17:55.925512: train_loss -0.5745 
+2023-07-24 01:17:55.925694: val_loss -0.4715 
+2023-07-24 01:17:55.925791: Pseudo dice [0.8548, 0.653, 0.5559] 
+2023-07-24 01:17:55.925879: Epoch time: 213.41 s 
+2023-07-24 01:17:55.925951: Yayy! New best EMA pseudo Dice: 0.623 
+2023-07-24 01:17:58.448946:  
+2023-07-24 01:17:58.449071: Epoch 21 
+2023-07-24 01:17:58.449180: Current learning rate: 0.00981 
+2023-07-24 01:21:29.617508: train_loss -0.5761 
+2023-07-24 01:21:29.617684: val_loss -0.4919 
+2023-07-24 01:21:29.617774: Pseudo dice [0.8628, 0.7072, 0.4682] 
+2023-07-24 01:21:29.617859: Epoch time: 211.17 s 
+2023-07-24 01:21:29.617927: Yayy! New best EMA pseudo Dice: 0.6286 
+2023-07-24 01:21:32.232873:  
+2023-07-24 01:21:32.232998: Epoch 22 
+2023-07-24 01:21:32.233115: Current learning rate: 0.0098 
+2023-07-24 01:24:54.282193: train_loss -0.5831 
+2023-07-24 01:24:54.282391: val_loss -0.5023 
+2023-07-24 01:24:54.282501: Pseudo dice [0.8587, 0.6942, 0.4825] 
+2023-07-24 01:24:54.282606: Epoch time: 202.05 s 
+2023-07-24 01:24:54.282692: Yayy! New best EMA pseudo Dice: 0.6336 
+2023-07-24 01:24:57.618637:  
+2023-07-24 01:24:57.618829: Epoch 23 
+2023-07-24 01:24:57.618946: Current learning rate: 0.00979 
+2023-07-24 01:28:25.124920: train_loss -0.5864 
+2023-07-24 01:28:25.125104: val_loss -0.5029 
+2023-07-24 01:28:25.125192: Pseudo dice [0.8623, 0.7014, 0.5049] 
+2023-07-24 01:28:25.125279: Epoch time: 207.51 s 
+2023-07-24 01:28:25.125346: Yayy! New best EMA pseudo Dice: 0.6392 
+2023-07-24 01:28:29.015366:  
+2023-07-24 01:28:29.015487: Epoch 24 
+2023-07-24 01:28:29.015594: Current learning rate: 0.00978 
+2023-07-24 01:32:03.197192: train_loss -0.5843 
+2023-07-24 01:32:03.197452: val_loss -0.478 
+2023-07-24 01:32:03.197542: Pseudo dice [0.8586, 0.6702, 0.5603] 
+2023-07-24 01:32:03.197691: Epoch time: 214.18 s 
+2023-07-24 01:32:03.197757: Yayy! New best EMA pseudo Dice: 0.6449 
+2023-07-24 01:32:05.726707:  
+2023-07-24 01:32:05.726868: Epoch 25 
+2023-07-24 01:32:05.726998: Current learning rate: 0.00977 
+2023-07-24 01:35:41.851906: train_loss -0.5882 
+2023-07-24 01:35:41.852082: val_loss -0.4719 
+2023-07-24 01:35:41.852175: Pseudo dice [0.862, 0.6743, 0.3966] 
+2023-07-24 01:35:41.852259: Epoch time: 216.13 s 
+2023-07-24 01:35:43.534244:  
+2023-07-24 01:35:43.534373: Epoch 26 
+2023-07-24 01:35:43.534485: Current learning rate: 0.00977 
+2023-07-24 01:39:02.738217: train_loss -0.6018 
+2023-07-24 01:39:02.743619: val_loss -0.4994 
+2023-07-24 01:39:02.743780: Pseudo dice [0.8766, 0.733, 0.4566] 
+2023-07-24 01:39:02.743932: Epoch time: 199.21 s 
+2023-07-24 01:39:02.744023: Yayy! New best EMA pseudo Dice: 0.6493 
+2023-07-24 01:39:05.142646:  
+2023-07-24 01:39:05.142778: Epoch 27 
+2023-07-24 01:39:05.142897: Current learning rate: 0.00976 
+2023-07-24 01:42:29.737434: train_loss -0.5995 
+2023-07-24 01:42:29.737619: val_loss -0.4867 
+2023-07-24 01:42:29.737704: Pseudo dice [0.8689, 0.689, 0.4957] 
+2023-07-24 01:42:29.737784: Epoch time: 204.6 s 
+2023-07-24 01:42:29.737848: Yayy! New best EMA pseudo Dice: 0.6528 
+2023-07-24 01:42:32.621387:  
+2023-07-24 01:42:32.621503: Epoch 28 
+2023-07-24 01:42:32.621617: Current learning rate: 0.00975 
+2023-07-24 01:46:07.936492: train_loss -0.5998 
+2023-07-24 01:46:07.936731: val_loss -0.4998 
+2023-07-24 01:46:07.943375: Pseudo dice [0.8649, 0.6936, 0.5489] 
+2023-07-24 01:46:07.943589: Epoch time: 215.32 s 
+2023-07-24 01:46:07.943666: Yayy! New best EMA pseudo Dice: 0.6578 
+2023-07-24 01:46:12.240499:  
+2023-07-24 01:46:12.240789: Epoch 29 
+2023-07-24 01:46:12.240907: Current learning rate: 0.00974 
+2023-07-24 01:49:49.765490: train_loss -0.6192 
+2023-07-24 01:49:49.765681: val_loss -0.4884 
+2023-07-24 01:49:49.765781: Pseudo dice [0.8677, 0.6745, 0.5335] 
+2023-07-24 01:49:49.765875: Epoch time: 217.53 s 
+2023-07-24 01:49:49.765948: Yayy! New best EMA pseudo Dice: 0.6612 
+2023-07-24 01:49:54.074142:  
+2023-07-24 01:49:54.074317: Epoch 30 
+2023-07-24 01:49:54.074434: Current learning rate: 0.00973 
+2023-07-24 01:53:33.757728: train_loss -0.6115 
+2023-07-24 01:53:33.758031: val_loss -0.4838 
+2023-07-24 01:53:33.758127: Pseudo dice [0.8786, 0.6666, 0.5728] 
+2023-07-24 01:53:33.758209: Epoch time: 219.69 s 
+2023-07-24 01:53:33.758281: Yayy! New best EMA pseudo Dice: 0.6656 
+2023-07-24 01:53:37.008034:  
+2023-07-24 01:53:37.008158: Epoch 31 
+2023-07-24 01:53:37.008272: Current learning rate: 0.00972 
+2023-07-24 01:57:03.417002: train_loss -0.625 
+2023-07-24 01:57:03.417286: val_loss -0.5044 
+2023-07-24 01:57:03.417374: Pseudo dice [0.8686, 0.7071, 0.5431] 
+2023-07-24 01:57:03.417525: Epoch time: 206.41 s 
+2023-07-24 01:57:03.417588: Yayy! New best EMA pseudo Dice: 0.6697 
+2023-07-24 01:57:05.575807:  
+2023-07-24 01:57:05.575927: Epoch 32 
+2023-07-24 01:57:05.576024: Current learning rate: 0.00971 
+2023-07-24 02:00:42.226546: train_loss -0.6184 
+2023-07-24 02:00:42.232471: val_loss -0.4894 
+2023-07-24 02:00:42.232700: Pseudo dice [0.8702, 0.7033, 0.4937] 
+2023-07-24 02:00:42.232790: Epoch time: 216.65 s 
+2023-07-24 02:00:42.232924: Yayy! New best EMA pseudo Dice: 0.6716 
+2023-07-24 02:00:45.014342:  
+2023-07-24 02:00:45.014468: Epoch 33 
+2023-07-24 02:00:45.014586: Current learning rate: 0.0097 
+2023-07-24 02:04:19.757532: train_loss -0.6217 
+2023-07-24 02:04:19.757771: val_loss -0.4804 
+2023-07-24 02:04:19.757856: Pseudo dice [0.8748, 0.693, 0.5362] 
+2023-07-24 02:04:19.758005: Epoch time: 214.74 s 
+2023-07-24 02:04:19.758069: Yayy! New best EMA pseudo Dice: 0.6746 
+2023-07-24 02:04:24.430918:  
+2023-07-24 02:04:24.431120: Epoch 34 
+2023-07-24 02:04:24.431232: Current learning rate: 0.00969 
+2023-07-24 02:08:00.604949: train_loss -0.6087 
+2023-07-24 02:08:00.605173: val_loss -0.4999 
+2023-07-24 02:08:00.605262: Pseudo dice [0.8614, 0.7024, 0.5684] 
+2023-07-24 02:08:00.605341: Epoch time: 216.18 s 
+2023-07-24 02:08:00.605400: Yayy! New best EMA pseudo Dice: 0.6782 
+2023-07-24 02:08:05.228923:  
+2023-07-24 02:08:05.229156: Epoch 35 
+2023-07-24 02:08:05.229259: Current learning rate: 0.00968 
+2023-07-24 02:11:46.128471: train_loss -0.6278 
+2023-07-24 02:11:46.128723: val_loss -0.4975 
+2023-07-24 02:11:46.128807: Pseudo dice [0.8697, 0.6883, 0.5635] 
+2023-07-24 02:11:46.128886: Epoch time: 220.9 s 
+2023-07-24 02:11:46.128947: Yayy! New best EMA pseudo Dice: 0.6811 
+2023-07-24 02:11:48.352022:  
+2023-07-24 02:11:48.352140: Epoch 36 
+2023-07-24 02:11:48.352254: Current learning rate: 0.00968 
+2023-07-24 02:15:18.130978: train_loss -0.6341 
+2023-07-24 02:15:18.131156: val_loss -0.514 
+2023-07-24 02:15:18.131239: Pseudo dice [0.8717, 0.7153, 0.5774] 
+2023-07-24 02:15:18.131315: Epoch time: 209.78 s 
+2023-07-24 02:15:18.131555: Yayy! New best EMA pseudo Dice: 0.6852 
+2023-07-24 02:15:20.282215:  
+2023-07-24 02:15:20.282343: Epoch 37 
+2023-07-24 02:15:20.282458: Current learning rate: 0.00967 
+2023-07-24 02:18:53.374968: train_loss -0.6275 
+2023-07-24 02:18:53.375212: val_loss -0.5119 
+2023-07-24 02:18:53.375304: Pseudo dice [0.867, 0.6981, 0.5894] 
+2023-07-24 02:18:53.375387: Epoch time: 213.09 s 
+2023-07-24 02:18:53.375454: Yayy! New best EMA pseudo Dice: 0.6885 
+2023-07-24 02:18:56.726088:  
+2023-07-24 02:18:56.726211: Epoch 38 
+2023-07-24 02:18:56.726327: Current learning rate: 0.00966 
+2023-07-24 02:22:22.025652: train_loss -0.6328 
+2023-07-24 02:22:22.031740: val_loss -0.503 
+2023-07-24 02:22:22.032003: Pseudo dice [0.8649, 0.7161, 0.5203] 
+2023-07-24 02:22:22.032098: Epoch time: 205.3 s 
+2023-07-24 02:22:22.032236: Yayy! New best EMA pseudo Dice: 0.6897 
+2023-07-24 02:22:25.923321:  
+2023-07-24 02:22:25.923668: Epoch 39 
+2023-07-24 02:22:25.923775: Current learning rate: 0.00965 
+2023-07-24 02:25:55.050353: train_loss -0.6391 
+2023-07-24 02:25:55.050546: val_loss -0.5015 
+2023-07-24 02:25:55.050636: Pseudo dice [0.8618, 0.6972, 0.566] 
+2023-07-24 02:25:55.050725: Epoch time: 209.13 s 
+2023-07-24 02:25:55.050815: Yayy! New best EMA pseudo Dice: 0.6915 
+2023-07-24 02:25:57.131437:  
+2023-07-24 02:25:57.131554: Epoch 40 
+2023-07-24 02:25:57.131677: Current learning rate: 0.00964 
+2023-07-24 02:29:24.101611: train_loss -0.6453 
+2023-07-24 02:29:24.101807: val_loss -0.4857 
+2023-07-24 02:29:24.101905: Pseudo dice [0.8591, 0.6952, 0.5601] 
+2023-07-24 02:29:24.102006: Epoch time: 206.97 s 
+2023-07-24 02:29:24.102089: Yayy! New best EMA pseudo Dice: 0.6929 
+2023-07-24 02:29:26.722635:  
+2023-07-24 02:29:26.722777: Epoch 41 
+2023-07-24 02:29:26.722900: Current learning rate: 0.00963 
+2023-07-24 02:33:01.503387: train_loss -0.6384 
+2023-07-24 02:33:01.503580: val_loss -0.495 
+2023-07-24 02:33:01.503671: Pseudo dice [0.8646, 0.7111, 0.4851] 
+2023-07-24 02:33:01.503757: Epoch time: 214.78 s 
+2023-07-24 02:33:03.298025:  
+2023-07-24 02:33:03.298147: Epoch 42 
+2023-07-24 02:33:03.298252: Current learning rate: 0.00962 
+2023-07-24 02:36:28.531214: train_loss -0.6454 
+2023-07-24 02:36:28.531463: val_loss -0.4999 
+2023-07-24 02:36:28.531549: Pseudo dice [0.8693, 0.691, 0.6198] 
+2023-07-24 02:36:28.531719: Epoch time: 205.23 s 
+2023-07-24 02:36:28.531906: Yayy! New best EMA pseudo Dice: 0.6957 
+2023-07-24 02:36:30.675420:  
+2023-07-24 02:36:30.675563: Epoch 43 
+2023-07-24 02:36:30.675693: Current learning rate: 0.00961 
+2023-07-24 02:39:57.254115: train_loss -0.6449 
+2023-07-24 02:39:57.254383: val_loss -0.5029 
+2023-07-24 02:39:57.254475: Pseudo dice [0.8713, 0.6894, 0.6393] 
+2023-07-24 02:39:57.254557: Epoch time: 206.58 s 
+2023-07-24 02:39:57.254622: Yayy! New best EMA pseudo Dice: 0.6995 
+2023-07-24 02:39:59.978240:  
+2023-07-24 02:39:59.978516: Epoch 44 
+2023-07-24 02:39:59.978635: Current learning rate: 0.0096 
+2023-07-24 02:43:33.330351: train_loss -0.6418 
+2023-07-24 02:43:33.330585: val_loss -0.4937 
+2023-07-24 02:43:33.330672: Pseudo dice [0.8727, 0.6956, 0.5291] 
+2023-07-24 02:43:33.330842: Epoch time: 213.35 s 
+2023-07-24 02:43:35.411677:  
+2023-07-24 02:43:35.411899: Epoch 45 
+2023-07-24 02:43:35.412007: Current learning rate: 0.00959 
+2023-07-24 02:47:16.638136: train_loss -0.6484 
+2023-07-24 02:47:16.638322: val_loss -0.5065 
+2023-07-24 02:47:16.638409: Pseudo dice [0.8781, 0.7181, 0.5566] 
+2023-07-24 02:47:16.638503: Epoch time: 221.23 s 
+2023-07-24 02:47:16.638575: Yayy! New best EMA pseudo Dice: 0.7013 
+2023-07-24 02:47:18.875849:  
+2023-07-24 02:47:18.876126: Epoch 46 
+2023-07-24 02:47:18.876246: Current learning rate: 0.00959 
+2023-07-24 02:50:43.664102: train_loss -0.6594 
+2023-07-24 02:50:43.664368: val_loss -0.5021 
+2023-07-24 02:50:43.664471: Pseudo dice [0.8832, 0.7081, 0.5237] 
+2023-07-24 02:50:43.664641: Epoch time: 204.79 s 
+2023-07-24 02:50:43.664771: Yayy! New best EMA pseudo Dice: 0.7016 
+2023-07-24 02:50:45.913680:  
+2023-07-24 02:50:45.913988: Epoch 47 
+2023-07-24 02:50:45.914099: Current learning rate: 0.00958 
+2023-07-24 02:54:09.407397: train_loss -0.6559 
+2023-07-24 02:54:09.407646: val_loss -0.4876 
+2023-07-24 02:54:09.407736: Pseudo dice [0.8737, 0.6605, 0.6181] 
+2023-07-24 02:54:09.407889: Epoch time: 203.49 s 
+2023-07-24 02:54:09.407957: Yayy! New best EMA pseudo Dice: 0.7032 
+2023-07-24 02:54:12.936934:  
+2023-07-24 02:54:12.937068: Epoch 48 
+2023-07-24 02:54:12.937182: Current learning rate: 0.00957 
+2023-07-24 02:57:47.539666: train_loss -0.6583 
+2023-07-24 02:57:47.539865: val_loss -0.5048 
+2023-07-24 02:57:47.540035: Pseudo dice [0.8792, 0.7214, 0.4783] 
+2023-07-24 02:57:47.540195: Epoch time: 214.6 s 
+2023-07-24 02:57:49.232162:  
+2023-07-24 02:57:49.232288: Epoch 49 
+2023-07-24 02:57:49.232400: Current learning rate: 0.00956 
+2023-07-24 03:01:31.963185: train_loss -0.6562 
+2023-07-24 03:01:31.963373: val_loss -0.5071 
+2023-07-24 03:01:31.963463: Pseudo dice [0.8811, 0.7189, 0.5632] 
+2023-07-24 03:01:31.963546: Epoch time: 222.73 s 
+2023-07-24 03:01:32.441789: Yayy! New best EMA pseudo Dice: 0.7041 
+2023-07-24 03:01:34.494628:  
+2023-07-24 03:01:34.494856: Epoch 50 
+2023-07-24 03:01:34.494962: Current learning rate: 0.00955 
+2023-07-24 03:05:00.456790: train_loss -0.66 
+2023-07-24 03:05:00.457008: val_loss -0.4885 
+2023-07-24 03:05:00.457094: Pseudo dice [0.8722, 0.7199, 0.3882] 
+2023-07-24 03:05:00.457232: Epoch time: 205.96 s 
+2023-07-24 03:05:01.920852:  
+2023-07-24 03:05:01.921130: Epoch 51 
+2023-07-24 03:05:01.921252: Current learning rate: 0.00954 
+2023-07-24 03:08:21.863105: train_loss -0.6615 
+2023-07-24 03:08:21.863394: val_loss -0.5063 
+2023-07-24 03:08:21.863482: Pseudo dice [0.8652, 0.6923, 0.5877] 
+2023-07-24 03:08:21.863767: Epoch time: 199.94 s 
+2023-07-24 03:08:23.565506:  
+2023-07-24 03:08:23.565624: Epoch 52 
+2023-07-24 03:08:23.565736: Current learning rate: 0.00953 
+2023-07-24 03:11:58.618505: train_loss -0.6541 
+2023-07-24 03:11:58.618745: val_loss -0.5208 
+2023-07-24 03:11:58.618862: Pseudo dice [0.8769, 0.7111, 0.5391] 
+2023-07-24 03:11:58.619015: Epoch time: 215.05 s 
+2023-07-24 03:12:00.422971:  
+2023-07-24 03:12:00.423093: Epoch 53 
+2023-07-24 03:12:00.423211: Current learning rate: 0.00952 
+2023-07-24 03:15:34.016732: train_loss -0.6717 
+2023-07-24 03:15:34.016971: val_loss -0.4904 
+2023-07-24 03:15:34.017070: Pseudo dice [0.8808, 0.7097, 0.4256] 
+2023-07-24 03:15:34.017220: Epoch time: 213.59 s 
+2023-07-24 03:15:36.404579:  
+2023-07-24 03:15:36.404704: Epoch 54 
+2023-07-24 03:15:36.404819: Current learning rate: 0.00951 
+2023-07-24 03:19:13.253185: train_loss -0.677 
+2023-07-24 03:19:13.253387: val_loss -0.4902 
+2023-07-24 03:19:13.253475: Pseudo dice [0.8606, 0.7035, 0.5965] 
+2023-07-24 03:19:13.253560: Epoch time: 216.85 s 
+2023-07-24 03:19:14.880905:  
+2023-07-24 03:19:14.881205: Epoch 55 
+2023-07-24 03:19:14.881327: Current learning rate: 0.0095 
+2023-07-24 03:22:41.606614: train_loss -0.6675 
+2023-07-24 03:22:41.606848: val_loss -0.4941 
+2023-07-24 03:22:41.606937: Pseudo dice [0.873, 0.7194, 0.3798] 
+2023-07-24 03:22:41.607017: Epoch time: 206.73 s 
+2023-07-24 03:22:45.052177:  
+2023-07-24 03:22:45.052385: Epoch 56 
+2023-07-24 03:22:45.052505: Current learning rate: 0.00949 
+2023-07-24 03:26:17.637499: train_loss -0.6827 
+2023-07-24 03:26:17.637738: val_loss -0.5062 
+2023-07-24 03:26:17.637826: Pseudo dice [0.8737, 0.71, 0.6042] 
+2023-07-24 03:26:17.637978: Epoch time: 212.59 s 
+2023-07-24 03:26:21.340287:  
+2023-07-24 03:26:21.340517: Epoch 57 
+2023-07-24 03:26:21.340631: Current learning rate: 0.00949 
+2023-07-24 03:29:47.100365: train_loss -0.6838 
+2023-07-24 03:29:47.100567: val_loss -0.4964 
+2023-07-24 03:29:47.100666: Pseudo dice [0.8891, 0.7083, 0.4048] 
+2023-07-24 03:29:47.100763: Epoch time: 205.76 s 
+2023-07-24 03:29:48.555212:  
+2023-07-24 03:29:48.555346: Epoch 58 
+2023-07-24 03:29:48.555466: Current learning rate: 0.00948 
+2023-07-24 03:33:20.722145: train_loss -0.6697 
+2023-07-24 03:33:20.722324: val_loss -0.5062 
+2023-07-24 03:33:20.722407: Pseudo dice [0.8666, 0.6973, 0.5627] 
+2023-07-24 03:33:20.722488: Epoch time: 212.17 s 
+2023-07-24 03:33:23.647374:  
+2023-07-24 03:33:23.647504: Epoch 59 
+2023-07-24 03:33:23.647635: Current learning rate: 0.00947 
+2023-07-24 03:36:54.070232: train_loss -0.6887 
+2023-07-24 03:36:54.070504: val_loss -0.5134 
+2023-07-24 03:36:54.070593: Pseudo dice [0.8732, 0.7361, 0.4845] 
+2023-07-24 03:36:54.070743: Epoch time: 210.42 s 
+2023-07-24 03:36:56.444733:  
+2023-07-24 03:36:56.444867: Epoch 60 
+2023-07-24 03:36:56.444985: Current learning rate: 0.00946 
+2023-07-24 03:40:22.488861: train_loss -0.6985 
+2023-07-24 03:40:22.498857: val_loss -0.4992 
+2023-07-24 03:40:22.499064: Pseudo dice [0.8846, 0.7076, 0.5453] 
+2023-07-24 03:40:22.499147: Epoch time: 206.05 s 
+2023-07-24 03:40:25.415841:  
+2023-07-24 03:40:25.415988: Epoch 61 
+2023-07-24 03:40:25.416104: Current learning rate: 0.00945 
+2023-07-24 03:43:58.801075: train_loss -0.6876 
+2023-07-24 03:43:58.801260: val_loss -0.5122 
+2023-07-24 03:43:58.801349: Pseudo dice [0.8586, 0.716, 0.5498] 
+2023-07-24 03:43:58.801434: Epoch time: 213.39 s 
+2023-07-24 03:44:01.958030:  
+2023-07-24 03:44:01.970955: Epoch 62 
+2023-07-24 03:44:01.971086: Current learning rate: 0.00944 
+2023-07-24 03:47:44.738477: train_loss -0.6943 
+2023-07-24 03:47:44.738670: val_loss -0.5106 
+2023-07-24 03:47:44.738753: Pseudo dice [0.882, 0.7326, 0.544] 
+2023-07-24 03:47:44.738875: Epoch time: 222.78 s 
+2023-07-24 03:47:48.039309:  
+2023-07-24 03:47:48.039480: Epoch 63 
+2023-07-24 03:47:48.039597: Current learning rate: 0.00943 
+2023-07-24 03:51:16.951300: train_loss -0.6731 
+2023-07-24 03:51:16.951505: val_loss -0.4852 
+2023-07-24 03:51:16.951604: Pseudo dice [0.8708, 0.7122, 0.4841] 
+2023-07-24 03:51:16.951797: Epoch time: 208.91 s 
+2023-07-24 03:51:18.621170:  
+2023-07-24 03:51:18.621321: Epoch 64 
+2023-07-24 03:51:18.621441: Current learning rate: 0.00942 
+2023-07-24 03:54:44.957338: train_loss -0.6761 
+2023-07-24 03:54:44.957540: val_loss -0.4724 
+2023-07-24 03:54:44.957629: Pseudo dice [0.8598, 0.6921, 0.5167] 
+2023-07-24 03:54:44.957714: Epoch time: 206.34 s 
+2023-07-24 03:54:46.812274:  
+2023-07-24 03:54:46.812412: Epoch 65 
+2023-07-24 03:54:46.812517: Current learning rate: 0.00941 
+2023-07-24 03:58:23.950075: train_loss -0.6927 
+2023-07-24 03:58:23.950717: val_loss -0.5027 
+2023-07-24 03:58:23.951065: Pseudo dice [0.8701, 0.7152, 0.4785] 
+2023-07-24 03:58:23.951216: Epoch time: 217.14 s 
+2023-07-24 03:58:25.802012:  
+2023-07-24 03:58:25.802195: Epoch 66 
+2023-07-24 03:58:25.802299: Current learning rate: 0.0094 
+2023-07-24 04:01:56.722209: train_loss -0.6963 
+2023-07-24 04:01:56.722415: val_loss -0.5062 
+2023-07-24 04:01:56.722507: Pseudo dice [0.8726, 0.7042, 0.5969] 
+2023-07-24 04:01:56.722586: Epoch time: 210.92 s 
+2023-07-24 04:01:59.771096:  
+2023-07-24 04:01:59.771243: Epoch 67 
+2023-07-24 04:01:59.771358: Current learning rate: 0.00939 
+2023-07-24 04:05:40.925208: train_loss -0.6952 
+2023-07-24 04:05:40.925490: val_loss -0.5088 
+2023-07-24 04:05:40.925585: Pseudo dice [0.8802, 0.729, 0.5521] 
+2023-07-24 04:05:40.925745: Epoch time: 221.16 s 
+2023-07-24 04:05:43.897002:  
+2023-07-24 04:05:43.898616: Epoch 68 
+2023-07-24 04:05:43.898738: Current learning rate: 0.00939 
+2023-07-24 04:09:15.657769: train_loss -0.6889 
+2023-07-24 04:09:15.658007: val_loss -0.516 
+2023-07-24 04:09:15.658100: Pseudo dice [0.8782, 0.7278, 0.5643] 
+2023-07-24 04:09:15.658307: Epoch time: 211.76 s 
+2023-07-24 04:09:15.658453: Yayy! New best EMA pseudo Dice: 0.7051 
+2023-07-24 04:09:18.116199:  
+2023-07-24 04:09:18.116321: Epoch 69 
+2023-07-24 04:09:18.116441: Current learning rate: 0.00938 
+2023-07-24 04:12:42.753379: train_loss -0.6904 
+2023-07-24 04:12:42.753681: val_loss -0.4671 
+2023-07-24 04:12:42.753862: Pseudo dice [0.8749, 0.7138, 0.3681] 
+2023-07-24 04:12:42.753966: Epoch time: 204.64 s 
+2023-07-24 04:12:44.327600:  
+2023-07-24 04:12:44.327721: Epoch 70 
+2023-07-24 04:12:44.327840: Current learning rate: 0.00937 
+2023-07-24 04:16:22.761208: train_loss -0.6958 
+2023-07-24 04:16:22.761458: val_loss -0.5012 
+2023-07-24 04:16:22.761544: Pseudo dice [0.8879, 0.7126, 0.5304] 
+2023-07-24 04:16:22.761694: Epoch time: 218.43 s 
+2023-07-24 04:16:24.628846:  
+2023-07-24 04:16:24.629004: Epoch 71 
+2023-07-24 04:16:24.629131: Current learning rate: 0.00936 
+2023-07-24 04:19:50.557256: train_loss -0.6919 
+2023-07-24 04:19:50.557556: val_loss -0.4675 
+2023-07-24 04:19:50.557651: Pseudo dice [0.8698, 0.6855, 0.5056] 
+2023-07-24 04:19:50.557837: Epoch time: 205.93 s 
+2023-07-24 04:19:53.142438:  
+2023-07-24 04:19:53.142620: Epoch 72 
+2023-07-24 04:19:53.142735: Current learning rate: 0.00935 
+2023-07-24 04:23:22.464141: train_loss -0.6977 
+2023-07-24 04:23:22.464365: val_loss -0.5149 
+2023-07-24 04:23:22.464458: Pseudo dice [0.8859, 0.7116, 0.5175] 
+2023-07-24 04:23:22.464544: Epoch time: 209.32 s 
+2023-07-24 04:23:24.605744:  
+2023-07-24 04:23:24.605880: Epoch 73 
+2023-07-24 04:23:24.605996: Current learning rate: 0.00934 
+2023-07-24 04:26:50.364227: train_loss -0.6983 
+2023-07-24 04:26:50.364492: val_loss -0.4938 
+2023-07-24 04:26:50.364594: Pseudo dice [0.8637, 0.7213, 0.5451] 
+2023-07-24 04:26:50.364779: Epoch time: 205.76 s 
+2023-07-24 04:26:52.800122:  
+2023-07-24 04:26:52.800305: Epoch 74 
+2023-07-24 04:26:52.800421: Current learning rate: 0.00933 
+2023-07-24 04:30:26.828684: train_loss -0.6956 
+2023-07-24 04:30:26.828918: val_loss -0.4781 
+2023-07-24 04:30:26.829004: Pseudo dice [0.8634, 0.6982, 0.4993] 
+2023-07-24 04:30:26.829283: Epoch time: 214.03 s 
+2023-07-24 04:30:28.715835:  
+2023-07-24 04:30:28.716055: Epoch 75 
+2023-07-24 04:30:28.716175: Current learning rate: 0.00932 
+2023-07-24 04:33:56.996001: train_loss -0.6978 
+2023-07-24 04:33:56.996192: val_loss -0.5014 
+2023-07-24 04:33:56.996281: Pseudo dice [0.8721, 0.6953, 0.5428] 
+2023-07-24 04:33:56.996363: Epoch time: 208.28 s 
+2023-07-24 04:34:01.112788:  
+2023-07-24 04:34:01.112929: Epoch 76 
+2023-07-24 04:34:01.113038: Current learning rate: 0.00931 
+2023-07-24 04:37:36.118190: train_loss -0.6992 
+2023-07-24 04:37:36.118396: val_loss -0.5014 
+2023-07-24 04:37:36.118491: Pseudo dice [0.8768, 0.7055, 0.5652] 
+2023-07-24 04:37:36.118583: Epoch time: 215.01 s 
+2023-07-24 04:37:39.211814:  
+2023-07-24 04:37:39.211969: Epoch 77 
+2023-07-24 04:37:39.212115: Current learning rate: 0.0093 
+2023-07-24 04:41:10.694273: train_loss -0.7058 
+2023-07-24 04:41:10.694464: val_loss -0.4895 
+2023-07-24 04:41:10.694552: Pseudo dice [0.8602, 0.7049, 0.5613] 
+2023-07-24 04:41:10.694637: Epoch time: 211.48 s 
+2023-07-24 04:41:13.013998:  
+2023-07-24 04:41:13.014146: Epoch 78 
+2023-07-24 04:41:13.014262: Current learning rate: 0.0093 
+2023-07-24 04:44:39.537027: train_loss -0.7016 
+2023-07-24 04:44:39.537224: val_loss -0.4884 
+2023-07-24 04:44:39.537313: Pseudo dice [0.8797, 0.7206, 0.4807] 
+2023-07-24 04:44:39.537394: Epoch time: 206.52 s 
+2023-07-24 04:44:41.752397:  
+2023-07-24 04:44:41.752547: Epoch 79 
+2023-07-24 04:44:41.752668: Current learning rate: 0.00929 
+2023-07-24 04:48:09.494605: train_loss -0.6964 
+2023-07-24 04:48:09.494830: val_loss -0.5074 
+2023-07-24 04:48:09.494932: Pseudo dice [0.8874, 0.7203, 0.6069] 
+2023-07-24 04:48:09.495023: Epoch time: 207.74 s 
+2023-07-24 04:48:11.746876:  
+2023-07-24 04:48:11.747036: Epoch 80 
+2023-07-24 04:48:11.747147: Current learning rate: 0.00928 
+2023-07-24 04:51:42.348668: train_loss -0.7065 
+2023-07-24 04:51:42.348896: val_loss -0.4837 
+2023-07-24 04:51:42.348983: Pseudo dice [0.8784, 0.7032, 0.5621] 
+2023-07-24 04:51:42.349133: Epoch time: 210.6 s 
+2023-07-24 04:51:42.349199: Yayy! New best EMA pseudo Dice: 0.7061 
+2023-07-24 04:51:44.484021:  
+2023-07-24 04:51:44.484140: Epoch 81 
+2023-07-24 04:51:44.484257: Current learning rate: 0.00927 
+2023-07-24 04:55:09.696577: train_loss -0.714 
+2023-07-24 04:55:09.696886: val_loss -0.491 
+2023-07-24 04:55:09.696984: Pseudo dice [0.8814, 0.7251, 0.4554] 
+2023-07-24 04:55:09.697072: Epoch time: 205.21 s 
+2023-07-24 04:55:11.259889:  
+2023-07-24 04:55:11.260105: Epoch 82 
+2023-07-24 04:55:11.260218: Current learning rate: 0.00926 
+2023-07-24 04:58:42.167684: train_loss -0.7151 
+2023-07-24 04:58:42.167853: val_loss -0.5149 
+2023-07-24 04:58:42.167938: Pseudo dice [0.8747, 0.6999, 0.632] 
+2023-07-24 04:58:42.168024: Epoch time: 210.91 s 
+2023-07-24 04:58:42.168089: Yayy! New best EMA pseudo Dice: 0.7073 
+2023-07-24 04:58:46.756355:  
+2023-07-24 04:58:46.756490: Epoch 83 
+2023-07-24 04:58:46.756597: Current learning rate: 0.00925 
+2023-07-24 05:02:22.008022: train_loss -0.7232 
+2023-07-24 05:02:22.008212: val_loss -0.5229 
+2023-07-24 05:02:22.008301: Pseudo dice [0.8914, 0.7236, 0.6229] 
+2023-07-24 05:02:22.008386: Epoch time: 215.25 s 
+2023-07-24 05:02:22.008452: Yayy! New best EMA pseudo Dice: 0.7112 
+2023-07-24 05:02:24.066390:  
+2023-07-24 05:02:24.066524: Epoch 84 
+2023-07-24 05:02:24.066619: Current learning rate: 0.00924 
+2023-07-24 05:05:50.327326: train_loss -0.725 
+2023-07-24 05:05:50.327611: val_loss -0.5159 
+2023-07-24 05:05:50.327698: Pseudo dice [0.8756, 0.7106, 0.5817] 
+2023-07-24 05:05:50.327851: Epoch time: 206.26 s 
+2023-07-24 05:05:50.327918: Yayy! New best EMA pseudo Dice: 0.7123 
+2023-07-24 05:05:54.485906:  
+2023-07-24 05:05:54.486033: Epoch 85 
+2023-07-24 05:05:54.486146: Current learning rate: 0.00923 
+2023-07-24 05:09:37.322994: train_loss -0.723 
+2023-07-24 05:09:37.323187: val_loss -0.5029 
+2023-07-24 05:09:37.323277: Pseudo dice [0.8783, 0.7171, 0.4587] 
+2023-07-24 05:09:37.323362: Epoch time: 222.84 s 
+2023-07-24 05:09:38.723594:  
+2023-07-24 05:09:38.723815: Epoch 86 
+2023-07-24 05:09:38.723936: Current learning rate: 0.00922 
+2023-07-24 05:13:09.316705: train_loss -0.7182 
+2023-07-24 05:13:09.316886: val_loss -0.4834 
+2023-07-24 05:13:09.316982: Pseudo dice [0.8828, 0.7206, 0.4715] 
+2023-07-24 05:13:09.317072: Epoch time: 210.59 s 
+2023-07-24 05:13:12.422567:  
+2023-07-24 05:13:12.422688: Epoch 87 
+2023-07-24 05:13:12.422828: Current learning rate: 0.00921 
+2023-07-24 05:16:46.310607: train_loss -0.7222 
+2023-07-24 05:16:46.310821: val_loss -0.502 
+2023-07-24 05:16:46.310914: Pseudo dice [0.8901, 0.6998, 0.6513] 
+2023-07-24 05:16:46.310997: Epoch time: 213.89 s 
+2023-07-24 05:16:48.065801:  
+2023-07-24 05:16:48.065928: Epoch 88 
+2023-07-24 05:16:48.066038: Current learning rate: 0.0092 
+2023-07-24 05:20:26.412961: train_loss -0.7227 
+2023-07-24 05:20:26.413243: val_loss -0.5113 
+2023-07-24 05:20:26.413336: Pseudo dice [0.885, 0.7263, 0.6205] 
+2023-07-24 05:20:26.413419: Epoch time: 218.35 s 
+2023-07-24 05:20:26.413479: Yayy! New best EMA pseudo Dice: 0.7149 
+2023-07-24 05:20:28.466174:  
+2023-07-24 05:20:28.466462: Epoch 89 
+2023-07-24 05:20:28.466586: Current learning rate: 0.0092 
+2023-07-24 05:23:53.870799: train_loss -0.7281 
+2023-07-24 05:23:53.871042: val_loss -0.5177 
+2023-07-24 05:23:53.871127: Pseudo dice [0.8774, 0.7281, 0.5178] 
+2023-07-24 05:23:53.871280: Epoch time: 205.41 s 
+2023-07-24 05:23:56.666574:  
+2023-07-24 05:23:56.666709: Epoch 90 
+2023-07-24 05:23:56.666846: Current learning rate: 0.00919 
+2023-07-24 05:27:24.511108: train_loss -0.7268 
+2023-07-24 05:27:24.511282: val_loss -0.4883 
+2023-07-24 05:27:24.511378: Pseudo dice [0.8808, 0.7023, 0.5429] 
+2023-07-24 05:27:24.511463: Epoch time: 207.85 s 
+2023-07-24 05:27:26.253584:  
+2023-07-24 05:27:26.253713: Epoch 91 
+2023-07-24 05:27:26.253826: Current learning rate: 0.00918 
+2023-07-24 05:31:00.680078: train_loss -0.7187 
+2023-07-24 05:31:00.680262: val_loss -0.5126 
+2023-07-24 05:31:00.680357: Pseudo dice [0.8854, 0.734, 0.5313] 
+2023-07-24 05:31:00.680444: Epoch time: 214.43 s 
+2023-07-24 05:31:03.875111:  
+2023-07-24 05:31:03.875342: Epoch 92 
+2023-07-24 05:31:03.875453: Current learning rate: 0.00917 
+2023-07-24 05:34:36.700777: train_loss -0.7261 
+2023-07-24 05:34:36.701067: val_loss -0.4824 
+2023-07-24 05:34:36.701155: Pseudo dice [0.8677, 0.7068, 0.5625] 
+2023-07-24 05:34:36.701310: Epoch time: 212.83 s 
+2023-07-24 05:34:38.725615:  
+2023-07-24 05:34:38.725760: Epoch 93 
+2023-07-24 05:34:38.725878: Current learning rate: 0.00916 
+2023-07-24 05:38:14.723124: train_loss -0.7303 
+2023-07-24 05:38:14.723329: val_loss -0.4918 
+2023-07-24 05:38:14.723435: Pseudo dice [0.8725, 0.7201, 0.5379] 
+2023-07-24 05:38:14.723536: Epoch time: 216.0 s 
+2023-07-24 05:38:18.938028:  
+2023-07-24 05:38:18.938344: Epoch 94 
+2023-07-24 05:38:18.938459: Current learning rate: 0.00915 
+2023-07-24 05:41:49.722474: train_loss -0.7321 
+2023-07-24 05:41:49.722725: val_loss -0.5192 
+2023-07-24 05:41:49.722838: Pseudo dice [0.8869, 0.73, 0.5719] 
+2023-07-24 05:41:49.722987: Epoch time: 210.79 s 
+2023-07-24 05:41:49.723047: Yayy! New best EMA pseudo Dice: 0.7151 
+2023-07-24 05:41:52.238441:  
+2023-07-24 05:41:52.238770: Epoch 95 
+2023-07-24 05:41:52.238896: Current learning rate: 0.00914 
+2023-07-24 05:45:28.216917: train_loss -0.7294 
+2023-07-24 05:45:28.217144: val_loss -0.4859 
+2023-07-24 05:45:28.217231: Pseudo dice [0.8868, 0.7071, 0.502] 
+2023-07-24 05:45:28.217372: Epoch time: 215.98 s 
+2023-07-24 05:45:30.203134:  
+2023-07-24 05:45:30.203313: Epoch 96 
+2023-07-24 05:45:30.203428: Current learning rate: 0.00913 
+2023-07-24 05:49:07.118325: train_loss -0.7275 
+2023-07-24 05:49:07.130988: val_loss -0.4979 
+2023-07-24 05:49:07.131268: Pseudo dice [0.8742, 0.7169, 0.5796] 
+2023-07-24 05:49:07.131369: Epoch time: 216.92 s 
+2023-07-24 05:49:10.592634:  
+2023-07-24 05:49:10.592791: Epoch 97 
+2023-07-24 05:49:10.592889: Current learning rate: 0.00912 
+2023-07-24 05:52:37.449055: train_loss -0.7377 
+2023-07-24 05:52:37.449250: val_loss -0.4932 
+2023-07-24 05:52:37.449338: Pseudo dice [0.878, 0.7154, 0.5691] 
+2023-07-24 05:52:37.449421: Epoch time: 206.86 s 
+2023-07-24 05:52:37.449489: Yayy! New best EMA pseudo Dice: 0.7151 
+2023-07-24 05:52:39.613982:  
+2023-07-24 05:52:39.614139: Epoch 98 
+2023-07-24 05:52:39.614302: Current learning rate: 0.00911 
+2023-07-24 05:56:12.741833: train_loss -0.725 
+2023-07-24 05:56:12.742075: val_loss -0.5039 
+2023-07-24 05:56:12.742163: Pseudo dice [0.8862, 0.711, 0.6208] 
+2023-07-24 05:56:12.742243: Epoch time: 213.13 s 
+2023-07-24 05:56:12.742309: Yayy! New best EMA pseudo Dice: 0.7175 
+2023-07-24 05:56:16.737276:  
+2023-07-24 05:56:16.737576: Epoch 99 
+2023-07-24 05:56:16.737698: Current learning rate: 0.0091 
+2023-07-24 05:59:45.462096: train_loss -0.7306 
+2023-07-24 05:59:45.462285: val_loss -0.5266 
+2023-07-24 05:59:45.462388: Pseudo dice [0.8861, 0.7386, 0.513] 
+2023-07-24 05:59:45.462479: Epoch time: 208.73 s 
+2023-07-24 05:59:50.668950:  
+2023-07-24 05:59:50.669163: Epoch 100 
+2023-07-24 05:59:50.669280: Current learning rate: 0.0091 
+2023-07-24 06:03:33.285699: train_loss -0.7382 
+2023-07-24 06:03:33.285881: val_loss -0.5096 
+2023-07-24 06:03:33.285975: Pseudo dice [0.8703, 0.712, 0.5639] 
+2023-07-24 06:03:33.286058: Epoch time: 222.62 s 
+2023-07-24 06:03:35.851426:  
+2023-07-24 06:03:35.851692: Epoch 101 
+2023-07-24 06:03:35.851813: Current learning rate: 0.00909 
+2023-07-24 06:07:07.771703: train_loss -0.7398 
+2023-07-24 06:07:07.771932: val_loss -0.5006 
+2023-07-24 06:07:07.772034: Pseudo dice [0.8863, 0.723, 0.5644] 
+2023-07-24 06:07:07.772126: Epoch time: 211.92 s 
+2023-07-24 06:07:07.772207: Yayy! New best EMA pseudo Dice: 0.7176 
+2023-07-24 06:07:09.785565:  
+2023-07-24 06:07:09.785925: Epoch 102 
+2023-07-24 06:07:09.786036: Current learning rate: 0.00908 
+2023-07-24 06:10:43.597014: train_loss -0.7306 
+2023-07-24 06:10:43.597273: val_loss -0.5015 
+2023-07-24 06:10:43.599555: Pseudo dice [0.8817, 0.7251, 0.5061] 
+2023-07-24 06:10:43.599782: Epoch time: 213.81 s 
+2023-07-24 06:10:45.487350:  
+2023-07-24 06:10:45.487549: Epoch 103 
+2023-07-24 06:10:45.487668: Current learning rate: 0.00907 
+2023-07-24 06:14:14.858136: train_loss -0.7477 
+2023-07-24 06:14:14.858318: val_loss -0.4896 
+2023-07-24 06:14:14.858405: Pseudo dice [0.8898, 0.7226, 0.5152] 
+2023-07-24 06:14:14.858487: Epoch time: 209.37 s 
+2023-07-24 06:14:18.636439:  
+2023-07-24 06:14:18.636607: Epoch 104 
+2023-07-24 06:14:18.636723: Current learning rate: 0.00906 
+2023-07-24 06:17:56.407210: train_loss -0.7288 
+2023-07-24 06:17:56.407393: val_loss -0.4908 
+2023-07-24 06:17:56.407491: Pseudo dice [0.8777, 0.7242, 0.4401] 
+2023-07-24 06:17:56.407592: Epoch time: 217.77 s 
+2023-07-24 06:17:59.395841:  
+2023-07-24 06:17:59.406929: Epoch 105 
+2023-07-24 06:17:59.407068: Current learning rate: 0.00905 
+2023-07-24 06:21:34.379379: train_loss -0.7297 
+2023-07-24 06:21:34.379545: val_loss -0.5001 
+2023-07-24 06:21:34.379629: Pseudo dice [0.8812, 0.7174, 0.5466] 
+2023-07-24 06:21:34.379707: Epoch time: 215.0 s 
+2023-07-24 06:21:36.480978:  
+2023-07-24 06:21:36.481113: Epoch 106 
+2023-07-24 06:21:36.481229: Current learning rate: 0.00904 
+2023-07-24 06:24:59.495173: train_loss -0.7319 
+2023-07-24 06:24:59.495373: val_loss -0.4684 
+2023-07-24 06:24:59.495463: Pseudo dice [0.8719, 0.699, 0.4954] 
+2023-07-24 06:24:59.495545: Epoch time: 203.02 s 
+2023-07-24 06:25:01.416497:  
+2023-07-24 06:25:01.416672: Epoch 107 
+2023-07-24 06:25:01.416785: Current learning rate: 0.00903 
+2023-07-24 06:28:38.100931: train_loss -0.7425 
+2023-07-24 06:28:38.101100: val_loss -0.5066 
+2023-07-24 06:28:38.101199: Pseudo dice [0.8828, 0.7349, 0.551] 
+2023-07-24 06:28:38.101286: Epoch time: 216.69 s 
+2023-07-24 06:28:39.838402:  
+2023-07-24 06:28:39.838579: Epoch 108 
+2023-07-24 06:28:39.838694: Current learning rate: 0.00902 
+2023-07-24 06:32:19.977618: train_loss -0.7335 
+2023-07-24 06:32:19.990841: val_loss -0.4957 
+2023-07-24 06:32:19.990945: Pseudo dice [0.8713, 0.7344, 0.4371] 
+2023-07-24 06:32:19.991140: Epoch time: 220.14 s 
+2023-07-24 06:32:22.127137:  
+2023-07-24 06:32:22.127293: Epoch 109 
+2023-07-24 06:32:22.127414: Current learning rate: 0.00901 
+2023-07-24 06:35:56.862900: train_loss -0.7247 
+2023-07-24 06:35:56.863097: val_loss -0.4827 
+2023-07-24 06:35:56.863201: Pseudo dice [0.8801, 0.6793, 0.5831] 
+2023-07-24 06:35:56.863297: Epoch time: 214.74 s 
+2023-07-24 06:36:00.448839:  
+2023-07-24 06:36:00.449008: Epoch 110 
+2023-07-24 06:36:00.449116: Current learning rate: 0.009 
+2023-07-24 06:39:25.954977: train_loss -0.7307 
+2023-07-24 06:39:25.955158: val_loss -0.4855 
+2023-07-24 06:39:25.955249: Pseudo dice [0.8795, 0.6975, 0.5762] 
+2023-07-24 06:39:25.955334: Epoch time: 205.51 s 
+2023-07-24 06:39:28.934479:  
+2023-07-24 06:39:28.934601: Epoch 111 
+2023-07-24 06:39:28.934713: Current learning rate: 0.009 
+2023-07-24 06:42:53.226294: train_loss -0.7364 
+2023-07-24 06:42:53.226513: val_loss -0.4781 
+2023-07-24 06:42:53.226613: Pseudo dice [0.8844, 0.7035, 0.4998] 
+2023-07-24 06:42:53.226707: Epoch time: 204.29 s 
+2023-07-24 06:42:57.119614:  
+2023-07-24 06:42:57.119749: Epoch 112 
+2023-07-24 06:42:57.119859: Current learning rate: 0.00899 
+2023-07-24 06:46:27.868778: train_loss -0.7428 
+2023-07-24 06:46:27.868973: val_loss -0.5043 
+2023-07-24 06:46:27.869063: Pseudo dice [0.8869, 0.6979, 0.6164] 
+2023-07-24 06:46:27.869147: Epoch time: 210.75 s 
+2023-07-24 06:46:29.738551:  
+2023-07-24 06:46:29.738678: Epoch 113 
+2023-07-24 06:46:29.738820: Current learning rate: 0.00898 
+2023-07-24 06:49:47.016800: train_loss -0.7404 
+2023-07-24 06:49:47.017059: val_loss -0.484 
+2023-07-24 06:49:47.017146: Pseudo dice [0.8878, 0.7123, 0.5259] 
+2023-07-24 06:49:47.017297: Epoch time: 197.28 s 
+2023-07-24 06:49:49.028085:  
+2023-07-24 06:49:49.028212: Epoch 114 
+2023-07-24 06:49:49.028322: Current learning rate: 0.00897 
+2023-07-24 06:53:22.595026: train_loss -0.7403 
+2023-07-24 06:53:22.595240: val_loss -0.4923 
+2023-07-24 06:53:22.595333: Pseudo dice [0.8744, 0.7065, 0.5055] 
+2023-07-24 06:53:22.595417: Epoch time: 213.57 s 
+2023-07-24 06:53:25.630225:  
+2023-07-24 06:53:25.630350: Epoch 115 
+2023-07-24 06:53:25.630466: Current learning rate: 0.00896 
+2023-07-24 06:56:54.846274: train_loss -0.7407 
+2023-07-24 06:56:54.846479: val_loss -0.5206 
+2023-07-24 06:56:54.846571: Pseudo dice [0.8874, 0.7354, 0.4706] 
+2023-07-24 06:56:54.846656: Epoch time: 209.22 s 
+2023-07-24 06:56:58.530087:  
+2023-07-24 06:56:58.530329: Epoch 116 
+2023-07-24 06:56:58.530463: Current learning rate: 0.00895 
+2023-07-24 07:00:33.757893: train_loss -0.7397 
+2023-07-24 07:00:33.758083: val_loss -0.4963 
+2023-07-24 07:00:33.758173: Pseudo dice [0.8784, 0.7196, 0.5139] 
+2023-07-24 07:00:33.758259: Epoch time: 215.23 s 
+2023-07-24 07:00:36.559549:  
+2023-07-24 07:00:36.559805: Epoch 117 
+2023-07-24 07:00:36.559918: Current learning rate: 0.00894 
+2023-07-24 07:04:09.798198: train_loss -0.737 
+2023-07-24 07:04:09.798404: val_loss -0.4986 
+2023-07-24 07:04:09.809834: Pseudo dice [0.879, 0.706, 0.5266] 
+2023-07-24 07:04:09.810041: Epoch time: 213.24 s 
+2023-07-24 07:04:13.458022:  
+2023-07-24 07:04:13.458441: Epoch 118 
+2023-07-24 07:04:13.458563: Current learning rate: 0.00893 
+2023-07-24 07:07:55.203243: train_loss -0.7376 
+2023-07-24 07:07:55.203503: val_loss -0.5011 
+2023-07-24 07:07:55.203593: Pseudo dice [0.8852, 0.7273, 0.5817] 
+2023-07-24 07:07:55.203745: Epoch time: 221.75 s 
+2023-07-24 07:07:57.539746:  
+2023-07-24 07:07:57.539875: Epoch 119 
+2023-07-24 07:07:57.539999: Current learning rate: 0.00892 
+2023-07-24 07:11:23.694811: train_loss -0.7511 
+2023-07-24 07:11:23.706887: val_loss -0.5173 
+2023-07-24 07:11:23.707143: Pseudo dice [0.8818, 0.7369, 0.559] 
+2023-07-24 07:11:23.707327: Epoch time: 206.16 s 
+2023-07-24 07:11:26.298614:  
+2023-07-24 07:11:26.298933: Epoch 120 
+2023-07-24 07:11:26.299044: Current learning rate: 0.00891 
+2023-07-24 07:14:58.226911: train_loss -0.7376 
+2023-07-24 07:14:58.227126: val_loss -0.4984 
+2023-07-24 07:14:58.227228: Pseudo dice [0.8841, 0.7261, 0.4873] 
+2023-07-24 07:14:58.227322: Epoch time: 211.93 s 
+2023-07-24 07:15:00.409765:  
+2023-07-24 07:15:00.409939: Epoch 121 
+2023-07-24 07:15:00.410051: Current learning rate: 0.0089 
+2023-07-24 07:18:26.223230: train_loss -0.7331 
+2023-07-24 07:18:26.223434: val_loss -0.4727 
+2023-07-24 07:18:26.223520: Pseudo dice [0.8632, 0.6888, 0.5576] 
+2023-07-24 07:18:26.223612: Epoch time: 205.81 s 
+2023-07-24 07:18:28.232954:  
+2023-07-24 07:18:28.233098: Epoch 122 
+2023-07-24 07:18:28.233214: Current learning rate: 0.00889 
+2023-07-24 07:22:00.937126: train_loss -0.739 
+2023-07-24 07:22:00.937339: val_loss -0.4894 
+2023-07-24 07:22:00.937483: Pseudo dice [0.8769, 0.7199, 0.4559] 
+2023-07-24 07:22:00.937608: Epoch time: 212.71 s 
+2023-07-24 07:22:04.448639:  
+2023-07-24 07:22:04.448863: Epoch 123 
+2023-07-24 07:22:04.448967: Current learning rate: 0.00889 
+2023-07-24 07:25:39.226343: train_loss -0.7513 
+2023-07-24 07:25:39.226538: val_loss -0.4662 
+2023-07-24 07:25:39.226624: Pseudo dice [0.8728, 0.7032, 0.4345] 
+2023-07-24 07:25:39.226705: Epoch time: 214.78 s 
+2023-07-24 07:25:41.940106:  
+2023-07-24 07:25:41.940430: Epoch 124 
+2023-07-24 07:25:41.940552: Current learning rate: 0.00888 
+2023-07-24 07:29:31.867979: train_loss -0.7385 
+2023-07-24 07:29:31.868199: val_loss -0.4822 
+2023-07-24 07:29:31.868285: Pseudo dice [0.8879, 0.7072, 0.4662] 
+2023-07-24 07:29:31.868367: Epoch time: 229.93 s 
+2023-07-24 07:29:33.666378:  
+2023-07-24 07:29:33.666722: Epoch 125 
+2023-07-24 07:29:33.666862: Current learning rate: 0.00887 
+2023-07-24 07:33:13.897824: train_loss -0.735 
+2023-07-24 07:33:13.898085: val_loss -0.5056 
+2023-07-24 07:33:13.898172: Pseudo dice [0.8899, 0.7388, 0.4273] 
+2023-07-24 07:33:13.898322: Epoch time: 220.23 s 
+2023-07-24 07:33:15.701629:  
+2023-07-24 07:33:15.701919: Epoch 126 
+2023-07-24 07:33:15.702039: Current learning rate: 0.00886 
+2023-07-24 07:36:46.580624: train_loss -0.7412 
+2023-07-24 07:36:46.590938: val_loss -0.4682 
+2023-07-24 07:36:46.591583: Pseudo dice [0.8875, 0.707, 0.464] 
+2023-07-24 07:36:46.591727: Epoch time: 210.88 s 
+2023-07-24 07:36:49.059450:  
+2023-07-24 07:36:49.059957: Epoch 127 
+2023-07-24 07:36:49.060068: Current learning rate: 0.00885 
+2023-07-24 07:40:16.085344: train_loss -0.7467 
+2023-07-24 07:40:16.085670: val_loss -0.5007 
+2023-07-24 07:40:16.085762: Pseudo dice [0.8923, 0.7309, 0.4699] 
+2023-07-24 07:40:16.085928: Epoch time: 207.03 s 
+2023-07-24 07:40:18.069315:  
+2023-07-24 07:40:18.069484: Epoch 128 
+2023-07-24 07:40:18.069586: Current learning rate: 0.00884 
+2023-07-24 07:43:48.285688: train_loss -0.7613 
+2023-07-24 07:43:48.285896: val_loss -0.5026 
+2023-07-24 07:43:48.285983: Pseudo dice [0.8685, 0.7138, 0.5262] 
+2023-07-24 07:43:48.286072: Epoch time: 210.22 s 
+2023-07-24 07:43:50.040725:  
+2023-07-24 07:43:50.040847: Epoch 129 
+2023-07-24 07:43:50.040961: Current learning rate: 0.00883 
+2023-07-24 07:47:20.600014: train_loss -0.749 
+2023-07-24 07:47:20.600197: val_loss -0.4715 
+2023-07-24 07:47:20.600303: Pseudo dice [0.8669, 0.7066, 0.5142] 
+2023-07-24 07:47:20.600403: Epoch time: 210.56 s 
+2023-07-24 07:47:22.825845:  
+2023-07-24 07:47:22.825982: Epoch 130 
+2023-07-24 07:47:22.826100: Current learning rate: 0.00882 
+2023-07-24 07:51:01.366344: train_loss -0.7542 
+2023-07-24 07:51:01.372711: val_loss -0.4867 
+2023-07-24 07:51:01.373079: Pseudo dice [0.8709, 0.7212, 0.5275] 
+2023-07-24 07:51:01.373174: Epoch time: 218.54 s 
+2023-07-24 07:51:04.639824:  
+2023-07-24 07:51:04.640065: Epoch 131 
+2023-07-24 07:51:04.640186: Current learning rate: 0.00881 
+2023-07-24 07:54:25.996458: train_loss -0.7539 
+2023-07-24 07:54:25.996660: val_loss -0.493 
+2023-07-24 07:54:25.996764: Pseudo dice [0.879, 0.7073, 0.5086] 
+2023-07-24 07:54:25.996862: Epoch time: 201.36 s 
+2023-07-24 07:54:27.833304:  
+2023-07-24 07:54:27.833434: Epoch 132 
+2023-07-24 07:54:27.833548: Current learning rate: 0.0088 
+2023-07-24 07:58:06.308678: train_loss -0.7424 
+2023-07-24 07:58:06.308868: val_loss -0.4781 
+2023-07-24 07:58:06.308955: Pseudo dice [0.8749, 0.7132, 0.4636] 
+2023-07-24 07:58:06.309040: Epoch time: 218.48 s 
+2023-07-24 07:58:07.995495:  
+2023-07-24 07:58:07.995820: Epoch 133 
+2023-07-24 07:58:07.995934: Current learning rate: 0.00879 
+2023-07-24 08:01:39.319222: train_loss -0.7583 
+2023-07-24 08:01:39.319408: val_loss -0.4887 
+2023-07-24 08:01:39.319496: Pseudo dice [0.8776, 0.6941, 0.6211] 
+2023-07-24 08:01:39.319587: Epoch time: 211.32 s 
+2023-07-24 08:01:42.469862:  
+2023-07-24 08:01:42.470145: Epoch 134 
+2023-07-24 08:01:42.470260: Current learning rate: 0.00879 
+2023-07-24 08:05:12.546540: train_loss -0.7622 
+2023-07-24 08:05:12.546793: val_loss -0.487 
+2023-07-24 08:05:12.546890: Pseudo dice [0.877, 0.6954, 0.6036] 
+2023-07-24 08:05:12.547057: Epoch time: 210.08 s 
+2023-07-24 08:05:15.318394:  
+2023-07-24 08:05:15.318724: Epoch 135 
+2023-07-24 08:05:15.318935: Current learning rate: 0.00878 
+2023-07-24 08:08:54.008573: train_loss -0.757 
+2023-07-24 08:08:54.008773: val_loss -0.4737 
+2023-07-24 08:08:54.008857: Pseudo dice [0.8687, 0.6997, 0.5345] 
+2023-07-24 08:08:54.008935: Epoch time: 218.69 s 
+2023-07-24 08:08:57.009470:  
+2023-07-24 08:08:57.009609: Epoch 136 
+2023-07-24 08:08:57.009723: Current learning rate: 0.00877 
+2023-07-24 08:12:31.627689: train_loss -0.7468 
+2023-07-24 08:12:31.628009: val_loss -0.4989 
+2023-07-24 08:12:31.628209: Pseudo dice [0.8809, 0.7079, 0.5713] 
+2023-07-24 08:12:31.628382: Epoch time: 214.62 s 
+2023-07-24 08:12:34.747013:  
+2023-07-24 08:12:34.747181: Epoch 137 
+2023-07-24 08:12:34.747300: Current learning rate: 0.00876 
+2023-07-24 08:16:01.029350: train_loss -0.7588 
+2023-07-24 08:16:01.029534: val_loss -0.4855 
+2023-07-24 08:16:01.029621: Pseudo dice [0.8791, 0.6898, 0.6106] 
+2023-07-24 08:16:01.029705: Epoch time: 206.28 s 
+2023-07-24 08:16:02.771766:  
+2023-07-24 08:16:02.771899: Epoch 138 
+2023-07-24 08:16:02.772012: Current learning rate: 0.00875 
+2023-07-24 08:19:44.406471: train_loss -0.747 
+2023-07-24 08:19:44.406666: val_loss -0.5084 
+2023-07-24 08:19:44.406777: Pseudo dice [0.8869, 0.7204, 0.5542] 
+2023-07-24 08:19:44.406874: Epoch time: 221.64 s 
+2023-07-24 08:19:47.105821:  
+2023-07-24 08:19:47.105972: Epoch 139 
+2023-07-24 08:19:47.106068: Current learning rate: 0.00874 
+2023-07-24 08:23:18.237113: train_loss -0.7528 
+2023-07-24 08:23:18.237421: val_loss -0.4871 
+2023-07-24 08:23:18.237525: Pseudo dice [0.8776, 0.706, 0.5431] 
+2023-07-24 08:23:18.237715: Epoch time: 211.13 s 
+2023-07-24 08:23:20.150906:  
+2023-07-24 08:23:20.151026: Epoch 140 
+2023-07-24 08:23:20.151122: Current learning rate: 0.00873 
+2023-07-24 08:26:46.623374: train_loss -0.7637 
+2023-07-24 08:26:46.623651: val_loss -0.4917 
+2023-07-24 08:26:46.623740: Pseudo dice [0.8785, 0.6989, 0.6665] 
+2023-07-24 08:26:46.623897: Epoch time: 206.47 s 
+2023-07-24 08:26:48.784806:  
+2023-07-24 08:26:48.785008: Epoch 141 
+2023-07-24 08:26:48.785114: Current learning rate: 0.00872 
+2023-07-24 08:30:30.098001: train_loss -0.7605 
+2023-07-24 08:30:30.098207: val_loss -0.499 
+2023-07-24 08:30:30.098298: Pseudo dice [0.8795, 0.7093, 0.5429] 
+2023-07-24 08:30:30.098378: Epoch time: 221.31 s 
+2023-07-24 08:30:32.113620:  
+2023-07-24 08:30:32.113780: Epoch 142 
+2023-07-24 08:30:32.113915: Current learning rate: 0.00871 
+2023-07-24 08:33:58.876559: train_loss -0.7434 
+2023-07-24 08:33:58.876756: val_loss -0.4936 
+2023-07-24 08:33:58.876848: Pseudo dice [0.8844, 0.7121, 0.5416] 
+2023-07-24 08:33:58.876932: Epoch time: 206.76 s 
+2023-07-24 08:34:01.494818:  
+2023-07-24 08:34:01.495366: Epoch 143 
+2023-07-24 08:34:01.495486: Current learning rate: 0.0087 
+2023-07-24 08:37:26.398371: train_loss -0.7416 
+2023-07-24 08:37:26.398579: val_loss -0.5108 
+2023-07-24 08:37:26.398680: Pseudo dice [0.8855, 0.7182, 0.5946] 
+2023-07-24 08:37:26.398790: Epoch time: 204.91 s 
+2023-07-24 08:37:30.655529:  
+2023-07-24 08:37:30.655930: Epoch 144 
+2023-07-24 08:37:30.656043: Current learning rate: 0.00869 
+2023-07-24 08:40:55.134679: train_loss -0.7574 
+2023-07-24 08:40:55.134889: val_loss -0.4785 
+2023-07-24 08:40:55.134991: Pseudo dice [0.8803, 0.7162, 0.3907] 
+2023-07-24 08:40:55.135085: Epoch time: 204.48 s 
+2023-07-24 08:40:57.261309:  
+2023-07-24 08:40:57.261564: Epoch 145 
+2023-07-24 08:40:57.261678: Current learning rate: 0.00868 
+2023-07-24 08:44:23.101116: train_loss -0.7516 
+2023-07-24 08:44:23.106944: val_loss -0.4902 
+2023-07-24 08:44:23.107199: Pseudo dice [0.8784, 0.7066, 0.5606] 
+2023-07-24 08:44:23.107291: Epoch time: 205.84 s 
+2023-07-24 08:44:25.099223:  
+2023-07-24 08:44:25.099430: Epoch 146 
+2023-07-24 08:44:25.099553: Current learning rate: 0.00868 
+2023-07-24 08:47:56.039377: train_loss -0.759 
+2023-07-24 08:47:56.039689: val_loss -0.5251 
+2023-07-24 08:47:56.039789: Pseudo dice [0.8786, 0.7236, 0.5975] 
+2023-07-24 08:47:56.039928: Epoch time: 210.94 s 
+2023-07-24 08:47:58.670197:  
+2023-07-24 08:47:58.670312: Epoch 147 
+2023-07-24 08:47:58.670412: Current learning rate: 0.00867 
+2023-07-24 08:51:23.394095: train_loss -0.7657 
+2023-07-24 08:51:23.394388: val_loss -0.4863 
+2023-07-24 08:51:23.394477: Pseudo dice [0.8613, 0.7275, 0.4779] 
+2023-07-24 08:51:23.394634: Epoch time: 204.72 s 
+2023-07-24 08:51:25.151827:  
+2023-07-24 08:51:25.152007: Epoch 148 
+2023-07-24 08:51:25.152129: Current learning rate: 0.00866 
+2023-07-24 08:55:00.305970: train_loss -0.759 
+2023-07-24 08:55:00.306156: val_loss -0.4877 
+2023-07-24 08:55:00.306260: Pseudo dice [0.8708, 0.7051, 0.5229] 
+2023-07-24 08:55:00.306348: Epoch time: 215.16 s 
+2023-07-24 08:55:03.166358:  
+2023-07-24 08:55:03.166571: Epoch 149 
+2023-07-24 08:55:03.166703: Current learning rate: 0.00865 
+2023-07-24 08:58:36.158555: train_loss -0.7584 
+2023-07-24 08:58:36.163698: val_loss -0.5075 
+2023-07-24 08:58:36.163839: Pseudo dice [0.8796, 0.7186, 0.6129] 
+2023-07-24 08:58:36.163924: Epoch time: 212.99 s 
+2023-07-24 08:58:39.431387:  
+2023-07-24 08:58:39.431509: Epoch 150 
+2023-07-24 08:58:39.431633: Current learning rate: 0.00864 
+2023-07-24 09:02:08.121246: train_loss -0.7586 
+2023-07-24 09:02:08.121416: val_loss -0.5081 
+2023-07-24 09:02:08.121502: Pseudo dice [0.8847, 0.727, 0.6269] 
+2023-07-24 09:02:08.121582: Epoch time: 208.69 s 
+2023-07-24 09:02:10.686369:  
+2023-07-24 09:02:10.686602: Epoch 151 
+2023-07-24 09:02:10.686719: Current learning rate: 0.00863 
+2023-07-24 09:05:44.706747: train_loss -0.7629 
+2023-07-24 09:05:44.706952: val_loss -0.4625 
+2023-07-24 09:05:44.707042: Pseudo dice [0.8698, 0.6773, 0.509] 
+2023-07-24 09:05:44.707127: Epoch time: 214.02 s 
+2023-07-24 09:05:48.504527:  
+2023-07-24 09:05:48.504706: Epoch 152 
+2023-07-24 09:05:48.504832: Current learning rate: 0.00862 
+2023-07-24 09:09:19.069351: train_loss -0.7578 
+2023-07-24 09:09:19.069588: val_loss -0.5 
+2023-07-24 09:09:19.069682: Pseudo dice [0.8778, 0.7217, 0.5765] 
+2023-07-24 09:09:19.069833: Epoch time: 210.57 s 
+2023-07-24 09:09:21.075281:  
+2023-07-24 09:09:21.075402: Epoch 153 
+2023-07-24 09:09:21.075518: Current learning rate: 0.00861 
+2023-07-24 09:12:58.820027: train_loss -0.7531 
+2023-07-24 09:12:58.826884: val_loss -0.4773 
+2023-07-24 09:12:58.827152: Pseudo dice [0.8738, 0.7111, 0.5466] 
+2023-07-24 09:12:58.827244: Epoch time: 217.75 s 
+2023-07-24 09:13:02.862645:  
+2023-07-24 09:13:02.862877: Epoch 154 
+2023-07-24 09:13:02.862992: Current learning rate: 0.0086 
+2023-07-24 09:16:28.130270: train_loss -0.7474 
+2023-07-24 09:16:28.130463: val_loss -0.4756 
+2023-07-24 09:16:28.130564: Pseudo dice [0.8795, 0.7165, 0.489] 
+2023-07-24 09:16:28.130655: Epoch time: 205.27 s 
+2023-07-24 09:16:30.896615:  
+2023-07-24 09:16:30.896794: Epoch 155 
+2023-07-24 09:16:30.896921: Current learning rate: 0.00859 
+2023-07-24 09:20:10.606297: train_loss -0.7662 
+2023-07-24 09:20:10.609706: val_loss -0.4819 
+2023-07-24 09:20:10.609968: Pseudo dice [0.8773, 0.711, 0.5174] 
+2023-07-24 09:20:10.610058: Epoch time: 219.71 s 
+2023-07-24 09:20:13.662477:  
+2023-07-24 09:20:13.662828: Epoch 156 
+2023-07-24 09:20:13.662953: Current learning rate: 0.00858 
+2023-07-24 09:23:55.274102: train_loss -0.7626 
+2023-07-24 09:23:55.274285: val_loss -0.4917 
+2023-07-24 09:23:55.274372: Pseudo dice [0.8786, 0.7163, 0.5242] 
+2023-07-24 09:23:55.274455: Epoch time: 221.61 s 
+2023-07-24 09:23:56.839917:  
+2023-07-24 09:23:56.840061: Epoch 157 
+2023-07-24 09:23:56.840175: Current learning rate: 0.00858 
+2023-07-24 09:27:20.148015: train_loss -0.7542 
+2023-07-24 09:27:20.148191: val_loss -0.5016 
+2023-07-24 09:27:20.148277: Pseudo dice [0.8869, 0.7229, 0.5561] 
+2023-07-24 09:27:20.148360: Epoch time: 203.31 s 
+2023-07-24 09:27:21.869529:  
+2023-07-24 09:27:21.869649: Epoch 158 
+2023-07-24 09:27:21.869765: Current learning rate: 0.00857 
+2023-07-24 09:30:58.994606: train_loss -0.7622 
+2023-07-24 09:30:58.994871: val_loss -0.4855 
+2023-07-24 09:30:58.994982: Pseudo dice [0.8842, 0.7165, 0.4867] 
+2023-07-24 09:30:58.995074: Epoch time: 217.13 s 
+2023-07-24 09:31:03.135907:  
+2023-07-24 09:31:03.136067: Epoch 159 
+2023-07-24 09:31:03.136214: Current learning rate: 0.00856 

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/Untitled.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f69d6289-cca0-4546-baf8-c3e0bd052370",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \u001b[0m\u001b[01;34mProstate158\u001b[0m/                  create_nnunet_dataset.py   \u001b[01;34mnnUNet_results\u001b[0m/\n",
+      "'Untitled (2) (1) (2).ipynb'   \u001b[01;34mnnUNet_preprocessed\u001b[0m/\n",
+      " Untitled.ipynb                \u001b[01;34mnnUNet_raw\u001b[0m/\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d358c520-7bfb-4846-a9a7-e01a151b6912",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "ename": "RepositoryNotFoundError",
+     "evalue": "404 Client Error. (Request ID: Root=1-64be540a-13608ebb4f5e72723d9907a8;bec748ef-4d79-4099-914c-5e806f83f5e0)\n\nRepository Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py:261\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 261\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
+      "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m api \u001b[38;5;241m=\u001b[39m HfApi()\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Upload all the content from the local folder to your remote Space.\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# By default, files are uploaded at the root of the repo\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupload_folder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43musername/my-cool-space\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspace\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    825\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    827\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:3509\u001b[0m, in \u001b[0;36mHfApi.upload_folder\u001b[0;34m(self, repo_id, folder_path, path_in_repo, commit_message, commit_description, token, repo_type, revision, create_pr, parent_commit, allow_patterns, ignore_patterns, delete_patterns, multi_commits, multi_commits_verbose, run_as_future)\u001b[0m\n\u001b[1;32m   3497\u001b[0m     pr_url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_commits_on_pr(\n\u001b[1;32m   3498\u001b[0m         repo_id\u001b[38;5;241m=\u001b[39mrepo_id,\n\u001b[1;32m   3499\u001b[0m         repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3506\u001b[0m         verbose\u001b[38;5;241m=\u001b[39mmulti_commits_verbose,\n\u001b[1;32m   3507\u001b[0m     )\n\u001b[1;32m   3508\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3509\u001b[0m     commit_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_commit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3512\u001b[0m \u001b[43m        \u001b[49m\u001b[43moperations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_operations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3513\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_message\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3514\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcommit_description\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3515\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3516\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3517\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3518\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparent_commit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent_commit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3519\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3520\u001b[0m     pr_url \u001b[38;5;241m=\u001b[39m commit_info\u001b[38;5;241m.\u001b[39mpr_url\n\u001b[1;32m   3522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;129;01mand\u001b[39;00m pr_url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    825\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    827\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:2675\u001b[0m, in \u001b[0;36mHfApi.create_commit\u001b[0;34m(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)\u001b[0m\n\u001b[1;32m   2672\u001b[0m warn_on_overwriting_operations(operations)\n\u001b[1;32m   2674\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2675\u001b[0m     upload_modes \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_upload_modes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2676\u001b[0m \u001b[43m        \u001b[49m\u001b[43madditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madditions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2679\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2680\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2681\u001b[0m \u001b[43m        \u001b[49m\u001b[43mendpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2682\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2683\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2684\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   2685\u001b[0m     e\u001b[38;5;241m.\u001b[39mappend_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/_commit_api.py:483\u001b[0m, in \u001b[0;36mfetch_upload_modes\u001b[0;34m(additions, repo_type, repo_id, token, revision, endpoint, create_pr)\u001b[0m\n\u001b[1;32m    465\u001b[0m payload \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    466\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m    467\u001b[0m         {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    474\u001b[0m     ]\n\u001b[1;32m    475\u001b[0m }\n\u001b[1;32m    477\u001b[0m resp \u001b[38;5;241m=\u001b[39m get_session()\u001b[38;5;241m.\u001b[39mpost(\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/api/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/preupload/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrevision\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    479\u001b[0m     json\u001b[38;5;241m=\u001b[39mpayload,\n\u001b[1;32m    480\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m    481\u001b[0m     params\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreate_pr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1\u001b[39m\u001b[38;5;124m\"\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    482\u001b[0m )\n\u001b[0;32m--> 483\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    484\u001b[0m preupload_info \u001b[38;5;241m=\u001b[39m _validate_preupload_info(resp\u001b[38;5;241m.\u001b[39mjson())\n\u001b[1;32m    485\u001b[0m upload_modes\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m]: file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muploadMode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m preupload_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles\u001b[39m\u001b[38;5;124m\"\u001b[39m]})\n",
+      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py:293\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepoNotFound\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m401\u001b[39m:\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;66;03m# 401 is misleading as it is returned for:\u001b[39;00m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;66;03m#    - private and gated repos if user is not authenticated\u001b[39;00m\n\u001b[1;32m    282\u001b[0m     \u001b[38;5;66;03m#    - missing repos\u001b[39;00m\n\u001b[1;32m    283\u001b[0m     \u001b[38;5;66;03m# => for now, we process them as `RepoNotFound` anyway.\u001b[39;00m\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;66;03m# See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9\u001b[39;00m\n\u001b[1;32m    285\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    286\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    287\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m make sure you are authenticated.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    292\u001b[0m     )\n\u001b[0;32m--> 293\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RepositoryNotFoundError(message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    295\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m400\u001b[39m:\n\u001b[1;32m    296\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    297\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m endpoint:\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m endpoint_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    298\u001b[0m     )\n",
+      "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-64be540a-13608ebb4f5e72723d9907a8;bec748ef-4d79-4099-914c-5e806f83f5e0)\n\nRepository Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case."
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "api = HfApi()\n",
+    "\n",
+    "api.create_repo(\n",
+    "    repo_id=\"osbm/prostate158_nnUNet_results_3d_fullres2\",\n",
+    "    repo_type=\"dataset\",\n",
+    "    exist_ok=True,\n",
+    "    private=False,\n",
+    ")\n",
+    "# Upload all the content from the local folder to your remote Space.\n",
+    "# By default, files are uploaded at the root of the repo\n",
+    "api.upload_folder(\n",
+    "    folder_path=\"\",\n",
+    "    repo_id=\"username/my-cool-space\",\n",
+    "    repo_type=\"space\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dc0670f-31b2-4b0d-b756-043b798d865e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/checkpoint_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b1c91cbd76cbc97c1a5c93420cf3496051fab1e0972623ab6a21416aab02550
+size 356790417

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/debug.json

@@ -0,0 +1,52 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f974c2f5780>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f974c2f5090>",
+    "dataloader_train.num_processes": "4",
+    "dataloader_train.transform": "Compose ( [Convert3DTo2DTransform( apply_to_keys = ('data', 'seg') ), 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 = [256, 256], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-3.141592653589793, 3.141592653589793), angle_y = (0, 0), angle_z = (0, 0), 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 ), Convert2DTo3DTransform( apply_to_keys = ('data', 'seg') ), 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 = (0,) ), 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], [1.0, 0.5, 0.5], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f974c2f4880>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f974c2f4be0>",
+    "dataloader_val.num_processes": "2",
+    "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], [1.0, 0.5, 0.5], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A10G",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f974d1d1a50>",
+    "hostname": "s-osbm-jupyter-f0b83-8689bbb555-5t6kn",
+    "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 0x7f974d1d1c00>",
+    "local_rank": "0",
+    "log_file": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/training_log_2023_7_24_09_31_46.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f974d1d1ae0>",
+    "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 0x7f974d1d1b40>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[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]], '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': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "3",
+    "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    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": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1",
+    "output_folder_base": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[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]], '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': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}",
+    "preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset001_Prostate158/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset001_Prostate158",
+    "save_every": "50",
+    "torch_version": "2.0.1+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/training_log_2023_7_24_09_31_46.txt

@@ -0,0 +1,170 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}} 
+ 
+2023-07-24 09:31:48.662707: unpacking dataset... 
+2023-07-24 09:31:51.254056: unpacking done... 
+2023-07-24 09:31:51.254646: do_dummy_2d_data_aug: True 
+2023-07-24 09:31:51.255391: Using splits from existing split file: nnUNet_preprocessed/Dataset001_Prostate158/splits_final.json 
+2023-07-24 09:31:51.255736: The split file contains 5 splits. 
+2023-07-24 09:31:51.255795: Desired fold for training: 1 
+2023-07-24 09:31:51.255847: This split has 111 training and 28 validation cases. 
+2023-07-24 09:31:55.429484: Unable to plot network architecture: 
+2023-07-24 09:31:55.429709: module 'torch.onnx' has no attribute '_optimize_trace' 
+2023-07-24 09:31:55.479973:  
+2023-07-24 09:31:55.480082: Epoch 0 
+2023-07-24 09:31:55.480217: Current learning rate: 0.01 
+2023-07-24 09:35:51.149318: train_loss -0.0686 
+2023-07-24 09:35:51.149595: val_loss -0.1977 
+2023-07-24 09:35:51.149694: Pseudo dice [0.6772, 0.3986, 0.0] 
+2023-07-24 09:35:51.149858: Epoch time: 235.67 s 
+2023-07-24 09:35:51.149975: Yayy! New best EMA pseudo Dice: 0.3586 
+2023-07-24 09:35:52.944725:  
+2023-07-24 09:35:52.944855: Epoch 1 
+2023-07-24 09:35:52.945019: Current learning rate: 0.00999 
+2023-07-24 09:39:26.457915: train_loss -0.2509 
+2023-07-24 09:39:26.458209: val_loss -0.2695 
+2023-07-24 09:39:26.458309: Pseudo dice [0.7352, 0.5579, 0.0] 
+2023-07-24 09:39:26.458386: Epoch time: 213.51 s 
+2023-07-24 09:39:26.458445: Yayy! New best EMA pseudo Dice: 0.3658 
+2023-07-24 09:39:30.325983:  
+2023-07-24 09:39:30.326120: Epoch 2 
+2023-07-24 09:39:30.326235: Current learning rate: 0.00998 
+2023-07-24 09:42:59.369360: train_loss -0.3283 
+2023-07-24 09:42:59.369581: val_loss -0.2917 
+2023-07-24 09:42:59.369678: Pseudo dice [0.7702, 0.5453, 0.0] 
+2023-07-24 09:42:59.369768: Epoch time: 209.04 s 
+2023-07-24 09:42:59.369841: Yayy! New best EMA pseudo Dice: 0.3731 
+2023-07-24 09:43:02.898393:  
+2023-07-24 09:43:02.898701: Epoch 3 
+2023-07-24 09:43:02.898844: Current learning rate: 0.00997 
+2023-07-24 09:46:30.423032: train_loss -0.3623 
+2023-07-24 09:46:30.423313: val_loss -0.362 
+2023-07-24 09:46:30.423406: Pseudo dice [0.8084, 0.6354, 0.0] 
+2023-07-24 09:46:30.423570: Epoch time: 207.53 s 
+2023-07-24 09:46:30.423706: Yayy! New best EMA pseudo Dice: 0.3839 
+2023-07-24 09:46:32.679859:  
+2023-07-24 09:46:32.679986: Epoch 4 
+2023-07-24 09:46:32.680083: Current learning rate: 0.00996 
+2023-07-24 09:50:16.112560: train_loss -0.4055 
+2023-07-24 09:50:16.112745: val_loss -0.3814 
+2023-07-24 09:50:16.112836: Pseudo dice [0.8423, 0.6152, 0.3433] 
+2023-07-24 09:50:16.112920: Epoch time: 223.43 s 
+2023-07-24 09:50:16.112987: Yayy! New best EMA pseudo Dice: 0.4056 
+2023-07-24 09:50:18.759251:  
+2023-07-24 09:50:18.759383: Epoch 5 
+2023-07-24 09:50:18.759503: Current learning rate: 0.00995 
+2023-07-24 09:53:41.007815: train_loss -0.4426 
+2023-07-24 09:53:41.007999: val_loss -0.4302 
+2023-07-24 09:53:41.008113: Pseudo dice [0.8359, 0.6439, 0.4462] 
+2023-07-24 09:53:41.008215: Epoch time: 202.25 s 
+2023-07-24 09:53:41.008303: Yayy! New best EMA pseudo Dice: 0.4292 
+2023-07-24 09:53:43.767199:  
+2023-07-24 09:53:43.767324: Epoch 6 
+2023-07-24 09:53:43.767419: Current learning rate: 0.00995 
+2023-07-24 09:57:16.562120: train_loss -0.4301 
+2023-07-24 09:57:16.562339: val_loss -0.4084 
+2023-07-24 09:57:16.562424: Pseudo dice [0.8011, 0.6376, 0.48] 
+2023-07-24 09:57:16.562501: Epoch time: 212.8 s 
+2023-07-24 09:57:16.562591: Yayy! New best EMA pseudo Dice: 0.4502 
+2023-07-24 09:57:19.469062:  
+2023-07-24 09:57:19.469288: Epoch 7 
+2023-07-24 09:57:19.469407: Current learning rate: 0.00994 
+2023-07-24 10:00:48.194506: train_loss -0.4578 
+2023-07-24 10:00:48.194689: val_loss -0.4189 
+2023-07-24 10:00:48.194808: Pseudo dice [0.8137, 0.6457, 0.4479] 
+2023-07-24 10:00:48.194900: Epoch time: 208.73 s 
+2023-07-24 10:00:48.194967: Yayy! New best EMA pseudo Dice: 0.4688 
+2023-07-24 10:00:52.373474:  
+2023-07-24 10:00:52.373721: Epoch 8 
+2023-07-24 10:00:52.373865: Current learning rate: 0.00993 
+2023-07-24 10:04:19.517230: train_loss -0.498 
+2023-07-24 10:04:19.517421: val_loss -0.4187 
+2023-07-24 10:04:19.517504: Pseudo dice [0.8555, 0.6358, 0.3124] 
+2023-07-24 10:04:19.517583: Epoch time: 207.15 s 
+2023-07-24 10:04:19.517648: Yayy! New best EMA pseudo Dice: 0.482 
+2023-07-24 10:04:21.806987:  
+2023-07-24 10:04:21.807117: Epoch 9 
+2023-07-24 10:04:21.807218: Current learning rate: 0.00992 
+2023-07-24 10:07:51.359195: train_loss -0.5027 
+2023-07-24 10:07:51.370858: val_loss -0.4306 
+2023-07-24 10:07:51.371073: Pseudo dice [0.8521, 0.6531, 0.3801] 
+2023-07-24 10:07:51.371159: Epoch time: 209.55 s 
+2023-07-24 10:07:51.371223: Yayy! New best EMA pseudo Dice: 0.4967 
+2023-07-24 10:07:54.287527:  
+2023-07-24 10:07:54.287659: Epoch 10 
+2023-07-24 10:07:54.287776: Current learning rate: 0.00991 
+2023-07-24 10:11:21.020047: train_loss -0.5007 
+2023-07-24 10:11:21.020299: val_loss -0.4555 
+2023-07-24 10:11:21.020390: Pseudo dice [0.8525, 0.6574, 0.5038] 
+2023-07-24 10:11:21.020547: Epoch time: 206.73 s 
+2023-07-24 10:11:21.020615: Yayy! New best EMA pseudo Dice: 0.5141 
+2023-07-24 10:11:23.595996:  
+2023-07-24 10:11:23.596125: Epoch 11 
+2023-07-24 10:11:23.596240: Current learning rate: 0.0099 
+2023-07-24 10:14:50.060935: train_loss -0.5114 
+2023-07-24 10:14:50.061202: val_loss -0.4435 
+2023-07-24 10:14:50.061292: Pseudo dice [0.8367, 0.6786, 0.3972] 
+2023-07-24 10:14:50.061454: Epoch time: 206.47 s 
+2023-07-24 10:14:50.061521: Yayy! New best EMA pseudo Dice: 0.5265 
+2023-07-24 10:14:54.044929:  
+2023-07-24 10:14:54.045076: Epoch 12 
+2023-07-24 10:14:54.045194: Current learning rate: 0.00989 
+2023-07-24 10:18:35.033546: train_loss -0.5262 
+2023-07-24 10:18:35.033730: val_loss -0.4482 
+2023-07-24 10:18:35.033818: Pseudo dice [0.8485, 0.6596, 0.5738] 
+2023-07-24 10:18:35.033900: Epoch time: 220.99 s 
+2023-07-24 10:18:35.033965: Yayy! New best EMA pseudo Dice: 0.5432 
+2023-07-24 10:18:37.229037:  
+2023-07-24 10:18:37.229155: Epoch 13 
+2023-07-24 10:18:37.229270: Current learning rate: 0.00988 
+2023-07-24 10:22:07.718349: train_loss -0.5297 
+2023-07-24 10:22:07.718728: val_loss -0.4473 
+2023-07-24 10:22:07.718979: Pseudo dice [0.8557, 0.6552, 0.5333] 
+2023-07-24 10:22:07.719130: Epoch time: 210.49 s 
+2023-07-24 10:22:07.719215: Yayy! New best EMA pseudo Dice: 0.557 
+2023-07-24 10:22:12.377048:  
+2023-07-24 10:22:12.377682: Epoch 14 
+2023-07-24 10:22:12.377826: Current learning rate: 0.00987 
+2023-07-24 10:25:35.042624: train_loss -0.544 
+2023-07-24 10:25:35.042880: val_loss -0.4672 
+2023-07-24 10:25:35.042980: Pseudo dice [0.8415, 0.6909, 0.5861] 
+2023-07-24 10:25:35.043072: Epoch time: 202.67 s 
+2023-07-24 10:25:35.043142: Yayy! New best EMA pseudo Dice: 0.5719 
+2023-07-24 10:25:39.141425:  
+2023-07-24 10:25:39.141566: Epoch 15 
+2023-07-24 10:25:39.141699: Current learning rate: 0.00986 
+2023-07-24 10:29:17.006086: train_loss -0.536 
+2023-07-24 10:29:17.006276: val_loss -0.458 
+2023-07-24 10:29:17.006367: Pseudo dice [0.8684, 0.6802, 0.4725] 
+2023-07-24 10:29:17.006453: Epoch time: 217.87 s 
+2023-07-24 10:29:17.006521: Yayy! New best EMA pseudo Dice: 0.5821 
+2023-07-24 10:29:19.086107:  
+2023-07-24 10:29:19.086326: Epoch 16 
+2023-07-24 10:29:19.086447: Current learning rate: 0.00986 
+2023-07-24 10:32:52.937337: train_loss -0.5573 
+2023-07-24 10:32:52.937526: val_loss -0.4805 
+2023-07-24 10:32:52.937625: Pseudo dice [0.8545, 0.6796, 0.6108] 
+2023-07-24 10:32:52.937717: Epoch time: 213.85 s 
+2023-07-24 10:32:52.937808: Yayy! New best EMA pseudo Dice: 0.5954 
+2023-07-24 10:32:55.486784:  
+2023-07-24 10:32:55.486909: Epoch 17 
+2023-07-24 10:32:55.487025: Current learning rate: 0.00985 
+2023-07-24 10:36:29.203138: train_loss -0.5585 
+2023-07-24 10:36:29.203351: val_loss -0.4583 
+2023-07-24 10:36:29.203443: Pseudo dice [0.8662, 0.6791, 0.4972] 
+2023-07-24 10:36:29.203535: Epoch time: 213.72 s 
+2023-07-24 10:36:29.203619: Yayy! New best EMA pseudo Dice: 0.604 
+2023-07-24 10:36:34.055434:  
+2023-07-24 10:36:34.055742: Epoch 18 
+2023-07-24 10:36:34.055855: Current learning rate: 0.00984 

Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,342 @@
+{
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