add model weights and logs

model_training/nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all/.ipynb_checkpoints/training_log_2025_10_10_01_01_57-checkpoint.txt

@@ -0,0 +1,146 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2025-10-10 01:01:58.822350: Using torch.compile... 
+2025-10-10 01:01:59.568636: do_dummy_2d_data_aug: False 
+
+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': [112, 128, 128], 'median_image_size_in_voxels': [391.0, 512.0, 512.0], 'spacing': [2.5, 0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset101_FinalSet', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 0.87109375, 0.87109375], 'original_median_shape_after_transp': [391, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1567.0, 'mean': 30.700855255126953, 'median': 39.0, 'min': -1024.0, 'percentile_00_5': -95.0, 'percentile_99_5': 116.0, 'std': 39.6912956237793}}} 
+ 
+2025-10-10 01:02:00.254874: Unable to plot network architecture: nnUNet_compile is enabled! 
+2025-10-10 01:02:00.262840:  
+2025-10-10 01:02:00.263093: Epoch 0 
+2025-10-10 01:02:00.263730: Current learning rate: 0.01 
+2025-10-10 01:02:52.677278: train_loss 0.4693 
+2025-10-10 01:02:52.677473: val_loss 0.3563 
+2025-10-10 01:02:52.677547: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-10-10 01:02:52.677599: Epoch time: 52.42 s 
+2025-10-10 01:02:52.677628: Yayy! New best EMA pseudo Dice: 0.0 
+2025-10-10 01:02:53.382452:  
+2025-10-10 01:02:53.382595: Epoch 1 
+2025-10-10 01:02:53.382679: Current learning rate: 0.00999 
+2025-10-10 01:03:05.693481: train_loss 0.341 
+2025-10-10 01:03:05.693637: val_loss 0.3249 
+2025-10-10 01:03:05.693710: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-10-10 01:03:05.693762: Epoch time: 12.31 s 
+2025-10-10 01:03:06.324357:  
+2025-10-10 01:03:06.324465: Epoch 2 
+2025-10-10 01:03:06.324546: Current learning rate: 0.00998 
+2025-10-10 01:03:18.689049: train_loss 0.3171 
+2025-10-10 01:03:18.689218: val_loss 0.3228 
+2025-10-10 01:03:18.689289: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-10-10 01:03:18.689340: Epoch time: 12.37 s 
+2025-10-10 01:03:19.348528:  
+2025-10-10 01:03:19.348741: Epoch 3 
+2025-10-10 01:03:19.348851: Current learning rate: 0.00997 
+2025-10-10 01:03:31.769313: train_loss 0.3046 
+2025-10-10 01:03:31.769485: val_loss 0.2864 
+2025-10-10 01:03:31.769561: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.0052), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-10-10 01:03:31.769661: Epoch time: 12.42 s 
+2025-10-10 01:03:31.769700: Yayy! New best EMA pseudo Dice: 9.999999747378752e-05 
+2025-10-10 01:03:32.571169:  
+2025-10-10 01:03:32.571304: Epoch 4 
+2025-10-10 01:03:32.571398: Current learning rate: 0.00996 
+2025-10-10 01:03:45.008398: train_loss 0.2734 
+2025-10-10 01:03:45.008551: val_loss 0.2431 
+2025-10-10 01:03:45.008620: Pseudo dice [np.float32(0.0), np.float32(0.0), np.float32(0.5296), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] 
+2025-10-10 01:03:45.008673: Epoch time: 12.44 s 
+2025-10-10 01:03:45.008704: Yayy! New best EMA pseudo Dice: 0.005900000222027302 
+2025-10-10 01:03:45.816751:  
+2025-10-10 01:03:45.816903: Epoch 5 
+2025-10-10 01:03:45.816992: Current learning rate: 0.00995 
+2025-10-10 01:03:58.279692: train_loss 0.2456 
+2025-10-10 01:03:58.279913: val_loss 0.2106 
+2025-10-10 01:03:58.280334: Pseudo dice [np.float32(0.2983), np.float32(0.0), np.float32(0.629), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0006)] 
+2025-10-10 01:03:58.280401: Epoch time: 12.46 s 
+2025-10-10 01:03:58.280439: Yayy! New best EMA pseudo Dice: 0.015699999406933784 
+2025-10-10 01:03:59.066660:  
+2025-10-10 01:03:59.066812: Epoch 6 
+2025-10-10 01:03:59.066964: Current learning rate: 0.00995 
+2025-10-10 01:04:11.536456: train_loss 0.2018 
+2025-10-10 01:04:11.536756: val_loss 0.1539 
+2025-10-10 01:04:11.536861: Pseudo dice [np.float32(0.5945), np.float32(0.0), np.float32(0.6986), np.float32(0.0), np.float32(0.0), np.float32(0.0011), np.float32(0.0), np.float32(0.0), np.float32(0.3696)] 
+2025-10-10 01:04:11.536930: Epoch time: 12.47 s 
+2025-10-10 01:04:11.536964: Yayy! New best EMA pseudo Dice: 0.032600000500679016 
+2025-10-10 01:04:12.321670:  
+2025-10-10 01:04:12.321822: Epoch 7 
+2025-10-10 01:04:12.321927: Current learning rate: 0.00994 
+2025-10-10 01:04:24.823251: train_loss 0.1426 
+2025-10-10 01:04:24.823452: val_loss 0.122 
+2025-10-10 01:04:24.823526: Pseudo dice [np.float32(0.6552), np.float32(0.0), np.float32(0.684), np.float32(0.0), np.float32(0.1185), np.float32(0.13), np.float32(0.0), np.float32(0.0), np.float32(0.5228)] 
+2025-10-10 01:04:24.823584: Epoch time: 12.5 s 
+2025-10-10 01:04:24.823619: Yayy! New best EMA pseudo Dice: 0.052799999713897705 
+2025-10-10 01:04:25.620759:  
+2025-10-10 01:04:25.620902: Epoch 8 
+2025-10-10 01:04:25.620995: Current learning rate: 0.00993 
+2025-10-10 01:04:38.122212: train_loss 0.0939 
+2025-10-10 01:04:38.122365: val_loss 0.0189 
+2025-10-10 01:04:38.122432: Pseudo dice [np.float32(0.7589), np.float32(0.0), np.float32(0.792), np.float32(0.0), np.float32(0.576), np.float32(0.0743), np.float32(0.0), np.float32(0.0), np.float32(0.598)] 
+2025-10-10 01:04:38.122488: Epoch time: 12.5 s 
+2025-10-10 01:04:38.122518: Yayy! New best EMA pseudo Dice: 0.07859999686479568 
+2025-10-10 01:04:38.935607:  
+2025-10-10 01:04:38.935712: Epoch 9 
+2025-10-10 01:04:38.935801: Current learning rate: 0.00992 
+2025-10-10 01:04:51.429118: train_loss 0.0677 
+2025-10-10 01:04:51.429311: val_loss 0.0416 
+2025-10-10 01:04:51.429384: Pseudo dice [np.float32(0.7192), np.float32(0.0), np.float32(0.757), np.float32(0.0), np.float32(0.4609), np.float32(0.3806), np.float32(0.0532), np.float32(0.0118), np.float32(0.5489)] 
+2025-10-10 01:04:51.429441: Epoch time: 12.49 s 
+2025-10-10 01:04:51.429474: Yayy! New best EMA pseudo Dice: 0.10329999774694443 
+2025-10-10 01:04:52.220202:  
+2025-10-10 01:04:52.220456: Epoch 10 
+2025-10-10 01:04:52.220555: Current learning rate: 0.00991 
+2025-10-10 01:05:04.743607: train_loss 0.037 
+2025-10-10 01:05:04.743788: val_loss -0.0382 
+2025-10-10 01:05:04.744165: Pseudo dice [np.float32(0.777), np.float32(0.0), np.float32(0.7957), np.float32(0.0763), np.float32(0.6634), np.float32(0.4366), np.float32(0.0901), np.float32(0.0009), np.float32(0.5299)] 
+2025-10-10 01:05:04.744234: Epoch time: 12.52 s 
+2025-10-10 01:05:04.744266: Yayy! New best EMA pseudo Dice: 0.13040000200271606 
+2025-10-10 01:05:05.533273:  
+2025-10-10 01:05:05.533397: Epoch 11 
+2025-10-10 01:05:05.533483: Current learning rate: 0.0099 
+2025-10-10 01:05:18.056954: train_loss 0.0095 
+2025-10-10 01:05:18.057138: val_loss -0.0461 
+2025-10-10 01:05:18.057213: Pseudo dice [np.float32(0.7797), np.float32(0.0), np.float32(0.8266), np.float32(0.31), np.float32(0.7487), np.float32(0.3754), np.float32(0.4726), np.float32(0.1103), np.float32(0.6531)] 
+2025-10-10 01:05:18.057270: Epoch time: 12.52 s 
+2025-10-10 01:05:18.057301: Yayy! New best EMA pseudo Dice: 0.164900004863739 
+2025-10-10 01:05:19.283666:  
+2025-10-10 01:05:19.283898: Epoch 12 
+2025-10-10 01:05:19.283999: Current learning rate: 0.00989 
+2025-10-10 01:05:31.667317: train_loss -0.0153 
+2025-10-10 01:05:31.667476: val_loss -0.076 
+2025-10-10 01:05:31.667547: Pseudo dice [np.float32(0.784), np.float32(0.0), np.float32(0.8209), np.float32(0.2568), np.float32(0.6728), np.float32(0.5426), np.float32(0.6018), np.float32(0.1444), np.float32(0.6122)] 
+2025-10-10 01:05:31.667600: Epoch time: 12.38 s 
+2025-10-10 01:05:31.667629: Yayy! New best EMA pseudo Dice: 0.19769999384880066 
+2025-10-10 01:05:32.464355:  
+2025-10-10 01:05:32.464503: Epoch 13 
+2025-10-10 01:05:32.464578: Current learning rate: 0.00988 
+2025-10-10 01:05:44.843656: train_loss -0.0454 
+2025-10-10 01:05:44.843891: val_loss -0.0821 
+2025-10-10 01:05:44.843974: Pseudo dice [np.float32(0.721), np.float32(0.0), np.float32(0.7782), np.float32(0.4168), np.float32(0.6777), np.float32(0.5887), np.float32(0.5534), np.float32(0.0746), np.float32(0.6663)] 
+2025-10-10 01:05:44.844036: Epoch time: 12.38 s 
+2025-10-10 01:05:44.844068: Yayy! New best EMA pseudo Dice: 0.22769999504089355 
+2025-10-10 01:05:45.645461:  
+2025-10-10 01:05:45.645612: Epoch 14 
+2025-10-10 01:05:45.645688: Current learning rate: 0.00987 
+2025-10-10 01:05:58.043095: train_loss -0.077 
+2025-10-10 01:05:58.043283: val_loss -0.1018 
+2025-10-10 01:05:58.043360: Pseudo dice [np.float32(0.7999), np.float32(0.0135), np.float32(0.8192), np.float32(0.4498), np.float32(0.7035), np.float32(0.4472), np.float32(0.458), np.float32(0.3218), np.float32(0.6268)] 
+2025-10-10 01:05:58.043417: Epoch time: 12.4 s 
+2025-10-10 01:05:58.043450: Yayy! New best EMA pseudo Dice: 0.2563999891281128 
+2025-10-10 01:05:58.857145:  
+2025-10-10 01:05:58.857291: Epoch 15 
+2025-10-10 01:05:58.857357: Current learning rate: 0.00986 
+2025-10-10 01:06:11.235147: train_loss -0.11 
+2025-10-10 01:06:11.235322: val_loss -0.1833 
+2025-10-10 01:06:11.235394: Pseudo dice [np.float32(0.8483), np.float32(0.57), np.float32(0.8643), np.float32(0.3281), np.float32(0.7543), np.float32(0.7414), np.float32(0.6258), np.float32(0.3824), np.float32(0.6234)] 
+2025-10-10 01:06:11.235450: Epoch time: 12.38 s 
+2025-10-10 01:06:11.235480: Yayy! New best EMA pseudo Dice: 0.2946000099182129 
+2025-10-10 01:06:12.051040:  
+2025-10-10 01:06:12.051180: Epoch 16 
+2025-10-10 01:06:12.051282: Current learning rate: 0.00986 

model_training/nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all/checkpoint_best.pth

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model_training/nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all/checkpoint_latest.pth

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model_training/nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all/debug.json

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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 128, 128], 'median_image_size_in_voxels': [391.0, 512.0, 512.0], 'spacing': [2.5, 0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90800,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0xf2e431454ee0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0xf2e431454d90>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0xf2e431454f70>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0xf2e431454df0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'class_1': 1, 'class_2': 2, 'class_3': 3, 'class_4': 4, 'class_5': 5, 'class_6': 6, 'class_7': 7, 'class_8': 8, 'class_9': 9}, 'numTraining': 21, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GH200 480GB",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0xf2e433eb10c0>",
+    "hostname": "192-222-57-227",
+    "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 0xf2e433eb0f70>",
+    "local_rank": "0",
+    "log_file": "nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all/training_log_2025_10_10_01_01_57.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0xf2e433eb0fa0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0xf2e433d31090>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset101_FinalSet', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 0.87109375, 0.87109375], 'original_median_shape_after_transp': [391, 512, 512], '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': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [216, 193, 193], 'spacing': [4.515278086673537, 2.310432649036382, 2.310432649036382], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 128, 128], 'median_image_size_in_voxels': [391.0, 512.0, 512.0], 'spacing': [2.5, 0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1567.0, 'mean': 30.700855255126953, 'median': 39.0, 'min': -1024.0, 'percentile_00_5': -95.0, 'percentile_99_5': 116.0, 'std': 39.6912956237793}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'class_1': 1, 'class_2': 2, 'class_3': 3, 'class_4': 4, 'class_5': 5, 'class_6': 6, 'class_7': 7, 'class_8': 8, 'class_9': 9}, 'numTraining': 21, 'file_ending': '.nii.gz'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_all",
+    "output_folder_base": "nnUNet_results/Dataset101_FinalSet/nnUNetTrainer__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset101_FinalSet', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 0.87109375, 0.87109375], 'original_median_shape_after_transp': [391, 512, 512], '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': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [216, 193, 193], 'spacing': [4.515278086673537, 2.310432649036382, 2.310432649036382], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 128, 128], 'median_image_size_in_voxels': [391.0, 512.0, 512.0], 'spacing': [2.5, 0.87109375, 0.87109375], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1567.0, 'mean': 30.700855255126953, 'median': 39.0, 'min': -1024.0, 'percentile_00_5': -95.0, 'percentile_99_5': 116.0, 'std': 39.6912956237793}}}",
+    "preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset101_FinalSet/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset101_FinalSet",
+    "probabilistic_oversampling": "False",
+    "save_every": "50",
+    "torch_version": "2.7.1+cu128",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}