Replicating MOOSE dir structure

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/progress.png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

MOOSE-Drop-In/{nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres → Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres}/dataset.json

@@ -1,5 +1,5 @@
 {
-    "name": "Dataset145_Fast_organs",
+    "name": "Dataset123_Organs",
     "description": "",
     "reference": "",
     "licence": "hands off!",

MOOSE-Drop-In/{nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres → Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres}/fold_all/checkpoint_best.pth

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MOOSE-Drop-In/{nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres → Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres}/fold_all/checkpoint_final.pth

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MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/debug.json

@@ -0,0 +1,53 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7ce4acf10dc0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ce4aedabf10>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7ce4acf10850>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ce4aedabf70>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'name': 'Dataset123_Organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100 80GB PCIe",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7ce4af783790>",
+    "hostname": "metazoo",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7ce4af783520>",
+    "local_rank": "0",
+    "log_file": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_results/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all/training_log_2025_5_5_01_25_42.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7ce4aedaaa10>",
+    "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 0x7ce4acff4a60>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset123_Organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [649, 318, 318], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [318.0, 318.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [319, 156, 156], 'spacing': [3.049191159690605, 3.049191159690605, 3.049191159690605], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3033.969970703125, 'mean': -300.0020751953125, 'median': -1.0779765844345093, 'min': -1331.90234375, 'percentile_00_5': -959.2193603515625, 'percentile_99_5': 195.26037063598665, 'std': 406.9311828613281}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'name': 'Dataset123_Organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "2000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_results/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/fold_all",
+    "output_folder_base": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_results/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset123_Organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [649, 318, 318], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [318.0, 318.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 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3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [224, 96, 96], 'median_image_size_in_voxels': [649.0, 318.0, 318.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 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'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3033.969970703125, 'mean': -300.0020751953125, 'median': -1.0779765844345093, 'min': -1331.90234375, 'percentile_00_5': -959.2193603515625, 'percentile_99_5': 195.26037063598665, 'std': 406.9311828613281}}}",
+    "preprocessed_dataset_folder": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_preprocessed/Dataset123_Organs/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/media/datalagoon/researchers/Manel/MOOSE/1_6k_models/nnunet_preprocessed/Dataset123_Organs",
+    "save_every": "50",
+    "torch_version": "2.5.1+cu124",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

MOOSE-Drop-In/Dataset123_Organs/nnUNetTrainer_2000epochs_NoMirroring__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,521 @@
+{
+    "dataset_name": "Dataset123_Organs",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
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+        1.5,
+        1.5
+    ],
+    "original_median_shape_after_transp": [
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+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 30,
+            "patch_size": [
+                320,
+                320
+            ],
+            "median_image_size_in_voxels": [
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+            "spacing": [
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+                1.5
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+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
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+                "force_separate_z": null
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+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
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+                        2,
+                        2,
+                        2,
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+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+                    "norm_op_kwargs": {
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+                        "affine": true
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+                    "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": [
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+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
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+                "order": 3,
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+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
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+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
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+                        [
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+                        [
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+                        [
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+                            2
+                        ],
+                        [
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+                            1,
+                            1
+                        ]
+                    ],
+                    "n_conv_per_stage": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
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+            ],
+            "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,
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+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
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+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet",
+                "arch_kwargs": {
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+                    "features_per_stage": [
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+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
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+                            3
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+                            3
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+                            2
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+                        [
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+                            1,
+                            1
+                        ]
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+                        2,
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        2,
+                        2,
+                        2,
+                        2,
+                        2
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3033.969970703125,
+            "mean": -300.0020751953125,
+            "median": -1.0779765844345093,
+            "min": -1331.90234375,
+            "percentile_00_5": -959.2193603515625,
+            "percentile_99_5": 195.26037063598665,
+            "std": 406.9311828613281
+        }
+    }
+}

MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/debug.json

@@ -1,52 +0,0 @@
-{
-    "_best_ema": "None",
-    "batch_size": "4",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 8902,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c63580>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62da0>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [176, 80, 80], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7ebf99c636a0>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7ebf99c62bc0>",
-    "dataloader_val.num_processes": "6",
-    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
-    "dataset_json": "{'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "fold": "all",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA A100 80GB PCIe",
-    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7ebfa1d77730>",
-    "hostname": "metazoo",
-    "inference_allowed_mirroring_axes": "(0, 1, 2)",
-    "initial_lr": "0.01",
-    "is_cascaded": "False",
-    "is_ddp": "False",
-    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7ebfa1d77a30>",
-    "local_rank": "0",
-    "log_file": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_36_38.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7ebfa1d778b0>",
-    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7ebfa1da26e0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'name': 'Dataset145_Fast_organs', 'description': '', 'reference': '', 'licence': 'hands off!', 'release': '0.0', 'labels': {'background': '0', 'adrenal_gland_left': '1', 'adrenal_gland_right': '2', 'bladder': '3', 'brain': '4', 'gallbladder': '5', 'kidney_left': '6', 'kidney_right': '7', 'liver': '8', 'lung_lower_lobe_left': '9', 'lung_lower_lobe_right': '10', 'lung_middle_lobe_right': '11', 'lung_upper_lobe_left': '12', 'lung_upper_lobe_right': '13', 'pancreas': '14', 'spleen': '15', 'stomach': '16', 'thyroid_left': '17', 'thyroid_right': '18', 'trachea': '19'}, 'numTraining': 1683, 'file_ending': '.nii.gz', 'channel_names': {'0': 'CT'}}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "PlainConvUNet",
-    "num_epochs": "2000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all",
-    "output_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_results/Dataset145_Fast_organs/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "plans_manager": "{'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 492, 'patch_size': [80, 80], 'median_image_size_in_voxels': [80.0, 80.0], 'spacing': [6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}}",
-    "preprocessed_dataset_folder": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/media/zooguard/crucial-nvme-3/PROJECTS/Manuel/MOOSE/fast_organs_model/nnunet_preprocessed/Dataset145_Fast_organs",
-    "save_every": "50",
-    "torch_version": "2.3.1+cu121",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}

MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_8_27_12_35_49.txt

@@ -1,22 +0,0 @@
-
-#######################################################################
-Please cite the following paper when using nnU-Net:
-Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
-#######################################################################
- 
-
-This is the configuration used by this training:
-Configuration name: 3d_fullres
- {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 4, 'patch_size': [176, 80, 80], 'median_image_size_in_voxels': [162.0, 80.0, 80.0], 'spacing': [6.0, 6.0, 6.0], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2], 'num_pool_per_axis': [4, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False} 
- 
-These are the global plan.json settings:
- {'dataset_name': 'Dataset145_Fast_organs', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [6.0, 6.0, 6.0], 'original_median_shape_after_transp': [162, 80, 80], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 2981.83154296875, 'mean': -306.5704650878906, 'median': -7.578986644744873, 'min': -1138.905029296875, 'percentile_00_5': -952.3096923828125, 'percentile_99_5': 193.60693359375, 'std': 407.40484619140625}}} 
- 
-2024-08-27 12:35:52.178022: unpacking dataset... 
-2024-08-27 12:36:03.383381: unpacking done... 
-2024-08-27 12:36:03.384249: do_dummy_2d_data_aug: False 
-2024-08-27 12:36:03.404946: Unable to plot network architecture: 
-2024-08-27 12:36:03.405035: No module named 'hiddenlayer' 
-2024-08-27 12:36:03.410572:  
-2024-08-27 12:36:03.410657: Epoch 0 
-2024-08-27 12:36:03.410768: Current learning rate: 0.01 

MOOSE-Drop-In/nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres/plans.json

@@ -1,272 +0,0 @@
-{
-    "dataset_name": "Dataset145_Fast_organs",
-    "plans_name": "nnUNetPlans",
-    "original_median_spacing_after_transp": [
-        6.0,
-        6.0,
-        6.0
-    ],
-    "original_median_shape_after_transp": [
-        162,
-        80,
-        80
-    ],
-    "image_reader_writer": "SimpleITKIO",
-    "transpose_forward": [
-        0,
-        1,
-        2
-    ],
-    "transpose_backward": [
-        0,
-        1,
-        2
-    ],
-    "configurations": {
-        "2d": {
-            "data_identifier": "nnUNetPlans_2d",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 492,
-            "patch_size": [
-                80,
-                80
-            ],
-            "median_image_size_in_voxels": [
-                80.0,
-                80.0
-            ],
-            "spacing": [
-                6.0,
-                6.0
-            ],
-            "normalization_schemes": [
-                "CTNormalization"
-            ],
-            "use_mask_for_norm": [
-                false
-            ],
-            "UNet_class_name": "PlainConvUNet",
-            "UNet_base_num_features": 32,
-            "n_conv_per_stage_encoder": [
-                2,
-                2,
-                2,
-                2,
-                2
-            ],
-            "n_conv_per_stage_decoder": [
-                2,
-                2,
-                2,
-                2
-            ],
-            "num_pool_per_axis": [
-                4,
-                4
-            ],
-            "pool_op_kernel_sizes": [
-                [
-                    1,
-                    1
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2
-                ]
-            ],
-            "conv_kernel_sizes": [
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3
-                ]
-            ],
-            "unet_max_num_features": 512,
-            "resampling_fn_data": "resample_data_or_seg_to_shape",
-            "resampling_fn_seg": "resample_data_or_seg_to_shape",
-            "resampling_fn_data_kwargs": {
-                "is_seg": false,
-                "order": 3,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_seg_kwargs": {
-                "is_seg": true,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
-            "resampling_fn_probabilities_kwargs": {
-                "is_seg": false,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "batch_dice": true
-        },
-        "3d_fullres": {
-            "data_identifier": "nnUNetPlans_3d_fullres",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 4,
-            "patch_size": [
-                176,
-                80,
-                80
-            ],
-            "median_image_size_in_voxels": [
-                162.0,
-                80.0,
-                80.0
-            ],
-            "spacing": [
-                6.0,
-                6.0,
-                6.0
-            ],
-            "normalization_schemes": [
-                "CTNormalization"
-            ],
-            "use_mask_for_norm": [
-                false
-            ],
-            "UNet_class_name": "PlainConvUNet",
-            "UNet_base_num_features": 32,
-            "n_conv_per_stage_encoder": [
-                2,
-                2,
-                2,
-                2,
-                2
-            ],
-            "n_conv_per_stage_decoder": [
-                2,
-                2,
-                2,
-                2
-            ],
-            "num_pool_per_axis": [
-                4,
-                4,
-                4
-            ],
-            "pool_op_kernel_sizes": [
-                [
-                    1,
-                    1,
-                    1
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ],
-                [
-                    2,
-                    2,
-                    2
-                ]
-            ],
-            "conv_kernel_sizes": [
-                [
-                    3,
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3,
-                    3
-                ],
-                [
-                    3,
-                    3,
-                    3
-                ]
-            ],
-            "unet_max_num_features": 320,
-            "resampling_fn_data": "resample_data_or_seg_to_shape",
-            "resampling_fn_seg": "resample_data_or_seg_to_shape",
-            "resampling_fn_data_kwargs": {
-                "is_seg": false,
-                "order": 3,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_seg_kwargs": {
-                "is_seg": true,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
-            "resampling_fn_probabilities_kwargs": {
-                "is_seg": false,
-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "batch_dice": false
-        }
-    },
-    "experiment_planner_used": "ExperimentPlanner",
-    "label_manager": "LabelManager",
-    "foreground_intensity_properties_per_channel": {
-        "0": {
-            "max": 2981.83154296875,
-            "mean": -306.5704650878906,
-            "median": -7.578986644744873,
-            "min": -1138.905029296875,
-            "percentile_00_5": -952.3096923828125,
-            "percentile_99_5": 193.60693359375,
-            "std": 407.40484619140625
-        }
-    }
-}