Upload Dataset006_JSC_UCSD_PTGB/nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres/fold_0/debug.json with huggingface_hub

Dataset006_JSC_UCSD_PTGB/nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres/fold_0/debug.json

@@ -0,0 +1,55 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "cls_class_num": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [167.0, 137.0, 147.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True], '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}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90501,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x1542a053cf20>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataBalancedLoaderCLS object at 0x1542a0ad6960>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x1542a053e0f0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataBalancedLoaderCLS object at 0x1542a053cb30>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'T1post', '1': 'FLAIR', '2': 'ADC_4000_or_ADC_vendor'}, 'labels': {'background': 0, 'tumor': 1}, 'classification_labels': {'idh_mutation_status': {'0': 'IDH Wild-Type', '1': 'IDH Mutant'}}, 'numTraining': 184, 'file_ending': '.nii.gz', 'description': 'UCSD Post-Treatment Glioblastoma \u2014 total cellular tumor segmentation on multi-modal MRI (T1post, FLAIR, ADC) with IDH mutation status classification (184 cases, -1 indicates unknown/masked)', 'reference': 'Gagnon L, Gupta D, Nguyen U, Correia de Verdier M, Saluja R, Mastorakos G, White N, Goodwill V, McDonald CR, Beaumont T, Conlin C. The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) annotated multimodal MRI Dataset. Scientific Data. 2026 Jan 6.', 'license': 'CC-BY-NC 4.0', 'name': 'Dataset006_JSC_UCSD_PTGB', 'release': '1.0'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA H100 80GB HBM3 MIG 3g.40gb",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x1542a0c115b0>",
+    "hostname": "fc10910",
+    "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 0x1542a0c11580>",
+    "local_rank": "0",
+    "log_file": "/home/cyyu/scratch/cvpr26/nnUNet_data/nnUNet_results/Dataset006_JSC_UCSD_PTGB/nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres/fold_0/training_log_2026_4_9_19_54_21.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x1542a0c11520>",
+    "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 0x1542a0a86660>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset006_JSC_UCSD_PTGB', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [167, 137, 147], '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': 127, 'patch_size': [160, 160], 'median_image_size_in_voxels': [137.0, 147.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True], '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, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[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]], '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.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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [167.0, 137.0, 147.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True], '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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 36194.34375, 'mean': 1363.3490090893256, 'median': 1028.380615234375, 'min': 0.0, 'percentile_00_5': 289.9832458496094, 'percentile_99_5': 9439.935546875, 'std': 1268.081885380825}, '1': {'max': 3002.71240234375, 'mean': 1031.556858805927, 'median': 1010.5509643554688, 'min': 0.0, 'percentile_00_5': 348.52703857421875, 'percentile_99_5': 1751.8995361328125, 'std': 243.61353362402886}, '2': {'max': 0.0009840750135481358, 'mean': 0.0005000616207076369, 'median': 0.0005012508481740952, 'min': 0.0, 'percentile_00_5': 0.0002148466883227229, 'percentile_99_5': 0.0007649839972145855, 'std': 0.00011056813138906645}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'T1post', '1': 'FLAIR', '2': 'ADC_4000_or_ADC_vendor'}, 'labels': {'background': 0, 'tumor': 1}, 'classification_labels': {'idh_mutation_status': {'0': 'IDH Wild-Type', '1': 'IDH Mutant'}}, 'numTraining': 184, 'file_ending': '.nii.gz', 'description': 'UCSD Post-Treatment Glioblastoma \u2014 total cellular tumor segmentation on multi-modal MRI (T1post, FLAIR, ADC) with IDH mutation status classification (184 cases, -1 indicates unknown/masked)', 'reference': 'Gagnon L, Gupta D, Nguyen U, Correia de Verdier M, Saluja R, Mastorakos G, White N, Goodwill V, McDonald CR, Beaumont T, Conlin C. The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) annotated multimodal MRI Dataset. Scientific Data. 2026 Jan 6.', 'license': 'CC-BY-NC 4.0', 'name': 'Dataset006_JSC_UCSD_PTGB', 'release': '1.0'}, 'device': device(type='cuda')}",
+    "network": "SegmentationNetworkFusionClassificationHead",
+    "num_epochs": "1000",
+    "num_input_channels": "3",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "18",
+    "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": "/home/cyyu/scratch/cvpr26/nnUNet_data/nnUNet_results/Dataset006_JSC_UCSD_PTGB/nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/home/cyyu/scratch/cvpr26/nnUNet_data/nnUNet_results/Dataset006_JSC_UCSD_PTGB/nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset006_JSC_UCSD_PTGB', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [167, 137, 147], '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': 127, 'patch_size': [160, 160], 'median_image_size_in_voxels': [137.0, 147.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True], '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, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[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]], '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.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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [167.0, 137.0, 147.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [True, True, True], '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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 36194.34375, 'mean': 1363.3490090893256, 'median': 1028.380615234375, 'min': 0.0, 'percentile_00_5': 289.9832458496094, 'percentile_99_5': 9439.935546875, 'std': 1268.081885380825}, '1': {'max': 3002.71240234375, 'mean': 1031.556858805927, 'median': 1010.5509643554688, 'min': 0.0, 'percentile_00_5': 348.52703857421875, 'percentile_99_5': 1751.8995361328125, 'std': 243.61353362402886}, '2': {'max': 0.0009840750135481358, 'mean': 0.0005000616207076369, 'median': 0.0005012508481740952, 'min': 0.0, 'percentile_00_5': 0.0002148466883227229, 'percentile_99_5': 0.0007649839972145855, 'std': 0.00011056813138906645}}}",
+    "preprocessed_dataset_folder": "/home/cyyu/scratch/cvpr26/nnUNet_data/nnUNet_preprocessed/Dataset006_JSC_UCSD_PTGB/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/home/cyyu/scratch/cvpr26/nnUNet_data/nnUNet_preprocessed/Dataset006_JSC_UCSD_PTGB",
+    "probabilistic_oversampling": "False",
+    "save_every": "50",
+    "torch_version": "2.7.1",
+    "val_cases": "36",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}