Add AortaSeg24 nnUNet checkpoints, plans and dataset json

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/dataset.json

@@ -0,0 +1,38 @@
+{
+    "description": "Dataset824_AortaSeg24_CTA_50",
+    "labels": {
+        "background": 0,
+        "Zone 0": 1,
+        "Innominate": 2,
+        "Zone 1": 3,
+        "Left Common Carotid": 4,
+        "Zone 2": 5,
+        "Left Subclavian Artery": 6,
+        "Zone 3": 7,
+        "Zone 4": 8,
+        "Zone 5": 9,
+        "Zone 6": 10,
+        "Celiac Artery": 11,
+        "Zone 7": 12,
+        "SMA": 13,
+        "Zone 8": 14,
+        "Right Renal Artery": 15,
+        "Left Renal Artery": 16,
+        "Zone 9": 17,
+        "Zone 10 R(Right Common Iliac Artery)": 18,
+        "Zone 10 L(Left Common Iliac Artery)": 19,
+        "Right Internal Iliac Artery": 20,
+        "Left Internal Iliac Artery": 21,
+        "Zone 11 R(Right Extarnal Iliac Artery)": 22,
+        "Zone 11 L(Left Extarnal Iliac Artery)": 23
+    },
+    "licence": "None",
+    "name": "Dataset824_AortaSeg24_CTA_50",
+    "numTraining": 50,
+    "reference": "None",
+    "release": "1.0 05/07/2024",
+    "channel_names": {
+        "0": "CT"
+    },
+    "file_ending": ".mha"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/dataset_fingerprint.json

@@ -0,0 +1,518 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 8733.0,
+            "mean": 208.1422119140625,
+            "median": 209.0,
+            "min": -2048.0,
+            "percentile_00_5": -23.0,
+            "percentile_99_5": 507.0,
+            "std": 108.94955444335938
+        }
+    },
+    "median_relative_size_after_cropping": 1.0,
+    "shapes_after_crop": [
+        [
+            724,
+            435,
+            435
+        ],
+        [
+            715,
+            516,
+            516
+        ],
+        [
+            801,
+            400,
+            400
+        ],
+        [
+            725,
+            500,
+            500
+        ],
+        [
+            713,
+            476,
+            476
+        ],
+        [
+            800,
+            490,
+            490
+        ],
+        [
+            736,
+            437,
+            437
+        ],
+        [
+            696,
+            400,
+            400
+        ],
+        [
+            659,
+            400,
+            400
+        ],
+        [
+            697,
+            400,
+            400
+        ],
+        [
+            656,
+            447,
+            447
+        ],
+        [
+            676,
+            400,
+            400
+        ],
+        [
+            578,
+            437,
+            437
+        ],
+        [
+            633,
+            439,
+            439
+        ],
+        [
+            667,
+            439,
+            439
+        ],
+        [
+            699,
+            455,
+            455
+        ],
+        [
+            687,
+            455,
+            455
+        ],
+        [
+            746,
+            410,
+            410
+        ],
+        [
+            697,
+            471,
+            471
+        ],
+        [
+            618,
+            500,
+            500
+        ],
+        [
+            718,
+            453,
+            453
+        ],
+        [
+            763,
+            488,
+            488
+        ],
+        [
+            682,
+            500,
+            500
+        ],
+        [
+            743,
+            490,
+            490
+        ],
+        [
+            701,
+            423,
+            423
+        ],
+        [
+            709,
+            412,
+            412
+        ],
+        [
+            713,
+            392,
+            392
+        ],
+        [
+            783,
+            420,
+            420
+        ],
+        [
+            696,
+            482,
+            482
+        ],
+        [
+            697,
+            433,
+            433
+        ],
+        [
+            639,
+            476,
+            476
+        ],
+        [
+            749,
+            498,
+            498
+        ],
+        [
+            628,
+            400,
+            400
+        ],
+        [
+            650,
+            400,
+            400
+        ],
+        [
+            641,
+            418,
+            418
+        ],
+        [
+            714,
+            466,
+            466
+        ],
+        [
+            726,
+            471,
+            471
+        ],
+        [
+            663,
+            490,
+            490
+        ],
+        [
+            701,
+            423,
+            423
+        ],
+        [
+            627,
+            496,
+            496
+        ],
+        [
+            663,
+            400,
+            400
+        ],
+        [
+            770,
+            502,
+            502
+        ],
+        [
+            644,
+            430,
+            430
+        ],
+        [
+            661,
+            423,
+            423
+        ],
+        [
+            697,
+            389,
+            389
+        ],
+        [
+            659,
+            490,
+            490
+        ],
+        [
+            711,
+            425,
+            425
+        ],
+        [
+            733,
+            449,
+            449
+        ],
+        [
+            613,
+            468,
+            468
+        ],
+        [
+            711,
+            500,
+            500
+        ]
+    ],
+    "spacings": [
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_0/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f70b256b5b0a9ddfa54bdc8cebf5937a54fff768828b6e9bf5d683ce8ee4dc0
+size 68419018

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_0/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "0.7552952504524513",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "501",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f1da901a550>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f1da901a580>",
+    "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, 112, 112], 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 ), 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 0x7f1da901a700>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f1da901a220>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f1da90cb9d0>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7f1dae21ac40>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_0/training_log_2024_9_22_02_56_38.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7f1dae16c280>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f1da90c1c40>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_1/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0a7e60d523e4e117fa41ac0857a2263ea0859cf6822d8dc4edda2a7e5321831
+size 134678822

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_1/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f294f27d790>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f294f27dfd0>",
+    "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, 112, 112], 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 ), 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 0x7f294f27d2e0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f294f27df70>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f294f3ebf70>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7f29544c1ee0>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_1/training_log_2024_9_22_02_58_39.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7f29544cd8b0>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f294f35deb0>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_1",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_2/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:549925a409acd7bc67295c07f29ff65727b541361b02e2e7dac808254a5e2a7b
+size 134679078

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_2/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "0.7527695994282939",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "280",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f0aa13484c0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f0aa1348460>",
+    "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, 112, 112], 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 ), 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 0x7f0aa1348070>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f0aa1348250>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "2",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f0aa653cd30>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7f0aa654bb20>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_2/training_log_2024_9_24_05_00_01.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7f0aa14636a0>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f0aa13ba430>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_2",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_3/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a07cc159c3f0041cc9b6011fa7721de4cf00077e4378863e5a94a502f562fcfa
+size 134678950

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_3/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fad3519f760>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fad3519ffd0>",
+    "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, 112, 112], 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 ), 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 0x7fad3519f250>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fad3519ff70>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "3",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fad3530df40>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7fad3a3e3eb0>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_3/training_log_2024_9_24_13_34_00.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7fad3a3f0880>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fad3527fe80>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_3",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_4/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:964e8b52095c1f9d28760d23bdb6d5193ea828e11f3a8ca73224c9593a53ad77
+size 134679014

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_4/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "0.69261330300579",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "65",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f5b8bff13a0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f5b8bff15e0>",
+    "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, 112, 112], 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 ), 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 0x7f5b8bff1e50>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f5b8bff1dc0>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "4",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f5b8c102fa0>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7f5b91214a30>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_4/training_log_2024_9_24_06_41_57.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7f5b9115c550>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f5b8c071f10>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 4, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_4",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_all/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b068b6a2b7376aa2c208a29a5c98451bf7603f65a3cedaa797d9fb1707fd416c
+size 134679078

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_all/debug.json

@@ -0,0 +1,63 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "bti_level_dict": "{'inclusion': {2: [], 3: [], 4: [], 5: [], 6: []}, 'exclusion': {2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}, 'connection': {2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}}",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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, 'num_classes_hierarchical': [2, 3, 5, 9, 17, 24]}",
+    "configuration_name": "3d_fullres",
+    "connection_level_dict": "{2: [[1, 2]], 3: [[1, 2], [3, 4]], 4: [[1, 2], [3, 4], [5, 6]], 5: [[1, 2], [5, 6], [7, 8], [9, 10], [13, 14], [15, 16]], 6: [[3, 5], [10, 11], [12, 14]]}",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f7fadfba760>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f7fadfbac70>",
+    "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, 112, 112], 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 ), 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 0x7f7fadfba7f0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f7fadfbac40>",
+    "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": "{'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "exclusion_level_dict": "{2: [], 3: [], 4: [[7, 8]], 5: [[3, 4], [11, 12]], 6: [[4, 6], [15, 16], [20, 22], [21, 23]]}",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f7fae128f40>",
+    "hierarchical_K_dict": "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 2}",
+    "hierarchical_weight_dict": "{1: 2, 2: 4, 3: 8, 4: 16, 5: 32, 6: 64}",
+    "hostname": "9accf9197411",
+    "inclusion_level_dict": "{2: [], 3: [], 4: [], 5: [], 6: []}",
+    "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 0x7f7fb5202eb0>",
+    "level_epochs_list": "[0, 1, 2, 4, 7, 11]",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_all/training_log_2024_9_23_09_28_33.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger_hierarchical.nnUNetLogger_Hierarchical object at 0x7f7fb520f880>",
+    "loss": "DeepSupervisionWrapper()",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f7fae09ae80>",
+    "max_level": "6",
+    "min_level": "1",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_fullres', 'fold': 'all', 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_50', 'labels': {'background': 0, 'Zone 0': 1, 'Innominate': 2, 'Zone 1': 3, 'Left Common Carotid': 4, 'Zone 2': 5, 'Left Subclavian Artery': 6, 'Zone 3': 7, 'Zone 4': 8, 'Zone 5': 9, 'Zone 6': 10, 'Celiac Artery': 11, 'Zone 7': 12, 'SMA': 13, 'Zone 8': 14, 'Right Renal Artery': 15, 'Left Renal Artery': 16, 'Zone 9': 17, 'Zone 10 R(Right Common Iliac Artery)': 18, 'Zone 10 L(Left Common Iliac Artery)': 19, 'Right Internal Iliac Artery': 20, 'Left Internal Iliac Artery': 21, 'Zone 11 R(Right Extarnal Iliac Artery)': 22, 'Zone 11 L(Left Extarnal Iliac Artery)': 23}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 05/07/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/fold_all",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset824_AortaSeg24_CTA_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 15, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [279, 177, 177], 'spacing': [2.5000803453253524, 2.5000803453253524, 2.5000803453253524], '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': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [176, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset824_AortaSeg24_CTA_50",
+    "save_every": "1",
+    "target_level_dict": "{1: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n        19, 20, 21, 22, 23], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2, 3, 4, 5, 6], device='cuda:0'), tensor([ 7,  8,  9, 10, 11], device='cuda:0'), tensor([12, 13, 14, 15, 16, 17], device='cuda:0'), tensor([18, 19, 20, 21, 22, 23], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 3, 5], device='cuda:0'), tensor([2, 4, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10, 11], device='cuda:0'), tensor([12, 14, 17], device='cuda:0'), tensor([13, 15, 16], device='cuda:0'), tensor([18, 20, 22], device='cuda:0'), tensor([19, 21, 23], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "torch_version": "2.2.0+cu118",
+    "transform_levels_dict": "{1: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0')], 2: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')], 3: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0')], 4: [tensor([0], device='cuda:0'), tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0'), tensor([5, 6], device='cuda:0'), tensor([7, 8], device='cuda:0'), tensor([ 9, 10], device='cuda:0'), tensor([11, 12], device='cuda:0'), tensor([13, 14], device='cuda:0'), tensor([15, 16], device='cuda:0')], 5: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([3, 5], device='cuda:0'), tensor([2], device='cuda:0'), tensor([4, 6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10, 11], device='cuda:0'), tensor([12, 14], device='cuda:0'), tensor([17], device='cuda:0'), tensor([13], device='cuda:0'), tensor([15, 16], device='cuda:0'), tensor([18], device='cuda:0'), tensor([20, 22], device='cuda:0'), tensor([19], device='cuda:0'), tensor([21, 23], device='cuda:0')], 6: [tensor([0], device='cuda:0'), tensor([1], device='cuda:0'), tensor([2], device='cuda:0'), tensor([3], device='cuda:0'), tensor([4], device='cuda:0'), tensor([5], device='cuda:0'), tensor([6], device='cuda:0'), tensor([7], device='cuda:0'), tensor([8], device='cuda:0'), tensor([9], device='cuda:0'), tensor([10], device='cuda:0'), tensor([11], device='cuda:0'), tensor([12], device='cuda:0'), tensor([13], device='cuda:0'), tensor([14], device='cuda:0'), tensor([15], device='cuda:0'), tensor([16], device='cuda:0'), tensor([17], device='cuda:0'), tensor([18], device='cuda:0'), tensor([19], device='cuda:0'), tensor([20], device='cuda:0'), tensor([21], device='cuda:0'), tensor([22], device='cuda:0'), tensor([23], device='cuda:0')]}",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset824_AortaSeg24_CTA_50/nnUNetTrainer_Hierarchical_CBDC_AortaSeg23_NoMirroring_500epochs__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,420 @@
+{
+    "dataset_name": "Dataset824_AortaSeg24_CTA_50",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        697,
+        443,
+        443
+    ],
+    "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": 15,
+            "patch_size": [
+                448,
+                448
+            ],
+            "median_image_size_in_voxels": [
+                443.0,
+                443.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "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,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 0,
+                "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_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                176,
+                112,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                279,
+                177,
+                177
+            ],
+            "spacing": [
+                2.5000803453253524,
+                2.5000803453253524,
+                2.5000803453253524
+            ],
+            "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": 0,
+                "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,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                176,
+                112,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                697.0,
+                443.0,
+                443.0
+            ],
+            "spacing": [
+                1.0,
+                1.0,
+                1.0
+            ],
+            "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": 0,
+                "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_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 8733.0,
+            "mean": 208.1422119140625,
+            "median": 209.0,
+            "min": -2048.0,
+            "percentile_00_5": -23.0,
+            "percentile_99_5": 507.0,
+            "std": 108.94955444335938
+        }
+    }
+}

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/dataset.json

@@ -0,0 +1,16 @@
+{
+    "description": "Dataset824_AortaSeg24_CTA_bin_50",
+    "labels": {
+        "background": 0,
+        "vessel": 1
+    },
+    "licence": "None",
+    "name": "Dataset824_AortaSeg24_CTA_50",
+    "numTraining": 50,
+    "reference": "None",
+    "release": "1.0 24/09/2024",
+    "channel_names": {
+        "0": "CT"
+    },
+    "file_ending": ".mha"
+}

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/dataset_fingerprint.json

@@ -0,0 +1,518 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 8733.0,
+            "mean": 208.1422119140625,
+            "median": 209.0,
+            "min": -2048.0,
+            "percentile_00_5": -23.0,
+            "percentile_99_5": 507.0,
+            "std": 108.94955444335938
+        }
+    },
+    "median_relative_size_after_cropping": 1.0,
+    "shapes_after_crop": [
+        [
+            724,
+            435,
+            435
+        ],
+        [
+            715,
+            516,
+            516
+        ],
+        [
+            801,
+            400,
+            400
+        ],
+        [
+            725,
+            500,
+            500
+        ],
+        [
+            713,
+            476,
+            476
+        ],
+        [
+            800,
+            490,
+            490
+        ],
+        [
+            736,
+            437,
+            437
+        ],
+        [
+            696,
+            400,
+            400
+        ],
+        [
+            659,
+            400,
+            400
+        ],
+        [
+            697,
+            400,
+            400
+        ],
+        [
+            656,
+            447,
+            447
+        ],
+        [
+            676,
+            400,
+            400
+        ],
+        [
+            578,
+            437,
+            437
+        ],
+        [
+            633,
+            439,
+            439
+        ],
+        [
+            667,
+            439,
+            439
+        ],
+        [
+            699,
+            455,
+            455
+        ],
+        [
+            687,
+            455,
+            455
+        ],
+        [
+            746,
+            410,
+            410
+        ],
+        [
+            697,
+            471,
+            471
+        ],
+        [
+            618,
+            500,
+            500
+        ],
+        [
+            718,
+            453,
+            453
+        ],
+        [
+            763,
+            488,
+            488
+        ],
+        [
+            682,
+            500,
+            500
+        ],
+        [
+            743,
+            490,
+            490
+        ],
+        [
+            701,
+            423,
+            423
+        ],
+        [
+            709,
+            412,
+            412
+        ],
+        [
+            713,
+            392,
+            392
+        ],
+        [
+            783,
+            420,
+            420
+        ],
+        [
+            696,
+            482,
+            482
+        ],
+        [
+            697,
+            433,
+            433
+        ],
+        [
+            639,
+            476,
+            476
+        ],
+        [
+            749,
+            498,
+            498
+        ],
+        [
+            628,
+            400,
+            400
+        ],
+        [
+            650,
+            400,
+            400
+        ],
+        [
+            641,
+            418,
+            418
+        ],
+        [
+            714,
+            466,
+            466
+        ],
+        [
+            726,
+            471,
+            471
+        ],
+        [
+            663,
+            490,
+            490
+        ],
+        [
+            701,
+            423,
+            423
+        ],
+        [
+            627,
+            496,
+            496
+        ],
+        [
+            663,
+            400,
+            400
+        ],
+        [
+            770,
+            502,
+            502
+        ],
+        [
+            644,
+            430,
+            430
+        ],
+        [
+            661,
+            423,
+            423
+        ],
+        [
+            697,
+            389,
+            389
+        ],
+        [
+            659,
+            490,
+            490
+        ],
+        [
+            711,
+            425,
+            425
+        ],
+        [
+            733,
+            449,
+            449
+        ],
+        [
+            613,
+            468,
+            468
+        ],
+        [
+            711,
+            500,
+            500
+        ]
+    ],
+    "spacings": [
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ],
+        [
+            1.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_0/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:510cac4ca804b23c5d9d893e0221b63384c15c398ff53eeaf3948175a9c75611
+size 244946622

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_0/debug.json

@@ -0,0 +1,52 @@
+{
+    "_best_ema": "0.9226536018639016",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}",
+    "configuration_name": "3d_lowres",
+    "cudnn_version": 8700,
+    "current_epoch": "319",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f18a5bb71f0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f18a5af9730>",
+    "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 = [192, 112, 112], 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 ), 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], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f18a39c57c0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f18a5bb7c40>",
+    "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], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'description': 'Dataset824_AortaSeg24_CTA_bin_50', 'labels': {'background': 0, 'vessel': 1}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 24/09/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f18a394ab80>",
+    "hostname": "9accf9197411",
+    "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 0x7f18ad52b880>",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_0/training_log_2024_9_25_09_12_11.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f18a6798ca0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_and_CBDC_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n    (cbdice): SoftcbDiceLoss(\n      (t_skeletonize): Skeletonize()\n      (m_skeletonize): SoftSkeletonize()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f18a39f0520>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset825_AortaSeg24_CTA_bin_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_lowres', 'fold': 0, 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_bin_50', 'labels': {'background': 0, 'vessel': 1}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 24/09/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_0",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset825_AortaSeg24_CTA_bin_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset825_AortaSeg24_CTA_bin_50/nnUNetPlans_3d_lowres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset825_AortaSeg24_CTA_bin_50",
+    "save_every": "1",
+    "torch_version": "2.2.0+cu118",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_all/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db6da8221079b11c1e800cc6e02b89d21ecfb237282640d21a7270528821ee91
+size 244947518

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_all/debug.json

@@ -0,0 +1,52 @@
+{
+    "_best_ema": "0.8991154532626939",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}",
+    "configuration_name": "3d_lowres",
+    "cudnn_version": 8700,
+    "current_epoch": "19",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f39e010da60>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f39e010d760>",
+    "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 = [192, 112, 112], 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 ), 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], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f39e010db20>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f39e010d5b0>",
+    "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], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'description': 'Dataset824_AortaSeg24_CTA_bin_50', 'labels': {'background': 0, 'vessel': 1}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 24/09/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce RTX 3090",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f39e01ed1c0>",
+    "hostname": "9accf9197411",
+    "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 0x7f39e718ad90>",
+    "local_rank": "0",
+    "log_file": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_all/training_log_2024_9_23_10_19_36.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f39e7196be0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_and_CBDC_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n    (cbdice): SoftcbDiceLoss(\n      (t_skeletonize): Skeletonize()\n      (m_skeletonize): SoftSkeletonize()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f39dfd9ac40>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset825_AortaSeg24_CTA_bin_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}, 'configuration': '3d_lowres', 'fold': 'all', 'dataset_json': {'description': 'Dataset824_AortaSeg24_CTA_bin_50', 'labels': {'background': 0, 'vessel': 1}, 'licence': 'None', 'name': 'Dataset824_AortaSeg24_CTA_50', 'numTraining': 50, 'reference': 'None', 'release': '1.0 24/09/2024', 'channel_names': {'0': 'CT'}, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "500",
+    "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    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": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/fold_all",
+    "output_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_results/Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset825_AortaSeg24_CTA_bin_50', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [697, 443, 443], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [443.0, 443.0], 'spacing': [1.0, 1.0], '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, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [287, 183, 183], 'spacing': [2.4272624711896627, 2.4272624711896627, 2.4272624711896627], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 112, 112], 'median_image_size_in_voxels': [697.0, 443.0, 443.0], 'spacing': [1.0, 1.0, 1.0], '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, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 4, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 1, 1]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 0, '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_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 8733.0, 'mean': 208.1422119140625, 'median': 209.0, 'min': -2048.0, 'percentile_00_5': -23.0, 'percentile_99_5': 507.0, 'std': 108.94955444335938}}}",
+    "preprocessed_dataset_folder": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset825_AortaSeg24_CTA_bin_50/nnUNetPlans_3d_lowres",
+    "preprocessed_dataset_folder_base": "/root/data/spc/Dataset/nnUNet/nnUNet_preprocessed/Dataset825_AortaSeg24_CTA_bin_50",
+    "save_every": "1",
+    "torch_version": "2.2.0+cu118",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset825_AortaSeg24_CTA_bin_50/nnUNetTrainer_CE_DC_CBDC_NoMirroring_500epochs__nnUNetPlans__3d_lowres/plans.json

@@ -0,0 +1,444 @@
+{
+    "dataset_name": "Dataset825_AortaSeg24_CTA_bin_50",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        697,
+        443,
+        443
+    ],
+    "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": 16,
+            "patch_size": [
+                448,
+                448
+            ],
+            "median_image_size_in_voxels": [
+                443.0,
+                443.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "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,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 0,
+                "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_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                192,
+                112,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                287,
+                183,
+                183
+            ],
+            "spacing": [
+                2.4272624711896627,
+                2.4272624711896627,
+                2.4272624711896627
+            ],
+            "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,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                4,
+                4
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    1,
+                    1
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 0,
+                "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,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                192,
+                112,
+                112
+            ],
+            "median_image_size_in_voxels": [
+                697.0,
+                443.0,
+                443.0
+            ],
+            "spacing": [
+                1.0,
+                1.0,
+                1.0
+            ],
+            "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,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                4,
+                4
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    1,
+                    1
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 0,
+                "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_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 8733.0,
+            "mean": 208.1422119140625,
+            "median": 209.0,
+            "min": -2048.0,
+            "percentile_00_5": -23.0,
+            "percentile_99_5": 507.0,
+            "std": 108.94955444335938
+        }
+    }
+}