Upload folder using huggingface_hub

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+models/upsample_ashs_pmc_t2/model.dat filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1 @@
+CRASHS Template and Model Package by Paul Yushkevich is marked with CC0 1.0 Universal. To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/

README.md

@@ -1,10 +1,6 @@
----
-license: cc0-1.0
-tags:
-- medical
-- hippocampus
-- ASHS
-- CRASHS
-- MRI
-pretty_name: CRASHS Template Package
----
+CRASHS Template and Model Package
+=================================
+
+This folder contains the templates and deep learning models needed to run CRASHS (cortical reconstruction for automated segmentation of hippocampal subfields). Please see [CRASHS github page](https://github.com/pyushkevich/crashs) for details on using this dataset.
+
+This package is compatible with CRASHS version 0.2.5 and later

Version.txt

@@ -0,0 +1 @@
+2024-08-30

models/nnunet_t1_wm/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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+    "file_ending": ".nii.gz"
+}

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models/nnunet_t1_wm/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_final.pth

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models/nnunet_t1_wm/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/debug.json

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+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
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+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
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+    "output_folder": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}",
+    "preprocessed_dataset_folder": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT",
+    "save_every": "50",
+    "torch_version": "1.13.0+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
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+{
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+    "batch_size": "2",
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+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f8456116b60>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f8456115c30>",
+    "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 = [64, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 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 0x7f8456115b70>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f8456115cc0>",
+    "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], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "Quadro RTX 5000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f84560b3430>",
+    "hostname": "lambda-picsl",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f84560b2dd0>",
+    "local_rank": "0",
+    "log_file": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_1/training_log_2023_8_3_05_04_34.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f84560b36d0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f84561145e0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "400",
+    "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": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_1",
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+    "preprocessed_dataset_folder": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/nnUNetPlans_3d_fullres",
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+    "torch_version": "1.13.0+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
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+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f63222bec50>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f63222bdd20>",
+    "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 = [64, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
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+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f63222bddb0>",
+    "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], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "2",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "Quadro RTX 5000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f632225f520>",
+    "hostname": "lambda-picsl",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f632225eec0>",
+    "local_rank": "0",
+    "log_file": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_2/training_log_2023_8_3_11_44_51.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f632225f7c0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f63222bc6d0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "400",
+    "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)",
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+    "output_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
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+    "was_initialized": "True",
+    "weight_decay": "3e-05"
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+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fd917552980>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd917551d80>",
+    "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 = [64, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 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 0x7fd917551cc0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd917551e10>",
+    "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], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "3",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "Quadro RTX 5000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fd9174f3580>",
+    "hostname": "lambda-picsl",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fd9174f2f20>",
+    "local_rank": "0",
+    "log_file": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_3/training_log_2023_8_3_18_25_42.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fd9174f3820>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fd917550730>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "400",
+    "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": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_3",
+    "output_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}",
+    "preprocessed_dataset_folder": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT",
+    "save_every": "50",
+    "torch_version": "1.13.0+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

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models/nnunet_t1_wm/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_4/debug.json

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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8500,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fe9773c2b60>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fe9773c1cf0>",
+    "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 = [64, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 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 0x7fe9773c1a50>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fe9773c1d80>",
+    "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], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "4",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "Quadro RTX 5000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fe97735f4f0>",
+    "hostname": "lambda-picsl",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fe97735ee90>",
+    "local_rank": "0",
+    "log_file": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_4/training_log_2023_8_4_01_06_51.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fe97735f790>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fe9773c06a0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}, 'configuration': '3d_fullres', 'fold': 4, 'dataset_json': {'channel_names': {'0': '3TT1'}, 'labels': {'background': 0, 'WM': 1}, 'numTraining': 58, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "400",
+    "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": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres/fold_4",
+    "output_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_trained_models/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer400Epoch__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 145, 'patch_size': [128, 128], 'median_image_size_in_voxels': [113.0, 113.0], 'spacing': [0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], 'normalization_schemes': ['ZScoreNormalization'], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}}",
+    "preprocessed_dataset_folder": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT",
+    "save_every": "50",
+    "torch_version": "1.13.0+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

models/nnunet_t1_wm/Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,294 @@
+{
+    "dataset_name": "Dataset303_3TT1WMSegASHSGT",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        0.9765625,
+        0.5,
+        0.48828125
+    ],
+    "original_median_shape_after_transp": [
+        64,
+        113,
+        113
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        1,
+        0,
+        2
+    ],
+    "transpose_backward": [
+        1,
+        0,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 145,
+            "patch_size": [
+                128,
+                128
+            ],
+            "median_image_size_in_voxels": [
+                113.0,
+                113.0
+            ],
+            "spacing": [
+                0.5,
+                0.48828125
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "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,
+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    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
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                64,
+                128,
+                128
+            ],
+            "median_image_size_in_voxels": [
+                64.0,
+                113.0,
+                113.0
+            ],
+            "spacing": [
+                0.9765625,
+                0.5,
+                0.48828125
+            ],
+            "normalization_schemes": [
+                "ZScoreNormalization"
+            ],
+            "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": [
+                4,
+                5,
+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    1,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    1,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": false
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 685.0,
+            "mean": 356.69915771484375,
+            "median": 396.0,
+            "min": 117.0,
+            "percentile_00_5": 149.0,
+            "percentile_99_5": 570.0,
+            "std": 120.57451629638672
+        }
+    }
+}

models/nnunet_t1_wm/config.json

@@ -0,0 +1,7 @@
+{
+  "model": "Dataset303_3TT1WMSegASHSGT/nnUNetTrainer__nnUNetPlans__3d_fullres",
+  "trainer": "nnUNetTrainer400Epoch",
+  "target_orientation": "ASL",
+  "target_spacing": [1.0, 0.5, 0.5]
+}
+

models/upsample_ashs_pmc_t2/config.json

@@ -0,0 +1 @@
+{"config": {"patch_size": [5, 32, 32], "upsample_factor": [5, 1, 1]}, "id_train": ["100551R", "104937L", "106049L", "106312R", "113909R", "116748R", "117243R", "117667R", "118374L", "118430R", "119349L", "119359R", "119933L", "120126L", "120267L", "120937L", "121250L"], "epochs": 2000, "batch_size": 64}

models/upsample_ashs_pmc_t2/model.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e93f327a87805ef9abff4358e07dc6b156153df93bb929b5c97d73d00cfb709
+size 40489817

templates/ashs_pmc_alveus/template.json

@@ -0,0 +1,19 @@
+{
+    "ashs_label_type": {
+        "wm": [15],
+        "gm": [1, 2, 4, 8, 10, 11, 12, 13],
+        "bg": [0, 3, 7, 14]
+    },
+    "labels_for_surface_matching": [15, 1, 2, 4, 8, 10, 11, 12, 13],
+    "preprocessing": {
+        "mode": "t2_alveus",
+        "t2_alveus_param": {
+            "dg_labels": [3],
+            "cortex_labels": [11, 12, 13],
+            "suberc_labels": [8, 10],
+            "ca_labels": [1, 2, 4],
+            "upsample_model": "upsample_ashs_pmc_t2"
+        },
+        "nnunet_wm": "nnunet_t1_wm"
+    }
+}

templates/ashs_pmc_t1/ashs_template_flip.mat

@@ -0,0 +1,4 @@
+-0.992704 0.120567 0.00126359 -5.55375
+0.120571 0.9927 0.00299536 0.381202
+0.000893229 -0.00312585 0.999995 0.121834
+0 0 0 1

templates/ashs_pmc_t1/template.json

@@ -0,0 +1,18 @@
+{
+    "ashs_label_type": {
+        "wm": [20],
+        "gm": [1, 2, 10, 11, 12, 13],
+        "bg": [0, 4, 6, 7, 9, 14, 16, 17, 18, 19]    
+    },
+    "labels_for_surface_matching": [ 20, 1, 2, 10, 11, 12, 13 ],
+    "preprocessing": {
+        "mode": "t1_add_wm",
+        "t1_add_wm_param": {
+            "cortex_labels": [11, 12],
+            "suberc_labels": [10],
+            "hipp_labels": [1]
+        },
+        "nnunet_wm": "nnunet_t1_wm"
+    }
+}
+

templates/exvivo_phg_94t/template.json

@@ -0,0 +1,56 @@
+{
+    "sides": {
+        "left": {
+            "mesh": "template_shoot_left.vtk",
+            "mesh_reduced": "template_shoot_reduced_left.vtk"
+        },
+        "right": {
+            "mesh": "template_shoot_right.vtk",
+            "mesh_reduced": "template_shoot_reduced_right.vtk"
+        }
+    },
+    "registration": {
+        "varifold_sigma": 2.0,
+        "lddmm_sigma": 2.0,
+        "lddmm_gamma": 0.05,
+        "lddmm_nt": 10,
+        "jacobian_penalty": 10.0,
+        "lddmm_maxiter": 250,
+        "affine_maxiter": 50
+    },
+    "cruise": {
+        "inflate_maxiter": 2000,
+        "inflate_reduction": 4000,
+        "laminar_n_layers": 10,
+        "laminar_method": "distance-preserving"
+    },
+    "template_build": {
+        "schedule": [
+            10,
+            10,
+            20,
+            200
+        ],
+        "remeshing_edge_length_pct": 2.0,
+        "root_sigma_factor": 2.4,
+        "remeshing_feature_angle": 180.0
+    },
+    "ashs_label_type": {
+        "wm": [
+            10
+        ],
+        "gm": [
+            1,
+            3
+        ],
+        "bg": [
+            0,
+            5
+        ]
+    },
+    "labels_for_surface_matching": [
+        10,
+        1,
+        3
+    ]
+}