diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json
deleted file mode 100644
index eb7b5fafd0abe77d0eaaa20fa981e33fdb34c9ed..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json
+++ /dev/null
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\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json
deleted file mode 100644
index 35b188a77f5820783e8dc6896b87f03b68ef7cf4..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json
+++ /dev/null
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diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth
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--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth
+++ /dev/null
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diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json
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--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "None",
-    "aim_run": "<Run#-9187275223076144166 name=31ee65d636234e48b931cb61 repo=<Repo#-3893665497386652479 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
-    "batch_size": "2",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e4f8cf10>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4f8dcd0>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e4f8c090>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4f8c250>",
-    "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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '01a79fd07dfb4718b450ee50'}",
-    "decoder_type": "standard",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "False",
-    "fold": "0",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA H100",
-    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x15543db00e50>",
-    "hostname": "g024",
-    "image_loss_weight": "0.5",
-    "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 0x15543db00f50>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2025_8_14_18_17_20.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e69d990>",
-    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x15543c15b7d0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0",
-    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png
deleted file mode 100644
index e9af218b59918ce87bd27dd891d648d6548ef008..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png
+++ /dev/null
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diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json
deleted file mode 100644
index d933cd753b831547bac32060a1341a6e6bd99136..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 52.80768787193642,
-        "std": 39.25411217734592,
-        "max": 349.4295323444315,
-        "min": 30.53782153506361,
-        "25pc": 39.67259547669727,
-        "50pc": 44.417437064864046,
-        "75pc": 56.57117028196328,
-        "count": 65
-    },
-    "psnr": {
-        "mean": 32.25481819350785,
-        "std": 2.9858789867732187,
-        "max": 36.561440579128075,
-        "min": 17.068229372936194,
-        "25pc": 30.947542147774367,
-        "50pc": 32.57369524597034,
-        "75pc": 34.17962467969445,
-        "count": 65
-    },
-    "ms_ssim": {
-        "mean": 0.9536834487302785,
-        "std": 0.08019347284318636,
-        "max": 0.9915810123537975,
-        "min": 0.3802523438307981,
-        "25pc": 0.9586479045957355,
-        "50pc": 0.9741513563984106,
-        "75pc": 0.9824337046998175,
-        "count": 65
-    },
-    "DICE": {
-        "mean": 0.8256282861416157,
-        "std": 0.06491444113684663,
-        "max": 0.9275574088096619,
-        "min": 0.5898815393447876,
-        "25pc": 0.7742491364479065,
-        "50pc": 0.8340623378753662,
-        "75pc": 0.8758383393287659,
-        "count": 65
-    },
-    "HD95": {
-        "mean": 4.208385368493887,
-        "std": 3.814029574946056,
-        "max": 30.388620376586914,
-        "min": 1.1522142887115479,
-        "25pc": 2.4057328701019287,
-        "50pc": 3.2774264812469482,
-        "75pc": 4.871885299682617,
-        "count": 65
-    }
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth
deleted file mode 100644
index 30efc744e2b86455bc008cdd9c1fe11e4b7a351b..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8cc8763c8bc6c2caf172da97e564771fb83199b817092ed9e5d600bbdec58ae1
-size 815580205
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json
deleted file mode 100644
index 94f73d47f7866e713fada9c18dd588e261e9b065..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "None",
-    "aim_run": "<Run#1996752835076131023 name=ab23b330243e4ecbba185376 repo=<Repo#-7360065323854941642 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
-    "batch_size": "2",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15521cf89f90>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15521cf88d50>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15521cf89510>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15521cf892d0>",
-    "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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': 'ab23b330243e4ecbba185376'}",
-    "decoder_type": "standard",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "False",
-    "fold": "1",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA H100",
-    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x1554407a4950>",
-    "hostname": "g016",
-    "image_loss_weight": "0.5",
-    "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 0x15543db005d0>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2025_8_14_18_28_49.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6ac8d0>",
-    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x15543c18cc90>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1",
-    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
\ No newline at end of file
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deleted file mode 100644
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+++ /dev/null
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diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json
deleted file mode 100644
index ce7fd775a6f0be8643106d9879494f4f4fc0800d..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 51.74281859670549,
-        "std": 14.81055265884747,
-        "max": 105.18706017900627,
-        "min": 29.480414002127183,
-        "25pc": 41.12862731518998,
-        "50pc": 49.6834193239368,
-        "75pc": 57.898092486924966,
-        "count": 64
-    },
-    "psnr": {
-        "mean": 31.857202630198294,
-        "std": 2.547837450189115,
-        "max": 36.365145176287974,
-        "min": 25.080774344952033,
-        "25pc": 30.07883834750593,
-        "50pc": 32.14347279859011,
-        "75pc": 33.53926451697998,
-        "count": 64
-    },
-    "ms_ssim": {
-        "mean": 0.9554992098191104,
-        "std": 0.05442613121036117,
-        "max": 0.9913923436807068,
-        "min": 0.5931477719026477,
-        "25pc": 0.9557171065697516,
-        "50pc": 0.9686996016968981,
-        "75pc": 0.978514011510029,
-        "count": 64
-    },
-    "DICE": {
-        "mean": 0.8221774445846677,
-        "std": 0.09407676486178415,
-        "max": 0.9378559589385986,
-        "min": 0.5096656084060669,
-        "25pc": 0.7995712906122208,
-        "50pc": 0.8502130508422852,
-        "75pc": 0.876771479845047,
-        "count": 64
-    },
-    "HD95": {
-        "mean": 3.7033023349940777,
-        "std": 2.586202359487684,
-        "max": 15.125686645507812,
-        "min": 1.2324292659759521,
-        "25pc": 2.0685394406318665,
-        "50pc": 2.5963653326034546,
-        "75pc": 4.235003709793091,
-        "count": 64
-    }
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth
deleted file mode 100644
index fba5b2aa8d0695434d926272b97d2ee47cf0ed0d..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:55f5e061d25a0361343a74af7a06a91ae3f2d443bfec1c1ffe29f244c5fc8491
-size 815580653
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json
deleted file mode 100644
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--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "None",
-    "aim_run": "<Run#6229970060413143018 name=23a615eee95f47b990c7017f repo=<Repo#2200379000171752251 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
-    "batch_size": "2",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e9fdb710>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e9fda710>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e9fdae50>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1552302a99d0>",
-    "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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '23a615eee95f47b990c7017f'}",
-    "decoder_type": "standard",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "False",
-    "fold": "2",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA H100",
-    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x15543e727690>",
-    "hostname": "g079",
-    "image_loss_weight": "0.5",
-    "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 0x15543db00dd0>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2025_8_14_18_29_02.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e77f6d0>",
-    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x15543c1b72d0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2",
-    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png
deleted file mode 100644
index 7ab1c0b41e1fe7f4a705242b37bdeb6720494663..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 647707
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json
deleted file mode 100644
index 1f24b9f83a047d4c438b8b797020ae65f0c68474..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 51.33464563133104,
-        "std": 35.177256693998366,
-        "max": 318.7569394134721,
-        "min": 26.757598185068293,
-        "25pc": 39.384197501680035,
-        "50pc": 46.33890513891107,
-        "75pc": 56.157680116206336,
-        "count": 64
-    },
-    "psnr": {
-        "mean": 32.42280396534713,
-        "std": 2.7018856678595,
-        "max": 38.18073568426472,
-        "min": 18.90129554039322,
-        "25pc": 31.349622397840733,
-        "50pc": 32.48367799565817,
-        "75pc": 33.688548231588065,
-        "count": 64
-    },
-    "ms_ssim": {
-        "mean": 0.9545687285280778,
-        "std": 0.08365836263484329,
-        "max": 0.9913876003283747,
-        "min": 0.34326933918698904,
-        "25pc": 0.9590733214870808,
-        "50pc": 0.9710989029121344,
-        "75pc": 0.9829548158383792,
-        "count": 64
-    },
-    "DICE": {
-        "mean": 0.7963443796543288,
-        "std": 0.1273409456045245,
-        "max": 0.9353682994842529,
-        "min": 0.0017798985354602337,
-        "25pc": 0.7674169540405273,
-        "50pc": 0.8182075917720795,
-        "75pc": 0.8725696057081223,
-        "count": 64
-    },
-    "HD95": {
-        "mean": 5.118571551516652,
-        "std": 10.49953236007353,
-        "max": 84.93049621582031,
-        "min": 1.264313817024231,
-        "25pc": 2.2895336747169495,
-        "50pc": 2.951563239097595,
-        "75pc": 4.755865693092346,
-        "count": 64
-    }
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth
deleted file mode 100644
index c17ad31d5864cb6e6b5e36e99d3b6f0e9991b750..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2c152e1058aefa9078809b64b7293a3099b6a37200727cb6fb4bbad9f19ee49d
-size 815580781
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json
deleted file mode 100644
index a8319ec1c49b817e021628a05e453ed74fc3e642..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "None",
-    "aim_run": "<Run#-6899666458330705255 name=14b0284affa943b7aa2e906f repo=<Repo#-6645048960822042994 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
-    "batch_size": "2",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e4f6c690>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4f6c4d0>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e4f6cf90>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4f6c810>",
-    "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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '14b0284affa943b7aa2e906f'}",
-    "decoder_type": "standard",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "False",
-    "fold": "3",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA H100",
-    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x15543e70d710>",
-    "hostname": "g095",
-    "image_loss_weight": "0.5",
-    "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 0x15543db00a10>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2025_8_14_18_29_12.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e745410>",
-    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x15543e71e710>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3",
-    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png
deleted file mode 100644
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--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png
+++ /dev/null
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-oid sha256:88e8cc876e46a466d2add49380f4a6abd2bbebea6728a15b630c08b810b7ca36
-size 759915
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json
deleted file mode 100644
index d3133cde063b429e5a6e3942e3f83aac5b83c819..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 59.6831403703124,
-        "std": 13.282268575250406,
-        "max": 135.26334083952912,
-        "min": 43.70561993673689,
-        "25pc": 50.90544320106635,
-        "50pc": 57.59766416973342,
-        "75pc": 63.61140017268492,
-        "count": 65
-    },
-    "psnr": {
-        "mean": 30.41879186477032,
-        "std": 1.7822310331910258,
-        "max": 33.303479514662435,
-        "min": 21.938598217631913,
-        "25pc": 29.489967207335592,
-        "50pc": 30.549653942101994,
-        "75pc": 31.678537891293097,
-        "count": 65
-    },
-    "ms_ssim": {
-        "mean": 0.972537866604284,
-        "std": 0.023400539772748585,
-        "max": 0.9896922348656185,
-        "min": 0.8256950304841117,
-        "25pc": 0.972346153083238,
-        "50pc": 0.9793511650351168,
-        "75pc": 0.9834134511089297,
-        "count": 65
-    },
-    "DICE": {
-        "mean": 0.8757181415191063,
-        "std": 0.04152858997642459,
-        "max": 0.9331912994384766,
-        "min": 0.7493325471878052,
-        "25pc": 0.8536831736564636,
-        "50pc": 0.885571300983429,
-        "75pc": 0.907253623008728,
-        "count": 65
-    },
-    "HD95": {
-        "mean": 1.9038109889397254,
-        "std": 0.8689346520839407,
-        "max": 4.7832865715026855,
-        "min": 1.1818181276321411,
-        "25pc": 1.3928370475769043,
-        "50pc": 1.54265558719635,
-        "75pc": 2.0603861808776855,
-        "count": 65
-    }
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth
deleted file mode 100644
index b2d99689e56e71edcda12d4ed8a86e9e5bb2be74..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1b46968b0572da62b2da28ecf87b21939c19136f2106018f8392cfc0edbfe41a
-size 815580781
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json
deleted file mode 100644
index e14db809fa5b39d5fb4109771dc804fccf501a97..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.07174573",
-    "aim_run": "<Run#5217745144174963812 name=b4c2c1523f4f43458d997185 repo=<Repo#3104583568250827973 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
-    "batch_size": "2",
-    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "17",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15518f3244d0>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15518f4e0190>",
-    "dataloader_train.num_processes": "12",
-    "dataloader_train.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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15518f326b90>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15518f327090>",
-    "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' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
-    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': 'b4c2c1523f4f43458d997185'}",
-    "decoder_type": "standard",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "False",
-    "fold": "4",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA H100",
-    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x155400e75f90>",
-    "hostname": "g011",
-    "image_loss_weight": "0.5",
-    "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 0x1553fe039910>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/training_log_2025_8_14_22_11_54.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x155400e8f2d0>",
-    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
-    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x1553fe3b0410>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}, 'configuration': '3d_fullres', 'fold': 4, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 321, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.009855884118426006\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4",
-    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [81, 450, 450], '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': 13, 'patch_size': [512, 448], 'median_image_size_in_voxels': [450.0, 450.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 224, 192], 'median_image_size_in_voxels': [81, 298, 298], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 224, 192], 'median_image_size_in_voxels': [81.0, 450.0, 450.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -330.8976135253906, 'median': -243.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 563.0, 'std': 371.2642822265625}}}",
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-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}
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diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json
deleted file mode 100644
index 751073fdca90d7648534b4287ba12fbf8d699e85..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 59.88798023950074,
-        "std": 57.783745732152084,
-        "max": 402.1487391215082,
-        "min": 24.99876303329541,
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-        "50pc": 47.523179480329034,
-        "75pc": 59.01712512618472,
-        "count": 64
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-        "mean": 31.940514605997574,
-        "std": 3.8244056625891694,
-        "max": 38.61016131020203,
-        "min": 16.166608488671578,
-        "25pc": 30.502047685085795,
-        "50pc": 32.46838725951116,
-        "75pc": 33.98167476379149,
-        "count": 64
-    },
-    "ms_ssim": {
-        "mean": 0.9390399525508067,
-        "std": 0.11888193307610521,
-        "max": 0.9928031769753466,
-        "min": 0.2866173907995687,
-        "25pc": 0.9541345219833262,
-        "50pc": 0.9710010874743243,
-        "75pc": 0.9787920313898105,
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-    "DICE": {
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-        "std": 0.10473576728843335,
-        "max": 0.9104868173599243,
-        "min": 0.38233712315559387,
-        "25pc": 0.7617079466581345,
-        "50pc": 0.8164933323860168,
-        "75pc": 0.870903417468071,
-        "count": 64
-    },
-    "HD95": {
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-        "std": 5.548372165918884,
-        "max": 34.284122467041016,
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-        "25pc": 2.258346378803253,
-        "50pc": 3.13424813747406,
-        "75pc": 4.478186011314392,
-        "count": 64
-    }
-}
\ No newline at end of file
diff --git a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json b/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json
deleted file mode 100644
index 50e2c9f3ed7b45a897c4c512d4721da21702777e..0000000000000000000000000000000000000000
--- a/Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json
+++ /dev/null
@@ -1,521 +0,0 @@
-{
-    "dataset_name": "Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
-    "plans_name": "nnUNetResEncUNetLPlans",
-    "original_median_spacing_after_transp": [
-        3.0,
-        1.0,
-        1.0
-    ],
-    "original_median_shape_after_transp": [
-        81,
-        450,
-        450
-    ],
-    "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": 13,
-            "patch_size": [
-                512,
-                448
-            ],
-            "median_image_size_in_voxels": [
-                450.0,
-                450.0
-            ],
-            "spacing": [
-                1.0,
-                1.0
-            ],
-            "normalization_schemes": [
-                "CTNormalizationClippingSynthrad2025"
-            ],
-            "use_mask_for_norm": [
-                false
-            ],
-            "resampling_fn_data": "resample_data_or_seg_to_shape",
-            "resampling_fn_seg": "resample_data_or_seg_to_shape",
-            "resampling_fn_data_kwargs": {
-                "is_seg": false,
-                "order": 3,
-                "order_z": 0,
-                "force_separate_z": null
-            },
-            "resampling_fn_seg_kwargs": {
-                "is_seg": true,
-                "order": 1,
-                "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
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-            "architecture": {
-                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
-                "arch_kwargs": {
-                    "n_stages": 7,
-                    "features_per_stage": [
-                        32,
-                        64,
-                        128,
-                        256,
-                        512,
-                        512,
-                        512
-                    ],
-                    "conv_op": "torch.nn.modules.conv.Conv2d",
-                    "kernel_sizes": [
-                        [
-                            3,
-                            3
-                        ],
-                        [
-                            3,
-                            3
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-                        [
-                            3,
-                            3
-                        ],
-                        [
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-                            3
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-                    "strides": [
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-                            1
-                        ],
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-                            2,
-                            2
-                        ],
-                        [
-                            2,
-                            2
-                        ],
-                        [
-                            2,
-                            2
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-                        [
-                            2,
-                            2
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-                        [
-                            2,
-                            2
-                        ],
-                        [
-                            2,
-                            2
-                        ]
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-                    "n_blocks_per_stage": [
-                        1,
-                        3,
-                        4,
-                        6,
-                        6,
-                        6,
-                        6
-                    ],
-                    "n_conv_per_stage_decoder": [
-                        1,
-                        1,
-                        1,
-                        1,
-                        1,
-                        1
-                    ],
-                    "conv_bias": true,
-                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
-                    "norm_op_kwargs": {
-                        "eps": 1e-05,
-                        "affine": true
-                    },
-                    "dropout_op": null,
-                    "dropout_op_kwargs": null,
-                    "nonlin": "torch.nn.LeakyReLU",
-                    "nonlin_kwargs": {
-                        "inplace": true
-                    }
-                },
-                "_kw_requires_import": [
-                    "conv_op",
-                    "norm_op",
-                    "dropout_op",
-                    "nonlin"
-                ]
-            },
-            "batch_dice": true
-        },
-        "3d_lowres": {
-            "data_identifier": "nnUNetResEncUNetLPlans_3d_lowres",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 2,
-            "patch_size": [
-                56,
-                224,
-                192
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-            "median_image_size_in_voxels": [
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-                298,
-                298
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-            "spacing": [
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-                1.512589724855112,
-                1.512589724855112
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-            "normalization_schemes": [
-                "CTNormalizationClippingSynthrad2025"
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-            "use_mask_for_norm": [
-                false
-            ],
-            "resampling_fn_data": "resample_data_or_seg_to_shape",
-            "resampling_fn_seg": "resample_data_or_seg_to_shape",
-            "resampling_fn_data_kwargs": {
-                "is_seg": false,
-                "order": 3,
-                "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": {
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-                "order": 1,
-                "order_z": 0,
-                "force_separate_z": null
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-            "architecture": {
-                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
-                "arch_kwargs": {
-                    "n_stages": 6,
-                    "features_per_stage": [
-                        32,
-                        64,
-                        128,
-                        256,
-                        320,
-                        320
-                    ],
-                    "conv_op": "torch.nn.modules.conv.Conv3d",
-                    "kernel_sizes": [
-                        [
-                            3,
-                            3,
-                            3
-                        ],
-                        [
-                            3,
-                            3,
-                            3
-                        ],
-                        [
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-                    "strides": [
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-                            2
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-                            2
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-                            1,
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-                            2
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-                        [
-                            1,
-                            2,
-                            2
-                        ]
-                    ],
-                    "n_blocks_per_stage": [
-                        1,
-                        3,
-                        4,
-                        6,
-                        6,
-                        6
-                    ],
-                    "n_conv_per_stage_decoder": [
-                        1,
-                        1,
-                        1,
-                        1,
-                        1
-                    ],
-                    "conv_bias": true,
-                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
-                    "norm_op_kwargs": {
-                        "eps": 1e-05,
-                        "affine": true
-                    },
-                    "dropout_op": null,
-                    "dropout_op_kwargs": null,
-                    "nonlin": "torch.nn.LeakyReLU",
-                    "nonlin_kwargs": {
-                        "inplace": true
-                    }
-                },
-                "_kw_requires_import": [
-                    "conv_op",
-                    "norm_op",
-                    "dropout_op",
-                    "nonlin"
-                ]
-            },
-            "batch_dice": false,
-            "next_stage": "3d_cascade_fullres"
-        },
-        "3d_fullres": {
-            "data_identifier": "nnUNetPlans_3d_fullres",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 2,
-            "patch_size": [
-                40,
-                224,
-                192
-            ],
-            "median_image_size_in_voxels": [
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-                450.0
-            ],
-            "spacing": [
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-                1.0,
-                1.0
-            ],
-            "normalization_schemes": [
-                "CTNormalizationClippingSynthrad2025"
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-            "use_mask_for_norm": [
-                false
-            ],
-            "resampling_fn_data": "resample_data_or_seg_to_shape",
-            "resampling_fn_seg": "resample_data_or_seg_to_shape",
-            "resampling_fn_data_kwargs": {
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-                "order": 3,
-                "order_z": 0,
-                "force_separate_z": null
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-                "order_z": 0,
-                "force_separate_z": null
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-            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
-            "resampling_fn_probabilities_kwargs": {
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-                "order": 1,
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-            "architecture": {
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-                    "features_per_stage": [
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-                        256,
-                        320,
-                        320
-                    ],
-                    "conv_op": "torch.nn.modules.conv.Conv3d",
-                    "kernel_sizes": [
-                        [
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-                            3,
-                            3
-                        ],
-                        [
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-                    "strides": [
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-                            1
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\ No newline at end of file