diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json
deleted file mode 100644
index cc4fbecccdefaf960c81cc2f2c7e2ea247071c4f..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json
+++ /dev/null
@@ -1,14 +0,0 @@
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\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json
deleted file mode 100644
index b0f5e56b08233470185d7c001d03dd89fed3f25a..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json
+++ /dev/null
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diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth
deleted file mode 100644
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json
deleted file mode 100644
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.06327102",
-    "aim_run": "<Run#-3275266343258779429 name=71a4eb65476745ac89c8118f repo=<Repo#-6524686033397964869 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, 449.0, 449.0], '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": "1000",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e4e80550>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4e80910>",
-    "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 0x1551e4e755d0>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e4e80450>",
-    "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': 309, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '71a4eb65476745ac89c8118f'}",
-    "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 0x15543e707710>",
-    "hostname": "g020",
-    "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 0x15543db015d0>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2025_8_15_18_18_19.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6a9790>",
-    "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 0x15543faf54d0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 309, '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: 1.995262314968881e-05\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/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540_synthrad2025_task2_CBCT_AB_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': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json
deleted file mode 100644
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 55.60064848848964,
-        "std": 14.338210868800594,
-        "max": 105.47389638122534,
-        "min": 35.20833348049252,
-        "25pc": 44.134254115405234,
-        "50pc": 53.92539230685208,
-        "75pc": 62.27850375865349,
-        "count": 62
-    },
-    "psnr": {
-        "mean": 30.48458344031393,
-        "std": 2.38001206819551,
-        "max": 35.77302372613302,
-        "min": 25.190641684003367,
-        "25pc": 28.694927361710747,
-        "50pc": 29.964147419029842,
-        "75pc": 32.48056910462868,
-        "count": 62
-    },
-    "ms_ssim": {
-        "mean": 0.9189507095920884,
-        "std": 0.05408709159150134,
-        "max": 0.9836779174167589,
-        "min": 0.715160057895457,
-        "25pc": 0.8933213351088732,
-        "50pc": 0.9349496548077856,
-        "75pc": 0.9545689319733478,
-        "count": 62
-    },
-    "DICE": {
-        "mean": 0.7588220357162053,
-        "std": 0.15028017475619626,
-        "max": 0.9240115880966187,
-        "min": 0.12273936718702316,
-        "25pc": 0.7277467846870422,
-        "50pc": 0.8152719140052795,
-        "75pc": 0.8395445346832275,
-        "count": 62
-    },
-    "HD95": {
-        "mean": 5.62839973168295,
-        "std": 4.9812358838641115,
-        "max": 27.221315383911133,
-        "min": 1.3090169429779053,
-        "25pc": 2.8109169006347656,
-        "50pc": 4.001410961151123,
-        "75pc": 6.309501647949219,
-        "count": 62
-    }
-}
\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth
deleted file mode 100644
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--- a/Dataset540/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
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diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.0656996",
-    "aim_run": "<Run#7840238524482879097 name=d67d9bffe97749aeabbb5f77 repo=<Repo#-3728560219381671007 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, 449.0, 449.0], '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": "950",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1552305785d0>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x155230578fd0>",
-    "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 0x1552305791d0>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15523057ba10>",
-    "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': 309, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '7ede7879ce8b4ac5aa179a6a'}",
-    "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 0x15543e693bd0>",
-    "hostname": "g014",
-    "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 0x15543db01150>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/training_log_2025_8_15_18_25_04.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6a9510>",
-    "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 0x15543db5b290>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 309, '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: 0.0006867729140492686\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/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540_synthrad2025_task2_CBCT_AB_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': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png
deleted file mode 100644
index 659cbe161b733e47479702493eda65f1f1565e96..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4726175d412e476be3f6307c084171181dda281662da0999e3aa2c9ba2be1a72
-size 539512
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json
deleted file mode 100644
index 59e24b00a88d7e6120552f0de81d29b86432b94f..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_1/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 53.55272689190319,
-        "std": 14.889341766841387,
-        "max": 122.86352309898407,
-        "min": 30.416893515619734,
-        "25pc": 43.1023531612861,
-        "50pc": 51.06755808296815,
-        "75pc": 60.72060011192438,
-        "count": 62
-    },
-    "psnr": {
-        "mean": 30.976497183908666,
-        "std": 2.2584403248631495,
-        "max": 35.89901188876336,
-        "min": 23.252552883227978,
-        "25pc": 29.74020113989821,
-        "50pc": 31.370777952339942,
-        "75pc": 32.65058521268283,
-        "count": 62
-    },
-    "ms_ssim": {
-        "mean": 0.9257386513947641,
-        "std": 0.05426036772193981,
-        "max": 0.9871172003174392,
-        "min": 0.6888293559782649,
-        "25pc": 0.9136096677059793,
-        "50pc": 0.9405245997158069,
-        "75pc": 0.9546367508874889,
-        "count": 62
-    },
-    "DICE": {
-        "mean": 0.7907230057677285,
-        "std": 0.09843220115897036,
-        "max": 0.9307920932769775,
-        "min": 0.42861416935920715,
-        "25pc": 0.7511935234069824,
-        "50pc": 0.8232013583183289,
-        "75pc": 0.8472089767456055,
-        "count": 62
-    },
-    "HD95": {
-        "mean": 5.159833487917165,
-        "std": 2.932905965974733,
-        "max": 15.669718742370605,
-        "min": 1.196161150932312,
-        "25pc": 3.1113007068634033,
-        "50pc": 4.230724811553955,
-        "75pc": 6.683786392211914,
-        "count": 62
-    }
-}
\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth
deleted file mode 100644
index 8bbed4221daeeae42edeceb3b82d107788ade950..0000000000000000000000000000000000000000
--- a/Dataset540/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 sha256:4ef999ec808ef6ec3aac4136b4ffaeaf0832a900857edb9c006cba3caf5fa372
-size 815581357
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json
deleted file mode 100644
index 716311d729edd553d402920da64258440d8adcc8..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.068972714",
-    "aim_run": "<Run#4954960653440782911 name=53940e89e55e48b1a8c92b62 repo=<Repo#-3056837629437348974 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, 449.0, 449.0], '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": "950",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15543c19d310>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551f4d49950>",
-    "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 0x1551f4d49c90>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15543c19d110>",
-    "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': 309, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': 'f17931503c984281b1e4d510'}",
-    "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 0x15543db01090>",
-    "hostname": "g018",
-    "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 0x15543db01210>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2025_8_15_18_25_04.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e68df50>",
-    "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 0x15543c1885d0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 309, '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: 0.0006867729140492686\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/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540_synthrad2025_task2_CBCT_AB_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': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png
+++ /dev/null
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-size 571722
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json
deleted file mode 100644
index 145d7ba6215300aa15aa210669f1433066fb895c..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 57.01654723077147,
-        "std": 17.622638874816897,
-        "max": 143.86217318377788,
-        "min": 30.19067215921589,
-        "25pc": 46.552292844688836,
-        "50pc": 55.79211682433204,
-        "75pc": 67.60596397847009,
-        "count": 62
-    },
-    "psnr": {
-        "mean": 30.488147366127464,
-        "std": 2.7070511008506726,
-        "max": 36.14905491889264,
-        "min": 21.888288703089877,
-        "25pc": 28.616781478342578,
-        "50pc": 30.36708218893584,
-        "75pc": 32.39591542406915,
-        "count": 62
-    },
-    "ms_ssim": {
-        "mean": 0.914863735480796,
-        "std": 0.0703194162744839,
-        "max": 0.987453209185812,
-        "min": 0.6110426850795952,
-        "25pc": 0.9057893543937303,
-        "50pc": 0.9342285837658622,
-        "75pc": 0.9558997511725557,
-        "count": 62
-    },
-    "DICE": {
-        "mean": 0.7534704665342967,
-        "std": 0.1304653140881235,
-        "max": 0.9473499059677124,
-        "min": 0.2837992310523987,
-        "25pc": 0.700099989771843,
-        "50pc": 0.7787275016307831,
-        "75pc": 0.8359065353870392,
-        "count": 62
-    },
-    "HD95": {
-        "mean": 6.240646302700043,
-        "std": 3.83524675527322,
-        "max": 17.518985748291016,
-        "min": 1.0,
-        "25pc": 3.014153778553009,
-        "50pc": 5.6401801109313965,
-        "75pc": 8.471923351287842,
-        "count": 62
-    }
-}
\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth
deleted file mode 100644
index f5ec33634d56fb46352025e7d27af74b902747e5..0000000000000000000000000000000000000000
--- a/Dataset540/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
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-size 815581741
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json
deleted file mode 100644
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.06857173",
-    "aim_run": "<Run#673879363958945887 name=0d8fb8744ca64e53b7691d6b repo=<Repo#-4523144353147373464 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, 449.0, 449.0], '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": "950",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551dce8f490>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551dce8dbd0>",
-    "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 0x1551dce8d810>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551dce8d610>",
-    "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': 309, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '35f73ede5632413992b21416'}",
-    "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 0x15543e717390>",
-    "hostname": "g020",
-    "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 0x15543db01790>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/training_log_2025_8_15_18_24_18.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6958d0>",
-    "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 0x15543c187350>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 309, '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: 0.0006867729140492686\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/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540_synthrad2025_task2_CBCT_AB_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': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png
deleted file mode 100644
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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/progress.png
+++ /dev/null
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diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json
deleted file mode 100644
index 73c5dca1098a33ed09e6ebb905c56df729dbf39b..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_3/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 56.71793795595987,
-        "std": 12.926959239082308,
-        "max": 100.08358536039722,
-        "min": 39.08826491363383,
-        "25pc": 47.3806929298052,
-        "50pc": 54.57368186078197,
-        "75pc": 61.234397706107444,
-        "count": 62
-    },
-    "psnr": {
-        "mean": 30.35647354740913,
-        "std": 2.041547611009348,
-        "max": 34.23448236668267,
-        "min": 25.124795623364186,
-        "25pc": 29.532969038069165,
-        "50pc": 30.454974931340733,
-        "75pc": 31.733575819196595,
-        "count": 62
-    },
-    "ms_ssim": {
-        "mean": 0.9213780089998237,
-        "std": 0.04198867328050704,
-        "max": 0.9744976763119004,
-        "min": 0.7398955680442516,
-        "25pc": 0.9129013904192553,
-        "50pc": 0.9319182617945962,
-        "75pc": 0.9481228491182435,
-        "count": 62
-    },
-    "DICE": {
-        "mean": 0.7493938769473404,
-        "std": 0.13625626351480594,
-        "max": 0.961596667766571,
-        "min": 0.0,
-        "25pc": 0.7069389820098877,
-        "50pc": 0.7764418721199036,
-        "75pc": 0.8240805864334106,
-        "count": 62
-    },
-    "HD95": {
-        "mean": 6.003821134567261,
-        "std": 3.725983822404894,
-        "max": 18.105030059814453,
-        "min": 0.0,
-        "25pc": 3.6449646949768066,
-        "50pc": 5.065055847167969,
-        "75pc": 7.41800594329834,
-        "count": 62
-    }
-}
\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth
deleted file mode 100644
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--- a/Dataset540/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 sha256:4521728138a26cc8f7c8acc976fa46c3d931ffdddb1ca9ee41ba09c5c7c40044
-size 815581421
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json
deleted file mode 100644
index f75d3c30f2eae3be2df5b29abeb365c5bb90bbbf..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/debug.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-    "_best_ema": "0.08793011",
-    "aim_run": "<Run#-4971232533756805270 name=f1962a41887c438899eb10c4 repo=<Repo#4165875965791107709 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, 449.0, 449.0], '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": "16",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x1551e1fe2990>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e1fe3310>",
-    "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 0x1551e1fe3f90>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551e1fe2cd0>",
-    "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': 309, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': 'f1962a41887c438899eb10c4'}",
-    "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 0x15543e70df50>",
-    "hostname": "g027",
-    "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 0x15543db00710>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/training_log_2025_8_14_21_19_33.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e69db50>",
-    "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 0x155230546890>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}, 'configuration': '3d_fullres', 'fold': 4, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 309, '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: 0.009864898188698403\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/Dataset540_synthrad2025_task2_CBCT_AB_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/Dataset540_synthrad2025_task2_CBCT_AB_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': 'Dataset540_synthrad2025_task2_CBCT_AB_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, 449, 449], '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': [449.0, 449.0], '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, 297, 297], '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, 449.0, 449.0], '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': -239.3903045654297, 'median': -185.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 479.0, 'std': 328.2787170410156}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset540_synthrad2025_task2_CBCT_AB_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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--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/progress.png
+++ /dev/null
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diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json
deleted file mode 100644
index e673598f97f60b52ce458f49222d80f17d73abf0..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_4/validation/summary.json
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 56.27332791745465,
-        "std": 20.172097278234112,
-        "max": 171.63834684961154,
-        "min": 29.370366386008243,
-        "25pc": 46.96331618370054,
-        "50pc": 51.68690135311662,
-        "75pc": 62.467438127664224,
-        "count": 61
-    },
-    "psnr": {
-        "mean": 30.559476270897356,
-        "std": 2.3920149796228998,
-        "max": 36.610363673994065,
-        "min": 20.802903374486114,
-        "25pc": 29.11417039503029,
-        "50pc": 30.779790266594,
-        "75pc": 31.892711484291347,
-        "count": 61
-    },
-    "ms_ssim": {
-        "mean": 0.9244688257164735,
-        "std": 0.05160669106703707,
-        "max": 0.9877655932279906,
-        "min": 0.6644621959050216,
-        "25pc": 0.913027593453776,
-        "50pc": 0.9352244769962569,
-        "75pc": 0.9544957511678459,
-        "count": 61
-    },
-    "DICE": {
-        "mean": 0.7694484567055937,
-        "std": 0.11448899144058666,
-        "max": 0.9566950798034668,
-        "min": 0.32794564962387085,
-        "25pc": 0.7302031517028809,
-        "50pc": 0.7830876111984253,
-        "75pc": 0.8363634943962097,
-        "count": 61
-    },
-    "HD95": {
-        "mean": 5.0484582596137875,
-        "std": 3.159580380071128,
-        "max": 19.093908309936523,
-        "min": 1.0,
-        "25pc": 2.8694076538085938,
-        "50pc": 4.541381359100342,
-        "75pc": 6.609293460845947,
-        "count": 61
-    }
-}
\ No newline at end of file
diff --git a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json b/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json
deleted file mode 100644
index b1690d28d07e5df93275b0863342443c6ad587ee..0000000000000000000000000000000000000000
--- a/Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json
+++ /dev/null
@@ -1,521 +0,0 @@
-{
-    "dataset_name": "Dataset540_synthrad2025_task2_CBCT_AB_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,
-        449,
-        449
-    ],
-    "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": [
-                449.0,
-                449.0
-            ],
-            "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
-            },
-            "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
-                        ],
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-                        [
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-                            3
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-                    ],
-                    "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": 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
-            ],
-            "median_image_size_in_voxels": [
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-                297,
-                297
-            ],
-            "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": 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
-            },
-            "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
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-                            3,
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-                            3
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-                        [
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-                            3
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-                    "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": 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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-                449.0,
-                449.0
-            ],
-            "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": 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": {
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-                    "features_per_stage": [
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-                        64,
-                        128,
-                        256,
-                        320,
-                        320
-                    ],
-                    "conv_op": "torch.nn.modules.conv.Conv3d",
-                    "kernel_sizes": [
-                        [
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-                            3,
-                            3
-                        ],
-                        [
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-                            3
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-                        [
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-                        ],
-                        [
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-                        ]
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-                    "strides": [
-                        [
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-                            1
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-                    "n_conv_per_stage_decoder": [
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-    "label_manager": "LabelManager",
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-            "min": -1024.0,
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-            "std": 328.2787170410156
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-}
\ No newline at end of file