Delete Dataset262/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2

Dataset262/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth

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Dataset262/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json

@@ -1,58 +0,0 @@
-{
-    "_best_ema": "None",
-    "aim_run": "<Run#7325676299133527188 name=59407d6c5463471fa96d8244 repo=<Repo#1366112406700853824 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': [56, 192, 160], 'median_image_size_in_voxels': [89.0, 296.0, 279.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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': False}",
-    "configuration_name": "3d_fullres",
-    "cudnn_version": 90501,
-    "current_epoch": "0",
-    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x155230259950>",
-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x15523025a850>",
-    "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 0x155230259d50>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1552302a27d0>",
-    "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': 'MR'}, 'numTraining': 221, 'file_ending': '.mha'}",
-    "debug_custom": "{'run_hash': '59407d6c5463471fa96d8244'}",
-    "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 0x15543daeeb50>",
-    "hostname": "g031",
-    "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 0x15543daeec50>",
-    "local_rank": "0",
-    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2025_8_13_01_36_48.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6a4b90>",
-    "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, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 32, 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 0x15543db3f290>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [89, 296, 279], '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': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [296.0, 279.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 192, 160], 'median_image_size_in_voxels': [89.0, 296.0, 279.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4142.0, 'mean': 75.25260162353516, 'median': 0.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 1222.0, 'std': 197.03660583496094}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'MR'}, 'numTraining': 221, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
-    "network": "ResidualEncoderUNet",
-    "num_epochs": "1000",
-    "num_input_channels": "1",
-    "num_iterations_per_epoch": "250",
-    "num_val_iterations_per_epoch": "50",
-    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
-    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked/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/Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "perception_masked": "True",
-    "plans_manager": "{'dataset_name': 'Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [89, 296, 279], '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': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [296.0, 279.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [56, 192, 160], 'median_image_size_in_voxels': [89.0, 296.0, 279.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4142.0, 'mean': 75.25260162353516, 'median': 0.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 1222.0, 'std': 197.03660583496094}}}",
-    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset262_synthrad2025_task1_MR_HN_pre_v2r_stitched_masked",
-    "save_every": "50",
-    "torch_version": "2.7.1+cu126",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}

Dataset262/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json

@@ -1,52 +0,0 @@
-{
-    "mae": {
-        "mean": 74.65292392337206,
-        "std": 27.21601270921252,
-        "max": 180.22424320443434,
-        "min": 50.03639991050344,
-        "25pc": 55.20103966286182,
-        "50pc": 60.57595211267504,
-        "75pc": 95.06778034175494,
-        "count": 44
-    },
-    "psnr": {
-        "mean": 28.46884222939042,
-        "std": 2.740618880237753,
-        "max": 31.788027766490927,
-        "min": 21.581445444266258,
-        "25pc": 25.956340082853398,
-        "50pc": 29.589134728975914,
-        "75pc": 30.78935038430233,
-        "count": 44
-    },
-    "ms_ssim": {
-        "mean": 0.9470716773141884,
-        "std": 0.04705583669583442,
-        "max": 0.9837417913911773,
-        "min": 0.7408830509741683,
-        "25pc": 0.9323297519024558,
-        "50pc": 0.9680664744199681,
-        "75pc": 0.9773946177781223,
-        "count": 44
-    },
-    "DICE": {
-        "mean": 0.8484438739039681,
-        "std": 0.11799668647372488,
-        "max": 0.9286674857139587,
-        "min": 0.18524354696273804,
-        "25pc": 0.821153312921524,
-        "50pc": 0.8878902792930603,
-        "75pc": 0.911092683672905,
-        "count": 44
-    },
-    "HD95": {
-        "mean": 2.199756370349364,
-        "std": 1.6979196164112982,
-        "max": 11.68980884552002,
-        "min": 1.0883883237838745,
-        "25pc": 1.2982812225818634,
-        "50pc": 1.5779862999916077,
-        "75pc": 2.510884642601013,
-        "count": 44
-    }
-}