diff --git a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth b/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth deleted file mode 100644 index c0ebfc1c7034e9880ac299dfabf6c326b43cb98a..0000000000000000000000000000000000000000 --- a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6ddbbcfe44a62d106d0243a899198adb832469613c676788d033ea0946338631 -size 815581549 diff --git a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json b/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json deleted file mode 100644 index 2a62db09d1239c6902462cdb8eec0dd7f69c0e8c..0000000000000000000000000000000000000000 --- a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/debug.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "_best_ema": "None", - "aim_run": ">", - "batch_size": "2", - "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 192, 224], 'median_image_size_in_voxels': [99.0, 442.0, 465.0], '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': True}", - "configuration_name": "3d_fullres", - "cudnn_version": 90501, - "current_epoch": "0", - "dataloader_train": "", - "dataloader_train.generator": "", - "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": "", - "dataloader_val.generator": "", - "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': 175, 'file_ending': '.mha'}", - "debug_custom": "{'run_hash': 'df86de6bad9d4af88cd79bf4'}", - "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": "", - "hostname": "g100", - "image_loss_weight": "0.5", - "inference_allowed_mirroring_axes": "None", - "initial_lr": "0.01", - "is_cascaded": "False", - "is_ddp": "False", - "label_manager": "", - "local_rank": "0", - "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/training_log_2025_8_13_01_35_04.txt", - "logger": "", - "loss": "SynPerceptionLoss(\n (seg_model): ResidualEncoderUNet(\n (encoder): ResidualEncoder(\n (stem): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (stages): Sequential(\n (0): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (1): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (2): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (3): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (4): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (5): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n )\n (decoder): UNetDecoder(\n (encoder): ResidualEncoder(\n (stem): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (stages): Sequential(\n (0): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (1): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (2): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (3): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (4): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n (1): ConvDropoutNormReLU(\n (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n (5): StackedResidualBlocks(\n (blocks): Sequential(\n (0): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n (skip): Sequential(\n (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n )\n )\n (1): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (2): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (3): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (4): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n (5): BasicBlockD(\n (conv1): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n (conv2): ConvDropoutNormReLU(\n (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (all_modules): Sequential(\n (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n )\n )\n (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n )\n (stages): ModuleList(\n (0): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (1): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (2): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (3): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n (4): StackedConvBlocks(\n (convs): Sequential(\n (0): ConvDropoutNormReLU(\n (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n (all_modules): Sequential(\n (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n (2): LeakyReLU(negative_slope=0.01, inplace=True)\n )\n )\n )\n )\n )\n (transpconvs): ModuleList(\n (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n )\n (seg_layers): ModuleList(\n (0): Conv3d(320, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n (1): Conv3d(256, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n (2): Conv3d(128, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n (3): Conv3d(64, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n (4): Conv3d(32, 62, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n )\n )\n )\n (L1): L1Loss()\n (image_loss): myMaskedMSE(\n (mse): myMSE()\n )\n)", - "lr_scheduler": "", - "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [99, 442, 465], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 13, 'patch_size': [448, 512], 'median_image_size_in_voxels': [442.0, 465.0], '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_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [99, 292, 307], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], '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': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 192, 224], 'median_image_size_in_voxels': [99.0, 442.0, 465.0], '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': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4142.0, 'mean': 166.43568420410156, 'median': 4.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 1590.0, 'std': 346.18096923828125}}}, 'configuration': '3d_fullres', 'fold': 2, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'MR'}, 'numTraining': 175, '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/Dataset260_synthrad2025_task1_MR_AB_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/Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres", - "oversample_foreground_percent": "0.33", - "perception_masked": "True", - "plans_manager": "{'dataset_name': 'Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [99, 442, 465], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 13, 'patch_size': [448, 512], 'median_image_size_in_voxels': [442.0, 465.0], '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_lowres': {'data_identifier': 'nnUNetResEncUNetLPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [99, 292, 307], 'spacing': [3.0, 1.512589724855112, 1.512589724855112], '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': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [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, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [40, 192, 224], 'median_image_size_in_voxels': [99.0, 442.0, 465.0], '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': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4142.0, 'mean': 166.43568420410156, 'median': 4.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 1590.0, 'std': 346.18096923828125}}}", - "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked/nnUNetPlans_3d_fullres", - "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset260_synthrad2025_task1_MR_AB_pre_v2r_stitched_masked", - "save_every": "50", - "torch_version": "2.7.1+cu126", - "unpack_dataset": "True", - "was_initialized": "True", - "weight_decay": "3e-05" -} \ No newline at end of file diff --git a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png b/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png deleted file mode 100644 index b3f8ec70170edf03bc326b1725a460099fb58a37..0000000000000000000000000000000000000000 --- a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/progress.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4bb5e2f9910d354333c544dd25b038a9c6d58f2ad2ace3e32d972e9b95936c57 -size 530372 diff --git a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json b/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json deleted file mode 100644 index 724e73f3eb8e90850640484f70eae9ab703f60bc..0000000000000000000000000000000000000000 --- a/Dataset260/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_2/validation/summary.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "mae": { - "mean": 84.74803688363573, - "std": 40.047402284729955, - "max": 263.04195372841815, - "min": 40.32802880932887, - "25pc": 63.28146353775634, - "50pc": 72.06488595910446, - "75pc": 89.78496781277786, - "count": 35 - }, - "psnr": { - "mean": 26.885433281824522, - "std": 2.6544000856675587, - "max": 31.854206682238477, - "min": 19.393644306850135, - "25pc": 25.196885045914968, - "50pc": 27.44324508788327, - "75pc": 28.4630928707804, - "count": 35 - }, - "ms_ssim": { - "mean": 0.8115448857014917, - "std": 0.12441964379373294, - "max": 0.9412200398725382, - "min": 0.42626283493743744, - "25pc": 0.7605181879936237, - "50pc": 0.8653771175752596, - "75pc": 0.8976873502779543, - "count": 35 - }, - "DICE": { - "mean": 0.6612528319869723, - "std": 0.12250841573151652, - "max": 0.808124840259552, - "min": 0.14218591153621674, - "25pc": 0.6211503744125366, - "50pc": 0.68855881690979, - "75pc": 0.740378350019455, - "count": 35 - }, - "HD95": { - "mean": 7.289406333650861, - "std": 5.135283095630513, - "max": 25.59170150756836, - "min": 2.4629297256469727, - "25pc": 4.024229168891907, - "50pc": 5.817408561706543, - "75pc": 8.499962329864502, - "count": 35 - } -} \ No newline at end of file