Predictor: Model: classpath: Model:UNetpp UNetpp: outputs_criterions: None pretrained: false Dataset: groups_src: Volume_0: groups_dest: Volume: transforms: TensorCast: dtype: int16 inverse: false ResampleToResolution: spacing: - 1 - 1 - 3 inverse: true Clip: min_value: min max_value: percentile:99.5 save_clip_min: false save_clip_max: false mask: None Normalize: lazy: false channels: None min_value: -1 max_value: 1 inverse: false patch_transforms: None is_input: true augmentations: DataAugmentation_0: data_augmentations: Flip: f_prob: - 0 - 0.5 - 0.5 prob: 1 nb: 2 Patch: patch_size: - 1 - 512 - 512 overlap: None mask: None pad_value: -1 extend_slice: 2 subset: None filter: None dataset_filenames: - ./Dataset:mha use_cache: false batch_size: 8 outputs_dataset: Head:Tanh: OutputDataset: name_class: OutSameAsGroupDataset before_reduction_transforms: UnNormalize: min_value: -1024 max_value: 3071 TensorCast: dtype: int16 inverse: false after_reduction_transforms: InferenceStack: dataset: Predictions/ImpactSynth/Output:mha name: InferenceStack mode: mean final_transforms: None dataset_filename: Output:mha inverse_transform: true group: sCT same_as_group: Volume_0:Volume patch_combine: None reduction: Concat Concat: {} train_name: ImpactSynth manual_seed: 32 gpu_checkpoints: None images_log: None combine: Concat autocast: false data_log: None Concat: {}