pytorch_connectomics/tutorials/fiber_linghu26.yaml at master · PytorchConnectomics/pytorch_connectomics · GitHub

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_base_:
- ../connectomics/config/all_profiles.yaml

experiment_name: fiber_mednext_bcs
description: Fiber segmentation with MedNeXt and multi-task learning (Binary + Contour + SDT)

default:
  system:
    num_gpus: -1
    num_workers: -1
    seed: 42
  model:
    arch:
      profile: mednext_s
    out_channels: 3
    input_size:
    - 32
    - 128
    - 128
    output_size:
    - 32
    - 128
    - 128
    loss:
      deep_supervision: false
      losses:
      - function: WeightedBCEWithLogitsLoss
        weight: 1.0
        kwargs: {reduction: mean}
        pred_slice: "0:1"
        target_slice: "0:1"
      - function: DiceLoss
        weight: 0.5
        kwargs: {sigmoid: true, smooth_nr: 1e-5, smooth_dr: 1e-5}
        pred_slice: "0:1"
        target_slice: "0:1"
      - function: WeightedBCEWithLogitsLoss
        weight: 1.0
        kwargs: {reduction: mean}
        pred_slice: "1:2"
        target_slice: "1:2"
      - function: DiceLoss
        weight: 0.5
        kwargs: {sigmoid: true, smooth_nr: 1e-5, smooth_dr: 1e-5}
        pred_slice: "1:2"
        target_slice: "1:2"
      - function: WeightedMSELoss
        weight: 4.0
        kwargs: {tanh: true}
        pred_slice: "2:3"
        target_slice: "2:3"
    mednext:
      size: S
      kernel_size: 3
  data:
    dataloader:
      use_preloaded_cache_train: true
      persistent_workers: true
      patch_size: [32, 128, 128]
    nnunet_preprocessing:
      enabled: true
      crop_to_nonzero: true
      normalization: zscore
      normalization_use_nonzero_mask: true
      clip_percentile_low: 0.005
      clip_percentile_high: 0.995
    label_transform:
      targets:
      - name: binary
        kwargs: {}
      - name: instance_boundary
        kwargs:
          thickness: 1
          edge_mode: all
          mode: 2d
      - name: skeleton_aware_edt
        kwargs:
          resolution:
          - 40
          - 16
          - 16
          alpha: 1
          bg_value: -1.0
          relabel: true
    augmentation:
      flip:
        enabled: true
      rotate:
        enabled: true
        spatial_axes:
        - 1
        - 2
      intensity:
        enabled: true
        gaussian_noise_prob: 0
        gaussian_noise_std: 0.5
        shift_intensity_prob: 0.5
        shift_intensity_offset: 0.1
        contrast_prob: 0.5
        contrast_range: [0.9, 1.1]
  inference:
    system:
      num_workers: 1
    sliding_window:
      window_size:
      - 32
      - 128
      - 128
      overlap: 0.5
      blending: gaussian
      sigma_scale: 0.25
      padding_mode: reflect
    test_time_augmentation:
      enabled: true
      channel_activations:
      - {channels: "0:2", activation: sigmoid}
      - {channels: "2:3", activation: tanh}

  decoding:
    steps:
    - name: decode_binary_contour_distance_watershed
      kwargs:
        binary_threshold:
        - 0.9
        - 0.85
        contour_threshold:
        - 0.8
        - 1.1
        distance_threshold:
        - 0.5
        - -0.5
        min_instance_size: 100
        min_seed_size: 20
        prediction_scale: 1
train:
  optimization:
    max_epochs: 500
    n_steps_per_epoch: 1000
    gradient_clip_val: 0.5
    accumulate_grad_batches: 1
    precision: "16-mixed"
    log_every_n_steps: 100
    optimizer:
      lr: 0.0003
      weight_decay: 0.01
      eps: 1.0e-08
    scheduler:
      name: WarmupCosineLR
      warmup_epochs: 3
      warmup_start_lr: 1.0e-05
      min_lr: 1.0e-06
    ema:
      enabled: true
      decay: 0.999
      validate_with_ema: true
  monitor:
    nan_detection:
      enabled: true
      debug_on_nan: false
    logging:
      scalar:
        loss:
        - train_loss_total_epoch
        loss_every_n_steps: 10
      images:
        max_images: 2
        num_slices: 4
        log_every_n_epochs: 5
    checkpoint:
      save_top_k: 3
      save_every_n_epochs: 5
    early_stopping:
      monitor: train_loss_total_epoch
      patience: 100
      min_delta: 1.0e-05
      threshold: 0.01
      divergence_threshold: 100.0
  data:
    train:
      path: /projects/weilab/dataset/barcode/2026/PT37_round2/
      image:
      - "*raw.tif"
      label:
      - "*mask.tif"
      resolution:
      - 40
      - 16
      - 16

test:
  data:
    test:
      path: /projects/weilab/dataset/barcode/2026/PT37_round2/
      image:
      - "*raw.tif"
      label:
      - "*mask.tif"
      resolution:
      - 40
      - 16
      - 16
  evaluation:
    enabled: true
    metrics:
    - adapted_rand