---
imports:
- "$import glob"
- "$import json"
- "$import os"
- "$import ignite"
- "$from scipy import ndimage"
input_channels: 1
output_classes: 3
arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'"
arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))"
bundle_root: "."
ckpt_dir: "$@bundle_root + '/models'"
output_dir: "$@bundle_root + '/eval'"
dataset_dir: "/workspace/data/msd/Task07_Pancreas"
data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'"
train_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='training',
  base_dir=@dataset_dir)"
val_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation',
  base_dir=@dataset_dir)"
device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
dints_space:
  _target_: monai.networks.nets.TopologyInstance
  channel_mul: 1
  num_blocks: 12
  num_depths: 4
  use_downsample: true
  arch_code:
  - "$@arch_ckpt['arch_code_a']"
  - "$@arch_ckpt['arch_code_c']"
  device: "$torch.device('cuda')"
network_def:
  _target_: monai.networks.nets.DiNTS
  dints_space: "@dints_space"
  in_channels: "@input_channels"
  num_classes: "@output_classes"
  use_downsample: true
  node_a: "$@arch_ckpt['node_a']"
network: "$@network_def.to(@device)"
loss:
  _target_: DiceCELoss
  include_background: false
  to_onehot_y: true
  softmax: true
  squared_pred: true
  batch: true
  smooth_nr: 1.0e-05
  smooth_dr: 1.0e-05
optimizer:
  _target_: torch.optim.SGD
  params: "$@network.parameters()"
  momentum: 0.9
  weight_decay: 4.0e-05
  lr: 0.025
lr_scheduler:
  _target_: torch.optim.lr_scheduler.StepLR
  optimizer: "@optimizer"
  step_size: 80
  gamma: 0.5
image_key: image
label_key: label
val_interval: 10
train:
  deterministic_transforms:
  - _target_: LoadImaged
    keys:
    - "@image_key"
    - "@label_key"
  - _target_: EnsureChannelFirstd
    keys:
    - "@image_key"
    - "@label_key"
  - _target_: Orientationd
    keys:
    - "@image_key"
    - "@label_key"
    axcodes: RAS
  - _target_: Spacingd
    keys:
    - "@image_key"
    - "@label_key"
    pixdim:
    - 1
    - 1
    - 1
    mode:
    - bilinear
    - nearest
    align_corners:
    - true
    - true
  - _target_: CastToTyped
    keys: "@image_key"
    dtype: "$torch.float32"
  - _target_: ScaleIntensityRanged
    keys: "@image_key"
    a_min: -87
    a_max: 199
    b_min: 0
    b_max: 1
    clip: true
  - _target_: CastToTyped
    keys:
    - "@image_key"
    - "@label_key"
    dtype:
    - "$np.float16"
    - "$np.uint8"
  - _target_: CopyItemsd
    keys: "@label_key"
    times: 1
    names:
    - label4crop
  - _target_: Lambdad
    keys: label4crop
    func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype),
      iterations=48).astype(float) for _k in range(s)]), axis=0)"
    overwrite: true
  - _target_: EnsureTyped
    keys:
    - "@image_key"
    - "@label_key"
  - _target_: CastToTyped
    keys: "@image_key"
    dtype: "$torch.float32"
  - _target_: SpatialPadd
    keys:
    - "@image_key"
    - "@label_key"
    - label4crop
    spatial_size:
    - 96
    - 96
    - 96
    mode:
    - reflect
    - constant
    - constant
  random_transforms:
  - _target_: RandCropByLabelClassesd
    keys:
    - "@image_key"
    - "@label_key"
    label_key: label4crop
    num_classes: "@output_classes"
    ratios: "$[1,] * @output_classes"
    spatial_size:
    - 96
    - 96
    - 96
    num_samples: 1
  - _target_: Lambdad
    keys: label4crop
    func: "$lambda x: 0"
  - _target_: RandRotated
    keys:
    - "@image_key"
    - "@label_key"
    range_x: 0.3
    range_y: 0.3
    range_z: 0.3
    mode:
    - bilinear
    - nearest
    prob: 0.2
  - _target_: RandZoomd
    keys:
    - "@image_key"
    - "@label_key"
    min_zoom: 0.8
    max_zoom: 1.2
    mode:
    - trilinear
    - nearest
    align_corners:
    - true
    -
    prob: 0.16
  - _target_: RandGaussianSmoothd
    keys: "@image_key"
    sigma_x:
    - 0.5
    - 1.15
    sigma_y:
    - 0.5
    - 1.15
    sigma_z:
    - 0.5
    - 1.15
    prob: 0.15
  - _target_: RandScaleIntensityd
    keys: "@image_key"
    factors: 0.3
    prob: 0.5
  - _target_: RandShiftIntensityd
    keys: "@image_key"
    offsets: 0.1
    prob: 0.5
  - _target_: RandGaussianNoised
    keys: "@image_key"
    std: 0.01
    prob: 0.15
  - _target_: RandFlipd
    keys:
    - "@image_key"
    - "@label_key"
    spatial_axis: 0
    prob: 0.5
  - _target_: RandFlipd
    keys:
    - "@image_key"
    - "@label_key"
    spatial_axis: 1
    prob: 0.5
  - _target_: RandFlipd
    keys:
    - "@image_key"
    - "@label_key"
    spatial_axis: 2
    prob: 0.5
  - _target_: CastToTyped
    keys:
    - "@image_key"
    - "@label_key"
    dtype:
    - "$torch.float32"
    - "$torch.uint8"
  - _target_: ToTensord
    keys:
    - "@image_key"
    - "@label_key"
  preprocessing:
    _target_: Compose
    transforms: "$@train#deterministic_transforms + @train#random_transforms"
  dataset:
    _target_: CacheDataset
    data: "@train_datalist"
    transform: "@train#preprocessing"
    cache_rate: 0.125
    num_workers: 4
  dataloader:
    _target_: DataLoader
    dataset: "@train#dataset"
    batch_size: 2
    shuffle: true
    num_workers: 4
  inferer:
    _target_: SimpleInferer
  postprocessing:
    _target_: Compose
    transforms:
    - _target_: Activationsd
      keys: pred
      softmax: true
    - _target_: AsDiscreted
      keys:
      - pred
      - label
      argmax:
      - true
      - false
      to_onehot: "@output_classes"
  handlers:
  - _target_: LrScheduleHandler
    lr_scheduler: "@lr_scheduler"
    print_lr: true
  - _target_: ValidationHandler
    validator: "@validate#evaluator"
    epoch_level: true
    interval: "@val_interval"
  - _target_: StatsHandler
    tag_name: train_loss
    output_transform: "$monai.handlers.from_engine(['loss'], first=True)"
  - _target_: TensorBoardStatsHandler
    log_dir: "@output_dir"
    tag_name: train_loss
    output_transform: "$monai.handlers.from_engine(['loss'], first=True)"
  key_metric:
    train_accuracy:
      _target_: ignite.metrics.Accuracy
      output_transform: "$monai.handlers.from_engine(['pred', 'label'])"
  trainer:
    _target_: SupervisedTrainer
    max_epochs: 400
    device: "@device"
    train_data_loader: "@train#dataloader"
    network: "@network"
    loss_function: "@loss"
    optimizer: "@optimizer"
    inferer: "@train#inferer"
    postprocessing: "@train#postprocessing"
    key_train_metric: "@train#key_metric"
    train_handlers: "@train#handlers"
    amp: true
validate:
  preprocessing:
    _target_: Compose
    transforms: "%train#deterministic_transforms"
  dataset:
    _target_: CacheDataset
    data: "@val_datalist"
    transform: "@validate#preprocessing"
    cache_rate: 0.125
  dataloader:
    _target_: DataLoader
    dataset: "@validate#dataset"
    batch_size: 1
    shuffle: false
    num_workers: 4
  inferer:
    _target_: SlidingWindowInferer
    roi_size:
    - 96
    - 96
    - 96
    sw_batch_size: 6
    overlap: 0.625
  postprocessing: "%train#postprocessing"
  handlers:
  - _target_: StatsHandler
    iteration_log: false
  - _target_: TensorBoardStatsHandler
    log_dir: "@output_dir"
    iteration_log: false
  - _target_: CheckpointSaver
    save_dir: "@ckpt_dir"
    save_dict:
      model: "@network"
    save_key_metric: true
    key_metric_filename: model.pt
  key_metric:
    val_mean_dice:
      _target_: MeanDice
      include_background: false
      output_transform: "$monai.handlers.from_engine(['pred', 'label'])"
  additional_metrics:
    val_accuracy:
      _target_: ignite.metrics.Accuracy
      output_transform: "$monai.handlers.from_engine(['pred', 'label'])"
  evaluator:
    _target_: SupervisedEvaluator
    device: "@device"
    val_data_loader: "@validate#dataloader"
    network: "@network"
    inferer: "@validate#inferer"
    postprocessing: "@validate#postprocessing"
    key_val_metric: "@validate#key_metric"
    additional_metrics: "@validate#additional_metrics"
    val_handlers: "@validate#handlers"
    amp: true
initialize:
- "$monai.utils.set_determinism(seed=123)"
run:
- "$@train#trainer.run()"