smoke.yaml

# @package __global__
defaults:
  - augmentation: base_more

module: RetinaUNetV001
predictor: BoxPredictorSelective
plan: D3V001_3d

planners:
  2d: [D2C002]
  3d: [D3V001] # [D3C002LR15, D3C002LR20] [D3C002NR, D3C002RibFrac] [D2C002, D3C002]

augment_cfg:
  augmentation: ${augmentation}
  num_train_batches_per_epoch: ${trainer_cfg.num_train_batches_per_epoch}
  num_val_batches_per_epoch: ${trainer_cfg.num_val_batches_per_epoch}

  dataloader: "DataLoader{}DOffset"
  oversample_foreground_percent: 0.5 # ratio of fg and bg in batches
  dataloader_kwargs: {}

  num_threads: ${oc.env:det_num_threads, "12"}
  num_cached_per_thread: 2
  multiprocessing: True # only deactivate this if debugging

trainer_cfg:
  gpus: 1 # number of gpus
  accelerator: ddp # distributed backend
  precision: 16 # mixed precision
  amp_backend: native # mixed precision backend
  amp_level: O1 # when mixed precision backend is APEX use O1
  # Per default training is deterministic, non-deterministic allows
  # cudnn.benchmark which can give up to 20% performance. Set this to false
  # to perform non-deterministic training
  deterministic: False
  benchmark: False

  # fp16: True # enable fp16 training. Makes sense for supported hardware only!
  monitor_key: "mAP_IoU_0.10_0.50_0.05_MaxDet_100" # used to determine the best model
  monitor_mode: "max" # metric operation mode "min" or "max"

  max_num_epochs: 2 # max number of epochs
  num_train_batches_per_epoch: 20 # number of train batches per epoch
  num_val_batches_per_epoch: 10 # number of val batches per epoch

  initial_lr: 0.01 # initial learning rate to start with
  sgd_momentum: 0.9 # momentum term
  sgd_nesterov: True # nesterov momentum
  weight_decay: 3.e-5 # weight decay for optimizer
  momentum: 0.9 # momentum term

  warm_iterations: 4000 # number of iterations with warmup
  warm_lr: 1.e-6 # learning rate to start warmup from

  poly_gamma: 0.9

  swa_epochs: 2 # number of epochs to run swa with cyclic learning rate

model_cfg:
  encoder_kwargs: {} # keyword arguments passed to encoder
  decoder_kwargs: # keyword arguments passed to decoder
    min_out_channels: 8
    upsampling_mode: "transpose"

    num_lateral: 1
    norm_lateral: False
    activation_lateral: False

    num_out: 1
    norm_out: False
    activation_out: False

  head_kwargs: {} # keyword arguments to passed to head

  head_classifier_kwargs: # keyword arguments passed to classifier in head
    num_convs: 2
    norm_channels_per_group: 16
    norm_affine: True
    reduction: "mean"
    loss_weight: 1.
    # gamma: 1.
    # alpha: 0.75
    # reduction: "sum"
    # loss_weight: 0.3
    prior_prob: 0.01

  head_regressor_kwargs: # keyword arguments passed to regressor in head
    num_convs: 2
    norm_channels_per_group: 16
    norm_affine: True
    reduction: "sum"
    loss_weight: 1.
    learn_scale: True

  head_sampler_kwargs: # keyword arguments passed to sampler
    batch_size_per_image: 32 # number of anchors sampled per image
    positive_fraction: 0.33 # defines ratio between positive and negative anchors
    # hard negatives are sampled from a pool of size:
    # batch_size_per_image * (1 - positive_fraction) * pool_size
    pool_size: 20
    min_neg: 1 # minimum number of negative anchors sampled per image

  segmenter_kwargs:
    dice_kwargs:
      batch_dice: True

  matcher_kwargs: # keyword arguments passed to matcher
    num_candidates: 4
    center_in_gt: False

  plan_arch_overwrites: {} # overwrite arguments of architecture
  plan_anchors_overwrites: {} # overwrite arguments of anchors

debug:
  num_cases_val: 2 # only predict two cases for validation results