train_config.yaml

---
defaults:
  - _self_
  - model_config/hrnet_w48@model.params.nn_module.hrnet_config
metadata:
  experiment_name: line
  run_name: extreme_heatpoints2
  description: detect two end points for each line
  experimenter: RUILONG
data_params:
  input_size: [960, 540] #[960, 544]
  stride: 4
  sigma: 1
  num_keypoint_pairs: 23
  batch_size: 8
  num_workers: 8
  pin_memory: true
  augmentations:
    brightness: [0.8, 1.2]
    color: [0.8, 1.2]
    contrast: [0.8, 1.2]
    gauss_noise_sigma: 30.0
    prob: 0.5
data:
  train: /workdir/data/dataset/train
  val: /workdir/data/dataset/valid
model:
  _target_: src.models.line.metamodel.EHMMetaModel
  params:
    device: cuda:0
    nn_module:
      num_refinement_stages: 0
      num_heatmaps: ${model.params.nn_module.hrnet_config.num_classes}
    loss:
      num_refinement_stages: ${model.params.nn_module.num_refinement_stages}
      gmse_w: 1.
      awing_w: 1.
      sigma: 4
    optimizer:
      lr: 0.001
    prediction_transform:
      scale: ${data_params.stride}
      sigma: 6
    amp: True
    pretrain: /workdir/data/experiments/line_extreme_heatpoints2/model-035-0.834300.pth # null or path
train_params:
  max_epochs: 100
  early_stopping_epochs: 50
  reduce_lr_factor: 0.8
  reduce_lr_patience: 8
  monitor_metric: val_acc # val_acc
  monitor_metric_better: max # max