train_config.yaml

TARGET: "cityscapes" # cityscapes or kitti 

PATHS:
  CITYSCAPES:
    path_masks: "./data/cityscapes/gtFine/gtFine"
    path_img: "./data/cityscapes/leftImg8bit"
    # test_root_path: "./data/cityscapes/leftImg8bit/test"
    test_root_path: "/samsung_drive/semantic_segmentation/data/cityscapes/demovideo/leftImg8bit/demoVideo"
    val_cities: ["ulm", "bremen", "aachen"] # for cityscapes only
    train_root_path: "./data/cityscapes/leftImg8bit/train"
    
  KITTI:
    path_masks: "./data/kitti/data_road/training/gt_image_2"
    path_img: "./data/kitti/data_road/training/image_2"
    test_root_path: "./data/kitti/data_road/testing/image_2"
    val_frac: 0.2
    mode: "lane" # lane or road in case of kitti-lanes dataset

DATASET:
  hard_augs: False # add random snow / rain / fog
  orig_size: [1024, 2048]
  resize: [512, 1024] # or []
  select_classes: []
  train_on_cats: True # Must be False if select_classes is not empty else --> train on all classes

MODEL:
  mode: "UNET" # UNET or FPN
  backbone: "resnext50"
  num_classes: 8
  unet_res_blocks_decoder: False # adds residual blocks inside decoder --> net become heavier 

TRAINING:  
  model_path: "/samsung_drive/semantic_segmentation/UNET_2x_downsize"
  load_checkpoint: "/samsung_drive/semantic_segmentation/UNET_4x_downsize/last_model.pth"
  # load_checkpoint: ""
  weights_decay: 0.0
  batch_size: 6
  accumulation_batches: 1
  freeze_n_iters: 0
  load_optimizer_state: False
  lr: 0.001
  num_epochs: 40
  devices_ids: [0, 1]
  key_metric: "dice"
  base_threshold: .0
  activate: False
  bce_loss_weight: .8
  class_weights: [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125]
  scheduler_patience: 3

EVAL:
  test_mode: True # doesn't calculate metrics
  images_morphing: True # morph images and masks
  model_path: "/samsung_drive/semantic_segmentation/UNET_2x_downsize/best_model.pth"
  eval_images_path: "/samsung_drive/semantic_segmentation/UNET_2x_downsize/eval_images"
  test_images_path: "/samsung_drive/semantic_segmentation/UNET_2x_downsize/test_images"  
  device: "cuda:0" # cpu or cuda
  apply_tta: True

  activate: False
  base_threshold: 0.0

  # activate: True
  # base_threshold: 0.5

  drop_clusters: 70 # no. pixels on one side; if <= 0 --> doesn't apply