config_adapt.yaml

Dataset:
  dataset: Kitti
#  dataset: RobotCar
  dataset_path: USER/data/kitti/odometry/dataset
  frame_ids: [ 0, -1, 1 ]
  scales: [ 0, 1, 2, 3 ] # Provided by dataloader
  height: 192
  width: 640

DepthPosePrediction:
  train_set: all
  val_set: 0
#  train_set: 2015-08-12-15-04-18
#  val_set: 2015-08-12-15-04-18
  resnet_depth: 18
  resnet_pose: 18
  resnet_pretrained: true
  scales: [ 0, 1, 2, 3 ]  # Network size
  learning_rate: 0.0001
  scheduler_step_size: 15
  num_workers: 0
  num_epochs: 20
  min_depth: .1
  max_depth:
  disparity_smoothness: .001
  velocity_loss_scaling: .05
  mask_dynamic: False
  save_frequency: -1
  save_val_depth: false
  save_val_depth_batches: 0
  multiple_gpus: false
  gpu_ids:
  batch_size: 3
  log_path: ./log/slam/c_k9
  load_weights_folder: ./log/cityscapes/models/weights_025
  use_wandb: False

ReplayBuffer:
  maximize_diversity: True
  max_buffer_size: 100
  similarity_threshold: .95
  similarity_sampling: False
  load_path: ./log/slam/c_k9/replay_buffer

LoopClosureDetection:
  detection_threshold: .99
  id_threshold: 250
  num_matches: 1

Slam:
  dataset_sequence: 6
  adaptation: True
  adaptation_epochs: 5
  min_distance: .2
  start_frame: 0  # Start mapping after this frame
  logging: true
  do_loop_closures: true
  keyframe_frequency: 5
  lc_distance_poses: 150  # min num consec poses between lc checks