dagger_settings.yaml

log_dir: '/tmp/train_7m_object_straight/'
quad_name: 'hummingbird'
odometry_topic: 'ground_truth/odometry'
rgb_topic: 'agile_autonomy/unity_rgb'
depth_topic: 'agile_autonomy/sgm_depth'
use_rgb: False # Use camera images
use_depth: True # Use depth images
state_dim: 3 # x,y,z
out_seq_len: 10 # Number of steps in the future to predict
future_time: 5.0 # in seconds, how much in the future to track the reference
predict_state_number: [ ] # Empty list means will predict all states in out_seq_len. Otherwise it will train only for the single state. Only supported mode is len(predict_state_number) == 1.
seq_len: 1 # History Len. Reactive (hist = 1) works best.
modes: 3 # Number of modes
img_width: 224
img_height: 224
data_generation:
  max_rollouts: 100
  train_every_n_rollouts: 10
  expert_folder: "../data_generation/data"
  increase_net_usage_every_n_rollouts: 10
checkpoint:
  resume_training: True
  resume_file: "models/ckpt-50"
train:
  max_training_epochs: 5
  batch_size: 8
  data_save_freq: 15
  top_trajectories: 3
  summary_freq: 400
  train_dir: "data/train"
  val_dir: "data/val" # you should fill this with some validation data
  log_images: False
  freeze_backbone: False
  save_every_n_epochs: 5
  ref_frame: 'bf'
  track_global_traj: False
  spacings: [6,5,4] # mt
test_time:
  execute_nw_predictions: True
  perform_inference: True # If `False` completely disables network forward passes
  accept_thresh: 0.9 # when ratio btw other and best score is larger than this, other trajectory will be sent as well.
  input_update_freq: 15 #Hz
  fallback_radius_expert: 10 #m distance to trajectory to switch back. Put to zero when training from scratch
  network_frequency: 15.0 #Hz
  crashed_thr: 0.20 # 10 cm from a point is crash
verbose: False # saving net inputs
inputs:
  position: False
  attitude: True
  bodyrates: True
  velocity_frame: 'bf'
  pitch_angle: 0
unity:
  random_seed: 0
  # x,y,z, yaw
  # forest
  unity_start_pos: [[-20.,20.,0.,0]]