opt_cvo.yaml

---
### This setup is for generating qualitative result from trained weights. 
### Set "depth meta setup" according to the setting in training. 
### Always set true in "cvo meta setup" and "use_normal_v3" to get pcd and normal file outputs. 
# system setup
model_name: "mono_model_KITTI_Lidar"
log_frequency: 100
cuda_n: 0
save_pic_intv: 4000
save_pcd_intv: 0
server: "sunny"
disable_log: false

# training setup
batch_size: 4
iters_per_update: 2
scheduler_step_size: 10
num_layers: 50
num_workers: 4

# depth meta setup
depth_in_loss: true
disp_in_loss: false
min_depth: 1.0
ref_depth: 10
depth_ref_mode: true

# cvo meta setup
# cvo_loss: false
# cvo_loss_dense: false
# dense_flat_grid: false
# supervised_by_gt_depth: false
cvo_loss: true
cvo_loss_dense: true
dense_flat_grid: true
supervised_by_gt_depth: true

# cvo params
use_normal_v3: true
norm_in_dist: false
neighbor_range: 2
normal_nrange: 9
ell_basedist: 10.0
random_ell: true
ell_geo: 0.1
ell_min: 0.05

# dataset setup
dataset: 
    - kitti
dataset_val: 
    - kitti
split_train:
    - eigen_zhou
split_val:
    - eigen_zhou
# load_weights_folder: 
# load_weights_folder_parent: "/media/sda1/minghanz/tmp/mono_model_KITTI_Lidar/models_Tue Feb 25 00:40:29 2020/weights_19"
save_pcd_pic_mode: 
    - train