defaults:
  - data: disk_megadepth # megadepth+acdc or megadepth+tokyo
  - backbones: vgg
  - upsampler: hypercolumn_features # interpolate_sparse2D
  - matcher: concurrent_mnn_poselib
  - descriptor_loss: contrastive_loss # none to deactivate
  - inl_th: constant # exp_decay
  - _self_

project_name: ???
name: ???

hydra:
  run:
    dir: ${oc.env:OUTPUT_DIR}/${project_name}/${name}/${oc.env:SLURM_JOB_ID}/${now:%Y-%m-%d}/${now:%H-%M-%S}
output_dir: ${hydra:runtime.output_dir}

num_gpus: 1
# precision: "32-true"
precision: "bf16-mixed" # numerically more stable
# precision: "16-mixed"

log_interval: 50 # log every N steps/ batches
wandb_mode: online
val_interval: 2000
conf_inference:
  threshold: 0.5
  top_k: 2048

desc_loss_weight: 5.0 # 0.0 to deactivate, also deactivates 1x1 conv

num_workers: 8
batch_size: 6

transformation_model: fundamental

network:
  _target_: ripe.models.ripe.RIPE
  _partial_: true
  window_size: 8
  non_linearity_dect:
    _target_: torch.nn.Identity
    # _target_: torch.nn.ReLU
  desc_shares:
    null
    # - 64
    # - 64
    # - 64
    # - 64

lr: 0.001 # 0.001 makes it somewhat unstable
fp_penalty: -1e-7 # -1e-7
kp_penalty: -7e-7 # -7e-7
num_grad_accs: 4
reward_type: inlier # inlier_ratio , inlier+inlier_ratio
no_filtering_negatives: False
descriptor_dim: 256

lr_scheduler:
  _partial_: true
  _target_: ripe.scheduler.linearLR.StepLinearLR
  num_steps: ${num_steps}
  initial_lr: ${lr}
  final_lr: 1e-6

use_whitening: false

selected_only: False

padding_filter_mode: ignore
# padding_filter_mode: punish

num_steps: 80000

alpha_scheduler: # 1.0 after 1/3 of the steps
  _target_: ripe.scheduler.linear_with_plateaus.LinearWithPlateaus
  start_val: 0.0
  end_val: 1.0
  steps_total: ${num_steps}
  rel_length_start_plateau: 0.0
  rel_length_end_plateu: 0.6666666

beta_scheduler: # linear increase over all steps
  _target_: ripe.scheduler.linear_with_plateaus.LinearWithPlateaus
  start_val: 0.0
  end_val: 1.0
  steps_total: ${num_steps}
  rel_length_start_plateau: 0.0
  rel_length_end_plateu: 0.0