train_gnnet_cmu.yaml

data:
  name: cmu
  min_overlap: 0.3
  max_overlap: 1.0
  max_num_points3D: 512
  force_num_points3D: true
  max_baseline: 7.0
  resize: 512
  resize_by: min
  crop: 512
  optimal_crop: false
  batch_size: 4
  num_workers: 6
  seed: 1
model:
  name: gnnet
  extractor:
    name: unet
    encoder: vgg16
    decoder: [64, 64, 64, 32]
    output_scales: [0, 2, 4]
    output_dim: [32, 128, 128]
    freeze_batch_normalization: false
    do_average_pooling: false
    compute_uncertainty: false
    checkpointed: true
  optimizer:
    num_iters: 15
    pad: 3
    lambda_: 0.01
    verbose: false
    loss_fn: scaled_barron(0, 0.1)
    jacobi_scaling: false
  normalize_features: true
  loss:
    margin_positive: 0.2
    margin_negative: 1
    num_top_negative_sampling: 200
    gauss_newton_magnitude: 1.0
    gauss_newton_weight: 0.1
    contrastive_weight: 1
train:
  seed: 0
  epochs: 200
  log_every_iter: 50
  eval_every_iter: 500
  dataset_callback_fn: sample_new_items
  lr: 1.0e-06
  median_metrics:
  - loss/reprojection_error
  - loss/reprojection_error/init
  - R_error
  - t_error