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CONFIGURATIONS.md

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Configuration details

Config files contain the model hyperparameters. Here we describe every parameter along with its default value.

Our config is organized as follows:

  • gpus (List[int, ...]) - ids of gpus to run training on, default=[0, 1]

  • data:

    • root_path (str) - root directory for MegaDepth data

    • train_list_path (str) - path to the file with train split, ex.: megadepth_train_2.0.txt

    • val_list_path (str) - path to the file with validation split, ex.: megadepth_valid_2.0.txt

    • test_list_path (str) - path to the file with test split

    • batch_size_per_gpu (int) - number of image pairs in a batch per gpu, default=4

    • dataloader_workers_per_gpu (int) - number of workers per one gpu, default=4

    • target_size (List[int, int]) - shape of input image after resizing, default=[ 960, 720 ]

    • val_max_pairs_per_scene (int) - max number of image pairs retrieved from the same scene, default=50

    • features_dir --optional-- [ONLY if features were CACHED] (str) - path to the saved cached features directory

    • max_keypoints --optional-- [ONLY if features were CACHED] (int) - max number of keypoints to detect, default=1024

  • logging:

    • root_path (str) - directory, where experiment's logs will be saved
    • name (str) - experiment name
    • val_frequency (int) - number of iterations for frequency of computing validation, default=10000
    • train_logs_steps (int) - number of iterations for frquency of logging results on train, default=50

  • train:

    • grad_clip (float) - clip gradients' global norm to <= threshold, default=10.0

    • precision (int) - mixed precision configuration, combines the use of both 32 and 16 bit floating points, default=32

    • gt_positive_threshold (float) - threshold value for ignoring match, default=3.

    • gt_negative_threshold (float) - threshold value for an unmatched match, default=5.

    • margin (float) - margin for the criterion, default=null

    • nll_weight (float) - weight for the proportion of NLL loss, default=1.0

    • metric_weight (float) - weight for the proportion of metric loss, default=0.0

    • lr (float) - starting learning rate, default=1.0e-4

    • scheduler_gamma (float) - value used to decay lr, default=0.999994

    • use_cached_features --optional-- [ONLY if features were CACHED] (bool) - flag that enables training with cached features

  • evaluation:

    • epipolar_dist_threshold (float) - threshold for epipolar distance metric, default=5.0e-4
    • camera_auc_thresholds (List[float,...]) - thresholds for area under the curve metric, pose error in degrees, default=[5.0, 10.0, 20.0]
    • camera_auc_ransac_inliers_threshold(float) - sampson error, default=2.0

  • inference:

    • match_threshold (float) - threshold for a match, default=0.2

  • superglue:

    • descriptor_dim (int) - dimensionality of descriptors, default=128
    • laf_to_sideinfo_method (str) - ability to include geometry info from detector for each keypoint in positional encoding, options: [none, rotation, scale, scale_rotation, affine], default= none
    • positional_encoding:
      • hidden_layers_sizes (List[int, ...]) - input shape for hidden layers in MLP net for positional encoding, default=[32, 64, 128]
      • output_size (int) - dimensionality of returned output, in most cases should correspond descriptor_dim, default=128
    • attention_gnn:
      • num_stages (int) - number of attention stages (layers), 1 stage = SELF-attn + CROSS-attn, default=12
      • num_heads (int) - number of attention heads, default=4
      • embed_dim (int) - corresponds to descriptor_dim, default=128
      • attention (str) - method for attention, options: [linear, softmax], default='linear'
      • use_offset (bool) - flag for usage of offset attention https://arxiv.org/abs/2012.09688, default=False
    • dustbin_score_init (float) - dustbin score, default=1.0
    • otp:
      • num_iters(int) - number of iterations for differentiable Optimal Transport solver (Sinkhorn matrix scaling algorithm), default=20
      • reg (float) - regularization value for Sinkhorn, default=1.0
    • residual (bool) - flag for enabling combining local descriptor with context-aware descriptor, default=True

This part is set in seperate config files from config/features and config/features_online

For each feature extractor, options: [OPENCV_SIFT, SuperPointNet, SuperPointNetBn, OPENCVDoGAffNetHardNet], default='OPENCV_SIFT', this section varies, so please look in yaml files for more details.

Example of the general setup for SuperPoint case:

  • name (str) - method name for descriptor
  • max_keypoints (int) - maximum number of keypoints, default=1024
  • descriptor_dim (int) - dimensionality of descriptors
  • nms_kernel (int) - size of the kernel for non-maximum suppression convolution, default=3
  • remove_borders_size (int) - the number of border-neighboring pixels to skip for keypoint detection, default=4
  • keypoint_threshold (float) - threshold of score confidence for keypoint to be considered, default=0.0
  • weights (str) - path to the weights, option for pretrained SuperPoint weights