third_party/gim/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml

data:
    name: homographies
    homography:
        difficulty: 0.7
        max_angle: 45
        patch_shape: [640, 480]
    photometric:
        p: 0.75
    train_size: 900000
    val_size: 1000
    batch_size: 160  # 20 per 10GB of GPU mem (12 for triplet)
    num_workers: 15
model:
    name: gluefactory.models.two_view_pipeline
    extractor:
        name: gluefactory.models.lines.wireframe
        trainable: False
        point_extractor:
            name: gluefactory.models.extractors.superpoint_open
            # name: disk
            # chunk: 10
            max_num_keypoints: 1000
            force_num_keypoints: true
            trainable: False
        line_extractor:
            name: gluefactory.models.lines.lsd
            max_num_lines: 250
            force_num_lines: True
            min_length: 15
            trainable: False
        wireframe_params:
            merge_points: True
            merge_line_endpoints: True
            nms_radius: 4
    detector:
        name: null
    descriptor:
        name: null
    ground_truth:
        name: gluefactory.models.matchers.homography_matcher
        trainable: False
        use_points: True
        use_lines: True
        th_positive: 3
        th_negative: 5
    matcher:
        name: gluefactory.models.matchers.gluestick
        input_dim: 256  # 128 for DISK
        descriptor_dim: 256  # 128 for DISK
        inter_supervision: [2, 5]
        GNN_layers: [
            self, cross, self, cross, self, cross,
            self, cross, self, cross, self, cross,
            self, cross, self, cross, self, cross,
        ]
        checkpointed: true
train:
    seed: 0
    epochs: 200
    log_every_iter: 400
    eval_every_iter: 700
    save_every_iter: 1400
    lr: 1e-4
    lr_schedule:
        type: exp  # exp or multi_step
        start: 200e3
        exp_div_10: 200e3
        gamma: 0.5
        step: 50e3
        n_steps: 4
    submodules: []
    # clip_grad: 10  # Use only with mixed precision
    # load_experiment: