ga_faster_r50_fpn_1x_coco.py

_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
    rpn_head=dict(
        _delete_=True,
        type='GARPNHead',
        in_channels=256,
        feat_channels=256,
        approx_anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=8,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        square_anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            scales=[8],
            strides=[4, 8, 16, 32, 64]),
        anchor_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.07, 0.07, 0.14, 0.14]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.07, 0.07, 0.11, 0.11]),
        loc_filter_thr=0.01,
        loss_loc=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
    roi_head=dict(
        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))))
# model training and testing settings
train_cfg = dict(
    rpn=dict(
        ga_assigner=dict(
            type='ApproxMaxIoUAssigner',
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            ignore_iof_thr=-1),
        ga_sampler=dict(
            type='RandomSampler',
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False),
        allowed_border=-1,
        center_ratio=0.2,
        ignore_ratio=0.5),
    rpn_proposal=dict(max_num=300),
    rcnn=dict(
        assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
        sampler=dict(type='RandomSampler', num=256)))
test_cfg = dict(rpn=dict(max_num=300), rcnn=dict(score_thr=1e-3))
optimizer_config = dict(
    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))