ga_retinanet_r50_caffe_fpn_1x_coco.py

_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
    bbox_head=dict(
        _delete_=True,
        type='GARetinaHead',
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        approx_anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128]),
        square_anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            scales=[4],
            strides=[8, 16, 32, 64, 128]),
        anchor_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        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='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
    # training and testing settings
    train_cfg=dict(
        ga_assigner=dict(
            type='ApproxMaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0.4,
            ignore_iof_thr=-1),
        ga_sampler=dict(
            type='RandomSampler',
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False),
        assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
        center_ratio=0.2,
        ignore_ratio=0.5))
optimizer_config = dict(
    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))