Method __init__

(self,
                 num_classes,
                 in_channels,
                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
                                 (512, INF)),
                 center_sampling=False,
                 center_sample_radius=1.5,
                 sync_num_pos=True,
                 gradient_mul=0.1,
                 bbox_norm_type='reg_denom',
                 loss_cls_fl=dict(
                     type='FocalLoss',
                     use_sigmoid=True,
                     gamma=2.0,
                     alpha=0.25,
                     loss_weight=1.0),
                 use_vfl=True,
                 loss_cls=dict(
                     type='VarifocalLoss',
                     use_sigmoid=True,
                     alpha=0.75,
                     gamma=2.0,
                     iou_weighted=True,
                     loss_weight=1.0),
                 loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
                 loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
                 use_atss=True,
                 reg_decoded_bbox=True,
                 anchor_generator=dict(
                     type='AnchorGenerator',
                     ratios=[1.0],
                     octave_base_scale=8,
                     scales_per_octave=1,
                     center_offset=0.0,
                     strides=[8, 16, 32, 64, 128]),
                 init_cfg=dict(
                     type='Normal',
                     layer='Conv2d',
                     std=0.01,
                     override=dict(
                         type='Normal',
                         name='vfnet_cls',
                         std=0.01,
                         bias_prob=0.01)),
                 **kwargs)