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(nms_post=1000, max_per_img=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(