| _base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' | |
| model = dict( | |
| rpn_head=dict( | |
| _delete_=True, | |
| type='CascadeRPNHead', | |
| num_stages=2, | |
| stages=[ | |
| dict( | |
| type='StageCascadeRPNHead', | |
| in_channels=256, | |
| feat_channels=256, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8], | |
| ratios=[1.0], | |
| strides=[4, 8, 16, 32, 64]), | |
| adapt_cfg=dict(type='dilation', dilation=3), | |
| bridged_feature=True, | |
| sampling=False, | |
| with_cls=False, | |
| reg_decoded_bbox=True, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=(.0, .0, .0, .0), | |
| target_stds=(0.1, 0.1, 0.5, 0.5)), | |
| loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)), | |
| dict( | |
| type='StageCascadeRPNHead', | |
| in_channels=256, | |
| feat_channels=256, | |
| adapt_cfg=dict(type='offset'), | |
| bridged_feature=False, | |
| sampling=True, | |
| with_cls=True, | |
| reg_decoded_bbox=True, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=(.0, .0, .0, .0), | |
| target_stds=(0.05, 0.05, 0.1, 0.1)), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=True, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)) | |
| ]), | |
| train_cfg=dict(rpn=[ | |
| dict( | |
| assigner=dict( | |
| type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| debug=False), | |
| dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.7, | |
| min_pos_iou=0.3, | |
| ignore_iof_thr=-1, | |
| iou_calculator=dict(type='BboxOverlaps2D')), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.5, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=False), | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| debug=False) | |
| ]), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=2000, | |
| max_per_img=2000, | |
| nms=dict(type='nms', iou_threshold=0.8), | |
| min_bbox_size=0))) | |
| optimizer_config = dict( | |
| _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) | |