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# model settings
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model = dict(
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type='RPN',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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style='pytorch',
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=5),
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rpn_head=dict(
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type='RPNHead',
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in_channels=256,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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scales=[8],
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ratios=[0.5, 1.0, 2.0],
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strides=[4, 8, 16, 32, 64]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[.0, .0, .0, .0],
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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# model training and testing settings
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train_cfg=dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=0,
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rpn=dict(
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nms_pre=2000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0)))
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