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- _base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- # model settings
- norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
- model = dict(
- backbone=dict(
- depth=101,
- init_cfg=dict(type='Pretrained',
- checkpoint='torchvision://resnet101')),
- bbox_head=dict(
- _delete_=True,
- type='SABLRetinaHead',
- 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]),
- norm_cfg=norm_cfg,
- bbox_coder=dict(
- type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
- loss_bbox_reg=dict(
- type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='ApproxMaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.4,
- min_pos_iou=0.0,
- ignore_iof_thr=-1),
- allowed_border=-1,
- pos_weight=-1,
- debug=False))
- # optimizer
- optim_wrapper = dict(
- optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
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