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- _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
- conv_cfg = dict(type='ConvWS')
- norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
- model = dict(
- backbone=dict(
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- init_cfg=dict(
- type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')),
- neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg),
- roi_head=dict(
- bbox_head=dict(
- type='Shared4Conv1FCBBoxHead',
- conv_out_channels=256,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg),
- mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg)))
- # learning policy
- max_epochs = 24
- train_cfg = dict(max_epochs=max_epochs)
- # learning rate
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_epochs,
- by_epoch=True,
- milestones=[16, 22],
- gamma=0.1)
- ]
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