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- _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
- model = dict(
- backbone=dict(
- _delete_=True,
- type='HRNet',
- extra=dict(
- stage1=dict(
- num_modules=1,
- num_branches=1,
- block='BOTTLENECK',
- num_blocks=(4, ),
- num_channels=(64, )),
- stage2=dict(
- num_modules=1,
- num_branches=2,
- block='BASIC',
- num_blocks=(4, 4),
- num_channels=(32, 64)),
- stage3=dict(
- num_modules=4,
- num_branches=3,
- block='BASIC',
- num_blocks=(4, 4, 4),
- num_channels=(32, 64, 128)),
- stage4=dict(
- num_modules=3,
- num_branches=4,
- block='BASIC',
- num_blocks=(4, 4, 4, 4),
- num_channels=(32, 64, 128, 256))),
- init_cfg=dict(
- type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
- neck=dict(
- _delete_=True,
- type='HRFPN',
- in_channels=[32, 64, 128, 256],
- out_channels=256))
- # learning policy
- max_epochs = 20
- train_cfg = dict(max_epochs=max_epochs)
- 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, 19],
- gamma=0.1)
- ]
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