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| _base_ = '../rtmdet_m_syncbn_fast_8xb32-300e_coco.py' | |
| teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco/rtmdet_l_syncbn_fast_8xb32-300e_coco_20230102_135928-ee3abdc4.pth' # noqa: E501 | |
| norm_cfg = dict(type='BN', affine=False, track_running_stats=False) | |
| model = dict( | |
| _delete_=True, | |
| _scope_='mmrazor', | |
| type='FpnTeacherDistill', | |
| architecture=dict( | |
| cfg_path='mmyolo::rtmdet/rtmdet_m_syncbn_fast_8xb32-300e_coco.py'), | |
| teacher=dict( | |
| cfg_path='mmyolo::rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py'), | |
| teacher_ckpt=teacher_ckpt, | |
| distiller=dict( | |
| type='ConfigurableDistiller', | |
| # `recorders` are used to record various intermediate results during | |
| # the model forward. | |
| student_recorders=dict( | |
| fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'), | |
| fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'), | |
| fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv'), | |
| ), | |
| teacher_recorders=dict( | |
| fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'), | |
| fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'), | |
| fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv')), | |
| # `connectors` are adaptive layers which usually map teacher's and | |
| # students features to the same dimension. | |
| connectors=dict( | |
| fpn0_s=dict( | |
| type='ConvModuleConnector', | |
| in_channel=192, | |
| out_channel=256, | |
| bias=False, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None), | |
| fpn0_t=dict( | |
| type='NormConnector', in_channels=256, norm_cfg=norm_cfg), | |
| fpn1_s=dict( | |
| type='ConvModuleConnector', | |
| in_channel=192, | |
| out_channel=256, | |
| bias=False, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None), | |
| fpn1_t=dict( | |
| type='NormConnector', in_channels=256, norm_cfg=norm_cfg), | |
| fpn2_s=dict( | |
| type='ConvModuleConnector', | |
| in_channel=192, | |
| out_channel=256, | |
| bias=False, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None), | |
| fpn2_t=dict( | |
| type='NormConnector', in_channels=256, norm_cfg=norm_cfg)), | |
| distill_losses=dict( | |
| loss_fpn0=dict(type='ChannelWiseDivergence', loss_weight=1), | |
| loss_fpn1=dict(type='ChannelWiseDivergence', loss_weight=1), | |
| loss_fpn2=dict(type='ChannelWiseDivergence', loss_weight=1)), | |
| # `loss_forward_mappings` are mappings between distill loss forward | |
| # arguments and records. | |
| loss_forward_mappings=dict( | |
| loss_fpn0=dict( | |
| preds_S=dict( | |
| from_student=True, recorder='fpn0', connector='fpn0_s'), | |
| preds_T=dict( | |
| from_student=False, recorder='fpn0', connector='fpn0_t')), | |
| loss_fpn1=dict( | |
| preds_S=dict( | |
| from_student=True, recorder='fpn1', connector='fpn1_s'), | |
| preds_T=dict( | |
| from_student=False, recorder='fpn1', connector='fpn1_t')), | |
| loss_fpn2=dict( | |
| preds_S=dict( | |
| from_student=True, recorder='fpn2', connector='fpn2_s'), | |
| preds_T=dict( | |
| from_student=False, recorder='fpn2', | |
| connector='fpn2_t'))))) | |
| find_unused_parameters = True | |
| custom_hooks = [ | |
| dict( | |
| type='EMAHook', | |
| ema_type='ExpMomentumEMA', | |
| momentum=0.0002, | |
| update_buffers=True, | |
| strict_load=False, | |
| priority=49), | |
| dict( | |
| type='mmdet.PipelineSwitchHook', | |
| switch_epoch=_base_.max_epochs - _base_.num_epochs_stage2, | |
| switch_pipeline=_base_.train_pipeline_stage2), | |
| # stop distillation after the 280th epoch | |
| dict(type='mmrazor.StopDistillHook', stop_epoch=280) | |
| ] | |