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YOLO-World3
/
third_party
/mmyolo
/configs
/rtmdet
/distillation
/kd_tiny_rtmdet_s_neck_300e_coco.py
_base_ = '../rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py' | |
teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco/rtmdet_s_syncbn_fast_8xb32-300e_coco_20221230_182329-0a8c901a.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_tiny_syncbn_fast_8xb32-300e_coco.py'), | |
teacher=dict( | |
cfg_path='mmyolo::rtmdet/rtmdet_s_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=96, | |
out_channel=128, | |
bias=False, | |
norm_cfg=norm_cfg, | |
act_cfg=None), | |
fpn0_t=dict( | |
type='NormConnector', in_channels=128, norm_cfg=norm_cfg), | |
fpn1_s=dict( | |
type='ConvModuleConnector', | |
in_channel=96, | |
out_channel=128, | |
bias=False, | |
norm_cfg=norm_cfg, | |
act_cfg=None), | |
fpn1_t=dict( | |
type='NormConnector', in_channels=128, norm_cfg=norm_cfg), | |
fpn2_s=dict( | |
type='ConvModuleConnector', | |
in_channel=96, | |
out_channel=128, | |
bias=False, | |
norm_cfg=norm_cfg, | |
act_cfg=None), | |
fpn2_t=dict( | |
type='NormConnector', in_channels=128, 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) | |
] | |