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kd_logits_resnet50_mobilenet-v2_8xb32_in1k.py
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kd_logits_resnet50_mobilenet-v2_8xb32_in1k.py
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_base_ = ['mmcls::mobilenet_v2/mobilenet-v2_8xb32_in1k.py']
student = _base_.model
teacher_ckpt = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth' # noqa: E501
model = dict(
_scope_='mmrazor',
_delete_=True,
type='SingleTeacherDistill',
data_preprocessor=dict(
type='ImgDataPreprocessor',
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
bgr_to_rgb=True),
architecture=student,
teacher=dict(
cfg_path='mmcls::resnet/resnet50_8xb32_in1k.py', pretrained=False),
teacher_ckpt=teacher_ckpt,
distiller=dict(
type='ConfigurableDistiller',
student_recorders=dict(
fc=dict(type='ModuleOutputs', source='head.fc')),
teacher_recorders=dict(
fc=dict(type='ModuleOutputs', source='head.fc')),
distill_losses=dict(
loss_kl=dict(type='KLDivergence', tau=1, loss_weight=3)),
loss_forward_mappings=dict(
loss_kl=dict(
preds_S=dict(from_student=True, recorder='fc'),
preds_T=dict(from_student=False, recorder='fc')))))
find_unused_parameters = True
val_cfg = dict(_delete_=True, type='mmrazor.SingleTeacherDistillValLoop')