_base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py'  # noqa

# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth'  # noqa

model = dict(
    backbone=dict(
        _delete_=True,
        type='mmcls.ConvNeXt',
        arch='small',
        out_indices=[0, 1, 2, 3],
        drop_path_rate=0.6,
        layer_scale_init_value=1.0,
        gap_before_final_norm=False,
        init_cfg=dict(
            type='Pretrained', checkpoint=checkpoint_file,
            prefix='backbone.')))

optim_wrapper = dict(paramwise_cfg={
    'decay_rate': 0.7,
    'decay_type': 'layer_wise',
    'num_layers': 12
})

_base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py'  # noqa

# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth'  # noqa

model = dict(
    backbone=dict(
        _delete_=True,
        type='mmcls.ConvNeXt',
        arch='small',
        out_indices=[0, 1, 2, 3],
        drop_path_rate=0.6,
        layer_scale_init_value=1.0,
        gap_before_final_norm=False,
        init_cfg=dict(
            type='Pretrained', checkpoint=checkpoint_file,
            prefix='backbone.')))

optim_wrapper = dict(paramwise_cfg={
    'decay_rate': 0.7,
    'decay_type': 'layer_wise',
    'num_layers': 12
})
