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An example to implement a new backbone with OpenMMLab framework.
My some configs:
base = [
'../base/models/cascade_rcnn_r50_fpn.py'
]
pretrained='./pretrained_model/convnext-base_3rdparty_32xb128_in1k.pth'
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
model = dict(
type='CascadeRCNN',
backbone=dict(
delete=True,
type='mmcls.ConvNeXt',
arch='base',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.5,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=pretrained)),
neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
out_channels=256,
num_outs=5),
But meet a error:RuntimeError: Given groups=1, weight of size [256, 96, 1, 1], expected input[4, 128, 200, 200] to have 96 channels, but got 128 channels instead
假设你提到这种方式比较好用,那为什么你们官方的mmcls,mmdet,mmseg这些项目不使用这种方式,而是每个项目单独拉出来搞 ?
或者能否列举通过这种方式复现相关论文或者工程化的案例 ?
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