模型脱离mmpretrain框架，放在自己写的模型里微调，但是报错backbone的所有参数都不能参与梯度计算。

[yaoyuan10475]

我想将mmpretrain的模型拿出来，放入自己写的模型当作backbone，但是在训练的时候，报错backbone不能参与梯度计算。
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class UpernetDniov2(nn.Module):
def init(self, cfg, *args, **kwargs) -> None:
super().init(*args, **kwargs)
self.backbone = VisionTransformer(**cfg.model.backbone)
self.decode_head = UPerHead(**cfg.model.decode_head)
def forward(self, x):
feat = self.backbone(x)
outs = self.decode_head(feat)
return outs
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RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel, and by

making sure all forward function outputs participate in calculating loss.

If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable).

Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 ...
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