你好，非常想知道单卡单机训练时该怎么修改代码，自己尝试直接单卡运行，在此处报错
Traceback (most recent call last):
File "train.py", line 429, in
train()
File "train.py", line 292, in train
out, out16, out32, detail8 = net(im)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 885, in forward
inputs, kwargs = self.to_kwargs(inputs, kwargs, self.device_ids[0])
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 992, in to_kwargs
inputs = self._recursive_to(inputs, device_id) if inputs else []
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 986, in _recursive_to
res = to_map(inputs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 977, in to_map
return list(zip(*map(to_map, obj)))
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 955, in to_map
if obj.device == torch.device("cuda", target_gpu):
RuntimeError: Device index must not be negative
将local_rank默认值改为0后又有如下报错：
Traceback (most recent call last):
File "train.py", line 429, in
train()
File "train.py", line 292, in train
out, out16, out32, detail8 = net(im)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 886, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/STDC-Seg/models/model_stages.py", line 272, in forward
feat_res2, feat_res4, feat_res8, feat_res16, feat_cp8, feat_cp16 = self.cp(x)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/STDC-Seg/models/model_stages.py", line 141, in forward
avg = self.conv_avg(avg)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/STDC-Seg/models/model_stages.py", line 31, in forward
x = self.bn(x)
File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/root/STDC-Seg/modules/bn.py", line 118, in forward
return inplace_abn_sync(x, self.weight, self.bias, self.running_mean, self.running_var,
RuntimeError: Some elements marked as dirty during the forward method were not returned as output. The inputs that are modified inplace must all be outputs of the Function.
不知道该怎么解决，非常想知道怎么解决，或者希望能指点要去掉分布式训练该怎么修train.py，万分感谢！
你好，请问你最后解决了这个问题，在单卡单机上训练成功了没有？
RuntimeError: Some elements marked as dirty during the forward method were not returned as output. The inputs that are modified inplace must all be outputs of the Function.
@yang-stephen @Lee6384
报上述错误，BatchNorm2d函数不对，建议使用torch官方正则化函数，即nn.BatchNorm2d （model_stages.py里）
MichaelFan01
