Seems like a bug while training my own module...

[fhfhfh999]

==== Epoch [ 1 / 10 ] START ====
<<<= Phase: train =>>>
D:\Data\quickNAT_pytorch-master\venv\lib\site-packages\torch\optim\lr_scheduler.py:131: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of lr_scheduler.step() before optimizer.step(). "
Traceback (most recent call last):
File "D:\Data\quickNAT_pytorch-master\run.py", line 187, in
train(train_params, common_params, data_params, net_params)
File "D:\Data\quickNAT_pytorch-master\run.py", line 57, in train
solver.train(train_loader, val_loader)
File "D:\Data\quickNAT_pytorch-master\solver.py", line 113, in train
output = model(X)
File "D:\Data\quickNAT_pytorch-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Data\quickNAT_pytorch-master\quicknat.py", line 49, in forward
e1, out1, ind1 = self.encode1.forward(input)
File "D:\Data\quickNAT_pytorch-master\venv\lib\site-packages\nn_common_modules\modules.py", line 151, in forward
out_block = self.SELayer(out_block, weights)
File "D:\Data\quickNAT_pytorch-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
TypeError: forward() takes 2 positional arguments but 3 were given

I don't know whether it is a bug or my wrong operation...

[fhfhfh999]

pytorch version cause this bug.