我下面有这段代码，但我找不到阻止梯度计算的就地操作。
for epoch in range(nepoch):
model.train()
scheduler.step()
for batch1 in loader1:
torch.ones(len(batch1[0]), dtype=torch.float)
x, label = batch1
x = x1.to('cuda', non_blocking=True)
optimizer.zero_grad()
pred = model(x)
pred = pred.squeeze() if pred.ndimension() > 1 else pred
label = (label.float()).cuda(cuda0)
weights = torch.ones(len(label))
loss_fun = torch.nn.BCEWithLogitsLoss(weight=weights.cuda(cuda0))
score = loss_fun(pred, label)
label = np.array(np.round(label.cpu().detach())).astype(bool)
pred = np.array(pred.cpu().detach()>0).astype(bool)
torch.autograd.set_detect_anomaly(True)
score.backward()
optimizer.step()
最后，我弹出了一个错误：
Warning: Error detected in MulBackward0. Traceback of forward call that caused the error:
File "train.py", line 98, in <module>
pred = model(x)
File "/home/anatole2/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/anatole2/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py", line 100, in forward
input = module(input)
File "/home/anatole2/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/anatole2/best/PCEN_pytorch.py", line 30, in forward
filtered[i] = filtered[i] + (1-exp(self.log_s)) * filtered[i-1]
(print_stack at /pytorch/torch/csrc/autograd/python_anomaly_mode.cpp:60)
Traceback (most recent call last):
File "train.py", line 116, in <module>
score.backward()
File "/home/anatole2/miniconda3/lib/python3.7/site-packages/torch/tensor.py", line 198, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/anatole2/miniconda3/lib/python3.7/site-packages/torch/autograd/__init__.py", line 100, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [16, 1, 80]], which is output 0 of SelectBackward, is at version 378; expected version 377 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
如果你能帮我，那就太好了！
就地操作似乎在这一行：
File "/home/anatole2/best/PCEN_pytorch.py", line 30, in forward
filtered[i] = filtered[i] + (1-exp(self.log_s)) * filtered[i-1]
请注意，它使用filtered[i]中的值，然后将结果存储在filtered[i]中。这就是到位的意义；新值覆盖旧值。
要修复它，你需要做这样的事情：
filtered_new = torch.zeros_like(filtered)
...
filtered_new[i] = filtered[i] + (1-exp(self.log_s)) * filtered[i-1]
让这件事有点复杂的是，它似乎在一个循环中(我假设i是循环计数器(，它可能使用了上一次循环中的值。修改后的版本还没有到位，但可能也不会产生与原始版本相同的结果。所以你可能不得不这样做：
filtered_new[i] = filtered[i] + (1-exp(self.log_s)) * filtered_new[i-1]
如果不看到更多的代码，就不可能解决这个问题，但基本上，环顾四周，用创建新张量来存储计算结果的操作来替换任何改变现有张量的操作。