如何使用numpy函数作为PyTorch中的loss函数,并避免在运行时出错?

TL;博士

with torch.no_grad():
    predFeats = self(x)
    targetFeats = self(target)
    loss = torch.tensor(np.corrcoef(predFeats.cpu().numpy(),
                                    targetFeats.cpu().numpy())[1][1]).float()

RuntimeError 通过分离张量( predFeats 和 targetFeats i、 e.在没有梯度和梯度函数的情况下获得张量数据的副本( grad_fn ).

因此,与其

torch.Tensor.detach(np.corrcoef(x.numpy(), y.numpy())) # Detaches a newly created tensor!
# x and y still may have gradients. Hence the first error.

什么都不做,做什么

# Detaches x and y properly
torch.Tensor(np.corrcoef(x.detach().numpy(), y.detach().numpy()))

就像你正确地修复的那样,让我们禁用渐变。

torch.no_grad()

现在,计算特征。

predFeats = self(x) # No need for the explicit .forward() call
targetFeats = self(target)

我发现打破你的最后一排很有帮助。

loss = np.corrcoef(predFeats.numpy(), targetFeats.numpy()) # We don't need to detach

# Notice that we don't need to cast the arguments to fp32
# since the `corrcoef` casts them to fp64 anyway.

print(loss.shape, loss.dtype) # A 2-dimensional fp64 matrix

loss = loss[1][1]
print(type(loss)) # Output: numpy.float64
# Loss now just a simple fp64 number

这就是问题所在!

因为,当我们

loss = torch.from_numpy(loss)

我们正在传递一个号码( numpy.float64 )而它期望一个numpy张量( np.ndarray ).

只需更换 from_numpy() tensor() 创建方法。

loss = torch.tensor(loss)

rowvar=False corrcoef 因为PyTorch张量中的行通常表示观测值。