BCELoss（）期望您的输出是概率，即使用sigmoid激活。
BCEWithLogitsLoss（）期望您的输出是logits，即没有sigmoid激活。
我想你可能计算错了（比如精度）。这里我给予你一个基于你的代码的简单例子：
概率：

dummy_x = torch.randn(1000,1)
dummy_y = (dummy_x > 0).type(torch.float)

model1 = nn.Sequential(
    nn.Linear(1, 1),
    nn.Sigmoid()
)
criterion1 = nn.BCELoss()
optimizer = torch.optim.Adam(model1.parameters(), 0.001)

def binary_accuracy(preds, y, logits=False):
    if logits:
        rounded_preds = torch.round(torch.sigmoid(preds))
    else:
        rounded_preds = torch.round(preds)
    correct = (rounded_preds == y).float()
    accuracy = correct.sum() / len(y)
    return accuracy

for e in range(2000):
    y_hat = model1(dummy_x)
    loss = criterion1(y_hat, dummy_y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if e != 0 and e % 100==0:
        print(f"Epoch: {e}, Loss: {loss:.4f}")
        print(f"Epoch: {e}, Acc: {binary_accuracy(y_hat, dummy_y)}")

#Result:
Epoch: 100, Loss: 0.5840
Epoch: 100, Acc: 0.5839999914169312
Epoch: 200, Loss: 0.5423
Epoch: 200, Acc: 0.6499999761581421
...
Epoch: 1800, Loss: 0.2862
Epoch: 1800, Acc: 0.9950000047683716
Epoch: 1900, Loss: 0.2793
Epoch: 1900, Acc: 0.9929999709129333

现在用logits

model2 = nn.Linear(1, 1)
criterion2 = nn.BCEWithLogitsLoss()
optimizer2 = torch.optim.Adam(model2.parameters(), 0.001)
for e in range(2000):
    y_hat = model2(dummy_x)
    loss = criterion2(y_hat, dummy_y)
    optimizer2.zero_grad()
    loss.backward()
    optimizer2.step()

    if e != 0 and e % 100==0:
        print(f"Epoch: {e}, Loss: {loss:.4f}")
        print(f"Epoch: {e}, Acc: {binary_accuracy(y_hat, dummy_y, logits=True)}")

#Results: 
Epoch: 100, Loss: 1.1042
Epoch: 100, Acc: 0.007000000216066837
Epoch: 200, Loss: 1.0484
Epoch: 200, Acc: 0.01899999938905239
...
Epoch: 1800, Loss: 0.5019
Epoch: 1800, Acc: 0.9879999756813049
Epoch: 1900, Loss: 0.4844
Epoch: 1900, Acc: 0.9879999756813049