I am having trouble training a model with BECLoss. This is my model and below that is my training script.
class StartDetector(nn.Module):
    CHECKPOINT_FILENAME_PATTERN = 'StartDetector-{}.tar'

    def __init__(self):
        super(StartDetector, self).__init__()

        self.cnn1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=1, padding=1)

        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=1)

        self.cnn3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=1)
        self.relu3 = nn.ReLU()

        self.dropout = nn.Dropout(0.3)
        self.fcl1 = nn.Linear(64*9*9, 1)
        self.sigmoid = nn.Sigmoid()


    def forward(self, x):

        out = self.cnn1(x)
        out = self.relu1(out)
        out = self.maxpool1(out)
        out = self.cnn2(out)
        out = self.relu2(out)
        out = self.maxpool2(out)
        out = self.cnn3(out)
        out = self.relu3(out)

        out = out.view(out.size(0), -1)

        out = self.dropout(out)
        out = self.fcl1(out)
        out = self.sigmoid(out)

        return out

    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    evaluator = Evaluator(val_loader)

    while True:
        for batch_idx, (images, labels) in enumerate(train_loader):
            start_time = time.time()
            images, labels = (Variable(images.to(device)),
                              Variable(labels.to(device)))

            logits = model.train()(images)
            loss = criterion(logits.squeeze(1).float(), labels.float())

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            step += 1

I had to change both the logits and labels to floats in order to solve the
RuntimeError: Expected object of scalar type Float but got scalar type Long for argument #2 'target'

Now that the error is gone my model is just not learning. The model seems to do fine using CrossEntropyLoss.
Can anyone give my some guidance on how to use BCELoss for binary classification?