I want to calculate IoU for my segmentation task this is the model used
class UNetModel(nn.Module):
    def __init__(self, input_channels, nclasses, threshold=.6):
        super().__init__()

        self.threshold =  threshold

        # go down
        self.conv1 = conv_bn_leru(input_channels,64)
        self.conv2 = conv_bn_leru(64, 128)
        self.conv3 = conv_bn_leru(128, 256)
        self.conv4 = conv_bn_leru(256, 512)
        self.conv5 = conv_bn_leru(512, 1024)
        self.down_pooling = nn.MaxPool2d(2)

        # go up
        self.up_pool6 = up_pooling(1024, 512)
        self.conv6 = conv_bn_leru(1024, 512)
        self.up_pool7 = up_pooling(512, 256)
        self.conv7 = conv_bn_leru(512, 256)
        self.up_pool8 = up_pooling(256, 128)
        self.conv8 = conv_bn_leru(256, 128)
        self.up_pool9 = up_pooling(128, 64)
        self.conv9 = conv_bn_leru(128, 64)

        self.conv10 = nn.Conv2d(64, nclasses, 1)

        # test weight init
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_out')
                if m.bias is not None:
                    m.bias.data.zero_()


    def forward(self, x):
        # go down
        x1 = self.conv1(x)
        p1 = self.down_pooling(x1)
        x2 = self.conv2(p1)
        p2 = self.down_pooling(x2)
        x3 = self.conv3(p2)
        p3 = self.down_pooling(x3)
        x4 = self.conv4(p3)
        p4 = self.down_pooling(x4)
        x5 = self.conv5(p4)

        # go up
        p6 = self.up_pool6(x5)
        x6 = torch.cat([p6, x4], dim=1)
        x6 = self.conv6(x6)

        p7 = self.up_pool7(x6)
        x7 = torch.cat([p7, x3], dim=1)
        x7 = self.conv7(x7)

        p8 = self.up_pool8(x7)
        x8 = torch.cat([p8, x2], dim=1)
        x8 = self.conv8(x8)

        p9 = self.up_pool9(x8)
        x9 = torch.cat([p9, x1], dim=1)
        x9 = self.conv9(x9)

        output = self.conv10(x9)
        output = torch.sigmoid(output)

        return output

this is the function that computes IoU:
def iou_pytorch(outputs, labels):
    
    outputs = outputs.squeeze(1)  # BATCH x 1 x H x W => BATCH x H x W
    
    intersection = (outputs & labels).float().sum((1, 2))  
    union = (outputs | labels).float().sum((1, 2))        
    
    iou = (intersection + SMOOTH) / (union + SMOOTH)  # We smooth our devision to avoid 0/0
    
    thresholded = torch.clamp(20 * (iou - 0.5), 0, 10).ceil() / 10 
    
    return thresholded.mean()

this is the part where I compute the IoU
def test(model, validation_loader, criterion, device):
    """evaluate the current state of the model

    Args:
        model (torch.nn.Module): the model you want to to train
        validation_loader (torch.utils.data.DataLoader): validation-set dataloader
        criterion (callable): the loss function used to evaluate the model
        device (torch.device): device used for calculations 'CPU' or 'GPU'

    """
    with torch.no_grad():
        model.eval()
        test_loss = 0
        iou = 0
        for idx, data in enumerate(validation_loader):
            mask, image = data['mask'].to(device), data['image'].to(device)
            output = model(image)
            # sum up batch loss
            test_loss += criterion(output, mask).item()
            output = (output > 0.7).type(torch.cuda.DoubleTensor) 
            iou += iou_pytorch(output, mask.type(torch.cuda.DoubleTensor))
        
        iou /= len(validation_loader.dataset)
        test_loss /= len(validation_loader.dataset)
    return test_loss, iou

But RuntimeError: cbitand is only supported for integer type tensors Raises.