Correct way to calculate Dice Loss for binary segmentation

vision
1

On some examples I see Dice Loss calculated leaving the batch out (inputs.reshape(inputs.shape[0], -1):

def dice_loss(inputs, target):
    num = target.size(0)
    inputs = inputs.reshape(num, -1)
    target = target.reshape(num, -1)


    intersection = (inputs * target).sum(1)
    union = inputs.sum(1) + target.sum(1)
    dice = (2. * intersection) / (union + 1e-8)
    dice = dice.sum() / num
    return 1 - dice

Other examples they flatten the entire tensor (inputs.reshape(-1)):

def dice_loss(inputs, target):
   
    inputs = inputs.reshape( -1)
    target = target.reshape(-1)


    intersection = (inputs * target).sum()
    union = inputs.sum() + target.sum()
    dice = (2. * intersection) / (union + 1e-8)
    dice = dice.sum() / num
    return 1 - dice

and I am confused