here is part of code of mask rcnn found in github, in the predict function，the batchnorm layer is set to eval mode in the training process, but why?
as I know, batchnorm in the eval mode will not update running mean and running variance, in this implementation, batchnorm is always set to eval mode in training process and test process, so is that mean the running mean and running variance is always the initial value, whats the initial value? and what about gamma and beta?
def predict(self, input, mode):
    molded_images = input[0]
    image_metas = input[1]

    if mode == 'inference':
        self.eval()
    elif mode == 'training':
        self.train()

        # Set batchnorm always in eval mode during training
        def set_bn_eval(m):
            classname = m.__class__.__name__
            if classname.find('BatchNorm') != -1:
                m.eval()

        self.apply(set_bn_eval)