layers_nearest_2.py 文件源码

def batchnorm(bottom, is_train, num_reference, epsilon=1e-3, decay=0.999, name=None):
    """ virtual batch normalization (poor man's version)
    the first half is the true batch, the second half is the reference batch.
    When num_reference = 0, it is just typical batch normalization.  
    To use virtual batch normalization in test phase, "update_popmean.py" needed to be executed first 
    (in order to store the mean and variance of the reference batch into pop_mean and pop_variance of batchnorm.)
    """

    batch_size = bottom.get_shape().as_list()[0]
    inst_size = batch_size - num_reference
    instance_weight = np.ones([batch_size])

    if inst_size > 0:
        reference_weight = 1.0 - (1.0 / ( num_reference + 1.0))
        instance_weight[0:inst_size] = 1.0 - reference_weight
        instance_weight[inst_size:] = reference_weight
    else:
        decay = 0.0

    return slim.batch_norm(bottom, activation_fn=None, is_training=is_train, decay=decay, scale=True, scope=name, batch_weights=instance_weight)