正则化方法之DropBlock

正则化方法之DropBlock

论文：
https://arxiv.org/pdf/1810.12890.pdf
Github：
https://github.com/miguelvr/dropblock
https://github.com/DHZS/tf-dropblock

自己的复现：

import tensorflow as tf
 
 
def compute_gamma(keep_prob,feat_size_h,feat_size_w,block_size_h,block_size_w):
    feat_size_h=tf.to_float(feat_size_h)
    feat_size_w=tf.to_float(feat_size_w)
    gamma=(1-keep_prob)*(feat_size_h*feat_size_w)/(block_size_h*block_size_w)/((feat_size_h-block_size_h+1)*(feat_size_w-block_size_w+1))
    return gamma
 
def compute_keep_prob(now_step=0,start_value=1.0,stop_value=0.75,nr_steps=100000,trainable=False):
    prob_values = tf.linspace(start=start_value, stop=stop_value, num=nr_steps)
    prob=tf.where(tf.less(now_step,nr_steps),prob_values[now_step],prob_values[-1])
    keep_prob=tf.where(tf.equal(trainable,False),start_value,prob)
    return keep_prob
 
def bernoulli(shape,gamma=0):
    mask=tf.cast(tf.random_uniform(shape, minval=0, maxval=1, dtype=tf.float32)<gamma,tf.float32)
    return mask
 
def compute_block_mask(shape,padding,gamma,block_size_shape):
    mask=bernoulli(shape,gamma)
    mask = tf.pad(mask, padding,"CONSTANT")
    mask = tf.nn.max_pool(mask, [1,block_size_shape[0],block_size_shape[1], 1], [1, 1, 1, 1], 'SAME')
    mask = 1 - mask
    return mask
 
 
 
def DropBlock(inputs,keep_prob,block_size_shape,feat_size_shape):
    #keep_prob:keep ratio,float32
    #block_size_shape:[height,width]
    #feat_size_shape:[batch,height,width,channel]
    gamma=compute_gamma(keep_prob,feat_size_shape[1],feat_size_shape[2],block_size_shape[0],block_size_shape[1])
    shape=[feat_size_shape[0],feat_size_shape[1]-block_size_shape[0]+1,feat_size_shape[2]-block_size_shape[1]+1,feat_size_shape[3]]
    bottom = (block_size_shape[0]-1) // 2
    right = (block_size_shape[1]-1) // 2
 
    top = (block_size_shape[0]-1) // 2
    left = (block_size_shape[1]-1) // 2
 
    padding = [[0, 0], [top, bottom], [left, right], [0, 0]]
    mask=compute_block_mask(shape,padding,gamma,block_size_shape)
 
    normalize_mask=mask*tf.to_float(tf.size(mask)) / tf.reduce_sum(mask)
    
    outputs=inputs*normalize_mask
 
    return outputs

调用：

......
pool=*****
now_step=tf.Variable(0,trainable=False,name="now_step")
keep_prob=compute_keep_prob(now_step=now_step,trainable=True)
dropblock=DropBlock(pool,keep_prob,[3,3],tf.shape(pool))
......