【tensorflow】MTCNN网络基本函数bbox_ohem&landmark_ohem()

全栈程序员-站长 • 2022年5月29日下午11:00 • 未分类 • 阅读 44

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tf.gather：用一个一维的索引数组，将张量中对应索引的向量提取出来

import tensorflow as tf
import numpy as np
a = tf.constant([1,2,3,4])
b = tf.square(a)
with tf.Session() as sess:
    print("b:%s" % sess.run(b))
# b:[ 1  4  9 16]

import numpy as np
import tensorflow as tf
def bbox_ohem(bbox_pred,bbox_target,label):
    '''
    :param bbox_pred:
    :param bbox_target:
    :param label: class label
    :return: mean euclidean loss for all the pos and part examples
    '''
    zeros_index = tf.zeros_like(label, dtype=tf.float32)
    ones_index = tf.ones_like(label, dtype=tf.float32)
    #获取pos样本和part样本
    valid_inds = tf.where(tf.equal(tf.abs(label),1),ones_index,zeros_index)
    #(batch,)
    #计算平方和（按行）tf.square(bbox_pred-bbox_target): 求每个数的平方值
    square_error = tf.square(bbox_pred-bbox_target)
    square_error = tf.reduce_sum(square_error,axis=1)
    with tf.Session() as sess:
        print("bbox_pred-bbox_target:%s"%(sess.run(bbox_pred-bbox_target)))
        print("square_error:%s" % (sess.run(square_error)))
    # 计算pos样本和part样本的数量
    num_valid = tf.reduce_sum(valid_inds)
    keep_num = tf.cast(num_valid, dtype=tf.int32)
    # 去掉neg样本和landmark样本的平方和
    square_error = square_error*valid_inds
    # 获取前K个样本的索引，K为pos和part样本的数量
    _, k_index = tf.nn.top_k(square_error, k=keep_num)
    # 将所有pos样本和part样本的平方和提取出来
    square_error = tf.gather(square_error, k_index)
    # 返回均值
    return tf.reduce_mean(square_error)

bbox_pred = tf.random_uniform([2,4],10,100,seed = 100)
bbox_target = tf.random_uniform([2,4],15,150,seed = 100)
with tf.Session() as sess:
    print("cls_prob:%s"%(sess.run(bbox_pred)))
label = np.array([1,0])
bbox_ohem(bbox_pred,bbox_target,label)

在这里插入图片描述

landmark_ohem：作用就是返回landmark的损失，用的是landmark样本。

def landmark_ohem(landmark_pred,landmark_target,label):
    '''

    :param landmark_pred:
    :param landmark_target:
    :param label:
    :return: mean euclidean loss
    '''
    #keep label =-2  then do landmark detection
    ones = tf.ones_like(label,dtype=tf.float32)
    zeros = tf.zeros_like(label,dtype=tf.float32)
    valid_inds = tf.where(tf.equal(label,-2),ones,zeros)
    square_error = tf.square(landmark_pred-landmark_target)
    square_error = tf.reduce_sum(square_error,axis=1)
    num_valid = tf.reduce_sum(valid_inds)
    #keep_num = tf.cast(num_valid*num_keep_radio,dtype=tf.int32)
    keep_num = tf.cast(num_valid, dtype=tf.int32)
    square_error = square_error*valid_inds
    _, k_index = tf.nn.top_k(square_error, k=keep_num)
    square_error = tf.gather(square_error, k_index)
    return tf.reduce_mean(square_error)

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