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How did we get here?
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mode 100644 index 00000000..dfdf7142 --- /dev/null +++ b/exp/training_order.html @@ -0,0 +1,55 @@ + + + + + + 训练指令 | ljh blog + + + + + + + + + + + + diff --git a/index.html b/index.html new file mode 100644 index 00000000..f15670dc --- /dev/null +++ b/index.html @@ -0,0 +1,38 @@ + + + + + + ljh blog + + + + + + + + +
    hero

    + ljh blog +

    + welcomg to my blog +

    + 快速上手 → +

    读书

    书籍的力量

    技术

    码农的记录

    生活

    随笔随想

    + + + diff --git a/paper/index.html b/paper/index.html new file mode 100644 index 00000000..5a7a30e9 --- /dev/null +++ b/paper/index.html @@ -0,0 +1,30 @@ + + + + + + ljh blog + + + + + + + + + + + + diff --git a/paper/paper.html b/paper/paper.html new file mode 100644 index 00000000..174d79bf --- /dev/null +++ b/paper/paper.html @@ -0,0 +1,41 @@ + + + + + + ljh blog + + + + + + + + +

    paper_name

    A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations

    一段视频输入talknet得到可能的说话人的帧序列(有可能是错误的,因此需要后续的匹配操作),将这些帧序列进行聚类,由于meld数据集包含6个不同的说话人,因此,预先从数据集中得到每个人20张不同角度的人脸图片,针对聚类完成得到可能说话人的帧序列,经由resnet-50(pretrained on face recongnition dataset)提取对应的特征,计算相似度得到最终的结果

    image-20240320140335166image-20240320134318989

    • EMT_DLFR

      前向两次,分别得到完整模态,缺失模态的结果

      在不同epoch下的同一样本,缺失的time step是一样的。而cutoff则是不一样的。能否在训练时人为构造更多的缺失样本,一方面可以避免过拟合,一方面模拟更多真实世界的缺失模态情况

      跨层参数共享,global2local的attention,将注意力的计算降低至线性复杂度

    • Robust-MSA

      端到端的方式;情感词的识别与replace;asr error;语音的背景噪声(人声分离?);图像中人脸角度,不同光照,人脸缺失,运动模糊

    • NIAT

      三种模态在对齐的情况下,设置了三种不一样的缺失情况:三种模态缺失的位置均不一致(随机位置缺失);三种模态缺失的位置一致(连续片段缺失,随机位置缺失)

    • 离线数据增强与在线数据增强 (opens new window)

    • xlnet (opens new window)

    • UniMF:提出不同的mask机制,解决未对齐序列缺失模态的问题

      图中橘色部分有:
      +$$
      +Y_{[multi]} = CA_{L->[multi]}([multi],Z_{L})+CA_{A->[multi]}([multi],Z_{A})+SA([multi])
      +$$
      image-20240311144051942

      构造两种不同的mask,一种学习模态内部的交互,一种学习模态间的交互。并采用balence策略

      image-20240311150509143

    • meta_noise

      元学习相关资料:https://zhuanlan.zhihu.com/p/72920138,https://zhuanlan.zhihu.com/p/57864886

    • mask autoencoder

      https://zhuanlan.zhihu.com/p/439554945

    • top-k准确率的计算

      https://zhuanlan.zhihu.com/p/340760336

    • 项目使用的分类代码

      https://github.com/bubbliiiing/classification-pytorch

    • my paper

      单一视角(intra) vs 多视角(inter)

      有的时候,我们仅仅通过单一一种模态,便能做出很好的感情推断,无需其他模态的帮助:

      only inf from text:

      1. a lot of sad parts
      2. there is sad part

      但有时我们需要借助其他模态的帮助(交互信息)从而准确判断主体的情感:

      inf from text and visual:

      netural text: AND THERE BUT THEYRE NOT HUGE GLARING FLAWS

      negative video: frown

    • 半监督的paper

      第一篇,关键是balence所生成的样本类别数目,作者对每一类选取top-k置信度的样本,保证了重新训练时样本数量的平衡

    • 门控机制

      image-20240314174857894
    + + + diff --git a/paper/paper_writing.html b/paper/paper_writing.html new file mode 100644 index 00000000..076af51d --- /dev/null +++ b/paper/paper_writing.html @@ -0,0 +1,38 @@ + + + + + + ljh blog + + + + + + + + +

    关于splncs04引用格式,该风格是根据文章作者首字母进行排序后进行引用,有时候我们不需要排序,而是根据引用顺序在文章中标号即可,因此需要修改splncs04文件,将SORTZ注释改为splncs04_unsort格式即可

    https://zhuanlan.zhihu.com/p/427371287

    + + + diff --git a/prepare4work/index.html b/prepare4work/index.html new file mode 100644 index 00000000..1c05fbc9 --- /dev/null +++ b/prepare4work/index.html @@ -0,0 +1,30 @@ + + + + + + ljh blog + + + + + + + + + + + + diff --git a/prepare4work/pre4work.html b/prepare4work/pre4work.html new file mode 100644 index 00000000..2e5896a2 --- /dev/null +++ b/prepare4work/pre4work.html @@ -0,0 +1,49 @@ + + + + + + * | ljh blog + + + + + + + + +

    #


    1. 跨层参数共享

      image-20240229120637462

    优点:需保存的参数量降低,减少存储成本。减少训练时间

    缺点:推理时间仍然一样

    image-20240229120702532

    共享不同参数层的结果

    img
    1. 词向量因式分解

      引入一全连接层,将词向量维度降维,达到降低transformer中隐层维度的目的

    2. 渐进式知识蒸馏

      MobileBERT:学生模型开始学习教师模型的第一层,接下来学习教师模型的第二层,而此时学生模型的第一层权重是不参与更新的。依此类推。

    3. 注意力机制的改进(longformer)

      • 滑动窗口注意力

        当前token仅与左右k/2个token窗口内的token计算注意力

      • 扩张窗口注意力

        在窗口内不是与所有token计算注意力而是进行采样,与窗口内被采样到的token计算注意力(采样间隔)

      • 全局+滑动窗口

        将QKV映射两种不同的子空间,这两种不同的QK,分别计算全局(与具体任务有关,如具有全局视角的CLS,则该token可以关注其他所有token的信息)和滑动窗口注意力

    # idea


    1. 缺失模态下的情感分析

    首先,预训练,基于MLM策略(动态掩码(Robeta)/静态掩码)

    接着,基于预训练好的的重建模型,提取特征,模态融合模块,进行情感预测

    1. 相关的掩码策略(NLP,BART)
    • 单词掩码,在输入文本中随机采样一部分单词,并且替换为掩码标记如[MASK]

    • 单词删除,随机采样一部分单词并且删除。在预测缺失单词的同时还需要确定缺失单词的位置

    • 句子排列变换。将多个句子的顺序随机打乱

    • 文档旋转变换

    • 文本填充

    • 实体级别掩码,在普通的单词掩码中,模型只需要根据J.与Rowling就可以做出正确的预测,而无法学习到更高层次的语义

      image-20240229165723875

      image-20240229160945980

    1. 门控策略
    • 输入信号经非线性变换映射至[0,1],得到门控向量,与原始输入进行元素点乘
    1. bert相关
    • 词向量编码,块编码,位置编码

      词向量编码由独热向量经由词向量矩阵(可学习参数矩阵)映射得到

      块编码指的是当前token属于哪一句子,属于句子0则编码为0,属于句子1则编码为1,以此类推。运用块向量矩阵进行映射得到最终的块编码

      位置编码则是将按单词顺序转换为独热编码,再经由位置向量矩阵转换为位置编码

      image-20240304161829784
    • MLM实现

      在得到掩码位置的隐向量后,经由词向量矩阵,映射到词表空间,之后经softmax操作得到词表中的词对应该掩码位置的概率,取max即为预测的单词,模型对第i个掩码位置的预测概率的计算如下:
      +$$
      +P(i)=Softmax(h_{i}W_{}^{T}+b)
      +$$

      整词掩码:掩码的最小粒度为单词级而非wordpiece级,提升了mlm任务的难度,模型在预测掩码词时,需要更多依靠上下文信息,有助于提升模型对输入文本语义信息的挖掘

      需要注意的是,整词掩码中,子词的掩码方式可以是替换为[MASK],随机词或者保留原词,三选一;各个子词均会受到掩码;掩码的方式受到概率控制

    • 下游任务

      抽取式阅读理解:将问题和篇章输入至模型,问题在前,篇章在后,篇章超过最大允许输入长度时,分段输入。在获取得到每个位置起点、终点位置的概率,分别取top-k个,假设起点终点位置相互独立,则联合概率为二者相乘,得到一个[start,end,prob]的三元组,数量为k*k个,添加限制条件start<end,筛选得到最终的结果。

    # code


    1. 链表

      环的入口节点,如果一链表中包含环,如何确定环的起点

      image-20240304212702894

      解法:双指针,通过快慢指针找到处于环中的节点,快指针每次走两步,慢指针每次走一步,由于链表存在环,因此最终肯定会碰上(快的会追上慢的,rectify:快指针多走n步,随后两个指针每次都是走一步),重复的位置就是处于环中的节点。接着如果快慢指针每次走的步长相差为环的长度的整数倍,则最终必会在入口处碰上(rectify:初始时,快指针多走环的长度的步长,接着每次都走一步),问题转化为寻找环的长度的问题,环的长度可以通过单指针实现,在步骤一中得到的起始节点出发,走一遍,并进行计数,最终就能得到环的长度。接着进行步骤二就能找到入口节点。


    链表中倒数第K个节点

    解法:双指针,快指针初始多走k(k-1)步,接着每次都走一步,快指针到终点时,慢指针刚好指向倒数第k个节点。

    image-20240304214814123


    调整数组顺序使奇数位于偶数前面

    解法:双指针,奇偶(分别向中心移动),奇奇(while到奇数左指针+1),偶偶(右指针-1),偶奇(交换)

    2,1,4,5,3

    3,1,4,5,2

    3,1,5,4,2


    • 删除链表的节点:给定单向链表的头指针和一个节点指针,定义一个函数在O(1)时间内删除该节点

    解法:有了要删除的节点指针,可以知道该节点的下一个节点,把下一个节点的值赋值给要删除的节点,将当前节点的next指向下下个节点,除此之外还需要把待删除节点指向的下一个节点删除。倘若在尾部,只能顺序查找删除

    image-20240304221018613

    时间复杂度的分析

    ((n-1)*O(1)+O(n))/n=O(1a)

    • 删除已排序链表中重复的节点

    双指针


    打印从1到最大的n位数

    常规做法会出现越界问题,考点是字符串的数字加法及终止条件的判定


    数值的整数次方

    解法:先举例子


    二进制中1的个数

    解法:输入的二进制数与0001进行与运算,若为1则说明最低位存在1,接着左移一位,得到0010,即与输入的二进制数的次低位进行与运算,判断次低位是否为1,以此类推


    反转链表

    解法:由于当前节点和下个节点的顺序反转后,链表出现了断裂,即下个节点与下下个节点之间的链接出现断裂,因此需要记录三个节点的信息,

    prev_node = nullptr
    +while(p->node!=nullptr)
    +{
    +next_node = p->node->next
    +p->node->next = prev_node
    +prev_node = p->node
    +p->node = next_node
    +}
    +

    合并两个排序链表

    解法:比较两个链表头节点的大小,将小的一个作为合并后链表的头节点,接着继续合并链表中的剩余节点

    image-20240307135602509
    mergehead
    +if phead1->value < phead2->value:
    +	mergehead=phead1
    +	mergehead=
    +

    树的子结构

    **

    二叉树的镜像

    + + + diff --git a/tec/index.html b/tec/index.html new file mode 100644 index 00000000..f6749a81 --- /dev/null +++ b/tec/index.html @@ -0,0 +1,30 @@ + + + + + + ljh blog + + + + + + + + + + + + diff --git a/tec/linux/index.html b/tec/linux/index.html new file mode 100644 index 00000000..4567a7f2 --- /dev/null +++ b/tec/linux/index.html @@ -0,0 +1,30 @@ + + + + + + ljh blog + + + + + + + + + + + + diff --git a/tec/linux/top.html b/tec/linux/top.html new file mode 100644 index 00000000..fb555b08 --- /dev/null +++ b/tec/linux/top.html @@ -0,0 +1,35 @@ + + + + + + ljh blog + + + + + + + + +
    top
    +

    输入top,查看资源使用情况

    image-20240320150851293

    输入m,按照内存从大到小排列则输入shift+m

    image-20240320150925159

    输入c,全部显示command列(可能出现部分遮挡的情况,可以适当拖动窗口)

    image-20240320151014799

    按q退出

    + + +

    # 训练指令

    image-20240330172228350

    train.sh 文件下修改运行train.py的参数,如--nothing,--dataset,--fusion_mode

    tee:保存命令行的输出结果,结果保存在./trainlog_mosei/mylog.log路径下,-a代表以追加的形式

    bash train_origin.sh --nothing --dataset mosei --fusion_mode inter_intra 2>&1 | tee -a ./trainlog_mosei/mylog.log
    +
    bash train_tcp.sh 2>&1 | tee -a ./trainlog_mosei/mylog_tcp.log
    +

    消融实验

    bash train_origin.sh --nothing --dataset mosei --fusion_mode inter_intra --without_modality t 2>&1 | tee -a ./trainlog_mosei/mylog_wt.log
    +

    # 可视化指令

    visualize.sh文件下,可视化注意力机制的结果,通过运行visualize.py文件,可指定参数 --visualize 样本名,得到对应样本的attention_weight,为一字典类型,包含global(fusion_mode为global时); inter,intra(fusion_mode为inter_intra时)模型的注意力结果,shape为(layers, bsz, num_heads, q_len, kv_len)

    总共涉及到2个文件:visualize.py,model_attenvisualize.py

    bash visualize.sh 2>&1 | tee -a ./visualizelog_mosei/mylog.log
    +

    # 对齐操作

    1. 首先,使用d\数据集\mosei\raw\audio\full目录下的whisper_rec.py获取所需要得到time_stamps的音频文件(修改音频路径,存储路径)

    2. 接着运行服务器上目录为**/autodl-tmp/alpaca-lora/ljh/code/**下的sim2.py文件,获取得到对齐结果,存储在pkl文件中(修改存储路径)

    3. 接着本地对视频(d\数据集\mosei\load.py)、音频进行clip(d\数据集\mosei\clip-audio.py)(修改存储路径)

    # 自训练

    基于预训练模型,跑一遍推理;输出csv文件,每类选取top_k个样本(confidence),扩充训练集,重新训练模型,冻结regression头,重新训练p次

    首先进行预训练,通过修改模型参数保存路径,保存到指定路径下

    bash train.sh 2>&1 | tee -a ./trainlog_mosei/mylog.log
    +

    从指定路径下加载模型参数,运行以下指令,会读取/root/autodl-tmp/alpaca-lora/ljh/DPC_KNN/data/ami/ami_audiofilter.csv文件,推理ami数据集,得到伪标签,保存结果在/root/autodl-tmp/alpaca-lora/ljh/code/distill_gate/SFIN_origin/ami_result/ami_pred.csv文件中,运行目录下的analysis.py文件,会得到滤除后的top-10样本的预测,即每个类别选取10个高置信度的样本,最终得到filter_ami_pred.csv文件(以追加的形式),基于该文件重新训练模型,指令的运行顺序如下:

    bash train.sh 2>&1 | tee -a ./trainlog_mosei/mylog.log # 模型参数保存路径指定,每次重新训练路径最好保持不一致
    +python inf_ami_args.py # 输入ami_audiofilter.csv,模型参数;输出:ami_pred.csv
    +python analysis.py # 输入:ami_pred.csv 追加的形式输出:filter_ami_pred.csv,同时更新ami_auidofilter.csv,即剩余的未推理的ami样本
    +bash train.sh 2>&1 | tee -a ./trainlog_mosei/mylog.log # 输入:filter_ami_pred.csv,模型参数保存路径指定,指定为retrain模式
    +python inf_ami_args.py # 输入ami_audiofilter.csv,模型参数;输出:ami_pred.csv
    +python analysis.py # 输入:ami_pred.csv 追加的形式输出:filter_ami_pred.csv,同时更新ami_auidofilter.csv,即剩余的未推理的ami样本
    +

    现在retrain的想法是,val head损失由mosei数据集提供,emo head的损失由mosei+ami数据集提供。

    已封装成相关指令,循环自训练的指令

    bash train.sh --nothing --dataset mosei --fusion_mode inter_intra --retrain 2>&1 | tee -a ./trainlog_mosei/mylog.log
    +

    每次重新自训练前,需要更新初始模型,路径在model_single/mosei/inter_intramodelf1.pt中,需更换为初始模型,路径在model_single_originvideo/mosei/inter_intramodelf.pt,更换**ami_audiofilter.csv为初始版本,删除ami_result/ami_pred.csvami_result/filter_ami_pred.csv**

    # 画图

    让坐标轴的label始终对准每一小格的中间,思路:首先得到每一个grid的size,然后获取坐标的起止,让每个刻度都加grid_size/2,终止刻度也要加0.5,传入即可

    # 获取横坐标轴的范围
    +x_min, x_max = plt.xlim()# -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5
    +# 计算一个小格的长度
    +grid_size = (x_max - x_min) / len(words) #word为每一小格放置的label
    +ticks = np.arange(x_min + grid_size/2, x_max + grid_size/2 , grid_size)
    +

    image-20240325160500471

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