diff --git a/404.html b/404.html new file mode 100644 index 00000000..3453c7ca --- /dev/null +++ b/404.html @@ -0,0 +1,20 @@ + + + + + + ljh blog + + + + + + + + +

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b/assets/js/vendors~docsearch.11055bee.js @@ -0,0 +1,3 @@ +(window.webpackJsonp=window.webpackJsonp||[]).push([[9],{307:function(t,e,n){ +/*! docsearch 2.6.3 | © Algolia | github.com/algolia/docsearch */ +var r;"undefined"!=typeof self&&self,r=function(){return function(t){var e={};function n(r){if(e[r])return e[r].exports;var i=e[r]={i:r,l:!1,exports:{}};return t[r].call(i.exports,i,i.exports,n),i.l=!0,i.exports}return n.m=t,n.c=e,n.d=function(t,e,r){n.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:r})},n.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return n.d(e,"a",e),e},n.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},n.p="",n(n.s=22)}([function(t,e,n){"use strict";var r,i=n(1);function s(t){return t.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}t.exports={isArray:null,isFunction:null,isObject:null,bind:null,each:null,map:null,mixin:null,isMsie:function(t){if(void 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mode 100644 index 00000000..f0864fc3 --- /dev/null +++ b/exp/training_order.html @@ -0,0 +1,55 @@ + + + + + + 训练指令 | ljh blog + + + + + + + + +
ljh blog

# 训练指令

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.csv，ami_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

+ ← + + 阅读的相关文章 + + → +

+ + + diff --git a/index.html b/index.html new file mode 100644 index 00000000..f7bfd8f2 --- /dev/null +++ b/index.html @@ -0,0 +1,38 @@ + + + + + + ljh blog + + + + + + + + +
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

+ MIT Licensed | Copyright © 2024-present ljh +
+ + + diff --git a/paper/index.html b/paper/index.html new file mode 100644 index 00000000..e52832af --- /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..89cdb2db --- /dev/null +++ b/paper/paper.html @@ -0,0 +1,41 @@ + + + + + + ljh blog + + + + + + + + +
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

+ ← + + 文章的撰写 + + → +

+ + + diff --git a/paper/paper_writing.html b/paper/paper_writing.html new file mode 100644 index 00000000..99f30ef3 --- /dev/null +++ b/paper/paper_writing.html @@ -0,0 +1,38 @@ + + + + + + ljh blog + + + + + + + + +
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

+ ← + + prepare4work + + → +

+ + + diff --git a/prepare4work/index.html b/prepare4work/index.html new file mode 100644 index 00000000..2e91a70b --- /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..b32944a0 --- /dev/null +++ b/prepare4work/pre4work.html @@ -0,0 +1,49 @@ + + + + + + * | ljh blog + + + + + + + + +
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..fea3a254 --- /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..ba3f7dec --- /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..c59dc591 --- /dev/null +++ b/tec/linux/top.html @@ -0,0 +1,35 @@ + + + + + + ljh blog + + + + + + + + +
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 + + → +

+ + +