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炒锅加油放入牛肉,双面炒到变色,倒入鸡蛋
鸡蛋成型翻面
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两个鸡蛋和瘦肉,加水搅拌
放进汤锅蒸25min
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加水和酱油版
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第一次
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方法2:
炒锅冷水加料酒、姜片煮排骨、玉米、胡萝卜,中火煮10分钟
加盐、小火煮20分钟
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方法3:【不错】
炒锅冷水加料酒、姜片煮排骨、玉米,中火煮10分钟
加盐、胡萝卜,小火煮20-30分钟
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1 | github登录 |
hybrlk
https://www.mixamo.com/#/
https://www.jiqizhixin.com/articles/2022-05-24-6
https://www.shuzhiduo.com/A/q4zVywajdK/
https://xungejiang.com/2019/07/26/pytorch-imagenet/
V100-PICE/V100/K80
1 | vim ~/.bashrc |
1 | https://pytorch.org/get-started/previous-versions/ |
1 | conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch |
pip install memory_profiler
python -m memory_profiler xxx.py
添加@profile
mprof run xxx.py
mprof plot XXX.dat
export DISPLAY="tmux show-env | sed -n 's/^DISPLAY=//p'
"
top -p 进程ID 查看内存占用
参考:https://blog.csdn.net/qq_18254385/article/details/90401181
1 | np.random.rand(3,4) #随机生成矩阵 |
1 | pic = cv2.resize(pic, (400, 400), interpolation=cv2.INTER_CUBIC) |
1 | matplotlib.pyplot.imshow |
https://blog.csdn.net/ztf312/article/details/102474190
https://blog.csdn.net/guduruyu/article/details/60868501
https://www.cnblogs.com/HuZihu/p/9481068.html
1 | tensorboard --logdir=path --port=6005 |
bce
https://blog.csdn.net/tmk_01/article/details/80844260
指标
https://www.jianshu.com/p/b960305718f1
https://blog.csdn.net/geter_CS/article/details/79849386
https://www.cnblogs.com/jiaxin359/p/8627530.html
https://zhuanlan.zhihu.com/p/87768945
AP:
https://zhuanlan.zhihu.com/p/33372046
AUC:
https://zhuanlan.zhihu.com/p/267901426
多分类混淆矩阵
https://blog.csdn.net/m0_38061927/article/details/77198990
多分类指标
https://zhuanlan.zhihu.com/p/59862986
https://zhuanlan.zhihu.com/p/147663370
https://zhuanlan.zhihu.com/p/51125423
库
http://d0evi1.com/sklearn/model_evaluation/
https://sklearn.apachecn.org/docs/master/32.html(很有用)
https://scikit-learn.org.cn/view/93.html
1 | print(next(model.parameters()).device) #模型 |
1 | cpu_imgs.cuda() |
https://blog.csdn.net/weixin_43135178/article/details/118768531
https://www.cnblogs.com/xiaodai0/p/10413711.html
1 | if seed == 0: |
https://www.daimajiaoliu.com/daima/479c201301003e4
代码:
https://github.com/tczhangzhi/pytorch-distributed
https://github.com/Xianchao-Wu/pytorch-distributed/tree/master
参考
https://zhuanlan.zhihu.com/p/98535650
https://zhuanlan.zhihu.com/p/105755472
https://zhuanlan.zhihu.com/p/343891349
https://blog.csdn.net/lgzlgz3102/article/details/107054314?ivk_sa=1024320u
参数更新
https://zhuanlan.zhihu.com/p/350501860
https://zhuanlan.zhihu.com/p/129912419
https://zhuanlan.zhihu.com/p/76638962(重要)
1 | for task in tqdm(tasks): |
sitk_image = sitk.ReadImage(im)
orig_volume = sitk.GetArrayFromImage(sitk_image) # mod, z, y, x
sitk_image.GetDimension()
mod_num = sitk_image.GetSize()
original_shape = orig_volume.shape
以上两个x,y,z位置不一致
sitk_image.GetOrigin() ???
sitk_image.GetSpacing()
sitk_refer.GetDirection() ???
sitk.Resample
输入图像对这一层输出的神经元的影响有多大
公式:(N-1)_RF = (N_RF - 1) * stride + kernel
AvgEER
Equal error rate
特征提取
一类分类器one-class classifier
end-to-end solutions
training strategies
deepfake audio 的可持续学习
DFWF
deepfake音频检测的任务不同于传统的持续学习场景。 任务和类别的数量没有增加,只是数据分布发生了变化
灾难性遗忘catastrophic forgetting-多条件训练Multi-condition training-持续学习continual learning
(增量更新和不断学习)
boundary forgeries replay (BoFoRy)
在类边界选择有代表性的假音频样本进行回放
选择正确区分的假音频
保证存储在缓冲区中的选定样本接近类边界,并被正确分类为假音频。
缓冲区仅保存虚假类别(因为真实音频在不同场景下更加一致,而不同类型的假音频差异很大)
普通
类别增加,边界越接近中心
中心比边界更容易学习特征
deepfake
类别固定,更关注边界
真实数据分布集中,存储假数据的边界样本
假音频的特征向量与所有真实真实的平均特征向量的距离被用作它与类边界的接近程度的近似度量。
获取地址:
pytorch:https://hub.docker.com/r/pytorch/pytorch/tags?page=1&ordering=last_updated
centos:https://hub.docker.com/_/centos?tab=tags&page=2&ordering=-last_updated
下载centos7.5:
1 | sudo docker pull centos:centos7.5.1804 |
查看docker存储路径
1 | docker info | grep "Docker Root Dir" |
查看镜像
1 | docker image ls |
查看容器
1 | docker ps |
停止容器
1 | docker stop 容器ID |
删除容器
1 | docker rm 容器ID |
查看容器详细信息
1 | docker inspect 容器ID |
1 | nvidia-docker run -d -it -p 12000-12010:12000-12010 -p 宿主机端口:docker端口 --privileged -v 宿主机路径:/home/repos -v /data:/data -v /data_static:/data_static -v /data_activate:/data_activate --name XXX 镜像ID /bin/bash |
1 | -itd i交互式 t终端 d后台 |
1 | docker exec -it --privileged XXX /bin/bash |
1 | docker commit 容器ID -a "XXX" deepfake:pytorch1.4-cuda10.1-cudnn7-deepfake-devel |
参考:https://qastack.cn/programming/20845056/how-can-i-expose-more-than-1-port-with-docker
1 | watch -n 0.1 nvidia-smi |
1 | docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi |
1 | nvidia-docker run XXXX |
1 | docker run -d -it --gpus all XXX |
安装vim
1 | apt-get update |
安装ping
1 | apt-get install iputils-ping |
tag:
缺失模块。
1、请确保node版本大于6.2
2、在博客根目录(注意不是yilia根目录)执行以下命令:
npm i hexo-generator-json-content --save
3、在根目录_config.yml里添加配置:
jsonContent: meta: false pages: false posts: title: true date: true path: true text: false raw: false content: false slug: false updated: false comments: false link: false permalink: false excerpt: false categories: false tags: true