├── .github └── workflows │ └── make_pages.yml ├── .gitignore ├── LICENSE ├── Makefile ├── README.md ├── docs ├── .buildinfo ├── .nojekyll ├── _images │ ├── 3.4.1.png │ ├── 3.4.2.png │ ├── 3.5.1lossfunciton.png │ ├── 3.5.2.png │ ├── 3.6.1.png │ ├── 3.6.2.png │ ├── 5.2.1unet.png │ ├── AI&ML&DL.jpg │ ├── AI.jpg │ ├── Anaconda.png │ ├── Architecture.png │ ├── CPU&NPU.png │ ├── DDP.png │ ├── DP.png │ ├── Hyper_parameters.png │ ├── LSTM-arch.jpg │ ├── LSTM-gate_f.jpg │ ├── LSTM-gate_m.jpg │ ├── LSTM-gate_o.jpg │ ├── LSTM-update.jpg │ ├── Lenna_original.png │ ├── Linux_GPU.png │ ├── Linux_source.png │ ├── MLU.jpg │ ├── Nvidia.png │ ├── Patch_Merging.png │ ├── Patch_Merging_dim.png │ ├── Patch_embedding.png │ ├── Pycharm.png │ ├── Pytorch.png │ ├── Window_Partition_Reverse.png │ ├── acc_wandb.png │ ├── aug_seq.png │ ├── backends.png │ ├── bias0.png │ ├── block.jpg │ ├── block1.jpg │ ├── cam.png │ ├── cam_dog.png │ ├── check_linux.png │ ├── check_windows.png │ ├── choose_envs.png │ ├── code_Architecture.png │ ├── coords.png │ ├── dadiannao.jpg │ ├── data_parllel.png │ ├── diannao.jpg │ ├── different_size.png │ ├── dog.png │ ├── download.png │ ├── env_list.png │ ├── envs1.png │ ├── envs2.png │ ├── expansion.jpg │ ├── expansion1.jpg │ ├── fashion-mnist-sprite.png │ ├── finetune.png │ ├── float16.jpg │ ├── frameworks.png │ ├── ft_activate.png │ ├── ft_gradient.png │ ├── gpu.png │ ├── image-20220805033034092.png │ ├── image-20220805144702521.png │ ├── image-20220805144722375.png │ ├── image-20220805145218241.png │ ├── image-20220805150721875.png │ ├── image-20220812162810305.png │ ├── image_batch.png │ ├── install.png │ ├── kernel_vis.png │ ├── layer2.png │ ├── layer3.png │ ├── layer4.png │ ├── list.png │ ├── logo2.jpg │ ├── main_compare.png │ ├── model_parllel.png │ ├── node.png │ ├── output_11_2.png │ ├── output_13_1.png │ ├── output_13_2.png │ ├── output_15_2.png │ ├── output_17_2.png │ ├── output_19_2.png │ ├── output_1_2.png │ ├── output_21_1.png │ ├── output_3_1.png │ ├── output_44_1.png │ ├── output_5_2.png │ ├── output_7_0.png │ ├── output_9_2.png │ ├── prompt.png │ ├── qrcode.jpeg │ ├── relative_pos_code.png │ ├── rotate.png │ ├── screenshot.png │ ├── shidiannao2.jpg │ ├── shortcut.jpg │ ├── shortcut1.jpg │ ├── source_ok.png │ ├── split.png │ ├── ssh_tunnel_UI.png │ ├── table.png │ ├── tb_hist.png │ ├── tb_image.png │ ├── tb_image_grid.png │ ├── tb_images.png │ ├── tb_model.png │ ├── tb_scalar.png │ ├── tb_twolines.png │ ├── torchaudio_feature_extractions.png │ ├── vit_cls_pos.png │ ├── wandb_api_keys.png │ ├── wandb_config.png │ ├── wandb_sys.png │ ├── xshell_set.png │ └── xshell_ui.png ├── _sources │ ├── index.md.txt │ ├── 第一章 │ │ ├── 1.1 PyTorch简介.md.txt │ │ ├── 1.2 PyTorch的安装.md.txt │ │ ├── 1.3 PyTorch相关资源.md.txt │ │ └── index.md.txt │ ├── 第七章 │ │ ├── 7.1 可视化网络结构.md.txt │ │ ├── 7.2 CNN卷积层可视化.md.txt │ │ ├── 7.3 使用TensorBoard可视化训练过程.md.txt │ │ ├── 7.4 使用wandb可视化训练过程.md.txt │ │ └── index.md.txt │ ├── 第三章 │ │ ├── 3.1 思考：完成深度学习的必要部分.md.txt │ │ ├── 3.2 基本配置.md.txt │ │ ├── 3.3 数据读入.md.txt │ │ ├── 3.4 模型构建.md.txt │ │ ├── 3.5 模型初始化.md.txt │ │ ├── 3.6 损失函数.md.txt │ │ ├── 3.7 训练与评估.md.txt │ │ ├── 3.8 可视化.md.txt │ │ ├── 3.9 优化器.md.txt │ │ └── index.md.txt │ ├── 第九章 │ │ ├── 9.1 使用ONNX进行部署并推理.md.txt │ │ ├── 9.1 图像分类.md.txt │ │ ├── 9.2 目标检测.md.txt │ │ ├── 9.3 图像分割.md.txt │ │ └── index.md.txt │ ├── 第二章 │ │ ├── 2.1 张量.md.txt │ │ ├── 2.2 自动求导.md.txt │ │ ├── 2.3 并行计算简介.md.txt │ │ ├── 2.4 AI硬件加速设备.md.txt │ │ ├── 2.4 其他加速设备.md.txt │ │ └── index.md.txt │ ├── 第五章 │ │ ├── 5.1 PyTorch模型定义的方式.md.txt │ │ ├── 5.2 利用模型块快速搭建复杂网络.md.txt │ │ ├── 5.3 PyTorch修改模型.md.txt │ │ ├── 5.4 PyTorh模型保存与读取.md.txt │ │ └── index.md.txt │ ├── 第八章 │ │ ├── 8.1 本章简介.md.txt │ │ ├── 8.2 图像 - torchvision.md.txt │ │ ├── 8.3 视频 - PyTorchVideo.md.txt │ │ ├── 8.4 文本 - torchtext.md.txt │ │ ├── 8.5 音频 - torchaudio.md.txt │ │ ├── index.md.txt │ │ └── transforms实操.md.txt │ ├── 第六章 │ │ ├── 6.1 自定义损失函数.md.txt │ │ ├── 6.2 动态调整学习率.md.txt │ │ ├── 6.3 模型微调-timm.md.txt │ │ ├── 6.3 模型微调-torchvision.md.txt │ │ ├── 6.4 半精度训练.md.txt │ │ ├── 6.5 数据增强-imgaug.md.txt │ │ ├── 6.6 使用argparse进行调参.md.txt │ │ ├── 6.7 PyTorch模型定义与进阶训练技巧.md.txt │ │ ├── PyTorch模型定义与进阶训练技巧.md.txt │ │ └── index.md.txt │ ├── 第十章 │ │ ├── 10.1 图像分类.md.txt │ │ ├── 10.2 目标检测.md.txt │ │ ├── 10.3 图像分割.md.txt │ │ ├── LSTM解读及实战.md.txt │ │ ├── RNN详解及其实现.md.txt │ │ ├── ResNet源码解读.md.txt │ │ ├── Swin-Transformer解读.md.txt │ │ ├── Transformer 解读.md.txt │ │ ├── ViT解读.md.txt │ │ ├── YOLO系列解读 │ │ │ ├── MMYOLO实现 │ │ │ │ ├── README.md.txt │ │ │ │ ├── model_design.md.txt │ │ │ │ ├── rtmdet_description.md.txt │ │ │ │ └── yolov5_description.md.txt │ │ │ └── torch实现 │ │ │ │ └── README.md.txt │ │ └── index.md.txt │ ├── 第四章 │ │ ├── 4.1 ResNet.md.txt │ │ ├── 4.2 GoogleNet.md.txt │ │ ├── 4.3 DenseNet.md.txt │ │ ├── 4.4 FashionMNIST图像分类.md.txt │ │ ├── index.md.txt │ │ └── 基础实战——FashionMNIST时装分类.md.txt │ └── 第零章 │ │ ├── 0.1 人工智能简史.md.txt │ │ ├── 0.1 深度学习基础知识.md.txt │ │ ├── 0.2 数学基础知识.md.txt │ │ ├── 0.2 评价指标.md.txt │ │ ├── 0.3 常用包的学习.md.txt │ │ ├── 0.4 Jupyter相关操作.md.txt │ │ └── index.md.txt ├── _static │ ├── basic.css │ ├── doctools.js │ ├── documentation_options.js │ ├── file.png │ ├── images │ │ ├── logo_binder.svg │ │ ├── logo_colab.png │ │ ├── logo_deepnote.svg │ │ └── logo_jupyterhub.svg │ ├── jquery-3.5.1.js │ ├── jquery.js │ ├── language_data.js │ ├── locales │ │ ├── ar │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── bg │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── bn │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ca │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── cs │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── da │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── de │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── el │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── eo │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── es │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── et │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── fi │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── fr │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── hr │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── id │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── it │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── iw │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ja │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ko │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── lt │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── lv │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ml │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── mr │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ms │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── nl │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── no │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── pl │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── pt │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ro │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ru │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── sk │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── sl │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── sr │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── sv │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ta │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── te │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── tg │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── th │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── tl │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── tr │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── uk │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── ur │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── vi │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ ├── zh_CN │ │ │ └── LC_MESSAGES │ │ │ │ └── booktheme.po │ │ └── zh_TW │ │ │ └── LC_MESSAGES │ │ │ └── booktheme.po │ ├── minus.png │ ├── mystnb.css │ ├── plot_directive.css │ ├── plus.png │ ├── pygments.css │ ├── sbt-webpack-macros.html │ ├── scripts │ │ ├── pydata-sphinx-theme.js │ │ ├── sphinx-book-theme.js │ │ └── sphinx-book-theme.js.map │ ├── searchtools.js │ ├── styles │ │ ├── pydata-sphinx-theme.css │ │ ├── sphinx-book-theme.css │ │ └── theme.css │ ├── togglebutton.css │ ├── togglebutton.js │ ├── underscore-1.13.1.js │ ├── underscore.js │ ├── vendor │ │ └── fontawesome │ │ │ └── 5.13.0 │ │ │ ├── LICENSE.txt │ │ │ ├── css │ │ │ └── all.min.css │ │ │ └── webfonts │ │ │ ├── fa-brands-400.eot │ │ │ ├── fa-brands-400.svg │ │ │ ├── fa-brands-400.ttf │ │ │ ├── fa-brands-400.woff │ │ │ ├── fa-brands-400.woff2 │ │ │ ├── fa-regular-400.eot │ │ │ ├── fa-regular-400.svg │ │ │ ├── fa-regular-400.ttf │ │ │ ├── fa-regular-400.woff │ │ │ ├── fa-regular-400.woff2 │ │ │ ├── fa-solid-900.eot │ │ │ ├── fa-solid-900.svg │ │ │ ├── fa-solid-900.ttf │ │ │ ├── fa-solid-900.woff │ │ │ └── fa-solid-900.woff2 │ └── webpack-macros.html ├── genindex.html ├── index.html ├── objects.inv ├── search.html ├── searchindex.js ├── 第一章 │ ├── 1.1 PyTorch简介.html │ ├── 1.2 PyTorch的安装.html │ ├── 1.3 PyTorch相关资源.html │ └── index.html ├── 第七章 │ ├── 7.1 可视化网络结构.html │ ├── 7.2 CNN卷积层可视化.html │ ├── 7.3 使用TensorBoard可视化训练过程.html │ ├── 7.4 使用wandb可视化训练过程.html │ └── index.html ├── 第三章 │ ├── 3.1 思考：完成深度学习的必要部分.html │ ├── 3.2 基本配置.html │ ├── 3.3 数据读入.html │ ├── 3.4 模型构建.html │ ├── 3.5 模型初始化.html │ ├── 3.6 损失函数.html │ ├── 3.7 训练与评估.html │ ├── 3.8 可视化.html │ ├── 3.9 优化器.html │ └── index.html ├── 第九章 │ ├── 9.1 使用ONNX进行部署并推理.html │ ├── figures │ │ ├── frameworks.png │ │ ├── grapgflow.png │ │ ├── node.png │ │ ├── pipeline.jpg │ │ └── screenshot.png │ └── index.html ├── 第二章 │ ├── 2.1 张量.html │ ├── 2.2 自动求导.html │ ├── 2.3 并行计算简介.html │ ├── 2.4 AI硬件加速设备.html │ ├── 2.4 其他加速设备.html │ └── index.html ├── 第五章 │ ├── 5.1 PyTorch模型定义的方式.html │ ├── 5.2 利用模型块快速搭建复杂网络.html │ ├── 5.3 PyTorch修改模型.html │ ├── 5.4 PyTorh模型保存与读取.html │ └── index.html ├── 第八章 │ ├── 8.1 本章简介.html │ ├── 8.2 图像 - torchvision.html │ ├── 8.3 视频 - PyTorchVideo.html │ ├── 8.4 文本 - torchtext.html │ ├── 8.5 音频 - torchaudio.html │ ├── index.html │ └── transforms实操.html ├── 第六章 │ ├── 6.1 自定义损失函数.html │ ├── 6.2 动态调整学习率.html │ ├── 6.3 模型微调-timm.html │ ├── 6.3 模型微调-torchvision.html │ ├── 6.4 半精度训练.html │ ├── 6.5 数据增强-imgaug.html │ ├── 6.6 使用argparse进行调参.html │ ├── 6.7 PyTorch模型定义与进阶训练技巧.html │ ├── PyTorch模型定义与进阶训练技巧.html │ └── index.html ├── 第十章 │ ├── 10.1 图像分类.html │ ├── 10.2 目标检测.html │ ├── 10.3 图像分割.html │ ├── LSTM解读及实战.html │ ├── RNN详解及其实现.html │ ├── ResNet源码解读.html │ ├── Swin-Transformer解读.html │ ├── Transformer 解读.html │ ├── ViT解读.html │ ├── YOLO系列解读 │ │ ├── MMYOLO实现 │ │ │ ├── README.html │ │ │ ├── model_design.html │ │ │ ├── rtmdet_description.html │ │ │ └── yolov5_description.html │ │ └── torch实现 │ │ │ └── README.html │ └── index.html ├── 第四章 │ ├── 4.1 ResNet.html │ ├── 4.2 GoogleNet.html │ ├── 4.3 DenseNet.html │ ├── 4.4 FashionMNIST图像分类.html │ ├── index.html │ └── 基础实战——FashionMNIST时装分类.html └── 第零章 │ ├── 0.1 人工智能简史.html │ ├── 0.1 深度学习基础知识.html │ ├── 0.2 数学基础知识.html │ ├── 0.2 评价指标.html │ ├── 0.3 常用包的学习.html │ ├── 0.4 Jupyter相关操作.html │ ├── figures │ ├── AI&ML&DL.jpg │ ├── AI.jpg │ ├── IOU.png │ ├── PR.png │ ├── confusion_matrix.png │ ├── image-20220716235443528.png │ ├── image-20220716235510697.png │ ├── image-20220716235540827.png │ ├── image-20220716235931573.png │ ├── image-20220717000530228.png │ ├── image-20220717001243904.png │ ├── image-20220804170455298.png │ ├── image-20220804171027983.png │ ├── image-20220805024106987.png │ ├── image-20220805030345613.png │ ├── image-20220805030406608.png │ ├── image-20220805031209481.png │ ├── image-20220805033034092.png │ ├── image-20220805144702521.png │ ├── image-20220805144722375.png │ ├── image-20220805144803441.png │ ├── image-20220805145218241.png │ ├── image-20220805150721875.png │ ├── image-20220805151600140.png │ ├── image-20220805152331777.png │ ├── image-20220805152507891.png │ ├── image-20220812162810305.png │ ├── image-20220812171025719.png │ └── normalized_confusion_matrix.png │ └── index.html ├── make.bat ├── notebook ├── README.md ├── 第二章 PyTorch基础知识 │ └── 代码演示：PyTorch基础知识.ipynb ├── 第八章 PyTorch生态简介 │ ├── figures │ │ └── lenna.jpg │ └── transforms.ipynb ├── 第六章 PyTorch进阶训练技巧 │ ├── PyTorch模型定义与进阶训练技巧.ipynb │ ├── img │ │ ├── Lenna.jpg │ │ ├── UI.png │ │ ├── UI2.png │ │ ├── UI3.png │ │ ├── demo1.png │ │ ├── demo1ex.png │ │ ├── demo2.png │ │ ├── demo3.png │ │ ├── demo4.png │ │ ├── demo5.png │ │ ├── demo6.png │ │ ├── demo7.png │ │ ├── photo.png │ │ ├── plots.gif │ │ ├── start.png │ │ └── visdom.png │ ├── 使用argparse调参 │ │ └── config.py │ └── 利用Visdom可视化训练过程.ipynb ├── 第十章 常见网络代码的解读 │ └── LSTM实战.ipynb ├── 第四章 PyTorch基础实战 │ └── 图像分类 │ │ ├── FashionMNIST时装分类实战 │ │ ├── fashion-mnist-sprite.png │ │ └── 基础实战——FashionMNIST时装分类.ipynb │ │ └── 果蔬分类 │ │ └── 基础实战——果蔬分类.ipynb └── 第零章 前置知识补充 │ └── 进阶操作.ipynb └── source ├── conf.py ├── figures └── qrcode.jpeg ├── index.md ├── 第一章 ├── 1.1 PyTorch简介.md ├── 1.2 PyTorch的安装.md ├── 1.3 PyTorch相关资源.md ├── figures │ ├── Anaconda.png │ ├── Cuda.png │ ├── Linux_GPU.png │ ├── Linux_source.png │ ├── Nvidia.png │ ├── Pycharm.png │ ├── Pytorch.png │ ├── check_linux.png │ ├── check_windows.png │ ├── choose_envs.png │ ├── download.png │ ├── env_list.png │ ├── envs1.png │ ├── envs2.png │ ├── gpu.png │ ├── install.png │ ├── main_compare.png │ ├── pip换源.png │ ├── prompt.png │ ├── source_ok.png │ ├── table.png │ ├── vim.png │ └── 创建新的环境.png └── index.md ├── 第七章 ├── 7.1 可视化网络结构.md ├── 7.2 CNN卷积层可视化.md ├── 7.3 使用TensorBoard可视化训练过程.md ├── 7.4 使用wandb可视化训练过程.md ├── 7.5 使用SwanLab可视化训练过程.md ├── figures │ ├── acc_wandb.png │ ├── aswanlab_mnist_3.png │ ├── cam.png │ ├── cam_dog.png │ ├── dog.png │ ├── ft_activate.png │ ├── ft_gradient.png │ ├── kernel_vis.png │ ├── layer2.png │ ├── layer3.png │ ├── layer4.png │ ├── ssh_tunnel_UI.png │ ├── swanlab_hello_world.png │ ├── swanlab_login.png │ ├── swanlab_mnist_1.png │ ├── swanlab_mnist_2.png │ ├── swanlab_yolo_1.png │ ├── swanlab_yolo_2.png │ ├── swanlab_yolo_3.png │ ├── tb_hist.png │ ├── tb_image.png │ ├── tb_image_grid.png │ ├── tb_images.png │ ├── tb_model.png │ ├── tb_scalar.png │ ├── tb_twolines.png │ ├── wandb_api_keys.png │ ├── wandb_config.png │ ├── wandb_sys.png │ ├── xshell_set.png │ └── xshell_ui.png └── index.md ├── 第三章 ├── 3.1 思考：完成深度学习的必要部分.md ├── 3.2 基本配置.md ├── 3.3 数据读入.md ├── 3.4 模型构建.md ├── 3.5 模型初始化.md ├── 3.6 损失函数.md ├── 3.7 训练与评估.md ├── 3.8 可视化.md ├── 3.9 优化器.md ├── figures │ ├── 3.4.1.png │ ├── 3.4.2.png │ ├── 3.5.1lossfunciton.png │ ├── 3.5.2.png │ ├── 3.6.1.png │ └── 3.6.2.png └── index.md ├── 第九章 ├── 9.1 使用ONNX进行部署并推理.md ├── figures │ ├── frameworks.png │ ├── grapgflow.png │ ├── node.png │ ├── pipeline.jpg │ └── screenshot.png └── index.md ├── 第二章 ├── 2.1 张量.md ├── 2.2 自动求导.md ├── 2.3 并行计算简介.md ├── 2.4 AI硬件加速设备.md ├── figures │ ├── CPU&NPU.png │ ├── DDP.png │ ├── DP.png │ ├── FloorPlan.jpg │ ├── MLU.jpg │ ├── MMU.jpg │ ├── TPU.jpg │ ├── accelerator.jpg │ ├── backends.png │ ├── dadiannao.jpg │ ├── data_parllel.png │ ├── diannao.jpg │ ├── model_parllel.png │ ├── shidiannao.jpg │ ├── shidiannao2.jpg │ └── split.png └── index.md ├── 第五章 ├── 5.1 PyTorch模型定义的方式.md ├── 5.2 利用模型块快速搭建复杂网络.md ├── 5.3 PyTorch修改模型.md ├── 5.4 PyTorh模型保存与读取.md ├── figures │ ├── 5.2.1unet.png │ ├── resnet.png │ └── resnet_simplified.png └── index.md ├── 第八章 ├── 8.1 本章简介.md ├── 8.2 图像 - torchvision.md ├── 8.3 视频 - PyTorchVideo.md ├── 8.4 文本 - torchtext.md ├── 8.5 音频 - torchaudio.md ├── figures │ ├── Lenna.jpg │ ├── list.png │ ├── logo.jpg │ ├── logo2.jpg │ ├── output_11_2.png │ ├── output_13_1.png │ ├── output_15_2.png │ ├── output_17_2.png │ ├── output_19_2.png │ ├── output_1_2.png │ ├── output_21_1.png │ ├── output_3_1.png │ ├── output_5_2.png │ ├── output_7_0.png │ ├── output_9_2.png │ └── torchaudio_feature_extractions.png └── index.md ├── 第六章 ├── 6.1 自定义损失函数.md ├── 6.2 动态调整学习率.md ├── 6.3 模型微调-timm.md ├── 6.3 模型微调-torchvision.md ├── 6.4 半精度训练.md ├── 6.5 数据增强-imgaug.md ├── 6.6 使用argparse进行调参.md ├── figures │ ├── Lenna.jpg │ ├── Lenna_original.png │ ├── aug_seq.png │ ├── different_size.png │ ├── finetune.png │ ├── float16.jpg │ ├── image_batch.png │ ├── output_44_1.png │ └── rotate.png └── index.md ├── 第十章 ├── 10.1 图像分类.md ├── 10.2 目标检测.md ├── 10.3 图像分割.md ├── LSTM解读及实战.md ├── RNN详解及其实现.md ├── ResNet源码解读.md ├── Swin-Transformer解读.md ├── Transformer 解读.md ├── ViT解读.md ├── YOLO系列解读 │ ├── MMYOLO实现 │ │ ├── README.md │ │ ├── model_design.md │ │ ├── resources │ │ │ ├── mmyolo-logo.png │ │ │ ├── qq_group_qrcode.jpg │ │ │ └── zhihu_qrcode.jpg │ │ ├── rtmdet_description.md │ │ └── yolov5_description.md │ └── torch实现 │ │ └── README.md ├── figures │ ├── Architecture.png │ ├── Hyper_parameters.png │ ├── LSTM-arch.jpg │ ├── LSTM-gate_f.jpg │ ├── LSTM-gate_m.jpg │ ├── LSTM-gate_o.jpg │ ├── LSTM-update.jpg │ ├── Mask.png │ ├── Net.png │ ├── Patch_Merging.png │ ├── Patch_Merging_dim.png │ ├── Patch_embedding.png │ ├── Shift_window_index.png │ ├── Shifted_Window.png │ ├── Swin-T&ViT.png │ ├── Swin-T.png │ ├── Swin-T_block.png │ ├── W-MSA.png │ ├── Window_Partition_Reverse.png │ ├── bias0.png │ ├── block.jpg │ ├── code_Architecture.png │ ├── coords.png │ ├── expansion.jpg │ ├── relative_pos_bias_table.png │ ├── relative_pos_code.png │ ├── shortcut.jpg │ ├── shortcut2.jpg │ ├── torch_roll.png │ ├── transformer_Decoder.png │ ├── transformer_Encoder.png │ ├── transformer_Multi-Head attention.png │ ├── transformer_Multi-Head attention_compute.png │ ├── transformer_Multi-Head visual.jpg │ ├── transformer_architecture.png │ ├── transformer_attention.png │ ├── transformer_attention_compute.png │ ├── transformer_attention_compute_2.png │ ├── transformer_datalink.png │ ├── transformer_position_embedding.png │ ├── transformer_selfattention.jpg │ ├── vit_cls_pos.png │ ├── vit_framework.png │ ├── vit_msa.png │ ├── vit_sa.png │ ├── vit_transformer.png │ ├── 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