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输入 YAML 格式的网络定义，生成 SVG 架构图","maintainers":[{"name":"icyfenix","email":"icyfenix@gmail.com"}],"readme":"# 神经网络架构可视化工具\n\n一个纯前端的神经网络架构可视化工具，输入 YAML 格式的网络定义，生成美观的 SVG 架构图。\n\n## 效果展示\n\n![AlexNet 架构图](assets/alexnet.svg)\n\n## 功能特性\n\n- ✅ 支持多种层类型：Input、Conv、Pool、FC、Output、Embedding、Attention 等\n- ✅ 支持复杂拓扑结构：残差块（Residual）、并行块（Parallel）、重复块（Stack）\n- ✅ 支持多种布局：水平、垂直、自动换行\n- ✅ 支持分组显示（Sections）\n- ✅ 预置模板：AlexNet、VGG16、ResNet18、Transformer、GoogLeNet、Inception Module\n- ✅ 纯前端实现，无需后端依赖\n- ✅ 支持外部 API 调用\n\n## 文件结构\n\n```\nnn-arch/\n├── index.html          # 主页面（编辑器 + 预览）\n├── src/                # 源代码目录\n│   ├── styles.css      # CSS 样式\n│   ├── app.js          # 主逻辑（页面交互）\n│   ├── parser.js       # YAML 解析器\n│   ├── layout.js       # 布局算法\n│   ├── svg-generator.js # SVG 生成器\n│   ├── templates.js    # 预置模板\n│   ├── nn-arch.js      # 外部调用 API\n│   └── lib/\n│     └ js-yaml.min.js  # YAML 解析库（本地）\n└── tests/              # 测试文件\n```\n\n## 使用方式\n\n### 方式一：npm 安装\n\n```bash\nnpm install @icyfenix-dmla/nn-arch\n```\n\n在 Node.js 中使用：\n\n```javascript\nconst NNArch = require('@icyfenix-dmla/nn-arch');\n\n// 从 YAML 生成 SVG\nconst yaml = `\nname: SimpleNet\nlayout: horizontal\nlayers:\n  - {name: Input, type: input, size: \"224x224x3\"}\n  - {name: Conv1, type: conv, kernel: 3, channels: 64, act: ReLU}\n  - {name: Output, type: output, size: 10}\n`;\nconst svg = NNArch.generateFromYaml(yaml);\n\n// 从预置模板生成\nconst vggSvg = NNArch.generateFromTemplate('vgg16');\n```\n\n### 方式二：直接打开网页\n\n在浏览器中打开 `index.html`，即可使用可视化界面：\n\n1. 在 YAML 编辑器中输入网络定义\n2. 点击\"生成 SVG\"按钮\n3. 在预览区域查看生成的架构图\n4. 使用 +/- 按钮调整缩放比例\n5. 点击\"下载 SVG\"保存文件\n\n### 方式三：外部 API 聃用（浏览器）\n\n在你的网页中引入相关文件后，调用 `NNArch` 对象的方法：\n\n```html\n<!-- 引入依赖 -->\n<script src=\"src/lib/js-yaml.min.js\"></script>\n<script src=\"src/parser.js\"></script>\n<script src=\"src/layout.js\"></script>\n<script src=\"src/svg-generator.js\"></script>\n<script src=\"src/templates.js\"></script>\n<script src=\"src/nn-arch.js\"></script>\n\n<!-- 使用 API -->\n<script>\n  // 从 YAML 生成 SVG\n  const yaml = `\nname: SimpleNet\nlayout: horizontal\nlayers:\n  - {name: Input, type: input, size: \"224x224x3\"}\n  - {name: Conv1, type: conv, kernel: 3, channels: 64, act: ReLU}\n  - {name: Output, type: output, size: 10}\n`;\n  const svg = NNArch.generateFromYaml(yaml);\n  document.getElementById('container').innerHTML = svg;\n\n  // 从预置模板生成\n  const vggSvg = NNArch.generateFromTemplate('vgg16');\n\n  // 下载 SVG\n  NNArch.downloadSvg(svg, 'MyNetwork');\n</script>\n```\n\n### API 方法\n\n| 方法 | 参数 | 返回值 | 说明 |\n|------|------|--------|------|\n| `generateFromYaml(yamlText)` | YAML 文本 | SVG 字符串 | 从 YAML 生成 SVG |\n| `generateFromTemplate(key)` | 模板键名 | SVG 字符串 | 从预置模板生成 |\n| `getTemplateList()` | 无 | 数组 | 获取可用模板列表 |\n| `getTemplateYaml(key)` | 模板键名 | YAML 文本 | 获取模板 YAML |\n| `downloadSvg(svg, filename)` | SVG、文件名 | 无 | 下载 SVG 文件 |\n\n**可用模板：** `alexnet`, `vgg16`, `resnet18`, `transformer`, `googlenet`, `inception_module`\n\n## YAML 格式说明\n\n> **注意：** YAML inline 格式（如 `{name: Conv1, ...}`）中，冒号后必须有空格。例如 `out: \"3*3\"` 是正确的，而 `out:\"3*3\"` 会被错误解析为 `out:\"3*3\"` 整体作为 key。\n\n### 基本结构\n\n```yaml\nname: 网络名称\nlayout: horizontal | vertical    # 布局方式（可选，默认 horizontal）\n\nsections:                        # 分组区域（可选）\n  - name: 特征提取器\n    layers: [Input, Conv1, Conv2]\n    row_label: \"Flatten: 9216\"   # section 后的标注（可选）\n\nlayers:                          # 所有层定义\n  - {name: Input, type: input, size: \"224x224x3\"}\n  - {name: Conv1, type: conv, kernel: 11, stride: 4, channels: 96, out: \"55x55x96\", act: ReLU}\n  - {name: Pool1, type: pool, kernel: 3, stride: 2, out: \"27x27x96\"}\n  - {name: FC1, type: fc, size: 4096, act: ReLU, dropout: true}\n  - {name: Output, type: output, size: 1000, act: Softmax}\n```\n\n### 支持的层类型\n\n| 类型 | 必要参数 | 可选参数 |\n|------|----------|----------|\n| `input` | `size` | - |\n| `conv` | `kernel`, `channels` | `stride`, `out`, `act`, `pool` |\n| `pool` | `kernel` | `stride`, `out`, `type` |\n| `fc` | `size` | `act`, `dropout` |\n| `output` | `size` | `act` |\n| `embedding` | `size` | - |\n| `attention` | `heads` | `type` |\n| `note` | - | `name`（显示的文本内容），`label`（额外提示信息） |\n\n#### note 类型说明\n\n`note` 类型用于在架构图中添加注释说明：\n- 使用虚线边框，灰色填充\n- 显示 `name` 属性的文本内容（斜体样式）\n- 可选 `label` 参数显示额外提示信息（在 name 下方，灰色小字）\n- **不参与数据流连接**，无箭头连接到其他层\n\n```yaml\n# 在 output 后添加注释说明\nlayers_after_blocks:\n  - {id: output, name: Output, type: output, size: \"28x28x256\"}\n  - {id: note, name: \"用于下一阶段输入\", type: note, label: \"Batch=128\"}\n\n# 带 label 的注释示例\nlayers:\n  - {id: input, name: Input, type: input, size: \"224x224x3\"}\n  - {id: note1, name: \"预处理\", type: note, label: \"归一化 + 随机裁剪\"}\n```\n\n### 支持的块类型\n\n除了基本层类型外，还支持复杂的拓扑结构块：\n\n| 类型 | 说明 | 必要参数 |\n|------|------|----------|\n| `residual` | 残差块：主路径 + 跳跃连接 | `main` |\n| `parallel` | 并行块：多个分支并行执行 | `branches` |\n| `stack` | 重复块：层结构的多次重复 | `layers`, `repeat` |\n\n#### 块属性说明\n\n| 属性 | 适用类型 | 说明 |\n|------|----------|------|\n| `style` | residual | 布局样式：`arc`（弧形）或 `parallel`（并行） |\n| `expand` | stack | 是否全部展开：`true` 显示每层，`false` 显示简化形式 |\n| `main` | residual | 主路径层列表 |\n| `branches` | parallel | 分支层列表（单层对象或多层数组） |\n| `layers` | stack | 重复的层列表 |\n| `skip` | residual | 跳跃连接类型：`identity`（恒等）或 `projection`（投影） |\n| `merge` | residual, parallel | 合并方式：`add`（相加）或 `concat`（拼接） |\n| `repeat` | stack | 重复次数 |\n\n#### YAML 示例\n\n```yaml\nblocks:\n  # 残差块示例\n  - name: ResBlock\n    type: residual\n    style: arc              # arc 或 parallel\n    main:\n      - {id: conv1, name: conv1, type: conv, kernel: 3, channels: 64}\n      - {id: conv2, name: conv2, type: conv, kernel: 3, channels: 64}\n    skip: identity\n    merge: add\n    act: ReLU\n\n  # 并行块示例（单层分支）\n  - name: MultiHead\n    type: parallel\n    branches:\n      - {id: q, name: Q, type: fc, size: 64}\n      - {id: k, name: K, type: fc, size: 64}\n      - {id: v, name: V, type: fc, size: 64}\n    merge: concat\n\n  # Inception 模块示例（多层分支）\n  - name: Inception\n    type: parallel\n    branches:\n      # 分支1: 单层 1x1 conv\n      - {id: branch_1x1, name: \"1×1\", type: conv, kernel: 1, channels: 64}\n      # 分支2: 多层 3x3 reduce + conv\n      - [\n          {id: branch_3x3r, name: \"3×3 reduce\", type: conv, kernel: 1, channels: 96},\n          {id: branch_3x3, name: \"3×3\", type: conv, kernel: 3, channels: 128}\n        ]\n      # 分支3: 多层 5x5 reduce + conv\n      - [\n          {id: branch_5x5r, name: \"5×5 reduce\", type: conv, kernel: 1, channels: 16},\n          {id: branch_5x5, name: \"5×5\", type: conv, kernel: 5, channels: 32}\n        ]\n      # 分支4: 多层 pool + 1x1 conv\n      - [\n          {id: branch_pool, name: Pool, type: pool, kernel: 3},\n          {id: branch_pool_proj, name: \"Pool+1×1\", type: conv, kernel: 1, channels: 32}\n        ]\n    merge: concat\n\n  # 重复块示例\n  - name: EncoderBlock\n    type: stack\n    repeat: 6\n    expand: false           # true 全部展开\n    layers:\n      - {id: ff1, name: FF1, type: fc, size: 2048}\n```\n\n### 换行机制\n\n当 section 内的层超过 6 个时，会自动换行显示，换行处会有折线连接。\n\n## 测试\n\n```bash\nnode tests/test-runner.js\n```\n\n或打开 `tests/test-runner.html` 在浏览器中运行测试。\n\n## 许可证\n\nMIT License","readmeFilename":"README.md"}