792 lines
33 KiB
Plaintext
792 lines
33 KiB
Plaintext
---
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import BaseLayout from '../../../layouts/BaseLayout.astro';
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import Header from '../../../components/Header.astro';
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import Footer from '../../../components/Footer.astro';
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import ReportSection from '../../../components/report/ReportSection.astro';
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import GlassTable from '../../../components/common/GlassTable.astro';
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import MathFormula from '../../../components/common/MathFormula.astro';
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import CodeBlock from '../../../components/common/CodeBlock.astro';
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import Container from '../../../components/Container.astro';
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import AnimatedElement from '../../../components/AnimatedElement.astro';
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import ReportSidebar from '../../../components/report/ReportSidebar.astro';
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import {
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getSectionBaseDelay,
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getChildDelay
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} from '../../../scripts/animation-timing';
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const SECTION_DELAYS = {
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HERO: 200,
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OVERVIEW: getSectionBaseDelay(0),
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JOURNEY: getSectionBaseDelay(1),
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TECHNOLOGY: getSectionBaseDelay(2),
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OUTCOMES: getSectionBaseDelay(3),
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FUTURE: getSectionBaseDelay(4),
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APPENDIX: getSectionBaseDelay(5)
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};
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const reportConfig = {
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title: 'RoRD-Layout-Recognation 项目综合技术报告',
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subtitle: '围绕旋转鲁棒局部特征的 IC 版图识别解决方案，从历程、架构、创新到未来规划的全景梳理。',
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description: 'RoRD-Layout-Recognation 项目的完整历程、核心技术方案、当前成果与未来规划。',
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version: '版本 1.3 (完整版)',
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date: '2025年10月5日',
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type: '综合技术报告'
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};
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const journeyPhases = [
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{
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id: 'phase-1',
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title: '第一阶段：开题与技术选型',
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period: '2025年6月',
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summary: '锁定服务设计-工艺协同优化 (DTCO) 的最终目标，完成 RoRD 技术路线的调研与论证。',
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highlights: [
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'对 U-Net、YOLO、ViT、SuperPoint、RoRD 等方案开展比选，聚焦版图识别的旋转鲁棒与数据稀缺两大核心痛点。',
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'确立 “RoRD + 自监督 + 几何约束” 的总体策略，为后续工作奠定方向。'
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]
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},
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{
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id: 'phase-2',
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title: '第二阶段：模型适配与损失函数设计',
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period: '2025年7月',
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summary: '针对 IC 版图的几何特性，裁剪 RoRD 模块并构建几何感知损失体系。',
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highlights: [
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'移除正交视图生成模块，结合滑动窗口与图像金字塔实现多尺度匹配。',
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'设计复合损失函数，引导模型学习几何描述子而非纹理特征。'
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]
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},
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{
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id: 'phase-3',
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title: '第三阶段：架构现代化与性能提升',
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period: '2025年9月',
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summary: '全面工程化改造，提升项目可维护性、实验效率与推理速度。',
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highlights: [
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'引入 FPN 与 NMS，大幅度提升多尺度匹配效率。',
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'采用 YAML 配置 + 模块解耦策略，结合 TensorBoard 构建实验追踪闭环。'
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]
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},
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{
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id: 'phase-4',
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title: '当前状态：技术成熟与可扩展性',
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period: '2025年10月',
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summary: '核心组件稳定运行，具备大规模实验与快速应用的工程能力。',
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highlights: [
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'FPN、NMS、配置中心、模块化代码、实验追踪等特性均已落地。',
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'进入持续优化与学术成果产出的关键阶段。'
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]
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}
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];
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const publicationPlan = {
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headers: ['目标会议', '投稿截止 (预计)', '结果通知 (预计)', '会议召开 (预计)', '策略说明'],
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rows: [
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['ICCAD 2026', '2026年5月中下旬', '2026年8月上旬', '2026年10月底', '首要目标，窗口合适，聚焦旋转鲁棒识别创新。'],
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['DATE 2027', '2026年9月中旬', '2026年12月中旬', '2027年3-4月', 'Plan A：若 ICCAD 未中，依据评审意见快速迭代。'],
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['DAC 2027', '2026年11月中下旬', '2027年2月底', '2027年6-7月', 'Plan B：留出 3 个月强化实验与对比。'],
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['ASP-DAC 2028', '2027年7月中旬', '2027年10月中旬', '2028年1月下旬', '进一步打磨模型与工业验证的备选路径。'],
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['IEEE TCAD', '滚动', '-', '-', '最终方案：若多轮会议未果，汇总为期刊稿件。']
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]
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};
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const futureRoadmap = [
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{
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title: '数据策略 (高优先级)',
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items: [
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'引入弹性变形与版图缺陷模拟，真实再现制造侧形变。',
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'实现程序化版图生成器 (gdstk)，构造大规模合成数据集。'
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]
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},
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{
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title: '训练策略 (高优先级)',
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items: [
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'引入基于不确定度的损失权重自动加权，动态平衡多任务目标。',
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'强化困难样本采样策略，提升描述子学习效率。'
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]
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},
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{
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title: '模型架构 (中优先级)',
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items: [
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'尝试 ResNet / EfficientNet 等现代骨干网络替代 VGG。',
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'探索注意力机制，引导模型聚焦关键几何结构。'
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]
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}
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];
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const resourceGuidance = {
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headers: ['资源类型', '需求规格', '说明'],
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rows: [
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['数据集（启动阶段）', '100-200 张', '高分辨率版图，完成功能验证。'],
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['数据集（初步可用）', '1,000-2,000 张', '学习稳定的几何描述子。'],
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['数据集（生产级）', '5,000-10,000+ 张', '覆盖多工艺、多设计风格。'],
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['GPU（入门级）', 'RTX 3060 / 4060', '小规模试验、功能验证。'],
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['GPU（主流级）', 'RTX 3080 / 4070', '推荐配置，兼顾效率与成本。'],
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['GPU（专业级）', 'RTX 3090 / 4090 / A6000', '大规模实验或生产部署。'],
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['显存需求', '≥ 12 GB', 'Batch Size = 8，Patch = 256×256。'],
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['CPU / 内存', '8 核 / 32 GB', '确保数据预处理不成瓶颈。']
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]
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};
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const timeEstimation = {
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headers: ['训练阶段', '时间估算', '说明'],
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rows: [
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['单个 Epoch', '15-25 分钟', 'RTX 3080，2000 张图像。'],
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['总训练时间', '约 16.7 小时', '50 Epoch @ 20 分钟 / Epoch。'],
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['实际收敛时间', '10-20 小时', '启用早停 (patience = 10)。'],
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['数据增强调优', '1-2 周', '调节尺度、亮度、噪声参数。'],
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['损失函数权重', '1-2 周', '平衡 BCE / Triplet / Manhattan 等。'],
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['超参数搜索', '2-4 周', '学习率、批大小、优化器。'],
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['模型架构微调', '2-4 周', '测试不同骨干与注意力模块。'],
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['总调优时间', '1.5-3 个月', '达到生产级部署标准。']
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]
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};
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// 代码示例静态对象
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const codeExamples = {
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fpnForward: {
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title: "FPN 前向推理",
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language: "python",
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showLineNumbers: true,
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code: `def forward(self, x: torch.Tensor, return_pyramid: bool = False):
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if not return_pyramid:
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features = self.backbone(x)
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detection_map = self.detection_head(features)
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descriptors = self.descriptor_head(features)
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return detection_map, descriptors
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c2, c3, c4 = self._extract_c234(x)
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p4 = self.lateral_c4(c4)
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p3 = self.lateral_c3(c3) + F.interpolate(p4, size=c3.shape[-2:], mode="nearest")
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p2 = self.lateral_c2(c2) + F.interpolate(p3, size=c2.shape[-2:], mode="nearest")
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p4 = self.smooth_p4(p4)
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p3 = self.smooth_p3(p3)
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p2 = self.smooth_p2(p2)
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pyramid = {}
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if 4 in self.fpn_levels:
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pyramid['P4'] = (self.det_head_fpn(p4), self.desc_head_fpn(p4), 8)
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if 3 in self.fpn_levels:
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pyramid['P3'] = (self.det_head_fpn(p3), self.desc_head_fpn(p3), 4)
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if 2 in self.fpn_levels:
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pyramid['P2'] = (self.det_head_fpn(p2), self.desc_head_fpn(p2), 2)
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return pyramid`
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},
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geometricLoss: {
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title: "几何感知损失实现",
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language: "python",
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showLineNumbers: true,
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code: `def compute_description_loss(desc_original, desc_rotated, H, margin=1.0):
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# 旋转一致性与困难负样本挖掘
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negative_list = []
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for angle in [90, 180, 270]:
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rotated_coords = rotate_coords(manhattan_coords, angle)
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negative_list.append(rotated_coords)
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geometric_triplet = triplet_loss(anchor, positive, hardest_negative)
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manhattan_loss = compute_manhattan_alignment(anchor, positive)
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sparsity_loss = torch.mean(torch.abs(anchor)) + torch.mean(torch.abs(positive))
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binary_loss = torch.mean(torch.abs(torch.sign(anchor) - torch.sign(positive)))
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return geometric_triplet + 0.1 * manhattan_loss + 0.01 * sparsity_loss + 0.05 * binary_loss`
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},
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trainingLoop: {
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title: "自监督训练循环",
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language: "python",
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showLineNumbers: true,
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code: `for epoch in range(epochs):
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model.train()
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for original, rotated, H in train_dataloader:
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original, rotated, H = original.cuda(), rotated.cuda(), H.cuda()
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det_o, desc_o = model(original)
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det_r, desc_r = model(rotated)
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det_loss = compute_detection_loss(det_o, det_r, H)
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desc_loss = compute_description_loss(desc_o, desc_r, H)
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loss = det_loss + desc_loss
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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optimizer.step()
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scheduler.step(validation_metric)
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if early_stopper.should_stop():
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break`
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},
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pyramidExtraction: {
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title: "多尺度推理提取",
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language: "python",
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showLineNumbers: true,
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code: `def extract_from_pyramid(model, image_tensor, kp_thresh, nms_cfg):
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with torch.no_grad():
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pyramid = model(image_tensor, return_pyramid=True)
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keypoints, descriptors = [], []
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for level_name, (det, desc, stride) in pyramid.items():
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kps, descs = decode_level(det, desc, stride, kp_thresh)
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if nms_cfg.get('enabled', False):
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keep = radius_nms(kps, det['scores'], nms_cfg['radius'])
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kps, descs = kps[keep], descs[keep]
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keypoints.append(kps)
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descriptors.append(descs)
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return torch.cat(keypoints, dim=0), torch.cat(descriptors, dim=0)`
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},
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yamlConfig: {
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title: "configs/base_config.yaml",
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language: "yaml",
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showLineNumbers: true,
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code: `model:
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backbone: resnet34
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fpn:
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enabled: true
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out_channels: 256
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levels: [2, 3, 4]
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matching:
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keypoint_threshold: 0.5
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nms:
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enabled: true
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radius: 4
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min_inliers: 15
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logging:
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use_tensorboard: true
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log_dir: runs
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experiment_name: baseline`
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},
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projectStructure: {
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title: "项目目录结构",
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language: "text",
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showLineNumbers: false,
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code: `RoRD-Layout-Recognation/
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├── configs/
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│ └── base_config.yaml # YAML 配置中心
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├── data/
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│ └── ic_dataset.py # 数据集定义
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├── docs/ # 文档目录
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├── models/
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│ └── rord.py # RoRD 模型 (含 FPN)
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├── utils/ # 通用工具
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├── losses.py # 几何感知损失函数
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├── train.py # 训练脚本
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├── evaluate.py # 评估脚本
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├── match.py # 模板匹配脚本
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├── pyproject.toml # 依赖定义
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└── README.md`
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},
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uvWorkflow: {
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title: "uv 工程流程",
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language: "bash",
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showLineNumbers: false,
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code: `# 安装全部依赖
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uv sync
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# 训练管线
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uv run python train.py --config configs/your_exp_config.yaml
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# 版图模板匹配
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uv run python match.py \\
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--config configs/your_exp_config.yaml \\
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--model_path path/to/model.pth \\
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--layout path/to/layout.png \\
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--template path/to/template.png
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# 启动 TensorBoard
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uv run tensorboard --logdir runs`
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},
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jsonAnnotation: {
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title: "验证集 JSON 标注",
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language: "json",
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showLineNumbers: true,
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code: `{
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"boxes": [
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{"template": "template1.png", "x": 100, "y": 200, "width": 50, "height": 50},
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{"template": "template2.png", "x": 300, "y": 400, "width": 60, "height": 60}
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]
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}`
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}
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};
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---
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<BaseLayout
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title={`${reportConfig.title} - Jiao77`}
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description={reportConfig.description}
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type="report"
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>
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<Header
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pageTitle={reportConfig.title}
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showPageTitle={true}
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description={reportConfig.subtitle}
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/>
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<main class="report-main">
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<div class="report-layout container mx-auto px-4">
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<ReportSidebar title="报告目录" toggleLabel="目录" />
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<div class="report-content" data-report-content>
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|
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<!-- 报告标题区域 -->
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<section id="intro" class="report-header scroll-mt-16">
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<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.HERO} trigger="load">
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||
<Container
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||
variant="glass"
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||
size="full"
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||
padding="xl"
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className="text-center mb-12 mt-24"
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>
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<p class="text-sm font-medium text-gray-500 mb-3">{reportConfig.version}</p>
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<h1 class="text-4xl md:text-5xl font-extrabold mb-4 text-[#3d5366]">
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{reportConfig.title}
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</h1>
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<p class="text-lg text-[#4a5f75] max-w-3xl mx-auto mb-6">
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||
{reportConfig.subtitle}
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||
</p>
|
||
<div class="flex flex-wrap items-center justify-center gap-4 text-sm text-[#5a7186]">
|
||
<span class="flex items-center gap-2">
|
||
<i class="fas fa-calendar-alt"></i>
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||
{reportConfig.date}
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||
</span>
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||
<span class="flex items-center gap-2">
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||
<i class="fas fa-file-alt"></i>
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||
{reportConfig.type}
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||
</span>
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</div>
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</Container>
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</AnimatedElement>
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||
</section>
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<!-- 项目概述 -->
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<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.OVERVIEW} trigger="scroll">
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<section id="overview">
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<ReportSection
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title="1. 项目概述"
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subtitle="RoRD-Layout-Recognation：面向 IC 版图识别的旋转鲁棒方案"
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level={2}
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>
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<div class="space-y-10 text-[#4a5f75] leading-relaxed">
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<div>
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<p class="mb-4">
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<strong>RoRD-Layout-Recognation</strong> 致力于通过深度改造 RoRD (Rotation-Robust Descriptors) 模型，构建一套对 IC 版图模板具备高精度、低延迟、零/少样本能力的识别系统，为设计-工艺协同优化 (DTCO) 提供自动化支撑。
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</p>
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</div>
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<div>
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<h3 class="section-subtitle">1.1 项目目标</h3>
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<ul class="space-y-3">
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<li><strong>高精度模板匹配：</strong> 在复杂版图中准确定位所有模板实例及其 8 种方向与尺度。</li>
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<li><strong>高效率推理能力：</strong> 针对大尺寸 GDSII 版图实现近实时匹配，加速工业落地。</li>
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<li><strong>零/少样本泛化：</strong> 支持新模板的快速识别，减少大规模标注依赖。</li>
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<li><strong>标准化研发流程：</strong> 构建数据准备、训练、调参与评估的完整可复现实验管线。</li>
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</ul>
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</div>
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<div>
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<h3 class="section-subtitle">1.2 主要困难与挑战</h3>
|
||
<ul class="space-y-3">
|
||
<li><strong>数据稀缺：</strong> 高质量标注版图昂贵且难获取，限制了监督式方法。</li>
|
||
<li><strong>几何多变：</strong> 模板存在旋转与镜像的全方向变化，要求描述子具备高度鲁棒性。</li>
|
||
<li><strong>结构复杂：</strong> 版图呈现曼哈顿网格、二值稀疏特征与大量重复结构，区别于自然图像。</li>
|
||
<li><strong>快速演进：</strong> 工艺与 IP 库持续更新，模型需具备持续适配能力。</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
<!-- 项目历程 -->
|
||
<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.JOURNEY} trigger="scroll">
|
||
<section id="journey">
|
||
<ReportSection
|
||
title="2. 项目历程与当前状态"
|
||
subtitle="从选型破题到工程化落地的完整路径"
|
||
level={2}
|
||
>
|
||
<div class="grid md:grid-cols-2 gap-6">
|
||
{journeyPhases.map((phase, index) => (
|
||
<AnimatedElement
|
||
animation="fadeInUp"
|
||
delay={getChildDelay(SECTION_DELAYS.JOURNEY, index)}
|
||
trigger="scroll"
|
||
>
|
||
<Container variant="glass" padding="lg" className="journey-card">
|
||
<div class="flex items-start justify-between gap-4 mb-3">
|
||
<h3 class="text-xl font-bold text-[#526b83]">{phase.title}</h3>
|
||
<span class="text-sm font-medium text-gray-500">{phase.period}</span>
|
||
</div>
|
||
<p class="text-[#4a5f75] mb-3">{phase.summary}</p>
|
||
<ul class="space-y-2 text-[#5a7186]">
|
||
{phase.highlights.map(point => (
|
||
<li class="flex gap-2">
|
||
<span class="text-[#6b88a5] font-semibold">•</span>
|
||
<span>{point}</span>
|
||
</li>
|
||
))}
|
||
</ul>
|
||
</Container>
|
||
</AnimatedElement>
|
||
))}
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
<!-- 技术实现 -->
|
||
<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.TECHNOLOGY} trigger="scroll">
|
||
<section id="technology">
|
||
<ReportSection
|
||
title="3. 技术实现与创新点"
|
||
subtitle="旋转鲁棒、多尺度特征与几何约束的深度融合"
|
||
level={2}
|
||
>
|
||
<div class="space-y-12 text-[#4a5f75] leading-relaxed">
|
||
<Container variant="glass" padding="lg" className="tech-block">
|
||
<h3 class="block-title">3.1 模型架构：从 VGG 到 FPN</h3>
|
||
<p class="mb-4">模型采用残差网络骨干，并行关键点检测头与描述子生成头。通过引入特征金字塔网络 (FPN)，实现单次前向即可获得多尺度特征 (P2 / P3 / P4)。</p>
|
||
<ul class="space-y-2 mb-4">
|
||
<li><strong>横向连接：</strong> 将 backbone 的 C2 / C3 / C4 特征映射至统一通道数。</li>
|
||
<li><strong>自顶向下融合：</strong> 通过上采样与逐层平滑构建金字塔，避免尺度偏移。</li>
|
||
<li><strong>共享头部：</strong> 在各层共享检测头与描述子头，提升效率与一致性。</li>
|
||
</ul>
|
||
<CodeBlock
|
||
title={codeExamples.fpnForward.title}
|
||
language={codeExamples.fpnForward.language}
|
||
showLineNumbers={codeExamples.fpnForward.showLineNumbers}
|
||
code={codeExamples.fpnForward.code}
|
||
/>
|
||
</Container>
|
||
|
||
<Container variant="glass" padding="lg" className="tech-block">
|
||
<h3 class="block-title">3.2 核心创新：几何感知损失函数</h3>
|
||
<p class="mb-3">为了引导模型学习几何结构而非纹理，我们构建了检测损失与描述子损失的复合体系：</p>
|
||
<div class="space-y-3">
|
||
<MathFormula formula="L_{\text{total}} = L_{\text{det}} + L_{\text{desc}}" />
|
||
<MathFormula formula="L_{\text{det}} = \operatorname{BCE}(D_{\text{o}}, \mathcal{W}(D_{\text{r}}, H^{-1})) + 0.1\,\operatorname{SmoothL1}(D_{\text{o}}, \mathcal{W}(D_{\text{r}}, H^{-1}))" />
|
||
<MathFormula formula="L_{\text{desc}} = L_{\text{triplet}} + 0.1L_{\text{manhattan}} + 0.01L_{\text{sparse}} + 0.05L_{\text{binary}}" />
|
||
</div>
|
||
<ul class="space-y-2 mt-4">
|
||
<li><strong>L<sub>triplet</sub>：</strong> 采用 L1 范数的几何 Triplet Loss，突出旋转一致性。</li>
|
||
<li><strong>L<sub>manhattan</sub>：</strong> 约束 90° 旋转后的描述子一致，解决重复结构混淆。</li>
|
||
<li><strong>L<sub>sparse</sub>：</strong> 稀疏化正则抑制空白区域噪声。</li>
|
||
<li><strong>L<sub>binary</sub>：</strong> 基于符号的一致性，强化几何边界表达。</li>
|
||
</ul>
|
||
<CodeBlock
|
||
className="mt-4"
|
||
title={codeExamples.geometricLoss.title}
|
||
language={codeExamples.geometricLoss.language}
|
||
showLineNumbers={codeExamples.geometricLoss.showLineNumbers}
|
||
code={codeExamples.geometricLoss.code}
|
||
/>
|
||
</Container>
|
||
|
||
<Container variant="glass" padding="lg" className="tech-block">
|
||
<h3 class="block-title">3.3 训练策略：自监督与稳定性</h3>
|
||
<ul class="space-y-2 mb-4">
|
||
<li><strong>自监督框架：</strong> 通过随机几何变换自动生成 (original, rotated, H) 训练对，完全摆脱人工标注。</li>
|
||
<li><strong>数据增强：</strong> 组合尺度抖动、Sobel 边缘增强、亮度对比度调整、高斯噪声等策略。</li>
|
||
<li><strong>稳定性机制：</strong> 采用梯度裁剪、早停与 ReduceLROnPlateau 学习率调度，确保训练可控。</li>
|
||
</ul>
|
||
<CodeBlock
|
||
title={codeExamples.trainingLoop.title}
|
||
language={codeExamples.trainingLoop.language}
|
||
showLineNumbers={codeExamples.trainingLoop.showLineNumbers}
|
||
code={codeExamples.trainingLoop.code}
|
||
/>
|
||
</Container>
|
||
|
||
<Container variant="glass" padding="lg" className="tech-block">
|
||
<h3 class="block-title">3.4 推理与匹配：高效的多实例检测</h3>
|
||
<p class="mb-4">推理阶段利用 FPN、多尺度关键点提取、半径 NMS 与 RANSAC，实现快速稳定的多实例匹配。</p>
|
||
<ul class="space-y-2 mb-4">
|
||
<li><strong>extract_from_pyramid：</strong> 一次性提取所有尺度的关键点与描述子。</li>
|
||
<li><strong>radius_nms：</strong> 基于关键点分数与空间距离过滤重复点。</li>
|
||
<li><strong>MNN + RANSAC：</strong> 先进行互最近邻匹配，再利用 RANSAC 估计单应矩阵并剔除外点。</li>
|
||
<li><strong>多实例循环：</strong> 针对每个模板实例迭代匹配，逐步屏蔽已检测区域。</li>
|
||
</ul>
|
||
<CodeBlock
|
||
title={codeExamples.pyramidExtraction.title}
|
||
language={codeExamples.pyramidExtraction.language}
|
||
showLineNumbers={codeExamples.pyramidExtraction.showLineNumbers}
|
||
code={codeExamples.pyramidExtraction.code}
|
||
/>
|
||
</Container>
|
||
|
||
<Container variant="glass" padding="lg" className="tech-block">
|
||
<h3 class="block-title">3.5 工程化：配置驱动与实验追踪</h3>
|
||
<ul class="space-y-2 mb-4">
|
||
<li><strong>YAML 配置中心：</strong> 使用 <code>OmegaConf</code> 统一管理 <code>configs/base_config.yaml</code>，实现参数解耦与一键复现实验。</li>
|
||
<li><strong>模块化代码：</strong> 数据集 (<code>data/</code>)、损失函数 (<code>losses.py</code>)、模型 (<code>models/</code>) 与工具 (<code>utils/</code>) 独立维护。</li>
|
||
<li><strong>TensorBoard 集成：</strong> 训练、评估、匹配脚本均写入关键指标，支持端到端可视化与对比分析。</li>
|
||
</ul>
|
||
<CodeBlock
|
||
title={codeExamples.yamlConfig.title}
|
||
language={codeExamples.yamlConfig.language}
|
||
showLineNumbers={codeExamples.yamlConfig.showLineNumbers}
|
||
code={codeExamples.yamlConfig.code}
|
||
/>
|
||
</Container>
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
<!-- 项目成果 -->
|
||
<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.OUTCOMES} trigger="scroll">
|
||
<section id="outcomes">
|
||
<ReportSection
|
||
title="4. 项目成果与预期效果"
|
||
subtitle="精度、效率、鲁棒性与可扩展性的综合提升"
|
||
level={2}
|
||
>
|
||
<div class="space-y-6 text-[#4a5f75] leading-relaxed">
|
||
<Container variant="glass" padding="lg">
|
||
<h3 class="block-title">当前核心能力</h3>
|
||
<ul class="space-y-2">
|
||
<li><strong>多实例检测：</strong> 可在大型版图中准确定位同一模板的多个实例。</li>
|
||
<li><strong>旋转鲁棒性：</strong> 对 0°-360° 旋转和镜像变换具备稳定识别能力。</li>
|
||
<li><strong>高效推理：</strong> FPN 将传统图像金字塔的多次推理精简为单次前向。</li>
|
||
<li><strong>可视化评估：</strong> Precision / Recall / F1 指标及调参记录全部纳入 TensorBoard。</li>
|
||
</ul>
|
||
</Container>
|
||
|
||
<Container variant="glass" padding="lg">
|
||
<h3 class="block-title">预期量化目标</h3>
|
||
<ul class="space-y-2">
|
||
<li><strong>精度：</strong> 已训练模板或相似风格验证集 F1 ≥ 95%。</li>
|
||
<li><strong>速度：</strong> 百万门级版图、单模板匹配时间控制在 1 分钟内 (V100 / A100)。</li>
|
||
<li><strong>鲁棒性：</strong> 对轻微线宽变化、金属填充差异保持稳定识别效果。</li>
|
||
<li><strong>可扩展性：</strong> 直接匹配新模板时仍保持 ≥ 85% F1，无需重新训练。</li>
|
||
</ul>
|
||
</Container>
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
<!-- 未来规划 -->
|
||
<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.FUTURE} trigger="scroll">
|
||
<section id="future">
|
||
<ReportSection
|
||
title="5. 未来工作与展望"
|
||
subtitle="技术优化路线与学术产出计划"
|
||
level={2}
|
||
>
|
||
<div class="space-y-10 text-[#4a5f75] leading-relaxed">
|
||
<div>
|
||
<h3 class="section-subtitle">5.1 技术优化路线图</h3>
|
||
<div class="grid md:grid-cols-2 gap-6">
|
||
{futureRoadmap.map((block, index) => (
|
||
<AnimatedElement
|
||
animation="fadeInUp"
|
||
delay={getChildDelay(SECTION_DELAYS.FUTURE, index)}
|
||
trigger="scroll"
|
||
>
|
||
<Container variant="glass" padding="lg" className="roadmap-card">
|
||
<h4 class="text-lg font-semibold text-[#526b83] mb-3">{block.title}</h4>
|
||
<ul class="space-y-2">
|
||
{block.items.map(item => (
|
||
<li class="flex gap-2">
|
||
<span class="text-[#6b88a5] font-semibold">•</span>
|
||
<span>{item}</span>
|
||
</li>
|
||
))}
|
||
</ul>
|
||
</Container>
|
||
</AnimatedElement>
|
||
))}
|
||
</div>
|
||
</div>
|
||
|
||
<div>
|
||
<h3 class="section-subtitle">5.2 学术产出与论文计划</h3>
|
||
<Container variant="glass" padding="lg" className="mb-6">
|
||
<h4 class="text-lg font-semibold text-[#526b83] mb-3">论文核心贡献点</h4>
|
||
<ol class="list-decimal list-inside space-y-2">
|
||
<li>首次将旋转鲁棒局部特征 (RoRD) 引入 IC 版图识别，并验证其在工业场景下的有效性。</li>
|
||
<li>提出面向版图几何的复合损失体系，显著提升二值稀疏、重复结构的描述能力。</li>
|
||
<li>融合 FPN + NMS 等现代技术，将匹配效率与多实例检测性能提升到实用级别。</li>
|
||
</ol>
|
||
</Container>
|
||
|
||
<GlassTable
|
||
headers={publicationPlan.headers}
|
||
rows={publicationPlan.rows}
|
||
caption="EDA 领域主要会议投稿计划"
|
||
/>
|
||
</div>
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
<!-- 附录 -->
|
||
<AnimatedElement animation="fadeInUp" delay={SECTION_DELAYS.APPENDIX} trigger="scroll">
|
||
<section id="appendix">
|
||
<ReportSection
|
||
title="6. 附录"
|
||
subtitle="项目结构、使用指南与数据要求"
|
||
level={2}
|
||
>
|
||
<div class="space-y-10 text-[#4a5f75] leading-relaxed">
|
||
<div>
|
||
<h3 class="section-subtitle">A. 项目结构</h3>
|
||
<CodeBlock
|
||
title={codeExamples.projectStructure.title}
|
||
language={codeExamples.projectStructure.language}
|
||
showLineNumbers={codeExamples.projectStructure.showLineNumbers}
|
||
code={codeExamples.projectStructure.code}
|
||
/>
|
||
</div>
|
||
|
||
<div>
|
||
<h3 class="section-subtitle">B. 快速使用指南</h3>
|
||
<p class="mb-4">推荐使用 <code>uv</code> 管理虚拟环境，按以下步骤启用训练与推理:</p>
|
||
<CodeBlock
|
||
title={codeExamples.uvWorkflow.title}
|
||
language={codeExamples.uvWorkflow.language}
|
||
showLineNumbers={codeExamples.uvWorkflow.showLineNumbers}
|
||
code={codeExamples.uvWorkflow.code}
|
||
/>
|
||
</div>
|
||
|
||
<div>
|
||
<h3 class="section-subtitle">C. 数据集要求</h3>
|
||
<ul class="space-y-2 mb-4">
|
||
<li><strong>训练数据：</strong> 仅需大量无标注 PNG 版图，依靠自监督生成训练对。</li>
|
||
<li><strong>验证数据：</strong> 提供模板图、版图图与描述模板位置的 JSON 标注。</li>
|
||
</ul>
|
||
<p class="mb-2">JSON 标注示例：</p>
|
||
<CodeBlock
|
||
title={codeExamples.jsonAnnotation.title}
|
||
language={codeExamples.jsonAnnotation.language}
|
||
showLineNumbers={codeExamples.jsonAnnotation.showLineNumbers}
|
||
code={codeExamples.jsonAnnotation.code}
|
||
/>
|
||
</div>
|
||
|
||
<div>
|
||
<h3 class="section-subtitle">D. 资源规划建议</h3>
|
||
<GlassTable
|
||
headers={resourceGuidance.headers}
|
||
rows={resourceGuidance.rows}
|
||
caption="实验资源配置建议"
|
||
/>
|
||
</div>
|
||
|
||
<div>
|
||
<h3 class="section-subtitle">E. 训练时间评估</h3>
|
||
<GlassTable
|
||
headers={timeEstimation.headers}
|
||
rows={timeEstimation.rows}
|
||
caption="关键阶段时间估算"
|
||
/>
|
||
</div>
|
||
</div>
|
||
</ReportSection>
|
||
</section>
|
||
</AnimatedElement>
|
||
|
||
</div>
|
||
</div>
|
||
</main>
|
||
|
||
<Footer />
|
||
</BaseLayout>
|
||
|
||
<style>
|
||
.report-layout {
|
||
display: flex;
|
||
flex-direction: column;
|
||
gap: var(--report-sidebar-gap);
|
||
position: relative;
|
||
width: 100%;
|
||
}
|
||
|
||
.report-content {
|
||
position: relative;
|
||
width: 100%;
|
||
min-width: 0;
|
||
margin: 0 auto;
|
||
}
|
||
|
||
.container {
|
||
max-width: 1200px;
|
||
width: 100%;
|
||
}
|
||
|
||
.report-header {
|
||
padding: 4rem 0 2rem 0;
|
||
text-align: center;
|
||
}
|
||
|
||
.section-subtitle {
|
||
font-size: 1.25rem;
|
||
font-weight: 700;
|
||
color: #526b83;
|
||
margin-bottom: 1rem;
|
||
}
|
||
|
||
.block-title {
|
||
font-size: 1.35rem;
|
||
font-weight: 700;
|
||
color: #526b83;
|
||
margin-bottom: 1rem;
|
||
}
|
||
|
||
.tech-block {
|
||
backdrop-filter: blur(16px);
|
||
}
|
||
|
||
.journey-card {
|
||
min-height: 220px;
|
||
}
|
||
|
||
.roadmap-card {
|
||
min-height: 180px;
|
||
}
|
||
|
||
:global(:root) {
|
||
--max-content-width: 1200px;
|
||
--report-sidebar-min-width: 260px;
|
||
--report-sidebar-base-width: 300px;
|
||
--report-sidebar-max-width: 320px;
|
||
--report-sidebar-gap: 2.5rem;
|
||
--report-content-padding: 1rem;
|
||
}
|
||
|
||
@media (min-width: 1280px) {
|
||
.report-main {
|
||
padding-left: clamp(
|
||
calc(var(--report-sidebar-base-width) + var(--report-sidebar-gap)),
|
||
calc((100vw - var(--max-content-width)) / 2 + var(--report-content-padding)),
|
||
calc(var(--report-sidebar-max-width) + 3rem)
|
||
);
|
||
padding-right: clamp(
|
||
var(--report-content-padding),
|
||
calc((100vw - var(--max-content-width)) / 2 + var(--report-content-padding)),
|
||
2rem
|
||
);
|
||
}
|
||
|
||
.report-layout {
|
||
max-width: var(--max-content-width);
|
||
margin: 0 auto;
|
||
position: relative;
|
||
}
|
||
|
||
.report-content {
|
||
max-width: 100%;
|
||
width: 100%;
|
||
margin-left: 0;
|
||
}
|
||
|
||
.container {
|
||
padding-left: var(--report-content-padding);
|
||
padding-right: var(--report-content-padding);
|
||
}
|
||
}
|
||
|
||
@media (max-width: 768px) {
|
||
.section-subtitle {
|
||
font-size: 1.1rem;
|
||
}
|
||
}
|
||
</style> |