【Hackathon 7th PPSCI No.7】No.7 AI-aided geometric design of anti-infection catheters 论文复现 #986
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ADream-ki
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感谢提交PR，有一些修改建议如下，另外提交的代码请使用pre-commit格式化（参考文档：https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/development/#1）
docs/zh/examples/catheter.md
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| === "模型训练命令" | ||
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| ``sh python laplace2d.py `` |
docs/zh/examples/catheter.md
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| | 预训练模型 | 指标 | | ||
| | :----------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------- | | ||
| | [GeoFno.pdparams](https://paddle-orghttps://aistudio.baidu.com/datasetdetail/291940.bj.bcebos.com/paddlescience/models/laplace2d/laplace2d_pretrained.pdparams) | Test loss(LPLoss): 0.07319313049316406 | |
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- laplace案例模版的内容请删除干净
- 预训练模型可以放aistudio或者本PR里，我会帮忙上传
docs/zh/examples/laplace2d.md
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examples/geofno/conf/geofno.yaml
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| hydra: | ||
| run: | ||
| # dynamic output directory according to running time and override name | ||
| dir: outputs_geofno/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname} | ||
| job: | ||
| name: ${mode} # name of logfile | ||
| chdir: false # keep current working directory unchanged | ||
| callbacks: | ||
| init_callback: | ||
| _target_: ppsci.utils.callbacks.InitCallback | ||
| sweep: | ||
| # output directory for multirun | ||
| dir: ${geofno.run.dir} | ||
| subdir: ./ | ||
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| hydra: | |
| run: | |
| # dynamic output directory according to running time and override name | |
| dir: outputs_geofno/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname} | |
| job: | |
| name: ${mode} # name of logfile | |
| chdir: false # keep current working directory unchanged | |
| callbacks: | |
| init_callback: | |
| _target_: ppsci.utils.callbacks.InitCallback | |
| sweep: | |
| # output directory for multirun | |
| dir: ${geofno.run.dir} | |
| subdir: ./ | |
| defaults: | |
| - ppsci_default | |
| - TRAIN: train_default | |
| - TRAIN/ema: ema_default | |
| - TRAIN/swa: swa_default | |
| - EVAL: eval_default | |
| - INFER: infer_default | |
| - hydra/job/config/override_dirname/exclude_keys: exclude_keys_default | |
| - _self_ | |
| hydra: | |
| run: | |
| # dynamic output directory according to running time and override name | |
| dir: outputs_geofno/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname} | |
| job: | |
| name: ${mode} # name of logfile | |
| chdir: false # keep current working directory unchanged | |
| callbacks: | |
| init_callback: | |
| _target_: ppsci.utils.callbacks.InitCallback | |
| sweep: | |
| # output directory for multirun | |
| dir: ${geofno.run.dir} | |
| subdir: ./ | |
examples/geofno/conf/geofno.yaml
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| x_path: "examples/geofno/data/training/x_1d_structured_mesh.npy" | ||
| y_path: "examples/geofno/data/training/y_1d_structured_mesh.npy" | ||
| para_path: "examples/geofno/data/training/data_info.npy" | ||
| output_path: "examples/geofno/data/training/density_1d_data.npy" |
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请删除路径中的"examples/geofno/"前缀，一般默认当前路径在examples/geofno/下
examples/geofno/geofno.py
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| # initialize solver OK | ||
| solver = ppsci.solver.Solver( | ||
| model, | ||
| constraint, | ||
| cfg.output_dir, | ||
| optimizer, | ||
| epochs=cfg.TRAIN.epochs, | ||
| iters_per_epoch=ITERS_PER_EPOCH, | ||
| eval_during_train=cfg.TRAIN.eval_during_train, | ||
| validator=l2rel_validator, | ||
| save_freq=cfg.TRAIN.save_freq, | ||
| seed=cfg.seed, | ||
| ) |
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| # initialize solver OK | |
| solver = ppsci.solver.Solver( | |
| model, | |
| constraint, | |
| cfg.output_dir, | |
| optimizer, | |
| epochs=cfg.TRAIN.epochs, | |
| iters_per_epoch=ITERS_PER_EPOCH, | |
| eval_during_train=cfg.TRAIN.eval_during_train, | |
| validator=l2rel_validator, | |
| save_freq=cfg.TRAIN.save_freq, | |
| seed=cfg.seed, | |
| ) | |
| # initialize solver OK | |
| solver = ppsci.solver.Solver( | |
| model, | |
| constraint, | |
| cfg.output_dir, | |
| optimizer, | |
| epochs=cfg.TRAIN.epochs, | |
| iters_per_epoch=ITERS_PER_EPOCH, | |
| eval_during_train=cfg.TRAIN.eval_during_train, | |
| validator=l2rel_validator, | |
| save_freq=cfg.TRAIN.save_freq, | |
| ) |
examples/geofno/geofno.py
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| # set random seed for reproducibility | ||
| ppsci.utils.misc.set_random_seed(cfg.seed) | ||
| # initialize logger | ||
| logger.init_logger("ppsci", osp.join(cfg.output_dir, f"{cfg.mode}.log"), "info") | ||
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examples/geofno/geofno.py
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| plt.legend() | ||
| plt.tight_layout() | ||
| plt.savefig("Validation."+str(sample_id)+".pdf") |
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| plt.savefig("Validation."+str(sample_id)+".pdf") | |
| plt.savefig(f"Validation.{sample_id}.pdf") |
examples/geofno/geofno.py
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| raise ValueError( | ||
| f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'" | ||
| ) |
examples/geofno/utilities3.py
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| def count_params(model): | ||
| c = 0 | ||
| for p in model.parameters(): | ||
| c += np.prod(p.shape) # 使用paddle.prod计算参数的总数 | ||
| return c |
docs/zh/examples/catheter.md
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| | 预训练模型 | 指标 | | ||
| | :----------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------- | | ||
| | [GeoFno.pdparams](https://paddle-orghttps://aistudio.baidu.com/datasetdetail/291940.bj.bcebos.com/paddlescience/models/laplace2d/laplace2d_pretrained.pdparams) | Test loss(LPLoss): 0.07319313049316406 | |
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wangguan1995
reviewed
Sep 5, 2024
examples/geofno/utilities3.py
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| # loss function with rel/abs Lp loss | ||
| class LpLoss(nn.Layer): |
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删除utilities3.py
复用：ppsci.loss.L2Loss
如果不能复用，新建一个/workspace/wangguan/Hackathon_7/ppsci/loss/Lp.py
docs/zh/examples/laplace2d.md
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wangguan1995
reviewed
Sep 5, 2024
docs/zh/examples/catheter.md
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| ## 1. 背景简介 | ||
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| [论文](https://www.science.org/doi/pdf/10.1126/sciadv.adj1741)提出了一种基于人工智能的导尿管几何设计方法，可以有效抑制细菌逆流，从而预防 CAUTI。该方法通过流体动力学和粒子动力学模拟研究细菌运动机制，并使用人工智能模型优化导尿管几何形状，使其能够最大程度地抑制细菌逆流。实验结果表明，该设计能够将细菌逆流的程度降低一到两个数量级，具有安全、有效、易于实施和成本低等优点，有望成为未来 CAUTI 预防的重要手段。 |
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人工智能辅助的抗感染导管几何创新设计
在狭窄管道内的流体环境中，细菌能借助流体动力学作用逆流迁移，对使用植入性导管的患者构成泌尿道感染的严重威胁。尽管已有提议采用涂层与结构化表面来抑制导管内的细菌滋生，但遗憾的是，至今尚无一种表面结构或涂层技术能从根本上解决污染难题。鉴于此，我们依据逆流游动的物理原理，创新性地提出了一种几何设计方案，并通过人工智能模型对细菌流入动力学进行预测与优化。相较于传统模拟方法，所采用的傅立叶神经算子人工智能技术实现了显著的速度提升。
在准二维微流体实验中，我们以大肠杆菌为对象，验证了该设计的抗感染机制，并在临床相关流速下，通过3D打印的导管原型对其有效性进行了评估。实验结果显示，我们的导管设计在抑制导管上游端细菌污染方面，实现了1-2个数量级的提升，有望大幅延长导管的安全留置时间，并整体降低导管相关性尿路感染的风险。
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| 可以看到模型预测结果与真实结果基本一致，模型泛化效果良好。 | ||
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Training Test Loss 也放一下
加在aistudio那里了 |
luotao1
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docs/zh/examples/catheter.md
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| # Catheter | |||
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| <a href="https://aistudio.baidu.com/aistudio/projectdetail/6169897?sUid=455441&shared=1&ts=1684122038217" class="md-button md-button--primary" style>AI Studio 快速体验</a> | |||
examples/geofno/catheter.py
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| ################################################################ | ||
| # fourier layer | ||
| ################################################################ | ||
| class SpectralConv1d(nn.Layer): |
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神经网络的代码，应归类到：ppsci/arch
调用方式如图：
这样方便网络算法直接，快速的横向对比，对用户友好，别的数据集也可以用这种网络~
examples/geofno/geofno.py
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建议把这个案例文件，以及文件夹，命名为catheter
当前的catheter.py，因为其包含了geofno网络架构，建议命名为geofno
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数据集没有公开，另外需要【挂载】到aistudio项目上 |
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抱歉抱歉，忘了公开数据集了，现在已经可以了，请review @wangguan1995 |
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