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1.6 KiB
1.6 KiB
该项目是《Problem-independent machine learning (PIML)-based topology optimization—A universal approach》的python复现
环境依赖
PyTorch 2.1.0 CUDA 12.1 Ubuntu 20.04 （没有用到新特性，所以应该对旧版本兼容）
# matplotlib==3.8.0
# numpy==1.26.2
# scikit_learn==1.3.0
# torch==2.1.0
pip install -r requirements.txt
Usage
TODO: [done] 用argparse模块管理网络参数
Train
python train.py
# --mod [mod1 mod2 mod3] 参数选择训练数据，默认mod1
# e.g. python train.py --mod mod1
Test
python test.py
# --mod [mod1 mod2 mod3] 参数选择测试数据，默认mod3
# --pretrained_model_path <xxx_opt.pt> 选择预训练模型，默认./checkpoints/ANN_mod1/ANN_mod1_opt.pt
# e.g. python test.py --mod mod3 --pretrained_model_path ./checkpoints/ANN_mod1/ANN_mod1_opt.pt
TopOpt with EMsFEA net
python topopt_EMsFEA.py
数据集
通过经典二维拓扑优化代码生成的三组形变、密度数据
mod1: 
mod2: 
mod3: 
项目结构
.
|-- README.md
|-- checkpoints
| `-- ...
|-- datasets
| |-- train
| | `--resolution
| | |--u
| | `--xPhys
| |-- test
|-- models
| |-- ANN.py
| |-- AutoEncoder.py
| |-- CNN.py
| `-- __init__.py
|-- options
| |-- __init__.py
| |-- base_options.py
| |-- test_options.py
| `-- train_options.py
|-- requirements.txt
|-- results
|-- test.py
|-- topopt_EMsFEA.py
|-- train.py
|-- utils
| |-- data_loader.py
| |-- data_standardizer.py
| |-- topopt_88.py
| `-- utils.py
`-- visualization.ipynb