supervised_layout_benchmark/README.md

supervised_layout_benchmark

Introduction

This project aims to establish a deep neural network (DNN) surrogate modeling benchmark for the temperature field prediction of heat source layout (HSL-TFP) task, providing a set of representative DNN surrogates as baselines as well as the original code files for easy start and comparison.

Running Requirements

• Software

  • python：
  • cuda：
  • pytorch：

• Hardware

  • A single GPU with at least 4GB.

Environment construction

• pip install -r requirements.txt

A quick start

The training, test and visualization can be accessed by running main.py file.

• The data is available at the server address: \\192.168.2.1\mnt/share1/layout_data/v1.0/data/（refer to Readme for samples). Remember to modify variable data_root in the configuration file config/config_complex_net.yml to the right server address.

• Training

  python main.py -m train
  

  or

  python main.py --mode=train
  

• Test

  python main.py -m test --test_check_num=21
  

  or

  python main.py --mode=test --test_check_num=21
  

  where variable test_check_num is the number of the saved model for test.

• Prediction visualization

  python main.py -m plot --test_check_num=21
  

  or

  python main.py --mode=plot --test_check_num=21
  

  where variable test_check_num is the number of the saved model for plotting.

Project architecture

• config: the configuration file
• notebook: the test file for notebook
• outputs: the output results by test and plot module. The test results is saved at outputs/*.csv and the plotting figures is saved at outputs/predict_plot/.
• src: including surrogate model, training and testing files.
  • test.py: testing files.
  • train.py: training files.
  • plot.py: prediction visualization files.
  • data: data preprocessing and data loading files.
  • metric: evaluation metric file. (For details, see Readme for metric)
  • models: DNN surrogate models for the HSL-TFP task.
  • utils: useful tool function files.

One tiny example

One tiny example for training and testing can be accessed based on the following instruction.

• Some training and testing data are available at samples/data.
• Based on the original configuration file, run python main.py directly for a quick experience of this tiny example.