Xianqi ed5b16ab15 | ||
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config | ||
docker | ||
outputs | ||
samples | ||
src | ||
tests | ||
.gitignore | ||
.gitlab-ci.yml | ||
LICENSE | ||
README.md | ||
README_CN.md | ||
main.py | ||
requirements.txt |
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 variabledata_root
in the configuration fileconfig/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 filenotebook
: the test file fornotebook
outputs
: the output results bytest
andplot
module. The test results is saved atoutputs/*.csv
and the plotting figures is saved atoutputs/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.