87 lines
2.6 KiB
Markdown
87 lines
2.6 KiB
Markdown
# 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](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/samples/README.md)). Remember to modify variable `data_root` in the configuration file `config/config_complex_net.yml` to the right server address.
|
||
|
||
- Training
|
||
|
||
```python
|
||
python main.py -m train
|
||
```
|
||
|
||
or
|
||
|
||
```python
|
||
python main.py --mode=train
|
||
```
|
||
|
||
- Test
|
||
|
||
```python
|
||
python main.py -m test --test_check_num=21
|
||
```
|
||
|
||
or
|
||
|
||
```python
|
||
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
|
||
python main.py -m plot --test_check_num=21
|
||
```
|
||
|
||
or
|
||
```python
|
||
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](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/src/metric/README.md))
|
||
- `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. |