diff --git a/LICENCE b/LICENCE new file mode 100644 index 0000000..871b41d --- /dev/null +++ b/LICENCE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 shendu-sw + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md index 81f2343..2a63249 100644 --- a/README.md +++ b/README.md @@ -17,20 +17,17 @@ This project provides the implementation of the paper "TFRD: A Benchmark Dataset pip install -r requirements.txt ``` -Others should note that -`torch-cluster`, -`torch-scatter`, -`torch-sparse` package are also required for implementation of GCNs. The installation of the three packages should follow the version of `torch`, `cuda` ([download](https://pytorch-geometric.com/whl/torch-1.5.0.html)). +`torch-cluster`, `torch-scatter`, `torch-sparse` package are also required for implementation of GCNs. The installation of the three packages should follow the version of `torch`, `cuda` [[download](https://pytorch-geometric.com/whl/torch-1.5.0.html)]. ## Running -> All the methods for TFR-HSS task can be accessed by ruuning `main.py` file +> All the methods for TFR-HSS task can be accessed by running `main.py` file + +* The data root is put in `data_root` in configuration file `config/config.yml` . ### Image-based and Vector-based methods > The image-based and vector-based methods are following the same command. -- The data root is put in `data_root` in configuration file `config/config.yml` . - - Training ``` @@ -87,12 +84,13 @@ Others should note that > Only testing is permitted for point-based methods. -- Running Command +- Testing ``` python main.py ``` - if you want to plot the reconstruction result, you can use the following command + +* Testing with reconstruction visualization ``` python main.py --plot @@ -101,16 +99,21 @@ Others should note that ## Project architecture - `config`: the configuration file - - `data.yml` describes the setups of the layout domain and heat sources - - `config.yml` describes other configurations -- `samples`: tiny examples + - `config.yml` describes configurations + - `model_name`: model for reconstruction + - `backbone`: backbone network, used only for deep surrogate models + - `data_root`: root path of data + - `train_list`: train samples + - `test_list`: test samples + - others +- `samples`: examples - `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. - - `point.py`: Model and testing files for point-based methods - - `DeepRegression.py`: Model configurations. + - `point.py`: Model and testing files for point-based methods. + - `DeepRegression.py`: Model configurations for image-based and vector-based methods. - `data`: data preprocessing and data loading files. - `models`: interpolation and machine learning models for the TFR-HSS task. - `utils`: useful tool function files.