update README.md

README.md

 # pytorch-DCRNN
-This is a TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: 
-Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR 2018.
+
+The original tensorflow implementation: [liyaguang/DCRNN](https://github.com/liyaguang/DCRNN),
+
+This repo is still under development.
+
+PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
+Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926), ICLR 2018.
+
+
+## Requirements
+- scipy>=0.19.0
+- numpy>=1.12.1
+- pandas>=0.19.2
+- torch>=0.4.x
+- tqdm
+- pytable
+
+
+## Data Preparation
+The traffic data files for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY), i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
+put into the `data/` folder.
+The `*.h5` files store the data in `panads.DataFrame` using the `HDF5` file format. Here is an example:
+
+|                     | sensor_0 | sensor_1 | sensor_2 | sensor_n |
+|:-------------------:|:--------:|:--------:|:--------:|:--------:|
+| 2018/01/01 00:00:00 |   60.0   |   65.0   |   70.0   |    ...   |
+| 2018/01/01 00:05:00 |   61.0   |   64.0   |   65.0   |    ...   |
+| 2018/01/01 00:10:00 |   63.0   |   65.0   |   60.0   |    ...   |
+|         ...         |    ...   |    ...   |    ...   |    ...   |
+
+
+Here is an article about [Using HDF5 with Python](https://medium.com/@jerilkuriakose/using-hdf5-with-python-6c5242d08773).
+
+Run the following commands to generate train/test/val dataset at  `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.
+```bash
+# Create data directories
+mkdir -p data/{METR-LA,PEMS-BAY}
+
+# METR-LA
+python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
+
+# PEMS-BAY
+python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
+```
+
+## Graph Construction
+ As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
+ supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
+```bash
+python -m scripts.gen_adj_mx  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
+    --output_pkl_filename=data/sensor_graph/adj_mx.pkl
+```
+Besides, the locations of sensors in Los Angeles, i.e., METR-LA, are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
+
+## Run the Pre-trained Model on METR-LA
+
+```bash
+# METR-LA
+python run_demo.py --config_filename=data/model/pretrained/METR-LA/config.yaml
+
+# PEMS-BAY
+python run_demo.py --config_filename=data/model/pretrained/PEMS-BAY/config.yaml
+```
+The generated prediction of DCRNN is in `data/results/dcrnn_predictions`.
+
+
+## Model Training
+For now, training is only supported for METR-LA dataset due to data availability.
+```bash
+# METR-LA
+python train_DCRNN.py 
+```
+Each epoch takes about 2min(~ 130 seconds) on a single RTX 2080 Ti for METR-LA. 
+
+There is a chance that the training loss will explode, the temporary workaround is to restart from the last saved model before the explosion, or to decrease the learning rate earlier in the learning rate schedule. 
+
+
+## Eval baseline methods
+```bash
+# METR-LA
+python -m scripts.eval_baseline_methods --traffic_reading_filename=data/metr-la.h5
+```
+
+More details are being added ...
+
+## Citation
+
+If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:
+```
+@inproceedings{li2018dcrnn_traffic,
+  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
+  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
+  booktitle={International Conference on Learning Representations (ICLR '18)},
+  year={2018}
+}
+```
+