Use run_model

The script run_model.py used for training and evaluating a single model is provided in the root directory of the framework, and a series of command line parameters are provided to allow users to adjust the running parameter configuration.

When run the run_model.py, you must specify the following three parameters, namely task, dataset and model. That is:

python run_model.py --task=[task_name] --model=[model_name] --dataset=[dataset_name]

Furthermore, the script supports the input of the following command line parameters to adjust the parameter settings of the pipeline.

Supporting parameters:

  • task: The name of the task to be performed, including traffic_state_pred, traj_loc_pred, eta, map_matching, road_representation. Defaults to traffic_state_pred.

  • model: The name of the model to be performed. Defaults to GRU. (supporting models)

  • dataset: The dataset to be performed. Defaults to METR_LA. (supporting datasets)

  • config_file：The name of user-defined configuration file. Defaults to None. (see more)

  • saved_model：Whether to save the trained model. Defaults to True.

  • train：If the model has been pre-trained, whether to retrain the model. Defaults to True.

  • batch_size：The training and evaluation batch size.

  • train_rate：The proportion of the training set to the total dataset. (The order of division is training set, validation set, test set).

  • eval_rate：The proportion of the validation set.

  • learning_rate：Learning_rate. The default learning rate of different models may be different, please refer to the corresponding configuration file for details.

  • max_epoch：Maximum rounds of training. The default value varies with the model.

  • gpu：Whether to use GPU. Defaults to True.

  • gpu_id：The id of the GPU used. Defaults to 0.